From 3c7ec8997b33a9ceaa52ba56aecdb48e501ec73e Mon Sep 17 00:00:00 2001
From: Ailurus1 <ilyako25@gmail.com>
Date: Mon, 25 Sep 2023 00:06:54 +0300
Subject: [PATCH 001/305] Add TFLite frontend for TVM backend; Add input layer
 name processing via temporal file

---
 .../python/backend_tvm.py                     | 50 +++++++++++++++----
 .../python/main.py                            |  2 +-
 .../run_common.sh                             | 11 +++-
 3 files changed, 52 insertions(+), 11 deletions(-)

diff --git a/vision/classification_and_detection/python/backend_tvm.py b/vision/classification_and_detection/python/backend_tvm.py
index 61b755c67d..7e5a97787e 100644
--- a/vision/classification_and_detection/python/backend_tvm.py
+++ b/vision/classification_and_detection/python/backend_tvm.py
@@ -6,12 +6,11 @@
 import backend
 
 import tvm
+import numpy as np
 from tvm import auto_scheduler
 from tvm.contrib import graph_executor
 from tvm.runtime import vm as runtime_vm
 
-import numpy as np
-
 import os
 import multiprocessing
 
@@ -26,6 +25,8 @@ def __init__(self):
         self.executor_type = None
         self.max_batchsize = None
         self.pool = None
+        self.model_format = None
+        self.shape_dict = None
 
     def version(self):
         return tvm.__version__
@@ -36,7 +37,7 @@ def name(self):
 
     def image_format(self):
         """Requested image_format. Use a more popular layout NCHW"""
-        return "NCHW"
+        return "NHWC" if self.model_format == ".tflite" else "NCHW"
 
     def create_omp_args(self, arena_idx):
         idx_start = self.arena_size * arena_idx
@@ -58,13 +59,43 @@ def create_omp_args(self, arena_idx):
     def set_omp_envs(omp_args):
         for env_arg in omp_args:
             os.environ[env_arg[0]] = env_arg[1]
+                
+    @staticmethod
+    def vmobj_to_list(o):
+        if isinstance(o, tvm.nd.NDArray):
+            result = [o.numpy()]
+        elif isinstance(o, tvm.runtime.container.ADT):
+            result = []
+            for f in o:
+                result.extend(BackendTVM.vmobj_to_list(f))
+        elif isinstance(o, tvm.relay.backend.interpreter.ConstructorValue):
+            if o.constructor.name_hint == "Cons":
+                tl = BackendTVM.vmobj_to_list(o.fields[1])
+                hd = BackendTVM.vmobj_to_list(o.fields[0])
+                hd.extend(tl)
+                result = hd
+            elif o.constructor.name_hint == "Nil":
+                result = []
+            elif "tensor_nil" in o.constructor.name_hint:
+                result = [0]
+            elif "tensor" in o.constructor.name_hint:
+                result = [o.fields[0].numpy()]
+            else:
+                raise RuntimeError(f"Unknown object type: {o.constructor.name_hint}")
+        else:
+            raise RuntimeError(f"Unknown object type: {type(o)}")
+        return result
 
     def load_impl(self, model_path, inputs, outputs, max_batchsize):
 
         self.max_batchsize = max_batchsize
-
+        _, self.model_format = os.path.splitext(model_path)
+        
         work_dir = os.path.dirname(model_path)
         compiled_model = os.path.join(work_dir, 'model-tvm.so')
+        
+        with open(os.path.join(work_dir, "input_layer_name"), 'r') as file:
+            self.input_layer_name = file.read().strip()
 
         if compiled_model.endswith('.so') or compiled_model.endswith('.dylib'):
             if not os.path.isfile(compiled_model):
@@ -136,10 +167,12 @@ def predict_impl(self, feed):
         input_idx = self.inputs.index(iname)
         if self.executor_type == "virtual_machine":
             self.executor.set_input(
-                "main", **{"input_tensor:0": tvm.nd.array(item)})
-            result = self.executor.run()
-
-            return [result[0].asnumpy()[:batch_size], result[1].asnumpy()[:batch_size]]
+                "main", **{self.input_layer_name: tvm.nd.array(item)}
+            )
+            result = BackendTVM.vmobj_to_list(self.executor.run())
+            if self.model_format == ".tflite":
+                result = result[::-1]
+            return result
         else:
             self.executor.set_input(input_idx, tvm.nd.array(item))
             self.executor.run()
@@ -171,7 +204,6 @@ def load(self, model_path, inputs=None, outputs=None):
                                                        self.create_omp_args(0))
                                              )
 
-        # TODO(@apeskov): do we really have to return self ??
         return self
 
     def predict(self, feed):
diff --git a/vision/classification_and_detection/python/main.py b/vision/classification_and_detection/python/main.py
index 5cd5e60f38..1c2cd9a5ab 100755
--- a/vision/classification_and_detection/python/main.py
+++ b/vision/classification_and_detection/python/main.py
@@ -98,7 +98,7 @@
     "resnet50-pytorch": {
         "inputs": "image",
         "outputs": "ArgMax:0",
-        "dataset": "imagenet",
+        "dataset": "imagenet_pytorch",
         "backend": "tensorflow",
         "model-name": "resnet50",
     },
diff --git a/vision/classification_and_detection/run_common.sh b/vision/classification_and_detection/run_common.sh
index 041e29265a..fae0ba1b50 100755
--- a/vision/classification_and_detection/run_common.sh
+++ b/vision/classification_and_detection/run_common.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 
 if [ $# -lt 1 ]; then
-    echo "usage: $0 tf|onnxruntime|pytorch|tflite|tvm-onnx|tvm-pytorch [resnet50|mobilenet|ssd-mobilenet|ssd-resnet34|retinanet] [cpu|gpu]"
+    echo "usage: $0 tf|onnxruntime|pytorch|tflite|tvm-onnx|tvm-pytorch|tvm-tflite [resnet50|mobilenet|ssd-mobilenet|ssd-resnet34|retinanet] [cpu|gpu]"
     exit 1
 fi
 if [ "x$DATA_DIR" == "x" ]; then
@@ -137,6 +137,15 @@ if [ $name == "resnet50-tvm-pytorch" ] ; then
     extra_args="$extra_args --backend tvm"
 fi
 
+#
+# TVM with TFLite models
+#
+if [ $name == "resnet50-tvm-tflite" ] ; then
+    model_path="$MODEL_DIR/resnet50_v1.tflite"
+    profile=resnet50-tf
+    extra_args="$extra_args --backend tvm"
+fi
+
 #
 # ncnn
 #

From 15e673d97432ecb61c57d33b165b50a85cf061d2 Mon Sep 17 00:00:00 2001
From: Ailurus1 <ilyako25@gmail.com>
Date: Mon, 25 Sep 2023 07:41:44 +0300
Subject: [PATCH 002/305] Fix pytorch profile and add tvm-tflite

---
 vision/classification_and_detection/run_common.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vision/classification_and_detection/run_common.sh b/vision/classification_and_detection/run_common.sh
index fae0ba1b50..35d5720fd1 100755
--- a/vision/classification_and_detection/run_common.sh
+++ b/vision/classification_and_detection/run_common.sh
@@ -18,7 +18,7 @@ device="cpu"
 
 for i in $* ; do
     case $i in
-       tf|onnxruntime|tflite|pytorch|tvm-onnx|tvm-pytorch|ncnn) backend=$i; shift;;
+       tf|onnxruntime|tflite|pytorch|tvm-onnx|tvm-pytorch|tvm-tflite|ncnn) backend=$i; shift;;
        cpu|gpu) device=$i; shift;;
        gpu) device=gpu; shift;;
        resnet50|mobilenet|ssd-mobilenet|ssd-resnet34|ssd-resnet34-tf|retinanet) model=$i; shift;;
@@ -133,7 +133,7 @@ fi
 #
 if [ $name == "resnet50-tvm-pytorch" ] ; then
     model_path="$MODEL_DIR/resnet50_INT8bit_quantized.pt"
-    profile=resnet50-onnxruntime
+    profile=resnet50-pytorch
     extra_args="$extra_args --backend tvm"
 fi
 

From b50a8cc345e0d935a77d0223d457244156e44730 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 17 Oct 2023 11:42:30 -0500
Subject: [PATCH 003/305] Fix test01: Avoid passing with empty file (#1500)

---
 compliance/nvidia/TEST01/verify_accuracy.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/compliance/nvidia/TEST01/verify_accuracy.py b/compliance/nvidia/TEST01/verify_accuracy.py
index d670433c39..cfaca5dec4 100644
--- a/compliance/nvidia/TEST01/verify_accuracy.py
+++ b/compliance/nvidia/TEST01/verify_accuracy.py
@@ -120,10 +120,10 @@ def main():
         print("num_perf_log_qsl_idx_match = {:}".format(num_perf_log_qsl_idx_match))
         print("num_perf_log_data_mismatch = {:}".format(num_perf_log_data_mismatch))
         print("num_missing_qsl_idxs = {:}".format(num_missing_qsl_idxs))
-        if num_perf_log_data_mismatch > 0 :
-            print("TEST FAIL\n");
-        else :
-            print("TEST PASS\n");
+        if num_perf_log_data_mismatch == 0 and num_perf_log_qsl_idx_match > 0:
+            print("TEST PASS\n")
+        else:
+            print("TEST FAIL\n")
         exit()
 
     py33 = sys.version_info >= (3,3)
@@ -146,6 +146,7 @@ def main():
     #print(num_perf_lines)
     
     num_perf_log_data_mismatch = 0
+    num_perf_log_data_match = 0
     print("Each dot represents 1% completion:")
     for perf_line in range(0, num_perf_lines):
         if perf_line % int(num_perf_lines/100) == 0:
@@ -170,15 +171,17 @@ def main():
 
         if perf_md5sum != acc_md5sum:
             num_perf_log_data_mismatch += 1
+        else:
+            num_perf_log_data_match += 1
 
     print("")
     print("num_acc_log_entries = {:}".format(num_acc_log_entries))
     print("num_perf_log_data_mismatch = {:}".format(num_perf_log_data_mismatch))
     print("num_perf_log_entries = {:}".format(num_perf_log_entries))
-    if num_perf_log_data_mismatch > 0 :
-        print("TEST FAIL\n");
-    else :
-        print("TEST PASS\n");
+    if num_perf_log_data_mismatch == 0 and num_perf_log_data_match > 0:
+        print("TEST PASS\n")
+    else:
+        print("TEST FAIL\n")
 
 if __name__ == '__main__':
 	main()

From 531f7c442038159beedcc3f753090f0a00a296a6 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 31 Oct 2023 16:57:14 +0000
Subject: [PATCH 004/305] Support ROCm backend for bert onnxruntime
 implementation (#1509)

* Fix reported power efficiency for inferred results, #1494

* Refactor get_inferred_result code

* Support other execution providers for bert-onnxruntime
---
 language/bert/onnxruntime_SUT.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/language/bert/onnxruntime_SUT.py b/language/bert/onnxruntime_SUT.py
index f37bdb7839..2b45e68df3 100644
--- a/language/bert/onnxruntime_SUT.py
+++ b/language/bert/onnxruntime_SUT.py
@@ -42,8 +42,8 @@ def __init__(self, args):
             else:
                 model_path = "build/data/bert_tf_v1_1_large_fp32_384_v2/model.onnx"
         if len(onnxruntime.get_all_providers()) > 1 and os.environ.get("USE_GPU", "yes").lower() not in [ "0", "false", "off", "no" ]:
-            #Currently considering only CUDAExecutionProvider
-            self.sess = onnxruntime.InferenceSession(model_path, self.options, providers=['CUDAExecutionProvider'])
+            preferred_execution_provider = os.environ.get("ONNXRUNTIME_PREFERRED_EXECUTION_PROVIDER", "CUDAExecutionProvider")
+            self.sess = onnxruntime.InferenceSession(model_path, self.options, providers=[ preferred_execution_provider ])
         else:
             self.sess = onnxruntime.InferenceSession(model_path, self.options, providers=["CPUExecutionProvider"])
 

From eea9ee7d36f0ad03526b440d7ae0a65c195c66f3 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 31 Oct 2023 17:01:45 +0000
Subject: [PATCH 005/305] Fixes #1402, make rnnt run with pytorch >=2.0 (#1506)

---
 .../rnnt/pytorch/parts/features.py            | 33 ++++++++++---------
 1 file changed, 17 insertions(+), 16 deletions(-)

diff --git a/speech_recognition/rnnt/pytorch/parts/features.py b/speech_recognition/rnnt/pytorch/parts/features.py
index 99092678e9..5a4decd615 100644
--- a/speech_recognition/rnnt/pytorch/parts/features.py
+++ b/speech_recognition/rnnt/pytorch/parts/features.py
@@ -97,12 +97,12 @@ def __init__(self, sample_rate=8000, window_size=0.02, window_stride=0.01,
 #        print("PADDING: {}".format(pad_to))
 
         torch_windows = {
-            'hann': torch.hann_window,
-            'hamming': torch.hamming_window,
-            'blackman': torch.blackman_window,
-            'bartlett': torch.bartlett_window,
-            'none': None,
-        }
+                'hann': torch.hann_window,
+                'hamming': torch.hamming_window,
+                'blackman': torch.blackman_window,
+                'bartlett': torch.bartlett_window,
+                'none': None,
+                }
 
         self.win_length = int(sample_rate * window_size)  # frame size
         self.hop_length = int(sample_rate * window_stride)
@@ -123,16 +123,16 @@ def __init__(self, sample_rate=8000, window_size=0.02, window_stride=0.01,
         window_tensor = window_fn(self.win_length,
                                   periodic=False) if window_fn else None
         filterbanks = torch.tensor(
-            librosa.filters.mel(sr=sample_rate, n_fft=self.n_fft, n_mels=nfilt, fmin=lowfreq,
-                                fmax=highfreq), dtype=torch.float).unsqueeze(0)
+                librosa.filters.mel(sr=sample_rate, n_fft=self.n_fft, n_mels=nfilt, fmin=lowfreq,
+                                    fmax=highfreq), dtype=torch.float).unsqueeze(0)
         # self.fb = filterbanks
         # self.window = window_tensor
         self.register_buffer("fb", filterbanks)
         self.register_buffer("window", window_tensor)
         # Calculate maximum sequence length (# frames)
         max_length = 1 + math.ceil(
-            (max_duration * sample_rate - self.win_length) / self.hop_length
-        )
+                (max_duration * sample_rate - self.win_length) / self.hop_length
+                )
         max_pad = 16 - (max_length % 16)
         self.max_length = max_length + max_pad
 
@@ -141,9 +141,9 @@ def get_seq_len(self, seq_len):
         seq_len = (seq_len + self.frame_splicing - 1) // self.frame_splicing
         return seq_len
 
-    @torch.no_grad()
     def forward(self, inp: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
-        x, seq_len = inp
+        with torch.no_grad():
+            x, seq_len = inp
 
         dtype = x.dtype
 
@@ -162,7 +162,8 @@ def forward(self, inp: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
         # do stft
         x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length,
                        win_length=self.win_length,
-                       center=True, window=self.window.to(dtype=torch.float))
+                       center=True, window=self.window.to(dtype=torch.float), return_complex = True)
+        x = torch.view_as_real(x)
 
         # get power spectrum
         x = x.pow(2).sum(-1)
@@ -244,9 +245,9 @@ def from_config(cls, cfg, log=False):
 
 class FeatureFactory(object):
     featurizers = {
-        "logfbank": FilterbankFeatures,
-        "fbank": FilterbankFeatures,
-    }
+            "logfbank": FilterbankFeatures,
+            "fbank": FilterbankFeatures,
+            }
 
     def __init__(self):
         pass

From 91c60acc08635c5ea6f1bc4b0cc60b2447c77f35 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 7 Nov 2023 17:41:18 +0000
Subject: [PATCH 006/305] Update README.md, added model categories (#1507)

Co-authored-by: Miro <mirhodak@amd.com>
---
 README.md | 104 +++++++++++++++++++++++++++---------------------------
 1 file changed, 52 insertions(+), 52 deletions(-)

diff --git a/README.md b/README.md
index dc9d01ca16..8f83e3fbdd 100644
--- a/README.md
+++ b/README.md
@@ -20,15 +20,15 @@ Those doing power submissions must use the `master` branch of the [MLCommons pow
 
 You can see the individual Readme files in the reference app for more details.
 
-| model | reference app | framework | dataset |
-| ---- | ---- | ---- | ---- |
-| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, onnx, tvm, ncnn | imagenet2012 |
-| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800|
-| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 |
-| dlrm-v2 | [recommendation/dlrm_v2](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm_v2/pytorch) | pytorch | Multihot Criteo Terabyte |
-| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 |
-| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus |
-| gpt-j | [language/gpt-j](https://github.com/mlcommons/inference/tree/master/language/gpt-j)| pytorch | CNN-Daily Mail |
+| model | reference app | framework | dataset | category
+| ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, onnx, tvm, ncnn | imagenet2012 | edge,datacenter |
+| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge,datacenter |
+| dlrm-v2 | [recommendation/dlrm_v2](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm_v2/pytorch) | pytorch | Multihot Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 | edge,datacenter |
+| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus | edge,datacenter |
+| gpt-j | [language/gpt-j](https://github.com/mlcommons/inference/tree/master/language/gpt-j)| pytorch | CNN-Daily Mail | edge,datacenter |
 
 
 ## MLPerf Inference v3.0 (submission 03/03/2023)
@@ -36,14 +36,14 @@ Please use the v3.0 tag (```git checkout v3.0```) if you would like to reproduce
 
 You can see the individual Readme files in the reference app for more details.
 
-| model | reference app | framework | dataset |
-| ---- | ---- | ---- | ---- |
-| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, onnx, tvm | imagenet2012 |
-| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800|
-| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 |
-| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm/pytorch) | pytorch, tensorflow(?)) | Criteo Terabyte |
-| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 |
-| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus |
+| model | reference app | framework | dataset | category
+| ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, onnx, tvm | imagenet2012 | edge,datacenter |
+| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge,datacenter |
+| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm/pytorch) | pytorch, tensorflow | Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 | edge,datacenter |
+| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus | edge,datacenter |
 
 
 ## MLPerf Inference v2.1 (submission 08/05/2022)
@@ -51,14 +51,14 @@ Use the r2.1 branch (```git checkout r2.1```) if you want to submit or reproduce
 
 See the individual Readme files in the reference app for details.
 
-| model | reference app | framework | dataset |
-| ---- | ---- | ---- | ---- |
-| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, pytorch, onnx | imagenet2012 |
-| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800|
-| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 |
-| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm/pytorch) | pytorch, tensorflow(?), onnx(?) | Criteo Terabyte |
-| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 |
-| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus |
+| model | reference app | framework | dataset | category
+| ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, onnx | imagenet2012 | edge,datacenter |
+| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge,datacenter |
+| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm/pytorch) | pytorch, tensorflow | Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 | edge,datacenter |
+| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus | edge,datacenter |
 
 
 ## MLPerf Inference v2.0 (submission 02/25/2022)
@@ -66,15 +66,15 @@ Use the r2.0 branch (```git checkout r2.0```) if you want to submit or reproduce
 
 See the individual Readme files in the reference app for details.
 
-| model | reference app | framework | dataset |
-| ---- | ---- | ---- | ---- |
-| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, pytorch, onnx | imagenet2012 |
-| ssd-mobilenet 300x300 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, pytorch, onnx| coco resized to 300x300 | 
-| ssd-resnet34 1200x1200 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, pytorch, onnx | coco resized to 1200x1200|
-| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 |
-| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm/pytorch) | pytorch, tensorflow(?), onnx(?) | Criteo Terabyte |
-| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 |
-| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus |
+| model | reference app | framework | dataset | category |
+| ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, onnx | imagenet2012 | edge,datacenter |
+| ssd-mobilenet 300x300 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, pytorch, onnx| coco resized to 300x300 | edge |
+| ssd-resnet34 1200x1200 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, pytorch, onnx | coco resized to 1200x1200| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge,datacenter |
+| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm/pytorch) | pytorch, tensorflow | Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 | edge,datacenter |
+| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus | edge,datacenter |
 
 
 ## MLPerf Inference v1.1 (submission 08/13/2021)
@@ -82,30 +82,30 @@ Use the r1.1 branch (```git checkout r1.1```) if you want to submit or reproduce
 
 See the individual Readme files in the reference app for details.
 
-| model | reference app | framework | dataset |
-| ---- | ---- | ---- | ---- |
-| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.1/vision/classification_and_detection) | tensorflow, pytorch, onnx | imagenet2012 |
-| ssd-mobilenet 300x300 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.1/vision/classification_and_detection) | tensorflow, pytorch, onnx| coco resized to 300x300 | 
-| ssd-resnet34 1200x1200 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.1/vision/classification_and_detection) | tensorflow, pytorch, onnx | coco resized to 1200x1200|
-| bert | [language/bert](https://github.com/mlcommons/inference/tree/r1.1/language/bert) | tensorflow, pytorch, onnx | squad-1.1 |
-| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/r1.1/recommendation/dlrm/pytorch) | pytorch, tensorflow(?), onnx(?) | Criteo Terabyte |
-| 3d-unet | [vision/medical_imaging/3d-unet](https://github.com/mlcommons/inference/tree/r1.1/vision/medical_imaging/3d-unet) | pytorch, tensorflow(?), onnx(?) | BraTS 2019 |
-| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/r1.1/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus |
+| model | reference app | framework | dataset | category |
+| ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.1/vision/classification_and_detection) | tensorflow, onnx | imagenet2012 | edge,datacenter |
+| ssd-mobilenet 300x300 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.1/vision/classification_and_detection) | tensorflow, pytorch, onnx| coco resized to 300x300 | edge |
+| ssd-resnet34 1200x1200 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.1/vision/classification_and_detection) | tensorflow, pytorch, onnx | coco resized to 1200x1200| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/r1.1/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge,datacenter |
+| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/r1.1/recommendation/dlrm/pytorch) | pytorch, tensorflow | Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet](https://github.com/mlcommons/inference/tree/r1.1/vision/medical_imaging/3d-unet) | pytorch, tensorflow(?), onnx(?) | BraTS 2019 | edge,datacenter |
+| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/r1.1/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus | edge,datacenter |
 
 ## MLPerf Inference v1.0 (submission 03/19/2021)
 Use the r1.0 branch (```git checkout r1.0```) if you want to submit or reproduce v1.0 results.
 
 See the individual Readme files in the reference app for details.
 
-| model | reference app | framework | dataset |
-| ---- | ---- | ---- | ---- |
-| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.0/vision/classification_and_detection) | tensorflow, pytorch, onnx | imagenet2012 |
-| ssd-mobilenet 300x300 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.0/vision/classification_and_detection) | tensorflow, pytorch, onnx| coco resized to 300x300 | 
-| ssd-resnet34 1200x1200 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.0/vision/classification_and_detection) | tensorflow, pytorch, onnx | coco resized to 1200x1200|
-| bert | [language/bert](https://github.com/mlcommons/inference/tree/r1.0/language/bert) | tensorflow, pytorch, onnx | squad-1.1 |
-| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/r1.0/recommendation/dlrm/pytorch) | pytorch, tensorflow(?), onnx(?) | Criteo Terabyte |
-| 3d-unet | [vision/medical_imaging/3d-unet](https://github.com/mlcommons/inference/tree/r1.0/vision/medical_imaging/3d-unet) | pytorch, tensorflow(?), onnx(?) | BraTS 2019 |
-| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/r1.0/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus |
+| model | reference app | framework | dataset | category |
+| ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.0/vision/classification_and_detection) | tensorflow, onnx | imagenet2012 | edge,datacenter |
+| ssd-mobilenet 300x300 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.0/vision/classification_and_detection) | tensorflow, pytorch, onnx| coco resized to 300x300 | edge |
+| ssd-resnet34 1200x1200 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/r1.0/vision/classification_and_detection) | tensorflow, pytorch, onnx | coco resized to 1200x1200| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/r1.0/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge,datacenter |
+| dlrm | [recommendation/dlrm](https://github.com/mlcommons/inference/tree/r1.0/recommendation/dlrm/pytorch) | pytorch, tensorflow(?) | Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet](https://github.com/mlcommons/inference/tree/r1.0/vision/medical_imaging/3d-unet) | pytorch, tensorflow(?), onnx(?) | BraTS 2019 | edge,datacenter |
+| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/r1.0/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus | edge,datacenter |
 
 
 ## MLPerf Inference v0.7 (submission 9/18/2020)

From 272c9d9b5da4b5779cd47b9915b781e82fd2200c Mon Sep 17 00:00:00 2001
From: badhri-intel <107150316+badhri-intel@users.noreply.github.com>
Date: Tue, 7 Nov 2023 09:53:11 -0800
Subject: [PATCH 007/305] Update README.md (#1446)

Co-authored-by: Miro <mirhodak@amd.com>
---
 language/gpt-j/README.md | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/language/gpt-j/README.md b/language/gpt-j/README.md
index a8fb44ad1c..cc46135f25 100644
--- a/language/gpt-j/README.md
+++ b/language/gpt-j/README.md
@@ -99,6 +99,13 @@ Apache License Version 2.0.
 
 ### Datasets & Models:
 
-To the extent that any data, datasets or models are referenced by Intel or accessed using tools or code on this site such data, datasets and models are provided by the third party indicated as the source of such content. Intel does not create the data, datasets, or models, provide a license to any third-party data, datasets, or models referenced, and does not warrant their accuracy or quality.  By accessing such data, dataset(s) or model(s) you agree to the terms associated with that content and that your use complies with the applicable license. DATASETS [https://github.com/badhri-intel/inference/blob/gpt-j/ref_implementation/language/gpt-j/DATASETS_MODELS.md]
+To the extent that any data, datasets or models are referenced by Intel or accessed using tools or code on this site such data, datasets and models are provided by the third party indicated as the source of such content. Intel does not create the data, datasets, or models, provide a license to any third-party data, datasets, or models referenced, and does not warrant their accuracy or quality.  By accessing such data, dataset(s) or model(s) you agree to the terms associated with that content and that your use complies with the applicable license. 
+
+This is a comprehensive list of public datasets and models used by this repository.
+
+| Name (Link/Source) | Framework | Use Case |
+|--------------------| --------- | -------- |
+| [cnn_dailymail (Hugging Face)](https://huggingface.co/datasets/cnn_dailymail) | PyTorch | Text Summarization |
+| [gpt-j-6b (Hugging Face)](https://huggingface.co/EleutherAI/gpt-j-6b) | PyTorch | Text Summarization |
 
 Intel expressly disclaims the accuracy, adequacy, or completeness of any data, datasets or models, and is not liable for any errors, omissions, or defects in such content, or for any reliance thereon. Intel also expressly disclaims any warranty of non-infringement with respect to such data, dataset(s), or model(s). Intel is not liable for any liability or damages relating to your use of such data, datasets or models.

From 0e39a7963b8b6fd3f16a70bde8229b6771a73320 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 21 Nov 2023 17:39:32 +0000
Subject: [PATCH 008/305] RAY_SUT for bert (#1517)

* ray

* ray

* ray

* use all gpus

* ray

* tensorrt

---------

Co-authored-by: sakria9 <i@sustech.moe>
---
 language/bert/ray_SUT.py | 182 +++++++++++++++++++++++++++++++++++++++
 language/bert/run.py     |   7 +-
 2 files changed, 188 insertions(+), 1 deletion(-)
 create mode 100644 language/bert/ray_SUT.py

diff --git a/language/bert/ray_SUT.py b/language/bert/ray_SUT.py
new file mode 100644
index 0000000000..fd53dc07c0
--- /dev/null
+++ b/language/bert/ray_SUT.py
@@ -0,0 +1,182 @@
+# coding=utf-8
+# Copyright 2021 Arm Limited and affiliates.
+# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import array
+import json
+import os
+import sys
+sys.path.insert(0, os.path.join(os.getcwd(), "DeepLearningExamples", "PyTorch", "LanguageModeling", "BERT"))
+sys.path.insert(0, os.getcwd())
+
+import mlperf_loadgen as lg
+import numpy as np
+import torch
+import torch_tensorrt
+import transformers
+from transformers import BertConfig, BertForQuestionAnswering
+from squad_QSL import get_squad_QSL
+
+import ray
+from ray.util.actor_pool import ActorPool
+
+
+@ray.remote(num_cpus=1,num_gpus=1)
+class TorchPredictor:
+    def __init__(self, config_json, model_file, batch_size):
+        print("init", os.getpid(), torch.cuda.device_count())
+        self.pid = os.getpid()
+        self.dev_cnt = torch.cuda.device_count()
+
+        config = BertConfig(
+            attention_probs_dropout_prob=config_json["attention_probs_dropout_prob"],
+            hidden_act=config_json["hidden_act"],
+            hidden_dropout_prob=config_json["hidden_dropout_prob"],
+            hidden_size=config_json["hidden_size"],
+            initializer_range=config_json["initializer_range"],
+            intermediate_size=config_json["intermediate_size"],
+            max_position_embeddings=config_json["max_position_embeddings"],
+            num_attention_heads=config_json["num_attention_heads"],
+            num_hidden_layers=config_json["num_hidden_layers"],
+            type_vocab_size=config_json["type_vocab_size"],
+            vocab_size=config_json["vocab_size"])
+
+        self.dev = torch.device("cuda")
+        self.model = BertForQuestionAnswering(config)
+        self.model.to(self.dev)
+        self.model.eval()
+        self.model.load_state_dict(torch.load(model_file), strict=False)
+        # tensor rt
+        batch_input_ids = torch.LongTensor(np.zeros((batch_size, 384))).to(self.dev)
+        traced_mlm_model = torch.jit.trace(self.model, [batch_input_ids, batch_input_ids, batch_input_ids], strict=False)
+        self.trt_model = torch_tensorrt.compile(traced_mlm_model,
+            inputs=[
+                torch_tensorrt.Input(shape=[batch_size, 384], dtype=torch.int32),
+                torch_tensorrt.Input(shape=[batch_size, 384], dtype=torch.int32),
+                torch_tensorrt.Input(shape=[batch_size, 384], dtype=torch.int32),
+        ],
+        enabled_precisions= {torch.float32},
+        workspace_size=2000000000,
+        truncate_long_and_double=True)
+
+        print("done loading")
+
+    # Logic for inference on 1 batch of data.
+    def forward(self, batch):
+        input_ids=torch.from_numpy(batch["input_ids"]).to(self.dev)
+        attention_mask=torch.from_numpy(batch["attention_mask"]).to(self.dev)
+        token_type_ids=torch.from_numpy(batch["token_type_ids"]).to(self.dev)
+        with torch.inference_mode():
+            # pytorch
+            # model_output = self.model.forward(input_ids=input_ids,
+            #     attention_mask=attention_mask,
+            #     token_type_ids=token_type_ids)
+            # start_scores = model_output.start_logits
+            # end_scores = model_output.end_logits
+            
+            # tensor rt
+            trt_output = self.trt_model(input_ids, attention_mask, token_type_ids)
+            start_scores = trt_output["start_logits"]
+            end_scores = trt_output["end_logits"]
+            
+            batch_ret = torch.stack([start_scores, end_scores], axis=-1).cpu().numpy()
+            return {
+                "output": batch_ret
+            }
+    
+    def ready(self):
+        pass
+
+class BERT_Ray_SUT():
+    def __init__(self, args):
+        with open("bert_config.json") as f:
+            config_json = json.load(f)
+        model_file = os.environ.get("ML_MODEL_FILE_WITH_PATH", "build/data/bert_tf_v1_1_large_fp32_384_v2/model.pytorch")
+
+        print("Constructing SUT...")
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        print("Finished constructing SUT.")
+        self.qsl = get_squad_QSL(args.max_examples)
+
+        ray.init()
+        self.batch_size = 10
+        resources = ray.cluster_resources()
+        num_gpus = int(resources.get('GPU', 0))
+        self.actor_list = [TorchPredictor.remote(config_json, model_file, self.batch_size) for _ in range(num_gpus)]
+        self.pool = ActorPool(self.actor_list)
+
+        samples = []
+        for i in range(self.qsl.count):
+            sample = {}
+            eval_features = self.qsl.get_features(i)
+            sample["input_ids"] = np.array(eval_features.input_ids).astype(np.int32)
+            sample["attention_mask"] = np.array(eval_features.input_mask).astype(np.int32)
+            sample["token_type_ids"] = np.array(eval_features.segment_ids).astype(np.int32)
+            samples.append(sample)
+        self.samples = samples
+        
+        print("Waiting Actors init")
+        for actor in self.actor_list:
+            ray.get(actor.ready.remote())
+        print("BERT_Ray_SUT construct complete")
+
+    def issue_queries(self, query_samples):
+        batch_samples = []
+        i = 0
+        while i < len(query_samples):
+            batch_sample = {
+                "input_ids": np.array([
+                    self.samples[query_sample.index]["input_ids"]
+                    for query_sample in query_samples[i:i+self.batch_size]]),
+                "attention_mask": np.array([
+                    self.samples[query_sample.index]["attention_mask"]
+                    for query_sample in query_samples[i:i+self.batch_size]]),
+                "token_type_ids": np.array([
+                    self.samples[query_sample.index]["token_type_ids"]
+                    for query_sample in query_samples[i:i+self.batch_size]]),
+            }
+            batch_samples.append(batch_sample)
+            i = i + self.batch_size
+
+        # print("samples len", len(batch_samples))
+        batch_inference_results = list(self.pool.map_unordered(lambda a, v: a.forward.remote(v), batch_samples))
+
+        results = []
+        for batch_inference_result in batch_inference_results:
+            batch_inference_result = batch_inference_result["output"]
+            for inference_result in batch_inference_result:
+                response_array = array.array("B", inference_result.tobytes())
+                bi = response_array.buffer_info()
+                results.append(bi)
+        
+        # print("results len", len(results))
+        
+        responses = []
+        for i in range(len(query_samples)):
+            # print(query_samples[i].index)
+            bi = results[i]
+            response = lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])
+            responses.append(response)
+        lg.QuerySamplesComplete(responses)
+
+    def flush_queries(self):
+        pass
+
+    def __del__(self):
+        print("Finished destroying SUT.")
+
+def get_ray_sut(args):
+    return BERT_Ray_SUT(args)
diff --git a/language/bert/run.py b/language/bert/run.py
index 843da58375..bc82a73250 100644
--- a/language/bert/run.py
+++ b/language/bert/run.py
@@ -26,7 +26,7 @@
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--backend", choices=["tf", "pytorch", "onnxruntime", "tf_estimator"], default="tf", help="Backend")
+        "--backend", choices=["tf", "pytorch", "onnxruntime", "tf_estimator", "ray"], default="tf", help="Backend")
     parser.add_argument("--scenario", choices=["SingleStream", "Offline",
                         "Server", "MultiStream"], default="Offline", help="Scenario")
     parser.add_argument("--accuracy", action="store_true",
@@ -76,6 +76,11 @@ def main():
     elif args.backend == "onnxruntime":
         from onnxruntime_SUT import get_onnxruntime_sut
         sut = get_onnxruntime_sut(args)
+    elif args.backend == "ray":
+        assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
+        assert not args.profile, "Profiling is only supported by onnxruntime backend!"
+        from ray_SUT import get_ray_sut
+        sut = get_ray_sut(args)
     else:
         raise ValueError("Unknown backend: {:}".format(args.backend))
 

From 2458b9c0e36d078025f7f07f306343bcdbd2d13c Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 21 Nov 2023 17:43:06 +0000
Subject: [PATCH 009/305] Updated the v3.1 details in the main readme (#1514)

Co-authored-by: Mitchelle Rasquinha <80070689+mrasquinha-g@users.noreply.github.com>
---
 README.md | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 8f83e3fbdd..c635c73b0e 100644
--- a/README.md
+++ b/README.md
@@ -13,12 +13,16 @@ Please see the [MLPerf Inference benchmark paper](https://arxiv.org/abs/1911.025
     primaryClass={cs.LG}
 }
 ```
+## MLPerf Inference v4.0 (submission deadline February 23, 2024)
+
+Code freeze coming soon...
+
 ## MLPerf Inference v3.1 (submission August 18, 2023)
-Please use the master branch for v3.1 submission. You can use the latest commit or any commit since [f7df3acb6880b6b3a92cd5a444d173137aa5d8ca](https://github.com/mlcommons/inference/tree/f7df3acb6880b6b3a92cd5a444d173137aa5d8ca) for doing the submission. v3.1 tag will be released once submissions are over for reproducibility. 
+Please use [v3.1 tag](https://github.com/mlcommons/inference/releases/tag/v3.1) (```git checkout v3.1```) if you would like to reproduce the v3.1 results. 
 
-Those doing power submissions must use the `master` branch of the [MLCommons power-dev](https://github.com/mlcommons/power-dev) repository and checkout to [e9e16b1299ef61a2a5d8b9abf5d759309293c440](https://github.com/mlcommons/power-dev/tree/e9e16b1299ef61a2a5d8b9abf5d759309293c440). 
+For reproducing power submissions please use the `master` branch of the [MLCommons power-dev](https://github.com/mlcommons/power-dev) repository and checkout to [e9e16b1299ef61a2a5d8b9abf5d759309293c440](https://github.com/mlcommons/power-dev/tree/e9e16b1299ef61a2a5d8b9abf5d759309293c440). 
 
-You can see the individual Readme files in the reference app for more details.
+You can see the individual README files in the benchmark task folders for more details regarding the benchmarks. For reproducing the submitted results please see the README files under the respective submitter folders in the [inference v3.1 results repository](https://github.com/mlcommons/inference_results_v3.1).
 
 | model | reference app | framework | dataset | category
 | ---- | ---- | ---- | ---- | ---- |

From 0f71999f0b72cc02d667efd5f009521540fd14be Mon Sep 17 00:00:00 2001
From: ljk3210 <140800687+ljk3210@users.noreply.github.com>
Date: Wed, 22 Nov 2023 01:58:52 +0800
Subject: [PATCH 010/305] Rm pybind11 submodule (#1501)

* Rm pybind11 submodule

* Rm py36 (EOL at Dec 2021)

---------

Co-authored-by: Mitchelle Rasquinha <80070689+mrasquinha-g@users.noreply.github.com>
---
 .github/workflows/build_wheels.yml | 10 +++-------
 .gitmodules                        |  6 ------
 pyproject.toml                     |  3 +++
 third_party/pybind                 |  1 -
 4 files changed, 6 insertions(+), 14 deletions(-)
 create mode 100755 pyproject.toml
 delete mode 160000 third_party/pybind

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index dbd52f8ff1..71dd4c8cdf 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -20,20 +20,16 @@ jobs:
       - name: Install requirements
         run: python -m pip install cibuildwheel==2.11.4 twine==4.0.2
 
-      - name: Init pybind11 submodule
-        run: |
-          git submodule init third_party/pybind
-          git submodule update third_party/pybind
-
       - name: Build wheels
         run: python -m cibuildwheel loadgen/ --output-dir wheels
         env:
           CIBW_ENVIRONMENT: "CFLAGS='-std=c++14'"
-          CIBW_BUILD: 'cp3{6,7,8,9,10}-*'
+          CIBW_BUILD: 'cp3{7,8,9,10,11}-*'
 
       - uses: actions/upload-artifact@v3
         with:
           path: ./wheels/*.whl 
 
       - name: Publish package to PyPI
-        run: python -m twine upload wheels/* -u __token__ -p ${{ secrets.PYPI_API_TOKEN }}
\ No newline at end of file
+        run: python -m twine upload wheels/* -u __token__ -p ${{ secrets.PYPI_API_TOKEN }}
+
diff --git a/.gitmodules b/.gitmodules
index 5e9799fd28..db27fd3ae5 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,9 +1,3 @@
-[submodule "third_party/gn"]
-        path = third_party/gn
-        url = https://gn.googlesource.com/gn
-[submodule "third_party/pybind"]
-	path = third_party/pybind
-	url = https://github.com/pybind/pybind11.git
 [submodule "language/bert/DeepLearningExamples"]
 	path = language/bert/DeepLearningExamples
 	url = https://github.com/NVIDIA/DeepLearningExamples.git
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100755
index 0000000000..45eec865bc
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools>=42", "wheel", "pybind11==2.11.1"]
+build-backend = "setuptools.build_meta:__legacy__"
diff --git a/third_party/pybind b/third_party/pybind
deleted file mode 160000
index 25abf7efba..0000000000
--- a/third_party/pybind
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 25abf7efba0b2990f5a6dfb0a31bc65c0f2f4d17

From 74c88dfe058c25fc22dc4c33ba35c9a77c030053 Mon Sep 17 00:00:00 2001
From: Saheli Bhattacharjee <saheli@krai.ai>
Date: Fri, 8 Dec 2023 15:01:28 +0000
Subject: [PATCH 011/305] Fix test score comparison for Server scenario,
 resolves #1529

---
 compliance/nvidia/TEST05/verify_performance.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/compliance/nvidia/TEST05/verify_performance.py b/compliance/nvidia/TEST05/verify_performance.py
index 8e24397969..763737f776 100644
--- a/compliance/nvidia/TEST05/verify_performance.py
+++ b/compliance/nvidia/TEST05/verify_performance.py
@@ -139,7 +139,7 @@ def main():
        (ref_mode == "MultiStream" and float(ref_score) <= 1600000):
         threshold = 0.20
         
-    if (ref_mode == "Offline" and float(test_score) > float(ref_score) * (1 - threshold)) or ("Stream" in ref_mode and float(test_score) < float(ref_score) * (1 + threshold)):
+    if (ref_mode in [ "Offline", "Server" ] and float(test_score) > float(ref_score) * (1 - threshold)) or ("Stream" in ref_mode and float(test_score) < float(ref_score) * (1 + threshold)):
         print("TEST PASS")
     else:
         print("TEST FAIL: Test score invalid")

From 4c231dca4ff0a983aaabfff011b86e6aeca33598 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 12 Dec 2023 16:32:03 +0000
Subject: [PATCH 012/305] Update mlperf.conf

Modifying the min queries constraint for the Offline scenario.
---
 mlperf.conf | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/mlperf.conf b/mlperf.conf
index 04a17ef836..6929e10b8b 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -57,10 +57,19 @@ gptj.Server.target_latency = 20000
 
 *.Offline.target_latency_percentile = 90
 *.Offline.min_duration = 600000
+
 # In Offline scenario, we always have one query. But LoadGen maps this to
-# min_sample_count internally in Offline scenario, so set this to 24576 since
-# the rule requires that Offline scenario run for at least 24576 samples.
-*.Offline.min_query_count = 24576
+# min_sample_count internally in Offline scenario. If the dataset size is larger 
+# than 24576 we limit the min_query_count to 24576 and otherwise we use 
+# the dataset size as the limit
+
+resnet50.Offline.min_query_count = 24576
+retinanet.Offline.min_query_count = 24576
+dlrm-v2.Offline.min_query_count = 24576
+bert.Offline.min_query_count = 10833
+gptj.Offline.min_query_count = 13368
+rnnt.Offline.min_query_count = 2513
+3d-unet.Offline.min_query_count = 43
 
 # These fields should be defined and overridden by user.conf.
 *.SingleStream.target_latency = 10

From 9382f573e1f042ad7800ed430e26749d9853cc3e Mon Sep 17 00:00:00 2001
From: interestingLSY <2659723130@qq.com>
Date: Wed, 13 Dec 2023 02:03:53 +0800
Subject: [PATCH 013/305] Bugfix and enhancement for ray-BERT SUT (#1531)

* fix: bugfix in ray_SUT.py. Now it yields correct result in accuracy test

* feat: allow to change batch_size in ray_SUT

* feat: use FP16 in ray_SUT to get an additional 3x speedup

* feat: add a reminder when no existing RAY cluster is detected
---
 language/bert/ray_SUT.py | 42 ++++++++++++++++++++++++++--------------
 1 file changed, 27 insertions(+), 15 deletions(-)

diff --git a/language/bert/ray_SUT.py b/language/bert/ray_SUT.py
index fd53dc07c0..d03e52f50d 100644
--- a/language/bert/ray_SUT.py
+++ b/language/bert/ray_SUT.py
@@ -34,6 +34,9 @@
 from ray.util.actor_pool import ActorPool
 
 
+# Adjustable Parameters
+BATCH_SIZE = 16    # Note. num_samples (called "test_query_count" in CM) must be a multiple of batch_size
+
 @ray.remote(num_cpus=1,num_gpus=1)
 class TorchPredictor:
     def __init__(self, config_json, model_file, batch_size):
@@ -68,7 +71,7 @@ def __init__(self, config_json, model_file, batch_size):
                 torch_tensorrt.Input(shape=[batch_size, 384], dtype=torch.int32),
                 torch_tensorrt.Input(shape=[batch_size, 384], dtype=torch.int32),
         ],
-        enabled_precisions= {torch.float32},
+        enabled_precisions= {torch.float32, torch.float16},
         workspace_size=2000000000,
         truncate_long_and_double=True)
 
@@ -111,10 +114,23 @@ def __init__(self, args):
         print("Finished constructing SUT.")
         self.qsl = get_squad_QSL(args.max_examples)
 
-        ray.init()
-        self.batch_size = 10
+        try:
+            ray.init(address="auto")
+        except:
+            print("WARN: Cannot connect to existing Ray cluster.")
+            print("We are going to start a new RAY cluster, but pay attention that")
+            print("the cluster contains only one node.")
+            print("If you want to use multiple nodes, please start the cluster manually via:")
+            print("\tOn the head node, run `ray start --head`")
+            print("\tOn other nodes, run `ray start --address=<head node IP>:6379`")
+            ray.init()
+            
+        self.batch_size = BATCH_SIZE
         resources = ray.cluster_resources()
         num_gpus = int(resources.get('GPU', 0))
+        
+        print(f"The cluster has {num_gpus} GPUs.")
+        
         self.actor_list = [TorchPredictor.remote(config_json, model_file, self.batch_size) for _ in range(num_gpus)]
         self.pool = ActorPool(self.actor_list)
 
@@ -134,6 +150,10 @@ def __init__(self, args):
         print("BERT_Ray_SUT construct complete")
 
     def issue_queries(self, query_samples):
+        if len(query_samples) % self.batch_size != 0:
+            print("ERROR: batch size must be a multiple of the number of samples")
+            sys.exit(1)
+            
         batch_samples = []
         i = 0
         while i < len(query_samples):
@@ -154,23 +174,15 @@ def issue_queries(self, query_samples):
         # print("samples len", len(batch_samples))
         batch_inference_results = list(self.pool.map_unordered(lambda a, v: a.forward.remote(v), batch_samples))
 
-        results = []
+        cur_query_index = 0
         for batch_inference_result in batch_inference_results:
             batch_inference_result = batch_inference_result["output"]
             for inference_result in batch_inference_result:
                 response_array = array.array("B", inference_result.tobytes())
                 bi = response_array.buffer_info()
-                results.append(bi)
-        
-        # print("results len", len(results))
-        
-        responses = []
-        for i in range(len(query_samples)):
-            # print(query_samples[i].index)
-            bi = results[i]
-            response = lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])
-            responses.append(response)
-        lg.QuerySamplesComplete(responses)
+                response = lg.QuerySampleResponse(query_samples[cur_query_index].id, bi[0], bi[1])
+                lg.QuerySamplesComplete([response])
+                cur_query_index += 1
 
     def flush_queries(self):
         pass

From fb839ed502f5d2d42d92f1af618c43f61a035a8d Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 12 Dec 2023 18:04:43 +0000
Subject: [PATCH 014/305] Update Submission_Guidelines.md (#1521)

* Update Submission_Guidelines.md

Fix the issue with the open division model specification

* Update Submission_Guidelines.md
---
 Submission_Guidelines.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Submission_Guidelines.md b/Submission_Guidelines.md
index acb59bed69..15818cc39a 100644
--- a/Submission_Guidelines.md
+++ b/Submission_Guidelines.md
@@ -1,6 +1,6 @@
 ## Submission Rules
 
-The MLPerf inference submission rules are spread between the [MLCommons policies](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc) and the [MLCommons Inference policies](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc) documents. Further, the rules related to power submissions are given [here](https://github.com/mlcommons/inference_policies/blob/master/power_measurement.adoc). The below points are a summary taken from the official rules to act as a checklist for the submitters.
+The MLPerf inference submission rules are spread between the [MLCommons policies](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc) and the [MLCommons Inference policies](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc) documents. Further, the rules related to power submissions are given [here](https://github.com/mlcommons/inference_policies/blob/master/power_measurement.adoc). The below points are a summary taken from the official rules to act as a checklist for the submitter - please see the original rules for any clarification.
 
 
 ## Hardware requirements
@@ -26,11 +26,11 @@ The previous MLPerf inference results are aggregated in [Collective Knowledge pl
 
 ### Expected time to do benchmark runs
 1. Closed submission under data enter needs offline and server scenario runs with a minimum of ten minutes needed for both. 
-2. Closed submission under edge category needs single stream, multi-stream (only for R50 and retinanet), and offline scenarios. A minimum of ten minutes are needed for each scenario. 
+2. Closed submission under the edge category needs single stream, multi-stream (only for R50 and retinanet), and offline scenarios. A minimum of ten minutes is needed for each scenario. 
 3. Further two (three for ResNet50) compliance runs are needed for closed division, each taking at least 10 minutes for each scenario.
 4. SingleStream, MultiStream and Server scenarios use early stopping and so can always finish around 10 minutes
 5. Offline scenario needs a minimum of 24756 input queries to be processed -- can take hours for low-performing models like 3dunet, LLMs, etc.
-6. Open division has no accuracy constraints, no compliance runs required, and can be submitted for any single scenario as well. There is no constraint on the model used also except that the model must be trained on the dataset used in the corresponding MLPerf inference task.
+6. Open division has no accuracy constraints, no required compliance runs, and can be submitted for any single scenario. There is no constraint on the model used except that the model accuracy must be validated on the accuracy dataset used in the corresponding MLPerf inference task [or must be preapproved](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc#412-relaxed-constraints-for-the-open-division).
 7. Power submission needs an extra ranging mode to determine the peak current usage and this often doubles the overall experiment run time.
 
 

From bfe664f19f901222dba8fba88e12ee8884a598c7 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 12 Dec 2023 22:33:53 +0000
Subject: [PATCH 015/305] Support GPU (Nvidia and AMD) run for RNNT reference
 implementation (#1525)

* Fixes #1402, make rnnt run with pytorch >=2.0

* improve rnnt
---
 speech_recognition/rnnt/pytorch/decoders.py   |  7 ++++---
 .../rnnt/pytorch/model_separable_rnnt.py      | 20 ++++++++++++-------
 speech_recognition/rnnt/pytorch/rnn.py        |  7 +++++--
 speech_recognition/rnnt/pytorch_SUT.py        |  3 +++
 4 files changed, 25 insertions(+), 12 deletions(-)

diff --git a/speech_recognition/rnnt/pytorch/decoders.py b/speech_recognition/rnnt/pytorch/decoders.py
index 7f6d405688..56745d7ecd 100644
--- a/speech_recognition/rnnt/pytorch/decoders.py
+++ b/speech_recognition/rnnt/pytorch/decoders.py
@@ -16,7 +16,7 @@
 from typing import List, Optional, Tuple
 
 import torch
-
+import os
 import torch.nn.functional as F
 from model_separable_rnnt import label_collate
 
@@ -42,6 +42,7 @@ def __init__(self, blank_index, model, max_symbols_per_step=30):
         self._model = model
         self._blank_id = blank_index
         self._SOS = -1
+        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
         assert max_symbols_per_step > 0
         self._max_symbols_per_step = max_symbols_per_step
 
@@ -64,7 +65,7 @@ def forward(self, x: torch.Tensor, out_lens: torch.Tensor) -> Tuple[torch.Tensor
 
         output: List[List[int]] = []
         for batch_idx in range(logits.size(0)):
-            inseq = logits[batch_idx, :, :].unsqueeze(1)
+            inseq = logits[batch_idx, :, :].unsqueeze(1).to(self.dev)
             # inseq: TxBxF
             logitlen = logits_lens[batch_idx]
             sentence = self._greedy_decode(inseq, logitlen)
@@ -76,7 +77,7 @@ def _greedy_decode(self, x: torch.Tensor, out_len: torch.Tensor) -> List[int]:
         hidden: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
         label: List[int] = []
         for time_idx in range(int(out_len.item())):
-            f = x[time_idx, :, :].unsqueeze(0)
+            f = x[time_idx, :, :].unsqueeze(0).to(self.dev)
 
             not_blank = True
             symbols_added = 0
diff --git a/speech_recognition/rnnt/pytorch/model_separable_rnnt.py b/speech_recognition/rnnt/pytorch/model_separable_rnnt.py
index 68a0ed6b5e..39e181358a 100644
--- a/speech_recognition/rnnt/pytorch/model_separable_rnnt.py
+++ b/speech_recognition/rnnt/pytorch/model_separable_rnnt.py
@@ -2,7 +2,7 @@
 
 import numpy as np
 import torch
-
+import os
 from rnn import rnn
 from rnn import StackTime
 
@@ -58,6 +58,8 @@ def __init__(self, in_features, encoder_n_hidden,
                  forget_gate_bias, norm, rnn_type, encoder_stack_time_factor,
                  dropout):
         super().__init__()
+        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+
         self.pre_rnn = rnn(
             rnn=rnn_type,
             input_size=in_features,
@@ -80,7 +82,7 @@ def __init__(self, in_features, encoder_n_hidden,
         )
 
     def forward(self, x_padded: torch.Tensor, x_lens: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x_padded, _ = self.pre_rnn(x_padded, None)
+        x_padded, _ = self.pre_rnn(x_padded.to(self.dev), None)
         x_padded, x_lens = self.stack_time(x_padded, x_lens)
         # (T, B, H)
         x_padded, _ = self.post_rnn(x_padded, None)
@@ -92,6 +94,8 @@ class Prediction(torch.nn.Module):
     def __init__(self, vocab_size, n_hidden, pred_rnn_layers,
                  forget_gate_bias, norm, rnn_type, dropout):
         super().__init__()
+        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+
         self.embed = torch.nn.Embedding(vocab_size - 1, n_hidden)
         self.n_hidden = n_hidden
         self.dec_rnn = rnn(
@@ -129,9 +133,9 @@ def forward(self, y: Optional[torch.Tensor],
             assert state is None
             # Hacky, no way to determine this right now!
             B = 1
-            y = torch.zeros((B, 1, self.n_hidden), dtype=torch.float32)
+            y = torch.zeros((B, 1, self.n_hidden), dtype=torch.float32).to(self.dev)
         else:
-            y = self.embed(y)
+            y = self.embed(y.to(self.dev)).to(self.dev)
 
         # if state is None:
         #    batch = y.size(0)
@@ -141,7 +145,7 @@ def forward(self, y: Optional[torch.Tensor],
         #        for _ in range(self.pred_rnn_layers)
         #    ]
 
-        y = y.transpose(0, 1)  # .contiguous()   # (U + 1, B, H)
+        y = y.transpose(0, 1).to(self.dev)  # .contiguous()   # (U + 1, B, H)
         g, hid = self.dec_rnn(y, state)
         g = g.transpose(0, 1)  # .contiguous()   # (B, U + 1, H)
         # del y, state
@@ -151,6 +155,8 @@ class Joint(torch.nn.Module):
     def __init__(self, vocab_size, pred_n_hidden, enc_n_hidden,
                  joint_n_hidden, dropout):
         super().__init__()
+        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+
         layers = [
             torch.nn.Linear(pred_n_hidden + enc_n_hidden, joint_n_hidden),
             torch.nn.ReLU(),
@@ -173,10 +179,10 @@ def forward(self, f: torch.Tensor, g: torch.Tensor):
         B, T, H = f.shape
         B, U_, H2 = g.shape
 
-        f = f.unsqueeze(dim=2)   # (B, T, 1, H)
+        f = f.unsqueeze(dim=2).to(self.dev)   # (B, T, 1, H)
         f = f.expand((B, T, U_, H))
 
-        g = g.unsqueeze(dim=1)   # (B, 1, U + 1, H)
+        g = g.unsqueeze(dim=1).to(self.dev)   # (B, 1, U + 1, H)
         g = g.expand((B, T, U_, H2))
 
         inp = torch.cat([f, g], dim=3)   # (B, T, U, 2H)
diff --git a/speech_recognition/rnnt/pytorch/rnn.py b/speech_recognition/rnnt/pytorch/rnn.py
index 39e2121dad..6bc825011a 100644
--- a/speech_recognition/rnnt/pytorch/rnn.py
+++ b/speech_recognition/rnnt/pytorch/rnn.py
@@ -14,7 +14,7 @@
 # limitations under the License.
 
 import torch
-
+import os
 from typing import Optional, Tuple
 
 
@@ -55,6 +55,7 @@ def __init__(self, input_size, hidden_size, num_layers, dropout, forget_gate_bia
             A `torch.nn.LSTM`.
         """
         super(LstmDrop, self).__init__()
+        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
 
         self.lstm = torch.nn.LSTM(
             input_size=input_size,
@@ -93,10 +94,12 @@ class StackTime(torch.nn.Module):
     def __init__(self, factor):
         super().__init__()
         self.factor = int(factor)
+        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+
 
     def forward(self, x, x_lens):
         # T, B, U
-        r = torch.transpose(x, 0, 1)
+        r = torch.transpose(x, 0, 1).to(self.dev)
         s = r.shape
         zeros = torch.zeros(
             s[0], (-s[1]) % self.factor, s[2], dtype=r.dtype, device=r.device)
diff --git a/speech_recognition/rnnt/pytorch_SUT.py b/speech_recognition/rnnt/pytorch_SUT.py
index da8b496192..429d742342 100644
--- a/speech_recognition/rnnt/pytorch_SUT.py
+++ b/speech_recognition/rnnt/pytorch_SUT.py
@@ -50,6 +50,8 @@ def __init__(self, config_toml, checkpoint_path, dataset_dir,
         rnnt_vocab = add_blank_label(dataset_vocab)
         featurizer_config = config['input_eval']
 
+        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
         self.qsl = AudioQSLInMemory(dataset_dir,
                                     manifest_filepath,
@@ -69,6 +71,7 @@ def __init__(self, config_toml, checkpoint_path, dataset_dir,
         )
         model.load_state_dict(load_and_migrate_checkpoint(checkpoint_path),
                               strict=True)
+        model.to(self.dev)
         model.eval()
         model.encoder = torch.jit.script(model.encoder)
         model.encoder = torch.jit._recursive.wrap_cpp_module(

From fea5b14a20261685d09c2937cde67637df0390b7 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 12 Dec 2023 23:11:13 +0000
Subject: [PATCH 016/305] Add loadgen over the network support for bert
 (onnxruntime and pytorch) (#1524)

* Added loadgen over the network support for bert onnxruntime

* Support pytorch bert LON

* Support pytorch bert LON

* Support pytorch bert LON

* Cleanup

* Fix offline scenario for LON

* unify the network QDLs

* unify the network QDLs

* Bug fix

* Make issue query non blocking

* Fix nargs for sut_server

* Cleanup

* Update README.md
---
 language/bert/README.md          |  29 +++++++
 language/bert/bert_QDL.py        | 128 +++++++++++++++++++++++++++++++
 language/bert/network_LON.py     |  45 +++++++++++
 language/bert/onnxruntime_SUT.py |  66 +++++++++++-----
 language/bert/pytorch_SUT.py     |  50 ++++++++----
 language/bert/run.py             | 117 ++++++++++++++++------------
 lon/network_SUT.py               |  67 ++++++++++++++++
 7 files changed, 420 insertions(+), 82 deletions(-)
 create mode 100644 language/bert/bert_QDL.py
 create mode 100644 language/bert/network_LON.py
 create mode 100644 lon/network_SUT.py

diff --git a/language/bert/README.md b/language/bert/README.md
index b5576e4ffb..1c5fefe3ae 100644
--- a/language/bert/README.md
+++ b/language/bert/README.md
@@ -33,6 +33,8 @@ Please run the following commands:
 - `make launch_docker`: launch docker container with an interaction session.
 - `python3 run.py --backend=[tf|pytorch|onnxruntime|tf_estimator] --scenario=[Offline|SingleStream|MultiStream|Server] [--accuracy] [--quantized]`: run the harness inside the docker container. Performance or Accuracy results will be printed in console.
 
+* ENV variable `CM_MAX_NUM_THREADS` can be used to control the number of parallel threads issuing queries.
+
 ## Details
 
 - SUT implementations are in [tf_SUT.py](tf_SUT.py), [tf_estimator_SUT.py](tf_estimator_SUT.py) and [pytorch_SUT.py](pytorch_SUT.py). QSL implementation is in [squad_QSL.py](squad_QSL.py).
@@ -43,6 +45,33 @@ Please run the following commands:
 - The script [tf_freeze_bert.py] freezes the TensorFlow model into pb file.
 - The script [bert_tf_to_pytorch.py] converts the TensorFlow model into the PyTorch `BertForQuestionAnswering` module in [HuggingFace Transformers](https://github.com/huggingface/transformers) and also exports the model to [ONNX](https://github.com/onnx/onnx) format.
 
+## Loadgen over the Network 
+
+The below CM command will launch the SUT server
+
+```
+cm run script --tags=generate-run-cmds,inference --model=bert-99 --backend=pytorch  \
+--rerun --adr.mlperf-implementation.version=custom \
+--adr.mlperf-implementation.tags=_repo.https://github.com/GATEOVerflow/inference \
+--mode=performance --device=cuda --quiet --test_query_count=1000 --network=sut
+```
+
+Once the SUT server is launched, the below command can be run on the loadgen node to do issue queries to the SUT nodes. In this command `-sut_servers` has just the localhost address - it can be changed to a comma-separated list of any hostname/IP in the network. 
+
+
+```
+cm run script --tags=generate-run-cmds,inference --model=bert-99 --backend=pytorch  --rerun \
+--adr.mlperf-implementation.version=custom \
+--adr.mlperf-implementation.tags=_repo.https://github.com/GATEOVerflow/inference \
+--mode=performance --device=cuda --quiet --test_query_count=1000  \
+--sut_servers,=http://localhost:8000 --network=lon
+```
+
+If you are not using CM, just add `--network=lon` along with your normal run command on the SUT side.
+On the loadgen node, add `--network=lon` option and `--sut_server <IP1> <IP2>` to the normal command to connect to SUT nodes at IP addresses IP1, IP2 etc. 
+
+Loadgen over the network works for `onnxruntime` and `pytorch` backends.
+
 ## License
 
 Apache License 2.0
diff --git a/language/bert/bert_QDL.py b/language/bert/bert_QDL.py
new file mode 100644
index 0000000000..87d93b4fcf
--- /dev/null
+++ b/language/bert/bert_QDL.py
@@ -0,0 +1,128 @@
+# Copyright 2023 MLCommons. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#   
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+
+import threading
+import requests
+import array
+import time
+import os
+import sys
+sys.path.insert(0, os.getcwd())
+import numpy as np
+
+import mlperf_loadgen as lg
+import squad_QSL
+from time import sleep
+
+class bert_QDL:
+    """QDL acting as a proxy to the SUT.
+    This QDL communicates with the SUT via HTTP.
+    It uses two endpoints to communicate with the SUT:
+    - /predict/ : Send a query to the SUT and get a response.
+    - /getname/ : Get the name of the SUT. Send a getname to the SUT and get a response.
+    """
+
+    def __init__(self, qsl: squad_QSL.SQuAD_v1_QSL, sut_server_addr: list):
+        """
+        Constructor for the QDL.
+        Args:
+            qsl: The QSL to use.
+            sut_server_addr: A list of addresses of the SUT.
+        """
+        self.qsl = qsl
+        self.quantized = False
+ 
+        # Construct QDL from the python binding
+        self.qdl = lg.ConstructQDL(
+            self.issue_query, self.flush_queries, self.client_get_name)
+        self.sut_server_addr = sut_server_addr
+        self.num_nodes = len(sut_server_addr)
+
+        # For round robin between the SUTs:
+        self.next_sut_id = 0
+        self.lock = threading.Lock()
+
+    def issue_query(self, query_samples):
+        """Process the query to send to the SUT"""
+        threading.Thread(target=self.process_query_async,
+                         args=[query_samples]).start()
+
+    def flush_queries(self):
+        """Flush the queries. Dummy implementation."""
+        pass
+
+    def process_query_async(self, query_samples):
+        """
+        This function is called by the Loadgen in a separate thread.
+        It is responsible for
+            1. Creating a query for the SUT, by reading the features from the QSL.
+            2. Sending the query to the SUT.
+            3. Waiting for the response from the SUT.
+            4. Deserializing the response.
+            5. Calling mlperf_loadgen.QuerySamplesComplete(query_samples, response)
+        Args:
+            query_samples: A list of QuerySample objects.
+        """
+
+        max_num_threads = int(os.environ.get('CM_MAX_NUM_THREADS', os.cpu_count()))
+
+        for i in range(len(query_samples)):
+            eval_features = self.qsl.get_features(query_samples[i].index)
+            encoded_eval_features = {
+                    "input_ids": eval_features.input_ids,
+                    "input_mask": eval_features.input_mask,
+                    "segment_ids": eval_features.segment_ids
+                    }
+            n = threading.active_count()
+            while n >= max_num_threads:
+                sleep(0.0001)
+                n = threading.active_count()
+            threading.Thread(target=self.client_predict_worker,
+                         args=[encoded_eval_features, query_samples[i].id]).start()
+
+
+    def get_sut_id_round_robin(self):
+        """Get the SUT id in round robin."""
+        with self.lock:
+            res = self.next_sut_id
+            self.next_sut_id = (self.next_sut_id + 1) % self.num_nodes
+        return res
+
+    def client_predict_worker(self, query, query_id):
+        """Serialize the query, send it to the SUT in round robin, and return the deserialized response."""
+        url = '{}/predict/'.format(self.sut_server_addr[self.get_sut_id_round_robin()])
+        responses = []
+        response = requests.post(url, json={'query': query})
+        output = response.json()['result']
+        output = np.array(output).astype(np.float32)
+        response_array = array.array("B", output.tobytes())
+        bi = response_array.buffer_info()
+
+        responses.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
+        lg.QuerySamplesComplete(responses)
+
+    def client_get_name(self):
+        """Get the name of the SUT from ALL the SUTS."""
+        if len(self.sut_server_addr) == 1:
+            return requests.post(f'{self.sut_server_addr[0]}/getname/').json()['name']
+    
+        sut_names = [requests.post(f'{addr}/getname/').json()['name'] for addr in self.sut_server_addr]
+        return "Multi-node SUT: " + ', '.join(sut_names)
+
+    def __del__(self):
+        lg.DestroyQDL(self.qdl)
+
+
diff --git a/language/bert/network_LON.py b/language/bert/network_LON.py
new file mode 100644
index 0000000000..fa6113e1ad
--- /dev/null
+++ b/language/bert/network_LON.py
@@ -0,0 +1,45 @@
+# Copyright 2023 MLCommons. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#   
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+
+import sys
+import os
+sys.path.insert(0, os.getcwd())
+from absl import app
+import squad_QSL
+import mlperf_loadgen as lg
+import bert_QDL
+
+def set_args(argv, g_settings, g_log_settings, g_audit_conf, g_sut_server, g_backend, g_total_count_override=None, g_perf_count_override=None):
+
+    global settings, log_settings, audit_conf, sut_server, total_count_override, perf_count_override, backend
+    sys.argv = sys.argv[0:1]
+    settings = g_settings
+    log_settings = g_log_settings
+    audit_conf = g_audit_conf
+    sut_server = g_sut_server
+    total_count_override = g_total_count_override
+    perf_count_override = g_perf_count_override
+    backend = g_backend
+
+def main(argv):
+        qsl = squad_QSL.get_squad_QSL(total_count_override, perf_count_override)
+        qdl = bert_QDL.bert_QDL(qsl, sut_server_addr=sut_server)
+
+        lg.StartTestWithLogSettings(qdl.qdl, qsl.qsl, settings, log_settings, audit_conf)
+
+
+if __name__ == "__main__":
+    app.run(main)
diff --git a/language/bert/onnxruntime_SUT.py b/language/bert/onnxruntime_SUT.py
index 2b45e68df3..a016c1f7b7 100644
--- a/language/bert/onnxruntime_SUT.py
+++ b/language/bert/onnxruntime_SUT.py
@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import threading
 import array
 import json
 import os
@@ -25,10 +26,12 @@
 import onnxruntime
 from transformers import BertConfig, BertForQuestionAnswering
 from squad_QSL import get_squad_QSL
+from time import sleep
 
 class BERT_ONNXRuntime_SUT():
     def __init__(self, args):
         self.profile = args.profile
+        self.network = args.network
         self.options = onnxruntime.SessionOptions()
         self.options.enable_profiling = args.profile
 
@@ -54,27 +57,52 @@ def __init__(self, args):
         self.qsl = get_squad_QSL(args.max_examples)
 
     def issue_queries(self, query_samples):
+        max_num_threads = int(os.environ.get('CM_MAX_NUM_THREADS', os.cpu_count()))
+
         for i in range(len(query_samples)):
             eval_features = self.qsl.get_features(query_samples[i].index)
-            if self.quantized:
-                fd = {
-                    "input_ids": np.array(eval_features.input_ids).astype(np.int64)[np.newaxis, :],
-                    "attention_mask": np.array(eval_features.input_mask).astype(np.int64)[np.newaxis, :],
-                    "token_type_ids": np.array(eval_features.segment_ids).astype(np.int64)[np.newaxis, :]
-                }
-            else:
-                fd = {
-                    "input_ids": np.array(eval_features.input_ids).astype(np.int64)[np.newaxis, :],
-                    "input_mask": np.array(eval_features.input_mask).astype(np.int64)[np.newaxis, :],
-                    "segment_ids": np.array(eval_features.segment_ids).astype(np.int64)[np.newaxis, :]
-                }
-            scores = self.sess.run([o.name for o in self.sess.get_outputs()], fd)
-            output = np.stack(scores, axis=-1)[0]
-
-            response_array = array.array("B", output.tobytes())
-            bi = response_array.buffer_info()
-            response = lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])
-            lg.QuerySamplesComplete([response])
+            n = threading.active_count()
+            while n >= max_num_threads:
+                #sleep(0.01)
+                n = threading.active_count()
+            threading.Thread(target=self.process_sample,
+                         args=[eval_features, query_samples[i].id]).start()
+
+    def process_sample(self, eval_features, query_id=None):
+
+        '''For Loadgen over the network'''
+        if self.network == "sut":
+            input_ids = eval_features['input_ids']
+            input_mask = eval_features['input_mask']
+            segment_ids = eval_features['segment_ids']
+        else:
+            input_ids = eval_features.input_ids
+            input_mask = eval_features.input_mask
+            segment_ids = eval_features.segment_ids
+
+        if self.quantized:
+            fd = {
+                "input_ids": np.array(input_ids).astype(np.int64)[np.newaxis, :],
+                "attention_mask": np.array(input_mask).astype(np.int64)[np.newaxis, :],
+                "token_type_ids": np.array(segment_ids).astype(np.int64)[np.newaxis, :]
+            }
+        else:
+            fd = {
+                "input_ids": np.array(input_ids).astype(np.int64)[np.newaxis, :],
+                "input_mask": np.array(input_mask).astype(np.int64)[np.newaxis, :],
+                "segment_ids": np.array(segment_ids).astype(np.int64)[np.newaxis, :]
+            }
+
+        scores = self.sess.run([o.name for o in self.sess.get_outputs()], fd)
+        output = np.stack(scores, axis=-1)[0]
+
+        if self.network == "sut":
+            return output.tolist()
+
+        response_array = array.array("B", output.tobytes())
+        bi = response_array.buffer_info()
+        response = lg.QuerySampleResponse(query_id, bi[0], bi[1])
+        lg.QuerySamplesComplete([response])
 
     def flush_queries(self):
         pass
diff --git a/language/bert/pytorch_SUT.py b/language/bert/pytorch_SUT.py
index 329fceabd6..81908ea6d3 100644
--- a/language/bert/pytorch_SUT.py
+++ b/language/bert/pytorch_SUT.py
@@ -48,6 +48,7 @@ def __init__(self, args):
             type_vocab_size=config_json["type_vocab_size"],
             vocab_size=config_json["vocab_size"])
 
+        self.network = args.network
         self.dev = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
         self.version = transformers.__version__
 
@@ -65,23 +66,40 @@ def __init__(self, args):
         self.qsl = get_squad_QSL(args.max_examples)
 
     def issue_queries(self, query_samples):
+        for i in range(len(query_samples)):
+            eval_features = self.qsl.get_features(query_samples[i].index)
+            self.process_sample(eval_features, query_samples[i].id)
+
+    def process_sample(self, sample_input, query_id = None):
+
+        if self.network == "sut":
+            input_ids = sample_input['input_ids']
+            input_mask = sample_input['input_mask']
+            segment_ids = sample_input['segment_ids']
+        else:
+            input_ids = sample_input.input_ids
+            input_mask = sample_input.input_mask
+            segment_ids = sample_input.segment_ids
+
         with torch.no_grad():
-            for i in range(len(query_samples)):
-                eval_features = self.qsl.get_features(query_samples[i].index)
-                model_output = self.model.forward(input_ids=torch.LongTensor(eval_features.input_ids).unsqueeze(0).to(self.dev),
-                    attention_mask=torch.LongTensor(eval_features.input_mask).unsqueeze(0).to(self.dev),
-                    token_type_ids=torch.LongTensor(eval_features.segment_ids).unsqueeze(0).to(self.dev))
-                if self.version >= '4.0.0':
-                    start_scores = model_output.start_logits
-                    end_scores = model_output.end_logits
-                else:
-                    start_scores, end_scores = model_output
-                output = torch.stack([start_scores, end_scores], axis=-1).squeeze(0).cpu().numpy()
-
-                response_array = array.array("B", output.tobytes())
-                bi = response_array.buffer_info()
-                response = lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])
-                lg.QuerySamplesComplete([response])
+            model_output = self.model.forward(input_ids=torch.LongTensor(input_ids).unsqueeze(0).to(self.dev),
+                attention_mask=torch.LongTensor(input_mask).unsqueeze(0).to(self.dev),
+                token_type_ids=torch.LongTensor(segment_ids).unsqueeze(0).to(self.dev))
+            if self.version >= '4.0.0':
+                start_scores = model_output.start_logits
+                end_scores = model_output.end_logits
+            else:
+                start_scores, end_scores = model_output
+            output = torch.stack([start_scores, end_scores], axis=-1).squeeze(0).cpu().numpy()
+
+            if self.network == "sut":
+                return output.tolist()
+    
+            response_array = array.array("B", output.tobytes())
+            bi = response_array.buffer_info()
+            response = lg.QuerySampleResponse(query_id, bi[0], bi[1])
+            lg.QuerySamplesComplete([response])
+
 
     def flush_queries(self):
         pass
diff --git a/language/bert/run.py b/language/bert/run.py
index bc82a73250..5cdaa56b17 100644
--- a/language/bert/run.py
+++ b/language/bert/run.py
@@ -21,14 +21,16 @@
 import os
 import sys
 sys.path.insert(0, os.getcwd())
-
+sys.path.insert(0, os.path.join(os.getcwd(), "..", "..", "lon"))
+from absl import app
+from absl import flags
 
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--backend", choices=["tf", "pytorch", "onnxruntime", "tf_estimator", "ray"], default="tf", help="Backend")
+            "--backend", choices=["tf", "pytorch", "onnxruntime", "tf_estimator", "ray"], default="tf", help="Backend")
     parser.add_argument("--scenario", choices=["SingleStream", "Offline",
-                        "Server", "MultiStream"], default="Offline", help="Scenario")
+                                               "Server", "MultiStream"], default="Offline", help="Scenario")
     parser.add_argument("--accuracy", action="store_true",
                         help="enable accuracy pass")
     parser.add_argument("--quantized", action="store_true",
@@ -36,53 +38,61 @@ def get_args():
     parser.add_argument("--profile", action="store_true",
                         help="enable profiling (only valid for onnxruntime backend)")
     parser.add_argument(
-        "--mlperf_conf", default="build/mlperf.conf", help="mlperf rules config")
+            "--mlperf_conf", default="build/mlperf.conf", help="mlperf rules config")
     parser.add_argument("--user_conf", default="user.conf",
                         help="user config for user LoadGen settings such as target QPS")
     parser.add_argument("--audit_conf", default="audit.conf",
                         help="audit config for LoadGen settings during compliance runs")
     parser.add_argument("--max_examples", type=int,
                         help="Maximum number of examples to consider (not limited by default)")
+    parser.add_argument("--network", choices=["sut","lon",None], default=None, help="Loadgen network mode")
+    parser.add_argument('--node', type=str, default="")
+    parser.add_argument('--port', type=int, default=8000)
+    parser.add_argument('--sut_server', nargs="*", default= ['http://localhost:8000'],
+                    help='Address of the server(s) under test.')
+
     args = parser.parse_args()
     return args
 
 
 scenario_map = {
-    "SingleStream": lg.TestScenario.SingleStream,
-    "Offline": lg.TestScenario.Offline,
-    "Server": lg.TestScenario.Server,
-    "MultiStream": lg.TestScenario.MultiStream
-}
-
+        "SingleStream": lg.TestScenario.SingleStream,
+        "Offline": lg.TestScenario.Offline,
+        "Server": lg.TestScenario.Server,
+        "MultiStream": lg.TestScenario.MultiStream
+        }
 
 def main():
     args = get_args()
 
-    if args.backend == "pytorch":
-        assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
-        assert not args.profile, "Profiling is only supported by onnxruntime backend!"
-        from pytorch_SUT import get_pytorch_sut
-        sut = get_pytorch_sut(args)
-    elif args.backend == "tf":
-        assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
-        assert not args.profile, "Profiling is only supported by onnxruntime backend!"
-        from tf_SUT import get_tf_sut
-        sut = get_tf_sut(args)
-    elif args.backend == "tf_estimator":
-        assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
-        assert not args.profile, "Profiling is only supported by onnxruntime backend!"
-        from tf_estimator_SUT import get_tf_estimator_sut
-        sut = get_tf_estimator_sut()
-    elif args.backend == "onnxruntime":
-        from onnxruntime_SUT import get_onnxruntime_sut
-        sut = get_onnxruntime_sut(args)
-    elif args.backend == "ray":
-        assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
-        assert not args.profile, "Profiling is only supported by onnxruntime backend!"
-        from ray_SUT import get_ray_sut
-        sut = get_ray_sut(args)
-    else:
-        raise ValueError("Unknown backend: {:}".format(args.backend))
+    sut = None
+
+    if not args.network or args.network == "sut":
+        if args.backend == "pytorch":
+            assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
+            assert not args.profile, "Profiling is only supported by onnxruntime backend!"
+            from pytorch_SUT import get_pytorch_sut
+            sut = get_pytorch_sut(args)
+        elif args.backend == "tf":
+            assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
+            assert not args.profile, "Profiling is only supported by onnxruntime backend!"
+            from tf_SUT import get_tf_sut
+            sut = get_tf_sut(args)
+        elif args.backend == "tf_estimator":
+            assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
+            assert not args.profile, "Profiling is only supported by onnxruntime backend!"
+            from tf_estimator_SUT import get_tf_estimator_sut
+            sut = get_tf_estimator_sut()
+        elif args.backend == "onnxruntime":
+            from onnxruntime_SUT import get_onnxruntime_sut
+            sut = get_onnxruntime_sut(args)
+        elif args.backend == "ray":
+            assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
+            assert not args.profile, "Profiling is only supported by onnxruntime backend!"
+            from ray_SUT import get_ray_sut
+            sut = get_ray_sut(args)
+        else:
+            raise ValueError("Unknown backend: {:}".format(args.backend))
 
     settings = lg.TestSettings()
     settings.scenario = scenario_map[args.scenario]
@@ -105,22 +115,35 @@ def main():
     log_settings.log_output = log_output_settings
     log_settings.enable_trace = True
 
-    print("Running LoadGen test...")
-    lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings, args.audit_conf)
-    if args.accuracy and not os.environ.get("SKIP_VERIFY_ACCURACY"):
-        cmd = "python3 {:}/accuracy-squad.py {}".format(
-            os.path.dirname(os.path.abspath(__file__)),
-            '--max_examples {}'.format(
-                args.max_examples) if args.max_examples else '')
-        subprocess.check_call(cmd, shell=True)
+    if args.network == "lon":
+        from network_LON import app, set_args, main as app_main
+        set_args(args, settings, log_settings, args.audit_conf, args.sut_server, args.backend, args.max_examples)
+        app.run(app_main)
+
+    elif args.network == "sut":
+        from network_SUT import app, node, set_backend
+        node = args.node
+        set_backend(sut)
+        app.run(debug=False, port=args.port)
+
+    else:
+        print("Running LoadGen test...")
+        lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings, args.audit_conf)
+        if args.accuracy and not os.environ.get("SKIP_VERIFY_ACCURACY"):
+            cmd = "python3 {:}/accuracy-squad.py {}".format(
+                os.path.dirname(os.path.abspath(__file__)),
+                '--max_examples {}'.format(
+                    args.max_examples) if args.max_examples else '')
+            subprocess.check_call(cmd, shell=True)
 
     print("Done!")
 
-    print("Destroying SUT...")
-    lg.DestroySUT(sut.sut)
+    if sut:
+        print("Destroying SUT...")
+        lg.DestroySUT(sut.sut)
 
-    print("Destroying QSL...")
-    lg.DestroyQSL(sut.qsl.qsl)
+        print("Destroying QSL...")
+        lg.DestroyQSL(sut.qsl.qsl)
 
 
 if __name__ == "__main__":
diff --git a/lon/network_SUT.py b/lon/network_SUT.py
new file mode 100644
index 0000000000..1935151495
--- /dev/null
+++ b/lon/network_SUT.py
@@ -0,0 +1,67 @@
+# Copyright 2023 MLCommons. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import array
+import json
+import os
+import sys
+sys.path.insert(0, os.getcwd())
+
+
+import argparse
+from flask import Flask, request, jsonify
+
+
+app = Flask(__name__)
+
+
+node = ""
+
+def set_backend(b):
+    global backend
+    backend = b
+
+def preprocess(query):
+    """[SUT Node] A dummy preprocess."""
+    # Here may come for example batching, tokenization, resizing, normalization, etc.
+    response = query
+    return response
+
+
+def dnn_model(query):
+    # Here may come for example a call to a dnn model such as resnet, bert, etc.
+    response = backend.process_sample(query)
+    return response
+
+
+def postprocess(query):
+    """[SUT Node] A dummy postprocess."""
+    # Here may come for example a postprocessing call, e.g., NMS, detokenization, etc.
+    response = query
+    return response
+
+
+@app.route('/predict/', methods=['POST'])
+def predict():
+    """Receives a query (e.g., a text) runs inference, and returns a prediction."""
+    query = request.get_json(force=True)['query']
+    result = postprocess(dnn_model(preprocess(query)))
+    return jsonify(result=result)
+
+
+@app.route('/getname/', methods=['POST', 'GET'])
+def getname():
+    """Returns the name of the SUT."""
+    return jsonify(name=f'Demo SUT (Network SUT) node' + (' ' + node) if node else '')

From 330a8f3b37060a026c29c9193178cfac8a8eb067 Mon Sep 17 00:00:00 2001
From: Thomas Atta-Fosu <thomas.atta-fosu@intel.com>
Date: Wed, 13 Dec 2023 07:32:19 -0800
Subject: [PATCH 017/305] Tattafosu/llama2-70b reference implementation (#1526)

* Add files

* Add data processor script

* Update README.md

* Update README.md

Fetch Loadgen changes for first token latencies

* Update Readme

Add data download and processing

* Reshape input tokens

* minimize debug logs

* Destroy sut

* clean up dataset

* Skip prompt during stream

* Fix post-processing for Accuracy run

* Add accuracy evaluation
---
 language/llama2-70b/README.md            | 131 +++++++++++
 language/llama2-70b/SUT.py               | 285 +++++++++++++++++++++++
 language/llama2-70b/dataset.py           |  87 +++++++
 language/llama2-70b/evaluate-accuracy.py | 102 ++++++++
 language/llama2-70b/main.py              |  96 ++++++++
 language/llama2-70b/mlperf.conf          |  74 ++++++
 language/llama2-70b/processorca.py       | 239 +++++++++++++++++++
 language/llama2-70b/run_offline.sh       |  12 +
 language/llama2-70b/run_server.sh        |  12 +
 language/llama2-70b/user.conf            |  11 +
 10 files changed, 1049 insertions(+)
 create mode 100644 language/llama2-70b/README.md
 create mode 100644 language/llama2-70b/SUT.py
 create mode 100644 language/llama2-70b/dataset.py
 create mode 100644 language/llama2-70b/evaluate-accuracy.py
 create mode 100644 language/llama2-70b/main.py
 create mode 100644 language/llama2-70b/mlperf.conf
 create mode 100644 language/llama2-70b/processorca.py
 create mode 100644 language/llama2-70b/run_offline.sh
 create mode 100644 language/llama2-70b/run_server.sh
 create mode 100644 language/llama2-70b/user.conf

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
new file mode 100644
index 0000000000..7827fd4b6b
--- /dev/null
+++ b/language/llama2-70b/README.md
@@ -0,0 +1,131 @@
+# Reference Implementation for Llama-v2-70B
+
+**Basic implementation for Llama-v2-70B. Few noteworthy items:**
+
++ Processing of Validation dataset is not finalized yet. Decision on input token lengths is pending
++ Streamer for communicating with loadgen has quite some overhead. This is only meant to provide functional implementation
+
+
+## Prepare environment
+```
+conda create -n llama2-70b python=3.9
+conda activate llama2-70b
+
+# Install packages
+conda install pybind11==2.10.4 -c conda-forge -y
+python -m pip install torch==2.2.0.dev20231006+cpu --index-url https://download.pytorch.org/whl/nightly/cpu
+pip install transformers==4.31.0 nltk==3.8.1 evaluate==0.4.0 absl-py==1.4.0 rouge-score==0.1.2 sentencepiece==0.1.99 accelerate==0.21.0
+
+export CUR_DIR=${PWD}
+cd <inference-repo-root>/loadgen
+
+# Need to fetch Pablo's changes
+git fetch origin pull/1523/head:llm-server
+git merge llm-server
+
+python -m pip install .
+```
+
+
+## Get Model
++ For now, MLCommons is not hosting the checkpoing, so you must first go to [llama2-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to huggingface (if you don't have account, you'd need to create one). **Please note your authentication credentials** as you may be required to provide them when cloninng below
++ Requires Git Large Files Storage
+```
+export CHECKPOINT_PATH=${PWD}/Llama-2-70b-chat-hf
+git lfs install
+git clone https://huggingface.co/meta-llama/Llama-2-70b-chat-hf ${CHECKPOINT_PATH}
+
+```
+
+## Get Dataset
+
+```
+# First get the `open-orca` parquet from huggingface
+export OPENORCA_DATASET=${PWD}/open-orca
+git clone https://huggingface.co/datasets/Open-Orca/OpenOrca ${OPENORCA_DATASET}
+
+export OPENORCA_PARQUET=${OPENORCA_DATASET}/1M-GPT4-Augmented.parquet
+EXPORT_DIR=${PWD}/processed-openorca
+export DATASET_PATH=${PWD}/processed-data.pkl
+
+# Process the dataset according the Taskforce's agreed criteria
+python3 processorca.py --dataset_pq_path=${OPENORCA_PARQUET} --model_dir=${CHECKPOINT_PATH} --seqlen_limit=2048 --export_dir=${EXPORT_DIR} --num_total_samples=24576
+
+mv ${EXPORT_DIR}/open_orca_gpt4_tokenized_llama.sampled_24576.pkl ${DATASET_PATH}
+```
+
+
+## Run Performance Benchmarks
+
+### Offline
+```
+python -u main.py --scenario Offline \
+                --model-path ${CHECKPOINT_PATH} \
+                --mlperf-conf mlperf.conf \
+                --user-conf user.conf \
+                --total-sample-count 24576 \
+                --device cpu \
+		--dataset-path ${DATASET_PATH} \
+                --output-log-dir offline-logs
+
+```
+
+### Server
+```
+python -u main.py --scenario Server \
+                --model-path ${CHECKPOINT_PATH} \
+                --mlperf-conf mlperf.conf \
+                --user-conf user.conf \
+                --total-sample-count 24576 \
+                --device cpu \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir server-logs
+```
+
+
+## Run Accuracy Benchmarks
+
+### Offline
+```
+OUTPUT_LOG_DIR=offline-accuracy-logs
+
+python -u main.py --scenario Offline \
+                --model-path ${CHECKPOINT_PATH} \
+                --accuracy \
+                --mlperf-conf mlperf.conf \
+                --user-conf user.conf \
+                --total-sample-count 24576 \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir ${OUTPUT_LOG_DIR} \
+                --device cpu
+
+
+ACCURACY_LOG_FILE=${OUTPUT_LOG_DIR}/mlperf_log_accuracy.json
+if [ -e ${ACCURACY_LOG_FILE} ]; then
+        python evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
+                --mlperf-accuracy-file ${ACCURACY_LOG_FILE} --dataset-file ${DATASET_PATH} --dtype int32
+fi
+```
+
+### Server
+```
+OUTPUT_LOG_DIR=server-accuracy-logs
+
+python -u main.py --scenario Server \
+                --model-path ${CHECKPOINT_PATH} \
+                --accuracy \
+                --mlperf-conf mlperf.conf \
+                --user-conf user.conf \
+                --total-sample-count 24576 \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir ${OUTPUT_LOG_DIR} \
+                --device cpu
+
+
+ACCURACY_LOG_FILE=${OUTPUT_LOG_DIR}/mlperf_log_accuracy.json
+if [ -e ${ACCURACY_LOG_FILE} ]; then
+        python evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
+                --mlperf-accuracy-file ${ACCURACY_LOG_FILE} --dataset-file ${DATASET_PATH} --dtype int32
+fi
+```
+
diff --git a/language/llama2-70b/SUT.py b/language/llama2-70b/SUT.py
new file mode 100644
index 0000000000..d29f789bf7
--- /dev/null
+++ b/language/llama2-70b/SUT.py
@@ -0,0 +1,285 @@
+import os
+import time
+import numpy as np
+import array
+import torch
+from torch.nn.functional import pad
+from torch.utils.data import DataLoader
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
+from transformers.generation.streamers import BaseStreamer
+
+import threading
+import queue
+
+import logging
+from typing import TYPE_CHECKING, Optional, List
+
+import mlperf_loadgen as lg
+from dataset import Dataset
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Llama-70B-SUT")
+
+gen_kwargs = {
+    "early_stopping": True,
+    "max_new_tokens": 1024,
+    "min_new_tokens": 1,
+    "num_beams": 1,
+    "do_sample": False
+}
+
+
+
+class FirstTokenStreamer(BaseStreamer):
+    """ Streams first tokens to a 'holder' """
+
+    def __init__(self, first_token, tokens_cache=[], is_first_token=True, response_ids=[] ):
+        """ Response ids added to 'sign' the first token"""
+
+        self.first_token = first_token # Queue for first token
+        self.is_first_token = is_first_token
+
+        # Cache for subsequent generated tokens
+        self.tokens_cache = tokens_cache
+
+        self.response_ids = response_ids
+
+        self.is_prompt = True # The first tokens sent to the streamer are actually the input prompts
+
+    def put(self, value):
+        """ Caches the tokens as they're generated. Assumes bs=1 """
+
+        # Prompts are streamed first so we need to skip the first time value that arrives
+        if self.is_prompt:
+            self.is_prompt = False
+            return
+
+        value = value.item()
+        if self.is_first_token:
+
+            # Add generated first token together with its query response_id to first tokens queue
+            self.first_token.put((value, self.response_ids[0]))
+
+            self.is_first_token = False
+            return
+
+        self.tokens_cache.append(value)
+
+
+    def end(self):
+        pass
+
+    def get_out_tokens(self):
+        return self.tokens_cache
+
+
+class SUT():
+    def __init__(self, model_path=None, dtype="bfloat16", device="cpu", total_sample_count=24576, dataset_path=None, workers=1):
+
+        self.model_path = model_path or "meta-llama/Llama-2-70b-chat-hf"
+        self.device = device
+
+        # dtype
+        if dtype == 'bfloat16':
+            self.amp_enabled = True
+            self.amp_dtype = torch.bfloat16
+        elif dtype == 'float16':
+            self.amp_enabled = True
+            self.amp_dtype = torch.float16
+        else:
+            self.amp_enabled = False
+            self.amp_dtype = torch.float32
+
+        if 'cuda' in self.device:
+            assert torch.cuda.is_available(), "torch gpu is not available, exiting..."
+
+        self.dataset_path = dataset_path
+        self.data_object = Dataset(self.model_path, dataset_path=self.dataset_path, total_sample_count=total_sample_count)
+        self.qsl = lg.ConstructQSL(self.data_object.total_sample_count, self.data_object.perf_count,
+                                   self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
+
+        self.load_model()
+
+        self.num_workers = workers
+        self.worker_threads = [None] * self.num_workers
+        self.query_queue = queue.Queue()
+
+
+    def start(self):
+        
+        # Create worker threads
+        for j in range(self.num_workers):
+            worker = threading.Thread(target=self.process_queries)
+            worker.start()
+            self.worker_threads[j] = worker
+
+    def stop(self):
+        for _ in range(self.num_workers):
+            self.query_queue.put(None)
+
+        for worker in self.worker_threads:
+            worker.join()
+
+
+    def process_queries(self):
+        """Processor of the queued queries. User may choose to add batching logic """
+
+        while True:
+            qitem = self.query_queue.get()
+            if qitem is None:
+                break
+
+            # TODO: If batching, call collator to batch the inputs here
+            input_ids_tensor = self.data_object.input_ids[qitem.index]
+            input_masks_tensor = self.data_object.attention_masks[qitem.index]
+            input_len = [self.data_object.input_lens[qitem.index]]
+
+
+            pred_output_tokens = self.model.generate(
+                                                input_ids=input_ids_tensor,
+                                                attention_mask=input_masks_tensor,
+                                                pad_token_id=self.tokenizer.pad_token_id,
+                                                **gen_kwargs
+                                                )
+
+            processed_output = self.data_object.postProcess(pred_output_tokens, input_len)
+
+            response_array = array.array("B", processed_output[0].tobytes())
+            bi = response_array.buffer_info()
+            response = [lg.QuerySampleResponse(
+                qitem.id, bi[0], bi[1])]
+            lg.QuerySamplesComplete(response)
+
+
+    def load_model(self):
+        self.model = LlamaForCausalLM.from_pretrained(
+            self.model_path,
+            device_map= "auto" if self.device=="cpu" else None,
+            low_cpu_mem_usage=True if self.device=="cpu" else False,
+            torch_dtype=self.amp_dtype
+        )
+
+        self.device = torch.device(self.device)
+        self.model.to(self.device)
+
+        self.model.eval()
+        self.model = self.model.to(memory_format=torch.channels_last)
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_path,
+            model_max_length=1024,
+            padding_side="left",
+            use_fast=False,)
+
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+    def get_sut(self):
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        return self.sut
+
+    def get_qsl(self):
+        return self.qsl
+
+
+    def predict(self,**kwargs):
+        raise NotImplementedError
+
+
+    def issue_queries(self, query_samples):
+        """ Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
+
+        list_prompts_tokens = []
+        list_prompts_attn_masks = []
+
+        for i in range(len(query_samples)):
+            self.query_queue.put(query_samples[i])
+
+
+    def flush_queries(self):
+        pass
+
+    def __del__(self):
+        pass
+
+
+class SUTServer(SUT):
+    def __init__(self, model_path=None, dtype="bfloat16", device="cpu", total_sample_count=24576, dataset_path=None, workers=1):
+
+        super().__init__(model_path=model_path, dtype=dtype, device=device, total_sample_count=total_sample_count, dataset_path=dataset_path, workers=workers)
+
+        self.first_token_queue = queue.Queue()
+
+    def start(self):
+
+        # Create worker threads
+        for j in range(self.num_workers):
+            worker = threading.Thread(target=self.process_queries)
+            worker.start()
+            self.worker_threads[j] = worker
+
+        # Create first token response thread
+        self.ft_response_thread = threading.Thread(target=self.process_first_tokens)
+        self.ft_response_thread.start()
+
+
+    def process_first_tokens(self):
+
+        while True:
+            first_token_item = self.first_token_queue.get()
+
+            if first_token_item is None:
+                log.info("Exiting First token response thread")
+                break
+
+            first_tokens, response_id = first_token_item
+
+            response_data = array.array("B", np.array(first_tokens, np.float32).tobytes())
+            bi = response_data.buffer_info()
+            response = [lg.QuerySampleResponse(response_id, bi[0], bi[1])]
+            lg.FirstTokenComplete(response)
+
+    def process_queries(self):
+        """Processor of the queued queries. User may choose to add batching logic """
+        while True:
+
+            qitem = self.query_queue.get()
+            if qitem is None:
+                break
+            
+            input_ids_tensor = self.data_object.input_ids[qitem.index]
+            input_masks_tensor = self.data_object.attention_masks[qitem.index]
+
+            #TODO: This PoC is super slow with significant overhead. Best to create a patch to `generate`
+            tokens_cache = []
+            tokens_streamer = FirstTokenStreamer(self.first_token_queue, tokens_cache=tokens_cache, is_first_token=True, response_ids=[qitem.id])
+
+            _ = self.model.generate(    input_ids=input_ids_tensor,
+                                        attention_mask=input_masks_tensor,
+                                        pad_token_id=self.tokenizer.pad_token_id,
+                                        streamer = tokens_streamer,
+                                        **gen_kwargs
+                                        )
+
+            output_tokens = tokens_streamer.get_out_tokens()
+
+            response_array = array.array("B", np.array(output_tokens, np.int32).tobytes())
+            bi = response_array.buffer_info()
+            response = [lg.QuerySampleResponse(
+                qitem.id, bi[0], bi[1])]
+            lg.QuerySamplesComplete(response)
+    
+
+    def issue_queries(self, query_samples):
+
+        self.query_queue.put(query_samples[0])
+
+
+    def stop(self):
+        for _ in range(self.num_workers):
+            self.query_queue.put(None)
+
+        for worker in self.worker_threads:
+            worker.join()
+
+        self.first_token_queue.put(None)
+        self.ft_response_thread.join()
diff --git a/language/llama2-70b/dataset.py b/language/llama2-70b/dataset.py
new file mode 100644
index 0000000000..9b54520509
--- /dev/null
+++ b/language/llama2-70b/dataset.py
@@ -0,0 +1,87 @@
+import os
+import time
+import numpy as np
+import torch
+from datasets import load_dataset, load_from_disk
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from torch.nn.functional import pad
+from torch.utils.data import DataLoader
+from typing import Optional, Dict, Sequence
+import io
+#import utils
+import copy
+
+import logging
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Llama-70B-Dataset")
+
+import random
+
+class Dataset():
+    def __init__(self, model_name=None, total_sample_count=24576, perf_count_override=None, dataset_path=None, device="cpu"):
+        self.model_name = model_name or "meta-llama/Llama-2-70b-chat-hf"
+        self.dataset_path = dataset_path
+        self.max_length = 1024
+        self.device = device
+
+        #self.total_sample_count = total_sample_count
+
+        self.load_tokenizer()
+        self.load_processed_dataset()
+
+        self.total_sample_count = min(len(self.input_ids), total_sample_count)
+        self.perf_count = perf_count_override or self.total_sample_count
+
+    def load_tokenizer(self):
+        """ Returns tokenizer """
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_name,
+            model_max_length=1024,
+            padding_side="left",
+            use_fast=False,)
+
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+    def load_processed_dataset(self):
+        if not os.path.isfile(self.dataset_path):
+            log.warn("Processed pickle file {} not found. Please check that the path is correct".format(self.dataset_path))
+
+        import pandas as pd
+        processed_data = pd.read_pickle(self.dataset_path)
+
+        input_tokens = processed_data['tok_input']
+
+        self.input_ids = []
+        self.input_lens = []
+        self.attention_masks = []
+
+        for ids in input_tokens:
+            input_ids = torch.tensor(ids, dtype=torch.int32).view(1,-1).to(self.device)
+            attn_mask = torch.ones_like(input_ids)
+            self.input_ids.append(input_ids)
+            self.attention_masks.append(attn_mask)
+            self.input_lens.append(input_ids.shape[-1])
+
+
+    def postProcess(self, out_tokens, input_seq_lens=None, query_id_list=None, sample_index_list=None):
+        """ Postprocesses output prediction """
+
+        #TODO: Create response object in postProcess(?)
+        preds = []
+        for i in range(out_tokens.shape[0]):
+            #pred = out_tokens[i].reshape(-1).cpu().numpy() # Slice up to original input length as below?
+
+            input_len = input_seq_lens[i] if input_seq_lens else 0
+            pred = out_tokens[i, input_len:].reshape(-1).cpu().numpy()
+            preds.append(pred)
+        
+        return preds
+
+    def LoadSamplesToRam(self, sample_list):
+        pass
+
+    def UnloadSamplesFromRam(self, sample_list):
+        pass
+
+    def __del__(self):
+        pass
diff --git a/language/llama2-70b/evaluate-accuracy.py b/language/llama2-70b/evaluate-accuracy.py
new file mode 100644
index 0000000000..cd0571eee1
--- /dev/null
+++ b/language/llama2-70b/evaluate-accuracy.py
@@ -0,0 +1,102 @@
+import argparse
+from transformers import AutoTokenizer
+import nltk
+import evaluate
+import numpy as np
+import json
+
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint-path", required=True,
+                        help="Path to Llama2-70b-hf-chat checkpoint")
+    parser.add_argument("--mlperf-accuracy-file", required=True,
+                        help="path to mlperf_log_accuracy.json")
+    parser.add_argument("--dataset-file", required=True,
+                        help="path to processed openorca validation set")
+    parser.add_argument("--verbose", action="store_true",
+                        help="verbose messages")
+    parser.add_argument("--dtype", default="int64",
+                        help="dtype of the accuracy log", choices=["int32", "int64", "float"])
+    args = parser.parse_args()
+    return args
+
+
+def get_groundtruth(processed_dataset_file):
+    import pandas as pd
+    data = pd.read_pickle(processed_dataset_file)
+    ground_truths = data['output']
+    return ground_truths
+
+def postprocess_text(preds, targets):
+    preds = [pred.strip() for pred in preds]
+    targets = [target.strip() for target in targets]
+
+    # rougeLSum expects newline after each sentence
+    preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
+    targets = ["\n".join(nltk.sent_tokenize(target)) for target in targets]
+
+    return preds, targets
+
+
+
+def main():
+
+    args = get_args()
+    dataset_path = args.dataset_file
+    checkpoint_path = args.checkpoint_path
+    metric = evaluate.load("rouge")
+    nltk.download('punkt')
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        checkpoint_path,
+        model_max_length=2048,
+        padding_side="left",
+        use_fast=False,)
+
+
+    targets = get_groundtruth(args.dataset_file)
+
+    target_required = []
+    preds_token_ids = []
+
+    eval_dtype = np.int64
+    if args.dtype == "int32":
+        eval_dtype = np.int32
+    elif args.dtype == "float":
+        eval_dtype = np.float32
+
+    with open(args.mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+    seen = set()
+    for pred in results:
+        qsl_idx = pred['qsl_idx']
+        if qsl_idx in seen:
+            continue
+
+        seen.add(qsl_idx)
+        target = targets[qsl_idx]
+        target_required.append(target)
+        pred = np.frombuffer( bytes.fromhex(pred['data']), eval_dtype)
+
+        preds_token_ids.append(pred)
+
+    preds_decoded_text = tokenizer.batch_decode(
+        preds_token_ids, skip_special_tokens=True)
+
+    preds, targets = postprocess_text(preds_decoded_text, target_required)
+
+    result = metric.compute(
+        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
+    result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
+    prediction_lens = [len(pred) for pred in preds]
+    result["gen_len"] = np.sum(prediction_lens)
+    result["gen_num"] = len(preds)
+    print("\nResults\n")
+    print(result)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/language/llama2-70b/main.py b/language/llama2-70b/main.py
new file mode 100644
index 0000000000..d039d04635
--- /dev/null
+++ b/language/llama2-70b/main.py
@@ -0,0 +1,96 @@
+import subprocess
+import mlperf_loadgen as lg
+import argparse
+import os
+import logging
+import sys
+from SUT import SUT, SUTServer
+
+sys.path.insert(0, os.getcwd())
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Llama-70B-MAIN")
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--scenario", type=str, choices=["Offline", "Server"], default="Offline", help="Scenario")
+    parser.add_argument("--model-path", type=str, default="meta-llama/Llama-2-70b-chat-hf", help="Model name")
+    parser.add_argument("--dataset-path", type=str, default=None, help="")
+    parser.add_argument("--accuracy", action="store_true", help="Run accuracy mode")
+    parser.add_argument("--dtype", type=str, default="float32", help="data type of the model, choose from float16, bfloat16 and float32")
+    parser.add_argument("--device", type=str,  choices=["cpu", "cuda:0"], default="cpu", help="device to use")
+    parser.add_argument("--audit-conf", type=str, default="audit.conf", help="audit config for LoadGen settings during compliance runs")
+    parser.add_argument("--mlperf-conf", type=str, default="mlperf.conf", help="mlperf rules config")
+    parser.add_argument("--user-conf", type=str, default="user.conf", help="user config for user LoadGen settings such as target QPS")
+    parser.add_argument("--total-sample-count", type=int, default=24576, help="Number of samples to use in benchmark.") # TODO: This interpretation of 'total-sample-count' is a little misleading. Fix it
+    parser.add_argument("--output-log-dir", type=str, default="output-logs", help="Where logs are saved")
+    parser.add_argument("--enable-log-trace", action="store_true", help="Enable log tracing. This file can become quite large")
+    parser.add_argument("--num-workers", type=int, default=1, help="Number of workers to process queries")
+
+    args = parser.parse_args()
+    return args
+
+
+scenario_map = {
+    "offline": lg.TestScenario.Offline,
+    "server": lg.TestScenario.Server,
+    }
+
+sut_map = {
+        "offline": SUT,
+        "server": SUTServer
+        }
+
+def main():
+    args = get_args()
+
+    settings = lg.TestSettings()
+    settings.scenario = scenario_map[args.scenario.lower()]
+    # Need to update the conf
+    settings.FromConfig(args.mlperf_conf, "llama-v2-70b", args.scenario)
+    settings.FromConfig(args.user_conf, "llama-v2-70b", args.scenario)
+
+    if args.accuracy:
+        settings.mode = lg.TestMode.AccuracyOnly
+        log.warning("Accuracy run will generate the accuracy logs, but the evaluation of the log is not completed yet")
+    else:
+        settings.mode = lg.TestMode.PerformanceOnly
+
+    os.makedirs(args.output_log_dir, exist_ok=True)
+    log_output_settings = lg.LogOutputSettings()
+    log_output_settings.outdir = args.output_log_dir
+    log_output_settings.copy_summary_to_stdout = True
+    log_settings = lg.LogSettings()
+    log_settings.log_output = log_output_settings
+    log_settings.enable_trace = args.enable_log_trace
+
+    sut_cls = sut_map[args.scenario.lower()]
+
+    sut = sut_cls(
+        model_path=args.model_path,
+        dtype=args.dtype,
+        dataset_path=args.dataset_path,
+        total_sample_count=args.total_sample_count,
+        device=args.device,
+    )
+
+    # Start sut before loadgen starts
+    sut.start()
+    lgSUT = lg.ConstructSUT(sut.issue_queries, sut.flush_queries)
+    log.info("Starting Benchmark run")
+    lg.StartTestWithLogSettings(lgSUT, sut.qsl, settings, log_settings, args.audit_conf)
+
+    # Stop sut after completion
+    sut.stop()
+
+    log.info("Run Completed!")
+
+    log.info("Destroying SUT...")
+    lg.DestroySUT(lgSUT)
+
+    log.info("Destroying QSL...")
+    lg.DestroyQSL(sut.qsl)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/language/llama2-70b/mlperf.conf b/language/llama2-70b/mlperf.conf
new file mode 100644
index 0000000000..3533fcabda
--- /dev/null
+++ b/language/llama2-70b/mlperf.conf
@@ -0,0 +1,74 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+
+# Set performance_sample_count for each model.
+# User can optionally set this to higher values in user.conf.
+resnet50.*.performance_sample_count_override = 1024
+ssd-mobilenet.*.performance_sample_count_override = 256
+retinanet.*.performance_sample_count_override = 64
+bert.*.performance_sample_count_override = 10833
+dlrm.*.performance_sample_count_override = 204800
+dlrm-v2.*.performance_sample_count_override = 204800
+rnnt.*.performance_sample_count_override = 2513
+# set to 0 to let entire sample set to be performance sample
+3d-unet.*.performance_sample_count_override = 0
+
+# Set seeds. The seeds will be distributed two weeks before the submission.
+*.*.qsl_rng_seed = 148687905518835231
+*.*.sample_index_rng_seed = 520418551913322573
+*.*.schedule_rng_seed = 811580660758947900
+# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission.
+*.*.test05_qsl_rng_seed = 793197339507417767
+*.*.test05_sample_index_rng_seed = 255610748586851044
+*.*.test05_schedule_rng_seed = 352213341366340113
+
+
+*.SingleStream.target_latency_percentile = 90
+*.SingleStream.min_duration = 600000
+#*.SingleStream.min_query_count = 1024
+
+*.MultiStream.target_latency_percentile = 99
+*.MultiStream.samples_per_query = 8
+*.MultiStream.min_duration = 600000
+#*.MultiStream.min_query_count = 270336
+*.MultiStream.min_query_count = 662
+retinanet.MultiStream.target_latency = 528
+
+# 3D-UNet uses equal issue mode
+3d-unet.*.sample_concatenate_permutation = 1
+
+# GPT-J uses equal issue mode for Single-Stream
+gptj.SingleStream.sample_concatenate_permutation = 1
+
+*.Server.target_latency = 10
+*.Server.target_latency_percentile = 99
+*.Server.target_duration = 0
+*.Server.min_duration = 600000
+#*.Server.min_query_count = 270336
+resnet50.Server.target_latency = 15
+retinanet.Server.target_latency = 100
+bert.Server.target_latency = 130
+dlrm.Server.target_latency = 60
+dlrm-v2.Server.target_latency = 60
+rnnt.Server.target_latency = 1000
+gptj.Server.target_latency = 20000
+
+# Falcon Server scenario requires two latency constraints
+llama-v2-70b.Server.target_latency = 2000
+llama-v2-70b.Server.ttft_latency = 2000
+llama-v2-70b.Server.tpot_latency = 200
+
+*.Offline.target_latency_percentile = 90
+*.Offline.min_duration = 600000
+# In Offline scenario, we always have one query. But LoadGen maps this to
+# min_sample_count internally in Offline scenario, so set this to 24576 since
+# the rule requires that Offline scenario run for at least 24576 samples.
+*.Offline.min_query_count = 24576
+
+# These fields should be defined and overridden by user.conf.
+*.SingleStream.target_latency = 10
+*.MultiStream.target_latency = 80
+*.Server.target_qps = 1.0
+*.Offline.target_qps = 1.0
diff --git a/language/llama2-70b/processorca.py b/language/llama2-70b/processorca.py
new file mode 100644
index 0000000000..0cc9c3f8ab
--- /dev/null
+++ b/language/llama2-70b/processorca.py
@@ -0,0 +1,239 @@
+# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import time
+import argparse
+import pandas as pd
+import numpy as np
+from dataclasses import dataclass
+from pathlib import Path
+from transformers import LlamaTokenizerFast
+from typing import Dict
+
+llama_prompt_system = "<s>[INST] <<SYS>>\n{}\n<</SYS>>\n\n{} [/INST]"
+llama_prompt_no_system = "<s>[INST] {} [/INST]"
+
+def format_llama_input(row):
+    if row['system_prompt']:
+        return llama_prompt_system.format(row['system_prompt'], row['question'])
+    else:
+        return llama_prompt_no_system.format(row['question'])
+
+def is_english(s):
+    for c in s:
+        allowed = c.isascii()
+        allowed = allowed or (c in ['’', '–', '“', '”', '—'])  # Taken from Habana: Unicode quotes and hyphens
+        if not allowed:
+            return False
+    return True
+
+
+@dataclass
+class Keyphrase:
+    col: str
+    phrase: str
+    startswith: bool = False
+    case: bool = False
+
+
+class OpenOrcaDatasetGenerator:
+    def __init__(self,
+                 pq_path: os.PathLike,
+                 model_dir: os.PathLike,
+                 io_token_limit: int):
+        self.pq_path = Path(pq_path)
+        self.model_dir = Path(model_dir)
+        self.io_token_limit = io_token_limit
+        self.keyphrases = []
+
+    def load_parquet(self) -> pd.DataFrame:
+        llama_tokenizer = LlamaTokenizerFast.from_pretrained(self.model_dir)
+
+        tik = time.time()
+        df = pd.read_parquet(self.pq_path)
+        print(f"Tokenizing input")
+        df.rename(columns={'response': 'output'}, inplace=True)
+        df['input'] = df.apply(format_llama_input, axis=1)
+        df['tok_input'] = df['input'].apply(lambda x: llama_tokenizer(x)['input_ids'] if isinstance(x, str) else [])
+        df['tok_output'] = df['output'].apply(lambda x: llama_tokenizer(x)['input_ids'] if isinstance(x, str) else [])
+        tok = time.time()
+        print(f"Loaded parquet and tokenized in {tok-tik} sec.")
+        return df
+
+    def filter_english(self, df: pd.DataFrame) -> pd.DataFrame:
+        df['input_english'] = df['input'].apply(is_english)
+        df['output_english'] = df['output'].apply(is_english)
+        df['all_english'] = df['input_english'] & df['output_english']
+
+        # Filter based on english tokens
+        df = df[df['all_english']].drop(["input_english", "output_english", "all_english"], axis=1)
+        return df.reset_index(drop=True)
+
+    def filter_seqlen_oob(self, df: pd.DataFrame) -> pd.DataFrame:
+        df['tok_input_length'] = df['tok_input'].apply(lambda x: len(x))
+        df['tok_output_length'] = df['tok_output'].apply(lambda x: len(x))
+
+        # Filter based on sequence length (2048, 2048)
+        df = df[df["tok_input_length"] < self.io_token_limit]
+        df = df[df["tok_output_length"] < self.io_token_limit]
+        return df.reset_index(drop=True)
+
+    def filter_short_expected_response(self, df: pd.DataFrame) -> pd.DataFrame:
+        # We have found that short expected responses (such as for yes/no and true/false questions, or multiple choice
+        # questions where the expected response is just the choice, i.e. (B)), disproportionately have lower Rouge1
+        # scores (< 0.02).
+
+        # Filter out 1 and 2 word expected responses. We've seen best results when this is filtered to >= 6, but it is
+        # hard to justify removing that many samples.
+        df = df[df["tok_output_length"] >= 3]
+        return df.reset_index(drop=True)
+
+    def filter_bad_prompts(self, df: pd.DataFrame, only_niv_t0: bool = True) -> pd.DataFrame:
+        # Some prompts underperform and cause very bad Rouge scores for a significant percentage of samples with these
+        # prompts. See Jupyter notebook for analysis.
+        # These generally only affect NIV and t0 and do not exist in flan or cot.
+        # Set 'only_niv_t0' to True to explicitly only remove these prompts from niv and t0 samples.
+        bad_prompts = ['',
+                       'You are an AI assistant that follows instruction extremely well. Help as much as you can.',
+                       'You are an AI assistant. Provide a detailed answer so user don’t need to search outside to understand the answer.',
+                       "You are an AI assistant. Provide a detailed answer so user don't need to search outside to understand the answer.",
+                       'User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer.',
+                       'Explain how you used the definition to come up with the answer.',
+                       ]
+        for prompt in bad_prompts:
+            criteria = (df.system_prompt == prompt)
+            if only_niv_t0:
+                criteria = criteria & ((df.origin == "niv") | (df.origin == "t0"))
+            df = df[~criteria]
+
+        return df.reset_index(drop=True)
+
+    def register_keyphrase(self, keyphrase: Keyphrase):
+        self.keyphrases.append(keyphrase)
+
+    def filter_keyphrases(self, df: pd.DataFrame) -> pd.DataFrame:
+        # Filter out registered keyphrases. This is unused for the final dataset as there are no registered keyphrases.
+        for kp in self.keyphrases:
+            if kp.startswith:
+                selector = df[kp.col].str.startswith(kp.phrase)
+            else:
+                selector = df[kp.col].str.contains(kp.phrase, case=kp.case)
+            df = df[~selector]
+        return df.reset_index(drop=True)
+
+    def set_origins(self, df: pd.DataFrame) -> pd.DataFrame:
+        get_sample_origin = lambda x: x.split(".")[0]
+        df['origin'] = df['id'].apply(get_sample_origin)
+        return df
+
+    def _per_origin_split(self, df: pd.DataFrame) -> Dict[str, pd.DataFrame]:
+        print(f"Unique sample origin datasets: {df.origin.unique()}")
+        dfs_by_origin = dict(tuple(df.groupby('origin')))
+        for origin, sub_df in dfs_by_origin.items():
+            sub_df = sub_df.reset_index(drop=True, inplace=True)
+        return dfs_by_origin
+
+    def _get_sampling(self, df, N, rng_seed: int = 1337):
+        _N = min(df.shape[0], N)
+        if _N < N:
+            raise RuntimeError(f"Not enough samples. Requires {N - _N} more.")
+        return df.sample(n=_N, random_state=rng_seed)
+
+    def sample(self, dfs_by_origin: Dict[str, pd.DataFrame], n_total, rng_seed: int = 1337) -> pd.DataFrame:
+        nways = len(dfs_by_origin)
+        assert n_total % nways == 0, f"Total number of samples ({n_total}) must be divisible by n_origins ({nways})"
+
+        split_size = n_total // nways
+        samplings = []
+        for origin, df in dfs_by_origin.items():
+            print(f"Sampling {split_size} from {origin}")
+            sample = self._get_sampling(df, split_size, rng_seed=rng_seed)
+            samplings.append(sample)
+
+        sampled_df = pd.concat(samplings)
+        sampled_df = sampled_df.reset_index(drop=True)
+        return sampled_df
+
+    def generate(self, export_dir: os.PathLike, n_samples: int = 24576, use_cached: bool = True):
+        export_dir = Path(export_dir)
+        if not export_dir.exists():
+            print(f"Creating {export_dir}")
+            export_dir.mkdir(parents=True)
+        if export_dir.is_file():
+            raise ValueError(f"Cannot export to file {export_dir}. Must be a directory.")
+
+        full_fpath = export_dir / f"open_orca_gpt4_tokenized_llama.full.pkl"
+        if full_fpath.exists() and use_cached:
+            df = pd.read_pickle(full_fpath)
+        else:
+            df = self.load_parquet()
+            df = self.set_origins(df)
+
+            # Apply filters
+            df = self.filter_english(df)
+            df = self.filter_seqlen_oob(df)
+            df = self.filter_short_expected_response(df)
+            df = self.filter_bad_prompts(df)
+            df = self.filter_keyphrases(df)
+            df.to_pickle(full_fpath)
+
+        dfs_by_origin = self._per_origin_split(df)
+
+        # Export base files
+        for origin, sub_df in dfs_by_origin.items():
+            print(f"Subset '{origin}' has {sub_df.shape[0]} samples")
+            origin_fpath = export_dir / f"open_orca_gpt4_tokenized_llama.{origin}.pkl"
+            if not origin_fpath.exists() or not use_cached:
+                sub_df.to_pickle(origin_fpath)
+
+        # Strategy:
+        # After some analysis, we found that OpenOrca's dataset has a skewed "origin-dataset" distribution:
+        # cot and niv have significantly fewer samples (71K and 58K) compared to flan and t0 (375K and 278K).
+        # cot has a higher rouge score from a 100k sampling (of the whole dataset) than the rest, while niv has lower.
+        # Sample from each dataset equally.
+        sampled_df = self.sample(dfs_by_origin, n_samples)
+        sampled_fpath = export_dir / f"open_orca_gpt4_tokenized_llama.sampled_{n_samples}.pkl"
+        sampled_df.to_pickle(sampled_fpath)
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset_pq_path', type=str,
+                        default='/raid/data/mlperf-llm/OpenOrca/1M-GPT4-Augmented.parquet',
+                        help="the path to the open_orca GPT4 parquet.")
+    parser.add_argument('--model_dir', type=str, default='/raid/data/mlperf-llm/Llama-2-70b-chat-hf')
+    parser.add_argument('--seqlen_limit', type=int, default=2048, help="Upper limit of the input/output sequence lengths")
+    parser.add_argument('--export_dir', type=str,
+                        default="/raid/data/mlperf-llm/OpenOrca/llama/filtered",
+                        help="Path to the output pkl file.")
+    parser.add_argument('--num_total_samples', type=int, default=24576, help="Number of samples to generate")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_arguments()
+    ds_gen = OpenOrcaDatasetGenerator(
+        pq_path=args.dataset_pq_path,
+        model_dir=args.model_dir,
+        io_token_limit=args.seqlen_limit,
+    )
+    ds_gen.generate(
+        export_dir=args.export_dir,
+        n_samples=args.num_total_samples,
+    )
+
+    # Sample command to run:
+    # python3 processorca.py --dataset_pq_path=/raid/data/mlperf-llm/OpenOrca/1M-GPT4-Augmented.parquet --model_dir=/raid/data/mlperf-llm/Llama-2-70b-chat-hf --seqlen_limit=2048 --export_dir=/raid/data/mlperf-llm/OpenOrca/llama/filtered --num_total_samples=24576
diff --git a/language/llama2-70b/run_offline.sh b/language/llama2-70b/run_offline.sh
new file mode 100644
index 0000000000..2ea28c4369
--- /dev/null
+++ b/language/llama2-70b/run_offline.sh
@@ -0,0 +1,12 @@
+
+
+CHECKPOINT_PATH="${CHECKPOINT_PATH:-meta-llama/Llama-2-70b-chat-hf}"
+DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
+
+python -u main.py --scenario Offline \
+		--model-path ${CHECKPOINT_PATH} \
+		--mlperf-conf mlperf.conf \
+		--user-conf user.conf \
+		--total-sample-count 24576 \
+		--dataset-path ${DATASET_PATH} \
+		--device cpu 2>&1 | tee server_log.log
diff --git a/language/llama2-70b/run_server.sh b/language/llama2-70b/run_server.sh
new file mode 100644
index 0000000000..a71bf371e7
--- /dev/null
+++ b/language/llama2-70b/run_server.sh
@@ -0,0 +1,12 @@
+
+
+CHECKPOINT_PATH="${CHECKPOINT_PATH:-meta-llama/Llama-2-70b-chat-hf}"
+DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
+
+python -u main.py --scenario Server \
+		--model-path ${CHECKPOINT_PATH} \
+		--mlperf-conf mlperf.conf \
+		--user-conf user.conf \
+		--total-sample-count 24576 \
+		--dataset-path ${DATASET_PATH} \
+		--device cpu 2>&1 | tee server_log.log
diff --git a/language/llama2-70b/user.conf b/language/llama2-70b/user.conf
new file mode 100644
index 0000000000..bb97c437a9
--- /dev/null
+++ b/language/llama2-70b/user.conf
@@ -0,0 +1,11 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+#
+*.Offline.min_duration = 600000
+*.Offline.min_query_count = 2000
+
+*.Server.target_qps = 0.5
+*.Server.min_duration = 120000
+*.Server.min_query_count = 100

From 55bebbfcb2a82ee64048674a2d9862ee0ab672eb Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 13 Dec 2023 14:31:30 -0500
Subject: [PATCH 018/305] Stable Diffusion Reference Implementation (#1519)

* Stable Diffusion Reference Implementation

* Add calibration images

* Remove refiner from pipeline

* Minor fixes for SD

* Add SD variables to mlperf.conf

* Rename calibration file

* Add accuracy coco script + minor accuracy fixes
---
 .../COCO-2014/coco_cal_captions_list.txt      |  500 ++
 mlperf.conf                                   |    2 +
 text_to_image/README.md                       |   82 +
 text_to_image/backend.py                      |   21 +
 text_to_image/backend_debug.py                |   28 +
 text_to_image/backend_pytorch.py              |  383 ++
 text_to_image/coco.py                         |  190 +
 .../coco2014/captions/captions_source.tsv     | 5001 +++++++++++++++++
 text_to_image/dataset.py                      |   67 +
 text_to_image/dockerfile.gpu                  |   21 +
 text_to_image/main.py                         |  486 ++
 text_to_image/requirements.txt                |    7 +
 text_to_image/tools/accuracy_coco.py          |  101 +
 text_to_image/tools/check_latents.py          |   22 +
 text_to_image/tools/coco.py                   |  150 +
 text_to_image/tools/coco_calibration.py       |  122 +
 .../tools/coco_generate_calibration.py        |   95 +
 .../tools/download-coco-2014-calibration.sh   |   15 +
 text_to_image/tools/download-coco-2014.sh     |   36 +
 text_to_image/tools/latent.py                 |   97 +
 text_to_image/tools/latents.npy               |  Bin 0 -> 262272 bytes
 text_to_image/tools/latents.pt                |  Bin 0 -> 263324 bytes
 text_to_image/user.conf                       |    4 +
 23 files changed, 7430 insertions(+)
 create mode 100644 calibration/COCO-2014/coco_cal_captions_list.txt
 create mode 100644 text_to_image/README.md
 create mode 100644 text_to_image/backend.py
 create mode 100644 text_to_image/backend_debug.py
 create mode 100644 text_to_image/backend_pytorch.py
 create mode 100644 text_to_image/coco.py
 create mode 100644 text_to_image/coco2014/captions/captions_source.tsv
 create mode 100644 text_to_image/dataset.py
 create mode 100644 text_to_image/dockerfile.gpu
 create mode 100644 text_to_image/main.py
 create mode 100644 text_to_image/requirements.txt
 create mode 100644 text_to_image/tools/accuracy_coco.py
 create mode 100644 text_to_image/tools/check_latents.py
 create mode 100644 text_to_image/tools/coco.py
 create mode 100644 text_to_image/tools/coco_calibration.py
 create mode 100644 text_to_image/tools/coco_generate_calibration.py
 create mode 100755 text_to_image/tools/download-coco-2014-calibration.sh
 create mode 100755 text_to_image/tools/download-coco-2014.sh
 create mode 100644 text_to_image/tools/latent.py
 create mode 100644 text_to_image/tools/latents.npy
 create mode 100644 text_to_image/tools/latents.pt
 create mode 100644 text_to_image/user.conf

diff --git a/calibration/COCO-2014/coco_cal_captions_list.txt b/calibration/COCO-2014/coco_cal_captions_list.txt
new file mode 100644
index 0000000000..42dd6ff2e1
--- /dev/null
+++ b/calibration/COCO-2014/coco_cal_captions_list.txt
@@ -0,0 +1,500 @@
+282
+655
+1919
+2449
+3994
+4219
+5933
+7631
+8165
+8560
+11546
+12209
+12214
+12310
+12824
+16062
+16859
+16963
+19857
+20118
+22609
+22759
+25891
+34997
+35274
+37691
+38242
+38276
+43640
+43812
+46608
+51634
+54503
+57560
+61381
+61482
+64888
+68709
+72435
+74694
+75279
+75409
+75664
+76297
+76700
+77959
+78032
+79803
+82363
+84527
+90667
+91685
+96724
+98481
+99060
+99425
+100234
+100859
+106223
+106965
+109094
+109822
+110271
+111258
+113365
+116179
+118646
+119116
+120604
+122972
+123104
+123235
+123407
+127408
+129239
+130755
+132461
+132928
+135203
+135647
+135956
+137747
+137832
+139916
+145736
+147243
+147892
+153816
+155022
+155047
+158009
+158557
+159299
+164206
+165028
+165957
+165985
+167864
+170830
+171117
+176908
+180042
+185826
+185932
+189915
+194228
+195160
+195713
+197848
+199178
+199200
+199908
+199921
+202501
+204193
+209966
+214011
+214229
+214586
+214867
+217065
+217295
+217484
+220690
+220722
+223208
+225982
+226008
+228286
+229410
+231454
+231675
+232564
+233822
+238605
+238703
+239720
+241728
+242835
+248687
+254060
+255054
+257247
+260915
+262962
+263381
+264082
+266208
+266360
+270221
+271934
+274449
+277740
+278168
+279180
+284113
+284902
+285577
+286143
+287011
+288123
+288148
+288191
+289544
+289820
+289895
+297749
+299866
+302385
+306804
+307451
+308370
+311176
+311859
+312484
+313043
+315973
+317878
+321959
+322372
+324354
+324846
+326131
+327858
+328638
+330754
+331208
+331351
+331947
+332345
+332615
+332621
+339395
+344215
+345425
+345429
+345857
+346481
+348287
+351866
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+353202
+353718
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+367550
+371196
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+372654
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+380251
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+391654
+393623
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+413745
+414198
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+613388
+613400
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+614269
+615842
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+625106
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+628498
+628880
+631688
+632659
+635606
+637587
+638626
+642074
+644960
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+651665
+651985
+654081
+656276
+657434
+663501
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+667323
+667340
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+677498
+677552
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+686937
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+688866
+691418
+694109
+695364
+696603
+697984
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+708905
+709307
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+711279
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+721026
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+731931
+732747
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+734384
+734434
+736607
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+740521
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+747307
+748541
+749382
+752604
+752650
+754008
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+768541
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+769988
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+808924
+809930
+811475
+812529
+813097
+813327
+829358
\ No newline at end of file
diff --git a/mlperf.conf b/mlperf.conf
index 6929e10b8b..58611b2b31 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -12,6 +12,7 @@ bert.*.performance_sample_count_override = 10833
 dlrm.*.performance_sample_count_override = 204800
 dlrm-v2.*.performance_sample_count_override = 204800
 rnnt.*.performance_sample_count_override = 2513
+stable-diffusion-xl.*.performance_sample_count_override = 5000
 # set to 0 to let entire sample set to be performance sample
 3d-unet.*.performance_sample_count_override = 0
 
@@ -54,6 +55,7 @@ dlrm.Server.target_latency = 60
 dlrm-v2.Server.target_latency = 60
 rnnt.Server.target_latency = 1000
 gptj.Server.target_latency = 20000
+stable-diffusion-xl.Server.target_latency = 20000
 
 *.Offline.target_latency_percentile = 90
 *.Offline.min_duration = 600000
diff --git a/text_to_image/README.md b/text_to_image/README.md
new file mode 100644
index 0000000000..fd20ce3d4c
--- /dev/null
+++ b/text_to_image/README.md
@@ -0,0 +1,82 @@
+# MLPerf™ Inference Benchmarks for Text to Image
+
+This is the reference implementation for MLPerf Inference text to image
+
+## Supported Models
+
+| model | accuracy | dataset | model link | model source | precision | notes |
+| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
+| StableDiffusion | Torch | - | Coco2014 | - | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp16 | NCHW||
+
+## Dataset
+
+| Data | Description |
+| ---- | ---- | 
+| Coco-2014 | We use a subset of 5000 images and captions of the coco 2014 validation dataset, so that there is exaclty one caption per image. The model takes as input the caption of the image and generates an image from it. The original images and the generated images are used to compute FID score. The caption and the generated images are used to compute the CLIP score. We provide a [script](tools/coco.py) to automatically download the dataset |
+| Coco-2014 (calibration) | We use a subset of 100 images and captions of the coco 2014 training dataset, so that there is exaclty one caption per image. The subset was generated using this [script](tools/coco_generate_calibration.py). We provide the [caption ids](../calibration/COCO-2014/coco_cal_images_list.txt) and a [script](tools/coco_calibration.py) to download them. |
+
+
+## Setup
+Set the following helper variables
+```bash
+export ROOT=$PWD/inference
+export SD_FOLDER=$PWD/inference/text_to_image
+export LOADGEN_FOLDER=$PWD/inference/loadgen
+```
+### Clone the repository
+**TEMPORARLY:**
+```bash
+git clone --recurse-submodules https://github.com/pgmpablo157321/inference.git --branch stable_diffusion_reference --depth 1
+```
+**KEEP FOR LATER:**
+```bash
+git clone --recurse-submodules https://github.com/mlcommmons/inference.git --depth 1
+```
+Finally copy the `mlperf.conf` file to the stable diffusion folder
+```bash
+cp $ROOT/mlperf.conf $SD_FOLDER
+```
+
+### Install requirements (only for running without using docker)
+Install requirements:
+```bash
+cd SD_FOLDER
+pip install -r requirements.txt
+```
+Install loadgen:
+```bash
+cd LOADGEN_FOLDER
+CFLAGS="-std=c++14" python setup.py install
+```
+
+### Download dataset
+```bash
+cd $SD_FOLDER/tools
+./download-coco-2014.sh -n <number_of_workers>
+```
+For debugging you can download only a part of all the images in the dataset
+```bash
+cd $SD_FOLDER/tools
+./download-coco-2014.sh -m <max_number_of_images>
+```
+If the file [captions.tsv](coco2014/captions/captions.tsv) can be found in the script, it will be used to download the target dataset subset, otherwise it will be generated. We recommend you to have this file for consistency.
+
+### Run the benchmark
+#### Local run
+```bash
+python3 main.py --dataset "coco-1024" --dataset-path coco2014 --profile stable-diffusion-xl-pytorch [--model-path <TODO: provide model weights>] [--dtype <fp32, fp16 or bf16>] [--device <cuda or cpu>] [--time 600] [--scenario SingleStream]
+```
+#### Run using docker
+```bash
+cd $SD_FOLDER
+# Build the container
+docker build . -t sd_mlperf_inference
+#Run the container
+docker run --rm -it --gpus=all -v $SD_FOLDER:/workspace sd_mlperf_inference bash
+```
+Inside the container run the following:
+```bash
+python3 main.py --dataset "coco-1024" --dataset-path coco2014 --profile stable-diffusion-xl-pytorch [--model-path <TODO: provide model weights>] [--dtype <fp32, fp16 or bf16>] [--device <cuda or cpu>] [--time 600] [--scenario SingleStream]
+```
+
+
diff --git a/text_to_image/backend.py b/text_to_image/backend.py
new file mode 100644
index 0000000000..58e4f9fa6c
--- /dev/null
+++ b/text_to_image/backend.py
@@ -0,0 +1,21 @@
+"""
+abstract backend class
+"""
+
+
+class Backend:
+    def __init__(self):
+        self.inputs = []
+        self.outputs = []
+
+    def version(self):
+        raise NotImplementedError("Backend:version")
+
+    def name(self):
+        raise NotImplementedError("Backend:name")
+
+    def load(self, model_path, inputs=None, outputs=None):
+        raise NotImplementedError("Backend:load")
+
+    def predict(self, feed):
+        raise NotImplementedError("Backend:predict")
diff --git a/text_to_image/backend_debug.py b/text_to_image/backend_debug.py
new file mode 100644
index 0000000000..3041a563ee
--- /dev/null
+++ b/text_to_image/backend_debug.py
@@ -0,0 +1,28 @@
+import torch
+import backend
+
+
+class BackendDebug(backend.Backend):
+    def __init__(self, image_size=[3, 1024, 1024], **kwargs):
+        super(BackendDebug, self).__init__()
+        self.image_size = image_size
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "debug-SUT"
+
+    def image_format(self):
+        return "NCHW"
+
+    def load(self):
+        return self
+
+    def predict(self, prompts):
+        images = []
+        with torch.no_grad():
+            for prompt in prompts:
+                image = torch.randn(self.image_size)
+                images.append(image)
+        return images
diff --git a/text_to_image/backend_pytorch.py b/text_to_image/backend_pytorch.py
new file mode 100644
index 0000000000..eccbf246e6
--- /dev/null
+++ b/text_to_image/backend_pytorch.py
@@ -0,0 +1,383 @@
+from typing import Optional, List, Union
+import os
+import torch
+import logging
+import backend
+from diffusers import StableDiffusionXLPipeline
+from diffusers import EulerDiscreteScheduler
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("backend-pytorch")
+
+
+class BackendPytorch(backend.Backend):
+    def __init__(
+        self,
+        model_path=None,
+        model_id="xl",
+        guidance=8,
+        steps=20,
+        device="cuda",
+        precision="fp32",
+        negative_prompt="normal quality, low quality, worst quality, low res, blurry, nsfw, nude",
+    ):
+        super(BackendPytorch, self).__init__()
+        self.model_path = model_path
+        if model_id == "xl":
+            self.model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+        else:
+            raise ValueError(f"{model_id} is not a valid model id")
+
+        self.device = device if torch.cuda.is_available() else "cpu"
+        if precision == "fp16":
+            self.dtype = torch.float16
+        elif precision == "bf16":
+            self.dtype = torch.bfloat16
+        else:
+            self.dtype = torch.float32
+
+        if torch.cuda.is_available():
+            self.local_rank = 0
+            self.world_size = 1
+
+        self.guidance = guidance
+        self.steps = steps
+        self.negative_prompt = negative_prompt
+        self.max_length_neg_prompt = 77
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "pytorch-SUT"
+
+    def image_format(self):
+        return "NCHW"
+
+    def load(self):
+        if self.model_path is None:
+            log.warning(
+                "Model path not provided, running with default hugging face weights\n"
+                "This may not be valid for official submissions"
+            )
+            self.scheduler = EulerDiscreteScheduler.from_pretrained(
+                self.model_id, subfolder="scheduler"
+            )
+            self.pipe = StableDiffusionXLPipeline.from_pretrained(
+                self.model_id,
+                scheduler=self.scheduler,
+                safety_checker=None,
+                add_watermarker=False,
+                variant="fp16" if (self.dtype == torch.float16) else None,
+                torch_dtype=self.dtype,
+            )
+            # self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)
+        else:
+            self.scheduler = EulerDiscreteScheduler.from_pretrained(
+                os.path.join(self.model_path, "checkpoint_scheduler"),
+                subfolder="scheduler",
+            )
+            self.pipe = StableDiffusionXLPipeline.from_pretrained(
+                os.path.join(self.model_path, "checkpoint_pipe"),
+                scheduler=self.scheduler,
+                safety_checker=None,
+                add_watermarker=False,
+                variant="fp16" if (self.dtype == torch.float16) else None,
+                torch_dtype=self.dtype,
+            )
+            # self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)
+
+        self.pipe.to(self.device)
+        #self.pipe.set_progress_bar_config(disable=True)
+
+        self.negative_prompt_tokens = self.pipe.tokenizer(
+            self.convert_prompt(self.negative_prompt, self.pipe.tokenizer),
+            padding="max_length",
+            max_length=self.max_length_neg_prompt,
+            truncation=True,
+            return_tensors="pt",
+        )
+        self.negative_prompt_tokens_2 = self.pipe.tokenizer_2(
+            self.convert_prompt(self.negative_prompt, self.pipe.tokenizer_2),
+            padding="max_length",
+            max_length=self.max_length_neg_prompt,
+            truncation=True,
+            return_tensors="pt",
+        )
+        return self
+
+    def convert_prompt(self, prompt, tokenizer):
+        tokens = tokenizer.tokenize(prompt)
+        unique_tokens = set(tokens)
+        for token in unique_tokens:
+            if token in tokenizer.added_tokens_encoder:
+                replacement = token
+                i = 1
+                while f"{token}_{i}" in tokenizer.added_tokens_encoder:
+                    replacement += f" {token}_{i}"
+                    i += 1
+
+                prompt = prompt.replace(token, replacement)
+
+        return prompt
+
+    def encode_tokens(
+        self,
+        pipe: StableDiffusionXLPipeline,
+        text_input: torch.Tensor,
+        text_input_2: Optional[torch.Tensor] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[torch.Tensor] = None,
+        negative_prompt_2: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the input tokens into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            lora_scale (`float`, *optional*):
+                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        """
+        device = device or pipe._execution_device
+        batch_size = text_input.input_ids.shape[0]
+
+        # Define tokenizers and text encoders
+        tokenizers = (
+            [pipe.tokenizer, pipe.tokenizer_2]
+            if pipe.tokenizer is not None
+            else [pipe.tokenizer_2]
+        )
+        text_encoders = (
+            [pipe.text_encoder, pipe.text_encoder_2]
+            if pipe.text_encoder is not None
+            else [pipe.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            text_input_2 = text_input_2 or text_input
+
+            # textual inversion: procecss multi-vector tokens if necessary
+            prompt_embeds_list = []
+            text_inputs_list = [text_input, text_input_2]
+            for text_inputs, tokenizer, text_encoder in zip(
+                text_inputs_list, tokenizers, text_encoders
+            ):
+                text_input_ids = text_inputs.input_ids
+                prompt_embeds = text_encoder(
+                    text_input_ids.to(device), output_hidden_states=True
+                )
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                if clip_skip is None:
+                    prompt_embeds = prompt_embeds.hidden_states[-2]
+                else:
+                    # "2" because SDXL always indexes from the penultimate layer.
+                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+
+                prompt_embeds_list.append(prompt_embeds)
+
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = (
+            negative_prompt is None and pipe.config.force_zeros_for_empty_prompt
+        )
+        if (
+            do_classifier_free_guidance
+            and negative_prompt_embeds is None
+            and zero_out_negative_prompt
+        ):
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # normalize str to list
+            negative_prompt_inputs = (
+                negative_prompt.input_ids.repeat(batch_size, 1)
+                if (len(negative_prompt.input_ids.shape) == 1)
+                else negative_prompt.input_ids
+            )
+            negative_prompt_2_inputs = (
+                negative_prompt_2.input_ids.repeat(batch_size, 1)
+                if (len(negative_prompt_2.input_ids.shape) == 1)
+                else negative_prompt_2.input_ids
+            )
+
+            uncond_inputs = [negative_prompt_inputs, negative_prompt_2_inputs]
+
+            negative_prompt_embeds_list = []
+            for uncond_input, tokenizer, text_encoder in zip(
+                uncond_inputs, tokenizers, text_encoders
+            ):
+                negative_prompt_embeds = text_encoder(
+                    uncond_input.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        if pipe.text_encoder_2 is not None:
+            prompt_embeds = prompt_embeds.to(
+                dtype=pipe.text_encoder_2.dtype, device=device
+            )
+        else:
+            prompt_embeds = prompt_embeds.to(dtype=pipe.unet.dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(
+            bs_embed * num_images_per_prompt, seq_len, -1
+        )
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            if pipe.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(
+                    dtype=pipe.text_encoder_2.dtype, device=device
+                )
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(
+                    dtype=pipe.unet.dtype, device=device
+                )
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(
+                1, num_images_per_prompt, 1
+            )
+            negative_prompt_embeds = negative_prompt_embeds.view(
+                batch_size * num_images_per_prompt, seq_len, -1
+            )
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(
+            1, num_images_per_prompt
+        ).view(bs_embed * num_images_per_prompt, -1)
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(
+                1, num_images_per_prompt
+            ).view(bs_embed * num_images_per_prompt, -1)
+        return (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        )
+
+    def predict(self, inputs):
+        images = []
+        with torch.no_grad():
+            for prompt in inputs:
+                assert isinstance(prompt, dict)
+                text_input = prompt["input_tokens"]
+                text_input_2 = prompt["input_tokens_2"]
+                latents_input = prompt["latents"].to(self.dtype)
+                (
+                    prompt_embeds,
+                    negative_prompt_embeds,
+                    pooled_prompt_embeds,
+                    negative_pooled_prompt_embeds,
+                ) = self.encode_tokens(
+                    self.pipe,
+                    text_input,
+                    text_input_2,
+                    negative_prompt=self.negative_prompt_tokens,
+                    negative_prompt_2=self.negative_prompt_tokens_2,
+                )
+                image = self.pipe(
+                    prompt_embeds=prompt_embeds,
+                    negative_prompt_embeds=negative_prompt_embeds,
+                    pooled_prompt_embeds=pooled_prompt_embeds,
+                    negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+                    guidance_scale=self.guidance,
+                    num_inference_steps=self.steps,
+                    output_type="pt",
+                    latents=latents_input,
+                ).images[0]
+                images.append(image)
+        return images
+
+
+if __name__ == "__main__":
+    from coco import Coco
+
+    backend = BackendPytorch(precision="fp16")
+    backend = backend.load()
+    dataset = Coco(
+        "coco2014/",
+        name="coco-1024",
+        pipe_tokenizer=backend.pipe.tokenizer,
+        pipe_tokenizer_2=backend.pipe.tokenizer_2,
+    )
+    dataset.load_query_samples([0, 1, 2, 3, 4])
+    items, _ = dataset.get_samples([0, 1, 2, 3, 4])
+    captions = dataset.captions_df["caption"].iloc[[0, 1, 2, 3, 4]]
+    images_1 = backend.predict(items)
+    images_2 = []
+    for i in range(5):
+        image = backend.pipe(
+            captions[i],
+            negative_prompt = backend.negative_prompt,
+            output_type="pil",
+            latents=items[i]["latents"].to(backend.dtype),
+            guidance_scale=backend.guidance,
+            num_inference_steps=backend.steps,
+        ).images[0]
+        images_2.append(image)
+
+    os.makedirs("tmp/", exist_ok=True)
+    for i, image in enumerate(images_1):
+        image.save(f"tmp/image_{i}.png")
+
+    for i, image in enumerate(images_2):
+        image.save(f"tmp/image_{i+5}.png")
diff --git a/text_to_image/coco.py b/text_to_image/coco.py
new file mode 100644
index 0000000000..61d61aa3e9
--- /dev/null
+++ b/text_to_image/coco.py
@@ -0,0 +1,190 @@
+"""
+implementation of coco dataset
+"""
+
+# pylint: disable=unused-argument,missing-docstring
+
+import json
+import logging
+import os
+import time
+
+from PIL import Image
+import numpy as np
+import pandas as pd
+import dataset
+
+import torch
+from torchvision import transforms
+from torchmetrics.multimodal.clip_score import CLIPScore
+from torchmetrics.image.fid import FrechetInceptionDistance
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("coco")
+
+
+class Coco(dataset.Dataset):
+    def __init__(
+        self,
+        data_path,
+        name=None,
+        image_size=None,
+        use_preprocessed=False,
+        pre_process=None,
+        pipe_tokenizer=None,
+        pipe_tokenizer_2=None,
+        latent_dtype=torch.float32,
+        latent_device="cuda",
+        latent_framework="torch",
+        **kwargs,
+    ):
+        super().__init__()
+        self.captions_df = pd.read_csv(f"{data_path}/captions/captions.tsv", sep="\t")
+        self.image_size = image_size
+        self.use_preprocessed = use_preprocessed
+        self.preprocessed_dir = os.path.abspath(f"{data_path}/preprocessed/")
+        self.img_dir = os.path.abspath(f"{data_path}/validation/data/")
+        self.name = name
+
+        # Preprocess prompts
+        self.captions_df["input_tokens"] = self.captions_df["caption"].apply(
+            lambda x: self.preprocess(x, pipe_tokenizer)
+        )
+        self.captions_df["input_tokens_2"] = self.captions_df["caption"].apply(
+            lambda x: self.preprocess(x, pipe_tokenizer_2)
+        )
+        self.latent_dtype = latent_dtype
+        self.latent_device = latent_device if torch.cuda.is_available() else "cpu"
+        if latent_framework == "torch":
+            self.latents = (
+                torch.load(f"{data_path}/latents/latents.pt")
+                .to(latent_dtype)
+                .to(latent_device)
+            )
+        elif latent_framework == "numpy":
+            self.latents = (
+                torch.Tensor(np.load(f"{data_path}/latents/latents.npy"))
+                .to(latent_dtype)
+                .to(latent_device)
+            )
+        if self.use_preprocessed:
+            os.makedirs(self.preprocessed_dir, exist_ok=True)
+            self.captions_df["preprocessed_path"] = self.captions_df["file_name"].apply(
+                lambda x: self.preprocess_images(x)
+            )
+
+    def preprocess(self, prompt, tokenizer):
+        converted_prompt = self.convert_prompt(prompt, tokenizer)
+        return tokenizer(
+            converted_prompt,
+            padding="max_length",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+    def preprocess_images(self, file_name):
+        img = Image.open(self.img_dir + "/" + file_name)
+        img = np.asarray(img)
+        if len(img.shape) == 2:
+            img = np.expand_dims(img, axis=-1)
+        tensor = torch.Tensor(img.transpose([2,0,1])).to(torch.uint8)
+        if tensor.shape[0] == 1:
+            tensor = tensor.repeat(3,1,1)
+        target_name = file_name.split(".")[0]
+        target_path = self.preprocessed_dir + "/" + target_name + ".pt"
+        if not os.path.exists(target_path):
+            torch.save(tensor, target_path)
+        return target_path
+
+    def convert_prompt(self, prompt, tokenizer):
+        tokens = tokenizer.tokenize(prompt)
+        unique_tokens = set(tokens)
+        for token in unique_tokens:
+            if token in tokenizer.added_tokens_encoder:
+                replacement = token
+                i = 1
+                while f"{token}_{i}" in tokenizer.added_tokens_encoder:
+                    replacement += f" {token}_{i}"
+                    i += 1
+
+                prompt = prompt.replace(token, replacement)
+
+        return prompt
+
+    def get_item(self, id):
+        return dict(self.captions_df.loc[id], latents=self.latents)
+
+    def get_item_count(self):
+        return len(self.captions_df)
+
+    def get_imgs(self, id_list):
+        image_list = []
+        if self.use_preprocessed:
+            for id in id_list:
+                image_list.append(
+                    torch.load(self.captions_df.loc[id]["preprocessed_path"])
+                )
+        else:
+            convert_tensor = transforms.ToTensor()
+            for id in id_list:
+                img = Image.open(
+                    self.img_dir + "/" + self.captions_df.loc[id]["file_name"]
+                )
+                tensor = convert_tensor(img)
+                if tensor.shape[0] == 1:
+                    tensor = tensor.repeat(3,1,1)
+                image_list.append(convert_tensor(img))
+        return image_list
+
+    def get_captions(self, id_list):
+        return [self.get_item(id)["caption"] for id in id_list]
+    
+    def get_item_loc(self, id):
+        if self.use_preprocessed:
+            return self.captions_df.loc[id]["preprocessed_path"]
+        else:
+            return self.img_dir + "/" + self.captions_df.loc[id]["file_name"]
+
+class PostProcessCoco:
+    def __init__(self, device="cpu", dtype = torch.uint8):
+        self.results = []
+        self.good = 0
+        self.total = 0
+        self.content_ids = []
+        self.device = device if torch.cuda.is_available() else "cpu"
+        self.dtype = dtype
+
+    def add_results(self, results):
+        self.results.extend(results)
+
+    def __call__(self, results, ids, expected=None, result_dict=None):
+        self.content_ids.extend(ids)
+        return [(t*255).round().to(self.dtype).to(self.device) for t in results]
+
+    def start(self):
+        self.results = []
+
+    def finalize(self, result_dict, ds=None, output_dir=None):
+        fid = FrechetInceptionDistance(feature=2048)
+        fid.to(self.device)
+        clip = CLIPScore(model_name_or_path="openai/clip-vit-base-patch32")
+        clip.to(self.device)
+        dataset_size = ds.get_item_count()
+        log.info("Accumulating results")
+        for i in range(0, dataset_size):
+            target = (
+                torch.stack(ds.get_imgs([self.content_ids[i]]))
+                .to(self.dtype)
+                .to(self.device)
+            )
+            captions = ds.get_captions([self.content_ids[i]])
+            generated = torch.stack([self.results[i]]).to(self.dtype).to(self.device)
+            fid.update(target, real=True)
+            fid.update(generated, real=False)
+            clip.update(generated, captions)
+        result_dict["FID_SCORE"] = float(fid.compute().item())
+        result_dict["CLIP_SCORE"] = float(clip.compute().item())
+
+        return result_dict
diff --git a/text_to_image/coco2014/captions/captions_source.tsv b/text_to_image/coco2014/captions/captions_source.tsv
new file mode 100644
index 0000000000..70f2b005ab
--- /dev/null
+++ b/text_to_image/coco2014/captions/captions_source.tsv
@@ -0,0 +1,5001 @@
+id	image_id	caption	height	width	file_name	coco_url
+70	227049	A city at night with people walking around.	640	480	COCO_val2014_000000227049.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227049.jpg
+277	288229	The large clock was prominently displaying the time.	640	425	COCO_val2014_000000288229.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000288229.jpg
+310	132510	A person in winter gear riding a snowboard.	500	358	COCO_val2014_000000132510.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132510.jpg
+392	412355	A small bird is perched on an empty bird feeder.	459	640	COCO_val2014_000000412355.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412355.jpg
+414	91495	Baseballs players sliding to base and jumping during the game.	447	640	COCO_val2014_000000091495.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000091495.jpg
+639	411768	A woman playing tennis in a white outfit	640	510	COCO_val2014_000000411768.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000411768.jpg
+689	38031	A woman looking at her phone next to a car being towed.	375	500	COCO_val2014_000000038031.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038031.jpg
+699	71988	A man in a wheelchair and another sitting on a bench that is overlooking the water.	380	640	COCO_val2014_000000071988.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000071988.jpg
+748	459912	Rows of motor bikes and helmets in a city	427	640	COCO_val2014_000000459912.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000459912.jpg
+939	226419	A random plane in the sky flying alone	426	640	COCO_val2014_000000226419.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226419.jpg
+1063	129159	A large clock tower rises up above the surrounding buildings.	640	480	COCO_val2014_000000129159.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129159.jpg
+1072	306073	A boy and two females standing in a circle playing tennis.	428	640	COCO_val2014_000000306073.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306073.jpg
+1159	128675	A man holding one water ski at the lake.	480	640	COCO_val2014_000000128675.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000128675.jpg
+1175	533537	A clock outside in front of some buildings.	640	427	COCO_val2014_000000533537.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533537.jpg
+1241	555905	A bedroom that looks like a hotel room includes a television and bed.	480	640	COCO_val2014_000000555905.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000555905.jpg
+1258	56592	A young man in a white shirt is playing tennis.	500	354	COCO_val2014_000000056592.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056592.jpg
+1359	78488	A yellow fire hydrant in front of a bike leaning against four poles	640	481	COCO_val2014_000000078488.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078488.jpg
+1375	281148	A parakeet perching atop a pair of used Vans shoes.	480	640	COCO_val2014_000000281148.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000281148.jpg
+1403	573349	There are several people walking in a street parade.	480	640	COCO_val2014_000000573349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573349.jpg
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+39631	269682	A traffic light over a street with a sign that reads K.	480	640	COCO_val2014_000000269682.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000269682.jpg
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+39898	418949	The ballplayer waits in the batter's box, watching the umpire defend his call.	640	480	COCO_val2014_000000418949.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000418949.jpg
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+40461	232598	A man holding a pizza pie with a section missing.	640	640	COCO_val2014_000000232598.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000232598.jpg
+40556	514537	A wild animal drinks water from the toilet bowl.	640	481	COCO_val2014_000000514537.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000514537.jpg
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+40772	246233	A massive wooden ship beached on snow covered ground.	583	640	COCO_val2014_000000246233.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000246233.jpg
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+41211	543895	Two men squatting outside a store looking at a frisbee.	427	640	COCO_val2014_000000543895.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543895.jpg
+41886	206613	A baseball player swings his bat after a hit.	428	640	COCO_val2014_000000206613.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000206613.jpg
+42154	234945	A woman talking on a cellular phone with someone nearby.	333	500	COCO_val2014_000000234945.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000234945.jpg
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+45286	149676	this image does not have a picture associated with it to describe	480	640	COCO_val2014_000000149676.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000149676.jpg
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+49504	301429	a couple of black dogs laying down in a white chair	481	640	COCO_val2014_000000301429.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301429.jpg
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+49689	208946	The building stands tall against a clear blue sky.	640	426	COCO_val2014_000000208946.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208946.jpg
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+50409	442894	Two giraffe in front of a painting of the outdoors at a zoo.	359	640	COCO_val2014_000000442894.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442894.jpg
+50424	80714	A pretty woman sitting at a desk using a laptop computer.	480	640	COCO_val2014_000000080714.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080714.jpg
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+50576	287970	A large elephant standing in front of the capital building in Washington DC.	640	513	COCO_val2014_000000287970.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287970.jpg
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+50875	346006	Yellow utility truck parked inside a warehouse by itself.	480	640	COCO_val2014_000000346006.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000346006.jpg
+50876	541025	Baseball players prepare to react to a pitch.	375	500	COCO_val2014_000000541025.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000541025.jpg
+50935	358427	Two pieces of broccoli being held up on a fork	427	640	COCO_val2014_000000358427.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000358427.jpg
+51079	436467	A pan filled with veggies and a block of butter.	427	640	COCO_val2014_000000436467.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000436467.jpg
+51119	403378	A crying woman looking at herself in a mirror.	425	640	COCO_val2014_000000403378.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000403378.jpg
+51193	424219	Two large giraffe standing next to each other and an ostrich.	427	640	COCO_val2014_000000424219.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000424219.jpg
+51216	230008	a traffic motorcycle cop waits to give a ticket	360	640	COCO_val2014_000000230008.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000230008.jpg
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+51405	314370	A kitchen filled with clutter all over it's counter.	427	640	COCO_val2014_000000314370.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000314370.jpg
+51693	168234	A car and a bus are near a street light at night.	500	375	COCO_val2014_000000168234.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168234.jpg
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+51975	542211	A red fire hydrant between two trees in front of a building.	640	640	COCO_val2014_000000542211.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000542211.jpg
+52248	510152	A man flying off the side of a cliff on a snowboard.	640	427	COCO_val2014_000000510152.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000510152.jpg
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+52410	539326	Cattle grazing on a green grass covered field.	427	640	COCO_val2014_000000539326.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000539326.jpg
+52466	213905	Two women walking on top of a tennis court holding tennis racquets.	640	357	COCO_val2014_000000213905.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213905.jpg
+52478	211317	Giraffe standing on a grass covered field next to a hill.	500	400	COCO_val2014_000000211317.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211317.jpg
+52543	65798	A baseball team playing a baseball game in front of a crowd.	480	640	COCO_val2014_000000065798.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000065798.jpg
+52609	473994	two feet and a white dog carrying a frisbee	375	500	COCO_val2014_000000473994.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473994.jpg
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+52718	115823	Two men are playing together in a park.	428	640	COCO_val2014_000000115823.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000115823.jpg
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+52948	293794	Man at street market with table full of bananas.	640	428	COCO_val2014_000000293794.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000293794.jpg
+53214	264919	A man is making a phone call while surrounded by balloons.	640	427	COCO_val2014_000000264919.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264919.jpg
+53244	114717	A baby is looking at a teddy bear that is next to it.	376	640	COCO_val2014_000000114717.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114717.jpg
+53370	224000	A flock of sheep crosses a dusty road.	381	640	COCO_val2014_000000224000.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224000.jpg
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+54086	147482	Little girl observing three octopus kites being flown	427	640	COCO_val2014_000000147482.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000147482.jpg
+54152	113588	several young students working at a desk with multiple computers	640	425	COCO_val2014_000000113588.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000113588.jpg
+54232	301148	A family gathers around a birthday cake and poses for picture.	427	640	COCO_val2014_000000301148.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301148.jpg
+54342	54679	A woman holds a pink and blue kite in her hand	640	456	COCO_val2014_000000054679.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054679.jpg
+54561	545250	There are refrigerators lined up to be trashed	480	640	COCO_val2014_000000545250.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000545250.jpg
+54678	106335	Several vehicles turning the corner on a busy intersection.	427	640	COCO_val2014_000000106335.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000106335.jpg
+54838	54123	The zebras are standing around and grazing in the pasture.	428	640	COCO_val2014_000000054123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054123.jpg
+56132	479155	Two young kids interacting in the grocery store produce section.	332	500	COCO_val2014_000000479155.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000479155.jpg
+56835	570848	a person in tennis wear holding a tennis racket	538	640	COCO_val2014_000000570848.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000570848.jpg
+57033	80163	A boy playing tennis in a tennis court.	500	375	COCO_val2014_000000080163.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080163.jpg
+57175	534151	an elephant in a field of grass with trees near by	480	640	COCO_val2014_000000534151.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534151.jpg
+57279	47775	a bathroom with two sinks two mirrors and a bath tub	480	640	COCO_val2014_000000047775.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000047775.jpg
+57329	469492	An old fashioned biplane with its propeller spinning.	640	640	COCO_val2014_000000469492.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000469492.jpg
+57348	333737	A man with a white umbrella posing with some foliage.	640	480	COCO_val2014_000000333737.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333737.jpg
+57366	118363	A LIVING ROOM WITH FURNITURE, A FIREPLACE, AND A LARGE SCENIC WINDOW	426	640	COCO_val2014_000000118363.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118363.jpg
+57456	378814	A Giraffe looking down as another giraffe in the background in looking through a fence	425	640	COCO_val2014_000000378814.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000378814.jpg
+57483	205670	A MAN ON A SURF BOARD IN THE WATER WITH HILLS AND RV'S IN THE BACKGROUND	424	640	COCO_val2014_000000205670.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205670.jpg
+57706	328111	The cars are traveling in opposite directions down the two way road.	480	640	COCO_val2014_000000328111.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328111.jpg
+57775	331475	Two plates of food sitting on the table	480	640	COCO_val2014_000000331475.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000331475.jpg
+57896	240800	A fire hydrant in front of a graffitti written brick wall.	640	480	COCO_val2014_000000240800.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000240800.jpg
+58304	150413	Two dogs survey the framing of a house addition	480	640	COCO_val2014_000000150413.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150413.jpg
+58410	401320	A train pulling into a train station pulling a line of train cars behind it.	428	640	COCO_val2014_000000401320.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401320.jpg
+58638	120509	A woman smiling while holding a sandwich while sitting down.	333	500	COCO_val2014_000000120509.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120509.jpg
+58652	60431	A man walking across a baseball field with a small child behind him.	427	640	COCO_val2014_000000060431.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000060431.jpg
+58671	187411	A herd of sheep grazing on a pile of hay in a field.	480	640	COCO_val2014_000000187411.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187411.jpg
+58718	323460	A red plastic basket with two hot dogs on it.	480	640	COCO_val2014_000000323460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000323460.jpg
+58739	93476	A clock is near a light post on a street.	480	640	COCO_val2014_000000093476.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000093476.jpg
+58772	466602	A snow skier is standing on the snow.	640	480	COCO_val2014_000000466602.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000466602.jpg
+58776	327919	A bus loading and unloading passengers in front of a Pizza Hut	424	640	COCO_val2014_000000327919.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000327919.jpg
+58799	379977	A brown horse is walking around in the grassy mountain.	640	425	COCO_val2014_000000379977.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379977.jpg
+58977	568846	A man giving a little boy a hair cut.	429	640	COCO_val2014_000000568846.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000568846.jpg
+59370	449132	A black and white spotted horse in a pasture.	480	640	COCO_val2014_000000449132.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449132.jpg
+60376	529590	A bearded man in glasses and his cat	400	500	COCO_val2014_000000529590.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529590.jpg
+60378	399628	Two children looking at a steam train engine	640	480	COCO_val2014_000000399628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000399628.jpg
+60406	505891	A domestic cat lounges on a quilt covering a bed.	480	640	COCO_val2014_000000505891.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000505891.jpg
+60649	490405	A man stacks oranges on a cart i an open air market.	640	480	COCO_val2014_000000490405.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000490405.jpg
+61043	134199	A young elephant walks near a herd of other elephants.	480	640	COCO_val2014_000000134199.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134199.jpg
+61061	419312	Full course dinner served on large plate including drinks and dessert.	500	375	COCO_val2014_000000419312.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419312.jpg
+61449	78490	a close up of a woman next to a horse	425	640	COCO_val2014_000000078490.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078490.jpg
+61589	171298	Three yellow coach buses parked in a line.	480	640	COCO_val2014_000000171298.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000171298.jpg
+61599	146965	a room wit ha chair a bed multiple windows	399	600	COCO_val2014_000000146965.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146965.jpg
+61760	433652	A small teddy bear is on a strange chair.	640	427	COCO_val2014_000000433652.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000433652.jpg
+61841	21989	A group of people in ski wear and skis standing in the snow next to each other.	480	640	COCO_val2014_000000021989.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000021989.jpg
+62021	64523	A black and white photograph of a female tennis club.	444	640	COCO_val2014_000000064523.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064523.jpg
+62239	266601	a group of guys sitting around a table eating a meal	480	640	COCO_val2014_000000266601.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000266601.jpg
+62443	116825	A cat leans over a desk and paws at an army knife.	360	640	COCO_val2014_000000116825.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116825.jpg
+62547	528984	A group of people skiing down a ski slope.	480	640	COCO_val2014_000000528984.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000528984.jpg
+62636	292995	A tray of hot dogs with different toppings on them.	426	640	COCO_val2014_000000292995.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000292995.jpg
+63047	476120	The back of a microwave and it's plugs.	427	640	COCO_val2014_000000476120.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000476120.jpg
+63048	69106	Three zebras standing in front of a wall.	334	500	COCO_val2014_000000069106.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000069106.jpg
+63342	232900	A red and white bus sits in front of a large, stone house.	480	640	COCO_val2014_000000232900.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000232900.jpg
+63622	492102	A passenger train traveling through a tunnel covered with a forest.	375	500	COCO_val2014_000000492102.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000492102.jpg
+63759	468169	A hitter swings his bat to hit the ball.	640	363	COCO_val2014_000000468169.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000468169.jpg
+64236	75001	three people on bicycles lined up behind one another along a road.	415	640	COCO_val2014_000000075001.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000075001.jpg
+64379	204435	A hazard sign on top of a crossing gate in front of a building.	426	640	COCO_val2014_000000204435.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000204435.jpg
+64466	125072	Cattle grazing on grass near a lake surrounded by mountain..	314	640	COCO_val2014_000000125072.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125072.jpg
+64476	44361	A bathroom with a large white tub and his and her sinks.	428	640	COCO_val2014_000000044361.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000044361.jpg
+64517	368153	Two blonde horses standing in a field grazing on grass.	335	500	COCO_val2014_000000368153.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000368153.jpg
+64634	82787	Two buses passing each other by while traveling under a bridge.	480	640	COCO_val2014_000000082787.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000082787.jpg
+64637	211063	A humming bird mid flight preparing to land on a tree	614	640	COCO_val2014_000000211063.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211063.jpg
+64864	182541	two people sailing down a stream in a blue canoe.	426	640	COCO_val2014_000000182541.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182541.jpg
+64918	74120	a woman with a red hat is eating a hot dog	361	640	COCO_val2014_000000074120.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000074120.jpg
+65016	171970	two men riding in the back of a blue truck while driving down the street.	332	500	COCO_val2014_000000171970.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000171970.jpg
+65032	80756	A table is set with the fixings for bacon, lettuce and tomato sandwiches.	320	400	COCO_val2014_000000080756.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080756.jpg
+65083	5706	a bird  with a long beak flying low over the ocean.	346	640	COCO_val2014_000000005706.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005706.jpg
+65085	64390	a street sign attached to a metal pole next to a street.	640	480	COCO_val2014_000000064390.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064390.jpg
+66428	168491	People in public playing with video game controllers.	480	640	COCO_val2014_000000168491.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168491.jpg
+66558	446497	Person in a field under cloudy skies flying a kite.	480	640	COCO_val2014_000000446497.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000446497.jpg
+66631	217039	Closeup of view of the cabin and wing of a large commercial airliner.	427	640	COCO_val2014_000000217039.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000217039.jpg
+66677	505344	A man is bending down by the water with a frisbee.	480	640	COCO_val2014_000000505344.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000505344.jpg
+66811	363343	A plate with a picture of a movie character painted on it in the sand	640	640	COCO_val2014_000000363343.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000363343.jpg
+66990	51628	People are lying down watching television with their socks on.	480	640	COCO_val2014_000000051628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000051628.jpg
+67018	374060	A small airplane getting ready to take off in field	427	640	COCO_val2014_000000374060.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000374060.jpg
+67119	62025	An image of a hotel bathroom that is ugly.	640	480	COCO_val2014_000000062025.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062025.jpg
+67135	380247	A small kitchen inside of a dark office	454	640	COCO_val2014_000000380247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000380247.jpg
+67206	481928	Yellow and red trolleys with people standing about.	428	640	COCO_val2014_000000481928.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000481928.jpg
+67569	109516	A colorful bird is sitting on a tree.	640	427	COCO_val2014_000000109516.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000109516.jpg
+67729	68080	A tennis play is being watched by a small crowd.	640	480	COCO_val2014_000000068080.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068080.jpg
+67730	34464	People are at the beach near the ocean edge.	480	640	COCO_val2014_000000034464.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034464.jpg
+67735	267515	Toothbrushes and toothpaste lay on the counter by the sink.	427	640	COCO_val2014_000000267515.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267515.jpg
+67736	155154	The bathroom is small and white and gray.	500	375	COCO_val2014_000000155154.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000155154.jpg
+67826	489764	A woman and a man playing a video game in the living room	538	640	COCO_val2014_000000489764.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000489764.jpg
+67944	477906	A snowboarder in a plaid black jacket it catching air.	349	640	COCO_val2014_000000477906.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000477906.jpg
+68028	381721	A man and two cats sit on a bench beside a body of water.	375	500	COCO_val2014_000000381721.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000381721.jpg
+68217	295574	A man taking pics in a bathroom with a cellphone	640	480	COCO_val2014_000000295574.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295574.jpg
+68266	579201	A knife with an apple engaged in it next to an apple computer	427	640	COCO_val2014_000000579201.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000579201.jpg
+68414	76738	A man posing with a mouse and keyboard	375	500	COCO_val2014_000000076738.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000076738.jpg
+68536	247788	A young infant boy in bed is leaning towards an electronic device to adjust it.	466	640	COCO_val2014_000000247788.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000247788.jpg
+68923	577212	A kitchen area is being built that contains a deep sink, a dishwasher, and cabinets.	375	500	COCO_val2014_000000577212.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577212.jpg
+68948	71711	A large airplane museum with old war planes.	427	640	COCO_val2014_000000071711.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000071711.jpg
+69623	111806	Characters from Cat in the Hat in front of a man and child.	500	436	COCO_val2014_000000111806.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111806.jpg
+69702	474600	Large giraffe standing in the shade underneath a tree in a field.	500	375	COCO_val2014_000000474600.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474600.jpg
+69883	237060	A huge airplane looks small against a wide open concrete area.	427	640	COCO_val2014_000000237060.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237060.jpg
+70038	415646	An attic bedroom with a bed and a couch.	480	640	COCO_val2014_000000415646.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415646.jpg
+70162	170716	The girl is cutting pizza in her kitchen, the melted cheese clinging  to the knife.	436	640	COCO_val2014_000000170716.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170716.jpg
+70430	57812	A cat and a kitten laying on a bed next to a laptop.	427	640	COCO_val2014_000000057812.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000057812.jpg
+70881	307510	A man taking a swing at a tennis ball.	640	427	COCO_val2014_000000307510.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000307510.jpg
+71082	220670	A pizza with tomatoes is on a round plate.	426	640	COCO_val2014_000000220670.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000220670.jpg
+71113	45835	A group of women enjoying wine and cheese.	472	640	COCO_val2014_000000045835.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000045835.jpg
+71333	338625	A double city bus is pulled up to a bus stop.	480	640	COCO_val2014_000000338625.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000338625.jpg
+71469	568276	A close up of pizzas resting on serving trays on a table.	427	640	COCO_val2014_000000568276.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000568276.jpg
+71670	45387	A baboon is sitting eating a banana on the ground.	640	459	COCO_val2014_000000045387.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000045387.jpg
+71928	303667	A man holding a cell phone in a crowded mall.	480	640	COCO_val2014_000000303667.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303667.jpg
+71934	144243	A group of children playing soccer on a field.	427	640	COCO_val2014_000000144243.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000144243.jpg
+72111	110509	A young girl using a cellphone while sitting next to luggage.	561	640	COCO_val2014_000000110509.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000110509.jpg
+72308	553436	Two men and a dog in a kitchen.	640	480	COCO_val2014_000000553436.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000553436.jpg
+72632	479008	A group of passengers sitting on an airplane in flight	333	500	COCO_val2014_000000479008.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000479008.jpg
+72644	532686	A white bathroom with a walk in shower and a toilet.	480	640	COCO_val2014_000000532686.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532686.jpg
+72690	440514	A large clock tower on top of a church next to a road.	480	640	COCO_val2014_000000440514.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000440514.jpg
+72742	364493	A large bed sitting in a bedroom next to a window.	480	640	COCO_val2014_000000364493.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000364493.jpg
+72957	457449	The top of the head of a man sitting in front of disorganized computer desk	480	640	COCO_val2014_000000457449.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000457449.jpg
+73032	191169	A beautiful young woman bending over while holding a back cat.	640	426	COCO_val2014_000000191169.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000191169.jpg
+73242	118097	A man thinks as he stands in front of a sign.	234	351	COCO_val2014_000000118097.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118097.jpg
+73333	102466	A boat going through the water in a canal.	332	500	COCO_val2014_000000102466.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000102466.jpg
+73385	23807	Two people sitting on a dock eating sandwiches	480	640	COCO_val2014_000000023807.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000023807.jpg
+73676	217082	A dog with his leash attached to a bench	383	640	COCO_val2014_000000217082.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000217082.jpg
+74282	67002	A store filled with merchandise with workers and customers standing inside of it.	427	640	COCO_val2014_000000067002.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067002.jpg
+74374	25529	A plate of food with a sandwich and fries.	427	640	COCO_val2014_000000025529.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025529.jpg
+74376	88682	A group of people set on the ground talking in a park.	640	428	COCO_val2014_000000088682.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000088682.jpg
+74542	226761	Two boys standing in front of a couch with Wii controllers.	640	502	COCO_val2014_000000226761.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226761.jpg
+74648	507313	A white park bench sitting in the middle of a forest.	375	500	COCO_val2014_000000507313.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507313.jpg
+74678	339678	A man holding an umbrella next to a frozen over fire hydrant.	640	640	COCO_val2014_000000339678.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000339678.jpg
+74737	577527	A boat with a bed set by a set of windows.	480	640	COCO_val2014_000000577527.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577527.jpg
+74810	487181	A young man crossing a street next to bikers.	427	640	COCO_val2014_000000487181.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487181.jpg
+75343	224664	A group of people flying kites on top of a sandy beach.	480	640	COCO_val2014_000000224664.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224664.jpg
+75651	374266	A brown horse standing behind a chain link fence on green grass.	480	640	COCO_val2014_000000374266.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000374266.jpg
+75687	198447	A person standing in front of a train holding a pair of skis.	480	640	COCO_val2014_000000198447.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198447.jpg
+75876	23017	A blender filled with liquid sitting on a kitchen counter.	500	333	COCO_val2014_000000023017.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000023017.jpg
+76006	133002	A man riding a motorcycle with a woman on the back.	481	640	COCO_val2014_000000133002.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133002.jpg
+76288	374567	A yellow and green train on a train track.	424	640	COCO_val2014_000000374567.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000374567.jpg
+76348	448897	A pizza sitting on top of a white plate on a wooden table.	480	640	COCO_val2014_000000448897.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448897.jpg
+76699	566264	A dog swimming in the ocean by itself.	428	640	COCO_val2014_000000566264.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000566264.jpg
+76794	18888	A wooden bench in front of some trees.	480	640	COCO_val2014_000000018888.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018888.jpg
+76910	35599	Cafe tables with table cloths and orange umbrellas over them.	640	447	COCO_val2014_000000035599.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000035599.jpg
+77044	37149	There is a woman looking at two vases that are on display in a glass case.	394	640	COCO_val2014_000000037149.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000037149.jpg
+77099	287748	An Equestrian jumping their horse over a white jump.	479	640	COCO_val2014_000000287748.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287748.jpg
+77330	539140	The airborne tennis player hits the tennis ball with his racket.	427	640	COCO_val2014_000000539140.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000539140.jpg
+77476	181013	Two men are playing frisbee with one man on the offense and another guarding.	356	500	COCO_val2014_000000181013.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000181013.jpg
+77578	40426	There are fruits in a blend and a carton of icecream next to it.	480	640	COCO_val2014_000000040426.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000040426.jpg
+78214	498350	Motorcycle rider rides a rocky trail in the forest.	427	640	COCO_val2014_000000498350.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498350.jpg
+79492	300214	A man in a suit and tie is sitting on a bench.	640	425	COCO_val2014_000000300214.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000300214.jpg
+79504	305634	Street corner signs above a red stop sign.	640	428	COCO_val2014_000000305634.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000305634.jpg
+79505	175785	Bride and groom with knife cutting wedding cake.	428	640	COCO_val2014_000000175785.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175785.jpg
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+79994	66427	A road sign on top of a stop sign	480	640	COCO_val2014_000000066427.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000066427.jpg
+80129	120340	a man putting his bike on a rack in front of the bus	480	640	COCO_val2014_000000120340.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120340.jpg
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+80585	121663	City bus on an empty city street near a STOP sign.	360	640	COCO_val2014_000000121663.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000121663.jpg
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+81037	294605	Small plate of food with mixed vegetables and meat.	640	640	COCO_val2014_000000294605.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294605.jpg
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+81257	498716	Two cats laying near a vase and two candles.	500	357	COCO_val2014_000000498716.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498716.jpg
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+81578	401951	A building has a fire truck near it on the street with a man on a lift.	480	640	COCO_val2014_000000401951.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401951.jpg
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+82080	110196	a freeway traffic sign writtien in an asian language	427	640	COCO_val2014_000000110196.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000110196.jpg
+82207	506115	Blonde girls on a bus hold deflated soccer balls as a brown haired girl sits in the row of seats in front of them.	480	640	COCO_val2014_000000506115.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000506115.jpg
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+82838	227801	Two tall teddy bears standing next to each other with a person kneeling between them.	411	548	COCO_val2014_000000227801.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227801.jpg
+82886	40102	Two giraffe standing next to each other on a grassy field.	640	480	COCO_val2014_000000040102.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000040102.jpg
+83001	395665	A tennis player gets prepared to hit the ball.	427	640	COCO_val2014_000000395665.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000395665.jpg
+83076	335810	A plate is piled high of orange slices while a bunch of bananas sits next to it.	429	640	COCO_val2014_000000335810.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335810.jpg
+83406	393979	The herd of zebras are traveling together as they walk, eat, and stand.	426	640	COCO_val2014_000000393979.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000393979.jpg
+83536	183379	Large jetliners parked on an airport tarmac outside of an airport.	480	640	COCO_val2014_000000183379.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000183379.jpg
+83617	164969	A pizza in a box with toppings that are divided into thirds.	428	640	COCO_val2014_000000164969.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000164969.jpg
+83672	31118	Christmas decorations surrounding a clock tower in a town square.	427	640	COCO_val2014_000000031118.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000031118.jpg
+84290	511002	A large red stop sign next to a box sitting on a sidewalk.	480	640	COCO_val2014_000000511002.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000511002.jpg
+84586	160740	A bunch of cars driving down a street surrounded by tall buildings.	640	480	COCO_val2014_000000160740.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160740.jpg
+84633	18491	A boy catches a ball as a player slides to the base.	427	640	COCO_val2014_000000018491.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018491.jpg
+84681	495973	Two computer keyboards sitting on top of a table next to a computer mouse.	375	500	COCO_val2014_000000495973.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000495973.jpg
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+84696	231281	a baseball swinging a baseball bat at a ball	480	640	COCO_val2014_000000231281.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231281.jpg
+84817	364284	A man holding a blue, red and green frisbee in his hands.	480	640	COCO_val2014_000000364284.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000364284.jpg
+84845	477805	A man with glasses sitting in front of a laptop computer.	427	640	COCO_val2014_000000477805.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000477805.jpg
+85015	301061	A person is moving green hay towards an elephant that is inside the back of a white truck.	640	480	COCO_val2014_000000301061.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301061.jpg
+85074	283186	A helmeted and goggled skier leans to get around an obstacle.	418	640	COCO_val2014_000000283186.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000283186.jpg
+85138	423201	Small black dog with paw on man's knee.	480	640	COCO_val2014_000000423201.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000423201.jpg
+85139	315338	Young man with backpack holding skateboard going down escalator.	640	436	COCO_val2014_000000315338.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315338.jpg
+85147	170041	A tiled shower, molded plastic bathtub, shelf, mirror, wooden vanity, lamp, and sink make up a beige colored bathroom.	333	500	COCO_val2014_000000170041.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170041.jpg
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+85762	192730	Three cows are standing on the pavement next to a gate with three structures in the background.	424	640	COCO_val2014_000000192730.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192730.jpg
+85826	165014	A dinner plate with sliced beets and bread.	640	480	COCO_val2014_000000165014.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000165014.jpg
+86201	180653	The young couple is enjoying their skiing trip.	480	640	COCO_val2014_000000180653.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000180653.jpg
+86296	182176	Double decker city bus passing under an overpass.	427	640	COCO_val2014_000000182176.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182176.jpg
+86297	51309	Small horse standing behind a large group of horses eating.	360	640	COCO_val2014_000000051309.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000051309.jpg
+86313	244760	A white and black horse standing next to a wooden fence in a field.	640	458	COCO_val2014_000000244760.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000244760.jpg
+86364	122602	A table with scissors and a bottle of podge next to a cutting board.	375	500	COCO_val2014_000000122602.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000122602.jpg
+86380	453626	A red stop sign on a gate next to a grass covered field.	640	484	COCO_val2014_000000453626.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453626.jpg
+86623	304744	Man placing items on a dining room table in front of other people.	640	426	COCO_val2014_000000304744.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000304744.jpg
+86626	34257	A market display case filled with different colored vegetables.	309	500	COCO_val2014_000000034257.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034257.jpg
+87099	203781	A family standing next to each other wearing ski equipment.	480	640	COCO_val2014_000000203781.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203781.jpg
+87157	495984	A white toilet sitting in the corner of a bathroom.	640	480	COCO_val2014_000000495984.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000495984.jpg
+87314	153103	Meat sitting on a plate with green vegetables on the side.	360	640	COCO_val2014_000000153103.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000153103.jpg
+87467	353562	A family of sheep standing next to each other on a lush green field.	480	640	COCO_val2014_000000353562.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000353562.jpg
+87488	301282	A little girl about to eat something off of a spoon.	640	427	COCO_val2014_000000301282.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301282.jpg
+87527	146541	A zebra laying on a dirt ground next to a wooden fence.	480	640	COCO_val2014_000000146541.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146541.jpg
+87716	376545	A young man flipping a skateboard through the air.	424	640	COCO_val2014_000000376545.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000376545.jpg
+87828	334391	A furry dog standing next to a fire hydrant.	480	640	COCO_val2014_000000334391.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334391.jpg
+88149	317441	A street sign above an orange detour sign.	480	640	COCO_val2014_000000317441.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000317441.jpg
+88183	17379	a flat screen TV embedded in a restroom mirror.	640	478	COCO_val2014_000000017379.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000017379.jpg
+88188	190637	a small child laying in bed playing nintendo wii	640	428	COCO_val2014_000000190637.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190637.jpg
+88377	313990	A large clock tower towering above a city with clocks on every side.	640	359	COCO_val2014_000000313990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000313990.jpg
+88618	320150	a man taking a picture of prizes in a claw machine game	480	640	COCO_val2014_000000320150.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000320150.jpg
+89096	334074	A group of zebras hidden among the trees on a hillside	480	640	COCO_val2014_000000334074.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334074.jpg
+89343	446285	Skateboarder sticks his tongue out while he rides his skateboard down the rail.	640	426	COCO_val2014_000000446285.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000446285.jpg
+90056	266082	A red fire hydrant in front of a skyscraper	500	375	COCO_val2014_000000266082.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000266082.jpg
+90113	39659	A breakfast of bagels, eggs, friend potatoes and fresh fruit along side glasses of juice and water, with a basket of jelly packets to the side.	375	500	COCO_val2014_000000039659.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039659.jpg
+90489	331686	an image of a group of policeman on motorcylces	640	640	COCO_val2014_000000331686.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000331686.jpg
+90872	60569	A smiling woman looks to be playing a Wii.	476	640	COCO_val2014_000000060569.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000060569.jpg
+91057	393804	A roast meat sandwich with a green sauce sits on a plate with something fried to the side.	480	640	COCO_val2014_000000393804.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000393804.jpg
+91114	137826	A red double decker tourist bus goes around town	480	640	COCO_val2014_000000137826.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000137826.jpg
+91306	279549	Am an taking a swing at a tennis ball	426	640	COCO_val2014_000000279549.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279549.jpg
+91476	14723	A picture of man in the air on a snowboard.	279	448	COCO_val2014_000000014723.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000014723.jpg
+92043	147518	A bathroom that has magazine rack and small cabinet.	640	480	COCO_val2014_000000147518.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000147518.jpg
+92451	566038	A bird sits on an outside windowsill overlooking a parking lot.	384	480	COCO_val2014_000000566038.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000566038.jpg
+92557	385194	A baseball players stands at home plate, waiting for the next pitch.	640	426	COCO_val2014_000000385194.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000385194.jpg
+92843	46011	a small air plane on an air plane run way	360	640	COCO_val2014_000000046011.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000046011.jpg
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+93230	226588	a person riding skis on a snowy slope	425	640	COCO_val2014_000000226588.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226588.jpg
+93763	32375	Two teddy bears on a couch with a pillow	416	640	COCO_val2014_000000032375.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032375.jpg
+93783	581654	A dog approaches two sheep in front of onlookers.	427	640	COCO_val2014_000000581654.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000581654.jpg
+93787	203294	A tour bus with a cats face painted on the front of it	480	640	COCO_val2014_000000203294.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203294.jpg
+94222	347276	Two plates have brocolli on them that is smothered in cheese.	480	640	COCO_val2014_000000347276.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347276.jpg
+94223	417465	An adult black bear let's his long tongue protrude.	425	640	COCO_val2014_000000417465.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000417465.jpg
+94239	227073	A woman standing on a beach near the water	612	612	COCO_val2014_000000227073.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227073.jpg
+94391	34020	Person down hill skiing in well groomed snow	427	640	COCO_val2014_000000034020.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034020.jpg
+94569	29793	Two young people laying in two beds in a bedroom under blankets.	310	500	COCO_val2014_000000029793.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029793.jpg
+94711	31749	A demonic looking chucky like doll standing next to a white clock.	640	640	COCO_val2014_000000031749.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000031749.jpg
+94712	315191	An airplane doing maneuvers with white smoke pouring out of it's back end.	399	640	COCO_val2014_000000315191.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315191.jpg
+94734	552217	A woman laying in her bed touching the alarm clock	334	500	COCO_val2014_000000552217.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552217.jpg
+94767	559576	A street sign showing how to drive around a divide	640	480	COCO_val2014_000000559576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559576.jpg
+95365	492110	A man looking at a laptop with a confused look	427	640	COCO_val2014_000000492110.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000492110.jpg
+95655	315641	An older woman sitting at a table cutting up donuts.	480	640	COCO_val2014_000000315641.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315641.jpg
+95761	347018	A baseball player holding a baseball bat standing on home plate.	640	512	COCO_val2014_000000347018.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347018.jpg
+96017	454982	Two laptop computers sit side by side on a desk.	640	640	COCO_val2014_000000454982.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000454982.jpg
+96100	550117	An older woman sitting at a table in front of a pizza.	640	491	COCO_val2014_000000550117.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550117.jpg
+96123	114586	A cheesy pizza sitting on top of a white plate.	375	500	COCO_val2014_000000114586.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114586.jpg
+96231	507750	A person riding skis into a pool of water down a ski slope.	392	640	COCO_val2014_000000507750.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507750.jpg
+96484	179430	A street sign with two streets and two block numbers.	427	640	COCO_val2014_000000179430.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000179430.jpg
+96577	498627	A man wearing a USA shirt skiing in the sun.	640	427	COCO_val2014_000000498627.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498627.jpg
+96678	275501	A bird standing in the sand and another bird landing on a blanket in the sand.	427	640	COCO_val2014_000000275501.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000275501.jpg
+96708	519635	Smiling lady typing on laptop on high back bench and table.	429	640	COCO_val2014_000000519635.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519635.jpg
+96711	446209	A man and three children playing with a kite on the grass and other people riding bikes on a nearby road.	640	638	COCO_val2014_000000446209.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000446209.jpg
+96861	385181	A white dish with a tomato based casserole	480	640	COCO_val2014_000000385181.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000385181.jpg
+96913	561795	Two men in the bed of a pickup truck working on the vehicle.	480	640	COCO_val2014_000000561795.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000561795.jpg
+96941	173183	Street signs, corner of Lynn and Bigelow.taken 11.01.2009 23:58.	480	640	COCO_val2014_000000173183.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173183.jpg
+96946	38721	A woman wearing a blouse and devil horns hold a cell phone to her ear.	640	427	COCO_val2014_000000038721.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038721.jpg
+97085	398567	A desk with a computer, a keyboard, a mouse, a bobble head, speakers and a lava lamp on it.	477	640	COCO_val2014_000000398567.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398567.jpg
+97116	147740	A woman walking while carrying bananas on her hat.	480	640	COCO_val2014_000000147740.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000147740.jpg
+97195	124390	Two giraffes standing outdoors near a brick building.	640	480	COCO_val2014_000000124390.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000124390.jpg
+97264	506872	People dressed in costumes standing in a field.	400	500	COCO_val2014_000000506872.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000506872.jpg
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+97377	238071	S&B train number 3815 coming down the track, country area.	333	500	COCO_val2014_000000238071.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000238071.jpg
+97388	500703	People are waiting for the train to arrive	640	427	COCO_val2014_000000500703.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500703.jpg
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+97664	540733	World flags decorate a busy urban street corner.	427	640	COCO_val2014_000000540733.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540733.jpg
+97711	310999	Green bananas are growing on a banana tree.	480	640	COCO_val2014_000000310999.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000310999.jpg
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+97900	241691	A giraffe standing next to a covered structure.	428	640	COCO_val2014_000000241691.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000241691.jpg
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+98413	177529	God looking at orange cat that is standing on a stool.	439	640	COCO_val2014_000000177529.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177529.jpg
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+99677	320249	THERE IS A MOTORCYLCE WITH PEOPLE STANDING ON THE SIDE OF THE STREET	458	640	COCO_val2014_000000320249.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000320249.jpg
+99777	301765	Two street signs on a post beneath power lines.	480	640	COCO_val2014_000000301765.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301765.jpg
+99794	260182	A window that looks out onto an intersection with several shops.	640	428	COCO_val2014_000000260182.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260182.jpg
+99849	463226	There are crafts and craft supplies on a table	378	640	COCO_val2014_000000463226.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000463226.jpg
+99914	438989	A PERSON IS THROWING A FRISBEE IN THE GRASS	480	640	COCO_val2014_000000438989.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000438989.jpg
+99974	490337	Bright red umbrella open on the sand of a beach.	426	640	COCO_val2014_000000490337.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000490337.jpg
+100380	461838	A surfer coming out of the water with his surfboard.	640	426	COCO_val2014_000000461838.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000461838.jpg
+100483	100083	Fast food burger and fries sitting on the wrappers.	480	640	COCO_val2014_000000100083.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000100083.jpg
+100484	277048	Some taxi cabs parked along the side of a road.	375	500	COCO_val2014_000000277048.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000277048.jpg
+100729	415823	half of an orange in a small bowl.	612	612	COCO_val2014_000000415823.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415823.jpg
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+100797	262394	Motorcycle parked along the street next to a building.	480	640	COCO_val2014_000000262394.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000262394.jpg
+100800	202503	one zebra laying down in the dirt outside	427	640	COCO_val2014_000000202503.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000202503.jpg
+100802	570349	BLACK, BLUE, WHITE, RED, ORANGE RED AND YELLOW AIRPLANE	640	640	COCO_val2014_000000570349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000570349.jpg
+100850	291686	A smiling man holding a fork and knife	480	640	COCO_val2014_000000291686.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000291686.jpg
+100997	449102	Baseball player throwing a pitch from the mound	427	640	COCO_val2014_000000449102.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449102.jpg
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+101250	288224	A sandwich on a plate near a bowl of chips and glass of water	480	640	COCO_val2014_000000288224.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000288224.jpg
+101440	299042	Two sheep are standing side by side behind a fence.	428	640	COCO_val2014_000000299042.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000299042.jpg
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+101566	524282	A women's tennis doubles match on a professional court	480	640	COCO_val2014_000000524282.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000524282.jpg
+102123	477741	A vandalized abandoned building at a stop sign	480	640	COCO_val2014_000000477741.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000477741.jpg
+102159	69535	Two halves of an orange sit on wood.	428	640	COCO_val2014_000000069535.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000069535.jpg
+102443	577417	there is a treat sign says alameda street	427	640	COCO_val2014_000000577417.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577417.jpg
+102525	280424	Three zebras standing near each other in a penned in area.	333	500	COCO_val2014_000000280424.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000280424.jpg
+102676	561366	There is a phone on top of a calculator	480	640	COCO_val2014_000000561366.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000561366.jpg
+103012	501142	there is a cow standing in the water by a boat	313	500	COCO_val2014_000000501142.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000501142.jpg
+103400	347544	There is an old multi colored volvo parked next to a motel	480	640	COCO_val2014_000000347544.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347544.jpg
+103553	516998	Two men standing next to a busted fire hydrant.	375	500	COCO_val2014_000000516998.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516998.jpg
+103809	54305	A giraffe walks through a line of cars in a parking lot.	640	466	COCO_val2014_000000054305.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054305.jpg
+103981	13948	Two zebras are seen running on a path together.	428	640	COCO_val2014_000000013948.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013948.jpg
+104325	376278	A zebra is lying on the ground with other zebras.	427	640	COCO_val2014_000000376278.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000376278.jpg
+104587	499200	A surfer is in the middle of riding a wave.	427	640	COCO_val2014_000000499200.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000499200.jpg
+105027	552612	A herd of sheep grazing on a lush green field.	612	612	COCO_val2014_000000552612.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552612.jpg
+105100	566920	A cooked italian pie is on round white plate on the table with silver spoons and forks randomly scattered around it.	426	640	COCO_val2014_000000566920.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000566920.jpg
+105220	218091	A bedroom with a large bed sitting next to a black dresser.	429	640	COCO_val2014_000000218091.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218091.jpg
+105391	294284	a herd of elephants walking down a desert road	480	640	COCO_val2014_000000294284.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294284.jpg
+105589	412384	A child holding an umbrella in front of a house.	640	478	COCO_val2014_000000412384.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412384.jpg
+105626	170651	A pair of trains parked at a station.	427	640	COCO_val2014_000000170651.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170651.jpg
+105696	301912	A piece of cake on a plate with some juice by it	421	640	COCO_val2014_000000301912.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301912.jpg
+105810	384258	Two zebra standing on a dirty road next to a grass covered field.	480	640	COCO_val2014_000000384258.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384258.jpg
+105822	550864	A modern kitchen with stainless steel fridge, wooden cabinets, and overhead lighting is separated from the dining room by a bar.	500	375	COCO_val2014_000000550864.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550864.jpg
+106052	89648	A crowd of people sitting down next to each other.	427	640	COCO_val2014_000000089648.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089648.jpg
+106398	529065	A man riding a skateboard down a metal hang rail.	640	425	COCO_val2014_000000529065.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529065.jpg
+106418	480379	A man standing on top of a  oat in a river.	516	640	COCO_val2014_000000480379.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000480379.jpg
+106552	40087	A yellow fire hydrant sits by the side of the road.	333	500	COCO_val2014_000000040087.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000040087.jpg
+106554	291458	A clock tower gives the time in a city square.	640	389	COCO_val2014_000000291458.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000291458.jpg
+106595	427338	A city made out of stone brick with large arches.	396	640	COCO_val2014_000000427338.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427338.jpg
+106610	14056	A group of people playing in the water next to a beach.	612	612	COCO_val2014_000000014056.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000014056.jpg
+106735	506707	A baseball player runs into home plate during a game.	427	640	COCO_val2014_000000506707.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000506707.jpg
+106837	530317	Two air boats driving next to each other on the water	480	640	COCO_val2014_000000530317.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530317.jpg
+106843	301347	Two giraffe standing next to a chain link fence.	427	640	COCO_val2014_000000301347.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301347.jpg
+107147	82338	A man on motorcycle driving past cows on the street	480	640	COCO_val2014_000000082338.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000082338.jpg
+107148	107227	A woman riding a horse with number 26 on it	640	480	COCO_val2014_000000107227.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107227.jpg
+107198	162855	A young woman holding a white frisbee while standing on a grass field.	427	640	COCO_val2014_000000162855.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000162855.jpg
+107245	396754	A group of people horse back riding on a sandy beach.	427	640	COCO_val2014_000000396754.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000396754.jpg
+107387	493695	A city street filled with traffic at night.	322	500	COCO_val2014_000000493695.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493695.jpg
+107392	302760	A bathroom sink with a marble counter top under a large mirror.	375	500	COCO_val2014_000000302760.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000302760.jpg
+107437	506552	Group of folks playing bowling on Wii sports	370	640	COCO_val2014_000000506552.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000506552.jpg
+107553	558955	A black and white soccer ball sits in the grass.	388	640	COCO_val2014_000000558955.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558955.jpg
+107616	466936	A man is sitting down outdoors smoking a cigarette.	334	500	COCO_val2014_000000466936.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000466936.jpg
+108015	266091	A small garden area features a few springs of growth and a small busy plant and a few bricks.	375	500	COCO_val2014_000000266091.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000266091.jpg
+108134	155007	a teenager playing in a skate park with a skateboard	640	480	COCO_val2014_000000155007.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000155007.jpg
+108197	509609	A cat cuddles an elephant toy on the bed.	640	419	COCO_val2014_000000509609.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000509609.jpg
+108207	434788	@ couple of kids walking in the snow	320	568	COCO_val2014_000000434788.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000434788.jpg
+108300	201622	The horses are out for a walk on the road.	360	640	COCO_val2014_000000201622.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000201622.jpg
+108505	461491	A baseball player wearing a catchers mitt while catching a baseball.	373	640	COCO_val2014_000000461491.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000461491.jpg
+108577	291209	A man walking on top of a baseball field wearing a catchers mitt.	427	640	COCO_val2014_000000291209.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000291209.jpg
+108635	383337	A hand reaching into a box of donuts on a counter.	480	640	COCO_val2014_000000383337.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000383337.jpg
+108677	13832	close up of a chicken sandwich and fries on a plate	640	640	COCO_val2014_000000013832.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013832.jpg
+108914	111416	Three young men holding surfboards walking on a beach.	428	640	COCO_val2014_000000111416.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111416.jpg
+109068	54671	A couple laying on a big bed in a bedroom.	426	640	COCO_val2014_000000054671.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054671.jpg
+109069	387173	A person riding on the water in a red row boat.	480	640	COCO_val2014_000000387173.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000387173.jpg
+109167	5476	A group of people walking towards a beach while carrying surfboards.	476	640	COCO_val2014_000000005476.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005476.jpg
+109351	206135	A herd of cattle sitting in front of a church with a steeple.	500	332	COCO_val2014_000000206135.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000206135.jpg
+109406	533281	A wood fire oven with a long metal spatula hanging on the wall next to it.	500	375	COCO_val2014_000000533281.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533281.jpg
+109424	357475	A white toilet sitting next to a white bath tub.	480	640	COCO_val2014_000000357475.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000357475.jpg
+109461	435334	horses grazing on a lush green field surrounded by trees.	480	640	COCO_val2014_000000435334.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000435334.jpg
+109609	79490	A bench next to a stone wall and a dirt road.	480	640	COCO_val2014_000000079490.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000079490.jpg
+109620	546245	A bed covered in pillows sitting underneath a window.	374	500	COCO_val2014_000000546245.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546245.jpg
+109722	64196	A woman standing at a table preparing food.	480	640	COCO_val2014_000000064196.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064196.jpg
+109729	194756	A man driving a boat down a river next to shore.	480	640	COCO_val2014_000000194756.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000194756.jpg
+109995	45966	A stop sign posted in a foreign language	640	480	COCO_val2014_000000045966.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000045966.jpg
+110291	138115	An old man making duck lips while holding an orange.	621	448	COCO_val2014_000000138115.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000138115.jpg
+110295	142667	People look at various food trucks in the city.	612	612	COCO_val2014_000000142667.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000142667.jpg
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+110376	398028	A ballplayer on deck practicing for his turn at bat	640	428	COCO_val2014_000000398028.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398028.jpg
+110448	221713	A curly haired man standing on the side of the street.	640	480	COCO_val2014_000000221713.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000221713.jpg
+110474	55072	A single giraffe looks over the green brush.	427	640	COCO_val2014_000000055072.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000055072.jpg
+110618	483249	A couple of cats sleeping on a car hood.	333	500	COCO_val2014_000000483249.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000483249.jpg
+110766	385036	Fresh fruit and vegetables have been gathered from the market	440	640	COCO_val2014_000000385036.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000385036.jpg
+110801	526359	A group of couches in a tidy living room.	480	640	COCO_val2014_000000526359.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000526359.jpg
+110802	227742	A man in white skiing in the snow.	640	427	COCO_val2014_000000227742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227742.jpg
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+111048	300538	Two small children brushing their teeth at a bathroom sink	480	640	COCO_val2014_000000300538.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000300538.jpg
+111138	228504	People on a beach with a kite flying in the air.	426	640	COCO_val2014_000000228504.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000228504.jpg
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+111848	163025	This is one man with two different sides to him.	640	427	COCO_val2014_000000163025.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000163025.jpg
+111896	568559	A black cat is sitting on a wood desk.	375	500	COCO_val2014_000000568559.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000568559.jpg
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+112318	193743	A man standing on a tennis court waves to someone.	640	427	COCO_val2014_000000193743.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000193743.jpg
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+112877	169700	A clock shows it is just after 10:00 o'clock.	640	480	COCO_val2014_000000169700.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000169700.jpg
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+113279	505528	A horse pulling a coach in an indoor arena.	425	640	COCO_val2014_000000505528.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000505528.jpg
+113364	471415	White sheep grazing in a grass pasture near stone walls.	375	500	COCO_val2014_000000471415.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000471415.jpg
+113634	294712	Two young child skiers are headed down a small slope.	375	500	COCO_val2014_000000294712.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294712.jpg
+113848	348594	A group of people are playing frisbee on the beach	375	500	COCO_val2014_000000348594.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000348594.jpg
+114589	478736	Red street lights on an empty intersection with no traffic	480	640	COCO_val2014_000000478736.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000478736.jpg
+114827	518177	a tray of food going into an oven	441	640	COCO_val2014_000000518177.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000518177.jpg
+114915	278095	A man power sliding on a long board	640	640	COCO_val2014_000000278095.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000278095.jpg
+115029	398222	Many people are gathered together for the dinner party.	427	640	COCO_val2014_000000398222.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398222.jpg
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+115233	422982	A couple uses a knife to cut the cake.	424	640	COCO_val2014_000000422982.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000422982.jpg
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+115617	517610	An open with plants sitting in front of it on a ledge.	640	512	COCO_val2014_000000517610.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000517610.jpg
+115876	415967	The dark mirror shows a bathroom in it's reflection.	640	478	COCO_val2014_000000415967.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415967.jpg
+115878	31000	Many oranges have been placed inside a bowl.	480	640	COCO_val2014_000000031000.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000031000.jpg
+115922	129175	A group of people on a gondola underneath a bridge.	430	640	COCO_val2014_000000129175.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129175.jpg
+116020	576070	A bed in a bedroom between two lamps in front of a tv on a dresser.	333	500	COCO_val2014_000000576070.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000576070.jpg
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+116149	257853	A canopy bed is dressed in all pink bedding.	640	484	COCO_val2014_000000257853.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000257853.jpg
+116177	502275	A brown cat is sitting on a plastic bag.	427	640	COCO_val2014_000000502275.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000502275.jpg
+116315	172690	People are playing frisbee and volleyball at the beach.	359	640	COCO_val2014_000000172690.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000172690.jpg
+116328	534127	A man in a field ducking from a frisbee	360	640	COCO_val2014_000000534127.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534127.jpg
+116368	235200	A picture of some animals eating some grass.	480	640	COCO_val2014_000000235200.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000235200.jpg
+116585	377577	A picture of a person holding an umbrella.	428	640	COCO_val2014_000000377577.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000377577.jpg
+116874	275270	A very clean sink that is in the bathroom.	451	640	COCO_val2014_000000275270.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000275270.jpg
+116922	432626	A man standing in a room next to a man laying on the ground.	640	480	COCO_val2014_000000432626.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000432626.jpg
+117000	533958	A white plate topped with a fried egg next to a salad.	640	640	COCO_val2014_000000533958.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533958.jpg
+117217	373007	A man riding a skateboard up the side of a cement ramp.	640	426	COCO_val2014_000000373007.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000373007.jpg
+117221	144365	a window shoing a man standing alone on a train platform	480	640	COCO_val2014_000000144365.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000144365.jpg
+117248	264753	A picture of two buildings and a street in black-and-white.	423	640	COCO_val2014_000000264753.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264753.jpg
+117330	345068	A hospital room filled with empty beds and medical equipment.	457	640	COCO_val2014_000000345068.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345068.jpg
+117419	401816	A group of three men walking horses on a paved country road.	480	640	COCO_val2014_000000401816.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401816.jpg
+117460	504561	A woman holding a pink jacket next to a man wearing a tie.	640	480	COCO_val2014_000000504561.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000504561.jpg
+117500	519479	A man in  library looking at his laptop.	513	500	COCO_val2014_000000519479.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519479.jpg
+117569	1761	Two planes flying in the sky over a bridge.	640	427	COCO_val2014_000000001761.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001761.jpg
+117583	258588	A boy holding a tennis racquet on a tennis court.	375	500	COCO_val2014_000000258588.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000258588.jpg
+117661	126340	A young boy with a bowl haircut holding  a Nintendo Wii controller.	480	640	COCO_val2014_000000126340.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000126340.jpg
+117715	29886	Two people sitting at a table having a conversation.	640	630	COCO_val2014_000000029886.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029886.jpg
+117775	9379	A bus stop next to a curvy road surrounded by traffic lights.	426	640	COCO_val2014_000000009379.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009379.jpg
+117840	176730	A young swimmer walks across surf boards being held.	427	640	COCO_val2014_000000176730.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000176730.jpg
+117909	10114	A picture of a very nice kitchen that is white.	640	480	COCO_val2014_000000010114.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010114.jpg
+118015	99961	A boat parked in a field with long green grass.	480	640	COCO_val2014_000000099961.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099961.jpg
+118020	266246	A young girl outside in the rain holding an umbrella.	360	640	COCO_val2014_000000266246.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000266246.jpg
+118092	303937	Apples and oranges sitting next to each other on a shelf.	307	640	COCO_val2014_000000303937.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303937.jpg
+118192	284605	A cat under a blanket looking at something.	480	640	COCO_val2014_000000284605.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000284605.jpg
+118247	467475	A formation of birds in a sky at sunset.	375	500	COCO_val2014_000000467475.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000467475.jpg
+118259	467990	A gray and yellow train traveling along the tracks	480	640	COCO_val2014_000000467990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000467990.jpg
+118326	220160	A close-up of the streetlamp showing to red street signs.	375	500	COCO_val2014_000000220160.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000220160.jpg
+118701	344762	A building with a grey hound road sign on it	480	640	COCO_val2014_000000344762.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344762.jpg
+118789	4498	A young boy riding a skateboard on a street in front of a house.	640	468	COCO_val2014_000000004498.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000004498.jpg
+118848	514550	a man on a skateboard skates on the sides of a skate bowl	640	424	COCO_val2014_000000514550.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000514550.jpg
+119092	213932	A picture of a motorcycle on the street.	429	640	COCO_val2014_000000213932.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213932.jpg
+119581	172911	A car seat covered in a piece of cake and frosting.	427	640	COCO_val2014_000000172911.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000172911.jpg
+119645	218889	A group of young people wearing ski equipment while standing on a snow covered ski slope.	303	640	COCO_val2014_000000218889.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218889.jpg
+120130	246248	A red stop sign on the side of a brick road.	480	640	COCO_val2014_000000246248.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000246248.jpg
+120255	137622	A young boy on the skateboard at the skate park.	480	640	COCO_val2014_000000137622.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000137622.jpg
+120256	173693	A jockey riding a horse on a horse racing track.	480	640	COCO_val2014_000000173693.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173693.jpg
+120268	546664	A man surfing the waves on an artificial wave pool.	640	480	COCO_val2014_000000546664.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546664.jpg
+120306	92335	two kayakers enjoy the clear open water	330	640	COCO_val2014_000000092335.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092335.jpg
+120319	446301	A beautiful woman in a short skirt holding a green tennis ball and a tennis racquet.	428	640	COCO_val2014_000000446301.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000446301.jpg
+120356	161370	A living room filled with nice furniture and a persian rug.	360	640	COCO_val2014_000000161370.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000161370.jpg
+120386	146742	A beautiful young lady laying on top of a park bench.	436	640	COCO_val2014_000000146742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146742.jpg
+120395	376754	The man is catching a wave at the ocean.	426	640	COCO_val2014_000000376754.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000376754.jpg
+120492	134459	A group of people playing Frisbee in a field.	428	640	COCO_val2014_000000134459.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134459.jpg
+120709	559261	A tennis player reaches for a tennis ball.	500	375	COCO_val2014_000000559261.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559261.jpg
+120955	580363	A close up view of sheets that are on a bed	480	640	COCO_val2014_000000580363.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000580363.jpg
+121390	177246	A close-up of a waffle shaped cone with a banana in it.	612	612	COCO_val2014_000000177246.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177246.jpg
+121483	400516	An Asian street scene with many people on the street.	427	640	COCO_val2014_000000400516.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000400516.jpg
+121611	296471	Cat sitting on the ground in between an open refrigerator.	492	640	COCO_val2014_000000296471.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000296471.jpg
+121635	34522	A close-up of a hawk with a group of people in the background.	453	640	COCO_val2014_000000034522.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034522.jpg
+121646	128373	Various stuffed animals behind a pane of glass	640	456	COCO_val2014_000000128373.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000128373.jpg
+121804	313603	A close-up of headphones next to a keyboard on a desk.	480	640	COCO_val2014_000000313603.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000313603.jpg
+122075	247880	Group of surfing equipment sitting next to a beach.	480	640	COCO_val2014_000000247880.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000247880.jpg
+122262	267363	A man sitting in the driver seat of a truck on a cell phone.	480	640	COCO_val2014_000000267363.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267363.jpg
+122413	556278	A street intersection at night with no vehicles on the road.	424	640	COCO_val2014_000000556278.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000556278.jpg
+122597	416256	A cat laying on the floor next to a keyboard.	375	500	COCO_val2014_000000416256.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000416256.jpg
+123044	487418	a close up of a person eating a hot dog	640	640	COCO_val2014_000000487418.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487418.jpg
+123241	226170	a close up of a plate of food like peas and broccoli	640	480	COCO_val2014_000000226170.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226170.jpg
+123478	31151	Three demonic looking dummies standing next to each other on a snow covered slope.	383	640	COCO_val2014_000000031151.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000031151.jpg
+123563	232453	A large green and yellow truck parked next to a similar green and yellow truck.	480	640	COCO_val2014_000000232453.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000232453.jpg
+123614	394458	Two baby gray elephants standing in front of each other.	427	640	COCO_val2014_000000394458.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000394458.jpg
+123720	314792	A pink doll house filled with miniature furniture.	480	640	COCO_val2014_000000314792.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000314792.jpg
+123728	182021	A man holding a baseball bat while standing on a baseball field.	360	640	COCO_val2014_000000182021.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182021.jpg
+123862	321692	A group of horses tied up to post under a big green tree.	439	640	COCO_val2014_000000321692.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000321692.jpg
+123871	166340	A man flying a kite while standing next to his small white dog on a beach.	500	375	COCO_val2014_000000166340.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000166340.jpg
+124006	168010	Luggage sitting on the ground on a dirty and grass field.	464	640	COCO_val2014_000000168010.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168010.jpg
+124030	160199	A giraffe standing in front of a stone building.	425	640	COCO_val2014_000000160199.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160199.jpg
+124116	209180	A group of baseball players playing a game of baseball.	640	426	COCO_val2014_000000209180.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209180.jpg
+124125	92244	A doll in a dress standing between two parking meters.	480	640	COCO_val2014_000000092244.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092244.jpg
+124126	235399	a white dog is sitting in the back of a truck and a mattress	375	500	COCO_val2014_000000235399.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000235399.jpg
+124247	25972	A blue love seat sitting on a trailer attached to a bike.	481	640	COCO_val2014_000000025972.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025972.jpg
+124413	54603	A mirror sitting on top of a wooden dresser.	419	640	COCO_val2014_000000054603.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054603.jpg
+124457	184499	A man and a woman sitting in a booth having milk shakes and cake.	640	640	COCO_val2014_000000184499.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000184499.jpg
+124539	291930	a man wearing leathers and his black motorcycle	480	640	COCO_val2014_000000291930.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000291930.jpg
+124580	328368	an old man with a white dog on a leash and a bench	500	356	COCO_val2014_000000328368.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328368.jpg
+124624	126257	little dude shredding the gnar on flat ground	612	612	COCO_val2014_000000126257.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000126257.jpg
+124680	211825	A man blowing out candles on a cake.	480	640	COCO_val2014_000000211825.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211825.jpg
+124784	388795	Two desks rigged to look like the drivers seats of race cars are at the foot of a bed.	333	500	COCO_val2014_000000388795.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000388795.jpg
+124957	376751	Skies skiing near a chair lift and a mountain behind.	480	640	COCO_val2014_000000376751.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000376751.jpg
+125419	264962	three zebras and a gazelle drinking at a watering hole	473	640	COCO_val2014_000000264962.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264962.jpg
+125509	529069	A bathroom toilet with a light fixture overhead	640	427	COCO_val2014_000000529069.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529069.jpg
+125653	152543	A group of young people are sitting on a concrete divider.	407	640	COCO_val2014_000000152543.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152543.jpg
+125676	172556	An instructor teaching people how to surf on the beach	480	640	COCO_val2014_000000172556.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000172556.jpg
+126316	538236	A tray filled with croissants with hotdogs in the middle	428	640	COCO_val2014_000000538236.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000538236.jpg
+126957	9857	The lamp is on next to a bed with a quilt.	480	640	COCO_val2014_000000009857.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009857.jpg
+127014	154587	A motorcycle carrying a bicycle is parked in the gravel.	462	640	COCO_val2014_000000154587.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000154587.jpg
+127437	533026	a bathroom with a cat underneath a bath tub	640	359	COCO_val2014_000000533026.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533026.jpg
+127556	328354	A dilapidated room with a plaster chunked floor is equipped with debris ridden chairs and a tiny vintage refrigerator.	424	640	COCO_val2014_000000328354.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328354.jpg
+127743	448600	There is one piece of cake on the plate.	612	612	COCO_val2014_000000448600.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448600.jpg
+128540	261062	the door to the room is open to the outside	439	640	COCO_val2014_000000261062.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000261062.jpg
+128559	540694	A giraffe standing in a lot filled with cars.	480	640	COCO_val2014_000000540694.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540694.jpg
+129628	29709	A photo of a dining venue outside complete with tables, chairs and umbrellas.	375	500	COCO_val2014_000000029709.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029709.jpg
+129737	32300	A picture of a kid picking up a ball.	480	640	COCO_val2014_000000032300.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032300.jpg
+129813	345618	A picture of some food on a plate.	640	480	COCO_val2014_000000345618.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345618.jpg
+130023	126876	A picture of a plane is flying in the air.	480	640	COCO_val2014_000000126876.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000126876.jpg
+130077	574376	A pretty young lady kneeling down on top of a surfboard.	360	640	COCO_val2014_000000574376.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000574376.jpg
+130171	328745	Two brown bears sitting on top of a black and white checkered bed.	426	640	COCO_val2014_000000328745.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328745.jpg
+130196	263858	Three boats filled with people floating down a river.	427	640	COCO_val2014_000000263858.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000263858.jpg
+130341	334140	A close up of a woven basket on a bicycle.	381	640	COCO_val2014_000000334140.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334140.jpg
+130438	493652	Living room decorated for Christmas with gifts galore	399	640	COCO_val2014_000000493652.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493652.jpg
+130452	456323	A bedroom with a bike parked in it	480	640	COCO_val2014_000000456323.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000456323.jpg
+130578	311913	A pair of horses running through a grassy field	427	640	COCO_val2014_000000311913.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000311913.jpg
+130741	290234	A person that is on his cell phone in a car.	241	500	COCO_val2014_000000290234.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000290234.jpg
+130906	338562	A red fire hydrant in front of a shopping center.	500	499	COCO_val2014_000000338562.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000338562.jpg
+131041	334743	Three double decker busses are parked in front of a building.	480	640	COCO_val2014_000000334743.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334743.jpg
+131266	424445	A fighter jet taking off from an airport runway.	427	640	COCO_val2014_000000424445.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000424445.jpg
+131640	524392	A kitchen scene with focus on the refrigerator.	640	480	COCO_val2014_000000524392.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000524392.jpg
+131941	202275	Two dogs pulling on the same play toy in a park	427	640	COCO_val2014_000000202275.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000202275.jpg
+132906	74465	A cargo train is sitting at a train station.	360	640	COCO_val2014_000000074465.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000074465.jpg
+133077	373574	A TV sitting on top of a table next to a laptop computer.	326	512	COCO_val2014_000000373574.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000373574.jpg
+133299	501281	A man with a tennis racquet walking in the street.	480	640	COCO_val2014_000000501281.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000501281.jpg
+133449	12101	A toilet bowl with a sign on the top	375	500	COCO_val2014_000000012101.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000012101.jpg
+133491	89558	A train station with a blue train at the station.	640	640	COCO_val2014_000000089558.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089558.jpg
+133792	421970	An expressway sign is informing of how much further to the next destination.	375	500	COCO_val2014_000000421970.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421970.jpg
+134282	465074	A woman with a hat is using an umbrella.	640	428	COCO_val2014_000000465074.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000465074.jpg
+134450	517128	inside a airplane hangar,a stealth plane hanging -up.	424	640	COCO_val2014_000000517128.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000517128.jpg
+134742	444982	Two zebra grazing the grass in their enclosure.	480	640	COCO_val2014_000000444982.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444982.jpg
+134931	552866	A picture of a very long and tall building.	427	640	COCO_val2014_000000552866.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552866.jpg
+135046	400238	A group of people standing around with two of them holding the Nintendo Wii remote.	480	640	COCO_val2014_000000400238.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000400238.jpg
+135531	397640	A man on horseback herds cattle on yellow grass.	427	640	COCO_val2014_000000397640.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397640.jpg
+135676	514681	A body of water with boats in city buildings in the background.	459	640	COCO_val2014_000000514681.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000514681.jpg
+135806	552744	A black-and-white photo of a girl with a teddy bear on her shoulder.	427	640	COCO_val2014_000000552744.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552744.jpg
+135817	468736	A close-up of a plate of pasta and meat with a piece of broccoli.	480	640	COCO_val2014_000000468736.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000468736.jpg
+135819	473783	Multiple train tracks with a train on one of the tracks.	480	640	COCO_val2014_000000473783.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473783.jpg
+135864	393858	A plane that is flying in the air.	301	640	COCO_val2014_000000393858.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000393858.jpg
+135955	469713	An animal that is licking the neck of another animal.	427	640	COCO_val2014_000000469713.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000469713.jpg
+136305	329421	A tent that is in some grass with a table.	640	640	COCO_val2014_000000329421.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000329421.jpg
+136333	344029	A bus that is driving down the road.	480	640	COCO_val2014_000000344029.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344029.jpg
+136480	62154	A table with a piece of construction paper, scissors and sewing thread.	480	640	COCO_val2014_000000062154.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062154.jpg
+136521	129595	A man holding up a cardboard sign next to a woman.	373	500	COCO_val2014_000000129595.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129595.jpg
+137382	422622	A group of skiers adjusts their gear during a heavy snow.	427	640	COCO_val2014_000000422622.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000422622.jpg
+137420	209419	A white dog laying on top of a Siamese cat on a stone wall.	546	640	COCO_val2014_000000209419.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209419.jpg
+137477	315001	A flock of birds flying over the ocean next to a beach.	639	640	COCO_val2014_000000315001.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315001.jpg
+137520	555456	A person riding a snowboard on a snow covered slope.	427	640	COCO_val2014_000000555456.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000555456.jpg
+137601	139684	A living room filled with furniture and a flat screen tv.	333	500	COCO_val2014_000000139684.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000139684.jpg
+137746	401163	The dog lies on the ground, while the two men talk to each other.	427	640	COCO_val2014_000000401163.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401163.jpg
+137826	427901	four people playing tennis on a tennis court.	429	640	COCO_val2014_000000427901.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427901.jpg
+138081	143576	A cook is in the kitchen preparing a big pot of food.	425	640	COCO_val2014_000000143576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143576.jpg
+138291	156292	A woman standing in front of an outdoor wall decorated with images and clocks.	640	428	COCO_val2014_000000156292.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000156292.jpg
+138902	561037	A cruiser bike in line with others at a biker gathering	509	640	COCO_val2014_000000561037.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000561037.jpg
+139624	11300	The little boy sits on the ground next to the skateboard.	425	640	COCO_val2014_000000011300.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000011300.jpg
+139892	508443	A half eaten pizza muffin is sitting on a plate with others untouched.	640	428	COCO_val2014_000000508443.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000508443.jpg
+141016	434958	A man is in a car holding a black cat.	640	427	COCO_val2014_000000434958.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000434958.jpg
+141694	257359	A train is stopped in front of an industrial area.	418	640	COCO_val2014_000000257359.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000257359.jpg
+141928	50655	A bathroom with a narrow toilet and hanging towels.	640	428	COCO_val2014_000000050655.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000050655.jpg
+142277	429717	A woman lays on her bed with her dog while using the mirror to take a picture.	375	500	COCO_val2014_000000429717.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000429717.jpg
+142494	118584	some elephants in a field of grass near a tree	360	480	COCO_val2014_000000118584.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118584.jpg
+142627	89188	two dogs are at a door trying to get outside	640	480	COCO_val2014_000000089188.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089188.jpg
+142777	44575	A man coaching a tee ball practice.	401	640	COCO_val2014_000000044575.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000044575.jpg
+143183	448359	a woman holding a tennis racket approaching the net	428	640	COCO_val2014_000000448359.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448359.jpg
+143293	488522	There is a knife cutting into a cake.	500	375	COCO_val2014_000000488522.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000488522.jpg
+143406	155035	The three giraffe stand next to a pile of logs.	480	640	COCO_val2014_000000155035.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000155035.jpg
+144141	61831	A pair of parking meters with a coffee cup between them	640	427	COCO_val2014_000000061831.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000061831.jpg
+144619	42215	There are two yellow lemons and five green lemons and also two oranges	318	640	COCO_val2014_000000042215.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000042215.jpg
+144726	339368	A city street scene with a green bus coming up a street, with ocean in the background.	418	640	COCO_val2014_000000339368.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000339368.jpg
+144858	49660	A male tennis player jumping in the air to serve a ball on a clay court with spectators looking on.	500	334	COCO_val2014_000000049660.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000049660.jpg
+145394	37109	A piece of cheesecake and 2 pieces of an English muffin.	612	612	COCO_val2014_000000037109.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000037109.jpg
+145556	209028	A small brown and white bulldog riding a skateboard.	640	480	COCO_val2014_000000209028.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209028.jpg
+145582	218470	A traffic light next to a street light below a tall building.	640	480	COCO_val2014_000000218470.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218470.jpg
+145657	195648	A park bench surrounded by a green forest of trees.	640	458	COCO_val2014_000000195648.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195648.jpg
+145684	55167	A yellow and blue bus traveling down a road next to a park.	360	640	COCO_val2014_000000055167.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000055167.jpg
+145693	545550	A little kid eating a slice of cheesy pizza.	523	640	COCO_val2014_000000545550.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000545550.jpg
+145817	288983	A large giraffe standing behind a wire fence.	640	425	COCO_val2014_000000288983.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000288983.jpg
+145822	126123	A man standing next to a bear on a Chain leash.	412	640	COCO_val2014_000000126123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000126123.jpg
+145885	549518	two bananas sitting on top of a table next to a white bowl.	640	480	COCO_val2014_000000549518.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000549518.jpg
+146018	562139	A lighted kitchen looks shiny clean and nice.	361	640	COCO_val2014_000000562139.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000562139.jpg
+146083	300437	A picture of some pizza on a table.	480	640	COCO_val2014_000000300437.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000300437.jpg
+146258	67213	A lot of people that are looking at a pool.	485	640	COCO_val2014_000000067213.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067213.jpg
+146323	503470	A man and a woman laying in bed on top of each other.	640	426	COCO_val2014_000000503470.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000503470.jpg
+146324	413971	Two plates filled with hot dogs sitting on a wooden counter next to drinks.	425	640	COCO_val2014_000000413971.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000413971.jpg
+146587	350251	A metal microwave sitting on a counter top.	427	640	COCO_val2014_000000350251.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000350251.jpg
+146620	473731	A picture of a horse that is in some dirt.	424	640	COCO_val2014_000000473731.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473731.jpg
+146633	154420	A clean bathroom with blue tiles on wall and counter.	640	428	COCO_val2014_000000154420.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000154420.jpg
+146649	222825	A kitchen with a stove top oven next to a white fridge.	640	427	COCO_val2014_000000222825.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000222825.jpg
+146762	577030	A large white couch in a living room in front of a TV.	479	640	COCO_val2014_000000577030.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577030.jpg
+147039	109819	An elephant walks next to a bus down a busy street.	480	640	COCO_val2014_000000109819.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000109819.jpg
+147145	86320	A bathroom sink sitting under a large mirror.	336	500	COCO_val2014_000000086320.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000086320.jpg
+147205	341969	A man holding a smart phone inside of his pocket.	411	640	COCO_val2014_000000341969.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341969.jpg
+147321	419624	A long white train traveling through a lush green hillside.	428	640	COCO_val2014_000000419624.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419624.jpg
+147364	468548	Factory where bananas are being prepared for market	426	640	COCO_val2014_000000468548.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000468548.jpg
+147427	37709	There is a stop sign and two street name signs on one poll	480	640	COCO_val2014_000000037709.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000037709.jpg
+147487	477792	AN IMAGE OF A BUNCH OF PRETTY RED ROSES IN A BLUE BUCKET	375	500	COCO_val2014_000000477792.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000477792.jpg
+147648	445009	Looking down into a living room where a dog is sleeping on the floor	480	640	COCO_val2014_000000445009.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000445009.jpg
+147676	179273	A plate topped with different kinds of foods on a table.	302	400	COCO_val2014_000000179273.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000179273.jpg
+147715	254123	An empty plastic container and a plastic fork left on a bench.	375	500	COCO_val2014_000000254123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000254123.jpg
+147744	532085	The glaring lights of cars and street lights.	375	500	COCO_val2014_000000532085.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532085.jpg
+147930	26378	An older man takes a pizza out of the oven.	375	500	COCO_val2014_000000026378.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026378.jpg
+147931	468337	A woman sitting on a surfboard in the ocean.	446	640	COCO_val2014_000000468337.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000468337.jpg
+147985	154154	A man riding a skateboard up the side of a ramp.	427	640	COCO_val2014_000000154154.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000154154.jpg
+148031	532690	A woman playing a Wii game with other people.	640	528	COCO_val2014_000000532690.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532690.jpg
+148180	192660	Benches of fruit on display at a market	480	640	COCO_val2014_000000192660.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192660.jpg
+148183	276804	A small lit baseball field at night with people playing baseball.	640	512	COCO_val2014_000000276804.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000276804.jpg
+148414	388237	A young person riding a skateboard down the street	427	640	COCO_val2014_000000388237.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000388237.jpg
+148949	201916	A brown sheep standing in front of a pile of rock.	425	640	COCO_val2014_000000201916.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000201916.jpg
+149095	464751	The man stands in the bathroom with no shirt on brushing his teeth.	333	500	COCO_val2014_000000464751.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464751.jpg
+149257	401892	A young boy trying to get his striped kite to fly.	375	500	COCO_val2014_000000401892.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401892.jpg
+149263	378809	A rear view mirror on the side of a yellow bus.	640	436	COCO_val2014_000000378809.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000378809.jpg
+149499	1398	A cat rubbing up against the camera persons legs.	482	407	COCO_val2014_000000001398.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001398.jpg
+150035	580248	Two surfers with their heads disguised at Joe Biden and President Obama hold a surfboard	480	640	COCO_val2014_000000580248.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000580248.jpg
+150254	379552	A hand in a bowl full of broccoli.	425	640	COCO_val2014_000000379552.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379552.jpg
+150293	447585	Two men stand by a large desert pizza.	640	427	COCO_val2014_000000447585.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000447585.jpg
+150405	554620	A woman with blonde hair holding an open umbrella.	640	426	COCO_val2014_000000554620.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000554620.jpg
+150780	122239	A small boy with blonde hair eats an apple	426	640	COCO_val2014_000000122239.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000122239.jpg
+150783	214527	A train station with a large clock on the front of the building.	425	640	COCO_val2014_000000214527.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214527.jpg
+150908	518383	A person riding a snowboard down a snow covered slope.	480	640	COCO_val2014_000000518383.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000518383.jpg
+151027	193704	A green motel sign hanging from a fake cactus.	555	640	COCO_val2014_000000193704.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000193704.jpg
+151188	499105	A young lady learning to play tennis preparing to hit a ball	500	375	COCO_val2014_000000499105.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000499105.jpg
+151435	85852	A little girl holding a green umbrella on a sidewalk.	640	427	COCO_val2014_000000085852.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000085852.jpg
+151845	277094	Two cops standing in front of a street on a cross walk.	480	640	COCO_val2014_000000277094.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000277094.jpg
+151920	303928	a walk in shower with a seat next to a white sink.	640	480	COCO_val2014_000000303928.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303928.jpg
+152000	450709	a snowboarder in a white jacket making way down the hill	637	640	COCO_val2014_000000450709.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450709.jpg
+152072	551358	A bed in a redroom next to a door with windows.	640	480	COCO_val2014_000000551358.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000551358.jpg
+152228	389174	Some people that are sitting on a bench.	427	640	COCO_val2014_000000389174.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000389174.jpg
+152341	289943	A white street sign hanging from the side of a pole.	426	640	COCO_val2014_000000289943.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289943.jpg
+152634	503277	A stop sign sitting next to a street sign on the side of a road.	640	596	COCO_val2014_000000503277.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000503277.jpg
+152745	246701	A white toilet sitting next to a tub next to a sink	500	375	COCO_val2014_000000246701.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000246701.jpg
+153050	412621	A person that is doing a trick in the air.	524	640	COCO_val2014_000000412621.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412621.jpg
+153052	80131	A group of people sitting around a table in the kitchen.	480	640	COCO_val2014_000000080131.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080131.jpg
+153074	346596	A bike with a basket sitting on snow covered ground.	215	640	COCO_val2014_000000346596.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000346596.jpg
+153328	154202	Several different types of electronics sprawled out on a bed.	480	640	COCO_val2014_000000154202.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000154202.jpg
+153371	534128	A sleeping area of a train car with a seat belt buckle.	425	640	COCO_val2014_000000534128.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534128.jpg
+153411	210342	A crowd of people laying on a beach next to surfboards.	425	640	COCO_val2014_000000210342.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000210342.jpg
+153534	3209	A woman riding a horse next to a group of people.	640	640	COCO_val2014_000000003209.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000003209.jpg
+153568	87156	a yellow tram some mountains snow and trees	426	640	COCO_val2014_000000087156.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087156.jpg
+153591	47570	A family sitting down at a dinner table eating food.	428	640	COCO_val2014_000000047570.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000047570.jpg
+153625	529944	A woman kneeling down on the floor before an oven.	480	640	COCO_val2014_000000529944.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529944.jpg
+153751	425555	A woman in a wheel chair sitting next to a red fire hydrant.	426	640	COCO_val2014_000000425555.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000425555.jpg
+153784	234691	A large clock tower with a giant clock and two statues.	640	427	COCO_val2014_000000234691.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000234691.jpg
+154121	188945	A man riding a skateboard down the side of a road.	148	640	COCO_val2014_000000188945.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188945.jpg
+154286	81287	A green and beige bus traveling down a busy city street.	480	640	COCO_val2014_000000081287.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000081287.jpg
+154304	181742	A red fire hydrant sitting on a muddy green hillside.	640	480	COCO_val2014_000000181742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000181742.jpg
+154305	209995	A woman with blonde hair standing next to a bar making drinks.	480	640	COCO_val2014_000000209995.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209995.jpg
+154406	431165	A baby elephant standing in front of a brick building.	427	640	COCO_val2014_000000431165.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431165.jpg
+154762	149196	A pile of stuffed animals hanging from the ceiling of a store.	478	640	COCO_val2014_000000149196.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000149196.jpg
+154773	473623	A white porcelain toilet with the lid down, in a bathroom.	640	480	COCO_val2014_000000473623.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473623.jpg
+154871	87444	A cow sitting in a penn inside of a building.	480	640	COCO_val2014_000000087444.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087444.jpg
+154935	574845	A mattress sitting in the corner between two buildings.	480	640	COCO_val2014_000000574845.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000574845.jpg
+155818	503255	The bench at the tree offers a respite and a scenic autumnal view of a grand valley	640	427	COCO_val2014_000000503255.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000503255.jpg
+156403	440554	A baseball player lower his glove for a ground ball.	400	640	COCO_val2014_000000440554.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000440554.jpg
+157010	10693	an image of a woman holding an umbrella with her coat on	427	640	COCO_val2014_000000010693.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010693.jpg
+157018	386187	a person getting a drink with a person behind the bar	480	640	COCO_val2014_000000386187.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000386187.jpg
+157020	330098	a bike sitting in front of colorful signs	427	640	COCO_val2014_000000330098.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000330098.jpg
+157551	581899	Two bright green passenger trains pass by with people standing in the doorways.	480	640	COCO_val2014_000000581899.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000581899.jpg
+157668	236323	Contents of a refrigerator inside compartments on the door.	375	500	COCO_val2014_000000236323.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000236323.jpg
+157675	11742	A glass of red wine sits beside the bottle for a refill as a piece of cake goes untouched in the back.	640	467	COCO_val2014_000000011742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000011742.jpg
+157689	373029	An aircraft is overtaken by maintenance workers as it sits on a landing.	480	640	COCO_val2014_000000373029.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000373029.jpg
+157757	543620	A catcher, umpire, and batter playing on a baseball field.	285	640	COCO_val2014_000000543620.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543620.jpg
+157816	296176	A man in a field flying an airplane shaped kite.	480	640	COCO_val2014_000000296176.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000296176.jpg
+157896	393874	The older man uses a computer near library equipment.	640	481	COCO_val2014_000000393874.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000393874.jpg
+159027	60687	A brown cow lays down in the grass	480	640	COCO_val2014_000000060687.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000060687.jpg
+159457	109894	a clock on a pole on a city street near buildings	640	429	COCO_val2014_000000109894.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000109894.jpg
+159733	13465	Two people doing a skateboard trick above a concrete set of stairs.	640	426	COCO_val2014_000000013465.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013465.jpg
+159792	141828	Square table with a table top on three sides.	375	500	COCO_val2014_000000141828.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000141828.jpg
+160007	443347	A young lady showing off the hot dogs she just bought.	640	480	COCO_val2014_000000443347.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000443347.jpg
+160167	217783	a large air plane on a snowy surface with a sky background	480	640	COCO_val2014_000000217783.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000217783.jpg
+160220	325347	A man on a tennis court playing tennis	359	640	COCO_val2014_000000325347.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325347.jpg
+160475	67868	A park bench submerged in water in a flood.	429	640	COCO_val2014_000000067868.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067868.jpg
+160481	542374	Two bottles of apple cider, an apple and a garnished entree.	427	640	COCO_val2014_000000542374.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000542374.jpg
+160659	257370	two people are playing a baseball video game	480	640	COCO_val2014_000000257370.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000257370.jpg
+161166	124876	a person riding a surf board on a wave	480	640	COCO_val2014_000000124876.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000124876.jpg
+161533	47828	A blue lighted bridge over water at night.	318	640	COCO_val2014_000000047828.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000047828.jpg
+161647	183338	a man is serving in a game of tennis	425	640	COCO_val2014_000000183338.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000183338.jpg
+161662	504020	a pack of zebra underneath a shaded tree	427	640	COCO_val2014_000000504020.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000504020.jpg
+161674	378860	A slice of pizza is loaded with pepperoni and resting by itself.	333	500	COCO_val2014_000000378860.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000378860.jpg
+161718	555669	A smiling woman nonetheless cringes as a dog licks her ear.	640	533	COCO_val2014_000000555669.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000555669.jpg
+162146	480036	many bikes parked in a parking lot very close to each other	375	500	COCO_val2014_000000480036.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000480036.jpg
+162159	7952	A truck crosses train tracks near an oncoming train.	428	640	COCO_val2014_000000007952.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000007952.jpg
+162593	434129	Elaborate Toronto Blue Jays professional baseball cap cake	478	640	COCO_val2014_000000434129.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000434129.jpg
+162658	474571	A smart phone sitting on a table next to a bottle of corona beer.	640	428	COCO_val2014_000000474571.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474571.jpg
+163025	410019	A boy does tricks on a scooter.	640	425	COCO_val2014_000000410019.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000410019.jpg
+163026	507301	A cat sits on an open laptop.	640	480	COCO_val2014_000000507301.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507301.jpg
+163195	384616	a red fire hydrant near a dirt road with trees in the background	480	640	COCO_val2014_000000384616.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384616.jpg
+163426	153568	A close up of a stop sign with two hand written notes taped to it.	400	500	COCO_val2014_000000153568.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000153568.jpg
+163619	122120	A young man on a skateboard doing tricks	640	480	COCO_val2014_000000122120.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000122120.jpg
+164625	7522	An adult zebra and a younger zebra in Savannah	428	640	COCO_val2014_000000007522.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000007522.jpg
+164787	389974	A woman cooking with a large white bot and a cooking board with meat in it in a small kitchen.	425	640	COCO_val2014_000000389974.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000389974.jpg
+165050	413810	A skier riding down a snow covered slope.	640	480	COCO_val2014_000000413810.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000413810.jpg
+165138	388770	A flock of birds standing on top of a metal set of bars.	424	640	COCO_val2014_000000388770.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000388770.jpg
+165451	188416	A black computer keyboard sitting on a table.	427	640	COCO_val2014_000000188416.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188416.jpg
+165515	398758	A tiger cat sitting on the arm of a sofa in a living room.	480	640	COCO_val2014_000000398758.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398758.jpg
+165540	69121	A bathroom sink surrounded by a stone counter top.	290	450	COCO_val2014_000000069121.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000069121.jpg
+165558	107140	A woman puts her fork to a plate with an omelette and toast.	478	640	COCO_val2014_000000107140.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107140.jpg
+165597	447200	Two small dogs on leashes walking on a brick pathway.	480	640	COCO_val2014_000000447200.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000447200.jpg
+165920	406616	A wooden cutting board topped with veggies and a knife.	426	640	COCO_val2014_000000406616.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406616.jpg
+166244	306466	A TV on top of a wooden dresser next to a desk.	480	640	COCO_val2014_000000306466.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306466.jpg
+166320	430670	A man riding a skateboard down a set of steps.	640	427	COCO_val2014_000000430670.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000430670.jpg
+166443	265378	A white car parked in a parking space.	640	480	COCO_val2014_000000265378.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000265378.jpg
+166643	530583	A couple of glass items that are in a room.	640	480	COCO_val2014_000000530583.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530583.jpg
+166857	13232	A black-and-white photo of two teddy bears sitting on a piano.	427	640	COCO_val2014_000000013232.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013232.jpg
+167009	135972	The stainless steel black and aluminum toilet is opened.	640	480	COCO_val2014_000000135972.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000135972.jpg
+167179	525077	The large aircraft flies through the cloudy sky.	326	640	COCO_val2014_000000525077.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000525077.jpg
+167229	58651	A green train traveling through a train yard.	380	640	COCO_val2014_000000058651.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000058651.jpg
+167567	497238	A man riding a skateboard on a stone bench.	500	333	COCO_val2014_000000497238.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497238.jpg
+167603	402685	A plate with a piece chicken and some broccoli	640	533	COCO_val2014_000000402685.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000402685.jpg
+167924	391548	A person that is posing for a picture.	408	320	COCO_val2014_000000391548.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000391548.jpg
+167970	425369	A young woman leaning on a ski pole as she puts on her skis	640	428	COCO_val2014_000000425369.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000425369.jpg
+167993	415840	A silver and red train traveling down a busy city street.	428	640	COCO_val2014_000000415840.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415840.jpg
+168201	216944	A young girl laying on her stomach reading a book.	427	640	COCO_val2014_000000216944.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216944.jpg
+168280	557644	A row of parked bicycles sitting on a side walk.	640	427	COCO_val2014_000000557644.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000557644.jpg
+168616	54955	A small boy leaning up against a window on a train	445	640	COCO_val2014_000000054955.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054955.jpg
+168632	298994	The vegetable are laid out neatly at the table.	428	640	COCO_val2014_000000298994.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000298994.jpg
+168651	342060	The suitcases are on the pavement next to the brown bench.	480	640	COCO_val2014_000000342060.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000342060.jpg
+168697	87553	A couple of people that are in the grass.	333	500	COCO_val2014_000000087553.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087553.jpg
+168851	162799	A female tennis player on the court holding a racket.	640	471	COCO_val2014_000000162799.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000162799.jpg
+168925	47597	A clock sits on top of some books.	640	480	COCO_val2014_000000047597.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000047597.jpg
+169047	415843	A row of three wall mounted urinals in a bathroom.	360	640	COCO_val2014_000000415843.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415843.jpg
+169099	92167	A bird flying in the ocean with the sun in the background.	324	500	COCO_val2014_000000092167.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092167.jpg
+169427	148444	A close-up of a plate of food that has been eaten.	480	640	COCO_val2014_000000148444.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000148444.jpg
+169458	127182	A kitchen filled with metal appliances and a window.	640	427	COCO_val2014_000000127182.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000127182.jpg
+169802	282437	A herd of zebras standing together in a plain.	479	640	COCO_val2014_000000282437.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000282437.jpg
+169940	404738	A ceramic pot is laying in the grass	480	640	COCO_val2014_000000404738.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000404738.jpg
+170046	399666	A man jumping through the air catching a frisbee.	415	500	COCO_val2014_000000399666.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000399666.jpg
+170240	276836	A white toilet with a wooden toilet seat.	640	480	COCO_val2014_000000276836.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000276836.jpg
+170469	344439	A traffic light with a couple of green street signs mounted to it's sides.	640	426	COCO_val2014_000000344439.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344439.jpg
+170492	484627	A room filled with single beds and tow large walls of windows.	421	640	COCO_val2014_000000484627.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000484627.jpg
+170541	528496	A plate filled with a cut in half sandwich on top of a black table.	427	640	COCO_val2014_000000528496.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000528496.jpg
+170769	177258	A white tray filled with plates of food next to a cat.	427	640	COCO_val2014_000000177258.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177258.jpg
+170907	245915	A herd of elephants splashing and playing in a river.	333	500	COCO_val2014_000000245915.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000245915.jpg
+171006	260470	A counter topped with a large white frosted cake sitting next to small pastry.	427	640	COCO_val2014_000000260470.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260470.jpg
+171010	92093	Hundreds of people gathered around looking at motorcycles.	414	640	COCO_val2014_000000092093.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092093.jpg
+171145	258998	A large gray cat sitting in front of a mirror next to a pink bowl.	640	428	COCO_val2014_000000258998.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000258998.jpg
+171461	196506	A woman decorating a double layer cake on a table.	640	498	COCO_val2014_000000196506.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000196506.jpg
+171485	130826	Two men sitting next to each other on a wooden bench.	480	640	COCO_val2014_000000130826.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000130826.jpg
+171775	472623	A young man riding a bike on top of a beach.	640	425	COCO_val2014_000000472623.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472623.jpg
+172035	499428	A large red double decker bus traveling down a road.	427	640	COCO_val2014_000000499428.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000499428.jpg
+172140	93670	A man wearing a brown suit and brown tie.	427	640	COCO_val2014_000000093670.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000093670.jpg
+172688	554607	Hotdogs are cooking on a roaster with condiments arranged alongside.	480	640	COCO_val2014_000000554607.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000554607.jpg
+172933	135167	Scrambled eggs are accompanying dinner foods on a plate.	612	612	COCO_val2014_000000135167.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000135167.jpg
+173165	155179	an upclose photo of a cake with chocolate pieces	468	640	COCO_val2014_000000155179.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000155179.jpg
+173295	565395	"A man in glasses sitting next to a woman and holding up a red frisbee that says ""Cheese Shop"" on it."	480	640	COCO_val2014_000000565395.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565395.jpg
+174271	116037	An baseball player is swinging at a moving ball while the catcher behind him is ready to catch it.	411	640	COCO_val2014_000000116037.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116037.jpg
+174731	450599	A man in sandals playing tennis with two rackets	456	640	COCO_val2014_000000450599.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450599.jpg
+175368	269020	an image of zebras playing in the snow	339	640	COCO_val2014_000000269020.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000269020.jpg
+176073	143129	A man and giraffes are sitting on the grass.	427	640	COCO_val2014_000000143129.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143129.jpg
+176238	302221	A stuffed animal has been placed inside of blankets.	480	640	COCO_val2014_000000302221.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000302221.jpg
+177603	443034	A third of the chocolate cake has been eaten.	427	640	COCO_val2014_000000443034.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000443034.jpg
+177637	521943	A small boy practicing hitting a baseball off of a ball stand	468	640	COCO_val2014_000000521943.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000521943.jpg
+177986	445944	A man with a suit stands behind a row of chairs in the middle of the afternoon.	640	428	COCO_val2014_000000445944.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000445944.jpg
+178238	124013	Two young girls riding on a horse together with an older girl walking next to the horse.	475	500	COCO_val2014_000000124013.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000124013.jpg
+178815	53505	A toilet sitting underneath a medicine cabinet in a bathroom.	640	428	COCO_val2014_000000053505.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000053505.jpg
+178829	120380	A few small kids playing a game of Wii.	640	640	COCO_val2014_000000120380.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120380.jpg
+178866	229858	A deer standing next to a small deer in a forest.	555	640	COCO_val2014_000000229858.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000229858.jpg
+179018	12639	A little boy about to hit a baseball during a game.	640	480	COCO_val2014_000000012639.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000012639.jpg
+179176	160482	A couple of people on a small boat in the water.	494	640	COCO_val2014_000000160482.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160482.jpg
+179630	331807	A cutting tray with some lemons and a knife.	500	500	COCO_val2014_000000331807.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000331807.jpg
+179661	505530	A large elephant laying on top of a rocky field.	435	640	COCO_val2014_000000505530.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000505530.jpg
+179673	307073	A person standing on a stage with a dog flying through the air catching a frisbee.	428	640	COCO_val2014_000000307073.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000307073.jpg
+179876	158846	A man serving the ball to the opponent in a tennis match.	640	427	COCO_val2014_000000158846.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158846.jpg
+179907	140439	A clear, glass vase full of fresh cut tulips	640	428	COCO_val2014_000000140439.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000140439.jpg
+180068	521427	A person standing next to a gray and brown dog.	425	640	COCO_val2014_000000521427.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000521427.jpg
+180100	53916	Two zebra standing next to each other in a park.	428	640	COCO_val2014_000000053916.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000053916.jpg
+180163	146667	A woman digging through the storage compartment on her moped.	539	640	COCO_val2014_000000146667.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146667.jpg
+180271	358817	A yellow traffic light above a street next to houses.	480	640	COCO_val2014_000000358817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000358817.jpg
+180340	80357	The carrot cake has white frosting and is sprinkled with walnuts.	413	640	COCO_val2014_000000080357.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080357.jpg
+180631	145956	a large clock tower towering above a city under a cloudy blue sky.	396	640	COCO_val2014_000000145956.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000145956.jpg
+180654	247369	A highway filled with lots of traffic next to a lush green park.	480	640	COCO_val2014_000000247369.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000247369.jpg
+180661	34930	A beautiful young lady walking down a street in the rain.	550	600	COCO_val2014_000000034930.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034930.jpg
+180836	432843	A metal street pole with a street sign hanging off the side of it.	427	640	COCO_val2014_000000432843.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000432843.jpg
+180886	487295	a burger and a pickle sit on a plate	480	640	COCO_val2014_000000487295.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487295.jpg
+180889	111878	A white bowl filled with vegetables on top of a wooden table.	640	426	COCO_val2014_000000111878.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111878.jpg
+181149	157102	The young child is cutting up some paper.	640	480	COCO_val2014_000000157102.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000157102.jpg
+181269	452622	Six vases of various sizes are beige and brown.	500	481	COCO_val2014_000000452622.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000452622.jpg
+181283	549224	A pizza sitting on top of a table with slices missing.	480	640	COCO_val2014_000000549224.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000549224.jpg
+181295	356428	A couple of people walking through a market filled with food.	281	500	COCO_val2014_000000356428.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000356428.jpg
+181332	183657	An orange in a bowl near a puddle of water.	375	500	COCO_val2014_000000183657.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000183657.jpg
+181494	12179	A giraffe standing on a lush green hillside.	640	427	COCO_val2014_000000012179.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000012179.jpg
+181849	30851	A beautiful young lady taking a selfie in a mirror.	375	500	COCO_val2014_000000030851.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000030851.jpg
+181933	268943	A lake filled with water and a kite flying over itl	500	333	COCO_val2014_000000268943.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268943.jpg
+182169	335194	Many people are walking up and down the boardwalk.	426	640	COCO_val2014_000000335194.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335194.jpg
+182188	535106	A toddler sitting on a living room floor surrounded by toys.	403	640	COCO_val2014_000000535106.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000535106.jpg
+182426	489829	A person standing inside of a clock shop with lots of clocks.	612	612	COCO_val2014_000000489829.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000489829.jpg
+182599	367582	A man wearing a white shirt and tie looking at a paper.	430	279	COCO_val2014_000000367582.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000367582.jpg
+182741	573891	Two teddy bears are sitting on a couch.	426	640	COCO_val2014_000000573891.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573891.jpg
+182920	506052	a small hand held television set being held by a hand	426	640	COCO_val2014_000000506052.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000506052.jpg
+182979	171546	Blonde haired boy doing a jump while riding a skate board.	427	640	COCO_val2014_000000171546.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000171546.jpg
+183073	421558	A no parking sign mounted to the side of a pole.	480	640	COCO_val2014_000000421558.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421558.jpg
+183409	558510	People in red and white shirts at a tennis court holding rackets.	426	640	COCO_val2014_000000558510.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558510.jpg
+183710	382797	A shirtless man in a hat making luch	500	375	COCO_val2014_000000382797.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382797.jpg
+184011	119876	A man dressed loudly is using his cell phone.	640	427	COCO_val2014_000000119876.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000119876.jpg
+184108	293810	The meal consists of meat with brocolli on the side.	375	500	COCO_val2014_000000293810.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000293810.jpg
+184559	62151	Horses stand and drink from pond water near the road.	429	640	COCO_val2014_000000062151.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062151.jpg
+185501	377368	A cake shaped as a Teddy Bear on a wooden table.	481	640	COCO_val2014_000000377368.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000377368.jpg
+185530	398237	A small child is holding onto lawn decorations.	480	640	COCO_val2014_000000398237.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398237.jpg
+185933	51376	Soup, salad and sandwich sitting on a plate.	480	640	COCO_val2014_000000051376.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000051376.jpg
+186148	222908	A living room filled with furniture and lots of windows.	426	640	COCO_val2014_000000222908.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000222908.jpg
+186170	80757	An orange tabby cat is curled up fast asleep on the top of a sofa cushion.	375	500	COCO_val2014_000000080757.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080757.jpg
+186335	45229	A kitchen filled with a wooden cabinet and a large window.	480	640	COCO_val2014_000000045229.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000045229.jpg
+186386	283268	A white plate topped with a toy hot dog in a toy bun.	427	640	COCO_val2014_000000283268.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000283268.jpg
+186465	237350	A beautiful young lady brushing her teeth in front of a mirror.	640	562	COCO_val2014_000000237350.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237350.jpg
+186629	132059	A yellow tray topped with a cup of coffee and a donut.	478	640	COCO_val2014_000000132059.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132059.jpg
+186630	317448	A man flying through the air while riding a skateboard.	640	426	COCO_val2014_000000317448.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000317448.jpg
+187211	164515	Three motorcycles parked next to a motor home.	427	640	COCO_val2014_000000164515.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000164515.jpg
+187288	443281	A TOILET IS IN THE BATHROOM DOOR WAY	640	480	COCO_val2014_000000443281.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000443281.jpg
+187479	26185	A man with a surfboard walking on the shore near an ocean.	226	640	COCO_val2014_000000026185.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026185.jpg
+187766	277710	A person on skis going down a mountain slope.	427	640	COCO_val2014_000000277710.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000277710.jpg
+187776	291983	A large clock tower with two clocks mounted to it's sides.	480	640	COCO_val2014_000000291983.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000291983.jpg
+187845	205183	A duck walking along a paved road next to a patch of grass.	427	640	COCO_val2014_000000205183.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205183.jpg
+187911	204502	A wooden table topped with a bottle of alcohol and a glass.	640	480	COCO_val2014_000000204502.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000204502.jpg
+188077	533805	A plane is flying above the airport terminal	480	640	COCO_val2014_000000533805.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533805.jpg
+188542	272049	A large yellow dump truck sitting on green lawn.	427	640	COCO_val2014_000000272049.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000272049.jpg
+188732	541631	A empty street with a crosswalk at night.	640	427	COCO_val2014_000000541631.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000541631.jpg
+188775	343692	A motorcycle in a garage-like area by the wall.	640	480	COCO_val2014_000000343692.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000343692.jpg
+188977	546854	a pitcher of orange juice with orange slices in it	474	640	COCO_val2014_000000546854.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546854.jpg
+189247	261426	A black square plate topped with a cut in half chicken sandwich.	480	640	COCO_val2014_000000261426.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000261426.jpg
+189464	425412	A close up of a street sign stating street table.	640	480	COCO_val2014_000000425412.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000425412.jpg
+189733	364133	Three different colored suitcases are on the ground next to each other.	427	640	COCO_val2014_000000364133.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000364133.jpg
+190013	316715	Beautiful gray clouds with light cascading out of them.	375	500	COCO_val2014_000000316715.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000316715.jpg
+190403	28039	A herd of zebra standing next to each other drinking from a river.	383	640	COCO_val2014_000000028039.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028039.jpg
+190551	16959	A carrying a surfboard while walking across a beach next to the ocean.	474	640	COCO_val2014_000000016959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000016959.jpg
+190616	474039	A baby sitting in between to large stuffed animals.	401	640	COCO_val2014_000000474039.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474039.jpg
+190763	408773	A man in a plaid shirt riding a skateboard.	640	475	COCO_val2014_000000408773.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000408773.jpg
+190814	407259	Men in uniform watching two other men in uniform cut a cake.	457	640	COCO_val2014_000000407259.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000407259.jpg
+191153	47225	A group of small stuffed animals sitting on top of a table.	480	640	COCO_val2014_000000047225.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000047225.jpg
+191354	411587	The man is in the water with a boat.	640	640	COCO_val2014_000000411587.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000411587.jpg
+191355	186977	The young boy is holding a tennis racquet.	381	500	COCO_val2014_000000186977.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000186977.jpg
+191588	5694	A white parrot standing next to a jungle covered hillside.	480	640	COCO_val2014_000000005694.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005694.jpg
+191933	129707	A person holding an old phone showing it to the camera.	640	480	COCO_val2014_000000129707.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129707.jpg
+192206	129632	A close up of a zebra grazing in the leaves.	334	500	COCO_val2014_000000129632.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129632.jpg
+192207	472821	Two zebras grazing in the grass under the trees.	480	640	COCO_val2014_000000472821.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472821.jpg
+192308	14845	A river with boats docked and houses in the background.	480	640	COCO_val2014_000000014845.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000014845.jpg
+192397	163451	A young child has a cell phone up to their ear.	640	334	COCO_val2014_000000163451.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000163451.jpg
+192465	519275	a person getting ready to hit a tennis serve	640	511	COCO_val2014_000000519275.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519275.jpg
+192640	237597	A small elephant licking a blue metal fence.	513	640	COCO_val2014_000000237597.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237597.jpg
+192659	555396	A man wearing a shirt and tie near a forest.	640	427	COCO_val2014_000000555396.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000555396.jpg
+192759	444273	Two zebra standing next to each other on a lush green hillside.	457	640	COCO_val2014_000000444273.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444273.jpg
+193107	414008	A brown and white dog runs on some brown grass near a Frisbee that is just sailing above the ground.	427	640	COCO_val2014_000000414008.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000414008.jpg
+193240	503595	A piece of meat on a plate next to broccoli.	612	612	COCO_val2014_000000503595.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000503595.jpg
+193307	126272	A luggage cart sitting inside of a brick building.	480	640	COCO_val2014_000000126272.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000126272.jpg
+193400	479067	A close up of an sandwich and soup on a wooden table.	612	612	COCO_val2014_000000479067.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000479067.jpg
+193437	79924	A purple and green fire hydrant next to a black and green fire hydrant.	640	427	COCO_val2014_000000079924.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000079924.jpg
+193488	489815	A large clock tower with two clocks mounted on it's sides.	640	427	COCO_val2014_000000489815.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000489815.jpg
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+200998	92212	a group of cars parked on a street nest to street signs	640	480	COCO_val2014_000000092212.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092212.jpg
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+201081	346717	A man flying through the air while riding a skateboard.	427	640	COCO_val2014_000000346717.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000346717.jpg
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+202088	371873	A table topped with donuts and a cup of coffee.	426	640	COCO_val2014_000000371873.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000371873.jpg
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+202163	274014	a number of people riding skis on a snowy surface	333	500	COCO_val2014_000000274014.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000274014.jpg
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+202559	420827	A group of firefighters near fire engines parked on a street.	427	640	COCO_val2014_000000420827.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000420827.jpg
+202606	540133	A boy in yellow shirt playing a game with a Nintendo Wii controller.	640	480	COCO_val2014_000000540133.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540133.jpg
+202692	9270	A young girl eating a piece of cake off of a plate.	640	424	COCO_val2014_000000009270.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009270.jpg
+202924	552824	A car parked outside of a gas station with chrome rims.	640	438	COCO_val2014_000000552824.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552824.jpg
+202944	297372	A small brown dog wearing a leash next to a bike.	403	500	COCO_val2014_000000297372.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297372.jpg
+202979	292789	a close up of a skate board on a tree branch	480	640	COCO_val2014_000000292789.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000292789.jpg
+203026	488743	Cupped hands holding an orange with a green leaf attached to it.	640	426	COCO_val2014_000000488743.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000488743.jpg
+203065	25252	An old church building in an urban environment	640	480	COCO_val2014_000000025252.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025252.jpg
+203084	200627	A plate of food that includes rice and various vegetables.	480	640	COCO_val2014_000000200627.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000200627.jpg
+203199	165675	Slices of ham on a black tray next to a knife	612	612	COCO_val2014_000000165675.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000165675.jpg
+203400	372794	A yellow and green train pulling out of a tunnel next to a red car.	480	640	COCO_val2014_000000372794.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372794.jpg
+203487	455859	A little girl in a pink shirt standing near a blue metal sculpture.	480	640	COCO_val2014_000000455859.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000455859.jpg
+203501	507065	A little boy sitting on a wooden bench eating half a sandwich.	612	612	COCO_val2014_000000507065.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507065.jpg
+203604	325368	A red street sign next to a no parking sign.	640	480	COCO_val2014_000000325368.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325368.jpg
+204308	447549	Three giraffes in a field with trees in the background.	427	640	COCO_val2014_000000447549.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000447549.jpg
+204619	433182	A train pulling into a train station next to a platform.	480	640	COCO_val2014_000000433182.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000433182.jpg
+204620	43555	A man riding a skateboard on the side of a planter.	429	640	COCO_val2014_000000043555.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000043555.jpg
+204670	240287	A man with a beard standing on the beach with a surfboard under his right arm and he is looking out into the ocean.	425	640	COCO_val2014_000000240287.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000240287.jpg
+204683	522235	A man sitting in a brown c hair holding a game controller.	426	640	COCO_val2014_000000522235.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000522235.jpg
+204801	212116	A group of young ladies riding on the back of a horse.	354	500	COCO_val2014_000000212116.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000212116.jpg
+204834	443689	A man with his arms crossing wearing a shirt and tie.	640	470	COCO_val2014_000000443689.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000443689.jpg
+204885	387470	A pile of luggage sitting next to a  lamp.	426	640	COCO_val2014_000000387470.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000387470.jpg
+205143	179151	People are standing in a shopping district with stores.	500	375	COCO_val2014_000000179151.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000179151.jpg
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+205380	532994	A horse drawn fire truck traveling across a field.	429	640	COCO_val2014_000000532994.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532994.jpg
+205398	73450	Airport workers loading luggage onto into an airplane's cargo hold.	427	640	COCO_val2014_000000073450.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000073450.jpg
+205526	87481	A fire hydrant covered in snow on a sidewalk.	612	612	COCO_val2014_000000087481.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087481.jpg
+205543	165164	A man standing in front of a pile of bananas.	427	640	COCO_val2014_000000165164.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000165164.jpg
+205883	504248	A bathroom with a tub next to a toilet and a sink.	480	640	COCO_val2014_000000504248.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000504248.jpg
+205973	530777	A tall brick clock tower sitting in a park.	427	640	COCO_val2014_000000530777.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530777.jpg
+206052	557965	Two people riding on the backs of brown horses.	427	640	COCO_val2014_000000557965.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000557965.jpg
+206057	450263	A woman sitting on a bench reading a paper.	480	640	COCO_val2014_000000450263.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450263.jpg
+206195	237745	a man with a hat riding on a surf board	640	458	COCO_val2014_000000237745.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237745.jpg
+206693	245460	a small boy with a hat playing with a remote	480	640	COCO_val2014_000000245460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000245460.jpg
+207038	534335	A foot long hot dog next to a pile of fries.	375	500	COCO_val2014_000000534335.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534335.jpg
+207246	489798	A group of people sitting around a table.	426	640	COCO_val2014_000000489798.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000489798.jpg
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+207548	230262	A brown cow standing on the side of a road.	427	640	COCO_val2014_000000230262.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000230262.jpg
+207556	276381	A woman in a dress laying on a bed on the dirt.	480	640	COCO_val2014_000000276381.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000276381.jpg
+207595	467742	A woman carrying a red purse filled with hot dogs.	479	640	COCO_val2014_000000467742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000467742.jpg
+207651	350231	A woman sitting at a table next to a table.	480	640	COCO_val2014_000000350231.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000350231.jpg
+207671	246576	A young man brushing his teeth in front of a mirror.	621	640	COCO_val2014_000000246576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000246576.jpg
+207678	303926	A woman holding a child on a skateboard.	427	640	COCO_val2014_000000303926.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303926.jpg
+207690	550322	A metal cup filled with scissors and two ladels.	640	427	COCO_val2014_000000550322.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550322.jpg
+207724	407386	A black and white cat in front of a laptop and a monitor.	480	640	COCO_val2014_000000407386.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000407386.jpg
+207762	97659	A traffic light under a cloudy blue sky.	640	427	COCO_val2014_000000097659.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000097659.jpg
+207847	256094	A white toilet sitting in a bathroom next to a wall.	640	425	COCO_val2014_000000256094.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000256094.jpg
+207886	466005	A jetliner flying above in a light blue sky.	400	600	COCO_val2014_000000466005.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000466005.jpg
+207979	322106	A woman sitting next to a man next to luggage.	612	612	COCO_val2014_000000322106.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000322106.jpg
+207989	386092	A metal juicer sitting on top of a table next to veggies.	640	486	COCO_val2014_000000386092.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000386092.jpg
+208012	156846	A group of people sitting around a long table	480	640	COCO_val2014_000000156846.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000156846.jpg
+208050	493864	A man standing on a wet beach holding a blue board.	640	480	COCO_val2014_000000493864.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493864.jpg
+208131	264654	A set of three pieces of luggage filled with clothing.	375	500	COCO_val2014_000000264654.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264654.jpg
+208214	98392	A large clock tower in the middle of a street.	640	480	COCO_val2014_000000098392.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000098392.jpg
+208235	438737	A grocery store filled with lots of fresh produce.	480	640	COCO_val2014_000000438737.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000438737.jpg
+208283	352228	A large white semi truck parked next to a green truck.	425	640	COCO_val2014_000000352228.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000352228.jpg
+208763	134738	A woman with glasses drying her hair with a blowdryer.	480	640	COCO_val2014_000000134738.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134738.jpg
+208806	290269	A black and white dog chasing sheep in a field.	640	481	COCO_val2014_000000290269.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000290269.jpg
+208832	563882	an image of a person on their cellular phone	425	640	COCO_val2014_000000563882.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000563882.jpg
+209282	484042	A man in black wet suit holding a surfboard under his arm.	544	363	COCO_val2014_000000484042.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000484042.jpg
+209365	457884	A group of kids playing a game of baseball.	458	640	COCO_val2014_000000457884.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000457884.jpg
+209366	515485	A woman in a red shirt and white shorts playing tennis.	426	640	COCO_val2014_000000515485.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000515485.jpg
+209389	200699	A blacn and white cow sticking its head over a fence.	423	640	COCO_val2014_000000200699.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000200699.jpg
+209468	12231	A plate of food that includes meat and broccoli.	480	640	COCO_val2014_000000012231.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000012231.jpg
+209867	384723	A young boy tries out skateboarding tricks on a road	428	640	COCO_val2014_000000384723.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384723.jpg
+210261	260232	A traffic light on a table with a checkered cloth.	612	612	COCO_val2014_000000260232.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260232.jpg
+210586	374342	A man is throwing up a tennis ball and going to hit it with a racket.	640	480	COCO_val2014_000000374342.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000374342.jpg
+211134	137961	Gray cat lying on a chair looking at the camera.	480	640	COCO_val2014_000000137961.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000137961.jpg
+211472	531816	a little girl in pink holding onto a pink teddy bear	640	426	COCO_val2014_000000531816.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000531816.jpg
+212343	347039	A clock tower stands over a city landscape.	640	496	COCO_val2014_000000347039.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347039.jpg
+212851	211186	A living room with a fireplace and chandelier.	480	640	COCO_val2014_000000211186.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211186.jpg
+212914	214255	An orange traffic cone sitting in the middle of traffic.	500	354	COCO_val2014_000000214255.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214255.jpg
+213061	109707	This is a living room with a gray couch and yellow chair.	427	640	COCO_val2014_000000109707.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000109707.jpg
+213376	84123	A tall green street sign sitting on the side of a road.	375	500	COCO_val2014_000000084123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000084123.jpg
+213382	399538	A boat is sitting in the water at a dock	424	640	COCO_val2014_000000399538.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000399538.jpg
+213421	518440	Hot dogs in buns with fruit salad on a plate	428	640	COCO_val2014_000000518440.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000518440.jpg
+213602	122588	A wooden table topped with a computer monitor and two keyboards.	480	640	COCO_val2014_000000122588.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000122588.jpg
+213667	172886	A bathroom with two sinks mounted on a wall.	427	640	COCO_val2014_000000172886.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000172886.jpg
+213960	573853	A pizza with thick crust sitting on top of a pan.	480	640	COCO_val2014_000000573853.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573853.jpg
+213978	417085	A big cow and a smaller cow standing beside a road.	360	640	COCO_val2014_000000417085.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000417085.jpg
+214113	237399	A cat standing on top of a laptop computer.	427	640	COCO_val2014_000000237399.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237399.jpg
+214144	412764	A crowd of people standing around a field.	357	500	COCO_val2014_000000412764.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412764.jpg
+214245	185201	A butcher cutting up the corpse of a dead animal.	427	640	COCO_val2014_000000185201.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000185201.jpg
+214525	439472	A living room filled with furniture and lamps.	426	640	COCO_val2014_000000439472.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000439472.jpg
+214566	397109	A park bench next to a serene lake.	479	640	COCO_val2014_000000397109.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397109.jpg
+214714	481064	Two men standing on a lush green field playing a game of frisbee.	640	520	COCO_val2014_000000481064.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000481064.jpg
+214759	458790	A view from the inside of a train from a side window.	375	500	COCO_val2014_000000458790.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000458790.jpg
+215211	179397	A factory with a large smoke stack next to a train.	429	640	COCO_val2014_000000179397.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000179397.jpg
+215293	148353	A couple of people walking over a bridge going over a street.	640	640	COCO_val2014_000000148353.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000148353.jpg
+215373	256505	A woman riding a bike while wearing really short shorts.	545	640	COCO_val2014_000000256505.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000256505.jpg
+215504	208236	A brown dog fighting over a deflated ball with a darker brown dog.	480	640	COCO_val2014_000000208236.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208236.jpg
+215694	285957	A giraffe standing in a lush green field.	500	375	COCO_val2014_000000285957.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000285957.jpg
+215899	277717	A man standing near a bridge going over water.	428	640	COCO_val2014_000000277717.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000277717.jpg
+215902	44171	A woman eating a giant doughnut covered in sprinkles.	480	640	COCO_val2014_000000044171.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000044171.jpg
+216176	93946	A mall girl and boy sitting in front of a pan topped with pizza.	478	640	COCO_val2014_000000093946.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000093946.jpg
+216177	191614	A horse standing in a lush green grass field.	612	612	COCO_val2014_000000191614.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000191614.jpg
+216343	439180	A group of people riding horses down a small road.	360	640	COCO_val2014_000000439180.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000439180.jpg
+216499	218678	A man on a white surfboard riding on a wave.	427	640	COCO_val2014_000000218678.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218678.jpg
+216571	208473	A small bird standing on top of a sandy beach.	425	640	COCO_val2014_000000208473.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208473.jpg
+216675	9171	A man riding down a snow covered slope in the snow.	480	640	COCO_val2014_000000009171.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009171.jpg
+216719	29906	A single broccoli floret in a larger garden.	640	427	COCO_val2014_000000029906.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029906.jpg
+216720	279769	A peeled banana on a table next to an unpeeled banana.	480	640	COCO_val2014_000000279769.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279769.jpg
+216897	278890	A woman using a smart phone with her two hands.	480	640	COCO_val2014_000000278890.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000278890.jpg
+216942	68381	A bike parked in front of a window on the side of a building.	640	480	COCO_val2014_000000068381.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068381.jpg
+217028	564109	A group of giraffe standing next to each other on a field.	375	500	COCO_val2014_000000564109.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000564109.jpg
+217058	383607	A green arrow sign pointing up next to a stop sign.	640	419	COCO_val2014_000000383607.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000383607.jpg
+217068	378701	A row of up right surfboards inside of a building.	209	500	COCO_val2014_000000378701.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000378701.jpg
+217151	565031	A group of bikers parked in a parking lot under large lights.	480	640	COCO_val2014_000000565031.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565031.jpg
+217178	562735	A cup filled with a glass at on top of a table.	500	375	COCO_val2014_000000562735.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000562735.jpg
+217331	263178	A clear glass bowl filled with fruit on a wooden table..	539	640	COCO_val2014_000000263178.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000263178.jpg
+217361	162035	A sign with teddy bears on it is advertising a tea room.	375	500	COCO_val2014_000000162035.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000162035.jpg
+217450	441023	a person on a small boat with another boat in the background	480	640	COCO_val2014_000000441023.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000441023.jpg
+217720	233075	A group of knives mounted to a kitchen wall.	480	640	COCO_val2014_000000233075.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000233075.jpg
+217722	3832	A white boat floating on a lake under mountains.	426	640	COCO_val2014_000000003832.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000003832.jpg
+217792	62353	Two giraffe walking next to each other in a forest.	480	640	COCO_val2014_000000062353.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062353.jpg
+217900	225480	Two brown and white cows standing next to each other.	427	640	COCO_val2014_000000225480.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000225480.jpg
+217970	491131	Wooden and black bench on the side of the road.	480	640	COCO_val2014_000000491131.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491131.jpg
+218005	74135	A man standing next to another man as they prepare food.	541	640	COCO_val2014_000000074135.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000074135.jpg
+218042	157170	Four mountain goats standing on a hill in front of the country side.	480	640	COCO_val2014_000000157170.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000157170.jpg
+218067	115721	A dog laying down on top of a couch cushion.	480	640	COCO_val2014_000000115721.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000115721.jpg
+218211	75187	A woman sitting on top of a bench looking beautiful.	427	640	COCO_val2014_000000075187.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000075187.jpg
+218240	333140	A black cat sitting at a computer desk topped with a monitor.	450	640	COCO_val2014_000000333140.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333140.jpg
+218419	382030	A wig sitting next to a pair of shoes on top of a table.	375	500	COCO_val2014_000000382030.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382030.jpg
+218562	533137	A young boy standing on the top of a sky slope.	375	500	COCO_val2014_000000533137.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533137.jpg
+218677	492363	A cakeshop sign sitting outside of a cakeshop store.	640	480	COCO_val2014_000000492363.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000492363.jpg
+218738	45687	Two laptop computers sitting on top of a desk.	396	640	COCO_val2014_000000045687.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000045687.jpg
+218838	17364	A woman and a man walking down a street carrying luggage.	428	640	COCO_val2014_000000017364.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000017364.jpg
+218967	248300	A man standing next to a girl eating a piece of cake.	538	640	COCO_val2014_000000248300.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248300.jpg
+218971	38816	A view into a bathroom from inside a kitchen.	480	640	COCO_val2014_000000038816.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038816.jpg
+219042	170629	A yellow bus is parked near people on the side of the road.	431	640	COCO_val2014_000000170629.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170629.jpg
+219096	505156	A man riding skis on top of snow covered ground.	425	640	COCO_val2014_000000505156.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000505156.jpg
+219214	424268	A couple of young men walking down a street together.	428	640	COCO_val2014_000000424268.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000424268.jpg
+219277	552837	A horse is on a brushy hillside on the gravel.	427	640	COCO_val2014_000000552837.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552837.jpg
+219279	451284	Some hot chocolate is in front of a computer.	480	640	COCO_val2014_000000451284.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451284.jpg
+219432	124979	A man laying on top of a wall next to a bike.	422	640	COCO_val2014_000000124979.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000124979.jpg
+219840	273188	A red stop sign sitting next to a wooden fence.	640	480	COCO_val2014_000000273188.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273188.jpg
+219877	143927	A ceramic coffee cup inside of a silver microwave oven.	428	640	COCO_val2014_000000143927.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143927.jpg
+219886	201120	A table topped with pieces of cloth and large piece of paper.	482	640	COCO_val2014_000000201120.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000201120.jpg
+219969	406959	A large clock sitting in front of a tall building.	640	425	COCO_val2014_000000406959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406959.jpg
+220004	129786	A long white couch in front of a wooden table.	480	640	COCO_val2014_000000129786.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129786.jpg
+220090	281800	A man riding a skateboard over the top of a cement block.	429	640	COCO_val2014_000000281800.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000281800.jpg
+220266	397375	A dog sitting in the passenger seat of a car.	425	640	COCO_val2014_000000397375.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397375.jpg
+220291	387286	A person riding a paddle boat on top of a wave in the ocean.	428	640	COCO_val2014_000000387286.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000387286.jpg
+220448	153092	Two birds are standing in the water together.	640	374	COCO_val2014_000000153092.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000153092.jpg
+220459	26017	A traffic light next to a very tall building near a street.	640	480	COCO_val2014_000000026017.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026017.jpg
+220638	368505	A tall white clock tower inside of a tall building.	640	424	COCO_val2014_000000368505.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000368505.jpg
+220775	330955	A kid eating a chili cheese dog while wearing sunglasses.	427	640	COCO_val2014_000000330955.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000330955.jpg
+220920	316648	A crowd of people standing under a give way sign.	640	425	COCO_val2014_000000316648.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000316648.jpg
+220985	372150	The Big Ben clock tower underneath a cloudy blue sky.	640	428	COCO_val2014_000000372150.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372150.jpg
+221005	262054	A large kitchen filled with white appliances with lots of counter space.	425	640	COCO_val2014_000000262054.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000262054.jpg
+221051	540816	A large passenger jet flying through a blue sky.	401	640	COCO_val2014_000000540816.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540816.jpg
+221068	478356	A woman holding a sign sitting on top of a truck.	516	640	COCO_val2014_000000478356.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000478356.jpg
+221073	345848	A group of people are on bikes on a busy street.	640	426	COCO_val2014_000000345848.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345848.jpg
+221159	411215	A train traveling on a lush green hillside.	480	640	COCO_val2014_000000411215.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000411215.jpg
+221283	237464	A group of people flying kites over a beach next to the ocean.	427	640	COCO_val2014_000000237464.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237464.jpg
+221361	399582	Two men standing in a living room next to a couch holding Wii controllers.	375	500	COCO_val2014_000000399582.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000399582.jpg
+221365	407150	A pile of garbage sitting next to a trash can.	640	640	COCO_val2014_000000407150.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000407150.jpg
+221407	384015	Two men standing next to each other holding Wii game controllers.	427	640	COCO_val2014_000000384015.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384015.jpg
+221435	259056	a man flying through the air while riding a snowboard.	418	640	COCO_val2014_000000259056.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000259056.jpg
+221446	306893	A long train traveling down a city street with tall buildings.	368	500	COCO_val2014_000000306893.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306893.jpg
+221483	200681	A dog jumping up into the air to catch a blue frisbee.	640	427	COCO_val2014_000000200681.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000200681.jpg
+221570	180953	A large purple and red passenger jet flying through a blue sky..	427	640	COCO_val2014_000000180953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000180953.jpg
+221684	35498	A mouse sitting on the side of a computer monitor.	375	500	COCO_val2014_000000035498.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000035498.jpg
+221741	199449	A living room with hard wood floors filled with furniture.	480	640	COCO_val2014_000000199449.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000199449.jpg
+221833	344621	A living room filled with furniture and  set of glass doors.	333	500	COCO_val2014_000000344621.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344621.jpg
+221861	570515	a male in a gray shirt is moving a pot in the oven	480	640	COCO_val2014_000000570515.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000570515.jpg
+221879	551338	A woman standing in a park holding a yellow frisbee.	427	640	COCO_val2014_000000551338.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000551338.jpg
+221900	158563	A man standing next to a yellow fire hydrant.	480	640	COCO_val2014_000000158563.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158563.jpg
+221926	35401	A parking lot filled with cars next to a large traffic light.	375	500	COCO_val2014_000000035401.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000035401.jpg
+221961	376247	A couple of children riding on the back of a horse posing with their mom, her cow and a small dog.	564	640	COCO_val2014_000000376247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000376247.jpg
+221974	256569	A couple of people standing in a field playing with a frisbee.	480	640	COCO_val2014_000000256569.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000256569.jpg
+222070	480591	A group of men and women playing a game on the Nintendo Wii.	480	640	COCO_val2014_000000480591.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000480591.jpg
+222114	215622	A beautiful woman sitting in front of a laptop computer.	640	426	COCO_val2014_000000215622.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000215622.jpg
+222147	190204	A train with people sitting on it traveling through a countryside.	426	640	COCO_val2014_000000190204.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190204.jpg
+222178	181576	a person sitting at a table eating food from a plate.	640	427	COCO_val2014_000000181576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000181576.jpg
+222225	389400	A herd of cattle walking across a muddy field.	489	640	COCO_val2014_000000389400.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000389400.jpg
+222307	366615	A dog wearing a witches costume standing on a grass field.	480	640	COCO_val2014_000000366615.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000366615.jpg
+222329	233033	An outdoor with a patio with chairs and a wooden deck.	480	640	COCO_val2014_000000233033.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000233033.jpg
+222424	138070	A living room filled with furniture and a flat screen TV.	427	640	COCO_val2014_000000138070.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000138070.jpg
+222566	90646	A man standing next to a dummy wearing clothes.	640	480	COCO_val2014_000000090646.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000090646.jpg
+222582	53677	A man standing next to a small airplane with two dogs.	463	640	COCO_val2014_000000053677.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000053677.jpg
+222660	474410	Four jets flying in formation in a blue sky.	426	640	COCO_val2014_000000474410.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474410.jpg
+222703	318373	A group of people walking down a sidewalk next to a wagon of little children.	427	640	COCO_val2014_000000318373.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000318373.jpg
+222720	208825	Closeup of a piece of cake and fork on a plate.	480	640	COCO_val2014_000000208825.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208825.jpg
+222812	166918	A group of people sitting down at a dining room table next to dishes.	640	480	COCO_val2014_000000166918.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000166918.jpg
+222826	337143	A herd of giraffe standing next to each other at a zoo.	359	640	COCO_val2014_000000337143.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000337143.jpg
+222910	529083	A couple of red buses traveling over a bridge spanning a rivers width.	480	640	COCO_val2014_000000529083.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529083.jpg
+223029	453622	A living room filled with furniture and lamps.	424	640	COCO_val2014_000000453622.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453622.jpg
+223106	423113	A person in wetsuit on white surfboards in water.	449	640	COCO_val2014_000000423113.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000423113.jpg
+223557	324740	A brown teddy bear sitting in a chair wearing a blue shirt and red hat.	640	464	COCO_val2014_000000324740.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000324740.jpg
+223587	216520	a baby elephant walking on a dirt field next to a fenced enclosure.	480	640	COCO_val2014_000000216520.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216520.jpg
+224064	118507	A hallway with yellow painted walls and a light mounted on a wall.	640	480	COCO_val2014_000000118507.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118507.jpg
+224176	427245	A couple of women sitting at a table next to drinks.	612	612	COCO_val2014_000000427245.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427245.jpg
+224220	320180	an image of a woman sitting down on a couch with laptop	427	640	COCO_val2014_000000320180.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000320180.jpg
+224379	371322	A river with men riding paddle and surf boards on top of it.	426	640	COCO_val2014_000000371322.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000371322.jpg
+224779	240775	A yellow double decker bus driving down a street	425	640	COCO_val2014_000000240775.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000240775.jpg
+224780	349184	A woman sitting on top of a wooden bench near a park.	640	428	COCO_val2014_000000349184.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000349184.jpg
+224968	77402	A white plate topped with food and eating utensils.	427	640	COCO_val2014_000000077402.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000077402.jpg
+225153	381262	A couple of men sitting on a cement block on top of a skateboard.	427	640	COCO_val2014_000000381262.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000381262.jpg
+225211	478550	A man riding skis down a snow covered ski slope.	426	640	COCO_val2014_000000478550.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000478550.jpg
+225382	88269	A plate holding a grilled cheese sandwich and bowl of soup.	480	640	COCO_val2014_000000088269.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000088269.jpg
+225435	459064	A brown and white dog in window sill looking outside.	427	640	COCO_val2014_000000459064.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000459064.jpg
+225783	382717	"Two dogs on a truck with frame that reads ""cockleburs galore."""	483	640	COCO_val2014_000000382717.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382717.jpg
+226187	360346	A small propeller airplane taking off from a runway.	412	640	COCO_val2014_000000360346.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360346.jpg
+226272	364703	a red train on a train track below power lines.	425	640	COCO_val2014_000000364703.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000364703.jpg
+226717	369310	A toddler boy is on a white pad playing a video game.	640	395	COCO_val2014_000000369310.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369310.jpg
+226820	571839	A mantle topped with two vases and a golden clock.	480	640	COCO_val2014_000000571839.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000571839.jpg
+226937	142323	A blue street sign next to a power pole.	480	640	COCO_val2014_000000142323.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000142323.jpg
+227106	489611	A young boy holding Nintendo Wii game controllers.	622	640	COCO_val2014_000000489611.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000489611.jpg
+227157	87740	People riding on elephants that are walking through a river.	480	640	COCO_val2014_000000087740.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087740.jpg
+227221	493334	A sheet cake with a frosting cell phone on it.	311	500	COCO_val2014_000000493334.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493334.jpg
+227801	460505	A cat sitting in a black piece of luggage.	375	500	COCO_val2014_000000460505.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460505.jpg
+228228	119469	A herd of sheep grazing on a lush green filed.	457	640	COCO_val2014_000000119469.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000119469.jpg
+228409	347664	A herd of cattle walking across a sandy beach near the ocean.	427	640	COCO_val2014_000000347664.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347664.jpg
+229125	521306	A boy with glasses playing a game with a Nintendo Wii controller.	640	480	COCO_val2014_000000521306.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000521306.jpg
+229149	93068	A cart full of bananas next to boy in red hat.	640	480	COCO_val2014_000000093068.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000093068.jpg
+229440	426070	a person sitting on a couch in a living room	427	640	COCO_val2014_000000426070.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000426070.jpg
+229696	318171	A dog sitting on a grassy hillside by a path.	480	640	COCO_val2014_000000318171.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000318171.jpg
+229780	366529	A man in white jacket and red tie and a woman in a white headdress.	428	640	COCO_val2014_000000366529.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000366529.jpg
+230110	69196	many people walking into an opened air plane	640	512	COCO_val2014_000000069196.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000069196.jpg
+230170	264161	many different items including game boy cartridges	312	500	COCO_val2014_000000264161.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264161.jpg
+230285	524110	A group of people on small boats with lights as it is getting dark outside.	427	640	COCO_val2014_000000524110.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000524110.jpg
+230808	171349	An unfinished bathroom with a boarded window and exposed pipes.	480	640	COCO_val2014_000000171349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000171349.jpg
+231756	323399	A square piece of pizza on top of a plate.	424	640	COCO_val2014_000000323399.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000323399.jpg
+231809	111435	A tall building with a massive golden clock on it's face.	512	640	COCO_val2014_000000111435.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111435.jpg
+231978	151844	A red double decker bus traveling down a street.	495	640	COCO_val2014_000000151844.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000151844.jpg
+232124	388707	A desktop computer monitor sitting on top of a desk.	480	640	COCO_val2014_000000388707.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000388707.jpg
+232210	513115	many small boats in a large body of water	480	640	COCO_val2014_000000513115.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000513115.jpg
+232259	430656	A young man holding a Nintendo Wii game controller.	640	353	COCO_val2014_000000430656.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000430656.jpg
+232273	506987	A man sitting in a car holding up a cellphone camera.	375	500	COCO_val2014_000000506987.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000506987.jpg
+232431	223574	Emergency vehicles are on the scene of the accident.	479	640	COCO_val2014_000000223574.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000223574.jpg
+232501	532527	A table topped with vegetables and a pitcher.	640	480	COCO_val2014_000000532527.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532527.jpg
+232548	244246	A commercial aircraft flies low above street traffic.	640	480	COCO_val2014_000000244246.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000244246.jpg
+232614	222828	A group of women standing next to each other carrying luggage.	480	640	COCO_val2014_000000222828.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000222828.jpg
+232731	520956	A tall building with a massive clock on it's face.	640	426	COCO_val2014_000000520956.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000520956.jpg
+232819	28692	A man flying through while riding a skateboard.	427	640	COCO_val2014_000000028692.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028692.jpg
+232893	480663	A woman smiles while sitting in a leisure area under an sun umbrella.	500	375	COCO_val2014_000000480663.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000480663.jpg
+232902	134964	A cake with icing and cartoon cake toppers.	428	640	COCO_val2014_000000134964.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134964.jpg
+232916	469634	A man riding a skateboard down the middle of a street.	428	640	COCO_val2014_000000469634.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000469634.jpg
+233032	118741	A woman standing on a green field flying a colorful kite.	426	640	COCO_val2014_000000118741.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118741.jpg
+233245	224281	A stuffed teddy bear sitting on top of a cloth bag.	640	480	COCO_val2014_000000224281.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224281.jpg
+234479	241187	A man's hands working on a laptop computer with a television screen in the background.	640	427	COCO_val2014_000000241187.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000241187.jpg
+234497	42091	A woman in sunglasses and hat standing by plant.	480	640	COCO_val2014_000000042091.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000042091.jpg
+234498	389604	An adult and baby elephant standing in a grassy field.	640	425	COCO_val2014_000000389604.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000389604.jpg
+234823	189168	A batter at a baseball game gets ready for the pitch.	427	640	COCO_val2014_000000189168.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000189168.jpg
+235115	541687	some people are sitting at a table on a phone and laptop	375	500	COCO_val2014_000000541687.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000541687.jpg
+235624	436858	A child skier is headed down a small slope on their skies.	359	640	COCO_val2014_000000436858.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000436858.jpg
+236681	199540	A women holding up a very large pair of scissors .	640	480	COCO_val2014_000000199540.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000199540.jpg
+236818	433373	two plates of food on a table with chairs	612	612	COCO_val2014_000000433373.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000433373.jpg
+237401	39865	a man riding a big wave on his surfboard	338	640	COCO_val2014_000000039865.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039865.jpg
+237902	267115	A white dog sitting on a ledge of a window.	500	375	COCO_val2014_000000267115.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267115.jpg
+238604	470604	The little boy is posing with tennis rackets.	631	640	COCO_val2014_000000470604.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000470604.jpg
+238918	351530	People seated on wooden chairs and a bicycle parked nearby.	378	640	COCO_val2014_000000351530.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000351530.jpg
+239309	521849	A grey and blue fire hydrant on a sidewalk.	640	427	COCO_val2014_000000521849.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000521849.jpg
+239698	91545	A man with hoodie skateboarding on a lonely road.	424	640	COCO_val2014_000000091545.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000091545.jpg
+240474	508713	Many cows are standing and lying on a bench.	428	640	COCO_val2014_000000508713.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000508713.jpg
+240583	193380	Many boats are docked at the harbor of a large city.	480	640	COCO_val2014_000000193380.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000193380.jpg
+240713	542803	The city bus in en route to Graham Street.	427	640	COCO_val2014_000000542803.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000542803.jpg
+240911	545293	A single park bench is by small trees.	640	480	COCO_val2014_000000545293.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000545293.jpg
+242080	262987	Two triangular street signs on grass next to brick pathway.	500	375	COCO_val2014_000000262987.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000262987.jpg
+243152	201163	Three men sitting on motorcycles on a sidewalk.	427	640	COCO_val2014_000000201163.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000201163.jpg
+244255	223357	a close up of a pot with flowers in it	478	640	COCO_val2014_000000223357.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000223357.jpg
+245193	565233	Man sitting on his bed while using his laptop.	428	640	COCO_val2014_000000565233.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565233.jpg
+245242	492886	A knitted stuffed animal face down on a magazine.	480	640	COCO_val2014_000000492886.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000492886.jpg
+245635	329373	A group of young men standing around an open box of pizza.	480	640	COCO_val2014_000000329373.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000329373.jpg
+245799	519611	a close up of a polar bear on a rock formation	480	640	COCO_val2014_000000519611.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519611.jpg
+245862	537889	two billboards with a sky background and people walking near by	426	640	COCO_val2014_000000537889.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000537889.jpg
+246094	267076	a small child watching an elephant behind a fence	478	640	COCO_val2014_000000267076.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267076.jpg
+246724	202225	A couch, chair, dining room table and television sitting in a room.	425	640	COCO_val2014_000000202225.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000202225.jpg
+246735	10785	A little girl with bows in her hair eating a plate full of broccoli.	429	640	COCO_val2014_000000010785.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010785.jpg
+246840	423588	A group of people snowboarding in the mountains.	427	640	COCO_val2014_000000423588.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000423588.jpg
+246846	333095	A large group of people eating at a table.	480	640	COCO_val2014_000000333095.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333095.jpg
+246997	87377	pretty cat hiding inside of a brown suitcase	640	480	COCO_val2014_000000087377.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087377.jpg
+247508	107205	A man standing on one foot holding a skateboard behind him.	500	323	COCO_val2014_000000107205.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107205.jpg
+247600	431323	Several peeled vegetables are sitting in a baking dish.	480	640	COCO_val2014_000000431323.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431323.jpg
+247696	234500	A child eating a sandwich with relish on it.	427	640	COCO_val2014_000000234500.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000234500.jpg
+248153	180729	A plate sits on a table in front of a woman.	640	427	COCO_val2014_000000180729.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000180729.jpg
+248470	491556	A man and a woman sit in an industrial workshop with a dog.	434	640	COCO_val2014_000000491556.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491556.jpg
+248558	19324	Black and white photograph of officers on horses.	428	640	COCO_val2014_000000019324.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000019324.jpg
+248714	121611	A living area with table, chairs and a sofa.	425	640	COCO_val2014_000000121611.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000121611.jpg
+249137	243190	A variety of foods are mixed in a bowl.	425	640	COCO_val2014_000000243190.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000243190.jpg
+249561	4375	A crowd of skiers gathers on the top of the slopes.	333	500	COCO_val2014_000000004375.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000004375.jpg
+249677	213247	a woman sitting in a car while holding onto a cake in a plastic container	612	612	COCO_val2014_000000213247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213247.jpg
+250808	134596	a person doing a headstand on a concrete floor	446	640	COCO_val2014_000000134596.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134596.jpg
+251228	325598	A black cat is curled up and sleeping.	640	357	COCO_val2014_000000325598.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325598.jpg
+251428	560332	A suitcase sitting on a bench in front of a bed.	375	500	COCO_val2014_000000560332.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000560332.jpg
+251752	164305	A teddy bear in a pink dress and pink shoes.	400	500	COCO_val2014_000000164305.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000164305.jpg
+252469	341128	Several hoofed animals much on hay in the stable.	480	640	COCO_val2014_000000341128.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341128.jpg
+252576	541279	Two potatoes sit on a cluttered kitchen counter.	480	640	COCO_val2014_000000541279.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000541279.jpg
+252824	495348	some zebras a giraffe and other animals and some bushes and trees	278	640	COCO_val2014_000000495348.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000495348.jpg
+253090	510651	A young couple smile as they sit in a nice restaurant.	480	640	COCO_val2014_000000510651.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000510651.jpg
+253237	557552	Several motor scooters parked in a row on the side of a street.	480	640	COCO_val2014_000000557552.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000557552.jpg
+253794	453824	This room is decorated in a simple yet elegant style.	480	640	COCO_val2014_000000453824.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453824.jpg
+254131	233139	A display of clocks inside a shop window.	427	640	COCO_val2014_000000233139.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000233139.jpg
+254159	390215	A plate of chicken with herbs and steamed broccoli.	426	640	COCO_val2014_000000390215.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000390215.jpg
+254589	136077	Two zebra feasting on a dead animal in a dry grass field..	427	640	COCO_val2014_000000136077.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000136077.jpg
+254618	277592	A tall giraffe standing in front of two giant stones.	640	480	COCO_val2014_000000277592.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000277592.jpg
+255486	82449	A hot dog covered in mustard and cheese sits next to french fries.	425	640	COCO_val2014_000000082449.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000082449.jpg
+255535	420914	A long haired dog walking through the grass in the back yard.	375	500	COCO_val2014_000000420914.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000420914.jpg
+255933	243355	a zebra looks at the grass at a zoo	640	480	COCO_val2014_000000243355.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000243355.jpg
+256203	198992	A pair of red scissors on a white surface.	375	500	COCO_val2014_000000198992.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198992.jpg
+256588	168692	Two boys carrying a surfboard on the beach.	427	640	COCO_val2014_000000168692.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168692.jpg
+256802	486369	A trash truck is next to a glass with a straw.	480	640	COCO_val2014_000000486369.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000486369.jpg
+256980	579226	A large clock on the side of a building has biblical pictures.	480	640	COCO_val2014_000000579226.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000579226.jpg
+257357	174313	a guy sitting on a motor cycle next to some trees	640	480	COCO_val2014_000000174313.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174313.jpg
+257489	187745	a small yellow Cessna plane flying on a clear day	480	640	COCO_val2014_000000187745.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187745.jpg
+257490	365207	rear view mirror reflection showing a dog in the back seat	588	640	COCO_val2014_000000365207.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365207.jpg
+257555	295138	Young skateboarder showing off learned skills next to building.	423	640	COCO_val2014_000000295138.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295138.jpg
+258192	518725	A sliced sandwich with a pickle on a plate.	480	640	COCO_val2014_000000518725.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000518725.jpg
+259590	288174	Young boy on blue skateboard in parking lot.	427	640	COCO_val2014_000000288174.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000288174.jpg
+259641	143792	A little dog is smiling yet panting with a frisbee at his side.	427	640	COCO_val2014_000000143792.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143792.jpg
+259805	192362	male skier sliding down a cold snowy hill surrounded by evergreen trees	640	427	COCO_val2014_000000192362.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192362.jpg
+259937	540186	A view of a very large bathroom with mirrored walls.	640	621	COCO_val2014_000000540186.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540186.jpg
+260222	489728	a person eating with chopsticks and reading books in their living room	480	640	COCO_val2014_000000489728.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000489728.jpg
+260603	290619	Banana and plastic drink container sitting on a table.	480	640	COCO_val2014_000000290619.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000290619.jpg
+260807	287235	A cop riding on the back of a brown horse down a street.	480	640	COCO_val2014_000000287235.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287235.jpg
+260871	537574	A little pit bull dog is sitting on a floor board of a car.	640	426	COCO_val2014_000000537574.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000537574.jpg
+261213	134940	A large crowd of people around a very large illustration.	427	640	COCO_val2014_000000134940.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134940.jpg
+261243	37945	A young boy holding a toothbrush in his mouth.	640	406	COCO_val2014_000000037945.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000037945.jpg
+261408	262016	A man is baking bread in a room full of bread.	428	640	COCO_val2014_000000262016.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000262016.jpg
+261727	561491	A woman holding up a pregnancy test near a plate of food.	640	480	COCO_val2014_000000561491.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000561491.jpg
+261806	163447	a pair of vases with flowers are under a chandelier	640	427	COCO_val2014_000000163447.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000163447.jpg
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+261993	352496	A man on skis with several other skiers on a hill.	640	480	COCO_val2014_000000352496.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000352496.jpg
+262245	542570	A man and a woman standing in front of a kitchen table.	640	425	COCO_val2014_000000542570.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000542570.jpg
+262425	279696	A man standing next to a circus clown.	447	640	COCO_val2014_000000279696.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279696.jpg
+262730	89109	A cute brown dog laying on a blanket.	612	612	COCO_val2014_000000089109.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089109.jpg
+262893	25453	A vintage truck is parked at an outdoor event.	427	640	COCO_val2014_000000025453.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025453.jpg
+263321	247477	Person lying on brown leather couch in sparsely fille room.	480	640	COCO_val2014_000000247477.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000247477.jpg
+263385	443737	An old style jet plane on a tarmac.	435	640	COCO_val2014_000000443737.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000443737.jpg
+263911	455666	A young man, who is dressed formally, is posing for the camera.	640	426	COCO_val2014_000000455666.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000455666.jpg
+263965	313916	As two skiers make their way downhill a pair of skis are left vacant in the back.	443	640	COCO_val2014_000000313916.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000313916.jpg
+264064	131612	A girl riding a horse is jumping the log.	400	600	COCO_val2014_000000131612.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000131612.jpg
+264083	385873	Three pizzas sitting next to each other in boxes.	480	640	COCO_val2014_000000385873.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000385873.jpg
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+265340	340179	A group of people sitting around a  table having a meal.	393	500	COCO_val2014_000000340179.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000340179.jpg
+265371	470545	a close up of a plate of food with pizza	640	427	COCO_val2014_000000470545.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000470545.jpg
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+265581	165029	A man with a snowboard next to a man with a mask.	469	640	COCO_val2014_000000165029.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000165029.jpg
+265610	341857	Two men and a woman posing for a picture.	612	612	COCO_val2014_000000341857.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341857.jpg
+266054	307342	A group of people riding up a ski lift.	480	640	COCO_val2014_000000307342.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000307342.jpg
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+266413	443160	The would is dressed up wearing a hat and tie.	500	333	COCO_val2014_000000443160.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000443160.jpg
+266578	515821	Two boys play Wii while seated on a couch in a crowded room.	640	428	COCO_val2014_000000515821.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000515821.jpg
+266966	120285	A living room filled with furniture next to a fire place.	425	640	COCO_val2014_000000120285.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120285.jpg
+267088	419117	A plate topped with lots of food on a table.	360	640	COCO_val2014_000000419117.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419117.jpg
+267089	281448	Two sheep standing next to each other on a grass field.	334	500	COCO_val2014_000000281448.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000281448.jpg
+267370	513098	A group of planes flying in sequence in the sky.	360	640	COCO_val2014_000000513098.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000513098.jpg
+267488	276841	A woman holding two halves of a donut, one in each hand.	640	478	COCO_val2014_000000276841.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000276841.jpg
+267543	132612	These four zebra are walking in a field.	480	640	COCO_val2014_000000132612.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132612.jpg
+267681	280107	This people are standing near a man who is in the water.	435	640	COCO_val2014_000000280107.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000280107.jpg
+267806	554595	A man laying on top of a sandy beach laying next to a surfboard.	376	500	COCO_val2014_000000554595.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000554595.jpg
+267824	200567	A baseball player holding a bat standing on a field.	427	640	COCO_val2014_000000200567.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000200567.jpg
+267952	209835	A woman looking down at a set of luggage to her right.	640	477	COCO_val2014_000000209835.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209835.jpg
+268238	44029	A man riding skis down the side of a snow covered slope.	427	640	COCO_val2014_000000044029.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000044029.jpg
+268330	382629	Two wire racks filled with donuts and donut holes.	640	480	COCO_val2014_000000382629.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382629.jpg
+268757	423395	A man holding a bottle and a group of people near an umbrella.	424	640	COCO_val2014_000000423395.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000423395.jpg
+268911	52565	a cow stares as it stands next to a body of water	458	640	COCO_val2014_000000052565.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000052565.jpg
+268919	273052	A sheep lying on a hill side next to a tree.	334	500	COCO_val2014_000000273052.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273052.jpg
+268940	53774	skateboarder who is about to land from a jump of some type	427	640	COCO_val2014_000000053774.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000053774.jpg
+268989	176606	A goat alongside a house, trying to go through the bushes	480	640	COCO_val2014_000000176606.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000176606.jpg
+269080	215596	A bathtub sitting under a chandelier and next to a pair of windows and a mirror in a bathroom.	640	480	COCO_val2014_000000215596.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000215596.jpg
+269587	316123	Children are playing soccer in a field of grass.	640	426	COCO_val2014_000000316123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000316123.jpg
+270136	337502	a pizza with a lot of cheese on it on a oven board	427	640	COCO_val2014_000000337502.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000337502.jpg
+270451	40602	A couple of women playing a game on the Wii.	480	640	COCO_val2014_000000040602.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000040602.jpg
+270631	116434	A silhouette of a man surfing over waves.	640	471	COCO_val2014_000000116434.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116434.jpg
+270728	5617	A couple of cats laying on top of pillows.	480	640	COCO_val2014_000000005617.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005617.jpg
+270974	280392	Two people stand on the beach, surfboards in hand.	640	427	COCO_val2014_000000280392.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000280392.jpg
+271180	448810	A street sign with six different locations on it.	375	500	COCO_val2014_000000448810.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448810.jpg
+271522	64703	A large sandwich filled with meat on a plate.	480	640	COCO_val2014_000000064703.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064703.jpg
+271930	88349	A small pizza sitting on top of a blue plate.	480	640	COCO_val2014_000000088349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000088349.jpg
+271982	156487	A highway with a Rue Paul St. sign beside it.	480	640	COCO_val2014_000000156487.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000156487.jpg
+272189	345380	A dog jumping in the air catching a Frisbee in its mouth.	427	640	COCO_val2014_000000345380.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345380.jpg
+272486	284296	Three giraffe's leaning over to get a sip of water.	427	640	COCO_val2014_000000284296.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000284296.jpg
+272538	322959	Bread and fruit are on a table with a knife and fork.	612	612	COCO_val2014_000000322959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000322959.jpg
+272603	304817	Snowboard stuck in a thick collection of trees.	480	640	COCO_val2014_000000304817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000304817.jpg
+272779	9145	Two ducklings sit next to each other in grass.	324	640	COCO_val2014_000000009145.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009145.jpg
+272822	58797	A toilet is in a dirty bathroom with a sink.	351	500	COCO_val2014_000000058797.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000058797.jpg
+273001	573968	A person on a snowboard is standing on a rail.	480	640	COCO_val2014_000000573968.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573968.jpg
+273162	160703	A table topped with two pizzas covered in sauce and toppings.	640	480	COCO_val2014_000000160703.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160703.jpg
+273284	550952	A group of four surfboards laying on top of a beach.	329	500	COCO_val2014_000000550952.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550952.jpg
+273543	305319	A woman laying on top of a surfboard in the ocean.	413	640	COCO_val2014_000000305319.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000305319.jpg
+273614	305004	A young man riding a white surfboard on top of a wave.	360	640	COCO_val2014_000000305004.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000305004.jpg
+273670	273711	A table topped with plates, bowls and containers of food.	480	640	COCO_val2014_000000273711.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273711.jpg
+273853	33441	A child is pushing a loaded luggage cart.	612	612	COCO_val2014_000000033441.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000033441.jpg
+273975	308394	An older woman riding a train while sitting under it's window.	428	640	COCO_val2014_000000308394.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000308394.jpg
+274339	435533	A bathroom toilet with yellow liquid inside and a dirty tub.	478	640	COCO_val2014_000000435533.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000435533.jpg
+274379	61972	A large metal clock hanging with chains from a roof.	375	500	COCO_val2014_000000061972.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000061972.jpg
+274503	372249	A black bird sitting on top of a wooden fence.	375	500	COCO_val2014_000000372249.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372249.jpg
+274547	390072	A large bouquet of flowers in a vase one being a sunflower.	640	427	COCO_val2014_000000390072.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000390072.jpg
+274626	60899	The young boy is playing in the living room of his house.	640	360	COCO_val2014_000000060899.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000060899.jpg
+274636	296731	A group of people flying over the top of a snow covered ski slope.	427	640	COCO_val2014_000000296731.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000296731.jpg
+274698	164497	Adult and baby buffalo graze on a plain.	480	640	COCO_val2014_000000164497.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000164497.jpg
+274815	493704	The woman is riding her horse on the beach by the water.	640	639	COCO_val2014_000000493704.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493704.jpg
+275067	566998	A cat holding a remote control while laying on a couch.	427	640	COCO_val2014_000000566998.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000566998.jpg
+275090	26982	Two motorcycles parked next to each other on a lush green field.	427	640	COCO_val2014_000000026982.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026982.jpg
+275485	158994	Two big white city buses parked near the curb.	425	640	COCO_val2014_000000158994.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158994.jpg
+275916	448448	Two giraffes grazing on some green leaves in the field.	480	640	COCO_val2014_000000448448.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448448.jpg
+276924	84158	A bear standing at the border between the shade and the sunshine.	640	428	COCO_val2014_000000084158.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000084158.jpg
+276998	401384	Signs at a resort pointing in many directions	640	640	COCO_val2014_000000401384.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401384.jpg
+277296	73326	An open refrigerator including bottles of various food.	480	640	COCO_val2014_000000073326.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000073326.jpg
+277703	414201	A very happy older couple cutting a cake together.	640	425	COCO_val2014_000000414201.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000414201.jpg
+277842	230177	A double deck bus, with a convertible top.	427	640	COCO_val2014_000000230177.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000230177.jpg
+277874	577887	Part of a pizza is displayed showing it's toppings.	423	640	COCO_val2014_000000577887.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577887.jpg
+278075	32597	A cat sits in a living room with a television in the background.	612	612	COCO_val2014_000000032597.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032597.jpg
+278152	242529	A bicycle is parked near a body of water.	480	640	COCO_val2014_000000242529.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242529.jpg
+278687	301991	A bride is eating steak at a table.	640	640	COCO_val2014_000000301991.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301991.jpg
+278872	581062	A man rides his skateboard near a drain pipe system.	375	500	COCO_val2014_000000581062.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000581062.jpg
+279159	303847	A carved wooden elephant next to a book.	480	640	COCO_val2014_000000303847.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303847.jpg
+279291	305329	a baseball player holding a bat on a field	640	427	COCO_val2014_000000305329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000305329.jpg
+279377	502644	A woman sitting on a toilet with a mask on.	640	429	COCO_val2014_000000502644.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000502644.jpg
+279608	134691	A dirty bathroom consisting of a toilet, bath tub and sink.	640	427	COCO_val2014_000000134691.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134691.jpg
+279707	545441	A young girl tries to comb her own hair.	427	640	COCO_val2014_000000545441.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000545441.jpg
+280373	3244	Camera and computers set up on a desk	338	640	COCO_val2014_000000003244.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000003244.jpg
+280462	160104	A table with bread and cheese and plates and knife.	612	612	COCO_val2014_000000160104.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160104.jpg
+280531	313562	A woman selling raw carrots at an outdoor market	480	640	COCO_val2014_000000313562.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000313562.jpg
+280752	580953	A desk with a laptop, computer monitor and a keyboard.	480	640	COCO_val2014_000000580953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000580953.jpg
+280910	182366	Four pans of food sitting on a stove top.	425	640	COCO_val2014_000000182366.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182366.jpg
+281017	401860	Vegetable and cheese slice of pizza on white plate.	427	640	COCO_val2014_000000401860.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401860.jpg
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+282021	279420	Child on skis with tips pointing at each other.	480	640	COCO_val2014_000000279420.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279420.jpg
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+283253	43611	GUY SKATE BOARDING DOWN THE STREET IN THE DARK	400	500	COCO_val2014_000000043611.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000043611.jpg
+283627	325586	A cell phone and a banana on a rock.	480	640	COCO_val2014_000000325586.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325586.jpg
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+287625	442822	Several rows of boats docked on a body of water.	427	640	COCO_val2014_000000442822.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442822.jpg
+287660	9807	A gray and white sitting in front of a TV.	425	640	COCO_val2014_000000009807.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009807.jpg
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+288055	147624	A person in a shirt and tie is holding a can.	640	427	COCO_val2014_000000147624.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000147624.jpg
+288109	537692	A collection of scissors and candles place on a table.	375	500	COCO_val2014_000000537692.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000537692.jpg
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+288290	146509	The front of a business that sells teddy bears.	640	480	COCO_val2014_000000146509.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146509.jpg
+288430	259576	A man flies his kite on a hill overlooking a bridge.	480	640	COCO_val2014_000000259576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000259576.jpg
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+288467	535682	A woman lounging on a beach chair holding two phones.	480	640	COCO_val2014_000000535682.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000535682.jpg
+288478	116589	A group of zebras stay close to each other near a log.	375	500	COCO_val2014_000000116589.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116589.jpg
+288574	325505	A kitchen counter top with many different appliances.	427	640	COCO_val2014_000000325505.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325505.jpg
+288644	280976	A kneeling down on a baseball field next to grass.	640	512	COCO_val2014_000000280976.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000280976.jpg
+288742	46869	A household bathroom with vanity sink, toilet and tub and shower.	640	480	COCO_val2014_000000046869.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000046869.jpg
+288743	477172	Skier jumping moguls on his run down the mountain.	402	640	COCO_val2014_000000477172.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000477172.jpg
+288839	202201	Some people that are picking out from a large variety of doughnuts.	333	500	COCO_val2014_000000202201.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000202201.jpg
+288939	165229	Unplugged old style tube television sitting on a floor.	425	640	COCO_val2014_000000165229.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000165229.jpg
+288966	449433	A small cake on a table with a sparkling candle.	612	612	COCO_val2014_000000449433.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449433.jpg
+288994	212895	a single giraffe standing in the middle of a field	640	480	COCO_val2014_000000212895.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000212895.jpg
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+289093	475572	There are is a picture of a couple, a beany baby bear, and pot of grass on a mantle.	431	640	COCO_val2014_000000475572.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000475572.jpg
+289150	419080	A sign sitting next to a sidewalk with a garbage can, traffic cone, and several fire hydrants on it.	640	480	COCO_val2014_000000419080.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419080.jpg
+289195	401758	A dog kissing a cat on top of a cement road.	640	481	COCO_val2014_000000401758.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401758.jpg
+289229	211054	A train traveling past a factory with tons of smoke pouring out of it's stacks.	387	640	COCO_val2014_000000211054.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211054.jpg
+289233	234794	A vintage STOP sign in black & white	480	640	COCO_val2014_000000234794.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000234794.jpg
+289292	88432	Some street signs near a road with a truck.	640	480	COCO_val2014_000000088432.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000088432.jpg
+289294	361157	a cat rubbing its face against a bottle.c	439	500	COCO_val2014_000000361157.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000361157.jpg
+289404	398661	A view of a kitchen shows large and small appliances and pans on the cabinets.	427	640	COCO_val2014_000000398661.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398661.jpg
+289434	378311	A line of ovens are on a wall in a kitchen.	640	427	COCO_val2014_000000378311.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000378311.jpg
+289669	146253	A group of skiers trudging up a snow covered hill.	428	640	COCO_val2014_000000146253.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146253.jpg
+289712	322353	A cat sitting on top of a window sill over a sink.	416	500	COCO_val2014_000000322353.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000322353.jpg
+289786	133000	A city view of an E-train on a track	427	640	COCO_val2014_000000133000.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133000.jpg
+289890	347766	A computer desk has two monitors, mice, keyboards, cell phone, speakers, and webcam.	480	640	COCO_val2014_000000347766.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347766.jpg
+289995	507211	A surfer rides a wave on a sunny day.	453	640	COCO_val2014_000000507211.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507211.jpg
+290054	459825	A crowded work desk placed against a brick wall.	428	640	COCO_val2014_000000459825.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000459825.jpg
+290065	573391	A bear walking in the forest in the early morning	480	640	COCO_val2014_000000573391.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573391.jpg
+290276	546203	A person in a coat and goggles carrying a snowboard.	640	481	COCO_val2014_000000546203.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546203.jpg
+290450	409291	A girl with a baseball glove is standing in the grass.	640	428	COCO_val2014_000000409291.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000409291.jpg
+290545	299319	Two women walking side by side holding umbrellas.	421	640	COCO_val2014_000000299319.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000299319.jpg
+290708	74573	A parking meter on the side of a street near a window.	480	640	COCO_val2014_000000074573.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000074573.jpg
+290959	552752	A man hangs off a stationary train indoors.	640	404	COCO_val2014_000000552752.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552752.jpg
+291533	33405	A woman is walking her two dogs on the beach.	482	640	COCO_val2014_000000033405.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000033405.jpg
+291657	242619	A white plate topped with sauced covered food next to broccoli.	640	640	COCO_val2014_000000242619.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242619.jpg
+291883	410932	A red stop sign sitting on the side of a road.	480	640	COCO_val2014_000000410932.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000410932.jpg
+292125	184139	A bed with a blanket and pillows on top of it.	480	640	COCO_val2014_000000184139.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000184139.jpg
+292193	172935	A person flying through the air whale riding skis.	364	488	COCO_val2014_000000172935.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000172935.jpg
+292290	357176	Two riders on horses kicking up dust while training.	612	612	COCO_val2014_000000357176.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000357176.jpg
+293032	472253	A group of people in a conference room watching a televisions.	375	500	COCO_val2014_000000472253.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472253.jpg
+293352	124707	A kitchen area with a table, dishwasher and stove.	426	640	COCO_val2014_000000124707.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000124707.jpg
+294146	560797	NASA men making first, ceremonial pitch at a baseball game.	409	640	COCO_val2014_000000560797.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000560797.jpg
+294392	522061	A stove on a wall in a small kitchen.	500	337	COCO_val2014_000000522061.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000522061.jpg
+294644	334085	A man and a kids at a table with pizza.	428	640	COCO_val2014_000000334085.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334085.jpg
+294687	493805	Two giraffes in a zoo look at their surroundings	512	640	COCO_val2014_000000493805.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493805.jpg
+294902	519329	A man holding a tray of glasses with candy canes.	640	428	COCO_val2014_000000519329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519329.jpg
+295326	194097	A woman sitting with a boy cutting a cake.	526	640	COCO_val2014_000000194097.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000194097.jpg
+295473	52368	Several benches in a park area, with flowers and an umbrella above a table	640	428	COCO_val2014_000000052368.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000052368.jpg
+295486	259814	Four people on horses race inside an enclosed area	480	640	COCO_val2014_000000259814.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000259814.jpg
+296256	187624	A person and young girl with a frog umbrella standing next to water.	640	479	COCO_val2014_000000187624.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187624.jpg
+296706	482978	an image of a subway bus going thru the subway station	360	640	COCO_val2014_000000482978.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000482978.jpg
+296722	529256	Two dogs laying on a bed with a red comforter.	426	640	COCO_val2014_000000529256.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529256.jpg
+297337	325351	2 men sit on the couch, video game controllers in hands.	427	640	COCO_val2014_000000325351.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325351.jpg
+297384	452179	The twin beds with the blue spread are pushed together.	640	480	COCO_val2014_000000452179.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000452179.jpg
+297587	565563	Many cars are parked along the sidewalk of a narrow street.	480	640	COCO_val2014_000000565563.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565563.jpg
+297835	557709	A Baseball player is attempting to take his best swing while the players behind him look on.	373	640	COCO_val2014_000000557709.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000557709.jpg
+297921	408534	a man in the ocean surfing holding on to a kite.	426	640	COCO_val2014_000000408534.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000408534.jpg
+298257	75434	A man walks on the beach holding a surfboard on his head.	640	427	COCO_val2014_000000075434.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000075434.jpg
+298714	46870	A motorcycle leaning on a car in street.	457	640	COCO_val2014_000000046870.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000046870.jpg
+298715	312509	Various birds flying over a body of water next to trees.	426	640	COCO_val2014_000000312509.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000312509.jpg
+299289	199125	some small boats in a body of water	432	640	COCO_val2014_000000199125.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000199125.jpg
+299311	286458	Two images of open suitecases full of toiletries.	310	455	COCO_val2014_000000286458.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000286458.jpg
+299358	343031	A black steam engine pulling a small passenger car.	427	640	COCO_val2014_000000343031.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000343031.jpg
+299515	178317	Two ladies standing on skis besides a chair lift.	640	480	COCO_val2014_000000178317.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000178317.jpg
+299516	187384	A red bowl in the center of a plate with olives and lemons on the outer plate.	480	640	COCO_val2014_000000187384.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187384.jpg
+299537	470298	A polar bear in water with a round cage that contains an orange round object.	426	640	COCO_val2014_000000470298.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000470298.jpg
+299832	205531	A dog rests on a pillow on the ledge of a window.	640	426	COCO_val2014_000000205531.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205531.jpg
+299870	54761	Three young boys sitting down with some luggage.	425	640	COCO_val2014_000000054761.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054761.jpg
+299871	500116	A group of people standing together on a field smiling.	428	640	COCO_val2014_000000500116.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500116.jpg
+300295	151651	A bathroom with a row of wall mounted sinks.	425	640	COCO_val2014_000000151651.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000151651.jpg
+300501	298691	a big bowl on a table filled with broccoli	427	640	COCO_val2014_000000298691.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000298691.jpg
+300537	529166	A man in tiny, red shorts lifts his leg as a yellow Frisbee hovers near him.	500	333	COCO_val2014_000000529166.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529166.jpg
+300615	460126	Two giraffes stand in a dirt yard next to a wooden structure.	400	500	COCO_val2014_000000460126.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460126.jpg
+300780	157124	Various sink cabinets lined up in a warehouse	424	640	COCO_val2014_000000157124.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000157124.jpg
+300809	453954	A city bus driving on the street in the snow.	612	612	COCO_val2014_000000453954.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453954.jpg
+301345	391415	There is a red car being towed on a truck	480	640	COCO_val2014_000000391415.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000391415.jpg
+301618	494584	A small white bird flying over the water.	421	640	COCO_val2014_000000494584.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000494584.jpg
+301807	426268	A large long train with man inside on the track.	427	640	COCO_val2014_000000426268.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000426268.jpg
+302077	369849	Three horse grazing on grass near a street sign.	427	640	COCO_val2014_000000369849.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369849.jpg
+302101	167964	A lit street lamp at an intersection with a stop sign.	375	500	COCO_val2014_000000167964.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000167964.jpg
+302658	34749	A truck hauling a large load to a job site on a winding mountain road.	480	640	COCO_val2014_000000034749.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034749.jpg
+302770	513417	An elephant is standing next to a rock	427	640	COCO_val2014_000000513417.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000513417.jpg
+302962	236338	Many elephants being paraded through an area of spectators.	480	640	COCO_val2014_000000236338.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000236338.jpg
+302996	144870	A bedroom area with a bed, mirror and window to patio.	480	640	COCO_val2014_000000144870.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000144870.jpg
+303139	383748	A clock in wooden frame covered by a wreath.	427	640	COCO_val2014_000000383748.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000383748.jpg
+303238	435179	Some men find a ship wreaked on the beach.	426	640	COCO_val2014_000000435179.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000435179.jpg
+303261	343073	A mirror and soap dispenser in a bathroom.	500	375	COCO_val2014_000000343073.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000343073.jpg
+303316	84749	A cat curled up in the shade on a door stoop.	480	640	COCO_val2014_000000084749.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000084749.jpg
+303380	298697	Horses are standing in an open field in the grass.	480	640	COCO_val2014_000000298697.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000298697.jpg
+303489	227227	A woman sitting in a car holding a small white dog.	334	500	COCO_val2014_000000227227.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227227.jpg
+303557	227781	A group of people sit next to horses.	428	640	COCO_val2014_000000227781.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227781.jpg
+303777	396217	Two dogs resting comfortably on a tiled floor.	375	500	COCO_val2014_000000396217.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000396217.jpg
+304132	546928	A close up view of a cheesy pizza in front of a person.	480	640	COCO_val2014_000000546928.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546928.jpg
+304492	450932	The reflection of a woman is in a window and beyond her is a white and red train.	375	500	COCO_val2014_000000450932.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450932.jpg
+304563	530099	A orange calico cat on top of a vehicle.	375	500	COCO_val2014_000000530099.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530099.jpg
+304775	519055	A man throwing a baseball while people watch from chairs behind him.	521	640	COCO_val2014_000000519055.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519055.jpg
+304947	236730	A zebra staring blankly near a watering hole.	640	480	COCO_val2014_000000236730.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000236730.jpg
+305020	370839	A man with remote standing in a room.	480	640	COCO_val2014_000000370839.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000370839.jpg
+305067	437049	The people are at tables on an outdoor patio.	640	429	COCO_val2014_000000437049.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000437049.jpg
+305422	56983	a woman stares as a bird flys through the air	428	640	COCO_val2014_000000056983.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056983.jpg
+305506	472643	Children cover a parking meter in a red knitted garment.	554	640	COCO_val2014_000000472643.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472643.jpg
+305529	390902	A man standing on a tennis court holding a racquet.	500	333	COCO_val2014_000000390902.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000390902.jpg
+305769	191013	A large clock on the side of a building above cars on the street.	640	474	COCO_val2014_000000191013.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000191013.jpg
+305854	60093	Cars driving on a road near a traffic light.	640	595	COCO_val2014_000000060093.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000060093.jpg
+305946	487333	A cat sitting on couch biting into a banana.	478	640	COCO_val2014_000000487333.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487333.jpg
+306346	120935	A herd of zebra standing near some bushes and rocks.	425	640	COCO_val2014_000000120935.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120935.jpg
+306435	382462	A bunch of soccer players in uniform playing soccer.	429	640	COCO_val2014_000000382462.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382462.jpg
+306436	342380	A red kite sitting on top of a lush green field.	480	640	COCO_val2014_000000342380.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000342380.jpg
+306458	452891	A white dog standing on top of a wooden bench.	640	478	COCO_val2014_000000452891.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000452891.jpg
+306965	163306	there are two polar bears standing near each other	427	640	COCO_val2014_000000163306.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000163306.jpg
+307051	359220	A person at a table eating some food.	480	640	COCO_val2014_000000359220.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000359220.jpg
+307450	359465	A pair of skis in the open water.	480	640	COCO_val2014_000000359465.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000359465.jpg
+308565	379716	A small bowl of broccoli and some toast.	480	640	COCO_val2014_000000379716.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379716.jpg
+308699	468018	A person jumping up with a kite in their hands.	333	500	COCO_val2014_000000468018.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000468018.jpg
+308959	396820	A woman holding an umbrella in a square.	640	480	COCO_val2014_000000396820.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000396820.jpg
+309060	148282	A baby in a carriage on street next to market.	480	360	COCO_val2014_000000148282.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000148282.jpg
+309305	534801	Two men in a market area with various fruits and vegetables.	480	640	COCO_val2014_000000534801.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534801.jpg
+309584	536034	Many small boats in the water in a harbor.	480	640	COCO_val2014_000000536034.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000536034.jpg
+309645	270297	A small train going down a steel track.	427	640	COCO_val2014_000000270297.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000270297.jpg
+309842	274084	A man stands in a boat near a flock of ducks.	480	640	COCO_val2014_000000274084.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000274084.jpg
+310189	304828	The lower half of a very tall giraffe in a grassy area.	480	640	COCO_val2014_000000304828.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000304828.jpg
+310948	52232	A bed in a hotel room with doors leading to a balcony.	480	640	COCO_val2014_000000052232.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000052232.jpg
+310950	214519	Two side by side urinals mounted on a wall.	360	640	COCO_val2014_000000214519.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214519.jpg
+311273	421201	Doorway view of a bathroom that includes a toilet and tub.	640	478	COCO_val2014_000000421201.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421201.jpg
+311505	431352	Closed toilet in a narrow, tiled bathroom, with sink and shower stall.	640	427	COCO_val2014_000000431352.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431352.jpg
+311523	577614	Two students study on two separate bench tables	480	640	COCO_val2014_000000577614.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577614.jpg
+312029	108224	A red bus on side of street next to a building.	480	640	COCO_val2014_000000108224.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000108224.jpg
+312123	479727	A semi busy street in a delightful neighborhood	518	640	COCO_val2014_000000479727.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000479727.jpg
+312173	296848	A man in ski gear skiing on snow.	428	640	COCO_val2014_000000296848.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000296848.jpg
+312225	563258	Various colored vases with black and white photographs on wall.	366	640	COCO_val2014_000000563258.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000563258.jpg
+312550	418578	Group of teddy bears with holiday clothes on a red carpet.	333	500	COCO_val2014_000000418578.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000418578.jpg
+312779	260330	The kitchen has two refrigerators and a stove in it.	480	640	COCO_val2014_000000260330.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260330.jpg
+313007	491058	A skateboarder holds out his arms as he pops up the front of his board while standing on the back.	640	427	COCO_val2014_000000491058.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491058.jpg
+313152	402123	A baby elephant walks in a fenced area next to old wood and a purple sun umbrella on the other side on the fence.	480	640	COCO_val2014_000000402123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000402123.jpg
+313266	425462	A baseball player captured while throwing a ball.	640	427	COCO_val2014_000000425462.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000425462.jpg
+313404	267812	Several giraffes, including an infant, stand in an exhibit.	640	428	COCO_val2014_000000267812.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267812.jpg
+313579	545730	a herd of zebras crossing a shallow part of a river	427	640	COCO_val2014_000000545730.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000545730.jpg
+313846	529698	a close up of a motorcycle parked on a street filled with people	494	640	COCO_val2014_000000529698.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529698.jpg
+313970	542077	Two street lights on a corner pole in a town.	375	500	COCO_val2014_000000542077.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000542077.jpg
+314314	409566	a bathroom with a toilet and a sink and a bath tub	640	480	COCO_val2014_000000409566.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000409566.jpg
+314605	496541	A large white sheep and two baby black sheep in barn	478	640	COCO_val2014_000000496541.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000496541.jpg
+314849	508370	A bicycle parked on top of a baby dolls head.	640	427	COCO_val2014_000000508370.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000508370.jpg
+314947	32193	Three giraffe standing next to two zebra on a lush green field.	480	640	COCO_val2014_000000032193.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032193.jpg
+315131	266977	A man carrying a surfboard across the beach	640	425	COCO_val2014_000000266977.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000266977.jpg
+315289	303744	A person flying a kite next to a lake and trees	427	640	COCO_val2014_000000303744.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303744.jpg
+315458	419369	A man hitting a tennis ball with a racket	427	640	COCO_val2014_000000419369.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419369.jpg
+315587	383341	a yellow plate of sliced bananas and butterflies on them	480	640	COCO_val2014_000000383341.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000383341.jpg
+315595	314541	A large green field with a bench in the foreground.	425	640	COCO_val2014_000000314541.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000314541.jpg
+315612	254119	A colorful cuckoo clock with the clock hands at six o'clock.	640	480	COCO_val2014_000000254119.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000254119.jpg
+315710	39081	A person walking their dog on the beach	480	640	COCO_val2014_000000039081.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039081.jpg
+315752	575219	a cocker spaniel puppy laying on top of controlers	612	612	COCO_val2014_000000575219.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575219.jpg
+315761	100895	These children are sleeping in the same bed.	612	612	COCO_val2014_000000100895.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000100895.jpg
+315873	283119	a dog laying down eating food out of a bowl	400	500	COCO_val2014_000000283119.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000283119.jpg
+316439	265208	A tray topped with two sandwiches, pie and a plate of coleslaw.	375	500	COCO_val2014_000000265208.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000265208.jpg
+316440	208817	A man riding on top of a surfboard in the ocean.	480	640	COCO_val2014_000000208817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208817.jpg
+316679	485483	Some pancakes with icecream and bananas and a coffee	640	480	COCO_val2014_000000485483.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000485483.jpg
+316844	295728	A clock has four faces on each side of an information booth.	640	512	COCO_val2014_000000295728.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295728.jpg
+316867	346482	the propeller of a plane flying in the air	500	375	COCO_val2014_000000346482.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000346482.jpg
+317014	451835	A toddler in a grassy area playing with a Frisbee.	426	640	COCO_val2014_000000451835.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451835.jpg
+317183	431832	Batter, catcher, and umpire are ready for the pitcher to throw the ball.	428	640	COCO_val2014_000000431832.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431832.jpg
+317513	530212	A rack of assorted  colorful neck ties.	500	334	COCO_val2014_000000530212.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530212.jpg
+317594	529434	A piece of luggage with a rainbow strap and a ticket on it.	500	400	COCO_val2014_000000529434.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529434.jpg
+317734	218310	Two multicolored birds perched on a wooden branch.	640	420	COCO_val2014_000000218310.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218310.jpg
+317970	337439	A woman rolling her luggage through an airport.	375	500	COCO_val2014_000000337439.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000337439.jpg
+318066	566155	A bathroom area with a crate full of water and an in-floor urinal.	480	640	COCO_val2014_000000566155.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000566155.jpg
+318125	75234	A man swinging a tennis racket at a tennis ball.	426	640	COCO_val2014_000000075234.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000075234.jpg
+318467	506454	A park bench that is sitting in grass under a tree.	480	640	COCO_val2014_000000506454.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000506454.jpg
+318743	158498	A tablet sitting on top of a computer desk.	374	500	COCO_val2014_000000158498.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158498.jpg
+318929	63965	A woman holding a plate of cake covered in frosting.	427	640	COCO_val2014_000000063965.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000063965.jpg
+318953	355123	a frying pan with an egg that has a double yolk	480	640	COCO_val2014_000000355123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000355123.jpg
+319122	242940	A stop sign at an intersection, with Hammer time and a person's picture displayed on the sign	480	640	COCO_val2014_000000242940.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242940.jpg
+319181	157653	A man talking on a cell phone while walking down a street.	428	640	COCO_val2014_000000157653.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000157653.jpg
+319245	412531	a single parking meter sitting next to some plants and a tree	480	640	COCO_val2014_000000412531.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412531.jpg
+319319	454610	Two boats filled with lots of fresh produce floating on a river.	333	500	COCO_val2014_000000454610.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000454610.jpg
+319420	64898	Two children who are on wake boards in the ocean.	427	640	COCO_val2014_000000064898.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064898.jpg
+319423	85813	The couple is skiing down the hill together.	640	609	COCO_val2014_000000085813.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000085813.jpg
+319614	353067	A man riding a skateboard through the air at a skate park.	640	424	COCO_val2014_000000353067.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000353067.jpg
+319684	29030	An upside down and right side up picture of a boy holding a phone.	483	640	COCO_val2014_000000029030.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029030.jpg
+319685	226278	A man on a tennis court at night time.	640	478	COCO_val2014_000000226278.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226278.jpg
+319699	259216	The woman has many bananas and other fruit at her stand.	482	640	COCO_val2014_000000259216.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000259216.jpg
+320658	231161	In a grassy field stand 2 giraffes and 2 llamas.	500	375	COCO_val2014_000000231161.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231161.jpg
+320780	483375	a large body of water with boats floating within it	362	640	COCO_val2014_000000483375.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000483375.jpg
+320797	562155	A person in a car wearing dark glasses, a shirt, and tie.	640	480	COCO_val2014_000000562155.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000562155.jpg
+320802	538792	A red and black motorcycle parked near another bike.	480	640	COCO_val2014_000000538792.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000538792.jpg
+320877	377427	A man at a table with several dishes of food.	640	480	COCO_val2014_000000377427.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000377427.jpg
+320948	50942	two males on computers and a gray keyboard	480	640	COCO_val2014_000000050942.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000050942.jpg
+321762	241319	A bathroom sink with two toothbrushes facing each other.	476	640	COCO_val2014_000000241319.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000241319.jpg
+322153	421681	A couple of suit cases sitting on top of a wooden block.	375	500	COCO_val2014_000000421681.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421681.jpg
+322195	337662	A few people in a kitchen preparing a table and checking food in an oven.	427	640	COCO_val2014_000000337662.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000337662.jpg
+322483	491765	A plate of fried fish over a bed of french fries with some sauce in a can.	480	640	COCO_val2014_000000491765.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491765.jpg
+322611	458487	A baseball player holding a bat next to a ball.	428	640	COCO_val2014_000000458487.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000458487.jpg
+322616	294032	A couple of men sitting at a table talking on cell phones.	480	640	COCO_val2014_000000294032.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294032.jpg
+322617	21483	A yellow and white train traveling down train tracks.	480	640	COCO_val2014_000000021483.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000021483.jpg
+322713	230701	A man and woman sitting at a table with cellphone and beers.	375	500	COCO_val2014_000000230701.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000230701.jpg
+322828	169331	A person and some vehicles on a road.	480	640	COCO_val2014_000000169331.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000169331.jpg
+322922	553075	A white plate holding a cheese pizza on a table.	375	500	COCO_val2014_000000553075.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000553075.jpg
+323146	277080	The woman is eating a piece of pizza.	457	640	COCO_val2014_000000277080.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000277080.jpg
+323180	248767	People standing on the corner with lights and pedestrian crossing near a construction site.	375	500	COCO_val2014_000000248767.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248767.jpg
+323329	409181	A herd of zebra standing on a field.	426	640	COCO_val2014_000000409181.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000409181.jpg
+323547	479078	A brown horse being jumped over a fence rail in a competition.	400	600	COCO_val2014_000000479078.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000479078.jpg
+323781	432724	A couple of wild animals and a zebra standing on grass.	424	640	COCO_val2014_000000432724.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000432724.jpg
+324162	349841	A public bus driving on a city street.	483	640	COCO_val2014_000000349841.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000349841.jpg
+324484	139306	A footlong sandwich on a piece of paper next to a serrated knife.	480	640	COCO_val2014_000000139306.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000139306.jpg
+324531	40042	A cat sitting behind a pair of slippers on a blue towel.	640	534	COCO_val2014_000000040042.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000040042.jpg
+324781	178578	A person wearing a beige ski hat driving a car.	480	640	COCO_val2014_000000178578.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000178578.jpg
+324919	494269	A bathroom with a toilet, sink and trashcan in it.	640	427	COCO_val2014_000000494269.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000494269.jpg
+325099	194559	A bed with several items of cloth arranged as hearts on top.	426	640	COCO_val2014_000000194559.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000194559.jpg
+325358	228011	A brown and white cow walking down a busy street.	480	640	COCO_val2014_000000228011.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000228011.jpg
+325468	310669	Two big brown horses pulling a cart filled with people.	375	500	COCO_val2014_000000310669.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000310669.jpg
+325521	202825	A woman in yellow shirt and white shorts holding a pole.	640	409	COCO_val2014_000000202825.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000202825.jpg
+326107	91267	A room with a couch, chair and television.	427	640	COCO_val2014_000000091267.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000091267.jpg
+326206	294035	A boy standing up on a open refrigerator.	640	456	COCO_val2014_000000294035.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294035.jpg
+326534	285433	A toilet with a black seat cover next to a sink.	480	640	COCO_val2014_000000285433.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000285433.jpg
+326954	363241	A lady eating a slice of deep dish pizza.	640	425	COCO_val2014_000000363241.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000363241.jpg
+327158	547102	A group of flowers in a vase sitting on a well varnished table.	640	640	COCO_val2014_000000547102.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000547102.jpg
+327598	122606	A bus going to crosstown parked on side of road.	480	640	COCO_val2014_000000122606.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000122606.jpg
+327731	138397	Many old fashion nick-knacks are all neatly placed.	480	640	COCO_val2014_000000138397.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000138397.jpg
+327906	279437	A small neat bedroom for an adult	500	375	COCO_val2014_000000279437.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279437.jpg
+328083	381529	two men in suits standing next to each other	427	640	COCO_val2014_000000381529.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000381529.jpg
+328120	56092	Horses kicking up dirt and grass, behind a fence.	259	640	COCO_val2014_000000056092.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056092.jpg
+328181	412551	A view of a street at night shows a yellow fire hydrant.	425	640	COCO_val2014_000000412551.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412551.jpg
+328637	383223	A group of men on a field playing baseball.	333	500	COCO_val2014_000000383223.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000383223.jpg
+328843	213843	A chocolate cake with cream and wafers on top.	480	640	COCO_val2014_000000213843.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213843.jpg
+329334	306749	A man and baby are by an oversize teddy bear.	640	428	COCO_val2014_000000306749.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306749.jpg
+329450	333924	A man on a surfboard on a wave in the water.	271	640	COCO_val2014_000000333924.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333924.jpg
+330458	139872	A dog laying on grass near a flying disc.	480	640	COCO_val2014_000000139872.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000139872.jpg
+331102	6460	a person riding a surf board on a wave	427	640	COCO_val2014_000000006460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000006460.jpg
+331844	26640	Someone is using a small grill to melt his sandwich.	640	433	COCO_val2014_000000026640.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026640.jpg
+332428	172408	A man in black hovers over a skateboard while they are both in the air.	428	640	COCO_val2014_000000172408.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000172408.jpg
+332740	210276	A cat in a suitcase during a sunny day.	640	480	COCO_val2014_000000210276.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000210276.jpg
+332807	29833	A woman is holding a small bunch of fruit.	640	480	COCO_val2014_000000029833.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029833.jpg
+332817	430583	A rusty old red fire hydrant in the weeds.	375	500	COCO_val2014_000000430583.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000430583.jpg
+333142	137120	A man flying through the air while riding a snowboard.	425	640	COCO_val2014_000000137120.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000137120.jpg
+333211	460361	a baseball batter at citifield is about to hit the baseball	359	640	COCO_val2014_000000460361.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460361.jpg
+333538	169690	A little girl sitting on top of a bench in a  park.	640	427	COCO_val2014_000000169690.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000169690.jpg
+333837	41011	A man in an English suit and bowler riding a horse.	500	469	COCO_val2014_000000041011.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000041011.jpg
+334239	72832	There are many flavors of donuts on the paper.	427	640	COCO_val2014_000000072832.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000072832.jpg
+334310	242510	A large white dog statue sitting next to a clock.	458	640	COCO_val2014_000000242510.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242510.jpg
+334330	458388	three donuts and five donut holes on a plate	427	640	COCO_val2014_000000458388.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000458388.jpg
+334534	13168	The high speed passenger train is bound for New York.	612	612	COCO_val2014_000000013168.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013168.jpg
+334648	44952	three men dressed like cowboys riding on horses	434	640	COCO_val2014_000000044952.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000044952.jpg
+334866	386227	Cows and sheep graze a snowy field on a cloudy day	309	640	COCO_val2014_000000386227.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000386227.jpg
+335187	94651	A couple of cats standing on top of a desk.	480	640	COCO_val2014_000000094651.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000094651.jpg
+335196	32577	A woman standing on a tennis court holding a racquet.	375	500	COCO_val2014_000000032577.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032577.jpg
+335320	551622	Two small elephants standing in a river soaking wet.	393	500	COCO_val2014_000000551622.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000551622.jpg
+335397	256859	a baby zebra walking between two bigger zebras	427	640	COCO_val2014_000000256859.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000256859.jpg
+335469	110618	A snowboarder with his snowboard attached to his feet sitting on a slope.	427	640	COCO_val2014_000000110618.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000110618.jpg
+335491	345136	a toddler wearing a hat sitting in the dry grass	427	640	COCO_val2014_000000345136.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345136.jpg
+335875	39726	A danish dessert with frosting that is partially eaten	427	640	COCO_val2014_000000039726.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039726.jpg
+336094	421431	there is a an standing on top of a mountain	375	500	COCO_val2014_000000421431.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421431.jpg
+336444	122390	a cat laying down stretched out near a laptop	375	500	COCO_val2014_000000122390.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000122390.jpg
+336762	365047	A train moving along a track near a field.	413	640	COCO_val2014_000000365047.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365047.jpg
+336835	49759	a boy is preparing to serve a volley ball	457	640	COCO_val2014_000000049759.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000049759.jpg
+337058	472216	A lady skiing with other ladies on a snowy hill.	640	425	COCO_val2014_000000472216.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472216.jpg
+337076	350959	People walking in a field surrounded by a stone wall near a cow.	340	640	COCO_val2014_000000350959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000350959.jpg
+337427	459136	A steel bench sitting next to a wall.	640	480	COCO_val2014_000000459136.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000459136.jpg
+337462	24652	A group of cattle grazing in a field behind a fence.	480	640	COCO_val2014_000000024652.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000024652.jpg
+337594	299838	A man wearing a blue shirt and white shorts playing tennis.	499	640	COCO_val2014_000000299838.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000299838.jpg
+337744	215906	a woman in a gray top is playing tennis	640	426	COCO_val2014_000000215906.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000215906.jpg
+338047	64842	A beautiful woman standing in a field on green grass.	500	333	COCO_val2014_000000064842.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064842.jpg
+338068	109434	A bathroom with a large mirror above a white sink.	640	427	COCO_val2014_000000109434.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000109434.jpg
+338136	223764	A woman brushing her teeth while wearing a towel on her head.	460	640	COCO_val2014_000000223764.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000223764.jpg
+338168	249964	A group of brown cows standing on top of a lush green field.	640	493	COCO_val2014_000000249964.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000249964.jpg
+338239	222322	A group of people walking along  a beach covered in umbrellas.	431	640	COCO_val2014_000000222322.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000222322.jpg
+338466	81766	A large black dog with long hair standing on steps.	480	640	COCO_val2014_000000081766.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000081766.jpg
+338503	171962	A city bus driving down a road with bikes fixed to it's front.	480	640	COCO_val2014_000000171962.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000171962.jpg
+338552	530439	a man sitting at a table using a laptop computer.	258	459	COCO_val2014_000000530439.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530439.jpg
+338574	96785	A couple of women standing next to each other in front of a store.	435	640	COCO_val2014_000000096785.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000096785.jpg
+338826	248382	A woman in orange sari on a park bench.	640	426	COCO_val2014_000000248382.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248382.jpg
+338849	334769	A man holding a tennis racquet no a tennis court.	640	427	COCO_val2014_000000334769.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334769.jpg
+339168	5644	A boy flying through the air with his skateboard as it flips around.	427	640	COCO_val2014_000000005644.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005644.jpg
+339290	108864	Two elephants cross tusks in confrontation in a field	480	640	COCO_val2014_000000108864.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000108864.jpg
+339479	161990	Train with four cars traveling through the wooded area.	427	640	COCO_val2014_000000161990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000161990.jpg
+339903	203845	An old brown building has a clock on it.	438	640	COCO_val2014_000000203845.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203845.jpg
+340081	26045	A long line of white urinals in a public restroom.	344	459	COCO_val2014_000000026045.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026045.jpg
+340308	13632	A small round ceramic glass vase on a surface.	640	424	COCO_val2014_000000013632.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013632.jpg
+340366	530259	Plates with food on, a cup of coffee, and plastic utensils on a wood table.	480	640	COCO_val2014_000000530259.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530259.jpg
+340703	286001	A doll sitting at a table with a Happy New Year note	455	500	COCO_val2014_000000286001.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000286001.jpg
+340732	549055	A man on a surfboard in the water.	304	640	COCO_val2014_000000549055.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000549055.jpg
+340733	99807	A person with skis in the snow with two dogs.	427	640	COCO_val2014_000000099807.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099807.jpg
+340773	187533	A man on a surfboard is on top of a wave.	640	480	COCO_val2014_000000187533.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187533.jpg
+341164	210081	Snow skiers in line skiing down a mountain range.	480	640	COCO_val2014_000000210081.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000210081.jpg
+341503	479848	There are condiments at a hot dog stand	640	424	COCO_val2014_000000479848.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000479848.jpg
+341543	509192	A woman is standing on a kitchen floor.	640	359	COCO_val2014_000000509192.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000509192.jpg
+341900	453507	A black cat wearing a little party hat.	426	640	COCO_val2014_000000453507.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453507.jpg
+341988	469603	A brown horse looking out of a window in his stall.	640	428	COCO_val2014_000000469603.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000469603.jpg
+342246	86835	The large building has many windows and a white door.	427	640	COCO_val2014_000000086835.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000086835.jpg
+342444	27438	Two sheep one with a bell tied to its neck walking in a green field.	315	640	COCO_val2014_000000027438.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000027438.jpg
+342765	114891	A small dog lies on a pillow near a toy banana.	480	640	COCO_val2014_000000114891.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114891.jpg
+343006	335450	Two brown ponies in grassy field next to a fence.	428	640	COCO_val2014_000000335450.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335450.jpg
+343060	165351	Sliced bananas sit on top of a pastry dish on a plate.	612	612	COCO_val2014_000000165351.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000165351.jpg
+343336	125755	A tray of cupcakes is set on top of a kitchen counter.	480	640	COCO_val2014_000000125755.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125755.jpg
+343430	566450	An old style refrigerator that is holding beverages.	424	640	COCO_val2014_000000566450.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000566450.jpg
+343559	158288	Two litle girls laying in a bed with beige sheets.	424	640	COCO_val2014_000000158288.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158288.jpg
+343697	119345	a train sitting on a train track next to some trees	426	640	COCO_val2014_000000119345.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000119345.jpg
+343825	261178	A clock tower looms underneath a clear sky.	427	640	COCO_val2014_000000261178.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000261178.jpg
+344004	316365	A person working at an airport, outside of an airplane.	500	375	COCO_val2014_000000316365.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000316365.jpg
+344070	373075	A shirtless man grilling a lot of chicken breasts.	480	640	COCO_val2014_000000373075.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000373075.jpg
+344096	237230	A couple vases of flowers on a counter next to a bar.	383	640	COCO_val2014_000000237230.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237230.jpg
+344132	380834	A black and white photo of four people, one rowing along a lake.	359	640	COCO_val2014_000000380834.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000380834.jpg
+344469	240329	An unmanned boat is in the ocean water.	360	640	COCO_val2014_000000240329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000240329.jpg
+345218	448205	A renovated propeller airplane flying in a blue sky	384	640	COCO_val2014_000000448205.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448205.jpg
+345314	487422	A tall giraffe in a green grassy field.	640	480	COCO_val2014_000000487422.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487422.jpg
+345322	540006	A woman is preparing food as two young boys stand by and watch.	640	480	COCO_val2014_000000540006.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540006.jpg
+345356	258346	A dreary, rainy day by the water, with people under an umbrella.	640	428	COCO_val2014_000000258346.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000258346.jpg
+345378	183166	Two people riding a scooter on the road while holding an umbrella.	480	640	COCO_val2014_000000183166.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000183166.jpg
+345418	484434	many people standing together holding open umbrellas	324	640	COCO_val2014_000000484434.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000484434.jpg
+345426	16356	two kids standing outside flying a kite during the day	640	427	COCO_val2014_000000016356.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000016356.jpg
+345488	289073	A close up of a red fire hydrant next to a light pole.	500	326	COCO_val2014_000000289073.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289073.jpg
+345701	268322	A cabana-styled bedroom looks light and airy.	366	640	COCO_val2014_000000268322.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268322.jpg
+345983	305978	A giraffe is walking in a grassy field as a cow is laying down.	480	640	COCO_val2014_000000305978.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000305978.jpg
+346186	107247	The double decker bus is heading south through the mountainous region of California.	400	640	COCO_val2014_000000107247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107247.jpg
+346340	50006	Looking out over a bay with many tourist boats moored	480	640	COCO_val2014_000000050006.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000050006.jpg
+346693	213351	A desk with three computer monitors, stacks of books and a phone.	427	640	COCO_val2014_000000213351.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213351.jpg
+346968	466585	A pier stands in the ocean while people wade in the water.	435	640	COCO_val2014_000000466585.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000466585.jpg
+347236	116279	A large jetliner flying through a cloudy blue sky.	425	640	COCO_val2014_000000116279.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116279.jpg
+347384	386204	Woman with a backpack on sits on the curb moving her skateboard	392	640	COCO_val2014_000000386204.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000386204.jpg
+347387	353321	A woman sitting next to a  man on top of a purple bench.	480	640	COCO_val2014_000000353321.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000353321.jpg
+347427	203131	A pizza and coffee on a tray of some sort.	425	640	COCO_val2014_000000203131.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203131.jpg
+347512	1176	View of a skateboarder igrinding a rail through a lens	640	426	COCO_val2014_000000001176.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001176.jpg
+347520	372706	There are some cars and trucks parked at a bus depot	429	640	COCO_val2014_000000372706.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372706.jpg
+347581	367128	A pregnant women takes a selfie wearing a black tank top.	640	478	COCO_val2014_000000367128.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000367128.jpg
+347825	522232	a person washing a parked motorcycle on a concrete road	640	480	COCO_val2014_000000522232.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000522232.jpg
+347936	230976	A chalk sign is placed outside on a wall advertising a restaurant.	640	480	COCO_val2014_000000230976.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000230976.jpg
+348059	85298	A little girl standing on a tennis court holding a racquet.	640	424	COCO_val2014_000000085298.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000085298.jpg
+348302	229385	A red fire hydrant stands on the sidewalk by the street.	640	480	COCO_val2014_000000229385.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000229385.jpg
+348308	257137	A white bowl filled with rice and vegetables.	480	640	COCO_val2014_000000257137.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000257137.jpg
+348401	437393	a carousel horse has a rose in its mane	640	480	COCO_val2014_000000437393.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000437393.jpg
+348402	494345	a cat is climbing from a window sill on top of a toilet seat	480	640	COCO_val2014_000000494345.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000494345.jpg
+348632	469609	A pole that has a bunch of street signs on it.	375	500	COCO_val2014_000000469609.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000469609.jpg
+348675	38574	A tray topped with a Micheal Jackson hat and matching glove.	528	640	COCO_val2014_000000038574.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038574.jpg
+348827	567603	A yellow cat is looking at a black cat with something on its head.	457	500	COCO_val2014_000000567603.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000567603.jpg
+348874	332300	A clock mounted on the side of a tall building under a blue sky.	478	640	COCO_val2014_000000332300.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000332300.jpg
+348983	351609	a bunch of already peeled oranges grouped together	333	500	COCO_val2014_000000351609.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000351609.jpg
+348998	213605	A bus parked next to a white building with red trimming.	640	480	COCO_val2014_000000213605.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213605.jpg
+349295	369345	a black couch with a red pillow and blue blanket	480	640	COCO_val2014_000000369345.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369345.jpg
+349328	174371	A woman in camouflage showing a device to young women.	511	640	COCO_val2014_000000174371.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174371.jpg
+349580	297595	A person on a surfboard is surfing on a crashing wave.	427	640	COCO_val2014_000000297595.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297595.jpg
+349600	464515	The couple is riding the motorcycle together.	325	640	COCO_val2014_000000464515.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464515.jpg
+349660	312868	A desk with two laptops and a computer monitor with keyboard.	480	640	COCO_val2014_000000312868.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000312868.jpg
+349782	78741	A grey and white cat sitting next to keyboard and computer monitor.	449	600	COCO_val2014_000000078741.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078741.jpg
+349819	504142	Black and white puppy snuggled between a blanket and a pillow.	484	640	COCO_val2014_000000504142.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000504142.jpg
+349915	263211	A giraffe walking through a lush green field.	426	640	COCO_val2014_000000263211.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000263211.jpg
+349929	304725	A traffic light sitting next to a chair on a hard wood floor.	600	400	COCO_val2014_000000304725.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000304725.jpg
+349968	283038	A red stop sign sitting on top of a green planter.	281	500	COCO_val2014_000000283038.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000283038.jpg
+350417	73153	A person talking on a phone with a tree in the foreground.	640	427	COCO_val2014_000000073153.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000073153.jpg
+350593	109945	A bowl of granola with raspberries and honey on top with a spoon.	640	640	COCO_val2014_000000109945.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000109945.jpg
+350687	233882	A small red plane sitting on top of an airport tarmac.	304	640	COCO_val2014_000000233882.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000233882.jpg
+350889	575473	A white toilet sitting inside of a bathroom.	640	480	COCO_val2014_000000575473.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575473.jpg
+350998	412510	A man in black shirt using urinal in bathroom.	640	480	COCO_val2014_000000412510.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412510.jpg
+350999	421569	A brown and white dog on street next to a blue car.	388	640	COCO_val2014_000000421569.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421569.jpg
+351165	220739	"A green and white street sign that reads ""lincoln av e."""	427	640	COCO_val2014_000000220739.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000220739.jpg
+351355	223335	A man riding a motorcycle on a racing track.	427	640	COCO_val2014_000000223335.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000223335.jpg
+351641	381599	Five jumbo jets are parked at a large airport.	427	640	COCO_val2014_000000381599.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000381599.jpg
+351658	517306	Two people sitting behind a large pizza on a table.	480	640	COCO_val2014_000000517306.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000517306.jpg
+351662	193449	The bed covered in many sheets and blankets is the only furniture in the room.	427	640	COCO_val2014_000000193449.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000193449.jpg
+351798	425136	The little girl poses with her dog for a photo.	426	640	COCO_val2014_000000425136.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000425136.jpg
+351846	14439	Some people in a grass field flying a kite in the sky.	404	640	COCO_val2014_000000014439.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000014439.jpg
+352426	305060	a person getting a piece of pizza pie	426	640	COCO_val2014_000000305060.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000305060.jpg
+352547	508846	A cat hiding in a basket of some sort.	434	640	COCO_val2014_000000508846.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000508846.jpg
+352569	167854	The Asian woman is standing on the beach holding an opened parasol in each hand.	480	640	COCO_val2014_000000167854.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000167854.jpg
+352572	349837	The refrigerators are lined up against the wall.	333	500	COCO_val2014_000000349837.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000349837.jpg
+352662	548962	A flat screen TV sitting in a living room.	480	640	COCO_val2014_000000548962.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000548962.jpg
+352664	214199	A man surfing on a wave during the day.	427	640	COCO_val2014_000000214199.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214199.jpg
+353022	4795	A cat observing a computer screen next to a laptop and a cordless phone.	480	640	COCO_val2014_000000004795.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000004795.jpg
+353162	528091	A man with a tennis racket on a court.	500	482	COCO_val2014_000000528091.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000528091.jpg
+353758	320658	A cat sleeping on top of an open laptop computer.	375	500	COCO_val2014_000000320658.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000320658.jpg
+354069	406329	A blue and white bus parked in front of a brick house.	427	640	COCO_val2014_000000406329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406329.jpg
+354227	295440	A woman pouring wine into glasses on a wooden table.	640	427	COCO_val2014_000000295440.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295440.jpg
+354331	261068	A lady doing something with animals in an enclosed area.	429	640	COCO_val2014_000000261068.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000261068.jpg
+354386	273889	A woman walking along a tennis court holding a tennis racquet.	640	463	COCO_val2014_000000273889.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273889.jpg
+354628	445048	A person is snow skiing and is in the middle of a jump.	426	640	COCO_val2014_000000445048.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000445048.jpg
+354632	21361	a stuffed animal sitting on a chair in front of a laptop	436	500	COCO_val2014_000000021361.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000021361.jpg
+354735	569452	A young boy doing a trick on his skate board.	640	429	COCO_val2014_000000569452.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000569452.jpg
+354772	347163	Traffic stop sign with dilapidated building behind it.	427	640	COCO_val2014_000000347163.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347163.jpg
+355084	475586	A man wearing a purple tie holding his hand up to his face.	640	640	COCO_val2014_000000475586.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000475586.jpg
+355288	120905	A red fire hydrant with a green cap with a bird on top of it.	427	640	COCO_val2014_000000120905.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120905.jpg
+355302	522622	A group of young children running on a field.	427	640	COCO_val2014_000000522622.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000522622.jpg
+355323	218874	A man fishing while standing on some rocks next to the ocean.	429	640	COCO_val2014_000000218874.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218874.jpg
+355352	460145	A herd of sheep walking down a road in front of a silver truck.	480	640	COCO_val2014_000000460145.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460145.jpg
+355559	109146	A couple of brown horses standing in front of a building.	428	640	COCO_val2014_000000109146.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000109146.jpg
+355616	181065	A herd of sheep grazing on a lush green hillside.	433	640	COCO_val2014_000000181065.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000181065.jpg
+355648	199977	The airplane in the sky is doing tricks while spitting out smoke.	640	480	COCO_val2014_000000199977.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000199977.jpg
+355710	110638	A couple of large elephants standing next to a baby elephant.	427	640	COCO_val2014_000000110638.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000110638.jpg
+355759	311180	The man's hand is bigger then the scissors he is holding.	640	480	COCO_val2014_000000311180.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000311180.jpg
+355873	395928	A wooden bench sitting next to a fence near a forest.	479	640	COCO_val2014_000000395928.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000395928.jpg
+355886	218637	A motorcycle parked in front of a house.	640	427	COCO_val2014_000000218637.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218637.jpg
+355910	328921	A group of three dummy heads covered in hats.	359	640	COCO_val2014_000000328921.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328921.jpg
+355936	150265	A group of people walking down a sidewalk.	640	480	COCO_val2014_000000150265.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150265.jpg
+356085	59740	A table topped with lots of vegetables of different color and kind.	427	640	COCO_val2014_000000059740.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000059740.jpg
+356118	140658	A brick building with a tall clock tower beside it.	640	480	COCO_val2014_000000140658.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000140658.jpg
+356514	573759	Guy riding in his boat with thee dogs	365	500	COCO_val2014_000000573759.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573759.jpg
+356838	564563	A view of birds gliding through the air.	282	500	COCO_val2014_000000564563.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000564563.jpg
+356861	365511	The street sign is clearly visible for all to see.	480	640	COCO_val2014_000000365511.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365511.jpg
+356888	41998	A picture of a large building with people milling about.	640	480	COCO_val2014_000000041998.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000041998.jpg
+357661	560830	A black and white dog on a brown tile floor next to counter.	640	360	COCO_val2014_000000560830.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000560830.jpg
+357824	270918	A sharp steeple on a building is lit in the sky.	640	480	COCO_val2014_000000270918.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000270918.jpg
+358332	568653	A shiny black motorcycle with gleaming chrome is parked on a city sidewalk.	640	426	COCO_val2014_000000568653.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000568653.jpg
+358582	519703	A group of ferry and sailboats are sitting on the water.	428	640	COCO_val2014_000000519703.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519703.jpg
+358982	375128	A fire hydrant is in the dark by a sidewalk.	480	640	COCO_val2014_000000375128.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000375128.jpg
+359096	272117	Four people at a table with a birthday cake that has just been lit.	428	640	COCO_val2014_000000272117.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000272117.jpg
+359219	452158	Sheep grazing on the side of a country road	425	640	COCO_val2014_000000452158.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000452158.jpg
+359372	252048	A horse is standing in the stables in the hay	640	480	COCO_val2014_000000252048.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000252048.jpg
+359829	214692	A large crowd watching a professional baseball game	480	640	COCO_val2014_000000214692.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214692.jpg
+359923	473215	A banana sundae sits in a dessert glass.	640	480	COCO_val2014_000000473215.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473215.jpg
+360412	64189	A boy in black jacket jumping a ramp on a skateboard.	500	375	COCO_val2014_000000064189.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064189.jpg
+360727	15883	Cook preparing a meal in folded bread at a food stall.	640	427	COCO_val2014_000000015883.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000015883.jpg
+360921	25457	A very cluttered area with several books and tools.	426	640	COCO_val2014_000000025457.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025457.jpg
+361121	225791	a man on a skate board on a rail	640	427	COCO_val2014_000000225791.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000225791.jpg
+361360	62661	A taxi cab parked next to a fire hydrant.	334	500	COCO_val2014_000000062661.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062661.jpg
+361448	84241	A group of women in kitchen next to pot.	396	640	COCO_val2014_000000084241.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000084241.jpg
+361449	17365	Two people on skis in snowy area next to a barn.	480	640	COCO_val2014_000000017365.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000017365.jpg
+361590	310059	A television playing Battlestar Galactica with pizza on a plate on the floor.	640	426	COCO_val2014_000000310059.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000310059.jpg
+361604	473370	A laptop on a desk in a classroom.	480	640	COCO_val2014_000000473370.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473370.jpg
+361612	398537	A man in black jacket holding a kite in front of another person.	427	640	COCO_val2014_000000398537.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398537.jpg
+361614	407097	Black and white photograph of potted plants and gardening tools.	480	640	COCO_val2014_000000407097.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000407097.jpg
+361634	72376	A man gets ready to throw a frisbee.	480	640	COCO_val2014_000000072376.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000072376.jpg
+361717	256743	A long haired snowboarder poses standing with his board.	425	640	COCO_val2014_000000256743.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000256743.jpg
+361790	105291	The stop sign at the corner of North Main and Schoolhouse	480	640	COCO_val2014_000000105291.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000105291.jpg
+361929	141271	A man throwing a tennis serve into the air.	333	500	COCO_val2014_000000141271.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000141271.jpg
+361977	509891	A man scrubs down an elephant in a water hole.	424	640	COCO_val2014_000000509891.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000509891.jpg
+362068	264769	A couple pairs of scissors and a person sitting.	334	500	COCO_val2014_000000264769.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264769.jpg
+362196	337525	Wet black and grey dog retrieving an orange ball.	425	640	COCO_val2014_000000337525.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000337525.jpg
+362488	408047	City bus loading on a busy metropolitan street.	426	640	COCO_val2014_000000408047.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000408047.jpg
+362496	237735	A sleek gray plane flying through a cloudy sky.	426	640	COCO_val2014_000000237735.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237735.jpg
+362511	437898	A medium sized home kitchen with wood cabinets.	427	640	COCO_val2014_000000437898.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000437898.jpg
+362718	372817	a group of people with umbrellas walk on a side walk	429	640	COCO_val2014_000000372817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372817.jpg
+362896	530987	A group of people riding on top of three elephants.	480	640	COCO_val2014_000000530987.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530987.jpg
+362999	205190	two trains sit motionless at a train depot	423	640	COCO_val2014_000000205190.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205190.jpg
+363027	423187	A plane that is flying high up in the air.	427	640	COCO_val2014_000000423187.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000423187.jpg
+363075	312890	A couple of zebras are walking through a field.	640	480	COCO_val2014_000000312890.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000312890.jpg
+363088	208406	a man performs a trick on a skate board as people look on	333	500	COCO_val2014_000000208406.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208406.jpg
+363170	82718	A man is kite surfing in the ocean.	640	640	COCO_val2014_000000082718.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000082718.jpg
+363282	448849	A boat filled with people floating down a canal.	480	640	COCO_val2014_000000448849.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448849.jpg
+364253	300972	three people on a gravel ground playing frisbee	360	640	COCO_val2014_000000300972.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000300972.jpg
+364772	380057	Many posters are placed on a wall near a busy street.	480	640	COCO_val2014_000000380057.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000380057.jpg
+364940	514396	Photo of a cat stuck high up in a tree.	426	640	COCO_val2014_000000514396.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000514396.jpg
+364957	45388	The boy is playing with his colorful kite.	480	640	COCO_val2014_000000045388.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000045388.jpg
+365002	189067	This expert skateboarder is skateboarding on a railing.	636	640	COCO_val2014_000000189067.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000189067.jpg
+365520	174871	A photograph of a cat and mouse on top of a dog.	512	640	COCO_val2014_000000174871.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174871.jpg
+365897	419653	A buffet of casserole dishes on a kitchen counter.	480	640	COCO_val2014_000000419653.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419653.jpg
+366111	539717	A young man on the beach holding a plate of onion rings and a hot dog sandwich.	500	375	COCO_val2014_000000539717.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000539717.jpg
+366262	503196	A stop sign with road signs sitting on top of it is displayed.	480	640	COCO_val2014_000000503196.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000503196.jpg
+366551	419544	Bus 811 is pulling into a bus stop.	483	640	COCO_val2014_000000419544.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419544.jpg
+366732	297074	A man holds up a Polish sausage on a bun.	375	500	COCO_val2014_000000297074.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297074.jpg
+366900	323760	A toilet is shown in a bare room.	500	375	COCO_val2014_000000323760.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000323760.jpg
+367159	380143	Are the dogs being friendly with each other or chasing each other?	427	640	COCO_val2014_000000380143.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000380143.jpg
+367360	324670	A seagull sitting on the pier with the light house behind him.	427	640	COCO_val2014_000000324670.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000324670.jpg
+367448	566756	Little girl stands in the room looking at a cell phone	640	480	COCO_val2014_000000566756.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000566756.jpg
+367501	497076	A man is riding an elephant down the street.	480	640	COCO_val2014_000000497076.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497076.jpg
+367520	439781	Elephants stand together along a wall in a zoo.	480	640	COCO_val2014_000000439781.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000439781.jpg
+367564	416862	a store with a fridge and some cabinets and a customer	426	640	COCO_val2014_000000416862.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000416862.jpg
+367598	345137	A pat sitting on top of a counter filled with vegetables.	424	640	COCO_val2014_000000345137.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345137.jpg
+367761	167347	A person walks on a bridge with a kite.	480	640	COCO_val2014_000000167347.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000167347.jpg
+367847	235833	A toilet is shown against a wall with wires.	640	629	COCO_val2014_000000235833.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000235833.jpg
+368050	514437	A building is shown with trees in front of it.	612	612	COCO_val2014_000000514437.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000514437.jpg
+368147	108408	Rows of suitcases on carpet with crate in background.	640	427	COCO_val2014_000000108408.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000108408.jpg
+368308	388487	A patch of wild flowers growing next to a stop sign.	375	500	COCO_val2014_000000388487.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000388487.jpg
+368425	184388	A youngster is flying a kite on the beach.	388	640	COCO_val2014_000000184388.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000184388.jpg
+368509	557323	A man dressed in green kicks a soccer ball.	494	640	COCO_val2014_000000557323.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000557323.jpg
+368938	556505	A wet black dog with its face covered in sand.	612	612	COCO_val2014_000000556505.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000556505.jpg
+369268	150639	A man sits in a car while on a phone.	480	640	COCO_val2014_000000150639.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150639.jpg
+369969	330681	a home made stop sign in the middle of nowhere	640	427	COCO_val2014_000000330681.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000330681.jpg
+370005	404476	A young man is sitting in a chair and has mismatched outfit and a name badge.	640	444	COCO_val2014_000000404476.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000404476.jpg
+370270	127129	A table full of food and two glasses with drinks .	480	640	COCO_val2014_000000127129.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000127129.jpg
+370282	282772	some black and white pillows on a white bed	500	374	COCO_val2014_000000282772.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000282772.jpg
+370579	2240	A pile of teddy bears and dolls in a toy box.	600	600	COCO_val2014_000000002240.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000002240.jpg
+370867	26665	A herd of zebras standing together in a field.	427	640	COCO_val2014_000000026665.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026665.jpg
+371077	5076	Large tree with wooden picnic bench in wilderness area	640	414	COCO_val2014_000000005076.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005076.jpg
+371140	238045	The three green buses are parked next to each other.	429	640	COCO_val2014_000000238045.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000238045.jpg
+371185	71466	a chair with a pillow a mirror on the wall and a set of curtains	500	375	COCO_val2014_000000071466.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000071466.jpg
+371479	515586	There is a bed in the room next to the night stand.	426	640	COCO_val2014_000000515586.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000515586.jpg
+371533	21503	Crackers with spread on table with remote in background	480	640	COCO_val2014_000000021503.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000021503.jpg
+371567	510579	two surfers looking at the dark stormy looking sky	443	640	COCO_val2014_000000510579.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000510579.jpg
+371715	577712	Three people stand in front of the giraffe zoo section.	425	640	COCO_val2014_000000577712.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577712.jpg
+371797	34121	A basket of red fruit, a basket of ginger, a basket of bananas, and a basket of seed pods.	415	640	COCO_val2014_000000034121.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034121.jpg
+371849	143440	some people and a snowboarder in a blue jacket	426	640	COCO_val2014_000000143440.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143440.jpg
+372508	384823	A computer sits on a small wooden desk.	480	640	COCO_val2014_000000384823.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384823.jpg
+372510	10428	A sink is shown in front of a frame covered wall.	640	427	COCO_val2014_000000010428.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010428.jpg
+372611	34904	A book on a wooden bench on a street.	425	640	COCO_val2014_000000034904.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034904.jpg
+372613	71123	A large jumbo jet with huge wings is on the runway.	425	640	COCO_val2014_000000071123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000071123.jpg
+372616	164848	A child on skis is skiing on the snow.	428	640	COCO_val2014_000000164848.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000164848.jpg
+372620	546829	A small white dog on a wooden bench.	425	640	COCO_val2014_000000546829.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546829.jpg
+372655	82765	A bed and window in a small room.	640	426	COCO_val2014_000000082765.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000082765.jpg
+373173	129855	A bare-chested man on turbulent ocean waves surfing	200	300	COCO_val2014_000000129855.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129855.jpg
+373202	152866	Several slices of pizza on a white paper plate.	480	640	COCO_val2014_000000152866.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152866.jpg
+373328	304853	A middle-aged man assembles a green kite at his office.	426	640	COCO_val2014_000000304853.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000304853.jpg
+373348	439481	A group of people near some people riding horses.	480	640	COCO_val2014_000000439481.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000439481.jpg
+373510	561903	A dog standing by a television in a room.	640	480	COCO_val2014_000000561903.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000561903.jpg
+373654	17425	Three people with a video game remote in their hands.	480	640	COCO_val2014_000000017425.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000017425.jpg
+373713	66502	A group of women on various color surfboards in water.	427	640	COCO_val2014_000000066502.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000066502.jpg
+373740	526004	A small infant sitting on someones lap at a table and eating a pizza crust.	640	480	COCO_val2014_000000526004.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000526004.jpg
+373771	9483	A man in a suit standing next to a control board and computer.	480	640	COCO_val2014_000000009483.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009483.jpg
+373815	29558	A man and boy in grassy area with a kite.	480	640	COCO_val2014_000000029558.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029558.jpg
+373861	301376	A street area with a building and cars.	500	333	COCO_val2014_000000301376.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301376.jpg
+374016	418961	Closeup of a decorative gold and grey clock.	427	640	COCO_val2014_000000418961.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000418961.jpg
+374087	25743	Two elephants in a dirt area next to concrete walls.	480	640	COCO_val2014_000000025743.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025743.jpg
+374135	437239	A group of people standing on a field playing disc golf.	425	640	COCO_val2014_000000437239.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000437239.jpg
+374584	49269	A horse in a pasture of tall grass observing an energetic dog.	640	431	COCO_val2014_000000049269.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000049269.jpg
+374617	117098	A red motorcycle on display at a show.	510	640	COCO_val2014_000000117098.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000117098.jpg
+374827	573320	Two people are playing a WII video game.	480	640	COCO_val2014_000000573320.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573320.jpg
+374873	227316	A clock tower is on top of an old building.	640	480	COCO_val2014_000000227316.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227316.jpg
+374914	135673	Two planes are close together in the sky.	427	640	COCO_val2014_000000135673.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000135673.jpg
+375036	198530	A group of people on skis stand on the snow.	480	640	COCO_val2014_000000198530.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198530.jpg
+375038	94944	People are standing on top of a snowy mountain.	427	640	COCO_val2014_000000094944.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000094944.jpg
+375833	519046	A giraffe standing in front of green brush.	640	427	COCO_val2014_000000519046.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519046.jpg
+375853	363508	An elderly giraffe with a mottled forehead looks at the camera.	480	640	COCO_val2014_000000363508.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000363508.jpg
+376004	213586	A man is in a tv frame and making a funny face.	427	640	COCO_val2014_000000213586.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213586.jpg
+376128	263382	A lamp with a red wall with a large clock on it.	480	640	COCO_val2014_000000263382.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000263382.jpg
+376397	562507	A pizza is served on a dish above a table.	375	500	COCO_val2014_000000562507.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000562507.jpg
+376461	97988	"Man standing under banner that reads ""Run for Rights"""	612	612	COCO_val2014_000000097988.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000097988.jpg
+376520	511892	A kitchen area with oven, pots and various cabinets.	427	640	COCO_val2014_000000511892.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000511892.jpg
+376583	408696	A man sitting on a toilet that isn't connected to anything.	480	640	COCO_val2014_000000408696.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000408696.jpg
+376707	133464	A laptop and phone on the table of someones balcony.	640	480	COCO_val2014_000000133464.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133464.jpg
+376724	513765	A group of children sit at a craft table.	414	640	COCO_val2014_000000513765.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000513765.jpg
+376827	39606	A person on a skateboard does an air trick.	500	500	COCO_val2014_000000039606.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039606.jpg
+376858	28903	A cat is sitting on a white toilet.	640	640	COCO_val2014_000000028903.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028903.jpg
+377291	526801	A cluttered desk with a laptop and a computer on it.	483	640	COCO_val2014_000000526801.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000526801.jpg
+377323	174103	A grouo of four snowboards out in front of a red colors building.	408	640	COCO_val2014_000000174103.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174103.jpg
+377818	502063	A table with a vase on it in an empty space near a man.	373	640	COCO_val2014_000000502063.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000502063.jpg
+377901	198079	A group of people sitting on a bench in front of a restaurant.	480	640	COCO_val2014_000000198079.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198079.jpg
+377934	304929	A stop sign over a grassy area next to trees.	402	600	COCO_val2014_000000304929.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000304929.jpg
+377987	260547	A blonde lady holding a smart phone laughing.	500	375	COCO_val2014_000000260547.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260547.jpg
+378091	565742	A vast and grassy field with sheep grazing.	361	640	COCO_val2014_000000565742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565742.jpg
+378172	174904	A Christmas ornament shaped like a donut on a Christmas tree.	428	640	COCO_val2014_000000174904.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174904.jpg
+378240	500559	Two wooden benches with a tree in background.	489	640	COCO_val2014_000000500559.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500559.jpg
+378412	394840	Dude on horseback leads herd of sheep with two dogs	480	640	COCO_val2014_000000394840.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000394840.jpg
+378445	113556	A shelf in a room full of books	640	480	COCO_val2014_000000113556.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000113556.jpg
+378605	261318	A BOY ON HIS PHONE OUTSIDE NEAR A RED CHAIR.	640	427	COCO_val2014_000000261318.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000261318.jpg
+378782	369386	A red and green walled bathroom with a toilet.	375	500	COCO_val2014_000000369386.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369386.jpg
+378811	203063	A herd of sheep grazing in a snow covered field.	359	640	COCO_val2014_000000203063.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203063.jpg
+379319	397318	A group of people sitting at a table with plates and soda.	433	640	COCO_val2014_000000397318.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397318.jpg
+379411	203687	"A circular green and white sign that reads ""car parking"" in front of cows."	427	640	COCO_val2014_000000203687.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203687.jpg
+379948	69021	A dog is in a living room sitting on the back of a couch.	457	640	COCO_val2014_000000069021.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000069021.jpg
+380312	150599	A woman using a laptop computer at night.	480	640	COCO_val2014_000000150599.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150599.jpg
+380377	455381	A white white topped with pasta, mushrooms and broccoli.	504	640	COCO_val2014_000000455381.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000455381.jpg
+380628	423618	A carrot and a knife are resting on a cutting board.	480	640	COCO_val2014_000000423618.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000423618.jpg
+380823	404820	A man in a wet suit is surfing a wave.	471	640	COCO_val2014_000000404820.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000404820.jpg
+380836	578330	A man in glasses is eating a sandwich.	640	480	COCO_val2014_000000578330.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000578330.jpg
+381448	177935	A large white kitchen range with gold towels on the front.	640	480	COCO_val2014_000000177935.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177935.jpg
+381785	507663	People are on grass playing some sort of game.	356	500	COCO_val2014_000000507663.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507663.jpg
+382501	444444	A woman standing in front of a brown horse.	480	640	COCO_val2014_000000444444.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444444.jpg
+382524	305135	A large sign and some cars on a street.	480	640	COCO_val2014_000000305135.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000305135.jpg
+382587	176841	A photo of a person cutting chicken with scissors.	462	640	COCO_val2014_000000176841.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000176841.jpg
+382716	169076	A television is on a tv stand next to a bureau.	612	612	COCO_val2014_000000169076.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000169076.jpg
+382806	333703	A laptop on the top of a surfboard.	216	450	COCO_val2014_000000333703.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333703.jpg
+383035	534173	A woman in plaid shirt talking on a cellphone.	375	500	COCO_val2014_000000534173.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534173.jpg
+383042	125286	A street sign sitting on top of a snow covered traffic lights.	640	480	COCO_val2014_000000125286.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125286.jpg
+383270	492323	A baseball player standing in the outfield waiting for a play.	334	500	COCO_val2014_000000492323.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000492323.jpg
+383492	482275	A man and woman cutting a slice of cake by trees.	480	640	COCO_val2014_000000482275.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000482275.jpg
+383713	265151	A man with some suitcases on the street.	640	480	COCO_val2014_000000265151.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000265151.jpg
+383941	34428	A man riding a snowboard down a snow covered slope.	500	335	COCO_val2014_000000034428.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034428.jpg
+384011	75636	A pizza and a salad are sitting on a table.	450	600	COCO_val2014_000000075636.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000075636.jpg
+384012	326075	People are walking around at a technology convention.	640	480	COCO_val2014_000000326075.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000326075.jpg
+384020	505385	A man is in a black and white public restroom.	375	500	COCO_val2014_000000505385.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000505385.jpg
+384154	425371	A white sink sitting under a mirror in a bathroom.	480	640	COCO_val2014_000000425371.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000425371.jpg
+384155	432176	A wooden bench sitting next to an entrance.	480	640	COCO_val2014_000000432176.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000432176.jpg
+384614	147590	A glass of wine on a wooden table.	640	427	COCO_val2014_000000147590.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000147590.jpg
+384711	99747	A couple of bathrooms one with a standing toilet next to one with a sitting toilet.	480	640	COCO_val2014_000000099747.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099747.jpg
+384798	202572	a close up of a stuffed animal on the ground	640	427	COCO_val2014_000000202572.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000202572.jpg
+384958	30925	A giraffe stands next to a tree and a lake.	640	420	COCO_val2014_000000030925.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000030925.jpg
+385248	207548	Two children sit on a wooden bench in a forest.	480	640	COCO_val2014_000000207548.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000207548.jpg
+385365	318561	A herd of sheep in an open field.	448	640	COCO_val2014_000000318561.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000318561.jpg
+385484	75673	A dog going to the bathroom in the park.	640	426	COCO_val2014_000000075673.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000075673.jpg
+385802	269006	A red double decker bus driving past very tall buildings.	640	480	COCO_val2014_000000269006.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000269006.jpg
+385848	306467	The four men are performing in costumes.	480	640	COCO_val2014_000000306467.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306467.jpg
+385900	449675	A plate of food and fork on a table.	426	640	COCO_val2014_000000449675.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449675.jpg
+385961	92648	A town square is full of people riding their bikes and skateboarding.	425	640	COCO_val2014_000000092648.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092648.jpg
+385986	544334	A man on a surfboard in the water.	471	640	COCO_val2014_000000544334.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000544334.jpg
+386323	479030	A large long train on a steel track.	424	640	COCO_val2014_000000479030.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000479030.jpg
+386633	204256	A cell phone and book on a couch.	375	500	COCO_val2014_000000204256.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000204256.jpg
+386689	195598	A small toy train on a model track.	425	640	COCO_val2014_000000195598.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195598.jpg
+386717	1083	A group of cows on a grassy field.	427	640	COCO_val2014_000000001083.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001083.jpg
+386890	104965	A stop sign and a cart on the side of the road.	267	400	COCO_val2014_000000104965.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000104965.jpg
+386959	384661	An oven with a stove, pots and utensils, and a refrigerator next to it.	375	500	COCO_val2014_000000384661.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384661.jpg
+386960	27620	A laptop sitting on a desk next to a chair and another computer.	480	640	COCO_val2014_000000027620.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000027620.jpg
+387160	156809	A yellow fire hydrant sitting in a patch of green grass.	640	480	COCO_val2014_000000156809.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000156809.jpg
+387413	563295	A propeller plane flying through a gray sky.	389	640	COCO_val2014_000000563295.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000563295.jpg
+387509	374829	A man holding a baseball bat as another gives signs.	427	640	COCO_val2014_000000374829.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000374829.jpg
+387527	438364	A red double decker bus driving down a city street.	480	640	COCO_val2014_000000438364.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000438364.jpg
+387592	45959	A bicycle parking sign on a pole at the corner of a street.	640	480	COCO_val2014_000000045959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000045959.jpg
+387896	55810	A guy with a messy hairdo wears business attire.	640	480	COCO_val2014_000000055810.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000055810.jpg
+387910	81274	A girl in pink shirt sitting at a table with green cup.	425	640	COCO_val2014_000000081274.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000081274.jpg
+388071	294451	A market area with various crates of vegetables.	480	640	COCO_val2014_000000294451.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294451.jpg
+388098	364166	2 Zebras standing next to each other in plaines	375	500	COCO_val2014_000000364166.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000364166.jpg
+388117	28540	Couple wearing hats enjoy a calm elephant ride with tour guide in front	480	640	COCO_val2014_000000028540.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028540.jpg
+388258	150024	A wooden box wit clock hanging from ceiling of building.	427	640	COCO_val2014_000000150024.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150024.jpg
+388983	27146	A very long kitchen looks kind of messy.	238	640	COCO_val2014_000000027146.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000027146.jpg
+389113	113860	A large boat with people on the back in the water.	480	640	COCO_val2014_000000113860.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000113860.jpg
+389141	520528	A child holding a baseball on a baseball field	426	640	COCO_val2014_000000520528.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000520528.jpg
+389142	421908	A person skiing down a hill with poles	427	640	COCO_val2014_000000421908.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421908.jpg
+389267	520694	A cat sitting on top of a suitcase on a bed.	535	640	COCO_val2014_000000520694.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000520694.jpg
+389524	324634	Man and woman texting on cell phones.	500	339	COCO_val2014_000000324634.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000324634.jpg
+390200	308543	A hand holding two spoons over a metal sink.	426	640	COCO_val2014_000000308543.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000308543.jpg
+390376	140454	a big sail boat in the sea with other boats	480	640	COCO_val2014_000000140454.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000140454.jpg
+390681	118594	A Texas Longhorn bull grazing on grass.	480	640	COCO_val2014_000000118594.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118594.jpg
+390979	371555	A smiling woman reclines in a chair with a newspaper.	640	511	COCO_val2014_000000371555.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000371555.jpg
+390980	152628	Two women in wetsuits pose with a surfboard.	640	462	COCO_val2014_000000152628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152628.jpg
+391208	552947	The man in a costume suit is talking on a cell phone.	640	480	COCO_val2014_000000552947.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552947.jpg
+391394	34438	A young skier headed down to the ski lodge.	480	640	COCO_val2014_000000034438.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034438.jpg
+391639	254824	A picture of a building appearing to be made of legos	640	480	COCO_val2014_000000254824.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000254824.jpg
+391757	384012	A batter up at the plate in a baseball game	512	640	COCO_val2014_000000384012.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384012.jpg
+391836	296906	Snowboarder in blue coat performing aerial trick in alpine area.	426	640	COCO_val2014_000000296906.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000296906.jpg
+391918	360798	A wood paneled living room with a stone fireplace.	427	640	COCO_val2014_000000360798.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360798.jpg
+391920	451940	A giraffe in front of a field of sheep.	424	640	COCO_val2014_000000451940.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451940.jpg
+392167	434662	A skier bending down to gain momentum as she goes down the slope.	427	640	COCO_val2014_000000434662.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000434662.jpg
+392181	442456	A man sits on the tires of a car that rests on the ground.	356	640	COCO_val2014_000000442456.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442456.jpg
+392466	492075	Two giraffes eat grass out of a cage hanging from the ceiling.	640	493	COCO_val2014_000000492075.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000492075.jpg
+392501	502012	A group of white work trucks with advertising parked together	425	640	COCO_val2014_000000502012.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000502012.jpg
+392616	285212	A grown and white dog on floor next to person's shoes.	427	640	COCO_val2014_000000285212.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000285212.jpg
+392623	289842	The woman throws a tennis ball into the air during a serve.	427	640	COCO_val2014_000000289842.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289842.jpg
+392625	382207	Many plates of different foods are on the table.	640	480	COCO_val2014_000000382207.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382207.jpg
+392657	243171	A young adult male in shorts and tennis shoes is playing tennis.	441	640	COCO_val2014_000000243171.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000243171.jpg
+393125	133869	A woman talks on her cell phone on the busy sidewalk.	427	640	COCO_val2014_000000133869.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133869.jpg
+393199	137677	a bird flying thru the sky with mountains in the background	427	640	COCO_val2014_000000137677.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000137677.jpg
+393265	538595	a group of horses graze on some grass	466	640	COCO_val2014_000000538595.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000538595.jpg
+393304	334767	a group of beginner snow skiers having class	333	500	COCO_val2014_000000334767.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334767.jpg
+393590	362712	two people standing up near a couch playing nintendo wii	375	500	COCO_val2014_000000362712.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000362712.jpg
+393669	247317	Two people are finishing a meal in a restaurant.	425	640	COCO_val2014_000000247317.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000247317.jpg
+393891	354832	Three photos of people throwing and catching Frisbees.	226	640	COCO_val2014_000000354832.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000354832.jpg
+393966	30843	two horses that are standing near each other and having an interaction.	640	333	COCO_val2014_000000030843.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000030843.jpg
+394003	484331	A young boy with baseball bat raised waiting for the ball	426	640	COCO_val2014_000000484331.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000484331.jpg
+394339	539089	two pans of dinner rolls baking in a large oven	640	428	COCO_val2014_000000539089.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000539089.jpg
+394487	83614	A Mac mini and a keyboard are on a table.	640	427	COCO_val2014_000000083614.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000083614.jpg
+394533	365129	A sunset behind a hill with a bay of boats in the foreground.	480	640	COCO_val2014_000000365129.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365129.jpg
+394727	405174	A cat sitting on the keyboard of a computer on a table	414	640	COCO_val2014_000000405174.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000405174.jpg
+394782	29913	A bicycle is lying on the sidewalk beside a fire hydrant.	428	640	COCO_val2014_000000029913.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029913.jpg
+394834	134255	a bus and some cars at a park like setting	453	578	COCO_val2014_000000134255.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134255.jpg
+394835	484414	a boy jumping his skateboard over a red box	449	640	COCO_val2014_000000484414.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000484414.jpg
+395188	117014	A snowboarder is in the middle of a jump through the air.	426	640	COCO_val2014_000000117014.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000117014.jpg
+395202	427615	a close up of a child sleeping holding a stuffed animal	480	640	COCO_val2014_000000427615.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427615.jpg
+395330	563083	a person is playing with their smartphone or tablet	427	640	COCO_val2014_000000563083.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000563083.jpg
+395396	295945	A man standing with a yellow motorcycle in park	573	640	COCO_val2014_000000295945.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295945.jpg
+395421	456725	A man taking a swing at a  baseball	373	500	COCO_val2014_000000456725.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000456725.jpg
+395657	417742	An adult skier is holding the hand of a child skier.	640	360	COCO_val2014_000000417742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000417742.jpg
+395717	215396	The young baby is sitting next to the large teddy bear.	403	640	COCO_val2014_000000215396.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000215396.jpg
+395802	521540	A banana is on a plate with cookies and peanut butter.	480	640	COCO_val2014_000000521540.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000521540.jpg
+395930	579321	The person walking the dog has dressed it in a torn shirt.	498	640	COCO_val2014_000000579321.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000579321.jpg
+395949	444869	A fry pan with a mixture of vegetables in it.	426	640	COCO_val2014_000000444869.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444869.jpg
+395953	306693	A small yellow car parked between two large yellow trucks.	273	640	COCO_val2014_000000306693.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306693.jpg
+396044	330091	A snowboarder is sliding quickly along a snowy hill.	426	640	COCO_val2014_000000330091.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000330091.jpg
+396045	233977	A bus is passing through an intersection beside a building.	480	640	COCO_val2014_000000233977.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000233977.jpg
+396403	286813	A child in a cart behind small pony on race track.	480	640	COCO_val2014_000000286813.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000286813.jpg
+396701	30685	Motor vehicles on roadway in urban city setting.	427	640	COCO_val2014_000000030685.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000030685.jpg
+396991	388258	There is a bike that is parked by a bench, and an airplane that is high in the sky	478	640	COCO_val2014_000000388258.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000388258.jpg
+397046	111749	a cut in half sandwich sitting on top of a plate.	426	640	COCO_val2014_000000111749.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111749.jpg
+397049	301735	The big bird is on the beach and the dog is barking at it.	480	640	COCO_val2014_000000301735.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301735.jpg
+397170	369185	A living room has a couch, a chair, and a wood stove.	426	640	COCO_val2014_000000369185.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369185.jpg
+397213	331049	A small brown horse standing  on a hilltop	640	426	COCO_val2014_000000331049.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000331049.jpg
+397270	420929	The two white birds are flying in the clear sky.	427	640	COCO_val2014_000000420929.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000420929.jpg
+397896	578962	A busy city street filled with cars at night	480	640	COCO_val2014_000000578962.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000578962.jpg
+398043	410428	A herd of sheep are grazing in a green field.	427	640	COCO_val2014_000000410428.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000410428.jpg
+398643	455040	A harbor with a passenger ship and  and grain silos	360	640	COCO_val2014_000000455040.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000455040.jpg
+398910	349622	A man is skateboarding up a ramp in a skatepark.	640	429	COCO_val2014_000000349622.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000349622.jpg
+399209	318464	A couple of bicycles sitting in a large garden.	427	640	COCO_val2014_000000318464.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000318464.jpg
+399210	63478	A bathroom vanity sink with person products around it.	500	400	COCO_val2014_000000063478.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000063478.jpg
+399724	552153	A small plane flying over the woods near a mountain	424	640	COCO_val2014_000000552153.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552153.jpg
+399924	385997	A living room filled with furniture and a large window.	480	640	COCO_val2014_000000385997.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000385997.jpg
+400055	140435	An old mattress near parked trucks in the grass	640	480	COCO_val2014_000000140435.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000140435.jpg
+400434	381247	a building with a clock near the ceiling and skylights	640	480	COCO_val2014_000000381247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000381247.jpg
+400436	447147	doughnuts baked in an oven on top of aluminum foil	428	640	COCO_val2014_000000447147.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000447147.jpg
+400456	575156	A man in a white shirt has his hands on another man's shirt collar.	426	640	COCO_val2014_000000575156.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575156.jpg
+400477	267349	A plate and a bowl hold several types of fruit.	333	500	COCO_val2014_000000267349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267349.jpg
+400520	397842	A small dog running to catch a frisbee in the air.	427	640	COCO_val2014_000000397842.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397842.jpg
+400548	168200	Commercial jet airplane flying in clear blue skies.	480	640	COCO_val2014_000000168200.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168200.jpg
+400618	372009	A brown teddy bear lays on a bed spread.	360	640	COCO_val2014_000000372009.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372009.jpg
+400645	102805	A man in holds a Frisbee in the parking lot by the river.	426	640	COCO_val2014_000000102805.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000102805.jpg
+400910	150703	A paper with a hot dog on it next to a cup of beer.	480	640	COCO_val2014_000000150703.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150703.jpg
+401215	226400	Two cats are sitting in a door way.	640	480	COCO_val2014_000000226400.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226400.jpg
+401287	459921	A woman is holding a camera by a bathroom.	640	480	COCO_val2014_000000459921.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000459921.jpg
+401297	6854	A poster that indicates the letter S stands for sandwich.	500	375	COCO_val2014_000000006854.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000006854.jpg
+401575	263403	A surfer flies off the crest of a wave.	394	638	COCO_val2014_000000263403.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000263403.jpg
+401847	160927	A large boat floating down a river next to a walkway.	427	640	COCO_val2014_000000160927.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160927.jpg
+401864	192247	A herd of animals walking along a lush green field.	427	640	COCO_val2014_000000192247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192247.jpg
+402175	1532	A street scene with focus on the street signs on an overpass.	480	640	COCO_val2014_000000001532.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001532.jpg
+402176	516004	An electric train pulling into the train station.	640	480	COCO_val2014_000000516004.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516004.jpg
+402265	155012	A dog laying under an office desk with two computers, a bag and other small objects.	426	640	COCO_val2014_000000155012.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000155012.jpg
+402343	240418	A man on a surfboard surfing on a wave.	428	640	COCO_val2014_000000240418.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000240418.jpg
+402473	195974	A snowboarder is in the air while people watch.	640	426	COCO_val2014_000000195974.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195974.jpg
+402520	137490	A small bird sitting on the handle of a car door.	640	398	COCO_val2014_000000137490.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000137490.jpg
+402578	315319	A kitchen with a large clock mounted to it's wall.	359	640	COCO_val2014_000000315319.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315319.jpg
+402596	186632	A flat screen TV sitting in a living room in front of a dining room table.	640	480	COCO_val2014_000000186632.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000186632.jpg
+402650	31980	A pizza sitting on top of a white plate on a table.	333	500	COCO_val2014_000000031980.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000031980.jpg
+402657	84431	A slice of pizza sitting on top of a white plate.	640	361	COCO_val2014_000000084431.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000084431.jpg
+402741	101933	Two men riding on a motorcycle with a young girl.	480	640	COCO_val2014_000000101933.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000101933.jpg
+402881	10386	Guy takes a picture with his back to the river and mountains	480	640	COCO_val2014_000000010386.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010386.jpg
+402921	214975	A green truck cake sitting on top of a table.	427	640	COCO_val2014_000000214975.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214975.jpg
+402932	518348	Two giraffe standing next to each other on a dirt field.	333	500	COCO_val2014_000000518348.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000518348.jpg
+402945	276549	A family of giraffe standing next to each other.	427	640	COCO_val2014_000000276549.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000276549.jpg
+402992	482730	A pile of carrots and other vegetables on  a tray.	426	640	COCO_val2014_000000482730.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000482730.jpg
+403002	133999	A double decker bus driving down a street next to a tall building.	424	640	COCO_val2014_000000133999.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133999.jpg
+403022	57363	a red and yellow fire truck and some buildings	480	640	COCO_val2014_000000057363.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000057363.jpg
+403041	423613	a male in a gray suit checked tie and silver watch	424	283	COCO_val2014_000000423613.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000423613.jpg
+403048	215278	a woman in a pink top a refrigerator and some shelves	480	640	COCO_val2014_000000215278.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000215278.jpg
+403067	158497	A kitchen filled with lots of dogs near a dishwasher.	426	640	COCO_val2014_000000158497.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158497.jpg
+403235	379014	A tall sign sitting next to a traffic light at night.	480	640	COCO_val2014_000000379014.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379014.jpg
+403354	351133	A woman holding a tooth brush while wearing black.	500	374	COCO_val2014_000000351133.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000351133.jpg
+403386	1840	A bunch of colorful umbrellas hanging from trees in a forest.	427	640	COCO_val2014_000000001840.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001840.jpg
+403426	15259	A white vase filled with purple and green flowers.	427	640	COCO_val2014_000000015259.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000015259.jpg
+403599	422212	A wall with three different framed photos of tennis players and a tennis racket under each frame.	228	500	COCO_val2014_000000422212.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000422212.jpg
+403651	229270	A man with a scarf is next to a suitcase.	356	640	COCO_val2014_000000229270.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000229270.jpg
+404173	134193	A tennis player in white is in action with the ball.	352	296	COCO_val2014_000000134193.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134193.jpg
+404779	357385	A man holding a chocolate covered doughnut while sitting in a chair.	500	375	COCO_val2014_000000357385.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000357385.jpg
+405026	203350	Two small children in a double stroller looking at each other.	426	640	COCO_val2014_000000203350.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203350.jpg
+405058	575367	A simple kitchen with wooden counters and stove.	480	640	COCO_val2014_000000575367.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575367.jpg
+405256	373356	A photo of a man swinging a tennis racket.	640	507	COCO_val2014_000000373356.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000373356.jpg
+406012	268350	Girl walking through a living room with a huge leather sectional.	427	640	COCO_val2014_000000268350.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268350.jpg
+406067	491683	There is only one horse in the grassy field.	500	366	COCO_val2014_000000491683.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491683.jpg
+406340	409016	A woman's feet leave the ground as she swings her tennis racket.	640	403	COCO_val2014_000000409016.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000409016.jpg
+406376	494852	A man in grey shirt doing a trick on skateboard.	427	640	COCO_val2014_000000494852.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000494852.jpg
+406558	211674	Red double decker bus with people standing on top.	406	640	COCO_val2014_000000211674.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211674.jpg
+406610	228506	A cat laying bed in a small room.	451	640	COCO_val2014_000000228506.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000228506.jpg
+406887	341775	A single elephant that is walking in an enclosure.	427	640	COCO_val2014_000000341775.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341775.jpg
+407147	90367	a close up of a dog sitting wearing a hat	640	480	COCO_val2014_000000090367.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000090367.jpg
+407266	120826	A train is on the track waiting in the middle of the afternoon.	425	640	COCO_val2014_000000120826.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120826.jpg
+408050	341048	A panda eats a bamboo stalk in the forest.	427	640	COCO_val2014_000000341048.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341048.jpg
+408093	440461	Two double-decker buses followed by two smaller trucks.	480	640	COCO_val2014_000000440461.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000440461.jpg
+408412	340209	A man wearing a hat standing outside taking a picture.	281	500	COCO_val2014_000000340209.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000340209.jpg
+408474	109005	An elephant and a rhinoceros stand not far from each other.	428	640	COCO_val2014_000000109005.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000109005.jpg
+408757	399988	A man wearing a green shirt playing tennis.	640	480	COCO_val2014_000000399988.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000399988.jpg
+408838	511546	The colorful cake with candles is shaped like a horse.	428	640	COCO_val2014_000000511546.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000511546.jpg
+408938	1903	A hot dog is in a styrofoam box.	375	500	COCO_val2014_000000001903.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001903.jpg
+409047	128168	The school bus is remodeled with bright paint.	426	640	COCO_val2014_000000128168.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000128168.jpg
+409433	122046	A woman is sitting in a chair with an umbrella.	640	427	COCO_val2014_000000122046.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000122046.jpg
+409453	545475	A cat thats lieing in a pile of shoes by the door.	426	640	COCO_val2014_000000545475.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000545475.jpg
+409615	25228	A woman sitting on a surfboard in the ocean.	436	640	COCO_val2014_000000025228.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025228.jpg
+409671	436636	A group of surfers in the water trying to catch a wave	423	640	COCO_val2014_000000436636.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000436636.jpg
+409910	71877	a guy doing a skateboard trick along a concrete railing	640	428	COCO_val2014_000000071877.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000071877.jpg
+410308	184405	A man is flying a kite in a field	512	640	COCO_val2014_000000184405.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000184405.jpg
+410565	375503	A stair case sitting next to a wooden desk with a computer on it.	480	640	COCO_val2014_000000375503.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000375503.jpg
+410986	312033	A young boy holding a baseball bat on a field.	457	640	COCO_val2014_000000312033.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000312033.jpg
+410996	14831	A brown and white animal laying on top of a bed.	640	480	COCO_val2014_000000014831.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000014831.jpg
+411202	254368	A man laying in bed holding a plate of food.	640	392	COCO_val2014_000000254368.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000254368.jpg
+411372	132686	A sky filled with lots of colorful flying kites.	640	480	COCO_val2014_000000132686.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132686.jpg
+411425	491835	A woman is waiting for a train.	640	427	COCO_val2014_000000491835.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491835.jpg
+411517	404805	A woman on a boogie board riding a wave.	427	640	COCO_val2014_000000404805.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000404805.jpg
+411661	255827	A bed that is made with a stuffed animal on it .	640	480	COCO_val2014_000000255827.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000255827.jpg
+411869	89761	A toilet seat behind a tree in the open outdoor.	640	480	COCO_val2014_000000089761.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089761.jpg
+411924	66817	A white plate topped with meat veggies and rice with sauce.	612	612	COCO_val2014_000000066817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000066817.jpg
+412439	114147	A group of people riding motor scooters down a street.	480	640	COCO_val2014_000000114147.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114147.jpg
+412720	349822	A woman running through a city while carrying a Frisbee.	333	500	COCO_val2014_000000349822.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000349822.jpg
+412793	413321	An explorer or adventurer hikes in a remote area in challenging weather.	375	500	COCO_val2014_000000413321.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000413321.jpg
+413227	101246	Group of people watching kites being flown.	375	500	COCO_val2014_000000101246.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000101246.jpg
+413246	47713	A series of collectible items are sitting on display.	427	640	COCO_val2014_000000047713.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000047713.jpg
+413619	351967	The image is a large building with many clock along the front.	640	427	COCO_val2014_000000351967.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000351967.jpg
+413630	264392	A dog sits on a seat in the middle of a room.	427	640	COCO_val2014_000000264392.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264392.jpg
+413754	7155	Someone's suitcase left outside against the wall opened up	640	450	COCO_val2014_000000007155.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000007155.jpg
+413895	464339	A brick fire place with a painting on top of it.	485	640	COCO_val2014_000000464339.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464339.jpg
+414119	415723	The outdoor show features a dog who can catch a frisbee.	332	500	COCO_val2014_000000415723.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415723.jpg
+414151	175653	A person trying to get up after falling while skiing.	480	640	COCO_val2014_000000175653.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175653.jpg
+414176	91402	Guy out in the water rides through a wave on his surfboard	425	640	COCO_val2014_000000091402.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000091402.jpg
+414199	156098	A late night, long-exposure picture of a city street.	428	640	COCO_val2014_000000156098.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000156098.jpg
+414707	46099	A stop sign that looks likes it made of crinkled paper.	500	333	COCO_val2014_000000046099.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000046099.jpg
+414866	561783	A clean, organized kitchen decorated in white, brown, and red.	480	640	COCO_val2014_000000561783.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000561783.jpg
+415065	560908	A hotel room scene with a double bed and a view of the patio.	386	640	COCO_val2014_000000560908.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000560908.jpg
+415104	14940	A small cat laying on a couch in a room.	375	500	COCO_val2014_000000014940.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000014940.jpg
+415183	38678	A woman in striped shirt holding a plate with a bagel on it.	640	480	COCO_val2014_000000038678.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038678.jpg
+415564	27648	A row of white urinals in bathroom below windows.	640	480	COCO_val2014_000000027648.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000027648.jpg
+415576	450282	A man is shoving a large piece of food in to his mouth.	428	640	COCO_val2014_000000450282.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450282.jpg
+415859	527022	A painting of a man herding sheep in the field.	480	640	COCO_val2014_000000527022.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000527022.jpg
+416086	552503	Three pots of bright colorful food cooking on a black stove top.	480	640	COCO_val2014_000000552503.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552503.jpg
+416427	59635	Woman in wet suit considers waves near forested coastline.	640	486	COCO_val2014_000000059635.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000059635.jpg
+416798	334809	Blue and white plate with meat and brussel sprouts.	428	640	COCO_val2014_000000334809.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334809.jpg
+416978	20487	Two teenage boys are flying kites on top of a hill.	500	375	COCO_val2014_000000020487.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000020487.jpg
+417525	464283	A young man and woman drink wine at a wine bar.	426	640	COCO_val2014_000000464283.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464283.jpg
+417739	117093	A very nice looking motorcycle parked near some bushes.	451	640	COCO_val2014_000000117093.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000117093.jpg
+418088	96420	Brown horse pulling a white and red cart on a street.	428	640	COCO_val2014_000000096420.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000096420.jpg
+418090	464263	A window view of a garden at a workstation.	480	640	COCO_val2014_000000464263.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464263.jpg
+418091	18014	A cooked pizza sitting in a pizza box.	640	640	COCO_val2014_000000018014.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018014.jpg
+418171	397268	I am unable to see the image above.	480	640	COCO_val2014_000000397268.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397268.jpg
+418196	419790	a number of people on bikes under a traffic light	360	640	COCO_val2014_000000419790.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419790.jpg
+418416	400489	A dog and a cat are playing in a field	332	500	COCO_val2014_000000400489.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000400489.jpg
+418839	393180	a person sitting on the ground in front of a stove	427	640	COCO_val2014_000000393180.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000393180.jpg
+418892	549109	Black and white dog sitting on a futon near a TV.	480	640	COCO_val2014_000000549109.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000549109.jpg
+418960	86001	A batter is swinging his bat while a catcher squats behind him.	529	640	COCO_val2014_000000086001.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000086001.jpg
+419325	363344	Four birds are walking on the sand near water.	480	640	COCO_val2014_000000363344.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000363344.jpg
+419784	206068	Grapes, kiwi, bananas and tangerines are sliced in a pile.	426	640	COCO_val2014_000000206068.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000206068.jpg
+420716	192062	A street sign surrounded by orange and red leaves	500	375	COCO_val2014_000000192062.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192062.jpg
+420895	132389	A  white dog is looking at the camera	428	640	COCO_val2014_000000132389.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132389.jpg
+420922	175212	A bathroom with a low roof diagonally located above it	640	478	COCO_val2014_000000175212.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175212.jpg
+420941	127761	A rowboat set next to a dock in a river.	500	488	COCO_val2014_000000127761.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000127761.jpg
+421296	137188	A cat laying down on a surface in a room.	426	640	COCO_val2014_000000137188.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000137188.jpg
+421475	482225	Clear plate of fries next to a bacon cheeseburger.	480	640	COCO_val2014_000000482225.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000482225.jpg
+421829	99645	A cat next to a television in a room.	480	640	COCO_val2014_000000099645.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099645.jpg
+421978	161840	Three cats sitting in a window waiting for their master to return.	457	640	COCO_val2014_000000161840.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000161840.jpg
+421982	508373	A skier is about to start a race.	375	500	COCO_val2014_000000508373.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000508373.jpg
+421984	9801	A person near a bike and a car on a street.	640	633	COCO_val2014_000000009801.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009801.jpg
+422471	264420	A elephant that is stepping on to the road.	427	640	COCO_val2014_000000264420.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264420.jpg
+422688	120478	Street sign on overhanging post displayed above roadway.	375	500	COCO_val2014_000000120478.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120478.jpg
+422769	364665	a person with a black oven mit is taking a pan out of the oven	347	500	COCO_val2014_000000364665.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000364665.jpg
+422830	316189	A catcher kneeling on the field throwing the baseball back to the pitcher.	480	640	COCO_val2014_000000316189.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000316189.jpg
+422852	526555	A group of people some with designed clothing, some with plain clothing on.	404	640	COCO_val2014_000000526555.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000526555.jpg
+422947	216218	A toilet that is sitting in a bathroom with the light on.	640	428	COCO_val2014_000000216218.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216218.jpg
+422949	456991	A man that is sitting at a table with various items on it.	426	640	COCO_val2014_000000456991.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000456991.jpg
+423080	482134	Yellow and red floral arrangement displayed in bathroom.	640	480	COCO_val2014_000000482134.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000482134.jpg
+423200	464312	A man holding a bat while standing near home base.	436	640	COCO_val2014_000000464312.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464312.jpg
+423201	519542	A woman riding on the back of a purple motor scooter.	500	259	COCO_val2014_000000519542.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519542.jpg
+423217	122745	A stop sign is lit up in the dark of night.	640	480	COCO_val2014_000000122745.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000122745.jpg
+423241	503926	A cluttered living room with a tree in it	640	480	COCO_val2014_000000503926.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000503926.jpg
+423709	550529	A motorbike sitting in front of a wine display case	612	612	COCO_val2014_000000550529.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550529.jpg
+423929	223587	A plate of chicken and mushrooms next to vegetables.	480	640	COCO_val2014_000000223587.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000223587.jpg
+424159	328797	A kitchen with lots of dishes and appliances next to cooking utensils.	480	640	COCO_val2014_000000328797.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328797.jpg
+424443	544639	a black and white zebra some rocks and plants	480	640	COCO_val2014_000000544639.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000544639.jpg
+424503	219271	Large group of people surrounding a truck on a mountain.	500	350	COCO_val2014_000000219271.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000219271.jpg
+424810	408906	Two personal sized pizzas are being prepared on a pan.	427	640	COCO_val2014_000000408906.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000408906.jpg
+425234	280888	A group of people on a field playing soccer.	425	640	COCO_val2014_000000280888.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000280888.jpg
+425278	175535	a white plate with some food and two trays of sauce	640	478	COCO_val2014_000000175535.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175535.jpg
+425834	463522	A horse pulling  carriage travels through a busy area in a city	480	640	COCO_val2014_000000463522.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000463522.jpg
+426128	320951	A bathroom with a toilet, sink, counter and large mirror	640	428	COCO_val2014_000000320951.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000320951.jpg
+426834	569931	A zebra in the wild walking towards a dirt road.	332	500	COCO_val2014_000000569931.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000569931.jpg
+427017	543330	A table with cut up vegetables and cheese with it's rind cut off.	480	640	COCO_val2014_000000543330.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543330.jpg
+427292	529549	A herd of elephants standing next to each other on a field.	427	640	COCO_val2014_000000529549.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529549.jpg
+427438	38865	Two white plates topped with meat and veggies next to bottles.	480	640	COCO_val2014_000000038865.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038865.jpg
+427605	307936	A small elephant walking across a dry grass field.	480	640	COCO_val2014_000000307936.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000307936.jpg
+427672	139094	A man riding on the back of an orange fork lift.	481	640	COCO_val2014_000000139094.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000139094.jpg
+427779	471097	A white car parked next to a parking meter in front of a parking garage.	480	640	COCO_val2014_000000471097.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000471097.jpg
+427810	268356	A tall clock tower sticking out of the roof of a building.	425	640	COCO_val2014_000000268356.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268356.jpg
+427864	284257	A man standing in a room next to a metal and red pole.	427	640	COCO_val2014_000000284257.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000284257.jpg
+427878	315196	A man riding a bike down a street with a woman.	301	640	COCO_val2014_000000315196.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315196.jpg
+427898	578093	a white yellow and blue train at an empty train station	480	640	COCO_val2014_000000578093.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000578093.jpg
+428094	579693	There is a toilet in a narrow bathroom.	480	640	COCO_val2014_000000579693.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000579693.jpg
+428149	173044	A very tall clock tower sitting next to a tall glass building.	640	359	COCO_val2014_000000173044.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173044.jpg
+428194	53949	Men holding tennis rackets are shaking hands above a net.	640	480	COCO_val2014_000000053949.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000053949.jpg
+428213	344654	There is an elephant shaped figure next to other decorations.	427	640	COCO_val2014_000000344654.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344654.jpg
+428622	356456	a cargo and train on the train tracks	399	640	COCO_val2014_000000356456.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000356456.jpg
+428752	275332	A man does tricks on his skateboard while people look at the scenery.	427	640	COCO_val2014_000000275332.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000275332.jpg
+428792	13428	A plate topped with broccoli and a red onion.	500	335	COCO_val2014_000000013428.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013428.jpg
+428876	369442	A group of people standing around a herd of animals in a city.	480	640	COCO_val2014_000000369442.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369442.jpg
+429082	516766	A white teddy bear sitting next to a brown teddy bear.	640	427	COCO_val2014_000000516766.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516766.jpg
+429084	175494	A large brown and black dog laying on top of a bed.	480	640	COCO_val2014_000000175494.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175494.jpg
+429334	253699	A piece of cake sitting on top of a plate covered in marshmallow with a lit candle sticking out of it.	640	442	COCO_val2014_000000253699.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000253699.jpg
+429426	86696	A wooden center island in a kitchen next to two flat screen TVs.	360	640	COCO_val2014_000000086696.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000086696.jpg
+429856	306477	A street sign leaned over with the words High Gate Avenue on it.	480	640	COCO_val2014_000000306477.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306477.jpg
+429885	120162	a man walking out of the water with his surfboard	480	640	COCO_val2014_000000120162.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120162.jpg
+429955	6220	A dark table has a large arrangement of food.	425	640	COCO_val2014_000000006220.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000006220.jpg
+430532	274352	A large group of women are at a dining table.	612	612	COCO_val2014_000000274352.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000274352.jpg
+430533	454562	A man is playing baseball before a crowd.	289	500	COCO_val2014_000000454562.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000454562.jpg
+430574	28885	People stroller through the walkway near a giraffe.	500	400	COCO_val2014_000000028885.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028885.jpg
+430872	379077	A brown dog looks up at the camera as he stands in the snow.	480	640	COCO_val2014_000000379077.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379077.jpg
+431449	485687	a white and black sea bird standing in very shallow water	640	427	COCO_val2014_000000485687.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000485687.jpg
+431507	414698	a male tennis player volleying a tennis ball at a match	427	640	COCO_val2014_000000414698.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000414698.jpg
+431604	279677	A person with a hat on flying a kite by a plane flying in the sky.	500	375	COCO_val2014_000000279677.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279677.jpg
+431920	387431	Sidewalk in city with store fronts and clock.	640	480	COCO_val2014_000000387431.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000387431.jpg
+432825	370561	A man that is sitting in the sand near surfboards.	500	333	COCO_val2014_000000370561.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000370561.jpg
+433459	415536	A bus is driving on a wet road with many green trees on the roadside.	480	640	COCO_val2014_000000415536.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415536.jpg
+433530	114055	A man is doing a trick at the skatepark.	640	457	COCO_val2014_000000114055.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114055.jpg
+433863	129864	A man sits in a car with a cat in his lap.	480	640	COCO_val2014_000000129864.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129864.jpg
+434239	438046	A large white bed in a red room	478	640	COCO_val2014_000000438046.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000438046.jpg
+434292	334941	A woman leaning up against an outside wall using her cell phone.	640	359	COCO_val2014_000000334941.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334941.jpg
+434478	260307	Some street signs near a big bridge overpass.	480	640	COCO_val2014_000000260307.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260307.jpg
+434500	335288	A dog wrapping its leg around a stuffed bear.	375	500	COCO_val2014_000000335288.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335288.jpg
+434507	495053	A sign post at a corner in front of a building.	423	640	COCO_val2014_000000495053.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000495053.jpg
+434765	271620	a tray with various foods and beverages on it	480	640	COCO_val2014_000000271620.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000271620.jpg
+434768	51741	A white toilet and sink in a room.	480	640	COCO_val2014_000000051741.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000051741.jpg
+435029	219170	A laptop, keyboard, monitor and speakers on a hardwood floor.	480	320	COCO_val2014_000000219170.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000219170.jpg
+435346	191112	A person holds a robot in front of a ball near a goal.	427	640	COCO_val2014_000000191112.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000191112.jpg
+435464	456236	Some food that is laying on a white plate.	334	500	COCO_val2014_000000456236.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000456236.jpg
+435574	244151	a person riding a skate board on a city street	500	335	COCO_val2014_000000244151.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000244151.jpg
+435823	231828	A close up view of a giraffe's face.	640	639	COCO_val2014_000000231828.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231828.jpg
+436328	323726	A batter in the batter's box next to the catcher and umpire.	428	640	COCO_val2014_000000323726.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000323726.jpg
+436420	335827	A close up of a very large church with a clock.	500	375	COCO_val2014_000000335827.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335827.jpg
+436509	560440	A view of a crosswalk that leads into a intersection.	503	640	COCO_val2014_000000560440.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000560440.jpg
+436520	295051	a close up of a cat on a desk near stuffed animals	640	484	COCO_val2014_000000295051.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295051.jpg
+436870	407822	A man is posing with two young ladies, while holding his drink.	426	640	COCO_val2014_000000407822.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000407822.jpg
+436964	5325	A lady holding an umbrella is walking with a lady in the wheel chair.	375	500	COCO_val2014_000000005325.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005325.jpg
+437077	83958	A giraffe is sitting down in the wild.	500	332	COCO_val2014_000000083958.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000083958.jpg
+437351	174594	A man stands at the base with a bat.	457	640	COCO_val2014_000000174594.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174594.jpg
+437465	175612	A figurine is standing on the pavement and carrying a skateboard.	640	427	COCO_val2014_000000175612.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175612.jpg
+437548	21764	A man riding a skateboard down the side of a ramp.	640	427	COCO_val2014_000000021764.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000021764.jpg
+437581	232610	Smiling girl wearing glasses, with a cellphone up to her ear.	500	333	COCO_val2014_000000232610.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000232610.jpg
+437622	130791	A horse and bug-gee is riding next to the buildings.	640	481	COCO_val2014_000000130791.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000130791.jpg
+437787	162144	A couple of people with ski's standing in the snow.	478	640	COCO_val2014_000000162144.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000162144.jpg
+437813	159944	A cat that is laying with its head down on a mouse.	400	500	COCO_val2014_000000159944.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000159944.jpg
+437826	119709	Someone is preparing many donuts at one time.	640	480	COCO_val2014_000000119709.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000119709.jpg
+437886	69911	A pair of sneakers hang in the background at this street corner.	427	640	COCO_val2014_000000069911.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000069911.jpg
+437965	401850	A horse drawn carriage goes down this street surrounded with tall buildings.	640	472	COCO_val2014_000000401850.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401850.jpg
+438127	448964	Multiple city buses parked in a storage area.	478	640	COCO_val2014_000000448964.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448964.jpg
+438227	111944	A guy on skis with another skier farther behind him.	480	640	COCO_val2014_000000111944.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111944.jpg
+438277	393421	A room is shown with a bed and luggage.	500	281	COCO_val2014_000000393421.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000393421.jpg
+438307	198563	A cat sitting on a toilet seat while the cover is up.	480	640	COCO_val2014_000000198563.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198563.jpg
+438614	66711	A coupe of road signs near a downtown area or highway.	640	535	COCO_val2014_000000066711.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000066711.jpg
+438819	498032	A person standing in a room with a remote.	464	640	COCO_val2014_000000498032.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498032.jpg
+438994	420882	The inside of a building is shown with benches.	640	480	COCO_val2014_000000420882.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000420882.jpg
+439179	1803	A modern day bathroom with a unusual sink	500	500	COCO_val2014_000000001803.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001803.jpg
+439797	227741	A man with a racket plays on a court.	427	640	COCO_val2014_000000227741.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227741.jpg
+439836	178744	A Chicago logo boat is traveling in the water.	428	640	COCO_val2014_000000178744.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000178744.jpg
+439953	226498	A group of people on a field play a game of soccer.	478	640	COCO_val2014_000000226498.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226498.jpg
+440045	162021	Three people in white shirts and ties on skateboards.	427	640	COCO_val2014_000000162021.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000162021.jpg
+440160	309633	Various luggage on couch and floor next to a bookshelf.	480	640	COCO_val2014_000000309633.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000309633.jpg
+440362	99592	A man with a skateboard flying a kite.	618	640	COCO_val2014_000000099592.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099592.jpg
+440516	261036	A boy standing in the grass with a frisbee.	500	333	COCO_val2014_000000261036.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000261036.jpg
+440689	170595	A plate with a peanut butter sandwich, ham, and tomatoes.	612	612	COCO_val2014_000000170595.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170595.jpg
+440700	398304	The steering wheel of a bike with many gadgets parked in a mud room.	480	640	COCO_val2014_000000398304.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398304.jpg
+441012	521863	A skateboarder is seen near a crowd through the glass door.	500	375	COCO_val2014_000000521863.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000521863.jpg
+441033	42492	A kitchen with a center stove top island.	480	640	COCO_val2014_000000042492.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000042492.jpg
+441233	43073	A young boy wearing a blue shirt standing next to a woman.	484	640	COCO_val2014_000000043073.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000043073.jpg
+441302	284282	A blender is sitting next to a printer.	333	500	COCO_val2014_000000284282.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000284282.jpg
+441744	23754	A person on a surfboard jumping out of the water	427	640	COCO_val2014_000000023754.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000023754.jpg
+442110	460188	Many people skiing and snowboarding in the snow.	333	500	COCO_val2014_000000460188.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460188.jpg
+442759	434930	A kid eating a doughnut at a table.	640	636	COCO_val2014_000000434930.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000434930.jpg
+442921	38259	Plane in window with cloudy sky and chairs	640	608	COCO_val2014_000000038259.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038259.jpg
+442964	524977	A metal stove and a counter in a room.	333	500	COCO_val2014_000000524977.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000524977.jpg
+443138	408221	A very big elephant that is keeping an eye on the photographer.	425	640	COCO_val2014_000000408221.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000408221.jpg
+443223	360329	Some bananas are hanging from a banana tree.	500	500	COCO_val2014_000000360329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360329.jpg
+443257	572233	People are sitting at tables for a reception.	427	640	COCO_val2014_000000572233.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000572233.jpg
+443348	388299	A baby smiles with it's stuffed animal.	480	640	COCO_val2014_000000388299.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000388299.jpg
+443352	136143	A man wearing a red shirt is throwing a frisbee	480	640	COCO_val2014_000000136143.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000136143.jpg
+443444	90040	some boats are in the water buildings and a person	361	640	COCO_val2014_000000090040.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000090040.jpg
+443712	498650	A herd of elephants standing on leaf covered ground.	640	427	COCO_val2014_000000498650.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498650.jpg
+443807	563775	A woman with purple hair standing next to a hipster.	640	480	COCO_val2014_000000563775.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000563775.jpg
+444055	559247	Several people in a lunch line with hot dogs and buns.	480	640	COCO_val2014_000000559247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559247.jpg
+444066	573605	One glass of wine is full and one glass is empty.	480	640	COCO_val2014_000000573605.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573605.jpg
+444273	205811	Several people are sitting down eating eating a lunch.	459	640	COCO_val2014_000000205811.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205811.jpg
+444281	12471	That small plane looks like it is ready for takeoff.	480	640	COCO_val2014_000000012471.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000012471.jpg
+444314	455004	She is holding up a peace sign in the snow.	412	266	COCO_val2014_000000455004.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000455004.jpg
+444617	224595	I think that the plane has just taken off.	480	640	COCO_val2014_000000224595.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224595.jpg
+444784	441156	Men on a safari with an elephant and photo equipment.	480	640	COCO_val2014_000000441156.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000441156.jpg
+445213	517523	A 787 airplane is landing at the airport.	435	640	COCO_val2014_000000517523.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000517523.jpg
+445417	1270	Kids playing a game of baseball while people watch.	480	640	COCO_val2014_000000001270.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001270.jpg
+445525	520787	Two slices of pizza are being served on a plate.	640	480	COCO_val2014_000000520787.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000520787.jpg
+445588	35279	there is a tennis racket and a laptop on the floor	463	640	COCO_val2014_000000035279.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000035279.jpg
+445863	87493	a cell phone painted on a wall near a bike	500	375	COCO_val2014_000000087493.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087493.jpg
+446040	273556	An image looking down in to a toilet.	375	500	COCO_val2014_000000273556.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273556.jpg
+446127	152176	A bike is parked alongside the lake shore.	427	640	COCO_val2014_000000152176.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152176.jpg
+446259	461973	People on a platform watch a train go by	428	640	COCO_val2014_000000461973.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000461973.jpg
+446400	182339	A young product representative gives out Frisbees at a show	640	480	COCO_val2014_000000182339.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182339.jpg
+446430	257864	A white plate topped with a sandwich and other food.	640	480	COCO_val2014_000000257864.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000257864.jpg
+446476	483027	People are eating at an outdoors restaurant in a black and white photo.	480	640	COCO_val2014_000000483027.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000483027.jpg
+446483	449379	Cats curled up and asleep on a bed during the day	640	480	COCO_val2014_000000449379.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449379.jpg
+446525	245094	A California king sized bed sits under a white canopy.	425	640	COCO_val2014_000000245094.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000245094.jpg
+446552	561311	A chef holding a platter with green leaves around the edge.	425	640	COCO_val2014_000000561311.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000561311.jpg
+446572	345401	The toilet is in a separate area away from the sink.	640	427	COCO_val2014_000000345401.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345401.jpg
+446618	374669	stop sign that says no vehicles past posts in front of building	640	480	COCO_val2014_000000374669.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000374669.jpg
+446860	39211	A man sitting at a table with a laptop computer.	375	500	COCO_val2014_000000039211.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039211.jpg
+447080	530600	A group of stuffed animals piled on top of each other.	428	640	COCO_val2014_000000530600.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530600.jpg
+447249	555197	A person is going down the mountain on skis.	427	640	COCO_val2014_000000555197.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000555197.jpg
+447431	553420	a number of street signs with a business sign in the background	375	500	COCO_val2014_000000553420.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000553420.jpg
+447563	372718	A clear vase that has some flowers in it.	638	640	COCO_val2014_000000372718.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372718.jpg
+447678	528181	A couch sitting in a living room under two windows.	375	500	COCO_val2014_000000528181.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000528181.jpg
+447766	577149	there are many zebras walking on the side of this road	413	640	COCO_val2014_000000577149.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577149.jpg
+447821	216726	A series of photos showing different cakes and glasses.	480	640	COCO_val2014_000000216726.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216726.jpg
+447881	223314	brown cattle grazing on a yellow grassy area	406	640	COCO_val2014_000000223314.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000223314.jpg
+447991	359110	A broken down building with a clock that collapsed.	374	640	COCO_val2014_000000359110.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000359110.jpg
+448229	550349	A bus about to stop to pick up passengers.	640	480	COCO_val2014_000000550349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550349.jpg
+448261	329614	a double deckered bus on a city street	208	500	COCO_val2014_000000329614.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000329614.jpg
+448262	22628	a person holding a surf board on a beach	424	640	COCO_val2014_000000022628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000022628.jpg
+448282	23019	a few big buildings with roads next to them	428	640	COCO_val2014_000000023019.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000023019.jpg
+448446	315976	a red double decker bus is seen coming up the street	612	612	COCO_val2014_000000315976.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315976.jpg
+448662	52982	A person standing next to a large elephant.	480	640	COCO_val2014_000000052982.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000052982.jpg
+448753	484842	A whole cake and a piece in a plate by a person.	429	640	COCO_val2014_000000484842.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000484842.jpg
+449135	118811	A person sitting at a table with some drinks.	640	425	COCO_val2014_000000118811.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118811.jpg
+449535	143822	two plastic cases for a wii, for different purposes	480	640	COCO_val2014_000000143822.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143822.jpg
+449720	35705	A woman rests against pillows as she uses a laptop.	375	500	COCO_val2014_000000035705.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000035705.jpg
+449781	419856	a black bicycle parked near a large branch carrying bananas	480	640	COCO_val2014_000000419856.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419856.jpg
+449935	85940	This is an RV kitchen area next to a tv.	480	640	COCO_val2014_000000085940.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000085940.jpg
+450020	352495	A little girl sitting in front of a table full of dishes.	428	640	COCO_val2014_000000352495.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000352495.jpg
+450172	38118	A man on his skis on a snowy slope.	427	640	COCO_val2014_000000038118.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038118.jpg
+450201	188522	An old pickup truck sitting in a field falling apart.	426	640	COCO_val2014_000000188522.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188522.jpg
+450349	489687	A cat sitting on top of a a black car.	352	500	COCO_val2014_000000489687.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000489687.jpg
+450384	183914	A man with a camera has a smoke in his mouth.	400	500	COCO_val2014_000000183914.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000183914.jpg
+450476	93419	A bear standing in grass next to rocks, behind a wire fence.	640	480	COCO_val2014_000000093419.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000093419.jpg
+450481	75006	two guys sitting in a restaurant with a large half eaten pizza	480	640	COCO_val2014_000000075006.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000075006.jpg
+450868	5037	A passenger bus pulling up to the side of a street.	425	640	COCO_val2014_000000005037.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005037.jpg
+451024	215394	A cat napping inside of a large planter.	480	640	COCO_val2014_000000215394.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000215394.jpg
+451090	442667	A large bus and a truck on a street.	480	640	COCO_val2014_000000442667.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442667.jpg
+451815	325587	A dog walking toward the camera behind a pair of shoes near a couch.	640	480	COCO_val2014_000000325587.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325587.jpg
+451975	124621	Train traveling through countryside near tall brick structure.	640	480	COCO_val2014_000000124621.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000124621.jpg
+452034	234934	Eggs, orange and vegetables are sitting on a plate.	289	385	COCO_val2014_000000234934.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000234934.jpg
+452680	18366	Several buses and cars driving down a busy street.	480	640	COCO_val2014_000000018366.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018366.jpg
+452714	307683	A blanket that has different types of food.	428	640	COCO_val2014_000000307683.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000307683.jpg
+453115	450695	A large long train on a steel track.	448	640	COCO_val2014_000000450695.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450695.jpg
+453144	40275	there is a monk standing in front of a building	640	640	COCO_val2014_000000040275.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000040275.jpg
+453188	484641	A breakfast plate with toast, eggs and bacon.	640	640	COCO_val2014_000000484641.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000484641.jpg
+453208	372819	Four smaller dogs are running away from the women on the bench.	426	640	COCO_val2014_000000372819.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372819.jpg
+453526	333101	A young girl with a top hat on is playing with an object.	466	640	COCO_val2014_000000333101.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333101.jpg
+453921	573962	A picture of rush hour traffic on the streets.	427	640	COCO_val2014_000000573962.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573962.jpg
+454108	108338	Two elephants are standing by the trees in the wild.	415	640	COCO_val2014_000000108338.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000108338.jpg
+454232	35429	A young boy seems to be trying to feed a giraffe.	480	640	COCO_val2014_000000035429.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000035429.jpg
+454240	138814	A man on the tennis court is about to use his racket.	640	427	COCO_val2014_000000138814.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000138814.jpg
+454546	298812	A little girl holding an umbrella is standing on the sidewalk	640	426	COCO_val2014_000000298812.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000298812.jpg
+454659	531633	there is a man walking on the beach holding two surf boards	427	640	COCO_val2014_000000531633.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000531633.jpg
+454774	259879	A cat sitting on a window sill in a room.	500	367	COCO_val2014_000000259879.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000259879.jpg
+454820	529968	A cat is sitting on a motorcycle looking in the mirror.	480	640	COCO_val2014_000000529968.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529968.jpg
+454867	420658	A stop sign is hanging from a post.	480	640	COCO_val2014_000000420658.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000420658.jpg
+454870	162627	A girl that is sitting on a horse.	480	640	COCO_val2014_000000162627.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000162627.jpg
+455164	5535	A man walking down the street beside a big bus.	480	640	COCO_val2014_000000005535.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005535.jpg
+455416	221910	A stone staircase and window in a building.	640	426	COCO_val2014_000000221910.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000221910.jpg
+455620	276852	A zebra that is bent over eating grass.	332	500	COCO_val2014_000000276852.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000276852.jpg
+455963	85434	Cross country skiers on snow covered course during race.	480	640	COCO_val2014_000000085434.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000085434.jpg
+455982	576253	a man with eye glasses who is holding a wii remote	480	640	COCO_val2014_000000576253.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000576253.jpg
+456107	1153	A banana that is sitting in a bowl on the table.	375	500	COCO_val2014_000000001153.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001153.jpg
+456111	399873	A woman is cutting a sheet with scissors.	426	640	COCO_val2014_000000399873.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000399873.jpg
+456173	56426	A modern sink is on top of a bathroom counter top.	640	425	COCO_val2014_000000056426.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056426.jpg
+456479	355276	A bear that is laying down in the dirt.	428	640	COCO_val2014_000000355276.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000355276.jpg
+457028	273784	An older man standing next to a garage and a young boy.	426	640	COCO_val2014_000000273784.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273784.jpg
+457114	568505	A bed with a pillow at the tip is neatly made.	480	640	COCO_val2014_000000568505.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000568505.jpg
+457211	342403	A young man kneeling on top of a lush green field.	375	500	COCO_val2014_000000342403.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000342403.jpg
+458134	498346	A dog grabs for a Frisbee in the park.	375	500	COCO_val2014_000000498346.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498346.jpg
+458543	221561	A stuffed bear standing on top of a bed.	375	500	COCO_val2014_000000221561.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000221561.jpg
+458544	1799	A large green truck on a city street.	480	640	COCO_val2014_000000001799.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001799.jpg
+458550	96241	there is a small black train and people looking at it	427	640	COCO_val2014_000000096241.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000096241.jpg
+458678	173274	A man wearing glasses, dress shirt and a tie is smiling in content.	640	640	COCO_val2014_000000173274.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173274.jpg
+458695	230936	A small boat in the water and a person.	360	640	COCO_val2014_000000230936.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000230936.jpg
+458987	337984	A couple of sinks in a large room.	429	640	COCO_val2014_000000337984.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000337984.jpg
+459035	531350	A man wearing a tie up against a wall.	401	640	COCO_val2014_000000531350.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000531350.jpg
+459283	212226	A person in a the reflection of a truck mirror.	335	500	COCO_val2014_000000212226.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000212226.jpg
+459337	86884	a white plate and a piece of white cake	480	640	COCO_val2014_000000086884.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000086884.jpg
+459415	10449	A group of three elephants are standing in the water.	480	640	COCO_val2014_000000010449.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010449.jpg
+459504	382009	A person on a court with a tennis racket.	427	640	COCO_val2014_000000382009.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382009.jpg
+459850	571835	A grey tabby kitten inside a black suit case.	431	640	COCO_val2014_000000571835.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000571835.jpg
+459865	559778	there is a german sherpard sitting on the floor with a stuffed bear	375	500	COCO_val2014_000000559778.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559778.jpg
+460091	81862	A black computer keyboard on top of a white surface.	427	640	COCO_val2014_000000081862.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000081862.jpg
+460168	234889	Two large trains at a modernized train station, people walking about.	480	640	COCO_val2014_000000234889.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000234889.jpg
+460280	53058	there is a older man sitting and using a lap top	640	640	COCO_val2014_000000053058.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000053058.jpg
+460281	87998	there is a black motorcycle parked outside in the dirt	480	640	COCO_val2014_000000087998.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087998.jpg
+460442	297380	a female in a black top and a child in a green shirt	426	640	COCO_val2014_000000297380.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297380.jpg
+460594	214241	a horse with a long mane looking at the camera	640	479	COCO_val2014_000000214241.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214241.jpg
+460595	160886	a banana sitting next to a can of tomato soup	500	375	COCO_val2014_000000160886.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160886.jpg
+460707	203705	A laptop computer on a white desk with various office work and two cups beside it.	375	500	COCO_val2014_000000203705.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203705.jpg
+460801	218855	A bunch of people are sitting around while a guy plays a video game	480	640	COCO_val2014_000000218855.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218855.jpg
+461270	297017	A mother duck with her ducklings in a pond.	413	640	COCO_val2014_000000297017.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297017.jpg
+461372	159662	A person on a court with a tennis racket.	640	425	COCO_val2014_000000159662.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000159662.jpg
+461844	209142	Green salad with broccoli and peas with fork and bowl	425	640	COCO_val2014_000000209142.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209142.jpg
+461913	382855	A person sitting on the ground with a cell phone.	427	640	COCO_val2014_000000382855.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382855.jpg
+461924	188534	Assorted chinese food in a Styrofoam container on a table.	480	640	COCO_val2014_000000188534.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188534.jpg
+462148	3217	A wooden bench on the ground next to some trees.	640	480	COCO_val2014_000000003217.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000003217.jpg
+462176	128704	A man picking up trash with a garbage bag at a food court.	457	640	COCO_val2014_000000128704.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000128704.jpg
+462206	190783	A teddy bear lying on the ground by some leaves.	480	640	COCO_val2014_000000190783.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190783.jpg
+462292	180984	A man and a woman playing frisbee in the grass.	438	500	COCO_val2014_000000180984.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000180984.jpg
+462823	108982	A blue and yellow colored bird perches on a tree branch.	427	640	COCO_val2014_000000108982.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000108982.jpg
+463040	321705	A herd of sheep standing on top of a lush green field grazing.	480	640	COCO_val2014_000000321705.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000321705.jpg
+463282	201241	A polar bear keeping cool in the water.	480	640	COCO_val2014_000000201241.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000201241.jpg
+463459	333697	Two street signs on the corner of a stone wall.	640	426	COCO_val2014_000000333697.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333697.jpg
+463516	334332	A man holding a gigantic donut in a napkin.	480	640	COCO_val2014_000000334332.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334332.jpg
+463633	9647	there are many men preparing to cut a red ribbon	528	640	COCO_val2014_000000009647.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009647.jpg
+463757	258209	there is a man wearing a suit sitting on a street bench	426	640	COCO_val2014_000000258209.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000258209.jpg
+463944	181499	A hand is holding a cd by a laptop computer.	513	640	COCO_val2014_000000181499.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000181499.jpg
+464075	165012	Little girl is outside ready to play with someone.	640	427	COCO_val2014_000000165012.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000165012.jpg
+464262	332087	A surfer is on top of a wave.	427	640	COCO_val2014_000000332087.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000332087.jpg
+464589	502318	A man with a TV in the middle of no where.	375	500	COCO_val2014_000000502318.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000502318.jpg
+464669	362240	Two motorcycles parked beside one another in a garage.	640	480	COCO_val2014_000000362240.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000362240.jpg
+464690	357578	A fire/rescue truck in line in traffic	480	640	COCO_val2014_000000357578.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000357578.jpg
+464723	18864	Three giraffes are eating and grooming each other.	333	500	COCO_val2014_000000018864.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018864.jpg
+464796	290768	A kitchen with a counter, window, stove and cutlery.	612	612	COCO_val2014_000000290768.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000290768.jpg
+465042	89643	Many blowup kits being flown at a beach	333	500	COCO_val2014_000000089643.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089643.jpg
+465216	498975	Street signs at an intersection, silhouetted against trees and a blue sky	480	640	COCO_val2014_000000498975.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498975.jpg
+465352	391735	A man kneeling down beside the street and a yellow building.	424	640	COCO_val2014_000000391735.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000391735.jpg
+465407	515682	A glass field with a few chairs and vehicles in the distance, along with people standing in the field and one kite being flow in the air.	428	640	COCO_val2014_000000515682.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000515682.jpg
+465408	77123	The side of an airplane that is parked, and an Air China sign on the side of the plane.	480	640	COCO_val2014_000000077123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000077123.jpg
+465429	193875	Some people are sitting on a car that's been wrapped in plastic	480	640	COCO_val2014_000000193875.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000193875.jpg
+465476	262119	A man flying through the air on a skateboard.	375	500	COCO_val2014_000000262119.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000262119.jpg
+466041	533532	there are many bags and luggages on the floor	480	640	COCO_val2014_000000533532.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533532.jpg
+466513	421535	People on skis are entering a snow tunnel.	360	640	COCO_val2014_000000421535.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421535.jpg
+466586	170640	A man sitting on a bed with a dog while holding a cell phone.	480	640	COCO_val2014_000000170640.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170640.jpg
+466883	459164	Two calculators are next to a computer laptop.	480	640	COCO_val2014_000000459164.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000459164.jpg
+466895	36990	there is a red and a yellow bus parked under a tree	381	640	COCO_val2014_000000036990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000036990.jpg
+466981	568879	One of the two monitors next to the laptops is on.	480	640	COCO_val2014_000000568879.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000568879.jpg
+467011	549965	a close up of a bench on a city street	427	640	COCO_val2014_000000549965.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000549965.jpg
+467307	188414	A person with long blonde hair wearing a blue shirt with a brown tie and blue white flowers.	640	480	COCO_val2014_000000188414.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188414.jpg
+467448	311789	The black cat with white patches is sleeping on a computer keyboard.	427	640	COCO_val2014_000000311789.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000311789.jpg
+467740	175251	A toddler in the bathroom brushing his teeth	612	612	COCO_val2014_000000175251.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175251.jpg
+468021	556597	a big jumbo jet sits parked next to another plane	480	640	COCO_val2014_000000556597.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000556597.jpg
+468055	552573	A man splashing in the water with a giant umbrella.	640	625	COCO_val2014_000000552573.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552573.jpg
+468255	521692	A man is standing on top of a snow covered hill.	480	640	COCO_val2014_000000521692.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000521692.jpg
+468463	519945	a small boats in a body of water	427	640	COCO_val2014_000000519945.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519945.jpg
+468538	69077	A brown dog sits in the snow near a red table and black chairs.	512	640	COCO_val2014_000000069077.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000069077.jpg
+468555	451305	a small zebra on dirt ground near a tree	480	640	COCO_val2014_000000451305.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451305.jpg
+468668	204650	A large air plane smoking and parked in the snow .	480	640	COCO_val2014_000000204650.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000204650.jpg
+468845	533261	a orange sits in some a puddle of water	375	500	COCO_val2014_000000533261.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533261.jpg
+469049	26374	A man in a blue uniform kicking the soccer ball	640	456	COCO_val2014_000000026374.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026374.jpg
+469232	136768	baseball players practicing their batting skills in a filled arena	427	640	COCO_val2014_000000136768.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000136768.jpg
+469669	338191	Several different fire hydrants that have been painted various colors.	640	635	COCO_val2014_000000338191.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000338191.jpg
+470906	132791	A man that is standing in the dirt.	427	640	COCO_val2014_000000132791.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132791.jpg
+471135	540544	A lady in red is playing the Wii.	640	480	COCO_val2014_000000540544.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540544.jpg
+471272	152759	a white toilet sits next to a dresser	640	428	COCO_val2014_000000152759.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152759.jpg
+471307	213033	A man standing near the water with an umbrella.	640	480	COCO_val2014_000000213033.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213033.jpg
+471352	337247	Two bananas and an apple are next to an Amazon box.	480	640	COCO_val2014_000000337247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000337247.jpg
+471392	154342	a kid stands on a hillside while flying a kite	640	480	COCO_val2014_000000154342.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000154342.jpg
+471469	454561	A train that is on the tracks next to the trees.	640	480	COCO_val2014_000000454561.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000454561.jpg
+471488	225715	The room was clean with the bed neatly made.	431	640	COCO_val2014_000000225715.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000225715.jpg
+471545	577995	A bed with two books and pillows on it.	480	640	COCO_val2014_000000577995.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577995.jpg
+471840	524742	A wall covered in graffiti next to a sidewalk.	480	640	COCO_val2014_000000524742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000524742.jpg
+471983	297520	Hot dogs, buns and croissants on a BBQ grill	480	640	COCO_val2014_000000297520.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297520.jpg
+472185	63595	A tennis player in a red shirt is serving the ball.	427	640	COCO_val2014_000000063595.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000063595.jpg
+472257	43693	a vintage photo of man standing in the middle of some waves	480	640	COCO_val2014_000000043693.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000043693.jpg
+472306	210832	a person jumping a skate board into the air	334	500	COCO_val2014_000000210832.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000210832.jpg
+472313	492366	A group of people that are standing next to a horse and cart.	414	640	COCO_val2014_000000492366.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000492366.jpg
+472344	330493	Person in restaurant with flooring and blinds with tables	425	640	COCO_val2014_000000330493.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000330493.jpg
+472400	400925	A man that is laying down with his tongue out.	427	640	COCO_val2014_000000400925.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000400925.jpg
+472572	293034	A group of people that are standing around giraffes.	480	640	COCO_val2014_000000293034.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000293034.jpg
+472600	68674	Baby sits inside an empty suitcase on top of a bed.	418	640	COCO_val2014_000000068674.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068674.jpg
+472605	556248	Birds walking around on a large rock by the ocean	360	640	COCO_val2014_000000556248.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000556248.jpg
+472680	361497	A airplane mirror where clouds can be seen.	640	427	COCO_val2014_000000361497.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000361497.jpg
+472689	405213	A skyscraper reflecting the blue sky near a street lamp	427	640	COCO_val2014_000000405213.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000405213.jpg
+472765	306733	Bathroom with sink, shower, towels, curtain, and more with window	426	640	COCO_val2014_000000306733.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306733.jpg
+472945	253408	two people sitting down at a table near wine glasses	480	640	COCO_val2014_000000253408.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000253408.jpg
+472946	475667	a giraffe in a field with another in the background	640	476	COCO_val2014_000000475667.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000475667.jpg
+473208	323370	A baked sandwich, french fries, coleslaw, and silverware.	480	640	COCO_val2014_000000323370.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000323370.jpg
+473536	31061	A young man is sitting down with his skateboard.	640	427	COCO_val2014_000000031061.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000031061.jpg
+473730	215652	A girl is sitting on a bed and has each hand on a notepad.	640	478	COCO_val2014_000000215652.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000215652.jpg
+474067	76026	A giraffe that is standing up in the grass.	640	426	COCO_val2014_000000076026.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000076026.jpg
+474156	335589	A bed that has different objects on it.	640	640	COCO_val2014_000000335589.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335589.jpg
+474204	530494	A group of doughnuts that are on a conveyor.	480	640	COCO_val2014_000000530494.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530494.jpg
+474992	424776	a grey colored jet plane flying over a snowy mountain range.	426	640	COCO_val2014_000000424776.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000424776.jpg
+475151	267625	The man is wearing a hat and tie of a man wearing a hat.	640	480	COCO_val2014_000000267625.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267625.jpg
+475478	384350	a white and red plane parked on a tarmac	480	640	COCO_val2014_000000384350.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384350.jpg
+475479	372577	a tennis player holding a tennis racket on a court	640	480	COCO_val2014_000000372577.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372577.jpg
+475656	398138	Kites fly high in the air over a park.	428	640	COCO_val2014_000000398138.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398138.jpg
+475764	451879	Baseball players in action, one person dressed in green running while the others in white are looking and reaching toward the ball.	438	500	COCO_val2014_000000451879.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451879.jpg
+475804	579918	There are several multicolored sun umbrellas and this boy is holding one	427	640	COCO_val2014_000000579918.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000579918.jpg
+476076	74759	A group of people sitting on the backs of elephants.	427	640	COCO_val2014_000000074759.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000074759.jpg
+476119	119476	a man talking to a group of people and standing in front of a screen	425	640	COCO_val2014_000000119476.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000119476.jpg
+476183	430036	A man flying through the air while riding skis.	426	640	COCO_val2014_000000430036.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000430036.jpg
+476462	219329	Tall tower with a clock in the front near a sunny sky.	612	612	COCO_val2014_000000219329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000219329.jpg
+476934	384554	This is a giant metal refrigerator and freezer.	480	640	COCO_val2014_000000384554.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384554.jpg
+477318	158849	A group of people wait in line to use a machine.	480	640	COCO_val2014_000000158849.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158849.jpg
+477456	78820	Food in a pain is shown up close.	612	612	COCO_val2014_000000078820.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078820.jpg
+477789	318524	A rusty side car of a train that has been vandalized with artwork.	480	640	COCO_val2014_000000318524.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000318524.jpg
+477839	455847	A woman and a child with red ski gear and a helmet wearing skis.	500	375	COCO_val2014_000000455847.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000455847.jpg
+477917	213361	A seagull drinks water from a pool of water.	451	640	COCO_val2014_000000213361.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213361.jpg
+477921	450596	A living room with a long beige couch and a tan chair with a mop laying sideways on the chair.	426	640	COCO_val2014_000000450596.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450596.jpg
+477928	202860	A woman holding a vintage teddy bear in her hands.	640	426	COCO_val2014_000000202860.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000202860.jpg
+477929	195815	A large dog sitting in a  chair next to a glass top table.	480	640	COCO_val2014_000000195815.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195815.jpg
+477939	224531	A boat floats in the water near the shore.	425	640	COCO_val2014_000000224531.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224531.jpg
+478042	559462	A man and a woman dressed in wedding dresses.	640	426	COCO_val2014_000000559462.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559462.jpg
+478321	221610	A living room with a floral area rug and a fire place.	427	640	COCO_val2014_000000221610.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000221610.jpg
+478396	450790	A pizza with pineapple and meat topping on a metal pizza pan.	480	640	COCO_val2014_000000450790.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450790.jpg
+478675	559483	A man holding a skateboard while wearing a yellow shirt.	612	612	COCO_val2014_000000559483.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559483.jpg
+478833	142934	A couple of people walking up the side of a snow covered mountain.	480	640	COCO_val2014_000000142934.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000142934.jpg
+478912	330845	A man riding a boat on top of a swimming pool.	640	427	COCO_val2014_000000330845.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000330845.jpg
+478948	361265	A woman walking across a street holding a camera.	427	640	COCO_val2014_000000361265.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000361265.jpg
+479040	301415	A woman holding up a carrot while wearing a scarf.	375	500	COCO_val2014_000000301415.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301415.jpg
+479151	167095	A teddy bear sitting on top of an arch.	640	427	COCO_val2014_000000167095.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000167095.jpg
+479208	449990	a view of a woman dressed nicely and holding a baseball bat and a man squating awaiting the pitch of a ball	493	500	COCO_val2014_000000449990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449990.jpg
+479279	274528	A blue truck with a large poster on it's side.	427	640	COCO_val2014_000000274528.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000274528.jpg
+479301	190391	a hot dog in a bun covered with mustard sitting in a small container	427	640	COCO_val2014_000000190391.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190391.jpg
+479371	136572	A couple of motorcycles parked next to each other.	480	640	COCO_val2014_000000136572.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000136572.jpg
+479477	285844	A man standing on top of a tennis court holding a racquet.	640	428	COCO_val2014_000000285844.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000285844.jpg
+479494	310611	A giraffe standing inside of a cage near a building.	500	375	COCO_val2014_000000310611.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000310611.jpg
+479596	242673	A highway filled with lots of traffic next to buildings.	480	640	COCO_val2014_000000242673.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242673.jpg
+479670	310369	A group of women sitting next to eachother.	422	640	COCO_val2014_000000310369.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000310369.jpg
+479674	189209	Someone is holding a pair of green handled scissors.	500	316	COCO_val2014_000000189209.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000189209.jpg
+480020	454642	Two people are riding motor scooters in a parking lot.	375	500	COCO_val2014_000000454642.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000454642.jpg
+480049	392506	A woman sitting behind bananas, cucumbers and a pan.	640	480	COCO_val2014_000000392506.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000392506.jpg
+480232	500471	A cat laying on top of a suitcase laying on the floor.	480	640	COCO_val2014_000000500471.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500471.jpg
+480358	158708	A bike sitting next to a cup filled with coffee.	427	640	COCO_val2014_000000158708.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158708.jpg
+480403	563696	A person sitting in front of a laptop computer on a  desk.	428	640	COCO_val2014_000000563696.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000563696.jpg
+480605	164602	A man and a woman standing in front of a mirror.	480	640	COCO_val2014_000000164602.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000164602.jpg
+480658	457817	A male tennis player in action on the court.	473	640	COCO_val2014_000000457817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000457817.jpg
+481145	294403	A woman under an umbrella on a rainy day	619	640	COCO_val2014_000000294403.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294403.jpg
+481205	49096	Woman sitting at table at outdoor venue with additional photos of food served for consumption.	612	612	COCO_val2014_000000049096.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000049096.jpg
+481273	328730	Two men enjoy glasses of beer at a gathering.	428	640	COCO_val2014_000000328730.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328730.jpg
+481466	444366	A wooden chair sitting in front of a window.	500	375	COCO_val2014_000000444366.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444366.jpg
+481671	150872	A man flying a kite on the beach.	640	360	COCO_val2014_000000150872.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150872.jpg
+481823	337551	Dogs playing at a birthday with one dog wearing a hat	480	640	COCO_val2014_000000337551.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000337551.jpg
+481901	546987	A women who is taking a picture of herself with a cell phone.	375	500	COCO_val2014_000000546987.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546987.jpg
+482045	303685	A street vendor with ties hanging from a rod	467	640	COCO_val2014_000000303685.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303685.jpg
+482102	284426	There are cattle on the bank by the river.	363	640	COCO_val2014_000000284426.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000284426.jpg
+482220	56713	Lone zebra as seen through narrow opening in enclosed area.	640	427	COCO_val2014_000000056713.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056713.jpg
+482305	218357	A very bright light shining down on some signs at night.	640	480	COCO_val2014_000000218357.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218357.jpg
+482316	550423	Two men in suits look like they are singing	426	640	COCO_val2014_000000550423.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550423.jpg
+482468	389273	A woman throwing a frisbee on a field.	410	640	COCO_val2014_000000389273.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000389273.jpg
+482495	542163	A man riding a wave on a surfboard.	458	640	COCO_val2014_000000542163.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000542163.jpg
+482995	254016	A row of doughnuts being passed through a fryer.	640	480	COCO_val2014_000000254016.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000254016.jpg
+483047	264554	Commode near white shower curtain in residential bathroom setting.	500	334	COCO_val2014_000000264554.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264554.jpg
+483206	192453	a kitchen with a brown oven and wooden cabinetry	640	361	COCO_val2014_000000192453.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192453.jpg
+483471	519838	A bicycle leaning up against a tree in a park.	300	400	COCO_val2014_000000519838.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519838.jpg
+483510	464682	a plate with a sandwich that has a toothpick in it.	426	640	COCO_val2014_000000464682.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464682.jpg
+483601	9262	A cart of finger foods including sandwiches with a group of teddy bears around the plates.	459	640	COCO_val2014_000000009262.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009262.jpg
+484920	132953	A man in black surfing in wild choppy water.	360	640	COCO_val2014_000000132953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132953.jpg
+484958	46245	a happy looking polar bear swiiming in water	425	640	COCO_val2014_000000046245.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000046245.jpg
+485423	146773	A dog is looking at a pink, flowered, multiple layered cake.	640	478	COCO_val2014_000000146773.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146773.jpg
+485655	52132	A room with an older model television in it.	375	500	COCO_val2014_000000052132.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000052132.jpg
+485663	484303	CARTOON DEPICTION OF A SNOW SKIER GOING DOWN THE SLOPES	343	640	COCO_val2014_000000484303.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000484303.jpg
+485901	90802	CLOSE UP SHOT OF A BOTTLE AND A WINE GLASS	640	480	COCO_val2014_000000090802.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000090802.jpg
+485930	464357	A shirtless man on a surfboard riding a wave	414	640	COCO_val2014_000000464357.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464357.jpg
+486032	327918	An oven with some pots of cooking food items.	360	640	COCO_val2014_000000327918.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000327918.jpg
+486168	325031	a balloon elephant sits in the middle of a park area	608	640	COCO_val2014_000000325031.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325031.jpg
+486231	460131	A big pile of assorted fresh produce close together.	424	640	COCO_val2014_000000460131.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460131.jpg
+486455	528069	A distant view from high up in the sky in a window.	500	379	COCO_val2014_000000528069.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000528069.jpg
+486705	394677	A person bent over sleeping in a chair next to others.	375	500	COCO_val2014_000000394677.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000394677.jpg
+486868	88183	Two guys sitting on couches in a living room	480	640	COCO_val2014_000000088183.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000088183.jpg
+486949	242946	A group of women sitting at a table with plates of food	480	640	COCO_val2014_000000242946.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242946.jpg
+487152	7961	Some is holding a bottle of wine next to a huge hot dog covered in chili.	640	437	COCO_val2014_000000007961.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000007961.jpg
+487173	25181	A train station with an awning is depicted with a train on the right platform.	427	640	COCO_val2014_000000025181.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025181.jpg
+487191	322796	A brown dog carrying a black frisbee in its mouth	375	500	COCO_val2014_000000322796.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000322796.jpg
+487544	493286	an old car in front of an old air plane	359	640	COCO_val2014_000000493286.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493286.jpg
+487660	402623	An adorable dog standing on top of a lawn chair next to a BBQ with little hot dogs grilling on it.	486	640	COCO_val2014_000000402623.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000402623.jpg
+487666	134213	A plaque mounted to the side of a building with a clock on it.	429	640	COCO_val2014_000000134213.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134213.jpg
+487686	213475	A street scene in the evening with a lone car and traffic light showing red.	640	426	COCO_val2014_000000213475.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213475.jpg
+487766	545867	Several lambs and sheep standing on hay and eating it.	480	640	COCO_val2014_000000545867.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000545867.jpg
+487995	211069	Two giraffe moving very quickly in the woods.	480	640	COCO_val2014_000000211069.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211069.jpg
+488064	474021	A couple of guys playing on the nintendo wii at a gathering.	375	500	COCO_val2014_000000474021.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474021.jpg
+488234	252716	A frisbee that is laying in the grass near a tree.	640	426	COCO_val2014_000000252716.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000252716.jpg
+488366	15140	A man and woman lie on the couch as a cat sits between her lap.	375	500	COCO_val2014_000000015140.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000015140.jpg
+488404	225299	A person flying through the air while riding down a snow slope.	486	640	COCO_val2014_000000225299.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000225299.jpg
+488533	174511	A computer monitor sitting on top of a wooden desk.	480	640	COCO_val2014_000000174511.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174511.jpg
+488764	402115	The young man is eating a hamburger with a pizza folded into it.	480	640	COCO_val2014_000000402115.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000402115.jpg
+488839	393478	The green and yellow train is rounding the bend of a track.	500	375	COCO_val2014_000000393478.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000393478.jpg
+488888	64409	A pile of bananas sitting on top of a white table.	411	640	COCO_val2014_000000064409.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064409.jpg
+488986	540483	Elephants walking in a field with a lot of grass.	425	640	COCO_val2014_000000540483.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540483.jpg
+489181	184121	Four male subjects, young and old, that are enjoying winter sports.	518	640	COCO_val2014_000000184121.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000184121.jpg
+489282	125707	A man preparing food in a restaurant kitchen.	425	640	COCO_val2014_000000125707.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125707.jpg
+489335	93730	a tennis player on a court wit ha racket	427	640	COCO_val2014_000000093730.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000093730.jpg
+489410	20779	a person riding on a horse drawn carriage	480	640	COCO_val2014_000000020779.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000020779.jpg
+489494	271917	A bus approaching an intersection at a green light.	500	375	COCO_val2014_000000271917.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000271917.jpg
+489710	158882	A person riding a wave on top of a surfboard.	400	640	COCO_val2014_000000158882.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158882.jpg
+489934	446850	Two people who are playing tennis on a tennis court.	429	640	COCO_val2014_000000446850.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000446850.jpg
+490036	311746	A field filled with purple flowers and green plants.	334	500	COCO_val2014_000000311746.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000311746.jpg
+490476	42661	A man riding a snowboard down a snow covered slope.	640	426	COCO_val2014_000000042661.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000042661.jpg
+490715	268247	A vase with a flower sitting on top of a wooden table.	640	427	COCO_val2014_000000268247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268247.jpg
+490741	410735	Various different pictures of food in a bowl.	512	640	COCO_val2014_000000410735.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000410735.jpg
+490748	315899	A man that is standing up holding a cellphone.	640	426	COCO_val2014_000000315899.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315899.jpg
+490788	192905	A group of zebras that are in the grass.	425	640	COCO_val2014_000000192905.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192905.jpg
+491450	528033	A brown chair with two stuffed animals on top of it.	482	500	COCO_val2014_000000528033.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000528033.jpg
+491520	315824	A bear is sitting in the grass with a cub.	332	500	COCO_val2014_000000315824.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315824.jpg
+491753	92173	A black and white cat laying on top of a desk.	478	640	COCO_val2014_000000092173.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092173.jpg
+491766	88970	A man serving a tennis ball on top of a tennis court.	500	333	COCO_val2014_000000088970.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000088970.jpg
+492361	249301	Two guys are riding a motorcycle down the street.	413	640	COCO_val2014_000000249301.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000249301.jpg
+492516	1138	A living room has a couch, chair, fireplace, windows, and a potted plant.	375	500	COCO_val2014_000000001138.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001138.jpg
+492540	381792	A bath tub sitting next to a white toilet.	375	500	COCO_val2014_000000381792.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000381792.jpg
+492617	498274	Large sized public transit bus with two levels.	480	640	COCO_val2014_000000498274.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498274.jpg
+492730	83172	A woman holding a racquet on top of a tennis court.	427	640	COCO_val2014_000000083172.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000083172.jpg
+493029	315790	a room with a computer monitor and a black cat on a chair	480	640	COCO_val2014_000000315790.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315790.jpg
+493704	307531	a dog running towards a frisbee in the air	332	500	COCO_val2014_000000307531.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000307531.jpg
+493988	443278	Three zebras roam a field together side by side.	480	640	COCO_val2014_000000443278.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000443278.jpg
+494572	24805	A panda bear with its hand to its mouth.	640	427	COCO_val2014_000000024805.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000024805.jpg
+494622	472046	A kitchen with a stove, sink, table, chairs and couches.	425	640	COCO_val2014_000000472046.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472046.jpg
+494641	107105	"A white motorcycle is next to a store front that says ""Macau welcomes you""."	480	640	COCO_val2014_000000107105.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107105.jpg
+494643	528251	A black and brown dog sits on a white rug with a toy donut in front of him.	480	640	COCO_val2014_000000528251.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000528251.jpg
+494736	482507	A dog carrying a frisbee in its mouth on a field.	427	640	COCO_val2014_000000482507.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000482507.jpg
+494935	266099	A large commercial airplane parked on the runway	480	640	COCO_val2014_000000266099.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000266099.jpg
+495035	70332	A man with an evil smirk on his face wearing a vest.	640	359	COCO_val2014_000000070332.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000070332.jpg
+495120	186009	A brown sheep standing on top of a lush green field.	480	640	COCO_val2014_000000186009.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000186009.jpg
+495277	185397	A person holding a doughnut in their left hand.	399	500	COCO_val2014_000000185397.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000185397.jpg
+495458	78979	That aircraft is for display not for riding.	468	640	COCO_val2014_000000078979.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078979.jpg
+495467	193162	a man on a three wheeler following behind a two dogs and a cow	428	640	COCO_val2014_000000193162.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000193162.jpg
+495646	303253	A large brown dog laying on top of a raft.	360	640	COCO_val2014_000000303253.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303253.jpg
+495707	138095	a hand feeding food to two giraffes behind a fence	492	640	COCO_val2014_000000138095.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000138095.jpg
+495844	41200	THERE ARE SOME THINGS THAT ARE IN A TRAY	640	480	COCO_val2014_000000041200.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000041200.jpg
+495970	572907	A man standing on a tennis court holding a racquet.	640	476	COCO_val2014_000000572907.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000572907.jpg
+496021	385359	THERE IS A BOY IN  A BLUE SWEATER LOOKING AT SOMETHING	374	500	COCO_val2014_000000385359.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000385359.jpg
+496071	231534	A large jetliner sitting on top of an airport tarmac.	427	640	COCO_val2014_000000231534.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231534.jpg
+496549	51167	There is a blue and yellow transit bus stopped to pick up passengers.	480	640	COCO_val2014_000000051167.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000051167.jpg
+496749	574135	A MAN IS PLAYING BASE BALL ON THE FIELD	640	427	COCO_val2014_000000574135.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000574135.jpg
+497038	182424	A guy looking down at a big knife he is holding.	500	333	COCO_val2014_000000182424.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182424.jpg
+497117	427870	There is a skateboarder at the top of a small ramp.	640	429	COCO_val2014_000000427870.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427870.jpg
+497315	439015	This is a close up of a zebra's face.	426	640	COCO_val2014_000000439015.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000439015.jpg
+497317	353149	A very cute cat sitting on a park bench.	480	640	COCO_val2014_000000353149.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000353149.jpg
+497648	224862	A white bus sitting on top of a grass field.	298	451	COCO_val2014_000000224862.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224862.jpg
+497665	461007	A wooden bench sitting up against a lush green park.	500	375	COCO_val2014_000000461007.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000461007.jpg
+497701	333630	THERE IS A MAN THAT IS PLAYING MUSICAL INSTURMENTS	500	375	COCO_val2014_000000333630.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333630.jpg
+497728	15660	there is a man on a surf board in the ocean	348	640	COCO_val2014_000000015660.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000015660.jpg
+497876	14151	skiier jumping with skis split in a v-shape over the olympic circles	500	431	COCO_val2014_000000014151.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000014151.jpg
+498018	391620	A WOMAN IS STANDIGN NEXT TO A WHITE HORSE	588	640	COCO_val2014_000000391620.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000391620.jpg
+498022	554711	A woman laying in bed while using a laptop computer.	640	480	COCO_val2014_000000554711.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000554711.jpg
+498193	345869	THERE IS A TOWER CLOCK THAT IS ON DISPLAY	640	508	COCO_val2014_000000345869.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345869.jpg
+498332	70987	A woman with beautiful breast sitting next to another woman and pizzas.	480	640	COCO_val2014_000000070987.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000070987.jpg
+498513	155743	A zebra in captivity grazing in its exhibit.	479	640	COCO_val2014_000000155743.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000155743.jpg
+498906	426166	A light blue bicycle chained to a pole on the sidewalk in front of a red building.	332	500	COCO_val2014_000000426166.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000426166.jpg
+499074	177539	A man in striped shorts leaning against a board on a beach.	427	640	COCO_val2014_000000177539.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177539.jpg
+499157	81035	A woman with a red sweater, seated and holding a small dog over a railing while looking into a body of water.	480	640	COCO_val2014_000000081035.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000081035.jpg
+500025	355263	Someone has hung those stuffed Animals from the top of the bridge.	480	640	COCO_val2014_000000355263.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000355263.jpg
+500284	468501	A group of women playing video games together	375	500	COCO_val2014_000000468501.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000468501.jpg
+500320	61316	a kitchen with a nasty dirty kitchen with a light beam through the window	463	640	COCO_val2014_000000061316.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000061316.jpg
+500661	426948	A giraffe is crossing a dirt road and walking away from the trees.	568	640	COCO_val2014_000000426948.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000426948.jpg
+500765	283743	A display of chocolate donuts and some pink and white cookies.	443	640	COCO_val2014_000000283743.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000283743.jpg
+500833	475064	The front of a restaurant with someone holding up food	427	640	COCO_val2014_000000475064.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000475064.jpg
+501071	417074	The parade features these people riding or even standing atop elephants	424	640	COCO_val2014_000000417074.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000417074.jpg
+501248	489909	A crate with cucumbers, oranges and a banana in it.	429	640	COCO_val2014_000000489909.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000489909.jpg
+501344	438220	A silver, blue, and orange train is moving past a platform at the train station.	427	640	COCO_val2014_000000438220.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000438220.jpg
+501552	17409	A place mat is displaying an ornate glass along with a bottle of liquor.	640	425	COCO_val2014_000000017409.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000017409.jpg
+501731	1739	A truck that is sitting out in the woods.	453	640	COCO_val2014_000000001739.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001739.jpg
+501821	154155	Several pieces of fruit including:  a pineapple, apples and pears.	640	480	COCO_val2014_000000154155.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000154155.jpg
+501986	413959	A horse that is standing with a cart near birds.	398	640	COCO_val2014_000000413959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000413959.jpg
+502221	289486	a number of giraffes in a field near trees	314	640	COCO_val2014_000000289486.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289486.jpg
+502292	404027	A man holding a tennis racquet while on a tennis court.	427	640	COCO_val2014_000000404027.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000404027.jpg
+502464	318523	A display in a doughnut shop filled with doughnuts.	480	640	COCO_val2014_000000318523.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000318523.jpg
+502465	31773	Two computers that are sitting on a desk.	480	640	COCO_val2014_000000031773.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000031773.jpg
+502491	212646	A white stove top oven sitting inside of a kitchen.	448	640	COCO_val2014_000000212646.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000212646.jpg
+502534	279221	A yellow furniture moving truck parked next to a traffic light.	426	640	COCO_val2014_000000279221.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279221.jpg
+502541	485129	A man that is on a surfboard in the water.	640	640	COCO_val2014_000000485129.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000485129.jpg
+502560	322895	A living room that has a bunch of different couches.	480	640	COCO_val2014_000000322895.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000322895.jpg
+502637	229000	two people standing very close to a microwave	375	500	COCO_val2014_000000229000.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000229000.jpg
+502842	200739	A photo with many buildings off of a street with trucks parked in front.	404	500	COCO_val2014_000000200739.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000200739.jpg
+502910	115060	A golden clock in a case on display.	478	640	COCO_val2014_000000115060.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000115060.jpg
+502938	522175	A couple of horses standing next to each other on a field.	427	640	COCO_val2014_000000522175.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000522175.jpg
+503101	542248	A giraffe standing next to a forest on top of a field.	480	640	COCO_val2014_000000542248.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000542248.jpg
+503329	501005	derek jeter swings his bat at a baseball	396	640	COCO_val2014_000000501005.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000501005.jpg
+503403	18563	A woman standing in front of an oven near another woman.	512	528	COCO_val2014_000000018563.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018563.jpg
+503412	143068	Kites being used by people on a beach.	213	640	COCO_val2014_000000143068.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143068.jpg
+503660	236370	A crowd of people sitting in a room on to of a wooden floor.	480	640	COCO_val2014_000000236370.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000236370.jpg
+503680	455597	a person in a kitchen with a stove some pots and pans	446	640	COCO_val2014_000000455597.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000455597.jpg
+503741	11887	A motorcycle with a trailer parked in a spot	480	640	COCO_val2014_000000011887.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000011887.jpg
+503772	277239	A classroom that is empty and has tables and chairs.	480	640	COCO_val2014_000000277239.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000277239.jpg
+503820	288161	a kitchen with an electonic device above the sink	334	640	COCO_val2014_000000288161.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000288161.jpg
+503932	143974	A cow walking across a river as a boat approaches on the water.	418	640	COCO_val2014_000000143974.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143974.jpg
+504000	76257	a man on a motorcycle driving down a track	427	640	COCO_val2014_000000076257.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000076257.jpg
+504175	572462	A group of people standing inside of a moving tram.	612	612	COCO_val2014_000000572462.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000572462.jpg
+504249	549936	A herd of cattle standing on top of a dirt and grass field.	389	640	COCO_val2014_000000549936.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000549936.jpg
+504286	28994	A man riding a surfboard on top of a wave.	477	640	COCO_val2014_000000028994.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028994.jpg
+504415	553072	A fleet of red and blue planes with a Canadien logo.	195	640	COCO_val2014_000000553072.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000553072.jpg
+504773	456679	A man riding on the back of an elephant in a  jungle.	427	640	COCO_val2014_000000456679.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000456679.jpg
+504842	208778	A CGI man sitting on top of a CGI hospital bed.	286	500	COCO_val2014_000000208778.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208778.jpg
+504974	365745	A black bomb squad truck driving down a  road.	480	640	COCO_val2014_000000365745.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365745.jpg
+505067	19167	Oranges and a leaf are in a silver colander.	499	432	COCO_val2014_000000019167.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000019167.jpg
+505410	472472	A man standing on a beach holding a white frisbee.	428	640	COCO_val2014_000000472472.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472472.jpg
+506104	575174	A kid is standing on a tennis court with a racket.	427	640	COCO_val2014_000000575174.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575174.jpg
+506227	485390	A herd of sheep grazing in a pasture.	424	640	COCO_val2014_000000485390.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000485390.jpg
+506239	16410	Yellow flowers in a vase on a windowsill.	640	480	COCO_val2014_000000016410.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000016410.jpg
+506525	516178	A large white building with a clock in it is surrounded by palm trees and red flags.	480	640	COCO_val2014_000000516178.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516178.jpg
+506640	192554	A sink, soap, toilet and a mirror	480	640	COCO_val2014_000000192554.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192554.jpg
+506735	539971	A black and white cat slightly opens its eyes on a red blanket in a bed.	480	640	COCO_val2014_000000539971.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000539971.jpg
+506779	341017	A boy sitting on top of a truck with a bunch of goats.	480	640	COCO_val2014_000000341017.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341017.jpg
+506812	279388	A giraffe is eating from a leafy tree.	480	640	COCO_val2014_000000279388.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279388.jpg
+506915	402903	A reflection on the exterior window of a vehicle.	333	500	COCO_val2014_000000402903.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000402903.jpg
+506942	405882	A cake sits on a table, ready to be cut.	390	500	COCO_val2014_000000405882.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000405882.jpg
+506943	235093	A kite flies high in the air near some docks.	480	640	COCO_val2014_000000235093.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000235093.jpg
+506970	233521	A woman sitting down holding a Nintendo Wii controller.	427	640	COCO_val2014_000000233521.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000233521.jpg
+507664	247216	A baseball player is pitching a ball.	353	500	COCO_val2014_000000247216.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000247216.jpg
+508063	12927	A man riding a board on top of a wave in the ocean.	425	640	COCO_val2014_000000012927.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000012927.jpg
+508112	127749	A cat laying its head against a teddy bear.	480	640	COCO_val2014_000000127749.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000127749.jpg
+508222	38190	A pan with a pastry covered in meat and veggies.	480	640	COCO_val2014_000000038190.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038190.jpg
+508317	464814	a living room filled with couches, chairs, tv, and windows	425	640	COCO_val2014_000000464814.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464814.jpg
+508339	272953	A brown horse grazing on a lush green field.	484	640	COCO_val2014_000000272953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000272953.jpg
+508345	98683	A lush green field with people flying kites over it.	480	640	COCO_val2014_000000098683.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000098683.jpg
+508655	68300	A plate of spaghetti with red sauce and broccoli.	480	640	COCO_val2014_000000068300.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068300.jpg
+508670	368294	A woman making cigars in a factory with a pink towel on her head.	426	640	COCO_val2014_000000368294.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000368294.jpg
+508879	180662	A young child sitting in front of a bowl of dessert holding up two ice cream bars.	480	640	COCO_val2014_000000180662.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000180662.jpg
+508897	475248	A metal counter top with a pan filled with food.	480	640	COCO_val2014_000000475248.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000475248.jpg
+509045	235836	A boy playing baseball about to hit a ball.	640	427	COCO_val2014_000000235836.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000235836.jpg
+509306	203345	A couple of people standing next to a food cart filled with smoke.	500	500	COCO_val2014_000000203345.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203345.jpg
+509349	341309	A woman wearing a white sweater standing next to a man in a jacket.	640	583	COCO_val2014_000000341309.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341309.jpg
+509394	146910	A bald man holding a hot dog in a white wrapper.	640	592	COCO_val2014_000000146910.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146910.jpg
+509400	67995	A cat laying on top of a blanket near a wall.	425	640	COCO_val2014_000000067995.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067995.jpg
+509489	206958	a room showing a toilet and a sink	640	480	COCO_val2014_000000206958.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000206958.jpg
+509596	408818	A red caboose with a man hanging off the back of it.	426	640	COCO_val2014_000000408818.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000408818.jpg
+509820	105655	A table filled with some tasty looking pizza.	612	612	COCO_val2014_000000105655.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000105655.jpg
+510003	87567	A kitchen table is lined with cooking materials.	428	640	COCO_val2014_000000087567.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087567.jpg
+510271	86135	A bike with tanks on it sitting next to cars.	480	640	COCO_val2014_000000086135.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000086135.jpg
+510286	411184	A very large bridge spanning over the width of a river.	480	640	COCO_val2014_000000411184.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000411184.jpg
+510445	515421	A woman with nice breast holding a book while sitting next to a dog.	428	640	COCO_val2014_000000515421.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000515421.jpg
+511117	298440	A large jetliner flying through a blue sky with two engines.	640	640	COCO_val2014_000000298440.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000298440.jpg
+511334	6810	A black and white picture of a building with a clock outside.	427	640	COCO_val2014_000000006810.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000006810.jpg
+511344	345507	A large amount of motorcycles are parked by a sidewalk.	426	640	COCO_val2014_000000345507.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345507.jpg
+511517	433845	A single surfer is riding a surfboard on a wave.	480	640	COCO_val2014_000000433845.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000433845.jpg
+511561	7506	A giraffe spreads its legs in order to get its head closer to the ground.	640	425	COCO_val2014_000000007506.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000007506.jpg
+511874	139917	A boy playing a WII game at Christmas time.	479	640	COCO_val2014_000000139917.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000139917.jpg
+512483	56504	A group of people that are holding onto sheep.	427	640	COCO_val2014_000000056504.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056504.jpg
+512494	315792	A big giraffe up close looking at you.	400	500	COCO_val2014_000000315792.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315792.jpg
+512512	292428	A man's feet resting on a skateboard	428	640	COCO_val2014_000000292428.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000292428.jpg
+512631	519112	An animal eating something on the ground	640	426	COCO_val2014_000000519112.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519112.jpg
+512960	133631	The old, adult elephant stands near a wire fence.	640	425	COCO_val2014_000000133631.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133631.jpg
+513493	537301	two colorful parrots sitting on some tree branches while one chews on it	375	500	COCO_val2014_000000537301.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000537301.jpg
+513504	354098	This toilet has a lot of wadded up tissue paper in it.	375	500	COCO_val2014_000000354098.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000354098.jpg
+513570	129285	A number of trains sitting in a warehouse	480	640	COCO_val2014_000000129285.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129285.jpg
+513630	142483	A few pieces of pizza sitting on a plate	440	640	COCO_val2014_000000142483.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000142483.jpg
+513680	67587	Three giraffes are sitting on the ground.	333	500	COCO_val2014_000000067587.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067587.jpg
+513694	519460	a plate with a sandwich, salad, and some soup sitting on a table	480	640	COCO_val2014_000000519460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519460.jpg
+513777	110330	a crowd of people getting on a  tour bus	427	640	COCO_val2014_000000110330.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000110330.jpg
+514161	152360	A dark night at a market with bananas hanging from a stand.	433	640	COCO_val2014_000000152360.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152360.jpg
+514329	171464	A parking lot with a car, three buses, and a semi.	480	640	COCO_val2014_000000171464.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000171464.jpg
+514339	190115	the man is sitting with a salad in front of him and smiling.	500	500	COCO_val2014_000000190115.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190115.jpg
+514340	326572	the plate has a lite candle and it says happy birthday on it	640	478	COCO_val2014_000000326572.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000326572.jpg
+514347	567053	a plane is parked on a asphalt ready to fly	427	640	COCO_val2014_000000567053.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000567053.jpg
+514508	164759	a living room with a long couch in it	480	535	COCO_val2014_000000164759.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000164759.jpg
+514906	189376	a room filled desks and computers a man and woman sitting listening and anther man with a camera	480	640	COCO_val2014_000000189376.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000189376.jpg
+514934	427986	A corgi dog running on a lawn to catch a Frisbee	640	538	COCO_val2014_000000427986.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427986.jpg
+514974	395745	A sandwich with meat, vegetables and dressing is sitting on a plate.	480	640	COCO_val2014_000000395745.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000395745.jpg
+515164	163585	The bench on the side of the street is empty.	427	640	COCO_val2014_000000163585.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000163585.jpg
+515245	389869	a person walks along a beach as some bird play in the water	456	640	COCO_val2014_000000389869.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000389869.jpg
+515381	155714	A pizza with sauce, spinach and cheese on a pan.	640	480	COCO_val2014_000000155714.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000155714.jpg
+515585	335578	A tennis player hits a ball in an indoor area.	441	640	COCO_val2014_000000335578.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335578.jpg
+515800	209468	A woman in pajamas using her laptop on the stove top in the kitchen	480	640	COCO_val2014_000000209468.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209468.jpg
+515882	421028	A gray cat lying on a carpet in a room	523	640	COCO_val2014_000000421028.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421028.jpg
+516110	549683	A man trying to snowboard in the dark.	640	508	COCO_val2014_000000549683.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000549683.jpg
+516312	156232	Two people in yellow vests and helmets riding horses.	581	640	COCO_val2014_000000156232.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000156232.jpg
+516891	315660	a tennis player pacing on the court waiting for a ball	480	640	COCO_val2014_000000315660.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315660.jpg
+517379	272688	The woman and baby are watching the elephant.	427	640	COCO_val2014_000000272688.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000272688.jpg
+517499	531499	a big empty kitchen with only a refrigerator stove and a sink	427	640	COCO_val2014_000000531499.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000531499.jpg
+517615	406189	This zoo exhibit is home to a large group of girrafes	480	640	COCO_val2014_000000406189.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406189.jpg
+517652	214124	a close up view of a pie with strussel toppings	480	640	COCO_val2014_000000214124.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214124.jpg
+517722	278550	a dog rests in a basket on a bike	308	311	COCO_val2014_000000278550.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000278550.jpg
+517738	486118	Some girls who are standing together, two of them have umbrellas.	427	640	COCO_val2014_000000486118.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000486118.jpg
+517935	78313	The shelves above the cabinet have porcelain dishes on them.	426	640	COCO_val2014_000000078313.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078313.jpg
+517952	272958	A man on a wake board that is up in the air.	429	640	COCO_val2014_000000272958.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000272958.jpg
+518169	253740	A stainless steel stove that is in a kitchen.	375	500	COCO_val2014_000000253740.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000253740.jpg
+518284	581022	A group of men that are sitting on a couch.	360	640	COCO_val2014_000000581022.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000581022.jpg
+518355	460461	Four guys with skate boards sitting on a park bench.	429	640	COCO_val2014_000000460461.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460461.jpg
+518416	499716	a herd of zebras sniff around a caged area	409	640	COCO_val2014_000000499716.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000499716.jpg
+518518	330053	a lady observing a woman carrying two large bags and a man doing karate	428	640	COCO_val2014_000000330053.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000330053.jpg
+518574	329337	A stop sign above other signs in front of a house.	640	480	COCO_val2014_000000329337.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000329337.jpg
+518680	575465	A group of men standing on top of a baseball field playing a game of baseball.	640	426	COCO_val2014_000000575465.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575465.jpg
+518686	92332	A man holding and pointing to a jumbo remote control in clear package in a store aisle.	480	640	COCO_val2014_000000092332.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092332.jpg
+518742	362005	A man flying into the air on top of a skateboard ramp.	425	640	COCO_val2014_000000362005.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000362005.jpg
+518940	18239	Several horses are seen milling about this grassy area.	356	640	COCO_val2014_000000018239.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018239.jpg
+518957	99734	Men standing in their boats that are in the water.	335	500	COCO_val2014_000000099734.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099734.jpg
+519075	406647	A motorcycle is parked amongst the cars at a store.	375	500	COCO_val2014_000000406647.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406647.jpg
+519140	246512	A black dog laying on top of a puddle of mud.	640	640	COCO_val2014_000000246512.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000246512.jpg
+519291	248678	A banana peal on a piece of board.	426	640	COCO_val2014_000000248678.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248678.jpg
+519372	20541	A crow approaches a cardboard pizza box on the ground and opens it	500	329	COCO_val2014_000000020541.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000020541.jpg
+519420	479762	Three adult zebra are walking through the dirt path.	480	640	COCO_val2014_000000479762.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000479762.jpg
+519422	406332	A small bear holding a larger bear around the neck while standing on a rock in a river.	428	640	COCO_val2014_000000406332.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406332.jpg
+519450	152582	There are some colorful contraptions sitting in the sand on a beach	318	640	COCO_val2014_000000152582.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152582.jpg
+519454	561650	Baseball player standing in front of a fence holding a bat.	443	640	COCO_val2014_000000561650.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000561650.jpg
+519596	457580	Several people sitting around a table that has papers and name cards on it.	480	640	COCO_val2014_000000457580.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000457580.jpg
+519910	370697	A clean sink is in the middle of the counter.	480	640	COCO_val2014_000000370697.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000370697.jpg
+519939	430856	A herd of sheep standing on top of a dirt field.	538	640	COCO_val2014_000000430856.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000430856.jpg
+519945	565374	An old man sitting down playing an accordion next to parked bikes.	360	640	COCO_val2014_000000565374.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565374.jpg
+520291	110683	Two big giraffes walking in a grassy area with a baby giraffe.	427	640	COCO_val2014_000000110683.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000110683.jpg
+520354	213650	the corner of a roof with trees in the background	480	360	COCO_val2014_000000213650.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213650.jpg
+520666	294247	A passenger jet in flight in a clear blue sky.	427	640	COCO_val2014_000000294247.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294247.jpg
+520781	172648	a man leaning over the back of a truck in front of buildings	480	640	COCO_val2014_000000172648.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000172648.jpg
+520855	145020	Donald Duck talking to Minney Mouse at Disney World at Christmas.	361	640	COCO_val2014_000000145020.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000145020.jpg
+520867	222757	A house filled with windows and a platform.	480	640	COCO_val2014_000000222757.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000222757.jpg
+520913	402922	vendors standing at fruit stands at outdoor market.	375	500	COCO_val2014_000000402922.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000402922.jpg
+520987	400803	A man standing on top of an orange boat on a river.	427	640	COCO_val2014_000000400803.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000400803.jpg
+521386	239843	Two street signs on top of a stop sign.	375	500	COCO_val2014_000000239843.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000239843.jpg
+521443	551633	a little girl playing with a soccer ball	640	424	COCO_val2014_000000551633.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000551633.jpg
+521444	236182	a sidewalk with a fence, street signs, and a lot of trees along it	640	480	COCO_val2014_000000236182.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000236182.jpg
+521561	126631	A tennis player with racket and a bag with a baby	480	640	COCO_val2014_000000126631.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000126631.jpg
+521615	321973	A meat wrapped, pineapple and banana on a table	480	640	COCO_val2014_000000321973.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000321973.jpg
+521769	150875	A large stone bench sitting next to rose bushes.	422	640	COCO_val2014_000000150875.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150875.jpg
+521858	116023	A tennis player holding a racket and ball	427	640	COCO_val2014_000000116023.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116023.jpg
+521932	120747	a living room with hardwood foors , a tv and table	428	640	COCO_val2014_000000120747.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120747.jpg
+522070	120918	A group of elephants standing next to each other .	640	426	COCO_val2014_000000120918.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120918.jpg
+522141	456046	A train covered in graffiti sitting on top of train tracks.	480	640	COCO_val2014_000000456046.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000456046.jpg
+522325	289423	Two younger people playing a game of frisbee.	426	640	COCO_val2014_000000289423.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289423.jpg
+522551	438851	A bag is shown with random small objects lined up.	640	457	COCO_val2014_000000438851.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000438851.jpg
+522552	262396	A cat is sitting on top of a computer chair which is covered in hair.	640	534	COCO_val2014_000000262396.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000262396.jpg
+522764	452084	A lunch bowl with bread, sausage, tomato, and egg.	639	640	COCO_val2014_000000452084.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000452084.jpg
+522877	112066	A sexy young woman holding a tennis racquet on a tennis court.	427	640	COCO_val2014_000000112066.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000112066.jpg
+523257	562419	Large ornamental white clock sitting on a building.	427	640	COCO_val2014_000000562419.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000562419.jpg
+523394	333240	A cheese covered pizza sitting on top of a counter.	480	640	COCO_val2014_000000333240.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333240.jpg
+523761	295124	A group of people riding skis across a snow covered slope.	311	640	COCO_val2014_000000295124.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295124.jpg
+523933	579453	Beautiful yellow flowers with orange centers in a blue vase.	500	336	COCO_val2014_000000579453.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000579453.jpg
+523934	200938	A tall clock tower with a wind vane on top.	640	427	COCO_val2014_000000200938.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000200938.jpg
+523935	23666	A toilet and bathtub in a dark room with abnormal plumbing.	640	480	COCO_val2014_000000023666.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000023666.jpg
+524264	201685	A young man is surfing on an ocean wave.	427	640	COCO_val2014_000000201685.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000201685.jpg
+524303	220266	a grey cat sitting by a round mirror	640	451	COCO_val2014_000000220266.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000220266.jpg
+524616	208050	a group of people that are standing up drinking something	512	640	COCO_val2014_000000208050.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208050.jpg
+524689	82821	Paddle boats parked at the edge of a river or lake	427	640	COCO_val2014_000000082821.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000082821.jpg
+525096	575817	A toilet that is covered in some filth, and some filth on the ground.	621	640	COCO_val2014_000000575817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575817.jpg
+525121	216863	A group of skateboarders on top of a ramp.	427	640	COCO_val2014_000000216863.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216863.jpg
+525393	343068	a bed that is in a room with brick walls	480	640	COCO_val2014_000000343068.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000343068.jpg
+525537	540159	A very nice looking living room with a couch.	480	640	COCO_val2014_000000540159.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540159.jpg
+525650	94055	Some fancy dressed folks on some horses in a crowd.	400	640	COCO_val2014_000000094055.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000094055.jpg
+525958	111347	A toddler smiles in front of a toy train set on a table.	383	640	COCO_val2014_000000111347.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111347.jpg
+525986	398236	A big very tasty looking sandwich filled with some meat.	480	640	COCO_val2014_000000398236.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398236.jpg
+526205	455720	Some people gathered at a very big table with a big cake.	366	640	COCO_val2014_000000455720.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000455720.jpg
+526434	99540	a lift truck sits parked next to a pine tree	427	640	COCO_val2014_000000099540.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099540.jpg
+526999	510955	A very pretty lady with a oven mitt in a kitchen.	640	428	COCO_val2014_000000510955.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000510955.jpg
+527075	117788	A kitchen with lots of objects on the counter.	426	640	COCO_val2014_000000117788.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000117788.jpg
+527131	136181	A white bed with a turquoise blanket, with a two piece painting hanging above it.	366	640	COCO_val2014_000000136181.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000136181.jpg
+527241	353510	A couple of older folks standing in a kitchen.	480	640	COCO_val2014_000000353510.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000353510.jpg
+527303	532129	A cheese pizza sitting on top of white paper and a large plate.	427	640	COCO_val2014_000000532129.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532129.jpg
+527398	22929	A sepia toned photo of a baby snuggling with a giant teddy bear.	640	541	COCO_val2014_000000022929.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000022929.jpg
+527435	570594	A man walking with two elephants out side.	400	640	COCO_val2014_000000570594.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000570594.jpg
+527821	94619	A young man skateboarding while listening to music.	640	640	COCO_val2014_000000094619.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000094619.jpg
+527974	457078	A room with a desktop computer and a picture of a mummy.	480	640	COCO_val2014_000000457078.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000457078.jpg
+528359	415964	A large red stop sign on a metal pole.	333	500	COCO_val2014_000000415964.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415964.jpg
+528461	64865	A couple of zebra standing on top of a grass covered field.	429	640	COCO_val2014_000000064865.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064865.jpg
+528671	365866	A toilet and container in a small room.	480	640	COCO_val2014_000000365866.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365866.jpg
+528782	182236	A laptop computer is open with a cup of coffee next to it.	480	640	COCO_val2014_000000182236.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182236.jpg
+528861	65981	A large train is coming down the track between a beautiful mountain.	480	640	COCO_val2014_000000065981.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000065981.jpg
+529107	68291	A herd of sheep standing below very tall buildings.	640	480	COCO_val2014_000000068291.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068291.jpg
+529169	480400	A laptop and keyboard sitting on a cluttered desk.	480	640	COCO_val2014_000000480400.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000480400.jpg
+529272	245162	A road with cars and buses near a building.	514	640	COCO_val2014_000000245162.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000245162.jpg
+529772	224329	Two trains traveling down tracks with black smoke pouring out of the top of them.	383	640	COCO_val2014_000000224329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224329.jpg
+530111	483348	A bathroom stall that is made of wood.	332	500	COCO_val2014_000000483348.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000483348.jpg
+530528	325027	A hand is lifting up the top bread of a sub sandwich.	427	640	COCO_val2014_000000325027.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325027.jpg
+530531	318386	A plate holds dinner including green beans and potatoes.	480	500	COCO_val2014_000000318386.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000318386.jpg
+530598	326995	A couple of ducks standing on the side of a road.	480	640	COCO_val2014_000000326995.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000326995.jpg
+530614	125412	Three brown bears walking and standing near a snowy mountain.	271	640	COCO_val2014_000000125412.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125412.jpg
+530628	110721	a large plane is docked at the airport and connected	418	640	COCO_val2014_000000110721.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000110721.jpg
+530640	22488	A bird perched on top of a leafless tree under a blue sky.	400	640	COCO_val2014_000000022488.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000022488.jpg
+530783	302298	A large and small giraffe standing in a grassy field.	640	425	COCO_val2014_000000302298.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000302298.jpg
+530821	240175	A white bath tub sitting next to a sink in a bathroom.	480	640	COCO_val2014_000000240175.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000240175.jpg
+530867	215781	A bird sitting on a branch of plants.	426	640	COCO_val2014_000000215781.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000215781.jpg
+530885	221190	A large building that has a train passing by it.	480	640	COCO_val2014_000000221190.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000221190.jpg
+531204	322352	A dining room with some plants are seen.	375	500	COCO_val2014_000000322352.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000322352.jpg
+531250	100909	A baseball player holding a bat on top of a field.	640	472	COCO_val2014_000000100909.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000100909.jpg
+531382	326781	A white toilet and a sink in a room.	640	480	COCO_val2014_000000326781.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000326781.jpg
+531759	462466	a lady lighting candles on a birthday cake	425	640	COCO_val2014_000000462466.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000462466.jpg
+531834	1063	A giraffe walking across a lush green field.	640	480	COCO_val2014_000000001063.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001063.jpg
+531886	474054	A man flying through the air while riding a skateboard.	640	480	COCO_val2014_000000474054.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474054.jpg
+532007	276731	A street sign hanging from the side of a metal pole.	428	640	COCO_val2014_000000276731.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000276731.jpg
+532215	205906	A nightstand with a couole phones and watches and clocks on it.	425	640	COCO_val2014_000000205906.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205906.jpg
+532233	93405	A clock mounted on the side of a building.	427	640	COCO_val2014_000000093405.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000093405.jpg
+532285	435205	A pregnant with writing on her stomach while holding a brown teddy bear.	333	500	COCO_val2014_000000435205.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000435205.jpg
+532355	522713	a bench sitting in the grass facing the water and boats	480	640	COCO_val2014_000000522713.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000522713.jpg
+532394	387042	A man swinging a tennis racquet on top of a tennis court.	640	427	COCO_val2014_000000387042.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000387042.jpg
+532560	491478	A sandwich sitting on top of a white plate.	480	640	COCO_val2014_000000491478.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491478.jpg
+532722	513789	A bed sitting in a bedroom under a window.	640	518	COCO_val2014_000000513789.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000513789.jpg
+532821	449676	A person standing on a beach flying a kite on the beach.	640	428	COCO_val2014_000000449676.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449676.jpg
+533226	173959	A group of children playing a game of soccer.	427	640	COCO_val2014_000000173959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173959.jpg
+533245	3742	Apple in and over-flowing a bowl on a windowsill with a rose in a vase.	480	640	COCO_val2014_000000003742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000003742.jpg
+533287	289573	An older couple getting married in front of a crowd.	405	468	COCO_val2014_000000289573.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289573.jpg
+533294	175382	A group of people standing inside of a shop on a tile floor.	360	640	COCO_val2014_000000175382.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175382.jpg
+533439	385196	A group of people riding skateboards across a walkway.	427	640	COCO_val2014_000000385196.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000385196.jpg
+533463	51095	A black cat standing inside of a piece of luggage.	500	375	COCO_val2014_000000051095.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000051095.jpg
+533472	153976	A young man riding a skateboard into the air.	223	640	COCO_val2014_000000153976.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000153976.jpg
+533499	118432	A crowd of people flying kites on top of a field.	427	640	COCO_val2014_000000118432.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118432.jpg
+533608	309474	A large ship driving behind a smaller ship.	360	640	COCO_val2014_000000309474.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000309474.jpg
+533662	192806	a woman standing by the road while talking on a cell phone	640	480	COCO_val2014_000000192806.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192806.jpg
+533747	46144	An worn out rainbow colored park bench seat	334	500	COCO_val2014_000000046144.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000046144.jpg
+533755	2171	A group of people riding boards in the ocean.	427	640	COCO_val2014_000000002171.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000002171.jpg
+534188	175570	A bus turning a corner at an intersection.	428	640	COCO_val2014_000000175570.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175570.jpg
+534216	530875	A young man on a skateboard in a skate park.	640	480	COCO_val2014_000000530875.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000530875.jpg
+534624	274917	A group of men are cutting a cake.	457	640	COCO_val2014_000000274917.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000274917.jpg
+535023	136400	The Stop sign has been hit and is leaning to the side.	640	480	COCO_val2014_000000136400.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000136400.jpg
+535432	17207	A bus stuck in traffic on a busy highway.	480	640	COCO_val2014_000000017207.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000017207.jpg
+535657	475893	Dead plants on a sidewalk of a busy city street.	478	640	COCO_val2014_000000475893.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000475893.jpg
+535788	559006	A big brown bear swims in the water.	333	500	COCO_val2014_000000559006.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559006.jpg
+535990	46155	this is a tennis player about to hit a tennis ball	429	640	COCO_val2014_000000046155.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000046155.jpg
+536345	111817	a cake with some cup cakes around it	496	640	COCO_val2014_000000111817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111817.jpg
+536630	109753	A yellow clock that is on the floor.	640	443	COCO_val2014_000000109753.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000109753.jpg
+537242	280687	a square plate that has some food on it	229	305	COCO_val2014_000000280687.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000280687.jpg
+537354	445019	A man with a baseball bat that is on a field.	426	640	COCO_val2014_000000445019.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000445019.jpg
+537483	122252	a vehicle got an accident and a police officer looking	614	640	COCO_val2014_000000122252.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000122252.jpg
+537785	367569	A living room has a fireplace and a piano.	640	469	COCO_val2014_000000367569.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000367569.jpg
+538013	66568	Two teddy bears lie propped up against a wall.	480	640	COCO_val2014_000000066568.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000066568.jpg
+538144	172649	A boy is riding a skateboard on a wall.	427	640	COCO_val2014_000000172649.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000172649.jpg
+538215	543528	A big plane flying low  on a cloudy sky	480	640	COCO_val2014_000000543528.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543528.jpg
+538245	350663	A boy swinging a baseball bat on a field.	427	640	COCO_val2014_000000350663.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000350663.jpg
+538345	157155	The officer is riding a horse threw the streets.	480	640	COCO_val2014_000000157155.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000157155.jpg
+538428	22420	A library or store is full of books.	480	640	COCO_val2014_000000022420.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000022420.jpg
+539149	356920	A man holding an umbrella while walking down a street.	640	448	COCO_val2014_000000356920.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000356920.jpg
+539224	473153	a building with some trees surrounding it	493	640	COCO_val2014_000000473153.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473153.jpg
+539281	217005	a large amount of plants growing in a forest	332	500	COCO_val2014_000000217005.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000217005.jpg
+539510	4093	Lot of the building, behind the fence, full of garbage	640	399	COCO_val2014_000000004093.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000004093.jpg
+539885	234938	A group of wooden benches sitting on top of a wooden deck.	480	640	COCO_val2014_000000234938.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000234938.jpg
+539899	52016	A person standing by a building with a doughnut.	640	480	COCO_val2014_000000052016.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000052016.jpg
+539924	113757	A group of students sitting in front of laptop computers.	427	640	COCO_val2014_000000113757.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000113757.jpg
+539968	272666	A young boy wearing camouflage sitting in a  doorway.	640	479	COCO_val2014_000000272666.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000272666.jpg
+540156	143010	A red rose sticking out of a glass vase.	640	480	COCO_val2014_000000143010.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143010.jpg
+540309	427655	a horse is pulling a carriage with people down a street	640	427	COCO_val2014_000000427655.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427655.jpg
+540483	139468	There are two cars on the road at this intersection.	640	433	COCO_val2014_000000139468.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000139468.jpg
+540516	216437	A laptop computer sitting on top of a wooden desk.	500	375	COCO_val2014_000000216437.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216437.jpg
+540566	209044	Two young children are playing a video game.	480	640	COCO_val2014_000000209044.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209044.jpg
+540889	361587	A man riding a wave on top of a surfboard.	427	640	COCO_val2014_000000361587.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000361587.jpg
+541275	485865	a couple of zebras stand next to some horses	480	640	COCO_val2014_000000485865.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000485865.jpg
+541432	116083	Adults walking along with kids, all carrying bags	437	640	COCO_val2014_000000116083.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116083.jpg
+541461	227656	A laptop computer on a table in the passenger cabin of an airplane.	640	480	COCO_val2014_000000227656.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227656.jpg
+541563	308974	A kite flying above in a crystal clear blue sky.	427	640	COCO_val2014_000000308974.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000308974.jpg
+541624	497568	A commercial plane being serviced by two people.	424	640	COCO_val2014_000000497568.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497568.jpg
+541781	462890	A baby laying on it's back holding a toothbrush.	425	640	COCO_val2014_000000462890.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000462890.jpg
+541851	509514	A small child thats eating and watching Mickey mouse on a phone.	640	595	COCO_val2014_000000509514.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000509514.jpg
+542124	5001	A group of people cutting a ribbon on a street.	480	640	COCO_val2014_000000005001.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005001.jpg
+542142	25603	A girl with some food and drink at a table.	480	640	COCO_val2014_000000025603.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025603.jpg
+542349	297314	A pitcher winds up to throw a ball.	423	640	COCO_val2014_000000297314.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297314.jpg
+542986	345252	A boy waring headphones is playing a game on his computer.	479	640	COCO_val2014_000000345252.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345252.jpg
+543019	48143	some zebras dirt tall brown grass bushes and trees	427	640	COCO_val2014_000000048143.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000048143.jpg
+543209	421286	a plate with cut slices of pizza and peppeoni	640	478	COCO_val2014_000000421286.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421286.jpg
+543210	64116	a large field full of sheep out in the outdoors	360	640	COCO_val2014_000000064116.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064116.jpg
+543348	33325	Three people are sitting on a bench in the subway.	375	500	COCO_val2014_000000033325.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000033325.jpg
+543390	536028	A man on a surfboard in the middle of body of water.	640	480	COCO_val2014_000000536028.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000536028.jpg
+543457	283516	a sign with a clock in it hang off a building	640	499	COCO_val2014_000000283516.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000283516.jpg
+543572	13597	A piece of chocolate cake on a plate.	425	640	COCO_val2014_000000013597.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013597.jpg
+544427	188044	Two people are standing at a subway stop as a train goes by.	428	640	COCO_val2014_000000188044.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188044.jpg
+544451	442661	a giraffe stares out of a glass window	480	640	COCO_val2014_000000442661.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442661.jpg
+544461	481670	Two skiers are traveling down a snowy mountain.	424	640	COCO_val2014_000000481670.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000481670.jpg
+544478	499423	A very tall church tower with a clock on the side of it.	640	480	COCO_val2014_000000499423.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000499423.jpg
+544693	88967	A three way sign is posted on an outdoor pole.	640	427	COCO_val2014_000000088967.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000088967.jpg
+545458	38070	A white toilet sitting next to a trash can in a restroom.	375	500	COCO_val2014_000000038070.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038070.jpg
+545921	178413	looking out a window from inside the ski lodge	480	640	COCO_val2014_000000178413.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000178413.jpg
+546390	117836	A pizza on top of a white plate covered in greens.	480	640	COCO_val2014_000000117836.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000117836.jpg
+546782	56684	a brown donut on some white paper on a red counter	426	640	COCO_val2014_000000056684.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056684.jpg
+546953	465868	A gray cat standing on top of a dirt road.	480	640	COCO_val2014_000000465868.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000465868.jpg
+547303	326814	A photo of a cat in black and white. The person taking the picture is visible in the mirror.	640	425	COCO_val2014_000000326814.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000326814.jpg
+547376	447460	A young boy eating a hot dog on top of a paper in a basket.	375	500	COCO_val2014_000000447460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000447460.jpg
+547603	412587	A man and little kids flying kites in a cloudy sky.	640	398	COCO_val2014_000000412587.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412587.jpg
+547604	488697	A couple of young men playing a game of frisbee.	428	640	COCO_val2014_000000488697.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000488697.jpg
+547616	64030	A flock of ducks floating on top of a lake.	475	640	COCO_val2014_000000064030.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064030.jpg
+547734	61596	A sandwich sitting on top  of paper filled with food.	480	640	COCO_val2014_000000061596.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000061596.jpg
+548075	348474	A woman standing next to a group of horses on a field.	480	640	COCO_val2014_000000348474.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000348474.jpg
+548085	130291	A woman adjusting a mans tie as he stands at an exit.	640	428	COCO_val2014_000000130291.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000130291.jpg
+548172	404479	A large jetliner sitting on top of an airport runway.	427	640	COCO_val2014_000000404479.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000404479.jpg
+548335	197609	Two hot dogs sitting on top of tissue paper.	480	640	COCO_val2014_000000197609.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000197609.jpg
+548397	558457	A young boy standing next to a pile of green things.	480	640	COCO_val2014_000000558457.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558457.jpg
+548499	498463	A metallic refrigerator freezer sitting in a kitchen.	480	640	COCO_val2014_000000498463.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498463.jpg
+548526	210522	A group of friends standing next to each other at night.	480	640	COCO_val2014_000000210522.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000210522.jpg
+548587	537206	A man laying on the ground in front of a motorcycle.	480	640	COCO_val2014_000000537206.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000537206.jpg
+549092	273698	A stack of pan cakes sitting on top of a plate.	480	640	COCO_val2014_000000273698.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273698.jpg
+549465	5328	A white toilet sitting next to a white sink.	640	430	COCO_val2014_000000005328.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005328.jpg
+549571	187822	A person riding a skateboard on the side of an empty pool.	612	612	COCO_val2014_000000187822.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187822.jpg
+549875	237723	A white bath tub sitting under a window next to a toilet.	640	480	COCO_val2014_000000237723.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237723.jpg
+549897	185210	A city street at night filled with lots of traffic.	428	640	COCO_val2014_000000185210.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000185210.jpg
+550016	301014	two cats sitting side by side eating out of a dish	291	500	COCO_val2014_000000301014.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301014.jpg
+550201	338826	A fire station with a flag pole in front of it.	427	640	COCO_val2014_000000338826.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000338826.jpg
+550445	559454	a female wearing short shorts and cutting a cake with a knife	375	500	COCO_val2014_000000559454.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559454.jpg
+550539	497664	A white and blue bus sitting on the side of tall buildings.	414	640	COCO_val2014_000000497664.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497664.jpg
+550549	98895	A room with a toilet, a door and shoes in it.	480	640	COCO_val2014_000000098895.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000098895.jpg
+550684	79906	A room with two couches, a table, several pillows and two pictures on the wall.	480	640	COCO_val2014_000000079906.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000079906.jpg
+551093	236945	A kitten playing with a video game remote control.	480	640	COCO_val2014_000000236945.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000236945.jpg
+551645	418700	a few boats are docked by a large pier	640	427	COCO_val2014_000000418700.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000418700.jpg
+551649	371650	A boy is preparing to hit a tennis ball.	428	640	COCO_val2014_000000371650.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000371650.jpg
+551658	89924	a large teddy bear that is next to a log	640	427	COCO_val2014_000000089924.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089924.jpg
+551662	359140	a small toilet in a bathroom with tile walls	640	480	COCO_val2014_000000359140.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000359140.jpg
+552256	40881	A bowl of chicken and vegetables is shown.	427	640	COCO_val2014_000000040881.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000040881.jpg
+552297	336324	A very messy desk with a computer and mouse.	480	640	COCO_val2014_000000336324.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000336324.jpg
+552763	442250	The man is coaching the kids on how to play soccer.	414	640	COCO_val2014_000000442250.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442250.jpg
+552786	564404	A man who looks like two face with a creepy smirk on his face.	640	640	COCO_val2014_000000564404.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000564404.jpg
+552873	412676	cars parked near a all black corner store	499	640	COCO_val2014_000000412676.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412676.jpg
+553135	54592	Two people with snow gear on, carrying equipment.	418	640	COCO_val2014_000000054592.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054592.jpg
+553272	407057	Vintage picture of a man and a horse on the farm.	404	640	COCO_val2014_000000407057.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000407057.jpg
+554708	475389	A man sitting on a motor bike smiling with a friend close to him	640	426	COCO_val2014_000000475389.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000475389.jpg
+554843	542035	A bedroom with a bed, rug, lamp and pillows.	427	640	COCO_val2014_000000542035.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000542035.jpg
+554861	106331	A baseball player has swung his bat during a game.	333	500	COCO_val2014_000000106331.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000106331.jpg
+554981	397274	People with umbrellas walking down a street on a rainy day.	640	480	COCO_val2014_000000397274.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397274.jpg
+555011	106356	A dog sitting up straight on a bed looking stoic.	480	640	COCO_val2014_000000106356.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000106356.jpg
+555189	62817	A person holding a cell phone in their hand with three other phones around.	334	500	COCO_val2014_000000062817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062817.jpg
+555194	508006	Two women standing behind a birthday cake that is sparking.	522	640	COCO_val2014_000000508006.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000508006.jpg
+555398	363188	People dressed as zombies walking down the street.	425	640	COCO_val2014_000000363188.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000363188.jpg
+556049	257458	A refrigerator with a box sitting on top and a stove with a shelf above it that has a toy red truck and candles.	640	480	COCO_val2014_000000257458.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000257458.jpg
+556632	569016	A runway has many old-fashioned planes and helicopters.	319	640	COCO_val2014_000000569016.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000569016.jpg
+556906	210448	A pair of zebra's looking on at the camera in a pin.	429	640	COCO_val2014_000000210448.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000210448.jpg
+557278	398174	a young blonde standing and playing with her cell phone	428	640	COCO_val2014_000000398174.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000398174.jpg
+558407	323847	A bird stands on a rock, gazing out over the water.	640	543	COCO_val2014_000000323847.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000323847.jpg
+558799	580027	There is a tub, a sink, and a shower inside a bathroom.	350	490	COCO_val2014_000000580027.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000580027.jpg
+558925	56455	A man sitting at a desk with a computer holding a cell phone.	640	480	COCO_val2014_000000056455.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056455.jpg
+559173	210915	A man riding a surfboard on a wave in the ocean.	480	640	COCO_val2014_000000210915.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000210915.jpg
+559672	341164	A man who is riding on a skateboard.	640	640	COCO_val2014_000000341164.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341164.jpg
+560015	355471	There is a bear sitting next to a bird.	640	545	COCO_val2014_000000355471.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000355471.jpg
+560064	389721	The giraffes wander around their cage and eat from the trough on a wall.	640	480	COCO_val2014_000000389721.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000389721.jpg
+560096	354303	A large group of zebra standing in a dirt field.	480	640	COCO_val2014_000000354303.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000354303.jpg
+560227	510340	A red motorcycle is on the sidewalk in front of a business with ads on the windows.	480	640	COCO_val2014_000000510340.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000510340.jpg
+560228	10694	A cat stares from behind a large TV.	375	500	COCO_val2014_000000010694.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010694.jpg
+560513	132615	A baseball player is swinging and about to run.	427	640	COCO_val2014_000000132615.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132615.jpg
+560552	25990	a child sitting on the ground with a flower	640	420	COCO_val2014_000000025990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025990.jpg
+560720	248457	there's someone sitting under that umbrella and someone taking a picture	480	640	COCO_val2014_000000248457.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248457.jpg
+561250	147576	A couple of boats and car on a street.	480	640	COCO_val2014_000000147576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000147576.jpg
+561411	384188	A couple of suitcases at a station with a train.	640	480	COCO_val2014_000000384188.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384188.jpg
+561441	214306	A big brown dog looking out a pretty glass door.	424	640	COCO_val2014_000000214306.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214306.jpg
+561864	441286	A person on a surf board riding a wave.	493	640	COCO_val2014_000000441286.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000441286.jpg
+562074	508230	People in matching shirts standing under umbrellas in the sun	640	439	COCO_val2014_000000508230.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000508230.jpg
+562285	86946	A large umbrella and a chair on a street.	640	480	COCO_val2014_000000086946.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000086946.jpg
+562622	573395	a group of surfers are riding waves in the ocean	360	640	COCO_val2014_000000573395.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573395.jpg
+562842	261824	a dog in a Santa hat on a pillow	427	640	COCO_val2014_000000261824.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000261824.jpg
+562913	534733	A chair and a couch in a room.	424	640	COCO_val2014_000000534733.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534733.jpg
+563053	467978	The dog is outside barking at the sheep.	427	640	COCO_val2014_000000467978.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000467978.jpg
+563319	20598	Small children are eating from plates of pizza.	424	640	COCO_val2014_000000020598.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000020598.jpg
+564332	403255	A small white boat in the open water.	384	640	COCO_val2014_000000403255.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000403255.jpg
+564549	52412	A jetliner wing flying over the top of a parking.	426	640	COCO_val2014_000000052412.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000052412.jpg
+564562	192047	A white sink in a corner underneath a small mirror and light.	640	480	COCO_val2014_000000192047.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192047.jpg
+564659	196046	A group of people sitting on motor bikes on a street.	386	640	COCO_val2014_000000196046.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000196046.jpg
+564908	347453	all the vehicles in this town are red	335	500	COCO_val2014_000000347453.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347453.jpg
+564918	169166	A couple of people walking down a sidewalk with stars.	640	427	COCO_val2014_000000169166.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000169166.jpg
+564928	363106	two people standing near one another playing nintendo wii	427	640	COCO_val2014_000000363106.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000363106.jpg
+565321	229388	A man holding an umbrella in one hand and a cell phone up to his ear in the other.	640	480	COCO_val2014_000000229388.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000229388.jpg
+565379	47524	A person with glasses and a shirt in a room.	428	640	COCO_val2014_000000047524.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000047524.jpg
+565436	487385	Two plates filled with food siting beside a book.	478	640	COCO_val2014_000000487385.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487385.jpg
+565443	163777	The fur on this dog is long enough to cover his eyes.	480	640	COCO_val2014_000000163777.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000163777.jpg
+565850	323515	A platter topped with sliced vegetables with ranch dip in the center.	480	640	COCO_val2014_000000323515.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000323515.jpg
+565931	416700	a man is reaching back with a tennis racket	332	500	COCO_val2014_000000416700.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000416700.jpg
+565938	461953	A long hot dog on a plate on a table.	191	500	COCO_val2014_000000461953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000461953.jpg
+566795	237071	A man standing on a tennis court holding a tennis racquet.	427	640	COCO_val2014_000000237071.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237071.jpg
+566826	459301	A man holding two ski poles and two skis.	591	392	COCO_val2014_000000459301.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000459301.jpg
+566864	464018	a couple of stuffed bears are sitting with each other	480	640	COCO_val2014_000000464018.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464018.jpg
+566890	527535	A dessert topped with whipped cream sits on a table.	427	640	COCO_val2014_000000527535.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000527535.jpg
+566912	574184	A cat laying on a bed next to an opened laptop.	375	500	COCO_val2014_000000574184.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000574184.jpg
+567074	476349	A man riding a snowboard down the side of a snow covered slope.	480	640	COCO_val2014_000000476349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000476349.jpg
+567343	190898	A building with large windows sitting inside of a building.	640	426	COCO_val2014_000000190898.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190898.jpg
+567359	129001	A bath tub sitting next to a sink in a bathroom.	640	480	COCO_val2014_000000129001.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129001.jpg
+567395	539975	An orange bus parked next to a building.	411	640	COCO_val2014_000000539975.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000539975.jpg
+567459	359851	A Pokemon and Mickey Mouse standing next to a light pole.	640	480	COCO_val2014_000000359851.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000359851.jpg
+567472	528098	A man wearing a straw hat and playing the guitar.	480	640	COCO_val2014_000000528098.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000528098.jpg
+567583	208524	A black and yellow train traveling down tracks.	348	640	COCO_val2014_000000208524.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208524.jpg
+567801	170116	A wall mounted with a list and other papers.	480	640	COCO_val2014_000000170116.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170116.jpg
+568025	524373	A giraffe standing next to a palm tree on a field.	480	640	COCO_val2014_000000524373.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000524373.jpg
+568054	260166	A group of people sitting on the back of an elephant.	480	640	COCO_val2014_000000260166.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260166.jpg
+568354	369913	A woman holding shears in one hand a tile in the other.	500	440	COCO_val2014_000000369913.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369913.jpg
+568458	134119	Some very cute giraffes in a big grass field.	428	640	COCO_val2014_000000134119.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134119.jpg
+568674	85329	An image of a very cute girl with face piercings.	449	640	COCO_val2014_000000085329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000085329.jpg
+568818	548528	a parking meter standing in the cold snow	640	427	COCO_val2014_000000548528.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000548528.jpg
+569054	476514	two people in a field wearing a dress and a suit and tie	640	426	COCO_val2014_000000476514.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000476514.jpg
+569078	552253	A very big polar bear under water with an item.	640	626	COCO_val2014_000000552253.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552253.jpg
+569146	128450	I am unable to see the image above.	428	640	COCO_val2014_000000128450.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000128450.jpg
+569164	342765	Man and woman look into open refrigerator that contains drinks	480	640	COCO_val2014_000000342765.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000342765.jpg
+569401	342271	A family riding on the back of raft.	382	640	COCO_val2014_000000342271.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000342271.jpg
+569560	279259	Man in shorts running with a necktie on top	640	428	COCO_val2014_000000279259.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279259.jpg
+569854	271076	A weather vane with a clock near a body of water.	640	480	COCO_val2014_000000271076.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000271076.jpg
+569988	295409	Two friends are playing the Nintendo Wii in the living room	427	640	COCO_val2014_000000295409.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295409.jpg
+570184	108440	A train that is on a rail over a bridge.	434	640	COCO_val2014_000000108440.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000108440.jpg
+570669	280607	A pizza that is sitting on a table.	375	500	COCO_val2014_000000280607.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000280607.jpg
+570678	413349	A man that has a skateboard that is in the air.	640	640	COCO_val2014_000000413349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000413349.jpg
+570786	267628	A train traveling down tracks near a rural area.	360	640	COCO_val2014_000000267628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267628.jpg
+570815	176034	Large wall with an antique finish window and an old clock above it.	640	480	COCO_val2014_000000176034.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000176034.jpg
+570984	552901	The bathroom in the cabin needs to be remodeled.	480	640	COCO_val2014_000000552901.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000552901.jpg
+571273	26609	A city street line with very tall buildings.	640	480	COCO_val2014_000000026609.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026609.jpg
+571373	429281	a table full of bananas being sold outside	640	480	COCO_val2014_000000429281.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000429281.jpg
+571374	481582	a group of people are with a horse in a stable	427	640	COCO_val2014_000000481582.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000481582.jpg
+571382	415076	A clock that is on the side of a building.	480	640	COCO_val2014_000000415076.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415076.jpg
+571748	43494	A close up of a grill loaded with burgers and hot dogs.	480	640	COCO_val2014_000000043494.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000043494.jpg
+571749	519691	A very big truck with a bunch of stuff in the back.	400	400	COCO_val2014_000000519691.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519691.jpg
+572132	238291	Two black cats sit on a windowsill looking into the backyard garden.	427	640	COCO_val2014_000000238291.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000238291.jpg
+572183	281625	There are a lot of people sitting by the fountain.	640	480	COCO_val2014_000000281625.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000281625.jpg
+572198	176466	The young girl is standing by the fire hydrant in curlers.	427	640	COCO_val2014_000000176466.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000176466.jpg
+572485	271576	A dog standing on top of a boat in a body of water.	428	640	COCO_val2014_000000271576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000271576.jpg
+572529	341299	A group of people that are outside of an airplane.	425	640	COCO_val2014_000000341299.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341299.jpg
+572592	494800	A flower hanging from the side of a banana plant.	427	640	COCO_val2014_000000494800.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000494800.jpg
+572623	553471	An alarm clock sitting under a large hammer.	640	427	COCO_val2014_000000553471.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000553471.jpg
+572675	168837	A rose that is laying down on a bed.	406	640	COCO_val2014_000000168837.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168837.jpg
+572682	391446	A toy boat sits on the ground.	427	640	COCO_val2014_000000391446.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000391446.jpg
+572948	444275	A microwave oven sitting next to a device near a fence.	480	640	COCO_val2014_000000444275.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444275.jpg
+573144	424998	A city street with cars parked on it under a ball building.	526	640	COCO_val2014_000000424998.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000424998.jpg
+573153	321437	A clock on a pole sitting next to tall buildings.	640	480	COCO_val2014_000000321437.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000321437.jpg
+573181	441054	The bear is alone in the woods outside.	480	640	COCO_val2014_000000441054.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000441054.jpg
+573229	195510	A busy street is full of cars and a bus.	424	640	COCO_val2014_000000195510.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195510.jpg
+573243	223554	Some very cute big birds by the water.	427	640	COCO_val2014_000000223554.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000223554.jpg
+573348	438628	A very cute black cat laying by a keyboard.	375	500	COCO_val2014_000000438628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000438628.jpg
+573423	72050	A city street at night with a green signal.	332	500	COCO_val2014_000000072050.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000072050.jpg
+573643	37624	A crowd shot of a tennis match at night time.	612	612	COCO_val2014_000000037624.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000037624.jpg
+573781	125485	A group of boats floating on a body of water.	427	640	COCO_val2014_000000125485.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125485.jpg
+573920	287331	a dog is jumping up to catch a frisbee	401	500	COCO_val2014_000000287331.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287331.jpg
+574251	428992	A stove with two kettles and a pan of pizza on it.	500	333	COCO_val2014_000000428992.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000428992.jpg
+574485	520124	some people are standing together around some food	427	640	COCO_val2014_000000520124.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000520124.jpg
+574550	524979	A young boy tossing a ball into the air.	640	397	COCO_val2014_000000524979.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000524979.jpg
+574558	280022	A man hitting a tennis ball with a tennis racquet.	640	427	COCO_val2014_000000280022.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000280022.jpg
+574656	67255	A red stop sign sitting above a four way sign.	480	640	COCO_val2014_000000067255.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067255.jpg
+574711	311518	A small green boat in a pond outside an office building.	427	640	COCO_val2014_000000311518.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000311518.jpg
+574874	568111	A bedroom with a bed under a window next to a green rug.	640	473	COCO_val2014_000000568111.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000568111.jpg
+575159	462982	A white toilet in a bathroom next to a  sink.	640	480	COCO_val2014_000000462982.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000462982.jpg
+575160	423484	A man walking down a road in front of a double decker bus.	428	640	COCO_val2014_000000423484.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000423484.jpg
+575166	532575	A dog rests his head on the edge of a boat at sea.	427	640	COCO_val2014_000000532575.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532575.jpg
+575331	69965	A man riding a wave on top of a surfboard.	360	640	COCO_val2014_000000069965.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000069965.jpg
+575371	469793	A man preparing food inside of a kitchen with a  small bird on his shoulder like snow white while wearing a cheap chefs hat.	640	561	COCO_val2014_000000469793.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000469793.jpg
+575459	162495	Tall building with clock and spire behind white bricked building.	640	427	COCO_val2014_000000162495.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000162495.jpg
+575497	198935	A dog sits on grass next to a fire hydrant while a person holds a leash and a bag filled with dog poop	614	640	COCO_val2014_000000198935.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198935.jpg
+575649	382041	a man in yellow is doing a trick on a skateboard	361	640	COCO_val2014_000000382041.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382041.jpg
+576501	112517	A tall building with a clock on it near a cemetery.	500	375	COCO_val2014_000000112517.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000112517.jpg
+576670	8010	The two blurry surfers are walking on the beach	443	640	COCO_val2014_000000008010.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000008010.jpg
+576716	46616	The two surfers are ready to go out on the water	640	318	COCO_val2014_000000046616.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000046616.jpg
+576738	58851	The bench is empty at night in the park	426	640	COCO_val2014_000000058851.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000058851.jpg
+577619	95295	a couple of people are sitting on a boat	375	500	COCO_val2014_000000095295.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000095295.jpg
+578049	40924	a couple of naked guys in a room together	393	600	COCO_val2014_000000040924.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000040924.jpg
+578094	417055	We are looking at an ornate old clock tower.	481	481	COCO_val2014_000000417055.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000417055.jpg
+578457	27642	a bunch of motorcycles are parked on the street together	480	640	COCO_val2014_000000027642.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000027642.jpg
+578523	62839	The person is walking in the alley alone	640	426	COCO_val2014_000000062839.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062839.jpg
+578636	10205	The man is playing video games in the living room	426	640	COCO_val2014_000000010205.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010205.jpg
+578867	267910	a black laptop and a notepad on a desk	480	640	COCO_val2014_000000267910.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267910.jpg
+579121	116017	Two well dressed standing together while on writes a note.	457	640	COCO_val2014_000000116017.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116017.jpg
+579602	78032	Benches on a paved deck overlooking a waterway	383	640	COCO_val2014_000000078032.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078032.jpg
+579609	350073	a couple of planes are out on a runway	480	640	COCO_val2014_000000350073.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000350073.jpg
+579711	196924	Living room with furniture with garage door at one end.	480	640	COCO_val2014_000000196924.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000196924.jpg
+580181	120356	The man stands in front of a television playing a video game.	480	640	COCO_val2014_000000120356.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120356.jpg
+580432	21060	The computer desk has a small pad of notes, and an mp3 player on it.	375	500	COCO_val2014_000000021060.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000021060.jpg
+580546	296404	A couple of giraffe standing by a tree in the grass.	333	500	COCO_val2014_000000296404.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000296404.jpg
+580759	203095	a bunch of food is layed out next to an oven	480	640	COCO_val2014_000000203095.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000203095.jpg
+580853	460378	The kitchen counters are almost down with the remodel.	480	640	COCO_val2014_000000460378.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460378.jpg
+581056	389197	A boy sliding down a waterslide at a waterpark.	426	640	COCO_val2014_000000389197.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000389197.jpg
+581405	143767	A collection of products displayed on a bed.	612	612	COCO_val2014_000000143767.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143767.jpg
+581426	51223	A couple of men running across a lush green field.	335	500	COCO_val2014_000000051223.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000051223.jpg
+581508	299305	a toilet a toilet brush tiles and a door handle	325	500	COCO_val2014_000000299305.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000299305.jpg
+581733	34114	A white microwave oven sitting inside of a wall.	480	640	COCO_val2014_000000034114.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034114.jpg
+582013	369997	A man riding a motorcycle across a lush green park.	428	640	COCO_val2014_000000369997.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369997.jpg
+582099	373705	A number of young people are enjoying themselves around a fountain, not far from a fire hydrant.	427	640	COCO_val2014_000000373705.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000373705.jpg
+582107	386929	A kitchen with a tile floor and a metallic sink.	640	427	COCO_val2014_000000386929.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000386929.jpg
+582116	344222	A jar filled with different types of fruit on a table.	640	428	COCO_val2014_000000344222.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344222.jpg
+582195	387562	A giraffe walking across a dirt and grass field.	360	640	COCO_val2014_000000387562.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000387562.jpg
+582231	151558	A traffic light hanging from the side of a wooden pole.	640	427	COCO_val2014_000000151558.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000151558.jpg
+583192	58690	A boy playing a multicolored piano on the street.	480	640	COCO_val2014_000000058690.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000058690.jpg
+583307	108748	A tennis player swinging his racket a low ball.	306	500	COCO_val2014_000000108748.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000108748.jpg
+583327	80462	some lightening is striking off in the distance	427	640	COCO_val2014_000000080462.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080462.jpg
+583405	151358	A newspaper that has sunglasses on top of it sitting in front of books.	640	480	COCO_val2014_000000151358.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000151358.jpg
+583407	125655	Several parked bikes sitting in the grass near a tree.	480	640	COCO_val2014_000000125655.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125655.jpg
+583539	454457	The skateboarder makes a jump above a rail and stairs.	640	427	COCO_val2014_000000454457.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000454457.jpg
+583736	352491	A train sitting idle in an empty train station	480	640	COCO_val2014_000000352491.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000352491.jpg
+584015	571384	a small group of people that are in front of a bus	612	612	COCO_val2014_000000571384.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000571384.jpg
+584333	37678	A train sitting under a display inside a building.	640	439	COCO_val2014_000000037678.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000037678.jpg
+584334	224557	A plastic tray that has a sub and chips on it.	480	640	COCO_val2014_000000224557.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224557.jpg
+584399	346067	A yellow and black bird perched on top of a wooden branch.	484	500	COCO_val2014_000000346067.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000346067.jpg
+584463	343753	A group of people walking under a parking lot with skateboards.	640	480	COCO_val2014_000000343753.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000343753.jpg
+584647	563758	a teddy bear that has a red scarf on	640	429	COCO_val2014_000000563758.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000563758.jpg
+584658	182162	A living room with hard wood flooring and black furniture.	425	640	COCO_val2014_000000182162.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182162.jpg
+584941	153527	A row of wooden benches sitting on the side of a road.	375	500	COCO_val2014_000000153527.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000153527.jpg
+584958	295016	A kitten sitting inside of a coffee cup.	640	503	COCO_val2014_000000295016.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295016.jpg
+585047	260172	A display case filled with bakes goods in front of a store.	480	640	COCO_val2014_000000260172.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260172.jpg
+585091	517515	Several baskets filled with food sitting on a table.	500	375	COCO_val2014_000000517515.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000517515.jpg
+585287	573113	A pair of shoes hanging over a street sign.	426	640	COCO_val2014_000000573113.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573113.jpg
+585288	483013	A brown bear walking across a river near a river.	640	427	COCO_val2014_000000483013.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000483013.jpg
+585404	384808	A man taking his picture in a bathroom mirror.	500	375	COCO_val2014_000000384808.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384808.jpg
+585417	487897	Two people sitting at a table with a cake in front of them.	457	640	COCO_val2014_000000487897.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487897.jpg
+585553	190690	A yellow and white cat sitting next to a book on a bed.	444	640	COCO_val2014_000000190690.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190690.jpg
+585614	317062	A desktop computer sitting on top of a desk.	482	640	COCO_val2014_000000317062.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000317062.jpg
+585784	499157	A flat screen TV mounted to a wall over a lamp.	428	640	COCO_val2014_000000499157.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000499157.jpg
+586312	464286	Several cows stick their heads out of a pen to feed.	640	519	COCO_val2014_000000464286.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464286.jpg
+586314	287649	A cat sits on a desk next to a keyboard.	480	640	COCO_val2014_000000287649.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287649.jpg
+586370	402528	A jar of mustard next to a small plate of a sandwich	426	640	COCO_val2014_000000402528.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000402528.jpg
+586491	416308	A crowd of people standing on a beach flying kites.	480	640	COCO_val2014_000000416308.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000416308.jpg
+586647	194188	An elephant is walking around his exhibit at the zoo	426	640	COCO_val2014_000000194188.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000194188.jpg
+586711	491215	The snow boarder is near the top of the ramp.	640	574	COCO_val2014_000000491215.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491215.jpg
+586814	348091	A bedroom with a bed next to a closet.	480	640	COCO_val2014_000000348091.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000348091.jpg
+586855	231644	A couple of people flying kites on top of a beach.	640	480	COCO_val2014_000000231644.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231644.jpg
+586921	130130	A computer desk holding a monitor and keyboard in front of blinds.	375	500	COCO_val2014_000000130130.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000130130.jpg
+587146	91045	a couple of men that are posing for a picture	480	640	COCO_val2014_000000091045.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000091045.jpg
+587202	564088	a few people that are inside a fence	424	640	COCO_val2014_000000564088.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000564088.jpg
+587494	39202	A very pretty girl working with some food in a kitchen.	428	640	COCO_val2014_000000039202.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039202.jpg
+587839	51461	A man holding a skateboard in front of a sun setting.	365	640	COCO_val2014_000000051461.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000051461.jpg
+587950	444491	A man riding up the side of an empty swimming pool.	426	640	COCO_val2014_000000444491.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444491.jpg
+588012	232950	A man is looking at a sliced pizza.	480	640	COCO_val2014_000000232950.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000232950.jpg
+588250	568979	A man walking next to an elephant on a green field.	640	482	COCO_val2014_000000568979.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000568979.jpg
+588271	536168	Trains on a track, one with freight boxes.	612	612	COCO_val2014_000000536168.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000536168.jpg
+588592	359843	A fire hydrant in the grass near the curb with the remnants of melting snow	479	640	COCO_val2014_000000359843.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000359843.jpg
+588619	191288	A woman riding on the back of a brown horse over an obstacle.	400	600	COCO_val2014_000000191288.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000191288.jpg
+588628	298637	A person flying through the air while riding skis.	330	500	COCO_val2014_000000298637.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000298637.jpg
+588663	409088	A paper sitting on top of a white plate near a small bird.	640	359	COCO_val2014_000000409088.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000409088.jpg
+588729	298540	A patch of grass with lots of bikes parked on it.	480	640	COCO_val2014_000000298540.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000298540.jpg
+588863	532126	A white toilet sitting next to a bath tub with a chair in it.	478	640	COCO_val2014_000000532126.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532126.jpg
+588864	98283	A person riding a skateboard on top of a blue floor.	375	500	COCO_val2014_000000098283.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000098283.jpg
+588872	216603	A white plate with two fried eggs and greens.	480	640	COCO_val2014_000000216603.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216603.jpg
+589223	9170	A cat drinking out of a white toilet bowl.	383	640	COCO_val2014_000000009170.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009170.jpg
+589225	130619	a giraffe with a man sitting in front of the girraffe	500	333	COCO_val2014_000000130619.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000130619.jpg
+589398	183716	Two little girls are dressed in uniform preparing for the day	500	353	COCO_val2014_000000183716.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000183716.jpg
+589481	282037	A baseball player pitching a ball on top of a field.	449	640	COCO_val2014_000000282037.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000282037.jpg
+589493	140270	A person is riding a horse around on grasses	400	600	COCO_val2014_000000140270.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000140270.jpg
+589506	104982	A man holding a smart phone in his right hand.	281	500	COCO_val2014_000000104982.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000104982.jpg
+589526	322357	A man standing on a tennis court holding a racquet.	375	500	COCO_val2014_000000322357.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000322357.jpg
+589858	564129	A red train parked in front of a loading platform next to passengers.	480	640	COCO_val2014_000000564129.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000564129.jpg
+589878	320864	A boy wearing a helmet skateboards through water.	640	427	COCO_val2014_000000320864.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000320864.jpg
+589937	354132	A man standing next to a motorcycle on the side of a hill.	427	640	COCO_val2014_000000354132.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000354132.jpg
+589973	223413	A cat laying on the ground next to a woman's shoes.	640	469	COCO_val2014_000000223413.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000223413.jpg
+590086	208363	a group of people sitting together eating pizza	480	640	COCO_val2014_000000208363.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208363.jpg
+590211	275219	a gray cat is seen laying on top of a blue tube	435	500	COCO_val2014_000000275219.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000275219.jpg
+590721	573647	A woman standing on a tennis court holding a racquet.	427	640	COCO_val2014_000000573647.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573647.jpg
+591780	384995	A young boy riding skis on top of a snow covered slope.	640	480	COCO_val2014_000000384995.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384995.jpg
+591977	514222	A young boy eating a custard covered donut.	612	612	COCO_val2014_000000514222.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000514222.jpg
+592275	543082	A sailboarder holds on to the sail in the ocean.	427	640	COCO_val2014_000000543082.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543082.jpg
+592295	142346	An adult skier towing a small child by a ski pole.	480	640	COCO_val2014_000000142346.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000142346.jpg
+592722	396617	A boy skate boards down a neighborhood street.	424	640	COCO_val2014_000000396617.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000396617.jpg
+593026	19253	A photo of a grassy hillside with several sheep grazing.	568	640	COCO_val2014_000000019253.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000019253.jpg
+593463	192964	A skateboarder performing a trick on an indoor ramp.	640	428	COCO_val2014_000000192964.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192964.jpg
+593599	532145	A herd of cattle standing on top of a grass covered field.	427	640	COCO_val2014_000000532145.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532145.jpg
+593771	306582	A group of cows standing next to a  line of laundry drying.	480	640	COCO_val2014_000000306582.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306582.jpg
+594256	134328	An airplane flying through a blue sky next to a helicopter.	372	640	COCO_val2014_000000134328.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134328.jpg
+594399	357356	A man tossing a frisbee standing on a stone path in the woods.	500	332	COCO_val2014_000000357356.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000357356.jpg
+594436	572956	A row of motorcycles parked next to a building.	333	500	COCO_val2014_000000572956.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000572956.jpg
+594546	174771	Front end of a white vehicle in a parking lot	428	640	COCO_val2014_000000174771.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174771.jpg
+594582	57542	A woman sitting at a table with others using laptops.	480	640	COCO_val2014_000000057542.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000057542.jpg
+594911	345941	A desert on a plate with a spoon on the side of the plate.	640	640	COCO_val2014_000000345941.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345941.jpg
+595282	293390	A VERY NICE BATHROOM IN THE MIDDLE OF A REMODEL	480	640	COCO_val2014_000000293390.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000293390.jpg
+595293	544876	two males are sitting on a wooden bench eating donuts	480	640	COCO_val2014_000000544876.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000544876.jpg
+596082	211046	The train rides down the tracks near a huge building in the city.	480	640	COCO_val2014_000000211046.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211046.jpg
+596242	252459	A picture of a window opened and something on it.	424	640	COCO_val2014_000000252459.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000252459.jpg
+596373	573823	a laptop computer on a desk and a tv	427	640	COCO_val2014_000000573823.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573823.jpg
+596426	279838	The woman using a cell phone touches her long hair.	464	640	COCO_val2014_000000279838.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279838.jpg
+596976	221017	A rooster is walking on a grassy beach.	480	640	COCO_val2014_000000221017.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000221017.jpg
+597013	231349	The sign on the door says the train is out of service.	427	640	COCO_val2014_000000231349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231349.jpg
+597015	50882	A fat man is playing golf on the Wii.	358	500	COCO_val2014_000000050882.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000050882.jpg
+597024	111609	A man is sleeping with the covers pulled up high.	429	640	COCO_val2014_000000111609.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111609.jpg
+597079	97922	A picture of dinner of steak and potatoes by a keyboard.	375	500	COCO_val2014_000000097922.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000097922.jpg
+597173	16733	A pizza next to two calzone's in boxes on a table.	375	640	COCO_val2014_000000016733.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000016733.jpg
+597176	133386	some people are on skateboards at a skate park	429	640	COCO_val2014_000000133386.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133386.jpg
+597207	539141	a red truck a yellow crane some water boats and trees	506	640	COCO_val2014_000000539141.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000539141.jpg
+597244	98248	Close up from behind of a young giraffe standing with front legs splayed and head the ground by a fence outside.	640	480	COCO_val2014_000000098248.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000098248.jpg
+597246	176118	Close up of a plate with broccoli and shrimp, with some broth.	427	640	COCO_val2014_000000176118.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000176118.jpg
+597283	213961	Two cows walking along the sand dunes at a beach.	480	640	COCO_val2014_000000213961.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213961.jpg
+597299	4731	A man holding a baby up in a kitchen.	425	640	COCO_val2014_000000004731.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000004731.jpg
+597316	198733	A guy jumping in the air on a skateboard.	640	480	COCO_val2014_000000198733.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198733.jpg
+597334	290289	A living room with many chairs and couches.	480	640	COCO_val2014_000000290289.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000290289.jpg
+597367	32688	A pair of giraffes and standing with a skyline in the background.	426	640	COCO_val2014_000000032688.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032688.jpg
+597403	293027	a black and white cat hugging a handbag	480	640	COCO_val2014_000000293027.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000293027.jpg
+597409	3378	a person is doing a trick on a skateboard	640	427	COCO_val2014_000000003378.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000003378.jpg
+597435	558229	A bus driver cleans off the windshield of the bus.	427	640	COCO_val2014_000000558229.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558229.jpg
+597470	513242	A man wearing a nice pink shirt with a pink tie.	640	480	COCO_val2014_000000513242.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000513242.jpg
+597583	105879	A man carrying a surfboard on top of the ocean.	640	443	COCO_val2014_000000105879.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000105879.jpg
+597588	244099	A person riding a horse in an empty area of dried grass.	428	640	COCO_val2014_000000244099.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000244099.jpg
+597596	301582	A man holding his cell phone in his hand.	480	640	COCO_val2014_000000301582.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301582.jpg
+597672	440455	a man watching his dog checking something on the desk	425	640	COCO_val2014_000000440455.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000440455.jpg
+597731	546463	A full view of a kitchen with stove and counters.	480	640	COCO_val2014_000000546463.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546463.jpg
+597754	131617	A computer animated girl brushes her teeth while two adults watch.	500	500	COCO_val2014_000000131617.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000131617.jpg
+597786	547839	a photo of a train heading down the tracks	480	640	COCO_val2014_000000547839.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000547839.jpg
+597901	57415	A green and yellow train sitting under a metal structure.	425	640	COCO_val2014_000000057415.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000057415.jpg
+597905	97577	A full view of a nice and comfortable bed with pillows.	360	640	COCO_val2014_000000097577.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000097577.jpg
+597945	242610	A man standing next to a blue and brown bed.	480	640	COCO_val2014_000000242610.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242610.jpg
+597973	533462	A person is sleeping on one side of the bed.	480	640	COCO_val2014_000000533462.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533462.jpg
+597996	471868	A man is riding a motorcycle down a wet sidewalk.	427	640	COCO_val2014_000000471868.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000471868.jpg
+598095	357916	a young man is about to throw a ball.	333	500	COCO_val2014_000000357916.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000357916.jpg
+598102	323355	A young kid is holding a box of pizza.	640	480	COCO_val2014_000000323355.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000323355.jpg
+598123	195897	A restroom with a toilet basin, toilet paper and a mirror	640	480	COCO_val2014_000000195897.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195897.jpg
+598152	102478	A group of baseball players with a bat and ball at the pitch	375	640	COCO_val2014_000000102478.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000102478.jpg
+598203	534045	An old green fire hydrant sits on a grassy curb.	640	427	COCO_val2014_000000534045.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534045.jpg
+598204	395210	"A large clock near a house is marked, ""1802""."	480	640	COCO_val2014_000000395210.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000395210.jpg
+598495	436795	A man eating a hotdog in a kitchen	480	640	COCO_val2014_000000436795.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000436795.jpg
+598557	500434	Two surfers swimming out into the white waves	426	640	COCO_val2014_000000500434.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500434.jpg
+598561	242208	A woman is talking on her phone while walking.	427	640	COCO_val2014_000000242208.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242208.jpg
+598605	159320	Cars are travelling on a foreign city street.	400	640	COCO_val2014_000000159320.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000159320.jpg
+598640	550666	People gathering at an old building to eat on a sunny day	426	640	COCO_val2014_000000550666.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550666.jpg
+598876	227413	Two jack o lanterns are outside on a rail.	402	600	COCO_val2014_000000227413.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227413.jpg
+598950	50746	A person is laying tennis with racket in hand	415	640	COCO_val2014_000000050746.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000050746.jpg
+599073	330410	This couple is playing wrestling on their Wii.	640	480	COCO_val2014_000000330410.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000330410.jpg
+599094	245430	there is a opening on the street of the building n the city	640	480	COCO_val2014_000000245430.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000245430.jpg
+599220	573406	a group of light resting on a post with a street sign on it	640	383	COCO_val2014_000000573406.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573406.jpg
+599383	539938	A train traveling down tracks near a rural countryside.	424	640	COCO_val2014_000000539938.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000539938.jpg
+599424	396851	Crowd of people walking near trucks outside of large complex.	427	640	COCO_val2014_000000396851.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000396851.jpg
+599436	312103	A man holding a slice of pizza while wearing glasses.	375	500	COCO_val2014_000000312103.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000312103.jpg
+599685	328802	A zebra is in a shady tree lined enclosure.	480	640	COCO_val2014_000000328802.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328802.jpg
+599876	265635	A pretty young lady holding a briefcase in front of maps on a wall.	640	640	COCO_val2014_000000265635.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000265635.jpg
+599877	480179	A pile of luggage and trunks sitting under a cloudy blue sky.	640	480	COCO_val2014_000000480179.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000480179.jpg
+599997	188346	A green fire truck sitting on top of an airport tarmac.	427	640	COCO_val2014_000000188346.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188346.jpg
+600145	123415	An elephant standing next to a smaller elephant.	427	640	COCO_val2014_000000123415.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000123415.jpg
+600158	198775	A red and black smart car parked next to a parking meter.	480	640	COCO_val2014_000000198775.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198775.jpg
+600380	491130	A man riding a snowboard on top of a snow covered slope.	640	443	COCO_val2014_000000491130.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491130.jpg
+600458	216096	A person falling off of a surfboard while other surfers watch.	281	500	COCO_val2014_000000216096.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216096.jpg
+600640	198234	Group of young adults sitting in chairs while some hold wii remotes	375	500	COCO_val2014_000000198234.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198234.jpg
+600991	215778	There is a lap top computer on the desk	427	640	COCO_val2014_000000215778.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000215778.jpg
+601228	374702	A man cutting up food on top of a plate.	640	428	COCO_val2014_000000374702.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000374702.jpg
+601240	406404	A black and white photo of a man in the air with his skateboard.	428	640	COCO_val2014_000000406404.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406404.jpg
+601246	157053	An adorable little girl holding a Nintendo Wii game controller over head.	640	428	COCO_val2014_000000157053.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000157053.jpg
+601509	333321	The sign is telling us where the train station is located.	640	425	COCO_val2014_000000333321.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333321.jpg
+601725	265677	There are a lot of people flying their kites together in the field.	428	640	COCO_val2014_000000265677.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000265677.jpg
+602086	532426	A dog  attached to a motor cycle by a leash.	333	500	COCO_val2014_000000532426.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532426.jpg
+602166	449634	A couple of train engines carrying carts down a set of tracks.	427	640	COCO_val2014_000000449634.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449634.jpg
+602347	296426	A very tall building with people walking around below it.	468	640	COCO_val2014_000000296426.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000296426.jpg
+602492	102947	A den with chairs, a table, couch and television.	176	640	COCO_val2014_000000102947.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000102947.jpg
+602536	32990	A kitchen sink sitting next to a toilet.	640	426	COCO_val2014_000000032990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032990.jpg
+602788	63488	A young man ready to hit a home run.	428	640	COCO_val2014_000000063488.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000063488.jpg
+603257	63403	A close up of black and white zebra stripes.	427	640	COCO_val2014_000000063403.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000063403.jpg
+603264	161635	A man riding a wave on top of a surfboard.	427	640	COCO_val2014_000000161635.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000161635.jpg
+603291	440709	A see through container filled with lots of oranges.	612	612	COCO_val2014_000000440709.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000440709.jpg
+603366	216677	A white plate with a cut in half sandwich.	375	500	COCO_val2014_000000216677.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216677.jpg
+603457	241868	An adult zebra and a child zebra walking through a lake.	313	640	COCO_val2014_000000241868.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000241868.jpg
+603458	26926	An orange fire hydrant with a face and bow tie drawn on it.	640	423	COCO_val2014_000000026926.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000026926.jpg
+603505	559950	An adorable dog looking out the side of a car door window.	480	640	COCO_val2014_000000559950.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559950.jpg
+603586	535993	a glass of water is sitting on a wood table	429	640	COCO_val2014_000000535993.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000535993.jpg
+603640	456261	A man laying on top of a green bed next to a wall.	500	375	COCO_val2014_000000456261.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000456261.jpg
+603642	279927	A group of people standing next to each other near a ground of people on a green patch fo grass.	442	640	COCO_val2014_000000279927.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279927.jpg
+603769	75791	a couple of bears are standing by a fence	427	640	COCO_val2014_000000075791.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000075791.jpg
+603887	273329	a number of people standing around a table with wine bottles	427	640	COCO_val2014_000000273329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273329.jpg
+604083	191689	A brow bear walking next to a large rock in a field.	480	640	COCO_val2014_000000191689.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000191689.jpg
+604090	250001	A man standing on a tennis court holding a tennis racquet	426	640	COCO_val2014_000000250001.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000250001.jpg
+604392	293011	a cake decorated with an airplane has lit candles in it	480	640	COCO_val2014_000000293011.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000293011.jpg
+604445	352936	A crow sitting on the top of a bench	629	640	COCO_val2014_000000352936.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000352936.jpg
+604500	239894	A person wearing chaps with the rear end out and a stuffed bear hanging off their back	640	426	COCO_val2014_000000239894.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000239894.jpg
+604599	114453	Caucasian girl talking on a cell phone in public.	333	500	COCO_val2014_000000114453.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114453.jpg
+604610	90707	A white bowl filled with lots of green broccoli.	480	640	COCO_val2014_000000090707.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000090707.jpg
+604745	450396	A man in a pink tie next to a woman with sunglasses on her head.	480	640	COCO_val2014_000000450396.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450396.jpg
+604872	138241	A burrito sitting on top of a white plate.	640	480	COCO_val2014_000000138241.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000138241.jpg
+604934	443218	A baby with his binky sleeping on a pillow in the middle of a bed.	427	640	COCO_val2014_000000443218.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000443218.jpg
+604956	59479	Lamps are the only lighting in this kitchen.	427	640	COCO_val2014_000000059479.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000059479.jpg
+605295	579003	A shower stall with interesting tile is the focal point.	640	480	COCO_val2014_000000579003.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000579003.jpg
+605405	549171	A young woman is curling her long hair.	640	359	COCO_val2014_000000549171.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000549171.jpg
+605444	44825	In what country do they allow bicycles on a passenger train?	375	500	COCO_val2014_000000044825.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000044825.jpg
+605532	149304	A bathroom with both a toilet and a urinal in it.	512	343	COCO_val2014_000000149304.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000149304.jpg
+605647	365426	Photograph of an outdoor arena that looks great.	426	640	COCO_val2014_000000365426.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365426.jpg
+605781	112329	A traffic signal and signs attached to a pole.	598	640	COCO_val2014_000000112329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000112329.jpg
+605997	220900	Several horses and donkeys are grazing in a field.	480	640	COCO_val2014_000000220900.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000220900.jpg
+606233	99828	A vase filled with white and yellow flowers.	445	640	COCO_val2014_000000099828.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099828.jpg
+606383	87399	A clock on display on a shelf in front of a mirror.	640	480	COCO_val2014_000000087399.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087399.jpg
+606529	415016	A young boy holding a baseball bat on a field.	375	500	COCO_val2014_000000415016.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415016.jpg
+606602	107094	A man riding skis on top of snow covered ground.	426	640	COCO_val2014_000000107094.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107094.jpg
+606631	28702	A man standing with his arms folded while smiling.	640	481	COCO_val2014_000000028702.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028702.jpg
+606720	266385	A golden block sitting in a  room on red carpeting.	640	480	COCO_val2014_000000266385.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000266385.jpg
+606728	123545	a man is holding a racket looking up at the ball	640	428	COCO_val2014_000000123545.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000123545.jpg
+606758	177143	A blue table sitting inside of a restaurant with a menu on top of it.	640	480	COCO_val2014_000000177143.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177143.jpg
+606768	342814	A couple of green buses driving next to a roller coaster.	427	640	COCO_val2014_000000342814.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000342814.jpg
+607016	339852	A black and white dog standing on top of a seat.	480	640	COCO_val2014_000000339852.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000339852.jpg
+607017	149912	A man kneeling down next to a brown cow.	640	458	COCO_val2014_000000149912.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000149912.jpg
+607090	68950	A man riding a paddle board along a river.	480	640	COCO_val2014_000000068950.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068950.jpg
+607358	184972	A man who is wearing a tie with some words on it.	640	474	COCO_val2014_000000184972.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000184972.jpg
+607562	543713	A long horn sheep standing on top of a field.	481	640	COCO_val2014_000000543713.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543713.jpg
+607947	518604	A large bird sitting on top of a tree.	480	640	COCO_val2014_000000518604.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000518604.jpg
+608013	254838	The cat is eating the sandwich on the plate.	427	640	COCO_val2014_000000254838.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000254838.jpg
+608331	87929	A look of a bathroom sink through an open door.	500	375	COCO_val2014_000000087929.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087929.jpg
+608534	408288	A man riding a board on top of a foamy wave.	640	486	COCO_val2014_000000408288.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000408288.jpg
+608729	559348	PEOPLE ARE SITTING IN THE STANDS WATCHING A BASEBALL GAME	480	640	COCO_val2014_000000559348.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559348.jpg
+608927	201418	A young man standing in front of a luggage carousel at an airport.	375	500	COCO_val2014_000000201418.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000201418.jpg
+608988	411821	An item inside the picture which appears to be truly astounding.	640	484	COCO_val2014_000000411821.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000411821.jpg
+609009	228683	An open door and a cat inside a house.	640	480	COCO_val2014_000000228683.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000228683.jpg
+609019	241317	A man pushing a cart filled with lots of fresh fruit.	427	640	COCO_val2014_000000241317.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000241317.jpg
+609020	4021	A baseball field full of baseball players standing on a field.	412	640	COCO_val2014_000000004021.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000004021.jpg
+609077	441862	Man playing WII while and infant and dog interact.	480	640	COCO_val2014_000000441862.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000441862.jpg
+609139	237316	The tiny white bathroom has a toilet and pedestal sink.	500	375	COCO_val2014_000000237316.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237316.jpg
+609228	171494	Some sort of figure in fur walking upright on two feet.	640	599	COCO_val2014_000000171494.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000171494.jpg
+609386	492552	A man with a striped tie and a black blazer	640	530	COCO_val2014_000000492552.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000492552.jpg
+609496	548337	A group of people sitting at tables eating pizza.	640	480	COCO_val2014_000000548337.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000548337.jpg
+609890	184606	A dog is standing in floodwater beside a house.	426	640	COCO_val2014_000000184606.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000184606.jpg
+609897	231831	A black and white cat sitting on top of a chair.	640	481	COCO_val2014_000000231831.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231831.jpg
+609933	464476	A laptop computer sitting on top of a table.	375	500	COCO_val2014_000000464476.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464476.jpg
+609975	507550	A flock of birds standing on top of a body of water.	428	640	COCO_val2014_000000507550.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507550.jpg
+610533	174987	A train is being doodled with green and blue.	480	640	COCO_val2014_000000174987.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174987.jpg
+610549	82940	A pair of woman paddling on surfboards in the ocean.	640	457	COCO_val2014_000000082940.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000082940.jpg
+610550	371135	A woman hitting a tennis ball on a court.	640	512	COCO_val2014_000000371135.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000371135.jpg
+610564	205230	An individual is in the open view in the image.	333	500	COCO_val2014_000000205230.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205230.jpg
+610874	125958	a person is riding a horse by the beach	426	640	COCO_val2014_000000125958.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125958.jpg
+611074	64016	A man has a crow sitting on his hand.	427	640	COCO_val2014_000000064016.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064016.jpg
+611080	491836	A large body of water with a boat on it.	425	640	COCO_val2014_000000491836.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491836.jpg
+611458	129420	Three men wearing black and ties stand and smile at something out of the frame.	427	640	COCO_val2014_000000129420.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129420.jpg
+611565	268124	A bunch of horses that are standing in the water.	480	640	COCO_val2014_000000268124.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268124.jpg
+611569	507318	Grapes, banana, and kiwi are displayed on a plate.	427	640	COCO_val2014_000000507318.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507318.jpg
+611641	369799	A group of kids that are sitting together.	434	500	COCO_val2014_000000369799.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369799.jpg
+611761	540619	A white toilet sitting inside of a bathroom.	640	328	COCO_val2014_000000540619.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540619.jpg
+611787	522380	A cat sitting on top of a blue piece of luggage.	480	640	COCO_val2014_000000522380.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000522380.jpg
+611910	18476	A tie that is sitting on top of a shirt.	347	500	COCO_val2014_000000018476.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018476.jpg
+611955	551737	A man that is standing in the grass.	500	333	COCO_val2014_000000551737.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000551737.jpg
+612313	178370	A social occasion of people are uncovered before the open day.	375	500	COCO_val2014_000000178370.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000178370.jpg
+612563	388983	There is a pickle slice and a hot dog on a piece of bread in a tray.	500	375	COCO_val2014_000000388983.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000388983.jpg
+612659	133867	A parade on the street with a person riding an elephant.	500	470	COCO_val2014_000000133867.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133867.jpg
+612929	558524	The large, very old jar is on display behind glass.	640	480	COCO_val2014_000000558524.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558524.jpg
+612978	442536	Two zebras that are standing in the grass.	454	640	COCO_val2014_000000442536.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442536.jpg
+612999	241528	A small sink inside of a very clean and white bathroom.	640	480	COCO_val2014_000000241528.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000241528.jpg
+613014	477919	A store selling both ripe and unripe bananas.	378	640	COCO_val2014_000000477919.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000477919.jpg
+613135	103485	A group of young people playing a game of soccer.	433	640	COCO_val2014_000000103485.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000103485.jpg
+613157	372620	A young man riding a skateboard in a car garage.	427	640	COCO_val2014_000000372620.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372620.jpg
+613181	422725	A man standing on one leg on a tennis court.	427	640	COCO_val2014_000000422725.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000422725.jpg
+613237	338054	a teddy bear handing from a persons hand	424	640	COCO_val2014_000000338054.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000338054.jpg
+613494	550103	People standing around while someone flies a kit	640	426	COCO_val2014_000000550103.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550103.jpg
+613693	405440	A circuit board sitting on top of a map.	333	500	COCO_val2014_000000405440.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000405440.jpg
+613706	227438	This hotel room is neat, clean, and the bed has been made.	427	640	COCO_val2014_000000227438.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227438.jpg
+613728	510449	A brown bear walking in an open area.	427	640	COCO_val2014_000000510449.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000510449.jpg
+613849	276552	A man cutting food with a knife and a fork	640	480	COCO_val2014_000000276552.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000276552.jpg
+613938	407909	A photo of a person being taken in this picture.	512	640	COCO_val2014_000000407909.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000407909.jpg
+614007	166913	A woman is helping another play a video game.	427	640	COCO_val2014_000000166913.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000166913.jpg
+614120	529917	A bunch of people wearing the same things are standing in line.	426	640	COCO_val2014_000000529917.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529917.jpg
+614294	234785	A dog walking down the street with a bunch of birds flying around.	356	500	COCO_val2014_000000234785.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000234785.jpg
+614325	107661	Two men are playing Frisbee together on a field.	428	640	COCO_val2014_000000107661.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107661.jpg
+614577	92053	Three plates, spoons and glasses on a dining table	427	640	COCO_val2014_000000092053.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092053.jpg
+614754	357542	A man kneeling with his dog in the snow.	640	481	COCO_val2014_000000357542.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000357542.jpg
+614911	558671	A woman cooking food on a stove while a dog watches.	480	640	COCO_val2014_000000558671.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558671.jpg
+614985	254711	A baseball player swinging a bat during a baseball game.	479	640	COCO_val2014_000000254711.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000254711.jpg
+615011	140843	Two soccer teams playing a soccer match in a stadium.	361	640	COCO_val2014_000000140843.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000140843.jpg
+615073	434083	A horse standing next to a man in a hat.	600	400	COCO_val2014_000000434083.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000434083.jpg
+615377	325114	A table with a plate of food and a urinal basin	480	640	COCO_val2014_000000325114.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325114.jpg
+615711	550055	An airplane getting ready to take off at the airport	425	640	COCO_val2014_000000550055.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550055.jpg
+616467	391637	A motor bike parked in front of a building with a cover on it	625	640	COCO_val2014_000000391637.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000391637.jpg
+616649	224742	A view of a couple types of toilet items.	480	640	COCO_val2014_000000224742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224742.jpg
+616955	575606	a small metallic toilet in a tile bathroom	640	480	COCO_val2014_000000575606.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575606.jpg
+617093	147758	a motorcycle is parked near a building by the curb	426	640	COCO_val2014_000000147758.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000147758.jpg
+617209	160283	a small white toilet is in a tiny bathroom	640	480	COCO_val2014_000000160283.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160283.jpg
+617210	39357	a man is holding a tennis racket on a court	593	640	COCO_val2014_000000039357.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039357.jpg
+617362	514914	a fancy bathroom with some tile walls	366	640	COCO_val2014_000000514914.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000514914.jpg
+617506	440171	A man is sitting on the back of an elephant.	640	411	COCO_val2014_000000440171.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000440171.jpg
+617534	152808	Some cut vegetables are kept in a tray on the table.	480	640	COCO_val2014_000000152808.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152808.jpg
+617722	142999	a green blanket a white bed red pillows and pictures	500	358	COCO_val2014_000000142999.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000142999.jpg
+617820	199055	A person on skateboard skating on the pavement.	640	480	COCO_val2014_000000199055.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000199055.jpg
+618583	417597	A person holding a bowl of rice, broccoli, carrots, peas and chicken.	480	640	COCO_val2014_000000417597.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000417597.jpg
+618679	361730	A large group of people flying kites beside a body of water	480	640	COCO_val2014_000000361730.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000361730.jpg
+618729	216325	A man rides a wave on his surfboard while other surfers watch from the shore.	480	640	COCO_val2014_000000216325.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216325.jpg
+618934	442323	A hotel has a small tv on the dresser.	480	640	COCO_val2014_000000442323.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442323.jpg
+618988	56344	Two computer monitors on a desk with a keyboard and mouse	480	640	COCO_val2014_000000056344.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056344.jpg
+619297	66685	Two surfboards on a beach in an urban setting.	480	640	COCO_val2014_000000066685.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000066685.jpg
+619325	461256	Woman stirring boiling pan of food on back burner.	426	640	COCO_val2014_000000461256.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000461256.jpg
+619517	543385	Three zebras eating hay at a wildlife habitat.	487	640	COCO_val2014_000000543385.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543385.jpg
+619807	516476	A bus driving down a street next to a white van.	425	640	COCO_val2014_000000516476.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516476.jpg
+620094	433998	A pitcher winds up in preparation to throw a pitch.	427	640	COCO_val2014_000000433998.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000433998.jpg
+620776	560178	A person holding a bit vegetable in their left hand.	480	640	COCO_val2014_000000560178.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000560178.jpg
+621046	514979	A very large elephant standing next to a baby elephant.	427	640	COCO_val2014_000000514979.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000514979.jpg
+621300	83850	That pizza is four times the size of a normal pizza.	480	640	COCO_val2014_000000083850.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000083850.jpg
+621356	130645	A foreign bus traveling down a street in front of jeep.	441	640	COCO_val2014_000000130645.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000130645.jpg
+621435	248152	One person on a beach flying their kite in the air.	480	640	COCO_val2014_000000248152.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248152.jpg
+621666	415393	a man on a skate board looks like he is falling	425	640	COCO_val2014_000000415393.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415393.jpg
+622057	410934	A broccoli plant has very large leaves.	480	640	COCO_val2014_000000410934.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000410934.jpg
+622058	566941	People laugh at the antics of an elephant.	480	640	COCO_val2014_000000566941.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000566941.jpg
+622098	114458	a child in a bed with a striped sweater and colorful blanket	480	640	COCO_val2014_000000114458.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114458.jpg
+622178	301575	Text written on a photo of a cat shutting in front of a television	534	640	COCO_val2014_000000301575.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301575.jpg
+622469	197279	A clock sitting on top of a sidewalk.	640	480	COCO_val2014_000000197279.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000197279.jpg
+622546	15690	A couple of elephants standing next to each other.	426	640	COCO_val2014_000000015690.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000015690.jpg
+623052	328433	A bird perched on the back of a white bench.	480	640	COCO_val2014_000000328433.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328433.jpg
+623068	134649	A white toilet sitting under a bathroom window.	640	480	COCO_val2014_000000134649.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134649.jpg
+623258	419560	Two giraffes near a tree in the wild.	427	640	COCO_val2014_000000419560.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419560.jpg
+623391	315018	A woman takes a selfie picture with a giraffe in the background.	427	640	COCO_val2014_000000315018.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315018.jpg
+623499	211722	A moustached skate board rider holds his board	496	500	COCO_val2014_000000211722.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211722.jpg
+623553	36277	A cat standing inside of a dresser drawer.	640	423	COCO_val2014_000000036277.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000036277.jpg
+623564	110857	A bedroom with a white bed on a frame next to a window.	369	640	COCO_val2014_000000110857.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000110857.jpg
+623642	370890	A skier makes a jump on a very steep hill.	425	640	COCO_val2014_000000370890.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000370890.jpg
+624125	200284	A surfer lying on his surfboard as a wave approaches from behind.	450	640	COCO_val2014_000000200284.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000200284.jpg
+624224	2239	a little girl holding onto a tennis racket with both hands	424	640	COCO_val2014_000000002239.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000002239.jpg
+624450	410456	a surfing instructor teaches students with surfboards on a beach in front of large hotel buildings.	480	640	COCO_val2014_000000410456.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000410456.jpg
+624476	173204	Two skiers race while a crowd looks on.	479	640	COCO_val2014_000000173204.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173204.jpg
+624699	401429	A man standing over a surfboard in the ocean.	480	640	COCO_val2014_000000401429.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401429.jpg
+624745	228300	A group of young men sitting next to each other on a wall.	374	640	COCO_val2014_000000228300.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000228300.jpg
+624908	427135	A man kicking a soccer ball on a field.	433	640	COCO_val2014_000000427135.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427135.jpg
+625399	192291	A bathroom with walk-in shower, deep tub and sink overlooks other townhouses	640	480	COCO_val2014_000000192291.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192291.jpg
+625492	364557	After a long day surfing, this couple walks down the shoreline.	427	640	COCO_val2014_000000364557.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000364557.jpg
+625728	81738	Someone about to cut and birthday cake with blue and yellow frosting.	480	640	COCO_val2014_000000081738.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000081738.jpg
+625739	125983	A woman dancing in front of a large mirror.	640	458	COCO_val2014_000000125983.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125983.jpg
+625764	301956	A large clock sitting over a lobby in a building.	640	480	COCO_val2014_000000301956.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301956.jpg
+626321	400161	A bunch of remotes that are in cloth holders.	480	640	COCO_val2014_000000400161.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000400161.jpg
+626387	249356	A bathroom with a toilet, shower stall and a mirror.	640	480	COCO_val2014_000000249356.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000249356.jpg
+626711	248341	Which of the four desserts would you choose?	480	640	COCO_val2014_000000248341.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248341.jpg
+626889	300303	A locomotive crossing on a street with the arm down to stop traffic as the train is passing through.	427	640	COCO_val2014_000000300303.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000300303.jpg
+627111	473110	A train engine carrying a cart down a track.	427	640	COCO_val2014_000000473110.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473110.jpg
+627292	256156	A white toilet sitting in a bathroom next to a tile wall.	640	480	COCO_val2014_000000256156.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000256156.jpg
+627307	76416	A double decker bus driving past very tall buildings.	480	640	COCO_val2014_000000076416.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000076416.jpg
+627327	123704	A glass filled with different flavored candy canes.	333	500	COCO_val2014_000000123704.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000123704.jpg
+627471	331329	Munching in the grass is a daily habit.	375	500	COCO_val2014_000000331329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000331329.jpg
+627474	130419	A woman standing next to two bags of luggage.	481	640	COCO_val2014_000000130419.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000130419.jpg
+627812	268044	Giraffes have a very long tongue to go with their long neck.	480	640	COCO_val2014_000000268044.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268044.jpg
+627882	465549	Two young people standing in a living room holding game controllers.	428	640	COCO_val2014_000000465549.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000465549.jpg
+628273	244167	A brown cow in a field of grass near two red barns.	480	640	COCO_val2014_000000244167.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000244167.jpg
+628423	273586	Three surfers take their positions to ride an ocean wave.	428	640	COCO_val2014_000000273586.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273586.jpg
+628638	314108	Old photograph showing group of men with horse drawn cart outside of business.	450	640	COCO_val2014_000000314108.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000314108.jpg
+629384	29056	A sign for a state fair is under a street sign.	480	640	COCO_val2014_000000029056.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029056.jpg
+629537	345456	Passengers departing a train at a busy train station	344	640	COCO_val2014_000000345456.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345456.jpg
+629548	279165	A couple of zebras eating grass from a small grass area.	426	640	COCO_val2014_000000279165.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000279165.jpg
+629766	173514	A snow boarder is standing on a snow covered mountain.	480	640	COCO_val2014_000000173514.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173514.jpg
+629872	288378	A picturesque scene complete with a picnic table, mountain, and body of water	426	640	COCO_val2014_000000288378.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000288378.jpg
+630080	566324	The computer desk in the bedroom is beside a television.	480	640	COCO_val2014_000000566324.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000566324.jpg
+630363	270738	a tennis player swings his racket at a tennis ball on a tennis court	601	640	COCO_val2014_000000270738.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000270738.jpg
+630459	424464	The sculpture is of five people in clown costumes riding one long bike.	480	640	COCO_val2014_000000424464.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000424464.jpg
+630627	565929	A tower with clocks on the walls at the top is standing in the garden.	640	426	COCO_val2014_000000565929.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565929.jpg
+630679	105689	a closeup of a slice of pizza covered with various ingredients	612	612	COCO_val2014_000000105689.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000105689.jpg
+630804	140231	a work desk with display with graphs, notebooks, and keyboard	480	640	COCO_val2014_000000140231.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000140231.jpg
+630962	284043	This quaint backyard has a shed and a bench	500	486	COCO_val2014_000000284043.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000284043.jpg
+630991	66508	A city street with a storm drain, fire hydrant, and manhole cover.	376	500	COCO_val2014_000000066508.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000066508.jpg
+631017	116173	The boy is on his surfboard in the water riding it.	427	640	COCO_val2014_000000116173.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116173.jpg
+631170	160447	a couple of giraffes walk next to a stone wall	426	640	COCO_val2014_000000160447.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160447.jpg
+631498	387551	a row of boats are lined up in the water	480	640	COCO_val2014_000000387551.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000387551.jpg
+631667	487050	The statues were of famous old baseball players.	640	480	COCO_val2014_000000487050.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487050.jpg
+631873	544825	Five people are riding in a red boat on the water.	424	640	COCO_val2014_000000544825.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000544825.jpg
+632204	334178	The pipe smoker enjoys his nightly  smoky ritual.	480	640	COCO_val2014_000000334178.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334178.jpg
+632420	453755	A table topped with piles of dishes under a window.	374	500	COCO_val2014_000000453755.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453755.jpg
+632571	123511	A close up of an orange in a red serving bowl.	339	500	COCO_val2014_000000123511.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000123511.jpg
+632611	487943	a man doing a big jump with his skateboard	476	640	COCO_val2014_000000487943.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487943.jpg
+632627	572571	a pitcher throwing a ball towards a batter during a Dodgers game	375	500	COCO_val2014_000000572571.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000572571.jpg
+632810	264191	Some rocks are collected in a corner in this room	640	427	COCO_val2014_000000264191.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264191.jpg
+632845	420775	a man wearing a backpack and green jacket	428	640	COCO_val2014_000000420775.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000420775.jpg
+632946	362778	These scantily clad people are enjoying a party	640	480	COCO_val2014_000000362778.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000362778.jpg
+632959	233266	A man in a wetsuit rides a wave in the ocean	480	640	COCO_val2014_000000233266.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000233266.jpg
+633062	432273	A traffic light and cars on a street.	325	500	COCO_val2014_000000432273.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000432273.jpg
+633081	255479	Two men in wet suits carry wake boards across a sandy beach.	427	640	COCO_val2014_000000255479.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000255479.jpg
+633241	297620	A large open room with a long white staircase.	612	612	COCO_val2014_000000297620.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297620.jpg
+633471	443723	A black and silver computer mouse with a black background.	480	640	COCO_val2014_000000443723.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000443723.jpg
+633995	464366	The action at a professional baseball game played on a field of grass.	426	640	COCO_val2014_000000464366.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464366.jpg
+634026	388945	Close up view of baked pizza served on table.	427	640	COCO_val2014_000000388945.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000388945.jpg
+634215	95113	A skier stands posing on a flat area in front of the lodge.	640	480	COCO_val2014_000000095113.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000095113.jpg
+634414	365194	A man is swatting at an object with a bat.	427	640	COCO_val2014_000000365194.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365194.jpg
+634773	318911	Several sheep are grazing on grass outside in a fenced in area.	503	640	COCO_val2014_000000318911.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000318911.jpg
+634973	397121	A green heron perches on a stump in a swamp.	512	640	COCO_val2014_000000397121.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397121.jpg
+635154	192714	A street light at an intersection in a small town.	480	640	COCO_val2014_000000192714.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000192714.jpg
+635383	103499	A person with a parasol is walking on a crosswalk.	425	640	COCO_val2014_000000103499.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000103499.jpg
+635426	259037	A man dressed in baseball clothing and holding a baseball glove.	640	480	COCO_val2014_000000259037.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000259037.jpg
+635450	183949	A panda is eating bamboo on the ground.	426	640	COCO_val2014_000000183949.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000183949.jpg
+635553	191919	A group of cows are grazing on the grass.	480	640	COCO_val2014_000000191919.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000191919.jpg
+635556	400719	A group of people in a city park many are flying kites.	427	640	COCO_val2014_000000400719.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000400719.jpg
+635693	49068	A woman in riding gear riding a horse in a dirt arena.	610	640	COCO_val2014_000000049068.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000049068.jpg
+635916	22271	People are standing in front of a building across the street from the western sign	480	640	COCO_val2014_000000022271.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000022271.jpg
+635973	80596	An empty handbag lying flat on a table.	360	640	COCO_val2014_000000080596.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080596.jpg
+636218	477700	the small plane is hanging in a building	419	640	COCO_val2014_000000477700.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000477700.jpg
+636284	494580	the plate has a piece of fish on the side of it	480	640	COCO_val2014_000000494580.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000494580.jpg
+636285	36494	the people are all in a restaurant some are sitting	434	640	COCO_val2014_000000036494.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000036494.jpg
+636489	81561	A large point tower towering over a traffic light.	640	427	COCO_val2014_000000081561.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000081561.jpg
+636581	356937	A cross country skier traveling past many evergreen trees.	427	640	COCO_val2014_000000356937.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000356937.jpg
+636726	385856	A black and brown dog laying on the ground.	471	640	COCO_val2014_000000385856.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000385856.jpg
+636966	546342	Altered black and white photograph of a boy swinging a racquet	640	640	COCO_val2014_000000546342.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546342.jpg
+636974	494473	A street intersection with a stop and street signs.	640	480	COCO_val2014_000000494473.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000494473.jpg
+637156	35594	An elephant mother with a baby elephant beneath her reaches for branches with her trunk.	480	640	COCO_val2014_000000035594.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000035594.jpg
+637367	170519	A zebra is standing away from two adult giraffe.	428	640	COCO_val2014_000000170519.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170519.jpg
+637500	527054	Woman wearing polka dot stockings and pink dress posing with an umbrella.	480	640	COCO_val2014_000000527054.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000527054.jpg
+637661	573179	A black and white photo of a boy and a horse.	640	529	COCO_val2014_000000573179.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573179.jpg
+637709	776	Three teddy bears, each a different color, snuggling together.	640	428	COCO_val2014_000000000776.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000000776.jpg
+637936	346759	A red and white plane is sitting on the runway.	480	640	COCO_val2014_000000346759.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000346759.jpg
+637951	555387	the carrots are laying next to a plate	427	640	COCO_val2014_000000555387.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000555387.jpg
+638083	87244	the fire hydrant has on the side of the road	500	375	COCO_val2014_000000087244.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087244.jpg
+638132	463303	the fire hydrant is behind a rock  and flowers	500	333	COCO_val2014_000000463303.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000463303.jpg
+638259	250608	the bus is stopped at the bus stop	421	640	COCO_val2014_000000250608.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000250608.jpg
+638298	379158	the room has a glass shower as well as a red cross kit	640	630	COCO_val2014_000000379158.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379158.jpg
+638340	335851	As the horses graze, their friend the dog looks on.	512	640	COCO_val2014_000000335851.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335851.jpg
+638443	70619	A train parked in front of a train station.	427	640	COCO_val2014_000000070619.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000070619.jpg
+638652	537513	A bed sitting in a bedroom next to a lamp.	425	640	COCO_val2014_000000537513.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000537513.jpg
+639049	481886	A pizza covered in leafy greens on a table.	640	480	COCO_val2014_000000481886.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000481886.jpg
+639057	430125	A man eating a slice of pizza next to food stands.	360	640	COCO_val2014_000000430125.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000430125.jpg
+639268	250246	A white toilet filled with bags of garbage.	640	480	COCO_val2014_000000250246.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000250246.jpg
+639272	287959	a sandwich that has something green and red in it	480	640	COCO_val2014_000000287959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287959.jpg
+639440	222146	The two cats are looking for something good to eat.	480	640	COCO_val2014_000000222146.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000222146.jpg
+639512	30255	A boat traveling on water under a large bridge.	640	426	COCO_val2014_000000030255.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000030255.jpg
+639532	308793	Two skiers heading up the side of a mountain	480	640	COCO_val2014_000000308793.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000308793.jpg
+639790	149780	A woman playing  with a toilet seat and putting a normal chair over it.	640	427	COCO_val2014_000000149780.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000149780.jpg
+639820	197462	A fire hydrant that has some writing on it.	612	612	COCO_val2014_000000197462.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000197462.jpg
+640003	558528	Men are taking pictures of a skater grinding on a car	427	640	COCO_val2014_000000558528.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558528.jpg
+640203	96711	A woman is sitting outdoors eating some food.	427	640	COCO_val2014_000000096711.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000096711.jpg
+640300	362779	The refrigerator and the stove are both made of stainless steel.	426	640	COCO_val2014_000000362779.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000362779.jpg
+640326	425848	A prepared dinner on a plate sits on a table.	480	640	COCO_val2014_000000425848.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000425848.jpg
+640589	268059	The woman stands cooking at a pot over a fire.	425	640	COCO_val2014_000000268059.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268059.jpg
+640686	190760	A person offering a donut to a cat.	640	427	COCO_val2014_000000190760.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190760.jpg
+640708	245497	A man flying through the air on a skateboard.	332	500	COCO_val2014_000000245497.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000245497.jpg
+640747	421370	A man on a motorcycle going down the street.	427	640	COCO_val2014_000000421370.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421370.jpg
+640911	225913	A couple of people standing at a counter with some food.	480	640	COCO_val2014_000000225913.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000225913.jpg
+641277	278867	A man is throwing a baseball near some water	395	450	COCO_val2014_000000278867.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000278867.jpg
+641309	278570	A picture of four red Asian fire hydrants lined up.	427	640	COCO_val2014_000000278570.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000278570.jpg
+641488	54605	a shake is sitting next to a cake	612	612	COCO_val2014_000000054605.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054605.jpg
+641646	135579	A white plate topped with mean, veggies and mashed potatoes.	480	640	COCO_val2014_000000135579.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000135579.jpg
+641823	301486	Assorted food items displayed on trays with condiments on table.	640	427	COCO_val2014_000000301486.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301486.jpg
+641905	74331	This is a case full of yellow bananas.	640	427	COCO_val2014_000000074331.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000074331.jpg
+642378	118542	A sad, young girl sits on her bed, moping.	480	640	COCO_val2014_000000118542.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118542.jpg
+642513	2212	Two dogs curled up asleep on a couch.	426	640	COCO_val2014_000000002212.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000002212.jpg
+642797	204802	Someone is standing in front of the tv playing the game	427	640	COCO_val2014_000000204802.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000204802.jpg
+642898	573105	A view of a red sports car with luggage on the back of it.	480	640	COCO_val2014_000000573105.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000573105.jpg
+643096	491867	A young bald man dressed sharply smiles happily	640	480	COCO_val2014_000000491867.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491867.jpg
+643271	391225	A man riding a wave on top of a surfboard.	427	640	COCO_val2014_000000391225.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000391225.jpg
+644042	114327	a stop sign sits on the corner of a street	480	360	COCO_val2014_000000114327.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114327.jpg
+644096	72514	A couple of cows tied to a fence in a city area.	425	640	COCO_val2014_000000072514.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000072514.jpg
+644400	46898	A bear is sitting in the grass in front of a rusty chain-link fence	426	640	COCO_val2014_000000046898.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000046898.jpg
+644619	479195	A fire hydrant on the side of the road, it looks old.	427	640	COCO_val2014_000000479195.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000479195.jpg
+644990	344013	This player swings his racket during a match	427	640	COCO_val2014_000000344013.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344013.jpg
+645060	132042	a man is sitting down playing video games	500	375	COCO_val2014_000000132042.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132042.jpg
+645375	502982	A tennis player with a racket at the court	479	640	COCO_val2014_000000502982.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000502982.jpg
+645502	419008	a cat is standing next to a picture of a fish	500	375	COCO_val2014_000000419008.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419008.jpg
+646185	448786	a train that is on a train track	612	612	COCO_val2014_000000448786.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448786.jpg
+646323	391067	a piece of bread with some other food on it	427	640	COCO_val2014_000000391067.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000391067.jpg
+646862	78959	A strange plant hanging off a banana tree.	640	384	COCO_val2014_000000078959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078959.jpg
+646894	160057	A while plate holds a large sandwich and pickle.	480	640	COCO_val2014_000000160057.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160057.jpg
+647039	67310	A male skater jumps in the air at a skate park.	640	486	COCO_val2014_000000067310.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067310.jpg
+647366	444543	two white and black birds a nest and some trees	424	640	COCO_val2014_000000444543.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444543.jpg
+647596	231617	A red and yellow bus sits on the back of a flatbed truck, driving down the highway.	480	640	COCO_val2014_000000231617.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231617.jpg
+647700	457572	A girl lays on a bed next to her glasses looking in the mirror	640	480	COCO_val2014_000000457572.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000457572.jpg
+647706	578788	A young man riding a skateboard on a hand rail	333	500	COCO_val2014_000000578788.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000578788.jpg
+648167	239857	A woman is about to throw a frisbee in the dirt.	333	500	COCO_val2014_000000239857.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000239857.jpg
+648249	111031	A man and woman are playing a video game together.	640	598	COCO_val2014_000000111031.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000111031.jpg
+648401	286450	A large black train on a train track.	480	640	COCO_val2014_000000286450.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000286450.jpg
+648900	253506	A white tow truck with another car behind it.	612	612	COCO_val2014_000000253506.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000253506.jpg
+648902	139953	An arrangement of donuts and other food sits on a table.	612	612	COCO_val2014_000000139953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000139953.jpg
+649006	113058	A young man laying on a sofa using a laptop.	480	640	COCO_val2014_000000113058.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000113058.jpg
+649299	380487	A little girl is sitting on her bed that has a flower bed spread.	360	480	COCO_val2014_000000380487.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000380487.jpg
+649447	460251	A tv remote control sits on a table.	640	639	COCO_val2014_000000460251.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460251.jpg
+649526	107964	A man doing a trick on a skate board.	425	640	COCO_val2014_000000107964.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107964.jpg
+649656	268541	a man in a suit drinking a cup of coffee	430	430	COCO_val2014_000000268541.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268541.jpg
+649665	538891	A female tennis player is on the court playing.	640	427	COCO_val2014_000000538891.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000538891.jpg
+649838	213091	A kitchen with white cabinets and a grey counter top.	480	640	COCO_val2014_000000213091.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213091.jpg
+649847	256031	A surfer is depicted riding a wave on a surf board.	432	640	COCO_val2014_000000256031.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000256031.jpg
+650140	496548	The skier is wearing a blue snowsuit and the person behind her is wearing red.	480	640	COCO_val2014_000000496548.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000496548.jpg
+650560	565034	A computer on a desk with multiple speakers	480	640	COCO_val2014_000000565034.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565034.jpg
+650664	578203	There is a bowl of fresh fruit on the table.	457	640	COCO_val2014_000000578203.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000578203.jpg
+650910	49740	A child baseball player tries to hit the ball	508	640	COCO_val2014_000000049740.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000049740.jpg
+651322	353509	a person that is on a horse in a field	400	640	COCO_val2014_000000353509.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000353509.jpg
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+651499	174425	A small slice of pizza remains on the plate	480	640	COCO_val2014_000000174425.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000174425.jpg
+651512	492196	A boat tugs a barge behind it through the water.	360	640	COCO_val2014_000000492196.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000492196.jpg
+652213	28273	The little black kitten is sitting inside a black bag.	480	640	COCO_val2014_000000028273.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028273.jpg
+652241	288465	Sheep in the process of getting sheered of their wool	359	640	COCO_val2014_000000288465.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000288465.jpg
+652290	237558	This aerial shot shows a person crossing the street with an umbrella over their head.	640	521	COCO_val2014_000000237558.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237558.jpg
+652425	403975	A baby is sitting on the floor with a small cake in front of him.	427	640	COCO_val2014_000000403975.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000403975.jpg
+652588	553129	The organized kitchen of a busy family in a suburban home	427	640	COCO_val2014_000000553129.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000553129.jpg
+652841	502	A furry, black bear standing in a rocky, weedy, area in the wild.	427	640	COCO_val2014_000000000502.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000000502.jpg
+652866	168662	A woman is looking over a ledge where a small bird is perched.	480	640	COCO_val2014_000000168662.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168662.jpg
+652979	306159	point of view shot of man using a small urinal in bathroom	640	480	COCO_val2014_000000306159.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306159.jpg
+653440	133693	Traffic and street signs sit on the pole	409	640	COCO_val2014_000000133693.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133693.jpg
+653871	516865	Several skiers on the slop in mid jump.	285	500	COCO_val2014_000000516865.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516865.jpg
+653877	38572	A man is performing in a tank with dolphins	480	640	COCO_val2014_000000038572.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038572.jpg
+653907	367680	Cars, truck and carriages are traveling on the road	338	450	COCO_val2014_000000367680.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000367680.jpg
+653974	579362	A man sits on a bench on a blue bench.	640	454	COCO_val2014_000000579362.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000579362.jpg
+653978	244754	a living room with some fancy curtains behind a couch	332	500	COCO_val2014_000000244754.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000244754.jpg
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+654387	67463	A truck that is sitting in the road.	640	480	COCO_val2014_000000067463.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067463.jpg
+654632	162393	A man that is standing on the court with a racquet.	500	375	COCO_val2014_000000162393.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000162393.jpg
+654987	133571	A horse is standing next to a metal box.	427	640	COCO_val2014_000000133571.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133571.jpg
+655623	194153	a women that has a umbrella in her hand	640	476	COCO_val2014_000000194153.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000194153.jpg
+655743	138832	A large piece of fruit is growing on a tree.	640	480	COCO_val2014_000000138832.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000138832.jpg
+656032	442478	A young man performing a stunt on a skateboard in a skate pit.	500	335	COCO_val2014_000000442478.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442478.jpg
+656200	508602	Bird standing on car roof near covered pavilion.	429	500	COCO_val2014_000000508602.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000508602.jpg
+656253	121812	This hazy picture depicts traffic on a busy street.	375	500	COCO_val2014_000000121812.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000121812.jpg
+656275	302155	The people carry their travel suitcase between them.	428	640	COCO_val2014_000000302155.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000302155.jpg
+656304	213399	a little giraffe sitting on the ground next to a somewhat bigger one	427	640	COCO_val2014_000000213399.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213399.jpg
+656314	331764	These red and yellow flowers are in a white vase	640	480	COCO_val2014_000000331764.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000331764.jpg
+656347	463388	It is a great day for these youngsters to have tennis lessons.	332	500	COCO_val2014_000000463388.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000463388.jpg
+656495	494578	Wood post lined up in the water with birds perched on them.	429	640	COCO_val2014_000000494578.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000494578.jpg
+656653	188028	A large amount of boats are parked in the marina	425	640	COCO_val2014_000000188028.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188028.jpg
+656824	523426	This bathroom has gray walls and white fixtures.	640	480	COCO_val2014_000000523426.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000523426.jpg
+656969	412639	Two people are standing and talking to each other with a bus in the background.	478	640	COCO_val2014_000000412639.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000412639.jpg
+657236	329653	Group of chocolate covered strawberries on square plate.	544	640	COCO_val2014_000000329653.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000329653.jpg
+657475	193884	A red stop sign sitting on the side of a road.	480	640	COCO_val2014_000000193884.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000193884.jpg
+657724	119802	stuffed animals are sitting together in a street corner	427	640	COCO_val2014_000000119802.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000119802.jpg
+657916	33904	A teddy bear is positioned to read a text book.	480	640	COCO_val2014_000000033904.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000033904.jpg
+658263	38210	a skier is prepared to go down a mountain.	640	428	COCO_val2014_000000038210.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038210.jpg
+658270	16838	A small model figurine set for Back to the Future.	427	640	COCO_val2014_000000016838.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000016838.jpg
+658874	304355	this clean bathroom has all white fixtures in it	640	480	COCO_val2014_000000304355.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000304355.jpg
+659130	207785	Obama speaking to a crowd of Obama supporters.	640	428	COCO_val2014_000000207785.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000207785.jpg
+659251	272253	Whatever is in the cup does not please this woman.	500	316	COCO_val2014_000000272253.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000272253.jpg
+659282	147346	A man in a trench coat, hat, and carrying an umbrella pointing to the shirt collar of another man.	333	500	COCO_val2014_000000147346.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000147346.jpg
+659335	445557	A plate of donuts on a white plate rests on a counter.	480	640	COCO_val2014_000000445557.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000445557.jpg
+659637	472445	The lights are on above the mirror of the bathroom sink.	480	640	COCO_val2014_000000472445.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472445.jpg
+659682	321877	A blond woman hold a red Wii controller.	640	425	COCO_val2014_000000321877.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000321877.jpg
+659800	445766	this fire hydrant has a painting on it	640	428	COCO_val2014_000000445766.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000445766.jpg
+659903	500657	A little league baseman couching by his base.	427	640	COCO_val2014_000000500657.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500657.jpg
+659957	89355	Black and white picture of two blond women walking on beach with surf board.	516	640	COCO_val2014_000000089355.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089355.jpg
+660087	474934	Two horses on a beach near the water	480	640	COCO_val2014_000000474934.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474934.jpg
+660099	72752	A dining room table and four wood chairs.	500	375	COCO_val2014_000000072752.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000072752.jpg
+660202	390704	a person riding a snow board on a snowy surface	480	640	COCO_val2014_000000390704.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000390704.jpg
+660253	413811	People at a beach playing in the water or carrying water boards.	427	640	COCO_val2014_000000413811.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000413811.jpg
+660336	165056	Two giraffes are standing together under the trees.	480	640	COCO_val2014_000000165056.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000165056.jpg
+660701	405778	A herd of zebras is grazing in a grassy field.	480	640	COCO_val2014_000000405778.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000405778.jpg
+660757	321718	A woman takes a dish out of the oven proudly.	612	612	COCO_val2014_000000321718.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000321718.jpg
+660862	39914	A woman and small child are trying to fly a kite.	640	487	COCO_val2014_000000039914.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039914.jpg
+660864	21563	A sliced pizza with cheese on it is shown on a plate.	427	640	COCO_val2014_000000021563.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000021563.jpg
+660896	364567	a person riding a horse jumping over an obstacle	447	640	COCO_val2014_000000364567.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000364567.jpg
+660951	303264	people in a terminal with suitcases taking some pictures	375	500	COCO_val2014_000000303264.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303264.jpg
+661315	176226	Zebras, an elephant and some giraffes stand together in the grass.	480	640	COCO_val2014_000000176226.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000176226.jpg
+661370	531299	a person riding a skate board on a city street	640	427	COCO_val2014_000000531299.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000531299.jpg
+661410	133380	A small orange bird standing on a collection of rocks.	398	640	COCO_val2014_000000133380.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133380.jpg
+661558	114303	A bathroom area with a tub, shelves and a sink.	500	500	COCO_val2014_000000114303.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114303.jpg
+661680	500723	a cat on a chair on the ground	640	480	COCO_val2014_000000500723.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500723.jpg
+661800	429802	A red fire hydrant placed near a building	640	395	COCO_val2014_000000429802.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000429802.jpg
+662204	236016	A bedroom with a dresser, tv, and a lamp in it.	426	640	COCO_val2014_000000236016.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000236016.jpg
+662312	273246	a number of people on a beach with a body of water	425	640	COCO_val2014_000000273246.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000273246.jpg
+662345	554900	a close up of a toilet with tiled walls	640	428	COCO_val2014_000000554900.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000554900.jpg
+662802	205232	The large crowd stands on the beach underneath flying kites.	480	640	COCO_val2014_000000205232.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205232.jpg
+662824	438481	A traffic light with a blue sign underneath it	640	480	COCO_val2014_000000438481.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000438481.jpg
+663107	173490	A white toilet sitting below a bathroom window.	480	640	COCO_val2014_000000173490.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173490.jpg
+663184	407521	there is a man riding a skateboard over a ramp	320	480	COCO_val2014_000000407521.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000407521.jpg
+663687	510275	A banana peel laying next to a glass bottle.	468	640	COCO_val2014_000000510275.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000510275.jpg
+663821	369082	A kitchen with a stove, oven, cabinets and blender.	480	640	COCO_val2014_000000369082.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369082.jpg
+663931	491098	A formally set dining table is adorned with fresh tulips.	480	640	COCO_val2014_000000491098.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491098.jpg
+663967	10014	A city bus parked next to a crowd of people.	426	640	COCO_val2014_000000010014.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010014.jpg
+664010	10393	a man is riding a white bike down the street	480	640	COCO_val2014_000000010393.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010393.jpg
+664061	431401	A vase sitting on top of a counter.	640	433	COCO_val2014_000000431401.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431401.jpg
+664064	567197	an old black and white photo of cars parked near a building	407	500	COCO_val2014_000000567197.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000567197.jpg
+664220	420916	a red park bench that is by some water	400	640	COCO_val2014_000000420916.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000420916.jpg
+664794	139721	A cat calmly sitting on a counter next to some scissors.	480	640	COCO_val2014_000000139721.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000139721.jpg
+664807	487435	A woman riding skis down a snow covered slope.	375	500	COCO_val2014_000000487435.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487435.jpg
+665051	427666	Three bananas that are sitting next to a laptop and cellphones.	427	640	COCO_val2014_000000427666.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427666.jpg
+665265	345211	Two people playing tennis with a referee and another man watching.	375	500	COCO_val2014_000000345211.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345211.jpg
+665321	569273	Fire hydrant sitting in the middle of a hiking area on the side of a gorge.	480	640	COCO_val2014_000000569273.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000569273.jpg
+665690	383359	A person is trying to hit a ball with a tennis racket.	427	640	COCO_val2014_000000383359.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000383359.jpg
+665707	118124	A man with glasses and a tie clip sits in his car.	640	480	COCO_val2014_000000118124.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118124.jpg
+665714	548722	A zebra in scrub land with small trees behind.	480	640	COCO_val2014_000000548722.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000548722.jpg
+665801	428856	There is a battleship floating in the water.	427	640	COCO_val2014_000000428856.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000428856.jpg
+665888	457394	A white plate filled with lots of pasta.	427	640	COCO_val2014_000000457394.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000457394.jpg
+665958	526222	A woman swinging a racket at a tennis ball.	376	640	COCO_val2014_000000526222.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000526222.jpg
+666141	213785	Two people who are diving for a Frisbee.	333	500	COCO_val2014_000000213785.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213785.jpg
+666254	217521	A cat walking past a wooden desk next to another cat.	500	375	COCO_val2014_000000217521.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000217521.jpg
+667158	44579	A man holding a phone up to his ear.	500	375	COCO_val2014_000000044579.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000044579.jpg
+667186	314125	a plate of vegetables sits next to a trey of pizza	640	480	COCO_val2014_000000314125.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000314125.jpg
+667245	103747	A man riding skis on top of a snow covered slope.	427	640	COCO_val2014_000000103747.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000103747.jpg
+667536	179045	A young child sitting on top of a couch.	478	640	COCO_val2014_000000179045.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000179045.jpg
+667724	214494	Someone who is applying some chocolate on a cake.	640	448	COCO_val2014_000000214494.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214494.jpg
+668014	432378	A group of people standing outside of a bus.	508	640	COCO_val2014_000000432378.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000432378.jpg
+668082	190648	A messy and unmade bed and a red chair	480	640	COCO_val2014_000000190648.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190648.jpg
+668105	297046	A train crosses an intersection while cars wait.	401	640	COCO_val2014_000000297046.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297046.jpg
+668554	375554	BMX bikers waiting in sandy area for something with gear to ride.	425	640	COCO_val2014_000000375554.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000375554.jpg
+668595	326243	A baseball player throwing a pitch wearing a glove.	640	512	COCO_val2014_000000326243.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000326243.jpg
+668840	214503	a rainbow umbrella that has people under it	500	375	COCO_val2014_000000214503.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214503.jpg
+668841	118209	a couple of horse that are next to a fence	480	640	COCO_val2014_000000118209.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118209.jpg
+668877	488785	A vase with flowers on the table	640	480	COCO_val2014_000000488785.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000488785.jpg
+669286	56872	A man in sunglasses and chains is holding a microphone to his mouth.	640	427	COCO_val2014_000000056872.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000056872.jpg
+669614	450359	A family is in the kitchen preparing a meal.	480	640	COCO_val2014_000000450359.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450359.jpg
+669645	503799	A man standing on a skateboard riding it down a sidewalk.	426	640	COCO_val2014_000000503799.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000503799.jpg
+669696	262686	A man pointing at some food on a trey.	640	427	COCO_val2014_000000262686.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000262686.jpg
+669925	175443	A Christmas tree with lights and teddy bear	640	480	COCO_val2014_000000175443.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000175443.jpg
+670007	216636	A viw of a cake with pieces cut off of it.	427	640	COCO_val2014_000000216636.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216636.jpg
+670065	358976	A cat laying on top of a couch.	480	640	COCO_val2014_000000358976.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000358976.jpg
+670071	200770	A man catching a frisbee while standing in a park.	386	500	COCO_val2014_000000200770.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000200770.jpg
+670079	338560	A blue fire hydrant posed on a street corner in a city.	640	480	COCO_val2014_000000338560.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000338560.jpg
+670712	560365	A cooked pizza with different toppings sitting on a pan.	480	640	COCO_val2014_000000560365.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000560365.jpg
+670738	98328	Two zebras pushing against each other in a field.	480	640	COCO_val2014_000000098328.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000098328.jpg
+670909	553081	A family of giraffes stand together beside a wood fence.	425	640	COCO_val2014_000000553081.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000553081.jpg
+671256	314689	A group of young children sitting on top of a bed.	427	640	COCO_val2014_000000314689.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000314689.jpg
+671312	504005	A pan filled with an assortment of vegetables and a wooden spoon.	480	640	COCO_val2014_000000504005.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000504005.jpg
+671323	173545	People are sitting under umbrellas while riding elephants.	640	428	COCO_val2014_000000173545.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173545.jpg
+671327	458365	A white car is parked in the street at night time.	463	640	COCO_val2014_000000458365.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000458365.jpg
+671336	560794	A bathroom with a sink under a mirror.	640	426	COCO_val2014_000000560794.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000560794.jpg
+671471	84530	A group of men sitting next to each other.	403	640	COCO_val2014_000000084530.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000084530.jpg
+671499	177919	A man riding a skateboard down cement steps.	640	427	COCO_val2014_000000177919.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177919.jpg
+671978	476628	an oven with the door open and a closed pot on the iside	612	612	COCO_val2014_000000476628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000476628.jpg
+671983	231855	A group of people riding on the backs of motorcycles.	425	640	COCO_val2014_000000231855.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231855.jpg
+672308	397617	Two women walking together in the rain with umbrellas.	640	427	COCO_val2014_000000397617.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397617.jpg
+672488	578168	Several bench seats lined up in the car of a vehicle.	375	500	COCO_val2014_000000578168.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000578168.jpg
+672550	282113	A fuzzy white goat is grazing in the grass	412	640	COCO_val2014_000000282113.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000282113.jpg
+672554	180798	a cellphone sitting on a blue mesh blanket	500	375	COCO_val2014_000000180798.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000180798.jpg
+672934	493395	A rose blooms next to a train track.	640	428	COCO_val2014_000000493395.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493395.jpg
+673052	569274	a couple of kids that are eating at a table	480	640	COCO_val2014_000000569274.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000569274.jpg
+673185	534018	A large crowd of people stand in the snow outside a building.	480	640	COCO_val2014_000000534018.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534018.jpg
+673218	78107	A cat sits in front of television intently looking at it.	480	640	COCO_val2014_000000078107.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078107.jpg
+673237	543681	A clock displays the time outside of a bank.	640	480	COCO_val2014_000000543681.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543681.jpg
+673403	288202	A polar bear laying on a large stack of ice.	640	480	COCO_val2014_000000288202.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000288202.jpg
+674151	12706	A crowd of pedestrians around a French monument.	640	427	COCO_val2014_000000012706.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000012706.jpg
+674195	339851	A young man in a yellow shirt and black shorts hitting a tennis ball in a tennis match in a stadium.	428	640	COCO_val2014_000000339851.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000339851.jpg
+674357	379488	A person on a surf board riding a wave.	425	640	COCO_val2014_000000379488.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379488.jpg
+674368	68740	A very cute small fire hydrant by a small road.	640	385	COCO_val2014_000000068740.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068740.jpg
+674853	101860	A group of men on a field playing baseball.	640	480	COCO_val2014_000000101860.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000101860.jpg
+674992	444576	A man and a dog ride in a convertible.	436	640	COCO_val2014_000000444576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444576.jpg
+675089	347953	A bird is flying away from the person	427	640	COCO_val2014_000000347953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347953.jpg
+675112	272718	a person para sailing on the ocean waves	454	640	COCO_val2014_000000272718.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000272718.jpg
+675166	551959	A man is feeding a griaffe off of the stand	404	640	COCO_val2014_000000551959.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000551959.jpg
+675288	474953	A man gets ready to hit the pitch being thrown	426	640	COCO_val2014_000000474953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474953.jpg
+675429	27610	Four people are looking at a cell phone.	426	640	COCO_val2014_000000027610.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000027610.jpg
+675576	72536	A person is taking a slice of pizza inside.	480	640	COCO_val2014_000000072536.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000072536.jpg
+675580	502024	A white controller for the Nintendo Wii game system.	640	480	COCO_val2014_000000502024.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000502024.jpg
+675772	354278	A fire hydrant is painted to look like a dalmation	640	490	COCO_val2014_000000354278.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000354278.jpg
+675957	287436	People standing over a barrel with several fruit in it.	513	640	COCO_val2014_000000287436.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287436.jpg
+675993	231732	A sink with a toothbrush holder, soap and a mirror around it.	426	640	COCO_val2014_000000231732.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000231732.jpg
+676036	345353	A person sitting at a table with two plates of food.	480	640	COCO_val2014_000000345353.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345353.jpg
+676695	574425	Two old fashioned buses parked on the side of the road.	422	640	COCO_val2014_000000574425.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000574425.jpg
+676709	288847	A cat peeking over a table while in the chair	427	640	COCO_val2014_000000288847.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000288847.jpg
+676815	14135	A man skate bording going up on a ramp.	427	640	COCO_val2014_000000014135.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000014135.jpg
+677038	113095	A skateboarder rides his skateboard across a parking lot.	640	428	COCO_val2014_000000113095.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000113095.jpg
+677213	451496	A motorcycle near a curb with a building in its mirror.	426	640	COCO_val2014_000000451496.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451496.jpg
+677624	79139	A man serving a tennis ball on top of a tennis court.	375	500	COCO_val2014_000000079139.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000079139.jpg
+677939	360181	a man that is jumping his skateboard on some concrete	500	372	COCO_val2014_000000360181.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360181.jpg
+678058	362293	a horse and buggy that is going down a road	425	640	COCO_val2014_000000362293.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000362293.jpg
+678273	419048	Some very cute cows in a nice shaded area.	480	640	COCO_val2014_000000419048.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000419048.jpg
+678345	100977	Two women each feeding a giraffe at their enclosure.	480	640	COCO_val2014_000000100977.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000100977.jpg
+678418	338591	A big clock on top of a big strange structure.	640	480	COCO_val2014_000000338591.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000338591.jpg
+678448	60641	Four boxes of pizza are opened on the table	480	640	COCO_val2014_000000060641.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000060641.jpg
+678482	305464	there is a large bucket and a small pot in this bathroom	375	500	COCO_val2014_000000305464.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000305464.jpg
+678756	442161	Three people pose for a picture while drinking wine	375	500	COCO_val2014_000000442161.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442161.jpg
+678838	481281	a horse that is tied up to a post	612	612	COCO_val2014_000000481281.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000481281.jpg
+678847	409346	two ladies filling their plates with lots of food	425	640	COCO_val2014_000000409346.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000409346.jpg
+678864	525988	A small teddy bear holding a toy camera.	428	640	COCO_val2014_000000525988.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000525988.jpg
+678899	138995	A boy is dressed up in a tie and is holding a stick.	640	478	COCO_val2014_000000138995.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000138995.jpg
+678955	478386	An old truck is parked in front of the station	428	640	COCO_val2014_000000478386.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000478386.jpg
+679246	449839	A bride and groom posing by a tree	640	428	COCO_val2014_000000449839.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449839.jpg
+679485	128112	a man that is on a snowy trail	640	425	COCO_val2014_000000128112.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000128112.jpg
+679524	146123	A guy on a surf board riding a wave.	427	640	COCO_val2014_000000146123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146123.jpg
+679896	49881	a couple of people on some motorcycles	612	612	COCO_val2014_000000049881.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000049881.jpg
+680073	302302	A very big cute looking bird sitting on a pole.	640	478	COCO_val2014_000000302302.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000302302.jpg
+680168	213086	A man holding an oven door open while he looks in it.	500	375	COCO_val2014_000000213086.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213086.jpg
+680360	515022	A red and yellow train on the train tracks.	441	640	COCO_val2014_000000515022.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000515022.jpg
+681330	522418	A woman wearing a net on her head cutting a cake.	480	640	COCO_val2014_000000522418.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000522418.jpg
+681376	61268	A train going down the track near a city	424	640	COCO_val2014_000000061268.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000061268.jpg
+681390	22797	An airplane close to the ground with its landing gear dropped	427	640	COCO_val2014_000000022797.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000022797.jpg
+681467	442952	A bench sitting in the middle of a lake.	400	640	COCO_val2014_000000442952.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000442952.jpg
+681571	254516	A baseball player holding a bat next to a dugout.	427	640	COCO_val2014_000000254516.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000254516.jpg
+681651	282768	A man on a skateboard with a dog on the sidewalk	500	330	COCO_val2014_000000282768.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000282768.jpg
+681684	211891	A train sitting inside of a train station near a skylight.	360	640	COCO_val2014_000000211891.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211891.jpg
+681697	379554	a toilet that has some bubbles coming out of it	281	500	COCO_val2014_000000379554.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379554.jpg
+681737	401024	A pair of hands in the process of cutting hair	538	640	COCO_val2014_000000401024.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401024.jpg
+681800	498638	A boy serving a tennis ball on the court	500	400	COCO_val2014_000000498638.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498638.jpg
+681841	554021	A man in holding a tube next to a shirt.	640	480	COCO_val2014_000000554021.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000554021.jpg
+681920	345071	There are several people standing next to motor cycles.	480	640	COCO_val2014_000000345071.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345071.jpg
+682168	571847	A tray with a donut and coffee on it	427	640	COCO_val2014_000000571847.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000571847.jpg
+682579	355919	A slice of pepperoni and sausage pizza and a drink in a disposable cup.	500	375	COCO_val2014_000000355919.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000355919.jpg
+682686	538454	A man prepares to kick a soccer ball for three children.	612	612	COCO_val2014_000000538454.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000538454.jpg
+682693	92768	A man sitting on a bench holding a wooden guitar.	427	640	COCO_val2014_000000092768.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000092768.jpg
+682695	340503	A giant man standing  under large colorful kites.	500	332	COCO_val2014_000000340503.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000340503.jpg
+683484	53123	A tennis player has just attempted to hit the ball.	640	427	COCO_val2014_000000053123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000053123.jpg
+683816	98385	A table with a plate that has food on it and a mug.	360	640	COCO_val2014_000000098385.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000098385.jpg
+683880	178948	A dog stands on a street corner near a group of people.	640	480	COCO_val2014_000000178948.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000178948.jpg
+683931	543041	Six donuts of varies flavors in a box	358	500	COCO_val2014_000000543041.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543041.jpg
+684192	2153	Batter preparing to swing at pitch during major game.	480	640	COCO_val2014_000000002153.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000002153.jpg
+684419	348522	Man sitting on large block checking cell phone.	426	640	COCO_val2014_000000348522.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000348522.jpg
+684588	224391	Lone elephant walking on dirt path near bushy area.	480	640	COCO_val2014_000000224391.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000224391.jpg
+684603	369093	The pizza is on wax paper near a paper plate.	480	640	COCO_val2014_000000369093.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000369093.jpg
+684666	57878	a lady eating at a table holding up the peace sign	480	640	COCO_val2014_000000057878.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000057878.jpg
+684740	541813	A chair sitting under a picture hanging on a  wall.	427	640	COCO_val2014_000000541813.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000541813.jpg
+684843	168834	Cute little cat sitting inside of a bidet	418	640	COCO_val2014_000000168834.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168834.jpg
+685124	476300	Several elephants walking in the dirt near grass.	375	500	COCO_val2014_000000476300.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000476300.jpg
+685395	478731	A man with glasses and a tie on staring.	640	480	COCO_val2014_000000478731.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000478731.jpg
+685498	161447	A woman and three men at a table with most of the pizza gone.	423	640	COCO_val2014_000000161447.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000161447.jpg
+685624	35966	The large blue truck is old with rusted parts.	426	640	COCO_val2014_000000035966.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000035966.jpg
+685880	113678	A dog that is wearing a red hat.	270	500	COCO_val2014_000000113678.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000113678.jpg
+685937	301506	A sandwich being browned on a buttered grill.	427	640	COCO_val2014_000000301506.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301506.jpg
+685982	464134	A baseball field; young children are the batter and the catcher while the umpire and the pitcher are grown men.	375	500	COCO_val2014_000000464134.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464134.jpg
+686268	544121	A desk topped with a laptop computer next to another laptop.	480	640	COCO_val2014_000000544121.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000544121.jpg
+686522	378126	Women are running after the ball in a soccer game.	427	640	COCO_val2014_000000378126.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000378126.jpg
+686533	30270	a white tub is in an outside area	427	640	COCO_val2014_000000030270.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000030270.jpg
+686630	45108	A couple of cats taking a nap on top of a bed.	640	478	COCO_val2014_000000045108.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000045108.jpg
+686646	28463	A table topped with breakfast food and a cup of orange juice.	612	612	COCO_val2014_000000028463.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028463.jpg
+686864	406570	A pile of apples sitting next to a pile of green apples.	640	427	COCO_val2014_000000406570.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406570.jpg
+686880	162523	A man who is performing a trick on a skateboard.	500	339	COCO_val2014_000000162523.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000162523.jpg
+686947	458950	A very skinny red fire hydrant with silver top and sides.	423	640	COCO_val2014_000000458950.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000458950.jpg
+686964	97679	A car with a snowboard on it's ski rack parked in a foggy parking lot next to two cars with empty ski racks.	480	640	COCO_val2014_000000097679.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000097679.jpg
+687134	529348	Two zebras are eating grass under an umbrella.	480	640	COCO_val2014_000000529348.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529348.jpg
+687369	181870	this lady is standing next to her brown horse	439	640	COCO_val2014_000000181870.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000181870.jpg
+687496	377097	there is a man that is posing for a picture in front of donuts	480	640	COCO_val2014_000000377097.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000377097.jpg
+687600	219984	A woman flying a kite in the blue sky.	640	427	COCO_val2014_000000219984.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000219984.jpg
+687915	284885	Train traveling on tracks through city near glass buildings.	425	640	COCO_val2014_000000284885.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000284885.jpg
+688024	94760	a man trying to zip a woman into a suitcase	400	600	COCO_val2014_000000094760.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000094760.jpg
+688028	289938	A hot dog cart across from the Radio City Music Hall.	426	640	COCO_val2014_000000289938.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289938.jpg
+688529	152609	A living room with tv and speakers, sofa and coffee table	480	640	COCO_val2014_000000152609.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152609.jpg
+688537	142472	A city filled with lots of vehicles and pedestrian traffic.	480	640	COCO_val2014_000000142472.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000142472.jpg
+688725	118113	this is a very dark picture of a room with a shelf	640	480	COCO_val2014_000000118113.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118113.jpg
+688746	266502	a small plane with a propellor sitting on a runway	480	640	COCO_val2014_000000266502.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000266502.jpg
+688820	52925	a small cat stands on some snowy plank	464	640	COCO_val2014_000000052925.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000052925.jpg
+689041	283659	A man riding a kiteboard on top of a wave in the ocean.	426	640	COCO_val2014_000000283659.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000283659.jpg
+689363	565245	a processing machine with a bunch of doughnuts on it	482	640	COCO_val2014_000000565245.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565245.jpg
+689529	32610	a desk top with a bunch of electronics on it	427	640	COCO_val2014_000000032610.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032610.jpg
+689777	21614	Mixed assortment of fish rice and vegetables with carrots.	612	612	COCO_val2014_000000021614.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000021614.jpg
+689808	217517	A city filled with lots of tall white buildings.	273	500	COCO_val2014_000000217517.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000217517.jpg
+689819	251798	there is a man holding up a blow dryer	480	640	COCO_val2014_000000251798.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000251798.jpg
+689830	82554	A large cheesy pizza sitting on top of a white plate.	480	640	COCO_val2014_000000082554.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000082554.jpg
+690171	564336	a man at a restaurant using his laptop	360	640	COCO_val2014_000000564336.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000564336.jpg
+690348	354662	a tiled bathroom with a toilet and scale in it	640	427	COCO_val2014_000000354662.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000354662.jpg
+690810	417144	A pile of fresh fruits and vegetables sitting on display.	480	640	COCO_val2014_000000417144.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000417144.jpg
+690958	403020	A giraffe lying on the ground in a zoo pin.	640	360	COCO_val2014_000000403020.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000403020.jpg
+691085	543034	there is a dog and a cat sitting in a basket	500	385	COCO_val2014_000000543034.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000543034.jpg
+691303	474186	A cut in half sandwich sitting on top of a basket.	640	640	COCO_val2014_000000474186.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474186.jpg
+691419	491203	A white toilet sitting under a window next to a chair.	427	640	COCO_val2014_000000491203.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000491203.jpg
+691603	360400	A woman texting on a phone, in the back of vehicle.	640	426	COCO_val2014_000000360400.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360400.jpg
+691639	181462	A dead cow corpse laying on top of a beach.	480	640	COCO_val2014_000000181462.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000181462.jpg
+691795	156999	A giraffe is bending its neck over a wood log.	621	640	COCO_val2014_000000156999.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000156999.jpg
+691841	407879	A pizza sitting on a table, with a spatula in the back.	480	640	COCO_val2014_000000407879.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000407879.jpg
+692086	451155	A woman sitting in front of a laptop computer.	480	640	COCO_val2014_000000451155.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451155.jpg
+692168	531000	A giraffe with its teeth on a fence.	427	640	COCO_val2014_000000531000.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000531000.jpg
+692234	394199	A woman sitting on top of a red motorcycle.	480	640	COCO_val2014_000000394199.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000394199.jpg
+692247	574357	The motorcyclist in a helmet is looking over the side of a bridge.	428	640	COCO_val2014_000000574357.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000574357.jpg
+692369	188146	A bike parked in front of a doorway.	480	640	COCO_val2014_000000188146.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188146.jpg
+692430	349199	A group of people enjoying a day at the beach.	426	640	COCO_val2014_000000349199.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000349199.jpg
+692480	331185	A person is riding his motorcycle on the dirt road.	426	640	COCO_val2014_000000331185.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000331185.jpg
+692631	477195	A pizza with pepperoni and spinach leaves by a beer.	480	640	COCO_val2014_000000477195.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000477195.jpg
+693018	81303	The skier wearing goggles is riding through the snow with ski poles.	412	640	COCO_val2014_000000081303.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000081303.jpg
+693578	230619	a dog with blue eyes standing in front of an oven	332	500	COCO_val2014_000000230619.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000230619.jpg
+693744	265001	A couple of birds sitting on a tree, with a blurry background.	428	640	COCO_val2014_000000265001.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000265001.jpg
+693747	360951	A computer workstation with a laptop and a desktop computer.	427	640	COCO_val2014_000000360951.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360951.jpg
+693760	575243	A person walking down a street while holding an umbrella.	427	640	COCO_val2014_000000575243.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575243.jpg
+694627	150224	Family and friends are together at the beach.	427	640	COCO_val2014_000000150224.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150224.jpg
+694741	515993	this is blueberry pie from the old days	428	640	COCO_val2014_000000515993.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000515993.jpg
+694768	424174	Bananas sitting on top of apples, pears, and other fruits.	640	480	COCO_val2014_000000424174.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000424174.jpg
+694769	135680	A flower in a vase next to a window.	640	300	COCO_val2014_000000135680.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000135680.jpg
+695257	217071	A bed with a purple blanket is pictured in this image.	480	640	COCO_val2014_000000217071.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000217071.jpg
+695258	27755	A stop sign sits in front of a billboard in a quiet area.	334	500	COCO_val2014_000000027755.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000027755.jpg
+695547	483374	A man and a woman looking at cell phones.	332	500	COCO_val2014_000000483374.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000483374.jpg
+695609	281259	A young boy holding a baseball bat on top of a cement area.	640	480	COCO_val2014_000000281259.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000281259.jpg
+695875	252659	A group of tennis players at a professional tennis game, its nobles.	429	640	COCO_val2014_000000252659.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000252659.jpg
+695918	338600	Man with tennis racket on green turf in front of a goal	375	500	COCO_val2014_000000338600.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000338600.jpg
+695988	473767	Railway crossing sign and vacant train tracks and road	500	332	COCO_val2014_000000473767.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473767.jpg
+696148	576966	A group of raft boats sitting on a river side.	424	640	COCO_val2014_000000576966.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000576966.jpg
+696253	495519	Person riding a four wheeler on a beach towards a bridge.	375	500	COCO_val2014_000000495519.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000495519.jpg
+696406	220872	A man standing next to a train going through grand central station	374	640	COCO_val2014_000000220872.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000220872.jpg
+696580	373539	this is a piece of meat and green beans on a plate	428	640	COCO_val2014_000000373539.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000373539.jpg
+696649	292990	Baseball players on a field and near empty stadium.	390	640	COCO_val2014_000000292990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000292990.jpg
+696752	453330	A bird stands on the ground among dried leaves.	480	640	COCO_val2014_000000453330.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453330.jpg
+696757	284628	A lidless toilet is shown caked in dirt or other filth.	480	640	COCO_val2014_000000284628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000284628.jpg
+696800	497014	The dog is playing catch with his master.	375	500	COCO_val2014_000000497014.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497014.jpg
+696844	395230	A small white car with a small white dog riding in it.	417	640	COCO_val2014_000000395230.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000395230.jpg
+696882	123360	A group of people walking down a walkway.	480	640	COCO_val2014_000000123360.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000123360.jpg
+696982	413666	A group of people riding skateboards down the middle of a road.	358	640	COCO_val2014_000000413666.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000413666.jpg
+697001	428111	A man riding a skateboard up the side of a ramp.	429	640	COCO_val2014_000000428111.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000428111.jpg
+697045	478567	there are several people on the field playing soccer together	425	640	COCO_val2014_000000478567.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000478567.jpg
+697166	54909	Two people walk on a path carrying umbrellas.	427	640	COCO_val2014_000000054909.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054909.jpg
+697169	44151	A bottle of water sits on an empty bench.	640	427	COCO_val2014_000000044151.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000044151.jpg
+697314	469464	there is a boy that is playing a video game	528	640	COCO_val2014_000000469464.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000469464.jpg
+697569	363039	this is a bird flying in the sky with its wings	640	426	COCO_val2014_000000363039.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000363039.jpg
+698111	446354	A pan of mini vegetables is being cooked on a stove.	480	640	COCO_val2014_000000446354.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000446354.jpg
+698179	278435	A man riding a red scooter down the street.	640	427	COCO_val2014_000000278435.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000278435.jpg
+698372	219283	A teddy bear sitting in front of a pile of boxes.	500	500	COCO_val2014_000000219283.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000219283.jpg
+698562	146601	a conveyer belt full of doughnuts rolling down the machine	480	640	COCO_val2014_000000146601.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146601.jpg
+698660	516020	a couple of trains parked on some tracks under a closed roof	374	640	COCO_val2014_000000516020.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516020.jpg
+699032	328421	an elephant standing next to some other smaller animals in a field of grass	480	640	COCO_val2014_000000328421.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000328421.jpg
+699055	353666	A woman in a red dress holding a cell phone.	640	611	COCO_val2014_000000353666.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000353666.jpg
+699179	370233	a man standing in a bathroom looking into a mirror	333	500	COCO_val2014_000000370233.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000370233.jpg
+699199	297933	a man standing on a skate board on the top of a ramp	425	640	COCO_val2014_000000297933.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297933.jpg
+699614	474003	A bathroom with outdated fixtures and a clothes hamper in the middle of the floor.	480	640	COCO_val2014_000000474003.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000474003.jpg
+699847	153343	two big stuffed bears sitting behind a chained fenced	480	640	COCO_val2014_000000153343.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000153343.jpg
+700129	498630	A baseball player with a bat in his hand being watched by a crowd of people.	640	428	COCO_val2014_000000498630.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498630.jpg
+700175	240918	Pink bike sits on a guard rail by the river.	332	500	COCO_val2014_000000240918.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000240918.jpg
+700299	223789	a rome with rose flowers and a sink	500	346	COCO_val2014_000000223789.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000223789.jpg
+700799	283264	A pitcher is throwing the ball in a baseball game.	428	640	COCO_val2014_000000283264.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000283264.jpg
+700862	40468	A guy on his surfboard surfing through the ocean.	382	640	COCO_val2014_000000040468.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000040468.jpg
+701127	290293	a long subway with people in it is lit up	326	640	COCO_val2014_000000290293.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000290293.jpg
+701514	304330	a man that is petting a big giraffe	640	480	COCO_val2014_000000304330.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000304330.jpg
+702014	182362	A young man is eating food while using the computer.	480	640	COCO_val2014_000000182362.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182362.jpg
+702132	525546	a lady that has a pink racket in hand	640	545	COCO_val2014_000000525546.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000525546.jpg
+702154	339019	so many people at the beach relaxing and swimming	316	640	COCO_val2014_000000339019.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000339019.jpg
+702369	121519	A man brushes his teeth in the dark of night.	333	500	COCO_val2014_000000121519.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000121519.jpg
+702394	126563	A chocolate cake topped with lots of raspberries.	427	640	COCO_val2014_000000126563.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000126563.jpg
+702539	59547	A woman holding two rainbow slices of cake.	640	428	COCO_val2014_000000059547.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000059547.jpg
+702574	493797	A dog sitting on the side of an area where boats are docked in the water.	360	640	COCO_val2014_000000493797.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493797.jpg
+702991	98095	A man is playing with a Wii controller as his friends watch.	500	375	COCO_val2014_000000098095.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000098095.jpg
+703009	50355	a bunch of cars that are paying a toll	480	640	COCO_val2014_000000050355.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000050355.jpg
+703237	19723	A table full of food including bread, vegetables and fish.	640	480	COCO_val2014_000000019723.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000019723.jpg
+703316	180135	A man holding a microphone in one hand while he holds a skateboard in the other.	500	333	COCO_val2014_000000180135.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000180135.jpg
+703407	581702	A close up of a red and black bird on a rock.	508	640	COCO_val2014_000000581702.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000581702.jpg
+703577	19817	A white toilet tin a bathroom sitting next to a sink.	640	427	COCO_val2014_000000019817.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000019817.jpg
+703593	230056	A flock of birds walking along a beach near water.	198	500	COCO_val2014_000000230056.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000230056.jpg
+704096	89285	Girl tennis player getting ready to serve ball.	640	426	COCO_val2014_000000089285.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089285.jpg
+704209	42421	A white cat standing next to a cluster of palm trees.	640	427	COCO_val2014_000000042421.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000042421.jpg
+704249	236784	two dogs laying down on a brown couch	400	500	COCO_val2014_000000236784.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000236784.jpg
+704372	100199	A store bought pizza baking in the oven.	427	640	COCO_val2014_000000100199.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000100199.jpg
+704492	352290	A brown horse standing on top of a lush green hillside.	427	640	COCO_val2014_000000352290.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000352290.jpg
+704659	120099	A man sitting at a table with lots of items on it.	640	480	COCO_val2014_000000120099.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120099.jpg
+704724	327961	A couple of horses standing and laying on top of a lush green hillside.	500	375	COCO_val2014_000000327961.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000327961.jpg
+704816	84460	Girl siting on a bench in the woods in the fall.	427	640	COCO_val2014_000000084460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000084460.jpg
+704921	144985	A street scene with a trucking going by.	404	640	COCO_val2014_000000144985.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000144985.jpg
+704922	73445	A street scene with a horse and carriage.	418	640	COCO_val2014_000000073445.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000073445.jpg
+705046	427802	A close up of dozens of of oranges stacked.	375	500	COCO_val2014_000000427802.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427802.jpg
+705061	115866	various food items in bowls on white counter	480	640	COCO_val2014_000000115866.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000115866.jpg
+705126	306412	A woman is standing next to a wall with a blue umbrella.	424	640	COCO_val2014_000000306412.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000306412.jpg
+705198	343903	food on a white plate with a colored place mat and pink table cloth	612	612	COCO_val2014_000000343903.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000343903.jpg
+705199	182683	A black and white cat is sitting looking out a window.	427	640	COCO_val2014_000000182683.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000182683.jpg
+705286	191017	A group of people standing on top of a field flying kites.	612	612	COCO_val2014_000000191017.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000191017.jpg
+705779	378667	a person in orange is standing in skies	640	428	COCO_val2014_000000378667.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000378667.jpg
+706064	28452	An open refrigerator door on a kitchen floor.	640	480	COCO_val2014_000000028452.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028452.jpg
+706286	72797	A man hitting a tennis ball with a racquet.	640	438	COCO_val2014_000000072797.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000072797.jpg
+706432	160828	there is a woman that is riding a surf board in the water	365	640	COCO_val2014_000000160828.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160828.jpg
+706599	196053	A woman holding a tennis racquet on top of a court.	500	333	COCO_val2014_000000196053.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000196053.jpg
+706815	68005	a dog with a plate of food on the ground	480	640	COCO_val2014_000000068005.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068005.jpg
+706964	322864	A yellow taxi cab sitting below tall buildings.	640	427	COCO_val2014_000000322864.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000322864.jpg
+707209	500952	A red train traveling under a bridge on train tracks.	425	640	COCO_val2014_000000500952.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500952.jpg
+707302	168355	A man sitting at a table in front of bowls of spices.	424	640	COCO_val2014_000000168355.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168355.jpg
+707418	446917	A person riding a racing bicycle with a banana and sunscreen in their back pocket.	427	640	COCO_val2014_000000446917.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000446917.jpg
+707429	461410	A cat laying on top of a white bed on a pillow.	480	640	COCO_val2014_000000461410.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000461410.jpg
+707537	196660	A surfer, surfboard under his arm, near a no swimming sign on a beach.	640	424	COCO_val2014_000000196660.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000196660.jpg
+707559	417043	A bench sitting on the side of a road.	480	640	COCO_val2014_000000417043.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000417043.jpg
+707709	18462	a man waiting for a bus at a stop on a corner	640	480	COCO_val2014_000000018462.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018462.jpg
+707738	160669	A baseball player holding a bat next to home plate.	427	640	COCO_val2014_000000160669.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000160669.jpg
+708087	227460	Some water that is reflecting a building near it.	500	375	COCO_val2014_000000227460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227460.jpg
+708130	186240	An elephant standing next to a bail of hay.	640	399	COCO_val2014_000000186240.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000186240.jpg
+708180	505371	A brown bear walking in white and black boots.	640	480	COCO_val2014_000000505371.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000505371.jpg
+708348	426836	Someone flying a kite right above the trees.	640	640	COCO_val2014_000000426836.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000426836.jpg
+708383	114479	A few pieces of luggage sit, stacked neatly, on the ground.	640	480	COCO_val2014_000000114479.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114479.jpg
+708485	482158	A street full of confetti and streamers with a building in the background.	640	427	COCO_val2014_000000482158.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000482158.jpg
+708731	173598	A pitcher throwing a ball during a baseball game.	480	640	COCO_val2014_000000173598.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173598.jpg
+708878	387208	A woman eating a chili dog during the day.	640	480	COCO_val2014_000000387208.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000387208.jpg
+708978	343466	An empty kitchen in a house with a window in the corner.	240	320	COCO_val2014_000000343466.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000343466.jpg
+709060	166509	A traffic light sitting on the side of a road next to a tree.	444	640	COCO_val2014_000000166509.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000166509.jpg
+709229	324554	A duck floating on top of a body of water.	427	640	COCO_val2014_000000324554.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000324554.jpg
+709622	144715	Locomotive pulling cars on tracks in outdoor area.	360	640	COCO_val2014_000000144715.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000144715.jpg
+709675	55544	A bare kitchen in the midst of a paint job.	427	640	COCO_val2014_000000055544.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000055544.jpg
+709677	537857	A young girl in a dress and a yellow sunhat holds onto a kite string.	640	429	COCO_val2014_000000537857.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000537857.jpg
+709900	422240	a man is holding a baseball bat in front of a banner	480	640	COCO_val2014_000000422240.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000422240.jpg
+709901	576461	a car is driving down a busy street	375	500	COCO_val2014_000000576461.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000576461.jpg
+709944	188696	A parking meter sitting on the side of a road.	450	413	COCO_val2014_000000188696.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188696.jpg
+710665	187475	A person holding a hot dog with lots of fixings on it.	383	640	COCO_val2014_000000187475.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187475.jpg
+710678	55013	two guys and two girls watching a computer	436	640	COCO_val2014_000000055013.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000055013.jpg
+710706	353935	some food is laying on a white and blue plate	640	478	COCO_val2014_000000353935.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000353935.jpg
+710900	315257	A bird with outstretched blue wings is sitting on some bird feeder.	514	640	COCO_val2014_000000315257.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315257.jpg
+711005	542511	a statue of person on top of a building with a clock	434	640	COCO_val2014_000000542511.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000542511.jpg
+711109	248468	A group of horses that are standing in the dirt.	640	511	COCO_val2014_000000248468.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248468.jpg
+711338	354537	A blue vehicle with a dog sitting in the driver's seat.	427	640	COCO_val2014_000000354537.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000354537.jpg
+711464	140840	many kites with different shapes all laying on the ground	281	500	COCO_val2014_000000140840.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000140840.jpg
+711554	342146	A man sanding next to a cow in a pen.	480	640	COCO_val2014_000000342146.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000342146.jpg
+711649	534734	A temporary tennis court has been erected in the town square.	480	640	COCO_val2014_000000534734.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534734.jpg
+711766	347612	A red stop sign sitting next to a tree.	640	480	COCO_val2014_000000347612.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347612.jpg
+711768	560890	A group of people standing around each other.	428	640	COCO_val2014_000000560890.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000560890.jpg
+711776	145325	A desk chair sitting up against a desk.	360	640	COCO_val2014_000000145325.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000145325.jpg
+712971	30675	The train engine number 6309 is operated by BNSF.	360	640	COCO_val2014_000000030675.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000030675.jpg
+713078	202231	A man holding up holding an object inside of a plastic case.	500	375	COCO_val2014_000000202231.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000202231.jpg
+713237	114907	A lady explains the process of milking a cow.	425	640	COCO_val2014_000000114907.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000114907.jpg
+713273	550514	A group of people standing on top of a snow covered ski slope.	480	640	COCO_val2014_000000550514.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550514.jpg
+713274	1029	A large jet flying through a cloudy sky.	640	443	COCO_val2014_000000001029.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000001029.jpg
+713393	249792	A man wearing a red neck tie while wearing a checkered shirt.	622	640	COCO_val2014_000000249792.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000249792.jpg
+713477	558677	A large jetliner sitting on top of an airport tarmac.	480	640	COCO_val2014_000000558677.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558677.jpg
+713744	511138	A giraffe that is standing in the grass.	320	240	COCO_val2014_000000511138.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000511138.jpg
+713886	190499	A pretty young lady holding a tooth brush in her mouth.	640	480	COCO_val2014_000000190499.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190499.jpg
+713956	34874	A man and a woman standing next to each other.	332	500	COCO_val2014_000000034874.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000034874.jpg
+714565	118366	A person that is laying down on a surfboard in the water.	427	640	COCO_val2014_000000118366.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000118366.jpg
+714728	31878	A group of different appliances sitting on top of a table.	479	640	COCO_val2014_000000031878.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000031878.jpg
+714905	475330	The stuffed bears are dress as a turkey and a pumpkin.	333	500	COCO_val2014_000000475330.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000475330.jpg
+715108	345029	a female tennis player in a black top playing tennis	425	640	COCO_val2014_000000345029.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000345029.jpg
+715582	318204	A man walking an animal across a beach.	480	640	COCO_val2014_000000318204.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000318204.jpg
+715891	67536	A computer keyboard sitting on top of a table.	423	640	COCO_val2014_000000067536.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067536.jpg
+716083	375914	A yellow end sign sitting on the side of a field.	640	454	COCO_val2014_000000375914.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000375914.jpg
+716332	292741	A television with a video game logo sits in front of two trays with controllers.	480	640	COCO_val2014_000000292741.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000292741.jpg
+716337	462315	A yellow truck that was recently on fire and put out by the fire department.	427	640	COCO_val2014_000000462315.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000462315.jpg
+716678	193387	A cake sitting on top of a pan on a table.	333	500	COCO_val2014_000000193387.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000193387.jpg
+716684	121632	A pizza is being eaten with a knife and fork.	480	640	COCO_val2014_000000121632.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000121632.jpg
+717053	66336	A woman holding a pink umbrella over her left shoulder.	640	427	COCO_val2014_000000066336.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000066336.jpg
+717101	515555	a large clock is hanging up on a wall	429	640	COCO_val2014_000000515555.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000515555.jpg
+717102	309579	a bunch of people are flying kites in a field	398	600	COCO_val2014_000000309579.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000309579.jpg
+717106	500298	A counter topped with lots of fresh vegetables.	333	500	COCO_val2014_000000500298.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500298.jpg
+717107	207180	A group of people walking around a train station.	457	640	COCO_val2014_000000207180.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000207180.jpg
+717169	465718	A computer desk topped with two monitors and a laptop.	429	640	COCO_val2014_000000465718.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000465718.jpg
+717263	61605	A man standing on train tracks as a train approaches.	421	640	COCO_val2014_000000061605.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000061605.jpg
+717396	507975	Some very pretty horses with riders in the grass.	480	640	COCO_val2014_000000507975.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507975.jpg
+717655	205585	A man riding on the back of horses while standing.	428	640	COCO_val2014_000000205585.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205585.jpg
+717682	67785	A boy stares at a pizza with a birthday candle in it.	612	612	COCO_val2014_000000067785.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000067785.jpg
+718351	189966	A kitten standing on the edge of a small table in front of a mirror with an elaborately carved wooden frame.	640	556	COCO_val2014_000000189966.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000189966.jpg
+718439	86988	Someone who is teetering on one foot while holding a tennis racket.	640	480	COCO_val2014_000000086988.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000086988.jpg
+719002	108698	A cat is sitting in the window on a rail.	640	480	COCO_val2014_000000108698.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000108698.jpg
+719089	311902	A woman preparing food while standing in a kitchen.	640	427	COCO_val2014_000000311902.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000311902.jpg
+719119	13132	A pair of men wearing waders stand in shallow water.	604	640	COCO_val2014_000000013132.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013132.jpg
+719120	380552	A young woman sits on a horse on a dirt road.	612	612	COCO_val2014_000000380552.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000380552.jpg
+719325	281628	A man wearing a short skirt and tighty whities.	490	640	COCO_val2014_000000281628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000281628.jpg
+719383	167989	a lady preparing pizza on a wooden table	612	612	COCO_val2014_000000167989.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000167989.jpg
+719477	367087	A small wooden and brick structure next to a field.	480	640	COCO_val2014_000000367087.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000367087.jpg
+719515	462755	A group of people walking around a lush green field.	375	500	COCO_val2014_000000462755.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000462755.jpg
+719561	472298	a man and a woman sitting on a small boat	387	640	COCO_val2014_000000472298.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472298.jpg
+719618	20992	A woman holding food up to her mouth.	333	500	COCO_val2014_000000020992.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000020992.jpg
+719852	148957	A box contains six donuts with varying types of glazes and toppings.	480	640	COCO_val2014_000000148957.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000148957.jpg
+719861	451919	A dog sitting on to of a window sill.	640	480	COCO_val2014_000000451919.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451919.jpg
+719930	287714	A white bathroom sink sitting under a bathroom mirror.	480	640	COCO_val2014_000000287714.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287714.jpg
+719997	380459	A person standing in front of a plate of food.	480	640	COCO_val2014_000000380459.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000380459.jpg
+720032	286708	a black and white cat wearing a little purple hat	481	640	COCO_val2014_000000286708.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000286708.jpg
+720137	312191	There are many birds flying above the water.	295	640	COCO_val2014_000000312191.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000312191.jpg
+720314	33900	A man riding skis on top of  a snow covered field.	427	640	COCO_val2014_000000033900.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000033900.jpg
+720770	440189	A man standing on top of a sandy beach holding a paddle.	480	640	COCO_val2014_000000440189.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000440189.jpg
+721244	188460	A young man sitting with a laptop computer on his lap.	480	640	COCO_val2014_000000188460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000188460.jpg
+721249	497067	A young boy kicking a soccer ball across a field.	623	640	COCO_val2014_000000497067.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497067.jpg
+721313	497281	A woman swinging a tennis racquet on a tennis court.	359	640	COCO_val2014_000000497281.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497281.jpg
+721314	461885	A woman riding on the back of a brown horse through a forest.	480	640	COCO_val2014_000000461885.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000461885.jpg
+721334	335515	A white plate topped with different types of food.	426	640	COCO_val2014_000000335515.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335515.jpg
+721342	320978	A store filled with lots of fresh produce on display.	512	640	COCO_val2014_000000320978.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000320978.jpg
+721534	211425	A little girl wearing a pink jacket holding two ski poles.	480	640	COCO_val2014_000000211425.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000211425.jpg
+721551	476313	A bathroom counter with two sinks under two mirrors.	480	640	COCO_val2014_000000476313.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000476313.jpg
+721597	463347	A trash can covered in an umbrella between two roads.	640	424	COCO_val2014_000000463347.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000463347.jpg
+721753	246452	A couple of birds having sex on top of a river.	426	640	COCO_val2014_000000246452.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000246452.jpg
+721866	317969	A herd of sheep walking across a road.	283	500	COCO_val2014_000000317969.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000317969.jpg
+721903	502737	A person holding up a chocolate doughnut with a face drawn on it.	640	480	COCO_val2014_000000502737.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000502737.jpg
+721962	576136	A brown dog eating food from a dog bowl.	375	500	COCO_val2014_000000576136.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000576136.jpg
+721980	38963	A kitchen with lots of clutter a door with a large window.	427	640	COCO_val2014_000000038963.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000038963.jpg
+721987	78823	A dog standing on the back of a brown car.	480	640	COCO_val2014_000000078823.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000078823.jpg
+722047	533589	A blue refrigerator sitting up against a shelf with bottles of wine.	480	640	COCO_val2014_000000533589.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000533589.jpg
+722236	550051	people on the street near a sea with waters	427	640	COCO_val2014_000000550051.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550051.jpg
+722572	88848	a couple of pictures that have some people and some fire hyrdons	640	640	COCO_val2014_000000088848.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000088848.jpg
+722591	472904	A table with a fall arrangement sits in a large room.	640	424	COCO_val2014_000000472904.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472904.jpg
+722628	238199	A young man jumping off a staircase while riding a skate board.	426	640	COCO_val2014_000000238199.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000238199.jpg
+723419	235788	A vintage VW bus that is red and white.	427	640	COCO_val2014_000000235788.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000235788.jpg
+723686	260447	A very long shiny train on a bridge over some cars.	416	640	COCO_val2014_000000260447.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260447.jpg
+723689	258915	A man lounging in a field where cows are grazing.	640	428	COCO_val2014_000000258915.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000258915.jpg
+723818	423508	two benches, a small statue and some street signs stand between a street curb and the brick facade of a large building.	640	427	COCO_val2014_000000423508.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000423508.jpg
+723835	101053	a big bus that has some people by it	480	640	COCO_val2014_000000101053.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000101053.jpg
+723836	565570	a man that is standing on a basebal lfield	604	640	COCO_val2014_000000565570.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000565570.jpg
+723984	347936	A tabletop is full of homemeade desserts and they look appetizing.	480	640	COCO_val2014_000000347936.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347936.jpg
+725233	570659	A stove and a sink in a room.	480	640	COCO_val2014_000000570659.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000570659.jpg
+725235	521655	A person on some skis in the snow.	376	640	COCO_val2014_000000521655.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000521655.jpg
+725960	303320	A man throwing a frisbee to his friend standing in his yard.	437	640	COCO_val2014_000000303320.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303320.jpg
+726558	557596	A woman that is playing tennis on a court.	640	423	COCO_val2014_000000557596.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000557596.jpg
+726816	352007	A man in a top hat steering a horse-drawn carriage through the snow.	612	612	COCO_val2014_000000352007.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000352007.jpg
+726896	287874	A passenger train travels down the tracks at a stop.	434	640	COCO_val2014_000000287874.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287874.jpg
+727265	451561	There is a vase of flowers setting in the snow.	334	500	COCO_val2014_000000451561.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451561.jpg
+727295	422998	A person makes a sandwich on a paper plate.	427	640	COCO_val2014_000000422998.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000422998.jpg
+727712	410168	A dog wearing a yellow hat with a smiley face on it.	640	428	COCO_val2014_000000410168.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000410168.jpg
+727735	335233	Laptop computer and other electronic equipment displayed on small wooden desk.	428	640	COCO_val2014_000000335233.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335233.jpg
+727859	91136	Some very cute sheep by some very big trees.	480	640	COCO_val2014_000000091136.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000091136.jpg
+727924	351875	A bunch of people gathered together in a very big crowd.	427	640	COCO_val2014_000000351875.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000351875.jpg
+727963	260150	A person sitting in front of a plate of pancakes.	436	640	COCO_val2014_000000260150.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260150.jpg
+727964	570678	A person in cold weather gear riding on a snowboard down a slope.	427	640	COCO_val2014_000000570678.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000570678.jpg
+728397	208844	Several stuffed animals handing from a metal rail.	480	640	COCO_val2014_000000208844.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000208844.jpg
+728521	244005	An older white motorhome pulled over on the side of a highway.	428	640	COCO_val2014_000000244005.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000244005.jpg
+728550	235000	A man with a helmet riding on a motorcycle with a basket on it.	457	640	COCO_val2014_000000235000.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000235000.jpg
+728915	236717	A parked silver Nissan Titan four door pickup truck.	375	500	COCO_val2014_000000236717.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000236717.jpg
+729015	360101	a plate of food with two sandwiches	640	518	COCO_val2014_000000360101.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360101.jpg
+729160	347836	A man and boy ride horse and stir up dust.	480	640	COCO_val2014_000000347836.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000347836.jpg
+729474	352937	A baby holding a toothbrush in it's mouth.	640	480	COCO_val2014_000000352937.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000352937.jpg
+729726	364190	One slice is taken from the thick crust pizza.	640	427	COCO_val2014_000000364190.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000364190.jpg
+729755	348263	The child smiles while posing in a baseball uniform.	640	417	COCO_val2014_000000348263.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000348263.jpg
+729791	503826	A computer that is on a wooden desk.	480	640	COCO_val2014_000000503826.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000503826.jpg
+729846	226883	An animal grazing on a lush field of green grass.	480	640	COCO_val2014_000000226883.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226883.jpg
+729950	289899	A group of people ride in a canal past buildings.	480	640	COCO_val2014_000000289899.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289899.jpg
+730029	265125	A woman holding a black umbrella as she stands behind a car.	427	640	COCO_val2014_000000265125.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000265125.jpg
+730207	83925	A child is playing with a ball in a batting cage.	480	640	COCO_val2014_000000083925.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000083925.jpg
+730272	33652	A pizza sitting on top of a pizza pan.	480	640	COCO_val2014_000000033652.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000033652.jpg
+730430	97465	A person holding up a smart phone in their hands.	360	640	COCO_val2014_000000097465.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000097465.jpg
+730537	6874	A group of giraffes and a couple of deer stand in the dust.	457	640	COCO_val2014_000000006874.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000006874.jpg
+730543	324390	Some meat and vegetables are arranged on a white plate.	640	424	COCO_val2014_000000324390.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000324390.jpg
+730663	112437	A woman touching a decoration on top of a batman cake.	480	640	COCO_val2014_000000112437.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000112437.jpg
+730675	143516	A plate topped with food next to trays of drinks.	480	640	COCO_val2014_000000143516.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000143516.jpg
+730895	431686	An all way stop sign on a sidewalk area.	640	427	COCO_val2014_000000431686.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431686.jpg
+731372	459034	An older man sitting next to an older woman, both of them are holding Wii controllers.	424	640	COCO_val2014_000000459034.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000459034.jpg
+731401	562261	A man stretching out yelling while catching a Frisbee.	426	640	COCO_val2014_000000562261.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000562261.jpg
+731628	456931	Many yellow and green lemons are stacked in baskets.	612	612	COCO_val2014_000000456931.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000456931.jpg
+731835	267946	A blue sign giving directions to a public toilet.	332	500	COCO_val2014_000000267946.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267946.jpg
+731880	229311	A stainless steel kitchen sink on a black granite countertop.	375	500	COCO_val2014_000000229311.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000229311.jpg
+731941	260257	A red stop sign sitting below a tall building.	500	375	COCO_val2014_000000260257.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260257.jpg
+732407	546325	A couch with pillows sitting in a living room.	640	480	COCO_val2014_000000546325.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546325.jpg
+732626	401446	A woman wearing a dress and holding an umbrella.	480	640	COCO_val2014_000000401446.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401446.jpg
+732671	547136	An airplane having maintenance done on the tarmac.	612	612	COCO_val2014_000000547136.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000547136.jpg
+732746	460294	A group of women walking across a parking lot with umbrellas.	425	640	COCO_val2014_000000460294.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460294.jpg
+732974	297698	A man is skiing in mid jump onto a ramp.	441	640	COCO_val2014_000000297698.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297698.jpg
+733324	357586	A man with a small teddy bear peeking from a backpack.	427	640	COCO_val2014_000000357586.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000357586.jpg
+733447	527407	Photoshop has allowed the photographer to creatively identify the name of the Italian city in his shot.	496	640	COCO_val2014_000000527407.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000527407.jpg
+733484	370170	A salad of noodles, asparagus and meat in a dish.	427	640	COCO_val2014_000000370170.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000370170.jpg
+733560	102904	The living room has two couches and an easy chair.	480	640	COCO_val2014_000000102904.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000102904.jpg
+733612	262371	A cat laying on top of a bag sitting on a floor.	480	640	COCO_val2014_000000262371.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000262371.jpg
+733777	166277	A cat drinking out of a glass on top of a table.	640	480	COCO_val2014_000000166277.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000166277.jpg
+733839	363673	A young lady standing on a  tennis court with a racquet.	640	427	COCO_val2014_000000363673.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000363673.jpg
+733855	312866	A train covered in graffiti sitting on top of  train tracks.	480	640	COCO_val2014_000000312866.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000312866.jpg
+734183	335690	A horse head sitting on a counter top next to drinks and food.	640	427	COCO_val2014_000000335690.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335690.jpg
+734231	362189	A basket filled with ripe bananas on top of a table.	427	640	COCO_val2014_000000362189.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000362189.jpg
+734241	263758	A large propeller airplane sitting on top of a tarmac.	427	640	COCO_val2014_000000263758.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000263758.jpg
+734253	310735	A couple of tennis players on a large, fenced-in outdoor court.	480	640	COCO_val2014_000000310735.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000310735.jpg
+734358	289323	A man holding a cart seat while standing next to a little girl.	640	480	COCO_val2014_000000289323.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289323.jpg
+734388	297787	A person in a room with knives and scissors hanging on the wall.	425	640	COCO_val2014_000000297787.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000297787.jpg
+734454	348447	A cook in a restaurant kitchen poses with food.	427	640	COCO_val2014_000000348447.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000348447.jpg
+734504	195800	A street with people and cows walking down it.	480	640	COCO_val2014_000000195800.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195800.jpg
+734562	391029	A bird is sitting on a bench in the grass.	427	640	COCO_val2014_000000391029.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000391029.jpg
+734740	243034	A very tall clock tower towering over a city at night.	640	427	COCO_val2014_000000243034.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000243034.jpg
+734810	295269	a trey with some cookies on top of it	460	640	COCO_val2014_000000295269.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295269.jpg
+735187	428847	A bike parked next to a red door on the front of a house.	418	640	COCO_val2014_000000428847.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000428847.jpg
+735237	515751	A person is riding a skateboard down the street.	425	640	COCO_val2014_000000515751.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000515751.jpg
+735358	554291	A small kitten standing in a  blue hat on top of a table.	428	640	COCO_val2014_000000554291.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000554291.jpg
+735670	430762	A black pen is laying on top of a red pair of scissors.	335	500	COCO_val2014_000000430762.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000430762.jpg
+735884	134066	a couple of elephants fighting in a grass area	428	640	COCO_val2014_000000134066.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000134066.jpg
+736107	251537	There is a carrot next to some greens on the red cutting board.	640	640	COCO_val2014_000000251537.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000251537.jpg
+736448	28535	a person riding a surf board on a body of water	427	640	COCO_val2014_000000028535.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000028535.jpg
+736500	30383	The fire hydrant is across the street from street lamps.	640	427	COCO_val2014_000000030383.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000030383.jpg
+736922	307598	a yellow and green train, at stop lights.	426	640	COCO_val2014_000000307598.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000307598.jpg
+737088	485480	a man is holding a bat at a baseball game	281	640	COCO_val2014_000000485480.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000485480.jpg
+737224	255315	Pregnant mother and daughter playing with Nintendo wii controllers.	640	427	COCO_val2014_000000255315.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000255315.jpg
+737225	221700	A pair of red scissors sitting on a newspaper.	479	640	COCO_val2014_000000221700.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000221700.jpg
+737364	43758	a couple of sheep graze on some grass	426	640	COCO_val2014_000000043758.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000043758.jpg
+737633	24851	A picture of two stop lights that are red at a busy intersection.	591	640	COCO_val2014_000000024851.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000024851.jpg
+737891	505573	A dog wears a tie while on the steps	640	359	COCO_val2014_000000505573.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000505573.jpg
+737935	79047	A statue of a bear sitting in the middle of a sidewalk.	640	480	COCO_val2014_000000079047.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000079047.jpg
+737980	74460	a couple of horses grazing on some green grass	426	640	COCO_val2014_000000074460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000074460.jpg
+738173	376939	a little boy in shallow water on a surf board	640	427	COCO_val2014_000000376939.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000376939.jpg
+738225	308587	People are in the water with kites flying over them.	640	480	COCO_val2014_000000308587.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000308587.jpg
+738414	385641	a small laptop is sitting on a desk	422	500	COCO_val2014_000000385641.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000385641.jpg
+738466	274612	A row of colorful bikes parked under two umbrellas.	480	640	COCO_val2014_000000274612.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000274612.jpg
+738491	173383	A two layer cake sitting on top of a table.	425	640	COCO_val2014_000000173383.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173383.jpg
+738705	450758	A herd of giraffe running across a field.	479	640	COCO_val2014_000000450758.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000450758.jpg
+738871	344633	a couple of people riding on the backs of brown horses.	426	640	COCO_val2014_000000344633.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344633.jpg
+738928	177207	A group of people riding skis on top of a snow covered ski slope.	299	448	COCO_val2014_000000177207.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177207.jpg
+739125	558449	A potted plant sitting on a concrete overview.	480	640	COCO_val2014_000000558449.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558449.jpg
+739361	85750	A clock sits against a window in a white wall.	427	640	COCO_val2014_000000085750.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000085750.jpg
+739426	64146	A scary man wears a hat made out of bananas.	640	458	COCO_val2014_000000064146.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000064146.jpg
+739686	528331	Two mean stand in a park, one of them wearing a baseball glove.	427	640	COCO_val2014_000000528331.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000528331.jpg
+739877	317797	a girl in a white dress and a big yellow animal	640	478	COCO_val2014_000000317797.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000317797.jpg
+739904	451679	An off white smoker being tended to by a guy and people with their back turned in the background.	480	640	COCO_val2014_000000451679.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000451679.jpg
+739937	15559	A woman standing on ice holding skis in flip flops.	480	640	COCO_val2014_000000015559.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000015559.jpg
+740008	472143	A young couple sit and enjoy their food outside a busy shopping area.	640	640	COCO_val2014_000000472143.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472143.jpg
+740032	94168	A yellow and red train on the railroad tracks.	426	640	COCO_val2014_000000094168.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000094168.jpg
+740033	9175	A woman holding a tiger kite with people standing around watching and talking.	480	640	COCO_val2014_000000009175.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009175.jpg
+740458	125318	A tray of doughnuts is sitting next to a fryer.	428	640	COCO_val2014_000000125318.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000125318.jpg
+740590	326081	A dog laying on the ground with a pink frisbee in it's mouth.	640	480	COCO_val2014_000000326081.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000326081.jpg
+740859	105885	A busted fire hydrant spewing water out onto a street.	480	640	COCO_val2014_000000105885.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000105885.jpg
+741053	540869	A man riding on the back of a red motorcycle.	426	640	COCO_val2014_000000540869.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540869.jpg
+741129	341196	A man riding a wave on top of a surfboard.	451	640	COCO_val2014_000000341196.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341196.jpg
+741879	267571	a giraffe standing on a sandy beach in front of some rocks	480	640	COCO_val2014_000000267571.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267571.jpg
+742247	358796	With their legs up on the furniture and the tv on, they sat in a living room setting of a house.	480	640	COCO_val2014_000000358796.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000358796.jpg
+742331	341676	Two microwave ovens one marked with man symbol and the other for woman.	375	500	COCO_val2014_000000341676.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000341676.jpg
+743300	558083	A bathroom that has a white toilet with the seat lifted up and various rolls of toilet paper on the top of the toilet tank.	500	375	COCO_val2014_000000558083.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558083.jpg
+745102	221543	A clock displaying the time in a passenger bus.	375	500	COCO_val2014_000000221543.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000221543.jpg
+745824	522990	Horse carriage being led by man in busy city.	375	500	COCO_val2014_000000522990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000522990.jpg
+746111	502663	Food truck with customers ordering them with friends.	427	640	COCO_val2014_000000502663.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000502663.jpg
+747006	576654	A person in a dry area with a sail high in the sky	427	640	COCO_val2014_000000576654.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000576654.jpg
+747020	389477	A piece of lasagna with cheese and vegetable.	480	640	COCO_val2014_000000389477.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000389477.jpg
+747040	298924	A plate of food with meat and white noodles.	612	612	COCO_val2014_000000298924.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000298924.jpg
+747134	360698	a commercial plane is on the airport runway	427	640	COCO_val2014_000000360698.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360698.jpg
+747205	581262	A fire hydrant and some debris on the street.	480	640	COCO_val2014_000000581262.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000581262.jpg
+747316	527313	A person prepares to eat a large hamburger.	640	480	COCO_val2014_000000527313.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000527313.jpg
+747402	571596	The view through the door of a small, tiled bathroom.	640	480	COCO_val2014_000000571596.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000571596.jpg
+747411	126833	A man sitting on a bench near a pole.	640	426	COCO_val2014_000000126833.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000126833.jpg
+747446	250108	A computer that is on a desk near a window.	481	640	COCO_val2014_000000250108.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000250108.jpg
+747489	198271	A large tower with a clock and a blue banner	640	480	COCO_val2014_000000198271.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198271.jpg
+747554	58223	A giraffe sticking it's head in a van.	343	500	COCO_val2014_000000058223.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000058223.jpg
+747558	101884	Ivy is growing up the walls of a building.	426	640	COCO_val2014_000000101884.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000101884.jpg
+747585	500330	A black dishwasher sitting next to a microwave.	640	427	COCO_val2014_000000500330.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500330.jpg
+747723	546760	this is the under side of a red train	425	640	COCO_val2014_000000546760.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546760.jpg
+747771	427189	this is a few heads of broccoli in a box	334	500	COCO_val2014_000000427189.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000427189.jpg
+747905	550576	The men are both diving to catch the frisbee	361	640	COCO_val2014_000000550576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000550576.jpg
+747927	201141	A man that is sitting down in a fruit stand.	640	425	COCO_val2014_000000201141.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000201141.jpg
+748005	538163	A man and woman that are around a table.	375	500	COCO_val2014_000000538163.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000538163.jpg
+748100	360716	The skiing woman is jumping in the air.	640	591	COCO_val2014_000000360716.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360716.jpg
+748108	447152	this is a train riding down th rails	389	640	COCO_val2014_000000447152.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000447152.jpg
+748197	331113	The meat and broccoli on the plate is covered with steak sauce.	480	640	COCO_val2014_000000331113.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000331113.jpg
+748500	546052	Two men and a baby at a Christmas party.	480	640	COCO_val2014_000000546052.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000546052.jpg
+748517	314865	A refrigerator that is in an otherwise empty room.	375	500	COCO_val2014_000000314865.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000314865.jpg
+748548	344410	A couple of men that are standing in the grass with a frisbee.	427	640	COCO_val2014_000000344410.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344410.jpg
+748595	213156	A sandwich that is sitting on a plate.	480	640	COCO_val2014_000000213156.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213156.jpg
+748672	446984	A giant pair of scissors hangs over the heads of the bicyclists.	446	640	COCO_val2014_000000446984.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000446984.jpg
+748831	141651	A woman that is sitting on the back of a horse.	640	555	COCO_val2014_000000141651.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000141651.jpg
+748963	466561	Young man batting during a sunny baseball game.	427	640	COCO_val2014_000000466561.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000466561.jpg
+749055	350721	Two men fall to the ground during an ultimate Frisbee game	427	640	COCO_val2014_000000350721.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000350721.jpg
+749057	198915	This is an intersection sign between green lake drive and one other	404	640	COCO_val2014_000000198915.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198915.jpg
+749291	132288	A man that is on a box with a skateboard.	612	612	COCO_val2014_000000132288.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000132288.jpg
+749412	507223	A man that is standing in the dirt with a bat.	640	480	COCO_val2014_000000507223.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000507223.jpg
+749519	373810	Two teddy bears that are hanging on a line.	480	640	COCO_val2014_000000373810.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000373810.jpg
+749609	184771	A gold and white dog sticking its head out a car window.	480	640	COCO_val2014_000000184771.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000184771.jpg
+749714	362079	A giraffe that is standing in the grass.	640	496	COCO_val2014_000000362079.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000362079.jpg
+749720	307564	Tea time in the dollhouse is much like the real thing.	480	640	COCO_val2014_000000307564.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000307564.jpg
+749729	71618	A toilet that has its seat open in a bathroom.	640	480	COCO_val2014_000000071618.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000071618.jpg
+749795	138755	A clock that is on the side of a tower.	427	640	COCO_val2014_000000138755.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000138755.jpg
+749831	214742	A man that is holding a baseball bat.	313	640	COCO_val2014_000000214742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000214742.jpg
+749870	402931	this is two people skiing down a hill	427	640	COCO_val2014_000000402931.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000402931.jpg
+750085	468773	The cat is sitting next to the computer mouse.	640	480	COCO_val2014_000000468773.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000468773.jpg
+750206	204232	A man that has a skateboard jumping in the air.	640	425	COCO_val2014_000000204232.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000204232.jpg
+750300	187658	A pizza that is sitting on a metal platter.	640	482	COCO_val2014_000000187658.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187658.jpg
+750302	12748	A man and child next to a horse.	640	480	COCO_val2014_000000012748.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000012748.jpg
+750398	228450	A black and white image of a laptop beside a coffee mug.	640	640	COCO_val2014_000000228450.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000228450.jpg
+750621	81224	this is horses racing each other on dirt	388	640	COCO_val2014_000000081224.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000081224.jpg
+750740	444568	A little boy with headphones in front of a computer.	500	338	COCO_val2014_000000444568.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444568.jpg
+751167	378614	A print ad for the Pizzeria La Crescia.	480	640	COCO_val2014_000000378614.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000378614.jpg
+751529	531803	A person on skis standing on the snow next to some dogs.	640	480	COCO_val2014_000000531803.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000531803.jpg
+751624	559101	A baby in a softly colorful padded chair, holding a red plastic toy.	333	500	COCO_val2014_000000559101.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559101.jpg
+751771	62109	Three young men prepare to race bicycles back in the early nineteenth century as a crowd of mean watch.	358	500	COCO_val2014_000000062109.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062109.jpg
+751992	55772	HAM AND EGG ON A PANCAKE, WITH A DISH OF YOGURT	640	480	COCO_val2014_000000055772.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000055772.jpg
+752882	186130	A person cutting up an ice cream cake sitting in the box on a table.	480	640	COCO_val2014_000000186130.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000186130.jpg
+753266	571544	a black cat is standing on a small desk	480	640	COCO_val2014_000000571544.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000571544.jpg
+753531	117396	A portable bathroom has a gray plastic urinal.	375	500	COCO_val2014_000000117396.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000117396.jpg
+753685	39551	A woman that is standing on a tennis court with a racquet.	427	640	COCO_val2014_000000039551.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039551.jpg
+753691	330140	A round picture of a train station with a tall building across the street.	612	612	COCO_val2014_000000330140.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000330140.jpg
+753851	408120	A little girl that is standing with an umbrella.	427	640	COCO_val2014_000000408120.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000408120.jpg
+753949	372633	A group of people riding skis on a snowy slope.	640	480	COCO_val2014_000000372633.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372633.jpg
+754235	291834	a person jumping over a ramp with a skateboard	640	578	COCO_val2014_000000291834.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000291834.jpg
+754673	107831	a apple sits on a floor with a knife stuck inside of it	500	334	COCO_val2014_000000107831.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107831.jpg
+754714	313240	a desk with a computer, books, paperwork and shelves with DVDs	480	640	COCO_val2014_000000313240.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000313240.jpg
+755267	185821	A giraffe that is standing in a field.	480	640	COCO_val2014_000000185821.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000185821.jpg
+755308	517938	A baseball player hitting a pitch with catcher and umpire behind him.	428	640	COCO_val2014_000000517938.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000517938.jpg
+755575	144706	A picture of a street light through a rainy lens.	480	640	COCO_val2014_000000144706.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000144706.jpg
+755856	121384	A seagull swims toward a rocky shore in a lake.	356	640	COCO_val2014_000000121384.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000121384.jpg
+755974	108681	A cat sleeping on top of a laptop keyboard.	480	640	COCO_val2014_000000108681.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000108681.jpg
+756023	504224	A young man crafting something on the lawn outside.	480	640	COCO_val2014_000000504224.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000504224.jpg
+756143	248953	a man riding a rail with a snowboard	375	500	COCO_val2014_000000248953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248953.jpg
+756145	32888	The living room is decked out with various types of black leather.	425	640	COCO_val2014_000000032888.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032888.jpg
+756195	281188	The blender is mixing several different fresh ingredients.	640	480	COCO_val2014_000000281188.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000281188.jpg
+756247	177714	A plate of cooked food in seen in this image.	409	640	COCO_val2014_000000177714.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177714.jpg
+756284	4396	a teddy bear wearing a green robe	640	427	COCO_val2014_000000004396.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000004396.jpg
+756595	48133	a bunch of people on skis going through the snow	640	480	COCO_val2014_000000048133.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000048133.jpg
+756623	561164	A street corner with a sign and windows on the building.	640	526	COCO_val2014_000000561164.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000561164.jpg
+756625	177575	A stir fry bowl with broccoli and beef cooking.	424	640	COCO_val2014_000000177575.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000177575.jpg
+756742	319221	a tray filled with assorted plates and bowls, most with veggies in it	426	640	COCO_val2014_000000319221.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000319221.jpg
+756947	166563	A large statue of an elephant is attached to a business sign.	408	640	COCO_val2014_000000166563.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000166563.jpg
+757180	335981	A few people walking in a store near each other.	480	640	COCO_val2014_000000335981.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335981.jpg
+757252	242934	A large living room is seen in this image.	227	500	COCO_val2014_000000242934.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242934.jpg
+757296	195042	A bathroom that has a sink with a glass bowl.	640	360	COCO_val2014_000000195042.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195042.jpg
+757343	431521	A llama looks at the camera from a fenced in, grassy area.	403	640	COCO_val2014_000000431521.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431521.jpg
+757344	190494	A horse-drawn carriage moves past a historical building.	416	640	COCO_val2014_000000190494.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190494.jpg
+757413	287506	Several people ride on elephants in a mountainous area.	480	640	COCO_val2014_000000287506.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000287506.jpg
+757471	37675	Horses graze in front of a large building amid snow.	415	640	COCO_val2014_000000037675.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000037675.jpg
+757506	195002	A young girl eats a doughnut near another woman.	640	480	COCO_val2014_000000195002.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195002.jpg
+758291	195275	Tennis player and white outfit holding up a racket and a ball.	640	427	COCO_val2014_000000195275.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000195275.jpg
+758440	10324	a fridge is open with some food inside of it	640	425	COCO_val2014_000000010324.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000010324.jpg
+758490	520427	a man stands next to a bunch of parked bike	480	640	COCO_val2014_000000520427.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000520427.jpg
+758541	519193	A kid holding a video game controller in his hand.	480	640	COCO_val2014_000000519193.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000519193.jpg
+758645	146836	A passenger train traveling on rail road tracks.	480	640	COCO_val2014_000000146836.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000146836.jpg
+758654	190441	A flock of white sheep in a green field.	480	640	COCO_val2014_000000190441.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190441.jpg
+758874	309467	Two people cross country skiing on a snow covered hill.	326	640	COCO_val2014_000000309467.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000309467.jpg
+758993	76161	Two men wearing ties holding glasses filled with beverages.	428	640	COCO_val2014_000000076161.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000076161.jpg
+759163	504074	Women that are sitting outside on a patio area and smoking cigarettes.	427	640	COCO_val2014_000000504074.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000504074.jpg
+759505	199783	a bed room with two bends and two lamps	480	640	COCO_val2014_000000199783.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000199783.jpg
+759806	72989	The clock for sale has  birds displayed on it.	640	446	COCO_val2014_000000072989.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000072989.jpg
+760037	219632	A black curly haired dog is playing with a frisbee.	426	640	COCO_val2014_000000219632.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000219632.jpg
+760183	495357	A dog sitting on top of a motorcycle in an intersection.	479	640	COCO_val2014_000000495357.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000495357.jpg
+760389	8181	a motorcycle parked in a field with a sky background	480	640	COCO_val2014_000000008181.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000008181.jpg
+760481	115106	A bird sits on a roof with a tree nearby.	375	500	COCO_val2014_000000115106.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000115106.jpg
+760551	19786	One person standing and another bending over in the foreground.	375	500	COCO_val2014_000000019786.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000019786.jpg
+760653	267169	A baby elephant being fed a bottle of milk.	428	640	COCO_val2014_000000267169.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000267169.jpg
+760731	384981	A group of people gathered to celebrate a birthday.	480	640	COCO_val2014_000000384981.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000384981.jpg
+760796	150317	A person passes an expanse of snow on skis while another watches in the distance.	640	480	COCO_val2014_000000150317.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150317.jpg
+760831	430961	A baseball player preparing to throw a pitch during a game.	319	450	COCO_val2014_000000430961.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000430961.jpg
+761007	535198	A large airplane flies overhead through the sky.	640	480	COCO_val2014_000000535198.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000535198.jpg
+761184	167082	a person riding a skate board on a rail	429	640	COCO_val2014_000000167082.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000167082.jpg
+761185	453756	a collage of photos with bowl and pots	453	640	COCO_val2014_000000453756.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453756.jpg
+761239	154028	The animals are grazing and eating in the grass.	480	640	COCO_val2014_000000154028.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000154028.jpg
+761349	70685	a group of elephants near a body of water	427	640	COCO_val2014_000000070685.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000070685.jpg
+761625	406665	A man sniffing wine inside of a glass.	500	378	COCO_val2014_000000406665.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406665.jpg
+761728	53542	People standing around a two story build with a clock on top of it.	428	640	COCO_val2014_000000053542.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000053542.jpg
+761864	120964	A man wearing a North Carolina basketball jersey performs skateboard tricks	640	429	COCO_val2014_000000120964.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000120964.jpg
+761919	357254	The clock has pictures of different birds on the numbers.	480	640	COCO_val2014_000000357254.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000357254.jpg
+761932	495379	Close up of a red fire hydrant near a sidewalk.	439	640	COCO_val2014_000000495379.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000495379.jpg
+762040	57843	some dessert is laying out on a yellow and white plate	427	640	COCO_val2014_000000057843.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000057843.jpg
+762061	325602	Two teenagers on riding skateboards down at a park.	640	496	COCO_val2014_000000325602.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000325602.jpg
+762064	499884	Five young people are posing at the tennis net.	480	640	COCO_val2014_000000499884.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000499884.jpg
+762093	372495	An odd bathroom with stone walls, and two rolls of toilet paper	480	640	COCO_val2014_000000372495.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000372495.jpg
+762193	516628	Interior bedroom scene of yellow walls, white curtains and a mattress.	640	480	COCO_val2014_000000516628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516628.jpg
+762245	462953	Two teddy bears sitting on top of a pile of suitcases.	480	640	COCO_val2014_000000462953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000462953.jpg
+762280	77591	People holding racquets playing tennis on a court.	480	640	COCO_val2014_000000077591.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000077591.jpg
+762408	17484	A woman hitting a tennis ball with a racquet.	640	427	COCO_val2014_000000017484.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000017484.jpg
+762570	62474	Two trains beside each other on two tracks that run beside each other.	389	640	COCO_val2014_000000062474.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062474.jpg
+762715	382309	Small slice of pizza sitting on a table next to the bottle of beer.	612	612	COCO_val2014_000000382309.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000382309.jpg
+762773	431355	A toilet is shown with a black ruck.	500	375	COCO_val2014_000000431355.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431355.jpg
+762820	289702	Large black dog sitting in front of a big mirror.	427	640	COCO_val2014_000000289702.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000289702.jpg
+763038	536416	A woman stands on a surfboard on the beach near another woman.	427	640	COCO_val2014_000000536416.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000536416.jpg
+763081	99269	Miniature fruit and chocolate chip cookies are ready for the doll house occupants.	450	600	COCO_val2014_000000099269.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099269.jpg
+763214	421471	We are looking at a small airline toilet.	480	640	COCO_val2014_000000421471.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000421471.jpg
+763236	294305	Two clocks on top of a tower at night time.	428	640	COCO_val2014_000000294305.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294305.jpg
+763509	158109	Two black cat sitting in front of a window facing the backyard.	375	500	COCO_val2014_000000158109.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000158109.jpg
+763746	527486	A female tennis player holding a ball and a racquet	500	333	COCO_val2014_000000527486.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000527486.jpg
+764358	256250	two giraffes in a field next to some standing water	482	640	COCO_val2014_000000256250.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000256250.jpg
+764401	300725	a pigeon perched on the arm of a bench	480	640	COCO_val2014_000000300725.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000300725.jpg
+764550	333018	Person riding a bicycle while walking two dogs.	478	500	COCO_val2014_000000333018.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333018.jpg
+764560	187585	a young man is on his skateboard doing a trick	436	640	COCO_val2014_000000187585.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187585.jpg
+764591	225378	The soccer player in blue edges out her opponent.	426	640	COCO_val2014_000000225378.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000225378.jpg
+764766	547309	a night time photo of a cab over truck	480	640	COCO_val2014_000000547309.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000547309.jpg
+765219	98497	A traffic sign that has a picture of a man holding a surfboard on it.	480	640	COCO_val2014_000000098497.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000098497.jpg
+765282	286903	Herd of zebras walking single file in grass land.	429	640	COCO_val2014_000000286903.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000286903.jpg
+765452	497626	a made bed with four pillows and lamp on a table	640	478	COCO_val2014_000000497626.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497626.jpg
+765621	508482	The open doors looking into a subway from the station.	640	480	COCO_val2014_000000508482.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000508482.jpg
+765683	116750	a train car parked in a train yard	425	640	COCO_val2014_000000116750.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116750.jpg
+765688	327771	A bed is sitting on a wooden frame.	558	558	COCO_val2014_000000327771.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000327771.jpg
+765798	405146	a kitchen counter covered with cleaning supplies and other items	480	640	COCO_val2014_000000405146.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000405146.jpg
+765932	539683	A gray cat sitting in a white sink in a bathroom.	480	640	COCO_val2014_000000539683.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000539683.jpg
+766099	101013	A blue car driving down a street past parked cars.	427	640	COCO_val2014_000000101013.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000101013.jpg
+766262	247609	a wood bench is sitting near a pole	360	640	COCO_val2014_000000247609.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000247609.jpg
+766401	213162	A tennis player approaches the ball with her racket.	640	426	COCO_val2014_000000213162.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000213162.jpg
+766449	525184	Young woman affectionately holding white teddy bear with car.	500	375	COCO_val2014_000000525184.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000525184.jpg
+766548	463669	A black and white cat laying on top of a blue blanket.	480	640	COCO_val2014_000000463669.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000463669.jpg
+766672	99810	A boy is eating donut holes while sitting at a dinner table.	332	500	COCO_val2014_000000099810.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099810.jpg
+766769	5213	Atv parked alongside rural roadway with trees lining landscape.	375	500	COCO_val2014_000000005213.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005213.jpg
+766781	80303	A large green truck parked on top of a desert field.	424	640	COCO_val2014_000000080303.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080303.jpg
+766803	497928	Flat screen fixed on a all opposite a nice couch and living room.	360	640	COCO_val2014_000000497928.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497928.jpg
+766978	83768	a laptop sitting in a chair next to a big tv	427	640	COCO_val2014_000000083768.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000083768.jpg
+767000	89078	Female snowboarder riding down a trail smiling at camera.	640	479	COCO_val2014_000000089078.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089078.jpg
+767064	54204	People with horse drawn carriage on side of city street.	445	640	COCO_val2014_000000054204.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054204.jpg
+767205	332292	The skateboarder is checking his technique in the mirror.	425	640	COCO_val2014_000000332292.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000332292.jpg
+767459	510898	A bunch of yellow and red apples are shining.	640	478	COCO_val2014_000000510898.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000510898.jpg
+767888	446861	A row of traffic lights hanging over a suburban intersection.	427	640	COCO_val2014_000000446861.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000446861.jpg
+767917	375430	A stack of pancakes that are sitting on a plate.	427	640	COCO_val2014_000000375430.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000375430.jpg
+767992	198724	A dog that is standing on a frisbee.	466	640	COCO_val2014_000000198724.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000198724.jpg
+768195	577932	A woman walking next to a man riding a pink bike.	543	640	COCO_val2014_000000577932.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577932.jpg
+768391	303566	A bunch of boxes that have fruit inside of them.	426	640	COCO_val2014_000000303566.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303566.jpg
+768568	295478	A woman walking a small white dog down a street.	640	512	COCO_val2014_000000295478.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000295478.jpg
+768653	444073	Two sheep that are standing in the grass.	332	500	COCO_val2014_000000444073.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444073.jpg
+768733	557329	A bird is perched on a branch in a tree.	640	640	COCO_val2014_000000557329.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000557329.jpg
+769169	406681	The dogs are looking like they are interested in the game.	427	640	COCO_val2014_000000406681.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000406681.jpg
+769261	168593	There are two refrigerators in this dirty, rundown kitchen.	480	640	COCO_val2014_000000168593.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168593.jpg
+769331	25807	The paddle surfer is looking for a big wave.	426	640	COCO_val2014_000000025807.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000025807.jpg
+769751	557360	Some bunches of red bananas on a stalk.	640	640	COCO_val2014_000000557360.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000557360.jpg
+770024	8762	Street lights and a lit up street sign at an intersection.	427	640	COCO_val2014_000000008762.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000008762.jpg
+770082	119677	A square of cheesecake on a marble cutting board with a two-pronged fork.	480	640	COCO_val2014_000000119677.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000119677.jpg
+770226	497187	A view of a keyboard from up close and on the side.	427	640	COCO_val2014_000000497187.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000497187.jpg
+770558	509270	There are two kites flying in the wind.	640	424	COCO_val2014_000000509270.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000509270.jpg
+771228	444755	Pug wearing tie as bearded man stares at camera	640	640	COCO_val2014_000000444755.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000444755.jpg
+771266	294437	A computer sits on in front of a television set.	374	500	COCO_val2014_000000294437.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000294437.jpg
+771390	171942	A variety of desserts arranged in a row.	432	640	COCO_val2014_000000171942.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000171942.jpg
+771399	85376	A single green motorcycle Ina full parking lot	640	480	COCO_val2014_000000085376.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000085376.jpg
+771557	152465	A fire hydrant with a tire underneath it.	516	640	COCO_val2014_000000152465.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000152465.jpg
+771735	514787	A couple of photos of a group of people playing with interactive a gaming unit..	500	500	COCO_val2014_000000514787.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000514787.jpg
+771875	448492	A price sign for broccoli is in front of some radishes.	426	640	COCO_val2014_000000448492.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000448492.jpg
+771914	636	A toilet seat sits on top of a hole in the ground.	640	480	COCO_val2014_000000000636.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000000636.jpg
+771919	244197	Two men next to each other on their laptops.	428	640	COCO_val2014_000000244197.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000244197.jpg
+772507	248310	Two tooth brushes sitting beside each other on a table.	468	640	COCO_val2014_000000248310.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248310.jpg
+772562	540473	A man standing next to a woman in front of a couch.	333	500	COCO_val2014_000000540473.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000540473.jpg
+773434	390283	Man sitting in an innertube with a hat on the Sidneyville.	640	427	COCO_val2014_000000390283.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000390283.jpg
+773516	424641	The people are riding bicycles in the bicycle lane of the road.	425	640	COCO_val2014_000000424641.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000424641.jpg
+773743	39267	A woman is kneeling down next to a toilet.	640	501	COCO_val2014_000000039267.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039267.jpg
+773771	437514	a wood bench is next to a tree	556	640	COCO_val2014_000000437514.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000437514.jpg
+773773	529457	a white and black fire hydrant is on a curb	640	415	COCO_val2014_000000529457.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529457.jpg
+773808	233298	A large commercial airliner is flying through the sky.	480	640	COCO_val2014_000000233298.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000233298.jpg
+773960	371564	A man standing under an umbrella  in the rain using a cell phone.	428	640	COCO_val2014_000000371564.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000371564.jpg
+774027	314704	A black and white photo of a baseball game.	408	640	COCO_val2014_000000314704.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000314704.jpg
+774332	112751	A beach with waves coming in, and two people in wet suits and carrying surfboards, from the back, walking to the water.	470	640	COCO_val2014_000000112751.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000112751.jpg
+774404	74591	A box with a variety leafy green and root vegetables in it.	480	640	COCO_val2014_000000074591.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000074591.jpg
+774420	237277	Folded red cloth with a plate on top containing a bran muffin, small tin of cream cheese, and a butter knife, behind which are a red apple and a glass of ice water.	308	385	COCO_val2014_000000237277.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000237277.jpg
+774684	150098	A horse standing in the grass in a fenced in area.	640	512	COCO_val2014_000000150098.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000150098.jpg
+774714	523175	A bowl that has soup and a spoon in it.	375	500	COCO_val2014_000000523175.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000523175.jpg
+774748	523819	a stop sign a white and green street sign and some trees	500	500	COCO_val2014_000000523819.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000523819.jpg
+775064	257084	a person on a skate board ride down a ramp	333	500	COCO_val2014_000000257084.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000257084.jpg
+775336	155199	a black cat is laying on a couch	640	480	COCO_val2014_000000155199.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000155199.jpg
+775458	351130	a baseball player swings his bat at a ball	480	640	COCO_val2014_000000351130.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000351130.jpg
+775509	241602	A bathroom with a spa tub, sink, toilet, yellow stripes on the floor, and flower decorations on the walls.	427	640	COCO_val2014_000000241602.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000241602.jpg
+775510	59386	A group of giraffes stand together feeding on leaves in a large park.	640	427	COCO_val2014_000000059386.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000059386.jpg
+775609	197775	a van with the back open and appliances in the back	480	640	COCO_val2014_000000197775.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000197775.jpg
+775776	48104	A person riding on top of an elephant near a tree.	640	480	COCO_val2014_000000048104.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000048104.jpg
+775844	401221	An airplane in the sky with a group of clouds behind it.	427	640	COCO_val2014_000000401221.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000401221.jpg
+776020	187695	a person is holding a hotdog with toppings	640	480	COCO_val2014_000000187695.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187695.jpg
+776233	404273	A giraffe walking close to a pool of water.	640	419	COCO_val2014_000000404273.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000404273.jpg
+776354	62703	a female in a green top and a laptop	480	640	COCO_val2014_000000062703.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062703.jpg
+776518	532295	a snow skier in a red jacket carrying skis and snow	640	427	COCO_val2014_000000532295.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532295.jpg
+776658	116951	A train driving on the tracks near trees.	418	640	COCO_val2014_000000116951.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116951.jpg
+777002	205238	A lady in a black and orange Halloween costume.	640	427	COCO_val2014_000000205238.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205238.jpg
+777053	29080	A sitting man holding a cell phone near a ship.	426	640	COCO_val2014_000000029080.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029080.jpg
+777055	554579	Three women wearing long dresses standing behind a dog.	640	494	COCO_val2014_000000554579.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000554579.jpg
+777281	209829	A person standing on a surfboard in the water.	480	640	COCO_val2014_000000209829.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209829.jpg
+777282	311190	A dog wearing a hat while in a car.	500	375	COCO_val2014_000000311190.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000311190.jpg
+777922	536000	Several young girls sitting on a couch and playing Wii.	319	500	COCO_val2014_000000536000.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000536000.jpg
+777995	577826	Two skateboarders riding their boards on a sidewalk.	426	640	COCO_val2014_000000577826.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577826.jpg
+777996	308167	A fire hydrant next to a low brick wall.	640	480	COCO_val2014_000000308167.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000308167.jpg
+777999	94951	A yellow fire hydrant with a wet paint sign.	640	640	COCO_val2014_000000094951.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000094951.jpg
+778048	166096	A clock tower is seen in front of a tall building.	640	427	COCO_val2014_000000166096.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000166096.jpg
+778232	466347	airplanes floating in the lake on a cloudy day	426	640	COCO_val2014_000000466347.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000466347.jpg
+778270	365317	A man that is standing on a court with a racquet.	427	640	COCO_val2014_000000365317.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365317.jpg
+778490	473720	A large black dog standing inside of a kitchen.	480	640	COCO_val2014_000000473720.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000473720.jpg
+778572	277812	Two women who are standing under a umbrella.	640	399	COCO_val2014_000000277812.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000277812.jpg
+778587	124654	A restroom view of a toilet, sink, and shower curtain.	640	480	COCO_val2014_000000124654.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000124654.jpg
+778717	68646	A little boy that is sitting in front of a laptop.	428	640	COCO_val2014_000000068646.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068646.jpg
+778752	58937	A wooden table filled with wine glasses and a bowl of salad.	480	640	COCO_val2014_000000058937.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000058937.jpg
+778823	308900	A BASEBALL GAME IN AN INNER CITY BALL PARK	480	640	COCO_val2014_000000308900.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000308900.jpg
+779012	94379	A bunch of glasses that are sitting on a table.	480	640	COCO_val2014_000000094379.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000094379.jpg
+779157	246066	a woman sitting on a wooden park bench smiling at the camera	459	640	COCO_val2014_000000246066.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000246066.jpg
+779609	93120	Three young boys dressed in formal clothes, two are standing one is sitting	519	640	COCO_val2014_000000093120.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000093120.jpg
+779755	190788	A pizza that is sitting on a table.	478	640	COCO_val2014_000000190788.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000190788.jpg
+779857	225617	A bed with lots of pillows and a blanket.	480	640	COCO_val2014_000000225617.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000225617.jpg
+779899	154193	A woman sitting at a table with a  pizza in front of her.	480	640	COCO_val2014_000000154193.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000154193.jpg
+780145	187776	A bunch of people are gathered around many tables inside of a building.	427	640	COCO_val2014_000000187776.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000187776.jpg
+780469	54164	a man that is surfing on some water	427	640	COCO_val2014_000000054164.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054164.jpg
+780482	61171	A herd of black and white cows eating dry grass.	480	640	COCO_val2014_000000061171.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000061171.jpg
+780664	275393	A tennis player prepares to hit a forehand shot in a tennis match.	427	640	COCO_val2014_000000275393.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000275393.jpg
+780694	326021	A woman with a teddy bear sitting at a booth outside.	640	525	COCO_val2014_000000326021.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000326021.jpg
+780771	103723	a person standing close to an elephant	640	480	COCO_val2014_000000103723.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000103723.jpg
+780818	209610	A train that is sitting on the tracks.	440	640	COCO_val2014_000000209610.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000209610.jpg
+781105	183519	A bunch of people inside a crowded subway train.	480	640	COCO_val2014_000000183519.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000183519.jpg
+781182	351840	A dog is sitting in the passenger seat of a car.	640	480	COCO_val2014_000000351840.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000351840.jpg
+781300	245102	A bright red light that is on the side of a snow covered street.	427	640	COCO_val2014_000000245102.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000245102.jpg
+781490	397628	a stop sign and across the street are some vehicles	480	640	COCO_val2014_000000397628.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397628.jpg
+781664	339253	A woman flying a kite in a blue sky.	375	500	COCO_val2014_000000339253.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000339253.jpg
+781872	32322	A look at a white refrigerator and brown cabinets.	375	500	COCO_val2014_000000032322.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000032322.jpg
+782051	54011	a fire hydrgon that is out in the bushes	425	640	COCO_val2014_000000054011.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054011.jpg
+782053	36032	a person that is snowboarding down a hill	500	333	COCO_val2014_000000036032.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000036032.jpg
+782141	216326	Young baby sleeping in innocent white drapes around him	480	640	COCO_val2014_000000216326.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000216326.jpg
+782155	101096	A oool lookign stop sign by a murky bog hill	501	640	COCO_val2014_000000101096.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000101096.jpg
+782227	54618	A train is passing another train sitting on a track.	361	640	COCO_val2014_000000054618.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000054618.jpg
+782305	336182	A woman sitting on a bed brushing her teeth.	480	640	COCO_val2014_000000336182.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000336182.jpg
+782588	255990	sheep in a pen with some hay to eat	426	640	COCO_val2014_000000255990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000255990.jpg
+782784	431456	Long view of tennis match with man diving for ball in match	375	500	COCO_val2014_000000431456.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431456.jpg
+782881	155912	a guy on a bicycle and a guy flying a kite	427	640	COCO_val2014_000000155912.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000155912.jpg
+783173	329533	a couple of bananas and some other fruit	640	480	COCO_val2014_000000329533.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000329533.jpg
+783229	558864	A white plate topped with carrots, potatoes and dumplings.	480	640	COCO_val2014_000000558864.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558864.jpg
+783245	428698	a living room that has some furniture in it	427	640	COCO_val2014_000000428698.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000428698.jpg
+783390	68205	A man riding a skateboard up the side of a ramp.	500	500	COCO_val2014_000000068205.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000068205.jpg
+783521	178747	A bus drives over a muddy road with trees on the side of it.	480	640	COCO_val2014_000000178747.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000178747.jpg
+783688	395006	a bench near a table near a tree	359	640	COCO_val2014_000000395006.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000395006.jpg
+784516	453065	A woman watching TV with an arm reaching from under the couch.	333	500	COCO_val2014_000000453065.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453065.jpg
+784743	242427	A room with tables and laptops and a power point screen.	640	480	COCO_val2014_000000242427.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000242427.jpg
+784800	578500	a living room that has some couches and tables in it	290	640	COCO_val2014_000000578500.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000578500.jpg
+784869	173138	A man riding a board on top of a wave.	427	640	COCO_val2014_000000173138.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000173138.jpg
+784874	107257	An old stove and trash bags sit on the curb.	640	427	COCO_val2014_000000107257.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000107257.jpg
+784901	87561	A white toilet sitting next to a bathroom sink.	640	478	COCO_val2014_000000087561.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000087561.jpg
+785047	316047	a spin brush tooth brush on the sink	334	500	COCO_val2014_000000316047.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000316047.jpg
+785365	435309	A broken red smart phone sitting on a table.	633	640	COCO_val2014_000000435309.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000435309.jpg
+785524	462211	a sign in a cityscape light sup in the rain	640	480	COCO_val2014_000000462211.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000462211.jpg
+786067	8042	A boy and girl playing a game with remote controllers.	360	640	COCO_val2014_000000008042.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000008042.jpg
+786173	11169	A large cow walks over a fox in the grass.	427	640	COCO_val2014_000000011169.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000011169.jpg
+786838	468902	A dog playing with a calf in the grass.	640	426	COCO_val2014_000000468902.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000468902.jpg
+786859	11494	A double deckers buss full of people taking part in a parade.	480	640	COCO_val2014_000000011494.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000011494.jpg
+786957	449879	A man sits on a motorcycle holding a cat.	500	375	COCO_val2014_000000449879.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000449879.jpg
+787184	19032	A guy holding a horse while a little girl pets it.	441	640	COCO_val2014_000000019032.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000019032.jpg
+787203	55806	there are two zebras eating on a little bit of loose grass	408	500	COCO_val2014_000000055806.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000055806.jpg
+787218	80185	some parked bicycles and two women on a bench and a book	530	640	COCO_val2014_000000080185.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000080185.jpg
+787255	498641	A dog that is laying down on its side.	480	640	COCO_val2014_000000498641.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000498641.jpg
+787672	360540	A store with some umbrellas next to a motor scooter.	427	640	COCO_val2014_000000360540.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000360540.jpg
+787744	196878	A newspaper called The Queenslander with a holiday dinner scene on it.	640	474	COCO_val2014_000000196878.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000196878.jpg
+787787	559102	A couple of kids playing with a racquet in front of a the camera.	640	424	COCO_val2014_000000559102.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000559102.jpg
+787835	457119	The birds are on and all along the bench outside.	500	375	COCO_val2014_000000457119.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000457119.jpg
+787877	72753	The two teddy bears are sitting together on the red tablecloth.	480	621	COCO_val2014_000000072753.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000072753.jpg
+787979	301082	A woman brushes her teeth over the sink while sitting in her bed.	480	640	COCO_val2014_000000301082.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000301082.jpg
+788077	370444	The bathroom has a blue trash can on the floor	500	375	COCO_val2014_000000370444.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000370444.jpg
+788080	517987	a man that is sitting on a skateboard and drinking something	640	427	COCO_val2014_000000517987.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000517987.jpg
+788135	402658	a plate of food that is on a table	427	640	COCO_val2014_000000402658.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000402658.jpg
+788224	116455	a motorcycle that has some sticks on his back	640	640	COCO_val2014_000000116455.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116455.jpg
+788611	484145	A man standing with a glass in his hand.	334	500	COCO_val2014_000000484145.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000484145.jpg
+788663	260922	A photo of an apartment building with a clock on it	427	640	COCO_val2014_000000260922.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000260922.jpg
+788696	42383	A white toilet sitting next to a white bath tub.	640	480	COCO_val2014_000000042383.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000042383.jpg
+788955	367747	A train on some tracks above a city area.	427	640	COCO_val2014_000000367747.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000367747.jpg
+789119	504690	A group of zebras and wildebeest grazing in a field.	333	500	COCO_val2014_000000504690.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000504690.jpg
+789345	75576	A cat that is laying near a pair of slippers.	375	500	COCO_val2014_000000075576.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000075576.jpg
+789673	363875	A man that is sitting on a moped.	640	558	COCO_val2014_000000363875.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000363875.jpg
+789786	466763	a woman in glasses with a blow dryer	480	640	COCO_val2014_000000466763.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000466763.jpg
+789891	315350	A man swinging a tennis racquet on a court.	427	640	COCO_val2014_000000315350.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315350.jpg
+790098	116466	a small child is getting his hair blow dried	425	640	COCO_val2014_000000116466.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000116466.jpg
+790236	139883	The animal food tray and cage is empty.	427	640	COCO_val2014_000000139883.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000139883.jpg
+790237	22724	The building has a tall tower with a clock.	480	640	COCO_val2014_000000022724.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000022724.jpg
+790319	575931	Young boy on the back of his father under an umbrella.	612	612	COCO_val2014_000000575931.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000575931.jpg
+790603	334509	A man that is standing in front of a grill with meat.	480	640	COCO_val2014_000000334509.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334509.jpg
+790821	532503	Man and woman on an ice covered bridge.	640	640	COCO_val2014_000000532503.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000532503.jpg
+790884	495681	A kitchen sink sitting underneath a large wooden window.	640	545	COCO_val2014_000000495681.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000495681.jpg
+791048	418548	a man is holding up a box of doughnuts	500	401	COCO_val2014_000000418548.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000418548.jpg
+791223	119460	Many appliances on the shell of a store with containers	640	640	COCO_val2014_000000119460.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000119460.jpg
+791260	119452	a spotty banana sitting on top of an orange and some other fruit	480	640	COCO_val2014_000000119452.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000119452.jpg
+791276	513072	Man preforming stunt on skateboard on a road.	640	426	COCO_val2014_000000513072.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000513072.jpg
+791282	170442	Inside of a bedroom with a bed and lamp.	480	640	COCO_val2014_000000170442.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170442.jpg
+791467	17073	Two people walking down the sidewalk with each other near a Street	640	433	COCO_val2014_000000017073.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000017073.jpg
+791852	233727	A bus coming around the corner on a city street.	427	640	COCO_val2014_000000233727.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000233727.jpg
+792069	191053	The young child is wearing a tie and smiling	640	428	COCO_val2014_000000191053.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000191053.jpg
+792546	376059	a man fixes a tie with a button up shirt	640	413	COCO_val2014_000000376059.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000376059.jpg
+792849	62831	A group of zebra's eating hay from a trough.	375	500	COCO_val2014_000000062831.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000062831.jpg
+792873	165133	A young man poses with a surfboard next to water.	612	612	COCO_val2014_000000165133.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000165133.jpg
+793122	362737	A closeup view of a pizza, with a fork near it.	500	375	COCO_val2014_000000362737.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000362737.jpg
+793169	179690	a dog that is sitting at a keyboard	640	427	COCO_val2014_000000179690.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000179690.jpg
+793180	487349	a woman is making some food in a bowl	612	612	COCO_val2014_000000487349.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000487349.jpg
+793270	379869	a couple of cats that are sitting on a fence	428	640	COCO_val2014_000000379869.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379869.jpg
+793480	89258	a vase that has some flowers in it	640	504	COCO_val2014_000000089258.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000089258.jpg
+793558	376123	A baseball player throwing a ball on a mound.	366	640	COCO_val2014_000000376123.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000376123.jpg
+793716	226744	a glove that has a bunch of baseballs in it	427	640	COCO_val2014_000000226744.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226744.jpg
+793783	172327	A person swimming in the waves of a body of water.	425	640	COCO_val2014_000000172327.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000172327.jpg
+793831	334713	A man kneeling in the grass next to an animal.	640	427	COCO_val2014_000000334713.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000334713.jpg
+793837	344832	a little boy that is standing at home plate	640	480	COCO_val2014_000000344832.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344832.jpg
+793937	147256	A stir fry bowl of broccoli and a bowl of meatballs.	477	640	COCO_val2014_000000147256.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000147256.jpg
+794378	18957	A small boy pushes a pizza into an oven.	557	640	COCO_val2014_000000018957.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000018957.jpg
+794496	264992	Two bags are full of fruits on the table.	480	640	COCO_val2014_000000264992.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000264992.jpg
+794553	113672	Two cows are beside a group of people standing near a parking lot.	480	640	COCO_val2014_000000113672.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000113672.jpg
+794720	169598	A bathroom shower, toilet and mirror that is decorated in tan tile.	640	480	COCO_val2014_000000169598.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000169598.jpg
+794733	461467	Two women singing at a dinner table with a large piece of pie in the middle.	299	500	COCO_val2014_000000461467.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000461467.jpg
+794991	184266	The man in the green shirt and cap is walking with a tennis racket.	427	640	COCO_val2014_000000184266.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000184266.jpg
+795002	168953	Man sitting at a table looking at silver cell phones in a circle.	500	332	COCO_val2014_000000168953.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000168953.jpg
+795137	29524	A girl in a soccer uniform playing a soccer game.	640	459	COCO_val2014_000000029524.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000029524.jpg
+795327	249668	A seagull on the waters edge of a beach holding a fish in it's mouth.	434	640	COCO_val2014_000000249668.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000249668.jpg
+795754	453722	room with wood floors and a stone fire place	427	640	COCO_val2014_000000453722.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000453722.jpg
+795853	373333	A motorcycle is parked on the beach near the ocean.	426	640	COCO_val2014_000000373333.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000373333.jpg
+795946	167510	A stop sign at the intersection of Lyndon Ave and South.	427	640	COCO_val2014_000000167510.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000167510.jpg
+796216	342397	Skier performing jump on high altitude ski slope.	427	640	COCO_val2014_000000342397.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000342397.jpg
+796283	148785	A young boy with a downed kite on a string	357	640	COCO_val2014_000000148785.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000148785.jpg
+796345	335065	Adults and children gathered at large family meal.	427	640	COCO_val2014_000000335065.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000335065.jpg
+796814	94531	a group of giraffes that are inside the fence	480	640	COCO_val2014_000000094531.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000094531.jpg
+796953	128568	A floral arrangement with flowering twigs in an orange vase.	640	480	COCO_val2014_000000128568.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000128568.jpg
+796956	250924	a bookshelf that has a bunch of books on it	375	500	COCO_val2014_000000250924.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000250924.jpg
+797263	52689	a bunch of people that are skiing down a hill	427	640	COCO_val2014_000000052689.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000052689.jpg
+797385	274687	A bicycle is standing next to a bed in a room.	480	640	COCO_val2014_000000274687.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000274687.jpg
+797521	99927	a clock that is sitting on top of a table	612	612	COCO_val2014_000000099927.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000099927.jpg
+797605	381134	a person that is spiting something up in a toilet	427	640	COCO_val2014_000000381134.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000381134.jpg
+797653	276263	People passed out on the floor of a bathroom with urinals and a toilet.	640	480	COCO_val2014_000000276263.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000276263.jpg
+797777	275111	Two women on a park bench while a man on a cell phone walks down the sidewalk.	511	640	COCO_val2014_000000275111.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000275111.jpg
+797811	303726	a bathroom that has a sink and a toilet in it	640	480	COCO_val2014_000000303726.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000303726.jpg
+797812	435707	a lap top that has a wire attached to it	480	640	COCO_val2014_000000435707.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000435707.jpg
+797908	141158	a person that is skiing across some snow	480	640	COCO_val2014_000000141158.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000141158.jpg
+798012	133559	a number of baseball players in a field	436	640	COCO_val2014_000000133559.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000133559.jpg
+798044	374990	A woman standing in a doorway as a dog turns and looks at her.	471	640	COCO_val2014_000000374990.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000374990.jpg
+798164	129903	A street sign with the words Manuel and Idalgo on it.	427	640	COCO_val2014_000000129903.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000129903.jpg
+798408	234660	Two white bullet trains parked at a train station.	480	640	COCO_val2014_000000234660.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000234660.jpg
+798462	63791	A group of men playing soccer in a park.	426	640	COCO_val2014_000000063791.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000063791.jpg
+798875	535262	A large elephant in mid stride in the desert	480	640	COCO_val2014_000000535262.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000535262.jpg
+799253	549932	some people standing in front of a building with some suitcases	640	640	COCO_val2014_000000549932.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000549932.jpg
+799301	77394	a couple of people that are sitting on a couch	640	480	COCO_val2014_000000077394.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000077394.jpg
+799317	424422	a couple of men that are walking around on some grass	427	640	COCO_val2014_000000424422.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000424422.jpg
+799392	121745	a plate that has a orange slice on it	480	640	COCO_val2014_000000121745.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000121745.jpg
+799393	527447	a mountain that has some skiiers on it	640	480	COCO_val2014_000000527447.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000527447.jpg
+799434	202981	Group of people standing on a Snowmountain with skis on.	480	640	COCO_val2014_000000202981.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000202981.jpg
+800080	170474	A man who is about to server a tennis ball.	476	640	COCO_val2014_000000170474.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000170474.jpg
+800193	240783	A plate that has a pile of doughnuts on it.	480	640	COCO_val2014_000000240783.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000240783.jpg
+800274	483965	a close up of a person eating a hot dog	428	640	COCO_val2014_000000483965.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000483965.jpg
+800336	333430	a white stove is next to a window	427	640	COCO_val2014_000000333430.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000333430.jpg
+800424	397786	some people are sitting on a bench in front of a boat	428	640	COCO_val2014_000000397786.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397786.jpg
+800426	226801	A train traveling down tracks next to a bridge.	425	640	COCO_val2014_000000226801.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000226801.jpg
+800441	460676	A man and young boy with tennis rackets on a blue tennis court.	409	286	COCO_val2014_000000460676.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000460676.jpg
+800556	433423	a cat siting on the ground watching a tv	640	427	COCO_val2014_000000433423.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000433423.jpg
+800585	131493	People on the shore as boats sit in the water.	500	375	COCO_val2014_000000131493.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000131493.jpg
+800653	103837	a person riding a horse on a track	428	640	COCO_val2014_000000103837.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000103837.jpg
+801097	329806	A stoplight in a city with trees in background	480	640	COCO_val2014_000000329806.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000329806.jpg
+801234	19443	a motorcycle is hanging in the air with plants on it	359	640	COCO_val2014_000000019443.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000019443.jpg
+801383	554100	There was a sandwich and cheese and meet served.	426	640	COCO_val2014_000000554100.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000554100.jpg
+801471	399091	a bench surrounded by different types of plant life	480	640	COCO_val2014_000000399091.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000399091.jpg
+801706	135902	A red and yellow train is shown on the train tracks.	425	640	COCO_val2014_000000135902.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000135902.jpg
+802493	531023	a bath room with a toilet and a towel rack	640	426	COCO_val2014_000000531023.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000531023.jpg
+802608	9448	A little girl holds up a big blue umbrella.	640	551	COCO_val2014_000000009448.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000009448.jpg
+802648	379605	An airplane sitting on the water parked at the docks	480	640	COCO_val2014_000000379605.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000379605.jpg
+802782	48653	Man on a snowboard, mid air going down a snowy slope.	426	640	COCO_val2014_000000048653.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000048653.jpg
+802954	500940	People are sailing on a small boat in the water.	480	640	COCO_val2014_000000500940.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500940.jpg
+803459	344308	An elegant bedroom with a draped, decorated bed.	480	640	COCO_val2014_000000344308.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000344308.jpg
+803460	205089	The interior of a studio apartment decorated with various pieces of furniture.	480	640	COCO_val2014_000000205089.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000205089.jpg
+803570	283744	A string of Christmas garland hangs below a display of stuffed animals.	333	500	COCO_val2014_000000283744.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000283744.jpg
+803659	315722	a pizza with lots of ingredients on a table in a room lit by dark lighting	640	480	COCO_val2014_000000315722.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000315722.jpg
+803719	280363	A living area with a bed and a large window.	480	640	COCO_val2014_000000280363.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000280363.jpg
+804177	241141	"The street sign says ""Can't Stop The Dance""."	500	375	COCO_val2014_000000241141.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000241141.jpg
+804199	516540	Twin fire plugs are planted just a few feet apart on the grassy strip	640	427	COCO_val2014_000000516540.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516540.jpg
+804615	4988	A public bus parked at a bus stop	480	640	COCO_val2014_000000004988.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000004988.jpg
+804666	431954	A young girl, holding  a teddy bear at an outdoor event.	640	480	COCO_val2014_000000431954.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000431954.jpg
+804713	252582	A small airplane flying through the clear blue sky.	612	612	COCO_val2014_000000252582.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000252582.jpg
+805122	250227	two long lines of boys paddle a canoe	427	640	COCO_val2014_000000250227.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000250227.jpg
+805325	207093	a cat laying on part of a suitcase	375	500	COCO_val2014_000000207093.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000207093.jpg
+805355	392391	Two people in a park who are playing frisbee.	420	640	COCO_val2014_000000392391.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000392391.jpg
+805401	69568	A smiling old lady holds a pizza on a plate.	640	481	COCO_val2014_000000069568.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000069568.jpg
+805436	531018	a bridge with some people on it and another riding a see doo under it	426	640	COCO_val2014_000000531018.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000531018.jpg
+806158	397069	A black and white shot of a toothbrush on the sink rim	318	500	COCO_val2014_000000397069.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397069.jpg
+806910	5064	A baseball player swinging a bat during a game.	427	640	COCO_val2014_000000005064.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000005064.jpg
+806996	218811	A kitchen area with a stove, counter and kids kitchen set.	428	640	COCO_val2014_000000218811.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000218811.jpg
+807106	500100	The dog is pulling a woman on a skateboard.	492	500	COCO_val2014_000000500100.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000500100.jpg
+807113	462632	Two laptops that are sitting on a desk.	428	640	COCO_val2014_000000462632.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000462632.jpg
+807220	351034	A new android phone is next to another android phone.	480	640	COCO_val2014_000000351034.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000351034.jpg
+807272	15987	Two elephants are touching trunks through a fence.	408	640	COCO_val2014_000000015987.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000015987.jpg
+807746	415360	A puppy laying on a skateboard on a brick sidewalk.	495	640	COCO_val2014_000000415360.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000415360.jpg
+807804	365385	A toothbrush in a cup sitting next to a sink.	427	640	COCO_val2014_000000365385.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000365385.jpg
+807909	383533	A man and woman dragging surf boards through the water	427	640	COCO_val2014_000000383533.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000383533.jpg
+808101	506656	a guy standing in front of a few horses.	480	640	COCO_val2014_000000506656.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000506656.jpg
+808172	219487	a couple of men are playing with a frisbee	427	640	COCO_val2014_000000219487.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000219487.jpg
+808420	227326	A man and woman that is standing near a cake.	407	640	COCO_val2014_000000227326.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227326.jpg
+808718	397225	a couple of kids are holding up pink umbrellas	424	640	COCO_val2014_000000397225.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000397225.jpg
+808766	227851	An elderly woman fanning herself while sitting outside.	480	640	COCO_val2014_000000227851.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000227851.jpg
+808922	558064	a woman is sitting with a statue on a bench	389	640	COCO_val2014_000000558064.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000558064.jpg
+809078	304300	A man operating a very small park train going down a track.	480	640	COCO_val2014_000000304300.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000304300.jpg
+809861	556426	a white bed pillow window and some trees	640	480	COCO_val2014_000000556426.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000556426.jpg
+810227	493623	A pair of high heels sitting near a vase with flowers in it.	332	500	COCO_val2014_000000493623.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000493623.jpg
+810553	39963	A bedroom contains a bed, a table with a computer, and speakers.	411	640	COCO_val2014_000000039963.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039963.jpg
+810573	248655	a person is standing in the snow in skis	640	480	COCO_val2014_000000248655.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000248655.jpg
+810801	496239	a small child is playing with a game controller	428	640	COCO_val2014_000000496239.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000496239.jpg
+811102	36477	Close up on bunches of yellow ripe bananas.	375	500	COCO_val2014_000000036477.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000036477.jpg
+811216	282942	The woman is pointing at a big cake.	425	640	COCO_val2014_000000282942.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000282942.jpg
+811496	516342	some people sitting on some benches flowers and buildings	437	640	COCO_val2014_000000516342.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000516342.jpg
+811654	577522	a bird stands on top of a laptop keyboard	640	640	COCO_val2014_000000577522.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000577522.jpg
+811874	8170	A refrigerator and a stove occupy a kitchen.	218	640	COCO_val2014_000000008170.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000008170.jpg
+811947	320461	a guy doing tricks on a skateboard on a skateboard ramp covered in graffitti	427	640	COCO_val2014_000000320461.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000320461.jpg
+812081	20912	A collection of several items that are placed near each other.	375	500	COCO_val2014_000000020912.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000020912.jpg
+812101	39106	Four young adults standing and playing a video game.	444	640	COCO_val2014_000000039106.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000039106.jpg
+812148	268742	Dishes lined up on a narrow shelf above a radiator.	288	640	COCO_val2014_000000268742.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000268742.jpg
+812252	414664	A horse standing in the grass near trees in the woods.	640	634	COCO_val2014_000000414664.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000414664.jpg
+812380	527098	Several boats in the water near an amusement park.	428	640	COCO_val2014_000000527098.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000527098.jpg
+812710	302185	There is s toilet scrubber next to the toilet in the stall.	640	480	COCO_val2014_000000302185.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000302185.jpg
+812812	505728	A woman prepares to pour batter into lined muffin tins.	429	640	COCO_val2014_000000505728.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000505728.jpg
+812905	499618	People walking on the street next to a truck with caged animals in it.	360	640	COCO_val2014_000000499618.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000499618.jpg
+813014	534829	The bottom view of people's body and a puppy on a leash.	427	640	COCO_val2014_000000534829.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000534829.jpg
+813020	104544	There are two elephants standing near the water.	426	640	COCO_val2014_000000104544.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000104544.jpg
+813052	229948	a wagon that is being pulled by some horse	428	640	COCO_val2014_000000229948.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000229948.jpg
+813090	509020	A man wearing glasses on the lap top.	480	640	COCO_val2014_000000509020.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000509020.jpg
+813472	338007	A man wearing glasses holding a Wii controller in one of his hands	480	640	COCO_val2014_000000338007.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000338007.jpg
+813661	180515	A pole that has a street sign on it.	356	500	COCO_val2014_000000180515.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000180515.jpg
+814045	246442	a women that is walking around with a umbrella	427	640	COCO_val2014_000000246442.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000246442.jpg
+814169	257382	a big yellow school bus parked in front of some trees.	426	640	COCO_val2014_000000257382.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000257382.jpg
+815150	464092	Several benches next to each other in front of bushes.	480	640	COCO_val2014_000000464092.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000464092.jpg
+815510	472803	A box of donuts are sitting on a desk.	612	612	COCO_val2014_000000472803.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000472803.jpg
+815545	181888	Children are playing tennis on the street on a nice day.	480	640	COCO_val2014_000000181888.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000181888.jpg
+815580	529592	Two men in yellow shirts in a Frisbee competition.	611	640	COCO_val2014_000000529592.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000529592.jpg
+815878	204100	A woman is picking bananas from a basket.	281	500	COCO_val2014_000000204100.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000204100.jpg
+815892	13081	A man and a young boy working on laptop computers.	480	640	COCO_val2014_000000013081.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000013081.jpg
+816625	310553	A man riding on the back of a motorcycle with a woman.	427	640	COCO_val2014_000000310553.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000310553.jpg
+829300	117061	A person in a silly shirt sits at a table so they can eat some pizza	500	375	COCO_val2014_000000117061.jpg	http://images.cocodataset.org/val2014/COCO_val2014_000000117061.jpg
diff --git a/text_to_image/dataset.py b/text_to_image/dataset.py
new file mode 100644
index 0000000000..f1165058ad
--- /dev/null
+++ b/text_to_image/dataset.py
@@ -0,0 +1,67 @@
+"""
+dataset related classes and methods
+"""
+
+# pylint: disable=unused-argument,missing-docstring
+
+import logging
+import sys
+import time
+
+import cv2
+import numpy as np
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("dataset")
+
+
+class Dataset:
+    def __init__(self):
+        self.arrival = None
+        self.image_list = []
+        self.caption_list = []
+        self.items_inmemory = {}
+        self.last_loaded = -1
+
+    def preprocess(self, use_cache=True):
+        raise NotImplementedError("Dataset:preprocess")
+
+    def get_item_count(self):
+        return len(self.image_list)
+
+    def get_list(self):
+        raise NotImplementedError("Dataset:get_list")
+
+    def load_query_samples(self, sample_list):
+        self.items_inmemory = {}
+        for sample in sample_list:
+            self.items_inmemory[sample] = self.get_item(sample)
+        self.last_loaded = time.time()
+
+    def unload_query_samples(self, sample_list):
+        if sample_list:
+            for sample in sample_list:
+                if sample in self.items_inmemory:
+                    del self.items_inmemory[sample]
+        else:
+            self.items_inmemory = {}
+
+    def get_samples(self, id_list):
+        data = [
+            {
+                "input_tokens": self.items_inmemory[id]["input_tokens"],
+                "input_tokens_2": self.items_inmemory[id]["input_tokens_2"],
+                "latents": self.items_inmemory[id]["latents"],
+            }
+            for id in id_list
+        ]
+        images = [self.items_inmemory[id]["file_name"] for id in id_list]
+        return data, images
+
+    def get_item(self, id):
+        raise NotImplementedError("Dataset:get_item")
+
+
+def preprocess(img):
+    return img
diff --git a/text_to_image/dockerfile.gpu b/text_to_image/dockerfile.gpu
new file mode 100644
index 0000000000..875cdf76c5
--- /dev/null
+++ b/text_to_image/dockerfile.gpu
@@ -0,0 +1,21 @@
+ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:23.08-py3
+FROM ${FROM_IMAGE_NAME}
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# apt dependencies
+RUN apt-get update
+RUN apt-get install -y ffmpeg libsm6 libxext6
+
+# install LDM
+COPY . /diffusion
+RUN cd /diffusion && \
+    pip install --no-cache-dir -r requirements.txt
+
+# install loadgen
+RUN cd /tmp && \
+    git clone --recursive https://github.com/mlcommons/inference && \
+    cd inference/loadgen && \
+    pip install pybind11 && \
+    CFLAGS="-std=c++14" python setup.py install && \
+    rm -rf mlperf
\ No newline at end of file
diff --git a/text_to_image/main.py b/text_to_image/main.py
new file mode 100644
index 0000000000..a3ea97d4a1
--- /dev/null
+++ b/text_to_image/main.py
@@ -0,0 +1,486 @@
+"""
+mlperf inference benchmarking tool
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import array
+import collections
+import json
+import logging
+import os
+import sys
+import threading
+import time
+from queue import Queue
+
+import mlperf_loadgen as lg
+import numpy as np
+import torch
+
+import dataset
+import coco
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("main")
+
+NANO_SEC = 1e9
+MILLI_SEC = 1000
+
+SUPPORTED_DATASETS = {
+    "coco-1024": (
+        coco.Coco,
+        dataset.preprocess,
+        coco.PostProcessCoco(),
+        {"image_size": [3, 1024, 1024]},
+    )
+}
+
+
+SUPPORTED_PROFILES = {
+    "defaults": {
+        "dataset": "coco-1024",
+        "backend": "pytorch",
+        "model-name": "stable-diffusion-xl",
+    },
+    "debug": {
+        "dataset": "coco-1024",
+        "backend": "debug",
+        "model-name": "stable-diffusion-xl",
+    },
+    "stable-diffusion-xl-pytorch": {
+        "dataset": "coco-1024",
+        "backend": "pytorch",
+        "model-name": "stable-diffusion-xl",
+    },
+    "stable-diffusion-xl-pytorch-dist": {
+        "dataset": "coco-1024",
+        "backend": "pytorch-dist",
+        "model-name": "stable-diffusion-xl",
+    },
+}
+
+SCENARIO_MAP = {
+    "SingleStream": lg.TestScenario.SingleStream,
+    "MultiStream": lg.TestScenario.MultiStream,
+    "Server": lg.TestScenario.Server,
+    "Offline": lg.TestScenario.Offline,
+}
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", choices=SUPPORTED_DATASETS.keys(), help="dataset")
+    parser.add_argument("--dataset-path", required=True, help="path to the dataset")
+    parser.add_argument(
+        "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
+    )
+    parser.add_argument(
+        "--scenario",
+        default="SingleStream",
+        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())),
+    )
+    parser.add_argument(
+        "--max-batchsize",
+        type=int,
+        default=1,
+        help="max batch size in a single inference",
+    )
+    parser.add_argument("--threads", default=1, type=int, help="threads")
+    parser.add_argument(
+        "--use_preprocessed_dataset",
+        action="store_true",
+        help="use preprocessed dataset instead of the original",
+    )
+    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
+    parser.add_argument(
+        "--find-peak-performance",
+        action="store_true",
+        help="enable finding peak performance pass",
+    )
+    parser.add_argument("--backend", help="Name of the backend")
+    parser.add_argument("--model-name", help="Name of the model")
+    parser.add_argument("--output", default="output", help="test results")
+    parser.add_argument("--qps", type=int, help="target qps")
+    parser.add_argument("--model-path", help="Path to model weights")
+
+    parser.add_argument(
+        "--dtype",
+        default="fp32",
+        choices=["fp32", "fp16", "bf16"],
+        help="dtype of the model",
+    )
+    parser.add_argument(
+        "--device",
+        default="cuda",
+        choices=["cuda", "cpu"],
+        help="device to run the benchmark",
+    )
+    parser.add_argument(
+        "--latent-framework",
+        default="torch",
+        choices=["torch", "numpy"],
+        help="framework to load the latents",
+    )
+
+    # file to use mlperf rules compliant parameters
+    parser.add_argument(
+        "--mlperf_conf", default="mlperf.conf", help="mlperf rules config"
+    )
+    # file for user LoadGen settings such as target QPS
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+    # file for LoadGen audit settings
+    parser.add_argument(
+        "--audit_conf", default="audit.config", help="config for LoadGen audit settings"
+    )
+
+    # below will override mlperf rules compliant settings - don't use for official submission
+    parser.add_argument("--time", type=int, help="time to scan in seconds")
+    parser.add_argument("--count", type=int, help="dataset items to use")
+    parser.add_argument("--debug", action="store_true", help="debug")
+    parser.add_argument(
+        "--performance-sample-count", type=int, help="performance sample count", default=5000
+    )
+    parser.add_argument(
+        "--max-latency", type=float, help="mlperf max latency in pct tile"
+    )
+    parser.add_argument(
+        "--samples-per-query",
+        default=8,
+        type=int,
+        help="mlperf multi-stream samples per query",
+    )
+    args = parser.parse_args()
+
+    # don't use defaults in argparser. Instead we default to a dict, override that with a profile
+    # and take this as default unless command line give
+    defaults = SUPPORTED_PROFILES["defaults"]
+
+    if args.profile:
+        profile = SUPPORTED_PROFILES[args.profile]
+        defaults.update(profile)
+    for k, v in defaults.items():
+        kc = k.replace("-", "_")
+        if getattr(args, kc) is None:
+            setattr(args, kc, v)
+
+    if args.scenario not in SCENARIO_MAP:
+        parser.error("valid scanarios:" + str(list(SCENARIO_MAP.keys())))
+    return args
+
+
+def get_backend(backend, **kwargs):
+    if backend == "pytorch":
+        from backend_pytorch import BackendPytorch
+
+        backend = BackendPytorch(**kwargs)
+
+    elif backend == "debug":
+        from backend_debug import BackendDebug
+
+        backend = BackendDebug()
+    else:
+        raise ValueError("unknown backend: " + backend)
+    return backend
+
+
+class Item:
+    """An item that we queue for processing by the thread pool."""
+
+    def __init__(self, query_id, content_id, inputs, img=None):
+        self.query_id = query_id
+        self.content_id = content_id
+        self.img = img
+        self.inputs = inputs
+        self.start = time.time()
+
+
+class RunnerBase:
+    def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
+        self.take_accuracy = False
+        self.ds = ds
+        self.model = model
+        self.post_process = post_proc
+        self.threads = threads
+        self.take_accuracy = False
+        self.max_batchsize = max_batchsize
+        self.result_timing = []
+
+    def handle_tasks(self, tasks_queue):
+        pass
+
+    def start_run(self, result_dict, take_accuracy):
+        self.result_dict = result_dict
+        self.result_timing = []
+        self.take_accuracy = take_accuracy
+        self.post_process.start()
+
+    def run_one_item(self, qitem: Item):
+        # run the prediction
+        processed_results = []
+        try:
+            results = self.model.predict(qitem.inputs)
+            processed_results = self.post_process(
+                results, qitem.content_id, qitem.inputs, self.result_dict
+            )
+            if self.take_accuracy:
+                self.post_process.add_results(processed_results)
+            self.result_timing.append(time.time() - qitem.start)
+        except Exception as ex:  # pylint: disable=broad-except
+            src = [self.ds.get_item_loc(i) for i in qitem.content_id]
+            log.error("thread: failed on contentid=%s, %s", src, ex)
+            # since post_process will not run, fake empty responses
+            processed_results = [[]] * len(qitem.query_id)
+        finally:
+            response_array_refs = []
+            response = []
+            for idx, query_id in enumerate(qitem.query_id):
+                response_array = array.array(
+                    "B", np.array(processed_results[idx], np.float32).tobytes()
+                )
+                response_array_refs.append(response_array)
+                bi = response_array.buffer_info()
+                response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
+            lg.QuerySamplesComplete(response)
+
+    def enqueue(self, query_samples):
+        idx = [q.index for q in query_samples]
+        query_id = [q.id for q in query_samples]
+        if len(query_samples) < self.max_batchsize:
+            data, label = self.ds.get_samples(idx)
+            self.run_one_item(Item(query_id, idx, data, label))
+        else:
+            bs = self.max_batchsize
+            for i in range(0, len(idx), bs):
+                data, label = self.ds.get_samples(idx[i : i + bs])
+                self.run_one_item(
+                    Item(query_id[i : i + bs], idx[i : i + bs], data, label)
+                )
+
+    def finish(self):
+        pass
+
+
+class QueueRunner(RunnerBase):
+    def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
+        super().__init__(model, ds, threads, post_proc, max_batchsize)
+        self.tasks = Queue(maxsize=threads * 4)
+        self.workers = []
+        self.result_dict = {}
+
+        for _ in range(self.threads):
+            worker = threading.Thread(target=self.handle_tasks, args=(self.tasks,))
+            worker.daemon = True
+            self.workers.append(worker)
+            worker.start()
+
+    def handle_tasks(self, tasks_queue):
+        """Worker thread."""
+        while True:
+            qitem = tasks_queue.get()
+            if qitem is None:
+                # None in the queue indicates the parent want us to exit
+                tasks_queue.task_done()
+                break
+            self.run_one_item(qitem)
+            tasks_queue.task_done()
+
+    def enqueue(self, query_samples):
+        idx = [q.index for q in query_samples]
+        query_id = [q.id for q in query_samples]
+        if len(query_samples) < self.max_batchsize:
+            data, label = self.ds.get_samples(idx)
+            self.tasks.put(Item(query_id, idx, data, label))
+        else:
+            bs = self.max_batchsize
+            for i in range(0, len(idx), bs):
+                ie = i + bs
+                data, label = self.ds.get_samples(idx[i:ie])
+                self.tasks.put(Item(query_id[i:ie], idx[i:ie], data, label))
+
+    def finish(self):
+        # exit all threads
+        for _ in self.workers:
+            self.tasks.put(None)
+        for worker in self.workers:
+            worker.join()
+
+
+def main():
+    args = get_args()
+
+    log.info(args)
+
+    # find backend
+    backend = get_backend(
+        args.backend,
+        precision=args.dtype,
+        device=args.device,
+        model_path=args.model_path,
+    )
+    if args.dtype == "fp16":
+        dtype = torch.float16
+    elif args.dtype == "bf16":
+        dtype = torch.bfloat16
+    else:
+        dtype = torch.float32
+
+    # --count applies to accuracy mode only and can be used to limit the number of images
+    # for testing.
+    count_override = False
+    count = args.count
+    if count:
+        count_override = True
+
+    # load model to backend
+    model = backend.load()
+
+    # dataset to use
+    dataset_class, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
+    if args.use_preprocessed_dataset:
+        pre_proc = None
+    ds = dataset_class(
+        data_path=args.dataset_path,
+        name=args.dataset,
+        pre_process=pre_proc,
+        use_preprocessed=args.use_preprocessed_dataset,
+        count=count,
+        threads=args.threads,
+        pipe_tokenizer=model.pipe.tokenizer,
+        pipe_tokenizer_2=model.pipe.tokenizer_2,
+        latent_dtype=dtype,
+        latent_device=args.device,
+        latent_framework=args.latent_framework,
+        **kwargs,
+    )
+    final_results = {
+        "runtime": model.name(),
+        "version": model.version(),
+        "time": int(time.time()),
+        "args": vars(args),
+        "cmdline": str(args),
+    }
+
+    mlperf_conf = os.path.abspath(args.mlperf_conf)
+    if not os.path.exists(mlperf_conf):
+        log.error("{} not found".format(mlperf_conf))
+        sys.exit(1)
+
+    user_conf = os.path.abspath(args.user_conf)
+    if not os.path.exists(user_conf):
+        log.error("{} not found".format(user_conf))
+        sys.exit(1)
+
+    audit_config = os.path.abspath(args.audit_conf)
+    if args.output:
+        output_dir = os.path.abspath(args.output)
+        os.makedirs(output_dir, exist_ok=True)
+        os.chdir(output_dir)
+
+    #
+    # make one pass over the dataset to validate accuracy
+    #
+    count = ds.get_item_count()
+
+    # warmup
+    ds.load_query_samples([0])
+    for i in range(5):
+        captions, _ = ds.get_samples([0])
+        _ = backend.predict(captions)
+    ds.unload_query_samples(None)
+
+    scenario = SCENARIO_MAP[args.scenario]
+    runner_map = {
+        lg.TestScenario.SingleStream: RunnerBase,
+        lg.TestScenario.MultiStream: QueueRunner,
+        lg.TestScenario.Server: QueueRunner,
+        lg.TestScenario.Offline: QueueRunner,
+    }
+    runner = runner_map[scenario](
+        model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize
+    )
+
+    def issue_queries(query_samples):
+        runner.enqueue(query_samples)
+
+    def flush_queries():
+        pass
+
+    log_output_settings = lg.LogOutputSettings()
+    log_output_settings.outdir = output_dir
+    log_output_settings.copy_summary_to_stdout = False
+    log_settings = lg.LogSettings()
+    log_settings.enable_trace = args.debug
+    log_settings.log_output = log_output_settings
+
+    settings = lg.TestSettings()
+    settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
+    settings.FromConfig(user_conf, args.model_name, args.scenario)
+    settings.scenario = scenario
+    settings.mode = lg.TestMode.PerformanceOnly
+    if args.accuracy:
+        settings.mode = lg.TestMode.AccuracyOnly
+    if args.find_peak_performance:
+        settings.mode = lg.TestMode.FindPeakPerformance
+
+    if args.time:
+        # override the time we want to run
+        settings.min_duration_ms = args.time * MILLI_SEC
+        settings.max_duration_ms = args.time * MILLI_SEC
+
+    if args.qps:
+        qps = float(args.qps)
+        settings.server_target_qps = qps
+        settings.offline_expected_qps = qps
+
+    if count_override:
+        settings.min_query_count = count
+        settings.max_query_count = count
+
+    if args.samples_per_query:
+        settings.multi_stream_samples_per_query = args.samples_per_query
+    if args.max_latency:
+        settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_expected_latency_ns = int(args.max_latency * NANO_SEC)
+
+    performance_sample_count = (
+        args.performance_sample_count
+        if args.performance_sample_count
+        else min(count, 500)
+    )
+    sut = lg.ConstructSUT(issue_queries, flush_queries)
+    qsl = lg.ConstructQSL(
+        count, performance_sample_count, ds.load_query_samples, ds.unload_query_samples
+    )
+
+    log.info("starting {}".format(scenario))
+    result_dict = {"scenario": str(scenario)}
+    runner.start_run(result_dict, args.accuracy)
+
+    lg.StartTestWithLogSettings(sut, qsl, settings, log_settings, audit_config)
+
+    if args.accuracy:
+        post_proc.finalize(result_dict, ds, output_dir=args.output)
+        final_results["accuracy_results"] = result_dict
+
+    runner.finish()
+    lg.DestroyQSL(qsl)
+    lg.DestroySUT(sut)
+
+    #
+    # write final results
+    #
+    if args.output:
+        with open("results.json", "w") as f:
+            json.dump(final_results, f, sort_keys=True, indent=4)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/text_to_image/requirements.txt b/text_to_image/requirements.txt
new file mode 100644
index 0000000000..46812291bd
--- /dev/null
+++ b/text_to_image/requirements.txt
@@ -0,0 +1,7 @@
+diffusers==0.21.2
+transformers==4.33.2
+accelerate==0.23.0
+open-clip-torch==2.7.0
+opencv-python==4.8.1.78
+pycocotools==2.0.7
+torchmetrics[image]==1.2.0
diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
new file mode 100644
index 0000000000..c6714785f3
--- /dev/null
+++ b/text_to_image/tools/accuracy_coco.py
@@ -0,0 +1,101 @@
+"""
+Tool to calculate accuracy for loadgen accuracy output found in mlperf_log_accuracy.json
+We assume that loadgen's query index is in the same order as
+the images in coco's captions/captions.tsv.
+"""
+
+
+import argparse
+import json
+import os
+
+from PIL import Image
+import numpy as np
+import pandas as pd
+import torch
+from torchvision import transforms
+from torchmetrics.multimodal.clip_score import CLIPScore
+from torchmetrics.image.fid import FrechetInceptionDistance
+
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
+    parser.add_argument("--dataset-dir", required=True, help="openimages directory")
+    parser.add_argument("--verbose", action="store_true", help="verbose messages")
+    parser.add_argument("--output-file", default="coco-results.json", help="path to output file")
+    parser.add_argument("--use-preprocessed", action="store_true")
+    parser.add_argument("--device", default="cpu", choices=["gpu", "cpu"])
+    args = parser.parse_args()
+    return args
+
+
+def preprocess_image(img_dir, file_name):
+    img = Image.open(img_dir + "/" + file_name)
+    img = np.asarray(img)
+    if len(img.shape) == 2:
+        img = np.expand_dims(img, axis=-1)
+    tensor = torch.Tensor(np.asarray(img).transpose([2,0,1])).to(torch.uint8)
+    if tensor.shape[0] == 1:
+        tensor = tensor.repeat(3,1,1)
+    return tensor.unsqueeze(0)
+
+def main():
+    args = get_args()
+    annotations_file = os.environ.get('DATASET_ANNOTATIONS_FILE_PATH')
+    if not annotations_file:
+        annotations_file = os.path.join(args.dataset_dir, "captions", "captions.tsv")
+    
+    result_dict = {}
+
+    # Load dataset annotations
+    df_captions = pd.read_csv(annotations_file, sep="\t")
+
+    # Load model outputs
+    with open(args.mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+    # set device
+    device = args.device if torch.cuda.is_available() else "cpu"
+    if device == "gpu":
+        device = "cuda"
+
+    # Load torchmetrics modules
+    fid = FrechetInceptionDistance(feature=2048)
+    fid.to(device)
+    clip = CLIPScore(model_name_or_path="openai/clip-vit-base-patch32")
+    clip.to(device)
+
+    image_ids = set()
+    seen = set()
+    no_results = 0
+    for j in results:
+        idx = j['qsl_idx']
+        if idx in seen:
+            continue
+        seen.add(idx)
+
+        # Load generated image
+        generated_img = np.frombuffer(bytes.fromhex(j['data']), np.float32).reshape(1, 3, 1024, 1024)
+        generated_img = torch.Tensor(generated_img).to(torch.uint8).to(device)
+        # Load Ground Truth
+        caption = df_captions.iloc[idx]["caption"]
+        if args.use_preprocessed:
+            file_name = df_captions.iloc[idx]["file_name"].replace(".jpg", ".pt")
+            target_img = torch.load(args.dataset_dir + "/preprocessed/" + file_name).to(torch.uint8).to(device)
+        else:
+            target_img = preprocess_image(args.dataset_dir + "/validation/data", df_captions.iloc[idx]["file_name"]).to(torch.uint8).to(device)
+        fid.update(target_img, real=True)
+        fid.update(generated_img, real=False)
+        clip.update(generated_img, caption)
+        
+    result_dict["FID_SCORE"] = float(fid.compute().item())
+    result_dict["CLIP_SCORE"] = float(clip.compute().item())
+
+    with open(args.output_file, "w") as fp:
+        json.dump(result_dict, fp, sort_keys=True, indent=4)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/text_to_image/tools/check_latents.py b/text_to_image/tools/check_latents.py
new file mode 100644
index 0000000000..d8fbd652cc
--- /dev/null
+++ b/text_to_image/tools/check_latents.py
@@ -0,0 +1,22 @@
+import torch
+import numpy as np
+import argparse
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--torch-input", type=str, default="latents.pt")
+    parser.add_argument("--numpy-input", type=str, default="latents.npy")
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == "__main__":
+    args = get_args()
+    torch_latents = torch.load(args.torch_input)
+    numpy_latents = torch.Tensor(np.load(args.numpy_input))
+    print(f"Torch Latents: {torch_latents}\nShape: {torch_latents.shape}")
+    print(f"Numpy Latents: {numpy_latents}\nShape: {numpy_latents.shape}")
+    assert torch_latents.shape == numpy_latents.shape
+    assert (numpy_latents == torch_latents).all().item()
+    print("All tests passed!")
diff --git a/text_to_image/tools/coco.py b/text_to_image/tools/coco.py
new file mode 100644
index 0000000000..2c25d70266
--- /dev/null
+++ b/text_to_image/tools/coco.py
@@ -0,0 +1,150 @@
+import argparse
+import json
+import logging
+from multiprocessing import Pool
+import pandas as pd
+import os
+import tqdm
+import urllib.request
+import zipfile
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("coco")
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset-dir", default="./coco-2014", help="Dataset download location"
+    )
+    parser.add_argument(
+        "--tsv-path", default=None, help="Precomputed tsv file location"
+    )
+    parser.add_argument(
+        "--max-images",
+        default=5000,
+        type=int,
+        help="Maximun number of images to download",
+    )
+    parser.add_argument("--num-workers", default=1, type=int, help="Number of processes to download images")
+    parser.add_argument(
+        "--allow-duplicate-images",
+        action="store_true",
+        help="Allow mulple captions per image"
+    )
+    parser.add_argument(
+        "--latents-path-torch", default="latents.pt", type=str, help="Path to pytorch latents"
+    )
+    parser.add_argument(
+        "--latents-path-numpy", default="latents.npy", type=str, help="Path to numpy latents"
+    )
+    parser.add_argument(
+        "--seed", type=int, default=2023, help="Seed to choose the dataset"
+    )
+    parser.add_argument(
+        "--keep-raw", action="store_true", help="Keep raw folder"
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def download_img(args):
+    img_url, target_folder, file_name = args
+    if os.path.exists(target_folder + file_name):
+        log.warning(f"Image {file_name} found locally, skipping download")
+    else:
+        urllib.request.urlretrieve(img_url, target_folder + file_name)
+
+
+if __name__ == "__main__":
+    args = get_args()
+    dataset_dir = os.path.abspath(args.dataset_dir)
+    # Check if the annotation dataframe is there
+    if os.path.exists(f"{dataset_dir}/captions/captions_source.tsv"):
+        df_annotations = pd.read_csv(f"{dataset_dir}/captions/captions_source.tsv", sep="\t")
+        df_annotations = df_annotations.iloc[: args.max_images]
+    elif os.path.exists(f"{dataset_dir}/../captions_source.tsv"):
+        os.makedirs(f"{dataset_dir}/captions/", exist_ok=True)
+        os.system(f"cp {dataset_dir}/../captions_source.tsv {dataset_dir}/captions/")
+        df_annotations = pd.read_csv(f"{dataset_dir}/captions/captions_source.tsv", sep="\t")
+        df_annotations = df_annotations.iloc[: args.max_images]
+    elif args.tsv_path is not None and os.path.exists(f"{args.tsv_path}"):
+        file_name = args.tsv_path.split("/")[-1]
+        os.makedirs(f"{dataset_dir}/captions/", exist_ok=True)
+        os.system(f"cp {args.tsv_path} {dataset_dir}/captions/")
+        df_annotations = pd.read_csv(f"{dataset_dir}/captions/{file_name}", sep="\t")
+        df_annotations = df_annotations.iloc[: args.max_images]
+    else:
+        # Check if raw annotations file already exist
+        if not os.path.exists(f"{dataset_dir}/raw/annotations/captions_val2014.json"):
+            # Download annotations
+            os.makedirs(f"{dataset_dir}/raw/", exist_ok=True)
+            os.makedirs(f"{dataset_dir}/download_aux/", exist_ok=True)
+            os.system(
+                f"cd {dataset_dir}/download_aux/ && \
+                    wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip --show-progress"
+            )
+
+            # Unzip file
+            with zipfile.ZipFile(
+                f"{dataset_dir}/download_aux/annotations_trainval2014.zip", "r"
+            ) as zip_ref:
+                zip_ref.extractall(f"{dataset_dir}/raw/")
+
+        # Move captions to target folder
+        os.makedirs(f"{dataset_dir}/captions/", exist_ok=True)
+        os.system(
+            f"mv {dataset_dir}/raw/annotations/captions_val2014.json {dataset_dir}/captions/"
+        )
+        if not args.keep_raw:
+            os.system(f"rm -rf {dataset_dir}/raw")
+        os.system(f"rm -rf {dataset_dir}/download_aux")
+        # Convert to dataframe format and extract the relevant fields
+        with open(f"{dataset_dir}/captions/captions_val2014.json") as f:
+            captions = json.load(f)
+            annotations = captions["annotations"]
+            images = captions["images"]
+        df_annotations = pd.DataFrame(annotations)
+        df_images = pd.DataFrame(images)
+        if not args.allow_duplicate_images:
+            df_annotations = df_annotations.drop_duplicates(
+                subset=["image_id"], keep="first"
+            )
+        # Sort, shuffle and choose the final dataset
+        df_annotations = df_annotations.sort_values(by=["id"])
+        df_annotations = df_annotations.sample(
+            frac=1, random_state=args.seed
+        ).reset_index(drop=True)
+        df_annotations = df_annotations.iloc[: args.max_images]
+        df_annotations['caption'] = df_annotations['caption'].apply(lambda x: x.replace('\n', '').strip())
+        df_annotations = (
+            df_annotations.merge(
+                df_images, how="inner", left_on="image_id", right_on="id"
+            )
+            .drop(["id_y"], axis=1)
+            .rename(columns={"id_x": "id"})
+            .sort_values(by=["id"])
+            .reset_index(drop=True)
+        )
+    # Download images
+    os.makedirs(f"{dataset_dir}/validation/data/", exist_ok=True)
+    tasks = [
+        (row["coco_url"], f"{dataset_dir}/validation/data/", row["file_name"])
+        for i, row in df_annotations.iterrows()
+    ]
+    pool = Pool(processes=args.num_workers)
+    [_ for _ in tqdm.tqdm(pool.imap_unordered(download_img, tasks), total=len(tasks))]
+    # Finalize annotations
+    df_annotations[
+        ["id", "image_id", "caption", "height", "width", "file_name", "coco_url"]
+    ].to_csv(f"{dataset_dir}/captions/captions.tsv", sep="\t", index=False)
+
+    if os.path.exists(args.latents_path_torch):
+        os.makedirs(f"{dataset_dir}/latents/", exist_ok=True)
+        os.system(f"cp {args.latents_path_torch} {dataset_dir}/latents/")
+
+    if os.path.exists(args.latents_path_numpy):
+        os.makedirs(f"{dataset_dir}/latents/", exist_ok=True)
+        os.system(f"cp {args.latents_path_numpy} {dataset_dir}/latents/")
diff --git a/text_to_image/tools/coco_calibration.py b/text_to_image/tools/coco_calibration.py
new file mode 100644
index 0000000000..e80b33dfe5
--- /dev/null
+++ b/text_to_image/tools/coco_calibration.py
@@ -0,0 +1,122 @@
+import argparse
+import json
+import logging
+from multiprocessing import Pool
+import numpy as np
+import pandas as pd
+import os
+import tqdm
+import urllib.request
+import zipfile
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("coco")
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset-dir", default="./coco-2014", help="Dataset download location"
+    )
+    parser.add_argument(
+        "--tsv-path", default=None, help="Precomputed tsv file location"
+    )
+    parser.add_argument("--num-workers", default=1, type=int, help="Number of processes to download images")
+    parser.add_argument(
+        "--calibration-dir", default=None, help="Calibration ids location"
+    )
+    parser.add_argument(
+        "--keep-raw", action="store_true", help="Keep the raw dataset"
+    )
+    parser.add_argument(
+        "--download-images", action="store_true", help="Download the calibration set"
+    )
+    args = parser.parse_args()
+    return args
+
+
+def download_img(args):
+    img_url, target_folder, file_name = args
+    if os.path.exists(target_folder + file_name):
+        log.warning(f"Image {file_name} found locally, skipping download")
+    else:
+        urllib.request.urlretrieve(img_url, target_folder + file_name)
+
+
+if __name__ == "__main__":
+    args = get_args()
+    dataset_dir = os.path.abspath(args.dataset_dir)
+    calibration_dir = args.calibration_dir if args.calibration_dir is not None else os.path.join(os.path.dirname(__file__), "..", "..", "calibration", "COCO-2014")
+    # Check if the annotation dataframe is there
+    if os.path.exists(f"{dataset_dir}/calibration/captions.tsv"):
+        df_annotations = pd.read_csv(f"{dataset_dir}/calibration/captions.tsv", sep="\t")
+    elif args.tsv_path is not None and os.path.exists(f"{args.tsv_path}"):
+        os.makedirs(f"{dataset_dir}/calibration/", exist_ok=True)
+        os.system(f"cp {args.tsv_path} {dataset_dir}/calibration/")
+        df_annotations = pd.read_csv(f"{dataset_dir}/calibration/captions.tsv", sep="\t")
+    else:
+        # Check if raw annotations file already exist
+        if not os.path.exists(f"{dataset_dir}/raw/annotations/captions_train2014.json"):
+            # Download annotations
+            os.makedirs(f"{dataset_dir}/raw/", exist_ok=True)
+            os.makedirs(f"{dataset_dir}/download_aux/", exist_ok=True)
+            os.system(
+                f"cd {dataset_dir}/download_aux/ && \
+                    wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip --show-progress"
+            )
+
+            # Unzip file
+            with zipfile.ZipFile(
+                f"{dataset_dir}/download_aux/annotations_trainval2014.zip", "r"
+            ) as zip_ref:
+                zip_ref.extractall(f"{dataset_dir}/raw/")
+
+        # Move captions to target folder
+        os.makedirs(f"{dataset_dir}/captions/", exist_ok=True)
+        os.system(
+            f"mv {dataset_dir}/raw/annotations/captions_train2014.json {dataset_dir}/captions/"
+        )
+        if not args.keep_raw:
+            os.system(f"rm -rf {dataset_dir}/raw")
+        os.system(f"rm -rf {dataset_dir}/download_aux")
+        # Convert to dataframe format and extract the relevant fields
+        with open(f"{dataset_dir}/captions/captions_train2014.json") as f:
+            captions = json.load(f)
+            annotations = captions["annotations"]
+            images = captions["images"]
+        df_annotations = pd.DataFrame(annotations)
+        df_images = pd.DataFrame(images)
+
+        # Calibration images 
+        with open(f"{calibration_dir}/coco_cal_images_list.txt") as f:
+            calibration_ids = f.readlines()
+            calibration_ids = [int(id.replace('\n', '')) for id in calibration_ids]
+            calibration_ids = calibration_ids
+
+        df_annotations = df_annotations[np.isin(df_annotations["id"], calibration_ids)]
+        df_annotations = df_annotations.sort_values(by=["id"])
+        df_annotations['caption'] = df_annotations['caption'].apply(lambda x: x.replace('\n', '').strip())
+        df_annotations = (
+            df_annotations.merge(
+                df_images, how="inner", left_on="image_id", right_on="id"
+            )
+            .drop(["id_y"], axis=1)
+            .rename(columns={"id_x": "id"})
+            .sort_values(by=["id"])
+            .reset_index(drop=True)
+        )
+    # Download images
+    os.makedirs(f"{dataset_dir}/calibration/", exist_ok=True)
+    if args.download_images:
+        os.makedirs(f"{dataset_dir}/calibration/data/", exist_ok=True)
+        tasks = [
+            (row["coco_url"], f"{dataset_dir}/calibration/data/", row["file_name"])
+            for i, row in df_annotations.iterrows()
+        ]
+        pool = Pool(processes=args.num_workers)
+        [_ for _ in tqdm.tqdm(pool.imap_unordered(download_img, tasks), total=len(tasks))]
+    # Finalize annotations
+    df_annotations[
+        ["id", "image_id", "caption", "height", "width", "file_name"]
+    ].to_csv(f"{dataset_dir}/calibration/captions.tsv", sep="\t", index=False)
diff --git a/text_to_image/tools/coco_generate_calibration.py b/text_to_image/tools/coco_generate_calibration.py
new file mode 100644
index 0000000000..a2a7254c0c
--- /dev/null
+++ b/text_to_image/tools/coco_generate_calibration.py
@@ -0,0 +1,95 @@
+import argparse
+import json
+import logging
+from multiprocessing import Pool
+import numpy as np
+import pandas as pd
+import os
+import tqdm
+import urllib.request
+import zipfile
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("coco")
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset-dir", default="./coco-2014", help="Dataset download location"
+    )
+    parser.add_argument(
+        "--max-images",
+        default=500,
+        type=int,
+        help="Size of the calibration dataset",
+    )
+    parser.add_argument(
+        "--calibration-dir", default=None, help="Calibration ids location"
+    )
+    parser.add_argument(
+        "--seed", type=int, default=2023, help="Seed to choose the dataset"
+    )
+    parser.add_argument(
+        "--keep-raw", action="store_true", help="Keep raw folder"
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == "__main__":
+    args = get_args()
+    dataset_dir = os.path.abspath(args.dataset_dir)
+    calibration_dir = args.calibration_dir if args.calibration_dir is not None else os.path.join(os.path.dirname(__file__), "..", "..", "calibration", "COCO-2014")
+    
+    # Check if raw annotations file already exist
+    if not os.path.exists(f"{dataset_dir}/raw/annotations/captions_train2014.json"):
+        # Download annotations
+        os.makedirs(f"{dataset_dir}/raw/", exist_ok=True)
+        os.makedirs(f"{dataset_dir}/download_aux/", exist_ok=True)
+        os.system(
+            f"cd {dataset_dir}/download_aux/ && \
+                wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip --show-progress"
+        )
+
+        # Unzip file
+        with zipfile.ZipFile(
+            f"{dataset_dir}/download_aux/annotations_trainval2014.zip", "r"
+        ) as zip_ref:
+            zip_ref.extractall(f"{dataset_dir}/raw/")
+
+    # Convert to dataframe format and extract the relevant fields
+    with open(f"{dataset_dir}/raw/annotations/captions_train2014.json") as f:
+        captions = json.load(f)
+        annotations = captions["annotations"]
+        images = captions["images"]
+    df_annotations = pd.DataFrame(annotations)
+    df_images = pd.DataFrame(images)
+
+    # Calibration images 
+    df_annotations = df_annotations.drop_duplicates(
+        subset=["image_id"], keep="first"
+    )
+    # Sort, shuffle and choose the final dataset
+    df_annotations = df_annotations.sort_values(by=["id"])
+    df_annotations = df_annotations.sample(frac=1, random_state=args.seed).reset_index(drop=True)
+    df_annotations = df_annotations.iloc[: args.max_images]
+    df_annotations['caption'] = df_annotations['caption'].apply(lambda x: x.replace('\n', '').strip())
+    df_annotations = (
+        df_annotations.merge(
+            df_images, how="inner", left_on="image_id", right_on="id"
+        )
+        .drop(["id_y"], axis=1)
+        .rename(columns={"id_x": "id"})
+        .sort_values(by=["id"])
+        .reset_index(drop=True)
+    )
+    # Save ids
+    with open(f"{calibration_dir}/coco_cal_images_list.txt", "w+") as f:
+        s = "\n".join([str(_) for _ in df_annotations["id"].values])
+        f.write(s)
+    # Remove Folder
+    os.system(f"rm -rf {dataset_dir}")
+
diff --git a/text_to_image/tools/download-coco-2014-calibration.sh b/text_to_image/tools/download-coco-2014-calibration.sh
new file mode 100755
index 0000000000..9d1edf7dea
--- /dev/null
+++ b/text_to_image/tools/download-coco-2014-calibration.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+: "${DOWNLOAD_PATH:=../coco2014}"
+
+while [ "$1" != "" ]; do
+    case $1 in
+        -d | --download-path )       shift
+                                     DOWNLOAD_PATH=$1
+                                     ;;
+    esac
+done
+
+
+python3 coco_calibration.py \
+    --dataset-dir ${DOWNLOAD_PATH}
diff --git a/text_to_image/tools/download-coco-2014.sh b/text_to_image/tools/download-coco-2014.sh
new file mode 100755
index 0000000000..3ad8bdcf57
--- /dev/null
+++ b/text_to_image/tools/download-coco-2014.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+
+: "${DOWNLOAD_PATH:=../coco2014}"
+: "${MAX_IMAGES:=5000}"
+: "${NUM_WORKERS:=1}"
+
+while [ "$1" != "" ]; do
+    case $1 in
+        -d | --download-path )       shift
+                                     DOWNLOAD_PATH=$1
+                                     ;;
+    esac
+    case $1 in
+        -m | --max-images  )        shift
+                                      MAX_IMAGES=$1
+                                      ;;
+    esac
+    case $1 in
+        -n | --num-workers  )        shift
+                                      NUM_WORKERS=$1
+                                      ;;
+    esac
+    shift
+done
+
+if [ -z ${MAX_IMAGES} ];
+then
+    python3 coco.py \
+        --dataset-dir ${DOWNLOAD_PATH} \
+        --num-workers ${NUM_WORKERS}
+else
+    python3 coco.py \
+        --dataset-dir ${DOWNLOAD_PATH} \
+        --max-images ${MAX_IMAGES} \
+        --num-workers ${NUM_WORKERS}
+fi
\ No newline at end of file
diff --git a/text_to_image/tools/latent.py b/text_to_image/tools/latent.py
new file mode 100644
index 0000000000..dcbfc97613
--- /dev/null
+++ b/text_to_image/tools/latent.py
@@ -0,0 +1,97 @@
+import argparse
+import torch
+import numpy as np
+from diffusers.utils.torch_utils import randn_tensor
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--batch-size", type=int, default=1, help="Batch size of the latent"
+    )
+    parser.add_argument(
+        "--num-channels-latents", type=int, default=4, help="Batch size of the latent"
+    )
+    parser.add_argument("--height", type=int, default=1024, help="Height of the image")
+    parser.add_argument("--width", type=int, default=1024, help="Width of the image")
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        default="fp32",
+        help="Type of the latent",
+        choices=["fp32", "fp16", "bf16"],
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=0,
+        help="Random seed to make the latent deterministic",
+    )
+    parser.add_argument(
+        "--vae-scale-factor",
+        type=int,
+        default=8,
+        help="Variational Autoencoder scale factor, obtainded from model inspection",
+    )
+    parser.add_argument("--output-type", type=str, default="pt", choices=["pt", "np"])
+    args = parser.parse_args()
+    return args
+
+
+def create_latents(
+    batch_size,
+    num_channels_latents,
+    height,
+    width,
+    dtype,
+    generator,
+    vae_scale_factor,
+) -> torch.Tensor:
+    shape = (
+        batch_size,
+        num_channels_latents,
+        height // vae_scale_factor,
+        width // vae_scale_factor,
+    )
+    latents = randn_tensor(shape, generator=generator, dtype=dtype)
+    return latents
+
+
+if __name__ == "__main__":
+    args = get_args()
+    batch_size = args.batch_size
+    num_channels_latents = args.num_channels_latents
+    height = args.height
+    width = args.width
+    if args.dtype == "fp32":
+        dtype = torch.float32
+    elif args.dtype == "fp16":
+        dtype = torch.float16
+    elif args.dtype == "bf16":
+        dtype = torch.bfloat16
+    else:
+        raise ValueError(f"Dtype: {args.dtype} is not supported")
+    seed = args.seed
+    generator = torch.Generator().manual_seed(seed)
+    vae_scale_factor = args.vae_scale_factor
+    if args.output_type == "pt":
+        save_path = "latents.pt"
+    elif args.output_type == "np":
+        save_path = "latents.npy"
+    else:
+        raise ValueError(f"Output Type: {args.output_type} is not supported")
+    latents = create_latents(
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        generator,
+        vae_scale_factor,
+    )
+    if args.output_type == "pt":
+        torch.save(latents, save_path)
+    elif args.output_type == "np":
+        np.save(save_path, latents.detach().cpu().numpy())
+    else:
+        raise ValueError(f"Output Type: {args.output_type} is not supported")
diff --git a/text_to_image/tools/latents.npy b/text_to_image/tools/latents.npy
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literal 0
HcmV?d00001

diff --git a/text_to_image/user.conf b/text_to_image/user.conf
new file mode 100644
index 0000000000..07a10bbe21
--- /dev/null
+++ b/text_to_image/user.conf
@@ -0,0 +1,4 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds

From 4b1d1156c23965172ae56eacdd8372f8897eb771 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 19 Dec 2023 12:25:38 -0500
Subject: [PATCH 019/305] Match clip and fid score calculation (#1536)

* Add SD model links in README

* Match clip and fid score calculation
---
 notes.txt                                |   0
 text_to_image/README.md                  |  35 ++-
 text_to_image/backend_pytorch.py         |  37 +--
 text_to_image/coco.py                    | 100 +++----
 text_to_image/main.py                    |  10 +-
 text_to_image/requirements.txt           |   1 +
 text_to_image/tools/accuracy_coco.py     |  46 ++-
 text_to_image/tools/clip/clip_encoder.py | 105 +++++++
 text_to_image/tools/coco.py              |  18 +-
 text_to_image/tools/fid/README.md        |   1 +
 text_to_image/tools/fid/fid_score.py     | 362 +++++++++++++++++++++++
 text_to_image/tools/fid/inception.py     | 341 +++++++++++++++++++++
 text_to_image/tools/val2014.npz          | Bin 0 -> 32000912 bytes
 13 files changed, 923 insertions(+), 133 deletions(-)
 create mode 100644 notes.txt
 create mode 100644 text_to_image/tools/clip/clip_encoder.py
 create mode 100644 text_to_image/tools/fid/README.md
 create mode 100644 text_to_image/tools/fid/fid_score.py
 create mode 100644 text_to_image/tools/fid/inception.py
 create mode 100644 text_to_image/tools/val2014.npz

diff --git a/notes.txt b/notes.txt
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/text_to_image/README.md b/text_to_image/README.md
index fd20ce3d4c..923cff41c7 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -6,7 +6,7 @@ This is the reference implementation for MLPerf Inference text to image
 
 | model | accuracy | dataset | model link | model source | precision | notes |
 | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
-| StableDiffusion | Torch | - | Coco2014 | - | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp16 | NCHW||
+| StableDiffusion | Torch | - | Coco2014 | - | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp32 | NCHW||
 
 ## Dataset
 
@@ -22,6 +22,7 @@ Set the following helper variables
 export ROOT=$PWD/inference
 export SD_FOLDER=$PWD/inference/text_to_image
 export LOADGEN_FOLDER=$PWD/inference/loadgen
+export MODEL_PATH=$PWD/inference/text_to_image/model/
 ```
 ### Clone the repository
 **TEMPORARLY:**
@@ -49,6 +50,21 @@ cd LOADGEN_FOLDER
 CFLAGS="-std=c++14" python setup.py install
 ```
 
+### Download model
+
+We host two checkpoints ([fp32](https://cloud.mlcommons.org/index.php/s/DjnCSGyNBkWA4Ro) and [f16](https://cloud.mlcommons.org/index.php/s/LCdW5RM6wgGWbxC)) that are a snapshot of the [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) pipeline at the time of the release of the benchmark. Download them and move them to your model path.
+
+```bash
+mkdir $MODEL_PATH
+cd $MODEL_PATH
+# For fp32
+mv <path_to_download>/stable_diffusion_fp32.zip .
+unzip stable_diffusion_fp32.zip
+# For fp16
+mv <path_to_download>/stable_diffusion_fp16.zip .
+unzip stable_diffusion_fp16.zip
+```
+
 ### Download dataset
 ```bash
 cd $SD_FOLDER/tools
@@ -64,19 +80,26 @@ If the file [captions.tsv](coco2014/captions/captions.tsv) can be found in the s
 ### Run the benchmark
 #### Local run
 ```bash
-python3 main.py --dataset "coco-1024" --dataset-path coco2014 --profile stable-diffusion-xl-pytorch [--model-path <TODO: provide model weights>] [--dtype <fp32, fp16 or bf16>] [--device <cuda or cpu>] [--time 600] [--scenario SingleStream]
+# Go to the benchmark folder
+cd $SD_FOLDER
+# Run the benchmark
+python3 main.py --dataset "coco-1024" --dataset-path coco2014 --profile stable-diffusion-xl-pytorch --model-path model/ [--dtype <fp32, fp16 or bf16>] [--device <cuda or cpu>] [--time <time>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 #### Run using docker
 ```bash
+# Go to the benchmark folder
 cd $SD_FOLDER
 # Build the container
 docker build . -t sd_mlperf_inference
-#Run the container
+# Run the container
 docker run --rm -it --gpus=all -v $SD_FOLDER:/workspace sd_mlperf_inference bash
 ```
 Inside the container run the following:
 ```bash
-python3 main.py --dataset "coco-1024" --dataset-path coco2014 --profile stable-diffusion-xl-pytorch [--model-path <TODO: provide model weights>] [--dtype <fp32, fp16 or bf16>] [--device <cuda or cpu>] [--time 600] [--scenario SingleStream]
+python3 main.py --dataset "coco-1024" --dataset-path coco2014 --profile stable-diffusion-xl-pytorch --model-path model/ [--dtype <fp32, fp16 or bf16>] [--device <cuda or cpu>] [--time <time>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+```
+#### Accuracy run
+Add the `--accuracy` to the command to run the benchmark
+```bash
+python3 main.py --dataset "coco-1024" --dataset-path coco2014 --profile stable-diffusion-xl-pytorch --accuracy --model-path model/ [--dtype <fp32, fp16 or bf16>] [--device <cuda or cpu>] [--time <time>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
-
-
diff --git a/text_to_image/backend_pytorch.py b/text_to_image/backend_pytorch.py
index eccbf246e6..2455fa21e2 100644
--- a/text_to_image/backend_pytorch.py
+++ b/text_to_image/backend_pytorch.py
@@ -20,6 +20,7 @@ def __init__(
         device="cuda",
         precision="fp32",
         negative_prompt="normal quality, low quality, worst quality, low res, blurry, nsfw, nude",
+        seed=42
     ):
         super(BackendPytorch, self).__init__()
         self.model_path = model_path
@@ -44,6 +45,7 @@ def __init__(
         self.steps = steps
         self.negative_prompt = negative_prompt
         self.max_length_neg_prompt = 77
+        self.seed = seed
 
     def version(self):
         return torch.__version__
@@ -343,41 +345,8 @@ def predict(self, inputs):
                     num_inference_steps=self.steps,
                     output_type="pt",
                     latents=latents_input,
+                    generator=torch.Generator(device=self.device).manual_seed(self.seed)
                 ).images[0]
                 images.append(image)
         return images
 
-
-if __name__ == "__main__":
-    from coco import Coco
-
-    backend = BackendPytorch(precision="fp16")
-    backend = backend.load()
-    dataset = Coco(
-        "coco2014/",
-        name="coco-1024",
-        pipe_tokenizer=backend.pipe.tokenizer,
-        pipe_tokenizer_2=backend.pipe.tokenizer_2,
-    )
-    dataset.load_query_samples([0, 1, 2, 3, 4])
-    items, _ = dataset.get_samples([0, 1, 2, 3, 4])
-    captions = dataset.captions_df["caption"].iloc[[0, 1, 2, 3, 4]]
-    images_1 = backend.predict(items)
-    images_2 = []
-    for i in range(5):
-        image = backend.pipe(
-            captions[i],
-            negative_prompt = backend.negative_prompt,
-            output_type="pil",
-            latents=items[i]["latents"].to(backend.dtype),
-            guidance_scale=backend.guidance,
-            num_inference_steps=backend.steps,
-        ).images[0]
-        images_2.append(image)
-
-    os.makedirs("tmp/", exist_ok=True)
-    for i, image in enumerate(images_1):
-        image.save(f"tmp/image_{i}.png")
-
-    for i, image in enumerate(images_2):
-        image.save(f"tmp/image_{i+5}.png")
diff --git a/text_to_image/coco.py b/text_to_image/coco.py
index 61d61aa3e9..0ff5024816 100644
--- a/text_to_image/coco.py
+++ b/text_to_image/coco.py
@@ -15,9 +15,8 @@
 import dataset
 
 import torch
-from torchvision import transforms
-from torchmetrics.multimodal.clip_score import CLIPScore
-from torchmetrics.image.fid import FrechetInceptionDistance
+from tools.clip.clip_encoder import CLIPEncoder
+from tools.fid.fid_score import compute_fid
 
 
 logging.basicConfig(level=logging.INFO)
@@ -30,7 +29,6 @@ def __init__(
         data_path,
         name=None,
         image_size=None,
-        use_preprocessed=False,
         pre_process=None,
         pipe_tokenizer=None,
         pipe_tokenizer_2=None,
@@ -42,7 +40,6 @@ def __init__(
         super().__init__()
         self.captions_df = pd.read_csv(f"{data_path}/captions/captions.tsv", sep="\t")
         self.image_size = image_size
-        self.use_preprocessed = use_preprocessed
         self.preprocessed_dir = os.path.abspath(f"{data_path}/preprocessed/")
         self.img_dir = os.path.abspath(f"{data_path}/validation/data/")
         self.name = name
@@ -68,11 +65,6 @@ def __init__(
                 .to(latent_dtype)
                 .to(latent_device)
             )
-        if self.use_preprocessed:
-            os.makedirs(self.preprocessed_dir, exist_ok=True)
-            self.captions_df["preprocessed_path"] = self.captions_df["file_name"].apply(
-                lambda x: self.preprocess_images(x)
-            )
 
     def preprocess(self, prompt, tokenizer):
         converted_prompt = self.convert_prompt(prompt, tokenizer)
@@ -84,14 +76,18 @@ def preprocess(self, prompt, tokenizer):
             return_tensors="pt",
         )
 
-    def preprocess_images(self, file_name):
-        img = Image.open(self.img_dir + "/" + file_name)
+    def image_to_tensor(self, img):
         img = np.asarray(img)
         if len(img.shape) == 2:
             img = np.expand_dims(img, axis=-1)
-        tensor = torch.Tensor(img.transpose([2,0,1])).to(torch.uint8)
+        tensor = torch.Tensor(img.transpose([2, 0, 1])).to(torch.uint8)
         if tensor.shape[0] == 1:
-            tensor = tensor.repeat(3,1,1)
+            tensor = tensor.repeat(3, 1, 1)
+        return tensor
+
+    def preprocess_images(self, file_name):
+        img = Image.open(self.img_dir + "/" + file_name)
+        tensor = self.image_to_tensor(img)
         target_name = file_name.split(".")[0]
         target_path = self.preprocessed_dir + "/" + target_name + ".pt"
         if not os.path.exists(target_path):
@@ -119,72 +115,70 @@ def get_item(self, id):
     def get_item_count(self):
         return len(self.captions_df)
 
+    def get_img(self, id):
+        img = Image.open(self.img_dir + "/" + self.captions_df.loc[id]["file_name"])
+        return self.image_to_tensor(img)
+
     def get_imgs(self, id_list):
         image_list = []
-        if self.use_preprocessed:
-            for id in id_list:
-                image_list.append(
-                    torch.load(self.captions_df.loc[id]["preprocessed_path"])
-                )
-        else:
-            convert_tensor = transforms.ToTensor()
-            for id in id_list:
-                img = Image.open(
-                    self.img_dir + "/" + self.captions_df.loc[id]["file_name"]
-                )
-                tensor = convert_tensor(img)
-                if tensor.shape[0] == 1:
-                    tensor = tensor.repeat(3,1,1)
-                image_list.append(convert_tensor(img))
+        for id in id_list:
+            image_list.append(self.get_img(id))
         return image_list
 
+    def get_caption(self, i):
+        return self.get_item(i)["caption"]
+
     def get_captions(self, id_list):
-        return [self.get_item(id)["caption"] for id in id_list]
-    
+        return [self.get_caption(id) for id in id_list]
+
     def get_item_loc(self, id):
-        if self.use_preprocessed:
-            return self.captions_df.loc[id]["preprocessed_path"]
-        else:
-            return self.img_dir + "/" + self.captions_df.loc[id]["file_name"]
+        return self.img_dir + "/" + self.captions_df.loc[id]["file_name"]
+
 
 class PostProcessCoco:
-    def __init__(self, device="cpu", dtype = torch.uint8):
+    def __init__(
+        self, device="cpu", dtype="uint8", statistics_path=os.path.join(os.path.dirname(__file__), "tools", "val2014.npz")
+    ):
         self.results = []
         self.good = 0
         self.total = 0
         self.content_ids = []
+        self.clip_scores = []
+        self.fid_scores = []
         self.device = device if torch.cuda.is_available() else "cpu"
-        self.dtype = dtype
+        if dtype == "uint8":
+            self.dtype = torch.uint8
+            self.numpy_dtype = np.uint8
+        else:
+            raise ValueError(f"dtype must be one of: uint8")
+        self.statistics_path = statistics_path
 
     def add_results(self, results):
         self.results.extend(results)
 
     def __call__(self, results, ids, expected=None, result_dict=None):
         self.content_ids.extend(ids)
-        return [(t*255).round().to(self.dtype).to(self.device) for t in results]
+        return [
+            (t.cpu().permute(1, 2, 0).float().numpy() * 255).round().astype(self.numpy_dtype)
+            for t in results
+        ]
 
     def start(self):
         self.results = []
 
     def finalize(self, result_dict, ds=None, output_dir=None):
-        fid = FrechetInceptionDistance(feature=2048)
-        fid.to(self.device)
-        clip = CLIPScore(model_name_or_path="openai/clip-vit-base-patch32")
-        clip.to(self.device)
+        clip = CLIPEncoder(device=self.device)
         dataset_size = ds.get_item_count()
         log.info("Accumulating results")
         for i in range(0, dataset_size):
-            target = (
-                torch.stack(ds.get_imgs([self.content_ids[i]]))
-                .to(self.dtype)
-                .to(self.device)
+            caption = ds.get_caption(self.content_ids[i])
+            generated = Image.fromarray(self.results[i])
+            self.clip_scores.append(
+                100 * clip.get_clip_score(caption, generated).item()
             )
-            captions = ds.get_captions([self.content_ids[i]])
-            generated = torch.stack([self.results[i]]).to(self.dtype).to(self.device)
-            fid.update(target, real=True)
-            fid.update(generated, real=False)
-            clip.update(generated, captions)
-        result_dict["FID_SCORE"] = float(fid.compute().item())
-        result_dict["CLIP_SCORE"] = float(clip.compute().item())
+
+        fid_score = compute_fid(self.results, self.statistics_path, self.device)
+        result_dict["FID_SCORE"] = fid_score
+        result_dict["CLIP_SCORE"] = np.mean(self.clip_scores)
 
         return result_dict
diff --git a/text_to_image/main.py b/text_to_image/main.py
index a3ea97d4a1..367e12e264 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -90,11 +90,6 @@ def get_args():
         help="max batch size in a single inference",
     )
     parser.add_argument("--threads", default=1, type=int, help="threads")
-    parser.add_argument(
-        "--use_preprocessed_dataset",
-        action="store_true",
-        help="use preprocessed dataset instead of the original",
-    )
     parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
     parser.add_argument(
         "--find-peak-performance",
@@ -243,7 +238,7 @@ def run_one_item(self, qitem: Item):
             response = []
             for idx, query_id in enumerate(qitem.query_id):
                 response_array = array.array(
-                    "B", np.array(processed_results[idx], np.float32).tobytes()
+                    "B", np.array(processed_results[idx], np.uint8).tobytes()
                 )
                 response_array_refs.append(response_array)
                 bi = response_array.buffer_info()
@@ -344,13 +339,10 @@ def main():
 
     # dataset to use
     dataset_class, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
-    if args.use_preprocessed_dataset:
-        pre_proc = None
     ds = dataset_class(
         data_path=args.dataset_path,
         name=args.dataset,
         pre_process=pre_proc,
-        use_preprocessed=args.use_preprocessed_dataset,
         count=count,
         threads=args.threads,
         pipe_tokenizer=model.pipe.tokenizer,
diff --git a/text_to_image/requirements.txt b/text_to_image/requirements.txt
index 46812291bd..a0a8507731 100644
--- a/text_to_image/requirements.txt
+++ b/text_to_image/requirements.txt
@@ -5,3 +5,4 @@ open-clip-torch==2.7.0
 opencv-python==4.8.1.78
 pycocotools==2.0.7
 torchmetrics[image]==1.2.0
+scipy==1.9.1
diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
index c6714785f3..a7c9828c61 100644
--- a/text_to_image/tools/accuracy_coco.py
+++ b/text_to_image/tools/accuracy_coco.py
@@ -13,9 +13,8 @@
 import numpy as np
 import pandas as pd
 import torch
-from torchvision import transforms
-from torchmetrics.multimodal.clip_score import CLIPScore
-from torchmetrics.image.fid import FrechetInceptionDistance
+from clip.clip_encoder import CLIPEncoder
+from fid.fid_score import compute_fid
 
 
 
@@ -23,10 +22,10 @@ def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
     parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--dataset-dir", required=True, help="openimages directory")
+    parser.add_argument("--dataset-dir", required=True, help="coco directory")
+    parser.add_argument("--statistics-path", default=None, help="path to statistics")
     parser.add_argument("--verbose", action="store_true", help="verbose messages")
     parser.add_argument("--output-file", default="coco-results.json", help="path to output file")
-    parser.add_argument("--use-preprocessed", action="store_true")
     parser.add_argument("--device", default="cpu", choices=["gpu", "cpu"])
     args = parser.parse_args()
     return args
@@ -62,15 +61,16 @@ def main():
     if device == "gpu":
         device = "cuda"
 
-    # Load torchmetrics modules
-    fid = FrechetInceptionDistance(feature=2048)
-    fid.to(device)
-    clip = CLIPScore(model_name_or_path="openai/clip-vit-base-patch32")
-    clip.to(device)
+    # set statistics path
+    statistics_path = args.statistics_path
+    if args.statistics_path is None:
+        statistics_path = os.path.join(os.path.dirname(__file__), "val2014.npz")
 
-    image_ids = set()
+    # Load torchmetrics modules
+    clip = CLIPEncoder(device=device)
+    clip_scores = []
     seen = set()
-    no_results = 0
+    result_list = []
     for j in results:
         idx = j['qsl_idx']
         if idx in seen:
@@ -78,21 +78,19 @@ def main():
         seen.add(idx)
 
         # Load generated image
-        generated_img = np.frombuffer(bytes.fromhex(j['data']), np.float32).reshape(1, 3, 1024, 1024)
-        generated_img = torch.Tensor(generated_img).to(torch.uint8).to(device)
+        generated_img = np.frombuffer(bytes.fromhex(j['data']), np.uint8).reshape(1024, 1024, 3)
+        result_list.append(generated_img)
+        generated_img = Image.fromarray(generated_img)
+        # generated_img = torch.Tensor(generated_img).to(torch.uint8).to(device)
         # Load Ground Truth
         caption = df_captions.iloc[idx]["caption"]
-        if args.use_preprocessed:
-            file_name = df_captions.iloc[idx]["file_name"].replace(".jpg", ".pt")
-            target_img = torch.load(args.dataset_dir + "/preprocessed/" + file_name).to(torch.uint8).to(device)
-        else:
-            target_img = preprocess_image(args.dataset_dir + "/validation/data", df_captions.iloc[idx]["file_name"]).to(torch.uint8).to(device)
-        fid.update(target_img, real=True)
-        fid.update(generated_img, real=False)
-        clip.update(generated_img, caption)
+        clip_scores.append(
+            100 * clip.get_clip_score(caption, generated_img).item()
+        )
+    fid_score = compute_fid(result_list, statistics_path, device)
         
-    result_dict["FID_SCORE"] = float(fid.compute().item())
-    result_dict["CLIP_SCORE"] = float(clip.compute().item())
+    result_dict["FID_SCORE"] = fid_score
+    result_dict["CLIP_SCORE"] = np.mean(clip_scores)
 
     with open(args.output_file, "w") as fp:
         json.dump(result_dict, fp, sort_keys=True, indent=4)
diff --git a/text_to_image/tools/clip/clip_encoder.py b/text_to_image/tools/clip/clip_encoder.py
new file mode 100644
index 0000000000..da24ada271
--- /dev/null
+++ b/text_to_image/tools/clip/clip_encoder.py
@@ -0,0 +1,105 @@
+import torch
+import torch.nn as nn
+
+import open_clip
+from PIL import Image
+from typing import Union, List, Optional, Tuple
+
+
+class CLIPEncoder(nn.Module):
+    """
+    A class for encoding images and texts using a specified CLIP model and computing the similarity between them.
+    
+    Attributes:
+    -----------
+    clip_version: str
+        The version of the CLIP model to be used.
+    pretrained: str
+        The pre-trained weights to load.
+    model: nn.Module
+        The CLIP model.
+    preprocess: Callable
+        The preprocessing transform to apply to the input image.
+    device: str
+        The device to which the model is moved.
+    """
+    def __init__(self, 
+                 clip_version: str = 'ViT-B/32',
+                 pretrained: Optional[str] = '',
+                 cache_dir: Optional[str] = None,
+                 device: str = 'cpu'):
+        """
+        Initializes the CLIPEncoder with the specified CLIP model version and pre-trained weights.
+        
+        Parameters:
+        -----------
+        clip_version: str, optional
+            The version of the CLIP model to be used. Defaults to 'ViT-B/32'.
+        pretrained: str, optional
+            The pre-trained weights to load. If not provided, it defaults based on clip_version.
+        cache_dir: str, optional
+            The directory to cache the model. Defaults to None.
+        device: str, optional
+            The device to which the model is moved. Defaults to 'cuda'.
+        """
+        super().__init__()
+
+        self.clip_version = clip_version
+        self.pretrained = pretrained if pretrained else self._get_default_pretrained()
+        
+        self.model, _, self.preprocess = open_clip.create_model_and_transforms(self.clip_version,
+                                                                               pretrained=self.pretrained,
+                                                                               cache_dir=cache_dir)
+
+        self.model.eval()
+        self.model.to(device)
+        self.device = device
+
+    def _get_default_pretrained(self) -> str:
+        """Returns the default pretrained weights based on the clip_version."""
+        if self.clip_version == 'ViT-H-14':
+            return 'laion2b_s32b_b79k'
+        elif self.clip_version == 'ViT-g-14':
+            return 'laion2b_s12b_b42k'
+        else:
+            return 'openai'
+
+    @torch.no_grad()
+    def get_clip_score(self, text: Union[str, List[str]], image: Union[Image.Image, torch.Tensor]) -> torch.Tensor:
+        """
+        Computes the similarity score between the given text(s) and image using the CLIP model.
+        
+        Parameters:
+        -----------
+        text: Union[str, List[str]]
+            The text or list of texts to compare with the image.
+        image: Image.Image
+            The input image.
+            
+        Returns:
+        --------
+        torch.Tensor
+            The similarity score between the text(s) and image.
+        """
+        # Preprocess the image and move it to the specified device
+        image = self.preprocess(image).unsqueeze(0).to(self.device)
+        
+        # Normalize the image features
+        image_features = self.model.encode_image(image).float()
+        image_features /= image_features.norm(dim=-1, keepdim=True)
+
+        # If a single text string is provided, convert it to a list
+        if not isinstance(text, (list, tuple)):
+            text = [text]
+        
+        # Tokenize the text and move it to the specified device
+        text = open_clip.tokenize(text).to(self.device)
+        
+        # Normalize the text features
+        text_features = self.model.encode_text(text).float()
+        text_features /= text_features.norm(dim=-1, keepdim=True)
+        
+        # Compute the similarity between the image and text features
+        similarity = image_features @ text_features.T
+
+        return similarity
\ No newline at end of file
diff --git a/text_to_image/tools/coco.py b/text_to_image/tools/coco.py
index 2c25d70266..c11f2225ce 100644
--- a/text_to_image/tools/coco.py
+++ b/text_to_image/tools/coco.py
@@ -45,6 +45,9 @@ def get_args():
     parser.add_argument(
         "--keep-raw", action="store_true", help="Keep raw folder"
     )
+    parser.add_argument(
+        "--download-images", action="store_true", help="Download the calibration set"
+    )
 
     args = parser.parse_args()
     return args
@@ -129,13 +132,14 @@ def download_img(args):
             .reset_index(drop=True)
         )
     # Download images
-    os.makedirs(f"{dataset_dir}/validation/data/", exist_ok=True)
-    tasks = [
-        (row["coco_url"], f"{dataset_dir}/validation/data/", row["file_name"])
-        for i, row in df_annotations.iterrows()
-    ]
-    pool = Pool(processes=args.num_workers)
-    [_ for _ in tqdm.tqdm(pool.imap_unordered(download_img, tasks), total=len(tasks))]
+    if args.download_images:
+        os.makedirs(f"{dataset_dir}/validation/data/", exist_ok=True)
+        tasks = [
+            (row["coco_url"], f"{dataset_dir}/validation/data/", row["file_name"])
+            for i, row in df_annotations.iterrows()
+        ]
+        pool = Pool(processes=args.num_workers)
+        [_ for _ in tqdm.tqdm(pool.imap_unordered(download_img, tasks), total=len(tasks))]
     # Finalize annotations
     df_annotations[
         ["id", "image_id", "caption", "height", "width", "file_name", "coco_url"]
diff --git a/text_to_image/tools/fid/README.md b/text_to_image/tools/fid/README.md
new file mode 100644
index 0000000000..3936f2bfef
--- /dev/null
+++ b/text_to_image/tools/fid/README.md
@@ -0,0 +1 @@
+This is a copy from https://github.com/mseitzer/pytorch-fid/ with the modifications made here https://github.com/ahmadki/mlperf_sd_inference and some additional modifications for taking as dataset of tensors as input
\ No newline at end of file
diff --git a/text_to_image/tools/fid/fid_score.py b/text_to_image/tools/fid/fid_score.py
new file mode 100644
index 0000000000..ce7cfe4b00
--- /dev/null
+++ b/text_to_image/tools/fid/fid_score.py
@@ -0,0 +1,362 @@
+"""Calculates the Frechet Inception Distance (FID) to evalulate GANs
+
+The FID metric calculates the distance between two distributions of images.
+Typically, we have summary statistics (mean & covariance matrix) of one
+of these distributions, while the 2nd distribution is given by a GAN.
+
+When run as a stand-alone program, it compares the distribution of
+images that are stored as PNG/JPEG at a specified location with a
+distribution given by summary statistics (in pickle format).
+
+The FID is calculated by assuming that X_1 and X_2 are the activations of
+the pool_3 layer of the inception net for generated samples and real world
+samples respectively.
+
+See --help to see further details.
+
+Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead
+of Tensorflow
+
+Copyright 2018 Institute of Bioinformatics, JKU Linz
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import os
+import sys
+sys.path.append(os.path.dirname(__file__))
+import pathlib
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+from typing import Any
+
+import numpy as np
+import random
+import torch
+import torchvision.transforms as TF
+from PIL import Image
+from scipy import linalg
+from torch.nn.functional import adaptive_avg_pool2d
+
+
+try:
+    from tqdm import tqdm
+except ImportError:
+    # If tqdm is not available, provide a mock version of it
+    def tqdm(x):
+        return x
+
+
+from inception import InceptionV3
+
+
+class ImagesDataset(torch.utils.data.Dataset):
+    def __init__(self, imgs, transforms=None):
+        self.imgs = imgs
+        self.transforms = transforms
+
+    def __len__(self):
+        return len(self.imgs)
+
+    def __getitem__(self, i):
+        img = self.imgs[i]
+        if self.transforms is not None:
+            img = self.transforms(img)
+        return img
+    
+
+def get_activations(
+    files, model, batch_size=50, dims=2048, device="cpu", num_workers=1
+):
+    """Calculates the activations of the pool_3 layer for all images.
+
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : Batch size of images for the model to process at once.
+                     Make sure that the number of samples is a multiple of
+                     the batch size, otherwise some samples are ignored. This
+                     behavior is retained to match the original FID score
+                     implementation.
+    -- dims        : Dimensionality of features returned by Inception
+    -- device      : Device to run calculations
+    -- num_workers : Number of parallel dataloader workers
+
+    Returns:
+    -- A numpy array of dimension (num images, dims) that contains the
+       activations of the given tensor when feeding inception with the
+       query tensor.
+    """
+    model.eval()
+
+    if batch_size > len(files):
+        print(
+            (
+                "Warning: batch size is bigger than the data size. "
+                "Setting batch size to data size"
+            )
+        )
+        batch_size = len(files)
+
+    dataset = ImagesDataset(files, transforms=TF.ToTensor())
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        drop_last=False,
+        num_workers=num_workers,
+    )
+
+    pred_arr = np.empty((len(files), dims))
+
+    start_idx = 0
+
+    for batch in tqdm(dataloader):
+        batch = batch.to(device)
+
+        with torch.no_grad():
+            pred = model(batch)[0]
+
+        # If model output is not scalar, apply global spatial average pooling.
+        # This happens if you choose a dimensionality not equal 2048.
+        if pred.size(2) != 1 or pred.size(3) != 1:
+            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
+
+        pred = pred.squeeze(3).squeeze(2).cpu().numpy()
+
+        pred_arr[start_idx : start_idx + pred.shape[0]] = pred
+
+        start_idx = start_idx + pred.shape[0]
+
+    return pred_arr
+
+
+def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+    """Numpy implementation of the Frechet Distance.
+    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
+    and X_2 ~ N(mu_2, C_2) is
+            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
+
+    Stable version by Dougal J. Sutherland.
+
+    Params:
+    -- mu1   : Numpy array containing the activations of a layer of the
+               inception net (like returned by the function 'get_predictions')
+               for generated samples.
+    -- mu2   : The sample mean over activations, precalculated on an
+               representative data set.
+    -- sigma1: The covariance matrix over activations for generated samples.
+    -- sigma2: The covariance matrix over activations, precalculated on an
+               representative data set.
+
+    Returns:
+    --   : The Frechet Distance.
+    """
+
+    mu1 = np.atleast_1d(mu1)
+    mu2 = np.atleast_1d(mu2)
+
+    sigma1 = np.atleast_2d(sigma1)
+    sigma2 = np.atleast_2d(sigma2)
+
+    assert (
+        mu1.shape == mu2.shape
+    ), "Training and test mean vectors have different lengths"
+    assert (
+        sigma1.shape == sigma2.shape
+    ), "Training and test covariances have different dimensions"
+
+    diff = mu1 - mu2
+
+    # Product might be almost singular
+    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
+    if not np.isfinite(covmean).all():
+        msg = (
+            "fid calculation produces singular product; "
+            "adding %s to diagonal of cov estimates"
+        ) % eps
+        print(msg)
+        offset = np.eye(sigma1.shape[0]) * eps
+        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
+
+    # Numerical error might give slight imaginary component
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            m = np.max(np.abs(covmean.imag))
+            raise ValueError("Imaginary component {}".format(m))
+        covmean = covmean.real
+
+    tr_covmean = np.trace(covmean)
+
+    return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
+
+
+def calculate_activation_statistics(
+    files, model, batch_size=50, dims=2048, device="cpu", num_workers=1
+):
+    """Calculation of the statistics used by the FID.
+    Params:
+    -- files       : List of image files paths
+    -- model       : Instance of inception model
+    -- batch_size  : The images numpy array is split into batches with
+                     batch size batch_size. A reasonable batch size
+                     depends on the hardware.
+    -- dims        : Dimensionality of features returned by Inception
+    -- device      : Device to run calculations
+    -- num_workers : Number of parallel dataloader workers
+
+    Returns:
+    -- mu    : The mean over samples of the activations of the pool_3 layer of
+               the inception model.
+    -- sigma : The covariance matrix of the activations of the pool_3 layer of
+               the inception model.
+    """
+    act = get_activations(files, model, batch_size, dims, device, num_workers)
+    mu = np.mean(act, axis=0)
+    sigma = np.cov(act, rowvar=False)
+    return mu, sigma
+
+
+def compute_statistics_of_path(
+    path,
+    model,
+    batch_size,
+    dims,
+    device,
+    num_workers=1,
+    subset_size=None,
+    shuffle_seed=None,
+):
+    if path.endswith(".npz"):
+        with np.load(path) as f:
+            m, s = f["mu"][:], f["sigma"][:]
+    else:
+        path = pathlib.Path(path)
+        files = sorted(
+            [file for ext in IMAGE_EXTENSIONS for file in path.glob("*.{}".format(ext))]
+        )
+        if subset_size is not None:
+            random.seed(shuffle_seed)
+            files = random.sample(files, subset_size)
+        m, s = calculate_activation_statistics(
+            files, model, batch_size, dims, device, num_workers
+        )
+
+    return m, s
+
+
+def calculate_fid_given_paths(
+    paths, batch_size, device, dims, num_workers=1, subset_size=None, shuffle_seed=None
+):
+    """Calculates the FID of two paths"""
+    for p in paths:
+        if not os.path.exists(p):
+            raise RuntimeError("Invalid path: %s" % p)
+
+    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+
+    model = InceptionV3([block_idx]).to(device)
+
+    m1, s1 = compute_statistics_of_path(
+        paths[0],
+        model,
+        batch_size,
+        dims,
+        device,
+        num_workers,
+        subset_size,
+        shuffle_seed,
+    )
+    m2, s2 = compute_statistics_of_path(
+        paths[1],
+        model,
+        batch_size,
+        dims,
+        device,
+        num_workers,
+        subset_size,
+        shuffle_seed,
+    )
+    fid_value = calculate_frechet_distance(m1, s1, m2, s2)
+
+    return fid_value
+
+
+def save_fid_stats(paths, batch_size, device, dims, num_workers=1):
+    """Calculates the FID of two paths"""
+    if not os.path.exists(paths[0]):
+        raise RuntimeError("Invalid path: %s" % paths[0])
+
+    if os.path.exists(paths[1]):
+        raise RuntimeError("Existing output file: %s" % paths[1])
+
+    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+
+    model = InceptionV3([block_idx]).to(device)
+
+    print(f"Saving statistics for {paths[0]}")
+
+    m1, s1 = compute_statistics_of_path(
+        paths[0], model, batch_size, dims, device, num_workers, None, None
+    )
+
+    np.savez_compressed(paths[1], mu=m1, sigma=s1)
+
+
+def compute_fid(
+    results,
+    statistics_path,
+    device,
+    dims=2048,
+    num_workers=1,
+    batch_size=1,
+    subset_size=None,
+    shuffle_seed=None
+):
+    imgs = [Image.fromarray(e).convert("RGB") for e in results]
+    device = torch.device(device if torch.cuda.is_available() else "cpu")
+    if num_workers is None:
+        try:
+            num_cpus = len(os.sched_getaffinity(0))
+        except AttributeError:
+            # os.sched_getaffinity is not available under Windows, use
+            # os.cpu_count instead (which may not return the *available* number
+            # of CPUs).
+            num_cpus = os.cpu_count()
+
+        num_workers = min(num_cpus, 8) if num_cpus is not None else 0
+    else:
+        num_workers = num_workers
+    assert statistics_path.endswith(".npz")
+
+    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
+
+    model = InceptionV3([block_idx]).to(device)
+
+    m1, s1 = compute_statistics_of_path(
+        statistics_path,
+        model,
+        batch_size,
+        dims,
+        device,
+        num_workers,
+        subset_size,
+        shuffle_seed,
+    )
+
+    m2, s2 = calculate_activation_statistics(
+        imgs, model, batch_size, dims, device, num_workers
+    )
+
+    fid_value = calculate_frechet_distance(m1, s1, m2, s2)
+
+    return fid_value
+
diff --git a/text_to_image/tools/fid/inception.py b/text_to_image/tools/fid/inception.py
new file mode 100644
index 0000000000..cc56870522
--- /dev/null
+++ b/text_to_image/tools/fid/inception.py
@@ -0,0 +1,341 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+try:
+    from torchvision.models.utils import load_state_dict_from_url
+except ImportError:
+    from torch.utils.model_zoo import load_url as load_state_dict_from_url
+
+# Inception weights ported to Pytorch from
+# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
+FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'  # noqa: E501
+
+
+class InceptionV3(nn.Module):
+    """Pretrained InceptionV3 network returning feature maps"""
+
+    # Index of default block of inception to return,
+    # corresponds to output of final average pooling
+    DEFAULT_BLOCK_INDEX = 3
+
+    # Maps feature dimensionality to their output blocks indices
+    BLOCK_INDEX_BY_DIM = {
+        64: 0,   # First max pooling features
+        192: 1,  # Second max pooling featurs
+        768: 2,  # Pre-aux classifier features
+        2048: 3  # Final average pooling features
+    }
+
+    def __init__(self,
+                 output_blocks=(DEFAULT_BLOCK_INDEX,),
+                 resize_input=True,
+                 normalize_input=True,
+                 requires_grad=False,
+                 use_fid_inception=True):
+        """Build pretrained InceptionV3
+
+        Parameters
+        ----------
+        output_blocks : list of int
+            Indices of blocks to return features of. Possible values are:
+                - 0: corresponds to output of first max pooling
+                - 1: corresponds to output of second max pooling
+                - 2: corresponds to output which is fed to aux classifier
+                - 3: corresponds to output of final average pooling
+        resize_input : bool
+            If true, bilinearly resizes input to width and height 299 before
+            feeding input to model. As the network without fully connected
+            layers is fully convolutional, it should be able to handle inputs
+            of arbitrary size, so resizing might not be strictly needed
+        normalize_input : bool
+            If true, scales the input from range (0, 1) to the range the
+            pretrained Inception network expects, namely (-1, 1)
+        requires_grad : bool
+            If true, parameters of the model require gradients. Possibly useful
+            for finetuning the network
+        use_fid_inception : bool
+            If true, uses the pretrained Inception model used in Tensorflow's
+            FID implementation. If false, uses the pretrained Inception model
+            available in torchvision. The FID Inception model has different
+            weights and a slightly different structure from torchvision's
+            Inception model. If you want to compute FID scores, you are
+            strongly advised to set this parameter to true to get comparable
+            results.
+        """
+        super(InceptionV3, self).__init__()
+
+        self.resize_input = resize_input
+        self.normalize_input = normalize_input
+        self.output_blocks = sorted(output_blocks)
+        self.last_needed_block = max(output_blocks)
+
+        assert self.last_needed_block <= 3, \
+            'Last possible output block index is 3'
+
+        self.blocks = nn.ModuleList()
+
+        if use_fid_inception:
+            inception = fid_inception_v3()
+        else:
+            inception = _inception_v3(weights='DEFAULT')
+
+        # Block 0: input to maxpool1
+        block0 = [
+            inception.Conv2d_1a_3x3,
+            inception.Conv2d_2a_3x3,
+            inception.Conv2d_2b_3x3,
+            nn.MaxPool2d(kernel_size=3, stride=2)
+        ]
+        self.blocks.append(nn.Sequential(*block0))
+
+        # Block 1: maxpool1 to maxpool2
+        if self.last_needed_block >= 1:
+            block1 = [
+                inception.Conv2d_3b_1x1,
+                inception.Conv2d_4a_3x3,
+                nn.MaxPool2d(kernel_size=3, stride=2)
+            ]
+            self.blocks.append(nn.Sequential(*block1))
+
+        # Block 2: maxpool2 to aux classifier
+        if self.last_needed_block >= 2:
+            block2 = [
+                inception.Mixed_5b,
+                inception.Mixed_5c,
+                inception.Mixed_5d,
+                inception.Mixed_6a,
+                inception.Mixed_6b,
+                inception.Mixed_6c,
+                inception.Mixed_6d,
+                inception.Mixed_6e,
+            ]
+            self.blocks.append(nn.Sequential(*block2))
+
+        # Block 3: aux classifier to final avgpool
+        if self.last_needed_block >= 3:
+            block3 = [
+                inception.Mixed_7a,
+                inception.Mixed_7b,
+                inception.Mixed_7c,
+                nn.AdaptiveAvgPool2d(output_size=(1, 1))
+            ]
+            self.blocks.append(nn.Sequential(*block3))
+
+        for param in self.parameters():
+            param.requires_grad = requires_grad
+
+    def forward(self, inp):
+        """Get Inception feature maps
+
+        Parameters
+        ----------
+        inp : torch.autograd.Variable
+            Input tensor of shape Bx3xHxW. Values are expected to be in
+            range (0, 1)
+
+        Returns
+        -------
+        List of torch.autograd.Variable, corresponding to the selected output
+        block, sorted ascending by index
+        """
+        outp = []
+        x = inp
+
+        if self.resize_input:
+            x = F.interpolate(x,
+                              size=(299, 299),
+                              mode='bilinear',
+                              align_corners=False)
+
+        if self.normalize_input:
+            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
+
+        for idx, block in enumerate(self.blocks):
+            x = block(x)
+            if idx in self.output_blocks:
+                outp.append(x)
+
+            if idx == self.last_needed_block:
+                break
+
+        return outp
+
+
+def _inception_v3(*args, **kwargs):
+    """Wraps `torchvision.models.inception_v3`"""
+    try:
+        version = tuple(map(int, torchvision.__version__.split('.')[:2]))
+    except ValueError:
+        # Just a caution against weird version strings
+        version = (0,)
+
+    # Skips default weight inititialization if supported by torchvision
+    # version. See https://github.com/mseitzer/pytorch-fid/issues/28.
+    if version >= (0, 6):
+        kwargs['init_weights'] = False
+
+    # Backwards compatibility: `weights` argument was handled by `pretrained`
+    # argument prior to version 0.13.
+    if version < (0, 13) and 'weights' in kwargs:
+        if kwargs['weights'] == 'DEFAULT':
+            kwargs['pretrained'] = True
+        elif kwargs['weights'] is None:
+            kwargs['pretrained'] = False
+        else:
+            raise ValueError(
+                'weights=={} not supported in torchvision {}'.format(
+                    kwargs['weights'], torchvision.__version__
+                )
+            )
+        del kwargs['weights']
+
+    return torchvision.models.inception_v3(*args, **kwargs)
+
+
+def fid_inception_v3():
+    """Build pretrained Inception model for FID computation
+
+    The Inception model for FID computation uses a different set of weights
+    and has a slightly different structure than torchvision's Inception.
+
+    This method first constructs torchvision's Inception and then patches the
+    necessary parts that are different in the FID Inception model.
+    """
+    inception = _inception_v3(num_classes=1008,
+                              aux_logits=False,
+                              weights=None)
+    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
+    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
+    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
+    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
+    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
+    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
+    inception.Mixed_7b = FIDInceptionE_1(1280)
+    inception.Mixed_7c = FIDInceptionE_2(2048)
+
+    state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
+    inception.load_state_dict(state_dict)
+    return inception
+
+
+class FIDInceptionA(torchvision.models.inception.InceptionA):
+    """InceptionA block patched for FID computation"""
+    def __init__(self, in_channels, pool_features):
+        super(FIDInceptionA, self).__init__(in_channels, pool_features)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch5x5 = self.branch5x5_1(x)
+        branch5x5 = self.branch5x5_2(branch5x5)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionC(torchvision.models.inception.InceptionC):
+    """InceptionC block patched for FID computation"""
+    def __init__(self, in_channels, channels_7x7):
+        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch7x7 = self.branch7x7_1(x)
+        branch7x7 = self.branch7x7_2(branch7x7)
+        branch7x7 = self.branch7x7_3(branch7x7)
+
+        branch7x7dbl = self.branch7x7dbl_1(x)
+        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
+        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_1(torchvision.models.inception.InceptionE):
+    """First InceptionE block patched for FID computation"""
+    def __init__(self, in_channels):
+        super(FIDInceptionE_1, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: Tensorflow's average pool does not use the padded zero's in
+        # its average calculation
+        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
+                                   count_include_pad=False)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
+
+
+class FIDInceptionE_2(torchvision.models.inception.InceptionE):
+    """Second InceptionE block patched for FID computation"""
+    def __init__(self, in_channels):
+        super(FIDInceptionE_2, self).__init__(in_channels)
+
+    def forward(self, x):
+        branch1x1 = self.branch1x1(x)
+
+        branch3x3 = self.branch3x3_1(x)
+        branch3x3 = [
+            self.branch3x3_2a(branch3x3),
+            self.branch3x3_2b(branch3x3),
+        ]
+        branch3x3 = torch.cat(branch3x3, 1)
+
+        branch3x3dbl = self.branch3x3dbl_1(x)
+        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
+        branch3x3dbl = [
+            self.branch3x3dbl_3a(branch3x3dbl),
+            self.branch3x3dbl_3b(branch3x3dbl),
+        ]
+        branch3x3dbl = torch.cat(branch3x3dbl, 1)
+
+        # Patch: The FID Inception model uses max pooling instead of average
+        # pooling. This is likely an error in this specific Inception
+        # implementation, as other Inception models use average pooling here
+        # (which matches the description in the paper).
+        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
+        branch_pool = self.branch_pool(branch_pool)
+
+        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
+        return torch.cat(outputs, 1)
\ No newline at end of file
diff --git a/text_to_image/tools/val2014.npz b/text_to_image/tools/val2014.npz
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O0BZmM01r#(0RRBn?x5`e

literal 0
HcmV?d00001


From 678ed4fbada4f3ee1414005f88a7f30e1e38e4d9 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 19 Dec 2023 12:29:40 -0500
Subject: [PATCH 020/305] [WIP] Loadgen changes first token (#1523)

* LLM reference implementation

* Loadgen changes for token based metrics (TTFT)

* Fix Bug: Correctly report number of tokens

* Record first token in accuracy mode

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 loadgen/bindings/c_api.cc                     |  16 ++
 loadgen/bindings/c_api.h                      |   8 +
 loadgen/bindings/python_api.cc                |  28 +++-
 .../token_metrics/py_demo_multi_stream.py     | 124 ++++++++++++++
 .../demos/token_metrics/py_demo_offline.py    | 118 +++++++++++++
 loadgen/demos/token_metrics/py_demo_server.py | 116 +++++++++++++
 .../token_metrics/py_demo_single_stream.py    | 113 +++++++++++++
 loadgen/issue_query_controller.h              |   3 +
 loadgen/loadgen.cc                            |  92 ++++++++++-
 loadgen/loadgen.h                             |   6 +-
 loadgen/logging.cc                            | 156 +++++++++++++++++-
 loadgen/logging.h                             |  17 ++
 loadgen/query_sample.h                        |  24 +++
 loadgen/results.cc                            | 121 +++++++++++++-
 loadgen/results.h                             |  22 ++-
 loadgen/test_settings.h                       |   2 +
 loadgen/test_settings_internal.cc             |   5 +-
 loadgen/test_settings_internal.h              |   1 +
 18 files changed, 951 insertions(+), 21 deletions(-)
 create mode 100644 loadgen/demos/token_metrics/py_demo_multi_stream.py
 create mode 100644 loadgen/demos/token_metrics/py_demo_offline.py
 create mode 100644 loadgen/demos/token_metrics/py_demo_server.py
 create mode 100644 loadgen/demos/token_metrics/py_demo_single_stream.py

diff --git a/loadgen/bindings/c_api.cc b/loadgen/bindings/c_api.cc
index d8f4523387..4101c7b96e 100644
--- a/loadgen/bindings/c_api.cc
+++ b/loadgen/bindings/c_api.cc
@@ -155,6 +155,22 @@ void QuerySamplesCompleteResponseCb(QuerySampleResponse* responses,
       });
 }
 
+void FirstTokenComplete(QuerySampleResponse* responses,
+                          size_t response_count) {
+  mlperf::FirstTokenComplete(responses, response_count);
+}
+
+void FirstTokenCompleteResponseCb(QuerySampleResponse* responses,
+                                    size_t response_count,
+                                    ResponseCallback response_cb,
+                                    ClientData client_data) {
+  mlperf::FirstTokenComplete(
+      responses, response_count,
+      [client_data, response_cb](QuerySampleResponse* response) {
+        response_cb(client_data, response);
+      });
+}
+
 void RegisterIssueQueryThread() { mlperf::RegisterIssueQueryThread(); }
 
 }  // namespace c
diff --git a/loadgen/bindings/c_api.h b/loadgen/bindings/c_api.h
index 75e69902a1..8c730e96a9 100644
--- a/loadgen/bindings/c_api.h
+++ b/loadgen/bindings/c_api.h
@@ -49,6 +49,14 @@ void QuerySamplesCompleteResponseCb(QuerySampleResponse* responses,
                                     ResponseCallback response_cb,
                                     ClientData client_data);
 
+void FirstTokenComplete(QuerySampleResponse* responses,
+                          size_t response_count);
+                        
+void FirstTokenCompleteResponseCb(QuerySampleResponse* responses,
+                                    size_t response_count,
+                                    ResponseCallback response_cb,
+                                    ClientData client_data);
+
 /// \brief Create an opaque SUT pointer based on C callbacks.
 void* ConstructSUT(ClientData client_data, const char* name, size_t name_length,
                    IssueQueryCallback issue_cb,
diff --git a/loadgen/bindings/python_api.cc b/loadgen/bindings/python_api.cc
index ca946becdf..816f7a4f61 100644
--- a/loadgen/bindings/python_api.cc
+++ b/loadgen/bindings/python_api.cc
@@ -258,6 +258,12 @@ void QuerySamplesComplete(std::vector<QuerySampleResponse> responses,
   mlperf::QuerySamplesComplete(responses.data(), responses.size(), response_cb);
 }
 
+void FirstTokenComplete(std::vector<QuerySampleResponse> responses,
+                          ResponseCallback response_cb = {}) {
+  pybind11::gil_scoped_release gil_releaser;
+  mlperf::FirstTokenComplete(responses.data(), responses.size(), response_cb);
+}
+
 PYBIND11_MODULE(mlperf_loadgen, m) {
   m.doc() = "MLPerf Inference load generator.";
 
@@ -325,10 +331,11 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
                      &TestSettings::performance_sample_count_override)
       .def_readwrite("test05",
                      &TestSettings::test05)
-      .def_readwrite("test05_qsl_rng_seed", &TestSettings::qsl_rng_seed)
+      .def_readwrite("test05_qsl_rng_seed", &TestSettings::test05_qsl_rng_seed)
       .def_readwrite("test05_sample_index_rng_seed",
-                     &TestSettings::sample_index_rng_seed)
-      .def_readwrite("test05_schedule_rng_seed", &TestSettings::schedule_rng_seed)
+                     &TestSettings::test05_sample_index_rng_seed)
+      .def_readwrite("test05_schedule_rng_seed", &TestSettings::test05_schedule_rng_seed)
+      .def_readwrite("use_token_latencies", &TestSettings::use_token_latencies)
       .def("FromConfig", &TestSettings::FromConfig, "FromConfig.");
 
   pybind11::enum_<LoggingMode>(m, "LoggingMode")
@@ -379,22 +386,30 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
   pybind11::class_<QuerySampleResponse>(m, "QuerySampleResponse")
       .def(pybind11::init<>())
       .def(pybind11::init<ResponseId, uintptr_t, size_t>())
+      .def(pybind11::init<ResponseId, uintptr_t, size_t, int64_t>())
       .def_readwrite("id", &QuerySampleResponse::id)
       .def_readwrite("data", &QuerySampleResponse::data)
       .def_readwrite("size", &QuerySampleResponse::size)
+      .def_readwrite("n_tokens", &QuerySampleResponse::n_tokens)
       .def(pybind11::pickle(
           [](const QuerySampleResponse& qsr) {  // __getstate__
             /* Return a tuple that fully encodes state of object*/
             return pybind11::make_tuple(qsr.id, qsr.data, qsr.size);
           },
           [](pybind11::tuple t) {  // __setstate__
-            if (t.size() != 3)
+            if ((t.size() != 3) || (t.size() != 4))
               throw std::runtime_error("Invalid state for QuerySampleResponse");
             /* Create a new C++ instance*/
             QuerySampleResponse q;
             q.id = t[0].cast<uintptr_t>();
             q.data = t[1].cast<uintptr_t>();
             q.size = t[2].cast<size_t>();
+            if (t.size() == 4){
+              q.n_tokens = t[3].cast<int64_t>();
+            }
+            else{
+              q.n_tokens = 0;
+            }
             return q;
           }));
 
@@ -439,6 +454,11 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
         "IssueQuery calls have finished.",
         pybind11::arg("responses"),
         pybind11::arg("response_cb") = ResponseCallback{});
+  m.def("FirstTokenComplete", &py::FirstTokenComplete,
+        "Called by the SUT to indicate that tokens from some combination of"
+        "IssueQuery calls have finished.",
+        pybind11::arg("responses"),
+        pybind11::arg("response_cb") = ResponseCallback{});
 }
 
 }  // namespace py
diff --git a/loadgen/demos/token_metrics/py_demo_multi_stream.py b/loadgen/demos/token_metrics/py_demo_multi_stream.py
new file mode 100644
index 0000000000..a6350e53db
--- /dev/null
+++ b/loadgen/demos/token_metrics/py_demo_multi_stream.py
@@ -0,0 +1,124 @@
+# Copyright 2019 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Python demo showing how to use the MLPerf Inference load generator bindings.
+"""
+
+from __future__ import print_function
+
+import argparse
+import threading
+import time
+import numpy as np
+import array
+
+import mlperf_loadgen
+
+from datetime import datetime
+
+# Global var
+NUM_AGENTS = 8
+LOOPBACK_LATENCY_S = .001
+
+def f(x, y):
+    return (4 + 3*x*y + x**3 + y**2)
+
+def create_responses(n, m, mod = 4):
+    r = []
+    for i in range(n):
+        r.append([f(i,j) for j in range(m + (i%mod))])
+    return r
+responses = create_responses(1024, 20)
+
+def load_samples_to_ram(query_samples):
+    del query_samples
+    return
+
+
+def unload_samples_from_ram(query_samples):
+    del query_samples
+    return
+
+
+# Processes queries in NUM_AGENTS slices that complete at different times.
+def process_query_async(query_samples, i_slice):
+    time.sleep(LOOPBACK_LATENCY_S * (i_slice + 1))
+    query_responses = []
+    samples_to_complete = query_samples[i_slice:len(query_samples):NUM_AGENTS]
+    for j, s in enumerate(samples_to_complete):
+        response_array = np.array(responses[s.index], np.int32)
+        token = response_array[0]
+        time.sleep(.0002)
+        response_token = array.array("B", token.tobytes())
+        response_token_info = response_token.buffer_info()
+        response_token_data = response_token_info[0]
+        response_token_size = response_token_info[1] * response_token.itemsize
+        mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
+        time.sleep(.02)
+        n_tokens = len(response_array)
+        response_array = array.array("B", response_array.tobytes())
+        response_info = response_array.buffer_info()
+        response_data = response_info[0]
+        response_size = response_info[1] * response_array.itemsize
+        query_responses.append(
+            mlperf_loadgen.QuerySampleResponse(
+                s.id, response_data, response_size, n_tokens))
+    mlperf_loadgen.QuerySamplesComplete(query_responses)
+
+
+def issue_query(query_samples):
+    for i in range(8):
+        threading.Thread(target=process_query_async,
+                         args=(query_samples, i)).start()
+
+
+def flush_queries():
+    pass
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument("--expected-latency", type=int, default=8000000)
+    parser.add_argument("--samples-per-query", type=int, default=8)
+    parser.add_argument("--min-query-count", type=int, default=100)
+    parser.add_argument("--min-duration-ms", type=int, default=30000)
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    settings = mlperf_loadgen.TestSettings()
+    settings.scenario = mlperf_loadgen.TestScenario.MultiStream
+    if args.mode == "performance":
+        settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
+    else:
+        settings.mode = mlperf_loadgen.TestMode.AccuracyOnly
+    settings.multi_stream_expected_latency_ns = args.expected_latency
+    settings.multi_stream_samples_per_query = args.samples_per_query
+    settings.min_query_count = args.min_query_count
+    settings.min_duration_ms = args.min_duration_ms
+    settings.use_token_latencies = True
+
+    sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
+    qsl = mlperf_loadgen.ConstructQSL(
+        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+    mlperf_loadgen.StartTest(sut, qsl, settings)
+    mlperf_loadgen.DestroyQSL(qsl)
+    mlperf_loadgen.DestroySUT(sut)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/loadgen/demos/token_metrics/py_demo_offline.py b/loadgen/demos/token_metrics/py_demo_offline.py
new file mode 100644
index 0000000000..cc8ba569ea
--- /dev/null
+++ b/loadgen/demos/token_metrics/py_demo_offline.py
@@ -0,0 +1,118 @@
+# Copyright 2019 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Python demo showing how to use the MLPerf Inference load generator bindings.
+"""
+
+from __future__ import print_function
+
+import argparse
+import threading
+import time
+import numpy as np
+import array
+
+import mlperf_loadgen
+
+
+def f(x, y):
+    return (4 + 3*x*y + x**3 + y**2)
+
+def create_responses(n, m, mod = 4):
+    r = []
+    for i in range(n):
+        r.append([f(i,j) for j in range(m + (i%mod))])
+    return r
+responses = create_responses(1024, 20)
+
+def load_samples_to_ram(query_samples):
+    del query_samples
+    return
+
+
+def unload_samples_from_ram(query_samples):
+    del query_samples
+    return
+
+
+# Processes queries in 3 slices that complete at different times.
+def process_query_async(query_samples, i_slice):
+    time.sleep(3 * (i_slice + 1))
+    query_responses = []
+    samples_to_complete = query_samples[i_slice:len(query_samples):3]
+    for s in samples_to_complete:
+        response_array = np.array(responses[s.index], np.int32)
+        token = response_array[0]
+        time.sleep(.0002)
+        response_token = array.array("B", token.tobytes())
+        response_token_info = response_token.buffer_info()
+        response_token_data = response_token_info[0]
+        response_token_size = response_token_info[1] * response_token.itemsize
+        mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
+        time.sleep(.02)
+        n_tokens = len(response_array)
+        response_array = array.array("B", response_array.tobytes())
+        response_info = response_array.buffer_info()
+        response_data = response_info[0]
+        response_size = response_info[1] * response_array.itemsize
+        query_responses.append(
+            mlperf_loadgen.QuerySampleResponse(
+                s.id, response_data, response_size, n_tokens))
+    mlperf_loadgen.QuerySamplesComplete(query_responses)
+
+
+def issue_query(query_samples):
+    threading.Thread(target=process_query_async,
+                     args=(query_samples, 0)).start()
+    threading.Thread(target=process_query_async,
+                     args=(query_samples, 1)).start()
+    threading.Thread(target=process_query_async,
+                     args=(query_samples, 2)).start()
+
+
+def flush_queries():
+    pass
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument("--expected-qps", type=int, default=1000)
+    parser.add_argument("--min-duration-ms", type=int, default=30000)
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    settings = mlperf_loadgen.TestSettings()
+    settings.scenario = mlperf_loadgen.TestScenario.Offline
+    if args.mode == "performance":
+        settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
+    else:
+        settings.mode = mlperf_loadgen.TestMode.AccuracyOnly
+    settings.offline_expected_qps = args.expected_qps
+    settings.min_duration_ms = args.min_duration_ms
+    settings.use_token_latencies = True
+
+    sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
+    qsl = mlperf_loadgen.ConstructQSL(
+        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+    mlperf_loadgen.StartTest(sut, qsl, settings)
+    mlperf_loadgen.DestroyQSL(qsl)
+    mlperf_loadgen.DestroySUT(sut)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/loadgen/demos/token_metrics/py_demo_server.py b/loadgen/demos/token_metrics/py_demo_server.py
new file mode 100644
index 0000000000..8879863e08
--- /dev/null
+++ b/loadgen/demos/token_metrics/py_demo_server.py
@@ -0,0 +1,116 @@
+# Copyright 2019 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Python demo showing how to use the MLPerf Inference load generator bindings.
+"""
+
+from __future__ import print_function
+
+import argparse
+import array
+import threading
+import time
+import numpy as np
+
+from absl import app
+import mlperf_loadgen
+
+def f(x, y):
+    return (4 + 3*x*y + x**3 + y**2)
+
+def create_responses(n, m, mod = 4):
+    r = []
+    for i in range(n):
+        r.append([f(i,j) for j in range(m + (i%mod))])
+    return r
+responses = create_responses(1024, 20)
+
+def load_samples_to_ram(query_samples):
+    del query_samples
+    return
+
+
+def unload_samples_from_ram(query_samples):
+    del query_samples
+    return
+
+
+def process_query_async(query_samples):
+    """Processes the list of queries."""
+    query_responses = []
+    for s in query_samples:
+        response_array = np.array(responses[s.index], np.int32)
+        token = response_array[0]
+        time.sleep(.0002)
+        response_token = array.array("B", token.tobytes())
+        response_token_info = response_token.buffer_info()
+        response_token_data = response_token_info[0]
+        response_token_size = response_token_info[1] * response_token.itemsize
+        mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
+        time.sleep(.02)
+        n_tokens = len(response_array)
+        response_array = array.array("B", response_array.tobytes())
+        response_info = response_array.buffer_info()
+        response_data = response_info[0]
+        response_size = response_info[1] * response_array.itemsize
+        # print(f"Reported size python: {n_tokens}")
+        query_responses.append(
+            mlperf_loadgen.QuerySampleResponse(
+                s.id, response_data, response_size, n_tokens))
+    mlperf_loadgen.QuerySamplesComplete(query_responses)
+
+
+def issue_query(query_samples):
+    threading.Thread(target=process_query_async,
+                     args=[query_samples]).start()
+
+
+def flush_queries():
+    pass
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument("--target-qps", type=int, default=100)
+    parser.add_argument("--target-latency-ns", type=int, default=100000000)
+    parser.add_argument("--min-query-count", type=int, default=100)
+    parser.add_argument("--min-duration-ms", type=int, default=30000)
+    return parser.parse_args()
+
+def main():
+    args = get_args()
+    settings = mlperf_loadgen.TestSettings()
+    settings.scenario = mlperf_loadgen.TestScenario.Server
+    if args.mode == "performance":
+        settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
+    else:
+        settings.mode = mlperf_loadgen.TestMode.AccuracyOnly
+    settings.server_target_qps = args.target_qps
+    settings.server_target_latency_ns = args.target_latency_ns
+    settings.min_query_count = args.min_query_count
+    settings.min_duration_ms = args.min_duration_ms
+    settings.use_token_latencies = True
+
+    sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
+    qsl = mlperf_loadgen.ConstructQSL(
+        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+    mlperf_loadgen.StartTest(sut, qsl, settings)
+    mlperf_loadgen.DestroyQSL(qsl)
+    mlperf_loadgen.DestroySUT(sut)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/loadgen/demos/token_metrics/py_demo_single_stream.py b/loadgen/demos/token_metrics/py_demo_single_stream.py
new file mode 100644
index 0000000000..e4aa4226fe
--- /dev/null
+++ b/loadgen/demos/token_metrics/py_demo_single_stream.py
@@ -0,0 +1,113 @@
+# Copyright 2019 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Python demo showing how to use the MLPerf Inference load generator bindings.
+"""
+
+from __future__ import print_function
+
+import argparse
+import array
+import threading
+import time
+import numpy as np
+
+from absl import app
+import mlperf_loadgen
+
+def f(x, y):
+    return (4 + 3*x*y + x**3 + y**2)
+
+def create_responses(n, m, mod = 4):
+    r = []
+    for i in range(n):
+        r.append([f(i,j) for j in range(m + (i%mod))])
+    return r
+responses = create_responses(1024, 20)
+
+def load_samples_to_ram(query_samples):
+    del query_samples
+    return
+
+
+def unload_samples_from_ram(query_samples):
+    del query_samples
+    return
+
+
+def process_query_async(query_samples):
+    """Processes the list of queries."""
+    query_responses = []
+    for s in query_samples:
+        response_array = np.array(responses[s.index], np.int32)
+        time.sleep(.0002)
+        token = response_array[:1]
+        response_token = array.array("B", token.tobytes())
+        response_token_info = response_token.buffer_info()
+        response_token_data = response_token_info[0]
+        response_token_size = response_token_info[1] * response_token.itemsize
+        mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
+        time.sleep(.02)
+        n_tokens = len(response_array)
+        response_array = array.array("B", response_array.tobytes())
+        response_info = response_array.buffer_info()
+        response_data = response_info[0]
+        response_size = response_info[1] * response_array.itemsize
+        query_responses.append(
+            mlperf_loadgen.QuerySampleResponse(
+                s.id, response_data, response_size, n_tokens))
+    mlperf_loadgen.QuerySamplesComplete(query_responses)
+
+
+def issue_query(query_samples):
+    threading.Thread(target=process_query_async,
+                     args=[query_samples]).start()
+
+
+def flush_queries():
+    pass
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument("--expected-latency", type=int, default=2050000)
+    parser.add_argument("--min-query-count", type=int, default=100)
+    parser.add_argument("--min-duration-ms", type=int, default=30000)
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    settings = mlperf_loadgen.TestSettings()
+    settings.scenario = mlperf_loadgen.TestScenario.SingleStream
+    if args.mode == "performance":
+        settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
+    else:
+        settings.mode = mlperf_loadgen.TestMode.AccuracyOnly
+    settings.single_stream_expected_latency_ns = args.expected_latency
+    settings.min_query_count = args.min_query_count
+    settings.min_duration_ms = args.min_duration_ms
+    settings.use_token_latencies = True
+
+    sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
+    qsl = mlperf_loadgen.ConstructQSL(
+        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+    mlperf_loadgen.StartTest(sut, qsl, settings)
+    mlperf_loadgen.DestroyQSL(qsl)
+    mlperf_loadgen.DestroySUT(sut)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/loadgen/issue_query_controller.h b/loadgen/issue_query_controller.h
index b095497139..e723234cd7 100644
--- a/loadgen/issue_query_controller.h
+++ b/loadgen/issue_query_controller.h
@@ -70,6 +70,9 @@ struct ResponseDelegate {
   virtual void SampleComplete(SampleMetadata*, QuerySampleResponse*,
                               PerfClock::time_point,
                               const ResponseCallback&) = 0;
+  virtual void TokenComplete(SampleMetadata*, QuerySampleResponse*,
+                              PerfClock::time_point,
+                              const ResponseCallback&) = 0;
   virtual void QueryComplete() = 0;
   std::atomic<size_t> queries_completed{0};
 };
diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index 1346707278..3517e78d1b 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -96,10 +96,10 @@ struct ResponseDelegateDetailed : public ResponseDelegate {
       uint8_t* src_end = src_begin + response->size;
       sample_data_copy = new std::vector<uint8_t>(src_begin, src_end);
     }
-    Log([sample, complete_begin_time, sample_data_copy](AsyncLog& log) {
+    int64_t n_tokens = response->n_tokens;
+    Log([sample, complete_begin_time, sample_data_copy, n_tokens](AsyncLog& log) {
       QueryMetadata* query = sample->query_metadata;
       DurationGeneratorNs sched{query->scheduled_time};
-
       if (scenario == TestScenario::Server) {
         // Trace the server scenario as a stacked graph via counter events.
         DurationGeneratorNs issued{query->issued_start_time};
@@ -128,6 +128,45 @@ struct ResponseDelegateDetailed : public ResponseDelegate {
       // thread and potentially destroy the metadata being used above.
       QuerySampleLatency latency = sched.delta(complete_begin_time);
       log.RecordSampleCompletion(sample->sequence_id, complete_begin_time,
+                                 latency, n_tokens);
+    });
+  }
+
+    void TokenComplete(SampleMetadata* sample, QuerySampleResponse* response,
+                      PerfClock::time_point complete_begin_time,
+                      const ResponseCallback& response_cb) override {
+    // Using a raw pointer here should help us hit the std::function
+    // small buffer optimization code path when we aren't copying data.
+    // For some reason, using std::unique_ptr<std::vector> wasn't moving
+    // into the lambda; even with C++14.
+    std::vector<uint8_t>* token_data_copy = nullptr;
+    if (mode == TestMode::AccuracyOnly) {
+      uint8_t* src_begin = reinterpret_cast<uint8_t*>(response->data);
+      uint8_t* src_end = src_begin + response->size;
+      token_data_copy = new std::vector<uint8_t>(src_begin, src_end);
+    }
+    Log([sample, complete_begin_time, token_data_copy](AsyncLog& log) {
+      QueryMetadata* query = sample->query_metadata;
+      DurationGeneratorNs sched{query->scheduled_time};
+      if (scenario == TestScenario::Server) {
+        DurationGeneratorNs issued{query->issued_start_time};
+        log.TraceCounterEvent("Token_Latency", query->scheduled_time, "issue_delay",
+                              sched.delta(query->issued_start_time),
+                              "issue_to_done",
+                              issued.delta(complete_begin_time));
+      }else{
+        log.TraceSample("Token", sample->sequence_id, query->scheduled_time,
+                      complete_begin_time, "sample_seq", sample->sequence_id,
+                      "query_seq", query->sequence_id, "sample_idx",
+                      sample->sample_index, "issue_start_ns",
+                      sched.delta(query->issued_start_time), "complete_ns",
+                      sched.delta(complete_begin_time));
+      }
+      if (token_data_copy) {
+        log.CacheToken(sample->sequence_id, LogBinaryAsHexString{token_data_copy});
+      }
+      QuerySampleLatency latency = sched.delta(complete_begin_time);
+      log.RecordTokenCompletion(sample->sequence_id, complete_begin_time,
                                  latency);
     });
   }
@@ -482,6 +521,13 @@ PerformanceResult IssueQueries(SystemUnderTest* sut,
   std::vector<QuerySampleLatency> sample_latencies(
       GlobalLogger().GetLatenciesBlocking(expected_latencies));
 
+  std::vector<QuerySampleLatency> first_token_latencies(
+    GlobalLogger().GetTokenLatencies(expected_latencies));
+
+  std::vector<int64_t> tokens_per_sample(
+    GlobalLogger().GetTokensPerSample(expected_latencies));
+
+
   // Log contention counters after every test as a sanity check.
   GlobalLogger().LogContentionAndAllocations();
 
@@ -529,12 +575,17 @@ PerformanceResult IssueQueries(SystemUnderTest* sut,
   }
 
   return PerformanceResult{std::move(sample_latencies),
-                           std::move(query_latencies),
-                           queries_issued,
-                           max_latency,
-                           final_query_scheduled_time,
-                           final_query_issued_time,
-                           final_query_all_samples_done_time};
+                          std::move(query_latencies),
+                          queries_issued,
+                          max_latency,
+                          final_query_scheduled_time,
+                          final_query_issued_time,
+                          final_query_all_samples_done_time,
+                          TokenPerformanceResults{
+                            first_token_latencies,
+                            tokens_per_sample
+                          }
+                        };
 }
 
 void LoadSamplesToRam(QuerySampleLibrary* qsl,
@@ -1103,6 +1154,8 @@ void StartTest(SystemUnderTest* sut, QuerySampleLibrary* qsl,
                               &log_outputs.accuracy_out,
                               log_settings.log_output.copy_detail_to_stdout,
                               log_settings.log_output.copy_summary_to_stdout);
+            
+  GlobalLogger().SetUseTokens(requested_settings.use_token_latencies);
 
   if (log_settings.enable_trace) {
     GlobalLogger().StartNewTrace(&log_outputs.trace_out, PerfClock::now());
@@ -1246,6 +1299,29 @@ void QuerySamplesComplete(QuerySampleResponse* responses, size_t response_count,
     query->response_delegate->SampleComplete(sample, response, timestamp,
                                              response_cb);
   }
+  // PerfClock::time_point end_timestamp = PerfClock::now();
+  // mlperf::samples_overhead_acum += (end_timestamp - timestamp).count();
+}
+
+void FirstTokenComplete(QuerySampleResponse* responses, size_t response_count,
+                          const ResponseCallback& response_cb) {
+  PerfClock::time_point timestamp = PerfClock::now();
+
+  auto tracer = MakeScopedTracer(
+      [](AsyncTrace& trace) { trace("FirstTokenComplete"); });
+
+  const QuerySampleResponse* end = responses + response_count;
+
+  // Log samples.
+  for (QuerySampleResponse* response = responses; response < end; response++) {
+    loadgen::SampleMetadata* sample =
+        reinterpret_cast<loadgen::SampleMetadata*>(response->id);
+    loadgen::QueryMetadata* query = sample->query_metadata;
+    query->response_delegate->TokenComplete(sample, response, timestamp,
+                                             response_cb);
+  }
+  // PerfClock::time_point end_timestamp = PerfClock::now();
+  // mlperf::tokens_overhead_acum += (end_timestamp - timestamp).count();
 }
 
 }  // namespace mlperf
diff --git a/loadgen/loadgen.h b/loadgen/loadgen.h
index f29394e12f..f0e6554900 100644
--- a/loadgen/loadgen.h
+++ b/loadgen/loadgen.h
@@ -65,6 +65,9 @@ using ResponseCallback = std::function<void(QuerySampleResponse*)>;
 void QuerySamplesComplete(QuerySampleResponse* responses, size_t response_count,
                           const ResponseCallback& response_cb = {});
 
+void FirstTokenComplete(QuerySampleResponse* responses, size_t response_count,
+                          const ResponseCallback& response_cb = {});
+
 ///
 /// \brief Starts the test against SUT with the specified settings.
 /// \details This is the C++ entry point. See mlperf::c::StartTest for the
@@ -92,7 +95,8 @@ void AbortTest();
 /// C entry point.
 ///
 void RegisterIssueQueryThread();
-
+// inline long long samples_overhead_acum;
+// inline long long tokens_overhead_acum;
 /// @}
 
 }  // namespace mlperf
diff --git a/loadgen/logging.cc b/loadgen/logging.cc
index f04737149f..3ab5437de7 100644
--- a/loadgen/logging.cc
+++ b/loadgen/logging.cc
@@ -89,6 +89,7 @@ constexpr std::chrono::milliseconds kLogPollPeriod(10);
 constexpr size_t kTlsLogReservedEntryCount = 1024;
 
 constexpr auto kInvalidLatency = std::numeric_limits<QuerySampleLatency>::min();
+constexpr auto nTokenInvalid = std::numeric_limits<int64_t>::min();
 
 }  // namespace
 
@@ -284,12 +285,28 @@ void AsyncLog::LogAccuracy(uint64_t seq_id, const QuerySampleIndex qsl_idx,
     return;
   }
   *accuracy_out_ << (accuracy_needs_comma_ ? ",\n{ " : "\n{ ");
-  LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
+  if (!use_tokens_){
+    LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
           response);
+  } else {
+    const size_t i = seq_id - latencies_first_sample_sequence_id_;
+    LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
+          response, "token_data", token_records_[i]);
+  }
+  
   *accuracy_out_ << " }";
   accuracy_needs_comma_ = true;
 }
 
+void AsyncLog::CacheToken(uint64_t seq_id, const LogBinaryAsHexString& response){
+  std::unique_lock<std::mutex> lock(token_record_mutex_);
+  const size_t i = seq_id - latencies_first_sample_sequence_id_;
+  if (token_records_.size() <= i) {
+    token_records_.resize(i + 1);
+  }
+  token_records_[i] = response;
+}
+
 void AsyncLog::Flush() {
   {
     std::unique_lock<std::mutex> lock(log_mutex_);
@@ -330,11 +347,14 @@ void AsyncLog::RestartLatencyRecording(uint64_t first_sample_sequence_id,
   max_completion_timstamp_ = PerfClock::now();
   latencies_first_sample_sequence_id_ = first_sample_sequence_id;
   latencies_.reserve(latencies_to_reserve);
+  token_latencies_.reserve(latencies_to_reserve);
+  tokens_per_sample_.reserve(latencies_to_reserve);
 }
 
 void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
                                       PerfClock::time_point completion_time,
-                                      QuerySampleLatency latency) {
+                                      QuerySampleLatency latency,
+                                      int64_t n_tokens = 0) {
   std::unique_lock<std::mutex> lock(latencies_mutex_);
 
   max_latency_ = std::max(max_latency_, latency);
@@ -381,7 +401,42 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
     // the error above.
     return;
   }
+  
+  if (use_tokens_){
+    if(token_latencies_.size() <= i){
+      MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
+                          "Attempted to record a sample latency before it's first token latency");
+    }else if (token_latencies_[i] == kInvalidLatency){
+      MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
+                          "Attempted to record a sample latency before it's first token latency");
+    }
 
+    if (tokens_per_sample_.size() <= i) {
+      // TODO: Reserve in advance.
+      tokens_per_sample_.resize(i + 1, nTokenInvalid);
+    } else if (tokens_per_sample_[i] != nTokenInvalid) {
+      // Call LogErrorSync here since this kind of error could result in a
+      // segfault in the near future.
+  #if USE_NEW_LOGGING_FORMAT
+      MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
+                            "Attempted to complete a sample twice.");
+  #else
+      GlobalLogger().LogErrorSync("Attempted to complete a sample twice.");
+  #endif
+
+      // Return without recording the latency again to avoid potentially
+      // ending the test before the SUT is actually done, which could result
+      // in a segfault.
+      // If the SUT recorded the wrong sample, the test will hang and see
+      // the error above.
+      return;
+    } else if (n_tokens <= 0){
+      MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
+                            "Attempted to record a negative number of tokens");
+      n_tokens = 0;
+    }
+    tokens_per_sample_[i] = n_tokens;
+  }
   latencies_[i] = latency;
   latencies_recorded_++;
   if (AllLatenciesRecorded()) {
@@ -389,6 +444,75 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
   }
 }
 
+void AsyncLog::RecordTokenCompletion(uint64_t sample_sequence_id,
+                                      PerfClock::time_point completion_time,
+                                      QuerySampleLatency latency) {
+  std::unique_lock<std::mutex> lock(token_latencies_mutex_);
+  //std::unique_lock<std::mutex> lock(latencies_mutex_);
+  //max_latency_ = std::max(max_latency_, latency);
+
+  //max_completion_timstamp_ =
+  //    std::max(max_completion_timstamp_, completion_time);
+
+  if (sample_sequence_id < latencies_first_sample_sequence_id_) {
+    // Call LogErrorSync here since this kind of error could result in a
+    // segfault in the near future.
+#if USE_NEW_LOGGING_FORMAT
+    std::stringstream ss;
+    ss << "Received completion for an old sample."
+       << " Min expected id: " << latencies_first_sample_sequence_id_
+       << " Actual id: " << sample_sequence_id;
+    MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime", ss.str());
+#else
+    GlobalLogger().LogErrorSync(
+        "Received completion for an old sample.", "Min expected id",
+        latencies_first_sample_sequence_id_, "Actual id", sample_sequence_id);
+#endif
+    return;
+  }
+
+  const size_t i = sample_sequence_id - latencies_first_sample_sequence_id_;
+  
+  if (latencies_.size() > i){
+    if (latencies_[i] != kInvalidLatency){
+#if USE_NEW_LOGGING_FORMAT
+    MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
+                          "Attempted to record token latency after sample was completed");
+#else
+    GlobalLogger().LogErrorSync("Attempted to record token latency after sample was completed");
+#endif
+
+    // Return without recording the latency again to avoid potentially
+    // ending the test before the SUT is actually done, which could result
+    // in a segfault.
+    // If the SUT recorded the wrong sample, the test will hang and see
+    // the error above.
+    return;
+    }
+  }
+  if (token_latencies_.size() <= i) {
+    // TODO: Reserve in advance.
+    token_latencies_.resize(i + 1, kInvalidLatency);
+  } else if (token_latencies_[i] != kInvalidLatency) {
+    // Call LogErrorSync here since this kind of error could result in a
+    // segfault in the near future.
+#if USE_NEW_LOGGING_FORMAT
+    MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
+                          "Attempted to complete a sample twice.");
+#else
+    GlobalLogger().LogErrorSync("Attempted to complete a sample twice.");
+#endif
+
+    // Return without recording the latency again to avoid potentially
+    // ending the test before the SUT is actually done, which could result
+    // in a segfault.
+    // If the SUT recorded the wrong sample, the test will hang and see
+    // the error above.
+    return;
+  }
+  token_latencies_[i] = latency;
+}
+
 std::vector<QuerySampleLatency> AsyncLog::GetLatenciesBlocking(
     size_t expected_count) {
   std::vector<QuerySampleLatency> latencies;
@@ -439,6 +563,18 @@ std::vector<QuerySampleLatency> AsyncLog::GetLatenciesBlocking(
   return latencies;
 }
 
+std::vector<QuerySampleLatency> AsyncLog::GetTokenLatencies(size_t expected_count) {
+  std::vector<QuerySampleLatency> token_latencies;
+  token_latencies.swap(token_latencies_);
+  return token_latencies;
+}
+
+std::vector<int64_t> AsyncLog::GetTokensPerSample(size_t expected_count) {
+  std::vector<int64_t> tokens_per_sample;
+  tokens_per_sample.swap(tokens_per_sample_);
+  return tokens_per_sample;
+}
+
 PerfClock::time_point AsyncLog::GetMaxCompletionTime() {
   return max_completion_timstamp_;
 }
@@ -448,6 +584,10 @@ QuerySampleLatency AsyncLog::GetMaxLatencySoFar() {
   return max_latency_;
 }
 
+void AsyncLog::SetUseTokens(bool use_tokens){
+  use_tokens_ = use_tokens;
+}
+
 /// \brief Records a single thread using thread-local storage and submits
 /// entries to the central Logger.
 ///
@@ -757,6 +897,14 @@ std::vector<QuerySampleLatency> Logger::GetLatenciesBlocking(
     size_t expected_count) {
   return async_logger_.GetLatenciesBlocking(expected_count);
 }
+std::vector<QuerySampleLatency> Logger::GetTokenLatencies(
+    size_t expected_count) {
+  return async_logger_.GetTokenLatencies(expected_count);
+}
+std::vector<QuerySampleLatency> Logger::GetTokensPerSample(
+    size_t expected_count) {
+  return async_logger_.GetTokensPerSample(expected_count);
+}
 
 PerfClock::time_point Logger::GetMaxCompletionTime() {
   return async_logger_.GetMaxCompletionTime();
@@ -766,6 +914,10 @@ QuerySampleLatency Logger::GetMaxLatencySoFar() {
   return async_logger_.GetMaxLatencySoFar();
 }
 
+void Logger::SetUseTokens(bool use_tokens){
+  async_logger_.SetUseTokens(use_tokens);
+}
+
 TlsLogger* Logger::GetTlsLoggerThatRequestedSwap(size_t slot, size_t next_id) {
   uintptr_t slot_value = thread_swap_request_slots_[slot].load();
   if (SwapRequestSlotIsReadable(slot_value)) {
diff --git a/loadgen/logging.h b/loadgen/logging.h
index 23b70f14c5..f741ea62a9 100644
--- a/loadgen/logging.h
+++ b/loadgen/logging.h
@@ -227,6 +227,7 @@ class AsyncLog {
 
   void LogAccuracy(uint64_t seq_id, const QuerySampleIndex qsl_idx,
                    const LogBinaryAsHexString& response);
+  void CacheToken(uint64_t seq_id, const LogBinaryAsHexString& response);
 
   template <typename... Args>
   void LogSummary(const std::string& message, const Args... args);
@@ -313,11 +314,18 @@ class AsyncLog {
   void RestartLatencyRecording(uint64_t first_sample_sequence_id,
                                size_t latencies_to_reserve);
   void RecordSampleCompletion(uint64_t sample_sequence_id,
+                              PerfClock::time_point completion_time,
+                              QuerySampleLatency latency,
+                              int64_t n_tokens);
+  void RecordTokenCompletion(uint64_t sample_sequence_id,
                               PerfClock::time_point completion_time,
                               QuerySampleLatency latency);
   std::vector<QuerySampleLatency> GetLatenciesBlocking(size_t expected_count);
+  std::vector<QuerySampleLatency> GetTokenLatencies(size_t expected_count);
+  std::vector<int64_t> GetTokensPerSample(size_t expected_count);
   PerfClock::time_point GetMaxCompletionTime();
   QuerySampleLatency GetMaxLatencySoFar();
+  void SetUseTokens(bool use_tokens);
 
  private:
   void WriteAccuracyHeaderLocked();
@@ -358,6 +366,7 @@ class AsyncLog {
   bool error_flagged_ = false;
   size_t log_warning_count_ = 0;
   bool warning_flagged_ = false;
+  bool use_tokens_ = false;
 
   std::mutex trace_mutex_;
   std::unique_ptr<ChromeTracer> tracer_;
@@ -369,9 +378,14 @@ class AsyncLog {
   PerfClock::time_point scoped_end_;
 
   std::mutex latencies_mutex_;
+  std::mutex token_latencies_mutex_;
+  std::mutex token_record_mutex_;
   std::condition_variable all_latencies_recorded_;
   uint64_t latencies_first_sample_sequence_id_ = 0;
   std::vector<QuerySampleLatency> latencies_;
+  std::vector<QuerySampleLatency> token_latencies_;
+  std::vector<LogBinaryAsHexString> token_records_;
+  std::vector<int64_t> tokens_per_sample_;
   QuerySampleLatency max_latency_ = 0;
   PerfClock::time_point max_completion_timstamp_;
   size_t latencies_recorded_ = 0;
@@ -404,8 +418,11 @@ class Logger {
   void RestartLatencyRecording(uint64_t first_sample_sequence_id,
                                size_t latencies_to_reserve);
   std::vector<QuerySampleLatency> GetLatenciesBlocking(size_t expected_count);
+  std::vector<QuerySampleLatency> GetTokenLatencies(size_t expected_count);
+  std::vector<int64_t> GetTokensPerSample(size_t expected_count);
   PerfClock::time_point GetMaxCompletionTime();
   QuerySampleLatency GetMaxLatencySoFar();
+  void SetUseTokens(bool use_tokens);
 
  private:
   friend AsyncLog;
diff --git a/loadgen/query_sample.h b/loadgen/query_sample.h
index 9a0bb3751f..371b49e975 100644
--- a/loadgen/query_sample.h
+++ b/loadgen/query_sample.h
@@ -21,6 +21,7 @@ limitations under the License.
 
 #include <stddef.h>
 #include <stdint.h>
+#include <iostream>
 
 namespace mlperf {
 
@@ -49,6 +50,29 @@ struct QuerySampleResponse {
   ResponseId id;
   uintptr_t data;
   size_t size;  ///< Size in bytes.
+  int64_t n_tokens;
+  public:
+    QuerySampleResponse(ResponseId id, uintptr_t data, size_t size, int64_t n_tokens)
+    : id(id), 
+    data(data), 
+    size(size), 
+    n_tokens(n_tokens){
+      //std::cout << "Initialized with 4 arguments, n_tokens: " << n_tokens <<"\n";
+    };
+    QuerySampleResponse(ResponseId id, uintptr_t data, size_t size)
+    : id(id), 
+    data(data), 
+    size(size), 
+    n_tokens(0){
+      //std::cout << "Initialized with 3 arguments, n_tokens: " << n_tokens <<"\n";
+    };
+    QuerySampleResponse()
+    : id(0), 
+    data(0), 
+    size(0), 
+    n_tokens(0){
+      //std::cout << "Initialized with 0 arguments, n_tokens: " << n_tokens <<"\n";
+    };
 };
 
 /// \brief A latency in nanoseconds, as recorded by the loadgen.
diff --git a/loadgen/results.cc b/loadgen/results.cc
index 4c2f07ed67..d705d312fc 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -55,6 +55,10 @@ void PerformanceSummary::ProcessLatencies() {
                          max_latency);
   }
 
+  if (settings.use_token_latencies){
+    ProcessTokenLatencies();
+  }
+
   // MultiStream only after this point.
   if (settings.scenario != TestScenario::MultiStream) {
     return;
@@ -78,6 +82,45 @@ void PerformanceSummary::ProcessLatencies() {
   }
 }
 
+void PerformanceSummary::ProcessTokenLatencies() {
+  if (pr.token_results.first_token_latencies.empty()) {
+    return;
+  }
+  constexpr auto nTokenInvalid = std::numeric_limits<int64_t>::min();
+  token_count = 0;
+  for (auto n_tokens: pr.token_results.tokens_per_sample){
+    if (n_tokens != nTokenInvalid)
+      token_count += n_tokens;
+  }
+  QuerySampleLatency accumulated_first_token_latency = 0;
+  for (auto latency : pr.token_results.first_token_latencies) {
+    accumulated_first_token_latency += latency;
+  }
+  first_token_latency_mean = accumulated_first_token_latency / sample_count;
+  std::sort(pr.token_results.first_token_latencies.begin(), 
+    pr.token_results.first_token_latencies.end());
+  
+  token_target_latency_percentile.sample_latency =
+      pr.token_results.first_token_latencies[sample_count * token_target_latency_percentile.percentile];
+  first_token_latency_min = pr.token_results.first_token_latencies.front();
+  first_token_latency_max = pr.token_results.first_token_latencies.back();
+  for (auto& lp : token_latency_percentiles) {
+    assert(lp.percentile >= 0.0);
+    assert(lp.percentile < 1.0);
+    lp.sample_latency = pr.token_results.first_token_latencies[sample_count * lp.percentile];
+  }
+
+  if (settings.scenario == TestScenario::Server) {
+    // TODO: Maybe another target latency needs to be added?
+    QuerySampleLatency max_latency = settings.target_latency.count() + 1;
+    overlatency_first_token_count =
+        pr.token_results.first_token_latencies.end() -
+        std::lower_bound(pr.token_results.first_token_latencies.begin(), pr.token_results.first_token_latencies.end(),
+                         max_latency);
+  }
+
+}
+
 bool PerformanceSummary::EarlyStopping(std::string* recommendation) {
   recommendation->clear();
 
@@ -299,10 +342,10 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
 
   switch (settings.scenario) {
     case TestScenario::SingleStream: {
-      summary(DoubleToString(target_latency_percentile.percentile * 100, 0) +
-                  "th percentile latency (ns) : ",
-              target_latency_percentile.sample_latency);
-      break;
+        summary(DoubleToString(target_latency_percentile.percentile * 100, 0) +
+                    "th percentile latency (ns) : ",
+                target_latency_percentile.sample_latency);
+        break;
     }
     case TestScenario::MultiStream: {
       summary(DoubleToString(target_latency_percentile.percentile * 100, 0) +
@@ -332,6 +375,28 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
     }
   }
 
+  if (settings.use_token_latencies){
+    switch (settings.scenario) {
+      case TestScenario::SingleStream: {
+      summary(DoubleToString(token_target_latency_percentile.percentile * 100, 0) +
+                  "th first token percentile latency (ns) : ",
+              token_target_latency_percentile.sample_latency);
+      break;
+      }
+      case TestScenario::MultiStream: {
+        summary(DoubleToString(token_target_latency_percentile.percentile * 100, 0) +
+                    "th first token percentile latency (ns) : ",
+                token_target_latency_percentile.sample_latency);
+        break;
+      }
+      case TestScenario::Offline: {
+        double tokens_per_second = token_count / pr.max_latency;
+        summary("Tokens per second: ", tokens_per_second);
+        break;
+      }
+    }
+  }
+
   std::string min_duration_recommendation;
   std::string perf_constraints_recommendation;
   std::string early_stopping_recommendation;
@@ -414,6 +479,32 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
           lp.sample_latency);
     }
   }
+  if (settings.use_token_latencies){
+    summary("");
+    if (settings.scenario == TestScenario::SingleStream) {
+    double tps_w_lg = token_count / pr.final_query_issued_time;
+    double tps_wo_lg = ((double)token_count) / (QuerySampleLatencyToSeconds(sample_latency_mean) * sample_count);
+    summary("TPS w/ loadgen overhead         : " + DoubleToString(tps_w_lg));
+    summary("TPS w/o loadgen overhead        : " + DoubleToString(tps_wo_lg));
+    
+    } else if (settings.scenario == TestScenario::Server) {
+      double tps_as_completed =
+          token_count / pr.final_query_all_samples_done_time;
+      summary("Completed tokens per second    : ",
+              DoubleToString(tps_as_completed));
+    }
+
+    
+    summary("Min First Token latency (ns)    : ", first_token_latency_min);
+    summary("Max First Token latency (ns)    : ", first_token_latency_max);
+    summary("Mean First Token latency (ns)   : ", first_token_latency_mean);
+    for (auto& lp : token_latency_percentiles) {
+      summary(
+          DoubleToString(lp.percentile * 100) + " percentile latency (ns)   : ",
+          lp.sample_latency);
+    }
+    
+  }
 
   summary(
       "\n"
@@ -537,6 +628,28 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
                    "_percentile_latency_ns",
                lp.sample_latency);
   }
+  // Detailed first token latencies
+  if (settings.use_token_latencies){
+    MLPERF_LOG(detail, "result_first_token_min_latency_ns", first_token_latency_min);
+    MLPERF_LOG(detail, "result_first_token_max_latency_ns", first_token_latency_max);
+    MLPERF_LOG(detail, "result_first_token_mean_latency_ns", first_token_latency_mean);
+    for (auto& lp : token_latency_percentiles) {
+      MLPERF_LOG(detail,
+                  "result_" + DoubleToString(lp.percentile * 100) +
+                      "_percentile_latency_ns",
+                  lp.sample_latency);
+    }
+    double tps_w_lg = ((double)token_count) / pr.final_query_issued_time;
+    double tps_wo_lg= ((double)token_count) / (sample_latency_mean * sample_count);
+    MLPERF_LOG(detail, "result_token_throughput_with_loadgen_overhead", tps_w_lg);
+    MLPERF_LOG(detail, "result_token_throughput", tps_wo_lg);
+    double tpot = sample_count * (sample_latency_mean - first_token_latency_mean) / ((double)token_count);
+    MLPERF_LOG(detail, "result_time_to_output_token", tpot);
+    if (settings.scenario == TestScenario::Offline) {
+      double tokens_per_second = token_count / pr.max_latency;
+      MLPERF_LOG(detail, "result_tokens_per_second", tokens_per_second);
+    }
+  }
 #endif
 }
 
diff --git a/loadgen/results.h b/loadgen/results.h
index aa9e9e0173..69825a9d30 100644
--- a/loadgen/results.h
+++ b/loadgen/results.h
@@ -25,6 +25,13 @@ limitations under the License.
 namespace mlperf {
 namespace loadgen {
 
+/// \brief Contains the performance results for benchmarks that have
+/// token based metrics
+struct TokenPerformanceResults {
+  std::vector<QuerySampleLatency> first_token_latencies;
+  std::vector<int64_t> tokens_per_sample;
+};
+
 /// \brief Provides performance results that are independent of scenario
 /// and other context.
 struct PerformanceResult {
@@ -35,6 +42,7 @@ struct PerformanceResult {
   double final_query_scheduled_time;         // seconds from start.
   double final_query_issued_time;            // seconds from start.
   double final_query_all_samples_done_time;  // seconds from start.
+  TokenPerformanceResults token_results;
 };
 
 
@@ -72,6 +80,18 @@ struct PerformanceSummary {
   QuerySampleLatency early_stopping_latency_ss = 0;
   QuerySampleLatency early_stopping_latency_ms = 0;
 
+  // Set by ProcessTokenLatencies
+  size_t token_count = 0;
+  size_t overlatency_first_token_count = 0;
+  QuerySampleLatency first_token_latency_min;
+  QuerySampleLatency first_token_latency_max;
+  QuerySampleLatency first_token_latency_mean;
+
+  // Latency token target percentile
+  PercentileEntry token_target_latency_percentile{settings.target_latency_percentile};
+  PercentileEntry token_latency_percentiles[6] = {{.50}, {.90}, {.95},
+                                                  {.97}, {.99}, {.999}};
+
 #if defined(_WIN32) || defined(WIN32) || defined(_WIN64) || defined(WIN64)
   // MSVC complains if there is no explicit constructor.
   // (target_latency_percentile above depends on construction with settings)
@@ -81,6 +101,7 @@ struct PerformanceSummary {
       : sut_name(sut_name_arg), settings(settings_arg), pr(pr_arg){};
 #endif
   void ProcessLatencies();
+  void ProcessTokenLatencies();
 
   bool MinDurationMet(std::string* recommendation);
   bool EarlyStopping(std::string* recommendation);
@@ -91,7 +112,6 @@ struct PerformanceSummary {
   void LogSummary(AsyncSummary& summary);
   void LogDetail(AsyncDetail& detail);
 };
-
 }  // namespace loadgen
 }  // namespace mlperf
 
diff --git a/loadgen/test_settings.h b/loadgen/test_settings.h
index b359ae8438..ccae5327a7 100644
--- a/loadgen/test_settings.h
+++ b/loadgen/test_settings.h
@@ -262,6 +262,8 @@ struct TestSettings {
   uint64_t performance_issue_same_index = 0;
   /// \brief Overrides QSL->PerformanceSampleCount() when non-zero
   uint64_t performance_sample_count_override = 0;
+  /// \brief Measure token latencies
+  bool use_token_latencies = false;
   /**@}*/
 };
 
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 9d6cb423e1..0a4b9af6ea 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -48,7 +48,8 @@ TestSettingsInternal::TestSettingsInternal(
       performance_issue_same(requested.performance_issue_same),
       performance_issue_same_index(requested.performance_issue_same_index),
       performance_sample_count(0),
-      sample_concatenate_permutation(false){
+      sample_concatenate_permutation(false),
+      use_token_latencies(requested.use_token_latencies){
   // Target QPS, target latency, and max_async_queries.
   switch (requested.scenario) {
     case TestScenario::SingleStream:
@@ -672,6 +673,8 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
   lookupkv(model, scenario, "test05_sample_index_rng_seed", &test05_sample_index_rng_seed,
            nullptr);
   lookupkv(model, scenario, "test05_schedule_rng_seed", &test05_schedule_rng_seed, nullptr);
+  if (lookupkv(model, scenario, "use_token_latencies", &val, nullptr))
+    use_token_latencies = (val == 1) ? true : false;
 
   // keys that apply to SingleStream
   lookupkv(model, "SingleStream", "target_latency_percentile", nullptr,
diff --git a/loadgen/test_settings_internal.h b/loadgen/test_settings_internal.h
index e13f296708..6bc1c3cf39 100644
--- a/loadgen/test_settings_internal.h
+++ b/loadgen/test_settings_internal.h
@@ -82,6 +82,7 @@ struct TestSettingsInternal {
   uint64_t performance_sample_count;
 
   bool sample_concatenate_permutation;
+  bool use_token_latencies = false;
 };
 
 /// \brief A namespace of collections of FindPeakPerformance helper functions,

From 94b0cc48752b215f37b0ab211207e3a56cd2a7ae Mon Sep 17 00:00:00 2001
From: nv-alicheng <107440104+nv-alicheng@users.noreply.github.com>
Date: Mon, 8 Jan 2024 12:31:10 -0800
Subject: [PATCH 021/305] Update processorca for new rules and patching
 potential TTFT exploit. Enable GPU for Offline scenario in Llama-v2 Reference
 Implementation. (#1544)

* Add environment files for GPU

* Update flags to enable running on device=gpu

* Enable BS>1 on GPU, update processorca for new rules and first token workaround

* Dump outputs and add script to consolidate into single pickle file for analysis

* Add option to continue run if prior session was killed

* Small comment fix, fix default flags to match original reference implementation

* Generalize launch scripts for outside users, update README

* Minor fix: comments and default arguments

* Add accuracy target to README

* Add calibration dataset generation to processorca.py

* Update language/llama2-70b/README.md

Co-authored-by: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>

* Make calibration rng seed a kwarg

---------

Co-authored-by: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
---
 language/llama2-70b/Dockerfile             |  48 +++++++
 language/llama2-70b/README.md              |  79 +++++++++++-
 language/llama2-70b/SUT.py                 | 141 ++++++++++++++++-----
 language/llama2-70b/build.sh               |   8 ++
 language/llama2-70b/consolidate_results.py | 116 +++++++++++++++++
 language/llama2-70b/dataset.py             |  21 ++-
 language/llama2-70b/launch.sh              |  37 ++++++
 language/llama2-70b/processorca.py         |  44 ++++++-
 language/llama2-70b/run_accuracy.sh        |  22 ++++
 language/llama2-70b/run_offline.sh         |   2 -
 language/llama2-70b/with_the_same_user     |  39 ++++++
 11 files changed, 514 insertions(+), 43 deletions(-)
 create mode 100644 language/llama2-70b/Dockerfile
 create mode 100644 language/llama2-70b/build.sh
 create mode 100644 language/llama2-70b/consolidate_results.py
 create mode 100644 language/llama2-70b/launch.sh
 create mode 100644 language/llama2-70b/run_accuracy.sh
 create mode 100755 language/llama2-70b/with_the_same_user

diff --git a/language/llama2-70b/Dockerfile b/language/llama2-70b/Dockerfile
new file mode 100644
index 0000000000..b04910d732
--- /dev/null
+++ b/language/llama2-70b/Dockerfile
@@ -0,0 +1,48 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
+SHELL ["/bin/bash", "-c"]
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+
+ENV TZ=US/Pacific
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
+RUN rm -rf /var/lib/apt/lists/* && rm /etc/apt/sources.list.d/* \
+ && apt update \
+ && apt install -y --no-install-recommends build-essential autoconf \
+        libtool git ccache curl wget pkg-config sudo ca-certificates \
+        automake libssl-dev bc python3-dev python3-pip google-perftools \
+        gdb libglib2.0-dev clang sshfs libre2-dev libboost-dev \
+        libnuma-dev numactl sysstat sshpass ntpdate less iputils-ping \
+ && apt -y autoremove \
+ && apt remove -y cmake \
+ && apt install -y --no-install-recommends pkg-config zip g++ zlib1g-dev \
+        unzip libarchive-dev
+RUN apt install -y --no-install-recommends rsync
+
+# Install setuptools
+RUN python3 -m pip install --upgrade pip \
+    && python3 -m pip install --upgrade setuptools wheel virtualenv
+
+# Install conda
+WORKDIR /tmp
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.5.2-0-Linux-x86_64.sh \
+    && bash Miniconda3-* -b -p /opt/miniconda3
+ENV PATH="$PATH:/opt/miniconda3/bin"
+RUN conda create -n llama2-70b python=3.10
+RUN chmod -R 777 /opt/miniconda3
diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 7827fd4b6b..0fefec95fa 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -7,6 +7,9 @@
 
 
 ## Prepare environment
+
+For a CPU-only run:
+
 ```
 conda create -n llama2-70b python=3.9
 conda activate llama2-70b
@@ -26,9 +29,35 @@ git merge llm-server
 python -m pip install .
 ```
 
+For a GPU-based run:
+
+A dockerfile is provided, along with scripts to help launch it. First, add any docker volume mounts you want in
+`launch.sh`. There is a section at the top of the file that looks like:
+```
+# Add any volume mounts here with the following syntax
+# /path/to/src:/path/to/dir/in/container
+MOUNTS=(
+    $MLCOMMONS_REPO_PATH:$MLCOMMONS_REPO_PATH
+)
+```
+
+For example if you have a raid space located at `/raid/data` on your local machine, you can add it to the same path in the container like so:
+```
+# Add any volume mounts here with the following syntax
+# /path/to/src:/path/to/dir/in/container
+MOUNTS=(
+    $MLCOMMONS_REPO_PATH:$MLCOMMONS_REPO_PATH
+    /raid/data:/raid/data
+)
+```
+Once you have added all your mounts, launch the container with `bash launch.sh`.
+
+Inside the container, set up the environment with `bash build.sh`. This will install all the dependencies from the
+CPU-only setup, as well as any GPU versions for applicable libraries like PyTorch.
+
 
 ## Get Model
-+ For now, MLCommons is not hosting the checkpoing, so you must first go to [llama2-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to huggingface (if you don't have account, you'd need to create one). **Please note your authentication credentials** as you may be required to provide them when cloninng below
++ For now, MLCommons is not hosting the checkpoint, so you must first go to [llama2-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to huggingface (if you don't have account, you'd need to create one). **Please note your authentication credentials** as you may be required to provide them when cloninng below
 + Requires Git Large Files Storage
 ```
 export CHECKPOINT_PATH=${PWD}/Llama-2-70b-chat-hf
@@ -49,7 +78,7 @@ EXPORT_DIR=${PWD}/processed-openorca
 export DATASET_PATH=${PWD}/processed-data.pkl
 
 # Process the dataset according the Taskforce's agreed criteria
-python3 processorca.py --dataset_pq_path=${OPENORCA_PARQUET} --model_dir=${CHECKPOINT_PATH} --seqlen_limit=2048 --export_dir=${EXPORT_DIR} --num_total_samples=24576
+python3 processorca.py --dataset_pq_path=${OPENORCA_PARQUET} --model_dir=${CHECKPOINT_PATH} --seqlen_limit=1024 --export_dir=${EXPORT_DIR} --num_total_samples=24576
 
 mv ${EXPORT_DIR}/open_orca_gpt4_tokenized_llama.sampled_24576.pkl ${DATASET_PATH}
 ```
@@ -65,11 +94,24 @@ python -u main.py --scenario Offline \
                 --user-conf user.conf \
                 --total-sample-count 24576 \
                 --device cpu \
-		--dataset-path ${DATASET_PATH} \
+                --dataset-path ${DATASET_PATH} \
                 --output-log-dir offline-logs
 
 ```
 
+For a GPU-based run:
+```
+python3 -u main.py --scenario Offline \
+        --model-path ${CHECKPOINT_PATH} \
+        --mlperf-conf mlperf.conf \
+        --user-conf user.conf \
+        --total-sample-count 24576 \
+        --dataset-path ${DATASET_PATH} \
+        --output-log-dir offline-logs \
+        --dtype float32 \
+        --device cuda:0 2>&1 | tee offline_performance_log.log
+```
+
 ### Server
 ```
 python -u main.py --scenario Server \
@@ -82,6 +124,8 @@ python -u main.py --scenario Server \
                 --output-log-dir server-logs
 ```
 
+The ServerSUT was not tested for GPU runs.
+
 
 ## Run Accuracy Benchmarks
 
@@ -89,6 +133,8 @@ python -u main.py --scenario Server \
 ```
 OUTPUT_LOG_DIR=offline-accuracy-logs
 
+mkdir -p "run_outputs"  # The script will dump all the outputs to 'run_outputs'.
+
 python -u main.py --scenario Offline \
                 --model-path ${CHECKPOINT_PATH} \
                 --accuracy \
@@ -105,8 +151,23 @@ if [ -e ${ACCURACY_LOG_FILE} ]; then
         python evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
                 --mlperf-accuracy-file ${ACCURACY_LOG_FILE} --dataset-file ${DATASET_PATH} --dtype int32
 fi
+
+# Optional: Create a pickled pandas DataFrame that is the original dataset with extra columns with output data from the
+# accuracy run. The following columns will be added:
+# - "gen_output_tok_id": A list of ints representing the tokenized output sequence.
+# - "gen_output_text": A str representing the untokenized output sequence.
+# - "gen_output_tok_len": An int representing the number of output tokens.
+# - "rouge1": The rouge1 score for this sample
+# - "rouge2": The rouge2 score for this sample
+# - "rougeL": The rougeL score for this sample
+# This file will by default be saved to 'full_output.pkl'. You can modify this with --output-pkl-path.
+python consolidate_results.py --dataset-path ${DATASET_PATH} --model-dir ${CHECKPOINT_PATH}
 ```
 
+For the GPU run - The above steps have been automated in `run_accuracy.sh`. You can also modify this script to use
+`--device cpu` to adapt it to a CPU-only run.
+
+
 ### Server
 ```
 OUTPUT_LOG_DIR=server-accuracy-logs
@@ -129,3 +190,15 @@ if [ -e ${ACCURACY_LOG_FILE} ]; then
 fi
 ```
 
+The ServerSUT was not tested for GPU runs. You can try setting `--device cuda:0`, but YMMV.
+
+
+## Accuracy Target
+Running the GPU implementation in FP32 precision resulted in the following FP32 accuracy targets (normalized to a 0-100
+scale from a 0.0-1.0 scale):
+- Rouge1: 43.88
+- Rouge2: 21.7108
+- RougeL: 28.2502
+- RougeLsum: 41.4821
+
+This was run an 8xH100 node. Total runtime was ~4.5 days.
diff --git a/language/llama2-70b/SUT.py b/language/llama2-70b/SUT.py
index d29f789bf7..9cf009c084 100644
--- a/language/llama2-70b/SUT.py
+++ b/language/llama2-70b/SUT.py
@@ -8,11 +8,15 @@
 from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
 from transformers.generation.streamers import BaseStreamer
 
+import pickle
+import time
 import threading
+import tqdm
 import queue
 
 import logging
 from typing import TYPE_CHECKING, Optional, List
+from pathlib import Path
 
 import mlperf_loadgen as lg
 from dataset import Dataset
@@ -74,11 +78,26 @@ def get_out_tokens(self):
 
 
 class SUT():
-    def __init__(self, model_path=None, dtype="bfloat16", device="cpu", total_sample_count=24576, dataset_path=None, workers=1):
+    def __init__(self,
+                 model_path=None,
+                 dtype="bfloat16",
+                 device="cpu",
+                 batch_size=None,
+                 total_sample_count=24576,
+                 dataset_path=None,
+                 use_cached_outputs=False,  # Set this to True *only for test accuracy runs* in case your prior session was killed partway through
+                 workers=1):
 
         self.model_path = model_path or "meta-llama/Llama-2-70b-chat-hf"
         self.device = device
 
+        if not batch_size:
+            if device == "cpu":
+                batch_size = 1
+            else:
+                batch_size = 32  # Reduce to 8 if using 4 GPUs, 16 for 8.
+        self.batch_size = batch_size
+
         # dtype
         if dtype == 'bfloat16':
             self.amp_enabled = True
@@ -94,7 +113,10 @@ def __init__(self, model_path=None, dtype="bfloat16", device="cpu", total_sample
             assert torch.cuda.is_available(), "torch gpu is not available, exiting..."
 
         self.dataset_path = dataset_path
-        self.data_object = Dataset(self.model_path, dataset_path=self.dataset_path, total_sample_count=total_sample_count)
+        self.data_object = Dataset(self.model_path,
+                                   dataset_path=self.dataset_path,
+                                   total_sample_count=total_sample_count,
+                                   device=self.device)
         self.qsl = lg.ConstructQSL(self.data_object.total_sample_count, self.data_object.perf_count,
                                    self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
 
@@ -104,9 +126,12 @@ def __init__(self, model_path=None, dtype="bfloat16", device="cpu", total_sample
         self.worker_threads = [None] * self.num_workers
         self.query_queue = queue.Queue()
 
+        self.use_cached_outputs = use_cached_outputs
+        self.sample_counter = 0
+        self.sample_counter_lock = threading.Lock()
+
 
     def start(self):
-        
         # Create worker threads
         for j in range(self.num_workers):
             worker = threading.Thread(target=self.process_queries)
@@ -129,38 +154,90 @@ def process_queries(self):
             if qitem is None:
                 break
 
-            # TODO: If batching, call collator to batch the inputs here
-            input_ids_tensor = self.data_object.input_ids[qitem.index]
-            input_masks_tensor = self.data_object.attention_masks[qitem.index]
-            input_len = [self.data_object.input_lens[qitem.index]]
-
-
-            pred_output_tokens = self.model.generate(
-                                                input_ids=input_ids_tensor,
-                                                attention_mask=input_masks_tensor,
-                                                pad_token_id=self.tokenizer.pad_token_id,
-                                                **gen_kwargs
-                                                )
-
-            processed_output = self.data_object.postProcess(pred_output_tokens, input_len)
-
-            response_array = array.array("B", processed_output[0].tobytes())
-            bi = response_array.buffer_info()
-            response = [lg.QuerySampleResponse(
-                qitem.id, bi[0], bi[1])]
-            lg.QuerySamplesComplete(response)
+            query_ids = [q.index for q in qitem]
+
+            fname = "q" + "_".join([str(i) for i in query_ids])
+            fname = f"run_outputs/{fname}.pkl"
+            _p = Path(fname)
+            if self.use_cached_outputs and _p.exists():
+                # Read cache
+                with _p.open(mode="rb") as f:
+                    d = pickle.load(f)
+                processed_output = d["outputs"]
+                tik1 = None
+                tik2 = None
+                tik3 = None
+                tok = None
+            else:
+                # Construct / collate batch
+                max_seq_len = 1024
+
+                tik1 = time.time()
+
+                input_ids_tensor = []
+                input_masks_tensor = []
+                input_len = []
+                for q in qitem:
+                    input_ids_tensor.append(pad(self.data_object.input_ids[q.index],
+                                                (0, max_seq_len - self.data_object.input_lens[q.index], 0, 0),
+                                                value=self.tokenizer.pad_token_id))
+                    input_masks_tensor.append(pad(self.data_object.attention_masks[q.index],
+                                                  (0, max_seq_len - self.data_object.input_lens[q.index], 0, 0),
+                                                 value=0))
+                    input_len.append(self.data_object.input_lens[q.index])
+                input_ids_tensor = torch.cat(input_ids_tensor)
+                input_masks_tensor = torch.cat(input_masks_tensor)
+
+                assert input_ids_tensor.shape == input_masks_tensor.shape
+                assert input_ids_tensor.shape[0] <= self.batch_size
+
+                tik2 = time.time()
+
+                pred_output_tokens = self.model.generate(
+                    input_ids=input_ids_tensor,
+                    attention_mask=input_masks_tensor,
+                    pad_token_id=self.tokenizer.pad_token_id,
+                    **gen_kwargs
+                )
+
+                tik3 = time.time()
+
+                processed_output = self.data_object.postProcess(pred_output_tokens,
+                                                                input_seq_lens=input_len,
+                                                                query_id_list=query_ids)
+
+            for i in range(len(qitem)):
+                response_array = array.array("B", processed_output[i].tobytes())
+                bi = response_array.buffer_info()
+                response = [lg.QuerySampleResponse(qitem[i].id, bi[0], bi[1])]
+                lg.QuerySamplesComplete(response)
+
+            tok = time.time()
+
+            with self.sample_counter_lock:
+                self.sample_counter += len(qitem)
+                print(f"Samples run: {self.sample_counter}")
+                if tik1:
+                    print(f"\tBatchMaker time: {tik2 - tik1}")
+                    print(f"\tInference time: {tik3 - tik2}")
+                    print(f"\tPostprocess time: {tok - tik3}")
+                    print(f"\t==== Total time: {tok - tik1}")
+                else:
+                    print(f"\tLoaded from cache: {_p}")
 
 
     def load_model(self):
         self.model = LlamaForCausalLM.from_pretrained(
             self.model_path,
-            device_map= "auto" if self.device=="cpu" else None,
-            low_cpu_mem_usage=True if self.device=="cpu" else False,
+            device_map="auto",
+            low_cpu_mem_usage=True,
             torch_dtype=self.amp_dtype
         )
+        print("Loaded model")
 
         self.device = torch.device(self.device)
-        self.model.to(self.device)
+        if self.device == "cpu":
+            self.model = self.model.to(self.device)  # Force CPU if your system has GPU and you specifically want CPU-only run
 
         self.model.eval()
         self.model = self.model.to(memory_format=torch.channels_last)
@@ -172,6 +249,7 @@ def load_model(self):
             use_fast=False,)
 
         self.tokenizer.pad_token = self.tokenizer.eos_token
+        print("Loaded tokenizer")
 
     def get_sut(self):
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
@@ -191,8 +269,11 @@ def issue_queries(self, query_samples):
         list_prompts_tokens = []
         list_prompts_attn_masks = []
 
-        for i in range(len(query_samples)):
-            self.query_queue.put(query_samples[i])
+        print(f"IssueQuery started with {len(query_samples)} samples")
+        while len(query_samples) > 0:
+            self.query_queue.put(query_samples[:self.batch_size])
+            query_samples = query_samples[self.batch_size:]
+        print(f"IssueQuery done")
 
 
     def flush_queries(self):
@@ -245,7 +326,7 @@ def process_queries(self):
             qitem = self.query_queue.get()
             if qitem is None:
                 break
-            
+
             input_ids_tensor = self.data_object.input_ids[qitem.index]
             input_masks_tensor = self.data_object.attention_masks[qitem.index]
 
@@ -267,7 +348,7 @@ def process_queries(self):
             response = [lg.QuerySampleResponse(
                 qitem.id, bi[0], bi[1])]
             lg.QuerySamplesComplete(response)
-    
+
 
     def issue_queries(self, query_samples):
 
diff --git a/language/llama2-70b/build.sh b/language/llama2-70b/build.sh
new file mode 100644
index 0000000000..87afb992fa
--- /dev/null
+++ b/language/llama2-70b/build.sh
@@ -0,0 +1,8 @@
+set -e
+
+conda install pybind11==2.10.4 -c conda-forge -y
+conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch-nightly -c nvidia
+python -m pip install transformers==4.31.0 nltk==3.8.1 evaluate==0.4.0 absl-py==1.4.0 rouge-score==0.1.2 sentencepiece==0.1.99 accelerate==0.21.0
+
+
+cd ../../loadgen && python3 -m pip install .
diff --git a/language/llama2-70b/consolidate_results.py b/language/llama2-70b/consolidate_results.py
new file mode 100644
index 0000000000..ad5fbe4119
--- /dev/null
+++ b/language/llama2-70b/consolidate_results.py
@@ -0,0 +1,116 @@
+import argparse
+import evaluate
+import glob
+import nltk
+import numpy as np
+import os
+import pandas as pd
+import pickle
+
+from pathlib import Path
+from transformers import LlamaTokenizerFast
+from tqdm import tqdm
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset-path", type=str, default=None, help="Path to .pkl generated by processorca.py")
+    parser.add_argument("--run-outputs", type=str, default="run_outputs", help="Output dir generated by accuracy run.")
+    parser.add_argument("--model-dir", type=str, default=None, help="Path to Llamav2 HuggingFace repo clone")
+    parser.add_argument("--output-pkl-path", type=str, default="full_output.pkl", help="Path to dump output to")
+    args = parser.parse_args()
+    return args
+
+
+def load_dataset(p: os.PathLike):
+    print(f"Loading from {p}...")
+    return pd.read_pickle(p)
+
+
+def load_run_outputs(p: os.PathLike):
+    g = glob.glob(str(Path(p) / "q*.pkl"))
+
+    by_query_idx = dict()
+    for pkl_file in g:
+        print(f"Loading from {pkl_file}...")
+        with open(pkl_file, 'rb') as f:
+            d = pickle.load(f)
+        assert len(d["query_ids"]) == len(d["outputs"])
+
+        for i in range(len(d["query_ids"])):
+            qid = d["query_ids"][i]
+            assert qid not in by_query_idx
+            by_query_idx[qid] = d["outputs"][i]
+
+    return by_query_idx
+
+
+def main(args):
+    # Set up decode and evaluation objects
+    tokenizer = LlamaTokenizerFast.from_pretrained(args.model_dir)
+    metric = evaluate.load("rouge")
+    nltk.download("punkt")
+
+    # Load Data
+    df = load_dataset(args.dataset_path)
+    run_outputs = load_run_outputs(args.run_outputs)
+    assert len(run_outputs) == 24576
+
+    # Set up columns to add
+    output_tok_ids_col = [None] * 24576
+    output_text_col = [None] * 24576
+    output_lens = [None] * 24576
+
+    # Process data
+    no_eos_ids = []
+    for qid, output in tqdm(run_outputs.items()):
+        L = list(output)
+        # Prune trailing 2s (EOS token)
+        try:
+            first2 = L.index(2)
+            L = L[:first2]
+        except ValueError:
+            # Do nothing
+            no_eos_ids.append(qid)
+
+        assert L[-1] != 2
+        output_tok_ids_col[qid] = L
+        output_lens[qid] = len(L)
+
+        # Decode tokens
+        output_text_col[qid] = tokenizer.decode(output_tok_ids_col[qid], skip_special_tokens=True)
+    print(f"Found {len(no_eos_ids)} samples with no EOS token")
+
+    print("Calculating rouge scores...")
+    _preproc = lambda s: "\n".join(nltk.sent_tokenize(s.strip()))
+    preds = list(map(_preproc, output_text_col))
+    targets = list(map(_preproc, list(df["output"])))
+    rouge_scores = metric.compute(predictions=preds,
+                                  references=targets,
+                                  use_stemmer=True,
+                                  use_aggregator=False)
+
+    assert len(rouge_scores["rouge1"]) == 24576
+    assert len(rouge_scores["rouge2"]) == 24576
+    assert len(rouge_scores["rougeL"]) == 24576
+
+    agg = {k: round(np.mean(v) * 100, 4) for k, v in rouge_scores.items()}
+    print(agg)
+    print("Avg output seqlen:", np.mean(output_lens))
+
+    # Set columns
+    df["gen_output_tok_id"] = output_tok_ids_col
+    df["gen_output_text"] = output_text_col
+    df["gen_output_tok_len"] = output_lens
+    df["rouge1"] = rouge_scores["rouge1"]
+    df["rouge2"] = rouge_scores["rouge2"]
+    df["rougeL"] = rouge_scores["rougeL"]
+
+    p = Path(args.output_pkl_path)
+    p.parent.mkdir(exist_ok=True)
+    df.to_pickle(p)
+    print(f"Dumped to {p}")
+
+
+if __name__ == "__main__":
+    main(get_args())
diff --git a/language/llama2-70b/dataset.py b/language/llama2-70b/dataset.py
index 9b54520509..a59fd7f552 100644
--- a/language/llama2-70b/dataset.py
+++ b/language/llama2-70b/dataset.py
@@ -10,6 +10,7 @@
 import io
 #import utils
 import copy
+import pickle
 
 import logging
 logging.basicConfig(level=logging.INFO)
@@ -46,6 +47,7 @@ def load_processed_dataset(self):
         if not os.path.isfile(self.dataset_path):
             log.warn("Processed pickle file {} not found. Please check that the path is correct".format(self.dataset_path))
 
+        print("Loading dataset...")
         import pandas as pd
         processed_data = pd.read_pickle(self.dataset_path)
 
@@ -61,12 +63,14 @@ def load_processed_dataset(self):
             self.input_ids.append(input_ids)
             self.attention_masks.append(attn_mask)
             self.input_lens.append(input_ids.shape[-1])
+        print("Finished loading dataset.")
 
 
     def postProcess(self, out_tokens, input_seq_lens=None, query_id_list=None, sample_index_list=None):
         """ Postprocesses output prediction """
 
         #TODO: Create response object in postProcess(?)
+        """
         preds = []
         for i in range(out_tokens.shape[0]):
             #pred = out_tokens[i].reshape(-1).cpu().numpy() # Slice up to original input length as below?
@@ -74,8 +78,21 @@ def postProcess(self, out_tokens, input_seq_lens=None, query_id_list=None, sampl
             input_len = input_seq_lens[i] if input_seq_lens else 0
             pred = out_tokens[i, input_len:].reshape(-1).cpu().numpy()
             preds.append(pred)
-        
-        return preds
+        """
+        # Everything is padded to max_len (1024), so prune the input and parse to numpy
+        output_seq = out_tokens[:, 1024:].cpu().numpy()
+        assert len(query_id_list) == output_seq.shape[0]
+
+        # Save outputs
+        fname = "q" + "_".join([str(i) for i in query_id_list])
+        fname = f"run_outputs/{fname}.pkl"
+        with open(fname, mode='wb') as f:
+            d = {"query_ids": query_id_list,
+                 "outputs": output_seq}
+            print(f"Saving outputs to {fname}")
+            pickle.dump(d, f)
+
+        return output_seq
 
     def LoadSamplesToRam(self, sample_list):
         pass
diff --git a/language/llama2-70b/launch.sh b/language/llama2-70b/launch.sh
new file mode 100644
index 0000000000..c3389c5162
--- /dev/null
+++ b/language/llama2-70b/launch.sh
@@ -0,0 +1,37 @@
+#!/bin/bash
+
+MLCOMMONS_REPO_PATH="$(dirname "$(dirname "$PWD")")"
+
+# Add any volume mounts here with the following syntax
+# /path/to/src:/path/to/dir/in/container
+MOUNTS=(
+    $MLCOMMONS_REPO_PATH:$MLCOMMONS_REPO_PATH
+)
+
+# Set up docker environment file for current user
+rm -f .docker_env
+echo "CI_BUILD_USER=`id -u -n`" >> .docker_env
+echo "CI_BUILD_UID=`id -u`" >> .docker_env
+echo "CI_BUILD_GROUP=`id -g -n`" >> .docker_env
+echo "CI_BUILD_GID=`id -g`" >> .docker_env
+cat .docker_env
+
+# Build container
+docker build . -t llm/gpubringup
+
+# Build mount flags
+declare -a MOUNT_FLAGS
+for _mount in ${MOUNTS[@]}; do
+    _split=($(echo $_mount | tr ':' '\n'));
+    MOUNT_FLAGS+=("--mount type=bind,source=${_split[0]},target=${_split[1]}");
+done
+
+set -x
+nvidia-docker run -it --rm --net=host --runtime=nvidia --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
+  --cap-add=SYS_PTRACE --cap-add=SYS_ADMIN --cap-add=DAC_READ_SEARCH \
+  --security-opt seccomp=unconfined \
+  -w $PWD \
+  --env-file `pwd`/.docker_env \
+  ${MOUNT_FLAGS[*]} \
+  llm/gpubringup \
+  bash ./with_the_same_user
diff --git a/language/llama2-70b/processorca.py b/language/llama2-70b/processorca.py
index 0cc9c3f8ab..91dec5d510 100644
--- a/language/llama2-70b/processorca.py
+++ b/language/llama2-70b/processorca.py
@@ -19,6 +19,7 @@
 import pandas as pd
 import numpy as np
 from dataclasses import dataclass
+from functools import partial
 from pathlib import Path
 from transformers import LlamaTokenizerFast
 from typing import Dict
@@ -41,6 +42,19 @@ def is_english(s):
     return True
 
 
+def _tokenize_helper(x, llama_tokenizer=None, append_response_init_token=True):
+    if not isinstance(x, str):
+        return []
+
+    tokens = llama_tokenizer(x)["input_ids"]
+
+    if append_response_init_token:
+        # Workaround to enable cheat checking for first token: Llama always outputs token 29871 first
+        # It is possible for submitters to just immediately output this token to achieve a very fast TTFT.
+        tokens.append(29871)
+    return tokens
+
+
 @dataclass
 class Keyphrase:
     col: str
@@ -53,11 +67,13 @@ class OpenOrcaDatasetGenerator:
     def __init__(self,
                  pq_path: os.PathLike,
                  model_dir: os.PathLike,
-                 io_token_limit: int):
+                 io_token_limit: int,
+                 calibration_subset_size: int = 1000):
         self.pq_path = Path(pq_path)
         self.model_dir = Path(model_dir)
         self.io_token_limit = io_token_limit
         self.keyphrases = []
+        self.calibration_subset_size = calibration_subset_size
 
     def load_parquet(self) -> pd.DataFrame:
         llama_tokenizer = LlamaTokenizerFast.from_pretrained(self.model_dir)
@@ -67,8 +83,11 @@ def load_parquet(self) -> pd.DataFrame:
         print(f"Tokenizing input")
         df.rename(columns={'response': 'output'}, inplace=True)
         df['input'] = df.apply(format_llama_input, axis=1)
-        df['tok_input'] = df['input'].apply(lambda x: llama_tokenizer(x)['input_ids'] if isinstance(x, str) else [])
-        df['tok_output'] = df['output'].apply(lambda x: llama_tokenizer(x)['input_ids'] if isinstance(x, str) else [])
+
+        input_tokenizer = partial(_tokenize_helper, llama_tokenizer=llama_tokenizer)
+        output_tokenizer = partial(_tokenize_helper, llama_tokenizer=llama_tokenizer, append_response_init_token=False)
+        df['tok_input'] = df['input'].apply(input_tokenizer)
+        df['tok_output'] = df['output'].apply(output_tokenizer)
         tok = time.time()
         print(f"Loaded parquet and tokenized in {tok-tik} sec.")
         return df
@@ -167,7 +186,11 @@ def sample(self, dfs_by_origin: Dict[str, pd.DataFrame], n_total, rng_seed: int
         sampled_df = sampled_df.reset_index(drop=True)
         return sampled_df
 
-    def generate(self, export_dir: os.PathLike, n_samples: int = 24576, use_cached: bool = True):
+    def generate(self,
+                 export_dir: os.PathLike,
+                 n_samples: int = 24576,
+                 use_cached: bool = True,
+                 calib_rng_seed: int = 12345):
         export_dir = Path(export_dir)
         if not export_dir.exists():
             print(f"Creating {export_dir}")
@@ -208,6 +231,13 @@ def generate(self, export_dir: os.PathLike, n_samples: int = 24576, use_cached:
         sampled_fpath = export_dir / f"open_orca_gpt4_tokenized_llama.sampled_{n_samples}.pkl"
         sampled_df.to_pickle(sampled_fpath)
 
+        # Calibration dataset
+        calib_ds = sampled_df.sample(n=self.calibration_subset_size,
+                                     random_state=calib_rng_seed)
+        calib_ds = calib_ds.reset_index(drop=True)
+        calib_fpath = export_dir / f"open_orca_gpt4_tokenized_llama.calibration_{self.calibration_subset_size}.pkl"
+        calib_ds.to_pickle(calib_fpath)
+
 
 def parse_arguments():
     parser = argparse.ArgumentParser()
@@ -215,11 +245,12 @@ def parse_arguments():
                         default='/raid/data/mlperf-llm/OpenOrca/1M-GPT4-Augmented.parquet',
                         help="the path to the open_orca GPT4 parquet.")
     parser.add_argument('--model_dir', type=str, default='/raid/data/mlperf-llm/Llama-2-70b-chat-hf')
-    parser.add_argument('--seqlen_limit', type=int, default=2048, help="Upper limit of the input/output sequence lengths")
+    parser.add_argument('--seqlen_limit', type=int, default=1024, help="Upper limit of the input/output sequence lengths")
     parser.add_argument('--export_dir', type=str,
                         default="/raid/data/mlperf-llm/OpenOrca/llama/filtered",
                         help="Path to the output pkl file.")
     parser.add_argument('--num_total_samples', type=int, default=24576, help="Number of samples to generate")
+    parser.add_argument('--calibration_subset_size', type=int, default=1000, help="Number of samples for calibration subset")
     return parser.parse_args()
 
 
@@ -229,6 +260,7 @@ def parse_arguments():
         pq_path=args.dataset_pq_path,
         model_dir=args.model_dir,
         io_token_limit=args.seqlen_limit,
+        calibration_subset_size=args.calibration_subset_size,
     )
     ds_gen.generate(
         export_dir=args.export_dir,
@@ -236,4 +268,4 @@ def parse_arguments():
     )
 
     # Sample command to run:
-    # python3 processorca.py --dataset_pq_path=/raid/data/mlperf-llm/OpenOrca/1M-GPT4-Augmented.parquet --model_dir=/raid/data/mlperf-llm/Llama-2-70b-chat-hf --seqlen_limit=2048 --export_dir=/raid/data/mlperf-llm/OpenOrca/llama/filtered --num_total_samples=24576
+    # python3 processorca.py --dataset_pq_path=/raid/data/mlperf-llm/OpenOrca/1M-GPT4-Augmented.parquet --model_dir=/raid/data/mlperf-llm/Llama-2-70b-chat-hf --seqlen_limit=1024 --export_dir=/raid/data/mlperf-llm/OpenOrca/llama/filtered --num_total_samples=24576
diff --git a/language/llama2-70b/run_accuracy.sh b/language/llama2-70b/run_accuracy.sh
new file mode 100644
index 0000000000..b4f7f8ad96
--- /dev/null
+++ b/language/llama2-70b/run_accuracy.sh
@@ -0,0 +1,22 @@
+CHECKPOINT_PATH="${CHECKPOINT_PATH:-meta-llama/Llama-2-70b-chat-hf}"
+DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
+
+mkdir -p "run_outputs"
+
+python3 -u main.py --scenario Offline \
+        --model-path ${CHECKPOINT_PATH} \
+        --accuracy \
+        --mlperf-conf mlperf.conf \
+        --user-conf user.conf \
+        --total-sample-count 24576 \
+        --dataset-path ${DATASET_PATH} \
+        --output-log-dir offline_accuracy_loadgen_logs \
+        --dtype float32 \
+        --device cuda:0 2>&1 | tee offline_accuracy_log.log
+
+python3 evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
+        --mlperf-accuracy-file offline_accuracy_loadgen_logs/mlperf_log_accuracy.json \
+        --dataset-file ${DATASET_PATH} \
+        --dtype int32
+
+python3 consolidate_results.py --dataset-path ${DATASET_PATH} --model-dir ${CHECKPOINT_PATH}
diff --git a/language/llama2-70b/run_offline.sh b/language/llama2-70b/run_offline.sh
index 2ea28c4369..7153ea7cab 100644
--- a/language/llama2-70b/run_offline.sh
+++ b/language/llama2-70b/run_offline.sh
@@ -1,5 +1,3 @@
-
-
 CHECKPOINT_PATH="${CHECKPOINT_PATH:-meta-llama/Llama-2-70b-chat-hf}"
 DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
 
diff --git a/language/llama2-70b/with_the_same_user b/language/llama2-70b/with_the_same_user
new file mode 100755
index 0000000000..6b98baf7dc
--- /dev/null
+++ b/language/llama2-70b/with_the_same_user
@@ -0,0 +1,39 @@
+#!/usr/bin/env bash
+# wkong: manually set the user info in env first
+
+set -ex
+
+if [ -z "$@" ]; then
+  COMMAND=(bash)
+else
+  COMMAND=("$@")
+fi
+
+apt-get update && apt-get install -y sudo
+
+getent group "${CI_BUILD_GID}" || addgroup --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}"
+getent passwd "${CI_BUILD_UID}" || adduser --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" --gecos "${CI_BUILD_USER} (generated by with_the_same_user script)" --disabled-password --quiet "${CI_BUILD_USER}"
+
+usermod -a -G dip "${CI_BUILD_USER}"
+usermod -a -G sudo "${CI_BUILD_USER}"
+usermod -a -G root "${CI_BUILD_USER}"
+
+echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+mkdir -p /home/"${CI_BUILD_USER}"
+touch /home/"${CI_BUILD_USER}"/.bashrc
+echo 'export PATH="$PATH:/opt/miniconda3/bin"' >> /home/"${CI_BUILD_USER}"/.bashrc
+
+sudo -H -u "#${CI_BUILD_UID}" --preserve-env \
+  PATH="${PATH}" \
+  LD_LIBRARY_PATH="${LD_LIBRARY_PATH}" \
+  PYTHONPATH="${PYTHONPATH}" \
+  bash -c "conda init bash"
+
+echo 'conda activate llama2-70b' >> /home/"${CI_BUILD_USER}"/.bashrc
+
+
+sudo -H -u "#${CI_BUILD_UID}" --preserve-env \
+  PATH="${PATH}" \
+  LD_LIBRARY_PATH="${LD_LIBRARY_PATH}" \
+  PYTHONPATH="${PYTHONPATH}" \
+  ${COMMAND[@]}

From f4e5458a1c82549438d1a1484fd593943ac8fdbc Mon Sep 17 00:00:00 2001
From: rachithaps <109756569+rachithaps@users.noreply.github.com>
Date: Mon, 8 Jan 2024 12:32:00 -0800
Subject: [PATCH 022/305] Remove generator seed (#1542)

---
 text_to_image/backend_pytorch.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/text_to_image/backend_pytorch.py b/text_to_image/backend_pytorch.py
index 2455fa21e2..e75a78f42d 100644
--- a/text_to_image/backend_pytorch.py
+++ b/text_to_image/backend_pytorch.py
@@ -20,7 +20,6 @@ def __init__(
         device="cuda",
         precision="fp32",
         negative_prompt="normal quality, low quality, worst quality, low res, blurry, nsfw, nude",
-        seed=42
     ):
         super(BackendPytorch, self).__init__()
         self.model_path = model_path
@@ -45,7 +44,6 @@ def __init__(
         self.steps = steps
         self.negative_prompt = negative_prompt
         self.max_length_neg_prompt = 77
-        self.seed = seed
 
     def version(self):
         return torch.__version__
@@ -345,7 +343,6 @@ def predict(self, inputs):
                     num_inference_steps=self.steps,
                     output_type="pt",
                     latents=latents_input,
-                    generator=torch.Generator(device=self.device).manual_seed(self.seed)
                 ).images[0]
                 images.append(image)
         return images

From e82d0c976be06c9e6a3a1236acec003b48e4cb93 Mon Sep 17 00:00:00 2001
From: ljk3210 <ljk321_0@hotmail.com>
Date: Tue, 5 Dec 2023 04:11:35 +0000
Subject: [PATCH 023/305] Fix cibuildwheel

---
 .github/workflows/build_wheels.yml       | 5 +----
 pyproject.toml => loadgen/pyproject.toml | 4 ++++
 2 files changed, 5 insertions(+), 4 deletions(-)
 rename pyproject.toml => loadgen/pyproject.toml (59%)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 71dd4c8cdf..3c14198e81 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -18,13 +18,10 @@ jobs:
       - uses: actions/setup-python@v3
 
       - name: Install requirements
-        run: python -m pip install cibuildwheel==2.11.4 twine==4.0.2
+        run: python -m pip install cibuildwheel==2.16.2 twine==4.0.2
 
       - name: Build wheels
         run: python -m cibuildwheel loadgen/ --output-dir wheels
-        env:
-          CIBW_ENVIRONMENT: "CFLAGS='-std=c++14'"
-          CIBW_BUILD: 'cp3{7,8,9,10,11}-*'
 
       - uses: actions/upload-artifact@v3
         with:
diff --git a/pyproject.toml b/loadgen/pyproject.toml
similarity index 59%
rename from pyproject.toml
rename to loadgen/pyproject.toml
index 45eec865bc..f62b2c5f7a 100755
--- a/pyproject.toml
+++ b/loadgen/pyproject.toml
@@ -1,3 +1,7 @@
 [build-system]
 requires = ["setuptools>=42", "wheel", "pybind11==2.11.1"]
 build-backend = "setuptools.build_meta:__legacy__"
+
+[tool.cibuildwheel]
+environment = "CFLAGS='-std=c++14'"
+build = "cp3{7,8,9,10,11}-*"

From 31dcb5797c12801e07244084f3a51987df1e8206 Mon Sep 17 00:00:00 2001
From: mlcommons-bot <74634038+mlcommons-bot@users.noreply.github.com>
Date: Tue, 9 Jan 2024 20:34:37 -0600
Subject: [PATCH 024/305] =?UTF-8?q?=F0=9F=94=84=20synced=20local=20'tools/?=
 =?UTF-8?q?submission/power/power=5Fchecker.py'=20with=20remote=20'complia?=
 =?UTF-8?q?nce/check.py'=20(#1513)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: mlcommons-bot <null>
---
 tools/submission/power/power_checker.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/tools/submission/power/power_checker.py b/tools/submission/power/power_checker.py
index 6f19e06e76..5adcd197c6 100755
--- a/tools/submission/power/power_checker.py
+++ b/tools/submission/power/power_checker.py
@@ -49,6 +49,9 @@ class CheckerWarning(Exception):
     "YokogawaWT330": 52,
     "YokogawaWT330E": 52,
     "YokogawaWT330_multichannel": 77,
+    "YokogawaWT210_DC": 508,
+    "YokogawaWT310_DC": 549,
+    "YokogawaWT330_DC": 586,
 }
 
 RANGING_MODE = "ranging"
@@ -81,7 +84,7 @@ class CheckerWarning(Exception):
     re.compile(r"Uncertainty \d+.\d+%, which is above 1.00% limit for the last sample!")
 ]
 
-TIME_DELTA_TOLERANCE = 500  # in milliseconds
+TIME_DELTA_TOLERANCE = 800  # in milliseconds
 
 
 def _normalize(path: str) -> str:
@@ -393,8 +396,8 @@ def get_avg_power(power_path: str, run_path: str) -> Tuple[float, float]:
         )
 
         # convert to UTC
-        power_begin = datetime.fromtimestamp(power_begin, tz = timezone.utc)
-        power_end = datetime.fromtimestamp(power_end, tz = timezone.utc)
+        power_begin = datetime.fromtimestamp(power_begin, tz=timezone.utc)
+        power_end = datetime.fromtimestamp(power_end, tz=timezone.utc)
 
         detail_log_fname = os.path.join(run_path, "mlperf_log_detail.txt")
         datetime_format = "%m-%d-%Y %H:%M:%S.%f"
@@ -654,12 +657,12 @@ def get_msg_without_time(line: str) -> Optional[str]:
             continue
         if (not start_ranging_time) and (start_ranging_line == msg):
             start_ranging_time = get_time_from_line(
-                line, date_regexp, file_path, 0 #timezone_offset
+                line, date_regexp, file_path, 0  # timezone_offset
             )
         if (not stop_ranging_time) and bool(start_ranging_time):
             if ": Completed test" == msg:
                 stop_ranging_time = get_time_from_line(
-                    line, date_regexp, file_path, 0 #timezone_offset
+                    line, date_regexp, file_path, 0  # timezone_offset
                 )
                 break
 
@@ -674,7 +677,7 @@ def get_msg_without_time(line: str) -> Optional[str]:
             try:
                 log_time = None
                 log_time = get_time_from_line(
-                    line, date_regexp, file_path, 0 #timezone_offset
+                    line, date_regexp, file_path, 0  # timezone_offset
                 )
             except LineWithoutTimeStamp:
                 assert (

From acfc99c494cd30e74d324da552d018afb67928af Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Fri, 5 Jan 2024 17:19:06 -0500
Subject: [PATCH 025/305] Add option to save generated images

---
 text_to_image/coco.py              | 20 +++++++++++++++++-
 text_to_image/main.py              | 12 +++++++++++
 text_to_image/tools/sample_ids.py  | 33 ++++++++++++++++++++++++++++++
 text_to_image/tools/sample_ids.txt | 10 +++++++++
 4 files changed, 74 insertions(+), 1 deletion(-)
 create mode 100644 text_to_image/tools/sample_ids.py
 create mode 100644 text_to_image/tools/sample_ids.txt

diff --git a/text_to_image/coco.py b/text_to_image/coco.py
index 0ff5024816..fa89ba2152 100644
--- a/text_to_image/coco.py
+++ b/text_to_image/coco.py
@@ -162,13 +162,31 @@ def __call__(self, results, ids, expected=None, result_dict=None):
             (t.cpu().permute(1, 2, 0).float().numpy() * 255).round().astype(self.numpy_dtype)
             for t in results
         ]
+    
+    def save_images(self, ids, ds):
+        info = []
+        idx = {}
+        for i, id in enumerate(self.content_ids):
+            if id in ids:
+                idx[id] = i
+        if not os.path.exists("images/"):
+            os.makedirs("images/", exist_ok=True)
+        for id in ids:
+            caption = ds.get_caption(id)
+            generated = Image.fromarray(self.results[idx[id]])
+            image_path_tmp = f"images/{self.content_ids[id]}.png"
+            generated.save(image_path_tmp)
+            info.append((self.content_ids[id], caption))
+        with open("images/captions.txt", "w+") as f:
+            for id, caption in info:
+                f.write(f"{id}  {caption}\n")
 
     def start(self):
         self.results = []
 
     def finalize(self, result_dict, ds=None, output_dir=None):
         clip = CLIPEncoder(device=self.device)
-        dataset_size = ds.get_item_count()
+        dataset_size = len(self.results)
         log.info("Accumulating results")
         for i in range(0, dataset_size):
             caption = ds.get_caption(self.content_ids[i])
diff --git a/text_to_image/main.py b/text_to_image/main.py
index 367e12e264..1c0d35bc50 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -135,6 +135,11 @@ def get_args():
     parser.add_argument(
         "--audit_conf", default="audit.config", help="config for LoadGen audit settings"
     )
+    # arguments to save images
+    # pass this argument for official submission
+    # parser.add_argument("--output-images", action="store_true", help="Store a subset of the generated images")
+    # do not modify this argument for official submission
+    parser.add_argument("--ids-path", help="Path to caption ids", default="tools/sample_ids.txt")
 
     # below will override mlperf rules compliant settings - don't use for official submission
     parser.add_argument("--time", type=int, help="time to scan in seconds")
@@ -371,6 +376,12 @@ def main():
         sys.exit(1)
 
     audit_config = os.path.abspath(args.audit_conf)
+    
+    if args.accuracy:
+        ids_path = os.path.abspath(args.ids_path)
+        with open(ids_path) as f:
+            saved_images_ids = [int(_) for _ in f.readlines()]
+
     if args.output:
         output_dir = os.path.abspath(args.output)
         os.makedirs(output_dir, exist_ok=True)
@@ -461,6 +472,7 @@ def flush_queries():
     if args.accuracy:
         post_proc.finalize(result_dict, ds, output_dir=args.output)
         final_results["accuracy_results"] = result_dict
+        post_proc.save_images(saved_images_ids, ds)
 
     runner.finish()
     lg.DestroyQSL(qsl)
diff --git a/text_to_image/tools/sample_ids.py b/text_to_image/tools/sample_ids.py
new file mode 100644
index 0000000000..3b0548fd2c
--- /dev/null
+++ b/text_to_image/tools/sample_ids.py
@@ -0,0 +1,33 @@
+
+import argparse
+import numpy as np
+import pandas as pd
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--tsv-path", default="../coco2014/captions/captions_source.tsv", help="Dataset download location"
+    )
+    parser.add_argument(
+        "--output-path", default="sample_ids.txt", help="Dataset download location"
+    )
+    parser.add_argument(
+        "--n", type=int, default=10, help="Dataset download location"
+    )
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == "__main__":
+    args = get_args()
+    np.random.seed(42)
+    df_annotations = pd.read_csv(f"{args.tsv_path}", sep="\t")
+    sample_ids = list(np.random.choice(df_annotations.shape[0], args.n))
+    with open(args.output_path, "w+") as f:
+        for i, sample in enumerate(sample_ids):
+            if i != (len(sample_ids)-1):
+                f.write(str(sample) + "\n")
+            else:
+                f.write(str(sample))
+    
\ No newline at end of file
diff --git a/text_to_image/tools/sample_ids.txt b/text_to_image/tools/sample_ids.txt
new file mode 100644
index 0000000000..a8bd670457
--- /dev/null
+++ b/text_to_image/tools/sample_ids.txt
@@ -0,0 +1,10 @@
+860
+3772
+3092
+466
+4426
+3444
+3171
+2919
+130
+1685
\ No newline at end of file

From 1e7c0779b2f863284f0cc370a5c2a67aa553144a Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 4 Jan 2024 18:01:52 -0500
Subject: [PATCH 026/305] Fix SD readme and calibration script

---
 mlperf.conf                                   |  1 +
 text_to_image/README.md                       | 24 ++++++++++++-------
 text_to_image/tools/coco_calibration.py       |  4 ++--
 .../tools/download-coco-2014-calibration.sh   | 24 +++++++++++++++++--
 4 files changed, 41 insertions(+), 12 deletions(-)

diff --git a/mlperf.conf b/mlperf.conf
index 58611b2b31..3bf8c0db73 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -72,6 +72,7 @@ bert.Offline.min_query_count = 10833
 gptj.Offline.min_query_count = 13368
 rnnt.Offline.min_query_count = 2513
 3d-unet.Offline.min_query_count = 43
+stable-diffusion-xl.Offline.min_query_count = 5000
 
 # These fields should be defined and overridden by user.conf.
 *.SingleStream.target_latency = 10
diff --git a/text_to_image/README.md b/text_to_image/README.md
index 923cff41c7..26393f54f7 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -5,15 +5,15 @@ This is the reference implementation for MLPerf Inference text to image
 ## Supported Models
 
 | model | accuracy | dataset | model link | model source | precision | notes |
-| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
-| StableDiffusion | Torch | - | Coco2014 | - | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp32 | NCHW||
+| ---- | ---- | ---- | ---- | ---- | ---- | ---- |
+| StableDiffusion | - | Coco2014 | [fp32](https://cloud.mlcommons.org/index.php/s/DjnCSGyNBkWA4Ro) and [f16](https://cloud.mlcommons.org/index.php/s/LCdW5RM6wgGWbxC) | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp32 | NCHW |
 
 ## Dataset
 
 | Data | Description |
 | ---- | ---- | 
 | Coco-2014 | We use a subset of 5000 images and captions of the coco 2014 validation dataset, so that there is exaclty one caption per image. The model takes as input the caption of the image and generates an image from it. The original images and the generated images are used to compute FID score. The caption and the generated images are used to compute the CLIP score. We provide a [script](tools/coco.py) to automatically download the dataset |
-| Coco-2014 (calibration) | We use a subset of 100 images and captions of the coco 2014 training dataset, so that there is exaclty one caption per image. The subset was generated using this [script](tools/coco_generate_calibration.py). We provide the [caption ids](../calibration/COCO-2014/coco_cal_images_list.txt) and a [script](tools/coco_calibration.py) to download them. |
+| Coco-2014 (calibration) | We use a subset of 500 captions and images of the coco 2014 training dataset, so that there is exaclty one caption per image. The subset was generated using this [script](tools/coco_generate_calibration.py). We provide the [caption ids](../calibration/COCO-2014/coco_cal_captions_list.txt) and a [script](tools/coco_calibration.py) to download them. |
 
 
 ## Setup
@@ -25,11 +25,6 @@ export LOADGEN_FOLDER=$PWD/inference/loadgen
 export MODEL_PATH=$PWD/inference/text_to_image/model/
 ```
 ### Clone the repository
-**TEMPORARLY:**
-```bash
-git clone --recurse-submodules https://github.com/pgmpablo157321/inference.git --branch stable_diffusion_reference --depth 1
-```
-**KEEP FOR LATER:**
 ```bash
 git clone --recurse-submodules https://github.com/mlcommmons/inference.git --depth 1
 ```
@@ -77,6 +72,19 @@ cd $SD_FOLDER/tools
 ```
 If the file [captions.tsv](coco2014/captions/captions.tsv) can be found in the script, it will be used to download the target dataset subset, otherwise it will be generated. We recommend you to have this file for consistency.
 
+#### Calibration dataset
+
+We provide a script to download the calibration captions and images. To download only the captions:
+```bash
+cd $SD_FOLDER/tools
+./download-coco-2014-calibration.sh
+```
+To download only the captions and images:
+```bash
+cd $SD_FOLDER/tools
+./download-coco-2014-calibration.sh -i -n <number_of_workers>
+```
+
 ### Run the benchmark
 #### Local run
 ```bash
diff --git a/text_to_image/tools/coco_calibration.py b/text_to_image/tools/coco_calibration.py
index e80b33dfe5..c8417f52a3 100644
--- a/text_to_image/tools/coco_calibration.py
+++ b/text_to_image/tools/coco_calibration.py
@@ -89,7 +89,7 @@ def download_img(args):
         df_images = pd.DataFrame(images)
 
         # Calibration images 
-        with open(f"{calibration_dir}/coco_cal_images_list.txt") as f:
+        with open(f"{calibration_dir}/coco_cal_captions_list.txt") as f:
             calibration_ids = f.readlines()
             calibration_ids = [int(id.replace('\n', '')) for id in calibration_ids]
             calibration_ids = calibration_ids
@@ -118,5 +118,5 @@ def download_img(args):
         [_ for _ in tqdm.tqdm(pool.imap_unordered(download_img, tasks), total=len(tasks))]
     # Finalize annotations
     df_annotations[
-        ["id", "image_id", "caption", "height", "width", "file_name"]
+        ["id", "image_id", "caption", "height", "width", "file_name", "coco_url"]
     ].to_csv(f"{dataset_dir}/calibration/captions.tsv", sep="\t", index=False)
diff --git a/text_to_image/tools/download-coco-2014-calibration.sh b/text_to_image/tools/download-coco-2014-calibration.sh
index 9d1edf7dea..00aa66e9cc 100755
--- a/text_to_image/tools/download-coco-2014-calibration.sh
+++ b/text_to_image/tools/download-coco-2014-calibration.sh
@@ -8,8 +8,28 @@ while [ "$1" != "" ]; do
                                      DOWNLOAD_PATH=$1
                                      ;;
     esac
+    case $1 in
+        -i | --images )
+                                     IMAGES=1
+                                     ;;
+    esac
+    case $1 in
+        -n | --num-workers  )        shift
+                                      NUM_WORKERS=$1
+                                      ;;
+    esac
+    shift
 done
 
+if [ -z ${IMAGES} ];
+then
+    python3 coco_calibration.py \
+        --dataset-dir ${DOWNLOAD_PATH} \
+        --num-workers ${NUM_WORKERS}
 
-python3 coco_calibration.py \
-    --dataset-dir ${DOWNLOAD_PATH}
+else
+    python3 coco_calibration.py \
+        --dataset-dir ${DOWNLOAD_PATH} \
+        --download-images \
+        --num-workers ${NUM_WORKERS}
+fi

From 007ec3cd68b36a51b122da787b3f77eb4d559856 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 16 Jan 2024 12:43:43 -0500
Subject: [PATCH 027/305] Add batched inference to stable diffusion (#1553)

Co-authored-by: Miro <mirhodak@amd.com>
---
 text_to_image/backend_pytorch.py | 62 +++++++++++++++++++++++++-------
 text_to_image/main.py            |  1 +
 2 files changed, 50 insertions(+), 13 deletions(-)

diff --git a/text_to_image/backend_pytorch.py b/text_to_image/backend_pytorch.py
index e75a78f42d..300d7e58b5 100644
--- a/text_to_image/backend_pytorch.py
+++ b/text_to_image/backend_pytorch.py
@@ -17,6 +17,7 @@ def __init__(
         model_id="xl",
         guidance=8,
         steps=20,
+        batch_size=1,
         device="cuda",
         precision="fp32",
         negative_prompt="normal quality, low quality, worst quality, low res, blurry, nsfw, nude",
@@ -44,6 +45,7 @@ def __init__(
         self.steps = steps
         self.negative_prompt = negative_prompt
         self.max_length_neg_prompt = 77
+        self.batch_size = batch_size
 
     def version(self):
         return torch.__version__
@@ -313,20 +315,30 @@ def encode_tokens(
             pooled_prompt_embeds,
             negative_pooled_prompt_embeds,
         )
-
-    def predict(self, inputs):
-        images = []
-        with torch.no_grad():
-            for prompt in inputs:
+    
+    def prepare_inputs(self, inputs, i):
+        if self.batch_size == 1:
+            return self.encode_tokens(
+                self.pipe,
+                inputs[i]["input_tokens"],
+                inputs[i]["input_tokens_2"],
+                negative_prompt=self.negative_prompt_tokens,
+                negative_prompt_2=self.negative_prompt_tokens_2,
+            )
+        else:
+            prompt_embeds = []
+            negative_prompt_embeds = []
+            pooled_prompt_embeds = []
+            negative_pooled_prompt_embeds = []
+            for prompt in inputs[i:min(i+self.batch_size, len(inputs))]:
                 assert isinstance(prompt, dict)
                 text_input = prompt["input_tokens"]
                 text_input_2 = prompt["input_tokens_2"]
-                latents_input = prompt["latents"].to(self.dtype)
                 (
-                    prompt_embeds,
-                    negative_prompt_embeds,
-                    pooled_prompt_embeds,
-                    negative_pooled_prompt_embeds,
+                    p_e,
+                    n_p_e,
+                    p_p_e,
+                    n_p_p_e,
                 ) = self.encode_tokens(
                     self.pipe,
                     text_input,
@@ -334,7 +346,31 @@ def predict(self, inputs):
                     negative_prompt=self.negative_prompt_tokens,
                     negative_prompt_2=self.negative_prompt_tokens_2,
                 )
-                image = self.pipe(
+                prompt_embeds.append(p_e)
+                negative_prompt_embeds.append(n_p_e)
+                pooled_prompt_embeds.append(p_p_e)
+                negative_pooled_prompt_embeds.append(n_p_p_e)
+
+
+            prompt_embeds = torch.cat(prompt_embeds)
+            negative_prompt_embeds = torch.cat(negative_prompt_embeds)
+            pooled_prompt_embeds = torch.cat(pooled_prompt_embeds)
+            negative_pooled_prompt_embeds = torch.cat(negative_pooled_prompt_embeds)
+            return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+    def predict(self, inputs):
+        images = []
+        with torch.no_grad():
+            for i in range(0, len(inputs), self.batch_size):
+                latents_input = [inputs[idx]["latents"] for idx in range(i, min(i+self.batch_size, len(inputs)))]
+                latents_input = torch.cat(latents_input).to(self.device)
+                (
+                    prompt_embeds,
+                    negative_prompt_embeds,
+                    pooled_prompt_embeds,
+                    negative_pooled_prompt_embeds,
+                ) = self.prepare_inputs(inputs, i)
+                generated = self.pipe(
                     prompt_embeds=prompt_embeds,
                     negative_prompt_embeds=negative_prompt_embeds,
                     pooled_prompt_embeds=pooled_prompt_embeds,
@@ -343,7 +379,7 @@ def predict(self, inputs):
                     num_inference_steps=self.steps,
                     output_type="pt",
                     latents=latents_input,
-                ).images[0]
-                images.append(image)
+                ).images
+                images.extend(generated)
         return images
 
diff --git a/text_to_image/main.py b/text_to_image/main.py
index 1c0d35bc50..0b93ceb21f 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -324,6 +324,7 @@ def main():
         precision=args.dtype,
         device=args.device,
         model_path=args.model_path,
+        batch_size=args.max_batchsize
     )
     if args.dtype == "fp16":
         dtype = torch.float16

From 8e36925bd36a503e39fcbbc488e9e46126f079ed Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 17 Jan 2024 12:08:50 -0500
Subject: [PATCH 028/305] Submission checker version 4.0 (#1560)

* Submission checker version 4.0

* Fix Llama2-70b name

* Add 4.0 random seeds
---
 language/llama2-70b/README.md          |   4 +-
 language/llama2-70b/main.py            |   4 +-
 language/llama2-70b/mlperf.conf        |   6 +-
 loadgen/CMakeLists.txt                 |   4 +-
 loadgen/setup.py                       |   4 +-
 loadgen/version_generator.py           |   2 +-
 mlperf.conf                            |  12 +-
 text_to_image/tools/sample_ids.py      |   5 +-
 text_to_image/tools/sample_ids.txt     |  20 +-
 tools/submission/submission_checker.py | 280 ++++++++++++++++++++++++-
 10 files changed, 307 insertions(+), 34 deletions(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 0fefec95fa..6c4b1c36a0 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -1,6 +1,6 @@
-# Reference Implementation for Llama-v2-70B
+# Reference Implementation for llama2-70b
 
-**Basic implementation for Llama-v2-70B. Few noteworthy items:**
+**Basic implementation for llama2-70b. Few noteworthy items:**
 
 + Processing of Validation dataset is not finalized yet. Decision on input token lengths is pending
 + Streamer for communicating with loadgen has quite some overhead. This is only meant to provide functional implementation
diff --git a/language/llama2-70b/main.py b/language/llama2-70b/main.py
index d039d04635..bf1def8063 100644
--- a/language/llama2-70b/main.py
+++ b/language/llama2-70b/main.py
@@ -47,8 +47,8 @@ def main():
     settings = lg.TestSettings()
     settings.scenario = scenario_map[args.scenario.lower()]
     # Need to update the conf
-    settings.FromConfig(args.mlperf_conf, "llama-v2-70b", args.scenario)
-    settings.FromConfig(args.user_conf, "llama-v2-70b", args.scenario)
+    settings.FromConfig(args.mlperf_conf, "llama2-70b", args.scenario)
+    settings.FromConfig(args.user_conf, "llama2-70b", args.scenario)
 
     if args.accuracy:
         settings.mode = lg.TestMode.AccuracyOnly
diff --git a/language/llama2-70b/mlperf.conf b/language/llama2-70b/mlperf.conf
index 3533fcabda..28c19bddf6 100644
--- a/language/llama2-70b/mlperf.conf
+++ b/language/llama2-70b/mlperf.conf
@@ -56,9 +56,9 @@ rnnt.Server.target_latency = 1000
 gptj.Server.target_latency = 20000
 
 # Falcon Server scenario requires two latency constraints
-llama-v2-70b.Server.target_latency = 2000
-llama-v2-70b.Server.ttft_latency = 2000
-llama-v2-70b.Server.tpot_latency = 200
+llama2-70b.Server.target_latency = 2000
+llama2-70b.Server.ttft_latency = 2000
+llama2-70b.Server.tpot_latency = 200
 
 *.Offline.target_latency_percentile = 90
 *.Offline.min_duration = 600000
diff --git a/loadgen/CMakeLists.txt b/loadgen/CMakeLists.txt
index cd6846e163..977068cd86 100644
--- a/loadgen/CMakeLists.txt
+++ b/loadgen/CMakeLists.txt
@@ -3,8 +3,8 @@ cmake_minimum_required(VERSION 3.1)
 project(mlperf_loadgen)
 
 # The mlperf_loadgen version.
-set(mlperf_loadgen_VERSION_MAJOR 3)
-set(mlperf_loadgen_VERSION_MINOR 1)
+set(mlperf_loadgen_VERSION_MAJOR 4)
+set(mlperf_loadgen_VERSION_MINOR 0)
 message("mlperf_loadgen v${mlperf_loadgen_VERSION_MAJOR}.${mlperf_loadgen_VERSION_MINOR}")
 
 # Set build options. NB: CXX_STANDARD is supported since CMake 3.1.
diff --git a/loadgen/setup.py b/loadgen/setup.py
index 676258b810..d7b8224de4 100644
--- a/loadgen/setup.py
+++ b/loadgen/setup.py
@@ -76,13 +76,13 @@
 
 mlperf_loadgen_module = Pybind11Extension(
         "mlperf_loadgen",
-        define_macros=[("MAJOR_VERSION", "3"), ("MINOR_VERSION", "1")],
+        define_macros=[("MAJOR_VERSION", "4"), ("MINOR_VERSION", "0")],
         include_dirs=[".", get_include()],
         sources=mlperf_loadgen_sources,
         depends=mlperf_loadgen_headers)
 
 setup(name="mlperf_loadgen",
-      version="3.1",
+      version="4.0",
       description="MLPerf Inference LoadGen python bindings",
       url="https://mlcommons.org/",
       cmdclass={"build_ext": build_ext},
diff --git a/loadgen/version_generator.py b/loadgen/version_generator.py
index 331780f022..a34657a64a 100644
--- a/loadgen/version_generator.py
+++ b/loadgen/version_generator.py
@@ -94,7 +94,7 @@ def generate_loadgen_version_definitions(cc_filename, loadgen_root):
     ofile.write("// DO NOT EDIT: Autogenerated by version_generator.py.\n\n")
     ofile.write("#include <string>\n\n")
     ofile.write("namespace mlperf {\n\n")
-    ofile.write(func_def("Version", "\"3.1\""))
+    ofile.write(func_def("Version", "\"4.0\""))
 
     date_time_now_local = datetime.datetime.now().isoformat()
     date_time_now_utc = datetime.datetime.utcnow().isoformat()
diff --git a/mlperf.conf b/mlperf.conf
index 3bf8c0db73..a73ddd4a78 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -17,13 +17,13 @@ stable-diffusion-xl.*.performance_sample_count_override = 5000
 3d-unet.*.performance_sample_count_override = 0
 
 # Set seeds. The seeds will be distributed two weeks before the submission.
-*.*.qsl_rng_seed = 148687905518835231
-*.*.sample_index_rng_seed = 520418551913322573
-*.*.schedule_rng_seed = 811580660758947900
+*.*.qsl_rng_seed = 13281865557512327830
+*.*.sample_index_rng_seed = 198141574272810017
+*.*.schedule_rng_seed = 7575108116881280410
 # Set seeds for TEST_05. The seeds will be distributed two weeks before the submission.
-*.*.test05_qsl_rng_seed = 793197339507417767
-*.*.test05_sample_index_rng_seed = 255610748586851044
-*.*.test05_schedule_rng_seed = 352213341366340113
+*.*.test05_qsl_rng_seed = 2376919268182438552
+*.*.test05_sample_index_rng_seed = 11176391829184272374
+*.*.test05_schedule_rng_seed = 3911940905271271337
 
 
 *.SingleStream.target_latency_percentile = 90
diff --git a/text_to_image/tools/sample_ids.py b/text_to_image/tools/sample_ids.py
index 3b0548fd2c..e1d6effb4b 100644
--- a/text_to_image/tools/sample_ids.py
+++ b/text_to_image/tools/sample_ids.py
@@ -15,13 +15,16 @@ def get_args():
     parser.add_argument(
         "--n", type=int, default=10, help="Dataset download location"
     )
+    parser.add_argument(
+        "--seed", "-s", type=int, default=926019364, help="Dataset download location"
+    )
     args = parser.parse_args()
     return args
 
 
 if __name__ == "__main__":
     args = get_args()
-    np.random.seed(42)
+    np.random.seed(args.seed)
     df_annotations = pd.read_csv(f"{args.tsv_path}", sep="\t")
     sample_ids = list(np.random.choice(df_annotations.shape[0], args.n))
     with open(args.output_path, "w+") as f:
diff --git a/text_to_image/tools/sample_ids.txt b/text_to_image/tools/sample_ids.txt
index a8bd670457..65c9f5641a 100644
--- a/text_to_image/tools/sample_ids.txt
+++ b/text_to_image/tools/sample_ids.txt
@@ -1,10 +1,10 @@
-860
-3772
-3092
-466
-4426
-3444
-3171
-2919
-130
-1685
\ No newline at end of file
+4459
+4015
+2705
+1682
+4048
+4683
+3757
+1578
+3319
+95
\ No newline at end of file
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 6c22659160..7131415a45 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1049,6 +1049,184 @@
             "gpt3-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
         },
     },
+    "v4.0": {
+        "models": [
+            "resnet",
+            "retinanet",
+            "rnnt",
+            "bert-99",
+            "bert-99.9",
+            "dlrm-v2-99",
+            "dlrm-v2-99.9",
+            "3d-unet-99",
+            "3d-unet-99.9",
+            "gptj-99",
+            "gptj-99.9",
+            "llama2-70b-99",
+            "llama2-70b-99.9",
+            "stable-diffusion-xl",
+        ],
+        "required-scenarios-datacenter": {
+            "resnet": ["Server", "Offline"],
+            "retinanet": ["Server", "Offline"],
+            "rnnt": ["Server", "Offline"],
+            "bert-99": ["Server", "Offline"],
+            "bert-99.9": ["Server", "Offline"],
+            "dlrm-v2-99": ["Server", "Offline"],
+            "dlrm-v2-99.9": ["Server", "Offline"],
+            "3d-unet-99": ["Offline"],
+            "3d-unet-99.9": ["Offline"],
+            "gptj-99": ["Server", "Offline"],
+            "gptj-99.9": ["Server", "Offline"],
+            "llama2-70b-99": ["Server", "Offline"],
+            "llama2-70b-99.9": ["Server", "Offline"],
+            "stable-diffusion-xl": ["Server", "Offline"],
+        },
+        "optional-scenarios-datacenter": {},
+        "required-scenarios-edge": {
+            "resnet": ["SingleStream", "MultiStream", "Offline"],
+            "retinanet": ["SingleStream", "MultiStream", "Offline"],
+            "rnnt": ["SingleStream", "Offline"],
+            "bert-99": ["SingleStream", "Offline"],
+            "3d-unet-99": ["SingleStream", "Offline"],
+            "3d-unet-99.9": ["SingleStream", "Offline"],
+            "gptj-99": ["SingleStream", "Offline"],
+            "gptj-99.9": ["SingleStream", "Offline"],
+            "stable-diffusion-xl": ["SingleStream", "Offline"],
+        },
+        "optional-scenarios-edge": {},
+        "required-scenarios-datacenter-edge": {
+            "resnet": ["SingleStream", "Offline", "MultiStream", "Server"],
+            "retinanet": ["SingleStream", "Offline", "MultiStream", "Server"],
+            "rnnt": ["SingleStream", "Offline", "Server"],
+            "bert-99": ["SingleStream", "Offline", "Server"],
+            "bert-99.9": ["Offline", "Server"],
+            "dlrm-v2-99": ["Offline", "Server"],
+            "dlrm-v2-99.9": ["Offline", "Server"],
+            "3d-unet-99": ["SingleStream", "Offline"],
+            "3d-unet-99.9": ["SingleStream", "Offline"],
+            "gptj-99": ["SingleStream", "Offline", "Server"],
+            "gptj-99.9": ["SingleStream", "Offline", "Server"],
+            "llama2-70b-99": ["Server", "Offline"],
+            "llama2-70b-99.9": ["Server", "Offline"],
+            "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
+        },
+        "optional-scenarios-datacenter-edge": {},
+        "accuracy-target": {
+            "resnet": ("acc", 76.46 * 0.99),
+            "retinanet": ("mAP", 37.55 * 0.99),
+            "rnnt": ("WER", (100 - 7.452) * 0.99),
+            "bert-99": ("F1", 90.874 * 0.99),
+            "bert-99.9": ("F1", 90.874 * 0.999),
+            "dlrm-v2-99": ("AUC", 80.31 * 0.99),
+            "dlrm-v2-99.9": ("AUC", 80.31 * 0.999),
+            "3d-unet-99": ("DICE", 0.86170 * 0.99),
+            "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
+            "gptj-99" : ("ROUGE1", 42.9865 * 0.99, "ROUGE2", 20.1235 * 0.99, "ROUGEL", 29.9881 * 0.99, "GEN_LEN", 4016878*0.9),
+            "gptj-99.9" : ("ROUGE1", 42.9865 * 0.999, "ROUGE2", 20.1235 * 0.999, "ROUGEL", 29.9881 * 0.999, "GEN_LEN", 4016878*0.9),
+            # TODO: Replace with metrics for llama2-70b
+            "llama2-70b-99" : ("ROUGE1", 43.88 * 0.99, "ROUGE2", 21.7108 * 0.99, "ROUGEL", 28.2502 * 0.99, "tokens_per_sample", 839.4*0.9),
+            "llama2-70b-99.9" : ("ROUGE1", 43.88 * 0.999, "ROUGE2", 21.7108 * 0.999, "ROUGEL", 28.2502 * 0.999, "tokens_per_sample", 839.4*0.9),
+            "stable-diffusion-xl": ("CLIP_SCORE", 31.68631873, "FID_SCORE", 23.01085758)
+        },
+        "accuracy-upper-limit": {
+            "stable-diffusion-xl": ("CLIP_SCORE", 31.81331801, "FID_SCORE", 23.95007626)
+        },
+        "performance-sample-count": {
+            "resnet": 1024,
+            "retinanet": 64,
+            "rnnt": 2513,
+            "bert-99": 10833,
+            "bert-99.9": 10833,
+            "dlrm-v2-99": 204800,
+            "dlrm-v2-99.9": 204800,
+            "3d-unet-99": 43,
+            "3d-unet-99.9": 43,
+            "gptj-99": 13368,
+            "gptj-99.9": 13368,
+            "llama2-70b-99": 24576,
+            "llama2-70b-99.9": 24576,
+            "stable-diffusion-xl": 5000
+        },
+        # TODO: Update this list.
+        "model_mapping": {
+            # map model names to the official mlperf model class
+            "ssd-mobilenet": "ssd-small",
+            "ssd-resnet34": "retinanet",
+            "mobilenet": "resnet",
+            "resnet50": "resnet",
+            "ssd_resnet101_v1_fpn_640x640": "ssd-small",
+            "ssd_resnet101_v1_fpn_1024x1024": "ssd-large",
+            "ssd_resnet152_v1_fpn_640x640": "ssd-small",
+            "ssd_resnet152_v1_fpn_1024x1024": "ssd-large",
+            "rcnn-resnet50-lowproposals-coco": "ssd-large",
+            "rcnn-inception-resnet-v2-lowproposals-coco": "ssd-large",
+            "rcnn-inception-v2-coco": "ssd-large",
+            "rcnn-nas-lowproposals-coco": "ssd-large",
+            "rcnn-resnet101-lowproposals-coco": "ssd-large",
+            "ssd_mobilenet_v1_coco": "ssd-small",
+            "ssd_mobilenet_v1_fpn_640x640": "ssd-small",
+            "ssd_mobilenet_v1_quantized_coco": "ssd-small",
+            "ssd_mobilenet_v2_320x320": "ssd-small",
+            "ssd_mobilenet_v2_fpnlite_320x320": "ssd-small",
+            "ssd_mobilenet_v2_fpnlite_640x640": "ssd-small",
+            "ssd_resnet50_v1_fpn_640x640": "ssd-small",
+            "ssd_resnet50_v1_fpn_1024x1024": "ssd-large",
+        },
+        "seeds": {
+            # TODO: Update random seeds
+            "qsl_rng_seed": 13281865557512327830,
+            "sample_index_rng_seed": 198141574272810017,
+            "schedule_rng_seed": 7575108116881280410,
+        },
+        "test05_seeds": {
+            # TODO: Update random seeds
+            "qsl_rng_seed": 2376919268182438552,
+            "sample_index_rng_seed": 11176391829184272374,
+            "schedule_rng_seed": 3911940905271271337,
+        },
+        "ignore_errors": [],
+        "latency-constraint": {
+            "resnet": {"Server": 15000000},
+            "retinanet": {"Server": 100000000},
+            "rnnt": {"Server": 1000000000},
+            "bert-99": {"Server": 130000000},
+            "bert-99.9": {"Server": 130000000},
+            "dlrm-v2-99": {"Server": 60000000},
+            "dlrm-v2-99.9": {"Server": 60000000},
+            "gptj-99": {"Server": 20000000000},
+            "gptj-99.9": {"Server": 20000000000},
+            "llama2-70b-99": {"Server": 20000000000},
+            "llama2-70b-99.9": {"Server": 20000000000},
+            "stable-diffusion-xl" : {"Server": 20000000000}
+        },
+        "min-queries": {
+            "resnet": {
+                "SingleStream": 1024,
+                "MultiStream": 270336,
+                "Server": 270336,
+                "Offline": 1,
+            },
+            "retinanet": {
+                "SingleStream": 1024,
+                "MultiStream": 270336,
+                "Server": 270336,
+                "Offline": 1,
+            },
+            "rnnt": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "dlrm-v2-99": {"Server": 270336, "Offline": 1},
+            "dlrm-v2-99.9": {"Server": 270336, "Offline": 1},
+            "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
+            "3d-unet-99.9": {"SingleStream": 1024, "Offline": 1},
+            "gptj-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "gptj-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama2-70b-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama2-70b-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "stable-diffusion-xl": {"SingleStream": 1024, "Server": 270336, "Offline": 1}
+        },
+    },
 }
 
 VALID_DIVISIONS = ["open", "closed", "network"]
@@ -1069,6 +1247,24 @@
     "accuracy.txt",
     "mlperf_log_accuracy.json",
 ]
+REQUIRED_ACC_BENCHMARK = {
+    "stable-diffusion-xl": {
+        "v4.0": {
+            "images": [
+                "4459",
+                "4015",
+                "2705",
+                "1682",
+                "4048",
+                "4683",
+                "3757",
+                "1578",
+                "3319",
+                "95"
+            ]
+        }
+    }
+}
 REQUIRED_MEASURE_FILES = ["mlperf.conf", "user.conf", "README.md"]
 REQUIRED_POWER_MEASURE_FILES = ["analyzer_table.*", "power_settings.*"]
 MS_TO_NS = 1000 * 1000
@@ -1156,6 +1352,24 @@
         "MultiStreamLegacy": "effective_samples_per_query",
         "MultiStream": "early_stopping_latency_ms",
         "Server": "result_scheduled_samples_per_sec",
+    },
+    "v4.0": {
+        "Offline": "result_samples_per_second",
+        "SingleStream": "early_stopping_latency_ss",
+        "MultiStreamLegacy": "effective_samples_per_query",
+        "MultiStream": "early_stopping_latency_ms",
+        "Server": "result_scheduled_samples_per_sec",
+    }
+}
+
+RESULT_FIELD_BENCHMARK_OVERWRITE = {
+    "llama2-70b-99": {
+        "Offline": "result_tokens_per_second",
+        "Server": "result_scheduled_samples_per_sec",
+    },
+    "llama2-70b-99.9": {
+        "Offline": "result_tokens_per_second",
+        "Server": "result_scheduled_samples_per_sec",
     }
 }
 
@@ -1170,7 +1384,11 @@
     "ROUGE1": r".*'rouge1':\s([\d.]+).*",
     "ROUGE2": r".*'rouge2':\s([\d.]+).*",
     "ROUGEL": r".*'rougeL':\s([\d.]+).*",
+    "ROUGELSUM": r".*'rougeLsum':\s([\d.]+).*",
     "GEN_LEN": r".*'gen_len':\s([\d.]+).*",
+    "TOKENS_PER_SAMPLE": r".*'tokens_per_sample':\s([\d.]+).*",
+    "CLIP_SCORE": r".*'CLIP_SCORE':\s([\d.]+).*",
+    "FID_SCORE": r".*'FID_SCORE':\s([\d.]+).*",
 }
 
 SYSTEM_DESC_REQUIRED_FIELDS = [
@@ -1309,6 +1527,7 @@ def __init__(
         self.seeds = self.base["seeds"]
         self.test05_seeds = self.base["test05_seeds"]
         self.accuracy_target = self.base["accuracy-target"]
+        self.accuracy_upper_limit = self.base["accuracy-upper-limit"]
         self.performance_sample_count = self.base["performance-sample-count"]
         self.latency_constraint = self.base.get("latency-constraint", {})
         self.min_queries = self.base.get("min-queries", {})
@@ -1394,6 +1613,9 @@ def get_accuracy_target(self, model):
         if model not in self.accuracy_target:
             raise ValueError("model not known: " + model)
         return self.accuracy_target[model]
+    
+    def get_accuracy_upper_limit(self, model):
+        return self.accuracy_upper_limit.get(model, None)
 
     def get_performance_sample_count(self, model):
         model = self.get_mlperf_model(model)
@@ -1442,7 +1664,7 @@ def get_args():
     parser.add_argument("--input", required=True, help="submission directory")
     parser.add_argument(
         "--version",
-        default="v3.1",
+        default="v4.0",
         choices=list(MODEL_CONFIG.keys()),
         help="mlperf version",
     )
@@ -1522,6 +1744,23 @@ def list_files_recursively(*path):
     ]
 
 
+def check_extra_files(path, target_files):
+    missing_files = []
+    check_pass = True
+    folders = list_dir(path)
+    for dir in target_files.keys():
+        if dir not in folders:
+            check_pass = False
+            missing_files.append(os.path.join(path, dir))
+        else:
+            files = [f.split(".")[0] for f in list_files(os.path.join(path, dir))]
+            for target_file in target_files[dir]:
+                if target_file not in files:
+                    check_pass = False
+                    missing_files.append(f"{os.path.join(path, dir, target_file)}.png")
+    return check_pass, missing_files
+
+
 def split_path(m):
     return m.replace("\\", "/").split("/")
 
@@ -1574,12 +1813,18 @@ def check_accuracy_dir(config, model, path, verbose):
     result_acc = None
     hash_val = None
     target = config.get_accuracy_target(model)
+    acc_upper_limit = config.get_accuracy_upper_limit(model)
     patterns = []
     acc_targets = []
+    if acc_upper_limit is not None:
+        acc_limits = []
+        acc_limit_check = True
     for i in range(0, len(target), 2):
         acc_type, acc_target = target[i:i+2]
         patterns.append(ACC_PATTERN[acc_type])
         acc_targets.append(acc_target)
+        if acc_upper_limit is not None:
+            acc_limits.append(acc_upper_limit[i+1])
     acc_seen = [False for _ in acc_targets]
     with open(os.path.join(path, "accuracy.txt"), "r", encoding="utf-8") as f:
         for line in f:
@@ -1596,12 +1841,17 @@ def check_accuracy_dir(config, model, path, verbose):
                 elif acc is not None:
                     all_accuracy_valid = False
                     log.warning("%s accuracy not met: expected=%f, found=%s", path, acc_target, acc)
+                if acc is not None and acc_upper_limit is not None and float(acc) > acc_limits[i]:
+                    acc_limit_check = False
+                    log.warning("%s accuracy not met: upper limit=%f, found=%s", path, acc_limits[i], acc)
                 if i == 0 and acc:
                     result_acc = acc
                 acc = None
             if all(acc_seen) and hash_val:
                 break;
         is_valid = all_accuracy_valid & all(acc_seen)
+        if acc_upper_limit is not None:
+            is_valid &= acc_limit_check
 
     if not hash_val:
         log.error("%s not hash value for mlperf_log_accuracy.json", path)
@@ -1678,6 +1928,9 @@ def check_performance_dir(
             else scenario
         )
         res = float(mlperf_log[RESULT_FIELD_NEW[config.version][scenario_for_res]])
+        if model in RESULT_FIELD_BENCHMARK_OVERWRITE and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[model]:
+            res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[model][scenario_for_res]])
+
         latency_99_percentile = mlperf_log["result_99.00_percentile_latency_ns"]
         latency_mean = mlperf_log["result_mean_latency_ns"]
         if scenario in ["MultiStream"]:
@@ -2638,6 +2891,14 @@ def log_result(
                                 acc_path,
                                 debug or is_closed_or_network,
                             )
+                            if mlperf_model in REQUIRED_ACC_BENCHMARK:
+                                if config.version in REQUIRED_ACC_BENCHMARK[mlperf_model]:
+                                    extra_files_pass, missing_files = check_extra_files(acc_path, REQUIRED_ACC_BENCHMARK[mlperf_model][config.version])
+                                    if not extra_files_pass:
+                                        log.error(
+                                            "%s expected to have the following extra files (%s)", acc_path, missing_files
+                                        )
+                                        accuracy_is_valid = False
                             if not accuracy_is_valid and not is_closed_or_network:
                                 if debug:
                                     log.warning(
@@ -3216,20 +3477,29 @@ def check_compliance_dir(
         "retinanet",
         "gptj-99",
         "gptj-99.9",
-        "gpt3-99",
-        "gpt3-99.9",
+        "llama2-70b-99",
+        "llama2-70b-99.9",
+        "stable-diffusion-xl"
+
     ]:
         test_list.remove("TEST04")
 
     if model in [
         "gptj-99",
         "gptj-99.9",
-        "gpt3-99",
-        "gpt3-99.9",
+        "llama2-70b-99",
+        "llama2-70b-99.9",
+        "stable-diffusion-xl"
     ]:
         test_list.remove("TEST05")
         test_list.remove("TEST01") 
 
+    if model in [
+        "llama2-70b-99",
+        "llama2-70b-99.9",
+    ]:
+        test_list.append("TEST06") 
+
     # Check performance of all Tests
     for test in test_list:
         test_dir = os.path.join(compliance_dir, test)

From 853219495509d68125f063d664383d83597c19b8 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 17 Jan 2024 12:16:34 -0500
Subject: [PATCH 029/305] Add compliance tests table (#1561)

Co-authored-by: Miro <mirhodak@amd.com>
---
 compliance/nvidia/README.md | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/compliance/nvidia/README.md b/compliance/nvidia/README.md
index f45db59870..e3a751e983 100755
--- a/compliance/nvidia/README.md
+++ b/compliance/nvidia/README.md
@@ -5,6 +5,7 @@ This repository provides the compliance tests that need to be run by the submitt
 1. [Introduction](#introduction)
 2. [Test Infrastructure](#Test-Infrastructure)
 3. [Test Methodology](#Test-Methodology)
+4. [Tests Required for each Benchmark](#tests-required-for-each-benchmark)
 
 ## Introduction
 The purpose of compliance testing is to ensure a basic level of compliance with a subset of the MLPerf rules. The tests are designed to be complementary to third-party auditing which will be introduced in future rounds of MLPerf. The tests are not meant to root-cause issues with the submission, but can help detect anomalies in the submission that need to be investigated further by the submitter.
@@ -25,5 +26,16 @@ Run the provided python-based verification script to ensure that the compliance
 ## Test Submission
 The `run_verification.py` found in each test directory will copy the test files to be submitted to the directory specified. These files will follow the directory structure specified in [https://github.com/mlperf/policies/blob/master/submission_rules.adoc#562-inference](https://github.com/mlperf/policies/blob/master/submission_rules.adoc#562-inference) under `audit/` and are must be uploaded in order for the submission to be considered valid.
 
+## Tests Required for each Benchmark
 
-
+| model | Required Compliance Tests
+| ---- | ---- |
+| resnet50-v1.5 | [TEST01](./TEST01/), [TEST04](./TEST04/), [TEST05](./TEST05/) |
+| retinanet 800x800 | [TEST01](./TEST01/), [TEST05](./TEST05/) |
+| bert | [TEST01](./TEST01/), [TEST05](./TEST05/) |
+| dlrm-v2 | [TEST01](./TEST01/), [TEST05](./TEST05/) |
+| 3d-unet | [TEST01](./TEST01/), [TEST05](./TEST05/) |
+| rnnt | [TEST01](./TEST01/), [TEST05](./TEST05/) |
+| gpt-j | - |
+| stable-diffusion-xl | - |
+| Llama2-70b | [TEST06]() |

From 751712d286517efce6980fb5f3870a11fa8836a2 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Fri, 19 Jan 2024 14:39:57 -0500
Subject: [PATCH 030/305] Fix Bug: Calling FirstTokenComplete optional for
 Offline (#1547)

Co-authored-by: Miro <mirhodak@amd.com>
---
 .../demos/token_metrics/py_demo_offline.py    |  2 +-
 loadgen/loadgen.cc                            |  2 +
 loadgen/logging.cc                            | 19 +++++--
 loadgen/logging.h                             |  3 +
 loadgen/results.cc                            | 57 ++++++++++---------
 5 files changed, 50 insertions(+), 33 deletions(-)

diff --git a/loadgen/demos/token_metrics/py_demo_offline.py b/loadgen/demos/token_metrics/py_demo_offline.py
index cc8ba569ea..d0bbd2c495 100644
--- a/loadgen/demos/token_metrics/py_demo_offline.py
+++ b/loadgen/demos/token_metrics/py_demo_offline.py
@@ -60,7 +60,7 @@ def process_query_async(query_samples, i_slice):
         response_token_info = response_token.buffer_info()
         response_token_data = response_token_info[0]
         response_token_size = response_token_info[1] * response_token.itemsize
-        mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
+        # mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
         time.sleep(.02)
         n_tokens = len(response_array)
         response_array = array.array("B", response_array.tobytes())
diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index 3517e78d1b..7db118613d 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -1156,6 +1156,8 @@ void StartTest(SystemUnderTest* sut, QuerySampleLibrary* qsl,
                               log_settings.log_output.copy_summary_to_stdout);
             
   GlobalLogger().SetUseTokens(requested_settings.use_token_latencies);
+  bool needs_first_token = (requested_settings.scenario != TestScenario::Offline);
+  GlobalLogger().SetNeedsFirstToken(needs_first_token);
 
   if (log_settings.enable_trace) {
     GlobalLogger().StartNewTrace(&log_outputs.trace_out, PerfClock::now());
diff --git a/loadgen/logging.cc b/loadgen/logging.cc
index 3ab5437de7..d990e7c4dc 100644
--- a/loadgen/logging.cc
+++ b/loadgen/logging.cc
@@ -285,7 +285,7 @@ void AsyncLog::LogAccuracy(uint64_t seq_id, const QuerySampleIndex qsl_idx,
     return;
   }
   *accuracy_out_ << (accuracy_needs_comma_ ? ",\n{ " : "\n{ ");
-  if (!use_tokens_){
+  if (!use_tokens_ || !needs_first_token_){
     LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
           response);
   } else {
@@ -403,10 +403,10 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
   }
   
   if (use_tokens_){
-    if(token_latencies_.size() <= i){
+    if(needs_first_token_ && (token_latencies_.size() <= i)){
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
                           "Attempted to record a sample latency before it's first token latency");
-    }else if (token_latencies_[i] == kInvalidLatency){
+    }else if (needs_first_token_ && (token_latencies_[i] == kInvalidLatency)){
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
                           "Attempted to record a sample latency before it's first token latency");
     }
@@ -430,7 +430,10 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
       // If the SUT recorded the wrong sample, the test will hang and see
       // the error above.
       return;
-    } else if (n_tokens <= 0){
+    } else if (n_tokens == 0){
+      MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
+                            "n_tokens argument missing or attempted to record 0 as number of tokens");
+    } else if (n_tokens < 0){
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
                             "Attempted to record a negative number of tokens");
       n_tokens = 0;
@@ -588,6 +591,10 @@ void AsyncLog::SetUseTokens(bool use_tokens){
   use_tokens_ = use_tokens;
 }
 
+void AsyncLog::SetNeedsFirstToken(bool needs_first_token){
+  needs_first_token_ = needs_first_token;
+}
+
 /// \brief Records a single thread using thread-local storage and submits
 /// entries to the central Logger.
 ///
@@ -918,6 +925,10 @@ void Logger::SetUseTokens(bool use_tokens){
   async_logger_.SetUseTokens(use_tokens);
 }
 
+void Logger::SetNeedsFirstToken(bool needs_first_token){
+  async_logger_.SetNeedsFirstToken(needs_first_token);
+}
+
 TlsLogger* Logger::GetTlsLoggerThatRequestedSwap(size_t slot, size_t next_id) {
   uintptr_t slot_value = thread_swap_request_slots_[slot].load();
   if (SwapRequestSlotIsReadable(slot_value)) {
diff --git a/loadgen/logging.h b/loadgen/logging.h
index f741ea62a9..d10f574d60 100644
--- a/loadgen/logging.h
+++ b/loadgen/logging.h
@@ -326,6 +326,7 @@ class AsyncLog {
   PerfClock::time_point GetMaxCompletionTime();
   QuerySampleLatency GetMaxLatencySoFar();
   void SetUseTokens(bool use_tokens);
+  void SetNeedsFirstToken(bool needs_first_token);
 
  private:
   void WriteAccuracyHeaderLocked();
@@ -367,6 +368,7 @@ class AsyncLog {
   size_t log_warning_count_ = 0;
   bool warning_flagged_ = false;
   bool use_tokens_ = false;
+  bool needs_first_token_ = false;
 
   std::mutex trace_mutex_;
   std::unique_ptr<ChromeTracer> tracer_;
@@ -423,6 +425,7 @@ class Logger {
   PerfClock::time_point GetMaxCompletionTime();
   QuerySampleLatency GetMaxLatencySoFar();
   void SetUseTokens(bool use_tokens);
+  void SetNeedsFirstToken(bool needs_first_token);
 
  private:
   friend AsyncLog;
diff --git a/loadgen/results.cc b/loadgen/results.cc
index d705d312fc..21fd2c90ae 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -83,15 +83,15 @@ void PerformanceSummary::ProcessLatencies() {
 }
 
 void PerformanceSummary::ProcessTokenLatencies() {
-  if (pr.token_results.first_token_latencies.empty()) {
-    return;
-  }
   constexpr auto nTokenInvalid = std::numeric_limits<int64_t>::min();
   token_count = 0;
   for (auto n_tokens: pr.token_results.tokens_per_sample){
     if (n_tokens != nTokenInvalid)
       token_count += n_tokens;
   }
+  if (pr.token_results.first_token_latencies.empty()) {
+    return;
+  }
   QuerySampleLatency accumulated_first_token_latency = 0;
   for (auto latency : pr.token_results.first_token_latencies) {
     accumulated_first_token_latency += latency;
@@ -494,16 +494,16 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
               DoubleToString(tps_as_completed));
     }
 
-    
-    summary("Min First Token latency (ns)    : ", first_token_latency_min);
-    summary("Max First Token latency (ns)    : ", first_token_latency_max);
-    summary("Mean First Token latency (ns)   : ", first_token_latency_mean);
-    for (auto& lp : token_latency_percentiles) {
-      summary(
-          DoubleToString(lp.percentile * 100) + " percentile latency (ns)   : ",
-          lp.sample_latency);
+    if (settings.scenario != TestScenario::Offline) {
+      summary("Min First Token latency (ns)    : ", first_token_latency_min);
+      summary("Max First Token latency (ns)    : ", first_token_latency_max);
+      summary("Mean First Token latency (ns)   : ", first_token_latency_mean);
+      for (auto& lp : token_latency_percentiles) {
+        summary(
+            DoubleToString(lp.percentile * 100) + " percentile latency (ns)   : ",
+            lp.sample_latency);
+      }
     }
-    
   }
 
   summary(
@@ -630,22 +630,23 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
   }
   // Detailed first token latencies
   if (settings.use_token_latencies){
-    MLPERF_LOG(detail, "result_first_token_min_latency_ns", first_token_latency_min);
-    MLPERF_LOG(detail, "result_first_token_max_latency_ns", first_token_latency_max);
-    MLPERF_LOG(detail, "result_first_token_mean_latency_ns", first_token_latency_mean);
-    for (auto& lp : token_latency_percentiles) {
-      MLPERF_LOG(detail,
-                  "result_" + DoubleToString(lp.percentile * 100) +
-                      "_percentile_latency_ns",
-                  lp.sample_latency);
-    }
-    double tps_w_lg = ((double)token_count) / pr.final_query_issued_time;
-    double tps_wo_lg= ((double)token_count) / (sample_latency_mean * sample_count);
-    MLPERF_LOG(detail, "result_token_throughput_with_loadgen_overhead", tps_w_lg);
-    MLPERF_LOG(detail, "result_token_throughput", tps_wo_lg);
-    double tpot = sample_count * (sample_latency_mean - first_token_latency_mean) / ((double)token_count);
-    MLPERF_LOG(detail, "result_time_to_output_token", tpot);
-    if (settings.scenario == TestScenario::Offline) {
+    if (settings.scenario != TestScenario::Offline) {
+      MLPERF_LOG(detail, "result_first_token_min_latency_ns", first_token_latency_min);
+      MLPERF_LOG(detail, "result_first_token_max_latency_ns", first_token_latency_max);
+      MLPERF_LOG(detail, "result_first_token_mean_latency_ns", first_token_latency_mean);
+      for (auto& lp : token_latency_percentiles) {
+        MLPERF_LOG(detail,
+                    "result_" + DoubleToString(lp.percentile * 100) +
+                        "_percentile_latency_ns",
+                    lp.sample_latency);
+      }
+      double tps_w_lg = ((double)token_count) / pr.final_query_issued_time;
+      double tps_wo_lg= ((double)token_count) / (sample_latency_mean * sample_count);
+      MLPERF_LOG(detail, "result_token_throughput_with_loadgen_overhead", tps_w_lg);
+      MLPERF_LOG(detail, "result_token_throughput", tps_wo_lg);
+      double tpot = sample_count * (sample_latency_mean - first_token_latency_mean) / ((double)token_count);
+      MLPERF_LOG(detail, "result_time_to_output_token", tpot);
+    } else {
       double tokens_per_second = token_count / pr.max_latency;
       MLPERF_LOG(detail, "result_tokens_per_second", tokens_per_second);
     }

From ebec78c32a0d593c21468772fd3557740ee71a35 Mon Sep 17 00:00:00 2001
From: Michal Szutenberg <37601244+szutenberg@users.noreply.github.com>
Date: Fri, 19 Jan 2024 20:41:01 +0100
Subject: [PATCH 031/305] [llama] Extend evaluate-accuracy.py (#1552)

* [llama] Extend evaluate-accuracy.py

* add reporting accuracy relative to the target
* add reporting tokens_per_sample
* add reporting amount of generated tokens

* Remove reporting accuracy

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 language/llama2-70b/evaluate-accuracy.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/language/llama2-70b/evaluate-accuracy.py b/language/llama2-70b/evaluate-accuracy.py
index cd0571eee1..cf4cb2c054 100644
--- a/language/llama2-70b/evaluate-accuracy.py
+++ b/language/llama2-70b/evaluate-accuracy.py
@@ -71,6 +71,7 @@ def main():
         results = json.load(f)
 
     seen = set()
+    gen_tok_len = 0
     for pred in results:
         qsl_idx = pred['qsl_idx']
         if qsl_idx in seen:
@@ -81,6 +82,7 @@ def main():
         target_required.append(target)
         pred = np.frombuffer( bytes.fromhex(pred['data']), eval_dtype)
 
+        gen_tok_len += len(pred)
         preds_token_ids.append(pred)
 
     preds_decoded_text = tokenizer.batch_decode(
@@ -92,8 +94,15 @@ def main():
         predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
     result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
     prediction_lens = [len(pred) for pred in preds]
-    result["gen_len"] = np.sum(prediction_lens)
-    result["gen_num"] = len(preds)
+    gen_num = len(preds)
+
+    result = {**result,
+              'gen_len': np.sum(prediction_lens),
+              'gen_num': gen_num,
+              'gen_tok_len': gen_tok_len,
+              'tokens_per_sample': round(gen_tok_len / gen_num, 1)
+              }
+
     print("\nResults\n")
     print(result)
 

From c65150e89c2ab199212825b508778119726f9535 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Fri, 19 Jan 2024 14:41:52 -0500
Subject: [PATCH 032/305] Add compliance TEST06 (#1556)

Co-authored-by: Miro <mirhodak@amd.com>
---
 compliance/nvidia/TEST06/README.md           |  48 +++++++
 compliance/nvidia/TEST06/audit.config        |  11 ++
 compliance/nvidia/TEST06/run_verification.py | 128 +++++++++++++++++++
 3 files changed, 187 insertions(+)
 create mode 100644 compliance/nvidia/TEST06/README.md
 create mode 100644 compliance/nvidia/TEST06/audit.config
 create mode 100644 compliance/nvidia/TEST06/run_verification.py

diff --git a/compliance/nvidia/TEST06/README.md b/compliance/nvidia/TEST06/README.md
new file mode 100644
index 0000000000..a93312f94d
--- /dev/null
+++ b/compliance/nvidia/TEST06/README.md
@@ -0,0 +1,48 @@
+# Test 06 - Verify consistency of the output of the Llama-v2-70b
+This repository provides the config files and scripts to run and verify TEST 06 - Verify consistency of the Llama-v2-70b output.
+
+# Table of contents
+1. [Introduction](#introduction)
+2. [Requisites](#Requisites)
+2. [Instructions](#instructions)
+
+## Introduction
+
+The purpose of this test is to ensure the consistency of the output of the Llama2 model and avoid a potential EOS exploit. This test will make a performance run, with a limit of 100 samples and logging them into `mlperf_log_accuracy.json`. To achieve a passing result in this test, two criteria must be met:
+- In the case the first token is reported independently (not applicable for Offline scenario), it should match for every query with the first token of the model output.
+- For each query, the model output should only end with zero or one EOS token
+
+## Requisites
+
+For this test, you need to be able to run the `Llama2-70b` benchmark. Therefore all it's requirements are also required for this test. Additionally, you need to have `numpy` installed.
+```
+pip install numpy
+```
+
+## Instructions
+### Part I
+Run the Llama-v2-70b benchmark with the provided audit.config in the corresponding subdirectory. Note that audit.config must be copied to the directory where the benchmark is being run from. Verification that audit.config was properly read can be done by checking that loadgen has found audit.config in mlperf_log_detail.txt
+
+### Part II
+Run the verification script
+```
+python3 run_verification.py -c COMPLIANCE_DIR -o OUTPUT_DIR -s SCENARIO
+```
+- COMPLIANCE_DIR: Specifies the path to the directory containing the logs from the compliance test run. 
+- OUTPUT_DIR: Specifies the path to the output directory where compliance logs will be uploaded from,   i.e. `inference_results_v0.7/closed/NVIDIA/compliance/TEST06/llama2-70b/Offline`
+- SCENARIO: Specifies the scenario the benchmark was run. One of ["Offline", "Server", "SingleStream", "MultiStream"]
+
+Expected output
+```
+First token check pass: True                
+EOS check pass: True             
+TEST06 verification complete   
+```
+
+Or:
+
+```
+First token check pass: Skipped                
+EOS check pass: True             
+TEST06 verification complete     
+```
\ No newline at end of file
diff --git a/compliance/nvidia/TEST06/audit.config b/compliance/nvidia/TEST06/audit.config
new file mode 100644
index 0000000000..c8888be506
--- /dev/null
+++ b/compliance/nvidia/TEST06/audit.config
@@ -0,0 +1,11 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+
+# mode dictionary (0 = submission, 1 = accuracy, 2 = performance, 3 = find peak perf)
+*.*.mode = 2
+*.*.accuracy_log_rng_seed = 720381539243781796
+*.*.accuracy_log_sampling_target = 100
+*.*.min_query_count = 100
+*.*.min_duration = 0
\ No newline at end of file
diff --git a/compliance/nvidia/TEST06/run_verification.py b/compliance/nvidia/TEST06/run_verification.py
new file mode 100644
index 0000000000..9d4cd1ad74
--- /dev/null
+++ b/compliance/nvidia/TEST06/run_verification.py
@@ -0,0 +1,128 @@
+#! /usr/bin/env python3
+# Copyright 2018-2022 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+import os
+import sys
+import shutil
+import argparse
+import json
+
+import numpy as np
+
+EOS_TOKEN = 2
+DTYPE_MAP = {
+    "int64": np.int64,
+    "int32": np.int32,
+    "int16": np.int16,
+    "float32": np.float32
+}
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--compliance_dir", "-c", 
+                        help="Specifies the path to the directory containing the logs from the compliance test run.",
+                        required=True)
+    parser.add_argument("--output_dir", "-o",
+                        help="Specifies the path to the output directory where compliance logs will be uploaded from, i.e. inference_results_v0.7/closed/NVIDIA/compliance/T4x8/resnet/Offline.",
+                        required=True)
+    parser.add_argument("--dtype", "-d", default="int64", choices=["int64", "int32", "int16", "float32"])
+    parser.add_argument("--scenario", "-s", required=True, choices=["Offline", "Server", "SingleStream", "MultiStream"])
+    args = parser.parse_args()
+    return args
+
+def eos_check(acc_data, dtype):
+    for sample in acc_data:
+        data = np.frombuffer(bytes.fromhex(sample["data"]), dtype=dtype)
+        i = data.shape[0] - 1
+        n_eos_tokens = 0
+        while (i > 0):
+            if data[i] == EOS_TOKEN:
+                n_eos_tokens += 1
+            if n_eos_tokens >= 2:
+                return False
+            if data[i] != EOS_TOKEN:
+                break
+            i-=1
+    return True
+
+def first_token_check(acc_data, dtype):
+    for sample in acc_data:
+        data = np.frombuffer(bytes.fromhex(sample["data"]), dtype=dtype)
+        token_data = np.frombuffer(bytes.fromhex(sample["token_data"]), dtype=dtype)
+        print(token_data)
+        for t1, t2 in zip(data, token_data):
+            if t1 != t2:
+                return False
+        
+    return True
+
+def main():
+    args = get_args()
+    accuracy_file = os.path.join(args.compliance_dir, "mlperf_log_accuracy.json")
+    
+    with open(accuracy_file, "r") as acc_json:
+        acc_data = json.load(acc_json)
+    
+    try:
+        eos_pass = eos_check(acc_data, DTYPE_MAP[args.dtype])
+    except Exception:
+        print("Unexpected error occured while doing the EOS check")
+        eos_pass = False
+
+    need_first_token_check = (args.scenario != "Offline")
+    first_token_pass = True
+    if need_first_token_check:
+        try:
+            first_token_pass = first_token_check(acc_data, DTYPE_MAP[args.dtype])
+        except Exception:
+            print("Unexpected error occured while doing the first token check")
+            first_token_pass = False
+
+    # Construct output based on the results of checks
+    output = ""
+    # Add first token check
+    if need_first_token_check:
+        output += f"First token check pass: {first_token_pass}\n"
+    else:
+        output += f"First token check pass: Skipped\n"
+    
+    # Add EOS check
+    output += f"EOS check pass: {eos_pass}\n"
+
+    if eos_pass and first_token_pass:
+        output += "TEST06 verification complete\n"
+    else:
+        output += "TEST06 verification failed\n"
+
+    # Output test output to console and folder
+    output_dir = args.output_dir
+    output_accuracy_dir = os.path.join(args.output_dir, "accuracy")
+    
+    if not os.path.isdir(output_dir):
+        os.makedirs(output_dir)
+    if not os.path.isdir(output_accuracy_dir):
+        os.makedirs(output_accuracy_dir)
+
+    with open(os.path.join(output_dir, "verify_accuracy.txt"), "w") as f:
+        f.write(output)
+
+    try:
+        shutil.copy2(accuracy_file,output_accuracy_dir)
+    except Exception:
+        print("Exception occured trying to copy " + accuracy_file + " to " + output_accuracy_dir)
+    print(output)
+
+if __name__ == "__main__":
+    main()

From 5e2883bbbc71641a774b0617e94e0846c1570044 Mon Sep 17 00:00:00 2001
From: nv-alicheng <107440104+nv-alicheng@users.noreply.github.com>
Date: Fri, 19 Jan 2024 11:44:41 -0800
Subject: [PATCH 033/305] Use left padding instead of right padding for BS>1 on
 Llama v2 (#1572)

---
 language/llama2-70b/SUT.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/language/llama2-70b/SUT.py b/language/llama2-70b/SUT.py
index 9cf009c084..30fb21f8f9 100644
--- a/language/llama2-70b/SUT.py
+++ b/language/llama2-70b/SUT.py
@@ -179,10 +179,10 @@ def process_queries(self):
                 input_len = []
                 for q in qitem:
                     input_ids_tensor.append(pad(self.data_object.input_ids[q.index],
-                                                (0, max_seq_len - self.data_object.input_lens[q.index], 0, 0),
+                                                (max_seq_len - self.data_object.input_lens[q.index], 0, 0, 0),
                                                 value=self.tokenizer.pad_token_id))
                     input_masks_tensor.append(pad(self.data_object.attention_masks[q.index],
-                                                  (0, max_seq_len - self.data_object.input_lens[q.index], 0, 0),
+                                                  (max_seq_len - self.data_object.input_lens[q.index], 0, 0, 0),
                                                  value=0))
                     input_len.append(self.data_object.input_lens[q.index])
                 input_ids_tensor = torch.cat(input_ids_tensor)

From 8c27e558115a20026313d3c58319965eb69c1947 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Fri, 19 Jan 2024 12:29:06 -0800
Subject: [PATCH 034/305] Fix llama2 token_per_sample typo and number (#1573)

---
 tools/submission/submission_checker.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 7131415a45..e3679cd308 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1124,9 +1124,8 @@
             "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
             "gptj-99" : ("ROUGE1", 42.9865 * 0.99, "ROUGE2", 20.1235 * 0.99, "ROUGEL", 29.9881 * 0.99, "GEN_LEN", 4016878*0.9),
             "gptj-99.9" : ("ROUGE1", 42.9865 * 0.999, "ROUGE2", 20.1235 * 0.999, "ROUGEL", 29.9881 * 0.999, "GEN_LEN", 4016878*0.9),
-            # TODO: Replace with metrics for llama2-70b
-            "llama2-70b-99" : ("ROUGE1", 43.88 * 0.99, "ROUGE2", 21.7108 * 0.99, "ROUGEL", 28.2502 * 0.99, "tokens_per_sample", 839.4*0.9),
-            "llama2-70b-99.9" : ("ROUGE1", 43.88 * 0.999, "ROUGE2", 21.7108 * 0.999, "ROUGEL", 28.2502 * 0.999, "tokens_per_sample", 839.4*0.9),
+            "llama2-70b-99" : ("ROUGE1", 43.88 * 0.99, "ROUGE2", 21.7108 * 0.99, "ROUGEL", 28.2502 * 0.99, "TOKENS_PER_SAMPLE", 293.3*0.9),
+            "llama2-70b-99.9" : ("ROUGE1", 43.88 * 0.999, "ROUGE2", 21.7108 * 0.999, "ROUGEL", 28.2502 * 0.999, "TOKENS_PER_SAMPLE", 293.3*0.9),
             "stable-diffusion-xl": ("CLIP_SCORE", 31.68631873, "FID_SCORE", 23.01085758)
         },
         "accuracy-upper-limit": {

From 4ad3fe9c598ed5155a945f61132faa63ea949589 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Fri, 19 Jan 2024 20:36:41 +0000
Subject: [PATCH 035/305] Update mlperf.conf to have llama2 latency constraints
 (#1570)

* Update mlperf.conf to have llama2 latency constraints

* Update mlperf.conf

---------

Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
---
 mlperf.conf | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/mlperf.conf b/mlperf.conf
index a73ddd4a78..fe743a8dde 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -37,6 +37,7 @@ stable-diffusion-xl.*.performance_sample_count_override = 5000
 *.MultiStream.min_query_count = 662
 retinanet.MultiStream.target_latency = 528
 
+
 # 3D-UNet uses equal issue mode
 3d-unet.*.sample_concatenate_permutation = 1
 
@@ -56,6 +57,10 @@ dlrm-v2.Server.target_latency = 60
 rnnt.Server.target_latency = 1000
 gptj.Server.target_latency = 20000
 stable-diffusion-xl.Server.target_latency = 20000
+# Falcon Server scenario requires two latency constraints
+llama2-70b.Server.target_latency = 2000
+llama2-70b.Server.ttft_latency = 2000
+llama2-70b.Server.tpot_latency = 200
 
 *.Offline.target_latency_percentile = 90
 *.Offline.min_duration = 600000
@@ -73,6 +78,7 @@ gptj.Offline.min_query_count = 13368
 rnnt.Offline.min_query_count = 2513
 3d-unet.Offline.min_query_count = 43
 stable-diffusion-xl.Offline.min_query_count = 5000
+llama2-70b.Offline.min_query_count = 24576
 
 # These fields should be defined and overridden by user.conf.
 *.SingleStream.target_latency = 10

From f4ff573dba933b03a9b88fa997cf0474e2382cca Mon Sep 17 00:00:00 2001
From: nvyihengz <109830892+nvyihengz@users.noreply.github.com>
Date: Fri, 19 Jan 2024 12:47:50 -0800
Subject: [PATCH 036/305] Use explicit caption path for SDXL accuracy script
 and print our FID and CLIP (#1567)

---
 text_to_image/tools/accuracy_coco.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
index a7c9828c61..ad5c93c0cb 100644
--- a/text_to_image/tools/accuracy_coco.py
+++ b/text_to_image/tools/accuracy_coco.py
@@ -22,7 +22,7 @@ def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
     parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--dataset-dir", required=True, help="coco directory")
+    parser.add_argument("--caption-path", default="coco2014/captions/captions_source.tsv", help="path to coco captions")
     parser.add_argument("--statistics-path", default=None, help="path to statistics")
     parser.add_argument("--verbose", action="store_true", help="verbose messages")
     parser.add_argument("--output-file", default="coco-results.json", help="path to output file")
@@ -43,14 +43,11 @@ def preprocess_image(img_dir, file_name):
 
 def main():
     args = get_args()
-    annotations_file = os.environ.get('DATASET_ANNOTATIONS_FILE_PATH')
-    if not annotations_file:
-        annotations_file = os.path.join(args.dataset_dir, "captions", "captions.tsv")
-    
+
     result_dict = {}
 
     # Load dataset annotations
-    df_captions = pd.read_csv(annotations_file, sep="\t")
+    df_captions = pd.read_csv(args.caption_path, sep="\t")
 
     # Load model outputs
     with open(args.mlperf_accuracy_file, "r") as f:
@@ -88,12 +85,13 @@ def main():
             100 * clip.get_clip_score(caption, generated_img).item()
         )
     fid_score = compute_fid(result_list, statistics_path, device)
-        
+
     result_dict["FID_SCORE"] = fid_score
     result_dict["CLIP_SCORE"] = np.mean(clip_scores)
+    print(f"Accuracy Results: {result_dict}")
 
     with open(args.output_file, "w") as fp:
         json.dump(result_dict, fp, sort_keys=True, indent=4)
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()

From 16d312bb6cb31e2e9a6f7c9fed68f45efa59d437 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Fri, 19 Jan 2024 20:51:52 +0000
Subject: [PATCH 037/305] Create requirements.txt (#1557)

Adding pybind11 dependency requirement
---
 loadgen/requirements.txt | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 loadgen/requirements.txt

diff --git a/loadgen/requirements.txt b/loadgen/requirements.txt
new file mode 100644
index 0000000000..e47c59fd7c
--- /dev/null
+++ b/loadgen/requirements.txt
@@ -0,0 +1 @@
+pybind11

From f21c39da92555eaf1eedf6c5f76d59893785b59e Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Mon, 22 Jan 2024 16:12:40 +0000
Subject: [PATCH 038/305] Fix offline_min_samples in submission checker and
 #1569 (#1571)

* Fix offline_min_samples in submission checker and #1569

* Removed mlperf.conf from llama2 directory to avoid confusion

* Update submission_checker.py
---
 language/llama2-70b/README.md          |  5 ++
 language/llama2-70b/mlperf.conf        | 74 --------------------------
 tools/submission/submission_checker.py | 22 +++++++-
 3 files changed, 25 insertions(+), 76 deletions(-)
 delete mode 100644 language/llama2-70b/mlperf.conf

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 6c4b1c36a0..a9a150b4cb 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -8,6 +8,11 @@
 
 ## Prepare environment
 
+Copy the mlperf.conf file to this folder.
+```
+cp ../../mlperf.conf .
+```
+
 For a CPU-only run:
 
 ```
diff --git a/language/llama2-70b/mlperf.conf b/language/llama2-70b/mlperf.conf
deleted file mode 100644
index 28c19bddf6..0000000000
--- a/language/llama2-70b/mlperf.conf
+++ /dev/null
@@ -1,74 +0,0 @@
-# The format of this config file is 'key = value'.
-# The key has the format 'model.scenario.key'. Value is mostly int64_t.
-# Model maybe '*' as wildcard. In that case the value applies to all models.
-# All times are in milli seconds
-
-# Set performance_sample_count for each model.
-# User can optionally set this to higher values in user.conf.
-resnet50.*.performance_sample_count_override = 1024
-ssd-mobilenet.*.performance_sample_count_override = 256
-retinanet.*.performance_sample_count_override = 64
-bert.*.performance_sample_count_override = 10833
-dlrm.*.performance_sample_count_override = 204800
-dlrm-v2.*.performance_sample_count_override = 204800
-rnnt.*.performance_sample_count_override = 2513
-# set to 0 to let entire sample set to be performance sample
-3d-unet.*.performance_sample_count_override = 0
-
-# Set seeds. The seeds will be distributed two weeks before the submission.
-*.*.qsl_rng_seed = 148687905518835231
-*.*.sample_index_rng_seed = 520418551913322573
-*.*.schedule_rng_seed = 811580660758947900
-# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission.
-*.*.test05_qsl_rng_seed = 793197339507417767
-*.*.test05_sample_index_rng_seed = 255610748586851044
-*.*.test05_schedule_rng_seed = 352213341366340113
-
-
-*.SingleStream.target_latency_percentile = 90
-*.SingleStream.min_duration = 600000
-#*.SingleStream.min_query_count = 1024
-
-*.MultiStream.target_latency_percentile = 99
-*.MultiStream.samples_per_query = 8
-*.MultiStream.min_duration = 600000
-#*.MultiStream.min_query_count = 270336
-*.MultiStream.min_query_count = 662
-retinanet.MultiStream.target_latency = 528
-
-# 3D-UNet uses equal issue mode
-3d-unet.*.sample_concatenate_permutation = 1
-
-# GPT-J uses equal issue mode for Single-Stream
-gptj.SingleStream.sample_concatenate_permutation = 1
-
-*.Server.target_latency = 10
-*.Server.target_latency_percentile = 99
-*.Server.target_duration = 0
-*.Server.min_duration = 600000
-#*.Server.min_query_count = 270336
-resnet50.Server.target_latency = 15
-retinanet.Server.target_latency = 100
-bert.Server.target_latency = 130
-dlrm.Server.target_latency = 60
-dlrm-v2.Server.target_latency = 60
-rnnt.Server.target_latency = 1000
-gptj.Server.target_latency = 20000
-
-# Falcon Server scenario requires two latency constraints
-llama2-70b.Server.target_latency = 2000
-llama2-70b.Server.ttft_latency = 2000
-llama2-70b.Server.tpot_latency = 200
-
-*.Offline.target_latency_percentile = 90
-*.Offline.min_duration = 600000
-# In Offline scenario, we always have one query. But LoadGen maps this to
-# min_sample_count internally in Offline scenario, so set this to 24576 since
-# the rule requires that Offline scenario run for at least 24576 samples.
-*.Offline.min_query_count = 24576
-
-# These fields should be defined and overridden by user.conf.
-*.SingleStream.target_latency = 10
-*.MultiStream.target_latency = 80
-*.Server.target_qps = 1.0
-*.Offline.target_qps = 1.0
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index e3679cd308..e61590e340 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1281,6 +1281,23 @@
     "compliance_accuracy.txt",
 ]
 
+OFFLINE_MIN_SPQ_SINCE_V4 = {
+    "resnet": 24576,
+    "retinanet": 24576,
+    "bert-99": 10833,
+    "bert-99.9": 10833,
+    "dlrm-v2-99": 24576,
+    "dlrm-v2-99.9": 24576,
+    "3d-unet-99": 43,
+    "3d-unet-99.9": 43,
+    "rnnt": 2513,
+    "gptj-99": 13368,
+    "gptj-99.9": 13368,
+    "llama2-70b-99": 24576,
+    "llama2-70b-99.9": 24576,
+    "stable-diffusion-xl": 5000
+}
+
 SCENARIO_MAPPING = {
     "singlestream": "SingleStream",
     "multistream": "MultiStream",
@@ -1526,7 +1543,7 @@ def __init__(
         self.seeds = self.base["seeds"]
         self.test05_seeds = self.base["test05_seeds"]
         self.accuracy_target = self.base["accuracy-target"]
-        self.accuracy_upper_limit = self.base["accuracy-upper-limit"]
+        self.accuracy_upper_limit = self.base.get("accuracy-upper-limit", {})
         self.performance_sample_count = self.base["performance-sample-count"]
         self.latency_constraint = self.base.get("latency-constraint", {})
         self.min_queries = self.base.get("min-queries", {})
@@ -2082,7 +2099,8 @@ def check_performance_dir(
                 )
                 is_valid = False
 
-        if scenario == "Offline" and (samples_per_query < OFFLINE_MIN_SPQ):
+        if config.version in ["v0.5", "v0.7", "v1.0", "v1.1", "v2.0", "v2.1", "v3.0", "v3.1"] and scenario == "Offline" and (samples_per_query < OFFLINE_MIN_SPQ) or \
+        scenario == "Offline" and (samples_per_query < OFFLINE_MIN_SPQ_SINCE_V4[model]):
             log.error(
                 "%s Required minimum samples per query not met by user config, Expected=%s, Found=%s",
                 fname,

From 4d0e24659edc0c96623b9325efe813dc065092aa Mon Sep 17 00:00:00 2001
From: nvyihengz <109830892+nvyihengz@users.noreply.github.com>
Date: Tue, 23 Jan 2024 08:55:46 -0800
Subject: [PATCH 039/305] Add TEST01 for stable diffusion XL (#1574)

---
 .../nvidia/TEST01/stable-diffusion-xl/audit.config       | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 compliance/nvidia/TEST01/stable-diffusion-xl/audit.config

diff --git a/compliance/nvidia/TEST01/stable-diffusion-xl/audit.config b/compliance/nvidia/TEST01/stable-diffusion-xl/audit.config
new file mode 100644
index 0000000000..7e7cfdf55a
--- /dev/null
+++ b/compliance/nvidia/TEST01/stable-diffusion-xl/audit.config
@@ -0,0 +1,9 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+
+# mode dictionary (0 = submission, 1 = accuracy, 2 = performance, 3 = find peak perf)
+*.*.mode = 2
+*.*.accuracy_log_rng_seed = 720381539243781796
+*.*.accuracy_log_sampling_target = 128

From 901ce67aad45c62a2cb840cc5826b805a442cbf3 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 25 Jan 2024 00:31:46 +0000
Subject: [PATCH 040/305] Fixes for report generation and submission checker
 for models without compliance tests (#1576)

* Fix offline_min_samples in submission checker and #1569

* Removed mlperf.conf from llama2 directory to avoid confusion

* Update submission_checker.py

* Fixes for 4.0

* Cleanup compliance dir check for models without compliance tests
---
 tools/submission/generate_final_report.py |  4 +++
 tools/submission/preprocess_submission.py |  2 +-
 tools/submission/submission_checker.py    | 35 ++++++++++++-----------
 3 files changed, 23 insertions(+), 18 deletions(-)

diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 7d1200a307..70ca9affc4 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -127,6 +127,9 @@ def main():
           '3d-unet-99.9': ['Offline'],
           'gptj-99': ['Server', 'Offline'],
           'gptj-99.9': ['Server', 'Offline'],
+          'stable-diffusion-xl': ['Server', 'Offline'],
+          'llama2-70b-99': ['Server', 'Offline'],
+          'llama2-70b-99.9': ['Server', 'Offline'],
       },
       'edge': {
           'resnet': ['SingleStream', 'MultiStream', 'Offline'],
@@ -140,6 +143,7 @@ def main():
           '3d-unet-99.9': ['SingleStream', 'Offline'],
           'gptj-99': ['SingleStream', 'Offline'],
           'gptj-99.9': ['SingleStream', 'Offline'],
+          'stable-diffusion-xl': ['SingleStream', 'Offline'],
       }
   }
 
diff --git a/tools/submission/preprocess_submission.py b/tools/submission/preprocess_submission.py
index f81cfae0af..d6e0212530 100644
--- a/tools/submission/preprocess_submission.py
+++ b/tools/submission/preprocess_submission.py
@@ -44,7 +44,7 @@ def get_args():
         default=False, action="store_true")
     parser.add_argument(
         "--version",
-        default="v3.1",
+        default="v4.0",
         choices=list(checker.MODEL_CONFIG.keys()),
         help="mlperf version")
     parser.add_argument("--submitter", help="filter to submitter")
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index e61590e340..2d00b6a0ef 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -3071,24 +3071,21 @@ def log_result(
                                 model_name,
                                 scenario,
                             )
-                            if not os.path.exists(compliance_dir) and "gptj" not in model_name:
-                                log.error("no compliance dir for %s", name)
+                            if not check_compliance_dir(
+                                compliance_dir,
+                                mlperf_model,
+                                scenario_fixed,
+                                config,
+                                division,
+                                system_json,
+                                name
+                            ):
+                                log.error(
+                                    "compliance dir %s has issues", compliance_dir
+                                )
                                 results[name] = None
                             else:
-                                if not check_compliance_dir(
-                                    compliance_dir,
-                                    mlperf_model,
-                                    scenario_fixed,
-                                    config,
-                                    division,
-                                    system_json,
-                                ):
-                                    log.error(
-                                        "compliance dir %s has issues", compliance_dir
-                                    )
-                                    results[name] = None
-                                else:
-                                    compliance = 1
+                                compliance = 1
 
                         if results.get(name):
                             if accuracy_is_valid:
@@ -3476,7 +3473,7 @@ def check_compliance_acc_dir(test_dir, model, config):
 
 
 def check_compliance_dir(
-    compliance_dir, model, scenario, config, division, system_json
+    compliance_dir, model, scenario, config, division, system_json, name
 ):
     compliance_perf_pass = True
     compliance_perf_dir_pass = True
@@ -3517,6 +3514,10 @@ def check_compliance_dir(
     ]:
         test_list.append("TEST06") 
 
+    if test_list and not os.path.exists(compliance_dir):
+        log.error("no compliance dir for %s: %s", name, compliance_dir)
+        return False
+
     # Check performance of all Tests
     for test in test_list:
         test_dir = os.path.join(compliance_dir, test)

From 190413d97b5a75b167a88e2edf5933a3f015fb0d Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 25 Jan 2024 09:57:00 -0500
Subject: [PATCH 041/305] Add Llama2 checks and log additional values (#1578)

---
 loadgen/bindings/python_api.cc         |  2 ++
 loadgen/results.cc                     | 16 ++++++++++++--
 loadgen/test_settings.h                |  3 +++
 loadgen/test_settings_internal.cc      | 18 +++++++++++++++-
 loadgen/test_settings_internal.h       |  2 ++
 mlperf.conf                            |  1 +
 tools/submission/submission_checker.py | 29 ++++++++++++++++++++++++++
 7 files changed, 68 insertions(+), 3 deletions(-)

diff --git a/loadgen/bindings/python_api.cc b/loadgen/bindings/python_api.cc
index 816f7a4f61..cfe24bd3c7 100644
--- a/loadgen/bindings/python_api.cc
+++ b/loadgen/bindings/python_api.cc
@@ -336,6 +336,8 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
                      &TestSettings::test05_sample_index_rng_seed)
       .def_readwrite("test05_schedule_rng_seed", &TestSettings::test05_schedule_rng_seed)
       .def_readwrite("use_token_latencies", &TestSettings::use_token_latencies)
+      .def_readwrite("ttft_latency", &TestSettings::server_ttft_latency)
+      .def_readwrite("tpot_latency", &TestSettings::server_tpot_latency)
       .def("FromConfig", &TestSettings::FromConfig, "FromConfig.");
 
   pybind11::enum_<LoggingMode>(m, "LoggingMode")
diff --git a/loadgen/results.cc b/loadgen/results.cc
index 21fd2c90ae..5048ac72eb 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -636,16 +636,28 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
       MLPERF_LOG(detail, "result_first_token_mean_latency_ns", first_token_latency_mean);
       for (auto& lp : token_latency_percentiles) {
         MLPERF_LOG(detail,
-                    "result_" + DoubleToString(lp.percentile * 100) +
+                    "result_first_token_" + DoubleToString(lp.percentile * 100) +
                         "_percentile_latency_ns",
                     lp.sample_latency);
+        if ((lp.percentile == .999) & (lp.sample_latency > settings.server_ttft_latency)){
+          MLPERF_LOG_WARNING(detail, "warning_generic_message", 
+            "Value for result_first_token_" + DoubleToString(lp.percentile * 100) +
+            "_percentile_latency_ns greater than target time_to_first_token"
+          );
+        }
       }
       double tps_w_lg = ((double)token_count) / pr.final_query_issued_time;
       double tps_wo_lg= ((double)token_count) / (sample_latency_mean * sample_count);
       MLPERF_LOG(detail, "result_token_throughput_with_loadgen_overhead", tps_w_lg);
       MLPERF_LOG(detail, "result_token_throughput", tps_wo_lg);
-      double tpot = sample_count * (sample_latency_mean - first_token_latency_mean) / ((double)token_count);
+      uint64_t tpot = sample_count * (sample_latency_mean - first_token_latency_mean) / (token_count);
       MLPERF_LOG(detail, "result_time_to_output_token", tpot);
+      if (tpot > settings.server_tpot_latency){
+        MLPERF_LOG_WARNING(detail, "warning_generic_message",
+          "Value for result_time_to_output_token "
+          "greater than target time_to_output_token"
+        );
+      }
     } else {
       double tokens_per_second = token_count / pr.max_latency;
       MLPERF_LOG(detail, "result_tokens_per_second", tokens_per_second);
diff --git a/loadgen/test_settings.h b/loadgen/test_settings.h
index ccae5327a7..b0018380d3 100644
--- a/loadgen/test_settings.h
+++ b/loadgen/test_settings.h
@@ -264,6 +264,9 @@ struct TestSettings {
   uint64_t performance_sample_count_override = 0;
   /// \brief Measure token latencies
   bool use_token_latencies = false;
+  /// Token latency parameters
+  uint64_t server_ttft_latency = 100000000;
+  uint64_t server_tpot_latency = 100000000;
   /**@}*/
 };
 
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 0a4b9af6ea..95e53b7315 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -49,7 +49,9 @@ TestSettingsInternal::TestSettingsInternal(
       performance_issue_same_index(requested.performance_issue_same_index),
       performance_sample_count(0),
       sample_concatenate_permutation(false),
-      use_token_latencies(requested.use_token_latencies){
+      use_token_latencies(requested.use_token_latencies),
+      server_ttft_latency(requested.server_ttft_latency),
+      server_tpot_latency(requested.server_tpot_latency){
   // Target QPS, target latency, and max_async_queries.
   switch (requested.scenario) {
     case TestScenario::SingleStream:
@@ -330,6 +332,14 @@ void LogRequestedTestSettings(const TestSettings &s) {
                s.performance_issue_same_index);
     MLPERF_LOG(detail, "requested_performance_sample_count_override",
                s.performance_sample_count_override);
+    // Token latencies specific values
+    if (s.use_token_latencies){
+      MLPERF_LOG(detail, "requested_use_token_latencies", s.use_token_latencies);
+      if (s.scenario != TestScenario::Offline){
+        MLPERF_LOG(detail, "requested_server_ttft_latency", s.server_ttft_latency);
+        MLPERF_LOG(detail, "requested_server_tpot_latency", s.server_tpot_latency);
+      }
+    }
 #else
     detail("");
     detail("Requested Settings:");
@@ -673,8 +683,14 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
   lookupkv(model, scenario, "test05_sample_index_rng_seed", &test05_sample_index_rng_seed,
            nullptr);
   lookupkv(model, scenario, "test05_schedule_rng_seed", &test05_schedule_rng_seed, nullptr);
+
+  // keys that apply to token metrics
   if (lookupkv(model, scenario, "use_token_latencies", &val, nullptr))
     use_token_latencies = (val == 1) ? true : false;
+    if (use_token_latencies){
+      lookupkv(model, "Server", "ttft_latency", &server_ttft_latency, nullptr, 1000 * 1000);
+      lookupkv(model, "Server", "tpot_latency", &server_tpot_latency, nullptr, 1000 * 1000);
+    }
 
   // keys that apply to SingleStream
   lookupkv(model, "SingleStream", "target_latency_percentile", nullptr,
diff --git a/loadgen/test_settings_internal.h b/loadgen/test_settings_internal.h
index 6bc1c3cf39..3392dd59d8 100644
--- a/loadgen/test_settings_internal.h
+++ b/loadgen/test_settings_internal.h
@@ -83,6 +83,8 @@ struct TestSettingsInternal {
 
   bool sample_concatenate_permutation;
   bool use_token_latencies = false;
+  uint64_t server_ttft_latency;
+  uint64_t server_tpot_latency;
 };
 
 /// \brief A namespace of collections of FindPeakPerformance helper functions,
diff --git a/mlperf.conf b/mlperf.conf
index fe743a8dde..8c6f53849e 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -58,6 +58,7 @@ rnnt.Server.target_latency = 1000
 gptj.Server.target_latency = 20000
 stable-diffusion-xl.Server.target_latency = 20000
 # Falcon Server scenario requires two latency constraints
+llama2-70b.*.use_token_latencies = 1
 llama2-70b.Server.target_latency = 2000
 llama2-70b.Server.ttft_latency = 2000
 llama2-70b.Server.tpot_latency = 200
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 2d00b6a0ef..bd716fbd00 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1389,6 +1389,17 @@
     }
 }
 
+LLAMA2_LATENCY_LIMITS = {
+    "interactive": {
+        "ttft": 500,
+        "tpot": 50
+    },
+    "conversational": {
+        "ttft": 2000,
+        "tpot": 200
+    }
+}
+
 ACC_PATTERN = {
     "acc": r"^accuracy=([\d\.]+).*",
     "AUC": r"^AUC=([\d\.]+).*",
@@ -1891,6 +1902,19 @@ def check_accuracy_dir(config, model, path, verbose):
     return is_valid, result_acc
 
 
+def extra_check_llama2(mlperf_log, scenario):
+    if (mlperf_log["use_token_latencies"]):
+        if scenario == "Offline":
+            # For offline no further checks are necessary
+            return None, True
+        else:
+            for constraint, limits in LLAMA2_LATENCY_LIMITS.items():
+                if mlperf_log["result_first_token_99.9_percentile_latency_ns"] < limits["ttft"] and mlperf_log["result_time_to_output_token"] < limits["tpot"]:
+                    return constraint, True
+    else:
+        return None, False
+            
+
 def get_performance_metric(
     config, model, path, scenario_fixed, division, system_json, has_power=False
 ):
@@ -1911,6 +1935,8 @@ def get_performance_metric(
     )
 
     res = float(mlperf_log[RESULT_FIELD_NEW[config.version][scenario_for_res]])
+    if model in RESULT_FIELD_BENCHMARK_OVERWRITE and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[model]:
+        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[model][scenario_for_res]])
 
     inferred = False
     if scenario_fixed != scenario:
@@ -1946,6 +1972,9 @@ def check_performance_dir(
         res = float(mlperf_log[RESULT_FIELD_NEW[config.version][scenario_for_res]])
         if model in RESULT_FIELD_BENCHMARK_OVERWRITE and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[model]:
             res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[model][scenario_for_res]])
+        
+        if model in ["llama2-70b-99", "llama2-70b-99.9"]:
+            llama_constraint, is_valid = extra_check_llama2(mlperf_log, scenario_fixed)
 
         latency_99_percentile = mlperf_log["result_99.00_percentile_latency_ns"]
         latency_mean = mlperf_log["result_mean_latency_ns"]

From 27ef43ae504f11b40878473e4fabafc0e1f01066 Mon Sep 17 00:00:00 2001
From: mlcommons-bot <74634038+mlcommons-bot@users.noreply.github.com>
Date: Thu, 25 Jan 2024 18:13:38 -0600
Subject: [PATCH 042/305] =?UTF-8?q?=F0=9F=94=84=20synced=20local=20'tools/?=
 =?UTF-8?q?submission/power/sources=5Fchecksums.json'=20with=20remote=20'c?=
 =?UTF-8?q?ompliance/sources=5Fchecksums.json'=20(#1582)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: mlcommons-bot <null>
---
 tools/submission/power/sources_checksums.json | 38 +------------------
 1 file changed, 2 insertions(+), 36 deletions(-)

diff --git a/tools/submission/power/sources_checksums.json b/tools/submission/power/sources_checksums.json
index 0ae4cc0205..78a240f1ad 100644
--- a/tools/submission/power/sources_checksums.json
+++ b/tools/submission/power/sources_checksums.json
@@ -1,38 +1,4 @@
 [
-    {
-        "server.py": "c3f90f2f7eeb4db30727556d0c815ebc89b3d28b",
-        "__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
-        "client.py": "33ca4f26368777ac06e01f9567b714a4b8063886",
-        "tests/unit/test_source_hashes.py": "00468a2907583c593e6574a1f6b404e4651c221a",
-        "tests/unit/__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
-        "tests/unit/test_server.py": "948c1995d4008bc2aa6c4046a34ffa3858d6d671",
-        "lib/time_sync.py": "3210db56eb0ff0df57bf4293dc4d4b03fffd46f1",
-        "lib/source_hashes.py": "60a2e02193209e8d392803326208d5466342da18",
-        "lib/common.py": "611d8b29633d331eb19c9455ea3b5fa3284ed6df",
-        "lib/server.py": "8054263a14dedddcf8e1c01adc19596c21bad591",
-        "lib/__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
-        "lib/summary.py": "aa92f0a3f975eecd44d3c0cd0236342ccc9f941d",
-        "lib/client.py": "c146491755e219a28d440b31f83998dbd5532483",
-        "lib/external/ntplib.py": "4da8f970656505a40483206ef2b5d3dd5e81711d",
-        "lib/external/__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709"
-    },
-    {
-        "__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
-        "client.py": "33ca4f26368777ac06e01f9567b714a4b8063886",
-        "lib/__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
-        "lib/client.py": "ac2aa093c8e8bbc9569b9e2a3471bc64e58a2258",
-        "lib/common.py": "611d8b29633d331eb19c9455ea3b5fa3284ed6df",
-        "lib/external/__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
-        "lib/external/ntplib.py": "4da8f970656505a40483206ef2b5d3dd5e81711d",
-        "lib/server.py": "c7af63c31bb2fbedea4345f571f6e3507d268ada",
-        "lib/source_hashes.py": "60a2e02193209e8d392803326208d5466342da18",
-        "lib/summary.py": "aa92f0a3f975eecd44d3c0cd0236342ccc9f941d",
-        "lib/time_sync.py": "122eba67a9abc85635223e054def53be1367ade2",
-        "server.py": "c3f90f2f7eeb4db30727556d0c815ebc89b3d28b",
-        "tests/unit/__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
-        "tests/unit/test_server.py": "948c1995d4008bc2aa6c4046a34ffa3858d6d671",
-        "tests/unit/test_source_hashes.py": "00468a2907583c593e6574a1f6b404e4651c221a"
-    },
     {
         "__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
         "client.py": "33ca4f26368777ac06e01f9567b714a4b8063886",
@@ -41,7 +7,7 @@
         "lib/common.py": "611d8b29633d331eb19c9455ea3b5fa3284ed6df",
         "lib/external/__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
         "lib/external/ntplib.py": "4da8f970656505a40483206ef2b5d3dd5e81711d",
-        "lib/server.py": "c7af63c31bb2fbedea4345f571f6e3507d268ada",
+        "lib/server.py": "99303c836c683aa9017ec565104e636161d02acb",
         "lib/source_hashes.py": "60a2e02193209e8d392803326208d5466342da18",
         "lib/summary.py": "aa92f0a3f975eecd44d3c0cd0236342ccc9f941d",
         "lib/time_sync.py": "80894ef2389e540781ff78de94db16aa4203a14e",
@@ -50,4 +16,4 @@
         "tests/unit/test_server.py": "948c1995d4008bc2aa6c4046a34ffa3858d6d671",
         "tests/unit/test_source_hashes.py": "00468a2907583c593e6574a1f6b404e4651c221a"
     }
-]
+]
\ No newline at end of file

From 9b8006ff2f0ade58cbc1935fbda967a19b2e363b Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 25 Jan 2024 19:26:57 -0500
Subject: [PATCH 043/305] Fix image list mismatch (#1579)

Co-authored-by: Miro <mirhodak@amd.com>
---
 text_to_image/coco.py                  | 4 ++--
 tools/submission/submission_checker.py | 2 ++
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/text_to_image/coco.py b/text_to_image/coco.py
index fa89ba2152..b2c9d6dfc2 100644
--- a/text_to_image/coco.py
+++ b/text_to_image/coco.py
@@ -174,9 +174,9 @@ def save_images(self, ids, ds):
         for id in ids:
             caption = ds.get_caption(id)
             generated = Image.fromarray(self.results[idx[id]])
-            image_path_tmp = f"images/{self.content_ids[id]}.png"
+            image_path_tmp = f"images/{self.content_ids[idx[id]]}.png"
             generated.save(image_path_tmp)
-            info.append((self.content_ids[id], caption))
+            info.append((self.content_ids[idx[id]], caption))
         with open("images/captions.txt", "w+") as f:
             for id, caption in info:
                 f.write(f"{id}  {caption}\n")
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index bd716fbd00..c4c66a4aa9 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1785,6 +1785,8 @@ def check_extra_files(path, target_files):
                 if target_file not in files:
                     check_pass = False
                     missing_files.append(f"{os.path.join(path, dir, target_file)}.png")
+            if "captions" not in files:
+                missing_files.append(f"{os.path.join(path, dir, 'captions.txt')}")
     return check_pass, missing_files
 
 

From 180014ad5724de84fd16a42bef69e8f58faf9e4f Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Thu, 25 Jan 2024 19:08:54 -0800
Subject: [PATCH 044/305] #1558 update llama2 reference fp32 accuracy (#1583)

Co-authored-by: Miro <mirhodak@amd.com>
---
 language/llama2-70b/README.md          | 12 ++++++------
 tools/submission/submission_checker.py |  4 ++--
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index a9a150b4cb..2614749a76 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -195,15 +195,15 @@ if [ -e ${ACCURACY_LOG_FILE} ]; then
 fi
 ```
 
-The ServerSUT was not tested for GPU runs. You can try setting `--device cuda:0`, but YMMV.
+The ServerSUT was not tested for GPU runs.
 
 
 ## Accuracy Target
 Running the GPU implementation in FP32 precision resulted in the following FP32 accuracy targets (normalized to a 0-100
 scale from a 0.0-1.0 scale):
-- Rouge1: 43.88
-- Rouge2: 21.7108
-- RougeL: 28.2502
-- RougeLsum: 41.4821
+- Rouge1: 44.4312
+- Rouge2: 22.0352
+- RougeL: 28.6162
+- Tokens per sample: 294.45
 
-This was run an 8xH100 node. Total runtime was ~4.5 days.
+This was run on a DGX-H100 node. Total runtime was ~4.5 days.
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index c4c66a4aa9..b6cd599df1 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1124,8 +1124,8 @@
             "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
             "gptj-99" : ("ROUGE1", 42.9865 * 0.99, "ROUGE2", 20.1235 * 0.99, "ROUGEL", 29.9881 * 0.99, "GEN_LEN", 4016878*0.9),
             "gptj-99.9" : ("ROUGE1", 42.9865 * 0.999, "ROUGE2", 20.1235 * 0.999, "ROUGEL", 29.9881 * 0.999, "GEN_LEN", 4016878*0.9),
-            "llama2-70b-99" : ("ROUGE1", 43.88 * 0.99, "ROUGE2", 21.7108 * 0.99, "ROUGEL", 28.2502 * 0.99, "TOKENS_PER_SAMPLE", 293.3*0.9),
-            "llama2-70b-99.9" : ("ROUGE1", 43.88 * 0.999, "ROUGE2", 21.7108 * 0.999, "ROUGEL", 28.2502 * 0.999, "TOKENS_PER_SAMPLE", 293.3*0.9),
+            "llama2-70b-99" : ("ROUGE1", 44.4312 * 0.99, "ROUGE2", 22.0352 * 0.99, "ROUGEL", 28.6162 * 0.99, "TOKENS_PER_SAMPLE", 294.45*0.9),
+            "llama2-70b-99.9" : ("ROUGE1", 44.4312 * 0.999, "ROUGE2", 22.0352 * 0.999, "ROUGEL", 28.6162 * 0.999, "TOKENS_PER_SAMPLE", 294.45*0.9),
             "stable-diffusion-xl": ("CLIP_SCORE", 31.68631873, "FID_SCORE", 23.01085758)
         },
         "accuracy-upper-limit": {

From 523316e5a49f458945e8c559de597df48abbf272 Mon Sep 17 00:00:00 2001
From: mlcommons-bot <74634038+mlcommons-bot@users.noreply.github.com>
Date: Fri, 26 Jan 2024 11:34:08 -0600
Subject: [PATCH 045/305] =?UTF-8?q?=F0=9F=94=84=20synced=20local=20'tools/?=
 =?UTF-8?q?submission/power/power=5Fchecker.py'=20with=20remote=20'complia?=
 =?UTF-8?q?nce/check.py'=20(#1587)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: mlcommons-bot <null>
---
 tools/submission/power/power_checker.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tools/submission/power/power_checker.py b/tools/submission/power/power_checker.py
index 5adcd197c6..93d9d6fb98 100755
--- a/tools/submission/power/power_checker.py
+++ b/tools/submission/power/power_checker.py
@@ -408,6 +408,8 @@ def get_avg_power(power_path: str, run_path: str) -> Tuple[float, float]:
 
         with open(spl_fname) as f:
             for line in f:
+                if not line.startswith("Time"):
+                    continue
                 timestamp = (
                     datetime.strptime(line.split(",")[1], datetime_format)
                 ).replace(tzinfo=timezone.utc)

From 3ad853426528a3d692ea34a72b81a7c4fed0346e Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Fri, 26 Jan 2024 19:10:13 +0000
Subject: [PATCH 046/305] Update the main README.md for 4.0 (#1586)

---
 README.md | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c635c73b0e..f1cfa2721d 100644
--- a/README.md
+++ b/README.md
@@ -15,7 +15,23 @@ Please see the [MLPerf Inference benchmark paper](https://arxiv.org/abs/1911.025
 ```
 ## MLPerf Inference v4.0 (submission deadline February 23, 2024)
 
-Code freeze coming soon...
+There is an extra one-week extension allowed only for the llama2-70b submissions. For submissions, please use the master branch and any commit since the [4.0 seed release](https://github.com/mlcommons/inference/commit/8e36925bd36a503e39fcbbc488e9e46126f079ed) although it is best to use the latest commit. v4.0 tag will be created from the master branch after the result publication.
+
+For power submissions please use [SPEC PTD 1.10](https://github.com/mlcommons/power/tree/main/inference_v1.0) (needs special access) and any commit of the power-dev repository after the [code-freeze](https://github.com/mlcommons/power-dev/commit/4e026f43481f46ad57d2464d28924018444b0428)
+
+| model | reference app | framework | dataset | category
+| ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, onnx, tvm, ncnn | imagenet2012 | edge,datacenter |
+| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge,datacenter |
+| dlrm-v2 | [recommendation/dlrm_v2](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm_v2/pytorch) | pytorch | Multihot Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 | edge,datacenter |
+| rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus | edge,datacenter |
+| gpt-j | [language/gpt-j](https://github.com/mlcommons/inference/tree/master/language/gpt-j)| pytorch | CNN-Daily Mail | edge,datacenter |
+| stable-diffusion-xl | [text_to_image] (https://github.com/mlcommons/inference/tree/master/text_to_image) | pytorch | COCO 2014| edge,datacenter |
+| llama2-70b | [language/llama2-70b](https://github.com/mlcommons/inference/tree/master/language/llama2-70b) | pytorch | OpenOrca | datacenter |
+
+* Framework here is given for the reference implementation. Submitters are free to use their own frameworks to run the benchmark.
 
 ## MLPerf Inference v3.1 (submission August 18, 2023)
 Please use [v3.1 tag](https://github.com/mlcommons/inference/releases/tag/v3.1) (```git checkout v3.1```) if you would like to reproduce the v3.1 results. 

From a04b1f52c2b0baa628bd4b2d1ddec245b6e65008 Mon Sep 17 00:00:00 2001
From: Anton Lokhmotov <psyhtest@users.noreply.github.com>
Date: Tue, 30 Jan 2024 18:01:24 +0000
Subject: [PATCH 047/305] Ignore trailing whitespace lines in spl.txt files
 (#1584)

* Ignore trailing whitespace lines in spl.txt files.

* Remove fix from sync'ed power_checker.py.

* Reformat according to black.
---
 tools/submission/submission_checker.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index b6cd599df1..ac76c5dea5 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -2261,6 +2261,8 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
     power_list = []
     with open(spl_fname) as f:
         for line in f:
+            if not line.startswith("Time"):
+                continue
             timestamp = (
                 datetime.datetime.strptime(line.split(",")[1], datetime_format)
                 + server_timezone

From 4bdf56f883bafa08cd571cc0f515a7362afa0b79 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 1 Feb 2024 00:33:13 +0000
Subject: [PATCH 048/305] Add stable diffusion and llama2 to the final
 spreadsheet (#1589)

---
 tools/submission/generate_final_report.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 70ca9affc4..13a01fe553 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -14,8 +14,8 @@ def get_args():
   """Parse commandline."""
   parser = argparse.ArgumentParser()
   parser.add_argument('--input', required=True, help='results csv from checker')
-  parser.add_argument('--version', default='3.1', help='mlperf version')
-  parser.add_argument('--repository', default='submissions_inference_3.1', help='mlperf repository')
+  parser.add_argument('--version', default='4.0', help='mlperf version')
+  parser.add_argument('--repository', default='submissions_inference_4.0', help='mlperf repository')
   args = parser.parse_args()
   return args
 
@@ -104,7 +104,7 @@ def main():
                    [
                        'resnet', 'retinanet', '3d-unet-99', '3d-unet-99.9',
                        'rnnt', 'bert-99', 'bert-99.9', 'dlrm-v2-99', 'dlrm-v2-99.9',
-                       'gptj-99', 'gptj-99.9'
+                       'gptj-99', 'gptj-99.9', 'stable-diffusion-xl', 'llama2-70b-99', 'llama2-70b-99.9'
                    ], ['SingleStream', 'MultiStream', 'Server', 'Offline'],
                    [
                        'Latency (ms)',

From 3a902e598e5a17013c26fea975c0a60e6d7feae1 Mon Sep 17 00:00:00 2001
From: nvyihengz <109830892+nvyihengz@users.noreply.github.com>
Date: Thu, 1 Feb 2024 06:29:59 -0800
Subject: [PATCH 049/305] Add support to dump 10 compliance images during
 accuracy run for SDXL (#1591)

* Add support to dump 10 compliance images during accuracy run for SDXL

* Fix typo

* Dump caption.txt in the same path
---
 text_to_image/tools/accuracy_coco.py | 23 ++++++++++++++++++++++-
 1 file changed, 22 insertions(+), 1 deletion(-)

diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
index ad5c93c0cb..f831c4b6e2 100644
--- a/text_to_image/tools/accuracy_coco.py
+++ b/text_to_image/tools/accuracy_coco.py
@@ -26,6 +26,7 @@ def get_args():
     parser.add_argument("--statistics-path", default=None, help="path to statistics")
     parser.add_argument("--verbose", action="store_true", help="verbose messages")
     parser.add_argument("--output-file", default="coco-results.json", help="path to output file")
+    parser.add_argument("--compliance-images-path", required=False, help="path to dump 10 stable diffusion xl compliance images")
     parser.add_argument("--device", default="cpu", choices=["gpu", "cpu"])
     args = parser.parse_args()
     return args
@@ -43,7 +44,6 @@ def preprocess_image(img_dir, file_name):
 
 def main():
     args = get_args()
-
     result_dict = {}
 
     # Load dataset annotations
@@ -63,6 +63,22 @@ def main():
     if args.statistics_path is None:
         statistics_path = os.path.join(os.path.dirname(__file__), "val2014.npz")
 
+    # Set compliance images path
+    dump_compliance_images = False
+    if args.compliance_images_path:
+        if not os.path.exists(args.compliance_images_path):
+            os.makedirs(args.compliance_images_path)
+        dump_compliance_images = True
+        compliance_images_idx_list = []
+        with open(os.path.join(os.path.dirname(__file__), "sample_ids.txt"), 'r') as compliance_id_file:
+            for line in compliance_id_file:
+                idx = int(line.strip())
+                compliance_images_idx_list.append(idx)
+        # Dump caption.txt
+        with open(os.path.join(args.compliance_images_path, "captions.txt"), "w+") as caption_file:
+            for idx in compliance_images_idx_list:
+                caption_file.write(f"{idx}  {df_captions.iloc[idx]['caption']}\n")
+
     # Load torchmetrics modules
     clip = CLIPEncoder(device=device)
     clip_scores = []
@@ -78,6 +94,11 @@ def main():
         generated_img = np.frombuffer(bytes.fromhex(j['data']), np.uint8).reshape(1024, 1024, 3)
         result_list.append(generated_img)
         generated_img = Image.fromarray(generated_img)
+
+        # Dump compliance images
+        if dump_compliance_images and idx in compliance_images_idx_list:
+            generated_img.save(os.path.join(args.compliance_images_path, f"{idx}.png"))
+
         # generated_img = torch.Tensor(generated_img).to(torch.uint8).to(device)
         # Load Ground Truth
         caption = df_captions.iloc[idx]["caption"]

From cc3daae3c7f18d7eedc5ffbc271d324e5aca6233 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Thu, 1 Feb 2024 10:56:34 -0800
Subject: [PATCH 050/305] #1598: fix token and sample logging for Llama2 when
 accuracy_log_sampling_target is enabled (#1599)

---
 compliance/nvidia/TEST06/run_verification.py | 1 -
 loadgen/loadgen.cc                           | 7 ++++++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/compliance/nvidia/TEST06/run_verification.py b/compliance/nvidia/TEST06/run_verification.py
index 9d4cd1ad74..afa4081d32 100644
--- a/compliance/nvidia/TEST06/run_verification.py
+++ b/compliance/nvidia/TEST06/run_verification.py
@@ -61,7 +61,6 @@ def first_token_check(acc_data, dtype):
     for sample in acc_data:
         data = np.frombuffer(bytes.fromhex(sample["data"]), dtype=dtype)
         token_data = np.frombuffer(bytes.fromhex(sample["token_data"]), dtype=dtype)
-        print(token_data)
         for t1, t2 in zip(data, token_data):
             if t1 != t2:
                 return False
diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index 7db118613d..c9785b78fe 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -140,7 +140,12 @@ struct ResponseDelegateDetailed : public ResponseDelegate {
     // For some reason, using std::unique_ptr<std::vector> wasn't moving
     // into the lambda; even with C++14.
     std::vector<uint8_t>* token_data_copy = nullptr;
-    if (mode == TestMode::AccuracyOnly) {
+    double accuracy_log_val =
+        sample->accuracy_log_val + accuracy_log_offset < 1.0
+            ? sample->accuracy_log_val + accuracy_log_offset
+            : sample->accuracy_log_val + accuracy_log_offset - 1.0;
+    if (mode == TestMode::AccuracyOnly ||
+        accuracy_log_val <= accuracy_log_prob) {
       uint8_t* src_begin = reinterpret_cast<uint8_t*>(response->data);
       uint8_t* src_end = src_begin + response->size;
       token_data_copy = new std::vector<uint8_t>(src_begin, src_end);

From 473053fc9f1b8c5edf60e11ec74607126938fbc4 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Fri, 2 Feb 2024 09:25:07 -0500
Subject: [PATCH 051/305] Fix loadgen token metrics latency constrains (#1596)

* Fix loadgen token metrics latency constrains

* Update perf constraints check for token metrics

* Add equal issue mode for LLMs models
---
 language/llama2-70b/user.conf     |   2 +
 loadgen/loadgen.cc                |   4 +
 loadgen/logging.cc                |  38 ++++++-
 loadgen/logging.h                 |   3 +
 loadgen/results.cc                | 162 +++++++++++++++++++++++-------
 loadgen/results.h                 |  12 ++-
 loadgen/test_settings_internal.cc |   7 +-
 mlperf.conf                       |   4 +-
 8 files changed, 193 insertions(+), 39 deletions(-)

diff --git a/language/llama2-70b/user.conf b/language/llama2-70b/user.conf
index bb97c437a9..945082fe98 100644
--- a/language/llama2-70b/user.conf
+++ b/language/llama2-70b/user.conf
@@ -9,3 +9,5 @@
 *.Server.target_qps = 0.5
 *.Server.min_duration = 120000
 *.Server.min_query_count = 100
+
+llama2-70b.Server.sample_concatenate_permutation = 1
\ No newline at end of file
diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index c9785b78fe..1d7d7420d9 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -529,6 +529,9 @@ PerformanceResult IssueQueries(SystemUnderTest* sut,
   std::vector<QuerySampleLatency> first_token_latencies(
     GlobalLogger().GetTokenLatencies(expected_latencies));
 
+  std::vector<QuerySampleLatency> time_per_output_token_arr(
+    GlobalLogger().GetTimePerOutputToken(expected_latencies));
+
   std::vector<int64_t> tokens_per_sample(
     GlobalLogger().GetTokensPerSample(expected_latencies));
 
@@ -588,6 +591,7 @@ PerformanceResult IssueQueries(SystemUnderTest* sut,
                           final_query_all_samples_done_time,
                           TokenPerformanceResults{
                             first_token_latencies,
+                            time_per_output_token_arr,
                             tokens_per_sample
                           }
                         };
diff --git a/loadgen/logging.cc b/loadgen/logging.cc
index d990e7c4dc..bd8e377fab 100644
--- a/loadgen/logging.cc
+++ b/loadgen/logging.cc
@@ -349,6 +349,7 @@ void AsyncLog::RestartLatencyRecording(uint64_t first_sample_sequence_id,
   latencies_.reserve(latencies_to_reserve);
   token_latencies_.reserve(latencies_to_reserve);
   tokens_per_sample_.reserve(latencies_to_reserve);
+  time_per_output_token_.reserve(latencies_to_reserve);
 }
 
 void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
@@ -430,15 +431,40 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
       // If the SUT recorded the wrong sample, the test will hang and see
       // the error above.
       return;
-    } else if (n_tokens == 0){
+    } 
+    if (n_tokens == 0){
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
                             "n_tokens argument missing or attempted to record 0 as number of tokens");
     } else if (n_tokens < 0){
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
                             "Attempted to record a negative number of tokens");
       n_tokens = 0;
+    } else if (n_tokens == 1){
+      MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
+                            "Number of tokens need to be greater than 1");
+      n_tokens = 0;
+    }
+    if (time_per_output_token_.size() <= i){
+      time_per_output_token_.resize(i + 1, kInvalidLatency);
+    } else if (time_per_output_token_[i] != kInvalidLatency) {
+      // Call LogErrorSync here since this kind of error could result in a
+      // segfault in the near future.
+  #if USE_NEW_LOGGING_FORMAT
+      MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
+                            "Attempted to complete a sample twice.");
+  #else
+      GlobalLogger().LogErrorSync("Attempted to complete a sample twice.");
+  #endif
+
+      // Return without recording the latency again to avoid potentially
+      // ending the test before the SUT is actually done, which could result
+      // in a segfault.
+      // If the SUT recorded the wrong sample, the test will hang and see
+      // the error above.
+      return;
     }
     tokens_per_sample_[i] = n_tokens;
+    time_per_output_token_[i] = (latency - token_latencies_[i]) / (n_tokens - 1);
   }
   latencies_[i] = latency;
   latencies_recorded_++;
@@ -572,6 +598,12 @@ std::vector<QuerySampleLatency> AsyncLog::GetTokenLatencies(size_t expected_coun
   return token_latencies;
 }
 
+std::vector<QuerySampleLatency> AsyncLog::GetTimePerOutputToken(size_t expected_count){
+  std::vector<QuerySampleLatency> tpot_latencies;
+  tpot_latencies.swap(time_per_output_token_);
+  return tpot_latencies;
+}
+
 std::vector<int64_t> AsyncLog::GetTokensPerSample(size_t expected_count) {
   std::vector<int64_t> tokens_per_sample;
   tokens_per_sample.swap(tokens_per_sample_);
@@ -908,6 +940,10 @@ std::vector<QuerySampleLatency> Logger::GetTokenLatencies(
     size_t expected_count) {
   return async_logger_.GetTokenLatencies(expected_count);
 }
+std::vector<QuerySampleLatency> Logger::GetTimePerOutputToken(
+    size_t expected_count) {
+  return async_logger_.GetTimePerOutputToken(expected_count);
+}
 std::vector<QuerySampleLatency> Logger::GetTokensPerSample(
     size_t expected_count) {
   return async_logger_.GetTokensPerSample(expected_count);
diff --git a/loadgen/logging.h b/loadgen/logging.h
index d10f574d60..b9c032a72e 100644
--- a/loadgen/logging.h
+++ b/loadgen/logging.h
@@ -322,6 +322,7 @@ class AsyncLog {
                               QuerySampleLatency latency);
   std::vector<QuerySampleLatency> GetLatenciesBlocking(size_t expected_count);
   std::vector<QuerySampleLatency> GetTokenLatencies(size_t expected_count);
+  std::vector<QuerySampleLatency> GetTimePerOutputToken(size_t expected_count);
   std::vector<int64_t> GetTokensPerSample(size_t expected_count);
   PerfClock::time_point GetMaxCompletionTime();
   QuerySampleLatency GetMaxLatencySoFar();
@@ -386,6 +387,7 @@ class AsyncLog {
   uint64_t latencies_first_sample_sequence_id_ = 0;
   std::vector<QuerySampleLatency> latencies_;
   std::vector<QuerySampleLatency> token_latencies_;
+  std::vector<QuerySampleLatency> time_per_output_token_;
   std::vector<LogBinaryAsHexString> token_records_;
   std::vector<int64_t> tokens_per_sample_;
   QuerySampleLatency max_latency_ = 0;
@@ -421,6 +423,7 @@ class Logger {
                                size_t latencies_to_reserve);
   std::vector<QuerySampleLatency> GetLatenciesBlocking(size_t expected_count);
   std::vector<QuerySampleLatency> GetTokenLatencies(size_t expected_count);
+  std::vector<QuerySampleLatency> GetTimePerOutputToken(size_t expected_count);
   std::vector<int64_t> GetTokensPerSample(size_t expected_count);
   PerfClock::time_point GetMaxCompletionTime();
   QuerySampleLatency GetMaxLatencySoFar();
diff --git a/loadgen/results.cc b/loadgen/results.cc
index 5048ac72eb..d5173f4410 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -97,8 +97,15 @@ void PerformanceSummary::ProcessTokenLatencies() {
     accumulated_first_token_latency += latency;
   }
   first_token_latency_mean = accumulated_first_token_latency / sample_count;
+  QuerySampleLatency accumulated_tpot = 0;
+  for (auto latency : pr.token_results.time_per_output_token_arr) {
+    accumulated_tpot += latency;
+  }
+  time_per_output_token_mean = accumulated_tpot / sample_count;
   std::sort(pr.token_results.first_token_latencies.begin(), 
     pr.token_results.first_token_latencies.end());
+  std::sort(pr.token_results.time_per_output_token_arr.begin(),
+    pr.token_results.time_per_output_token_arr.end());
   
   token_target_latency_percentile.sample_latency =
       pr.token_results.first_token_latencies[sample_count * token_target_latency_percentile.percentile];
@@ -110,6 +117,16 @@ void PerformanceSummary::ProcessTokenLatencies() {
     lp.sample_latency = pr.token_results.first_token_latencies[sample_count * lp.percentile];
   }
 
+  target_tpot_percentile.sample_latency =
+      pr.token_results.time_per_output_token_arr[sample_count * target_tpot_percentile.percentile];
+  time_per_output_token_min = pr.token_results.time_per_output_token_arr.front();
+  time_per_output_token_max = pr.token_results.time_per_output_token_arr.back();
+  for (auto& lp : tpot_percentiles) {
+    assert(lp.percentile >= 0.0);
+    assert(lp.percentile < 1.0);
+    lp.sample_latency = pr.token_results.time_per_output_token_arr[sample_count * lp.percentile];
+  }
+
   if (settings.scenario == TestScenario::Server) {
     // TODO: Maybe another target latency needs to be added?
     QuerySampleLatency max_latency = settings.target_latency.count() + 1;
@@ -121,10 +138,12 @@ void PerformanceSummary::ProcessTokenLatencies() {
 
 }
 
-bool PerformanceSummary::EarlyStopping(std::string* recommendation) {
+bool PerformanceSummary::EarlyStopping(std::string* recommendation, int64_t queries_issued, 
+                                        std::vector<QuerySampleLatency>* sample_latencies,
+                                        std::vector<QuerySampleLatency>* query_latencies,
+                                        std::chrono::nanoseconds target_latency) {
   recommendation->clear();
 
-  int64_t queries_issued = pr.queries_issued;
   MinPassingQueriesFinder find_min_passing;
   double confidence = 0.99;
   double tolerance = 0.0;
@@ -155,7 +174,7 @@ bool PerformanceSummary::EarlyStopping(std::string* recommendation) {
         }
       }
       QuerySampleLatency percentile_estimate =
-          pr.sample_latencies[queries_issued - t];
+          (*sample_latencies)[queries_issued - t];
       *recommendation =
           " * Processed at least " + std::to_string(h_min + 1) + " queries (" +
           std::to_string(queries_issued) + ").\n" + " * Would discard " +
@@ -187,7 +206,7 @@ bool PerformanceSummary::EarlyStopping(std::string* recommendation) {
             break;
           }
         }
-        percentile_estimate = pr.sample_latencies[queries_issued - t];
+        percentile_estimate = (*sample_latencies)[queries_issued - t];
         *recommendation +=
             "\n * Early stopping " +
             DoubleToString(multi_stream_percentile * 100, 0) +
@@ -198,9 +217,9 @@ bool PerformanceSummary::EarlyStopping(std::string* recommendation) {
     }
     case TestScenario::Server: {
       int64_t t =
-          std::count_if(pr.sample_latencies.begin(), pr.sample_latencies.end(),
+          std::count_if((*sample_latencies).begin(), (*sample_latencies).end(),
                         [=](auto const& latency) {
-                          return latency > settings.target_latency.count();
+                          return latency > target_latency.count();
                         });
       int64_t h = find_min_passing(t, target_latency_percentile.percentile,
                                    tolerance, confidence);
@@ -239,7 +258,7 @@ bool PerformanceSummary::EarlyStopping(std::string* recommendation) {
         }
       }
       QuerySampleLatency percentile_estimate =
-          pr.query_latencies[queries_issued - t];
+          (*query_latencies)[queries_issued - t];
       *recommendation =
           " * Processed at least " + std::to_string(h_min + 1) + " queries (" +
           std::to_string(queries_issued) + ").\n" + " * Would discard " +
@@ -317,10 +336,28 @@ bool PerformanceSummary::PerfConstraintsMet(std::string* recommendation) {
       break;
     case TestScenario::Server:
       ProcessLatencies();
-      if (target_latency_percentile.sample_latency >
-          settings.target_latency.count()) {
-        *recommendation = "Reduce target QPS to improve latency.";
-        perf_constraints_met = false;
+      if (!settings.use_token_latencies){
+        if (target_latency_percentile.sample_latency >
+            settings.target_latency.count()) {
+          *recommendation = "Reduce target QPS to improve latency.";
+          perf_constraints_met = false;
+        }
+      } else {
+        if ( token_target_latency_percentile.sample_latency >
+            settings.server_ttft_latency) {
+          *recommendation = "TTFT constrain not met: Reduce target QPS to improve latency.";
+          perf_constraints_met = false;
+        }
+
+        if ( target_tpot_percentile.sample_latency >
+            settings.server_tpot_latency) {
+          if (recommendation->empty()){
+            *recommendation = "TPOT constrain not met: Reduce target QPS to improve latency.";
+          } else {
+            recommendation->append("\n * TPOT constrain not met: Reduce target QPS to improve latency.");
+          }
+          perf_constraints_met = false;
+        }
       }
       break;
     case TestScenario::Offline:
@@ -400,10 +437,30 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
   std::string min_duration_recommendation;
   std::string perf_constraints_recommendation;
   std::string early_stopping_recommendation;
+  std::string early_stopping_ttft_recommendation;
+  std::string early_stopping_tpot_recommendation;
 
   bool min_duration_met = MinDurationMet(&min_duration_recommendation);
   bool min_queries_met = MinQueriesMet() && MinSamplesMet();
-  bool early_stopping_met = EarlyStopping(&early_stopping_recommendation);
+  bool early_stopping_met = true;
+  if (!settings.use_token_latencies){
+    early_stopping_met = EarlyStopping(&early_stopping_recommendation,
+                                        pr.queries_issued, 
+                                        &pr.sample_latencies, 
+                                        &pr.query_latencies,
+                                        settings.target_latency);
+  } else {
+    early_stopping_met = EarlyStopping(&early_stopping_tpot_recommendation,
+                                        pr.queries_issued, 
+                                        &pr.token_results.time_per_output_token_arr, 
+                                        &pr.query_latencies,
+                                        std::chrono::nanoseconds(settings.server_tpot_latency)) && 
+                          EarlyStopping(&early_stopping_ttft_recommendation,
+                                        pr.queries_issued, 
+                                        &pr.token_results.first_token_latencies, 
+                                        &pr.query_latencies,
+                                        std::chrono::nanoseconds(settings.server_ttft_latency));
+  }
   bool perf_constraints_met =
       PerfConstraintsMet(&perf_constraints_recommendation);
   bool all_constraints_met = min_duration_met && min_queries_met &&
@@ -435,8 +492,15 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
   if (settings.scenario == TestScenario::SingleStream ||
       settings.scenario == TestScenario::Server ||
       settings.scenario == TestScenario::MultiStream) {
-    summary("Early Stopping Result:");
-    summary(early_stopping_recommendation);
+    if (!settings.use_token_latencies){
+      summary("Early Stopping Result:");
+      summary(early_stopping_recommendation);
+    } else {
+      summary("TTFT Early Stopping Result:");
+      summary(early_stopping_ttft_recommendation);
+      summary("TPOT Early Stopping Result:");
+      summary(early_stopping_tpot_recommendation);
+    }
   }
 
   summary(
@@ -490,17 +554,26 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
     } else if (settings.scenario == TestScenario::Server) {
       double tps_as_completed =
           token_count / pr.final_query_all_samples_done_time;
-      summary("Completed tokens per second    : ",
+      summary("Completed tokens per second                 : ",
               DoubleToString(tps_as_completed));
     }
 
     if (settings.scenario != TestScenario::Offline) {
-      summary("Min First Token latency (ns)    : ", first_token_latency_min);
-      summary("Max First Token latency (ns)    : ", first_token_latency_max);
-      summary("Mean First Token latency (ns)   : ", first_token_latency_mean);
+      summary("Min First Token latency (ns)                : ", first_token_latency_min);
+      summary("Max First Token latency (ns)                : ", first_token_latency_max);
+      summary("Mean First Token latency (ns)               : ", first_token_latency_mean);
       for (auto& lp : token_latency_percentiles) {
         summary(
-            DoubleToString(lp.percentile * 100) + " percentile latency (ns)   : ",
+            DoubleToString(lp.percentile * 100) + " percentile first token latency (ns)   : ",
+            lp.sample_latency);
+      }
+      summary("");
+      summary("Min Time to Output Token (ns)                : ", time_per_output_token_min);
+      summary("Max Time to Output Token (ns)                : ", time_per_output_token_max);
+      summary("Mean Time to Output Token (ns)               : ", time_per_output_token_mean);
+      for (auto& lp : tpot_percentiles) {
+        summary(
+            DoubleToString(lp.percentile * 100) + " percentile time to output token (ns)   : ",
             lp.sample_latency);
       }
     }
@@ -522,11 +595,31 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
   std::string min_duration_recommendation;
   std::string perf_constraints_recommendation;
   std::string early_stopping_recommendation;
+  std::string early_stopping_ttft_recommendation;
+  std::string early_stopping_tpot_recommendation;
   bool min_duration_met = MinDurationMet(&min_duration_recommendation);
   bool min_queries_met = MinQueriesMet() && MinSamplesMet();
   bool perf_constraints_met =
       PerfConstraintsMet(&perf_constraints_recommendation);
-  bool early_stopping_met = EarlyStopping(&early_stopping_recommendation);
+  bool early_stopping_met = true;
+  if (!settings.use_token_latencies){
+    early_stopping_met = EarlyStopping(&early_stopping_recommendation,
+                                        pr.queries_issued, 
+                                        &pr.sample_latencies, 
+                                        &pr.query_latencies,
+                                        settings.target_latency);
+  } else {
+    early_stopping_met = EarlyStopping(&early_stopping_tpot_recommendation,
+                                        pr.queries_issued, 
+                                        &pr.token_results.time_per_output_token_arr, 
+                                        &pr.query_latencies,
+                                        std::chrono::nanoseconds(settings.server_tpot_latency)) && 
+                          EarlyStopping(&early_stopping_ttft_recommendation,
+                                        pr.queries_issued, 
+                                        &pr.token_results.first_token_latencies, 
+                                        &pr.query_latencies,
+                                        std::chrono::nanoseconds(settings.server_ttft_latency));
+  }
   bool all_constraints_met = min_duration_met && min_queries_met &&
                              perf_constraints_met && early_stopping_met;
 
@@ -554,8 +647,12 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
   }
   std::replace(early_stopping_recommendation.begin(),
                early_stopping_recommendation.end(), '\n', ' ');
-  MLPERF_LOG(detail, "early_stopping_result", early_stopping_recommendation);
-
+  if (!settings.use_token_latencies){
+    MLPERF_LOG(detail, "early_stopping_result", early_stopping_recommendation);
+  } else{
+    MLPERF_LOG(detail, "early_stopping_ttft_result", early_stopping_ttft_recommendation);
+    MLPERF_LOG(detail, "early_stopping_tpot_result", early_stopping_tpot_recommendation);
+  }
   // Report number of queries
   MLPERF_LOG(detail, "result_query_count", query_count);
   if (settings.scenario == TestScenario::Server) {
@@ -639,25 +736,20 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
                     "result_first_token_" + DoubleToString(lp.percentile * 100) +
                         "_percentile_latency_ns",
                     lp.sample_latency);
-        if ((lp.percentile == .999) & (lp.sample_latency > settings.server_ttft_latency)){
-          MLPERF_LOG_WARNING(detail, "warning_generic_message", 
-            "Value for result_first_token_" + DoubleToString(lp.percentile * 100) +
-            "_percentile_latency_ns greater than target time_to_first_token"
-          );
-        }
       }
       double tps_w_lg = ((double)token_count) / pr.final_query_issued_time;
       double tps_wo_lg= ((double)token_count) / (sample_latency_mean * sample_count);
       MLPERF_LOG(detail, "result_token_throughput_with_loadgen_overhead", tps_w_lg);
       MLPERF_LOG(detail, "result_token_throughput", tps_wo_lg);
-      uint64_t tpot = sample_count * (sample_latency_mean - first_token_latency_mean) / (token_count);
-      MLPERF_LOG(detail, "result_time_to_output_token", tpot);
-      if (tpot > settings.server_tpot_latency){
-        MLPERF_LOG_WARNING(detail, "warning_generic_message",
-          "Value for result_time_to_output_token "
-          "greater than target time_to_output_token"
-        );
+      for (auto& lp : tpot_percentiles) {
+        MLPERF_LOG(detail,
+                    "result_time_per_output_token_" + DoubleToString(lp.percentile * 100) +
+                        "_percentile_ns",
+                    lp.sample_latency);
       }
+      MLPERF_LOG(detail, "result_time_to_output_token_min", time_per_output_token_min);
+      MLPERF_LOG(detail, "result_time_to_output_token_max", time_per_output_token_max);
+      MLPERF_LOG(detail, "result_time_to_output_token_mean", time_per_output_token_mean);
     } else {
       double tokens_per_second = token_count / pr.max_latency;
       MLPERF_LOG(detail, "result_tokens_per_second", tokens_per_second);
diff --git a/loadgen/results.h b/loadgen/results.h
index 69825a9d30..38bbe32d49 100644
--- a/loadgen/results.h
+++ b/loadgen/results.h
@@ -29,6 +29,7 @@ namespace loadgen {
 /// token based metrics
 struct TokenPerformanceResults {
   std::vector<QuerySampleLatency> first_token_latencies;
+  std::vector<QuerySampleLatency> time_per_output_token_arr;
   std::vector<int64_t> tokens_per_sample;
 };
 
@@ -86,11 +87,17 @@ struct PerformanceSummary {
   QuerySampleLatency first_token_latency_min;
   QuerySampleLatency first_token_latency_max;
   QuerySampleLatency first_token_latency_mean;
+  QuerySampleLatency time_per_output_token_min;
+  QuerySampleLatency time_per_output_token_max;
+  QuerySampleLatency time_per_output_token_mean;
 
   // Latency token target percentile
   PercentileEntry token_target_latency_percentile{settings.target_latency_percentile};
   PercentileEntry token_latency_percentiles[6] = {{.50}, {.90}, {.95},
                                                   {.97}, {.99}, {.999}};
+  PercentileEntry target_tpot_percentile{settings.target_latency_percentile};
+  PercentileEntry tpot_percentiles[6] = {{.50}, {.90}, {.95},
+                                        {.97}, {.99}, {.999}};
 
 #if defined(_WIN32) || defined(WIN32) || defined(_WIN64) || defined(WIN64)
   // MSVC complains if there is no explicit constructor.
@@ -104,7 +111,10 @@ struct PerformanceSummary {
   void ProcessTokenLatencies();
 
   bool MinDurationMet(std::string* recommendation);
-  bool EarlyStopping(std::string* recommendation);
+  bool EarlyStopping(std::string* recommendation, int64_t queries_issued, 
+                      std::vector<QuerySampleLatency>* sample_latencies,
+                      std::vector<QuerySampleLatency>* query_latencies,
+                      std::chrono::nanoseconds target_latency);
   bool MinQueriesMet();
   bool MinSamplesMet();
   bool HasPerfConstraints();
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 95e53b7315..814814df80 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -482,7 +482,12 @@ void TestSettingsInternal::LogAllSettings() const {
 void TestSettingsInternal::LogSummary(AsyncSummary &summary) const {
   summary("samples_per_query : ", samples_per_query);
   summary("target_qps : ", target_qps);
-  summary("target_latency (ns): ", target_latency.count());
+  if (!use_token_latencies){
+    summary("target_latency (ns): ", target_latency.count());
+  } else {
+    summary("ttft_latency (ns): ", server_ttft_latency);
+    summary("tpot_latency (ns): ", server_tpot_latency);
+  }
   summary("max_async_queries : ", max_async_queries);
   summary("min_duration (ms): ", min_duration.count());
   summary("max_duration (ms): ", max_duration.count());
diff --git a/mlperf.conf b/mlperf.conf
index 8c6f53849e..c02222668c 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -43,6 +43,7 @@ retinanet.MultiStream.target_latency = 528
 
 # GPT-J uses equal issue mode for Single-Stream
 gptj.SingleStream.sample_concatenate_permutation = 1
+gptj.Server.sample_concatenate_permutation = 1
 
 *.Server.target_latency = 10
 *.Server.target_latency_percentile = 99
@@ -59,7 +60,8 @@ gptj.Server.target_latency = 20000
 stable-diffusion-xl.Server.target_latency = 20000
 # Falcon Server scenario requires two latency constraints
 llama2-70b.*.use_token_latencies = 1
-llama2-70b.Server.target_latency = 2000
+# Only ttft and tpot are tracked for the llama2-70b benchmark therefore target_latency = 0
+llama2-70b.Server.target_latency = 0
 llama2-70b.Server.ttft_latency = 2000
 llama2-70b.Server.tpot_latency = 200
 

From 104d85531af7ae2a07ded36dd6e8c7f2c7746d06 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 6 Feb 2024 12:38:31 -0500
Subject: [PATCH 052/305] Add sample length check to test06 (#1603)

* Add sample length check to test06

* Remove spaces in token metrics recomendation

* Add important item to Llama readme

* Fix Bug: number of tokens logged before computing them

* Fix typo: lenght -> length
---
 compliance/nvidia/TEST06/README.md           |  5 ++++-
 compliance/nvidia/TEST06/run_verification.py | 16 +++++++++++++++-
 language/llama2-70b/README.md                |  4 +++-
 language/llama2-70b/SUT.py                   |  4 ++--
 loadgen/loadgen.cc                           |  2 +-
 loadgen/logging.cc                           | 14 ++++++++++----
 loadgen/logging.h                            |  2 +-
 loadgen/results.cc                           |  4 ++++
 8 files changed, 40 insertions(+), 11 deletions(-)

diff --git a/compliance/nvidia/TEST06/README.md b/compliance/nvidia/TEST06/README.md
index a93312f94d..a867e75273 100644
--- a/compliance/nvidia/TEST06/README.md
+++ b/compliance/nvidia/TEST06/README.md
@@ -8,9 +8,10 @@ This repository provides the config files and scripts to run and verify TEST 06
 
 ## Introduction
 
-The purpose of this test is to ensure the consistency of the output of the Llama2 model and avoid a potential EOS exploit. This test will make a performance run, with a limit of 100 samples and logging them into `mlperf_log_accuracy.json`. To achieve a passing result in this test, two criteria must be met:
+The purpose of this test is to ensure the consistency of the output of the Llama2 model and avoid a potential EOS exploit. This test will make a performance run, with a limit of 100 samples and logging them into `mlperf_log_accuracy.json`. To achieve a passing result in this test, three criteria must be met:
 - In the case the first token is reported independently (not applicable for Offline scenario), it should match for every query with the first token of the model output.
 - For each query, the model output should only end with zero or one EOS token
+- The number of reported tokens should match with the length of it's
 
 ## Requisites
 
@@ -36,6 +37,7 @@ Expected output
 ```
 First token check pass: True                
 EOS check pass: True             
+Sample length check pass: True  
 TEST06 verification complete   
 ```
 
@@ -44,5 +46,6 @@ Or:
 ```
 First token check pass: Skipped                
 EOS check pass: True             
+Sample length check pass: True  
 TEST06 verification complete     
 ```
\ No newline at end of file
diff --git a/compliance/nvidia/TEST06/run_verification.py b/compliance/nvidia/TEST06/run_verification.py
index afa4081d32..698d5d6dc7 100644
--- a/compliance/nvidia/TEST06/run_verification.py
+++ b/compliance/nvidia/TEST06/run_verification.py
@@ -67,6 +67,15 @@ def first_token_check(acc_data, dtype):
         
     return True
 
+def sample_len_check(acc_data, dtype):
+    for sample in acc_data:
+        data = np.frombuffer(bytes.fromhex(sample["data"]), dtype=dtype)
+        token_count = int(sample["token_count"])
+        if len(data) != token_count:
+            return False
+    return True
+
+
 def main():
     args = get_args()
     accuracy_file = os.path.join(args.compliance_dir, "mlperf_log_accuracy.json")
@@ -89,6 +98,8 @@ def main():
             print("Unexpected error occured while doing the first token check")
             first_token_pass = False
 
+    sample_len_pass = sample_len_check(acc_data, DTYPE_MAP[args.dtype])
+
     # Construct output based on the results of checks
     output = ""
     # Add first token check
@@ -100,7 +111,10 @@ def main():
     # Add EOS check
     output += f"EOS check pass: {eos_pass}\n"
 
-    if eos_pass and first_token_pass:
+    # Add sample length check
+    output += f"Sample length check pass: {sample_len_pass}\n"
+
+    if eos_pass and first_token_pass and sample_len_pass:
         output += "TEST06 verification complete\n"
     else:
         output += "TEST06 verification failed\n"
diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 2614749a76..979488e91a 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -4,7 +4,9 @@
 
 + Processing of Validation dataset is not finalized yet. Decision on input token lengths is pending
 + Streamer for communicating with loadgen has quite some overhead. This is only meant to provide functional implementation
-
++ For custom/optimized implementations of this benchmark it is important to include the :
+        - For server scenario, it is necesary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
+        - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
 
 ## Prepare environment
 
diff --git a/language/llama2-70b/SUT.py b/language/llama2-70b/SUT.py
index 30fb21f8f9..228324d734 100644
--- a/language/llama2-70b/SUT.py
+++ b/language/llama2-70b/SUT.py
@@ -342,11 +342,11 @@ def process_queries(self):
                                         )
 
             output_tokens = tokens_streamer.get_out_tokens()
-
+            n_tokens = len(output_tokens)
             response_array = array.array("B", np.array(output_tokens, np.int32).tobytes())
             bi = response_array.buffer_info()
             response = [lg.QuerySampleResponse(
-                qitem.id, bi[0], bi[1])]
+                qitem.id, bi[0], bi[1], n_tokens)]
             lg.QuerySamplesComplete(response)
 
 
diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index 1d7d7420d9..7b4fcbbd2f 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -120,7 +120,7 @@ struct ResponseDelegateDetailed : public ResponseDelegate {
 
       if (sample_data_copy) {
         log.LogAccuracy(sample->sequence_id, sample->sample_index,
-                        LogBinaryAsHexString{sample_data_copy});
+                        LogBinaryAsHexString{sample_data_copy}, n_tokens);
         delete sample_data_copy;
       }
 
diff --git a/loadgen/logging.cc b/loadgen/logging.cc
index bd8e377fab..d33074c016 100644
--- a/loadgen/logging.cc
+++ b/loadgen/logging.cc
@@ -279,19 +279,25 @@ void AsyncLog::StopTrace() {
 }
 
 void AsyncLog::LogAccuracy(uint64_t seq_id, const QuerySampleIndex qsl_idx,
-                           const LogBinaryAsHexString& response) {
+                           const LogBinaryAsHexString& response,
+                           int64_t n_tokens = 0) {
   std::unique_lock<std::mutex> lock(log_mutex_);
   if (!accuracy_out_) {
     return;
   }
   *accuracy_out_ << (accuracy_needs_comma_ ? ",\n{ " : "\n{ ");
-  if (!use_tokens_ || !needs_first_token_){
+  if (!use_tokens_){
     LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
           response);
-  } else {
+  } else if (!needs_first_token_)
+  {
+    LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
+          response, "token_count", n_tokens);
+  }
+  else {
     const size_t i = seq_id - latencies_first_sample_sequence_id_;
     LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
-          response, "token_data", token_records_[i]);
+          response, "token_data", token_records_[i], "token_count", n_tokens);
   }
   
   *accuracy_out_ << " }";
diff --git a/loadgen/logging.h b/loadgen/logging.h
index b9c032a72e..e628258592 100644
--- a/loadgen/logging.h
+++ b/loadgen/logging.h
@@ -226,7 +226,7 @@ class AsyncLog {
   void SetCurrentPidTid(uint64_t pid, uint64_t tid);
 
   void LogAccuracy(uint64_t seq_id, const QuerySampleIndex qsl_idx,
-                   const LogBinaryAsHexString& response);
+                   const LogBinaryAsHexString& response, int64_t n_tokens);
   void CacheToken(uint64_t seq_id, const LogBinaryAsHexString& response);
 
   template <typename... Args>
diff --git a/loadgen/results.cc b/loadgen/results.cc
index d5173f4410..4fbfa74b16 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -650,6 +650,10 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
   if (!settings.use_token_latencies){
     MLPERF_LOG(detail, "early_stopping_result", early_stopping_recommendation);
   } else{
+    std::replace(early_stopping_ttft_recommendation.begin(),
+               early_stopping_ttft_recommendation.end(), '\n', ' ');
+    std::replace(early_stopping_tpot_recommendation.begin(),
+                early_stopping_tpot_recommendation.end(), '\n', ' ');
     MLPERF_LOG(detail, "early_stopping_ttft_result", early_stopping_ttft_recommendation);
     MLPERF_LOG(detail, "early_stopping_tpot_result", early_stopping_tpot_recommendation);
   }

From 357ccefca4db55ff373e029d5d1e3e6246bde5d4 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Tue, 6 Feb 2024 20:24:06 -0800
Subject: [PATCH 053/305] Enable equal issue mode for LLM benchmarks (#1610)

* Enable equal issue mode for LLM benchmarks

* Reduce min_query_count to 1 for server/MS/SS

* Remove scenario

* Remove min_query_count so default is used; revoke padding change for equal issue offline

* Pad min_queries, not samples_per_query for non-offline

* Add documentation to the sample equal issue
---
 loadgen/loadgen.cc | 45 +++++++++++++++++++++++++++------------------
 mlperf.conf        | 13 ++++++-------
 2 files changed, 33 insertions(+), 25 deletions(-)

diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index 7b4fcbbd2f..464f2a741b 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -227,10 +227,8 @@ auto SampleDistribution<TestMode::PerformanceOnly>(size_t sample_count,
              auto& gen) mutable { return dist(gen); };
 }
 
-/// \brief SampleDistribution for 3D-UNet SingleStream, for v2.0
-// FIXME: meant for 3D UNet SingleStream only at the moment but the logic should
-// work for others
-// TODO: consolidate the distribution generator after v2.0
+/// \brief Sample across the dataset, and ensure coverage of each of the samples.
+// Useful for non-uniform dataset (e.g. Llama2, GPTJ, 3d-unet)
 auto SampleDistributionEqualIssue(size_t sample_count, size_t set_size,
                                   std::mt19937* rng) {
   std::vector<size_t> indices;
@@ -306,8 +304,6 @@ std::vector<QueryMetadata> GenerateQueries(
   auto sample_distribution_unique = SampleDistribution<TestMode::AccuracyOnly>(
       loaded_sample_set.sample_distribution_end, sample_stride, &sample_rng);
 
-  // FIXME: Only used for v2.0 3D-UNet KiTS19 SingleStream
-  // TODO: Need to consolidate the code for any generic usage after v2.0
   auto sample_distribution_equal_issue =
       SampleDistributionEqualIssue(min_queries,
                                    loaded_samples.size(),
@@ -317,14 +313,24 @@ std::vector<QueryMetadata> GenerateQueries(
       ScheduleDistribution<scenario>(settings.target_qps);
 
   // When sample_concatenate_permutation is turned on, pad to a multiple of the
-  // complete dataset to ensure complete fairness.
-  // FIXME: Only override this for Offline; fix after v2.0
-  if (settings.sample_concatenate_permutation &&
-      scenario == TestScenario::Offline &&
-      samples_per_query % loaded_samples.size() != 0) {
-    size_t pad_size =
+  // complete dataset to ensure fairness.
+  auto enable_equal_issue = settings.sample_concatenate_permutation;
+  if (enable_equal_issue)
+  {
+    if (scenario == TestScenario::Offline &&
+      samples_per_query % loaded_samples.size() != 0)
+    {
+      // In offline mode, we pad samples_per_query
+      size_t pad_size =
         (loaded_samples.size() - samples_per_query % loaded_samples.size());
-    samples_per_query += pad_size;
+      samples_per_query += pad_size;
+    }
+    else if (min_queries % loaded_samples.size() != 0)
+    {
+      // In Server, SingleStream, MultiStream mode, the min_queries should be padded
+      size_t pad_size = (loaded_samples.size() - min_queries % loaded_samples.size());
+      min_queries += pad_size;
+    }
   }
 
   std::vector<QuerySampleIndex> samples(samples_per_query);
@@ -380,16 +386,12 @@ std::vector<QueryMetadata> GenerateQueries(
         }
       }
     } else {
-      // FIXME: only used for v2.0 3D-UNet KiTS19 SingleStream
-      // TODO: consolidate after v2.0
-      auto equal_issue = settings.sample_concatenate_permutation &&
-                         scenario == TestScenario::SingleStream;
       for (auto& s : samples) {
         s = loaded_samples[settings.performance_issue_unique
                            ? sample_distribution_unique(sample_rng)
                            : settings.performance_issue_same
                              ? same_sample
-                             : equal_issue
+                             : enable_equal_issue
                                ? sample_distribution_equal_issue(sample_rng)
                                : sample_distribution(sample_rng)];
       }
@@ -397,6 +399,13 @@ std::vector<QueryMetadata> GenerateQueries(
     queries.emplace_back(samples, timestamp, response_delegate, sequence_gen);
     prev_timestamp = timestamp;
     timestamp += schedule_distribution(schedule_rng);
+    // In equal_issue mode, the min_queries will be bumped up by a multiple of the dataset size
+    // if the test time has not met the threshold.
+    if (enable_equal_issue && (queries.size() >= min_queries) &&
+      (prev_timestamp < gen_duration) && (scenario != TestScenario::Offline))
+    {
+      min_queries += loaded_samples.size();
+    }
   }
 
   // See if we need to create a "remainder" query for offline+accuracy to
diff --git a/mlperf.conf b/mlperf.conf
index c02222668c..7e286b565e 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -12,6 +12,8 @@ bert.*.performance_sample_count_override = 10833
 dlrm.*.performance_sample_count_override = 204800
 dlrm-v2.*.performance_sample_count_override = 204800
 rnnt.*.performance_sample_count_override = 2513
+gptj.*.performance_sample_count_override = 13368
+llama2-70b.*.performance_sample_count_override = 24576
 stable-diffusion-xl.*.performance_sample_count_override = 5000
 # set to 0 to let entire sample set to be performance sample
 3d-unet.*.performance_sample_count_override = 0
@@ -28,28 +30,25 @@ stable-diffusion-xl.*.performance_sample_count_override = 5000
 
 *.SingleStream.target_latency_percentile = 90
 *.SingleStream.min_duration = 600000
-#*.SingleStream.min_query_count = 1024
 
 *.MultiStream.target_latency_percentile = 99
 *.MultiStream.samples_per_query = 8
 *.MultiStream.min_duration = 600000
-#*.MultiStream.min_query_count = 270336
 *.MultiStream.min_query_count = 662
 retinanet.MultiStream.target_latency = 528
 
-
-# 3D-UNet uses equal issue mode
+# 3D-UNet uses equal issue mode because it has non-uniform inputs
 3d-unet.*.sample_concatenate_permutation = 1
 
-# GPT-J uses equal issue mode for Single-Stream
-gptj.SingleStream.sample_concatenate_permutation = 1
+# LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario
 gptj.Server.sample_concatenate_permutation = 1
+gptj.SingleStream.sample_concatenate_permutation = 1
+llama2-70b.Server.sample_concatenate_permutation = 1
 
 *.Server.target_latency = 10
 *.Server.target_latency_percentile = 99
 *.Server.target_duration = 0
 *.Server.min_duration = 600000
-#*.Server.min_query_count = 270336
 resnet50.Server.target_latency = 15
 retinanet.Server.target_latency = 100
 bert.Server.target_latency = 130

From 44285d9a44041c3d4f7df0acf1e72c375e9fcbd2 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 6 Feb 2024 23:47:02 -0500
Subject: [PATCH 054/305] Set completed samples per second as llama metric
 (#1613)

---
 loadgen/results.cc                     | 22 +++++++++++++++++++---
 tools/submission/submission_checker.py |  4 ++--
 2 files changed, 21 insertions(+), 5 deletions(-)

diff --git a/loadgen/results.cc b/loadgen/results.cc
index 4fbfa74b16..55cc83a36f 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -399,10 +399,18 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
       //    the 1 second time point; but that would be the 1001th sample in
       //    the stream. Given the first 1001 queries, the QPS is
       //    1000 queries / 1 second.
+      // TODO: make a more permanent solution
+      if (settings.use_token_latencies){
+      double qps_as_completed =
+        (sample_count - 1) / pr.final_query_all_samples_done_time;
+      summary("Completed samples per second    : ",
+              DoubleToString(qps_as_completed));
+    } else {
       double qps_as_scheduled =
           (sample_count - 1) / pr.final_query_scheduled_time;
       summary("Scheduled samples per second : ",
               DoubleToString(qps_as_scheduled));
+    }
       break;
     }
     case TestScenario::Offline: {
@@ -516,10 +524,18 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
     summary("QPS w/o loadgen overhead        : " + DoubleToString(qps_wo_lg));
     summary("");
   } else if (settings.scenario == TestScenario::Server) {
-    double qps_as_completed =
+    // TODO: make a more permanent solution
+    if (!settings.use_token_latencies){
+      double qps_as_completed =
         (sample_count - 1) / pr.final_query_all_samples_done_time;
-    summary("Completed samples per second    : ",
-            DoubleToString(qps_as_completed));
+      summary("Completed samples per second    : ",
+              DoubleToString(qps_as_completed));
+    } else {
+      double qps_as_scheduled =
+          (sample_count - 1) / pr.final_query_scheduled_time;
+      summary("Scheduled samples per second : ",
+              DoubleToString(qps_as_scheduled));
+    }
     summary("");
   } else if (settings.scenario == TestScenario::MultiStream) {
     summary("Per-query latency:  ");
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index ac76c5dea5..5d75da9b75 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1381,11 +1381,11 @@
 RESULT_FIELD_BENCHMARK_OVERWRITE = {
     "llama2-70b-99": {
         "Offline": "result_tokens_per_second",
-        "Server": "result_scheduled_samples_per_sec",
+        "Server": "result_completed_samples_per_sec",
     },
     "llama2-70b-99.9": {
         "Offline": "result_tokens_per_second",
-        "Server": "result_scheduled_samples_per_sec",
+        "Server": "result_completed_samples_per_sec",
     }
 }
 

From d45a66c33bb0b6548d6172b38645bf94ffe24b39 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 7 Feb 2024 09:27:20 -0500
Subject: [PATCH 055/305] Add upper limit to tokens per sample (#1612)

---
 tools/submission/submission_checker.py | 27 ++++++++++++++++++++------
 1 file changed, 21 insertions(+), 6 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 5d75da9b75..a4b9a1421b 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1129,7 +1129,9 @@
             "stable-diffusion-xl": ("CLIP_SCORE", 31.68631873, "FID_SCORE", 23.01085758)
         },
         "accuracy-upper-limit": {
-            "stable-diffusion-xl": ("CLIP_SCORE", 31.81331801, "FID_SCORE", 23.95007626)
+            "stable-diffusion-xl": ("CLIP_SCORE", 31.81331801, "FID_SCORE", 23.95007626),
+            "llama2-70b-99" : ("TOKENS_PER_SAMPLE", 294.45*1.1),
+            "llama2-70b-99.9" : ("TOKENS_PER_SAMPLE", 294.45*1.1)
         },
         "performance-sample-count": {
             "resnet": 1024,
@@ -1847,13 +1849,17 @@ def check_accuracy_dir(config, model, path, verbose):
     acc_targets = []
     if acc_upper_limit is not None:
         acc_limits = []
+        up_patterns = []
         acc_limit_check = True
+        for i in range(0, len(acc_upper_limit), 2):
+            acc_type, acc_target = acc_upper_limit[i:i+2]
+            acc_limits.append(acc_target)
+            up_patterns.append(ACC_PATTERN[acc_type])
+
     for i in range(0, len(target), 2):
         acc_type, acc_target = target[i:i+2]
         patterns.append(ACC_PATTERN[acc_type])
         acc_targets.append(acc_target)
-        if acc_upper_limit is not None:
-            acc_limits.append(acc_upper_limit[i+1])
     acc_seen = [False for _ in acc_targets]
     with open(os.path.join(path, "accuracy.txt"), "r", encoding="utf-8") as f:
         for line in f:
@@ -1870,12 +1876,21 @@ def check_accuracy_dir(config, model, path, verbose):
                 elif acc is not None:
                     all_accuracy_valid = False
                     log.warning("%s accuracy not met: expected=%f, found=%s", path, acc_target, acc)
-                if acc is not None and acc_upper_limit is not None and float(acc) > acc_limits[i]:
-                    acc_limit_check = False
-                    log.warning("%s accuracy not met: upper limit=%f, found=%s", path, acc_limits[i], acc)
                 if i == 0 and acc:
                     result_acc = acc
                 acc = None
+            if acc_upper_limit is not None:
+                for i, (pattern, acc_limit) in enumerate(zip(up_patterns, acc_limits)):
+                    m = re.match(pattern, line)
+                    if m:
+                        acc = m.group(1)
+                    m = re.match(r"^hash=([\w\d]+)$", line)
+                    if m:
+                        hash_val = m.group(1)
+                    if acc is not None and acc_upper_limit is not None and float(acc) > acc_limit:
+                        acc_limit_check = False
+                        log.warning("%s accuracy not met: upper limit=%f, found=%s", path, acc_limit, acc)
+                    acc = None
             if all(acc_seen) and hash_val:
                 break;
         is_valid = all_accuracy_valid & all(acc_seen)

From d7dba0826ac96a0e8bcae525086961e47c409a38 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 7 Feb 2024 09:50:16 -0500
Subject: [PATCH 056/305] Remove loadgen warnings (#1608)

Co-authored-by: Miro <mirhodak@amd.com>
---
 loadgen/results.cc                | 2 ++
 loadgen/test_settings_internal.cc | 3 ++-
 loadgen/test_settings_internal.h  | 4 ++--
 3 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/loadgen/results.cc b/loadgen/results.cc
index 55cc83a36f..445de89015 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -439,6 +439,8 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
         summary("Tokens per second: ", tokens_per_second);
         break;
       }
+      case TestScenario::Server:
+        break;
     }
   }
 
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 814814df80..2bcf62c290 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -690,12 +690,13 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
   lookupkv(model, scenario, "test05_schedule_rng_seed", &test05_schedule_rng_seed, nullptr);
 
   // keys that apply to token metrics
-  if (lookupkv(model, scenario, "use_token_latencies", &val, nullptr))
+  if (lookupkv(model, scenario, "use_token_latencies", &val, nullptr)){
     use_token_latencies = (val == 1) ? true : false;
     if (use_token_latencies){
       lookupkv(model, "Server", "ttft_latency", &server_ttft_latency, nullptr, 1000 * 1000);
       lookupkv(model, "Server", "tpot_latency", &server_tpot_latency, nullptr, 1000 * 1000);
     }
+  }
 
   // keys that apply to SingleStream
   lookupkv(model, "SingleStream", "target_latency_percentile", nullptr,
diff --git a/loadgen/test_settings_internal.h b/loadgen/test_settings_internal.h
index 3392dd59d8..5222f3156c 100644
--- a/loadgen/test_settings_internal.h
+++ b/loadgen/test_settings_internal.h
@@ -83,8 +83,8 @@ struct TestSettingsInternal {
 
   bool sample_concatenate_permutation;
   bool use_token_latencies = false;
-  uint64_t server_ttft_latency;
-  uint64_t server_tpot_latency;
+  int64_t server_ttft_latency;
+  int64_t server_tpot_latency;
 };
 
 /// \brief A namespace of collections of FindPeakPerformance helper functions,

From b0777f0bd3b7e5cd6a51d670d8b4df656cf6fb29 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 7 Feb 2024 15:04:47 +0000
Subject: [PATCH 057/305] Update README.md - remove unwanted lines in CM
 commands (#1601)

* Update README.md

No longer need custom fork as the relevant changes are in the inference repository

* Update dataset.py

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 language/bert/README.md        | 4 ----
 language/llama2-70b/dataset.py | 2 ++
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/language/bert/README.md b/language/bert/README.md
index 1c5fefe3ae..144e6d9d05 100644
--- a/language/bert/README.md
+++ b/language/bert/README.md
@@ -51,8 +51,6 @@ The below CM command will launch the SUT server
 
 ```
 cm run script --tags=generate-run-cmds,inference --model=bert-99 --backend=pytorch  \
---rerun --adr.mlperf-implementation.version=custom \
---adr.mlperf-implementation.tags=_repo.https://github.com/GATEOVerflow/inference \
 --mode=performance --device=cuda --quiet --test_query_count=1000 --network=sut
 ```
 
@@ -61,8 +59,6 @@ Once the SUT server is launched, the below command can be run on the loadgen nod
 
 ```
 cm run script --tags=generate-run-cmds,inference --model=bert-99 --backend=pytorch  --rerun \
---adr.mlperf-implementation.version=custom \
---adr.mlperf-implementation.tags=_repo.https://github.com/GATEOVerflow/inference \
 --mode=performance --device=cuda --quiet --test_query_count=1000  \
 --sut_servers,=http://localhost:8000 --network=lon
 ```
diff --git a/language/llama2-70b/dataset.py b/language/llama2-70b/dataset.py
index a59fd7f552..4b1b1bb910 100644
--- a/language/llama2-70b/dataset.py
+++ b/language/llama2-70b/dataset.py
@@ -84,6 +84,8 @@ def postProcess(self, out_tokens, input_seq_lens=None, query_id_list=None, sampl
         assert len(query_id_list) == output_seq.shape[0]
 
         # Save outputs
+        if not os.path.exists("run_outputs"):
+            os.makedirs("run_outputs")
         fname = "q" + "_".join([str(i) for i in query_id_list])
         fname = f"run_outputs/{fname}.pkl"
         with open(fname, mode='wb') as f:

From 3190d09a148bc1d72c9d946e4cf3b54db0d0e60a Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 7 Feb 2024 15:12:27 +0000
Subject: [PATCH 058/305] Typo fix in README.md (#1588)

Co-authored-by: Miro <mirhodak@amd.com>
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f1cfa2721d..dde466980d 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@ For power submissions please use [SPEC PTD 1.10](https://github.com/mlcommons/po
 | 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 | edge,datacenter |
 | rnnt | [speech_recognition/rnnt](https://github.com/mlcommons/inference/tree/master/speech_recognition/rnnt) | pytorch | OpenSLR LibriSpeech Corpus | edge,datacenter |
 | gpt-j | [language/gpt-j](https://github.com/mlcommons/inference/tree/master/language/gpt-j)| pytorch | CNN-Daily Mail | edge,datacenter |
-| stable-diffusion-xl | [text_to_image] (https://github.com/mlcommons/inference/tree/master/text_to_image) | pytorch | COCO 2014| edge,datacenter |
+| stable-diffusion-xl | [text_to_image](https://github.com/mlcommons/inference/tree/master/text_to_image) | pytorch | COCO 2014| edge,datacenter |
 | llama2-70b | [language/llama2-70b](https://github.com/mlcommons/inference/tree/master/language/llama2-70b) | pytorch | OpenOrca | datacenter |
 
 * Framework here is given for the reference implementation. Submitters are free to use their own frameworks to run the benchmark.

From 840435a1abe8be823e0a5a18ad3173bc520aa24f Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 7 Feb 2024 15:13:05 +0000
Subject: [PATCH 059/305] Update README.md with CM commands to download
 stable-diffusion, gptj and dlrmv2 models (#1604)

* Update README.md

Add CM commands to download Stable diffusion models

* Update README.md

* Update README.md
---
 language/gpt-j/README.md                 | 8 ++++++++
 recommendation/dlrm_v2/pytorch/README.md | 9 +++++++++
 text_to_image/README.md                  | 8 ++++++++
 3 files changed, 25 insertions(+)

diff --git a/language/gpt-j/README.md b/language/gpt-j/README.md
index cc46135f25..58af37ba31 100644
--- a/language/gpt-j/README.md
+++ b/language/gpt-j/README.md
@@ -74,6 +74,14 @@ Please download the fine-tuned GPT-J checkpoint from [here](https://cloud.mlcomm
 wget https://cloud.mlcommons.org/index.php/s/QAZ2oM94MkFtbQx/download --output-document checkpoint.zip
 ```
 
+The following MLCommons CM commands can be used to programmatically download the model checkpoint. 
+
+```
+pip install cmind
+cm pull repo mlcommons@ck
+cm run script --tags=get,ml-model,gptj,_pytorch,_rclone -j
+```
+
 ### Running the Benchmark
 Replace the model and dataset path arguments with your corresponding paths. For evaluating the ROUGE score after the run, include --accuracy as shown below. For user specific target qps, please include user.conf.
 ```
diff --git a/recommendation/dlrm_v2/pytorch/README.md b/recommendation/dlrm_v2/pytorch/README.md
index 313bf1e1f2..64d2af2497 100755
--- a/recommendation/dlrm_v2/pytorch/README.md
+++ b/recommendation/dlrm_v2/pytorch/README.md
@@ -79,6 +79,15 @@ You can download the weights by running:
 wget https://cloud.mlcommons.org/index.php/s/XzfSeLgW8FYfR3S/download -O weights.zip
 unzip weights.zip
 ```
+
+The following MLCommons CM commands can be used to programmatically download the model checkpoint. 
+
+```
+pip install cmind
+cm pull repo mlcommons@ck
+cm run script --tags=get,ml-model,dlrm,_pytorch,_weight_sharded,_rclone -j
+```
+
 (optional) To speed up future downloads, we recommend you save the weights in a bucket (E.g GCP, AWS). For example, after saving the checkpoint in a GCP bucket, you can download the weights faster by running:
 ```
 export BUCKET_NAME=<BUCKET_CONTAINING_MODEL>
diff --git a/text_to_image/README.md b/text_to_image/README.md
index 26393f54f7..b8573624c3 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -8,6 +8,14 @@ This is the reference implementation for MLPerf Inference text to image
 | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
 | StableDiffusion | - | Coco2014 | [fp32](https://cloud.mlcommons.org/index.php/s/DjnCSGyNBkWA4Ro) and [f16](https://cloud.mlcommons.org/index.php/s/LCdW5RM6wgGWbxC) | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp32 | NCHW |
 
+The following MLCommons CM commands can be used to programmatically download the model checkpoints. 
+
+```
+pip install cmind
+cm pull repo mlcommons@ck
+cm run script --tags=get,ml-model,sdxl,_fp16,_rclone -j
+cm run script --tags=get,ml-model,sdxl,_fp32,_rclone -j
+```
 ## Dataset
 
 | Data | Description |

From 817dd961221d3ca284b024368130bd5dc5e4b54a Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Wed, 7 Feb 2024 20:50:01 -0800
Subject: [PATCH 060/305] Turn equal issue mode off for TEST06 (#1615)

* Turn equal issue mode off for Llama2 TEST06

* Add TEST06 to the output dir
---
 compliance/nvidia/TEST06/audit.config        | 4 +++-
 compliance/nvidia/TEST06/run_verification.py | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/compliance/nvidia/TEST06/audit.config b/compliance/nvidia/TEST06/audit.config
index c8888be506..090ec03b14 100644
--- a/compliance/nvidia/TEST06/audit.config
+++ b/compliance/nvidia/TEST06/audit.config
@@ -8,4 +8,6 @@
 *.*.accuracy_log_rng_seed = 720381539243781796
 *.*.accuracy_log_sampling_target = 100
 *.*.min_query_count = 100
-*.*.min_duration = 0
\ No newline at end of file
+*.*.min_duration = 0
+# Turn off equal issue mode for TEST06
+*.*.sample_concatenate_permutation = 0
\ No newline at end of file
diff --git a/compliance/nvidia/TEST06/run_verification.py b/compliance/nvidia/TEST06/run_verification.py
index 698d5d6dc7..6cdb082e65 100644
--- a/compliance/nvidia/TEST06/run_verification.py
+++ b/compliance/nvidia/TEST06/run_verification.py
@@ -120,7 +120,7 @@ def main():
         output += "TEST06 verification failed\n"
 
     # Output test output to console and folder
-    output_dir = args.output_dir
+    output_dir = os.path.join(args.output_dir, "TEST06")
     output_accuracy_dir = os.path.join(args.output_dir, "accuracy")
     
     if not os.path.isdir(output_dir):

From 0ed5190e82e1ca1cf95626e50e61cb6678c7f2ff Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Thu, 8 Feb 2024 11:29:58 -0800
Subject: [PATCH 061/305] Fix submission checker and TEST06 for Llama2 (#1616)

* Fix submission checker and TEST06 for Llama2

* Remove redundant line

* Move test_dir check
---
 compliance/nvidia/TEST06/run_verification.py |   2 +-
 tools/submission/submission_checker.py       | 212 ++++++++++---------
 2 files changed, 118 insertions(+), 96 deletions(-)

diff --git a/compliance/nvidia/TEST06/run_verification.py b/compliance/nvidia/TEST06/run_verification.py
index 6cdb082e65..03c60c2efb 100644
--- a/compliance/nvidia/TEST06/run_verification.py
+++ b/compliance/nvidia/TEST06/run_verification.py
@@ -121,7 +121,7 @@ def main():
 
     # Output test output to console and folder
     output_dir = os.path.join(args.output_dir, "TEST06")
-    output_accuracy_dir = os.path.join(args.output_dir, "accuracy")
+    output_accuracy_dir = os.path.join(output_dir, "accuracy")
     
     if not os.path.isdir(output_dir):
         os.makedirs(output_dir)
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index a4b9a1421b..0bae5b0d45 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1392,13 +1392,10 @@
 }
 
 LLAMA2_LATENCY_LIMITS = {
-    "interactive": {
-        "ttft": 500,
-        "tpot": 50
-    },
+    # We might add interactive in the next round. Latency in ns
     "conversational": {
-        "ttft": 2000,
-        "tpot": 200
+        "ttft": 2000 * 1000000,
+        "tpot": 200 * 1000000
     }
 }
 
@@ -1920,16 +1917,20 @@ def check_accuracy_dir(config, model, path, verbose):
 
 
 def extra_check_llama2(mlperf_log, scenario):
-    if (mlperf_log["use_token_latencies"]):
+    if (mlperf_log["requested_use_token_latencies"]):
         if scenario == "Offline":
             # For offline no further checks are necessary
             return None, True
         else:
             for constraint, limits in LLAMA2_LATENCY_LIMITS.items():
-                if mlperf_log["result_first_token_99.9_percentile_latency_ns"] < limits["ttft"] and mlperf_log["result_time_to_output_token"] < limits["tpot"]:
+                if mlperf_log["result_first_token_99.00_percentile_latency_ns"] < limits["ttft"] and mlperf_log["result_time_per_output_token_99.00_percentile_ns"] < limits["tpot"]:
                     return constraint, True
     else:
+        log.error(f'use_token_latencies flag needs to be enabled for Llama2 benchmark')
         return None, False
+
+    log.error(f'Failed Llama2 extra check for TTFT and TPOT. TTFT 99-tile: {mlperf_log["result_first_token_99.00_percentile_latency_ns"]}, TPOT 99-tile: {mlperf_log["result_time_per_output_token_99.00_percentile_ns"]}')
+    return None, False
             
 
 def get_performance_metric(
@@ -2986,6 +2987,7 @@ def log_result(
                             n = ["run_1"]
 
                         for i in n:
+                            is_valid = True
                             perf_path = os.path.join(name, "performance", i)
                             if not os.path.exists(perf_path):
                                 log.error("%s is missing", perf_path)
@@ -3432,90 +3434,106 @@ def check_compliance_acc_dir(test_dir, model, config):
     if not os.path.exists(fname):
         log.error("%s is missing in %s", fname, test_dir)
     else:
-        # Accuracy can fail for TEST01
-        is_valid = True
-        with open(fname, "r") as f:
-            for line in f:
-                # look for: TEST PASS
-                if "TEST PASS" in line:
-                    acc_passed = True
-                    break
-        if acc_passed == False:
-            log.info(
-                "Compliance test accuracy check (deterministic mode) in %s failed",
-                test_dir,
-            )
+        if "TEST01" in test_dir:
+            # Accuracy can fail for TEST01
+            is_valid = True
+            with open(fname, "r") as f:
+                for line in f:
+                    # look for: TEST PASS
+                    if "TEST PASS" in line:
+                        acc_passed = True
+                        break
+            if acc_passed == False:
+                log.info(
+                    "Compliance test accuracy check (deterministic mode) in %s failed",
+                    test_dir,
+                )
 
-        # Check Accuracy dir
-        test_acc_path = os.path.join(test_dir, "accuracy")
-        if not os.path.exists(test_acc_path):
-            log.error("%s has no accuracy directory", test_dir)
-            is_valid = False
-        else:
-            diff = files_diff(
-                list_files(test_acc_path),
-                REQUIRED_TEST01_ACC_FILES_1
-                if acc_passed
-                else REQUIRED_TEST01_ACC_FILES,
-            )
-            if diff:
-                log.error("%s has file list mismatch (%s)", test_acc_path, diff)
+            # Check Accuracy dir
+            test_acc_path = os.path.join(test_dir, "accuracy")
+            if not os.path.exists(test_acc_path):
+                log.error("%s has no accuracy directory", test_dir)
                 is_valid = False
-            elif not acc_passed:
-                target = config.get_accuracy_target(model)
-                patterns = []
-                acc_types = []
-                for i in range(0, len(target), 2):
-                    acc_type = target[i:i+2]
-                    acc_types.append(acc_type)
-                    patterns.append(ACC_PATTERN[acc_type[0]])
-                acc_seen = [False for _ in acc_type]
-
-
-
-                more_accurate = model.find("99.9")
-                if more_accurate == -1:
-                    required_delta_perc = 1
-                else:
-                    required_delta_perc = 0.1
-                
-                acc_baseline = {
-                    acc_type: 0 for acc_type in acc_types
-                }
-                acc_compliance = {
-                    acc_type: 0 for acc_type in acc_types
-                }
-                with open(
-                    os.path.join(test_acc_path, "baseline_accuracy.txt"),
-                    "r",
-                    encoding="utf-8",
-                ) as f:
-                    for line in f:
-                        for acc_type, pattern in zip(acc_types, patterns):
-                            m = re.match(pattern, line)
-                            if m:
-                                acc_baseline[acc_type] = float(m.group(1))
-                with open(
-                    os.path.join(test_acc_path, "compliance_accuracy.txt"),
-                    "r",
-                    encoding="utf-8",
-                ) as f:
-                    for line in f:
-                        for acc_type, pattern in zip(acc_types, patterns):
-                            m = re.match(pattern, line)
-                            if m:
-                                acc_compliance[acc_type] = float(m.group(1))
-                for acc_type in acc_types:
-                    if acc_baseline[acc_type] == 0 or acc_compliance[acc_type] == 0:
-                        is_valid = False
-                        break
+            else:
+                diff = files_diff(
+                    list_files(test_acc_path),
+                    REQUIRED_TEST01_ACC_FILES_1
+                    if acc_passed
+                    else REQUIRED_TEST01_ACC_FILES,
+                )
+                if diff:
+                    log.error("%s has file list mismatch (%s)", test_acc_path, diff)
+                    is_valid = False
+                elif not acc_passed:
+                    target = config.get_accuracy_target(model)
+                    patterns = []
+                    acc_types = []
+                    for i in range(0, len(target), 2):
+                        acc_type = target[i:i+2]
+                        acc_types.append(acc_type)
+                        patterns.append(ACC_PATTERN[acc_type[0]])
+                    acc_seen = [False for _ in acc_type]
+
+                    more_accurate = model.find("99.9")
+                    if more_accurate == -1:
+                        required_delta_perc = 1
                     else:
-                        delta_perc = abs(1 - acc_baseline[acc_type] / acc_compliance[acc_type]) * 100
-                        if delta_perc <= required_delta_perc:
-                            is_valid = True
-                        else:
+                        required_delta_perc = 0.1
+                    acc_baseline = {
+                        acc_type: 0 for acc_type in acc_types
+                    }
+                    acc_compliance = {
+                        acc_type: 0 for acc_type in acc_types
+                    }
+                    with open(
+                        os.path.join(test_acc_path, "baseline_accuracy.txt"),
+                        "r",
+                        encoding="utf-8",
+                    ) as f:
+                        for line in f:
+                            for acc_type, pattern in zip(acc_types, patterns):
+                                m = re.match(pattern, line)
+                                if m:
+                                    acc_baseline[acc_type] = float(m.group(1))
+                    with open(
+                        os.path.join(test_acc_path, "compliance_accuracy.txt"),
+                        "r",
+                        encoding="utf-8",
+                    ) as f:
+                        for line in f:
+                            for acc_type, pattern in zip(acc_types, patterns):
+                                m = re.match(pattern, line)
+                                if m:
+                                    acc_compliance[acc_type] = float(m.group(1))
+                    for acc_type in acc_types:
+                        if acc_baseline[acc_type] == 0 or acc_compliance[acc_type] == 0:
                             is_valid = False
                             break
+                        else:
+                            delta_perc = abs(1 - acc_baseline[acc_type] / acc_compliance[acc_type]) * 100
+                            if delta_perc <= required_delta_perc:
+                                is_valid = True
+                            else:
+                                is_valid = False
+                                break
+        elif "TEST06" in test_dir:
+            """
+            Expected output
+            First token check pass: True (or First token check pass: Skipped)
+            EOS check pass: True
+            TEST06 verification complete
+            """
+            with open(fname, "r") as f:
+                lines = f.readlines()
+            lines = [line.strip() for line in lines]
+            first_token_pass = "First token check pass: True" in lines or "First token check pass: Skipped" in lines
+            eos_pass = "EOS check pass: True" in lines
+            length_check_pass = "Sample length check pass: True" in lines
+            is_valid = first_token_pass and eos_pass and length_check_pass
+            if not is_valid:
+                log.error(f"TEST06 accuracy check failed. first_token_check: {first_token_pass} eos_check: {eos_pass} length_check: {length_check_pass}.")
+        else:
+            raise NotImplemented(f"{test_dir} is neither TEST01 and TEST06, which doesn't require accuracy check")
 
     return is_valid
 
@@ -3566,13 +3584,16 @@ def check_compliance_dir(
         log.error("no compliance dir for %s: %s", name, compliance_dir)
         return False
 
-    # Check performance of all Tests
+    # Check performance of all Tests (except for TEST06)
     for test in test_list:
         test_dir = os.path.join(compliance_dir, test)
         if not os.path.exists(test_dir):
             log.error("Missing %s in compliance dir %s", test, compliance_dir)
             compliance_perf_dir_pass = False
         else:
+            # TEST06 has no performance test.
+            if "TEST06" in test_list:
+                continue
             try:
                 compliance_perf_dir = os.path.join(
                     compliance_dir, test, "performance", "run_1"
@@ -3595,13 +3616,14 @@ def check_compliance_dir(
                 and compliance_perf_valid
             )
 
-    if "TEST01" in test_list:
-        # Check accuracy for TEST01
-        compliance_acc_pass = check_compliance_acc_dir(
-            os.path.join(compliance_dir, "TEST01"), model, config
-        )
-    else:
-        compliance_acc_pass= True
+    compliance_acc_pass= True
+    for test in ["TEST01", "TEST06"]:
+        if test in test_list:
+            # Check accuracy for TEST01
+            compliance_acc_pass &= check_compliance_acc_dir(
+                os.path.join(compliance_dir, test), model, config
+            )
+
 
     return compliance_perf_pass and compliance_acc_pass and compliance_perf_dir_pass
 

From f06b920eac2170df38b5502c7443f50c9afa6045 Mon Sep 17 00:00:00 2001
From: Jinho Suh <83969361+nv-jinhosuh@users.noreply.github.com>
Date: Mon, 12 Feb 2024 13:47:52 -0600
Subject: [PATCH 062/305] Bugfix: equal-issue mode on offline causing accuracy
 run to fail (3D-UNet) (#1624)

Currently 3D-UNet is the only workload using equal-issue mode on Offline scenario.
Recent code change on LLM equal-issue mode caused 3D-UNet accuracy run to run more than 1 queries, causing the accuracy log to bloat and fail the accuracy checking script.
This change fixes the problem described above.
---
 loadgen/loadgen.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index 464f2a741b..76e59151b2 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -315,7 +315,7 @@ std::vector<QueryMetadata> GenerateQueries(
   // When sample_concatenate_permutation is turned on, pad to a multiple of the
   // complete dataset to ensure fairness.
   auto enable_equal_issue = settings.sample_concatenate_permutation;
-  if (enable_equal_issue)
+  if (mode != TestMode::AccuracyOnly && enable_equal_issue)
   {
     if (scenario == TestScenario::Offline &&
       samples_per_query % loaded_samples.size() != 0)

From f9a643c0a0e920588da1b51a1d822e1071a9dbec Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Mon, 12 Feb 2024 14:49:27 -0500
Subject: [PATCH 063/305] Add number of tokens to offline (#1623)

---
 language/llama2-70b/SUT.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/language/llama2-70b/SUT.py b/language/llama2-70b/SUT.py
index 228324d734..4ff78c38c4 100644
--- a/language/llama2-70b/SUT.py
+++ b/language/llama2-70b/SUT.py
@@ -207,9 +207,10 @@ def process_queries(self):
                                                                 query_id_list=query_ids)
 
             for i in range(len(qitem)):
+                n_tokens = processed_output[i].shape[0]
                 response_array = array.array("B", processed_output[i].tobytes())
                 bi = response_array.buffer_info()
-                response = [lg.QuerySampleResponse(qitem[i].id, bi[0], bi[1])]
+                response = [lg.QuerySampleResponse(qitem[i].id, bi[0], bi[1], n_tokens)]
                 lg.QuerySamplesComplete(response)
 
             tok = time.time()

From 486a629ea4d5c5150f452d0b0a196bf71fd2021e Mon Sep 17 00:00:00 2001
From: Jinho Suh <83969361+nv-jinhosuh@users.noreply.github.com>
Date: Thu, 15 Feb 2024 14:46:50 -0600
Subject: [PATCH 064/305] Hotfix: DLRMv2 Audit Test01 fallback failure (#1626)

* Hotfix: DLRMv2 Audit Test01 fallback failure

DLRMv2 Audit TEST01 may go to fallback route and the accuracy check script (accuracy-dlrm.py) didn't expect this to happen. It always expects entire sample set to be in the accuracy log while Audit TEST01 would generate subset only.

This fixes the Audit TEST01 failure described above.

* typo fix
---
 recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py b/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py
index 873f6a0e10..ce662071f5 100644
--- a/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py
+++ b/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py
@@ -43,7 +43,9 @@ def get_targets(args, qsl_indices):
     with open(args.aggregation_trace_file) as f:
         for line in f:
             sample_boundaries.append(sample_boundaries[-1] + int(line.split(", ")[2]))
-    assert len(sample_boundaries) == len(qsl_indices) + 1, "Number of samples in trace file does not match number of samples in loadgen accuracy log!"
+    if len(sample_boundaries) != len(qsl_indices) + 1:
+        print("Warning: number of samples in trace file ({}) does not match number of samples ({}) in "
+              "loadgen accuracy log!".format(len(sample_boundaries)-1, len(qsl_indices)))
     # Get all the ground truth labels in the original order in day_23
     print("Parsing ground truth labels from day_23 file...")
     ground_truths = []

From de31ee2cc35db7704f11e615cf5ee63a82f17b0a Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 20 Feb 2024 17:25:36 +0000
Subject: [PATCH 065/305] Fix preprocess_submission script to copy the code
 path while inferring low accuracy results (#1627)

---
 tools/submission/preprocess_submission.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/tools/submission/preprocess_submission.py b/tools/submission/preprocess_submission.py
index d6e0212530..fee2aadde8 100644
--- a/tools/submission/preprocess_submission.py
+++ b/tools/submission/preprocess_submission.py
@@ -247,6 +247,13 @@ def infer_scenario_results(filter_submitter, noinfer_low_accuracy_results, confi
 
                                     shutil.copytree(high_accuracy_model_path, \
                                             low_accuracy_model_path)
+                                high_accuracy_model_code_path = os.path.join(log_path, "..", \
+                                        "code", model)
+                                low_accuracy_model_code_path = os.path.join(log_path, "..", \
+                                        "code", low_accuracy_model)
+                                if not os.path.exists(low_accuracy_model_code_path):
+                                    shutil.copytree(high_accuracy_model_code_path, \
+                                            low_accuracy_model_code_path)
 
 
 

From 268bc9dc8a3c0a96bbb7d38482c0ce5016507633 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 13 Feb 2024 11:20:33 -0500
Subject: [PATCH 066/305] Add batching to SD reference

---
 text_to_image/main.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/text_to_image/main.py b/text_to_image/main.py
index 0b93ceb21f..32425762ff 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -394,11 +394,18 @@ def main():
     count = ds.get_item_count()
 
     # warmup
-    ds.load_query_samples([0])
+    syntetic_str = "Lorem ipsum dolor sit amet, consectetur adipiscing elit"
+    latents_pt = torch.rand(ds.latents.shape, dtype=dtype).to(args.device)
+    warmup_samples = [
+        {
+            "input_tokens": ds.preprocess(syntetic_str, model.pipe.tokenizer),
+            "input_tokens_2": ds.preprocess(syntetic_str, model.pipe.tokenizer_2),
+            "latents": latents_pt,
+        }
+        for _ in range(args.max_batchsize)
+    ]
     for i in range(5):
-        captions, _ = ds.get_samples([0])
-        _ = backend.predict(captions)
-    ds.unload_query_samples(None)
+        _ = backend.predict(warmup_samples)
 
     scenario = SCENARIO_MAP[args.scenario]
     runner_map = {

From 5d0c221255b3c86759bbf7e20df521c4bc7c8469 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Wed, 21 Feb 2024 12:13:25 -0600
Subject: [PATCH 067/305] Add Rclone-Cloudflare download instructions to
 README.md

---
 text_to_image/README.md | 51 ++++++++++++++++++++++++++++++++---------
 1 file changed, 40 insertions(+), 11 deletions(-)

diff --git a/text_to_image/README.md b/text_to_image/README.md
index b8573624c3..2430674592 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -4,18 +4,11 @@ This is the reference implementation for MLPerf Inference text to image
 
 ## Supported Models
 
-| model | accuracy | dataset | model link | model source | precision | notes |
-| ---- | ---- | ---- | ---- | ---- | ---- | ---- |
-| StableDiffusion | - | Coco2014 | [fp32](https://cloud.mlcommons.org/index.php/s/DjnCSGyNBkWA4Ro) and [f16](https://cloud.mlcommons.org/index.php/s/LCdW5RM6wgGWbxC) | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp32 | NCHW |
+| model | accuracy | dataset | model source | precision | notes |
+| ---- | ---- | ---- | ---- | ---- | ---- |
+| StableDiffusion | - | Coco2014 | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp32 | NCHW |
 
-The following MLCommons CM commands can be used to programmatically download the model checkpoints. 
 
-```
-pip install cmind
-cm pull repo mlcommons@ck
-cm run script --tags=get,ml-model,sdxl,_fp16,_rclone -j
-cm run script --tags=get,ml-model,sdxl,_fp32,_rclone -j
-```
 ## Dataset
 
 | Data | Description |
@@ -55,7 +48,43 @@ CFLAGS="-std=c++14" python setup.py install
 
 ### Download model
 
-We host two checkpoints ([fp32](https://cloud.mlcommons.org/index.php/s/DjnCSGyNBkWA4Ro) and [f16](https://cloud.mlcommons.org/index.php/s/LCdW5RM6wgGWbxC)) that are a snapshot of the [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) pipeline at the time of the release of the benchmark. Download them and move them to your model path.
+We host two checkpoints (fp32 and fp16) that are a snapshot of the [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) pipeline at the time of the release of the benchmark. Download them and move them to your model path.
+
+#### CM method
+
+The following MLCommons CM commands can be used to programmatically download the model checkpoints. 
+
+```
+pip install cmind
+cm pull repo mlcommons@ck
+cm run script --tags=get,ml-model,sdxl,_fp16,_rclone -j
+cm run script --tags=get,ml-model,sdxl,_fp32,_rclone -j
+```
+#### Manual method
+
+The above command automatically runs a set of Rclone commands to download the data from a Cloudflare R2 bucket. However, if you'd like to run the Rclone commands manually, you can do so as follows:
+
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
+```
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
+```
+Once Rclone is installed, run the following command to authenticate with the bucket:
+```
+rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+```
+You can then navigate in the terminal to your desired download directory and run the following commands to download the dataset and checkpoints:
+
+**`fp32`**
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/stable_diffusion_fp32 ./stable_diffusion_fp32 -P
+```
+**`fp16`**
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/stable_diffusion_fp16 ./stable_diffusion_fp16 -P
+```
+
+#### Move to model path
 
 ```bash
 mkdir $MODEL_PATH

From dc94ae31918b68aaaf2071b8bc1e3a10548521d4 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Wed, 21 Feb 2024 12:33:02 -0600
Subject: [PATCH 068/305] Add Rclone-Cloudflare download instructiosn to
 README.md

---
 recommendation/dlrm_v2/pytorch/README.md | 45 +++++++++++++-----------
 1 file changed, 25 insertions(+), 20 deletions(-)

diff --git a/recommendation/dlrm_v2/pytorch/README.md b/recommendation/dlrm_v2/pytorch/README.md
index 64d2af2497..6de850b819 100755
--- a/recommendation/dlrm_v2/pytorch/README.md
+++ b/recommendation/dlrm_v2/pytorch/README.md
@@ -67,18 +67,15 @@ cd $HOME/mlcommons/inference/loadgen
 CFLAGS="-std=c++14" python setup.py develop --user
 ```
 
+
 ### Downloading model weights
 
-File name | framework | Size in bytes (`du *`) | MD5 hash (`md5sum *`)
--|-|-|-
+framework | Size in bytes (`du *`) | MD5 hash (`md5sum *`)
+-|-|-
 N/A | pytorch | <2GB | -
-[weight_sharded](https://cloud.mlcommons.org/index.php/s/XzfSeLgW8FYfR3S/download) | pytorch | 97.31GB | -
+ pytorch | 97.31GB | -
 
-You can download the weights by running:
-```
-wget https://cloud.mlcommons.org/index.php/s/XzfSeLgW8FYfR3S/download -O weights.zip
-unzip weights.zip
-```
+#### CM method
 
 The following MLCommons CM commands can be used to programmatically download the model checkpoint. 
 
@@ -88,24 +85,32 @@ cm pull repo mlcommons@ck
 cm run script --tags=get,ml-model,dlrm,_pytorch,_weight_sharded,_rclone -j
 ```
 
-(optional) To speed up future downloads, we recommend you save the weights in a bucket (E.g GCP, AWS). For example, after saving the checkpoint in a GCP bucket, you can download the weights faster by running:
+#### Manual method
+
+The above command automatically runs a set of Rclone commands to download the data from a Cloudflare R2 bucket. However, if you'd like to run the Rclone commands manually, you can do so as follows:
+
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
 ```
-export BUCKET_NAME=<BUCKET_CONTAINING_MODEL>
-cd $HOME/mlcommons/inference/recommendation/dlrm_v2/pytorch/model/
-gsutil -m cp -r "gs://$BUCKET_NAME/model_weights/*" .
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
 ```
+Once Rclone is installed, run the following command to authenticate with the bucket:
+```
+rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+```
+You can then navigate in the terminal to your desired download directory and run the following commands to download the model weights:
 
-### Downloading dataset
-| Original dataset | download link |
-| ---- | ---- |
-| Criteo Terabyte (day 23) | https://labs.criteo.com/2013/12/download-terabyte-click-logs/ |
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/model_weights ./model_weights -P
+```
 
+#### (optional) 
 
-1. The Criteo fake dataset can be created in place of the real datasets in order to facilitate debugging and testing. We provide a fake (random) data generator that can be used to quickly generate data samples in a format compatible with the original dataset. Please use the following script in `./tools` to quickly create random samples for the corresponding models, which will be placed into `./fake_criteo` directory
+To speed up future downloads, we recommend you save the weights in a bucket (E.g GCP, AWS). For example, after saving the checkpoint in a GCP bucket, you can download the weights faster by running:
 ```
-./make_fake_criteo.sh
-mv ./fake_criteo .. && cd ..
-export DATA_DIR=./fake_criteo
+export BUCKET_NAME=<BUCKET_CONTAINING_MODEL>
+cd $HOME/mlcommons/inference/recommendation/dlrm_v2/pytorch/model/
+gsutil -m cp -r "gs://$BUCKET_NAME/model_weights/*" .
 ```
 
 

From ab747c42bc233f0331a8c673cf5ba2ab068dc95d Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Wed, 21 Feb 2024 12:33:40 -0600
Subject: [PATCH 069/305] Minor wording edit to README.md

---
 text_to_image/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/text_to_image/README.md b/text_to_image/README.md
index 2430674592..e353bfac01 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -73,7 +73,7 @@ Once Rclone is installed, run the following command to authenticate with the buc
 ```
 rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
 ```
-You can then navigate in the terminal to your desired download directory and run the following commands to download the dataset and checkpoints:
+You can then navigate in the terminal to your desired download directory and run the following commands to download the checkpoints:
 
 **`fp32`**
 ```

From d037f22f408db7e0e6b0fa98baea04ef159955a7 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Wed, 21 Feb 2024 12:47:23 -0600
Subject: [PATCH 070/305] Add Rclone-Cloudflare download instructions to
 README.md

---
 language/gpt-j/README.md | 26 ++++++++++++++++++++++----
 1 file changed, 22 insertions(+), 4 deletions(-)

diff --git a/language/gpt-j/README.md b/language/gpt-j/README.md
index 58af37ba31..cbb5b3e3c2 100644
--- a/language/gpt-j/README.md
+++ b/language/gpt-j/README.md
@@ -68,11 +68,9 @@ pip install datasets
 python prepare-calibration.py --calibration-list-file calibration-list.txt --output-dir </path/to/output-folder>
 ```
 ### Download GPT-J model
-Please download the fine-tuned GPT-J checkpoint from [here](https://cloud.mlcommons.org/index.php/s/QAZ2oM94MkFtbQx) and extract it as model/. The download_gptj.py only downloads the default huggingface model which is not fine-tuned on CNN-Daily mail dataset. 
+Please download the fine-tuned GPT-J checkpoint using the instructions below. The download_gptj.py only downloads the default huggingface model which is not fine-tuned on CNN-Daily mail dataset. 
 
-```
-wget https://cloud.mlcommons.org/index.php/s/QAZ2oM94MkFtbQx/download --output-document checkpoint.zip
-```
+#### CM method
 
 The following MLCommons CM commands can be used to programmatically download the model checkpoint. 
 
@@ -82,6 +80,26 @@ cm pull repo mlcommons@ck
 cm run script --tags=get,ml-model,gptj,_pytorch,_rclone -j
 ```
 
+#### Manual method
+
+The above command automatically runs a set of Rclone commands to download the data from a Cloudflare R2 bucket. However, if you'd like to run the Rclone commands manually, you can do so as follows:
+
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
+```
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
+```
+Once Rclone is installed, run the following command to authenticate with the bucket:
+```
+rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+```
+You can then navigate in the terminal to your desired download directory and run the following commands to download the model checkpoint:
+
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/gpt-j ./model -P
+```
+
+
 ### Running the Benchmark
 Replace the model and dataset path arguments with your corresponding paths. For evaluating the ROUGE score after the run, include --accuracy as shown below. For user specific target qps, please include user.conf.
 ```

From 147a91a69fb9857dea70e4be0cc9fd19961cea21 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Wed, 21 Feb 2024 13:03:58 -0600
Subject: [PATCH 071/305] Add Rclone-GDrive download instructions to README.md

---
 language/llama2-70b/README.md | 28 ++--------------------------
 1 file changed, 2 insertions(+), 26 deletions(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 979488e91a..8c6a0c27c5 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -63,32 +63,8 @@ Inside the container, set up the environment with `bash build.sh`. This will ins
 CPU-only setup, as well as any GPU versions for applicable libraries like PyTorch.
 
 
-## Get Model
-+ For now, MLCommons is not hosting the checkpoint, so you must first go to [llama2-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to huggingface (if you don't have account, you'd need to create one). **Please note your authentication credentials** as you may be required to provide them when cloninng below
-+ Requires Git Large Files Storage
-```
-export CHECKPOINT_PATH=${PWD}/Llama-2-70b-chat-hf
-git lfs install
-git clone https://huggingface.co/meta-llama/Llama-2-70b-chat-hf ${CHECKPOINT_PATH}
-
-```
-
-## Get Dataset
-
-```
-# First get the `open-orca` parquet from huggingface
-export OPENORCA_DATASET=${PWD}/open-orca
-git clone https://huggingface.co/datasets/Open-Orca/OpenOrca ${OPENORCA_DATASET}
-
-export OPENORCA_PARQUET=${OPENORCA_DATASET}/1M-GPT4-Augmented.parquet
-EXPORT_DIR=${PWD}/processed-openorca
-export DATASET_PATH=${PWD}/processed-data.pkl
-
-# Process the dataset according the Taskforce's agreed criteria
-python3 processorca.py --dataset_pq_path=${OPENORCA_PARQUET} --model_dir=${CHECKPOINT_PATH} --seqlen_limit=1024 --export_dir=${EXPORT_DIR} --num_total_samples=24576
-
-mv ${EXPORT_DIR}/open_orca_gpt4_tokenized_llama.sampled_24576.pkl ${DATASET_PATH}
-```
+## Get Model and Dataset
+MLCommons hosts the model and preprocessed dataset for download. You must first agree to the [confidentiality notice](https://docs.google.com/forms/d/e/1FAIpQLSc_8VIvRmXM3I8KQaYnKf7gy27Z63BBoI_I1u02f4lw6rBp3g/viewform), then follow the link to a directory containing Rclone download instructions.
 
 
 ## Run Performance Benchmarks

From 15d14c95c86c7a42215c162827bab2daaec61da1 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Wed, 21 Feb 2024 16:23:00 -0600
Subject: [PATCH 072/305] Add new and old instructions to README.md

---
 language/llama2-70b/README.md | 56 +++++++++++++++++++++++++++++++++--
 1 file changed, 54 insertions(+), 2 deletions(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 8c6a0c27c5..f648b1b5d8 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -63,8 +63,60 @@ Inside the container, set up the environment with `bash build.sh`. This will ins
 CPU-only setup, as well as any GPU versions for applicable libraries like PyTorch.
 
 
-## Get Model and Dataset
-MLCommons hosts the model and preprocessed dataset for download. You must first agree to the [confidentiality notice](https://docs.google.com/forms/d/e/1FAIpQLSc_8VIvRmXM3I8KQaYnKf7gy27Z63BBoI_I1u02f4lw6rBp3g/viewform), then follow the link to a directory containing Rclone download instructions.
+## Get Model
+### MLCommons Members Download
+MLCommons hosts the model and preprocessed dataset for download exclusively by MLCommons Members. You must first agree to the [confidentiality notice](https://docs.google.com/forms/d/e/1FAIpQLSc_8VIvRmXM3I8KQaYnKf7gy27Z63BBoI_I1u02f4lw6rBp3g/viewform), then follow the link to a directory containing Rclone download instructions.
+
+
+### External Download
++ First go to [llama2-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloninng below.
++ Requires Git Large Files Storage
+```
+export CHECKPOINT_PATH=${PWD}/Llama-2-70b-chat-hf
+git lfs install
+git clone https://huggingface.co/meta-llama/Llama-2-70b-chat-hf ${CHECKPOINT_PATH}
+
+```
+
+## Get Dataset
+
+### Preprocessed
+
+You can use Rclone to download the preprocessed dataset from a Cloudflare R2 bucket.
+
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
+```
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
+```
+Once Rclone is installed, run the following command to authenticate with the bucket:
+```
+rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+```
+You can then navigate in the terminal to your desired download directory and run the following commands to download the model checkpoint:
+
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/open_orca ./open_orca -P
+```
+
+### Unprocessed
+
+You can also download and process the dataset yourself as follows:
+
+```
+# First get the `open-orca` parquet from huggingface
+export OPENORCA_DATASET=${PWD}/open-orca
+git clone https://huggingface.co/datasets/Open-Orca/OpenOrca ${OPENORCA_DATASET}
+
+export OPENORCA_PARQUET=${OPENORCA_DATASET}/1M-GPT4-Augmented.parquet
+EXPORT_DIR=${PWD}/processed-openorca
+export DATASET_PATH=${PWD}/processed-data.pkl
+
+# Process the dataset according the Taskforce's agreed criteria
+python3 processorca.py --dataset_pq_path=${OPENORCA_PARQUET} --model_dir=${CHECKPOINT_PATH} --seqlen_limit=1024 --export_dir=${EXPORT_DIR} --num_total_samples=24576
+
+mv ${EXPORT_DIR}/open_orca_gpt4_tokenized_llama.sampled_24576.pkl ${DATASET_PATH}
+```
 
 
 ## Run Performance Benchmarks

From 46a35c2e5535252a300f98b4c238a9f1e96ccb7d Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Wed, 21 Feb 2024 17:09:29 -0600
Subject: [PATCH 073/305] Tweak language in README.md

---
 language/llama2-70b/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index f648b1b5d8..3fdf71ce2c 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -93,7 +93,7 @@ Once Rclone is installed, run the following command to authenticate with the buc
 ```
 rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
 ```
-You can then navigate in the terminal to your desired download directory and run the following commands to download the model checkpoint:
+You can then navigate in the terminal to your desired download directory and run the following command to download the dataset:
 
 ```
 rclone copy mlc-inference:mlcommons-inference-wg-public/open_orca ./open_orca -P

From c0bd844fd6d1f11c0ec035fd52f07895d4146f3e Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Wed, 21 Feb 2024 17:10:08 -0600
Subject: [PATCH 074/305] Language tweak in README.md

---
 language/gpt-j/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/gpt-j/README.md b/language/gpt-j/README.md
index cbb5b3e3c2..061067027a 100644
--- a/language/gpt-j/README.md
+++ b/language/gpt-j/README.md
@@ -93,7 +93,7 @@ Once Rclone is installed, run the following command to authenticate with the buc
 ```
 rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
 ```
-You can then navigate in the terminal to your desired download directory and run the following commands to download the model checkpoint:
+You can then navigate in the terminal to your desired download directory and run the following command to download the model checkpoint:
 
 ```
 rclone copy mlc-inference:mlcommons-inference-wg-public/gpt-j ./model -P

From 8219069f83c2396be1c1b4b4c8f80cca756db5ca Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Wed, 21 Feb 2024 17:11:15 -0600
Subject: [PATCH 075/305] Minor language tweak in README.md

---
 recommendation/dlrm_v2/pytorch/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/recommendation/dlrm_v2/pytorch/README.md b/recommendation/dlrm_v2/pytorch/README.md
index 6de850b819..12937c0c86 100755
--- a/recommendation/dlrm_v2/pytorch/README.md
+++ b/recommendation/dlrm_v2/pytorch/README.md
@@ -98,7 +98,7 @@ Once Rclone is installed, run the following command to authenticate with the buc
 ```
 rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
 ```
-You can then navigate in the terminal to your desired download directory and run the following commands to download the model weights:
+You can then navigate in the terminal to your desired download directory and run the following command to download the model weights:
 
 ```
 rclone copy mlc-inference:mlcommons-inference-wg-public/model_weights ./model_weights -P

From e39003a9c4c89a2215db0ca57ad7a57b16f9a785 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Fri, 23 Feb 2024 13:55:22 -0600
Subject: [PATCH 076/305] Fix typo in README.md

---
 language/llama2-70b/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 3fdf71ce2c..b539b3e3cf 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -69,7 +69,7 @@ MLCommons hosts the model and preprocessed dataset for download exclusively by M
 
 
 ### External Download
-+ First go to [llama2-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloninng below.
++ First go to [llama2-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloning below.
 + Requires Git Large Files Storage
 ```
 export CHECKPOINT_PATH=${PWD}/Llama-2-70b-chat-hf

From 7fc059bf9160645746e50c0e94d1d91c9541ebb4 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 14 Mar 2024 22:47:19 +0000
Subject: [PATCH 077/305] Count error when logging errors:
 submission_checker.py

---
 tools/submission/submission_checker.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 0bae5b0d45..c79963096a 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -2169,6 +2169,7 @@ def check_performance_dir(
                 required_min_duration,
                 min_duration,
             )
+            is_valid = False
 
     inferred = False
     if scenario_fixed != scenario:
@@ -2945,12 +2946,16 @@ def log_result(
                                 "process_accuracy.py",
                                 acc_path,
                             )
+                            errors += 1
+                            continue
                         else:
                             diff = files_diff(list_files(acc_path), REQUIRED_ACC_FILES)
                             if diff:
                                 log.error(
                                     "%s has file list mismatch (%s)", acc_path, diff
                                 )
+                                errors += 1
+                                continue
                             accuracy_is_valid, acc = check_accuracy_dir(
                                 config,
                                 mlperf_model,

From b3c617ca77915babc09f8470fad82cdfeb669036 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 28 Feb 2024 12:45:24 +0000
Subject: [PATCH 078/305] Fixes #1648, restrict loadgen uncommitted error
 message to within the loadgen directory

---
 loadgen/version_generator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/version_generator.py b/loadgen/version_generator.py
index a34657a64a..4de930a633 100644
--- a/loadgen/version_generator.py
+++ b/loadgen/version_generator.py
@@ -43,7 +43,7 @@ def func_def(name, string):
 def generate_loadgen_version_definitions_git(ofile, git_command):
     git_rev = os.popen(git_command + "rev-parse --short=10 HEAD").read()
     git_commit_date = os.popen(git_command + "log --format=\"%cI\" -n 1").read()
-    git_status = os.popen(git_command + "status -s -uno").read()
+    git_status = os.popen(git_command + "status -s -uno .").read()
     git_log = subprocess.Popen(
         git_command + "log --pretty=oneline -n 16 --no-decorate", stdout=subprocess.PIPE, shell=True, encoding='ascii', errors="ignore" ).stdout.read()
     ofile.write(func_def("GitRevision", "\"" + git_rev[0:-1] + "\""))

From 31c3b37da5e93571915deab8570ed8dbe6f17723 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 2 May 2024 06:18:14 +0100
Subject: [PATCH 079/305] Update test-rnnt.yml (#1688)

Stopping the github action for rnnt
---
 .github/workflows/test-rnnt.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test-rnnt.yml b/.github/workflows/test-rnnt.yml
index bbaf5bf528..a765739009 100755
--- a/.github/workflows/test-rnnt.yml
+++ b/.github/workflows/test-rnnt.yml
@@ -5,7 +5,7 @@ name: Test for MLPerf inference rnnt submission generation using CM script autom
 
 on:
   pull_request:
-    branches: [ "master", "dev" ]
+    branches: [ "master-retired", "dev-retired" ]
     paths:
       - speech_recognition/rnnt/**
       - tools/submission/**

From 0ab25310584705ffc513a6c8d94fad207355b4c3 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 7 May 2024 11:22:08 -0500
Subject: [PATCH 080/305] Move old benchmarks to retired benchmarks folder
 (#1687)

* Move old benchmarks to retired benchmarks folder

* Add never adopted benchmarks

* Add mobilenet to retired benchmarks folder
---
 .github/workflows/test-rnnt.yml               |    0
 .../language/gpt3/megatron/DATASETS_MODELS.md |    8 +
 .../language/gpt3/megatron/LICENSE            |  201 ++++
 .../language/gpt3/megatron/README.md          |  139 +++
 .../language/gpt3/megatron/backend.py         |  186 +++
 .../gpt3/megatron/calibration-list.txt        | 1000 +++++++++++++++++
 .../language/gpt3/megatron/dataset.py         |  167 +++
 .../language/gpt3/megatron/download_cnndm.py  |   57 +
 .../language/gpt3/megatron/evaluation.py      |   76 ++
 .../language/gpt3/megatron/main.py            |  112 ++
 .../language/gpt3/megatron/mlperf.conf        |   68 ++
 .../gpt3/megatron/prepare-calibration.py      |   59 +
 .../language/gpt3/megatron/requirements.txt   |    7 +
 .../gpt3/megatron/run_generation_server.sh    |   32 +
 .../megatron/run_generation_server_debug.sh   |   26 +
 .../gpt3/megatron/text_generation_server.py   |  270 +++++
 .../language/gpt3/megatron/user.conf          |    4 +
 .../language/gpt3/megatron/utils.py           |   15 +
 .../dlrm/pytorch/.dockerignore                |    0
 .../recommendation}/dlrm/pytorch/.gitignore   |    0
 .../recommendation}/dlrm/pytorch/README.md    |    0
 .../dlrm/pytorch/VERSION_NUMBER               |    0
 .../dlrm/pytorch/docker_cpu/Dockerfile        |    0
 .../pytorch/docker_cpu/build_docker_cpu.sh    |    0
 .../dlrm/pytorch/docker_cpu/run_docker_cpu.sh |    0
 .../dlrm/pytorch/docker_gpu/Dockerfile        |    0
 .../pytorch/docker_gpu/build_docker_gpu.sh    |    0
 .../dlrm/pytorch/docker_gpu/run_docker_gpu.sh |    0
 .../dlrm/pytorch/python/__init__.py           |    0
 .../dlrm/pytorch/python/backend.py            |    0
 .../pytorch/python/backend_onnxruntime.py     |    0
 .../pytorch/python/backend_pytorch_native.py  |    0
 .../dlrm/pytorch/python/backend_tf.py         |    0
 .../dlrm/pytorch/python/criteo.py             |    0
 .../dlrm/pytorch/python/dataset.py            |    0
 .../dlrm/pytorch/python/main.py               |    0
 .../dlrm/pytorch/python/tf_dlrm.py            |    0
 .../dlrm/pytorch/python/version.py            |    0
 .../dlrm/pytorch/run_and_time.sh              |    0
 .../dlrm/pytorch/run_common.sh                |    0
 .../dlrm/pytorch/run_helper.sh                |    0
 .../recommendation}/dlrm/pytorch/run_local.sh |    0
 .../recommendation}/dlrm/pytorch/setup.py     |    0
 .../dlrm/pytorch/tools/accuracy-dlrm.py       |    0
 .../dlrm/pytorch/tools/dist_quantile.txt      |    0
 .../pytorch/tools/dist_trace_verification.txt |    0
 .../dlrm/pytorch/tools/make_fake_criteo.sh    |    0
 .../dlrm/pytorch/tools/quickgen.py            |    0
 .../recommendation}/dlrm/pytorch/user.conf    |    0
 .../recommendation}/dlrm/tf/README.md         |    0
 .../recommendation}/dlrm/tf/__init__.py       |    0
 .../recommendation}/dlrm/tf/common.py         |    0
 .../recommendation}/dlrm/tf/dataloader.py     |    0
 .../recommendation}/dlrm/tf/dlrm.py           |    0
 .../dlrm/tf/dlrm_embedding_runner.py          |    0
 .../recommendation}/dlrm/tf/dlrm_main.py      |    0
 .../recommendation}/dlrm/tf/feature_config.py |    0
 .../recommendation}/dlrm/tf/mlp_log.py        |    0
 .../tf/roc_metrics/python/roc_metrics.clif    |    0
 .../dlrm/tf/roc_metrics/roc_metrics.cc        |    0
 .../dlrm/tf/roc_metrics/roc_metrics.h         |    0
 .../dlrm/tf/train_and_eval_runner.py          |    0
 .../recommendation}/dlrm/tf/utils.py          |    0
 .../speech_recognition}/rnnt/QSL.py           |    0
 .../speech_recognition}/rnnt/README.md        |    0
 .../speech_recognition}/rnnt/README_cm.md     |    0
 .../speech_recognition}/rnnt/accuracy_eval.py |    0
 .../rnnt/dev-clean-wav.json                   |    0
 .../speech_recognition}/rnnt/environment.yml  |    0
 .../rnnt/optional_harness_ck/README.md        |    0
 .../rnnt/pytorch/Dockerfile                   |    0
 .../speech_recognition}/rnnt/pytorch/LICENSE  |    0
 .../speech_recognition}/rnnt/pytorch/NOTICE   |    0
 .../rnnt/pytorch/configs/rnnt.toml            |    0
 .../rnnt/pytorch/dataset.py                   |    0
 .../rnnt/pytorch/decoders.py                  |    0
 .../rnnt/pytorch/helpers.py                   |    0
 .../rnnt/pytorch/metrics.py                   |    0
 .../rnnt/pytorch/model_separable_rnnt.py      |    0
 .../rnnt/pytorch/parts/features.py            |    0
 .../rnnt/pytorch/parts/manifest.py            |    0
 .../rnnt/pytorch/parts/segment.py             |    0
 .../rnnt/pytorch/parts/text/LICENSE           |    0
 .../rnnt/pytorch/parts/text/__init__.py       |    0
 .../rnnt/pytorch/parts/text/cleaners.py       |    0
 .../rnnt/pytorch/parts/text/numbers.py        |    0
 .../rnnt/pytorch/preprocessing.py             |    0
 .../speech_recognition}/rnnt/pytorch/rnn.py   |    0
 .../rnnt/pytorch/scripts/docker/build.sh      |    0
 .../rnnt/pytorch/scripts/docker/launch.sh     |    0
 .../pytorch/scripts/download_librispeech.sh   |    0
 .../rnnt/pytorch/scripts/evaluation.sh        |    0
 .../rnnt/pytorch/scripts/inference.sh         |    0
 .../pytorch/scripts/inference_benchmark.sh    |    0
 .../pytorch/scripts/preprocess_librispeech.sh |    0
 .../rnnt/pytorch/scripts/train.sh             |    0
 .../rnnt/pytorch/scripts/train_benchmark.sh   |    0
 .../rnnt/pytorch/utils/__init__.py            |    0
 .../rnnt/pytorch/utils/convert_librispeech.py |    0
 .../pytorch/utils/download_librispeech.py     |    0
 .../rnnt/pytorch/utils/download_utils.py      |    0
 .../pytorch/utils/inference_librispeech.csv   |    0
 .../pytorch/utils/librispeech-inference.csv   |    0
 .../rnnt/pytorch/utils/librispeech.csv        |    0
 .../rnnt/pytorch/utils/preprocessing_utils.py |    0
 .../speech_recognition}/rnnt/pytorch_SUT.py   |    0
 .../speech_recognition}/rnnt/run.py           |    0
 .../speech_recognition}/rnnt/run.sh           |    0
 .../speech_recognition}/rnnt/user.conf        |    0
 .../translation}/gnmt/.gitignore              |    0
 .../translation}/gnmt/README.md               |    0
 .../translation}/gnmt/mlcube/mlcube.yaml      |    0
 .../gnmt/mlcube/workspace/parameters.yaml     |    0
 .../translation}/gnmt/tensorflow/Dockerfile   |    0
 .../translation}/gnmt/tensorflow/README.md    |    0
 .../gnmt/tensorflow/download_dataset.sh       |    0
 .../gnmt/tensorflow/download_trained_model.sh |    0
 .../gnmt/tensorflow/generic_loadgen.py        |    0
 .../gnmt/tensorflow/loadgen_gnmt.py           |    0
 .../translation}/gnmt/tensorflow/mlcube.py    |    0
 .../gnmt/tensorflow/nmt/__init__.py           |    0
 .../gnmt/tensorflow/nmt/attention_model.py    |    0
 .../gnmt/tensorflow/nmt/gnmt_model.py         |    0
 .../gnmt/tensorflow/nmt/inference.py          |    0
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 .../translation}/gnmt/tensorflow/nmt/model.py |    0
 .../gnmt/tensorflow/nmt/model_helper.py       |    0
 .../gnmt/tensorflow/nmt/model_test.py         |    0
 .../translation}/gnmt/tensorflow/nmt/nmt.py   |    0
 .../gnmt/tensorflow/nmt/nmt_test.py           |    0
 .../gnmt/tensorflow/nmt/scripts/__init__.py   |    0
 .../gnmt/tensorflow/nmt/scripts/bleu.py       |    0
 .../gnmt/tensorflow/nmt/scripts/rouge.py      |    0
 .../standard_hparams/wmt16_gnmt_4_layer.json  |    0
 .../translation}/gnmt/tensorflow/nmt/train.py |    0
 .../gnmt/tensorflow/nmt/utils/__init__.py     |    0
 .../tensorflow/nmt/utils/common_test_utils.py |    0
 .../tensorflow/nmt/utils/evaluation_utils.py  |    0
 .../nmt/utils/evaluation_utils_test.py        |    0
 .../tensorflow/nmt/utils/iterator_utils.py    |    0
 .../nmt/utils/iterator_utils_test.py          |    0
 .../gnmt/tensorflow/nmt/utils/misc_utils.py   |    0
 .../tensorflow/nmt/utils/misc_utils_test.py   |    0
 .../gnmt/tensorflow/nmt/utils/nmt_utils.py    |    0
 .../nmt/utils/standard_hparams_utils.py       |    0
 .../gnmt/tensorflow/nmt/utils/vocab_utils.py  |    0
 .../tensorflow/nmt/utils/vocab_utils_test.py  |    0
 .../gnmt/tensorflow/preprocess_input.sh       |    0
 .../gnmt/tensorflow/process_accuracy.py       |    0
 .../gnmt/tensorflow/requirements.txt          |    0
 .../translation}/gnmt/tensorflow/run.sh       |    0
 .../translation}/gnmt/tensorflow/run_task.py  |    0
 .../gnmt/tensorflow/train_gnmt.txt            |    0
 .../gnmt/tensorflow/verify_dataset.sh         |    0
 .../.dockerignore                             |    3 +
 .../classification_and_detection/.gitignore   |   19 +
 .../Dockerfile.cpu                            |   44 +
 .../Dockerfile.gpu                            |   55 +
 .../GettingStarted.ipynb                      |  413 +++++++
 .../classification_and_detection/README.md    |  270 +++++
 .../VERSION_NUMBER                            |    1 +
 .../classification/README.md                  |  421 +++++++
 .../classification/onnx/README.md             |  239 ++++
 .../classification/tf-cpp/README.md           |  367 ++++++
 .../classification/tf-py/README.md            |  181 +++
 .../classification/tflite/README.md           |  320 ++++++
 .../optional_harness_ck/detection/README.md   |  177 +++
 .../detection/tf-py/README.md                 |  252 +++++
 .../detection/tflite/README.md                |  183 +++
 .../python/__init__.py                        |    0
 .../python/backend.py                         |   23 +
 .../python/backend_null.py                    |   34 +
 .../python/backend_onnxruntime.py             |   47 +
 .../python/backend_pytorch.py                 |   64 ++
 .../python/backend_pytorch_native.py          |   57 +
 .../python/backend_tf.py                      |   63 ++
 .../python/backend_tflite.py                  |   62 +
 .../python/coco.py                            |  343 ++++++
 .../python/dataset.py                         |  273 +++++
 .../python/imagenet.py                        |   95 ++
 .../python/main.py                            |  565 ++++++++++
 .../python/models/anchor_generator.py         |  394 +++++++
 .../python/models/base_model_r34.py           |  204 ++++
 .../python/models/convert_tf_weights.py       |  137 +++
 .../python/models/ssd_mobilenet_v1.py         |  253 +++++
 .../python/models/ssd_r34.py                  |  351 ++++++
 .../python/models/utils.py                    |  198 ++++
 .../python/pycoco.py                          |  433 +++++++
 .../python/version.py                         |    3 +
 .../run_and_time.sh                           |   26 +
 .../run_common.sh                             |  115 ++
 .../run_helper.sh                             |   18 +
 .../classification_and_detection/run_local.sh |   13 +
 .../classification_and_detection/setup.py     |   85 ++
 .../tools/accuracy-coco.py                    |  125 +++
 .../tools/accuracy-imagenet.py                |   74 ++
 .../tools/calibrate_torchvision_model.py      |   77 ++
 .../tools/ci-run.sh                           |   78 ++
 .../tools/coco-analyze.py                     |  134 +++
 .../tools/convert-to-onnx.sh                  |   50 +
 .../tools/lglog2csv.py                        |   69 ++
 .../tools/make_fake_imagenet.sh               |   27 +
 .../tools/mobilenet-to-onnx.sh                |   30 +
 .../tools/pylintrc                            |  320 ++++++
 .../tools/resnet50-to-tflite.sh               |   26 +
 .../tools/resnet_save.py                      |  298 +++++
 .../tools/ssd-nhwc.py                         |  267 +++++
 .../classification_and_detection/user.conf    |    6 +
 208 files changed, 10809 insertions(+)
 mode change 100755 => 100644 .github/workflows/test-rnnt.yml
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/DATASETS_MODELS.md
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/LICENSE
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/README.md
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/backend.py
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/calibration-list.txt
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/dataset.py
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/download_cnndm.py
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/evaluation.py
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/main.py
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/mlperf.conf
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/prepare-calibration.py
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/requirements.txt
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/run_generation_server.sh
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/run_generation_server_debug.sh
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/text_generation_server.py
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/user.conf
 create mode 100644 retired_benchmarks/never_adopted/language/gpt3/megatron/utils.py
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/.dockerignore (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/.gitignore (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/README.md (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/VERSION_NUMBER (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/docker_cpu/Dockerfile (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/docker_cpu/build_docker_cpu.sh (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/docker_cpu/run_docker_cpu.sh (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/docker_gpu/Dockerfile (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/docker_gpu/build_docker_gpu.sh (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/docker_gpu/run_docker_gpu.sh (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/__init__.py (100%)
 mode change 100755 => 100644
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/backend.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/backend_onnxruntime.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/backend_pytorch_native.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/backend_tf.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/criteo.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/dataset.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/main.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/tf_dlrm.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/python/version.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/run_and_time.sh (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/run_common.sh (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/run_helper.sh (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/run_local.sh (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/setup.py (100%)
 rename {recommendation => retired_benchmarks/recommendation}/dlrm/pytorch/tools/accuracy-dlrm.py (100%)
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diff --git a/.github/workflows/test-rnnt.yml b/.github/workflows/test-rnnt.yml
old mode 100755
new mode 100644
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/DATASETS_MODELS.md b/retired_benchmarks/never_adopted/language/gpt3/megatron/DATASETS_MODELS.md
new file mode 100644
index 0000000000..6cf9edae17
--- /dev/null
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@@ -0,0 +1,8 @@
+# Datasets
+
+This is a comprehensive list of public datasets and models used by this repository.
+
+| Name (Link/Source) | Framework | Use Case |
+|--------------------| --------- | -------- |
+| [cnn_dailymail (Hugging Face)](https://huggingface.co/datasets/cnn_dailymail) | PyTorch | Text Summarization |
+| [gpt-3-175b (Megatron implementation)](https://github.com/mlcommons/training/tree/master/large_language_model/megatron-lm) | PyTorch | Text Summarization |
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/LICENSE b/retired_benchmarks/never_adopted/language/gpt3/megatron/LICENSE
new file mode 100644
index 0000000000..25ae411062
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/LICENSE
@@ -0,0 +1,201 @@
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+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/README.md b/retired_benchmarks/never_adopted/language/gpt3/megatron/README.md
new file mode 100644
index 0000000000..23900f715c
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/README.md
@@ -0,0 +1,139 @@
+# GPT-3 Reference Implementation
+
+## Setup Instructions
+
+```bash
+WORK_DIR=$PWD
+# Create Environment (conda)
+conda create -n llm python=3.9 -y
+conda activate llm
+```
+
+### Download repositories
+```bash
+cd $HOME
+git clone --recurse-submodules https://github.com/mlcommons/inference.git --depth 1
+git clone https://github.com/NVIDIA/apex.git
+git clone https://github.com/mlcommons/training.git --branch inference-megatron
+cd $HOME/apex
+git checkout -b language 2d8302a6c12e202f7b40b13a43daa95f326fd0ea
+```
+
+
+### install requirements
+```bash
+pip install torch==1.13.0 torchvision==0.14.0 datasets evaluate accelerate simplejson nltk rouge_score pybind11 Ninja numpy==1.19.5 sentencepiece  zarr tensorstore
+pip install git+https://github.com/NVIDIA/mlperf-common.git
+pip install git+https://github.com/mlperf/logging.git
+sudo apt install pybind11-dev
+```
+
+#### install apex
+For `pip >= 23.1`
+```bash
+cd $HOME/apex
+pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
+```
+Otherwise
+```bash
+cd $HOME/apex
+pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./
+```
+**Warning:** Make sure the Nvidia driver version and the pytorch's version of cuda match
+
+This step takes a several minutes. You can cache this step by running:
+For `pip >= 23.1`
+```bash
+cd $HOME/apex
+pip wheel -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
+```
+Otherwise
+```bash
+cd $HOME/apex
+pip wheel -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./
+```
+Afterthat, you can store the whl file and simply run
+```bash
+pip install <wheel_output_file>.whl
+```
+
+### Build Loadgen
+```sh
+cd $HOME/inference/loadgen
+CFLAGS="-std=c++14 -O3" python setup.py develop
+```
+
+
+### Download & Process Dataset
+Downloads the raw data, processes and saves it as json file inside data/
+```bash
+cd $HOME/inference/language/gpt3/megatron
+python download_cnndm.py
+```
+### Calibration
+Downloads CNN-Daily Mail dataset and creates the calibration dataset (JSON) for post-training quantization
+```bash
+cd $HOME/inference/language/gpt3/megatron
+pip install datasets
+python prepare-calibration.py --calibration-list-file calibration-list.txt --output-dir </path/to/output-folder>
+```
+### Download tokenizer files
+TODO: Share tokenizer links
+
+Temporary private link:
+```bash
+cd $HOME/inference/language/gpt3/megatron/data/
+gsutil cp gs://mlperf-llm-public2/vocab/c4_en_301_5Mexp2_spm.model .
+```
+### Download GPT-3 model
+TODO: Share checkpoint link
+
+Temporary private link:
+```bash
+cd $HOME/inference/language/gpt3/megatron/
+mkdir model
+cd $HOME/inference/language/gpt3/megatron/model/
+gcloud auth login
+# gcloud storage cp "gs://mlperf-llm-public2/nv_gpt3ckpt_00011000_megatron_06162023/language_model*" .
+gsutil -m rsync -r "gs://mlperf-llm-public2/nv_gpt3ckpt_00011000_megatron_06162023/" .
+gsutil cp gs://mlperf-llm-public2/nv_gpt3ckpt_00011000_megatron_06162023/metadata.json .
+```
+### Running the Benchmark - Megatron
+First set the `MEGATRON_PATH` environment variable:
+```bash
+export MEGATRON_PATH=$HOME/training/large_language_model/megatron-lm
+```
+In one terminal, run the text generation server. For this 8 gpus are necessary:
+```bash
+cd $HOME/inference/language/gpt3/megatron/
+./run_generation_server.sh
+```
+You can make a debug run with one gpu:
+```bash
+cd $HOME/inference/language/gpt3/megatron/
+./run_generation_server_debug.sh
+```
+
+In another terminal run the benchmark. This will query the server each time a query for the SUT is generated
+```bash
+cd $HOME/inference/language/gpt3/megatron/
+python main.py --scenario=[Offline | Server | SingleStream] --model-path=./model/ --dataset-path=./data/cnn_eval.json [--accuracy] --max_examples=[Maximum number of examples to consider]
+```
+### Evaluate accuracy run 
+Evaluates the ROGUE scores from the accuracy logs. Only applicable when specifiying [--accuracy] while running main.py
+```bash
+pip install rouge_score
+python evaluation.py --mlperf-accuracy-file ./build/logs/mlperf_log_accuracy.json --dataset-file ./data/cnn_eval.json
+```
+
+### Reference Model - ROUGE scores
+TODO: Compute rouge scores
+
+### License:
+Apache License Version 2.0.
+
+### Datasets & Models:
+
+To the extent that any data, datasets or models are referenced by Intel or accessed using tools or code on this site such data, datasets and models are provided by the third party indicated as the source of such content. Intel does not create the data, datasets, or models, provide a license to any third-party data, datasets, or models referenced, and does not warrant their accuracy or quality.  By accessing such data, dataset(s) or model(s) you agree to the terms associated with that content and that your use complies with the applicable license. DATASETS [https://github.com/badhri-intel/inference/blob/gpt-j/ref_implementation/language/gpt-j/DATASETS_MODELS.md]
+
+Intel expressly disclaims the accuracy, adequacy, or completeness of any data, datasets or models, and is not liable for any errors, omissions, or defects in such content, or for any reliance thereon. Intel also expressly disclaims any warranty of non-infringement with respect to such data, dataset(s), or model(s). Intel is not liable for any liability or damages relating to your use of such data, datasets or models.
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/backend.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/backend.py
new file mode 100644
index 0000000000..39605cd7bb
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/backend.py
@@ -0,0 +1,186 @@
+import array
+import torch
+import requests
+import json
+import os
+import sys
+import numpy as np
+
+import mlperf_loadgen as lg
+from dataset import Dataset
+
+
+class SUT_base:
+    def __init__(
+        self,
+        dataset_path,
+        max_examples,
+        args,
+    ):
+        # TODO : Pass model file name to init instead of args
+        print("Loading PyTorch model...")
+        self.model_name = "Megatron-LM"
+        self.dataset_path = dataset_path
+        self.url = "http://localhost:5000/api"
+        self.headers = {"Content-Type": "application/json"}
+
+        self.data_object = Dataset(
+            self.dataset_path, total_count_override=max_examples, args=args
+        )
+
+        self.qsl = lg.ConstructQSL(
+            self.data_object.count,
+            self.data_object.perf_count,
+            self.data_object.LoadSamplesToRam,
+            self.data_object.UnloadSamplesFromRam,
+        )
+
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+
+    def issue_queries(self, query_samples):
+        print("Number of Samples in query_samples : ", len(query_samples))
+
+        for i in range(len(query_samples)):
+            index = query_samples[i].index
+            input_ids_tensor = self.data_object.source_encoded_input_ids[index]
+            # input_masks_tensor = self.data_object.source_encoded_attn_masks[index]
+            input_length_tensor = self.data_object.source_encoded_input_id_lengths[
+                index
+            ]
+
+            pred_output_batch = self.inference_call(
+                input_ids_tensor, input_length_tensor
+            )
+
+            response_array = array.array("B", pred_output_batch[0].tobytes())
+            bi = response_array.buffer_info()
+            response = [lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])]
+            lg.QuerySamplesComplete(response)
+            if i % 5 == 0:
+                print("Completed : ", i)
+
+    def inference_call(self, input_ids_tensor, input_length_tensor):
+        """Common for all scenarios"""
+        data = {"input_ids": input_ids_tensor, "input_length": input_length_tensor}
+        response = requests.put(self.url, data=json.dumps(data), headers=self.headers)
+        if response.status_code != 200:
+            # TODO: Manage exeption
+            return None
+        else:
+            output = response.json()["output"]
+        output = np.asarray(output)
+        return output
+
+    def flush_queries(self):
+        pass
+
+    def __del__(self):
+        print("Finished destroying SUT.")
+
+
+class SUT_Offline(SUT_base):
+    def __init__(
+        self, dataset_path, max_examples, args,
+    ):
+        SUT_base.__init__(
+            self,
+            dataset_path,
+            max_examples,
+            args,
+        )
+
+    """IssueQuery and inference methods implemented in Base class"""
+
+
+class SUT_Server(SUT_base):
+    def __init__(
+        self, dataset_path, max_examples, args,
+    ):
+
+        SUT_base.__init__(
+            self,
+            dataset_path,
+            max_examples,
+            args,
+        )
+        self.total_samples_done = 0
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        print("SUT Server")
+
+    def issue_queries(self, query_samples):
+
+        index = query_samples[0].index
+        input_ids_tensor = self.data_object.source_encoded_input_ids[index]
+        # input_masks_tensor = self.data_object.source_encoded_attn_masks[index]
+        input_length_tensor = self.data_object.source_encoded_input_id_lengths[index]
+
+        pred_output_batch = (
+            self.inference_call(input_ids_tensor, input_length_tensor)
+        )
+
+        response_array = array.array("B", pred_output_batch[0].tobytes())
+        bi = response_array.buffer_info()
+        responses = [lg.QuerySampleResponse(query_samples[0].id, bi[0], bi[1])]
+        lg.QuerySamplesComplete(responses)
+        self.total_samples_done += 1
+        if self.total_samples_done % 5 == 0:
+            print("Completed : ", self.total_samples_done)
+
+
+class SUT_SingleStream(SUT_base):
+    def __init__(
+        self, dataset_path, max_examples, args,
+    ):
+        SUT_base.__init__(
+            self,
+            dataset_path,
+            max_examples,
+            args,
+        )
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        self.total_samples_done = 0
+
+    def issue_queries(self, query_samples):
+
+        index = query_samples[0].index
+        input_ids_tensor = self.data_object.source_encoded_input_ids[index]
+        # input_masks_tensor = self.data_object.source_encoded_attn_masks[index]
+        input_length_tensor = self.data_object.source_encoded_input_id_lengths[index]
+
+        pred_output_batch = (
+            self.inference_call(input_ids_tensor, input_length_tensor)
+        )
+
+        response_array = array.array("B", pred_output_batch[0].tobytes())
+        bi = response_array.buffer_info()
+        responses = [lg.QuerySampleResponse(query_samples[0].id, bi[0], bi[1])]
+        lg.QuerySamplesComplete(responses)
+        self.total_samples_done += 1
+        if self.total_samples_done % 5 == 0:
+            print("Completed : ", self.total_samples_done)
+
+
+def get_SUT(
+    scenario,
+    dataset_path,
+    max_examples,
+    args,
+):
+    if scenario == "Offline":
+        return SUT_Offline(
+            dataset_path,
+            max_examples,
+            args,
+        )
+    elif scenario == "Server":
+        return SUT_Server(
+            dataset_path,
+            max_examples,
+            args,
+        )
+    elif scenario == "SingleStream":
+        return SUT_SingleStream(
+            dataset_path,
+            max_examples,
+            args,
+        )
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/calibration-list.txt b/retired_benchmarks/never_adopted/language/gpt3/megatron/calibration-list.txt
new file mode 100644
index 0000000000..fc5d258845
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/calibration-list.txt
@@ -0,0 +1,1000 @@
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diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/dataset.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/dataset.py
new file mode 100644
index 0000000000..f8bf443883
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/dataset.py
@@ -0,0 +1,167 @@
+import os
+import sys
+
+sys.path.append(os.environ["MEGATRON_PATH"])
+from megatron.tokenizer import build_tokenizer
+from megatron.utils import get_ltor_masks_and_position_ids
+import torch
+import argparse
+import utils
+import json
+
+from argparse import Namespace
+
+
+PROMPT_DICT = {
+    "prompt_input": (
+        "{instruction}{input}"
+    )
+}
+
+
+class Dataset:
+    def __init__(
+        self,
+        dataset_path,
+        batch_size=1,
+        args=Namespace(),
+        gen_kwards={},
+        total_count_override=None,
+        perf_count_override=None,
+        debug=False,
+    ):
+        print("Constructing QSL")
+
+        self.dataset = "cnn_dailymail"
+        self.dataset_path = dataset_path
+        self.batch_size = batch_size
+        self.debug = debug
+        self.gen_kwards = gen_kwards
+
+        ## TODO: provide arguments in command line
+        args.rank = 0
+        args.tokenizer_type = "SentencePieceTokenizer"
+        args.vocab_extra_ids = 0
+        if 'make_vocab_size_divisible_by' not in vars(args):
+            args.make_vocab_size_divisible_by = 128
+        if 'tensor_model_parallel_size' not in vars(args):
+            args.tensor_model_parallel_size = 8
+        if 'tokenizer_model' not in vars(args):
+            args.tokenizer_model = "./data/c4_en_301_5Mexp2_spm.model"
+        
+        
+        self.tokenizer = build_tokenizer(args)
+
+        self.list_data_dict = utils.jload(self.dataset_path)
+        self.max_input_tokens = 2048
+
+        prompt_input = PROMPT_DICT["prompt_input"]
+        self.sources = [
+            prompt_input.format_map(example) for example in self.list_data_dict
+        ]
+        self.targets = [f"{example['output']}" for example in self.list_data_dict]
+
+        (
+            self.source_encoded_input_ids,
+            # self.source_encoded_attn_masks,
+            self.source_encoded_input_id_lengths,
+        ) = self.encode_samples()
+        self.count = total_count_override or len(self.sources)
+        self.perf_count = perf_count_override or self.count
+
+    def _build_attention_mask(self, tokens):
+        """Build the attention mask and postition ids for the input tokens."""
+
+        # Since we are not interested in loss-mask and reset attention/position
+        # is also False, eod_token is not used so it is safe to set it to None.
+        attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
+            data=tokens,
+            eod_token=None,
+            reset_position_ids=False,
+            reset_attention_mask=False,
+            eod_mask_loss=False,
+        )
+        return attention_mask
+
+    def tokenize_prompts(self, prompts, tokens_to_generate, add_BOS):
+        """Given a set of prompts and number of tokens to generate:
+        - tokenize prompts
+        - set the sequence length to be the max of length of prompts
+        plus the number of tokens we would like to generate
+        - pad all the sequences to this length so we can convert them
+        into a 2D tensor.
+        """
+
+        # Tokenize all the prompts.
+        if add_BOS:
+            prompts_tokens = [
+                [self.tokenizer.eod] + self.tokenizer.tokenize(prompt)
+                for prompt in prompts
+            ]
+        else:
+            prompts_tokens = [self.tokenizer.tokenize(prompt)[:self.max_input_tokens] for prompt in prompts]
+
+        # Now we have a list of list of tokens which each list has a different
+        # size. We want to extend this list to:
+        #   - incorporate the tokens that need to be generated
+        #   - make all the sequences equal length.
+        # Get the prompts length.
+        prompts_length = [len(prompt_tokens) for prompt_tokens in prompts_tokens]
+        # Get the max prompts length.
+        max_prompt_len = max(prompts_length)
+        # Number of tokens in the each sample of the batch.
+        samples_length = max_prompt_len + tokens_to_generate
+        # Now update the list of list to be of the same size: samples_length.
+        for prompt_tokens, prompt_length in zip(prompts_tokens, prompts_length):
+            padding_size = samples_length - prompt_length
+            prompt_tokens.extend([self.tokenizer.pad] * padding_size)
+
+        # Now we are in a structured format, we can convert to tensors.
+        # prompts_tokens_tensor = torch.LongTensor(prompts_tokens)
+        # prompts_length_tensor = torch.LongTensor(prompts_length)
+
+        return prompts_tokens, prompts_length
+
+    def encode_samples(self):
+        print("Encoding Samples")
+
+        total_samples = len(self.sources)
+
+        source_encoded_input_ids = []
+        source_encoded_input_id_lengths = []
+        # source_encoded_attn_masks = []
+
+        for i in range(total_samples):
+            if i % 100 == 0 and self.debug:
+                print("Sentence:")
+                print(self.sources[i])
+                print(
+                    "--------------------------------------------------------------------------------"
+                )
+            tokens, length = self.tokenize_prompts(
+                [self.sources[i]], self.gen_kwards.get("max_new_tokens", 128), None
+            )
+            # attn_mask = self._build_attention_mask(tokens)
+            source_encoded_input_ids.append(tokens)
+            # source_encoded_attn_masks.append(attn_mask)
+            source_encoded_input_id_lengths.append(length)
+            if i % 100 == 0 and self.debug:
+                print(f"Tokens: {tokens}")
+                print(f"Original length: {length}")
+                # input("...")
+
+        return (
+            source_encoded_input_ids,
+            # source_encoded_attn_masks,
+            source_encoded_input_id_lengths,
+        )
+
+    def LoadSamplesToRam(self, sample_list):
+        pass
+
+    def UnloadSamplesFromRam(self, sample_list):
+        pass
+
+    def __del__(self):
+        print("Finished destroying QSL.")
+
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/download_cnndm.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/download_cnndm.py
new file mode 100644
index 0000000000..ec47912eae
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/download_cnndm.py
@@ -0,0 +1,57 @@
+# experiment config
+dataset_id = "cnn_dailymail"
+dataset_config = "3.0.0"
+text_column = "article"
+summary_column = "highlights"
+
+from datasets import load_dataset
+
+import numpy as np
+import os
+import simplejson as json
+import sys
+
+save_dataset_path = os.environ.get("DATASET_CNNDM_PATH", "data")
+
+# Check whether the specified path exists or not
+isExist = os.path.exists(save_dataset_path)
+if not isExist:
+    # Create a new directory because it does not exist
+    os.makedirs(save_dataset_path)
+
+# Load dataset from the hub
+dataset = load_dataset(dataset_id, name=dataset_config)
+
+instruction_template = "summarize: "
+
+
+def preprocess_function(sample, padding="max_length"):
+    # create list of samples
+    inputs = []
+
+    for i in range(0, len(sample[text_column])):
+        x = dict()
+        x["instruction"] = instruction_template
+        x["input"] = sample[text_column][i]
+        x["output"] = sample[summary_column][i]
+        inputs.append(x)
+    model_inputs = dict()
+    model_inputs["text"] = inputs
+
+    return model_inputs
+
+
+# process dataset
+tokenized_dataset = dataset.map(
+    preprocess_function, batched=True, remove_columns=list(dataset["train"].features)
+)
+
+# save dataset to disk
+
+with open(os.path.join(save_dataset_path, "cnn_eval.json"), "w") as write_f:
+    json.dump(
+        tokenized_dataset["validation"]["text"], write_f, indent=4, ensure_ascii=False
+    )
+
+
+print("Dataset saved in ", save_dataset_path)
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/evaluation.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/evaluation.py
new file mode 100644
index 0000000000..4e32289fcb
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/evaluation.py
@@ -0,0 +1,76 @@
+from dataset import Dataset 
+import numpy as np
+import json
+import nltk
+
+import evaluate
+import argparse
+from argparse import Namespace
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
+    parser.add_argument("--dataset-file", required=True, help="path to cnn_eval.json")
+    parser.add_argument(
+        "--tokenizer-model",
+        default="./data/c4_en_301_5Mexp2_spm.model",
+        help="Path to tokenizer model",
+    )
+    parser.add_argument("--verbose", action="store_true", help="verbose messages")
+    args = parser.parse_args()
+    return args
+
+def postprocess_text(preds, targets):
+    preds = [pred.strip() for pred in preds]
+    targets = [target.strip() for target in targets]
+
+    # rougeLSum expects newline after each sentence
+    preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
+    targets = ["\n".join(nltk.sent_tokenize(target)) for target in targets]
+
+    return preds, targets
+
+
+def main():
+    args = get_args()
+    dataset_path = args.dataset_file
+    metric = evaluate.load("rouge")
+    nltk.download('punkt')
+    
+    dataset_args = Namespace(tokenizer_model = args.tokenizer_model)
+    data_object = Dataset(dataset_path, args=dataset_args)
+
+    targets = data_object.targets
+
+    with open(args.mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+
+    target_required = []
+    preds_token_ids = []
+
+    for pred in results:
+        qsl_idx = pred['qsl_idx']
+        target = targets[qsl_idx]
+        target_required.append(target)
+        preds = np.frombuffer(bytes.fromhex(pred['data']), np.int64).tolist()
+        preds = [int(p) for p in preds]
+        preds_token_ids.append(preds)
+        
+
+    preds_decoded_text = [data_object.tokenizer.detokenize(ids) for ids in preds_token_ids]
+    preds, targets = postprocess_text(preds_decoded_text, target_required)
+
+
+    result = metric.compute(predictions=preds, references=targets, use_stemmer=True,use_aggregator=False)
+    result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
+    prediction_lens = [len(pred) for pred in preds]
+    result["gen_len"] = np.sum(prediction_lens)
+    result["gen_num"] = len(preds)
+    print("\nResults\n")
+    print(result)
+
+if __name__ == "__main__":
+    main()
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/main.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/main.py
new file mode 100644
index 0000000000..183278e52b
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/main.py
@@ -0,0 +1,112 @@
+import subprocess
+import mlperf_loadgen as lg
+import argparse
+import os
+import sys
+
+import sys
+from backend import get_SUT
+
+sys.path.insert(0, os.getcwd())
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--scenario",
+        choices=["SingleStream", "Offline", "Server", "MultiStream"],
+        default="Offline",
+        help="Scenario",
+    )
+    parser.add_argument("--dataset-path", default="./data/cnn_eval.json", help="")
+    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
+    parser.add_argument(
+        "--mlperf_conf", default="mlperf.conf", help="mlperf rules config"
+    )
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+    parser.add_argument(
+        "--max_examples",
+        type=int,
+        default=13368,
+        help="Maximum number of examples to consider (not limited by default)",
+    )
+    parser.add_argument(
+        "--make-vocab-size-divisible-by",
+        type=int,
+        default=128,
+        help="Make the vocab size divisible by",
+    )
+    parser.add_argument(
+        "--tensor-model-parallel-size",
+        type=int,
+        default=8,
+        help="Degree of tensor model parallelism.",
+    )
+    parser.add_argument(
+        "--tokenizer-model",
+        default="./data/c4_en_301_5Mexp2_spm.model",
+        help="Path to tokenizer model",
+    )
+    args = parser.parse_args()
+    return args
+
+
+scenario_map = {
+    "SingleStream": lg.TestScenario.SingleStream,
+    "Offline": lg.TestScenario.Offline,
+    "Server": lg.TestScenario.Server,
+    "MultiStream": lg.TestScenario.MultiStream,
+}
+
+
+def main():
+    args = get_args()
+    os.environ["RANK"] = "0"
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29500"
+
+    sut = get_SUT(
+        scenario=args.scenario,
+        dataset_path=args.dataset_path,
+        max_examples=args.max_examples,
+        args=args,
+    )
+
+    settings = lg.TestSettings()
+    settings.scenario = scenario_map[args.scenario]
+    # Need to update the conf
+    settings.FromConfig(args.mlperf_conf, "gpt3", args.scenario)
+    settings.FromConfig(args.user_conf, "gpt3", args.scenario)
+
+    if args.accuracy:
+        settings.mode = lg.TestMode.AccuracyOnly
+    else:
+        settings.mode = lg.TestMode.PerformanceOnly
+    log_path = os.environ.get("LOG_PATH")
+    if not log_path:
+        log_path = "build/logs"
+    if not os.path.exists(log_path):
+        os.makedirs(log_path)
+    log_output_settings = lg.LogOutputSettings()
+    log_output_settings.outdir = log_path
+    log_output_settings.copy_summary_to_stdout = True
+    log_settings = lg.LogSettings()
+    log_settings.log_output = log_output_settings
+    log_settings.enable_trace = True
+
+    lg.StartTestWithLogSettings(sut.sut, sut.qsl, settings, log_settings)
+    print("Test Done!")
+
+    print("Destroying SUT...")
+    lg.DestroySUT(sut.sut)
+
+    print("Destroying QSL...")
+    lg.DestroyQSL(sut.qsl)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/mlperf.conf b/retired_benchmarks/never_adopted/language/gpt3/megatron/mlperf.conf
new file mode 100644
index 0000000000..c31c0d7e2c
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/mlperf.conf
@@ -0,0 +1,68 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+
+# Set performance_sample_count for each model.
+# User can optionally set this to higher values in user.conf.
+resnet50.*.performance_sample_count_override = 1024
+ssd-mobilenet.*.performance_sample_count_override = 256
+retinanet.*.performance_sample_count_override = 64
+bert.*.performance_sample_count_override = 10833
+dlrm.*.performance_sample_count_override = 204800
+rnnt.*.performance_sample_count_override = 2513
+# set to 0 to let entire sample set to be performance sample
+3d-unet.*.performance_sample_count_override = 0
+
+# Set seeds. The seeds will be distributed two weeks before the submission.
+# 0x8ad40a335b00b614
+*.*.qsl_rng_seed = 10003631887983097364
+# 0xee765861048ca2ba
+*.*.sample_index_rng_seed = 17183018601990103738
+# 0xa867c8b40e66b636
+*.*.schedule_rng_seed = 12134888396634371638
+# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission.
+#0xcb41409ba0dd1540
+*.*.test05_qsl_rng_seed = 14646058500348515648
+#0x10c102904c81c5a2
+*.*.test05_sample_index_rng_seed = 1207248993894122914
+#0xa4db284011bcd876
+*.*.test05_schedule_rng_seed = 11879132697760422006
+
+
+*.SingleStream.target_latency_percentile = 90
+*.SingleStream.min_duration = 600000
+*.SingleStream.min_query_count = 1024
+
+*.MultiStream.target_latency_percentile = 99
+*.MultiStream.samples_per_query = 8
+*.MultiStream.min_duration = 600000
+*.MultiStream.min_query_count = 270336
+retinanet.MultiStream.target_latency = 528
+
+# 3D-UNet uses equal issue mode
+3d-unet.*.sample_concatenate_permutation = 1
+
+*.Server.target_latency = 10
+*.Server.target_latency_percentile = 99
+*.Server.target_duration = 0
+*.Server.min_duration = 600000
+*.Server.min_query_count = 270336
+resnet50.Server.target_latency = 15
+retinanet.Server.target_latency = 100
+bert.Server.target_latency = 130
+dlrm.Server.target_latency = 30
+rnnt.Server.target_latency = 1000
+
+*.Offline.target_latency_percentile = 90
+*.Offline.min_duration = 600000
+# In Offline scenario, we always have one query. But LoadGen maps this to
+# min_sample_count internally in Offline scenario, so set this to 24576 since
+# the rule requires that Offline scenario run for at least 24576 samples.
+*.Offline.min_query_count = 24576
+
+# These fields should be defined and overridden by user.conf.
+*.SingleStream.target_latency = 10
+*.MultiStream.target_latency = 80
+*.Server.target_qps = 1.0
+*.Offline.target_qps = 1.0
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/prepare-calibration.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/prepare-calibration.py
new file mode 100644
index 0000000000..846e492d38
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/prepare-calibration.py
@@ -0,0 +1,59 @@
+import os
+import sys
+import json
+from argparse import ArgumentParser
+from datasets import load_dataset
+
+def get_args():
+    parser = ArgumentParser()
+    parser.add_argument("--calibration-list-file", required=True, help="Path to calibration list")
+    parser.add_argument("--output-dir", help="Output directory", default="calibration-data")
+
+    return parser.parse_args()
+
+dataset_id='cnn_dailymail'
+version='3.0.0'
+split='train'
+
+instruction_template="Summarize the following news article:"
+
+def check_path(path):
+    return os.path.exists(path)
+
+def prepare_calibration_data(calibration_list_file, output_dir):
+    if not check_path(calibration_list_file):
+        print("Calibration list file not found: {}".format(calibration_list_file))
+        sys.exit(1)
+
+    dataset = load_dataset("cnn_dailymail", name="3.0.0", split='train')
+    train = dict((x['id'], x) for x in dataset)
+
+    
+    with open(calibration_list_file, 'r') as fid:
+        calibration_ids = fid.read().splitlines()
+
+    inputs = []
+    for id in calibration_ids:
+        calibration_sample = train[id]
+        x = dict()
+        x["instruction"] = instruction_template
+        x["input"] = calibration_sample["article"]
+        x["output"] = calibration_sample["highlights"]
+        inputs.append(x)
+    
+    if not os.path.isdir(output_dir):
+        os.makedirs(output_dir)
+
+    output_path = os.path.join(output_dir,"cnn_dailymail_calibration.json")
+    with open(output_path, 'w') as write_f:
+        json.dump(inputs, write_f, indent=4, ensure_ascii=False)
+
+    print("Calibration data saved at {}".format(output_path))
+
+def main():
+
+    args = get_args()
+    prepare_calibration_data(args.calibration_list_file, args.output_dir)
+
+if __name__=="__main__":
+    main()
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/requirements.txt b/retired_benchmarks/never_adopted/language/gpt3/megatron/requirements.txt
new file mode 100644
index 0000000000..660aac27ed
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/requirements.txt
@@ -0,0 +1,7 @@
+transformers
+datasets
+evaluate
+accelerate
+simplejson
+nltk
+rouge_score
\ No newline at end of file
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/run_generation_server.sh b/retired_benchmarks/never_adopted/language/gpt3/megatron/run_generation_server.sh
new file mode 100644
index 0000000000..4411537295
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/run_generation_server.sh
@@ -0,0 +1,32 @@
+#!/bin/bash
+# This example will start serving the 175B model.
+DISTRIBUTED_ARGS="--nproc_per_node 8 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+CHECKPOINT=$HOME/inference/language/gpt3/megatron/model/
+TOKENIZER_MODEL_FILE=$HOME/inference/language/gpt3/megatron/data/c4_en_301_5Mexp2_spm.model
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+pip install flask-restful
+
+torchrun $DISTRIBUTED_ARGS text_generation_server.py   \
+       --tensor-model-parallel-size 8  \
+       --pipeline-model-parallel-size 1  \
+       --num-layers 96  \
+       --hidden-size 12288  \
+       --num-attention-heads 96  \
+       --max-position-embeddings 2048  \
+       --tokenizer-type SentencePieceTokenizer  \
+       --micro-batch-size 1  \
+       --seq-length 2048  \
+       --tokenizer-model $TOKENIZER_MODEL_FILE \
+       --seed 42  \
+       --use-ext-ckpt  \
+       --no-load-rng  \
+       --fp16  \
+       --use-beam-search  \
+       --load ${CHECKPOINT}
\ No newline at end of file
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/run_generation_server_debug.sh b/retired_benchmarks/never_adopted/language/gpt3/megatron/run_generation_server_debug.sh
new file mode 100644
index 0000000000..b891662346
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/run_generation_server_debug.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+# This example will start serving the 345M model.
+DISTRIBUTED_ARGS="--nproc_per_node 1 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+TOKENIZER_MODEL_FILE=$HOME/inference/language/gpt3/megatron/data/c4_en_301_5Mexp2_spm.model
+
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+
+pip install flask-restful
+
+torchrun $DISTRIBUTED_ARGS text_generation_server.py   \
+       --tensor-model-parallel-size 1  \
+       --pipeline-model-parallel-size 1  \
+       --num-layers 24  \
+       --hidden-size 1024  \
+       --num-attention-heads 16  \
+       --max-position-embeddings 4096  \
+       --tokenizer-type SentencePieceTokenizer  \
+       --micro-batch-size 1  \
+       --seq-length 1024  \
+       --tokenizer-model $TOKENIZER_MODEL_FILE \
+       --seed 42 
\ No newline at end of file
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/text_generation_server.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/text_generation_server.py
new file mode 100644
index 0000000000..9639384359
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/text_generation_server.py
@@ -0,0 +1,270 @@
+import os
+import sys
+
+sys.path.append(os.environ["MEGATRON_PATH"])
+
+import datetime
+import torch
+import json
+import threading
+from flask import Flask, request, jsonify, current_app
+from flask_restful import Resource, Api
+from megatron import get_args
+from megatron.text_generation.generation import (
+    generate_tokens_probs_and_return_on_first_stage,
+    beam_search_and_return_on_first_stage
+)
+
+
+from megatron import get_args
+from megatron import print_rank_0
+from megatron import mpu
+from megatron.checkpointing import load_checkpoint
+from megatron.initialize import initialize_megatron
+from megatron.model import GPTModel
+from megatron.training import get_model
+import torch
+
+
+GENERATE_NUM = 0
+BEAM_NUM = 1
+lock = threading.Lock()
+
+
+class MegatronGenerate(Resource):
+    def __init__(self, model, gen_kwargs, log=None):
+        self.model = model
+        self.log = log
+        self.gen_kwargs = gen_kwargs
+        self.use_beam_search = self.gen_kwargs.get("use_beam_search", None)
+
+    @staticmethod
+    def send_do_generate():
+        choice = torch.cuda.LongTensor([GENERATE_NUM])
+        torch.distributed.broadcast(choice, 0)
+
+    @staticmethod
+    def send_do_beam_search():
+        choice = torch.cuda.LongTensor([BEAM_NUM])
+        torch.distributed.broadcast(choice, 0)
+
+    @staticmethod
+    def sync_input(input_ids, input_length):
+        input_length_tensor = torch.cuda.LongTensor(input_length)
+        torch.distributed.broadcast(input_length_tensor, 0)
+        input_ids_tensor = torch.cuda.LongTensor(input_ids)
+        torch.distributed.broadcast(input_ids_tensor, 0)
+        return input_ids_tensor, input_length_tensor
+
+    def put(self):
+        args = get_args()
+        if not "input_ids" in request.get_json():
+            return "input_ids argument required", 400
+
+        if not "input_length" in request.get_json():
+            return "input_length is required", 400
+
+        input_ids = request.get_json()["input_ids"]
+        input_length = request.get_json()["input_length"]
+
+        with lock:  # Need to get lock to keep multiple threads from hitting code
+
+            if self.log:
+                print("request IP: " + str(request.remote_addr))
+                print("start time: ", datetime.datetime.now())
+
+            try:
+                if self.use_beam_search:
+                    try:
+                        MegatronGenerate.send_do_beam_search()  # Tell other ranks we're doing beam_search
+                        input_ids_tensor, input_length_tensor = MegatronGenerate.sync_input(
+                            input_ids, input_length
+                        )
+                        (
+                            output_tokens,
+                            _,
+                        ) = beam_search_and_return_on_first_stage(
+                            self.model,
+                            input_ids_tensor,
+                            input_length_tensor,
+                            beam_size=self.gen_kwargs.get("beam_size", 4),
+                            stop_token = self.gen_kwargs.get("beam_stop_token", 1),
+                            num_return_gen = self.gen_kwargs.get("beam_num_return_gen", 1),
+                            length_penalty = self.gen_kwargs.get("beam_length_penalty", 1),
+                            min_length = self.gen_kwargs.get("min_new_tokens", 30),
+                        )
+                        output_batch_truncated = []
+                        for data, source_len in zip(output_tokens, input_length_tensor):
+                            output_batch_truncated.append(
+                                data[source_len:].cpu().numpy().tolist()
+                            )
+                        if self.log:
+                            print("end time: ", datetime.datetime.now())
+                        return jsonify({"output": output_batch_truncated})
+                    except Exception as e:
+                        print(str(e))
+                        print("ERROR")
+                        return jsonify({"output": [[]], "is_error": True})
+                else:
+                    try:
+                        MegatronGenerate.send_do_generate()  # Tell other ranks we're doing generate
+                        input_ids_tensor, input_length_tensor = MegatronGenerate.sync_input(
+                            input_ids, input_length
+                        )
+                        (
+                            output_tokens,
+                            _,
+                            _,
+                        ) = generate_tokens_probs_and_return_on_first_stage(
+                            self.model,
+                            input_ids_tensor,
+                            input_length_tensor,
+                            top_k=self.gen_kwargs.get("top_k", 4),
+                            temperature=self.gen_kwargs.get("temperature", 0.0),
+                            min_length = gen_kwargs.get("min_new_tokens", 30),
+                        )
+                        output_batch_truncated = []
+                        for data, source_len in zip(output_tokens, input_length_tensor):
+                            output_batch_truncated.append(
+                                data[source_len:].cpu().numpy().tolist()
+                            )
+                        if self.log:
+                            print("end time: ", datetime.datetime.now())
+                        return jsonify({"output": output_batch_truncated})
+                    except Exception as e:
+                        print(str(e))
+                        print("ERROR")
+                        return jsonify({"output": [[]], "is_error": True})
+
+            except ValueError as ve:
+                return ve.args[0]
+
+
+class MegatronServer(object):
+    def __init__(self, model, gen_kwargs):
+        self.app = Flask(__name__, static_url_path="")
+        api = Api(self.app)
+        api.add_resource(
+            MegatronGenerate, "/api", resource_class_args=[model, gen_kwargs]
+        )
+
+    def run(self, url):
+        self.app.run(url, threaded=True, debug=False)
+
+
+def model_provider(pre_process=True, post_process=True):
+    """Build the model."""
+
+    print_rank_0("building GPT model ...")
+    model = GPTModel(
+        num_tokentypes=0,
+        parallel_output=False,
+        pre_process=pre_process,
+        post_process=post_process,
+    )
+
+    return model
+
+
+def add_text_generate_args(parser):
+    group = parser.add_argument_group(title='text generation')
+    group.add_argument("--use-beam-search", action = "store_true")
+    return parser
+
+
+if __name__ == "__main__":
+    initialize_megatron(
+        extra_args_provider=add_text_generate_args,
+        args_defaults={
+            "tokenizer_type": "SentencePieceTokenizer",
+            "no_load_rng": True,
+            "no_load_optim": True,
+        }
+    )
+
+    args = get_args()
+    gen_kwargs = {
+        "early_stopping": True,
+        "max_new_tokens": 128,
+        "min_new_tokens": 30,
+        "top_k": 40,
+        "temperature": 0.5,
+        "use_beam_search": args.use_beam_search,
+        "beam_size": 4,
+        "beam_stop_token": 1,
+        "beam_num_return_gen": 1,
+        "beam_length_penalty": 1
+    }
+    if args.num_layers_per_virtual_pipeline_stage is not None:
+        print("Interleaved pipeline schedule is not yet supported for text generation.")
+        exit()
+    # Set up model and load checkpoint
+    model = get_model(model_provider, wrap_with_ddp=False)
+
+    if args.load is not None:
+        _ = load_checkpoint(model, None, None)
+
+    assert len(model) == 1, "Above condition should have caught this"
+    model = model[0]
+    if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0:
+        server = MegatronServer(model, gen_kwargs)
+        server.run("127.0.0.1")
+
+    while True:
+        choice = torch.cuda.LongTensor(1)
+        input_length_tensor = torch.cuda.LongTensor(1)
+        torch.distributed.broadcast(choice, 0)
+        if choice[0].item() == 0:
+            # Greedy or top-k
+            try:
+                torch.distributed.broadcast(input_length_tensor, 0)
+                input_ids_tensor = torch.cuda.LongTensor(
+                    [
+                        [
+                            0
+                            for _ in range(
+                                input_length_tensor[0].item()
+                                + gen_kwargs.get("max_new_tokens")
+                            )
+                        ]
+                    ]
+                )
+                torch.distributed.broadcast(input_ids_tensor, 0)
+                generate_tokens_probs_and_return_on_first_stage(
+                    model,
+                    input_ids_tensor,
+                    input_length_tensor,
+                    top_k=gen_kwargs.get("top_k", 4),
+                    temperature=gen_kwargs.get("temperature", 1.0),
+                    min_length = gen_kwargs.get("min_new_tokens", 30),
+                )
+            except ValueError as ve:
+                pass
+        elif choice[0].item() == 1:
+            # Beam search
+            try:
+                torch.distributed.broadcast(input_length_tensor, 0)
+                input_ids_tensor = torch.cuda.LongTensor(
+                    [
+                        [
+                            0
+                            for _ in range(
+                                input_length_tensor[0].item()
+                                + gen_kwargs.get("max_new_tokens")
+                            )
+                        ]
+                    ]
+                )
+                torch.distributed.broadcast(input_ids_tensor, 0)
+                beam_search_and_return_on_first_stage(
+                    model,
+                    input_ids_tensor,
+                    input_length_tensor,
+                    beam_size=gen_kwargs.get("beam_size", 4),
+                    stop_token = gen_kwargs.get("beam_stop_token", 1),
+                    num_return_gen = gen_kwargs.get("beam_num_return_gen", 1),
+                    length_penalty = gen_kwargs.get("beam_length_penalty", 1),
+                    min_length = gen_kwargs.get("min_new_tokens", 30),
+                )
+            except ValueError as ve:
+                pass
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/user.conf b/retired_benchmarks/never_adopted/language/gpt3/megatron/user.conf
new file mode 100644
index 0000000000..07a10bbe21
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/user.conf
@@ -0,0 +1,4 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/utils.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/utils.py
new file mode 100644
index 0000000000..0fb2fbcd3a
--- /dev/null
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/utils.py
@@ -0,0 +1,15 @@
+import json
+import os
+import io
+
+def _make_r_io_base(f, mode: str):
+    if not isinstance(f, io.IOBase):
+        f = open(f, mode=mode, encoding='utf-8')
+    return f
+
+def jload(f, mode="r"):
+    """Load a .json file into a dictionary."""
+    f = _make_r_io_base(f, mode)
+    jdict = json.load(f)
+    f.close()
+    return jdict
diff --git a/recommendation/dlrm/pytorch/.dockerignore b/retired_benchmarks/recommendation/dlrm/pytorch/.dockerignore
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rename to retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/iterator_utils_test.py
diff --git a/translation/gnmt/tensorflow/nmt/utils/misc_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils.py
similarity index 100%
rename from translation/gnmt/tensorflow/nmt/utils/misc_utils.py
rename to retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils.py
diff --git a/translation/gnmt/tensorflow/nmt/utils/misc_utils_test.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils_test.py
similarity index 100%
rename from translation/gnmt/tensorflow/nmt/utils/misc_utils_test.py
rename to retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils_test.py
diff --git a/translation/gnmt/tensorflow/nmt/utils/nmt_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/nmt_utils.py
similarity index 100%
rename from translation/gnmt/tensorflow/nmt/utils/nmt_utils.py
rename to retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/nmt_utils.py
diff --git a/translation/gnmt/tensorflow/nmt/utils/standard_hparams_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/standard_hparams_utils.py
similarity index 100%
rename from translation/gnmt/tensorflow/nmt/utils/standard_hparams_utils.py
rename to retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/standard_hparams_utils.py
diff --git a/translation/gnmt/tensorflow/nmt/utils/vocab_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils.py
similarity index 100%
rename from translation/gnmt/tensorflow/nmt/utils/vocab_utils.py
rename to retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils.py
diff --git a/translation/gnmt/tensorflow/nmt/utils/vocab_utils_test.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils_test.py
similarity index 100%
rename from translation/gnmt/tensorflow/nmt/utils/vocab_utils_test.py
rename to retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils_test.py
diff --git a/translation/gnmt/tensorflow/preprocess_input.sh b/retired_benchmarks/translation/gnmt/tensorflow/preprocess_input.sh
similarity index 100%
rename from translation/gnmt/tensorflow/preprocess_input.sh
rename to retired_benchmarks/translation/gnmt/tensorflow/preprocess_input.sh
diff --git a/translation/gnmt/tensorflow/process_accuracy.py b/retired_benchmarks/translation/gnmt/tensorflow/process_accuracy.py
similarity index 100%
rename from translation/gnmt/tensorflow/process_accuracy.py
rename to retired_benchmarks/translation/gnmt/tensorflow/process_accuracy.py
diff --git a/translation/gnmt/tensorflow/requirements.txt b/retired_benchmarks/translation/gnmt/tensorflow/requirements.txt
similarity index 100%
rename from translation/gnmt/tensorflow/requirements.txt
rename to retired_benchmarks/translation/gnmt/tensorflow/requirements.txt
diff --git a/translation/gnmt/tensorflow/run.sh b/retired_benchmarks/translation/gnmt/tensorflow/run.sh
similarity index 100%
rename from translation/gnmt/tensorflow/run.sh
rename to retired_benchmarks/translation/gnmt/tensorflow/run.sh
diff --git a/translation/gnmt/tensorflow/run_task.py b/retired_benchmarks/translation/gnmt/tensorflow/run_task.py
similarity index 100%
rename from translation/gnmt/tensorflow/run_task.py
rename to retired_benchmarks/translation/gnmt/tensorflow/run_task.py
diff --git a/translation/gnmt/tensorflow/train_gnmt.txt b/retired_benchmarks/translation/gnmt/tensorflow/train_gnmt.txt
similarity index 100%
rename from translation/gnmt/tensorflow/train_gnmt.txt
rename to retired_benchmarks/translation/gnmt/tensorflow/train_gnmt.txt
diff --git a/translation/gnmt/tensorflow/verify_dataset.sh b/retired_benchmarks/translation/gnmt/tensorflow/verify_dataset.sh
similarity index 100%
rename from translation/gnmt/tensorflow/verify_dataset.sh
rename to retired_benchmarks/translation/gnmt/tensorflow/verify_dataset.sh
diff --git a/retired_benchmarks/vision/classification_and_detection/.dockerignore b/retired_benchmarks/vision/classification_and_detection/.dockerignore
new file mode 100644
index 0000000000..91916221ca
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/.dockerignore
@@ -0,0 +1,3 @@
+preprocessed
+python/preprocessed
+mlperf_*
diff --git a/retired_benchmarks/vision/classification_and_detection/.gitignore b/retired_benchmarks/vision/classification_and_detection/.gitignore
new file mode 100755
index 0000000000..0ab826da33
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/.gitignore
@@ -0,0 +1,19 @@
+.coverage*
+*.pyc
+.idea
+build
+dist
+bin
+obj
+.ipynb_checkpoints
+__pycache__
+*.pyc
+*.swp
+.cache
+.eggs
+*.egg-info
+run.sh
+preprocessed
+python/preprocessed
+output
+mlperf_log_*
diff --git a/retired_benchmarks/vision/classification_and_detection/Dockerfile.cpu b/retired_benchmarks/vision/classification_and_detection/Dockerfile.cpu
new file mode 100755
index 0000000000..b435c61430
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/Dockerfile.cpu
@@ -0,0 +1,44 @@
+FROM ubuntu:16.04
+
+ENV PYTHON_VERSION=3.7
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PATH /opt/anaconda3/bin:$PATH
+
+WORKDIR /root
+ENV HOME /root
+
+RUN apt-get update
+
+RUN apt-get install -y --no-install-recommends \
+      git \
+      build-essential \
+      software-properties-common \
+      ca-certificates \
+      wget \
+      curl \
+      htop \
+      zip \
+      unzip
+
+RUN cd /opt && \
+    wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh -O miniconda.sh && \
+    /bin/bash ./miniconda.sh -b -p /opt/anaconda3 && \
+    rm miniconda.sh && \
+    /opt/anaconda3/bin/conda clean -tipsy && \
+    ln -s /opt/anaconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
+    echo ". /opt/anaconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
+    echo "conda activate base" >> ~/.bashrc && \
+    conda config --set always_yes yes --set changeps1 no
+
+RUN conda install pytorch-cpu torchvision-cpu -c pytorch
+RUN pip install future pillow onnx opencv-python-headless tensorflow onnxruntime
+RUN pip install Cython && pip install pycocotools
+RUN cd /tmp && \
+    git clone --recursive https://github.com/mlcommons/inference && \
+    cd inference/loadgen && \
+    pip install pybind11 && \
+    CFLAGS="-std=c++14" python setup.py install && \
+    rm -rf mlperf
+
+ENTRYPOINT ["/bin/bash"]
diff --git a/retired_benchmarks/vision/classification_and_detection/Dockerfile.gpu b/retired_benchmarks/vision/classification_and_detection/Dockerfile.gpu
new file mode 100755
index 0000000000..5ba2c680ca
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/Dockerfile.gpu
@@ -0,0 +1,55 @@
+FROM nvidia/cuda:11.1-cudnn8-devel-ubuntu16.04
+
+ENV PYTHON_VERSION=3.7
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PATH /opt/anaconda3/bin:$PATH
+
+WORKDIR /root
+ENV HOME /root
+
+RUN apt-get update
+
+RUN apt-get install -y --no-install-recommends \
+      git \
+      build-essential \
+      software-properties-common \
+      ca-certificates \
+      wget \
+      curl \
+      htop \
+      zip \
+      unzip
+
+RUN cd /opt && \
+    wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh -O miniconda.sh && \
+    /bin/bash ./miniconda.sh -b -p /opt/anaconda3 && \
+    rm miniconda.sh && \
+    /opt/anaconda3/bin/conda clean -tipsy && \
+    ln -s /opt/anaconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
+    echo ". /opt/anaconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
+    echo "conda activate base" >> ~/.bashrc && \
+    conda config --set always_yes yes --set changeps1 no
+
+RUN pip install --upgrade pip && \
+    pip install cython future pillow onnx opencv-python-headless && \
+    ln -s /usr/local/cuda/lib64 /usr/local/cuda/lib && \
+    cp /usr/lib/x86_64-linux-gnu/libnccl* /usr/local/cuda/lib && \
+    ldconfig
+
+RUN conda install pytorch torchvision -c pytorch
+RUN pip install tensorflow onnxruntime-gpu
+RUN pip install Cython && pip install pycocotools
+
+
+RUN cd /tmp && \
+    git clone --recursive https://github.com/mlcommons/inference && \
+    cd inference/loadgen && \
+    pip install pybind11 && \
+    CFLAGS="-std=c++14" python setup.py install && \
+    rm -rf mlperf
+
+RUN  echo "/usr/local/cuda/compat" >> /etc/ld.so.conf.d/cuda-10-0.conf && \
+    ldconfig
+
+ENTRYPOINT ["/bin/bash"]
diff --git a/retired_benchmarks/vision/classification_and_detection/GettingStarted.ipynb b/retired_benchmarks/vision/classification_and_detection/GettingStarted.ipynb
new file mode 100755
index 0000000000..85419b6931
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/GettingStarted.ipynb
@@ -0,0 +1,413 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A short tutorial how to use the mlperf inference reference benchmark"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We wrapped all inference models into a single benchmark app. The benchmark app will read the propper dataset, preprocesses it and interface with the backend. Traffic is generated by loadgen, which depending on the desired mode drives the desired traffic to the benchmark app. \n",
+    "\n",
+    "To run this notebook, pick a directory and clone the mlperf source tree:\n",
+    "```\n",
+    "cd /tmp\n",
+    "git clone https://github.com/mlperf/inference.git\n",
+    "cd inference/v0.5/classification_and_detection\n",
+    "jupyter notebook \n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "root = os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running develop\n",
+      "running egg_info\n",
+      "writing mlperf_loadgen.egg-info/PKG-INFO\n",
+      "writing dependency_links to mlperf_loadgen.egg-info/dependency_links.txt\n",
+      "writing top-level names to mlperf_loadgen.egg-info/top_level.txt\n",
+      "reading manifest file 'mlperf_loadgen.egg-info/SOURCES.txt'\n",
+      "writing manifest file 'mlperf_loadgen.egg-info/SOURCES.txt'\n",
+      "running build_ext\n",
+      "building 'mlperf_loadgen' extension\n",
+      "gcc -pthread -B /opt/anaconda3/compiler_compat -Wl,--sysroot=/ -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -std=c++14 -fPIC -DMAJOR_VERSION=0 -DMINOR_VERSION=5 -I. -I../third_party/pybind/include -I/opt/anaconda3/include/python3.7m -c loadgen.cc -o build/temp.linux-x86_64-3.7/loadgen.o\n",
+      "\u001b[01m\u001b[Kcc1plus:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcommand line option ‘\u001b[01m\u001b[K-Wstrict-prototypes\u001b[m\u001b[K’ is valid for C/ObjC but not for C++\n",
+      "...\n",
+      "Using /opt/anaconda3/lib/python3.7/site-packages\n",
+      "Finished processing dependencies for mlperf-inference==0.1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cd ../../loadgen; CFLAGS=\"-std=c++14\" python setup.py develop; cd {root}\n",
+    "!python setup.py develop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The benchmark app uses a shell script to simplify command line options and the user can pick backend, model and device:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "usage: ./run_local.sh tf|onnxruntime|pytorch|tflite [resnet50|mobilenet|ssd-mobilenet|ssd-resnet34|ssd-resnet34] [cpu|gpu]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./run_local.sh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before running the benchmark, device on model and dataset and set the environment variable ```MODEL_DIR``` and ```DATA_DIR```. \n",
+    "\n",
+    "For this tutorial we use onnxruntime (tensorflow and pytorch will work as well), mobilenet and a fake imagetnet dataset with a few images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: onnxruntime in /opt/anaconda3/lib/python3.7/site-packages (0.4.0)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install onnxruntime"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 1 - download the model. You find the links to the models [here](https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection#supported-models)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget -q https://zenodo.org/record/3157894/files/mobilenet_v1_1.0_224.onnx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 2 - download the dataset. For this tutorial we create a small, fake dataset that pretends to be imagenet.\n",
+    "Normally you'd need to download imagenet2012/valiation for image classification or coco2017/valiation for object detections.\n",
+    "\n",
+    "Links and instructions how to download the datasets can be found in the [README](https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection#datasets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!tools/make_fake_imagenet.sh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 3 - tell the benchmark where to find model and data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ['MODEL_DIR'] = root\n",
+    "os.environ['DATA_DIR'] = os.path.join(root, \"fake_imagenet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For mlperf submission number of queries, time, latencies and percentiles are given and we default to those settings. But for this tuturial we pass in some extra options to make things go quicker.\n",
+    "run_local.sh will look for the evironment variable EXTRA_OPS and add this to the arguments. You can also add additional arguments in the command line.\n",
+    "The options below will limit the time that the benchmarks run to 10 seconds and adds accuracy reporting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['EXTRA_OPS'] =\"--queries-offline 20 --time 10 --max-latency 0.2\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 4 - run the benchmark."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:main:Namespace(accuracy=True, backend='onnxruntime', cache=0, count=None, data_format=None, dataset='imagenet_mobilenet', dataset_list=None, dataset_path='/home/gs/inference/v0.5/classification_and_detection/fake_imagenet', inputs=None, max_batchsize=32, max_latency=[0.2], model='/home/gs/inference/v0.5/classification_and_detection/mobilenet_v1_1.0_224.onnx', output='/home/gs/inference/v0.5/classification_and_detection/output/mobilenet-onnxruntime-cpu/results.json', outputs=['MobilenetV1/Predictions/Reshape_1:0'], profile='mobilenet-onnxruntime', qps=10, queries_multi=24576, queries_offline=20, queries_single=1024, scenario=[TestScenario.SingleStream], threads=8, time=10)\n",
+      "INFO:imagenet:loaded 8 images, cache=0, took=0.2sec\n",
+      "INFO:main:starting TestScenario.SingleStream\n",
+      "TestScenario.SingleStream qps=69.75, mean=0.0115, time=0.11, acc=62.50, queries=8, tiles=50.0:0.0112,80.0:0.0115,90.0:0.0121,95.0:0.0129,99.0:0.0135,99.9:0.0137\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./run_local.sh onnxruntime mobilenet cpu --accuracy "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The line ```Accuracy``` reports accuracy or mAP together with some latencies in various percentiles so you can insight how this run was. Above accuracy was 87.5%.\n",
+    "\n",
+    "The line ```TestScenario.SingleStream-1.0``` reports the latency and qps seen during the benchmark.\n",
+    "\n",
+    "For submission the official logging is found in [mlperf_log_summary.txt](mlperf_log_summary.txt) and [mlperf_log_detail.txt](mlperf_log_detail.txt)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you read over the mlperf inference rules guide you'll find multiple scenarios to be run for the inference benchmarks:\n",
+    "\n",
+    "|scenario|description|\n",
+    "|:---|:---|\n",
+    "|SingleStream|The LoadGen sends the next query as soon as the SUT completes the previous one|\n",
+    "|MultiStream|The LoadGen sends a new query every Latency Constraint, if the SUT has completed the prior query. Otherwise, the new query is dropped. Such an event is one overtime query.|\n",
+    "|Server|The LoadGen sends new queries to the SUT according to a Poisson distribution. Overtime queries must not exceed 2x the latency bound.|\n",
+    "|Offline|The LoadGen sends all queries to the SUT at one time.|\n",
+    "\n",
+    "We can run those scenario using the ```--scenario``` option in the command line, for example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:main:Namespace(accuracy=False, backend='onnxruntime', cache=0, count=None, data_format=None, dataset='imagenet_mobilenet', dataset_list=None, dataset_path='/home/gs/inference/v0.5/classification_and_detection/fake_imagenet', inputs=None, max_batchsize=32, max_latency=[0.2], model='/home/gs/inference/v0.5/classification_and_detection/mobilenet_v1_1.0_224.onnx', output='/home/gs/inference/v0.5/classification_and_detection/output/mobilenet-onnxruntime-cpu/results.json', outputs=['MobilenetV1/Predictions/Reshape_1:0'], profile='mobilenet-onnxruntime', qps=10, queries_multi=24576, queries_offline=20, queries_single=1024, scenario=[TestScenario.Offline], threads=8, time=10)\n",
+      "INFO:imagenet:loaded 8 images, cache=0, took=0.0sec\n",
+      "INFO:main:starting TestScenario.Offline\n",
+      "TestScenario.Offline qps=44.11, mean=2.3486, time=2.49, queries=110, tiles=50.0:2.4500,80.0:2.4687,90.0:2.4687,95.0:2.4687,99.0:2.4687,99.9:2.4687\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./run_local.sh onnxruntime mobilenet cpu --scenario Offline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Additional logfiles\n",
+    "\n",
+    "We log some additional information [here](output/mobilenet-onnxruntime-cpu/results.json) which can be used to plot graphs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Under the hood\n",
+    "\n",
+    "In case you wonder what the run_local.sh does, it only assembles the command line for the python based benchmark app. Command ine options for the app are documented [here](https://github.com/mlperf/inference/blob/master/cloud/image_classification)\n",
+    "\n",
+    "Calling\n",
+    "```\n",
+    "!bash -x ./run_local.sh onnxruntime mobilenet cpu  --accuracy \n",
+    "```\n",
+    "will results in the following command line:\n",
+    "```\n",
+    "python python/main.py --profile mobilenet-onnxruntime --model /tmp/inference/cloud/image_classification/mobilenet_v1_1.0_224.onnx --dataset-path /tmp/inference/cloud/image_classification/fake_imagenet --output /tmp/inference/cloud/image_classification/output/mobilenet-onnxruntime-cpu/results.json --queries-offline 20 --time 10 --max-latency 0.2 --accuracy\n",
+    "```\n",
+    "During testing you can change some of the options to have faster test cycles but for final submission use the defaults."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using docker\n",
+    "\n",
+    "Instead of run_local.sh you can use run_and_time.sh which does have the same options but instead of running local will run the benchmark under docker."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sending build context to Docker daemon  18.54MB\n",
+      "Step 1/15 : FROM ubuntu:16.04\n",
+      " ---> bd3d4369aebc\n",
+      "Step 2/15 : ENV PYTHON_VERSION=3.7\n",
+      " ---> Using cache\n",
+      " ---> e25f214201a2\n",
+      "Step 3/15 : ENV LANG C.UTF-8\n",
+      " ---> Using cache\n",
+      " ---> 12986ee696e1\n",
+      "Step 4/15 : ENV LC_ALL C.UTF-8\n",
+      " ---> Using cache\n",
+      " ---> 1460535b24e1\n",
+      "Step 5/15 : ENV PATH /opt/anaconda3/bin:$PATH\n",
+      " ---> Using cache\n",
+      " ---> f4c922578fdf\n",
+      "Step 6/15 : WORKDIR /root\n",
+      " ---> Using cache\n",
+      " ---> fb0ec9a436a5\n",
+      "Step 7/15 : ENV HOME /root\n",
+      " ---> Using cache\n",
+      " ---> edeb7c15ebfb\n",
+      "Step 8/15 : RUN apt-get update\n",
+      " ---> Using cache\n",
+      " ---> 42da1a4fa3fd\n",
+      "Step 9/15 : RUN apt-get install -y --no-install-recommends       git       build-essential       software-properties-common       ca-certificates       wget       curl       htop       zip       unzip\n",
+      " ---> Using cache\n",
+      " ---> a1de66a3c7bd\n",
+      "Step 10/15 : RUN cd /opt &&     wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh -O miniconda.sh &&     /bin/bash ./miniconda.sh -b -p /opt/anaconda3 &&     rm miniconda.sh &&     /opt/anaconda3/bin/conda clean -tipsy &&     ln -s /opt/anaconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh &&     echo \". /opt/anaconda3/etc/profile.d/conda.sh\" >> ~/.bashrc &&     echo \"conda activate base\" >> ~/.bashrc &&     conda config --set always_yes yes --set changeps1 no\n",
+      " ---> Using cache\n",
+      " ---> b3a1fa068421\n",
+      "Step 11/15 : RUN conda install pytorch-cpu torchvision-cpu -c pytorch\n",
+      " ---> Using cache\n",
+      " ---> 0f7c294fe4c8\n",
+      "Step 12/15 : RUN pip install future pillow onnx opencv-python-headless tensorflow onnxruntime\n",
+      " ---> Using cache\n",
+      " ---> 160977b84ece\n",
+      "Step 13/15 : RUN pip install Cython && pip install pycocotools\n",
+      " ---> Using cache\n",
+      " ---> ffc479fc7d11\n",
+      "Step 14/15 : RUN cd /tmp &&     git clone https://github.com/mlperf/inference &&     cd inference/loadgen &&     pip install pybind11 &&     CFLAGS=\"-std=c++14\" python setup.py install &&     rm -rf mlperf\n",
+      " ---> Using cache\n",
+      " ---> 20eb0ce678b0\n",
+      "Step 15/15 : ENTRYPOINT [\"/bin/bash\"]\n",
+      " ---> Using cache\n",
+      " ---> 9440a8884457\n",
+      "Successfully built 9440a8884457\n",
+      "Successfully tagged mlperf-infer-imgclassify-cpu:latest\n",
+      "Clearing caches.\n",
+      "3\n",
+      "STARTING RUN AT 2019-07-23 04:09:29 PM\n",
+      "INFO:main:Namespace(accuracy=False, backend='onnxruntime', cache=0, count=None, data_format=None, dataset='imagenet_mobilenet', dataset_list=None, dataset_path='/home/gs/inference/v0.5/classification_and_detection/fake_imagenet', inputs=None, max_batchsize=32, max_latency=[0.2], model='/home/gs/inference/v0.5/classification_and_detection/mobilenet_v1_1.0_224.onnx', output='/output/results.json', outputs=['MobilenetV1/Predictions/Reshape_1:0'], profile='mobilenet-onnxruntime', qps=10, queries_multi=24576, queries_offline=20, queries_single=1024, scenario=[TestScenario.SingleStream], threads=8, time=10)\n",
+      "INFO:imagenet:loaded 8 images, cache=0, took=0.3sec\n",
+      "INFO:main:starting TestScenario.SingleStream\n",
+      "TestScenario.SingleStream qps=37.18, mean=0.0268, time=10.09, queries=375, tiles=50.0:0.0261,80.0:0.0262,90.0:0.0266,95.0:0.0271,99.0:0.0385,99.9:0.0823\n",
+      "ENDING RUN AT 2019-07-23 04:09:45 PM\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./run_and_time.sh onnxruntime mobilenet cpu "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preparing for offical submision\n",
+    "\n",
+    "TODO."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/retired_benchmarks/vision/classification_and_detection/README.md b/retired_benchmarks/vision/classification_and_detection/README.md
new file mode 100755
index 0000000000..45009870fd
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/README.md
@@ -0,0 +1,270 @@
+# MLPerf Inference Benchmarks for Image Classification and Object Detection Tasks
+
+This is the reference implementation for MLPerf Inference benchmarks.
+
+You can find a short tutorial how to use this benchmark [here](https://github.com/mlperf/inference/blob/master/vision/classification_and_detection/GettingStarted.ipynb).
+
+## Supported Models
+
+| model | framework | accuracy | dataset | model link | model source | precision | notes |
+| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | tensorflow | 76.456% | imagenet2012 validation | [from zenodo](https://zenodo.org/record/2535873/files/resnet50_v1.pb) | [mlperf](https://github.com/mlperf/training/tree/master/image_classification), [tensorflow](https://github.com/tensorflow/models/tree/master/official/resnet) | fp32 | NHWC. More information on resnet50 v1.5 can be found [here](https://github.com/tensorflow/models/tree/master/official/resnet).||
+| resnet50-v1.5 | onnx | 76.456% | imagenet2012 validation | from zenodo: [opset-8](https://zenodo.org/record/2592612/files/resnet50_v1.onnx), [opset-11](https://zenodo.org/record/4735647/files/resnet50_v1.onnx) | [from zenodo](https://zenodo.org/record/2535873/files/resnet50_v1.pb) converted with [this script](https://github.com/mlcommons/inference/blob/master/vision/classification_and_detection/tools/convert-to-onnx.sh) | fp32 | NCHW, tested on pytorch and onnxruntime |
+| resnet50-v1.5 | pytorch | 76.014% | imagenet2012 validation | [from zenodo](https://zenodo.org/record/4588417/files/resnet50-19c8e357.pth) | [from TorchVision](https://github.com/pytorch/vision/blob/v0.8.2/torchvision/models/resnet.py) | fp32 | NCHW |
+| resnet50-v1.5 | pytorch | 75.790% | imagenet2012 validation | [from zenodo](https://zenodo.org/record/4589637/files/resnet50_INT8bit_quantized.pt) | Edgecortix [quantization script](tools/calibrate_torchvision_model.py) | A: int8, W: uint8 | NCHW |
+| mobilenet-v1 (depreciated since mlperf-v0.7)| tensorflow | 71.676% | imagenet2012 validation | [from zenodo](https://zenodo.org/record/2269307/files/mobilenet_v1_1.0_224.tgz) | [from tensorflow](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz) | fp32 | NHWC |
+| mobilenet-v1 quantized (depreciated since mlperf-v0.7)| tensorflow | 70.694% | imagenet2012 validation | [from zenodo](https://zenodo.org/record/2269307/files/mobilenet_v1_1.0_224_quant.tgz) | [from tensorflow](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz) | int8 | NHWC |
+| mobilenet-v1 (depreciated since mlperf-v0.7)| tflite | 71.676% | imagenet2012 validation | [from zenodo](https://zenodo.org/record/2269307/files/mobilenet_v1_1.0_224.tgz) | [from tensorflow](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz) | fp32 | NHWC |
+| mobilenet-v1 quantized (depreciated since mlperf-v0.7)| tflite | 70.762% | imagenet2012 validation | [from zenodo](https://zenodo.org/record/2269307/files/mobilenet_v1_1.0_224_quant.tgz) | [from tensorflow](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz) | int8 | NHWC |
+| mobilenet-v1 (depreciated since mlperf-v0.7)| onnx | 71.676% | imagenet2012 validation | from zenodo: [opset-8](https://zenodo.org/record/3157894/files/mobilenet_v1_1.0_224.onnx), [opset-11](https://zenodo.org/record/4735651/files/mobilenet_v1_1.0_224.onnx) | [from tensorflow](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz) converted with [this script](https://github.com/mlcommons/inference/blob/master/vision/classification_and_detection/tools/convert-to-onnx.sh) | fp32 | NCHW, tested on pytorch and onnxruntime |
+| mobilenet-v1 (depreciated since mlperf-v0.7)| onnx, pytorch | 70.9% | imagenet2012 validation | [from zenodo](https://zenodo.org/record/3353417/files/Quantized%20MobileNet.zip) | ??? | int8 | ??? |
+| ssd-mobilenet 300x300 | tensorflow | mAP 0.23 | coco resized to 300x300 | [from tensorflow](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz) | [from tensorflow](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz) | fp32 | NHWC |
+| ssd-mobilenet 300x300 quantized finetuned | tensorflow | mAP 0.23594 | coco resized to 300x300 | [from zenodo](https://zenodo.org/record/3252084/files/mobilenet_v1_ssd_8bit_finetuned.tar.gz) | Habana | int8 | ??? |
+| ssd-mobilenet 300x300 symmetrically quantized finetuned | tensorflow | mAP 0.234 | coco resized to 300x300 | [from zenodo](https://zenodo.org/record/3401714/files/ssd_mobilenet_v1_quant_ft_no_zero_point_frozen_inference_graph.pb) | Habana | int8 | ??? |
+| ssd-mobilenet 300x300 | pytorch | mAP 0.23 | coco resized to 300x300 | [from zenodo](https://zenodo.org/record/3239977/files/ssd_mobilenet_v1.pytorch) | [from tensorflow](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz) | fp32 | NHWC |
+| ssd-mobilenet 300x300 | onnx | mAP 0.23 | coco resized to 300x300 | from zenodo [opset-8](https://zenodo.org/record/3163026/files/ssd_mobilenet_v1_coco_2018_01_28.onnx), [opset-11](https://zenodo.org/record/4735652/files/ssd_mobilenet_v1_coco_2018_01_28.onnx) | [from tensorflow](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz) converted using [this script](https://github.com/mlcommons/inference/blob/master/vision/classification_and_detection/tools/convert-to-onnx.sh) | fp32 | NHWC, tested on onnxruntime, some runtime warnings |
+| ssd-mobilenet 300x300 | onnx, pytorch | mAP 0.23 | coco resized to 300x300  | [from zenodo](https://zenodo.org/record/3252084/files/mobilenet_v1_ssd_8bit_finetuned.tar.gz) | ??? | int8 | ??? |
+| ssd-resnet34 1200x1200 | tensorflow | mAP 0.20 | coco resized to 1200x1200| [from zenodo](https://zenodo.org/record/3345892/files/tf_ssd_resnet34_22.1.zip?download=1) | [from mlperf](https://github.com/mlperf/inference/tree/master/others/cloud/single_stage_detector/tensorflow), [training model](https://github.com/lji72/inference/tree/tf_ssd_resent34_align_onnx/others/cloud/single_stage_detector/tensorflow) | fp32 | NCHW |
+| ssd-resnet34 1200x1200 | pytorch | mAP 0.20 | coco resized to 1200x1200 | [from zenodo](https://zenodo.org/record/3236545/files/resnet34-ssd1200.pytorch) | [from mlperf](https://github.com/mlperf/inference/tree/master/others/cloud/single_stage_detector/pytorch) | fp32 | NCHW |
+| ssd-resnet34 1200x1200 | onnx | mAP 0.20 | coco resized to 1200x1200 | from zenodo [opset-8](https://zenodo.org/record/3228411/files/resnet34-ssd1200.onnx) | [from mlperf](https://github.com/mlperf/inference/tree/master/others/cloud/single_stage_detector) converted using the these [instructions](https://github.com/BowenBao/inference/tree/master/cloud/single_stage_detector/pytorch#6-onnx) | fp32 | Converted from pytorch model. |
+| ssd-resnet34 1200x1200 | onnx | mAP 0.20 | coco resized to 1200x1200 | from zenodo [opset-11](https://zenodo.org/record/4735664/files/ssd_resnet34_mAP_20.2.onnx) | [from zenodo](https://zenodo.org/record/3345892/files/tf_ssd_resnet34_22.1.zip) converted using [this script](https://github.com/mlcommons/inference/blob/master/vision/classification_and_detection/tools/convert-to-onnx.sh) | fp32 | Converted from the tensorflow model and uses the same interface as the tensorflow model. |
+
+## Disclaimer
+This benchmark app is a reference implementation that is not meant to be the fastest implementation possible.
+It is written in python which might make it less suitable for lite models like mobilenet or large number of cpu's.
+We are thinking to provide a c++ implementation with identical functionality in the near future.
+
+## Tools for preparing datasets and validating accuracy
+The reference implementation includes all required pre-processing of datasets.
+It also includes a ```--accuracy``` option to validate accuracy as required by mlperf.
+If you are not using the reference implementation, a few scripts will help:
+### Prepare the coco dataset 
+The tool is [here](../../tools/upscale_coco).
+You can run it for ssd-mobilenet like:
+```
+python upscale_coco.py --inputs /data/coco/ --outputs /data/coco-300 --size 300 300 --format png
+```
+and for ssd-resnet34 like:
+```
+python upscale_coco.py --inputs /data/coco/ --outputs /data/coco-1200 --size 1200 1200 --format png
+```
+### Prepare the imagenet dataset 
+to come.
+
+### Validate accuracy for resnet50 and mobilenet benchmarks
+The tool is [here](tools/accuracy-imagenet.py). You can run it like:
+```
+python tools/accuracy-imagenet.py --mlperf-accuracy-file mlperf_log_accuracy.json --imagenet-val-file /data/imagenet2012/val_map.txt
+```
+
+### Validate accuracy for ssd-mobilenet and ssd-resnet34 benchmarks
+The tool is [here](tools/accuracy-coco.py). You can run it like:
+```
+python tools/accuracy-coco.py --mlperf-accuracy-file mlperf_log_accuracy.json --coco-dir /data/coco --use-inv-map
+```
+
+## Datasets
+| dataset | download link | 
+| ---- | ---- | 
+| imagenet2012 (validation) | http://image-net.org/challenges/LSVRC/2012/ | 
+| coco (validation) | http://images.cocodataset.org/zips/val2017.zip | 
+| coco (annotations) | http://images.cocodataset.org/annotations/annotations_trainval2017.zip |
+
+### Using Collective Knowledge (CK)
+
+Alternatively, you can download the datasets using the [Collective Knowledge](http://cknowledge.org)
+framework (CK) for collaborative and reproducible research.
+
+First, install CK and pull its repositories containing dataset packages:
+```bash
+$ python -m pip install ck --user
+$ ck version
+V1.9.8.1
+$ ck pull repo:ck-env
+```
+
+#### ImageNet 2012 validation dataset
+Download the original dataset and auxiliaries:
+```bash
+$ ck install package --tags=image-classification,dataset,imagenet,val,original,full
+$ ck install package --tags=image-classification,dataset,imagenet,aux
+```
+Copy the labels next to the images:
+```bash
+$ ck locate env --tags=image-classification,dataset,imagenet,val,original,full
+/home/dvdt/CK-TOOLS/dataset-imagenet-ilsvrc2012-val
+$ ck locate env --tags=image-classification,dataset,imagenet,aux
+/home/dvdt/CK-TOOLS/dataset-imagenet-ilsvrc2012-aux
+$ cp `ck locate env --tags=aux`/val.txt `ck locate env --tags=val`/val_map.txt
+```
+
+#### COCO 2017 validation dataset
+```bash
+$ ck install package --tags=object-detection,dataset,coco,2017,val,original
+$ ck locate env --tags=object-detection,dataset,coco,2017,val,original
+/home/dvdt/CK-TOOLS/dataset-coco-2017-val
+```
+
+## Prerequisites and Installation
+We support [tensorfow+tflite](https://github.com/tensorflow/tensorflow), [onnxruntime](https://github.com/Microsoft/onnxruntime)  and [pytoch](http://pytorch.org) backend's with the same benchmark tool.
+Support for other backends can be easily added.
+
+The following steps are **only** needed if you run the benchmark **without Docker**.
+
+Python 3.5, 3.6 or 3.7 is supported and we recommend to use Anaconda (See [Dockerfile](Dockerfile.cpu) for a minimal Anaconda install).
+
+Install the desired backend.
+For tensorflow:
+```
+pip install tensorflow or pip install tensorflow-gpu
+```
+For onnxruntime:
+```
+pip install onnxruntime or pip install onnxruntime-gpu
+```
+
+Build and install the benchmark:
+```
+cd ../../loadgen; CFLAGS="-std=c++14" python setup.py develop --user; cd ../vision/classification_and_detection
+
+python setup.py develop
+```
+
+
+## Running the benchmark
+### One time setup
+
+Download the model and dataset for the model you want to benchmark.
+
+Both local and docker environment need to set 2 environment variables:
+```
+export MODEL_DIR=YourModelFileLocation
+export DATA_DIR=YourImageNetLocation
+```
+
+
+### Run local
+```
+./run_local.sh backend model device
+
+backend is one of [tf|onnxruntime|pytorch|tflite]
+model is one of [resnet50|mobilenet|ssd-mobilenet|ssd-resnet34]
+device is one of [cpu|gpu]
+
+
+For example:
+
+./run_local.sh tf resnet50 gpu
+```
+
+### Run as Docker container
+```
+./run_and_time.sh backend model device
+
+backend is one of [tf|onnxruntime|pytorch|tflite]
+model is one of [resnet50|mobilenet|ssd-mobilenet|ssd-resnet34]
+device is one of [cpu|gpu]
+
+For example:
+
+./run_and_time.sh tf resnet50 gpu
+```
+This will build and run the benchmark.
+
+### Examples for testing
+During development running the full benchmark is unpractical. Some options to help:
+
+```--count``` limits the number of items in the dataset used for accuracy pass
+
+```--time``` limits the time the benchmark runs
+
+```--accuracy``` enables accuracy pass
+
+```--max-latency``` the latency used for Server mode
+
+So if you want to tune for example Server mode, try:
+```
+./run_local.sh tf resnet50 gpu --count 100 --time 60 --scenario Server --qps 200 --max-latency 0.1
+or
+./run_local.sh tf ssd-mobilenet gpu --count 100 --time 60 --scenario Server --qps 100 --max-latency 0.1
+
+```
+
+If you want run with accuracy pass, try:
+```
+./run_local.sh tf ssd-mobilenet gpu --accuracy --time 60 --scenario Server --qps 100 --max-latency 0.2
+```
+
+
+### Usage
+```
+usage: main.py [-h]
+    [--mlperf_conf ../../mlperf.conf]
+    [--user_conf user.conf]
+    [--dataset {imagenet,imagenet_mobilenet,coco,coco-300,coco-1200,coco-1200-onnx,coco-1200-pt,coco-1200-tf}]
+    --dataset-path DATASET_PATH [--dataset-list DATASET_LIST]
+    [--data-format {NCHW,NHWC}]
+    [--profile {defaults,resnet50-tf,resnet50-onnxruntime,mobilenet-tf,mobilenet-onnxruntime,ssd-mobilenet-tf,ssd-mobilenet-onnxruntime,ssd-resnet34-tf,ssd-resnet34-pytorch,ssd-resnet34-onnxruntime}]
+    [--scenario list of SingleStream,MultiStream,Server,Offline]
+    [--max-batchsize MAX_BATCHSIZE]
+    --model MODEL [--output OUTPUT] [--inputs INPUTS]
+    [--outputs OUTPUTS] [--backend BACKEND] [--threads THREADS]
+    [--time TIME] [--count COUNT] [--qps QPS]
+    [--max-latency MAX_LATENCY] [--cache CACHE] [--accuracy]
+```
+
+```--mlperf_conf```
+the mlperf config file to use for rules compliant parameters, defaults to ../../mlperf.conf
+
+```--user_conf```
+the user config file to use for user LoadGen settings such as target QPS, defaults to user.conf
+
+```--dataset```
+use the specified dataset. Currently we only support ImageNet.
+
+```--dataset-path```
+path to the dataset.
+
+```--data-format {NCHW,NHWC}```
+data-format of the model (default: the backends prefered format).
+
+```--scenario {SingleStream,MultiStream,Server,Offline}```
+comma separated list of benchmark modes.
+
+```--profile {resnet50-tf,resnet50-onnxruntime,mobilenet-tf,mobilenet-onnxruntime,ssd-mobilenet-tf,ssd-mobilenet-onnxruntime,ssd-resnet34-tf,ssd-resnet34-onnxruntime}```
+this fills in default command line options with the once specified in the profile. Command line options that follow may override the those.
+
+```--model MODEL```
+the model file.
+
+```--inputs INPUTS```
+comma separated input name list in case the model format does not provide the input names. This is needed for tensorflow since the graph does not specify the inputs.
+
+```--outputs OUTPUTS```
+comma separated output name list in case the model format does not provide the output names. This is needed for tensorflow since the graph does not specify the outputs.
+
+```--output OUTPUT]```
+location of the JSON output.
+
+```--backend BACKEND```
+which backend to use. Currently supported is tensorflow, onnxruntime, pytorch and tflite.
+
+```--threads THREADS```
+number of worker threads to use (default: the number of processors in the system).
+
+```--count COUNT```
+Number of images the dataset we use (default: use all images in the dataset).
+
+```--qps QPS```
+Expected QPS.
+
+```--max-latency MAX_LATENCY```
+comma separated list of which latencies (in seconds) we try to reach in the 99 percentile (deault: 0.01,0.05,0.100).
+
+```--max-batchsize MAX_BATCHSIZE```
+maximum batchsize we generate to backend (default: 128).
+
+
+## License
+
+[Apache License 2.0](LICENSE)
diff --git a/retired_benchmarks/vision/classification_and_detection/VERSION_NUMBER b/retired_benchmarks/vision/classification_and_detection/VERSION_NUMBER
new file mode 100644
index 0000000000..6e8bf73aa5
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/VERSION_NUMBER
@@ -0,0 +1 @@
+0.1.0
diff --git a/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/README.md b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/README.md
new file mode 100644
index 0000000000..bc63302c9c
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/README.md
@@ -0,0 +1,421 @@
+[![compatibility](https://github.com/ctuning/ck-guide-images/blob/master/ck-compatible.svg)](https://github.com/ctuning/ck)
+[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
+
+# MLPerf Inference - Image Classification
+
+MLPerf Inference v0.5 uses MobileNets-v1-1.0-224 (called MobileNet in what follows) and ResNet50-v1.5 (called ResNet in what follows).
+
+# Table of contents
+
+1. [Installation](#installation)
+    - [Install prerequisites](#installation-debian) (Debian-specific)
+    - [Install CK workflows](#installation-workflows) (universal)
+1. [Benchmarking](#benchmarking)
+    - [via TensorFlow Lite](tflite/README.md)
+    - [via TensorFlow (C++)](tf-cpp/README.md)
+    - [via TensorFlow (Python)](tf-py/README.md)
+    - [via ONNX](onnx/README.md)
+1. [Understanding the anatomy of a benchmark](#anatomy)
+1. [Inspecting experimental results](#results)
+
+<a name="installation"></a>
+# Installation
+
+**NB:** If you would like to get a feel of CK workflows, you can skip
+installation instructions and try [benchmarking](#benchmarking)
+instructions on available Docker images:
+- TensorFlow Lite:
+    - [Debian 9](https://github.com/ctuning/ck-mlperf/tree/master/docker/image-classification-tflite.debian-9)
+    - [CentOS 7](https://github.com/ctuning/ck-mlperf/tree/master/docker/image-classification-tflite.centos-7)
+    - [Ubuntu 16.04](https://github.com/ctuning/ck-mlperf/tree/master/docker/image-classification-tflite.ubuntu-16.04)
+    - [Ubuntu 18.04](https://github.com/ctuning/ck-mlperf/tree/master/docker/image-classification-tflite.ubuntu-18.04)
+    - [Ubuntu 18.04 - Dashboard](https://github.com/ctuning/ck-mlperf/tree/master/docker/image-classification-tflite.dashboard.ubuntu-18.04)
+- TensorFlow (C++):
+    - [Debian 9](https://github.com/ctuning/ck-mlperf/tree/master/docker/image-classification-tf-cpp.debian-9)
+- Arm NN:
+    - [Debian 9](https://github.com/ARM-software/armnn-mlperf/tree/master/docker/image-classification-armnn-tflite.debian-9)
+
+Even if you would like to run CK workflows natively (e.g. on an Arm-based
+development board or Android phone), you may wish to have a quick look into the
+latest Dockerfile's to check for latest updates e.g. system-specific
+dependencies.
+
+<a name="installation-debian"></a>
+## Debian
+
+- Common tools and libraries.
+- [Python](https://www.python.org/), [pip](https://pypi.org/project/pip/), [NumPy](https://numpy.org/), [Collective Knowledge](https://cknowledge.org) (CK).
+- (Optional) [Android SDK](https://developer.android.com/studio/), [Android NDK](https://developer.android.com/ndk/).
+
+### Install common tools and libraries
+```bash
+$ sudo apt install git wget libz-dev curl
+$ sudo apt install gcc g++ autoconf autogen libtool
+```
+
+### Install Python 3 and the latest pip
+```bash
+$ sudo apt install python3 python3-pip
+$ sudo python3 -m pip install --upgrade pip
+```
+
+**NB:** Care must be taken not to mix Python 3 and Python 2 packages.
+If your system uses Python 2 by default, we recommend you prefix
+all CK commands, for example, with `CK_PYTHON=python3` for CK to run under Python 3:
+```
+$ python --version
+Python 2.7.13
+$ ck python_version
+2.7.13 (default, Sep 26 2018, 18:42:22)
+[GCC 6.3.0 20170516]
+$ CK_PYTHON=python3 ck python_version
+3.5.3 (default, Sep 27 2018, 17:25:39)
+[GCC 6.3.0 20170516]
+```
+Similarly, if you use multiple Python 3 versions (e.g. 3.5 and 3.6), we recommend
+you stick to one of them for consistency:
+```
+$ CK_PYTHON=python3.5 ck python_version
+3.5.2 (default, Nov 12 2018, 13:43:14)
+[GCC 5.4.0 20160609]
+$ CK_PYTHON=python3.6 ck python_version
+3.6.7 (default, Oct 25 2018, 09:16:13)
+[GCC 5.4.0 20160609]
+```
+
+### Install required Python 3 packages
+Choose one of the following installation options:
+1. system-wide via pip;
+1. user-space via pip;
+1. user-space via CK.
+
+With the first two options, packages get installed via pip and get registered
+with CK later (typically, on the first run of a program).
+
+With the last option, packages also get installed via pip but get registered
+with CK at the same time (so there is less chance of mixing things up).
+
+#### Option 1: system-wide installation via pip (under `/usr`)
+```bash
+$ sudo python3 -m pip install numpy ck
+```
+#### Option 2: user-space installation via pip (under `$HOME`)
+```bash
+$ python3 -m pip install numpy ck --user
+```
+#### Option 3: User-space installation via CK (under `$HOME` and `$CK_TOOLS`)
+Install CK via pip (or [from GitHub](https://github.com/ctuning/ck#installation)):
+```bash
+$ python3 -m pip install ck --user
+$ ck version
+V1.10.3
+```
+
+Install and register Python packages with CK:
+```bash
+$ ck pull repo:ck-env
+$ ck detect soft:compiler.python --full_path=`which python3`
+$ ck install package --tags=lib,python-package,numpy
+```
+
+If the above dependencies have been installed on a clean system, you should be
+able to inspect the registered CK environments e.g. as follows:
+```
+$ ck show env --tags=python-package
+Env UID:         Target OS: Bits: Name:                     Version: Tags:
+
+4e82bab01c8ee3b7   linux-64    64 Python NumPy library      1.16.2   64bits,host-os-linux-64,lib,needs-python,needs-python-3.5.2,numpy,python-package,target-os-linux-64,v1,v1.16,v1.16.2,vmaster
+
+$ ck cat env --tags=python-package | grep PYTHONPATH
+export PYTHONPATH=/home/anton/CK_TOOLS/lib-python-numpy-compiler.python-3.5.2-linux-64/build:${PYTHONPATH}
+```
+
+### [Optional] Install Android SDK and NDK
+
+You can optionally target Android API 23 (v6.0 "Marshmallow") devices using the
+`--target_os=android23-arm64` flag
+(or [similar](https://source.android.com/setup/start/build-numbers)), when using
+the TensorFlow Lite benchmark (recommended) and TensorFlow (C++) benchmark (not recommended).
+
+On Debian Linux, you can install the [Android SDK](https://developer.android.com/studio/) and the [Android NDK](https://developer.android.com/ndk/) as follows:
+```
+$ sudo apt install android-sdk
+$ adb version
+Android Debug Bridge version 1.0.36
+Revision 1:7.0.0+r33-2
+$ sudo apt install google-android-ndk-installer
+```
+**NB:** On Ubuntu 18.04, NDK r13b gets installed. On Ubuntu 16.04, download [NDK r18b](https://dl.google.com/android/repository/android-ndk-r18b-linux-x86_64.zip) and extract it into e.g. `/usr/local`. NDK r18c only supports LLVM, which currently requires a CK quirk to work properly (removing a dependency on `soft:compiler.gcc.android.ndk` from `soft:compiler.llvm.android.ndk`).
+
+<a name="installation-workflows"></a>
+## Install CK workflows for MLPerf
+
+### Pull CK repositories
+```bash
+$ ck pull repo:ck-mlperf
+```
+**NB:** Transitive dependencies include [repo:ck-tensorflow](https://github.com/ctuning/ck-tensorflow).
+
+### Install a small dataset (500 images)
+```bash
+$ ck install package:imagenet-2012-val-min
+```
+**NB:** ImageNet dataset descriptions are in [repo:ck-env](https://github.com/ctuning/ck-env).
+
+### Install the full dataset (50,000 images)
+```bash
+$ ck install package:imagenet-2012-val
+```
+
+**NB:** If you already have the ImageNet validation dataset downloaded in a directory e.g. `$HOME/ilsvrc2012-val/`, you can simply detect it as follows:
+```bash
+$ ck detect soft:dataset.imagenet.val --full_path=$HOME/ilsvrc2012-val/ILSVRC2012_val_00000001.JPEG
+```
+
+### Preprocess datasets
+
+ImageNet can be preprocessed in many different ways,
+which can significantly affect the resulting accuracy.
+We currently support 3 different preprocessing methods:
+```
+$ ck install package --tags=dataset,imagenet,preprocessed
+
+More than one package or version found:
+
+ 0) dataset-imagenet-preprocessed-using-tensorflow (fac1d0d5f4e69a85)
+ 1) dataset-imagenet-preprocessed-using-pillow (a6a4613ba6dfd570)
+ 2) dataset-imagenet-preprocessed-using-opencv (4932bbdd2ac7a17b)
+
+Please select the package to install [ hit return for "0" ]: 
+```
+
+Preprocessing using OpenCV (option 2) is the current official method.
+You can perform it directly by adding the `using-opencv` tag as follows:
+```
+$ ck install package --tags=dataset,imagenet,preprocessed,using-opencv --ask
+```
+
+You can locate the preprocessed files on disk using the same tags as follows:
+```
+$ ck cat env --tags=dataset,imagenet,preprocessed,using-opencv | grep CK_ENV_DATASET_IMAGENET_PREPROCESSED_DIR
+export CK_ENV_DATASET_IMAGENET_PREPROCESSED_DIR=/datasets/dataset-imagenet-preprocessed-using-opencv
+```
+
+CK installs all the dependencies automatically. (More information on recommended choices for dependencies can be provided on demand.)
+
+#### Summary of preprocessing methods
+
+The table below summarizes the available methods.
+
+| Model                | Pillow            | OpenCV universal         | OpenCV for MobileNet         | OpenCV for ResNet         | TensorFlow         |
+|-|-|-|-|-|-|
+| Matches official?    | No                | No                       | Yes                          | Yes                       | No                 |
+| Additional tags      | `using-pillow`    | `using-opencv,universal` | `using-opencv,for-mobilenet` | `using-opencv,for-resnet` | `using-tensorflow` |
+| Supported models     | ResNet, MobileNet | ResNet, MobileNet        | MobileNet only               | ResNet only               | ResNet only        |
+| Supported platforms  | x86, arm          | x86                      | x86                          | x86                       | x86 (prebuilt TF)  |
+| Data format          | rgb8 (int8)       | rgb8 (int8)              | rgbf32 (float32)             | rgbf32 (float32)          | rgbf32 (float32)   |
+| Data size            | 7.1G              | 7.1G                     | 29G                          | 29G                       | 29G                |
+
+The official code [preprocesses](https://github.com/mlperf/inference/blob/master/v0.5/classification_and_detection/python/dataset.py) images using OpenCV. ResNet and MobileNet require different preprocessing after resizing: ResNet requires [means to be subtracted](https://github.com/mlperf/inference/blob/master/v0.5/classification_and_detection/python/dataset.py#L178); MobileNet requires [normalization to the (-1, 1) range](https://github.com/mlperf/inference/blob/master/v0.5/classification_and_detection/python/dataset.py#L195). In addition, the official ResNet preprocessing uses [area interpolation](https://github.com/mlperf/inference/blob/master/v0.5/classification_and_detection/python/dataset.py#L172), instead of the default [bilinear interpolation](https://github.com/mlperf/inference/blob/master/v0.5/classification_and_detection/python/dataset.py#L154). The same behaviour can be reproduced via CK. Preprocessed images for both MobileNet and ResNet, however, require 58G on disk, as pixels are stored as 32-bit floats.
+
+An alternative, dubbed OpenCV (universal), uses bilinear interpolation and stores pixels as 8-bit integers. At load time, however, minor additional processing may be required depending on the model (e.g. see sample [code for normalization](https://github.com/ctuning/ck-tensorflow/blob/master/program/image-classification-tflite/benchmark.h#L463) and [code for subtracting means](https://github.com/ctuning/ck-tensorflow/blob/master/program/image-classification-tflite/benchmark.h#L469)).
+
+
+#### Accuracy on the ImageNet 2012 validation set
+
+The table below shows the accuracy on the ImageNet 2012 validation set
+(50,000 images) measured [via TensorFlow (C++)](tf-cpp/README.md).
+
+| Model                   | Metric | Pillow  | OpenCV universal | OpenCV for MobileNet | OpenCV for ResNet | TensorFlow |
+|-|-|-|-|-|-|-|
+| ResNet                  |  Top1  | 0.76170 | 0.76422          | N/A                  | 0.76456           | 0.76522    |
+|                         |  Top5  | 0.92866 | 0.93074          | N/A                  | 0.93016           | 0.93066    |
+| MobileNet non-quantized |  Top1  | 0.71226 | 0.71676          | 0.71676              | N/A               | N/A        |
+|                         |  Top5  | 0.89834 | 0.90118          | 0.90118              | N/A               | N/A        |
+| MobileNet quantized     |  Top1  | 0.70348 | 0.70700          | 0.70694              | N/A               | N/A        |
+|                         |  Top5  | 0.89376 | 0.89594          | 0.89594              | N/A               | N/A        |
+
+Considering Top1, the universal OpenCV method is slightly less accurate for ResNet, but slightly more accurate for MobileNet quantized than the official code. The TensorFlow method is most accurate for ResNet, but is not suitable for MobileNet. The Pillow method is least accurate, but can be used on Arm platforms. The difference between the universal OpenCV and the TensorFlow methods on ResNet is exactly 0.1% or 50 images.
+
+#### Detect datasets preprocessed on a different machine
+
+**TODO**
+
+
+<a name="benchmarking"></a>
+## Benchmarking
+
+You can benchmark MobileNet using one of the available options:
+- [via TensorFlow Lite](tflite/README.md)
+- [via TensorFlow (C++)](tf-cpp/README.md)
+- [via TensorFlow (Python)](tf-py/README.md)
+- [via ONNX](onnx/README.md)
+
+Please come back here if you would like to learn about [the anatomy of a benchmark](#anatomy), or
+how to inspect and visualize [experimental results](#results).
+
+<a name="anatomy"></a>
+## The anatomy of a benchmark
+
+While the componentized nature of CK workflows streamlines
+[installation](#installation) and [benchmarking](#benchmarking), it also makes
+it less obvious what the components are and where they are stored. This section
+describes the anatomy of a benchmark in terms of its components. We use the
+[TFLite MobileNet implementation](tflite/README.md) as a running example.
+
+### Model
+
+To search for the CK entry of an installed model, use `ck search env` with the same tags you used to install it e.g.:
+```
+$ ck search env --tags=model,tflite,mlperf,mobilenet,quantized
+local:env:3f0ca5c4d25b4ea3
+```
+
+To view more information about the CK entry, use `ck show env` e.g.:
+```
+$ ck show env --tags=model,tflite,mlperf,mobilenet,quantized
+Env UID:         Target OS: Bits: Name:                                                                Version:                   Tags:
+
+3f0ca5c4d25b4ea3   linux-64    64 TensorFlow model and weights (mobilenet-v1-1.0-224-quant-2018_08_02) 1_1.0_224_quant_2018_08_02 2018_08_02,64bits,downloaded,host-os-linux-64,mlperf,mobilenet,mobilenet-v1,mobilenet-v1-1.0-224,model,nhwc,python,quantised,quantized,target-os-linux-64,tensorflowmodel,tf,tflite,v1,v1.1,v1.1.0,v1.1.0.224,v1.1.0.224.0,v1.1.0.224.0.2018,v1.1.0.224.0.2018.8,v1.1.0.224.0.2018.8.2,weights
+```
+
+To view the environment variables set up by the CK entry, use `ck cat env` e.g.:
+```
+$ ck cat `ck search env --tags=model,tflite,mlperf,mobilenet,quantized`
+#! /bin/bash
+#
+# --------------------[ TensorFlow model and weights (mobilenet-v1-1.0-224-quant-2018_08_02) ver. 1_1.0_224_quant_2018_08_02, /home/anton/CK_REPOS/local/env/3f0ca5c4d25b4ea3/env.sh ]--------------------
+# Tags: 2018_08_02,64bits,downloaded,host-os-linux-64,mlperf,mobilenet,mobilenet-v1,mobilenet-v1-1.0-224,model,nhwc,python,quantised,quantized,target-os-linux-64,tensorflowmodel,tf,tflite,v1,v1.1,v1.1.0,v1.1.0.224,v1.1.0.224.0,v1.1.0.224.0.2018,v1.1.0.224.0.2018.8,v1.1.0.224.0.2018.8.2,weights
+#
+# CK generated script
+
+if [ "$1" != "1" ]; then if [ "$CK_ENV_TENSORFLOW_MODEL_SET" == "1" ]; then return; fi; fi
+
+# Soft UOA           = model.tensorflow.py (439b9f1757f27091)  (tensorflowmodel,model,weights,python,tf,tflite,nhwc,mobilenet,mobilenet-v1,mobilenet-v1-1.0-224,2018_08_02,quantized,quantised,mlperf,downloaded,host-os-linux-64,target-os-linux-64,64bits,v1,v1.1,v1.1.0,v1.1.0.224,v1.1.0.224.0,v1.1.0.224.0.2018,v1.1.0.224.0.2018.8,v1.1.0.224.0.2018.8.2)
+# Host OS UOA        = linux-64 (4258b5fe54828a50)
+# Target OS UOA      = linux-64 (4258b5fe54828a50)
+# Target OS bits     = 64
+# Tool version       = 1_1.0_224_quant_2018_08_02
+# Tool split version = [1, 1, 0, 224, 0, 2018, 8, 2]
+
+export CK_ENV_TENSORFLOW_MODEL_IMAGE_HEIGHT=224
+export CK_ENV_TENSORFLOW_MODEL_IMAGE_WIDTH=224
+export CK_ENV_TENSORFLOW_MODEL_INPUT_LAYER_NAME=input
+export CK_ENV_TENSORFLOW_MODEL_MOBILENET_MULTIPLIER=1.0
+export CK_ENV_TENSORFLOW_MODEL_MOBILENET_RESOLUTION=224
+export CK_ENV_TENSORFLOW_MODEL_MOBILENET_VERSION=1
+export CK_ENV_TENSORFLOW_MODEL_MODULE=/home/anton/CK_TOOLS/model-tf-mlperf-mobilenet-quantized-downloaded/mobilenet-model.py
+export CK_ENV_TENSORFLOW_MODEL_NORMALIZE_DATA=YES
+export CK_ENV_TENSORFLOW_MODEL_OUTPUT_LAYER_NAME=MobilenetV1/Predictions/Reshape_1
+export CK_ENV_TENSORFLOW_MODEL_ROOT=/home/anton/CK_TOOLS/model-tf-mlperf-mobilenet-quantized-downloaded
+export CK_ENV_TENSORFLOW_MODEL_TFLITE_FILENAME=mobilenet_v1_1.0_224_quant.tflite
+export CK_ENV_TENSORFLOW_MODEL_TFLITE_FILEPATH=/home/anton/CK_TOOLS/model-tf-mlperf-mobilenet-quantized-downloaded/mobilenet_v1_1.0_224_quant.tflite
+export CK_ENV_TENSORFLOW_MODEL_TF_FROZEN_FILENAME=mobilenet_v1_1.0_224_quant_frozen.pb
+export CK_ENV_TENSORFLOW_MODEL_TF_FROZEN_FILEPATH=/home/anton/CK_TOOLS/model-tf-mlperf-mobilenet-quantized-downloaded/mobilenet_v1_1.0_224_quant_frozen.pb
+export CK_ENV_TENSORFLOW_MODEL_WEIGHTS=/home/anton/CK_TOOLS/model-tf-mlperf-mobilenet-quantized-downloaded/mobilenet_v1_1.0_224_quant.ckpt
+export CK_ENV_TENSORFLOW_MODEL_WEIGHTS_ARE_CHECKPOINTS=YES
+export CK_MODEL_DATA_LAYOUT=NHWC
+
+export CK_ENV_TENSORFLOW_MODEL_SET=1
+```
+
+To inspect the model's files on disk, use `ck locate env` e.g.:
+```
+$ ck locate env --tags=model,tflite,mlperf,mobilenet,quantized
+/home/anton/CK_TOOLS/model-tf-mlperf-mobilenet-quantized-downloaded
+$ ls -la `ck locate env --tags=model,tflite,mlperf,mobilenet,quantized`
+total 43524
+drwxr-xr-x  2 anton dvdt     4096 Mar 25 12:31 .
+drwxrwxr-x 18 anton dvdt     4096 Mar 25 12:32 ..
+-rw-rw-r--  1 anton dvdt     2240 Mar 25 12:31 ck-install.json
+-rw-rw-r--  1 anton dvdt     3477 Mar 25 12:31 mobilenet-model.py
+-rw-rw-r--  1 anton dvdt    20309 Mar 25 12:31 mobilenet_v1.py
+-rw-r--r--  1 anton dvdt 17020468 Aug  3  2018 mobilenet_v1_1.0_224_quant.ckpt.data-00000-of-00001
+-rw-r--r--  1 anton dvdt    14644 Aug  3  2018 mobilenet_v1_1.0_224_quant.ckpt.index
+-rw-r--r--  1 anton dvdt  5143394 Aug  3  2018 mobilenet_v1_1.0_224_quant.ckpt.meta
+-rw-r--r--  1 anton dvdt  4276352 Aug  3  2018 mobilenet_v1_1.0_224_quant.tflite
+-rw-r--r--  1 anton dvdt   885850 Aug  3  2018 mobilenet_v1_1.0_224_quant_eval.pbtxt
+-rw-r--r--  1 anton dvdt 17173742 Aug  3  2018 mobilenet_v1_1.0_224_quant_frozen.pb
+-rw-r--r--  1 anton dvdt       89 Aug  3  2018 mobilenet_v1_1.0_224_quant_info.txt
+```
+
+**NB:** The TFLite weights are in the `mobilenet_v1_1.0_224*.tflite` file. Only
+the TFLite weights are different between the `2018_02_22` and `2018_08_02`
+MobileNets-v1 packages. We have adopted the latter for MLPerf Inference v0.5.
+
+
+<a name="results"></a>
+## Inspecting and visualizing experimental results
+
+### Inspecting recorded experimental results
+
+If you run the same command several times selecting different models (quantized
+or non-quantized) or datasets (500 images or 50,000 images), CK will create
+several _experimental points_ in the same repository e.g.:
+```bash
+$ ck find local:experiment:mlperf-mobilenet-tf-cpp-accuracy
+/home/anton/CK_REPOS/local/experiment/mlperf-mobilenet-tf-cpp-accuracy
+$ ck list_points local:experiment:mlperf-mobilenet-tf-cpp-accuracy
+78dae6354e471199
+918c80bc5d4906b0
+```
+You can then retrieve various run parameters from such experimental points.
+
+#### Accuracy
+You can quickly inspect the accuracy recorded for a particular point as follows:
+```bash
+$ grep \"run\": -A2 /home/anton/CK_REPOS/local/experiment/mlperf-mobilenet-tf-cpp-accuracy/ckp-918c80bc5d4906b0.0001.json
+      "run": {
+        "accuracy_top1": 0.718,
+        "accuracy_top5": 0.9,
+$ grep \"run\": -A2 /home/anton/CK_REPOS/local/experiment/mlperf-mobilenet-tf-cpp-accuracy/ckp-78dae6354e471199.0001.json
+      "run": {
+        "accuracy_top1": 0.704,
+        "accuracy_top5": 0.898,
+```
+
+#### Model
+You can quickly inspect the model used for a particular point as follows:
+```bash
+$ grep RUN_OPT_GRAPH_FILE /home/anton/CK_REPOS/local/experiment/mlperf-mobilenet-tf-cpp-accuracy/ckp-918c80bc5d4906b0.0001.json
+      "RUN_OPT_GRAPH_FILE": "/home/anton/CK_TOOLS/model-tf-mlperf-mobilenet-downloaded/mobilenet_v1_1.0_224_frozen.pb",
+$ grep RUN_OPT_GRAPH_FILE /home/anton/CK_REPOS/local/experiment/mlperf-mobilenet-tf-cpp-accuracy/ckp-78dae6354e471199.0001.json
+      "RUN_OPT_GRAPH_FILE": "/home/anton/CK_TOOLS/model-tf-mlperf-mobilenet-quantized-downloaded/mobilenet_v1_1.0_224_quant_frozen.pb",
+```
+As expected, the lower accuracy comes from the quantized model.
+
+#### Dataset
+Unfortunately, the dataset path is recorded only to `pipeline.json`.
+This file gets overwritten on each run of `ck benchmark`, so only
+the dataset used in the latest command can be retrieved:
+```bash
+$ grep \"CK_ENV_DATASET_IMAGENET_VAL\": /home/anton/CK_REPOS/local/experiment/mlperf-mobilenet-tf-cpp-accuracy/pipeline.json
+          "CK_ENV_DATASET_IMAGENET_VAL": "/home/anton/CK_TOOLS/dataset-imagenet-ilsvrc2012-val-min"
+```
+
+#### Batch count
+You can, however, check the batch count e.g.:
+```bash
+$ grep CK_BATCH_COUNT /home/anton/CK_REPOS/local/experiment/mlperf-mobilenet-tf-cpp-accuracy/ckp-78dae6354e471199.0001.json
+      "CK_BATCH_COUNT": "500",
+```
+
+#### Image cropping
+By default, input images preprocessed for a client program [get cropped](https://github.com/ctuning/ck-tensorflow/tree/master/program/image-classification-tf-cpp#ck_crop_percent) by 87.5%:
+```bash
+$ grep CK_CROP_PERCENT /home/anton/CK_REPOS/local/experiment/mlperf-mobilenet-tf-cpp-accuracy/ckp-78dae6354e471199.0001.json
+      "CK_CROP_PERCENT": 87.5,
+```
+
+This can be changed by passing e.g. `--env.CK_CROP_PERCENT=100` to `ck
+benchmark` (but see [here](https://github.com/mlperf/inference/tree/21efaf57d55ccd78e77b87c668bb09d47564bb6a/v0.5/classification_and_detection/optional_harness_ck/classification/tf-cpp#reference-accuracy) how this can make results worse).
+
+
+### Visualizing experimental results
+
+If you have collected some experimental results on your machine (or transferred to it results collected from elsewhere), you can visualize them as follows:
+```
+$ ck display dashboard --scenario=mlperf.mobilenets
+```
+
+You can also see a demo CK [MobileNets](http://cknowledge.org/dashboard/mlperf.mobilenets) dashboard online.
diff --git a/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/onnx/README.md b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/onnx/README.md
new file mode 100644
index 0000000000..bfce26e758
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/onnx/README.md
@@ -0,0 +1,239 @@
+# Image Classification via ONNX
+
+1. [Installation instructions](#installation)
+2. [Benchmarking instructions](#benchmarking)
+3. [Reference accuracy](#accuracy)
+4. [Further information](#further-info)
+
+
+<a name="installation"></a>
+## Installation instructions
+
+Please follow the [common installation instructions](../README.md#installation) first.
+
+### Install ONNX
+
+Install the ONNX library and runtime:
+```
+$ ck install package --tags=lib,python-package,onnx
+$ ck install package --tags=lib,python-package,onnxruntime
+```
+
+### Install the MobileNet model for ONNX
+
+To select interactively from one of the non-quantized and quantized MobileNets-v1-1.0-224 models:
+```
+$ ck install package --tags=model,onnx,mlperf,mobilenet
+
+More than one package or version found:
+
+ 0) model-onnx-mlperf-mobilenet  Version 1_1.0_224_2018_08_02  (b47f4980eefabffa)
+ 1) model-onnx-convert-from-tf (22b1d864174bf743), Variations: mobilenet
+
+Please select the package to install [ hit return for "0" ]: 
+```
+Option 1 downloads the TF model and converts it to ONNX. Option 0 uses a pre-converted ONNX model.
+
+We recommend [converting models on-the-fly](https://github.com/ctuning/ck-mlperf/blob/master/package/model-onnx-convert-from-tf/README.md), as you can additionally control the data layout as follows:
+- NHWC:
+```
+$ ck install package --tags=onnx,model,mobilenet,converted,nhwc
+```
+- NCHW
+```
+$ ck install package --tags=onnx,model,mobilenet,converted,nchw
+```
+Note that without the layout tags (`nhwc` or `nchw`), the layout is selected nondeterministically.
+
+#### Bonus
+
+##### Install the ResNet model
+
+You can similarly convert ResNet as follows:
+- NHWC
+```
+$ ck install package --tags=onnx,model,resnet,converted,nhwc
+```
+- NCHW
+```
+$ ck install package --tags=onnx,model,resnet,converted,nchw
+```
+
+You can benchmark ResNet exactly in the same way as MobileNet.
+Just replace `mobilenet` with `resnet` in the [benchmarking instructions](#benchmarking) below.
+
+
+### Run the ONNX Image Classification client
+
+#### MobileNet, NHWC
+```
+$ ck run program:image-classification-onnx-py
+...
+*** Dependency 3 = weights (ONNX model):
+
+More than one environment found for "ONNX model" with tags="model,image-classification,onnx" and setup={"host_os_uoa": "linux-64", "target_os_uoa": "linux-64", "target_os_bits": "64"}:
+
+ 0) ONNX-from-TF model (MLPerf MobileNet) - v1_1.0_224_2018_08_02 (64bits,converted,converted-from-tf,host-os-linux-64,image-classification,mlperf,mobilenet,model,nhwc,onnx,target-os-linux-64,v1,v1.1,v1.1
+.0,v1.1.0.224,v1.1.0.224.2018,v1.1.0.224.2018.8,v1.1.0.224.2018.8.2 (f18d48538fbfbd46))
+                                       - Depends on "python" (env UOA=7c8bbf2343208d88, tags="compiler,python", version=3.6.7)
+                                       - Depends on "lib-python-numpy" (env UOA=fe9d0436cbfd34c8, tags="lib,python-package,numpy", version=1.16.2)
+                                       - Depends on "lib-tensorflow" (env UOA=9c34f3f9b9b8dfd4, tags="lib,tensorflow,vprebuilt", version=1.13.1)
+                                       - Depends on "lib-python-onnx" (env UOA=c9a3c5ad5de9adcb, tags="lib,python-package,onnx", version=1.4.1)
+                                       - Depends on "lib-python-tf2onnx" (env UOA=44dd6b520ae81482, tags="lib,python-package,tf2onnx", version=1.4.1)
+                                       - Depends on "model-source" (env UOA=e5cf6f254447a629, tags="model,image-classification,tf", version=1_1.0_224_2018_08_02)
+
+ 1) ONNX-from-TF model (MLPerf MobileNet) - v1_1.0_224_2018_08_02 (64bits,converted,converted-from-tf,host-os-linux-64,image-classification,mlperf,mobilenet,model,nchw,onnx,target-os-linux-64,v1,v1.1,v1.1
+.0,v1.1.0.224,v1.1.0.224.2018,v1.1.0.224.2018.8,v1.1.0.224.2018.8.2 (2e1b5534351b7e33))
+                                       - Depends on "python" (env UOA=7c8bbf2343208d88, tags="compiler,python", version=3.6.7)
+                                       - Depends on "lib-python-numpy" (env UOA=fe9d0436cbfd34c8, tags="lib,python-package,numpy", version=1.16.2)
+                                       - Depends on "lib-tensorflow" (env UOA=9c34f3f9b9b8dfd4, tags="lib,tensorflow,vprebuilt", version=1.13.1)
+                                       - Depends on "lib-python-onnx" (env UOA=c9a3c5ad5de9adcb, tags="lib,python-package,onnx", version=1.4.1)
+                                       - Depends on "lib-python-tf2onnx" (env UOA=44dd6b520ae81482, tags="lib,python-package,tf2onnx", version=1.4.1)
+                                       - Depends on "model-source" (env UOA=e5cf6f254447a629, tags="model,image-classification,tf", version=1_1.0_224_2018_08_02)
+
+
+Select one of the options for "ONNX model" with tags="model,image-classification,onnx" and setup={"host_os_uoa": "linux-64", "target_os_uoa": "linux-64", "target_os_bits": "64"} [ hit return for "0" ]: 0
+
+    Resolved. CK environment UID = f18d48538fbfbd46 (version 1_1.0_224_2018_08_02)
+...
+--------------------------------
+Process results in predictions
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.84 - (65) n01751748 sea snake
+0.08 - (58) n01737021 water snake
+0.04 - (34) n01665541 leatherback turtle, leatherback, leather...
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (57) n01735189 garter snake, grass snake
+---------------------------------------
+
+Summary:
+-------------------------------
+Graph loaded in 0.018571s
+All images loaded in 0.001246s
+All images classified in 0.188061s
+Average classification time: 0.188061s
+Accuracy top 1: 1.0 (1 of 1)
+Accuracy top 5: 1.0 (1 of 1)
+--------------------------------
+```
+
+#### MobileNet, NCHW
+```
+$ ck run program:image-classification-onnx-py
+...
+*** Dependency 3 = weights (ONNX model):
+
+More than one environment found for "ONNX model" with tags="model,image-classification,onnx" and setup={"host_os_uoa": "linux-64", "target_os_uoa": "linux-64", "target_os_bits": "64"}:
+
+ 0) ONNX-from-TF model (MLPerf MobileNet) - v1_1.0_224_2018_08_02 (64bits,converted,converted-from-tf,host-os-linux-64,image-classification,mlperf,mobilenet,model,nhwc,onnx,target-os-linux-64,v1,v1.1,v1.1
+.0,v1.1.0.224,v1.1.0.224.2018,v1.1.0.224.2018.8,v1.1.0.224.2018.8.2 (f18d48538fbfbd46))
+                                       - Depends on "python" (env UOA=7c8bbf2343208d88, tags="compiler,python", version=3.6.7)
+                                       - Depends on "lib-python-numpy" (env UOA=fe9d0436cbfd34c8, tags="lib,python-package,numpy", version=1.16.2)
+                                       - Depends on "lib-tensorflow" (env UOA=9c34f3f9b9b8dfd4, tags="lib,tensorflow,vprebuilt", version=1.13.1)
+                                       - Depends on "lib-python-onnx" (env UOA=c9a3c5ad5de9adcb, tags="lib,python-package,onnx", version=1.4.1)
+                                       - Depends on "lib-python-tf2onnx" (env UOA=44dd6b520ae81482, tags="lib,python-package,tf2onnx", version=1.4.1)
+                                       - Depends on "model-source" (env UOA=e5cf6f254447a629, tags="model,image-classification,tf", version=1_1.0_224_2018_08_02)
+
+ 1) ONNX-from-TF model (MLPerf MobileNet) - v1_1.0_224_2018_08_02 (64bits,converted,converted-from-tf,host-os-linux-64,image-classification,mlperf,mobilenet,model,nchw,onnx,target-os-linux-64,v1,v1.1,v1.1
+.0,v1.1.0.224,v1.1.0.224.2018,v1.1.0.224.2018.8,v1.1.0.224.2018.8.2 (2e1b5534351b7e33))
+                                       - Depends on "python" (env UOA=7c8bbf2343208d88, tags="compiler,python", version=3.6.7)
+                                       - Depends on "lib-python-numpy" (env UOA=fe9d0436cbfd34c8, tags="lib,python-package,numpy", version=1.16.2)
+                                       - Depends on "lib-tensorflow" (env UOA=9c34f3f9b9b8dfd4, tags="lib,tensorflow,vprebuilt", version=1.13.1)
+                                       - Depends on "lib-python-onnx" (env UOA=c9a3c5ad5de9adcb, tags="lib,python-package,onnx", version=1.4.1)
+                                       - Depends on "lib-python-tf2onnx" (env UOA=44dd6b520ae81482, tags="lib,python-package,tf2onnx", version=1.4.1)
+                                       - Depends on "model-source" (env UOA=e5cf6f254447a629, tags="model,image-classification,tf", version=1_1.0_224_2018_08_02)
+
+
+Select one of the options for "ONNX model" with tags="model,image-classification,onnx" and setup={"host_os_uoa": "linux-64", "target_os_uoa": "linux-64", "target_os_bits": "64"} [ hit return for "0" ]: 1
+
+    Resolved. CK environment UID = 2e1b5534351b7e33 (version 1_1.0_224_2018_08_02)
+...
+--------------------------------
+Process results in predictions
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.84 - (65) n01751748 sea snake
+0.08 - (58) n01737021 water snake
+0.04 - (34) n01665541 leatherback turtle, leatherback, leather...
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (57) n01735189 garter snake, grass snake
+---------------------------------------
+
+Summary:
+-------------------------------
+Graph loaded in 0.018411s
+All images loaded in 0.001247s
+All images classified in 0.189969s
+Average classification time: 0.189969s
+Accuracy top 1: 1.0 (1 of 1)
+Accuracy top 5: 1.0 (1 of 1)
+--------------------------------
+```
+
+<a name="benchmarking"></a>
+## Benchmarking instructions
+
+### Benchmark the performance
+```
+$ ck benchmark program:image-classification-onnx-py --cmd_key=preprocessed \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 \
+--record --record_repo=local --record_uoa=mlperf-image-classification-mobilenet-onnx-py-performance \
+--tags=mlperf,image-classification,mobilenet,onnx-py,performance \
+--skip_print_timers --skip_stat_analysis --process_multi_keys
+```
+
+**NB:** When using the batch count of **N**, the program classifies **N** images, but
+the slow first run is not taken into account when computing the average
+classification time e.g.:
+```bash
+$ ck benchmark program:image-classification-onnx-py --cmd_key=preprocessed \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2
+...
+Batch 1 of 2
+Batch loaded in 0.001307s
+Batch classified in 0.186297s
+
+Batch 2 of 2
+Batch loaded in 0.000721s
+Batch classified in 0.029533s
+...
+Summary:
+-------------------------------
+Graph loaded in 0.018409s
+All images loaded in 0.002028s
+All images classified in 0.029533s
+Average classification time: 0.029533s
+Accuracy top 1: 0.5 (1 of 2)
+Accuracy top 5: 1.0 (2 of 2)
+--------------------------------
+```
+
+### Benchmark the accuracy
+```bash
+$ ck benchmark program:image-classification-onnx-py --cmd_key=preprocessed \
+--repetitions=1  --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--record --record_repo=local --record_uoa=mlperf-image-classification-mobilenet-onnx-py-accuracy \
+--tags=mlperf,image-classification,mobilenet,onnx-py,accuracy \
+--skip_print_timers --skip_stat_analysis --process_multi_keys
+```
+**NB:** For the `imagenet-2012-val-min` dataset, change `--env.CK_BATCH_COUNT=50000`
+to `--env.CK_BATCH_COUNT=500` (or drop completely to test on a single image as if
+with `--env.CK_BATCH_COUNT=1`).
+
+
+<a name="accuracy"></a>
+## Reference accuracy
+**TODO**
+
+<a name="further-info"></a>
+## Further information
+
+### Using Collective Knowledge
+See the [common instructions](../README.md) for information on how to use Collective Knowledge
+to learn about [the anatomy of a benchmark](../README.md#anatomy), or
+to inspect and visualize [experimental results](../README.md#results).
+
+### Using the client program
+
+See [`ck-mlperf:program:image-classification-onnx-py`](https://github.com/ctuning/ck-mlperf/tree/master/program/image-classification-onnx-py) for more details about the client program.
diff --git a/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/tf-cpp/README.md b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/tf-cpp/README.md
new file mode 100644
index 0000000000..75d1c6c852
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/tf-cpp/README.md
@@ -0,0 +1,367 @@
+# Image Classification via TensorFlow (C++)
+
+1. [Installation instructions](#installation)
+2. [Benchmarking instructions](#benchmarking)
+3. [Reference accuracy](#accuracy)
+4. [Further information](#further-info)
+
+
+<a name="installation"></a>
+## Installation instructions
+
+Please follow the [common installation instructions](../README.md#installation) first.
+
+### Install TensorFlow (C++)
+
+Install TensorFlow (C++) v1.13.1 from source:
+```bash
+$ ck install package:lib-tensorflow-1.13.1-src-static [--target_os=android23-arm64]
+```
+**NB:** The ResNet model has a [known issue with v1.14.0](https://github.com/ctuning/ck-tensorflow/blob/master/package/lib-tensorflow-1.14.0-src-static/README.md).
+
+### Install models for TensorFlow (C++)
+
+#### ResNet
+
+To install the [ResNet50-v1.5 model](https://zenodo.org/record/2535873):
+```bash
+$ ck install package --tags=model,tf,mlperf,resnet
+```
+
+#### MobileNet
+To select interactively from one of the non-quantized and quantized MobileNets-v1-1.0-224 models:
+```
+$ ck install package --tags=model,tf,mlperf,mobilenet
+```
+
+##### MobileNet non-quantized
+
+To install the non-quantized MobileNet model from:
+- [zenodo.org](https://zenodo.org/record/2269307/files/mobilenet_v1_1.0_224.tgz) (default):
+```bash
+$ ck install package --tags=model,tf,mlperf,mobilenet,non-quantized,from-zenodo
+```
+- [tensorflow.org](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz)
+```bash
+$ ck install package --tags=model,tf,mlperf,mobilenet,non-quantized,from-google
+```
+
+##### MobileNet quantized
+
+To install the quantized MobileNet model from:
+- [zenodo.org](https://zenodo.org/record/2269307/files/mobilenet_v1_1.0_224_quant.tgz) (default):
+```bash
+$ ck install package --tags=model,tf,mlperf,mobilenet,quantized,from-zenodo
+```
+- [tensorflow.org](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz)
+```bash
+$ ck install package --tags=model,tf,mlperf,mobilenet,quantized,from-google
+```
+
+##### Bonus: other MobileNets models
+
+You can also install any other MobileNets model compatible with TensorFlow (C++) as follows:
+```bash
+$ ck install package --tags=tensorflowmodel,mobilenet,frozen --no_tags=mobilenet-all
+```
+
+### Compile the TensorFlow (C++) Image Classification client
+
+Compile the client. (For Android, append e.g. `--target_os=android23-arm64` to the command.)
+
+```bash
+$ ck compile program:image-classification-tf-cpp --speed
+```
+
+### Run the TensorFlow (C++) Image Classification client
+
+Run the client. (For Android, connect an Android device to your host machine via USB and append e.g. `--target_os=android23-arm64` to the command).
+
+If you have preprocessed input data using more than one method (OpenCV, Pillow or TensorFlow), you need to select the particular preprocessed dataset. Note that the TensorFlow preprocessing method is not applicable to the MobileNet models.
+
+#### ResNet
+
+##### OpenCV preprocessing
+```bash
+$ ck run program:image-classification-tf-cpp \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=resnet
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.95 - (65) n01751748 sea snake
+0.01 - (58) n01737021 water snake
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (66) n01753488 horned viper, cerastes, sand viper, horn...
+0.00 - (60) n01740131 night snake, Hypsiglena torquata
+---------------------------------------
+```
+
+##### Pillow preprocessing
+```bash
+$ ck run program:image-classification-tf-cpp \
+--dep_add_tags.images=preprocessed,using-pillow --dep_add_tags.weights=resnet
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.95 - (65) n01751748 sea snake
+0.02 - (58) n01737021 water snake
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (60) n01740131 night snake, Hypsiglena torquata
+0.01 - (66) n01753488 horned viper, cerastes, sand viper, horn...
+---------------------------------------
+```
+
+##### TensorFlow preprocessing
+```bash
+$ ck run program:image-classification-tf-cpp \
+--dep_add_tags.images=preprocessed,using-tf --dep_add_tags.weights=resnet
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.95 - (65) n01751748 sea snake
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (58) n01737021 water snake
+0.01 - (66) n01753488 horned viper, cerastes, sand viper, horn...
+0.00 - (60) n01740131 night snake, Hypsiglena torquata
+---------------------------------------
+```
+
+#### MobileNet non-quantized
+
+##### OpenCV preprocessing
+```bash
+$ ck run program:image-classification-tf-cpp \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,non-quantized
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.86 - (65) n01751748 sea snake
+0.05 - (58) n01737021 water snake
+0.04 - (34) n01665541 leatherback turtle, leatherback, leather...
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (57) n01735189 garter snake, grass snake
+---------------------------------------
+```
+
+##### Pillow preprocessing
+```bash
+$ ck run program:image-classification-tf-cpp \
+--dep_add_tags.images=preprocessed,using-pillow --dep_add_tags.weights=mobilenet,non-quantized
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.87 - (65) n01751748 sea snake
+0.06 - (34) n01665541 leatherback turtle, leatherback, leather...
+0.04 - (58) n01737021 water snake
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (57) n01735189 garter snake, grass snake
+---------------------------------------
+```
+
+##### TensorFlow preprocessing (**NOT APPLICABLE!**)
+```bash
+$ ck run program:image-classification-tf-cpp \
+--dep_add_tags.images=preprocessed,using-tf --dep_add_tags.weights=mobilenet,non-quantized
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.67 - (616) n03627232 knot
+0.08 - (584) n03476684 hair slide
+0.06 - (488) n02999410 chain
+0.02 - (792) n04208210 shovel
+0.02 - (549) n03291819 envelope
+---------------------------------------
+```
+
+#### MobileNet quantized
+
+##### OpenCV preprocessing
+```bash
+$ ck run program:image-classification-tf-cpp \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,quantized
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.91 - (65) n01751748 sea snake
+0.05 - (58) n01737021 water snake
+0.03 - (34) n01665541 leatherback turtle, leatherback, leather...
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.00 - (57) n01735189 garter snake, grass snake
+---------------------------------------
+```
+
+##### Pillow preprocessing
+```bash
+$ ck run program:image-classification-tf-cpp \
+--dep_add_tags.images=preprocessed,using-pillow --dep_add_tags.weights=mobilenet,quantized
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.92 - (65) n01751748 sea snake
+0.04 - (58) n01737021 water snake
+0.02 - (34) n01665541 leatherback turtle, leatherback, leather...
+0.00 - (390) n02526121 eel
+0.00 - (54) n01729322 hognose snake, puff adder, sand viper
+---------------------------------------
+```
+
+##### TensorFlow preprocessing (**NOT APPLICABLE!**)
+```bash
+$ ck run program:image-classification-tf-cpp \
+--dep_add_tags.images=preprocessed,using-tf --dep_add_tags.weights=mobilenet,quantized
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.55 - (616) n03627232 knot
+0.39 - (488) n02999410 chain
+0.01 - (71) n01770393 scorpion
+0.01 - (310) n02219486 ant, emmet, pismire
+0.01 - (695) n03874599 padlock
+---------------------------------------
+```
+
+<a name="benchmarking"></a>
+## Benchmarking instructions
+
+### Benchmark the performance
+
+**NB:** When using the batch count of **N**, the program classifies **N** images, but
+the slow first run is not taken into account when computing the average
+classification time e.g.:
+```bash
+$ ck benchmark program:image-classification-tf-cpp \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2
+...
+Processing batches...
+
+Batch 1 of 2
+Batch loaded in 0.00341696 s
+Batch classified in 0.355268 s
+
+Batch 2 of 2
+Batch loaded in 0.00335902 s
+Batch classified in 0.0108837 s
+...
+Summary:
+-------------------------------
+Graph loaded in 0.053440s
+All images loaded in 0.006776s
+All images classified in 0.366151s
+Average classification time: 0.010884s
+Accuracy top 1: 0.5 (1 of 2)
+Accuracy top 5: 1.0 (2 of 2)
+--------------------------------
+```
+
+#### ResNet
+```
+$ ck benchmark program:image-classification-tf-cpp --speed \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tf-cpp-performance-using-opencv-resnet \
+--tags=mlperf,image-classification,tf-cpp,performance,using-opencv,resnet \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=resnet
+```
+
+#### MobileNet non-quantized
+```
+$ ck benchmark program:image-classification-tf-cpp --speed \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tf-cpp-performance-using-opencv-mobilenet-non-quantized \
+--tags=mlperf,image-classification,tf-cpp,performance,using-opencv,mobilenet,non-quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,non-quantized
+```
+
+#### MobileNet quantized
+```
+$ ck benchmark program:image-classification-tf-cpp --speed \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tf-cpp-performance-using-opencv-mobilenet-quantized \
+--tags=mlperf,image-classification,tf-cpp,performance,using-opencv,mobilenet,quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,quantized
+```
+
+
+### Benchmark the accuracy
+
+**NB:** For the `imagenet-2012-val-min` dataset, change `--env.CK_BATCH_COUNT=50000`
+to `--env.CK_BATCH_COUNT=500` (or drop completely to test on a single image as if
+with `--env.CK_BATCH_COUNT=1`).
+
+#### ResNet
+```bash
+$ ck benchmark program:image-classification-tf-cpp --speed \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tf-cpp-accuracy-using-opencv-resnet \
+--tags=mlperf,image-classification,tf-cpp,accuracy,using-opencv,resnet \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=resnet
+```
+
+#### MobileNet non-quantized
+```bash
+$ ck benchmark program:image-classification-tf-cpp --speed \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tf-cpp-accuracy-using-opencv-mobilenet-non-quantized \
+--tags=mlperf,image-classification,tf-cpp,accuracy,using-opencv,mobilenet,non-quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,non-quantized
+```
+
+#### MobileNet quantized
+```bash
+$ ck benchmark program:image-classification-tf-cpp --speed \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tf-cpp-accuracy-using-opencv-mobilenet-quantized \
+--tags=mlperf,image-classification,tf-cpp,accuracy,using-opencv,mobilenet,quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,quantized
+```
+
+
+<a name="accuracy"></a>
+## Reference accuracy
+
+### Example: universal OpenCV preprocessing (default), MobileNet non-quantized
+```bash
+$ ck benchmark program:image-classification-tf-cpp \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tf-cpp-accuracy-using-opencv-mobilenet-non-quantized \
+--tags=mlperf,image-classification,tf-cpp,accuracy,using-opencv,mobilenet,non-quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,non-quantized
+```
+
+### ImageNet validation dataset (50,000 images)
+
+```bash
+$ ck benchmark program:image-classification-tf-cpp \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--dep_add_tags.images=preprocessed,using-opencv \
+--record_uoa=mlperf-image-classification-tf-cpp-accuracy \
+--tags=mlperf,image-classification,tf-cpp,accuracy
+```
+
+| Model                   | Metric | Pillow  | OpenCV universal | OpenCV for MobileNet | OpenCV for ResNet | TensorFlow |
+|-|-|-|-|-|-|-|
+| ResNet                  |  Top1  | 0.76170 | 0.76422          | N/A                  | 0.76456           | 0.76522    |
+|                         |  Top5  | 0.92866 | 0.93074          | N/A                  | 0.93016           | 0.93066    |
+| MobileNet non-quantized |  Top1  | 0.71226 | 0.71676          | 0.71676              | N/A               | N/A        |
+|                         |  Top5  | 0.89834 | 0.90118          | 0.90118              | N/A               | N/A        |
+| MobileNet quantized     |  Top1  | 0.70348 | 0.70700          | 0.70694              | N/A               | N/A        |
+|                         |  Top5  | 0.89376 | 0.89594          | 0.89594              | N/A               | N/A        |
+
+
+<a name="further-info"></a>
+## Further information
+
+### Using Collective Knowledge
+See the [common instructions](../README.md) for information on how to use Collective Knowledge
+to learn about [the anatomy of a benchmark](../README.md#anatomy), or
+to inspect and visualize [experimental results](../README.md#results).
+
+### Using the client program
+See [`ck-tensorflow:program:image-classification-tf-cpp`](https://github.com/ctuning/ck-tensorflow/tree/master/program/image-classification-tf-cpp) for more details about the client program.
diff --git a/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/tf-py/README.md b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/tf-py/README.md
new file mode 100644
index 0000000000..cfc5c7632b
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/tf-py/README.md
@@ -0,0 +1,181 @@
+# Image Classification via TensorFlow (Python)
+
+1. [Installation instructions](#installation)
+2. [Benchmarking instructions](#benchmarking)
+3. [Reference accuracy](#accuracy)
+4. [Further information](#further-info)
+
+
+<a name="installation"></a>
+## Installation instructions
+
+Please follow the [common installation instructions](../README.md#installation) first.
+
+### Install TensorFlow (Python)
+
+Install TensorFlow (Python) from an `x86_64` binary package:
+```
+$ ck install package:lib-tensorflow-1.13.1-cpu
+```
+or from source:
+```
+$ ck install package:lib-tensorflow-1.13.1-src-cpu
+```
+
+### Install the MobileNet model for TensorFlow (Python)
+
+To select interactively from one of the non-quantized and quantized MobileNets-v1-1.0-224 models:
+```
+$ ck install package --tags=model,tf,mlperf,mobilenet
+```
+
+To install the [non-quantized model](https://zenodo.org/record/2269307/files/mobilenet_v1_1.0_224.tgz) directly:
+```
+$ ck install package --tags=model,tf,mlperf,mobilenet,non-quantized
+```
+
+To install the [quantized model](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz) directly:
+```
+$ ck install package --tags=model,tf,mlperf,mobilenet,quantized
+```
+
+#### Bonus
+
+##### Install other MobileNets models
+You can also install any other MobileNets model compatible with TensorFlow (Python) as follows:
+```
+$ ck install package --tags=tensorflowmodel,mobilenet --no_tags=mobilenet-all
+```
+**NB:** This excludes "uber" packages which can be used to install all models in the sets `v1-2018-02-22` (16 models), `v1[-2018-06-14]` (16 models) and `v2` (22 models) in one go:
+```
+$ ck search package --tags=tensorflowmodel,mobilenet-all
+ck-tensorflow:package:tensorflowmodel-mobilenet-v1-2018_02_22
+ck-tensorflow:package:tensorflowmodel-mobilenet-v2
+ck-tensorflow:package:tensorflowmodel-mobilenet-v1
+```
+
+### Run the TensorFlow (Python) Image Classification client
+
+Run the client:
+
+- with the non-quantized model:
+```
+$ ck run program:image-classification-tf-py
+...
+*** Dependency 4 = weights (TensorFlow-Python model and weights):
+...
+    Resolved. CK environment UID = f934f3a3faaf4d73 (version 1_1.0_224_2018_02_22)
+...
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.82 - (65) n01751748 sea snake
+0.10 - (58) n01737021 water snake
+0.04 - (34) n01665541 leatherback turtle, leatherback, leather...
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (57) n01735189 garter snake, grass snake
+---------------------------------------
+
+Summary:
+-------------------------------
+Graph loaded in 0.855126s
+All images loaded in 0.001089s
+All images classified in 0.116698s
+Average classification time: 0.116698s
+Accuracy top 1: 1.0 (1 of 1)
+Accuracy top 5: 1.0 (1 of 1)
+--------------------------------
+```
+
+- with the quantized model:
+```
+$ ck run program:image-classification-tf-py
+...
+*** Dependency 4 = weights (TensorFlow-Python model and weights):
+...
+    Resolved. CK environment UID = b18ad885d440dc77 (version 1_1.0_224_quant_2018_08_02)
+...
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.16 - (60) n01740131 night snake, Hypsiglena torquata
+0.10 - (600) n03532672 hook, claw
+0.07 - (58) n01737021 water snake
+0.05 - (398) n02666196 abacus
+0.05 - (79) n01784675 centipede
+---------------------------------------
+
+Summary:
+-------------------------------
+Graph loaded in 1.066851s
+All images loaded in 0.001507s
+All images classified in 0.178281s
+Average classification time: 0.178281s
+Accuracy top 1: 0.0 (0 of 1)
+Accuracy top 5: 0.0 (0 of 1)
+--------------------------------
+```
+
+<a name="benchmarking"></a>
+## Benchmarking instructions
+
+### Benchmark the performance
+```
+$ ck benchmark program:image-classification-tf-py \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 \
+--record --record_repo=local --record_uoa=mlperf-image-classification-mobilenet-tf-py-performance \
+--tags=mlperf,image-classification,mobilenet,tf-py,performance \
+--skip_print_timers --skip_stat_analysis --process_multi_keys
+```
+
+**NB:** When using the batch count of **N**, the program classifies **N** images, but
+the slow first run is not taken into account when computing the average
+classification time e.g.:
+```bash
+$ ck benchmark program:image-classification-tf-py \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2
+...
+Weights loaded in 0.293122s
+
+Batch 1 of 2
+Batch loaded in 0.001036s
+Batch classified in 0.121501s
+
+Batch 2 of 2
+Batch loaded in 0.001257s
+Batch classified in 0.013995s
+...
+Summary:
+-------------------------------
+Graph loaded in 1.115745s
+All images loaded in 0.002293s
+All images classified in 0.013995s
+Average classification time: 0.013995s
+Accuracy top 1: 0.5 (1 of 2)
+Accuracy top 5: 1.0 (2 of 2)
+--------------------------------
+```
+
+### Benchmark the accuracy
+```bash
+$ ck benchmark program:image-classification-tf-py \
+--repetitions=1  --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--record --record_repo=local --record_uoa=mlperf-image-classification-mobilenet-tf-py-accuracy \
+--tags=mlperf,image-classification,mobilenet,tf-py,accuracy \
+--skip_print_timers --skip_stat_analysis --process_multi_keys
+```
+**NB:** For the `imagenet-2012-val-min` dataset, change `--env.CK_BATCH_COUNT=50000`
+to `--env.CK_BATCH_COUNT=500` (or drop completely to test on a single image as if
+with `--env.CK_BATCH_COUNT=1`).
+
+
+<a name="accuracy"></a>
+## Reference accuracy
+**TODO**
+
+<a name="further-info"></a>
+## Further information
+
+### Using Collective Knowledge
+See the [common instructions](../README.md) for information on how to use Collective Knowledge
+to learn about [the anatomy of a benchmark](../README.md#anatomy), or
+to inspect and visualize [experimental results](../README.md#results).
+
+### Using the client program
+See [`ck-tensorflow:program:image-classification-tf-py`](https://github.com/ctuning/ck-tensorflow/tree/master/program/image-classification-tf-py) for more details about the client program.
diff --git a/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/tflite/README.md b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/tflite/README.md
new file mode 100644
index 0000000000..f6933dd9c8
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/classification/tflite/README.md
@@ -0,0 +1,320 @@
+# Image Classification via TensorFlow Lite (TFLite)
+
+1. [Installation instructions](#installation)
+2. [Benchmarking instructions](#benchmarking)
+3. [Reference accuracy](#accuracy)
+4. [Further information](#further-info)
+
+
+<a name="installation"></a>
+## Installation instructions
+
+Please follow the [common installation instructions](../README.md#installation) first.
+
+### Install TFLite
+
+Install TFLite v1.13.1 from source:
+```
+$ ck install package --tags=lib,tflite,v1.13.1,vsrc [--target_os=android23-arm64]
+```
+**NB:** TFLite v1.14.0 has [many known issues on Arm platforms](https://github.com/ctuning/ck-tensorflow/blob/master/package/lib-tflite-1.14.0-src-static/README.md), and does not work for Android yet.
+
+You can also install TFLite v0.1.7 from a prebuilt binary package for your target e.g.:
+```
+$ ck list package:lib-tflite-prebuilt*
+lib-tflite-prebuilt-0.1.7-linux-aarch64
+lib-tflite-prebuilt-0.1.7-linux-x64
+lib-tflite-prebuilt-0.1.7-android-arm64
+$ ck install package:lib-tflite-prebuilt-0.1.7-android-arm64 [--target_os=android23-arm64]
+```
+
+**NB:** Please [let us know](info@dividiti.com) if you would like us to create
+prebuilt packages for TFLite 1.13.1.
+
+
+### Install the models for TFLite
+
+#### ResNet
+
+To install the ResNet50-v1.5 model:
+```bash
+$ ck install package --tags=model,tflite,mlperf,resnet
+
+More than one package or version found:
+
+ 0) model-tflite-mlperf-resnet-no-argmax  Version 1.5  (afb43014ef38f646)
+ 1) model-tflite-mlperf-resnet  Version 1.5  (d60d4e9a84151271)
+ 2) model-tflite-convert-from-tf (35e84375ac48dcb1), Variations: resnet
+
+Please select the package to install [ hit return for "0" ]:
+```
+
+Option 0 will download a TFLite model preconverted from the TF model.  During
+the conversion, the `ArgMax` operator causing an
+[issue](https://github.com/ARM-software/armnn/issues/150) with ArmNN v19.02
+and v19.05 was excluded.
+
+Option 1 will download a TFLite model preconverted from the TF model, but
+including the `ArgMax` operator. This variant can be used with ArmNN once
+the above issue is resolved.
+
+Option 2 will download the TF model and convert it to TFLite, while excluding
+the `ArgMax` operator.
+
+
+#### MobileNet
+
+To select interactively from one of the non-quantized and quantized
+MobileNets-v1-1.0-224 models:
+```
+$ ck install package --tags=model,tflite,mlperf,mobilenet
+
+More than one package or version found:
+
+ 0) model-tf-mlperf-mobilenet  Version 1_1.0_224_2018_08_02  (05c4dcbbbf872ecf)
+ 1) model-tf-mlperf-mobilenet-quantized  Version 1_1.0_224_quant_2018_08_02  (3013bdc96184bf3b)
+ 2) model-tflite-convert-from-tf (35e84375ac48dcb1), Variations: mobilenet
+
+Please select the package to install [ hit return for "0" ]:
+```
+Options 0 and 1 will download the official non-quantized and quantized models.
+Option 2 will download the official TF model and convert it to TFLite.
+
+**NB:** Option 2 is only viable on x86 platforms, as it depends on using a
+prebuilt version of TF.  While this constraint could be relaxed to use a
+version of TF built from source, building TF from source takes a long time on
+Arm platforms (as well as [not being officially
+supported](https://github.com/tensorflow/tensorflow/issues/25607#issuecomment-466583730)).
+
+##### MobileNet non-quantized
+
+To install the non-quantized MobileNet model from:
+- [zenodo.org](https://zenodo.org/record/2269307/files/mobilenet_v1_1.0_224.tgz) (default):
+```bash
+$ ck install package --tags=model,tflite,mlperf,mobilenet,non-quantized,from-zenodo
+```
+- [tensorflow.org](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz)
+```bash
+$ ck install package --tags=model,tflite,mlperf,mobilenet,non-quantized,from-google
+```
+
+##### MobileNet quantized
+
+To install the quantized MobileNet model from:
+- [zenodo.org](https://zenodo.org/record/2269307/files/mobilenet_v1_1.0_224_quant.tgz) (default):
+```bash
+$ ck install package --tags=model,tflite,mlperf,mobilenet,quantized,from-zenodo
+```
+- [tensorflow.org](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz)
+```bash
+$ ck install package --tags=model,tflite,mlperf,mobilenet,quantized,from-google
+```
+
+#### Bonus: other MobileNets models
+You can also install any other MobileNets model compatible with TFLite as follows:
+```
+$ ck install package --tags=tensorflowmodel,mobilenet,tflite
+```
+
+### Compile the TFLite Image Classification client
+
+Compile the client. (For Android, append e.g. `--target_os=android23-arm64` to the command.)
+
+```bash
+$ ck compile program:image-classification-tflite --speed
+```
+
+### Run the TFLite Image Classification client
+
+Run the client. (For Android, connect an Android device to your host machine via USB and append e.g. `--target_os=android23-arm64` to the command).
+
+If you have preprocessed input data using more than one method (OpenCV, Pillow or TensorFlow), you need to select the particular preprocessed dataset. Note that the TensorFlow preprocessing method is not applicable to the MobileNet models.
+
+#### ResNet
+
+##### OpenCV preprocessing
+```bash
+$ ck run program:image-classification-tflite \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=resnet
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.95 - (65) n01751748 sea snake
+0.01 - (58) n01737021 water snake
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (66) n01753488 horned viper, cerastes, sand viper, horn...
+0.00 - (60) n01740131 night snake, Hypsiglena torquata
+---------------------------------------
+```
+
+#### MobileNet non-quantized
+
+##### OpenCV preprocessing
+```bash
+$ ck run program:image-classification-tflite \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,non-quantized
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.86 - (65) n01751748 sea snake
+0.05 - (58) n01737021 water snake
+0.04 - (34) n01665541 leatherback turtle, leatherback, leather...
+0.01 - (54) n01729322 hognose snake, puff adder, sand viper
+0.01 - (57) n01735189 garter snake, grass snake
+---------------------------------------
+```
+
+#### MobileNet quantized
+
+##### OpenCV preprocessing
+```bash
+$ ck run program:image-classification-tflite \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,quantized
+...
+---------------------------------------
+ILSVRC2012_val_00000001.JPEG - (65) n01751748 sea snake
+0.88 - (65) n01751748 sea snake
+0.07 - (34) n01665541 leatherback turtle, leatherback, leather...
+0.03 - (58) n01737021 water snake
+0.00 - (54) n01729322 hognose snake, puff adder, sand viper
+0.00 - (0) n01440764 tench, Tinca tinca
+---------------------------------------
+```
+**NB:** The prediction from `tflite` differs from that from `tf-cpp`.
+
+<a name="benchmarking"></a>
+## Benchmarking instructions
+
+### Benchmark the performance
+
+**NB:** When using the batch count of **N**, the program classifies **N** images, but
+the slow first run is not taken into account when computing the average
+classification time e.g.:
+```
+$ ck benchmark program:image-classification-tflite \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2
+...
+Processing batches...
+
+Batch 1 of 2
+
+Batch loaded in 0.00802251 s
+Batch classified in 0.16831 s
+
+Batch 2 of 2
+
+Batch loaded in 0.00776105 s
+Batch classified in 0.0762354 s
+...
+Summary:
+-------------------------------
+Graph loaded in 0.000663s
+All images loaded in 0.015784s
+All images classified in 0.244545s
+Average classification time: 0.076235s
+Accuracy top 1: 0.5 (1 of 2)
+Accuracy top 5: 1.0 (2 of 2)
+--------------------------------
+```
+
+#### ResNet
+```
+$ ck benchmark program:image-classification-tflite --speed \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tflite-performance-using-opencv-resnet \
+--tags=mlperf,image-classification,tflite,performance,using-opencv,resnet \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=resnet
+```
+
+#### MobileNet non-quantized
+```
+$ ck benchmark program:image-classification-tflite --speed \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tflite-performance-using-opencv-mobilenet-non-quantized \
+--tags=mlperf,image-classification,tflite,performance,using-opencv,mobilenet,non-quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,non-quantized
+```
+
+#### MobileNet quantized
+```
+$ ck benchmark program:image-classification-tflite --speed \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tflite-performance-using-opencv-mobilenet-quantized \
+--tags=mlperf,image-classification,tflite,performance,using-opencv,mobilenet,quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,quantized
+```
+
+### Benchmark the accuracy
+
+**NB:** For the `imagenet-2012-val-min` dataset, change `--env.CK_BATCH_COUNT=50000`
+to `--env.CK_BATCH_COUNT=500` (or drop completely to test on a single image as if
+with `--env.CK_BATCH_COUNT=1`).
+
+#### ResNet
+```bash
+$ ck benchmark program:image-classification-tflite --speed \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tflite-accuracy-using-opencv-resnet \
+--tags=mlperf,image-classification,tflite,accuracy,using-opencv,resnet \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=resnet
+```
+
+#### MobileNet non-quantized
+```bash
+$ ck benchmark program:image-classification-tflite --speed \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tflite-accuracy-using-opencv-mobilenet-non-quantized \
+--tags=mlperf,image-classification,tflite,accuracy,using-opencv,mobilenet,non-quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,non-quantized
+```
+
+#### MobileNet quantized
+```bash
+$ ck benchmark program:image-classification-tflite --speed \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tflite-accuracy-using-opencv-mobilenet-quantized \
+--tags=mlperf,image-classification,tflite,accuracy,using-opencv,mobilenet,quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,quantized
+```
+
+
+<a name="accuracy"></a>
+## Reference accuracy
+
+### Example: OpenCV preprocessing (default), MobileNet non-quantized
+```bash
+$ ck benchmark program:image-classification-tflite \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=50000 \
+--skip_print_timers --skip_stat_analysis --process_multi_keys --record --record_repo=local \
+--record_uoa=mlperf-image-classification-tflite-accuracy-using-opencv-mobilenet-non-quantized \
+--tags=mlperf,image-classification,tflite,accuracy,using-opencv,mobilenet,non-quantized \
+--dep_add_tags.images=preprocessed,using-opencv --dep_add_tags.weights=mobilenet,non-quantized
+```
+
+### ImageNet validation dataset (50,000 images)
+
+| Model                   | Metric | Pillow  | OpenCV universal | OpenCV for MobileNet                                         | OpenCV for ResNet | TensorFlow |
+|-|-|-|-|-|-|-|
+| ResNet                  |  Top1  | 0.76170 | 0.76422          | N/A                                                          | 0.76456           | 0.76522    |
+|                         |  Top5  | 0.92866 | 0.93074          | N/A                                                          | 0.93016           | 0.93066    |
+| MobileNet non-quantized |  Top1  | 0.71226 | 0.71676          | 0.71676                                                      | N/A               | N/A        |
+|                         |  Top5  | 0.89834 | 0.90118          | 0.90118                                                      | N/A               | N/A        |
+| MobileNet quantized     |  Top1  | 0.70502 | 0.70762          | N/A ([bug?](https://github.com/ctuning/ck-mlperf/issues/40)) | N/A               | N/A        |
+|                         |  Top5  | 0.89118 | 0.89266          | N/A ([bug?](https://github.com/ctuning/ck-mlperf/issues/40)) | N/A               | N/A        |
+
+
+<a name="further-info"></a>
+## Further information
+### Using Collective Knowledge
+See the [common instructions](../README.md) for information on how to use Collective Knowledge
+to learn about [the anatomy of a benchmark](../README.md#anatomy), or
+to inspect and visualize [experimental results](../README.md#results).
+
+### Using the client program
+See [`ck-tensorflow:program:image-classification-tflite`](https://github.com/ctuning/ck-tensorflow/tree/master/program/image-classification-tflite) for more details about the client program.
diff --git a/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/detection/README.md b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/detection/README.md
new file mode 100644
index 0000000000..7a7cf33219
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/detection/README.md
@@ -0,0 +1,177 @@
+[![compatibility](https://github.com/ctuning/ck-guide-images/blob/master/ck-compatible.svg)](https://github.com/ctuning/ck)
+[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
+
+# MLPerf Inference - Object Detection
+
+MLPerf Inference v0.5 uses SSD-MobileNet-v1-1.0-224 (called SSD-MobileNet in what follows) and SSD-ResNet34 (called SSD-ResNet in what follows).
+
+# Table of contents
+
+1. [Installation](#installation)
+    - [Install prerequisites](#installation-debian) (Debian-specific)
+    - [Install CK workflows](#installation-workflows) (universal)
+1. [Benchmarking](#benchmarking)
+    - [via TensorFlow (Python)](tf-py/README.md)
+    - [via TensorFlow Lite](tflite/README.md)
+
+<a name="installation"></a>
+# Installation
+
+**NB:** If you would like to get a feel of CK workflows, you can skip
+installation instructions and try [benchmarking](#benchmarking)
+instructions on available Docker images:
+- TensorFlow Lite:
+    - [Debian 9](https://github.com/ctuning/ck-mlperf/tree/master/docker/object-detection-tflite.debian-9)
+- Arm NN:
+    - [Debian 9](https://github.com/ARM-software/armnn-mlperf/tree/master/docker/object-detection-armnn-tflite.debian-9)
+
+Even if you would like to run CK workflows natively (e.g. on an Arm-based
+development board or Android phone), you may wish to have a quick look into the
+latest Dockerfile's to check for latest updates e.g. system-specific
+dependencies.
+
+<a name="installation-debian"></a>
+## Debian
+
+- Common tools and libraries.
+- [Python](https://www.python.org/), [pip](https://pypi.org/project/pip/), [SciPy](https://www.scipy.org/), [Collective Knowledge](https://cknowledge.org) (CK).
+- (Optional) [Android SDK](https://developer.android.com/studio/), [Android NDK](https://developer.android.com/ndk/).
+
+### Install common tools and libraries
+```bash
+$ sudo apt install git wget libz-dev curl cmake
+$ sudo apt install gcc g++ autoconf autogen libtool
+```
+
+### Install Python 3 and the latest pip
+```bash
+$ sudo apt install python3 python3-pip
+$ sudo python3 -m pip install --upgrade pip
+```
+**NB:** Python 3 is needed for the [COCO API](https://github.com/cocodataset/cocoapi)
+used to evaluate object detection accuracy on the [COCO dataset](http://cocodataset.org).
+
+**NB:** Care must be taken not to mix Python 3 and Python 2 packages.
+If your system uses Python 2 by default, we recommend you prefix
+all CK commands, for example, with `CK_PYTHON=python3` for CK to run under Python 3:
+```
+$ python --version
+Python 2.7.13
+$ ck python_version
+2.7.13 (default, Sep 26 2018, 18:42:22)
+[GCC 6.3.0 20170516]
+$ CK_PYTHON=python3 ck python_version
+3.5.3 (default, Sep 27 2018, 17:25:39)
+[GCC 6.3.0 20170516]
+```
+Similarly, if you use multiple Python 3 versions (e.g. 3.5 and 3.6), we recommend
+you stick to one of them for consistency:
+```
+$ CK_PYTHON=python3.5 ck python_version
+3.5.2 (default, Nov 12 2018, 13:43:14)
+[GCC 5.4.0 20160609]
+$ CK_PYTHON=python3.6 ck python_version
+3.6.7 (default, Oct 25 2018, 09:16:13)
+[GCC 5.4.0 20160609]
+```
+
+### Install required Python 3 packages
+Choose one of the following installation options:
+1. system-wide via pip;
+1. user-space via pip;
+1. user-space via CK.
+
+With the first two options, packages get installed via pip and get registered
+with CK later (typically, on the first run of a program).
+
+With the last option, packages also get installed via pip but get registered
+with CK at the same time (so there is less chance of mixing things up).
+
+#### Option 1: system-wide installation via pip (under `/usr`)
+```bash
+$ sudo python3 -m pip install cython scipy==1.2.1 matplotlib pillow ck
+```
+#### Option 2: user-space installation via pip (under `$HOME`)
+```bash
+$ python3 -m pip install cython scipy==1.2.1 matplotlib pillow ck --user
+```
+#### Option 3: user-space installation via CK (under `$HOME` and `$CK_TOOLS`)
+Install CK via pip (or [from GitHub](https://github.com/ctuning/ck#installation)):
+```bash
+$ python3 -m pip install ck --user
+$ ck version
+V1.9.7
+```
+Install and register Python packages with CK:
+```bash
+$ ck pull repo:ck-env
+$ ck detect soft:compiler.python --full_path=`which python3`
+$ ck install package --tags=lib,python-package,numpy
+$ ck install package --tags=lib,python-package,scipy --force_version=1.2.1
+$ ck install package --tags=lib,python-package,matplotlib
+$ ck install package --tags=lib,python-package,pillow
+$ ck install package --tags=lib,python-package,cython
+```
+
+If the above dependencies have been installed on a clean system, you should be
+able to inspect the registered CK environments e.g. as follows:
+```
+$ ck show env --tags=python-package
+Env UID:         Target OS: Bits: Name:                     Version: Tags:
+
+4e82bab01c8ee3b7   linux-64    64 Python NumPy library      1.16.2   64bits,host-os-linux-64,lib,needs-python,needs-python-3.5.2,numpy,python-package,target-os-linux-64,v1,v1.16,v1.16.2,vmaster
+66642698751a2fcf   linux-64    64 Python SciPy library      1.2.1    64bits,host-os-linux-64,lib,needs-python,needs-python-3.5.2,python-package,scipy,target-os-linux-64,v1,v1.2,v1.2.1,vmaster
+78e8a1bfb4eb052c   linux-64    64 Python Matplotlib library 3.0.3    64bits,host-os-linux-64,lib,matplotlib,needs-python,needs-python-3.5.2,python-package,target-os-linux-64,v3,v3.0,v3.0.3,vmaster
+a6f9c25377710f6f   linux-64    64 Python Pillow library     6.0.0    64bits,PIL,host-os-linux-64,lib,needs-python,needs-python-3.5.2,pillow,python-package,target-os-linux-64,v6,v6.0,v6.0.0,vmaster
+498dbe464d051b44   linux-64    64 Python Cython library     0.29.9   64bits,cython,host-os-linux-64,lib,needs-python,needs-python-3.5.2,python-package,target-os-linux-64,v0,v0.29,v0.29.9,vmaster
+
+$ ck cat env --tags=python-package | grep PYTHONPATH
+export PYTHONPATH=/home/anton/CK_TOOLS/lib-python-numpy-compiler.python-3.5.2-linux-64/build:${PYTHONPATH}
+export PYTHONPATH=/home/anton/CK_TOOLS/lib-python-scipy-compiler.python-3.5.2-linux-64/build:${PYTHONPATH}
+export PYTHONPATH=/home/anton/CK_TOOLS/lib-python-matplotlib-compiler.python-3.5.2-linux-64/build:${PYTHONPATH}
+export PYTHONPATH=/home/anton/CK_TOOLS/lib-python-pillow-compiler.python-3.5.2-linux-64/build:${PYTHONPATH}
+export PYTHONPATH=/home/anton/CK_TOOLS/lib-python-cython-compiler.python-3.5.2-linux-64/build:${PYTHONPATH}
+```
+
+<a name="installation-workflows"></a>
+## Install CK workflows
+
+### Pull CK repositories
+```bash
+$ ck pull repo:ck-mlperf
+```
+**NB:** Transitive dependencies include [repo:ck-tensorflow](https://github.com/ctuning/ck-tensorflow).
+
+To update all CK repositories (e.g. after a bug fix):
+```
+$ ck pull repo --all
+```
+
+### Install the COCO 2017 validation dataset (5,000 images)
+```bash
+$ ck install package --tags=object-detection,dataset,coco.2017,val,original,full
+```
+**NB:** COCO dataset descriptions are in [repo:ck-env](https://github.com/ctuning/ck-env).
+
+**NB:** If you have previously installed the COCO 2017 validation dataset via CK to e.g. `$HOME/coco/`, you can simply detect it as follows:
+```bash
+$ ck detect soft:dataset.coco.2017.val --full_path=$HOME/coco/val2017/000000000139.jpg
+```
+(CK also places annotations under `annotations/val2017/`.)
+
+### Preprocess the COCO 2017 validation dataset (first 50 images)
+```bash
+$ ck install package --tags=object-detection,dataset,coco.2017,preprocessed,first.50
+```
+
+### Preprocess the COCO 2017 validation dataset (all 5,000 images)
+```bash
+$ ck install package --tags=object-detection,dataset,coco.2017,preprocessed,full
+```
+
+<a name="benchmarking"></a>
+## Benchmarking
+
+You can benchmark SSD-MobileNet using one of the available options:
+- [via TensorFlow (Python)](tf-py/README.md)
+- [via TensorFlow Lite](tflite/README.md)
diff --git a/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/detection/tf-py/README.md b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/detection/tf-py/README.md
new file mode 100644
index 0000000000..1dd5324ce0
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/detection/tf-py/README.md
@@ -0,0 +1,252 @@
+# Object Detection via TensorFlow (Python)
+
+1. [Installation instructions](#installation)
+2. [Benchmarking instructions](#benchmarking)
+3. [Reference accuracy](#accuracy)
+4. [Further information](#further-info)
+
+
+<a name="installation"></a>
+## Installation instructions
+
+Please follow the common [installation instructions](../README.md#installation) first.
+
+### Install additional Python packages in user-space
+```
+$ python -m pip install gast --user
+$ python -m pip install astor --user
+$ python -m pip install termcolor --user
+$ python -m pip install tensorflow-estimator==1.13.0 --user
+$ python -m pip install keras_applications==1.0.4 --no-deps --user
+$ python -m pip install keras_preprocessing==1.0.2 --no-deps --user
+```
+
+### Install TensorFlow (Python)
+
+Install TensorFlow (Python) from an `x86_64` binary package:
+```bash
+$ ck install package:lib-tensorflow-1.13.1-cpu
+```
+or from source:
+```bash
+$ ck install package:lib-tensorflow-1.13.1-src-cpu
+```
+
+### Install the SSD-MobileNet model
+
+To select interactively from one of the non-quantized and quantized SSD-MobileNet models:
+```
+$ ck install package --tags=model,tf,object-detection,mlperf,ssd-mobilenet
+```
+
+#### Install the [non-quantized model](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz) directly
+```bash
+$ ck install package --tags=model,tf,object-detection,mlperf,ssd-mobilenet,non-quantized
+```
+
+#### Install the quantized finetuned model (courtesy of [Habana](https://habana.ai/)) directly
+```bash
+$ ck install package --tags=model,tf,object-detection,mlperf,ssd-mobilenet,quantized,finetuned
+```
+
+### Run the TensorFlow (Python) Object Detection client on 50 images
+```bash
+$ ck run program:object-detection-tf-py --env.CK_BATCH_COUNT=50
+...
+********************************************************************************
+* Process results
+********************************************************************************
+
+Convert results to coco ...
+
+Evaluate metrics as coco ...
+loading annotations into memory...
+Done (t=0.55s)
+creating index...
+index created!
+Loading and preparing results...
+DONE (t=0.00s)
+creating index...
+index created!
+Running per image evaluation...
+Evaluate annotation type *bbox*
+DONE (t=0.12s).
+Accumulating evaluation results...
+DONE (t=0.22s).
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.315
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.439
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.331
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.064
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.184
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.689
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.296
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.322
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.323
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.066
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.187
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.711
+
+Summary:
+-------------------------------
+Graph loaded in 0.748657s
+All images loaded in 12.052568s
+All images detected in 1.251245s
+Average detection time: 0.025536s
+mAP: 0.3148934914889957
+Recall: 0.3225293342489256
+--------------------------------
+```
+
+<a name="benchmarking"></a>
+## Benchmarking instructions
+
+### Benchmark the performance
+```bash
+$ ck benchmark program:object-detection-tf-py \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 --env.CK_METRIC_TYPE=COCO \
+--record --record_repo=local --record_uoa=mlperf-object-detection-ssd-mobilenet-tf-py-performance \
+--tags=mlperf,object-detection,ssd-mobilenet,tf-py,performance \
+--skip_print_timers --skip_stat_analysis --process_multi_keys
+```
+
+**NB:** When using the batch count of **N**, the program runs object detection
+on **N** images, but the slow first run is not taken into account when
+computing the average detection time e.g.:
+```bash
+$ ck benchmark program:object-detection-tf-py \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2
+...
+Graph loaded in 0.7420s
+
+Detect image: 000000000139.jpg (1 of 2)
+Detected in 1.9351s
+
+Detect image: 000000000285.jpg (2 of 2)
+Detected in 0.0284s
+...
+Summary:
+-------------------------------
+Graph loaded in 0.741997s
+All images loaded in 0.604377s
+All images detected in 0.028387s
+Average detection time: 0.028387s
+mAP: 0.15445544554455443
+Recall: 0.15363636363636363
+--------------------------------
+```
+
+### Benchmark the accuracy
+```bash
+$ ck benchmark program:object-detection-tf-py \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=5000 --env.CK_METRIC_TYPE=COCO \
+--record --record_repo=local --record_uoa=mlperf-object-detection-ssd-mobilenet-tf-py-accuracy \
+--tags=mlperf,object-detection,ssd-mobilenet,tf-py,accuracy \
+--skip_print_timers --skip_stat_analysis --process_multi_keys
+```
+
+<a name="accuracy"></a>
+## Reference accuracy
+
+### SSD-MobileNet non-quantized
+```
+********************************************************************************
+* Process results
+********************************************************************************
+
+Convert results to coco ...
+
+Evaluate metrics as coco ...
+loading annotations into memory...
+Done (t=0.49s)
+creating index...
+index created!
+Loading and preparing results...
+DONE (t=0.07s)
+creating index...
+index created!
+Running per image evaluation...
+Evaluate annotation type *bbox*
+DONE (t=12.46s).
+Accumulating evaluation results...
+DONE (t=2.09s).
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.231
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.349
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.252
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.166
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.531
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.209
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.262
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.263
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.023
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.190
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.604
+
+Summary:
+-------------------------------
+Graph loaded in 0.753200s
+All images loaded in 1193.981655s
+All images detected in 123.461871s
+Average detection time: 0.024697s
+mAP: 0.23111107753357035
+Recall: 0.26304841188725403
+--------------------------------
+```
+
+### SSD-MobileNet quantized finetuned
+```
+********************************************************************************
+* Process results
+********************************************************************************
+
+Convert results to coco ...
+
+Evaluate metrics as coco ...
+loading annotations into memory...
+Done (t=0.48s)
+creating index...
+index created!
+Loading and preparing results...
+DONE (t=0.18s)
+creating index...
+index created!
+Running per image evaluation...
+Evaluate annotation type *bbox*
+DONE (t=12.74s).
+Accumulating evaluation results...
+DONE (t=2.13s).
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.236
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.361
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.259
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.166
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.546
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.212
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.268
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.269
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.025
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.191
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
+
+Summary:
+-------------------------------
+Graph loaded in 1.091316s
+All images loaded in 1150.996266s
+All images detected in 122.103661s
+Average detection time: 0.024426s
+mAP: 0.23594222525632427
+Recall: 0.26864982712779556
+--------------------------------
+```
+
+
+<a name="further-info"></a>
+## Further information
+
+### Using Collective Knowledge
+See the [common MobileNet instructions](../../../object_classification/mobilenets/README.md) for information on how to use Collective Knowledge
+to learn about [the anatomy of a benchmark](../../../object_clasification/mobilenets/README.md#the-anatomy-of-a-benchmark), or
+to inspect and visualize [experimental results](../../../object_clasification/mobilenets/README.md#inspecting-recorded-experimental-results).
+
+### Using the client program
+See [`ck-tensorflow:program:object-detection-tf-py`](https://github.com/ctuning/ck-tensorflow/tree/master/program/object-detection-tf-py) for more details about the client program.
diff --git a/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/detection/tflite/README.md b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/detection/tflite/README.md
new file mode 100644
index 0000000000..9ffff4c78d
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/optional_harness_ck/detection/tflite/README.md
@@ -0,0 +1,183 @@
+# Object Detection via TensorFlow Lite
+
+1. [Installation instructions](#installation)
+2. [Benchmarking instructions](#benchmarking)
+3. [Reference accuracy](#accuracy)
+4. [Further information](#further-info)
+
+
+<a name="installation"></a>
+## Installation instructions
+
+Please follow the common [installation instructions](../README.md#installation) first.
+
+### Install TensorFlow Lite (TFLite)
+
+Install TFLite from source:
+```
+$ ck install package:lib-tflite-1.13.1-src-static [--target_os=android23-arm64]
+```
+
+**NB:** Currently we have no TFLite 1.13.1 prebuilt packages.
+Please [let us know](info@dividiti.com) if you would like us to create some.
+
+
+### Install the SSD-MobileNet models for TFLite
+
+To select interactively from one of the non-quantized and quantized SSD-MobileNets-v1-1.0-224 models adopted for MLPerf Inference v0.5:
+```
+$ ck install package --tags=model,tflite,object-detection,mlperf,ssd-mobilenet
+```
+
+To install the non-quantized model directly:
+```
+$ ck install package --tags=model,tflite,object-detection,mlperf,ssd-mobilenet,non-quantized
+```
+**NB:** This TFLite model has been [converted](https://github.com/ctuning/ck-mlperf/blob/master/package/model-tflite-mlperf-ssd-mobilenet/README.md) from the [original TF model](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz), by adapting instructions in [Google's blog post](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193).
+
+### Compile the TFLite Object Detection client
+```
+$ ck compile program:object-detection-tflite [--target_os=android23-arm64]
+```
+
+### Run the TFLite Object Detection client on 50 images
+
+Run the client (if required, connect an Android device to your host machine via USB):
+```
+$ ck run program:object-detection-tflite --env.CK_BATCH_COUNT=50 \
+[--target_os=android23-arm64]
+...
+********************************************************************************
+* Process results
+********************************************************************************
+
+Convert results to coco ...
+
+Evaluate metrics as coco ...
+loading annotations into memory...
+Done (t=0.45s)
+creating index...
+index created!
+Loading and preparing results...
+DONE (t=0.00s)
+creating index...
+index created!
+Running per image evaluation...
+Evaluate annotation type *bbox*
+DONE (t=0.11s).
+Accumulating evaluation results...
+DONE (t=0.22s).
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.293
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.408
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.300
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.062
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.196
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.639
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.278
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.302
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.302
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.063
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.198
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.668
+
+Summary:
+-------------------------------
+Graph loaded in 0.000000s
+All images loaded in 0.000000s
+All images detected in 0.000000s
+Average detection time: 0.000000s
+mAP: 0.2931519685807111
+Recall: 0.3022676916450782
+--------------------------------
+```
+**NB:** We are working on resolving the difference in mAP between the TF and
+TFLite versions (31.5% vs. 29.3%), as well as resolving the timing issue (all
+zeros).
+
+<a name="benchmarking"></a>
+## Benchmarking instructions
+
+### Benchmark the performance
+```bash
+$ ck benchmark program:object-detection-tflite \
+--repetitions=10 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=2 --env.CK_METRIC_TYPE=COCO \
+--record --record_repo=local --record_uoa=mlperf-object-detection-ssd-mobilenet-tflite-performance \
+--tags=mlperf,object-detection,ssd-mobilenet,tflite,performance \
+--skip_print_timers --skip_stat_analysis --process_multi_keys
+```
+**NB:** When using the batch count of **N**, the program runs object detection
+on **N** images, but the slow first run is not taken into account when
+computing the average detection time.
+
+### Benchmark the accuracy
+```bash
+$ ck benchmark program:object-detection-tflite \
+--repetitions=1 --env.CK_BATCH_SIZE=1 --env.CK_BATCH_COUNT=5000 --env.CK_METRIC_TYPE=COCO \
+--record --record_repo=local --record_uoa=mlperf-object-detection-ssd-mobilenet-tflite-accuracy \
+--tags=mlperf,object-detection,ssd-mobilenet,tflite,accuracy \
+--skip_print_timers --skip_stat_analysis --process_multi_keys
+```
+
+<a name="accuracy"></a>
+## Reference accuracy
+
+### SSD-MobileNet non-quantized
+```
+********************************************************************************
+* Process results
+********************************************************************************
+
+Convert results to coco ...
+
+Evaluate metrics as coco ...
+loading annotations into memory...
+Done (t=0.50s)
+creating index...
+index created!
+Loading and preparing results...
+DONE (t=0.12s)
+creating index...
+index created!
+Running per image evaluation...
+Evaluate annotation type *bbox*
+DONE (t=12.81s).
+Accumulating evaluation results...
+DONE (t=2.10s).
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.223
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.341
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.247
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.015
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.160
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.515
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.203
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.255
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.255
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.182
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.593
+
+Summary:
+-------------------------------
+Graph loaded in 0.000000s
+All images loaded in 0.000000s
+All images detected in 0.000000s
+Average detection time: 0.000000s
+mAP: 0.22349680978666922
+Recall: 0.2550505369422975
+--------------------------------
+```
+**NB:** We are working on resolving the difference in mAP between the TF and
+TFLite versions (23.11% vs. 22.35%), as well as resolving the timing issue (all
+zeros). Both versions [use the same parameters](https://github.com/ctuning/ck-mlperf/blob/master/package/model-tflite-mlperf-ssd-mobilenet/README.md): `score_threshold=0.3`, etc.
+
+
+<a name="further-info"></a>
+## Further information
+
+### Using Collective Knowledge
+See the [common MobileNet instructions](../../../object_classification/mobilenets/README.md) for information on how to use Collective Knowledge
+to learn about [the anatomy of a benchmark](../../../object_clasification/mobilenets/README.md#the-anatomy-of-a-benchmark), or
+to inspect and visualize [experimental results](../../../object_clasification/mobilenets/README.md#inspecting-recorded-experimental-results).
+
+### Using the client program
+See [`ck-tensorflow:program:object-detection-tflite`](https://github.com/ctuning/ck-tensorflow/tree/master/program/object-detection-tflite) for more details about the client program.
diff --git a/retired_benchmarks/vision/classification_and_detection/python/__init__.py b/retired_benchmarks/vision/classification_and_detection/python/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend.py b/retired_benchmarks/vision/classification_and_detection/python/backend.py
new file mode 100755
index 0000000000..955eddb888
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend.py
@@ -0,0 +1,23 @@
+"""
+abstract backend class
+"""
+
+
+# pylint: disable=unused-argument,missing-docstring
+
+class Backend():
+    def __init__(self):
+        self.inputs = []
+        self.outputs = []
+
+    def version(self):
+        raise NotImplementedError("Backend:version")
+
+    def name(self):
+        raise NotImplementedError("Backend:name")
+
+    def load(self, model_path, inputs=None, outputs=None):
+        raise NotImplementedError("Backend:load")
+
+    def predict(self, feed):
+        raise NotImplementedError("Backend:predict")
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend_null.py b/retired_benchmarks/vision/classification_and_detection/python/backend_null.py
new file mode 100755
index 0000000000..ed5817070f
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend_null.py
@@ -0,0 +1,34 @@
+"""
+null backend
+"""
+
+# pylint: disable=unused-argument,missing-docstring,useless-super-delegation
+
+import time
+
+import backend
+
+
+class BackendNull(backend.Backend):
+    def __init__(self):
+        super(BackendNull, self).__init__()
+
+    def version(self):
+        return "-"
+
+    def name(self):
+        return "null"
+
+    def image_format(self):
+        return "NHWC"
+
+    def load(self, model_path, inputs=None, outputs=None):
+        self.outputs = ["output"]
+        self.inputs = ["input"]
+        return self
+
+    def predict(self, feed):
+        # yield to give the thread that feeds our queue a chance to run
+        time.sleep(0)
+        # return something fake
+        return [[0]]
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend_onnxruntime.py b/retired_benchmarks/vision/classification_and_detection/python/backend_onnxruntime.py
new file mode 100755
index 0000000000..66b8fdaae4
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend_onnxruntime.py
@@ -0,0 +1,47 @@
+"""
+onnxruntime backend (https://github.com/microsoft/onnxruntime)
+"""
+
+# pylint: disable=unused-argument,missing-docstring,useless-super-delegation
+
+import onnxruntime as rt
+
+import backend
+
+
+class BackendOnnxruntime(backend.Backend):
+    def __init__(self):
+        super(BackendOnnxruntime, self).__init__()
+
+    def version(self):
+        return rt.__version__
+
+    def name(self):
+        """Name of the runtime."""
+        return "onnxruntime"
+
+    def image_format(self):
+        """image_format. For onnx it is always NCHW."""
+        return "NCHW"
+
+    def load(self, model_path, inputs=None, outputs=None):
+        """Load model and find input/outputs from the model file."""
+        opt = rt.SessionOptions()
+        # enable level 3 optimizations
+        # FIXME: enable below once onnxruntime 0.5 is released
+        # opt.set_graph_optimization_level(3)
+        self.sess = rt.InferenceSession(model_path, opt)
+        # get input and output names
+        if not inputs:
+            self.inputs = [meta.name for meta in self.sess.get_inputs()]
+        else:
+            self.inputs = inputs
+        if not outputs:
+            self.outputs = [meta.name for meta in self.sess.get_outputs()]
+        else:
+            self.outputs = outputs
+        return self
+
+    def predict(self, feed):
+        """Run the prediction."""
+        return self.sess.run(self.outputs, feed)
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch.py b/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch.py
new file mode 100755
index 0000000000..02b010ac71
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch.py
@@ -0,0 +1,64 @@
+"""
+pytoch/caffe2 backend via onnx
+https://pytorch.org/docs/stable/onnx.html
+"""
+
+# pylint: disable=unused-argument,missing-docstring,useless-super-delegation
+
+from threading import Lock
+
+import caffe2.python.onnx.backend
+import onnx
+import torch  # needed to get version and cuda setup
+
+import backend
+
+
+class BackendPytorch(backend.Backend):
+    def __init__(self):
+        super(BackendPytorch, self).__init__()
+        self.sess = None
+        self.model = None
+        self.lock = Lock()
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "pytorch"
+
+    def image_format(self):
+        return "NCHW"
+
+    def load(self, model_path, inputs=None, outputs=None):
+        self.model = onnx.load(model_path)
+
+        # find inputs from the model if not passed in by config
+        if inputs:
+            self.inputs = inputs
+        else:
+            self.inputs = []
+            initializers = set()
+            for i in self.model.graph.initializer:
+                initializers.add(i.name)
+            for i in self.model.graph.input:
+                if i.name not in initializers:
+                    self.inputs.append(i.name)
+        # find outputs from the model if not passed in by config
+        if outputs:
+            self.outputs = outputs
+        else:
+            self.outputs = []
+            for i in self.model.graph.output:
+                self.outputs.append(i.name)
+
+        # prepare the backend
+        device = "CUDA:0" if torch.cuda.is_available() else "CPU"
+        self.sess = caffe2.python.onnx.backend.prepare(self.model, device)
+        return self
+
+    def predict(self, feed):
+        self.lock.acquire()
+        res = self.sess.run(feed)
+        self.lock.release()
+        return res
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch_native.py b/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch_native.py
new file mode 100755
index 0000000000..f631ac5d3d
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch_native.py
@@ -0,0 +1,57 @@
+"""
+pytoch native backend 
+"""
+# pylint: disable=unused-argument,missing-docstring
+import torch  # currently supports pytorch1.0
+import backend
+
+
+
+class BackendPytorchNative(backend.Backend):
+    def __init__(self):
+        super(BackendPytorchNative, self).__init__()
+        self.sess = None
+        self.model = None
+        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "pytorch-native"
+
+    def image_format(self):
+        return "NCHW"
+
+    def load(self, model_path, inputs=None, outputs=None):
+        self.model = torch.load(model_path,map_location=lambda storage, loc: storage)
+        self.model.eval()
+        # find inputs from the model if not passed in by config
+        if inputs:
+            self.inputs = inputs
+        else:
+            self.inputs = []
+            initializers = set()
+            for i in self.model.graph.initializer:
+                initializers.add(i.name)
+            for i in self.model.graph.input:
+                if i.name not in initializers:
+                    self.inputs.append(i.name)
+        # find outputs from the model if not passed in by config
+        if outputs:
+            self.outputs = outputs
+        else:
+            self.outputs = []
+            for i in self.model.graph.output:
+                self.outputs.append(i.name)
+
+        # prepare the backend
+        self.model = self.model.to(self.device)
+        return self
+
+        
+    def predict(self, feed):
+        key=[key for key in feed.keys()][0]    
+        feed[key] = torch.tensor(feed[key]).float().to(self.device)
+        with torch.no_grad():
+            output = self.model(feed[key])    
+        return output
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend_tf.py b/retired_benchmarks/vision/classification_and_detection/python/backend_tf.py
new file mode 100755
index 0000000000..05245a4547
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend_tf.py
@@ -0,0 +1,63 @@
+"""
+tensorflow backend (https://github.com/tensorflow/tensorflow)
+"""
+
+# pylint: disable=unused-argument,missing-docstring,useless-super-delegation
+
+import tensorflow as tf
+from tensorflow import dtypes
+from tensorflow.python.tools.optimize_for_inference_lib import optimize_for_inference
+
+import os
+import backend
+
+
+class BackendTensorflow(backend.Backend):
+    def __init__(self):
+        super(BackendTensorflow, self).__init__()
+
+    def version(self):
+        return tf.__version__ + "/" + tf.__git_version__
+
+    def name(self):
+        return "tensorflow"
+
+    def image_format(self):
+        # By default tensorflow uses NHWC (and the cpu implementation only does NHWC)
+        return "NHWC"
+
+    def load(self, model_path, inputs=None, outputs=None):
+        # there is no input/output meta data i the graph so it need to come from config.
+        if not inputs:
+            raise ValueError("BackendTensorflow needs inputs")
+        if not outputs:
+            raise ValueError("BackendTensorflow needs outputs")
+        self.outputs = outputs
+        self.inputs = inputs
+
+        infer_config = tf.compat.v1.ConfigProto()
+        infer_config.intra_op_parallelism_threads = int(os.environ['TF_INTRA_OP_PARALLELISM_THREADS']) \
+                if 'TF_INTRA_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
+        infer_config.inter_op_parallelism_threads = int(os.environ['TF_INTER_OP_PARALLELISM_THREADS']) \
+                if 'TF_INTER_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
+        infer_config.use_per_session_threads = 1
+
+        # TODO: support checkpoint and saved_model formats?
+        graph_def = tf.compat.v1.GraphDef()
+        with tf.compat.v1.gfile.FastGFile(model_path, "rb") as f:
+            graph_def.ParseFromString(f.read())
+        for as_datatype_enum in [dtypes.float32.as_datatype_enum, dtypes.uint8.as_datatype_enum]:
+            try:
+                optimized_graph_def = optimize_for_inference(graph_def, [item.split(':')[0] for item in inputs],
+                        [item.split(':')[0] for item in outputs], as_datatype_enum, False)
+                graph_def = optimized_graph_def
+                break
+            except ValueError:
+                pass
+
+        g = tf.compat.v1.import_graph_def(graph_def, name='')
+        self.sess = tf.compat.v1.Session(graph=g, config=infer_config)
+        return self
+
+    def predict(self, feed):
+        return self.sess.run(self.outputs, feed_dict=feed)
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend_tflite.py b/retired_benchmarks/vision/classification_and_detection/python/backend_tflite.py
new file mode 100755
index 0000000000..7c8c78c139
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend_tflite.py
@@ -0,0 +1,62 @@
+"""
+tflite backend (https://github.com/tensorflow/tensorflow/lite)
+"""
+
+# pylint: disable=unused-argument,missing-docstring,useless-super-delegation
+
+from threading import Lock
+
+try:
+    # try dedicated tflite package first
+    import tflite_runtime
+    import tflite_runtime.interpreter as tflite
+    _version = tflite_runtime.__version__
+    _git_version = tflite_runtime.__git_version__
+except:
+    # fall back to tflite bundled in tensorflow
+    import tensorflow as tf
+    from tensorflow.lite.python import interpreter as tflite
+    _version = tf.__version__
+    _git_version = tf.__git_version__
+
+
+import backend
+
+
+class BackendTflite(backend.Backend):
+    def __init__(self):
+        super(BackendTflite, self).__init__()
+        self.sess = None
+        self.lock = Lock()
+
+    def version(self):
+        return _version + "/" + _git_version
+
+    def name(self):
+        return "tflite"
+
+    def image_format(self):
+        # tflite is always NHWC
+        return "NHWC"
+
+    def load(self, model_path, inputs=None, outputs=None):
+        self.sess = tflite.Interpreter(model_path=model_path)
+        self.sess.allocate_tensors()
+        # keep input/output name to index mapping
+        self.input2index = {i["name"]: i["index"] for i in self.sess.get_input_details()}
+        self.output2index = {i["name"]: i["index"] for i in self.sess.get_output_details()}
+        # keep input/output names
+        self.inputs = list(self.input2index.keys())
+        self.outputs = list(self.output2index.keys())
+        return self
+
+    def predict(self, feed):
+        self.lock.acquire()
+        # set inputs
+        for k, v in self.input2index.items():
+            self.sess.set_tensor(v, feed[k])
+        self.sess.invoke()
+        # get results
+        res = [self.sess.get_tensor(v) for _, v in self.output2index.items()]
+        self.lock.release()
+        return res
diff --git a/retired_benchmarks/vision/classification_and_detection/python/coco.py b/retired_benchmarks/vision/classification_and_detection/python/coco.py
new file mode 100644
index 0000000000..4080158744
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/coco.py
@@ -0,0 +1,343 @@
+"""
+implementation of imagenet dataset
+"""
+
+# pylint: disable=unused-argument,missing-docstring
+
+import json
+import logging
+import os
+import time
+
+import cv2
+import numpy as np
+from pycocotools.cocoeval import COCOeval
+import pycoco
+import dataset
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("coco")
+
+
+class Coco(dataset.Dataset):
+    def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
+                 image_format="NHWC", pre_process=None, count=None, cache_dir=None,use_label_map=False):
+        super().__init__()
+        self.image_size = image_size
+        self.image_list = []
+        self.label_list = []
+        self.image_ids = []
+        self.image_sizes = []
+        self.count = count
+        self.use_cache = use_cache
+        self.data_path = data_path
+        self.pre_process = pre_process
+        self.use_label_map=use_label_map
+        if not cache_dir:
+            cache_dir = os.getcwd()
+        self.cache_dir = os.path.join(cache_dir, "preprocessed", name, image_format)
+        # input images are in HWC
+        self.need_transpose = True if image_format == "NCHW" else False
+        not_found = 0 
+        empty_80catageories = 0
+        if image_list is None:
+            # by default look for val_map.txt
+            image_list = os.path.join(data_path, "annotations/instances_val2017.json")
+        self.annotation_file = image_list
+        if self.use_label_map:
+            # for pytorch
+            label_map = {}
+            with open(self.annotation_file) as fin:
+                annotations = json.load(fin)
+            for cnt, cat in enumerate(annotations["categories"]):
+                label_map[cat["id"]] = cnt + 1
+
+        os.makedirs(self.cache_dir, exist_ok=True)
+        start = time.time()
+        images = {}
+        with open(image_list, "r") as f:
+            coco = json.load(f)
+        for i in coco["images"]:
+            images[i["id"]] = {"file_name": i["file_name"],
+                               "height": i["height"],
+                               "width": i["width"],
+                               "bbox": [],
+                               "category": []}
+        for a in coco["annotations"]:
+            i = images.get(a["image_id"])
+            if i is None:
+                continue
+            catagory_ids = label_map[a.get("category_id")] if self.use_label_map else a.get("category_id")
+            i["category"].append(catagory_ids)
+            i["bbox"].append(a.get("bbox"))
+
+        for image_id, img in images.items():
+            image_name = os.path.join("val2017", img["file_name"])
+            src = os.path.join(data_path, image_name)
+            if not os.path.exists(src):
+                # if the image does not exists ignore it
+                not_found += 1
+                continue
+            if len(img["category"])==0 and self.use_label_map: 
+                #if an image doesn't have any of the 81 categories in it    
+                empty_80catageories += 1 #should be 48 images - thus the validation sert has 4952 images
+                continue 
+
+            os.makedirs(os.path.dirname(os.path.join(self.cache_dir, image_name)), exist_ok=True)
+            dst = os.path.join(self.cache_dir, image_name)
+            if not os.path.exists(dst + ".npy"):
+                # cache a preprocessed version of the image
+                img_org = cv2.imread(src)
+                processed = self.pre_process(img_org, need_transpose=self.need_transpose, dims=self.image_size)
+                np.save(dst, processed)
+
+            self.image_ids.append(image_id)
+            self.image_list.append(image_name)
+            self.image_sizes.append((img["height"], img["width"]))
+            self.label_list.append((img["category"], img["bbox"]))
+
+            # limit the dataset if requested
+            if self.count and len(self.image_list) >= self.count:
+                break
+
+        time_taken = time.time() - start
+        if not self.image_list:
+            log.error("no images in image list found")
+            raise ValueError("no images in image list found")
+        if not_found > 0:
+            log.info("reduced image list, %d images not found", not_found)
+        if empty_80catageories > 0:
+            log.info("reduced image list, %d images without any of the 80 categories", empty_80catageories)
+
+        log.info("loaded {} images, cache={}, took={:.1f}sec".format(
+            len(self.image_list), use_cache, time_taken))
+
+        self.label_list = np.array(self.label_list)
+
+    def get_item(self, nr):
+        """Get image by number in the list."""
+        dst = os.path.join(self.cache_dir, self.image_list[nr])
+        img = np.load(dst + ".npy")
+        return img, self.label_list[nr]
+
+    def get_item_loc(self, nr):
+        src = os.path.join(self.data_path, self.image_list[nr])
+        return src
+
+
+class PostProcessCoco:
+    """
+    Post processing for tensorflow ssd-mobilenet style models
+    """
+    def __init__(self):
+        self.results = []
+        self.good = 0
+        self.total = 0
+        self.content_ids = []
+        self.use_inv_map = False
+
+    def add_results(self, results):
+        self.results.extend(results)
+
+    def __call__(self, results, ids, expected=None, result_dict=None, ):
+        # results come as:
+        #   tensorflow, ssd-mobilenet: num_detections,detection_boxes,detection_scores,detection_classes
+        processed_results = []
+        # batch size
+        bs = len(results[0])
+        for idx in range(0, bs):
+            # keep the content_id from loadgen to handle content_id's without results
+            self.content_ids.append(ids[idx])
+            processed_results.append([])
+            detection_num = int(results[0][idx])
+            detection_boxes = results[1][idx]
+            detection_classes = results[3][idx]
+            expected_classes = expected[idx][0]
+            for detection in range(0, detection_num):
+                detection_class = int(detection_classes[detection])
+                if detection_class in expected_classes:
+                    self.good += 1
+                box = detection_boxes[detection]
+                processed_results[idx].append([float(ids[idx]),
+                                              box[0], box[1], box[2], box[3],
+                                              results[2][idx][detection],
+                                              float(detection_class)])
+                self.total += 1
+        return processed_results
+
+    def start(self):
+        self.results = []
+        self.good = 0
+        self.total = 0
+
+    def finalize(self, result_dict, ds=None, output_dir=None):
+        result_dict["good"] += self.good
+        result_dict["total"] += self.total
+
+        if self.use_inv_map:
+            # for pytorch
+            label_map = {}
+            with open(ds.annotation_file) as fin:
+                annotations = json.load(fin)
+            for cnt, cat in enumerate(annotations["categories"]):
+                label_map[cat["id"]] = cnt + 1
+            inv_map = {v:k for k,v in label_map.items()}
+
+        detections = []
+        image_indices = []
+        for batch in range(0, len(self.results)):
+            image_indices.append(self.content_ids[batch])
+            for idx in range(0, len(self.results[batch])):
+                detection = self.results[batch][idx]
+                # this is the index of the coco image
+                image_idx = int(detection[0])
+                if image_idx != self.content_ids[batch]:
+                    # working with the coco index/id is error prone - extra check to make sure it is consistent
+                    log.error("image_idx missmatch, lg={} / result={}".format(image_idx, self.content_ids[batch]))
+                # map the index to the coco image id
+                detection[0] = ds.image_ids[image_idx]
+                height, width = ds.image_sizes[image_idx]
+                # box comes from model as: ymin, xmin, ymax, xmax
+                ymin = detection[1] * height
+                xmin = detection[2] * width
+                ymax = detection[3] * height
+                xmax = detection[4] * width
+                # pycoco wants {imageID,x1,y1,w,h,score,class}
+                detection[1] = xmin
+                detection[2] = ymin
+                detection[3] = xmax - xmin
+                detection[4] = ymax - ymin
+                if self.use_inv_map:
+                    cat_id = inv_map.get(int(detection[6]), -1)
+                    if cat_id == -1:
+                        # FIXME:
+                        log.info("finalize can't map category {}".format(int(detection[6])))
+                    detection[6] =  cat_id
+                detections.append(np.array(detection))
+
+        # map indices to coco image id's
+        image_ids = [ds.image_ids[i]  for i in image_indices]
+        self.results = []
+        cocoGt = pycoco.COCO(ds.annotation_file)
+        cocoDt = cocoGt.loadRes(np.array(detections))
+        cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+        cocoEval.params.imgIds = image_ids
+        cocoEval.evaluate()
+        cocoEval.accumulate()
+        cocoEval.summarize()
+        result_dict["mAP"] = cocoEval.stats[0]
+
+
+class PostProcessCocoPt(PostProcessCoco):
+    """
+    Post processing required by ssd-resnet34 / pytorch
+    """
+    def __init__(self,use_inv_map,score_threshold):
+        super().__init__()
+        self.use_inv_map = use_inv_map
+        self.score_threshold = score_threshold
+        
+    def __call__(self, results, ids, expected=None, result_dict=None):
+        # results come as:
+        #   detection_boxes,detection_classes,detection_scores
+
+        processed_results = []
+        # batch size
+        bs = len(results[0])
+        for idx in range(0, bs):
+            self.content_ids.append(ids[idx])
+            processed_results.append([])
+            detection_boxes = results[0][idx]
+            detection_classes = results[1][idx]
+            expected_classes = expected[idx][0]
+            scores = results[2][idx]
+            #for detection in range(0, len(expected_classes)):
+            for detection in range(0, len(scores)):
+                if scores[detection] < self.score_threshold:
+                    break
+                detection_class = int(detection_classes[detection])
+                if detection_class in expected_classes:
+                    self.good += 1
+                box = detection_boxes[detection]
+                # comes from model as:  0=xmax 1=ymax 2=xmin 3=ymin
+                processed_results[idx].append([float(ids[idx]),
+                                              box[1], box[0], box[3], box[2],
+                                              scores[detection],
+                                              float(detection_class)])
+                self.total += 1
+        return processed_results
+
+
+class PostProcessCocoOnnx(PostProcessCoco):
+    """
+    Post processing required by ssd-resnet34 / onnx
+    """
+    def __init__(self):
+        super().__init__()
+
+    def __call__(self, results, ids, expected=None, result_dict=None):
+        # results come as:
+        #   onnx (from pytorch ssd-resnet34): detection_boxes,detection_classes,detection_scores
+
+        processed_results = []
+
+        # batch size
+        bs = len(results[0])
+        for idx in range(0, bs):
+            self.content_ids.append(ids[idx])
+            processed_results.append([])
+            detection_boxes = results[0][idx]
+            detection_classes = results[1][idx]
+            expected_classes = expected[idx][0]
+            scores = results[2][idx]
+            for detection in range(0, len(scores)):
+                if scores[detection] < 0.5:
+                    break
+                detection_class = int(detection_classes[detection])
+                if detection_class in expected_classes:
+                    self.good += 1
+                box = detection_boxes[detection]
+                # comes from model as:  0=xmax 1=ymax 2=xmin 3=ymin
+                processed_results[idx].append([float(ids[idx]),
+                                              box[1], box[0], box[3], box[2],
+                                              scores[detection],
+                                              float(detection_class)])
+                self.total += 1
+        return processed_results
+
+class PostProcessCocoTf(PostProcessCoco):
+    """
+    Post processing required by ssd-resnet34 / pytorch
+    """
+    def __init__(self):
+        super().__init__()
+        self.use_inv_map = True
+
+    def __call__(self, results, ids, expected=None, result_dict=None):
+        # results come as:
+        #   detection_boxes,detection_classes,detection_scores
+
+        processed_results = []
+        # batch size
+        bs = len(results[0])
+        for idx in range(0, bs):
+            self.content_ids.append(ids[idx])
+            processed_results.append([])
+            detection_boxes = results[0][idx]
+            detection_classes = results[1][idx]
+            expected_classes = expected[idx][0]
+            scores = results[2][idx]
+            for detection in range(0, len(scores)):
+                if scores[detection] < 0.05:
+                    break
+                detection_class = int(detection_classes[detection])
+                if detection_class in expected_classes:
+                    self.good += 1
+                box = detection_boxes[detection]
+                # comes from model as:  0=xmax 1=ymax 2=xmin 3=ymin
+                processed_results[idx].append([float(ids[idx]),
+                                              box[0], box[1], box[2], box[3],
+                                              scores[detection],
+                                              float(detection_class)])
+                self.total += 1
+        return processed_results
diff --git a/retired_benchmarks/vision/classification_and_detection/python/dataset.py b/retired_benchmarks/vision/classification_and_detection/python/dataset.py
new file mode 100755
index 0000000000..dce968a3de
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/dataset.py
@@ -0,0 +1,273 @@
+"""
+dataset related classes and methods
+"""
+
+# pylint: disable=unused-argument,missing-docstring
+
+import logging
+import sys
+import time
+
+import cv2
+import numpy as np
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("dataset")
+
+class Item():
+    def __init__(self, label, img, idx):
+        self.label = label
+        self.img = img
+        self.idx = idx
+        self.start = time.time()
+
+
+def usleep(sec):
+    if sys.platform == 'win32':
+        # on windows time.sleep() doesn't work to well
+        import ctypes
+        kernel32 = ctypes.windll.kernel32
+        timer = kernel32.CreateWaitableTimerA(ctypes.c_void_p(), True, ctypes.c_void_p())
+        delay = ctypes.c_longlong(int(-1 * (10 * 1000000 * sec)))
+        kernel32.SetWaitableTimer(timer, ctypes.byref(delay), 0, ctypes.c_void_p(), ctypes.c_void_p(), False)
+        kernel32.WaitForSingleObject(timer, 0xffffffff)
+    else:
+        time.sleep(sec)
+
+
+class Dataset():
+    def __init__(self):
+        self.arrival = None
+        self.image_list = []
+        self.label_list = []
+        self.image_list_inmemory = {}
+        self.last_loaded = -1
+
+    def preprocess(self, use_cache=True):
+        raise NotImplementedError("Dataset:preprocess")
+
+    def get_item_count(self):
+        return len(self.image_list)
+
+    def get_list(self):
+        raise NotImplementedError("Dataset:get_list")
+
+    def load_query_samples(self, sample_list):
+        self.image_list_inmemory = {}
+        for sample in sample_list:
+            self.image_list_inmemory[sample], _ = self.get_item(sample)
+        self.last_loaded = time.time()
+
+    def unload_query_samples(self, sample_list):
+        if sample_list:
+            for sample in sample_list:
+                if sample in self.image_list_inmemory :
+                    del self.image_list_inmemory[sample]
+        else:
+            self.image_list_inmemory = {}
+
+    def get_samples(self, id_list):
+        data = np.array([self.image_list_inmemory[id] for id in id_list])
+        return data, self.label_list[id_list]
+
+    def get_item_loc(self, id):
+        raise NotImplementedError("Dataset:get_item_loc")
+
+
+#
+# Post processing
+#
+class PostProcessCommon:
+    def __init__(self, offset=0):
+        self.offset = offset
+        self.good = 0
+        self.total = 0
+
+    def __call__(self, results, ids, expected=None, result_dict=None):
+        processed_results = []
+        n = len(results[0])
+        for idx in range(0, n):
+            result = results[0][idx] + self.offset
+            processed_results.append([result])
+            if result == expected[idx]:
+                self.good += 1
+        self.total += n
+        return processed_results
+
+    def add_results(self, results):
+        pass
+
+    def start(self):
+        self.good = 0
+        self.total = 0
+
+    def finalize(self, results, ds=False,  output_dir=None):
+        results["good"] = self.good
+        results["total"] = self.total
+
+
+class PostProcessArgMax:
+    def __init__(self, offset=0):
+        self.offset = offset
+        self.good = 0
+        self.total = 0
+
+    def __call__(self, results, ids, expected=None, result_dict=None):
+        processed_results = []
+        results = np.argmax(results[0], axis=1)
+        n = results.shape[0]
+        for idx in range(0, n):
+            result = results[idx] + self.offset
+            processed_results.append([result])
+            if result == expected[idx]:
+                self.good += 1
+        self.total += n
+        return processed_results
+
+    def add_results(self, results):
+        pass
+
+    def start(self):
+        self.good = 0
+        self.total = 0
+
+    def finalize(self, results, ds=False, output_dir=None):
+        results["good"] = self.good
+        results["total"] = self.total
+
+
+#
+# pre-processing
+#
+
+def center_crop(img, out_height, out_width):
+    height, width, _ = img.shape
+    left = int((width - out_width) / 2)
+    right = int((width + out_width) / 2)
+    top = int((height - out_height) / 2)
+    bottom = int((height + out_height) / 2)
+    img = img[top:bottom, left:right]
+    return img
+
+
+def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
+    height, width, _ = img.shape
+    new_height = int(100. * out_height / scale)
+    new_width = int(100. * out_width / scale)
+    if height > width:
+        w = new_width
+        h = int(new_height * height / width)
+    else:
+        h = new_height
+        w = int(new_width * width / height)
+    img = cv2.resize(img, (w, h), interpolation=inter_pol)
+    return img
+
+
+def pre_process_vgg(img, dims=None, need_transpose=False):
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    output_height, output_width, _ = dims
+    cv2_interpol = cv2.INTER_AREA
+    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2_interpol)
+    img = center_crop(img, output_height, output_width)
+    img = np.asarray(img, dtype='float32')
+
+    # normalize image
+    means = np.array([123.68, 116.78, 103.94], dtype=np.float32)
+    img -= means
+
+    # transpose if needed
+    if need_transpose:
+        img = img.transpose([2, 0, 1])
+    return img
+
+
+def pre_process_mobilenet(img, dims=None, need_transpose=False):
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    output_height, output_width, _ = dims
+    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR)
+    img = center_crop(img, output_height, output_width)
+    img = np.asarray(img, dtype='float32')
+
+    img /= 255.0
+    img -= 0.5
+    img *= 2
+
+    # transpose if needed
+    if need_transpose:
+        img = img.transpose([2, 0, 1])
+    return img
+
+
+def pre_process_imagenet_pytorch(img, dims=None, need_transpose=False):
+    from PIL import Image
+    import torchvision.transforms.functional as F
+
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = Image.fromarray(img)
+    img = F.resize(img, 256, Image.BILINEAR)
+    img = F.center_crop(img, 224)
+    img = F.to_tensor(img)
+    img = F.normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], inplace=False)
+    if not need_transpose:
+        img = img.permute(1, 2, 0) # NHWC
+    img = np.asarray(img, dtype='float32')
+    return img
+
+
+def maybe_resize(img, dims):
+    img = np.array(img, dtype=np.float32)
+    if len(img.shape) < 3 or img.shape[2] != 3:
+        # some images might be grayscale
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    if dims != None:
+        im_height, im_width, _ = dims
+        img = cv2.resize(img, (im_width, im_height), interpolation=cv2.INTER_LINEAR)
+    return img
+
+
+def pre_process_coco_mobilenet(img, dims=None, need_transpose=False):
+    img = maybe_resize(img, dims)
+    img = np.asarray(img, dtype=np.uint8)
+    # transpose if needed
+    if need_transpose:
+        img = img.transpose([2, 0, 1])
+    return img
+
+
+def pre_process_coco_pt_mobilenet(img, dims=None, need_transpose=False):
+    img = maybe_resize(img, dims)
+    img -= 127.5
+    img /= 127.5
+    # transpose if needed
+    if need_transpose:
+        img = img.transpose([2, 0, 1])
+    return img
+
+
+def pre_process_coco_resnet34(img, dims=None, need_transpose=False):
+    img = maybe_resize(img, dims)
+    mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
+    std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
+
+    img = img / 255. - mean
+    img = img / std
+
+    if need_transpose:
+        img = img.transpose([2, 0, 1])
+
+    return img
+
+
+def pre_process_coco_resnet34_tf(img, dims=None, need_transpose=False):
+    img = maybe_resize(img, dims)
+    mean = np.array([123.68, 116.78, 103.94], dtype=np.float32)
+    img = img - mean
+    if need_transpose:
+        img = img.transpose([2, 0, 1])
+
+    return img
diff --git a/retired_benchmarks/vision/classification_and_detection/python/imagenet.py b/retired_benchmarks/vision/classification_and_detection/python/imagenet.py
new file mode 100755
index 0000000000..57865c6b7c
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/imagenet.py
@@ -0,0 +1,95 @@
+"""
+implementation of imagenet dataset
+"""
+
+# pylint: disable=unused-argument,missing-docstring
+
+import logging
+import os
+import re
+import time
+
+import cv2
+import numpy as np
+
+import dataset
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("imagenet")
+
+
+class Imagenet(dataset.Dataset):
+
+    def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
+                 image_format="NHWC", pre_process=None, count=None, cache_dir=None):
+        super(Imagenet, self).__init__()
+        if image_size is None:
+            self.image_size = [224, 224, 3]
+        else:
+            self.image_size = image_size
+        if not cache_dir:
+            cache_dir = os.getcwd()
+        self.image_list = []
+        self.label_list = []
+        self.count = count
+        self.use_cache = use_cache
+        self.cache_dir = os.path.join(cache_dir, "preprocessed", name, image_format)
+        self.data_path = data_path
+        self.pre_process = pre_process
+        # input images are in HWC
+        self.need_transpose = True if image_format == "NCHW" else False
+
+        not_found = 0
+        if image_list is None:
+            # by default look for val_map.txt
+            image_list = os.path.join(data_path, "val_map.txt")
+
+        os.makedirs(self.cache_dir, exist_ok=True)
+
+        start = time.time()
+        with open(image_list, 'r') as f:
+            for s in f:
+                image_name, label = re.split(r"\s+", s.strip())
+                src = os.path.join(data_path, image_name)
+                if not os.path.exists(src):
+                    # if the image does not exists ignore it
+                    not_found += 1
+                    continue
+                os.makedirs(os.path.dirname(os.path.join(self.cache_dir, image_name)), exist_ok=True)
+                dst = os.path.join(self.cache_dir, image_name)
+                if not os.path.exists(dst + ".npy"):
+                    # cache a preprocessed version of the image
+                    # TODO: make this multi threaded ?
+                    img_org = cv2.imread(src)
+                    processed = self.pre_process(img_org, need_transpose=self.need_transpose, dims=self.image_size)
+                    np.save(dst, processed)
+                
+                self.image_list.append(image_name)
+                self.label_list.append(int(label))
+
+                # limit the dataset if requested
+                if self.count and len(self.image_list) >= self.count:
+                    break
+
+        time_taken = time.time() - start
+        if not self.image_list:
+            log.error("no images in image list found")
+            raise ValueError("no images in image list found")
+        if not_found > 0:
+            log.info("reduced image list, %d images not found", not_found)
+
+        log.info("loaded {} images, cache={}, took={:.1f}sec".format(
+            len(self.image_list), use_cache, time_taken))
+
+        self.label_list = np.array(self.label_list)
+
+    def get_item(self, nr):
+        """Get image by number in the list."""
+        dst = os.path.join(self.cache_dir, self.image_list[nr])
+        img = np.load(dst + ".npy")
+        return img, self.label_list[nr]
+
+    def get_item_loc(self, nr):
+        src = os.path.join(self.data_path, self.image_list[nr])
+        return src
+
diff --git a/retired_benchmarks/vision/classification_and_detection/python/main.py b/retired_benchmarks/vision/classification_and_detection/python/main.py
new file mode 100755
index 0000000000..904a8b74a8
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/main.py
@@ -0,0 +1,565 @@
+"""
+mlperf inference benchmarking tool
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import array
+import collections
+import json
+import logging
+import os
+import sys
+import threading
+import time
+from queue import Queue
+
+import mlperf_loadgen as lg
+import numpy as np
+
+import dataset
+import imagenet
+import coco
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("main")
+
+NANO_SEC = 1e9
+MILLI_SEC = 1000
+
+# pylint: disable=missing-docstring
+
+# the datasets we support
+SUPPORTED_DATASETS = {
+    "imagenet":
+        (imagenet.Imagenet, dataset.pre_process_vgg, dataset.PostProcessCommon(offset=-1),
+         {"image_size": [224, 224, 3]}),
+    "imagenet_mobilenet":
+        (imagenet.Imagenet, dataset.pre_process_mobilenet, dataset.PostProcessArgMax(offset=-1),
+         {"image_size": [224, 224, 3]}),
+    "imagenet_pytorch":
+        (imagenet.Imagenet, dataset.pre_process_imagenet_pytorch, dataset.PostProcessArgMax(offset=0),
+         {"image_size": [224, 224, 3]}),
+    "coco-300":
+        (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(),
+         {"image_size": [300, 300, 3]}),
+    "coco-300-pt":
+        (coco.Coco, dataset.pre_process_coco_pt_mobilenet, coco.PostProcessCocoPt(False,0.3),
+         {"image_size": [300, 300, 3]}),         
+    "coco-1200":
+        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCoco(),
+         {"image_size": [1200, 1200, 3]}),
+    "coco-1200-onnx":
+        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoOnnx(),
+         {"image_size": [1200, 1200, 3]}),
+    "coco-1200-pt":
+        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoPt(True,0.05),
+         {"image_size": [1200, 1200, 3],"use_label_map": True}),
+    "coco-1200-tf":
+        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoTf(),
+         {"image_size": [1200, 1200, 3],"use_label_map": False}),
+}
+
+# pre-defined command line options so simplify things. They are used as defaults and can be
+# overwritten from command line
+
+SUPPORTED_PROFILES = {
+    "defaults": {
+        "dataset": "imagenet",
+        "backend": "tensorflow",
+        "cache": 0,
+        "max-batchsize": 32,
+    },
+
+    # resnet
+    "resnet50-tf": {
+        "inputs": "input_tensor:0",
+        "outputs": "ArgMax:0",
+        "dataset": "imagenet",
+        "backend": "tensorflow",
+        "model-name": "resnet50",
+    },
+    "resnet50-pytorch": {
+        "inputs": "image",
+        "outputs": "ArgMax:0",
+        "dataset": "imagenet",
+        "backend": "tensorflow",
+        "model-name": "resnet50",
+    },
+    "resnet50-onnxruntime": {
+        "dataset": "imagenet",
+        "outputs": "ArgMax:0",
+        "backend": "onnxruntime",
+        "model-name": "resnet50",
+    },
+
+    # mobilenet
+    "mobilenet-tf": {
+        "inputs": "input:0",
+        "outputs": "MobilenetV1/Predictions/Reshape_1:0",
+        "dataset": "imagenet_mobilenet",
+        "backend": "tensorflow",
+        "model-name": "mobilenet",
+    },
+    "mobilenet-onnxruntime": {
+        "dataset": "imagenet_mobilenet",
+        "outputs": "MobilenetV1/Predictions/Reshape_1:0",
+        "backend": "onnxruntime",
+        "model-name": "mobilenet",
+    },
+
+    # ssd-mobilenet
+    "ssd-mobilenet-tf": {
+        "inputs": "image_tensor:0",
+        "outputs": "num_detections:0,detection_boxes:0,detection_scores:0,detection_classes:0",
+        "dataset": "coco-300",
+        "backend": "tensorflow",
+        "model-name": "ssd-mobilenet",
+    },
+    "ssd-mobilenet-pytorch": {
+        "inputs": "image",
+        "outputs": "bboxes,labels,scores",
+        "dataset": "coco-300-pt",
+        "backend": "pytorch-native",
+        "model-name": "ssd-mobilenet",
+    },
+    "ssd-mobilenet-onnxruntime": {
+        "dataset": "coco-300",
+        "outputs": "num_detections:0,detection_boxes:0,detection_scores:0,detection_classes:0",
+        "backend": "onnxruntime",
+        "data-format": "NHWC",
+        "model-name": "ssd-mobilenet",
+    },
+
+    # ssd-resnet34
+    "ssd-resnet34-tf": {
+        "inputs": "image:0",
+        "outputs": "detection_bboxes:0,detection_classes:0,detection_scores:0",
+        "dataset": "coco-1200-tf",
+        "backend": "tensorflow",
+        "data-format": "NCHW",
+        "model-name": "ssd-resnet34",
+    },
+    "ssd-resnet34-pytorch": {
+        "inputs": "image",
+        "outputs": "bboxes,labels,scores",
+        "dataset": "coco-1200-pt",
+        "backend": "pytorch-native",
+        "model-name": "ssd-resnet34",
+    },
+    "ssd-resnet34-onnxruntime": {
+        "dataset": "coco-1200-onnx",
+        "inputs": "image",
+        "outputs": "bboxes,labels,scores",
+        "backend": "onnxruntime",
+        "data-format": "NCHW",
+        "max-batchsize": 1,
+        "model-name": "ssd-resnet34",
+    },
+    "ssd-resnet34-onnxruntime-tf": {
+        "dataset": "coco-1200-tf",
+        "inputs": "image:0",
+        "outputs": "detection_bboxes:0,detection_classes:0,detection_scores:0",
+        "backend": "onnxruntime",
+        "data-format": "NHWC",
+        "model-name": "ssd-resnet34",
+    },
+}
+
+SCENARIO_MAP = {
+    "SingleStream": lg.TestScenario.SingleStream,
+    "MultiStream": lg.TestScenario.MultiStream,
+    "Server": lg.TestScenario.Server,
+    "Offline": lg.TestScenario.Offline,
+}
+
+last_timeing = []
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", choices=SUPPORTED_DATASETS.keys(), help="dataset")
+    parser.add_argument("--dataset-path", required=True, help="path to the dataset")
+    parser.add_argument("--dataset-list", help="path to the dataset list")
+    parser.add_argument("--data-format", choices=["NCHW", "NHWC"], help="data format")
+    parser.add_argument("--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles")
+    parser.add_argument("--scenario", default="SingleStream",
+                        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())))
+    parser.add_argument("--max-batchsize", type=int, help="max batch size in a single inference")
+    parser.add_argument("--model", required=True, help="model file")
+    parser.add_argument("--output", default="output", help="test results")
+    parser.add_argument("--inputs", help="model inputs")
+    parser.add_argument("--outputs", help="model outputs")
+    parser.add_argument("--backend", help="runtime to use")
+    parser.add_argument("--model-name", help="name of the mlperf model, ie. resnet50")
+    parser.add_argument("--threads", default=os.cpu_count(), type=int, help="threads")
+    parser.add_argument("--qps", type=int, help="target qps")
+    parser.add_argument("--cache", type=int, default=0, help="use cache")
+    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
+    parser.add_argument("--find-peak-performance", action="store_true", help="enable finding peak performance pass")
+    parser.add_argument("--debug", action="store_true", help="debug, turn traces on")
+
+    # file to use mlperf rules compliant parameters
+    parser.add_argument("--mlperf_conf", default="../../mlperf.conf", help="mlperf rules config")
+    # file for user LoadGen settings such as target QPS
+    parser.add_argument("--user_conf", default="user.conf", help="user config for user LoadGen settings such as target QPS")
+
+    # below will override mlperf rules compliant settings - don't use for official submission
+    parser.add_argument("--time", type=int, help="time to scan in seconds")
+    parser.add_argument("--count", type=int, help="dataset items to use")
+    parser.add_argument("--max-latency", type=float, help="mlperf max latency in pct tile")
+    parser.add_argument("--samples-per-query", type=int, help="mlperf multi-stream sample per query")
+    args = parser.parse_args()
+
+    # don't use defaults in argparser. Instead we default to a dict, override that with a profile
+    # and take this as default unless command line give
+    defaults = SUPPORTED_PROFILES["defaults"]
+
+    if args.profile:
+        profile = SUPPORTED_PROFILES[args.profile]
+        defaults.update(profile)
+    for k, v in defaults.items():
+        kc = k.replace("-", "_")
+        if getattr(args, kc) is None:
+            setattr(args, kc, v)
+    if args.inputs:
+        args.inputs = args.inputs.split(",")
+    if args.outputs:
+        args.outputs = args.outputs.split(",")
+
+    if args.scenario not in SCENARIO_MAP:
+        parser.error("valid scanarios:" + str(list(SCENARIO_MAP.keys())))
+    return args
+
+
+def get_backend(backend):
+    if backend == "tensorflow":
+        from backend_tf import BackendTensorflow
+        backend = BackendTensorflow()
+    elif backend == "onnxruntime":
+        from backend_onnxruntime import BackendOnnxruntime
+        backend = BackendOnnxruntime()
+    elif backend == "null":
+        from backend_null import BackendNull
+        backend = BackendNull()
+    elif backend == "pytorch":
+        from backend_pytorch import BackendPytorch
+        backend = BackendPytorch()
+    elif backend == "pytorch-native":
+        from backend_pytorch_native import BackendPytorchNative
+        backend = BackendPytorchNative()      
+    elif backend == "tflite":
+        from backend_tflite import BackendTflite
+        backend = BackendTflite()
+    else:
+        raise ValueError("unknown backend: " + backend)
+    return backend
+
+
+class Item:
+    """An item that we queue for processing by the thread pool."""
+
+    def __init__(self, query_id, content_id, img, label=None):
+        self.query_id = query_id
+        self.content_id = content_id
+        self.img = img
+        self.label = label
+        self.start = time.time()
+
+
+class RunnerBase:
+    def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
+        self.take_accuracy = False
+        self.ds = ds
+        self.model = model
+        self.post_process = post_proc
+        self.threads = threads
+        self.take_accuracy = False
+        self.max_batchsize = max_batchsize
+        self.result_timing = []
+
+    def handle_tasks(self, tasks_queue):
+        pass
+
+    def start_run(self, result_dict, take_accuracy):
+        self.result_dict = result_dict
+        self.result_timing = []
+        self.take_accuracy = take_accuracy
+        self.post_process.start()
+
+    def run_one_item(self, qitem):
+        # run the prediction
+        processed_results = []
+        try:
+            results = self.model.predict({self.model.inputs[0]: qitem.img})
+            processed_results = self.post_process(results, qitem.content_id, qitem.label, self.result_dict)
+            if self.take_accuracy:
+                self.post_process.add_results(processed_results)
+                self.result_timing.append(time.time() - qitem.start)
+        except Exception as ex:  # pylint: disable=broad-except
+            src = [self.ds.get_item_loc(i) for i in qitem.content_id]
+            log.error("thread: failed on contentid=%s, %s", src, ex)
+            # since post_process will not run, fake empty responses
+            processed_results = [[]] * len(qitem.query_id)
+        finally:
+            response_array_refs = []
+            response = []
+            for idx, query_id in enumerate(qitem.query_id):
+                response_array = array.array("B", np.array(processed_results[idx], np.float32).tobytes())
+                response_array_refs.append(response_array)
+                bi = response_array.buffer_info()
+                response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
+            lg.QuerySamplesComplete(response)
+
+    def enqueue(self, query_samples):
+        idx = [q.index for q in query_samples]
+        query_id = [q.id for q in query_samples]
+        if len(query_samples) < self.max_batchsize:
+            data, label = self.ds.get_samples(idx)
+            self.run_one_item(Item(query_id, idx, data, label))
+        else:
+            bs = self.max_batchsize
+            for i in range(0, len(idx), bs):
+                data, label = self.ds.get_samples(idx[i:i+bs])
+                self.run_one_item(Item(query_id[i:i+bs], idx[i:i+bs], data, label))
+
+    def finish(self):
+        pass
+
+
+class QueueRunner(RunnerBase):
+    def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
+        super().__init__(model, ds, threads, post_proc, max_batchsize)
+        self.tasks = Queue(maxsize=threads * 4)
+        self.workers = []
+        self.result_dict = {}
+
+        for _ in range(self.threads):
+            worker = threading.Thread(target=self.handle_tasks, args=(self.tasks,))
+            worker.daemon = True
+            self.workers.append(worker)
+            worker.start()
+
+    def handle_tasks(self, tasks_queue):
+        """Worker thread."""
+        while True:
+            qitem = tasks_queue.get()
+            if qitem is None:
+                # None in the queue indicates the parent want us to exit
+                tasks_queue.task_done()
+                break
+            self.run_one_item(qitem)
+            tasks_queue.task_done()
+
+    def enqueue(self, query_samples):
+        idx = [q.index for q in query_samples]
+        query_id = [q.id for q in query_samples]
+        if len(query_samples) < self.max_batchsize:
+            data, label = self.ds.get_samples(idx)
+            self.tasks.put(Item(query_id, idx, data, label))
+        else:
+            bs = self.max_batchsize
+            for i in range(0, len(idx), bs):
+                ie = i + bs
+                data, label = self.ds.get_samples(idx[i:ie])
+                self.tasks.put(Item(query_id[i:ie], idx[i:ie], data, label))
+
+    def finish(self):
+        # exit all threads
+        for _ in self.workers:
+            self.tasks.put(None)
+        for worker in self.workers:
+            worker.join()
+
+
+def add_results(final_results, name, result_dict, result_list, took, show_accuracy=False):
+    percentiles = [50., 80., 90., 95., 99., 99.9]
+    buckets = np.percentile(result_list, percentiles).tolist()
+    buckets_str = ",".join(["{}:{:.4f}".format(p, b) for p, b in zip(percentiles, buckets)])
+
+    if result_dict["total"] == 0:
+        result_dict["total"] = len(result_list)
+
+    # this is what we record for each run
+    result = {
+        "took": took,
+        "mean": np.mean(result_list),
+        "percentiles": {str(k): v for k, v in zip(percentiles, buckets)},
+        "qps": len(result_list) / took,
+        "count": len(result_list),
+        "good_items": result_dict["good"],
+        "total_items": result_dict["total"],
+    }
+    acc_str = ""
+    if show_accuracy:
+        result["accuracy"] = 100. * result_dict["good"] / result_dict["total"]
+        acc_str = ", acc={:.3f}%".format(result["accuracy"])
+        if "mAP" in result_dict:
+            result["mAP"] = 100. * result_dict["mAP"]
+            acc_str += ", mAP={:.3f}%".format(result["mAP"])
+
+    # add the result to the result dict
+    final_results[name] = result
+
+    # to stdout
+    print("{} qps={:.2f}, mean={:.4f}, time={:.3f}{}, queries={}, tiles={}".format(
+        name, result["qps"], result["mean"], took, acc_str,
+        len(result_list), buckets_str))
+
+
+def main():
+    global last_timeing
+    args = get_args()
+
+    log.info(args)
+
+    # find backend
+    backend = get_backend(args.backend)
+
+    # override image format if given
+    image_format = args.data_format if args.data_format else backend.image_format()
+
+    # --count applies to accuracy mode only and can be used to limit the number of images
+    # for testing. For perf model we always limit count to 200.
+    count_override = False
+    count = args.count
+    if count:
+        count_override = True
+
+    # dataset to use
+    wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
+    ds = wanted_dataset(data_path=args.dataset_path,
+                        image_list=args.dataset_list,
+                        name=args.dataset,
+                        image_format=image_format,
+                        pre_process=pre_proc,
+                        use_cache=args.cache,
+                        count=count, **kwargs)
+    # load model to backend
+    model = backend.load(args.model, inputs=args.inputs, outputs=args.outputs)
+    final_results = {
+        "runtime": model.name(),
+        "version": model.version(),
+        "time": int(time.time()),
+        "cmdline": str(args),
+    }
+
+    mlperf_conf = os.path.abspath(args.mlperf_conf)
+    if not os.path.exists(mlperf_conf):
+        log.error("{} not found".format(mlperf_conf))
+        sys.exit(1)
+
+    user_conf = os.path.abspath(args.user_conf)
+    if not os.path.exists(user_conf):
+        log.error("{} not found".format(user_conf))
+        sys.exit(1)
+
+    if args.output:
+        output_dir = os.path.abspath(args.output)
+        os.makedirs(output_dir, exist_ok=True)
+        os.chdir(output_dir)
+
+    #
+    # make one pass over the dataset to validate accuracy
+    #
+    count = ds.get_item_count()
+
+    # warmup
+    ds.load_query_samples([0])
+    for _ in range(5):
+        img, _ = ds.get_samples([0])
+        _ = backend.predict({backend.inputs[0]: img})
+    ds.unload_query_samples(None)
+
+    scenario = SCENARIO_MAP[args.scenario]
+    runner_map = {
+        lg.TestScenario.SingleStream: RunnerBase,
+        lg.TestScenario.MultiStream: QueueRunner,
+        lg.TestScenario.Server: QueueRunner,
+        lg.TestScenario.Offline: QueueRunner
+    }
+    runner = runner_map[scenario](model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize)
+
+    def issue_queries(query_samples):
+        runner.enqueue(query_samples)
+
+    def flush_queries():
+        pass
+
+    def process_latencies(latencies_ns):
+        # called by loadgen to show us the recorded latencies
+        global last_timeing
+        last_timeing = [t / NANO_SEC for t in latencies_ns]
+
+    log_output_settings = lg.LogOutputSettings()
+    log_output_settings.outdir = output_dir
+    log_output_settings.copy_summary_to_stdout = False
+    log_settings = lg.LogSettings()
+    log_settings.enable_trace = args.debug
+    log_settings.log_output = log_output_settings
+
+    settings = lg.TestSettings()
+    settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
+    settings.FromConfig(user_conf, args.model_name, args.scenario)
+    settings.scenario = scenario
+    settings.mode = lg.TestMode.PerformanceOnly
+    if args.accuracy:
+        settings.mode = lg.TestMode.AccuracyOnly
+    if args.find_peak_performance:
+        settings.mode = lg.TestMode.FindPeakPerformance
+
+    if args.time:
+        # override the time we want to run
+        settings.min_duration_ms = args.time * MILLI_SEC
+        settings.max_duration_ms = args.time * MILLI_SEC
+
+    if args.qps:
+        qps = float(args.qps)
+        settings.server_target_qps = qps
+        settings.offline_expected_qps = qps
+
+    if count_override:
+        settings.min_query_count = count
+        settings.max_query_count = count
+
+    if args.samples_per_query:
+        settings.multi_stream_samples_per_query = args.samples_per_query
+    if args.max_latency:
+        settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_target_latency_ns = int(args.max_latency * NANO_SEC)
+
+    sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies)
+    qsl = lg.ConstructQSL(count, min(count, 500), ds.load_query_samples, ds.unload_query_samples)
+
+    log.info("starting {}".format(scenario))
+    result_dict = {"good": 0, "total": 0, "scenario": str(scenario)}
+    runner.start_run(result_dict, args.accuracy)
+
+    lg.StartTestWithLogSettings(sut, qsl, settings, log_settings)
+
+    if not last_timeing:
+        last_timeing = runner.result_timing
+    if args.accuracy:
+        post_proc.finalize(result_dict, ds, output_dir=args.output)
+
+    add_results(final_results, "{}".format(scenario),
+                result_dict, last_timeing, time.time() - ds.last_loaded, args.accuracy)
+
+    runner.finish()
+    lg.DestroyQSL(qsl)
+    lg.DestroySUT(sut)
+
+    #
+    # write final results
+    #
+    if args.output:
+        with open("results.json", "w") as f:
+            json.dump(final_results, f, sort_keys=True, indent=4)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/anchor_generator.py b/retired_benchmarks/vision/classification_and_detection/python/models/anchor_generator.py
new file mode 100644
index 0000000000..9a2d9d4904
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/anchor_generator.py
@@ -0,0 +1,394 @@
+import torch
+import numpy as np
+
+
+# The following functions were taken from 
+# https://github.com/tensorflow/models/tree/master/research/object_detection
+# with minor modifications so that they use
+# torch operations instead
+
+def expanded_shape(orig_shape, start_dim, num_dims):
+    s = (1,) * num_dims
+    return orig_shape[:start_dim] + s + orig_shape[start_dim:]
+
+
+def meshgrid(x, y):
+    """Tiles the contents of x and y into a pair of grids.
+    Multidimensional analog of numpy.meshgrid, giving the same behavior if x and y
+    are vectors. Generally, this will give:
+    xgrid(i1, ..., i_m, j_1, ..., j_n) = x(j_1, ..., j_n)
+    ygrid(i1, ..., i_m, j_1, ..., j_n) = y(i_1, ..., i_m)
+    Keep in mind that the order of the arguments and outputs is reverse relative
+    to the order of the indices they go into, done for compatibility with numpy.
+    The output tensors have the same shapes.  Specifically:
+    xgrid.get_shape() = y.get_shape().concatenate(x.get_shape())
+    ygrid.get_shape() = y.get_shape().concatenate(x.get_shape())
+    Args:
+    x: A tensor of arbitrary shape and rank. xgrid will contain these values
+       varying in its last dimensions.
+    y: A tensor of arbitrary shape and rank. ygrid will contain these values
+       varying in its first dimensions.
+    Returns:
+    A tuple of tensors (xgrid, ygrid).
+    """
+    x = torch.as_tensor(x)
+    y = torch.as_tensor(y)
+    x_exp_shape = expanded_shape(x.shape, 0, y.dim())
+    y_exp_shape = expanded_shape(y.shape, y.dim(), x.dim())
+
+    xgrid = torch.reshape(x, x_exp_shape).repeat(*y_exp_shape)
+    ygrid = torch.reshape(y, y_exp_shape).repeat(*x_exp_shape)
+    new_shape = y.shape + x.shape
+    xgrid = xgrid.reshape(new_shape)
+    ygrid = ygrid.reshape(new_shape)
+
+    return xgrid, ygrid
+
+
+def tile_anchors(grid_height,
+                 grid_width,
+                 scales,
+                 aspect_ratios,
+                 base_anchor_size,
+                 anchor_stride,
+                 anchor_offset):
+  """Create a tiled set of anchors strided along a grid in image space.
+  This op creates a set of anchor boxes by placing a "basis" collection of
+  boxes with user-specified scales and aspect ratios centered at evenly
+  distributed points along a grid.  The basis collection is specified via the
+  scale and aspect_ratios arguments.  For example, setting scales=[.1, .2, .2]
+  and aspect ratios = [2,2,1/2] means that we create three boxes: one with scale
+  .1, aspect ratio 2, one with scale .2, aspect ratio 2, and one with scale .2
+  and aspect ratio 1/2.  Each box is multiplied by "base_anchor_size" before
+  placing it over its respective center.
+  Grid points are specified via grid_height, grid_width parameters as well as
+  the anchor_stride and anchor_offset parameters.
+  Args:
+    grid_height: size of the grid in the y direction (int or int scalar tensor)
+    grid_width: size of the grid in the x direction (int or int scalar tensor)
+    scales: a 1-d  (float) tensor representing the scale of each box in the
+      basis set.
+    aspect_ratios: a 1-d (float) tensor representing the aspect ratio of each
+      box in the basis set.  The length of the scales and aspect_ratios tensors
+      must be equal.
+    base_anchor_size: base anchor size as [height, width]
+      (float tensor of shape [2])
+    anchor_stride: difference in centers between base anchors for adjacent grid
+                   positions (float tensor of shape [2])
+    anchor_offset: center of the anchor with scale and aspect ratio 1 for the
+                   upper left element of the grid, this should be zero for
+                   feature networks with only VALID padding and even receptive
+                   field size, but may need some additional calculation if other
+                   padding is used (float tensor of shape [2])
+  Returns:
+    a BoxList holding a collection of N anchor boxes
+  """
+  aspect_ratios = torch.as_tensor(aspect_ratios, dtype=torch.float32)
+  scales = torch.as_tensor(scales, dtype=torch.float32)
+
+  ratio_sqrts = torch.sqrt(aspect_ratios)
+  heights = scales / ratio_sqrts * base_anchor_size[0]
+  widths = scales * ratio_sqrts * base_anchor_size[1]
+
+  # Get a grid of box centers
+  y_centers = torch.arange(grid_height, dtype=torch.float32)
+  y_centers = y_centers * anchor_stride[0] + anchor_offset[0]
+  x_centers = torch.arange(grid_width, dtype=torch.float32)
+  x_centers = x_centers * anchor_stride[1] + anchor_offset[1]
+
+  x_centers, y_centers = meshgrid(x_centers, y_centers)
+
+  widths_grid, x_centers_grid = meshgrid(widths, x_centers)
+  heights_grid, y_centers_grid = meshgrid(heights, y_centers)
+
+  bbox_centers = torch.stack([y_centers_grid, x_centers_grid], dim=3)
+  bbox_sizes = torch.stack([heights_grid, widths_grid], dim=3)
+  bbox_centers = torch.reshape(bbox_centers, [-1, 2])
+  bbox_sizes = torch.reshape(bbox_sizes, [-1, 2])
+  bbox_corners = _center_size_bbox_to_corners_bbox(bbox_centers, bbox_sizes)
+  return bbox_corners
+
+
+def _center_size_bbox_to_corners_bbox(centers, sizes):
+  """Converts bbox center-size representation to corners representation.
+  Args:
+    centers: a tensor with shape [N, 2] representing bounding box centers
+    sizes: a tensor with shape [N, 2] representing bounding boxes
+  Returns:
+    corners: tensor with shape [N, 4] representing bounding boxes in corners
+      representation
+  """
+  return torch.cat([centers - .5 * sizes, centers + .5 * sizes], 1)
+
+
+def create_ssd_anchors(num_layers=6,
+                       min_scale=0.2,
+                       max_scale=0.95,
+                       scales=None,
+                       aspect_ratios=(1.0, 2.0, 1.0 / 2, 3.0, 1.0 / 3),
+                       interpolated_scale_aspect_ratio=1.0,
+                       base_anchor_size=None,
+                       anchor_strides=None,
+                       anchor_offsets=None,
+                       reduce_boxes_in_lowest_layer=True):
+  """Creates MultipleGridAnchorGenerator for SSD anchors.
+  This function instantiates a MultipleGridAnchorGenerator that reproduces
+  ``default box`` construction proposed by Liu et al in the SSD paper.
+  See Section 2.2 for details. Grid sizes are assumed to be passed in
+  at generation time from finest resolution to coarsest resolution --- this is
+  used to (linearly) interpolate scales of anchor boxes corresponding to the
+  intermediate grid sizes.
+  Anchors that are returned by calling the `generate` method on the returned
+  MultipleGridAnchorGenerator object are always in normalized coordinates
+  and clipped to the unit square: (i.e. all coordinates lie in [0, 1]x[0, 1]).
+  Args:
+    num_layers: integer number of grid layers to create anchors for (actual
+      grid sizes passed in at generation time)
+    min_scale: scale of anchors corresponding to finest resolution (float)
+    max_scale: scale of anchors corresponding to coarsest resolution (float)
+    scales: As list of anchor scales to use. When not None and not empty,
+      min_scale and max_scale are not used.
+    aspect_ratios: list or tuple of (float) aspect ratios to place on each
+      grid point.
+    interpolated_scale_aspect_ratio: An additional anchor is added with this
+      aspect ratio and a scale interpolated between the scale for a layer
+      and the scale for the next layer (1.0 for the last layer).
+      This anchor is not included if this value is 0.
+    base_anchor_size: base anchor size as [height, width].
+      The height and width values are normalized to the minimum dimension of the
+      input height and width, so that when the base anchor height equals the
+      base anchor width, the resulting anchor is square even if the input image
+      is not square.
+    anchor_strides: list of pairs of strides in pixels (in y and x directions
+      respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
+      means that we want the anchors corresponding to the first layer to be
+      strided by 25 pixels and those in the second layer to be strided by 50
+      pixels in both y and x directions. If anchor_strides=None, they are set to
+      be the reciprocal of the corresponding feature map shapes.
+    anchor_offsets: list of pairs of offsets in pixels (in y and x directions
+      respectively). The offset specifies where we want the center of the
+      (0, 0)-th anchor to lie for each layer. For example, setting
+      anchor_offsets=[(10, 10), (20, 20)]) means that we want the
+      (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
+      and likewise that we want the (0, 0)-th anchor of the second layer to lie
+      at (25, 25) in pixel space. If anchor_offsets=None, then they are set to
+      be half of the corresponding anchor stride.
+    reduce_boxes_in_lowest_layer: a boolean to indicate whether the fixed 3
+      boxes per location is used in the lowest layer.
+  Returns:
+    a MultipleGridAnchorGenerator
+  """
+  if base_anchor_size is None:
+    base_anchor_size = [1.0, 1.0]
+  base_anchor_size = torch.tensor(base_anchor_size, dtype=torch.float32)
+  box_specs_list = []
+  if scales is None or not scales:
+    scales = [min_scale + (max_scale - min_scale) * i / (num_layers - 1)
+              for i in range(num_layers)] + [1.0]
+  else:
+    # Add 1.0 to the end, which will only be used in scale_next below and used
+    # for computing an interpolated scale for the largest scale in the list.
+    scales += [1.0]
+
+  for layer, scale, scale_next in zip(
+      range(num_layers), scales[:-1], scales[1:]):
+    layer_box_specs = []
+    if layer == 0 and reduce_boxes_in_lowest_layer:
+      layer_box_specs = [(0.1, 1.0), (scale, 2.0), (scale, 0.5)]
+    else:
+      for aspect_ratio in aspect_ratios:
+        layer_box_specs.append((scale, aspect_ratio))
+      # Add one more anchor, with a scale between the current scale, and the
+      # scale for the next layer, with a specified aspect ratio (1.0 by
+      # default).
+      if interpolated_scale_aspect_ratio > 0.0:
+        layer_box_specs.append((np.sqrt(scale*scale_next),
+                                interpolated_scale_aspect_ratio))
+    box_specs_list.append(layer_box_specs)
+
+  return MultipleGridAnchorGenerator(box_specs_list, base_anchor_size,
+                                     anchor_strides, anchor_offsets)
+
+class MultipleGridAnchorGenerator(object):
+  """Generate a grid of anchors for multiple CNN layers."""
+
+  def __init__(self,
+               box_specs_list,
+               base_anchor_size=None,
+               anchor_strides=None,
+               anchor_offsets=None,
+               clip_window=None):
+    """Constructs a MultipleGridAnchorGenerator.
+    To construct anchors, at multiple grid resolutions, one must provide a
+    list of feature_map_shape_list (e.g., [(8, 8), (4, 4)]), and for each grid
+    size, a corresponding list of (scale, aspect ratio) box specifications.
+    For example:
+    box_specs_list = [[(.1, 1.0), (.1, 2.0)],  # for 8x8 grid
+                      [(.2, 1.0), (.3, 1.0), (.2, 2.0)]]  # for 4x4 grid
+    To support the fully convolutional setting, we pass grid sizes in at
+    generation time, while scale and aspect ratios are fixed at construction
+    time.
+    Args:
+      box_specs_list: list of list of (scale, aspect ratio) pairs with the
+        outside list having the same number of entries as feature_map_shape_list
+        (which is passed in at generation time).
+      base_anchor_size: base anchor size as [height, width]
+                        (length-2 float tensor, default=[1.0, 1.0]).
+                        The height and width values are normalized to the
+                        minimum dimension of the input height and width, so that
+                        when the base anchor height equals the base anchor
+                        width, the resulting anchor is square even if the input
+                        image is not square.
+      anchor_strides: list of pairs of strides in pixels (in y and x directions
+        respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
+        means that we want the anchors corresponding to the first layer to be
+        strided by 25 pixels and those in the second layer to be strided by 50
+        pixels in both y and x directions. If anchor_strides=None, they are set
+        to be the reciprocal of the corresponding feature map shapes.
+      anchor_offsets: list of pairs of offsets in pixels (in y and x directions
+        respectively). The offset specifies where we want the center of the
+        (0, 0)-th anchor to lie for each layer. For example, setting
+        anchor_offsets=[(10, 10), (20, 20)]) means that we want the
+        (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
+        and likewise that we want the (0, 0)-th anchor of the second layer to
+        lie at (25, 25) in pixel space. If anchor_offsets=None, then they are
+        set to be half of the corresponding anchor stride.
+      clip_window: a tensor of shape [4] specifying a window to which all
+        anchors should be clipped. If clip_window is None, then no clipping
+        is performed.
+    Raises:
+      ValueError: if box_specs_list is not a list of list of pairs
+      ValueError: if clip_window is not either None or a tensor of shape [4]
+    """
+    if isinstance(box_specs_list, list) and all(
+        [isinstance(list_item, list) for list_item in box_specs_list]):
+      self._box_specs = box_specs_list
+    else:
+      raise ValueError('box_specs_list is expected to be a '
+                       'list of lists of pairs')
+    if base_anchor_size is None:
+      base_anchor_size = torch.tensor([256, 256], dtype=torch.float32)
+    self._base_anchor_size = base_anchor_size
+    self._anchor_strides = anchor_strides
+    self._anchor_offsets = anchor_offsets
+    if clip_window is not None and list(clip_window.shape) != [4]:
+      raise ValueError('clip_window must either be None or a shape [4] tensor')
+    self._clip_window = clip_window
+    self._scales = []
+    self._aspect_ratios = []
+    for box_spec in self._box_specs:
+      if not all([isinstance(entry, tuple) and len(entry) == 2
+                  for entry in box_spec]):
+        raise ValueError('box_specs_list is expected to be a '
+                         'list of lists of pairs')
+      scales, aspect_ratios = zip(*box_spec)
+      self._scales.append(scales)
+      self._aspect_ratios.append(aspect_ratios)
+
+    for arg, arg_name in zip([self._anchor_strides, self._anchor_offsets],
+                             ['anchor_strides', 'anchor_offsets']):
+      if arg and not (isinstance(arg, list) and
+                      len(arg) == len(self._box_specs)):
+        raise ValueError('%s must be a list with the same length '
+                         'as self._box_specs' % arg_name)
+      if arg and not all([
+          isinstance(list_item, tuple) and len(list_item) == 2
+          for list_item in arg
+      ]):
+        raise ValueError('%s must be a list of pairs.' % arg_name)
+
+
+  def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
+    """Generates a collection of bounding boxes to be used as anchors.
+    The number of anchors generated for a single grid with shape MxM where we
+    place k boxes over each grid center is k*M^2 and thus the total number of
+    anchors is the sum over all grids. In our box_specs_list example
+    (see the constructor docstring), we would place two boxes over each grid
+    point on an 8x8 grid and three boxes over each grid point on a 4x4 grid and
+    thus end up with 2*8^2 + 3*4^2 = 176 anchors in total. The layout of the
+    output anchors follows the order of how the grid sizes and box_specs are
+    specified (with box_spec index varying the fastest, followed by width
+    index, then height index, then grid index).
+    Args:
+      feature_map_shape_list: list of pairs of convnet layer resolutions in the
+        format [(height_0, width_0), (height_1, width_1), ...]. For example,
+        setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
+        correspond to an 8x8 layer followed by a 7x7 layer.
+      im_height: the height of the image to generate the grid for. If both
+        im_height and im_width are 1, the generated anchors default to
+        absolute coordinates, otherwise normalized coordinates are produced.
+      im_width: the width of the image to generate the grid for. If both
+        im_height and im_width are 1, the generated anchors default to
+        absolute coordinates, otherwise normalized coordinates are produced.
+    Returns:
+      boxes_list: a list of BoxLists each holding anchor boxes corresponding to
+        the input feature map shapes.
+    Raises:
+      ValueError: if feature_map_shape_list, box_specs_list do not have the same
+        length.
+      ValueError: if feature_map_shape_list does not consist of pairs of
+        integers
+    """
+    if not (isinstance(feature_map_shape_list, list)
+            and len(feature_map_shape_list) == len(self._box_specs)):
+      raise ValueError('feature_map_shape_list must be a list with the same '
+                       'length as self._box_specs')
+    if not all([isinstance(list_item, tuple) and len(list_item) == 2
+                for list_item in feature_map_shape_list]):
+      raise ValueError('feature_map_shape_list must be a list of pairs.')
+
+    im_height = float(im_height)
+    im_width = float(im_width)
+
+    if not self._anchor_strides:
+      anchor_strides = [(1.0 / float(pair[0]), 1.0 / float(pair[1]))
+                        for pair in feature_map_shape_list]
+    else:
+      anchor_strides = [(float(stride[0]) / im_height,
+                         float(stride[1]) / im_width)
+                        for stride in self._anchor_strides]
+    if not self._anchor_offsets:
+      anchor_offsets = [(0.5 * stride[0], 0.5 * stride[1])
+                        for stride in anchor_strides]
+    else:
+      anchor_offsets = [(float(offset[0]) / im_height,
+                         float(offset[1]) / im_width)
+                        for offset in self._anchor_offsets]
+
+    for arg, arg_name in zip([anchor_strides, anchor_offsets],
+                             ['anchor_strides', 'anchor_offsets']):
+      if not (isinstance(arg, list) and len(arg) == len(self._box_specs)):
+        raise ValueError('%s must be a list with the same length '
+                         'as self._box_specs' % arg_name)
+      if not all([isinstance(list_item, tuple) and len(list_item) == 2
+                  for list_item in arg]):
+        raise ValueError('%s must be a list of pairs.' % arg_name)
+
+    anchor_grid_list = []
+    min_im_shape = min(im_height, im_width)
+    scale_height = min_im_shape / im_height
+    scale_width = min_im_shape / im_width
+    base_anchor_size = [
+        scale_height * self._base_anchor_size[0],
+        scale_width * self._base_anchor_size[1]
+    ]
+    for feature_map_index, (grid_size, scales, aspect_ratios, stride,
+                            offset) in enumerate(
+                                zip(feature_map_shape_list, self._scales,
+                                    self._aspect_ratios, anchor_strides,
+                                    anchor_offsets)):
+      tiled_anchors = tile_anchors(
+          grid_height=grid_size[0],
+          grid_width=grid_size[1],
+          scales=scales,
+          aspect_ratios=aspect_ratios,
+          base_anchor_size=base_anchor_size,
+          anchor_stride=stride,
+          anchor_offset=offset)
+      if self._clip_window is not None:
+        raise NotImplementedError("Oups!")
+      num_anchors_in_layer = len(tiled_anchors)
+      anchor_indices = feature_map_index * torch.ones(num_anchors_in_layer)
+      anchor_grid_list.append(tiled_anchors)
+
+    return anchor_grid_list
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/base_model_r34.py b/retired_benchmarks/vision/classification_and_detection/python/models/base_model_r34.py
new file mode 100644
index 0000000000..ea224a7caa
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/base_model_r34.py
@@ -0,0 +1,204 @@
+"""
+    Load the vgg16 weight and save it to special file
+"""
+
+#from torchvision.models.vgg import vgg16
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+from torch.autograd import Variable
+from collections import OrderedDict
+
+from torchvision.models.resnet import resnet18, resnet34, resnet50
+
+def _ModifyConvStrideDilation(conv, stride=(1, 1), padding=None):
+    conv.stride = stride
+
+    if padding is not None:
+        conv.padding = padding
+
+def _ModifyBlock(block, bottleneck=False, **kwargs):
+    for m in list(block.children()):
+        if bottleneck:
+           _ModifyConvStrideDilation(m.conv2, **kwargs)
+        else:
+           _ModifyConvStrideDilation(m.conv1, **kwargs)
+
+        if m.downsample is not None:
+            # need to make sure no padding for the 1x1 residual connection
+            _ModifyConvStrideDilation(list(m.downsample.children())[0], **kwargs)
+
+class ResNet18(nn.Module):
+    def __init__(self):
+        super().__init__()
+        rn18 = resnet18(pretrained=True)
+
+
+        # discard last Resnet block, avrpooling and classification FC
+        # layer1 = up to and including conv3 block
+        self.layer1 = nn.Sequential(*list(rn18.children())[:6])
+        # layer2 = conv4 block only
+        self.layer2 = nn.Sequential(*list(rn18.children())[6:7])
+
+        # modify conv4 if necessary
+        # Always deal with stride in first block
+        modulelist = list(self.layer2.children())
+        _ModifyBlock(modulelist[0], stride=(1,1))
+
+    def forward(self, data):
+        layer1_activation = self.layer1(data)
+        x = layer1_activation
+        layer2_activation = self.layer2(x)
+
+        # Only need the output of conv4
+        return [layer2_activation]
+
+class ResNet34(nn.Module):
+    def __init__(self):
+        super().__init__()
+        rn34 = resnet34(pretrained=True)
+
+        # discard last Resnet block, avrpooling and classification FC
+        self.layer1 = nn.Sequential(*list(rn34.children())[:6])
+        self.layer2 = nn.Sequential(*list(rn34.children())[6:7])
+        # modify conv4 if necessary
+        # Always deal with stride in first block
+        modulelist = list(self.layer2.children())
+        _ModifyBlock(modulelist[0], stride=(1,1))
+
+
+    def forward(self, data):
+        layer1_activation = self.layer1(data)
+        x = layer1_activation
+        layer2_activation = self.layer2(x)
+
+        return [layer2_activation]
+
+class L2Norm(nn.Module):
+    """
+       Scale shall be learnable according to original paper
+       scale: initial scale number
+       chan_num: L2Norm channel number (norm over all channels)
+    """
+    def __init__(self, scale=20, chan_num=512):
+        super(L2Norm, self).__init__()
+        # Scale across channels
+        self.scale = \
+            nn.Parameter(torch.Tensor([scale]*chan_num).view(1, chan_num, 1, 1))
+
+    def forward(self, data):
+        # normalize accross channel
+        return self.scale*data*data.pow(2).sum(dim=1, keepdim=True).clamp(min=1e-12).rsqrt()
+
+
+
+def tailor_module(src_model, src_dir, tgt_model, tgt_dir):
+    state = torch.load(src_dir)
+    src_model.load_state_dict(state)
+    src_state = src_model.state_dict()
+    # only need features
+    keys1 = src_state.keys()
+    keys1 = [k for k in src_state.keys() if k.startswith("features")]
+    keys2 = tgt_model.state_dict().keys()
+
+    assert len(keys1) == len(keys2)
+    state = OrderedDict()
+
+    for k1, k2 in zip(keys1, keys2):
+        # print(k1, k2)
+        state[k2] = src_state[k1]
+    #diff_keys = state.keys() - target_model.state_dict().keys()
+    #print("Different Keys:", diff_keys)
+    # Remove unecessary keys
+    #for k in diff_keys:
+    #    state.pop(k)
+    tgt_model.load_state_dict(state)
+    torch.save(tgt_model.state_dict(), tgt_dir)
+
+# Default vgg16 in pytorch seems different from ssd
+def make_layers(cfg, batch_norm=False):
+    layers = []
+    in_channels = 3
+    for v in cfg:
+        if v == 'M':
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+        elif v == 'C':
+            # Notice ceil_mode is true
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
+        else:
+            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+            if batch_norm:
+                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
+            else:
+                layers += [conv2d, nn.ReLU(inplace=True)]
+            in_channels = v
+    return layers
+
+class Loss(nn.Module):
+    """
+        Implements the loss as the sum of the followings:
+        1. Confidence Loss: All labels, with hard negative mining
+        2. Localization Loss: Only on positive labels
+        Suppose input dboxes has the shape 8732x4
+    """
+
+    def __init__(self, dboxes):
+        super(Loss, self).__init__()
+        self.scale_xy = 1.0/dboxes.scale_xy
+        self.scale_wh = 1.0/dboxes.scale_wh
+
+        self.sl1_loss = nn.SmoothL1Loss(reduce=False)
+        self.dboxes = nn.Parameter(dboxes(order="xywh").transpose(0, 1).unsqueeze(dim = 0),
+            requires_grad=False)
+        # Two factor are from following links
+        # http://jany.st/post/2017-11-05-single-shot-detector-ssd-from-scratch-in-tensorflow.html
+        self.con_loss = nn.CrossEntropyLoss(reduce=False)
+
+    def _loc_vec(self, loc):
+        """
+            Generate Location Vectors
+        """
+        gxy = self.scale_xy*(loc[:, :2, :] - self.dboxes[:, :2, :])/self.dboxes[:, 2:, ]
+        gwh = self.scale_wh*(loc[:, 2:, :]/self.dboxes[:, 2:, :]).log()
+
+        return torch.cat((gxy, gwh), dim=1).contiguous()
+
+    def forward(self, ploc, plabel, gloc, glabel):
+        """
+            ploc, plabel: Nx4x8732, Nxlabel_numx8732
+                predicted location and labels
+
+            gloc, glabel: Nx4x8732, Nx8732
+                ground truth location and labels
+        """
+
+        mask = glabel > 0
+        pos_num = mask.sum(dim=1)
+        vec_gd = self._loc_vec(gloc)
+        # sum on four coordinates, and mask
+        sl1 = self.sl1_loss(ploc, vec_gd).sum(dim=1)
+        sl1 = (mask.float()*sl1).sum(dim=1)
+
+        # hard negative mining
+        con = self.con_loss(plabel, glabel)
+
+        # postive mask will never selected
+        con_neg = con.clone()
+        con_neg[mask] = 0
+        _, con_idx = con_neg.sort(dim=1, descending=True)
+        _, con_rank = con_idx.sort(dim=1)
+
+        # number of negative three times positive
+        neg_num = torch.clamp(3*pos_num, max=mask.size(1)).unsqueeze(-1)
+        neg_mask = con_rank < neg_num
+
+        closs = (con*(mask.float() + neg_mask.float())).sum(dim=1)
+
+        # avoid no object detected
+        total_loss = sl1 + closs
+        num_mask = (pos_num > 0).float()
+        pos_num = pos_num.float().clamp(min=1e-6)
+
+        ret = (total_loss*num_mask/pos_num).mean(dim=0)
+        return ret
+
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/convert_tf_weights.py b/retired_benchmarks/vision/classification_and_detection/python/models/convert_tf_weights.py
new file mode 100644
index 0000000000..02c51bf1bb
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/convert_tf_weights.py
@@ -0,0 +1,137 @@
+import torch
+
+import re
+from collections import OrderedDict
+
+
+def remap_tf_base_names(orig_weights):
+    prefix = "backbone."
+
+    # convs
+    weights = {
+        k: v for k, v in orig_weights.items() if "FeatureExtractor/MobilenetV1" in k
+    }
+    convs = {
+        k: v
+        for k, v in weights.items()
+        if "batchnorm" not in k and "pointwise_" not in k
+    }
+
+    matcher = re.compile("(.*)Conv2d_(\d+)")
+    mapping = {}
+    for k in convs.keys():
+        l = matcher.match(k).group(2)
+        name = "pointwise" if "pointwise" in k else "depthwise"
+        if l == "0":
+            name = "0"
+        mapping[k] = "{}{}.{}.weight".format(prefix, l, name)
+
+    # batch norm
+    weights = {
+        k: v
+        for k, v in orig_weights.items()
+        if "FeatureExtractor/MobilenetV1/MobilenetV1" in k
+    }
+    weights = {k: v for k, v in weights.items() if "pointwise_" not in k}
+    for k in weights.keys():
+        l = matcher.match(k).group(2)
+        name = "pointwise" if "pointwise" in k else "depthwise"
+        op = "scale" if "mul" in k else "bias"
+        if l == "0":
+            name = "0"
+        mapping[k] = "{}{}.{}/BatchNorm.{}".format(prefix, l, name, op)
+
+    return mapping
+
+
+def remap_tf_extras(orig_weights):
+    prefix = "extras."
+
+    weights = {
+        k: v for k, v in orig_weights.items() if "FeatureExtractor/MobilenetV1" in k
+    }
+    weights = {k: v for k, v in weights.items() if "pointwise_" in k}
+
+    matcher = re.compile("(.*)Conv2d_(\d+)_(\d)x(\d)")
+    mapping = {}
+    for k in weights.keys():
+        m = matcher.match(k)
+        l = int(m.group(2)) - 2
+        ks = int(m.group(3))
+        if ks == 1:
+            pos = 0
+        else:
+            pos = 2
+        wtype = "weight" if "weight" in k else "bias"
+        mapping[k] = "{}{}.{}.{}".format(prefix, l, pos, wtype)
+
+    return mapping
+
+
+def remap_tf_predictors(orig_weights):
+    mapping = {}
+
+    # regression
+    weights = {k: v for k, v in orig_weights.items() if "BoxPredictor" in k}
+    weights = {k: v for k, v in weights.items() if "BoxEncodingPredictor" in k}
+
+    matcher = re.compile("BoxPredictor_(\d+)")
+    for k in weights.keys():
+        pos = matcher.match(k).group(1)
+        wtype = "weight" if "weights" in k else "bias"
+        mapping[k] = "predictors.{}.regression.{}".format(pos, wtype)
+
+    # classification
+    weights = {k: v for k, v in orig_weights.items() if "BoxPredictor" in k}
+    weights = {k: v for k, v in weights.items() if "ClassPredictor" in k}
+
+    for k in weights.keys():
+        pos = matcher.match(k).group(1)
+        wtype = "weight" if "weights" in k else "bias"
+        mapping[k] = "predictors.{}.classification.{}".format(pos, wtype)
+
+    return mapping
+
+
+def remap_tf_names(weights):
+    layers_base = remap_tf_base_names(weights)
+    layers_extra = remap_tf_extras(weights)
+    layers_predictors = remap_tf_predictors(weights)
+
+    layers = {}
+    layers.update(layers_base)
+    layers.update(layers_extra)
+    layers.update(layers_predictors)
+
+    return layers
+
+
+def get_state_dict(weights):
+    layers = remap_tf_names(weights)
+    state_dict = OrderedDict()
+
+    for orig, new in layers.items():
+        weight = weights[orig]
+        weight = torch.as_tensor(weight, dtype=torch.float32)
+        if weight.dim() == 4:
+            p = (2, 3, 0, 1)
+            if "pointwise" in orig or "backbone.0." in new or "BoxPredictor" in orig:
+                p = (3, 2, 0, 1)
+            weight = weight.permute(*p).contiguous()
+        state_dict[new] = weight
+    return state_dict
+
+
+def read_tf_weights(frozen_model):
+    import tensorflow as tf
+    from tensorflow.python.framework import tensor_util
+    weights = {}
+    with tf.Session() as sess:
+        with tf.gfile.GFile(frozen_model, 'rb') as f:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(f.read())
+            tf.import_graph_def(graph_def)
+        for n in graph_def.node:
+            if n.op == 'Const':
+                weights[n.name] = tensor_util.MakeNdarray(n.attr['value'].tensor)
+    return weights
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py b/retired_benchmarks/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py
new file mode 100644
index 0000000000..dc77808c77
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py
@@ -0,0 +1,253 @@
+from collections import OrderedDict
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from models.anchor_generator import create_ssd_anchors
+from models.utils import Conv2d_tf
+from models.utils import BatchNorm2d
+from models.utils import BiasAdd
+from models.utils import nms
+from models.utils import decode_boxes
+
+
+def conv_bn(inp, oup, stride):
+    return nn.Sequential(
+        OrderedDict(
+            [
+                ("0", Conv2d_tf(inp, oup, 3, stride, padding="SAME", bias=False)),
+                ("0/BatchNorm", BiasAdd(oup)),
+                ("0/ReLU", nn.ReLU6(inplace=True)),
+            ]
+        )
+    )
+
+
+def conv_dw(inp, oup, stride):
+    return nn.Sequential(
+        OrderedDict(
+            [
+                (
+                    "depthwise",
+                    Conv2d_tf(
+                        inp, inp, 3, stride, padding="SAME", groups=inp, bias=False
+                    ),
+                ),
+                ("depthwise/BatchNorm", BatchNorm2d(inp)),
+                ("depthwise/ReLU", nn.ReLU6(inplace=True)),
+                ("pointwise", nn.Conv2d(inp, oup, 1, 1, 0, bias=False)),
+                ("pointwise/BatchNorm", BiasAdd(oup)),
+                ("pointwise/ReLU", nn.ReLU6(inplace=True)),
+            ]
+        )
+    )
+
+
+class MobileNetV1Base(nn.ModuleList):
+    def __init__(self, return_layers=[11, 13]):
+        super(MobileNetV1Base, self).__init__(
+            [
+                conv_bn(3, 32, 2),
+                conv_dw(32, 64, 1),
+                conv_dw(64, 128, 2),
+                conv_dw(128, 128, 1),
+                conv_dw(128, 256, 2),
+                conv_dw(256, 256, 1),
+                conv_dw(256, 512, 2),
+                conv_dw(512, 512, 1),
+                conv_dw(512, 512, 1),
+                conv_dw(512, 512, 1),
+                conv_dw(512, 512, 1),
+                conv_dw(512, 512, 1),
+                conv_dw(512, 1024, 2),
+                conv_dw(1024, 1024, 1),
+            ]
+        )
+        self.return_layers = return_layers
+
+    def forward(self, x):
+        out = []
+        for idx, module in enumerate(self):
+            x = module(x)
+            if idx in self.return_layers:
+                out.append(x)
+        return out
+
+
+class PredictionHead(nn.Module):
+    def __init__(self, in_channels, num_classes, num_anchors):
+        super(PredictionHead, self).__init__()
+        self.classification = nn.Conv2d(
+            in_channels, num_classes * num_anchors, kernel_size=1
+        )
+        self.regression = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1)
+
+        self.num_classes = num_classes
+        self.num_anchors = num_anchors
+
+    def forward(self, x):
+        bs = x.shape[0]
+        class_logits = self.classification(x)
+        box_regression = self.regression(x)
+
+        class_logits = class_logits.permute(0, 2, 3, 1).reshape(
+            bs, -1, self.num_classes
+        )
+        box_regression = box_regression.permute(0, 2, 3, 1).reshape(bs, -1, 4)
+
+        return class_logits, box_regression
+
+
+class Block(nn.Sequential):
+    def __init__(self, in_channels, mid_channels, out_channels):
+        super(Block, self).__init__(
+            nn.Conv2d(in_channels, out_channels=mid_channels, kernel_size=1),
+            nn.ReLU6(),
+            Conv2d_tf(
+                mid_channels, out_channels, kernel_size=3, stride=2, padding="SAME"
+            ),
+            nn.ReLU6(),
+        )
+
+
+class SSD(nn.Module):
+    def __init__(self, backbone, predictors, extras):
+        super(SSD, self).__init__()
+
+        self.backbone = backbone
+        self.extras = extras
+        self.predictors = predictors
+
+        # preprocess
+        self.image_size = 300
+        self.image_mean = 127.5
+        self.image_std = 127.5
+
+        self.coder_weights = torch.tensor((10, 10, 5, 5), dtype=torch.float32)
+        self._feature_map_shapes = None
+
+        # postprocess
+        self.nms_threshold = 0.6
+
+        # set it to 0.01 for better results but slower runtime
+        self.score_threshold = 0.3
+
+    def ssd_model(self, x):
+        feature_maps = self.backbone(x)
+
+        out = feature_maps[-1]
+        for module in self.extras:
+            out = module(out)
+            feature_maps.append(out)
+
+        results = []
+        for feature, module in zip(feature_maps, self.predictors):
+            results.append(module(feature))
+
+        class_logits, box_regression = list(zip(*results))
+        class_logits = torch.cat(class_logits, 1)
+        box_regression = torch.cat(box_regression, 1)
+
+        scores = torch.sigmoid(class_logits)
+        box_regression = box_regression.squeeze(0)
+
+        shapes = [o.shape[-2:] for o in feature_maps]
+        if shapes != self._feature_map_shapes:
+            # generate anchors for the sizes of the feature map
+            priors = create_ssd_anchors()._generate(shapes)
+            priors = torch.cat(priors, dim=0)
+            self.priors = priors.to(scores)
+            self._feature_map_shapes = shapes
+
+        self.coder_weights = self.coder_weights.to(scores)
+        if box_regression.dim()==2:
+            box_regression = box_regression[None]
+        boxes = decode_boxes(box_regression, self.priors, self.coder_weights)
+        # add a batch dimension
+        return scores, boxes
+
+
+    def forward(self, images):
+        """
+        Arguments:
+            images (torch.Tensor[N,C,H,W]):
+        """
+
+        scores, boxes = self.ssd_model(images)
+        list_boxes=[]; list_labels=[]; list_scores=[]
+        for b in range(len(scores)):
+            bboxes, blabels, bscores = self.filter_results(scores[b], boxes[b])
+            list_boxes.append(bboxes)
+            list_labels.append(blabels.long())
+            list_scores.append(bscores)
+        #boxes = self.rescale_boxes(boxes, height, width)
+        return [list_boxes, list_labels, list_scores]
+
+    def filter_results(self, scores, boxes):
+        # in order to avoid custom C++ extensions
+        # we use an NMS implementation written purely
+        # on python. This implementation is faster on the
+        # CPU, which is why we run this part on the CPU
+        cpu_device = torch.device("cpu")
+        #boxes = boxes[0]
+        #scores = scores[0]
+        boxes = boxes.to(cpu_device)
+        scores = scores.to(cpu_device)
+        selected_box_probs = []
+        labels = []
+        for class_index in range(1, scores.size(1)):
+            probs = scores[:, class_index]
+            mask = probs > self.score_threshold
+            probs = probs[mask]
+            subset_boxes = boxes[mask, :]
+            box_probs = torch.cat([subset_boxes, probs.reshape(-1, 1)], dim=1)
+            box_probs = nms(box_probs, self.nms_threshold)
+            selected_box_probs.append(box_probs)
+            labels.append(
+                torch.full((box_probs.size(0),), class_index, dtype=torch.int64)
+            )
+        selected_box_probs = torch.cat(selected_box_probs)
+        labels = torch.cat(labels)
+        return selected_box_probs[:, :4], labels, selected_box_probs[:, 4]
+
+    def rescale_boxes(self, boxes, height, width):
+        boxes[:, 0] *= width
+        boxes[:, 1] *= height
+        boxes[:, 2] *= width
+        boxes[:, 3] *= height
+        return boxes
+
+
+def create_mobilenetv1_ssd(num_classes):
+    backbone = MobileNetV1Base()
+
+    extras = nn.ModuleList(
+        [
+            Block(1024, 256, 512),
+            Block(512, 128, 256),
+            Block(256, 128, 256),
+            Block(256, 64, 128),
+        ]
+    )
+
+    predictors = nn.ModuleList(
+        [
+            PredictionHead(in_channels, num_classes, num_anchors)
+            for in_channels, num_anchors in zip(
+                (512, 1024, 512, 256, 256, 128), (3, 6, 6, 6, 6, 6)
+            )
+        ]
+    )
+
+    return SSD(backbone, predictors, extras)
+
+
+def get_tf_pretrained_mobilenet_ssd(weights_file):
+    from models.convert_tf_weights import get_state_dict, read_tf_weights
+
+    model = create_mobilenetv1_ssd(91)
+    weights = read_tf_weights(weights_file)
+    state_dict = get_state_dict(weights)
+    model.load_state_dict(state_dict)
+    return model
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/ssd_r34.py b/retired_benchmarks/vision/classification_and_detection/python/models/ssd_r34.py
new file mode 100644
index 0000000000..63e596b4a5
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/ssd_r34.py
@@ -0,0 +1,351 @@
+import torch
+import torch.nn as nn
+from models.base_model_r34 import ResNet34
+import numpy as np
+from math import sqrt, ceil
+import itertools
+import torch.nn.functional as F
+
+##Inspired by https://github.com/kuangliu/pytorch-ssd
+
+class Encoder(object):
+    """
+        Transform between (bboxes, lables) <-> SSD output
+        
+        dboxes: default boxes in size 8732 x 4, 
+            encoder: input ltrb format, output xywh format
+            decoder: input xywh format, output ltrb format 
+
+        decode:
+            input  : bboxes_in (Tensor 8732 x 4), scores_in (Tensor 8732 x nitems)
+            output : bboxes_out (Tensor nboxes x 4), labels_out (Tensor nboxes)
+            criteria : IoU threshold of bboexes
+            max_output : maximum number of output bboxes
+    """
+
+    def __init__(self, dboxes):
+        self.dboxes = dboxes(order="ltrb")
+        self.dboxes_xywh = dboxes(order="xywh").unsqueeze(dim=0)
+        self.nboxes = self.dboxes.size(0)
+        #print("# Bounding boxes: {}".format(self.nboxes))
+        self.scale_xy = torch.tensor(dboxes.scale_xy)
+        self.scale_wh = torch.tensor(dboxes.scale_wh)
+    
+    
+    def decode_batch(self, bboxes_in, scores_in,  criteria = 0.45, max_output=200):
+        self.dboxes = self.dboxes.to(bboxes_in)
+        self.dboxes_xywh = self.dboxes_xywh.to(bboxes_in)
+        bboxes, probs = scale_back_batch(bboxes_in, scores_in,self.scale_xy,self.scale_wh,self.dboxes_xywh)
+        boxes = []; labels=[]; scores=[]
+        for bbox, prob in zip(bboxes.split(1, 0), probs.split(1, 0)):
+            bbox = bbox.squeeze(0)
+            prob = prob.squeeze(0)
+            dbox,dlabel,dscore=self.decode_single(bbox, prob, criteria, max_output)
+            boxes.append(dbox)
+            labels.append(dlabel)
+            scores.append(dscore)
+           
+        return [boxes,labels,scores]
+
+    # perform non-maximum suppression
+    def decode_single(self, bboxes_in, scores_in, criteria, max_output, max_num=200):
+        # Reference to https://github.com/amdegroot/ssd.pytorch
+       
+        bboxes_out = []        
+        scores_out = []
+        labels_out = []
+
+        for i, score in enumerate(scores_in.split(1, 1)):
+            # skip background
+            if i == 0: continue
+            
+            score = score.squeeze(1)
+            mask = score > 0.05
+
+            bboxes, score = bboxes_in[mask, :], score[mask]
+            if score.size(0) == 0: continue
+
+            score_sorted, score_idx_sorted = score.sort(dim=0)
+        
+            # select max_output indices
+            score_idx_sorted = score_idx_sorted[-max_num:]
+            candidates = []
+        
+            while score_idx_sorted.numel() > 0:
+                idx = score_idx_sorted[-1].item()
+                bboxes_sorted = bboxes[score_idx_sorted, :]
+                bboxes_idx = bboxes[idx, :].unsqueeze(dim=0)
+                iou_sorted = calc_iou_tensor(bboxes_sorted, bboxes_idx).squeeze()
+                # we only need iou < criteria 
+                score_idx_sorted = score_idx_sorted[iou_sorted < criteria]
+                candidates.append(idx)
+
+            bboxes_out.append(bboxes[candidates, :])
+            scores_out.append(score[candidates])
+            labels_out.extend([i]*len(candidates))
+
+        bboxes_out, labels_out, scores_out = torch.cat(bboxes_out, dim=0), \
+               torch.tensor(labels_out, dtype=torch.long), \
+               torch.cat(scores_out, dim=0)
+
+
+        _, max_ids = scores_out.sort(dim=0)
+        max_ids = max_ids[-max_output:]
+        return bboxes_out[max_ids, :], labels_out[max_ids], scores_out[max_ids]
+
+@torch.jit.script
+def calc_iou_tensor(box1, box2):
+    """ Calculation of IoU based on two boxes tensor,
+        Reference to https://github.com/kuangliu/pytorch-ssd
+        input:
+            box1 (N, 4) 
+            box2 (M, 4)
+        output:
+            IoU (N, M)
+    """
+    N = box1.size(0)
+    M = box2.size(0)
+
+    be1 = box1.unsqueeze(1).expand(-1, M, -1)
+    be2 = box2.unsqueeze(0).expand(N, -1, -1)
+
+    # Left Top & Right Bottom
+    lt = torch.max(be1[:,:,:2], be2[:,:,:2])
+    rb = torch.min(be1[:,:,2:], be2[:,:,2:])
+    delta = rb - lt
+    delta.clone().masked_fill_(delta < 0,0)
+    intersect = delta[:,:,0]*delta[:,:,1]
+    delta1 = be1[:,:,2:] - be1[:,:,:2]
+    area1 = delta1[:,:,0]*delta1[:,:,1]
+    delta2 = be2[:,:,2:] - be2[:,:,:2]
+    area2 = delta2[:,:,0]*delta2[:,:,1]
+
+    iou = intersect/(area1 + area2 - intersect)
+    return iou
+
+@torch.jit.script
+def scale_back_batch(bboxes_in, scores_in,scale_xy,scale_wh,dboxes_xywh):
+    """
+        Do scale and transform from xywh to ltrb
+        suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox
+    """     
+    bboxes_in = bboxes_in.permute(0, 2, 1)
+    scores_in = scores_in.permute(0, 2, 1)
+
+    bboxes_in[:, :, :2] = scale_xy*bboxes_in[:, :, :2]
+    bboxes_in[:, :, 2:] = scale_wh*bboxes_in[:, :, 2:]
+    bboxes_in[:, :, :2] = bboxes_in[:, :, :2]*dboxes_xywh[:, :, 2:] + dboxes_xywh[:, :, :2]
+    bboxes_in[:, :, 2:] = bboxes_in[:, :, 2:].exp()*dboxes_xywh[:, :, 2:]
+    # Transform format to ltrb 
+    l, t, r, b = bboxes_in[:, :, 0] - 0.5*bboxes_in[:, :, 2],\
+                 bboxes_in[:, :, 1] - 0.5*bboxes_in[:, :, 3],\
+                 bboxes_in[:, :, 0] + 0.5*bboxes_in[:, :, 2],\
+                 bboxes_in[:, :, 1] + 0.5*bboxes_in[:, :, 3]
+    bboxes_in[:, :, 0] = l
+    bboxes_in[:, :, 1] = t
+    bboxes_in[:, :, 2] = r
+    bboxes_in[:, :, 3] = b
+    return bboxes_in, F.softmax(scores_in, dim=-1)
+
+
+class DefaultBoxes(object):
+    def __init__(self, fig_size, feat_size, steps, scales, aspect_ratios, \
+                       scale_xy=0.1, scale_wh=0.2):
+
+        self.feat_size = feat_size
+        self.fig_size_w,self.fig_size_h = fig_size
+
+        self.scale_xy_ = scale_xy
+        self.scale_wh_ = scale_wh
+        
+        # According to https://github.com/weiliu89/caffe
+        # Calculation method slightly different from paper
+        self.steps_w = [st[0] for st in steps]
+        self.steps_h = [st[1] for st in steps]
+        self.scales = scales
+        fkw = self.fig_size_w//np.array(self.steps_w)
+        fkh = self.fig_size_h//np.array(self.steps_h)
+        self.aspect_ratios = aspect_ratios
+
+        self.default_boxes = []
+        # size of feature and number of feature
+        for idx, sfeat in enumerate(self.feat_size):
+            sfeat_w,sfeat_h=sfeat
+            sk1 = scales[idx][0]/self.fig_size_w
+            sk2 = scales[idx+1][1]/self.fig_size_h
+            sk3 = sqrt(sk1*sk2)
+            all_sizes = [(sk1, sk1), (sk3, sk3)]
+            for alpha in aspect_ratios[idx]:
+                w, h = sk1*sqrt(alpha), sk1/sqrt(alpha)
+                all_sizes.append((w, h))
+                all_sizes.append((h, w))
+            for w, h in all_sizes:
+                for i, j in itertools.product(range(sfeat_w), range(sfeat_h)):
+                    cx, cy = (j+0.5)/fkh[idx], (i+0.5)/fkw[idx]
+                    self.default_boxes.append((cx, cy, w, h)) 
+        self.dboxes = torch.tensor(self.default_boxes)
+        self.dboxes.clamp_(min=0, max=1)
+        # For IoU calculation
+        self.dboxes_ltrb = self.dboxes.clone()
+        self.dboxes_ltrb[:, 0] = self.dboxes[:, 0] - 0.5*self.dboxes[:, 2]
+        self.dboxes_ltrb[:, 1] = self.dboxes[:, 1] - 0.5*self.dboxes[:, 3]
+        self.dboxes_ltrb[:, 2] = self.dboxes[:, 0] + 0.5*self.dboxes[:, 2]
+        self.dboxes_ltrb[:, 3] = self.dboxes[:, 1] + 0.5*self.dboxes[:, 3]
+    
+    @property
+    def scale_xy(self):
+        return self.scale_xy_
+    
+    @property    
+    def scale_wh(self):
+        return self.scale_wh_
+
+    def __call__(self, order="ltrb"):
+        if order == "ltrb": return self.dboxes_ltrb
+        if order == "xywh": return self.dboxes
+
+def dboxes_R34_coco(figsize,strides):
+    feat_size = [[50, 50], [25, 25], [13, 13], [7, 7], [3, 3], [3, 3]]
+    steps=[(int(figsize[0]/fs[0]),int(figsize[1]/fs[1])) for fs in feat_size]
+    scales = [(int(s*figsize[0]/300),int(s*figsize[1]/300)) for s in [21, 45, 99, 153, 207, 261, 315]] 
+    aspect_ratios =  [[2], [2, 3], [2, 3], [2, 3], [2], [2]] 
+    dboxes = DefaultBoxes(figsize, feat_size, steps, scales, aspect_ratios)
+    return dboxes
+
+class SSD_R34(nn.Module):
+    """
+        Build a SSD module to take 300x300 image input,
+        and output 8732 per class bounding boxes
+
+        vggt: pretrained vgg16 (partial) model
+        label_num: number of classes (including background 0)
+    """
+    def __init__(self, label_num=81, backbone='resnet34', model_path="./resnet34-333f7ec4.pth",strides=[3,3 ,2 ,2 ,2 ,2],extract_shapes=False):
+
+        super(SSD_R34, self).__init__()
+
+        self.label_num = label_num
+        self.strides = strides
+        if backbone == 'resnet34':
+            self.model = ResNet34()
+            out_channels = 256
+            self.out_chan = [out_channels, 512, 512, 256, 256, 256]
+        else:
+            raise ValueError('Invalid backbone chosen')
+
+        self._build_additional_features(self.out_chan)
+        self.extract_shapes=extract_shapes
+        # after l2norm, conv7, conv8_2, conv9_2, conv10_2, conv11_2
+        # classifer 1, 2, 3, 4, 5 ,6
+
+        self.num_defaults = [4, 6, 6, 6, 4, 4]
+        self.loc = []
+        self.conf = []
+        for nd, oc in zip(self.num_defaults, self.out_chan):
+            self.loc.append(nn.Conv2d(oc, nd*4, kernel_size=3, padding=1,stride=self.strides[0]))
+            self.conf.append(nn.Conv2d(oc, nd*label_num, kernel_size=3, padding=1,stride=self.strides[1]))
+
+        self.loc = nn.ModuleList(self.loc)
+        self.conf = nn.ModuleList(self.conf)
+        if not extract_shapes:
+            self.size=(1200,1200)
+            dboxes = dboxes_R34_coco(list(self.size),[3,3,2,2,2,2])
+            self.encoder = Encoder(dboxes)
+        # intitalize all weights
+        self._init_weights()
+        self.device = 1
+    def _build_additional_features(self, input_channels):
+        idx = 0
+        self.additional_blocks = []
+        
+        self.additional_blocks.append(nn.Sequential(
+            nn.Conv2d(input_channels[idx], 256, kernel_size=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, input_channels[idx+1], kernel_size=3, padding=1,stride=self.strides[2]),
+            nn.ReLU(inplace=True),
+        ))
+        idx += 1
+
+        self.additional_blocks.append(nn.Sequential(
+            nn.Conv2d(input_channels[idx], 256, kernel_size=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, input_channels[idx+1], kernel_size=3, padding=1, stride=self.strides[3]),
+            nn.ReLU(inplace=True),
+        ))
+        idx += 1
+
+        # conv9_1, conv9_2
+        self.additional_blocks.append(nn.Sequential(
+            nn.Conv2d(input_channels[idx], 128, kernel_size=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, input_channels[idx+1], kernel_size=3, padding=1, stride=self.strides[4]),
+            nn.ReLU(inplace=True),
+        ))
+        idx += 1
+
+        # conv10_1, conv10_2
+        self.additional_blocks.append(nn.Sequential(
+            nn.Conv2d(input_channels[idx], 128, kernel_size=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, input_channels[idx+1], kernel_size=3,stride=self.strides[5]),
+            nn.ReLU(inplace=True),
+        ))
+        idx += 1
+
+
+
+        # conv11_1, conv11_2
+        self.additional_blocks.append(nn.Sequential(
+            nn.Conv2d(input_channels[idx], 128, kernel_size=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, input_channels[idx+1], kernel_size=3),
+            nn.ReLU(inplace=True),
+        ))
+
+        self.additional_blocks = nn.ModuleList(self.additional_blocks)
+
+    def _init_weights(self):
+
+        layers = [
+            *self.additional_blocks,
+            *self.loc, *self.conf]
+
+        for layer in layers:
+            for param in layer.parameters():
+                if param.dim() > 1: nn.init.xavier_uniform_(param)
+
+    # Shape the classifier to the view of bboxes
+    def bbox_view(self, src, loc, conf,extract_shapes=False):
+        ret = []
+        features_shapes = []
+        for s, l, c in zip(src, loc, conf):
+            ret.append((l(s).view(s.size(0), 4, -1), c(s).view(s.size(0), self.label_num, -1)))
+            # extract shapes for prior box initliziation 
+            if extract_shapes:
+                ls=l(s)
+                features_shapes.append([ls.shape[2],ls.shape[3]])
+        locs, confs = list(zip(*ret))
+        locs, confs = torch.cat(locs, 2).contiguous(), torch.cat(confs, 2).contiguous()
+        return locs, confs,features_shapes
+
+    def forward(self, data):
+        layers = self.model(data)
+
+        # last result from network goes into additional blocks
+        x = layers[-1]
+        
+        additional_results = []
+        for i, l in enumerate(self.additional_blocks):
+            
+            x = l(x)
+            additional_results.append(x)
+
+        src = [*layers, *additional_results]
+        # Feature maps sizes depend on the image size. For 300x300 with strides=[1,1,2,2,2,1] it is 38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4, 1x1x4 
+        locs, confs,features_shapes = self.bbox_view(src, self.loc, self.conf,extract_shapes=self.extract_shapes)
+        if self.extract_shapes:
+            return locs, confs,features_shapes
+        else:    
+            # For SSD 300 with strides=[1,1,2,2,2,1] , shall return nbatch x 8732 x {nlabels, nlocs} results 
+            results=self.encoder.decode_batch(locs, confs, 0.50, 200) #[0]
+            return results #locs, confs,features_shapes
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/utils.py b/retired_benchmarks/vision/classification_and_detection/python/models/utils.py
new file mode 100644
index 0000000000..940722075a
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/utils.py
@@ -0,0 +1,198 @@
+import torch
+import math
+
+from torch import nn
+from torch.nn import functional as F
+
+
+class BatchNorm2d(nn.Module):
+    """
+    Fixed version of BatchNorm2d, which has only the scale and bias
+    """
+
+    def __init__(self, out):
+        super(BatchNorm2d, self).__init__()
+        self.register_buffer("scale", torch.ones(out))
+        self.register_buffer("bias", torch.zeros(out))
+
+    #@torch.jit.script_method
+    def forward(self, x):
+        scale = self.scale.view(1, -1, 1, 1)
+        bias = self.bias.view(1, -1, 1, 1)
+        return x * scale + bias
+
+
+class BiasAdd(nn.Module):
+    """
+    Fixed version of BatchNorm2d, which has only the scale and bias
+    """
+
+    def __init__(self, out):
+        super(BiasAdd, self).__init__()
+        self.register_buffer("bias", torch.zeros(out))
+
+    #@torch.jit.script_method
+    def forward(self, x):
+        bias = self.bias.view(1, -1, 1, 1)
+        return x + bias
+
+
+class Conv2d_tf(nn.Conv2d):
+    """
+    Conv2d with the padding behavior from TF
+    """
+
+    def __init__(self, *args, **kwargs):
+        super(Conv2d_tf, self).__init__(*args, **kwargs)
+        self.padding = kwargs.get("padding", "SAME")
+
+    def _compute_padding(self, input, dim):
+        input_size = input.size(dim + 2)
+        filter_size = self.weight.size(dim + 2)
+        effective_filter_size = (filter_size - 1) * self.dilation[dim] + 1
+        out_size = (input_size + self.stride[dim] - 1) // self.stride[dim]
+        total_padding = max(
+            0, (out_size - 1) * self.stride[dim] + effective_filter_size - input_size
+        )
+        additional_padding = int(total_padding % 2 != 0)
+
+        return additional_padding, total_padding
+
+    def forward(self, input):
+        #import pdb; pdb.set_trace()
+        if self.padding == "VALID":
+            return F.conv2d(
+                input,
+                self.weight,
+                self.bias,
+                self.stride,
+                padding=0,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+        rows_odd, padding_rows = self._compute_padding(input, dim=0)
+        cols_odd, padding_cols = self._compute_padding(input, dim=1)
+        if rows_odd or cols_odd:
+            input = F.pad(input, [0, cols_odd, 0, rows_odd])
+
+        return F.conv2d(
+            input,
+            self.weight,
+            self.bias,
+            self.stride,
+            padding=(padding_rows // 2, padding_cols // 2),
+            dilation=self.dilation,
+            groups=self.groups,
+        )
+
+
+def box_area(left_top, right_bottom):
+    """Compute the areas of rectangles given two corners.
+
+    Args:
+        left_top (N, 2): left top corner.
+        right_bottom (N, 2): right bottom corner.
+
+    Returns:
+        area (N): return the area.
+    """
+    hw = torch.clamp(right_bottom - left_top, min=0.0)
+    return hw[..., 0] * hw[..., 1]
+
+
+def box_iou(boxes0, boxes1, eps=1e-5):
+    """Return intersection-over-union (Jaccard index) of boxes.
+
+    Args:
+        boxes0 (N, 4): ground truth boxes.
+        boxes1 (N or 1, 4): predicted boxes.
+        eps: a small number to avoid 0 as denominator.
+    Returns:
+        iou (N): IoU values.
+    """
+    overlap_left_top = torch.max(boxes0[..., :2], boxes1[..., :2])
+    overlap_right_bottom = torch.min(boxes0[..., 2:], boxes1[..., 2:])
+
+    overlap_area = box_area(overlap_left_top, overlap_right_bottom)
+    area0 = box_area(boxes0[..., :2], boxes0[..., 2:])
+    area1 = box_area(boxes1[..., :2], boxes1[..., 2:])
+    return overlap_area / (area0 + area1 - overlap_area + eps)
+
+
+def nms(box_scores, iou_threshold):
+    """
+
+    Args:
+        box_scores (N, 5): boxes in corner-form and probabilities.
+        iou_threshold: intersection over union threshold.
+    Returns:
+         picked: a list of indexes of the kept boxes
+    """
+    scores = box_scores[:, -1]
+    boxes = box_scores[:, :-1]
+    picked = []
+    _, indexes = scores.sort(descending=True)
+    while len(indexes) > 0:
+        current = indexes[0]
+        picked.append(current.item())
+        if len(indexes) == 1:
+            break
+        current_box = boxes[current, :]
+        indexes = indexes[1:]
+        rest_boxes = boxes[indexes, :]
+        iou = box_iou(rest_boxes, current_box.unsqueeze(0))
+        indexes = indexes[iou <= iou_threshold]
+
+    return box_scores[picked, :]
+
+
+@torch.jit.script
+def decode_boxes(rel_codes, boxes, weights):
+    # type: (torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor
+
+    # perform some unpacking to make it JIT-fusion friendly
+    
+    #rel_codes=rel_codes[0][None]
+    wx = weights[1]
+    wy = weights[0]
+    ww = weights[3]
+    wh = weights[2]
+
+    boxes_x1 = boxes[:, 1].unsqueeze(1).unsqueeze(0)
+    boxes_y1 = boxes[:, 0].unsqueeze(1).unsqueeze(0)
+    boxes_x2 = boxes[:, 3].unsqueeze(1).unsqueeze(0)
+    boxes_y2 = boxes[:, 2].unsqueeze(1).unsqueeze(0)
+
+    dx = rel_codes[:,:, 1].unsqueeze(2)
+    dy = rel_codes[:,:, 0].unsqueeze(2)
+    dw = rel_codes[:,:, 3].unsqueeze(2)
+    dh = rel_codes[:,:, 2].unsqueeze(2)
+
+    # implementation starts here
+    widths = boxes_x2 - boxes_x1
+    heights = boxes_y2 - boxes_y1
+    ctr_x = boxes_x1 + 0.5 * widths
+    ctr_y = boxes_y1 + 0.5 * heights
+
+    dx = dx / wx
+    dy = dy / wy
+    dw = dw / ww
+    dh = dh / wh
+
+    pred_ctr_x = dx * widths + ctr_x
+    #import pdb; pdb.set_trace()
+    pred_ctr_y = dy * heights + ctr_y
+    pred_w = torch.exp(dw) * widths
+    pred_h = torch.exp(dh) * heights
+
+    pred_boxes = torch.cat(
+        [
+            pred_ctr_x - 0.5 * pred_w,
+            pred_ctr_y - 0.5 * pred_h,
+            pred_ctr_x + 0.5 * pred_w,
+            pred_ctr_y + 0.5 * pred_h,
+        ],
+        dim=2,
+    )
+    #import pdb; pdb.set_trace()
+    return pred_boxes
diff --git a/retired_benchmarks/vision/classification_and_detection/python/pycoco.py b/retired_benchmarks/vision/classification_and_detection/python/pycoco.py
new file mode 100644
index 0000000000..9318635696
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/pycoco.py
@@ -0,0 +1,433 @@
+__author__ = 'tylin'
+__version__ = '2.0'
+# Interface for accessing the Microsoft COCO dataset.
+
+# Microsoft COCO is a large image dataset designed for object detection,
+# segmentation, and caption generation. pycocotools is a Python API that
+# assists in loading, parsing and visualizing the annotations in COCO.
+# Please visit http://mscoco.org/ for more information on COCO, including
+# for the data, paper, and tutorials. The exact format of the annotations
+# is also described on the COCO website. For example usage of the pycocotools
+# please see pycocotools_demo.ipynb. In addition to this API, please download both
+# the COCO images and annotations in order to run the demo.
+
+# An alternative to using the API is to load the annotations directly
+# into Python dictionary
+# Using the API provides additional utility functions. Note that this API
+# supports both *instance* and *caption* annotations. In the case of
+# captions not all functions are defined (e.g. categories are undefined).
+
+# The following API functions are defined:
+#  COCO       - COCO api class that loads COCO annotation file and prepare data structures.
+#  decodeMask - Decode binary mask M encoded via run-length encoding.
+#  encodeMask - Encode binary mask M using run-length encoding.
+#  getAnnIds  - Get ann ids that satisfy given filter conditions.
+#  getCatIds  - Get cat ids that satisfy given filter conditions.
+#  getImgIds  - Get img ids that satisfy given filter conditions.
+#  loadAnns   - Load anns with the specified ids.
+#  loadCats   - Load cats with the specified ids.
+#  loadImgs   - Load imgs with the specified ids.
+#  annToMask  - Convert segmentation in an annotation to binary mask.
+#  showAnns   - Display the specified annotations.
+#  loadRes    - Load algorithm results and create API for accessing them.
+#  download   - Download COCO images from mscoco.org server.
+# Throughout the API "ann"=annotation, "cat"=category, and "img"=image.
+# Help on each functions can be accessed by: "help COCO>function".
+
+# See also COCO>decodeMask,
+# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds,
+# COCO>getImgIds, COCO>loadAnns, COCO>loadCats,
+# COCO>loadImgs, COCO>annToMask, COCO>showAnns
+
+# Microsoft COCO Toolbox.      version 2.0
+# Data, paper, and tutorials available at:  http://mscoco.org/
+# Code written by Piotr Dollar and Tsung-Yi Lin, 2014.
+# Licensed under the Simplified BSD License [see bsd.txt]
+
+import json
+import time
+#import matplotlib.pyplot as plt
+#from matplotlib.collections import PatchCollection
+#from matplotlib.patches import Polygon
+import numpy as np
+import copy
+import itertools
+from pycocotools import mask as maskUtils
+import os
+from collections import defaultdict
+import sys
+PYTHON_VERSION = sys.version_info[0]
+if PYTHON_VERSION == 2:
+    from urllib import urlretrieve
+elif PYTHON_VERSION == 3:
+    from urllib.request import urlretrieve
+
+
+def _isArrayLike(obj):
+    return hasattr(obj, '__iter__') and hasattr(obj, '__len__')
+
+
+class COCO:
+    def __init__(self, annotation_file=None):
+        """
+        Constructor of Microsoft COCO helper class for reading and visualizing annotations.
+        :param annotation_file (str): location of annotation file
+        :param image_folder (str): location to the folder that hosts images.
+        :return:
+        """
+        # load dataset
+        self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()
+        self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)
+        if not annotation_file == None:
+            print('loading annotations into memory...')
+            tic = time.time()
+            dataset = json.load(open(annotation_file, 'r'))
+            assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))
+            print('Done (t={:0.2f}s)'.format(time.time()- tic))
+            self.dataset = dataset
+            self.createIndex()
+
+    def createIndex(self):
+        # create index
+        print('creating index...')
+        anns, cats, imgs = {}, {}, {}
+        imgToAnns,catToImgs = defaultdict(list),defaultdict(list)
+        if 'annotations' in self.dataset:
+            for ann in self.dataset['annotations']:
+                imgToAnns[ann['image_id']].append(ann)
+                anns[ann['id']] = ann
+
+        if 'images' in self.dataset:
+            for img in self.dataset['images']:
+                imgs[img['id']] = img
+
+        if 'categories' in self.dataset:
+            for cat in self.dataset['categories']:
+                cats[cat['id']] = cat
+
+        if 'annotations' in self.dataset and 'categories' in self.dataset:
+            for ann in self.dataset['annotations']:
+                catToImgs[ann['category_id']].append(ann['image_id'])
+
+        print('index created!')
+
+        # create class members
+        self.anns = anns
+        self.imgToAnns = imgToAnns
+        self.catToImgs = catToImgs
+        self.imgs = imgs
+        self.cats = cats
+
+    def info(self):
+        """
+        Print information about the annotation file.
+        :return:
+        """
+        for key, value in self.dataset['info'].items():
+            print('{}: {}'.format(key, value))
+
+    def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
+        """
+        Get ann ids that satisfy given filter conditions. default skips that filter
+        :param imgIds  (int array)     : get anns for given imgs
+               catIds  (int array)     : get anns for given cats
+               areaRng (float array)   : get anns for given area range (e.g. [0 inf])
+               iscrowd (boolean)       : get anns for given crowd label (False or True)
+        :return: ids (int array)       : integer array of ann ids
+        """
+        imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
+        catIds = catIds if _isArrayLike(catIds) else [catIds]
+
+        if len(imgIds) == len(catIds) == len(areaRng) == 0:
+            anns = self.dataset['annotations']
+        else:
+            if not len(imgIds) == 0:
+                lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]
+                anns = list(itertools.chain.from_iterable(lists))
+            else:
+                anns = self.dataset['annotations']
+            anns = anns if len(catIds)  == 0 else [ann for ann in anns if ann['category_id'] in catIds]
+            anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
+        if not iscrowd == None:
+            ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
+        else:
+            ids = [ann['id'] for ann in anns]
+        return ids
+
+    def getCatIds(self, catNms=[], supNms=[], catIds=[]):
+        """
+        filtering parameters. default skips that filter.
+        :param catNms (str array)  : get cats for given cat names
+        :param supNms (str array)  : get cats for given supercategory names
+        :param catIds (int array)  : get cats for given cat ids
+        :return: ids (int array)   : integer array of cat ids
+        """
+        catNms = catNms if _isArrayLike(catNms) else [catNms]
+        supNms = supNms if _isArrayLike(supNms) else [supNms]
+        catIds = catIds if _isArrayLike(catIds) else [catIds]
+
+        if len(catNms) == len(supNms) == len(catIds) == 0:
+            cats = self.dataset['categories']
+        else:
+            cats = self.dataset['categories']
+            cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name']          in catNms]
+            cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
+            cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id']            in catIds]
+        ids = [cat['id'] for cat in cats]
+        return ids
+
+    def getImgIds(self, imgIds=[], catIds=[]):
+        '''
+        Get img ids that satisfy given filter conditions.
+        :param imgIds (int array) : get imgs for given ids
+        :param catIds (int array) : get imgs with all given cats
+        :return: ids (int array)  : integer array of img ids
+        '''
+        imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
+        catIds = catIds if _isArrayLike(catIds) else [catIds]
+
+        if len(imgIds) == len(catIds) == 0:
+            ids = self.imgs.keys()
+        else:
+            ids = set(imgIds)
+            for i, catId in enumerate(catIds):
+                if i == 0 and len(ids) == 0:
+                    ids = set(self.catToImgs[catId])
+                else:
+                    ids &= set(self.catToImgs[catId])
+        return list(ids)
+
+    def loadAnns(self, ids=[]):
+        """
+        Load anns with the specified ids.
+        :param ids (int array)       : integer ids specifying anns
+        :return: anns (object array) : loaded ann objects
+        """
+        if _isArrayLike(ids):
+            return [self.anns[id] for id in ids]
+        elif type(ids) == int:
+            return [self.anns[ids]]
+
+    def loadCats(self, ids=[]):
+        """
+        Load cats with the specified ids.
+        :param ids (int array)       : integer ids specifying cats
+        :return: cats (object array) : loaded cat objects
+        """
+        if _isArrayLike(ids):
+            return [self.cats[id] for id in ids]
+        elif type(ids) == int:
+            return [self.cats[ids]]
+
+    def loadImgs(self, ids=[]):
+        """
+        Load anns with the specified ids.
+        :param ids (int array)       : integer ids specifying img
+        :return: imgs (object array) : loaded img objects
+        """
+        if _isArrayLike(ids):
+            return [self.imgs[id] for id in ids]
+        elif type(ids) == int:
+            return [self.imgs[ids]]
+
+    def showAnns(self, anns):
+        """
+        Display the specified annotations.
+        :param anns (array of object): annotations to display
+        :return: None
+        """
+        if len(anns) == 0:
+            return 0
+        if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
+            datasetType = 'instances'
+        elif 'caption' in anns[0]:
+            datasetType = 'captions'
+        else:
+            raise Exception('datasetType not supported')
+        if datasetType == 'instances':
+            ax = plt.gca()
+            ax.set_autoscale_on(False)
+            polygons = []
+            color = []
+            for ann in anns:
+                c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
+                if 'segmentation' in ann:
+                    if type(ann['segmentation']) == list:
+                        # polygon
+                        for seg in ann['segmentation']:
+                            poly = np.array(seg).reshape((int(len(seg)/2), 2))
+                            polygons.append(Polygon(poly))
+                            color.append(c)
+                    else:
+                        # mask
+                        t = self.imgs[ann['image_id']]
+                        if type(ann['segmentation']['counts']) == list:
+                            rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
+                        else:
+                            rle = [ann['segmentation']]
+                        m = maskUtils.decode(rle)
+                        img = np.ones( (m.shape[0], m.shape[1], 3) )
+                        if ann['iscrowd'] == 1:
+                            color_mask = np.array([2.0,166.0,101.0])/255
+                        if ann['iscrowd'] == 0:
+                            color_mask = np.random.random((1, 3)).tolist()[0]
+                        for i in range(3):
+                            img[:,:,i] = color_mask[i]
+                        ax.imshow(np.dstack( (img, m*0.5) ))
+                if 'keypoints' in ann and type(ann['keypoints']) == list:
+                    # turn skeleton into zero-based index
+                    sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
+                    kp = np.array(ann['keypoints'])
+                    x = kp[0::3]
+                    y = kp[1::3]
+                    v = kp[2::3]
+                    for sk in sks:
+                        if np.all(v[sk]>0):
+                            plt.plot(x[sk],y[sk], linewidth=3, color=c)
+                    plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
+                    plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)
+            p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
+            ax.add_collection(p)
+            p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)
+            ax.add_collection(p)
+        elif datasetType == 'captions':
+            for ann in anns:
+                print(ann['caption'])
+
+    def loadRes(self, resFile):
+        """
+        Load result file and return a result api object.
+        :param   resFile (str)     : file name of result file
+        :return: res (obj)         : result api object
+        """
+        res = COCO()
+        res.dataset['images'] = [img for img in self.dataset['images']]
+
+        print('Loading and preparing results...')
+        tic = time.time()
+        if type(resFile) == str: #or type(resFile) == unicode:
+            anns = json.load(open(resFile))
+        elif type(resFile) == np.ndarray:
+            anns = self.loadNumpyAnnotations(resFile)
+        else:
+            anns = resFile
+        assert type(anns) == list, 'results in not an array of objects'
+        annsImgIds = [ann['image_id'] for ann in anns]
+        assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
+               'Results do not correspond to current coco set'
+        if 'caption' in anns[0]:
+            imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
+            res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
+            for id, ann in enumerate(anns):
+                ann['id'] = id+1
+        elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
+            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+            for id, ann in enumerate(anns):
+                bb = ann['bbox']
+                x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
+                if not 'segmentation' in ann:
+                    ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
+                ann['area'] = bb[2]*bb[3]
+                ann['id'] = id+1
+                ann['iscrowd'] = 0
+        elif 'segmentation' in anns[0]:
+            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+            for id, ann in enumerate(anns):
+                # now only support compressed RLE format as segmentation results
+                ann['area'] = maskUtils.area(ann['segmentation'])
+                if not 'bbox' in ann:
+                    ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
+                ann['id'] = id+1
+                ann['iscrowd'] = 0
+        elif 'keypoints' in anns[0]:
+            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+            for id, ann in enumerate(anns):
+                s = ann['keypoints']
+                x = s[0::3]
+                y = s[1::3]
+                x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y)
+                ann['area'] = (x1-x0)*(y1-y0)
+                ann['id'] = id + 1
+                ann['bbox'] = [x0,y0,x1-x0,y1-y0]
+        print('DONE (t={:0.2f}s)'.format(time.time()- tic))
+
+        res.dataset['annotations'] = anns
+        res.createIndex()
+        return res
+
+    def download(self, tarDir = None, imgIds = [] ):
+        '''
+        Download COCO images from mscoco.org server.
+        :param tarDir (str): COCO results directory name
+               imgIds (list): images to be downloaded
+        :return:
+        '''
+        if tarDir is None:
+            print('Please specify target directory')
+            return -1
+        if len(imgIds) == 0:
+            imgs = self.imgs.values()
+        else:
+            imgs = self.loadImgs(imgIds)
+        N = len(imgs)
+        if not os.path.exists(tarDir):
+            os.makedirs(tarDir)
+        for i, img in enumerate(imgs):
+            tic = time.time()
+            fname = os.path.join(tarDir, img['file_name'])
+            if not os.path.exists(fname):
+                urlretrieve(img['coco_url'], fname)
+            print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic))
+
+    def loadNumpyAnnotations(self, data):
+        """
+        Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
+        :param  data (numpy.ndarray)
+        :return: annotations (python nested list)
+        """
+        print('Converting ndarray to lists...')
+        assert(type(data) == np.ndarray)
+        print(data.shape)
+        assert(data.shape[1] == 7)
+        N = data.shape[0]
+        ann = []
+        for i in range(N):
+            if i % 1000000 == 0:
+                print('{}/{}'.format(i,N))
+            ann += [{
+                'image_id'  : int(data[i, 0]),
+                'bbox'  : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ],
+                'score' : data[i, 5],
+                'category_id': int(data[i, 6]),
+                }]
+        return ann
+
+    def annToRLE(self, ann):
+        """
+        Convert annotation which can be polygons, uncompressed RLE to RLE.
+        :return: binary mask (numpy 2D array)
+        """
+        t = self.imgs[ann['image_id']]
+        h, w = t['height'], t['width']
+        segm = ann['segmentation']
+        if type(segm) == list:
+            # polygon -- a single object might consist of multiple parts
+            # we merge all parts into one mask rle code
+            rles = maskUtils.frPyObjects(segm, h, w)
+            rle = maskUtils.merge(rles)
+        elif type(segm['counts']) == list:
+            # uncompressed RLE
+            rle = maskUtils.frPyObjects(segm, h, w)
+        else:
+            # rle
+            rle = ann['segmentation']
+        return rle
+
+    def annToMask(self, ann):
+        """
+        Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
+        :return: binary mask (numpy 2D array)
+        """
+        rle = self.annToRLE(ann)
+        m = maskUtils.decode(rle)
+        return m
diff --git a/retired_benchmarks/vision/classification_and_detection/python/version.py b/retired_benchmarks/vision/classification_and_detection/python/version.py
new file mode 100644
index 0000000000..1152dbb417
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/python/version.py
@@ -0,0 +1,3 @@
+
+version = '0.1.0'
+git_version = '05df3bae82ef9fc933277385eb778e3f22cd0c6a'
diff --git a/retired_benchmarks/vision/classification_and_detection/run_and_time.sh b/retired_benchmarks/vision/classification_and_detection/run_and_time.sh
new file mode 100755
index 0000000000..50af82cb76
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/run_and_time.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+source run_common.sh
+
+dockercmd=docker
+if [ $device == "gpu" ]; then
+    runtime="--runtime=nvidia"
+fi
+
+# copy the config to cwd so the docker contrainer has access
+cp ../../mlperf.conf .
+
+OUTPUT_DIR=`pwd`/output/$name
+if [ ! -d $OUTPUT_DIR ]; then
+    mkdir -p $OUTPUT_DIR
+fi
+
+image=mlperf-infer-imgclassify-$device
+docker build  -t $image -f Dockerfile.$device .
+opts="--mlperf_conf ./mlperf.conf --profile $profile $common_opt --model $model_path \
+    --dataset-path $DATA_DIR --output $OUTPUT_DIR $extra_args $EXTRA_OPS $@"
+
+docker run $runtime -e opts="$opts" \
+    -v $DATA_DIR:$DATA_DIR -v $MODEL_DIR:$MODEL_DIR -v `pwd`:/mlperf \
+    -v $OUTPUT_DIR:/output -v /proc:/host_proc \
+    -t $image:latest /mlperf/run_helper.sh 2>&1 | tee $OUTPUT_DIR/output.txt
diff --git a/retired_benchmarks/vision/classification_and_detection/run_common.sh b/retired_benchmarks/vision/classification_and_detection/run_common.sh
new file mode 100755
index 0000000000..6071019abe
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/run_common.sh
@@ -0,0 +1,115 @@
+#!/bin/bash
+
+if [ $# -lt 1 ]; then
+    echo "usage: $0 tf|onnxruntime|pytorch|tflite [resnet50|mobilenet|ssd-mobilenet|ssd-resnet34] [cpu|gpu]"
+    exit 1
+fi
+if [ "x$DATA_DIR" == "x" ]; then
+    echo "DATA_DIR not set" && exit 1
+fi
+if [ "x$MODEL_DIR" == "x" ]; then
+    echo "MODEL_DIR not set" && exit 1
+fi
+
+# defaults
+backend=tf
+model=resnet50
+device="cpu"
+
+for i in $* ; do
+    case $i in
+       tf|onnxruntime|tflite|pytorch) backend=$i; shift;;
+       cpu|gpu) device=$i; shift;;
+       gpu) device=gpu; shift;;
+       resnet50|mobilenet|ssd-mobilenet|ssd-resnet34|ssd-resnet34-tf) model=$i; shift;;
+    esac
+done
+
+if [ $device == "cpu" ] ; then
+    export CUDA_VISIBLE_DEVICES=""
+fi
+
+name="$model-$backend"
+extra_args=""
+
+#
+# tensorflow
+#
+if [ $name == "resnet50-tf" ] ; then
+    model_path="$MODEL_DIR/resnet50_v1.pb"
+    profile=resnet50-tf
+fi
+if [ $name == "mobilenet-tf" ] ; then
+    model_path="$MODEL_DIR/mobilenet_v1_1.0_224_frozen.pb"
+    profile=mobilenet-tf
+fi
+if [ $name == "ssd-mobilenet-tf" ] ; then
+    model_path="$MODEL_DIR/ssd_mobilenet_v1_coco_2018_01_28.pb"
+    profile=ssd-mobilenet-tf
+fi
+if [ $name == "ssd-resnet34-tf" ] ; then
+    model_path="$MODEL_DIR/resnet34_tf.22.1.pb"
+    profile=ssd-resnet34-tf
+fi
+
+#
+# onnxruntime
+#
+if [ $name == "resnet50-onnxruntime" ] ; then
+    model_path="$MODEL_DIR/resnet50_v1.onnx"
+    profile=resnet50-onnxruntime
+fi
+if [ $name == "mobilenet-onnxruntime" ] ; then
+    model_path="$MODEL_DIR/mobilenet_v1_1.0_224.onnx"
+    profile=mobilenet-onnxruntime
+fi
+if [ $name == "ssd-mobilenet-onnxruntime" ] ; then
+    model_path="$MODEL_DIR/ssd_mobilenet_v1_coco_2018_01_28.onnx"
+    profile=ssd-mobilenet-onnxruntime
+fi
+if [ $name == "ssd-resnet34-onnxruntime" ] ; then
+    # use onnx model converted from pytorch
+    model_path="$MODEL_DIR/resnet34-ssd1200.onnx"
+    profile=ssd-resnet34-onnxruntime
+fi
+if [ $name == "ssd-resnet34-tf-onnxruntime" ] ; then
+    # use onnx model converted from tensorflow
+    model_path="$MODEL_DIR/ssd_resnet34_mAP_20.2.onnx"
+    profile=ssd-resnet34-onnxruntime-tf
+fi
+
+#
+# pytorch
+#
+if [ $name == "resnet50-pytorch" ] ; then
+    model_path="$MODEL_DIR/resnet50-19c8e357.pth"
+    profile=resnet50-pytorch
+    extra_args="$extra_args --backend pytorch"
+fi
+if [ $name == "mobilenet-pytorch" ] ; then
+    model_path="$MODEL_DIR/mobilenet_v1_1.0_224.onnx"
+    profile=mobilenet-onnxruntime
+    extra_args="$extra_args --backend pytorch"
+fi
+if [ $name == "ssd-resnet34-pytorch" ] ; then
+    model_path="$MODEL_DIR/resnet34-ssd1200.pytorch"
+    profile=ssd-resnet34-pytorch
+fi
+
+
+#
+# tflite
+#
+if [ $name == "resnet50-tflite" ] ; then
+    model_path="$MODEL_DIR/resnet50_v1.tflite"
+    profile=resnet50-tf
+    extra_args="$extra_args --backend tflite"
+fi
+if [ $name == "mobilenet-tflite" ] ; then
+    model_path="$MODEL_DIR/mobilenet_v1_1.0_224.tflite"
+    profile=mobilenet-tf
+    extra_args="$extra_args --backend tflite"
+fi
+
+name="$backend-$device/$model"
+EXTRA_OPS="$extra_args $EXTRA_OPS"
diff --git a/retired_benchmarks/vision/classification_and_detection/run_helper.sh b/retired_benchmarks/vision/classification_and_detection/run_helper.sh
new file mode 100755
index 0000000000..1804869236
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/run_helper.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+echo "Clearing caches."
+sync && echo 3 | tee /host_proc/sys/vm/drop_caches
+
+
+cd /root
+
+common_opt=""
+
+start_fmt=$(date +%Y-%m-%d\ %r)
+echo "STARTING RUN AT $start_fmt"
+
+cd /mlperf
+python python/main.py $opts --output /output
+
+end_fmt=$(date +%Y-%m-%d\ %r)
+echo "ENDING RUN AT $end_fmt"
diff --git a/retired_benchmarks/vision/classification_and_detection/run_local.sh b/retired_benchmarks/vision/classification_and_detection/run_local.sh
new file mode 100755
index 0000000000..c014fc1a3d
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/run_local.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+source ./run_common.sh
+
+common_opt="--mlperf_conf ../../mlperf.conf"
+dataset="--dataset-path $DATA_DIR"
+OUTPUT_DIR=`pwd`/output/$name
+if [ ! -d $OUTPUT_DIR ]; then
+    mkdir -p $OUTPUT_DIR
+fi
+
+python python/main.py --profile $profile $common_opt --model $model_path $dataset \
+    --output $OUTPUT_DIR $EXTRA_OPS $@
diff --git a/retired_benchmarks/vision/classification_and_detection/setup.py b/retired_benchmarks/vision/classification_and_detection/setup.py
new file mode 100644
index 0000000000..c1e2fbcf07
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/setup.py
@@ -0,0 +1,85 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT license.
+
+import distutils.command.build
+import os
+import subprocess
+from collections import namedtuple
+from textwrap import dedent
+
+import setuptools.command.build_py
+import setuptools.command.develop
+import setuptools.command.install
+from setuptools import setup, find_packages, Command
+
+TOP_DIR = os.path.realpath(os.path.dirname(__file__))
+SRC_DIR = os.path.join(TOP_DIR, 'python')
+
+try:
+    git_version = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=TOP_DIR).decode('ascii').strip()
+except (OSError, subprocess.CalledProcessError):
+    git_version = None
+
+with open(os.path.join(TOP_DIR, 'VERSION_NUMBER')) as version_file:
+    VersionInfo = namedtuple('VersionInfo', ['version', 'git_version'])(
+        version=version_file.read().strip(),
+        git_version=git_version
+    )
+
+
+class create_version(Command):
+    user_options = []
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+    def run(self):
+        with open(os.path.join(SRC_DIR, 'version.py'), 'w') as f:
+            f.write(dedent('''
+            version = '{version}'
+            git_version = '{git_version}'
+            '''.format(**dict(VersionInfo._asdict()))))
+
+
+class build_py(setuptools.command.build_py.build_py):
+    def run(self):
+        self.run_command('create_version')
+        setuptools.command.build_py.build_py.run(self)
+
+
+class build(distutils.command.build.build):
+    def run(self):
+        self.run_command('build_py')
+
+
+class develop(setuptools.command.develop.develop):
+    def run(self):
+        self.run_command('create_version')
+        self.run_command('build')
+        setuptools.command.develop.develop.run(self)
+
+
+cmdclass = {
+    'create_version': create_version,
+    'build_py': build_py,
+    'build': build,
+    'develop': develop,
+}
+
+setup(
+    name="mlperf-inference",
+    version=VersionInfo.version,
+    description='mlperf inference benchmark',
+    setup_requires=['pytest-runner'],
+    tests_require=['graphviz', 'parameterized', 'pytest', 'pytest-cov', 'pyyaml'],
+    cmdclass=cmdclass,
+    packages=find_packages(),
+    author='guschmue@microsoft.com',
+    author_email='guschmue@microsoft.com',
+    url='https://github.com/mlperf/inference',
+    install_requires=['numpy>=1.14.1', 'onnx>=1.5', 'pybind11', 'Cython',
+                        'pycocotools', 'mlperf_loadgen', 'opencv-python-headless']
+)
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/accuracy-coco.py b/retired_benchmarks/vision/classification_and_detection/tools/accuracy-coco.py
new file mode 100644
index 0000000000..1e15999f1d
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/accuracy-coco.py
@@ -0,0 +1,125 @@
+"""
+Tool to calculate accuracy for loadgen accuracy output found in mlperf_log_accuracy.json
+We assume that loadgen's query index is in the same order as
+the images in coco's annotations/instances_val2017.json.
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import json
+import os
+
+import numpy as np
+
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+# pylint: disable=missing-docstring
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
+    parser.add_argument("--coco-dir", required=True, help="coco directory")
+    parser.add_argument("--verbose", action="store_true", help="verbose messages")
+    parser.add_argument("--output-file", default="coco-results.json", help="path to output file")
+    parser.add_argument("--use-inv-map", action="store_true", help="use inverse label map")
+    parser.add_argument("--remove-48-empty-images", action="store_true", help="used in case you removed 48 empty images while preprocessing the dataset")
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    cocoGt = COCO(os.path.join(args.coco_dir, "annotations/instances_val2017.json"))
+
+    if args.use_inv_map:
+        inv_map = [0] + cocoGt.getCatIds() # First label in inv_map is not used
+
+    with open(args.mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+    detections = []
+    image_ids = set()
+    seen = set()
+    no_results = 0
+    if args.remove_48_empty_images:        
+        im_ids = []
+        for i in cocoGt.getCatIds():
+            im_ids += cocoGt.catToImgs[i]
+        im_ids = list(set(im_ids))
+        image_map = [cocoGt.imgs[id] for id in im_ids]
+    else:
+        image_map = cocoGt.dataset["images"]
+
+    for j in results:
+        idx = j['qsl_idx']
+        # de-dupe in case loadgen sends the same image multiple times
+        if idx in seen:
+            continue
+        seen.add(idx)
+
+        # reconstruct from mlperf accuracy log
+        # what is written by the benchmark is an array of float32's:
+        # id, box[0], box[1], box[2], box[3], score, detection_class
+        # note that id is a index into instances_val2017.json, not the actual image_id
+        data = np.frombuffer(bytes.fromhex(j['data']), np.float32)
+        if len(data) < 7:
+            # handle images that had no results
+            image = image_map[idx]
+            # by adding the id to image_ids we make pycoco aware of the no-result image
+            image_ids.add(image["id"])
+            no_results += 1
+            if args.verbose:
+                print("no results: {}, idx={}".format(image["coco_url"], idx))
+            continue
+
+        for i in range(0, len(data), 7):
+            image_idx, ymin, xmin, ymax, xmax, score, label = data[i:i + 7]
+            image = image_map[idx]
+            image_idx = int(image_idx)
+            if image_idx != idx:
+                print("ERROR: loadgen({}) and payload({}) disagree on image_idx".format(idx, image_idx))
+            image_id = image["id"]
+            height, width = image["height"], image["width"]
+            ymin *= height
+            xmin *= width
+            ymax *= height
+            xmax *= width
+            loc = os.path.join(args.coco_dir, "val2017", image["file_name"])
+            label = int(label)
+            if args.use_inv_map:
+                label = inv_map[label]
+            # pycoco wants {imageID,x1,y1,w,h,score,class}
+            detections.append({
+                "image_id": image_id,
+                "image_loc": loc,
+                "category_id": label,
+                "bbox": [float(xmin), float(ymin), float(xmax - xmin), float(ymax - ymin)],
+                "score": float(score)})
+            image_ids.add(image_id)
+
+    with open(args.output_file, "w") as fp:
+        json.dump(detections, fp, sort_keys=True, indent=4)
+
+    cocoDt = cocoGt.loadRes(args.output_file) # Load from file to bypass error with Python3
+    cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+    cocoEval.params.imgIds = list(image_ids)
+    cocoEval.evaluate()
+    cocoEval.accumulate()
+    cocoEval.summarize()
+
+    print("mAP={:.3f}%".format(100. * cocoEval.stats[0]))
+    if args.verbose:
+        print("found {} results".format(len(results)))
+        print("found {} images".format(len(image_ids)))
+        print("found {} images with no results".format(no_results))
+        print("ignored {} dupes".format(len(results) - len(seen)))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/accuracy-imagenet.py b/retired_benchmarks/vision/classification_and_detection/tools/accuracy-imagenet.py
new file mode 100644
index 0000000000..1879e0f09a
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/accuracy-imagenet.py
@@ -0,0 +1,74 @@
+"""
+Tool to calculate accuracy for loadgen accuracy output found in mlperf_log_accuracy.json
+We assume that loadgen's query index is in the same order as the images in imagenet2012/val_map.txt.
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import json
+
+import numpy as np
+
+
+# pylint: disable=missing-docstring
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
+    parser.add_argument("--imagenet-val-file", required=True, help="path to imagenet val_map.txt")
+    parser.add_argument("--verbose", action="store_true", help="verbose messages")
+    parser.add_argument("--dtype", default="float32", choices=["float32", "int32", "int64"], help="data type of the label")
+    args = parser.parse_args()
+    return args
+
+dtype_map = {
+    "float32": np.float32,
+    "int32": np.int32,
+    "int64": np.int64
+}
+
+def main():
+    args = get_args()
+
+    imagenet = []
+    with open(args.imagenet_val_file, "r") as f:
+        for line in f:
+            cols = line.strip().split()
+            imagenet.append((cols[0], int(cols[1])))
+
+    with open(args.mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+    seen = set()
+    good = 0
+    for j in results:
+        idx = j['qsl_idx']
+
+        # de-dupe in case loadgen sends the same image multiple times
+        if idx in seen:
+            continue
+        seen.add(idx)
+
+        # get the expected label and image
+        img, label = imagenet[idx]
+
+        # reconstruct label from mlperf accuracy log
+        data = np.frombuffer(bytes.fromhex(j['data']), dtype_map[args.dtype])
+        found = int(data[0])
+        if label == found:
+            good += 1
+        else:
+            if args.verbose:
+                print("{}, expected: {}, found {}".format(img, label, found))
+
+    print("accuracy={:.3f}%, good={}, total={}".format(100. * good / len(seen), good, len(seen)))
+    if args.verbose:
+        print("found and ignored {} dupes".format(len(results) - len(seen)))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/calibrate_torchvision_model.py b/retired_benchmarks/vision/classification_and_detection/tools/calibrate_torchvision_model.py
new file mode 100644
index 0000000000..815e5fe206
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/calibrate_torchvision_model.py
@@ -0,0 +1,77 @@
+import argparse
+import os
+from tqdm import tqdm
+from PIL import Image
+
+import torch
+from torch.utils.data import DataLoader, Dataset
+
+import torchvision.transforms as transforms
+from torchvision.models.quantization import *
+
+
+class CalibrationDataset(Dataset):
+    def __init__(self, root, files, transform):
+        with open(files, 'r') as f:
+            self.files = [os.path.join(root, fn.strip()) for fn in f.readlines()]
+        self.transform = transform
+
+    def __getitem__(self, idx):
+        image = Image.open(self.files[idx]).convert('RGB')
+        image = self.transform(image)
+        return image
+
+    def __len__(self):
+        return len(self.files)
+
+
+def quantize_model(model, dataloader, backend='fbgemm'):
+    if backend not in torch.backends.quantized.supported_engines:
+        raise RuntimeError("Quantized backend not supported ")
+    torch.backends.quantized.engine = backend
+    model.cpu()
+    model.eval()
+    model.fuse_model()
+
+    # Make sure that weight qconfig matches that of the serialized models
+    model.qconfig = torch.quantization.get_default_qconfig(backend)
+    torch.quantization.prepare(model, inplace=True)
+    print('calibrating...')
+    for x in tqdm(dataloader):
+        model(x)
+    print('calibration DONE!')
+    torch.quantization.convert(model, inplace=True)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', type=str, default='resnet50')
+    parser.add_argument('--image-dir', type=str, default='imagenet/val')
+    parser.add_argument('--image-list', type=str, default='../../calibration/ImageNet/cal_image_list_option_1.txt')
+    args = parser.parse_args()
+    print(args)
+
+    transform = transforms.Compose([                                                   
+        transforms.Resize(256),                                                        
+        transforms.CenterCrop(224),                                                    
+        transforms.ToTensor(),                                                         
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),   
+    ])                                                                                 
+
+    dataset = CalibrationDataset(root=args.image_dir, files=args.image_list, transform=transform)
+    dataloader = DataLoader(dataset, batch_size=1)
+
+    model = eval(args.model)(pretrained=True, progress=True, quantize=False)
+    quantize_model(model, dataloader)
+    print(model)
+
+    inp = torch.rand(1, 3, 224, 224)
+    script_module = torch.jit.trace(model, inp)
+    save_path = f'{args.model}.pt'
+    torch.jit.save(script_module, save_path)
+    print(f'saved: {save_path}')
+
+
+if __name__=='__main__':
+    main()
+
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/ci-run.sh b/retired_benchmarks/vision/classification_and_detection/tools/ci-run.sh
new file mode 100755
index 0000000000..ad8bda3f08
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/ci-run.sh
@@ -0,0 +1,78 @@
+#!/bin/bash
+
+# where to find stuff
+export DATA_ROOT=`pwd`/data
+export MODEL_DIR=`pwd`/models
+
+export DATA_ROOT=/data
+export MODEL_DIR=$HOME/resnet_for_mlperf
+
+
+# options for official runs
+gopt="--max-batchsize 32 --samples-per-query 2 --threads 2"
+gopt="$gopt $@"
+
+result=output/results.csv
+
+function one_run {
+    # args: mode framework device model ...
+    scenario=$1; shift
+    model=$3
+    system_desc=$1-$2
+    case $model in
+    "mobilenet")
+      official_model="mobilenet"
+      acc_cmd="tools/accuracy-imagenet.py --imagenet-val-file $DATA_ROOT/imagenet2012/val_map.txt";;
+    "resnet50")
+      official_model="resnet"
+      acc_cmd="tools/accuracy-imagenet.py --imagenet-val-file $DATA_ROOT/imagenet2012/val_map.txt";;
+    "ssd-mobilenet")
+      official_model="ssd-small"
+      acc_cmd="tools/accuracy-coco.py --coco-dir $DATA_ROOT/coco";;
+    "ssd-resnet34")
+      official_model="ssd-large"
+      acc_cmd="tools/accuracy-coco.py --use-inv-map --coco-dir $DATA_ROOT/coco";;
+    "gnmt")
+      official_model="gnmt";;
+    esac
+    echo "====== $official_model/$scenario ====="
+    output_dir=output/$system_desc/$official_model/$scenario
+
+    # accuracy run
+    ./run_local.sh $@ --scenario $scenario --accuracy --output $output_dir/accuracy
+    python $acc_cmd --verbose --mlperf-accuracy-file $output_dir/accuracy/mlperf_log_accuracy.json \
+           >  $output_dir/accuracy/accuracy.txt
+    cat $output_dir/accuracy/accuracy.txt
+
+    # performance run
+    ./run_local.sh $@ --scenario $scenario --output $output_dir/performance
+    
+    # summary to csv
+    python tools/lglog2csv.py --input $output_dir/performance/mlperf_log_summary.txt --runtime "$1-$2" \
+      --machine $HOSTNAME --model $3 --name $1-$2-py >> $result
+}
+
+function one_model {
+    # args: framework device model ...
+    one_run SingleStream $@
+    one_run MultiStream $@
+    one_run Server $@
+    one_run Offline $@
+}
+
+
+mkdir output
+echo "build,date,machine,runtime,model,mode,qps,mean,latency_90,latency_99" > $result
+
+# TODO: add gnmt
+
+# using imagenet
+export DATA_DIR=$DATA_ROOT/imagenet2012
+#one_model onnxruntime cpu mobilenet $gopt
+#one_model tf gpu resnet50 $gopt
+
+# using coco
+export DATA_DIR=$DATA_ROOT/coco
+#one_model tf gpu ssd-mobilenet $gopt
+one_model tf gpu ssd-resnet34 $gopt
+#one_model onnxruntime cpu ssd-resnet34 $gopt
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/coco-analyze.py b/retired_benchmarks/vision/classification_and_detection/tools/coco-analyze.py
new file mode 100755
index 0000000000..7f22daa84e
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/coco-analyze.py
@@ -0,0 +1,134 @@
+"""
+coco result analyzer
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import collections
+import json
+import os
+import time
+import numpy as np
+
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+from PIL import Image, ImageFont, ImageDraw, ImageColor
+
+
+# pylint: disable=missing-docstring
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output", required=True, help="output directory")
+    parser.add_argument("--input", required=True, help="input json")
+    parser.add_argument("--coco", help="coco dataset root")
+    args = parser.parse_args()
+    return args
+
+
+def annotate_image(results, cocoGt, output):
+    os.makedirs(output, exist_ok=True)
+
+    new_results = collections.defaultdict(list)
+    for result in results:
+        new_results[result['image_id']].append(result)
+    print("Unique images = {}".format(len(new_results)))
+    results = new_results
+
+    for k, result in results.items():
+        draw = None
+        image = None
+        for v in result:
+            box = v['bbox']
+            score = v['score']
+            predicted_class = v["category_id"]
+            try:
+                predicted_class = cocoGt.loadCats(predicted_class)[0]["name"]
+            except Exception as ex:
+                print("category {} not found, image {}".format(predicted_class, v["image_loc"]))
+            # predicted_class = self.class_names[c]
+            # "image_loc": "/home/gs/data/coco300/val2017/000000397133.jpg",
+            if not draw:
+                image = Image.open(v['image_loc'])
+                if image.mode != 'RGB':
+                    image = image.convert('RGB')
+
+                draw = ImageDraw.Draw(image)
+            # font = ImageFont.truetype(font='FreeMono.ttf',
+            #            size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
+            try:
+                left, top, w, h = box
+                bottom = top + h
+                right = left + w
+                top = max(0, np.floor(top + 0.5).astype('int32'))
+                left = max(0, np.floor(left + 0.5).astype('int32'))
+                bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
+                right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
+                label = '{} {:.2f}'.format(predicted_class, score)
+                # label_size = draw.textsize(label, font)
+                label_size = draw.textsize(label)
+
+                if top - label_size[1] >= 0:
+                    text_origin = tuple(np.array([left, top - label_size[1]]))
+                else:
+                    text_origin = tuple(np.array([left, top + 1]))
+
+                color = ImageColor.getrgb("red")
+                thickness = 0
+                draw.rectangle([left + thickness, top + thickness, right - thickness, bottom - thickness], outline=color)
+                draw.text(text_origin, label, fill=color)  # , font=font)
+            except Exception as ex:
+                print("{} failed, ex {}".format(v['image_loc'], ex))
+        image.save(os.path.join(output, os.path.basename(v['image_loc'])))
+        del draw
+
+
+def calculate_map(results, cocoGt, output):
+    # bbox is expected as:
+    # x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
+
+    cocoDt = cocoGt.loadRes(results)
+    cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+    cocoEval.evaluate()
+    cocoEval.accumulate()
+    cocoEval.summarize()
+
+    all_metrics = {
+        "DetectionBoxes_Precision/mAP": cocoEval.stats[0],
+        "DetectionBoxes_Precision/mAP@.50IOU": cocoEval.stats[1],
+        "DetectionBoxes_Precision/mAP@.75IOU": cocoEval.stats[2],
+        "DetectionBoxes_Precision/mAP (small)": cocoEval.stats[3],
+        "DetectionBoxes_Precision/mAP (medium)": cocoEval.stats[4],
+        "DetectionBoxes_Precision/mAP (large)": cocoEval.stats[5],
+        "DetectionBoxes_Recall/AR@1": cocoEval.stats[6],
+        "DetectionBoxes_Recall/AR@10": cocoEval.stats[7],
+        "DetectionBoxes_Recall/AR@100": cocoEval.stats[8],
+        "DetectionBoxes_Recall/AR@100 (small)": cocoEval.stats[9],
+        "DetectionBoxes_Recall/AR@100 (medium)": cocoEval.stats[10],
+        "DetectionBoxes_Recall/AR@100 (large)": cocoEval.stats[11]
+    }
+
+    mAP = all_metrics['DetectionBoxes_Precision/mAP']
+    recall = all_metrics['DetectionBoxes_Recall/AR@100']
+    print("mAP={}, recall={}".format(mAP, recall))
+
+
+def main():
+    args = get_args()
+
+    with open(args.input, "r") as f:
+        results = json.load(f)
+
+    annotation_file = os.path.join(args.coco, "annotations/instances_val2017.json")
+    cocoGt = COCO(annotation_file)
+    annotate_image(results, cocoGt, args.output)
+    calculate_map(args.input, cocoGt, args.output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/convert-to-onnx.sh b/retired_benchmarks/vision/classification_and_detection/tools/convert-to-onnx.sh
new file mode 100755
index 0000000000..c2b0ae9076
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/convert-to-onnx.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+
+# convert all mlperf inference models to onnx using
+# tf2onnx (https://github.com/onnx/tensorflow-onnx).
+# We assume tf2onnx is already installed (pip install -U tfonnx)
+#
+# by default we use opset 8 but if your runtime supports it opset 10 is a better choice.
+
+export CUDA_VISIBLE_DEVICES=""
+
+if [ $# -lt 1 ]; then
+    echo "Usage: convert.sh model_dir ..."
+    exit 1
+fi
+
+model_dir=$1
+shift
+
+opts="$@"
+
+#
+# resnet50
+#
+python -m tf2onnx.convert --input $model_dir/resnet50_v1.pb --output $model_dir/resnet50_v1.onnx \
+    --inputs-as-nchw input_tensor:0 \
+    --inputs input_tensor:0 \
+    --outputs ArgMax:0,softmax_tensor:0 --opset 8 $opts
+
+#
+# mobilenet
+#
+python -m tf2onnx.convert --input $model_dir/mobilenet_v1_1.0_224_frozen.pb --output  $model_dir/mobilenet_v1_1.0_224.onnx \
+    --inputs-as-nchw input:0 \
+    --inputs input:0 \
+    --outputs MobilenetV1/Predictions/Reshape_1:0 --opset 8 $opts
+
+#
+# ssd_mobilenet_v1_coco
+#
+python -m tf2onnx.convert --input  $model_dir/ssd_mobilenet_v1_coco_2018_01_28.pb --output  $model_dir/ssd_mobilenet_v1_coco_2018_01_28.onnx \
+    --inputs image_tensor:0 \
+    --outputs num_detections:0,detection_boxes:0,detection_scores:0,detection_classes:0 \
+    --opset 10 $opts
+
+#
+# ssd_resnet34 (from tensorflow)
+#
+python -m tf2onnx.convert --input  $model_dir/resnet34_tf.22.1.pb --output $model_dir/ssd_resnet34_mAP_20.2.onnx \
+    --inputs image:0 --outputs detection_bboxes:0,detection_scores:0,detection_classes:0  \
+    --opset 10 $opts
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/lglog2csv.py b/retired_benchmarks/vision/classification_and_detection/tools/lglog2csv.py
new file mode 100644
index 0000000000..901bd795de
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/lglog2csv.py
@@ -0,0 +1,69 @@
+"""
+collect mlperf loadgen output to csv
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import re
+import time
+
+
+# pylint: disable=missing-docstring
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input", required=True, help="input json")
+    parser.add_argument("--runtime", required=True, help="runtime")
+    parser.add_argument("--machine", required=True, help="machine")
+    parser.add_argument("--model", required=True, help="model")
+    parser.add_argument("--name", required=True, help="name")
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    # print("name,date,machine,runtime,model,mode,qps,mean,latency_90,latency_99")
+
+    now = int(time.time())
+
+    with open(args.input, "r") as fp:
+        mode, mean, latency_90, latency_99, qps = None, 0, 0, 0, 0
+        for line in fp:
+            m = re.match("^Scenario\s*:\s*(\w+).*", line)
+            if m:
+                mode = m.group(1)
+            m = re.match("^90.00 percentile latency.*:\s*(\d+).*", line)
+            if m:
+                latency_90 = m.group(1)
+            m = re.match("^99.00 percentile latency.*:\s*(\d+).*", line)
+            if m:
+                latency_99 = m.group(1)
+            m = re.match("^Mean latency.*:\s*(\d+).*", line)
+            if m:
+                mean = m.group(1)
+            m = re.match("^Completed samples per second.*:\s*(\d+).*", line)
+            if m:
+                qps = m.group(1)
+            m = re.match("^QPS w/ loadgen overhead.*:\s*(\d+).*", line)
+            if m:
+                qps = m.group(1)
+            m = re.match("^Samples per second.*:\s*(\d+).*", line)
+            if m:
+                qps = m.group(1)
+            m = re.match("Test Parameters Used.*", line)
+            if m:
+                print("{},{},{},{},{},{},{},{},{},{}".format(
+                    args.name, now, args.machine, args.runtime, args.model,
+                    mode, qps, mean, latency_90, latency_99))
+                mode, mean, latency_90, latency_99, qps = None, 0, 0, 0, 0
+
+
+if __name__ == "__main__":
+    main()
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/make_fake_imagenet.sh b/retired_benchmarks/vision/classification_and_detection/tools/make_fake_imagenet.sh
new file mode 100755
index 0000000000..d1c821929d
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/make_fake_imagenet.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+
+opt=-q
+
+mkdir -p fake_imagenet/val
+cd fake_imagenet/val
+wget $opt https://upload.wikimedia.org/wikipedia/commons/thumb/5/57/7weeks_old.JPG/800px-7weeks_old.JPG
+wget $opt https://upload.wikimedia.org/wikipedia/commons/thumb/1/15/Welsh_Springer_Spaniel.jpg/800px-Welsh_Springer_Spaniel.jpg
+wget $opt https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Jammlich_crop.jpg/800px-Jammlich_crop.jpg
+wget $opt https://upload.wikimedia.org/wikipedia/commons/thumb/1/15/Pumiforme.JPG/782px-Pumiforme.JPG
+wget $opt https://upload.wikimedia.org/wikipedia/commons/thumb/e/e1/Sardinian_Warbler.jpg/800px-Sardinian_Warbler.jpg
+wget $opt https://upload.wikimedia.org/wikipedia/commons/thumb/b/b8/Cacatua_moluccensis_-Cincinnati_Zoo-8a.jpg/512px-Cacatua_moluccensis_-Cincinnati_Zoo-8a.jpg
+wget $opt https://upload.wikimedia.org/wikipedia/commons/thumb/9/9f/20180630_Tesla_Model_S_70D_2015_midnight_blue_left_front.jpg/800px-20180630_Tesla_Model_S_70D_2015_midnight_blue_left_front.jpg
+wget $opt https://upload.wikimedia.org/wikipedia/commons/thumb/b/b8/Porsche_991_silver_IAA.jpg/800px-Porsche_991_silver_IAA.jpg
+cd ..
+
+cat > val_map.txt <<EOF
+val/800px-Porsche_991_silver_IAA.jpg 817
+val/512px-Cacatua_moluccensis_-Cincinnati_Zoo-8a.jpg 89
+val/800px-Sardinian_Warbler.jpg 13
+val/800px-7weeks_old.JPG 207
+val/800px-20180630_Tesla_Model_S_70D_2015_midnight_blue_left_front.jpg 817
+val/800px-Welsh_Springer_Spaniel.jpg 156
+val/800px-Jammlich_crop.jpg 233
+val/782px-Pumiforme.JPG 285
+EOF
+
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/mobilenet-to-onnx.sh b/retired_benchmarks/vision/classification_and_detection/tools/mobilenet-to-onnx.sh
new file mode 100755
index 0000000000..907aaa2aaf
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/mobilenet-to-onnx.sh
@@ -0,0 +1,30 @@
+#!/bin/bash
+
+# convert mobilenet to onnx
+
+python -c "import tf2onnx" > /dev/null 2>&1
+if [ $? -ne 0 ]; then
+    echo "We use tensorflow-onnx to convert tensorflow to onnx."
+    echo "See https://github.com/onnx/tensorflow-onnx for details."    
+    echo "Install with:"
+    echo "pip install tf2onnx"
+    echo "or"
+    echo "pip install https://github.com/onnx/tensorflow-onnx"
+    exit 1
+fi
+
+model=resnet50_v1
+tfmodel=mobilenet_v1_1.0_224_frozen.pb
+onnxmodel=mobilenet_v1_1.0_224.onnx
+url=http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz
+tgz=$(basename $url)
+
+if [ ! -r $tgz ]; then
+    wget  -q  $url
+    tar zxvf $tgz
+fi
+python -m tf2onnx.convert --input $tfmodel --output $onnxmodel \
+    --fold_const --opset 8 --verbose \
+    --inputs-as-nchw input:0 \
+    --inputs input:0 \
+    --outputs MobilenetV1/Predictions/Reshape_1:0
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/pylintrc b/retired_benchmarks/vision/classification_and_detection/tools/pylintrc
new file mode 100755
index 0000000000..955e353049
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/pylintrc
@@ -0,0 +1,320 @@
+# taken from tensorflow, but indent=4
+[MASTER]
+
+# Specify a configuration file.
+#rcfile=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Profiled execution.
+profile=no
+
+# Add files or directories to the blacklist. They should be base names, not
+# paths.
+ignore=CVS
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# List of plugins (as comma separated values of python modules names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+
+[MESSAGES CONTROL]
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time. See also the "--disable" option for examples.
+enable=indexing-exception,old-raise-syntax
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once).You can also use "--disable=all" to
+# disable everything first and then reenable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use"--disable=all --enable=classes
+# --disable=W"
+disable=design,similarities,no-self-use,attribute-defined-outside-init,locally-disabled,star-args,pointless-except,bad-option-value,global-statement,fixme,suppressed-message,useless-suppression,locally-enabled,no-member,no-name-in-module,import-error,unsubscriptable-object,unbalanced-tuple-unpacking,undefined-variable,not-context-manager,useless-object-inheritance
+
+
+# Set the cache size for astng objects.
+cache-size=500
+
+
+[REPORTS]
+
+# Set the output format. Available formats are text, parseable, colorized, msvs
+# (visual studio) and html. You can also give a reporter class, eg
+# mypackage.mymodule.MyReporterClass.
+output-format=text
+
+# Put messages in a separate file for each module / package specified on the
+# command line instead of printing them on stdout. Reports (if any) will be
+# written in a file name "pylint_global.[txt|html]".
+files-output=no
+
+# Tells whether to display a full report or only the messages
+reports=no
+
+# Python expression which should return a note less than 10 (10 is the highest
+# note). You have access to the variables errors warning, statement which
+# respectively contain the number of errors / warnings messages and the total
+# number of statements analyzed. This is used by the global evaluation report
+# (RP0004).
+evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
+
+# Add a comment according to your evaluation note. This is used by the global
+# evaluation report (RP0004).
+comment=no
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details
+#msg-template=
+
+
+[TYPECHECK]
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# mixin class is detected if its name ends with "mixin" (case insensitive).
+ignore-mixin-members=yes
+
+# List of classes names for which member attributes should not be checked
+# (useful for classes with attributes dynamically set).
+ignored-classes=SQLObject
+
+# When zope mode is activated, add a predefined set of Zope acquired attributes
+# to generated-members.
+zope=no
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E0201 when accessed. Python regular
+# expressions are accepted.
+generated-members=REQUEST,acl_users,aq_parent
+
+# List of decorators that create context managers from functions, such as
+# contextlib.contextmanager.
+contextmanager-decorators=contextlib.contextmanager,contextlib2.contextmanager
+
+
+[VARIABLES]
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# A regular expression matching the beginning of the name of dummy variables
+# (i.e. not used).
+dummy-variables-rgx=^\*{0,2}(_$|unused_|dummy_)
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid to define new builtins when possible.
+additional-builtins=
+
+
+[BASIC]
+
+# Required attributes for module, separated by a comma
+required-attributes=
+
+# List of builtins function names that should not be used, separated by a comma
+bad-functions=apply,input,reduce
+
+
+# Disable the report(s) with the given id(s).
+# All non-Google reports are disabled by default.
+disable-report=R0001,R0002,R0003,R0004,R0101,R0102,R0201,R0202,R0220,R0401,R0402,R0701,R0801,R0901,R0902,R0903,R0904,R0911,R0912,R0913,R0914,R0915,R0921,R0922,R0923
+
+# Regular expression which should only match correct module names
+module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
+
+# Regular expression which should only match correct module level names
+const-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$
+
+# Regular expression which should only match correct class names
+class-rgx=^_?[A-Z][a-zA-Z0-9]*$
+
+# Regular expression which should only match correct function names
+function-rgx=^(?:(?P<camel_case>_?[A-Z][a-zA-Z0-9]*)|(?P<snake_case>_?[a-z][a-z0-9_]*))$
+
+# Regular expression which should only match correct method names
+method-rgx=^(?:(?P<exempt>__[a-z0-9_]+__|next)|(?P<camel_case>_{0,2}[A-Z][a-zA-Z0-9]*)|(?P<snake_case>_{0,2}[a-z][a-z0-9_]*))$
+
+# Regular expression which should only match correct instance attribute names
+attr-rgx=^_{0,2}[a-z][a-z0-9_]*$
+
+# Regular expression which should only match correct argument names
+argument-rgx=^[a-z][a-z0-9_]*$
+
+# Regular expression which should only match correct variable names
+variable-rgx=^[a-z][a-z0-9_]*$
+
+# Regular expression which should only match correct attribute names in class
+# bodies
+class-attribute-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$
+
+# Regular expression which should only match correct list comprehension /
+# generator expression variable names
+inlinevar-rgx=^[a-z][a-z0-9_]*$
+
+# Good variable names which should always be accepted, separated by a comma
+good-names=main,_
+
+# Bad variable names which should always be refused, separated by a comma
+bad-names=
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=(__.*__|main)
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=10
+
+
+[FORMAT]
+
+# Maximum number of characters on a single line.
+max-line-length=120
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=y
+
+# List of optional constructs for which whitespace checking is disabled
+no-space-check=
+
+# Maximum number of lines in a module
+max-module-lines=99999
+
+# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+
+[SIMILARITIES]
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+# Ignore comments when computing similarities.
+ignore-comments=yes
+
+# Ignore docstrings when computing similarities.
+ignore-docstrings=yes
+
+# Ignore imports when computing similarities.
+ignore-imports=no
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=
+
+
+[IMPORTS]
+
+# Deprecated modules which should not be used, separated by a comma
+deprecated-modules=regsub,TERMIOS,Bastion,rexec,sets
+
+# Create a graph of every (i.e. internal and external) dependencies in the
+# given file (report RP0402 must not be disabled)
+import-graph=
+
+# Create a graph of external dependencies in the given file (report RP0402 must
+# not be disabled)
+ext-import-graph=
+
+# Create a graph of internal dependencies in the given file (report RP0402 must
+# not be disabled)
+int-import-graph=
+
+
+[CLASSES]
+
+# List of interface methods to ignore, separated by a comma. This is used for
+# instance to not check methods defines in Zope's Interface base class.
+ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,__new__,setUp
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls,class_
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=mcs
+
+
+[DESIGN]
+
+# Maximum number of arguments for function / method
+max-args=5
+
+# Argument names that match this expression will be ignored. Default to name
+# with leading underscore
+ignored-argument-names=_.*
+
+# Maximum number of locals for function / method body
+max-locals=15
+
+# Maximum number of return / yield for function / method body
+max-returns=6
+
+# Maximum number of branch for function / method body
+max-branches=12
+
+# Maximum number of statements in function / method body
+max-statements=50
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=2
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when being caught. Defaults to
+# "Exception"
+overgeneral-exceptions=Exception,StandardError,BaseException
+
+
+[AST]
+
+# Maximum line length for lambdas
+short-func-length=1
+
+# List of module members that should be marked as deprecated.
+# All of the string functions are listed in 4.1.4 Deprecated string functions
+# in the Python 2.4 docs.
+deprecated-members=string.atof,string.atoi,string.atol,string.capitalize,string.expandtabs,string.find,string.rfind,string.index,string.rindex,string.count,string.lower,string.split,string.rsplit,string.splitfields,string.join,string.joinfields,string.lstrip,string.rstrip,string.strip,string.swapcase,string.translate,string.upper,string.ljust,string.rjust,string.center,string.zfill,string.replace,sys.exitfunc
+
+
+[DOCSTRING]
+
+# List of exceptions that do not need to be mentioned in the Raises section of
+# a docstring.
+ignore-exceptions=AssertionError,NotImplementedError,StopIteration,TypeError
+
+
+
+[TOKENS]
+
+# Number of spaces of indent required when the last token on the preceding line
+# is an open (, [, or {.
+indent-after-paren=4
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/resnet50-to-tflite.sh b/retired_benchmarks/vision/classification_and_detection/tools/resnet50-to-tflite.sh
new file mode 100755
index 0000000000..39388fc1aa
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/resnet50-to-tflite.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+# convert resnet50 to tflite
+
+
+model=resnet50_v1
+tfmodel="$model.pb"
+url=https://zenodo.org/record/2535873/files/$tfmodel
+
+if [ ! -r $local ]; then
+    wget -o $local -q  $url
+fi
+
+tflite_convert --graph_def_file $tfmodel --output_file $model.tflite \
+    --input_arrays input_tensor \
+    --output_arrays ArgMax,softmax_tensor 
+
+tflite_convert --graph_def_file  $tfmodel --output_file $model"_quant.tflite" \
+    --input_arrays input_tensor \
+    --output_arrays ArgMax  \
+    --inference_type QUANTIZED_UINT8 --inference_input_type QUANTIZED_UINT8 \
+    --input_shape=1,224,224,3 \
+    --mean_values=128 \
+    --std_dev_values=128 \
+    --default_ranges_min=0 \
+    --default_ranges_max=6
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/resnet_save.py b/retired_benchmarks/vision/classification_and_detection/tools/resnet_save.py
new file mode 100755
index 0000000000..fca66ea269
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/resnet_save.py
@@ -0,0 +1,298 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains utility and supporting functions for ResNet.
+
+  This module contains ResNet code which does not directly build layers. This
+includes dataset management, hyperparameter and optimizer code, and argument
+parsing. Code for defining the ResNet layers can be found in resnet_model.py.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+import math
+import multiprocessing
+import os
+
+# pylint: disable=g-bad-import-order
+from absl import flags
+import tensorflow as tf
+from tensorflow.contrib.data.python.ops import threadpool
+
+from official.resnet import resnet_model
+from official.utils.flags import core as flags_core
+from official.utils.export import export
+from official.utils.logs import hooks_helper
+from official.utils.logs import logger
+from official.resnet import imagenet_preprocessing
+from official.utils.misc import distribution_utils
+from official.utils.misc import model_helpers
+
+
+
+
+def image_bytes_serving_input_fn(image_shape, dtype=tf.float32):
+  """Serving input fn for raw jpeg images."""
+
+  def _preprocess_image(image_bytes):
+    """Preprocess a single raw image."""
+    # Bounding box around the whole image.
+    bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=dtype, shape=[1, 1, 4])
+    height, width, num_channels = image_shape
+    image = imagenet_preprocessing.preprocess_image(
+        image_bytes, bbox, height, width, num_channels, is_training=False)
+    return image
+
+  image_bytes_list = tf.placeholder(
+      shape=[None], dtype=tf.string, name='input_tensor')
+  images = tf.map_fn(
+      _preprocess_image, image_bytes_list, back_prop=False, dtype=dtype)
+  return tf.estimator.export.TensorServingInputReceiver(
+      images, {'image_bytes': image_bytes_list})
+
+
+
+
+def resnet_model_fn(features, labels, mode, model_class,
+                    resnet_size, weight_decay, learning_rate_fn, momentum,
+                    data_format, resnet_version, loss_scale,
+                    loss_filter_fn=None, dtype=resnet_model.DEFAULT_DTYPE,
+                    fine_tune=False):
+  """Shared functionality for different resnet model_fns.
+
+  Initializes the ResnetModel representing the model layers
+  and uses that model to build the necessary EstimatorSpecs for
+  the `mode` in question. For training, this means building losses,
+  the optimizer, and the train op that get passed into the EstimatorSpec.
+  For evaluation and prediction, the EstimatorSpec is returned without
+  a train op, but with the necessary parameters for the given mode.
+
+  Args:
+    features: tensor representing input images
+    labels: tensor representing class labels for all input images
+    mode: current estimator mode; should be one of
+      `tf.estimator.ModeKeys.TRAIN`, `EVALUATE`, `PREDICT`
+    model_class: a class representing a TensorFlow model that has a __call__
+      function. We assume here that this is a subclass of ResnetModel.
+    resnet_size: A single integer for the size of the ResNet model.
+    weight_decay: weight decay loss rate used to regularize learned variables.
+    learning_rate_fn: function that returns the current learning rate given
+      the current global_step
+    momentum: momentum term used for optimization
+    data_format: Input format ('channels_last', 'channels_first', or None).
+      If set to None, the format is dependent on whether a GPU is available.
+    resnet_version: Integer representing which version of the ResNet network to
+      use. See README for details. Valid values: [1, 2]
+    loss_scale: The factor to scale the loss for numerical stability. A detailed
+      summary is present in the arg parser help text.
+    loss_filter_fn: function that takes a string variable name and returns
+      True if the var should be included in loss calculation, and False
+      otherwise. If None, batch_normalization variables will be excluded
+      from the loss.
+    dtype: the TensorFlow dtype to use for calculations.
+    fine_tune: If True only train the dense layers(final layers).
+
+  Returns:
+    EstimatorSpec parameterized according to the input params and the
+    current mode.
+  """
+
+  model = model_class(resnet_size, data_format, resnet_version=resnet_version,
+                      dtype=dtype)
+
+  logits = model(features, mode == tf.estimator.ModeKeys.TRAIN)
+
+  # This acts as a no-op if the logits are already in fp32 (provided logits are
+  # not a SparseTensor). If dtype is is low precision, logits must be cast to
+  # fp32 for numerical stability.
+  logits = tf.cast(logits, tf.float32)
+
+  predictions = {
+      'classes': tf.argmax(logits, axis=1),
+      'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
+  }
+
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    # Return the predictions and the specification for serving a SavedModel
+    return tf.estimator.EstimatorSpec(
+        mode=mode,
+        predictions=predictions,
+        export_outputs={
+            'predict': tf.estimator.export.PredictOutput(predictions)
+        })
+
+  # Calculate loss, which includes softmax cross entropy and L2 regularization.
+  cross_entropy = tf.losses.sparse_softmax_cross_entropy(
+      logits=logits, labels=labels)
+
+  # Create a tensor named cross_entropy for logging purposes.
+  tf.identity(cross_entropy, name='cross_entropy')
+
+  # If no loss_filter_fn is passed, assume we want the default behavior,
+  # which is that batch_normalization variables are excluded from loss.
+  def exclude_batch_norm(name):
+    return 'batch_normalization' not in name
+  loss_filter_fn = loss_filter_fn or exclude_batch_norm
+
+  # Add weight decay to the loss.
+  l2_loss = weight_decay * tf.add_n(
+      # loss is computed using fp32 for numerical stability.
+      [tf.nn.l2_loss(tf.cast(v, tf.float32)) for v in tf.trainable_variables()
+       if loss_filter_fn(v.name)])
+  tf.summary.scalar('l2_loss', l2_loss)
+  loss = cross_entropy + l2_loss
+
+  return tf.estimator.EstimatorSpec(
+      mode=mode,
+      predictions=predictions,
+      loss=loss,
+      train_op=train_op,
+      eval_metric_ops=metrics)
+
+
+def resnet_main(
+    flags_obj, model_function, input_function, dataset_name, shape=None):
+  """Shared main loop for ResNet Models.
+
+  Args:
+    flags_obj: An object containing parsed flags. See define_resnet_flags()
+      for details.
+    model_function: the function that instantiates the Model and builds the
+      ops for train/eval. This will be passed directly into the estimator.
+    input_function: the function that processes the dataset and returns a
+      dataset that the estimator can train on. This will be wrapped with
+      all the relevant flags for running and passed to estimator.
+    dataset_name: the name of the dataset for training and evaluation. This is
+      used for logging purpose.
+    shape: list of ints representing the shape of the images used for training.
+      This is only used if flags_obj.export_dir is passed.
+  """
+
+  print("RESNET MAIN")
+  model_helpers.apply_clean(flags.FLAGS)
+
+  # Ensures flag override logic is only executed if explicitly triggered.
+  if flags_obj.tf_gpu_thread_mode:
+    override_flags_and_set_envars_for_gpu_thread_pool(flags_obj)
+
+  # Creates session config. allow_soft_placement = True, is required for
+  # multi-GPU and is not harmful for other modes.
+  session_config = tf.ConfigProto(allow_soft_placement=True)
+
+  run_config = tf.estimator.RunConfig(
+      session_config=session_config,
+      save_checkpoints_secs=60*60*24)
+
+  # Initializes model with all but the dense layer from pretrained ResNet.
+  if flags_obj.pretrained_model_checkpoint_path is not None:
+    warm_start_settings = tf.estimator.WarmStartSettings(
+        flags_obj.pretrained_model_checkpoint_path,
+        vars_to_warm_start='^(?!.*dense)')
+  else:
+    warm_start_settings = None
+
+  classifier = tf.estimator.Estimator(
+      model_fn=model_function, model_dir=flags_obj.model_dir, config=run_config,
+      warm_start_from=warm_start_settings, params={
+          'resnet_size': int(flags_obj.resnet_size),
+          'data_format': flags_obj.data_format,
+          'batch_size': flags_obj.batch_size,
+          'resnet_version': int(flags_obj.resnet_version),
+          'loss_scale': flags_core.get_loss_scale(flags_obj),
+          'dtype': flags_core.get_tf_dtype(flags_obj),
+          'fine_tune': flags_obj.fine_tune
+      })
+
+  run_params = {
+      'batch_size': flags_obj.batch_size,
+      'dtype': flags_core.get_tf_dtype(flags_obj),
+      'resnet_size': flags_obj.resnet_size,
+      'resnet_version': flags_obj.resnet_version,
+      'synthetic_data': flags_obj.use_synthetic_data,
+      'train_epochs': flags_obj.train_epochs,
+  }
+
+  def input_fn_eval():
+    return input_function(
+        is_training=False,
+        data_dir=flags_obj.data_dir,
+        batch_size=distribution_utils.per_device_batch_size(
+            flags_obj.batch_size, flags_core.get_num_gpus(flags_obj)),
+        num_epochs=1,
+        dtype=flags_core.get_tf_dtype(flags_obj))
+
+  schedule, n_loops = [0], 1
+  if flags_obj.export_dir is not None:
+    # Exports a saved model for the given classifier.
+    export_dtype = flags_core.get_tf_dtype(flags_obj)
+    if flags_obj.image_bytes_as_serving_input:
+      input_receiver_fn = functools.partial(
+          image_bytes_serving_input_fn, shape, dtype=export_dtype)
+    else:
+      input_receiver_fn = export.build_tensor_serving_input_receiver_fn(
+          shape, batch_size=flags_obj.batch_size, dtype=export_dtype)
+    classifier.export_savedmodel(flags_obj.export_dir, input_receiver_fn,
+                                 strip_default_attrs=True)
+
+
+def define_resnet_flags(resnet_size_choices=None):
+  """Add flags and validators for ResNet."""
+  flags_core.define_base()
+  flags_core.define_performance(num_parallel_calls=False,
+                                tf_gpu_thread_mode=True,
+                                datasets_num_private_threads=True,
+                                datasets_num_parallel_batches=True)
+  flags_core.define_image()
+  flags_core.define_benchmark()
+  flags.adopt_module_key_flags(flags_core)
+
+  flags.DEFINE_enum(
+      name='resnet_version', short_name='rv', default='1',
+      enum_values=['1', '2'],
+      help=flags_core.help_wrap(
+          'Version of ResNet. (1 or 2) See README.md for details.'))
+  flags.DEFINE_bool(
+      name='fine_tune', short_name='ft', default=False,
+      help=flags_core.help_wrap(
+          'If True do not train any parameters except for the final layer.'))
+  flags.DEFINE_string(
+      name='pretrained_model_checkpoint_path', short_name='pmcp', default=None,
+      help=flags_core.help_wrap(
+          'If not None initialize all the network except the final layer with '
+          'these values'))
+  flags.DEFINE_boolean(
+      name='eval_only', default=False,
+      help=flags_core.help_wrap('Skip training and only perform evaluation on '
+                                'the latest checkpoint.'))
+  flags.DEFINE_boolean(
+      name='image_bytes_as_serving_input', default=False,
+      help=flags_core.help_wrap(
+          'If True exports savedmodel with serving signature that accepts '
+          'JPEG image bytes instead of a fixed size [HxWxC] tensor that '
+          'represents the image. The former is easier to use for serving at '
+          'the expense of image resize/cropping being done as part of model '
+          'inference. Note, this flag only applies to ImageNet and cannot '
+          'be used for CIFAR.'))
+
+  choice_kwargs = dict(
+      name='resnet_size', short_name='rs', default='50',
+      help=flags_core.help_wrap('The size of the ResNet model to use.'))
+
+  if resnet_size_choices is None:
+    flags.DEFINE_string(**choice_kwargs)
+  else:
+    flags.DEFINE_enum(enum_values=resnet_size_choices, **choice_kwargs)
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/ssd-nhwc.py b/retired_benchmarks/vision/classification_and_detection/tools/ssd-nhwc.py
new file mode 100644
index 0000000000..f255fd965f
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/tools/ssd-nhwc.py
@@ -0,0 +1,267 @@
+import os
+import sys
+import argparse
+import tensorflow as tf
+from tensorflow.core.framework import graph_pb2
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('pbfile')
+    return parser.parse_args()
+
+def insert_transpose(graph, a, b, to_nchw):
+    if not isinstance(b, list):
+        b = [b]
+    trans_perm = graph.node.add()
+    trans_perm.name = a.name + '/transpose/perm'
+    trans_perm.op = 'Const'
+    trans_perm.attr['dtype'].type = 3 # DT_INT32
+    trans_perm.attr['value'].tensor.dtype = 3 # DT_INT32
+    trans_perm.attr['value'].tensor.tensor_shape.dim.add()
+    trans_perm.attr['value'].tensor.tensor_shape.dim[0].size = 4
+    if to_nchw:
+        trans_perm.attr['value'].tensor.tensor_content = b'\000\000\000\000\003\000\000\000\001\000\000\000\002\000\000\000'
+    else:
+        trans_perm.attr['value'].tensor.tensor_content = b'\000\000\000\000\002\000\000\000\003\000\000\000\001\000\000\000'
+    
+    trans = graph.node.add()
+    trans.name = a.name + '/transpose'
+    trans.op = 'Transpose'
+    trans.input.append(a.name)
+    trans.input.append(trans_perm.name)
+    trans.attr['T'].type = 1
+    trans.attr['Tperm'].type = 3
+
+    for n in b:
+        inputs = []
+        for i in n.input:
+            if i == a.name:
+                inputs.append(trans.name)
+            else:
+                inputs.append(i)
+        cnt = len(n.input)
+        for i in range(0, cnt):
+            del n.input[0]
+        for i in range(0, cnt):
+            n.input.append(inputs[i])
+
+def convert_list_nhwc(l):
+    c = l.i[1]
+    h = l.i[2]
+    w = l.i[3]
+    l.i[1] = h
+    l.i[2] = w
+    l.i[3] = c
+    
+def convert_conv_nhwc(node_conv):
+    node_conv.attr['data_format'].s = b'NHWC'
+    convert_list_nhwc(node_conv.attr['dilations'].list)
+    convert_list_nhwc(node_conv.attr['strides'].list)
+
+def convert_general_nhwc(node):
+    node.attr['data_format'].s = b'NHWC'
+
+def convert_mp_nhwc(node_mp):
+    node_mp.attr['data_format'].s = b'NHWC'
+    convert_list_nhwc(node_mp.attr['ksize'].list)
+    convert_list_nhwc(node_mp.attr['strides'].list)
+
+def convert_image_nhwc(node_image):
+    c = node_image.attr['shape'].shape.dim[1].size
+    del node_image.attr['shape'].shape.dim[1]
+    d = node_image.attr['shape'].shape.dim.add()
+    d.size = c
+
+def init_node(n):
+    node = {}
+    node['node'] = n
+    node['inputs'] = []
+    node['outputs'] = []
+    return node
+
+def connect_nodes(n1, n2):
+    if n2['node'].name not in n1['outputs']:
+        n1['outputs'].append(n2['node'].name)
+        n2['inputs'].append(n1['node'].name)
+    else:
+        print('{} -> {} already connected'.format(n1['node'].name, n2['node'].name))
+
+def disconnect_nodes(n1, n2):
+    if n1['node'].name not in n2['inputs'] or n2['node'].name not in n1['outputs']:
+        print('{} -> {} not connected'.format(n1['node'].name, n2['node'].name))
+    for i in range(0, len(n1['outputs'])):
+        if n1['outputs'][i] == n2['node'].name:
+            del n1['outputs'][i]
+            break
+    for i in range(0, len(n2['inputs'])):
+        if n2['inputs'][i] == n1['node'].name:
+            del n2['inputs'][i]
+            break
+            
+def build_graph(graph):
+    node_map = {}
+    for n in graph.node:
+        node = init_node(n)
+        node_map[n.name] = node
+    for n in node_map:
+        for i in node_map[n]['node'].input:
+            if ':' in i:
+                i = i[:i.find(':')]
+            i = i.lstrip('^')
+            if i not in node_map:
+                print('node {} not found'.format(i))
+            else:
+                connect_nodes(node_map[i], node_map[n])
+    return node_map
+
+def trim_const_from_graph(node_map):
+    trim_list = []
+    for n in node_map:
+        if node_map[n]['node'].op == 'Const':
+            trim_list.append(n)
+    for n in trim_list:
+        print('trimming {}'.format(n))
+        for o in node_map[n]['outputs']:
+            disconnect_nodes(node_map[n], node_map[o])
+        del node_map[n]
+
+    trim_list = []
+    for n in node_map:
+        if node_map[n]['node'].op == 'Identity' and len(node_map[n]['inputs']) == 0:
+            trim_list.append(n)
+    for n in trim_list:
+        print('trimming {}'.format(n))
+        for o in node_map[n]['outputs']:
+            disconnect_nodes(node_map[n], node_map[o])
+        del node_map[n]
+
+
+def all_input_in_nhwc(n, node_map, nhwc_nodes):
+    for i in node_map[n]['inputs']:
+        if i not in nhwc_nodes:
+            return False
+    return True
+
+def all_output_in_nhwc(n, node_map, nhwc_nodes):
+    for o in node_map[n]['outputs']:
+        if o not in nhwc_nodes:
+            return False
+    return True
+
+def find_nhwc_region(node_map):
+    transpose_nhwc_nodes = {}
+    transpose_nchw_nodes = {}
+    nhwc_nodes = []
+
+    transpose_nhwc_nodes_append_list = []
+    transpose_nchw_nodes_append_list = []
+    for n in node_map:
+        if node_map[n]['node'].op == 'Conv2D':
+            transpose_nhwc_nodes_append_list.append(n)
+            transpose_nchw_nodes_append_list.append(n)
+            nhwc_nodes.append(n)
+    for n in transpose_nhwc_nodes_append_list:
+        if not all_input_in_nhwc(n, node_map, nhwc_nodes):
+            transpose_nhwc_nodes[n] = 1
+    for n in transpose_nchw_nodes_append_list:
+        if not all_output_in_nhwc(n, node_map, nhwc_nodes):
+            transpose_nchw_nodes[n] = 1
+
+    prev_cnt_nhwc_nodes = len(nhwc_nodes)
+    nhwc_op_list = ['Conv2D', 'Relu', 'FusedBatchNorm', 'MaxPool', 'BiasAdd', 'Add']
+    while True:
+        transpose_nchw_nodes_append_list = []
+        for n in transpose_nchw_nodes:
+            for o in node_map[n]['outputs']:
+                if o not in nhwc_nodes and node_map[o]['node'].op in nhwc_op_list:
+                    if all_input_in_nhwc(o, node_map, nhwc_nodes):
+                        nhwc_nodes.append(o)
+                        if o not in transpose_nchw_nodes_append_list:
+                            transpose_nchw_nodes_append_list.append(o)
+
+        transpose_nhwc_nodes_remove_list = []
+        transpose_nchw_nodes_remove_list = []
+        for n in transpose_nhwc_nodes:
+            if (all_input_in_nhwc(n, node_map, nhwc_nodes) and
+                n not in transpose_nhwc_nodes_remove_list):
+                transpose_nhwc_nodes_remove_list.append(n)
+        for n in transpose_nhwc_nodes_remove_list:
+            del transpose_nhwc_nodes[n]
+
+        for n in transpose_nchw_nodes:
+            if (all_output_in_nhwc(n, node_map, nhwc_nodes) and
+                n not in transpose_nchw_nodes_remove_list):
+                transpose_nchw_nodes_remove_list.append(n)
+        for n in transpose_nchw_nodes_remove_list:
+            del transpose_nchw_nodes[n]
+
+        for n in transpose_nchw_nodes_append_list:
+            if not all_output_in_nhwc(n, node_map, nhwc_nodes):
+                transpose_nchw_nodes[n] = 1
+
+        if len(nhwc_nodes) == prev_cnt_nhwc_nodes:
+            break
+        prev_cnt_nhwc_nodes = len(nhwc_nodes)
+                        
+    print('\n\nTranspose to NHWC at nodes:')
+    for n in transpose_nhwc_nodes:
+        print('    {}'.format(n))
+    
+    print('\n\nTranspose to NCHW at nodes:')
+    for n in transpose_nchw_nodes:
+        print('    {}'.format(n))
+    
+    return nhwc_nodes, transpose_nhwc_nodes, transpose_nchw_nodes
+
+def main():
+    args = get_args()
+
+    graph = graph_pb2.GraphDef()
+    with open(args.pbfile, 'rb') as f:
+        graph.ParseFromString(f.read())
+
+    node_map = build_graph(graph)
+    trim_const_from_graph(node_map)
+
+    nhwc_nodes, transpose_nhwc_nodes, transpose_nchw_nodes = find_nhwc_region(node_map)
+
+    nhwc_op_list = ['Conv2D', 'Relu', 'FusedBatchNorm', 'MaxPool', 'BiasAdd', 'Add']
+    for n in nhwc_nodes:
+        if node_map[n]['node'].op == 'Conv2D':
+            convert_conv_nhwc(node_map[n]['node'])
+        elif node_map[n]['node'].op in ['FusedBatchNorm', 'BiasAdd']:
+            convert_general_nhwc(node_map[n]['node'])
+        elif node_map[n]['node'].op == 'MaxPool':
+            convert_mp_nhwc(node_map[n]['node'])
+      
+    done_nhwc = False
+    if len(transpose_nhwc_nodes) == 1:
+        for n in transpose_nhwc_nodes:
+            if len(node_map[n]['inputs']) == 1 and node_map[n]['inputs'][0] == 'image':
+                image_outputs = []
+                for o in node_map['image']['outputs']:
+                    if o != n:
+                        image_outputs.append(node_map[o]['node'])
+                insert_transpose(graph, node_map['image']['node'], image_outputs, True)
+                convert_image_nhwc(node_map['image']['node'])
+                done_nhwc = True
+
+    if not done_nhwc:
+        for n in transpose_nhwc_nodes:
+            for i in node_map[n]['inputs']:
+                if i not in nhwc_nodes:
+                    insert_transpose(graph, node_map[i]['node'], node_map[n]['node'], False)
+
+    for n in transpose_nchw_nodes:
+        node_outputs = []
+        for o in node_map[n]['outputs']:
+            if o not in nhwc_nodes:
+                node_outputs.append(node_map[o]['node'])
+        insert_transpose(graph, node_map[n]['node'], node_outputs, True)
+
+    with open(args.pbfile+'.patch', 'wb') as f:
+        f.write(graph.SerializeToString())
+
+if __name__ == '__main__':
+    main()
+
diff --git a/retired_benchmarks/vision/classification_and_detection/user.conf b/retired_benchmarks/vision/classification_and_detection/user.conf
new file mode 100644
index 0000000000..545569c1ac
--- /dev/null
+++ b/retired_benchmarks/vision/classification_and_detection/user.conf
@@ -0,0 +1,6 @@
+# Please set these fields depending on the performance of your system to
+# override default LoadGen settings.
+*.SingleStream.target_latency = 10
+*.Server.target_qps = 1.0
+*.Offline.target_qps = 1.0
+*.MultiStream.samples_per_query = 4
\ No newline at end of file

From 87ba8cb8a6a4f6525f26255fa513d902b17ab060 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 11 Apr 2024 09:41:12 +0100
Subject: [PATCH 081/305] Create test-submission-checker.yml

---
 .github/workflows/test-submission-checker.yml | 36 +++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 .github/workflows/test-submission-checker.yml

diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
new file mode 100644
index 0000000000..26322d8588
--- /dev/null
+++ b/.github/workflows/test-submission-checker.yml
@@ -0,0 +1,36 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Test for MLPerf inference submission checker using CM script automation
+
+on:
+  pull_request:
+    branches: [ "master", "dev" ]
+    paths:
+      - tools/submission/**
+      - .github/workflows/test-submission-checker.yml
+      - '!**.md'
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [ "3.8", "3.11", "3.12" ]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python3 -m pip install cmind
+        cm pull repo mlcommons@ck
+        git clone https://github.com/mlcommons/inference_results_v4.0
+    - name: Test MLPerf inference submission checker
+      run: |
+        cm run script --tags=run,mlperf,inference,submission,checker --input=`pwd`/inference_results_v4.0 --quiet 

From 5e2539a50a43d55a053e1e3d85b6e8ac41dd6b3c Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 21 May 2024 17:33:33 +0100
Subject: [PATCH 082/305] Adding a github.io documentaion site with CM commands
 for all major implementations (#1701)

* Support batch-size in llama2 run

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-Cloudflare download instructiosn to README.md

* Minor wording edit to README.md

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-GDrive download instructions to README.md

* Add new and old instructions to README.md

* Tweak language in README.md

* Language tweak in README.md

* Minor language tweak in README.md

* Fix typo in README.md

* Count error when logging errors: submission_checker.py

* Fixes #1648, restrict loadgen uncommitted error message to within the loadgen directory

* Update test-rnnt.yml (#1688)

Stopping the github action for rnnt

* Added docs init

Added github action for website publish

Update benchmark documentation

Update publish.yaml

Update publish.yaml

Update benchmark documentation

Improved the submission documentation

Fix taskname

Removed unused images

* Fix benchmark URLs

* Fix links

* Add _full variation to run commands

* Added script flow diagram

* Added docker setup command for CM, extra run options

* Added support for docker options in the docs

* Added --quiet to the CM run_cmds in docs

---------

Co-authored-by: Nathan Wasson <nathanw@mlcommons.org>
---
 .github/workflows/publish.yaml                |  33 +++++
 docs/README.md                                |   9 ++
 .../image_classification/resnet50.md          |  68 +++++++++
 docs/benchmarks/index.md                      |  28 ++++
 docs/benchmarks/language/bert.md              |  73 ++++++++++
 docs/benchmarks/language/gpt-j.md             |  57 ++++++++
 docs/benchmarks/language/llama2-70b.md        |  52 +++++++
 docs/benchmarks/medical_imaging/3d-unet.md    |  60 ++++++++
 docs/benchmarks/object_detection/retinanet.md |  63 ++++++++
 docs/benchmarks/recommendation/dlrm-v2.md     |  36 +++++
 docs/benchmarks/text_to_image/sdxl.md         |  49 +++++++
 docs/changelog/changelog.md                   |   2 +
 docs/changelog/index.md                       |   2 +
 docs/demos/index.md                           |   2 +
 docs/img/logo_v2.svg                          |   6 +
 docs/img/pages (80).png                       | Bin 0 -> 242952 bytes
 docs/index.md                                 |   1 +
 docs/install/index.md                         | 105 ++++++++++++++
 docs/requirements.txt                         |   4 +
 docs/submission/index.md                      |  88 ++++++++++++
 docs/submission/tools-readme.md               |   1 +
 docs/usage/index.md                           |   1 +
 language/llama2-70b/main.py                   |   2 +
 main.py                                       | 136 ++++++++++++++++++
 mkdocs.yml                                    |  66 +++++++++
 25 files changed, 944 insertions(+)
 create mode 100644 .github/workflows/publish.yaml
 create mode 100644 docs/README.md
 create mode 100644 docs/benchmarks/image_classification/resnet50.md
 create mode 100644 docs/benchmarks/index.md
 create mode 100644 docs/benchmarks/language/bert.md
 create mode 100644 docs/benchmarks/language/gpt-j.md
 create mode 100644 docs/benchmarks/language/llama2-70b.md
 create mode 100644 docs/benchmarks/medical_imaging/3d-unet.md
 create mode 100644 docs/benchmarks/object_detection/retinanet.md
 create mode 100644 docs/benchmarks/recommendation/dlrm-v2.md
 create mode 100644 docs/benchmarks/text_to_image/sdxl.md
 create mode 100644 docs/changelog/changelog.md
 create mode 100644 docs/changelog/index.md
 create mode 100644 docs/demos/index.md
 create mode 100644 docs/img/logo_v2.svg
 create mode 100644 docs/img/pages (80).png
 create mode 120000 docs/index.md
 create mode 100644 docs/install/index.md
 create mode 100644 docs/requirements.txt
 create mode 100644 docs/submission/index.md
 create mode 120000 docs/submission/tools-readme.md
 create mode 100644 docs/usage/index.md
 create mode 100644 main.py
 create mode 100644 mkdocs.yml

diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml
new file mode 100644
index 0000000000..214ac3611b
--- /dev/null
+++ b/.github/workflows/publish.yaml
@@ -0,0 +1,33 @@
+# This is a basic workflow to help you get started with Actions
+
+name: Publish site
+
+
+on:
+  release:
+    types: [published]
+  push:
+    branches:
+      - master
+      - docs
+
+jobs:
+
+  publish:
+    name: Publish the site
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository normally
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+
+      - name: Install Mkdocs
+        run: pip install -r docs/requirements.txt
+
+      - name: Run Mkdocs deploy
+        run: mkdocs gh-deploy --force
diff --git a/docs/README.md b/docs/README.md
new file mode 100644
index 0000000000..477c13a7bf
--- /dev/null
+++ b/docs/README.md
@@ -0,0 +1,9 @@
+# Documentation Website for MLPerf Inference using the unified CM interface
+
+## Commands to get the website running locally
+```
+git clone https://github.com/GATEOverflow/cm4mlperf-inference
+cd cm4mlperf-inference
+pip install -r requirements.txt
+mkdocs serve
+```
diff --git a/docs/benchmarks/image_classification/resnet50.md b/docs/benchmarks/image_classification/resnet50.md
new file mode 100644
index 0000000000..1a77db65ac
--- /dev/null
+++ b/docs/benchmarks/image_classification/resnet50.md
@@ -0,0 +1,68 @@
+# Image Classification using ResNet50 
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    ResNet50 validation run uses the Imagenet 2012 validation dataset consisting of 50,000 images.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,imagenet,validation -j
+    ```
+=== "Calibration"
+    ResNet50 calibration dataset consist of 500 images selected from the Imagenet 2012 validation dataset. There are 2 alternative options for the calibration dataset.
+
+    ### Get Calibration Dataset Using Option 1
+    ```
+    cm run script --tags=get,dataset,imagenet,calibration,_mlperf.option1 -j
+    ```
+    ### Get Calibration Dataset Using Option 2
+    ```
+    cm run script --tags=get,dataset,imagenet,calibration,_mlperf.option2 -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf ResNet50 Model
+
+=== "Tensorflow"
+
+    ### Tensorflow
+    ```
+    cm run script --tags=get,ml-model,resnet50,_tensorflow -j
+    ```
+=== "Onnx"
+
+    ### Onnx
+    ```
+    cm run script --tags=get,ml-model,resnet50,_onnx -j
+    ```
+
+## Benchmark Implementations
+=== "MLCommons-Python"
+    ### MLPerf Reference Implementation in Python
+    
+{{ mlperf_inference_implementation_readme (4, "resnet50", "reference") }}
+
+=== "Nvidia"
+    ### Nvidia MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "resnet50", "nvidia") }}
+
+=== "Intel"
+    ### Intel MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "resnet50", "intel") }}
+
+=== "Qualcomm"
+    ### Qualcomm AI100 MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "resnet50", "qualcomm") }}
+
+=== "MLCommon-C++"
+    ### MLPerf Modular Implementation in C++
+    
+{{ mlperf_inference_implementation_readme (4, "resnet50", "cpp") }}
diff --git a/docs/benchmarks/index.md b/docs/benchmarks/index.md
new file mode 100644
index 0000000000..c4a970c81c
--- /dev/null
+++ b/docs/benchmarks/index.md
@@ -0,0 +1,28 @@
+# MLPerf Inference Benchmarks
+
+Please visit the individual benchmark links to see the run commands using the unified CM interface.
+
+1. [Image Classification](image_classification/resnet50.md) using ResNet50 model and Imagenet-2012 dataset
+
+2. [Text to Image](text_to_image/sdxl.md) using Stable Diffusion model and Coco2014 dataset
+
+3. [Object Detection](object_detection/retinanet.md) using Retinanet model and OpenImages dataset
+
+4. [Image Segmentation](medical_imaging/3d-unet.md)  using 3d-unet model and KiTS19 dataset
+
+5. [Question Answering](language/bert.md) using Bert-Large model and Squad v1.1 dataset
+
+6. [Text Summarization](language/gpt-j.md) using GPT-J model and CNN Daily Mail dataset
+
+7. [Text Summarization](language/llama2-70b.md) using LLAMA2-70b model and OpenORCA dataset
+
+8. [Recommendation](recommendation/dlrm-v2.md) using DLRMv2 model and Criteo multihot dataset
+
+All the eight benchmarks can participate in the datacenter category.
+All the eight benchmarks except DLRMv2 and LLAMA2 and can participate in the edge category. 
+
+`bert`, `llama2-70b`, `dlrm_v2` and `3d-unet` has a high accuracy (99.9%) variant, where the benchmark run  must achieve a higher accuracy of at least `99.9%` of the FP32 reference model
+in comparison with the `99%` default accuracy requirement.
+
+The `dlrm_v2` benchmark has a high-accuracy variant only. If this accuracy is not met, the submission result can be submitted only to the open division.
+
diff --git a/docs/benchmarks/language/bert.md b/docs/benchmarks/language/bert.md
new file mode 100644
index 0000000000..e2aa0995dc
--- /dev/null
+++ b/docs/benchmarks/language/bert.md
@@ -0,0 +1,73 @@
+# Question Answering using Bert-Large
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    BERT validation run uses the SQuAD v1.1 dataset.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,squad,validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf Bert-Large Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,bert-large,_pytorch -j
+    ```
+=== "Onnx"
+
+    ### Onnx
+    ```
+    cm run script --tags=get,ml-model,bert-large,_onnx -j
+    ```
+=== "Tensorflow"
+
+    ### Tensorflow
+    ```
+    cm run script --tags=get,ml-model,bert-large,_tensorflow -j
+    ```
+
+## Benchmark Implementations
+=== "MLCommons-Python"
+    ### MLPerf Reference Implementation in Python
+    
+    BERT-99
+{{ mlperf_inference_implementation_readme (4, "bert-99", "reference") }}
+
+    BERT-99.9
+{{ mlperf_inference_implementation_readme (4, "bert-99.9", "reference") }}
+
+=== "Nvidia"
+    ### Nvidia MLPerf Implementation
+    
+    BERT-99
+{{ mlperf_inference_implementation_readme (4, "bert-99", "nvidia") }}
+
+    BERT-99.9
+{{ mlperf_inference_implementation_readme (4, "bert-99.9", "nvidia") }}
+
+=== "Intel"
+    ### Intel MLPerf Implementation
+    BERT-99
+{{ mlperf_inference_implementation_readme (4, "bert-99", "intel") }}
+
+    BERT-99.9
+{{ mlperf_inference_implementation_readme (4, "bert-99.9", "intel") }}
+
+=== "Qualcomm"
+    ### Qualcomm AI100 MLPerf Implementation
+
+    BERT-99
+{{ mlperf_inference_implementation_readme (4, "bert-99", "qualcomm") }}
+
+    BERT-99.9
+{{ mlperf_inference_implementation_readme (4, "bert-99.9", "qualcomm") }}
diff --git a/docs/benchmarks/language/gpt-j.md b/docs/benchmarks/language/gpt-j.md
new file mode 100644
index 0000000000..d1c351214c
--- /dev/null
+++ b/docs/benchmarks/language/gpt-j.md
@@ -0,0 +1,57 @@
+# Text Summarization using GPT-J
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    GPT-J validation run uses the CNNDM dataset.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,cnndm,validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf GPT-J Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,gptj,_pytorch -j
+    ```
+
+## Benchmark Implementations
+=== "MLCommons-Python"
+    ### MLPerf Reference Implementation in Python
+    
+    GPT-J-99
+{{ mlperf_inference_implementation_readme (4, "gptj-99", "reference") }}
+
+    GPTJ-99.9
+{{ mlperf_inference_implementation_readme (4, "gptj-99.9", "reference") }}
+
+=== "Nvidia"
+    ### Nvidia MLPerf Implementation
+    
+    GPTJ-99
+{{ mlperf_inference_implementation_readme (4, "gptj-99", "nvidia") }}
+
+    GPTJ-99.9
+{{ mlperf_inference_implementation_readme (4, "gptj-99.9", "nvidia") }}
+
+=== "Intel"
+    ### Intel MLPerf Implementation
+    GPTJ-99
+{{ mlperf_inference_implementation_readme (4, "gptj-99", "intel") }}
+
+
+=== "Qualcomm"
+    ### Qualcomm AI100 MLPerf Implementation
+
+    GPTJ-99
+{{ mlperf_inference_implementation_readme (4, "gptj-99", "qualcomm") }}
+
diff --git a/docs/benchmarks/language/llama2-70b.md b/docs/benchmarks/language/llama2-70b.md
new file mode 100644
index 0000000000..7f8052aef5
--- /dev/null
+++ b/docs/benchmarks/language/llama2-70b.md
@@ -0,0 +1,52 @@
+# Text Summarization using LLAMA2-70b
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    LLAMA2-70b validation run uses the Open ORCA dataset.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,openorca,validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf LLAMA2-70b Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,llama2-70b,_pytorch -j
+    ```
+
+## Benchmark Implementations
+=== "MLCommons-Python"
+    ### MLPerf Reference Implementation in Python
+    
+    LLAMA2-70b-99
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "reference") }}
+
+    LLAMA2-70b-99.9
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "reference") }}
+
+=== "Nvidia"
+    ### Nvidia MLPerf Implementation
+    
+    LLAMA2-70b-99
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "nvidia") }}
+
+    LLAMA2-70b-99.9
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "nvidia") }}
+
+
+=== "Qualcomm"
+    ### Qualcomm AI100 MLPerf Implementation
+
+    LLAMA2-70b-99
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "qualcomm") }}
+
diff --git a/docs/benchmarks/medical_imaging/3d-unet.md b/docs/benchmarks/medical_imaging/3d-unet.md
new file mode 100644
index 0000000000..bd3ccae406
--- /dev/null
+++ b/docs/benchmarks/medical_imaging/3d-unet.md
@@ -0,0 +1,60 @@
+# Medical Imaging using 3d-unet (KiTS 2019 kidney tumor segmentation task)
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    3d-unet validation run uses the KiTS19 dataset performing [KiTS 2019](https://kits19.grand-challenge.org/) kidney tumor segmentation task
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,kits19,validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf 3d-unet Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,3d-unet,_pytorch -j
+    ```
+=== "Onnx"
+
+    ### Onnx
+    ```
+    cm run script --tags=get,ml-model,3d-unet,_onnx -j
+    ```
+=== "Tensorflow"
+
+    ### Tensorflow
+    ```
+    cm run script --tags=get,ml-model,3d-unet,_tensorflow -j
+    ```
+
+## Benchmark Implementations
+=== "MLCommons-Python"
+    ### MLPerf Reference Implementation in Python
+
+    3d-unet-99.9    
+{{ mlperf_inference_implementation_readme (4, "3d-unet-99.9", "reference") }}
+
+=== "Nvidia"
+    ### Nvidia MLPerf Implementation
+    3d-unet-99 
+{{ mlperf_inference_implementation_readme (4, "3d-unet-99", "nvidia") }}
+
+    3d-unet-99.9 
+{{ mlperf_inference_implementation_readme (4, "3d-unet-99.9", "nvidia") }}
+
+=== "Intel"
+    ### Intel MLPerf Implementation
+    3d-unet-99
+{{ mlperf_inference_implementation_readme (4, "3d-unet-99", "intel") }}
+
+    3d-unet-99.9
+{{ mlperf_inference_implementation_readme (4, "3d-unet-99.9", "intel") }}
diff --git a/docs/benchmarks/object_detection/retinanet.md b/docs/benchmarks/object_detection/retinanet.md
new file mode 100644
index 0000000000..f500f616d5
--- /dev/null
+++ b/docs/benchmarks/object_detection/retinanet.md
@@ -0,0 +1,63 @@
+# Object Detection using Retinanet
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    Retinanet validation run uses the OpenImages v6 MLPerf validation dataset resized to 800x800 and consisting of 24,576 images.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,openimages,_validation -j
+    ```
+=== "Calibration"
+    Retinanet calibration dataset consist of 500 images selected from the OpenImages v6 dataset.
+
+    ```
+    cm run script --tags=get,dataset,openimages,_calibration -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf Retinanet Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,retinanet,_pytorch -j
+    ```
+=== "Onnx"
+
+    ### Onnx
+    ```
+    cm run script --tags=get,ml-model,retinanet,_onnx -j
+    ```
+
+## Benchmark Implementations
+=== "MLCommons-Python"
+    ### MLPerf Reference Implementation in Python
+    
+{{ mlperf_inference_implementation_readme (4, "retinanet", "reference") }}
+
+=== "Nvidia"
+    ### Nvidia MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "retinanet", "nvidia") }}
+
+=== "Intel"
+    ### Intel MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "retinanet", "intel") }}
+
+=== "Qualcomm"
+    ### Qualcomm AI100 MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "retinanet", "qualcomm") }}
+
+=== "MLCommon-C++"
+    ### MLPerf Modular Implementation in C++
+    
+{{ mlperf_inference_implementation_readme (4, "retinanet", "cpp") }}
diff --git a/docs/benchmarks/recommendation/dlrm-v2.md b/docs/benchmarks/recommendation/dlrm-v2.md
new file mode 100644
index 0000000000..1294b008b5
--- /dev/null
+++ b/docs/benchmarks/recommendation/dlrm-v2.md
@@ -0,0 +1,36 @@
+# Recommendation using DLRM v2
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    DLRM validation run uses the Criteo dataset (Day 23).
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,criteo,validation -j
+    ```
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf DLRM v2 Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,dlrm_v2,_pytorch -j
+    ```
+
+## Benchmark Implementations
+=== "MLCommons-Python"
+    ### MLPerf Reference Implementation in Python
+    
+{{ mlperf_inference_implementation_readme (4, "dlrm_v2-99.9", "reference") }}
+
+=== "Nvidia"
+    ### Nvidia MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "dlrm_v2-99.9", "nvidia") }}
+
diff --git a/docs/benchmarks/text_to_image/sdxl.md b/docs/benchmarks/text_to_image/sdxl.md
new file mode 100644
index 0000000000..2e9c95c665
--- /dev/null
+++ b/docs/benchmarks/text_to_image/sdxl.md
@@ -0,0 +1,49 @@
+# Text to Image using Stable Diffusion
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    Stable Diffusion validation run uses the Coco 2014 dataset.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,coco2014,_validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf Stable Diffusion Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,sdxl,_pytorch -j
+    ```
+
+## Benchmark Implementations
+=== "MLCommons-Python"
+    ### MLPerf Reference Implementation in Python
+    
+{{ mlperf_inference_implementation_readme (4, "sdxl", "reference") }}
+
+=== "Nvidia"
+    ### Nvidia MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "sdxl", "nvidia") }}
+
+=== "Intel"
+    ### Intel MLPerf Implementation
+    GPTJ-99
+{{ mlperf_inference_implementation_readme (4, "sdxl", "intel") }}
+
+
+=== "Qualcomm"
+    ### Qualcomm AI100 MLPerf Implementation
+
+    GPTJ-99
+{{ mlperf_inference_implementation_readme (4, "sdxl", "qualcomm") }}
+
diff --git a/docs/changelog/changelog.md b/docs/changelog/changelog.md
new file mode 100644
index 0000000000..8b9abcc4b8
--- /dev/null
+++ b/docs/changelog/changelog.md
@@ -0,0 +1,2 @@
+# Release Notes
+
diff --git a/docs/changelog/index.md b/docs/changelog/index.md
new file mode 100644
index 0000000000..f68abc5b10
--- /dev/null
+++ b/docs/changelog/index.md
@@ -0,0 +1,2 @@
+# What's New, What's Coming
+
diff --git a/docs/demos/index.md b/docs/demos/index.md
new file mode 100644
index 0000000000..1c23a5f604
--- /dev/null
+++ b/docs/demos/index.md
@@ -0,0 +1,2 @@
+# Demos
+
diff --git a/docs/img/logo_v2.svg b/docs/img/logo_v2.svg
new file mode 100644
index 0000000000..fb655c6278
--- /dev/null
+++ b/docs/img/logo_v2.svg
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" data-name="Layer 1" viewBox="0 0 1660 500">
+  <title>MLCommons</title>
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literal 0
HcmV?d00001

diff --git a/docs/index.md b/docs/index.md
new file mode 120000
index 0000000000..32d46ee883
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1 @@
+../README.md
\ No newline at end of file
diff --git a/docs/install/index.md b/docs/install/index.md
new file mode 100644
index 0000000000..ec76718d95
--- /dev/null
+++ b/docs/install/index.md
@@ -0,0 +1,105 @@
+# Installation
+We use MLCommons CM Automation framework to run MLPerf inference benchmarks.
+
+## CM Install
+
+We have successfully tested CM on
+
+* Ubuntu 18.x, 20.x, 22.x , 23.x, 
+* RedHat 8, RedHat 9, CentOS 8
+* macOS
+* Wndows 10, Windows 11
+ 
+=== "Ubuntu"
+    ### Ubuntu, Debian
+
+
+    ```bash
+       sudo apt update && sudo apt upgrade
+       sudo apt install python3 python3-pip python3-venv git wget curl
+    ```
+
+    **Note that you must set up virtual env on Ubuntu 23+ before using any Python project:**
+    ```bash
+       python3 -m venv cm
+       source cm/bin/activate
+    ```
+
+    You can now install CM via PIP:
+
+    ```bash
+       python3 -m pip install cmind
+    ```
+
+    You might need to do the following command to update the `PATH` to include the BIN paths from pip installs
+
+    ```bash
+       source $HOME/.profile
+    ```
+
+    You can check that CM is available by checking the `cm` command
+
+
+=== "Red Hat"
+    ### Red Hat
+
+    ```bash
+       sudo dnf update
+       sudo dnf install python3 python-pip git wget curl
+       python3 -m pip install cmind --user
+    ```
+
+=== "macOS"
+    ### macOS
+
+    *Note that CM currently does not work with Python installed from the Apple Store.
+     Please install Python via brew as described below.*
+
+    If `brew` package manager is not installed, please install it as follows (see details [here](https://brew.sh/)):
+    ```bash
+       /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+    ```
+
+    Don't forget to add brew to PATH environment as described in the end of the installation output.
+
+    Then install python, pip, git and wget:
+
+    ```bash
+       brew install python3 git wget curl
+       python3 -m pip install cmind
+    ```
+
+=== "Windows"
+
+    ### Windows
+    * Configure Windows 10+ to [support long paths](https://learn.microsoft.com/en-us/windows/win32/fileio/maximum-file-path-limitation?tabs=registry#enable-long-paths-in-windows-10-version-1607-and-later) from command line as admin:
+      <small>
+      ```bash
+         reg add "HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem" /v LongPathsEnabled /t REG_DWORD /d 1 /f
+      ```
+      </small>
+    * Download and install Git from [git-for-windows.github.io](https://git-for-windows.github.io).
+      * Configure Git to accept long file names: `git config --system core.longpaths true`
+    * Download and install Python 3+ from [www.python.org/downloads/windows](https://www.python.org/downloads/windows).
+      * Don't forget to select option to add Python binaries to PATH environment!
+      * Configure Windows to accept long fie names during Python installation!
+
+    * Install CM via PIP:
+
+    ```bash
+       python -m pip install cmind
+    ```
+
+    *Note that we [have reports](https://github.com/mlcommons/ck/issues/844) 
+     that CM does not work when Python was first installed from the Microsoft Store.
+     If CM fails to run, you can find a fix [here](https://stackoverflow.com/questions/57485491/python-python3-executes-in-command-prompt-but-does-not-run-correctly)*.
+
+Please visit the [official CM installation page](https://github.com/mlcommons/ck/blob/master/docs/installation.md) for more details
+
+## Download the CM MLOps Repository
+
+```bash
+   cm pull repo gateoverflow@cm4mlops
+```
+
+Now, you are ready to use the `cm` commands to run MLPerf inference as given in the [benchmarks](../benchmarks/index.md) page
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000000..39fab4e1ff
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,4 @@
+mkdocs-material
+swagger-markdown
+mkdocs-macros-plugin
+ruamel.yaml
diff --git a/docs/submission/index.md b/docs/submission/index.md
new file mode 100644
index 0000000000..b5ff530336
--- /dev/null
+++ b/docs/submission/index.md
@@ -0,0 +1,88 @@
+If you follow the `cm run` commands under the individual model pages in the [benchmarks](../benchmarks) directory, all the valid results will get aggregated to the `cm cache` folder. Once all the results across all the modelsare ready you can use the following command to generate a valid submission tree compliant with the [MLPerf requirements](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc#inference-1).
+
+## Generate actual submission tree
+
+=== "Closed Edge"
+    ### Closed Edge Submission
+    ```bash
+       cm run script -tags=generate,inference,submission \
+      --clean \
+      --preprocess_submission=yes \
+      --run-checker \
+      --submitter=MLCommons \
+      --tar=yes \
+      --env.CM_TAR_OUTFILE=submission.tar.gz \
+      --division=closed \
+      --category=edge \
+      --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+      --quiet
+    ```
+
+=== "Closed Datacenter"
+    ### Closed Datacenter Submission
+    ```bash
+       cm run script -tags=generate,inference,submission \
+      --clean \
+      --preprocess_submission=yes \
+      --run-checker \
+      --submitter=MLCommons \
+      --tar=yes \
+      --env.CM_TAR_OUTFILE=submission.tar.gz \
+      --division=closed \
+      --category=datacenter \
+      --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+      --quiet
+    ```
+=== "Open Edge"
+    ### Open Edge Submission
+    ```bash
+       cm run script -tags=generate,inference,submission \
+      --clean \
+      --preprocess_submission=yes \
+      --run-checker \
+      --submitter=MLCommons \
+      --tar=yes \
+      --env.CM_TAR_OUTFILE=submission.tar.gz \
+      --division=open \
+      --category=edge \
+      --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+      --quiet
+    ```
+=== "Open Datacenter"
+    ### Closed Datacenter Submission
+    ```bash
+       cm run script -tags=generate,inference,submission \
+      --clean \
+      --preprocess_submission=yes \
+      --run-checker \
+      --submitter=MLCommons \
+      --tar=yes \
+      --env.CM_TAR_OUTFILE=submission.tar.gz \
+      --division=open \
+      --category=datacenter \
+      --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+      --quiet
+    ```
+
+* Use `--hw_name="My system name"` to give a meaningful system name. Examples can be seen [here](https://github.com/mlcommons/inference_results_v3.0/tree/main/open/cTuning/systems)
+
+* Use `--submitter=<Your name>` if your organization is an official MLCommons member and would like to submit under your organization
+
+* Use `--hw_notes_extra` option to add additional notes like `--hw_notes_extra="Result taken by NAME" `
+
+The above command should generate "submission.tar.gz" if there are no submission checker issues and you can upload it to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission).
+
+## Aggregate Results in GitHub
+
+If you are collecting results across multiple systems you can generate different submissions and aggregate all of them to a GitHub repository (can be private) and use it to generate a single tar ball which can be uploaded to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission). 
+
+Run the following command after **replacing `--repo_url` with your GitHub repository URL**.
+
+```bash
+   cm run script --tags=push,github,mlperf,inference,submission \
+   --repo_url=https://github.com/GATEOverflow/mlperf_inference_submissions_v4.1 \
+   --commit_message="Results on <HW name> added by <Name>" \
+   --quiet
+```
+
+At the end, you can download the github repo and upload to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission).
diff --git a/docs/submission/tools-readme.md b/docs/submission/tools-readme.md
new file mode 120000
index 0000000000..d6f026eab8
--- /dev/null
+++ b/docs/submission/tools-readme.md
@@ -0,0 +1 @@
+../../tools/submission/README.md
\ No newline at end of file
diff --git a/docs/usage/index.md b/docs/usage/index.md
new file mode 100644
index 0000000000..2e92a6e00c
--- /dev/null
+++ b/docs/usage/index.md
@@ -0,0 +1 @@
+# Using CM for MLPerf Inference
diff --git a/language/llama2-70b/main.py b/language/llama2-70b/main.py
index bf1def8063..468ad067e7 100644
--- a/language/llama2-70b/main.py
+++ b/language/llama2-70b/main.py
@@ -23,6 +23,7 @@ def get_args():
     parser.add_argument("--mlperf-conf", type=str, default="mlperf.conf", help="mlperf rules config")
     parser.add_argument("--user-conf", type=str, default="user.conf", help="user config for user LoadGen settings such as target QPS")
     parser.add_argument("--total-sample-count", type=int, default=24576, help="Number of samples to use in benchmark.") # TODO: This interpretation of 'total-sample-count' is a little misleading. Fix it
+    parser.add_argument("--batch-size", type=int, default=1, help="Model batch-size to use in benchmark.")
     parser.add_argument("--output-log-dir", type=str, default="output-logs", help="Where logs are saved")
     parser.add_argument("--enable-log-trace", action="store_true", help="Enable log tracing. This file can become quite large")
     parser.add_argument("--num-workers", type=int, default=1, help="Number of workers to process queries")
@@ -69,6 +70,7 @@ def main():
     sut = sut_cls(
         model_path=args.model_path,
         dtype=args.dtype,
+        batch_size=args.batch_size,
         dataset_path=args.dataset_path,
         total_sample_count=args.total_sample_count,
         device=args.device,
diff --git a/main.py b/main.py
new file mode 100644
index 0000000000..a833153341
--- /dev/null
+++ b/main.py
@@ -0,0 +1,136 @@
+def define_env(env):
+
+   @env.macro
+   def mlperf_inference_implementation_readme(spaces, model, implementation):
+     pre_space = ""
+     for i in range(1,spaces):
+       pre_space  = pre_space + " "
+     f_pre_space = pre_space
+     pre_space += " "
+
+     content=""
+     if implementation == "reference":
+       devices = [ "CPU", "CUDA", "ROCm" ]
+       if model.lower() == "resnet50":
+         frameworks = [ "Onnxruntime", "Tensorflow", "Deepsparse" ]
+       elif model.lower() == "retinanet":
+         frameworks = [ "Onnxruntime", "Pytorch" ]
+       elif "bert" in model.lower():
+         frameworks = [ "Onnxruntime", "Pytorch", "Tensorflow" ]
+       else:
+         frameworks = [ "Pytorch" ]
+     elif implementation == "nvidia":
+       devices = [ "CUDA" ]
+       frameworks = [ "TensorRT" ]
+     elif implementation == "intel":
+       devices = [ "CPU" ]
+       frameworks = [ "Pytorch" ]
+     elif implementation == "qualcomm":
+       devices = [ "QAIC" ]
+       frameworks = [ "Glow" ]
+     elif implementation == "cpp":
+       devices = [ "CPU", "CUDA" ]
+       frameworks = [ "Onnxruntime" ]
+
+     if model.lower() == "bert-99.9":
+       categories = [ "Datacenter" ]
+     elif "dlrm" in model.lower() or "llama2" in model.lower():
+       categories = [ "Datacenter" ]
+     else:
+       categories = [ "Edge", "Datacenter" ]
+
+     for category in categories:
+       if category == "Edge":
+         scenarios = [ "Offline", "SingleStream" ]
+         if model.lower() in [ "resnet50", "retinanet" ]:
+           scenarios.append("Multistream")
+       elif category == "Datacenter":
+         scenarios = [ "Offline", "Server" ] 
+
+       content += f"{pre_space}=== \"{category.lower()}\"\n\n"
+
+       cur_space = pre_space + "    "
+       scenarios_string = ", ".join(scenarios)
+     
+       content += f"{cur_space}#### {category} category \n\n{cur_space} In the {category.lower()} category, {model} has {scenarios_string} scenarios and all the scenarios are mandatory for a closed division submission.\n\n"
+
+
+       for framework in frameworks:
+         cur_space1 = cur_space + "    "
+         content += f"{cur_space}=== \"{framework}\"\n"
+         content += f"{cur_space1}##### {framework} framework\n\n"
+
+         for device in devices:
+           if framework.lower() == "deepsparse":
+             if device.lower() != "cpu":
+               continue
+           cur_space2 = cur_space1 + "    "
+           content += f"{cur_space1}=== \"{device}\"\n"
+           content += f"{cur_space2}###### {device} device\n\n"
+         
+           content += f"{cur_space2}###### Docker Setup Command\n\n"
+           test_query_count=100
+           content += mlperf_inference_run_command(spaces+12, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True)
+           content += f"{cur_space2}The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.\n\n"
+           content += f"{cur_space2}<details>\n"
+           content += f"{cur_space2}<summary> Please click here to see more options for the docker launch </summary>\n\n"
+           content += f"{cur_space2}* `--docker_cm_repo <Custom CM repo URL>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
+           content += f"{cur_space2}* `--docker_cache=no`: to not use docker cache during the image build\n"
+
+           if device.lower() not in [ "cuda" ]:
+             content += f"{cur_space2}* `--docker_os=ubuntu`: ubuntu and rhel are supported. \n"
+             content += f"{cur_space2}* `--docker_os_version=20.04`: [20.04, 22.04] are supported for Ubuntu and [8, 9] for RHEL\n"
+
+           content += f"{cur_space2}</details>\n"
+           run_suffix = ""
+           run_suffix += f"\n{cur_space2}    ###### Run Options\n\n"
+           run_suffix += f"{cur_space2}     * Use `--division=closed` to do a closed division submission which includes compliance runs\n\n"
+           run_suffix += f"{cur_space2}     * Use `--rerun` to do a rerun even when a valid run exists\n\n"
+
+           for scenario in scenarios:
+             cur_space3 = cur_space2 + "    "
+             content += f"{cur_space2}=== \"{scenario}\"\n{cur_space3}####### {scenario}\n"
+             run_cmd = mlperf_inference_run_command(spaces+16, model, implementation, framework.lower(), category.lower(), scenario, device.lower(), "valid")
+             content += run_cmd
+             content += run_suffix
+
+           content += f"{cur_space2}=== \"All Scenarios\"\n{cur_space3}####### All Scenarios\n"
+           run_cmd = mlperf_inference_run_command(spaces+16, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), "valid")
+           content += run_cmd
+           content += run_suffix
+
+     return content
+
+     
+   @env.macro
+   def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False):
+     pre_space = ""
+     for i in range(1,spaces):
+       pre_space  = pre_space + " "
+     f_pre_space = pre_space
+     pre_space += "  "
+
+     if scenario == "All Scenarios":
+       scenario_variation_tag = ",_all-scenarios"
+       scenario_option = ""
+     else:
+       scenario_variation_tag = ""
+       scenario_option = f"\\\n {pre_space} --scenario={scenario}"
+
+     if docker:
+       docker_cmd_suffix = f" \\\n {pre_space} --docker --quiet"
+       docker_cmd_suffix += f" \\\n {pre_space} --test_query_count={test_query_count}"
+
+       docker_setup_cmd = f"\n{f_pre_space} ```bash\n{f_pre_space} cm run script --tags=run-mlperf,inference,_find-performance,_full{scenario_variation_tag} \\\n {pre_space} --model={model} \\\n {pre_space} --implementation={implementation} \\\n {pre_space} --framework={framework} \\\n {pre_space} --category={category} {scenario_option} \\\n {pre_space} --execution-mode=test \\\n {pre_space} --device={device} {docker_cmd_suffix}\n{f_pre_space} ```\n"
+
+       return docker_setup_cmd
+
+     else:
+       cmd_suffix = f"\\\n {pre_space} --quiet"
+
+       if execution_mode == "test":
+         cmd_suffix += f" \\\n {pre_space} --test_query_count={test_query_count}"
+
+       run_cmd = f"\n{f_pre_space} ```bash\n{f_pre_space} cm run script --tags=run-mlperf,inference{scenario_variation_tag} \\\n {pre_space} --model={model} \\\n {pre_space} --implementation={implementation} \\\n {pre_space} --framework={framework} \\\n {pre_space} --category={category} {scenario_option} \\\n {pre_space} --execution-mode={execution_mode} \\\n {pre_space} --device={device} {cmd_suffix}\n{f_pre_space} ```\n"
+
+       return run_cmd
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000000..0d0f641524
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,66 @@
+site_name: MLPerf Inference Documentation
+repo_url: https://github.com/mlcommons/inference
+theme:
+  name: material
+  logo: img/logo_v2.svg
+  favicon: img/logo_v2.svg
+  palette:
+    primary: deep purple
+    accent: green
+  features:
+    - content.tabs.link
+    - content.code.copy
+    - navigation.expand
+    - navigation.sections
+    - navigation.indexes
+    - navigation.instant
+    - navigation.tabs
+    - navigation.tabs.sticky
+    - navigation.top
+    - toc.follow
+nav:
+  - Inference:
+    - index.md
+  - Install:
+    - install/index.md
+    - Quick Start: install/quick-start.md
+  - Benchmarks:
+    - benchmarks/index.md
+    - Image Classification:
+      - ResNet50: benchmarks/image_classification/resnet50.md
+    - Text to Image:
+      - Stable Diffusion: benchmarks/text_to_image/sdxl.md
+    - Object Detection:
+      - RetinaNet: benchmarks/object_detection/retinanet.md
+    - Medical Imaging:
+      - 3d-unet: benchmarks/medical_imaging/3d-unet.md
+    - Language Processing:
+      - Bert-Large: benchmarks/language/bert.md
+      - GPT-J: benchmarks/language/gpt-j.md
+      - LLAMA2-70B: benchmarks/language/llama2-70b.md
+    - Recommendation:
+      - DLRM-v2: benchmarks/recommendation/dlrm-v2.md
+  - Submission:
+    - Submission Generation: submission/index.md
+  - Release Notes:
+    - What's New: changelog/index.md
+    - Changelog: changelog/changelog.md
+
+markdown_extensions:
+  - pymdownx.tasklist:
+      custom_checkbox: true
+  - pymdownx.details
+  - admonition
+  - attr_list
+  - def_list
+  - footnotes
+  - pymdownx.superfences:
+      custom_fences:
+        - name: mermaid
+          class: mermaid
+          format: !!python/name:pymdownx.superfences.fence_code_format
+  - pymdownx.tabbed:
+      alternate_style: true
+plugins:
+  - search
+  - macros

From 101bb5b2cc9c77eb64c4d2761b73578191fc3b5d Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 21 May 2024 17:55:49 +0100
Subject: [PATCH 083/305] Cleanup the README in the submission directory
 (#1689)

* Fix preprocess_submission script to copy the code path while inferring low accuracy results

* Update submission tools README.md

* Update README.md

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 tools/submission/README.md | 103 +++++++++++++++++++++----------------
 1 file changed, 58 insertions(+), 45 deletions(-)

diff --git a/tools/submission/README.md b/tools/submission/README.md
index a6b8351942..2459ab3634 100644
--- a/tools/submission/README.md
+++ b/tools/submission/README.md
@@ -1,40 +1,33 @@
 # Tools to check Submissions
 
-## `filter_errors.py` (Deprecated)
-### Summary
-Tool to remove manually verified ERRORs from the log file in the v0.7 submission.
+## `truncate_accuracy_log.py` (Mandatory)
 
-## `generate_final_report.py`
 ### Inputs
-**input**: Path to .csv output file of the [submission checker](#submissioncheckerpy)
+**input**: Path to the directory containing your submission. `closed`, `open` directories must be inside this directory. <br>
+**output**: Path to the directory to output the submission with truncated files <br>
+**submitter**: Organization name <br>
+**backup**: Path to the directory to store an unmodified copy of the truncated files <be>
+
 ### Summary
-Generates the spreadsheet with the format in which the final results will be published. This script can be used by running the following command:
+Takes a directory containing a submission and truncates `mlperf_log_accuracy.json` files. There are two ways to use this script. First, we could create a new submission directory with the truncated files by running:
 ```
-python3 generate_final_report.py --input <path-to-csv>
+python truncate_accuracy_log.py --input <original_submission_directory> --submitter <organization_name> --output <new_submission_directory>
+```
+Second, we could truncate the desired files and place and store a copy of the unmodified files in the backup repository.
+```
+python tools/submission/truncate_accuracy_log.py --input <original_submission_directory> --submitter <organization_name> --backup <safe_directory> 
 ```
 ### Outputs
-Spreadsheet with the results.
-
-## `log_parser.py` 
-### Summary
-Helper module for the submission checker. It parses the logs containing the results of the benchmark.
-
-## `pack_submission.sh` (Deprecated)
-### Summary
-Creates an encrypted tarball and generate the SHA1 of the tarball. Currently submissions do not need to be encrypted.
+Output directory with submission with truncated `mlperf_log_accuracy.json` files
 
-## `repository_checks.sh`
+## `preprocess_submission.py` (Optional)
 ### Inputs
-Takes as input the path of the directory to run the checks on.
+**input**: Path to directory containing your submission <br>
+**submitter**: Organization name <br>
 ### Summary
-Checks that a directory containing one or several submissions is able to be uploaded to github. This script can be used by running the following command:
-```
-./repository_checks.sh <path-to-folder>
-```
-### Outputs
-Logs in the console the errors that could cause problems uploading the submission to github.
+The input submission directory is modified with empty directories removed and low accuracy results inferred. Multistream and offline scenario results are also wherever possible. The original input directory is saved in a timestamped directory.
 
-## `submission_checker.py`
+## `submission_checker.py` (Mandatory)
 ### Inputs
 **input**: Path to the directory containing one or several submissions.<br>
 **version**: Checker version. E.g v1.1, v2.0, v2.1, v3.0, v3.1. <br>
@@ -45,13 +38,17 @@ Logs in the console the errors that could cause problems uploading the submissio
 **submission-exceptions**: Flag to ignore errors in submissions<br>
 
 The below input fields are off by default since v3.1 and are mandatory but can be turned on for debugging purposes
-**skip-power-check**: Flag to skip the extra power checks. This flag has no effect on non-power submissions <br>
+
+**skip-power-check**: Flag to skip the extra power checks. This flag has no effect on non-power submission results
+
 **skip-meaningful-fields-emptiness-check**: Flag to avoid checking if mandatory system description fields are empty
+
 **skip-empty-files-check**: Flag to avoid checking if mandatory measurement files are empty
-**skip-check-power-measure-files**: Flag to avoid checking is the requirement power measurement files are present
+
+**skip-check-power-measure-files**: Flag to avoid checking if the required power measurement files are present
 
 ### Summary
-Checks a directory that contains one or several submission. This script can be used by running the following command:
+Checks a directory that contains one or several submissions. This script can be used by running the following command:
 ```
 python3 submission_checker.py --input <path-to-folder> 
     [--version <version>]
@@ -64,29 +61,45 @@ python3 submission_checker.py --input <path-to-folder>
 
 ### Outputs
 - CSV file containing all the valid results in the directory.
-- It raises several errors and logs the results that are invalid.
+- It raises several errors and logs invalid results.
+
+
 
-## `truncate_accuracy_log.py`
+## `generate_final_report.py` (Optional)
 ### Inputs
-**input**: Path to directory containing your submission <br>
-**output**: Path to the directory to output the submission with truncated files <br>
-**submitter**: Organization name <br>
-**backup**: Path to the directory to store an unmodified copy of the truncated files <br>
+**input**: Path to .csv output file of the [submission checker](#submissioncheckerpy)
 ### Summary
-Takes a directory containing a submission and truncates `mlperf_log_accuracy.json` files. There are two ways to use this script. First, we could create a new submission directory with the truncated files by running:
-```
-python truncate_accuracy_log.py --input <original_submission_directory> --submitter <organization_name> --output <new_submission_directory>
-```
-Second, we could truncate the desired files and place and store a copy of the unmodified files in the backup repository.
+Generates the spreadsheet with the format in which the final results will be published. This script can be used by running the following command:
 ```
-python tools/submission/truncate_accuracy_log.py --input <original_submission_directory> --submitter <organization_name> --backup <safe_directory> 
+python3 generate_final_report.py --input <path-to-csv>
 ```
 ### Outputs
-Output directory with submission with truncated `mlperf_log_accuracy.json` files
+Spreadsheet with the results.
+
+
+## `log_parser.py` 
+### Summary
+Helper module for the submission checker. It parses the logs containing the results of the benchmark.
 
-## `preprocess_submission.py`
+## `filter_errors.py` (Deprecated)
+### Summary
+Tool to remove manually verified ERRORs from the log file in the v0.7 submission.
+
+
+
+## `pack_submission.sh` (Deprecated)
+### Summary
+Creates an encrypted tarball and generates the SHA1 of the tarball. Currently submissions do not need to be encrypted.
+
+## `repository_checks.sh` (Deprecated)
 ### Inputs
-**input**: Path to directory containing your submission <br>
-**submitter**: Organization name <br>
+Takes as input the path of the directory to run the checks on.
 ### Summary
-The input submission directory is modified with empty directories removed and low accuracy results inferred and also multistream and offline scenario results wherever possible. The original input directory is saved in a timestamped directory.
+Checks that a directory containing one or several submissions can be uploaded to github. This script can be used by running the following command:
+```
+./repository_checks.sh <path-to-folder>
+```
+### Outputs
+Logs in the console the errors that could cause problems uploading the submission to github.
+
+

From 8fcd0d0ea38587e5b3a5150a95829c2b13b64377 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 21 May 2024 20:32:55 +0100
Subject: [PATCH 084/305] Adds CM commands to run stable diffusion reference
 implementation (#1678)

Co-authored-by: Miro <mirhodak@amd.com>
---
 text_to_image/README.md | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/text_to_image/README.md b/text_to_image/README.md
index e353bfac01..cb2548e15f 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -13,10 +13,19 @@ This is the reference implementation for MLPerf Inference text to image
 
 | Data | Description |
 | ---- | ---- | 
-| Coco-2014 | We use a subset of 5000 images and captions of the coco 2014 validation dataset, so that there is exaclty one caption per image. The model takes as input the caption of the image and generates an image from it. The original images and the generated images are used to compute FID score. The caption and the generated images are used to compute the CLIP score. We provide a [script](tools/coco.py) to automatically download the dataset |
-| Coco-2014 (calibration) | We use a subset of 500 captions and images of the coco 2014 training dataset, so that there is exaclty one caption per image. The subset was generated using this [script](tools/coco_generate_calibration.py). We provide the [caption ids](../calibration/COCO-2014/coco_cal_captions_list.txt) and a [script](tools/coco_calibration.py) to download them. |
+| Coco-2014 | We use a subset of 5000 images and captions of the coco 2014 validation dataset, so that there is exactly one caption per image. The model takes as input the caption of the image and generates an image from it. The original images and the generated images are used to compute FID score. The caption and the generated images are used to compute the CLIP score. We provide a [script](tools/coco.py) to automatically download the dataset |
+| Coco-2014 (calibration) | We use a subset of 500 captions and images of the coco 2014 training dataset, so that there is exactly one caption per image. The subset was generated using this [script](tools/coco_generate_calibration.py). We provide the [caption ids](../calibration/COCO-2014/coco_cal_captions_list.txt) and a [script](tools/coco_calibration.py) to download them. |
 
+## Automated command to run the benchmark via MLCommons CM
 
+```
+python3 -m pip install cmind
+cm pull repo mlcommons@ck
+cm run script --tags=run-mlperf,inference  --model=sdxl --implementation=reference --backend=pytorch --device=cpu --precision=float32 --scenario=Offline --quiet
+```
+* `--device=cuda` can be used to run on Nvidia GPUs and `--device=rocm` can be used to run on AMD GPUs
+* `--precision=float16` or `--precision=bfloat16` can be used to change the model precision
+ 
 ## Setup
 Set the following helper variables
 ```bash

From fb65dde110562aefb4d5906a6a9e5869f469834b Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 21 May 2024 15:20:05 -0500
Subject: [PATCH 085/305] Gptj metric scaling (#1699)

* Enable inferring tokens per second for gptj

* Add inferred token latencies demos

* Use sample_count - 1 to infer completed tps

* Update mlperf.conf
---
 loadgen/bindings/python_api.cc                |   2 +
 .../token_metrics/py_demo_offline_inferred.py | 119 ++++++++++++++++++
 .../token_metrics/py_demo_server_inferred.py  | 116 +++++++++++++++++
 loadgen/results.cc                            |  48 ++++++-
 loadgen/test_settings.h                       |   3 +
 loadgen/test_settings_internal.cc             |  13 +-
 loadgen/test_settings_internal.h              |   3 +
 mlperf.conf                                   |  10 +-
 8 files changed, 307 insertions(+), 7 deletions(-)
 create mode 100644 loadgen/demos/token_metrics/py_demo_offline_inferred.py
 create mode 100644 loadgen/demos/token_metrics/py_demo_server_inferred.py

diff --git a/loadgen/bindings/python_api.cc b/loadgen/bindings/python_api.cc
index cfe24bd3c7..83e14e0ecb 100644
--- a/loadgen/bindings/python_api.cc
+++ b/loadgen/bindings/python_api.cc
@@ -338,6 +338,8 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
       .def_readwrite("use_token_latencies", &TestSettings::use_token_latencies)
       .def_readwrite("ttft_latency", &TestSettings::server_ttft_latency)
       .def_readwrite("tpot_latency", &TestSettings::server_tpot_latency)
+      .def_readwrite("infer_token_latencies", &TestSettings::infer_token_latencies)
+      .def_readwrite("token_latency_scaling_factor", &TestSettings::token_latency_scaling_factor)
       .def("FromConfig", &TestSettings::FromConfig, "FromConfig.");
 
   pybind11::enum_<LoggingMode>(m, "LoggingMode")
diff --git a/loadgen/demos/token_metrics/py_demo_offline_inferred.py b/loadgen/demos/token_metrics/py_demo_offline_inferred.py
new file mode 100644
index 0000000000..79390e1b84
--- /dev/null
+++ b/loadgen/demos/token_metrics/py_demo_offline_inferred.py
@@ -0,0 +1,119 @@
+# Copyright 2019 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Python demo showing how to use the MLPerf Inference load generator bindings.
+"""
+
+from __future__ import print_function
+
+import argparse
+import threading
+import time
+import numpy as np
+import array
+
+import mlperf_loadgen
+
+
+def f(x, y):
+    return (4 + 3*x*y + x**3 + y**2)
+
+def create_responses(n, m, mod = 4):
+    r = []
+    for i in range(n):
+        r.append([f(i,j) for j in range(m + (i%mod))])
+    return r
+responses = create_responses(1024, 20, mod = 3)
+
+def load_samples_to_ram(query_samples):
+    del query_samples
+    return
+
+
+def unload_samples_from_ram(query_samples):
+    del query_samples
+    return
+
+
+# Processes queries in 3 slices that complete at different times.
+def process_query_async(query_samples, i_slice):
+    time.sleep(3 * (i_slice + 1))
+    query_responses = []
+    samples_to_complete = query_samples[i_slice:len(query_samples):3]
+    for s in samples_to_complete:
+        response_array = np.array(responses[s.index], np.int32)
+        token = response_array[0]
+        time.sleep(.0002)
+        response_token = array.array("B", token.tobytes())
+        response_token_info = response_token.buffer_info()
+        response_token_data = response_token_info[0]
+        response_token_size = response_token_info[1] * response_token.itemsize
+        # mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
+        time.sleep(.02)
+        n_tokens = len(response_array)
+        response_array = array.array("B", response_array.tobytes())
+        response_info = response_array.buffer_info()
+        response_data = response_info[0]
+        response_size = response_info[1] * response_array.itemsize
+        query_responses.append(
+            mlperf_loadgen.QuerySampleResponse(
+                s.id, response_data, response_size))
+    mlperf_loadgen.QuerySamplesComplete(query_responses)
+
+
+def issue_query(query_samples):
+    threading.Thread(target=process_query_async,
+                     args=(query_samples, 0)).start()
+    threading.Thread(target=process_query_async,
+                     args=(query_samples, 1)).start()
+    threading.Thread(target=process_query_async,
+                     args=(query_samples, 2)).start()
+
+
+def flush_queries():
+    pass
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument("--expected-qps", type=int, default=1000)
+    parser.add_argument("--min-duration-ms", type=int, default=30000)
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    settings = mlperf_loadgen.TestSettings()
+    settings.scenario = mlperf_loadgen.TestScenario.Offline
+    if args.mode == "performance":
+        settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
+    else:
+        settings.mode = mlperf_loadgen.TestMode.AccuracyOnly
+    settings.offline_expected_qps = args.expected_qps
+    settings.min_duration_ms = args.min_duration_ms
+    settings.infer_token_latencies = 1
+    settings.token_latency_scaling_factor = 21
+
+    sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
+    qsl = mlperf_loadgen.ConstructQSL(
+        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+    mlperf_loadgen.StartTest(sut, qsl, settings)
+    mlperf_loadgen.DestroyQSL(qsl)
+    mlperf_loadgen.DestroySUT(sut)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/loadgen/demos/token_metrics/py_demo_server_inferred.py b/loadgen/demos/token_metrics/py_demo_server_inferred.py
new file mode 100644
index 0000000000..b4431ec9cc
--- /dev/null
+++ b/loadgen/demos/token_metrics/py_demo_server_inferred.py
@@ -0,0 +1,116 @@
+# Copyright 2019 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+"""Python demo showing how to use the MLPerf Inference load generator bindings.
+"""
+
+from __future__ import print_function
+
+import argparse
+import array
+import threading
+import time
+import numpy as np
+
+from absl import app
+import mlperf_loadgen
+
+def f(x, y):
+    return (4 + 3*x*y + x**3 + y**2)
+
+def create_responses(n, m, mod = 4):
+    r = []
+    for i in range(n):
+        r.append([f(i,j) for j in range(m + (i%mod))])
+    return r
+responses = create_responses(1024, 20, mod = 3)
+
+def load_samples_to_ram(query_samples):
+    del query_samples
+    return
+
+
+def unload_samples_from_ram(query_samples):
+    del query_samples
+    return
+
+
+def process_query_async(query_samples):
+    """Processes the list of queries."""
+    query_responses = []
+    for s in query_samples:
+        response_array = np.array(responses[s.index], np.int32)
+        token = response_array[0]
+        time.sleep(.0002)
+        response_token = array.array("B", token.tobytes())
+        response_token_info = response_token.buffer_info()
+        response_token_data = response_token_info[0]
+        response_token_size = response_token_info[1] * response_token.itemsize
+        time.sleep(.02)
+        n_tokens = len(response_array)
+        response_array = array.array("B", response_array.tobytes())
+        response_info = response_array.buffer_info()
+        response_data = response_info[0]
+        response_size = response_info[1] * response_array.itemsize
+        # print(f"Reported size python: {n_tokens}")
+        query_responses.append(
+            mlperf_loadgen.QuerySampleResponse(
+                s.id, response_data, response_size))
+    mlperf_loadgen.QuerySamplesComplete(query_responses)
+
+
+def issue_query(query_samples):
+    threading.Thread(target=process_query_async,
+                     args=[query_samples]).start()
+
+
+def flush_queries():
+    pass
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument("--target-qps", type=int, default=100)
+    parser.add_argument("--target-latency-ns", type=int, default=100000000)
+    parser.add_argument("--min-query-count", type=int, default=100)
+    parser.add_argument("--min-duration-ms", type=int, default=30000)
+    return parser.parse_args()
+
+def main():
+    args = get_args()
+    settings = mlperf_loadgen.TestSettings()
+    settings.scenario = mlperf_loadgen.TestScenario.Server
+    if args.mode == "performance":
+        settings.mode = mlperf_loadgen.TestMode.PerformanceOnly
+    else:
+        settings.mode = mlperf_loadgen.TestMode.AccuracyOnly
+    settings.server_target_qps = args.target_qps
+    settings.server_target_latency_ns = args.target_latency_ns
+    settings.min_query_count = args.min_query_count
+    settings.min_duration_ms = args.min_duration_ms
+    settings.infer_token_latencies = 1
+    settings.token_latency_scaling_factor = 21
+
+    sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
+    qsl = mlperf_loadgen.ConstructQSL(
+        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+    mlperf_loadgen.StartTest(sut, qsl, settings)
+    mlperf_loadgen.DestroyQSL(qsl)
+    mlperf_loadgen.DestroySUT(sut)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/loadgen/results.cc b/loadgen/results.cc
index 445de89015..00ea66558d 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -440,6 +440,32 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
         break;
       }
       case TestScenario::Server:
+        double tps_as_completed =
+            token_count / pr.final_query_all_samples_done_time;
+        summary("Completed tokens per second: ",
+                DoubleToString(tps_as_completed));
+        break;
+    }
+  }
+
+  if (settings.infer_token_latencies){
+    switch (settings.scenario) {
+      case TestScenario::SingleStream: {
+        break;
+      }
+      case TestScenario::MultiStream: {
+        break;
+      }
+      case TestScenario::Offline: {
+        double tokens_per_second = settings.token_latency_scaling_factor * sample_count / pr.max_latency;
+        summary("Tokens per second (inferred): ", tokens_per_second);
+        break;
+      }
+      case TestScenario::Server:
+        double tps_as_completed =
+          settings.token_latency_scaling_factor * (sample_count - 1) / pr.final_query_all_samples_done_time;
+        summary("Completed tokens per second (inferred): ",
+                DoubleToString(tps_as_completed));
         break;
     }
   }
@@ -777,8 +803,28 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
       MLPERF_LOG(detail, "result_tokens_per_second", tokens_per_second);
     }
   }
+
+  if (settings.infer_token_latencies){
+    switch (settings.scenario) {
+      case TestScenario::Server: {
+        double completed_tokens_per_second = (sample_count - 1) * settings.token_latency_scaling_factor / pr.final_query_all_samples_done_time;
+        MLPERF_LOG(detail, "result_inferred_completed_tokens_per_second", completed_tokens_per_second);
+        break;
+      }
+      case TestScenario::Offline: {
+        double tokens_per_second = sample_count * settings.token_latency_scaling_factor / pr.max_latency;
+        MLPERF_LOG(detail, "result_inferred_tokens_per_second", tokens_per_second);
+        break;
+      }
+      case TestScenario::SingleStream: {
+        break;
+      }
+      case TestScenario::MultiStream: {
+        break;
+      }
+  }
 #endif
 }
-
+}
 }  // namespace loadgen
 } // namespace mlperf
diff --git a/loadgen/test_settings.h b/loadgen/test_settings.h
index b0018380d3..8b209035cc 100644
--- a/loadgen/test_settings.h
+++ b/loadgen/test_settings.h
@@ -267,6 +267,9 @@ struct TestSettings {
   /// Token latency parameters
   uint64_t server_ttft_latency = 100000000;
   uint64_t server_tpot_latency = 100000000;
+  /// \brief Infer token latencies
+  bool infer_token_latencies = false;
+  uint64_t token_latency_scaling_factor;
   /**@}*/
 };
 
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 2bcf62c290..5a18c32f90 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -51,7 +51,9 @@ TestSettingsInternal::TestSettingsInternal(
       sample_concatenate_permutation(false),
       use_token_latencies(requested.use_token_latencies),
       server_ttft_latency(requested.server_ttft_latency),
-      server_tpot_latency(requested.server_tpot_latency){
+      server_tpot_latency(requested.server_tpot_latency),
+      infer_token_latencies(requested.infer_token_latencies),
+      token_latency_scaling_factor(requested.token_latency_scaling_factor){
   // Target QPS, target latency, and max_async_queries.
   switch (requested.scenario) {
     case TestScenario::SingleStream:
@@ -689,7 +691,7 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
            nullptr);
   lookupkv(model, scenario, "test05_schedule_rng_seed", &test05_schedule_rng_seed, nullptr);
 
-  // keys that apply to token metrics
+  // keys to measure token metrics
   if (lookupkv(model, scenario, "use_token_latencies", &val, nullptr)){
     use_token_latencies = (val == 1) ? true : false;
     if (use_token_latencies){
@@ -698,6 +700,13 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
     }
   }
 
+  // keys to infer token metrics
+  if (lookupkv(model, scenario, "infer_token_latencies", &val, nullptr)){
+    infer_token_latencies = (val == 1) ? true : false;
+    if (infer_token_latencies){
+      lookupkv(model, scenario, "token_latency_scaling_factor", &token_latency_scaling_factor, nullptr, 1);
+    }
+  }
   // keys that apply to SingleStream
   lookupkv(model, "SingleStream", "target_latency_percentile", nullptr,
            &single_stream_target_latency_percentile, 0.01);
diff --git a/loadgen/test_settings_internal.h b/loadgen/test_settings_internal.h
index 5222f3156c..d557e97069 100644
--- a/loadgen/test_settings_internal.h
+++ b/loadgen/test_settings_internal.h
@@ -85,6 +85,9 @@ struct TestSettingsInternal {
   bool use_token_latencies = false;
   int64_t server_ttft_latency;
   int64_t server_tpot_latency;
+
+  bool infer_token_latencies = false;
+  int64_t token_latency_scaling_factor;
 };
 
 /// \brief A namespace of collections of FindPeakPerformance helper functions,
diff --git a/mlperf.conf b/mlperf.conf
index 7e286b565e..dd835563d1 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -41,9 +41,8 @@ retinanet.MultiStream.target_latency = 528
 3d-unet.*.sample_concatenate_permutation = 1
 
 # LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario
-gptj.Server.sample_concatenate_permutation = 1
-gptj.SingleStream.sample_concatenate_permutation = 1
-llama2-70b.Server.sample_concatenate_permutation = 1
+gptj.*.sample_concatenate_permutation = 1
+llama2-70b.*.sample_concatenate_permutation = 1
 
 *.Server.target_latency = 10
 *.Server.target_latency_percentile = 99
@@ -57,8 +56,11 @@ dlrm-v2.Server.target_latency = 60
 rnnt.Server.target_latency = 1000
 gptj.Server.target_latency = 20000
 stable-diffusion-xl.Server.target_latency = 20000
-# Falcon Server scenario requires two latency constraints
+# Llama2-70b benchmarks measures token latencies
 llama2-70b.*.use_token_latencies = 1
+# gptj benchmark infers token latencies
+gptj.*.infer_token_latencies = 1
+gptj.*.token_latency_scaling_factor = 69
 # Only ttft and tpot are tracked for the llama2-70b benchmark therefore target_latency = 0
 llama2-70b.Server.target_latency = 0
 llama2-70b.Server.ttft_latency = 2000

From dac75963ab4fc8c7e21048af3c5be9be78f724f9 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 21 May 2024 21:21:09 +0100
Subject: [PATCH 086/305] Add CM support for LLAMA2 (#1645)

Co-authored-by: Miro <mirhodak@amd.com>
Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
---
 language/llama2-70b/README.md | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index b539b3e3cf..4ee06b577c 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -5,9 +5,20 @@
 + Processing of Validation dataset is not finalized yet. Decision on input token lengths is pending
 + Streamer for communicating with loadgen has quite some overhead. This is only meant to provide functional implementation
 + For custom/optimized implementations of this benchmark it is important to include the :
-        - For server scenario, it is necesary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
+        - For server scenario, it is necessary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
         - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
 
+## Automated command to run the benchmark via MLCommons CM 
+
+```
+python3 -m pip install cmind
+cm pull repo mlcommons@ck
+cm run script --tags=run-mlperf,inference  --model=llama2-70b-99 --implementation=reference --backend=pytorch --device=cpu --precision=float32 --scenario=Offline --quiet
+```
+* `--device=cuda` can be used to run on Nvidia GPUs and `--device=rocm` can be used to run on AMD GPUs
+* `--precision=float16` or `--precision=bfloat16` can be used to change the model precision
+* `--model=llama2-7b` can be used to run the llama2-7b variant (this is not the official MLPerf model and so not valid for a closed division submission)
+ 
 ## Prepare environment
 
 Copy the mlperf.conf file to this folder.

From fd8857ff0910098acba79aa89388fab761e0ddcc Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 21 May 2024 21:22:13 +0100
Subject: [PATCH 087/305] Support running loadgen over the network with
 external SUTs (#1641)

Currently the loadgen over the network is connecting only to the same host. This fix makes it connect to external SUTs in the network.
---
 language/bert/run.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/bert/run.py b/language/bert/run.py
index 5cdaa56b17..2afbe56f67 100644
--- a/language/bert/run.py
+++ b/language/bert/run.py
@@ -124,7 +124,7 @@ def main():
         from network_SUT import app, node, set_backend
         node = args.node
         set_backend(sut)
-        app.run(debug=False, port=args.port)
+        app.run(debug=False, port=args.port, host="0.0.0.0")
 
     else:
         print("Running LoadGen test...")

From e6a493d4bc5ac4223d18f8a042e082b4669595cc Mon Sep 17 00:00:00 2001
From: nvyihengz <109830892+nvyihengz@users.noreply.github.com>
Date: Tue, 21 May 2024 13:23:56 -0700
Subject: [PATCH 088/305] Update SDXL model name in the readme (#1652)

Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
---
 text_to_image/README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/text_to_image/README.md b/text_to_image/README.md
index cb2548e15f..bb46406a87 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -6,15 +6,15 @@ This is the reference implementation for MLPerf Inference text to image
 
 | model | accuracy | dataset | model source | precision | notes |
 | ---- | ---- | ---- | ---- | ---- | ---- |
-| StableDiffusion | - | Coco2014 | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp32 | NCHW |
+| Stable Diffusion XL 1.0 | - | Coco2014 | [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | fp32 | NCHW |
 
 
 ## Dataset
 
 | Data | Description |
-| ---- | ---- | 
-| Coco-2014 | We use a subset of 5000 images and captions of the coco 2014 validation dataset, so that there is exactly one caption per image. The model takes as input the caption of the image and generates an image from it. The original images and the generated images are used to compute FID score. The caption and the generated images are used to compute the CLIP score. We provide a [script](tools/coco.py) to automatically download the dataset |
-| Coco-2014 (calibration) | We use a subset of 500 captions and images of the coco 2014 training dataset, so that there is exactly one caption per image. The subset was generated using this [script](tools/coco_generate_calibration.py). We provide the [caption ids](../calibration/COCO-2014/coco_cal_captions_list.txt) and a [script](tools/coco_calibration.py) to download them. |
+| ---- | ---- |
+| Coco-2014 | We use a subset of 5000 images and captions of the coco 2014 validation dataset, so that there is exaclty one caption per image. The model takes as input the caption of the image and generates an image from it. The original images and the generated images are used to compute FID score. The caption and the generated images are used to compute the CLIP score. We provide a [script](tools/coco.py) to automatically download the dataset |
+| Coco-2014 (calibration) | We use a subset of 500 captions and images of the coco 2014 training dataset, so that there is exaclty one caption per image. The subset was generated using this [script](tools/coco_generate_calibration.py). We provide the [caption ids](../calibration/COCO-2014/coco_cal_captions_list.txt) and a [script](tools/coco_calibration.py) to download them. |
 
 ## Automated command to run the benchmark via MLCommons CM
 
@@ -61,7 +61,7 @@ We host two checkpoints (fp32 and fp16) that are a snapshot of the [Hugging Face
 
 #### CM method
 
-The following MLCommons CM commands can be used to programmatically download the model checkpoints. 
+The following MLCommons CM commands can be used to programmatically download the model checkpoints.
 
 ```
 pip install cmind

From f274a5523b4eb8450dfd59679c3f26121ea9d40e Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Tue, 21 May 2024 13:24:32 -0700
Subject: [PATCH 089/305] Add open_orca preprocessing steps (#1664)

* Add open_orca preprocessing steps

* Typo
---
 language/llama2-70b/README.md      |  8 +++++++-
 language/llama2-70b/processorca.py | 11 ++++++++++-
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 4ee06b577c..837f24ade0 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -112,7 +112,7 @@ rclone copy mlc-inference:mlcommons-inference-wg-public/open_orca ./open_orca -P
 
 ### Unprocessed
 
-You can also download and process the dataset yourself as follows:
+You can also download and process the dataset yourself following the command below:
 
 ```
 # First get the `open-orca` parquet from huggingface
@@ -129,6 +129,12 @@ python3 processorca.py --dataset_pq_path=${OPENORCA_PARQUET} --model_dir=${CHECK
 mv ${EXPORT_DIR}/open_orca_gpt4_tokenized_llama.sampled_24576.pkl ${DATASET_PATH}
 ```
 
+The script will perform the following steps on the original open_orca GPT4 dataset:
+- filter out all queries with non-ascii characters, except for normal unicode quotes and hyphens.
+- filter out all queries with out-of-bound input/output sequence lengths
+- filter out all queries with expected answers shorter than 2 words (known to cause issues for Llama2)
+- filter out all queries with prompts that generate bad output texts using Llama2 models
+- sample equally from the sub-dataset (i.e. COT, NIV, FLAN, T0) and form the final dataset.
 
 ## Run Performance Benchmarks
 
diff --git a/language/llama2-70b/processorca.py b/language/llama2-70b/processorca.py
index 91dec5d510..c850f89bda 100644
--- a/language/llama2-70b/processorca.py
+++ b/language/llama2-70b/processorca.py
@@ -24,6 +24,15 @@
 from transformers import LlamaTokenizerFast
 from typing import Dict
 
+__doc__ = """
+This script takes the open_orca GPT4 dataset parquet and perform the following preprocessing and filtering steps:
+1. filter out all queries with non-ascii characters, except for normal unicode quotes and hyphens.
+2. filter out all queries with out-of-bound input/output sequence lengths
+3. filter out all queries with expected answers shorter than 2 words (known to cause issues for Llama2)
+4. filter out all queries with prompts that generate bad output texts using Llama2 models
+4. sample equally from the sub-dataset (i.e. COT, NIV, FLAN, T0) and form the final dataset.
+"""
+
 llama_prompt_system = "<s>[INST] <<SYS>>\n{}\n<</SYS>>\n\n{} [/INST]"
 llama_prompt_no_system = "<s>[INST] {} [/INST]"
 
@@ -105,7 +114,7 @@ def filter_seqlen_oob(self, df: pd.DataFrame) -> pd.DataFrame:
         df['tok_input_length'] = df['tok_input'].apply(lambda x: len(x))
         df['tok_output_length'] = df['tok_output'].apply(lambda x: len(x))
 
-        # Filter based on sequence length (2048, 2048)
+        # Filter based on sequence length
         df = df[df["tok_input_length"] < self.io_token_limit]
         df = df[df["tok_output_length"] < self.io_token_limit]
         return df.reset_index(drop=True)

From 2a78b2fdc0407b70681771af94d68577f472db77 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 21 May 2024 21:25:17 +0100
Subject: [PATCH 090/305] =?UTF-8?q?Remove=20backwards=20compatibility=20fo?=
 =?UTF-8?q?r=20the=20submission=20checker.=20Update=20sub=E2=80=A6=20(#167?=
 =?UTF-8?q?7)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Remove backwards compatibility for the submission checker. Update submission_checker.py

It makes sense to remove the backwards compatibility of the submission checker in the master branch. We can always use a specific release of the inference repository to get the submission checker for that round of submission.

* Update submission_checker.py

* Removed some old configs. Update submission_checker.py

* Remove some more old configs. Update submission_checker.py

* Update submission_checker.py

* Removed legacy MS checks Update submission_checker.py

* Removed outdated model configs Update submission_checker.py

* Update submission_checker.py

* Update submission_checker.py

* Update submission_checker.py

* Update submission_checker.py
---
 tools/submission/submission_checker.py | 1614 +++---------------------
 1 file changed, 181 insertions(+), 1433 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index c79963096a..ef3e60cc20 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1,4 +1,4 @@
-"""A checker for mlperf inference submissions
+"""A checker for MLPerf Inference submissions
 """
 
 from __future__ import division
@@ -12,1043 +12,19 @@
 import os
 import re
 import sys
-from glob import glob
-
-from log_parser import MLPerfLog
-
-# pylint: disable=missing-docstring
-
-logging.basicConfig(level=logging.INFO)
-log = logging.getLogger("main")
-
-submission_checker_dir = os.path.dirname(os.path.realpath(__file__))
-
-MODEL_CONFIG = {
-    "v0.5": {
-        "models": ["ssd-small", "ssd-large", "mobilenet", "resnet", "gnmt"],
-        "required-scenarios-datacenter": {
-            # anything goes
-        },
-        "optional-scenarios-datacenter": {
-            # anything goes
-        },
-        "required-scenarios-edge": {
-            # anything goes
-        },
-        "optional-scenarios-edge": {
-            # anything goes
-        },
-        "accuracy-target": {
-            "mobilenet": ("acc", 71.68 * 0.98),
-            "resnet": ("acc", 76.46 * 0.99),
-            "ssd-small": ("mAP", 22 * 0.99),
-            "ssd-large": ("mAP", 20 * 0.99),
-            "gnmt": ("bleu", 23.9 * 0.99),
-        },
-        "performance-sample-count": {
-            "mobilenet": 1024,
-            "resnet": 1024,
-            "ssd-small": 256,
-            "ssd-large": 64,
-            "gnmt": 3903900,
-        },
-        "model_mapping": {
-            # map model names to the official mlperf model class
-            "ssd-mobilenet": "ssd-small",
-            "ssd-resnet34": "ssd-large",
-            "resnet50": "resnet",
-        },
-        "seeds": {
-            "qsl_rng_seed": 3133965575612453542,
-            "sample_index_rng_seed": 665484352860916858,
-            "schedule_rng_seed": 3622009729038561421,
-        },
-        "test05_seeds": {
-            "qsl_rng_seed": 195,
-            "sample_index_rng_seed": 235,
-            "schedule_rng_seed": 634,
-        },
-        "ignore_errors": [
-            "check for ERROR in detailed",
-            "Loadgen built with uncommitted changes",
-            "Ran out of generated queries to issue before the minimum query "
-            "count and test duration were reached",
-            "CAS failed",
-        ],
-    },
-    "v0.7": {
-        "models": [
-            "ssd-small",
-            "ssd-large",
-            "resnet",
-            "rnnt",
-            "bert-99",
-            "bert-99.9",
-            "dlrm-99",
-            "dlrm-99.9",
-            "3d-unet-99",
-            "3d-unet-99.9",
-        ],
-        "required-scenarios-datacenter": {
-            "resnet": ["Offline"],
-            "ssd-large": ["Offline"],
-            "rnnt": ["Offline"],
-            "bert-99": ["Offline"],
-            "bert-99.9": ["Offline"],
-            "dlrm-99": ["Offline"],
-            "dlrm-99.9": ["Offline"],
-            "3d-unet-99": ["Offline"],
-            "3d-unet-99.9": ["Offline"],
-        },
-        "optional-scenarios-datacenter": {
-            "resnet": ["Server"],
-            "ssd-large": ["Server"],
-            "rnnt": ["Server"],
-            "bert-99": ["Server"],
-            "bert-99.9": ["Server"],
-            "dlrm-99": ["Server"],
-            "dlrm-99.9": ["Server"],
-        },
-        "required-scenarios-edge": {
-            "resnet": ["SingleStream", "Offline"],
-            "ssd-small": ["SingleStream", "Offline"],
-            "ssd-large": ["SingleStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-edge": {
-            "resnet": ["MultiStream"],
-            "ssd-small": ["MultiStream"],
-            "ssd-large": ["MultiStream"],
-        },
-        "accuracy-target": {
-            "resnet": ("acc", 76.46 * 0.99),
-            "ssd-small": ("mAP", 22 * 0.99),
-            "ssd-large": ("mAP", 20 * 0.99),
-            "rnnt": ("WER", (100 - 7.452) * 0.99),
-            "bert-99": ("F1", 90.874 * 0.99),
-            "bert-99.9": ("F1", 90.874 * 0.999),
-            "dlrm-99": ("AUC", 80.25 * 0.99),
-            "dlrm-99.9": ("AUC", 80.25 * 0.999),
-            "3d-unet-99": ("DICE", 0.853 * 0.99),
-            "3d-unet-99.9": ("DICE", 0.853 * 0.999),
-        },
-        "performance-sample-count": {
-            "ssd-small": 256,
-            "ssd-large": 64,
-            "resnet": 1024,
-            "rnnt": 2513,
-            "bert-99": 10833,
-            "bert-99.9": 10833,
-            "dlrm-99": 204800,
-            "dlrm-99.9": 204800,
-            "3d-unet-99": 16,
-            "3d-unet-99.9": 16,
-        },
-        "model_mapping": {
-            # map model names to the official mlperf model class
-            "ssd-mobilenet": "ssd-small",
-            "ssd-resnet34": "ssd-large",
-            "mobilenet": "resnet",
-            "resnet50": "resnet",
-        },
-        "seeds": {
-            "qsl_rng_seed": 12786827339337101903,
-            "sample_index_rng_seed": 12640797754436136668,
-            "schedule_rng_seed": 3135815929913719677,
-        },
-        "test05_seeds": {
-            "qsl_rng_seed": 313588358309856706,
-            "sample_index_rng_seed": 471397156132239067,
-            "schedule_rng_seed": 413914573387865862,
-        },
-        "ignore_errors": [
-            "CAS failed",
-        ],
-        "latency-constraint": {
-            "resnet": {"Server": 15000000, "MultiStream": 50000000},
-            "ssd-small": {"MultiStream": 50000000},
-            "ssd-large": {"Server": 100000000, "MultiStream": 66000000},
-            "rnnt": {"Server": 1000000000},
-            "bert-99": {"Server": 130000000},
-            "bert-99.9": {"Server": 130000000},
-            "dlrm-99": {"Server": 30000000},
-            "dlrm-99.9": {"Server": 30000000},
-        },
-        "min-queries": {
-            "resnet": {
-                "SingleStream": 1024,
-                "Server": 270336,
-                "MultiStream": 270336,
-                "Offline": 1,
-            },
-            "ssd-small": {"SingleStream": 1024, "MultiStream": 270336, "Offline": 1},
-            "ssd-large": {
-                "SingleStream": 1024,
-                "Server": 270336,
-                "MultiStream": 270336,
-                "Offline": 1,
-            },
-            "rnnt": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "dlrm-99": {"Server": 270336, "Offline": 1},
-            "dlrm-99.9": {"Server": 270336, "Offline": 1},
-            "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
-            "3d-unet-99.9": {"SingleStream": 1024, "Offline": 1},
-        },
-    },
-    "v1.0": {
-        "models": [
-            "ssd-small",
-            "ssd-large",
-            "resnet",
-            "rnnt",
-            "bert-99",
-            "bert-99.9",
-            "dlrm-99",
-            "dlrm-99.9",
-            "3d-unet-99",
-            "3d-unet-99.9",
-        ],
-        "required-scenarios-datacenter": {
-            "resnet": ["Offline"],
-            "ssd-large": ["Offline"],
-            "rnnt": ["Offline"],
-            "bert-99": ["Offline"],
-            "bert-99.9": ["Offline"],
-            "dlrm-99": ["Offline"],
-            "dlrm-99.9": ["Offline"],
-            "3d-unet-99": ["Offline"],
-            "3d-unet-99.9": ["Offline"],
-        },
-        "optional-scenarios-datacenter": {
-            "resnet": ["Server"],
-            "ssd-large": ["Server"],
-            "rnnt": ["Server"],
-            "bert-99": ["Server"],
-            "bert-99.9": ["Server"],
-            "dlrm-99": ["Server"],
-            "dlrm-99.9": ["Server"],
-        },
-        "required-scenarios-edge": {
-            "resnet": ["SingleStream", "Offline"],
-            "ssd-small": ["SingleStream", "Offline"],
-            "ssd-large": ["SingleStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-edge": {
-            "resnet": ["MultiStream"],
-            "ssd-small": ["MultiStream"],
-            "ssd-large": ["MultiStream"],
-        },
-        "required-scenarios-datacenter-edge": {
-            "resnet": ["SingleStream", "Offline"],
-            "ssd-small": ["SingleStream", "Offline"],
-            "ssd-large": ["SingleStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "bert-99.9": ["Offline"],
-            "dlrm-99": ["Offline"],
-            "dlrm-99.9": ["Offline"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-datacenter-edge": {
-            "resnet": ["MultiStream", "Server"],
-            "ssd-small": ["MultiStream"],
-            "ssd-large": ["MultiStream", "Server"],
-            "rnnt": ["Server"],
-            "bert-99": ["Server"],
-            "bert-99.9": ["Server"],
-            "dlrm-99": ["Server"],
-            "dlrm-99.9": ["Server"],
-        },
-        "accuracy-target": {
-            "resnet": ("acc", 76.46 * 0.99),
-            "ssd-small": ("mAP", 22 * 0.99),
-            "ssd-large": ("mAP", 20 * 0.99),
-            "rnnt": ("WER", (100 - 7.452) * 0.99),
-            "bert-99": ("F1", 90.874 * 0.99),
-            "bert-99.9": ("F1", 90.874 * 0.999),
-            "dlrm-99": ("AUC", 80.25 * 0.99),
-            "dlrm-99.9": ("AUC", 80.25 * 0.999),
-            "3d-unet-99": ("DICE", 0.853 * 0.99),
-            "3d-unet-99.9": ("DICE", 0.853 * 0.999),
-        },
-        "performance-sample-count": {
-            "ssd-small": 256,
-            "ssd-large": 64,
-            "resnet": 1024,
-            "rnnt": 2513,
-            "bert-99": 10833,
-            "bert-99.9": 10833,
-            "dlrm-99": 204800,
-            "dlrm-99.9": 204800,
-            "3d-unet-99": 16,
-            "3d-unet-99.9": 16,
-        },
-        "model_mapping": {
-            # map model names to the official mlperf model class
-            "ssd-mobilenet": "ssd-small",
-            "ssd-resnet34": "ssd-large",
-            "mobilenet": "resnet",
-            "resnet50": "resnet",
-        },
-        "seeds": {
-            "qsl_rng_seed": 7322528924094909334,
-            "sample_index_rng_seed": 1570999273408051088,
-            "schedule_rng_seed": 3507442325620259414,
-        },
-        "test05_seeds": {
-            "qsl_rng_seed": 313588358309856706,
-            "sample_index_rng_seed": 471397156132239067,
-            "schedule_rng_seed": 413914573387865862,
-        },
-        "ignore_errors": [],
-        "latency-constraint": {
-            "resnet": {"Server": 15000000, "MultiStream": 50000000},
-            "ssd-small": {"MultiStream": 50000000},
-            "ssd-large": {"Server": 100000000, "MultiStream": 66000000},
-            "rnnt": {"Server": 1000000000},
-            "bert-99": {"Server": 130000000},
-            "bert-99.9": {"Server": 130000000},
-            "dlrm-99": {"Server": 30000000},
-            "dlrm-99.9": {"Server": 30000000},
-        },
-        "min-queries": {
-            "resnet": {
-                "SingleStream": 1024,
-                "Server": 270336,
-                "MultiStream": 270336,
-                "Offline": 1,
-            },
-            "ssd-small": {"SingleStream": 1024, "MultiStream": 270336, "Offline": 1},
-            "ssd-large": {
-                "SingleStream": 1024,
-                "Server": 270336,
-                "MultiStream": 270336,
-                "Offline": 1,
-            },
-            "rnnt": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "dlrm-99": {"Server": 270336, "Offline": 1},
-            "dlrm-99.9": {"Server": 270336, "Offline": 1},
-            "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
-            "3d-unet-99.9": {"SingleStream": 1024, "Offline": 1},
-        },
-    },
-    "v1.1": {
-        "models": [
-            "ssd-small",
-            "ssd-large",
-            "resnet",
-            "rnnt",
-            "bert-99",
-            "bert-99.9",
-            "dlrm-99",
-            "dlrm-99.9",
-            "3d-unet-99",
-            "3d-unet-99.9",
-        ],
-        "required-scenarios-datacenter": {
-            "resnet": ["Offline"],
-            "ssd-large": ["Offline"],
-            "rnnt": ["Offline"],
-            "bert-99": ["Offline"],
-            "bert-99.9": ["Offline"],
-            "dlrm-99": ["Offline"],
-            "dlrm-99.9": ["Offline"],
-            "3d-unet-99": ["Offline"],
-            "3d-unet-99.9": ["Offline"],
-        },
-        "optional-scenarios-datacenter": {
-            "resnet": ["Server"],
-            "ssd-large": ["Server"],
-            "rnnt": ["Server"],
-            "bert-99": ["Server"],
-            "bert-99.9": ["Server"],
-            "dlrm-99": ["Server"],
-            "dlrm-99.9": ["Server"],
-        },
-        "required-scenarios-edge": {
-            "resnet": ["SingleStream", "Offline"],
-            "ssd-small": ["SingleStream", "Offline"],
-            "ssd-large": ["SingleStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "required-scenarios-datacenter-edge": {
-            "resnet": ["SingleStream", "Offline"],
-            "ssd-small": ["SingleStream", "Offline"],
-            "ssd-large": ["SingleStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "bert-99.9": ["Offline"],
-            "dlrm-99": ["Offline"],
-            "dlrm-99.9": ["Offline"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-datacenter-edge": {
-            "resnet": ["Server"],
-            "ssd-large": ["Server"],
-            "rnnt": ["Server"],
-            "bert-99": ["Server"],
-            "bert-99.9": ["Server"],
-            "dlrm-99": ["Server"],
-            "dlrm-99.9": ["Server"],
-        },
-        "accuracy-target": {
-            "resnet": ("acc", 76.46 * 0.99),
-            "ssd-small": ("mAP", 22 * 0.99),
-            "ssd-large": ("mAP", 20 * 0.99),
-            "rnnt": ("WER", (100 - 7.452) * 0.99),
-            "bert-99": ("F1", 90.874 * 0.99),
-            "bert-99.9": ("F1", 90.874 * 0.999),
-            "dlrm-99": ("AUC", 80.25 * 0.99),
-            "dlrm-99.9": ("AUC", 80.25 * 0.999),
-            "3d-unet-99": ("DICE", 0.853 * 0.99),
-            "3d-unet-99.9": ("DICE", 0.853 * 0.999),
-        },
-        "performance-sample-count": {
-            "ssd-small": 256,
-            "ssd-large": 64,
-            "resnet": 1024,
-            "rnnt": 2513,
-            "bert-99": 10833,
-            "bert-99.9": 10833,
-            "dlrm-99": 204800,
-            "dlrm-99.9": 204800,
-            "3d-unet-99": 16,
-            "3d-unet-99.9": 16,
-        },
-        "model_mapping": {
-            # map model names to the official mlperf model class
-            "ssd-mobilenet": "ssd-small",
-            "ssd-resnet34": "ssd-large",
-            "mobilenet": "resnet",
-            "resnet50": "resnet",
-        },
-        "seeds": {
-            "qsl_rng_seed": 1624344308455410291,
-            "sample_index_rng_seed": 517984244576520566,
-            "schedule_rng_seed": 10051496985653635065,
-        },
-        "test05_seeds": {
-            "qsl_rng_seed": 313588358309856706,
-            "sample_index_rng_seed": 471397156132239067,
-            "schedule_rng_seed": 413914573387865862,
-        },
-        "ignore_errors": [],
-        "latency-constraint": {
-            "resnet": {"Server": 15000000, "MultiStream": 50000000},
-            "ssd-small": {"MultiStream": 50000000},
-            "ssd-large": {"Server": 100000000, "MultiStream": 66000000},
-            "rnnt": {"Server": 1000000000},
-            "bert-99": {"Server": 130000000},
-            "bert-99.9": {"Server": 130000000},
-            "dlrm-99": {"Server": 30000000},
-            "dlrm-99.9": {"Server": 30000000},
-        },
-        "min-queries": {
-            "resnet": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "ssd-small": {"SingleStream": 1024, "Offline": 1},
-            "ssd-large": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "rnnt": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "dlrm-99": {"Server": 270336, "Offline": 1},
-            "dlrm-99.9": {"Server": 270336, "Offline": 1},
-            "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
-            "3d-unet-99.9": {"SingleStream": 1024, "Offline": 1},
-        },
-    },
-    "v2.0": {
-        "models": [
-            "ssd-small",
-            "ssd-large",
-            "resnet",
-            "rnnt",
-            "bert-99",
-            "bert-99.9",
-            "dlrm-99",
-            "dlrm-99.9",
-            "3d-unet-99",
-            "3d-unet-99.9",
-        ],
-        # FIXME: required/optional scenarios for v2.0 needs to be filled up correctly; below lists are temporary
-        "required-scenarios-datacenter": {
-            "resnet": ["Server", "Offline"],
-            "ssd-large": ["Server", "Offline"],
-            "rnnt": ["Server", "Offline"],
-            "bert-99": ["Server", "Offline"],
-            "bert-99.9": ["Server", "Offline"],
-            "dlrm-99": ["Server", "Offline"],
-            "dlrm-99.9": ["Server", "Offline"],
-            "3d-unet-99": ["Offline"],
-            "3d-unet-99.9": ["Offline"],
-        },
-        "optional-scenarios-datacenter": {},
-        "required-scenarios-edge": {
-            "resnet": ["SingleStream", "MultiStream", "Offline"],
-            "ssd-small": ["SingleStream", "MultiStream", "Offline"],
-            "ssd-large": ["SingleStream", "MultiStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-edge": {},
-        "required-scenarios-datacenter-edge": {
-            "resnet": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "ssd-small": ["SingleStream", "Offline", "MultiStream"],
-            "ssd-large": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "rnnt": ["SingleStream", "Offline", "Server"],
-            "bert-99": ["SingleStream", "Offline", "Server"],
-            "bert-99.9": ["Offline", "Server"],
-            "dlrm-99": ["Offline", "Server"],
-            "dlrm-99.9": ["Offline", "Server"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-datacenter-edge": {},
-        "accuracy-target": {
-            "resnet": ("acc", 76.46 * 0.99),
-            "ssd-small": ("mAP", 22 * 0.99),
-            "ssd-large": ("mAP", 20 * 0.99),
-            "rnnt": ("WER", (100 - 7.452) * 0.99),
-            "bert-99": ("F1", 90.874 * 0.99),
-            "bert-99.9": ("F1", 90.874 * 0.999),
-            "dlrm-99": ("AUC", 80.25 * 0.99),
-            "dlrm-99.9": ("AUC", 80.25 * 0.999),
-            "3d-unet-99": ("DICE", 0.86331 * 0.99),
-            "3d-unet-99.9": ("DICE", 0.86331 * 0.999),
-        },
-        "performance-sample-count": {
-            "ssd-small": 256,
-            "ssd-large": 64,
-            "resnet": 1024,
-            "rnnt": 2513,
-            "bert-99": 10833,
-            "bert-99.9": 10833,
-            "dlrm-99": 204800,
-            "dlrm-99.9": 204800,
-            "3d-unet-99": 42,
-            "3d-unet-99.9": 42,
-        },
-        "model_mapping": {
-            # map model names to the official mlperf model class
-            "ssd-mobilenet": "ssd-small",
-            "ssd-resnet34": "ssd-large",
-            "mobilenet": "resnet",
-            "resnet50": "resnet",
-            "ssd_resnet101_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet101_v1_fpn_1024x1024": "ssd-large",
-            "ssd_resnet152_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet152_v1_fpn_1024x1024": "ssd-large",
-            "rcnn-resnet50-lowproposals-coco": "ssd-large",
-            "rcnn-inception-resnet-v2-lowproposals-coco": "ssd-large",
-            "rcnn-inception-v2-coco": "ssd-large",
-            "rcnn-nas-lowproposals-coco": "ssd-large",
-            "rcnn-resnet101-lowproposals-coco": "ssd-large",
-            "ssd_mobilenet_v1_coco": "ssd-small",
-            "ssd_mobilenet_v1_fpn_640x640": "ssd-small",
-            "ssd_mobilenet_v1_quantized_coco": "ssd-small",
-            "ssd_mobilenet_v2_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_1024x1024": "ssd-large",
-        },
-        "seeds": {
-            "qsl_rng_seed": 6655344265603136530,
-            "sample_index_rng_seed": 15863379492028895792,
-            "schedule_rng_seed": 12662793979680847247,
-        },
-        "test05_seeds": {
-            "qsl_rng_seed": 313588358309856706,
-            "sample_index_rng_seed": 471397156132239067,
-            "schedule_rng_seed": 413914573387865862,
-        },
-        "ignore_errors": [],
-        "latency-constraint": {
-            "resnet": {"Server": 15000000},
-            "ssd-large": {"Server": 100000000},
-            "rnnt": {"Server": 1000000000},
-            "bert-99": {"Server": 130000000},
-            "bert-99.9": {"Server": 130000000},
-            "dlrm-99": {"Server": 30000000},
-            "dlrm-99.9": {"Server": 30000000},
-        },
-        "min-queries": {
-            "resnet": {
-                "SingleStream": 1024,
-                "MultiStream": 270336,
-                "Server": 270336,
-                "Offline": 1,
-            },
-            "ssd-small": {"SingleStream": 1024, "MultiStream": 270336, "Offline": 1},
-            "ssd-large": {
-                "SingleStream": 1024,
-                "MultiStream": 270336,
-                "Server": 270336,
-                "Offline": 1,
-            },
-            "rnnt": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "dlrm-99": {"Server": 270336, "Offline": 1},
-            "dlrm-99.9": {"Server": 270336, "Offline": 1},
-            "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
-            "3d-unet-99.9": {"SingleStream": 1024, "Offline": 1},
-        },
-    },
-    "v2.1": {
-        "models": [
-            "resnet",
-            "retinanet",
-            "rnnt",
-            "bert-99",
-            "bert-99.9",
-            "dlrm-99",
-            "dlrm-99.9",
-            "3d-unet-99",
-            "3d-unet-99.9",
-        ],
-        "required-scenarios-datacenter": {
-            "resnet": ["Server", "Offline"],
-            "retinanet": ["Server", "Offline"],
-            "rnnt": ["Server", "Offline"],
-            "bert-99": ["Server", "Offline"],
-            "bert-99.9": ["Server", "Offline"],
-            "dlrm-99": ["Server", "Offline"],
-            "dlrm-99.9": ["Server", "Offline"],
-            "3d-unet-99": ["Offline"],
-            "3d-unet-99.9": ["Offline"],
-        },
-        "optional-scenarios-datacenter": {},
-        "required-scenarios-edge": {
-            "resnet": ["SingleStream", "MultiStream", "Offline"],
-            "retinanet": ["SingleStream", "MultiStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-edge": {},
-        "required-scenarios-datacenter-edge": {
-            "resnet": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "retinanet": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "rnnt": ["SingleStream", "Offline", "Server"],
-            "bert-99": ["SingleStream", "Offline", "Server"],
-            "bert-99.9": ["Offline", "Server"],
-            "dlrm-99": ["Offline", "Server"],
-            "dlrm-99.9": ["Offline", "Server"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-datacenter-edge": {},
-        "accuracy-target": {
-            "resnet": ("acc", 76.46 * 0.99),
-            "retinanet": ("mAP", 37.55 * 0.99),
-            "rnnt": ("WER", (100 - 7.452) * 0.99),
-            "bert-99": ("F1", 90.874 * 0.99),
-            "bert-99.9": ("F1", 90.874 * 0.999),
-            "dlrm-99": ("AUC", 80.25 * 0.99),
-            "dlrm-99.9": ("AUC", 80.25 * 0.999),
-            "3d-unet-99": ("DICE", 0.86170 * 0.99),
-            "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
-        },
-        "performance-sample-count": {
-            "resnet": 1024,
-            # TODO: Update perf sample count for retinanet
-            "retinanet": 64,
-            "rnnt": 2513,
-            "bert-99": 10833,
-            "bert-99.9": 10833,
-            "dlrm-99": 204800,
-            "dlrm-99.9": 204800,
-            "3d-unet-99": 43,
-            "3d-unet-99.9": 43,
-        },
-        # TODO: Update this list.
-        "model_mapping": {
-            # map model names to the official mlperf model class
-            "ssd-mobilenet": "ssd-small",
-            "ssd-resnet34": "retinanet",
-            "mobilenet": "resnet",
-            "resnet50": "resnet",
-            "ssd_resnet101_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet101_v1_fpn_1024x1024": "ssd-large",
-            "ssd_resnet152_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet152_v1_fpn_1024x1024": "ssd-large",
-            "rcnn-resnet50-lowproposals-coco": "ssd-large",
-            "rcnn-inception-resnet-v2-lowproposals-coco": "ssd-large",
-            "rcnn-inception-v2-coco": "ssd-large",
-            "rcnn-nas-lowproposals-coco": "ssd-large",
-            "rcnn-resnet101-lowproposals-coco": "ssd-large",
-            "ssd_mobilenet_v1_coco": "ssd-small",
-            "ssd_mobilenet_v1_fpn_640x640": "ssd-small",
-            "ssd_mobilenet_v1_quantized_coco": "ssd-small",
-            "ssd_mobilenet_v2_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_1024x1024": "ssd-large",
-        },
-        "seeds": {
-            "qsl_rng_seed": 14284205019438841327,
-            "sample_index_rng_seed": 4163916728725999944,
-            "schedule_rng_seed": 299063814864929621,
-        },
-        "test05_seeds": {
-            "qsl_rng_seed": 313588358309856706,
-            "sample_index_rng_seed": 471397156132239067,
-            "schedule_rng_seed": 413914573387865862,
-        },
-        "ignore_errors": [],
-        "latency-constraint": {
-            "resnet": {"Server": 15000000},
-            "retinanet": {"Server": 100000000},
-            "rnnt": {"Server": 1000000000},
-            "bert-99": {"Server": 130000000},
-            "bert-99.9": {"Server": 130000000},
-            "dlrm-99": {"Server": 30000000},
-            "dlrm-99.9": {"Server": 30000000},
-        },
-        "min-queries": {
-            "resnet": {
-                "SingleStream": 1024,
-                "MultiStream": 270336,
-                "Server": 270336,
-                "Offline": 1,
-            },
-            "retinanet": {
-                "SingleStream": 1024,
-                "MultiStream": 270336,
-                "Server": 270336,
-                "Offline": 1,
-            },
-            "rnnt": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "dlrm-99": {"Server": 270336, "Offline": 1},
-            "dlrm-99.9": {"Server": 270336, "Offline": 1},
-            "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
-            "3d-unet-99.9": {"SingleStream": 1024, "Offline": 1},
-        },
-    },
-    "v3.0": {
-        "models": [
-            "resnet",
-            "retinanet",
-            "rnnt",
-            "bert-99",
-            "bert-99.9",
-            "dlrm-99",
-            "dlrm-99.9",
-            "3d-unet-99",
-            "3d-unet-99.9",
-        ],
-        "required-scenarios-datacenter": {
-            "resnet": ["Server", "Offline"],
-            "retinanet": ["Server", "Offline"],
-            "rnnt": ["Server", "Offline"],
-            "bert-99": ["Server", "Offline"],
-            "bert-99.9": ["Server", "Offline"],
-            "dlrm-99": ["Server", "Offline"],
-            "dlrm-99.9": ["Server", "Offline"],
-            "3d-unet-99": ["Offline"],
-            "3d-unet-99.9": ["Offline"],
-        },
-        "optional-scenarios-datacenter": {},
-        "required-scenarios-edge": {
-            "resnet": ["SingleStream", "MultiStream", "Offline"],
-            "retinanet": ["SingleStream", "MultiStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-edge": {},
-        "required-scenarios-datacenter-edge": {
-            "resnet": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "retinanet": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "rnnt": ["SingleStream", "Offline", "Server"],
-            "bert-99": ["SingleStream", "Offline", "Server"],
-            "bert-99.9": ["Offline", "Server"],
-            "dlrm-99": ["Offline", "Server"],
-            "dlrm-99.9": ["Offline", "Server"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-datacenter-edge": {},
-        "accuracy-target": {
-            "resnet": ("acc", 76.46 * 0.99),
-            "retinanet": ("mAP", 37.55 * 0.99),
-            "rnnt": ("WER", (100 - 7.452) * 0.99),
-            "bert-99": ("F1", 90.874 * 0.99),
-            "bert-99.9": ("F1", 90.874 * 0.999),
-            "dlrm-99": ("AUC", 80.25 * 0.99),
-            "dlrm-99.9": ("AUC", 80.25 * 0.999),
-            "3d-unet-99": ("DICE", 0.86170 * 0.99),
-            "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
-        },
-        "performance-sample-count": {
-            "resnet": 1024,
-            # TODO: Update perf sample count for retinanet
-            "retinanet": 64,
-            "rnnt": 2513,
-            "bert-99": 10833,
-            "bert-99.9": 10833,
-            "dlrm-99": 204800,
-            "dlrm-99.9": 204800,
-            "3d-unet-99": 43,
-            "3d-unet-99.9": 43,
-        },
-        # TODO: Update this list.
-        "model_mapping": {
-            # map model names to the official mlperf model class
-            "ssd-mobilenet": "ssd-small",
-            "ssd-resnet34": "retinanet",
-            "mobilenet": "resnet",
-            "resnet50": "resnet",
-            "ssd_resnet101_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet101_v1_fpn_1024x1024": "ssd-large",
-            "ssd_resnet152_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet152_v1_fpn_1024x1024": "ssd-large",
-            "rcnn-resnet50-lowproposals-coco": "ssd-large",
-            "rcnn-inception-resnet-v2-lowproposals-coco": "ssd-large",
-            "rcnn-inception-v2-coco": "ssd-large",
-            "rcnn-nas-lowproposals-coco": "ssd-large",
-            "rcnn-resnet101-lowproposals-coco": "ssd-large",
-            "ssd_mobilenet_v1_coco": "ssd-small",
-            "ssd_mobilenet_v1_fpn_640x640": "ssd-small",
-            "ssd_mobilenet_v1_quantized_coco": "ssd-small",
-            "ssd_mobilenet_v2_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_1024x1024": "ssd-large",
-        },
-        "seeds": {
-            "qsl_rng_seed": 10003631887983097364,
-            "sample_index_rng_seed": 17183018601990103738,
-            "schedule_rng_seed": 12134888396634371638,
-        },
-        "test05_seeds": {
-            "qsl_rng_seed": 14646058500348515648,
-            "sample_index_rng_seed": 1207248993894122914,
-            "schedule_rng_seed": 11879132697760422006,
-        },
-        "ignore_errors": [],
-        "latency-constraint": {
-            "resnet": {"Server": 15000000},
-            "retinanet": {"Server": 100000000},
-            "rnnt": {"Server": 1000000000},
-            "bert-99": {"Server": 130000000},
-            "bert-99.9": {"Server": 130000000},
-            "dlrm-99": {"Server": 30000000},
-            "dlrm-99.9": {"Server": 30000000},
-        },
-        "min-queries": {
-            "resnet": {
-                "SingleStream": 1024,
-                "MultiStream": 270336,
-                "Server": 270336,
-                "Offline": 1,
-            },
-            "retinanet": {
-                "SingleStream": 1024,
-                "MultiStream": 270336,
-                "Server": 270336,
-                "Offline": 1,
-            },
-            "rnnt": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "dlrm-99": {"Server": 270336, "Offline": 1},
-            "dlrm-99.9": {"Server": 270336, "Offline": 1},
-            "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
-            "3d-unet-99.9": {"SingleStream": 1024, "Offline": 1},
-        },
-    },
-    "v3.1": {
-        "models": [
-            "resnet",
-            "retinanet",
-            "rnnt",
-            "bert-99",
-            "bert-99.9",
-            "dlrm-v2-99",
-            "dlrm-v2-99.9",
-            "3d-unet-99",
-            "3d-unet-99.9",
-            "gptj-99",
-            "gptj-99.9",
-            "gpt3-99",
-            "gpt3-99.9"
-        ],
-        "required-scenarios-datacenter": {
-            "resnet": ["Server", "Offline"],
-            "retinanet": ["Server", "Offline"],
-            "rnnt": ["Server", "Offline"],
-            "bert-99": ["Server", "Offline"],
-            "bert-99.9": ["Server", "Offline"],
-            "dlrm-v2-99": ["Server", "Offline"],
-            "dlrm-v2-99.9": ["Server", "Offline"],
-            "3d-unet-99": ["Offline"],
-            "3d-unet-99.9": ["Offline"],
-            "gptj-99": ["Server", "Offline"],
-            "gptj-99.9": ["Server", "Offline"],
-            "gpt3-99": ["Server", "Offline"],
-            "gpt3-99.9": ["Server", "Offline"],
-        },
-        "optional-scenarios-datacenter": {},
-        "required-scenarios-edge": {
-            "resnet": ["SingleStream", "MultiStream", "Offline"],
-            "retinanet": ["SingleStream", "MultiStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-            "gptj-99": ["SingleStream", "Offline"],
-            "gptj-99.9": ["SingleStream", "Offline"],
-        },
-        "optional-scenarios-edge": {},
-        "required-scenarios-datacenter-edge": {
-            "resnet": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "retinanet": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "rnnt": ["SingleStream", "Offline", "Server"],
-            "bert-99": ["SingleStream", "Offline", "Server"],
-            "bert-99.9": ["Offline", "Server"],
-            "dlrm-v2-99": ["Offline", "Server"],
-            "dlrm-v2-99.9": ["Offline", "Server"],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-            "gptj-99": ["SingleStream", "Offline", "Server"],
-            "gptj-99.9": ["SingleStream", "Offline", "Server"],
-            "gpt3-99": ["Server", "Offline"],
-            "gpt3-99.9": ["Server", "Offline"],
-        },
-        "optional-scenarios-datacenter-edge": {},
-        "accuracy-target": {
-            "resnet": ("acc", 76.46 * 0.99),
-            "retinanet": ("mAP", 37.55 * 0.99),
-            "rnnt": ("WER", (100 - 7.452) * 0.99),
-            "bert-99": ("F1", 90.874 * 0.99),
-            "bert-99.9": ("F1", 90.874 * 0.999),
-            "dlrm-v2-99": ("AUC", 80.31 * 0.99),
-            "dlrm-v2-99.9": ("AUC", 80.31 * 0.999),
-            "3d-unet-99": ("DICE", 0.86170 * 0.99),
-            "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
-            "gptj-99" : ("ROUGE1", 42.9865 * 0.99, "ROUGE2", 20.1235 * 0.99, "ROUGEL", 29.9881 * 0.99, "GEN_LEN", 4016878*0.9),
-            "gptj-99.9" : ("ROUGE1", 42.9865 * 0.999, "ROUGE2", 20.1235 * 0.999, "ROUGEL", 29.9881 * 0.999, "GEN_LEN", 4016878*0.9),
-            # TODO: Replace with metrics for GPT3
-            "gpt3-99" : ("ROUGE1", 42.9865 * 0.99, "ROUGE2", 20.1235 * 0.99, "ROUGEL", 29.9881 * 0.99, "GEN_LEN", 4016878*0.9),
-            "gpt3-99.9" : ("ROUGE1", 42.9865 * 0.999, "ROUGE2", 20.1235 * 0.999, "ROUGEL", 29.9881 * 0.999, "GEN_LEN", 4016878*0.9),
-        },
-        "performance-sample-count": {
-            "resnet": 1024,
-            "retinanet": 64,
-            "rnnt": 2513,
-            "bert-99": 10833,
-            "bert-99.9": 10833,
-            "dlrm-v2-99": 204800,
-            "dlrm-v2-99.9": 204800,
-            "3d-unet-99": 43,
-            "3d-unet-99.9": 43,
-            "gptj-99": 13368,
-            "gptj-99.9": 13368,
-            "gpt3-99": 13368,
-            "gpt3-99.9": 13368,
-        },
-        # TODO: Update this list.
-        "model_mapping": {
-            # map model names to the official mlperf model class
-            "ssd-mobilenet": "ssd-small",
-            "ssd-resnet34": "retinanet",
-            "mobilenet": "resnet",
-            "resnet50": "resnet",
-            "ssd_resnet101_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet101_v1_fpn_1024x1024": "ssd-large",
-            "ssd_resnet152_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet152_v1_fpn_1024x1024": "ssd-large",
-            "rcnn-resnet50-lowproposals-coco": "ssd-large",
-            "rcnn-inception-resnet-v2-lowproposals-coco": "ssd-large",
-            "rcnn-inception-v2-coco": "ssd-large",
-            "rcnn-nas-lowproposals-coco": "ssd-large",
-            "rcnn-resnet101-lowproposals-coco": "ssd-large",
-            "ssd_mobilenet_v1_coco": "ssd-small",
-            "ssd_mobilenet_v1_fpn_640x640": "ssd-small",
-            "ssd_mobilenet_v1_quantized_coco": "ssd-small",
-            "ssd_mobilenet_v2_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_1024x1024": "ssd-large",
-        },
-        "seeds": {
-            # TODO: Update random seeds
-            "qsl_rng_seed": 148687905518835231,
-            "sample_index_rng_seed": 520418551913322573,
-            "schedule_rng_seed": 811580660758947900,
-        },
-        "test05_seeds": {
-            # TODO: Update random seeds
-            "qsl_rng_seed": 793197339507417767,
-            "sample_index_rng_seed": 255610748586851044,
-            "schedule_rng_seed": 352213341366340113,
-        },
-        "ignore_errors": [],
-        "latency-constraint": {
-            "resnet": {"Server": 15000000},
-            "retinanet": {"Server": 100000000},
-            "rnnt": {"Server": 1000000000},
-            "bert-99": {"Server": 130000000},
-            "bert-99.9": {"Server": 130000000},
-            "dlrm-v2-99": {"Server": 60000000},
-            "dlrm-v2-99.9": {"Server": 60000000},
-            "gptj-99": {"Server": 20000000000},
-            "gptj-99.9": {"Server": 20000000000},
-            "gpt3-99": {"Server": 20000000000},
-            "gpt3-99.9": {"Server": 20000000000},
-        },
-        "min-queries": {
-            "resnet": {
-                "SingleStream": 1024,
-                "MultiStream": 270336,
-                "Server": 270336,
-                "Offline": 1,
-            },
-            "retinanet": {
-                "SingleStream": 1024,
-                "MultiStream": 270336,
-                "Server": 270336,
-                "Offline": 1,
-            },
-            "rnnt": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "dlrm-v2-99": {"Server": 270336, "Offline": 1},
-            "dlrm-v2-99.9": {"Server": 270336, "Offline": 1},
-            "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
-            "3d-unet-99.9": {"SingleStream": 1024, "Offline": 1},
-            "gptj-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "gptj-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "gpt3-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "gpt3-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-        },
-    },
+
+from glob import glob
+
+from log_parser import MLPerfLog
+
+# pylint: disable=missing-docstring
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("main")
+
+submission_checker_dir = os.path.dirname(os.path.realpath(__file__))
+
+MODEL_CONFIG = {
     "v4.0": {
         "models": [
             "resnet",
@@ -1152,27 +128,9 @@
         # TODO: Update this list.
         "model_mapping": {
             # map model names to the official mlperf model class
-            "ssd-mobilenet": "ssd-small",
             "ssd-resnet34": "retinanet",
             "mobilenet": "resnet",
-            "resnet50": "resnet",
-            "ssd_resnet101_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet101_v1_fpn_1024x1024": "ssd-large",
-            "ssd_resnet152_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet152_v1_fpn_1024x1024": "ssd-large",
-            "rcnn-resnet50-lowproposals-coco": "ssd-large",
-            "rcnn-inception-resnet-v2-lowproposals-coco": "ssd-large",
-            "rcnn-inception-v2-coco": "ssd-large",
-            "rcnn-nas-lowproposals-coco": "ssd-large",
-            "rcnn-resnet101-lowproposals-coco": "ssd-large",
-            "ssd_mobilenet_v1_coco": "ssd-small",
-            "ssd_mobilenet_v1_fpn_640x640": "ssd-small",
-            "ssd_mobilenet_v1_quantized_coco": "ssd-small",
-            "ssd_mobilenet_v2_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_320x320": "ssd-small",
-            "ssd_mobilenet_v2_fpnlite_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_640x640": "ssd-small",
-            "ssd_resnet50_v1_fpn_1024x1024": "ssd-large",
+            "resnet50": "resnet"
         },
         "seeds": {
             # TODO: Update random seeds
@@ -1315,62 +273,6 @@
 }
 
 RESULT_FIELD_NEW = {
-    "v0.5": {
-        "Offline": "result_samples_per_second",
-        "SingleStream": "result_90.00_percentile_latency_ns",
-        "MultiStreamLegacy": "effective_samples_per_query",
-        "MultiStream": "result_99.00_percentile_per_query_latency_ns",
-        "Server": "result_scheduled_samples_per_sec",
-    },
-    "v0.7": {
-        "Offline": "result_samples_per_second",
-        "SingleStream": "result_90.00_percentile_latency_ns",
-        "MultiStreamLegacy": "effective_samples_per_query",
-        "MultiStream": "result_99.00_percentile_per_query_latency_ns",
-        "Server": "result_scheduled_samples_per_sec",
-    },
-    "v1.0": {
-        "Offline": "result_samples_per_second",
-        "SingleStream": "result_90.00_percentile_latency_ns",
-        "MultiStreamLegacy": "effective_samples_per_query",
-        "MultiStream": "result_99.00_percentile_per_query_latency_ns",
-        "Server": "result_scheduled_samples_per_sec",
-    },
-    "v1.1": {
-        "Offline": "result_samples_per_second",
-        "SingleStream": "result_90.00_percentile_latency_ns",
-        "MultiStreamLegacy": "effective_samples_per_query",
-        "MultiStream": "result_99.00_percentile_per_query_latency_ns",
-        "Server": "result_scheduled_samples_per_sec",
-    },
-    "v2.0": {
-        "Offline": "result_samples_per_second",
-        "SingleStream": "early_stopping_latency_ss",
-        "MultiStreamLegacy": "effective_samples_per_query",
-        "MultiStream": "early_stopping_latency_ms",
-        "Server": "result_scheduled_samples_per_sec",
-    },
-    "v2.1": {
-        "Offline": "result_samples_per_second",
-        "SingleStream": "early_stopping_latency_ss",
-        "MultiStreamLegacy": "effective_samples_per_query",
-        "MultiStream": "early_stopping_latency_ms",
-        "Server": "result_scheduled_samples_per_sec",
-    },
-    "v3.0": {
-        "Offline": "result_samples_per_second",
-        "SingleStream": "early_stopping_latency_ss",
-        "MultiStreamLegacy": "effective_samples_per_query",
-        "MultiStream": "early_stopping_latency_ms",
-        "Server": "result_scheduled_samples_per_sec",
-    },
-    "v3.1": {
-        "Offline": "result_samples_per_second",
-        "SingleStream": "early_stopping_latency_ss",
-        "MultiStreamLegacy": "effective_samples_per_query",
-        "MultiStream": "early_stopping_latency_ms",
-        "Server": "result_scheduled_samples_per_sec",
-    },
     "v4.0": {
         "Offline": "result_samples_per_second",
         "SingleStream": "early_stopping_latency_ss",
@@ -1434,6 +336,25 @@
     "accelerator_memory_capacity",
     "framework",
     "operating_system",
+    "system_type",
+    "other_software_stack",
+    "host_processor_frequency",
+    "host_processor_caches",
+    "host_memory_configuration",
+    "host_processor_interconnect",
+    "host_networking",
+    "host_networking_topology",
+    "accelerator_frequency",
+    "accelerator_host_interconnect",
+    "accelerator_interconnect",
+    "accelerator_interconnect_topology",
+    "accelerator_memory_configuration",
+    "accelerator_on-chip_memories",
+    "cooling",
+    "hw_notes",
+    "sw_notes",
+    "host_network_card_count",
+    "system_type_detail"
 ]
 
 SYSTEM_DESC_MEANINGFUL_RESPONSE_REQUIRED_FIELDS = [
@@ -1465,31 +386,6 @@
     "other_software_stack",
 ]
 
-SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V1 = [
-    "system_type",
-    "other_software_stack",
-    "host_processor_frequency",
-    "host_processor_caches",
-    "host_memory_configuration",
-    "host_processor_interconnect",
-    "host_networking",
-    "host_networking_topology",
-    "accelerator_frequency",
-    "accelerator_host_interconnect",
-    "accelerator_interconnect",
-    "accelerator_interconnect_topology",
-    "accelerator_memory_configuration",
-    "accelerator_on-chip_memories",
-    "cooling",
-    "hw_notes",
-    "sw_notes",
-]
-
-SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V3_1 = [
-    "host_network_card_count",
-    "system_type_detail"
-]
-
 SYSTEM_DESC_REQUIRED_FIELDS_POWER = [
     "power_management",
     "filesystem",
@@ -1563,9 +459,7 @@ def __init__(
         self.skip_power_check = skip_power_check
 
     def set_type(self, submission_type):
-        if submission_type is None and self.version in ["v0.5"]:
-            return
-        elif submission_type == "datacenter":
+        if submission_type == "datacenter":
             self.required = self.base["required-scenarios-datacenter"]
             self.optional = self.base["optional-scenarios-datacenter"]
         elif submission_type == "edge":
@@ -1597,39 +491,24 @@ def get_mlperf_model(self, model, extra_model_mapping=None):
 
         # try to guess, keep this for backwards compatibility
         # TODO: Generalize this guess or remove it completely?
-        if (
-            "ssdlite" in model
-            or "ssd-inception" in model
-            or "yolo" in model
-            or "ssd-mobilenet" in model
-            or "ssd-resnet50" in model
-        ):
-            model = "ssd-small"
-        elif "mobilenet" in model:
-            model = "mobilenet"
-        elif "efficientnet" in model or "resnet50" in model:
+
+        if "mobilenet" in model or "efficientnet" in model or "resnet50" in model:
             model = "resnet"
-        elif "rcnn" in model:
-            model = "ssd-small"
         elif "bert-99.9" in model:
             model = "bert-99.9"
         elif "bert-99" in model:
             model = "bert-99"
-        # map again, for example v0.7 does not have mobilenet so it needs to be mapped to resnet
+        # map again
         mlperf_model = self.base["model_mapping"].get(model, model)
         return mlperf_model
 
     def get_required(self, model):
-        if self.version in ["v0.5"]:
-            return set()
         model = self.get_mlperf_model(model)
         if model not in self.required:
             return None
         return set(self.required[model])
 
     def get_optional(self, model):
-        if self.version in ["v0.5"]:
-            return set(["SingleStream", "MultiStream", "Server", "Offline"])
         model = self.get_mlperf_model(model)
         if model not in self.optional:
             return set()
@@ -1667,22 +546,14 @@ def get_min_query_count(self, model, scenario):
         return self.min_queries[model].get(scenario)
 
     def has_new_logging_format(self):
-        return self.version not in ["v0.5", "v0.7"]
+        return True
 
-    def uses_legacy_multistream(self):
-        return self.version in ["v0.5", "v0.7", "v1.0", "v1.1"]
 
     def uses_early_stopping(self, scenario):
-        return (self.version not in ["v0.5", "v0.7", "v1.0", "v1.1"]) and (
+        return (
             scenario in ["Server", "SingleStream", "MultiStream"]
         )
 
-    def has_query_count_in_log(self):
-        return self.version not in ["v0.5", "v0.7", "v1.0", "v1.1"]
-
-    def has_power_utc_timestamps(self):
-        return self.version not in ["v0.5", "v0.7", "v1.0"]
-
 
 def get_args():
     """Parse commandline."""
@@ -1799,38 +670,23 @@ def find_error_in_detail_log(config, fname):
         log.error("%s is missing", fname)
         is_valid = False
     else:
-        if config.has_new_logging_format():
-            mlperf_log = MLPerfLog(fname)
-            if mlperf_log.has_error():
-                if config.ignore_uncommited:
-                    has_other_errors = False
-                    for error in mlperf_log.get_errors():
-                        if (
-                            "Loadgen built with uncommitted changes!"
-                            not in error["value"]
-                        ):
-                            has_other_errors = True
-
-                log.error("%s contains errors:", fname)
+        mlperf_log = MLPerfLog(fname)
+        if mlperf_log.has_error():
+            if config.ignore_uncommited:
+                has_other_errors = False
                 for error in mlperf_log.get_errors():
-                    log.error("%s", error["value"])
+                    if (
+                        "Loadgen built with uncommitted changes!"
+                        not in error["value"]
+                    ):
+                        has_other_errors = True
 
-                if not config.ignore_uncommited or has_other_errors:
-                    is_valid = False
-        else:
-            with open(fname, "r") as f:
-                for line in f:
-                    # look for: ERROR
-                    if "ERROR" in line:
-                        if config.ignore_errors(line):
-                            if (
-                                "ERROR : Loadgen built with uncommitted changes!"
-                                in line
-                            ):
-                                log.warning("%s contains error: %s", fname, line)
-                            continue
-                        log.error("%s contains error: %s", fname, line)
-                        is_valid = False
+            log.error("%s contains errors:", fname)
+            for error in mlperf_log.get_errors():
+                log.error("%s", error["value"])
+
+            if not config.ignore_uncommited or has_other_errors:
+                is_valid = False
     return is_valid
 
 
@@ -1946,15 +802,10 @@ def get_performance_metric(
     ):
         is_valid = True
     scenario = mlperf_log["effective_scenario"]
-    scenario_for_res = (
-        "MultiStreamLegacy"
-        if scenario == "MultiStream" and config.uses_legacy_multistream()
-        else scenario
-    )
 
-    res = float(mlperf_log[RESULT_FIELD_NEW[config.version][scenario_for_res]])
+    res = float(mlperf_log[RESULT_FIELD_NEW[config.version][scenario]])
     if model in RESULT_FIELD_BENCHMARK_OVERWRITE and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[model]:
-        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[model][scenario_for_res]])
+        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[model][scenario]])
 
     inferred = False
     if scenario_fixed != scenario:
@@ -1969,66 +820,37 @@ def check_performance_dir(
     rt = {}
 
     # look for: Result is: VALID
-    if config.has_new_logging_format():
-        fname = os.path.join(path, "mlperf_log_detail.txt")
-        mlperf_log = MLPerfLog(fname)
-        if (
-            "result_validity" in mlperf_log.get_keys()
-            and mlperf_log["result_validity"] == "VALID"
-        ):
-            is_valid = True
-        performance_sample_count = mlperf_log["effective_performance_sample_count"]
-        qsl_rng_seed = mlperf_log["effective_qsl_rng_seed"]
-        sample_index_rng_seed = mlperf_log["effective_sample_index_rng_seed"]
-        schedule_rng_seed = mlperf_log["effective_schedule_rng_seed"]
-        scenario = mlperf_log["effective_scenario"]
-        scenario_for_res = (
-            "MultiStreamLegacy"
-            if scenario == "MultiStream" and config.uses_legacy_multistream()
-            else scenario
-        )
-        res = float(mlperf_log[RESULT_FIELD_NEW[config.version][scenario_for_res]])
-        if model in RESULT_FIELD_BENCHMARK_OVERWRITE and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[model]:
-            res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[model][scenario_for_res]])
+    fname = os.path.join(path, "mlperf_log_detail.txt")
+    mlperf_log = MLPerfLog(fname)
+    if (
+        "result_validity" in mlperf_log.get_keys()
+        and mlperf_log["result_validity"] == "VALID"
+    ):
+        is_valid = True
+    performance_sample_count = mlperf_log["effective_performance_sample_count"]
+    qsl_rng_seed = mlperf_log["effective_qsl_rng_seed"]
+    sample_index_rng_seed = mlperf_log["effective_sample_index_rng_seed"]
+    schedule_rng_seed = mlperf_log["effective_schedule_rng_seed"]
+    scenario = mlperf_log["effective_scenario"]
+
+    res = float(mlperf_log[RESULT_FIELD_NEW[config.version][scenario]])
+    if model in RESULT_FIELD_BENCHMARK_OVERWRITE and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[model]:
+        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[model][scenario]])
         
-        if model in ["llama2-70b-99", "llama2-70b-99.9"]:
-            llama_constraint, is_valid = extra_check_llama2(mlperf_log, scenario_fixed)
-
-        latency_99_percentile = mlperf_log["result_99.00_percentile_latency_ns"]
-        latency_mean = mlperf_log["result_mean_latency_ns"]
-        if scenario in ["MultiStream"]:
-            latency_99_percentile = mlperf_log[
-                "result_99.00_percentile_per_query_latency_ns"
-            ]
-            latency_mean = mlperf_log["result_mean_query_latency_ns"]
-        min_query_count = mlperf_log["effective_min_query_count"]
-        samples_per_query = mlperf_log["effective_samples_per_query"]
-        min_duration = mlperf_log["effective_min_duration_ms"]
-        sut_name = mlperf_log["sut_name"]
-    else:
-        fname = os.path.join(path, "mlperf_log_summary.txt")
-        with open(fname, "r") as f:
-            for line in f:
-                m = re.match(r"^Result\s+is\s*\:\s+VALID", line)
-                if m:
-                    is_valid = True
-                m = re.match(r"^\s*([\w\s.\(\)\/]+)\s*\:\s*([\w\+\.][\w\+\.\s]*)", line)
-                if m:
-                    rt[m.group(1).strip()] = m.group(2).strip()
-        performance_sample_count = int(rt["performance_sample_count"])
-        qsl_rng_seed = int(rt["qsl_rng_seed"])
-        sample_index_rng_seed = int(rt["sample_index_rng_seed"])
-        schedule_rng_seed = int(rt["schedule_rng_seed"])
-        scenario = rt["Scenario"].replace(" ", "")
-        res = float(rt[RESULT_FIELD[scenario]])
-        latency_99_percentile = int(rt["99.00 percentile latency (ns)"])
-        latency_mean = int(rt["Mean latency (ns)"])
-        min_query_count = int(rt["min_query_count"])
-        samples_per_query = int(rt["samples_per_query"])
-        min_duration = int(rt["min_duration (ms)"])
-        if scenario == "SingleStream":
-            qps_wo_loadgen_overhead = float(rt["QPS w/o loadgen overhead"])
-        sut_name = str(rt["System Under Test (SUT) name: "])
+    if model in ["llama2-70b-99", "llama2-70b-99.9"]:
+        llama_constraint, is_valid = extra_check_llama2(mlperf_log, scenario_fixed)
+
+    latency_99_percentile = mlperf_log["result_99.00_percentile_latency_ns"]
+    latency_mean = mlperf_log["result_mean_latency_ns"]
+    if scenario in ["MultiStream"]:
+        latency_99_percentile = mlperf_log[
+            "result_99.00_percentile_per_query_latency_ns"
+        ]
+        latency_mean = mlperf_log["result_mean_query_latency_ns"]
+    min_query_count = mlperf_log["effective_min_query_count"]
+    samples_per_query = mlperf_log["effective_samples_per_query"]
+    min_duration = mlperf_log["effective_min_duration_ms"]
+    sut_name = mlperf_log["sut_name"]
 
     # check if there are any errors in the detailed log
     fname = os.path.join(path, "mlperf_log_detail.txt")
@@ -2071,105 +893,95 @@ def check_performance_dir(
         )
         is_valid = False
 
-    if scenario == "SingleStream" or (
-        scenario == "MultiStream" and not config.uses_legacy_multistream()
-    ):
+    if scenario == "SingleStream" or scenario == "MultiStream":
         res /= MS_TO_NS
 
-    # Check if current scenario (and version) uses early stopping
+    # Check if the current scenario uses early stopping
     uses_early_stopping = config.uses_early_stopping(scenario)
 
-    if config.version != "v0.5":
-        # FIXME: for open we script this because open can submit in all scenarios
-        # not supported for v0.5
-
-        if uses_early_stopping:
-            # check if early_stopping condition was met
-            if not mlperf_log["early_stopping_met"]:
-                early_stopping_result = mlperf_log["early_stopping_result"]
-                log.error(
-                    "Early stopping condition was not met, msg=%s",
-                    early_stopping_result,
-                )
-
-            # If the scenario has a target latency (Server scenario), check
-            # that the target latency that was passed to the early stopping
-            # is less than the target latency.
-            target_latency = config.latency_constraint.get(model, dict()).get(scenario)
-            if target_latency:
-                early_stopping_latency_ns = mlperf_log["effective_target_latency_ns"]
-                log.info(
-                    "Target latency: %s, Early Stopping Latency: %s, Scenario: %s",
-                    target_latency,
-                    early_stopping_latency_ns,
-                    scenario,
-                )
-                if early_stopping_latency_ns > target_latency:
-                    log.error(
-                        "%s Latency constraint with early stopping not met, expected=%s, found=%s",
-                        fname,
-                        target_latency,
-                        early_stopping_latency_ns,
-                    )
-                    is_valid = False
+    if uses_early_stopping:
+        # check if early_stopping condition was met
+        if not mlperf_log["early_stopping_met"]:
+            early_stopping_result = mlperf_log["early_stopping_result"]
+            log.error(
+                "Early stopping condition was not met, msg=%s",
+                early_stopping_result,
+            )
 
-        else:
-            # check if the benchmark meets latency constraint
-            target_latency = config.latency_constraint.get(model, dict()).get(scenario)
+        # If the scenario has a target latency (Server scenario), check
+        # that the target latency that was passed to the early stopping
+        # is less than the target latency.
+        target_latency = config.latency_constraint.get(model, dict()).get(scenario)
+        if target_latency:
+            early_stopping_latency_ns = mlperf_log["effective_target_latency_ns"]
             log.info(
-                "Target latency: %s, Latency: %s, Scenario: %s",
+                "Target latency: %s, Early Stopping Latency: %s, Scenario: %s",
                 target_latency,
-                latency_99_percentile,
+                early_stopping_latency_ns,
                 scenario,
             )
-            if target_latency:
-                if latency_99_percentile > target_latency:
-                    log.error(
-                        "%s Latency constraint not met, expected=%s, found=%s",
-                        fname,
-                        target_latency,
-                        latency_99_percentile,
-                    )
-
-        # Check Minimum queries were issued to meet test duration
-        # Check if this run uses early stopping. If it does, get the
-        # min_queries from the detail log, otherwise get this value
-        # from the config
-        if not uses_early_stopping:
-            required_min_query_count = config.get_min_query_count(model, scenario)
-            if required_min_query_count and min_query_count < required_min_query_count:
+            if early_stopping_latency_ns > target_latency:
                 log.error(
-                    "%s Required minimum Query Count not met by user config, Expected=%s, Found=%s",
+                    "%s Latency constraint with early stopping not met, expected=%s, found=%s",
                     fname,
-                    required_min_query_count,
-                    min_query_count,
+                    target_latency,
+                    early_stopping_latency_ns,
                 )
                 is_valid = False
 
-        if config.version in ["v0.5", "v0.7", "v1.0", "v1.1", "v2.0", "v2.1", "v3.0", "v3.1"] and scenario == "Offline" and (samples_per_query < OFFLINE_MIN_SPQ) or \
-        scenario == "Offline" and (samples_per_query < OFFLINE_MIN_SPQ_SINCE_V4[model]):
+    else:
+        # check if the benchmark meets latency constraint
+        target_latency = config.latency_constraint.get(model, dict()).get(scenario)
+        log.info(
+            "Target latency: %s, Latency: %s, Scenario: %s",
+            target_latency,
+            latency_99_percentile,
+            scenario,
+        )
+        if target_latency:
+            if latency_99_percentile > target_latency:
+                log.error(
+                    "%s Latency constraint not met, expected=%s, found=%s",
+                    fname,
+                    target_latency,
+                    latency_99_percentile,
+                )
+
+    # Check Minimum queries were issued to meet test duration
+    # Check if this run uses early stopping. If it does, get the
+    # min_queries from the detail log, otherwise get this value
+    # from the config
+    if not uses_early_stopping:
+        required_min_query_count = config.get_min_query_count(model, scenario)
+        if required_min_query_count and min_query_count < required_min_query_count:
             log.error(
-                "%s Required minimum samples per query not met by user config, Expected=%s, Found=%s",
+                "%s Required minimum Query Count not met by user config, Expected=%s, Found=%s",
                 fname,
-                OFFLINE_MIN_SPQ,
-                samples_per_query,
+                required_min_query_count,
+                min_query_count,
             )
             is_valid = False
 
-        # Test duration of 600s is met
-        required_min_duration = (
-            TEST_DURATION_MS_PRE_1_0
-            if config.version in ["v0.5", "v0.7"]
-            else TEST_DURATION_MS
+    if scenario == "Offline" and (samples_per_query < OFFLINE_MIN_SPQ_SINCE_V4[model]):
+        log.error(
+            "%s Required minimum samples per query not met by user config, Expected=%s, Found=%s",
+            fname,
+            OFFLINE_MIN_SPQ,
+            samples_per_query,
         )
-        if min_duration < required_min_duration:
-            log.error(
-                "%s Test duration less than 600s in user config. expected=%s, found=%s",
-                fname,
-                required_min_duration,
-                min_duration,
-            )
-            is_valid = False
+        is_valid = False
+
+    # Test duration of 600s is met
+    required_min_duration = TEST_DURATION_MS
+
+    if min_duration < required_min_duration:
+        log.error(
+            "%s Test duration less than 600s in user config. expected=%s, found=%s",
+            fname,
+            required_min_duration,
+            min_duration,
+        )
+        is_valid = False
 
     inferred = False
     if scenario_fixed != scenario:
@@ -2180,7 +992,7 @@ def check_performance_dir(
     )
     is_valid &= is_network_mode_valid
     if is_network_system:
-        # for network mode verify the SUT name is valid, accodring to the rules (must include "Network SUT" in name)
+        # for network mode verify the SUT name is valid, according to the rules (must include "Network SUT" in name)
         if NETWORK_MODE_REQUIRED_SUBSTRING_IN_SUT_NAME not in sut_name:
             log.error(
                 f"{fname} invalid sut name for network mode. expecting the substring '{NETWORK_MODE_REQUIRED_SUBSTRING_IN_SUT_NAME}' got '{sut_name}'"
@@ -2212,13 +1024,13 @@ def get_inferred_result(scenario_fixed, scenario, res, mlperf_log, config, log_e
         res = qps_wo_loadgen_overhead
 
     if (
-        scenario_fixed in ["Offline"] and not config.uses_legacy_multistream()
+        scenario_fixed in ["Offline"]
     ) and scenario in ["MultiStream"]:
         inferred = True
         res = samples_per_query * S_TO_MS / (latency_mean / MS_TO_NS)
 
     if (
-        scenario_fixed in ["MultiStream"] and not config.uses_legacy_multistream()
+        scenario_fixed in ["MultiStream"]
     ) and scenario in ["SingleStream"]:
         inferred = True
         # samples_per_query does not match with the one reported in the logs
@@ -2238,16 +1050,9 @@ def get_inferred_result(scenario_fixed, scenario, res, mlperf_log, config, log_e
 
 def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
     # parse the power logs
-    if config.has_power_utc_timestamps():
-        server_timezone = datetime.timedelta(0)
-        client_timezone = datetime.timedelta(0)
-    else:
-        server_json_fname = os.path.join(power_path, "server.json")
-        with open(server_json_fname) as f:
-            server_timezone = datetime.timedelta(seconds=json.load(f)["timezone"])
-        client_json_fname = os.path.join(power_path, "client.json")
-        with open(client_json_fname) as f:
-            client_timezone = datetime.timedelta(seconds=json.load(f)["timezone"])
+    server_timezone = datetime.timedelta(0)
+    client_timezone = datetime.timedelta(0)
+    
     detail_log_fname = os.path.join(log_path, "mlperf_log_detail.txt")
     mlperf_log = MLPerfLog(detail_log_fname)
     datetime_format = "%m-%d-%Y %H:%M:%S.%f"
@@ -2260,20 +1065,8 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
         + client_timezone
     )
     # Obtain the scenario also from logs to check if power is inferred
-    if config.has_new_logging_format():
-        scenario = mlperf_log["effective_scenario"]
-    else:
-        rt = {}
-        fname = os.path.join(log_path, "mlperf_log_summary.txt")
-        with open(fname, "r") as f:
-            for line in f:
-                m = re.match(r"^Result\s+is\s*\:\s+VALID", line)
-                if m:
-                    is_valid = True
-                m = re.match(r"^\s*([\w\s.\(\)\/]+)\s*\:\s*([\w\+\.][\w\+\.\s]*)", line)
-                if m:
-                    rt[m.group(1).strip()] = m.group(2).strip()
-        scenario = rt["Scenario"].replace(" ", "")
+    scenario = mlperf_log["effective_scenario"]
+    
     spl_fname = os.path.join(log_path, "spl.txt")
     power_list = []
     with open(spl_fname) as f:
@@ -2311,26 +1104,9 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
                 "MultiStream",
                 "SingleStream",
             ], "Unknown scenario: {:}".format(scenario_fixed)
-            if not config.has_query_count_in_log():
-                # Before v2.0, LoadGen does NOT print out the actual number of queries in detail logs. There is a
-                # "generated_query_count", but LoadGen exits early when the min_duration has been met, so it is not equal to
-                # the actual number of queries. To work around it, make use of "result_qps_with_loadgen_overhead", which is
-                # defined as: (sample_count - 1) / pr.final_query_issued_time, where final_query_issued_time can be
-                # approximated by power_duration (off by one query worth of latency, which is in general negligible compared
-                # to 600-sec total runtime and can be offsetted by removing the "+1" when reconstructing the sample_count).
-                # As for MultiStream, it always runs for 270336 queries, so using "generated_query_count" as above is fine.
-                if scenario_fixed in ["MultiStream"]:
-                    num_queries = (
-                        mlperf_log["generated_query_count"]
-                        * mlperf_log["generated_samples_per_query"]
-                    )
-                elif scenario_fixed in ["SingleStream"]:
-                    num_queries = (
-                        mlperf_log["result_qps_with_loadgen_overhead"] * power_duration
-                    )
-            else:
-                # Starting from v2.0, LoadGen logs the actual number of issued queries.
-                num_queries = int(mlperf_log["result_query_count"])
+
+            num_queries = int(mlperf_log["result_query_count"])
+
             power_metric = avg_power * power_duration * 1000 / num_queries
 
             if scenario_fixed in ["SingleStream"]:
@@ -2340,7 +1116,6 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
 
             if (
                 scenario_fixed in ["MultiStream"]
-                and not config.uses_legacy_multistream()
             ) and scenario in ["SingleStream"]:
                 power_metric = (
                     avg_power * power_duration * samples_per_query * 1000 / num_queries
@@ -2380,6 +1155,7 @@ def check_power_dir(
         log.error("%s has file list mismatch (%s)", power_path, diff)
         is_valid = False
 
+    # uncomment to measure ranging mode power
     '''
     (
         is_valid,
@@ -2880,18 +1656,9 @@ def log_result(
 
                         if has_power:
                             log.info("Detected power logs for %s", name)
-                            if config.version in [
-                                "v1.0",
-                                "v1.1",
-                                "v2.0",
-                                "v2.1",
-                                "v3.0",
-                                "v3.1"
-                            ]:
-                                pass  # Submission checker was not enforcing this
                             # The power related system_desc_fields are not used by submitters currently.
                             # Turning this check off for now
-                            elif False and not check_system_desc_id_power(
+                            if False and not check_system_desc_id_power(
                                 name,
                                 system_json,
                                 submitter,
@@ -2983,13 +1750,7 @@ def log_result(
                                 log.error("%s, accuracy not valid", acc_path)
 
                         inferred = 0
-                        if scenario in ["Server"] and config.version in [
-                            "v0.5",
-                            "v0.7",
-                        ]:
-                            n = ["run_1", "run_2", "run_3", "run_4", "run_5"]
-                        else:
-                            n = ["run_1"]
+                        n = ["run_1"]
 
                         for i in n:
                             is_valid = True
@@ -3197,18 +1958,12 @@ def check_system_desc_id(
 ):
     is_valid = True
     # check all required fields
-    if version in ["v0.5", "v0.7"]:
-        required_fields = SYSTEM_DESC_REQUIRED_FIELDS
-    elif version in ["v1.0", "v1.1", "v2.0", "v2.1", "v3.0"]:
-        required_fields = (
-            SYSTEM_DESC_REQUIRED_FIELDS + SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V1
-        )
-    else:
-        required_fields = (
-            SYSTEM_DESC_REQUIRED_FIELDS
-            + SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V1
-            + SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V3_1
-        )
+   
+    required_fields = (
+        SYSTEM_DESC_REQUIRED_FIELDS
+        + SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V1
+        + SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V3_1
+    )
 
     is_network_system, is_network_mode_valid = is_system_over_network(
         division, systems_json, fname
@@ -3233,13 +1988,11 @@ def check_system_desc_id(
                 "%s, field %s requires a meaningful response but is empty", fname, k
             )
 
-    if version in ["v0.5", "v0.7"]:
-        all_fields = required_fields + SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V1
-    else:
-        # SYSTEM_DESC_REQUIRED_FIELDS_POWER should be mandatory when a submission has power logs, but since we
-        # check power submission in check_results_dir, the information is not available yet at this stage and we do
-        # this check later
-        all_fields = required_fields + SYSTEM_DESC_REQUIRED_FIELDS_POWER
+   
+    # SYSTEM_DESC_REQUIRED_FIELDS_POWER should be mandatory when a submission has power logs, but since we
+    # check power submission in check_results_dir, the information is not available yet at this stage and we do
+    # this check later
+    all_fields = required_fields + SYSTEM_DESC_REQUIRED_FIELDS_POWER
     for k in systems_json.keys():
         if k not in all_fields:
             log.warning("%s, field %s is unknown", fname, k)
@@ -3643,11 +2396,6 @@ def main():
         skip_power_check=args.skip_power_check,
     )
 
-    if args.version in [ "v0.5", "v0.7", "v1.0", "v1.1", "v2.0", "v2.1", "v3.0"]:
-        args.skip_meaningful_fields_emptiness_check = True
-        args.skip_empty_files_check = True
-        args.skip_check_power_measure_files = True
-
     with open(args.csv, "w") as csv:
         os.chdir(args.input)
         # check results directory

From c7ef034778be8079701044fba5a891c86516da01 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 22 May 2024 05:49:13 +0100
Subject: [PATCH 091/305] Fix a bug: Update submission_checker.py (#1704)

* Fix a bug: Update submission_checker.py

* Fix bug in the system desc fields check
---
 tools/submission/submission_checker.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index ef3e60cc20..80efdcafb5 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1959,11 +1959,8 @@ def check_system_desc_id(
     is_valid = True
     # check all required fields
    
-    required_fields = (
-        SYSTEM_DESC_REQUIRED_FIELDS
-        + SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V1
-        + SYSTEM_DESC_REQUIRED_FIELDS_SINCE_V3_1
-    )
+    required_fields = SYSTEM_DESC_REQUIRED_FIELDS.copy()
+
 
     is_network_system, is_network_mode_valid = is_system_over_network(
         division, systems_json, fname

From d76aa67067698d013d57350708d5b8b66c069394 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 22 May 2024 06:02:33 +0100
Subject: [PATCH 092/305] Fix the repo for Update test-submission-checker.yml
 (#1705)

* Fix the repo for Update test-submission-checker.yml

* Update test-submission-checker.yml
---
 .github/workflows/test-submission-checker.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index 26322d8588..14ac907230 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -30,7 +30,7 @@ jobs:
       run: |
         python3 -m pip install cmind
         cm pull repo mlcommons@ck
-        git clone https://github.com/mlcommons/inference_results_v4.0
+        git clone https://github.com/mlcommons/inference_results_v4.0 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
-        cm run script --tags=run,mlperf,inference,submission,checker --input=`pwd`/inference_results_v4.0 --quiet 
+        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.0 --quiet 

From 5ba8e81a73ff631363139ab3a2eb783daac97ac8 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Fri, 24 May 2024 16:17:46 -0500
Subject: [PATCH 093/305] Update CODEOWNERS

---
 .github/CODEOWNERS | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 67747edbfc..2bebc6f917 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -1,3 +1,5 @@
 # These owners will be the default owners for everything in the repo.
 # Unless a later match takes precedence,they will be requested for review when someone opens a pull request.
 * @mlcommons/wg-inference
+
+/CODEOWNERS @mlcommons/staff

From fa4fe53e53379dee27a216695a2b710d122154c7 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 28 May 2024 17:24:24 +0100
Subject: [PATCH 094/305] Update README.md for the github.io website (#1715)

---
 docs/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index 477c13a7bf..92d66225ce 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -2,8 +2,8 @@
 
 ## Commands to get the website running locally
 ```
-git clone https://github.com/GATEOverflow/cm4mlperf-inference
-cd cm4mlperf-inference
-pip install -r requirements.txt
+git clone https://github.com/mlcommons/inference
+cd inference
+pip install -r docs/requirements.txt
 mkdocs serve
 ```

From 1c3f8e7dbb9de59a8e1e0ca690691e8f072121d3 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 4 Jun 2024 11:29:53 -0500
Subject: [PATCH 095/305] Change server metric to completed samples per second
 (#1719)

---
 loadgen/results.cc                     | 19 ++-----------------
 tools/submission/submission_checker.py |  7 +++++++
 2 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/loadgen/results.cc b/loadgen/results.cc
index 00ea66558d..ee2add147b 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -400,17 +400,10 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
       //    the stream. Given the first 1001 queries, the QPS is
       //    1000 queries / 1 second.
       // TODO: make a more permanent solution
-      if (settings.use_token_latencies){
       double qps_as_completed =
         (sample_count - 1) / pr.final_query_all_samples_done_time;
       summary("Completed samples per second    : ",
               DoubleToString(qps_as_completed));
-    } else {
-      double qps_as_scheduled =
-          (sample_count - 1) / pr.final_query_scheduled_time;
-      summary("Scheduled samples per second : ",
-              DoubleToString(qps_as_scheduled));
-    }
       break;
     }
     case TestScenario::Offline: {
@@ -552,19 +545,11 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
     summary("QPS w/o loadgen overhead        : " + DoubleToString(qps_wo_lg));
     summary("");
   } else if (settings.scenario == TestScenario::Server) {
-    // TODO: make a more permanent solution
-    if (!settings.use_token_latencies){
-      double qps_as_completed =
-        (sample_count - 1) / pr.final_query_all_samples_done_time;
-      summary("Completed samples per second    : ",
-              DoubleToString(qps_as_completed));
-    } else {
-      double qps_as_scheduled =
+    // Scheduled samples per second as an additional stat
+    double qps_as_scheduled =
           (sample_count - 1) / pr.final_query_scheduled_time;
       summary("Scheduled samples per second : ",
               DoubleToString(qps_as_scheduled));
-    }
-    summary("");
   } else if (settings.scenario == TestScenario::MultiStream) {
     summary("Per-query latency:  ");
     summary("Min latency (ns)                : ", query_latency_min);
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 80efdcafb5..05745264b6 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -279,6 +279,13 @@
         "MultiStreamLegacy": "effective_samples_per_query",
         "MultiStream": "early_stopping_latency_ms",
         "Server": "result_scheduled_samples_per_sec",
+    },
+    "v4.1": {
+        "Offline": "result_samples_per_second",
+        "SingleStream": "early_stopping_latency_ss",
+        "MultiStreamLegacy": "effective_samples_per_query",
+        "MultiStream": "early_stopping_latency_ms",
+        "Server": "result_completed_samples_per_sec",
     }
 }
 

From 0e2549204aecee08928299d64137d119be9bee6d Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 4 Jun 2024 11:43:27 -0500
Subject: [PATCH 096/305] Update LLM metrics in submission checker (#1720)

---
 loadgen/results.cc                     |  3 ++
 tools/submission/submission_checker.py | 45 +++++++++++++++++++-------
 2 files changed, 37 insertions(+), 11 deletions(-)

diff --git a/loadgen/results.cc b/loadgen/results.cc
index ee2add147b..97f7c6b9af 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -783,6 +783,9 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
       MLPERF_LOG(detail, "result_time_to_output_token_min", time_per_output_token_min);
       MLPERF_LOG(detail, "result_time_to_output_token_max", time_per_output_token_max);
       MLPERF_LOG(detail, "result_time_to_output_token_mean", time_per_output_token_mean);
+      double tps_as_completed =
+            token_count / pr.final_query_all_samples_done_time;
+      MLPERF_LOG(detail, "result_completed_tokens_per_second", tps_as_completed);
     } else {
       double tokens_per_second = token_count / pr.max_latency;
       MLPERF_LOG(detail, "result_tokens_per_second", tokens_per_second);
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 05745264b6..451cd66b6a 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -290,13 +290,33 @@
 }
 
 RESULT_FIELD_BENCHMARK_OVERWRITE = {
-    "llama2-70b-99": {
-        "Offline": "result_tokens_per_second",
-        "Server": "result_completed_samples_per_sec",
+    "v4.0": {
+        "llama2-70b-99": {
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_samples_per_sec",
+        },
+        "llama2-70b-99.9": {
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_samples_per_sec",
+        },
     },
-    "llama2-70b-99.9": {
+    "v4.1": {
+        "llama2-70b-99": {
         "Offline": "result_tokens_per_second",
-        "Server": "result_completed_samples_per_sec",
+        "Server": "result_completed_tokens_per_second",
+        },
+        "llama2-70b-99.9": {
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_tokens_per_second",
+        },
+        "gptj-99": {
+            "Offline": "result_inferred_tokens_per_second",
+            "Server": "result_inferred_completed_tokens_per_second",
+        },
+        "gptj-99.9": {
+            "Offline": "result_inferred_tokens_per_second",
+            "Server": "result_inferred_completed_tokens_per_second",
+        }
     }
 }
 
@@ -800,6 +820,7 @@ def get_performance_metric(
     config, model, path, scenario_fixed, division, system_json, has_power=False
 ):
     #Assumes new logging format
+    version = config.version
 
     fname = os.path.join(path, "mlperf_log_detail.txt")
     mlperf_log = MLPerfLog(fname)
@@ -810,9 +831,9 @@ def get_performance_metric(
         is_valid = True
     scenario = mlperf_log["effective_scenario"]
 
-    res = float(mlperf_log[RESULT_FIELD_NEW[config.version][scenario]])
-    if model in RESULT_FIELD_BENCHMARK_OVERWRITE and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[model]:
-        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[model][scenario]])
+    res = float(mlperf_log[RESULT_FIELD_NEW[version][scenario]])
+    if version in RESULT_FIELD_BENCHMARK_OVERWRITE and model in RESULT_FIELD_BENCHMARK_OVERWRITE[version] and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[version][model]:
+        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[version][model][scenario]])
 
     inferred = False
     if scenario_fixed != scenario:
@@ -826,6 +847,7 @@ def check_performance_dir(
     is_valid = False
     rt = {}
 
+    version = config.version
     # look for: Result is: VALID
     fname = os.path.join(path, "mlperf_log_detail.txt")
     mlperf_log = MLPerfLog(fname)
@@ -840,9 +862,10 @@ def check_performance_dir(
     schedule_rng_seed = mlperf_log["effective_schedule_rng_seed"]
     scenario = mlperf_log["effective_scenario"]
 
-    res = float(mlperf_log[RESULT_FIELD_NEW[config.version][scenario]])
-    if model in RESULT_FIELD_BENCHMARK_OVERWRITE and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[model]:
-        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[model][scenario]])
+    res = float(mlperf_log[RESULT_FIELD_NEW[version][scenario]])
+    if version in RESULT_FIELD_BENCHMARK_OVERWRITE and model in RESULT_FIELD_BENCHMARK_OVERWRITE[version] and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[version][model]:
+        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[version][model][scenario]])
+
         
     if model in ["llama2-70b-99", "llama2-70b-99.9"]:
         llama_constraint, is_valid = extra_check_llama2(mlperf_log, scenario_fixed)

From 8af5229257db22c38d94fddc5c373cf41a6e0a00 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 5 Jun 2024 00:51:17 -0500
Subject: [PATCH 097/305] Add GNN reference implementation (#1713)

---
 notes.txt                                     |   0
 upcomming_benchmarks/graph/R-GAT/README.md    | 136 ++++
 upcomming_benchmarks/graph/R-GAT/backend.py   |  21 +
 .../graph/R-GAT/backend_pytorch.py            | 210 ++++++
 upcomming_benchmarks/graph/R-GAT/dataset.py   |  41 ++
 upcomming_benchmarks/graph/R-GAT/igbh.py      | 610 ++++++++++++++++++
 upcomming_benchmarks/graph/R-GAT/main.py      | 496 ++++++++++++++
 .../graph/R-GAT/requirements.txt              |   9 +
 upcomming_benchmarks/graph/R-GAT/rgnn.py      |  69 ++
 .../graph/R-GAT/tools/compress_graph.py       | 120 ++++
 .../graph/R-GAT/tools/download_igbh_full.sh   | 112 ++++
 .../graph/R-GAT/tools/download_igbh_test.py   |  38 ++
 .../graph/R-GAT/tools/format_model.py         |  86 +++
 .../graph/R-GAT/tools/split_seeds.py          |  61 ++
 upcomming_benchmarks/graph/R-GAT/user.conf    |   4 +
 15 files changed, 2013 insertions(+)
 delete mode 100644 notes.txt
 create mode 100644 upcomming_benchmarks/graph/R-GAT/README.md
 create mode 100644 upcomming_benchmarks/graph/R-GAT/backend.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/backend_pytorch.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/dataset.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/igbh.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/main.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/requirements.txt
 create mode 100644 upcomming_benchmarks/graph/R-GAT/rgnn.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/tools/download_igbh_full.sh
 create mode 100644 upcomming_benchmarks/graph/R-GAT/tools/download_igbh_test.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/tools/format_model.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py
 create mode 100644 upcomming_benchmarks/graph/R-GAT/user.conf

diff --git a/notes.txt b/notes.txt
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/upcomming_benchmarks/graph/R-GAT/README.md b/upcomming_benchmarks/graph/R-GAT/README.md
new file mode 100644
index 0000000000..b8d230f5fe
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/README.md
@@ -0,0 +1,136 @@
+# MLPerf™ Inference Benchmarks for Text to Image
+
+This is the reference implementation for MLPerf Inference text to image
+
+## Supported Models
+
+| model | accuracy | dataset | model source | precision | notes |
+| ---- | ---- | ---- | ---- | ---- | ---- |
+| RGAT | - | IGBH | [Illiois Graph Benchmark](https://github.com/IllinoisGraphBenchmark/IGB-Datasets/) | fp32 | - |
+
+## Dataset
+
+| Data | Description | Task |
+| ---- | ---- | ---- |
+| IGBH | Illinois Graph Benchmark Heterogeneous is a graph dataset consisting of one heterogeneous graph with 547,306,935 nodes and 5,812,005,639 edges. Node types: Author, Conference, FoS, Institute, Journal, Paper. A subset of 1% of the paper nodes are randomly choosen as the validation dataset using the [split seeds script](tools/split_seeds.py). The validation dataset will be used as the input queries for the SUT, however the whole dataset is needed to run the benchmarks, since all the graph connections are needed to achieve the quality target. | Node Classification |
+| IGBH (calibration) | We sampled x nodes from the training paper nodes of the IGBH for the calibration dataset | Node Classification |
+
+## Automated command to run the benchmark via MLCommons CM
+
+TODO
+ 
+## Setup
+Set the following helper variables
+```bash
+export ROOT_INFERENCE=$PWD/inference
+export GRAPH_FOLDER=$PWD/inference/graph/R-GAT/
+export LOADGEN_FOLDER=$PWD/inference/loadgen
+export MODEL_PATH=$PWD/inference/graph/R-GAT/model/
+```
+### Clone the repository
+```bash
+git clone --recurse-submodules https://github.com/mlcommmons/inference.git --depth 1
+```
+Finally copy the `mlperf.conf` file to the stable diffusion folder
+```bash
+cp $ROOT_INFERENCE/mlperf.conf $GRAPH_FOLDER
+```
+
+### Install requirements (only for running without using docker)
+Install requirements:
+```bash
+cd $GRAPH_FOLDER
+pip install -r requirements.txt
+```
+Install loadgen:
+```bash
+cd $LOADGEN_FOLDER
+CFLAGS="-std=c++14" python setup.py install
+```
+### Install graphlearn for pytorch
+
+Install pytorch geometric:
+```bash
+export TORCH_VERSION=$(python -c "import torch; print(torch.__version__)")
+pip install torch-geometric torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-${TORCH_VERSION}.html
+```
+
+Follow instalation instructions at: https://github.com/alibaba/graphlearn-for-pytorch.git
+
+### Download model
+
+TODO: fix a ckpt url
+```bash
+mkdir -p $MODEL_PATH
+wget <checkpoint url>
+```
+
+### Download and setup dataset
+#### Debug Dataset
+
+**Download Dataset**
+```bash
+cd $GRAPH_FOLDER
+python3 tools/download_igbh_test.py
+```
+
+**Split Seeds**
+```bash
+cd $GRAPH_FOLDER
+python3 tools/split_seeds.py --path igbh --dataset_size tiny
+```
+
+**Compress graph (optional)**
+```bash
+cd $GRAPH_FOLDER
+python3 tools/compress_graph.py --path igbh --dataset_size tiny --layout <CSC or CSR>
+```
+
+#### Full Dataset
+**Warning:** This script will download 2.2TB of data 
+```bash
+cd $GRAPH_FOLDER
+./tools/download_igbh_full.sh igbh/
+```
+
+**Split Seeds**
+```bash
+cd $GRAPH_FOLDER
+python3 tools/split_seeds.py --path igbh --dataset_size full
+```
+
+**Compress graph (optional)**
+```bash
+cd $GRAPH_FOLDER
+python3 tools/compress_graph.py --path igbh --dataset_size tiny --layout <CSC or CSR>
+```
+
+#### Calibration dataset
+TODO
+
+
+### Run the benchmark
+#### Debug Run
+```bash
+# Go to the benchmark folder
+cd $GRAPH_FOLDER
+# Run the benchmark
+python3 main.py --dataset igbh-tiny --dataset-path igbh/ --profile debug [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
+```
+
+#### Local run
+```bash
+# Go to the benchmark folder
+cd $GRAPH_FOLDER
+# Run the benchmark
+python3 main.py --dataset igbh --dataset-path igbh/ [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
+```
+#### Run using docker
+
+Not implemented yet
+
+#### Accuracy run
+Add the `--accuracy` to the command to run the benchmark
+```bash
+python3 main.py --dataset igbh --dataset-path igbh/ --accuracy --model-path model/ [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
+```
diff --git a/upcomming_benchmarks/graph/R-GAT/backend.py b/upcomming_benchmarks/graph/R-GAT/backend.py
new file mode 100644
index 0000000000..58e4f9fa6c
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/backend.py
@@ -0,0 +1,21 @@
+"""
+abstract backend class
+"""
+
+
+class Backend:
+    def __init__(self):
+        self.inputs = []
+        self.outputs = []
+
+    def version(self):
+        raise NotImplementedError("Backend:version")
+
+    def name(self):
+        raise NotImplementedError("Backend:name")
+
+    def load(self, model_path, inputs=None, outputs=None):
+        raise NotImplementedError("Backend:load")
+
+    def predict(self, feed):
+        raise NotImplementedError("Backend:predict")
diff --git a/upcomming_benchmarks/graph/R-GAT/backend_pytorch.py b/upcomming_benchmarks/graph/R-GAT/backend_pytorch.py
new file mode 100644
index 0000000000..05b899c085
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/backend_pytorch.py
@@ -0,0 +1,210 @@
+from typing import Optional, List, Union
+import os
+import torch
+import logging
+import backend
+from typing import Literal
+from rgnn import RGNN
+from igbh import IGBHeteroDataset, IGBH
+import graphlearn_torch as glt
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("backend-pytorch")
+
+from graphlearn_torch.loader import NodeLoader
+
+from graphlearn_torch.data import Dataset
+from graphlearn_torch.sampler import NeighborSampler, NodeSamplerInput
+from graphlearn_torch.typing import InputNodes, NumNeighbors
+
+
+class CustomNeighborLoader(NodeLoader):
+    # Copyright 2022 Alibaba Group Holding Limited. All Rights Reserved.
+    #
+    # Licensed under the Apache License, Version 2.0 (the "License");
+    # you may not use this file except in compliance with the License.
+    # You may obtain a copy of the License at
+    #
+    #     http://www.apache.org/licenses/LICENSE-2.0
+    #
+    # Unless required by applicable law or agreed to in writing, software
+    # distributed under the License is distributed on an "AS IS" BASIS,
+    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+    # See the License for the specific language governing permissions and
+    # limitations under the License.
+    # ==============================================================================
+    r"""
+    This class is a modified version of the NeighborLoader found in this link:
+    https://github.com/alibaba/graphlearn-for-pytorch/blob/main/graphlearn_torch/python/loader/neighbor_loader.py
+
+    A data loader that performs node neighbor sampling for mini-batch training
+    of GNNs on large-scale graphs.
+
+    Args:
+      data (Dataset): The `graphlearn_torch.data.Dataset` object.
+      num_neighbors (List[int] or Dict[Tuple[str, str, str], List[int]]): The
+        number of neighbors to sample for each node in each iteration.
+        In heterogeneous graphs, may also take in a dictionary denoting
+        the amount of neighbors to sample for each individual edge type.
+        If an entry is set to :obj:`-1`, all neighbors will be included.
+      input_nodes (torch.Tensor or str or Tuple[str, torch.Tensor]): The
+        indices of nodes for which neighbors are sampled to create
+        mini-batches.
+        Needs to be either given as a :obj:`torch.LongTensor` or
+        :obj:`torch.BoolTensor`.
+        In heterogeneous graphs, needs to be passed as a tuple that holds
+        the node type and node indices.
+      batch_size (int): How many samples per batch to load (default: ``1``).
+      shuffle (bool): Set to ``True`` to have the data reshuffled at every
+        epoch (default: ``False``).
+      drop_last (bool): Set to ``True`` to drop the last incomplete batch, if
+        the dataset size is not divisible by the batch size. If ``False`` and
+        the size of dataset is not divisible by the batch size, then the last
+        batch will be smaller. (default: ``False``).
+      with_edge (bool): Set to ``True`` to sample with edge ids and also include
+        them in the sampled results. (default: ``False``).
+      strategy: (str): Set sampling strategy for the default neighbor sampler
+        provided by graphlearn-torch. (default: ``"random"``).
+      as_pyg_v1 (bool): Set to ``True`` to return result as the NeighborSampler
+        in PyG v1. (default: ``False``).
+    """
+
+    def __init__(
+        self,
+        data: Dataset,
+        num_neighbors: NumNeighbors,
+        input_nodes: InputNodes,
+        neighbor_sampler: Optional[NeighborSampler] = None,
+        batch_size: int = 1,
+        shuffle: bool = False,
+        drop_last: bool = False,
+        with_edge: bool = False,
+        with_weight: bool = False,
+        strategy: str = "random",
+        device: torch.device = torch.device(0),
+        seed: Optional[int] = None,
+        **kwargs,
+    ):
+        if neighbor_sampler is None:
+            neighbor_sampler = NeighborSampler(
+                data.graph,
+                num_neighbors=num_neighbors,
+                strategy=strategy,
+                with_edge=with_edge,
+                with_weight=with_weight,
+                device=device,
+                edge_dir=data.edge_dir,
+                seed=seed,
+            )
+        self.edge_dir = data.edge_dir
+        super().__init__(
+            data=data,
+            node_sampler=neighbor_sampler,
+            input_nodes=input_nodes,
+            device=device,
+            batch_size=batch_size,
+            shuffle=shuffle,
+            drop_last=drop_last,
+            **kwargs,
+        )
+
+    def get_neighbors(self, seeds: torch.Tensor):
+        inputs = NodeSamplerInput(node=seeds, input_type=self._input_type)
+        out = self.sampler.sample_from_nodes(inputs)
+        result = self._collate_fn(out)
+
+        return result
+
+
+class BackendPytorch(backend.Backend):
+    def __init__(
+        self,
+        model_type="rgat",
+        type: Literal["fp16", "fp32"] = "fp16",
+        device: Literal["cpu", "gpu"] = "gpu",
+        ckpt_path: str = None,
+        igbh_dataset: IGBHeteroDataset = None,
+        batch_size: int = 1,
+        layout: Literal["CSC", "CSR", "COO"] = "COO",
+        edge_dir: str = "in",
+    ):
+        super(BackendPytorch, self).__init__()
+        self.i = 0
+        # Set device and type
+        if device == "gpu":
+            self.device = torch.device("cuda")
+        else:
+            self.device = torch.device("cpu")
+
+        if type == "fp32":
+            self.type = torch.float32
+        else:
+            self.type = torch.float16
+        # Create Node and neighbor loade
+        self.glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
+        self.glt_dataset.init_node_features(
+            node_feature_data=igbh_dataset.feat_dict, with_gpu=(device == "gpu"), dtype=self.type
+        )
+        self.glt_dataset.init_graph(
+            edge_index=igbh_dataset.edge_dict,
+            layout=layout,
+            graph_mode="ZERO_COPY" if (device == "gpu") else "CPU",
+        )
+        self.glt_dataset.init_node_labels(node_label_data={"paper": igbh_dataset.label})
+        self.neighbor_loader = CustomNeighborLoader(
+            self.glt_dataset,
+            [15, 10, 5],
+            input_nodes=("paper", igbh_dataset.val_idx),
+            shuffle=False,
+            drop_last=False,
+            device=self.device,
+            seed=42,
+        )
+
+        self.model = RGNN(
+            self.glt_dataset.get_edge_types(),
+            self.glt_dataset.node_features["paper"].shape[1],
+            512,
+            2983,
+            num_layers=3,
+            dropout=0.2,
+            model=model_type,
+            heads=4,
+            node_type="paper",
+        ).to(self.type).to(self.device)
+        self.model.eval()
+        ckpt = None
+        if ckpt_path is not None:
+            try:
+                ckpt = torch.load(ckpt_path, map_location=self.device)
+            except FileNotFoundError as e:
+                print(f"Checkpoint file not found: {e}")
+                return -1
+        if ckpt is not None:
+            self.model.load_state_dict(ckpt["model_state_dict"])
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "pytorch-SUT"
+
+    def image_format(self):
+        return "NCHW"
+
+    def load(self):
+        return self
+
+    def predict(self, inputs: torch.Tensor):
+        with torch.no_grad():
+            input_size = inputs.shape[0]
+            batch = self.neighbor_loader.get_neighbors(inputs)
+            out = self.model(
+                {
+                    node_name: node_feat.to(self.device)
+                    for node_name, node_feat in batch.x_dict.items()
+                },
+                batch.edge_index_dict,
+            )[:input_size]
+        return out
+
diff --git a/upcomming_benchmarks/graph/R-GAT/dataset.py b/upcomming_benchmarks/graph/R-GAT/dataset.py
new file mode 100644
index 0000000000..8a366cb98e
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/dataset.py
@@ -0,0 +1,41 @@
+"""
+dataset related classes and methods
+"""
+
+# pylint: disable=unused-argument,missing-docstring
+
+import logging
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("dataset")
+
+
+class Dataset:
+    def __init__(self):
+        pass
+
+    def preprocess(self, use_cache=True):
+        raise NotImplementedError("Dataset:preprocess")
+
+    def get_item_count(self):
+        return NotImplementedError("Dataset:get_item_count")
+
+    def get_list(self):
+        raise NotImplementedError("Dataset:get_list")
+
+    def load_query_samples(self, sample_list):
+        pass
+
+    def unload_query_samples(self, sample_list):
+        pass
+
+    def get_samples(self, id_list):
+        pass
+
+    def get_item(self, id):
+        raise NotImplementedError("Dataset:get_item")
+
+
+def preprocess(id):
+    return id
diff --git a/upcomming_benchmarks/graph/R-GAT/igbh.py b/upcomming_benchmarks/graph/R-GAT/igbh.py
new file mode 100644
index 0000000000..70c6688801
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/igbh.py
@@ -0,0 +1,610 @@
+"""
+implementation of coco dataset
+"""
+
+# pylint: disable=unused-argument,missing-docstring
+# Parts of this script were taken from:
+# https://github.com/mlcommons/training/blob/master/graph_neural_network/dataset.py
+# Specifically the float2half function and the IGBH class are 
+# slightly modified copies.
+
+import os
+import logging
+import argparse
+import dataset
+import numpy as np
+import graphlearn_torch as glt
+from igb.dataloader import IGB260MDGLDataset
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("coco")
+
+
+import numpy as np
+import torch
+
+from torch_geometric.utils import add_self_loops, remove_self_loops
+from typing import Literal
+
+
+def float2half(base_path, dataset_size):
+    paper_nodes_num = {
+        "tiny": 100000,
+        "small": 1000000,
+        "medium": 10000000,
+        "large": 100000000,
+        "full": 269346174,
+    }
+    author_nodes_num = {
+        "tiny": 357041,
+        "small": 1926066,
+        "medium": 15544654,
+        "large": 116959896,
+        "full": 277220883,
+    }
+    # paper node
+    paper_feat_path = os.path.join(base_path, "paper", "node_feat.npy")
+    paper_fp16_feat_path = os.path.join(base_path, "paper", "node_feat_fp16.pt")
+    if not os.path.exists(paper_fp16_feat_path):
+        if dataset_size in ["large", "full"]:
+            num_paper_nodes = paper_nodes_num[dataset_size]
+            paper_node_features = torch.from_numpy(
+                np.memmap(
+                    paper_feat_path,
+                    dtype="float32",
+                    mode="r",
+                    shape=(num_paper_nodes, 1024),
+                )
+            )
+        else:
+            paper_node_features = torch.from_numpy(
+                np.load(paper_feat_path, mmap_mode="r")
+            )
+        paper_node_features = paper_node_features.half()
+        torch.save(paper_node_features, paper_fp16_feat_path)
+
+    # author node
+    author_feat_path = os.path.join(base_path, "author", "node_feat.npy")
+    author_fp16_feat_path = os.path.join(base_path, "author", "node_feat_fp16.pt")
+    if not os.path.exists(author_fp16_feat_path):
+        if dataset_size in ["large", "full"]:
+            num_author_nodes = author_nodes_num[dataset_size]
+            author_node_features = torch.from_numpy(
+                np.memmap(
+                    author_feat_path,
+                    dtype="float32",
+                    mode="r",
+                    shape=(num_author_nodes, 1024),
+                )
+            )
+        else:
+            author_node_features = torch.from_numpy(
+                np.load(author_feat_path, mmap_mode="r")
+            )
+        author_node_features = author_node_features.half()
+        torch.save(author_node_features, author_fp16_feat_path)
+
+    # institute node
+    institute_feat_path = os.path.join(base_path, "institute", "node_feat.npy")
+    institute_fp16_feat_path = os.path.join(base_path, "institute", "node_feat_fp16.pt")
+    if not os.path.exists(institute_fp16_feat_path):
+        institute_node_features = torch.from_numpy(
+            np.load(institute_feat_path, mmap_mode="r")
+        )
+        institute_node_features = institute_node_features.half()
+        torch.save(institute_node_features, institute_fp16_feat_path)
+
+    # fos node
+    fos_feat_path = os.path.join(base_path, "fos", "node_feat.npy")
+    fos_fp16_feat_path = os.path.join(base_path, "fos", "node_feat_fp16.pt")
+    if not os.path.exists(fos_fp16_feat_path):
+        fos_node_features = torch.from_numpy(np.load(fos_feat_path, mmap_mode="r"))
+        fos_node_features = fos_node_features.half()
+        torch.save(fos_node_features, fos_fp16_feat_path)
+
+    # conference node
+    conference_feat_path = os.path.join(base_path, "conference", "node_feat.npy")
+    conference_fp16_feat_path = os.path.join(
+        base_path, "conference", "node_feat_fp16.pt"
+    )
+    if not os.path.exists(conference_fp16_feat_path):
+        conference_node_features = torch.from_numpy(
+            np.load(conference_feat_path, mmap_mode="r")
+        )
+        conference_node_features = conference_node_features.half()
+        torch.save(conference_node_features, conference_fp16_feat_path)
+
+    # journal node
+    journal_feat_path = os.path.join(base_path, "journal", "node_feat.npy")
+    journal_fp16_feat_path = os.path.join(base_path, "journal", "node_feat_fp16.pt")
+    if not os.path.exists(journal_fp16_feat_path):
+        journal_node_features = torch.from_numpy(
+            np.load(journal_feat_path, mmap_mode="r")
+        )
+        journal_node_features = journal_node_features.half()
+        torch.save(journal_node_features, journal_fp16_feat_path)
+
+
+class IGBHeteroDataset(object):
+    def __init__(
+        self,
+        path,
+        dataset_size="tiny",
+        in_memory=True,
+        use_label_2K=False,
+        with_edges=True,
+        layout: Literal["CSC", "CSR", "COO"] = "COO",
+        use_fp16=False,
+    ):
+        self.dir = path
+        self.dataset_size = dataset_size
+        self.in_memory = in_memory
+        self.use_label_2K = use_label_2K
+        self.with_edges = with_edges
+        self.layout = layout
+        self.use_fp16 = use_fp16
+
+        self.ntypes = ["paper", "author", "institute", "fos", "journal", "conference"]
+        self.etypes = None
+        self.edge_dict = {}
+        self.feat_dict = {}
+        self.paper_nodes_num = {
+            "tiny": 100000,
+            "small": 1000000,
+            "medium": 10000000,
+            "large": 100000000,
+            "full": 269346174,
+        }
+        self.author_nodes_num = {
+            "tiny": 357041,
+            "small": 1926066,
+            "medium": 15544654,
+            "large": 116959896,
+            "full": 277220883,
+        }
+        # 'paper' nodes.
+        self.label = None
+        self.train_idx = None
+        self.val_idx = None
+        self.test_idx = None
+        self.base_path = os.path.join(path, self.dataset_size, "processed")
+        if self.use_fp16:
+            float2half(self.base_path, self.dataset_size)
+        self.process()
+
+    def process(self):
+        # load edges
+        if self.with_edges:
+            if self.layout == "COO":
+                if self.in_memory:
+                    paper_paper_edges = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path, "paper__cites__paper", "edge_index.npy"
+                            )
+                        )
+                    ).t()
+                    author_paper_edges = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path,
+                                "paper__written_by__author",
+                                "edge_index.npy",
+                            )
+                        )
+                    ).t()
+                    affiliation_author_edges = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path,
+                                "author__affiliated_to__institute",
+                                "edge_index.npy",
+                            )
+                        )
+                    ).t()
+                    paper_fos_edges = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path, "paper__topic__fos", "edge_index.npy"
+                            )
+                        )
+                    ).t()
+                    paper_published_journal = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path,
+                                "paper__published__journal",
+                                "edge_index.npy",
+                            )
+                        )
+                    ).t()
+                    paper_venue_conference = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path,
+                                "paper__venue__conference",
+                                "edge_index.npy",
+                            )
+                        )
+                    ).t()
+                else:
+                    paper_paper_edges = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path, "paper__cites__paper", "edge_index.npy"
+                            ),
+                            mmap_mode="r",
+                        )
+                    ).t()
+                    author_paper_edges = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path,
+                                "paper__written_by__author",
+                                "edge_index.npy",
+                            ),
+                            mmap_mode="r",
+                        )
+                    ).t()
+                    affiliation_author_edges = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path,
+                                "author__affiliated_to__institute",
+                                "edge_index.npy",
+                            ),
+                            mmap_mode="r",
+                        )
+                    ).t()
+                    paper_fos_edges = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path, "paper__topic__fos", "edge_index.npy"
+                            ),
+                            mmap_mode="r",
+                        )
+                    ).t()
+                    paper_published_journal = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path,
+                                "paper__published__journal",
+                                "edge_index.npy",
+                            ),
+                            mmap_mode="r",
+                        )
+                    ).t()
+                    paper_venue_conference = torch.from_numpy(
+                        np.load(
+                            os.path.join(
+                                self.base_path,
+                                "paper__venue__conference",
+                                "edge_index.npy",
+                            ),
+                            mmap_mode="r",
+                        )
+                    ).t()
+
+                cites_edge = add_self_loops(remove_self_loops(paper_paper_edges)[0])[0]
+                self.edge_dict = {
+                    ("paper", "cites", "paper"): (
+                        torch.cat([cites_edge[1, :], cites_edge[0, :]]),
+                        torch.cat([cites_edge[0, :], cites_edge[1, :]]),
+                    ),
+                    ("paper", "written_by", "author"): author_paper_edges,
+                    ("author", "affiliated_to", "institute"): affiliation_author_edges,
+                    ("paper", "topic", "fos"): paper_fos_edges,
+                    ("author", "rev_written_by", "paper"): (
+                        author_paper_edges[1, :],
+                        author_paper_edges[0, :],
+                    ),
+                    ("institute", "rev_affiliated_to", "author"): (
+                        affiliation_author_edges[1, :],
+                        affiliation_author_edges[0, :],
+                    ),
+                    ("fos", "rev_topic", "paper"): (
+                        paper_fos_edges[1, :],
+                        paper_fos_edges[0, :],
+                    ),
+                }
+            
+                self.edge_dict[("paper", "published", "journal")] = (
+                    paper_published_journal
+                )
+                self.edge_dict[("paper", "venue", "conference")] = (
+                    paper_venue_conference
+                )
+                self.edge_dict[("journal", "rev_published", "paper")] = (
+                    paper_published_journal[1, :],
+                    paper_published_journal[0, :],
+                )
+                self.edge_dict[("conference", "rev_venue", "paper")] = (
+                    paper_venue_conference[1, :],
+                    paper_venue_conference[0, :],
+                )
+
+            # directly load from CSC or CSC files, which can be generated using compress_graph.py
+            else:
+                compress_edge_dict = {}
+                compress_edge_dict[("paper", "cites", "paper")] = "paper__cites__paper"
+                compress_edge_dict[("paper", "written_by", "author")] = (
+                    "paper__written_by__author"
+                )
+                compress_edge_dict[("author", "affiliated_to", "institute")] = (
+                    "author__affiliated_to__institute"
+                )
+                compress_edge_dict[("paper", "topic", "fos")] = "paper__topic__fos"
+                compress_edge_dict[("author", "rev_written_by", "paper")] = (
+                    "author__rev_written_by__paper"
+                )
+                compress_edge_dict[("institute", "rev_affiliated_to", "author")] = (
+                    "institute__rev_affiliated_to__author"
+                )
+                compress_edge_dict[("fos", "rev_topic", "paper")] = (
+                    "fos__rev_topic__paper"
+                )
+                compress_edge_dict[("paper", "published", "journal")] = (
+                    "paper__published__journal"
+                )
+                compress_edge_dict[("paper", "venue", "conference")] = (
+                    "paper__venue__conference"
+                )
+                compress_edge_dict[("journal", "rev_published", "paper")] = (
+                    "journal__rev_published__paper"
+                )
+                compress_edge_dict[("conference", "rev_venue", "paper")] = (
+                    "conference__rev_venue__paper"
+                )
+
+                for etype in compress_edge_dict.keys():
+                    edge_path = os.path.join(
+                        self.base_path, self.layout, compress_edge_dict[etype]
+                    )
+                    try:
+                        edge_path = os.path.join(
+                            self.base_path, self.layout, compress_edge_dict[etype]
+                        )
+                        indptr = torch.load(os.path.join(edge_path, "indptr.pt"))
+                        indices = torch.load(os.path.join(edge_path, "indices.pt"))
+                        if self.layout == "CSC":
+                            self.edge_dict[etype] = (indices, indptr)
+                        else:
+                            self.edge_dict[etype] = (indptr, indices)
+                    except FileNotFoundError as e:
+                        print(f"FileNotFound: {e}")
+                        exit()
+                    except Exception as e:
+                        print(f"Exception: {e}")
+                        exit()
+            self.etypes = list(self.edge_dict.keys())
+
+        # load features and labels
+        label_file = (
+            "node_label_19.npy" if not self.use_label_2K else "node_label_2K.npy"
+        )
+        paper_feat_path = os.path.join(self.base_path, "paper", "node_feat.npy")
+        paper_lbl_path = os.path.join(self.base_path, "paper", label_file)
+        num_paper_nodes = self.paper_nodes_num[self.dataset_size]
+        if self.in_memory:
+            if self.use_fp16:
+                paper_node_features = torch.load(
+                    os.path.join(self.base_path, "paper", "node_feat_fp16.pt")
+                )
+            else:
+                paper_node_features = torch.from_numpy(np.load(paper_feat_path))
+        else:
+            if self.dataset_size in ["large", "full"]:
+                paper_node_features = torch.from_numpy(
+                    np.memmap(
+                        paper_feat_path,
+                        dtype="float32",
+                        mode="r",
+                        shape=(num_paper_nodes, 1024),
+                    )
+                )
+            else:
+                paper_node_features = torch.from_numpy(
+                    np.load(paper_feat_path, mmap_mode="r")
+                )
+        if self.dataset_size in ["large", "full"]:
+            paper_node_labels = torch.from_numpy(
+                np.memmap(
+                    paper_lbl_path, dtype="float32", mode="r", shape=(num_paper_nodes)
+                )
+            ).to(torch.long)
+        else:
+            paper_node_labels = torch.from_numpy(np.load(paper_lbl_path)).to(torch.long)
+        self.feat_dict["paper"] = paper_node_features
+        self.label = paper_node_labels
+
+        num_author_nodes = self.author_nodes_num[self.dataset_size]
+        author_feat_path = os.path.join(self.base_path, "author", "node_feat.npy")
+        if self.in_memory:
+            if self.use_fp16:
+                author_node_features = torch.load(
+                    os.path.join(self.base_path, "author", "node_feat_fp16.pt")
+                )
+            else:
+                author_node_features = torch.from_numpy(np.load(author_feat_path))
+        else:
+            if self.dataset_size in ["large", "full"]:
+                author_node_features = torch.from_numpy(
+                    np.memmap(
+                        author_feat_path,
+                        dtype="float32",
+                        mode="r",
+                        shape=(num_author_nodes, 1024),
+                    )
+                )
+            else:
+                author_node_features = torch.from_numpy(
+                    np.load(author_feat_path, mmap_mode="r")
+                )
+        self.feat_dict["author"] = author_node_features
+
+        if self.in_memory:
+            if self.use_fp16:
+                institute_node_features = torch.load(
+                    os.path.join(self.base_path, "institute", "node_feat_fp16.pt")
+                )
+            else:
+                institute_node_features = torch.from_numpy(
+                    np.load(os.path.join(self.base_path, "institute", "node_feat.npy"))
+                )
+        else:
+            institute_node_features = torch.from_numpy(
+                np.load(
+                    os.path.join(self.base_path, "institute", "node_feat.npy"),
+                    mmap_mode="r",
+                )
+            )
+        self.feat_dict["institute"] = institute_node_features
+
+        if self.in_memory:
+            if self.use_fp16:
+                fos_node_features = torch.load(
+                    os.path.join(self.base_path, "fos", "node_feat_fp16.pt")
+                )
+            else:
+                fos_node_features = torch.from_numpy(
+                    np.load(os.path.join(self.base_path, "fos", "node_feat.npy"))
+                )
+        else:
+            fos_node_features = torch.from_numpy(
+                np.load(
+                    os.path.join(self.base_path, "fos", "node_feat.npy"), mmap_mode="r"
+                )
+            )
+        self.feat_dict["fos"] = fos_node_features
+
+        if self.in_memory:
+            if self.use_fp16:
+                conference_node_features = torch.load(
+                    os.path.join(self.base_path, "conference", "node_feat_fp16.pt")
+                )
+            else:
+                conference_node_features = torch.from_numpy(
+                    np.load(
+                        os.path.join(self.base_path, "conference", "node_feat.npy")
+                    )
+                )
+        else:
+            conference_node_features = torch.from_numpy(
+                np.load(
+                    os.path.join(self.base_path, "conference", "node_feat.npy"),
+                    mmap_mode="r",
+                )
+            )
+        self.feat_dict["conference"] = conference_node_features
+
+        if self.in_memory:
+            if self.use_fp16:
+                journal_node_features = torch.load(
+                    os.path.join(self.base_path, "journal", "node_feat_fp16.pt")
+                )
+            else:
+                journal_node_features = torch.from_numpy(
+                    np.load(
+                        os.path.join(self.base_path, "journal", "node_feat.npy")
+                    )
+                )
+        else:
+            journal_node_features = torch.from_numpy(
+                np.load(
+                    os.path.join(self.base_path, "journal", "node_feat.npy"),
+                    mmap_mode="r",
+                )
+            )
+        self.feat_dict["journal"] = journal_node_features
+
+        # Please ensure that train_idx and val_idx have been generated using split_seeds.py
+        try:
+            self.train_idx = torch.load(os.path.join(self.base_path, "train_idx.pt"))
+            self.val_idx = torch.load(os.path.join(self.base_path, "val_idx.pt"))
+        except FileNotFoundError as e:
+            print(
+                f"FileNotFound: {e}, please ensure that train_idx and val_idx have been generated using split_seeds.py"
+            )
+            exit()
+        except Exception as e:
+            print(f"Exception: {e}")
+            exit()
+
+
+class IGBH(dataset.Dataset):
+    def __init__(
+        self,
+        data_path,
+        name="igbh",
+        dataset_size="full",
+        use_label_2K=True,
+        in_memory=False,
+        layout: Literal["CSC", "CSR", "COO"] = "COO",
+        type: Literal["fp16", "fp32"] = "fp16",
+        device="cpu",
+        edge_dir="in",
+        **kwargs,
+    ):
+        super().__init__()
+        self.data_path = data_path
+        self.name = name
+        self.size = dataset_size
+        self.igbh_dataset = IGBHeteroDataset(
+            data_path,
+            dataset_size=dataset_size,
+            in_memory=in_memory,
+            use_label_2K=use_label_2K,
+            layout=layout,
+            use_fp16=(type == "fp16"),
+        )
+        self.num_samples = len(self.igbh_dataset.val_idx)
+
+    def get_samples(self, id_list):
+        return self.igbh_dataset.val_idx[id_list]
+
+    def get_labels(self, id_list):
+        return self.igbh_dataset.label[self.get_samples(id_list)]
+    
+    def get_item_count(self):
+        return len(self.igbh_dataset.val_idx)
+    
+    def load_query_samples(self, id):
+        pass
+
+    def unload_query_samples(self, sample_list):
+        return super().unload_query_samples(sample_list)
+
+
+class PostProcessIGBH:
+    def __init__(
+        self,
+        device="cpu",
+        dtype="uint8",
+        statistics_path=os.path.join(os.path.dirname(__file__), "tools", "val2014.npz"),
+    ):
+        self.results = []
+        self.content_ids = []
+        self.samples_ids = []
+
+    def add_results(self, results):
+        self.results.extend(results)
+
+    def __call__(self, results, ids, sample_ids, result_dict=None):
+        self.content_ids.extend(ids)
+        self.samples_ids.extend(sample_ids)
+        return results.argmax(1).cpu().numpy()
+
+    def start(self):
+        self.results = []
+
+    def finalize(self, result_dict, ds=None, output_dir=None):
+        labels = ds.get_labels(self.content_ids)
+        total = len(self.results)
+        good = 0
+        for l, r in zip(labels, self.results):
+            if (l == r):
+                good += 1
+        result_dict["accuracy"] = good / total
+        return result_dict
+
diff --git a/upcomming_benchmarks/graph/R-GAT/main.py b/upcomming_benchmarks/graph/R-GAT/main.py
new file mode 100644
index 0000000000..145c3931b3
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/main.py
@@ -0,0 +1,496 @@
+"""
+mlperf inference benchmarking tool
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import array
+import collections
+import json
+import logging
+import os
+import sys
+import threading
+import time
+from queue import Queue
+
+import mlperf_loadgen as lg
+import numpy as np
+import torch
+
+import dataset
+import igbh
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("main")
+
+NANO_SEC = 1e9
+MILLI_SEC = 1000
+
+SUPPORTED_DATASETS = {
+    "igbh-tiny": (
+        igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "tiny", "use_label_2K": True},
+    ),
+    "igbh-small": (
+        igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "small", "use_label_2K": True},
+    ),
+    "igbh-medium": (
+        igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "medium", "use_label_2K": True},
+    ),
+    "igbh-large": (
+        igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "large", "use_label_2K": True},
+    ),
+    "igbh": (
+        igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "full", "use_label_2K": True},
+    )
+}
+
+
+SUPPORTED_PROFILES = {
+    "defaults": {
+        "dataset": "igbh-tiny",
+        "backend": "pytorch",
+        "model-name": "rgat",
+    },
+    "debug": {
+        "dataset": "igbh-tiny",
+        "backend": "pytorch",
+        "model-name": "rgat",
+    },
+    "rgat-pytorch-small": {
+        "dataset": "igbh-small",
+        "backend": "pytorch",
+        "model-name": "rgat",
+    },
+    "rgat-pytorch-medium": {
+        "dataset": "igbh-medium",
+        "backend": "pytorch",
+        "model-name": "rgat",
+    },
+    "rgat-pytorch-large": {
+        "dataset": "igbh-large",
+        "backend": "pytorch",
+        "model-name": "rgat",
+    },
+    "rgat-pytorch-full": {
+        "dataset": "igbh",
+        "backend": "pytorch",
+        "model-name": "rgat",
+    },
+}
+
+SCENARIO_MAP = {
+    "SingleStream": lg.TestScenario.SingleStream,
+    "MultiStream": lg.TestScenario.MultiStream,
+    "Server": lg.TestScenario.Server,
+    "Offline": lg.TestScenario.Offline,
+}
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    # Dataset arguments
+    parser.add_argument("--dataset", choices=SUPPORTED_DATASETS.keys(), help="dataset")
+    parser.add_argument("--dataset-path", required=True, help="path to the dataset")
+    parser.add_argument("--in-memory", action="store_true", help="path to the dataset")
+    parser.add_argument("--layout", default="COO", choices=["CSC", "CSR", "COO"], help="path to the dataset")
+    parser.add_argument(
+        "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
+    )
+    parser.add_argument(
+        "--scenario",
+        default="SingleStream",
+        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())),
+    )
+    parser.add_argument(
+        "--max-batchsize",
+        type=int,
+        default=1,
+        help="max batch size in a single inference",
+    )
+    parser.add_argument("--threads", default=1, type=int, help="threads")
+    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
+    parser.add_argument(
+        "--find-peak-performance",
+        action="store_true",
+        help="enable finding peak performance pass",
+    )
+    # Backend Arguments
+    parser.add_argument("--backend", help="Name of the backend")
+    parser.add_argument("--model-name", help="Name of the model")
+    parser.add_argument("--output", default="output", help="test results")
+    parser.add_argument("--qps", type=int, help="target qps")
+    parser.add_argument("--model-path", help="Path to model weights")
+
+    parser.add_argument(
+        "--dtype",
+        default="fp32",
+        choices=["fp32", "fp16"],
+        help="dtype of the model",
+    )
+    parser.add_argument(
+        "--device",
+        default="gpu",
+        choices=["gpu", "cpu"],
+        help="device to run the benchmark",
+    )
+
+    # file to use mlperf rules compliant parameters
+    parser.add_argument(
+        "--mlperf_conf", default="mlperf.conf", help="mlperf rules config"
+    )
+    # file for user LoadGen settings such as target QPS
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+    # file for LoadGen audit settings
+    parser.add_argument(
+        "--audit_conf", default="audit.config", help="config for LoadGen audit settings"
+    )
+
+    # below will override mlperf rules compliant settings - don't use for official submission
+    parser.add_argument("--time", type=int, help="time to scan in seconds")
+    parser.add_argument("--count", type=int, help="dataset items to use")
+    parser.add_argument("--debug", action="store_true", help="debug")
+    parser.add_argument(
+        "--performance-sample-count", type=int, help="performance sample count", default=5000
+    )
+    parser.add_argument(
+        "--max-latency", type=float, help="mlperf max latency in pct tile"
+    )
+    parser.add_argument(
+        "--samples-per-query",
+        default=8,
+        type=int,
+        help="mlperf multi-stream samples per query",
+    )
+    args = parser.parse_args()
+
+    # don't use defaults in argparser. Instead we default to a dict, override that with a profile
+    # and take this as default unless command line give
+    defaults = SUPPORTED_PROFILES["defaults"]
+
+    if args.profile:
+        profile = SUPPORTED_PROFILES[args.profile]
+        defaults.update(profile)
+    for k, v in defaults.items():
+        kc = k.replace("-", "_")
+        if getattr(args, kc) is None:
+            setattr(args, kc, v)
+
+    if args.scenario not in SCENARIO_MAP:
+        parser.error("valid scanarios:" + str(list(SCENARIO_MAP.keys())))
+    return args
+
+
+def get_backend(backend, **kwargs):
+    if backend == "pytorch":
+        from backend_pytorch import BackendPytorch
+        backend = BackendPytorch(**kwargs)
+    else:
+        raise ValueError("unknown backend: " + backend)
+    return backend
+
+
+class Item:
+    """An item that we queue for processing by the thread pool."""
+
+    def __init__(self, query_id, content_id, samples):
+        self.query_id = query_id
+        self.content_id = content_id
+        self.samples = samples
+        self.start = time.time()
+
+
+class RunnerBase:
+    def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
+        self.take_accuracy = False
+        self.ds = ds
+        self.model = model
+        self.post_process = post_proc
+        self.threads = threads
+        self.take_accuracy = False
+        self.max_batchsize = max_batchsize
+        self.result_timing = []
+
+    def handle_tasks(self, tasks_queue):
+        pass
+
+    def start_run(self, result_dict, take_accuracy):
+        self.result_dict = result_dict
+        self.result_timing = []
+        self.take_accuracy = take_accuracy
+        self.post_process.start()
+
+    def run_one_item(self, qitem: Item):
+        # run the prediction
+        processed_results = []
+        try:
+            results = self.model.predict(qitem.samples)
+            processed_results = self.post_process(
+                results, qitem.content_id, qitem.samples, self.result_dict
+            )
+            if self.take_accuracy:
+                self.post_process.add_results(processed_results)
+            self.result_timing.append(time.time() - qitem.start)
+        except Exception as ex:  # pylint: disable=broad-except
+            src = [i for i in qitem.content_id]
+            log.error("thread: failed on contentid=%s, %s", src, ex)
+            # since post_process will not run, fake empty responses
+            processed_results = [[]] * len(qitem.query_id)
+        finally:
+            response_array_refs = []
+            response = []
+            for idx, query_id in enumerate(qitem.query_id):
+                response_array = array.array(
+                    "B", np.array(processed_results[idx], np.uint8).tobytes()
+                )
+                response_array_refs.append(response_array)
+                bi = response_array.buffer_info()
+                response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
+            lg.QuerySamplesComplete(response)
+
+    def enqueue(self, query_samples):
+        idx = [q.index for q in query_samples]
+        query_id = [q.id for q in query_samples]
+        if len(query_samples) < self.max_batchsize:
+            samples = self.ds.get_samples(idx)
+            self.run_one_item(Item(query_id, idx, samples))
+        else:
+            bs = self.max_batchsize
+            for i in range(0, len(idx), bs):
+                samples = self.ds.get_samples(idx[i : i + bs])
+                self.run_one_item(
+                    Item(query_id[i : i + bs], idx[i : i + bs], samples)
+                )
+
+    def finish(self):
+        pass
+
+
+class QueueRunner(RunnerBase):
+    def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
+        super().__init__(model, ds, threads, post_proc, max_batchsize)
+        self.tasks = Queue(maxsize=threads * 4)
+        self.workers = []
+        self.result_dict = {}
+
+        for _ in range(self.threads):
+            worker = threading.Thread(target=self.handle_tasks, args=(self.tasks,))
+            worker.daemon = True
+            self.workers.append(worker)
+            worker.start()
+
+    def handle_tasks(self, tasks_queue):
+        """Worker thread."""
+        while True:
+            qitem = tasks_queue.get()
+            if qitem is None:
+                # None in the queue indicates the parent want us to exit
+                tasks_queue.task_done()
+                break
+            self.run_one_item(qitem)
+            tasks_queue.task_done()
+
+    def enqueue(self, query_samples):
+        idx = [q.index for q in query_samples]
+        query_id = [q.id for q in query_samples]
+        if len(query_samples) < self.max_batchsize:
+            samples = self.ds.get_samples(idx)
+            self.tasks.put(Item(query_id, idx, samples))
+        else:
+            bs = self.max_batchsize
+            for i in range(0, len(idx), bs):
+                ie = i + bs
+                samples = self.ds.get_samples(idx[i:ie])
+                self.tasks.put(Item(query_id[i:ie], idx[i:ie], samples))
+
+    def finish(self):
+        # exit all threads
+        for _ in self.workers:
+            self.tasks.put(None)
+        for worker in self.workers:
+            worker.join()
+
+
+def main():
+    args = get_args()
+
+    log.info(args)
+
+    # dataset to use
+    dataset_class, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
+    ds = dataset_class(
+        data_path=args.dataset_path,
+        name=args.dataset,
+        in_memory=args.in_memory,
+        layout=args.layout,
+        type=args.dtype,
+        **kwargs,
+    )
+
+    # find backend
+    backend = get_backend(
+        args.backend,
+        type=args.dtype,
+        device=args.device,
+        ckpt_path=args.model_path,
+        batch_size=args.max_batchsize,
+        igbh_dataset=ds.igbh_dataset,
+        layout=args.layout
+    )
+
+    # --count applies to accuracy mode only and can be used to limit the number of images
+    # for testing.
+    count_override = False
+    count = args.count
+    if count:
+        count_override = True
+
+    # load model to backend
+    model = backend.load()
+
+    final_results = {
+        "runtime": model.name(),
+        "version": model.version(),
+        "time": int(time.time()),
+        "args": vars(args),
+        "cmdline": str(args),
+    }
+
+    mlperf_conf = os.path.abspath(args.mlperf_conf)
+    if not os.path.exists(mlperf_conf):
+        log.error("{} not found".format(mlperf_conf))
+        sys.exit(1)
+
+    user_conf = os.path.abspath(args.user_conf)
+    if not os.path.exists(user_conf):
+        log.error("{} not found".format(user_conf))
+        sys.exit(1)
+
+    audit_config = os.path.abspath(args.audit_conf)
+    
+
+    if args.output:
+        output_dir = os.path.abspath(args.output)
+        os.makedirs(output_dir, exist_ok=True)
+        os.chdir(output_dir)
+
+    #
+    # make one pass over the dataset to validate accuracy
+    #
+    count = ds.get_item_count()
+
+    # warmup
+    warmup_samples = torch.Tensor([0]).to(torch.int64).to(backend.device)
+    for i in range(5):
+        _ = backend.predict(warmup_samples)
+
+    scenario = SCENARIO_MAP[args.scenario]
+    runner_map = {
+        lg.TestScenario.SingleStream: RunnerBase,
+        lg.TestScenario.MultiStream: QueueRunner,
+        lg.TestScenario.Server: QueueRunner,
+        lg.TestScenario.Offline: QueueRunner,
+    }
+    runner = runner_map[scenario](
+        model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize
+    )
+
+    def issue_queries(query_samples):
+        runner.enqueue(query_samples)
+
+    def flush_queries():
+        pass
+
+    log_output_settings = lg.LogOutputSettings()
+    log_output_settings.outdir = output_dir
+    log_output_settings.copy_summary_to_stdout = False
+    log_settings = lg.LogSettings()
+    log_settings.enable_trace = args.debug
+    log_settings.log_output = log_output_settings
+
+    settings = lg.TestSettings()
+    settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
+    settings.FromConfig(user_conf, args.model_name, args.scenario)
+    settings.scenario = scenario
+    settings.mode = lg.TestMode.PerformanceOnly
+    if args.accuracy:
+        settings.mode = lg.TestMode.AccuracyOnly
+    if args.find_peak_performance:
+        settings.mode = lg.TestMode.FindPeakPerformance
+
+    if args.time:
+        # override the time we want to run
+        settings.min_duration_ms = args.time * MILLI_SEC
+        settings.max_duration_ms = args.time * MILLI_SEC
+
+    if args.qps:
+        qps = float(args.qps)
+        settings.server_target_qps = qps
+        settings.offline_expected_qps = qps
+
+    if count_override:
+        settings.min_query_count = count
+        settings.max_query_count = count
+
+    if args.samples_per_query:
+        settings.multi_stream_samples_per_query = args.samples_per_query
+    if args.max_latency:
+        settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_expected_latency_ns = int(args.max_latency * NANO_SEC)
+
+    performance_sample_count = (
+        args.performance_sample_count
+        if args.performance_sample_count
+        else min(count, 500)
+    )
+    sut = lg.ConstructSUT(issue_queries, flush_queries)
+    qsl = lg.ConstructQSL(
+        count, performance_sample_count, ds.load_query_samples, ds.unload_query_samples
+    )
+
+    log.info("starting {}".format(scenario))
+    result_dict = {"scenario": str(scenario)}
+    runner.start_run(result_dict, args.accuracy)
+
+    lg.StartTestWithLogSettings(sut, qsl, settings, log_settings, audit_config)
+
+    if args.accuracy:
+        post_proc.finalize(result_dict, ds, output_dir=args.output)
+        final_results["accuracy_results"] = result_dict
+
+    runner.finish()
+    lg.DestroyQSL(qsl)
+    lg.DestroySUT(sut)
+
+    #
+    # write final results
+    #
+    if args.output:
+        with open("results.json", "w") as f:
+            json.dump(final_results, f, sort_keys=True, indent=4)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/upcomming_benchmarks/graph/R-GAT/requirements.txt b/upcomming_benchmarks/graph/R-GAT/requirements.txt
new file mode 100644
index 0000000000..062b710cd8
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/requirements.txt
@@ -0,0 +1,9 @@
+colorama==0.4.6
+tqdm==4.66.4
+requests==2.32.2
+torch==2.1.0
+dgl==2.1.0
+pybind11==2.12.0
+PyYAML==6.0.1
+pydantic==2.7.1
+git+https://github.com/IllinoisGraphBenchmark/IGB-Datasets.git
\ No newline at end of file
diff --git a/upcomming_benchmarks/graph/R-GAT/rgnn.py b/upcomming_benchmarks/graph/R-GAT/rgnn.py
new file mode 100644
index 0000000000..8743fe3097
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/rgnn.py
@@ -0,0 +1,69 @@
+# This script was taken from:
+# https://github.com/mlcommons/training/blob/master/graph_neural_network/dataset.py
+
+import torch
+import torch.nn.functional as F
+
+from torch_geometric.nn import HeteroConv, GATConv, GCNConv, SAGEConv
+from torch_geometric.utils import trim_to_layer
+
+class RGNN(torch.nn.Module):
+  r""" [Relational GNN model](https://arxiv.org/abs/1703.06103).
+
+  Args:
+    etypes: edge types.
+    in_dim: input size.
+    h_dim: Dimension of hidden layer.
+    out_dim: Output dimension.
+    num_layers: Number of conv layers.
+    dropout: Dropout probability for hidden layers.
+    model: "rsage" or "rgat".
+    heads: Number of multi-head-attentions for GAT.
+    node_type: The predict node type for node classification.
+
+  """
+  def __init__(self, etypes, in_dim, h_dim, out_dim, num_layers=2,
+               dropout=0.2, model='rgat', heads=4, node_type=None, with_trim=False):
+    super().__init__()
+    self.node_type = node_type
+    if node_type is not None:
+      self.lin = torch.nn.Linear(h_dim, out_dim)
+
+    self.convs = torch.nn.ModuleList()
+    for i in range(num_layers):
+      in_dim = in_dim if i == 0 else h_dim
+      h_dim = out_dim if (i == (num_layers - 1) and node_type is None) else h_dim
+      if model == 'rsage':
+        self.convs.append(HeteroConv({
+            etype: SAGEConv(in_dim, h_dim, root_weight=False)
+            for etype in etypes}))
+      elif model == 'rgat':
+        self.convs.append(HeteroConv({
+            etype: GATConv(in_dim, h_dim // heads, heads=heads, add_self_loops=False)
+            for etype in etypes}))
+    self.dropout = torch.nn.Dropout(dropout)
+    self.with_trim = with_trim
+
+  def forward(self, x_dict, edge_index_dict, num_sampled_edges_dict=None,
+              num_sampled_nodes_dict=None):
+    for i, conv in enumerate(self.convs):
+      if self.with_trim:
+        x_dict, edge_index_dict, _ = trim_to_layer(
+          layer=i,
+          num_sampled_nodes_per_hop=num_sampled_nodes_dict,
+          num_sampled_edges_per_hop=num_sampled_edges_dict,
+          x=x_dict,
+          edge_index=edge_index_dict
+        )
+      for key in list(edge_index_dict.keys()):
+        if key[0] not in x_dict or key[-1] not in x_dict:
+          del edge_index_dict[key]
+          
+      x_dict = conv(x_dict, edge_index_dict)
+      if i != len(self.convs) - 1:
+        x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()}
+        x_dict = {key: self.dropout(x) for key, x in x_dict.items()}
+    if hasattr(self, 'lin'): # for node classification
+      return self.lin(x_dict[self.node_type])
+    else:
+      return x_dict
\ No newline at end of file
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py b/upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py
new file mode 100644
index 0000000000..252d8458bf
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py
@@ -0,0 +1,120 @@
+# This is a modified version of a script taken from: https://github.com/mlcommons/training/blob/master/graph_neural_network/compress_graph.py
+
+import argparse, datetime, os
+import numpy as np
+import torch
+import os.path as osp
+
+import graphlearn_torch as glt
+
+import sys
+sys.path.append(os.path.dirname(__file__))
+sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir))
+from igbh import float2half
+from igb.download import download_dataset
+from torch_geometric.utils import add_self_loops, remove_self_loops
+from typing import Literal
+
+
+class IGBHeteroDatasetCompress(object):
+  def __init__(self,
+               path,
+               dataset_size,
+               layout: Literal['CSC', 'CSR'] = 'CSC',):
+    self.dir = path
+    self.dataset_size = dataset_size
+    self.layout = layout
+
+    self.ntypes = ['paper', 'author', 'institute', 'fos']
+    self.etypes = None
+    self.edge_dict = {}
+    self.paper_nodes_num = {'tiny':100000, 'small':1000000, 'medium':10000000, 'large':100000000, 'full':269346174}
+    self.author_nodes_num = {'tiny':357041, 'small':1926066, 'medium':15544654, 'large':116959896, 'full':277220883}
+    if not osp.exists(osp.join(path, self.dataset_size, 'processed')):
+      download_dataset(path, 'heterogeneous', dataset_size)
+    self.process()
+
+  def process(self):
+    paper_paper_edges = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
+    'paper__cites__paper', 'edge_index.npy'))).t()
+    author_paper_edges = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
+    'paper__written_by__author', 'edge_index.npy'))).t()
+    affiliation_author_edges = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
+    'author__affiliated_to__institute', 'edge_index.npy'))).t()
+    paper_fos_edges = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
+    'paper__topic__fos', 'edge_index.npy'))).t()
+    paper_published_journal = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
+    'paper__published__journal', 'edge_index.npy'))).t()
+    paper_venue_conference = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
+    'paper__venue__conference', 'edge_index.npy'))).t()
+
+    cites_edge = add_self_loops(remove_self_loops(paper_paper_edges)[0])[0]
+    self.edge_dict = {
+        ('paper', 'cites', 'paper'): (torch.cat([cites_edge[1, :], cites_edge[0, :]]), torch.cat([cites_edge[0, :], cites_edge[1, :]])),
+        ('paper', 'written_by', 'author'): author_paper_edges,
+        ('author', 'affiliated_to', 'institute'): affiliation_author_edges,
+        ('paper', 'topic', 'fos'): paper_fos_edges,
+        ('author', 'rev_written_by', 'paper'): (author_paper_edges[1, :], author_paper_edges[0, :]),
+        ('institute', 'rev_affiliated_to', 'author'): (affiliation_author_edges[1, :], affiliation_author_edges[0, :]),
+        ('fos', 'rev_topic', 'paper'): (paper_fos_edges[1, :], paper_fos_edges[0, :])
+    }
+    self.edge_dict[('paper', 'published', 'journal')] = paper_published_journal
+    self.edge_dict[('paper', 'venue', 'conference')] = paper_venue_conference
+    self.edge_dict[('journal', 'rev_published', 'paper')] = (paper_published_journal[1, :], paper_published_journal[0, :])
+    self.edge_dict[('conference', 'rev_venue', 'paper')] = (paper_venue_conference[1, :], paper_venue_conference[0, :])
+    self.etypes = list(self.edge_dict.keys())
+
+    # init graphlearn_torch Dataset.
+    edge_dir = 'out' if self.layout == 'CSR' else 'in'
+    glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
+    glt_dataset.init_graph(
+      edge_index=self.edge_dict,
+      graph_mode='CPU',
+    )
+
+    # save the corresponding csr or csc file
+    compress_edge_dict = {}
+    compress_edge_dict[('paper', 'cites', 'paper')] = 'paper__cites__paper'
+    compress_edge_dict[('paper', 'written_by', 'author')] = 'paper__written_by__author'
+    compress_edge_dict[('author', 'affiliated_to', 'institute')] = 'author__affiliated_to__institute'
+    compress_edge_dict[('paper', 'topic', 'fos')] = 'paper__topic__fos'
+    compress_edge_dict[('author', 'rev_written_by', 'paper')] = 'author__rev_written_by__paper'
+    compress_edge_dict[('institute', 'rev_affiliated_to', 'author')] = 'institute__rev_affiliated_to__author'
+    compress_edge_dict[('fos', 'rev_topic', 'paper')] = 'fos__rev_topic__paper'
+    compress_edge_dict[('paper', 'published', 'journal')] = 'paper__published__journal'
+    compress_edge_dict[('paper', 'venue', 'conference')] = 'paper__venue__conference'
+    compress_edge_dict[('journal', 'rev_published', 'paper')] = 'journal__rev_published__paper'
+    compress_edge_dict[('conference', 'rev_venue', 'paper')] = 'conference__rev_venue__paper'
+
+    for etype in self.etypes:
+      graph = glt_dataset.get_graph(etype)
+      indptr, indices, _ = graph.export_topology()
+      path = os.path.join(self.dir, self.dataset_size, 'processed', self.layout, compress_edge_dict[etype])
+      if not os.path.exists(path):
+        os.makedirs(path)
+      torch.save(indptr, os.path.join(path, 'indptr.pt'))
+      torch.save(indices, os.path.join(path, 'indices.pt'))
+    path = os.path.join(self.dir, self.dataset_size, 'processed', self.layout)
+    print(f"The {self.layout} graph has been persisted in path: {path}")
+
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  root = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.realpath(__file__)))), 'data', 'igbh')
+  glt.utils.ensure_dir(root)
+  parser.add_argument('--path', type=str, default=root,
+      help='path containing the datasets')
+  parser.add_argument('--dataset_size', type=str, default='full',
+      choices=['tiny', 'small', 'medium', 'large', 'full'],
+      help='size of the datasets')
+  parser.add_argument("--layout", type=str, default='CSC')
+  parser.add_argument('--use_fp16', action="store_true",
+    help="convert the node/edge feature into fp16 format")
+  args = parser.parse_args()
+  print(f"Start constructing the {args.layout} graph...")
+  igbh_dataset = IGBHeteroDatasetCompress(args.path, args.dataset_size, args.layout)
+  if args.use_fp16:
+    base_path = osp.join(args.path, args.dataset_size, 'processed')
+    float2half(base_path, args.dataset_size)
+  
\ No newline at end of file
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/download_igbh_full.sh b/upcomming_benchmarks/graph/R-GAT/tools/download_igbh_full.sh
new file mode 100644
index 0000000000..7c9d9def8c
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/tools/download_igbh_full.sh
@@ -0,0 +1,112 @@
+#!/bin/bash
+# This script was taken from: https://github.com/IllinoisGraphBenchmark/IGB-Datasets/blob/main/igb/download_igbh600m.sh
+# Copy of licence for this ONLY this script
+# MIT License
+
+# Copyright (c) 2022 IMPACT Research Group and IllinoisGraphBenchmark
+
+# This license is applicable for all software codebases provided in the repository.
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+echo "IGBH600M download starting"
+mkdir -p $1/full/processed
+cd $1/full/processed
+
+# paper
+mkdir paper
+cd paper
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper/node_feat.npy
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper/node_label_19.npy
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper/node_label_2K.npy
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper/paper_id_index_mapping.npy
+cd ..
+
+# paper__cites__paper
+mkdir paper__cites__paper
+cd paper__cites__paper
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper__cites__paper/edge_index.npy
+cd ..
+
+# author
+mkdir author
+cd author
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/author/author_id_index_mapping.npy
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/author/node_feat.npy
+cd ..
+
+# conference
+mkdir conference
+cd conference
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/conference/conference_id_index_mapping.npy
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/conference/node_feat.npy
+cd ..
+
+# institute
+mkdir institute
+cd institute
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/institute/institute_id_index_mapping.npy
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/institute/node_feat.npy
+cd ..
+
+# journal
+mkdir journal
+cd journal
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/journal/journal_id_index_mapping.npy
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/journal/node_feat.npy
+cd ..
+
+# fos
+mkdir fos
+cd fos
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/fos/fos_id_index_mapping.npy
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/fos/node_feat.npy
+cd ..
+
+# author__affiliated_to__institute
+mkdir author__affiliated_to__institute
+cd author__affiliated_to__institute
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/author__affiliated_to__institute/edge_index.npy
+cd ..
+
+# paper__published__journal
+mkdir paper__published__journal
+cd paper__published__journal
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper__published__journal/edge_index.npy
+cd ..
+
+# paper__topic__fos
+mkdir paper__topic__fos
+cd paper__topic__fos
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper__topic__fos/edge_index.npy
+cd ..
+
+# paper__venue__conference
+mkdir paper__venue__conference
+cd paper__venue__conference
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper__venue__conference/edge_index.npy
+cd ..
+
+# paper__written_by__author
+mkdir paper__written_by__author
+cd paper__written_by__author
+wget -c https://igb-public.s3.us-east-2.amazonaws.com/IGBH/processed/paper__written_by__author/edge_index.npy
+cd ..
+
+echo "IGBH-IGBH download complete"
\ No newline at end of file
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/download_igbh_test.py b/upcomming_benchmarks/graph/R-GAT/tools/download_igbh_test.py
new file mode 100644
index 0000000000..bd39b186ab
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/tools/download_igbh_test.py
@@ -0,0 +1,38 @@
+import argparse
+import os
+from igb import download
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--target-path",
+        type=str,
+        default="igbh/",
+        help="Download path for the dataset",
+    )
+    parser.add_argument(
+        "--dataset-size",
+        type=str,
+        default="tiny",
+        choices=["tiny", "small", "medium"],
+        help="Size of the dataset, only full for official submissions",
+    )
+    parser.add_argument(
+        "--dataset-type",
+        type=str,
+        default="heterogeneous",
+        choices=["homogeneous", "heterogeneous"],
+    )
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = get_args()
+    os.makedirs(args.target_path, exist_ok=True)
+    download.download_dataset(
+        path=args.target_path,
+        dataset_size=args.dataset_size,
+        dataset_type=args.dataset_type,
+    )
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/format_model.py b/upcomming_benchmarks/graph/R-GAT/tools/format_model.py
new file mode 100644
index 0000000000..b7cd50507f
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/tools/format_model.py
@@ -0,0 +1,86 @@
+from typing import Literal
+import torch
+import os, sys
+sys.path.append(os.path.dirname(__file__))
+sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir))
+from rgnn import RGNN
+from igbh import IGBHeteroDataset, IGBH
+import graphlearn_torch as glt
+
+from graphlearn_torch.loader import NodeLoader
+
+from graphlearn_torch.data import Dataset
+from graphlearn_torch.sampler import NeighborSampler, NodeSamplerInput
+from graphlearn_torch.typing import InputNodes, NumNeighbors
+
+from collections import OrderedDict
+
+
+
+class Formatter:
+    def __init__(self,
+            model_type="rgat",
+            type: Literal["fp16", "fp32"] = "fp16",
+            device: Literal["cpu", "gpu"] = "gpu",
+            ckpt_path: str = None,
+            igbh_dataset: IGBHeteroDataset = None,
+            batch_size: int = 1,
+            layout: Literal["CSC", "CSR", "COO"] = "COO",
+            edge_dir: str = "in",
+        ) -> None:
+        if device == "gpu":
+            self.device = torch.device("cuda")
+        else:
+            self.device = torch.device("cpu")
+
+        if type == "fp32":
+            self.type = torch.float32
+        else:
+            self.type = torch.float16
+        # Create Node and neighbor loade
+        self.glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
+        self.glt_dataset.init_node_features(
+            node_feature_data=igbh_dataset.feat_dict, with_gpu=(device == "gpu"), dtype=self.type
+        )
+        self.glt_dataset.init_graph(
+            edge_index=igbh_dataset.edge_dict,
+            layout=layout,
+            graph_mode="ZERO_COPY" if (device == "gpu") else "CPU",
+        )
+        self.glt_dataset.init_node_labels(node_label_data={"paper": igbh_dataset.label})
+
+        self.model = RGNN(
+            self.glt_dataset.get_edge_types(),
+            self.glt_dataset.node_features["paper"].shape[1],
+            512,
+            2983,
+            num_layers=3,
+            dropout=0.2,
+            model=model_type,
+            heads=4,
+            node_type="paper",
+        ).to(self.type).to(self.device)
+        self.model.eval()
+        ckpt = None
+        if ckpt_path is not None:
+            try:
+                ckpt = torch.load(ckpt_path, map_location=self.device)
+            except FileNotFoundError as e:
+                print(f"Checkpoint file not found: {e}")
+                return -1
+        if ckpt is not None:
+            formatted_ckpt = OrderedDict()
+            formatted_ckpt["model_state_dict"] = OrderedDict()
+            for k in ckpt["model_state_dict"]:
+                if "lin_dst" in k:
+                    pass
+                elif "lin_src" in k:
+                    formatted_ckpt["model_state_dict"][str(k).replace("lin_src", "lin")] = ckpt["model_state_dict"][k]
+                else:
+                    formatted_ckpt["model_state_dict"][k] = ckpt["model_state_dict"][k]
+            self.model.load_state_dict(formatted_ckpt["model_state_dict"])
+            torch.save({'model_state_dict': self.model.state_dict()}, "model/FULL_model_seq_69294_formatted.ckpt")
+
+if __name__ == "__main__":
+    igbh = IGBHeteroDataset("igbh", use_label_2K=True)
+    f = Formatter(igbh_dataset=igbh, ckpt_path="model/FULL_model_seq_69294.ckpt", device="cpu")
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py b/upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py
new file mode 100644
index 0000000000..9dcfe71687
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py
@@ -0,0 +1,61 @@
+# Script taken from https://github.com/mlcommons/training/blob/master/graph_neural_network/split_seeds.py
+
+import argparse
+import os.path as osp
+import torch
+
+class SeedSplitter(object):
+  def __init__(self,
+               path,
+               dataset_size='tiny',
+               use_label_2K=True,
+               random_seed=42,
+               validation_frac=0.01):
+    self.path = path
+    self.dataset_size = dataset_size
+    self.use_label_2K = use_label_2K
+    self.random_seed = random_seed
+    self.validation_frac = validation_frac
+    self.paper_nodes_num = {'tiny':100000, 'small':1000000, 'medium':10000000, 'large':100000000, 'full':269346174}
+    self.process()
+  
+  def process(self):
+    torch.manual_seed(self.random_seed)
+    n_labeled_idx = self.paper_nodes_num[self.dataset_size]
+    if self.dataset_size == 'full':
+      if self.use_label_2K:
+          n_labeled_idx = 157675969
+      else:
+          n_labeled_idx = 227130858
+
+    shuffled_index = torch.randperm(n_labeled_idx)
+    n_train = int(n_labeled_idx * 0.6)
+    n_val = int(n_labeled_idx * self.validation_frac)
+
+    train_idx = shuffled_index[:n_train]
+    val_idx = shuffled_index[n_train : n_train + n_val]
+
+    path = osp.join(self.path, self.dataset_size, 'processed')
+    torch.save(train_idx, osp.join(path, 'train_idx.pt'))
+    torch.save(val_idx, osp.join(path, 'val_idx.pt'))
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  root = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.realpath(__file__)))), 'data', 'igbh')
+  parser.add_argument('--path', type=str, default=root,
+      help='path containing the datasets')
+  parser.add_argument('--dataset_size', type=str, default='full',
+      choices=['tiny', 'small', 'medium', 'large', 'full'],
+      help='size of the datasets')
+  parser.add_argument("--random_seed", type=int, default='42')
+  parser.add_argument('--num_classes', type=int, default=2983,
+      choices=[19, 2983], help='number of classes')
+  parser.add_argument("--validation_frac", type=float, default=0.005,
+      help="Fraction of labeled vertices to be used for validation.")
+  
+  args = parser.parse_args()
+  splitter = SeedSplitter(path=args.path,
+                          dataset_size=args.dataset_size,
+                          use_label_2K=(args.num_classes==2983),
+                          random_seed=args.random_seed,
+                          validation_frac=args.validation_frac)
\ No newline at end of file
diff --git a/upcomming_benchmarks/graph/R-GAT/user.conf b/upcomming_benchmarks/graph/R-GAT/user.conf
new file mode 100644
index 0000000000..07a10bbe21
--- /dev/null
+++ b/upcomming_benchmarks/graph/R-GAT/user.conf
@@ -0,0 +1,4 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds

From 70d667838a94337eafcab046410bf53e361b1da0 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Fri, 7 Jun 2024 17:54:47 +0100
Subject: [PATCH 098/305] Updated the docs (#1724)

* Support batch-size in llama2 run

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-Cloudflare download instructiosn to README.md

* Minor wording edit to README.md

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-GDrive download instructions to README.md

* Add new and old instructions to README.md

* Tweak language in README.md

* Language tweak in README.md

* Minor language tweak in README.md

* Fix typo in README.md

* Count error when logging errors: submission_checker.py

* Fixes #1648, restrict loadgen uncommitted error message to within the loadgen directory

* Update test-rnnt.yml (#1688)

Stopping the github action for rnnt

* Added docs init

Added github action for website publish

Update benchmark documentation

Update publish.yaml

Update publish.yaml

Update benchmark documentation

Improved the submission documentation

Fix taskname

Removed unused images

* Fix benchmark URLs

* Fix links

* Add _full variation to run commands

* Added script flow diagram

* Added docker setup command for CM, extra run options

* Added support for docker options in the docs

* Added --quiet to the CM run_cmds in docs

* Fix the test query count for cm commands

* Support ctuning-cpp implementation

* Added commands for mobilenet models

* Docs cleanup

* Docs cleanup

* Added separate files for dataset and models in the docs

* Remove redundant tab in the docs

* Fixes some WIP models in the docs

* Use the official docs page for CM installation

* Fix the deadlink in docs

* Fix indendation issue in docs

* Added dockerinfo for nvidia implementation

* Added run options for gptj

* Added execution environment tabs

* Cleanup of the docs

* Cleanup of the docs

* Reordered the sections of the docs page

* Removed an unnecessary heading in the docs

* Fixes the commands for datacenter

---------

Co-authored-by: Nathan Wasson <nathanw@mlcommons.org>
Co-authored-by: anandhu-eng <anandhukicks@gmail.com>
---
 .../image_classification/get-resnet50-data.md |  43 ++
 .../image_classification/mobilenets.md        |  59 +++
 .../image_classification/resnet50.md          |  54 +--
 docs/benchmarks/language/bert.md              |  45 +-
 docs/benchmarks/language/get-bert-data.md     |  38 ++
 docs/benchmarks/language/get-gptj-data.md     |  25 ++
 .../language/get-llama2-70b-data.md           |  26 ++
 docs/benchmarks/language/gpt-j.md             |  38 +-
 docs/benchmarks/language/llama2-70b.md        |  30 +-
 docs/benchmarks/medical_imaging/3d-unet.md    |  53 +--
 .../medical_imaging/get-3d-unet-data.md       |  38 ++
 .../object_detection/get-retinanet-data.md    |  38 ++
 docs/benchmarks/object_detection/retinanet.md |  49 +-
 docs/benchmarks/recommendation/dlrm-v2.md     |  36 +-
 .../recommendation/get-dlrm_v2-data.md        |  25 ++
 .../benchmarks/text_to_image/get-sdxl-data.md |  26 ++
 docs/benchmarks/text_to_image/sdxl.md         |  34 +-
 docs/install/index.md                         |  96 +---
 docs/submission/index.md                      |   2 +-
 docs/submission/tools-readme.md               |   1 -
 main.py                                       | 423 ++++++++++++------
 mkdocs.yml                                    |   3 -
 22 files changed, 667 insertions(+), 515 deletions(-)
 create mode 100644 docs/benchmarks/image_classification/get-resnet50-data.md
 create mode 100644 docs/benchmarks/image_classification/mobilenets.md
 create mode 100644 docs/benchmarks/language/get-bert-data.md
 create mode 100644 docs/benchmarks/language/get-gptj-data.md
 create mode 100644 docs/benchmarks/language/get-llama2-70b-data.md
 create mode 100644 docs/benchmarks/medical_imaging/get-3d-unet-data.md
 create mode 100644 docs/benchmarks/object_detection/get-retinanet-data.md
 create mode 100644 docs/benchmarks/recommendation/get-dlrm_v2-data.md
 create mode 100644 docs/benchmarks/text_to_image/get-sdxl-data.md
 delete mode 120000 docs/submission/tools-readme.md

diff --git a/docs/benchmarks/image_classification/get-resnet50-data.md b/docs/benchmarks/image_classification/get-resnet50-data.md
new file mode 100644
index 0000000000..d1f83ae9a9
--- /dev/null
+++ b/docs/benchmarks/image_classification/get-resnet50-data.md
@@ -0,0 +1,43 @@
+# Image Classification using ResNet50 
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    ResNet50 validation run uses the Imagenet 2012 validation dataset consisting of 50,000 images.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,imagenet,validation -j
+    ```
+=== "Calibration"
+    ResNet50 calibration dataset consist of 500 images selected from the Imagenet 2012 validation dataset. There are 2 alternative options for the calibration dataset.
+
+    ### Get Calibration Dataset Using Option 1
+    ```
+    cm run script --tags=get,dataset,imagenet,calibration,_mlperf.option1 -j
+    ```
+    ### Get Calibration Dataset Using Option 2
+    ```
+    cm run script --tags=get,dataset,imagenet,calibration,_mlperf.option2 -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf ResNet50 Model
+
+=== "Tensorflow"
+
+    ### Tensorflow
+    ```
+    cm run script --tags=get,ml-model,resnet50,_tensorflow -j
+    ```
+=== "Onnx"
+
+    ### Onnx
+    ```
+    cm run script --tags=get,ml-model,resnet50,_onnx -j
+    ```
+
diff --git a/docs/benchmarks/image_classification/mobilenets.md b/docs/benchmarks/image_classification/mobilenets.md
new file mode 100644
index 0000000000..1907c2a415
--- /dev/null
+++ b/docs/benchmarks/image_classification/mobilenets.md
@@ -0,0 +1,59 @@
+# Image Classification using Mobilenet models
+
+Mobilenet models are not official MLPerf models and so cannot be used for a Closed division MLPerf inference submission. But since they can be run with Imagenet dataset, we are allowed to use them for Open division submission. Only CPU runs are supported now. 
+
+## TFLite Backend
+
+=== "Mobilenet-V1"
+    ### Mobilenet V1
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v1 --adr.compiler.tags=gcc
+    ```
+=== "Mobilenet-V2"
+    ### Mobilenet V2
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v2 --adr.compiler.tags=gcc
+    ```
+=== "Mobilenet-V2"
+    ### Mobilenet V2
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v2 --adr.compiler.tags=gcc
+    ```
+=== "Mobilenets"
+    ### Mobilenet V1,V2,V3
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_mobilenet --adr.compiler.tags=gcc
+    ```
+=== "Efficientnet"
+    ### Efficientnet
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_efficientnet --adr.compiler.tags=gcc
+    ```
+
+## ARMNN Backend
+=== "Mobilenet-V1"
+    ### Mobilenet V1
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v1 --adr.compiler.tags=gcc
+    ```
+=== "Mobilenet-V2"
+    ### Mobilenet V2
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 --adr.compiler.tags=gcc
+    ```
+=== "Mobilenet-V2"
+    ### Mobilenet V2
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 --adr.compiler.tags=gcc
+    ```
+=== "Mobilenets"
+    ### Mobilenet V1,V2,V3
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet --adr.compiler.tags=gcc
+    ```
+=== "Efficientnet"
+    ### Efficientnet
+    ```bash
+       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_efficientnet --adr.compiler.tags=gcc
+    ```
+
diff --git a/docs/benchmarks/image_classification/resnet50.md b/docs/benchmarks/image_classification/resnet50.md
index 1a77db65ac..26875258dc 100644
--- a/docs/benchmarks/image_classification/resnet50.md
+++ b/docs/benchmarks/image_classification/resnet50.md
@@ -1,68 +1,26 @@
 # Image Classification using ResNet50 
 
-## Dataset
-
-The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
-
-=== "Validation"
-    ResNet50 validation run uses the Imagenet 2012 validation dataset consisting of 50,000 images.
-
-    ### Get Validation Dataset
-    ```
-    cm run script --tags=get,dataset,imagenet,validation -j
-    ```
-=== "Calibration"
-    ResNet50 calibration dataset consist of 500 images selected from the Imagenet 2012 validation dataset. There are 2 alternative options for the calibration dataset.
-
-    ### Get Calibration Dataset Using Option 1
-    ```
-    cm run script --tags=get,dataset,imagenet,calibration,_mlperf.option1 -j
-    ```
-    ### Get Calibration Dataset Using Option 2
-    ```
-    cm run script --tags=get,dataset,imagenet,calibration,_mlperf.option2 -j
-    ```
-
-## Model
-The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
-
-Get the Official MLPerf ResNet50 Model
-
-=== "Tensorflow"
-
-    ### Tensorflow
-    ```
-    cm run script --tags=get,ml-model,resnet50,_tensorflow -j
-    ```
-=== "Onnx"
-
-    ### Onnx
-    ```
-    cm run script --tags=get,ml-model,resnet50,_onnx -j
-    ```
-
-## Benchmark Implementations
 === "MLCommons-Python"
-    ### MLPerf Reference Implementation in Python
+    ## MLPerf Reference Implementation in Python
     
 {{ mlperf_inference_implementation_readme (4, "resnet50", "reference") }}
 
 === "Nvidia"
-    ### Nvidia MLPerf Implementation
+    ## Nvidia MLPerf Implementation
     
 {{ mlperf_inference_implementation_readme (4, "resnet50", "nvidia") }}
 
 === "Intel"
-    ### Intel MLPerf Implementation
+    ## Intel MLPerf Implementation
     
 {{ mlperf_inference_implementation_readme (4, "resnet50", "intel") }}
 
 === "Qualcomm"
-    ### Qualcomm AI100 MLPerf Implementation
+    ## Qualcomm AI100 MLPerf Implementation
     
 {{ mlperf_inference_implementation_readme (4, "resnet50", "qualcomm") }}
 
-=== "MLCommon-C++"
-    ### MLPerf Modular Implementation in C++
+=== "MLCommons-C++"
+    ## MLPerf Modular Implementation in C++
     
 {{ mlperf_inference_implementation_readme (4, "resnet50", "cpp") }}
diff --git a/docs/benchmarks/language/bert.md b/docs/benchmarks/language/bert.md
index e2aa0995dc..782340b3d9 100644
--- a/docs/benchmarks/language/bert.md
+++ b/docs/benchmarks/language/bert.md
@@ -1,44 +1,7 @@
 # Question Answering using Bert-Large
 
-## Dataset
-
-The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
-
-=== "Validation"
-    BERT validation run uses the SQuAD v1.1 dataset.
-
-    ### Get Validation Dataset
-    ```
-    cm run script --tags=get,dataset,squad,validation -j
-    ```
-
-## Model
-The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
-
-Get the Official MLPerf Bert-Large Model
-
-=== "Pytorch"
-
-    ### Pytorch
-    ```
-    cm run script --tags=get,ml-model,bert-large,_pytorch -j
-    ```
-=== "Onnx"
-
-    ### Onnx
-    ```
-    cm run script --tags=get,ml-model,bert-large,_onnx -j
-    ```
-=== "Tensorflow"
-
-    ### Tensorflow
-    ```
-    cm run script --tags=get,ml-model,bert-large,_tensorflow -j
-    ```
-
-## Benchmark Implementations
 === "MLCommons-Python"
-    ### MLPerf Reference Implementation in Python
+    ## MLPerf Reference Implementation in Python
     
     BERT-99
 {{ mlperf_inference_implementation_readme (4, "bert-99", "reference") }}
@@ -47,7 +10,7 @@ Get the Official MLPerf Bert-Large Model
 {{ mlperf_inference_implementation_readme (4, "bert-99.9", "reference") }}
 
 === "Nvidia"
-    ### Nvidia MLPerf Implementation
+    ## Nvidia MLPerf Implementation
     
     BERT-99
 {{ mlperf_inference_implementation_readme (4, "bert-99", "nvidia") }}
@@ -56,7 +19,7 @@ Get the Official MLPerf Bert-Large Model
 {{ mlperf_inference_implementation_readme (4, "bert-99.9", "nvidia") }}
 
 === "Intel"
-    ### Intel MLPerf Implementation
+    ## Intel MLPerf Implementation
     BERT-99
 {{ mlperf_inference_implementation_readme (4, "bert-99", "intel") }}
 
@@ -64,7 +27,7 @@ Get the Official MLPerf Bert-Large Model
 {{ mlperf_inference_implementation_readme (4, "bert-99.9", "intel") }}
 
 === "Qualcomm"
-    ### Qualcomm AI100 MLPerf Implementation
+    ## Qualcomm AI100 MLPerf Implementation
 
     BERT-99
 {{ mlperf_inference_implementation_readme (4, "bert-99", "qualcomm") }}
diff --git a/docs/benchmarks/language/get-bert-data.md b/docs/benchmarks/language/get-bert-data.md
new file mode 100644
index 0000000000..f5462b181e
--- /dev/null
+++ b/docs/benchmarks/language/get-bert-data.md
@@ -0,0 +1,38 @@
+# Question Answering using Bert-Large
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    BERT validation run uses the SQuAD v1.1 dataset.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,squad,validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf Bert-Large Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,bert-large,_pytorch -j
+    ```
+=== "Onnx"
+
+    ### Onnx
+    ```
+    cm run script --tags=get,ml-model,bert-large,_onnx -j
+    ```
+=== "Tensorflow"
+
+    ### Tensorflow
+    ```
+    cm run script --tags=get,ml-model,bert-large,_tensorflow -j
+    ```
+
diff --git a/docs/benchmarks/language/get-gptj-data.md b/docs/benchmarks/language/get-gptj-data.md
new file mode 100644
index 0000000000..9ea31feb47
--- /dev/null
+++ b/docs/benchmarks/language/get-gptj-data.md
@@ -0,0 +1,25 @@
+# Text Summarization using GPT-J
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    GPT-J validation run uses the CNNDM dataset.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,cnndm,validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf GPT-J Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,gptj,_pytorch -j
+    ```
diff --git a/docs/benchmarks/language/get-llama2-70b-data.md b/docs/benchmarks/language/get-llama2-70b-data.md
new file mode 100644
index 0000000000..4b04f7068b
--- /dev/null
+++ b/docs/benchmarks/language/get-llama2-70b-data.md
@@ -0,0 +1,26 @@
+# Text Summarization using LLAMA2-70b
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    LLAMA2-70b validation run uses the Open ORCA dataset.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,openorca,validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf LLAMA2-70b Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,llama2-70b,_pytorch -j
+    ```
+
diff --git a/docs/benchmarks/language/gpt-j.md b/docs/benchmarks/language/gpt-j.md
index d1c351214c..2eefbbc79e 100644
--- a/docs/benchmarks/language/gpt-j.md
+++ b/docs/benchmarks/language/gpt-j.md
@@ -1,57 +1,39 @@
 # Text Summarization using GPT-J
 
-## Dataset
 
-The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
-
-=== "Validation"
-    GPT-J validation run uses the CNNDM dataset.
-
-    ### Get Validation Dataset
-    ```
-    cm run script --tags=get,dataset,cnndm,validation -j
-    ```
-
-## Model
-The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
-
-Get the Official MLPerf GPT-J Model
-
-=== "Pytorch"
-
-    ### Pytorch
-    ```
-    cm run script --tags=get,ml-model,gptj,_pytorch -j
-    ```
-
-## Benchmark Implementations
 === "MLCommons-Python"
-    ### MLPerf Reference Implementation in Python
+    ## MLPerf Reference Implementation in Python
     
     GPT-J-99
+
 {{ mlperf_inference_implementation_readme (4, "gptj-99", "reference") }}
 
     GPTJ-99.9
+
 {{ mlperf_inference_implementation_readme (4, "gptj-99.9", "reference") }}
 
 === "Nvidia"
-    ### Nvidia MLPerf Implementation
+    ## Nvidia MLPerf Implementation
     
     GPTJ-99
+
 {{ mlperf_inference_implementation_readme (4, "gptj-99", "nvidia") }}
 
     GPTJ-99.9
+
 {{ mlperf_inference_implementation_readme (4, "gptj-99.9", "nvidia") }}
 
 === "Intel"
-    ### Intel MLPerf Implementation
+    ## Intel MLPerf Implementation
     GPTJ-99
+
 {{ mlperf_inference_implementation_readme (4, "gptj-99", "intel") }}
 
 
 === "Qualcomm"
-    ### Qualcomm AI100 MLPerf Implementation
+    ## Qualcomm AI100 MLPerf Implementation
 
     GPTJ-99
+
 {{ mlperf_inference_implementation_readme (4, "gptj-99", "qualcomm") }}
 
diff --git a/docs/benchmarks/language/llama2-70b.md b/docs/benchmarks/language/llama2-70b.md
index 7f8052aef5..f1785dcb35 100644
--- a/docs/benchmarks/language/llama2-70b.md
+++ b/docs/benchmarks/language/llama2-70b.md
@@ -1,32 +1,8 @@
 # Text Summarization using LLAMA2-70b
 
-## Dataset
 
-The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
-
-=== "Validation"
-    LLAMA2-70b validation run uses the Open ORCA dataset.
-
-    ### Get Validation Dataset
-    ```
-    cm run script --tags=get,dataset,openorca,validation -j
-    ```
-
-## Model
-The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
-
-Get the Official MLPerf LLAMA2-70b Model
-
-=== "Pytorch"
-
-    ### Pytorch
-    ```
-    cm run script --tags=get,ml-model,llama2-70b,_pytorch -j
-    ```
-
-## Benchmark Implementations
 === "MLCommons-Python"
-    ### MLPerf Reference Implementation in Python
+    ## MLPerf Reference Implementation in Python
     
     LLAMA2-70b-99
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "reference") }}
@@ -35,7 +11,7 @@ Get the Official MLPerf LLAMA2-70b Model
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "reference") }}
 
 === "Nvidia"
-    ### Nvidia MLPerf Implementation
+    ## Nvidia MLPerf Implementation
     
     LLAMA2-70b-99
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "nvidia") }}
@@ -45,7 +21,7 @@ Get the Official MLPerf LLAMA2-70b Model
 
 
 === "Qualcomm"
-    ### Qualcomm AI100 MLPerf Implementation
+    ## Qualcomm AI100 MLPerf Implementation
 
     LLAMA2-70b-99
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "qualcomm") }}
diff --git a/docs/benchmarks/medical_imaging/3d-unet.md b/docs/benchmarks/medical_imaging/3d-unet.md
index bd3ccae406..b58ea7f2e7 100644
--- a/docs/benchmarks/medical_imaging/3d-unet.md
+++ b/docs/benchmarks/medical_imaging/3d-unet.md
@@ -1,60 +1,33 @@
 # Medical Imaging using 3d-unet (KiTS 2019 kidney tumor segmentation task)
 
-## Dataset
 
-The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
-
-=== "Validation"
-    3d-unet validation run uses the KiTS19 dataset performing [KiTS 2019](https://kits19.grand-challenge.org/) kidney tumor segmentation task
-
-    ### Get Validation Dataset
-    ```
-    cm run script --tags=get,dataset,kits19,validation -j
-    ```
-
-## Model
-The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
-
-Get the Official MLPerf 3d-unet Model
-
-=== "Pytorch"
-
-    ### Pytorch
-    ```
-    cm run script --tags=get,ml-model,3d-unet,_pytorch -j
-    ```
-=== "Onnx"
+=== "MLCommons-Python"
+    ## MLPerf Reference Implementation in Python
 
-    ### Onnx
-    ```
-    cm run script --tags=get,ml-model,3d-unet,_onnx -j
-    ```
-=== "Tensorflow"
+    3d-unet-99
 
-    ### Tensorflow
-    ```
-    cm run script --tags=get,ml-model,3d-unet,_tensorflow -j
-    ```
+{{ mlperf_inference_implementation_readme (4, "3d-unet-99", "reference") }}
 
-## Benchmark Implementations
-=== "MLCommons-Python"
-    ### MLPerf Reference Implementation in Python
+    3d-unet-99.9
 
-    3d-unet-99.9    
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99.9", "reference") }}
 
 === "Nvidia"
-    ### Nvidia MLPerf Implementation
-    3d-unet-99 
+    ## Nvidia MLPerf Implementation
+    3d-unet-99
+
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99", "nvidia") }}
 
-    3d-unet-99.9 
+    3d-unet-99.9
+
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99.9", "nvidia") }}
 
 === "Intel"
-    ### Intel MLPerf Implementation
+    ## Intel MLPerf Implementation
     3d-unet-99
+
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99", "intel") }}
 
     3d-unet-99.9
+
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99.9", "intel") }}
diff --git a/docs/benchmarks/medical_imaging/get-3d-unet-data.md b/docs/benchmarks/medical_imaging/get-3d-unet-data.md
new file mode 100644
index 0000000000..efc6ce6ed8
--- /dev/null
+++ b/docs/benchmarks/medical_imaging/get-3d-unet-data.md
@@ -0,0 +1,38 @@
+# Medical Imaging using 3d-unet (KiTS 2019 kidney tumor segmentation task)
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    3d-unet validation run uses the KiTS19 dataset performing [KiTS 2019](https://kits19.grand-challenge.org/) kidney tumor segmentation task
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,kits19,validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf 3d-unet Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,3d-unet,_pytorch -j
+    ```
+=== "Onnx"
+
+    ### Onnx
+    ```
+    cm run script --tags=get,ml-model,3d-unet,_onnx -j
+    ```
+=== "Tensorflow"
+
+    ### Tensorflow
+    ```
+    cm run script --tags=get,ml-model,3d-unet,_tensorflow -j
+    ```
+
diff --git a/docs/benchmarks/object_detection/get-retinanet-data.md b/docs/benchmarks/object_detection/get-retinanet-data.md
new file mode 100644
index 0000000000..f2d4322104
--- /dev/null
+++ b/docs/benchmarks/object_detection/get-retinanet-data.md
@@ -0,0 +1,38 @@
+# Object Detection using Retinanet
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    Retinanet validation run uses the OpenImages v6 MLPerf validation dataset resized to 800x800 and consisting of 24,576 images.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,openimages,_validation -j
+    ```
+=== "Calibration"
+    Retinanet calibration dataset consist of 500 images selected from the OpenImages v6 dataset.
+
+    ```
+    cm run script --tags=get,dataset,openimages,_calibration -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf Retinanet Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,retinanet,_pytorch -j
+    ```
+=== "Onnx"
+
+    ### Onnx
+    ```
+    cm run script --tags=get,ml-model,retinanet,_onnx -j
+    ```
+
diff --git a/docs/benchmarks/object_detection/retinanet.md b/docs/benchmarks/object_detection/retinanet.md
index f500f616d5..383a2ec1b8 100644
--- a/docs/benchmarks/object_detection/retinanet.md
+++ b/docs/benchmarks/object_detection/retinanet.md
@@ -1,63 +1,26 @@
 # Object Detection using Retinanet
 
-## Dataset
-
-The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
-
-=== "Validation"
-    Retinanet validation run uses the OpenImages v6 MLPerf validation dataset resized to 800x800 and consisting of 24,576 images.
-
-    ### Get Validation Dataset
-    ```
-    cm run script --tags=get,dataset,openimages,_validation -j
-    ```
-=== "Calibration"
-    Retinanet calibration dataset consist of 500 images selected from the OpenImages v6 dataset.
-
-    ```
-    cm run script --tags=get,dataset,openimages,_calibration -j
-    ```
-
-## Model
-The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
-
-Get the Official MLPerf Retinanet Model
-
-=== "Pytorch"
-
-    ### Pytorch
-    ```
-    cm run script --tags=get,ml-model,retinanet,_pytorch -j
-    ```
-=== "Onnx"
-
-    ### Onnx
-    ```
-    cm run script --tags=get,ml-model,retinanet,_onnx -j
-    ```
-
-## Benchmark Implementations
 === "MLCommons-Python"
-    ### MLPerf Reference Implementation in Python
+    ## MLPerf Reference Implementation in Python
     
 {{ mlperf_inference_implementation_readme (4, "retinanet", "reference") }}
 
 === "Nvidia"
-    ### Nvidia MLPerf Implementation
+    ## Nvidia MLPerf Implementation
     
 {{ mlperf_inference_implementation_readme (4, "retinanet", "nvidia") }}
 
 === "Intel"
-    ### Intel MLPerf Implementation
+    ## Intel MLPerf Implementation
     
 {{ mlperf_inference_implementation_readme (4, "retinanet", "intel") }}
 
 === "Qualcomm"
-    ### Qualcomm AI100 MLPerf Implementation
+    ## Qualcomm AI100 MLPerf Implementation
     
 {{ mlperf_inference_implementation_readme (4, "retinanet", "qualcomm") }}
 
-=== "MLCommon-C++"
-    ### MLPerf Modular Implementation in C++
+=== "MLCommons-C++"
+    ## MLPerf Modular Implementation in C++
     
 {{ mlperf_inference_implementation_readme (4, "retinanet", "cpp") }}
diff --git a/docs/benchmarks/recommendation/dlrm-v2.md b/docs/benchmarks/recommendation/dlrm-v2.md
index 1294b008b5..18266615fd 100644
--- a/docs/benchmarks/recommendation/dlrm-v2.md
+++ b/docs/benchmarks/recommendation/dlrm-v2.md
@@ -1,36 +1,22 @@
 # Recommendation using DLRM v2
 
-## Dataset
-
-The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
-
-=== "Validation"
-    DLRM validation run uses the Criteo dataset (Day 23).
-
-    ### Get Validation Dataset
-    ```
-    cm run script --tags=get,dataset,criteo,validation -j
-    ```
-## Model
-The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
-
-Get the Official MLPerf DLRM v2 Model
-
-=== "Pytorch"
-
-    ### Pytorch
-    ```
-    cm run script --tags=get,ml-model,dlrm_v2,_pytorch -j
-    ```
 
 ## Benchmark Implementations
 === "MLCommons-Python"
-    ### MLPerf Reference Implementation in Python
-    
+    ## MLPerf Reference Implementation in Python
+
+    DLRM-v2-99
+{{ mlperf_inference_implementation_readme (4, "dlrm_v2-99", "reference") }}
+
+    DLRM-v2-99.9
 {{ mlperf_inference_implementation_readme (4, "dlrm_v2-99.9", "reference") }}
 
 === "Nvidia"
-    ### Nvidia MLPerf Implementation
+    ## Nvidia MLPerf Implementation
     
+    DLRM-v2-99
+{{ mlperf_inference_implementation_readme (4, "dlrm_v2-99", "nvidia") }}
+
+    DLRM-v2-99.9
 {{ mlperf_inference_implementation_readme (4, "dlrm_v2-99.9", "nvidia") }}
 
diff --git a/docs/benchmarks/recommendation/get-dlrm_v2-data.md b/docs/benchmarks/recommendation/get-dlrm_v2-data.md
new file mode 100644
index 0000000000..97464a164a
--- /dev/null
+++ b/docs/benchmarks/recommendation/get-dlrm_v2-data.md
@@ -0,0 +1,25 @@
+# Recommendation using DLRM v2
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    DLRM validation run uses the Criteo dataset (Day 23).
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,criteo,validation -j
+    ```
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf DLRM v2 Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,dlrm_v2,_pytorch -j
+    ```
+
diff --git a/docs/benchmarks/text_to_image/get-sdxl-data.md b/docs/benchmarks/text_to_image/get-sdxl-data.md
new file mode 100644
index 0000000000..830465d44d
--- /dev/null
+++ b/docs/benchmarks/text_to_image/get-sdxl-data.md
@@ -0,0 +1,26 @@
+# Text to Image using Stable Diffusion
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    Stable Diffusion validation run uses the Coco 2014 dataset.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,coco2014,_validation -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf Stable Diffusion Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,sdxl,_pytorch -j
+    ```
+
diff --git a/docs/benchmarks/text_to_image/sdxl.md b/docs/benchmarks/text_to_image/sdxl.md
index 2e9c95c665..2d84838d4a 100644
--- a/docs/benchmarks/text_to_image/sdxl.md
+++ b/docs/benchmarks/text_to_image/sdxl.md
@@ -1,49 +1,23 @@
 # Text to Image using Stable Diffusion
 
-## Dataset
 
-The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
-
-=== "Validation"
-    Stable Diffusion validation run uses the Coco 2014 dataset.
-
-    ### Get Validation Dataset
-    ```
-    cm run script --tags=get,dataset,coco2014,_validation -j
-    ```
-
-## Model
-The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
-
-Get the Official MLPerf Stable Diffusion Model
-
-=== "Pytorch"
-
-    ### Pytorch
-    ```
-    cm run script --tags=get,ml-model,sdxl,_pytorch -j
-    ```
-
-## Benchmark Implementations
 === "MLCommons-Python"
-    ### MLPerf Reference Implementation in Python
+    ## MLPerf Reference Implementation in Python
     
 {{ mlperf_inference_implementation_readme (4, "sdxl", "reference") }}
 
 === "Nvidia"
-    ### Nvidia MLPerf Implementation
+    ## Nvidia MLPerf Implementation
     
 {{ mlperf_inference_implementation_readme (4, "sdxl", "nvidia") }}
 
 === "Intel"
-    ### Intel MLPerf Implementation
-    GPTJ-99
+    ## Intel MLPerf Implementation
 {{ mlperf_inference_implementation_readme (4, "sdxl", "intel") }}
 
 
 === "Qualcomm"
-    ### Qualcomm AI100 MLPerf Implementation
+    ## Qualcomm AI100 MLPerf Implementation
 
-    GPTJ-99
 {{ mlperf_inference_implementation_readme (4, "sdxl", "qualcomm") }}
 
diff --git a/docs/install/index.md b/docs/install/index.md
index ec76718d95..10ed6e3250 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -1,100 +1,7 @@
 # Installation
 We use MLCommons CM Automation framework to run MLPerf inference benchmarks.
 
-## CM Install
-
-We have successfully tested CM on
-
-* Ubuntu 18.x, 20.x, 22.x , 23.x, 
-* RedHat 8, RedHat 9, CentOS 8
-* macOS
-* Wndows 10, Windows 11
- 
-=== "Ubuntu"
-    ### Ubuntu, Debian
-
-
-    ```bash
-       sudo apt update && sudo apt upgrade
-       sudo apt install python3 python3-pip python3-venv git wget curl
-    ```
-
-    **Note that you must set up virtual env on Ubuntu 23+ before using any Python project:**
-    ```bash
-       python3 -m venv cm
-       source cm/bin/activate
-    ```
-
-    You can now install CM via PIP:
-
-    ```bash
-       python3 -m pip install cmind
-    ```
-
-    You might need to do the following command to update the `PATH` to include the BIN paths from pip installs
-
-    ```bash
-       source $HOME/.profile
-    ```
-
-    You can check that CM is available by checking the `cm` command
-
-
-=== "Red Hat"
-    ### Red Hat
-
-    ```bash
-       sudo dnf update
-       sudo dnf install python3 python-pip git wget curl
-       python3 -m pip install cmind --user
-    ```
-
-=== "macOS"
-    ### macOS
-
-    *Note that CM currently does not work with Python installed from the Apple Store.
-     Please install Python via brew as described below.*
-
-    If `brew` package manager is not installed, please install it as follows (see details [here](https://brew.sh/)):
-    ```bash
-       /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
-    ```
-
-    Don't forget to add brew to PATH environment as described in the end of the installation output.
-
-    Then install python, pip, git and wget:
-
-    ```bash
-       brew install python3 git wget curl
-       python3 -m pip install cmind
-    ```
-
-=== "Windows"
-
-    ### Windows
-    * Configure Windows 10+ to [support long paths](https://learn.microsoft.com/en-us/windows/win32/fileio/maximum-file-path-limitation?tabs=registry#enable-long-paths-in-windows-10-version-1607-and-later) from command line as admin:
-      <small>
-      ```bash
-         reg add "HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem" /v LongPathsEnabled /t REG_DWORD /d 1 /f
-      ```
-      </small>
-    * Download and install Git from [git-for-windows.github.io](https://git-for-windows.github.io).
-      * Configure Git to accept long file names: `git config --system core.longpaths true`
-    * Download and install Python 3+ from [www.python.org/downloads/windows](https://www.python.org/downloads/windows).
-      * Don't forget to select option to add Python binaries to PATH environment!
-      * Configure Windows to accept long fie names during Python installation!
-
-    * Install CM via PIP:
-
-    ```bash
-       python -m pip install cmind
-    ```
-
-    *Note that we [have reports](https://github.com/mlcommons/ck/issues/844) 
-     that CM does not work when Python was first installed from the Microsoft Store.
-     If CM fails to run, you can find a fix [here](https://stackoverflow.com/questions/57485491/python-python3-executes-in-command-prompt-but-does-not-run-correctly)*.
-
-Please visit the [official CM installation page](https://github.com/mlcommons/ck/blob/master/docs/installation.md) for more details
+Please follow the [official installation page](https://docs.mlcommons.org/ck/install) to install CM
 
 ## Download the CM MLOps Repository
 
@@ -102,4 +9,5 @@ Please visit the [official CM installation page](https://github.com/mlcommons/ck
    cm pull repo gateoverflow@cm4mlops
 ```
 
+
 Now, you are ready to use the `cm` commands to run MLPerf inference as given in the [benchmarks](../benchmarks/index.md) page
diff --git a/docs/submission/index.md b/docs/submission/index.md
index b5ff530336..94287f5d27 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -1,4 +1,4 @@
-If you follow the `cm run` commands under the individual model pages in the [benchmarks](../benchmarks) directory, all the valid results will get aggregated to the `cm cache` folder. Once all the results across all the modelsare ready you can use the following command to generate a valid submission tree compliant with the [MLPerf requirements](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc#inference-1).
+If you follow the `cm run` commands under the individual model pages in the [benchmarks](../benchmarks/index.md) directory, all the valid results will get aggregated to the `cm cache` folder. Once all the results across all the modelsare ready you can use the following command to generate a valid submission tree compliant with the [MLPerf requirements](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc#inference-1).
 
 ## Generate actual submission tree
 
diff --git a/docs/submission/tools-readme.md b/docs/submission/tools-readme.md
deleted file mode 120000
index d6f026eab8..0000000000
--- a/docs/submission/tools-readme.md
+++ /dev/null
@@ -1 +0,0 @@
-../../tools/submission/README.md
\ No newline at end of file
diff --git a/main.py b/main.py
index a833153341..f73357d4eb 100644
--- a/main.py
+++ b/main.py
@@ -1,136 +1,291 @@
 def define_env(env):
 
-   @env.macro
-   def mlperf_inference_implementation_readme(spaces, model, implementation):
-     pre_space = ""
-     for i in range(1,spaces):
-       pre_space  = pre_space + " "
-     f_pre_space = pre_space
-     pre_space += " "
-
-     content=""
-     if implementation == "reference":
-       devices = [ "CPU", "CUDA", "ROCm" ]
-       if model.lower() == "resnet50":
-         frameworks = [ "Onnxruntime", "Tensorflow", "Deepsparse" ]
-       elif model.lower() == "retinanet":
-         frameworks = [ "Onnxruntime", "Pytorch" ]
-       elif "bert" in model.lower():
-         frameworks = [ "Onnxruntime", "Pytorch", "Tensorflow" ]
-       else:
-         frameworks = [ "Pytorch" ]
-     elif implementation == "nvidia":
-       devices = [ "CUDA" ]
-       frameworks = [ "TensorRT" ]
-     elif implementation == "intel":
-       devices = [ "CPU" ]
-       frameworks = [ "Pytorch" ]
-     elif implementation == "qualcomm":
-       devices = [ "QAIC" ]
-       frameworks = [ "Glow" ]
-     elif implementation == "cpp":
-       devices = [ "CPU", "CUDA" ]
-       frameworks = [ "Onnxruntime" ]
-
-     if model.lower() == "bert-99.9":
-       categories = [ "Datacenter" ]
-     elif "dlrm" in model.lower() or "llama2" in model.lower():
-       categories = [ "Datacenter" ]
-     else:
-       categories = [ "Edge", "Datacenter" ]
-
-     for category in categories:
-       if category == "Edge":
-         scenarios = [ "Offline", "SingleStream" ]
-         if model.lower() in [ "resnet50", "retinanet" ]:
-           scenarios.append("Multistream")
-       elif category == "Datacenter":
-         scenarios = [ "Offline", "Server" ] 
-
-       content += f"{pre_space}=== \"{category.lower()}\"\n\n"
-
-       cur_space = pre_space + "    "
-       scenarios_string = ", ".join(scenarios)
-     
-       content += f"{cur_space}#### {category} category \n\n{cur_space} In the {category.lower()} category, {model} has {scenarios_string} scenarios and all the scenarios are mandatory for a closed division submission.\n\n"
-
-
-       for framework in frameworks:
-         cur_space1 = cur_space + "    "
-         content += f"{cur_space}=== \"{framework}\"\n"
-         content += f"{cur_space1}##### {framework} framework\n\n"
-
-         for device in devices:
-           if framework.lower() == "deepsparse":
-             if device.lower() != "cpu":
-               continue
-           cur_space2 = cur_space1 + "    "
-           content += f"{cur_space1}=== \"{device}\"\n"
-           content += f"{cur_space2}###### {device} device\n\n"
-         
-           content += f"{cur_space2}###### Docker Setup Command\n\n"
-           test_query_count=100
-           content += mlperf_inference_run_command(spaces+12, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True)
-           content += f"{cur_space2}The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.\n\n"
-           content += f"{cur_space2}<details>\n"
-           content += f"{cur_space2}<summary> Please click here to see more options for the docker launch </summary>\n\n"
-           content += f"{cur_space2}* `--docker_cm_repo <Custom CM repo URL>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
-           content += f"{cur_space2}* `--docker_cache=no`: to not use docker cache during the image build\n"
-
-           if device.lower() not in [ "cuda" ]:
-             content += f"{cur_space2}* `--docker_os=ubuntu`: ubuntu and rhel are supported. \n"
-             content += f"{cur_space2}* `--docker_os_version=20.04`: [20.04, 22.04] are supported for Ubuntu and [8, 9] for RHEL\n"
-
-           content += f"{cur_space2}</details>\n"
-           run_suffix = ""
-           run_suffix += f"\n{cur_space2}    ###### Run Options\n\n"
-           run_suffix += f"{cur_space2}     * Use `--division=closed` to do a closed division submission which includes compliance runs\n\n"
-           run_suffix += f"{cur_space2}     * Use `--rerun` to do a rerun even when a valid run exists\n\n"
-
-           for scenario in scenarios:
-             cur_space3 = cur_space2 + "    "
-             content += f"{cur_space2}=== \"{scenario}\"\n{cur_space3}####### {scenario}\n"
-             run_cmd = mlperf_inference_run_command(spaces+16, model, implementation, framework.lower(), category.lower(), scenario, device.lower(), "valid")
-             content += run_cmd
-             content += run_suffix
-
-           content += f"{cur_space2}=== \"All Scenarios\"\n{cur_space3}####### All Scenarios\n"
-           run_cmd = mlperf_inference_run_command(spaces+16, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), "valid")
-           content += run_cmd
-           content += run_suffix
-
-     return content
-
-     
-   @env.macro
-   def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False):
-     pre_space = ""
-     for i in range(1,spaces):
-       pre_space  = pre_space + " "
-     f_pre_space = pre_space
-     pre_space += "  "
-
-     if scenario == "All Scenarios":
-       scenario_variation_tag = ",_all-scenarios"
-       scenario_option = ""
-     else:
-       scenario_variation_tag = ""
-       scenario_option = f"\\\n {pre_space} --scenario={scenario}"
-
-     if docker:
-       docker_cmd_suffix = f" \\\n {pre_space} --docker --quiet"
-       docker_cmd_suffix += f" \\\n {pre_space} --test_query_count={test_query_count}"
-
-       docker_setup_cmd = f"\n{f_pre_space} ```bash\n{f_pre_space} cm run script --tags=run-mlperf,inference,_find-performance,_full{scenario_variation_tag} \\\n {pre_space} --model={model} \\\n {pre_space} --implementation={implementation} \\\n {pre_space} --framework={framework} \\\n {pre_space} --category={category} {scenario_option} \\\n {pre_space} --execution-mode=test \\\n {pre_space} --device={device} {docker_cmd_suffix}\n{f_pre_space} ```\n"
-
-       return docker_setup_cmd
-
-     else:
-       cmd_suffix = f"\\\n {pre_space} --quiet"
-
-       if execution_mode == "test":
-         cmd_suffix += f" \\\n {pre_space} --test_query_count={test_query_count}"
-
-       run_cmd = f"\n{f_pre_space} ```bash\n{f_pre_space} cm run script --tags=run-mlperf,inference{scenario_variation_tag} \\\n {pre_space} --model={model} \\\n {pre_space} --implementation={implementation} \\\n {pre_space} --framework={framework} \\\n {pre_space} --category={category} {scenario_option} \\\n {pre_space} --execution-mode={execution_mode} \\\n {pre_space} --device={device} {cmd_suffix}\n{f_pre_space} ```\n"
-
-       return run_cmd
+    @env.macro
+    def mlperf_inference_implementation_readme(spaces, model, implementation):
+        pre_space = ""
+
+        for i in range(1,spaces):
+            pre_space  = pre_space + " "
+        f_pre_space = pre_space
+        pre_space += " "
+
+        content=""
+        scenarios = []
+        execution_envs = ["Docker","Native"]
+
+        if implementation == "reference":
+            devices = [ "CPU", "CUDA", "ROCm" ]
+            if model.lower() == "resnet50":
+                 frameworks = [ "Onnxruntime", "Tensorflow", "Deepsparse" ]
+            elif model.lower() == "retinanet":
+                 frameworks = [ "Onnxruntime", "Pytorch" ]
+            elif "bert" in model.lower():
+                 frameworks = [ "Onnxruntime", "Pytorch", "Tensorflow" ]
+            else:
+                 frameworks = [ "Pytorch" ]
+
+        elif implementation == "nvidia":
+            if model in [ "sdxl", "llama2-70b-99", "llama2-70b-99.9" ]:
+                 return pre_space+"    WIP"
+            devices = [ "CUDA" ]
+            frameworks = [ "TensorRT" ]
+
+        elif implementation == "intel":
+            if model not in [ "bert-99", "bert-99.9", "gptj-99", "gptj-99.9" ]:
+                 return pre_space+"    WIP"
+            devices = [ "CPU" ]
+            frameworks = [ "Pytorch" ]
+
+        elif implementation == "qualcomm":
+            if model not in [ "resnet50", "retinanet", "bert-99", "bert-99.9" ]:
+                 return pre_space+"    WIP"
+
+            devices = [ "QAIC" ]
+            frameworks = [ "Glow" ]
+
+        elif implementation == "cpp":
+            devices = [ "CPU", "CUDA" ]
+            frameworks = [ "Onnxruntime" ]
+
+        elif implementation == "ctuning-cpp":
+            scenarios = [ "SingleStream" ]
+            devices = [ "CPU" ]
+            if model.lower() == "resnet50":
+                 frameworks = [ "TFLite" ]
+            else:
+                 frameworks = []
+
+        if model.lower() == "bert-99.9":
+            categories = [ "Datacenter" ]
+        elif "dlrm" in model.lower() or "llama2" in model.lower():
+            categories = [ "Datacenter" ]
+        else:
+            categories = [ "Edge", "Datacenter" ]
+
+        for category in categories:
+            if category == "Edge" and not scenarios:
+                scenarios = [ "Offline", "SingleStream" ]
+                if model.lower() in [ "resnet50", "retinanet" ] and not "MultiStream" in scenarios:#MultiStream was duplicating
+                     scenarios.append("MultiStream")
+            elif category == "Datacenter":
+                 scenarios = [ "Offline", "Server" ] 
+
+            content += f"{pre_space}=== \"{category.lower()}\"\n\n"
+
+            cur_space = pre_space + "    "
+            scenarios_string = ", ".join(scenarios)
+
+            content += f"{cur_space}### {category} category \n\n{cur_space} In the {category.lower()} category, {model} has {scenarios_string} scenarios and all the scenarios are mandatory for a closed division submission.\n\n"
+
+
+            for framework in frameworks:
+                cur_space1 = cur_space + "    "
+                content += f"{cur_space}=== \"{framework}\"\n"
+                content += f"{cur_space1}#### {framework} framework\n\n"
+
+                for device in devices:
+                    if framework.lower() == "deepsparse":
+                        if device.lower() != "cpu":
+                             continue
+                    cur_space2 = cur_space1 + "    "
+                    cur_space3 = cur_space2 + "    "
+                    cur_space4 = cur_space3 + "    "
+                    
+                    content += f"{cur_space1}=== \"{device}\"\n"
+                    content += f"{cur_space2}##### {device} device\n\n"
+
+                    # to select the execution environments(currently Docker and Native)
+                    for execution_env in execution_envs:
+                        if (device == "ROCm" or implementation == "qualcomm") and execution_env == "Docker":
+                            continue  # docker not currently supported for Qualcomm implementation and ROCm device
+                        if implementation == "nvidia" and execution_env == "Native":
+                            continue  # Nvidia implementation only supports execution through docker
+                        content += f"{cur_space2}=== \"{execution_env}\"\n"
+                        content += f"{cur_space3}###### {execution_env} Environment\n\n"
+                        test_query_count=get_test_query_count(model, implementation, device)
+
+                        if "99.9" not in model: #not showing docker command as it is already done for the 99% variant
+                            if execution_env == "Native": # Native implementation steps through virtual environment
+                                content += f"{cur_space3}####### Setup a virtual environment for Python\n"
+                                content += get_venv_command(spaces+16)
+                                content += f"{cur_space3}####### Performance Estimation for Offline Scenario\n"
+                                content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True).replace("--docker ","")
+                                content += f"{cur_space3}The above command should do a test run of Offline scenario and record the estimated offline_target_qps.\n\n"
+
+                            else: # Docker implementation steps
+                                content += f"{cur_space3}####### Docker Container Build and Performance Estimation for Offline Scenario\n"
+                                docker_info = get_docker_info(spaces+16, model, implementation, device)
+                                content += docker_info
+                                content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True)
+                                content += f"{cur_space3}The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.\n\n"
+                                content += f"{cur_space3}<details>\n"
+                                content += f"{cur_space3}<summary> Please click here to see more options for the docker launch </summary>\n\n"
+                                content += f"{cur_space3}* `--docker_cm_repo <Custom CM repo URL>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
+                                content += f"{cur_space3}* `--docker_cache=no`: to not use docker cache during the image build\n"
+
+                                if device.lower() not in [ "cuda" ]:
+                                    content += f"{cur_space3}* `--docker_os=ubuntu`: ubuntu and rhel are supported. \n"
+                                    content += f"{cur_space3}* `--docker_os_version=20.04`: [20.04, 22.04] are supported for Ubuntu and [8, 9] for RHEL\n"
+
+                                content += f"{cur_space3}</details>\n"
+                        else:
+                            content += f"{cur_space3} You can reuse the same environment as described for {model.split('.')[0]}.\n"
+                            content += f"{cur_space3}###### Performance Estimation for Offline Scenario\n"
+                            content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True).replace("--docker ","")
+                            content += f"{cur_space3}The above command should do a test run of Offline scenario and record the estimated offline_target_qps.\n\n"
+
+
+                        run_suffix = ""
+                        run_suffix += f"{cur_space3}<details>\n"
+                        run_suffix += f"{cur_space3}<summary> Please click here to see more options for the RUN command</summary>\n\n"
+                        run_suffix += f"{cur_space3}* Use `--division=closed` to do a closed division submission which includes compliance runs\n\n"
+                        run_suffix += f"{cur_space3}* Use `--rerun` to do a rerun even when a valid run exists\n"  
+                        run_suffix += f"{cur_space3}</details>\n"
+
+                        for scenario in scenarios:
+                            content += f"{cur_space3}=== \"{scenario}\"\n{cur_space4}###### {scenario}\n\n"
+                            run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), scenario, device.lower(), "valid", scenarios)
+                            content += run_cmd
+                            #content += run_suffix
+ 
+                        content += f"{cur_space3}=== \"All Scenarios\"\n{cur_space4}###### All Scenarios\n\n"
+                        run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), "valid", scenarios)
+                        content += run_cmd
+                        content += run_suffix
+
+                    
+
+        readme_prefix = get_readme_prefix(spaces, model, implementation)
+
+        readme_suffix = get_readme_suffix(spaces, model, implementation)
+
+        return readme_prefix + content + readme_suffix
+
+    def get_test_query_count(model, implementation, device, num_devices=1):
+
+        if model == "resnet50":
+             p_range = 1000
+        elif model in [ "retinanet", "bert-99", "bert-99.9" ]:
+             p_range = 100
+        else:
+             p_range = 50
+
+        if device == "cuda":
+            p_range *= 40
+            p_range *= num_devices
+
+        return p_range
+
+    def get_readme_prefix(spaces, model, implementation):
+        readme_prefix = ""
+        pre_space="    "
+        #for i in range(1,spaces):
+        #     pre_space  = pre_space + " "
+        #pre_space += "  "
+
+        return readme_prefix
+    
+    def get_venv_command(spaces):
+      pre_space = " "*spaces
+      return f"""\n
+{pre_space}```bash
+{pre_space}cm run script --tags=install,python-venv --name=mlperf
+{pre_space}export CM_SCRIPT_EXTRA_CMD=\"--adr.python.name=mlperf\"
+{pre_space}```\n"""   
+
+    def get_docker_info(spaces, model, implementation, device):
+        info = ""
+        pre_space=""
+        for i in range(1,spaces):
+             pre_space  = pre_space + " "
+        pre_space += " "
+        #pre_space = "                "
+        if implementation == "nvidia":
+            info += f"\n{pre_space}!!! tip\n\n"
+            info+= f"{pre_space}    All the Nvidia benchmarks, except GPT-J and LLAMA2-70B, use the same Docker container. Therefore, if you have already executed the Docker setup command for any benchmark, you can skip the Docker setup command below and run the commands inside the existing Docker container. The Docker container for GPT-J and LLAMA2-70B is the same and can be used for the other benchmarks, but not vice versa. This is because TensorRT-LLM is built specifically for the LLM benchmarks. If you are already inside a Docker container, execute the below Docker setup command without the --docker option for performance estimation.\n\n"
+        return info
+
+    def get_readme_suffix(spaces, model, implementation):
+        readme_suffix = ""
+        pre_space=""
+        for i in range(1,spaces):
+             pre_space  = pre_space + " "
+        pre_space += "  "
+
+        if implementation == "reference":
+            if not model.endswith("-99"):
+                model_base_name = model.replace("-99.9","").replace("-99","")
+                readme_suffix+= f"{pre_space}* If you want to download the official MLPerf model and dataset for {model} you can follow [this README](get-{model_base_name}-data.md).\n"
+            if model == "resnet50":
+                 readme_suffix += f"{pre_space}* Please see [mobilenets.md](mobilenets.md) for running mobilenet models for Image Classification."
+        return readme_suffix
+
+    def get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scenarios = []):
+        extra_content = ""
+        f_pre_space += ""
+        if scenario == "Server" or (scenario == "All Scenarios" and "Server" in scenarios):
+            extra_content += f"{f_pre_space}    * `<SERVER_TARGET_QPS>` must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.\n"
+
+        if "gptj" in model and device == "cuda" and implementation == "reference":
+            extra_content += f"{f_pre_space}    * `--precision=[float16|bfloat16]` can help run on GPUs with less RAM \n"
+            extra_content += f"{f_pre_space}    * `--beam-size=1` Beam size of 4 is mandatory for a closed division submission but reducing the beam size can help in running the model on GPUs with lower device memory\n"
+        if extra_content:
+            extra_content = f"{f_pre_space}!!! tip\n\n" + extra_content
+
+        return extra_content
+
+    @env.macro
+    def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False, scenarios = []):
+        pre_space = ""
+        for i in range(1,spaces):
+             pre_space  = pre_space + " "
+        f_pre_space = pre_space
+        pre_space += "  "
+
+        if scenario == "All Scenarios":
+             scenario_variation_tag = ",_all-scenarios"
+             scenario_option = ""
+        else:
+            scenario_variation_tag = ""
+            scenario_option = f"\\\n{pre_space} --scenario={scenario}"
+
+        if scenario == "Server" or (scenario == "All Scenarios" and "Server" in scenarios):
+            scenario_option = f"\\\n{pre_space} --server_target_qps=<SERVER_TARGET_QPS>"
+
+        run_cmd_extra = get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scenarios)
+
+        if docker:
+            docker_cmd_suffix = f" \\\n{pre_space} --docker --quiet"
+            docker_cmd_suffix += f" \\\n{pre_space} --test_query_count={test_query_count}"
+
+            docker_setup_cmd = f"""\n
+{f_pre_space}```bash
+{f_pre_space}cm run script --tags=run-mlperf,inference,_find-performance,_full{scenario_variation_tag} \\
+{pre_space} --model={model} \\
+{pre_space} --implementation={implementation} \\
+{pre_space} --framework={framework} \\
+{pre_space} --category={category} {scenario_option} \\
+{pre_space} --execution_mode=test \\
+{pre_space} --device={device} {docker_cmd_suffix}
+{f_pre_space}```\n"""
+
+            return docker_setup_cmd + run_cmd_extra
+
+        else:
+            cmd_suffix = f"\\\n{pre_space} --quiet"
+
+            if execution_mode == "test":
+                cmd_suffix += f" \\\n {pre_space} --test_query_count={test_query_count}"
+
+            run_cmd = f"""\n
+{f_pre_space}```bash
+{f_pre_space}cm run script --tags=run-mlperf,inference{scenario_variation_tag} \\
+{pre_space} --model={model} \\
+{pre_space} --implementation={implementation} \\
+{pre_space} --framework={framework} \\
+{pre_space} --category={category} {scenario_option} \\
+{pre_space} --execution_mode={execution_mode} \\
+{pre_space} --device={device} {cmd_suffix}
+{f_pre_space}```\n"""
+
+            return run_cmd + run_cmd_extra
diff --git a/mkdocs.yml b/mkdocs.yml
index 0d0f641524..339a0657d0 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -19,11 +19,8 @@ theme:
     - navigation.top
     - toc.follow
 nav:
-  - Inference:
-    - index.md
   - Install:
     - install/index.md
-    - Quick Start: install/quick-start.md
   - Benchmarks:
     - benchmarks/index.md
     - Image Classification:

From ad25362ba4e6570e70723c4268262a6cb7f13cb8 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Sun, 16 Jun 2024 22:46:02 -0500
Subject: [PATCH 099/305] MoE reference implementation (#1714)

* MoE reference implementation

* Fix evaluation script targets

* Add evaluation script docker

* Rename model to mixtral-8x7b

* Format python files

* Add skip tokens flag

* Add MBXP stop sequence

* Add download link for preprocessed dataset
---
 language/mixtral-8x7b/Dockerfile           |  48 +++
 language/mixtral-8x7b/Dockerfile.eval      |  63 +++
 language/mixtral-8x7b/README.md            | 249 ++++++++++++
 language/mixtral-8x7b/SUT.py               | 449 +++++++++++++++++++++
 language/mixtral-8x7b/build.sh             |   8 +
 language/mixtral-8x7b/dataset.py           | 115 ++++++
 language/mixtral-8x7b/evaluate-accuracy.py | 217 ++++++++++
 language/mixtral-8x7b/evaluate_mbxp.py     | 154 +++++++
 language/mixtral-8x7b/launch.sh            |  37 ++
 language/mixtral-8x7b/main.py              | 168 ++++++++
 language/mixtral-8x7b/run_accuracy.sh      |  22 +
 language/mixtral-8x7b/run_offline.sh       |  10 +
 language/mixtral-8x7b/run_server.sh        |  12 +
 language/mixtral-8x7b/user.conf            |   5 +
 mlperf.conf                                |   9 +-
 15 files changed, 1565 insertions(+), 1 deletion(-)
 create mode 100644 language/mixtral-8x7b/Dockerfile
 create mode 100644 language/mixtral-8x7b/Dockerfile.eval
 create mode 100644 language/mixtral-8x7b/README.md
 create mode 100644 language/mixtral-8x7b/SUT.py
 create mode 100644 language/mixtral-8x7b/build.sh
 create mode 100644 language/mixtral-8x7b/dataset.py
 create mode 100644 language/mixtral-8x7b/evaluate-accuracy.py
 create mode 100644 language/mixtral-8x7b/evaluate_mbxp.py
 create mode 100644 language/mixtral-8x7b/launch.sh
 create mode 100644 language/mixtral-8x7b/main.py
 create mode 100644 language/mixtral-8x7b/run_accuracy.sh
 create mode 100644 language/mixtral-8x7b/run_offline.sh
 create mode 100644 language/mixtral-8x7b/run_server.sh
 create mode 100644 language/mixtral-8x7b/user.conf

diff --git a/language/mixtral-8x7b/Dockerfile b/language/mixtral-8x7b/Dockerfile
new file mode 100644
index 0000000000..b04910d732
--- /dev/null
+++ b/language/mixtral-8x7b/Dockerfile
@@ -0,0 +1,48 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
+SHELL ["/bin/bash", "-c"]
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+
+ENV TZ=US/Pacific
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
+RUN rm -rf /var/lib/apt/lists/* && rm /etc/apt/sources.list.d/* \
+ && apt update \
+ && apt install -y --no-install-recommends build-essential autoconf \
+        libtool git ccache curl wget pkg-config sudo ca-certificates \
+        automake libssl-dev bc python3-dev python3-pip google-perftools \
+        gdb libglib2.0-dev clang sshfs libre2-dev libboost-dev \
+        libnuma-dev numactl sysstat sshpass ntpdate less iputils-ping \
+ && apt -y autoremove \
+ && apt remove -y cmake \
+ && apt install -y --no-install-recommends pkg-config zip g++ zlib1g-dev \
+        unzip libarchive-dev
+RUN apt install -y --no-install-recommends rsync
+
+# Install setuptools
+RUN python3 -m pip install --upgrade pip \
+    && python3 -m pip install --upgrade setuptools wheel virtualenv
+
+# Install conda
+WORKDIR /tmp
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.5.2-0-Linux-x86_64.sh \
+    && bash Miniconda3-* -b -p /opt/miniconda3
+ENV PATH="$PATH:/opt/miniconda3/bin"
+RUN conda create -n llama2-70b python=3.10
+RUN chmod -R 777 /opt/miniconda3
diff --git a/language/mixtral-8x7b/Dockerfile.eval b/language/mixtral-8x7b/Dockerfile.eval
new file mode 100644
index 0000000000..9fc13772b8
--- /dev/null
+++ b/language/mixtral-8x7b/Dockerfile.eval
@@ -0,0 +1,63 @@
+# Use Ubuntu 22.04 as the base image
+FROM ubuntu:22.04
+ARG DEBIAN_FRONTEND=noninteractive
+
+# Update package lists
+RUN apt-get update
+
+# Install Python 3 and pip
+RUN apt-get install -y python3 python3-pip git
+
+# Set Python 3 as the default python interpreter
+RUN ln -s /usr/bin/python3 /usr/bin/python
+
+# Verify installation
+RUN python --version
+RUN pip --version
+RUN git --version
+
+# Install requirements
+RUN pip install transformers nltk==3.8.1 evaluate==0.4.0 absl-py==1.4.0 rouge-score==0.1.2 sentencepiece==0.1.99 accelerate==0.21.0 huggingface-cli
+
+# Clone and install mxeval
+RUN git clone https://github.com/amazon-science/mxeval.git
+RUN pip install -e mxeval
+
+# Get language dependencies
+RUN apt install -y wget
+
+# Ruby
+RUN apt install -y curl libssl-dev libreadline-dev zlib1g-dev autoconf bison build-essential libyaml-dev libreadline-dev libncurses5-dev libffi-dev libgdbm-dev
+
+# PHP
+RUN apt install -y software-properties-common ca-certificates lsb-release apt-transport-https
+RUN add-apt-repository ppa:ondrej/php
+RUN apt-get update
+RUN apt install -y php8.0
+# RUN apt install -y php-{pear,cgi,pdo,common,curl,mbstring,gd,mysqlnd,gettext,bcmath,json,xml,fpm,intl,zip}
+
+# JAVA
+RUN apt-get install -y openjdk-8-jdk
+
+# JAVASCRIPT
+RUN apt install -y npm
+
+# SCALA
+RUN apt-get install -y scala
+
+# C#
+RUN apt-get install -y dotnet6
+
+# Kotlin
+RUN apt install -y zip unzip
+
+SHELL ["/bin/bash", "-c"]
+WORKDIR "/mxeval"
+RUN sed -i 's/sudo//g' /mxeval/language_setup/ubuntu.sh
+RUN sed -i 's/source/PS1=1 source/g' /mxeval/language_setup/ubuntu.sh # Need this to make sure that the "source ~/.bashrc" lines work correctly
+RUN sed -i 's/npx tsc/tsc/g' /mxeval/mxeval/execution.py # npx tsc runs into permission issues
+
+RUN PATH="$HOME/.rbenv/bin:$PATH" bash /mxeval/language_setup/ubuntu.sh
+
+WORKDIR "/"
+CMD bash
diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
new file mode 100644
index 0000000000..e301891d9f
--- /dev/null
+++ b/language/mixtral-8x7b/README.md
@@ -0,0 +1,249 @@
+# Reference Implementation for Mixtral-8x7B-instruct-v0.1
+
+**Basic implementation for Mixtral-8x7B-instruct-v0.1. Few noteworthy items:**
+
++ Dataset was constructed by randomly sampling from the validation split of 3 datasets, open_orca_gpt4, GSM8k and MBXP. 5K samples from each one.
++ Streamer for communicating with loadgen has quite some overhead. This is only meant to provide functional implementation
++ For custom/optimized implementations of this benchmark it is important to include the :
+        - For server scenario, it is necessary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
+        - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
+
+## Automated command to run the benchmark via MLCommons CM 
+
+TODO
+ 
+## Prepare environment
+
+Copy the mlperf.conf file to this folder.
+```
+cp ../../mlperf.conf .
+```
+
+For a CPU-only run:
+
+```
+conda create -n Mixtral-8x7B python=3.9
+conda activate Mixtral-8x7B
+
+# Install packages
+conda install pybind11==2.10.4 -c conda-forge -y
+python -m pip install torch==2.2.0.dev20231006+cpu --index-url https://download.pytorch.org/whl/nightly/cpu
+pip install transformers==4.31.0 nltk==3.8.1 evaluate==0.4.0 absl-py==1.4.0 rouge-score==0.1.2 sentencepiece==0.1.99 accelerate==0.21.0
+pip install git+https://github.com/amazon-science/mxeval.git@e09974f990eeaf0c0e8f2b5eaff4be66effb2c86
+
+export CUR_DIR=${PWD}
+cd <inference-repo-root>/loadgen
+
+python -m pip install .
+```
+
+For a GPU-based run:
+
+A dockerfile is provided, along with scripts to help launch it. First, add any docker volume mounts you want in
+`launch.sh`. There is a section at the top of the file that looks like:
+```
+# Add any volume mounts here with the following syntax
+# /path/to/src:/path/to/dir/in/container
+MOUNTS=(
+    $MLCOMMONS_REPO_PATH:$MLCOMMONS_REPO_PATH
+)
+```
+
+For example if you have a raid space located at `/raid/data` on your local machine, you can add it to the same path in the container like so:
+```
+# Add any volume mounts here with the following syntax
+# /path/to/src:/path/to/dir/in/container
+MOUNTS=(
+    $MLCOMMONS_REPO_PATH:$MLCOMMONS_REPO_PATH
+    /raid/data:/raid/data
+)
+```
+Once you have added all your mounts, launch the container with `bash launch.sh`.
+
+Inside the container, set up the environment with `bash build.sh`. This will install all the dependencies from the
+CPU-only setup, as well as any GPU versions for applicable libraries like PyTorch.
+
+
+## Get Model
+### MLCommons Members Download
+
+TODO: Create MLCommons get fixed link.
+For now it can be downloaded from [Hugging Face](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1/tree/main)
+
+## Get Dataset
+
+TODO: Create scripts and procedure to download all of the parts of the dataset
+
+### Preprocessed
+
+#### Using Rclone
+We make many of the MLPerf infernce models and datasets available using Rclone. In order to keep compatibility, you can use Rclone to get the preprocessed dataset:
+
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
+```bash
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
+```
+Once Rclone is installed, cd into the folder where you want to place the dataset and run:
+```bash
+rclone copyurl https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_15k_v4.pkl ./ -a -P
+```
+#### Using wget
+
+Alternatively, you can simply cd into the folder where you want to place the dataset and run
+```bash
+wget https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_15k_v4.pkl
+```
+
+### Unprocessed
+
+TODO: Share instructions and scripts
+
+## Run Performance Benchmarks
+
+### Offline
+```
+python -u main.py --scenario Offline \
+                --model-path ${CHECKPOINT_PATH} \
+                --mlperf-conf mlperf.conf \
+                --user-conf user.conf \
+                --total-sample-count 15000 \
+                --device cpu \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir offline-logs
+
+```
+
+For a GPU-based run:
+```
+python3 -u main.py --scenario Offline \
+        --model-path ${CHECKPOINT_PATH} \
+        --mlperf-conf mlperf.conf \
+        --user-conf user.conf \
+        --total-sample-count 15000 \
+        --dataset-path ${DATASET_PATH} \
+        --output-log-dir offline-logs \
+        --dtype float32 \
+        --device cuda:0 2>&1 | tee offline_performance_log.log
+```
+
+### Server
+```
+python -u main.py --scenario Server \
+                --model-path ${CHECKPOINT_PATH} \
+                --mlperf-conf mlperf.conf \
+                --user-conf user.conf \
+                --total-sample-count 15000 \
+                --device cpu \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir server-logs
+```
+
+The ServerSUT was not tested for GPU runs.
+
+
+## Run Accuracy Benchmarks
+
+### Offline
+```
+OUTPUT_LOG_DIR=offline-accuracy-logs
+
+mkdir -p "run_outputs"  # The script will dump all the outputs to 'run_outputs'.
+
+python -u main.py --scenario Offline \
+                --model-path ${CHECKPOINT_PATH} \
+                --accuracy \
+                --mlperf-conf mlperf.conf \
+                --user-conf user.conf \
+                --total-sample-count 15000 \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir ${OUTPUT_LOG_DIR} \
+                --device cpu
+
+
+ACCURACY_LOG_FILE=${OUTPUT_LOG_DIR}/mlperf_log_accuracy.json
+if [ -e ${ACCURACY_LOG_FILE} ]; then
+        python evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
+                --mlperf-accuracy-file ${ACCURACY_LOG_FILE} --dataset-file ${DATASET_PATH} --dtype int32
+fi
+
+# Optional: Create a pickled pandas DataFrame that is the original dataset with extra columns with output data from the
+# accuracy run. The following columns will be added:
+# - "gen_output_tok_id": A list of ints representing the tokenized output sequence.
+# - "gen_output_text": A str representing the untokenized output sequence.
+# - "gen_output_tok_len": An int representing the number of output tokens.
+# - "rouge1": The rouge1 score for this sample
+# - "rouge2": The rouge2 score for this sample
+# - "rougeL": The rougeL score for this sample
+# This file will by default be saved to 'full_output.pkl'. You can modify this with --output-pkl-path.
+python consolidate_results.py --dataset-path ${DATASET_PATH} --model-dir ${CHECKPOINT_PATH}
+```
+
+For the GPU run - The above steps have been automated in `run_accuracy.sh`. You can also modify this script to use
+`--device cpu` to adapt it to a CPU-only run.
+
+
+### Server
+```
+OUTPUT_LOG_DIR=server-accuracy-logs
+
+python -u main.py --scenario Server \
+                --model-path ${CHECKPOINT_PATH} \
+                --accuracy \
+                --mlperf-conf mlperf.conf \
+                --user-conf user.conf \
+                --total-sample-count 15000 \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir ${OUTPUT_LOG_DIR} \
+                --device cpu
+
+
+ACCURACY_LOG_FILE=${OUTPUT_LOG_DIR}/mlperf_log_accuracy.json
+if [ -e ${ACCURACY_LOG_FILE} ]; then
+        python evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
+                --mlperf-accuracy-file ${ACCURACY_LOG_FILE} --dataset-file ${DATASET_PATH} --dtype int32
+fi
+```
+
+The ServerSUT was not tested for GPU runs.
+
+### Evaluation
+Recreating the enviroment for evaluating the quality metrics can be quite tedious. Therefore we provide a dockerfile and recommend using docker for this task.
+1. Build the evaluation container
+```bash
+docker build . -f Dockerfile.eval -t evaluation
+```
+2. Run the docker in interactive mode and with 
+```bash
+sudo docker run -it -v $(pwd):/eval -t evaluation
+```
+3. 
+```bash
+cd eval
+huggingface-cli login --token [huggingface_token]
+python -u evaluate-accuracy.py --checkpoint-path mistralai/Mixtral-8x7B-instruct-v0.1 \
+                --mlperf-accuracy-file [path_to_mlperf_accuracy_file] \
+                --dataset-file [path_to_dataset] \
+                --n_workers 8
+```
+
+
+## Accuracy Target
+
+Reference scores:
+Open Orca:
+```json
+{'rouge1': 45.4911, 'rouge2': 23.2829, 'rougeL': 30.3615, 'rougeLsum': 42.4333}
+```
+GSM8K:
+```json
+{'gsm8k_accuracy': 73.78}
+```
+MBXP:
+```json
+{'mbxp_accuracy': 60.16}
+```
+For official submissions, 99% of each reference score is enforced. Additionally, 90%-110% of the generated tokens_per_samples:
+```json
+{'tokens_per_sample': 145.9}
+```
\ No newline at end of file
diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
new file mode 100644
index 0000000000..9ed44dbf5a
--- /dev/null
+++ b/language/mixtral-8x7b/SUT.py
@@ -0,0 +1,449 @@
+import os
+import time
+import numpy as np
+import array
+import torch
+from torch.nn.functional import pad
+from torch.utils.data import DataLoader
+from transformers import AutoModelForCausalLM, AutoTokenizer, LogitsProcessor, LogitsProcessorList
+from transformers.generation.streamers import BaseStreamer
+
+import pickle
+import time
+import threading
+import tqdm
+import queue
+
+import logging
+from typing import TYPE_CHECKING, Optional, List
+from pathlib import Path
+
+import mlperf_loadgen as lg
+from dataset import Dataset
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Mixtral-8x7B-Instruct-v0.1")
+
+gen_kwargs = {
+    "early_stopping": True,
+    "max_new_tokens": 1024,
+    "min_new_tokens": 1,
+    "num_beams": 1,
+    "do_sample": False
+}
+
+class StopAfterSequence(LogitsProcessor):
+        """Logits processor (to use with HuggingFace `generate()` method :
+        https://huggingface.co/docs/transformers/v4.24.0/en/main_classes/
+        text_generation#transformers.generation_utils.GenerationMixin).
+
+        This logits processor makes that when the model generates a specified
+        stopping sequence, it stops generating new tokens
+
+        Args:
+            stop_seq (List[int]): ID of the space token.
+            eos_token_id (int): ID of the EOS token.
+            device (str): Device that the model is running
+        """
+        def __init__(self, eos_token_id: int, stop_seq: List[int] = [13, 13940, 28832, 13], device="cpu"):
+            super().__init__()
+            assert(len(stop_seq) >= 1)
+            self.device = device
+            self.stop_seq = torch.tensor(stop_seq, dtype=torch.long).to(device)
+            self.stop_seq_length = len(stop_seq)
+            self.eos_token_id = eos_token_id
+
+        def check_stop_condition(self, input_ids: torch.LongTensor):
+            stop_condition_met = (input_ids[:, -self.stop_seq_length:] == self.stop_seq).all(dim=1)
+            return stop_condition_met
+        
+        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+            if input_ids.size(1) > self.stop_seq_length:
+                forced_eos = torch.full((scores.size(1),), -float("inf")).to(self.device)
+                forced_eos[self.eos_token_id] = 0
+                scores[self.check_stop_condition(input_ids)] = forced_eos
+            return scores
+
+
+class FirstTokenStreamer(BaseStreamer):
+    """ Streams first tokens to a 'holder' """
+
+    def __init__(self, first_token, tokens_cache=[],
+                 is_first_token=True, response_ids=[]):
+        """ Response ids added to 'sign' the first token"""
+
+        self.first_token = first_token  # Queue for first token
+        self.is_first_token = is_first_token
+
+        # Cache for subsequent generated tokens
+        self.tokens_cache = tokens_cache
+
+        self.response_ids = response_ids
+
+        # The first tokens sent to the streamer are actually the input prompts
+        self.is_prompt = True
+
+    def put(self, value):
+        """ Caches the tokens as they're generated. Assumes bs=1 """
+
+        # Prompts are streamed first so we need to skip the first time value
+        # that arrives
+        if self.is_prompt:
+            self.is_prompt = False
+            return
+
+        value = value.item()
+        if self.is_first_token:
+
+            # Add generated first token together with its query response_id to
+            # first tokens queue
+            self.first_token.put((value, self.response_ids[0]))
+
+            self.is_first_token = False
+            return
+
+        self.tokens_cache.append(value)
+
+    def end(self):
+        pass
+
+    def get_out_tokens(self):
+        return self.tokens_cache
+
+
+class SUT():
+    def __init__(self,
+                 model_path=None,
+                 dtype="bfloat16",
+                 device="cpu",
+                 batch_size=None,
+                 total_sample_count=24576,
+                 dataset_path=None,
+                 use_cached_outputs=False,
+                 # Set this to True *only for test accuracy runs* in case your
+                 # prior session was killed partway through
+                 workers=1):
+
+        self.model_path = model_path or "mistralai/Mixtral-8x7B-Instruct-v0.1"
+        self.device = device
+
+        if not batch_size:
+            if device == "cpu":
+                batch_size = 1
+            else:
+                batch_size = 32  # Reduce to 8 if using 4 GPUs, 16 for 8.
+        self.batch_size = batch_size
+
+        # dtype
+        if dtype == 'bfloat16':
+            self.amp_enabled = True
+            self.amp_dtype = torch.bfloat16
+        elif dtype == 'float16':
+            self.amp_enabled = True
+            self.amp_dtype = torch.float16
+        else:
+            self.amp_enabled = False
+            self.amp_dtype = torch.float32
+
+        if 'cuda' in self.device:
+            assert torch.cuda.is_available(), "torch gpu is not available, exiting..."
+
+        self.dataset_path = dataset_path
+        self.data_object = Dataset(self.model_path,
+                                   dataset_path=self.dataset_path,
+                                   total_sample_count=total_sample_count,
+                                   device=self.device)
+        self.qsl = lg.ConstructQSL(self.data_object.total_sample_count, self.data_object.perf_count,
+                                   self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
+
+        self.load_model()
+
+        self.num_workers = workers
+        self.worker_threads = [None] * self.num_workers
+        self.query_queue = queue.Queue()
+
+        self.use_cached_outputs = use_cached_outputs
+        self.sample_counter = 0
+        self.sample_counter_lock = threading.Lock()
+
+    def start(self):
+        # Create worker threads
+        for j in range(self.num_workers):
+            worker = threading.Thread(target=self.process_queries)
+            worker.start()
+            self.worker_threads[j] = worker
+
+    def stop(self):
+        for _ in range(self.num_workers):
+            self.query_queue.put(None)
+
+        for worker in self.worker_threads:
+            worker.join()
+
+    def process_queries(self):
+        """Processor of the queued queries. User may choose to add batching logic """
+
+        while True:
+            qitem = self.query_queue.get()
+            if qitem is None:
+                break
+
+            query_ids = [q.index for q in qitem]
+
+            fname = "q" + "_".join([str(i) for i in query_ids])
+            fname = f"run_outputs/{fname}.pkl"
+            _p = Path(fname)
+            if self.use_cached_outputs and _p.exists():
+                # Read cache
+                with _p.open(mode="rb") as f:
+                    d = pickle.load(f)
+                processed_output = d["outputs"]
+                tik1 = None
+                tik2 = None
+                tik3 = None
+                tok = None
+            else:
+                # Construct / collate batch
+                max_seq_len = 1024
+
+                tik1 = time.time()
+
+                input_ids_tensor = []
+                input_masks_tensor = []
+                input_len = []
+                input_dataset = []
+                for q in qitem:
+                    input_ids_tensor.append(pad(self.data_object.input_ids[q.index],
+                                                (max_seq_len -
+                                                 self.data_object.input_lens[q.index], 0, 0, 0),
+                                                value=self.tokenizer.pad_token_id))
+                    input_masks_tensor.append(pad(self.data_object.attention_masks[q.index],
+                                                  (max_seq_len -
+                                                   self.data_object.input_lens[q.index], 0, 0, 0),
+                                                  value=0))
+                    input_len.append(self.data_object.input_lens[q.index])
+
+                    # In case we predict code generation, we can specify an additional stop sequence
+                    input_dataset.append(self.data_object.dataset_names[q.index])
+                input_ids_tensor = torch.cat(input_ids_tensor)
+                input_masks_tensor = torch.cat(input_masks_tensor)
+
+                assert input_ids_tensor.shape == input_masks_tensor.shape
+                assert input_ids_tensor.shape[0] <= self.batch_size
+
+                tik2 = time.time()
+                logits_processor = LogitsProcessorList([StopAfterSequence(self.tokenizer.eos_token_id, device=self.device)])
+                for i in range(len(input_ids_tensor)):
+                    ids, masks, dataset = input_ids_tensor[i:i+1], input_masks_tensor[i:i+1], input_dataset[i]
+                    pred_output_tokens = []
+                    if dataset == "MBXP":
+                        out = self.model.generate(
+                            input_ids=ids,
+                            attention_mask=masks,
+                            pad_token_id=self.tokenizer.pad_token_id,
+                            logits_processor=logits_processor,
+                            **gen_kwargs
+                        )
+                    else:
+                        out = self.model.generate(
+                            input_ids=ids,
+                            attention_mask=masks,
+                            pad_token_id=self.tokenizer.pad_token_id,
+                            **gen_kwargs
+                        )
+                    pred_output_tokens.append(out)
+                pred_output_tokens = torch.cat(pred_output_tokens)
+                tik3 = time.time()
+
+                processed_output = self.data_object.postProcess(pred_output_tokens,
+                                                                input_seq_lens=input_len,
+                                                                query_id_list=query_ids)
+
+            for i in range(len(qitem)):
+                n_tokens = processed_output[i].shape[0]
+                response_array = array.array(
+                    "B", processed_output[i].tobytes())
+                bi = response_array.buffer_info()
+                response = [
+                    lg.QuerySampleResponse(
+                        qitem[i].id,
+                        bi[0],
+                        bi[1],
+                        n_tokens)]
+                lg.QuerySamplesComplete(response)
+
+            tok = time.time()
+
+            with self.sample_counter_lock:
+                self.sample_counter += len(qitem)
+                print(f"Samples run: {self.sample_counter}")
+                if tik1:
+                    print(f"\tBatchMaker time: {tik2 - tik1}")
+                    print(f"\tInference time: {tik3 - tik2}")
+                    print(f"\tPostprocess time: {tok - tik3}")
+                    print(f"\t==== Total time: {tok - tik1}")
+                else:
+                    print(f"\tLoaded from cache: {_p}")
+
+    def load_model(self):
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_path,
+            device_map="auto",
+            low_cpu_mem_usage=True,
+            torch_dtype=self.amp_dtype
+        )
+        print("Loaded model")
+
+        self.device = torch.device(self.device)
+        if self.device == "cpu":
+            # Force CPU if your system has GPU and you specifically want
+            # CPU-only run
+            self.model = self.model.to(self.device)
+
+        self.model.eval()
+        self.model = self.model.to(memory_format=torch.channels_last)
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_path,
+            model_max_length=1024,
+            padding_side="left",
+            use_fast=False,)
+
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+        print("Loaded tokenizer")
+
+    def get_sut(self):
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        return self.sut
+
+    def get_qsl(self):
+        return self.qsl
+
+    def predict(self, **kwargs):
+        raise NotImplementedError
+
+    def issue_queries(self, query_samples):
+        """ Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
+
+        list_prompts_tokens = []
+        list_prompts_attn_masks = []
+
+        print(f"IssueQuery started with {len(query_samples)} samples")
+        while len(query_samples) > 0:
+            self.query_queue.put(query_samples[:self.batch_size])
+            query_samples = query_samples[self.batch_size:]
+        print(f"IssueQuery done")
+
+    def flush_queries(self):
+        pass
+
+    def __del__(self):
+        pass
+
+
+class SUTServer(SUT):
+    def __init__(self, model_path=None, dtype="bfloat16", device="cpu",
+                 total_sample_count=24576, dataset_path=None, workers=1):
+
+        super().__init__(
+            model_path=model_path,
+            dtype=dtype,
+            device=device,
+            total_sample_count=total_sample_count,
+            dataset_path=dataset_path,
+            workers=workers)
+
+        self.first_token_queue = queue.Queue()
+
+    def start(self):
+
+        # Create worker threads
+        for j in range(self.num_workers):
+            worker = threading.Thread(target=self.process_queries)
+            worker.start()
+            self.worker_threads[j] = worker
+
+        # Create first token response thread
+        self.ft_response_thread = threading.Thread(
+            target=self.process_first_tokens)
+        self.ft_response_thread.start()
+
+    def process_first_tokens(self):
+
+        while True:
+            first_token_item = self.first_token_queue.get()
+
+            if first_token_item is None:
+                log.info("Exiting First token response thread")
+                break
+
+            first_tokens, response_id = first_token_item
+
+            response_data = array.array("B", np.array(
+                first_tokens, np.float32).tobytes())
+            bi = response_data.buffer_info()
+            response = [lg.QuerySampleResponse(response_id, bi[0], bi[1])]
+            lg.FirstTokenComplete(response)
+
+    def process_queries(self):
+        """Processor of the queued queries. User may choose to add batching logic """
+        while True:
+
+            qitem = self.query_queue.get()
+            if qitem is None:
+                break
+
+            input_ids_tensor = self.data_object.input_ids[qitem.index]
+            input_masks_tensor = self.data_object.attention_masks[qitem.index]
+            dataset = self.data_object.dataset_names[qitem.index]
+
+            # TODO: This PoC is super slow with significant overhead. Best to
+            # create a patch to `generate`
+            tokens_cache = []
+            tokens_streamer = FirstTokenStreamer(
+                self.first_token_queue,
+                tokens_cache=tokens_cache,
+                is_first_token=True,
+                response_ids=[
+                    qitem.id])
+            
+            logits_processor = LogitsProcessorList([StopAfterSequence(self.tokenizer.eos_token_id, device=self.device)])
+            if dataset == "MBXP":
+                _ = self.model.generate(input_ids=input_ids_tensor,
+                                        attention_mask=input_masks_tensor,
+                                        pad_token_id=self.tokenizer.pad_token_id,
+                                        streamer=tokens_streamer,
+                                        logits_processor=logits_processor,
+                                        **gen_kwargs
+                                        )
+            else:
+                _ = self.model.generate(input_ids=input_ids_tensor,
+                                        attention_mask=input_masks_tensor,
+                                        pad_token_id=self.tokenizer.pad_token_id,
+                                        streamer=tokens_streamer,
+                                        **gen_kwargs
+                                        )
+
+            output_tokens = tokens_streamer.get_out_tokens()
+            n_tokens = len(output_tokens)
+            response_array = array.array(
+                "B", np.array(
+                    output_tokens, np.int32).tobytes())
+            bi = response_array.buffer_info()
+            response = [lg.QuerySampleResponse(
+                qitem.id, bi[0], bi[1], n_tokens)]
+            lg.QuerySamplesComplete(response)
+
+    def issue_queries(self, query_samples):
+
+        self.query_queue.put(query_samples[0])
+
+    def stop(self):
+        for _ in range(self.num_workers):
+            self.query_queue.put(None)
+
+        for worker in self.worker_threads:
+            worker.join()
+
+        self.first_token_queue.put(None)
+        self.ft_response_thread.join()
diff --git a/language/mixtral-8x7b/build.sh b/language/mixtral-8x7b/build.sh
new file mode 100644
index 0000000000..87afb992fa
--- /dev/null
+++ b/language/mixtral-8x7b/build.sh
@@ -0,0 +1,8 @@
+set -e
+
+conda install pybind11==2.10.4 -c conda-forge -y
+conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch-nightly -c nvidia
+python -m pip install transformers==4.31.0 nltk==3.8.1 evaluate==0.4.0 absl-py==1.4.0 rouge-score==0.1.2 sentencepiece==0.1.99 accelerate==0.21.0
+
+
+cd ../../loadgen && python3 -m pip install .
diff --git a/language/mixtral-8x7b/dataset.py b/language/mixtral-8x7b/dataset.py
new file mode 100644
index 0000000000..d2cafac639
--- /dev/null
+++ b/language/mixtral-8x7b/dataset.py
@@ -0,0 +1,115 @@
+import random
+import os
+import time
+import numpy as np
+import torch
+from datasets import load_dataset, load_from_disk
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from torch.nn.functional import pad
+from torch.utils.data import DataLoader
+from typing import Optional, Dict, Sequence
+import io
+# import utils
+import copy
+import pickle
+
+import logging
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Llama-70B-Dataset")
+
+
+class Dataset():
+    def __init__(self, model_name=None, total_sample_count=15000,
+                 perf_count_override=None, dataset_path=None, device="cpu"):
+        self.model_name = model_name or "mistralai/Mixtral-8x7B-v0.1"
+        self.dataset_path = dataset_path
+        self.max_length = 1024
+        self.device = device
+
+        # self.total_sample_count = total_sample_count
+
+        self.load_tokenizer()
+        self.load_processed_dataset()
+
+        self.total_sample_count = min(len(self.input_ids), total_sample_count)
+        self.perf_count = perf_count_override or self.total_sample_count
+
+    def load_tokenizer(self):
+        """ Returns tokenizer """
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_name,
+            model_max_length=1024,
+            padding_side="left",
+            use_fast=False,)
+
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+    def load_processed_dataset(self):
+        if not os.path.isfile(self.dataset_path):
+            log.warn(
+                "Processed pickle file {} not found. Please check that the path is correct".format(
+                    self.dataset_path))
+
+        print("Loading dataset...")
+        import pandas as pd
+        processed_data = pd.read_pickle(self.dataset_path)
+
+        input_tokens = processed_data['tok_input']
+
+        self.input_ids = []
+        self.input_lens = []
+        self.attention_masks = []
+        self.dataset_names = []
+
+        for ids in input_tokens:
+            input_ids = torch.tensor(ids, dtype=torch.int32).view(
+                1, -1).to(self.device)
+            attn_mask = torch.ones_like(input_ids)
+            self.input_ids.append(input_ids)
+            self.attention_masks.append(attn_mask)
+            self.input_lens.append(input_ids.shape[-1])
+
+        for dataset in processed_data['dataset']:
+            self.dataset_names.append(dataset)
+        print("Finished loading dataset.")
+
+    def postProcess(self, out_tokens, input_seq_lens=None,
+                    query_id_list=None, sample_index_list=None):
+        """ Postprocesses output prediction """
+
+        # TODO: Create response object in postProcess(?)
+        """
+        preds = []
+        for i in range(out_tokens.shape[0]):
+            #pred = out_tokens[i].reshape(-1).cpu().numpy() # Slice up to original input length as below?
+
+            input_len = input_seq_lens[i] if input_seq_lens else 0
+            pred = out_tokens[i, input_len:].reshape(-1).cpu().numpy()
+            preds.append(pred)
+        """
+        # Everything is padded to max_len (1024), so prune the input and parse
+        # to numpy
+        output_seq = out_tokens[:, 1024:].cpu().numpy()
+        assert len(query_id_list) == output_seq.shape[0]
+
+        # Save outputs
+        if not os.path.exists("run_outputs"):
+            os.makedirs("run_outputs")
+        fname = "q" + "_".join([str(i) for i in query_id_list])
+        fname = f"run_outputs/{fname}.pkl"
+        with open(fname, mode='wb') as f:
+            d = {"query_ids": query_id_list,
+                 "outputs": output_seq}
+            print(f"Saving outputs to {fname}")
+            pickle.dump(d, f)
+
+        return output_seq
+
+    def LoadSamplesToRam(self, sample_list):
+        pass
+
+    def UnloadSamplesFromRam(self, sample_list):
+        pass
+
+    def __del__(self):
+        pass
diff --git a/language/mixtral-8x7b/evaluate-accuracy.py b/language/mixtral-8x7b/evaluate-accuracy.py
new file mode 100644
index 0000000000..e20834c412
--- /dev/null
+++ b/language/mixtral-8x7b/evaluate-accuracy.py
@@ -0,0 +1,217 @@
+import argparse
+from transformers import AutoTokenizer
+import nltk
+import evaluate
+import numpy as np
+import pandas as pd
+import json
+import re
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint-path", required=True,
+                        help="Path to Llama2-70b-hf-chat checkpoint")
+    parser.add_argument("--mlperf-accuracy-file", required=True,
+                        help="path to mlperf_log_accuracy.json")
+    parser.add_argument("--dataset-file", required=True,
+                        help="path to processed validation dataset")
+    parser.add_argument("--n_workers", default=2, type=int,
+                        help="Number of workers used for the MBXP evaluation")
+    parser.add_argument("--verbose", action="store_true",
+                        help="verbose messages")
+    parser.add_argument("--dtype", default="int64",
+                        help="dtype of the accuracy log", choices=["int32", "int64", "float"])
+    args = parser.parse_args()
+    return args
+
+
+def get_groundtruth(processed_dataset_file):
+    data = pd.read_pickle(processed_dataset_file)
+    return data
+
+
+# Functions for evaluating GSM8K
+def find_numbers(x: str) -> list[str]:
+    """Finds all numbers in a string."""
+    # Search for number, possibly negative (hyphen), with thousand separators
+    # (comma), and with a decimal point (period inbetween digits).
+    numbers = re.compile(
+        r'-?[\d,]*\.?\d+',
+        re.MULTILINE | re.DOTALL | re.IGNORECASE,
+    ).findall(x)
+    return numbers
+
+
+def find_number(x: str,
+                answer_delimiter: str = 'The answer is') -> str:
+    """Finds the most relevant number in a string."""
+    # If model uses the answer delimiter, then select the first number following
+    # that format.
+    if answer_delimiter in x:
+        answer = x.split(answer_delimiter)[-1]
+        numbers = find_numbers(answer)
+        if numbers:
+            return numbers[0]
+
+    # In general, select the last number in the string.
+    numbers = find_numbers(x)
+    if numbers:
+        return numbers[-1]
+    return ''
+
+
+def maybe_remove_comma(x: str) -> str:
+    # Example: 5,600 -> 5600
+    return x.replace(',', '')
+
+
+def try_float(x: str):
+    try:
+        ret = float(x)
+    except BaseException:
+        ret = None
+    return ret
+
+# Functions for evaluating OpenOrca
+
+
+def postprocess_text(preds, targets):
+    preds = [pred.strip() for pred in preds]
+    targets = [target.strip() for target in targets]
+
+    # rougeLSum expects newline after each sentence
+    preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
+    targets = ["\n".join(nltk.sent_tokenize(target)) for target in targets]
+
+    return preds, targets
+
+# Functions for MBXP
+
+
+def create_mbxp_dict(row, response):
+    lang, entry_point = row["id"].split("_", 1)
+    return {
+        "lang": lang,
+        "prompt": row["input"],
+        "test_code": row["gt_output"],
+        "entry_point": entry_point,
+        "response": response
+    }
+
+
+def main():
+
+    args = get_args()
+    dataset_path = args.dataset_file
+    checkpoint_path = args.checkpoint_path
+    metric = evaluate.load("rouge")
+    nltk.download('punkt')
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        checkpoint_path,
+        model_max_length=2048,
+        padding_side="left",
+        use_fast=False,)
+
+    data = get_groundtruth(args.dataset_file)
+    query_types, gt_outputs = data["dataset"], data["gt_output"]
+
+    target_required_GSM8K = []
+    target_required_OpenOrca = []
+    results_MBXP = []
+    preds_token_GSM8K = []
+    preds_token_OpenOrca = []
+    preds_token_MBXP = []
+
+    eval_dtype = np.int64
+    if args.dtype == "int32":
+        eval_dtype = np.int32
+    elif args.dtype == "float":
+        eval_dtype = np.float32
+
+    with open(args.mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+    seen = set()
+    gen_tok_len = 0
+    gen_num = 0
+    for pred in results:
+        gen_num += 1
+        qsl_idx = pred['qsl_idx']
+        if qsl_idx in seen:
+            continue
+
+        seen.add(qsl_idx)
+
+        query_type = query_types.iloc[qsl_idx]
+        if query_type == "GSM8K":
+            target = gt_outputs.iloc[qsl_idx]
+            target_required_GSM8K.append(target)
+            pred = np.frombuffer(bytes.fromhex(pred['data']), eval_dtype)
+
+            gen_tok_len += len(pred)
+            preds_token_GSM8K.append(pred)
+        elif query_type == "OpenOrca":
+            target = gt_outputs.iloc[qsl_idx]
+            target_required_OpenOrca.append(target)
+            pred = np.frombuffer(bytes.fromhex(pred['data']), eval_dtype)
+
+            gen_tok_len += len(pred)
+            preds_token_OpenOrca.append(pred)
+        else:
+            target = data.iloc[qsl_idx]
+            pred = np.frombuffer(bytes.fromhex(pred['data']), eval_dtype)
+            pred_str = tokenizer.decode(pred, skip_special_tokens=True)
+            results_MBXP.append(create_mbxp_dict(target, pred_str))
+
+            gen_tok_len += len(pred)
+
+    # OpenOrca metric
+    preds_decoded_text = tokenizer.batch_decode(
+        preds_token_OpenOrca, skip_special_tokens=True)
+
+    preds, targets = postprocess_text(
+        preds_decoded_text, target_required_OpenOrca)
+    result = metric.compute(
+        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
+    result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
+    prediction_lens = [len(pred) for pred in preds]
+    # GSM8K metric
+    preds_decoded_text = tokenizer.batch_decode(
+        preds_token_GSM8K, skip_special_tokens=True)
+    pred_nums = [
+        maybe_remove_comma(
+            find_number(
+                pred_text.split("\nQ:")[0])) for pred_text in preds_decoded_text]
+    gsm8k_total = len(target_required_GSM8K)
+    correct = 0
+    for idx in range(len(target_required_GSM8K)):
+        ref = try_float(target_required_GSM8K[idx])
+        tgt = try_float(pred_nums[idx])
+        if tgt is None:
+            continue
+        correct += (ref == tgt)
+
+    gsm8k_accuracy = 100.0 * correct / gsm8k_total
+
+    # MBXP metric
+    from evaluate_mbxp import evaluate_mbxp
+    mbxp_accuracy = evaluate_mbxp(results_MBXP, args.n_workers)
+
+    result = {
+        **result,
+        'gen_len': np.sum(prediction_lens),
+        'gen_num': gen_num,
+        'gen_tok_len': gen_tok_len,
+        'tokens_per_sample': round(gen_tok_len / gen_num, 1),
+        'gsm8k_accuracy': gsm8k_accuracy,
+        'mbxp_accuracy': mbxp_accuracy
+    }
+
+    print("\nResults\n")
+    print(result)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/language/mixtral-8x7b/evaluate_mbxp.py b/language/mixtral-8x7b/evaluate_mbxp.py
new file mode 100644
index 0000000000..e7d55d1694
--- /dev/null
+++ b/language/mixtral-8x7b/evaluate_mbxp.py
@@ -0,0 +1,154 @@
+import argparse
+import json
+import multiprocessing
+import pickle
+import queue
+import re
+import timeit
+
+import pandas as pd
+from tqdm import tqdm
+
+from mxeval.execution import check_correctness as check_correctness_python
+from mxeval.execution import (
+    check_correctness_cpp,
+    check_correctness_csharp,
+    check_correctness_go,
+    check_correctness_java,
+    check_correctness_javascript,
+    check_correctness_kotlin,
+    check_correctness_perl,
+    check_correctness_php,
+    check_correctness_ruby,
+    check_correctness_scala,
+    check_correctness_swift,
+    check_correctness_typescript,
+)
+
+
+def postprocess_golang(code: str) -> str:
+    multi_line_imports = re.compile(
+        r"^import \(\n(.+)((?:\n.+)+)\n\)", re.MULTILINE)
+    line_imports = re.compile(r"^import \".*\"")
+    func_main = re.compile(r"^func main.*^}", re.MULTILINE | re.DOTALL)
+
+    code = code.replace("package main", "")  # Remove package main
+    code = multi_line_imports.sub("", code)
+    code = line_imports.sub("", code)
+    code = func_main.sub("", code)
+
+    return code
+
+
+def postprocess_scala(code: str) -> str:
+    code = code.replace("object Main extends App {", "")
+    code = "".join(code.splitlines(True)[:-1])
+    return code
+
+
+def postprocess_python(code: str) -> str:
+    return code.lstrip()
+
+
+def worker(inp_queue, out_queue):
+    while True:
+        try:
+            problem = inp_queue.get(timeout=5)
+        except queue.Empty:
+            break
+
+        key = f"{problem['lang']}_{problem['entry_point']}"
+        checker = eval(f"check_correctness_{problem['lang']}")
+
+        problem["task_id"] = key
+        problem["test"] = problem["test_code"]
+
+        solution = problem["response"]
+
+        try:
+            solution = solution[:solution.index("```")]
+        except ValueError:
+            # Happens when a code block isn't closed properly
+            pass
+
+        if problem["lang"] == "go":
+            solution = postprocess_golang(solution)
+        elif problem["lang"] == "python":
+            solution = postprocess_python(solution)
+        elif problem["lang"] == "scala":
+            solution = postprocess_scala(solution)
+
+        # Mixtral likes escaping underscores for some reason, so let's remove
+        # these
+        solution = solution.replace("\\_", "_")
+
+        # The evaluation script evaluates `code = prompt + solution + tests`
+        # But Mixtral regenerates the prompt in its output, so we should remove
+        # this
+        problem["prompt"] = ""
+        try:
+            result = checker(problem, solution, timeout=20.0)
+            out_queue.put(
+                (key,
+                 problem["lang"],
+                    result["passed"],
+                    result["result"],
+                    problem["response"]))
+        except Exception as e:
+            print(e)
+            out_queue.put(
+                (key, problem["lang"], False, "", problem["response"]))
+
+
+def evaluate_mbxp(results, n_workers):
+    by_lang = {}
+    for problem in results:
+        by_lang.setdefault(problem["lang"], []).append(problem)
+
+    inp_queue = multiprocessing.Queue()
+    out_queue = multiprocessing.Queue()
+
+    n_problems = 0
+
+    for lang, problems in by_lang.items():
+        if lang not in ["cpp", "python", "php",
+                        "javascript", "ruby", "typescript"]:
+            continue
+
+        n_problems += len(problems)
+        for problem in problems:
+            inp_queue.put(problem)
+
+    start = timeit.default_timer()
+    workers = []
+    for _ in range(n_workers):
+        w = multiprocessing.Process(target=worker, args=(inp_queue, out_queue))
+        w.start()
+        workers.append(w)
+
+    passes = {}
+    n_passed = 0
+    lang_passed = {}
+    lang_counts = {}
+    for i in tqdm(range(n_problems)):
+        key, lang, passed, result, response = out_queue.get()
+        passes[key] = {
+            "passed": passed,
+            "result": result,
+            "response": response}
+        n_passed += passed
+
+        lang_passed.setdefault(lang, 0)
+        lang_passed[lang] += passed
+
+        lang_counts.setdefault(lang, 0)
+        lang_counts[lang] += 1
+
+    end = timeit.default_timer()
+    print(f"Processed {n_problems} in {end - start}s")
+    print(f"{100 * n_passed / n_problems : .02f}% pass@1")
+    print(lang_passed, lang_counts)
+    with open("evaluated_test.json", "w") as f:
+        json.dump(passes, f, indent=2)
+
+    return 100 * n_passed / n_problems
diff --git a/language/mixtral-8x7b/launch.sh b/language/mixtral-8x7b/launch.sh
new file mode 100644
index 0000000000..c3389c5162
--- /dev/null
+++ b/language/mixtral-8x7b/launch.sh
@@ -0,0 +1,37 @@
+#!/bin/bash
+
+MLCOMMONS_REPO_PATH="$(dirname "$(dirname "$PWD")")"
+
+# Add any volume mounts here with the following syntax
+# /path/to/src:/path/to/dir/in/container
+MOUNTS=(
+    $MLCOMMONS_REPO_PATH:$MLCOMMONS_REPO_PATH
+)
+
+# Set up docker environment file for current user
+rm -f .docker_env
+echo "CI_BUILD_USER=`id -u -n`" >> .docker_env
+echo "CI_BUILD_UID=`id -u`" >> .docker_env
+echo "CI_BUILD_GROUP=`id -g -n`" >> .docker_env
+echo "CI_BUILD_GID=`id -g`" >> .docker_env
+cat .docker_env
+
+# Build container
+docker build . -t llm/gpubringup
+
+# Build mount flags
+declare -a MOUNT_FLAGS
+for _mount in ${MOUNTS[@]}; do
+    _split=($(echo $_mount | tr ':' '\n'));
+    MOUNT_FLAGS+=("--mount type=bind,source=${_split[0]},target=${_split[1]}");
+done
+
+set -x
+nvidia-docker run -it --rm --net=host --runtime=nvidia --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
+  --cap-add=SYS_PTRACE --cap-add=SYS_ADMIN --cap-add=DAC_READ_SEARCH \
+  --security-opt seccomp=unconfined \
+  -w $PWD \
+  --env-file `pwd`/.docker_env \
+  ${MOUNT_FLAGS[*]} \
+  llm/gpubringup \
+  bash ./with_the_same_user
diff --git a/language/mixtral-8x7b/main.py b/language/mixtral-8x7b/main.py
new file mode 100644
index 0000000000..396948ba06
--- /dev/null
+++ b/language/mixtral-8x7b/main.py
@@ -0,0 +1,168 @@
+import subprocess
+import mlperf_loadgen as lg
+import argparse
+import os
+import logging
+import sys
+from SUT import SUT, SUTServer
+
+sys.path.insert(0, os.getcwd())
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Mixtral-8x7B-Instruct-v0.1-MAIN")
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--scenario",
+        type=str,
+        choices=[
+            "Offline",
+            "Server"],
+        default="Offline",
+        help="Scenario")
+    parser.add_argument(
+        "--model-path",
+        type=str,
+        default="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        help="Model name")
+    parser.add_argument(
+        "--dataset-path",
+        type=str,
+        default=None,
+        help="path to processed validation dataset")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="Run accuracy mode")
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        default="float32",
+        help="data type of the model, choose from float16, bfloat16 and float32")
+    parser.add_argument(
+        "--device",
+        type=str,
+        choices=[
+            "cpu",
+            "cuda:0"],
+        default="cpu",
+        help="device to use")
+    parser.add_argument(
+        "--audit-conf",
+        type=str,
+        default="audit.conf",
+        help="audit config for LoadGen settings during compliance runs")
+    parser.add_argument(
+        "--mlperf-conf",
+        type=str,
+        default="mlperf.conf",
+        help="mlperf rules config")
+    parser.add_argument(
+        "--user-conf",
+        type=str,
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS")
+    # TODO: This interpretation of 'total-sample-count' is a little
+    # misleading. Fix it
+    parser.add_argument(
+        "--total-sample-count",
+        type=int,
+        default=24576,
+        help="Number of samples to use in benchmark.")
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=1,
+        help="Model batch-size to use in benchmark.")
+    parser.add_argument(
+        "--output-log-dir",
+        type=str,
+        default="output-logs",
+        help="Where logs are saved")
+    parser.add_argument(
+        "--enable-log-trace",
+        action="store_true",
+        help="Enable log tracing. This file can become quite large")
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        default=1,
+        help="Number of workers to process queries")
+
+    args = parser.parse_args()
+    return args
+
+
+scenario_map = {
+    "offline": lg.TestScenario.Offline,
+    "server": lg.TestScenario.Server,
+}
+
+sut_map = {
+    "offline": SUT,
+    "server": SUTServer
+}
+
+
+def main():
+    args = get_args()
+
+    settings = lg.TestSettings()
+    settings.scenario = scenario_map[args.scenario.lower()]
+    # Need to update the conf
+    settings.FromConfig(args.mlperf_conf, "mixtral-8x7b", args.scenario)
+    settings.FromConfig(args.user_conf, "mixtral-8x7b", args.scenario)
+
+    if args.accuracy:
+        settings.mode = lg.TestMode.AccuracyOnly
+        log.warning(
+            "Accuracy run will generate the accuracy logs, but the evaluation of the log is not completed yet")
+    else:
+        settings.mode = lg.TestMode.PerformanceOnly
+
+    os.makedirs(args.output_log_dir, exist_ok=True)
+    log_output_settings = lg.LogOutputSettings()
+    log_output_settings.outdir = args.output_log_dir
+    log_output_settings.copy_summary_to_stdout = True
+    log_settings = lg.LogSettings()
+    log_settings.log_output = log_output_settings
+    log_settings.enable_trace = args.enable_log_trace
+
+    sut_cls = sut_map[args.scenario.lower()]
+
+    sut = sut_cls(
+        model_path=args.model_path,
+        dtype=args.dtype,
+        batch_size=args.batch_size,
+        dataset_path=args.dataset_path,
+        total_sample_count=args.total_sample_count,
+        device=args.device,
+    )
+
+    # Start sut before loadgen starts
+    sut.start()
+    lgSUT = lg.ConstructSUT(sut.issue_queries, sut.flush_queries)
+    log.info("Starting Benchmark run")
+    lg.StartTestWithLogSettings(
+        lgSUT,
+        sut.qsl,
+        settings,
+        log_settings,
+        args.audit_conf)
+
+    # Stop sut after completion
+    sut.stop()
+
+    log.info("Run Completed!")
+
+    log.info("Destroying SUT...")
+    lg.DestroySUT(lgSUT)
+
+    log.info("Destroying QSL...")
+    lg.DestroyQSL(sut.qsl)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/language/mixtral-8x7b/run_accuracy.sh b/language/mixtral-8x7b/run_accuracy.sh
new file mode 100644
index 0000000000..80db6b604d
--- /dev/null
+++ b/language/mixtral-8x7b/run_accuracy.sh
@@ -0,0 +1,22 @@
+CHECKPOINT_PATH="${CHECKPOINT_PATH:mistralai/Mixtral-8x7B-Instruct-v0.1}"
+DATASET_PATH="${DATASET_PATH:dataset/2024_06_06_mixtral_15k_v4.pkl}"
+
+mkdir -p "run_outputs"
+
+python3 -u main.py --scenario Offline \
+        --model-path ${CHECKPOINT_PATH} \
+        --accuracy \
+        --mlperf-conf mlperf.conf \
+        --user-conf user.conf \
+        --total-sample-count 15000 \
+        --dataset-path ${DATASET_PATH} \
+        --output-log-dir offline_accuracy_loadgen_logs \
+        --dtype float32 \
+        --device cuda:0 2>&1 | tee offline_accuracy_log.log
+
+python3 evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
+        --mlperf-accuracy-file offline_accuracy_loadgen_logs/mlperf_log_accuracy.json \
+        --dataset-file ${DATASET_PATH} \
+        --dtype int32
+
+python3 consolidate_results.py --dataset-path ${DATASET_PATH} --model-dir ${CHECKPOINT_PATH}
diff --git a/language/mixtral-8x7b/run_offline.sh b/language/mixtral-8x7b/run_offline.sh
new file mode 100644
index 0000000000..f451fdc243
--- /dev/null
+++ b/language/mixtral-8x7b/run_offline.sh
@@ -0,0 +1,10 @@
+CHECKPOINT_PATH="${CHECKPOINT_PATH:mistralai/Mixtral-8x7B-Instruct-v0.1}"
+DATASET_PATH="${DATASET_PATH:dataset/2024_06_06_mixtral_15k_v4.pkl}"
+
+python -u main.py --scenario Offline \
+		--model-path ${CHECKPOINT_PATH} \
+		--mlperf-conf mlperf.conf \
+		--user-conf user.conf \
+		--total-sample-count 15000 \
+		--dataset-path ${DATASET_PATH} \
+		--device cpu 2>&1 | tee server_log.log
diff --git a/language/mixtral-8x7b/run_server.sh b/language/mixtral-8x7b/run_server.sh
new file mode 100644
index 0000000000..7c4e4a05e2
--- /dev/null
+++ b/language/mixtral-8x7b/run_server.sh
@@ -0,0 +1,12 @@
+
+
+CHECKPOINT_PATH="${CHECKPOINT_PATH:mistralai/Mixtral-8x7B-Instruct-v0.1}"
+DATASET_PATH="${DATASET_PATH:dataset/2024_06_06_mixtral_15k_v4.pkl}"
+
+python -u main.py --scenario Server \
+		--model-path ${CHECKPOINT_PATH} \
+		--mlperf-conf mlperf.conf \
+		--user-conf user.conf \
+		--total-sample-count 15000 \
+		--dataset-path ${DATASET_PATH} \
+		--device cpu 2>&1 | tee server_log.log
diff --git a/language/mixtral-8x7b/user.conf b/language/mixtral-8x7b/user.conf
new file mode 100644
index 0000000000..c5f3ca4888
--- /dev/null
+++ b/language/mixtral-8x7b/user.conf
@@ -0,0 +1,5 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+#
\ No newline at end of file
diff --git a/mlperf.conf b/mlperf.conf
index dd835563d1..5a3c78b22f 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -43,6 +43,7 @@ retinanet.MultiStream.target_latency = 528
 # LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario
 gptj.*.sample_concatenate_permutation = 1
 llama2-70b.*.sample_concatenate_permutation = 1
+mixtral-8x7B.*.sample_concatenate_permutation = 1
 
 *.Server.target_latency = 10
 *.Server.target_latency_percentile = 99
@@ -58,14 +59,19 @@ gptj.Server.target_latency = 20000
 stable-diffusion-xl.Server.target_latency = 20000
 # Llama2-70b benchmarks measures token latencies
 llama2-70b.*.use_token_latencies = 1
+mixtral-8x7b.*.use_token_latencies = 1
 # gptj benchmark infers token latencies
 gptj.*.infer_token_latencies = 1
 gptj.*.token_latency_scaling_factor = 69
-# Only ttft and tpot are tracked for the llama2-70b benchmark therefore target_latency = 0
+# Only ttft and tpot are tracked for the llama2-70b & mixtral-8x7B benchmark therefore target_latency = 0
 llama2-70b.Server.target_latency = 0
 llama2-70b.Server.ttft_latency = 2000
 llama2-70b.Server.tpot_latency = 200
 
+mixtral-8x7b.Server.target_latency = 0
+mixtral-8x7b.Server.ttft_latency = 2000
+mixtral-8x7b.Server.tpot_latency = 200
+
 *.Offline.target_latency_percentile = 90
 *.Offline.min_duration = 600000
 
@@ -83,6 +89,7 @@ rnnt.Offline.min_query_count = 2513
 3d-unet.Offline.min_query_count = 43
 stable-diffusion-xl.Offline.min_query_count = 5000
 llama2-70b.Offline.min_query_count = 24576
+mixtral-8x7b.Offline.min_query_count = 15000
 
 # These fields should be defined and overridden by user.conf.
 *.SingleStream.target_latency = 10

From 0a0f53160f3f9d9757ec5013afbac4cfbcf32053 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 18 Jun 2024 17:08:07 +0100
Subject: [PATCH 100/305] Documentation update (#1735)

---
 .../image_classification/mobilenets.md        | 30 ++++++++++++-------
 docs/install/index.md                         | 12 ++++++--
 2 files changed, 29 insertions(+), 13 deletions(-)

diff --git a/docs/benchmarks/image_classification/mobilenets.md b/docs/benchmarks/image_classification/mobilenets.md
index 1907c2a415..d676300dff 100644
--- a/docs/benchmarks/image_classification/mobilenets.md
+++ b/docs/benchmarks/image_classification/mobilenets.md
@@ -7,53 +7,63 @@ Mobilenet models are not official MLPerf models and so cannot be used for a Clos
 === "Mobilenet-V1"
     ### Mobilenet V1
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v1 --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v1 \
+     --adr.compiler.tags=gcc
     ```
 === "Mobilenet-V2"
     ### Mobilenet V2
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v2 --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v2 \
+     --adr.compiler.tags=gcc
     ```
 === "Mobilenet-V2"
     ### Mobilenet V2
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v2 --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v2 \
+     --adr.compiler.tags=gcc
     ```
 === "Mobilenets"
     ### Mobilenet V1,V2,V3
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_mobilenet --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_mobilenet \
+     --adr.compiler.tags=gcc
     ```
 === "Efficientnet"
     ### Efficientnet
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_efficientnet --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_efficientnet \
+     --adr.compiler.tags=gcc
     ```
 
 ## ARMNN Backend
 === "Mobilenet-V1"
     ### Mobilenet V1
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v1 --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v1 \
+     --adr.compiler.tags=gcc
     ```
 === "Mobilenet-V2"
     ### Mobilenet V2
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 \
+     --adr.compiler.tags=gcc
     ```
 === "Mobilenet-V2"
     ### Mobilenet V2
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 \
+     --adr.compiler.tags=gcc
     ```
 === "Mobilenets"
     ### Mobilenet V1,V2,V3
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet \
+     --adr.compiler.tags=gcc
     ```
 === "Efficientnet"
     ### Efficientnet
     ```bash
-       cm run script --tags=run,mobilenet-models,_tflite,_armnn,_efficientnet --adr.compiler.tags=gcc
+    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_efficientnet \
+     --adr.compiler.tags=gcc
     ```
 
diff --git a/docs/install/index.md b/docs/install/index.md
index 10ed6e3250..9575e86c84 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -1,12 +1,18 @@
 # Installation
 We use MLCommons CM Automation framework to run MLPerf inference benchmarks.
 
-Please follow the [official installation page](https://docs.mlcommons.org/ck/install) to install CM
+CM needs `git`, `python3-pip` and `python3-venv` installed on your system. If any of these are absent, please follow the [official CM installation page](https://docs.mlcommons.org/ck/install) to install them. Once the dependencies are installed, do the following
 
-## Download the CM MLOps Repository
+## Activate a VENV for CM
+```bash
+   python3 -m venv cm
+   source cm/bin/activate
+```
+
+## Install CM and pulls any needed repositories
 
 ```bash
-   cm pull repo gateoverflow@cm4mlops
+   pip install cm4mlops
 ```
 
 

From 44ae828061bc1cfb24a71e3cf3eb5b9129238aea Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 18 Jun 2024 11:12:50 -0500
Subject: [PATCH 101/305] Add link to calibration dataset (#1733)

---
 language/mixtral-8x7b/README.md | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index e301891d9f..20cfe67c90 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -95,6 +95,20 @@ Alternatively, you can simply cd into the folder where you want to place the dat
 wget https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_15k_v4.pkl
 ```
 
+### Calibration dataset
+
+#### Using Rclone
+Rclone is installed, cd into the folder where you want to place the dataset and run:
+```bash
+rclone copyurl https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_15k_calibration_v4.pkl ./ -a -P
+```
+
+#### Using wget
+
+Alternatively, you can simply cd into the folder where you want to place the dataset and run
+```bash
+wget https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_15k_calibration_v4.pkl
+```
 ### Unprocessed
 
 TODO: Share instructions and scripts

From a536cd2be1f039a43e59b10384d4d2fc17583c62 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 20 Jun 2024 01:42:39 -0500
Subject: [PATCH 102/305] Update for v4.1: Add new seeds + update checker +
 update compliance test table (#1736)

---
 compliance/nvidia/README.md            |   5 +-
 mlperf.conf                            |  12 +-
 text_to_image/tools/sample_ids.py      |   2 +-
 text_to_image/tools/sample_ids.txt     |  20 +--
 tools/submission/submission_checker.py | 234 +++++++++++++++++++++++--
 5 files changed, 239 insertions(+), 34 deletions(-)

diff --git a/compliance/nvidia/README.md b/compliance/nvidia/README.md
index e3a751e983..6306761807 100755
--- a/compliance/nvidia/README.md
+++ b/compliance/nvidia/README.md
@@ -37,5 +37,6 @@ The `run_verification.py` found in each test directory will copy the test files
 | 3d-unet | [TEST01](./TEST01/), [TEST05](./TEST05/) |
 | rnnt | [TEST01](./TEST01/), [TEST05](./TEST05/) |
 | gpt-j | - |
-| stable-diffusion-xl | - |
-| Llama2-70b | [TEST06]() |
+| stable-diffusion-xl | [TEST01](./TEST01/), [TEST04](./TEST04/), [TEST05](./TEST05/) |
+| Llama2-70b | [TEST06](./TEST06/) |
+| mixtral-8x7b | [TEST06](./TEST06/) |
diff --git a/mlperf.conf b/mlperf.conf
index 5a3c78b22f..6487bfc238 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -19,13 +19,13 @@ stable-diffusion-xl.*.performance_sample_count_override = 5000
 3d-unet.*.performance_sample_count_override = 0
 
 # Set seeds. The seeds will be distributed two weeks before the submission.
-*.*.qsl_rng_seed = 13281865557512327830
-*.*.sample_index_rng_seed = 198141574272810017
-*.*.schedule_rng_seed = 7575108116881280410
+*.*.qsl_rng_seed = 3066443479025735752
+*.*.sample_index_rng_seed = 10688027786191513374
+*.*.schedule_rng_seed = 14962580496156340209
 # Set seeds for TEST_05. The seeds will be distributed two weeks before the submission.
-*.*.test05_qsl_rng_seed = 2376919268182438552
-*.*.test05_sample_index_rng_seed = 11176391829184272374
-*.*.test05_schedule_rng_seed = 3911940905271271337
+*.*.test05_qsl_rng_seed = 16799458546791641818
+*.*.test05_sample_index_rng_seed = 5453809927556429288
+*.*.test05_schedule_rng_seed = 5435552105434836064
 
 
 *.SingleStream.target_latency_percentile = 90
diff --git a/text_to_image/tools/sample_ids.py b/text_to_image/tools/sample_ids.py
index e1d6effb4b..8c440ec5b3 100644
--- a/text_to_image/tools/sample_ids.py
+++ b/text_to_image/tools/sample_ids.py
@@ -16,7 +16,7 @@ def get_args():
         "--n", type=int, default=10, help="Dataset download location"
     )
     parser.add_argument(
-        "--seed", "-s", type=int, default=926019364, help="Dataset download location"
+        "--seed", "-s", type=int, default=633994880, help="Dataset download location"
     )
     args = parser.parse_args()
     return args
diff --git a/text_to_image/tools/sample_ids.txt b/text_to_image/tools/sample_ids.txt
index 65c9f5641a..8bf3d2be8d 100644
--- a/text_to_image/tools/sample_ids.txt
+++ b/text_to_image/tools/sample_ids.txt
@@ -1,10 +1,10 @@
-4459
-4015
-2705
-1682
-4048
-4683
-3757
-1578
-3319
-95
\ No newline at end of file
+4655
+2569
+1303
+109
+4509
+3009
+2179
+1826
+2094
+3340
\ No newline at end of file
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 451cd66b6a..9263a5f687 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -186,6 +186,169 @@
             "stable-diffusion-xl": {"SingleStream": 1024, "Server": 270336, "Offline": 1}
         },
     },
+    "v4.1": {
+        "models": [
+            "resnet",
+            "retinanet",
+            "bert-99",
+            "bert-99.9",
+            "dlrm-v2-99",
+            "dlrm-v2-99.9",
+            "3d-unet-99",
+            "3d-unet-99.9",
+            "gptj-99",
+            "gptj-99.9",
+            "llama2-70b-99",
+            "llama2-70b-99.9",
+            "stable-diffusion-xl",
+            "mixtral-8x7b"
+        ],
+        "required-scenarios-datacenter": {
+            "resnet": ["Server", "Offline"],
+            "retinanet": ["Server", "Offline"],
+            "bert-99": ["Server", "Offline"],
+            "bert-99.9": ["Server", "Offline"],
+            "dlrm-v2-99": ["Server", "Offline"],
+            "dlrm-v2-99.9": ["Server", "Offline"],
+            "3d-unet-99": ["Offline"],
+            "3d-unet-99.9": ["Offline"],
+            "gptj-99": ["Server", "Offline"],
+            "gptj-99.9": ["Server", "Offline"],
+            "llama2-70b-99": ["Server", "Offline"],
+            "llama2-70b-99.9": ["Server", "Offline"],
+            "stable-diffusion-xl": ["Server", "Offline"],
+            "mixtral-8x7b": ["Server", "Offline"]
+        },
+        "optional-scenarios-datacenter": {},
+        "required-scenarios-edge": {
+            "resnet": ["SingleStream", "MultiStream", "Offline"],
+            "retinanet": ["SingleStream", "MultiStream", "Offline"],
+            "bert-99": ["SingleStream", "Offline"],
+            "3d-unet-99": ["SingleStream", "Offline"],
+            "3d-unet-99.9": ["SingleStream", "Offline"],
+            "gptj-99": ["SingleStream", "Offline"],
+            "gptj-99.9": ["SingleStream", "Offline"],
+            "stable-diffusion-xl": ["SingleStream", "Offline"],
+        },
+        "optional-scenarios-edge": {},
+        "required-scenarios-datacenter-edge": {
+            "resnet": ["SingleStream", "Offline", "MultiStream", "Server"],
+            "retinanet": ["SingleStream", "Offline", "MultiStream", "Server"],
+            "bert-99": ["SingleStream", "Offline", "Server"],
+            "bert-99.9": ["Offline", "Server"],
+            "dlrm-v2-99": ["Offline", "Server"],
+            "dlrm-v2-99.9": ["Offline", "Server"],
+            "3d-unet-99": ["SingleStream", "Offline"],
+            "3d-unet-99.9": ["SingleStream", "Offline"],
+            "gptj-99": ["SingleStream", "Offline", "Server"],
+            "gptj-99.9": ["SingleStream", "Offline", "Server"],
+            "llama2-70b-99": ["Server", "Offline"],
+            "llama2-70b-99.9": ["Server", "Offline"],
+            "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
+            "mixtral-8x7b": ["SingleStream""Server", "Offline"]
+        },
+        "optional-scenarios-datacenter-edge": {},
+        "accuracy-target": {
+            "resnet": ("acc", 76.46 * 0.99),
+            "retinanet": ("mAP", 37.55 * 0.99),
+            "bert-99": ("F1", 90.874 * 0.99),
+            "bert-99.9": ("F1", 90.874 * 0.999),
+            "dlrm-v2-99": ("AUC", 80.31 * 0.99),
+            "dlrm-v2-99.9": ("AUC", 80.31 * 0.999),
+            "3d-unet-99": ("DICE", 0.86170 * 0.99),
+            "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
+            "gptj-99" : ("ROUGE1", 42.9865 * 0.99, "ROUGE2", 20.1235 * 0.99, "ROUGEL", 29.9881 * 0.99, "GEN_LEN", 4016878*0.9),
+            "gptj-99.9" : ("ROUGE1", 42.9865 * 0.999, "ROUGE2", 20.1235 * 0.999, "ROUGEL", 29.9881 * 0.999, "GEN_LEN", 4016878*0.9),
+            "llama2-70b-99" : ("ROUGE1", 44.4312 * 0.99, "ROUGE2", 22.0352 * 0.99, "ROUGEL", 28.6162 * 0.99, "TOKENS_PER_SAMPLE", 294.45*0.9),
+            "llama2-70b-99.9" : ("ROUGE1", 44.4312 * 0.999, "ROUGE2", 22.0352 * 0.999, "ROUGEL", 28.6162 * 0.999, "TOKENS_PER_SAMPLE", 294.45*0.9),
+            "stable-diffusion-xl": ("CLIP_SCORE", 31.68631873, "FID_SCORE", 23.01085758),
+            # TODO: Mixtral metrics
+            # "mixtral-8x7b" : ("ROUGE1", X * 0.99, "ROUGE2", X * 0.99, "ROUGEL", X * 0.99, "TOKENS_PER_SAMPLE", X * 0.9, "gsm8k_accuracy": 73.78*0.99, "mbxp_accuracy": 60.12 * 0.99),
+        },
+        "accuracy-upper-limit": {
+            "stable-diffusion-xl": ("CLIP_SCORE", 31.81331801, "FID_SCORE", 23.95007626),
+            "llama2-70b-99" : ("TOKENS_PER_SAMPLE", 294.45*1.1),
+            "llama2-70b-99.9" : ("TOKENS_PER_SAMPLE", 294.45*1.1)
+            # "mixtral-8x7b" :("TOKENS_PER_SAMPLE", X * 0.9)
+        },
+        "performance-sample-count": {
+            "resnet": 1024,
+            "retinanet": 64,
+            "bert-99": 10833,
+            "bert-99.9": 10833,
+            "dlrm-v2-99": 204800,
+            "dlrm-v2-99.9": 204800,
+            "3d-unet-99": 43,
+            "3d-unet-99.9": 43,
+            "gptj-99": 13368,
+            "gptj-99.9": 13368,
+            "llama2-70b-99": 24576,
+            "llama2-70b-99.9": 24576,
+            "stable-diffusion-xl": 5000,
+            "mixtral-8x7b": 15000,
+        },
+        # TODO: Update this list.
+        "model_mapping": {
+            # map model names to the official mlperf model class
+            "ssd-resnet34": "retinanet",
+            "mobilenet": "resnet",
+            "resnet50": "resnet"
+        },
+        "seeds": {
+            # TODO: Update random seeds
+            "qsl_rng_seed": 3066443479025735752,
+            "sample_index_rng_seed": 10688027786191513374,
+            "schedule_rng_seed": 14962580496156340209,
+        },
+        "test05_seeds": {
+            # TODO: Update random seeds
+            "qsl_rng_seed": 16799458546791641818,
+            "sample_index_rng_seed": 5453809927556429288,
+            "schedule_rng_seed": 5435552105434836064,
+        },
+        "ignore_errors": [],
+        "latency-constraint": {
+            "resnet": {"Server": 15000000},
+            "retinanet": {"Server": 100000000},
+            "bert-99": {"Server": 130000000},
+            "bert-99.9": {"Server": 130000000},
+            "dlrm-v2-99": {"Server": 60000000},
+            "dlrm-v2-99.9": {"Server": 60000000},
+            "gptj-99": {"Server": 20000000000},
+            "gptj-99.9": {"Server": 20000000000},
+            "llama2-70b-99": {"Server": 20000000000},
+            "llama2-70b-99.9": {"Server": 20000000000},
+            "stable-diffusion-xl" : {"Server": 20000000000}
+            # TODO: Mixtral metrics
+            # "mixtral-8x7b" : {"Server": 20000000000}
+        },
+        "min-queries": {
+            "resnet": {
+                "SingleStream": 1024,
+                "MultiStream": 270336,
+                "Server": 270336,
+                "Offline": 1,
+            },
+            "retinanet": {
+                "SingleStream": 1024,
+                "MultiStream": 270336,
+                "Server": 270336,
+                "Offline": 1,
+            },
+            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "dlrm-v2-99": {"Server": 270336, "Offline": 1},
+            "dlrm-v2-99.9": {"Server": 270336, "Offline": 1},
+            "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
+            "3d-unet-99.9": {"SingleStream": 1024, "Offline": 1},
+            "gptj-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "gptj-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama2-70b-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama2-70b-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "stable-diffusion-xl": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "mixtral-8x7b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+        },
+    },
 }
 
 VALID_DIVISIONS = ["open", "closed", "network"]
@@ -221,6 +384,20 @@
                 "3319",
                 "95"
             ]
+        },
+        "v4.1": {
+            "images": [
+                "4655",
+                "2569",
+                "1303",
+                "109",
+                "4509",
+                "3009",
+                "2179",
+                "1826",
+                "2094",
+                "3340"
+            ]
         }
     }
 }
@@ -255,7 +432,8 @@
     "gptj-99.9": 13368,
     "llama2-70b-99": 24576,
     "llama2-70b-99.9": 24576,
-    "stable-diffusion-xl": 5000
+    "stable-diffusion-xl": 5000,
+    "mixtral-8x7b": 15000
 }
 
 SCENARIO_MAPPING = {
@@ -302,8 +480,8 @@
     },
     "v4.1": {
         "llama2-70b-99": {
-        "Offline": "result_tokens_per_second",
-        "Server": "result_completed_tokens_per_second",
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_tokens_per_second",
         },
         "llama2-70b-99.9": {
             "Offline": "result_tokens_per_second",
@@ -316,16 +494,33 @@
         "gptj-99.9": {
             "Offline": "result_inferred_tokens_per_second",
             "Server": "result_inferred_completed_tokens_per_second",
+        },
+        "mixtral-8x7b": {
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_tokens_per_second",
         }
     }
 }
 
-LLAMA2_LATENCY_LIMITS = {
-    # We might add interactive in the next round. Latency in ns
-    "conversational": {
-        "ttft": 2000 * 1000000,
-        "tpot": 200 * 1000000
-    }
+LLM_LATENCY_LIMITS = {
+    "llama2-70b-99":{
+        "conversational": {
+            "ttft": 2000 * 1000000,
+            "tpot": 200 * 1000000
+        }
+    },
+    "llama2-70b-99.9":{
+        "conversational": {
+            "ttft": 2000 * 1000000,
+            "tpot": 200 * 1000000
+        }
+    },
+    # "mixtral-8x7b":{
+    #     "conversational": {
+    #         "ttft": 2000 * 1000000,
+    #         "tpot": 200 * 1000000
+    #     }
+    # }
 }
 
 ACC_PATTERN = {
@@ -799,13 +994,13 @@ def check_accuracy_dir(config, model, path, verbose):
     return is_valid, result_acc
 
 
-def extra_check_llama2(mlperf_log, scenario):
+def extra_check_llm(mlperf_log, scenario, model):
     if (mlperf_log["requested_use_token_latencies"]):
         if scenario == "Offline":
             # For offline no further checks are necessary
             return None, True
         else:
-            for constraint, limits in LLAMA2_LATENCY_LIMITS.items():
+            for constraint, limits in LLM_LATENCY_LIMITS[model].items():
                 if mlperf_log["result_first_token_99.00_percentile_latency_ns"] < limits["ttft"] and mlperf_log["result_time_per_output_token_99.00_percentile_ns"] < limits["tpot"]:
                     return constraint, True
     else:
@@ -867,8 +1062,8 @@ def check_performance_dir(
         res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[version][model][scenario]])
 
         
-    if model in ["llama2-70b-99", "llama2-70b-99.9"]:
-        llama_constraint, is_valid = extra_check_llama2(mlperf_log, scenario_fixed)
+    if model in ["llama2-70b-99", "llama2-70b-99.9", "mixtral-8x7b"]:
+        llama_constraint, is_valid = extra_check_llm(mlperf_log, scenario_fixed, model)
 
     latency_99_percentile = mlperf_log["result_99.00_percentile_latency_ns"]
     latency_mean = mlperf_log["result_mean_latency_ns"]
@@ -2344,8 +2539,7 @@ def check_compliance_dir(
         "gptj-99.9",
         "llama2-70b-99",
         "llama2-70b-99.9",
-        "stable-diffusion-xl"
-
+        "mixtral-8x7b"
     ]:
         test_list.remove("TEST04")
 
@@ -2355,13 +2549,23 @@ def check_compliance_dir(
         "llama2-70b-99",
         "llama2-70b-99.9",
         "stable-diffusion-xl"
+        "mixtral-8x7b"
     ]:
         test_list.remove("TEST05")
+
+    if model in [
+        "gptj-99",
+        "gptj-99.9",
+        "llama2-70b-99",
+        "llama2-70b-99.9",
+        "mixtral-8x7b"
+    ]:
         test_list.remove("TEST01") 
 
     if model in [
         "llama2-70b-99",
         "llama2-70b-99.9",
+        "mixtral-8x7b"
     ]:
         test_list.append("TEST06") 
 

From 84e77719f117149940e326e92acd4f7d885090e8 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 20 Jun 2024 20:21:45 +0100
Subject: [PATCH 103/305] Fix build --sdist for loadgen (#1732)

* Fix build --sdist for loadgen

* Update build_wheels.yml

* Update build_wheels.yml

* Update build_wheels.yml

* Update setup.py

* Simplify the github actions based on latest CM

* Simplify the github actions based on latest CM

* Fix the version of submission checker in the github action
---
 .github/workflows/build_wheels.yml            |  8 +++++++-
 .github/workflows/test-bert.yml               |  6 ++----
 .github/workflows/test-loadgen.yml            |  5 ++---
 .github/workflows/test-resnet50.yml           |  6 ++----
 .github/workflows/test-retinanet.yml          |  6 ++----
 .github/workflows/test-submission-checker.yml |  2 +-
 .github/workflows/test-tvm.yml                |  6 ++----
 loadgen/pyproject.toml                        |  2 +-
 loadgen/setup.py                              | 12 +++++++++---
 loadgen/version_generator.py                  |  1 -
 10 files changed, 28 insertions(+), 26 deletions(-)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 3c14198e81..30a88f11b2 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -3,12 +3,18 @@ name: Build loadgen wheels and release them into PYPI
 on:
   release:
     types: [published]
+  push:
+    branches:
+      - master
+    paths:
+      - loadgen/setup.py
 
 jobs:
   build_wheels:
     name: Build wheels on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     strategy:
+      fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest, macOS-latest]
 
@@ -18,7 +24,7 @@ jobs:
       - uses: actions/setup-python@v3
 
       - name: Install requirements
-        run: python -m pip install cibuildwheel==2.16.2 twine==4.0.2
+        run: python -m pip install cibuildwheel twine
 
       - name: Build wheels
         run: python -m cibuildwheel loadgen/ --output-dir wheels
diff --git a/.github/workflows/test-bert.yml b/.github/workflows/test-bert.yml
index fb5ba3e515..6f6d77a397 100755
--- a/.github/workflows/test-bert.yml
+++ b/.github/workflows/test-bert.yml
@@ -30,9 +30,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cmind
-        cm pull repo mlcommons@ck
-        cm run script --quiet --tags=get,sys-utils-cm
+        python3 -m pip install cm4mlops
     - name: Test BERT and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.env.CM_GIT_CHECKOUT=${{ github.event.pull_request.head.ref }} --adr.inference-src.env.CM_GIT_URL=${{ github.event.pull_request.head.repo.html_url }} --target_qps=1
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }}
diff --git a/.github/workflows/test-loadgen.yml b/.github/workflows/test-loadgen.yml
index 125825e390..b010f82584 100755
--- a/.github/workflows/test-loadgen.yml
+++ b/.github/workflows/test-loadgen.yml
@@ -28,8 +28,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cmind
-        cm pull repo mlcommons@ck
+        python3 -m pip install cm4mlops
     - name: Test Loadgen
       run: |
-        cm run script --tags=get,mlperf,inference,loadgen --quiet --version=custom --adr.inference-src.env.CM_GIT_CHECKOUT=${{ github.event.pull_request.head.ref }} --adr.inference-src.env.CM_GIT_URL=${{ github.event.pull_request.head.repo.html_url }}
+        cm run script --tags=get,mlperf,inference,loadgen --quiet --version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }}
diff --git a/.github/workflows/test-resnet50.yml b/.github/workflows/test-resnet50.yml
index ab14d4ca50..b5d09a66ad 100755
--- a/.github/workflows/test-resnet50.yml
+++ b/.github/workflows/test-resnet50.yml
@@ -30,9 +30,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cmind
-        cm pull repo mlcommons@ck
-        cm run script --quiet --tags=get,sys-utils-cm
+        python3 -m pip install cm4mlops
     - name: Test Resnet50 and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.env.CM_GIT_CHECKOUT=${{ github.event.pull_request.head.ref }} --adr.inference-src.version=custom --adr.inference-src.env.CM_GIT_URL=${{ github.event.pull_request.head.repo.html_url }}
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom
diff --git a/.github/workflows/test-retinanet.yml b/.github/workflows/test-retinanet.yml
index 3afcf086d2..20b05cc25f 100755
--- a/.github/workflows/test-retinanet.yml
+++ b/.github/workflows/test-retinanet.yml
@@ -30,9 +30,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cmind
-        cm pull repo mlcommons@ck
-        cm run script --quiet --tags=get,sys-utils-cm
+        python3 -m pip install cm4mlops
     - name: Test Retinanet and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.env.CM_GIT_CHECKOUT=${{ github.event.pull_request.head.ref }} --adr.inference-src.env.CM_GIT_URL=${{ github.event.pull_request.head.repo.html_url }}
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }}
diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index 14ac907230..2d1b129034 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -33,4 +33,4 @@ jobs:
         git clone https://github.com/mlcommons/inference_results_v4.0 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
-        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.0 --quiet 
+        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.0 --version=r4.0 --quiet 
diff --git a/.github/workflows/test-tvm.yml b/.github/workflows/test-tvm.yml
index fc57c88120..b132fc0025 100755
--- a/.github/workflows/test-tvm.yml
+++ b/.github/workflows/test-tvm.yml
@@ -30,9 +30,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cmind
-        cm pull repo mlcommons@ck
-        cm run script --quiet --tags=get,sys-utils-cm
+        python3 -m pip install cm4mlops
     - name: Test Resnet50 TVM backend
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=5 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.env.CM_GIT_CHECKOUT=${{ github.event.pull_request.head.ref }} --adr.inference-src.env.CM_GIT_URL=${{ github.event.pull_request.head.repo.html_url }} --target_qps=1
+        cm run script --tags=run,mlperf,inference,generate-run-cmds --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=5 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }}
diff --git a/loadgen/pyproject.toml b/loadgen/pyproject.toml
index f62b2c5f7a..9be24eea6d 100755
--- a/loadgen/pyproject.toml
+++ b/loadgen/pyproject.toml
@@ -4,4 +4,4 @@ build-backend = "setuptools.build_meta:__legacy__"
 
 [tool.cibuildwheel]
 environment = "CFLAGS='-std=c++14'"
-build = "cp3{7,8,9,10,11}-*"
+build = "cp3{7,8,9,10,11,12}-*"
diff --git a/loadgen/setup.py b/loadgen/setup.py
index d7b8224de4..8dfc5b9f0e 100644
--- a/loadgen/setup.py
+++ b/loadgen/setup.py
@@ -40,6 +40,9 @@
     "query_sample_library.h",
     "system_under_test.h",
     "test_settings.h",
+    "issue_query_controller.h",
+    "early_stopping.h",
+    "query_dispatch_library.h",
 ]
 
 lib_headers = [
@@ -49,6 +52,8 @@
     "utils.h",
     "version.h",
     "results.h",
+    "bindings/c_api.h",
+    "version_generator.py"
 ]
 
 lib_sources = [
@@ -63,6 +68,7 @@
 ]
 
 lib_bindings = [
+    "bindings/c_api.cc",
     "bindings/python_api.cc",
 ]
 
@@ -76,13 +82,13 @@
 
 mlperf_loadgen_module = Pybind11Extension(
         "mlperf_loadgen",
-        define_macros=[("MAJOR_VERSION", "4"), ("MINOR_VERSION", "0")],
+        define_macros=[("MAJOR_VERSION", "4"), ("MINOR_VERSION", "1")],
         include_dirs=[".", get_include()],
         sources=mlperf_loadgen_sources,
         depends=mlperf_loadgen_headers)
 
-setup(name="mlperf_loadgen",
-      version="4.0",
+setup(name="mlcommons_loadgen",
+      version="4.1",
       description="MLPerf Inference LoadGen python bindings",
       url="https://mlcommons.org/",
       cmdclass={"build_ext": build_ext},
diff --git a/loadgen/version_generator.py b/loadgen/version_generator.py
index 4de930a633..2c5d11331a 100644
--- a/loadgen/version_generator.py
+++ b/loadgen/version_generator.py
@@ -69,7 +69,6 @@ def generate_loadgen_version_definitions_sha1(ofile, loadgen_root):
     sha1s = ""
     loadgen_files = (
         ["/bindings/" + s for s in os.listdir(loadgen_root + "/bindings")] +
-        ["/demos/" + s for s in os.listdir(loadgen_root + "/demos")] +
         ["/" + s for s in os.listdir(loadgen_root)])
     for fn in sorted(loadgen_files):
         full_fn = loadgen_root + fn

From 53f6475910954facecdce05399d0976e2bbcc931 Mon Sep 17 00:00:00 2001
From: Anton Lokhmotov <psyhtest@users.noreply.github.com>
Date: Thu, 20 Jun 2024 23:04:48 +0100
Subject: [PATCH 104/305] Set v4.1 as default version for submission checker.
 (#1737)

---
 tools/submission/submission_checker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 9263a5f687..b15a8596fd 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -783,7 +783,7 @@ def get_args():
     parser.add_argument("--input", required=True, help="submission directory")
     parser.add_argument(
         "--version",
-        default="v4.0",
+        default="v4.1",
         choices=list(MODEL_CONFIG.keys()),
         help="mlperf version",
     )

From 2a46c7afb88b89a6c5def96953902793996ee37b Mon Sep 17 00:00:00 2001
From: Michal Szutenberg <37601244+szutenberg@users.noreply.github.com>
Date: Mon, 24 Jun 2024 18:54:53 +0200
Subject: [PATCH 105/305] Small fixes in MoE eval script (#1743)

* Removed suffixes "_accuracy"
* Removed redundant code
* Similarly to llama we only check rouge1,rouge2 and rougeL scores
---
 language/mixtral-8x7b/README.md            | 6 +++---
 language/mixtral-8x7b/evaluate-accuracy.py | 6 ++----
 2 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 20cfe67c90..69f8035770 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -247,15 +247,15 @@ python -u evaluate-accuracy.py --checkpoint-path mistralai/Mixtral-8x7B-instruct
 Reference scores:
 Open Orca:
 ```json
-{'rouge1': 45.4911, 'rouge2': 23.2829, 'rougeL': 30.3615, 'rougeLsum': 42.4333}
+{'rouge1': 45.4911, 'rouge2': 23.2829, 'rougeL': 30.3615}
 ```
 GSM8K:
 ```json
-{'gsm8k_accuracy': 73.78}
+{'gsm8k': 73.78}
 ```
 MBXP:
 ```json
-{'mbxp_accuracy': 60.16}
+{'mbxp': 60.16}
 ```
 For official submissions, 99% of each reference score is enforced. Additionally, 90%-110% of the generated tokens_per_samples:
 ```json
diff --git a/language/mixtral-8x7b/evaluate-accuracy.py b/language/mixtral-8x7b/evaluate-accuracy.py
index e20834c412..d25b8178ba 100644
--- a/language/mixtral-8x7b/evaluate-accuracy.py
+++ b/language/mixtral-8x7b/evaluate-accuracy.py
@@ -193,11 +193,11 @@ def main():
             continue
         correct += (ref == tgt)
 
-    gsm8k_accuracy = 100.0 * correct / gsm8k_total
+    result['gsm8k'] = 100.0 * correct / gsm8k_total
 
     # MBXP metric
     from evaluate_mbxp import evaluate_mbxp
-    mbxp_accuracy = evaluate_mbxp(results_MBXP, args.n_workers)
+    result['mbxp'] = evaluate_mbxp(results_MBXP, args.n_workers)
 
     result = {
         **result,
@@ -205,8 +205,6 @@ def main():
         'gen_num': gen_num,
         'gen_tok_len': gen_tok_len,
         'tokens_per_sample': round(gen_tok_len / gen_num, 1),
-        'gsm8k_accuracy': gsm8k_accuracy,
-        'mbxp_accuracy': mbxp_accuracy
     }
 
     print("\nResults\n")

From 4a06d973e4ef8e2a5e9ca466478ce1ebd82a7a86 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 25 Jun 2024 11:37:27 -0500
Subject: [PATCH 106/305] Add missing moe checks to submission checker (#1744)

---
 tools/submission/submission_checker.py | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index b15a8596fd..d57b2bb585 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -245,7 +245,7 @@
             "llama2-70b-99": ["Server", "Offline"],
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
-            "mixtral-8x7b": ["SingleStream""Server", "Offline"]
+            "mixtral-8x7b": ["SingleStream", "Server", "Offline"]
         },
         "optional-scenarios-datacenter-edge": {},
         "accuracy-target": {
@@ -262,14 +262,13 @@
             "llama2-70b-99" : ("ROUGE1", 44.4312 * 0.99, "ROUGE2", 22.0352 * 0.99, "ROUGEL", 28.6162 * 0.99, "TOKENS_PER_SAMPLE", 294.45*0.9),
             "llama2-70b-99.9" : ("ROUGE1", 44.4312 * 0.999, "ROUGE2", 22.0352 * 0.999, "ROUGEL", 28.6162 * 0.999, "TOKENS_PER_SAMPLE", 294.45*0.9),
             "stable-diffusion-xl": ("CLIP_SCORE", 31.68631873, "FID_SCORE", 23.01085758),
-            # TODO: Mixtral metrics
-            # "mixtral-8x7b" : ("ROUGE1", X * 0.99, "ROUGE2", X * 0.99, "ROUGEL", X * 0.99, "TOKENS_PER_SAMPLE", X * 0.9, "gsm8k_accuracy": 73.78*0.99, "mbxp_accuracy": 60.12 * 0.99),
+            "mixtral-8x7b" : ("ROUGE1", 45.4911 * 0.99, "ROUGE2", 23.2829 * 0.99, "ROUGEL", 30.3615 * 0.99, "TOKENS_PER_SAMPLE", 145.9 * 0.9, "gsm8k_accuracy", 73.78*0.99, "mbxp_accuracy", 60.12 * 0.99),
         },
         "accuracy-upper-limit": {
             "stable-diffusion-xl": ("CLIP_SCORE", 31.81331801, "FID_SCORE", 23.95007626),
             "llama2-70b-99" : ("TOKENS_PER_SAMPLE", 294.45*1.1),
-            "llama2-70b-99.9" : ("TOKENS_PER_SAMPLE", 294.45*1.1)
-            # "mixtral-8x7b" :("TOKENS_PER_SAMPLE", X * 0.9)
+            "llama2-70b-99.9" : ("TOKENS_PER_SAMPLE", 294.45*1.1),
+            "mixtral-8x7b" : ("TOKENS_PER_SAMPLE", 145.9 * 1.1)
         },
         "performance-sample-count": {
             "resnet": 1024,
@@ -515,12 +514,12 @@
             "tpot": 200 * 1000000
         }
     },
-    # "mixtral-8x7b":{
-    #     "conversational": {
-    #         "ttft": 2000 * 1000000,
-    #         "tpot": 200 * 1000000
-    #     }
-    # }
+    "mixtral-8x7b":{
+        "conversational": {
+            "ttft": 2000 * 1000000,
+            "tpot": 200 * 1000000
+        }
+    }
 }
 
 ACC_PATTERN = {
@@ -539,6 +538,8 @@
     "TOKENS_PER_SAMPLE": r".*'tokens_per_sample':\s([\d.]+).*",
     "CLIP_SCORE": r".*'CLIP_SCORE':\s([\d.]+).*",
     "FID_SCORE": r".*'FID_SCORE':\s([\d.]+).*",
+    "gsm8k_accuracy": r"'gsm8k':\s([\d.]+).*",
+    "mbxp_accuracy": r"'mbxp':\s([\d.]+).*",
 }
 
 SYSTEM_DESC_REQUIRED_FIELDS = [

From eb6714e90ea04ee9e1d58db274f2f3cbde231560 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 25 Jun 2024 11:48:53 -0500
Subject: [PATCH 107/305] Quick fix: Exclude SD from test04 & test05 (#1742)

---
 tools/submission/submission_checker.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index d57b2bb585..9ed61a51c8 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -2540,6 +2540,7 @@ def check_compliance_dir(
         "gptj-99.9",
         "llama2-70b-99",
         "llama2-70b-99.9",
+        "stable-diffusion-xl",
         "mixtral-8x7b"
     ]:
         test_list.remove("TEST04")
@@ -2549,7 +2550,7 @@ def check_compliance_dir(
         "gptj-99.9",
         "llama2-70b-99",
         "llama2-70b-99.9",
-        "stable-diffusion-xl"
+        "stable-diffusion-xl",
         "mixtral-8x7b"
     ]:
         test_list.remove("TEST05")

From a31cced81df8f4b952a3ac1cea087c4cbaa7568b Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Tue, 25 Jun 2024 22:28:23 +0530
Subject: [PATCH 108/305] added batch size to SUT Server (#1750)

---
 language/llama2-70b/SUT.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/llama2-70b/SUT.py b/language/llama2-70b/SUT.py
index 4ff78c38c4..17faced405 100644
--- a/language/llama2-70b/SUT.py
+++ b/language/llama2-70b/SUT.py
@@ -285,7 +285,7 @@ def __del__(self):
 
 
 class SUTServer(SUT):
-    def __init__(self, model_path=None, dtype="bfloat16", device="cpu", total_sample_count=24576, dataset_path=None, workers=1):
+    def __init__(self, model_path=None, dtype="bfloat16", device="cpu", total_sample_count=24576, dataset_path=None, batch_size=None, workers=1):
 
         super().__init__(model_path=model_path, dtype=dtype, device=device, total_sample_count=total_sample_count, dataset_path=dataset_path, workers=workers)
 

From 145e77c7537a29a7b74d19cb8abf75afe5930b06 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 25 Jun 2024 17:59:47 +0100
Subject: [PATCH 109/305] Update Mixtral SUT.py (#1749)

Co-authored-by: Miro <mirhodak@amd.com>
---
 language/mixtral-8x7b/SUT.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
index 9ed44dbf5a..aaf0eecee2 100644
--- a/language/mixtral-8x7b/SUT.py
+++ b/language/mixtral-8x7b/SUT.py
@@ -301,7 +301,8 @@ def load_model(self):
             self.model = self.model.to(self.device)
 
         self.model.eval()
-        self.model = self.model.to(memory_format=torch.channels_last)
+        if self.device != "cpu":
+            self.model = self.model.to(memory_format=torch.channels_last)
 
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.model_path,

From 3c6fcec527c4c53631c1fd237a5c4e7d9f395326 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Tue, 25 Jun 2024 22:31:39 +0530
Subject: [PATCH 110/305] Updated documentation for mixtral-8x7b and main
 README (#1748)

* added mixtral in doc

* added get-mixtral-data in doc

* updated README for MLPerf Inference v4.1

* removed 4.0 deadline
---
 README.md                                     | 24 ++++++++++++++++++-
 .../language/get-mixtral-8x7b-data.md         | 23 ++++++++++++++++++
 docs/benchmarks/language/mixtral-8x7b.md      |  6 +++++
 main.py                                       |  4 ++--
 mkdocs.yml                                    |  1 +
 5 files changed, 55 insertions(+), 3 deletions(-)
 create mode 100644 docs/benchmarks/language/get-mixtral-8x7b-data.md
 create mode 100644 docs/benchmarks/language/mixtral-8x7b.md

diff --git a/README.md b/README.md
index dde466980d..e2f0fa8c0f 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,29 @@ Please see the [MLPerf Inference benchmark paper](https://arxiv.org/abs/1911.025
     primaryClass={cs.LG}
 }
 ```
-## MLPerf Inference v4.0 (submission deadline February 23, 2024)
+Please see [here](https://docs.mlcommons.org/inference/benchmarks/) for the MLPerf inference documentation website which includes automated commands to run MLPerf inference benchmarks using different implementations.
+
+## MLPerf Inference v4.1 (submission deadline July 26, 2024)
+
+For submissions, please use the master branch and any commit since the [4.1 seed release](https://github.com/mlcommons/inference/pull/1736/files) although it is best to use the latest commit. v4.1 tag will be created from the master branch after the result publication.
+
+For power submissions please use [SPEC PTD 1.10](https://github.com/mlcommons/power/tree/main/inference_v1.0) (needs special access) and any commit of the power-dev repository after the [code-freeze](https://github.com/mlcommons/power-dev/pull/325)
+
+| model | reference app | framework | dataset | category
+| ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, onnx, tvm, ncnn | imagenet2012 | edge,datacenter |
+| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge,datacenter |
+| dlrm-v2 | [recommendation/dlrm_v2](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm_v2/pytorch) | pytorch | Multihot Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 | edge,datacenter |
+| gpt-j | [language/gpt-j](https://github.com/mlcommons/inference/tree/master/language/gpt-j)| pytorch | CNN-Daily Mail | edge,datacenter |
+| stable-diffusion-xl | [text_to_image](https://github.com/mlcommons/inference/tree/master/text_to_image) | pytorch | COCO 2014| edge,datacenter |
+| llama2-70b | [language/llama2-70b](https://github.com/mlcommons/inference/tree/master/language/llama2-70b) | pytorch | OpenOrca | datacenter |
+| mixtral-8x7b | [language/mixtral-8x7b](https://github.com/mlcommons/inference/tree/master/language/mixtral-8x7b) | pytorch | OpenOrca, MBXP, GSM8K | datacenter |
+
+* Framework here is given for the reference implementation. Submitters are free to use their own frameworks to run the benchmark.
+
+## MLPerf Inference v4.0 (submission February 23, 2024)
 
 There is an extra one-week extension allowed only for the llama2-70b submissions. For submissions, please use the master branch and any commit since the [4.0 seed release](https://github.com/mlcommons/inference/commit/8e36925bd36a503e39fcbbc488e9e46126f079ed) although it is best to use the latest commit. v4.0 tag will be created from the master branch after the result publication.
 
diff --git a/docs/benchmarks/language/get-mixtral-8x7b-data.md b/docs/benchmarks/language/get-mixtral-8x7b-data.md
new file mode 100644
index 0000000000..601e5c1dd1
--- /dev/null
+++ b/docs/benchmarks/language/get-mixtral-8x7b-data.md
@@ -0,0 +1,23 @@
+## Dataset
+
+The benchmark implementation run command will automatically download the preprocessed validation and calibration datasets. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+    mixtral-8x7b validation run uses the combined dataset - Open ORCA, GSM8K and MBXP.
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset-mixtral,openorca-mbxp-gsm8k-combined -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf MIXTRAL-8x7b Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,mixtral -j
+    ```
\ No newline at end of file
diff --git a/docs/benchmarks/language/mixtral-8x7b.md b/docs/benchmarks/language/mixtral-8x7b.md
new file mode 100644
index 0000000000..eb138dc46f
--- /dev/null
+++ b/docs/benchmarks/language/mixtral-8x7b.md
@@ -0,0 +1,6 @@
+
+=== "MLCommons-Python"
+    ## MLPerf Reference Implementation in Python
+    
+    MIXTRAL-8x7b
+{{ mlperf_inference_implementation_readme (4, "mixtral-8x7b", "reference") }}
\ No newline at end of file
diff --git a/main.py b/main.py
index f73357d4eb..d208d07ce0 100644
--- a/main.py
+++ b/main.py
@@ -25,7 +25,7 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                  frameworks = [ "Pytorch" ]
 
         elif implementation == "nvidia":
-            if model in [ "sdxl", "llama2-70b-99", "llama2-70b-99.9" ]:
+            if model in [ "sdxl", "llama2-70b-99", "llama2-70b-99.9", "mixtral-8x7b" ]:
                  return pre_space+"    WIP"
             devices = [ "CUDA" ]
             frameworks = [ "TensorRT" ]
@@ -57,7 +57,7 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
 
         if model.lower() == "bert-99.9":
             categories = [ "Datacenter" ]
-        elif "dlrm" in model.lower() or "llama2" in model.lower():
+        elif "dlrm" in model.lower() or "llama2" in model.lower() or "mixtral" in model.lower():
             categories = [ "Datacenter" ]
         else:
             categories = [ "Edge", "Datacenter" ]
diff --git a/mkdocs.yml b/mkdocs.yml
index 339a0657d0..a5f75efa6c 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -35,6 +35,7 @@ nav:
       - Bert-Large: benchmarks/language/bert.md
       - GPT-J: benchmarks/language/gpt-j.md
       - LLAMA2-70B: benchmarks/language/llama2-70b.md
+      - MIXTRAL-8x7b: benchmarks/language/mixtral-8x7b.md
     - Recommendation:
       - DLRM-v2: benchmarks/recommendation/dlrm-v2.md
   - Submission:

From 5a604dd4c33d7b5284580e427ee07fb22c4f6d01 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Wed, 26 Jun 2024 00:20:17 +0530
Subject: [PATCH 111/305] Loadgen over Network implemented for GPT-J (#1702)

* Added sut network support files

Created initial file for network

Added arg node

Added arg port

Incorporated tqdm

Code clean

* Added provision for disk offload

Class call made proper

Set offload folder

Update backend.py

added offload state dict

Update offload conditions

Initial commit gptj network

Update backend.py

Update run.py

Fixed datatypes

set debug=True

debug

included statement for error checking

bug fix

Update request format

fixed format

Update backend.py

Update backend.py

* Non group encoding added

* Reorganised files and flow

Instead of processing the encoded tensors, texts are being passed, which is encoded in server.

Delete backend.py

File name changed

Delete run.py

* Server processes single text query rather than tensors

Accepts single text query

Changed return variable

Updated response datatype

* Added semaphore to process client requests

Renamed cache variable

f-string error fix

Update network_SUT.py

Semaphore declaration refined

Check for semaphore globally

Update size to GB

Semaphore initialisation bug fixed

Bug fix in getting model mem size

Updated KV_cache formula

Added semaphore support

* Run LON with external SUTs

* Delete run.py

* Update README for LON

Update README.md

* Removed confusing tags

* Modularised and seperated QSL and QDL as per network tags

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 language/gpt-j/GPTJ_QDL.py        | 132 ++++++++++++++++
 language/gpt-j/GPTJ_QSL.py        |  20 +++
 language/gpt-j/README.md          |  23 +++
 language/gpt-j/backend.py         | 202 -------------------------
 language/gpt-j/backend_PyTorch.py | 242 ++++++++++++++++++++++++++++++
 language/gpt-j/dataset.py         |  10 +-
 language/gpt-j/main.py            | 158 ++++++++++++++-----
 language/gpt-j/network_SUT.py     |  60 ++++++++
 language/gpt-j/systemStatus.py    |  14 ++
 language/gpt-j/tokenizer_GPTJ.py  |   8 +
 10 files changed, 619 insertions(+), 250 deletions(-)
 create mode 100644 language/gpt-j/GPTJ_QDL.py
 create mode 100644 language/gpt-j/GPTJ_QSL.py
 delete mode 100644 language/gpt-j/backend.py
 create mode 100644 language/gpt-j/backend_PyTorch.py
 create mode 100644 language/gpt-j/network_SUT.py
 create mode 100644 language/gpt-j/systemStatus.py
 create mode 100644 language/gpt-j/tokenizer_GPTJ.py

diff --git a/language/gpt-j/GPTJ_QDL.py b/language/gpt-j/GPTJ_QDL.py
new file mode 100644
index 0000000000..38ba2deba7
--- /dev/null
+++ b/language/gpt-j/GPTJ_QDL.py
@@ -0,0 +1,132 @@
+# For QDL
+import threading
+import requests
+from time import sleep
+import mlperf_loadgen as lg
+import os
+import numpy as np
+import array
+import time
+
+class GPTJ_QDL:
+    """QDL acting as a proxy to the SUT.
+    This QDL communicates with the SUT via HTTP.
+    It uses two endpoints to communicate with the SUT:
+    - /predict/ : Send a query to the SUT and get a response.
+    - /getname/ : Get the name of the SUT. Send a getname to the SUT and get a response.
+    """
+    def __init__(self, qsl, sut_server_addr: list, scenario: str):
+        self.scenario = scenario
+        self.sut_server_addr = sut_server_addr
+        self.num_nodes = len(sut_server_addr)
+        self.qsl = qsl
+
+        # Construct QDL from the python binding
+        self.qdl = lg.ConstructQDL(
+            self.issue_query, self.flush_queries, self.client_get_name)
+        print("Finished constructing QDL!")
+
+        # For round robin between the SUTs:
+        self.next_sut_id = 0
+        self.lock = threading.Lock()
+
+    def issue_query(self, query_samples):
+        """Process the query to send to the SUT"""
+        threading.Thread(target=self.process_query_async,
+                         args=[query_samples]).start()
+
+    def flush_queries(self):
+        """Flush the queries. Dummy implementation."""
+        pass
+
+    def process_query_async(self, query_samples):
+        """
+        This function is called by the Loadgen in a separate thread.
+        It is responsible for
+            1. Creating a query for the SUT, by reading the features from the QSL.
+            2. Sending the query to the SUT.
+            3. Waiting for the response from the SUT.
+            4. Deserializing the response.
+            5. Calling mlperf_loadgen.QuerySamplesComplete(query_samples, response)
+        Args:
+            query_samples: A list of QuerySample objects.
+        """
+
+        max_num_threads = int(os.environ.get('CM_MAX_NUM_THREADS', os.cpu_count()))
+        if self.scenario == "Offline":
+            # Client sends multiple requests using threads
+            # Pause when active thread equals the set max number of threads
+            # Only sends next request after recieving respose from server to any of the currently active threads 
+            print("Executing Offline scenario!")
+            for i in range(len(query_samples)):
+                index = query_samples[i].index
+                input_ids_tensor = self.qsl.data_object.source_encoded_input_ids[index]
+                input_masks_tensor = self.qsl.data_object.source_encoded_attn_masks[index]
+                text = self.qsl.data_object.sources[index]
+                query = {
+                    "input_text": text,
+                    "input_ids_tensor": input_ids_tensor.tolist(),
+                    "input_masks_tensor": input_masks_tensor.tolist()
+                }
+                n = threading.active_count()
+                while n >= max_num_threads:
+                    sleep(0.0001)
+                    n = threading.active_count()
+                threading.Thread(target=self.client_predict_worker,
+                             args=[query, query_samples[i].id]).start()
+        if self.scenario == "Server":
+            # Client sends request to server
+            # Number of samples can vary based on Poisson distribution
+            index = query_samples[0].index
+            input_ids_tensor = self.qsl.data_object.source_encoded_input_ids[index]
+            input_masks_tensor = self.qsl.data_object.source_encoded_attn_masks[index]
+            text = self.qsl.data_object.sources[index]
+            query = {
+                "input_text": text,
+                "input_ids_tensor": input_ids_tensor.tolist(),
+                "input_masks_tensor": input_masks_tensor.tolist()
+            }
+            self.client_predict_worker(query, query_samples[0].id)
+
+    def get_sut_id_round_robin(self):
+        """Get the SUT id in round robin."""
+        with self.lock:
+            res = self.next_sut_id
+            self.next_sut_id = (self.next_sut_id + 1) % self.num_nodes
+        return res
+    
+    def client_predict_worker(self, query, query_id):
+        """Serialize the query, send it to the SUT in round robin, and return the deserialized response."""
+        url = '{}/predict/'.format(self.sut_server_addr[self.get_sut_id_round_robin()])
+        responses = []
+        # Start the timer
+        startTime = time.time()
+        # Sending the request to the server through POST method
+        # Upon recieving the response, it is stored in response variable
+        response = requests.post(url, json={'query': query})
+        # Measure the response time
+        endTime = time.time()
+        # calculate the latency
+        print(f"Latency = {endTime-startTime}")
+        output = response.json()['result']
+        response_text = output["response_text"]
+        print(query["input_text"])
+        print(response_text)
+        
+        output_batch = np.array(output["pred_output_batch"]).astype(np.int32)
+        response_array = array.array("B", output_batch.tobytes())
+        bi = response_array.buffer_info()
+
+        responses.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
+        lg.QuerySamplesComplete(responses)
+    
+    def client_get_name(self):
+        """Get the name of the SUT from ALL the SUTS."""
+        if len(self.sut_server_addr) == 1:
+            return requests.post(f'{self.sut_server_addr[0]}/getname/').json()['name']
+    
+        sut_names = [requests.post(f'{addr}/getname/').json()['name'] for addr in self.sut_server_addr]
+        return "Multi-node SUT: " + ', '.join(sut_names)
+
+    def __del__(self):
+        lg.DestroyQDL(self.qdl)   
\ No newline at end of file
diff --git a/language/gpt-j/GPTJ_QSL.py b/language/gpt-j/GPTJ_QSL.py
new file mode 100644
index 0000000000..f74fe74930
--- /dev/null
+++ b/language/gpt-j/GPTJ_QSL.py
@@ -0,0 +1,20 @@
+import mlperf_loadgen as lg
+from dataset import Dataset
+
+class GPTJ_QSL():
+    def __init__(self, dataset_path: str, max_examples: int):
+        self.dataset_path = dataset_path
+        self.max_examples = max_examples
+
+        # creating data object for QSL
+        self.data_object = Dataset(
+                self.dataset_path, total_count_override=self.max_examples)
+        
+        # construct QSL from python binding
+        self.qsl = lg.ConstructQSL(self.data_object.count, self.data_object.perf_count,
+                                   self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
+
+        print("Finished constructing QSL.")
+
+def get_GPTJ_QSL(dataset_path: str, max_examples: int):
+    return GPTJ_QSL(dataset_path , max_examples)
\ No newline at end of file
diff --git a/language/gpt-j/README.md b/language/gpt-j/README.md
index 061067027a..7c5301482b 100644
--- a/language/gpt-j/README.md
+++ b/language/gpt-j/README.md
@@ -135,3 +135,26 @@ This is a comprehensive list of public datasets and models used by this reposito
 | [gpt-j-6b (Hugging Face)](https://huggingface.co/EleutherAI/gpt-j-6b) | PyTorch | Text Summarization |
 
 Intel expressly disclaims the accuracy, adequacy, or completeness of any data, datasets or models, and is not liable for any errors, omissions, or defects in such content, or for any reliance thereon. Intel also expressly disclaims any warranty of non-infringement with respect to such data, dataset(s), or model(s). Intel is not liable for any liability or damages relating to your use of such data, datasets or models.
+
+
+## Loadgen over the Network 
+
+The below CM command will launch the SUT server
+
+```
+cm run script --tags=run-mlperf,inference,_performance-only --model=gptj-99  \
+--backend=pytorch   --device=cuda --beam_size=1 --precision=bfloat16 \
+--network=sut --rerun --quiet --adr.compiler.tags=gcc 
+```
+
+#### Note: 
+In our experimentation, we found out that in addition to memory occupied by the model, KV cache of size around 6xbeam_size GB occupies the memory.
+
+Once the SUT server is launched, the below command can be run on the loadgen node to do issue queries to the SUT nodes. In this command `-sut_servers` has just the localhost address - it can be changed to a comma-separated list of any hostname/IP in the network. 
+
+```
+cm run script --tags=run-mlperf,inference,_performance-only --model=gptj-99 \
+--backend=pytorch  --test_query_count=30  \
+--network=lon  --rerun --quiet --scenario=Offline \
+--sut_servers,=http://localhost:8000 --adr.compiler.tags=gcc
+```
diff --git a/language/gpt-j/backend.py b/language/gpt-j/backend.py
deleted file mode 100644
index 7a01ee8a57..0000000000
--- a/language/gpt-j/backend.py
+++ /dev/null
@@ -1,202 +0,0 @@
-import os
-import time
-import numpy as np
-import array
-import torch
-from torch.nn.functional import pad
-from torch.utils.data import DataLoader
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import mlperf_loadgen as lg
-from dataset import Dataset
-
-
-gen_kwargs = {
-    "early_stopping": True,
-    "max_new_tokens": 128,
-    "min_new_tokens": 30,
-    "num_beams": int(os.environ.get("GPTJ_BEAM_SIZE", "4")), # only beam_size 4 is allowed for official submission
-}
-
-
-class SUT_base():
-    def __init__(self, model_path, dtype, dataset_path, max_examples, use_gpu=False):
-        # TODO : Pass model file name to init instead of args
-        print("Loading PyTorch model...")
-        self.model_name = "EleutherAI/gpt-j-6B"
-        self.dataset_path = dataset_path
-        self.model_path = model_path
-        self.use_gpu = use_gpu
-        # dtype
-        if dtype == 'bfloat16':
-            self.amp_enabled = True
-            self.amp_dtype = torch.bfloat16
-            print("BF16 autocast")
-        elif dtype == 'float16':
-            self.amp_enabled = True
-            self.amp_dtype = torch.float16
-        else:
-            self.amp_enabled = False
-            self.amp_dtype = torch.float32
-
-        self.model = AutoModelForCausalLM.from_pretrained(
-            self.model_path,
-            device_map="auto" if not self.use_gpu else None,
-            low_cpu_mem_usage=True if not self.use_gpu else False,
-            torch_dtype=self.amp_dtype
-        )
-
-        # Cast the model to GPU if the flag is set.
-        if self.use_gpu:
-            print(f"Casting models to GPU...")
-            assert torch.cuda.is_available(), "torch gpu is not available, exiting..."
-            self.device = torch.device("cuda:0")
-            self.model.to(self.device)
-
-        self.model.eval()
-        self.model = self.model.to(memory_format=torch.channels_last)
-
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            self.model_name,
-            model_max_length=1919,
-            padding_side="left",
-            use_fast=False,)
-        self.tokenizer.pad_token = self.tokenizer.eos_token
-
-        self.data_object = Dataset(
-            self.dataset_path, total_count_override=max_examples)
-        self.qsl = lg.ConstructQSL(self.data_object.count, self.data_object.perf_count,
-                                   self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
-
-        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
-
-    def issue_queries(self, query_samples):
-        print("Number of Samples in query_samples : ", len(query_samples))
-
-        total_samples_done = 0
-        list_prompts_tokens = []
-        list_prompts_attn_masks = []
-
-        for i in range(len(query_samples)):
-            index = query_samples[i].index
-            input_ids_tensor = self.data_object.source_encoded_input_ids[index]
-            input_masks_tensor = self.data_object.source_encoded_attn_masks[index]
-
-            # Cast to GPU
-            if self.use_gpu:
-                input_ids_tensor = input_ids_tensor.to(self.device)
-                input_masks_tensor = input_masks_tensor.to(self.device)
-
-            pred_output_batch = self.inference_call(
-                input_ids_tensor, input_masks_tensor).cpu().numpy()
-
-            response_array = array.array("B", pred_output_batch[0].tobytes())
-            bi = response_array.buffer_info()
-            response = [lg.QuerySampleResponse(
-                query_samples[i].id, bi[0], bi[1])]
-            lg.QuerySamplesComplete(response)
-            if i % 5 == 0:
-                print("Completed : ", i)
-
-    def inference_call(self, input_ids_tensor, input_masks_tensor):
-        ''' Common for all scenarios '''
-        torch_device_type = 'cuda' if self.use_gpu else 'cpu'
-
-        with torch.inference_mode(), torch.autocast(device_type=torch_device_type, enabled=self.amp_enabled, dtype=self.amp_dtype if self.amp_enabled else None):
-            input_batch = dict()
-            input_batch['input_ids'] = input_ids_tensor
-            input_batch['attention_mask'] = input_masks_tensor
-
-            output_batch = self.model.generate(
-                **input_batch, **gen_kwargs, pad_token_id=self.tokenizer.eos_token_id)
-
-            input_batch_lengths = [x.shape[0]
-                                   for x in input_batch["input_ids"]]
-
-            output_batch_lengths = [x.shape[0] for x in output_batch]
-
-            output_batch_truncated = []
-            for data, source_len in zip(output_batch, input_batch_lengths):
-                output_batch_truncated.append(data[source_len:])
-
-            output_batch_truncated = torch.stack(output_batch_truncated)
-
-        return output_batch_truncated
-
-    def flush_queries(self):
-        pass
-
-    def __del__(self):
-        print("Finished destroying SUT.")
-
-
-class SUT_Offline(SUT_base):
-    def __init__(self, model_path, dtype, dataset_path, max_examples, use_gpu):
-        SUT_base.__init__(self, model_path, dtype, dataset_path, max_examples, use_gpu)
-    '''IssueQuery and inference methods implemented in Base class'''
-
-
-class SUT_Server(SUT_base):
-    def __init__(self, model_path, dtype, dataset_path, max_examples, use_gpu):
-
-        SUT_base.__init__(self, model_path, dtype, dataset_path, max_examples, use_gpu)
-        self.total_samples_done = 0
-        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
-        print("SUT Server")
-
-    def issue_queries(self, query_samples):
-
-        index = query_samples[0].index
-        input_ids_tensor = self.data_object.source_encoded_input_ids[index]
-        input_masks_tensor = self.data_object.source_encoded_attn_masks[index]
-
-        if self.use_gpu:
-            input_ids_tensor = input_ids_tensor.to(self.device)
-            input_masks_tensor = input_masks_tensor.to(self.device)
-
-        pred_output_batch = self.inference_call(
-            input_ids_tensor, input_masks_tensor).cpu().numpy()
-
-        response_array = array.array("B", pred_output_batch.tobytes())
-        bi = response_array.buffer_info()
-        responses = [lg.QuerySampleResponse(query_samples[0].id, bi[0], bi[1])]
-        lg.QuerySamplesComplete(responses)
-        self.total_samples_done += 1
-        if self.total_samples_done % 5 == 0:
-            print("Completed : ", self.total_samples_done)
-
-
-class SUT_SingleStream(SUT_base):
-    def __init__(self, model_path, dtype, dataset_path, max_examples, use_gpu):
-        SUT_base.__init__(self, model_path, dtype, dataset_path, max_examples, use_gpu)
-        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
-        self.total_samples_done = 0
-
-    def issue_queries(self, query_samples):
-
-        index = query_samples[0].index
-        input_ids_tensor = self.data_object.source_encoded_input_ids[index]
-        input_masks_tensor = self.data_object.source_encoded_attn_masks[index]
-
-        if self.use_gpu:
-            input_ids_tensor = input_ids_tensor.to(self.device)
-            input_masks_tensor = input_masks_tensor.to(self.device)
-
-        pred_output_batch = self.inference_call(
-            input_ids_tensor, input_masks_tensor).cpu().numpy()
-
-        response_array = array.array("B", pred_output_batch.tobytes())
-        bi = response_array.buffer_info()
-        responses = [lg.QuerySampleResponse(query_samples[0].id, bi[0], bi[1])]
-        lg.QuerySamplesComplete(responses)
-        self.total_samples_done += 1
-        if self.total_samples_done % 5 == 0:
-            print("Completed : ", self.total_samples_done)
-
-
-def get_SUT(model_path, scenario, dtype, dataset_path, max_examples, use_gpu=False):
-    if scenario == "Offline":
-        return SUT_Offline(model_path, dtype, dataset_path, max_examples, use_gpu)
-    elif scenario == "Server":
-        return SUT_Server(model_path, dtype, dataset_path, max_examples, use_gpu)
-    elif scenario == "SingleStream":
-        return SUT_SingleStream(model_path, dtype, dataset_path, max_examples, use_gpu)
diff --git a/language/gpt-j/backend_PyTorch.py b/language/gpt-j/backend_PyTorch.py
new file mode 100644
index 0000000000..06002acff2
--- /dev/null
+++ b/language/gpt-j/backend_PyTorch.py
@@ -0,0 +1,242 @@
+import os
+import time
+import numpy as np
+import array
+import torch
+from torch.nn.functional import pad
+from torch.utils.data import DataLoader
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import mlperf_loadgen as lg
+from tqdm import tqdm
+from accelerate import disk_offload
+
+gen_kwargs = {
+    "early_stopping": True,
+    "max_new_tokens": 128,
+    "min_new_tokens": 30,
+    "num_beams": int(os.environ.get("GPTJ_BEAM_SIZE", "4")), # only beam_size 4 is allowed for official submission
+}
+
+
+class SUT_base():
+    def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu=False, network=None, qsl=None):
+        self.network = network
+        self.model_name = "EleutherAI/gpt-j-6B"
+        self.model_path = model_path
+        self.use_gpu = use_gpu
+        self.dataset_path = dataset_path
+        self.max_examples = max_examples
+        self.scenario = scenario
+        self.qsl = qsl
+        print("Loading PyTorch model...")
+            
+        # dtype
+        if dtype == 'bfloat16':
+            self.amp_enabled = True
+            self.amp_dtype = torch.bfloat16
+            print("BF16 autocast")
+        elif dtype == 'float16':
+            self.amp_enabled = True
+            self.amp_dtype = torch.float16
+        else:
+            self.amp_enabled = False
+            self.amp_dtype = torch.float32
+        try:
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.model_path,
+                device_map="auto" if not self.use_gpu else None,
+                low_cpu_mem_usage=True if not self.use_gpu else False,
+                torch_dtype=self.amp_dtype,
+                offload_folder="offload" if not self.use_gpu else None,    # specify offload folder when using devices with less RAM
+                offload_state_dict = True if not self.use_gpu else False   # to have some shards of the model to be on the disk
+            )
+        except ValueError as e: 
+            if "disk_offload" in str(e):
+                print("Offloading the whole model to disk...")
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    self.model_path,
+                    low_cpu_mem_usage=True if not self.use_gpu else False,
+                    torch_dtype=self.amp_dtype,
+                    offload_state_dict = True if not self.use_gpu else False
+                ).cpu()
+                disk_offload(model=self.model, offload_dir="offload")
+
+        # Cast the model to GPU if the flag is set.
+        if self.use_gpu:
+            print(f"Casting models to GPU...")
+            assert torch.cuda.is_available(), "torch gpu is not available, exiting..."
+            self.device = torch.device("cuda:0")
+            self.model.to(self.device)
+
+        self.model.eval()
+        try: # for systems with low ram, the below command gives error as some part is offloaded to disk
+            self.model = self.model.to(memory_format=torch.channels_last)
+        except:
+            pass
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_name,
+            model_max_length=1919,
+            padding_side="left",
+            use_fast=False,)
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        # calculate the memory size taken by the model 
+        self.total_mem_size = 0
+        parameters = list(self.model.parameters())
+        for param in tqdm(parameters):
+            self.total_mem_size += param.numel() * param.element_size()
+        self.total_mem_size = self.total_mem_size / (1024 ** 3)
+
+        # construct SUT
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+
+    def issue_queries(self, query_samples):
+        print("Number of Samples in query_samples : ", len(query_samples))
+
+        total_samples_done = 0
+        list_prompts_tokens = []
+        list_prompts_attn_masks = []
+
+        # Pass each query to inference_call function
+        # Activates only when scenario is Offline and network mode is None
+        for i in tqdm(range(len(query_samples))):
+            index = query_samples[i].index
+            input_ids_tensor = self.qsl.data_object.source_encoded_input_ids[index]
+            input_masks_tensor = self.qsl.data_object.source_encoded_attn_masks[index]
+            text = self.qsl.data_object.sources[index]
+            query = {
+                "input_text": text,
+                "input_ids_tensor": input_ids_tensor.tolist(),
+                "input_masks_tensor": input_masks_tensor.tolist()
+            }
+            self.inference_call(query, query_samples[i].id)
+
+    def inference_call(self, query, query_id=None):
+        ''' Common for all scenarios '''
+        torch_device_type = 'cuda' if self.use_gpu else 'cpu'
+
+        input_ids_tensor = torch.tensor(query["input_ids_tensor"])
+        input_masks_tensor = torch.tensor(query["input_masks_tensor"])
+
+        # Moves the tensor to CPU or GPU as per argument passed by user
+        input_ids_tensor = input_ids_tensor.to(torch_device_type)
+        input_masks_tensor = input_masks_tensor.to(torch_device_type)               
+
+            
+        with torch.inference_mode(), torch.autocast(device_type=torch_device_type, enabled=self.amp_enabled, dtype=self.amp_dtype if self.amp_enabled else None):
+            input_batch = dict()
+            input_batch['input_ids'] = input_ids_tensor
+            input_batch['attention_mask'] = input_masks_tensor
+
+            output_batch = self.model.generate(# takes Long and Int tensor datatype only
+                **input_batch, **gen_kwargs, pad_token_id=self.tokenizer.eos_token_id)
+
+            input_batch_lengths = [x.shape[0]
+                                   for x in input_batch["input_ids"]]
+
+            output_batch_lengths = [x.shape[0] for x in output_batch]
+
+            output_batch_truncated = []
+            for data, source_len in zip(output_batch, input_batch_lengths):
+                output_batch_truncated.append(data[source_len:])
+
+            output_batch_truncated = torch.stack(output_batch_truncated)
+            
+            # Loadgen monitors the reponse in corresponding functions
+            if ((self.scenario == "SingleStream" or self.scenario == "Server") and self.network == None):
+                return output_batch_truncated
+
+            pred_output_batch = output_batch_truncated.cpu().numpy()
+
+            decoded_outputs = [self.tokenizer.decode(output, skip_special_tokens=True) for output in pred_output_batch]
+            response_text = decoded_outputs[0]
+
+            # Loadgen monitors the response in GPT_QDL
+            if self.network == "sut":
+                return {"pred_output_batch":pred_output_batch.tolist(), "response_text": response_text}
+
+            response_array = array.array("B", pred_output_batch[0].tobytes())
+            bi = response_array.buffer_info()
+            response = lg.QuerySampleResponse(query_id, bi[0], bi[1])
+            lg.QuerySamplesComplete([response])
+
+    def flush_queries(self):
+        pass
+
+    def __del__(self):
+        print("Finished destroying SUT.")
+
+
+class SUT_Offline(SUT_base):
+    def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl):
+        SUT_base.__init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
+    '''IssueQuery and inference methods implemented in Base class'''
+
+
+class SUT_Server(SUT_base):
+    def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl):
+
+        SUT_base.__init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        self.total_samples_done = 0
+
+    def issue_queries(self, query_samples):
+        # The issue queries function is called multiple times by the loadgen as per Poisson Distribution
+        index = query_samples[0].index
+        input_ids_tensor = self.qsl.data_object.source_encoded_input_ids[index]
+        input_masks_tensor = self.qsl.data_object.source_encoded_attn_masks[index]
+        text = self.qsl.data_object.sources[index]
+        query = {
+            "input_ids_tensor": input_ids_tensor.tolist(),
+            "input_masks_tensor": input_masks_tensor.tolist()
+        }
+        pred_output_batch = self.inference_call(query, query_samples[0].id).cpu().numpy()
+        response_array = array.array("B", pred_output_batch.tobytes())
+        bi = response_array.buffer_info()
+        responses = [lg.QuerySampleResponse(query_samples[0].id, bi[0], bi[1])]
+        lg.QuerySamplesComplete(responses)
+
+        self.total_samples_done += 1
+        if self.total_samples_done % 5 == 0:
+            print("Completed : ", self.total_samples_done)
+
+class SUT_SingleStream(SUT_base):
+    def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl):
+        SUT_base.__init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        self.total_samples_done = 0
+
+    def issue_queries(self, query_samples):
+        # This function is called by the loadgen after completing the previous query
+        index = query_samples[0].index
+        input_ids_tensor = self.qsl.data_object.source_encoded_input_ids[index]
+        input_masks_tensor = self.qsl.data_object.source_encoded_attn_masks[index]
+        query = {
+            "input_ids_tensor": input_ids_tensor.tolist(),
+            "input_masks_tensor": input_masks_tensor.tolist()
+        }
+
+        pred_output_batch = self.inference_call(
+            query, query_samples[0].id).cpu().numpy()
+
+        response_array = array.array("B", pred_output_batch.tobytes())
+        bi = response_array.buffer_info()
+        responses = [lg.QuerySampleResponse(query_samples[0].id, bi[0], bi[1])]
+        lg.QuerySamplesComplete(responses)
+        
+        self.total_samples_done += 1
+        if self.total_samples_done % 5 == 0:
+            print("Completed : ", self.total_samples_done)
+
+
+
+
+
+def get_SUT(model_path, scenario, dtype, dataset_path, max_examples, use_gpu=False, network=None, qsl=None):
+    if scenario == "Offline":
+        return SUT_Offline(model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
+    elif scenario == "Server":
+        return SUT_Server(model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
+    elif scenario == "SingleStream":
+        return SUT_SingleStream(model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
\ No newline at end of file
diff --git a/language/gpt-j/dataset.py b/language/gpt-j/dataset.py
index 37d9cf3546..ad8da50102 100644
--- a/language/gpt-j/dataset.py
+++ b/language/gpt-j/dataset.py
@@ -3,7 +3,7 @@
 import numpy as np
 import torch
 from datasets import load_dataset, load_from_disk
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from tokenizer_GPTJ import get_transformer_autotokenizer
 from torch.nn.functional import pad
 from torch.utils.data import DataLoader
 from typing import Optional, Dict, Sequence
@@ -36,11 +36,7 @@ def __init__(self, dataset_path, batch_size=1, pad_val=1, pad_max=196, total_cou
         self.pad_val = pad_val
         self.pad_max = pad_max
 
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            self.model_name,
-            model_max_length=2048,
-            padding_side="left",
-            use_fast=False,)
+        self.tokenizer = get_transformer_autotokenizer(self.model_name)
         self.tokenizer.pad_token = self.tokenizer.eos_token
 
         self.list_data_dict = utils.jload(self.dataset_path)
@@ -80,4 +76,4 @@ def UnloadSamplesFromRam(self, sample_list):
         pass
 
     def __del__(self):
-        print("Finished destroying QSL.")
+        print("Finished destroying QSL.")
\ No newline at end of file
diff --git a/language/gpt-j/main.py b/language/gpt-j/main.py
index 367af52122..bf372e8125 100644
--- a/language/gpt-j/main.py
+++ b/language/gpt-j/main.py
@@ -2,12 +2,13 @@
 import mlperf_loadgen as lg
 import argparse
 import os
-
+import math
 import sys
-from backend import get_SUT
-sys.path.insert(0, os.getcwd())
-
+from backend_PyTorch import get_SUT
+from GPTJ_QDL import GPTJ_QDL
+from GPTJ_QSL import get_GPTJ_QSL
 
+# Function to parse the arguments passed during python file execution
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -34,10 +35,21 @@ def get_args():
                         help="user config for user LoadGen settings such as target QPS")
     parser.add_argument("--max_examples", type=int, default=13368,
                         help="Maximum number of examples to consider (not limited by default)")
+    parser.add_argument("--network", choices=["sut","lon",None], default=None, help="Loadgen network mode")
+    parser.add_argument('--node', type=str, default="")
+    parser.add_argument('--port', type=int, default=8000)
+    parser.add_argument('--sut_server', nargs="*", default= ['http://localhost:8000'],
+                    help='Address of the server(s) under test.')
     args = parser.parse_args()
     return args
 
+# Function to get the amount of temporary cache generated when running the GPT-J model
+# Varies with the beam size set(Estimate: 6GB x Beam size)
+def get_temp_cache():
+    beam_size = int(os.environ.get("GPTJ_BEAM_SIZE", "4"))
+    return 6 * beam_size
 
+# Map the loadgen scenario as per the option given by the user
 scenario_map = {
     "SingleStream": lg.TestScenario.SingleStream,
     "Offline": lg.TestScenario.Offline,
@@ -45,50 +57,114 @@ def get_args():
     "MultiStream": lg.TestScenario.MultiStream
 }
 
-
+# Main function triggered when the script is run
 def main():
     args = get_args()
+    qsl = None
+    if args.network != "sut":
+        # Gets the Query Data Loader and Query Sample Loader
+        # Responsible for loading(Query Sample Loader) and sending samples over the network(Query Data Loader) to the server
+        qsl = get_GPTJ_QSL(
+            dataset_path=args.dataset_path,
+            max_examples=args.max_examples
+        )
+        if args.network == "lon":
+            qdl = GPTJ_QDL(
+                sut_server_addr=args.sut_server,
+                scenario=args.scenario,
+                qsl = qsl
+            )
+
+        # Initiates and loads loadgen test settings and log path
+        settings = lg.TestSettings()
+        settings.scenario = scenario_map[args.scenario]
+        # Need to update the conf
+        settings.FromConfig(args.mlperf_conf, "gptj", args.scenario)
+        settings.FromConfig(args.user_conf, "gptj", args.scenario)
+
+        # Chosing test mode Accutacy/Performance
+        if args.accuracy:
+            settings.mode = lg.TestMode.AccuracyOnly
+        else:
+            settings.mode = lg.TestMode.PerformanceOnly
+
+        # Set log path
+        log_path = os.environ.get("LOG_PATH")
+        if not log_path:
+            log_path = "build/logs"
+        if not os.path.exists(log_path):
+            os.makedirs(log_path)
+
+        log_output_settings = lg.LogOutputSettings()
+        log_output_settings.outdir = log_path
+        log_output_settings.copy_summary_to_stdout = True
+        log_settings = lg.LogSettings()
+        log_settings.log_output = log_output_settings
+        log_settings.enable_trace = True
+
+    if args.network != "lon":
+        # Gets SUT.
+        # SUT only initialised when network is either None or is SUT as it is not needed in case of client(LON).
+        # Only the server loads the model.
+        sut = get_SUT(
+            model_path=args.model_path,
+            scenario=args.scenario,
+            dtype=args.dtype,
+            use_gpu=args.gpu,
+            network=args.network,
+            dataset_path=args.dataset_path,
+            max_examples=args.max_examples,
+            qsl=qsl # If args.network is None, then only QSL get passed to the SUT, else it will be None
+        )
+    
+    if args.network == "lon" and args.scenario == "SingleStream":
+        print("ERROR: Single stream scenario in Loadgen Over the Network is not supported!")
+
+    # If network option is LON, QDL is loaded and request is served to SUT based on the scenario given by user(Offline or SingleStream)
+    elif args.network == "lon":
+        lg.StartTestWithLogSettings(qdl.qdl, qsl.qsl, settings, log_settings, args.audit_conf)
+
+    # If network option is SUT, a flask server is initiated, request is processed and output is being sent back to LON client
+    elif args.network == "sut":
+        temp_cache = get_temp_cache()
+        from network_SUT import app, node, set_backend, set_semaphore
+        from systemStatus import get_cpu_memory_info, get_gpu_memory_info
+
+        # Calculating free memory inorder to determine the number of instances that can be run at a particular time
+        # lockVar contains the value of number of instances
+        # Formula:
+        #   Number of Instances = (free_memory_of_system - memory_size_taken_by_model)/temp_cache_size
+        # Based on the got value(lockVar) a semaphore is initialised
+        # Acquire and release of semaphore can be found in network_SUT.py
+        model_mem_size = sut.total_mem_size
+        if args.gpu:
+            free_mem = int(os.environ.get("CM_CUDA_DEVICE_PROP_GLOBAL_MEMORY", get_gpu_memory_info())) / (1024**3)
+        else:
+            free_mem = get_cpu_memory_info()
+        lockVar = math.floor((free_mem - model_mem_size)/temp_cache)
+        node = args.node
+        # Semaphore is set inorder to create multiple instances upon request incomming
+        set_semaphore(lockVar)
+        print(f"Set the semaphore lock variable to {lockVar}")
+        # Pass SUT as the backend
+        set_backend(sut)
+        app.run(debug=False, port=args.port, host="0.0.0.0")
 
-    sut = get_SUT(
-        model_path=args.model_path,
-        scenario=args.scenario,
-        dtype=args.dtype,
-        dataset_path=args.dataset_path,
-        max_examples=args.max_examples,
-        use_gpu=args.gpu,
-    )
-
-    settings = lg.TestSettings()
-    settings.scenario = scenario_map[args.scenario]
-    # Need to update the conf
-    settings.FromConfig(args.mlperf_conf, "gptj", args.scenario)
-    settings.FromConfig(args.user_conf, "gptj", args.scenario)
-
-    if args.accuracy:
-        settings.mode = lg.TestMode.AccuracyOnly
     else:
-        settings.mode = lg.TestMode.PerformanceOnly
-    log_path = os.environ.get("LOG_PATH")
-    if not log_path:
-        log_path = "build/logs"
-    if not os.path.exists(log_path):
-        os.makedirs(log_path)
-    log_output_settings = lg.LogOutputSettings()
-    log_output_settings.outdir = log_path
-    log_output_settings.copy_summary_to_stdout = True
-    log_settings = lg.LogSettings()
-    log_settings.log_output = log_output_settings
-    log_settings.enable_trace = True
-
-    lg.StartTestWithLogSettings(sut.sut, sut.qsl, settings, log_settings, args.audit_conf)
+        # Test not run in Loadgen Over the Network
+        print("Running LoadGen test...")
+        lg.StartTestWithLogSettings(sut.sut, qsl.qsl, settings, log_settings, args.audit_conf)
+
     print("Test Done!")
 
-    print("Destroying SUT...")
-    lg.DestroySUT(sut.sut)
+    if args.network != "lon":
+        print("Destroying SUT...")
+        lg.DestroySUT(sut.sut)
 
-    print("Destroying QSL...")
-    lg.DestroyQSL(sut.qsl)
+    if args.network != "sut":
+        print("Destroying QSL...")
+        lg.DestroyQSL(qsl.qsl)
 
 
 if __name__ == "__main__":
-    main()
+    main()
\ No newline at end of file
diff --git a/language/gpt-j/network_SUT.py b/language/gpt-j/network_SUT.py
new file mode 100644
index 0000000000..0372388ca0
--- /dev/null
+++ b/language/gpt-j/network_SUT.py
@@ -0,0 +1,60 @@
+from flask import Flask, request, jsonify
+import threading
+
+app = Flask(__name__)
+
+node = ""
+
+model_mem_size = None
+use_gpu = False
+free_mem = None
+
+# setting semaphore
+def set_semaphore(lockVar):
+    global semaphore
+    semaphore = threading.Semaphore(lockVar)
+
+
+def set_backend(b):
+    global backend
+    backend = b
+
+def preprocess(query):
+    """[SUT Node] A dummy preprocess."""
+    # Here may come for example batching, tokenization, resizing, normalization, etc.
+    response = query
+    return response
+
+
+def dnn_model(query):
+    # Here may come for example a call to a dnn model such as resnet, bert, etc.
+    response = backend.inference_call(query)
+    print(f"dnn model response:{response}")
+    return response
+
+
+def postprocess(query):
+    """[SUT Node] A dummy postprocess."""
+    # Here may come for example a postprocessing call, e.g., NMS, detokenization, etc.
+    response = query
+    return response
+
+
+@app.route('/predict/', methods=['POST'])
+def predict():
+    """Receives a query (e.g., a text) runs inference, and returns a prediction."""
+    semaphore.acquire() # wait to acquire semaphore    
+    try:
+        query = request.get_json(force=True)['query']
+        result = postprocess(dnn_model(preprocess(query)))
+        print(result)
+        return jsonify(result=result)
+    finally:
+        # semaphore is released after processing
+        semaphore.release()
+
+
+@app.route('/getname/', methods=['POST', 'GET'])
+def getname():
+    """Returns the name of the SUT."""
+    return jsonify(name=f'Demo SUT (Network SUT) node' + (' ' + node) if node else '')
\ No newline at end of file
diff --git a/language/gpt-j/systemStatus.py b/language/gpt-j/systemStatus.py
new file mode 100644
index 0000000000..de00d312f3
--- /dev/null
+++ b/language/gpt-j/systemStatus.py
@@ -0,0 +1,14 @@
+import psutil
+import torch
+
+def get_gpu_memory_info():
+    if torch.cuda.is_available():
+        device = torch.cuda.current_device()
+        total_memory = torch.cuda.get_device_properties(device).total_memory
+        allocated_memory = torch.cuda.memory_allocated(device)
+        free_memory = total_memory - allocated_memory
+    return free_memory / (1024**3)
+
+def get_cpu_memory_info():
+    mem = psutil.virtual_memory()
+    return mem.total / (1024**3)
\ No newline at end of file
diff --git a/language/gpt-j/tokenizer_GPTJ.py b/language/gpt-j/tokenizer_GPTJ.py
new file mode 100644
index 0000000000..0f12b3912c
--- /dev/null
+++ b/language/gpt-j/tokenizer_GPTJ.py
@@ -0,0 +1,8 @@
+from transformers import AutoTokenizer
+
+def get_transformer_autotokenizer(model_name: str):
+    return AutoTokenizer.from_pretrained(
+            model_name,
+            model_max_length=2048,
+            padding_side="left",
+            use_fast=False,)
\ No newline at end of file

From 799b30a3bc32985220e418127e7bed02b8095138 Mon Sep 17 00:00:00 2001
From: Andy Ye <andyye333@gmail.com>
Date: Tue, 25 Jun 2024 14:52:40 -0400
Subject: [PATCH 112/305] Add skipping logic (#1692)

Co-authored-by: Miro <mirhodak@amd.com>
---
 tools/submission/submission_checker.py    | 28 +++++++++++++++++++++--
 tools/submission/truncate_accuracy_log.py | 20 ++++++++++++++--
 2 files changed, 44 insertions(+), 4 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 9ed61a51c8..855024e683 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -831,6 +831,11 @@ def get_args():
         action="store_true",
         help="skips the check of extra files inside the root submission dir",
     )
+    parser.add_argument(
+        "--scenarios-to-skip",
+        help="Delimited list input of scenarios to skip. i.e. if you only have Offline results, pass in 'Server'",
+        type=str
+    )
     args = parser.parse_args()
     return args
 
@@ -1460,6 +1465,7 @@ def check_results_dir(
     skip_empty_files_check=False,
     skip_check_power_measure_files=False,
     skip_extra_files_in_root_check=False,
+    scenarios_to_skip=[]
 ):
     """
     Walk the results directory and do the checking.
@@ -1860,6 +1866,10 @@ def log_result(
                         # some submissions in v0.5 use lower case scenarios - map them for now
                         scenario_fixed = SCENARIO_MAPPING.get(scenario, scenario)
 
+                        # Skip scenario for debug purposes
+                        if scenario in scenarios_to_skip:
+                            continue
+
                         # we are looking at ./$division/$submitter/results/$system_desc/$model/$scenario,
                         #   ie ./closed/mlperf_org/results/t4-ort/bert/Offline
                         name = os.path.join(
@@ -1941,7 +1951,7 @@ def log_result(
                             )
                             errors += 1
                             continue
-                        else:
+                        elif scenario not in scenarios_to_skip:
                             diff = files_diff(list_files(acc_path), REQUIRED_ACC_FILES)
                             if diff:
                                 log.error(
@@ -2105,6 +2115,8 @@ def log_result(
                         # check if compliance dir is good for CLOSED division
                         compliance = 0 if is_closed_or_network else 1
                         if is_closed_or_network and not skip_compliance:
+                            if scenario in scenarios_to_skip:
+                                continue
                             compliance_dir = os.path.join(
                                 division,
                                 submitter,
@@ -2155,6 +2167,10 @@ def log_result(
                                 results[name] = None
                                 log.error("%s is OK but accuracy has issues", name)
 
+                    # Discard scenarios that we want to skip
+                    for scenario in scenarios_to_skip:
+                        required_scenarios.discard(scenario)
+
                     if required_scenarios:
                         name = os.path.join(results_path, system_desc, model_name)
                         if is_closed_or_network:
@@ -2629,6 +2645,13 @@ def main():
         skip_power_check=args.skip_power_check,
     )
 
+    if args.scenarios_to_skip:
+        scenarios_to_skip = [
+            scenario for scenario in args.scenarios_to_skip.split(',')
+        ]
+    else:
+        scenarios_to_skip = []
+
     with open(args.csv, "w") as csv:
         os.chdir(args.input)
         # check results directory
@@ -2641,7 +2664,8 @@ def main():
             args.skip_meaningful_fields_emptiness_check,
             args.skip_empty_files_check,
             args.skip_check_power_measure_files,
-            args.skip_extra_files_in_root_check
+            args.skip_extra_files_in_root_check,
+            scenarios_to_skip
         )
 
     # log results
diff --git a/tools/submission/truncate_accuracy_log.py b/tools/submission/truncate_accuracy_log.py
index 50b9b6977f..8cfe1414df 100755
--- a/tools/submission/truncate_accuracy_log.py
+++ b/tools/submission/truncate_accuracy_log.py
@@ -46,6 +46,12 @@ def get_args():
     parser.add_argument("--output", help="new submission directory")
     parser.add_argument("--submitter", required=True, help="filter to submitter")
     parser.add_argument("--backup", help="directory to store the original accuacy log")
+    parser.add_argument(
+        "--scenarios-to-skip",
+        help="Delimited list input of scenarios to skip. i.e. if you only have Offline results, pass in 'Server'",
+        type=str
+    )
+
 
     args = parser.parse_args()
     if not args.output and not args.backup:
@@ -104,7 +110,7 @@ def copy_submission_dir(src, dst, filter_submitter):
                             os.path.join(dst, division, submitter))
 
 
-def truncate_results_dir(filter_submitter, backup):
+def truncate_results_dir(filter_submitter, backup, scenarios_to_skip):
     """Walk result dir and 
        write a hash of mlperf_log_accuracy.json to accuracy.txt
        copy mlperf_log_accuracy.json to a backup location
@@ -131,6 +137,8 @@ def truncate_results_dir(filter_submitter, backup):
                 for system_desc in list_dir(log_path):
                     for model in list_dir(log_path, system_desc):
                         for scenario in list_dir(log_path, system_desc, model):
+                            if scenario in scenarios_to_skip:
+                                continue
                             for test in list_dir(log_path, system_desc, model, scenario):
 
                                 name = os.path.join(log_path, system_desc, model, scenario)
@@ -200,8 +208,16 @@ def main():
         src_dir = args.output
 
     os.chdir(src_dir)
+
+    if args.scenarios_to_skip:
+        scenarios_to_skip = [
+            scenario for scenario in args.scenarios_to_skip.split(',')
+        ]
+    else:
+        scenarios_to_skip = []
+
     # truncate results directory
-    truncate_results_dir(args.submitter, args.backup)
+    truncate_results_dir(args.submitter, args.backup, scenarios_to_skip)
 
     backup_location = args.output or args.backup
     log.info("Make sure you keep a backup of %s in case mlperf wants to see the original accuracy logs", backup_location)

From c74ec8cb7b53f40ef1fd018d01f332011fc4bbc5 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Tue, 25 Jun 2024 15:24:06 -0500
Subject: [PATCH 113/305] Add mixtral model download instructions to README.md
 (#1752)

---
 language/mixtral-8x7b/README.md | 28 +++++++++++++++++++---------
 1 file changed, 19 insertions(+), 9 deletions(-)

diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 69f8035770..5c72f84fb4 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -65,14 +65,27 @@ CPU-only setup, as well as any GPU versions for applicable libraries like PyTorc
 
 
 ## Get Model
-### MLCommons Members Download
 
-TODO: Create MLCommons get fixed link.
-For now it can be downloaded from [Hugging Face](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1/tree/main)
+### Checkpoint
 
-## Get Dataset
+#### Using Rclone
+
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
+```
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
+```
+Once Rclone is installed, run the following command to authenticate with the bucket:
+```
+rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+```
+You can then navigate in the terminal to your desired download directory and run the following command to download the model checkpoint:
+
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/mixtral_8x7b/mixtral-8x7b-instruct-v0.1 ./mixtral-8x7b-instruct-v0.1 -P
+```
 
-TODO: Create scripts and procedure to download all of the parts of the dataset
+## Get Dataset
 
 ### Preprocessed
 
@@ -109,9 +122,6 @@ Alternatively, you can simply cd into the folder where you want to place the dat
 ```bash
 wget https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_15k_calibration_v4.pkl
 ```
-### Unprocessed
-
-TODO: Share instructions and scripts
 
 ## Run Performance Benchmarks
 
@@ -260,4 +270,4 @@ MBXP:
 For official submissions, 99% of each reference score is enforced. Additionally, 90%-110% of the generated tokens_per_samples:
 ```json
 {'tokens_per_sample': 145.9}
-```
\ No newline at end of file
+```

From c62fc116d2591b0149df0104a8e16e9586cb244e Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 26 Jun 2024 15:16:45 +0100
Subject: [PATCH 114/305] Fix mixtral accuracy check run if there are no
 samples from openorca (#1754)

---
 language/mixtral-8x7b/evaluate-accuracy.py | 21 ++++++++++++++++-----
 1 file changed, 16 insertions(+), 5 deletions(-)

diff --git a/language/mixtral-8x7b/evaluate-accuracy.py b/language/mixtral-8x7b/evaluate-accuracy.py
index d25b8178ba..14b6054c2a 100644
--- a/language/mixtral-8x7b/evaluate-accuracy.py
+++ b/language/mixtral-8x7b/evaluate-accuracy.py
@@ -173,10 +173,17 @@ def main():
 
     preds, targets = postprocess_text(
         preds_decoded_text, target_required_OpenOrca)
-    result = metric.compute(
-        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
-    result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
-    prediction_lens = [len(pred) for pred in preds]
+    
+    if preds:
+        result = metric.compute(
+            predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
+        result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
+        prediction_lens = [len(pred) for pred in preds]
+
+    else:
+        result = {}
+        prediction_lens = []
+
     # GSM8K metric
     preds_decoded_text = tokenizer.batch_decode(
         preds_token_GSM8K, skip_special_tokens=True)
@@ -197,7 +204,11 @@ def main():
 
     # MBXP metric
     from evaluate_mbxp import evaluate_mbxp
-    result['mbxp'] = evaluate_mbxp(results_MBXP, args.n_workers)
+
+    if results_MBXP:
+        result['mbxp'] = evaluate_mbxp(results_MBXP, args.n_workers)
+    else:
+        result['mbxp'] = 0
 
     result = {
         **result,

From 763883b1ae9e7490fc1250f9478d0be6693b2e0b Mon Sep 17 00:00:00 2001
From: nvamberl <amberl@nvidia.com>
Date: Thu, 27 Jun 2024 22:24:35 +0800
Subject: [PATCH 115/305] Fix mlcommons pytorch accuracy check script OOM on
 Orin AGX 64g (#1745)

* Fix accuracy_coco.py OOM error on AGX Orin, reduce memoryfrom 70G to less than 10G.

* Fix mlcommons pytorch accuracy check script OOM on Orin AGX 64g: bug fixing
---
 text_to_image/tools/accuracy_coco.py | 136 ++++++++++++++++++++-------
 text_to_image/tools/fid/fid_score.py |   2 +-
 2 files changed, 105 insertions(+), 33 deletions(-)

diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
index f831c4b6e2..74a13f790c 100644
--- a/text_to_image/tools/accuracy_coco.py
+++ b/text_to_image/tools/accuracy_coco.py
@@ -14,7 +14,10 @@
 import pandas as pd
 import torch
 from clip.clip_encoder import CLIPEncoder
-from fid.fid_score import compute_fid
+from fid.inception import InceptionV3
+from fid.fid_score import compute_statistics_of_path, get_activations, calculate_frechet_distance
+from tqdm import tqdm
+import ijson
 
 
 
@@ -44,15 +47,10 @@ def preprocess_image(img_dir, file_name):
 
 def main():
     args = get_args()
-    result_dict = {}
 
     # Load dataset annotations
     df_captions = pd.read_csv(args.caption_path, sep="\t")
 
-    # Load model outputs
-    with open(args.mlperf_accuracy_file, "r") as f:
-        results = json.load(f)
-
     # set device
     device = args.device if torch.cuda.is_available() else "cpu"
     if device == "gpu":
@@ -79,39 +77,113 @@ def main():
             for idx in compliance_images_idx_list:
                 caption_file.write(f"{idx}  {df_captions.iloc[idx]['caption']}\n")
 
-    # Load torchmetrics modules
-    clip = CLIPEncoder(device=device)
+    # Compute accuracy
+    compute_accuracy(
+        args.mlperf_accuracy_file,
+        args.output_file,
+        device,
+        dump_compliance_images,
+        compliance_images_idx_list,
+        args.compliance_images_path,
+        df_captions,
+        statistics_path,
+    )
+    
+
+def compute_accuracy(
+    mlperf_accuracy_file, 
+    output_file,
+    device,
+    dump_compliance_images,
+    compliance_images_idx_list,
+    compliance_images_path,
+    df_captions,
+    statistics_path,
+    batch_size=8,
+    inception_dims=2048,
+    num_workers=1,
+):    
+    if num_workers is None:
+        try:
+            num_cpus = len(os.sched_getaffinity(0))
+        except AttributeError:
+            # os.sched_getaffinity is not available under Windows, use
+            # os.cpu_count instead (which may not return the *available* number
+            # of CPUs).
+            num_cpus = os.cpu_count()
+
+        num_workers = min(num_cpus, 8) if num_cpus is not None else 0
+    else:
+        num_workers = num_workers    
+
+    # Prepare models
+    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[inception_dims]
+    inception_model = InceptionV3([block_idx]).to(device)
+    clip_model = CLIPEncoder(device=device)
+    
     clip_scores = []
     seen = set()
-    result_list = []
-    for j in results:
-        idx = j['qsl_idx']
-        if idx in seen:
-            continue
-        seen.add(idx)
-
-        # Load generated image
-        generated_img = np.frombuffer(bytes.fromhex(j['data']), np.uint8).reshape(1024, 1024, 3)
-        result_list.append(generated_img)
-        generated_img = Image.fromarray(generated_img)
-
-        # Dump compliance images
-        if dump_compliance_images and idx in compliance_images_idx_list:
-            generated_img.save(os.path.join(args.compliance_images_path, f"{idx}.png"))
-
-        # generated_img = torch.Tensor(generated_img).to(torch.uint8).to(device)
-        # Load Ground Truth
-        caption = df_captions.iloc[idx]["caption"]
-        clip_scores.append(
-            100 * clip.get_clip_score(caption, generated_img).item()
-        )
-    fid_score = compute_fid(result_list, statistics_path, device)
+    result_batch = []
+    result_dict = {}
+    activations = np.empty((0, inception_dims))
+    
+    # Load model outputs
+    with open(mlperf_accuracy_file, "r") as f:
+        results = ijson.items(f, "item")
+
+        for j in tqdm(results):
+            idx = j['qsl_idx']
+            if idx in seen:
+                continue
+            seen.add(idx)
+
+            # Load generated image
+            generated_img = np.frombuffer(bytes.fromhex(j['data']), np.uint8).reshape(1024, 1024, 3)
+            generated_img = Image.fromarray(generated_img)
+
+            # Dump compliance images
+            if dump_compliance_images and idx in compliance_images_idx_list:
+                generated_img.save(os.path.join(compliance_images_path, f"{idx}.png"))
+
+            # Load Ground Truth
+            caption = df_captions.iloc[idx]["caption"]
+            clip_scores.append(
+                100 * clip_model.get_clip_score(caption, generated_img).item()
+            )
+
+            result_batch.append(generated_img.convert("RGB"))
+
+            if len(result_batch) == batch_size:
+                act = get_activations(result_batch, inception_model, batch_size, inception_dims, device, num_workers)
+                activations = np.append(activations, act, axis=0)
+                result_batch.clear()
+        
+        # Remaining data for last batch
+        if len(result_batch) > 0:
+            act = get_activations(result_batch, inception_model, len(result_batch), inception_dims, device, num_workers)
+            activations = np.append(activations, act, axis=0)
+            
+    m1, s1 = compute_statistics_of_path(
+        statistics_path,
+        inception_model,
+        batch_size,
+        inception_dims,
+        device,
+        num_workers,
+        None,
+        None,
+    )
+
+    m2 = np.mean(activations, axis=0)
+    s2 = np.cov(activations, rowvar=False)
+
+    fid_score = calculate_frechet_distance(m1, s1, m2, s2)
 
     result_dict["FID_SCORE"] = fid_score
     result_dict["CLIP_SCORE"] = np.mean(clip_scores)
     print(f"Accuracy Results: {result_dict}")
 
-    with open(args.output_file, "w") as fp:
+    with open(output_file, "w") as fp:
         json.dump(result_dict, fp, sort_keys=True, indent=4)
 
 if __name__ == "__main__":
diff --git a/text_to_image/tools/fid/fid_score.py b/text_to_image/tools/fid/fid_score.py
index ce7cfe4b00..d9f84d98da 100644
--- a/text_to_image/tools/fid/fid_score.py
+++ b/text_to_image/tools/fid/fid_score.py
@@ -119,7 +119,7 @@ def get_activations(
 
     start_idx = 0
 
-    for batch in tqdm(dataloader):
+    for batch in dataloader:
         batch = batch.to(device)
 
         with torch.no_grad():

From 9e2c9f642e6e12b74e7c08d2e099c8af0e542873 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 27 Jun 2024 15:32:02 +0100
Subject: [PATCH 116/305] Support rocm device for R50 and Retinanet (#1730)

Co-authored-by: Miro <mirhodak@amd.com>
Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
---
 vision/classification_and_detection/run_common.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vision/classification_and_detection/run_common.sh b/vision/classification_and_detection/run_common.sh
index 35d5720fd1..37552c71e0 100755
--- a/vision/classification_and_detection/run_common.sh
+++ b/vision/classification_and_detection/run_common.sh
@@ -19,7 +19,7 @@ device="cpu"
 for i in $* ; do
     case $i in
        tf|onnxruntime|tflite|pytorch|tvm-onnx|tvm-pytorch|tvm-tflite|ncnn) backend=$i; shift;;
-       cpu|gpu) device=$i; shift;;
+       cpu|gpu|rocm) device=$i; shift;;
        gpu) device=gpu; shift;;
        resnet50|mobilenet|ssd-mobilenet|ssd-resnet34|ssd-resnet34-tf|retinanet) model=$i; shift;;
     esac

From f43c24c5a57be60517c53c2c206013b985b11489 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 2 Jul 2024 11:02:31 -0500
Subject: [PATCH 117/305] Fix typo in Mixtral accuracy (#1760)

---
 language/mixtral-8x7b/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 5c72f84fb4..34402a9c30 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -265,7 +265,7 @@ GSM8K:
 ```
 MBXP:
 ```json
-{'mbxp': 60.16}
+{'mbxp': 60.12}
 ```
 For official submissions, 99% of each reference score is enforced. Additionally, 90%-110% of the generated tokens_per_samples:
 ```json

From 29edbb049081698fa07ebfe903a02404edb81e79 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 2 Jul 2024 17:45:50 +0100
Subject: [PATCH 118/305] Fixes #1761, llama2 and mixtral runtime error on CPU
 systems (#1762)

---
 language/llama2-70b/SUT.py   | 5 ++++-
 language/mixtral-8x7b/SUT.py | 4 +++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/language/llama2-70b/SUT.py b/language/llama2-70b/SUT.py
index 17faced405..9f9e4bf7bd 100644
--- a/language/llama2-70b/SUT.py
+++ b/language/llama2-70b/SUT.py
@@ -241,7 +241,10 @@ def load_model(self):
             self.model = self.model.to(self.device)  # Force CPU if your system has GPU and you specifically want CPU-only run
 
         self.model.eval()
-        self.model = self.model.to(memory_format=torch.channels_last)
+        try: # for systems with low ram, the below command gives error as some part is offloaded to disk
+            self.model = self.model.to(memory_format=torch.channels_last)
+        except:
+            pass
 
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.model_path,
diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
index aaf0eecee2..b4a2dc705b 100644
--- a/language/mixtral-8x7b/SUT.py
+++ b/language/mixtral-8x7b/SUT.py
@@ -301,8 +301,10 @@ def load_model(self):
             self.model = self.model.to(self.device)
 
         self.model.eval()
-        if self.device != "cpu":
+        try: # for systems with low ram, the below command gives error as some part is offloaded to disk
             self.model = self.model.to(memory_format=torch.channels_last)
+        except:
+            pass
 
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.model_path,

From 4a77003610175e414504fdfe43834b70470f6656 Mon Sep 17 00:00:00 2001
From: viraatc <viraatc@nvidia.com>
Date: Tue, 2 Jul 2024 21:43:45 -0700
Subject: [PATCH 119/305] fix: remove np type around output values (#1764)

fixes #1763
---
 language/mixtral-8x7b/evaluate-accuracy.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/language/mixtral-8x7b/evaluate-accuracy.py b/language/mixtral-8x7b/evaluate-accuracy.py
index 14b6054c2a..bcf638c887 100644
--- a/language/mixtral-8x7b/evaluate-accuracy.py
+++ b/language/mixtral-8x7b/evaluate-accuracy.py
@@ -177,7 +177,7 @@ def main():
     if preds:
         result = metric.compute(
             predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
-        result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
+        result = {k: float(round(np.mean(v) * 100, 4)) for k, v in result.items()}
         prediction_lens = [len(pred) for pred in preds]
 
     else:
@@ -212,7 +212,7 @@ def main():
 
     result = {
         **result,
-        'gen_len': np.sum(prediction_lens),
+        'gen_len': int(np.sum(prediction_lens)),
         'gen_num': gen_num,
         'gen_tok_len': gen_tok_len,
         'tokens_per_sample': round(gen_tok_len / gen_num, 1),

From 8c17da9b0d4b03e8e1c8aa356e279dd50e8c25d9 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 9 Jul 2024 17:56:47 +0100
Subject: [PATCH 120/305] Documentation Updates for Inference v4.1 (#1772)

* Fixes #1761, llama2 and mixtral runtime error on CPU systems

* Added mixtral to the benchmark list, improved benchmark docs

* Update docs for MLPerf inference v4.1

* Update docs for MLPerf inference v4.1

* Fix typo

* Gave direct link to implementation readmes

* Added tables detailing implementations

* Update vision README.md, split the frameworks into separate rows

* Update README.md

* pointed links to specific frameworks

* pointed links to specific frameworks

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

---------

Co-authored-by: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
---
 .github/workflows/test-submission-checker.yml |  3 +-
 Submission_Guidelines.md                      | 22 ++++------
 docs/benchmarks/index.md                      | 25 ++++++-----
 language/bert/README.md                       |  6 ++-
 language/bert/README_cm.md                    | 39 -----------------
 language/gpt-j/README.md                      |  9 ++--
 language/gpt-j/README_cm.md                   | 43 -------------------
 language/llama2-70b/README.md                 | 11 +----
 language/mixtral-8x7b/README.md               |  3 +-
 main.py                                       | 24 +++++++----
 mkdocs.yml                                    |  2 +-
 recommendation/dlrm_v2/pytorch/README.md      |  5 ++-
 .../3d-unet-brats19/.dockerignore             |  0
 .../3d-unet-brats19/.gitignore                |  0
 .../3d-unet-brats19/Dockerfile                |  0
 .../medical_imaging/3d-unet-brats19/Makefile  |  0
 .../medical_imaging/3d-unet-brats19/README.md |  0
 .../3d-unet-brats19/Task043_BraTS_2019.py     |  0
 .../3d-unet-brats19/accuracy-brats.py         |  0
 .../3d-unet-brats19/brats_QSL.py              |  0
 .../folds/fold0_validation.txt                |  0
 .../folds/fold1_validation.txt                |  0
 .../folds/fold2_validation.txt                |  0
 .../folds/fold3_validation.txt                |  0
 .../folds/fold4_validation.txt                |  0
 .../3d-unet-brats19/onnxruntime_SUT.py        |  0
 .../medical_imaging/3d-unet-brats19/ov_SUT.py |  0
 .../3d-unet-brats19/preprocess.py             |  0
 .../3d-unet-brats19/pytorch_SUT.py            |  0
 .../medical_imaging/3d-unet-brats19/run.py    |  0
 .../medical_imaging/3d-unet-brats19/tf_SUT.py |  0
 .../3d-unet-brats19/unet_onnx_to_tf.py        |  0
 .../3d-unet-brats19/unet_pytorch_to_onnx.py   |  0
 .../medical_imaging/3d-unet-brats19/user.conf |  0
 text_to_image/README.md                       | 13 ++----
 vision/classification_and_detection/README.md | 32 +++++++++++---
 .../README_cm_resnet50.md                     | 42 ------------------
 .../README_cm_retinanet.md                    | 38 ----------------
 vision/medical_imaging/3d-unet-brats19/nnUnet |  1 -
 .../medical_imaging/3d-unet-kits19/README.md  |  3 +-
 .../3d-unet-kits19/README_cm.md               | 39 -----------------
 41 files changed, 84 insertions(+), 276 deletions(-)
 delete mode 100644 language/bert/README_cm.md
 delete mode 100644 language/gpt-j/README_cm.md
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/.dockerignore (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/.gitignore (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/Dockerfile (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/Makefile (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/README.md (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/Task043_BraTS_2019.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/accuracy-brats.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/brats_QSL.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/folds/fold0_validation.txt (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/folds/fold1_validation.txt (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/folds/fold2_validation.txt (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/folds/fold3_validation.txt (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/folds/fold4_validation.txt (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/onnxruntime_SUT.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/ov_SUT.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/preprocess.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/pytorch_SUT.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/run.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/tf_SUT.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/unet_onnx_to_tf.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/unet_pytorch_to_onnx.py (100%)
 rename {vision => retired_benchmarks/vision}/medical_imaging/3d-unet-brats19/user.conf (100%)
 delete mode 100644 vision/classification_and_detection/README_cm_resnet50.md
 delete mode 100644 vision/classification_and_detection/README_cm_retinanet.md
 delete mode 160000 vision/medical_imaging/3d-unet-brats19/nnUnet
 delete mode 100644 vision/medical_imaging/3d-unet-kits19/README_cm.md

diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index 2d1b129034..31b8e4aacb 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -28,8 +28,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cmind
-        cm pull repo mlcommons@ck
+        python3 -m pip install cm4mlops
         git clone https://github.com/mlcommons/inference_results_v4.0 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
diff --git a/Submission_Guidelines.md b/Submission_Guidelines.md
index 15818cc39a..5c8cfee84a 100644
--- a/Submission_Guidelines.md
+++ b/Submission_Guidelines.md
@@ -18,40 +18,34 @@ The MLPerf inference submission rules are spread between the [MLCommons policies
 
 ### Is there an automatic way to run the MLPerf inference benchmarks?
 
-MLPerf inference submissions are expected on different hardware and related software stacks. For this reason, only reference implementations are provided by MLCommons and they can guide submitters to make their own optimal implementations for their software/hardware stack. Also, all the previous implementations are made available in the MLCommons Inference results repositories and they can also guide submitters in doing their own implementations.
-
-[The MLCommons taskforce on automation and reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md) has automated all the MLCommons inference tasks using the [MLCommons CM language](https://github.com/mlcommons/ck/blob/master/cm) and [this readme](https://github.com/mlcommons/ck/tree/master/docs/mlperf/inference) can guide you in running the reference implementations with very minimal effort. Currently, this automation supports MLCommons reference implementations, Nvidia implementations, and C++ implementations for Onnxruntime and TFLite. Feel free to join the [taskforce Discord channel](https://discord.gg/8jbEM4J6Ff) if you have any questions.
-
-The previous MLPerf inference results are aggregated in [Collective Knowledge platform (MLCommons CK playground)](platform) as [reproducible experiments](https://access.cknowledge.org/playground/?action=experiments)  and can be used by submitters to compare their results with the previous ones while adding various derived metrics (such as performance/watt) and constraints.
+MLPerf inference submissions are expected to be run on various hardware and supported software stacks. Therefore, MLCommons provides only reference implementations to guide submitters in creating optimal implementations for their specific software and hardware configurations. Additionally, all implementations used for MLPerf inference submissions are available in the MLCommons [Inference results](https://github.com/orgs/mlcommons/repositories?q=inference_results_v+sort%3Aname) repositories (under `closed/<submitter>/code` directory), offering further guidance for submitters developing their own implementations.
 
 ### Expected time to do benchmark runs
-1. Closed submission under data enter needs offline and server scenario runs with a minimum of ten minutes needed for both. 
+1. Closed submission under datacenter needs offline and server scenario runs with a minimum of ten minutes needed for both. 
 2. Closed submission under the edge category needs single stream, multi-stream (only for R50 and retinanet), and offline scenarios. A minimum of ten minutes is needed for each scenario. 
 3. Further two (three for ResNet50) compliance runs are needed for closed division, each taking at least 10 minutes for each scenario.
 4. SingleStream, MultiStream and Server scenarios use early stopping and so can always finish around 10 minutes
 5. Offline scenario needs a minimum of 24756 input queries to be processed -- can take hours for low-performing models like 3dunet, LLMs, etc.
 6. Open division has no accuracy constraints, no required compliance runs, and can be submitted for any single scenario. There is no constraint on the model used except that the model accuracy must be validated on the accuracy dataset used in the corresponding MLPerf inference task [or must be preapproved](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc#412-relaxed-constraints-for-the-open-division).
-7. Power submission needs an extra ranging mode to determine the peak current usage and this often doubles the overall experiment run time.
+7. Power submission needs an extra ranging mode to determine the peak current usage and this often doubles the overall experiment run time. If this overhead is too much, ranging run can be reduced to 5 minutes run using mechanisms like [this](https://github.com/mlcommons/cm4mlops/blob/main/script/benchmark-program-mlperf/customize.py#L18).
 
 
 ## Validity of the submission
 
 1. [MLCommons Inference submission checker](https://github.com/mlcommons/inference/blob/master/tools/submission/submission_checker.py) is provided to ensure that all submissions are passing the required checks.
-2. In the unlikely event that there is an error on the submission checker for your submission, please raise a Github issue [here](https://github.com/mlcommons/inference/issues)
+2. In the unlikely event that there is an error on the submission checker for your submission, please raise a GitHub issue [here](https://github.com/mlcommons/inference/issues)
 3. Any submission passing the submission checker is valid to go to the review discussions but submitters are still required to answer any queries and fix any issues being reported by other submitters.
 
 ### Reviewing other submissions
-1. Ensure that the `system_desc_id.json` file is having meaningful responses - submission_checker only checks for the existence of the fields.
+1. Ensure that the `system_desc_id.json` file is having meaningful responses - `submission_checker` only checks for the existence of the fields.
 2. For power submissions, `power settings` and `analyzer table` files are to be submitted, and even though the submission checker checks for the existence of these files, the content of [these files](https://github.com/mlcommons/inference_policies/blob/master/power_measurement.adoc#64-power-management-settings) must be checked manually for validity.
 3. README files in the submission directory must be checked to make sure that the instructions are reproducible.
 4. For closed datacenter submissions, [ECC RAM and Networking requirements](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc#constraints-for-the-closed-division) must be ensured.
 5. Submission checker might be reporting warnings and some of these warnings can warrant an answer from the submitter.
 
-## Changes from MLCommons Inference 3.0
+## Changes from MLCommons Inference 4.0
 
-1. Two new benchmarks GPT-J and GPT-3 and DLRMv2 replacing DLRM
-2. Submission checker is now checking for non-empty README files and mandatory system description and power-related fields
-3. New script is provided which can be used to infer scenario results and low-accuracy results from a high-accuracy result
-4. `min_query_count` is removed for all scenarios except offline due to early stopping. SingleStream now needs a minimum of 64 queries and MultiStream needs 662 queries as mandated by the early stopping criteria.
+1. One new benchmark in the datacenter category: Mixtral-8x7B. No changes in the edge category.
+2. For power submissions, there is no code change. 
 
 
diff --git a/docs/benchmarks/index.md b/docs/benchmarks/index.md
index c4a970c81c..39e3e72ac6 100644
--- a/docs/benchmarks/index.md
+++ b/docs/benchmarks/index.md
@@ -2,27 +2,26 @@
 
 Please visit the individual benchmark links to see the run commands using the unified CM interface.
 
-1. [Image Classification](image_classification/resnet50.md) using ResNet50 model and Imagenet-2012 dataset
+1. [Image Classification](image_classification/resnet50.md) using ResNet50-v1.5 model and Imagenet-2012 (224x224) validation dataset. Dataset size is 50,000 and QSL size is 1024. Reference model accuracy is 76.46%. Server scenario latency constraint is 15ms.
 
-2. [Text to Image](text_to_image/sdxl.md) using Stable Diffusion model and Coco2014 dataset
+2. [Text to Image](text_to_image/sdxl.md) using Stable Diffusion model and subset of Coco2014 dataset. Dataset size is 5000 amd QSL size is the same. Required accuracy for closed division is (23.01085758 <= FID <= 23.95007626, 32.68631873 <= CLIP <= 31.81331801).
 
-3. [Object Detection](object_detection/retinanet.md) using Retinanet model and OpenImages dataset
+3. [Object Detection](object_detection/retinanet.md) using Retinanet model and OpenImages dataset.Dataset size is 24781 and QSL size is 64. Reference model accuracy is 0.3755 mAP. Server scenario latency constraint is 100ms.
 
-4. [Image Segmentation](medical_imaging/3d-unet.md)  using 3d-unet model and KiTS19 dataset
+4. [Medical Image Segmentation](medical_imaging/3d-unet.md)  using 3d-unet model and KiTS2019 dataset. Dataset size is 42 and QSL size is the same. Reference model accuracy is 0.86330 mean DIXE score. Server scenario is not applicable.
 
-5. [Question Answering](language/bert.md) using Bert-Large model and Squad v1.1 dataset
+5. [Question Answering](language/bert.md) using Bert-Large model and Squad v1.1 dataset with 384 sequence length. Dataset size is 10833 and QSL size is the same. Reference model accuracy is f1 score = 90.874%. Server scenario latency constraint is 130ms.
 
-6. [Text Summarization](language/gpt-j.md) using GPT-J model and CNN Daily Mail dataset
+6. [Text Summarization](language/gpt-j.md) using GPT-J model and CNN Daily Mail v3.0.0 dataset. Dataset size is 13368 amd QSL size is the same. Reference model accuracy is (rouge1=42.9865, rouge2=20.1235, rougeL=29.9881, gen_len=4016878). Server scenario latency sconstraint is 20s.
 
-7. [Text Summarization](language/llama2-70b.md) using LLAMA2-70b model and OpenORCA dataset
+7. [Question Answering](language/llama2-70b.md) using LLAMA2-70b model and OpenORCA (GPT-4 split, max_seq_len=1024) dataset. Dataset size is 24576 and QSL size is the same. Reference model accuracy is (rouge1=44.4312, rouge2=22.0352, rougeL=28.6162, tokens_per_sample=294.45). Server scenario latency constraint is TTFT=2000ms, TPOT=200ms.
 
-8. [Recommendation](recommendation/dlrm-v2.md) using DLRMv2 model and Criteo multihot dataset
+8. [Question Answering, Math and Code Generation](language/mixtral-8x7b.md) using Mixtral-8x7B model and OpenORCA (5k samples of GPT-4 split, max_seq_len=2048), GSM8K (5k samples of the validation split, max_seq_len=2048), MBXP (5k samples of the validation split, max_seq_len=2048) datasets. Dataset size is 15000 and QSL size is the same. Reference model accuracy is (rouge1=45.4911, rouge2=23.2829, rougeL=30.3615, gsm8k accuracy = 73.78, mbxp accuracy = 60.12, tokens_per_sample=294.45). Server scenario latency constraint is TTFT=2000ms, TPOT=200ms.
 
-All the eight benchmarks can participate in the datacenter category.
-All the eight benchmarks except DLRMv2 and LLAMA2 and can participate in the edge category. 
+9. [Recommendation](recommendation/dlrm-v2.md) using DLRMv2 model and Synthetic Multihot Criteo dataset. Dataset size is 204800 and QSL size is the same. Reference model accuracy is AUC=80.31%. Server scenario latency constraint is 60 ms. 
+
+All the nine benchmarks can participate in the datacenter category.
+All the nine benchmarks except DLRMv2, LLAMA2 and Mixtral-8x7B and can participate in the edge category. 
 
 `bert`, `llama2-70b`, `dlrm_v2` and `3d-unet` has a high accuracy (99.9%) variant, where the benchmark run  must achieve a higher accuracy of at least `99.9%` of the FP32 reference model
 in comparison with the `99%` default accuracy requirement.
-
-The `dlrm_v2` benchmark has a high-accuracy variant only. If this accuracy is not met, the submission result can be submitted only to the open division.
-
diff --git a/language/bert/README.md b/language/bert/README.md
index 144e6d9d05..6e04686665 100644
--- a/language/bert/README.md
+++ b/language/bert/README.md
@@ -4,7 +4,7 @@ This is the reference implementation for MLPerf Inference benchmarks for Natural
 
 The chosen model is BERT-Large performing SQuAD v1.1 question answering task.
 
-Please see [this readme](README_cm.md) file for an automated way to run this benchmark out of the box and do an end-to-end submission with or without docker using the [MLCommons CM](https://github.com/mlcommons/ck/tree/master/cm) language.
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/bert) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
 ## Prerequisites
 
@@ -47,6 +47,10 @@ Please run the following commands:
 
 ## Loadgen over the Network 
 
+```
+pip install cm4mlops
+```
+
 The below CM command will launch the SUT server
 
 ```
diff --git a/language/bert/README_cm.md b/language/bert/README_cm.md
deleted file mode 100644
index f0284e68d8..0000000000
--- a/language/bert/README_cm.md
+++ /dev/null
@@ -1,39 +0,0 @@
-## Setup
-
-Please follow the MLCommons CK [installation guide](https://github.com/mlcommons/ck/blob/master/docs/installation.md) to install CM.
-
-Install MLCommons CK repository with automation workflows for MLPerf.
-
-```
-cm pull repo mlcommons@ck
-```
-
-# Bert
-
-Bert has two variants - `bert-99` and `bert-99.9` where the `99` and `99.9` specifies the required accuracy constraint with respect to the reference floating point model. `bert-99.9` model is applicable only on a datacenter system.
-
-In the edge category, bert-99 has Offline and SingleStream scenarios and in the datacenter category, both `bert-99` and `bert-99.9` have Offline and Server scenarios.
-
-
-## Run Command
-
-### One liner to do an end-to-end submission using the reference implementation
-
-The below command will automatically preprocess the dataset for a given backend, builds the loadgen, runs the inference for all the required scenarios and modes, and generate a submission folder out of the produced results. 
-
-** Please adjust the `target_qps` value as per your system performance to get a valid submission
-
-
-```
-cmr "run mlperf inference generate-run-cmds _submission" \
---quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference \
---backend=onnxruntime --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --target_qps=1 \
---category=edge --division=open
-```
-* Use `--device=cuda` to run the inference on Nvidia GPU
-* Use `--division=closed` to run all scenarios for the closed division
-* Use `--category=datacenter` to run datacenter scenarios. Both `bert-99` and `bert-99.9` are applicable to datacenter category whereas only `bert-99` is applicable to edge category
-* Use `--backend=tf` and `--backend=pytorch` to use tensorflow and pytorch backends respectively
-
-
-More details and commands to run different implementations like NVIDIA implementation can be seen [here](https://github.com/mlcommons/ck/tree/master/docs/mlperf/inference/bert).
diff --git a/language/gpt-j/README.md b/language/gpt-j/README.md
index 7c5301482b..9dc024a8e1 100644
--- a/language/gpt-j/README.md
+++ b/language/gpt-j/README.md
@@ -1,6 +1,6 @@
 # GPT-J Reference Implementation
 
-Please see [this readme](README_cm.md) file for an automated way to run this benchmark out of the box and do an end-to-end submission with or without docker using the [MLCommons CM](https://github.com/mlcommons/ck/tree/master/cm) language.
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/gpt-j) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
 ### Setup Instructions
 
@@ -75,8 +75,7 @@ Please download the fine-tuned GPT-J checkpoint using the instructions below. Th
 The following MLCommons CM commands can be used to programmatically download the model checkpoint. 
 
 ```
-pip install cmind
-cm pull repo mlcommons@ck
+pip install cm4mlops
 cm run script --tags=get,ml-model,gptj,_pytorch,_rclone -j
 ```
 
@@ -139,6 +138,10 @@ Intel expressly disclaims the accuracy, adequacy, or completeness of any data, d
 
 ## Loadgen over the Network 
 
+```
+pip install cm4mlops
+```
+
 The below CM command will launch the SUT server
 
 ```
diff --git a/language/gpt-j/README_cm.md b/language/gpt-j/README_cm.md
deleted file mode 100644
index 409fb47317..0000000000
--- a/language/gpt-j/README_cm.md
+++ /dev/null
@@ -1,43 +0,0 @@
-## Setup
-
-Please follow the MLCommons CK [installation guide](https://github.com/mlcommons/ck/blob/master/docs/installation.md) to install CM.
-
-Install MLCommons CK repository with automation workflows for MLPerf.
-
-```
-cm pull repo mlcommons@ck
-```
-
-# GPT-J
-
-Bert has two variants - `gpt-j-99` and `gpt-j-99.9` where the `99` and `99.9` specifies the required accuracy constraint with respect to the reference floating point model. `bert-99.9` model is applicable only on a datacenter system.
-
-In the edge category, gpt-j-99 has Offline and SingleStream scenarios and in the datacenter category, both `gpt-j-99` and `gpt-j-99.9` have Offline and Server scenarios.
-
-
-## Run Command
-
-### One liner to do an end-to-end submission using the reference implementation
-
-The below command will automatically preprocess the dataset for a given backend, builds the loadgen, runs the inference for all the required scenarios and modes, and generate a submission folder out of the produced results. 
-
-** Please modify the `--adr.gptj-model.checkpoint` value to the path containing the shared GPT-J checkpoint.**
-
-
-```
-cmr "run mlperf inference generate-run-cmds _submission" \
---quiet --submitter="MLCommons" --hw_name=default --model=gpt-j-99 --implementation=reference \
---backend=pytorch --device=cpu --scenario=Offline --adr.compiler.tags=gcc \
---category=edge --division=open --adr.gptj-model.checkpoint=$HOME/checkpoint-final \
---test_query_count=1 --execution_mode=test --precision=bfloat16 --offline_target_qps=1
-```
-
-* Use `--device=cuda` to run the inference on Nvidia GPU
-* Use `--division=closed` to run all scenarios for the closed division
-* Use `--scenario=SingleStream` to run SingleStream scenario
-* Use `--category=datacenter` to run datacenter scenarios. `--scenario=Server` and `--server_target_qps=<ACHIEVABLE_QPS>` should be given for server scenario
-* Can use `--adr.mlperf-inference-implementation.tags=_BEAM_SIZE.2` to change the beam size from the official value of 4 to 2 (can also be 1 or 3). This is allowed only for `open` submissions
-* Skip `--test_query_count=1 --execution_mode=test` to do a full valid run for submission
-
-
-More details and commands to run different implementations like NVIDIA implementation can be seen [here](https://github.com/mlcommons/ck/tree/master/docs/mlperf/inference/gpt-j).
diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 837f24ade0..b5d4440e41 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -2,22 +2,13 @@
 
 **Basic implementation for llama2-70b. Few noteworthy items:**
 
-+ Processing of Validation dataset is not finalized yet. Decision on input token lengths is pending
 + Streamer for communicating with loadgen has quite some overhead. This is only meant to provide functional implementation
 + For custom/optimized implementations of this benchmark it is important to include the :
         - For server scenario, it is necessary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
         - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
 
-## Automated command to run the benchmark via MLCommons CM 
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama2-70b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
-```
-python3 -m pip install cmind
-cm pull repo mlcommons@ck
-cm run script --tags=run-mlperf,inference  --model=llama2-70b-99 --implementation=reference --backend=pytorch --device=cpu --precision=float32 --scenario=Offline --quiet
-```
-* `--device=cuda` can be used to run on Nvidia GPUs and `--device=rocm` can be used to run on AMD GPUs
-* `--precision=float16` or `--precision=bfloat16` can be used to change the model precision
-* `--model=llama2-7b` can be used to run the llama2-7b variant (this is not the official MLPerf model and so not valid for a closed division submission)
  
 ## Prepare environment
 
diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 34402a9c30..1ebce04466 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -8,9 +8,8 @@
         - For server scenario, it is necessary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
         - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
 
-## Automated command to run the benchmark via MLCommons CM 
 
-TODO
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/mixtral-8x7b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
  
 ## Prepare environment
 
diff --git a/main.py b/main.py
index d208d07ce0..2f1c036fea 100644
--- a/main.py
+++ b/main.py
@@ -12,6 +12,10 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
         content=""
         scenarios = []
         execution_envs = ["Docker","Native"]
+        code_version="r4.1"
+
+        if model == "rnnt":
+            code_version="r4.0"
 
         if implementation == "reference":
             devices = [ "CPU", "CUDA", "ROCm" ]
@@ -31,8 +35,10 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
             frameworks = [ "TensorRT" ]
 
         elif implementation == "intel":
-            if model not in [ "bert-99", "bert-99.9", "gptj-99", "gptj-99.9" ]:
+            if model not in [ "bert-99", "bert-99.9", "gptj-99", "gptj-99.9", "resnet50", "retinanet", "3d-unet-99", "3d-unet-99.9" ]:
                  return pre_space+"    WIP"
+            if model in [ "bert-99", "bert-99.9", "retinanet", "3d-unet-99", "3d-unet-99.9" ]:
+                 code_version="r4.0"
             devices = [ "CPU" ]
             frameworks = [ "Pytorch" ]
 
@@ -109,14 +115,14 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                                 content += f"{cur_space3}####### Setup a virtual environment for Python\n"
                                 content += get_venv_command(spaces+16)
                                 content += f"{cur_space3}####### Performance Estimation for Offline Scenario\n"
-                                content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True).replace("--docker ","")
+                                content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, scenarios, code_version).replace("--docker ","")
                                 content += f"{cur_space3}The above command should do a test run of Offline scenario and record the estimated offline_target_qps.\n\n"
 
                             else: # Docker implementation steps
                                 content += f"{cur_space3}####### Docker Container Build and Performance Estimation for Offline Scenario\n"
                                 docker_info = get_docker_info(spaces+16, model, implementation, device)
                                 content += docker_info
-                                content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True)
+                                content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, scenarios, code_version)
                                 content += f"{cur_space3}The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.\n\n"
                                 content += f"{cur_space3}<details>\n"
                                 content += f"{cur_space3}<summary> Please click here to see more options for the docker launch </summary>\n\n"
@@ -131,7 +137,7 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                         else:
                             content += f"{cur_space3} You can reuse the same environment as described for {model.split('.')[0]}.\n"
                             content += f"{cur_space3}###### Performance Estimation for Offline Scenario\n"
-                            content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True).replace("--docker ","")
+                            content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, scenarios, code_version).replace("--docker ","")
                             content += f"{cur_space3}The above command should do a test run of Offline scenario and record the estimated offline_target_qps.\n\n"
 
 
@@ -144,12 +150,12 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
 
                         for scenario in scenarios:
                             content += f"{cur_space3}=== \"{scenario}\"\n{cur_space4}###### {scenario}\n\n"
-                            run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), scenario, device.lower(), "valid", scenarios)
+                            run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), scenario, device.lower(), "valid", 0, False, scenarios, code_version)
                             content += run_cmd
                             #content += run_suffix
  
                         content += f"{cur_space3}=== \"All Scenarios\"\n{cur_space4}###### All Scenarios\n\n"
-                        run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), "valid", scenarios)
+                        run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), "valid", scenarios, code_version)
                         content += run_cmd
                         content += run_suffix
 
@@ -235,7 +241,7 @@ def get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scen
         return extra_content
 
     @env.macro
-    def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False, scenarios = []):
+    def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False, scenarios = [], code_version="r4.1"):
         pre_space = ""
         for i in range(1,spaces):
              pre_space  = pre_space + " "
@@ -260,7 +266,7 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
 
             docker_setup_cmd = f"""\n
 {f_pre_space}```bash
-{f_pre_space}cm run script --tags=run-mlperf,inference,_find-performance,_full{scenario_variation_tag} \\
+{f_pre_space}cm run script --tags=run-mlperf,inference,_find-performance,_full,_{code_version}{scenario_variation_tag} \\
 {pre_space} --model={model} \\
 {pre_space} --implementation={implementation} \\
 {pre_space} --framework={framework} \\
@@ -279,7 +285,7 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
 
             run_cmd = f"""\n
 {f_pre_space}```bash
-{f_pre_space}cm run script --tags=run-mlperf,inference{scenario_variation_tag} \\
+{f_pre_space}cm run script --tags=run-mlperf,inference,_{code_version}{scenario_variation_tag} \\
 {pre_space} --model={model} \\
 {pre_space} --implementation={implementation} \\
 {pre_space} --framework={framework} \\
diff --git a/mkdocs.yml b/mkdocs.yml
index a5f75efa6c..90a3ce9fd8 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -35,7 +35,7 @@ nav:
       - Bert-Large: benchmarks/language/bert.md
       - GPT-J: benchmarks/language/gpt-j.md
       - LLAMA2-70B: benchmarks/language/llama2-70b.md
-      - MIXTRAL-8x7b: benchmarks/language/mixtral-8x7b.md
+      - MIXTRAL-8x7B: benchmarks/language/mixtral-8x7b.md
     - Recommendation:
       - DLRM-v2: benchmarks/recommendation/dlrm-v2.md
   - Submission:
diff --git a/recommendation/dlrm_v2/pytorch/README.md b/recommendation/dlrm_v2/pytorch/README.md
index 12937c0c86..647e6830c9 100755
--- a/recommendation/dlrm_v2/pytorch/README.md
+++ b/recommendation/dlrm_v2/pytorch/README.md
@@ -2,6 +2,8 @@
 
 This is the reference implementation for MLCommons Inference benchmarks.
 
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/recommendation/dlrm-v2/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
 ### Supported Models
 
 **TODO: Decide benchmark name**
@@ -80,8 +82,7 @@ N/A | pytorch | <2GB | -
 The following MLCommons CM commands can be used to programmatically download the model checkpoint. 
 
 ```
-pip install cmind
-cm pull repo mlcommons@ck
+pip install cm4mlops
 cm run script --tags=get,ml-model,dlrm,_pytorch,_weight_sharded,_rclone -j
 ```
 
diff --git a/vision/medical_imaging/3d-unet-brats19/.dockerignore b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/.dockerignore
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/.dockerignore
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/.dockerignore
diff --git a/vision/medical_imaging/3d-unet-brats19/.gitignore b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/.gitignore
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/.gitignore
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/.gitignore
diff --git a/vision/medical_imaging/3d-unet-brats19/Dockerfile b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Dockerfile
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/Dockerfile
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Dockerfile
diff --git a/vision/medical_imaging/3d-unet-brats19/Makefile b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Makefile
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/Makefile
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Makefile
diff --git a/vision/medical_imaging/3d-unet-brats19/README.md b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/README.md
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/README.md
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/README.md
diff --git a/vision/medical_imaging/3d-unet-brats19/Task043_BraTS_2019.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Task043_BraTS_2019.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/Task043_BraTS_2019.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Task043_BraTS_2019.py
diff --git a/vision/medical_imaging/3d-unet-brats19/accuracy-brats.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/accuracy-brats.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/accuracy-brats.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/accuracy-brats.py
diff --git a/vision/medical_imaging/3d-unet-brats19/brats_QSL.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/brats_QSL.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/brats_QSL.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/brats_QSL.py
diff --git a/vision/medical_imaging/3d-unet-brats19/folds/fold0_validation.txt b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold0_validation.txt
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/folds/fold0_validation.txt
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold0_validation.txt
diff --git a/vision/medical_imaging/3d-unet-brats19/folds/fold1_validation.txt b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold1_validation.txt
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/folds/fold1_validation.txt
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold1_validation.txt
diff --git a/vision/medical_imaging/3d-unet-brats19/folds/fold2_validation.txt b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold2_validation.txt
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/folds/fold2_validation.txt
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold2_validation.txt
diff --git a/vision/medical_imaging/3d-unet-brats19/folds/fold3_validation.txt b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold3_validation.txt
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/folds/fold3_validation.txt
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold3_validation.txt
diff --git a/vision/medical_imaging/3d-unet-brats19/folds/fold4_validation.txt b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold4_validation.txt
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/folds/fold4_validation.txt
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/folds/fold4_validation.txt
diff --git a/vision/medical_imaging/3d-unet-brats19/onnxruntime_SUT.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/onnxruntime_SUT.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/onnxruntime_SUT.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/onnxruntime_SUT.py
diff --git a/vision/medical_imaging/3d-unet-brats19/ov_SUT.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/ov_SUT.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/ov_SUT.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/ov_SUT.py
diff --git a/vision/medical_imaging/3d-unet-brats19/preprocess.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/preprocess.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/preprocess.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/preprocess.py
diff --git a/vision/medical_imaging/3d-unet-brats19/pytorch_SUT.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/pytorch_SUT.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/pytorch_SUT.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/pytorch_SUT.py
diff --git a/vision/medical_imaging/3d-unet-brats19/run.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/run.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/run.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/run.py
diff --git a/vision/medical_imaging/3d-unet-brats19/tf_SUT.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/tf_SUT.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/tf_SUT.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/tf_SUT.py
diff --git a/vision/medical_imaging/3d-unet-brats19/unet_onnx_to_tf.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_onnx_to_tf.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/unet_onnx_to_tf.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_onnx_to_tf.py
diff --git a/vision/medical_imaging/3d-unet-brats19/unet_pytorch_to_onnx.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_pytorch_to_onnx.py
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/unet_pytorch_to_onnx.py
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_pytorch_to_onnx.py
diff --git a/vision/medical_imaging/3d-unet-brats19/user.conf b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/user.conf
similarity index 100%
rename from vision/medical_imaging/3d-unet-brats19/user.conf
rename to retired_benchmarks/vision/medical_imaging/3d-unet-brats19/user.conf
diff --git a/text_to_image/README.md b/text_to_image/README.md
index bb46406a87..bdd1901295 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -1,6 +1,8 @@
 # MLPerf™ Inference Benchmarks for Text to Image
 
-This is the reference implementation for MLPerf Inference text to image
+This is the reference implementation for MLPerf Inference text to image.
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/text_to_image/sdxl) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
 ## Supported Models
 
@@ -16,15 +18,6 @@ This is the reference implementation for MLPerf Inference text to image
 | Coco-2014 | We use a subset of 5000 images and captions of the coco 2014 validation dataset, so that there is exaclty one caption per image. The model takes as input the caption of the image and generates an image from it. The original images and the generated images are used to compute FID score. The caption and the generated images are used to compute the CLIP score. We provide a [script](tools/coco.py) to automatically download the dataset |
 | Coco-2014 (calibration) | We use a subset of 500 captions and images of the coco 2014 training dataset, so that there is exaclty one caption per image. The subset was generated using this [script](tools/coco_generate_calibration.py). We provide the [caption ids](../calibration/COCO-2014/coco_cal_captions_list.txt) and a [script](tools/coco_calibration.py) to download them. |
 
-## Automated command to run the benchmark via MLCommons CM
-
-```
-python3 -m pip install cmind
-cm pull repo mlcommons@ck
-cm run script --tags=run-mlperf,inference  --model=sdxl --implementation=reference --backend=pytorch --device=cpu --precision=float32 --scenario=Offline --quiet
-```
-* `--device=cuda` can be used to run on Nvidia GPUs and `--device=rocm` can be used to run on AMD GPUs
-* `--precision=float16` or `--precision=bfloat16` can be used to change the model precision
  
 ## Setup
 Set the following helper variables
diff --git a/vision/classification_and_detection/README.md b/vision/classification_and_detection/README.md
index 8384c5aed2..12320b5fde 100755
--- a/vision/classification_and_detection/README.md
+++ b/vision/classification_and_detection/README.md
@@ -2,11 +2,32 @@
 
 This is the reference implementation for MLPerf Inference Classification and Object Detection benchmarks
 
-Please see the below readme files for an automated way to run this benchmark out of the box and do an end-to-end submission with or without docker using the [MLCommons CM](https://github.com/mlcommons/ck/tree/master/cm) language.
-1. [Resnet50](README_cm_resnet50.md)
-2. [Retinanet](README_cm_retinanet.md)
+## Automated Run Commands
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
-Alternatively, you can find a short tutorial on how to use this benchmark [here](https://github.com/mlperf/inference/blob/master/vision/classification_and_detection/GettingStarted.ipynb).
+### ResNet50
+
+| Implementation | Framework | Supported Devices | Supported Precision |
+| -------------- | --------- | ----------------- | ------------------- |
+| [Reference](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_3_1) | Onnxruntime | CPU, CUDA, ROCm | fp32 |
+| [Reference](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_3_2) | Tensorflow | CPU, CUDA, ROCm | fp32 |
+| [Reference](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_3_3) | Deepsparse | CPU | fp32, int8 |
+| [Nvidia](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_1_2) | TensorRT | CUDA | int8 |
+| [Intel](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_1_3) | PyTorch | CPU | int8 |
+| [MLCommons C++](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_1_5) | Onnxruntime | CPU, CUDA | fp32 |
+
+### Retinanet
+
+| Implementation | Framework | Supported Devices | Supported Precision |
+| -------------- | --------- | ----------------- | ------------------- |
+| [Reference](https://docs.mlcommons.org/inference/benchmarks/object_detection/retinanet/#__tabbed_3_1) | Onnxruntime | CPU, CUDA, ROCm | fp32 |
+| [Reference](https://docs.mlcommons.org/inference/benchmarks/object_detection/retinanet/#__tabbed_3_2) | PyTorch | CPU, CUDA, ROCm | fp32 |
+| [Nvidia](https://docs.mlcommons.org/inference/benchmarks/object_detection/retinanet/#__tabbed_1_2) | TensorRT | CUDA | int8 |
+| [Intel](https://docs.mlcommons.org/inference/benchmarks/object_detection/retinanet/#__tabbed_1_3) | PyTorch | CPU | int8 |
+| [MLCommons C++](https://docs.mlcommons.org/inference/benchmarks/object_detection/retinanet/#__tabbed_1_5) | Onnxruntime | CPU, CUDA | fp32 |
+
+
+Additionally, you can find a short tutorial on how to use this benchmark [here](https://github.com/mlperf/inference/blob/master/vision/classification_and_detection/GettingStarted.ipynb).
 
 
 ## Supported Models
@@ -48,8 +69,7 @@ Alternatively, you can find a short tutorial on how to use this benchmark [here]
 ## Disclaimer
 This benchmark app is a reference implementation that is not meant to be the fastest implementation possible.
 It is written in python which might make it less suitable for lite models like mobilenet or large number of cpu's.
-There is a [C++ implementation](https://github.com/mlcommons/ck/tree/master/cm-mlops/script/app-mlperf-inference-cpp) in
-[MLCommons CK repository](https://github.com/mlcommons/ck) which currently supports onnxruntime backend.
+There is a [C++ implementation](https://github.com/mlcommons/cm4mlops/tree/mlperf-inference/script/app-mlperf-inference-mlcommons-cpp) available which currently supports onnxruntime backend for CPUs and Nvidia GPUs.
 
 ## Tools for preparing datasets and validating accuracy
 The reference implementation includes all required pre-processing of datasets.
diff --git a/vision/classification_and_detection/README_cm_resnet50.md b/vision/classification_and_detection/README_cm_resnet50.md
deleted file mode 100644
index 2f1c14c68a..0000000000
--- a/vision/classification_and_detection/README_cm_resnet50.md
+++ /dev/null
@@ -1,42 +0,0 @@
-## Setup
-
-Please follow the MLCommons CK [installation guide](https://github.com/mlcommons/ck/blob/master/docs/installation.md) to install CM.
-
-Install MLCommons CK repository with automation workflows for MLPerf.
-
-```
-cm pull repo mlcommons@ck
-```
-
-# Resnet50
-
-## Get Imagenet Dataset
-
-We need to get imagenet full dataset to make image-classification submissions for MLPerf inference. Since this dataset is not publicly available via a URL please follow the instructions given [here](https://github.com/mlcommons/ck/blob/master/cm-mlops/script/get-dataset-imagenet-val/README-extra.md) to download the dataset and register in CM.
-
-In the edge category, ResNet50 has Offline, SingleStream, and MultiStream scenarios and in the datacenter category, it has Offline and Server scenarios. The below commands are assuming an edge category system. 
-
-## Run Command
-
-### One liner to do an end-to-end submission using the reference implementation
-
-The below command will automatically preprocess the dataset for a given backend, builds the loadgen, runs the inference for all the required scenarios and modes, and generate a submission folder out of the produced results. 
-
-** Please adjust the `target_qps` value as per your system performance to get a valid submission
-
-
-```
-cmr "run mlperf inference generate-run-cmds _submission" \
---quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference \
---backend=onnxruntime --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --target_qps=1 \
---category=edge --division=open
-```
-* Use `--device=cuda` to run the inference on Nvidia GPU
-* Use `--division=closed` to run all scenarios for the closed division (compliance tests are skipped for `_find-performance` mode)
-* Use `--category=datacenter` to run datacenter scenarios
-* Use `--backend=tf` or `--backend=tvm-onnx` to use tensorflow and tvm-onnx backends respectively
-
-
-More details and commands to run different implementations like NVIDIA implementation can be seen [here](https://github.com/mlcommons/ck/tree/master/docs/mlperf/inference/resnet50).
-
-
diff --git a/vision/classification_and_detection/README_cm_retinanet.md b/vision/classification_and_detection/README_cm_retinanet.md
deleted file mode 100644
index e782c539f1..0000000000
--- a/vision/classification_and_detection/README_cm_retinanet.md
+++ /dev/null
@@ -1,38 +0,0 @@
-## Setup
-
-Please follow the MLCommons CK [installation guide](https://github.com/mlcommons/ck/blob/master/docs/installation.md) to install CM.
-
-Install MLCommons CK repository with automation workflows for MLPerf
-
-```
-cm pull repo mlcommons@ck
-```
-
-# Retinanet
-
-In the edge category, Retinanet has Offline, SingleStream, and MultiStream scenarios and in the datacenter category, it has Offline and Server scenarios. The below commands are assuming an edge category system. 
-
-## Run Command
-
-### One liner to do an end-to-end submission using the reference implementation
-
-The below command will automatically preprocess the dataset for a given backend, builds the loadgen, runs the inference for all the required scenarios and modes, and generate a submission folder out of the produced results. 
-
-** Please adjust the `target_qps` value as per your system performance to get a valid submission
-
-```
-cmr "run mlperf inference generate-run-cmds _submission" \
---quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference \
---backend=onnxruntime --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --target_qps=1 \
---category=edge --division=open
-```
-
-* Use `--device=cuda` to run the inference on Nvidia GPU
-* Use `--division=closed` to run all scenarios for the closed division (compliance tests are skipped for `_find-performance` mode)
-* Use `--category=datacenter` to run datacenter scenarios
-* Use `--backend=pytorch` to use pytorch backend
-
-
-More details and commands to run different implementations like NVIDIA implementation can be seen [here](https://github.com/mlcommons/ck/tree/master/docs/mlperf/inference/retinanet).
-
-
diff --git a/vision/medical_imaging/3d-unet-brats19/nnUnet b/vision/medical_imaging/3d-unet-brats19/nnUnet
deleted file mode 160000
index b38c69b345..0000000000
--- a/vision/medical_imaging/3d-unet-brats19/nnUnet
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit b38c69b345b2f60cd0d053039669e8f988b0c0af
diff --git a/vision/medical_imaging/3d-unet-kits19/README.md b/vision/medical_imaging/3d-unet-kits19/README.md
index 39363d8530..ae2b816079 100644
--- a/vision/medical_imaging/3d-unet-kits19/README.md
+++ b/vision/medical_imaging/3d-unet-kits19/README.md
@@ -6,7 +6,8 @@ This model performing KiTS19 dataset for kidney tumor segmentation task is propo
 
 [3D-UNet BraTS19 model](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-brats19), which has been used for MLPerf-Inference v0.7 and v1.0, is the valid model for the submissions until the end of year 2021. Please use BraTS19 model for v.1.1 submission.
 
-Please see [this readme](README_cm.md) file for an automated way to run this benchmark out of the box and do an end-to-end submission with or without docker using the [MLCommons CM](https://github.com/mlcommons/ck/tree/master/cm) language.
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/medical_imaging/3d-unet/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
 
 ## Prerequisites
 
diff --git a/vision/medical_imaging/3d-unet-kits19/README_cm.md b/vision/medical_imaging/3d-unet-kits19/README_cm.md
deleted file mode 100644
index 772d0feb0a..0000000000
--- a/vision/medical_imaging/3d-unet-kits19/README_cm.md
+++ /dev/null
@@ -1,39 +0,0 @@
-## Setup
-
-Please follow the MLCommons CK [installation guide](https://github.com/mlcommons/ck/blob/master/docs/installation.md) to install CM.
-
-Install MLCommons CK repository with automation workflows for MLPerf.
-
-```
-cm pull repo mlcommons@ck
-```
-
-# 3d-unet
-
-3d-unet has two variants - `3d-unet-99` and `3d-unet-99.9` where the `99` and `99.9` specifies the required accuracy constraint with respect to the reference floating point model. Both the models are applicable to edge as well as datacenter category.
-
-In the edge category, Offline and SingleStream are the applicable scenarios and in the datacenter category, the applicable ones are Offline and Server scenarios.
-
-
-## Run Command
-
-### One liner to do an end-to-end submission using the reference implementation
-
-The below command will automatically preprocess the dataset for a given backend, builds the loadgen, runs the inference for all the required scenarios and modes, and generate a submission folder out of the produced results. 
-
-** Please adjust the `target_qps` value as per your system performance to get a valid submission
-
-
-```
-cmr "run mlperf inference generate-run-cmds _submission" \
---quiet --submitter="MLCommons" --hw_name=default --model=3d-unet-99 --implementation=reference \
---backend=onnxruntime --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --target_qps=1 \
---category=edge --division=open
-```
-* Use `--device=cuda` to run the inference on Nvidia GPU
-* Use `--division=closed` to run all scenarios for the closed division
-* Use `--category=datacenter` to run datacenter scenarios. 
-* Use `--backend=tf` and `--backend=pytorch` to use tensorflow and pytorch backends respectively
-
-
-More details and commands to run different implementations like NVIDIA implementation can be seen [here](https://github.com/mlcommons/ck/tree/master/docs/mlperf/inference/3d-unet).

From 36786abfa1a25a7eef649d2cf3c87a44db0fe984 Mon Sep 17 00:00:00 2001
From: nvamberl <amberl@nvidia.com>
Date: Wed, 10 Jul 2024 04:35:22 +0800
Subject: [PATCH 121/305] Add '--low_memory' flag for accuracy_coco.py to deal
 with data center and edge devices differently. (#1770)

* Add '--edge' flag for accuracy_coco.py to deal with data center and edge devices differently.

* Add '--low_memory' flag for accuracy_coco.py

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 text_to_image/tools/accuracy_coco.py | 94 +++++++++++++++++++++++-----
 1 file changed, 80 insertions(+), 14 deletions(-)

diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
index 74a13f790c..cdb4f5bebe 100644
--- a/text_to_image/tools/accuracy_coco.py
+++ b/text_to_image/tools/accuracy_coco.py
@@ -15,7 +15,7 @@
 import torch
 from clip.clip_encoder import CLIPEncoder
 from fid.inception import InceptionV3
-from fid.fid_score import compute_statistics_of_path, get_activations, calculate_frechet_distance
+from fid.fid_score import compute_fid, compute_statistics_of_path, get_activations, calculate_frechet_distance
 from tqdm import tqdm
 import ijson
 
@@ -31,6 +31,7 @@ def get_args():
     parser.add_argument("--output-file", default="coco-results.json", help="path to output file")
     parser.add_argument("--compliance-images-path", required=False, help="path to dump 10 stable diffusion xl compliance images")
     parser.add_argument("--device", default="cpu", choices=["gpu", "cpu"])
+    parser.add_argument("--low_memory", action="store_true", help="If device is has limited memory (<70G), use the memory saving path.")
     args = parser.parse_args()
     return args
 
@@ -78,19 +79,84 @@ def main():
                 caption_file.write(f"{idx}  {df_captions.iloc[idx]['caption']}\n")
 
     # Compute accuracy
-    compute_accuracy(
-        args.mlperf_accuracy_file,
-        args.output_file,
-        device,
-        dump_compliance_images,
-        compliance_images_idx_list,
-        args.compliance_images_path,
-        df_captions,
-        statistics_path,
-    )
-    
+    if args.low_memory:
+        print(f"Device has low memory, running memory saving path!")
+        compute_accuracy_low_memory(
+            args.mlperf_accuracy_file,
+            args.output_file,
+            device,
+            dump_compliance_images,
+            compliance_images_idx_list,
+            args.compliance_images_path,
+            df_captions,
+            statistics_path,
+        )
+    else:
+        compute_accuracy(
+            args.mlperf_accuracy_file,
+            args.output_file,
+            device,
+            dump_compliance_images,
+            compliance_images_idx_list,
+            args.compliance_images_path,
+            df_captions,
+            statistics_path,
+        )        
+
 
 def compute_accuracy(
+    mlperf_accuracy_file, 
+    output_file,
+    device,
+    dump_compliance_images,
+    compliance_images_idx_list,
+    compliance_images_path,
+    df_captions,
+    statistics_path,    
+):
+    # Load torchmetrics modules
+    clip = CLIPEncoder(device=device)
+    clip_scores = []
+    seen = set()
+    result_list = []
+    result_dict = {}
+
+    # Load model outputs
+    with open(mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+    for j in tqdm(results):
+        idx = j['qsl_idx']
+        if idx in seen:
+            continue
+        seen.add(idx)
+
+        # Load generated image
+        generated_img = np.frombuffer(bytes.fromhex(j['data']), np.uint8).reshape(1024, 1024, 3)
+        result_list.append(generated_img)
+        generated_img = Image.fromarray(generated_img)
+
+        # Dump compliance images
+        if dump_compliance_images and idx in compliance_images_idx_list:
+            generated_img.save(os.path.join(compliance_images_path, f"{idx}.png"))
+
+        # generated_img = torch.Tensor(generated_img).to(torch.uint8).to(device)
+        # Load Ground Truth
+        caption = df_captions.iloc[idx]["caption"]
+        clip_scores.append(
+            100 * clip.get_clip_score(caption, generated_img).item()
+        )
+    fid_score = compute_fid(result_list, statistics_path, device)
+
+    result_dict["FID_SCORE"] = fid_score
+    result_dict["CLIP_SCORE"] = np.mean(clip_scores)
+    print(f"Accuracy Results: {result_dict}")
+
+    with open(output_file, "w") as fp:
+        json.dump(result_dict, fp, sort_keys=True, indent=4)
+
+
+def compute_accuracy_low_memory(
     mlperf_accuracy_file, 
     output_file,
     device,
@@ -99,7 +165,7 @@ def compute_accuracy(
     compliance_images_path,
     df_captions,
     statistics_path,
-    batch_size=8,
+    batch_size=256,
     inception_dims=2048,
     num_workers=1,
 ):    
@@ -116,7 +182,7 @@ def compute_accuracy(
     else:
         num_workers = num_workers    
 
-    # Prepare models
+    # Load torchmetrics modules
     block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[inception_dims]
     inception_model = InceptionV3([block_idx]).to(device)
     clip_model = CLIPEncoder(device=device)

From 92dd3d24cf78d07aa31165f90c636d98c4adddcd Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 9 Jul 2024 21:46:52 +0100
Subject: [PATCH 122/305] Update test-submission-checker.yml (#1768)

* Update test-submission-checker.yml

* Update test-submission-checker.yml

* Fix submission checker code for v4.0
---
 .github/workflows/test-submission-checker.yml | 2 +-
 tools/submission/submission_checker.py        | 8 +++++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index 31b8e4aacb..da4192bddc 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -32,4 +32,4 @@ jobs:
         git clone https://github.com/mlcommons/inference_results_v4.0 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
-        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.0 --version=r4.0 --quiet 
+        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.0 --src_version=v4.0 --quiet 
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 855024e683..a02c6abfb7 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -2578,7 +2578,13 @@ def check_compliance_dir(
         "llama2-70b-99.9",
         "mixtral-8x7b"
     ]:
-        test_list.remove("TEST01") 
+        test_list.remove("TEST01")
+
+    if model in [
+        "stable-diffusion-xl"
+    ] and config.version in [ "v4.0" ]:
+        test_list.remove("TEST01")
+
 
     if model in [
         "llama2-70b-99",

From a0fc0da814457eddd3dfcdd173e847dbe318fea4 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 10 Jul 2024 16:59:17 -0500
Subject: [PATCH 123/305] Add stable diffusion to test04 with query count param
 (#1765)

---
 compliance/nvidia/TEST04/README.md     | 2 ++
 compliance/nvidia/TEST04/audit.config  | 5 +++++
 text_to_image/main.py                  | 2 ++
 tools/submission/submission_checker.py | 2 +-
 4 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/compliance/nvidia/TEST04/README.md b/compliance/nvidia/TEST04/README.md
index 604d2bfe56..130e02c16b 100644
--- a/compliance/nvidia/TEST04/README.md
+++ b/compliance/nvidia/TEST04/README.md
@@ -25,6 +25,8 @@ This test is not applicable for the following benchmarks whose performance is de
  5. retinanet (while retinanet input images are not variable, computation cost varies significantly due to the variance in qualified detections from the detector heads, which affect the NMS runtime)
  6. gpt-j
 
+**Warning:** For the stable diffusion benchmark, min_query_count is set to 500 to avoid a longer runtime. For loading benchmark specific configuration variables, you should load the audit configuration from your SUT code. You can do this by calling `settings.FromConfig(audit_config, model_name, scenario)`. This can't happend inside loadgen, because it is agnostic to the model that is being tested.
+
 ## Scenarios
 
  - This test is applicable for all scenarios.
diff --git a/compliance/nvidia/TEST04/audit.config b/compliance/nvidia/TEST04/audit.config
index b6e82b2cc1..e274b7c53b 100644
--- a/compliance/nvidia/TEST04/audit.config
+++ b/compliance/nvidia/TEST04/audit.config
@@ -18,3 +18,8 @@
 *.Server.performance_issue_unique = 0
 *.Server.performance_issue_same = 1
 *.Server.performance_issue_same_index = 3
+stable-diffusion-xl.Offline.min_query_count = 500
+# You can optionally set the target qps to match the expected query count 
+# in the min duration with the min_query_count. But take into account you
+# system expected qps, the min duration for this test is 10 minutes
+# stable-diffusion-xl.Offline.target_qps = 0.75
diff --git a/text_to_image/main.py b/text_to_image/main.py
index 32425762ff..3d81b3fb0a 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -434,6 +434,8 @@ def flush_queries():
     settings = lg.TestSettings()
     settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
     settings.FromConfig(user_conf, args.model_name, args.scenario)
+    if os.path.exists(audit_config):
+        settings.FromConfig(audit_config, args.model_name, args.scenario)
     settings.scenario = scenario
     settings.mode = lg.TestMode.PerformanceOnly
     if args.accuracy:
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index a02c6abfb7..6736f2ac31 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -2556,7 +2556,6 @@ def check_compliance_dir(
         "gptj-99.9",
         "llama2-70b-99",
         "llama2-70b-99.9",
-        "stable-diffusion-xl",
         "mixtral-8x7b"
     ]:
         test_list.remove("TEST04")
@@ -2584,6 +2583,7 @@ def check_compliance_dir(
         "stable-diffusion-xl"
     ] and config.version in [ "v4.0" ]:
         test_list.remove("TEST01")
+        test_list.remove("TEST04")
 
 
     if model in [

From f474c56f7727cdb99c76d5e464c5a7765ad9a6d3 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 10 Jul 2024 17:28:11 -0500
Subject: [PATCH 124/305] Remove newlines from recommendation (#1766)

---
 loadgen/results.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/loadgen/results.cc b/loadgen/results.cc
index 97f7c6b9af..3bbeda42a9 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -672,6 +672,8 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
       recommendation +=
           "The test exited early, before enough queries were issued.";
     }
+    std::replace(recommendation.begin(),
+               recommendation.end(), '\n', ' ');
     MLPERF_LOG(detail, "result_invalid_reason", recommendation);
   }
   std::replace(early_stopping_recommendation.begin(),

From e34bdc6e82181e855b8e34e710287654e58e6422 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 10 Jul 2024 17:29:26 -0500
Subject: [PATCH 125/305] Remove outdated warnings (#1767)

---
 language/llama2-70b/main.py   | 1 -
 language/mixtral-8x7b/main.py | 2 --
 2 files changed, 3 deletions(-)

diff --git a/language/llama2-70b/main.py b/language/llama2-70b/main.py
index 468ad067e7..15dbdd2edb 100644
--- a/language/llama2-70b/main.py
+++ b/language/llama2-70b/main.py
@@ -53,7 +53,6 @@ def main():
 
     if args.accuracy:
         settings.mode = lg.TestMode.AccuracyOnly
-        log.warning("Accuracy run will generate the accuracy logs, but the evaluation of the log is not completed yet")
     else:
         settings.mode = lg.TestMode.PerformanceOnly
 
diff --git a/language/mixtral-8x7b/main.py b/language/mixtral-8x7b/main.py
index 396948ba06..c28cdc536a 100644
--- a/language/mixtral-8x7b/main.py
+++ b/language/mixtral-8x7b/main.py
@@ -117,8 +117,6 @@ def main():
 
     if args.accuracy:
         settings.mode = lg.TestMode.AccuracyOnly
-        log.warning(
-            "Accuracy run will generate the accuracy logs, but the evaluation of the log is not completed yet")
     else:
         settings.mode = lg.TestMode.PerformanceOnly
 

From c83565d85d221bfb46cc7d5eef9dc89cf762d38e Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Wed, 10 Jul 2024 21:48:01 -0700
Subject: [PATCH 126/305] Fix loadgen version (#1775)

---
 loadgen/CMakeLists.txt       | 2 +-
 loadgen/version_generator.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/loadgen/CMakeLists.txt b/loadgen/CMakeLists.txt
index 977068cd86..35420e8064 100644
--- a/loadgen/CMakeLists.txt
+++ b/loadgen/CMakeLists.txt
@@ -4,7 +4,7 @@ project(mlperf_loadgen)
 
 # The mlperf_loadgen version.
 set(mlperf_loadgen_VERSION_MAJOR 4)
-set(mlperf_loadgen_VERSION_MINOR 0)
+set(mlperf_loadgen_VERSION_MINOR 1)
 message("mlperf_loadgen v${mlperf_loadgen_VERSION_MAJOR}.${mlperf_loadgen_VERSION_MINOR}")
 
 # Set build options. NB: CXX_STANDARD is supported since CMake 3.1.
diff --git a/loadgen/version_generator.py b/loadgen/version_generator.py
index 2c5d11331a..a013af0c6a 100644
--- a/loadgen/version_generator.py
+++ b/loadgen/version_generator.py
@@ -93,7 +93,7 @@ def generate_loadgen_version_definitions(cc_filename, loadgen_root):
     ofile.write("// DO NOT EDIT: Autogenerated by version_generator.py.\n\n")
     ofile.write("#include <string>\n\n")
     ofile.write("namespace mlperf {\n\n")
-    ofile.write(func_def("Version", "\"4.0\""))
+    ofile.write(func_def("Version", "\"4.1\""))
 
     date_time_now_local = datetime.datetime.now().isoformat()
     date_time_now_utc = datetime.datetime.utcnow().isoformat()

From f1c4d676e91909481b7551ce62d074377bbec068 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 16 Jul 2024 12:02:23 -0500
Subject: [PATCH 127/305] Fix accuracy script for no image generation (#1774)

---
 text_to_image/tools/accuracy_coco.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
index cdb4f5bebe..027efdafb4 100644
--- a/text_to_image/tools/accuracy_coco.py
+++ b/text_to_image/tools/accuracy_coco.py
@@ -64,6 +64,7 @@ def main():
 
     # Set compliance images path
     dump_compliance_images = False
+    compliance_images_idx_list = []
     if args.compliance_images_path:
         if not os.path.exists(args.compliance_images_path):
             os.makedirs(args.compliance_images_path)

From c60103b05acf9ea6432ef40857f21686299b5992 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Tue, 16 Jul 2024 12:51:50 -0700
Subject: [PATCH 128/305] #1777: TEST06 allows [eos_token_id, eos_token_id]
 case to WAR mixtral (#1778)

---
 compliance/nvidia/TEST06/README.md           |  8 ++++----
 compliance/nvidia/TEST06/run_verification.py | 13 +++++++------
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/compliance/nvidia/TEST06/README.md b/compliance/nvidia/TEST06/README.md
index a867e75273..9ca041cbd8 100644
--- a/compliance/nvidia/TEST06/README.md
+++ b/compliance/nvidia/TEST06/README.md
@@ -8,10 +8,10 @@ This repository provides the config files and scripts to run and verify TEST 06
 
 ## Introduction
 
-The purpose of this test is to ensure the consistency of the output of the Llama2 model and avoid a potential EOS exploit. This test will make a performance run, with a limit of 100 samples and logging them into `mlperf_log_accuracy.json`. To achieve a passing result in this test, three criteria must be met:
+The purpose of this test is to ensure the consistency of the output of the LLM (Llama2 and Mixtral) model and avoid a potential EOS exploit. This test will make a performance run, with a limit of 100 samples and logging them into `mlperf_log_accuracy.json`. To achieve a passing result in this test, three criteria must be met:
 - In the case the first token is reported independently (not applicable for Offline scenario), it should match for every query with the first token of the model output.
-- For each query, the model output should only end with zero or one EOS token
-- The number of reported tokens should match with the length of it's
+- For each query, the model output should only end with zero or one EOS token. The only exception for 2 EOS tokens is when the entire output sequences are EOS tokens (i.e. output is [eos_token_id, eos_token_id])
+- The number of reported tokens should match with the length of output sequence.
 
 ## Requisites
 
@@ -22,7 +22,7 @@ pip install numpy
 
 ## Instructions
 ### Part I
-Run the Llama-v2-70b benchmark with the provided audit.config in the corresponding subdirectory. Note that audit.config must be copied to the directory where the benchmark is being run from. Verification that audit.config was properly read can be done by checking that loadgen has found audit.config in mlperf_log_detail.txt
+Run the benchmark with the provided audit.config in the corresponding subdirectory. Note that audit.config must be copied to the directory where the benchmark is being run from. Verification that audit.config was properly read can be done by checking that loadgen has found audit.config in mlperf_log_detail.txt
 
 ### Part II
 Run the verification script
diff --git a/compliance/nvidia/TEST06/run_verification.py b/compliance/nvidia/TEST06/run_verification.py
index 03c60c2efb..b7fe7b5b51 100644
--- a/compliance/nvidia/TEST06/run_verification.py
+++ b/compliance/nvidia/TEST06/run_verification.py
@@ -21,7 +21,6 @@
 
 import numpy as np
 
-EOS_TOKEN = 2
 DTYPE_MAP = {
     "int64": np.int64,
     "int32": np.int32,
@@ -37,22 +36,24 @@ def get_args():
     parser.add_argument("--output_dir", "-o",
                         help="Specifies the path to the output directory where compliance logs will be uploaded from, i.e. inference_results_v0.7/closed/NVIDIA/compliance/T4x8/resnet/Offline.",
                         required=True)
+    parser.add_argument("--eos_token_id", '-e', default=2, help="EOS token id of the tokenizer")
     parser.add_argument("--dtype", "-d", default="int64", choices=["int64", "int32", "int16", "float32"])
     parser.add_argument("--scenario", "-s", required=True, choices=["Offline", "Server", "SingleStream", "MultiStream"])
     args = parser.parse_args()
     return args
 
-def eos_check(acc_data, dtype):
+def eos_check(acc_data, dtype, eos_token_id=2):
     for sample in acc_data:
         data = np.frombuffer(bytes.fromhex(sample["data"]), dtype=dtype)
         i = data.shape[0] - 1
         n_eos_tokens = 0
         while (i > 0):
-            if data[i] == EOS_TOKEN:
+            if data[i] == eos_token_id:
                 n_eos_tokens += 1
             if n_eos_tokens >= 2:
-                return False
-            if data[i] != EOS_TOKEN:
+                # Allow output to be [eos_token_id, eos_token_id]
+                return len(data) == 2
+            if data[i] != eos_token_id:
                 break
             i-=1
     return True
@@ -84,7 +85,7 @@ def main():
         acc_data = json.load(acc_json)
     
     try:
-        eos_pass = eos_check(acc_data, DTYPE_MAP[args.dtype])
+        eos_pass = eos_check(acc_data, DTYPE_MAP[args.dtype], args.eos_token_id)
     except Exception:
         print("Unexpected error occured while doing the EOS check")
         eos_pass = False

From c3c68ee61896e1e4d74b096565b627e14422337d Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Tue, 16 Jul 2024 15:42:18 -0500
Subject: [PATCH 129/305] Update Llama2 Members download instructions with new
 form. (#1781)

* Update Llama2 Members download instructions

* Add clarifying language

* Add member access details
---
 language/llama2-70b/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index b5d4440e41..458a0e50f3 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -67,7 +67,7 @@ CPU-only setup, as well as any GPU versions for applicable libraries like PyTorc
 
 ## Get Model
 ### MLCommons Members Download
-MLCommons hosts the model and preprocessed dataset for download exclusively by MLCommons Members. You must first agree to the [confidentiality notice](https://docs.google.com/forms/d/e/1FAIpQLSc_8VIvRmXM3I8KQaYnKf7gy27Z63BBoI_I1u02f4lw6rBp3g/viewform), then follow the link to a directory containing Rclone download instructions.
+MLCommons hosts the model and preprocessed dataset for download **exclusively by MLCommons Members**. You must first agree to the [confidentiality notice](https://llama2.mlcommons.org) using your organizational email address, then you will receive a link to a directory containing Rclone download instructions. _If you cannot access the form but you are part of a MLCommons Member organization, submit the [MLCommons subscription form](https://mlcommons.org/community/subscribe/) with your organizational email address and [associate a Google account](https://accounts.google.com/SignUpWithoutGmail) with your organizational email address._
 
 
 ### External Download

From 2ed9555be0c8beea97167bca46938d56aff19c1c Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Thu, 18 Jul 2024 13:02:45 -0700
Subject: [PATCH 130/305] Urgent: Mixtral submission checker error (#1790)

---
 tools/submission/submission_checker.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 6736f2ac31..3f0b0da5e7 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -538,8 +538,8 @@
     "TOKENS_PER_SAMPLE": r".*'tokens_per_sample':\s([\d.]+).*",
     "CLIP_SCORE": r".*'CLIP_SCORE':\s([\d.]+).*",
     "FID_SCORE": r".*'FID_SCORE':\s([\d.]+).*",
-    "gsm8k_accuracy": r"'gsm8k':\s([\d.]+).*",
-    "mbxp_accuracy": r"'mbxp':\s([\d.]+).*",
+    "gsm8k_accuracy": r".*'gsm8k':\s([\d.]+).*",
+    "mbxp_accuracy": r".*'mbxp':\s([\d.]+).*",
 }
 
 SYSTEM_DESC_REQUIRED_FIELDS = [

From 1c4cf2759e79291567cdbdfdcab9cbb864fbb1e2 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Fri, 19 Jul 2024 05:15:02 -0500
Subject: [PATCH 131/305] Fix mixtral readme (#1791)

---
 language/mixtral-8x7b/README.md | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 1ebce04466..341ae539a4 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -63,9 +63,15 @@ Inside the container, set up the environment with `bash build.sh`. This will ins
 CPU-only setup, as well as any GPU versions for applicable libraries like PyTorch.
 
 
-## Get Model
+## Model
 
-### Checkpoint
+| model | accuracy | model source | precision |
+| ---- | ---- | ---- | ---- |
+| Mixtral-8x7B-Instruct-v0.1 | [Accuracy target](#accuracy-target) | [Hugging Face](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | fp16 |
+
+**Important Note:** Files and configurations of the model have changed, and might change in the future. If you are going to get the model from Hugging Face or any external source, use a version of the model that exactly matches the one in this [commit](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1/commit/a60832cb6c88d5cb6e507680d0e9996fbad77050). We strongly recommend to get the model following the steps in the next section:
+
+### Get Checkpoint
 
 #### Using Rclone
 
@@ -243,8 +249,7 @@ sudo docker run -it -v $(pwd):/eval -t evaluation
 3. 
 ```bash
 cd eval
-huggingface-cli login --token [huggingface_token]
-python -u evaluate-accuracy.py --checkpoint-path mistralai/Mixtral-8x7B-instruct-v0.1 \
+python -u evaluate-accuracy.py --checkpoint-path [path_to_model_checkpoint] \
                 --mlperf-accuracy-file [path_to_mlperf_accuracy_file] \
                 --dataset-file [path_to_dataset] \
                 --n_workers 8

From 28144996310f5303a09355d55f238786737dc346 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Fri, 19 Jul 2024 11:36:14 -0500
Subject: [PATCH 132/305] Add eos padding to postprocessing (#1783)

---
 language/mixtral-8x7b/dataset.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/language/mixtral-8x7b/dataset.py b/language/mixtral-8x7b/dataset.py
index d2cafac639..4bc12d84a6 100644
--- a/language/mixtral-8x7b/dataset.py
+++ b/language/mixtral-8x7b/dataset.py
@@ -90,7 +90,14 @@ def postProcess(self, out_tokens, input_seq_lens=None,
         # Everything is padded to max_len (1024), so prune the input and parse
         # to numpy
         output_seq = out_tokens[:, 1024:].cpu().numpy()
+        aux_seq = []
         assert len(query_id_list) == output_seq.shape[0]
+        for i in range(len(output_seq)):
+            aux = output_seq[i]
+            while(len(output_seq[i]) <= 1):
+                aux = np.append(aux, self.tokenizer.eos_token_id)
+            aux_seq.append(aux)
+        output_seq = np.stack(aux_seq)
 
         # Save outputs
         if not os.path.exists("run_outputs"):

From a248595cf55d367a68a973bbe8616e4fb1f7f81f Mon Sep 17 00:00:00 2001
From: nvyihengz <109830892+nvyihengz@users.noreply.github.com>
Date: Mon, 22 Jul 2024 12:39:07 -0700
Subject: [PATCH 133/305] Fix SDXL readme and submission tool (#1798)

---
 compliance/nvidia/README.md               | 4 ++--
 tools/submission/generate_final_report.py | 6 ++++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/compliance/nvidia/README.md b/compliance/nvidia/README.md
index 6306761807..3abb256c18 100755
--- a/compliance/nvidia/README.md
+++ b/compliance/nvidia/README.md
@@ -10,7 +10,7 @@ This repository provides the compliance tests that need to be run by the submitt
 ## Introduction
 The purpose of compliance testing is to ensure a basic level of compliance with a subset of the MLPerf rules. The tests are designed to be complementary to third-party auditing which will be introduced in future rounds of MLPerf. The tests are not meant to root-cause issues with the submission, but can help detect anomalies in the submission that need to be investigated further by the submitter.
 
-Each compliance test must be run once for each submission run and the logs from the compliance test run must be uploaded along with the rest of the submission collateral. In MLPerf Inference v0.7, effort has been made to reduce the burden on submitters to perform compliance testing through improvements to documentation, scripting, and LoadGen's compliance functionality. More documentation is provided on the purpose of each test in the corresponding test directory, along with more detailed instructions. 
+Each compliance test must be run once for each submission run and the logs from the compliance test run must be uploaded along with the rest of the submission collateral. In MLPerf Inference v0.7, effort has been made to reduce the burden on submitters to perform compliance testing through improvements to documentation, scripting, and LoadGen's compliance functionality. More documentation is provided on the purpose of each test in the corresponding test directory, along with more detailed instructions.
 
 ## Test Infrastructure
 The compliance tests exercise functionality in LoadGen, triggered through the use of a config file that overrides LoadGen functionality. This enables LoadGen to run in a variety of compliance testing modes. When LoadGen::StartTest() is invoked, LoadGen checks if a `audit.config` file exists in the current working directory. If the file is found, LoadGen will log this event in `mlperf_log_detail.txt`.  The LoadGen settings that are used will be logged in `mlperf_log_summary.txt`. The configuration parameters in `audit.config` override any settings set by `mlperf.conf` or `user.conf`.
@@ -37,6 +37,6 @@ The `run_verification.py` found in each test directory will copy the test files
 | 3d-unet | [TEST01](./TEST01/), [TEST05](./TEST05/) |
 | rnnt | [TEST01](./TEST01/), [TEST05](./TEST05/) |
 | gpt-j | - |
-| stable-diffusion-xl | [TEST01](./TEST01/), [TEST04](./TEST04/), [TEST05](./TEST05/) |
+| stable-diffusion-xl | [TEST01](./TEST01/), [TEST04](./TEST04/) |
 | Llama2-70b | [TEST06](./TEST06/) |
 | mixtral-8x7b | [TEST06](./TEST06/) |
diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 13a01fe553..a39098b735 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -61,7 +61,7 @@ def main():
       lambda x: '=HYPERLINK("{}","details")'.format('/'.join(
           [base_url, x['Category'], x['Submitter'], 'results', x['Platform']])),
       axis=1)
-  
+
   # code url
   df['Code'] = df.apply(
       lambda x: '=HYPERLINK("{}","code")'.format('/'.join(
@@ -104,7 +104,8 @@ def main():
                    [
                        'resnet', 'retinanet', '3d-unet-99', '3d-unet-99.9',
                        'rnnt', 'bert-99', 'bert-99.9', 'dlrm-v2-99', 'dlrm-v2-99.9',
-                       'gptj-99', 'gptj-99.9', 'stable-diffusion-xl', 'llama2-70b-99', 'llama2-70b-99.9'
+                       'gptj-99', 'gptj-99.9', 'stable-diffusion-xl', 'llama2-70b-99', 'llama2-70b-99.9',
+                       'mixtral-8x7b',
                    ], ['SingleStream', 'MultiStream', 'Server', 'Offline'],
                    [
                        'Latency (ms)',
@@ -130,6 +131,7 @@ def main():
           'stable-diffusion-xl': ['Server', 'Offline'],
           'llama2-70b-99': ['Server', 'Offline'],
           'llama2-70b-99.9': ['Server', 'Offline'],
+          'mixtral-8x7b': ['Server', 'Offline'],
       },
       'edge': {
           'resnet': ['SingleStream', 'MultiStream', 'Offline'],

From 03657abe70423fdc1caabc64999ce71c96690ac8 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 23 Jul 2024 11:59:45 -0500
Subject: [PATCH 134/305] Fix typo in mlperf.conf (#1799)

---
 mlperf.conf | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mlperf.conf b/mlperf.conf
index 6487bfc238..10f7ae78f6 100644
--- a/mlperf.conf
+++ b/mlperf.conf
@@ -43,7 +43,7 @@ retinanet.MultiStream.target_latency = 528
 # LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario
 gptj.*.sample_concatenate_permutation = 1
 llama2-70b.*.sample_concatenate_permutation = 1
-mixtral-8x7B.*.sample_concatenate_permutation = 1
+mixtral-8x7b.*.sample_concatenate_permutation = 1
 
 *.Server.target_latency = 10
 *.Server.target_latency_percentile = 99

From 019f9ac3e6e25bc97fef2d5115bc28a51dded4dc Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 23 Jul 2024 16:13:22 -0500
Subject: [PATCH 135/305] Correct values for accuracy delta percentile (#1793)

Co-authored-by: Miro <mirhodak@amd.com>
---
 tools/submission/submission_checker.py | 32 +++++++++++++++++++++-----
 1 file changed, 26 insertions(+), 6 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 3f0b0da5e7..b422cbb59c 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -109,6 +109,12 @@
             "llama2-70b-99" : ("TOKENS_PER_SAMPLE", 294.45*1.1),
             "llama2-70b-99.9" : ("TOKENS_PER_SAMPLE", 294.45*1.1)
         },
+        "accuracy-delta-perc": {
+            "stable-diffusion-xl": {
+                "CLIP_SCORE": 1,
+                "FID_SCORE": 2
+            }
+        },
         "performance-sample-count": {
             "resnet": 1024,
             "retinanet": 64,
@@ -270,6 +276,12 @@
             "llama2-70b-99.9" : ("TOKENS_PER_SAMPLE", 294.45*1.1),
             "mixtral-8x7b" : ("TOKENS_PER_SAMPLE", 145.9 * 1.1)
         },
+        "accuracy-delta-perc": {
+            "stable-diffusion-xl": {
+                "CLIP_SCORE": 1,
+                "FID_SCORE": 2
+            }
+        },
         "performance-sample-count": {
             "resnet": 1024,
             "retinanet": 64,
@@ -672,6 +684,7 @@ def __init__(
         self.seeds = self.base["seeds"]
         self.test05_seeds = self.base["test05_seeds"]
         self.accuracy_target = self.base["accuracy-target"]
+        self.accuracy_delta_perc = self.base["accuracy-delta-perc"]
         self.accuracy_upper_limit = self.base.get("accuracy-upper-limit", {})
         self.performance_sample_count = self.base["performance-sample-count"]
         self.latency_constraint = self.base.get("latency-constraint", {})
@@ -767,6 +780,18 @@ def get_min_query_count(self, model, scenario):
         if model not in self.min_queries:
             raise ValueError("model not known: " + model)
         return self.min_queries[model].get(scenario)
+    
+    def get_delta_perc(self, model, metric):
+        if model in self.accuracy_delta_perc:
+            if metric in self.accuracy_delta_perc[model]:
+                return self.accuracy_delta_perc[model][metric]
+        
+        more_accurate = model.find("99.9")
+        if more_accurate == -1:
+            required_delta_perc = 1
+        else:
+            required_delta_perc = 0.1
+        return required_delta_perc
 
     def has_new_logging_format(self):
         return True
@@ -2470,12 +2495,6 @@ def check_compliance_acc_dir(test_dir, model, config):
                         acc_types.append(acc_type)
                         patterns.append(ACC_PATTERN[acc_type[0]])
                     acc_seen = [False for _ in acc_type]
-
-                    more_accurate = model.find("99.9")
-                    if more_accurate == -1:
-                        required_delta_perc = 1
-                    else:
-                        required_delta_perc = 0.1
                     acc_baseline = {
                         acc_type: 0 for acc_type in acc_types
                     }
@@ -2507,6 +2526,7 @@ def check_compliance_acc_dir(test_dir, model, config):
                             is_valid = False
                             break
                         else:
+                            required_delta_perc = config.get_delta_perc(model, acc_types)
                             delta_perc = abs(1 - acc_baseline[acc_type] / acc_compliance[acc_type]) * 100
                             if delta_perc <= required_delta_perc:
                                 is_valid = True

From 3fb6c3e1f6a387e097e12395145ce21cbe683822 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 24 Jul 2024 12:59:30 -0500
Subject: [PATCH 136/305] Check for equal issue mode in submission checker
 (#1802)

---
 loadgen/test_settings_internal.cc      |  4 ++
 tools/submission/submission_checker.py | 98 ++++++++++++++++++++++++--
 2 files changed, 96 insertions(+), 6 deletions(-)

diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 5a18c32f90..08d1d03dee 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -334,6 +334,8 @@ void LogRequestedTestSettings(const TestSettings &s) {
                s.performance_issue_same_index);
     MLPERF_LOG(detail, "requested_performance_sample_count_override",
                s.performance_sample_count_override);
+    MLPERF_LOG(detail, "requested_sample_concatenate_permutation",
+            s.sample_concatenate_permutation);
     // Token latencies specific values
     if (s.use_token_latencies){
       MLPERF_LOG(detail, "requested_use_token_latencies", s.use_token_latencies);
@@ -443,6 +445,8 @@ void TestSettingsInternal::LogEffectiveSettings() const {
                s.performance_issue_same_index);
     MLPERF_LOG(detail, "effective_performance_sample_count",
                s.performance_sample_count);
+    MLPERF_LOG(detail, "effective_sample_concatenate_permutation",
+               s.sample_concatenate_permutation);
 #else
     detail("");
     detail("Effective Settings:");
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index b422cbb59c..a0e996af4f 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -801,6 +801,21 @@ def uses_early_stopping(self, scenario):
         return (
             scenario in ["Server", "SingleStream", "MultiStream"]
         )
+    
+    def requires_equal_issue(self, model, division):
+        return (
+            division in ["closed", "network"] and
+            model in [
+                "3d-unet-99",
+                "3d-unet-99.9"
+                "gptj-99",
+                "gptj-99.9"
+                "llama2-70b-99",
+                "llama2-70b-99.9", 
+                "mixtral-8x7b"
+            ]
+            and self.version in ["v4.1"]
+        )
 
 
 def get_args():
@@ -1106,6 +1121,9 @@ def check_performance_dir(
     min_query_count = mlperf_log["effective_min_query_count"]
     samples_per_query = mlperf_log["effective_samples_per_query"]
     min_duration = mlperf_log["effective_min_duration_ms"]
+    equal_issue_used_check = (mlperf_log["effective_sample_concatenate_permutation"] == "true")
+    if not config.requires_equal_issue(model, division):
+        equal_issue_used_check = True
     sut_name = mlperf_log["sut_name"]
 
     # check if there are any errors in the detailed log
@@ -1151,6 +1169,7 @@ def check_performance_dir(
 
     if scenario == "SingleStream" or scenario == "MultiStream":
         res /= MS_TO_NS
+    
 
     # Check if the current scenario uses early stopping
     uses_early_stopping = config.uses_early_stopping(scenario)
@@ -1255,7 +1274,7 @@ def check_performance_dir(
             )
             is_valid = False
 
-    return is_valid, res, inferred
+    return is_valid, res, inferred, equal_issue_used_check
 
 def get_inferred_result(scenario_fixed, scenario, res, mlperf_log, config, log_error=False):
 
@@ -1946,18 +1965,21 @@ def log_result(
                             errors += 1
                             continue
                         else:
-                            if not check_measurement_dir(
+                            measurement_check, conf_equal_issue_check = check_measurement_dir(
+                                config,
                                 measurement_dir,
                                 name,
                                 system_desc,
                                 os.path.join(division, submitter),
                                 model_name,
                                 scenario,
+                                division,
                                 has_power,
                                 skip_meaningful_fields_emptiness_check,
                                 skip_empty_files_check,
                                 skip_check_power_measure_files,
-                            ):
+                            )
+                            if not measurement_check:
                                 log.error(
                                     "%s measurement_dir has issues", measurement_dir
                                 )
@@ -2039,7 +2061,7 @@ def log_result(
                                 continue
 
                             try:
-                                is_valid, r, is_inferred = check_performance_dir(
+                                is_valid, r, is_inferred, performance_equal_issue_check = check_performance_dir(
                                     config,
                                     mlperf_model,
                                     perf_path,
@@ -2053,6 +2075,16 @@ def log_result(
                                     log.info(
                                         "%s has inferred results, qps=%s", perf_path, r
                                     )
+
+                                # Check equal issue mode
+                                if not (conf_equal_issue_check or performance_equal_issue_check):
+                                    log.error(
+                                        "%s %s requires equal issue mode (sample_concatenate_permutation), expected=true, found=%s",
+                                        perf_path,
+                                        measurement_dir,
+                                        not (conf_equal_issue_check or performance_equal_issue_check),
+                                    )
+                                    is_valid, r = False, None
                             except Exception as e:
                                 log.error(
                                     "%s caused exception in check_performance_dir: %s",
@@ -2310,12 +2342,14 @@ def check_system_desc_id_power(
 
 
 def check_measurement_dir(
+    config,
     measurement_dir,
     fname,
     system_desc,
     root,
     model,
     scenario,
+    division,
     has_power,
     skip_meaningful_fields_emptiness_check,
     skip_empty_files_check,
@@ -2406,11 +2440,63 @@ def check_measurement_dir(
             if not os.path.exists(os.path.dirname(code_dir)):
                 log.error("%s is missing code_dir %s", fname, code_dir)
                 is_valid = False
+  
+        # Check equal issue mode 
+        equal_issue_used = False       
+        if "mlperf.conf" in files and config.requires_equal_issue(model, division):
+            with open(f"{measurement_dir}/mlperf.conf") as f:
+                lines = f.readlines()
+                conf_ref_model = model.replace("-99.9", "").replace("-99", "")
+                for line in lines:
+                    line = line.replace(" ", "").replace("\n", "")
+                    if line.startswith("#"):
+                        continue
+                    elif line == "":
+                        continue
+                    else:
+                        key, val = line.split("=")
+                        key.replace(" ", "")
+                        val.replace(" ", "")
+                        conf_model, conf_scenario, conf_key = key.split(".")
+                        if (
+                            (conf_key == "sample_concatenate_permutation") and 
+                            ((conf_model == conf_ref_model) or conf_model == "*") and
+                            ((conf_scenario == scenario) or conf_scenario == "*")
+                        ):
+                            if val.isnumeric():
+                                val = int(val)
+                                equal_issue_used = (val == 1)
+                                break
+
+        if "user.conf" in files and config.requires_equal_issue(model, division):
+            with open(f"{measurement_dir}/user.conf") as f:
+                lines = f.readlines()
+                conf_ref_model = model.replace("-99.9", "").replace("-99", "")
+                for line in lines:
+                    line = line.replace(" ", "").replace("\n", "")
+                    if line.startswith("#"):
+                        continue
+                    elif line == "":
+                        continue
+                    else:
+                        key, val = line.split("=")
+                        key.replace(" ", "")
+                        val.replace(" ", "")
+                        conf_model, conf_scenario, conf_key = key.split(".")
+                        if (
+                            (conf_key == "sample_concatenate_permutation") and 
+                            ((conf_model == conf_ref_model) or conf_model == "*") and
+                            ((conf_scenario == scenario) or conf_scenario == "*")
+                        ):
+                            if val.isnumeric():
+                                val = int(val)
+                                equal_issue_used = (val == 1)
+                                break
     else:
         log.error("%s is missing %s*.json", fname, system_desc)
         is_valid = False
 
-    return is_valid
+    return is_valid, equal_issue_used
 
 
 def check_compliance_perf_dir(test_dir):
@@ -2631,7 +2717,7 @@ def check_compliance_dir(
                 compliance_perf_dir = os.path.join(
                     compliance_dir, test, "performance", "run_1"
                 )
-                compliance_perf_valid, r, is_inferred = check_performance_dir(
+                compliance_perf_valid, r, is_inferred, _ = check_performance_dir(
                     config, model, compliance_perf_dir, scenario, division, system_json
                 )
                 if is_inferred:

From 379ee5345a25ee123fd5faec33285b6e8be0dadf Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 24 Jul 2024 22:02:58 -0500
Subject: [PATCH 137/305] Fix delta perf check (#1804)

---
 tools/submission/submission_checker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index a0e996af4f..bea5640239 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -2612,7 +2612,7 @@ def check_compliance_acc_dir(test_dir, model, config):
                             is_valid = False
                             break
                         else:
-                            required_delta_perc = config.get_delta_perc(model, acc_types)
+                            required_delta_perc = config.get_delta_perc(model, acc_type[0])
                             delta_perc = abs(1 - acc_baseline[acc_type] / acc_compliance[acc_type]) * 100
                             if delta_perc <= required_delta_perc:
                                 is_valid = True

From 8a94e52a0e5609e212d6f333a2e31fd2eed20d39 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Fri, 26 Jul 2024 15:29:02 +0100
Subject: [PATCH 138/305] Fix reference implementation to pass TEST06 (#1814)

* Update SUT.py

* Update run_verification.py

* Fix first token dtype for mixtral.
---
 compliance/nvidia/TEST06/run_verification.py | 2 +-
 language/llama2-70b/SUT.py                   | 2 +-
 language/mixtral-8x7b/SUT.py                 | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/compliance/nvidia/TEST06/run_verification.py b/compliance/nvidia/TEST06/run_verification.py
index b7fe7b5b51..092672a9a4 100644
--- a/compliance/nvidia/TEST06/run_verification.py
+++ b/compliance/nvidia/TEST06/run_verification.py
@@ -37,7 +37,7 @@ def get_args():
                         help="Specifies the path to the output directory where compliance logs will be uploaded from, i.e. inference_results_v0.7/closed/NVIDIA/compliance/T4x8/resnet/Offline.",
                         required=True)
     parser.add_argument("--eos_token_id", '-e', default=2, help="EOS token id of the tokenizer")
-    parser.add_argument("--dtype", "-d", default="int64", choices=["int64", "int32", "int16", "float32"])
+    parser.add_argument("--dtype", "-d", default="int32", choices=["int64", "int32", "int16", "float32"])
     parser.add_argument("--scenario", "-s", required=True, choices=["Offline", "Server", "SingleStream", "MultiStream"])
     args = parser.parse_args()
     return args
diff --git a/language/llama2-70b/SUT.py b/language/llama2-70b/SUT.py
index 9f9e4bf7bd..0bc1f6ad7f 100644
--- a/language/llama2-70b/SUT.py
+++ b/language/llama2-70b/SUT.py
@@ -318,7 +318,7 @@ def process_first_tokens(self):
 
             first_tokens, response_id = first_token_item
 
-            response_data = array.array("B", np.array(first_tokens, np.float32).tobytes())
+            response_data = array.array("B", np.array(first_tokens, np.int32).tobytes())
             bi = response_data.buffer_info()
             response = [lg.QuerySampleResponse(response_id, bi[0], bi[1])]
             lg.FirstTokenComplete(response)
diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
index b4a2dc705b..536ba46f57 100644
--- a/language/mixtral-8x7b/SUT.py
+++ b/language/mixtral-8x7b/SUT.py
@@ -383,7 +383,7 @@ def process_first_tokens(self):
             first_tokens, response_id = first_token_item
 
             response_data = array.array("B", np.array(
-                first_tokens, np.float32).tobytes())
+                first_tokens, np.int32).tobytes())
             bi = response_data.buffer_info()
             response = [lg.QuerySampleResponse(response_id, bi[0], bi[1])]
             lg.FirstTokenComplete(response)

From 725b3c0a5a13cccac49558f19c9a65676ebc89f4 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Fri, 26 Jul 2024 10:16:20 -0500
Subject: [PATCH 139/305] Don't check min_query_count with equal issue (#1810)

---
 tools/submission/submission_checker.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index bea5640239..95ee7d03d1 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -807,9 +807,9 @@ def requires_equal_issue(self, model, division):
             division in ["closed", "network"] and
             model in [
                 "3d-unet-99",
-                "3d-unet-99.9"
+                "3d-unet-99.9",
                 "gptj-99",
-                "gptj-99.9"
+                "gptj-99.9",
                 "llama2-70b-99",
                 "llama2-70b-99.9", 
                 "mixtral-8x7b"
@@ -1226,7 +1226,7 @@ def check_performance_dir(
     # Check if this run uses early stopping. If it does, get the
     # min_queries from the detail log, otherwise get this value
     # from the config
-    if not uses_early_stopping:
+    if not (uses_early_stopping or config.requires_equal_issue(model, division)):
         required_min_query_count = config.get_min_query_count(model, scenario)
         if required_min_query_count and min_query_count < required_min_query_count:
             log.error(

From 34884c5e78b8617cd4db312a85e8a42589def40b Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 15 Aug 2024 18:34:33 -0500
Subject: [PATCH 140/305] Fix accuracy in table, report multiple metrics
 (#1823)

---
 tools/submission/submission_checker.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 95ee7d03d1..a02b925b9b 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -962,12 +962,13 @@ def check_accuracy_dir(config, model, path, verbose):
     is_valid = False
     all_accuracy_valid = True
     acc = None
-    result_acc = None
+    result_acc = {}
     hash_val = None
     target = config.get_accuracy_target(model)
     acc_upper_limit = config.get_accuracy_upper_limit(model)
     patterns = []
     acc_targets = []
+    acc_types = []
     if acc_upper_limit is not None:
         acc_limits = []
         up_patterns = []
@@ -981,10 +982,11 @@ def check_accuracy_dir(config, model, path, verbose):
         acc_type, acc_target = target[i:i+2]
         patterns.append(ACC_PATTERN[acc_type])
         acc_targets.append(acc_target)
+        acc_types.append(acc_type)
     acc_seen = [False for _ in acc_targets]
     with open(os.path.join(path, "accuracy.txt"), "r", encoding="utf-8") as f:
         for line in f:
-            for i, (pattern, acc_target) in enumerate(zip(patterns, acc_targets)):
+            for i, (pattern, acc_target, acc_type) in enumerate(zip(patterns, acc_targets, acc_types)):
                 m = re.match(pattern, line)
                 if m:
                     acc = m.group(1)
@@ -997,8 +999,8 @@ def check_accuracy_dir(config, model, path, verbose):
                 elif acc is not None:
                     all_accuracy_valid = False
                     log.warning("%s accuracy not met: expected=%f, found=%s", path, acc_target, acc)
-                if i == 0 and acc:
-                    result_acc = acc
+                if acc:
+                    result_acc[acc_type] = acc
                 acc = None
             if acc_upper_limit is not None:
                 for i, (pattern, acc_limit) in enumerate(zip(up_patterns, acc_limits)):
@@ -2012,6 +2014,7 @@ def log_result(
                                 acc_path,
                                 debug or is_closed_or_network,
                             )
+                            acc = json.dumps(acc).replace(",", " ").replace('"', "").replace("{", "").replace("}", "")
                             if mlperf_model in REQUIRED_ACC_BENCHMARK:
                                 if config.version in REQUIRED_ACC_BENCHMARK[mlperf_model]:
                                     extra_files_pass, missing_files = check_extra_files(acc_path, REQUIRED_ACC_BENCHMARK[mlperf_model][config.version])

From c7db1c3f9a0ae0623200e05580d77a8759644812 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Fri, 16 Aug 2024 12:17:07 -0500
Subject: [PATCH 141/305] Fix units for LLM benchmarks (#1822)

Co-authored-by: Miro <mirhodak@amd.com>
---
 tools/submission/generate_final_report.py |  1 +
 tools/submission/submission_checker.py    | 34 ++++++++++++++++++++++-
 2 files changed, 34 insertions(+), 1 deletion(-)

diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index a39098b735..f02c1fa018 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -111,6 +111,7 @@ def main():
                        'Latency (ms)',
                        'Samples/s',
                        'Queries/s',
+                       'Tokens/s',
                        'millijoules',
                        'Watts',
                    ]]
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index a02b925b9b..692ff3f018 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1592,6 +1592,38 @@ def log_result(
         if system_json.get("sw_notes"):
             notes = notes + ". " if notes else ""
             notes = notes + system_json.get("sw_notes")
+        special_unit_dict = {
+            "gptj-99": {
+                "SingleStream": "Latency (ms)",
+                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            },
+            "gptj-99.9": {
+                "SingleStream": "Latency (ms)",
+                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            },
+            "llama2-70b-99" : {
+                "SingleStream": "Latency (ms)",
+                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            },
+            "llama2-70b-99.9" : {
+                "SingleStream": "Latency (ms)",
+                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            },
+            "mixtral-8x7b" : {
+                "SingleStream": "Latency (ms)",
+                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            }
+        }
         unit_dict = {
             "SingleStream": "Latency (ms)",
             "MultiStream": "Latency (ms)",
@@ -1604,7 +1636,7 @@ def log_result(
             "Offline": "Watts",
             "Server": "Watts",
         }
-        unit = unit_dict[scenario_fixed]
+        unit = special_unit_dict.get(model_name, unit_dict)[scenario_fixed]
         power_unit = power_unit_dict[scenario_fixed]
 
         csv.write(

From 73b02798219c794a735a7f2ddabbc3df9173352d Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 3 Sep 2024 17:45:04 +0100
Subject: [PATCH 142/305] Fix error with generate_final_report.py when the
 input CSV file is empty (#1827)

---
 tools/submission/generate_final_report.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index f02c1fa018..37b3d98988 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -25,6 +25,9 @@ def main():
 
   df = pd.read_csv(args.input).fillna('')
 
+  if df.empty:
+    return
+
   # rename some fields
   df.rename(
       columns={

From 81c2de69de4af90410cd1ba000fc5bd731bf6dee Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 3 Sep 2024 17:50:28 +0100
Subject: [PATCH 143/305] Documentation updates (#1821)

* Support batch-size in llama2 run

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-Cloudflare download instructiosn to README.md

* Minor wording edit to README.md

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-GDrive download instructions to README.md

* Add new and old instructions to README.md

* Tweak language in README.md

* Language tweak in README.md

* Minor language tweak in README.md

* Fix typo in README.md

* Count error when logging errors: submission_checker.py

* Fixes #1648, restrict loadgen uncommitted error message to within the loadgen directory

* Update test-rnnt.yml (#1688)

Stopping the github action for rnnt

* Added docs init

Added github action for website publish

Update benchmark documentation

Update publish.yaml

Update publish.yaml

Update benchmark documentation

Improved the submission documentation

Fix taskname

Removed unused images

* Fix benchmark URLs

* Fix links

* Add _full variation to run commands

* Added script flow diagram

* Added docker setup command for CM, extra run options

* Added support for docker options in the docs

* Added --quiet to the CM run_cmds in docs

* Fix the test query count for cm commands

* Support ctuning-cpp implementation

* Added commands for mobilenet models

* Docs cleanup

* Docs cleanup

* Added separate files for dataset and models in the docs

* Remove redundant tab in the docs

* Fixes some WIP models in the docs

* Use the official docs page for CM installation

* Fix the deadlink in docs

* Fix indendation issue in docs

* Added dockerinfo for nvidia implementation

* Added run options for gptj

* Added execution environment tabs

* Cleanup of the docs

* Cleanup of the docs

* Reordered the sections of the docs page

* Removed an unnecessary heading in the docs

* Fixes the commands for datacenter

* Fix the build --sdist for loadgen

* Fixes #1761, llama2 and mixtral runtime error on CPU systems

* Added mixtral to the benchmark list, improved benchmark docs

* Update docs for MLPerf inference v4.1

* Update docs for MLPerf inference v4.1

* Fix typo

* Gave direct link to implementation readmes

* Added tables detailing implementations

* Update vision README.md, split the frameworks into separate rows

* Update README.md

* pointed links to specific frameworks

* pointed links to specific frameworks

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* api support llama2

* Added request module and reduced max token len

* Fix for llama2 api server

* Update SUT_API offline to work for OpenAI

* Update SUT_API.py

* Minor fixes

* Fix json import in SUT_API.py

* Fix llama2 token length

* Added model name verification with server

* clean temp files

* support num_workers in LLAMA2 SUTs

* Remove batching from Offline SUT_API.py

* Update SUT_API.py

* Minor fixes for llama2 API

* Fix for llama2 API

* removed table of contents

* enabled llama2-nvidia + vllm-NM : WIP

* enabled dlrm for intel

* lower cased implementation

* added raw data input

* corrected data download commands

* renamed filename

* changes for bert and vllm

* documentation to work on custom repo and branch

* benchmark index page update

* enabled sdxl for nvidia and intel

* updated vllm server run cmd

* benchmark page information addition

* fix indendation issue

* Added submission categories

* update submission page - generate submission with or w/o using CM for benchmarking

* Updated kits dataset documentation

* Updated model parameters

* updation of information

* updated non cm based benchmark

* added info about hf password

* added links to model and access tokens

* Updated reference results structuree tree

* submission docs cleanup

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* added generic stubs deepsparse

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info (FID and CLIP data added)

* typo fix for bert deepsparse framework

* added min system requirements for models

* fixed code version

* changes for displaying reference and intel implementation tip

* added reference to installation page

* updated neural magic documentation

---------

Co-authored-by: Nathan Wasson <nathanw@mlcommons.org>
Co-authored-by: anandhu-eng <anandhukicks@gmail.com>
Co-authored-by: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>
---
 .../image_classification/get-resnet50-data.md |   5 +
 .../image_classification/mobilenets.md        |   5 +
 .../image_classification/resnet50.md          |   5 +
 docs/benchmarks/index.md                      |  27 -
 docs/benchmarks/language/bert.md              |  14 +-
 docs/benchmarks/language/get-bert-data.md     |   5 +
 docs/benchmarks/language/get-gptj-data.md     |   5 +
 .../language/get-llama2-70b-data.md           |   9 +
 .../language/get-mixtral-8x7b-data.md         |   5 +
 docs/benchmarks/language/gpt-j.md             |  13 +-
 docs/benchmarks/language/llama2-70b.md        |  20 +-
 docs/benchmarks/language/mixtral-8x7b.md      |   5 +-
 docs/benchmarks/medical_imaging/3d-unet.md    |  11 +-
 .../medical_imaging/get-3d-unet-data.md       |  14 +-
 .../object_detection/get-retinanet-data.md    |   5 +
 docs/benchmarks/object_detection/retinanet.md |   5 +
 docs/benchmarks/recommendation/dlrm-v2.md     |  25 +-
 ...et-dlrm_v2-data.md => get-dlrm-v2-data.md} |   9 +-
 .../benchmarks/text_to_image/get-sdxl-data.md |   5 +
 docs/benchmarks/text_to_image/sdxl.md         |  11 +-
 docs/changelog/index.md                       |   5 +
 docs/demos/index.md                           |   5 +
 docs/index.md                                 | 169 ++++++-
 docs/index_gh.md                              |   1 +
 docs/install/index.md                         |  14 +-
 docs/submission/index.md                      | 145 ++++--
 docs/usage/index.md                           |   5 +
 language/llama2-70b/SUT_API.py                | 465 ++++++++++++++++++
 language/llama2-70b/main.py                   |  71 ++-
 main.py                                       | 158 +++++-
 mkdocs.yml                                    |   2 +-
 31 files changed, 1093 insertions(+), 150 deletions(-)
 delete mode 100644 docs/benchmarks/index.md
 rename docs/benchmarks/recommendation/{get-dlrm_v2-data.md => get-dlrm-v2-data.md} (83%)
 mode change 120000 => 100644 docs/index.md
 create mode 120000 docs/index_gh.md
 create mode 100644 language/llama2-70b/SUT_API.py
 mode change 100644 => 100755 main.py

diff --git a/docs/benchmarks/image_classification/get-resnet50-data.md b/docs/benchmarks/image_classification/get-resnet50-data.md
index d1f83ae9a9..900379d5ae 100644
--- a/docs/benchmarks/image_classification/get-resnet50-data.md
+++ b/docs/benchmarks/image_classification/get-resnet50-data.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Image Classification using ResNet50 
 
 ## Dataset
diff --git a/docs/benchmarks/image_classification/mobilenets.md b/docs/benchmarks/image_classification/mobilenets.md
index d676300dff..f276008ef8 100644
--- a/docs/benchmarks/image_classification/mobilenets.md
+++ b/docs/benchmarks/image_classification/mobilenets.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Image Classification using Mobilenet models
 
 Mobilenet models are not official MLPerf models and so cannot be used for a Closed division MLPerf inference submission. But since they can be run with Imagenet dataset, we are allowed to use them for Open division submission. Only CPU runs are supported now. 
diff --git a/docs/benchmarks/image_classification/resnet50.md b/docs/benchmarks/image_classification/resnet50.md
index 26875258dc..62b966e0d1 100644
--- a/docs/benchmarks/image_classification/resnet50.md
+++ b/docs/benchmarks/image_classification/resnet50.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Image Classification using ResNet50 
 
 === "MLCommons-Python"
diff --git a/docs/benchmarks/index.md b/docs/benchmarks/index.md
deleted file mode 100644
index 39e3e72ac6..0000000000
--- a/docs/benchmarks/index.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# MLPerf Inference Benchmarks
-
-Please visit the individual benchmark links to see the run commands using the unified CM interface.
-
-1. [Image Classification](image_classification/resnet50.md) using ResNet50-v1.5 model and Imagenet-2012 (224x224) validation dataset. Dataset size is 50,000 and QSL size is 1024. Reference model accuracy is 76.46%. Server scenario latency constraint is 15ms.
-
-2. [Text to Image](text_to_image/sdxl.md) using Stable Diffusion model and subset of Coco2014 dataset. Dataset size is 5000 amd QSL size is the same. Required accuracy for closed division is (23.01085758 <= FID <= 23.95007626, 32.68631873 <= CLIP <= 31.81331801).
-
-3. [Object Detection](object_detection/retinanet.md) using Retinanet model and OpenImages dataset.Dataset size is 24781 and QSL size is 64. Reference model accuracy is 0.3755 mAP. Server scenario latency constraint is 100ms.
-
-4. [Medical Image Segmentation](medical_imaging/3d-unet.md)  using 3d-unet model and KiTS2019 dataset. Dataset size is 42 and QSL size is the same. Reference model accuracy is 0.86330 mean DIXE score. Server scenario is not applicable.
-
-5. [Question Answering](language/bert.md) using Bert-Large model and Squad v1.1 dataset with 384 sequence length. Dataset size is 10833 and QSL size is the same. Reference model accuracy is f1 score = 90.874%. Server scenario latency constraint is 130ms.
-
-6. [Text Summarization](language/gpt-j.md) using GPT-J model and CNN Daily Mail v3.0.0 dataset. Dataset size is 13368 amd QSL size is the same. Reference model accuracy is (rouge1=42.9865, rouge2=20.1235, rougeL=29.9881, gen_len=4016878). Server scenario latency sconstraint is 20s.
-
-7. [Question Answering](language/llama2-70b.md) using LLAMA2-70b model and OpenORCA (GPT-4 split, max_seq_len=1024) dataset. Dataset size is 24576 and QSL size is the same. Reference model accuracy is (rouge1=44.4312, rouge2=22.0352, rougeL=28.6162, tokens_per_sample=294.45). Server scenario latency constraint is TTFT=2000ms, TPOT=200ms.
-
-8. [Question Answering, Math and Code Generation](language/mixtral-8x7b.md) using Mixtral-8x7B model and OpenORCA (5k samples of GPT-4 split, max_seq_len=2048), GSM8K (5k samples of the validation split, max_seq_len=2048), MBXP (5k samples of the validation split, max_seq_len=2048) datasets. Dataset size is 15000 and QSL size is the same. Reference model accuracy is (rouge1=45.4911, rouge2=23.2829, rougeL=30.3615, gsm8k accuracy = 73.78, mbxp accuracy = 60.12, tokens_per_sample=294.45). Server scenario latency constraint is TTFT=2000ms, TPOT=200ms.
-
-9. [Recommendation](recommendation/dlrm-v2.md) using DLRMv2 model and Synthetic Multihot Criteo dataset. Dataset size is 204800 and QSL size is the same. Reference model accuracy is AUC=80.31%. Server scenario latency constraint is 60 ms. 
-
-All the nine benchmarks can participate in the datacenter category.
-All the nine benchmarks except DLRMv2, LLAMA2 and Mixtral-8x7B and can participate in the edge category. 
-
-`bert`, `llama2-70b`, `dlrm_v2` and `3d-unet` has a high accuracy (99.9%) variant, where the benchmark run  must achieve a higher accuracy of at least `99.9%` of the FP32 reference model
-in comparison with the `99%` default accuracy requirement.
diff --git a/docs/benchmarks/language/bert.md b/docs/benchmarks/language/bert.md
index 782340b3d9..57b1cf2246 100644
--- a/docs/benchmarks/language/bert.md
+++ b/docs/benchmarks/language/bert.md
@@ -1,36 +1,34 @@
+---
+hide:
+  - toc
+---
+
 # Question Answering using Bert-Large
 
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
     
-    BERT-99
 {{ mlperf_inference_implementation_readme (4, "bert-99", "reference") }}
 
-    BERT-99.9
 {{ mlperf_inference_implementation_readme (4, "bert-99.9", "reference") }}
 
 === "Nvidia"
     ## Nvidia MLPerf Implementation
     
-    BERT-99
 {{ mlperf_inference_implementation_readme (4, "bert-99", "nvidia") }}
 
-    BERT-99.9
 {{ mlperf_inference_implementation_readme (4, "bert-99.9", "nvidia") }}
 
 === "Intel"
     ## Intel MLPerf Implementation
-    BERT-99
+    
 {{ mlperf_inference_implementation_readme (4, "bert-99", "intel") }}
 
-    BERT-99.9
 {{ mlperf_inference_implementation_readme (4, "bert-99.9", "intel") }}
 
 === "Qualcomm"
     ## Qualcomm AI100 MLPerf Implementation
 
-    BERT-99
 {{ mlperf_inference_implementation_readme (4, "bert-99", "qualcomm") }}
 
-    BERT-99.9
 {{ mlperf_inference_implementation_readme (4, "bert-99.9", "qualcomm") }}
diff --git a/docs/benchmarks/language/get-bert-data.md b/docs/benchmarks/language/get-bert-data.md
index f5462b181e..fed6375720 100644
--- a/docs/benchmarks/language/get-bert-data.md
+++ b/docs/benchmarks/language/get-bert-data.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Question Answering using Bert-Large
 
 ## Dataset
diff --git a/docs/benchmarks/language/get-gptj-data.md b/docs/benchmarks/language/get-gptj-data.md
index 9ea31feb47..90591fb76e 100644
--- a/docs/benchmarks/language/get-gptj-data.md
+++ b/docs/benchmarks/language/get-gptj-data.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Text Summarization using GPT-J
 
 ## Dataset
diff --git a/docs/benchmarks/language/get-llama2-70b-data.md b/docs/benchmarks/language/get-llama2-70b-data.md
index 4b04f7068b..0214d95a5a 100644
--- a/docs/benchmarks/language/get-llama2-70b-data.md
+++ b/docs/benchmarks/language/get-llama2-70b-data.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Text Summarization using LLAMA2-70b
 
 ## Dataset
@@ -23,4 +28,8 @@ Get the Official MLPerf LLAMA2-70b Model
     ```
     cm run script --tags=get,ml-model,llama2-70b,_pytorch -j
     ```
+  
+!!! tip
+
+    Downloading llama2-70B model from Hugging Face will prompt you to enter the Hugging Face username and password. Please note that the password required is the [**access token**](https://huggingface.co/settings/tokens) generated for your account. Additionally, ensure that your account has access to the [llama2-70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) model.
 
diff --git a/docs/benchmarks/language/get-mixtral-8x7b-data.md b/docs/benchmarks/language/get-mixtral-8x7b-data.md
index 601e5c1dd1..1b2df1b9ea 100644
--- a/docs/benchmarks/language/get-mixtral-8x7b-data.md
+++ b/docs/benchmarks/language/get-mixtral-8x7b-data.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 ## Dataset
 
 The benchmark implementation run command will automatically download the preprocessed validation and calibration datasets. In case you want to download only the datasets, you can use the below commands.
diff --git a/docs/benchmarks/language/gpt-j.md b/docs/benchmarks/language/gpt-j.md
index 2eefbbc79e..4dcb3d70e2 100644
--- a/docs/benchmarks/language/gpt-j.md
+++ b/docs/benchmarks/language/gpt-j.md
@@ -1,31 +1,31 @@
+---
+hide:
+  - toc
+---
+
 # Text Summarization using GPT-J
 
 
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
-    
-    GPT-J-99
+
 
 {{ mlperf_inference_implementation_readme (4, "gptj-99", "reference") }}
 
-    GPTJ-99.9
 
 {{ mlperf_inference_implementation_readme (4, "gptj-99.9", "reference") }}
 
 === "Nvidia"
     ## Nvidia MLPerf Implementation
     
-    GPTJ-99
 
 {{ mlperf_inference_implementation_readme (4, "gptj-99", "nvidia") }}
 
-    GPTJ-99.9
 
 {{ mlperf_inference_implementation_readme (4, "gptj-99.9", "nvidia") }}
 
 === "Intel"
     ## Intel MLPerf Implementation
-    GPTJ-99
 
 {{ mlperf_inference_implementation_readme (4, "gptj-99", "intel") }}
 
@@ -33,7 +33,6 @@
 === "Qualcomm"
     ## Qualcomm AI100 MLPerf Implementation
 
-    GPTJ-99
 
 {{ mlperf_inference_implementation_readme (4, "gptj-99", "qualcomm") }}
 
diff --git a/docs/benchmarks/language/llama2-70b.md b/docs/benchmarks/language/llama2-70b.md
index f1785dcb35..0d9a0504de 100644
--- a/docs/benchmarks/language/llama2-70b.md
+++ b/docs/benchmarks/language/llama2-70b.md
@@ -1,28 +1,28 @@
+---
+hide:
+  - toc
+---
+
 # Text Summarization using LLAMA2-70b
 
 
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
     
-    LLAMA2-70b-99
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "reference") }}
 
-    LLAMA2-70b-99.9
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "reference") }}
 
 === "Nvidia"
     ## Nvidia MLPerf Implementation
     
-    LLAMA2-70b-99
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "nvidia") }}
 
-    LLAMA2-70b-99.9
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "nvidia") }}
 
+=== "Neural Magic"
+    ## Neural Magic MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "neuralmagic") }}
 
-=== "Qualcomm"
-    ## Qualcomm AI100 MLPerf Implementation
-
-    LLAMA2-70b-99
-{{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "qualcomm") }}
-
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "neuralmagic") }}
\ No newline at end of file
diff --git a/docs/benchmarks/language/mixtral-8x7b.md b/docs/benchmarks/language/mixtral-8x7b.md
index eb138dc46f..9f3bf2992b 100644
--- a/docs/benchmarks/language/mixtral-8x7b.md
+++ b/docs/benchmarks/language/mixtral-8x7b.md
@@ -1,6 +1,9 @@
+---
+hide:
+  - toc
+---
 
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
     
-    MIXTRAL-8x7b
 {{ mlperf_inference_implementation_readme (4, "mixtral-8x7b", "reference") }}
\ No newline at end of file
diff --git a/docs/benchmarks/medical_imaging/3d-unet.md b/docs/benchmarks/medical_imaging/3d-unet.md
index b58ea7f2e7..01a54c63e6 100644
--- a/docs/benchmarks/medical_imaging/3d-unet.md
+++ b/docs/benchmarks/medical_imaging/3d-unet.md
@@ -1,33 +1,32 @@
+---
+hide:
+  - toc
+---
+
 # Medical Imaging using 3d-unet (KiTS 2019 kidney tumor segmentation task)
 
 
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
 
-    3d-unet-99
 
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99", "reference") }}
 
-    3d-unet-99.9
 
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99.9", "reference") }}
 
 === "Nvidia"
     ## Nvidia MLPerf Implementation
-    3d-unet-99
 
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99", "nvidia") }}
 
-    3d-unet-99.9
 
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99.9", "nvidia") }}
 
 === "Intel"
     ## Intel MLPerf Implementation
-    3d-unet-99
 
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99", "intel") }}
 
-    3d-unet-99.9
 
 {{ mlperf_inference_implementation_readme (4, "3d-unet-99.9", "intel") }}
diff --git a/docs/benchmarks/medical_imaging/get-3d-unet-data.md b/docs/benchmarks/medical_imaging/get-3d-unet-data.md
index efc6ce6ed8..6c361f6f14 100644
--- a/docs/benchmarks/medical_imaging/get-3d-unet-data.md
+++ b/docs/benchmarks/medical_imaging/get-3d-unet-data.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Medical Imaging using 3d-unet (KiTS 2019 kidney tumor segmentation task)
 
 ## Dataset
@@ -7,9 +12,14 @@ The benchmark implementation run command will automatically download the validat
 === "Validation"
     3d-unet validation run uses the KiTS19 dataset performing [KiTS 2019](https://kits19.grand-challenge.org/) kidney tumor segmentation task
 
-    ### Get Validation Dataset
+    ### Get Validation Dataset(Original)
+    ```
+    cm run script --tags=get,dataset,kits19,_validation -j
+    ```
+
+    ### Get Validation Dataset(Preprocessed)
     ```
-    cm run script --tags=get,dataset,kits19,validation -j
+    cm run script --tags=get,dataset,kits19,preprocessed -j
     ```
 
 ## Model
diff --git a/docs/benchmarks/object_detection/get-retinanet-data.md b/docs/benchmarks/object_detection/get-retinanet-data.md
index f2d4322104..900fd572a7 100644
--- a/docs/benchmarks/object_detection/get-retinanet-data.md
+++ b/docs/benchmarks/object_detection/get-retinanet-data.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Object Detection using Retinanet
 
 ## Dataset
diff --git a/docs/benchmarks/object_detection/retinanet.md b/docs/benchmarks/object_detection/retinanet.md
index 383a2ec1b8..699d920504 100644
--- a/docs/benchmarks/object_detection/retinanet.md
+++ b/docs/benchmarks/object_detection/retinanet.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Object Detection using Retinanet
 
 === "MLCommons-Python"
diff --git a/docs/benchmarks/recommendation/dlrm-v2.md b/docs/benchmarks/recommendation/dlrm-v2.md
index 18266615fd..ce30810778 100644
--- a/docs/benchmarks/recommendation/dlrm-v2.md
+++ b/docs/benchmarks/recommendation/dlrm-v2.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Recommendation using DLRM v2
 
 
@@ -5,18 +10,20 @@
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
 
-    DLRM-v2-99
-{{ mlperf_inference_implementation_readme (4, "dlrm_v2-99", "reference") }}
+{{ mlperf_inference_implementation_readme (4, "dlrm-v2-99", "reference") }}
 
-    DLRM-v2-99.9
-{{ mlperf_inference_implementation_readme (4, "dlrm_v2-99.9", "reference") }}
+{{ mlperf_inference_implementation_readme (4, "dlrm-v2-99.9", "reference") }}
 
 === "Nvidia"
     ## Nvidia MLPerf Implementation
-    
-    DLRM-v2-99
-{{ mlperf_inference_implementation_readme (4, "dlrm_v2-99", "nvidia") }}
 
-    DLRM-v2-99.9
-{{ mlperf_inference_implementation_readme (4, "dlrm_v2-99.9", "nvidia") }}
+{{ mlperf_inference_implementation_readme (4, "dlrm-v2-99", "nvidia") }}
+
+{{ mlperf_inference_implementation_readme (4, "dlrm-v2-99.9", "nvidia") }}
+
+=== "Intel"
+    ## Intel MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "dlrm-v2-99", "intel") }}
 
+{{ mlperf_inference_implementation_readme (4, "dlrm-v2-99.9", "intel") }}
\ No newline at end of file
diff --git a/docs/benchmarks/recommendation/get-dlrm_v2-data.md b/docs/benchmarks/recommendation/get-dlrm-v2-data.md
similarity index 83%
rename from docs/benchmarks/recommendation/get-dlrm_v2-data.md
rename to docs/benchmarks/recommendation/get-dlrm-v2-data.md
index 97464a164a..1c44ec471c 100644
--- a/docs/benchmarks/recommendation/get-dlrm_v2-data.md
+++ b/docs/benchmarks/recommendation/get-dlrm-v2-data.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Recommendation using DLRM v2
 
 ## Dataset
@@ -9,7 +14,7 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset,criteo,validation -j
+    cm run script --tags=get,dataset,criteo,_validation -j
     ```
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
@@ -20,6 +25,6 @@ Get the Official MLPerf DLRM v2 Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,dlrm_v2,_pytorch -j
+    cm run script --tags=get,ml-model,dlrm,_pytorch -j
     ```
 
diff --git a/docs/benchmarks/text_to_image/get-sdxl-data.md b/docs/benchmarks/text_to_image/get-sdxl-data.md
index 830465d44d..f0d1376bd8 100644
--- a/docs/benchmarks/text_to_image/get-sdxl-data.md
+++ b/docs/benchmarks/text_to_image/get-sdxl-data.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Text to Image using Stable Diffusion
 
 ## Dataset
diff --git a/docs/benchmarks/text_to_image/sdxl.md b/docs/benchmarks/text_to_image/sdxl.md
index 2d84838d4a..575f4cabbf 100644
--- a/docs/benchmarks/text_to_image/sdxl.md
+++ b/docs/benchmarks/text_to_image/sdxl.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Text to Image using Stable Diffusion
 
 
@@ -15,9 +20,3 @@
     ## Intel MLPerf Implementation
 {{ mlperf_inference_implementation_readme (4, "sdxl", "intel") }}
 
-
-=== "Qualcomm"
-    ## Qualcomm AI100 MLPerf Implementation
-
-{{ mlperf_inference_implementation_readme (4, "sdxl", "qualcomm") }}
-
diff --git a/docs/changelog/index.md b/docs/changelog/index.md
index f68abc5b10..6033a48260 100644
--- a/docs/changelog/index.md
+++ b/docs/changelog/index.md
@@ -1,2 +1,7 @@
+---
+hide:
+  - toc
+---
+
 # What's New, What's Coming
 
diff --git a/docs/demos/index.md b/docs/demos/index.md
index 1c23a5f604..a9e6424ea4 100644
--- a/docs/demos/index.md
+++ b/docs/demos/index.md
@@ -1,2 +1,7 @@
+---
+hide:
+  - toc
+---
+
 # Demos
 
diff --git a/docs/index.md b/docs/index.md
deleted file mode 120000
index 32d46ee883..0000000000
--- a/docs/index.md
+++ /dev/null
@@ -1 +0,0 @@
-../README.md
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000000..11f2a52c21
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,168 @@
+# MLPerf Inference Benchmarks
+
+## Overview
+The currently valid [MLPerf Inference Benchmarks](index_gh.md) as of MLPerf inference v4.0 round are listed below, categorized by tasks. Under each model you can find its details like the dataset used, reference accuracy, server latency constraints etc.
+
+---
+
+## Image Classification
+### [ResNet50-v1.5](benchmarks/image_classification/resnet50.md)
+- **Dataset**: Imagenet-2012 (224x224) Validation
+    - **Dataset Size**: 50,000
+    - **QSL Size**: 1,024
+- **Number of Parameters**: 25.6 million
+- **FLOPs**: 3.8 billion
+- **Reference Model Accuracy**: 76.46% ACC
+- **Server Scenario Latency Constraint**: 15ms
+- **Equal Issue mode**: False
+- **High accuracy variant**: No
+- **Submission Category**: Datacenter, Edge
+
+---
+
+## Text to Image
+### [Stable Diffusion](benchmarks/text_to_image/sdxl.md)
+- **Dataset**: Subset of Coco2014
+    - **Dataset Size**: 5,000
+    - **QSL Size**: 5,000
+- **Number of Parameters**: 3.5 billion <!-- taken from https://stability.ai/news/stable-diffusion-sdxl-1-announcement -->
+- **FLOPs**: 1.28 - 2.4 trillion
+- **Reference Model Accuracy (fp32)**:  CLIP: 31.74981837, FID: 23.48046692
+- **Required Accuracy (Closed Division)**:
+    - CLIP: 31.68631873 ≤ CLIP ≤ 31.81331801 (within 0.2% of the reference model CLIP score)
+    - FID: 23.01085758 ≤ FID ≤ 23.95007626 (within 2% of the reference model FID score)
+- **Equal Issue mode**: False
+- **High accuracy variant**: No
+- **Submission Category**: Datacenter, Edge
+
+---
+
+## Object Detection
+### [Retinanet](benchmarks/object_detection/retinanet.md)
+- **Dataset**: OpenImages
+    - **Dataset Size**: 24,781
+    - **QSL Size**: 64
+- **Number of Parameters**: TBD
+- **Reference Model Accuracy (fp32) **: 0.3755 mAP
+- **Server Scenario Latency Constraint**: 100ms
+- **Equal Issue mode**: False
+- **High accuracy variant**: Yes
+- **Submission Category**: Datacenter, Edge
+
+---
+
+## Medical Image Segmentation
+### [3d-unet](benchmarks/medical_imaging/3d-unet.md) <!-- https://ar5iv.labs.arxiv.org/html/1809.10483v2 -->
+- **Dataset**: KiTS2019
+    - **Dataset Size**: 42
+    - **QSL Size**: 42
+- **Number of Parameters**: 32.5 million
+- **FLOPs**: 100-300 billion
+- **Reference Model Accuracy (fp32) **: 0.86330 Mean DICE Score
+- **Server Scenario**: Not Applicable
+- **Equal Issue mode**: True
+- **High accuracy variant**: Yes
+- **Submission Category**: Datacenter, Edge
+
+---
+
+## Language Tasks
+
+### Question Answering
+
+#### [Bert-Large](benchmarks/language/bert.md)
+- **Dataset**: Squad v1.1 (384 Sequence Length)
+    - **Dataset Size**: 10,833
+    - **QSL Size**: 10,833
+- **Number of Parameters**: 340 million <!-- taken from https://huggingface.co/transformers/v2.9.1/pretrained_models.html -->
+- **FLOPs**: ~128 billion
+- **Reference Model Accuracy (fp32) **: F1 Score = 90.874%
+- **Server Scenario Latency Constraint**: 130ms
+- **Equal Issue mode**: False
+- **High accuracy variant**: yes
+- **Submission Category**: Datacenter, Edge
+
+#### [LLAMA2-70B](benchmarks/language/llama2-70b.md)
+- **Dataset**: OpenORCA (GPT-4 split, max_seq_len=1024)
+    - **Dataset Size**: 24,576
+    - **QSL Size**: 24,576
+- **Number of Parameters**: 70 billion
+- **FLOPs**: ~500 trillion
+- **Reference Model Accuracy (fp32) **:
+    - Rouge1: 44.4312
+    - Rouge2: 22.0352
+    - RougeL: 28.6162
+    - Tokens_per_sample: 294.45
+- **Server Scenario Latency Constraint**:
+    - TTFT: 2000ms
+    - TPOT: 200ms
+- **Equal Issue mode**: True
+- **High accuracy variant**: Yes
+- **Submission Category**: Datacenter
+
+###  Text Summarization
+
+#### [GPT-J](benchmarks/language/gpt-j.md)
+- **Dataset**: CNN Daily Mail v3.0.0
+    - **Dataset Size**: 13,368
+    - **QSL Size**: 13,368
+- **Number of Parameters**: 6 billion
+- **FLOPs**: ~148 billion
+- **Reference Model Accuracy (fp32) **:
+    - Rouge1: 42.9865
+    - Rouge2: 20.1235
+    - RougeL: 29.9881
+    - Gen_len: 4,016,878
+- **Server Scenario Latency Constraint**: 20s
+- **Equal Issue mode**: True
+- **High accuracy variant**: Yes
+- **Submission Category**: Datacenter, Edge
+
+### Mixed Tasks (Question Answering, Math, and Code Generation)
+
+#### [Mixtral-8x7B](benchmarks/language/mixtral-8x7b.md)
+- **Datasets**:
+    - OpenORCA (5k samples of GPT-4 split, max_seq_len=2048)
+    - GSM8K (5k samples of the validation split, max_seq_len=2048)
+    - MBXP (5k samples of the validation split, max_seq_len=2048)
+    - **Dataset Size**: 15,000
+    - **QSL Size**: 15,000
+- **Number of Parameters**: 47 billion <!-- https://huggingface.co/blog/moe -->
+- **Reference Model Accuracy (fp16) **:
+    - OpenORCA
+        - Rouge1: 45.4911
+        - Rouge2: 23.2829
+        - RougeL: 30.3615
+    - GSM8K Accuracy: 73.78%
+    - MBXP Accuracy: 60.12%
+  - Tokens_per_sample: 294.45
+- **Server Scenario Latency Constraint**:
+    - TTFT: 2000ms
+    - TPOT: 200ms
+- **Equal Issue mode**: True
+- **High accuracy variant**: Yes
+- **Submission Category**: Datacenter
+
+---
+
+## Recommendation
+### [DLRM_v2](benchmarks/recommendation/dlrm-v2.md)
+- **Dataset**: Synthetic Multihot Criteo
+    - **Dataset Size**: 204,800
+    - **QSL Size**: 204,800
+- **Number of Parameters**: ~23 billion
+- **Reference Model Accuracy**: AUC = 80.31%
+- **Server Scenario Latency Constraint**: 60ms
+- **Equal Issue mode**: False
+- **High accuracy variant**: Yes
+- **Submission Category**: Datacenter
+
+---
+
+## Submission Categories
+- **Datacenter Category**: All the current inference benchmarks are applicable to the datacenter category.
+- **Edge Category**: All benchmarks except DLRMv2, LLAMA2-70B, and Mixtral-8x7B are applicable to the edge category.
+
+## High Accuracy Variants
+- **Benchmarks**: `bert`, `llama2-70b`, `gpt-j`,  `dlrm_v2`, and `3d-unet` have a normal accuracy variant as well as a high accuracy variant.
+- **Requirement**: Must achieve at least 99.9% of the reference model accuracy, compared to the default 99% accuracy requirement.
diff --git a/docs/index_gh.md b/docs/index_gh.md
new file mode 120000
index 0000000000..32d46ee883
--- /dev/null
+++ b/docs/index_gh.md
@@ -0,0 +1 @@
+../README.md
\ No newline at end of file
diff --git a/docs/install/index.md b/docs/install/index.md
index 9575e86c84..60377adee5 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -1,3 +1,8 @@
+---
+hide:
+  - toc
+---
+
 # Installation
 We use MLCommons CM Automation framework to run MLPerf inference benchmarks.
 
@@ -15,5 +20,12 @@ CM needs `git`, `python3-pip` and `python3-venv` installed on your system. If an
    pip install cm4mlops
 ```
 
+## To work on custom GitHub repo and branch
+
+```bash
+   pip install cmind && cm init --quiet --repo=mlcommons@cm4mlops --branch=mlperf-inference
+```
+
+Here, repo is in the format `githubUsername@githubRepo`.
 
-Now, you are ready to use the `cm` commands to run MLPerf inference as given in the [benchmarks](../benchmarks/index.md) page
+Now, you are ready to use the `cm` commands to run MLPerf inference as given in the [benchmarks](../index.md) page
diff --git a/docs/submission/index.md b/docs/submission/index.md
index 94287f5d27..a75bc32590 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -1,67 +1,122 @@
-If you follow the `cm run` commands under the individual model pages in the [benchmarks](../benchmarks/index.md) directory, all the valid results will get aggregated to the `cm cache` folder. Once all the results across all the modelsare ready you can use the following command to generate a valid submission tree compliant with the [MLPerf requirements](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc#inference-1).
+---
+hide:
+  - toc
+---
+
+=== "CM based benchmark"
+    If you have followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, all the valid results will get aggregated to the `cm cache` folder. The following command could be used to browse the structure of inference results folder generated by CM.
+    ### Get results folder structure
+    ```bash
+    cm find cache --tags=get,mlperf,inference,results,dir | xargs tree
+    ```
+=== "Non CM based benchmark"
+    If you have not followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, please make sure that the result directory is structured in the following way. 
+    ```
+    └── System description ID(SUT Name)
+        ├── system_meta.json
+        └── Benchmark
+            └── Scenario
+                ├── Performance
+                |   └── run_x/#1 run for all scenarios
+                |       ├── mlperf_log_summary.txt
+                |       └── mlperf_log_detail.txt
+                ├── Accuracy
+                |   ├── mlperf_log_summary.txt
+                |   ├── mlperf_log_detail.txt
+                |   ├── mlperf_log_accuracy.json
+                |   └── accuracy.txt
+                └── Compliance_Test_ID
+                    ├── Performance
+                    |   └── run_x/#1 run for all scenarios
+                    |       ├── mlperf_log_summary.txt
+                    |       └── mlperf_log_detail.txt
+                    ├── Accuracy
+                    |   ├── baseline_accuracy.txt
+                    |   ├── compliance_accuracy.txt
+                    |   ├── mlperf_log_accuracy.json
+                    |   └── accuracy.txt
+                    ├── verify_performance.txt
+                    └── verify_accuracy.txt #for TEST01 only
+    ```
+    
+    <details>
+    <summary>Click here if you are submitting in open division</summary>
+
+    * The `model_mapping.json` should be included inside the SUT folder which is used to map the custom model full name to the official model name. The format of json file is:
+
+    ```
+        {
+            "custom_model_name_for_model1":"official_model_name_for_model1",
+            "custom_model_name_for_model2":"official_model_name_for_model2",
+
+        }
+    ```
+    </details>
+
+Once all the results across all the models are ready you can use the following command to generate a valid submission tree compliant with the [MLPerf requirements](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc#inference-1).
 
 ## Generate actual submission tree
 
 === "Closed Edge"
     ### Closed Edge Submission
     ```bash
-       cm run script -tags=generate,inference,submission \
-      --clean \
-      --preprocess_submission=yes \
-      --run-checker \
-      --submitter=MLCommons \
-      --tar=yes \
-      --env.CM_TAR_OUTFILE=submission.tar.gz \
-      --division=closed \
-      --category=edge \
-      --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-      --quiet
+       cm run script --tags=generate,inference,submission \
+          --clean \
+          --preprocess_submission=yes \
+          --run-checker \
+          --submitter=MLCommons \
+          --tar=yes \
+          --env.CM_TAR_OUTFILE=submission.tar.gz \
+          --division=closed \
+          --category=edge \
+          --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+          --quiet
     ```
 
 === "Closed Datacenter"
     ### Closed Datacenter Submission
     ```bash
-       cm run script -tags=generate,inference,submission \
-      --clean \
-      --preprocess_submission=yes \
-      --run-checker \
-      --submitter=MLCommons \
-      --tar=yes \
-      --env.CM_TAR_OUTFILE=submission.tar.gz \
-      --division=closed \
-      --category=datacenter \
-      --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-      --quiet
+       cm run script --tags=generate,inference,submission \
+          --clean \
+          --preprocess_submission=yes \
+          --run-checker \
+          --submitter=MLCommons \
+          --tar=yes \
+          --env.CM_TAR_OUTFILE=submission.tar.gz \
+          --division=closed \
+          --category=datacenter \
+          --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+          --quiet
     ```
 === "Open Edge"
     ### Open Edge Submission
     ```bash
-       cm run script -tags=generate,inference,submission \
-      --clean \
-      --preprocess_submission=yes \
-      --run-checker \
-      --submitter=MLCommons \
-      --tar=yes \
-      --env.CM_TAR_OUTFILE=submission.tar.gz \
-      --division=open \
-      --category=edge \
-      --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-      --quiet
+       cm run script --tags=generate,inference,submission \
+          --clean \
+          --preprocess_submission=yes \
+          --run-checker \
+          --submitter=MLCommons \
+          --tar=yes \
+          --env.CM_TAR_OUTFILE=submission.tar.gz \
+          --division=open \
+          --category=edge \
+          --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+          --quiet
     ```
 === "Open Datacenter"
     ### Closed Datacenter Submission
     ```bash
-       cm run script -tags=generate,inference,submission \
-      --clean \
-      --preprocess_submission=yes \
-      --run-checker \
-      --submitter=MLCommons \
-      --tar=yes \
-      --env.CM_TAR_OUTFILE=submission.tar.gz \
-      --division=open \
-      --category=datacenter \
-      --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-      --quiet
+       cm run script --tags=generate,inference,submission \
+          --clean \
+          --preprocess_submission=yes \
+          --run-checker \
+          --submitter=MLCommons \
+          --tar=yes \
+          --env.CM_TAR_OUTFILE=submission.tar.gz \
+          --division=open \
+          --category=datacenter \
+          --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+          --quiet
     ```
 
 * Use `--hw_name="My system name"` to give a meaningful system name. Examples can be seen [here](https://github.com/mlcommons/inference_results_v3.0/tree/main/open/cTuning/systems)
diff --git a/docs/usage/index.md b/docs/usage/index.md
index 2e92a6e00c..01d54c3aa6 100644
--- a/docs/usage/index.md
+++ b/docs/usage/index.md
@@ -1 +1,6 @@
+---
+hide:
+  - toc
+---
+
 # Using CM for MLPerf Inference
diff --git a/language/llama2-70b/SUT_API.py b/language/llama2-70b/SUT_API.py
new file mode 100644
index 0000000000..da28d98cd3
--- /dev/null
+++ b/language/llama2-70b/SUT_API.py
@@ -0,0 +1,465 @@
+import os
+import time
+import numpy as np
+import array
+import torch
+from torch.nn.functional import pad
+from torch.utils.data import DataLoader
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
+from transformers.generation.streamers import BaseStreamer
+
+import json
+import pickle
+import time
+import threading
+import tqdm
+import queue
+
+import logging
+from typing import TYPE_CHECKING, Optional, List
+from pathlib import Path
+
+import more_itertools as mit
+from concurrent.futures.thread import ThreadPoolExecutor
+
+import requests
+from urllib3.exceptions import InsecureRequestWarning
+
+import mlperf_loadgen as lg
+from dataset import Dataset
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Llama-70B-SUT")
+
+gen_kwargs = {
+    "early_stopping": True,
+    "max_new_tokens": 1024,
+    "min_new_tokens": 1,
+    "num_beams": 1,
+    "do_sample": False
+}
+
+
+
+class FirstTokenStreamer(BaseStreamer):
+    """ Streams first tokens to a 'holder' """
+
+    def __init__(self, first_token, tokens_cache=[], is_first_token=True, response_ids=[] ):
+        """ Response ids added to 'sign' the first token"""
+
+        self.first_token = first_token # Queue for first token
+        self.is_first_token = is_first_token
+
+        # Cache for subsequent generated tokens
+        self.tokens_cache = tokens_cache
+
+        self.response_ids = response_ids
+
+        self.is_prompt = True # The first tokens sent to the streamer are actually the input prompts
+
+    def put(self, value):
+        """ Caches the tokens as they're generated. Assumes bs=1 """
+
+        # Prompts are streamed first so we need to skip the first time value that arrives
+        if self.is_prompt:
+            self.is_prompt = False
+            return
+
+        value = value.item()
+        if self.is_first_token:
+
+            # Add generated first token together with its query response_id to first tokens queue
+            self.first_token.put((value, self.response_ids[0]))
+
+            self.is_first_token = False
+            return
+
+        self.tokens_cache.append(value)
+
+
+    def end(self):
+        pass
+
+    def get_out_tokens(self):
+        return self.tokens_cache
+
+
+class SUT():
+    def __init__(self,
+                 model_path=None,
+                 api_server=None,
+                 api_model_name=None,
+                 dtype="bfloat16",
+                 device="cpu",
+                 batch_size=None,
+                 total_sample_count=24576,
+                 dataset_path=None,
+                 use_cached_outputs=False,  # Set this to True *only for test accuracy runs* in case your prior session was killed partway through
+                 workers=1):
+
+        self.model_path = model_path or "meta-llama/Llama-2-70b-chat-hf"
+        self.device = device
+        self.api_servers = []
+        if api_server:
+            self.api_servers.append(api_server)
+        self.api_model_name = api_model_name
+        self.device = device
+
+        batch_size = total_sample_count
+        self.batch_size = batch_size
+
+        # dtype
+        if dtype == 'bfloat16':
+            self.amp_enabled = True
+            self.amp_dtype = torch.bfloat16
+        elif dtype == 'float16':
+            self.amp_enabled = True
+            self.amp_dtype = torch.float16
+        else:
+            self.amp_enabled = False
+            self.amp_dtype = torch.float32
+
+        if 'cuda' in self.device:
+            assert torch.cuda.is_available(), "torch gpu is not available, exiting..."
+
+        self.dataset_path = dataset_path
+        self.data_object = Dataset(self.model_path,
+                                   dataset_path=self.dataset_path,
+                                   total_sample_count=total_sample_count,
+                                   device=self.device)
+        self.qsl = lg.ConstructQSL(self.data_object.total_sample_count, self.data_object.perf_count,
+                                   self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
+
+        #self.load_model()
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_path,
+            model_max_length=1024,
+            padding_side="left",
+            use_fast=True,) #changed from false
+
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        self.num_workers = workers
+        self.worker_threads = [None] * self.num_workers
+        self.query_queue = queue.Queue()
+
+        self.use_cached_outputs = use_cached_outputs
+        self.sample_counter = 0
+        self.sample_counter_lock = threading.Lock()
+
+
+    def start(self):
+        # Create worker threads
+        for j in range(self.num_workers):
+            worker = threading.Thread(target=self.process_queries)
+            worker.start()
+            self.worker_threads[j] = worker
+
+    def stop(self):
+        for _ in range(self.num_workers):
+            self.query_queue.put(None)
+
+        for worker in self.worker_threads:
+            worker.join()
+
+    def query_api_vllm(self, inputs, idx):
+        headers = {
+            'Content-Type': 'application/json',
+        }
+        json_data = {
+            "model": self.api_model_name,
+            "prompt": inputs,
+            "min_tokens": 1,
+            "max_tokens": 1024
+        }
+
+        response_code = 0
+        print(f"Server path {self.api_servers[idx]}/v1/completions")
+        while response_code != 200:
+            try:
+                response = requests.post(f"{self.api_servers[idx]}/v1/completions", headers=headers, json=json_data, verify=False)
+                response_code = response.status_code
+            except Exception as e:
+                print(e)
+                print("connection failure")
+                break
+        return [resp["text"] for resp in json.loads(response.text)["choices"]]
+
+    def api_action_handler(self, chunk, server_idx):
+        output = self.query_api_vllm(chunk, server_idx)
+        return output
+
+    def process_queries(self):
+        """Processor of the queued queries. User may choose to add batching logic """
+
+        while True:
+            qitem = self.query_queue.get()
+            if qitem is None:
+                break
+
+            query_ids = [q.index for q in qitem]
+
+            fname = "q" + "_".join([str(i) for i in query_ids])
+            fname = f"run_outputs/{fname}.pkl"
+            _p = Path(fname)
+            if self.use_cached_outputs and _p.exists():
+                # Read cache
+                with _p.open(mode="rb") as f:
+                    d = pickle.load(f)
+                processed_output = d["outputs"]
+                tik1 = None
+                tik2 = None
+                tik3 = None
+                tok = None
+            else:
+                # Construct / collate batch
+                max_seq_len = 1024
+
+                tik1 = time.time()
+
+                # OpenAI-API servers don't require padding and can take input tokens 
+                # directly, so we build our input_ids_tensor as a jagged list
+                input_ids_tensor = []
+                for q in qitem:
+                    #input_ids_tensor.append(self.data_object.input_ids[q.index].tolist())
+                    input_ids_tensor += self.data_object.input_ids[q.index].tolist()
+                
+                # NOTE(mgoin): I don't think this has to be a torch tensor
+                #input_ids_tensor = torch.cat(input_ids_tensor)
+
+                #print(input_ids_tensor)
+
+                assert len(input_ids_tensor) <= self.batch_size
+
+                tik2 = time.time()
+
+                # NOTE(mgoin): I don't think threading is necessary since we are submitting all queries in one request
+                # The API server should take care of mini-batches and scheduling
+                if self.api_servers:
+                    '''
+                    decoded = self.tokenizer.batch_decode(input_ids_tensor)
+                    cleaned = [entry.replace('</s>','').replace('<s>','') for entry in decoded]
+                    cleaned_chunks = [list(c) for c in mit.divide(len(self.api_servers), cleaned)]
+                    '''
+                    cleaned_chunks = [input_ids_tensor]
+                    with ThreadPoolExecutor(max_workers=len(self.api_servers)) as executor:
+                        #needs to be tested
+                        output_chunks = list(executor.map(self.api_action_handler,cleaned_chunks,range(len(self.api_servers))))
+                    output = []
+                    for row in output_chunks:
+                        output += row
+                else:
+                    print("Error: Specify at least one API to which the request is to be sent!")
+                    exit(1)
+
+                tik3 = time.time()
+
+            processed_output = self.tokenizer(output)['input_ids']
+            #for i in range(len(qitem)):
+            for i in range(len(processed_output)):
+                # NOTE(mgoin): Not optimal to make numpy arrays just to serialize
+                unpadded = np.array(processed_output[i])
+                n_tokens = unpadded.shape[0]
+                response_array = array.array("B", unpadded.tobytes())
+                bi = response_array.buffer_info()
+                response = [lg.QuerySampleResponse(qitem[i].id, bi[0], bi[1], n_tokens)]
+                lg.QuerySamplesComplete(response)
+
+            tok = time.time()
+
+            with self.sample_counter_lock:
+                self.sample_counter += len(qitem)
+                print(f"Samples run: {self.sample_counter}")
+                if tik1:
+                    print(f"\tBatchMaker time: {tik2 - tik1}")
+                    print(f"\tInference time: {tik3 - tik2}")
+                    print(f"\tPostprocess time: {tok - tik3}")
+                    print(f"\t==== Total time: {tok - tik1}")
+                else:
+                    print(f"\tLoaded from cache: {_p}")
+
+    def get_sut(self):
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        return self.sut
+
+    def get_qsl(self):
+        return self.qsl
+
+
+    def predict(self,**kwargs):
+        raise NotImplementedError
+
+
+    def issue_queries(self, query_samples):
+        """ Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
+
+        list_prompts_tokens = []
+        list_prompts_attn_masks = []
+
+        print(f"IssueQuery started with {len(query_samples)} samples")
+        while len(query_samples) > 0:
+            self.query_queue.put(query_samples[:self.batch_size])
+            query_samples = query_samples[self.batch_size:]
+        print(f"IssueQuery done")
+
+
+    def flush_queries(self):
+        pass
+
+    def __del__(self):
+        pass
+
+
+class SUTServer(SUT):
+    def __init__(self, model_path=None, api_server=None, api_model_name=None, dtype="bfloat16", device="cpu", total_sample_count=24576, dataset_path=None, batch_size=None, workers=1):
+
+        super().__init__(model_path=model_path, api_server=None, api_model_name=None, dtype=dtype, device=device, total_sample_count=total_sample_count, dataset_path=dataset_path, workers=workers)
+
+        with open(f"{self.model_path}/tokenizer.json", 'r') as token_file:
+            llama_tokenizer = json.load(token_file)
+        self.llama_vocab = llama_tokenizer["model"]["vocab"]
+
+        self.first_token_queue = queue.Queue()
+
+    def start(self):
+
+        # Create worker threads
+        for j in range(self.num_workers):
+            worker = threading.Thread(target=self.process_queries)
+            worker.start()
+            self.worker_threads[j] = worker
+
+        # Create first token response thread
+        self.ft_response_thread = threading.Thread(target=self.process_first_tokens)
+        self.ft_response_thread.start()
+
+
+    def process_first_tokens(self):
+
+        while True:
+            first_token_item = self.first_token_queue.get()
+
+            if first_token_item is None:
+                log.info("Exiting First token response thread")
+                break
+
+            first_tokens, response_id = first_token_item
+
+            response_data = array.array("B", np.array(first_tokens, np.float32).tobytes())
+            bi = response_data.buffer_info()
+            response = [lg.QuerySampleResponse(response_id, bi[0], bi[1])]
+            lg.FirstTokenComplete(response)
+
+    def stream_api_vllm(self, input, response_ids, idx):
+        headers = {
+            'Content-Type': 'application/json',
+        }
+
+        json_data = {
+            'model': '/opt/app-root/share/models',
+            'prompt': input,
+            'max_tokens': 1024,
+            'temperature': 0,
+            'stream': True,
+            'logprobs': 1
+        }
+
+        while True:
+            try:
+                token_cache = []
+                s = requests.Session()
+                first = True
+                with s.post(
+                    f'{self.api_servers[idx]}/v1/completions',
+                    headers=headers,
+                    json=json_data,
+                    verify=False,
+                    stream=True
+                ) as resp:
+                    for line in resp.iter_lines():
+                        if line:
+                            decoded = line.decode()
+                            if decoded.startswith("data") and "[DONE]" not in decoded:
+                                inter = json.loads(decoded[6:])["choices"][0]["logprobs"]
+                                if "top_logprobs" in inter:
+                                    token_s = list(inter["top_logprobs"][0].keys())[0]
+                                    token = self.llama_vocab[token_s]
+                                    if first:
+                                        self.first_token_queue.put((token, response_ids[0]))
+                                        first = False
+                                    token_cache.append(token)
+                s.close()
+                if token_cache:
+                    return token_cache
+            except Exception as e:
+                s.close()
+                print("Connection failure")
+                print(f"An exception occurred: {type(e).__name__}")
+                print(f"Exception details: {e}")
+   
+    def async_process_query(self, input_ids_tensor, qitem_id, idx):
+        decoded = self.tokenizer.decode(input_ids_tensor[0])
+        response_ids = [qitem_id]
+        output_tokens = self.stream_api_vllm(decoded, response_ids, idx)
+        n_tokens = len(output_tokens)
+        if n_tokens <= 1:
+            print("WARNING: caught low token count")
+            print(input_ids_tensor)
+            print(output_tokens)
+        response_array = array.array("B", np.array(output_tokens, np.int32).tobytes())
+        bi = response_array.buffer_info()
+        response = [lg.QuerySampleResponse(
+            qitem_id, bi[0], bi[1], n_tokens)]
+        lg.QuerySamplesComplete(response)
+        sys.exit()
+
+    def process_queries(self):
+        """Processor of the queued queries. User may choose to add batching logic """
+        server_idx = 0
+        while True:
+
+            qitem = self.query_queue.get()
+            if qitem is None:
+                break
+
+            input_ids_tensor = self.data_object.input_ids[qitem.index]
+            input_masks_tensor = self.data_object.attention_masks[qitem.index]
+
+            if self.api_servers:
+                threading.Thread(target=self.async_process_query, args=(input_ids_tensor, qitem.id, server_idx)).start()
+                server_idx = (server_idx + 1) % len(self.api_servers)
+            else:
+                #TODO: This PoC is super slow with significant overhead. Best to create a patch to `generate`
+                tokens_cache = []
+                tokens_streamer = FirstTokenStreamer(self.first_token_queue, tokens_cache=tokens_cache, is_first_token=True, response_ids=[qitem.id])
+
+                _ = self.model.generate(    input_ids=input_ids_tensor,
+                                            attention_mask=input_masks_tensor,
+                                            pad_token_id=self.tokenizer.pad_token_id,
+                                            streamer = tokens_streamer,
+                                            **gen_kwargs
+                                            )
+
+                output_tokens = tokens_streamer.get_out_tokens()
+
+                n_tokens = len(output_tokens)
+                response_array = array.array("B", np.array(output_tokens, np.int32).tobytes())
+                bi = response_array.buffer_info()
+                response = [lg.QuerySampleResponse(
+                    qitem.id, bi[0], bi[1], n_tokens)]
+                lg.QuerySamplesComplete(response)
+
+    def issue_queries(self, query_samples):
+
+        self.query_queue.put(query_samples[0])
+
+
+    def stop(self):
+        for _ in range(self.num_workers):
+            self.query_queue.put(None)
+
+        for worker in self.worker_threads:
+            worker.join()
+
+        self.first_token_queue.put(None)
+        self.ft_response_thread.join()
diff --git a/language/llama2-70b/main.py b/language/llama2-70b/main.py
index 15dbdd2edb..c1af88b952 100644
--- a/language/llama2-70b/main.py
+++ b/language/llama2-70b/main.py
@@ -4,13 +4,27 @@
 import os
 import logging
 import sys
-from SUT import SUT, SUTServer
+import requests
+import json
 
 sys.path.insert(0, os.getcwd())
 
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("Llama-70B-MAIN")
 
+# function to check the model name in server matches the user specified one
+def verify_model_name(user_specified_name, url):
+    response = requests.get(url)
+    if response.status_code == 200:
+        response_dict = response.json()
+        server_model_name = response_dict["data"][0]["id"]
+        if user_specified_name == server_model_name:
+            return {"matched":True, "error":False}
+        else:
+            return {"matched":False, "error":f"User specified {user_specified_name} and server model name {server_model_name} mismatch!"}
+    else:
+        return {"matched":False, "error":f"Failed to get a valid response. Status code: {response.status_code}"}
+
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--scenario", type=str, choices=["Offline", "Server"], default="Offline", help="Scenario")
@@ -27,6 +41,9 @@ def get_args():
     parser.add_argument("--output-log-dir", type=str, default="output-logs", help="Where logs are saved")
     parser.add_argument("--enable-log-trace", action="store_true", help="Enable log tracing. This file can become quite large")
     parser.add_argument("--num-workers", type=int, default=1, help="Number of workers to process queries")
+    parser.add_argument("--vllm", action="store_true", help="vllm mode")
+    parser.add_argument("--api-model-name", type=str, default="meta-llama/Llama-2-70b-chat-hf", help="Model name(specified in llm server)")
+    parser.add_argument("--api-server", type=str, default=None, help="Specify an api endpoint call to use api mode")
 
     args = parser.parse_args()
     return args
@@ -37,13 +54,15 @@ def get_args():
     "server": lg.TestScenario.Server,
     }
 
-sut_map = {
-        "offline": SUT,
-        "server": SUTServer
-        }
-
 def main():
     args = get_args()
+    
+    if args.vllm:
+        resp = verify_model_name(args.api_model_name, args.api_server+"/v1/models")
+        if resp["error"]:
+            print(f"\n\n\033[91mError:\033[0m", end=" ")
+            print(resp["error"])
+            sys.exit(1)
 
     settings = lg.TestSettings()
     settings.scenario = scenario_map[args.scenario.lower()]
@@ -64,16 +83,40 @@ def main():
     log_settings.log_output = log_output_settings
     log_settings.enable_trace = args.enable_log_trace
 
+    if args.vllm:
+        from SUT_API import SUT, SUTServer
+    else:
+        from SUT import SUT, SUTServer
+
+    sut_map = {
+        "offline": SUT,
+        "server": SUTServer
+        }
+
     sut_cls = sut_map[args.scenario.lower()]
 
-    sut = sut_cls(
-        model_path=args.model_path,
-        dtype=args.dtype,
-        batch_size=args.batch_size,
-        dataset_path=args.dataset_path,
-        total_sample_count=args.total_sample_count,
-        device=args.device,
-    )
+    if args.vllm:
+        sut = sut_cls(
+            model_path=args.model_path,
+            dtype=args.dtype,
+            batch_size=args.batch_size,
+            dataset_path=args.dataset_path,
+            total_sample_count=args.total_sample_count,
+            device=args.device,
+            api_server=args.api_server,
+            api_model_name=args.api_model_name,
+            workers=args.num_workers
+        )
+    else:
+        sut = sut_cls(
+            model_path=args.model_path,
+            dtype=args.dtype,
+            batch_size=args.batch_size,
+            dataset_path=args.dataset_path,
+            total_sample_count=args.total_sample_count,
+            device=args.device,
+            workers=args.num_workers
+        )
 
     # Start sut before loadgen starts
     sut.start()
diff --git a/main.py b/main.py
old mode 100644
new mode 100755
index 2f1c036fea..c9e3e1b566
--- a/main.py
+++ b/main.py
@@ -12,30 +12,44 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
         content=""
         scenarios = []
         execution_envs = ["Docker","Native"]
-        code_version="r4.1"
+        code_version="r4.1-dev"
 
         if model == "rnnt":
             code_version="r4.0"
 
         if implementation == "reference":
+            # Tip
+            if "99.9" not in model:
+                content += f"\n{pre_space}!!! tip\n\n"
+                content += f"{pre_space}    - MLCommons reference implementations are only meant to provide a rules compliant reference implementation for the submitters and in most cases are not best performing. If you want to benchmark any system, it is advisable to use the vendor MLPerf implementation for that system like Nvidia, Intel etc.\n\n"
+              
             devices = [ "CPU", "CUDA", "ROCm" ]
             if model.lower() == "resnet50":
                  frameworks = [ "Onnxruntime", "Tensorflow", "Deepsparse" ]
             elif model.lower() == "retinanet":
                  frameworks = [ "Onnxruntime", "Pytorch" ]
             elif "bert" in model.lower():
-                 frameworks = [ "Onnxruntime", "Pytorch", "Tensorflow" ]
+                 frameworks = [ "Pytorch", "Deepsparse" ]
             else:
                  frameworks = [ "Pytorch" ]
 
         elif implementation == "nvidia":
-            if model in [ "sdxl", "llama2-70b-99", "llama2-70b-99.9", "mixtral-8x7b" ]:
+            if model in [ "mixtral-8x7b" ]:
                  return pre_space+"    WIP"
             devices = [ "CUDA" ]
             frameworks = [ "TensorRT" ]
+        
+        elif implementation == "neuralmagic":
+            devices = [ "CUDA" ]
+            frameworks = [ "pytorch" ]
 
         elif implementation == "intel":
-            if model not in [ "bert-99", "bert-99.9", "gptj-99", "gptj-99.9", "resnet50", "retinanet", "3d-unet-99", "3d-unet-99.9" ]:
+            # Tip
+            if "99.9" not in model:
+                content += f"\n{pre_space}!!! tip\n\n"
+                content += f"{pre_space}    - Intel MLPerf inference implementation is available only for datacenter category and has been tested only on a limited number of systems. Most of the benchmarks using Intel implementation require at least Intel Sapphire Rapids or higher CPU generation.\n\n"
+                                
+            if model not in [ "bert-99", "bert-99.9", "gptj-99", "gptj-99.9", "resnet50", "retinanet", "3d-unet-99", "3d-unet-99.9", "dlrm-v2-99", "dlrm-v2-99.9", "sdxl" ]:
                  return pre_space+"    WIP"
             if model in [ "bert-99", "bert-99.9", "retinanet", "3d-unet-99", "3d-unet-99.9" ]:
                  code_version="r4.0"
@@ -68,6 +82,8 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
         else:
             categories = [ "Edge", "Datacenter" ]
 
+        # model name
+        content += f"{pre_space}{model.upper()}\n\n"
         for category in categories:
             if category == "Edge" and not scenarios:
                 scenarios = [ "Offline", "SingleStream" ]
@@ -100,6 +116,9 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                     content += f"{cur_space1}=== \"{device}\"\n"
                     content += f"{cur_space2}##### {device} device\n\n"
 
+                    # minimum system requirements
+                    content += get_min_system_requirements(cur_space2, model, implementation, device)
+
                     # to select the execution environments(currently Docker and Native)
                     for execution_env in execution_envs:
                         if (device == "ROCm" or implementation == "qualcomm") and execution_env == "Docker":
@@ -108,9 +127,18 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                             continue  # Nvidia implementation only supports execution through docker
                         content += f"{cur_space2}=== \"{execution_env}\"\n"
                         content += f"{cur_space3}###### {execution_env} Environment\n\n"
+                        # ref to cm installation
+                        content += f"{cur_space3}Please refer to the [installation page](../../install/index.md) to install CM for running the automated benchmark commands.\n\n"
                         test_query_count=get_test_query_count(model, implementation, device)
 
                         if "99.9" not in model: #not showing docker command as it is already done for the 99% variant
+                            if implementation == "neuralmagic":
+                                content += f"{cur_space3}####### Run the Inference Server\n"
+                                content += get_inference_server_run_cmd(spaces+16,implementation)
+                                # tips regarding the running of nural magic server
+                                content += f"\n{cur_space3}!!! tip\n\n"
+                                content += f"{cur_space3}    - Host and Port number of the server can be configured through `--host` and `--port`. Otherwise, server will run on default host `localhost` and port `8000`.\n\n"
+                                
                             if execution_env == "Native": # Native implementation steps through virtual environment
                                 content += f"{cur_space3}####### Setup a virtual environment for Python\n"
                                 content += get_venv_command(spaces+16)
@@ -126,7 +154,7 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                                 content += f"{cur_space3}The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.\n\n"
                                 content += f"{cur_space3}<details>\n"
                                 content += f"{cur_space3}<summary> Please click here to see more options for the docker launch </summary>\n\n"
-                                content += f"{cur_space3}* `--docker_cm_repo <Custom CM repo URL>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
+                                content += f"{cur_space3}* `--docker_cm_repo=<Custom CM repo URL>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
                                 content += f"{cur_space3}* `--docker_cache=no`: to not use docker cache during the image build\n"
 
                                 if device.lower() not in [ "cuda" ]:
@@ -146,7 +174,28 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                         run_suffix += f"{cur_space3}<summary> Please click here to see more options for the RUN command</summary>\n\n"
                         run_suffix += f"{cur_space3}* Use `--division=closed` to do a closed division submission which includes compliance runs\n\n"
                         run_suffix += f"{cur_space3}* Use `--rerun` to do a rerun even when a valid run exists\n"  
-                        run_suffix += f"{cur_space3}</details>\n"
+                        run_suffix += f"{cur_space3}</details>\n\n"
+
+                        if "bert" in model.lower() and framework == "deepsparse":
+                            run_suffix += f"{cur_space3}<details>\n"
+                            run_suffix += f"{cur_space3}<summary> Please click here for generic model stubs for bert deepsparse</summary>\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned95_quant-none-vnni\n\n" 
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50_quant-none-vnni\n\n" 
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned95_obs_quant-none\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50-none-vnni\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-base/pytorch/huggingface/squad/pruned90-none\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned90-none\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/bert-large/pytorch/huggingface/squad/pruned80_quant-none-vnni\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned95-none-vnni\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97-none\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/bert-large/pytorch/huggingface/squad/base-none\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none\n\n"
+                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base-none\n"
+                            run_suffix += f"{cur_space3}</details>\n"
+
+                        
 
                         for scenario in scenarios:
                             content += f"{cur_space3}=== \"{scenario}\"\n{cur_space4}###### {scenario}\n\n"
@@ -155,7 +204,7 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                             #content += run_suffix
  
                         content += f"{cur_space3}=== \"All Scenarios\"\n{cur_space4}###### All Scenarios\n\n"
-                        run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), "valid", scenarios, code_version)
+                        run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), "valid", 0, False, scenarios, code_version)
                         content += run_cmd
                         content += run_suffix
 
@@ -181,6 +230,55 @@ def get_test_query_count(model, implementation, device, num_devices=1):
             p_range *= num_devices
 
         return p_range
+    
+    def get_min_system_requirements(spaces, model, implementation, device):
+        model = model.lower()
+        min_sys_req_content = ""
+        min_sys_req_content += f"{spaces}<details>\n"
+        min_sys_req_content += f"{spaces}<summary>Please click here to see the minimum system requirements for running the benchmark</summary>\n\n"
+        # device memory
+        if device.lower() == "cuda" and (implementation.lower() == "nvidia" or implementation.lower() == "reference"):
+            if implementation.lower() == "nvidia":
+                if "dlrm" in model:
+                    device_memory = "24GB"
+                elif "llama2-70b" in model or "mixtral" in model:
+                    device_memory = "80GB"
+                elif "sdxl" in model or "gptj" in model:
+                    device_memory = "16GB"
+                else:
+                    device_memory = "8GB"
+            elif implementation.lower() == "reference":
+                if "dlrm" in model:
+                    device_memory = "2x80GB"
+                elif "llama2-70b" in model:
+                    device_memory = "8x80GB"
+                elif "mixtral" in model:
+                    device_memory = "4x80GB"
+                elif "sdxl" in model:
+                    device_memory = "24GB(fp32), 16GB(fp16)"
+                elif "gptj" in model:
+                    device_memory = "80GB(fp32). 40GB(fp16)"
+                else:
+                    device_memory = "8GB"
+            min_sys_req_content += f"{spaces}* **Device Memory**: {device_memory}\n\n"
+        # disk space
+        if "dlrm" in model:
+            disk_space = "500GB"
+        elif "llama2-70b" in model:
+            disk_space = "700GB"
+        elif "mixtral" in model:
+            disk_space = "100GB"
+        elif "retinanet" in model:
+            disk_space = "200GB"
+        else:
+            disk_space = "50GB"
+        min_sys_req_content += f"{spaces}* **Disk Space**: {disk_space}\n\n"
+        # System memory
+        if "dlrm" in model:
+            system_memory = "512GB"
+            min_sys_req_content += f"{spaces}* **System Memory(RAM+SWAP)**: {system_memory}\n\n"
+        min_sys_req_content += f"{spaces}</details>\n"
+        return min_sys_req_content
 
     def get_readme_prefix(spaces, model, implementation):
         readme_prefix = ""
@@ -191,6 +289,18 @@ def get_readme_prefix(spaces, model, implementation):
 
         return readme_prefix
     
+    def get_inference_server_run_cmd(spaces, implementation):
+        indent = " "*spaces + " "
+        if implementation == "neuralmagic":
+            pre_space = " "*spaces
+            return f"""\n
+{pre_space}```bash
+{pre_space}cm run script --tags=run,vllm-server \\
+{indent}--model=nm-testing/Llama-2-70b-chat-hf-FP8 \\
+{indent}--vllm_model_name=nm-testing/Llama-2-70b-chat-hf-FP8 \\
+{indent}--quiet
+{pre_space}```\n"""
+
     def get_venv_command(spaces):
       pre_space = " "*spaces
       return f"""\n
@@ -208,7 +318,7 @@ def get_docker_info(spaces, model, implementation, device):
         #pre_space = "                "
         if implementation == "nvidia":
             info += f"\n{pre_space}!!! tip\n\n"
-            info+= f"{pre_space}    All the Nvidia benchmarks, except GPT-J and LLAMA2-70B, use the same Docker container. Therefore, if you have already executed the Docker setup command for any benchmark, you can skip the Docker setup command below and run the commands inside the existing Docker container. The Docker container for GPT-J and LLAMA2-70B is the same and can be used for the other benchmarks, but not vice versa. This is because TensorRT-LLM is built specifically for the LLM benchmarks. If you are already inside a Docker container, execute the below Docker setup command without the --docker option for performance estimation.\n\n"
+            info+= f"{pre_space}    If ran with `--all_models=yes`, all the benchmark models of NVIDIA implementation could be run within the same container.\n\n"
         return info
 
     def get_readme_suffix(spaces, model, implementation):
@@ -241,7 +351,7 @@ def get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scen
         return extra_content
 
     @env.macro
-    def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False, scenarios = [], code_version="r4.1"):
+    def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False, scenarios = [], code_version="r4.1-dev"):
         pre_space = ""
         for i in range(1,spaces):
              pre_space  = pre_space + " "
@@ -256,13 +366,27 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
             scenario_option = f"\\\n{pre_space} --scenario={scenario}"
 
         if scenario == "Server" or (scenario == "All Scenarios" and "Server" in scenarios):
-            scenario_option = f"\\\n{pre_space} --server_target_qps=<SERVER_TARGET_QPS>"
+            scenario_option += f"\\\n{pre_space} --server_target_qps=<SERVER_TARGET_QPS>"
 
         run_cmd_extra = get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scenarios)
 
         if docker:
             docker_cmd_suffix = f" \\\n{pre_space} --docker --quiet"
             docker_cmd_suffix += f" \\\n{pre_space} --test_query_count={test_query_count}"
+            
+            if "bert" in model.lower() and framework == "deepsparse":
+                docker_cmd_suffix += f"\\\n{pre_space} --env.CM_MLPERF_NEURALMAGIC_MODEL_ZOO_STUB=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none"
+            if "llama2-70b" in model.lower():
+                if implementation == "nvidia":
+                    docker_cmd_suffix += f" \\\n{pre_space} --tp_size=2"
+                    docker_cmd_suffix += f" \\\n{pre_space} --nvidia_llama2_dataset_file_path=<PATH_TO_PICKE_FILE>"
+                elif implementation == "neuralmagic":
+                    docker_cmd_suffix += f" \\\n{pre_space} --api_server=http://localhost:8000"
+                    docker_cmd_suffix += f" \\\n{pre_space} --vllm_model_name=nm-testing/Llama-2-70b-chat-hf-FP8"
+                    docker_cmd_suffix += f" \\\n{pre_space} --adr.mlperf-implementation.tags=_repo.https://github.com/neuralmagic/inference,_branch.vllm"
+            
+            if "dlrm-v2" in model.lower() and implementation == "nvidia":
+                docker_cmd_suffix += f" \\\n{pre_space} --criteo_day23_raw_data_path=<PATH_TO_CRITEO_DAY23_RAW_DATA>"
 
             docker_setup_cmd = f"""\n
 {f_pre_space}```bash
@@ -283,6 +407,20 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
             if execution_mode == "test":
                 cmd_suffix += f" \\\n {pre_space} --test_query_count={test_query_count}"
 
+            if "bert" in model.lower() and framework == "deepsparse":
+                cmd_suffix += f"\\\n{pre_space} --env.CM_MLPERF_NEURALMAGIC_MODEL_ZOO_STUB=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none"
+            if "llama2-70b" in model.lower():
+                if implementation == "nvidia":
+                    cmd_suffix += f" \\\n{pre_space} --tp_size=<TP_SIZE>"
+                    cmd_suffix += f" \\\n{pre_space} --nvidia_llama2_dataset_file_path=<PATH_TO_PICKE_FILE>"
+                elif implementation == "neuralmagic":
+                    cmd_suffix += f" \\\n{pre_space} --api_server=http://localhost:8000"
+                    cmd_suffix += f" \\\n{pre_space} --vllm_model_name=nm-testing/Llama-2-70b-chat-hf-FP8"
+                    cmd_suffix += f" \\\n{pre_space} --adr.mlperf-implementation.tags=_repo.https://github.com/neuralmagic/inference,_branch.vllm"
+            
+            if "dlrm-v2" in model and implementation == "nvidia":
+                cmd_suffix += f" \\\n{pre_space} --criteo_day23_raw_data_path=<PATH_TO_CRITEO_DAY23_RAW_DATA>"
+
             run_cmd = f"""\n
 {f_pre_space}```bash
 {f_pre_space}cm run script --tags=run-mlperf,inference,_{code_version}{scenario_variation_tag} \\
diff --git a/mkdocs.yml b/mkdocs.yml
index 90a3ce9fd8..8e59acf63a 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -22,7 +22,7 @@ nav:
   - Install:
     - install/index.md
   - Benchmarks:
-    - benchmarks/index.md
+    - index.md
     - Image Classification:
       - ResNet50: benchmarks/image_classification/resnet50.md
     - Text to Image:

From cf5fddc5d0746bf3820eb0ab7294bbf709d788ab Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 24 Sep 2024 11:19:54 -0500
Subject: [PATCH 144/305] Enable systems to be marked as power only (#1850)

---
 tools/submission/submission_checker.py | 74 +++++++++++++++-----------
 1 file changed, 44 insertions(+), 30 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 692ff3f018..357ed64310 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -636,6 +636,7 @@
     "power_supply_details",
     "disk_drives",
     "disk_controllers",
+    "system_power_only",
 ]
 
 SYSTEM_DESC_MEANINGFUL_RESPONSE_REQUIRED_FIELDS_POWER = []
@@ -931,6 +932,18 @@ def check_extra_files(path, target_files):
 def split_path(m):
     return m.replace("\\", "/").split("/")
 
+def get_boolean(s):
+    if s is None:
+        return False
+    elif isinstance(s, bool):
+        return s
+    elif isinstance(s, str):
+        return (s.lower() == "true")
+    elif isinstance(s, int):
+        return bool(s)
+    else:
+        raise TypeError(f"Variable should be bool, string or int, got {type(s)} instead")
+
 
 def find_error_in_detail_log(config, fname):
     is_valid = True
@@ -1639,36 +1652,37 @@ def log_result(
         unit = special_unit_dict.get(model_name, unit_dict)[scenario_fixed]
         power_unit = power_unit_dict[scenario_fixed]
 
-        csv.write(
-            fmt.format(
-                submitter,
-                available,
-                division,
-                '"' + system_type + '"',
-                '"' + system_name + '"',
-                system_desc,
-                model_name,
-                mlperf_model,
-                scenario_fixed,
-                r,
-                acc,
-                system_json.get("number_of_nodes"),
-                '"' + system_json.get("host_processor_model_name") + '"',
-                system_json.get("host_processors_per_node"),
-                system_json.get("host_processor_core_count"),
-                '"' + system_json.get("accelerator_model_name") + '"',
-                '"' + str(system_json.get("accelerators_per_node")) + '"',
-                name.replace("\\", "/"),
-                '"' + system_json.get("framework", "") + '"',
-                '"' + system_json.get("operating_system", "") + '"',
-                '"' + notes + '"',
-                compliance,
-                errors,
-                config.version,
-                inferred,
-                power_metric > 0,
-                unit,
-            )
+        if (power_metric <= 0) or (not get_boolean(system_json.get("system_power_only"))):
+            csv.write(
+                fmt.format(
+                    submitter,
+                    available,
+                    division,
+                    '"' + system_type + '"',
+                    '"' + system_name + '"',
+                    system_desc,
+                    model_name,
+                    mlperf_model,
+                    scenario_fixed,
+                    r,
+                    acc,
+                    system_json.get("number_of_nodes"),
+                    '"' + system_json.get("host_processor_model_name") + '"',
+                    system_json.get("host_processors_per_node"),
+                    system_json.get("host_processor_core_count"),
+                    '"' + system_json.get("accelerator_model_name") + '"',
+                    '"' + str(system_json.get("accelerators_per_node")) + '"',
+                    name.replace("\\", "/"),
+                    '"' + system_json.get("framework", "") + '"',
+                    '"' + system_json.get("operating_system", "") + '"',
+                    '"' + notes + '"',
+                    compliance,
+                    errors,
+                    config.version,
+                    inferred,
+                    power_metric > 0,
+                    unit,
+                )
         )
 
         if power_metric > 0:

From 7d2f0c41e5cd79c9178702867392e38f57953338 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 24 Sep 2024 11:32:51 -0500
Subject: [PATCH 145/305] Update DLRM readme (#1811)

Co-authored-by: Miro <mirhodak@amd.com>
---
 recommendation/dlrm_v2/pytorch/README.md | 25 ++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/recommendation/dlrm_v2/pytorch/README.md b/recommendation/dlrm_v2/pytorch/README.md
index 647e6830c9..d93e15ac35 100755
--- a/recommendation/dlrm_v2/pytorch/README.md
+++ b/recommendation/dlrm_v2/pytorch/README.md
@@ -69,6 +69,31 @@ cd $HOME/mlcommons/inference/loadgen
 CFLAGS="-std=c++14" python setup.py develop --user
 ```
 
+### Download preprocessed Dataset
+
+Download the preprocessed dataset using Rclone.
+
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
+```
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
+```
+Once Rclone is installed, run the following command to authenticate with the bucket:
+```
+rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+```
+Prepare your dataset destination:
+```
+cd $HOME/mlcommons/inference/recommendation/dlrm_v2/pytorch/
+mkdir ./dataset && cd ./dataset
+mv <downloaded_file(s)> ./
+export DATA_DIR=./dataset
+```
+Download the dataset
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/dlrm_preprocessed ./dataset  -P
+```
+
 
 ### Downloading model weights
 

From c4d0b3ea98e6fe7252e50cb573f0d523da7979df Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 24 Sep 2024 17:39:18 +0100
Subject: [PATCH 146/305] Update docs: SCC24,  fix broken redirect  (#1843)

* Support batch-size in llama2 run

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-Cloudflare download instructiosn to README.md

* Minor wording edit to README.md

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-GDrive download instructions to README.md

* Add new and old instructions to README.md

* Tweak language in README.md

* Language tweak in README.md

* Minor language tweak in README.md

* Fix typo in README.md

* Count error when logging errors: submission_checker.py

* Fixes #1648, restrict loadgen uncommitted error message to within the loadgen directory

* Update test-rnnt.yml (#1688)

Stopping the github action for rnnt

* Added docs init

Added github action for website publish

Update benchmark documentation

Update publish.yaml

Update publish.yaml

Update benchmark documentation

Improved the submission documentation

Fix taskname

Removed unused images

* Fix benchmark URLs

* Fix links

* Add _full variation to run commands

* Added script flow diagram

* Added docker setup command for CM, extra run options

* Added support for docker options in the docs

* Added --quiet to the CM run_cmds in docs

* Fix the test query count for cm commands

* Support ctuning-cpp implementation

* Added commands for mobilenet models

* Docs cleanup

* Docs cleanup

* Added separate files for dataset and models in the docs

* Remove redundant tab in the docs

* Fixes some WIP models in the docs

* Use the official docs page for CM installation

* Fix the deadlink in docs

* Fix indendation issue in docs

* Added dockerinfo for nvidia implementation

* Added run options for gptj

* Added execution environment tabs

* Cleanup of the docs

* Cleanup of the docs

* Reordered the sections of the docs page

* Removed an unnecessary heading in the docs

* Fixes the commands for datacenter

* Fix the build --sdist for loadgen

* Fixes #1761, llama2 and mixtral runtime error on CPU systems

* Added mixtral to the benchmark list, improved benchmark docs

* Update docs for MLPerf inference v4.1

* Update docs for MLPerf inference v4.1

* Fix typo

* Gave direct link to implementation readmes

* Added tables detailing implementations

* Update vision README.md, split the frameworks into separate rows

* Update README.md

* pointed links to specific frameworks

* pointed links to specific frameworks

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* api support llama2

* Added request module and reduced max token len

* Fix for llama2 api server

* Update SUT_API offline to work for OpenAI

* Update SUT_API.py

* Minor fixes

* Fix json import in SUT_API.py

* Fix llama2 token length

* Added model name verification with server

* clean temp files

* support num_workers in LLAMA2 SUTs

* Remove batching from Offline SUT_API.py

* Update SUT_API.py

* Minor fixes for llama2 API

* Fix for llama2 API

* removed table of contents

* enabled llama2-nvidia + vllm-NM : WIP

* enabled dlrm for intel

* lower cased implementation

* added raw data input

* corrected data download commands

* renamed filename

* changes for bert and vllm

* documentation to work on custom repo and branch

* benchmark index page update

* enabled sdxl for nvidia and intel

* updated vllm server run cmd

* benchmark page information addition

* fix indendation issue

* Added submission categories

* update submission page - generate submission with or w/o using CM for benchmarking

* Updated kits dataset documentation

* Updated model parameters

* updation of information

* updated non cm based benchmark

* added info about hf password

* added links to model and access tokens

* Updated reference results structuree tree

* submission docs cleanup

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* added generic stubs deepsparse

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info (FID and CLIP data added)

* typo fix for bert deepsparse framework

* added min system requirements for models

* fixed code version

* changes for displaying reference and intel implementation tip

* added reference to installation page

* updated neural magic documentation

* Added links to the install page, redirect benchmarks page

* added tips about batch size and dataset for nvidia llama2

* fix conditions logic

* modified tips and additional run cmds

* sentence corrections

* Minor fix for the documentation

* fixed bug in deepsparse generic model stubs + styling

* added more information to stubs

* Added SCC24 readme, support reproducibility in the docs

* Made clear the custom CM repo URL format

* Support conditional implementation, setup and run tips

* Support rocm for sdxl

* Fix _short tag support

* Fix install URL

* Expose bfloat16 and float16 options for sdxl

* Expose download model to host option for sdxl

* IndySCC24 documentation added

* Improve the SCC24 docs

* Improve the support of short variation

* Improved the indyscc24 documentation

* Updated scc run commands

* removed test_query_count option for scc

* Remove scc24 in the main docs

* Remove scc24 in the main docs

* Fix docs: indendation issue on the submission page

* generalised code for skipping test query count

* Fixes for SCC24 docs

* Fix scenario text in main.py

* Fix links for scc24

* Fix links for scc24

* Improve the general docs

* Fix links for scc24

* Use float16 in scc24 doc

* Improve scc24 docs

* Improve scc24 docs

* Use float16 in scc24 doc

* fixed command bug

---------

Co-authored-by: Nathan Wasson <nathanw@mlcommons.org>
Co-authored-by: anandhu-eng <anandhukicks@gmail.com>
Co-authored-by: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>
---
 .../image_classification/mobilenets.md        |   2 +
 .../image_classification/resnet50.md          |   2 +
 docs/benchmarks/language/gpt-j.md             |   1 -
 docs/benchmarks/language/llama2-70b.md        |   3 +-
 docs/benchmarks/language/mixtral-8x7b.md      |   4 +-
 .../reproducibility/indyscc24-bert.md         |  48 ++++
 docs/benchmarks/medical_imaging/3d-unet.md    |   1 -
 docs/benchmarks/recommendation/dlrm-v2.md     |   4 +-
 .../text_to_image/reproducibility/scc24.md    |  96 +++++++
 docs/install/index.md                         |  26 +-
 docs/requirements.txt                         |   2 +
 docs/submission/index.md                      |  90 +++----
 main.py                                       | 238 +++++++++++-------
 mkdocs.yml                                    |  20 +-
 text_to_image/main.py                         |   2 +-
 15 files changed, 376 insertions(+), 163 deletions(-)
 create mode 100644 docs/benchmarks/language/reproducibility/indyscc24-bert.md
 create mode 100644 docs/benchmarks/text_to_image/reproducibility/scc24.md

diff --git a/docs/benchmarks/image_classification/mobilenets.md b/docs/benchmarks/image_classification/mobilenets.md
index f276008ef8..09840ef1d4 100644
--- a/docs/benchmarks/image_classification/mobilenets.md
+++ b/docs/benchmarks/image_classification/mobilenets.md
@@ -5,6 +5,8 @@ hide:
 
 # Image Classification using Mobilenet models
 
+Install CM following the [installation page](site:install).
+
 Mobilenet models are not official MLPerf models and so cannot be used for a Closed division MLPerf inference submission. But since they can be run with Imagenet dataset, we are allowed to use them for Open division submission. Only CPU runs are supported now. 
 
 ## TFLite Backend
diff --git a/docs/benchmarks/image_classification/resnet50.md b/docs/benchmarks/image_classification/resnet50.md
index 62b966e0d1..4172158dcc 100644
--- a/docs/benchmarks/image_classification/resnet50.md
+++ b/docs/benchmarks/image_classification/resnet50.md
@@ -3,8 +3,10 @@ hide:
   - toc
 ---
 
+
 # Image Classification using ResNet50 
 
+
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
     
diff --git a/docs/benchmarks/language/gpt-j.md b/docs/benchmarks/language/gpt-j.md
index 4dcb3d70e2..d2f5458152 100644
--- a/docs/benchmarks/language/gpt-j.md
+++ b/docs/benchmarks/language/gpt-j.md
@@ -5,7 +5,6 @@ hide:
 
 # Text Summarization using GPT-J
 
-
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
 
diff --git a/docs/benchmarks/language/llama2-70b.md b/docs/benchmarks/language/llama2-70b.md
index 0d9a0504de..e686937166 100644
--- a/docs/benchmarks/language/llama2-70b.md
+++ b/docs/benchmarks/language/llama2-70b.md
@@ -5,7 +5,6 @@ hide:
 
 # Text Summarization using LLAMA2-70b
 
-
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
     
@@ -25,4 +24,4 @@ hide:
     
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "neuralmagic") }}
 
-{{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "neuralmagic") }}
\ No newline at end of file
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "neuralmagic") }}
diff --git a/docs/benchmarks/language/mixtral-8x7b.md b/docs/benchmarks/language/mixtral-8x7b.md
index 9f3bf2992b..bdb26ae770 100644
--- a/docs/benchmarks/language/mixtral-8x7b.md
+++ b/docs/benchmarks/language/mixtral-8x7b.md
@@ -3,7 +3,9 @@ hide:
   - toc
 ---
 
+# Question Answering, Math, and Code Generation using Mixtral-8x7B
+
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
     
-{{ mlperf_inference_implementation_readme (4, "mixtral-8x7b", "reference") }}
\ No newline at end of file
+{{ mlperf_inference_implementation_readme (4, "mixtral-8x7b", "reference") }}
diff --git a/docs/benchmarks/language/reproducibility/indyscc24-bert.md b/docs/benchmarks/language/reproducibility/indyscc24-bert.md
new file mode 100644
index 0000000000..68215c5e14
--- /dev/null
+++ b/docs/benchmarks/language/reproducibility/indyscc24-bert.md
@@ -0,0 +1,48 @@
+---
+hide:
+  - toc
+---
+
+# Question and Answering using Bert Large for IndySCC 2024
+
+## Introduction
+
+This guide is designed for the [IndySCC 2024](https://sc24.supercomputing.org/students/indyscc/) to walk participants through running and optimizing the [MLPerf Inference Benchmark](https://arxiv.org/abs/1911.02549) using [Bert Large](https://github.com/mlcommons/inference/tree/master/language/bert#supported-models) across various software and hardware configurations. The goal is to maximize system throughput (measured in samples per second) without compromising accuracy.
+
+For a valid MLPerf inference submission, two types of runs are required: a performance run and an accuracy run. In this competition, we focus on the `Offline` scenario, where throughput is the key metric—higher values are better. The official MLPerf inference benchmark for Bert Large requires processing a minimum of 10833 samples in both performance and accuracy modes using the Squad v1.1 dataset. Setting up for Nvidia GPUs may take 2-3 hours but can be done offline. Your final output will be a tarball (`mlperf_submission.tar.gz`) containing MLPerf-compatible results, which you will submit to the SCC organizers for scoring.
+
+## Scoring
+
+In the SCC, your first objective will be to run a reference (unoptimized) Python implementation or a vendor-provided version (such as Nvidia's) of the MLPerf inference benchmark to secure a baseline score.
+
+Once the initial run is successful, you'll have the opportunity to optimize the benchmark further by maximizing system utilization, applying quantization techniques, adjusting ML frameworks, experimenting with batch sizes, and more, all of which can earn you additional points.
+
+Since vendor implementations of the MLPerf inference benchmark vary and are often limited to single-node benchmarking, teams will compete within their respective hardware categories (e.g., Nvidia GPUs, AMD GPUs). Points will be awarded based on the throughput achieved on your system.
+
+
+!!! info
+    Both MLPerf and CM automation are evolving projects.
+    If you encounter issues or have questions, please submit them [here](https://github.com/mlcommons/cm4mlops/issues)
+
+## Artifacts to submit to the SCC committee
+
+You will need to submit the following files:
+
+* `mlperf_submission_short.tar.gz` - automatically generated file with validated MLPerf results.
+* `mlperf_submission_short_summary.json` - automatically generated summary of MLPerf results.
+* `mlperf_submission_short.run` - CM commands to run MLPerf BERT inference benchmark saved to this file.
+* `mlperf_submission_short.tstamps` - execution timestamps before and after CM command saved to this file.
+* `mlperf_submission_short.md` - description of your platform and some highlights of the MLPerf benchmark execution.
+
+
+
+=== "MLCommons-Python"
+    ## MLPerf Reference Implementation in Python
+    
+{{ mlperf_inference_implementation_readme (4, "bert-99", "reference", extra_variation_tags=",_short", scenarios=["Offline"],categories=["Edge"], setup_tips=False) }}
+
+=== "Nvidia"
+    ## Nvidia MLPerf Implementation
+{{ mlperf_inference_implementation_readme (4, "bert-99", "nvidia", extra_variation_tags=",_short", scenarios=["Offline"],categories=["Edge"], setup_tips=False, implementation_tips=False) }}
+    
+
diff --git a/docs/benchmarks/medical_imaging/3d-unet.md b/docs/benchmarks/medical_imaging/3d-unet.md
index 01a54c63e6..72d5eed493 100644
--- a/docs/benchmarks/medical_imaging/3d-unet.md
+++ b/docs/benchmarks/medical_imaging/3d-unet.md
@@ -5,7 +5,6 @@ hide:
 
 # Medical Imaging using 3d-unet (KiTS 2019 kidney tumor segmentation task)
 
-
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
 
diff --git a/docs/benchmarks/recommendation/dlrm-v2.md b/docs/benchmarks/recommendation/dlrm-v2.md
index ce30810778..b539c1607e 100644
--- a/docs/benchmarks/recommendation/dlrm-v2.md
+++ b/docs/benchmarks/recommendation/dlrm-v2.md
@@ -5,8 +5,6 @@ hide:
 
 # Recommendation using DLRM v2
 
-
-## Benchmark Implementations
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
 
@@ -26,4 +24,4 @@ hide:
     
 {{ mlperf_inference_implementation_readme (4, "dlrm-v2-99", "intel") }}
 
-{{ mlperf_inference_implementation_readme (4, "dlrm-v2-99.9", "intel") }}
\ No newline at end of file
+{{ mlperf_inference_implementation_readme (4, "dlrm-v2-99.9", "intel") }}
diff --git a/docs/benchmarks/text_to_image/reproducibility/scc24.md b/docs/benchmarks/text_to_image/reproducibility/scc24.md
new file mode 100644
index 0000000000..bae4eceb39
--- /dev/null
+++ b/docs/benchmarks/text_to_image/reproducibility/scc24.md
@@ -0,0 +1,96 @@
+---
+hide:
+  - toc
+---
+
+# Text-to-Image with Stable Diffusion for Student Cluster Competition 2024
+
+## Introduction
+
+This guide is designed for the [Student Cluster Competition 2024](https://sc24.supercomputing.org/students/student-cluster-competition/) to walk participants through running and optimizing the [MLPerf Inference Benchmark](https://arxiv.org/abs/1911.02549) using [Stable Diffusion XL 1.0](https://github.com/mlcommons/inference/tree/master/text_to_image#supported-models) across various software and hardware configurations. The goal is to maximize system throughput (measured in samples per second) without compromising accuracy. Since the model performs poorly on CPUs, it is essential to run it on GPUs.
+
+For a valid MLPerf inference submission, two types of runs are required: a performance run and an accuracy run. In this competition, we focus on the `Offline` scenario, where throughput is the key metric—higher values are better. The official MLPerf inference benchmark for Stable Diffusion XL requires processing a minimum of 5,000 samples in both performance and accuracy modes using the COCO 2014 dataset. However, for SCC, we have reduced this and we also have two variants. `scc-base` variant has dataset size reduced to 50 samples, making it possible to complete both performance and accuracy runs in approximately 5-10 minutes. `scc-main` variant has dataset size of 500 and running it will fetch extra points as compared to running just the base variant. Setting up for Nvidia GPUs may take 2-3 hours but can be done offline. Your final output will be a tarball (`mlperf_submission.tar.gz`) containing MLPerf-compatible results, which you will submit to the SCC organizers for scoring.
+
+## Scoring
+
+In the SCC, your first objective will be to run `scc-base` variant for reference (unoptimized) Python implementation or a vendor-provided version (such as Nvidia's) of the MLPerf inference benchmark to secure a baseline score.
+
+Once the initial run is successful, you'll have the opportunity to optimize the benchmark further by maximizing system utilization, applying quantization techniques, adjusting ML frameworks, experimenting with batch sizes, and more, all of which can earn you additional points.
+
+Since vendor implementations of the MLPerf inference benchmark vary and are often limited to single-node benchmarking, teams will compete within their respective hardware categories (e.g., Nvidia GPUs, AMD GPUs). Points will be awarded based on the throughput achieved on your system.
+
+Additionally, significant bonus points will be awarded if your team enhances an existing implementation, adds support for new hardware (such as an unsupported GPU), enables multi-node execution, or adds/extends scripts to [cm4mlops repository](https://github.com/mlcommons/cm4mlops/tree/main/script) supporting new devices, frameworks, implementations etc. All improvements must be made publicly available under the Apache 2.0 license and submitted alongside your results to the SCC committee to earn these bonus points, contributing to the MLPerf community.
+
+
+!!! info
+    Both MLPerf and CM automation are evolving projects.
+    If you encounter issues or have questions, please submit them [here](https://github.com/mlcommons/cm4mlops/issues)
+
+## Artifacts to submit to the SCC committee
+
+You will need to submit the following files:
+
+* `mlperf_submission.run` - CM commands to run MLPerf inference benchmark saved to this file.
+* `mlperf_submission.md` - description of your platform and some highlights of the MLPerf benchmark execution.
+* `<Team Name>` under which results are pushed to the github repository. 
+
+
+## SCC interview
+
+You are encouraged to highlight and explain the obtained MLPerf inference throughput on your system
+and describe any improvements and extensions to this benchmark (such as adding new hardware backend
+or supporting multi-node execution) useful for the community and [MLCommons](https://mlcommons.org).
+
+## Run Commands
+
+=== "MLCommons-Python"
+    ## MLPerf Reference Implementation in Python
+    
+{{ mlperf_inference_implementation_readme (4, "sdxl", "reference", extra_variation_tags=",_short,_scc24-base", devices=["ROCm", "CUDA"],scenarios=["Offline"],categories=["Datacenter"], setup_tips=False, skip_test_query_count=True, extra_input_string="--precision=float16") }}
+
+=== "Nvidia"
+    ## Nvidia MLPerf Implementation
+{{ mlperf_inference_implementation_readme (4, "sdxl", "nvidia", extra_variation_tags=",_short,_scc24-base", scenarios=["Offline"],categories=["Datacenter"], setup_tips=False, implementation_tips=False, skip_test_query_count=True) }}
+
+!!! info
+    Once the above run is successful, you can change `_scc24-base` to `_scc24-main` to run the main variant.
+
+## Submission Commands
+
+### Generate actual submission tree
+
+```bash
+cm run script --tags=generate,inference,submission \
+   --clean \
+   --preprocess_submission=yes \
+   --run-checker \
+   --tar=yes \
+   --env.CM_TAR_OUTFILE=submission.tar.gz \
+   --division=open \
+   --category=datacenter \
+   --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+   --run_style=test \
+   --adr.submission-checker.tags=_short-run \
+   --quiet \
+   --submitter=<Team Name>
+```
+
+* Use `--hw_name="My system name"` to give a meaningful system name.
+
+
+### Push Results to GitHub
+
+Fork the repository URL at [https://github.com/gateoverflow/cm4mlperf-inference](https://github.com/gateoverflow/cm4mlperf-inference). 
+
+Run the following command after **replacing `--repo_url` with your GitHub fork URL**.
+
+```bash
+cm run script --tags=push,github,mlperf,inference,submission \
+   --repo_url=https://github.com/gateoverflow/cm4mlperf-inference \
+   --repo_branch=mlperf-inference-results-scc24 \
+   --commit_message="Results on system <HW Name>" \
+   --quiet
+```
+
+Once uploaded give a Pull Request to the origin repository. Github action will be running there and once 
+finished you can see your submitted results at [https://gateoverflow.github.io/cm4mlperf-inference](https://gateoverflow.github.io/cm4mlperf-inference).
diff --git a/docs/install/index.md b/docs/install/index.md
index 60377adee5..195521c7e2 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -8,24 +8,24 @@ We use MLCommons CM Automation framework to run MLPerf inference benchmarks.
 
 CM needs `git`, `python3-pip` and `python3-venv` installed on your system. If any of these are absent, please follow the [official CM installation page](https://docs.mlcommons.org/ck/install) to install them. Once the dependencies are installed, do the following
 
-## Activate a VENV for CM
+## Activate a Virtual ENV for CM
+This step is not mandatory as CM can use separate virtual environment for MLPerf inference. But the latest `pip` install requires this or else will need the `--break-system-packages` flag while installing `cm4mlops`.
+
 ```bash
    python3 -m venv cm
    source cm/bin/activate
 ```
 
 ## Install CM and pulls any needed repositories
-
-```bash
-   pip install cm4mlops
-```
-
-## To work on custom GitHub repo and branch
-
-```bash
-   pip install cmind && cm init --quiet --repo=mlcommons@cm4mlops --branch=mlperf-inference
-```
-
-Here, repo is in the format `githubUsername@githubRepo`.
+=== "Use the default fork of CM MLOps repository"
+    ```bash
+     pip install cm4mlops
+    ```
+
+=== "Use custom fork/branch of the CM MLOps repository"
+    ```bash
+     pip install cmind && cm init --quiet --repo=mlcommons@cm4mlops --branch=mlperf-inference
+    ```
+    Here, `repo` is in the format `githubUsername@githubRepo`.
 
 Now, you are ready to use the `cm` commands to run MLPerf inference as given in the [benchmarks](../index.md) page
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 39fab4e1ff..293abf164e 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -2,3 +2,5 @@ mkdocs-material
 swagger-markdown
 mkdocs-macros-plugin
 ruamel.yaml
+mkdocs-redirects
+mkdocs-site-urls
diff --git a/docs/submission/index.md b/docs/submission/index.md
index a75bc32590..4f6e05c255 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -60,63 +60,63 @@ Once all the results across all the models are ready you can use the following c
 === "Closed Edge"
     ### Closed Edge Submission
     ```bash
-       cm run script --tags=generate,inference,submission \
-          --clean \
-          --preprocess_submission=yes \
-          --run-checker \
-          --submitter=MLCommons \
-          --tar=yes \
-          --env.CM_TAR_OUTFILE=submission.tar.gz \
-          --division=closed \
-          --category=edge \
-          --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-          --quiet
+    cm run script --tags=generate,inference,submission \
+       --clean \
+       --preprocess_submission=yes \
+       --run-checker \
+       --submitter=MLCommons \
+       --tar=yes \
+       --env.CM_TAR_OUTFILE=submission.tar.gz \
+       --division=closed \
+       --category=edge \
+       --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+       --quiet
     ```
 
 === "Closed Datacenter"
     ### Closed Datacenter Submission
     ```bash
-       cm run script --tags=generate,inference,submission \
-          --clean \
-          --preprocess_submission=yes \
-          --run-checker \
-          --submitter=MLCommons \
-          --tar=yes \
-          --env.CM_TAR_OUTFILE=submission.tar.gz \
-          --division=closed \
-          --category=datacenter \
-          --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-          --quiet
+    cm run script --tags=generate,inference,submission \
+       --clean \
+       --preprocess_submission=yes \
+       --run-checker \
+       --submitter=MLCommons \
+       --tar=yes \
+       --env.CM_TAR_OUTFILE=submission.tar.gz \
+       --division=closed \
+       --category=datacenter \
+       --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+       --quiet
     ```
 === "Open Edge"
     ### Open Edge Submission
     ```bash
-       cm run script --tags=generate,inference,submission \
-          --clean \
-          --preprocess_submission=yes \
-          --run-checker \
-          --submitter=MLCommons \
-          --tar=yes \
-          --env.CM_TAR_OUTFILE=submission.tar.gz \
-          --division=open \
-          --category=edge \
-          --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-          --quiet
+    cm run script --tags=generate,inference,submission \
+       --clean \
+       --preprocess_submission=yes \
+       --run-checker \
+       --submitter=MLCommons \
+       --tar=yes \
+       --env.CM_TAR_OUTFILE=submission.tar.gz \
+       --division=open \
+       --category=edge \
+       --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+       --quiet
     ```
 === "Open Datacenter"
     ### Closed Datacenter Submission
     ```bash
-       cm run script --tags=generate,inference,submission \
-          --clean \
-          --preprocess_submission=yes \
-          --run-checker \
-          --submitter=MLCommons \
-          --tar=yes \
-          --env.CM_TAR_OUTFILE=submission.tar.gz \
-          --division=open \
-          --category=datacenter \
-          --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-          --quiet
+    cm run script --tags=generate,inference,submission \
+       --clean \
+       --preprocess_submission=yes \
+       --run-checker \
+       --submitter=MLCommons \
+       --tar=yes \
+       --env.CM_TAR_OUTFILE=submission.tar.gz \
+       --division=open \
+       --category=datacenter \
+       --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+       --quiet
     ```
 
 * Use `--hw_name="My system name"` to give a meaningful system name. Examples can be seen [here](https://github.com/mlcommons/inference_results_v3.0/tree/main/open/cTuning/systems)
@@ -134,7 +134,7 @@ If you are collecting results across multiple systems you can generate different
 Run the following command after **replacing `--repo_url` with your GitHub repository URL**.
 
 ```bash
-   cm run script --tags=push,github,mlperf,inference,submission \
+cm run script --tags=push,github,mlperf,inference,submission \
    --repo_url=https://github.com/GATEOverflow/mlperf_inference_submissions_v4.1 \
    --commit_message="Results on <HW name> added by <Name>" \
    --quiet
diff --git a/main.py b/main.py
index c9e3e1b566..aa8dd769e3 100755
--- a/main.py
+++ b/main.py
@@ -1,7 +1,7 @@
 def define_env(env):
 
     @env.macro
-    def mlperf_inference_implementation_readme(spaces, model, implementation):
+    def mlperf_inference_implementation_readme(spaces, model, implementation, *, implementation_tips=True, setup_tips=True, run_tips=True, skip_test_query_count=False, scenarios = [], devices=[], frameworks=[], categories=[], extra_variation_tags="", extra_input_string="", extra_docker_input_string=""):
         pre_space = ""
 
         for i in range(1,spaces):
@@ -10,28 +10,32 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
         pre_space += " "
 
         content=""
-        scenarios = []
+
         execution_envs = ["Docker","Native"]
         code_version="r4.1-dev"
+        implementation_run_options = []
 
         if model == "rnnt":
             code_version="r4.0"
 
         if implementation == "reference":
             # Tip
-            if "99.9" not in model:
+            if "99.9" not in model and implementation_tips:
                 content += f"\n{pre_space}!!! tip\n\n"
                 content += f"{pre_space}    - MLCommons reference implementations are only meant to provide a rules compliant reference implementation for the submitters and in most cases are not best performing. If you want to benchmark any system, it is advisable to use the vendor MLPerf implementation for that system like Nvidia, Intel etc.\n\n"
               
-            devices = [ "CPU", "CUDA", "ROCm" ]
-            if model.lower() == "resnet50":
-                 frameworks = [ "Onnxruntime", "Tensorflow", "Deepsparse" ]
-            elif model.lower() == "retinanet":
-                 frameworks = [ "Onnxruntime", "Pytorch" ]
-            elif "bert" in model.lower():
-                 frameworks = [ "Pytorch", "Deepsparse" ]
-            else:
-                 frameworks = [ "Pytorch" ]
+            if not devices:
+                devices = [ "CPU", "CUDA", "ROCm" ]
+
+            if not frameworks:
+                if model.lower() == "resnet50":
+                    frameworks = [ "Onnxruntime", "Tensorflow", "Deepsparse" ]
+                elif model.lower() == "retinanet":
+                    frameworks = [ "Onnxruntime", "Pytorch" ]
+                elif "bert" in model.lower():
+                    frameworks = [ "Pytorch", "Deepsparse" ]
+                else:
+                    frameworks = [ "Pytorch" ]
 
         elif implementation == "nvidia":
             if model in [ "mixtral-8x7b" ]:
@@ -45,7 +49,7 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
 
         elif implementation == "intel":
             # Tip
-            if "99.9" not in model:
+            if "99.9" not in model and implementation_tips:
                 content += f"\n{pre_space}!!! tip\n\n"
                 content += f"{pre_space}    - Intel MLPerf inference implementation is available only for datacenter category and has been tested only on a limited number of systems. Most of the benchmarks using Intel implementation require at least Intel Sapphire Rapids or higher CPU generation.\n\n"
                                 
@@ -64,7 +68,8 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
             frameworks = [ "Glow" ]
 
         elif implementation == "cpp":
-            devices = [ "CPU", "CUDA" ]
+            if not devices:
+                devices = [ "CPU", "CUDA" ]
             frameworks = [ "Onnxruntime" ]
 
         elif implementation == "ctuning-cpp":
@@ -75,22 +80,27 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
             else:
                  frameworks = []
 
-        if model.lower() == "bert-99.9":
-            categories = [ "Datacenter" ]
-        elif "dlrm" in model.lower() or "llama2" in model.lower() or "mixtral" in model.lower():
-            categories = [ "Datacenter" ]
-        else:
-            categories = [ "Edge", "Datacenter" ]
+        if not categories:
+            if model.lower() == "bert-99.9":
+                categories = [ "Datacenter" ]
+            elif "dlrm" in model.lower() or "llama2" in model.lower() or "mixtral" in model.lower():
+                categories = [ "Datacenter" ]
+            else:
+                categories = [ "Edge", "Datacenter" ]
 
         # model name
         content += f"{pre_space}{model.upper()}\n\n"
+
+        final_run_mode = "valid" if "short" not in extra_variation_tags else "test"
+
         for category in categories:
-            if category == "Edge" and not scenarios:
-                scenarios = [ "Offline", "SingleStream" ]
-                if model.lower() in [ "resnet50", "retinanet" ] and not "MultiStream" in scenarios:#MultiStream was duplicating
-                     scenarios.append("MultiStream")
-            elif category == "Datacenter":
-                 scenarios = [ "Offline", "Server" ] 
+            if not scenarios:
+                if category == "Edge" and not scenarios:
+                    scenarios = [ "Offline", "SingleStream" ]
+                    if model.lower() in [ "resnet50", "retinanet" ] and not "MultiStream" in scenarios:#MultiStream was duplicating
+                         scenarios.append("MultiStream")
+                elif category == "Datacenter":
+                     scenarios = [ "Offline", "Server" ] 
 
             content += f"{pre_space}=== \"{category.lower()}\"\n\n"
 
@@ -128,35 +138,49 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                         content += f"{cur_space2}=== \"{execution_env}\"\n"
                         content += f"{cur_space3}###### {execution_env} Environment\n\n"
                         # ref to cm installation
-                        content += f"{cur_space3}Please refer to the [installation page](../../install/index.md) to install CM for running the automated benchmark commands.\n\n"
-                        test_query_count=get_test_query_count(model, implementation, device)
+                        content += f"{cur_space3}Please refer to the [installation page](site:inference/install/) to install CM for running the automated benchmark commands.\n\n"
+                        test_query_count=get_test_query_count(model, implementation, device.lower())
 
                         if "99.9" not in model: #not showing docker command as it is already done for the 99% variant
                             if implementation == "neuralmagic":
                                 content += f"{cur_space3}####### Run the Inference Server\n"
                                 content += get_inference_server_run_cmd(spaces+16,implementation)
-                                # tips regarding the running of nural magic server
-                                content += f"\n{cur_space3}!!! tip\n\n"
-                                content += f"{cur_space3}    - Host and Port number of the server can be configured through `--host` and `--port`. Otherwise, server will run on default host `localhost` and port `8000`.\n\n"
+                                if run_tips:
+                                    # tips regarding the running of nural magic server
+                                    content += f"\n{cur_space3}!!! tip\n\n"
+                                    content += f"{cur_space3}    - Host and Port number of the server can be configured through `--host` and `--port` options. Otherwise, server will run on the default host `localhost` and port `8000`.\n\n"
                                 
+                            setup_run_cmd = mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, skip_test_query_count, scenarios, code_version, extra_variation_tags, extra_input_string, extra_docker_input_string)
+
                             if execution_env == "Native": # Native implementation steps through virtual environment
                                 content += f"{cur_space3}####### Setup a virtual environment for Python\n"
                                 content += get_venv_command(spaces+16)
                                 content += f"{cur_space3}####### Performance Estimation for Offline Scenario\n"
-                                content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, scenarios, code_version).replace("--docker ","")
+
+                                content += setup_run_cmd.replace("--docker ", "")
+
                                 content += f"{cur_space3}The above command should do a test run of Offline scenario and record the estimated offline_target_qps.\n\n"
 
                             else: # Docker implementation steps
                                 content += f"{cur_space3}####### Docker Container Build and Performance Estimation for Offline Scenario\n"
-                                docker_info = get_docker_info(spaces+16, model, implementation, device)
+                                docker_info = get_docker_info(spaces+16, model, implementation, device, setup_tips)
                                 content += docker_info
-                                content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, scenarios, code_version)
-                                content += f"{cur_space3}The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for each scenario.\n\n"
+
+                                content += setup_run_cmd
+
+                                if len(scenarios)  == 1:
+                                    scenario_text = f"""the {scenarios[0]} scenario"""
+                                else:
+                                    scenario_text = "each scenario"""
+                                content += f"{cur_space3}The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for {scenario_text}.\n\n"
                                 content += f"{cur_space3}<details>\n"
                                 content += f"{cur_space3}<summary> Please click here to see more options for the docker launch </summary>\n\n"
-                                content += f"{cur_space3}* `--docker_cm_repo=<Custom CM repo URL>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
+                                content += f"{cur_space3}* `--docker_cm_repo=<Custom CM GitHub repo URL in username@repo format>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
                                 content += f"{cur_space3}* `--docker_cache=no`: to not use docker cache during the image build\n"
 
+                                if implementation.lower() == "nvidia":
+                                    content += f"{cur_space3}* `--gpu_name=<Name of the GPU>` : The GPUs with supported configs in CM are `orin`, `rtx_4090`, `rtx_a6000`, `rtx_6000_ada`, `l4`, `t4`and `a100`. For other GPUs, default configuration as per the GPU memory will be used.\n"
+
                                 if device.lower() not in [ "cuda" ]:
                                     content += f"{cur_space3}* `--docker_os=ubuntu`: ubuntu and rhel are supported. \n"
                                     content += f"{cur_space3}* `--docker_os_version=20.04`: [20.04, 22.04] are supported for Ubuntu and [8, 9] for RHEL\n"
@@ -165,7 +189,8 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                         else:
                             content += f"{cur_space3} You can reuse the same environment as described for {model.split('.')[0]}.\n"
                             content += f"{cur_space3}###### Performance Estimation for Offline Scenario\n"
-                            content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, scenarios, code_version).replace("--docker ","")
+
+                            content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, skip_test_query_count, scenarios, code_version).replace("--docker ","")
                             content += f"{cur_space3}The above command should do a test run of Offline scenario and record the estimated offline_target_qps.\n\n"
 
 
@@ -174,45 +199,48 @@ def mlperf_inference_implementation_readme(spaces, model, implementation):
                         run_suffix += f"{cur_space3}<summary> Please click here to see more options for the RUN command</summary>\n\n"
                         run_suffix += f"{cur_space3}* Use `--division=closed` to do a closed division submission which includes compliance runs\n\n"
                         run_suffix += f"{cur_space3}* Use `--rerun` to do a rerun even when a valid run exists\n"  
+                        if implementation.lower() == "nvidia":
+                            run_suffix += f"{cur_space3}* `--gpu_name=<Name of the GPU>` : The GPUs with supported configs in CM are `orin`, `rtx_4090`, `rtx_a6000`, `rtx_6000_ada`, `l4`, `t4`and `a100`. For other GPUs, default configuration as per the GPU memory will be used.\n"
                         run_suffix += f"{cur_space3}</details>\n\n"
 
-                        if "bert" in model.lower() and framework == "deepsparse":
+                        if "bert" in model.lower() and framework.lower() == "deepsparse":
                             run_suffix += f"{cur_space3}<details>\n"
-                            run_suffix += f"{cur_space3}<summary> Please click here for generic model stubs for bert deepsparse</summary>\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned95_quant-none-vnni\n\n" 
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50_quant-none-vnni\n\n" 
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned95_obs_quant-none\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50-none-vnni\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-base/pytorch/huggingface/squad/pruned90-none\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned90-none\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/bert-large/pytorch/huggingface/squad/pruned80_quant-none-vnni\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned95-none-vnni\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97-none\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/bert-large/pytorch/huggingface/squad/base-none\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none\n\n"
-                            run_suffix += f"{cur_space3}* zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base-none\n"
+                            run_suffix += f"{cur_space3}<summary> Please click here to view available generic model stubs for bert deepsparse</summary>\n\n"
+                            run_suffix += f"{cur_space3}* **obert-large-pruned95_quant-none-vnni:** zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned95_quant-none-vnni\n\n" 
+                            run_suffix += f"{cur_space3}* **mobilebert-none-14layer_pruned50_quant-none-vnni:** zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50_quant-none-vnni\n\n" 
+                            run_suffix += f"{cur_space3}* **mobilebert-none-base_quant-none:** zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none\n\n"
+                            run_suffix += f"{cur_space3}* **bert-base-pruned95_obs_quant-none:** zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned95_obs_quant-none\n\n"
+                            run_suffix += f"{cur_space3}* **mobilebert-none-14layer_pruned50-none-vnni:** zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50-none-vnni\n\n"
+                            run_suffix += f"{cur_space3}* **obert-base-pruned90-none:** zoo:nlp/question_answering/obert-base/pytorch/huggingface/squad/pruned90-none\n\n"
+                            run_suffix += f"{cur_space3}* **obert-large-pruned97_quant-none:** zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none\n\n"
+                            run_suffix += f"{cur_space3}* **bert-base-pruned90-none:** zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned90-none\n\n"
+                            run_suffix += f"{cur_space3}* **bert-large-pruned80_quant-none-vnni:** zoo:nlp/question_answering/bert-large/pytorch/huggingface/squad/pruned80_quant-none-vnni\n\n"
+                            run_suffix += f"{cur_space3}* **obert-large-pruned95-none-vnni:** zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned95-none-vnni\n\n"
+                            run_suffix += f"{cur_space3}* **obert-large-pruned97-none:** zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97-none\n\n"
+                            run_suffix += f"{cur_space3}* **bert-large-base-none:** zoo:nlp/question_answering/bert-large/pytorch/huggingface/squad/base-none\n\n"
+                            run_suffix += f"{cur_space3}* **obert-large-base-none:** zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none\n\n"
+                            run_suffix += f"{cur_space3}* **mobilebert-none-base-none:** zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base-none\n"
                             run_suffix += f"{cur_space3}</details>\n"
 
                         
 
                         for scenario in scenarios:
                             content += f"{cur_space3}=== \"{scenario}\"\n{cur_space4}###### {scenario}\n\n"
-                            run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), scenario, device.lower(), "valid", 0, False, scenarios, code_version)
+                            run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), scenario, device.lower(), final_run_mode, test_query_count, False, skip_test_query_count, scenarios, code_version, extra_variation_tags, extra_input_string)
                             content += run_cmd
                             #content += run_suffix
- 
-                        content += f"{cur_space3}=== \"All Scenarios\"\n{cur_space4}###### All Scenarios\n\n"
-                        run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), "valid", 0, False, scenarios, code_version)
-                        content += run_cmd
-                        content += run_suffix
+
+                        if len(scenarios) > 1: 
+                            content += f"{cur_space3}=== \"All Scenarios\"\n{cur_space4}###### All Scenarios\n\n"
+                            run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), final_run_mode, test_query_count, False, skip_test_query_count, scenarios, code_version, extra_variation_tags, extra_input_string)
+                            content += run_cmd
+                            content += run_suffix
 
                     
 
-        readme_prefix = get_readme_prefix(spaces, model, implementation)
+        readme_prefix = get_readme_prefix(spaces, model, implementation, extra_variation_tags)
 
-        readme_suffix = get_readme_suffix(spaces, model, implementation)
+        readme_suffix = get_readme_suffix(spaces, model, implementation, extra_variation_tags)
 
         return readme_prefix + content + readme_suffix
 
@@ -223,10 +251,10 @@ def get_test_query_count(model, implementation, device, num_devices=1):
         elif model in [ "retinanet", "bert-99", "bert-99.9" ]:
              p_range = 100
         else:
-             p_range = 50
+             p_range = 10
 
         if device == "cuda":
-            p_range *= 40
+            p_range *= 5
             p_range *= num_devices
 
         return p_range
@@ -280,15 +308,6 @@ def get_min_system_requirements(spaces, model, implementation, device):
         min_sys_req_content += f"{spaces}</details>\n"
         return min_sys_req_content
 
-    def get_readme_prefix(spaces, model, implementation):
-        readme_prefix = ""
-        pre_space="    "
-        #for i in range(1,spaces):
-        #     pre_space  = pre_space + " "
-        #pre_space += "  "
-
-        return readme_prefix
-    
     def get_inference_server_run_cmd(spaces, implementation):
         indent = " "*spaces + " "
         if implementation == "neuralmagic":
@@ -309,26 +328,50 @@ def get_venv_command(spaces):
 {pre_space}export CM_SCRIPT_EXTRA_CMD=\"--adr.python.name=mlperf\"
 {pre_space}```\n"""   
 
-    def get_docker_info(spaces, model, implementation, device):
+    def get_docker_info(spaces, model, implementation, device, setup_tips=True):
         info = ""
         pre_space=""
         for i in range(1,spaces):
              pre_space  = pre_space + " "
         pre_space += " "
         #pre_space = "                "
-        if implementation == "nvidia":
+        if setup_tips:
             info += f"\n{pre_space}!!! tip\n\n"
-            info+= f"{pre_space}    If ran with `--all_models=yes`, all the benchmark models of NVIDIA implementation could be run within the same container.\n\n"
+
+            if model == "sdxl":
+                info+= f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
+
+            info+= f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
+            if implementation.lower() == "nvidia":
+                info+= f"{pre_space}    - Default batch size is assigned based on [GPU memory](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1129) or the [specified GPU](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1370). Please click more option for *docker launch* or *run command* to see how to specify the GPU name.\n\n"
+                info+= f"{pre_space}    - When run with `--all_models=yes`, all the benchmark models of NVIDIA implementation can be executed within the same container.\n\n"
+                if "llama2" in model.lower():
+                    info+= f"{pre_space}    - The dataset for NVIDIA's implementation of Llama2 is not publicly available. The user must fill [this](https://docs.google.com/forms/d/e/1FAIpQLSc_8VIvRmXM3I8KQaYnKf7gy27Z63BBoI_I1u02f4lw6rBp3g/viewform?pli=1&fbzx=-8842630989397184967) form and be verified as a MLCommons member to access the dataset.\n\n"
+                    info+= f"{pre_space}    - `PATH_TO_PICKE_FILE` should be replaced with path to the downloaded pickle file.\n\n"
+        else:
+            if model == "sdxl":
+                info += f"\n{pre_space}!!! tip\n\n"
+                info+= f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
+
         return info
 
-    def get_readme_suffix(spaces, model, implementation):
+    def get_readme_prefix(spaces, model, implementation, extra_variation_tags):
+        readme_prefix = ""
+        pre_space="    "
+        #for i in range(1,spaces):
+        #     pre_space  = pre_space + " "
+        #pre_space += "  "
+
+        return readme_prefix
+    
+    def get_readme_suffix(spaces, model, implementation, extra_variation_tags):
         readme_suffix = ""
         pre_space=""
         for i in range(1,spaces):
              pre_space  = pre_space + " "
         pre_space += "  "
 
-        if implementation == "reference":
+        if implementation == "reference" and not extra_variation_tags:
             if not model.endswith("-99"):
                 model_base_name = model.replace("-99.9","").replace("-99","")
                 readme_suffix+= f"{pre_space}* If you want to download the official MLPerf model and dataset for {model} you can follow [this README](get-{model_base_name}-data.md).\n"
@@ -336,22 +379,28 @@ def get_readme_suffix(spaces, model, implementation):
                  readme_suffix += f"{pre_space}* Please see [mobilenets.md](mobilenets.md) for running mobilenet models for Image Classification."
         return readme_suffix
 
-    def get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scenarios = []):
+    def get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scenarios = [], run_tips=True, extra_input_string=""):
         extra_content = ""
         f_pre_space += ""
         if scenario == "Server" or (scenario == "All Scenarios" and "Server" in scenarios):
             extra_content += f"{f_pre_space}    * `<SERVER_TARGET_QPS>` must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.\n"
-
-        if "gptj" in model and device == "cuda" and implementation == "reference":
-            extra_content += f"{f_pre_space}    * `--precision=[float16|bfloat16]` can help run on GPUs with less RAM \n"
+        if implementation == "reference" and model in [ "sdxl", "gptj-99", "gptj-99.9" ] and device in ["cuda", "rocm"] and "precision" not in extra_input_string:
+            extra_content += f"{f_pre_space}    * `--precision=float16` can help run on GPUs with less RAM / gives better performance \n"
+        if implementation == "reference" and model in [ "sdxl", "gptj-99", "gptj-99.9" ] and device in ["cpu"] and "precision" not in extra_input_string:
+            extra_content += f"{f_pre_space}    * `--precision=bfloat16` can give better performance \n"
+        if "gptj" in model and implementation == "reference":
             extra_content += f"{f_pre_space}    * `--beam-size=1` Beam size of 4 is mandatory for a closed division submission but reducing the beam size can help in running the model on GPUs with lower device memory\n"
         if extra_content:
             extra_content = f"{f_pre_space}!!! tip\n\n" + extra_content
 
-        return extra_content
+        if run_tips:
+            return extra_content
+        else:
+            return ""
+       
 
     @env.macro
-    def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False, scenarios = [], code_version="r4.1-dev"):
+    def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False, skip_test_query_count=False, scenarios = [], code_version="r4.1-dev", extra_variation_tags="", extra_input_string="", extra_docker_input_string=""):
         pre_space = ""
         for i in range(1,spaces):
              pre_space  = pre_space + " "
@@ -368,18 +417,20 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
         if scenario == "Server" or (scenario == "All Scenarios" and "Server" in scenarios):
             scenario_option += f"\\\n{pre_space} --server_target_qps=<SERVER_TARGET_QPS>"
 
-        run_cmd_extra = get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scenarios)
+        run_cmd_extra = get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scenarios, True, extra_input_string)
 
         if docker:
             docker_cmd_suffix = f" \\\n{pre_space} --docker --quiet"
-            docker_cmd_suffix += f" \\\n{pre_space} --test_query_count={test_query_count}"
-            
+            if not skip_test_query_count:
+                docker_cmd_suffix += f" \\\n{pre_space} --test_query_count={test_query_count}"
+            if extra_docker_input_string != "" or extra_input_string != "":
+                docker_cmd_suffix += f" \\\n{pre_space} {extra_docker_input_string} {extra_input_string}"
             if "bert" in model.lower() and framework == "deepsparse":
                 docker_cmd_suffix += f"\\\n{pre_space} --env.CM_MLPERF_NEURALMAGIC_MODEL_ZOO_STUB=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none"
             if "llama2-70b" in model.lower():
                 if implementation == "nvidia":
                     docker_cmd_suffix += f" \\\n{pre_space} --tp_size=2"
-                    docker_cmd_suffix += f" \\\n{pre_space} --nvidia_llama2_dataset_file_path=<PATH_TO_PICKE_FILE>"
+                    docker_cmd_suffix += f" \\\n{pre_space} --nvidia_llama2_dataset_file_path=<PATH_TO_PICKLE_FILE>"
                 elif implementation == "neuralmagic":
                     docker_cmd_suffix += f" \\\n{pre_space} --api_server=http://localhost:8000"
                     docker_cmd_suffix += f" \\\n{pre_space} --vllm_model_name=nm-testing/Llama-2-70b-chat-hf-FP8"
@@ -388,9 +439,14 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
             if "dlrm-v2" in model.lower() and implementation == "nvidia":
                 docker_cmd_suffix += f" \\\n{pre_space} --criteo_day23_raw_data_path=<PATH_TO_CRITEO_DAY23_RAW_DATA>"
 
+            if "short" in extra_variation_tags:
+                full_ds_needed_tag = ""
+            else:
+                full_ds_needed_tag = ",_full"
+
             docker_setup_cmd = f"""\n
 {f_pre_space}```bash
-{f_pre_space}cm run script --tags=run-mlperf,inference,_find-performance,_full,_{code_version}{scenario_variation_tag} \\
+{f_pre_space}cm run script --tags=run-mlperf,inference,_find-performance,{full_ds_needed_tag}_{code_version}{scenario_variation_tag}{extra_variation_tags} \\
 {pre_space} --model={model} \\
 {pre_space} --implementation={implementation} \\
 {pre_space} --framework={framework} \\
@@ -402,9 +458,9 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
             return docker_setup_cmd + run_cmd_extra
 
         else:
-            cmd_suffix = f"\\\n{pre_space} --quiet"
+            cmd_suffix = f"\\\n{pre_space} --quiet {extra_input_string}"
 
-            if execution_mode == "test":
+            if execution_mode == "test" and not skip_test_query_count:
                 cmd_suffix += f" \\\n {pre_space} --test_query_count={test_query_count}"
 
             if "bert" in model.lower() and framework == "deepsparse":
@@ -423,7 +479,7 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
 
             run_cmd = f"""\n
 {f_pre_space}```bash
-{f_pre_space}cm run script --tags=run-mlperf,inference,_{code_version}{scenario_variation_tag} \\
+{f_pre_space}cm run script --tags=run-mlperf,inference,_{code_version}{scenario_variation_tag}{extra_variation_tags} \\
 {pre_space} --model={model} \\
 {pre_space} --implementation={implementation} \\
 {pre_space} --framework={framework} \\
diff --git a/mkdocs.yml b/mkdocs.yml
index 8e59acf63a..95dfb6e861 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -19,25 +19,31 @@ theme:
     - navigation.top
     - toc.follow
 nav:
-  - Install:
-    - install/index.md
-  - Benchmarks:
+  - Home:
     - index.md
     - Image Classification:
       - ResNet50: benchmarks/image_classification/resnet50.md
     - Text to Image:
-      - Stable Diffusion: benchmarks/text_to_image/sdxl.md
+      - Stable Diffusion:
+        - Run Commands: benchmarks/text_to_image/sdxl.md
+        - Reproducibility:
+          - SCC24: benchmarks/text_to_image/reproducibility/scc24.md
     - Object Detection:
       - RetinaNet: benchmarks/object_detection/retinanet.md
     - Medical Imaging:
       - 3d-unet: benchmarks/medical_imaging/3d-unet.md
     - Language Processing:
-      - Bert-Large: benchmarks/language/bert.md
+      - Bert-Large:
+        - Run Commands: benchmarks/language/bert.md
+        - Reproducibility:
+          - IndySCC24: benchmarks/language/reproducibility/indyscc24-bert.md
       - GPT-J: benchmarks/language/gpt-j.md
       - LLAMA2-70B: benchmarks/language/llama2-70b.md
       - MIXTRAL-8x7B: benchmarks/language/mixtral-8x7b.md
     - Recommendation:
       - DLRM-v2: benchmarks/recommendation/dlrm-v2.md
+  - Install CM:
+    - install/index.md
   - Submission:
     - Submission Generation: submission/index.md
   - Release Notes:
@@ -62,3 +68,7 @@ markdown_extensions:
 plugins:
   - search
   - macros
+  - site-urls
+  - redirects:
+      redirect_maps:
+        'benchmarks/index.md': 'index.md'
diff --git a/text_to_image/main.py b/text_to_image/main.py
index 3d81b3fb0a..07cf174721 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -111,7 +111,7 @@ def get_args():
     parser.add_argument(
         "--device",
         default="cuda",
-        choices=["cuda", "cpu"],
+        choices=["cuda", "cpu", "rocm"],
         help="device to run the benchmark",
     )
     parser.add_argument(

From 54f3f93a73cc8ca5e3319ad87fb325e510574f56 Mon Sep 17 00:00:00 2001
From: nv-alicheng <107440104+nv-alicheng@users.noreply.github.com>
Date: Tue, 1 Oct 2024 09:32:43 -0700
Subject: [PATCH 147/305] Add binding for server_num_issue_query_threads
 parameter (#1862)

---
 loadgen/bindings/python_api.cc | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/loadgen/bindings/python_api.cc b/loadgen/bindings/python_api.cc
index 83e14e0ecb..f3155987a7 100644
--- a/loadgen/bindings/python_api.cc
+++ b/loadgen/bindings/python_api.cc
@@ -306,6 +306,8 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
                      &TestSettings::server_find_peak_qps_boundary_step_size)
       .def_readwrite("server_max_async_queries",
                      &TestSettings::server_max_async_queries)
+      .def_readwrite("server_num_issue_query_threads",
+                     &TestSettings::server_num_issue_query_threads)
       .def_readwrite("offline_expected_qps",
                      &TestSettings::offline_expected_qps)
       .def_readwrite("min_duration_ms", &TestSettings::min_duration_ms)

From 6eff09986e337ccf03f675c9f244d8ee93644e16 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 1 Oct 2024 17:41:47 +0100
Subject: [PATCH 148/305] Extend the final report generation script to output a
 json file of results (#1825)

* Extend the generate_final_reports script to output a json file of results

* Fix an error in merging perf and power results

* Export weight_data_types in the submission checker csv

* Fix positional argument

* Quote model precision info in submission checker csv

* Quote model precision info in submission checker csv

* Fix submission generation for v4.0
---
 tools/submission/generate_final_report.py | 78 +++++++++++++++++++++--
 tools/submission/submission_checker.py    | 19 ++++--
 2 files changed, 87 insertions(+), 10 deletions(-)

diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 37b3d98988..160d274245 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -8,7 +8,7 @@
 import re
 import numpy as np
 import pandas as pd
-
+import json
 
 def get_args():
   """Parse commandline."""
@@ -73,6 +73,7 @@ def main():
 
   output = args.input[:-4]
   writer = pd.ExcelWriter(output + '.xlsx', engine='xlsxwriter')
+  outjsondata = []
 
   indices = {}
   indices['closed'] = [
@@ -153,15 +154,18 @@ def main():
       }
   }
 
-  def MakeWorksheet(df, index, filter_dict, sheet_name):
+  def MakeWorksheet(df, index, filter_dict, sheet_name, outjsondata=[]):
     for key, value in filter_dict.items():
       if type(key) == tuple:
         key = list(key)
       df = df[value(df[key])]
-    df = df.pivot_table(index=index, columns=columns, values=['Result'])
-    df = df.fillna('')
     if df.size == 0:
       return
+    json_df = df.to_json(orient='records')
+    outjsondata += json.loads(json_df)
+
+    df = df.pivot_table(index=index, columns=columns, values=['Result'])
+    df = df.fillna('')
     for i, order in enumerate(columns_order):
       df = df.reindex(columns=order, level=i)
     df.to_excel(writer, sheet_name=sheet_name)
@@ -222,7 +226,7 @@ def MakeUniqueID(x):
                       NotEqual('millijoules/Stream')),
               ('Scenario', 'Model'):
                   Apply(FilterScenario, suite)
-          }, suite + ' - ' + category)
+          }, suite + ' - ' + category, outjsondata)
 
       MakeWorksheet(
           df, indices[category], {
@@ -230,8 +234,70 @@ def MakeUniqueID(x):
               'Suite': Contain(suite),
               'has_power': Equal(True),
               ('Scenario', 'Model'): Apply(FilterScenario, suite)
-          }, suite + ' - ' + category + ' - power')
+          }, suite + ' - ' + category + ' - power', outjsondata)
+
+  def reformatlink(data, key):
+    details = data[key]
+    details = details[details.find("(")+2:details.find(",")-1]
+    return details
+
+  for i,result in enumerate(outjsondata):
+    result['Details'] = reformatlink(result, "Details")
+    result['Code'] = reformatlink(result, "Code")
+    result_id = result.pop('ID')
+    outjsondata[i] = {'ID': result_id, **result}
+
+  outjsondata.sort(key=lambda x:x["Units"])
+  outjsondata.sort(key=lambda x:x["Scenario"])
+  outjsondata.sort(key=lambda x:x["UsedModel"])
+  outjsondata.sort(key=lambda x:x["ID"])
+
+  #remove duplicate perf results
+  keystomatch = ['ID', 'UsedModel', 'Scenario', 'Units']
+  i = 0
+  n = len(outjsondata)
+  while i < n:
+    result = outjsondata[i]
+    while i < n - 1 and all(result[key] == outjsondata[i+1][key] for key in keystomatch):
+      del(outjsondata[i+1])
+      n -= 1
+    i += 1
+
+  #merge perf and power results
+  keystomatch.pop()
+
+  for i in range(len(outjsondata)):
+    result = outjsondata[i]
+    if not result:
+      continue
+    if i < len(outjsondata) - 1:
+      if all(result[key] == outjsondata[i+1][key] for key in keystomatch):
+        #print(result)
+        #print(outjsondata[i+1])
+        if "Watts" in result['Units'] or "joules" in result['Units']:
+          result['Performance_Result'] = outjsondata[i+1]['Result']
+          result['Performance_Units'] = outjsondata[i+1]['Units']
+          result['Power_Result'] = result['Result']
+          result['Power_Units'] = result['Units']
+        else:
+          result['Power_Result'] = outjsondata[i+1]['Result']
+          result['Power_Units'] = outjsondata[i+1]['Units']
+          result['Performance_Result'] = result['Result']
+          result['Performance_Units'] = result['Units']
+        outjsondata[i+1] = {}
+        del(result['Result'])
+        del(result['Units'])
+
+  for i,result in enumerate(outjsondata):
+    if result.get('Result'):
+      result['Performance_Result'] = result['Result']
+      result['Performance_Units'] = result['Units']
+      del(result['Result'])
+      del(result['Units'])
 
+  outjsondata = [ i for i in outjsondata if i != {}]
+  with open(f"{output}_results.json", "w") as f:
+    f.write(json.dumps(outjsondata, indent=2))
   score_format = writer.book.add_format({'num_format': '#,##0.00'})
   bg_format = writer.book.add_format({'bg_color': '#efefef'})
   for ws in writer.book.worksheets():
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 357ed64310..32c3fe7521 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1575,6 +1575,7 @@ def check_results_dir(
         "inferred",
         "has_power",
         "Units",
+        "weight_data_types"
     ]
     fmt = ",".join(["{}"] * len(head)) + "\n"
     csv.write(",".join(head) + "\n")
@@ -1600,6 +1601,7 @@ def log_result(
         config,
         inferred=0,
         power_metric=0,
+        weight_data_types="fp32"
     ):
         notes = system_json.get("hw_notes", "")
         if system_json.get("sw_notes"):
@@ -1649,9 +1651,13 @@ def log_result(
             "Offline": "Watts",
             "Server": "Watts",
         }
-        unit = special_unit_dict.get(model_name, unit_dict)[scenario_fixed]
+        if config.version == "v4.0":
+            unit = unit_dict[scenario_fixed]
+        else:
+            unit = special_unit_dict.get(model_name, unit_dict)[scenario_fixed]
         power_unit = power_unit_dict[scenario_fixed]
 
+
         if (power_metric <= 0) or (not get_boolean(system_json.get("system_power_only"))):
             csv.write(
                 fmt.format(
@@ -1682,6 +1688,7 @@ def log_result(
                     inferred,
                     power_metric > 0,
                     unit,
+                    '"' + weight_data_types + '"',
                 )
         )
 
@@ -1715,6 +1722,7 @@ def log_result(
                     inferred,
                     power_metric > 0,
                     power_unit,
+                    '"' + weight_data_types + '"',
                 )
             )
 
@@ -1732,7 +1740,7 @@ def log_result(
         ]
         if len(files_outside_division) > 0 and not skip_extra_files_in_root_check:
             log.error(
-                "Root contains files outside division folder %s",
+                "Root contains files outside division folder %s. You can use '--skip-extra-files-in-root-check' to skip this check temporarily",
                 division,
                 files_outside_division,
             )
@@ -2013,7 +2021,7 @@ def log_result(
                             errors += 1
                             continue
                         else:
-                            measurement_check, conf_equal_issue_check = check_measurement_dir(
+                            measurement_check, conf_equal_issue_check, weight_data_types = check_measurement_dir(
                                 config,
                                 measurement_dir,
                                 name,
@@ -2268,6 +2276,7 @@ def log_result(
                                     config,
                                     inferred=inferred,
                                     power_metric=power_metric,
+                                    weight_data_types=weight_data_types
                                 )
                             else:
                                 results[name] = None
@@ -2464,9 +2473,11 @@ def check_measurement_dir(
                 end = len(".json")
                 break
 
+    weight_data_types = None
     if system_file:
         with open(os.path.join(measurement_dir, system_file), "r") as f:
             j = json.load(f)
+            weight_data_types = j['weight_data_types']
             for k in SYSTEM_IMP_REQUIRED_FILES:
                 if k not in j:
                     is_valid = False
@@ -2545,7 +2556,7 @@ def check_measurement_dir(
         log.error("%s is missing %s*.json", fname, system_desc)
         is_valid = False
 
-    return is_valid, equal_issue_used
+    return is_valid, equal_issue_used, weight_data_types
 
 
 def check_compliance_perf_dir(test_dir):

From a0874c100c54cbc54fb743ac8bf9fb5fadc64135 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 1 Oct 2024 17:42:31 +0100
Subject: [PATCH 149/305] Update build_wheels.yml (#1758)

Co-authored-by: Miro <mirhodak@amd.com>
Co-authored-by: Nathan Wasson <nathanw@mlcommons.org>
---
 .github/workflows/build_wheels.yml | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 30a88f11b2..114783f193 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -13,6 +13,10 @@ jobs:
   build_wheels:
     name: Build wheels on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
+    environment: release
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
     strategy:
       fail-fast: false
       matrix:
@@ -29,10 +33,11 @@ jobs:
       - name: Build wheels
         run: python -m cibuildwheel loadgen/ --output-dir wheels
 
-      - uses: actions/upload-artifact@v3
+      - name: Publish
+        uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          path: ./wheels/*.whl 
-
-      - name: Publish package to PyPI
-        run: python -m twine upload wheels/* -u __token__ -p ${{ secrets.PYPI_API_TOKEN }}
-
+          verify-metadata: true
+          skip-existing: true
+          packages-dir: wheels
+          repository-url: https://upload.pypi.org/legacy/
+          verbose: true

From 3ef1249b7f50a250c02c568342e0aea6638fc5a7 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 1 Oct 2024 17:45:41 +0100
Subject: [PATCH 150/305] Fix docs (#1853)

* Support batch-size in llama2 run

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-Cloudflare download instructiosn to README.md

* Minor wording edit to README.md

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-GDrive download instructions to README.md

* Add new and old instructions to README.md

* Tweak language in README.md

* Language tweak in README.md

* Minor language tweak in README.md

* Fix typo in README.md

* Count error when logging errors: submission_checker.py

* Fixes #1648, restrict loadgen uncommitted error message to within the loadgen directory

* Update test-rnnt.yml (#1688)

Stopping the github action for rnnt

* Added docs init

Added github action for website publish

Update benchmark documentation

Update publish.yaml

Update publish.yaml

Update benchmark documentation

Improved the submission documentation

Fix taskname

Removed unused images

* Fix benchmark URLs

* Fix links

* Add _full variation to run commands

* Added script flow diagram

* Added docker setup command for CM, extra run options

* Added support for docker options in the docs

* Added --quiet to the CM run_cmds in docs

* Fix the test query count for cm commands

* Support ctuning-cpp implementation

* Added commands for mobilenet models

* Docs cleanup

* Docs cleanup

* Added separate files for dataset and models in the docs

* Remove redundant tab in the docs

* Fixes some WIP models in the docs

* Use the official docs page for CM installation

* Fix the deadlink in docs

* Fix indendation issue in docs

* Added dockerinfo for nvidia implementation

* Added run options for gptj

* Added execution environment tabs

* Cleanup of the docs

* Cleanup of the docs

* Reordered the sections of the docs page

* Removed an unnecessary heading in the docs

* Fixes the commands for datacenter

* Fix the build --sdist for loadgen

* Fixes #1761, llama2 and mixtral runtime error on CPU systems

* Added mixtral to the benchmark list, improved benchmark docs

* Update docs for MLPerf inference v4.1

* Update docs for MLPerf inference v4.1

* Fix typo

* Gave direct link to implementation readmes

* Added tables detailing implementations

* Update vision README.md, split the frameworks into separate rows

* Update README.md

* pointed links to specific frameworks

* pointed links to specific frameworks

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* api support llama2

* Added request module and reduced max token len

* Fix for llama2 api server

* Update SUT_API offline to work for OpenAI

* Update SUT_API.py

* Minor fixes

* Fix json import in SUT_API.py

* Fix llama2 token length

* Added model name verification with server

* clean temp files

* support num_workers in LLAMA2 SUTs

* Remove batching from Offline SUT_API.py

* Update SUT_API.py

* Minor fixes for llama2 API

* Fix for llama2 API

* removed table of contents

* enabled llama2-nvidia + vllm-NM : WIP

* enabled dlrm for intel

* lower cased implementation

* added raw data input

* corrected data download commands

* renamed filename

* changes for bert and vllm

* documentation to work on custom repo and branch

* benchmark index page update

* enabled sdxl for nvidia and intel

* updated vllm server run cmd

* benchmark page information addition

* fix indendation issue

* Added submission categories

* update submission page - generate submission with or w/o using CM for benchmarking

* Updated kits dataset documentation

* Updated model parameters

* updation of information

* updated non cm based benchmark

* added info about hf password

* added links to model and access tokens

* Updated reference results structuree tree

* submission docs cleanup

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* added generic stubs deepsparse

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info (FID and CLIP data added)

* typo fix for bert deepsparse framework

* added min system requirements for models

* fixed code version

* changes for displaying reference and intel implementation tip

* added reference to installation page

* updated neural magic documentation

* Added links to the install page, redirect benchmarks page

* added tips about batch size and dataset for nvidia llama2

* fix conditions logic

* modified tips and additional run cmds

* sentence corrections

* Minor fix for the documentation

* fixed bug in deepsparse generic model stubs + styling

* added more information to stubs

* Added SCC24 readme, support reproducibility in the docs

* Made clear the custom CM repo URL format

* Support conditional implementation, setup and run tips

* Support rocm for sdxl

* Fix _short tag support

* Fix install URL

* Expose bfloat16 and float16 options for sdxl

* Expose download model to host option for sdxl

* IndySCC24 documentation added

* Improve the SCC24 docs

* Improve the support of short variation

* Improved the indyscc24 documentation

* Updated scc run commands

* removed test_query_count option for scc

* Remove scc24 in the main docs

* Remove scc24 in the main docs

* Fix docs: indendation issue on the submission page

* generalised code for skipping test query count

* Fixes for SCC24 docs

* Fix scenario text in main.py

* Fix links for scc24

* Fix links for scc24

* Improve the general docs

* Fix links for scc24

* Use float16 in scc24 doc

* Improve scc24 docs

* Improve scc24 docs

* Use float16 in scc24 doc

* fixed command bug

* Fix typo in docs

* Fix typo in docs

* Remove unnecessary indendation in docs

* initial commit for tip - native run CUDA

* Updated tip

---------

Co-authored-by: Nathan Wasson <nathanw@mlcommons.org>
Co-authored-by: anandhu-eng <anandhukicks@gmail.com>
Co-authored-by: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>
---
 docs/install/index.md | 4 ++--
 main.py               | 6 +++++-
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/docs/install/index.md b/docs/install/index.md
index 195521c7e2..1750d86e43 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -12,8 +12,8 @@ CM needs `git`, `python3-pip` and `python3-venv` installed on your system. If an
 This step is not mandatory as CM can use separate virtual environment for MLPerf inference. But the latest `pip` install requires this or else will need the `--break-system-packages` flag while installing `cm4mlops`.
 
 ```bash
-   python3 -m venv cm
-   source cm/bin/activate
+python3 -m venv cm
+source cm/bin/activate
 ```
 
 ## Install CM and pulls any needed repositories
diff --git a/main.py b/main.py
index aa8dd769e3..6a607cc101 100755
--- a/main.py
+++ b/main.py
@@ -140,6 +140,9 @@ def mlperf_inference_implementation_readme(spaces, model, implementation, *, imp
                         # ref to cm installation
                         content += f"{cur_space3}Please refer to the [installation page](site:inference/install/) to install CM for running the automated benchmark commands.\n\n"
                         test_query_count=get_test_query_count(model, implementation, device.lower())
+                        if device.lower() == "cuda" and execution_env.lower() == "native":
+                            content += f"\n{cur_space3}!!! tip\n\n"
+                            content += f"{cur_space3}    - It is advisable to use the commands in the Docker tab for CUDA. Run the below native command only if you are already on a CUDA setup with cuDNN and TensorRT installed.\n\n"
 
                         if "99.9" not in model: #not showing docker command as it is already done for the 99% variant
                             if implementation == "neuralmagic":
@@ -442,7 +445,8 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
             if "short" in extra_variation_tags:
                 full_ds_needed_tag = ""
             else:
-                full_ds_needed_tag = ",_full"
+                full_ds_needed_tag = "_full,"
+
 
             docker_setup_cmd = f"""\n
 {f_pre_space}```bash

From 224cfbf5c0e82cae6d48620025b7e1258ae3666a Mon Sep 17 00:00:00 2001
From: v-shobhit <161510941+v-shobhit@users.noreply.github.com>
Date: Tue, 1 Oct 2024 09:48:19 -0700
Subject: [PATCH 151/305] Fix typo in reference datatype (#1851)

Change reference accuracy target datatype from fp32 to fp16

Co-authored-by: Miro <mirhodak@amd.com>
Co-authored-by: Arjun Suresh <arjunsuresh1987@gmail.com>
---
 language/llama2-70b/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 458a0e50f3..07d3553445 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -237,7 +237,7 @@ The ServerSUT was not tested for GPU runs.
 
 
 ## Accuracy Target
-Running the GPU implementation in FP32 precision resulted in the following FP32 accuracy targets (normalized to a 0-100
+Running the GPU implementation in FP16 precision resulted in the following FP16 accuracy targets (normalized to a 0-100
 scale from a 0.0-1.0 scale):
 - Rouge1: 44.4312
 - Rouge2: 22.0352

From 92bd8198d15411d7fb7d7c27f8904bc5a0bcfe7a Mon Sep 17 00:00:00 2001
From: Michal Szutenberg <37601244+szutenberg@users.noreply.github.com>
Date: Tue, 1 Oct 2024 18:49:50 +0200
Subject: [PATCH 152/305] Fix warning in the submission checker (#1808)

Co-authored-by: Miro <mirhodak@amd.com>
Co-authored-by: Arjun Suresh <arjunsuresh1987@gmail.com>
---
 tools/submission/submission_checker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 32c3fe7521..52c85be7e5 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1256,7 +1256,7 @@ def check_performance_dir(
         log.error(
             "%s Required minimum samples per query not met by user config, Expected=%s, Found=%s",
             fname,
-            OFFLINE_MIN_SPQ,
+            OFFLINE_MIN_SPQ_SINCE_V4[model],
             samples_per_query,
         )
         is_valid = False

From e0fdec1c7a75c98cfc194f13d62ac4388d419c8a Mon Sep 17 00:00:00 2001
From: Miro <mirhodak@amd.com>
Date: Tue, 1 Oct 2024 11:51:09 -0500
Subject: [PATCH 153/305] Fix link in GettingStarted.ipynb (#1512)

Co-authored-by: Mitchelle Rasquinha <80070689+mrasquinha-g@users.noreply.github.com>
Co-authored-by: Arjun Suresh <arjunsuresh1987@gmail.com>
---
 vision/classification_and_detection/GettingStarted.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vision/classification_and_detection/GettingStarted.ipynb b/vision/classification_and_detection/GettingStarted.ipynb
index 0b921b2f63..874cce93fb 100755
--- a/vision/classification_and_detection/GettingStarted.ipynb
+++ b/vision/classification_and_detection/GettingStarted.ipynb
@@ -119,7 +119,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Step 1 - download the model. You find the links to the models [here](https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection#supported-models)."
+    "#### Step 1 - download the model. You find the links to the models [here](https://github.com/mlcommons/inference/blob/master/vision/classification_and_detection/README.md)."
    ]
   },
   {

From 518b454fd8647bfbd23a074e875e87353f33393e Mon Sep 17 00:00:00 2001
From: makaveli <39617050+makaveli10@users.noreply.github.com>
Date: Tue, 1 Oct 2024 22:23:18 +0530
Subject: [PATCH 154/305] Tflite tpu (#1449)

* add tpu support tflite backend

* tflite conversion & edgetpu compilation

* add pycoral interpreter

* update preprocess name

* Update run_common.sh | fix wrong else case code

* Revert version.py change

---------

Co-authored-by: Arjun Suresh <arjunsuresh1987@gmail.com>
Co-authored-by: Miro <mirhodak@amd.com>
---
 .../python/backend_tflite.py                  | 15 ++++-
 .../python/dataset.py                         | 12 ++++
 .../python/main.py                            | 15 ++++-
 .../run_common.sh                             | 23 +++++---
 .../classification_and_detection/run_local.sh |  5 ++
 .../tools/install_edgetpu_compiler.sh         |  6 ++
 .../tools/resnet50_tflite_edgetpu.py          | 55 +++++++++++++++++++
 7 files changed, 119 insertions(+), 12 deletions(-)
 create mode 100644 vision/classification_and_detection/tools/install_edgetpu_compiler.sh
 create mode 100644 vision/classification_and_detection/tools/resnet50_tflite_edgetpu.py

diff --git a/vision/classification_and_detection/python/backend_tflite.py b/vision/classification_and_detection/python/backend_tflite.py
index 7c8c78c139..bb8ed7d16e 100755
--- a/vision/classification_and_detection/python/backend_tflite.py
+++ b/vision/classification_and_detection/python/backend_tflite.py
@@ -19,7 +19,7 @@
     _version = tf.__version__
     _git_version = tf.__git_version__
 
-
+import numpy as np
 import backend
 
 
@@ -39,8 +39,13 @@ def image_format(self):
         # tflite is always NHWC
         return "NHWC"
 
-    def load(self, model_path, inputs=None, outputs=None):
-        self.sess = tflite.Interpreter(model_path=model_path)
+    def load(self, model_path, inputs=None, outputs=None, use_tpu=False):
+        self.use_tpu = use_tpu
+        if use_tpu:
+            from pycoral.utils.edgetpu import make_interpreter
+            self.sess = make_interpreter(model_path)
+        else:
+            self.sess = tflite.Interpreter(model_path=model_path)
         self.sess.allocate_tensors()
         # keep input/output name to index mapping
         self.input2index = {i["name"]: i["index"] for i in self.sess.get_input_details()}
@@ -54,6 +59,10 @@ def predict(self, feed):
         self.lock.acquire()
         # set inputs
         for k, v in self.input2index.items():
+            if self.use_tpu and self.sess.get_input_details()[v]['dtype'] == np.uint8:
+                input_scale, input_zero_point = self.sess.get_input_details()[v]["quantization"]
+                feed[k] = feed[k] / input_scale + input_zero_point
+                feed[k] = feed[k].astype(np.uint8)
             self.sess.set_tensor(v, feed[k])
         self.sess.invoke()
         # get results
diff --git a/vision/classification_and_detection/python/dataset.py b/vision/classification_and_detection/python/dataset.py
index 02aa654c25..0d82a99a3c 100755
--- a/vision/classification_and_detection/python/dataset.py
+++ b/vision/classification_and_detection/python/dataset.py
@@ -202,6 +202,18 @@ def pre_process_mobilenet(img, dims=None, need_transpose=False):
     return img
 
 
+def pre_process_imagenet_tflite_tpu(img, dims=None, need_transpose=False):
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = resize_with_aspectratio(img, 224, 224, inter_pol=cv2.INTER_LINEAR)
+    img = center_crop(img, 224, 224)
+    img = np.asarray(img, dtype='float32')
+   
+    img = img[..., ::-1]
+    means = np.array([103.939, 116.779, 123.68], dtype=np.float32)
+    img -= means
+    return img
+    
+
 def pre_process_imagenet_pytorch(img, dims=None, need_transpose=False):
     from PIL import Image
     import torchvision.transforms.functional as F
diff --git a/vision/classification_and_detection/python/main.py b/vision/classification_and_detection/python/main.py
index 1c2cd9a5ab..d6f16467ac 100755
--- a/vision/classification_and_detection/python/main.py
+++ b/vision/classification_and_detection/python/main.py
@@ -41,6 +41,9 @@
     "imagenet_mobilenet":
         (imagenet.Imagenet, dataset.pre_process_mobilenet, dataset.PostProcessArgMax(offset=-1),
          {"image_size": [224, 224, 3]}),
+    "imagenet_tflite_tpu":
+        (imagenet.Imagenet, dataset.pre_process_imagenet_tflite_tpu, dataset.PostProcessArgMax(offset=0),
+         {"image_size": [224, 224, 3]}),
     "imagenet_pytorch":
         (imagenet.Imagenet, dataset.pre_process_imagenet_pytorch, dataset.PostProcessArgMax(offset=0),
          {"image_size": [224, 224, 3]}),
@@ -114,6 +117,12 @@
         "backend": "ncnn",
         "model-name": "resnet50",
     },
+    "resnet50-tflite": {
+        "dataset": "imagenet_tflite_tpu",
+        "outputs": "ArgMax:0",
+        "backend": "tflite",
+        "model-name": "resnet50",
+    },
 
     # mobilenet
     "mobilenet-tf": {
@@ -231,6 +240,7 @@ def get_args():
     parser.add_argument("--inputs", help="model inputs")
     parser.add_argument("--outputs", help="model outputs")
     parser.add_argument("--backend", help="runtime to use")
+    parser.add_argument("--device", help="device to use")
     parser.add_argument("--model-name", help="name of the mlperf model, ie. resnet50")
     parser.add_argument("--threads", default=os.cpu_count(), type=int, help="threads")
     parser.add_argument("--qps", type=int, help="target qps")
@@ -500,7 +510,10 @@ def main():
                         threads=args.threads,
                         **kwargs)
     # load model to backend
-    model = backend.load(args.model, inputs=args.inputs, outputs=args.outputs)
+    if args.device == "tpu":
+        model = backend.load(args.model, inputs=args.inputs, outputs=args.outputs, use_tpu=True)
+    else:
+        model = backend.load(args.model, inputs=args.inputs, outputs=args.outputs)
     final_results = {
         "runtime": model.name(),
         "version": model.version(),
diff --git a/vision/classification_and_detection/run_common.sh b/vision/classification_and_detection/run_common.sh
index 37552c71e0..0d872c85af 100755
--- a/vision/classification_and_detection/run_common.sh
+++ b/vision/classification_and_detection/run_common.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 
 if [ $# -lt 1 ]; then
-    echo "usage: $0 tf|onnxruntime|pytorch|tflite|tvm-onnx|tvm-pytorch|tvm-tflite [resnet50|mobilenet|ssd-mobilenet|ssd-resnet34|retinanet] [cpu|gpu]"
+    echo "usage: $0 tf|onnxruntime|pytorch|tflite|tvm-onnx|tvm-pytorch|tvm-tflite [resnet50|mobilenet|ssd-mobilenet|ssd-resnet34|retinanet] [cpu|gpu|tpu]"
     exit 1
 fi
 if [ "x$DATA_DIR" == "x" ]; then
@@ -19,7 +19,7 @@ device="cpu"
 for i in $* ; do
     case $i in
        tf|onnxruntime|tflite|pytorch|tvm-onnx|tvm-pytorch|tvm-tflite|ncnn) backend=$i; shift;;
-       cpu|gpu|rocm) device=$i; shift;;
+       cpu|gpu|tpu|rocm) device=$i; shift;;
        gpu) device=gpu; shift;;
        resnet50|mobilenet|ssd-mobilenet|ssd-resnet34|ssd-resnet34-tf|retinanet) model=$i; shift;;
     esac
@@ -108,14 +108,21 @@ fi
 #
 # tflite
 #
-if [ $name == "resnet50-tflite" ] ; then
-    model_path="$MODEL_DIR/resnet50_v1.tflite"
-    profile=resnet50-tf
-    extra_args="$extra_args --backend tflite"
+if [ "$name" = "resnet50-tflite" ]; then
+  if [ "$device" = "tpu" ]; then
+      model_path="$MODEL_DIR/resnet50_quant_full_mlperf_edgetpu.tflite"
+      profile="resnet50-tflite"
+      extra_args="$extra_args --backend tflite --device tpu"
+  else
+      model_path="$MODEL_DIR/resnet50_v1.tflite"
+      profile="resnet50-tf"
+      extra_args="$extra_args --backend tflite"
+  fi
 fi
-if [ $name == "mobilenet-tflite" ] ; then
+
+if [ "$name" = "mobilenet-tflite" ]; then
     model_path="$MODEL_DIR/mobilenet_v1_1.0_224.tflite"
-    profile=mobilenet-tf
+    profile="mobilenet-tf"
     extra_args="$extra_args --backend tflite"
 fi
 
diff --git a/vision/classification_and_detection/run_local.sh b/vision/classification_and_detection/run_local.sh
index 16f905ce41..120b538c76 100755
--- a/vision/classification_and_detection/run_local.sh
+++ b/vision/classification_and_detection/run_local.sh
@@ -21,5 +21,10 @@ done
 
 cmd="python3 python/main.py --profile $profile $common_opt --model \"$model_path\" $dataset \
     --output \"$OUTPUT_DIR\" $EXTRA_OPS ${ARGS}"
+
+if [[ $EXTRA_OPS == *"tpu"* ]]; then
+    cmd="sudo $cmd"
+fi
+
 echo $cmd
 eval $cmd
diff --git a/vision/classification_and_detection/tools/install_edgetpu_compiler.sh b/vision/classification_and_detection/tools/install_edgetpu_compiler.sh
new file mode 100644
index 0000000000..dea1c9e18e
--- /dev/null
+++ b/vision/classification_and_detection/tools/install_edgetpu_compiler.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
+echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list
+apt-get update
+apt install edgetpu-compiler
\ No newline at end of file
diff --git a/vision/classification_and_detection/tools/resnet50_tflite_edgetpu.py b/vision/classification_and_detection/tools/resnet50_tflite_edgetpu.py
new file mode 100644
index 0000000000..b99c989a23
--- /dev/null
+++ b/vision/classification_and_detection/tools/resnet50_tflite_edgetpu.py
@@ -0,0 +1,55 @@
+import argparse
+import os
+import cv2
+import numpy as np
+
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.resnet50 import ResNet50, decode_predictions, preprocess_input
+
+
+def main(path_to_mlperf_calib_dataset):
+    # load tf model
+    model = ResNet50(weights='imagenet')
+
+    # read jpeg files from mlperf calib dataset
+    jpeg_files = []
+    for filename in os.listdir(path_to_mlperf_calib_dataset):
+        if filename.lower().endswith('.jpg') or filename.lower().endswith('.jpeg'):
+            jpeg_files.append(os.path.join(path_to_mlperf_calib_dataset, filename))
+    
+    # qunatization
+    def representative_data_gen():
+        for img_path in jpeg_files:
+            img = image.load_img(img_path, target_size=(224, 224))
+            x = image.img_to_array(img)
+            x = np.expand_dims(x, axis=0)
+            x = preprocess_input(x)
+            yield [x]
+
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    converter.representative_dataset = representative_data_gen
+    # Ensure that if any ops can't be quantized, the converter throws an error
+    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+
+    # full quant
+    converter.inference_input_type = tf.uint8
+    converter.inference_output_type = tf.uint8
+
+    tflite_model_quant = converter.convert()
+    with open('resnet50_quant_full_mlperf.tflite', 'wb') as f:
+        f.write(tflite_model_quant)
+
+
+if __name__=="__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--image-dir', type=str, default=None)
+    args = parser.parse_args()
+    print(args)
+    if args.image_dir is None or not os.path.exists(args.image_dir):
+        raise ValueError("Please provide a calibration dataset.")
+    main(args.image_dir)
+
+    # compile model for edge tpu
+    os.system("edgetpu_compiler resnet50_quant_full_mlperf.tflite")
\ No newline at end of file

From 41fa8aadd1ba0ecc97f6a519d8b42b04278e5f24 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 1 Oct 2024 12:00:17 -0500
Subject: [PATCH 155/305] Add format files github action (#1682)

Co-authored-by: Arjun Suresh <arjunsuresh1987@gmail.com>
---
 .github/scripts/format-cpp.sh | 26 +++++++++++++++++
 .github/scripts/format-py.sh  | 26 +++++++++++++++++
 .github/workflows/format.yml  | 55 +++++++++++++++++++++++++++++++++++
 3 files changed, 107 insertions(+)
 create mode 100755 .github/scripts/format-cpp.sh
 create mode 100755 .github/scripts/format-py.sh
 create mode 100644 .github/workflows/format.yml

diff --git a/.github/scripts/format-cpp.sh b/.github/scripts/format-cpp.sh
new file mode 100755
index 0000000000..aa36c75647
--- /dev/null
+++ b/.github/scripts/format-cpp.sh
@@ -0,0 +1,26 @@
+#================================================================
+# HEADER
+#================================================================
+# DESCRIPTION
+#    This is a script containing some commands to automatically 
+#    format the c/c++ code contained in one folder. 
+#    This will help to maintain good quality code in the github 
+#    repository. 
+# SET UP
+#    You need to allow the correct permissions for this file.
+#    This can be done by running:
+#    chmod u+x <path to file>
+# REQUIREMENTS
+#    clang-format
+#================================================================
+# END_OF_HEADER
+#================================================================
+
+
+# Checks all the modified c/c++ files, format them and adds them
+# to the commit.
+for FILE in $(git diff upstream/$1 --name-only | grep -E '.*\.(cc|cpp|h|hpp)$')
+do
+    clang-format -i -style=file $FILE
+    git add $FILE
+done
\ No newline at end of file
diff --git a/.github/scripts/format-py.sh b/.github/scripts/format-py.sh
new file mode 100755
index 0000000000..0a85078b70
--- /dev/null
+++ b/.github/scripts/format-py.sh
@@ -0,0 +1,26 @@
+#================================================================
+# HEADER
+#================================================================
+# DESCRIPTION
+#    This is a script containing some commands to automatically 
+#    format the c/c++ code contained in one folder. 
+#    This will help to maintain good quality code in the github 
+#    repository. 
+# SET UP
+#    You need to allow the correct permissions for this file.
+#    This can be done by running:
+#    chmod u+x <path to file>
+# REQUIREMENTS
+#    clang-format
+#================================================================
+# END_OF_HEADER
+#================================================================
+
+
+# Checks all the modified c/c++ files, format them and adds them
+# to the commit.
+for FILE in $(git diff upstream/$1 --name-only | grep -E '.*\.py$')
+do
+    autopep8 --in-place -a $FILE
+    git add $FILE
+done
\ No newline at end of file
diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
new file mode 100644
index 0000000000..4c88733312
--- /dev/null
+++ b/.github/workflows/format.yml
@@ -0,0 +1,55 @@
+
+name: "format"
+on:
+  pull_request:
+    branches: [ master ]
+    types: [opened, closed, synchronize]
+
+
+env:
+  python_version: "3.9"
+  repo: "mlcommons/inference"
+
+jobs:
+  format-code:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          ref: ${{ github.head_ref || github.ref_name }}
+          repository: ${{ github.event.pull_request.head.repo.full_name }}
+      - name: Set up Python ${{ env.python_version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ env.python_version }}
+
+      - name: Install dependencies
+        run: |
+          python3 -m pip install autopep8
+
+      - name: Grant permissions
+        run: |
+          chmod 777 "${GITHUB_WORKSPACE}/.github/scripts/format-cpp.sh"
+          chmod 777 "${GITHUB_WORKSPACE}/.github/scripts/format-py.sh"
+      
+      - name: Format Codebase
+        if: ${{ github.event.pull_request.base.repo.full_name == env.repo }}
+        run: |
+          git remote add upstream ${{ github.event.pull_request.base.repo.clone_url }}
+          git fetch upstream ${{ github.event.pull_request.base.ref }}
+          ".github/scripts/format-cpp.sh" "${{ github.event.pull_request.base.ref }}"
+          ".github/scripts/format-py.sh" "${{ github.event.pull_request.base.ref }}"
+
+      - name: Commit
+        run: |
+          cd ${GITHUB_WORKSPACE}
+          HAS_CHANGES=$(git diff --cached --name-only)
+          if [ ${#HAS_CHANGES} -gt 0 ]; then
+            git log
+            git config --global user.email "${GITHUB_ACTOR_ID}+${GITHUB_ACTOR}@users.noreply.github.com"
+            git config --global user.name "${GITHUB_ACTOR}"
+            git commit -m '[Automated Commit] Format Codebase'
+            git push origin ${{ github.head_ref || github.ref_name }}
+          fi
+
+      

From 976bb1ad9c7946be79507f3ff67955c27426af52 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 8 Oct 2024 11:53:20 -0500
Subject: [PATCH 156/305] Set correct remote repo (#1871)

---
 .github/workflows/format.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index 4c88733312..9e4675d2c8 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -49,7 +49,7 @@ jobs:
             git config --global user.email "${GITHUB_ACTOR_ID}+${GITHUB_ACTOR}@users.noreply.github.com"
             git config --global user.name "${GITHUB_ACTOR}"
             git commit -m '[Automated Commit] Format Codebase'
-            git push origin ${{ github.head_ref || github.ref_name }}
+            git push upstream ${{ github.head_ref || github.ref_name }}
           fi
 
       

From f5c8f1758374aeaba26b2e84d31690111cfdf054 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 8 Oct 2024 12:30:16 -0500
Subject: [PATCH 157/305] Fix bug: Loadgen ignoring token latency targets in
 user conf (#1874)

---
 language/mixtral-8x7b/user.conf   |  2 +-
 loadgen/test_settings_internal.cc | 14 +++++++-------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/language/mixtral-8x7b/user.conf b/language/mixtral-8x7b/user.conf
index c5f3ca4888..36b67b6eb5 100644
--- a/language/mixtral-8x7b/user.conf
+++ b/language/mixtral-8x7b/user.conf
@@ -2,4 +2,4 @@
 # The key has the format 'model.scenario.key'. Value is mostly int64_t.
 # Model maybe '*' as wildcard. In that case the value applies to all models.
 # All times are in milli seconds
-#
\ No newline at end of file
+#
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 08d1d03dee..ce1b9c4bec 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -698,18 +698,18 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
   // keys to measure token metrics
   if (lookupkv(model, scenario, "use_token_latencies", &val, nullptr)){
     use_token_latencies = (val == 1) ? true : false;
-    if (use_token_latencies){
-      lookupkv(model, "Server", "ttft_latency", &server_ttft_latency, nullptr, 1000 * 1000);
-      lookupkv(model, "Server", "tpot_latency", &server_tpot_latency, nullptr, 1000 * 1000);
-    }
+  }
+  if (use_token_latencies){
+    lookupkv(model, "Server", "ttft_latency", &server_ttft_latency, nullptr, 1000 * 1000);
+    lookupkv(model, "Server", "tpot_latency", &server_tpot_latency, nullptr, 1000 * 1000);
   }
 
   // keys to infer token metrics
   if (lookupkv(model, scenario, "infer_token_latencies", &val, nullptr)){
     infer_token_latencies = (val == 1) ? true : false;
-    if (infer_token_latencies){
-      lookupkv(model, scenario, "token_latency_scaling_factor", &token_latency_scaling_factor, nullptr, 1);
-    }
+  }
+  if (infer_token_latencies){
+    lookupkv(model, scenario, "token_latency_scaling_factor", &token_latency_scaling_factor, nullptr, 1);
   }
   // keys that apply to SingleStream
   lookupkv(model, "SingleStream", "target_latency_percentile", nullptr,

From ecb880167756cb4b36ad70766b8d3254bfb06d26 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 22 Oct 2024 17:58:13 +0100
Subject: [PATCH 158/305] [Postmortem 4.1] Make mlperf.conf static in loadgen,
 enable automatic Pypi release (#1882)

* Extend the generate_final_reports script to output a json file of results

* Fix an error in merging perf and power results

* Export weight_data_types in the submission checker csv

* Fix positional argument

* Quote model precision info in submission checker csv

* Quote model precision info in submission checker csv

* Fix submission generation for v4.0

* Autoload mlperf.conf in loadgen

Added VERSION file

* Fix loadgen version in R50 test

* Fix format for setup.py

* Added soft link to mlperf.conf in root

* Fix format with clang

* Fixed softlink for mlperf.conf

* Fixed Python API binding for default argument in FromConfig

* Fixed Python API binding for default argument in FromConfig

* Fix a bug in FromConfig

* Fix deprecation of pkg_resources

* Fix deprecation of pkg_resources

* Fix deprecation of pkg_resources

* Fix package info

* Fix package info

* Added __init__.py

* Fix package info

* Restrict the use of only one conf file in loadgen

* Remove mlperf.conf arg from the reference implementations

* Use custom loadgen version for the github actions (retinanet+bert)

* Added version patch

* Remove mlperf.conf for vision benchmarks

* Fix compilation warnings

* formatted by clang

* Modify the build_wheels action to use VERSION

* Fix build_wheels gh action

* Do version increment in github action only for linux

* VERSION -> VERSION.txt

* Fix the build_wheel github action on windows

* VERSION -> VERSION.txt

* Support python 3.13 loadgen build

* VERSION -> VERSION.txt

* VERSION update

* Build wheels on macos and windows but upload from only ubuntu

* Build wheels on macos and windows but upload from only ubuntu

* Build wheels on macos and windows but upload from only ubuntu

* Fixes for github action

* Fixes for github action

* Fixes for github action

* Fixes for github action

* Make version number consistent for all OS

* Make version number consistent for all OS

* Do not release pypi wheels from loadgen-dev branch

* Split the keys specific to mlperf.conf

* Split the keys specific to mlperf.conf

* Split the keys specific to mlperf.conf
---
 .github/workflows/build_wheels.yml            |  95 +++++-
 .github/workflows/test-bert.yml               |   2 +-
 .github/workflows/test-loadgen.yml            |   4 +-
 .github/workflows/test-resnet50.yml           |   3 +-
 .github/workflows/test-retinanet.yml          |   2 +-
 language/bert/run.py                          |  52 ++-
 language/gpt-j/main.py                        |  72 ++--
 language/llama2-70b/main.py                   | 124 +++++--
 language/mixtral-8x7b/main.py                 |   9 +-
 loadgen/CMakeLists.txt                        |  20 +-
 loadgen/MANIFEST.in                           |   2 +
 loadgen/VERSION.txt                           |   1 +
 loadgen/__init__.py                           |   0
 loadgen/benchmark/repro.cpp                   |  16 +-
 loadgen/bindings/python_api.cc                |  90 ++---
 loadgen/mlperf.conf                           |  98 ++++++
 loadgen/pyproject.toml                        |   2 +-
 loadgen/results.h                             |  33 +-
 loadgen/setup.py                              |  42 ++-
 loadgen/test_settings.h                       |  11 +-
 loadgen/test_settings_internal.cc             | 166 +++++----
 mlperf.conf                                   |  99 +-----
 recommendation/dlrm_v2/pytorch/python/main.py | 323 ++++++++++++++----
 text_to_image/main.py                         |  51 +--
 .../python/main.py                            | 191 ++++++++---
 .../classification_and_detection/run_local.sh |   2 +-
 26 files changed, 1040 insertions(+), 470 deletions(-)
 create mode 100644 loadgen/MANIFEST.in
 create mode 100644 loadgen/VERSION.txt
 create mode 100644 loadgen/__init__.py
 create mode 100644 loadgen/mlperf.conf
 mode change 100644 => 120000 mlperf.conf

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 114783f193..d7f98764ea 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -6,21 +6,69 @@ on:
   push:
     branches:
       - master
+      - loadgen-release
     paths:
-      - loadgen/setup.py
+      - loadgen/**
 
 jobs:
+  update_version:
+    name: Update version only on ubuntu but used by windows and macos
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      # Step 3: Check if VERSION.txt file has changed in this push
+      - name: Check if VERSION.txt file has changed
+        id: version_changed
+        run: |
+          if git diff --name-only HEAD~1 | grep -q "VERSION.txt"; then
+            echo "VERSION.txt file has been modified"
+            echo "::set-output name=version_changed::true"
+            new_version=$(cat VERSION.txt)
+          else
+            echo "VERSION file has NOT been modified"
+            echo "::set-output name=version_changed::false"
+          fi
+          echo "::set-output name=new_version::$new_version"
+
+      # Step 4: Increment version if VERSION was not changed
+      - name: Increment version if necessary
+        id: do_version_increment
+        if: steps.version_changed.outputs.version_changed == 'false'
+        run: |
+          cd loadgen
+          # Check if VERSION file exists, else initialize it
+          if [ ! -f VERSION.txt ]; then
+            echo "0.0.0" > VERSION.txt
+          fi
+
+          version=$(cat VERSION.txt)
+          IFS='.' read -r major minor patch <<< "$version"
+          patch=$((patch + 1))
+          new_version="$major.$minor.$patch"
+          echo $new_version > VERSION.txt
+          echo "New version: $new_version"
+          echo "::set-output name=new_version::$new_version"
+
+      # Step 5: Commit the updated version to the repository
+      - name: Commit updated version
+        if: steps.version_changed.outputs.version_changed == 'false'
+        run: |
+          cd loadgen
+          git config --global user.name "${{ github.actor }}"
+          git config --global user.email "${{ github.actor }}@users.noreply.github.com"
+          git add VERSION.txt
+          git commit -m "Increment version to ${{ steps.do_version_increment.outputs.new_version }}"
+          git push
+  
   build_wheels:
     name: Build wheels on ${{ matrix.os }}
+    needs: update_version
     runs-on: ${{ matrix.os }}
-    environment: release
-    permissions:
-      # IMPORTANT: this permission is mandatory for trusted publishing
-      id-token: write
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, windows-latest, macOS-latest]
+        os: [ubuntu-latest, windows-latest, macos-latest]
 
     steps:
       - uses: actions/checkout@v3
@@ -33,6 +81,41 @@ jobs:
       - name: Build wheels
         run: python -m cibuildwheel loadgen/ --output-dir wheels
 
+       # Save wheels as artifacts
+      - name: Upload built wheels
+        uses: actions/upload-artifact@v3
+        with:
+          name: wheels-${{ matrix.os }}
+          path: wheels
+
+  publish_wheels:
+    needs: build_wheels  # Wait for the build_wheels job to complete
+    runs-on: ubuntu-latest  # Only run this job on Linux
+    environment: release
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
+    steps:
+      - uses: actions/checkout@v3
+
+      # Download the built wheels from ubuntu
+      - name: Download Ubuntu wheels
+        uses: actions/download-artifact@v3
+        with:
+          name: wheels-ubuntu-latest
+          path: wheels
+      # Download the built wheels from macOS
+      - name: Download macOS wheels
+        uses: actions/download-artifact@v3
+        with:
+          name: wheels-macos-latest
+          path: wheels
+      # Download the built wheels from Windows
+      - name: Download Windows wheels
+        uses: actions/download-artifact@v3
+        with:
+          name: wheels-windows-latest
+          path: wheels
       - name: Publish
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
diff --git a/.github/workflows/test-bert.yml b/.github/workflows/test-bert.yml
index 6f6d77a397..52ec1dd4d9 100755
--- a/.github/workflows/test-bert.yml
+++ b/.github/workflows/test-bert.yml
@@ -33,4 +33,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test BERT and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }}
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }} --adr.loadgen.version=custom
diff --git a/.github/workflows/test-loadgen.yml b/.github/workflows/test-loadgen.yml
index b010f82584..09436cc020 100755
--- a/.github/workflows/test-loadgen.yml
+++ b/.github/workflows/test-loadgen.yml
@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v3
@@ -31,4 +31,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Loadgen
       run: |
-        cm run script --tags=get,mlperf,inference,loadgen --quiet --version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }}
+        cm run script --tags=get,mlperf,inference,loadgen --quiet --version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }} --adr.loadgen.tags=_no-compilation-warnings
diff --git a/.github/workflows/test-resnet50.yml b/.github/workflows/test-resnet50.yml
index b5d09a66ad..f1e3e6c394 100755
--- a/.github/workflows/test-resnet50.yml
+++ b/.github/workflows/test-resnet50.yml
@@ -8,6 +8,7 @@ on:
     branches: [ "master", "dev" ]
     paths:
       - vision/classification_and_detection/**
+      - loadgen/**
       - tools/submission/**
       - .github/workflows/test-resnet50.yml
       - '!**.md'
@@ -33,4 +34,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Resnet50 and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --adr.loadgen.version=custom
diff --git a/.github/workflows/test-retinanet.yml b/.github/workflows/test-retinanet.yml
index 20b05cc25f..a2f5bf8ec7 100755
--- a/.github/workflows/test-retinanet.yml
+++ b/.github/workflows/test-retinanet.yml
@@ -33,4 +33,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Retinanet and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }}
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }} --adr.loadgen.version=custom
diff --git a/language/bert/run.py b/language/bert/run.py
index 2afbe56f67..f0b376564b 100644
--- a/language/bert/run.py
+++ b/language/bert/run.py
@@ -15,6 +15,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from absl import flags
+from absl import app
 import subprocess
 import mlperf_loadgen as lg
 import argparse
@@ -22,13 +24,12 @@
 import sys
 sys.path.insert(0, os.getcwd())
 sys.path.insert(0, os.path.join(os.getcwd(), "..", "..", "lon"))
-from absl import app
-from absl import flags
+
 
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-            "--backend", choices=["tf", "pytorch", "onnxruntime", "tf_estimator", "ray"], default="tf", help="Backend")
+        "--backend", choices=["tf", "pytorch", "onnxruntime", "tf_estimator", "ray"], default="tf", help="Backend")
     parser.add_argument("--scenario", choices=["SingleStream", "Offline",
                                                "Server", "MultiStream"], default="Offline", help="Scenario")
     parser.add_argument("--accuracy", action="store_true",
@@ -37,30 +38,36 @@ def get_args():
                         help="use quantized model (only valid for onnxruntime backend)")
     parser.add_argument("--profile", action="store_true",
                         help="enable profiling (only valid for onnxruntime backend)")
-    parser.add_argument(
-            "--mlperf_conf", default="build/mlperf.conf", help="mlperf rules config")
     parser.add_argument("--user_conf", default="user.conf",
                         help="user config for user LoadGen settings such as target QPS")
     parser.add_argument("--audit_conf", default="audit.conf",
                         help="audit config for LoadGen settings during compliance runs")
     parser.add_argument("--max_examples", type=int,
                         help="Maximum number of examples to consider (not limited by default)")
-    parser.add_argument("--network", choices=["sut","lon",None], default=None, help="Loadgen network mode")
+    parser.add_argument(
+        "--network",
+        choices=[
+            "sut",
+            "lon",
+            None],
+        default=None,
+        help="Loadgen network mode")
     parser.add_argument('--node', type=str, default="")
     parser.add_argument('--port', type=int, default=8000)
-    parser.add_argument('--sut_server', nargs="*", default= ['http://localhost:8000'],
-                    help='Address of the server(s) under test.')
+    parser.add_argument('--sut_server', nargs="*", default=['http://localhost:8000'],
+                        help='Address of the server(s) under test.')
 
     args = parser.parse_args()
     return args
 
 
 scenario_map = {
-        "SingleStream": lg.TestScenario.SingleStream,
-        "Offline": lg.TestScenario.Offline,
-        "Server": lg.TestScenario.Server,
-        "MultiStream": lg.TestScenario.MultiStream
-        }
+    "SingleStream": lg.TestScenario.SingleStream,
+    "Offline": lg.TestScenario.Offline,
+    "Server": lg.TestScenario.Server,
+    "MultiStream": lg.TestScenario.MultiStream
+}
+
 
 def main():
     args = get_args()
@@ -96,7 +103,8 @@ def main():
 
     settings = lg.TestSettings()
     settings.scenario = scenario_map[args.scenario]
-    settings.FromConfig(args.mlperf_conf, "bert", args.scenario)
+    # mlperf.conf is automatically loaded by the loadgen
+    # settings.FromConfig(args.mlperf_conf, "bert", args.scenario)
     settings.FromConfig(args.user_conf, "bert", args.scenario)
 
     if args.accuracy:
@@ -117,7 +125,14 @@ def main():
 
     if args.network == "lon":
         from network_LON import app, set_args, main as app_main
-        set_args(args, settings, log_settings, args.audit_conf, args.sut_server, args.backend, args.max_examples)
+        set_args(
+            args,
+            settings,
+            log_settings,
+            args.audit_conf,
+            args.sut_server,
+            args.backend,
+            args.max_examples)
         app.run(app_main)
 
     elif args.network == "sut":
@@ -128,7 +143,12 @@ def main():
 
     else:
         print("Running LoadGen test...")
-        lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings, args.audit_conf)
+        lg.StartTestWithLogSettings(
+            sut.sut,
+            sut.qsl.qsl,
+            settings,
+            log_settings,
+            args.audit_conf)
         if args.accuracy and not os.environ.get("SKIP_VERIFY_ACCURACY"):
             cmd = "python3 {:}/accuracy-squad.py {}".format(
                 os.path.dirname(os.path.abspath(__file__)),
diff --git a/language/gpt-j/main.py b/language/gpt-j/main.py
index bf372e8125..d7c244d610 100644
--- a/language/gpt-j/main.py
+++ b/language/gpt-j/main.py
@@ -9,6 +9,8 @@
 from GPTJ_QSL import get_GPTJ_QSL
 
 # Function to parse the arguments passed during python file execution
+
+
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -20,7 +22,10 @@ def get_args():
         "--dataset-path", default="./data/cnn_eval.json", help="")
     parser.add_argument("--accuracy", action="store_true",
                         help="enable accuracy pass")
-    parser.add_argument("--dtype", default="float32", help="data type of the model, choose from float16, bfloat16 and float32")
+    parser.add_argument(
+        "--dtype",
+        default="float32",
+        help="data type of the model, choose from float16, bfloat16 and float32")
     parser.add_argument("--quantized", action="store_true",
                         help="use quantized model (only valid for onnxruntime backend)")
     parser.add_argument("--profile", action="store_true",
@@ -29,26 +34,34 @@ def get_args():
                         help="use GPU instead of CPU for the inference")
     parser.add_argument("--audit_conf", default="audit.conf",
                         help="audit config for LoadGen settings during compliance runs")
-    parser.add_argument(
-        "--mlperf_conf", default="mlperf.conf", help="mlperf rules config")
     parser.add_argument("--user_conf", default="user.conf",
                         help="user config for user LoadGen settings such as target QPS")
     parser.add_argument("--max_examples", type=int, default=13368,
                         help="Maximum number of examples to consider (not limited by default)")
-    parser.add_argument("--network", choices=["sut","lon",None], default=None, help="Loadgen network mode")
+    parser.add_argument(
+        "--network",
+        choices=[
+            "sut",
+            "lon",
+            None],
+        default=None,
+        help="Loadgen network mode")
     parser.add_argument('--node', type=str, default="")
     parser.add_argument('--port', type=int, default=8000)
-    parser.add_argument('--sut_server', nargs="*", default= ['http://localhost:8000'],
-                    help='Address of the server(s) under test.')
+    parser.add_argument('--sut_server', nargs="*", default=['http://localhost:8000'],
+                        help='Address of the server(s) under test.')
     args = parser.parse_args()
     return args
 
 # Function to get the amount of temporary cache generated when running the GPT-J model
 # Varies with the beam size set(Estimate: 6GB x Beam size)
+
+
 def get_temp_cache():
     beam_size = int(os.environ.get("GPTJ_BEAM_SIZE", "4"))
     return 6 * beam_size
 
+
 # Map the loadgen scenario as per the option given by the user
 scenario_map = {
     "SingleStream": lg.TestScenario.SingleStream,
@@ -58,12 +71,15 @@ def get_temp_cache():
 }
 
 # Main function triggered when the script is run
+
+
 def main():
     args = get_args()
     qsl = None
     if args.network != "sut":
         # Gets the Query Data Loader and Query Sample Loader
-        # Responsible for loading(Query Sample Loader) and sending samples over the network(Query Data Loader) to the server
+        # Responsible for loading(Query Sample Loader) and sending samples over
+        # the network(Query Data Loader) to the server
         qsl = get_GPTJ_QSL(
             dataset_path=args.dataset_path,
             max_examples=args.max_examples
@@ -72,14 +88,14 @@ def main():
             qdl = GPTJ_QDL(
                 sut_server_addr=args.sut_server,
                 scenario=args.scenario,
-                qsl = qsl
+                qsl=qsl
             )
 
         # Initiates and loads loadgen test settings and log path
         settings = lg.TestSettings()
         settings.scenario = scenario_map[args.scenario]
-        # Need to update the conf
-        settings.FromConfig(args.mlperf_conf, "gptj", args.scenario)
+        # mlperf conf is automatically loaded by the loadgen
+        # settings.FromConfig(args.mlperf_conf, "gptj", args.scenario)
         settings.FromConfig(args.user_conf, "gptj", args.scenario)
 
         # Chosing test mode Accutacy/Performance
@@ -114,17 +130,24 @@ def main():
             network=args.network,
             dataset_path=args.dataset_path,
             max_examples=args.max_examples,
-            qsl=qsl # If args.network is None, then only QSL get passed to the SUT, else it will be None
+            qsl=qsl  # If args.network is None, then only QSL get passed to the SUT, else it will be None
         )
-    
+
     if args.network == "lon" and args.scenario == "SingleStream":
         print("ERROR: Single stream scenario in Loadgen Over the Network is not supported!")
 
-    # If network option is LON, QDL is loaded and request is served to SUT based on the scenario given by user(Offline or SingleStream)
+    # If network option is LON, QDL is loaded and request is served to SUT
+    # based on the scenario given by user(Offline or SingleStream)
     elif args.network == "lon":
-        lg.StartTestWithLogSettings(qdl.qdl, qsl.qsl, settings, log_settings, args.audit_conf)
-
-    # If network option is SUT, a flask server is initiated, request is processed and output is being sent back to LON client
+        lg.StartTestWithLogSettings(
+            qdl.qdl,
+            qsl.qsl,
+            settings,
+            log_settings,
+            args.audit_conf)
+
+    # If network option is SUT, a flask server is initiated, request is
+    # processed and output is being sent back to LON client
     elif args.network == "sut":
         temp_cache = get_temp_cache()
         from network_SUT import app, node, set_backend, set_semaphore
@@ -138,12 +161,14 @@ def main():
         # Acquire and release of semaphore can be found in network_SUT.py
         model_mem_size = sut.total_mem_size
         if args.gpu:
-            free_mem = int(os.environ.get("CM_CUDA_DEVICE_PROP_GLOBAL_MEMORY", get_gpu_memory_info())) / (1024**3)
+            free_mem = int(os.environ.get(
+                "CM_CUDA_DEVICE_PROP_GLOBAL_MEMORY", get_gpu_memory_info())) / (1024**3)
         else:
             free_mem = get_cpu_memory_info()
-        lockVar = math.floor((free_mem - model_mem_size)/temp_cache)
+        lockVar = math.floor((free_mem - model_mem_size) / temp_cache)
         node = args.node
-        # Semaphore is set inorder to create multiple instances upon request incomming
+        # Semaphore is set inorder to create multiple instances upon request
+        # incomming
         set_semaphore(lockVar)
         print(f"Set the semaphore lock variable to {lockVar}")
         # Pass SUT as the backend
@@ -153,7 +178,12 @@ def main():
     else:
         # Test not run in Loadgen Over the Network
         print("Running LoadGen test...")
-        lg.StartTestWithLogSettings(sut.sut, qsl.qsl, settings, log_settings, args.audit_conf)
+        lg.StartTestWithLogSettings(
+            sut.sut,
+            qsl.qsl,
+            settings,
+            log_settings,
+            args.audit_conf)
 
     print("Test Done!")
 
@@ -167,4 +197,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/language/llama2-70b/main.py b/language/llama2-70b/main.py
index c1af88b952..cc808efb3b 100644
--- a/language/llama2-70b/main.py
+++ b/language/llama2-70b/main.py
@@ -13,37 +13,103 @@
 log = logging.getLogger("Llama-70B-MAIN")
 
 # function to check the model name in server matches the user specified one
+
+
 def verify_model_name(user_specified_name, url):
     response = requests.get(url)
     if response.status_code == 200:
         response_dict = response.json()
         server_model_name = response_dict["data"][0]["id"]
         if user_specified_name == server_model_name:
-            return {"matched":True, "error":False}
+            return {"matched": True, "error": False}
         else:
-            return {"matched":False, "error":f"User specified {user_specified_name} and server model name {server_model_name} mismatch!"}
+            return {"matched": False,
+                    "error": f"User specified {user_specified_name} and server model name {server_model_name} mismatch!"}
     else:
-        return {"matched":False, "error":f"Failed to get a valid response. Status code: {response.status_code}"}
+        return {"matched": False,
+                "error": f"Failed to get a valid response. Status code: {response.status_code}"}
+
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--scenario", type=str, choices=["Offline", "Server"], default="Offline", help="Scenario")
-    parser.add_argument("--model-path", type=str, default="meta-llama/Llama-2-70b-chat-hf", help="Model name")
+    parser.add_argument(
+        "--scenario",
+        type=str,
+        choices=[
+            "Offline",
+            "Server"],
+        default="Offline",
+        help="Scenario")
+    parser.add_argument(
+        "--model-path",
+        type=str,
+        default="meta-llama/Llama-2-70b-chat-hf",
+        help="Model name")
     parser.add_argument("--dataset-path", type=str, default=None, help="")
-    parser.add_argument("--accuracy", action="store_true", help="Run accuracy mode")
-    parser.add_argument("--dtype", type=str, default="float32", help="data type of the model, choose from float16, bfloat16 and float32")
-    parser.add_argument("--device", type=str,  choices=["cpu", "cuda:0"], default="cpu", help="device to use")
-    parser.add_argument("--audit-conf", type=str, default="audit.conf", help="audit config for LoadGen settings during compliance runs")
-    parser.add_argument("--mlperf-conf", type=str, default="mlperf.conf", help="mlperf rules config")
-    parser.add_argument("--user-conf", type=str, default="user.conf", help="user config for user LoadGen settings such as target QPS")
-    parser.add_argument("--total-sample-count", type=int, default=24576, help="Number of samples to use in benchmark.") # TODO: This interpretation of 'total-sample-count' is a little misleading. Fix it
-    parser.add_argument("--batch-size", type=int, default=1, help="Model batch-size to use in benchmark.")
-    parser.add_argument("--output-log-dir", type=str, default="output-logs", help="Where logs are saved")
-    parser.add_argument("--enable-log-trace", action="store_true", help="Enable log tracing. This file can become quite large")
-    parser.add_argument("--num-workers", type=int, default=1, help="Number of workers to process queries")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="Run accuracy mode")
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        default="float32",
+        help="data type of the model, choose from float16, bfloat16 and float32")
+    parser.add_argument(
+        "--device",
+        type=str,
+        choices=[
+            "cpu",
+            "cuda:0"],
+        default="cpu",
+        help="device to use")
+    parser.add_argument(
+        "--audit-conf",
+        type=str,
+        default="audit.conf",
+        help="audit config for LoadGen settings during compliance runs")
+    parser.add_argument(
+        "--user-conf",
+        type=str,
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS")
+    # TODO: This interpretation of 'total-sample-count' is a little
+    # misleading. Fix it
+    parser.add_argument(
+        "--total-sample-count",
+        type=int,
+        default=24576,
+        help="Number of samples to use in benchmark.")
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=1,
+        help="Model batch-size to use in benchmark.")
+    parser.add_argument(
+        "--output-log-dir",
+        type=str,
+        default="output-logs",
+        help="Where logs are saved")
+    parser.add_argument(
+        "--enable-log-trace",
+        action="store_true",
+        help="Enable log tracing. This file can become quite large")
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        default=1,
+        help="Number of workers to process queries")
     parser.add_argument("--vllm", action="store_true", help="vllm mode")
-    parser.add_argument("--api-model-name", type=str, default="meta-llama/Llama-2-70b-chat-hf", help="Model name(specified in llm server)")
-    parser.add_argument("--api-server", type=str, default=None, help="Specify an api endpoint call to use api mode")
+    parser.add_argument(
+        "--api-model-name",
+        type=str,
+        default="meta-llama/Llama-2-70b-chat-hf",
+        help="Model name(specified in llm server)")
+    parser.add_argument(
+        "--api-server",
+        type=str,
+        default=None,
+        help="Specify an api endpoint call to use api mode")
 
     args = parser.parse_args()
     return args
@@ -52,13 +118,16 @@ def get_args():
 scenario_map = {
     "offline": lg.TestScenario.Offline,
     "server": lg.TestScenario.Server,
-    }
+}
+
 
 def main():
     args = get_args()
-    
+
     if args.vllm:
-        resp = verify_model_name(args.api_model_name, args.api_server+"/v1/models")
+        resp = verify_model_name(
+            args.api_model_name,
+            args.api_server + "/v1/models")
         if resp["error"]:
             print(f"\n\n\033[91mError:\033[0m", end=" ")
             print(resp["error"])
@@ -66,8 +135,8 @@ def main():
 
     settings = lg.TestSettings()
     settings.scenario = scenario_map[args.scenario.lower()]
-    # Need to update the conf
-    settings.FromConfig(args.mlperf_conf, "llama2-70b", args.scenario)
+    # mlperf.conf is automatically loaded by the loadgen
+    # settings.FromConfig(args.mlperf_conf, "llama2-70b", args.scenario)
     settings.FromConfig(args.user_conf, "llama2-70b", args.scenario)
 
     if args.accuracy:
@@ -91,7 +160,7 @@ def main():
     sut_map = {
         "offline": SUT,
         "server": SUTServer
-        }
+    }
 
     sut_cls = sut_map[args.scenario.lower()]
 
@@ -122,7 +191,12 @@ def main():
     sut.start()
     lgSUT = lg.ConstructSUT(sut.issue_queries, sut.flush_queries)
     log.info("Starting Benchmark run")
-    lg.StartTestWithLogSettings(lgSUT, sut.qsl, settings, log_settings, args.audit_conf)
+    lg.StartTestWithLogSettings(
+        lgSUT,
+        sut.qsl,
+        settings,
+        log_settings,
+        args.audit_conf)
 
     # Stop sut after completion
     sut.stop()
diff --git a/language/mixtral-8x7b/main.py b/language/mixtral-8x7b/main.py
index c28cdc536a..3f2cd61b75 100644
--- a/language/mixtral-8x7b/main.py
+++ b/language/mixtral-8x7b/main.py
@@ -54,11 +54,6 @@ def get_args():
         type=str,
         default="audit.conf",
         help="audit config for LoadGen settings during compliance runs")
-    parser.add_argument(
-        "--mlperf-conf",
-        type=str,
-        default="mlperf.conf",
-        help="mlperf rules config")
     parser.add_argument(
         "--user-conf",
         type=str,
@@ -111,8 +106,8 @@ def main():
 
     settings = lg.TestSettings()
     settings.scenario = scenario_map[args.scenario.lower()]
-    # Need to update the conf
-    settings.FromConfig(args.mlperf_conf, "mixtral-8x7b", args.scenario)
+    # mlperf_conf is automatically loaded by the loadgen
+    # settings.FromConfig(args.mlperf_conf, "mixtral-8x7b", args.scenario)
     settings.FromConfig(args.user_conf, "mixtral-8x7b", args.scenario)
 
     if args.accuracy:
diff --git a/loadgen/CMakeLists.txt b/loadgen/CMakeLists.txt
index 35420e8064..66a68f6abe 100644
--- a/loadgen/CMakeLists.txt
+++ b/loadgen/CMakeLists.txt
@@ -1,10 +1,17 @@
-cmake_minimum_required(VERSION 3.1)
+cmake_minimum_required(VERSION 3.5)
 
 project(mlperf_loadgen)
 
-# The mlperf_loadgen version.
-set(mlperf_loadgen_VERSION_MAJOR 4)
-set(mlperf_loadgen_VERSION_MINOR 1)
+# Read the version file
+file(READ "${CMAKE_SOURCE_DIR}/VERSION.txt" VERSION_CONTENTS)
+
+# Extract the major and minor versions from the VERSION file (assuming "MAJOR.MINOR.PATCH" format)
+string(REGEX MATCH "^([0-9]+)\\.([0-9]+)" VERSION_MATCH ${VERSION_CONTENTS})
+
+# Set the variables for the major and minor versions
+set(mlperf_loadgen_VERSION_MAJOR "${CMAKE_MATCH_1}")
+set(mlperf_loadgen_VERSION_MINOR "${CMAKE_MATCH_2}")
+
 message("mlperf_loadgen v${mlperf_loadgen_VERSION_MAJOR}.${mlperf_loadgen_VERSION_MINOR}")
 
 # Set build options. NB: CXX_STANDARD is supported since CMake 3.1.
@@ -73,3 +80,8 @@ install(TARGETS mlperf_loadgen
 	DESTINATION ${CMAKE_INSTALL_PREFIX}/lib)
 install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/
 	DESTINATION ${CMAKE_INSTALL_PREFIX}/include FILES_MATCHING PATTERN "*.h")
+install(FILES ${CMAKE_CURRENT_SOURCE_DIR}/mlperf.conf
+        DESTINATION ${CMAKE_INSTALL_PREFIX}/include)
+
+# Define preprocessor macro with the path to mlperf.conf
+add_definitions(-DMLPERF_CONF_PATH=\"${CMAKE_INSTALL_PREFIX}/include/mlperf.conf\")
diff --git a/loadgen/MANIFEST.in b/loadgen/MANIFEST.in
new file mode 100644
index 0000000000..1cdd95c6d6
--- /dev/null
+++ b/loadgen/MANIFEST.in
@@ -0,0 +1,2 @@
+include mlperf.conf
+include VERSION.txt
diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
new file mode 100644
index 0000000000..9edf2a44f4
--- /dev/null
+++ b/loadgen/VERSION.txt
@@ -0,0 +1 @@
+4.1.7
diff --git a/loadgen/__init__.py b/loadgen/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/loadgen/benchmark/repro.cpp b/loadgen/benchmark/repro.cpp
index 1112ef961a..44ff53efa9 100644
--- a/loadgen/benchmark/repro.cpp
+++ b/loadgen/benchmark/repro.cpp
@@ -33,10 +33,10 @@ class QSL : public mlperf::QuerySampleLibrary {
   const std::string& Name() override { return mName; }
   size_t TotalSampleCount() override { return 1000000; }
   size_t PerformanceSampleCount() override { return TotalSampleCount(); }
-  void LoadSamplesToRam(
-      const std::vector<mlperf::QuerySampleIndex>& samples) override {}
+  void LoadSamplesToRam(const std::vector<mlperf::QuerySampleIndex>&) override {
+  }
   void UnloadSamplesFromRam(
-      const std::vector<mlperf::QuerySampleIndex>& samples) override {}
+      const std::vector<mlperf::QuerySampleIndex>&) override {}
 
  private:
   std::string mName{"Dummy QSL"};
@@ -51,14 +51,14 @@ class BasicSUT : public mlperf::SystemUnderTest {
   ~BasicSUT() override {}
   const std::string& Name() override { return mName; }
   void IssueQuery(const std::vector<mlperf::QuerySample>& samples) override {
-    int n = samples.size();
+    size_t n = samples.size();
     if (n > mResponses.size()) {
       std::cerr << "Warning: reallocating response buffer in BasicSUT. Maybe "
                    "you should initResponse with larger value!?"
                 << std::endl;
       initResponse(samples.size());
     }
-    for (int i = 0; i < n; i++) {
+    for (size_t i = 0; i < n; i++) {
       mResponses[i].id = samples[i].id;
     }
     mlperf::QuerySamplesComplete(mResponses.data(), n);
@@ -122,7 +122,7 @@ class QueueSUT : public mlperf::SystemUnderTest {
         }
 
         actualSize = std::min(maxSize, mIdQueue.size());
-        for (int i = 0; i < actualSize; i++) {
+        for (size_t i = 0; i < actualSize; i++) {
           responses[i].id = mIdQueue.front();
           mIdQueue.pop_front();
         }
@@ -166,7 +166,7 @@ class MultiBasicSUT : public mlperf::SystemUnderTest {
   const std::string& Name() override { return mName; }
   void IssueQuery(const std::vector<mlperf::QuerySample>& samples) override {
     int thread_idx = mThreadMap[std::this_thread::get_id()];
-    int n = samples.size();
+    size_t n = samples.size();
     auto& reponses = mResponses[thread_idx];
     if (n > reponses.size()) {
       std::cout
@@ -175,7 +175,7 @@ class MultiBasicSUT : public mlperf::SystemUnderTest {
           << std::endl;
       initResponse(samples.size());
     }
-    for (int i = 0; i < n; i++) {
+    for (size_t i = 0; i < n; i++) {
       reponses[i].id = samples[i].id;
     }
     mlperf::QuerySamplesComplete(reponses.data(), n);
diff --git a/loadgen/bindings/python_api.cc b/loadgen/bindings/python_api.cc
index f3155987a7..7f50f5f567 100644
--- a/loadgen/bindings/python_api.cc
+++ b/loadgen/bindings/python_api.cc
@@ -19,9 +19,9 @@ limitations under the License.
 #include <functional>
 
 #include "../loadgen.h"
+#include "../query_dispatch_library.h"
 #include "../query_sample.h"
 #include "../query_sample_library.h"
-#include "../query_dispatch_library.h"
 #include "../system_under_test.h"
 #include "../test_settings.h"
 #include "pybind11/functional.h"
@@ -133,34 +133,36 @@ class QuerySampleLibraryTrampoline : public QuerySampleLibrary {
 // A QDL that allows defining callbacks for
 // IssueQuery, FlushQueries, and Name methods.
 class QueryDispatchLibraryTrampoline : public QueryDispatchLibrary {
-  public:
-    QueryDispatchLibraryTrampoline(IssueQueryCallback issue_query_callback,
+ public:
+  QueryDispatchLibraryTrampoline(IssueQueryCallback issue_query_callback,
                                  FlushQueriesCallback flush_queries_callback,
                                  NameCallback name_callback)
-        : issue_query_callback_(issue_query_callback),
-          flush_queries_callback_(flush_queries_callback),
-          name_callback_(name_callback) {}
-
-    // Returns the name of the SUT. Name shall be returned over the network
-    // TODO: other bindings should also be fixed eventually to be used over the network 
-    const std::string& Name() override {
-      static std::string name; // HACK: avoid returning a reference to temporary.
-      pybind11::gil_scoped_acquire gil_acquirer; 
-      name = name_callback_(); // name_callback_() shall returned name over the network.
-      return name;
-    }
+      : issue_query_callback_(issue_query_callback),
+        flush_queries_callback_(flush_queries_callback),
+        name_callback_(name_callback) {}
+
+  // Returns the name of the SUT. Name shall be returned over the network
+  // TODO: other bindings should also be fixed eventually to be used over the
+  // network
+  const std::string& Name() override {
+    static std::string name;  // HACK: avoid returning a reference to temporary.
+    pybind11::gil_scoped_acquire gil_acquirer;
+    name = name_callback_();  // name_callback_() shall returned name over the
+                              // network.
+    return name;
+  }
 
-    void IssueQuery(const std::vector<QuerySample>& samples) override {
-        pybind11::gil_scoped_acquire gil_acquirer;
-        issue_query_callback_(samples);
-    }
+  void IssueQuery(const std::vector<QuerySample>& samples) override {
+    pybind11::gil_scoped_acquire gil_acquirer;
+    issue_query_callback_(samples);
+  }
 
-    void FlushQueries() override { flush_queries_callback_(); }
+  void FlushQueries() override { flush_queries_callback_(); }
 
-    protected:
-      IssueQueryCallback issue_query_callback_;
-      FlushQueriesCallback flush_queries_callback_;
-      NameCallback name_callback_;
+ protected:
+  IssueQueryCallback issue_query_callback_;
+  FlushQueriesCallback flush_queries_callback_;
+  NameCallback name_callback_;
 };
 
 }  // namespace
@@ -213,8 +215,8 @@ void DestroyQSL(uintptr_t qsl) {
 uintptr_t ConstructQDL(IssueQueryCallback issue_cb,
                        FlushQueriesCallback flush_queries_cb,
                        NameCallback name_callback) {
-  QueryDispatchLibraryTrampoline* qdl =
-      new QueryDispatchLibraryTrampoline(issue_cb, flush_queries_cb, name_callback);
+  QueryDispatchLibraryTrampoline* qdl = new QueryDispatchLibraryTrampoline(
+      issue_cb, flush_queries_cb, name_callback);
   return reinterpret_cast<uintptr_t>(qdl);
 }
 
@@ -223,7 +225,7 @@ void DestroyQDL(uintptr_t qdl) {
       reinterpret_cast<QueryDispatchLibraryTrampoline*>(qdl);
   delete qdl_cast;
 }
- 
+
 void StartTest(uintptr_t sut, uintptr_t qsl, mlperf::TestSettings test_settings,
                const std::string& audit_config_filename) {
   pybind11::gil_scoped_release gil_releaser;
@@ -259,7 +261,7 @@ void QuerySamplesComplete(std::vector<QuerySampleResponse> responses,
 }
 
 void FirstTokenComplete(std::vector<QuerySampleResponse> responses,
-                          ResponseCallback response_cb = {}) {
+                        ResponseCallback response_cb = {}) {
   pybind11::gil_scoped_release gil_releaser;
   mlperf::FirstTokenComplete(responses.data(), responses.size(), response_cb);
 }
@@ -331,18 +333,26 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
                      &TestSettings::performance_issue_same_index)
       .def_readwrite("performance_sample_count_override",
                      &TestSettings::performance_sample_count_override)
-      .def_readwrite("test05",
-                     &TestSettings::test05)
+      .def_readwrite("test05", &TestSettings::test05)
       .def_readwrite("test05_qsl_rng_seed", &TestSettings::test05_qsl_rng_seed)
       .def_readwrite("test05_sample_index_rng_seed",
                      &TestSettings::test05_sample_index_rng_seed)
-      .def_readwrite("test05_schedule_rng_seed", &TestSettings::test05_schedule_rng_seed)
+      .def_readwrite("test05_schedule_rng_seed",
+                     &TestSettings::test05_schedule_rng_seed)
       .def_readwrite("use_token_latencies", &TestSettings::use_token_latencies)
       .def_readwrite("ttft_latency", &TestSettings::server_ttft_latency)
       .def_readwrite("tpot_latency", &TestSettings::server_tpot_latency)
-      .def_readwrite("infer_token_latencies", &TestSettings::infer_token_latencies)
-      .def_readwrite("token_latency_scaling_factor", &TestSettings::token_latency_scaling_factor)
-      .def("FromConfig", &TestSettings::FromConfig, "FromConfig.");
+      .def_readwrite("infer_token_latencies",
+                     &TestSettings::infer_token_latencies)
+      .def_readwrite("token_latency_scaling_factor",
+                     &TestSettings::token_latency_scaling_factor)
+      .def("FromConfig", &TestSettings::FromConfig, pybind11::arg("path"),
+           pybind11::arg("model"), pybind11::arg("scenario"),
+           pybind11::arg("is_mlperf_conf") = false,
+           "This function configures settings from the given user "
+           "configuration file, model, and scenario. The is_mlperf_conf flag "
+           "should be set to false or else only the default mlperf_conf file "
+           "will be loaded by the loadgen.");
 
   pybind11::enum_<LoggingMode>(m, "LoggingMode")
       .value("AsyncPoll", LoggingMode::AsyncPoll)
@@ -410,10 +420,9 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
             q.id = t[0].cast<uintptr_t>();
             q.data = t[1].cast<uintptr_t>();
             q.size = t[2].cast<size_t>();
-            if (t.size() == 4){
+            if (t.size() == 4) {
               q.n_tokens = t[3].cast<int64_t>();
-            }
-            else{
+            } else {
               q.n_tokens = 0;
             }
             return q;
@@ -438,10 +447,11 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
   m.def("DestroyQSL", &py::DestroyQSL,
         "Destroy the object created by ConstructQSL.");
 
-  m.def("ConstructQDL", &py::ConstructQDL, 
-      "Construct the query sample library, communicating with the SUT over the network.");
+  m.def("ConstructQDL", &py::ConstructQDL,
+        "Construct the query sample library, communicating with the SUT over "
+        "the network.");
   m.def("DestroyQDL", &py::DestroyQDL,
-      "Destroy the object created by ConstructQDL.");
+        "Destroy the object created by ConstructQDL.");
 
   m.def("StartTest", &py::StartTest,
         "Run tests on a SUT created by ConstructSUT() with the provided QSL. "
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
new file mode 100644
index 0000000000..10f7ae78f6
--- /dev/null
+++ b/loadgen/mlperf.conf
@@ -0,0 +1,98 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+
+# Set performance_sample_count for each model.
+# User can optionally set this to higher values in user.conf.
+resnet50.*.performance_sample_count_override = 1024
+ssd-mobilenet.*.performance_sample_count_override = 256
+retinanet.*.performance_sample_count_override = 64
+bert.*.performance_sample_count_override = 10833
+dlrm.*.performance_sample_count_override = 204800
+dlrm-v2.*.performance_sample_count_override = 204800
+rnnt.*.performance_sample_count_override = 2513
+gptj.*.performance_sample_count_override = 13368
+llama2-70b.*.performance_sample_count_override = 24576
+stable-diffusion-xl.*.performance_sample_count_override = 5000
+# set to 0 to let entire sample set to be performance sample
+3d-unet.*.performance_sample_count_override = 0
+
+# Set seeds. The seeds will be distributed two weeks before the submission.
+*.*.qsl_rng_seed = 3066443479025735752
+*.*.sample_index_rng_seed = 10688027786191513374
+*.*.schedule_rng_seed = 14962580496156340209
+# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission.
+*.*.test05_qsl_rng_seed = 16799458546791641818
+*.*.test05_sample_index_rng_seed = 5453809927556429288
+*.*.test05_schedule_rng_seed = 5435552105434836064
+
+
+*.SingleStream.target_latency_percentile = 90
+*.SingleStream.min_duration = 600000
+
+*.MultiStream.target_latency_percentile = 99
+*.MultiStream.samples_per_query = 8
+*.MultiStream.min_duration = 600000
+*.MultiStream.min_query_count = 662
+retinanet.MultiStream.target_latency = 528
+
+# 3D-UNet uses equal issue mode because it has non-uniform inputs
+3d-unet.*.sample_concatenate_permutation = 1
+
+# LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario
+gptj.*.sample_concatenate_permutation = 1
+llama2-70b.*.sample_concatenate_permutation = 1
+mixtral-8x7b.*.sample_concatenate_permutation = 1
+
+*.Server.target_latency = 10
+*.Server.target_latency_percentile = 99
+*.Server.target_duration = 0
+*.Server.min_duration = 600000
+resnet50.Server.target_latency = 15
+retinanet.Server.target_latency = 100
+bert.Server.target_latency = 130
+dlrm.Server.target_latency = 60
+dlrm-v2.Server.target_latency = 60
+rnnt.Server.target_latency = 1000
+gptj.Server.target_latency = 20000
+stable-diffusion-xl.Server.target_latency = 20000
+# Llama2-70b benchmarks measures token latencies
+llama2-70b.*.use_token_latencies = 1
+mixtral-8x7b.*.use_token_latencies = 1
+# gptj benchmark infers token latencies
+gptj.*.infer_token_latencies = 1
+gptj.*.token_latency_scaling_factor = 69
+# Only ttft and tpot are tracked for the llama2-70b & mixtral-8x7B benchmark therefore target_latency = 0
+llama2-70b.Server.target_latency = 0
+llama2-70b.Server.ttft_latency = 2000
+llama2-70b.Server.tpot_latency = 200
+
+mixtral-8x7b.Server.target_latency = 0
+mixtral-8x7b.Server.ttft_latency = 2000
+mixtral-8x7b.Server.tpot_latency = 200
+
+*.Offline.target_latency_percentile = 90
+*.Offline.min_duration = 600000
+
+# In Offline scenario, we always have one query. But LoadGen maps this to
+# min_sample_count internally in Offline scenario. If the dataset size is larger 
+# than 24576 we limit the min_query_count to 24576 and otherwise we use 
+# the dataset size as the limit
+
+resnet50.Offline.min_query_count = 24576
+retinanet.Offline.min_query_count = 24576
+dlrm-v2.Offline.min_query_count = 24576
+bert.Offline.min_query_count = 10833
+gptj.Offline.min_query_count = 13368
+rnnt.Offline.min_query_count = 2513
+3d-unet.Offline.min_query_count = 43
+stable-diffusion-xl.Offline.min_query_count = 5000
+llama2-70b.Offline.min_query_count = 24576
+mixtral-8x7b.Offline.min_query_count = 15000
+
+# These fields should be defined and overridden by user.conf.
+*.SingleStream.target_latency = 10
+*.MultiStream.target_latency = 80
+*.Server.target_qps = 1.0
+*.Offline.target_qps = 1.0
diff --git a/loadgen/pyproject.toml b/loadgen/pyproject.toml
index 9be24eea6d..6f0ae06f0f 100755
--- a/loadgen/pyproject.toml
+++ b/loadgen/pyproject.toml
@@ -4,4 +4,4 @@ build-backend = "setuptools.build_meta:__legacy__"
 
 [tool.cibuildwheel]
 environment = "CFLAGS='-std=c++14'"
-build = "cp3{7,8,9,10,11,12}-*"
+build = "cp3{7,8,9,10,11,12,13}-*"
diff --git a/loadgen/results.h b/loadgen/results.h
index 38bbe32d49..6befea2c04 100644
--- a/loadgen/results.h
+++ b/loadgen/results.h
@@ -16,12 +16,12 @@ limitations under the License.
 #ifndef MLPERF_LOADGEN_RESULTS_H_
 #define MLPERF_LOADGEN_RESULTS_H_
 
+#include <string>
+#include <vector>
+
 #include "query_sample.h"
 #include "test_settings_internal.h"
 
-#include <vector>
-#include <string>
-
 namespace mlperf {
 namespace loadgen {
 
@@ -46,7 +46,6 @@ struct PerformanceResult {
   TokenPerformanceResults token_results;
 };
 
-
 /// \brief Wraps PerformanceResult with relevant context to change how
 /// it's interpreted and reported.
 struct PerformanceSummary {
@@ -84,20 +83,21 @@ struct PerformanceSummary {
   // Set by ProcessTokenLatencies
   size_t token_count = 0;
   size_t overlatency_first_token_count = 0;
-  QuerySampleLatency first_token_latency_min;
-  QuerySampleLatency first_token_latency_max;
-  QuerySampleLatency first_token_latency_mean;
-  QuerySampleLatency time_per_output_token_min;
-  QuerySampleLatency time_per_output_token_max;
-  QuerySampleLatency time_per_output_token_mean;
+  QuerySampleLatency first_token_latency_min = 0;
+  QuerySampleLatency first_token_latency_max = 0;
+  QuerySampleLatency first_token_latency_mean = 0;
+  QuerySampleLatency time_per_output_token_min = 0;
+  QuerySampleLatency time_per_output_token_max = 0;
+  QuerySampleLatency time_per_output_token_mean = 0;
 
   // Latency token target percentile
-  PercentileEntry token_target_latency_percentile{settings.target_latency_percentile};
+  PercentileEntry token_target_latency_percentile{
+      settings.target_latency_percentile};
   PercentileEntry token_latency_percentiles[6] = {{.50}, {.90}, {.95},
                                                   {.97}, {.99}, {.999}};
   PercentileEntry target_tpot_percentile{settings.target_latency_percentile};
   PercentileEntry tpot_percentiles[6] = {{.50}, {.90}, {.95},
-                                        {.97}, {.99}, {.999}};
+                                         {.97}, {.99}, {.999}};
 
 #if defined(_WIN32) || defined(WIN32) || defined(_WIN64) || defined(WIN64)
   // MSVC complains if there is no explicit constructor.
@@ -111,10 +111,10 @@ struct PerformanceSummary {
   void ProcessTokenLatencies();
 
   bool MinDurationMet(std::string* recommendation);
-  bool EarlyStopping(std::string* recommendation, int64_t queries_issued, 
-                      std::vector<QuerySampleLatency>* sample_latencies,
-                      std::vector<QuerySampleLatency>* query_latencies,
-                      std::chrono::nanoseconds target_latency);
+  bool EarlyStopping(std::string* recommendation, int64_t queries_issued,
+                     std::vector<QuerySampleLatency>* sample_latencies,
+                     std::vector<QuerySampleLatency>* query_latencies,
+                     std::chrono::nanoseconds target_latency);
   bool MinQueriesMet();
   bool MinSamplesMet();
   bool HasPerfConstraints();
@@ -125,5 +125,4 @@ struct PerformanceSummary {
 }  // namespace loadgen
 }  // namespace mlperf
 
-
 #endif
diff --git a/loadgen/setup.py b/loadgen/setup.py
index 8dfc5b9f0e..1e8cb0a8d6 100644
--- a/loadgen/setup.py
+++ b/loadgen/setup.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # =============================================================================
 
-## \file
+# \file
 #  \brief MLPerf Inference LoadGen python module setup.
 #  \details Creates a module that python can import.
 #  All source files are compiled by python"s C++ toolchain  without depending
@@ -24,12 +24,11 @@
 #  and binaries. Use one of the gn build targets instead if you want
 #  to avoid poluting the source tree.
 
-from setuptools import Extension
-from setuptools import setup
-from version_generator import generate_loadgen_version_definitions
+from setuptools import Extension, setup
 from pathlib import Path
 from pybind11 import get_include
 from pybind11.setup_helpers import Pybind11Extension, build_ext
+from version_generator import generate_loadgen_version_definitions
 
 generated_version_source_filename = "generated/version_generated.cc"
 generate_loadgen_version_definitions(generated_version_source_filename, ".")
@@ -77,21 +76,42 @@
 mlperf_loadgen_sources_no_gen = lib_sources + lib_bindings
 mlperf_loadgen_sources = (mlperf_loadgen_sources_no_gen +
                           [generated_version_source_filename])
-mlperf_long_description = (this_directory / "README.md").read_text(encoding="utf-8")
+mlperf_long_description = (
+    this_directory /
+    "README.md").read_text(
+        encoding="utf-8")
+
+config_file_path = Path(__file__).parent / "mlperf.conf"
+
+with open("VERSION.txt", "r") as f:
+    version = f.read()
+version_split = version.split(".")
+
+if len(version_split) < 2:
+    print("Version is incomplete. Needs a format like 4.1 in VERSION file")
 
 
 mlperf_loadgen_module = Pybind11Extension(
-        "mlperf_loadgen",
-        define_macros=[("MAJOR_VERSION", "4"), ("MINOR_VERSION", "1")],
-        include_dirs=[".", get_include()],
-        sources=mlperf_loadgen_sources,
-        depends=mlperf_loadgen_headers)
+    "mlperf_loadgen",
+    define_macros=[
+        ("MAJOR_VERSION",
+         version_split[0]),
+        ("MINOR_VERSION",
+         version_split[1]),
+        ("MLPERF_CONF_PATH",
+         f'"{config_file_path}"')],
+    include_dirs=[".", get_include()],
+    sources=mlperf_loadgen_sources,
+    depends=mlperf_loadgen_headers)
 
 setup(name="mlcommons_loadgen",
-      version="4.1",
+      version=version,
       description="MLPerf Inference LoadGen python bindings",
       url="https://mlcommons.org/",
       cmdclass={"build_ext": build_ext},
       ext_modules=[mlperf_loadgen_module],
+      packages=[''],
+      package_data={'': ['mlperf.conf']},
+      include_package_data=True,
       long_description=mlperf_long_description,
       long_description_content_type='text/markdown')
diff --git a/loadgen/test_settings.h b/loadgen/test_settings.h
index 8b209035cc..739b2947f9 100644
--- a/loadgen/test_settings.h
+++ b/loadgen/test_settings.h
@@ -180,10 +180,10 @@ struct TestSettings {
   double offline_expected_qps = 1;
   /// \brief Affects the order in which the samples of the dataset are chosen.
   /// If false it concatenates a single permutation of the dataset (or part
-  /// of it depending on QSL->PerformanceSampleCount()) several times up to the 
+  /// of it depending on QSL->PerformanceSampleCount()) several times up to the
   /// number of samples requested.
-  /// If true it concatenates a multiple permutation of the dataset (or a 
-  /// part of it depending on QSL->PerformanceSampleCount()) several times  
+  /// If true it concatenates a multiple permutation of the dataset (or a
+  /// part of it depending on QSL->PerformanceSampleCount()) several times
   /// up to the number of samples requested.
   bool sample_concatenate_permutation = false;
   /**@}*/
@@ -228,7 +228,8 @@ struct TestSettings {
   uint64_t accuracy_log_sampling_target = 0;
 
   /// \brief Variables for running test05 from native config. A boolean that
-  /// determines whether or not to run test05 and three random seed to run the test
+  /// determines whether or not to run test05 and three random seed to run the
+  /// test
   bool test05 = false;
   uint64_t test05_qsl_rng_seed = 0;
   uint64_t test05_sample_index_rng_seed = 0;
@@ -236,7 +237,7 @@ struct TestSettings {
 
   /// \brief Load mlperf parameter config from file.
   int FromConfig(const std::string &path, const std::string &model,
-                 const std::string &scenario);
+                 const std::string &scenario, bool is_mlperf_conf = false);
   /**@}*/
 
   // ==================================
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index ce1b9c4bec..3dd49c25d4 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -53,7 +53,7 @@ TestSettingsInternal::TestSettingsInternal(
       server_ttft_latency(requested.server_ttft_latency),
       server_tpot_latency(requested.server_tpot_latency),
       infer_token_latencies(requested.infer_token_latencies),
-      token_latency_scaling_factor(requested.token_latency_scaling_factor){
+      token_latency_scaling_factor(requested.token_latency_scaling_factor) {
   // Target QPS, target latency, and max_async_queries.
   switch (requested.scenario) {
     case TestScenario::SingleStream:
@@ -158,14 +158,15 @@ TestSettingsInternal::TestSettingsInternal(
   // performance_sample_count == 0 makes it to be equal to loaded_samples.size()
   if (sample_concatenate_permutation &&
       requested.scenario == TestScenario::SingleStream) {
-    // set slack larger for 3D-UNet KiTS19 distribution, i.e. 50% latency << 90% latency
+    // set slack larger for 3D-UNet KiTS19 distribution, i.e. 50% latency << 90%
+    // latency
     constexpr double kSlack = 2.0;
-    uint64_t expected_queries = kSlack * DurationToSeconds(target_duration) * target_qps;
-    min_query_count = min_query_count > expected_queries 
-                      ? min_query_count
-                      : expected_queries;
-    min_query_count += 
-        qsl_performance_sample_count - (min_query_count  % qsl_performance_sample_count);
+    uint64_t expected_queries =
+        kSlack * DurationToSeconds(target_duration) * target_qps;
+    min_query_count =
+        min_query_count > expected_queries ? min_query_count : expected_queries;
+    min_query_count += qsl_performance_sample_count -
+                       (min_query_count % qsl_performance_sample_count);
   }
 
   min_sample_count = min_query_count * samples_per_query;
@@ -335,13 +336,16 @@ void LogRequestedTestSettings(const TestSettings &s) {
     MLPERF_LOG(detail, "requested_performance_sample_count_override",
                s.performance_sample_count_override);
     MLPERF_LOG(detail, "requested_sample_concatenate_permutation",
-            s.sample_concatenate_permutation);
+               s.sample_concatenate_permutation);
     // Token latencies specific values
-    if (s.use_token_latencies){
-      MLPERF_LOG(detail, "requested_use_token_latencies", s.use_token_latencies);
-      if (s.scenario != TestScenario::Offline){
-        MLPERF_LOG(detail, "requested_server_ttft_latency", s.server_ttft_latency);
-        MLPERF_LOG(detail, "requested_server_tpot_latency", s.server_tpot_latency);
+    if (s.use_token_latencies) {
+      MLPERF_LOG(detail, "requested_use_token_latencies",
+                 s.use_token_latencies);
+      if (s.scenario != TestScenario::Offline) {
+        MLPERF_LOG(detail, "requested_server_ttft_latency",
+                   s.server_ttft_latency);
+        MLPERF_LOG(detail, "requested_server_tpot_latency",
+                   s.server_tpot_latency);
       }
     }
 #else
@@ -488,7 +492,7 @@ void TestSettingsInternal::LogAllSettings() const {
 void TestSettingsInternal::LogSummary(AsyncSummary &summary) const {
   summary("samples_per_query : ", samples_per_query);
   summary("target_qps : ", target_qps);
-  if (!use_token_latencies){
+  if (!use_token_latencies) {
     summary("target_latency (ns): ", target_latency.count());
   } else {
     summary("ttft_latency (ns): ", server_ttft_latency);
@@ -514,13 +518,28 @@ void TestSettingsInternal::LogSummary(AsyncSummary &summary) const {
 
 }  // namespace loadgen
 
-/// \todo The TestSettings::FromConfig definition belongs in a test_settings.cc
-/// file which doesn't yet exist. To avoid churn so close to the submission
-/// deadline, adding a test_settings.cc file has been deferred to v0.6.
 int TestSettings::FromConfig(const std::string &path, const std::string &model,
-                             const std::string &scenario) {
-  // TODO: move this method to a new file test_settings.cc
+                             const std::string &scenario, bool is_mlperf_conf) {
   std::map<std::string, std::string> kv;
+  static int configCount = 0;
+
+  if (!is_mlperf_conf) {
+    if (configCount == 0) {
+      // Only allow userConf as the single configFile and loadgen loads the
+      // mlperfConf automatically
+      FromConfig(MLPERF_CONF_PATH, model, scenario, true);
+    }
+
+    else {
+      LogDetail([](AsyncDetail &detail) {
+        std::stringstream ss;
+        ss << "Multiple conf files are used. This is not valid for official "
+              "submission.";
+        MLPERF_LOG_ERROR(detail, "error_invalid_config", ss.str());
+      });
+    }
+    configCount++;
+  }
 
   // lookup key/value pairs from config
   auto lookupkv = [&](const std::string &model, const std::string &scenario,
@@ -662,66 +681,57 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
         break;
     }
   }
-  lookupkv(model, scenario, "min_duration", &min_duration_ms, nullptr);
-  lookupkv(model, scenario, "max_duration", &max_duration_ms, nullptr);
-  lookupkv(model, scenario, "min_query_count", &min_query_count, nullptr);
-  lookupkv(model, scenario, "max_query_count", &max_query_count, nullptr);
-  lookupkv(model, scenario, "qsl_rng_seed", &qsl_rng_seed, nullptr);
-  lookupkv(model, scenario, "sample_index_rng_seed", &sample_index_rng_seed,
-           nullptr);
-  lookupkv(model, scenario, "schedule_rng_seed", &schedule_rng_seed, nullptr);
-  lookupkv(model, scenario, "accuracy_log_rng_seed", &accuracy_log_rng_seed,
-           nullptr);
-  lookupkv(model, scenario, "accuracy_log_probability", nullptr,
-           &accuracy_log_probability, 0.01);
-  lookupkv(model, scenario, "accuracy_log_sampling_target",
-           &accuracy_log_sampling_target, nullptr);
-  if (lookupkv(model, scenario, "print_timestamps", &val, nullptr))
-    print_timestamps = (val == 0) ? false : true;
-  if (lookupkv(model, scenario, "performance_issue_unique", &val, nullptr))
-    performance_issue_unique = (val == 0) ? false : true;
-  if (lookupkv(model, scenario, "performance_issue_same", &val, nullptr))
-    performance_issue_same = (val == 0) ? false : true;
-  lookupkv(model, scenario, "performance_issue_same_index",
-           &performance_issue_same_index, nullptr);
-  lookupkv(model, scenario, "performance_sample_count_override",
-           &performance_sample_count_override, nullptr);
-  if (lookupkv(model, scenario, "sample_concatenate_permutation", &val, nullptr))
-    sample_concatenate_permutation = (val == 1) ? true : false;
-  if (lookupkv(model, scenario, "test05", &val, nullptr))
-    test05 = (val == 1) ? true : false;
-  lookupkv(model, scenario, "test05_qsl_rng_seed", &test05_qsl_rng_seed, nullptr);
-  lookupkv(model, scenario, "test05_sample_index_rng_seed", &test05_sample_index_rng_seed,
-           nullptr);
-  lookupkv(model, scenario, "test05_schedule_rng_seed", &test05_schedule_rng_seed, nullptr);
-
-  // keys to measure token metrics
-  if (lookupkv(model, scenario, "use_token_latencies", &val, nullptr)){
+  if (is_mlperf_conf) {
+    lookupkv(model, scenario, "qsl_rng_seed", &qsl_rng_seed, nullptr);
+    lookupkv(model, scenario, "sample_index_rng_seed", &sample_index_rng_seed,
+             nullptr);
+    lookupkv(model, scenario, "schedule_rng_seed", &schedule_rng_seed, nullptr);
+    lookupkv(model, scenario, "accuracy_log_rng_seed", &accuracy_log_rng_seed,
+             nullptr);
+    lookupkv(model, scenario, "accuracy_log_probability", nullptr,
+             &accuracy_log_probability, 0.01);
+    lookupkv(model, scenario, "accuracy_log_sampling_target",
+             &accuracy_log_sampling_target, nullptr);
+    if (lookupkv(model, scenario, "sample_concatenate_permutation", &val,
+                 nullptr))
+      sample_concatenate_permutation = (val == 1) ? true : false;
+    if (lookupkv(model, scenario, "test05", &val, nullptr))
+      test05 = (val == 1) ? true : false;
+    lookupkv(model, scenario, "test05_qsl_rng_seed", &test05_qsl_rng_seed,
+             nullptr);
+    lookupkv(model, scenario, "test05_sample_index_rng_seed",
+             &test05_sample_index_rng_seed, nullptr);
+    lookupkv(model, scenario, "test05_schedule_rng_seed",
+             &test05_schedule_rng_seed, nullptr);
+  }
+
+  // keys that can be overriden in user.conf but will make the results eligibale
+  // only for open submission keys to measure token metrics
+  if (lookupkv(model, scenario, "use_token_latencies", &val, nullptr)) {
     use_token_latencies = (val == 1) ? true : false;
   }
-  if (use_token_latencies){
-    lookupkv(model, "Server", "ttft_latency", &server_ttft_latency, nullptr, 1000 * 1000);
-    lookupkv(model, "Server", "tpot_latency", &server_tpot_latency, nullptr, 1000 * 1000);
+  if (use_token_latencies) {
+    lookupkv(model, "Server", "ttft_latency", &server_ttft_latency, nullptr,
+             1000 * 1000);
+    lookupkv(model, "Server", "tpot_latency", &server_tpot_latency, nullptr,
+             1000 * 1000);
   }
 
   // keys to infer token metrics
-  if (lookupkv(model, scenario, "infer_token_latencies", &val, nullptr)){
+  if (lookupkv(model, scenario, "infer_token_latencies", &val, nullptr)) {
     infer_token_latencies = (val == 1) ? true : false;
   }
-  if (infer_token_latencies){
-    lookupkv(model, scenario, "token_latency_scaling_factor", &token_latency_scaling_factor, nullptr, 1);
+  if (infer_token_latencies) {
+    lookupkv(model, scenario, "token_latency_scaling_factor",
+             &token_latency_scaling_factor, nullptr, 1);
   }
   // keys that apply to SingleStream
   lookupkv(model, "SingleStream", "target_latency_percentile", nullptr,
            &single_stream_target_latency_percentile, 0.01);
-  lookupkv(model, "SingleStream", "target_latency", nullptr,
-           &single_stream_expected_latency_ns, 1000 * 1000);
 
   // keys that apply to MultiStream
   lookupkv(model, "MultiStream", "target_latency_percentile", nullptr,
            &multi_stream_target_latency_percentile, 0.01);
-  lookupkv(model, "MultiStream", "target_latency", nullptr,
-           &multi_stream_expected_latency_ns, 1000 * 1000);
   lookupkv(model, "MultiStream", "samples_per_query",
            &multi_stream_samples_per_query, nullptr, 1);
 
@@ -730,15 +740,37 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
            &server_target_latency_percentile, 0.01);
   lookupkv(model, "Server", "target_latency", &server_target_latency_ns,
            nullptr, 1000 * 1000);
-  lookupkv(model, "Server", "target_qps", nullptr, &server_target_qps);
+
+  // keys that can be overriden in user.conf (the provided values still need to
+  // pass the submission checker rules)
+  if (lookupkv(model, scenario, "performance_issue_unique", &val, nullptr))
+    performance_issue_unique = (val == 0) ? false : true;
+  if (lookupkv(model, scenario, "performance_issue_same", &val, nullptr))
+    performance_issue_same = (val == 0) ? false : true;
+  lookupkv(model, scenario, "performance_issue_same_index",
+           &performance_issue_same_index, nullptr);
+
   if (lookupkv(model, "Server", "coalesce_queries", &val, nullptr))
     server_coalesce_queries = (val == 0) ? false : true;
   if (lookupkv(model, "Server", "max_async_queries", &val, nullptr))
     server_max_async_queries = int(val);
 
-  // keys that apply to Offline
+  lookupkv(model, scenario, "min_duration", &min_duration_ms, nullptr);
+  lookupkv(model, scenario, "max_duration", &max_duration_ms, nullptr);
+  lookupkv(model, scenario, "min_query_count", &min_query_count, nullptr);
+  lookupkv(model, scenario, "max_query_count", &max_query_count, nullptr);
+  lookupkv(model, scenario, "performance_sample_count_override",
+           &performance_sample_count_override, nullptr);
+  lookupkv(model, "SingleStream", "target_latency", nullptr,
+           &single_stream_expected_latency_ns, 1000 * 1000);
+  lookupkv(model, "MultiStream", "target_latency", nullptr,
+           &multi_stream_expected_latency_ns, 1000 * 1000);
+  lookupkv(model, "Server", "target_qps", nullptr, &server_target_qps);
   lookupkv(model, "Offline", "target_qps", 0, &offline_expected_qps);
 
+  if (lookupkv(model, scenario, "print_timestamps", &val, nullptr))
+    print_timestamps = (val == 0) ? false : true;
+
   return 0;
 }
 
diff --git a/mlperf.conf b/mlperf.conf
deleted file mode 100644
index 10f7ae78f6..0000000000
--- a/mlperf.conf
+++ /dev/null
@@ -1,98 +0,0 @@
-# The format of this config file is 'key = value'.
-# The key has the format 'model.scenario.key'. Value is mostly int64_t.
-# Model maybe '*' as wildcard. In that case the value applies to all models.
-# All times are in milli seconds
-
-# Set performance_sample_count for each model.
-# User can optionally set this to higher values in user.conf.
-resnet50.*.performance_sample_count_override = 1024
-ssd-mobilenet.*.performance_sample_count_override = 256
-retinanet.*.performance_sample_count_override = 64
-bert.*.performance_sample_count_override = 10833
-dlrm.*.performance_sample_count_override = 204800
-dlrm-v2.*.performance_sample_count_override = 204800
-rnnt.*.performance_sample_count_override = 2513
-gptj.*.performance_sample_count_override = 13368
-llama2-70b.*.performance_sample_count_override = 24576
-stable-diffusion-xl.*.performance_sample_count_override = 5000
-# set to 0 to let entire sample set to be performance sample
-3d-unet.*.performance_sample_count_override = 0
-
-# Set seeds. The seeds will be distributed two weeks before the submission.
-*.*.qsl_rng_seed = 3066443479025735752
-*.*.sample_index_rng_seed = 10688027786191513374
-*.*.schedule_rng_seed = 14962580496156340209
-# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission.
-*.*.test05_qsl_rng_seed = 16799458546791641818
-*.*.test05_sample_index_rng_seed = 5453809927556429288
-*.*.test05_schedule_rng_seed = 5435552105434836064
-
-
-*.SingleStream.target_latency_percentile = 90
-*.SingleStream.min_duration = 600000
-
-*.MultiStream.target_latency_percentile = 99
-*.MultiStream.samples_per_query = 8
-*.MultiStream.min_duration = 600000
-*.MultiStream.min_query_count = 662
-retinanet.MultiStream.target_latency = 528
-
-# 3D-UNet uses equal issue mode because it has non-uniform inputs
-3d-unet.*.sample_concatenate_permutation = 1
-
-# LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario
-gptj.*.sample_concatenate_permutation = 1
-llama2-70b.*.sample_concatenate_permutation = 1
-mixtral-8x7b.*.sample_concatenate_permutation = 1
-
-*.Server.target_latency = 10
-*.Server.target_latency_percentile = 99
-*.Server.target_duration = 0
-*.Server.min_duration = 600000
-resnet50.Server.target_latency = 15
-retinanet.Server.target_latency = 100
-bert.Server.target_latency = 130
-dlrm.Server.target_latency = 60
-dlrm-v2.Server.target_latency = 60
-rnnt.Server.target_latency = 1000
-gptj.Server.target_latency = 20000
-stable-diffusion-xl.Server.target_latency = 20000
-# Llama2-70b benchmarks measures token latencies
-llama2-70b.*.use_token_latencies = 1
-mixtral-8x7b.*.use_token_latencies = 1
-# gptj benchmark infers token latencies
-gptj.*.infer_token_latencies = 1
-gptj.*.token_latency_scaling_factor = 69
-# Only ttft and tpot are tracked for the llama2-70b & mixtral-8x7B benchmark therefore target_latency = 0
-llama2-70b.Server.target_latency = 0
-llama2-70b.Server.ttft_latency = 2000
-llama2-70b.Server.tpot_latency = 200
-
-mixtral-8x7b.Server.target_latency = 0
-mixtral-8x7b.Server.ttft_latency = 2000
-mixtral-8x7b.Server.tpot_latency = 200
-
-*.Offline.target_latency_percentile = 90
-*.Offline.min_duration = 600000
-
-# In Offline scenario, we always have one query. But LoadGen maps this to
-# min_sample_count internally in Offline scenario. If the dataset size is larger 
-# than 24576 we limit the min_query_count to 24576 and otherwise we use 
-# the dataset size as the limit
-
-resnet50.Offline.min_query_count = 24576
-retinanet.Offline.min_query_count = 24576
-dlrm-v2.Offline.min_query_count = 24576
-bert.Offline.min_query_count = 10833
-gptj.Offline.min_query_count = 13368
-rnnt.Offline.min_query_count = 2513
-3d-unet.Offline.min_query_count = 43
-stable-diffusion-xl.Offline.min_query_count = 5000
-llama2-70b.Offline.min_query_count = 24576
-mixtral-8x7b.Offline.min_query_count = 15000
-
-# These fields should be defined and overridden by user.conf.
-*.SingleStream.target_latency = 10
-*.MultiStream.target_latency = 80
-*.Server.target_qps = 1.0
-*.Offline.target_qps = 1.0
diff --git a/mlperf.conf b/mlperf.conf
new file mode 120000
index 0000000000..58fc80e84e
--- /dev/null
+++ b/mlperf.conf
@@ -0,0 +1 @@
+loadgen/mlperf.conf
\ No newline at end of file
diff --git a/recommendation/dlrm_v2/pytorch/python/main.py b/recommendation/dlrm_v2/pytorch/python/main.py
index 53b8d9237a..d931c661ad 100755
--- a/recommendation/dlrm_v2/pytorch/python/main.py
+++ b/recommendation/dlrm_v2/pytorch/python/main.py
@@ -36,13 +36,13 @@
 SUPPORTED_DATASETS = {
     "debug":
         (multihot_criteo.MultihotCriteo, multihot_criteo.pre_process_criteo_dlrm, multihot_criteo.DlrmPostProcess(),
-         {"randomize": 'total',  "memory_map": True}),
+         {"randomize": 'total', "memory_map": True}),
     "multihot-criteo-sample":
         (multihot_criteo.MultihotCriteo, multihot_criteo.pre_process_criteo_dlrm, multihot_criteo.DlrmPostProcess(),
-         {"randomize": 'total',  "memory_map": True}),
+         {"randomize": 'total', "memory_map": True}),
     "multihot-criteo":
         (multihot_criteo.MultihotCriteo, multihot_criteo.pre_process_criteo_dlrm, multihot_criteo.DlrmPostProcess(),
-         {"randomize": 'total',  "memory_map": True}),
+         {"randomize": 'total', "memory_map": True}),
 }
 
 # pre-defined command line options so simplify things. They are used as defaults and can be
@@ -97,42 +97,104 @@ def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", help="name of the mlperf model, ie. dlrm")
-    parser.add_argument("--model-path", required=True, help="path to the model file")
-    parser.add_argument("--dataset", choices=SUPPORTED_DATASETS.keys(), help="dataset")
-    parser.add_argument("--dataset-path", required=True, help="path to the dataset")
-    parser.add_argument("--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles")
+    parser.add_argument(
+        "--model-path",
+        required=True,
+        help="path to the model file")
+    parser.add_argument(
+        "--dataset",
+        choices=SUPPORTED_DATASETS.keys(),
+        help="dataset")
+    parser.add_argument(
+        "--dataset-path",
+        required=True,
+        help="path to the dataset")
+    parser.add_argument(
+        "--profile",
+        choices=SUPPORTED_PROFILES.keys(),
+        help="standard profiles")
     parser.add_argument("--scenario", default="SingleStream",
                         help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())))
     parser.add_argument("--max-ind-range", type=int, default=-1)
-    parser.add_argument("--max-batchsize", type=int, help="max batch size in a single inference")
+    parser.add_argument(
+        "--max-batchsize",
+        type=int,
+        help="max batch size in a single inference")
     parser.add_argument("--output", help="test results")
     parser.add_argument("--inputs", help="model inputs (currently not used)")
     parser.add_argument("--outputs", help="model outputs (currently not used)")
     parser.add_argument("--backend", help="runtime to use")
     parser.add_argument("--use-gpu", action="store_true", default=False)
-    parser.add_argument("--threads", default=os.cpu_count(), type=int, help="threads")
-    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
-    parser.add_argument("--find-peak-performance", action="store_true", help="enable finding peak performance pass")
+    parser.add_argument(
+        "--threads",
+        default=os.cpu_count(),
+        type=int,
+        help="threads")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
+    parser.add_argument(
+        "--find-peak-performance",
+        action="store_true",
+        help="enable finding peak performance pass")
 
-    # file to use mlperf rules compliant parameters
-    parser.add_argument("--mlperf_conf", default="mlperf.conf", help="mlperf rules config")
     # file for user LoadGen settings such as target QPS
-    parser.add_argument("--user_conf", default="user.conf", help="user config for user LoadGen settings such as target QPS")
-
-    # below will override mlperf rules compliant settings - don't use for official submission
-    parser.add_argument("--duration", type=int, help="duration in milliseconds (ms)")
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS")
+
+    # below will override mlperf rules compliant settings - don't use for
+    # official submission
+    parser.add_argument(
+        "--duration",
+        type=int,
+        help="duration in milliseconds (ms)")
     parser.add_argument("--target-qps", type=int, help="target/expected qps")
-    parser.add_argument("--max-latency", type=float, help="mlperf max latency in pct tile")
-    parser.add_argument("--count-samples", type=int, help="dataset items to use")
-    parser.add_argument("--count-queries", type=int, help="number of queries to use")
-    parser.add_argument("--samples-per-query-multistream", default=8, type=int, help="query length for multi-stream scenario (in terms of aggregated samples)")
+    parser.add_argument(
+        "--max-latency",
+        type=float,
+        help="mlperf max latency in pct tile")
+    parser.add_argument(
+        "--count-samples",
+        type=int,
+        help="dataset items to use")
+    parser.add_argument(
+        "--count-queries",
+        type=int,
+        help="number of queries to use")
+    parser.add_argument(
+        "--samples-per-query-multistream",
+        default=8,
+        type=int,
+        help="query length for multi-stream scenario (in terms of aggregated samples)")
     # --samples-per-query-offline is equivalent to perf_sample_count
-    parser.add_argument("--samples-per-query-offline", type=int, default=2048, help="query length for offline scenario (in terms of aggregated samples)")
-    parser.add_argument("--samples-to-aggregate-fix", type=int, help="number of samples to be treated as one")
-    parser.add_argument("--samples-to-aggregate-min", type=int, help="min number of samples to be treated as one in random query size")
-    parser.add_argument("--samples-to-aggregate-max", type=int, help="max number of samples to be treated as one in random query size")
-    parser.add_argument("--samples-to-aggregate-quantile-file", type=str, help="distribution quantile used to generate number of samples to be treated as one in random query size")
-    parser.add_argument("--samples-to-aggregate-trace-file", type=str, default="dlrm_trace_of_aggregated_samples.txt")
+    parser.add_argument(
+        "--samples-per-query-offline",
+        type=int,
+        default=2048,
+        help="query length for offline scenario (in terms of aggregated samples)")
+    parser.add_argument(
+        "--samples-to-aggregate-fix",
+        type=int,
+        help="number of samples to be treated as one")
+    parser.add_argument(
+        "--samples-to-aggregate-min",
+        type=int,
+        help="min number of samples to be treated as one in random query size")
+    parser.add_argument(
+        "--samples-to-aggregate-max",
+        type=int,
+        help="max number of samples to be treated as one in random query size")
+    parser.add_argument(
+        "--samples-to-aggregate-quantile-file",
+        type=str,
+        help="distribution quantile used to generate number of samples to be treated as one in random query size")
+    parser.add_argument(
+        "--samples-to-aggregate-trace-file",
+        type=str,
+        default="dlrm_trace_of_aggregated_samples.txt")
     parser.add_argument("--numpy-rand-seed", type=int, default=123)
     parser.add_argument("--debug", action="store_true", default=False)
     args = parser.parse_args()
@@ -170,7 +232,7 @@ def get_backend(backend, dataset, use_gpu, debug):
             if dataset == "debug":
                 # 1. Syntetic debug dataset
                 backend = BackendDistPytorchNative(
-                    num_embeddings_per_feature = [2 for _ in range(26)],
+                    num_embeddings_per_feature=[2 for _ in range(26)],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
@@ -182,7 +244,33 @@ def get_backend(backend, dataset, use_gpu, debug):
             elif dataset == "multihot-criteo-sample":
                 # 2. Syntetic multihot criteo sample
                 backend = BackendDistPytorchNative(
-                    num_embeddings_per_feature = [40000000,39060,17295,7424,20265,3,7122,1543,63,40000000,3067956,405282,10,2209,11938,155,4,976,14,40000000,40000000,40000000,590152,12973,108,36],
+                    num_embeddings_per_feature=[
+                        40000000,
+                        39060,
+                        17295,
+                        7424,
+                        20265,
+                        3,
+                        7122,
+                        1543,
+                        63,
+                        40000000,
+                        3067956,
+                        405282,
+                        10,
+                        2209,
+                        11938,
+                        155,
+                        4,
+                        976,
+                        14,
+                        40000000,
+                        40000000,
+                        40000000,
+                        590152,
+                        12973,
+                        108,
+                        36],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
@@ -194,7 +282,33 @@ def get_backend(backend, dataset, use_gpu, debug):
             elif dataset == "multihot-criteo":
                 # 3. Syntetic multihot criteo
                 backend = BackendDistPytorchNative(
-                    num_embeddings_per_feature = [40000000,39060,17295,7424,20265,3,7122,1543,63,40000000,3067956,405282,10,2209,11938,155,4,976,14,40000000,40000000,40000000,590152,12973,108,36],
+                    num_embeddings_per_feature=[
+                        40000000,
+                        39060,
+                        17295,
+                        7424,
+                        20265,
+                        3,
+                        7122,
+                        1543,
+                        63,
+                        40000000,
+                        3067956,
+                        405282,
+                        10,
+                        2209,
+                        11938,
+                        155,
+                        4,
+                        976,
+                        14,
+                        40000000,
+                        40000000,
+                        40000000,
+                        590152,
+                        12973,
+                        108,
+                        36],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
@@ -204,12 +318,13 @@ def get_backend(backend, dataset, use_gpu, debug):
                     debug=debug
                 )
             else:
-                raise ValueError("only debug|multihot-criteo-sample|multihot-criteo dataset options are supported")
+                raise ValueError(
+                    "only debug|multihot-criteo-sample|multihot-criteo dataset options are supported")
         else:
             if dataset == "debug":
                 # 1. Syntetic debug dataset
                 backend = BackendPytorchNative(
-                    num_embeddings_per_feature = [2 for _ in range(26)],
+                    num_embeddings_per_feature=[2 for _ in range(26)],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
@@ -221,7 +336,33 @@ def get_backend(backend, dataset, use_gpu, debug):
             elif dataset == "multihot-criteo-sample":
                 # 2. Syntetic multihot criteo sample
                 backend = BackendPytorchNative(
-                    num_embeddings_per_feature = [40000000,39060,17295,7424,20265,3,7122,1543,63,40000000,3067956,405282,10,2209,11938,155,4,976,14,40000000,40000000,40000000,590152,12973,108,36],
+                    num_embeddings_per_feature=[
+                        40000000,
+                        39060,
+                        17295,
+                        7424,
+                        20265,
+                        3,
+                        7122,
+                        1543,
+                        63,
+                        40000000,
+                        3067956,
+                        405282,
+                        10,
+                        2209,
+                        11938,
+                        155,
+                        4,
+                        976,
+                        14,
+                        40000000,
+                        40000000,
+                        40000000,
+                        590152,
+                        12973,
+                        108,
+                        36],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
@@ -233,7 +374,33 @@ def get_backend(backend, dataset, use_gpu, debug):
             elif dataset == "multihot-criteo":
                 # 3. Syntetic multihot criteo
                 backend = BackendPytorchNative(
-                    num_embeddings_per_feature = [40000000,39060,17295,7424,20265,3,7122,1543,63,40000000,3067956,405282,10,2209,11938,155,4,976,14,40000000,40000000,40000000,590152,12973,108,36],
+                    num_embeddings_per_feature=[
+                        40000000,
+                        39060,
+                        17295,
+                        7424,
+                        20265,
+                        3,
+                        7122,
+                        1543,
+                        63,
+                        40000000,
+                        3067956,
+                        405282,
+                        10,
+                        2209,
+                        11938,
+                        155,
+                        4,
+                        976,
+                        14,
+                        40000000,
+                        40000000,
+                        40000000,
+                        590152,
+                        12973,
+                        108,
+                        36],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
@@ -243,7 +410,8 @@ def get_backend(backend, dataset, use_gpu, debug):
                     debug=debug
                 )
             else:
-                raise ValueError("only debug|multihot-criteo-sample|multihot-criteo dataset options are supported")
+                raise ValueError(
+                    "only debug|multihot-criteo-sample|multihot-criteo dataset options are supported")
 
     else:
         raise ValueError("unknown backend: " + backend)
@@ -253,7 +421,8 @@ def get_backend(backend, dataset, use_gpu, debug):
 class Item:
     """An item that we queue for processing by the thread pool."""
 
-    def __init__(self, query_id, content_id, features, batch_T=None, idx_offsets = None):
+    def __init__(self, query_id, content_id, features,
+                 batch_T=None, idx_offsets=None):
         self.query_id = query_id
         self.content_id = content_id
         self.features = features
@@ -261,6 +430,7 @@ def __init__(self, query_id, content_id, features, batch_T=None, idx_offsets = N
         self.idx_offsets = idx_offsets
         self.start = time.time()
 
+
 class RunnerBase:
     def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
         self.take_accuracy = False
@@ -285,7 +455,8 @@ def run_one_item(self, qitem):
         processed_results = []
         try:
             results = self.model.predict(qitem.features, qitem.content_id)
-            processed_results = self.post_process(results, qitem.batch_T, self.result_dict)
+            processed_results = self.post_process(
+                results, qitem.batch_T, self.result_dict)
             if self.take_accuracy:
                 self.post_process.add_results(processed_results)
             self.result_timing.append(time.time() - qitem.start)
@@ -305,7 +476,8 @@ def run_one_item(self, qitem):
                 # print("s,e:",s_idx,e_idx, len(processed_results))
                 s_idx = qitem.idx_offsets[idx]
                 e_idx = qitem.idx_offsets[idx + 1]
-                response_array = array.array("B", np.array(processed_results[s_idx:e_idx], np.float32).tobytes())
+                response_array = array.array("B", np.array(
+                    processed_results[s_idx:e_idx], np.float32).tobytes())
                 response_array_refs.append(response_array)
                 bi = response_array.buffer_info()
                 response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
@@ -314,20 +486,27 @@ def run_one_item(self, qitem):
     def enqueue(self, query_samples):
         idx = [q.index for q in query_samples]
         query_id = [q.id for q in query_samples]
-        #print(idx)
+        # print(idx)
         query_len = len(query_samples)
 
         if query_len < self.max_batchsize:
             samples, idx_offsets = self.ds.get_samples(idx)
             batch_T = [self.ds.get_labels(sample) for sample in samples]
-            self.run_one_item(Item(query_id, idx, samples, batch_T, idx_offsets))
+            self.run_one_item(
+                Item(
+                    query_id,
+                    idx,
+                    samples,
+                    batch_T,
+                    idx_offsets))
         else:
             bs = self.max_batchsize
             for i in range(0, query_len, bs):
                 ie = min(i + bs, query_len)
                 samples, idx_offsets = self.ds.get_samples(idx[i:ie])
                 batch_T = [self.ds.get_labels(sample) for sample in samples]
-                self.run_one_item(Item(query_id[i:ie], idx[i:ie], samples, batch_T, idx_offsets))
+                self.run_one_item(
+                    Item(query_id[i:ie], idx[i:ie], samples, batch_T, idx_offsets))
 
     def finish(self):
         pass
@@ -336,13 +515,16 @@ def finish(self):
 class QueueRunner(RunnerBase):
     def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
         super().__init__(model, ds, threads, post_proc, max_batchsize)
-        queue_size_multiplier = 4 #(args.samples_per_query_offline + max_batchsize - 1) // max_batchsize)
+        # (args.samples_per_query_offline + max_batchsize - 1) // max_batchsize)
+        queue_size_multiplier = 4
         self.tasks = JoinableQueue(maxsize=threads * queue_size_multiplier)
         self.workers = []
         self.result_dict = {}
 
         for _ in range(self.threads):
-            worker = threading.Thread(target=self.handle_tasks, args=(self.tasks,))
+            worker = threading.Thread(
+                target=self.handle_tasks, args=(
+                    self.tasks,))
             worker.daemon = True
             self.workers.append(worker)
             worker.start()
@@ -362,7 +544,7 @@ def enqueue(self, query_samples):
         idx = [q.index for q in query_samples]
         query_id = [q.id for q in query_samples]
         query_len = len(query_samples)
-        #print(idx)
+        # print(idx)
         if query_len < self.max_batchsize:
             samples, idx_offsets = self.ds.get_samples(idx)
             batch_T = [self.ds.get_labels(sample) for sample in samples]
@@ -373,7 +555,8 @@ def enqueue(self, query_samples):
                 ie = min(i + bs, query_len)
                 samples, idx_offsets = self.ds.get_samples(idx)
                 batch_T = [self.ds.get_labels(sample) for sample in samples]
-                self.tasks.put(Item(query_id[i:ie], idx[i:ie], samples, batch_T, idx_offsets))
+                self.tasks.put(
+                    Item(query_id[i:ie], idx[i:ie], samples, batch_T, idx_offsets))
 
     def finish(self):
         # exit all threads
@@ -383,11 +566,12 @@ def finish(self):
             worker.join()
 
 
-
-def add_results(final_results, name, result_dict, result_list, took, show_accuracy=False):
+def add_results(final_results, name, result_dict,
+                result_list, took, show_accuracy=False):
     percentiles = [50., 80., 90., 95., 99., 99.9]
     buckets = np.percentile(result_list, percentiles).tolist()
-    buckets_str = ",".join(["{}:{:.4f}".format(p, b) for p, b in zip(percentiles, buckets)])
+    buckets_str = ",".join(["{}:{:.4f}".format(p, b)
+                           for p, b in zip(percentiles, buckets)])
 
     if result_dict["total"] == 0:
         result_dict["total"] = len(result_list)
@@ -426,13 +610,17 @@ def main():
     log.info(args)
 
     # find backend
-    backend = get_backend(args.backend, args.dataset, args.use_gpu, debug=args.debug)
+    backend = get_backend(
+        args.backend,
+        args.dataset,
+        args.use_gpu,
+        debug=args.debug)
 
     # dataset to use
     wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
 
     # --count-samples can be used to limit the number of samples used for testing
-    ds = wanted_dataset(num_embeddings_per_feature=[40000000,39060,17295,7424,20265,3,7122,1543,63,40000000,3067956,405282,10,2209,11938,155,4,976,14,40000000,40000000,40000000,590152,12973,108,3],
+    ds = wanted_dataset(num_embeddings_per_feature=[40000000, 39060, 17295, 7424, 20265, 3, 7122, 1543, 63, 40000000, 3067956, 405282, 10, 2209, 11938, 155, 4, 976, 14, 40000000, 40000000, 40000000, 590152, 12973, 108, 3],
                         data_path=args.dataset_path,
                         name=args.dataset,
                         pre_process=pre_proc,  # currently an identity function
@@ -445,7 +633,10 @@ def main():
                         max_ind_range=args.max_ind_range,
                         **kwargs)
     # load model to backend
-    model = backend.load(args.model_path, inputs=args.inputs, outputs=args.outputs)
+    model = backend.load(
+        args.model_path,
+        inputs=args.inputs,
+        outputs=args.outputs)
     final_results = {
         "runtime": model.name(),
         "version": model.version(),
@@ -453,11 +644,6 @@ def main():
         "cmdline": str(args),
     }
 
-    mlperf_conf = os.path.abspath(args.mlperf_conf)
-    if not os.path.exists(mlperf_conf):
-        log.error("{} not found".format(mlperf_conf))
-        sys.exit(1)
-
     user_conf = os.path.abspath(args.user_conf)
     if not os.path.exists(user_conf):
         log.error("{} not found".format(user_conf))
@@ -489,7 +675,12 @@ def main():
         lg.TestScenario.Offline: QueueRunner
     }
 
-    runner = runner_map[scenario](model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize)
+    runner = runner_map[scenario](
+        model,
+        ds,
+        args.threads,
+        post_proc=post_proc,
+        max_batchsize=args.max_batchsize)
 
     def issue_queries(query_samples):
         runner.enqueue(query_samples)
@@ -498,7 +689,8 @@ def flush_queries():
         pass
 
     settings = lg.TestSettings()
-    settings.FromConfig(mlperf_conf, args.model_path, args.scenario)
+    # mlperf_conf is automatically loaded by the loadgen
+    # settings.FromConfig(mlperf_conf, args.model_path, args.scenario)
     settings.FromConfig(user_conf, args.model_path, args.scenario)
     settings.scenario = scenario
     settings.mode = lg.TestMode.PerformanceOnly
@@ -526,13 +718,22 @@ def flush_queries():
 
     if args.max_latency:
         settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
-        settings.multi_stream_expected_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_expected_latency_ns = int(
+            args.max_latency * NANO_SEC)
 
     sut = lg.ConstructSUT(issue_queries, flush_queries)
-    qsl = lg.ConstructQSL(count, min(count, args.samples_per_query_offline), ds.load_query_samples, ds.unload_query_samples)
+    qsl = lg.ConstructQSL(count,
+                          min(count,
+                              args.samples_per_query_offline),
+                          ds.load_query_samples,
+                          ds.unload_query_samples)
 
     log.info("starting {}".format(scenario))
-    result_dict = {"good": 0, "total": 0, "roc_auc": 0, "scenario": str(scenario)}
+    result_dict = {
+        "good": 0,
+        "total": 0,
+        "roc_auc": 0,
+        "scenario": str(scenario)}
     runner.start_run(result_dict, args.accuracy)
     lg.StartTest(sut, qsl, settings)
 
diff --git a/text_to_image/main.py b/text_to_image/main.py
index 07cf174721..57bfb56dd4 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -73,15 +73,22 @@
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--dataset", choices=SUPPORTED_DATASETS.keys(), help="dataset")
-    parser.add_argument("--dataset-path", required=True, help="path to the dataset")
+    parser.add_argument(
+        "--dataset",
+        choices=SUPPORTED_DATASETS.keys(),
+        help="dataset")
+    parser.add_argument(
+        "--dataset-path",
+        required=True,
+        help="path to the dataset")
     parser.add_argument(
         "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
     )
     parser.add_argument(
         "--scenario",
         default="SingleStream",
-        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())),
+        help="mlperf benchmark scenario, one of " +
+        str(list(SCENARIO_MAP.keys())),
     )
     parser.add_argument(
         "--max-batchsize",
@@ -90,7 +97,10 @@ def get_args():
         help="max batch size in a single inference",
     )
     parser.add_argument("--threads", default=1, type=int, help="threads")
-    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
     parser.add_argument(
         "--find-peak-performance",
         action="store_true",
@@ -121,10 +131,6 @@ def get_args():
         help="framework to load the latents",
     )
 
-    # file to use mlperf rules compliant parameters
-    parser.add_argument(
-        "--mlperf_conf", default="mlperf.conf", help="mlperf rules config"
-    )
     # file for user LoadGen settings such as target QPS
     parser.add_argument(
         "--user_conf",
@@ -139,9 +145,13 @@ def get_args():
     # pass this argument for official submission
     # parser.add_argument("--output-images", action="store_true", help="Store a subset of the generated images")
     # do not modify this argument for official submission
-    parser.add_argument("--ids-path", help="Path to caption ids", default="tools/sample_ids.txt")
+    parser.add_argument(
+        "--ids-path",
+        help="Path to caption ids",
+        default="tools/sample_ids.txt")
 
-    # below will override mlperf rules compliant settings - don't use for official submission
+    # below will override mlperf rules compliant settings - don't use for
+    # official submission
     parser.add_argument("--time", type=int, help="time to scan in seconds")
     parser.add_argument("--count", type=int, help="dataset items to use")
     parser.add_argument("--debug", action="store_true", help="debug")
@@ -259,9 +269,9 @@ def enqueue(self, query_samples):
         else:
             bs = self.max_batchsize
             for i in range(0, len(idx), bs):
-                data, label = self.ds.get_samples(idx[i : i + bs])
+                data, label = self.ds.get_samples(idx[i: i + bs])
                 self.run_one_item(
-                    Item(query_id[i : i + bs], idx[i : i + bs], data, label)
+                    Item(query_id[i: i + bs], idx[i: i + bs], data, label)
                 )
 
     def finish(self):
@@ -276,7 +286,9 @@ def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
         self.result_dict = {}
 
         for _ in range(self.threads):
-            worker = threading.Thread(target=self.handle_tasks, args=(self.tasks,))
+            worker = threading.Thread(
+                target=self.handle_tasks, args=(
+                    self.tasks,))
             worker.daemon = True
             self.workers.append(worker)
             worker.start()
@@ -366,18 +378,13 @@ def main():
         "cmdline": str(args),
     }
 
-    mlperf_conf = os.path.abspath(args.mlperf_conf)
-    if not os.path.exists(mlperf_conf):
-        log.error("{} not found".format(mlperf_conf))
-        sys.exit(1)
-
     user_conf = os.path.abspath(args.user_conf)
     if not os.path.exists(user_conf):
         log.error("{} not found".format(user_conf))
         sys.exit(1)
 
     audit_config = os.path.abspath(args.audit_conf)
-    
+
     if args.accuracy:
         ids_path = os.path.abspath(args.ids_path)
         with open(ids_path) as f:
@@ -432,7 +439,8 @@ def flush_queries():
     log_settings.log_output = log_output_settings
 
     settings = lg.TestSettings()
-    settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
+    # mlperf.conf is automatically loaded by the loadgen
+    # settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
     settings.FromConfig(user_conf, args.model_name, args.scenario)
     if os.path.exists(audit_config):
         settings.FromConfig(audit_config, args.model_name, args.scenario)
@@ -461,7 +469,8 @@ def flush_queries():
         settings.multi_stream_samples_per_query = args.samples_per_query
     if args.max_latency:
         settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
-        settings.multi_stream_expected_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_expected_latency_ns = int(
+            args.max_latency * NANO_SEC)
 
     performance_sample_count = (
         args.performance_sample_count
diff --git a/vision/classification_and_detection/python/main.py b/vision/classification_and_detection/python/main.py
index d6f16467ac..a41e0c1dff 100755
--- a/vision/classification_and_detection/python/main.py
+++ b/vision/classification_and_detection/python/main.py
@@ -51,20 +51,20 @@
         (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(),
          {"image_size": [300, 300, 3]}),
     "coco-300-pt":
-        (coco.Coco, dataset.pre_process_coco_pt_mobilenet, coco.PostProcessCocoPt(False,0.3),
+        (coco.Coco, dataset.pre_process_coco_pt_mobilenet, coco.PostProcessCocoPt(False, 0.3),
          {"image_size": [300, 300, 3]}),
     "openimages-300-retinanet":
-        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False,0.05,300,300), 
-        {"image_size": [300, 300, 3]}),
+        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False, 0.05, 300, 300),
+         {"image_size": [300, 300, 3]}),
     "openimages-800-retinanet":
-        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False,0.05,800,800), 
-        {"image_size": [800, 800, 3]}),
+        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False, 0.05, 800, 800),
+         {"image_size": [800, 800, 3]}),
     "openimages-1200-retinanet":
-        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False,0.05,1200,1200), 
-        {"image_size": [1200, 1200, 3]}),
+        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False, 0.05, 1200, 1200),
+         {"image_size": [1200, 1200, 3]}),
     "openimages-800-retinanet-onnx":
-        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False,0.05,800,800,False), 
-        {"image_size": [800, 800, 3]}),       
+        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False, 0.05, 800, 800, False),
+         {"image_size": [800, 800, 3]}),
     "coco-1200":
         (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCoco(),
          {"image_size": [1200, 1200, 3]}),
@@ -72,11 +72,11 @@
         (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoOnnx(),
          {"image_size": [1200, 1200, 3]}),
     "coco-1200-pt":
-        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoPt(True,0.05),
-         {"image_size": [1200, 1200, 3],"use_label_map": True}),
+        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoPt(True, 0.05),
+         {"image_size": [1200, 1200, 3], "use_label_map": True}),
     "coco-1200-tf":
         (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoTf(),
-         {"image_size": [1200, 1200, 3],"use_label_map": False}),
+         {"image_size": [1200, 1200, 3], "use_label_map": False}),
 }
 
 # pre-defined command line options so simplify things. They are used as defaults and can be
@@ -227,44 +227,102 @@
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--dataset", choices=SUPPORTED_DATASETS.keys(), help="dataset")
-    parser.add_argument("--dataset-path", required=True, help="path to the dataset")
+    parser.add_argument(
+        "--dataset",
+        choices=SUPPORTED_DATASETS.keys(),
+        help="dataset")
+    parser.add_argument(
+        "--dataset-path",
+        required=True,
+        help="path to the dataset")
     parser.add_argument("--dataset-list", help="path to the dataset list")
-    parser.add_argument("--data-format", choices=["NCHW", "NHWC"], help="data format")
-    parser.add_argument("--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles")
+    parser.add_argument(
+        "--data-format",
+        choices=[
+            "NCHW",
+            "NHWC"],
+        help="data format")
+    parser.add_argument(
+        "--profile",
+        choices=SUPPORTED_PROFILES.keys(),
+        help="standard profiles")
     parser.add_argument("--scenario", default="SingleStream",
                         help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())))
-    parser.add_argument("--max-batchsize", type=int, help="max batch size in a single inference")
+    parser.add_argument(
+        "--max-batchsize",
+        type=int,
+        help="max batch size in a single inference")
     parser.add_argument("--model", required=True, help="model file")
     parser.add_argument("--output", default="output", help="test results")
     parser.add_argument("--inputs", help="model inputs")
     parser.add_argument("--outputs", help="model outputs")
     parser.add_argument("--backend", help="runtime to use")
     parser.add_argument("--device", help="device to use")
-    parser.add_argument("--model-name", help="name of the mlperf model, ie. resnet50")
-    parser.add_argument("--threads", default=os.cpu_count(), type=int, help="threads")
+    parser.add_argument(
+        "--model-name",
+        help="name of the mlperf model, ie. resnet50")
+    parser.add_argument(
+        "--threads",
+        default=os.cpu_count(),
+        type=int,
+        help="threads")
     parser.add_argument("--qps", type=int, help="target qps")
     parser.add_argument("--cache", type=int, default=0, help="use cache")
-    parser.add_argument("--cache_dir", type=str, default=None, help="dir path for caching")
-    parser.add_argument("--preprocessed_dir", type=str, default=None, help="dir path for storing preprocessed images (overrides cache_dir)")
-    parser.add_argument("--use_preprocessed_dataset", action="store_true", help="use preprocessed dataset instead of the original")
-    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
-    parser.add_argument("--find-peak-performance", action="store_true", help="enable finding peak performance pass")
-    parser.add_argument("--debug", action="store_true", help="debug, turn traces on")
-
-    # file to use mlperf rules compliant parameters
-    parser.add_argument("--mlperf_conf", default="../../mlperf.conf", help="mlperf rules config")
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="dir path for caching")
+    parser.add_argument(
+        "--preprocessed_dir",
+        type=str,
+        default=None,
+        help="dir path for storing preprocessed images (overrides cache_dir)")
+    parser.add_argument(
+        "--use_preprocessed_dataset",
+        action="store_true",
+        help="use preprocessed dataset instead of the original")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
+    parser.add_argument(
+        "--find-peak-performance",
+        action="store_true",
+        help="enable finding peak performance pass")
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="debug, turn traces on")
+
     # file for user LoadGen settings such as target QPS
-    parser.add_argument("--user_conf", default="user.conf", help="user config for user LoadGen settings such as target QPS")
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS")
     # file for LoadGen audit settings
-    parser.add_argument("--audit_conf", default="audit.config", help="config for LoadGen audit settings")
+    parser.add_argument(
+        "--audit_conf",
+        default="audit.config",
+        help="config for LoadGen audit settings")
 
-    # below will override mlperf rules compliant settings - don't use for official submission
+    # below will override mlperf rules compliant settings - don't use for
+    # official submission
     parser.add_argument("--time", type=int, help="time to scan in seconds")
     parser.add_argument("--count", type=int, help="dataset items to use")
-    parser.add_argument("--performance-sample-count", type=int, help="performance sample count")
-    parser.add_argument("--max-latency", type=float, help="mlperf max latency in pct tile")
-    parser.add_argument("--samples-per-query", default=8, type=int, help="mlperf multi-stream samples per query")
+    parser.add_argument(
+        "--performance-sample-count",
+        type=int,
+        help="performance sample count")
+    parser.add_argument(
+        "--max-latency",
+        type=float,
+        help="mlperf max latency in pct tile")
+    parser.add_argument(
+        "--samples-per-query",
+        default=8,
+        type=int,
+        help="mlperf multi-stream samples per query")
     args = parser.parse_args()
 
     # don't use defaults in argparser. Instead we default to a dict, override that with a profile
@@ -306,7 +364,7 @@ def get_backend(backend):
         backend = BackendPytorch()
     elif backend == "pytorch-native":
         from backend_pytorch_native import BackendPytorchNative
-        backend = BackendPytorchNative()      
+        backend = BackendPytorchNative()
     elif backend == "tflite":
         from backend_tflite import BackendTflite
         backend = BackendTflite()
@@ -354,7 +412,8 @@ def run_one_item(self, qitem):
         processed_results = []
         try:
             results = self.model.predict({self.model.inputs[0]: qitem.img})
-            processed_results = self.post_process(results, qitem.content_id, qitem.label, self.result_dict)
+            processed_results = self.post_process(
+                results, qitem.content_id, qitem.label, self.result_dict)
             if self.take_accuracy:
                 self.post_process.add_results(processed_results)
             self.result_timing.append(time.time() - qitem.start)
@@ -367,7 +426,8 @@ def run_one_item(self, qitem):
             response_array_refs = []
             response = []
             for idx, query_id in enumerate(qitem.query_id):
-                response_array = array.array("B", np.array(processed_results[idx], np.float32).tobytes())
+                response_array = array.array("B", np.array(
+                    processed_results[idx], np.float32).tobytes())
                 response_array_refs.append(response_array)
                 bi = response_array.buffer_info()
                 response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
@@ -382,8 +442,9 @@ def enqueue(self, query_samples):
         else:
             bs = self.max_batchsize
             for i in range(0, len(idx), bs):
-                data, label = self.ds.get_samples(idx[i:i+bs])
-                self.run_one_item(Item(query_id[i:i+bs], idx[i:i+bs], data, label))
+                data, label = self.ds.get_samples(idx[i:i + bs])
+                self.run_one_item(
+                    Item(query_id[i:i + bs], idx[i:i + bs], data, label))
 
     def finish(self):
         pass
@@ -397,7 +458,9 @@ def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
         self.result_dict = {}
 
         for _ in range(self.threads):
-            worker = threading.Thread(target=self.handle_tasks, args=(self.tasks,))
+            worker = threading.Thread(
+                target=self.handle_tasks, args=(
+                    self.tasks,))
             worker.daemon = True
             self.workers.append(worker)
             worker.start()
@@ -434,10 +497,12 @@ def finish(self):
             worker.join()
 
 
-def add_results(final_results, name, result_dict, result_list, took, show_accuracy=False):
+def add_results(final_results, name, result_dict,
+                result_list, took, show_accuracy=False):
     percentiles = [50., 80., 90., 95., 99., 99.9]
     buckets = np.percentile(result_list, percentiles).tolist()
-    buckets_str = ",".join(["{}:{:.4f}".format(p, b) for p, b in zip(percentiles, buckets)])
+    buckets_str = ",".join(["{}:{:.4f}".format(p, b)
+                           for p, b in zip(percentiles, buckets)])
 
     if result_dict["total"] == 0:
         result_dict["total"] = len(result_list)
@@ -478,7 +543,7 @@ def main():
     # find backend
     backend = get_backend(args.backend)
 
-     # If TVM, pass max_batchsize to the backend
+    # If TVM, pass max_batchsize to the backend
     if args.backend.startswith('tvm'):
         backend.max_batchsize = args.max_batchsize
         backend.arena_num = args.threads
@@ -497,7 +562,7 @@ def main():
     # dataset to use
     wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
     if args.use_preprocessed_dataset:
-        pre_proc=None
+        pre_proc = None
     ds = wanted_dataset(data_path=args.dataset_path,
                         image_list=args.dataset_list,
                         name=args.dataset,
@@ -511,9 +576,16 @@ def main():
                         **kwargs)
     # load model to backend
     if args.device == "tpu":
-        model = backend.load(args.model, inputs=args.inputs, outputs=args.outputs, use_tpu=True)
+        model = backend.load(
+            args.model,
+            inputs=args.inputs,
+            outputs=args.outputs,
+            use_tpu=True)
     else:
-        model = backend.load(args.model, inputs=args.inputs, outputs=args.outputs)
+        model = backend.load(
+            args.model,
+            inputs=args.inputs,
+            outputs=args.outputs)
     final_results = {
         "runtime": model.name(),
         "version": model.version(),
@@ -522,11 +594,6 @@ def main():
         "cmdline": str(args),
     }
 
-    mlperf_conf = os.path.abspath(args.mlperf_conf)
-    if not os.path.exists(mlperf_conf):
-        log.error("{} not found".format(mlperf_conf))
-        sys.exit(1)
-
     user_conf = os.path.abspath(args.user_conf)
     if not os.path.exists(user_conf):
         log.error("{} not found".format(user_conf))
@@ -558,7 +625,12 @@ def main():
         lg.TestScenario.Server: QueueRunner,
         lg.TestScenario.Offline: QueueRunner
     }
-    runner = runner_map[scenario](model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize)
+    runner = runner_map[scenario](
+        model,
+        ds,
+        args.threads,
+        post_proc=post_proc,
+        max_batchsize=args.max_batchsize)
 
     def issue_queries(query_samples):
         runner.enqueue(query_samples)
@@ -574,7 +646,8 @@ def flush_queries():
     log_settings.log_output = log_output_settings
 
     settings = lg.TestSettings()
-    settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
+    # mlperf.conf is automatically loaded by the loadgen
+    # settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
     settings.FromConfig(user_conf, args.model_name, args.scenario)
     settings.scenario = scenario
     settings.mode = lg.TestMode.PerformanceOnly
@@ -601,11 +674,17 @@ def flush_queries():
         settings.multi_stream_samples_per_query = args.samples_per_query
     if args.max_latency:
         settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
-        settings.multi_stream_expected_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_expected_latency_ns = int(
+            args.max_latency * NANO_SEC)
 
-    performance_sample_count = args.performance_sample_count if args.performance_sample_count else min(count, 500)
+    performance_sample_count = args.performance_sample_count if args.performance_sample_count else min(
+        count, 500)
     sut = lg.ConstructSUT(issue_queries, flush_queries)
-    qsl = lg.ConstructQSL(count, performance_sample_count, ds.load_query_samples, ds.unload_query_samples)
+    qsl = lg.ConstructQSL(
+        count,
+        performance_sample_count,
+        ds.load_query_samples,
+        ds.unload_query_samples)
 
     log.info("starting {}".format(scenario))
     result_dict = {"good": 0, "total": 0, "scenario": str(scenario)}
diff --git a/vision/classification_and_detection/run_local.sh b/vision/classification_and_detection/run_local.sh
index 120b538c76..680160d334 100755
--- a/vision/classification_and_detection/run_local.sh
+++ b/vision/classification_and_detection/run_local.sh
@@ -2,7 +2,7 @@
 
 source ./run_common.sh
 
-common_opt="--mlperf_conf ../../mlperf.conf"
+common_opt=""
 dataset="--dataset-path $DATA_DIR"
 OUTPUT_DIR=${OUTPUT_DIR:-`pwd`/output/$name}
 if [ ! -d "$OUTPUT_DIR" ]; then

From f74d16f54131d9080b9e45f234cc23e0ebaaf20c Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Tue, 22 Oct 2024 13:38:02 -0700
Subject: [PATCH 159/305] Apply min_new_tokens=2 to mixtral-8x7b, address #1777
 (#1884)

---
 compliance/nvidia/TEST06/README.md           |  2 +-
 compliance/nvidia/TEST06/run_verification.py |  3 +--
 language/mixtral-8x7b/README.md              | 13 ++++++++-----
 language/mixtral-8x7b/SUT.py                 |  2 +-
 4 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/compliance/nvidia/TEST06/README.md b/compliance/nvidia/TEST06/README.md
index 9ca041cbd8..0fd29dd39f 100644
--- a/compliance/nvidia/TEST06/README.md
+++ b/compliance/nvidia/TEST06/README.md
@@ -10,7 +10,7 @@ This repository provides the config files and scripts to run and verify TEST 06
 
 The purpose of this test is to ensure the consistency of the output of the LLM (Llama2 and Mixtral) model and avoid a potential EOS exploit. This test will make a performance run, with a limit of 100 samples and logging them into `mlperf_log_accuracy.json`. To achieve a passing result in this test, three criteria must be met:
 - In the case the first token is reported independently (not applicable for Offline scenario), it should match for every query with the first token of the model output.
-- For each query, the model output should only end with zero or one EOS token. The only exception for 2 EOS tokens is when the entire output sequences are EOS tokens (i.e. output is [eos_token_id, eos_token_id])
+- For each query, the model output should only end with zero or one EOS token.
 - The number of reported tokens should match with the length of output sequence.
 
 ## Requisites
diff --git a/compliance/nvidia/TEST06/run_verification.py b/compliance/nvidia/TEST06/run_verification.py
index 092672a9a4..0f57d01f81 100644
--- a/compliance/nvidia/TEST06/run_verification.py
+++ b/compliance/nvidia/TEST06/run_verification.py
@@ -51,8 +51,7 @@ def eos_check(acc_data, dtype, eos_token_id=2):
             if data[i] == eos_token_id:
                 n_eos_tokens += 1
             if n_eos_tokens >= 2:
-                # Allow output to be [eos_token_id, eos_token_id]
-                return len(data) == 2
+                return False
             if data[i] != eos_token_id:
                 break
             i-=1
diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 341ae539a4..7b20b6ba0b 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -109,6 +109,9 @@ rclone copyurl https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06
 #### Using wget
 
 Alternatively, you can simply cd into the folder where you want to place the dataset and run
+
+TBD: The dataset is being replaced in v5.0 due to https://github.com/mlcommons/inference/issues/1777
+
 ```bash
 wget https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_15k_v4.pkl
 ```
@@ -261,17 +264,17 @@ python -u evaluate-accuracy.py --checkpoint-path [path_to_model_checkpoint] \
 Reference scores:
 Open Orca:
 ```json
-{'rouge1': 45.4911, 'rouge2': 23.2829, 'rougeL': 30.3615}
+{'rouge1': 45.5989, 'rouge2': 23.3526, 'rougeL': 30.4608}
 ```
 GSM8K:
 ```json
-{'gsm8k': 73.78}
+{'gsm8k': 73.66}
 ```
 MBXP:
 ```json
-{'mbxp': 60.12}
+{'mbxp': 60.16}
 ```
-For official submissions, 99% of each reference score is enforced. Additionally, 90%-110% of the generated tokens_per_samples:
+For official submissions, 99% of each reference score is enforced. Additionally, 90%-110% of the generated tokens_per_samples (counting all the non-EOS tokens):
 ```json
-{'tokens_per_sample': 145.9}
+{'tokens_per_sample': 144.84}
 ```
diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
index 536ba46f57..5c487a630c 100644
--- a/language/mixtral-8x7b/SUT.py
+++ b/language/mixtral-8x7b/SUT.py
@@ -27,7 +27,7 @@
 gen_kwargs = {
     "early_stopping": True,
     "max_new_tokens": 1024,
-    "min_new_tokens": 1,
+    "min_new_tokens": 2,
     "num_beams": 1,
     "do_sample": False
 }

From 0cde97774d85a291e26fd940089da841279356d7 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 29 Oct 2024 04:17:12 -0500
Subject: [PATCH 160/305] Format code files (#1681)

---
 calibration/BraTS/brats_cal_images_list.py    |    8 +-
 compliance/nvidia/TEST01/run_verification.py  |  134 +-
 compliance/nvidia/TEST01/verify_accuracy.py   |  153 +-
 .../nvidia/TEST01/verify_performance.py       |   79 +-
 compliance/nvidia/TEST04/run_verification.py  |   71 +-
 .../nvidia/TEST04/verify_performance.py       |   72 +-
 compliance/nvidia/TEST05/run_verification.py  |   76 +-
 .../nvidia/TEST05/verify_performance.py       |   82 +-
 compliance/nvidia/TEST06/run_verification.py  |   81 +-
 language/bert/accuracy-squad.py               |  210 +-
 language/bert/bert_QDL.py                     |   62 +-
 language/bert/bert_tf_to_pytorch.py           |   59 +-
 language/bert/create_squad_data.py            |  813 ++++---
 language/bert/evaluate_v1.1.py                |   75 +-
 language/bert/network_LON.py                  |   37 +-
 language/bert/onnxruntime_SUT.py              |   64 +-
 language/bert/pytorch_SUT.py                  |   71 +-
 language/bert/ray_SUT.py                      |  184 +-
 language/bert/run.py                          |  135 +-
 language/bert/squad_QSL.py                    |   47 +-
 language/bert/tf_SUT.py                       |   65 +-
 language/bert/tf_estimator_SUT.py             |  116 +-
 language/bert/tf_freeze_bert.py               |  150 +-
 language/gpt-j/GPTJ_QDL.py                    |   59 +-
 language/gpt-j/GPTJ_QSL.py                    |   19 +-
 language/gpt-j/backend_PyTorch.py             |  239 +-
 language/gpt-j/dataset.py                     |   38 +-
 language/gpt-j/download_cnndm.py              |   33 +-
 language/gpt-j/download_gptj.py               |   10 +-
 language/gpt-j/evaluation.py                  |   49 +-
 language/gpt-j/main.py                        |  122 +-
 language/gpt-j/network_SUT.py                 |   22 +-
 language/gpt-j/prepare-calibration.py         |   37 +-
 language/gpt-j/systemStatus.py                |    4 +-
 language/gpt-j/tokenizer_GPTJ.py              |   10 +-
 language/gpt-j/utils.py                       |    2 +
 language/llama2-70b/SUT.py                    |  224 +-
 language/llama2-70b/SUT_API.py                |  281 ++-
 language/llama2-70b/consolidate_results.py    |   43 +-
 language/llama2-70b/dataset.py                |   57 +-
 language/llama2-70b/evaluate-accuracy.py      |   66 +-
 language/llama2-70b/main.py                   |   71 +-
 language/llama2-70b/processorca.py            |  191 +-
 language/mixtral-8x7b/SUT.py                  |  292 ++-
 language/mixtral-8x7b/dataset.py              |   46 +-
 language/mixtral-8x7b/evaluate-accuracy.py    |  102 +-
 language/mixtral-8x7b/evaluate_mbxp.py        |   11 +-
 language/mixtral-8x7b/main.py                 |   52 +-
 loadgen/bindings/c_api.cc                     |    9 +-
 loadgen/bindings/c_api.h                      |   11 +-
 loadgen/demos/lon/py_demo_server_lon.py       |   52 +-
 loadgen/demos/lon/sut_over_network_demo.py    |   28 +-
 loadgen/demos/py_demo_multi_stream.py         |   14 +-
 loadgen/demos/py_demo_offline.py              |   20 +-
 loadgen/demos/py_demo_server.py               |    8 +-
 loadgen/demos/py_demo_single_stream.py        |   14 +-
 .../token_metrics/py_demo_multi_stream.py     |   44 +-
 .../demos/token_metrics/py_demo_offline.py    |   42 +-
 .../token_metrics/py_demo_offline_inferred.py |   43 +-
 loadgen/demos/token_metrics/py_demo_server.py |   40 +-
 .../token_metrics/py_demo_server_inferred.py  |   33 +-
 .../token_metrics/py_demo_single_stream.py    |   38 +-
 loadgen/docs/src/doxygen_html_generator.py    |    2 +-
 loadgen/issue_query_controller.cc             |   20 +-
 loadgen/issue_query_controller.h              |    4 +-
 loadgen/loadgen.cc                            |  129 +-
 loadgen/loadgen.h                             |    5 +-
 loadgen/logging.cc                            |  118 +-
 loadgen/logging.h                             |   58 +-
 loadgen/query_dispatch_library.h              |    6 +-
 loadgen/query_sample.h                        |   50 +-
 loadgen/results.cc                            |  291 ++-
 loadgen/setup.py                              |   44 +-
 loadgen/test_settings_internal.h              |    4 +-
 loadgen/tests/loadgen_test.h                  |    8 +-
 loadgen/tests/perftests_null_sut.py           |    3 +-
 loadgen/version_generator.py                  |   52 +-
 lon/network_SUT.py                            |   27 +-
 main.py                                       |  479 ++--
 .../dlrm_v2/pytorch/python/backend.py         |    4 +-
 .../python/backend_dist_pytorch_native.py     |   26 +-
 .../pytorch/python/backend_pytorch_native.py  |   24 +-
 .../dlrm_v2/pytorch/python/dataset.py         |   22 +-
 recommendation/dlrm_v2/pytorch/python/main.py |  242 +-
 .../dlrm_v2/pytorch/python/multihot_criteo.py |   81 +-
 .../dlrm_v2/pytorch/python/version.py         |    5 +-
 recommendation/dlrm_v2/pytorch/setup.py       |   71 +-
 .../dlrm_v2/pytorch/tools/accuracy-dlrm.py    |   81 +-
 .../dlrm_v2/pytorch/tools/quickgen.py         |   39 +-
 .../language/gpt3/megatron/backend.py         |   39 +-
 .../language/gpt3/megatron/dataset.py         |   50 +-
 .../language/gpt3/megatron/download_cnndm.py  |   11 +-
 .../language/gpt3/megatron/evaluation.py      |   39 +-
 .../language/gpt3/megatron/main.py            |   10 +-
 .../gpt3/megatron/prepare-calibration.py      |   37 +-
 .../gpt3/megatron/text_generation_server.py   |   93 +-
 .../language/gpt3/megatron/utils.py           |    4 +-
 .../dlrm/pytorch/python/backend.py            |    4 +-
 .../pytorch/python/backend_onnxruntime.py     |   19 +-
 .../pytorch/python/backend_pytorch_native.py  |   25 +-
 .../dlrm/pytorch/python/backend_tf.py         |   29 +-
 .../dlrm/pytorch/python/criteo.py             |  170 +-
 .../dlrm/pytorch/python/dataset.py            |   51 +-
 .../dlrm/pytorch/python/main.py               |  734 +++++-
 .../dlrm/pytorch/python/tf_dlrm.py            |  448 ++--
 .../dlrm/pytorch/python/version.py            |    5 +-
 .../recommendation/dlrm/pytorch/setup.py      |   71 +-
 .../dlrm/pytorch/tools/accuracy-dlrm.py       |   78 +-
 .../dlrm/pytorch/tools/quickgen.py            |  142 +-
 .../recommendation/dlrm/tf/common.py          |  420 ++--
 .../recommendation/dlrm/tf/dataloader.py      |  501 ++--
 .../recommendation/dlrm/tf/dlrm.py            |  701 +++---
 .../dlrm/tf/dlrm_embedding_runner.py          |  303 +--
 .../recommendation/dlrm/tf/dlrm_main.py       |  427 ++--
 .../recommendation/dlrm/tf/feature_config.py  |  271 ++-
 .../recommendation/dlrm/tf/mlp_log.py         |   61 +-
 .../dlrm/tf/train_and_eval_runner.py          | 1062 +++++----
 .../recommendation/dlrm/tf/utils.py           |  104 +-
 .../speech_recognition/rnnt/QSL.py            |   54 +-
 .../speech_recognition/rnnt/accuracy_eval.py  |   82 +-
 .../rnnt/pytorch/dataset.py                   |  151 +-
 .../rnnt/pytorch/decoders.py                  |   26 +-
 .../rnnt/pytorch/helpers.py                   |   45 +-
 .../rnnt/pytorch/metrics.py                   |   14 +-
 .../rnnt/pytorch/model_separable_rnnt.py      |  118 +-
 .../rnnt/pytorch/parts/features.py            |  168 +-
 .../rnnt/pytorch/parts/manifest.py            |  101 +-
 .../rnnt/pytorch/parts/segment.py             |   71 +-
 .../rnnt/pytorch/parts/text/__init__.py       |    2 +-
 .../rnnt/pytorch/parts/text/cleaners.py       |   62 +-
 .../rnnt/pytorch/parts/text/numbers.py        |   61 +-
 .../rnnt/pytorch/preprocessing.py             |   11 +-
 .../speech_recognition/rnnt/pytorch/rnn.py    |   47 +-
 .../rnnt/pytorch/utils/convert_librispeech.py |   87 +-
 .../pytorch/utils/download_librispeech.py     |   50 +-
 .../rnnt/pytorch/utils/download_utils.py      |   24 +-
 .../rnnt/pytorch/utils/preprocessing_utils.py |   55 +-
 .../speech_recognition/rnnt/pytorch_SUT.py    |   92 +-
 .../speech_recognition/rnnt/run.py            |   58 +-
 .../gnmt/tensorflow/generic_loadgen.py        |   39 +-
 .../gnmt/tensorflow/loadgen_gnmt.py           |  341 ++-
 .../translation/gnmt/tensorflow/mlcube.py     |   51 +-
 .../gnmt/tensorflow/nmt/attention_model.py    |  339 +--
 .../gnmt/tensorflow/nmt/gnmt_model.py         |  621 ++---
 .../gnmt/tensorflow/nmt/inference.py          |  496 ++--
 .../gnmt/tensorflow/nmt/inference_test.py     |  283 +--
 .../translation/gnmt/tensorflow/nmt/model.py  | 1733 +++++++-------
 .../gnmt/tensorflow/nmt/model_helper.py       | 1243 +++++-----
 .../gnmt/tensorflow/nmt/model_test.py         | 2100 +++++++++--------
 .../translation/gnmt/tensorflow/nmt/nmt.py    | 1544 +++++++-----
 .../gnmt/tensorflow/nmt/nmt_test.py           |  104 +-
 .../gnmt/tensorflow/nmt/scripts/bleu.py       |  276 ++-
 .../gnmt/tensorflow/nmt/scripts/rouge.py      |  566 +++--
 .../translation/gnmt/tensorflow/nmt/train.py  | 1559 ++++++------
 .../tensorflow/nmt/utils/common_test_utils.py |  218 +-
 .../tensorflow/nmt/utils/evaluation_utils.py  |  256 +-
 .../nmt/utils/evaluation_utils_test.py        |   69 +-
 .../tensorflow/nmt/utils/iterator_utils.py    |  492 ++--
 .../nmt/utils/iterator_utils_test.py          |  548 ++---
 .../gnmt/tensorflow/nmt/utils/misc_utils.py   |  233 +-
 .../tensorflow/nmt/utils/misc_utils_test.py   |   38 +-
 .../gnmt/tensorflow/nmt/utils/nmt_utils.py    |  232 +-
 .../nmt/utils/standard_hparams_utils.py       |  164 +-
 .../gnmt/tensorflow/nmt/utils/vocab_utils.py  |  272 ++-
 .../tensorflow/nmt/utils/vocab_utils_test.py  |   49 +-
 .../gnmt/tensorflow/process_accuracy.py       |   38 +-
 .../translation/gnmt/tensorflow/run_task.py   |  126 +-
 .../python/backend.py                         |    4 +-
 .../python/backend_pytorch_native.py          |   15 +-
 .../python/backend_tf.py                      |   36 +-
 .../python/backend_tflite.py                  |   12 +-
 .../python/coco.py                            |  183 +-
 .../python/dataset.py                         |   59 +-
 .../python/imagenet.py                        |   40 +-
 .../python/main.py                            |  261 +-
 .../python/models/anchor_generator.py         |  694 +++---
 .../python/models/base_model_r34.py           |   98 +-
 .../python/models/convert_tf_weights.py       |   14 +-
 .../python/models/ssd_mobilenet_v1.py         |   19 +-
 .../python/models/ssd_r34.py                  |  404 ++--
 .../python/models/utils.py                    |   25 +-
 .../python/pycoco.py                          |  355 +--
 .../python/version.py                         |    5 +-
 .../classification_and_detection/setup.py     |   71 +-
 .../tools/accuracy-coco.py                    |   80 +-
 .../tools/accuracy-imagenet.py                |   39 +-
 .../tools/calibrate_torchvision_model.py      |   53 +-
 .../tools/coco-analyze.py                     |   59 +-
 .../tools/lglog2csv.py                        |   31 +-
 .../tools/resnet_save.py                      |  542 +++--
 .../tools/ssd-nhwc.py                         |  230 +-
 .../3d-unet-brats19/Task043_BraTS_2019.py     |  110 +-
 .../3d-unet-brats19/accuracy-brats.py         |  154 +-
 .../3d-unet-brats19/brats_QSL.py              |   35 +-
 .../3d-unet-brats19/onnxruntime_SUT.py        |   34 +-
 .../medical_imaging/3d-unet-brats19/ov_SUT.py |   36 +-
 .../3d-unet-brats19/preprocess.py             |   98 +-
 .../3d-unet-brats19/pytorch_SUT.py            |   83 +-
 .../medical_imaging/3d-unet-brats19/run.py    |   95 +-
 .../medical_imaging/3d-unet-brats19/tf_SUT.py |   32 +-
 .../3d-unet-brats19/unet_onnx_to_tf.py        |   28 +-
 .../3d-unet-brats19/unet_pytorch_to_onnx.py   |   86 +-
 text_to_image/backend_pytorch.py              |   47 +-
 text_to_image/coco.py                         |   25 +-
 text_to_image/main.py                         |   12 +-
 text_to_image/tools/accuracy_coco.py          |  131 +-
 text_to_image/tools/clip/clip_encoder.py      |   53 +-
 text_to_image/tools/coco.py                   |   56 +-
 text_to_image/tools/coco_calibration.py       |   54 +-
 .../tools/coco_generate_calibration.py        |   38 +-
 text_to_image/tools/fid/fid_score.py          |   40 +-
 text_to_image/tools/fid/inception.py          |   87 +-
 text_to_image/tools/latent.py                 |   20 +-
 text_to_image/tools/sample_ids.py             |   15 +-
 tools/submission/filter_errors.py             |    6 +-
 tools/submission/generate_final_report.py     |  666 +++---
 tools/submission/log_parser.py                |   62 +-
 tools/submission/power/power_checker.py       |   77 +-
 tools/submission/preprocess_submission.py     |  301 ++-
 tools/submission/submission_checker.py        |  727 ++++--
 tools/submission/truncate_accuracy_log.py     |  120 +-
 tools/upscale_coco/coco.py                    |  348 +--
 tools/upscale_coco/upscale_coco.py            |   96 +-
 .../graph/R-GAT/backend_pytorch.py            |   44 +-
 upcomming_benchmarks/graph/R-GAT/igbh.py      |  141 +-
 upcomming_benchmarks/graph/R-GAT/main.py      |   56 +-
 upcomming_benchmarks/graph/R-GAT/rgnn.py      |  146 +-
 .../graph/R-GAT/tools/compress_graph.py       |  329 ++-
 .../graph/R-GAT/tools/format_model.py         |   92 +-
 .../graph/R-GAT/tools/split_seeds.py          |  142 +-
 .../python/backend.py                         |    4 +-
 .../python/backend_ncnn.py                    |    8 +-
 .../python/backend_onnxruntime.py             |   17 +-
 .../python/backend_pytorch_native.py          |    3 +-
 .../python/backend_tf.py                      |   44 +-
 .../python/backend_tflite.py                  |   20 +-
 .../python/backend_tvm.py                     |   84 +-
 .../python/coco.py                            |  186 +-
 .../python/dataset.py                         |   69 +-
 .../python/imagenet.py                        |   74 +-
 .../python/main.py                            |  303 ++-
 .../python/models/anchor_generator.py         |  694 +++---
 .../python/models/base_model_r34.py           |   98 +-
 .../python/models/convert_tf_weights.py       |   14 +-
 .../python/models/ssd_mobilenet_v1.py         |   19 +-
 .../python/models/ssd_r34.py                  |  404 ++--
 .../python/models/utils.py                    |   25 +-
 .../python/ncnn_models/__init__.py            |    2 +-
 .../python/ncnn_models/resnet50.py            |    8 +-
 .../python/openimages.py                      |  148 +-
 .../python/pycoco.py                          |  355 +--
 .../python/version.py                         |    5 +-
 vision/classification_and_detection/setup.py  |   71 +-
 .../tools/accuracy-coco.py                    |   80 +-
 .../tools/accuracy-imagenet.py                |   39 +-
 .../tools/accuracy-openimages.py              |   82 +-
 .../tools/calibrate_torchvision_model.py      |   53 +-
 .../tools/coco-analyze.py                     |   59 +-
 .../tools/lglog2csv.py                        |   31 +-
 .../tools/openimages.py                       |   39 +-
 .../tools/openimages_calibration.py           |   35 +-
 .../tools/resnet50_tflite_edgetpu.py          |   25 +-
 .../tools/resnet50_v1_to_ncnn.py              |    8 +-
 .../tools/resnet_save.py                      |  542 +++--
 .../tools/retinanet_pytorch_to_onnx.py        |    3 +-
 .../tools/ssd-nhwc.py                         |  230 +-
 .../3d-unet-kits19/accuracy_kits.py           |  171 +-
 .../3d-unet-kits19/base_SUT.py                |   37 +-
 .../3d-unet-kits19/global_vars.py             |   69 +-
 .../3d-unet-kits19/inference_utils.py         |   26 +-
 .../3d-unet-kits19/kits_QSL.py                |   24 +-
 .../3d-unet-kits19/onnxruntime_SUT.py         |   23 +-
 .../3d-unet-kits19/preprocess.py              |  294 ++-
 .../3d-unet-kits19/pytorch_SUT.py             |   10 +-
 .../3d-unet-kits19/pytorch_checkpoint_SUT.py  |  123 +-
 vision/medical_imaging/3d-unet-kits19/run.py  |  110 +-
 .../3d-unet-kits19/tensorflow_SUT.py          |    8 +-
 .../3d-unet-kits19/unet_onnx_to_tensorflow.py |   30 +-
 .../3d-unet-kits19/unet_pytorch_to_onnx.py    |  111 +-
 279 files changed, 24664 insertions(+), 16837 deletions(-)

diff --git a/calibration/BraTS/brats_cal_images_list.py b/calibration/BraTS/brats_cal_images_list.py
index c0a43403ce..a87539198f 100644
--- a/calibration/BraTS/brats_cal_images_list.py
+++ b/calibration/BraTS/brats_cal_images_list.py
@@ -1,12 +1,16 @@
 import numpy as np
+
 np.random.seed(0)
 images = []
 for i in [0, 2, 3, 4]:
-    with open("../../v0.7/medical_imaging/3d-unet/folds/fold{:d}_validation.txt".format(i)) as f:
+    with open(
+        "../../v0.7/medical_imaging/3d-unet/folds/fold{:d}_validation.txt".format(
+            i)
+    ) as f:
         for line in f:
             images.append(line.rstrip())
 indices = np.random.permutation(len(images))[:40]
 selected = sorted([images[idx] for idx in indices])
 with open("brats_cal_images_list.txt", "w") as f:
     for img in selected:
-        print(img, file=f)
\ No newline at end of file
+        print(img, file=f)
diff --git a/compliance/nvidia/TEST01/run_verification.py b/compliance/nvidia/TEST01/run_verification.py
index 4923ca7506..51f265e209 100644
--- a/compliance/nvidia/TEST01/run_verification.py
+++ b/compliance/nvidia/TEST01/run_verification.py
@@ -28,36 +28,45 @@
     "byte": np.byte,
     "float32": np.float32,
     "int32": np.int32,
-    "int64": np.int64
+    "int64": np.int64,
 }
 
-def main():
 
+def main():
 
-    py3 = sys.version_info >= (3,0)
+    py3 = sys.version_info >= (3, 0)
     # Parse arguments to identify the path to the accuracy logs from
     #   the accuracy and performance runs
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--results_dir", "-r",
+        "--results_dir",
+        "-r",
         help="Specifies the path to the corresponding results directory that contains the accuracy and performance subdirectories containing the submission logs, i.e. inference_results_v0.7/closed/NVIDIA/results/T4x8/resnet/Offline.",
-        required=True
+        required=True,
     )
     parser.add_argument(
-        "--compliance_dir", "-c",
+        "--compliance_dir",
+        "-c",
         help="Specifies the path to the directory containing the logs from the compliance test run.",
-        required=True
+        required=True,
     )
     parser.add_argument(
-        "--output_dir", "-o",
+        "--output_dir",
+        "-o",
         help="Specifies the path to the output directory where compliance logs will be uploaded from, i.e. inference_results_v0.7/closed/NVIDIA/compliance/T4x8/resnet/Offline.",
-        required=True
+        required=True,
     )
     parser.add_argument(
-        "--dtype", default="byte", choices=["byte", "float32", "int32", "int64"], help="data type of the label (not needed in unixmode")
+        "--dtype",
+        default="byte",
+        choices=["byte", "float32", "int32", "int64"],
+        help="data type of the label (not needed in unixmode",
+    )
     parser.add_argument(
-        "--unixmode", action="store_true",
-        help="Use UNIX commandline utilities to verify accuracy (uses less memory but much slower.")
+        "--unixmode",
+        action="store_true",
+        help="Use UNIX commandline utilities to verify accuracy (uses less memory but much slower.",
+    )
 
     args = parser.parse_args()
 
@@ -70,44 +79,81 @@ def main():
         unixmode = " --unixmode"
         for binary in ["wc", "md5sum", "grep", "awk", "sed", "head", "tail"]:
             missing_binary = False
-            if shutil.which(binary) == None:
-                print("Error: This script requires the {:} commandline utility".format(binary))
+            if shutil.which(binary) is None:
+                print(
+                    "Error: This script requires the {:} commandline utility".format(
+                        binary
+                    )
+                )
                 missing_binary = True
         if missing_binary:
             exit()
 
     dtype = args.dtype
 
-    verify_accuracy_binary = os.path.join(os.path.dirname(__file__),"verify_accuracy.py")
+    verify_accuracy_binary = os.path.join(
+        os.path.dirname(__file__), "verify_accuracy.py"
+    )
     # run verify accuracy
-    verify_accuracy_command = "python3 " + verify_accuracy_binary + " --dtype " + args.dtype + unixmode + " -r " + results_dir + "/accuracy/mlperf_log_accuracy.json" + " -t " + compliance_dir + "/mlperf_log_accuracy.json | tee verify_accuracy.txt"
+    verify_accuracy_command = (
+        "python3 "
+        + verify_accuracy_binary
+        + " --dtype "
+        + args.dtype
+        + unixmode
+        + " -r "
+        + results_dir
+        + "/accuracy/mlperf_log_accuracy.json"
+        + " -t "
+        + compliance_dir
+        + "/mlperf_log_accuracy.json | tee verify_accuracy.txt"
+    )
     try:
         os.system(verify_accuracy_command)
     except Exception:
-        print("Exception occurred trying to execute:\n  " + verify_accuracy_command)
+        print(
+            "Exception occurred trying to execute:\n  " +
+            verify_accuracy_command)
     # check if verify accuracy script passes
 
     accuracy_pass_command = "grep PASS verify_accuracy.txt"
     try:
-        accuracy_pass = "TEST PASS" in subprocess.check_output(accuracy_pass_command, shell=True).decode("utf-8")
+        accuracy_pass = "TEST PASS" in subprocess.check_output(
+            accuracy_pass_command, shell=True
+        ).decode("utf-8")
     except Exception:
         accuracy_pass = False
 
     # run verify performance
-    verify_performance_binary = os.path.join(os.path.dirname(__file__),"verify_performance.py")
-    verify_performance_command = "python3 " + verify_performance_binary + " -r " + results_dir + "/performance/run_1/mlperf_log_summary.txt" + " -t " + compliance_dir + "/mlperf_log_summary.txt | tee verify_performance.txt"
+    verify_performance_binary = os.path.join(
+        os.path.dirname(__file__), "verify_performance.py"
+    )
+    verify_performance_command = (
+        "python3 "
+        + verify_performance_binary
+        + " -r "
+        + results_dir
+        + "/performance/run_1/mlperf_log_summary.txt"
+        + " -t "
+        + compliance_dir
+        + "/mlperf_log_summary.txt | tee verify_performance.txt"
+    )
     try:
         os.system(verify_performance_command)
     except Exception:
-        print("Exception occurred trying to execute:\n  " + verify_performance_command)
+        print(
+            "Exception occurred trying to execute:\n  " +
+            verify_performance_command)
 
     # check if verify performance script passes
     performance_pass_command = "grep PASS verify_performance.txt"
     try:
-        performance_pass = "TEST PASS" in subprocess.check_output(performance_pass_command, shell=True).decode("utf-8")
+        performance_pass = "TEST PASS" in subprocess.check_output(
+            performance_pass_command, shell=True
+        ).decode("utf-8")
     except Exception:
         performance_pass = False
-    
+
     # setup output compliance directory structure
     output_accuracy_dir = os.path.join(output_dir, "accuracy")
     output_performance_dir = os.path.join(output_dir, "performance", "run_1")
@@ -123,28 +169,44 @@ def main():
         print("Exception occurred trying to create " + output_performance_dir)
 
     # copy compliance logs to output compliance directory
-    shutil.copy2("verify_accuracy.txt",output_dir)
-    shutil.copy2("verify_performance.txt",output_dir)
-    accuracy_file = os.path.join(compliance_dir,"mlperf_log_accuracy.json")
-    summary_file = os.path.join(compliance_dir,"mlperf_log_summary.txt")
-    detail_file = os.path.join(compliance_dir,"mlperf_log_detail.txt")
+    shutil.copy2("verify_accuracy.txt", output_dir)
+    shutil.copy2("verify_performance.txt", output_dir)
+    accuracy_file = os.path.join(compliance_dir, "mlperf_log_accuracy.json")
+    summary_file = os.path.join(compliance_dir, "mlperf_log_summary.txt")
+    detail_file = os.path.join(compliance_dir, "mlperf_log_detail.txt")
 
     try:
-        shutil.copy2(accuracy_file,output_accuracy_dir)
+        shutil.copy2(accuracy_file, output_accuracy_dir)
     except Exception:
-        print("Exception occured trying to copy " + accuracy_file + " to " + output_accuracy_dir)
+        print(
+            "Exception occured trying to copy "
+            + accuracy_file
+            + " to "
+            + output_accuracy_dir
+        )
     try:
-        shutil.copy2(summary_file,output_performance_dir)
+        shutil.copy2(summary_file, output_performance_dir)
     except Exception:
-        print("Exception occured trying to copy " + summary_file + " to " + output_performance_dir)
+        print(
+            "Exception occured trying to copy "
+            + summary_file
+            + " to "
+            + output_performance_dir
+        )
     try:
-        shutil.copy2(detail_file,output_performance_dir)
+        shutil.copy2(detail_file, output_performance_dir)
     except Exception:
-        print("Exception occured trying to copy " + detail_file + " to " + output_performance_dir)
+        print(
+            "Exception occured trying to copy "
+            + detail_file
+            + " to "
+            + output_performance_dir
+        )
 
     print("Accuracy check pass: {:}".format(accuracy_pass))
     print("Performance check pass: {:}".format(performance_pass))
     print("TEST01 verification complete")
 
-if __name__ == '__main__':
-	main()
+
+if __name__ == "__main__":
+    main()
diff --git a/compliance/nvidia/TEST01/verify_accuracy.py b/compliance/nvidia/TEST01/verify_accuracy.py
index cfaca5dec4..7f8a750a35 100644
--- a/compliance/nvidia/TEST01/verify_accuracy.py
+++ b/compliance/nvidia/TEST01/verify_accuracy.py
@@ -13,54 +13,65 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =============================================================================
+import numpy as np
+import json
+import argparse
 import os
 import subprocess
 import sys
 import shutil
-sys.path.append(os.getcwd())
 
-import argparse
-import json
+sys.path.append(os.getcwd())
 
-import numpy as np
 
 dtype_map = {
     "byte": np.byte,
     "float32": np.float32,
     "int32": np.int32,
-    "int64": np.int64
+    "int64": np.int64,
 }
 
+
 def main():
 
-    py3 = sys.version_info >= (3,0)
+    py3 = sys.version_info >= (3, 0)
     # Parse arguments to identify the path to the accuracy logs from
     #   the accuracy and performance runs
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--reference_accuracy", "-r",
+        "--reference_accuracy",
+        "-r",
         help="Specifies the path to the accuracy log from a submission/accuracy run.",
-        default=""
+        default="",
     )
     parser.add_argument(
-        "--test_accuracy", "-t",
+        "--test_accuracy",
+        "-t",
         help="Specifies the path to the accuracy log from a performance run with accuracy log sampling enabled.",
-        default=""
+        default="",
     )
     parser.add_argument(
-        "--dtype", default="byte", choices=["byte", "float32", "int32", "int64"], help="data type of the label")
+        "--dtype",
+        default="byte",
+        choices=["byte", "float32", "int32", "int64"],
+        help="data type of the label",
+    )
 
     parser.add_argument(
-        "--unixmode", action="store_true",
-        help="Use unix commandline utilities instead of python JSON library (uses less memory but much slower.")
+        "--unixmode",
+        action="store_true",
+        help="Use unix commandline utilities instead of python JSON library (uses less memory but much slower.",
+    )
 
     parser.add_argument(
-        "--fastmode", action="store_true",
-        help="This flag has been deprecated. This script runs in fastmode by default. Use --unixmode to run in low memory consumption mode.")
+        "--fastmode",
+        action="store_true",
+        help="This flag has been deprecated. This script runs in fastmode by default. Use --unixmode to run in low memory consumption mode.",
+    )
     args = parser.parse_args()
 
     print("Verifying accuracy. This might take a while...")
-    acc_log  = args.reference_accuracy
+    acc_log = args.reference_accuracy
     perf_log = args.test_accuracy
 
     if not args.unixmode:
@@ -80,10 +91,10 @@ def main():
 
         print("Reading accuracy mode results...")
         for sample in acc_data:
-            #print sample["qsl_idx"]
+            # print sample["qsl_idx"]
             qsl_idx = sample["qsl_idx"]
             data = sample["data"]
-            if data == '':
+            if data == "":
                 data = ""
             if qsl_idx in results_dict.keys():
                 num_acc_log_duplicate_keys += 1
@@ -95,13 +106,28 @@ def main():
         print("Reading performance mode results...")
         for sample in perf_data:
             qsl_idx = sample["qsl_idx"]
-            data = np.frombuffer(bytes.fromhex(sample['data']), dtype_map[args.dtype]) if py3 == True \
-                else np.frombuffer(bytearray.fromhex(sample['data']), dtype_map[args.dtype])
+            data = (
+                np.frombuffer(bytes.fromhex(
+                    sample["data"]), dtype_map[args.dtype])
+                if py3 == True
+                else np.frombuffer(
+                    bytearray.fromhex(sample["data"]), dtype_map[args.dtype]
+                )
+            )
 
             if qsl_idx in results_dict.keys():
                 num_perf_log_qsl_idx_match += 1
-                data_perf = np.frombuffer(bytes.fromhex(results_dict[qsl_idx]), dtype_map[args.dtype]) \
-                    if py3 == True else np.frombuffer(bytearray.fromhex(results_dict[qsl_idx]), dtype_map[args.dtype])
+                data_perf = (
+                    np.frombuffer(
+                        bytes.fromhex(
+                            results_dict[qsl_idx]), dtype_map[args.dtype]
+                    )
+                    if py3 == True
+                    else np.frombuffer(
+                        bytearray.fromhex(
+                            results_dict[qsl_idx]), dtype_map[args.dtype]
+                    )
+                )
                 if data_perf.size == 0 or data.size == 0:
                     if data_perf.size != data.size:
                         num_perf_log_data_mismatch += 1
@@ -112,13 +138,16 @@ def main():
 
             results_dict[sample["qsl_idx"]] = sample["data"]
 
-
         print("num_acc_log_entries = {:}".format(len(acc_data)))
-        print("num_acc_log_duplicate_keys = {:}".format(num_acc_log_duplicate_keys))
-        print("num_acc_log_data_mismatch = {:}".format(num_acc_log_data_mismatch))
+        print("num_acc_log_duplicate_keys = {:}".format(
+            num_acc_log_duplicate_keys))
+        print("num_acc_log_data_mismatch = {:}".format(
+            num_acc_log_data_mismatch))
         print("num_perf_log_entries = {:}".format(len(perf_data)))
-        print("num_perf_log_qsl_idx_match = {:}".format(num_perf_log_qsl_idx_match))
-        print("num_perf_log_data_mismatch = {:}".format(num_perf_log_data_mismatch))
+        print("num_perf_log_qsl_idx_match = {:}".format(
+            num_perf_log_qsl_idx_match))
+        print("num_perf_log_data_mismatch = {:}".format(
+            num_perf_log_data_mismatch))
         print("num_missing_qsl_idxs = {:}".format(num_missing_qsl_idxs))
         if num_perf_log_data_mismatch == 0 and num_perf_log_qsl_idx_match > 0:
             print("TEST PASS\n")
@@ -126,48 +155,76 @@ def main():
             print("TEST FAIL\n")
         exit()
 
-    py33 = sys.version_info >= (3,3)
+    py33 = sys.version_info >= (3, 3)
 
     if not py33:
         print("Error: This script requires Python v3.3 or later")
         exit()
 
-
     get_perf_lines_cmd = "wc -l " + perf_log + "| awk '{print $1}'"
-    num_perf_lines = int(subprocess.check_output(get_perf_lines_cmd, shell=True).decode("utf-8"))
+    num_perf_lines = int(
+        subprocess.check_output(get_perf_lines_cmd, shell=True).decode("utf-8")
+    )
 
     get_acc_lines_cmd = "wc -l " + acc_log + "| awk '{print $1}'"
-    num_acc_lines = int(subprocess.check_output(get_acc_lines_cmd, shell=True).decode("utf-8"))
+    num_acc_lines = int(
+        subprocess.check_output(get_acc_lines_cmd, shell=True).decode("utf-8")
+    )
 
     num_acc_log_entries = num_acc_lines - 2
     num_perf_log_entries = num_perf_lines - 2
-    #print(perf_qsl_idx)
-    #print(get_perf_lines_cmd)
-    #print(num_perf_lines)
-    
+    # print(perf_qsl_idx)
+    # print(get_perf_lines_cmd)
+    # print(num_perf_lines)
+
     num_perf_log_data_mismatch = 0
     num_perf_log_data_match = 0
     print("Each dot represents 1% completion:")
     for perf_line in range(0, num_perf_lines):
-        if perf_line % int(num_perf_lines/100) == 0:
+        if perf_line % int(num_perf_lines / 100) == 0:
             sys.stdout.write(".")
             sys.stdout.flush()
         # first and last line are brackets
-        if perf_line == 0 or perf_line == int(num_perf_lines)-1:
+        if perf_line == 0 or perf_line == int(num_perf_lines) - 1:
             continue
 
         # calculate md5sum of line in perf mode accuracy_log
-        perf_md5sum_cmd = "head -n " + str(perf_line + 1) + " " + perf_log + "| tail -n 1| sed -r 's/,//g' | sed -r 's/\"seq_id\" : \S+//g' | md5sum"
-        #print(perf_md5sum_cmd)
-        perf_md5sum = subprocess.check_output(perf_md5sum_cmd, shell=True).decode("utf-8")
+        perf_md5sum_cmd = (
+            "head -n "
+            + str(perf_line + 1)
+            + " "
+            + perf_log
+            + "| tail -n 1| sed -r 's/,//g' | sed -r 's/\"seq_id\" : \\S+//g' | md5sum"
+        )
+        # print(perf_md5sum_cmd)
+        perf_md5sum = subprocess.check_output(perf_md5sum_cmd, shell=True).decode(
+            "utf-8"
+        )
 
         # get qsl idx
-        get_qsl_idx_cmd = "head -n " + str(perf_line + 1) + " " + perf_log + "| tail -n 1| awk -F\": |,\" '{print $4}'"
-        qsl_idx = subprocess.check_output(get_qsl_idx_cmd, shell=True).decode("utf-8").rstrip()
+        get_qsl_idx_cmd = (
+            "head -n "
+            + str(perf_line + 1)
+            + " "
+            + perf_log
+            + "| tail -n 1| awk -F\": |,\" '{print $4}'"
+        )
+        qsl_idx = (
+            subprocess.check_output(get_qsl_idx_cmd, shell=True)
+            .decode("utf-8")
+            .rstrip()
+        )
 
         # calculate md5sum of line in acc mode accuracy_log
-        acc_md5sum_cmd = "grep \"qsl_idx\\\" : " + qsl_idx + ",\" " + acc_log + "| sed -r 's/,//g' | sed -r 's/\"seq_id\" : \S+//g' | md5sum"
-        acc_md5sum = subprocess.check_output(acc_md5sum_cmd, shell=True).decode("utf-8")
+        acc_md5sum_cmd = (
+            'grep "qsl_idx\\" : '
+            + qsl_idx
+            + '," '
+            + acc_log
+            + "| sed -r 's/,//g' | sed -r 's/\"seq_id\" : \\S+//g' | md5sum"
+        )
+        acc_md5sum = subprocess.check_output(
+            acc_md5sum_cmd, shell=True).decode("utf-8")
 
         if perf_md5sum != acc_md5sum:
             num_perf_log_data_mismatch += 1
@@ -176,12 +233,14 @@ def main():
 
     print("")
     print("num_acc_log_entries = {:}".format(num_acc_log_entries))
-    print("num_perf_log_data_mismatch = {:}".format(num_perf_log_data_mismatch))
+    print("num_perf_log_data_mismatch = {:}".format(
+        num_perf_log_data_mismatch))
     print("num_perf_log_entries = {:}".format(num_perf_log_entries))
     if num_perf_log_data_mismatch == 0 and num_perf_log_data_match > 0:
         print("TEST PASS\n")
     else:
         print("TEST FAIL\n")
 
-if __name__ == '__main__':
-	main()
+
+if __name__ == "__main__":
+    main()
diff --git a/compliance/nvidia/TEST01/verify_performance.py b/compliance/nvidia/TEST01/verify_performance.py
index 44fabddee0..02ee8753b6 100644
--- a/compliance/nvidia/TEST01/verify_performance.py
+++ b/compliance/nvidia/TEST01/verify_performance.py
@@ -13,27 +13,30 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =============================================================================
+import json
+import argparse
 import os
 import sys
 import re
+
 sys.path.append(os.getcwd())
 
-import argparse
-import json
 
 def main():
     # Parse arguments to identify the path to the accuracy logs from
     #   the accuracy and performance runs
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--reference_summary", "-r",
+        "--reference_summary",
+        "-r",
         help="Specifies the path to the summary log for the performance run.",
-        default=""
+        default="",
     )
     parser.add_argument(
-        "--test_summary", "-t",
+        "--test_summary",
+        "-t",
         help="Specifies the path to the summary log for this test.",
-        default=""
+        default="",
     )
     args = parser.parse_args()
 
@@ -42,68 +45,67 @@ def main():
     test_file = open(args.test_summary, "r")
     ref_score = 0
     test_score = 0
-    ref_mode = ''
-    test_mode = ''
+    ref_mode = ""
+    test_mode = ""
 
     for line in ref_file:
         if re.match("Scenario", line):
-            ref_mode = line.split(": ",1)[1].strip()
+            ref_mode = line.split(": ", 1)[1].strip()
             continue
 
         if ref_mode == "SingleStream":
             if re.match(".*Early stopping 90th percentile estimate", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
 
         if ref_mode == "MultiStream":
             if re.match(".*Early stopping 99th percentile estimate", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
 
         if ref_mode == "Server":
             if re.match("Completed samples per second", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
             if re.match("target_latency (ns)", line):
-                ref_target_latency = line.split(": ",1)[1].strip()
+                ref_target_latency = line.split(": ", 1)[1].strip()
                 continue
 
         if ref_mode == "Offline":
             if re.match("Samples per second", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
 
         if re.match("Result is", line):
-            valid = line.split(": ",1)[1].strip()
-            if valid == 'INVALID':
+            valid = line.split(": ", 1)[1].strip()
+            if valid == "INVALID":
                 sys.exit("TEST FAIL: Reference results are invalid")
 
-        if re.match("\d+ ERROR", line):
-            error = line.split(" ",1)[0].strip()
+        if re.match("\\d+ ERROR", line):
+            error = line.split(" ", 1)[0].strip()
             print("WARNING: " + error + " ERROR reported in reference results")
 
-
     for line in test_file:
         if re.match("Scenario", line):
-            test_mode = line.split(": ",1)[1].strip()
+            test_mode = line.split(": ", 1)[1].strip()
             continue
 
         if test_mode == "SingleStream":
             if re.match(".*Early stopping 90th percentile estimate", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
 
         if test_mode == "MultiStream":
             if re.match(".*Early stopping 99th percentile estimate", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
 
         if test_mode == "Server":
             if re.match("Completed samples per second", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
             if re.match("target_latency (ns)", line):
-                test_target_latency = line.split(": ",1)[1].strip()
+                test_target_latency = line.split(": ", 1)[1].strip()
                 if test_target_latency != ref_target_latency:
                     print("TEST FAIL: Server target latency mismatch")
                     sys.exit()
@@ -111,16 +113,16 @@ def main():
 
         if test_mode == "Offline":
             if re.match("Samples per second", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
 
         if re.match("Result is", line):
-            valid = line.split(": ",1)[1].strip()
-            if valid == 'INVALID':
+            valid = line.split(": ", 1)[1].strip()
+            if valid == "INVALID":
                 sys.exit("TEST FAIL: Test results are invalid")
-            
-        if re.match("\d+ ERROR", line):
-            error = line.split(" ",1)[0].strip()
+
+        if re.match("\\d+ ERROR", line):
+            error = line.split(" ", 1)[0].strip()
             print("WARNING: " + error + " ERROR reported in test results")
 
     if test_mode != ref_mode:
@@ -129,22 +131,23 @@ def main():
     print("reference score = {}".format(ref_score))
     print("test score = {}".format(test_score))
 
- 
     threshold = 0.10
 
     # In single-/multi-stream mode, latencies can be very short for high performance systems
     # and run-to-run variation due to external disturbances (OS) can be significant.
     # In this case we relax pass threshold to 20%
-    if (ref_mode == "SingleStream" and float(ref_score) <= 200000) or\
-       (ref_mode == "MultiStream" and float(ref_score) <= 1600000):
+    if (ref_mode == "SingleStream" and float(ref_score) <= 200000) or (
+        ref_mode == "MultiStream" and float(ref_score) <= 1600000
+    ):
         threshold = 0.20
-        
-    if float(test_score) < float(ref_score) * (1 + threshold) and\
-       float(test_score) > float(ref_score) * (1 - threshold):
+
+    if float(test_score) < float(ref_score) * (1 + threshold) and float(
+        test_score
+    ) > float(ref_score) * (1 - threshold):
         print("TEST PASS")
     else:
         print("TEST FAIL: Test score invalid")
 
-if __name__ == '__main__':
-	main()
 
+if __name__ == "__main__":
+    main()
diff --git a/compliance/nvidia/TEST04/run_verification.py b/compliance/nvidia/TEST04/run_verification.py
index 027cf7aa86..647f71f82a 100644
--- a/compliance/nvidia/TEST04/run_verification.py
+++ b/compliance/nvidia/TEST04/run_verification.py
@@ -21,26 +21,29 @@
 
 sys.path.append(os.getcwd())
 
-def main():
 
+def main():
 
     # Parse arguments to identify the path to the logs from
     #   the performance runs
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--results_dir", "-r",
+        "--results_dir",
+        "-r",
         help="Specifies the path to the corresponding results directory that contains the performance subdirectories containing the submission logs, i.e. inference_results_v0.7/closed/NVIDIA/results/T4x8/resnet/Offline.",
-        required=True
+        required=True,
     )
     parser.add_argument(
-        "--compliance_dir", "-c",
+        "--compliance_dir",
+        "-c",
         help="Specifies the path to the directory containing the logs from the compliance test run.",
-        required=True
+        required=True,
     )
     parser.add_argument(
-        "--output_dir", "-o",
+        "--output_dir",
+        "-o",
         help="Specifies the path to the output directory where compliance logs will be uploaded from, i.e. inference_results_v0.7/closed/NVIDIA/compliance/T4x8/resnet/Offline.",
-        required=True
+        required=True,
     )
 
     args = parser.parse_args()
@@ -51,20 +54,35 @@ def main():
     output_dir = os.path.join(args.output_dir, "TEST04")
 
     # run verify performance
-    verify_performance_binary = os.path.join(os.path.dirname(__file__),"verify_performance.py")
-    verify_performance_command = "python3 " + verify_performance_binary + " -r " + results_dir + "/performance/run_1/mlperf_log_summary.txt" + " -t " + compliance_dir + "/mlperf_log_summary.txt | tee verify_performance.txt"
+    verify_performance_binary = os.path.join(
+        os.path.dirname(__file__), "verify_performance.py"
+    )
+    verify_performance_command = (
+        "python3 "
+        + verify_performance_binary
+        + " -r "
+        + results_dir
+        + "/performance/run_1/mlperf_log_summary.txt"
+        + " -t "
+        + compliance_dir
+        + "/mlperf_log_summary.txt | tee verify_performance.txt"
+    )
     try:
         os.system(verify_performance_command)
     except Exception:
-        print("Exception occurred trying to execute:\n  " + verify_performance_command)
+        print(
+            "Exception occurred trying to execute:\n  " +
+            verify_performance_command)
 
     # check if verify performance script passes
     performance_pass_command = "grep PASS verify_performance.txt"
     try:
-        performance_pass = "TEST PASS" in subprocess.check_output(performance_pass_command, shell=True).decode("utf-8")
+        performance_pass = "TEST PASS" in subprocess.check_output(
+            performance_pass_command, shell=True
+        ).decode("utf-8")
     except Exception:
         performance_pass = False
-    
+
     # setup output compliance directory structure
     output_performance_dir = os.path.join(output_dir, "performance", "run_1")
     try:
@@ -74,21 +92,32 @@ def main():
         print("Exception occurred trying to create " + output_performance_dir)
 
     # copy compliance logs to output compliance directory
-    shutil.copy2("verify_performance.txt",output_dir)
-    summary_file = os.path.join(compliance_dir,"mlperf_log_summary.txt")
-    detail_file = os.path.join(compliance_dir,"mlperf_log_detail.txt")
+    shutil.copy2("verify_performance.txt", output_dir)
+    summary_file = os.path.join(compliance_dir, "mlperf_log_summary.txt")
+    detail_file = os.path.join(compliance_dir, "mlperf_log_detail.txt")
 
     try:
-        shutil.copy2(summary_file,output_performance_dir)
+        shutil.copy2(summary_file, output_performance_dir)
     except Exception:
-        print("Exception occured trying to copy " + summary_file + " to " + output_performance_dir)
+        print(
+            "Exception occured trying to copy "
+            + summary_file
+            + " to "
+            + output_performance_dir
+        )
     try:
-        shutil.copy2(detail_file,output_performance_dir)
+        shutil.copy2(detail_file, output_performance_dir)
     except Exception:
-        print("Exception occured trying to copy " + detail_file + " to " + output_performance_dir)
+        print(
+            "Exception occured trying to copy "
+            + detail_file
+            + " to "
+            + output_performance_dir
+        )
 
     print("Performance check pass: {:}".format(performance_pass))
     print("TEST04 verification complete")
 
-if __name__ == '__main__':
-	main()
+
+if __name__ == "__main__":
+    main()
diff --git a/compliance/nvidia/TEST04/verify_performance.py b/compliance/nvidia/TEST04/verify_performance.py
index 0aec33ace9..861ec609cc 100644
--- a/compliance/nvidia/TEST04/verify_performance.py
+++ b/compliance/nvidia/TEST04/verify_performance.py
@@ -13,26 +13,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =============================================================================
+import argparse
 import os
 import sys
 import re
+
 sys.path.append(os.getcwd())
 
-import argparse
 
 def main():
     # Parse arguments to identify the path to the accuracy logs from
     #   the accuracy and performance runs
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--reference_summary", "-r",
+        "--reference_summary",
+        "-r",
         help="Specifies the path to the summary log for the performance run.",
-        default=""
+        default="",
     )
     parser.add_argument(
-        "--test_summary", "-t",
+        "--test_summary",
+        "-t",
         help="Specifies the path to the summary log for this test.",
-        default=""
+        default="",
     )
     args = parser.parse_args()
 
@@ -41,72 +44,71 @@ def main():
     test_file = open(args.test_summary, "r")
     ref_score = 0
     test_score = 0
-    ref_mode = ''
-    test_mode = ''
+    ref_mode = ""
+    test_mode = ""
 
     for line in ref_file:
         if re.match("Scenario", line):
-            ref_mode = line.split(": ",1)[1].strip()
+            ref_mode = line.split(": ", 1)[1].strip()
             continue
 
         if ref_mode == "SingleStream":
             if re.match(".*Early stopping 90th percentile estimate", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 ref_score = 1e9 / float(ref_score)
                 continue
 
         if ref_mode == "MultiStream":
             if re.match(".*Early stopping 99th percentile estimate", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 ref_score = 1e9 / float(ref_score)
                 continue
 
         if ref_mode == "Server":
             if re.match("Completed samples per second", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
             if re.match("target_latency (ns)", line):
-                ref_target_latency = line.split(": ",1)[1].strip()
+                ref_target_latency = line.split(": ", 1)[1].strip()
                 continue
 
         if ref_mode == "Offline":
             if re.match("Samples per second", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
 
         if re.match("Result is", line):
-            valid = line.split(": ",1)[1].strip()
-            if valid == 'INVALID':
+            valid = line.split(": ", 1)[1].strip()
+            if valid == "INVALID":
                 sys.exit("TEST FAIL: Reference results are invalid")
 
-        if re.match("\d+ ERROR", line):
-            error = line.split(" ",1)[0].strip()
+        if re.match("\\d+ ERROR", line):
+            error = line.split(" ", 1)[0].strip()
             print("WARNING: " + error + " ERROR reported in reference results")
 
-
     for line in test_file:
         if re.match("Scenario", line):
-            test_mode = line.split(": ",1)[1].strip()
+            test_mode = line.split(": ", 1)[1].strip()
             continue
 
         if test_mode == "SingleStream":
             if re.match(".*Early stopping 90th percentile estimate", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 test_score = 1e9 / float(test_score)
                 continue
 
         if test_mode == "MultiStream":
             if re.match(".*Early stopping 99th percentile estimate", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 test_score = 1e9 / float(test_score)
                 continue
 
         if test_mode == "Server":
             if re.match("Completed samples per second", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
             if re.match("target_latency (ns)", line):
-                test_target_latency = line.split(": ",1)[1].strip()
+                test_target_latency = line.split(": ", 1)[1].strip()
                 if test_target_latency != ref_target_latency:
                     print("TEST FAIL: Server target latency mismatch")
                     sys.exit()
@@ -114,16 +116,16 @@ def main():
 
         if test_mode == "Offline":
             if re.match("Samples per second", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
 
         if re.match("Result is", line):
-            valid = line.split(": ",1)[1].strip()
-            if valid == 'INVALID':
+            valid = line.split(": ", 1)[1].strip()
+            if valid == "INVALID":
                 sys.exit("TEST FAIL: Test results are invalid")
-            
-        if re.match("\d+ ERROR", line):
-            error = line.split(" ",1)[0].strip()
+
+        if re.match("\\d+ ERROR", line):
+            error = line.split(" ", 1)[0].strip()
             print("WARNING: " + error + " ERROR reported in test results")
 
     if test_mode != ref_mode:
@@ -132,21 +134,21 @@ def main():
     print("reference score = {}".format(ref_score))
     print("test score = {}".format(test_score))
 
- 
     threshold = 0.10
 
     # In single-/multi-stream mode, latencies can be very short for high performance systems
     # and run-to-run variation due to external disturbances (OS) can be significant.
     # In this case we relax pass threshold to 20%
-    if (ref_mode == "SingleStream" and float(ref_score) <= 200000) or\
-       (ref_mode == "MultiStream" and float(ref_score) <= 1600000):
+    if (ref_mode == "SingleStream" and float(ref_score) <= 200000) or (
+        ref_mode == "MultiStream" and float(ref_score) <= 1600000
+    ):
         threshold = 0.20
-        
+
     if float(test_score) < float(ref_score) * (1 + threshold):
         print("TEST PASS")
     else:
         print("TEST FAIL: Test score invalid")
 
-if __name__ == '__main__':
-	main()
 
+if __name__ == "__main__":
+    main()
diff --git a/compliance/nvidia/TEST05/run_verification.py b/compliance/nvidia/TEST05/run_verification.py
index 7bf32fabc5..8041551879 100644
--- a/compliance/nvidia/TEST05/run_verification.py
+++ b/compliance/nvidia/TEST05/run_verification.py
@@ -24,26 +24,30 @@
 
 sys.path.append(os.getcwd())
 
-def main():
 
+def main():
 
-    py3 = sys.version_info >= (3,0)
-    # Parse arguments to identify the path to the logs from the performance runs
+    py3 = sys.version_info >= (3, 0)
+    # Parse arguments to identify the path to the logs from the performance
+    # runs
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--results_dir", "-r",
+        "--results_dir",
+        "-r",
         help="Specifies the path to the corresponding results directory that contains the performance subdirectories containing the submission logs, i.e. inference_results_v0.7/closed/NVIDIA/results/T4x8/resnet/Offline.",
-        required=True
+        required=True,
     )
     parser.add_argument(
-        "--compliance_dir", "-c",
+        "--compliance_dir",
+        "-c",
         help="Specifies the path to the directory containing the logs from the compliance test run.",
-        required=True
+        required=True,
     )
     parser.add_argument(
-        "--output_dir", "-o",
+        "--output_dir",
+        "-o",
         help="Specifies the path to the output directory where compliance logs will be uploaded from, i.e. inference_results_v0.7/closed/NVIDIA/compliance/T4x8/resnet/Offline.",
-        required=True
+        required=True,
     )
 
     args = parser.parse_args()
@@ -54,20 +58,35 @@ def main():
     output_dir = os.path.join(args.output_dir, "TEST05")
 
     # run verify performance
-    verify_performance_binary = os.path.join(os.path.dirname(__file__),"verify_performance.py")
-    verify_performance_command = "python3 " + verify_performance_binary + " -r " + results_dir + "/performance/run_1/mlperf_log_summary.txt" + " -t " + compliance_dir + "/mlperf_log_summary.txt | tee verify_performance.txt"
+    verify_performance_binary = os.path.join(
+        os.path.dirname(__file__), "verify_performance.py"
+    )
+    verify_performance_command = (
+        "python3 "
+        + verify_performance_binary
+        + " -r "
+        + results_dir
+        + "/performance/run_1/mlperf_log_summary.txt"
+        + " -t "
+        + compliance_dir
+        + "/mlperf_log_summary.txt | tee verify_performance.txt"
+    )
     try:
         os.system(verify_performance_command)
     except Exception:
-        print("Exception occurred trying to execute:\n  " + verify_performance_command)
+        print(
+            "Exception occurred trying to execute:\n  " +
+            verify_performance_command)
 
     # check if verify performance script passes
     performance_pass_command = "grep PASS verify_performance.txt"
     try:
-        performance_pass = "TEST PASS" in subprocess.check_output(performance_pass_command, shell=True).decode("utf-8")
+        performance_pass = "TEST PASS" in subprocess.check_output(
+            performance_pass_command, shell=True
+        ).decode("utf-8")
     except Exception:
         performance_pass = False
-    
+
     # setup output compliance directory structure
     output_performance_dir = os.path.join(output_dir, "performance", "run_1")
     try:
@@ -77,21 +96,32 @@ def main():
         print("Exception occurred trying to create " + output_performance_dir)
 
     # copy compliance logs to output compliance directory
-    shutil.copy2("verify_performance.txt",output_dir)
-    summary_file = os.path.join(compliance_dir,"mlperf_log_summary.txt")
-    detail_file = os.path.join(compliance_dir,"mlperf_log_detail.txt")
+    shutil.copy2("verify_performance.txt", output_dir)
+    summary_file = os.path.join(compliance_dir, "mlperf_log_summary.txt")
+    detail_file = os.path.join(compliance_dir, "mlperf_log_detail.txt")
 
     try:
-        shutil.copy2(summary_file,output_performance_dir)
+        shutil.copy2(summary_file, output_performance_dir)
     except Exception:
-        print("Exception occured trying to copy " + summary_file + " to " + output_performance_dir)
+        print(
+            "Exception occured trying to copy "
+            + summary_file
+            + " to "
+            + output_performance_dir
+        )
     try:
-        shutil.copy2(detail_file,output_performance_dir)
+        shutil.copy2(detail_file, output_performance_dir)
     except Exception:
-        print("Exception occured trying to copy " + detail_file + " to " + output_performance_dir)
+        print(
+            "Exception occured trying to copy "
+            + detail_file
+            + " to "
+            + output_performance_dir
+        )
 
     print("Performance check pass: {:}".format(performance_pass))
     print("TEST05 verification complete")
 
-if __name__ == '__main__':
-	main()
+
+if __name__ == "__main__":
+    main()
diff --git a/compliance/nvidia/TEST05/verify_performance.py b/compliance/nvidia/TEST05/verify_performance.py
index 763737f776..864a8af446 100644
--- a/compliance/nvidia/TEST05/verify_performance.py
+++ b/compliance/nvidia/TEST05/verify_performance.py
@@ -13,27 +13,30 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =============================================================================
+import json
+import argparse
 import os
 import sys
 import re
+
 sys.path.append(os.getcwd())
 
-import argparse
-import json
 
 def main():
     # Parse arguments to identify the path to the accuracy logs from
     #   the accuracy and performance runs
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--reference_summary", "-r",
+        "--reference_summary",
+        "-r",
         help="Specifies the path to the summary log for the performance run.",
-        default=""
+        default="",
     )
     parser.add_argument(
-        "--test_summary", "-t",
+        "--test_summary",
+        "-t",
         help="Specifies the path to the summary log for this test.",
-        default=""
+        default="",
     )
     args = parser.parse_args()
 
@@ -42,68 +45,67 @@ def main():
     test_file = open(args.test_summary, "r")
     ref_score = 0
     test_score = 0
-    ref_mode = ''
-    test_mode = ''
+    ref_mode = ""
+    test_mode = ""
 
     for line in ref_file:
         if re.match("Scenario", line):
-            ref_mode = line.split(": ",1)[1].strip()
+            ref_mode = line.split(": ", 1)[1].strip()
             continue
 
         if ref_mode == "SingleStream":
             if re.match(".*Early stopping 90th percentile estimate", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
 
         if ref_mode == "MultiStream":
             if re.match(".*Early stopping 99th percentile estimate", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
 
         if ref_mode == "Server":
             if re.match("Completed samples per second", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
             if re.match("target_latency (ns)", line):
-                ref_target_latency = line.split(": ",1)[1].strip()
+                ref_target_latency = line.split(": ", 1)[1].strip()
                 continue
 
         if ref_mode == "Offline":
             if re.match("Samples per second", line):
-                ref_score = line.split(": ",1)[1].strip()
+                ref_score = line.split(": ", 1)[1].strip()
                 continue
 
         if re.match("Result is", line):
-            valid = line.split(": ",1)[1].strip()
-            if valid == 'INVALID':
+            valid = line.split(": ", 1)[1].strip()
+            if valid == "INVALID":
                 sys.exit("TEST FAIL: Reference results are invalid")
 
-        if re.match("\d+ ERROR", line):
-            error = line.split(" ",1)[0].strip()
+        if re.match("\\d+ ERROR", line):
+            error = line.split(" ", 1)[0].strip()
             print("WARNING: " + error + " ERROR reported in reference results")
 
-
     for line in test_file:
         if re.match("Scenario", line):
-            test_mode = line.split(": ",1)[1].strip()
+            test_mode = line.split(": ", 1)[1].strip()
             continue
 
         if test_mode == "SingleStream":
             if re.match(".*Early stopping 90th percentile estimate", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
 
         if test_mode == "MultiStream":
             if re.match(".*Early stopping 99th percentile estimate", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
 
         if test_mode == "Server":
             if re.match("Completed samples per second", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
             if re.match("target_latency (ns)", line):
-                test_target_latency = line.split(": ",1)[1].strip()
+                test_target_latency = line.split(": ", 1)[1].strip()
                 if test_target_latency != ref_target_latency:
                     print("TEST FAIL: Server target latency mismatch")
                     sys.exit()
@@ -111,16 +113,16 @@ def main():
 
         if test_mode == "Offline":
             if re.match("Samples per second", line):
-                test_score = line.split(": ",1)[1].strip()
+                test_score = line.split(": ", 1)[1].strip()
                 continue
 
         if re.match("Result is", line):
-            valid = line.split(": ",1)[1].strip()
-            if valid == 'INVALID':
+            valid = line.split(": ", 1)[1].strip()
+            if valid == "INVALID":
                 sys.exit("TEST FAIL: Test results are invalid")
-            
-        if re.match("\d+ ERROR", line):
-            error = line.split(" ",1)[0].strip()
+
+        if re.match("\\d+ ERROR", line):
+            error = line.split(" ", 1)[0].strip()
             print("WARNING: " + error + " ERROR reported in test results")
 
     if test_mode != ref_mode:
@@ -129,21 +131,27 @@ def main():
     print("reference score = {}".format(ref_score))
     print("test score = {}".format(test_score))
 
- 
     threshold = 0.05
 
     # In single-/multi-stream mode, latencies can be very short for high performance systems
     # and run-to-run variation due to external disturbances (OS) can be significant.
     # In this case we relax pass threshold to 20%
-    if (ref_mode == "SingleStream" and float(ref_score) <= 200000) or\
-       (ref_mode == "MultiStream" and float(ref_score) <= 1600000):
+    if (ref_mode == "SingleStream" and float(ref_score) <= 200000) or (
+        ref_mode == "MultiStream" and float(ref_score) <= 1600000
+    ):
         threshold = 0.20
-        
-    if (ref_mode in [ "Offline", "Server" ] and float(test_score) > float(ref_score) * (1 - threshold)) or ("Stream" in ref_mode and float(test_score) < float(ref_score) * (1 + threshold)):
+
+    if (
+        ref_mode in ["Offline", "Server"]
+        and float(test_score) > float(ref_score) * (1 - threshold)
+    ) or (
+        "Stream" in ref_mode and float(test_score) < float(
+            ref_score) * (1 + threshold)
+    ):
         print("TEST PASS")
     else:
         print("TEST FAIL: Test score invalid")
 
-if __name__ == '__main__':
-	main()
 
+if __name__ == "__main__":
+    main()
diff --git a/compliance/nvidia/TEST06/run_verification.py b/compliance/nvidia/TEST06/run_verification.py
index 0f57d01f81..84bc2b73c4 100644
--- a/compliance/nvidia/TEST06/run_verification.py
+++ b/compliance/nvidia/TEST06/run_verification.py
@@ -25,48 +25,70 @@
     "int64": np.int64,
     "int32": np.int32,
     "int16": np.int16,
-    "float32": np.float32
+    "float32": np.float32,
 }
 
+
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--compliance_dir", "-c", 
-                        help="Specifies the path to the directory containing the logs from the compliance test run.",
-                        required=True)
-    parser.add_argument("--output_dir", "-o",
-                        help="Specifies the path to the output directory where compliance logs will be uploaded from, i.e. inference_results_v0.7/closed/NVIDIA/compliance/T4x8/resnet/Offline.",
-                        required=True)
-    parser.add_argument("--eos_token_id", '-e', default=2, help="EOS token id of the tokenizer")
-    parser.add_argument("--dtype", "-d", default="int32", choices=["int64", "int32", "int16", "float32"])
-    parser.add_argument("--scenario", "-s", required=True, choices=["Offline", "Server", "SingleStream", "MultiStream"])
+    parser.add_argument(
+        "--compliance_dir",
+        "-c",
+        help="Specifies the path to the directory containing the logs from the compliance test run.",
+        required=True,
+    )
+    parser.add_argument(
+        "--output_dir",
+        "-o",
+        help="Specifies the path to the output directory where compliance logs will be uploaded from, i.e. inference_results_v0.7/closed/NVIDIA/compliance/T4x8/resnet/Offline.",
+        required=True,
+    )
+    parser.add_argument(
+        "--eos_token_id", "-e", default=2, help="EOS token id of the tokenizer"
+    )
+    parser.add_argument(
+        "--dtype", "-d", default="int32", choices=["int64", "int32", "int16", "float32"]
+    )
+    parser.add_argument(
+        "--scenario",
+        "-s",
+        required=True,
+        choices=["Offline", "Server", "SingleStream", "MultiStream"],
+    )
     args = parser.parse_args()
     return args
 
+
 def eos_check(acc_data, dtype, eos_token_id=2):
     for sample in acc_data:
         data = np.frombuffer(bytes.fromhex(sample["data"]), dtype=dtype)
         i = data.shape[0] - 1
         n_eos_tokens = 0
-        while (i > 0):
+        while i > 0:
             if data[i] == eos_token_id:
                 n_eos_tokens += 1
             if n_eos_tokens >= 2:
                 return False
             if data[i] != eos_token_id:
                 break
-            i-=1
+            i -= 1
     return True
 
+
 def first_token_check(acc_data, dtype):
     for sample in acc_data:
         data = np.frombuffer(bytes.fromhex(sample["data"]), dtype=dtype)
-        token_data = np.frombuffer(bytes.fromhex(sample["token_data"]), dtype=dtype)
+        token_data = np.frombuffer(
+            bytes.fromhex(
+                sample["token_data"]),
+            dtype=dtype)
         for t1, t2 in zip(data, token_data):
             if t1 != t2:
                 return False
-        
+
     return True
 
+
 def sample_len_check(acc_data, dtype):
     for sample in acc_data:
         data = np.frombuffer(bytes.fromhex(sample["data"]), dtype=dtype)
@@ -78,22 +100,27 @@ def sample_len_check(acc_data, dtype):
 
 def main():
     args = get_args()
-    accuracy_file = os.path.join(args.compliance_dir, "mlperf_log_accuracy.json")
-    
+    accuracy_file = os.path.join(
+        args.compliance_dir,
+        "mlperf_log_accuracy.json")
+
     with open(accuracy_file, "r") as acc_json:
         acc_data = json.load(acc_json)
-    
+
     try:
-        eos_pass = eos_check(acc_data, DTYPE_MAP[args.dtype], args.eos_token_id)
+        eos_pass = eos_check(acc_data,
+                             DTYPE_MAP[args.dtype],
+                             args.eos_token_id)
     except Exception:
         print("Unexpected error occured while doing the EOS check")
         eos_pass = False
 
-    need_first_token_check = (args.scenario != "Offline")
+    need_first_token_check = args.scenario != "Offline"
     first_token_pass = True
     if need_first_token_check:
         try:
-            first_token_pass = first_token_check(acc_data, DTYPE_MAP[args.dtype])
+            first_token_pass = first_token_check(
+                acc_data, DTYPE_MAP[args.dtype])
         except Exception:
             print("Unexpected error occured while doing the first token check")
             first_token_pass = False
@@ -107,7 +134,7 @@ def main():
         output += f"First token check pass: {first_token_pass}\n"
     else:
         output += f"First token check pass: Skipped\n"
-    
+
     # Add EOS check
     output += f"EOS check pass: {eos_pass}\n"
 
@@ -122,7 +149,7 @@ def main():
     # Output test output to console and folder
     output_dir = os.path.join(args.output_dir, "TEST06")
     output_accuracy_dir = os.path.join(output_dir, "accuracy")
-    
+
     if not os.path.isdir(output_dir):
         os.makedirs(output_dir)
     if not os.path.isdir(output_accuracy_dir):
@@ -132,10 +159,16 @@ def main():
         f.write(output)
 
     try:
-        shutil.copy2(accuracy_file,output_accuracy_dir)
+        shutil.copy2(accuracy_file, output_accuracy_dir)
     except Exception:
-        print("Exception occured trying to copy " + accuracy_file + " to " + output_accuracy_dir)
+        print(
+            "Exception occured trying to copy "
+            + accuracy_file
+            + " to "
+            + output_accuracy_dir
+        )
     print(output)
 
+
 if __name__ == "__main__":
     main()
diff --git a/language/bert/accuracy-squad.py b/language/bert/accuracy-squad.py
index 3f3a01c08f..557f360fcb 100644
--- a/language/bert/accuracy-squad.py
+++ b/language/bert/accuracy-squad.py
@@ -36,30 +36,44 @@
 installed = {pkg.key for pkg in pkg_resources.working_set}
 if "tensorflow" in installed:
     import tensorflow
+
     sys.path.insert(
-        0, os.path.join(
+        0,
+        os.path.join(
             os.path.dirname(__file__),
-            "DeepLearningExamples", "TensorFlow", "LanguageModeling", "BERT"
-        )
+            "DeepLearningExamples",
+            "TensorFlow",
+            "LanguageModeling",
+            "BERT",
+        ),
     )
 elif "torch" in installed:
     import torch
+
     sys.path.insert(
-        0, os.path.join(
+        0,
+        os.path.join(
             os.path.dirname(__file__),
-            "DeepLearningExamples", "PyTorch", "LanguageModeling", "BERT"
-        )
+            "DeepLearningExamples",
+            "PyTorch",
+            "LanguageModeling",
+            "BERT",
+        ),
     )
+try:
+    import tokenization
+    from create_squad_data import convert_examples_to_features, read_squad_examples
+except ImportError:
+    raise Exception("Error importing local modules")
 
-import tokenization
-from create_squad_data import convert_examples_to_features, read_squad_examples
 
 max_seq_length = 384
 max_query_length = 64
 doc_stride = 128
 
 RawResult = collections.namedtuple(
-    "RawResult", ["unique_id", "start_logits", "end_logits"])
+    "RawResult", ["unique_id", "start_logits", "end_logits"]
+)
 
 dtype_map = {
     "int8": np.int8,
@@ -68,7 +82,7 @@
     "int64": np.int64,
     "float16": np.float16,
     "float32": np.float32,
-    "float64": np.float64
+    "float64": np.float64,
 }
 
 
@@ -103,7 +117,7 @@ def get_final_text(pred_text, orig_text, do_lower_case):
     def _strip_spaces(text):
         ns_chars = []
         ns_to_s_map = collections.OrderedDict()
-        for (i, c) in enumerate(text):
+        for i, c in enumerate(text):
             if c == " ":
                 continue
             ns_to_s_map[len(ns_chars)] = i
@@ -133,7 +147,7 @@ def _strip_spaces(text):
     # We then project the characters in `pred_text` back to `orig_text` using
     # the character-to-character alignment.
     tok_s_to_ns_map = {}
-    for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
+    for i, tok_index in six.iteritems(tok_ns_to_s_map):
         tok_s_to_ns_map[tok_index] = i
 
     orig_start_position = None
@@ -154,14 +168,16 @@ def _strip_spaces(text):
     if orig_end_position is None:
         return orig_text
 
-    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+    output_text = orig_text[orig_start_position: (orig_end_position + 1)]
     return output_text
 
 
 def _get_best_indexes(logits, n_best_size):
     """Get the n-best logits from a list."""
     index_and_score = sorted(
-        enumerate(logits), key=lambda x: x[1], reverse=True)
+        enumerate(logits),
+        key=lambda x: x[1],
+        reverse=True)
 
     best_indexes = []
     for i in range(len(index_and_score)):
@@ -194,8 +210,16 @@ def _compute_softmax(scores):
     return probs
 
 
-def write_predictions(all_examples, all_features, all_results, n_best_size,
-                      max_answer_length, do_lower_case, output_prediction_file, max_examples=None):
+def write_predictions(
+    all_examples,
+    all_features,
+    all_results,
+    n_best_size,
+    max_answer_length,
+    do_lower_case,
+    output_prediction_file,
+    max_examples=None,
+):
     """Write final predictions to the json file and log-odds of null if needed."""
     print("Writing predictions to: %s" % (output_prediction_file))
 
@@ -209,13 +233,14 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
 
     _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
         "PrelimPrediction",
-        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"],
+    )
 
     all_predictions = collections.OrderedDict()
     all_nbest_json = collections.OrderedDict()
     scores_diff_json = collections.OrderedDict()
 
-    for (example_index, example) in enumerate(all_examples):
+    for example_index, example in enumerate(all_examples):
         if max_examples and example_index == max_examples:
             break
 
@@ -227,7 +252,7 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
         min_null_feature_index = 0  # the paragraph slice with min mull score
         null_start_logit = 0  # the start logit at the slice with min null score
         null_end_logit = 0  # the end logit at the slice with min null score
-        for (feature_index, feature) in enumerate(features):
+        for feature_index, feature in enumerate(features):
             # FIX: During compliance/audit runs, we only generate a small subset of
             # all entries from the dataset. As a result, sometimes dict retrieval
             # fails because a key is missing.
@@ -237,7 +262,8 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
                 continue
             start_indexes = _get_best_indexes(result.start_logits, n_best_size)
             end_indexes = _get_best_indexes(result.end_logits, n_best_size)
-            # if we could have irrelevant answers, get the min score of irrelevant
+            # if we could have irrelevant answers, get the min score of
+            # irrelevant
             for start_index in start_indexes:
                 for end_index in end_indexes:
                     # We could hypothetically create invalid predictions, e.g., predict
@@ -251,7 +277,8 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
                         continue
                     if end_index not in feature.token_to_orig_map:
                         continue
-                    if not feature.token_is_max_context.get(start_index, False):
+                    if not feature.token_is_max_context.get(
+                            start_index, False):
                         continue
                     if end_index < start_index:
                         continue
@@ -264,15 +291,19 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
                             start_index=start_index,
                             end_index=end_index,
                             start_logit=result.start_logits[start_index],
-                            end_logit=result.end_logits[end_index]))
+                            end_logit=result.end_logits[end_index],
+                        )
+                    )
 
         prelim_predictions = sorted(
             prelim_predictions,
             key=lambda x: (x.start_logit + x.end_logit),
-            reverse=True)
+            reverse=True,
+        )
 
         _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-            "NbestPrediction", ["text", "start_logit", "end_logit"])
+            "NbestPrediction", ["text", "start_logit", "end_logit"]
+        )
 
         seen_predictions = {}
         nbest = []
@@ -280,10 +311,11 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
             if len(nbest) >= n_best_size:
                 break
             feature = features[pred.feature_index]
-            tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
+            tok_tokens = feature.tokens[pred.start_index: (pred.end_index + 1)]
             orig_doc_start = feature.token_to_orig_map[pred.start_index]
             orig_doc_end = feature.token_to_orig_map[pred.end_index]
-            orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
+            orig_tokens = example.doc_tokens[orig_doc_start: (
+                orig_doc_end + 1)]
             tok_text = " ".join(tok_tokens)
 
             # De-tokenize WordPieces that have been split off.
@@ -305,13 +337,18 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
                 _NbestPrediction(
                     text=final_text,
                     start_logit=pred.start_logit,
-                    end_logit=pred.end_logit))
+                    end_logit=pred.end_logit,
+                )
+            )
 
         # In very rare edge cases we could have no valid predictions. So we
         # just create a nonce prediction in this case to avoid failure.
         if not nbest:
             nbest.append(
-                _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+                _NbestPrediction(
+                    text="empty",
+                    start_logit=0.0,
+                    end_logit=0.0))
 
         assert len(nbest) >= 1
 
@@ -326,7 +363,7 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
         probs = _compute_softmax(total_scores)
 
         nbest_json = []
-        for (i, entry) in enumerate(nbest):
+        for i, entry in enumerate(nbest):
             output = collections.OrderedDict()
             output["text"] = entry.text
             output["probability"] = probs[i]
@@ -342,7 +379,9 @@ def write_predictions(all_examples, all_features, all_results, n_best_size,
         writer.write(json.dumps(all_predictions, indent=4) + "\n")
 
 
-def load_loadgen_log(log_path, eval_features, dtype=np.float32, output_transposed=False):
+def load_loadgen_log(
+    log_path, eval_features, dtype=np.float32, output_transposed=False
+):
     with open(log_path) as f:
         predictions = json.load(f)
 
@@ -350,23 +389,27 @@ def load_loadgen_log(log_path, eval_features, dtype=np.float32, output_transpose
     for prediction in predictions:
         qsl_idx = prediction["qsl_idx"]
         if output_transposed:
-            logits = np.frombuffer(bytes.fromhex(
-                prediction["data"]), dtype).reshape(2, -1)
+            logits = np.frombuffer(bytes.fromhex(prediction["data"]), dtype).reshape(
+                2, -1
+            )
             logits = np.transpose(logits)
         else:
-            logits = np.frombuffer(bytes.fromhex(
-                prediction["data"]), dtype).reshape(-1, 2)
+            logits = np.frombuffer(bytes.fromhex(prediction["data"]), dtype).reshape(
+                -1, 2
+            )
         # Pad logits to max_seq_length
         seq_length = logits.shape[0]
         start_logits = np.ones(max_seq_length) * -10000.0
         end_logits = np.ones(max_seq_length) * -10000.0
         start_logits[:seq_length] = logits[:, 0]
         end_logits[:seq_length] = logits[:, 1]
-        results.append(RawResult(
-            unique_id=eval_features[qsl_idx].unique_id,
-            start_logits=start_logits.tolist(),
-            end_logits=end_logits.tolist()
-        ))
+        results.append(
+            RawResult(
+                unique_id=eval_features[qsl_idx].unique_id,
+                start_logits=start_logits.tolist(),
+                end_logits=end_logits.tolist(),
+            )
+        )
 
     return results
 
@@ -374,38 +417,62 @@ def load_loadgen_log(log_path, eval_features, dtype=np.float32, output_transpose
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--vocab_file", default="build/data/bert_tf_v1_1_large_fp32_384_v2/vocab.txt", help="Path to vocab.txt")
+        "--vocab_file",
+        default="build/data/bert_tf_v1_1_large_fp32_384_v2/vocab.txt",
+        help="Path to vocab.txt",
+    )
+    parser.add_argument(
+        "--val_data", default="build/data/dev-v1.1.json", help="Path to validation data"
+    )
+    parser.add_argument(
+        "--log_file",
+        default="build/logs/mlperf_log_accuracy.json",
+        help="Path to LoadGen accuracy log",
+    )
+    parser.add_argument(
+        "--out_file",
+        default="build/result/predictions.json",
+        help="Path to output predictions file",
+    )
+    parser.add_argument(
+        "--features_cache_file",
+        default="eval_features.pickle",
+        help="Path to features' cache file",
+    )
+    parser.add_argument(
+        "--output_transposed", action="store_true", help="Transpose the output"
+    )
+    parser.add_argument(
+        "--output_dtype",
+        default="float32",
+        choices=dtype_map.keys(),
+        help="Output data type",
+    )
     parser.add_argument(
-        "--val_data", default="build/data/dev-v1.1.json", help="Path to validation data")
-    parser.add_argument("--log_file", default="build/logs/mlperf_log_accuracy.json",
-                        help="Path to LoadGen accuracy log")
-    parser.add_argument("--out_file", default="build/result/predictions.json",
-                        help="Path to output predictions file")
-    parser.add_argument("--features_cache_file",
-                        default="eval_features.pickle", help="Path to features' cache file")
-    parser.add_argument("--output_transposed",
-                        action="store_true", help="Transpose the output")
-    parser.add_argument("--output_dtype", default="float32",
-                        choices=dtype_map.keys(), help="Output data type")
-    parser.add_argument("--max_examples", type=int,
-                        help="Maximum number of examples to consider (not limited by default)")
+        "--max_examples",
+        type=int,
+        help="Maximum number of examples to consider (not limited by default)",
+    )
     args = parser.parse_args()
 
     output_dtype = dtype_map[args.output_dtype]
 
     print("Reading examples...")
-    eval_examples = read_squad_examples(input_file=args.val_data,
-                                        is_training=False, version_2_with_negative=False)
+    eval_examples = read_squad_examples(
+        input_file=args.val_data, is_training=False, version_2_with_negative=False
+    )
 
     eval_features = []
     # Load features if cached, convert from examples otherwise.
     cache_path = args.features_cache_file
     if os.path.exists(cache_path):
         print("Loading cached features from '%s'..." % cache_path)
-        with open(cache_path, 'rb') as cache_file:
+        with open(cache_path, "rb") as cache_file:
             eval_features = pickle.load(cache_file)
     else:
-        print("No cached features at '%s'... converting from examples..." % cache_path)
+        print(
+            "No cached features at '%s'... converting from examples..." %
+            cache_path)
 
         print("Creating tokenizer...")
         tokenizer = BertTokenizer(args.vocab_file)
@@ -423,25 +490,38 @@ def append_feature(feature):
             max_query_length=max_query_length,
             is_training=False,
             output_fn=append_feature,
-            verbose_logging=False)
+            verbose_logging=False,
+        )
 
         print("Caching features at '%s'..." % cache_path)
-        with open(cache_path, 'wb') as cache_file:
+        with open(cache_path, "wb") as cache_file:
             pickle.dump(eval_features, cache_file)
 
     print("Loading LoadGen logs...")
     results = load_loadgen_log(
-        args.log_file, eval_features, output_dtype, args.output_transposed)
+        args.log_file, eval_features, output_dtype, args.output_transposed
+    )
 
     print("Post-processing predictions...")
-    write_predictions(eval_examples, eval_features, results,
-                      20, 30, True, args.out_file, args.max_examples)
+    write_predictions(
+        eval_examples,
+        eval_features,
+        results,
+        20,
+        30,
+        True,
+        args.out_file,
+        args.max_examples,
+    )
 
     print("Evaluating predictions...")
     cmd = "python3 {:}/evaluate_v1.1.py {:} {:} {}".format(
-        os.path.dirname(os.path.abspath(__file__)), args.val_data,
-        args.out_file, '--max_examples {}'.format(
-            args.max_examples) if args.max_examples else '')
+        os.path.dirname(os.path.abspath(__file__)),
+        args.val_data,
+        args.out_file,
+        "--max_examples {}".format(
+            args.max_examples) if args.max_examples else "",
+    )
     subprocess.check_call(cmd, shell=True)
 
 
diff --git a/language/bert/bert_QDL.py b/language/bert/bert_QDL.py
index 87d93b4fcf..6e804a7998 100644
--- a/language/bert/bert_QDL.py
+++ b/language/bert/bert_QDL.py
@@ -5,7 +5,7 @@
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
-#   
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -14,18 +14,19 @@
 # =============================================================================
 
 
+from time import sleep
+import squad_QSL
+import mlperf_loadgen as lg
+import numpy as np
 import threading
 import requests
 import array
 import time
 import os
 import sys
+
 sys.path.insert(0, os.getcwd())
-import numpy as np
 
-import mlperf_loadgen as lg
-import squad_QSL
-from time import sleep
 
 class bert_QDL:
     """QDL acting as a proxy to the SUT.
@@ -44,10 +45,11 @@ def __init__(self, qsl: squad_QSL.SQuAD_v1_QSL, sut_server_addr: list):
         """
         self.qsl = qsl
         self.quantized = False
- 
+
         # Construct QDL from the python binding
         self.qdl = lg.ConstructQDL(
-            self.issue_query, self.flush_queries, self.client_get_name)
+            self.issue_query, self.flush_queries, self.client_get_name
+        )
         self.sut_server_addr = sut_server_addr
         self.num_nodes = len(sut_server_addr)
 
@@ -57,8 +59,9 @@ def __init__(self, qsl: squad_QSL.SQuAD_v1_QSL, sut_server_addr: list):
 
     def issue_query(self, query_samples):
         """Process the query to send to the SUT"""
-        threading.Thread(target=self.process_query_async,
-                         args=[query_samples]).start()
+        threading.Thread(
+            target=self.process_query_async,
+            args=[query_samples]).start()
 
     def flush_queries(self):
         """Flush the queries. Dummy implementation."""
@@ -77,22 +80,26 @@ def process_query_async(self, query_samples):
             query_samples: A list of QuerySample objects.
         """
 
-        max_num_threads = int(os.environ.get('CM_MAX_NUM_THREADS', os.cpu_count()))
+        max_num_threads = int(
+            os.environ.get(
+                "CM_MAX_NUM_THREADS",
+                os.cpu_count()))
 
         for i in range(len(query_samples)):
             eval_features = self.qsl.get_features(query_samples[i].index)
             encoded_eval_features = {
-                    "input_ids": eval_features.input_ids,
-                    "input_mask": eval_features.input_mask,
-                    "segment_ids": eval_features.segment_ids
-                    }
+                "input_ids": eval_features.input_ids,
+                "input_mask": eval_features.input_mask,
+                "segment_ids": eval_features.segment_ids,
+            }
             n = threading.active_count()
             while n >= max_num_threads:
                 sleep(0.0001)
                 n = threading.active_count()
-            threading.Thread(target=self.client_predict_worker,
-                         args=[encoded_eval_features, query_samples[i].id]).start()
-
+            threading.Thread(
+                target=self.client_predict_worker,
+                args=[encoded_eval_features, query_samples[i].id],
+            ).start()
 
     def get_sut_id_round_robin(self):
         """Get the SUT id in round robin."""
@@ -103,10 +110,11 @@ def get_sut_id_round_robin(self):
 
     def client_predict_worker(self, query, query_id):
         """Serialize the query, send it to the SUT in round robin, and return the deserialized response."""
-        url = '{}/predict/'.format(self.sut_server_addr[self.get_sut_id_round_robin()])
+        url = "{}/predict/".format(
+            self.sut_server_addr[self.get_sut_id_round_robin()])
         responses = []
-        response = requests.post(url, json={'query': query})
-        output = response.json()['result']
+        response = requests.post(url, json={"query": query})
+        output = response.json()["result"]
         output = np.array(output).astype(np.float32)
         response_array = array.array("B", output.tobytes())
         bi = response_array.buffer_info()
@@ -117,12 +125,14 @@ def client_predict_worker(self, query, query_id):
     def client_get_name(self):
         """Get the name of the SUT from ALL the SUTS."""
         if len(self.sut_server_addr) == 1:
-            return requests.post(f'{self.sut_server_addr[0]}/getname/').json()['name']
-    
-        sut_names = [requests.post(f'{addr}/getname/').json()['name'] for addr in self.sut_server_addr]
-        return "Multi-node SUT: " + ', '.join(sut_names)
+            return requests.post(
+                f"{self.sut_server_addr[0]}/getname/").json()["name"]
+
+        sut_names = [
+            requests.post(f"{addr}/getname/").json()["name"]
+            for addr in self.sut_server_addr
+        ]
+        return "Multi-node SUT: " + ", ".join(sut_names)
 
     def __del__(self):
         lg.DestroyQDL(self.qdl)
-
-
diff --git a/language/bert/bert_tf_to_pytorch.py b/language/bert/bert_tf_to_pytorch.py
index 4ec6d9ad62..cb70f7d388 100644
--- a/language/bert/bert_tf_to_pytorch.py
+++ b/language/bert/bert_tf_to_pytorch.py
@@ -25,19 +25,29 @@
 import sys
 import time
 
-sys.path.insert(0, os.path.join(os.getcwd(), "DeepLearningExamples", "TensorFlow", "LanguageModeling", "BERT"))
+sys.path.insert(
+    0,
+    os.path.join(
+        os.getcwd(), "DeepLearningExamples", "TensorFlow", "LanguageModeling", "BERT"
+    ),
+)
 sys.path.insert(0, os.getcwd())
 
-import numpy as np
-import torch
-from transformers import BertConfig, BertTokenizer, BertForQuestionAnswering
-import tensorflow as tf
+try:
+    import tensorflow as tf
+    from transformers import BertConfig, BertTokenizer, BertForQuestionAnswering
+    import torch
+    import numpy as np
+except ImportError:
+    raise Exception("Error importing local modules")
+
 
 def load_from_tf(config, tf_path):
     model = BertForQuestionAnswering(config)
     model.classifier = model.qa_outputs
 
-    # This part is copied from HuggingFace Transformers with a fix to bypass an error
+    # This part is copied from HuggingFace Transformers with a fix to bypass
+    # an error
     init_vars = tf.train.list_variables(tf_path)
     names = []
     arrays = []
@@ -67,7 +77,9 @@ def load_from_tf(config, tf_path):
             elif scope_names[0] == "output_weights":
                 pointer = getattr(pointer, "weight")
             elif scope_names[0] == "squad":
-                pointer = getattr(pointer, "classifier") # This line is causing the issue
+                pointer = getattr(
+                    pointer, "classifier"
+                )  # This line is causing the issue
             else:
                 try:
                     pointer = getattr(pointer, scope_names[0])
@@ -93,8 +105,11 @@ def load_from_tf(config, tf_path):
     del model.classifier
     return model
 
+
 def save_to_onnx(model):
-    tokenizer = BertTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+    tokenizer = BertTokenizer.from_pretrained(
+        "bert-large-uncased-whole-word-masking-finetuned-squad"
+    )
     model.eval()
 
     dummy_input = torch.ones((1, 384), dtype=torch.int64)
@@ -103,13 +118,21 @@ def save_to_onnx(model):
         (dummy_input, dummy_input, dummy_input),
         "build/data/bert_tf_v1_1_large_fp32_384_v2/model.onnx",
         verbose=True,
-        input_names = ["input_ids", "input_mask", "segment_ids"],
-        output_names = ["output_start_logits", "output_end_logits"],
+        input_names=["input_ids", "input_mask", "segment_ids"],
+        output_names=["output_start_logits", "output_end_logits"],
         opset_version=11,
-        dynamic_axes=({"input_ids": {0: "batch_size"}, "input_mask": {0: "batch_size"}, "segment_ids": {0: "batch_size"},
-            "output_start_logits": {0: "batch_size"}, "output_end_logits": {0: "batch_size"}})
+        dynamic_axes=(
+            {
+                "input_ids": {0: "batch_size"},
+                "input_mask": {0: "batch_size"},
+                "segment_ids": {0: "batch_size"},
+                "output_start_logits": {0: "batch_size"},
+                "output_end_logits": {0: "batch_size"},
+            }
+        ),
     )
 
+
 def main():
     with open("build/data/bert_tf_v1_1_large_fp32_384_v2/bert_config.json") as f:
         config_json = json.load(f)
@@ -125,11 +148,17 @@ def main():
         num_attention_heads=config_json["num_attention_heads"],
         num_hidden_layers=config_json["num_hidden_layers"],
         type_vocab_size=config_json["type_vocab_size"],
-        vocab_size=config_json["vocab_size"])
+        vocab_size=config_json["vocab_size"],
+    )
 
-    model = load_from_tf(config, "build/data/bert_tf_v1_1_large_fp32_384_v2/model.ckpt-5474")
-    torch.save(model.state_dict(), "build/data/bert_tf_v1_1_large_fp32_384_v2/model.pytorch")
+    model = load_from_tf(
+        config, "build/data/bert_tf_v1_1_large_fp32_384_v2/model.ckpt-5474"
+    )
+    torch.save(
+        model.state_dict(), "build/data/bert_tf_v1_1_large_fp32_384_v2/model.pytorch"
+    )
     save_to_onnx(model)
 
+
 if __name__ == "__main__":
     main()
diff --git a/language/bert/create_squad_data.py b/language/bert/create_squad_data.py
index 6792e7587c..c5e0856446 100644
--- a/language/bert/create_squad_data.py
+++ b/language/bert/create_squad_data.py
@@ -24,390 +24,443 @@
 import tokenization
 import six
 
+
 class SquadExample(object):
-  """A single training/test example for simple sequence classification.
-
-     For examples without an answer, the start and end position are -1.
-  """
-
-  def __init__(self,
-               qas_id,
-               question_text,
-               doc_tokens,
-               orig_answer_text=None,
-               start_position=None,
-               end_position=None,
-               is_impossible=False):
-    self.qas_id = qas_id
-    self.question_text = question_text
-    self.doc_tokens = doc_tokens
-    self.orig_answer_text = orig_answer_text
-    self.start_position = start_position
-    self.end_position = end_position
-    self.is_impossible = is_impossible
-
-  def __str__(self):
-    return self.__repr__()
-
-  def __repr__(self):
-    s = ""
-    s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
-    s += ", question_text: %s" % (
-        tokenization.printable_text(self.question_text))
-    s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
-    if self.start_position:
-      s += ", start_position: %d" % (self.start_position)
-    if self.start_position:
-      s += ", end_position: %d" % (self.end_position)
-    if self.start_position:
-      s += ", is_impossible: %r" % (self.is_impossible)
-    return s
+    """A single training/test example for simple sequence classification.
+
+    For examples without an answer, the start and end position are -1.
+    """
+
+    def __init__(
+        self,
+        qas_id,
+        question_text,
+        doc_tokens,
+        orig_answer_text=None,
+        start_position=None,
+        end_position=None,
+        is_impossible=False,
+    ):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.doc_tokens = doc_tokens
+        self.orig_answer_text = orig_answer_text
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        s = ""
+        s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
+        s += ", question_text: %s" % (
+            tokenization.printable_text(self.question_text))
+        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+        if self.start_position:
+            s += ", start_position: %d" % (self.start_position)
+        if self.start_position:
+            s += ", end_position: %d" % (self.end_position)
+        if self.start_position:
+            s += ", is_impossible: %r" % (self.is_impossible)
+        return s
+
 
 class InputFeatures(object):
-  """A single set of features of data."""
-
-  def __init__(self,
-               unique_id,
-               example_index,
-               doc_span_index,
-               tokens,
-               token_to_orig_map,
-               token_is_max_context,
-               input_ids,
-               input_mask,
-               segment_ids,
-               start_position=None,
-               end_position=None,
-               is_impossible=None):
-    self.unique_id = unique_id
-    self.example_index = example_index
-    self.doc_span_index = doc_span_index
-    self.tokens = tokens
-    self.token_to_orig_map = token_to_orig_map
-    self.token_is_max_context = token_is_max_context
-    self.input_ids = input_ids
-    self.input_mask = input_mask
-    self.segment_ids = segment_ids
-    self.start_position = start_position
-    self.end_position = end_position
-    self.is_impossible = is_impossible
-
-def read_squad_examples(input_file, is_training, version_2_with_negative=False):
-  """Read a SQuAD json file into a list of SquadExample."""
-  with open(input_file) as reader:
-    input_data = json.load(reader)["data"]
-
-  def is_whitespace(c):
-    if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
-      return True
-    return False
-
-  examples = []
-  for entry in input_data:
-    for paragraph in entry["paragraphs"]:
-      paragraph_text = paragraph["context"]
-      doc_tokens = []
-      char_to_word_offset = []
-      prev_is_whitespace = True
-      for c in paragraph_text:
-        if is_whitespace(c):
-          prev_is_whitespace = True
-        else:
-          if prev_is_whitespace:
-            doc_tokens.append(c)
-          else:
-            doc_tokens[-1] += c
-          prev_is_whitespace = False
-        char_to_word_offset.append(len(doc_tokens) - 1)
-
-      for qa in paragraph["qas"]:
-        qas_id = qa["id"]
-        question_text = qa["question"]
-        start_position = None
-        end_position = None
-        orig_answer_text = None
-        is_impossible = False
-        if is_training:
-
-          if version_2_with_negative:
-            is_impossible = qa["is_impossible"]
-          if (len(qa["answers"]) != 1) and (not is_impossible):
-            raise ValueError(
-                "For training, each question should have exactly 1 answer.")
-          if not is_impossible:
-            answer = qa["answers"][0]
-            orig_answer_text = answer["text"]
-            answer_offset = answer["answer_start"]
-            answer_length = len(orig_answer_text)
-            start_position = char_to_word_offset[answer_offset]
-            end_position = char_to_word_offset[answer_offset + answer_length -
-                                               1]
-            # Only add answers where the text can be exactly recovered from the
-            # document. If this CAN'T happen it's likely due to weird Unicode
-            # stuff so we will just skip the example.
-            #
-            # Note that this means for training mode, every example is NOT
-            # guaranteed to be preserved.
-            actual_text = " ".join(
-                doc_tokens[start_position:(end_position + 1)])
-            cleaned_answer_text = " ".join(
-                tokenization.whitespace_tokenize(orig_answer_text))
-            if actual_text.find(cleaned_answer_text) == -1:
-              print("Could not find answer: '%s' vs. '%s'",
-                                 actual_text, cleaned_answer_text)
-              continue
-          else:
-            start_position = -1
-            end_position = -1
-            orig_answer_text = ""
-
-        example = SquadExample(
-            qas_id=qas_id,
-            question_text=question_text,
-            doc_tokens=doc_tokens,
-            orig_answer_text=orig_answer_text,
-            start_position=start_position,
-            end_position=end_position,
-            is_impossible=is_impossible)
-        examples.append(example)
-
-  return examples
+    """A single set of features of data."""
+
+    def __init__(
+        self,
+        unique_id,
+        example_index,
+        doc_span_index,
+        tokens,
+        token_to_orig_map,
+        token_is_max_context,
+        input_ids,
+        input_mask,
+        segment_ids,
+        start_position=None,
+        end_position=None,
+        is_impossible=None,
+    ):
+        self.unique_id = unique_id
+        self.example_index = example_index
+        self.doc_span_index = doc_span_index
+        self.tokens = tokens
+        self.token_to_orig_map = token_to_orig_map
+        self.token_is_max_context = token_is_max_context
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+
+def read_squad_examples(input_file, is_training,
+                        version_2_with_negative=False):
+    """Read a SQuAD json file into a list of SquadExample."""
+    with open(input_file) as reader:
+        input_data = json.load(reader)["data"]
+
+    def is_whitespace(c):
+        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+            return True
+        return False
+
+    examples = []
+    for entry in input_data:
+        for paragraph in entry["paragraphs"]:
+            paragraph_text = paragraph["context"]
+            doc_tokens = []
+            char_to_word_offset = []
+            prev_is_whitespace = True
+            for c in paragraph_text:
+                if is_whitespace(c):
+                    prev_is_whitespace = True
+                else:
+                    if prev_is_whitespace:
+                        doc_tokens.append(c)
+                    else:
+                        doc_tokens[-1] += c
+                    prev_is_whitespace = False
+                char_to_word_offset.append(len(doc_tokens) - 1)
+
+            for qa in paragraph["qas"]:
+                qas_id = qa["id"]
+                question_text = qa["question"]
+                start_position = None
+                end_position = None
+                orig_answer_text = None
+                is_impossible = False
+                if is_training:
+
+                    if version_2_with_negative:
+                        is_impossible = qa["is_impossible"]
+                    if (len(qa["answers"]) != 1) and (not is_impossible):
+                        raise ValueError(
+                            "For training, each question should have exactly 1 answer."
+                        )
+                    if not is_impossible:
+                        answer = qa["answers"][0]
+                        orig_answer_text = answer["text"]
+                        answer_offset = answer["answer_start"]
+                        answer_length = len(orig_answer_text)
+                        start_position = char_to_word_offset[answer_offset]
+                        end_position = char_to_word_offset[
+                            answer_offset + answer_length - 1
+                        ]
+                        # Only add answers where the text can be exactly recovered from the
+                        # document. If this CAN'T happen it's likely due to weird Unicode
+                        # stuff so we will just skip the example.
+                        #
+                        # Note that this means for training mode, every example is NOT
+                        # guaranteed to be preserved.
+                        actual_text = " ".join(
+                            doc_tokens[start_position: (end_position + 1)]
+                        )
+                        cleaned_answer_text = " ".join(
+                            tokenization.whitespace_tokenize(orig_answer_text)
+                        )
+                        if actual_text.find(cleaned_answer_text) == -1:
+                            print(
+                                "Could not find answer: '%s' vs. '%s'",
+                                actual_text,
+                                cleaned_answer_text,
+                            )
+                            continue
+                    else:
+                        start_position = -1
+                        end_position = -1
+                        orig_answer_text = ""
+
+                example = SquadExample(
+                    qas_id=qas_id,
+                    question_text=question_text,
+                    doc_tokens=doc_tokens,
+                    orig_answer_text=orig_answer_text,
+                    start_position=start_position,
+                    end_position=end_position,
+                    is_impossible=is_impossible,
+                )
+                examples.append(example)
+
+    return examples
+
 
 def _check_is_max_context(doc_spans, cur_span_index, position):
-  """Check if this is the 'max context' doc span for the token."""
-
-  # Because of the sliding window approach taken to scoring documents, a single
-  # token can appear in multiple documents. E.g.
-  #  Doc: the man went to the store and bought a gallon of milk
-  #  Span A: the man went to the
-  #  Span B: to the store and bought
-  #  Span C: and bought a gallon of
-  #  ...
-  #
-  # Now the word 'bought' will have two scores from spans B and C. We only
-  # want to consider the score with "maximum context", which we define as
-  # the *minimum* of its left and right context (the *sum* of left and
-  # right context will always be the same, of course).
-  #
-  # In the example the maximum context for 'bought' would be span C since
-  # it has 1 left context and 3 right context, while span B has 4 left context
-  # and 0 right context.
-  best_score = None
-  best_span_index = None
-  for (span_index, doc_span) in enumerate(doc_spans):
-    end = doc_span.start + doc_span.length - 1
-    if position < doc_span.start:
-      continue
-    if position > end:
-      continue
-    num_left_context = position - doc_span.start
-    num_right_context = end - position
-    score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
-    if best_score is None or score > best_score:
-      best_score = score
-      best_span_index = span_index
-
-  return cur_span_index == best_span_index
-
-def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
-                         orig_answer_text):
-  """Returns tokenized answer spans that better match the annotated answer."""
-
-  # The SQuAD annotations are character based. We first project them to
-  # whitespace-tokenized words. But then after WordPiece tokenization, we can
-  # often find a "better match". For example:
-  #
-  #   Question: What year was John Smith born?
-  #   Context: The leader was John Smith (1895-1943).
-  #   Answer: 1895
-  #
-  # The original whitespace-tokenized answer will be "(1895-1943).". However
-  # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
-  # the exact answer, 1895.
-  #
-  # However, this is not always possible. Consider the following:
-  #
-  #   Question: What country is the top exporter of electornics?
-  #   Context: The Japanese electronics industry is the lagest in the world.
-  #   Answer: Japan
-  #
-  # In this case, the annotator chose "Japan" as a character sub-span of
-  # the word "Japanese". Since our WordPiece tokenizer does not split
-  # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
-  # in SQuAD, but does happen.
-  tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
-
-  for new_start in range(input_start, input_end + 1):
-    for new_end in range(input_end, new_start - 1, -1):
-      text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
-      if text_span == tok_answer_text:
-        return (new_start, new_end)
-
-  return (input_start, input_end)
-
-
-def convert_examples_to_features(examples, tokenizer, max_seq_length,
-                                 doc_stride, max_query_length, is_training,
-                                 output_fn, verbose_logging=False):
-  """Loads a data file into a list of `InputBatch`s."""
-
-  unique_id = 1000000000
-
-  for (example_index, example) in enumerate(examples):
-    query_tokens = tokenizer.tokenize(example.question_text)
-
-    if len(query_tokens) > max_query_length:
-      query_tokens = query_tokens[0:max_query_length]
-
-    tok_to_orig_index = []
-    orig_to_tok_index = []
-    all_doc_tokens = []
-    for (i, token) in enumerate(example.doc_tokens):
-      orig_to_tok_index.append(len(all_doc_tokens))
-      sub_tokens = tokenizer.tokenize(token)
-      for sub_token in sub_tokens:
-        tok_to_orig_index.append(i)
-        all_doc_tokens.append(sub_token)
-
-    tok_start_position = None
-    tok_end_position = None
-    if is_training and example.is_impossible:
-      tok_start_position = -1
-      tok_end_position = -1
-    if is_training and not example.is_impossible:
-      tok_start_position = orig_to_tok_index[example.start_position]
-      if example.end_position < len(example.doc_tokens) - 1:
-        tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
-      else:
-        tok_end_position = len(all_doc_tokens) - 1
-      (tok_start_position, tok_end_position) = _improve_answer_span(
-          all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
-          example.orig_answer_text)
-
-    # The -3 accounts for [CLS], [SEP] and [SEP]
-    max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
-
-    # We can have documents that are longer than the maximum sequence length.
-    # To deal with this we do a sliding window approach, where we take chunks
-    # of the up to our max length with a stride of `doc_stride`.
-    _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
-        "DocSpan", ["start", "length"])
-    doc_spans = []
-    start_offset = 0
-    while start_offset < len(all_doc_tokens):
-      length = len(all_doc_tokens) - start_offset
-      if length > max_tokens_for_doc:
-        length = max_tokens_for_doc
-      doc_spans.append(_DocSpan(start=start_offset, length=length))
-      if start_offset + length == len(all_doc_tokens):
-        break
-      start_offset += min(length, doc_stride)
-
-    for (doc_span_index, doc_span) in enumerate(doc_spans):
-      tokens = []
-      token_to_orig_map = {}
-      token_is_max_context = {}
-      segment_ids = []
-      tokens.append("[CLS]")
-      segment_ids.append(0)
-      for token in query_tokens:
-        tokens.append(token)
-        segment_ids.append(0)
-      tokens.append("[SEP]")
-      segment_ids.append(0)
-
-      for i in range(doc_span.length):
-        split_token_index = doc_span.start + i
-        token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
-
-        is_max_context = _check_is_max_context(doc_spans, doc_span_index,
-                                               split_token_index)
-        token_is_max_context[len(tokens)] = is_max_context
-        tokens.append(all_doc_tokens[split_token_index])
-        segment_ids.append(1)
-      tokens.append("[SEP]")
-      segment_ids.append(1)
-
-      input_ids = tokenizer.convert_tokens_to_ids(tokens)
-
-      # The mask has 1 for real tokens and 0 for padding tokens. Only real
-      # tokens are attended to.
-      input_mask = [1] * len(input_ids)
-
-      # Zero-pad up to the sequence length.
-      while len(input_ids) < max_seq_length:
-        input_ids.append(0)
-        input_mask.append(0)
-        segment_ids.append(0)
-
-      assert len(input_ids) == max_seq_length
-      assert len(input_mask) == max_seq_length
-      assert len(segment_ids) == max_seq_length
-
-      start_position = None
-      end_position = None
-      if is_training and not example.is_impossible:
-        # For training, if our document chunk does not contain an annotation
-        # we throw it out, since there is nothing to predict.
-        doc_start = doc_span.start
-        doc_end = doc_span.start + doc_span.length - 1
-        out_of_span = False
-        if not (tok_start_position >= doc_start and
-                tok_end_position <= doc_end):
-          out_of_span = True
-        if out_of_span:
-          start_position = 0
-          end_position = 0
-        else:
-          doc_offset = len(query_tokens) + 2
-          start_position = tok_start_position - doc_start + doc_offset
-          end_position = tok_end_position - doc_start + doc_offset
-
-      if is_training and example.is_impossible:
-        start_position = 0
-        end_position = 0
-
-      if verbose_logging and example_index < 20:
-        print("*** Example ***")
-        print("unique_id: %s" % (unique_id))
-        print("example_index: %s" % (example_index))
-        print("doc_span_index: %s" % (doc_span_index))
-        print("tokens: %s" % " ".join(
-            [tokenization.printable_text(x) for x in tokens]))
-        print("token_to_orig_map: %s" % " ".join(
-            ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
-        print("token_is_max_context: %s" % " ".join([
-            "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
-        ]))
-        print("input_ids: %s" % " ".join([str(x) for x in input_ids]))
-        print(
-            "input_mask: %s" % " ".join([str(x) for x in input_mask]))
-        print(
-            "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+    """Check if this is the 'max context' doc span for the token."""
+
+    # Because of the sliding window approach taken to scoring documents, a single
+    # token can appear in multiple documents. E.g.
+    #  Doc: the man went to the store and bought a gallon of milk
+    #  Span A: the man went to the
+    #  Span B: to the store and bought
+    #  Span C: and bought a gallon of
+    #  ...
+    #
+    # Now the word 'bought' will have two scores from spans B and C. We only
+    # want to consider the score with "maximum context", which we define as
+    # the *minimum* of its left and right context (the *sum* of left and
+    # right context will always be the same, of course).
+    #
+    # In the example the maximum context for 'bought' would be span C since
+    # it has 1 left context and 3 right context, while span B has 4 left context
+    # and 0 right context.
+    best_score = None
+    best_span_index = None
+    for span_index, doc_span in enumerate(doc_spans):
+        end = doc_span.start + doc_span.length - 1
+        if position < doc_span.start:
+            continue
+        if position > end:
+            continue
+        num_left_context = position - doc_span.start
+        num_right_context = end - position
+        score = min(num_left_context, num_right_context) + \
+            0.01 * doc_span.length
+        if best_score is None or score > best_score:
+            best_score = score
+            best_span_index = span_index
+
+    return cur_span_index == best_span_index
+
+
+def _improve_answer_span(
+    doc_tokens, input_start, input_end, tokenizer, orig_answer_text
+):
+    """Returns tokenized answer spans that better match the annotated answer."""
+
+    # The SQuAD annotations are character based. We first project them to
+    # whitespace-tokenized words. But then after WordPiece tokenization, we can
+    # often find a "better match". For example:
+    #
+    #   Question: What year was John Smith born?
+    #   Context: The leader was John Smith (1895-1943).
+    #   Answer: 1895
+    #
+    # The original whitespace-tokenized answer will be "(1895-1943).". However
+    # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
+    # the exact answer, 1895.
+    #
+    # However, this is not always possible. Consider the following:
+    #
+    #   Question: What country is the top exporter of electornics?
+    #   Context: The Japanese electronics industry is the lagest in the world.
+    #   Answer: Japan
+    #
+    # In this case, the annotator chose "Japan" as a character sub-span of
+    # the word "Japanese". Since our WordPiece tokenizer does not split
+    # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
+    # in SQuAD, but does happen.
+    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
+
+    for new_start in range(input_start, input_end + 1):
+        for new_end in range(input_end, new_start - 1, -1):
+            text_span = " ".join(doc_tokens[new_start: (new_end + 1)])
+            if text_span == tok_answer_text:
+                return (new_start, new_end)
+
+    return (input_start, input_end)
+
+
+def convert_examples_to_features(
+    examples,
+    tokenizer,
+    max_seq_length,
+    doc_stride,
+    max_query_length,
+    is_training,
+    output_fn,
+    verbose_logging=False,
+):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    unique_id = 1000000000
+
+    for example_index, example in enumerate(examples):
+        query_tokens = tokenizer.tokenize(example.question_text)
+
+        if len(query_tokens) > max_query_length:
+            query_tokens = query_tokens[0:max_query_length]
+
+        tok_to_orig_index = []
+        orig_to_tok_index = []
+        all_doc_tokens = []
+        for i, token in enumerate(example.doc_tokens):
+            orig_to_tok_index.append(len(all_doc_tokens))
+            sub_tokens = tokenizer.tokenize(token)
+            for sub_token in sub_tokens:
+                tok_to_orig_index.append(i)
+                all_doc_tokens.append(sub_token)
+
+        tok_start_position = None
+        tok_end_position = None
         if is_training and example.is_impossible:
-          print("impossible example")
+            tok_start_position = -1
+            tok_end_position = -1
         if is_training and not example.is_impossible:
-          answer_text = " ".join(tokens[start_position:(end_position + 1)])
-          print("start_position: %d" % (start_position))
-          print("end_position: %d" % (end_position))
-          print(
-              "answer: %s" % (tokenization.printable_text(answer_text)))
-
-      feature = InputFeatures(
-          unique_id=unique_id,
-          example_index=example_index,
-          doc_span_index=doc_span_index,
-          tokens=tokens,
-          token_to_orig_map=token_to_orig_map,
-          token_is_max_context=token_is_max_context,
-          input_ids=input_ids,
-          input_mask=input_mask,
-          segment_ids=segment_ids,
-          start_position=start_position,
-          end_position=end_position,
-          is_impossible=example.is_impossible)
-
-      # Run callback
-      output_fn(feature)
-
-      unique_id += 1
+            tok_start_position = orig_to_tok_index[example.start_position]
+            if example.end_position < len(example.doc_tokens) - 1:
+                tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
+            else:
+                tok_end_position = len(all_doc_tokens) - 1
+            (tok_start_position, tok_end_position) = _improve_answer_span(
+                all_doc_tokens,
+                tok_start_position,
+                tok_end_position,
+                tokenizer,
+                example.orig_answer_text,
+            )
+
+        # The -3 accounts for [CLS], [SEP] and [SEP]
+        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+        # We can have documents that are longer than the maximum sequence length.
+        # To deal with this we do a sliding window approach, where we take chunks
+        # of the up to our max length with a stride of `doc_stride`.
+        _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
+            "DocSpan", ["start", "length"]
+        )
+        doc_spans = []
+        start_offset = 0
+        while start_offset < len(all_doc_tokens):
+            length = len(all_doc_tokens) - start_offset
+            if length > max_tokens_for_doc:
+                length = max_tokens_for_doc
+            doc_spans.append(_DocSpan(start=start_offset, length=length))
+            if start_offset + length == len(all_doc_tokens):
+                break
+            start_offset += min(length, doc_stride)
+
+        for doc_span_index, doc_span in enumerate(doc_spans):
+            tokens = []
+            token_to_orig_map = {}
+            token_is_max_context = {}
+            segment_ids = []
+            tokens.append("[CLS]")
+            segment_ids.append(0)
+            for token in query_tokens:
+                tokens.append(token)
+                segment_ids.append(0)
+            tokens.append("[SEP]")
+            segment_ids.append(0)
+
+            for i in range(doc_span.length):
+                split_token_index = doc_span.start + i
+                token_to_orig_map[len(
+                    tokens)] = tok_to_orig_index[split_token_index]
+
+                is_max_context = _check_is_max_context(
+                    doc_spans, doc_span_index, split_token_index
+                )
+                token_is_max_context[len(tokens)] = is_max_context
+                tokens.append(all_doc_tokens[split_token_index])
+                segment_ids.append(1)
+            tokens.append("[SEP]")
+            segment_ids.append(1)
+
+            input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+            # The mask has 1 for real tokens and 0 for padding tokens. Only real
+            # tokens are attended to.
+            input_mask = [1] * len(input_ids)
+
+            # Zero-pad up to the sequence length.
+            while len(input_ids) < max_seq_length:
+                input_ids.append(0)
+                input_mask.append(0)
+                segment_ids.append(0)
+
+            assert len(input_ids) == max_seq_length
+            assert len(input_mask) == max_seq_length
+            assert len(segment_ids) == max_seq_length
+
+            start_position = None
+            end_position = None
+            if is_training and not example.is_impossible:
+                # For training, if our document chunk does not contain an annotation
+                # we throw it out, since there is nothing to predict.
+                doc_start = doc_span.start
+                doc_end = doc_span.start + doc_span.length - 1
+                out_of_span = False
+                if not (
+                    tok_start_position >= doc_start and tok_end_position <= doc_end
+                ):
+                    out_of_span = True
+                if out_of_span:
+                    start_position = 0
+                    end_position = 0
+                else:
+                    doc_offset = len(query_tokens) + 2
+                    start_position = tok_start_position - doc_start + doc_offset
+                    end_position = tok_end_position - doc_start + doc_offset
+
+            if is_training and example.is_impossible:
+                start_position = 0
+                end_position = 0
+
+            if verbose_logging and example_index < 20:
+                print("*** Example ***")
+                print("unique_id: %s" % (unique_id))
+                print("example_index: %s" % (example_index))
+                print("doc_span_index: %s" % (doc_span_index))
+                print(
+                    "tokens: %s"
+                    % " ".join([tokenization.printable_text(x) for x in tokens])
+                )
+                print(
+                    "token_to_orig_map: %s"
+                    % " ".join(
+                        [
+                            "%d:%d" % (x, y)
+                            for (x, y) in six.iteritems(token_to_orig_map)
+                        ]
+                    )
+                )
+                print(
+                    "token_is_max_context: %s"
+                    % " ".join(
+                        [
+                            "%d:%s" % (x, y)
+                            for (x, y) in six.iteritems(token_is_max_context)
+                        ]
+                    )
+                )
+                print("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+                print("input_mask: %s" %
+                      " ".join([str(x) for x in input_mask]))
+                print("segment_ids: %s" %
+                      " ".join([str(x) for x in segment_ids]))
+                if is_training and example.is_impossible:
+                    print("impossible example")
+                if is_training and not example.is_impossible:
+                    answer_text = " ".join(
+                        tokens[start_position: (end_position + 1)])
+                    print("start_position: %d" % (start_position))
+                    print("end_position: %d" % (end_position))
+                    print(
+                        "answer: %s" %
+                        (tokenization.printable_text(answer_text)))
+
+            feature = InputFeatures(
+                unique_id=unique_id,
+                example_index=example_index,
+                doc_span_index=doc_span_index,
+                tokens=tokens,
+                token_to_orig_map=token_to_orig_map,
+                token_is_max_context=token_is_max_context,
+                input_ids=input_ids,
+                input_mask=input_mask,
+                segment_ids=segment_ids,
+                start_position=start_position,
+                end_position=end_position,
+                is_impossible=example.is_impossible,
+            )
+
+            # Run callback
+            output_fn(feature)
+
+            unique_id += 1
diff --git a/language/bert/evaluate_v1.1.py b/language/bert/evaluate_v1.1.py
index 0e89c9623e..f309e47d8f 100644
--- a/language/bert/evaluate_v1.1.py
+++ b/language/bert/evaluate_v1.1.py
@@ -10,7 +10,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-# Source: https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py
+# Source:
+# https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py
 
 """ Official evaluation script for v1.1 of the SQuAD dataset. """
 from __future__ import print_function
@@ -24,15 +25,16 @@
 
 def normalize_answer(s):
     """Lower text and remove punctuation, articles and extra whitespace."""
+
     def remove_articles(text):
-        return re.sub(r'\b(a|an|the)\b', ' ', text)
+        return re.sub(r"\b(a|an|the)\b", " ", text)
 
     def white_space_fix(text):
-        return ' '.join(text.split())
+        return " ".join(text.split())
 
     def remove_punc(text):
         exclude = set(string.punctuation)
-        return ''.join(ch for ch in text if ch not in exclude)
+        return "".join(ch for ch in text if ch not in exclude)
 
     def lower(text):
         return text.lower()
@@ -54,7 +56,7 @@ def f1_score(prediction, ground_truth):
 
 
 def exact_match_score(prediction, ground_truth):
-    return (normalize_answer(prediction) == normalize_answer(ground_truth))
+    return normalize_answer(prediction) == normalize_answer(ground_truth)
 
 
 def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
@@ -68,46 +70,61 @@ def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
 def evaluate(dataset, predictions, max_examples=None):
     f1 = exact_match = total = 0
     for article in dataset:
-        if max_examples and max_examples==total: break
-        for paragraph in article['paragraphs']:
-            if max_examples and max_examples==total: break
-            for qa in paragraph['qas']:
+        if max_examples and max_examples == total:
+            break
+        for paragraph in article["paragraphs"]:
+            if max_examples and max_examples == total:
+                break
+            for qa in paragraph["qas"]:
                 total += 1
-                if max_examples and max_examples==total: break
-
-                if qa['id'] not in predictions:
-                    message = 'Unanswered question ' + qa['id'] + \
-                              ' will receive score 0.'
+                if max_examples and max_examples == total:
+                    break
+
+                if qa["id"] not in predictions:
+                    message = (
+                        "Unanswered question " +
+                        qa["id"] + " will receive score 0."
+                    )
                     print(message, file=sys.stderr)
                     continue
-                ground_truths = list(map(lambda x: x['text'], qa['answers']))
-                prediction = predictions[qa['id']]
+                ground_truths = list(map(lambda x: x["text"], qa["answers"]))
+                prediction = predictions[qa["id"]]
                 exact_match += metric_max_over_ground_truths(
-                    exact_match_score, prediction, ground_truths)
+                    exact_match_score, prediction, ground_truths
+                )
                 f1 += metric_max_over_ground_truths(
                     f1_score, prediction, ground_truths)
 
     exact_match = 100.0 * exact_match / total
     f1 = 100.0 * f1 / total
 
-    return {'exact_match': exact_match, 'f1': f1}
+    return {"exact_match": exact_match, "f1": f1}
 
 
-if __name__ == '__main__':
-    expected_version = '1.1'
+if __name__ == "__main__":
+    expected_version = "1.1"
     parser = argparse.ArgumentParser(
-        description='Evaluation for SQuAD ' + expected_version)
-    parser.add_argument('dataset_file', help='Dataset file')
-    parser.add_argument('prediction_file', help='Prediction File')
-    parser.add_argument('--max_examples', type=int, help='Maximum number of examples to consider (not limited by default)')
+        description="Evaluation for SQuAD " + expected_version
+    )
+    parser.add_argument("dataset_file", help="Dataset file")
+    parser.add_argument("prediction_file", help="Prediction File")
+    parser.add_argument(
+        "--max_examples",
+        type=int,
+        help="Maximum number of examples to consider (not limited by default)",
+    )
     args = parser.parse_args()
     with open(args.dataset_file) as dataset_file:
         dataset_json = json.load(dataset_file)
-        if (dataset_json['version'] != expected_version):
-            print('Evaluation expects v-' + expected_version +
-                  ', but got dataset with v-' + dataset_json['version'],
-                  file=sys.stderr)
-        dataset = dataset_json['data']
+        if dataset_json["version"] != expected_version:
+            print(
+                "Evaluation expects v-"
+                + expected_version
+                + ", but got dataset with v-"
+                + dataset_json["version"],
+                file=sys.stderr,
+            )
+        dataset = dataset_json["data"]
     with open(args.prediction_file) as prediction_file:
         predictions = json.load(prediction_file)
     print(json.dumps(evaluate(dataset, predictions, args.max_examples)))
diff --git a/language/bert/network_LON.py b/language/bert/network_LON.py
index fa6113e1ad..92b53a4113 100644
--- a/language/bert/network_LON.py
+++ b/language/bert/network_LON.py
@@ -5,7 +5,7 @@
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
-#   
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -14,15 +14,26 @@
 # =============================================================================
 
 
+import bert_QDL
+import mlperf_loadgen as lg
+import squad_QSL
+from absl import app
 import sys
 import os
+
 sys.path.insert(0, os.getcwd())
-from absl import app
-import squad_QSL
-import mlperf_loadgen as lg
-import bert_QDL
 
-def set_args(argv, g_settings, g_log_settings, g_audit_conf, g_sut_server, g_backend, g_total_count_override=None, g_perf_count_override=None):
+
+def set_args(
+    argv,
+    g_settings,
+    g_log_settings,
+    g_audit_conf,
+    g_sut_server,
+    g_backend,
+    g_total_count_override=None,
+    g_perf_count_override=None,
+):
 
     global settings, log_settings, audit_conf, sut_server, total_count_override, perf_count_override, backend
     sys.argv = sys.argv[0:1]
@@ -34,11 +45,17 @@ def set_args(argv, g_settings, g_log_settings, g_audit_conf, g_sut_server, g_bac
     perf_count_override = g_perf_count_override
     backend = g_backend
 
-def main(argv):
-        qsl = squad_QSL.get_squad_QSL(total_count_override, perf_count_override)
-        qdl = bert_QDL.bert_QDL(qsl, sut_server_addr=sut_server)
 
-        lg.StartTestWithLogSettings(qdl.qdl, qsl.qsl, settings, log_settings, audit_conf)
+def main(argv):
+    qsl = squad_QSL.get_squad_QSL(total_count_override, perf_count_override)
+    qdl = bert_QDL.bert_QDL(qsl, sut_server_addr=sut_server)
+
+    lg.StartTestWithLogSettings(
+        qdl.qdl,
+        qsl.qsl,
+        settings,
+        log_settings,
+        audit_conf)
 
 
 if __name__ == "__main__":
diff --git a/language/bert/onnxruntime_SUT.py b/language/bert/onnxruntime_SUT.py
index a016c1f7b7..598192509e 100644
--- a/language/bert/onnxruntime_SUT.py
+++ b/language/bert/onnxruntime_SUT.py
@@ -14,21 +14,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from time import sleep
+from squad_QSL import get_squad_QSL
+from transformers import BertConfig, BertForQuestionAnswering
+import onnxruntime
+import numpy as np
+import mlperf_loadgen as lg
 import threading
 import array
 import json
 import os
 import sys
+
 sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
-import numpy as np
-import onnxruntime
-from transformers import BertConfig, BertForQuestionAnswering
-from squad_QSL import get_squad_QSL
-from time import sleep
 
-class BERT_ONNXRuntime_SUT():
+class BERT_ONNXRuntime_SUT:
     def __init__(self, args):
         self.profile = args.profile
         self.network = args.network
@@ -44,11 +45,20 @@ def __init__(self, args):
                 model_path = "build/data/bert_tf_v1_1_large_fp32_384_v2/bert_large_v1_1_fake_quant.onnx"
             else:
                 model_path = "build/data/bert_tf_v1_1_large_fp32_384_v2/model.onnx"
-        if len(onnxruntime.get_all_providers()) > 1 and os.environ.get("USE_GPU", "yes").lower() not in [ "0", "false", "off", "no" ]:
-            preferred_execution_provider = os.environ.get("ONNXRUNTIME_PREFERRED_EXECUTION_PROVIDER", "CUDAExecutionProvider")
-            self.sess = onnxruntime.InferenceSession(model_path, self.options, providers=[ preferred_execution_provider ])
+        if len(onnxruntime.get_all_providers()) > 1 and os.environ.get(
+            "USE_GPU", "yes"
+        ).lower() not in ["0", "false", "off", "no"]:
+            preferred_execution_provider = os.environ.get(
+                "ONNXRUNTIME_PREFERRED_EXECUTION_PROVIDER", "CUDAExecutionProvider"
+            )
+            self.sess = onnxruntime.InferenceSession(
+                model_path, self.options, providers=[
+                    preferred_execution_provider]
+            )
         else:
-            self.sess = onnxruntime.InferenceSession(model_path, self.options, providers=["CPUExecutionProvider"])
+            self.sess = onnxruntime.InferenceSession(
+                model_path, self.options, providers=["CPUExecutionProvider"]
+            )
 
         print("Constructing SUT...")
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
@@ -57,24 +67,28 @@ def __init__(self, args):
         self.qsl = get_squad_QSL(args.max_examples)
 
     def issue_queries(self, query_samples):
-        max_num_threads = int(os.environ.get('CM_MAX_NUM_THREADS', os.cpu_count()))
+        max_num_threads = int(
+            os.environ.get(
+                "CM_MAX_NUM_THREADS",
+                os.cpu_count()))
 
         for i in range(len(query_samples)):
             eval_features = self.qsl.get_features(query_samples[i].index)
             n = threading.active_count()
             while n >= max_num_threads:
-                #sleep(0.01)
+                # sleep(0.01)
                 n = threading.active_count()
-            threading.Thread(target=self.process_sample,
-                         args=[eval_features, query_samples[i].id]).start()
+            threading.Thread(
+                target=self.process_sample, args=[
+                    eval_features, query_samples[i].id]
+            ).start()
 
     def process_sample(self, eval_features, query_id=None):
-
-        '''For Loadgen over the network'''
+        """For Loadgen over the network"""
         if self.network == "sut":
-            input_ids = eval_features['input_ids']
-            input_mask = eval_features['input_mask']
-            segment_ids = eval_features['segment_ids']
+            input_ids = eval_features["input_ids"]
+            input_mask = eval_features["input_mask"]
+            segment_ids = eval_features["segment_ids"]
         else:
             input_ids = eval_features.input_ids
             input_mask = eval_features.input_mask
@@ -84,13 +98,13 @@ def process_sample(self, eval_features, query_id=None):
             fd = {
                 "input_ids": np.array(input_ids).astype(np.int64)[np.newaxis, :],
                 "attention_mask": np.array(input_mask).astype(np.int64)[np.newaxis, :],
-                "token_type_ids": np.array(segment_ids).astype(np.int64)[np.newaxis, :]
+                "token_type_ids": np.array(segment_ids).astype(np.int64)[np.newaxis, :],
             }
         else:
             fd = {
                 "input_ids": np.array(input_ids).astype(np.int64)[np.newaxis, :],
                 "input_mask": np.array(input_mask).astype(np.int64)[np.newaxis, :],
-                "segment_ids": np.array(segment_ids).astype(np.int64)[np.newaxis, :]
+                "segment_ids": np.array(segment_ids).astype(np.int64)[np.newaxis, :],
             }
 
         scores = self.sess.run([o.name for o in self.sess.get_outputs()], fd)
@@ -109,8 +123,12 @@ def flush_queries(self):
 
     def __del__(self):
         if self.profile:
-            print("ONNX runtime profile dumped to: '{}'".format(self.sess.end_profiling()))
+            print(
+                "ONNX runtime profile dumped to: '{}'".format(
+                    self.sess.end_profiling())
+            )
         print("Finished destroying SUT.")
 
+
 def get_onnxruntime_sut(args):
     return BERT_ONNXRuntime_SUT(args)
diff --git a/language/bert/pytorch_SUT.py b/language/bert/pytorch_SUT.py
index 81908ea6d3..89660c9d96 100644
--- a/language/bert/pytorch_SUT.py
+++ b/language/bert/pytorch_SUT.py
@@ -19,17 +19,27 @@
 import json
 import os
 import sys
-sys.path.insert(0, os.path.join(os.getcwd(), "DeepLearningExamples", "PyTorch", "LanguageModeling", "BERT"))
+
+sys.path.insert(
+    0,
+    os.path.join(
+        os.getcwd(), "DeepLearningExamples", "PyTorch", "LanguageModeling", "BERT"
+    ),
+)
 sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
-import numpy as np
-import torch
-import transformers
-from transformers import BertConfig, BertForQuestionAnswering
-from squad_QSL import get_squad_QSL
+try:
+    from squad_QSL import get_squad_QSL
+    from transformers import BertConfig, BertForQuestionAnswering
+    import transformers
+    import torch
+    import numpy as np
+    import mlperf_loadgen as lg
+except ImportError:
+    raise Exception("Error importing local modules")
+
 
-class BERT_PyTorch_SUT():
+class BERT_PyTorch_SUT:
     def __init__(self, args):
         print("Loading BERT configs...")
         with open("bert_config.json") as f:
@@ -46,17 +56,24 @@ def __init__(self, args):
             num_attention_heads=config_json["num_attention_heads"],
             num_hidden_layers=config_json["num_hidden_layers"],
             type_vocab_size=config_json["type_vocab_size"],
-            vocab_size=config_json["vocab_size"])
+            vocab_size=config_json["vocab_size"],
+        )
 
         self.network = args.network
-        self.dev = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+        self.dev = (
+            torch.device("cuda:0") if torch.cuda.is_available(
+            ) else torch.device("cpu")
+        )
         self.version = transformers.__version__
 
         print("Loading PyTorch model...")
         self.model = BertForQuestionAnswering(config)
         self.model.to(self.dev)
         self.model.eval()
-        model_file = os.environ.get("ML_MODEL_FILE_WITH_PATH", "build/data/bert_tf_v1_1_large_fp32_384_v2/model.pytorch")
+        model_file = os.environ.get(
+            "ML_MODEL_FILE_WITH_PATH",
+            "build/data/bert_tf_v1_1_large_fp32_384_v2/model.pytorch",
+        )
         self.model.load_state_dict(torch.load(model_file), strict=False)
 
         print("Constructing SUT...")
@@ -70,42 +87,52 @@ def issue_queries(self, query_samples):
             eval_features = self.qsl.get_features(query_samples[i].index)
             self.process_sample(eval_features, query_samples[i].id)
 
-    def process_sample(self, sample_input, query_id = None):
+    def process_sample(self, sample_input, query_id=None):
 
         if self.network == "sut":
-            input_ids = sample_input['input_ids']
-            input_mask = sample_input['input_mask']
-            segment_ids = sample_input['segment_ids']
+            input_ids = sample_input["input_ids"]
+            input_mask = sample_input["input_mask"]
+            segment_ids = sample_input["segment_ids"]
         else:
             input_ids = sample_input.input_ids
             input_mask = sample_input.input_mask
             segment_ids = sample_input.segment_ids
 
         with torch.no_grad():
-            model_output = self.model.forward(input_ids=torch.LongTensor(input_ids).unsqueeze(0).to(self.dev),
-                attention_mask=torch.LongTensor(input_mask).unsqueeze(0).to(self.dev),
-                token_type_ids=torch.LongTensor(segment_ids).unsqueeze(0).to(self.dev))
-            if self.version >= '4.0.0':
+            model_output = self.model.forward(
+                input_ids=torch.LongTensor(
+                    input_ids).unsqueeze(0).to(self.dev),
+                attention_mask=torch.LongTensor(
+                    input_mask).unsqueeze(0).to(self.dev),
+                token_type_ids=torch.LongTensor(
+                    segment_ids).unsqueeze(0).to(self.dev),
+            )
+            if self.version >= "4.0.0":
                 start_scores = model_output.start_logits
                 end_scores = model_output.end_logits
             else:
                 start_scores, end_scores = model_output
-            output = torch.stack([start_scores, end_scores], axis=-1).squeeze(0).cpu().numpy()
+            output = (
+                torch.stack([start_scores, end_scores], axis=-1)
+                .squeeze(0)
+                .cpu()
+                .numpy()
+            )
 
             if self.network == "sut":
                 return output.tolist()
-    
+
             response_array = array.array("B", output.tobytes())
             bi = response_array.buffer_info()
             response = lg.QuerySampleResponse(query_id, bi[0], bi[1])
             lg.QuerySamplesComplete([response])
 
-
     def flush_queries(self):
         pass
 
     def __del__(self):
         print("Finished destroying SUT.")
 
+
 def get_pytorch_sut(args):
     return BERT_PyTorch_SUT(args)
diff --git a/language/bert/ray_SUT.py b/language/bert/ray_SUT.py
index d03e52f50d..6280d0effb 100644
--- a/language/bert/ray_SUT.py
+++ b/language/bert/ray_SUT.py
@@ -19,25 +19,35 @@
 import json
 import os
 import sys
-sys.path.insert(0, os.path.join(os.getcwd(), "DeepLearningExamples", "PyTorch", "LanguageModeling", "BERT"))
-sys.path.insert(0, os.getcwd())
-
-import mlperf_loadgen as lg
-import numpy as np
-import torch
-import torch_tensorrt
-import transformers
-from transformers import BertConfig, BertForQuestionAnswering
-from squad_QSL import get_squad_QSL
 
-import ray
-from ray.util.actor_pool import ActorPool
+sys.path.insert(
+    0,
+    os.path.join(
+        os.getcwd(), "DeepLearningExamples", "PyTorch", "LanguageModeling", "BERT"
+    ),
+)
+sys.path.insert(0, os.getcwd())
 
+try:
+    from ray.util.actor_pool import ActorPool
+    import ray
+    from squad_QSL import get_squad_QSL
+    from transformers import BertConfig, BertForQuestionAnswering
+    import transformers
+    import torch_tensorrt
+    import torch
+    import numpy as np
+    import mlperf_loadgen as lg
+except ImportError:
+    raise Exception("Error importing local modules")
 
 # Adjustable Parameters
-BATCH_SIZE = 16    # Note. num_samples (called "test_query_count" in CM) must be a multiple of batch_size
+# Note. num_samples (called "test_query_count" in CM) must be a multiple
+# of batch_size
+BATCH_SIZE = 16
+
 
-@ray.remote(num_cpus=1,num_gpus=1)
+@ray.remote(num_cpus=1, num_gpus=1)
 class TorchPredictor:
     def __init__(self, config_json, model_file, batch_size):
         print("init", os.getpid(), torch.cuda.device_count())
@@ -55,7 +65,8 @@ def __init__(self, config_json, model_file, batch_size):
             num_attention_heads=config_json["num_attention_heads"],
             num_hidden_layers=config_json["num_hidden_layers"],
             type_vocab_size=config_json["type_vocab_size"],
-            vocab_size=config_json["vocab_size"])
+            vocab_size=config_json["vocab_size"],
+        )
 
         self.dev = torch.device("cuda")
         self.model = BertForQuestionAnswering(config)
@@ -63,25 +74,46 @@ def __init__(self, config_json, model_file, batch_size):
         self.model.eval()
         self.model.load_state_dict(torch.load(model_file), strict=False)
         # tensor rt
-        batch_input_ids = torch.LongTensor(np.zeros((batch_size, 384))).to(self.dev)
-        traced_mlm_model = torch.jit.trace(self.model, [batch_input_ids, batch_input_ids, batch_input_ids], strict=False)
-        self.trt_model = torch_tensorrt.compile(traced_mlm_model,
+        batch_input_ids = torch.LongTensor(
+            np.zeros(
+                (batch_size, 384))).to(
+            self.dev)
+        traced_mlm_model = torch.jit.trace(
+            self.model,
+            [batch_input_ids, batch_input_ids, batch_input_ids],
+            strict=False,
+        )
+        self.trt_model = torch_tensorrt.compile(
+            traced_mlm_model,
             inputs=[
-                torch_tensorrt.Input(shape=[batch_size, 384], dtype=torch.int32),
-                torch_tensorrt.Input(shape=[batch_size, 384], dtype=torch.int32),
-                torch_tensorrt.Input(shape=[batch_size, 384], dtype=torch.int32),
-        ],
-        enabled_precisions= {torch.float32, torch.float16},
-        workspace_size=2000000000,
-        truncate_long_and_double=True)
+                torch_tensorrt.Input(
+                    shape=[
+                        batch_size,
+                        384],
+                    dtype=torch.int32),
+                torch_tensorrt.Input(
+                    shape=[
+                        batch_size,
+                        384],
+                    dtype=torch.int32),
+                torch_tensorrt.Input(
+                    shape=[
+                        batch_size,
+                        384],
+                    dtype=torch.int32),
+            ],
+            enabled_precisions={torch.float32, torch.float16},
+            workspace_size=2000000000,
+            truncate_long_and_double=True,
+        )
 
         print("done loading")
 
     # Logic for inference on 1 batch of data.
     def forward(self, batch):
-        input_ids=torch.from_numpy(batch["input_ids"]).to(self.dev)
-        attention_mask=torch.from_numpy(batch["attention_mask"]).to(self.dev)
-        token_type_ids=torch.from_numpy(batch["token_type_ids"]).to(self.dev)
+        input_ids = torch.from_numpy(batch["input_ids"]).to(self.dev)
+        attention_mask = torch.from_numpy(batch["attention_mask"]).to(self.dev)
+        token_type_ids = torch.from_numpy(batch["token_type_ids"]).to(self.dev)
         with torch.inference_mode():
             # pytorch
             # model_output = self.model.forward(input_ids=input_ids,
@@ -89,25 +121,29 @@ def forward(self, batch):
             #     token_type_ids=token_type_ids)
             # start_scores = model_output.start_logits
             # end_scores = model_output.end_logits
-            
+
             # tensor rt
-            trt_output = self.trt_model(input_ids, attention_mask, token_type_ids)
+            trt_output = self.trt_model(
+                input_ids, attention_mask, token_type_ids)
             start_scores = trt_output["start_logits"]
             end_scores = trt_output["end_logits"]
-            
-            batch_ret = torch.stack([start_scores, end_scores], axis=-1).cpu().numpy()
-            return {
-                "output": batch_ret
-            }
-    
+
+            batch_ret = torch.stack(
+                [start_scores, end_scores], axis=-1).cpu().numpy()
+            return {"output": batch_ret}
+
     def ready(self):
         pass
 
-class BERT_Ray_SUT():
+
+class BERT_Ray_SUT:
     def __init__(self, args):
         with open("bert_config.json") as f:
             config_json = json.load(f)
-        model_file = os.environ.get("ML_MODEL_FILE_WITH_PATH", "build/data/bert_tf_v1_1_large_fp32_384_v2/model.pytorch")
+        model_file = os.environ.get(
+            "ML_MODEL_FILE_WITH_PATH",
+            "build/data/bert_tf_v1_1_large_fp32_384_v2/model.pytorch",
+        )
 
         print("Constructing SUT...")
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
@@ -116,34 +152,45 @@ def __init__(self, args):
 
         try:
             ray.init(address="auto")
-        except:
+        except BaseException:
             print("WARN: Cannot connect to existing Ray cluster.")
             print("We are going to start a new RAY cluster, but pay attention that")
             print("the cluster contains only one node.")
-            print("If you want to use multiple nodes, please start the cluster manually via:")
+            print(
+                "If you want to use multiple nodes, please start the cluster manually via:"
+            )
             print("\tOn the head node, run `ray start --head`")
             print("\tOn other nodes, run `ray start --address=<head node IP>:6379`")
             ray.init()
-            
+
         self.batch_size = BATCH_SIZE
         resources = ray.cluster_resources()
-        num_gpus = int(resources.get('GPU', 0))
-        
+        num_gpus = int(resources.get("GPU", 0))
+
         print(f"The cluster has {num_gpus} GPUs.")
-        
-        self.actor_list = [TorchPredictor.remote(config_json, model_file, self.batch_size) for _ in range(num_gpus)]
+
+        self.actor_list = [
+            TorchPredictor.remote(config_json, model_file, self.batch_size)
+            for _ in range(num_gpus)
+        ]
         self.pool = ActorPool(self.actor_list)
 
         samples = []
         for i in range(self.qsl.count):
             sample = {}
             eval_features = self.qsl.get_features(i)
-            sample["input_ids"] = np.array(eval_features.input_ids).astype(np.int32)
-            sample["attention_mask"] = np.array(eval_features.input_mask).astype(np.int32)
-            sample["token_type_ids"] = np.array(eval_features.segment_ids).astype(np.int32)
+            sample["input_ids"] = np.array(
+                eval_features.input_ids).astype(
+                np.int32)
+            sample["attention_mask"] = np.array(eval_features.input_mask).astype(
+                np.int32
+            )
+            sample["token_type_ids"] = np.array(eval_features.segment_ids).astype(
+                np.int32
+            )
             samples.append(sample)
         self.samples = samples
-        
+
         print("Waiting Actors init")
         for actor in self.actor_list:
             ray.get(actor.ready.remote())
@@ -153,26 +200,38 @@ def issue_queries(self, query_samples):
         if len(query_samples) % self.batch_size != 0:
             print("ERROR: batch size must be a multiple of the number of samples")
             sys.exit(1)
-            
+
         batch_samples = []
         i = 0
         while i < len(query_samples):
             batch_sample = {
-                "input_ids": np.array([
-                    self.samples[query_sample.index]["input_ids"]
-                    for query_sample in query_samples[i:i+self.batch_size]]),
-                "attention_mask": np.array([
-                    self.samples[query_sample.index]["attention_mask"]
-                    for query_sample in query_samples[i:i+self.batch_size]]),
-                "token_type_ids": np.array([
-                    self.samples[query_sample.index]["token_type_ids"]
-                    for query_sample in query_samples[i:i+self.batch_size]]),
+                "input_ids": np.array(
+                    [
+                        self.samples[query_sample.index]["input_ids"]
+                        for query_sample in query_samples[i: i + self.batch_size]
+                    ]
+                ),
+                "attention_mask": np.array(
+                    [
+                        self.samples[query_sample.index]["attention_mask"]
+                        for query_sample in query_samples[i: i + self.batch_size]
+                    ]
+                ),
+                "token_type_ids": np.array(
+                    [
+                        self.samples[query_sample.index]["token_type_ids"]
+                        for query_sample in query_samples[i: i + self.batch_size]
+                    ]
+                ),
             }
             batch_samples.append(batch_sample)
             i = i + self.batch_size
 
         # print("samples len", len(batch_samples))
-        batch_inference_results = list(self.pool.map_unordered(lambda a, v: a.forward.remote(v), batch_samples))
+        batch_inference_results = list(
+            self.pool.map_unordered(
+                lambda a, v: a.forward.remote(v), batch_samples)
+        )
 
         cur_query_index = 0
         for batch_inference_result in batch_inference_results:
@@ -180,7 +239,9 @@ def issue_queries(self, query_samples):
             for inference_result in batch_inference_result:
                 response_array = array.array("B", inference_result.tobytes())
                 bi = response_array.buffer_info()
-                response = lg.QuerySampleResponse(query_samples[cur_query_index].id, bi[0], bi[1])
+                response = lg.QuerySampleResponse(
+                    query_samples[cur_query_index].id, bi[0], bi[1]
+                )
                 lg.QuerySamplesComplete([response])
                 cur_query_index += 1
 
@@ -190,5 +251,6 @@ def flush_queries(self):
     def __del__(self):
         print("Finished destroying SUT.")
 
+
 def get_ray_sut(args):
     return BERT_Ray_SUT(args)
diff --git a/language/bert/run.py b/language/bert/run.py
index f0b376564b..5a35148904 100644
--- a/language/bert/run.py
+++ b/language/bert/run.py
@@ -22,6 +22,7 @@
 import argparse
 import os
 import sys
+
 sys.path.insert(0, os.getcwd())
 sys.path.insert(0, os.path.join(os.getcwd(), "..", "..", "lon"))
 
@@ -29,33 +30,60 @@
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--backend", choices=["tf", "pytorch", "onnxruntime", "tf_estimator", "ray"], default="tf", help="Backend")
-    parser.add_argument("--scenario", choices=["SingleStream", "Offline",
-                                               "Server", "MultiStream"], default="Offline", help="Scenario")
-    parser.add_argument("--accuracy", action="store_true",
-                        help="enable accuracy pass")
-    parser.add_argument("--quantized", action="store_true",
-                        help="use quantized model (only valid for onnxruntime backend)")
-    parser.add_argument("--profile", action="store_true",
-                        help="enable profiling (only valid for onnxruntime backend)")
-    parser.add_argument("--user_conf", default="user.conf",
-                        help="user config for user LoadGen settings such as target QPS")
-    parser.add_argument("--audit_conf", default="audit.conf",
-                        help="audit config for LoadGen settings during compliance runs")
-    parser.add_argument("--max_examples", type=int,
-                        help="Maximum number of examples to consider (not limited by default)")
+        "--backend",
+        choices=["tf", "pytorch", "onnxruntime", "tf_estimator", "ray"],
+        default="tf",
+        help="Backend",
+    )
+    parser.add_argument(
+        "--scenario",
+        choices=["SingleStream", "Offline", "Server", "MultiStream"],
+        default="Offline",
+        help="Scenario",
+    )
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
+    parser.add_argument(
+        "--quantized",
+        action="store_true",
+        help="use quantized model (only valid for onnxruntime backend)",
+    )
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="enable profiling (only valid for onnxruntime backend)",
+    )
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+    parser.add_argument(
+        "--audit_conf",
+        default="audit.conf",
+        help="audit config for LoadGen settings during compliance runs",
+    )
+    parser.add_argument(
+        "--max_examples",
+        type=int,
+        help="Maximum number of examples to consider (not limited by default)",
+    )
     parser.add_argument(
         "--network",
-        choices=[
-            "sut",
-            "lon",
-            None],
+        choices=["sut", "lon", None],
         default=None,
-        help="Loadgen network mode")
-    parser.add_argument('--node', type=str, default="")
-    parser.add_argument('--port', type=int, default=8000)
-    parser.add_argument('--sut_server', nargs="*", default=['http://localhost:8000'],
-                        help='Address of the server(s) under test.')
+        help="Loadgen network mode",
+    )
+    parser.add_argument("--node", type=str, default="")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument(
+        "--sut_server",
+        nargs="*",
+        default=["http://localhost:8000"],
+        help="Address of the server(s) under test.",
+    )
 
     args = parser.parse_args()
     return args
@@ -65,7 +93,7 @@ def get_args():
     "SingleStream": lg.TestScenario.SingleStream,
     "Offline": lg.TestScenario.Offline,
     "Server": lg.TestScenario.Server,
-    "MultiStream": lg.TestScenario.MultiStream
+    "MultiStream": lg.TestScenario.MultiStream,
 }
 
 
@@ -76,27 +104,48 @@ def main():
 
     if not args.network or args.network == "sut":
         if args.backend == "pytorch":
-            assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
-            assert not args.profile, "Profiling is only supported by onnxruntime backend!"
+            assert (
+                not args.quantized
+            ), "Quantized model is only supported by onnxruntime backend!"
+            assert (
+                not args.profile
+            ), "Profiling is only supported by onnxruntime backend!"
             from pytorch_SUT import get_pytorch_sut
+
             sut = get_pytorch_sut(args)
         elif args.backend == "tf":
-            assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
-            assert not args.profile, "Profiling is only supported by onnxruntime backend!"
+            assert (
+                not args.quantized
+            ), "Quantized model is only supported by onnxruntime backend!"
+            assert (
+                not args.profile
+            ), "Profiling is only supported by onnxruntime backend!"
             from tf_SUT import get_tf_sut
+
             sut = get_tf_sut(args)
         elif args.backend == "tf_estimator":
-            assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
-            assert not args.profile, "Profiling is only supported by onnxruntime backend!"
+            assert (
+                not args.quantized
+            ), "Quantized model is only supported by onnxruntime backend!"
+            assert (
+                not args.profile
+            ), "Profiling is only supported by onnxruntime backend!"
             from tf_estimator_SUT import get_tf_estimator_sut
+
             sut = get_tf_estimator_sut()
         elif args.backend == "onnxruntime":
             from onnxruntime_SUT import get_onnxruntime_sut
+
             sut = get_onnxruntime_sut(args)
         elif args.backend == "ray":
-            assert not args.quantized, "Quantized model is only supported by onnxruntime backend!"
-            assert not args.profile, "Profiling is only supported by onnxruntime backend!"
+            assert (
+                not args.quantized
+            ), "Quantized model is only supported by onnxruntime backend!"
+            assert (
+                not args.profile
+            ), "Profiling is only supported by onnxruntime backend!"
             from ray_SUT import get_ray_sut
+
             sut = get_ray_sut(args)
         else:
             raise ValueError("Unknown backend: {:}".format(args.backend))
@@ -125,6 +174,7 @@ def main():
 
     if args.network == "lon":
         from network_LON import app, set_args, main as app_main
+
         set_args(
             args,
             settings,
@@ -132,11 +182,13 @@ def main():
             args.audit_conf,
             args.sut_server,
             args.backend,
-            args.max_examples)
+            args.max_examples,
+        )
         app.run(app_main)
 
     elif args.network == "sut":
         from network_SUT import app, node, set_backend
+
         node = args.node
         set_backend(sut)
         app.run(debug=False, port=args.port, host="0.0.0.0")
@@ -144,16 +196,17 @@ def main():
     else:
         print("Running LoadGen test...")
         lg.StartTestWithLogSettings(
-            sut.sut,
-            sut.qsl.qsl,
-            settings,
-            log_settings,
-            args.audit_conf)
+            sut.sut, sut.qsl.qsl, settings, log_settings, args.audit_conf
+        )
         if args.accuracy and not os.environ.get("SKIP_VERIFY_ACCURACY"):
             cmd = "python3 {:}/accuracy-squad.py {}".format(
                 os.path.dirname(os.path.abspath(__file__)),
-                '--max_examples {}'.format(
-                    args.max_examples) if args.max_examples else '')
+                (
+                    "--max_examples {}".format(args.max_examples)
+                    if args.max_examples
+                    else ""
+                ),
+            )
             subprocess.check_call(cmd, shell=True)
 
     print("Done!")
diff --git a/language/bert/squad_QSL.py b/language/bert/squad_QSL.py
index c751c33c61..a97961fe03 100644
--- a/language/bert/squad_QSL.py
+++ b/language/bert/squad_QSL.py
@@ -15,33 +15,41 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import pickle
+import mlperf_loadgen as lg
+from create_squad_data import read_squad_examples, convert_examples_to_features
+from transformers import BertTokenizer
 import os
 import sys
-sys.path.insert(0, os.getcwd())
 
-from transformers import BertTokenizer
-from create_squad_data import read_squad_examples, convert_examples_to_features
+sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
 
 # To support feature cache.
-import pickle
 
 max_seq_length = 384
 max_query_length = 64
 doc_stride = 128
 
-class SQuAD_v1_QSL():
-    def __init__(self, total_count_override=None, perf_count_override=None, cache_path='eval_features.pickle'):
+
+class SQuAD_v1_QSL:
+    def __init__(
+        self,
+        total_count_override=None,
+        perf_count_override=None,
+        cache_path="eval_features.pickle",
+    ):
         print("Constructing QSL...")
         eval_features = []
         # Load features if cached, convert from examples otherwise.
         if os.path.exists(cache_path):
             print("Loading cached features from '%s'..." % cache_path)
-            with open(cache_path, 'rb') as cache_file:
+            with open(cache_path, "rb") as cache_file:
                 eval_features = pickle.load(cache_file)
         else:
-            print("No cached features at '%s'... converting from examples..." % cache_path)
+            print(
+                "No cached features at '%s'... converting from examples..." % cache_path
+            )
 
             print("Creating tokenizer...")
             vocab_file = os.environ.get("VOCAB_FILE")
@@ -53,10 +61,14 @@ def __init__(self, total_count_override=None, perf_count_override=None, cache_pa
             dataset_file = os.environ.get("DATASET_FILE")
             if not dataset_file:
                 dataset_file = "build/data/dev-v1.1.json"
-            eval_examples = read_squad_examples(input_file=dataset_file,
-                is_training=False, version_2_with_negative=False)
+            eval_examples = read_squad_examples(
+                input_file=dataset_file,
+                is_training=False,
+                version_2_with_negative=False,
+            )
 
             print("Converting examples to features...")
+
             def append_feature(feature):
                 eval_features.append(feature)
 
@@ -68,16 +80,22 @@ def append_feature(feature):
                 max_query_length=max_query_length,
                 is_training=False,
                 output_fn=append_feature,
-                verbose_logging=False)
+                verbose_logging=False,
+            )
 
             print("Caching features at '%s'..." % cache_path)
-            with open(cache_path, 'wb') as cache_file:
+            with open(cache_path, "wb") as cache_file:
                 pickle.dump(eval_features, cache_file)
 
         self.eval_features = eval_features
         self.count = total_count_override or len(self.eval_features)
         self.perf_count = perf_count_override or self.count
-        self.qsl = lg.ConstructQSL(self.count, self.perf_count, self.load_query_samples, self.unload_query_samples)
+        self.qsl = lg.ConstructQSL(
+            self.count,
+            self.perf_count,
+            self.load_query_samples,
+            self.unload_query_samples,
+        )
         print("Finished constructing QSL.")
 
     def load_query_samples(self, sample_list):
@@ -92,5 +110,6 @@ def get_features(self, sample_id):
     def __del__(self):
         print("Finished destroying QSL.")
 
+
 def get_squad_QSL(total_count_override=None, perf_count_override=None):
     return SQuAD_v1_QSL(total_count_override, perf_count_override)
diff --git a/language/bert/tf_SUT.py b/language/bert/tf_SUT.py
index 2956e6d970..b59b8a2aee 100644
--- a/language/bert/tf_SUT.py
+++ b/language/bert/tf_SUT.py
@@ -18,31 +18,50 @@
 import array
 import os
 import sys
-sys.path.insert(0, os.path.join(os.getcwd(), "DeepLearningExamples", "TensorFlow", "LanguageModeling", "BERT"))
+
+sys.path.insert(
+    0,
+    os.path.join(
+        os.getcwd(), "DeepLearningExamples", "TensorFlow", "LanguageModeling", "BERT"
+    ),
+)
 sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
-import numpy as np
-import tensorflow as tf
-from tensorflow.python.platform import gfile
-from squad_QSL import get_squad_QSL
+try:
+    from squad_QSL import get_squad_QSL
+    from tensorflow.python.platform import gfile
+    import tensorflow as tf
+    import numpy as np
+    import mlperf_loadgen as lg
+except ImportError:
+    raise Exception("Error importing local modules")
+
 
-class BERT_TF_SUT():
+class BERT_TF_SUT:
     def __init__(self, args):
         print("Loading TF model...")
         infer_config = tf.compat.v1.ConfigProto()
-        infer_config.intra_op_parallelism_threads = int(os.environ['TF_INTRA_OP_PARALLELISM_THREADS']) \
-                if 'TF_INTRA_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
-        infer_config.inter_op_parallelism_threads = int(os.environ['TF_INTER_OP_PARALLELISM_THREADS']) \
-                if 'TF_INTER_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
+        infer_config.intra_op_parallelism_threads = (
+            int(os.environ["TF_INTRA_OP_PARALLELISM_THREADS"])
+            if "TF_INTRA_OP_PARALLELISM_THREADS" in os.environ
+            else os.cpu_count()
+        )
+        infer_config.inter_op_parallelism_threads = (
+            int(os.environ["TF_INTER_OP_PARALLELISM_THREADS"])
+            if "TF_INTER_OP_PARALLELISM_THREADS" in os.environ
+            else os.cpu_count()
+        )
         infer_config.use_per_session_threads = 1
         self.sess = tf.compat.v1.Session(config=infer_config)
-        model_file = os.environ.get('ML_MODEL_FILE_WITH_PATH', 'build/data/bert_tf_v1_1_large_fp32_384_v2/model.pb')
-        with gfile.FastGFile(model_file, 'rb') as f:
+        model_file = os.environ.get(
+            "ML_MODEL_FILE_WITH_PATH",
+            "build/data/bert_tf_v1_1_large_fp32_384_v2/model.pb",
+        )
+        with gfile.FastGFile(model_file, "rb") as f:
             graph_def = tf.compat.v1.GraphDef()
             graph_def.ParseFromString(f.read())
             self.sess.graph.as_default()
-            tf.import_graph_def(graph_def, name='')
+            tf.import_graph_def(graph_def, name="")
 
         print("Constructing SUT...")
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
@@ -53,20 +72,23 @@ def __init__(self, args):
     def issue_queries(self, query_samples):
         for i in range(len(query_samples)):
             eval_features = self.qsl.get_features(query_samples[i].index)
-            input_ids   = np.array([eval_features.input_ids])
-            input_mask  = np.array([eval_features.input_mask])
+            input_ids = np.array([eval_features.input_ids])
+            input_mask = np.array([eval_features.input_mask])
             segment_ids = np.array([eval_features.segment_ids])
             feeds = {
-                'input_ids:0':   input_ids,
-                'input_mask:0':  input_mask,
-                'segment_ids:0': segment_ids
+                "input_ids:0": input_ids,
+                "input_mask:0": input_mask,
+                "segment_ids:0": segment_ids,
             }
             result = self.sess.run(["logits:0"], feed_dict=feeds)
 
             logits = [float(x) for x in result[0].flat]
-            response_array = array.array("B", np.array(logits).astype(np.float32).tobytes())
+            response_array = array.array(
+                "B", np.array(logits).astype(np.float32).tobytes()
+            )
             bi = response_array.buffer_info()
-            response = lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])
+            response = lg.QuerySampleResponse(
+                query_samples[i].id, bi[0], bi[1])
             lg.QuerySamplesComplete([response])
 
     def flush_queries(self):
@@ -75,5 +97,6 @@ def flush_queries(self):
     def __del__(self):
         print("Finished destroying SUT.")
 
+
 def get_tf_sut(args):
     return BERT_TF_SUT(args)
diff --git a/language/bert/tf_estimator_SUT.py b/language/bert/tf_estimator_SUT.py
index a795da52cd..103fb7ca18 100644
--- a/language/bert/tf_estimator_SUT.py
+++ b/language/bert/tf_estimator_SUT.py
@@ -18,28 +18,44 @@
 import json
 import os
 import sys
-sys.path.insert(0, os.path.join(os.getcwd(), "DeepLearningExamples", "TensorFlow", "LanguageModeling", "BERT"))
+
+sys.path.insert(
+    0,
+    os.path.join(
+        os.getcwd(), "DeepLearningExamples", "TensorFlow", "LanguageModeling", "BERT"
+    ),
+)
 sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
-import modeling
-import numpy as np
-import tensorflow as tf
-from squad_QSL import get_squad_QSL
+try:
+    from squad_QSL import get_squad_QSL
+    import tensorflow as tf
+    import numpy as np
+    import modeling
+    import mlperf_loadgen as lg
+except ImportError:
+    raise Exception("Error importing local modules")
+
 
-# Allow TF to increase GPU memory usage dynamically to prevent cuBLAS init problems.
+# Allow TF to increase GPU memory usage dynamically to prevent cuBLAS init
+# problems.
 config = tf.compat.v1.ConfigProto()
 config.gpu_options.allow_growth = True
 session = tf.compat.v1.Session(config=config)
 
-class BERT_TF_ESTIMATOR_SUT():
+
+class BERT_TF_ESTIMATOR_SUT:
     def __init__(self, batch_size=8):
         print("Loading TF model...")
         bert_config = modeling.BertConfig.from_json_file("bert_config.json")
 
         model_fn = self.model_fn_builder(
             bert_config=bert_config,
-            init_checkpoint=os.environ.get("ML_MODEL_FILE_WITH_PATH", "build/data/bert_tf_v1_1_large_fp32_384_v2/model.ckpt-5474"))
+            init_checkpoint=os.environ.get(
+                "ML_MODEL_FILE_WITH_PATH",
+                "build/data/bert_tf_v1_1_large_fp32_384_v2/model.ckpt-5474",
+            ),
+        )
 
         self.estimator = tf.estimator.Estimator(model_fn=model_fn)
         self.batch_size = batch_size
@@ -55,7 +71,8 @@ def issue_queries(self, query_samples):
         input_mask = np.zeros((len(query_samples), 1, 384), dtype=np.int32)
         segment_ids = np.zeros((len(query_samples), 1, 384), dtype=np.int32)
         for sample_idx in range(len(query_samples)):
-            eval_features = self.qsl.get_features(query_samples[sample_idx].index)
+            eval_features = self.qsl.get_features(
+                query_samples[sample_idx].index)
             input_ids[sample_idx, ...] = np.array(eval_features.input_ids)
             input_mask[sample_idx, ...] = np.array(eval_features.input_mask)
             segment_ids[sample_idx, ...] = np.array(eval_features.segment_ids)
@@ -64,15 +81,18 @@ def input_fn():
             inputs = {
                 "input_ids": input_ids,
                 "input_mask": input_mask,
-                "segment_ids": segment_ids
+                "segment_ids": segment_ids,
             }
             return tf.data.Dataset.from_tensor_slices(inputs)
 
         for i, result in enumerate(self.estimator.predict(input_fn)):
             logits = [float(x) for x in result["logits"].flat]
-            response_array = array.array("B", np.array(logits).astype(np.float32).tobytes())
+            response_array = array.array(
+                "B", np.array(logits).astype(np.float32).tobytes()
+            )
             bi = response_array.buffer_info()
-            response = lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])
+            response = lg.QuerySampleResponse(
+                query_samples[i].id, bi[0], bi[1])
             lg.QuerySamplesComplete([response])
 
     def flush_queries(self):
@@ -81,33 +101,51 @@ def flush_queries(self):
     def __del__(self):
         print("Finished destroying SUT.")
 
-    def create_model(self, bert_config, is_training, input_ids, input_mask, segment_ids, use_one_hot_embeddings):
+    def create_model(
+        self,
+        bert_config,
+        is_training,
+        input_ids,
+        input_mask,
+        segment_ids,
+        use_one_hot_embeddings,
+    ):
         """Creates a classification model."""
         model = modeling.BertModel(
-                config=bert_config,
-                is_training=is_training,
-                input_ids=input_ids,
-                input_mask=input_mask,
-                token_type_ids=segment_ids,
-                use_one_hot_embeddings=use_one_hot_embeddings,
-                compute_type=tf.float32)
+            config=bert_config,
+            is_training=is_training,
+            input_ids=input_ids,
+            input_mask=input_mask,
+            token_type_ids=segment_ids,
+            use_one_hot_embeddings=use_one_hot_embeddings,
+            compute_type=tf.float32,
+        )
 
         final_hidden = model.get_sequence_output()
 
-        final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
+        final_hidden_shape = modeling.get_shape_list(
+            final_hidden, expected_rank=3)
         batch_size = final_hidden_shape[0]
         seq_length = final_hidden_shape[1]
         hidden_size = final_hidden_shape[2]
 
         output_weights = tf.get_variable(
-                "cls/squad/output_weights", [2, hidden_size],
-                initializer=tf.truncated_normal_initializer(stddev=0.02))
+            "cls/squad/output_weights",
+            [2, hidden_size],
+            initializer=tf.truncated_normal_initializer(stddev=0.02),
+        )
 
         output_bias = tf.get_variable(
-                "cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
-
-        final_hidden_matrix = tf.reshape(final_hidden, [batch_size * seq_length, hidden_size])
-        logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
+            "cls/squad/output_bias", [2], initializer=tf.zeros_initializer()
+        )
+
+        final_hidden_matrix = tf.reshape(
+            final_hidden, [batch_size * seq_length, hidden_size]
+        )
+        logits = tf.matmul(
+            final_hidden_matrix,
+            output_weights,
+            transpose_b=True)
         logits = tf.nn.bias_add(logits, output_bias)
 
         logits = tf.reshape(logits, [batch_size, seq_length, 2])
@@ -121,7 +159,9 @@ def create_model(self, bert_config, is_training, input_ids, input_mask, segment_
 
         # return (start_logits, end_logits)
 
-    def model_fn_builder(self, bert_config, init_checkpoint, use_one_hot_embeddings=False):
+    def model_fn_builder(
+        self, bert_config, init_checkpoint, use_one_hot_embeddings=False
+    ):
         """Returns `model_fn` closure for Estimator."""
 
         def model_fn(features, labels):  # pylint: disable=unused-argument
@@ -135,20 +175,24 @@ def model_fn(features, labels):  # pylint: disable=unused-argument
                 input_ids=input_ids,
                 input_mask=input_mask,
                 segment_ids=segment_ids,
-                use_one_hot_embeddings=use_one_hot_embeddings)
+                use_one_hot_embeddings=use_one_hot_embeddings,
+            )
 
             tvars = tf.compat.v1.trainable_variables()
 
             initialized_variable_names = {}
-            (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
+            (assignment_map, initialized_variable_names) = (
+                modeling.get_assignment_map_from_checkpoint(
+                    tvars, init_checkpoint)
+            )
 
-            tf.compat.v1.train.init_from_checkpoint(init_checkpoint, assignment_map)
+            tf.compat.v1.train.init_from_checkpoint(
+                init_checkpoint, assignment_map)
 
-            predictions = {
-                "logits": logits
-            }
+            predictions = {"logits": logits}
             output_spec = tf.estimator.EstimatorSpec(
-                mode=tf.estimator.ModeKeys.PREDICT, predictions=predictions)
+                mode=tf.estimator.ModeKeys.PREDICT, predictions=predictions
+            )
 
             return output_spec
 
diff --git a/language/bert/tf_freeze_bert.py b/language/bert/tf_freeze_bert.py
index 8a2e23bf53..f00d72dbe6 100644
--- a/language/bert/tf_freeze_bert.py
+++ b/language/bert/tf_freeze_bert.py
@@ -14,63 +14,82 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from tensorflow.tools.graph_transforms import TransformGraph
+from tensorflow.python.tools import optimize_for_inference_lib
+from tensorflow.python.framework import graph_util
+from tensorflow.python.framework import dtypes
+import tensorflow as tf
+import numpy as np
+import modeling
 import array
 import json
 import os
 import sys
-sys.path.insert(0, os.path.join(os.getcwd(), "DeepLearningExamples", "TensorFlow", "LanguageModeling", "BERT"))
-sys.path.insert(0, os.getcwd())
 
-import modeling
-import numpy as np
-import tensorflow as tf
+sys.path.insert(
+    0,
+    os.path.join(
+        os.getcwd(), "DeepLearningExamples", "TensorFlow", "LanguageModeling", "BERT"
+    ),
+)
+sys.path.insert(0, os.getcwd())
 
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import graph_util
-from tensorflow.python.tools import optimize_for_inference_lib
-from tensorflow.tools.graph_transforms import TransformGraph
 
 def save_model(fname, sess, graph=None):
-  def save(fname, graph_def):
-    pass
-    with tf.Graph().as_default() as g:
-        tf.import_graph_def(graph_def, name='')
-        graph_def = g.as_graph_def(add_shapes=True)
-    tf.train.write_graph(graph_def, ".", fname, as_text=False)
-
-  if graph == None:
-    graph_def = sess.graph_def
-  else:
-    graph_def = graph.as_graph_def(add_shapes=True)
-
-  input_nodes = ['IteratorGetNext:0', 'IteratorGetNext:1', 'IteratorGetNext:2']
-  output_nodes =  ['logits']
-
-  graph_def = graph_util.convert_variables_to_constants(
-      sess=sess,
-      input_graph_def=graph_def,
-      output_node_names=output_nodes)
-  graph_def = graph_util.remove_training_nodes(graph_def, protected_nodes=output_nodes)
-  graph_def = optimize_for_inference_lib.optimize_for_inference(graph_def, [], output_nodes, dtypes.float32.as_datatype_enum)
-
-  transforms = [
-    'remove_nodes(op=Identity, op=StopGradient)',
-    'fold_batch_norms',
-    'fold_old_batch_norms',
-  ]
-  graph_def = TransformGraph(graph_def, input_nodes, output_nodes, transforms)
-  save("build/data/bert_tf_v1_1_large_fp32_384_v2/model.pb", graph_def)
-
-def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, use_one_hot_embeddings):
+    def save(fname, graph_def):
+        pass
+        with tf.Graph().as_default() as g:
+            tf.import_graph_def(graph_def, name="")
+            graph_def = g.as_graph_def(add_shapes=True)
+        tf.train.write_graph(graph_def, ".", fname, as_text=False)
+
+    if graph is None:
+        graph_def = sess.graph_def
+    else:
+        graph_def = graph.as_graph_def(add_shapes=True)
+
+    input_nodes = [
+        "IteratorGetNext:0",
+        "IteratorGetNext:1",
+        "IteratorGetNext:2"]
+    output_nodes = ["logits"]
+
+    graph_def = graph_util.convert_variables_to_constants(
+        sess=sess, input_graph_def=graph_def, output_node_names=output_nodes
+    )
+    graph_def = graph_util.remove_training_nodes(
+        graph_def, protected_nodes=output_nodes
+    )
+    graph_def = optimize_for_inference_lib.optimize_for_inference(
+        graph_def, [], output_nodes, dtypes.float32.as_datatype_enum
+    )
+
+    transforms = [
+        "remove_nodes(op=Identity, op=StopGradient)",
+        "fold_batch_norms",
+        "fold_old_batch_norms",
+    ]
+    graph_def = TransformGraph(
+        graph_def,
+        input_nodes,
+        output_nodes,
+        transforms)
+    save("build/data/bert_tf_v1_1_large_fp32_384_v2/model.pb", graph_def)
+
+
+def create_model(
+    bert_config, is_training, input_ids, input_mask, segment_ids, use_one_hot_embeddings
+):
     """Creates a classification model."""
     model = modeling.BertModel(
-            config=bert_config,
-            is_training=is_training,
-            input_ids=input_ids,
-            input_mask=input_mask,
-            token_type_ids=segment_ids,
-            use_one_hot_embeddings=use_one_hot_embeddings,
-            compute_type=tf.float32)
+        config=bert_config,
+        is_training=is_training,
+        input_ids=input_ids,
+        input_mask=input_mask,
+        token_type_ids=segment_ids,
+        use_one_hot_embeddings=use_one_hot_embeddings,
+        compute_type=tf.float32,
+    )
 
     final_hidden = model.get_sequence_output()
 
@@ -80,26 +99,32 @@ def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, u
     hidden_size = final_hidden_shape[2]
 
     output_weights = tf.get_variable(
-            "cls/squad/output_weights", [2, hidden_size],
-            initializer=tf.truncated_normal_initializer(stddev=0.02))
+        "cls/squad/output_weights",
+        [2, hidden_size],
+        initializer=tf.truncated_normal_initializer(stddev=0.02),
+    )
 
     output_bias = tf.get_variable(
-            "cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
+        "cls/squad/output_bias", [2], initializer=tf.zeros_initializer()
+    )
 
-    final_hidden_matrix = tf.reshape(final_hidden, [batch_size * seq_length, hidden_size])
+    final_hidden_matrix = tf.reshape(
+        final_hidden, [batch_size * seq_length, hidden_size]
+    )
     logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
     logits = tf.nn.bias_add(logits, output_bias)
 
     logits = tf.reshape(logits, [batch_size, seq_length, 2], name="logits")
     return logits
 
+
 def main():
     bert_config = modeling.BertConfig.from_json_file("bert_config.json")
-    init_checkpoint="build/data/bert_tf_v1_1_large_fp32_384_v2/model.ckpt-5474"
+    init_checkpoint = "build/data/bert_tf_v1_1_large_fp32_384_v2/model.ckpt-5474"
 
-    input_ids = tf.placeholder(tf.int32, shape=(1,384), name="input_ids")
-    input_mask = tf.placeholder(tf.int32, shape=(1,384), name="input_mask")
-    segment_ids = tf.placeholder(tf.int32, shape=(1,384), name="segment_ids")
+    input_ids = tf.placeholder(tf.int32, shape=(1, 384), name="input_ids")
+    input_mask = tf.placeholder(tf.int32, shape=(1, 384), name="input_mask")
+    segment_ids = tf.placeholder(tf.int32, shape=(1, 384), name="segment_ids")
 
     logits = create_model(
         bert_config=bert_config,
@@ -107,24 +132,27 @@ def main():
         input_ids=input_ids,
         input_mask=input_mask,
         segment_ids=segment_ids,
-        use_one_hot_embeddings=False)
+        use_one_hot_embeddings=False,
+    )
 
     tvars = tf.compat.v1.trainable_variables()
 
     initialized_variable_names = {}
-    (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
+    (assignment_map, initialized_variable_names) = (
+        modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
+    )
 
     tf.compat.v1.train.init_from_checkpoint(init_checkpoint, assignment_map)
 
-    predictions = {
-        "logits": logits
-    }
+    predictions = {"logits": logits}
     output_spec = tf.estimator.EstimatorSpec(
-        mode=tf.estimator.ModeKeys.PREDICT, predictions=predictions)
+        mode=tf.estimator.ModeKeys.PREDICT, predictions=predictions
+    )
 
     with tf.Session() as sess:
         sess.run(tf.global_variables_initializer())
         save_model("bert_large_nv.pb", sess)
 
+
 if __name__ == "__main__":
     main()
diff --git a/language/gpt-j/GPTJ_QDL.py b/language/gpt-j/GPTJ_QDL.py
index 38ba2deba7..f75ece48d2 100644
--- a/language/gpt-j/GPTJ_QDL.py
+++ b/language/gpt-j/GPTJ_QDL.py
@@ -8,6 +8,7 @@
 import array
 import time
 
+
 class GPTJ_QDL:
     """QDL acting as a proxy to the SUT.
     This QDL communicates with the SUT via HTTP.
@@ -15,6 +16,7 @@ class GPTJ_QDL:
     - /predict/ : Send a query to the SUT and get a response.
     - /getname/ : Get the name of the SUT. Send a getname to the SUT and get a response.
     """
+
     def __init__(self, qsl, sut_server_addr: list, scenario: str):
         self.scenario = scenario
         self.sut_server_addr = sut_server_addr
@@ -23,7 +25,8 @@ def __init__(self, qsl, sut_server_addr: list, scenario: str):
 
         # Construct QDL from the python binding
         self.qdl = lg.ConstructQDL(
-            self.issue_query, self.flush_queries, self.client_get_name)
+            self.issue_query, self.flush_queries, self.client_get_name
+        )
         print("Finished constructing QDL!")
 
         # For round robin between the SUTs:
@@ -32,8 +35,9 @@ def __init__(self, qsl, sut_server_addr: list, scenario: str):
 
     def issue_query(self, query_samples):
         """Process the query to send to the SUT"""
-        threading.Thread(target=self.process_query_async,
-                         args=[query_samples]).start()
+        threading.Thread(
+            target=self.process_query_async,
+            args=[query_samples]).start()
 
     def flush_queries(self):
         """Flush the queries. Dummy implementation."""
@@ -52,28 +56,36 @@ def process_query_async(self, query_samples):
             query_samples: A list of QuerySample objects.
         """
 
-        max_num_threads = int(os.environ.get('CM_MAX_NUM_THREADS', os.cpu_count()))
+        max_num_threads = int(
+            os.environ.get(
+                "CM_MAX_NUM_THREADS",
+                os.cpu_count()))
         if self.scenario == "Offline":
             # Client sends multiple requests using threads
             # Pause when active thread equals the set max number of threads
-            # Only sends next request after recieving respose from server to any of the currently active threads 
+            # Only sends next request after recieving respose from server to
+            # any of the currently active threads
             print("Executing Offline scenario!")
             for i in range(len(query_samples)):
                 index = query_samples[i].index
                 input_ids_tensor = self.qsl.data_object.source_encoded_input_ids[index]
-                input_masks_tensor = self.qsl.data_object.source_encoded_attn_masks[index]
+                input_masks_tensor = self.qsl.data_object.source_encoded_attn_masks[
+                    index
+                ]
                 text = self.qsl.data_object.sources[index]
                 query = {
                     "input_text": text,
                     "input_ids_tensor": input_ids_tensor.tolist(),
-                    "input_masks_tensor": input_masks_tensor.tolist()
+                    "input_masks_tensor": input_masks_tensor.tolist(),
                 }
                 n = threading.active_count()
                 while n >= max_num_threads:
                     sleep(0.0001)
                     n = threading.active_count()
-                threading.Thread(target=self.client_predict_worker,
-                             args=[query, query_samples[i].id]).start()
+                threading.Thread(
+                    target=self.client_predict_worker, args=[
+                        query, query_samples[i].id]
+                ).start()
         if self.scenario == "Server":
             # Client sends request to server
             # Number of samples can vary based on Poisson distribution
@@ -84,7 +96,7 @@ def process_query_async(self, query_samples):
             query = {
                 "input_text": text,
                 "input_ids_tensor": input_ids_tensor.tolist(),
-                "input_masks_tensor": input_masks_tensor.tolist()
+                "input_masks_tensor": input_masks_tensor.tolist(),
             }
             self.client_predict_worker(query, query_samples[0].id)
 
@@ -94,39 +106,44 @@ def get_sut_id_round_robin(self):
             res = self.next_sut_id
             self.next_sut_id = (self.next_sut_id + 1) % self.num_nodes
         return res
-    
+
     def client_predict_worker(self, query, query_id):
         """Serialize the query, send it to the SUT in round robin, and return the deserialized response."""
-        url = '{}/predict/'.format(self.sut_server_addr[self.get_sut_id_round_robin()])
+        url = "{}/predict/".format(
+            self.sut_server_addr[self.get_sut_id_round_robin()])
         responses = []
         # Start the timer
         startTime = time.time()
         # Sending the request to the server through POST method
         # Upon recieving the response, it is stored in response variable
-        response = requests.post(url, json={'query': query})
+        response = requests.post(url, json={"query": query})
         # Measure the response time
         endTime = time.time()
         # calculate the latency
         print(f"Latency = {endTime-startTime}")
-        output = response.json()['result']
+        output = response.json()["result"]
         response_text = output["response_text"]
         print(query["input_text"])
         print(response_text)
-        
+
         output_batch = np.array(output["pred_output_batch"]).astype(np.int32)
         response_array = array.array("B", output_batch.tobytes())
         bi = response_array.buffer_info()
 
         responses.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
         lg.QuerySamplesComplete(responses)
-    
+
     def client_get_name(self):
         """Get the name of the SUT from ALL the SUTS."""
         if len(self.sut_server_addr) == 1:
-            return requests.post(f'{self.sut_server_addr[0]}/getname/').json()['name']
-    
-        sut_names = [requests.post(f'{addr}/getname/').json()['name'] for addr in self.sut_server_addr]
-        return "Multi-node SUT: " + ', '.join(sut_names)
+            return requests.post(
+                f"{self.sut_server_addr[0]}/getname/").json()["name"]
+
+        sut_names = [
+            requests.post(f"{addr}/getname/").json()["name"]
+            for addr in self.sut_server_addr
+        ]
+        return "Multi-node SUT: " + ", ".join(sut_names)
 
     def __del__(self):
-        lg.DestroyQDL(self.qdl)   
\ No newline at end of file
+        lg.DestroyQDL(self.qdl)
diff --git a/language/gpt-j/GPTJ_QSL.py b/language/gpt-j/GPTJ_QSL.py
index f74fe74930..657391e3f4 100644
--- a/language/gpt-j/GPTJ_QSL.py
+++ b/language/gpt-j/GPTJ_QSL.py
@@ -1,20 +1,27 @@
 import mlperf_loadgen as lg
 from dataset import Dataset
 
-class GPTJ_QSL():
+
+class GPTJ_QSL:
     def __init__(self, dataset_path: str, max_examples: int):
         self.dataset_path = dataset_path
         self.max_examples = max_examples
 
         # creating data object for QSL
         self.data_object = Dataset(
-                self.dataset_path, total_count_override=self.max_examples)
-        
+            self.dataset_path, total_count_override=self.max_examples
+        )
+
         # construct QSL from python binding
-        self.qsl = lg.ConstructQSL(self.data_object.count, self.data_object.perf_count,
-                                   self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
+        self.qsl = lg.ConstructQSL(
+            self.data_object.count,
+            self.data_object.perf_count,
+            self.data_object.LoadSamplesToRam,
+            self.data_object.UnloadSamplesFromRam,
+        )
 
         print("Finished constructing QSL.")
 
+
 def get_GPTJ_QSL(dataset_path: str, max_examples: int):
-    return GPTJ_QSL(dataset_path , max_examples)
\ No newline at end of file
+    return GPTJ_QSL(dataset_path, max_examples)
diff --git a/language/gpt-j/backend_PyTorch.py b/language/gpt-j/backend_PyTorch.py
index 06002acff2..49200c2096 100644
--- a/language/gpt-j/backend_PyTorch.py
+++ b/language/gpt-j/backend_PyTorch.py
@@ -14,12 +14,24 @@
     "early_stopping": True,
     "max_new_tokens": 128,
     "min_new_tokens": 30,
-    "num_beams": int(os.environ.get("GPTJ_BEAM_SIZE", "4")), # only beam_size 4 is allowed for official submission
+    "num_beams": int(
+        os.environ.get("GPTJ_BEAM_SIZE", "4")
+    ),  # only beam_size 4 is allowed for official submission
 }
 
 
-class SUT_base():
-    def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu=False, network=None, qsl=None):
+class SUT_base:
+    def __init__(
+        self,
+        model_path,
+        dtype,
+        dataset_path,
+        scenario,
+        max_examples,
+        use_gpu=False,
+        network=None,
+        qsl=None,
+    ):
         self.network = network
         self.model_name = "EleutherAI/gpt-j-6B"
         self.model_path = model_path
@@ -29,13 +41,13 @@ def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_
         self.scenario = scenario
         self.qsl = qsl
         print("Loading PyTorch model...")
-            
+
         # dtype
-        if dtype == 'bfloat16':
+        if dtype == "bfloat16":
             self.amp_enabled = True
             self.amp_dtype = torch.bfloat16
             print("BF16 autocast")
-        elif dtype == 'float16':
+        elif dtype == "float16":
             self.amp_enabled = True
             self.amp_dtype = torch.float16
         else:
@@ -47,17 +59,21 @@ def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_
                 device_map="auto" if not self.use_gpu else None,
                 low_cpu_mem_usage=True if not self.use_gpu else False,
                 torch_dtype=self.amp_dtype,
-                offload_folder="offload" if not self.use_gpu else None,    # specify offload folder when using devices with less RAM
-                offload_state_dict = True if not self.use_gpu else False   # to have some shards of the model to be on the disk
+                offload_folder=(
+                    "offload" if not self.use_gpu else None
+                ),  # specify offload folder when using devices with less RAM
+                offload_state_dict=(
+                    True if not self.use_gpu else False
+                ),  # to have some shards of the model to be on the disk
             )
-        except ValueError as e: 
+        except ValueError as e:
             if "disk_offload" in str(e):
                 print("Offloading the whole model to disk...")
                 self.model = AutoModelForCausalLM.from_pretrained(
                     self.model_path,
                     low_cpu_mem_usage=True if not self.use_gpu else False,
                     torch_dtype=self.amp_dtype,
-                    offload_state_dict = True if not self.use_gpu else False
+                    offload_state_dict=True if not self.use_gpu else False,
                 ).cpu()
                 disk_offload(model=self.model, offload_dir="offload")
 
@@ -69,24 +85,25 @@ def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_
             self.model.to(self.device)
 
         self.model.eval()
-        try: # for systems with low ram, the below command gives error as some part is offloaded to disk
+        try:  # for systems with low ram, the below command gives error as some part is offloaded to disk
             self.model = self.model.to(memory_format=torch.channels_last)
-        except:
+        except BaseException:
             pass
 
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.model_name,
             model_max_length=1919,
             padding_side="left",
-            use_fast=False,)
+            use_fast=False,
+        )
         self.tokenizer.pad_token = self.tokenizer.eos_token
 
-        # calculate the memory size taken by the model 
+        # calculate the memory size taken by the model
         self.total_mem_size = 0
         parameters = list(self.model.parameters())
         for param in tqdm(parameters):
             self.total_mem_size += param.numel() * param.element_size()
-        self.total_mem_size = self.total_mem_size / (1024 ** 3)
+        self.total_mem_size = self.total_mem_size / (1024**3)
 
         # construct SUT
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
@@ -108,29 +125,37 @@ def issue_queries(self, query_samples):
             query = {
                 "input_text": text,
                 "input_ids_tensor": input_ids_tensor.tolist(),
-                "input_masks_tensor": input_masks_tensor.tolist()
+                "input_masks_tensor": input_masks_tensor.tolist(),
             }
             self.inference_call(query, query_samples[i].id)
 
     def inference_call(self, query, query_id=None):
-        ''' Common for all scenarios '''
-        torch_device_type = 'cuda' if self.use_gpu else 'cpu'
+        """Common for all scenarios"""
+        torch_device_type = "cuda" if self.use_gpu else "cpu"
 
         input_ids_tensor = torch.tensor(query["input_ids_tensor"])
         input_masks_tensor = torch.tensor(query["input_masks_tensor"])
 
         # Moves the tensor to CPU or GPU as per argument passed by user
         input_ids_tensor = input_ids_tensor.to(torch_device_type)
-        input_masks_tensor = input_masks_tensor.to(torch_device_type)               
+        input_masks_tensor = input_masks_tensor.to(torch_device_type)
 
-            
-        with torch.inference_mode(), torch.autocast(device_type=torch_device_type, enabled=self.amp_enabled, dtype=self.amp_dtype if self.amp_enabled else None):
+        with torch.inference_mode(), torch.autocast(
+            device_type=torch_device_type,
+            enabled=self.amp_enabled,
+            dtype=self.amp_dtype if self.amp_enabled else None,
+        ):
             input_batch = dict()
-            input_batch['input_ids'] = input_ids_tensor
-            input_batch['attention_mask'] = input_masks_tensor
-
-            output_batch = self.model.generate(# takes Long and Int tensor datatype only
-                **input_batch, **gen_kwargs, pad_token_id=self.tokenizer.eos_token_id)
+            input_batch["input_ids"] = input_ids_tensor
+            input_batch["attention_mask"] = input_masks_tensor
+
+            output_batch = (
+                self.model.generate(  # takes Long and Int tensor datatype only
+                    **input_batch,
+                    **gen_kwargs,
+                    pad_token_id=self.tokenizer.eos_token_id,
+                )
+            )
 
             input_batch_lengths = [x.shape[0]
                                    for x in input_batch["input_ids"]]
@@ -142,19 +167,27 @@ def inference_call(self, query, query_id=None):
                 output_batch_truncated.append(data[source_len:])
 
             output_batch_truncated = torch.stack(output_batch_truncated)
-            
+
             # Loadgen monitors the reponse in corresponding functions
-            if ((self.scenario == "SingleStream" or self.scenario == "Server") and self.network == None):
+            if (
+                self.scenario == "SingleStream" or self.scenario == "Server"
+            ) and self.network is None:
                 return output_batch_truncated
 
             pred_output_batch = output_batch_truncated.cpu().numpy()
 
-            decoded_outputs = [self.tokenizer.decode(output, skip_special_tokens=True) for output in pred_output_batch]
+            decoded_outputs = [
+                self.tokenizer.decode(output, skip_special_tokens=True)
+                for output in pred_output_batch
+            ]
             response_text = decoded_outputs[0]
 
             # Loadgen monitors the response in GPT_QDL
             if self.network == "sut":
-                return {"pred_output_batch":pred_output_batch.tolist(), "response_text": response_text}
+                return {
+                    "pred_output_batch": pred_output_batch.tolist(),
+                    "response_text": response_text,
+                }
 
             response_array = array.array("B", pred_output_batch[0].tobytes())
             bi = response_array.buffer_info()
@@ -169,29 +202,73 @@ def __del__(self):
 
 
 class SUT_Offline(SUT_base):
-    def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl):
-        SUT_base.__init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
-    '''IssueQuery and inference methods implemented in Base class'''
+    def __init__(
+        self,
+        model_path,
+        dtype,
+        dataset_path,
+        scenario,
+        max_examples,
+        use_gpu,
+        network,
+        qsl,
+    ):
+        SUT_base.__init__(
+            self,
+            model_path,
+            dtype,
+            dataset_path,
+            scenario,
+            max_examples,
+            use_gpu,
+            network,
+            qsl,
+        )
+
+    """IssueQuery and inference methods implemented in Base class"""
 
 
 class SUT_Server(SUT_base):
-    def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl):
-
-        SUT_base.__init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
+    def __init__(
+        self,
+        model_path,
+        dtype,
+        dataset_path,
+        scenario,
+        max_examples,
+        use_gpu,
+        network,
+        qsl,
+    ):
+
+        SUT_base.__init__(
+            self,
+            model_path,
+            dtype,
+            dataset_path,
+            scenario,
+            max_examples,
+            use_gpu,
+            network,
+            qsl,
+        )
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
         self.total_samples_done = 0
 
     def issue_queries(self, query_samples):
-        # The issue queries function is called multiple times by the loadgen as per Poisson Distribution
+        # The issue queries function is called multiple times by the loadgen as
+        # per Poisson Distribution
         index = query_samples[0].index
         input_ids_tensor = self.qsl.data_object.source_encoded_input_ids[index]
         input_masks_tensor = self.qsl.data_object.source_encoded_attn_masks[index]
         text = self.qsl.data_object.sources[index]
         query = {
             "input_ids_tensor": input_ids_tensor.tolist(),
-            "input_masks_tensor": input_masks_tensor.tolist()
+            "input_masks_tensor": input_masks_tensor.tolist(),
         }
-        pred_output_batch = self.inference_call(query, query_samples[0].id).cpu().numpy()
+        pred_output_batch = (
+            self.inference_call(query, query_samples[0].id).cpu().numpy()
+        )
         response_array = array.array("B", pred_output_batch.tobytes())
         bi = response_array.buffer_info()
         responses = [lg.QuerySampleResponse(query_samples[0].id, bi[0], bi[1])]
@@ -201,42 +278,98 @@ def issue_queries(self, query_samples):
         if self.total_samples_done % 5 == 0:
             print("Completed : ", self.total_samples_done)
 
+
 class SUT_SingleStream(SUT_base):
-    def __init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl):
-        SUT_base.__init__(self, model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
+    def __init__(
+        self,
+        model_path,
+        dtype,
+        dataset_path,
+        scenario,
+        max_examples,
+        use_gpu,
+        network,
+        qsl,
+    ):
+        SUT_base.__init__(
+            self,
+            model_path,
+            dtype,
+            dataset_path,
+            scenario,
+            max_examples,
+            use_gpu,
+            network,
+            qsl,
+        )
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
         self.total_samples_done = 0
 
     def issue_queries(self, query_samples):
-        # This function is called by the loadgen after completing the previous query
+        # This function is called by the loadgen after completing the previous
+        # query
         index = query_samples[0].index
         input_ids_tensor = self.qsl.data_object.source_encoded_input_ids[index]
         input_masks_tensor = self.qsl.data_object.source_encoded_attn_masks[index]
         query = {
             "input_ids_tensor": input_ids_tensor.tolist(),
-            "input_masks_tensor": input_masks_tensor.tolist()
+            "input_masks_tensor": input_masks_tensor.tolist(),
         }
 
-        pred_output_batch = self.inference_call(
-            query, query_samples[0].id).cpu().numpy()
+        pred_output_batch = (
+            self.inference_call(query, query_samples[0].id).cpu().numpy()
+        )
 
         response_array = array.array("B", pred_output_batch.tobytes())
         bi = response_array.buffer_info()
         responses = [lg.QuerySampleResponse(query_samples[0].id, bi[0], bi[1])]
         lg.QuerySamplesComplete(responses)
-        
+
         self.total_samples_done += 1
         if self.total_samples_done % 5 == 0:
             print("Completed : ", self.total_samples_done)
 
 
-
-
-
-def get_SUT(model_path, scenario, dtype, dataset_path, max_examples, use_gpu=False, network=None, qsl=None):
+def get_SUT(
+    model_path,
+    scenario,
+    dtype,
+    dataset_path,
+    max_examples,
+    use_gpu=False,
+    network=None,
+    qsl=None,
+):
     if scenario == "Offline":
-        return SUT_Offline(model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
+        return SUT_Offline(
+            model_path,
+            dtype,
+            dataset_path,
+            scenario,
+            max_examples,
+            use_gpu,
+            network,
+            qsl,
+        )
     elif scenario == "Server":
-        return SUT_Server(model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
+        return SUT_Server(
+            model_path,
+            dtype,
+            dataset_path,
+            scenario,
+            max_examples,
+            use_gpu,
+            network,
+            qsl,
+        )
     elif scenario == "SingleStream":
-        return SUT_SingleStream(model_path, dtype, dataset_path, scenario, max_examples, use_gpu, network, qsl)
\ No newline at end of file
+        return SUT_SingleStream(
+            model_path,
+            dtype,
+            dataset_path,
+            scenario,
+            max_examples,
+            use_gpu,
+            network,
+            qsl,
+        )
diff --git a/language/gpt-j/dataset.py b/language/gpt-j/dataset.py
index ad8da50102..6d256407b2 100644
--- a/language/gpt-j/dataset.py
+++ b/language/gpt-j/dataset.py
@@ -25,8 +25,16 @@
 }
 
 
-class Dataset():
-    def __init__(self, dataset_path, batch_size=1, pad_val=1, pad_max=196, total_count_override=None, perf_count_override=None):
+class Dataset:
+    def __init__(
+        self,
+        dataset_path,
+        batch_size=1,
+        pad_val=1,
+        pad_max=196,
+        total_count_override=None,
+        perf_count_override=None,
+    ):
         print("Constructing QSL")
 
         self.dataset = "cnn_dailymail"
@@ -41,13 +49,19 @@ def __init__(self, dataset_path, batch_size=1, pad_val=1, pad_max=196, total_cou
 
         self.list_data_dict = utils.jload(self.dataset_path)
 
-        prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"]
-        self.sources = [prompt_input.format_map(
-            example) for example in self.list_data_dict]
+        prompt_input, prompt_no_input = (
+            PROMPT_DICT["prompt_input"],
+            PROMPT_DICT["prompt_no_input"],
+        )
+        self.sources = [
+            prompt_input.format_map(example) for example in self.list_data_dict
+        ]
         self.targets = [
             f"{example['output']}" for example in self.list_data_dict]
 
-        self.source_encoded_input_ids, self.source_encoded_attn_masks = self.encode_samples()
+        self.source_encoded_input_ids, self.source_encoded_attn_masks = (
+            self.encode_samples()
+        )
 
         self.count = total_count_override or len(self.sources)
         self.perf_count = perf_count_override or self.count
@@ -61,9 +75,13 @@ def encode_samples(self):
         source_encoded_attn_masks = []
 
         for i in range(total_samples):
-            source_encoded = self.tokenizer(self.sources[i], return_tensors="pt",
-                                            padding=True, truncation=True,
-                                            max_length=1919)
+            source_encoded = self.tokenizer(
+                self.sources[i],
+                return_tensors="pt",
+                padding=True,
+                truncation=True,
+                max_length=1919,
+            )
             source_encoded_input_ids.append(source_encoded.input_ids)
             source_encoded_attn_masks.append(source_encoded.attention_mask)
 
@@ -76,4 +94,4 @@ def UnloadSamplesFromRam(self, sample_list):
         pass
 
     def __del__(self):
-        print("Finished destroying QSL.")
\ No newline at end of file
+        print("Finished destroying QSL.")
diff --git a/language/gpt-j/download_cnndm.py b/language/gpt-j/download_cnndm.py
index fa47f1af8a..240db3cdb6 100644
--- a/language/gpt-j/download_cnndm.py
+++ b/language/gpt-j/download_cnndm.py
@@ -1,24 +1,24 @@
 # experiment config
+import sys
+import simplejson as json
+import os
+import numpy as np
+from transformers import AutoTokenizer
+from datasets import load_dataset, concatenate_datasets
 model_id = "EleutherAI/gpt-j-6b"
 dataset_id = "cnn_dailymail"
 dataset_config = "3.0.0"
 text_column = "article"
 summary_column = "highlights"
 
-from datasets import load_dataset, concatenate_datasets
-from transformers import AutoTokenizer
-import numpy as np
-import os
-import simplejson as json
-import sys
 
-save_dataset_path = os.environ.get('DATASET_CNNDM_PATH', "data")
+save_dataset_path = os.environ.get("DATASET_CNNDM_PATH", "data")
 
 # Check whether the specified path exists or not
 isExist = os.path.exists(save_dataset_path)
 if not isExist:
-   # Create a new directory because it does not exist
-   os.makedirs(save_dataset_path)
+    # Create a new directory because it does not exist
+    os.makedirs(save_dataset_path)
 
 # Load dataset from the hub
 dataset = load_dataset(dataset_id, name=dataset_config)
@@ -29,7 +29,6 @@
 tokenizer.model_max_length = 2048
 
 
-
 instruction_template = "Summarize the following news article:"
 
 prompt_length = len(tokenizer(instruction_template)["input_ids"])
@@ -51,14 +50,18 @@ def preprocess_function(sample, padding="max_length"):
 
     return model_inputs
 
+
 # process dataset
-tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=list(dataset["train"].features))
+tokenized_dataset = dataset.map(
+    preprocess_function, batched=True, remove_columns=list(dataset["train"].features)
+)
 
 # save dataset to disk
 
-with open(os.path.join(save_dataset_path,"cnn_eval.json"), 'w') as write_f:
-    json.dump(tokenized_dataset["validation"]["text"], write_f, indent=4, ensure_ascii=False)
-
+with open(os.path.join(save_dataset_path, "cnn_eval.json"), "w") as write_f:
+    json.dump(
+        tokenized_dataset["validation"]["text"], write_f, indent=4, ensure_ascii=False
+    )
 
-print("Dataset saved in ",save_dataset_path)
 
+print("Dataset saved in ", save_dataset_path)
diff --git a/language/gpt-j/download_gptj.py b/language/gpt-j/download_gptj.py
index 967862b41b..ac9a5df137 100644
--- a/language/gpt-j/download_gptj.py
+++ b/language/gpt-j/download_gptj.py
@@ -8,22 +8,24 @@
 #     print("Please provide a valid path for downloaded model")
 #     print("usage : python download_gptj.py <path_where_to_save_model>")
 #     exit()
-# else:  
+# else:
 #     model_path = sys.argv[1]
 #     if not os.path.exists(os.path.dirname(model_path)):
 #         print("Error : Please provide a valid path")
 #         exit()
 
 
-model_path = os.path.join(os.getcwd(),"model")
+model_path = os.path.join(os.getcwd(), "model")
 
 if not os.path.exists(os.path.dirname(model_path)):
     os.mkdir(model_path)
 
 model_name = "EleutherAI/gpt-j-6B"
-model = AutoModelForCausalLM.from_pretrained(model_name,device_map="auto",torchscript=True)  # torchscript will force `return_dict=False` to avoid jit errors
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, device_map="auto", torchscript=True
+)  # torchscript will force `return_dict=False` to avoid jit errors
 print("Loaded model")
 
 model.save_pretrained(model_path)
 
-print("Model downloaded and Saved in : ",model_path)
\ No newline at end of file
+print("Model downloaded and Saved in : ", model_path)
diff --git a/language/gpt-j/evaluation.py b/language/gpt-j/evaluation.py
index 1b37b3a3a4..3203359d0f 100644
--- a/language/gpt-j/evaluation.py
+++ b/language/gpt-j/evaluation.py
@@ -17,14 +17,23 @@
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True,
-                        help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--dataset-file", required=True,
-                        help="path to cnn_eval.json")
-    parser.add_argument("--verbose", action="store_true",
-                        help="verbose messages")
-    parser.add_argument("--dtype", default="int64",
-                        help="dtype of the accuracy log", choices=["int32", "int64"])
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--dataset-file",
+        required=True,
+        help="path to cnn_eval.json")
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--dtype",
+        default="int64",
+        help="dtype of the accuracy log",
+        choices=["int32", "int64"],
+    )
     args = parser.parse_args()
     return args
 
@@ -46,13 +55,14 @@ def main():
     model_name = "EleutherAI/gpt-j-6B"
     dataset_path = args.dataset_file
     metric = evaluate.load("rouge")
-    nltk.download('punkt')
+    nltk.download("punkt")
 
     tokenizer = AutoTokenizer.from_pretrained(
         model_name,
         model_max_length=2048,
         padding_side="left",
-        use_fast=False,)
+        use_fast=False,
+    )
     tokenizer.pad_token = tokenizer.eos_token
 
     data_object = Dataset(dataset_path)
@@ -66,11 +76,11 @@ def main():
     dedup_results = []
     seen = set()
     for result in results:
-        item = result['qsl_idx']
+        item = result["qsl_idx"]
         if item not in seen:
             seen.add(item)
             dedup_results.append(result)
-    results = dedup_results      
+    results = dedup_results
 
     target_required = []
     preds_token_ids = []
@@ -80,19 +90,24 @@ def main():
         eval_dtype = np.int32
 
     for pred in results:
-        qsl_idx = pred['qsl_idx']
+        qsl_idx = pred["qsl_idx"]
         target = targets[qsl_idx]
         target_required.append(target)
-        preds_token_ids.append(np.frombuffer(
-            bytes.fromhex(pred['data']), eval_dtype))
+        preds_token_ids.append(
+            np.frombuffer(
+                bytes.fromhex(
+                    pred["data"]),
+                eval_dtype))
 
     preds_decoded_text = tokenizer.batch_decode(
-        preds_token_ids, skip_special_tokens=True)
+        preds_token_ids, skip_special_tokens=True
+    )
 
     preds, targets = postprocess_text(preds_decoded_text, target_required)
 
     result = metric.compute(
-        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
+        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False
+    )
     result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
     prediction_lens = [len(pred) for pred in preds]
     result["gen_len"] = np.sum(prediction_lens)
diff --git a/language/gpt-j/main.py b/language/gpt-j/main.py
index d7c244d610..438c0854b3 100644
--- a/language/gpt-j/main.py
+++ b/language/gpt-j/main.py
@@ -14,45 +14,75 @@
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--backend", choices=["pytorch"], default="pytorch", help="Backend")
-    parser.add_argument("--scenario", choices=["SingleStream", "Offline",
-                        "Server"], default="Offline", help="Scenario")
+        "--backend", choices=["pytorch"], default="pytorch", help="Backend"
+    )
+    parser.add_argument(
+        "--scenario",
+        choices=["SingleStream", "Offline", "Server"],
+        default="Offline",
+        help="Scenario",
+    )
     parser.add_argument("--model-path", default="EleutherAI/gpt-j-6B", help="")
     parser.add_argument(
-        "--dataset-path", default="./data/cnn_eval.json", help="")
-    parser.add_argument("--accuracy", action="store_true",
-                        help="enable accuracy pass")
+        "--dataset-path",
+        default="./data/cnn_eval.json",
+        help="")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
     parser.add_argument(
         "--dtype",
         default="float32",
-        help="data type of the model, choose from float16, bfloat16 and float32")
-    parser.add_argument("--quantized", action="store_true",
-                        help="use quantized model (only valid for onnxruntime backend)")
-    parser.add_argument("--profile", action="store_true",
-                        help="enable profiling (only valid for onnxruntime backend)")
-    parser.add_argument("--gpu", action="store_true",
-                        help="use GPU instead of CPU for the inference")
-    parser.add_argument("--audit_conf", default="audit.conf",
-                        help="audit config for LoadGen settings during compliance runs")
-    parser.add_argument("--user_conf", default="user.conf",
-                        help="user config for user LoadGen settings such as target QPS")
-    parser.add_argument("--max_examples", type=int, default=13368,
-                        help="Maximum number of examples to consider (not limited by default)")
+        help="data type of the model, choose from float16, bfloat16 and float32",
+    )
+    parser.add_argument(
+        "--quantized",
+        action="store_true",
+        help="use quantized model (only valid for onnxruntime backend)",
+    )
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="enable profiling (only valid for onnxruntime backend)",
+    )
+    parser.add_argument(
+        "--gpu", action="store_true", help="use GPU instead of CPU for the inference"
+    )
+    parser.add_argument(
+        "--audit_conf",
+        default="audit.conf",
+        help="audit config for LoadGen settings during compliance runs",
+    )
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+    parser.add_argument(
+        "--max_examples",
+        type=int,
+        default=13368,
+        help="Maximum number of examples to consider (not limited by default)",
+    )
     parser.add_argument(
         "--network",
-        choices=[
-            "sut",
-            "lon",
-            None],
+        choices=["sut", "lon", None],
         default=None,
-        help="Loadgen network mode")
-    parser.add_argument('--node', type=str, default="")
-    parser.add_argument('--port', type=int, default=8000)
-    parser.add_argument('--sut_server', nargs="*", default=['http://localhost:8000'],
-                        help='Address of the server(s) under test.')
+        help="Loadgen network mode",
+    )
+    parser.add_argument("--node", type=str, default="")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument(
+        "--sut_server",
+        nargs="*",
+        default=["http://localhost:8000"],
+        help="Address of the server(s) under test.",
+    )
     args = parser.parse_args()
     return args
 
+
 # Function to get the amount of temporary cache generated when running the GPT-J model
 # Varies with the beam size set(Estimate: 6GB x Beam size)
 
@@ -67,7 +97,7 @@ def get_temp_cache():
     "SingleStream": lg.TestScenario.SingleStream,
     "Offline": lg.TestScenario.Offline,
     "Server": lg.TestScenario.Server,
-    "MultiStream": lg.TestScenario.MultiStream
+    "MultiStream": lg.TestScenario.MultiStream,
 }
 
 # Main function triggered when the script is run
@@ -81,14 +111,11 @@ def main():
         # Responsible for loading(Query Sample Loader) and sending samples over
         # the network(Query Data Loader) to the server
         qsl = get_GPTJ_QSL(
-            dataset_path=args.dataset_path,
-            max_examples=args.max_examples
+            dataset_path=args.dataset_path, max_examples=args.max_examples
         )
         if args.network == "lon":
             qdl = GPTJ_QDL(
-                sut_server_addr=args.sut_server,
-                scenario=args.scenario,
-                qsl=qsl
+                sut_server_addr=args.sut_server, scenario=args.scenario, qsl=qsl
             )
 
         # Initiates and loads loadgen test settings and log path
@@ -130,21 +157,20 @@ def main():
             network=args.network,
             dataset_path=args.dataset_path,
             max_examples=args.max_examples,
-            qsl=qsl  # If args.network is None, then only QSL get passed to the SUT, else it will be None
+            qsl=qsl,  # If args.network is None, then only QSL get passed to the SUT, else it will be None
         )
 
     if args.network == "lon" and args.scenario == "SingleStream":
-        print("ERROR: Single stream scenario in Loadgen Over the Network is not supported!")
+        print(
+            "ERROR: Single stream scenario in Loadgen Over the Network is not supported!"
+        )
 
     # If network option is LON, QDL is loaded and request is served to SUT
     # based on the scenario given by user(Offline or SingleStream)
     elif args.network == "lon":
         lg.StartTestWithLogSettings(
-            qdl.qdl,
-            qsl.qsl,
-            settings,
-            log_settings,
-            args.audit_conf)
+            qdl.qdl, qsl.qsl, settings, log_settings, args.audit_conf
+        )
 
     # If network option is SUT, a flask server is initiated, request is
     # processed and output is being sent back to LON client
@@ -161,8 +187,11 @@ def main():
         # Acquire and release of semaphore can be found in network_SUT.py
         model_mem_size = sut.total_mem_size
         if args.gpu:
-            free_mem = int(os.environ.get(
-                "CM_CUDA_DEVICE_PROP_GLOBAL_MEMORY", get_gpu_memory_info())) / (1024**3)
+            free_mem = int(
+                os.environ.get(
+                    "CM_CUDA_DEVICE_PROP_GLOBAL_MEMORY", get_gpu_memory_info()
+                )
+            ) / (1024**3)
         else:
             free_mem = get_cpu_memory_info()
         lockVar = math.floor((free_mem - model_mem_size) / temp_cache)
@@ -179,11 +208,8 @@ def main():
         # Test not run in Loadgen Over the Network
         print("Running LoadGen test...")
         lg.StartTestWithLogSettings(
-            sut.sut,
-            qsl.qsl,
-            settings,
-            log_settings,
-            args.audit_conf)
+            sut.sut, qsl.qsl, settings, log_settings, args.audit_conf
+        )
 
     print("Test Done!")
 
diff --git a/language/gpt-j/network_SUT.py b/language/gpt-j/network_SUT.py
index 0372388ca0..d98c2a2a0e 100644
--- a/language/gpt-j/network_SUT.py
+++ b/language/gpt-j/network_SUT.py
@@ -9,6 +9,7 @@
 use_gpu = False
 free_mem = None
 
+
 # setting semaphore
 def set_semaphore(lockVar):
     global semaphore
@@ -19,15 +20,18 @@ def set_backend(b):
     global backend
     backend = b
 
+
 def preprocess(query):
     """[SUT Node] A dummy preprocess."""
-    # Here may come for example batching, tokenization, resizing, normalization, etc.
+    # Here may come for example batching, tokenization, resizing,
+    # normalization, etc.
     response = query
     return response
 
 
 def dnn_model(query):
-    # Here may come for example a call to a dnn model such as resnet, bert, etc.
+    # Here may come for example a call to a dnn model such as resnet, bert,
+    # etc.
     response = backend.inference_call(query)
     print(f"dnn model response:{response}")
     return response
@@ -35,17 +39,18 @@ def dnn_model(query):
 
 def postprocess(query):
     """[SUT Node] A dummy postprocess."""
-    # Here may come for example a postprocessing call, e.g., NMS, detokenization, etc.
+    # Here may come for example a postprocessing call, e.g., NMS,
+    # detokenization, etc.
     response = query
     return response
 
 
-@app.route('/predict/', methods=['POST'])
+@app.route("/predict/", methods=["POST"])
 def predict():
     """Receives a query (e.g., a text) runs inference, and returns a prediction."""
-    semaphore.acquire() # wait to acquire semaphore    
+    semaphore.acquire()  # wait to acquire semaphore
     try:
-        query = request.get_json(force=True)['query']
+        query = request.get_json(force=True)["query"]
         result = postprocess(dnn_model(preprocess(query)))
         print(result)
         return jsonify(result=result)
@@ -54,7 +59,8 @@ def predict():
         semaphore.release()
 
 
-@app.route('/getname/', methods=['POST', 'GET'])
+@app.route("/getname/", methods=["POST", "GET"])
 def getname():
     """Returns the name of the SUT."""
-    return jsonify(name=f'Demo SUT (Network SUT) node' + (' ' + node) if node else '')
\ No newline at end of file
+    return jsonify(name=f"Demo SUT (Network SUT) node" +
+                   (" " + node) if node else "")
diff --git a/language/gpt-j/prepare-calibration.py b/language/gpt-j/prepare-calibration.py
index 846e492d38..91bcb0826f 100644
--- a/language/gpt-j/prepare-calibration.py
+++ b/language/gpt-j/prepare-calibration.py
@@ -4,32 +4,39 @@
 from argparse import ArgumentParser
 from datasets import load_dataset
 
+
 def get_args():
     parser = ArgumentParser()
-    parser.add_argument("--calibration-list-file", required=True, help="Path to calibration list")
-    parser.add_argument("--output-dir", help="Output directory", default="calibration-data")
+    parser.add_argument(
+        "--calibration-list-file", required=True, help="Path to calibration list"
+    )
+    parser.add_argument(
+        "--output-dir", help="Output directory", default="calibration-data"
+    )
 
     return parser.parse_args()
 
-dataset_id='cnn_dailymail'
-version='3.0.0'
-split='train'
 
-instruction_template="Summarize the following news article:"
+dataset_id = "cnn_dailymail"
+version = "3.0.0"
+split = "train"
+
+instruction_template = "Summarize the following news article:"
+
 
 def check_path(path):
     return os.path.exists(path)
 
+
 def prepare_calibration_data(calibration_list_file, output_dir):
     if not check_path(calibration_list_file):
         print("Calibration list file not found: {}".format(calibration_list_file))
         sys.exit(1)
 
-    dataset = load_dataset("cnn_dailymail", name="3.0.0", split='train')
-    train = dict((x['id'], x) for x in dataset)
+    dataset = load_dataset("cnn_dailymail", name="3.0.0", split="train")
+    train = dict((x["id"], x) for x in dataset)
 
-    
-    with open(calibration_list_file, 'r') as fid:
+    with open(calibration_list_file, "r") as fid:
         calibration_ids = fid.read().splitlines()
 
     inputs = []
@@ -40,20 +47,22 @@ def prepare_calibration_data(calibration_list_file, output_dir):
         x["input"] = calibration_sample["article"]
         x["output"] = calibration_sample["highlights"]
         inputs.append(x)
-    
+
     if not os.path.isdir(output_dir):
         os.makedirs(output_dir)
 
-    output_path = os.path.join(output_dir,"cnn_dailymail_calibration.json")
-    with open(output_path, 'w') as write_f:
+    output_path = os.path.join(output_dir, "cnn_dailymail_calibration.json")
+    with open(output_path, "w") as write_f:
         json.dump(inputs, write_f, indent=4, ensure_ascii=False)
 
     print("Calibration data saved at {}".format(output_path))
 
+
 def main():
 
     args = get_args()
     prepare_calibration_data(args.calibration_list_file, args.output_dir)
 
-if __name__=="__main__":
+
+if __name__ == "__main__":
     main()
diff --git a/language/gpt-j/systemStatus.py b/language/gpt-j/systemStatus.py
index de00d312f3..31c35e4f70 100644
--- a/language/gpt-j/systemStatus.py
+++ b/language/gpt-j/systemStatus.py
@@ -1,6 +1,7 @@
 import psutil
 import torch
 
+
 def get_gpu_memory_info():
     if torch.cuda.is_available():
         device = torch.cuda.current_device()
@@ -9,6 +10,7 @@ def get_gpu_memory_info():
         free_memory = total_memory - allocated_memory
     return free_memory / (1024**3)
 
+
 def get_cpu_memory_info():
     mem = psutil.virtual_memory()
-    return mem.total / (1024**3)
\ No newline at end of file
+    return mem.total / (1024**3)
diff --git a/language/gpt-j/tokenizer_GPTJ.py b/language/gpt-j/tokenizer_GPTJ.py
index 0f12b3912c..2ac93c4860 100644
--- a/language/gpt-j/tokenizer_GPTJ.py
+++ b/language/gpt-j/tokenizer_GPTJ.py
@@ -1,8 +1,10 @@
 from transformers import AutoTokenizer
 
+
 def get_transformer_autotokenizer(model_name: str):
     return AutoTokenizer.from_pretrained(
-            model_name,
-            model_max_length=2048,
-            padding_side="left",
-            use_fast=False,)
\ No newline at end of file
+        model_name,
+        model_max_length=2048,
+        padding_side="left",
+        use_fast=False,
+    )
diff --git a/language/gpt-j/utils.py b/language/gpt-j/utils.py
index 6e4e7e0368..ef6c25b6bd 100644
--- a/language/gpt-j/utils.py
+++ b/language/gpt-j/utils.py
@@ -2,11 +2,13 @@
 import os
 import io
 
+
 def _make_r_io_base(f, mode: str):
     if not isinstance(f, io.IOBase):
         f = open(f, mode=mode)
     return f
 
+
 def jload(f, mode="r"):
     """Load a .json file into a dictionary."""
     f = _make_r_io_base(f, mode)
diff --git a/language/llama2-70b/SUT.py b/language/llama2-70b/SUT.py
index 0bc1f6ad7f..983e803501 100644
--- a/language/llama2-70b/SUT.py
+++ b/language/llama2-70b/SUT.py
@@ -29,18 +29,19 @@
     "max_new_tokens": 1024,
     "min_new_tokens": 1,
     "num_beams": 1,
-    "do_sample": False
+    "do_sample": False,
 }
 
 
-
 class FirstTokenStreamer(BaseStreamer):
-    """ Streams first tokens to a 'holder' """
+    """Streams first tokens to a 'holder'"""
 
-    def __init__(self, first_token, tokens_cache=[], is_first_token=True, response_ids=[] ):
-        """ Response ids added to 'sign' the first token"""
+    def __init__(
+        self, first_token, tokens_cache=[], is_first_token=True, response_ids=[]
+    ):
+        """Response ids added to 'sign' the first token"""
 
-        self.first_token = first_token # Queue for first token
+        self.first_token = first_token  # Queue for first token
         self.is_first_token = is_first_token
 
         # Cache for subsequent generated tokens
@@ -48,12 +49,15 @@ def __init__(self, first_token, tokens_cache=[], is_first_token=True, response_i
 
         self.response_ids = response_ids
 
-        self.is_prompt = True # The first tokens sent to the streamer are actually the input prompts
+        self.is_prompt = (
+            True  # The first tokens sent to the streamer are actually the input prompts
+        )
 
     def put(self, value):
-        """ Caches the tokens as they're generated. Assumes bs=1 """
+        """Caches the tokens as they're generated. Assumes bs=1"""
 
-        # Prompts are streamed first so we need to skip the first time value that arrives
+        # Prompts are streamed first so we need to skip the first time value
+        # that arrives
         if self.is_prompt:
             self.is_prompt = False
             return
@@ -61,7 +65,8 @@ def put(self, value):
         value = value.item()
         if self.is_first_token:
 
-            # Add generated first token together with its query response_id to first tokens queue
+            # Add generated first token together with its query response_id to
+            # first tokens queue
             self.first_token.put((value, self.response_ids[0]))
 
             self.is_first_token = False
@@ -69,7 +74,6 @@ def put(self, value):
 
         self.tokens_cache.append(value)
 
-
     def end(self):
         pass
 
@@ -77,16 +81,20 @@ def get_out_tokens(self):
         return self.tokens_cache
 
 
-class SUT():
-    def __init__(self,
-                 model_path=None,
-                 dtype="bfloat16",
-                 device="cpu",
-                 batch_size=None,
-                 total_sample_count=24576,
-                 dataset_path=None,
-                 use_cached_outputs=False,  # Set this to True *only for test accuracy runs* in case your prior session was killed partway through
-                 workers=1):
+class SUT:
+    def __init__(
+        self,
+        model_path=None,
+        dtype="bfloat16",
+        device="cpu",
+        batch_size=None,
+        total_sample_count=24576,
+        dataset_path=None,
+        use_cached_outputs=False,
+        # Set this to True *only for test accuracy runs* in case your prior
+        # session was killed partway through
+        workers=1,
+    ):
 
         self.model_path = model_path or "meta-llama/Llama-2-70b-chat-hf"
         self.device = device
@@ -99,26 +107,32 @@ def __init__(self,
         self.batch_size = batch_size
 
         # dtype
-        if dtype == 'bfloat16':
+        if dtype == "bfloat16":
             self.amp_enabled = True
             self.amp_dtype = torch.bfloat16
-        elif dtype == 'float16':
+        elif dtype == "float16":
             self.amp_enabled = True
             self.amp_dtype = torch.float16
         else:
             self.amp_enabled = False
             self.amp_dtype = torch.float32
 
-        if 'cuda' in self.device:
+        if "cuda" in self.device:
             assert torch.cuda.is_available(), "torch gpu is not available, exiting..."
 
         self.dataset_path = dataset_path
-        self.data_object = Dataset(self.model_path,
-                                   dataset_path=self.dataset_path,
-                                   total_sample_count=total_sample_count,
-                                   device=self.device)
-        self.qsl = lg.ConstructQSL(self.data_object.total_sample_count, self.data_object.perf_count,
-                                   self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
+        self.data_object = Dataset(
+            self.model_path,
+            dataset_path=self.dataset_path,
+            total_sample_count=total_sample_count,
+            device=self.device,
+        )
+        self.qsl = lg.ConstructQSL(
+            self.data_object.total_sample_count,
+            self.data_object.perf_count,
+            self.data_object.LoadSamplesToRam,
+            self.data_object.UnloadSamplesFromRam,
+        )
 
         self.load_model()
 
@@ -130,7 +144,6 @@ def __init__(self,
         self.sample_counter = 0
         self.sample_counter_lock = threading.Lock()
 
-
     def start(self):
         # Create worker threads
         for j in range(self.num_workers):
@@ -145,9 +158,8 @@ def stop(self):
         for worker in self.worker_threads:
             worker.join()
 
-
     def process_queries(self):
-        """Processor of the queued queries. User may choose to add batching logic """
+        """Processor of the queued queries. User may choose to add batching logic"""
 
         while True:
             qitem = self.query_queue.get()
@@ -178,12 +190,32 @@ def process_queries(self):
                 input_masks_tensor = []
                 input_len = []
                 for q in qitem:
-                    input_ids_tensor.append(pad(self.data_object.input_ids[q.index],
-                                                (max_seq_len - self.data_object.input_lens[q.index], 0, 0, 0),
-                                                value=self.tokenizer.pad_token_id))
-                    input_masks_tensor.append(pad(self.data_object.attention_masks[q.index],
-                                                  (max_seq_len - self.data_object.input_lens[q.index], 0, 0, 0),
-                                                 value=0))
+                    input_ids_tensor.append(
+                        pad(
+                            self.data_object.input_ids[q.index],
+                            (
+                                max_seq_len -
+                                self.data_object.input_lens[q.index],
+                                0,
+                                0,
+                                0,
+                            ),
+                            value=self.tokenizer.pad_token_id,
+                        )
+                    )
+                    input_masks_tensor.append(
+                        pad(
+                            self.data_object.attention_masks[q.index],
+                            (
+                                max_seq_len -
+                                self.data_object.input_lens[q.index],
+                                0,
+                                0,
+                                0,
+                            ),
+                            value=0,
+                        )
+                    )
                     input_len.append(self.data_object.input_lens[q.index])
                 input_ids_tensor = torch.cat(input_ids_tensor)
                 input_masks_tensor = torch.cat(input_masks_tensor)
@@ -197,20 +229,28 @@ def process_queries(self):
                     input_ids=input_ids_tensor,
                     attention_mask=input_masks_tensor,
                     pad_token_id=self.tokenizer.pad_token_id,
-                    **gen_kwargs
+                    **gen_kwargs,
                 )
 
                 tik3 = time.time()
 
-                processed_output = self.data_object.postProcess(pred_output_tokens,
-                                                                input_seq_lens=input_len,
-                                                                query_id_list=query_ids)
+                processed_output = self.data_object.postProcess(
+                    pred_output_tokens,
+                    input_seq_lens=input_len,
+                    query_id_list=query_ids,
+                )
 
             for i in range(len(qitem)):
                 n_tokens = processed_output[i].shape[0]
-                response_array = array.array("B", processed_output[i].tobytes())
+                response_array = array.array(
+                    "B", processed_output[i].tobytes())
                 bi = response_array.buffer_info()
-                response = [lg.QuerySampleResponse(qitem[i].id, bi[0], bi[1], n_tokens)]
+                response = [
+                    lg.QuerySampleResponse(
+                        qitem[i].id,
+                        bi[0],
+                        bi[1],
+                        n_tokens)]
                 lg.QuerySamplesComplete(response)
 
             tok = time.time()
@@ -226,31 +266,33 @@ def process_queries(self):
                 else:
                     print(f"\tLoaded from cache: {_p}")
 
-
     def load_model(self):
         self.model = LlamaForCausalLM.from_pretrained(
             self.model_path,
             device_map="auto",
             low_cpu_mem_usage=True,
-            torch_dtype=self.amp_dtype
+            torch_dtype=self.amp_dtype,
         )
         print("Loaded model")
 
         self.device = torch.device(self.device)
         if self.device == "cpu":
-            self.model = self.model.to(self.device)  # Force CPU if your system has GPU and you specifically want CPU-only run
+            self.model = self.model.to(
+                self.device
+            )  # Force CPU if your system has GPU and you specifically want CPU-only run
 
         self.model.eval()
-        try: # for systems with low ram, the below command gives error as some part is offloaded to disk
+        try:  # for systems with low ram, the below command gives error as some part is offloaded to disk
             self.model = self.model.to(memory_format=torch.channels_last)
-        except:
+        except BaseException:
             pass
 
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.model_path,
             model_max_length=1024,
             padding_side="left",
-            use_fast=False,)
+            use_fast=False,
+        )
 
         self.tokenizer.pad_token = self.tokenizer.eos_token
         print("Loaded tokenizer")
@@ -262,24 +304,21 @@ def get_sut(self):
     def get_qsl(self):
         return self.qsl
 
-
-    def predict(self,**kwargs):
+    def predict(self, **kwargs):
         raise NotImplementedError
 
-
     def issue_queries(self, query_samples):
-        """ Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
+        """Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
 
         list_prompts_tokens = []
         list_prompts_attn_masks = []
 
         print(f"IssueQuery started with {len(query_samples)} samples")
         while len(query_samples) > 0:
-            self.query_queue.put(query_samples[:self.batch_size])
+            self.query_queue.put(query_samples[: self.batch_size])
             query_samples = query_samples[self.batch_size:]
         print(f"IssueQuery done")
 
-
     def flush_queries(self):
         pass
 
@@ -288,9 +327,25 @@ def __del__(self):
 
 
 class SUTServer(SUT):
-    def __init__(self, model_path=None, dtype="bfloat16", device="cpu", total_sample_count=24576, dataset_path=None, batch_size=None, workers=1):
-
-        super().__init__(model_path=model_path, dtype=dtype, device=device, total_sample_count=total_sample_count, dataset_path=dataset_path, workers=workers)
+    def __init__(
+        self,
+        model_path=None,
+        dtype="bfloat16",
+        device="cpu",
+        total_sample_count=24576,
+        dataset_path=None,
+        batch_size=None,
+        workers=1,
+    ):
+
+        super().__init__(
+            model_path=model_path,
+            dtype=dtype,
+            device=device,
+            total_sample_count=total_sample_count,
+            dataset_path=dataset_path,
+            workers=workers,
+        )
 
         self.first_token_queue = queue.Queue()
 
@@ -303,10 +358,10 @@ def start(self):
             self.worker_threads[j] = worker
 
         # Create first token response thread
-        self.ft_response_thread = threading.Thread(target=self.process_first_tokens)
+        self.ft_response_thread = threading.Thread(
+            target=self.process_first_tokens)
         self.ft_response_thread.start()
 
-
     def process_first_tokens(self):
 
         while True:
@@ -318,13 +373,15 @@ def process_first_tokens(self):
 
             first_tokens, response_id = first_token_item
 
-            response_data = array.array("B", np.array(first_tokens, np.int32).tobytes())
+            response_data = array.array(
+                "B", np.array(
+                    first_tokens, np.int32).tobytes())
             bi = response_data.buffer_info()
             response = [lg.QuerySampleResponse(response_id, bi[0], bi[1])]
             lg.FirstTokenComplete(response)
 
     def process_queries(self):
-        """Processor of the queued queries. User may choose to add batching logic """
+        """Processor of the queued queries. User may choose to add batching logic"""
         while True:
 
             qitem = self.query_queue.get()
@@ -334,31 +391,42 @@ def process_queries(self):
             input_ids_tensor = self.data_object.input_ids[qitem.index]
             input_masks_tensor = self.data_object.attention_masks[qitem.index]
 
-            #TODO: This PoC is super slow with significant overhead. Best to create a patch to `generate`
+            # TODO: This PoC is super slow with significant overhead. Best to
+            # create a patch to `generate`
             tokens_cache = []
-            tokens_streamer = FirstTokenStreamer(self.first_token_queue, tokens_cache=tokens_cache, is_first_token=True, response_ids=[qitem.id])
-
-            _ = self.model.generate(    input_ids=input_ids_tensor,
-                                        attention_mask=input_masks_tensor,
-                                        pad_token_id=self.tokenizer.pad_token_id,
-                                        streamer = tokens_streamer,
-                                        **gen_kwargs
-                                        )
+            tokens_streamer = FirstTokenStreamer(
+                self.first_token_queue,
+                tokens_cache=tokens_cache,
+                is_first_token=True,
+                response_ids=[qitem.id],
+            )
+
+            _ = self.model.generate(
+                input_ids=input_ids_tensor,
+                attention_mask=input_masks_tensor,
+                pad_token_id=self.tokenizer.pad_token_id,
+                streamer=tokens_streamer,
+                **gen_kwargs,
+            )
 
             output_tokens = tokens_streamer.get_out_tokens()
             n_tokens = len(output_tokens)
-            response_array = array.array("B", np.array(output_tokens, np.int32).tobytes())
+            response_array = array.array(
+                "B", np.array(output_tokens, np.int32).tobytes()
+            )
             bi = response_array.buffer_info()
-            response = [lg.QuerySampleResponse(
-                qitem.id, bi[0], bi[1], n_tokens)]
+            response = [
+                lg.QuerySampleResponse(
+                    qitem.id,
+                    bi[0],
+                    bi[1],
+                    n_tokens)]
             lg.QuerySamplesComplete(response)
 
-
     def issue_queries(self, query_samples):
 
         self.query_queue.put(query_samples[0])
 
-
     def stop(self):
         for _ in range(self.num_workers):
             self.query_queue.put(None)
diff --git a/language/llama2-70b/SUT_API.py b/language/llama2-70b/SUT_API.py
index da28d98cd3..0b1ebd0c98 100644
--- a/language/llama2-70b/SUT_API.py
+++ b/language/llama2-70b/SUT_API.py
@@ -36,18 +36,19 @@
     "max_new_tokens": 1024,
     "min_new_tokens": 1,
     "num_beams": 1,
-    "do_sample": False
+    "do_sample": False,
 }
 
 
-
 class FirstTokenStreamer(BaseStreamer):
-    """ Streams first tokens to a 'holder' """
+    """Streams first tokens to a 'holder'"""
 
-    def __init__(self, first_token, tokens_cache=[], is_first_token=True, response_ids=[] ):
-        """ Response ids added to 'sign' the first token"""
+    def __init__(
+        self, first_token, tokens_cache=[], is_first_token=True, response_ids=[]
+    ):
+        """Response ids added to 'sign' the first token"""
 
-        self.first_token = first_token # Queue for first token
+        self.first_token = first_token  # Queue for first token
         self.is_first_token = is_first_token
 
         # Cache for subsequent generated tokens
@@ -55,12 +56,15 @@ def __init__(self, first_token, tokens_cache=[], is_first_token=True, response_i
 
         self.response_ids = response_ids
 
-        self.is_prompt = True # The first tokens sent to the streamer are actually the input prompts
+        self.is_prompt = (
+            True  # The first tokens sent to the streamer are actually the input prompts
+        )
 
     def put(self, value):
-        """ Caches the tokens as they're generated. Assumes bs=1 """
+        """Caches the tokens as they're generated. Assumes bs=1"""
 
-        # Prompts are streamed first so we need to skip the first time value that arrives
+        # Prompts are streamed first so we need to skip the first time value
+        # that arrives
         if self.is_prompt:
             self.is_prompt = False
             return
@@ -68,7 +72,8 @@ def put(self, value):
         value = value.item()
         if self.is_first_token:
 
-            # Add generated first token together with its query response_id to first tokens queue
+            # Add generated first token together with its query response_id to
+            # first tokens queue
             self.first_token.put((value, self.response_ids[0]))
 
             self.is_first_token = False
@@ -76,7 +81,6 @@ def put(self, value):
 
         self.tokens_cache.append(value)
 
-
     def end(self):
         pass
 
@@ -84,18 +88,22 @@ def get_out_tokens(self):
         return self.tokens_cache
 
 
-class SUT():
-    def __init__(self,
-                 model_path=None,
-                 api_server=None,
-                 api_model_name=None,
-                 dtype="bfloat16",
-                 device="cpu",
-                 batch_size=None,
-                 total_sample_count=24576,
-                 dataset_path=None,
-                 use_cached_outputs=False,  # Set this to True *only for test accuracy runs* in case your prior session was killed partway through
-                 workers=1):
+class SUT:
+    def __init__(
+        self,
+        model_path=None,
+        api_server=None,
+        api_model_name=None,
+        dtype="bfloat16",
+        device="cpu",
+        batch_size=None,
+        total_sample_count=24576,
+        dataset_path=None,
+        use_cached_outputs=False,
+        # Set this to True *only for test accuracy runs* in case your prior
+        # session was killed partway through
+        workers=1,
+    ):
 
         self.model_path = model_path or "meta-llama/Llama-2-70b-chat-hf"
         self.device = device
@@ -109,33 +117,40 @@ def __init__(self,
         self.batch_size = batch_size
 
         # dtype
-        if dtype == 'bfloat16':
+        if dtype == "bfloat16":
             self.amp_enabled = True
             self.amp_dtype = torch.bfloat16
-        elif dtype == 'float16':
+        elif dtype == "float16":
             self.amp_enabled = True
             self.amp_dtype = torch.float16
         else:
             self.amp_enabled = False
             self.amp_dtype = torch.float32
 
-        if 'cuda' in self.device:
+        if "cuda" in self.device:
             assert torch.cuda.is_available(), "torch gpu is not available, exiting..."
 
         self.dataset_path = dataset_path
-        self.data_object = Dataset(self.model_path,
-                                   dataset_path=self.dataset_path,
-                                   total_sample_count=total_sample_count,
-                                   device=self.device)
-        self.qsl = lg.ConstructQSL(self.data_object.total_sample_count, self.data_object.perf_count,
-                                   self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
-
-        #self.load_model()
+        self.data_object = Dataset(
+            self.model_path,
+            dataset_path=self.dataset_path,
+            total_sample_count=total_sample_count,
+            device=self.device,
+        )
+        self.qsl = lg.ConstructQSL(
+            self.data_object.total_sample_count,
+            self.data_object.perf_count,
+            self.data_object.LoadSamplesToRam,
+            self.data_object.UnloadSamplesFromRam,
+        )
+
+        # self.load_model()
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.model_path,
             model_max_length=1024,
             padding_side="left",
-            use_fast=True,) #changed from false
+            use_fast=True,
+        )  # changed from false
 
         self.tokenizer.pad_token = self.tokenizer.eos_token
 
@@ -147,7 +162,6 @@ def __init__(self,
         self.sample_counter = 0
         self.sample_counter_lock = threading.Lock()
 
-
     def start(self):
         # Create worker threads
         for j in range(self.num_workers):
@@ -164,20 +178,25 @@ def stop(self):
 
     def query_api_vllm(self, inputs, idx):
         headers = {
-            'Content-Type': 'application/json',
+            "Content-Type": "application/json",
         }
         json_data = {
             "model": self.api_model_name,
             "prompt": inputs,
             "min_tokens": 1,
-            "max_tokens": 1024
+            "max_tokens": 1024,
         }
 
         response_code = 0
         print(f"Server path {self.api_servers[idx]}/v1/completions")
         while response_code != 200:
             try:
-                response = requests.post(f"{self.api_servers[idx]}/v1/completions", headers=headers, json=json_data, verify=False)
+                response = requests.post(
+                    f"{self.api_servers[idx]}/v1/completions",
+                    headers=headers,
+                    json=json_data,
+                    verify=False,
+                )
                 response_code = response.status_code
             except Exception as e:
                 print(e)
@@ -190,7 +209,7 @@ def api_action_handler(self, chunk, server_idx):
         return output
 
     def process_queries(self):
-        """Processor of the queued queries. User may choose to add batching logic """
+        """Processor of the queued queries. User may choose to add batching logic"""
 
         while True:
             qitem = self.query_queue.get()
@@ -217,52 +236,70 @@ def process_queries(self):
 
                 tik1 = time.time()
 
-                # OpenAI-API servers don't require padding and can take input tokens 
+                # OpenAI-API servers don't require padding and can take input tokens
                 # directly, so we build our input_ids_tensor as a jagged list
                 input_ids_tensor = []
                 for q in qitem:
-                    #input_ids_tensor.append(self.data_object.input_ids[q.index].tolist())
-                    input_ids_tensor += self.data_object.input_ids[q.index].tolist()
-                
+                    # input_ids_tensor.append(self.data_object.input_ids[q.index].tolist())
+                    input_ids_tensor += self.data_object.input_ids[q.index].tolist(
+                    )
+
                 # NOTE(mgoin): I don't think this has to be a torch tensor
-                #input_ids_tensor = torch.cat(input_ids_tensor)
+                # input_ids_tensor = torch.cat(input_ids_tensor)
 
-                #print(input_ids_tensor)
+                # print(input_ids_tensor)
 
                 assert len(input_ids_tensor) <= self.batch_size
 
                 tik2 = time.time()
 
                 # NOTE(mgoin): I don't think threading is necessary since we are submitting all queries in one request
-                # The API server should take care of mini-batches and scheduling
+                # The API server should take care of mini-batches and
+                # scheduling
                 if self.api_servers:
-                    '''
+                    """
                     decoded = self.tokenizer.batch_decode(input_ids_tensor)
                     cleaned = [entry.replace('</s>','').replace('<s>','') for entry in decoded]
                     cleaned_chunks = [list(c) for c in mit.divide(len(self.api_servers), cleaned)]
-                    '''
+                    """
                     cleaned_chunks = [input_ids_tensor]
-                    with ThreadPoolExecutor(max_workers=len(self.api_servers)) as executor:
-                        #needs to be tested
-                        output_chunks = list(executor.map(self.api_action_handler,cleaned_chunks,range(len(self.api_servers))))
+                    with ThreadPoolExecutor(
+                        max_workers=len(self.api_servers)
+                    ) as executor:
+                        # needs to be tested
+                        output_chunks = list(
+                            executor.map(
+                                self.api_action_handler,
+                                cleaned_chunks,
+                                range(len(self.api_servers)),
+                            )
+                        )
                     output = []
                     for row in output_chunks:
                         output += row
                 else:
-                    print("Error: Specify at least one API to which the request is to be sent!")
+                    print(
+                        "Error: Specify at least one API to which the request is to be sent!"
+                    )
                     exit(1)
 
                 tik3 = time.time()
 
-            processed_output = self.tokenizer(output)['input_ids']
-            #for i in range(len(qitem)):
+            processed_output = self.tokenizer(output)["input_ids"]
+            # for i in range(len(qitem)):
             for i in range(len(processed_output)):
-                # NOTE(mgoin): Not optimal to make numpy arrays just to serialize
+                # NOTE(mgoin): Not optimal to make numpy arrays just to
+                # serialize
                 unpadded = np.array(processed_output[i])
                 n_tokens = unpadded.shape[0]
                 response_array = array.array("B", unpadded.tobytes())
                 bi = response_array.buffer_info()
-                response = [lg.QuerySampleResponse(qitem[i].id, bi[0], bi[1], n_tokens)]
+                response = [
+                    lg.QuerySampleResponse(
+                        qitem[i].id,
+                        bi[0],
+                        bi[1],
+                        n_tokens)]
                 lg.QuerySamplesComplete(response)
 
             tok = time.time()
@@ -285,24 +322,21 @@ def get_sut(self):
     def get_qsl(self):
         return self.qsl
 
-
-    def predict(self,**kwargs):
+    def predict(self, **kwargs):
         raise NotImplementedError
 
-
     def issue_queries(self, query_samples):
-        """ Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
+        """Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
 
         list_prompts_tokens = []
         list_prompts_attn_masks = []
 
         print(f"IssueQuery started with {len(query_samples)} samples")
         while len(query_samples) > 0:
-            self.query_queue.put(query_samples[:self.batch_size])
+            self.query_queue.put(query_samples[: self.batch_size])
             query_samples = query_samples[self.batch_size:]
         print(f"IssueQuery done")
 
-
     def flush_queries(self):
         pass
 
@@ -311,11 +345,31 @@ def __del__(self):
 
 
 class SUTServer(SUT):
-    def __init__(self, model_path=None, api_server=None, api_model_name=None, dtype="bfloat16", device="cpu", total_sample_count=24576, dataset_path=None, batch_size=None, workers=1):
-
-        super().__init__(model_path=model_path, api_server=None, api_model_name=None, dtype=dtype, device=device, total_sample_count=total_sample_count, dataset_path=dataset_path, workers=workers)
-
-        with open(f"{self.model_path}/tokenizer.json", 'r') as token_file:
+    def __init__(
+        self,
+        model_path=None,
+        api_server=None,
+        api_model_name=None,
+        dtype="bfloat16",
+        device="cpu",
+        total_sample_count=24576,
+        dataset_path=None,
+        batch_size=None,
+        workers=1,
+    ):
+
+        super().__init__(
+            model_path=model_path,
+            api_server=None,
+            api_model_name=None,
+            dtype=dtype,
+            device=device,
+            total_sample_count=total_sample_count,
+            dataset_path=dataset_path,
+            workers=workers,
+        )
+
+        with open(f"{self.model_path}/tokenizer.json", "r") as token_file:
             llama_tokenizer = json.load(token_file)
         self.llama_vocab = llama_tokenizer["model"]["vocab"]
 
@@ -330,10 +384,10 @@ def start(self):
             self.worker_threads[j] = worker
 
         # Create first token response thread
-        self.ft_response_thread = threading.Thread(target=self.process_first_tokens)
+        self.ft_response_thread = threading.Thread(
+            target=self.process_first_tokens)
         self.ft_response_thread.start()
 
-
     def process_first_tokens(self):
 
         while True:
@@ -345,23 +399,25 @@ def process_first_tokens(self):
 
             first_tokens, response_id = first_token_item
 
-            response_data = array.array("B", np.array(first_tokens, np.float32).tobytes())
+            response_data = array.array(
+                "B", np.array(first_tokens, np.float32).tobytes()
+            )
             bi = response_data.buffer_info()
             response = [lg.QuerySampleResponse(response_id, bi[0], bi[1])]
             lg.FirstTokenComplete(response)
 
     def stream_api_vllm(self, input, response_ids, idx):
         headers = {
-            'Content-Type': 'application/json',
+            "Content-Type": "application/json",
         }
 
         json_data = {
-            'model': '/opt/app-root/share/models',
-            'prompt': input,
-            'max_tokens': 1024,
-            'temperature': 0,
-            'stream': True,
-            'logprobs': 1
+            "model": "/opt/app-root/share/models",
+            "prompt": input,
+            "max_tokens": 1024,
+            "temperature": 0,
+            "stream": True,
+            "logprobs": 1,
         }
 
         while True:
@@ -370,22 +426,28 @@ def stream_api_vllm(self, input, response_ids, idx):
                 s = requests.Session()
                 first = True
                 with s.post(
-                    f'{self.api_servers[idx]}/v1/completions',
+                    f"{self.api_servers[idx]}/v1/completions",
                     headers=headers,
                     json=json_data,
                     verify=False,
-                    stream=True
+                    stream=True,
                 ) as resp:
                     for line in resp.iter_lines():
                         if line:
                             decoded = line.decode()
-                            if decoded.startswith("data") and "[DONE]" not in decoded:
-                                inter = json.loads(decoded[6:])["choices"][0]["logprobs"]
+                            if decoded.startswith(
+                                    "data") and "[DONE]" not in decoded:
+                                inter = json.loads(decoded[6:])["choices"][0][
+                                    "logprobs"
+                                ]
                                 if "top_logprobs" in inter:
-                                    token_s = list(inter["top_logprobs"][0].keys())[0]
+                                    token_s = list(
+                                        inter["top_logprobs"][0].keys())[0]
                                     token = self.llama_vocab[token_s]
                                     if first:
-                                        self.first_token_queue.put((token, response_ids[0]))
+                                        self.first_token_queue.put(
+                                            (token, response_ids[0])
+                                        )
                                         first = False
                                     token_cache.append(token)
                 s.close()
@@ -396,7 +458,7 @@ def stream_api_vllm(self, input, response_ids, idx):
                 print("Connection failure")
                 print(f"An exception occurred: {type(e).__name__}")
                 print(f"Exception details: {e}")
-   
+
     def async_process_query(self, input_ids_tensor, qitem_id, idx):
         decoded = self.tokenizer.decode(input_ids_tensor[0])
         response_ids = [qitem_id]
@@ -406,15 +468,16 @@ def async_process_query(self, input_ids_tensor, qitem_id, idx):
             print("WARNING: caught low token count")
             print(input_ids_tensor)
             print(output_tokens)
-        response_array = array.array("B", np.array(output_tokens, np.int32).tobytes())
+        response_array = array.array(
+            "B", np.array(
+                output_tokens, np.int32).tobytes())
         bi = response_array.buffer_info()
-        response = [lg.QuerySampleResponse(
-            qitem_id, bi[0], bi[1], n_tokens)]
+        response = [lg.QuerySampleResponse(qitem_id, bi[0], bi[1], n_tokens)]
         lg.QuerySamplesComplete(response)
         sys.exit()
 
     def process_queries(self):
-        """Processor of the queued queries. User may choose to add batching logic """
+        """Processor of the queued queries. User may choose to add batching logic"""
         server_idx = 0
         while True:
 
@@ -426,34 +489,46 @@ def process_queries(self):
             input_masks_tensor = self.data_object.attention_masks[qitem.index]
 
             if self.api_servers:
-                threading.Thread(target=self.async_process_query, args=(input_ids_tensor, qitem.id, server_idx)).start()
+                threading.Thread(
+                    target=self.async_process_query,
+                    args=(input_ids_tensor, qitem.id, server_idx),
+                ).start()
                 server_idx = (server_idx + 1) % len(self.api_servers)
             else:
-                #TODO: This PoC is super slow with significant overhead. Best to create a patch to `generate`
+                # TODO: This PoC is super slow with significant overhead. Best
+                # to create a patch to `generate`
                 tokens_cache = []
-                tokens_streamer = FirstTokenStreamer(self.first_token_queue, tokens_cache=tokens_cache, is_first_token=True, response_ids=[qitem.id])
-
-                _ = self.model.generate(    input_ids=input_ids_tensor,
-                                            attention_mask=input_masks_tensor,
-                                            pad_token_id=self.tokenizer.pad_token_id,
-                                            streamer = tokens_streamer,
-                                            **gen_kwargs
-                                            )
+                tokens_streamer = FirstTokenStreamer(
+                    self.first_token_queue,
+                    tokens_cache=tokens_cache,
+                    is_first_token=True,
+                    response_ids=[qitem.id],
+                )
+
+                _ = self.model.generate(
+                    input_ids=input_ids_tensor,
+                    attention_mask=input_masks_tensor,
+                    pad_token_id=self.tokenizer.pad_token_id,
+                    streamer=tokens_streamer,
+                    **gen_kwargs,
+                )
 
                 output_tokens = tokens_streamer.get_out_tokens()
 
                 n_tokens = len(output_tokens)
-                response_array = array.array("B", np.array(output_tokens, np.int32).tobytes())
+                response_array = array.array(
+                    "B", np.array(output_tokens, np.int32).tobytes()
+                )
                 bi = response_array.buffer_info()
-                response = [lg.QuerySampleResponse(
-                    qitem.id, bi[0], bi[1], n_tokens)]
+                response = [
+                    lg.QuerySampleResponse(
+                        qitem.id, bi[0], bi[1], n_tokens)]
                 lg.QuerySamplesComplete(response)
 
     def issue_queries(self, query_samples):
 
         self.query_queue.put(query_samples[0])
 
-
     def stop(self):
         for _ in range(self.num_workers):
             self.query_queue.put(None)
diff --git a/language/llama2-70b/consolidate_results.py b/language/llama2-70b/consolidate_results.py
index ad5fbe4119..645b7bc57b 100644
--- a/language/llama2-70b/consolidate_results.py
+++ b/language/llama2-70b/consolidate_results.py
@@ -14,10 +14,30 @@
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--dataset-path", type=str, default=None, help="Path to .pkl generated by processorca.py")
-    parser.add_argument("--run-outputs", type=str, default="run_outputs", help="Output dir generated by accuracy run.")
-    parser.add_argument("--model-dir", type=str, default=None, help="Path to Llamav2 HuggingFace repo clone")
-    parser.add_argument("--output-pkl-path", type=str, default="full_output.pkl", help="Path to dump output to")
+    parser.add_argument(
+        "--dataset-path",
+        type=str,
+        default=None,
+        help="Path to .pkl generated by processorca.py",
+    )
+    parser.add_argument(
+        "--run-outputs",
+        type=str,
+        default="run_outputs",
+        help="Output dir generated by accuracy run.",
+    )
+    parser.add_argument(
+        "--model-dir",
+        type=str,
+        default=None,
+        help="Path to Llamav2 HuggingFace repo clone",
+    )
+    parser.add_argument(
+        "--output-pkl-path",
+        type=str,
+        default="full_output.pkl",
+        help="Path to dump output to",
+    )
     args = parser.parse_args()
     return args
 
@@ -33,7 +53,7 @@ def load_run_outputs(p: os.PathLike):
     by_query_idx = dict()
     for pkl_file in g:
         print(f"Loading from {pkl_file}...")
-        with open(pkl_file, 'rb') as f:
+        with open(pkl_file, "rb") as f:
             d = pickle.load(f)
         assert len(d["query_ids"]) == len(d["outputs"])
 
@@ -78,17 +98,18 @@ def main(args):
         output_lens[qid] = len(L)
 
         # Decode tokens
-        output_text_col[qid] = tokenizer.decode(output_tok_ids_col[qid], skip_special_tokens=True)
+        output_text_col[qid] = tokenizer.decode(
+            output_tok_ids_col[qid], skip_special_tokens=True
+        )
     print(f"Found {len(no_eos_ids)} samples with no EOS token")
 
     print("Calculating rouge scores...")
-    _preproc = lambda s: "\n".join(nltk.sent_tokenize(s.strip()))
+    def _preproc(s): return "\n".join(nltk.sent_tokenize(s.strip()))
     preds = list(map(_preproc, output_text_col))
     targets = list(map(_preproc, list(df["output"])))
-    rouge_scores = metric.compute(predictions=preds,
-                                  references=targets,
-                                  use_stemmer=True,
-                                  use_aggregator=False)
+    rouge_scores = metric.compute(
+        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False
+    )
 
     assert len(rouge_scores["rouge1"]) == 24576
     assert len(rouge_scores["rouge2"]) == 24576
diff --git a/language/llama2-70b/dataset.py b/language/llama2-70b/dataset.py
index 4b1b1bb910..0d8edc74ca 100644
--- a/language/llama2-70b/dataset.py
+++ b/language/llama2-70b/dataset.py
@@ -1,3 +1,4 @@
+import random
 import os
 import time
 import numpy as np
@@ -8,24 +9,32 @@
 from torch.utils.data import DataLoader
 from typing import Optional, Dict, Sequence
 import io
-#import utils
+
+# import utils
 import copy
 import pickle
 
 import logging
+
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("Llama-70B-Dataset")
 
-import random
 
-class Dataset():
-    def __init__(self, model_name=None, total_sample_count=24576, perf_count_override=None, dataset_path=None, device="cpu"):
+class Dataset:
+    def __init__(
+        self,
+        model_name=None,
+        total_sample_count=24576,
+        perf_count_override=None,
+        dataset_path=None,
+        device="cpu",
+    ):
         self.model_name = model_name or "meta-llama/Llama-2-70b-chat-hf"
         self.dataset_path = dataset_path
         self.max_length = 1024
         self.device = device
 
-        #self.total_sample_count = total_sample_count
+        # self.total_sample_count = total_sample_count
 
         self.load_tokenizer()
         self.load_processed_dataset()
@@ -34,42 +43,54 @@ def __init__(self, model_name=None, total_sample_count=24576, perf_count_overrid
         self.perf_count = perf_count_override or self.total_sample_count
 
     def load_tokenizer(self):
-        """ Returns tokenizer """
+        """Returns tokenizer"""
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.model_name,
             model_max_length=1024,
             padding_side="left",
-            use_fast=False,)
+            use_fast=False,
+        )
 
         self.tokenizer.pad_token = self.tokenizer.eos_token
 
     def load_processed_dataset(self):
         if not os.path.isfile(self.dataset_path):
-            log.warn("Processed pickle file {} not found. Please check that the path is correct".format(self.dataset_path))
+            log.warn(
+                "Processed pickle file {} not found. Please check that the path is correct".format(
+                    self.dataset_path
+                )
+            )
 
         print("Loading dataset...")
         import pandas as pd
+
         processed_data = pd.read_pickle(self.dataset_path)
 
-        input_tokens = processed_data['tok_input']
+        input_tokens = processed_data["tok_input"]
 
         self.input_ids = []
         self.input_lens = []
         self.attention_masks = []
 
         for ids in input_tokens:
-            input_ids = torch.tensor(ids, dtype=torch.int32).view(1,-1).to(self.device)
+            input_ids = torch.tensor(ids, dtype=torch.int32).view(
+                1, -1).to(self.device)
             attn_mask = torch.ones_like(input_ids)
             self.input_ids.append(input_ids)
             self.attention_masks.append(attn_mask)
             self.input_lens.append(input_ids.shape[-1])
         print("Finished loading dataset.")
 
+    def postProcess(
+        self,
+        out_tokens,
+        input_seq_lens=None,
+        query_id_list=None,
+        sample_index_list=None,
+    ):
+        """Postprocesses output prediction"""
 
-    def postProcess(self, out_tokens, input_seq_lens=None, query_id_list=None, sample_index_list=None):
-        """ Postprocesses output prediction """
-
-        #TODO: Create response object in postProcess(?)
+        # TODO: Create response object in postProcess(?)
         """
         preds = []
         for i in range(out_tokens.shape[0]):
@@ -79,7 +100,8 @@ def postProcess(self, out_tokens, input_seq_lens=None, query_id_list=None, sampl
             pred = out_tokens[i, input_len:].reshape(-1).cpu().numpy()
             preds.append(pred)
         """
-        # Everything is padded to max_len (1024), so prune the input and parse to numpy
+        # Everything is padded to max_len (1024), so prune the input and parse
+        # to numpy
         output_seq = out_tokens[:, 1024:].cpu().numpy()
         assert len(query_id_list) == output_seq.shape[0]
 
@@ -88,9 +110,8 @@ def postProcess(self, out_tokens, input_seq_lens=None, query_id_list=None, sampl
             os.makedirs("run_outputs")
         fname = "q" + "_".join([str(i) for i in query_id_list])
         fname = f"run_outputs/{fname}.pkl"
-        with open(fname, mode='wb') as f:
-            d = {"query_ids": query_id_list,
-                 "outputs": output_seq}
+        with open(fname, mode="wb") as f:
+            d = {"query_ids": query_id_list, "outputs": output_seq}
             print(f"Saving outputs to {fname}")
             pickle.dump(d, f)
 
diff --git a/language/llama2-70b/evaluate-accuracy.py b/language/llama2-70b/evaluate-accuracy.py
index cf4cb2c054..593ea95019 100644
--- a/language/llama2-70b/evaluate-accuracy.py
+++ b/language/llama2-70b/evaluate-accuracy.py
@@ -6,29 +6,41 @@
 import json
 
 
-
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--checkpoint-path", required=True,
-                        help="Path to Llama2-70b-hf-chat checkpoint")
-    parser.add_argument("--mlperf-accuracy-file", required=True,
-                        help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--dataset-file", required=True,
-                        help="path to processed openorca validation set")
-    parser.add_argument("--verbose", action="store_true",
-                        help="verbose messages")
-    parser.add_argument("--dtype", default="int64",
-                        help="dtype of the accuracy log", choices=["int32", "int64", "float"])
+    parser.add_argument(
+        "--checkpoint-path", required=True, help="Path to Llama2-70b-hf-chat checkpoint"
+    )
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--dataset-file",
+        required=True,
+        help="path to processed openorca validation set",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--dtype",
+        default="int64",
+        help="dtype of the accuracy log",
+        choices=["int32", "int64", "float"],
+    )
     args = parser.parse_args()
     return args
 
 
 def get_groundtruth(processed_dataset_file):
     import pandas as pd
+
     data = pd.read_pickle(processed_dataset_file)
-    ground_truths = data['output']
+    ground_truths = data["output"]
     return ground_truths
 
+
 def postprocess_text(preds, targets):
     preds = [pred.strip() for pred in preds]
     targets = [target.strip() for target in targets]
@@ -40,21 +52,20 @@ def postprocess_text(preds, targets):
     return preds, targets
 
 
-
 def main():
 
     args = get_args()
     dataset_path = args.dataset_file
     checkpoint_path = args.checkpoint_path
     metric = evaluate.load("rouge")
-    nltk.download('punkt')
+    nltk.download("punkt")
 
     tokenizer = AutoTokenizer.from_pretrained(
         checkpoint_path,
         model_max_length=2048,
         padding_side="left",
-        use_fast=False,)
-
+        use_fast=False,
+    )
 
     targets = get_groundtruth(args.dataset_file)
 
@@ -73,35 +84,38 @@ def main():
     seen = set()
     gen_tok_len = 0
     for pred in results:
-        qsl_idx = pred['qsl_idx']
+        qsl_idx = pred["qsl_idx"]
         if qsl_idx in seen:
             continue
 
         seen.add(qsl_idx)
         target = targets[qsl_idx]
         target_required.append(target)
-        pred = np.frombuffer( bytes.fromhex(pred['data']), eval_dtype)
+        pred = np.frombuffer(bytes.fromhex(pred["data"]), eval_dtype)
 
         gen_tok_len += len(pred)
         preds_token_ids.append(pred)
 
     preds_decoded_text = tokenizer.batch_decode(
-        preds_token_ids, skip_special_tokens=True)
+        preds_token_ids, skip_special_tokens=True
+    )
 
     preds, targets = postprocess_text(preds_decoded_text, target_required)
 
     result = metric.compute(
-        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
+        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False
+    )
     result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
     prediction_lens = [len(pred) for pred in preds]
     gen_num = len(preds)
 
-    result = {**result,
-              'gen_len': np.sum(prediction_lens),
-              'gen_num': gen_num,
-              'gen_tok_len': gen_tok_len,
-              'tokens_per_sample': round(gen_tok_len / gen_num, 1)
-              }
+    result = {
+        **result,
+        "gen_len": np.sum(prediction_lens),
+        "gen_num": gen_num,
+        "gen_tok_len": gen_tok_len,
+        "tokens_per_sample": round(gen_tok_len / gen_num, 1),
+    }
 
     print("\nResults\n")
     print(result)
diff --git a/language/llama2-70b/main.py b/language/llama2-70b/main.py
index cc808efb3b..1d795685f1 100644
--- a/language/llama2-70b/main.py
+++ b/language/llama2-70b/main.py
@@ -23,11 +23,15 @@ def verify_model_name(user_specified_name, url):
         if user_specified_name == server_model_name:
             return {"matched": True, "error": False}
         else:
-            return {"matched": False,
-                    "error": f"User specified {user_specified_name} and server model name {server_model_name} mismatch!"}
+            return {
+                "matched": False,
+                "error": f"User specified {user_specified_name} and server model name {server_model_name} mismatch!",
+            }
     else:
-        return {"matched": False,
-                "error": f"Failed to get a valid response. Status code: {response.status_code}"}
+        return {
+            "matched": False,
+            "error": f"Failed to get a valid response. Status code: {response.status_code}",
+        }
 
 
 def get_args():
@@ -35,16 +39,16 @@ def get_args():
     parser.add_argument(
         "--scenario",
         type=str,
-        choices=[
-            "Offline",
-            "Server"],
+        choices=["Offline", "Server"],
         default="Offline",
-        help="Scenario")
+        help="Scenario",
+    )
     parser.add_argument(
         "--model-path",
         type=str,
         default="meta-llama/Llama-2-70b-chat-hf",
-        help="Model name")
+        help="Model name",
+    )
     parser.add_argument("--dataset-path", type=str, default=None, help="")
     parser.add_argument(
         "--accuracy",
@@ -54,62 +58,68 @@ def get_args():
         "--dtype",
         type=str,
         default="float32",
-        help="data type of the model, choose from float16, bfloat16 and float32")
+        help="data type of the model, choose from float16, bfloat16 and float32",
+    )
     parser.add_argument(
         "--device",
         type=str,
-        choices=[
-            "cpu",
-            "cuda:0"],
+        choices=["cpu", "cuda:0"],
         default="cpu",
-        help="device to use")
+        help="device to use",
+    )
     parser.add_argument(
         "--audit-conf",
         type=str,
         default="audit.conf",
-        help="audit config for LoadGen settings during compliance runs")
+        help="audit config for LoadGen settings during compliance runs",
+    )
     parser.add_argument(
         "--user-conf",
         type=str,
         default="user.conf",
-        help="user config for user LoadGen settings such as target QPS")
+        help="user config for user LoadGen settings such as target QPS",
+    )
     # TODO: This interpretation of 'total-sample-count' is a little
     # misleading. Fix it
     parser.add_argument(
         "--total-sample-count",
         type=int,
         default=24576,
-        help="Number of samples to use in benchmark.")
+        help="Number of samples to use in benchmark.",
+    )
     parser.add_argument(
         "--batch-size",
         type=int,
         default=1,
-        help="Model batch-size to use in benchmark.")
+        help="Model batch-size to use in benchmark.",
+    )
     parser.add_argument(
-        "--output-log-dir",
-        type=str,
-        default="output-logs",
-        help="Where logs are saved")
+        "--output-log-dir", type=str, default="output-logs", help="Where logs are saved"
+    )
     parser.add_argument(
         "--enable-log-trace",
         action="store_true",
-        help="Enable log tracing. This file can become quite large")
+        help="Enable log tracing. This file can become quite large",
+    )
     parser.add_argument(
         "--num-workers",
         type=int,
         default=1,
-        help="Number of workers to process queries")
+        help="Number of workers to process queries",
+    )
     parser.add_argument("--vllm", action="store_true", help="vllm mode")
     parser.add_argument(
         "--api-model-name",
         type=str,
         default="meta-llama/Llama-2-70b-chat-hf",
-        help="Model name(specified in llm server)")
+        help="Model name(specified in llm server)",
+    )
     parser.add_argument(
         "--api-server",
         type=str,
         default=None,
-        help="Specify an api endpoint call to use api mode")
+        help="Specify an api endpoint call to use api mode",
+    )
 
     args = parser.parse_args()
     return args
@@ -157,10 +167,7 @@ def main():
     else:
         from SUT import SUT, SUTServer
 
-    sut_map = {
-        "offline": SUT,
-        "server": SUTServer
-    }
+    sut_map = {"offline": SUT, "server": SUTServer}
 
     sut_cls = sut_map[args.scenario.lower()]
 
@@ -174,7 +181,7 @@ def main():
             device=args.device,
             api_server=args.api_server,
             api_model_name=args.api_model_name,
-            workers=args.num_workers
+            workers=args.num_workers,
         )
     else:
         sut = sut_cls(
@@ -184,7 +191,7 @@ def main():
             dataset_path=args.dataset_path,
             total_sample_count=args.total_sample_count,
             device=args.device,
-            workers=args.num_workers
+            workers=args.num_workers,
         )
 
     # Start sut before loadgen starts
diff --git a/language/llama2-70b/processorca.py b/language/llama2-70b/processorca.py
index c850f89bda..4c0df05fb2 100644
--- a/language/llama2-70b/processorca.py
+++ b/language/llama2-70b/processorca.py
@@ -36,16 +36,21 @@
 llama_prompt_system = "<s>[INST] <<SYS>>\n{}\n<</SYS>>\n\n{} [/INST]"
 llama_prompt_no_system = "<s>[INST] {} [/INST]"
 
+
 def format_llama_input(row):
-    if row['system_prompt']:
-        return llama_prompt_system.format(row['system_prompt'], row['question'])
+    if row["system_prompt"]:
+        return llama_prompt_system.format(
+            row["system_prompt"], row["question"])
     else:
-        return llama_prompt_no_system.format(row['question'])
+        return llama_prompt_no_system.format(row["question"])
+
 
 def is_english(s):
     for c in s:
         allowed = c.isascii()
-        allowed = allowed or (c in ['’', '–', '“', '”', '—'])  # Taken from Habana: Unicode quotes and hyphens
+        allowed = allowed or (
+            c in ["’", "–", "“", "”", "—"]
+        )  # Taken from Habana: Unicode quotes and hyphens
         if not allowed:
             return False
     return True
@@ -59,7 +64,8 @@ def _tokenize_helper(x, llama_tokenizer=None, append_response_init_token=True):
 
     if append_response_init_token:
         # Workaround to enable cheat checking for first token: Llama always outputs token 29871 first
-        # It is possible for submitters to just immediately output this token to achieve a very fast TTFT.
+        # It is possible for submitters to just immediately output this token
+        # to achieve a very fast TTFT.
         tokens.append(29871)
     return tokens
 
@@ -73,11 +79,13 @@ class Keyphrase:
 
 
 class OpenOrcaDatasetGenerator:
-    def __init__(self,
-                 pq_path: os.PathLike,
-                 model_dir: os.PathLike,
-                 io_token_limit: int,
-                 calibration_subset_size: int = 1000):
+    def __init__(
+        self,
+        pq_path: os.PathLike,
+        model_dir: os.PathLike,
+        io_token_limit: int,
+        calibration_subset_size: int = 1000,
+    ):
         self.pq_path = Path(pq_path)
         self.model_dir = Path(model_dir)
         self.io_token_limit = io_token_limit
@@ -90,29 +98,37 @@ def load_parquet(self) -> pd.DataFrame:
         tik = time.time()
         df = pd.read_parquet(self.pq_path)
         print(f"Tokenizing input")
-        df.rename(columns={'response': 'output'}, inplace=True)
-        df['input'] = df.apply(format_llama_input, axis=1)
-
-        input_tokenizer = partial(_tokenize_helper, llama_tokenizer=llama_tokenizer)
-        output_tokenizer = partial(_tokenize_helper, llama_tokenizer=llama_tokenizer, append_response_init_token=False)
-        df['tok_input'] = df['input'].apply(input_tokenizer)
-        df['tok_output'] = df['output'].apply(output_tokenizer)
+        df.rename(columns={"response": "output"}, inplace=True)
+        df["input"] = df.apply(format_llama_input, axis=1)
+
+        input_tokenizer = partial(
+            _tokenize_helper,
+            llama_tokenizer=llama_tokenizer)
+        output_tokenizer = partial(
+            _tokenize_helper,
+            llama_tokenizer=llama_tokenizer,
+            append_response_init_token=False,
+        )
+        df["tok_input"] = df["input"].apply(input_tokenizer)
+        df["tok_output"] = df["output"].apply(output_tokenizer)
         tok = time.time()
         print(f"Loaded parquet and tokenized in {tok-tik} sec.")
         return df
 
     def filter_english(self, df: pd.DataFrame) -> pd.DataFrame:
-        df['input_english'] = df['input'].apply(is_english)
-        df['output_english'] = df['output'].apply(is_english)
-        df['all_english'] = df['input_english'] & df['output_english']
+        df["input_english"] = df["input"].apply(is_english)
+        df["output_english"] = df["output"].apply(is_english)
+        df["all_english"] = df["input_english"] & df["output_english"]
 
         # Filter based on english tokens
-        df = df[df['all_english']].drop(["input_english", "output_english", "all_english"], axis=1)
+        df = df[df["all_english"]].drop(
+            ["input_english", "output_english", "all_english"], axis=1
+        )
         return df.reset_index(drop=True)
 
     def filter_seqlen_oob(self, df: pd.DataFrame) -> pd.DataFrame:
-        df['tok_input_length'] = df['tok_input'].apply(lambda x: len(x))
-        df['tok_output_length'] = df['tok_output'].apply(lambda x: len(x))
+        df["tok_input_length"] = df["tok_input"].apply(lambda x: len(x))
+        df["tok_output_length"] = df["tok_output"].apply(lambda x: len(x))
 
         # Filter based on sequence length
         df = df[df["tok_input_length"] < self.io_token_limit]
@@ -129,22 +145,28 @@ def filter_short_expected_response(self, df: pd.DataFrame) -> pd.DataFrame:
         df = df[df["tok_output_length"] >= 3]
         return df.reset_index(drop=True)
 
-    def filter_bad_prompts(self, df: pd.DataFrame, only_niv_t0: bool = True) -> pd.DataFrame:
+    def filter_bad_prompts(
+        self, df: pd.DataFrame, only_niv_t0: bool = True
+    ) -> pd.DataFrame:
         # Some prompts underperform and cause very bad Rouge scores for a significant percentage of samples with these
         # prompts. See Jupyter notebook for analysis.
         # These generally only affect NIV and t0 and do not exist in flan or cot.
-        # Set 'only_niv_t0' to True to explicitly only remove these prompts from niv and t0 samples.
-        bad_prompts = ['',
-                       'You are an AI assistant that follows instruction extremely well. Help as much as you can.',
-                       'You are an AI assistant. Provide a detailed answer so user don’t need to search outside to understand the answer.',
-                       "You are an AI assistant. Provide a detailed answer so user don't need to search outside to understand the answer.",
-                       'User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer.',
-                       'Explain how you used the definition to come up with the answer.',
-                       ]
+        # Set 'only_niv_t0' to True to explicitly only remove these prompts
+        # from niv and t0 samples.
+        bad_prompts = [
+            "",
+            "You are an AI assistant that follows instruction extremely well. Help as much as you can.",
+            "You are an AI assistant. Provide a detailed answer so user don’t need to search outside to understand the answer.",
+            "You are an AI assistant. Provide a detailed answer so user don't need to search outside to understand the answer.",
+            "User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer.",
+            "Explain how you used the definition to come up with the answer.",
+        ]
         for prompt in bad_prompts:
-            criteria = (df.system_prompt == prompt)
+            criteria = df.system_prompt == prompt
             if only_niv_t0:
-                criteria = criteria & ((df.origin == "niv") | (df.origin == "t0"))
+                criteria = criteria & (
+                    (df.origin == "niv") | (
+                        df.origin == "t0"))
             df = df[~criteria]
 
         return df.reset_index(drop=True)
@@ -153,7 +175,8 @@ def register_keyphrase(self, keyphrase: Keyphrase):
         self.keyphrases.append(keyphrase)
 
     def filter_keyphrases(self, df: pd.DataFrame) -> pd.DataFrame:
-        # Filter out registered keyphrases. This is unused for the final dataset as there are no registered keyphrases.
+        # Filter out registered keyphrases. This is unused for the final
+        # dataset as there are no registered keyphrases.
         for kp in self.keyphrases:
             if kp.startswith:
                 selector = df[kp.col].str.startswith(kp.phrase)
@@ -163,13 +186,13 @@ def filter_keyphrases(self, df: pd.DataFrame) -> pd.DataFrame:
         return df.reset_index(drop=True)
 
     def set_origins(self, df: pd.DataFrame) -> pd.DataFrame:
-        get_sample_origin = lambda x: x.split(".")[0]
-        df['origin'] = df['id'].apply(get_sample_origin)
+        def get_sample_origin(x): return x.split(".")[0]
+        df["origin"] = df["id"].apply(get_sample_origin)
         return df
 
     def _per_origin_split(self, df: pd.DataFrame) -> Dict[str, pd.DataFrame]:
         print(f"Unique sample origin datasets: {df.origin.unique()}")
-        dfs_by_origin = dict(tuple(df.groupby('origin')))
+        dfs_by_origin = dict(tuple(df.groupby("origin")))
         for origin, sub_df in dfs_by_origin.items():
             sub_df = sub_df.reset_index(drop=True, inplace=True)
         return dfs_by_origin
@@ -180,9 +203,13 @@ def _get_sampling(self, df, N, rng_seed: int = 1337):
             raise RuntimeError(f"Not enough samples. Requires {N - _N} more.")
         return df.sample(n=_N, random_state=rng_seed)
 
-    def sample(self, dfs_by_origin: Dict[str, pd.DataFrame], n_total, rng_seed: int = 1337) -> pd.DataFrame:
+    def sample(
+        self, dfs_by_origin: Dict[str, pd.DataFrame], n_total, rng_seed: int = 1337
+    ) -> pd.DataFrame:
         nways = len(dfs_by_origin)
-        assert n_total % nways == 0, f"Total number of samples ({n_total}) must be divisible by n_origins ({nways})"
+        assert (
+            n_total % nways == 0
+        ), f"Total number of samples ({n_total}) must be divisible by n_origins ({nways})"
 
         split_size = n_total // nways
         samplings = []
@@ -195,17 +222,21 @@ def sample(self, dfs_by_origin: Dict[str, pd.DataFrame], n_total, rng_seed: int
         sampled_df = sampled_df.reset_index(drop=True)
         return sampled_df
 
-    def generate(self,
-                 export_dir: os.PathLike,
-                 n_samples: int = 24576,
-                 use_cached: bool = True,
-                 calib_rng_seed: int = 12345):
+    def generate(
+        self,
+        export_dir: os.PathLike,
+        n_samples: int = 24576,
+        use_cached: bool = True,
+        calib_rng_seed: int = 12345,
+    ):
         export_dir = Path(export_dir)
         if not export_dir.exists():
             print(f"Creating {export_dir}")
             export_dir.mkdir(parents=True)
         if export_dir.is_file():
-            raise ValueError(f"Cannot export to file {export_dir}. Must be a directory.")
+            raise ValueError(
+                f"Cannot export to file {export_dir}. Must be a directory."
+            )
 
         full_fpath = export_dir / f"open_orca_gpt4_tokenized_llama.full.pkl"
         if full_fpath.exists() and use_cached:
@@ -227,7 +258,8 @@ def generate(self,
         # Export base files
         for origin, sub_df in dfs_by_origin.items():
             print(f"Subset '{origin}' has {sub_df.shape[0]} samples")
-            origin_fpath = export_dir / f"open_orca_gpt4_tokenized_llama.{origin}.pkl"
+            origin_fpath = export_dir / \
+                f"open_orca_gpt4_tokenized_llama.{origin}.pkl"
             if not origin_fpath.exists() or not use_cached:
                 sub_df.to_pickle(origin_fpath)
 
@@ -237,29 +269,59 @@ def generate(self,
         # cot has a higher rouge score from a 100k sampling (of the whole dataset) than the rest, while niv has lower.
         # Sample from each dataset equally.
         sampled_df = self.sample(dfs_by_origin, n_samples)
-        sampled_fpath = export_dir / f"open_orca_gpt4_tokenized_llama.sampled_{n_samples}.pkl"
+        sampled_fpath = (
+            export_dir /
+            f"open_orca_gpt4_tokenized_llama.sampled_{n_samples}.pkl"
+        )
         sampled_df.to_pickle(sampled_fpath)
 
         # Calibration dataset
-        calib_ds = sampled_df.sample(n=self.calibration_subset_size,
-                                     random_state=calib_rng_seed)
+        calib_ds = sampled_df.sample(
+            n=self.calibration_subset_size, random_state=calib_rng_seed
+        )
         calib_ds = calib_ds.reset_index(drop=True)
-        calib_fpath = export_dir / f"open_orca_gpt4_tokenized_llama.calibration_{self.calibration_subset_size}.pkl"
+        calib_fpath = (
+            export_dir
+            / f"open_orca_gpt4_tokenized_llama.calibration_{self.calibration_subset_size}.pkl"
+        )
         calib_ds.to_pickle(calib_fpath)
 
 
 def parse_arguments():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--dataset_pq_path', type=str,
-                        default='/raid/data/mlperf-llm/OpenOrca/1M-GPT4-Augmented.parquet',
-                        help="the path to the open_orca GPT4 parquet.")
-    parser.add_argument('--model_dir', type=str, default='/raid/data/mlperf-llm/Llama-2-70b-chat-hf')
-    parser.add_argument('--seqlen_limit', type=int, default=1024, help="Upper limit of the input/output sequence lengths")
-    parser.add_argument('--export_dir', type=str,
-                        default="/raid/data/mlperf-llm/OpenOrca/llama/filtered",
-                        help="Path to the output pkl file.")
-    parser.add_argument('--num_total_samples', type=int, default=24576, help="Number of samples to generate")
-    parser.add_argument('--calibration_subset_size', type=int, default=1000, help="Number of samples for calibration subset")
+    parser.add_argument(
+        "--dataset_pq_path",
+        type=str,
+        default="/raid/data/mlperf-llm/OpenOrca/1M-GPT4-Augmented.parquet",
+        help="the path to the open_orca GPT4 parquet.",
+    )
+    parser.add_argument(
+        "--model_dir", type=str, default="/raid/data/mlperf-llm/Llama-2-70b-chat-hf"
+    )
+    parser.add_argument(
+        "--seqlen_limit",
+        type=int,
+        default=1024,
+        help="Upper limit of the input/output sequence lengths",
+    )
+    parser.add_argument(
+        "--export_dir",
+        type=str,
+        default="/raid/data/mlperf-llm/OpenOrca/llama/filtered",
+        help="Path to the output pkl file.",
+    )
+    parser.add_argument(
+        "--num_total_samples",
+        type=int,
+        default=24576,
+        help="Number of samples to generate",
+    )
+    parser.add_argument(
+        "--calibration_subset_size",
+        type=int,
+        default=1000,
+        help="Number of samples for calibration subset",
+    )
     return parser.parse_args()
 
 
@@ -277,4 +339,9 @@ def parse_arguments():
     )
 
     # Sample command to run:
-    # python3 processorca.py --dataset_pq_path=/raid/data/mlperf-llm/OpenOrca/1M-GPT4-Augmented.parquet --model_dir=/raid/data/mlperf-llm/Llama-2-70b-chat-hf --seqlen_limit=1024 --export_dir=/raid/data/mlperf-llm/OpenOrca/llama/filtered --num_total_samples=24576
+    # python3 processorca.py
+    # --dataset_pq_path=/raid/data/mlperf-llm/OpenOrca/1M-GPT4-Augmented.parquet
+    # --model_dir=/raid/data/mlperf-llm/Llama-2-70b-chat-hf
+    # --seqlen_limit=1024
+    # --export_dir=/raid/data/mlperf-llm/OpenOrca/llama/filtered
+    # --num_total_samples=24576
diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
index 5c487a630c..e12a4318fa 100644
--- a/language/mixtral-8x7b/SUT.py
+++ b/language/mixtral-8x7b/SUT.py
@@ -5,7 +5,12 @@
 import torch
 from torch.nn.functional import pad
 from torch.utils.data import DataLoader
-from transformers import AutoModelForCausalLM, AutoTokenizer, LogitsProcessor, LogitsProcessorList
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    LogitsProcessor,
+    LogitsProcessorList,
+)
 from transformers.generation.streamers import BaseStreamer
 
 import pickle
@@ -29,48 +34,61 @@
     "max_new_tokens": 1024,
     "min_new_tokens": 2,
     "num_beams": 1,
-    "do_sample": False
+    "do_sample": False,
 }
 
+
 class StopAfterSequence(LogitsProcessor):
-        """Logits processor (to use with HuggingFace `generate()` method :
-        https://huggingface.co/docs/transformers/v4.24.0/en/main_classes/
-        text_generation#transformers.generation_utils.GenerationMixin).
-
-        This logits processor makes that when the model generates a specified
-        stopping sequence, it stops generating new tokens
-
-        Args:
-            stop_seq (List[int]): ID of the space token.
-            eos_token_id (int): ID of the EOS token.
-            device (str): Device that the model is running
-        """
-        def __init__(self, eos_token_id: int, stop_seq: List[int] = [13, 13940, 28832, 13], device="cpu"):
-            super().__init__()
-            assert(len(stop_seq) >= 1)
-            self.device = device
-            self.stop_seq = torch.tensor(stop_seq, dtype=torch.long).to(device)
-            self.stop_seq_length = len(stop_seq)
-            self.eos_token_id = eos_token_id
-
-        def check_stop_condition(self, input_ids: torch.LongTensor):
-            stop_condition_met = (input_ids[:, -self.stop_seq_length:] == self.stop_seq).all(dim=1)
-            return stop_condition_met
-        
-        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-            if input_ids.size(1) > self.stop_seq_length:
-                forced_eos = torch.full((scores.size(1),), -float("inf")).to(self.device)
-                forced_eos[self.eos_token_id] = 0
-                scores[self.check_stop_condition(input_ids)] = forced_eos
-            return scores
+    """Logits processor (to use with HuggingFace `generate()` method :
+    https://huggingface.co/docs/transformers/v4.24.0/en/main_classes/
+    text_generation#transformers.generation_utils.GenerationMixin).
+
+    This logits processor makes that when the model generates a specified
+    stopping sequence, it stops generating new tokens
+
+    Args:
+        stop_seq (List[int]): ID of the space token.
+        eos_token_id (int): ID of the EOS token.
+        device (str): Device that the model is running
+    """
+
+    def __init__(
+        self,
+        eos_token_id: int,
+        stop_seq: List[int] = [13, 13940, 28832, 13],
+        device="cpu",
+    ):
+        super().__init__()
+        assert len(stop_seq) >= 1
+        self.device = device
+        self.stop_seq = torch.tensor(stop_seq, dtype=torch.long).to(device)
+        self.stop_seq_length = len(stop_seq)
+        self.eos_token_id = eos_token_id
+
+    def check_stop_condition(self, input_ids: torch.LongTensor):
+        stop_condition_met = (
+            input_ids[:, -self.stop_seq_length:] == self.stop_seq
+        ).all(dim=1)
+        return stop_condition_met
+
+    def __call__(
+        self, input_ids: torch.LongTensor, scores: torch.FloatTensor
+    ) -> torch.FloatTensor:
+        if input_ids.size(1) > self.stop_seq_length:
+            forced_eos = torch.full(
+                (scores.size(1),), -float("inf")).to(self.device)
+            forced_eos[self.eos_token_id] = 0
+            scores[self.check_stop_condition(input_ids)] = forced_eos
+        return scores
 
 
 class FirstTokenStreamer(BaseStreamer):
-    """ Streams first tokens to a 'holder' """
+    """Streams first tokens to a 'holder'"""
 
-    def __init__(self, first_token, tokens_cache=[],
-                 is_first_token=True, response_ids=[]):
-        """ Response ids added to 'sign' the first token"""
+    def __init__(
+        self, first_token, tokens_cache=[], is_first_token=True, response_ids=[]
+    ):
+        """Response ids added to 'sign' the first token"""
 
         self.first_token = first_token  # Queue for first token
         self.is_first_token = is_first_token
@@ -84,7 +102,7 @@ def __init__(self, first_token, tokens_cache=[],
         self.is_prompt = True
 
     def put(self, value):
-        """ Caches the tokens as they're generated. Assumes bs=1 """
+        """Caches the tokens as they're generated. Assumes bs=1"""
 
         # Prompts are streamed first so we need to skip the first time value
         # that arrives
@@ -111,18 +129,20 @@ def get_out_tokens(self):
         return self.tokens_cache
 
 
-class SUT():
-    def __init__(self,
-                 model_path=None,
-                 dtype="bfloat16",
-                 device="cpu",
-                 batch_size=None,
-                 total_sample_count=24576,
-                 dataset_path=None,
-                 use_cached_outputs=False,
-                 # Set this to True *only for test accuracy runs* in case your
-                 # prior session was killed partway through
-                 workers=1):
+class SUT:
+    def __init__(
+        self,
+        model_path=None,
+        dtype="bfloat16",
+        device="cpu",
+        batch_size=None,
+        total_sample_count=24576,
+        dataset_path=None,
+        use_cached_outputs=False,
+        # Set this to True *only for test accuracy runs* in case your
+        # prior session was killed partway through
+        workers=1,
+    ):
 
         self.model_path = model_path or "mistralai/Mixtral-8x7B-Instruct-v0.1"
         self.device = device
@@ -135,26 +155,32 @@ def __init__(self,
         self.batch_size = batch_size
 
         # dtype
-        if dtype == 'bfloat16':
+        if dtype == "bfloat16":
             self.amp_enabled = True
             self.amp_dtype = torch.bfloat16
-        elif dtype == 'float16':
+        elif dtype == "float16":
             self.amp_enabled = True
             self.amp_dtype = torch.float16
         else:
             self.amp_enabled = False
             self.amp_dtype = torch.float32
 
-        if 'cuda' in self.device:
+        if "cuda" in self.device:
             assert torch.cuda.is_available(), "torch gpu is not available, exiting..."
 
         self.dataset_path = dataset_path
-        self.data_object = Dataset(self.model_path,
-                                   dataset_path=self.dataset_path,
-                                   total_sample_count=total_sample_count,
-                                   device=self.device)
-        self.qsl = lg.ConstructQSL(self.data_object.total_sample_count, self.data_object.perf_count,
-                                   self.data_object.LoadSamplesToRam, self.data_object.UnloadSamplesFromRam)
+        self.data_object = Dataset(
+            self.model_path,
+            dataset_path=self.dataset_path,
+            total_sample_count=total_sample_count,
+            device=self.device,
+        )
+        self.qsl = lg.ConstructQSL(
+            self.data_object.total_sample_count,
+            self.data_object.perf_count,
+            self.data_object.LoadSamplesToRam,
+            self.data_object.UnloadSamplesFromRam,
+        )
 
         self.load_model()
 
@@ -181,7 +207,7 @@ def stop(self):
             worker.join()
 
     def process_queries(self):
-        """Processor of the queued queries. User may choose to add batching logic """
+        """Processor of the queued queries. User may choose to add batching logic"""
 
         while True:
             qitem = self.query_queue.get()
@@ -213,18 +239,38 @@ def process_queries(self):
                 input_len = []
                 input_dataset = []
                 for q in qitem:
-                    input_ids_tensor.append(pad(self.data_object.input_ids[q.index],
-                                                (max_seq_len -
-                                                 self.data_object.input_lens[q.index], 0, 0, 0),
-                                                value=self.tokenizer.pad_token_id))
-                    input_masks_tensor.append(pad(self.data_object.attention_masks[q.index],
-                                                  (max_seq_len -
-                                                   self.data_object.input_lens[q.index], 0, 0, 0),
-                                                  value=0))
+                    input_ids_tensor.append(
+                        pad(
+                            self.data_object.input_ids[q.index],
+                            (
+                                max_seq_len -
+                                self.data_object.input_lens[q.index],
+                                0,
+                                0,
+                                0,
+                            ),
+                            value=self.tokenizer.pad_token_id,
+                        )
+                    )
+                    input_masks_tensor.append(
+                        pad(
+                            self.data_object.attention_masks[q.index],
+                            (
+                                max_seq_len -
+                                self.data_object.input_lens[q.index],
+                                0,
+                                0,
+                                0,
+                            ),
+                            value=0,
+                        )
+                    )
                     input_len.append(self.data_object.input_lens[q.index])
 
-                    # In case we predict code generation, we can specify an additional stop sequence
-                    input_dataset.append(self.data_object.dataset_names[q.index])
+                    # In case we predict code generation, we can specify an
+                    # additional stop sequence
+                    input_dataset.append(
+                        self.data_object.dataset_names[q.index])
                 input_ids_tensor = torch.cat(input_ids_tensor)
                 input_masks_tensor = torch.cat(input_masks_tensor)
 
@@ -232,9 +278,16 @@ def process_queries(self):
                 assert input_ids_tensor.shape[0] <= self.batch_size
 
                 tik2 = time.time()
-                logits_processor = LogitsProcessorList([StopAfterSequence(self.tokenizer.eos_token_id, device=self.device)])
+                logits_processor = LogitsProcessorList(
+                    [StopAfterSequence(
+                        self.tokenizer.eos_token_id, device=self.device)]
+                )
                 for i in range(len(input_ids_tensor)):
-                    ids, masks, dataset = input_ids_tensor[i:i+1], input_masks_tensor[i:i+1], input_dataset[i]
+                    ids, masks, dataset = (
+                        input_ids_tensor[i: i + 1],
+                        input_masks_tensor[i: i + 1],
+                        input_dataset[i],
+                    )
                     pred_output_tokens = []
                     if dataset == "MBXP":
                         out = self.model.generate(
@@ -242,22 +295,24 @@ def process_queries(self):
                             attention_mask=masks,
                             pad_token_id=self.tokenizer.pad_token_id,
                             logits_processor=logits_processor,
-                            **gen_kwargs
+                            **gen_kwargs,
                         )
                     else:
                         out = self.model.generate(
                             input_ids=ids,
                             attention_mask=masks,
                             pad_token_id=self.tokenizer.pad_token_id,
-                            **gen_kwargs
+                            **gen_kwargs,
                         )
                     pred_output_tokens.append(out)
                 pred_output_tokens = torch.cat(pred_output_tokens)
                 tik3 = time.time()
 
-                processed_output = self.data_object.postProcess(pred_output_tokens,
-                                                                input_seq_lens=input_len,
-                                                                query_id_list=query_ids)
+                processed_output = self.data_object.postProcess(
+                    pred_output_tokens,
+                    input_seq_lens=input_len,
+                    query_id_list=query_ids,
+                )
 
             for i in range(len(qitem)):
                 n_tokens = processed_output[i].shape[0]
@@ -290,7 +345,7 @@ def load_model(self):
             self.model_path,
             device_map="auto",
             low_cpu_mem_usage=True,
-            torch_dtype=self.amp_dtype
+            torch_dtype=self.amp_dtype,
         )
         print("Loaded model")
 
@@ -301,16 +356,17 @@ def load_model(self):
             self.model = self.model.to(self.device)
 
         self.model.eval()
-        try: # for systems with low ram, the below command gives error as some part is offloaded to disk
+        try:  # for systems with low ram, the below command gives error as some part is offloaded to disk
             self.model = self.model.to(memory_format=torch.channels_last)
-        except:
+        except BaseException:
             pass
 
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.model_path,
             model_max_length=1024,
             padding_side="left",
-            use_fast=False,)
+            use_fast=False,
+        )
 
         self.tokenizer.pad_token = self.tokenizer.eos_token
         print("Loaded tokenizer")
@@ -326,14 +382,14 @@ def predict(self, **kwargs):
         raise NotImplementedError
 
     def issue_queries(self, query_samples):
-        """ Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
+        """Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
 
         list_prompts_tokens = []
         list_prompts_attn_masks = []
 
         print(f"IssueQuery started with {len(query_samples)} samples")
         while len(query_samples) > 0:
-            self.query_queue.put(query_samples[:self.batch_size])
+            self.query_queue.put(query_samples[: self.batch_size])
             query_samples = query_samples[self.batch_size:]
         print(f"IssueQuery done")
 
@@ -345,8 +401,15 @@ def __del__(self):
 
 
 class SUTServer(SUT):
-    def __init__(self, model_path=None, dtype="bfloat16", device="cpu",
-                 total_sample_count=24576, dataset_path=None, workers=1):
+    def __init__(
+        self,
+        model_path=None,
+        dtype="bfloat16",
+        device="cpu",
+        total_sample_count=24576,
+        dataset_path=None,
+        workers=1,
+    ):
 
         super().__init__(
             model_path=model_path,
@@ -354,7 +417,8 @@ def __init__(self, model_path=None, dtype="bfloat16", device="cpu",
             device=device,
             total_sample_count=total_sample_count,
             dataset_path=dataset_path,
-            workers=workers)
+            workers=workers,
+        )
 
         self.first_token_queue = queue.Queue()
 
@@ -382,14 +446,15 @@ def process_first_tokens(self):
 
             first_tokens, response_id = first_token_item
 
-            response_data = array.array("B", np.array(
-                first_tokens, np.int32).tobytes())
+            response_data = array.array(
+                "B", np.array(
+                    first_tokens, np.int32).tobytes())
             bi = response_data.buffer_info()
             response = [lg.QuerySampleResponse(response_id, bi[0], bi[1])]
             lg.FirstTokenComplete(response)
 
     def process_queries(self):
-        """Processor of the queued queries. User may choose to add batching logic """
+        """Processor of the queued queries. User may choose to add batching logic"""
         while True:
 
             qitem = self.query_queue.get()
@@ -407,34 +472,43 @@ def process_queries(self):
                 self.first_token_queue,
                 tokens_cache=tokens_cache,
                 is_first_token=True,
-                response_ids=[
-                    qitem.id])
-            
-            logits_processor = LogitsProcessorList([StopAfterSequence(self.tokenizer.eos_token_id, device=self.device)])
+                response_ids=[qitem.id],
+            )
+
+            logits_processor = LogitsProcessorList(
+                [StopAfterSequence(
+                    self.tokenizer.eos_token_id, device=self.device)]
+            )
             if dataset == "MBXP":
-                _ = self.model.generate(input_ids=input_ids_tensor,
-                                        attention_mask=input_masks_tensor,
-                                        pad_token_id=self.tokenizer.pad_token_id,
-                                        streamer=tokens_streamer,
-                                        logits_processor=logits_processor,
-                                        **gen_kwargs
-                                        )
+                _ = self.model.generate(
+                    input_ids=input_ids_tensor,
+                    attention_mask=input_masks_tensor,
+                    pad_token_id=self.tokenizer.pad_token_id,
+                    streamer=tokens_streamer,
+                    logits_processor=logits_processor,
+                    **gen_kwargs,
+                )
             else:
-                _ = self.model.generate(input_ids=input_ids_tensor,
-                                        attention_mask=input_masks_tensor,
-                                        pad_token_id=self.tokenizer.pad_token_id,
-                                        streamer=tokens_streamer,
-                                        **gen_kwargs
-                                        )
+                _ = self.model.generate(
+                    input_ids=input_ids_tensor,
+                    attention_mask=input_masks_tensor,
+                    pad_token_id=self.tokenizer.pad_token_id,
+                    streamer=tokens_streamer,
+                    **gen_kwargs,
+                )
 
             output_tokens = tokens_streamer.get_out_tokens()
             n_tokens = len(output_tokens)
             response_array = array.array(
-                "B", np.array(
-                    output_tokens, np.int32).tobytes())
+                "B", np.array(output_tokens, np.int32).tobytes()
+            )
             bi = response_array.buffer_info()
-            response = [lg.QuerySampleResponse(
-                qitem.id, bi[0], bi[1], n_tokens)]
+            response = [
+                lg.QuerySampleResponse(
+                    qitem.id,
+                    bi[0],
+                    bi[1],
+                    n_tokens)]
             lg.QuerySamplesComplete(response)
 
     def issue_queries(self, query_samples):
diff --git a/language/mixtral-8x7b/dataset.py b/language/mixtral-8x7b/dataset.py
index 4bc12d84a6..c8268d8d1d 100644
--- a/language/mixtral-8x7b/dataset.py
+++ b/language/mixtral-8x7b/dataset.py
@@ -9,18 +9,26 @@
 from torch.utils.data import DataLoader
 from typing import Optional, Dict, Sequence
 import io
+
 # import utils
 import copy
 import pickle
 
 import logging
+
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("Llama-70B-Dataset")
 
 
-class Dataset():
-    def __init__(self, model_name=None, total_sample_count=15000,
-                 perf_count_override=None, dataset_path=None, device="cpu"):
+class Dataset:
+    def __init__(
+        self,
+        model_name=None,
+        total_sample_count=15000,
+        perf_count_override=None,
+        dataset_path=None,
+        device="cpu",
+    ):
         self.model_name = model_name or "mistralai/Mixtral-8x7B-v0.1"
         self.dataset_path = dataset_path
         self.max_length = 1024
@@ -35,12 +43,13 @@ def __init__(self, model_name=None, total_sample_count=15000,
         self.perf_count = perf_count_override or self.total_sample_count
 
     def load_tokenizer(self):
-        """ Returns tokenizer """
+        """Returns tokenizer"""
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.model_name,
             model_max_length=1024,
             padding_side="left",
-            use_fast=False,)
+            use_fast=False,
+        )
 
         self.tokenizer.pad_token = self.tokenizer.eos_token
 
@@ -48,13 +57,16 @@ def load_processed_dataset(self):
         if not os.path.isfile(self.dataset_path):
             log.warn(
                 "Processed pickle file {} not found. Please check that the path is correct".format(
-                    self.dataset_path))
+                    self.dataset_path
+                )
+            )
 
         print("Loading dataset...")
         import pandas as pd
+
         processed_data = pd.read_pickle(self.dataset_path)
 
-        input_tokens = processed_data['tok_input']
+        input_tokens = processed_data["tok_input"]
 
         self.input_ids = []
         self.input_lens = []
@@ -69,13 +81,18 @@ def load_processed_dataset(self):
             self.attention_masks.append(attn_mask)
             self.input_lens.append(input_ids.shape[-1])
 
-        for dataset in processed_data['dataset']:
+        for dataset in processed_data["dataset"]:
             self.dataset_names.append(dataset)
         print("Finished loading dataset.")
 
-    def postProcess(self, out_tokens, input_seq_lens=None,
-                    query_id_list=None, sample_index_list=None):
-        """ Postprocesses output prediction """
+    def postProcess(
+        self,
+        out_tokens,
+        input_seq_lens=None,
+        query_id_list=None,
+        sample_index_list=None,
+    ):
+        """Postprocesses output prediction"""
 
         # TODO: Create response object in postProcess(?)
         """
@@ -94,7 +111,7 @@ def postProcess(self, out_tokens, input_seq_lens=None,
         assert len(query_id_list) == output_seq.shape[0]
         for i in range(len(output_seq)):
             aux = output_seq[i]
-            while(len(output_seq[i]) <= 1):
+            while len(output_seq[i]) <= 1:
                 aux = np.append(aux, self.tokenizer.eos_token_id)
             aux_seq.append(aux)
         output_seq = np.stack(aux_seq)
@@ -104,9 +121,8 @@ def postProcess(self, out_tokens, input_seq_lens=None,
             os.makedirs("run_outputs")
         fname = "q" + "_".join([str(i) for i in query_id_list])
         fname = f"run_outputs/{fname}.pkl"
-        with open(fname, mode='wb') as f:
-            d = {"query_ids": query_id_list,
-                 "outputs": output_seq}
+        with open(fname, mode="wb") as f:
+            d = {"query_ids": query_id_list, "outputs": output_seq}
             print(f"Saving outputs to {fname}")
             pickle.dump(d, f)
 
diff --git a/language/mixtral-8x7b/evaluate-accuracy.py b/language/mixtral-8x7b/evaluate-accuracy.py
index bcf638c887..3ea79cea89 100644
--- a/language/mixtral-8x7b/evaluate-accuracy.py
+++ b/language/mixtral-8x7b/evaluate-accuracy.py
@@ -10,18 +10,31 @@
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--checkpoint-path", required=True,
-                        help="Path to Llama2-70b-hf-chat checkpoint")
-    parser.add_argument("--mlperf-accuracy-file", required=True,
-                        help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--dataset-file", required=True,
-                        help="path to processed validation dataset")
-    parser.add_argument("--n_workers", default=2, type=int,
-                        help="Number of workers used for the MBXP evaluation")
-    parser.add_argument("--verbose", action="store_true",
-                        help="verbose messages")
-    parser.add_argument("--dtype", default="int64",
-                        help="dtype of the accuracy log", choices=["int32", "int64", "float"])
+    parser.add_argument(
+        "--checkpoint-path", required=True, help="Path to Llama2-70b-hf-chat checkpoint"
+    )
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--dataset-file", required=True, help="path to processed validation dataset"
+    )
+    parser.add_argument(
+        "--n_workers",
+        default=2,
+        type=int,
+        help="Number of workers used for the MBXP evaluation",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--dtype",
+        default="int64",
+        help="dtype of the accuracy log",
+        choices=["int32", "int64", "float"],
+    )
     args = parser.parse_args()
     return args
 
@@ -37,14 +50,13 @@ def find_numbers(x: str) -> list[str]:
     # Search for number, possibly negative (hyphen), with thousand separators
     # (comma), and with a decimal point (period inbetween digits).
     numbers = re.compile(
-        r'-?[\d,]*\.?\d+',
+        r"-?[\d,]*\.?\d+",
         re.MULTILINE | re.DOTALL | re.IGNORECASE,
     ).findall(x)
     return numbers
 
 
-def find_number(x: str,
-                answer_delimiter: str = 'The answer is') -> str:
+def find_number(x: str, answer_delimiter: str = "The answer is") -> str:
     """Finds the most relevant number in a string."""
     # If model uses the answer delimiter, then select the first number following
     # that format.
@@ -58,12 +70,12 @@ def find_number(x: str,
     numbers = find_numbers(x)
     if numbers:
         return numbers[-1]
-    return ''
+    return ""
 
 
 def maybe_remove_comma(x: str) -> str:
     # Example: 5,600 -> 5600
-    return x.replace(',', '')
+    return x.replace(",", "")
 
 
 def try_float(x: str):
@@ -73,6 +85,7 @@ def try_float(x: str):
         ret = None
     return ret
 
+
 # Functions for evaluating OpenOrca
 
 
@@ -86,6 +99,7 @@ def postprocess_text(preds, targets):
 
     return preds, targets
 
+
 # Functions for MBXP
 
 
@@ -96,7 +110,7 @@ def create_mbxp_dict(row, response):
         "prompt": row["input"],
         "test_code": row["gt_output"],
         "entry_point": entry_point,
-        "response": response
+        "response": response,
     }
 
 
@@ -106,13 +120,14 @@ def main():
     dataset_path = args.dataset_file
     checkpoint_path = args.checkpoint_path
     metric = evaluate.load("rouge")
-    nltk.download('punkt')
+    nltk.download("punkt")
 
     tokenizer = AutoTokenizer.from_pretrained(
         checkpoint_path,
         model_max_length=2048,
         padding_side="left",
-        use_fast=False,)
+        use_fast=False,
+    )
 
     data = get_groundtruth(args.dataset_file)
     query_types, gt_outputs = data["dataset"], data["gt_output"]
@@ -138,7 +153,7 @@ def main():
     gen_num = 0
     for pred in results:
         gen_num += 1
-        qsl_idx = pred['qsl_idx']
+        qsl_idx = pred["qsl_idx"]
         if qsl_idx in seen:
             continue
 
@@ -148,20 +163,20 @@ def main():
         if query_type == "GSM8K":
             target = gt_outputs.iloc[qsl_idx]
             target_required_GSM8K.append(target)
-            pred = np.frombuffer(bytes.fromhex(pred['data']), eval_dtype)
+            pred = np.frombuffer(bytes.fromhex(pred["data"]), eval_dtype)
 
             gen_tok_len += len(pred)
             preds_token_GSM8K.append(pred)
         elif query_type == "OpenOrca":
             target = gt_outputs.iloc[qsl_idx]
             target_required_OpenOrca.append(target)
-            pred = np.frombuffer(bytes.fromhex(pred['data']), eval_dtype)
+            pred = np.frombuffer(bytes.fromhex(pred["data"]), eval_dtype)
 
             gen_tok_len += len(pred)
             preds_token_OpenOrca.append(pred)
         else:
             target = data.iloc[qsl_idx]
-            pred = np.frombuffer(bytes.fromhex(pred['data']), eval_dtype)
+            pred = np.frombuffer(bytes.fromhex(pred["data"]), eval_dtype)
             pred_str = tokenizer.decode(pred, skip_special_tokens=True)
             results_MBXP.append(create_mbxp_dict(target, pred_str))
 
@@ -169,15 +184,21 @@ def main():
 
     # OpenOrca metric
     preds_decoded_text = tokenizer.batch_decode(
-        preds_token_OpenOrca, skip_special_tokens=True)
+        preds_token_OpenOrca, skip_special_tokens=True
+    )
 
     preds, targets = postprocess_text(
         preds_decoded_text, target_required_OpenOrca)
-    
+
     if preds:
         result = metric.compute(
-            predictions=preds, references=targets, use_stemmer=True, use_aggregator=False)
-        result = {k: float(round(np.mean(v) * 100, 4)) for k, v in result.items()}
+            predictions=preds,
+            references=targets,
+            use_stemmer=True,
+            use_aggregator=False,
+        )
+        result = {k: float(round(np.mean(v) * 100, 4))
+                  for k, v in result.items()}
         prediction_lens = [len(pred) for pred in preds]
 
     else:
@@ -186,11 +207,12 @@ def main():
 
     # GSM8K metric
     preds_decoded_text = tokenizer.batch_decode(
-        preds_token_GSM8K, skip_special_tokens=True)
+        preds_token_GSM8K, skip_special_tokens=True
+    )
     pred_nums = [
-        maybe_remove_comma(
-            find_number(
-                pred_text.split("\nQ:")[0])) for pred_text in preds_decoded_text]
+        maybe_remove_comma(find_number(pred_text.split("\nQ:")[0]))
+        for pred_text in preds_decoded_text
+    ]
     gsm8k_total = len(target_required_GSM8K)
     correct = 0
     for idx in range(len(target_required_GSM8K)):
@@ -198,24 +220,24 @@ def main():
         tgt = try_float(pred_nums[idx])
         if tgt is None:
             continue
-        correct += (ref == tgt)
+        correct += ref == tgt
 
-    result['gsm8k'] = 100.0 * correct / gsm8k_total
+    result["gsm8k"] = 100.0 * correct / gsm8k_total
 
     # MBXP metric
     from evaluate_mbxp import evaluate_mbxp
 
     if results_MBXP:
-        result['mbxp'] = evaluate_mbxp(results_MBXP, args.n_workers)
+        result["mbxp"] = evaluate_mbxp(results_MBXP, args.n_workers)
     else:
-        result['mbxp'] = 0
+        result["mbxp"] = 0
 
     result = {
         **result,
-        'gen_len': int(np.sum(prediction_lens)),
-        'gen_num': gen_num,
-        'gen_tok_len': gen_tok_len,
-        'tokens_per_sample': round(gen_tok_len / gen_num, 1),
+        "gen_len": int(np.sum(prediction_lens)),
+        "gen_num": gen_num,
+        "gen_tok_len": gen_tok_len,
+        "tokens_per_sample": round(gen_tok_len / gen_num, 1),
     }
 
     print("\nResults\n")
diff --git a/language/mixtral-8x7b/evaluate_mbxp.py b/language/mixtral-8x7b/evaluate_mbxp.py
index e7d55d1694..a230587fb6 100644
--- a/language/mixtral-8x7b/evaluate_mbxp.py
+++ b/language/mixtral-8x7b/evaluate_mbxp.py
@@ -66,7 +66,7 @@ def worker(inp_queue, out_queue):
         solution = problem["response"]
 
         try:
-            solution = solution[:solution.index("```")]
+            solution = solution[: solution.index("```")]
         except ValueError:
             # Happens when a code block isn't closed properly
             pass
@@ -89,11 +89,14 @@ def worker(inp_queue, out_queue):
         try:
             result = checker(problem, solution, timeout=20.0)
             out_queue.put(
-                (key,
-                 problem["lang"],
+                (
+                    key,
+                    problem["lang"],
                     result["passed"],
                     result["result"],
-                    problem["response"]))
+                    problem["response"],
+                )
+            )
         except Exception as e:
             print(e)
             out_queue.put(
diff --git a/language/mixtral-8x7b/main.py b/language/mixtral-8x7b/main.py
index 3f2cd61b75..cd16e70323 100644
--- a/language/mixtral-8x7b/main.py
+++ b/language/mixtral-8x7b/main.py
@@ -17,21 +17,22 @@ def get_args():
     parser.add_argument(
         "--scenario",
         type=str,
-        choices=[
-            "Offline",
-            "Server"],
+        choices=["Offline", "Server"],
         default="Offline",
-        help="Scenario")
+        help="Scenario",
+    )
     parser.add_argument(
         "--model-path",
         type=str,
         default="mistralai/Mixtral-8x7B-Instruct-v0.1",
-        help="Model name")
+        help="Model name",
+    )
     parser.add_argument(
         "--dataset-path",
         type=str,
         default=None,
-        help="path to processed validation dataset")
+        help="path to processed validation dataset",
+    )
     parser.add_argument(
         "--accuracy",
         action="store_true",
@@ -40,51 +41,55 @@ def get_args():
         "--dtype",
         type=str,
         default="float32",
-        help="data type of the model, choose from float16, bfloat16 and float32")
+        help="data type of the model, choose from float16, bfloat16 and float32",
+    )
     parser.add_argument(
         "--device",
         type=str,
-        choices=[
-            "cpu",
-            "cuda:0"],
+        choices=["cpu", "cuda:0"],
         default="cpu",
-        help="device to use")
+        help="device to use",
+    )
     parser.add_argument(
         "--audit-conf",
         type=str,
         default="audit.conf",
-        help="audit config for LoadGen settings during compliance runs")
+        help="audit config for LoadGen settings during compliance runs",
+    )
     parser.add_argument(
         "--user-conf",
         type=str,
         default="user.conf",
-        help="user config for user LoadGen settings such as target QPS")
+        help="user config for user LoadGen settings such as target QPS",
+    )
     # TODO: This interpretation of 'total-sample-count' is a little
     # misleading. Fix it
     parser.add_argument(
         "--total-sample-count",
         type=int,
         default=24576,
-        help="Number of samples to use in benchmark.")
+        help="Number of samples to use in benchmark.",
+    )
     parser.add_argument(
         "--batch-size",
         type=int,
         default=1,
-        help="Model batch-size to use in benchmark.")
+        help="Model batch-size to use in benchmark.",
+    )
     parser.add_argument(
-        "--output-log-dir",
-        type=str,
-        default="output-logs",
-        help="Where logs are saved")
+        "--output-log-dir", type=str, default="output-logs", help="Where logs are saved"
+    )
     parser.add_argument(
         "--enable-log-trace",
         action="store_true",
-        help="Enable log tracing. This file can become quite large")
+        help="Enable log tracing. This file can become quite large",
+    )
     parser.add_argument(
         "--num-workers",
         type=int,
         default=1,
-        help="Number of workers to process queries")
+        help="Number of workers to process queries",
+    )
 
     args = parser.parse_args()
     return args
@@ -95,10 +100,7 @@ def get_args():
     "server": lg.TestScenario.Server,
 }
 
-sut_map = {
-    "offline": SUT,
-    "server": SUTServer
-}
+sut_map = {"offline": SUT, "server": SUTServer}
 
 
 def main():
diff --git a/loadgen/bindings/c_api.cc b/loadgen/bindings/c_api.cc
index 4101c7b96e..0248a1c163 100644
--- a/loadgen/bindings/c_api.cc
+++ b/loadgen/bindings/c_api.cc
@@ -155,15 +155,14 @@ void QuerySamplesCompleteResponseCb(QuerySampleResponse* responses,
       });
 }
 
-void FirstTokenComplete(QuerySampleResponse* responses,
-                          size_t response_count) {
+void FirstTokenComplete(QuerySampleResponse* responses, size_t response_count) {
   mlperf::FirstTokenComplete(responses, response_count);
 }
 
 void FirstTokenCompleteResponseCb(QuerySampleResponse* responses,
-                                    size_t response_count,
-                                    ResponseCallback response_cb,
-                                    ClientData client_data) {
+                                  size_t response_count,
+                                  ResponseCallback response_cb,
+                                  ClientData client_data) {
   mlperf::FirstTokenComplete(
       responses, response_count,
       [client_data, response_cb](QuerySampleResponse* response) {
diff --git a/loadgen/bindings/c_api.h b/loadgen/bindings/c_api.h
index 8c730e96a9..0ee44fb71d 100644
--- a/loadgen/bindings/c_api.h
+++ b/loadgen/bindings/c_api.h
@@ -49,13 +49,12 @@ void QuerySamplesCompleteResponseCb(QuerySampleResponse* responses,
                                     ResponseCallback response_cb,
                                     ClientData client_data);
 
-void FirstTokenComplete(QuerySampleResponse* responses,
-                          size_t response_count);
-                        
+void FirstTokenComplete(QuerySampleResponse* responses, size_t response_count);
+
 void FirstTokenCompleteResponseCb(QuerySampleResponse* responses,
-                                    size_t response_count,
-                                    ResponseCallback response_cb,
-                                    ClientData client_data);
+                                  size_t response_count,
+                                  ResponseCallback response_cb,
+                                  ClientData client_data);
 
 /// \brief Create an opaque SUT pointer based on C callbacks.
 void* ConstructSUT(ClientData client_data, const char* name, size_t name_length,
diff --git a/loadgen/demos/lon/py_demo_server_lon.py b/loadgen/demos/lon/py_demo_server_lon.py
index bee0b2f8b4..1248215db1 100644
--- a/loadgen/demos/lon/py_demo_server_lon.py
+++ b/loadgen/demos/lon/py_demo_server_lon.py
@@ -32,8 +32,9 @@
 
 FLAGS = flags.FLAGS
 
-flags.DEFINE_list('sut_server', 'http://localhost:8000',
-                    'Address of the server(s) under test.')
+flags.DEFINE_list(
+    "sut_server", "http://localhost:8000", "Address of the server(s) under test."
+)
 
 
 class QSL:
@@ -41,9 +42,14 @@ class QSL:
 
     def __init__(self, total_sample_count, performance_sample_count):
         self.eval_features = {
-            i: f"what_is_my_dummy_feature_{i}?" for i in range(total_sample_count)}
+            i: f"what_is_my_dummy_feature_{i}?" for i in range(total_sample_count)
+        }
         self.qsl = mlperf_loadgen.ConstructQSL(
-            total_sample_count, performance_sample_count, self.load_samples_to_ram, self.unload_samples_from_ram)
+            total_sample_count,
+            performance_sample_count,
+            self.load_samples_to_ram,
+            self.unload_samples_from_ram,
+        )
 
     def get_features(self, sample_id):
         """Returns the feature for a given sample id."""
@@ -81,10 +87,11 @@ def __init__(self, qsl: QSL, sut_server_addr: list):
             sut_server_addr: A list of addresses of the SUT.
         """
         self.qsl = qsl
- 
+
         # Construct QDL from the python binding
         self.qdl = mlperf_loadgen.ConstructQDL(
-            self.issue_query, self.flush_queries, self.client_get_name)
+            self.issue_query, self.flush_queries, self.client_get_name
+        )
         self.sut_server_addr = sut_server_addr
         self.num_nodes = len(sut_server_addr)
 
@@ -94,8 +101,9 @@ def __init__(self, qsl: QSL, sut_server_addr: list):
 
     def issue_query(self, query_samples):
         """Process the query to send to the SUT"""
-        threading.Thread(target=self.process_query_async,
-                         args=[query_samples]).start()
+        threading.Thread(
+            target=self.process_query_async,
+            args=[query_samples]).start()
 
     def flush_queries(self):
         """Flush the queries. Dummy implementation."""
@@ -120,15 +128,16 @@ def process_query_async(self, query_samples):
             # Read features from the QSL
             features = self.qsl.get_features(s.index)
 
-            time.sleep(.001)  # Ensure a maximal rate of queries to the SUT
+            time.sleep(0.001)  # Ensure a maximal rate of queries to the SUT
 
             # Send the query to SUT in round robin
             # Wait for a response
             sut_result = self.client_predict(features, s.index)
-            response_array = array.array('B', sut_result.encode('utf-8'))
+            response_array = array.array("B", sut_result.encode("utf-8"))
             bi = response_array.buffer_info()
-            responses.append(mlperf_loadgen.QuerySampleResponse(
-                s.id, bi[0], bi[1]))
+            responses.append(
+                mlperf_loadgen.QuerySampleResponse(
+                    s.id, bi[0], bi[1]))
         mlperf_loadgen.QuerySamplesComplete(responses)
 
     def get_sut_id_round_robin(self):
@@ -140,17 +149,22 @@ def get_sut_id_round_robin(self):
 
     def client_predict(self, query, id):
         """Serialize the query, send it to the SUT in round robin, and return the deserialized response."""
-        url = '{}/predict/'.format(self.sut_server_addr[self.get_sut_id_round_robin()])
-        response = requests.post(url, json={'query': query, id: id})
-        return response.json()['result']
+        url = "{}/predict/".format(
+            self.sut_server_addr[self.get_sut_id_round_robin()])
+        response = requests.post(url, json={"query": query, id: id})
+        return response.json()["result"]
 
     def client_get_name(self):
         """Get the name of the SUT from ALL the SUTS."""
         if len(self.sut_server_addr) == 1:
-            return requests.post(f'{self.sut_server_addr[0]}/getname/').json()['name']
-    
-        sut_names = [requests.post(f'{addr}/getname/').json()['name'] for addr in self.sut_server_addr]
-        return "Multi-node SUT: " + ', '.join(sut_names)
+            return requests.post(
+                f"{self.sut_server_addr[0]}/getname/").json()["name"]
+
+        sut_names = [
+            requests.post(f"{addr}/getname/").json()["name"]
+            for addr in self.sut_server_addr
+        ]
+        return "Multi-node SUT: " + ", ".join(sut_names)
 
     def __del__(self):
         mlperf_loadgen.DestroyQDL(self.qdl)
diff --git a/loadgen/demos/lon/sut_over_network_demo.py b/loadgen/demos/lon/sut_over_network_demo.py
index 9922fc7650..55e5e038d5 100644
--- a/loadgen/demos/lon/sut_over_network_demo.py
+++ b/loadgen/demos/lon/sut_over_network_demo.py
@@ -24,7 +24,7 @@
 - /predict/ : Receives a query (e.g., a text) runs inference, and returns a prediction.
 - /getname/ : Get the name of the SUT.
 
-The current implementation is a dummy implementation, which does not use 
+The current implementation is a dummy implementation, which does not use
 a real DNN model, batching, or pre/postprocessing code,
 but rather just returns subset of the input query as a response,
 Yet, it illustrates the basic structure of a SUT server.
@@ -38,45 +38,51 @@
 
 
 node = ""
+
+
 def preprocess(query):
     """[SUT Node] A dummy preprocess."""
-    # Here may come for example batching, tokenization, resizing, normalization, etc.
+    # Here may come for example batching, tokenization, resizing,
+    # normalization, etc.
     response = query
     return response
 
 
 def dnn_model(query):
     """[SUT Node] A dummy DNN model."""
-    # Here may come for example a call to a dnn model such as resnet, bert, etc.
+    # Here may come for example a call to a dnn model such as resnet, bert,
+    # etc.
     response = query
     return response
 
 
 def postprocess(query):
     """[SUT Node] A dummy postprocess."""
-    # Here may come for example a postprocessing call, e.g., NMS, detokenization, etc.
+    # Here may come for example a postprocessing call, e.g., NMS,
+    # detokenization, etc.
     response = query
     return response
 
 
-@app.route('/predict/', methods=['POST'])
+@app.route("/predict/", methods=["POST"])
 def predict():
     """Receives a query (e.g., a text) runs inference, and returns a prediction."""
-    query = request.get_json(force=True)['query']
+    query = request.get_json(force=True)["query"]
     result = postprocess(dnn_model(preprocess(query)))
     return jsonify(result=result)
 
 
-@app.route('/getname/', methods=['POST', 'GET'])
+@app.route("/getname/", methods=["POST", "GET"])
 def getname():
     """Returns the name of the SUT."""
-    return jsonify(name=f'Demo SUT (Network SUT) node' + (' ' + node) if node else '')
+    return jsonify(name=f"Demo SUT (Network SUT) node" +
+                   (" " + node) if node else "")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--port', type=int, default=8000)
-    parser.add_argument('--node', type=str, default="")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument("--node", type=str, default="")
     args = parser.parse_args()
     node = args.node
     app.run(debug=False, port=args.port)
diff --git a/loadgen/demos/py_demo_multi_stream.py b/loadgen/demos/py_demo_multi_stream.py
index cd0addd4a8..f6082cad6b 100644
--- a/loadgen/demos/py_demo_multi_stream.py
+++ b/loadgen/demos/py_demo_multi_stream.py
@@ -28,7 +28,8 @@
 
 # Global var
 NUM_AGENTS = 8
-LOOPBACK_LATENCY_S = .001
+LOOPBACK_LATENCY_S = 0.001
+
 
 def load_samples_to_ram(query_samples):
     del query_samples
@@ -44,7 +45,8 @@ def unload_samples_from_ram(query_samples):
 def process_query_async(query_samples, i_slice):
     time.sleep(LOOPBACK_LATENCY_S * (i_slice + 1))
     responses = []
-    samples_to_complete = query_samples[i_slice:len(query_samples):NUM_AGENTS]
+    samples_to_complete = query_samples[i_slice: len(
+        query_samples): NUM_AGENTS]
     for j, s in enumerate(samples_to_complete):
         responses.append(mlperf_loadgen.QuerySampleResponse(s.id, 0, 0))
     mlperf_loadgen.QuerySamplesComplete(responses)
@@ -52,8 +54,9 @@ def process_query_async(query_samples, i_slice):
 
 def issue_query(query_samples):
     for i in range(8):
-        threading.Thread(target=process_query_async,
-                         args=(query_samples, i)).start()
+        threading.Thread(
+            target=process_query_async, args=(
+                query_samples, i)).start()
 
 
 def flush_queries():
@@ -72,7 +75,8 @@ def main(argv):
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
diff --git a/loadgen/demos/py_demo_offline.py b/loadgen/demos/py_demo_offline.py
index 46c6062190..909585edc3 100644
--- a/loadgen/demos/py_demo_offline.py
+++ b/loadgen/demos/py_demo_offline.py
@@ -39,19 +39,22 @@ def unload_samples_from_ram(query_samples):
 def process_query_async(query_samples, i_slice):
     time.sleep(3 * (i_slice + 1))
     responses = []
-    samples_to_complete = query_samples[i_slice:len(query_samples):3]
+    samples_to_complete = query_samples[i_slice: len(query_samples): 3]
     for s in samples_to_complete:
         responses.append(mlperf_loadgen.QuerySampleResponse(s.id, 0, 0))
     mlperf_loadgen.QuerySamplesComplete(responses)
 
 
 def issue_query(query_samples):
-    threading.Thread(target=process_query_async,
-                     args=(query_samples, 0)).start()
-    threading.Thread(target=process_query_async,
-                     args=(query_samples, 1)).start()
-    threading.Thread(target=process_query_async,
-                     args=(query_samples, 2)).start()
+    threading.Thread(
+        target=process_query_async, args=(
+            query_samples, 0)).start()
+    threading.Thread(
+        target=process_query_async, args=(
+            query_samples, 1)).start()
+    threading.Thread(
+        target=process_query_async, args=(
+            query_samples, 2)).start()
 
 
 def flush_queries():
@@ -67,7 +70,8 @@ def main(argv):
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
diff --git a/loadgen/demos/py_demo_server.py b/loadgen/demos/py_demo_server.py
index 3806a1fd0d..8b6f2b826b 100644
--- a/loadgen/demos/py_demo_server.py
+++ b/loadgen/demos/py_demo_server.py
@@ -36,7 +36,7 @@ def unload_samples_from_ram(query_samples):
 
 
 def process_query_async(query_samples):
-    time.sleep(.001)
+    time.sleep(0.001)
     responses = []
     for s in query_samples:
         responses.append(mlperf_loadgen.QuerySampleResponse(s.id, 0, 0))
@@ -44,8 +44,7 @@ def process_query_async(query_samples):
 
 
 def issue_query(query_samples):
-    threading.Thread(target=process_query_async,
-                     args=[query_samples]).start()
+    threading.Thread(target=process_query_async, args=[query_samples]).start()
 
 
 def flush_queries():
@@ -64,7 +63,8 @@ def main(argv):
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
diff --git a/loadgen/demos/py_demo_single_stream.py b/loadgen/demos/py_demo_single_stream.py
index a8ab45eb2d..8806271bd8 100644
--- a/loadgen/demos/py_demo_single_stream.py
+++ b/loadgen/demos/py_demo_single_stream.py
@@ -38,23 +38,24 @@ def unload_samples_from_ram(query_samples):
 
 def process_query_async(query_samples):
     """Processes the list of queries."""
-    time.sleep(.001)
+    time.sleep(0.001)
     responses = []
     response_array = array.array(
-        "f", [0, 1, 7, 8, 15, 16, 31, 32, 63, 64, 127, 128, 254, 255])
+        "f", [0, 1, 7, 8, 15, 16, 31, 32, 63, 64, 127, 128, 254, 255]
+    )
     response_info = response_array.buffer_info()
     response_data = response_info[0]
     response_size = response_info[1] * response_array.itemsize
     for s in query_samples:
         responses.append(
             mlperf_loadgen.QuerySampleResponse(
-                s.id, response_data, response_size))
+                s.id, response_data, response_size)
+        )
     mlperf_loadgen.QuerySamplesComplete(responses)
 
 
 def issue_query(query_samples):
-    threading.Thread(target=process_query_async,
-                     args=[query_samples]).start()
+    threading.Thread(target=process_query_async, args=[query_samples]).start()
 
 
 def flush_queries():
@@ -72,7 +73,8 @@ def main(argv):
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
diff --git a/loadgen/demos/token_metrics/py_demo_multi_stream.py b/loadgen/demos/token_metrics/py_demo_multi_stream.py
index a6350e53db..e4b083853c 100644
--- a/loadgen/demos/token_metrics/py_demo_multi_stream.py
+++ b/loadgen/demos/token_metrics/py_demo_multi_stream.py
@@ -30,18 +30,23 @@
 
 # Global var
 NUM_AGENTS = 8
-LOOPBACK_LATENCY_S = .001
+LOOPBACK_LATENCY_S = 0.001
+
 
 def f(x, y):
-    return (4 + 3*x*y + x**3 + y**2)
+    return 4 + 3 * x * y + x**3 + y**2
+
 
-def create_responses(n, m, mod = 4):
+def create_responses(n, m, mod=4):
     r = []
     for i in range(n):
-        r.append([f(i,j) for j in range(m + (i%mod))])
+        r.append([f(i, j) for j in range(m + (i % mod))])
     return r
+
+
 responses = create_responses(1024, 20)
 
+
 def load_samples_to_ram(query_samples):
     del query_samples
     return
@@ -56,17 +61,24 @@ def unload_samples_from_ram(query_samples):
 def process_query_async(query_samples, i_slice):
     time.sleep(LOOPBACK_LATENCY_S * (i_slice + 1))
     query_responses = []
-    samples_to_complete = query_samples[i_slice:len(query_samples):NUM_AGENTS]
+    samples_to_complete = query_samples[i_slice: len(
+        query_samples): NUM_AGENTS]
     for j, s in enumerate(samples_to_complete):
         response_array = np.array(responses[s.index], np.int32)
         token = response_array[0]
-        time.sleep(.0002)
+        time.sleep(0.0002)
         response_token = array.array("B", token.tobytes())
         response_token_info = response_token.buffer_info()
         response_token_data = response_token_info[0]
         response_token_size = response_token_info[1] * response_token.itemsize
-        mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
-        time.sleep(.02)
+        mlperf_loadgen.FirstTokenComplete(
+            [
+                mlperf_loadgen.QuerySampleResponse(
+                    s.id, response_token_data, response_token_size
+                )
+            ]
+        )
+        time.sleep(0.02)
         n_tokens = len(response_array)
         response_array = array.array("B", response_array.tobytes())
         response_info = response_array.buffer_info()
@@ -74,14 +86,17 @@ def process_query_async(query_samples, i_slice):
         response_size = response_info[1] * response_array.itemsize
         query_responses.append(
             mlperf_loadgen.QuerySampleResponse(
-                s.id, response_data, response_size, n_tokens))
+                s.id, response_data, response_size, n_tokens
+            )
+        )
     mlperf_loadgen.QuerySamplesComplete(query_responses)
 
 
 def issue_query(query_samples):
     for i in range(8):
-        threading.Thread(target=process_query_async,
-                         args=(query_samples, i)).start()
+        threading.Thread(
+            target=process_query_async, args=(
+                query_samples, i)).start()
 
 
 def flush_queries():
@@ -90,7 +105,9 @@ def flush_queries():
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument(
+        "--mode", choices=["performance", "accuracy"], default="performance"
+    )
     parser.add_argument("--expected-latency", type=int, default=8000000)
     parser.add_argument("--samples-per-query", type=int, default=8)
     parser.add_argument("--min-query-count", type=int, default=100)
@@ -114,7 +131,8 @@ def main():
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
diff --git a/loadgen/demos/token_metrics/py_demo_offline.py b/loadgen/demos/token_metrics/py_demo_offline.py
index d0bbd2c495..2e190cdd51 100644
--- a/loadgen/demos/token_metrics/py_demo_offline.py
+++ b/loadgen/demos/token_metrics/py_demo_offline.py
@@ -28,15 +28,19 @@
 
 
 def f(x, y):
-    return (4 + 3*x*y + x**3 + y**2)
+    return 4 + 3 * x * y + x**3 + y**2
 
-def create_responses(n, m, mod = 4):
+
+def create_responses(n, m, mod=4):
     r = []
     for i in range(n):
-        r.append([f(i,j) for j in range(m + (i%mod))])
+        r.append([f(i, j) for j in range(m + (i % mod))])
     return r
+
+
 responses = create_responses(1024, 20)
 
+
 def load_samples_to_ram(query_samples):
     del query_samples
     return
@@ -51,17 +55,17 @@ def unload_samples_from_ram(query_samples):
 def process_query_async(query_samples, i_slice):
     time.sleep(3 * (i_slice + 1))
     query_responses = []
-    samples_to_complete = query_samples[i_slice:len(query_samples):3]
+    samples_to_complete = query_samples[i_slice: len(query_samples): 3]
     for s in samples_to_complete:
         response_array = np.array(responses[s.index], np.int32)
         token = response_array[0]
-        time.sleep(.0002)
+        time.sleep(0.0002)
         response_token = array.array("B", token.tobytes())
         response_token_info = response_token.buffer_info()
         response_token_data = response_token_info[0]
         response_token_size = response_token_info[1] * response_token.itemsize
         # mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
-        time.sleep(.02)
+        time.sleep(0.02)
         n_tokens = len(response_array)
         response_array = array.array("B", response_array.tobytes())
         response_info = response_array.buffer_info()
@@ -69,17 +73,22 @@ def process_query_async(query_samples, i_slice):
         response_size = response_info[1] * response_array.itemsize
         query_responses.append(
             mlperf_loadgen.QuerySampleResponse(
-                s.id, response_data, response_size, n_tokens))
+                s.id, response_data, response_size, n_tokens
+            )
+        )
     mlperf_loadgen.QuerySamplesComplete(query_responses)
 
 
 def issue_query(query_samples):
-    threading.Thread(target=process_query_async,
-                     args=(query_samples, 0)).start()
-    threading.Thread(target=process_query_async,
-                     args=(query_samples, 1)).start()
-    threading.Thread(target=process_query_async,
-                     args=(query_samples, 2)).start()
+    threading.Thread(
+        target=process_query_async, args=(
+            query_samples, 0)).start()
+    threading.Thread(
+        target=process_query_async, args=(
+            query_samples, 1)).start()
+    threading.Thread(
+        target=process_query_async, args=(
+            query_samples, 2)).start()
 
 
 def flush_queries():
@@ -88,7 +97,9 @@ def flush_queries():
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument(
+        "--mode", choices=["performance", "accuracy"], default="performance"
+    )
     parser.add_argument("--expected-qps", type=int, default=1000)
     parser.add_argument("--min-duration-ms", type=int, default=30000)
     return parser.parse_args()
@@ -108,7 +119,8 @@ def main():
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
diff --git a/loadgen/demos/token_metrics/py_demo_offline_inferred.py b/loadgen/demos/token_metrics/py_demo_offline_inferred.py
index 79390e1b84..9325b84105 100644
--- a/loadgen/demos/token_metrics/py_demo_offline_inferred.py
+++ b/loadgen/demos/token_metrics/py_demo_offline_inferred.py
@@ -28,14 +28,18 @@
 
 
 def f(x, y):
-    return (4 + 3*x*y + x**3 + y**2)
+    return 4 + 3 * x * y + x**3 + y**2
 
-def create_responses(n, m, mod = 4):
+
+def create_responses(n, m, mod=4):
     r = []
     for i in range(n):
-        r.append([f(i,j) for j in range(m + (i%mod))])
+        r.append([f(i, j) for j in range(m + (i % mod))])
     return r
-responses = create_responses(1024, 20, mod = 3)
+
+
+responses = create_responses(1024, 20, mod=3)
+
 
 def load_samples_to_ram(query_samples):
     del query_samples
@@ -51,17 +55,17 @@ def unload_samples_from_ram(query_samples):
 def process_query_async(query_samples, i_slice):
     time.sleep(3 * (i_slice + 1))
     query_responses = []
-    samples_to_complete = query_samples[i_slice:len(query_samples):3]
+    samples_to_complete = query_samples[i_slice: len(query_samples): 3]
     for s in samples_to_complete:
         response_array = np.array(responses[s.index], np.int32)
         token = response_array[0]
-        time.sleep(.0002)
+        time.sleep(0.0002)
         response_token = array.array("B", token.tobytes())
         response_token_info = response_token.buffer_info()
         response_token_data = response_token_info[0]
         response_token_size = response_token_info[1] * response_token.itemsize
         # mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
-        time.sleep(.02)
+        time.sleep(0.02)
         n_tokens = len(response_array)
         response_array = array.array("B", response_array.tobytes())
         response_info = response_array.buffer_info()
@@ -69,17 +73,21 @@ def process_query_async(query_samples, i_slice):
         response_size = response_info[1] * response_array.itemsize
         query_responses.append(
             mlperf_loadgen.QuerySampleResponse(
-                s.id, response_data, response_size))
+                s.id, response_data, response_size)
+        )
     mlperf_loadgen.QuerySamplesComplete(query_responses)
 
 
 def issue_query(query_samples):
-    threading.Thread(target=process_query_async,
-                     args=(query_samples, 0)).start()
-    threading.Thread(target=process_query_async,
-                     args=(query_samples, 1)).start()
-    threading.Thread(target=process_query_async,
-                     args=(query_samples, 2)).start()
+    threading.Thread(
+        target=process_query_async, args=(
+            query_samples, 0)).start()
+    threading.Thread(
+        target=process_query_async, args=(
+            query_samples, 1)).start()
+    threading.Thread(
+        target=process_query_async, args=(
+            query_samples, 2)).start()
 
 
 def flush_queries():
@@ -88,7 +96,9 @@ def flush_queries():
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument(
+        "--mode", choices=["performance", "accuracy"], default="performance"
+    )
     parser.add_argument("--expected-qps", type=int, default=1000)
     parser.add_argument("--min-duration-ms", type=int, default=30000)
     return parser.parse_args()
@@ -109,7 +119,8 @@ def main():
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
diff --git a/loadgen/demos/token_metrics/py_demo_server.py b/loadgen/demos/token_metrics/py_demo_server.py
index 8879863e08..b564543cd8 100644
--- a/loadgen/demos/token_metrics/py_demo_server.py
+++ b/loadgen/demos/token_metrics/py_demo_server.py
@@ -27,16 +27,21 @@
 from absl import app
 import mlperf_loadgen
 
+
 def f(x, y):
-    return (4 + 3*x*y + x**3 + y**2)
+    return 4 + 3 * x * y + x**3 + y**2
+
 
-def create_responses(n, m, mod = 4):
+def create_responses(n, m, mod=4):
     r = []
     for i in range(n):
-        r.append([f(i,j) for j in range(m + (i%mod))])
+        r.append([f(i, j) for j in range(m + (i % mod))])
     return r
+
+
 responses = create_responses(1024, 20)
 
+
 def load_samples_to_ram(query_samples):
     del query_samples
     return
@@ -53,13 +58,19 @@ def process_query_async(query_samples):
     for s in query_samples:
         response_array = np.array(responses[s.index], np.int32)
         token = response_array[0]
-        time.sleep(.0002)
+        time.sleep(0.0002)
         response_token = array.array("B", token.tobytes())
         response_token_info = response_token.buffer_info()
         response_token_data = response_token_info[0]
         response_token_size = response_token_info[1] * response_token.itemsize
-        mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
-        time.sleep(.02)
+        mlperf_loadgen.FirstTokenComplete(
+            [
+                mlperf_loadgen.QuerySampleResponse(
+                    s.id, response_token_data, response_token_size
+                )
+            ]
+        )
+        time.sleep(0.02)
         n_tokens = len(response_array)
         response_array = array.array("B", response_array.tobytes())
         response_info = response_array.buffer_info()
@@ -68,27 +79,32 @@ def process_query_async(query_samples):
         # print(f"Reported size python: {n_tokens}")
         query_responses.append(
             mlperf_loadgen.QuerySampleResponse(
-                s.id, response_data, response_size, n_tokens))
+                s.id, response_data, response_size, n_tokens
+            )
+        )
     mlperf_loadgen.QuerySamplesComplete(query_responses)
 
 
 def issue_query(query_samples):
-    threading.Thread(target=process_query_async,
-                     args=[query_samples]).start()
+    threading.Thread(target=process_query_async, args=[query_samples]).start()
 
 
 def flush_queries():
     pass
 
+
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument(
+        "--mode", choices=["performance", "accuracy"], default="performance"
+    )
     parser.add_argument("--target-qps", type=int, default=100)
     parser.add_argument("--target-latency-ns", type=int, default=100000000)
     parser.add_argument("--min-query-count", type=int, default=100)
     parser.add_argument("--min-duration-ms", type=int, default=30000)
     return parser.parse_args()
 
+
 def main():
     args = get_args()
     settings = mlperf_loadgen.TestSettings()
@@ -105,7 +121,8 @@ def main():
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
@@ -113,4 +130,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-
diff --git a/loadgen/demos/token_metrics/py_demo_server_inferred.py b/loadgen/demos/token_metrics/py_demo_server_inferred.py
index b4431ec9cc..76461a75d6 100644
--- a/loadgen/demos/token_metrics/py_demo_server_inferred.py
+++ b/loadgen/demos/token_metrics/py_demo_server_inferred.py
@@ -27,15 +27,20 @@
 from absl import app
 import mlperf_loadgen
 
+
 def f(x, y):
-    return (4 + 3*x*y + x**3 + y**2)
+    return 4 + 3 * x * y + x**3 + y**2
+
 
-def create_responses(n, m, mod = 4):
+def create_responses(n, m, mod=4):
     r = []
     for i in range(n):
-        r.append([f(i,j) for j in range(m + (i%mod))])
+        r.append([f(i, j) for j in range(m + (i % mod))])
     return r
-responses = create_responses(1024, 20, mod = 3)
+
+
+responses = create_responses(1024, 20, mod=3)
+
 
 def load_samples_to_ram(query_samples):
     del query_samples
@@ -53,12 +58,12 @@ def process_query_async(query_samples):
     for s in query_samples:
         response_array = np.array(responses[s.index], np.int32)
         token = response_array[0]
-        time.sleep(.0002)
+        time.sleep(0.0002)
         response_token = array.array("B", token.tobytes())
         response_token_info = response_token.buffer_info()
         response_token_data = response_token_info[0]
         response_token_size = response_token_info[1] * response_token.itemsize
-        time.sleep(.02)
+        time.sleep(0.02)
         n_tokens = len(response_array)
         response_array = array.array("B", response_array.tobytes())
         response_info = response_array.buffer_info()
@@ -67,27 +72,31 @@ def process_query_async(query_samples):
         # print(f"Reported size python: {n_tokens}")
         query_responses.append(
             mlperf_loadgen.QuerySampleResponse(
-                s.id, response_data, response_size))
+                s.id, response_data, response_size)
+        )
     mlperf_loadgen.QuerySamplesComplete(query_responses)
 
 
 def issue_query(query_samples):
-    threading.Thread(target=process_query_async,
-                     args=[query_samples]).start()
+    threading.Thread(target=process_query_async, args=[query_samples]).start()
 
 
 def flush_queries():
     pass
 
+
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument(
+        "--mode", choices=["performance", "accuracy"], default="performance"
+    )
     parser.add_argument("--target-qps", type=int, default=100)
     parser.add_argument("--target-latency-ns", type=int, default=100000000)
     parser.add_argument("--min-query-count", type=int, default=100)
     parser.add_argument("--min-duration-ms", type=int, default=30000)
     return parser.parse_args()
 
+
 def main():
     args = get_args()
     settings = mlperf_loadgen.TestSettings()
@@ -105,7 +114,8 @@ def main():
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
@@ -113,4 +123,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-
diff --git a/loadgen/demos/token_metrics/py_demo_single_stream.py b/loadgen/demos/token_metrics/py_demo_single_stream.py
index e4aa4226fe..ca8d84591f 100644
--- a/loadgen/demos/token_metrics/py_demo_single_stream.py
+++ b/loadgen/demos/token_metrics/py_demo_single_stream.py
@@ -27,16 +27,21 @@
 from absl import app
 import mlperf_loadgen
 
+
 def f(x, y):
-    return (4 + 3*x*y + x**3 + y**2)
+    return 4 + 3 * x * y + x**3 + y**2
+
 
-def create_responses(n, m, mod = 4):
+def create_responses(n, m, mod=4):
     r = []
     for i in range(n):
-        r.append([f(i,j) for j in range(m + (i%mod))])
+        r.append([f(i, j) for j in range(m + (i % mod))])
     return r
+
+
 responses = create_responses(1024, 20)
 
+
 def load_samples_to_ram(query_samples):
     del query_samples
     return
@@ -52,14 +57,20 @@ def process_query_async(query_samples):
     query_responses = []
     for s in query_samples:
         response_array = np.array(responses[s.index], np.int32)
-        time.sleep(.0002)
+        time.sleep(0.0002)
         token = response_array[:1]
         response_token = array.array("B", token.tobytes())
         response_token_info = response_token.buffer_info()
         response_token_data = response_token_info[0]
         response_token_size = response_token_info[1] * response_token.itemsize
-        mlperf_loadgen.FirstTokenComplete([mlperf_loadgen.QuerySampleResponse(s.id, response_token_data, response_token_size)])
-        time.sleep(.02)
+        mlperf_loadgen.FirstTokenComplete(
+            [
+                mlperf_loadgen.QuerySampleResponse(
+                    s.id, response_token_data, response_token_size
+                )
+            ]
+        )
+        time.sleep(0.02)
         n_tokens = len(response_array)
         response_array = array.array("B", response_array.tobytes())
         response_info = response_array.buffer_info()
@@ -67,21 +78,25 @@ def process_query_async(query_samples):
         response_size = response_info[1] * response_array.itemsize
         query_responses.append(
             mlperf_loadgen.QuerySampleResponse(
-                s.id, response_data, response_size, n_tokens))
+                s.id, response_data, response_size, n_tokens
+            )
+        )
     mlperf_loadgen.QuerySamplesComplete(query_responses)
 
 
 def issue_query(query_samples):
-    threading.Thread(target=process_query_async,
-                     args=[query_samples]).start()
+    threading.Thread(target=process_query_async, args=[query_samples]).start()
 
 
 def flush_queries():
     pass
 
+
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mode", choices=["performance", "accuracy"], default="performance")
+    parser.add_argument(
+        "--mode", choices=["performance", "accuracy"], default="performance"
+    )
     parser.add_argument("--expected-latency", type=int, default=2050000)
     parser.add_argument("--min-query-count", type=int, default=100)
     parser.add_argument("--min-duration-ms", type=int, default=30000)
@@ -103,7 +118,8 @@ def main():
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024, 128, load_samples_to_ram, unload_samples_from_ram)
+        1024, 128, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
diff --git a/loadgen/docs/src/doxygen_html_generator.py b/loadgen/docs/src/doxygen_html_generator.py
index 405ac1e55a..4065d7bd0b 100644
--- a/loadgen/docs/src/doxygen_html_generator.py
+++ b/loadgen/docs/src/doxygen_html_generator.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # =============================================================================
 
-## \file
+# \file
 #  \brief A script that sets the environment variables expected by doxygen.cfg.
 #  \details This can be run manually without any arguments, but also allows a
 #  build system to customize the output directory.
diff --git a/loadgen/issue_query_controller.cc b/loadgen/issue_query_controller.cc
index 8e1e80f1ae..c1abea9d14 100644
--- a/loadgen/issue_query_controller.cc
+++ b/loadgen/issue_query_controller.cc
@@ -323,10 +323,10 @@ void IssueQueryController::StartIssueQueries(IssueQueryState* s) {
 
 template void IssueQueryController::StartIssueQueries<
     TestScenario::MultiStream>(IssueQueryState* s);
-template void IssueQueryController::StartIssueQueries<
-    TestScenario::Offline>(IssueQueryState* s);
-template void IssueQueryController::StartIssueQueries<
-    TestScenario::Server>(IssueQueryState* s);
+template void IssueQueryController::StartIssueQueries<TestScenario::Offline>(
+    IssueQueryState* s);
+template void IssueQueryController::StartIssueQueries<TestScenario::Server>(
+    IssueQueryState* s);
 template void IssueQueryController::StartIssueQueries<
     TestScenario::SingleStream>(IssueQueryState* s);
 
@@ -459,8 +459,8 @@ void IssueQueryController::IssueQueriesInternal(size_t query_stride,
 #if USE_NEW_LOGGING_FORMAT
             std::stringstream ss;
             ss << "IssueQueryThread " << thread_idx
-               << " Ending early: Too many outstanding queries."
-               << " issued " << queries_issued_total << " outstanding "
+               << " Ending early: Too many outstanding queries." << " issued "
+               << queries_issued_total << " outstanding "
                << queries_outstanding;
             MLPERF_LOG_ERROR(detail, "error_runtime", ss.str());
 #else
@@ -499,8 +499,8 @@ void IssueQueryController::IssueQueriesInternal(size_t query_stride,
 #if USE_NEW_LOGGING_FORMAT
         std::stringstream ss;
         ss << "IssueQueryThread " << thread_idx
-           << " Ending early: Max query count reached."
-           << " query_count " << queries_issued;
+           << " Ending early: Max query count reached." << " query_count "
+           << queries_issued;
         MLPERF_LOG_ERROR(detail, "error_runtime", ss.str());
 #else
         detail.Error("IssueQueryThread ", std::to_string(thread_idx),
@@ -519,8 +519,8 @@ void IssueQueryController::IssueQueriesInternal(size_t query_stride,
 #if USE_NEW_LOGGING_FORMAT
         std::stringstream ss;
         ss << "IssueQueryThread " << thread_idx
-           << " Ending early: Max test duration reached."
-           << " duration_ns " << duration.count();
+           << " Ending early: Max test duration reached." << " duration_ns "
+           << duration.count();
         MLPERF_LOG_ERROR(detail, "error_runtime", ss.str());
 #else
         detail.Error("IssueQueryThread ", std::to_string(thread_idx),
diff --git a/loadgen/issue_query_controller.h b/loadgen/issue_query_controller.h
index e723234cd7..5668c574ed 100644
--- a/loadgen/issue_query_controller.h
+++ b/loadgen/issue_query_controller.h
@@ -71,8 +71,8 @@ struct ResponseDelegate {
                               PerfClock::time_point,
                               const ResponseCallback&) = 0;
   virtual void TokenComplete(SampleMetadata*, QuerySampleResponse*,
-                              PerfClock::time_point,
-                              const ResponseCallback&) = 0;
+                             PerfClock::time_point,
+                             const ResponseCallback&) = 0;
   virtual void QueryComplete() = 0;
   std::atomic<size_t> queries_completed{0};
 };
diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index 76e59151b2..beda3a6c40 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -36,12 +36,12 @@ limitations under the License.
 #include "logging.h"
 #include "query_sample.h"
 #include "query_sample_library.h"
+#include "results.h"
 #include "system_under_test.h"
 #include "test_settings.h"
 #include "test_settings_internal.h"
 #include "utils.h"
 #include "version.h"
-#include "results.h"
 
 namespace mlperf {
 
@@ -97,7 +97,8 @@ struct ResponseDelegateDetailed : public ResponseDelegate {
       sample_data_copy = new std::vector<uint8_t>(src_begin, src_end);
     }
     int64_t n_tokens = response->n_tokens;
-    Log([sample, complete_begin_time, sample_data_copy, n_tokens](AsyncLog& log) {
+    Log([sample, complete_begin_time, sample_data_copy,
+         n_tokens](AsyncLog& log) {
       QueryMetadata* query = sample->query_metadata;
       DurationGeneratorNs sched{query->scheduled_time};
       if (scenario == TestScenario::Server) {
@@ -132,9 +133,9 @@ struct ResponseDelegateDetailed : public ResponseDelegate {
     });
   }
 
-    void TokenComplete(SampleMetadata* sample, QuerySampleResponse* response,
-                      PerfClock::time_point complete_begin_time,
-                      const ResponseCallback& response_cb) override {
+  void TokenComplete(SampleMetadata* sample, QuerySampleResponse* response,
+                     PerfClock::time_point complete_begin_time,
+                     const ResponseCallback& response_cb) override {
     // Using a raw pointer here should help us hit the std::function
     // small buffer optimization code path when we aren't copying data.
     // For some reason, using std::unique_ptr<std::vector> wasn't moving
@@ -155,24 +156,25 @@ struct ResponseDelegateDetailed : public ResponseDelegate {
       DurationGeneratorNs sched{query->scheduled_time};
       if (scenario == TestScenario::Server) {
         DurationGeneratorNs issued{query->issued_start_time};
-        log.TraceCounterEvent("Token_Latency", query->scheduled_time, "issue_delay",
-                              sched.delta(query->issued_start_time),
-                              "issue_to_done",
-                              issued.delta(complete_begin_time));
-      }else{
+        log.TraceCounterEvent(
+            "Token_Latency", query->scheduled_time, "issue_delay",
+            sched.delta(query->issued_start_time), "issue_to_done",
+            issued.delta(complete_begin_time));
+      } else {
         log.TraceSample("Token", sample->sequence_id, query->scheduled_time,
-                      complete_begin_time, "sample_seq", sample->sequence_id,
-                      "query_seq", query->sequence_id, "sample_idx",
-                      sample->sample_index, "issue_start_ns",
-                      sched.delta(query->issued_start_time), "complete_ns",
-                      sched.delta(complete_begin_time));
+                        complete_begin_time, "sample_seq", sample->sequence_id,
+                        "query_seq", query->sequence_id, "sample_idx",
+                        sample->sample_index, "issue_start_ns",
+                        sched.delta(query->issued_start_time), "complete_ns",
+                        sched.delta(complete_begin_time));
       }
       if (token_data_copy) {
-        log.CacheToken(sample->sequence_id, LogBinaryAsHexString{token_data_copy});
+        log.CacheToken(sample->sequence_id,
+                       LogBinaryAsHexString{token_data_copy});
       }
       QuerySampleLatency latency = sched.delta(complete_begin_time);
       log.RecordTokenCompletion(sample->sequence_id, complete_begin_time,
-                                 latency);
+                                latency);
     });
   }
 
@@ -227,7 +229,8 @@ auto SampleDistribution<TestMode::PerformanceOnly>(size_t sample_count,
              auto& gen) mutable { return dist(gen); };
 }
 
-/// \brief Sample across the dataset, and ensure coverage of each of the samples.
+/// \brief Sample across the dataset, and ensure coverage of each of the
+/// samples.
 // Useful for non-uniform dataset (e.g. Llama2, GPTJ, 3d-unet)
 auto SampleDistributionEqualIssue(size_t sample_count, size_t set_size,
                                   std::mt19937* rng) {
@@ -304,10 +307,8 @@ std::vector<QueryMetadata> GenerateQueries(
   auto sample_distribution_unique = SampleDistribution<TestMode::AccuracyOnly>(
       loaded_sample_set.sample_distribution_end, sample_stride, &sample_rng);
 
-  auto sample_distribution_equal_issue =
-      SampleDistributionEqualIssue(min_queries,
-                                   loaded_samples.size(),
-                                   &sample_rng);
+  auto sample_distribution_equal_issue = SampleDistributionEqualIssue(
+      min_queries, loaded_samples.size(), &sample_rng);
 
   auto schedule_distribution =
       ScheduleDistribution<scenario>(settings.target_qps);
@@ -315,20 +316,18 @@ std::vector<QueryMetadata> GenerateQueries(
   // When sample_concatenate_permutation is turned on, pad to a multiple of the
   // complete dataset to ensure fairness.
   auto enable_equal_issue = settings.sample_concatenate_permutation;
-  if (mode != TestMode::AccuracyOnly && enable_equal_issue)
-  {
+  if (mode != TestMode::AccuracyOnly && enable_equal_issue) {
     if (scenario == TestScenario::Offline &&
-      samples_per_query % loaded_samples.size() != 0)
-    {
+        samples_per_query % loaded_samples.size() != 0) {
       // In offline mode, we pad samples_per_query
       size_t pad_size =
-        (loaded_samples.size() - samples_per_query % loaded_samples.size());
+          (loaded_samples.size() - samples_per_query % loaded_samples.size());
       samples_per_query += pad_size;
-    }
-    else if (min_queries % loaded_samples.size() != 0)
-    {
-      // In Server, SingleStream, MultiStream mode, the min_queries should be padded
-      size_t pad_size = (loaded_samples.size() - min_queries % loaded_samples.size());
+    } else if (min_queries % loaded_samples.size() != 0) {
+      // In Server, SingleStream, MultiStream mode, the min_queries should be
+      // padded
+      size_t pad_size =
+          (loaded_samples.size() - min_queries % loaded_samples.size());
       min_queries += pad_size;
     }
   }
@@ -388,10 +387,9 @@ std::vector<QueryMetadata> GenerateQueries(
     } else {
       for (auto& s : samples) {
         s = loaded_samples[settings.performance_issue_unique
-                           ? sample_distribution_unique(sample_rng)
-                           : settings.performance_issue_same
-                             ? same_sample
-                             : enable_equal_issue
+                               ? sample_distribution_unique(sample_rng)
+                           : settings.performance_issue_same ? same_sample
+                           : enable_equal_issue
                                ? sample_distribution_equal_issue(sample_rng)
                                : sample_distribution(sample_rng)];
       }
@@ -399,11 +397,11 @@ std::vector<QueryMetadata> GenerateQueries(
     queries.emplace_back(samples, timestamp, response_delegate, sequence_gen);
     prev_timestamp = timestamp;
     timestamp += schedule_distribution(schedule_rng);
-    // In equal_issue mode, the min_queries will be bumped up by a multiple of the dataset size
-    // if the test time has not met the threshold.
+    // In equal_issue mode, the min_queries will be bumped up by a multiple of
+    // the dataset size if the test time has not met the threshold.
     if (enable_equal_issue && (queries.size() >= min_queries) &&
-      (prev_timestamp < gen_duration) && (scenario != TestScenario::Offline))
-    {
+        (prev_timestamp < gen_duration) &&
+        (scenario != TestScenario::Offline)) {
       min_queries += loaded_samples.size();
     }
   }
@@ -536,14 +534,13 @@ PerformanceResult IssueQueries(SystemUnderTest* sut,
       GlobalLogger().GetLatenciesBlocking(expected_latencies));
 
   std::vector<QuerySampleLatency> first_token_latencies(
-    GlobalLogger().GetTokenLatencies(expected_latencies));
+      GlobalLogger().GetTokenLatencies(expected_latencies));
 
   std::vector<QuerySampleLatency> time_per_output_token_arr(
-    GlobalLogger().GetTimePerOutputToken(expected_latencies));
+      GlobalLogger().GetTimePerOutputToken(expected_latencies));
 
   std::vector<int64_t> tokens_per_sample(
-    GlobalLogger().GetTokensPerSample(expected_latencies));
-
+      GlobalLogger().GetTokensPerSample(expected_latencies));
 
   // Log contention counters after every test as a sanity check.
   GlobalLogger().LogContentionAndAllocations();
@@ -591,19 +588,16 @@ PerformanceResult IssueQueries(SystemUnderTest* sut,
     }
   }
 
-  return PerformanceResult{std::move(sample_latencies),
-                          std::move(query_latencies),
-                          queries_issued,
-                          max_latency,
-                          final_query_scheduled_time,
-                          final_query_issued_time,
-                          final_query_all_samples_done_time,
-                          TokenPerformanceResults{
-                            first_token_latencies,
-                            time_per_output_token_arr,
-                            tokens_per_sample
-                          }
-                        };
+  return PerformanceResult{
+      std::move(sample_latencies),
+      std::move(query_latencies),
+      queries_issued,
+      max_latency,
+      final_query_scheduled_time,
+      final_query_issued_time,
+      final_query_all_samples_done_time,
+      TokenPerformanceResults{first_token_latencies, time_per_output_token_arr,
+                              tokens_per_sample}};
 }
 
 void LoadSamplesToRam(QuerySampleLibrary* qsl,
@@ -1172,9 +1166,10 @@ void StartTest(SystemUnderTest* sut, QuerySampleLibrary* qsl,
                               &log_outputs.accuracy_out,
                               log_settings.log_output.copy_detail_to_stdout,
                               log_settings.log_output.copy_summary_to_stdout);
-            
+
   GlobalLogger().SetUseTokens(requested_settings.use_token_latencies);
-  bool needs_first_token = (requested_settings.scenario != TestScenario::Offline);
+  bool needs_first_token =
+      (requested_settings.scenario != TestScenario::Offline);
   GlobalLogger().SetNeedsFirstToken(needs_first_token);
 
   if (log_settings.enable_trace) {
@@ -1235,7 +1230,7 @@ void StartTest(SystemUnderTest* sut, QuerySampleLibrary* qsl,
     test_settings.FromConfig(audit_config_filename, generic_model,
                              audit_scenario);
   }
-  if(test_settings.test05){
+  if (test_settings.test05) {
     // If the configuration indicates we are running test05,
     // random seeds
     LogDetail([](AsyncDetail& detail) {
@@ -1251,8 +1246,10 @@ void StartTest(SystemUnderTest* sut, QuerySampleLibrary* qsl,
     });
     test_settings.mode = TestMode::PerformanceOnly;
     test_settings.qsl_rng_seed = requested_settings.test05_qsl_rng_seed;
-    test_settings.sample_index_rng_seed = requested_settings.test05_sample_index_rng_seed;
-    test_settings.schedule_rng_seed = requested_settings.test05_schedule_rng_seed;
+    test_settings.sample_index_rng_seed =
+        requested_settings.test05_sample_index_rng_seed;
+    test_settings.schedule_rng_seed =
+        requested_settings.test05_schedule_rng_seed;
   }
 
   loadgen::TestSettingsInternal sanitized_settings(
@@ -1324,11 +1321,11 @@ void QuerySamplesComplete(QuerySampleResponse* responses, size_t response_count,
 }
 
 void FirstTokenComplete(QuerySampleResponse* responses, size_t response_count,
-                          const ResponseCallback& response_cb) {
+                        const ResponseCallback& response_cb) {
   PerfClock::time_point timestamp = PerfClock::now();
 
-  auto tracer = MakeScopedTracer(
-      [](AsyncTrace& trace) { trace("FirstTokenComplete"); });
+  auto tracer =
+      MakeScopedTracer([](AsyncTrace& trace) { trace("FirstTokenComplete"); });
 
   const QuerySampleResponse* end = responses + response_count;
 
@@ -1338,7 +1335,7 @@ void FirstTokenComplete(QuerySampleResponse* responses, size_t response_count,
         reinterpret_cast<loadgen::SampleMetadata*>(response->id);
     loadgen::QueryMetadata* query = sample->query_metadata;
     query->response_delegate->TokenComplete(sample, response, timestamp,
-                                             response_cb);
+                                            response_cb);
   }
   // PerfClock::time_point end_timestamp = PerfClock::now();
   // mlperf::tokens_overhead_acum += (end_timestamp - timestamp).count();
diff --git a/loadgen/loadgen.h b/loadgen/loadgen.h
index f0e6554900..84e02656c2 100644
--- a/loadgen/loadgen.h
+++ b/loadgen/loadgen.h
@@ -19,9 +19,8 @@ limitations under the License.
 
 #include <cstddef>
 #include <functional>
-#include <string>
 #include <numeric>
-
+#include <string>
 
 /// \brief Contains the loadgen API.
 namespace mlperf {
@@ -66,7 +65,7 @@ void QuerySamplesComplete(QuerySampleResponse* responses, size_t response_count,
                           const ResponseCallback& response_cb = {});
 
 void FirstTokenComplete(QuerySampleResponse* responses, size_t response_count,
-                          const ResponseCallback& response_cb = {});
+                        const ResponseCallback& response_cb = {});
 
 ///
 /// \brief Starts the test against SUT with the specified settings.
diff --git a/loadgen/logging.cc b/loadgen/logging.cc
index d33074c016..807c1954a8 100644
--- a/loadgen/logging.cc
+++ b/loadgen/logging.cc
@@ -286,25 +286,24 @@ void AsyncLog::LogAccuracy(uint64_t seq_id, const QuerySampleIndex qsl_idx,
     return;
   }
   *accuracy_out_ << (accuracy_needs_comma_ ? ",\n{ " : "\n{ ");
-  if (!use_tokens_){
+  if (!use_tokens_) {
     LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
-          response);
-  } else if (!needs_first_token_)
-  {
+            response);
+  } else if (!needs_first_token_) {
     LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
-          response, "token_count", n_tokens);
-  }
-  else {
+            response, "token_count", n_tokens);
+  } else {
     const size_t i = seq_id - latencies_first_sample_sequence_id_;
     LogArgs(accuracy_out_, "seq_id", seq_id, "qsl_idx", qsl_idx, "data",
-          response, "token_data", token_records_[i], "token_count", n_tokens);
+            response, "token_data", token_records_[i], "token_count", n_tokens);
   }
-  
+
   *accuracy_out_ << " }";
   accuracy_needs_comma_ = true;
 }
 
-void AsyncLog::CacheToken(uint64_t seq_id, const LogBinaryAsHexString& response){
+void AsyncLog::CacheToken(uint64_t seq_id,
+                          const LogBinaryAsHexString& response) {
   std::unique_lock<std::mutex> lock(token_record_mutex_);
   const size_t i = seq_id - latencies_first_sample_sequence_id_;
   if (token_records_.size() <= i) {
@@ -408,14 +407,16 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
     // the error above.
     return;
   }
-  
-  if (use_tokens_){
-    if(needs_first_token_ && (token_latencies_.size() <= i)){
+
+  if (use_tokens_) {
+    if (needs_first_token_ && (token_latencies_.size() <= i)) {
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
-                          "Attempted to record a sample latency before it's first token latency");
-    }else if (needs_first_token_ && (token_latencies_[i] == kInvalidLatency)){
+                            "Attempted to record a sample latency before it's "
+                            "first token latency");
+    } else if (needs_first_token_ && (token_latencies_[i] == kInvalidLatency)) {
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
-                          "Attempted to record a sample latency before it's first token latency");
+                            "Attempted to record a sample latency before it's "
+                            "first token latency");
     }
 
     if (tokens_per_sample_.size() <= i) {
@@ -424,12 +425,12 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
     } else if (tokens_per_sample_[i] != nTokenInvalid) {
       // Call LogErrorSync here since this kind of error could result in a
       // segfault in the near future.
-  #if USE_NEW_LOGGING_FORMAT
+#if USE_NEW_LOGGING_FORMAT
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
                             "Attempted to complete a sample twice.");
-  #else
+#else
       GlobalLogger().LogErrorSync("Attempted to complete a sample twice.");
-  #endif
+#endif
 
       // Return without recording the latency again to avoid potentially
       // ending the test before the SUT is actually done, which could result
@@ -437,30 +438,31 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
       // If the SUT recorded the wrong sample, the test will hang and see
       // the error above.
       return;
-    } 
-    if (n_tokens == 0){
+    }
+    if (n_tokens == 0) {
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
-                            "n_tokens argument missing or attempted to record 0 as number of tokens");
-    } else if (n_tokens < 0){
+                            "n_tokens argument missing or attempted to record "
+                            "0 as number of tokens");
+    } else if (n_tokens < 0) {
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
                             "Attempted to record a negative number of tokens");
       n_tokens = 0;
-    } else if (n_tokens == 1){
+    } else if (n_tokens == 1) {
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
                             "Number of tokens need to be greater than 1");
       n_tokens = 0;
     }
-    if (time_per_output_token_.size() <= i){
+    if (time_per_output_token_.size() <= i) {
       time_per_output_token_.resize(i + 1, kInvalidLatency);
     } else if (time_per_output_token_[i] != kInvalidLatency) {
       // Call LogErrorSync here since this kind of error could result in a
       // segfault in the near future.
-  #if USE_NEW_LOGGING_FORMAT
+#if USE_NEW_LOGGING_FORMAT
       MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
                             "Attempted to complete a sample twice.");
-  #else
+#else
       GlobalLogger().LogErrorSync("Attempted to complete a sample twice.");
-  #endif
+#endif
 
       // Return without recording the latency again to avoid potentially
       // ending the test before the SUT is actually done, which could result
@@ -470,7 +472,8 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
       return;
     }
     tokens_per_sample_[i] = n_tokens;
-    time_per_output_token_[i] = (latency - token_latencies_[i]) / (n_tokens - 1);
+    time_per_output_token_[i] =
+        (latency - token_latencies_[i]) / (n_tokens - 1);
   }
   latencies_[i] = latency;
   latencies_recorded_++;
@@ -480,14 +483,14 @@ void AsyncLog::RecordSampleCompletion(uint64_t sample_sequence_id,
 }
 
 void AsyncLog::RecordTokenCompletion(uint64_t sample_sequence_id,
-                                      PerfClock::time_point completion_time,
-                                      QuerySampleLatency latency) {
+                                     PerfClock::time_point completion_time,
+                                     QuerySampleLatency latency) {
   std::unique_lock<std::mutex> lock(token_latencies_mutex_);
-  //std::unique_lock<std::mutex> lock(latencies_mutex_);
-  //max_latency_ = std::max(max_latency_, latency);
+  // std::unique_lock<std::mutex> lock(latencies_mutex_);
+  // max_latency_ = std::max(max_latency_, latency);
 
-  //max_completion_timstamp_ =
-  //    std::max(max_completion_timstamp_, completion_time);
+  // max_completion_timstamp_ =
+  //     std::max(max_completion_timstamp_, completion_time);
 
   if (sample_sequence_id < latencies_first_sample_sequence_id_) {
     // Call LogErrorSync here since this kind of error could result in a
@@ -507,22 +510,24 @@ void AsyncLog::RecordTokenCompletion(uint64_t sample_sequence_id,
   }
 
   const size_t i = sample_sequence_id - latencies_first_sample_sequence_id_;
-  
-  if (latencies_.size() > i){
-    if (latencies_[i] != kInvalidLatency){
+
+  if (latencies_.size() > i) {
+    if (latencies_[i] != kInvalidLatency) {
 #if USE_NEW_LOGGING_FORMAT
-    MLPERF_LOG_ERROR_SYNC(GlobalLogger(), "error_runtime",
-                          "Attempted to record token latency after sample was completed");
+      MLPERF_LOG_ERROR_SYNC(
+          GlobalLogger(), "error_runtime",
+          "Attempted to record token latency after sample was completed");
 #else
-    GlobalLogger().LogErrorSync("Attempted to record token latency after sample was completed");
+      GlobalLogger().LogErrorSync(
+          "Attempted to record token latency after sample was completed");
 #endif
 
-    // Return without recording the latency again to avoid potentially
-    // ending the test before the SUT is actually done, which could result
-    // in a segfault.
-    // If the SUT recorded the wrong sample, the test will hang and see
-    // the error above.
-    return;
+      // Return without recording the latency again to avoid potentially
+      // ending the test before the SUT is actually done, which could result
+      // in a segfault.
+      // If the SUT recorded the wrong sample, the test will hang and see
+      // the error above.
+      return;
     }
   }
   if (token_latencies_.size() <= i) {
@@ -598,13 +603,15 @@ std::vector<QuerySampleLatency> AsyncLog::GetLatenciesBlocking(
   return latencies;
 }
 
-std::vector<QuerySampleLatency> AsyncLog::GetTokenLatencies(size_t expected_count) {
+std::vector<QuerySampleLatency> AsyncLog::GetTokenLatencies(
+    size_t expected_count) {
   std::vector<QuerySampleLatency> token_latencies;
   token_latencies.swap(token_latencies_);
   return token_latencies;
 }
 
-std::vector<QuerySampleLatency> AsyncLog::GetTimePerOutputToken(size_t expected_count){
+std::vector<QuerySampleLatency> AsyncLog::GetTimePerOutputToken(
+    size_t expected_count) {
   std::vector<QuerySampleLatency> tpot_latencies;
   tpot_latencies.swap(time_per_output_token_);
   return tpot_latencies;
@@ -625,11 +632,9 @@ QuerySampleLatency AsyncLog::GetMaxLatencySoFar() {
   return max_latency_;
 }
 
-void AsyncLog::SetUseTokens(bool use_tokens){
-  use_tokens_ = use_tokens;
-}
+void AsyncLog::SetUseTokens(bool use_tokens) { use_tokens_ = use_tokens; }
 
-void AsyncLog::SetNeedsFirstToken(bool needs_first_token){
+void AsyncLog::SetNeedsFirstToken(bool needs_first_token) {
   needs_first_token_ = needs_first_token;
 }
 
@@ -807,8 +812,7 @@ void Logger::CollectTlsLoggerStats(TlsLogger* tls_logger) {
   if (max_entry_vector_size > kTlsLogReservedEntryCount) {
 #if USE_NEW_LOGGING_FORMAT
     std::stringstream msg;
-    msg << "Logging allocation detected:"
-        << " tid: " << tls_logger->Tid()
+    msg << "Logging allocation detected:" << " tid: " << tls_logger->Tid()
         << " reserved_entries: " << kTlsLogReservedEntryCount
         << " max_entries: " << max_entry_vector_size;
     MLPERF_LOG_WARNING((*this), "warning_generic_message", msg.str());
@@ -963,11 +967,11 @@ QuerySampleLatency Logger::GetMaxLatencySoFar() {
   return async_logger_.GetMaxLatencySoFar();
 }
 
-void Logger::SetUseTokens(bool use_tokens){
+void Logger::SetUseTokens(bool use_tokens) {
   async_logger_.SetUseTokens(use_tokens);
 }
 
-void Logger::SetNeedsFirstToken(bool needs_first_token){
+void Logger::SetNeedsFirstToken(bool needs_first_token) {
   async_logger_.SetNeedsFirstToken(needs_first_token);
 }
 
diff --git a/loadgen/logging.h b/loadgen/logging.h
index e628258592..c5514562a5 100644
--- a/loadgen/logging.h
+++ b/loadgen/logging.h
@@ -119,13 +119,10 @@ class ChromeTracer {
   void AddCompleteEvent(const std::string& name, uint64_t pid, uint64_t tid,
                         PerfClock::time_point start, PerfClock::time_point end,
                         const Args... args) {
-    *out_ << "{\"name\":\"" << name << "\","
-          << "\"ph\":\"X\","
-          << "\"pid\":" << pid << ","
-          << "\"tid\":" << tid << ","
+    *out_ << "{\"name\":\"" << name << "\"," << "\"ph\":\"X\","
+          << "\"pid\":" << pid << "," << "\"tid\":" << tid << ","
           << "\"ts\":" << Micros(start - origin_).count() << ","
-          << "\"dur\":" << Micros(end - start).count() << ","
-          << "\"args\":{";
+          << "\"dur\":" << Micros(end - start).count() << "," << "\"args\":{";
     AddArgs(args...);
     *out_ << "}},\n";
   }
@@ -133,12 +130,9 @@ class ChromeTracer {
   template <typename... Args>
   void AddAsyncBeginEvent(const std::string& name, uint64_t pid, uint64_t id,
                           PerfClock::time_point time, const Args... args) {
-    *out_ << "{\"name\":\"" << name << "\","
-          << "\"cat\":\"default\","
-          << "\"ph\":\"b\","
-          << "\"pid\":" << pid << ","
-          << "\"id\":" << id << ","
-          << "\"ts\":" << Micros(time - origin_).count() << ","
+    *out_ << "{\"name\":\"" << name << "\"," << "\"cat\":\"default\","
+          << "\"ph\":\"b\"," << "\"pid\":" << pid << "," << "\"id\":" << id
+          << "," << "\"ts\":" << Micros(time - origin_).count() << ","
           << "\"args\":{";
     AddArgs(args...);
     *out_ << "}},\n";
@@ -147,12 +141,9 @@ class ChromeTracer {
   template <typename... Args>
   void AddAsyncInstantEvent(const std::string& name, uint64_t pid, uint64_t id,
                             PerfClock::time_point time, const Args... args) {
-    *out_ << "{\"name\":\"" << name << "\","
-          << "\"cat\":\"default\","
-          << "\"ph\":\"n\","
-          << "\"pid\":" << pid << ","
-          << "\"id\":" << id << ","
-          << "\"ts\":" << Micros(time - origin_).count() << ","
+    *out_ << "{\"name\":\"" << name << "\"," << "\"cat\":\"default\","
+          << "\"ph\":\"n\"," << "\"pid\":" << pid << "," << "\"id\":" << id
+          << "," << "\"ts\":" << Micros(time - origin_).count() << ","
           << "\"args\":{";
     AddArgs(args...);
     *out_ << "}},\n";
@@ -161,19 +152,15 @@ class ChromeTracer {
   template <typename... Args>
   void AddAsyncEndEvent(const std::string& name, uint64_t pid, uint64_t id,
                         PerfClock::time_point time) {
-    *out_ << "{\"name\":\"" << name << "\","
-          << "\"cat\":\"default\","
-          << "\"ph\":\"e\", "
-          << "\"pid\":" << pid << ","
-          << "\"id\":" << id << ","
-          << "\"ts\":" << Micros(time - origin_).count() << "},\n";
+    *out_ << "{\"name\":\"" << name << "\"," << "\"cat\":\"default\","
+          << "\"ph\":\"e\", " << "\"pid\":" << pid << "," << "\"id\":" << id
+          << "," << "\"ts\":" << Micros(time - origin_).count() << "},\n";
   }
 
   template <typename... Args>
   void AddCounterEvent(const std::string& name, uint64_t pid,
                        PerfClock::time_point time, const Args... args) {
-    *out_ << "{\"name\":\"" << name << "\","
-          << "\"ph\": \"C\","
+    *out_ << "{\"name\":\"" << name << "\"," << "\"ph\": \"C\","
           << "\"pid\":" << pid << ","
           << "\"ts\":" << Micros(time - origin_).count() << ","
           << "\"args\":{ ";
@@ -315,11 +302,10 @@ class AsyncLog {
                                size_t latencies_to_reserve);
   void RecordSampleCompletion(uint64_t sample_sequence_id,
                               PerfClock::time_point completion_time,
-                              QuerySampleLatency latency,
-                              int64_t n_tokens);
+                              QuerySampleLatency latency, int64_t n_tokens);
   void RecordTokenCompletion(uint64_t sample_sequence_id,
-                              PerfClock::time_point completion_time,
-                              QuerySampleLatency latency);
+                             PerfClock::time_point completion_time,
+                             QuerySampleLatency latency);
   std::vector<QuerySampleLatency> GetLatenciesBlocking(size_t expected_count);
   std::vector<QuerySampleLatency> GetTokenLatencies(size_t expected_count);
   std::vector<QuerySampleLatency> GetTimePerOutputToken(size_t expected_count);
@@ -734,15 +720,13 @@ void AsyncLog::LogDetail(const std::string& key, const T& value,
   }
   auto time_ns = (log_detail_time_ - log_origin_).count();
   for (auto os : detail_streams) {
-    *os << ":::MLLOG {"
-        << "\"key\": " << ArgValueTransform(key) << ", "
+    *os << ":::MLLOG {" << "\"key\": " << ArgValueTransform(key) << ", "
         << "\"value\": " << ArgValueTransform(value) << ", "
         << "\"time_ms\": " << ArgValueTransform(time_ns / 1000000ULL) << "."
         << std::setfill('0') << std::setw(6)
         << ArgValueTransform(time_ns % 1000000ULL) << ", "
         << "\"namespace\": \"mlperf::logging\", "
-        << "\"event_type\": \"POINT_IN_TIME\", "
-        << "\"metadata\": {"
+        << "\"event_type\": \"POINT_IN_TIME\", " << "\"metadata\": {"
         << "\"is_error\": " << ArgValueTransform(error_flagged_) << ", "
         << "\"is_warning\": " << ArgValueTransform(warning_flagged_) << ", "
         << "\"file\": \"" << file_name << "\", "
@@ -771,9 +755,9 @@ void AsyncLog::LogDetail(const std::string& message, const Args... args) {
     detail_streams.pop_back();
   }
   for (auto os : detail_streams) {
-    *os << "\"pid\": " << current_pid_ << ", "
-        << "\"tid\": " << current_tid_ << ", "
-        << "\"ts\": " << (log_detail_time_ - log_origin_).count() << "ns : ";
+    *os << "\"pid\": " << current_pid_ << ", " << "\"tid\": " << current_tid_
+        << ", " << "\"ts\": " << (log_detail_time_ - log_origin_).count()
+        << "ns : ";
     if (error_flagged_) {
       *os << "ERROR : ";
     } else if (warning_flagged_) {
diff --git a/loadgen/query_dispatch_library.h b/loadgen/query_dispatch_library.h
index 5f4f518c33..6c594efe07 100644
--- a/loadgen/query_dispatch_library.h
+++ b/loadgen/query_dispatch_library.h
@@ -25,8 +25,10 @@ namespace mlperf {
 /// \addtogroup LoadgenAPI
 /// @{
 
-/// \brief The interface a client implements for the LoadGen over the network to test. The API inherits the System_under_test.h API
-/// When working in LON mode the QueryDispatchLibrary class is used and natively Upcasted to the QueryDispatchLibrary class.
+/// \brief The interface a client implements for the LoadGen over the network to
+/// test. The API inherits the System_under_test.h API When working in LON mode
+/// the QueryDispatchLibrary class is used and natively Upcasted to the
+/// QueryDispatchLibrary class.
 
 class QueryDispatchLibrary : public SystemUnderTest {
  public:
diff --git a/loadgen/query_sample.h b/loadgen/query_sample.h
index 371b49e975..e740be99e0 100644
--- a/loadgen/query_sample.h
+++ b/loadgen/query_sample.h
@@ -21,6 +21,7 @@ limitations under the License.
 
 #include <stddef.h>
 #include <stdint.h>
+
 #include <iostream>
 
 namespace mlperf {
@@ -51,28 +52,33 @@ struct QuerySampleResponse {
   uintptr_t data;
   size_t size;  ///< Size in bytes.
   int64_t n_tokens;
-  public:
-    QuerySampleResponse(ResponseId id, uintptr_t data, size_t size, int64_t n_tokens)
-    : id(id), 
-    data(data), 
-    size(size), 
-    n_tokens(n_tokens){
-      //std::cout << "Initialized with 4 arguments, n_tokens: " << n_tokens <<"\n";
-    };
-    QuerySampleResponse(ResponseId id, uintptr_t data, size_t size)
-    : id(id), 
-    data(data), 
-    size(size), 
-    n_tokens(0){
-      //std::cout << "Initialized with 3 arguments, n_tokens: " << n_tokens <<"\n";
-    };
-    QuerySampleResponse()
-    : id(0), 
-    data(0), 
-    size(0), 
-    n_tokens(0){
-      //std::cout << "Initialized with 0 arguments, n_tokens: " << n_tokens <<"\n";
-    };
+
+ public:
+  QuerySampleResponse(ResponseId id, uintptr_t data, size_t size,
+                      int64_t n_tokens)
+      : id(id),
+        data(data),
+        size(size),
+        n_tokens(n_tokens){
+            // std::cout << "Initialized with 4 arguments, n_tokens: " <<
+            // n_tokens <<"\n";
+        };
+  QuerySampleResponse(ResponseId id, uintptr_t data, size_t size)
+      : id(id),
+        data(data),
+        size(size),
+        n_tokens(0){
+            // std::cout << "Initialized with 3 arguments, n_tokens: " <<
+            // n_tokens <<"\n";
+        };
+  QuerySampleResponse()
+      : id(0),
+        data(0),
+        size(0),
+        n_tokens(0){
+            // std::cout << "Initialized with 0 arguments, n_tokens: " <<
+            // n_tokens <<"\n";
+        };
 };
 
 /// \brief A latency in nanoseconds, as recorded by the loadgen.
diff --git a/loadgen/results.cc b/loadgen/results.cc
index 3bbeda42a9..817c5a379b 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -11,6 +11,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include "results.h"
+
 #include "early_stopping.h"
 #include "utils.h"
 
@@ -55,7 +56,7 @@ void PerformanceSummary::ProcessLatencies() {
                          max_latency);
   }
 
-  if (settings.use_token_latencies){
+  if (settings.use_token_latencies) {
     ProcessTokenLatencies();
   }
 
@@ -85,9 +86,8 @@ void PerformanceSummary::ProcessLatencies() {
 void PerformanceSummary::ProcessTokenLatencies() {
   constexpr auto nTokenInvalid = std::numeric_limits<int64_t>::min();
   token_count = 0;
-  for (auto n_tokens: pr.token_results.tokens_per_sample){
-    if (n_tokens != nTokenInvalid)
-      token_count += n_tokens;
+  for (auto n_tokens : pr.token_results.tokens_per_sample) {
+    if (n_tokens != nTokenInvalid) token_count += n_tokens;
   }
   if (pr.token_results.first_token_latencies.empty()) {
     return;
@@ -102,29 +102,37 @@ void PerformanceSummary::ProcessTokenLatencies() {
     accumulated_tpot += latency;
   }
   time_per_output_token_mean = accumulated_tpot / sample_count;
-  std::sort(pr.token_results.first_token_latencies.begin(), 
-    pr.token_results.first_token_latencies.end());
+  std::sort(pr.token_results.first_token_latencies.begin(),
+            pr.token_results.first_token_latencies.end());
   std::sort(pr.token_results.time_per_output_token_arr.begin(),
-    pr.token_results.time_per_output_token_arr.end());
-  
+            pr.token_results.time_per_output_token_arr.end());
+
   token_target_latency_percentile.sample_latency =
-      pr.token_results.first_token_latencies[sample_count * token_target_latency_percentile.percentile];
+      pr.token_results
+          .first_token_latencies[sample_count *
+                                 token_target_latency_percentile.percentile];
   first_token_latency_min = pr.token_results.first_token_latencies.front();
   first_token_latency_max = pr.token_results.first_token_latencies.back();
   for (auto& lp : token_latency_percentiles) {
     assert(lp.percentile >= 0.0);
     assert(lp.percentile < 1.0);
-    lp.sample_latency = pr.token_results.first_token_latencies[sample_count * lp.percentile];
+    lp.sample_latency =
+        pr.token_results.first_token_latencies[sample_count * lp.percentile];
   }
 
   target_tpot_percentile.sample_latency =
-      pr.token_results.time_per_output_token_arr[sample_count * target_tpot_percentile.percentile];
-  time_per_output_token_min = pr.token_results.time_per_output_token_arr.front();
+      pr.token_results
+          .time_per_output_token_arr[sample_count *
+                                     target_tpot_percentile.percentile];
+  time_per_output_token_min =
+      pr.token_results.time_per_output_token_arr.front();
   time_per_output_token_max = pr.token_results.time_per_output_token_arr.back();
   for (auto& lp : tpot_percentiles) {
     assert(lp.percentile >= 0.0);
     assert(lp.percentile < 1.0);
-    lp.sample_latency = pr.token_results.time_per_output_token_arr[sample_count * lp.percentile];
+    lp.sample_latency =
+        pr.token_results
+            .time_per_output_token_arr[sample_count * lp.percentile];
   }
 
   if (settings.scenario == TestScenario::Server) {
@@ -132,16 +140,17 @@ void PerformanceSummary::ProcessTokenLatencies() {
     QuerySampleLatency max_latency = settings.target_latency.count() + 1;
     overlatency_first_token_count =
         pr.token_results.first_token_latencies.end() -
-        std::lower_bound(pr.token_results.first_token_latencies.begin(), pr.token_results.first_token_latencies.end(),
+        std::lower_bound(pr.token_results.first_token_latencies.begin(),
+                         pr.token_results.first_token_latencies.end(),
                          max_latency);
   }
-
 }
 
-bool PerformanceSummary::EarlyStopping(std::string* recommendation, int64_t queries_issued, 
-                                        std::vector<QuerySampleLatency>* sample_latencies,
-                                        std::vector<QuerySampleLatency>* query_latencies,
-                                        std::chrono::nanoseconds target_latency) {
+bool PerformanceSummary::EarlyStopping(
+    std::string* recommendation, int64_t queries_issued,
+    std::vector<QuerySampleLatency>* sample_latencies,
+    std::vector<QuerySampleLatency>* query_latencies,
+    std::chrono::nanoseconds target_latency) {
   recommendation->clear();
 
   MinPassingQueriesFinder find_min_passing;
@@ -336,25 +345,29 @@ bool PerformanceSummary::PerfConstraintsMet(std::string* recommendation) {
       break;
     case TestScenario::Server:
       ProcessLatencies();
-      if (!settings.use_token_latencies){
+      if (!settings.use_token_latencies) {
         if (target_latency_percentile.sample_latency >
             settings.target_latency.count()) {
           *recommendation = "Reduce target QPS to improve latency.";
           perf_constraints_met = false;
         }
       } else {
-        if ( token_target_latency_percentile.sample_latency >
+        if (token_target_latency_percentile.sample_latency >
             settings.server_ttft_latency) {
-          *recommendation = "TTFT constrain not met: Reduce target QPS to improve latency.";
+          *recommendation =
+              "TTFT constrain not met: Reduce target QPS to improve latency.";
           perf_constraints_met = false;
         }
 
-        if ( target_tpot_percentile.sample_latency >
+        if (target_tpot_percentile.sample_latency >
             settings.server_tpot_latency) {
-          if (recommendation->empty()){
-            *recommendation = "TPOT constrain not met: Reduce target QPS to improve latency.";
+          if (recommendation->empty()) {
+            *recommendation =
+                "TPOT constrain not met: Reduce target QPS to improve latency.";
           } else {
-            recommendation->append("\n * TPOT constrain not met: Reduce target QPS to improve latency.");
+            recommendation->append(
+                "\n * TPOT constrain not met: Reduce target QPS to improve "
+                "latency.");
           }
           perf_constraints_met = false;
         }
@@ -379,10 +392,10 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
 
   switch (settings.scenario) {
     case TestScenario::SingleStream: {
-        summary(DoubleToString(target_latency_percentile.percentile * 100, 0) +
-                    "th percentile latency (ns) : ",
-                target_latency_percentile.sample_latency);
-        break;
+      summary(DoubleToString(target_latency_percentile.percentile * 100, 0) +
+                  "th percentile latency (ns) : ",
+              target_latency_percentile.sample_latency);
+      break;
     }
     case TestScenario::MultiStream: {
       summary(DoubleToString(target_latency_percentile.percentile * 100, 0) +
@@ -401,7 +414,7 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
       //    1000 queries / 1 second.
       // TODO: make a more permanent solution
       double qps_as_completed =
-        (sample_count - 1) / pr.final_query_all_samples_done_time;
+          (sample_count - 1) / pr.final_query_all_samples_done_time;
       summary("Completed samples per second    : ",
               DoubleToString(qps_as_completed));
       break;
@@ -413,16 +426,18 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
     }
   }
 
-  if (settings.use_token_latencies){
+  if (settings.use_token_latencies) {
     switch (settings.scenario) {
       case TestScenario::SingleStream: {
-      summary(DoubleToString(token_target_latency_percentile.percentile * 100, 0) +
-                  "th first token percentile latency (ns) : ",
-              token_target_latency_percentile.sample_latency);
-      break;
+        summary(DoubleToString(token_target_latency_percentile.percentile * 100,
+                               0) +
+                    "th first token percentile latency (ns) : ",
+                token_target_latency_percentile.sample_latency);
+        break;
       }
       case TestScenario::MultiStream: {
-        summary(DoubleToString(token_target_latency_percentile.percentile * 100, 0) +
+        summary(DoubleToString(token_target_latency_percentile.percentile * 100,
+                               0) +
                     "th first token percentile latency (ns) : ",
                 token_target_latency_percentile.sample_latency);
         break;
@@ -441,7 +456,7 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
     }
   }
 
-  if (settings.infer_token_latencies){
+  if (settings.infer_token_latencies) {
     switch (settings.scenario) {
       case TestScenario::SingleStream: {
         break;
@@ -450,13 +465,15 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
         break;
       }
       case TestScenario::Offline: {
-        double tokens_per_second = settings.token_latency_scaling_factor * sample_count / pr.max_latency;
+        double tokens_per_second = settings.token_latency_scaling_factor *
+                                   sample_count / pr.max_latency;
         summary("Tokens per second (inferred): ", tokens_per_second);
         break;
       }
       case TestScenario::Server:
-        double tps_as_completed =
-          settings.token_latency_scaling_factor * (sample_count - 1) / pr.final_query_all_samples_done_time;
+        double tps_as_completed = settings.token_latency_scaling_factor *
+                                  (sample_count - 1) /
+                                  pr.final_query_all_samples_done_time;
         summary("Completed tokens per second (inferred): ",
                 DoubleToString(tps_as_completed));
         break;
@@ -472,23 +489,20 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
   bool min_duration_met = MinDurationMet(&min_duration_recommendation);
   bool min_queries_met = MinQueriesMet() && MinSamplesMet();
   bool early_stopping_met = true;
-  if (!settings.use_token_latencies){
-    early_stopping_met = EarlyStopping(&early_stopping_recommendation,
-                                        pr.queries_issued, 
-                                        &pr.sample_latencies, 
-                                        &pr.query_latencies,
-                                        settings.target_latency);
+  if (!settings.use_token_latencies) {
+    early_stopping_met = EarlyStopping(
+        &early_stopping_recommendation, pr.queries_issued, &pr.sample_latencies,
+        &pr.query_latencies, settings.target_latency);
   } else {
-    early_stopping_met = EarlyStopping(&early_stopping_tpot_recommendation,
-                                        pr.queries_issued, 
-                                        &pr.token_results.time_per_output_token_arr, 
-                                        &pr.query_latencies,
-                                        std::chrono::nanoseconds(settings.server_tpot_latency)) && 
-                          EarlyStopping(&early_stopping_ttft_recommendation,
-                                        pr.queries_issued, 
-                                        &pr.token_results.first_token_latencies, 
-                                        &pr.query_latencies,
-                                        std::chrono::nanoseconds(settings.server_ttft_latency));
+    early_stopping_met =
+        EarlyStopping(&early_stopping_tpot_recommendation, pr.queries_issued,
+                      &pr.token_results.time_per_output_token_arr,
+                      &pr.query_latencies,
+                      std::chrono::nanoseconds(settings.server_tpot_latency)) &&
+        EarlyStopping(&early_stopping_ttft_recommendation, pr.queries_issued,
+                      &pr.token_results.first_token_latencies,
+                      &pr.query_latencies,
+                      std::chrono::nanoseconds(settings.server_ttft_latency));
   }
   bool perf_constraints_met =
       PerfConstraintsMet(&perf_constraints_recommendation);
@@ -521,7 +535,7 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
   if (settings.scenario == TestScenario::SingleStream ||
       settings.scenario == TestScenario::Server ||
       settings.scenario == TestScenario::MultiStream) {
-    if (!settings.use_token_latencies){
+    if (!settings.use_token_latencies) {
       summary("Early Stopping Result:");
       summary(early_stopping_recommendation);
     } else {
@@ -547,9 +561,9 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
   } else if (settings.scenario == TestScenario::Server) {
     // Scheduled samples per second as an additional stat
     double qps_as_scheduled =
-          (sample_count - 1) / pr.final_query_scheduled_time;
-      summary("Scheduled samples per second : ",
-              DoubleToString(qps_as_scheduled));
+        (sample_count - 1) / pr.final_query_scheduled_time;
+    summary("Scheduled samples per second : ",
+            DoubleToString(qps_as_scheduled));
   } else if (settings.scenario == TestScenario::MultiStream) {
     summary("Per-query latency:  ");
     summary("Min latency (ns)                : ", query_latency_min);
@@ -572,14 +586,16 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
           lp.sample_latency);
     }
   }
-  if (settings.use_token_latencies){
+  if (settings.use_token_latencies) {
     summary("");
     if (settings.scenario == TestScenario::SingleStream) {
-    double tps_w_lg = token_count / pr.final_query_issued_time;
-    double tps_wo_lg = ((double)token_count) / (QuerySampleLatencyToSeconds(sample_latency_mean) * sample_count);
-    summary("TPS w/ loadgen overhead         : " + DoubleToString(tps_w_lg));
-    summary("TPS w/o loadgen overhead        : " + DoubleToString(tps_wo_lg));
-    
+      double tps_w_lg = token_count / pr.final_query_issued_time;
+      double tps_wo_lg =
+          ((double)token_count) /
+          (QuerySampleLatencyToSeconds(sample_latency_mean) * sample_count);
+      summary("TPS w/ loadgen overhead         : " + DoubleToString(tps_w_lg));
+      summary("TPS w/o loadgen overhead        : " + DoubleToString(tps_wo_lg));
+
     } else if (settings.scenario == TestScenario::Server) {
       double tps_as_completed =
           token_count / pr.final_query_all_samples_done_time;
@@ -588,22 +604,28 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
     }
 
     if (settings.scenario != TestScenario::Offline) {
-      summary("Min First Token latency (ns)                : ", first_token_latency_min);
-      summary("Max First Token latency (ns)                : ", first_token_latency_max);
-      summary("Mean First Token latency (ns)               : ", first_token_latency_mean);
+      summary("Min First Token latency (ns)                : ",
+              first_token_latency_min);
+      summary("Max First Token latency (ns)                : ",
+              first_token_latency_max);
+      summary("Mean First Token latency (ns)               : ",
+              first_token_latency_mean);
       for (auto& lp : token_latency_percentiles) {
-        summary(
-            DoubleToString(lp.percentile * 100) + " percentile first token latency (ns)   : ",
-            lp.sample_latency);
+        summary(DoubleToString(lp.percentile * 100) +
+                    " percentile first token latency (ns)   : ",
+                lp.sample_latency);
       }
       summary("");
-      summary("Min Time to Output Token (ns)                : ", time_per_output_token_min);
-      summary("Max Time to Output Token (ns)                : ", time_per_output_token_max);
-      summary("Mean Time to Output Token (ns)               : ", time_per_output_token_mean);
+      summary("Min Time to Output Token (ns)                : ",
+              time_per_output_token_min);
+      summary("Max Time to Output Token (ns)                : ",
+              time_per_output_token_max);
+      summary("Mean Time to Output Token (ns)               : ",
+              time_per_output_token_mean);
       for (auto& lp : tpot_percentiles) {
-        summary(
-            DoubleToString(lp.percentile * 100) + " percentile time to output token (ns)   : ",
-            lp.sample_latency);
+        summary(DoubleToString(lp.percentile * 100) +
+                    " percentile time to output token (ns)   : ",
+                lp.sample_latency);
       }
     }
   }
@@ -631,23 +653,20 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
   bool perf_constraints_met =
       PerfConstraintsMet(&perf_constraints_recommendation);
   bool early_stopping_met = true;
-  if (!settings.use_token_latencies){
-    early_stopping_met = EarlyStopping(&early_stopping_recommendation,
-                                        pr.queries_issued, 
-                                        &pr.sample_latencies, 
-                                        &pr.query_latencies,
-                                        settings.target_latency);
+  if (!settings.use_token_latencies) {
+    early_stopping_met = EarlyStopping(
+        &early_stopping_recommendation, pr.queries_issued, &pr.sample_latencies,
+        &pr.query_latencies, settings.target_latency);
   } else {
-    early_stopping_met = EarlyStopping(&early_stopping_tpot_recommendation,
-                                        pr.queries_issued, 
-                                        &pr.token_results.time_per_output_token_arr, 
-                                        &pr.query_latencies,
-                                        std::chrono::nanoseconds(settings.server_tpot_latency)) && 
-                          EarlyStopping(&early_stopping_ttft_recommendation,
-                                        pr.queries_issued, 
-                                        &pr.token_results.first_token_latencies, 
-                                        &pr.query_latencies,
-                                        std::chrono::nanoseconds(settings.server_ttft_latency));
+    early_stopping_met =
+        EarlyStopping(&early_stopping_tpot_recommendation, pr.queries_issued,
+                      &pr.token_results.time_per_output_token_arr,
+                      &pr.query_latencies,
+                      std::chrono::nanoseconds(settings.server_tpot_latency)) &&
+        EarlyStopping(&early_stopping_ttft_recommendation, pr.queries_issued,
+                      &pr.token_results.first_token_latencies,
+                      &pr.query_latencies,
+                      std::chrono::nanoseconds(settings.server_ttft_latency));
   }
   bool all_constraints_met = min_duration_met && min_queries_met &&
                              perf_constraints_met && early_stopping_met;
@@ -672,21 +691,22 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
       recommendation +=
           "The test exited early, before enough queries were issued.";
     }
-    std::replace(recommendation.begin(),
-               recommendation.end(), '\n', ' ');
+    std::replace(recommendation.begin(), recommendation.end(), '\n', ' ');
     MLPERF_LOG(detail, "result_invalid_reason", recommendation);
   }
   std::replace(early_stopping_recommendation.begin(),
                early_stopping_recommendation.end(), '\n', ' ');
-  if (!settings.use_token_latencies){
+  if (!settings.use_token_latencies) {
     MLPERF_LOG(detail, "early_stopping_result", early_stopping_recommendation);
-  } else{
+  } else {
     std::replace(early_stopping_ttft_recommendation.begin(),
-               early_stopping_ttft_recommendation.end(), '\n', ' ');
+                 early_stopping_ttft_recommendation.end(), '\n', ' ');
     std::replace(early_stopping_tpot_recommendation.begin(),
-                early_stopping_tpot_recommendation.end(), '\n', ' ');
-    MLPERF_LOG(detail, "early_stopping_ttft_result", early_stopping_ttft_recommendation);
-    MLPERF_LOG(detail, "early_stopping_tpot_result", early_stopping_tpot_recommendation);
+                 early_stopping_tpot_recommendation.end(), '\n', ' ');
+    MLPERF_LOG(detail, "early_stopping_ttft_result",
+               early_stopping_ttft_recommendation);
+    MLPERF_LOG(detail, "early_stopping_tpot_result",
+               early_stopping_tpot_recommendation);
   }
   // Report number of queries
   MLPERF_LOG(detail, "result_query_count", query_count);
@@ -761,49 +781,64 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
                lp.sample_latency);
   }
   // Detailed first token latencies
-  if (settings.use_token_latencies){
+  if (settings.use_token_latencies) {
     if (settings.scenario != TestScenario::Offline) {
-      MLPERF_LOG(detail, "result_first_token_min_latency_ns", first_token_latency_min);
-      MLPERF_LOG(detail, "result_first_token_max_latency_ns", first_token_latency_max);
-      MLPERF_LOG(detail, "result_first_token_mean_latency_ns", first_token_latency_mean);
+      MLPERF_LOG(detail, "result_first_token_min_latency_ns",
+                 first_token_latency_min);
+      MLPERF_LOG(detail, "result_first_token_max_latency_ns",
+                 first_token_latency_max);
+      MLPERF_LOG(detail, "result_first_token_mean_latency_ns",
+                 first_token_latency_mean);
       for (auto& lp : token_latency_percentiles) {
         MLPERF_LOG(detail,
-                    "result_first_token_" + DoubleToString(lp.percentile * 100) +
-                        "_percentile_latency_ns",
-                    lp.sample_latency);
+                   "result_first_token_" + DoubleToString(lp.percentile * 100) +
+                       "_percentile_latency_ns",
+                   lp.sample_latency);
       }
       double tps_w_lg = ((double)token_count) / pr.final_query_issued_time;
-      double tps_wo_lg= ((double)token_count) / (sample_latency_mean * sample_count);
-      MLPERF_LOG(detail, "result_token_throughput_with_loadgen_overhead", tps_w_lg);
+      double tps_wo_lg =
+          ((double)token_count) / (sample_latency_mean * sample_count);
+      MLPERF_LOG(detail, "result_token_throughput_with_loadgen_overhead",
+                 tps_w_lg);
       MLPERF_LOG(detail, "result_token_throughput", tps_wo_lg);
       for (auto& lp : tpot_percentiles) {
         MLPERF_LOG(detail,
-                    "result_time_per_output_token_" + DoubleToString(lp.percentile * 100) +
-                        "_percentile_ns",
-                    lp.sample_latency);
+                   "result_time_per_output_token_" +
+                       DoubleToString(lp.percentile * 100) + "_percentile_ns",
+                   lp.sample_latency);
       }
-      MLPERF_LOG(detail, "result_time_to_output_token_min", time_per_output_token_min);
-      MLPERF_LOG(detail, "result_time_to_output_token_max", time_per_output_token_max);
-      MLPERF_LOG(detail, "result_time_to_output_token_mean", time_per_output_token_mean);
+      MLPERF_LOG(detail, "result_time_to_output_token_min",
+                 time_per_output_token_min);
+      MLPERF_LOG(detail, "result_time_to_output_token_max",
+                 time_per_output_token_max);
+      MLPERF_LOG(detail, "result_time_to_output_token_mean",
+                 time_per_output_token_mean);
       double tps_as_completed =
-            token_count / pr.final_query_all_samples_done_time;
-      MLPERF_LOG(detail, "result_completed_tokens_per_second", tps_as_completed);
+          token_count / pr.final_query_all_samples_done_time;
+      MLPERF_LOG(detail, "result_completed_tokens_per_second",
+                 tps_as_completed);
     } else {
       double tokens_per_second = token_count / pr.max_latency;
       MLPERF_LOG(detail, "result_tokens_per_second", tokens_per_second);
     }
   }
 
-  if (settings.infer_token_latencies){
+  if (settings.infer_token_latencies) {
     switch (settings.scenario) {
       case TestScenario::Server: {
-        double completed_tokens_per_second = (sample_count - 1) * settings.token_latency_scaling_factor / pr.final_query_all_samples_done_time;
-        MLPERF_LOG(detail, "result_inferred_completed_tokens_per_second", completed_tokens_per_second);
+        double completed_tokens_per_second =
+            (sample_count - 1) * settings.token_latency_scaling_factor /
+            pr.final_query_all_samples_done_time;
+        MLPERF_LOG(detail, "result_inferred_completed_tokens_per_second",
+                   completed_tokens_per_second);
         break;
       }
       case TestScenario::Offline: {
-        double tokens_per_second = sample_count * settings.token_latency_scaling_factor / pr.max_latency;
-        MLPERF_LOG(detail, "result_inferred_tokens_per_second", tokens_per_second);
+        double tokens_per_second = sample_count *
+                                   settings.token_latency_scaling_factor /
+                                   pr.max_latency;
+        MLPERF_LOG(detail, "result_inferred_tokens_per_second",
+                   tokens_per_second);
         break;
       }
       case TestScenario::SingleStream: {
@@ -812,9 +847,9 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
       case TestScenario::MultiStream: {
         break;
       }
-  }
+    }
 #endif
-}
+  }
 }
 }  // namespace loadgen
-} // namespace mlperf
+}  // namespace mlperf
diff --git a/loadgen/setup.py b/loadgen/setup.py
index 1e8cb0a8d6..662d47bc9c 100644
--- a/loadgen/setup.py
+++ b/loadgen/setup.py
@@ -52,7 +52,7 @@
     "version.h",
     "results.h",
     "bindings/c_api.h",
-    "version_generator.py"
+    "version_generator.py",
 ]
 
 lib_sources = [
@@ -74,8 +74,9 @@
 this_directory = Path(__file__).parent
 mlperf_loadgen_headers = public_headers + lib_headers
 mlperf_loadgen_sources_no_gen = lib_sources + lib_bindings
-mlperf_loadgen_sources = (mlperf_loadgen_sources_no_gen +
-                          [generated_version_source_filename])
+mlperf_loadgen_sources = mlperf_loadgen_sources_no_gen + [
+    generated_version_source_filename
+]
 mlperf_long_description = (
     this_directory /
     "README.md").read_text(
@@ -94,24 +95,25 @@
 mlperf_loadgen_module = Pybind11Extension(
     "mlperf_loadgen",
     define_macros=[
-        ("MAJOR_VERSION",
-         version_split[0]),
-        ("MINOR_VERSION",
-         version_split[1]),
-        ("MLPERF_CONF_PATH",
-         f'"{config_file_path}"')],
+        ("MAJOR_VERSION", version_split[0]),
+        ("MINOR_VERSION", version_split[1]),
+        ("MLPERF_CONF_PATH", f'"{config_file_path}"'),
+    ],
     include_dirs=[".", get_include()],
     sources=mlperf_loadgen_sources,
-    depends=mlperf_loadgen_headers)
+    depends=mlperf_loadgen_headers,
+)
 
-setup(name="mlcommons_loadgen",
-      version=version,
-      description="MLPerf Inference LoadGen python bindings",
-      url="https://mlcommons.org/",
-      cmdclass={"build_ext": build_ext},
-      ext_modules=[mlperf_loadgen_module],
-      packages=[''],
-      package_data={'': ['mlperf.conf']},
-      include_package_data=True,
-      long_description=mlperf_long_description,
-      long_description_content_type='text/markdown')
+setup(
+    name="mlcommons_loadgen",
+    version=version,
+    description="MLPerf Inference LoadGen python bindings",
+    url="https://mlcommons.org/",
+    cmdclass={"build_ext": build_ext},
+    ext_modules=[mlperf_loadgen_module],
+    packages=[""],
+    package_data={"": ["mlperf.conf"]},
+    include_package_data=True,
+    long_description=mlperf_long_description,
+    long_description_content_type="text/markdown",
+)
diff --git a/loadgen/test_settings_internal.h b/loadgen/test_settings_internal.h
index d557e97069..ab2773bd18 100644
--- a/loadgen/test_settings_internal.h
+++ b/loadgen/test_settings_internal.h
@@ -102,7 +102,7 @@ TestSettingsInternal MidOfBoundaries(
     const TestSettingsInternal &lower_bound_settings,
     const TestSettingsInternal &upper_bound_settings) {
   TestSettingsInternal mid_settings = lower_bound_settings;
-if (scenario == TestScenario::Server) {
+  if (scenario == TestScenario::Server) {
     assert(lower_bound_settings.target_qps < upper_bound_settings.target_qps);
     mid_settings.target_qps =
         lower_bound_settings.target_qps +
@@ -122,7 +122,7 @@ if (scenario == TestScenario::Server) {
 template <TestScenario scenario>
 bool IsFinished(const TestSettingsInternal &lower_bound_settings,
                 const TestSettingsInternal &upper_bound_settings) {
-if (scenario == TestScenario::Server) {
+  if (scenario == TestScenario::Server) {
     uint8_t precision = lower_bound_settings.requested
                             .server_find_peak_qps_decimals_of_precision;
     double l =
diff --git a/loadgen/tests/loadgen_test.h b/loadgen/tests/loadgen_test.h
index 00afcb63ac..777029b994 100644
--- a/loadgen/tests/loadgen_test.h
+++ b/loadgen/tests/loadgen_test.h
@@ -30,10 +30,10 @@ limitations under the License.
   static Test::StaticRegistrant t##name##scenario(        \
       #name "_" #scenario, test, __VA_ARGS__, mlperf::TestScenario::scenario)
 
-#define REGISTER_TEST_ALL_SCENARIOS(name, test, ...)                \
-  REGISTER_TEST_SCENARIO(name, SingleStream, test, __VA_ARGS__);    \
-  REGISTER_TEST_SCENARIO(name, MultiStream, test, __VA_ARGS__);     \
-  REGISTER_TEST_SCENARIO(name, Server, test, __VA_ARGS__);          \
+#define REGISTER_TEST_ALL_SCENARIOS(name, test, ...)             \
+  REGISTER_TEST_SCENARIO(name, SingleStream, test, __VA_ARGS__); \
+  REGISTER_TEST_SCENARIO(name, MultiStream, test, __VA_ARGS__);  \
+  REGISTER_TEST_SCENARIO(name, Server, test, __VA_ARGS__);       \
   REGISTER_TEST_SCENARIO(name, Offline, test, __VA_ARGS__);
 
 #define FAIL_IF(exp)                                              \
diff --git a/loadgen/tests/perftests_null_sut.py b/loadgen/tests/perftests_null_sut.py
index 5364c63657..115372e187 100644
--- a/loadgen/tests/perftests_null_sut.py
+++ b/loadgen/tests/perftests_null_sut.py
@@ -50,7 +50,8 @@ def main(argv):
 
     sut = mlperf_loadgen.ConstructSUT(issue_query, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        1024 * 1024, 1024, load_samples_to_ram, unload_samples_from_ram)
+        1024 * 1024, 1024, load_samples_to_ram, unload_samples_from_ram
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
diff --git a/loadgen/version_generator.py b/loadgen/version_generator.py
index a013af0c6a..49340408a3 100644
--- a/loadgen/version_generator.py
+++ b/loadgen/version_generator.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # =============================================================================
 
-## \file
+# \file
 #  \brief A script run by the build to generate the version definitions
 #  expected at link time.
 
@@ -27,27 +27,39 @@
 
 # Creates a C++ raw string literal using a delimiter that is very
 # unlikely to show up in a git stats.
-def make_raw_string(str) :
+def make_raw_string(str):
     delimeter = "LGVG_RSLD"
-    return "R\"" + delimeter + "(" + str + ")" + delimeter + "\""
+    return 'R"' + delimeter + "(" + str + ")" + delimeter + '"'
+
 
 def func_def(name, string):
-    return ("const std::string& Loadgen" + name + "() {\n" +
-            "  static const std::string str = " + string + ";\n" +
-            "  return str;\n" +
-            "}\n\n")
+    return (
+        "const std::string& Loadgen"
+        + name
+        + "() {\n"
+        + "  static const std::string str = "
+        + string
+        + ";\n"
+        + "  return str;\n"
+        + "}\n\n"
+    )
 
 
 # For clients that build the loadgen from the git respository without
 # any modifications.
 def generate_loadgen_version_definitions_git(ofile, git_command):
     git_rev = os.popen(git_command + "rev-parse --short=10 HEAD").read()
-    git_commit_date = os.popen(git_command + "log --format=\"%cI\" -n 1").read()
+    git_commit_date = os.popen(git_command + 'log --format="%cI" -n 1').read()
     git_status = os.popen(git_command + "status -s -uno .").read()
     git_log = subprocess.Popen(
-        git_command + "log --pretty=oneline -n 16 --no-decorate", stdout=subprocess.PIPE, shell=True, encoding='ascii', errors="ignore" ).stdout.read()
-    ofile.write(func_def("GitRevision", "\"" + git_rev[0:-1] + "\""))
-    ofile.write(func_def("GitCommitDate", "\"" + git_commit_date[0:-1] + "\""))
+        git_command + "log --pretty=oneline -n 16 --no-decorate",
+        stdout=subprocess.PIPE,
+        shell=True,
+        encoding="ascii",
+        errors="ignore",
+    ).stdout.read()
+    ofile.write(func_def("GitRevision", '"' + git_rev[0:-1] + '"'))
+    ofile.write(func_def("GitCommitDate", '"' + git_commit_date[0:-1] + '"'))
     ofile.write(func_def("GitStatus", make_raw_string(git_status[0:-1])))
     ofile.write(func_def("GitLog", make_raw_string(git_log[0:-1])))
 
@@ -55,7 +67,7 @@ def generate_loadgen_version_definitions_git(ofile, git_command):
 # For clients that might not import the loadgen code as the original git
 # repository.
 def generate_loadgen_verstion_definitions_git_stubs(ofile):
-    na = "\"NA\""
+    na = '"NA"'
     ofile.write(func_def("GitRevision", na))
     ofile.write(func_def("GitCommitDate", na))
     ofile.write(func_def("GitStatus", na))
@@ -67,9 +79,9 @@ def generate_loadgen_verstion_definitions_git_stubs(ofile):
 def generate_loadgen_version_definitions_sha1(ofile, loadgen_root):
     """Writes definition for Sha1OfFiles."""
     sha1s = ""
-    loadgen_files = (
-        ["/bindings/" + s for s in os.listdir(loadgen_root + "/bindings")] +
-        ["/" + s for s in os.listdir(loadgen_root)])
+    loadgen_files = [
+        "/bindings/" + s for s in os.listdir(loadgen_root + "/bindings")
+    ] + ["/" + s for s in os.listdir(loadgen_root)]
     for fn in sorted(loadgen_files):
         full_fn = loadgen_root + fn
         if not os.path.isfile(full_fn):
@@ -93,15 +105,15 @@ def generate_loadgen_version_definitions(cc_filename, loadgen_root):
     ofile.write("// DO NOT EDIT: Autogenerated by version_generator.py.\n\n")
     ofile.write("#include <string>\n\n")
     ofile.write("namespace mlperf {\n\n")
-    ofile.write(func_def("Version", "\"4.1\""))
+    ofile.write(func_def("Version", '"4.1"'))
 
     date_time_now_local = datetime.datetime.now().isoformat()
     date_time_now_utc = datetime.datetime.utcnow().isoformat()
-    ofile.write(func_def("BuildDateLocal", "\"" + date_time_now_local + "\""))
-    ofile.write(func_def("BuildDateUtc", "\"" + date_time_now_utc + "\""))
+    ofile.write(func_def("BuildDateLocal", '"' + date_time_now_local + '"'))
+    ofile.write(func_def("BuildDateUtc", '"' + date_time_now_utc + '"'))
 
-    git_dir = "--git-dir=\"" + loadgen_root + "/../.git\" "
-    git_work_tree = "--work-tree=\"" + loadgen_root + "/..\" "
+    git_dir = '--git-dir="' + loadgen_root + '/../.git" '
+    git_work_tree = '--work-tree="' + loadgen_root + '/.." '
     git_command = "git " + git_dir + git_work_tree
     git_status = os.popen(git_command + "status")
     git_status.read()
diff --git a/lon/network_SUT.py b/lon/network_SUT.py
index 1935151495..4cbb31eca9 100644
--- a/lon/network_SUT.py
+++ b/lon/network_SUT.py
@@ -13,15 +13,14 @@
 # limitations under the License.
 # =============================================================================
 
+from flask import Flask, request, jsonify
+import argparse
 import array
 import json
 import os
 import sys
-sys.path.insert(0, os.getcwd())
 
-
-import argparse
-from flask import Flask, request, jsonify
+sys.path.insert(0, os.getcwd())
 
 
 app = Flask(__name__)
@@ -29,39 +28,45 @@
 
 node = ""
 
+
 def set_backend(b):
     global backend
     backend = b
 
+
 def preprocess(query):
     """[SUT Node] A dummy preprocess."""
-    # Here may come for example batching, tokenization, resizing, normalization, etc.
+    # Here may come for example batching, tokenization, resizing,
+    # normalization, etc.
     response = query
     return response
 
 
 def dnn_model(query):
-    # Here may come for example a call to a dnn model such as resnet, bert, etc.
+    # Here may come for example a call to a dnn model such as resnet, bert,
+    # etc.
     response = backend.process_sample(query)
     return response
 
 
 def postprocess(query):
     """[SUT Node] A dummy postprocess."""
-    # Here may come for example a postprocessing call, e.g., NMS, detokenization, etc.
+    # Here may come for example a postprocessing call, e.g., NMS,
+    # detokenization, etc.
     response = query
     return response
 
 
-@app.route('/predict/', methods=['POST'])
+@app.route("/predict/", methods=["POST"])
 def predict():
     """Receives a query (e.g., a text) runs inference, and returns a prediction."""
-    query = request.get_json(force=True)['query']
+    query = request.get_json(force=True)["query"]
     result = postprocess(dnn_model(preprocess(query)))
     return jsonify(result=result)
 
 
-@app.route('/getname/', methods=['POST', 'GET'])
+@app.route("/getname/", methods=["POST", "GET"])
 def getname():
     """Returns the name of the SUT."""
-    return jsonify(name=f'Demo SUT (Network SUT) node' + (' ' + node) if node else '')
+    return jsonify(name=f"Demo SUT (Network SUT) node" +
+                   (" " + node) if node else "")
diff --git a/main.py b/main.py
index 6a607cc101..8fc8f202b7 100755
--- a/main.py
+++ b/main.py
@@ -1,92 +1,130 @@
 def define_env(env):
 
     @env.macro
-    def mlperf_inference_implementation_readme(spaces, model, implementation, *, implementation_tips=True, setup_tips=True, run_tips=True, skip_test_query_count=False, scenarios = [], devices=[], frameworks=[], categories=[], extra_variation_tags="", extra_input_string="", extra_docker_input_string=""):
+    def mlperf_inference_implementation_readme(
+        spaces,
+        model,
+        implementation,
+        *,
+        implementation_tips=True,
+        setup_tips=True,
+        run_tips=True,
+        skip_test_query_count=False,
+        scenarios=[],
+        devices=[],
+        frameworks=[],
+        categories=[],
+        extra_variation_tags="",
+        extra_input_string="",
+        extra_docker_input_string="",
+    ):
         pre_space = ""
 
-        for i in range(1,spaces):
-            pre_space  = pre_space + " "
+        for i in range(1, spaces):
+            pre_space = pre_space + " "
         f_pre_space = pre_space
         pre_space += " "
 
-        content=""
+        content = ""
 
-        execution_envs = ["Docker","Native"]
-        code_version="r4.1-dev"
+        execution_envs = ["Docker", "Native"]
+        code_version = "r4.1-dev"
         implementation_run_options = []
 
         if model == "rnnt":
-            code_version="r4.0"
+            code_version = "r4.0"
 
         if implementation == "reference":
             # Tip
             if "99.9" not in model and implementation_tips:
                 content += f"\n{pre_space}!!! tip\n\n"
                 content += f"{pre_space}    - MLCommons reference implementations are only meant to provide a rules compliant reference implementation for the submitters and in most cases are not best performing. If you want to benchmark any system, it is advisable to use the vendor MLPerf implementation for that system like Nvidia, Intel etc.\n\n"
-              
+
             if not devices:
-                devices = [ "CPU", "CUDA", "ROCm" ]
+                devices = ["CPU", "CUDA", "ROCm"]
 
             if not frameworks:
                 if model.lower() == "resnet50":
-                    frameworks = [ "Onnxruntime", "Tensorflow", "Deepsparse" ]
+                    frameworks = ["Onnxruntime", "Tensorflow", "Deepsparse"]
                 elif model.lower() == "retinanet":
-                    frameworks = [ "Onnxruntime", "Pytorch" ]
+                    frameworks = ["Onnxruntime", "Pytorch"]
                 elif "bert" in model.lower():
-                    frameworks = [ "Pytorch", "Deepsparse" ]
+                    frameworks = ["Pytorch", "Deepsparse"]
                 else:
-                    frameworks = [ "Pytorch" ]
+                    frameworks = ["Pytorch"]
 
         elif implementation == "nvidia":
-            if model in [ "mixtral-8x7b" ]:
-                 return pre_space+"    WIP"
-            devices = [ "CUDA" ]
-            frameworks = [ "TensorRT" ]
-        
+            if model in ["mixtral-8x7b"]:
+                return pre_space + "    WIP"
+            devices = ["CUDA"]
+            frameworks = ["TensorRT"]
+
         elif implementation == "neuralmagic":
-            devices = [ "CUDA" ]
-            frameworks = [ "pytorch" ]
+            devices = ["CUDA"]
+            frameworks = ["pytorch"]
 
         elif implementation == "intel":
             # Tip
             if "99.9" not in model and implementation_tips:
                 content += f"\n{pre_space}!!! tip\n\n"
                 content += f"{pre_space}    - Intel MLPerf inference implementation is available only for datacenter category and has been tested only on a limited number of systems. Most of the benchmarks using Intel implementation require at least Intel Sapphire Rapids or higher CPU generation.\n\n"
-                                
-            if model not in [ "bert-99", "bert-99.9", "gptj-99", "gptj-99.9", "resnet50", "retinanet", "3d-unet-99", "3d-unet-99.9", "dlrm-v2-99", "dlrm-v2-99.9", "sdxl" ]:
-                 return pre_space+"    WIP"
-            if model in [ "bert-99", "bert-99.9", "retinanet", "3d-unet-99", "3d-unet-99.9" ]:
-                 code_version="r4.0"
-            devices = [ "CPU" ]
-            frameworks = [ "Pytorch" ]
+
+            if model not in [
+                "bert-99",
+                "bert-99.9",
+                "gptj-99",
+                "gptj-99.9",
+                "resnet50",
+                "retinanet",
+                "3d-unet-99",
+                "3d-unet-99.9",
+                "dlrm-v2-99",
+                "dlrm-v2-99.9",
+                "sdxl",
+            ]:
+                return pre_space + "    WIP"
+            if model in [
+                "bert-99",
+                "bert-99.9",
+                "retinanet",
+                "3d-unet-99",
+                "3d-unet-99.9",
+            ]:
+                code_version = "r4.0"
+            devices = ["CPU"]
+            frameworks = ["Pytorch"]
 
         elif implementation == "qualcomm":
-            if model not in [ "resnet50", "retinanet", "bert-99", "bert-99.9" ]:
-                 return pre_space+"    WIP"
+            if model not in ["resnet50", "retinanet", "bert-99", "bert-99.9"]:
+                return pre_space + "    WIP"
 
-            devices = [ "QAIC" ]
-            frameworks = [ "Glow" ]
+            devices = ["QAIC"]
+            frameworks = ["Glow"]
 
         elif implementation == "cpp":
             if not devices:
-                devices = [ "CPU", "CUDA" ]
-            frameworks = [ "Onnxruntime" ]
+                devices = ["CPU", "CUDA"]
+            frameworks = ["Onnxruntime"]
 
         elif implementation == "ctuning-cpp":
-            scenarios = [ "SingleStream" ]
-            devices = [ "CPU" ]
+            scenarios = ["SingleStream"]
+            devices = ["CPU"]
             if model.lower() == "resnet50":
-                 frameworks = [ "TFLite" ]
+                frameworks = ["TFLite"]
             else:
-                 frameworks = []
+                frameworks = []
 
         if not categories:
             if model.lower() == "bert-99.9":
-                categories = [ "Datacenter" ]
-            elif "dlrm" in model.lower() or "llama2" in model.lower() or "mixtral" in model.lower():
-                categories = [ "Datacenter" ]
+                categories = ["Datacenter"]
+            elif (
+                "dlrm" in model.lower()
+                or "llama2" in model.lower()
+                or "mixtral" in model.lower()
+            ):
+                categories = ["Datacenter"]
             else:
-                categories = [ "Edge", "Datacenter" ]
+                categories = ["Edge", "Datacenter"]
 
         # model name
         content += f"{pre_space}{model.upper()}\n\n"
@@ -96,85 +134,130 @@ def mlperf_inference_implementation_readme(spaces, model, implementation, *, imp
         for category in categories:
             if not scenarios:
                 if category == "Edge" and not scenarios:
-                    scenarios = [ "Offline", "SingleStream" ]
-                    if model.lower() in [ "resnet50", "retinanet" ] and not "MultiStream" in scenarios:#MultiStream was duplicating
-                         scenarios.append("MultiStream")
+                    scenarios = ["Offline", "SingleStream"]
+                    if (
+                        model.lower() in ["resnet50", "retinanet"]
+                        and not "MultiStream" in scenarios
+                    ):  # MultiStream was duplicating
+                        scenarios.append("MultiStream")
                 elif category == "Datacenter":
-                     scenarios = [ "Offline", "Server" ] 
+                    scenarios = ["Offline", "Server"]
 
-            content += f"{pre_space}=== \"{category.lower()}\"\n\n"
+            content += f'{pre_space}=== "{category.lower()}"\n\n'
 
             cur_space = pre_space + "    "
             scenarios_string = ", ".join(scenarios)
 
             content += f"{cur_space}### {category} category \n\n{cur_space} In the {category.lower()} category, {model} has {scenarios_string} scenarios and all the scenarios are mandatory for a closed division submission.\n\n"
 
-
             for framework in frameworks:
                 cur_space1 = cur_space + "    "
-                content += f"{cur_space}=== \"{framework}\"\n"
+                content += f'{cur_space}=== "{framework}"\n'
                 content += f"{cur_space1}#### {framework} framework\n\n"
 
                 for device in devices:
                     if framework.lower() == "deepsparse":
                         if device.lower() != "cpu":
-                             continue
+                            continue
                     cur_space2 = cur_space1 + "    "
                     cur_space3 = cur_space2 + "    "
                     cur_space4 = cur_space3 + "    "
-                    
-                    content += f"{cur_space1}=== \"{device}\"\n"
+
+                    content += f'{cur_space1}=== "{device}"\n'
                     content += f"{cur_space2}##### {device} device\n\n"
 
                     # minimum system requirements
-                    content += get_min_system_requirements(cur_space2, model, implementation, device)
+                    content += get_min_system_requirements(
+                        cur_space2, model, implementation, device
+                    )
 
-                    # to select the execution environments(currently Docker and Native)
+                    # to select the execution environments(currently Docker and
+                    # Native)
                     for execution_env in execution_envs:
-                        if (device == "ROCm" or implementation == "qualcomm") and execution_env == "Docker":
+                        if (
+                            device == "ROCm" or implementation == "qualcomm"
+                        ) and execution_env == "Docker":
                             continue  # docker not currently supported for Qualcomm implementation and ROCm device
                         if implementation == "nvidia" and execution_env == "Native":
                             continue  # Nvidia implementation only supports execution through docker
-                        content += f"{cur_space2}=== \"{execution_env}\"\n"
+                        content += f'{cur_space2}=== "{execution_env}"\n'
                         content += f"{cur_space3}###### {execution_env} Environment\n\n"
                         # ref to cm installation
                         content += f"{cur_space3}Please refer to the [installation page](site:inference/install/) to install CM for running the automated benchmark commands.\n\n"
-                        test_query_count=get_test_query_count(model, implementation, device.lower())
-                        if device.lower() == "cuda" and execution_env.lower() == "native":
+                        test_query_count = get_test_query_count(
+                            model, implementation, device.lower()
+                        )
+                        if (
+                            device.lower() == "cuda"
+                            and execution_env.lower() == "native"
+                        ):
                             content += f"\n{cur_space3}!!! tip\n\n"
                             content += f"{cur_space3}    - It is advisable to use the commands in the Docker tab for CUDA. Run the below native command only if you are already on a CUDA setup with cuDNN and TensorRT installed.\n\n"
 
-                        if "99.9" not in model: #not showing docker command as it is already done for the 99% variant
+                        if (
+                            "99.9" not in model
+                        ):  # not showing docker command as it is already done for the 99% variant
                             if implementation == "neuralmagic":
-                                content += f"{cur_space3}####### Run the Inference Server\n"
-                                content += get_inference_server_run_cmd(spaces+16,implementation)
+                                content += (
+                                    f"{cur_space3}####### Run the Inference Server\n"
+                                )
+                                content += get_inference_server_run_cmd(
+                                    spaces + 16, implementation
+                                )
                                 if run_tips:
-                                    # tips regarding the running of nural magic server
+                                    # tips regarding the running of nural magic
+                                    # server
                                     content += f"\n{cur_space3}!!! tip\n\n"
                                     content += f"{cur_space3}    - Host and Port number of the server can be configured through `--host` and `--port` options. Otherwise, server will run on the default host `localhost` and port `8000`.\n\n"
-                                
-                            setup_run_cmd = mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, skip_test_query_count, scenarios, code_version, extra_variation_tags, extra_input_string, extra_docker_input_string)
 
-                            if execution_env == "Native": # Native implementation steps through virtual environment
+                            setup_run_cmd = mlperf_inference_run_command(
+                                spaces + 17,
+                                model,
+                                implementation,
+                                framework.lower(),
+                                category.lower(),
+                                "Offline",
+                                device.lower(),
+                                "test",
+                                test_query_count,
+                                True,
+                                skip_test_query_count,
+                                scenarios,
+                                code_version,
+                                extra_variation_tags,
+                                extra_input_string,
+                                extra_docker_input_string,
+                            )
+
+                            if (
+                                execution_env == "Native"
+                            ):  # Native implementation steps through virtual environment
                                 content += f"{cur_space3}####### Setup a virtual environment for Python\n"
-                                content += get_venv_command(spaces+16)
+                                content += get_venv_command(spaces + 16)
                                 content += f"{cur_space3}####### Performance Estimation for Offline Scenario\n"
 
-                                content += setup_run_cmd.replace("--docker ", "")
+                                content += setup_run_cmd.replace(
+                                    "--docker ", "")
 
                                 content += f"{cur_space3}The above command should do a test run of Offline scenario and record the estimated offline_target_qps.\n\n"
 
-                            else: # Docker implementation steps
+                            else:  # Docker implementation steps
                                 content += f"{cur_space3}####### Docker Container Build and Performance Estimation for Offline Scenario\n"
-                                docker_info = get_docker_info(spaces+16, model, implementation, device, setup_tips)
+                                docker_info = get_docker_info(
+                                    spaces + 16,
+                                    model,
+                                    implementation,
+                                    device,
+                                    setup_tips,
+                                )
                                 content += docker_info
 
                                 content += setup_run_cmd
 
-                                if len(scenarios)  == 1:
+                                if len(scenarios) == 1:
                                     scenario_text = f"""the {scenarios[0]} scenario"""
                                 else:
-                                    scenario_text = "each scenario"""
+                                    scenario_text = "each scenario" ""
                                 content += f"{cur_space3}The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for {scenario_text}.\n\n"
                                 content += f"{cur_space3}<details>\n"
                                 content += f"{cur_space3}<summary> Please click here to see more options for the docker launch </summary>\n\n"
@@ -184,7 +267,7 @@ def mlperf_inference_implementation_readme(spaces, model, implementation, *, imp
                                 if implementation.lower() == "nvidia":
                                     content += f"{cur_space3}* `--gpu_name=<Name of the GPU>` : The GPUs with supported configs in CM are `orin`, `rtx_4090`, `rtx_a6000`, `rtx_6000_ada`, `l4`, `t4`and `a100`. For other GPUs, default configuration as per the GPU memory will be used.\n"
 
-                                if device.lower() not in [ "cuda" ]:
+                                if device.lower() not in ["cuda"]:
                                     content += f"{cur_space3}* `--docker_os=ubuntu`: ubuntu and rhel are supported. \n"
                                     content += f"{cur_space3}* `--docker_os_version=20.04`: [20.04, 22.04] are supported for Ubuntu and [8, 9] for RHEL\n"
 
@@ -193,24 +276,40 @@ def mlperf_inference_implementation_readme(spaces, model, implementation, *, imp
                             content += f"{cur_space3} You can reuse the same environment as described for {model.split('.')[0]}.\n"
                             content += f"{cur_space3}###### Performance Estimation for Offline Scenario\n"
 
-                            content += mlperf_inference_run_command(spaces+17, model, implementation, framework.lower(), category.lower(), "Offline", device.lower(), "test", test_query_count, True, skip_test_query_count, scenarios, code_version).replace("--docker ","")
+                            content += mlperf_inference_run_command(
+                                spaces + 17,
+                                model,
+                                implementation,
+                                framework.lower(),
+                                category.lower(),
+                                "Offline",
+                                device.lower(),
+                                "test",
+                                test_query_count,
+                                True,
+                                skip_test_query_count,
+                                scenarios,
+                                code_version,
+                            ).replace("--docker ", "")
                             content += f"{cur_space3}The above command should do a test run of Offline scenario and record the estimated offline_target_qps.\n\n"
 
-
                         run_suffix = ""
                         run_suffix += f"{cur_space3}<details>\n"
                         run_suffix += f"{cur_space3}<summary> Please click here to see more options for the RUN command</summary>\n\n"
                         run_suffix += f"{cur_space3}* Use `--division=closed` to do a closed division submission which includes compliance runs\n\n"
-                        run_suffix += f"{cur_space3}* Use `--rerun` to do a rerun even when a valid run exists\n"  
+                        run_suffix += f"{cur_space3}* Use `--rerun` to do a rerun even when a valid run exists\n"
                         if implementation.lower() == "nvidia":
                             run_suffix += f"{cur_space3}* `--gpu_name=<Name of the GPU>` : The GPUs with supported configs in CM are `orin`, `rtx_4090`, `rtx_a6000`, `rtx_6000_ada`, `l4`, `t4`and `a100`. For other GPUs, default configuration as per the GPU memory will be used.\n"
                         run_suffix += f"{cur_space3}</details>\n\n"
 
-                        if "bert" in model.lower() and framework.lower() == "deepsparse":
+                        if (
+                            "bert" in model.lower()
+                            and framework.lower() == "deepsparse"
+                        ):
                             run_suffix += f"{cur_space3}<details>\n"
                             run_suffix += f"{cur_space3}<summary> Please click here to view available generic model stubs for bert deepsparse</summary>\n\n"
-                            run_suffix += f"{cur_space3}* **obert-large-pruned95_quant-none-vnni:** zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned95_quant-none-vnni\n\n" 
-                            run_suffix += f"{cur_space3}* **mobilebert-none-14layer_pruned50_quant-none-vnni:** zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50_quant-none-vnni\n\n" 
+                            run_suffix += f"{cur_space3}* **obert-large-pruned95_quant-none-vnni:** zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned95_quant-none-vnni\n\n"
+                            run_suffix += f"{cur_space3}* **mobilebert-none-14layer_pruned50_quant-none-vnni:** zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50_quant-none-vnni\n\n"
                             run_suffix += f"{cur_space3}* **mobilebert-none-base_quant-none:** zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none\n\n"
                             run_suffix += f"{cur_space3}* **bert-base-pruned95_obs_quant-none:** zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned95_obs_quant-none\n\n"
                             run_suffix += f"{cur_space3}* **mobilebert-none-14layer_pruned50-none-vnni:** zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/14layer_pruned50-none-vnni\n\n"
@@ -225,50 +324,84 @@ def mlperf_inference_implementation_readme(spaces, model, implementation, *, imp
                             run_suffix += f"{cur_space3}* **mobilebert-none-base-none:** zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base-none\n"
                             run_suffix += f"{cur_space3}</details>\n"
 
-                        
-
                         for scenario in scenarios:
-                            content += f"{cur_space3}=== \"{scenario}\"\n{cur_space4}###### {scenario}\n\n"
-                            run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), scenario, device.lower(), final_run_mode, test_query_count, False, skip_test_query_count, scenarios, code_version, extra_variation_tags, extra_input_string)
+                            content += f'{cur_space3}=== "{scenario}"\n{cur_space4}###### {scenario}\n\n'
+                            run_cmd = mlperf_inference_run_command(
+                                spaces + 21,
+                                model,
+                                implementation,
+                                framework.lower(),
+                                category.lower(),
+                                scenario,
+                                device.lower(),
+                                final_run_mode,
+                                test_query_count,
+                                False,
+                                skip_test_query_count,
+                                scenarios,
+                                code_version,
+                                extra_variation_tags,
+                                extra_input_string,
+                            )
                             content += run_cmd
-                            #content += run_suffix
-
-                        if len(scenarios) > 1: 
-                            content += f"{cur_space3}=== \"All Scenarios\"\n{cur_space4}###### All Scenarios\n\n"
-                            run_cmd = mlperf_inference_run_command(spaces+21, model, implementation, framework.lower(), category.lower(), "All Scenarios", device.lower(), final_run_mode, test_query_count, False, skip_test_query_count, scenarios, code_version, extra_variation_tags, extra_input_string)
+                            # content += run_suffix
+
+                        if len(scenarios) > 1:
+                            content += f'{cur_space3}=== "All Scenarios"\n{cur_space4}###### All Scenarios\n\n'
+                            run_cmd = mlperf_inference_run_command(
+                                spaces + 21,
+                                model,
+                                implementation,
+                                framework.lower(),
+                                category.lower(),
+                                "All Scenarios",
+                                device.lower(),
+                                final_run_mode,
+                                test_query_count,
+                                False,
+                                skip_test_query_count,
+                                scenarios,
+                                code_version,
+                                extra_variation_tags,
+                                extra_input_string,
+                            )
                             content += run_cmd
                             content += run_suffix
 
-                    
-
-        readme_prefix = get_readme_prefix(spaces, model, implementation, extra_variation_tags)
+        readme_prefix = get_readme_prefix(
+            spaces, model, implementation, extra_variation_tags
+        )
 
-        readme_suffix = get_readme_suffix(spaces, model, implementation, extra_variation_tags)
+        readme_suffix = get_readme_suffix(
+            spaces, model, implementation, extra_variation_tags
+        )
 
         return readme_prefix + content + readme_suffix
 
     def get_test_query_count(model, implementation, device, num_devices=1):
 
         if model == "resnet50":
-             p_range = 1000
-        elif model in [ "retinanet", "bert-99", "bert-99.9" ]:
-             p_range = 100
+            p_range = 1000
+        elif model in ["retinanet", "bert-99", "bert-99.9"]:
+            p_range = 100
         else:
-             p_range = 10
+            p_range = 10
 
         if device == "cuda":
             p_range *= 5
             p_range *= num_devices
 
         return p_range
-    
+
     def get_min_system_requirements(spaces, model, implementation, device):
         model = model.lower()
         min_sys_req_content = ""
         min_sys_req_content += f"{spaces}<details>\n"
         min_sys_req_content += f"{spaces}<summary>Please click here to see the minimum system requirements for running the benchmark</summary>\n\n"
         # device memory
-        if device.lower() == "cuda" and (implementation.lower() == "nvidia" or implementation.lower() == "reference"):
+        if device.lower() == "cuda" and (
+            implementation.lower() == "nvidia" or implementation.lower() == "reference"
+        ):
             if implementation.lower() == "nvidia":
                 if "dlrm" in model:
                     device_memory = "24GB"
@@ -307,14 +440,16 @@ def get_min_system_requirements(spaces, model, implementation, device):
         # System memory
         if "dlrm" in model:
             system_memory = "512GB"
-            min_sys_req_content += f"{spaces}* **System Memory(RAM+SWAP)**: {system_memory}\n\n"
+            min_sys_req_content += (
+                f"{spaces}* **System Memory(RAM+SWAP)**: {system_memory}\n\n"
+            )
         min_sys_req_content += f"{spaces}</details>\n"
         return min_sys_req_content
 
     def get_inference_server_run_cmd(spaces, implementation):
-        indent = " "*spaces + " "
+        indent = " " * spaces + " "
         if implementation == "neuralmagic":
-            pre_space = " "*spaces
+            pre_space = " " * spaces
             return f"""\n
 {pre_space}```bash
 {pre_space}cm run script --tags=run,vllm-server \\
@@ -324,72 +459,94 @@ def get_inference_server_run_cmd(spaces, implementation):
 {pre_space}```\n"""
 
     def get_venv_command(spaces):
-      pre_space = " "*spaces
-      return f"""\n
+        pre_space = " " * spaces
+        return f"""\n
 {pre_space}```bash
 {pre_space}cm run script --tags=install,python-venv --name=mlperf
 {pre_space}export CM_SCRIPT_EXTRA_CMD=\"--adr.python.name=mlperf\"
-{pre_space}```\n"""   
+{pre_space}```\n"""
 
-    def get_docker_info(spaces, model, implementation, device, setup_tips=True):
+    def get_docker_info(spaces, model, implementation,
+                        device, setup_tips=True):
         info = ""
-        pre_space=""
-        for i in range(1,spaces):
-             pre_space  = pre_space + " "
+        pre_space = ""
+        for i in range(1, spaces):
+            pre_space = pre_space + " "
         pre_space += " "
-        #pre_space = "                "
+        # pre_space = "                "
         if setup_tips:
             info += f"\n{pre_space}!!! tip\n\n"
 
             if model == "sdxl":
-                info+= f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
+                info += f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
 
-            info+= f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
+            info += f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
             if implementation.lower() == "nvidia":
-                info+= f"{pre_space}    - Default batch size is assigned based on [GPU memory](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1129) or the [specified GPU](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1370). Please click more option for *docker launch* or *run command* to see how to specify the GPU name.\n\n"
-                info+= f"{pre_space}    - When run with `--all_models=yes`, all the benchmark models of NVIDIA implementation can be executed within the same container.\n\n"
+                info += f"{pre_space}    - Default batch size is assigned based on [GPU memory](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1129) or the [specified GPU](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1370). Please click more option for *docker launch* or *run command* to see how to specify the GPU name.\n\n"
+                info += f"{pre_space}    - When run with `--all_models=yes`, all the benchmark models of NVIDIA implementation can be executed within the same container.\n\n"
                 if "llama2" in model.lower():
-                    info+= f"{pre_space}    - The dataset for NVIDIA's implementation of Llama2 is not publicly available. The user must fill [this](https://docs.google.com/forms/d/e/1FAIpQLSc_8VIvRmXM3I8KQaYnKf7gy27Z63BBoI_I1u02f4lw6rBp3g/viewform?pli=1&fbzx=-8842630989397184967) form and be verified as a MLCommons member to access the dataset.\n\n"
-                    info+= f"{pre_space}    - `PATH_TO_PICKE_FILE` should be replaced with path to the downloaded pickle file.\n\n"
+                    info += f"{pre_space}    - The dataset for NVIDIA's implementation of Llama2 is not publicly available. The user must fill [this](https://docs.google.com/forms/d/e/1FAIpQLSc_8VIvRmXM3I8KQaYnKf7gy27Z63BBoI_I1u02f4lw6rBp3g/viewform?pli=1&fbzx=-8842630989397184967) form and be verified as a MLCommons member to access the dataset.\n\n"
+                    info += f"{pre_space}    - `PATH_TO_PICKE_FILE` should be replaced with path to the downloaded pickle file.\n\n"
         else:
             if model == "sdxl":
                 info += f"\n{pre_space}!!! tip\n\n"
-                info+= f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
+                info += f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
 
         return info
 
     def get_readme_prefix(spaces, model, implementation, extra_variation_tags):
         readme_prefix = ""
-        pre_space="    "
-        #for i in range(1,spaces):
+        pre_space = "    "
+        # for i in range(1,spaces):
         #     pre_space  = pre_space + " "
-        #pre_space += "  "
+        # pre_space += "  "
 
         return readme_prefix
-    
+
     def get_readme_suffix(spaces, model, implementation, extra_variation_tags):
         readme_suffix = ""
-        pre_space=""
-        for i in range(1,spaces):
-             pre_space  = pre_space + " "
+        pre_space = ""
+        for i in range(1, spaces):
+            pre_space = pre_space + " "
         pre_space += "  "
 
         if implementation == "reference" and not extra_variation_tags:
             if not model.endswith("-99"):
-                model_base_name = model.replace("-99.9","").replace("-99","")
-                readme_suffix+= f"{pre_space}* If you want to download the official MLPerf model and dataset for {model} you can follow [this README](get-{model_base_name}-data.md).\n"
+                model_base_name = model.replace("-99.9", "").replace("-99", "")
+                readme_suffix += f"{pre_space}* If you want to download the official MLPerf model and dataset for {model} you can follow [this README](get-{model_base_name}-data.md).\n"
             if model == "resnet50":
-                 readme_suffix += f"{pre_space}* Please see [mobilenets.md](mobilenets.md) for running mobilenet models for Image Classification."
+                readme_suffix += f"{pre_space}* Please see [mobilenets.md](mobilenets.md) for running mobilenet models for Image Classification."
         return readme_suffix
 
-    def get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scenarios = [], run_tips=True, extra_input_string=""):
+    def get_run_cmd_extra(
+        f_pre_space,
+        model,
+        implementation,
+        device,
+        scenario,
+        scenarios=[],
+        run_tips=True,
+        extra_input_string="",
+    ):
         extra_content = ""
         f_pre_space += ""
-        if scenario == "Server" or (scenario == "All Scenarios" and "Server" in scenarios):
+        if scenario == "Server" or (
+            scenario == "All Scenarios" and "Server" in scenarios
+        ):
             extra_content += f"{f_pre_space}    * `<SERVER_TARGET_QPS>` must be determined manually. It is usually around 80% of the Offline QPS, but on some systems, it can drop below 50%. If a higher value is specified, the latency constraint will not be met, and the run will be considered invalid.\n"
-        if implementation == "reference" and model in [ "sdxl", "gptj-99", "gptj-99.9" ] and device in ["cuda", "rocm"] and "precision" not in extra_input_string:
+        if (
+            implementation == "reference"
+            and model in ["sdxl", "gptj-99", "gptj-99.9"]
+            and device in ["cuda", "rocm"]
+            and "precision" not in extra_input_string
+        ):
             extra_content += f"{f_pre_space}    * `--precision=float16` can help run on GPUs with less RAM / gives better performance \n"
-        if implementation == "reference" and model in [ "sdxl", "gptj-99", "gptj-99.9" ] and device in ["cpu"] and "precision" not in extra_input_string:
+        if (
+            implementation == "reference"
+            and model in ["sdxl", "gptj-99", "gptj-99.9"]
+            and device in ["cpu"]
+            and "precision" not in extra_input_string
+        ):
             extra_content += f"{f_pre_space}    * `--precision=bfloat16` can give better performance \n"
         if "gptj" in model and implementation == "reference":
             extra_content += f"{f_pre_space}    * `--beam-size=1` Beam size of 4 is mandatory for a closed division submission but reducing the beam size can help in running the model on GPUs with lower device memory\n"
@@ -400,34 +557,67 @@ def get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scen
             return extra_content
         else:
             return ""
-       
 
     @env.macro
-    def mlperf_inference_run_command(spaces, model, implementation, framework, category, scenario, device="cpu", execution_mode="test", test_query_count="20", docker=False, skip_test_query_count=False, scenarios = [], code_version="r4.1-dev", extra_variation_tags="", extra_input_string="", extra_docker_input_string=""):
+    def mlperf_inference_run_command(
+        spaces,
+        model,
+        implementation,
+        framework,
+        category,
+        scenario,
+        device="cpu",
+        execution_mode="test",
+        test_query_count="20",
+        docker=False,
+        skip_test_query_count=False,
+        scenarios=[],
+        code_version="r4.1-dev",
+        extra_variation_tags="",
+        extra_input_string="",
+        extra_docker_input_string="",
+    ):
         pre_space = ""
-        for i in range(1,spaces):
-             pre_space  = pre_space + " "
+        for i in range(1, spaces):
+            pre_space = pre_space + " "
         f_pre_space = pre_space
         pre_space += "  "
 
         if scenario == "All Scenarios":
-             scenario_variation_tag = ",_all-scenarios"
-             scenario_option = ""
+            scenario_variation_tag = ",_all-scenarios"
+            scenario_option = ""
         else:
             scenario_variation_tag = ""
             scenario_option = f"\\\n{pre_space} --scenario={scenario}"
 
-        if scenario == "Server" or (scenario == "All Scenarios" and "Server" in scenarios):
-            scenario_option += f"\\\n{pre_space} --server_target_qps=<SERVER_TARGET_QPS>"
-
-        run_cmd_extra = get_run_cmd_extra(f_pre_space, model, implementation, device, scenario, scenarios, True, extra_input_string)
+        if scenario == "Server" or (
+            scenario == "All Scenarios" and "Server" in scenarios
+        ):
+            scenario_option += (
+                f"\\\n{pre_space} --server_target_qps=<SERVER_TARGET_QPS>"
+            )
+
+        run_cmd_extra = get_run_cmd_extra(
+            f_pre_space,
+            model,
+            implementation,
+            device,
+            scenario,
+            scenarios,
+            True,
+            extra_input_string,
+        )
 
         if docker:
             docker_cmd_suffix = f" \\\n{pre_space} --docker --quiet"
             if not skip_test_query_count:
-                docker_cmd_suffix += f" \\\n{pre_space} --test_query_count={test_query_count}"
+                docker_cmd_suffix += (
+                    f" \\\n{pre_space} --test_query_count={test_query_count}"
+                )
             if extra_docker_input_string != "" or extra_input_string != "":
-                docker_cmd_suffix += f" \\\n{pre_space} {extra_docker_input_string} {extra_input_string}"
+                docker_cmd_suffix += (
+                    f" \\\n{pre_space} {extra_docker_input_string} {extra_input_string}"
+                )
             if "bert" in model.lower() and framework == "deepsparse":
                 docker_cmd_suffix += f"\\\n{pre_space} --env.CM_MLPERF_NEURALMAGIC_MODEL_ZOO_STUB=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none"
             if "llama2-70b" in model.lower():
@@ -435,10 +625,12 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
                     docker_cmd_suffix += f" \\\n{pre_space} --tp_size=2"
                     docker_cmd_suffix += f" \\\n{pre_space} --nvidia_llama2_dataset_file_path=<PATH_TO_PICKLE_FILE>"
                 elif implementation == "neuralmagic":
-                    docker_cmd_suffix += f" \\\n{pre_space} --api_server=http://localhost:8000"
+                    docker_cmd_suffix += (
+                        f" \\\n{pre_space} --api_server=http://localhost:8000"
+                    )
                     docker_cmd_suffix += f" \\\n{pre_space} --vllm_model_name=nm-testing/Llama-2-70b-chat-hf-FP8"
                     docker_cmd_suffix += f" \\\n{pre_space} --adr.mlperf-implementation.tags=_repo.https://github.com/neuralmagic/inference,_branch.vllm"
-            
+
             if "dlrm-v2" in model.lower() and implementation == "nvidia":
                 docker_cmd_suffix += f" \\\n{pre_space} --criteo_day23_raw_data_path=<PATH_TO_CRITEO_DAY23_RAW_DATA>"
 
@@ -447,7 +639,6 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
             else:
                 full_ds_needed_tag = "_full,"
 
-
             docker_setup_cmd = f"""\n
 {f_pre_space}```bash
 {f_pre_space}cm run script --tags=run-mlperf,inference,_find-performance,{full_ds_needed_tag}_{code_version}{scenario_variation_tag}{extra_variation_tags} \\
@@ -477,7 +668,7 @@ def mlperf_inference_run_command(spaces, model, implementation, framework, categ
                     cmd_suffix += f" \\\n{pre_space} --api_server=http://localhost:8000"
                     cmd_suffix += f" \\\n{pre_space} --vllm_model_name=nm-testing/Llama-2-70b-chat-hf-FP8"
                     cmd_suffix += f" \\\n{pre_space} --adr.mlperf-implementation.tags=_repo.https://github.com/neuralmagic/inference,_branch.vllm"
-            
+
             if "dlrm-v2" in model and implementation == "nvidia":
                 cmd_suffix += f" \\\n{pre_space} --criteo_day23_raw_data_path=<PATH_TO_CRITEO_DAY23_RAW_DATA>"
 
diff --git a/recommendation/dlrm_v2/pytorch/python/backend.py b/recommendation/dlrm_v2/pytorch/python/backend.py
index 955eddb888..6fc13454a8 100755
--- a/recommendation/dlrm_v2/pytorch/python/backend.py
+++ b/recommendation/dlrm_v2/pytorch/python/backend.py
@@ -2,10 +2,10 @@
 abstract backend class
 """
 
-
 # pylint: disable=unused-argument,missing-docstring
 
-class Backend():
+
+class Backend:
     def __init__(self):
         self.inputs = []
         self.outputs = []
diff --git a/recommendation/dlrm_v2/pytorch/python/backend_dist_pytorch_native.py b/recommendation/dlrm_v2/pytorch/python/backend_dist_pytorch_native.py
index e988c3d1ed..5ff11b2bf1 100644
--- a/recommendation/dlrm_v2/pytorch/python/backend_dist_pytorch_native.py
+++ b/recommendation/dlrm_v2/pytorch/python/backend_dist_pytorch_native.py
@@ -1,6 +1,7 @@
 """
 pytoch native backend for dlrm
 """
+
 import os
 import torch
 import backend
@@ -61,7 +62,6 @@ def __init__(
         else:
             print("Using CPU...")
 
-        
         # assert ngpus == 8, "Reference implementation only supports ngpus = 8"
         os.environ["RANK"] = "0"
         os.environ["MASTER_ADDR"] = "localhost"
@@ -80,7 +80,7 @@ def version(self):
 
     def name(self):
         return "pytorch-native-dlrm"
-    
+
     def load(self, model_path, inputs=None, outputs=None):
         # debug prints
         # print(model_path, inputs, outputs)
@@ -93,7 +93,6 @@ def load(self, model_path, inputs=None, outputs=None):
         self.dataset_cache = manager.dict()
         self.predictions_cache = [manager.dict() for _ in range(world_size)]
         self.main_lock = manager.Event()
-        
 
         # Create processes to load model
         ctx = mp.get_context("spawn")
@@ -102,7 +101,9 @@ def load(self, model_path, inputs=None, outputs=None):
             p = ctx.Process(
                 target=self.distributed_setup,
                 args=(
-                    rank, world_size, model_path,
+                    rank,
+                    world_size,
+                    model_path,
                 ),
             )
             p.start()
@@ -110,13 +111,15 @@ def load(self, model_path, inputs=None, outputs=None):
         self.main_lock.wait()
 
         return self
-        
+
     def distributed_setup(self, rank, world_size, model_path):
         print("Initializing process...")
         if self.use_gpu:
             self.device = torch.device(f"cuda:{rank}")
             torch.cuda.set_device(f"cuda:{rank}")
-        dist.init_process_group(backend=self.dist_backend, rank=rank, world_size=world_size)
+        dist.init_process_group(
+            backend=self.dist_backend, rank=rank, world_size=world_size
+        )
         pg = dist.group.WORLD
         print("Initializing embeddings...")
         eb_configs = [
@@ -155,16 +158,20 @@ def distributed_setup(self, rank, world_size, model_path):
             model, get_default_sharders(), dist.GroupMember.WORLD
         )
         dist_model = DistributedModelParallel(
-            module=model, device=self.device, plan=plan, env=ShardingEnv.from_process_group(pg),
+            module=model,
+            device=self.device,
+            plan=plan,
+            env=ShardingEnv.from_process_group(pg),
         )
         self.model = dist_model
         if not self.debug:
             print("Loading model weights...")
             from torchsnapshot import Snapshot
+
             snapshot = Snapshot(path=model_path)
             snapshot.restore(app_state={"model": self.model})
 
-            ### To understand the keys in snapshot, you can look at following code snippet.
+            # To understand the keys in snapshot, you can look at following code snippet.
             # d = snapshot.get_manifest()
             # for k, v in d.items():
             #     print(k, v)
@@ -173,7 +180,7 @@ def distributed_setup(self, rank, world_size, model_path):
         self.main_lock.set()
 
         # Main prediction loop
-        while(True):
+        while True:
             item = self.samples_q[rank].get()
             # If -1 is received terminate all subprocesses
             if item == -1:
@@ -199,7 +206,6 @@ def capture_output(self, id):
         self.dataset_cache.pop(id)
         return out
 
-
     def predict(self, samples, ids):
         outputs = []
         # If none is received terminate all subprocesses
diff --git a/recommendation/dlrm_v2/pytorch/python/backend_pytorch_native.py b/recommendation/dlrm_v2/pytorch/python/backend_pytorch_native.py
index aadbdc8f24..19f2b44af1 100755
--- a/recommendation/dlrm_v2/pytorch/python/backend_pytorch_native.py
+++ b/recommendation/dlrm_v2/pytorch/python/backend_pytorch_native.py
@@ -1,6 +1,7 @@
 """
 pytoch native backend for dlrm
 """
+
 import os
 import torch
 import backend
@@ -68,7 +69,7 @@ def __init__(
             self.dist_backend = "nccl"
             # torch.cuda.set_device(self.device)
         else:
-            #os.environ["WORLD_SIZE"] = "8"
+            # os.environ["WORLD_SIZE"] = "8"
             self.device: torch.device = torch.device("cpu")
             self.dist_backend = "gloo"
 
@@ -82,9 +83,12 @@ def load(self, model_path, inputs=None, outputs=None):
         # debug prints
         # print(model_path, inputs, outputs)
         print(f"Loading model from {model_path}")
-        
+
         print("Initializing embeddings...")
-        dist.init_process_group(backend=self.dist_backend, rank=0, world_size=1)
+        dist.init_process_group(
+            backend=self.dist_backend,
+            rank=0,
+            world_size=1)
         eb_configs = [
             EmbeddingBagConfig(
                 name=f"t_{feature_name}",
@@ -121,9 +125,7 @@ def load(self, model_path, inputs=None, outputs=None):
             model, get_default_sharders(), dist.GroupMember.WORLD
         )
         self.model = DistributedModelParallel(
-            module=model,
-            device=self.device,
-            plan=plan
+            module=model, device=self.device, plan=plan
         )
         # path_to_sharded_weights should have 2 subdirectories - batched and sharded
         # If we need to load the weights on different device or world size, we would need to change the process
@@ -133,26 +135,24 @@ def load(self, model_path, inputs=None, outputs=None):
         if not self.debug:
             print("Loading model weights...")
             from torchsnapshot import Snapshot
+
             snapshot = Snapshot(path=model_path)
             snapshot.restore(app_state={"model": self.model})
 
-            ### To understand the keys in snapshot, you can look at following code snippet.
+            # To understand the keys in snapshot, you can look at following code snippet.
             # d = snapshot.get_manifest()
             # for k, v in d.items():
             #     print(k, v)
         self.model.eval()
         return self
 
-    def predict(self, samples, ids = None):
+    def predict(self, samples, ids=None):
         outputs = []
         for batch in samples:
             batch_in = batch.to(self.device)
             with torch.no_grad():
-                _, (_, out, _) = self.model(
-                    batch_in
-                )
+                _, (_, out, _) = self.model(batch_in)
                 out = torch.sigmoid(out)
                 out = torch.reshape(out, (-1,))
                 outputs.append(out)
         return outputs
-
diff --git a/recommendation/dlrm_v2/pytorch/python/dataset.py b/recommendation/dlrm_v2/pytorch/python/dataset.py
index c19483739e..422ed46369 100755
--- a/recommendation/dlrm_v2/pytorch/python/dataset.py
+++ b/recommendation/dlrm_v2/pytorch/python/dataset.py
@@ -14,7 +14,8 @@
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("dataset")
 
-class Item():
+
+class Item:
     def __init__(self, label, img, idx):
         self.label = label
         self.img = img
@@ -23,19 +24,25 @@ def __init__(self, label, img, idx):
 
 
 def usleep(sec):
-    if sys.platform == 'win32':
+    if sys.platform == "win32":
         # on windows time.sleep() doesn't work to well
         import ctypes
+
         kernel32 = ctypes.windll.kernel32
-        timer = kernel32.CreateWaitableTimerA(ctypes.c_void_p(), True, ctypes.c_void_p())
+        timer = kernel32.CreateWaitableTimerA(
+            ctypes.c_void_p(), True, ctypes.c_void_p()
+        )
         delay = ctypes.c_longlong(int(-1 * (10 * 1000000 * sec)))
-        kernel32.SetWaitableTimer(timer, ctypes.byref(delay), 0, ctypes.c_void_p(), ctypes.c_void_p(), False)
-        kernel32.WaitForSingleObject(timer, 0xffffffff)
+        kernel32.SetWaitableTimer(
+            timer, ctypes.byref(
+                delay), 0, ctypes.c_void_p(), ctypes.c_void_p(), False
+        )
+        kernel32.WaitForSingleObject(timer, 0xFFFFFFFF)
     else:
         time.sleep(sec)
 
 
-class Dataset():
+class Dataset:
     def __init__(self):
         self.arrival = None
         self.image_list = []
@@ -92,7 +99,7 @@ def start(self):
         self.good = 0
         self.total = 0
 
-    def finalize(self, results, ds=False,  output_dir=None):
+    def finalize(self, results, ds=False, output_dir=None):
         results["good"] = self.good
         results["total"] = self.total
 
@@ -125,4 +132,3 @@ def start(self):
     def finalize(self, results, ds=False, output_dir=None):
         results["good"] = self.good
         results["total"] = self.total
-
diff --git a/recommendation/dlrm_v2/pytorch/python/main.py b/recommendation/dlrm_v2/pytorch/python/main.py
index d931c661ad..88c3c45b8a 100755
--- a/recommendation/dlrm_v2/pytorch/python/main.py
+++ b/recommendation/dlrm_v2/pytorch/python/main.py
@@ -34,15 +34,24 @@
 
 # the datasets we support
 SUPPORTED_DATASETS = {
-    "debug":
-        (multihot_criteo.MultihotCriteo, multihot_criteo.pre_process_criteo_dlrm, multihot_criteo.DlrmPostProcess(),
-         {"randomize": 'total', "memory_map": True}),
-    "multihot-criteo-sample":
-        (multihot_criteo.MultihotCriteo, multihot_criteo.pre_process_criteo_dlrm, multihot_criteo.DlrmPostProcess(),
-         {"randomize": 'total', "memory_map": True}),
-    "multihot-criteo":
-        (multihot_criteo.MultihotCriteo, multihot_criteo.pre_process_criteo_dlrm, multihot_criteo.DlrmPostProcess(),
-         {"randomize": 'total', "memory_map": True}),
+    "debug": (
+        multihot_criteo.MultihotCriteo,
+        multihot_criteo.pre_process_criteo_dlrm,
+        multihot_criteo.DlrmPostProcess(),
+        {"randomize": "total", "memory_map": True},
+    ),
+    "multihot-criteo-sample": (
+        multihot_criteo.MultihotCriteo,
+        multihot_criteo.pre_process_criteo_dlrm,
+        multihot_criteo.DlrmPostProcess(),
+        {"randomize": "total", "memory_map": True},
+    ),
+    "multihot-criteo": (
+        multihot_criteo.MultihotCriteo,
+        multihot_criteo.pre_process_criteo_dlrm,
+        multihot_criteo.DlrmPostProcess(),
+        {"randomize": "total", "memory_map": True},
+    ),
 }
 
 # pre-defined command line options so simplify things. They are used as defaults and can be
@@ -80,7 +89,7 @@
         "backend": "pytorch-native",
         "model": "dlrm",
         "max-batchsize": 2048,
-    }
+    },
 }
 
 SCENARIO_MAP = {
@@ -110,16 +119,18 @@ def get_args():
         required=True,
         help="path to the dataset")
     parser.add_argument(
-        "--profile",
-        choices=SUPPORTED_PROFILES.keys(),
-        help="standard profiles")
-    parser.add_argument("--scenario", default="SingleStream",
-                        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())))
+        "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
+    )
+    parser.add_argument(
+        "--scenario",
+        default="SingleStream",
+        help="mlperf benchmark scenario, one of " +
+        str(list(SCENARIO_MAP.keys())),
+    )
     parser.add_argument("--max-ind-range", type=int, default=-1)
     parser.add_argument(
-        "--max-batchsize",
-        type=int,
-        help="max batch size in a single inference")
+        "--max-batchsize", type=int, help="max batch size in a single inference"
+    )
     parser.add_argument("--output", help="test results")
     parser.add_argument("--inputs", help="model inputs (currently not used)")
     parser.add_argument("--outputs", help="model outputs (currently not used)")
@@ -137,13 +148,15 @@ def get_args():
     parser.add_argument(
         "--find-peak-performance",
         action="store_true",
-        help="enable finding peak performance pass")
+        help="enable finding peak performance pass",
+    )
 
     # file for user LoadGen settings such as target QPS
     parser.add_argument(
         "--user_conf",
         default="user.conf",
-        help="user config for user LoadGen settings such as target QPS")
+        help="user config for user LoadGen settings such as target QPS",
+    )
 
     # below will override mlperf rules compliant settings - don't use for
     # official submission
@@ -153,9 +166,8 @@ def get_args():
         help="duration in milliseconds (ms)")
     parser.add_argument("--target-qps", type=int, help="target/expected qps")
     parser.add_argument(
-        "--max-latency",
-        type=float,
-        help="mlperf max latency in pct tile")
+        "--max-latency", type=float, help="mlperf max latency in pct tile"
+    )
     parser.add_argument(
         "--count-samples",
         type=int,
@@ -168,33 +180,40 @@ def get_args():
         "--samples-per-query-multistream",
         default=8,
         type=int,
-        help="query length for multi-stream scenario (in terms of aggregated samples)")
+        help="query length for multi-stream scenario (in terms of aggregated samples)",
+    )
     # --samples-per-query-offline is equivalent to perf_sample_count
     parser.add_argument(
         "--samples-per-query-offline",
         type=int,
         default=2048,
-        help="query length for offline scenario (in terms of aggregated samples)")
+        help="query length for offline scenario (in terms of aggregated samples)",
+    )
     parser.add_argument(
         "--samples-to-aggregate-fix",
         type=int,
-        help="number of samples to be treated as one")
+        help="number of samples to be treated as one",
+    )
     parser.add_argument(
         "--samples-to-aggregate-min",
         type=int,
-        help="min number of samples to be treated as one in random query size")
+        help="min number of samples to be treated as one in random query size",
+    )
     parser.add_argument(
         "--samples-to-aggregate-max",
         type=int,
-        help="max number of samples to be treated as one in random query size")
+        help="max number of samples to be treated as one in random query size",
+    )
     parser.add_argument(
         "--samples-to-aggregate-quantile-file",
         type=str,
-        help="distribution quantile used to generate number of samples to be treated as one in random query size")
+        help="distribution quantile used to generate number of samples to be treated as one in random query size",
+    )
     parser.add_argument(
         "--samples-to-aggregate-trace-file",
         type=str,
-        default="dlrm_trace_of_aggregated_samples.txt")
+        default="dlrm_trace_of_aggregated_samples.txt",
+    )
     parser.add_argument("--numpy-rand-seed", type=int, default=123)
     parser.add_argument("--debug", action="store_true", default=False)
     args = parser.parse_args()
@@ -227,6 +246,7 @@ def get_backend(backend, dataset, use_gpu, debug):
     if backend == "pytorch-native":
         from backend_pytorch_native import BackendPytorchNative
         from backend_dist_pytorch_native import BackendDistPytorchNative
+
         n_cores = int(os.environ.get("WORLD_SIZE", 1))
         if n_cores > 1:
             if dataset == "debug":
@@ -239,7 +259,7 @@ def get_backend(backend, dataset, use_gpu, debug):
                     dense_arch_layer_sizes=[512, 256, 128],
                     over_arch_layer_sizes=[1024, 1024, 512, 256, 1],
                     use_gpu=use_gpu,
-                    debug=True
+                    debug=True,
                 )
             elif dataset == "multihot-criteo-sample":
                 # 2. Syntetic multihot criteo sample
@@ -270,14 +290,15 @@ def get_backend(backend, dataset, use_gpu, debug):
                         590152,
                         12973,
                         108,
-                        36],
+                        36,
+                    ],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
                     dense_arch_layer_sizes=[512, 256, 128],
                     over_arch_layer_sizes=[1024, 1024, 512, 256, 1],
                     use_gpu=use_gpu,
-                    debug=debug
+                    debug=debug,
                 )
             elif dataset == "multihot-criteo":
                 # 3. Syntetic multihot criteo
@@ -308,18 +329,20 @@ def get_backend(backend, dataset, use_gpu, debug):
                         590152,
                         12973,
                         108,
-                        36],
+                        36,
+                    ],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
                     dense_arch_layer_sizes=[512, 256, 128],
                     over_arch_layer_sizes=[1024, 1024, 512, 256, 1],
                     use_gpu=use_gpu,
-                    debug=debug
+                    debug=debug,
                 )
             else:
                 raise ValueError(
-                    "only debug|multihot-criteo-sample|multihot-criteo dataset options are supported")
+                    "only debug|multihot-criteo-sample|multihot-criteo dataset options are supported"
+                )
         else:
             if dataset == "debug":
                 # 1. Syntetic debug dataset
@@ -331,7 +354,7 @@ def get_backend(backend, dataset, use_gpu, debug):
                     dense_arch_layer_sizes=[512, 256, 128],
                     over_arch_layer_sizes=[1024, 1024, 512, 256, 1],
                     use_gpu=use_gpu,
-                    debug=True
+                    debug=True,
                 )
             elif dataset == "multihot-criteo-sample":
                 # 2. Syntetic multihot criteo sample
@@ -362,14 +385,15 @@ def get_backend(backend, dataset, use_gpu, debug):
                         590152,
                         12973,
                         108,
-                        36],
+                        36,
+                    ],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
                     dense_arch_layer_sizes=[512, 256, 128],
                     over_arch_layer_sizes=[1024, 1024, 512, 256, 1],
                     use_gpu=use_gpu,
-                    debug=debug
+                    debug=debug,
                 )
             elif dataset == "multihot-criteo":
                 # 3. Syntetic multihot criteo
@@ -400,18 +424,20 @@ def get_backend(backend, dataset, use_gpu, debug):
                         590152,
                         12973,
                         108,
-                        36],
+                        36,
+                    ],
                     embedding_dim=128,
                     dcn_num_layers=3,
                     dcn_low_rank_dim=512,
                     dense_arch_layer_sizes=[512, 256, 128],
                     over_arch_layer_sizes=[1024, 1024, 512, 256, 1],
                     use_gpu=use_gpu,
-                    debug=debug
+                    debug=debug,
                 )
             else:
                 raise ValueError(
-                    "only debug|multihot-criteo-sample|multihot-criteo dataset options are supported")
+                    "only debug|multihot-criteo-sample|multihot-criteo dataset options are supported"
+                )
 
     else:
         raise ValueError("unknown backend: " + backend)
@@ -456,7 +482,8 @@ def run_one_item(self, qitem):
         try:
             results = self.model.predict(qitem.features, qitem.content_id)
             processed_results = self.post_process(
-                results, qitem.batch_T, self.result_dict)
+                results, qitem.batch_T, self.result_dict
+            )
             if self.take_accuracy:
                 self.post_process.add_results(processed_results)
             self.result_timing.append(time.time() - qitem.start)
@@ -476,8 +503,10 @@ def run_one_item(self, qitem):
                 # print("s,e:",s_idx,e_idx, len(processed_results))
                 s_idx = qitem.idx_offsets[idx]
                 e_idx = qitem.idx_offsets[idx + 1]
-                response_array = array.array("B", np.array(
-                    processed_results[s_idx:e_idx], np.float32).tobytes())
+                response_array = array.array(
+                    "B", np.array(
+                        processed_results[s_idx:e_idx], np.float32).tobytes()
+                )
                 response_array_refs.append(response_array)
                 bi = response_array.buffer_info()
                 response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
@@ -506,7 +535,9 @@ def enqueue(self, query_samples):
                 samples, idx_offsets = self.ds.get_samples(idx[i:ie])
                 batch_T = [self.ds.get_labels(sample) for sample in samples]
                 self.run_one_item(
-                    Item(query_id[i:ie], idx[i:ie], samples, batch_T, idx_offsets))
+                    Item(query_id[i:ie], idx[i:ie],
+                         samples, batch_T, idx_offsets)
+                )
 
     def finish(self):
         pass
@@ -556,7 +587,9 @@ def enqueue(self, query_samples):
                 samples, idx_offsets = self.ds.get_samples(idx)
                 batch_T = [self.ds.get_labels(sample) for sample in samples]
                 self.tasks.put(
-                    Item(query_id[i:ie], idx[i:ie], samples, batch_T, idx_offsets))
+                    Item(query_id[i:ie], idx[i:ie],
+                         samples, batch_T, idx_offsets)
+                )
 
     def finish(self):
         # exit all threads
@@ -566,12 +599,14 @@ def finish(self):
             worker.join()
 
 
-def add_results(final_results, name, result_dict,
-                result_list, took, show_accuracy=False):
-    percentiles = [50., 80., 90., 95., 99., 99.9]
+def add_results(
+    final_results, name, result_dict, result_list, took, show_accuracy=False
+):
+    percentiles = [50.0, 80.0, 90.0, 95.0, 99.0, 99.9]
     buckets = np.percentile(result_list, percentiles).tolist()
-    buckets_str = ",".join(["{}:{:.4f}".format(p, b)
-                           for p, b in zip(percentiles, buckets)])
+    buckets_str = ",".join(
+        ["{}:{:.4f}".format(p, b) for p, b in zip(percentiles, buckets)]
+    )
 
     if result_dict["total"] == 0:
         result_dict["total"] = len(result_list)
@@ -588,19 +623,27 @@ def add_results(final_results, name, result_dict,
     }
     acc_str = ""
     if show_accuracy:
-        result["accuracy"] = 100. * result_dict["good"] / result_dict["total"]
+        result["accuracy"] = 100.0 * result_dict["good"] / result_dict["total"]
         acc_str = ", acc={:.3f}%".format(result["accuracy"])
         if "roc_auc" in result_dict:
-            result["roc_auc"] = 100. * result_dict["roc_auc"]
+            result["roc_auc"] = 100.0 * result_dict["roc_auc"]
             acc_str += ", auc={:.3f}%".format(result["roc_auc"])
 
     # add the result to the result dict
     final_results[name] = result
 
     # to stdout
-    print("{} qps={:.2f}, mean={:.4f}, time={:.3f}{}, queries={}, tiles={}".format(
-        name, result["qps"], result["mean"], took, acc_str,
-        len(result_list), buckets_str))
+    print(
+        "{} qps={:.2f}, mean={:.4f}, time={:.3f}{}, queries={}, tiles={}".format(
+            name,
+            result["qps"],
+            result["mean"],
+            took,
+            acc_str,
+            len(result_list),
+            buckets_str,
+        )
+    )
 
 
 def main():
@@ -620,18 +663,47 @@ def main():
     wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
 
     # --count-samples can be used to limit the number of samples used for testing
-    ds = wanted_dataset(num_embeddings_per_feature=[40000000, 39060, 17295, 7424, 20265, 3, 7122, 1543, 63, 40000000, 3067956, 405282, 10, 2209, 11938, 155, 4, 976, 14, 40000000, 40000000, 40000000, 590152, 12973, 108, 3],
-                        data_path=args.dataset_path,
-                        name=args.dataset,
-                        pre_process=pre_proc,  # currently an identity function
-                        count=args.count_samples,
-                        samples_to_aggregate_fix=args.samples_to_aggregate_fix,
-                        samples_to_aggregate_min=args.samples_to_aggregate_min,
-                        samples_to_aggregate_max=args.samples_to_aggregate_max,
-                        samples_to_aggregate_quantile_file=args.samples_to_aggregate_quantile_file,
-                        samples_to_aggregate_trace_file=args.samples_to_aggregate_trace_file,
-                        max_ind_range=args.max_ind_range,
-                        **kwargs)
+    ds = wanted_dataset(
+        num_embeddings_per_feature=[
+            40000000,
+            39060,
+            17295,
+            7424,
+            20265,
+            3,
+            7122,
+            1543,
+            63,
+            40000000,
+            3067956,
+            405282,
+            10,
+            2209,
+            11938,
+            155,
+            4,
+            976,
+            14,
+            40000000,
+            40000000,
+            40000000,
+            590152,
+            12973,
+            108,
+            3,
+        ],
+        data_path=args.dataset_path,
+        name=args.dataset,
+        pre_process=pre_proc,  # currently an identity function
+        count=args.count_samples,
+        samples_to_aggregate_fix=args.samples_to_aggregate_fix,
+        samples_to_aggregate_min=args.samples_to_aggregate_min,
+        samples_to_aggregate_max=args.samples_to_aggregate_max,
+        samples_to_aggregate_quantile_file=args.samples_to_aggregate_quantile_file,
+        samples_to_aggregate_trace_file=args.samples_to_aggregate_trace_file,
+        max_ind_range=args.max_ind_range,
+        **kwargs
+    )
     # load model to backend
     model = backend.load(
         args.model_path,
@@ -672,15 +744,12 @@ def main():
         lg.TestScenario.SingleStream: RunnerBase,
         lg.TestScenario.MultiStream: QueueRunner,
         lg.TestScenario.Server: QueueRunner,
-        lg.TestScenario.Offline: QueueRunner
+        lg.TestScenario.Offline: QueueRunner,
     }
 
     runner = runner_map[scenario](
-        model,
-        ds,
-        args.threads,
-        post_proc=post_proc,
-        max_batchsize=args.max_batchsize)
+        model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize
+    )
 
     def issue_queries(query_samples):
         runner.enqueue(query_samples)
@@ -722,11 +791,12 @@ def flush_queries():
             args.max_latency * NANO_SEC)
 
     sut = lg.ConstructSUT(issue_queries, flush_queries)
-    qsl = lg.ConstructQSL(count,
-                          min(count,
-                              args.samples_per_query_offline),
-                          ds.load_query_samples,
-                          ds.unload_query_samples)
+    qsl = lg.ConstructQSL(
+        count,
+        min(count, args.samples_per_query_offline),
+        ds.load_query_samples,
+        ds.unload_query_samples,
+    )
 
     log.info("starting {}".format(scenario))
     result_dict = {
@@ -745,14 +815,20 @@ def flush_queries():
     if args.accuracy:
         post_proc.finalize(result_dict, ds, output_dir=args.output)
 
-    add_results(final_results, "{}".format(scenario),
-                result_dict, last_timeing, time.time() - ds.last_loaded, args.accuracy)
+    add_results(
+        final_results,
+        "{}".format(scenario),
+        result_dict,
+        last_timeing,
+        time.time() - ds.last_loaded,
+        args.accuracy,
+    )
 
     runner.finish()
     lg.DestroyQSL(qsl)
     lg.DestroySUT(sut)
     # If multiple subprocesses are running the model send a signal to stop them
-    if (int(os.environ.get("WORLD_SIZE", 1)) > 1):
+    if int(os.environ.get("WORLD_SIZE", 1)) > 1:
         backend.predict(None, None)
 
     #
diff --git a/recommendation/dlrm_v2/pytorch/python/multihot_criteo.py b/recommendation/dlrm_v2/pytorch/python/multihot_criteo.py
index efbea8195e..c0f7ab3ad8 100755
--- a/recommendation/dlrm_v2/pytorch/python/multihot_criteo.py
+++ b/recommendation/dlrm_v2/pytorch/python/multihot_criteo.py
@@ -4,6 +4,13 @@
 
 # pylint: disable=unused-argument,missing-docstring
 
+from torchrec.datasets.criteo import (
+    CAT_FEATURE_COUNT,
+    DAYS,
+    DEFAULT_CAT_NAMES,
+    DEFAULT_INT_NAMES,
+)
+from dataset import Dataset
 import logging
 import os
 import sys
@@ -24,14 +31,6 @@
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("criteo")
 
-from dataset import Dataset
-from torchrec.datasets.criteo import (
-    CAT_FEATURE_COUNT,
-    DAYS,
-    DEFAULT_CAT_NAMES,
-    DEFAULT_INT_NAMES,
-)
-
 
 class MultihotCriteo(Dataset):
     def __init__(
@@ -74,19 +73,22 @@ def __init__(
         if name == "debug":
             stage_files = [
                 [os.path.join(data_path, f"day_{DAYS-1}_dense_debug.npy")],
-                [os.path.join(data_path, f"day_{DAYS-1}_sparse_multi_hot_debug.npz")],
+                [os.path.join(data_path,
+                              f"day_{DAYS-1}_sparse_multi_hot_debug.npz")],
                 [os.path.join(data_path, f"day_{DAYS-1}_labels_debug.npy")],
             ]
         elif name == "multihot-criteo-sample":
             stage_files = [
                 [os.path.join(data_path, f"day_{DAYS-1}_dense_sample.npy")],
-                [os.path.join(data_path, f"day_{DAYS-1}_sparse_multi_hot_sample.npz")],
+                [os.path.join(data_path,
+                              f"day_{DAYS-1}_sparse_multi_hot_sample.npz")],
                 [os.path.join(data_path, f"day_{DAYS-1}_labels_sample.npy")],
             ]
         elif name == "multihot-criteo":
             stage_files = [
                 [os.path.join(data_path, f"day_{DAYS-1}_dense.npy")],
-                [os.path.join(data_path, f"day_{DAYS-1}_sparse_multi_hot.npz")],
+                [os.path.join(data_path,
+                              f"day_{DAYS-1}_sparse_multi_hot.npz")],
                 [os.path.join(data_path, f"day_{DAYS-1}_labels.npy")],
             ]
         else:
@@ -112,7 +114,8 @@ def __init__(
         # of size samples_to_aggregate as an item we need to adjust the original dataset item_count.
         # On the other hand, data loader always returns number of batches.
         if self.use_fixed_size:
-            # the offsets for fixed query size will be generated on-the-fly later on
+            # the offsets for fixed query size will be generated on-the-fly
+            # later on
             print("Using fixed query size: " + str(self.samples_to_aggregate))
             self.num_aggregated_samples = (
                 self.num_individual_samples + self.samples_to_aggregate - 1
@@ -121,7 +124,8 @@ def __init__(
         else:
             # the offsets for variable query sizes will be pre-generated here
             if self.samples_to_aggregate_quantile_file is None:
-                # generate number of samples in a query from a uniform(min,max) distribution
+                # generate number of samples in a query from a uniform(min,max)
+                # distribution
                 print(
                     "Using variable query size: uniform distribution ("
                     + str(self.samples_to_aggregate_min)
@@ -160,7 +164,8 @@ def __init__(
                 # The inverse of its cdf with granularity of 0.05 can be written as
                 # quantile_p = [.05, .10, .15, .20, .25, .30, .35, .40, .45, .50, .55, .60, .65, .70, .75, .80, .85, .90, .95, 1.0] # p
                 # quantile_x = [100, 100, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 300, 300, 400, 500, 600, 700] # q(p) = x, such that f(x) >= p
-                # Notice that once we have quantile, we can apply inverse transform sampling method.
+                # Notice that once we have quantile, we can apply inverse
+                # transform sampling method.
                 print(
                     "Using variable query size: custom distribution (file "
                     + str(samples_to_aggregate_quantile_file)
@@ -186,7 +191,8 @@ def __init__(
                 self.random_offsets.append(int(qo))
 
                 # compute min and max number of samples
-                nas_max = (self.num_individual_samples + quantile[0] - 1) // quantile[0]
+                nas_max = (self.num_individual_samples +
+                           quantile[0] - 1) // quantile[0]
                 nas_min = (self.num_individual_samples + quantile[-1] - 1) // quantile[
                     -1
                 ]
@@ -203,7 +209,8 @@ def __init__(
 
         # limit number of items to count if needed
         if self.count is not None:
-            self.num_aggregated_samples = min(self.count, self.num_aggregated_samples)
+            self.num_aggregated_samples = min(
+                self.count, self.num_aggregated_samples)
 
         # dump the trace of aggregated samples
         if samples_to_aggregate_trace_file is not None:
@@ -268,7 +275,7 @@ def get_samples(self, id_list):
         for item in id_list:
             idx_offsets.append(idx_offsets[-1] + self.item_sizes[item])
         return [self.items_in_memory[item] for item in id_list], idx_offsets
-    
+
     def get_labels(self, sample):
         if isinstance(sample, list):
             labels = [s.labels for s in sample]
@@ -298,7 +305,7 @@ def __init__(
         self.batch_size = batch_size
         self.rank = rank
         self.world_size = world_size
-        self.split = (self.world_size > 1)
+        self.split = self.world_size > 1
 
         # Load arrays
         m = "r" if mmap_mode else None
@@ -320,7 +327,7 @@ def __init__(
 
         len_d0 = len(self.dense_arrs[0])
         second_half_start_index = int(len_d0 // 2 + len_d0 % 2)
-        if (stage == "val" and name == "multihot-criteo"):
+        if stage == "val" and name == "multihot-criteo":
             self.dense_arrs[0] = self.dense_arrs[0][:second_half_start_index, :]
             self.labels_arrs[0] = self.labels_arrs[0][:second_half_start_index, :]
             self.sparse_arrs[0] = [
@@ -364,7 +371,8 @@ def _load_from_npz(self, fname, npy_name):
         # read .npy header
         zf.open(npy_name, "r")
         version = np.lib.format.read_magic(zf.fp)
-        shape, fortran_order, dtype = np.lib.format._read_array_header(zf.fp, version)
+        shape, fortran_order, dtype = np.lib.format._read_array_header(
+            zf.fp, version)
         assert (
             dtype == "int32"
         ), f"sparse multi-hot dtype is {dtype} but should be int32"
@@ -380,20 +388,27 @@ def _load_from_npz(self, fname, npy_name):
         )
 
     def _np_arrays_to_batch(
-        self, dense: np.ndarray, sparse: List[np.ndarray], labels: np.ndarray,
+        self,
+        dense: np.ndarray,
+        sparse: List[np.ndarray],
+        labels: np.ndarray,
     ) -> Batch:
         batch_size = len(dense)
-        lengths = torch.ones((CAT_FEATURE_COUNT * batch_size), dtype=torch.int32)
+        lengths = torch.ones(
+            (CAT_FEATURE_COUNT * batch_size),
+            dtype=torch.int32)
         for k, multi_hot_size in enumerate(self.multi_hot_sizes):
-            lengths[k * batch_size : (k + 1) * batch_size] = multi_hot_size
-        offsets = torch.cumsum(torch.concat((torch.tensor([0]), lengths)), dim=0)
+            lengths[k * batch_size: (k + 1) * batch_size] = multi_hot_size
+        offsets = torch.cumsum(torch.concat(
+            (torch.tensor([0]), lengths)), dim=0)
         length_per_key = [
             batch_size * multi_hot_size for multi_hot_size in self.multi_hot_sizes
         ]
         offset_per_key = torch.cumsum(
             torch.concat((torch.tensor([0]), torch.tensor(length_per_key))), dim=0
         )
-        values = torch.concat([torch.from_numpy(feat).flatten() for feat in sparse])
+        values = torch.concat([torch.from_numpy(feat).flatten()
+                              for feat in sparse])
         return Batch(
             dense_features=torch.from_numpy(dense.copy()),
             sparse_features=KeyedJaggedTensor(
@@ -413,11 +428,16 @@ def load_batch(self, sample_list) -> Union[Batch, List[Batch]]:
         if self.split:
             batch = []
             n_samples = len(sample_list)
-            limits = [i*n_samples//self.world_size for i in range(self.world_size + 1)]
+            limits = [
+                i * n_samples // self.world_size for i in range(self.world_size + 1)
+            ]
             for i in range(self.world_size):
-                dense = self.dense_arrs[0][sample_list[limits[i]:limits[i+1]], :]
-                sparse = [arr[sample_list[limits[i]:limits[i+1]], :] for arr in self.sparse_arrs[0]]
-                labels = self.labels_arrs[0][sample_list[limits[i]:limits[i+1]], :]
+                dense = self.dense_arrs[0][sample_list[limits[i]: limits[i + 1]], :]
+                sparse = [
+                    arr[sample_list[limits[i]: limits[i + 1]], :]
+                    for arr in self.sparse_arrs[0]
+                ]
+                labels = self.labels_arrs[0][sample_list[limits[i]: limits[i + 1]], :]
                 batch.append(self._np_arrays_to_batch(dense, sparse, labels))
             return batch
         else:
@@ -445,7 +465,8 @@ def __call__(self, results, expected=None, result_dict=None):
         n = len(results)
         for idx in range(0, n):
             # NOTE: copy from GPU to CPU while post processing, if needed. Alternatively,
-            # we could do this on the output of predict function in backend_pytorch_native.py
+            # we could do this on the output of predict function in
+            # backend_pytorch_native.py
             result = results[idx].detach().cpu()
             target = expected[idx]
 
diff --git a/recommendation/dlrm_v2/pytorch/python/version.py b/recommendation/dlrm_v2/pytorch/python/version.py
index 1152dbb417..570348596c 100644
--- a/recommendation/dlrm_v2/pytorch/python/version.py
+++ b/recommendation/dlrm_v2/pytorch/python/version.py
@@ -1,3 +1,2 @@
-
-version = '0.1.0'
-git_version = '05df3bae82ef9fc933277385eb778e3f22cd0c6a'
+version = "0.1.0"
+git_version = "05df3bae82ef9fc933277385eb778e3f22cd0c6a"
diff --git a/recommendation/dlrm_v2/pytorch/setup.py b/recommendation/dlrm_v2/pytorch/setup.py
index c1e2fbcf07..758d874fbd 100644
--- a/recommendation/dlrm_v2/pytorch/setup.py
+++ b/recommendation/dlrm_v2/pytorch/setup.py
@@ -13,17 +13,20 @@
 from setuptools import setup, find_packages, Command
 
 TOP_DIR = os.path.realpath(os.path.dirname(__file__))
-SRC_DIR = os.path.join(TOP_DIR, 'python')
+SRC_DIR = os.path.join(TOP_DIR, "python")
 
 try:
-    git_version = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=TOP_DIR).decode('ascii').strip()
+    git_version = (
+        subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=TOP_DIR)
+        .decode("ascii")
+        .strip()
+    )
 except (OSError, subprocess.CalledProcessError):
     git_version = None
 
-with open(os.path.join(TOP_DIR, 'VERSION_NUMBER')) as version_file:
-    VersionInfo = namedtuple('VersionInfo', ['version', 'git_version'])(
-        version=version_file.read().strip(),
-        git_version=git_version
+with open(os.path.join(TOP_DIR, "VERSION_NUMBER")) as version_file:
+    VersionInfo = namedtuple("VersionInfo", ["version", "git_version"])(
+        version=version_file.read().strip(), git_version=git_version
     )
 
 
@@ -37,49 +40,67 @@ def finalize_options(self):
         pass
 
     def run(self):
-        with open(os.path.join(SRC_DIR, 'version.py'), 'w') as f:
-            f.write(dedent('''
+        with open(os.path.join(SRC_DIR, "version.py"), "w") as f:
+            f.write(
+                dedent(
+                    """
             version = '{version}'
             git_version = '{git_version}'
-            '''.format(**dict(VersionInfo._asdict()))))
+            """.format(
+                        **dict(VersionInfo._asdict())
+                    )
+                )
+            )
 
 
 class build_py(setuptools.command.build_py.build_py):
     def run(self):
-        self.run_command('create_version')
+        self.run_command("create_version")
         setuptools.command.build_py.build_py.run(self)
 
 
 class build(distutils.command.build.build):
     def run(self):
-        self.run_command('build_py')
+        self.run_command("build_py")
 
 
 class develop(setuptools.command.develop.develop):
     def run(self):
-        self.run_command('create_version')
-        self.run_command('build')
+        self.run_command("create_version")
+        self.run_command("build")
         setuptools.command.develop.develop.run(self)
 
 
 cmdclass = {
-    'create_version': create_version,
-    'build_py': build_py,
-    'build': build,
-    'develop': develop,
+    "create_version": create_version,
+    "build_py": build_py,
+    "build": build,
+    "develop": develop,
 }
 
 setup(
     name="mlperf-inference",
     version=VersionInfo.version,
-    description='mlperf inference benchmark',
-    setup_requires=['pytest-runner'],
-    tests_require=['graphviz', 'parameterized', 'pytest', 'pytest-cov', 'pyyaml'],
+    description="mlperf inference benchmark",
+    setup_requires=["pytest-runner"],
+    tests_require=[
+        "graphviz",
+        "parameterized",
+        "pytest",
+        "pytest-cov",
+        "pyyaml"],
     cmdclass=cmdclass,
     packages=find_packages(),
-    author='guschmue@microsoft.com',
-    author_email='guschmue@microsoft.com',
-    url='https://github.com/mlperf/inference',
-    install_requires=['numpy>=1.14.1', 'onnx>=1.5', 'pybind11', 'Cython',
-                        'pycocotools', 'mlperf_loadgen', 'opencv-python-headless']
+    author="guschmue@microsoft.com",
+    author_email="guschmue@microsoft.com",
+    url="https://github.com/mlperf/inference",
+    install_requires=[
+        "numpy>=1.14.1",
+        "onnx>=1.5",
+        "pybind11",
+        "Cython",
+        "pycocotools",
+        "mlperf_loadgen",
+        "opencv-python-headless",
+    ],
 )
diff --git a/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py b/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py
index ce662071f5..2420dab307 100644
--- a/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py
+++ b/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py
@@ -16,25 +16,39 @@
 
 # pylint: disable=missing-docstring
 
+
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--day-23-file", default=None,
-        help="path to day_23 file. If present, it is assumed that the accuracy log contains only the prediction, not the ground truth label.")
-    parser.add_argument("--aggregation-trace-file", default=None,
-        help="path to dlrm_trace_of_aggregated_samples.txt. Only needed if --day-23-file is specified")
-    parser.add_argument("--verbose", action="store_true", help="verbose messages")
-    parser.add_argument("--dtype", default="float32", choices=["float32", "int32", "int64"], help="data type of the label")
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--day-23-file",
+        default=None,
+        help="path to day_23 file. If present, it is assumed that the accuracy log contains only the prediction, not the ground truth label.",
+    )
+    parser.add_argument(
+        "--aggregation-trace-file",
+        default=None,
+        help="path to dlrm_trace_of_aggregated_samples.txt. Only needed if --day-23-file is specified",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--dtype",
+        default="float32",
+        choices=["float32", "int32", "int64"],
+        help="data type of the label",
+    )
     args = parser.parse_args()
     return args
 
 
-dtype_map = {
-    "float32": np.float32,
-    "int32": np.int32,
-    "int64": np.int64
-}
+dtype_map = {"float32": np.float32, "int32": np.int32, "int64": np.int64}
+
 
 def get_targets(args, qsl_indices):
     # Parse aggregation trace file to know the sample -> user-item pair mapping
@@ -42,10 +56,14 @@ def get_targets(args, qsl_indices):
     sample_boundaries = [0]
     with open(args.aggregation_trace_file) as f:
         for line in f:
-            sample_boundaries.append(sample_boundaries[-1] + int(line.split(", ")[2]))
+            sample_boundaries.append(
+                sample_boundaries[-1] + int(line.split(", ")[2]))
     if len(sample_boundaries) != len(qsl_indices) + 1:
-        print("Warning: number of samples in trace file ({}) does not match number of samples ({}) in "
-              "loadgen accuracy log!".format(len(sample_boundaries)-1, len(qsl_indices)))
+        print(
+            "Warning: number of samples in trace file ({}) does not match number of samples ({}) in "
+            "loadgen accuracy log!".format(
+                len(sample_boundaries) - 1, len(qsl_indices))
+        )
     # Get all the ground truth labels in the original order in day_23
     print("Parsing ground truth labels from day_23 file...")
     ground_truths = []
@@ -54,18 +72,22 @@ def get_targets(args, qsl_indices):
             if line_idx >= sample_boundaries[-1]:
                 break
             ground_truths.append(int(line.split("\t")[0]))
-    # Re-order the ground truth labels according to the qsl indices in the loadgen log.
+    # Re-order the ground truth labels according to the qsl indices in the
+    # loadgen log.
     print("Re-ordering ground truth labels...")
     targets = []
     for qsl_idx in qsl_indices:
-        for i in range(sample_boundaries[qsl_idx], sample_boundaries[qsl_idx + 1]):
+        for i in range(sample_boundaries[qsl_idx],
+                       sample_boundaries[qsl_idx + 1]):
             targets.append(ground_truths[i])
     return targets
 
+
 def main():
     args = get_args()
 
-    # If "--day-23-file" is specified, assume that the accuracy log contains only the prediction, not the ground truth label.
+    # If "--day-23-file" is specified, assume that the accuracy log contains
+    # only the prediction, not the ground truth label.
     log_contains_gt = args.day_23_file is None
 
     if log_contains_gt:
@@ -79,12 +101,12 @@ def main():
 
     seen = set()
     good = 0
-    total= 0
+    total = 0
     all_results = []
     all_targets = []
     qsl_indices = []
     for j in results:
-        idx = j['qsl_idx']
+        idx = j["qsl_idx"]
 
         # de-dupe in case loadgen sends the same sample multiple times
         if idx in seen:
@@ -93,7 +115,7 @@ def main():
         qsl_indices.append(idx)
 
         # reconstruct label from mlperf accuracy log
-        data = np.frombuffer(bytes.fromhex(j['data']), dtype_map[args.dtype])
+        data = np.frombuffer(bytes.fromhex(j["data"]), dtype_map[args.dtype])
 
         # data stores both predictions and targets
         output_count = 2 if log_contains_gt else 1
@@ -116,7 +138,11 @@ def main():
                     good += 1
                 else:
                     if args.verbose:
-                        print("{}:{}, expected: {}, found {}".format(idx, k, target, result.round()))
+                        print(
+                            "{}:{}, expected: {}, found {}".format(
+                                idx, k, target, result.round()
+                            )
+                        )
 
     if not log_contains_gt:
         all_targets = get_targets(args, qsl_indices)
@@ -131,9 +157,16 @@ def main():
     roc_auc = sklearn.metrics.roc_auc_score(all_targets, all_results)
     # compute accuracy metric
     acc = good / total
-    print("AUC={:.3f}%, accuracy={:.3f}%, good={}, total={}, queries={}".format(100. * roc_auc, 100. * acc, good, total, len(seen)))
+    print(
+        "AUC={:.3f}%, accuracy={:.3f}%, good={}, total={}, queries={}".format(
+            100.0 * roc_auc, 100.0 * acc, good, total, len(seen)
+        )
+    )
     if args.verbose:
-        print("found and ignored {} query dupes".format(len(results) - len(seen)))
+        print(
+            "found and ignored {} query dupes".format(
+                len(results) -
+                len(seen)))
 
 
 if __name__ == "__main__":
diff --git a/recommendation/dlrm_v2/pytorch/tools/quickgen.py b/recommendation/dlrm_v2/pytorch/tools/quickgen.py
index 895a7cedef..c9cf4cbd30 100644
--- a/recommendation/dlrm_v2/pytorch/tools/quickgen.py
+++ b/recommendation/dlrm_v2/pytorch/tools/quickgen.py
@@ -1,21 +1,26 @@
-'''
+"""
 quick generator of random samples for debugging
-'''
+"""
 
 import sys
 import argparse
 import numpy as np
 
+
 def quickgen(num_samples, num_t, num_d, multihot_sizes, text_file=None):
     # generate place holder random array, including dense features
-    dense_features = np.random.uniform(0., 9., size = (num_samples, num_d)).astype(np.float32)
+    dense_features = np.random.uniform(0.0, 9.0, size=(num_samples, num_d)).astype(
+        np.float32
+    )
     # generate targets
     labels = np.random.randint(0, 2, (num_samples, num_t), dtype=np.int32)
     # generate sparse features
     sparse_features = {}
     limit = 2
     for k, size in enumerate(multihot_sizes):
-        sparse_features[str(k)] = np.random.randint(0, limit, (num_samples, size), dtype=np.int32)
+        sparse_features[str(k)] = np.random.randint(
+            0, limit, (num_samples, size), dtype=np.int32
+        )
     # generate print format
     if text_file is not None:
         np.save(text_file + "_dense_debug.npy", dense_features)
@@ -24,28 +29,30 @@ def quickgen(num_samples, num_t, num_d, multihot_sizes, text_file=None):
 
     return dense_features, sparse_features, labels
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Quick generator of random samples for debugging."
     )
-    parser.add_argument("--num-samples",            type=int, default=4096)
-    parser.add_argument("--num-dense-features",     type=int, default=13)
-    parser.add_argument("--num-multihot-features",  type=str, default="4,3,2")
-    parser.add_argument("--num-targets",            type=int, default=1)
-    parser.add_argument("--day",                    type=int, default=23)
-    parser.add_argument("--numpy-rand-seed",        type=int, default=123)
-    parser.add_argument("--output-name",            type=str, default="day_")
-    parser.add_argument("--output-dir",             type=str, default="./")
+    parser.add_argument("--num-samples", type=int, default=4096)
+    parser.add_argument("--num-dense-features", type=int, default=13)
+    parser.add_argument("--num-multihot-features", type=str, default="4,3,2")
+    parser.add_argument("--num-targets", type=int, default=1)
+    parser.add_argument("--day", type=int, default=23)
+    parser.add_argument("--numpy-rand-seed", type=int, default=123)
+    parser.add_argument("--output-name", type=str, default="day_")
+    parser.add_argument("--output-dir", type=str, default="./")
     args = parser.parse_args()
 
     np.random.seed(args.numpy_rand_seed)
 
     out_name = args.output_name
-    multihot_sizes = np.fromstring(args.num_multihot_features, dtype=int, sep=",")
+    multihot_sizes = np.fromstring(
+        args.num_multihot_features, dtype=int, sep=",")
 
-    num_d   = args.num_dense_features
-    num_t   = args.num_targets
+    num_d = args.num_dense_features
+    num_t = args.num_targets
     out_dir = args.output_dir
-    text_file =  out_dir + out_name + str(args.day)
+    text_file = out_dir + out_name + str(args.day)
     print(text_file)
     quickgen(args.num_samples, num_t, num_d, multihot_sizes, text_file)
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/backend.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/backend.py
index 39605cd7bb..828d26d555 100644
--- a/retired_benchmarks/never_adopted/language/gpt3/megatron/backend.py
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/backend.py
@@ -54,15 +54,23 @@ def issue_queries(self, query_samples):
 
             response_array = array.array("B", pred_output_batch[0].tobytes())
             bi = response_array.buffer_info()
-            response = [lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])]
+            response = [
+                lg.QuerySampleResponse(
+                    query_samples[i].id,
+                    bi[0],
+                    bi[1])]
             lg.QuerySamplesComplete(response)
             if i % 5 == 0:
                 print("Completed : ", i)
 
     def inference_call(self, input_ids_tensor, input_length_tensor):
         """Common for all scenarios"""
-        data = {"input_ids": input_ids_tensor, "input_length": input_length_tensor}
-        response = requests.put(self.url, data=json.dumps(data), headers=self.headers)
+        data = {"input_ids": input_ids_tensor,
+                "input_length": input_length_tensor}
+        response = requests.put(
+            self.url,
+            data=json.dumps(data),
+            headers=self.headers)
         if response.status_code != 200:
             # TODO: Manage exeption
             return None
@@ -80,7 +88,10 @@ def __del__(self):
 
 class SUT_Offline(SUT_base):
     def __init__(
-        self, dataset_path, max_examples, args,
+        self,
+        dataset_path,
+        max_examples,
+        args,
     ):
         SUT_base.__init__(
             self,
@@ -94,7 +105,10 @@ def __init__(
 
 class SUT_Server(SUT_base):
     def __init__(
-        self, dataset_path, max_examples, args,
+        self,
+        dataset_path,
+        max_examples,
+        args,
     ):
 
         SUT_base.__init__(
@@ -114,9 +128,8 @@ def issue_queries(self, query_samples):
         # input_masks_tensor = self.data_object.source_encoded_attn_masks[index]
         input_length_tensor = self.data_object.source_encoded_input_id_lengths[index]
 
-        pred_output_batch = (
-            self.inference_call(input_ids_tensor, input_length_tensor)
-        )
+        pred_output_batch = self.inference_call(
+            input_ids_tensor, input_length_tensor)
 
         response_array = array.array("B", pred_output_batch[0].tobytes())
         bi = response_array.buffer_info()
@@ -129,7 +142,10 @@ def issue_queries(self, query_samples):
 
 class SUT_SingleStream(SUT_base):
     def __init__(
-        self, dataset_path, max_examples, args,
+        self,
+        dataset_path,
+        max_examples,
+        args,
     ):
         SUT_base.__init__(
             self,
@@ -147,9 +163,8 @@ def issue_queries(self, query_samples):
         # input_masks_tensor = self.data_object.source_encoded_attn_masks[index]
         input_length_tensor = self.data_object.source_encoded_input_id_lengths[index]
 
-        pred_output_batch = (
-            self.inference_call(input_ids_tensor, input_length_tensor)
-        )
+        pred_output_batch = self.inference_call(
+            input_ids_tensor, input_length_tensor)
 
         response_array = array.array("B", pred_output_batch[0].tobytes())
         bi = response_array.buffer_info()
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/dataset.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/dataset.py
index f8bf443883..5d67dd4117 100644
--- a/retired_benchmarks/never_adopted/language/gpt3/megatron/dataset.py
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/dataset.py
@@ -1,22 +1,17 @@
+from argparse import Namespace
+import json
+import utils
+import argparse
+import torch
+from megatron.utils import get_ltor_masks_and_position_ids
+from megatron.tokenizer import build_tokenizer
 import os
 import sys
 
 sys.path.append(os.environ["MEGATRON_PATH"])
-from megatron.tokenizer import build_tokenizer
-from megatron.utils import get_ltor_masks_and_position_ids
-import torch
-import argparse
-import utils
-import json
-
-from argparse import Namespace
 
 
-PROMPT_DICT = {
-    "prompt_input": (
-        "{instruction}{input}"
-    )
-}
+PROMPT_DICT = {"prompt_input": ("{instruction}{input}")}
 
 
 class Dataset:
@@ -38,18 +33,17 @@ def __init__(
         self.debug = debug
         self.gen_kwards = gen_kwards
 
-        ## TODO: provide arguments in command line
+        # TODO: provide arguments in command line
         args.rank = 0
         args.tokenizer_type = "SentencePieceTokenizer"
         args.vocab_extra_ids = 0
-        if 'make_vocab_size_divisible_by' not in vars(args):
+        if "make_vocab_size_divisible_by" not in vars(args):
             args.make_vocab_size_divisible_by = 128
-        if 'tensor_model_parallel_size' not in vars(args):
+        if "tensor_model_parallel_size" not in vars(args):
             args.tensor_model_parallel_size = 8
-        if 'tokenizer_model' not in vars(args):
+        if "tokenizer_model" not in vars(args):
             args.tokenizer_model = "./data/c4_en_301_5Mexp2_spm.model"
-        
-        
+
         self.tokenizer = build_tokenizer(args)
 
         self.list_data_dict = utils.jload(self.dataset_path)
@@ -59,7 +53,8 @@ def __init__(
         self.sources = [
             prompt_input.format_map(example) for example in self.list_data_dict
         ]
-        self.targets = [f"{example['output']}" for example in self.list_data_dict]
+        self.targets = [
+            f"{example['output']}" for example in self.list_data_dict]
 
         (
             self.source_encoded_input_ids,
@@ -99,20 +94,25 @@ def tokenize_prompts(self, prompts, tokens_to_generate, add_BOS):
                 for prompt in prompts
             ]
         else:
-            prompts_tokens = [self.tokenizer.tokenize(prompt)[:self.max_input_tokens] for prompt in prompts]
+            prompts_tokens = [
+                self.tokenizer.tokenize(prompt)[: self.max_input_tokens]
+                for prompt in prompts
+            ]
 
         # Now we have a list of list of tokens which each list has a different
         # size. We want to extend this list to:
         #   - incorporate the tokens that need to be generated
         #   - make all the sequences equal length.
         # Get the prompts length.
-        prompts_length = [len(prompt_tokens) for prompt_tokens in prompts_tokens]
+        prompts_length = [len(prompt_tokens)
+                          for prompt_tokens in prompts_tokens]
         # Get the max prompts length.
         max_prompt_len = max(prompts_length)
         # Number of tokens in the each sample of the batch.
         samples_length = max_prompt_len + tokens_to_generate
         # Now update the list of list to be of the same size: samples_length.
-        for prompt_tokens, prompt_length in zip(prompts_tokens, prompts_length):
+        for prompt_tokens, prompt_length in zip(
+                prompts_tokens, prompts_length):
             padding_size = samples_length - prompt_length
             prompt_tokens.extend([self.tokenizer.pad] * padding_size)
 
@@ -139,7 +139,8 @@ def encode_samples(self):
                     "--------------------------------------------------------------------------------"
                 )
             tokens, length = self.tokenize_prompts(
-                [self.sources[i]], self.gen_kwards.get("max_new_tokens", 128), None
+                [self.sources[i]], self.gen_kwards.get(
+                    "max_new_tokens", 128), None
             )
             # attn_mask = self._build_attention_mask(tokens)
             source_encoded_input_ids.append(tokens)
@@ -164,4 +165,3 @@ def UnloadSamplesFromRam(self, sample_list):
 
     def __del__(self):
         print("Finished destroying QSL.")
-
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/download_cnndm.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/download_cnndm.py
index ec47912eae..1c2303a977 100644
--- a/retired_benchmarks/never_adopted/language/gpt3/megatron/download_cnndm.py
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/download_cnndm.py
@@ -1,15 +1,14 @@
 # experiment config
+import sys
+import simplejson as json
+import os
+import numpy as np
+from datasets import load_dataset
 dataset_id = "cnn_dailymail"
 dataset_config = "3.0.0"
 text_column = "article"
 summary_column = "highlights"
 
-from datasets import load_dataset
-
-import numpy as np
-import os
-import simplejson as json
-import sys
 
 save_dataset_path = os.environ.get("DATASET_CNNDM_PATH", "data")
 
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/evaluation.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/evaluation.py
index 4e32289fcb..a1ad32b497 100644
--- a/retired_benchmarks/never_adopted/language/gpt3/megatron/evaluation.py
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/evaluation.py
@@ -1,4 +1,4 @@
-from dataset import Dataset 
+from dataset import Dataset
 import numpy as np
 import json
 import nltk
@@ -11,17 +11,26 @@
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--dataset-file", required=True, help="path to cnn_eval.json")
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--dataset-file",
+        required=True,
+        help="path to cnn_eval.json")
     parser.add_argument(
         "--tokenizer-model",
         default="./data/c4_en_301_5Mexp2_spm.model",
         help="Path to tokenizer model",
     )
-    parser.add_argument("--verbose", action="store_true", help="verbose messages")
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
     args = parser.parse_args()
     return args
 
+
 def postprocess_text(preds, targets):
     preds = [pred.strip() for pred in preds]
     targets = [target.strip() for target in targets]
@@ -37,9 +46,9 @@ def main():
     args = get_args()
     dataset_path = args.dataset_file
     metric = evaluate.load("rouge")
-    nltk.download('punkt')
-    
-    dataset_args = Namespace(tokenizer_model = args.tokenizer_model)
+    nltk.download("punkt")
+
+    dataset_args = Namespace(tokenizer_model=args.tokenizer_model)
     data_object = Dataset(dataset_path, args=dataset_args)
 
     targets = data_object.targets
@@ -47,24 +56,25 @@ def main():
     with open(args.mlperf_accuracy_file, "r") as f:
         results = json.load(f)
 
-
     target_required = []
     preds_token_ids = []
 
     for pred in results:
-        qsl_idx = pred['qsl_idx']
+        qsl_idx = pred["qsl_idx"]
         target = targets[qsl_idx]
         target_required.append(target)
-        preds = np.frombuffer(bytes.fromhex(pred['data']), np.int64).tolist()
+        preds = np.frombuffer(bytes.fromhex(pred["data"]), np.int64).tolist()
         preds = [int(p) for p in preds]
         preds_token_ids.append(preds)
-        
 
-    preds_decoded_text = [data_object.tokenizer.detokenize(ids) for ids in preds_token_ids]
+    preds_decoded_text = [
+        data_object.tokenizer.detokenize(ids) for ids in preds_token_ids
+    ]
     preds, targets = postprocess_text(preds_decoded_text, target_required)
 
-
-    result = metric.compute(predictions=preds, references=targets, use_stemmer=True,use_aggregator=False)
+    result = metric.compute(
+        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False
+    )
     result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
     prediction_lens = [len(pred) for pred in preds]
     result["gen_len"] = np.sum(prediction_lens)
@@ -72,5 +82,6 @@ def main():
     print("\nResults\n")
     print(result)
 
+
 if __name__ == "__main__":
     main()
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/main.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/main.py
index 183278e52b..2da5ea949d 100644
--- a/retired_benchmarks/never_adopted/language/gpt3/megatron/main.py
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/main.py
@@ -18,8 +18,14 @@ def get_args():
         default="Offline",
         help="Scenario",
     )
-    parser.add_argument("--dataset-path", default="./data/cnn_eval.json", help="")
-    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
+    parser.add_argument(
+        "--dataset-path",
+        default="./data/cnn_eval.json",
+        help="")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
     parser.add_argument(
         "--mlperf_conf", default="mlperf.conf", help="mlperf rules config"
     )
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/prepare-calibration.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/prepare-calibration.py
index 846e492d38..91bcb0826f 100644
--- a/retired_benchmarks/never_adopted/language/gpt3/megatron/prepare-calibration.py
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/prepare-calibration.py
@@ -4,32 +4,39 @@
 from argparse import ArgumentParser
 from datasets import load_dataset
 
+
 def get_args():
     parser = ArgumentParser()
-    parser.add_argument("--calibration-list-file", required=True, help="Path to calibration list")
-    parser.add_argument("--output-dir", help="Output directory", default="calibration-data")
+    parser.add_argument(
+        "--calibration-list-file", required=True, help="Path to calibration list"
+    )
+    parser.add_argument(
+        "--output-dir", help="Output directory", default="calibration-data"
+    )
 
     return parser.parse_args()
 
-dataset_id='cnn_dailymail'
-version='3.0.0'
-split='train'
 
-instruction_template="Summarize the following news article:"
+dataset_id = "cnn_dailymail"
+version = "3.0.0"
+split = "train"
+
+instruction_template = "Summarize the following news article:"
+
 
 def check_path(path):
     return os.path.exists(path)
 
+
 def prepare_calibration_data(calibration_list_file, output_dir):
     if not check_path(calibration_list_file):
         print("Calibration list file not found: {}".format(calibration_list_file))
         sys.exit(1)
 
-    dataset = load_dataset("cnn_dailymail", name="3.0.0", split='train')
-    train = dict((x['id'], x) for x in dataset)
+    dataset = load_dataset("cnn_dailymail", name="3.0.0", split="train")
+    train = dict((x["id"], x) for x in dataset)
 
-    
-    with open(calibration_list_file, 'r') as fid:
+    with open(calibration_list_file, "r") as fid:
         calibration_ids = fid.read().splitlines()
 
     inputs = []
@@ -40,20 +47,22 @@ def prepare_calibration_data(calibration_list_file, output_dir):
         x["input"] = calibration_sample["article"]
         x["output"] = calibration_sample["highlights"]
         inputs.append(x)
-    
+
     if not os.path.isdir(output_dir):
         os.makedirs(output_dir)
 
-    output_path = os.path.join(output_dir,"cnn_dailymail_calibration.json")
-    with open(output_path, 'w') as write_f:
+    output_path = os.path.join(output_dir, "cnn_dailymail_calibration.json")
+    with open(output_path, "w") as write_f:
         json.dump(inputs, write_f, indent=4, ensure_ascii=False)
 
     print("Calibration data saved at {}".format(output_path))
 
+
 def main():
 
     args = get_args()
     prepare_calibration_data(args.calibration_list_file, args.output_dir)
 
-if __name__=="__main__":
+
+if __name__ == "__main__":
     main()
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/text_generation_server.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/text_generation_server.py
index 9639384359..d4a2591004 100644
--- a/retired_benchmarks/never_adopted/language/gpt3/megatron/text_generation_server.py
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/text_generation_server.py
@@ -1,29 +1,24 @@
-import os
-import sys
-
-sys.path.append(os.environ["MEGATRON_PATH"])
-
-import datetime
-import torch
-import json
-import threading
-from flask import Flask, request, jsonify, current_app
-from flask_restful import Resource, Api
-from megatron import get_args
+from megatron.training import get_model
+from megatron.model import GPTModel
+from megatron.initialize import initialize_megatron
+from megatron.checkpointing import load_checkpoint
+from megatron import mpu
+from megatron import print_rank_0
 from megatron.text_generation.generation import (
     generate_tokens_probs_and_return_on_first_stage,
-    beam_search_and_return_on_first_stage
+    beam_search_and_return_on_first_stage,
 )
-
-
 from megatron import get_args
-from megatron import print_rank_0
-from megatron import mpu
-from megatron.checkpointing import load_checkpoint
-from megatron.initialize import initialize_megatron
-from megatron.model import GPTModel
-from megatron.training import get_model
+from flask_restful import Resource, Api
+from flask import Flask, request, jsonify, current_app
+import threading
+import json
 import torch
+import datetime
+import os
+import sys
+
+sys.path.append(os.environ["MEGATRON_PATH"])
 
 
 GENERATE_NUM = 0
@@ -76,9 +71,11 @@ def put(self):
             try:
                 if self.use_beam_search:
                     try:
-                        MegatronGenerate.send_do_beam_search()  # Tell other ranks we're doing beam_search
-                        input_ids_tensor, input_length_tensor = MegatronGenerate.sync_input(
-                            input_ids, input_length
+                        # Tell other ranks we're doing beam_search
+                        MegatronGenerate.send_do_beam_search()
+                        input_ids_tensor, input_length_tensor = (
+                            MegatronGenerate.sync_input(
+                                input_ids, input_length)
                         )
                         (
                             output_tokens,
@@ -88,13 +85,20 @@ def put(self):
                             input_ids_tensor,
                             input_length_tensor,
                             beam_size=self.gen_kwargs.get("beam_size", 4),
-                            stop_token = self.gen_kwargs.get("beam_stop_token", 1),
-                            num_return_gen = self.gen_kwargs.get("beam_num_return_gen", 1),
-                            length_penalty = self.gen_kwargs.get("beam_length_penalty", 1),
-                            min_length = self.gen_kwargs.get("min_new_tokens", 30),
+                            stop_token=self.gen_kwargs.get(
+                                "beam_stop_token", 1),
+                            num_return_gen=self.gen_kwargs.get(
+                                "beam_num_return_gen", 1
+                            ),
+                            length_penalty=self.gen_kwargs.get(
+                                "beam_length_penalty", 1
+                            ),
+                            min_length=self.gen_kwargs.get(
+                                "min_new_tokens", 30),
                         )
                         output_batch_truncated = []
-                        for data, source_len in zip(output_tokens, input_length_tensor):
+                        for data, source_len in zip(
+                                output_tokens, input_length_tensor):
                             output_batch_truncated.append(
                                 data[source_len:].cpu().numpy().tolist()
                             )
@@ -108,8 +112,9 @@ def put(self):
                 else:
                     try:
                         MegatronGenerate.send_do_generate()  # Tell other ranks we're doing generate
-                        input_ids_tensor, input_length_tensor = MegatronGenerate.sync_input(
-                            input_ids, input_length
+                        input_ids_tensor, input_length_tensor = (
+                            MegatronGenerate.sync_input(
+                                input_ids, input_length)
                         )
                         (
                             output_tokens,
@@ -120,11 +125,13 @@ def put(self):
                             input_ids_tensor,
                             input_length_tensor,
                             top_k=self.gen_kwargs.get("top_k", 4),
-                            temperature=self.gen_kwargs.get("temperature", 0.0),
-                            min_length = gen_kwargs.get("min_new_tokens", 30),
+                            temperature=self.gen_kwargs.get(
+                                "temperature", 0.0),
+                            min_length=gen_kwargs.get("min_new_tokens", 30),
                         )
                         output_batch_truncated = []
-                        for data, source_len in zip(output_tokens, input_length_tensor):
+                        for data, source_len in zip(
+                                output_tokens, input_length_tensor):
                             output_batch_truncated.append(
                                 data[source_len:].cpu().numpy().tolist()
                             )
@@ -167,8 +174,8 @@ def model_provider(pre_process=True, post_process=True):
 
 
 def add_text_generate_args(parser):
-    group = parser.add_argument_group(title='text generation')
-    group.add_argument("--use-beam-search", action = "store_true")
+    group = parser.add_argument_group(title="text generation")
+    group.add_argument("--use-beam-search", action="store_true")
     return parser
 
 
@@ -179,7 +186,7 @@ def add_text_generate_args(parser):
             "tokenizer_type": "SentencePieceTokenizer",
             "no_load_rng": True,
             "no_load_optim": True,
-        }
+        },
     )
 
     args = get_args()
@@ -193,7 +200,7 @@ def add_text_generate_args(parser):
         "beam_size": 4,
         "beam_stop_token": 1,
         "beam_num_return_gen": 1,
-        "beam_length_penalty": 1
+        "beam_length_penalty": 1,
     }
     if args.num_layers_per_virtual_pipeline_stage is not None:
         print("Interleaved pipeline schedule is not yet supported for text generation.")
@@ -236,7 +243,7 @@ def add_text_generate_args(parser):
                     input_length_tensor,
                     top_k=gen_kwargs.get("top_k", 4),
                     temperature=gen_kwargs.get("temperature", 1.0),
-                    min_length = gen_kwargs.get("min_new_tokens", 30),
+                    min_length=gen_kwargs.get("min_new_tokens", 30),
                 )
             except ValueError as ve:
                 pass
@@ -261,10 +268,10 @@ def add_text_generate_args(parser):
                     input_ids_tensor,
                     input_length_tensor,
                     beam_size=gen_kwargs.get("beam_size", 4),
-                    stop_token = gen_kwargs.get("beam_stop_token", 1),
-                    num_return_gen = gen_kwargs.get("beam_num_return_gen", 1),
-                    length_penalty = gen_kwargs.get("beam_length_penalty", 1),
-                    min_length = gen_kwargs.get("min_new_tokens", 30),
+                    stop_token=gen_kwargs.get("beam_stop_token", 1),
+                    num_return_gen=gen_kwargs.get("beam_num_return_gen", 1),
+                    length_penalty=gen_kwargs.get("beam_length_penalty", 1),
+                    min_length=gen_kwargs.get("min_new_tokens", 30),
                 )
             except ValueError as ve:
                 pass
diff --git a/retired_benchmarks/never_adopted/language/gpt3/megatron/utils.py b/retired_benchmarks/never_adopted/language/gpt3/megatron/utils.py
index 0fb2fbcd3a..b0ceeaebc8 100644
--- a/retired_benchmarks/never_adopted/language/gpt3/megatron/utils.py
+++ b/retired_benchmarks/never_adopted/language/gpt3/megatron/utils.py
@@ -2,11 +2,13 @@
 import os
 import io
 
+
 def _make_r_io_base(f, mode: str):
     if not isinstance(f, io.IOBase):
-        f = open(f, mode=mode, encoding='utf-8')
+        f = open(f, mode=mode, encoding="utf-8")
     return f
 
+
 def jload(f, mode="r"):
     """Load a .json file into a dictionary."""
     f = _make_r_io_base(f, mode)
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/python/backend.py b/retired_benchmarks/recommendation/dlrm/pytorch/python/backend.py
index 955eddb888..6fc13454a8 100755
--- a/retired_benchmarks/recommendation/dlrm/pytorch/python/backend.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/python/backend.py
@@ -2,10 +2,10 @@
 abstract backend class
 """
 
-
 # pylint: disable=unused-argument,missing-docstring
 
-class Backend():
+
+class Backend:
     def __init__(self):
         self.inputs = []
         self.outputs = []
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_onnxruntime.py b/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_onnxruntime.py
index 79ed48842a..e58de806a1 100755
--- a/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_onnxruntime.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_onnxruntime.py
@@ -11,7 +11,8 @@
 
 
 class BackendOnnxruntime(backend.Backend):
-    def __init__(self, m_spa, ln_emb, ln_bot, ln_top, use_gpu=False, mini_batch_size=1):
+    def __init__(self, m_spa, ln_emb, ln_bot, ln_top,
+                 use_gpu=False, mini_batch_size=1):
         super(BackendOnnxruntime, self).__init__()
 
     def version(self):
@@ -30,11 +31,11 @@ def load(self, model_path, inputs=None, outputs=None):
         # print("onnx load", model_path, inputs, outputs)
         self.sess = rt.InferenceSession(model_path, opt)
         # get input and output names
-        if True: #not inputs:
+        if True:  # not inputs:
             self.inputs = [meta.name for meta in self.sess.get_inputs()]
         else:
             self.inputs = inputs
-        if True: #not outputs:
+        if True:  # not outputs:
             self.outputs = [meta.name for meta in self.sess.get_outputs()]
         else:
             self.outputs = outputs
@@ -63,16 +64,22 @@ def predict(self, batch_dense_X, batch_lS_o, batch_lS_i):
             dict_inputs["offsets"] = batch_lS_o.numpy().astype(np.int64)
         else:  # list
             for i in range(len(batch_lS_o)):
-                dict_inputs["offsets_"+str(i)] = batch_lS_o[i].numpy().astype(np.int64)
+                dict_inputs["offsets_" + str(i)] = (
+                    batch_lS_o[i].numpy().astype(np.int64)
+                )
         if torch.is_tensor(batch_lS_i):
             dict_inputs["indices"] = batch_lS_i.numpy().astype(np.int64)
         else:  # list
             for i in range(len(batch_lS_i)):
-                dict_inputs["indices_"+str(i)] = batch_lS_i[i].numpy().astype(np.int64)
+                dict_inputs["indices_" + str(i)] = (
+                    batch_lS_i[i].numpy().astype(np.int64)
+                )
 
         # predict and return output
         # print("dict_inputs", dict_inputs)
-        output = self.sess.run(output_names=self.outputs, input_feed=dict_inputs)
+        output = self.sess.run(
+            output_names=self.outputs,
+            input_feed=dict_inputs)
         output = torch.tensor(output, requires_grad=False).view(-1, 1)
         # print("output", output)
         # print("output.shape", output.shape)
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_pytorch_native.py b/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_pytorch_native.py
index a0ae91d65f..d861c44dc6 100755
--- a/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_pytorch_native.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_pytorch_native.py
@@ -1,14 +1,17 @@
 """
 pytoch native backend for dlrm
 """
+
 # pylint: disable=unused-argument,missing-docstring
 import torch  # currently supports pytorch1.0
 import backend
 from dlrm_s_pytorch import DLRM_Net
 import numpy as np
 
+
 class BackendPytorchNative(backend.Backend):
-    def __init__(self, m_spa, ln_emb, ln_bot, ln_top, use_gpu=False, mini_batch_size=1):
+    def __init__(self, m_spa, ln_emb, ln_bot, ln_top,
+                 use_gpu=False, mini_batch_size=1):
         super(BackendPytorchNative, self).__init__()
         self.sess = None
         self.model = None
@@ -73,12 +76,12 @@ def load(self, model_path, inputs=None, outputs=None):
                 # note that the call to .to(device) has already happened
                 ld_model = torch.load(
                     model_path,
-                    map_location=torch.device('cuda')
+                    map_location=torch.device("cuda"),
                     # map_location=lambda storage, loc: storage.cuda(0)
                 )
         else:
             # when targeting inference on CPU
-            ld_model = torch.load(model_path, map_location=torch.device('cpu'))
+            ld_model = torch.load(model_path, map_location=torch.device("cpu"))
         # debug print
         # print(ld_model)
         dlrm.load_state_dict(ld_model["state_dict"])
@@ -114,13 +117,21 @@ def predict(self, batch_dense_X, batch_lS_o, batch_lS_i):
         if self.use_gpu:
             batch_dense_X = batch_dense_X.to(self.device)
 
-            batch_lS_i = [S_i.to(self.device) for S_i in batch_lS_i] if isinstance(batch_lS_i, list) \
+            batch_lS_i = (
+                [S_i.to(self.device) for S_i in batch_lS_i]
+                if isinstance(batch_lS_i, list)
                 else batch_lS_i.to(self.device)
+            )
 
-
-            batch_lS_o = [S_o.to(self.device) for S_o in batch_lS_o] if isinstance(batch_lS_o, list) \
+            batch_lS_o = (
+                [S_o.to(self.device) for S_o in batch_lS_o]
+                if isinstance(batch_lS_o, list)
                 else batch_lS_o.to(self.device)
+            )
 
         with torch.no_grad():
-             output = self.model(dense_x=batch_dense_X, lS_o=batch_lS_o, lS_i=batch_lS_i)
+            output = self.model(
+                dense_x=batch_dense_X,
+                lS_o=batch_lS_o,
+                lS_i=batch_lS_i)
         return output
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_tf.py b/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_tf.py
index 3b3bcf6196..9a699f65bc 100644
--- a/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_tf.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/python/backend_tf.py
@@ -1,9 +1,11 @@
 """
 pytoch native backend for dlrm
 """
+
 # pylint: disable=unused-argument,missing-docstring
 import torch  # currently supports pytorch1.0
 import backend
+
 # from dlrm_s_pytorch import DLRM_Net
 import tensorflow as tf
 from tf_dlrm import logits_fn, rand_features_np
@@ -12,6 +14,7 @@
 from typing import Dict, Any
 import sys
 
+
 class BackendTF(backend.Backend):
     def __init__(self, dim_embed, vocab_sizes, mlp_bottom, mlp_top):
         super(BackendTF, self).__init__()
@@ -55,9 +58,12 @@ def load(self, model_path, inputs=None, outputs=None):
 
         with self.graph.as_default():
 
-            features_int_np, features_cat_np = rand_features_np(1, num_d, num_s, minsize)
+            features_int_np, features_cat_np = rand_features_np(
+                1, num_d, num_s, minsize
+            )
 
-            features_int = tf.placeholder(tf.float32, [None, num_d], name="ph_1")
+            features_int = tf.placeholder(
+                tf.float32, [None, num_d], name="ph_1")
             features_cat = tf.placeholder(tf.int32, [None, num_s], name="ph_2")
 
             preds = logits_fn(features_int, features_cat, self.params)
@@ -68,7 +74,13 @@ def load(self, model_path, inputs=None, outputs=None):
             self.sess = tf.compat.v1.Session(graph=self.graph)
 
             self.sess.run(init_op)
-            self.sess.run(preds, feed_dict = {features_int : features_int_np, features_cat : features_cat_np} )
+            self.sess.run(
+                preds,
+                feed_dict={
+                    features_int: features_int_np,
+                    features_cat: features_cat_np,
+                },
+            )
 
         self.params["is_training"] = False
 
@@ -92,12 +104,15 @@ def predict(self, batch_dense_X, batch_lS_o, batch_lS_i):
 
         # print_op_preds = tf.print(estim.predictions, output_stream=sys.stdout)
 
-        out_operation = self.graph.get_operation_by_name('preds')
+        out_operation = self.graph.get_operation_by_name("preds")
 
-        ph_1 = self.graph.get_tensor_by_name('ph_1:0')
-        ph_2 = self.graph.get_tensor_by_name('ph_2:0')
+        ph_1 = self.graph.get_tensor_by_name("ph_1:0")
+        ph_2 = self.graph.get_tensor_by_name("ph_2:0")
 
-        np_tensor_out = out_operation.outputs[0].eval(session=self.sess, feed_dict = {ph_1 : np_tensor_int, ph_2 : np_tensor_cat})
+        np_tensor_out = out_operation.outputs[0].eval(
+            session=self.sess, feed_dict={
+                ph_1: np_tensor_int, ph_2: np_tensor_cat}
+        )
         # print("1st output element: ", np_tensor_out[:1])
 
         output = torch.from_numpy(np_tensor_out)
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/python/criteo.py b/retired_benchmarks/recommendation/dlrm/pytorch/python/criteo.py
index 3ea73b0aef..139edb8483 100755
--- a/retired_benchmarks/recommendation/dlrm/pytorch/python/criteo.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/python/criteo.py
@@ -4,6 +4,8 @@
 
 # pylint: disable=unused-argument,missing-docstring
 
+import data_loader_terabyte
+import dlrm_data_pytorch as dp
 import logging
 import os
 import sys
@@ -14,6 +16,7 @@
 import numpy as np
 import sklearn.metrics
 import inspect
+
 # pytorch
 import torch
 from torch.utils.data import Dataset, RandomSampler
@@ -23,44 +26,47 @@
 
 # add dlrm code path
 try:
-    dlrm_dir_path = os.environ['DLRM_DIR']
+    dlrm_dir_path = os.environ["DLRM_DIR"]
     sys.path.append(dlrm_dir_path)
 except KeyError:
     print("ERROR: Please set DLRM_DIR environment variable to the dlrm code location")
     sys.exit(0)
-#import dataset
-import dlrm_data_pytorch as dp
-import data_loader_terabyte
+# import dataset
 
 
 class Criteo(Dataset):
 
-    def __init__(self,
-                 data_path,
-                 name,
-                 pre_process,
-                 use_cache,
-                 count=None,
-                 samples_to_aggregate_fix=None,
-                 samples_to_aggregate_min=None,
-                 samples_to_aggregate_max=None,
-                 samples_to_aggregate_quantile_file=None,
-                 samples_to_aggregate_trace_file=None,
-                 test_num_workers=0,
-                 max_ind_range=-1,
-                 sub_sample_rate=0.0,
-                 mlperf_bin_loader=False,
-                 randomize="total",
-                 memory_map=False):
+    def __init__(
+        self,
+        data_path,
+        name,
+        pre_process,
+        use_cache,
+        count=None,
+        samples_to_aggregate_fix=None,
+        samples_to_aggregate_min=None,
+        samples_to_aggregate_max=None,
+        samples_to_aggregate_quantile_file=None,
+        samples_to_aggregate_trace_file=None,
+        test_num_workers=0,
+        max_ind_range=-1,
+        sub_sample_rate=0.0,
+        mlperf_bin_loader=False,
+        randomize="total",
+        memory_map=False,
+    ):
         super().__init__()
 
         self.count = count
         self.random_offsets = []
-        self.use_fixed_size = ((samples_to_aggregate_quantile_file is None) and
-                               (samples_to_aggregate_min is None or samples_to_aggregate_max is None))
+        self.use_fixed_size = (samples_to_aggregate_quantile_file is None) and (
+            samples_to_aggregate_min is None or samples_to_aggregate_max is None
+        )
         if self.use_fixed_size:
             # fixed size queries
-            self.samples_to_aggregate = 1 if samples_to_aggregate_fix is None else samples_to_aggregate_fix
+            self.samples_to_aggregate = (
+                1 if samples_to_aggregate_fix is None else samples_to_aggregate_fix
+            )
             self.samples_to_aggregate_min = None
             self.samples_to_aggregate_max = None
         else:
@@ -77,8 +83,11 @@ def __init__(self,
             raw_data_file = data_path + "/day"
             processed_data_file = data_path + "/terabyte_processed.npz"
         else:
-            raise ValueError("only kaggle|terabyte dataset options are supported")
-        self.use_mlperf_bin_loader = mlperf_bin_loader and memory_map and name == "terabyte"
+            raise ValueError(
+                "only kaggle|terabyte dataset options are supported")
+        self.use_mlperf_bin_loader = (
+            mlperf_bin_loader and memory_map and name == "terabyte"
+        )
         # debug prints
         # print("dataset filenames", raw_data_file, processed_data_file)
 
@@ -90,25 +99,26 @@ def __init__(self,
             split="test",
             raw_path=raw_data_file,
             pro_data=processed_data_file,
-            memory_map=memory_map
+            memory_map=memory_map,
         )
         self.num_individual_samples = len(self.test_data)
 
         if self.use_mlperf_bin_loader:
 
             test_file = data_path + "/terabyte_processed_test.bin"
-            counts_file = raw_data_file + '_fea_count.npz'
+            counts_file = raw_data_file + "_fea_count.npz"
 
             data_loader_terabyte.numpy_to_binary(
-                input_files=[raw_data_file + '_23_reordered.npz'],
+                input_files=[raw_data_file + "_23_reordered.npz"],
                 output_file_path=data_path + "/terabyte_processed_test.bin",
-                split="test")
+                split="test",
+            )
 
             self.test_data = data_loader_terabyte.CriteoBinDataset(
                 data_file=test_file,
                 counts_file=counts_file,
                 batch_size=self.samples_to_aggregate,
-                max_ind_range=max_ind_range
+                max_ind_range=max_ind_range,
             )
 
             self.test_loader = torch.utils.data.DataLoader(
@@ -138,32 +148,48 @@ def __init__(self,
         # of size samples_to_aggregate as an item we need to adjust the original dataset item_count.
         # On the other hand, data loader always returns number of batches.
         if self.use_fixed_size:
-            # the offsets for fixed query size will be generated on-the-fly later on
+            # the offsets for fixed query size will be generated on-the-fly
+            # later on
             print("Using fixed query size: " + str(self.samples_to_aggregate))
             if self.use_mlperf_bin_loader:
                 self.num_aggregated_samples = len(self.test_data)
                 # self.num_aggregated_samples2 = len(self.test_loader)
             else:
-                self.num_aggregated_samples = (self.num_individual_samples + self.samples_to_aggregate - 1) // self.samples_to_aggregate
+                self.num_aggregated_samples = (
+                    self.num_individual_samples + self.samples_to_aggregate - 1
+                ) // self.samples_to_aggregate
                 # self.num_aggregated_samples2 = len(self.test_loader)
         else:
             # the offsets for variable query sizes will be pre-generated here
             if self.samples_to_aggregate_quantile_file is None:
-                # generate number of samples in a query from a uniform(min,max) distribution
-                print("Using variable query size: uniform distribution (" + str(self.samples_to_aggregate_min) + "," + str(self.samples_to_aggregate_max) +  ")")
+                # generate number of samples in a query from a uniform(min,max)
+                # distribution
+                print(
+                    "Using variable query size: uniform distribution ("
+                    + str(self.samples_to_aggregate_min)
+                    + ","
+                    + str(self.samples_to_aggregate_max)
+                    + ")"
+                )
                 done = False
                 qo = 0
                 while done == False:
                     self.random_offsets.append(int(qo))
-                    qs = random.randint(self.samples_to_aggregate_min, self.samples_to_aggregate_max)
+                    qs = random.randint(
+                        self.samples_to_aggregate_min, self.samples_to_aggregate_max
+                    )
                     qo = min(qo + qs, self.num_individual_samples)
                     if qo >= self.num_individual_samples:
                         done = True
                 self.random_offsets.append(int(qo))
 
                 # compute min and max number of samples
-                nas_max = (self.num_individual_samples + self.samples_to_aggregate_min - 1) // self.samples_to_aggregate_min
-                nas_min = (self.num_individual_samples + self.samples_to_aggregate_max - 1) // self.samples_to_aggregate_max
+                nas_max = (
+                    self.num_individual_samples + self.samples_to_aggregate_min - 1
+                ) // self.samples_to_aggregate_min
+                nas_min = (
+                    self.num_individual_samples + self.samples_to_aggregate_max - 1
+                ) // self.samples_to_aggregate_max
             else:
                 # generate number of samples in a query from a custom distribution,
                 # with quantile (inverse of its cdf) given in the file. Note that
@@ -176,9 +202,14 @@ def __init__(self,
                 # The inverse of its cdf with granularity of 0.05 can be written as
                 # quantile_p = [.05, .10, .15, .20, .25, .30, .35, .40, .45, .50, .55, .60, .65, .70, .75, .80, .85, .90, .95, 1.0] # p
                 # quantile_x = [100, 100, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 300, 300, 400, 500, 600, 700] # q(p) = x, such that f(x) >= p
-                # Notice that once we have quantile, we can apply inverse transform sampling method.
-                print("Using variable query size: custom distribution (file " + str(samples_to_aggregate_quantile_file) + ")")
-                with open(self.samples_to_aggregate_quantile_file, 'r') as f:
+                # Notice that once we have quantile, we can apply inverse
+                # transform sampling method.
+                print(
+                    "Using variable query size: custom distribution (file "
+                    + str(samples_to_aggregate_quantile_file)
+                    + ")"
+                )
+                with open(self.samples_to_aggregate_quantile_file, "r") as f:
                     line = f.readline()
                     quantile = np.fromstring(line, dtype=int, sep=", ")
                 # debug prints
@@ -198,41 +229,53 @@ def __init__(self,
                 self.random_offsets.append(int(qo))
 
                 # compute min and max number of samples
-                nas_max = (self.num_individual_samples + quantile[0] - 1) // quantile[0]
-                nas_min = (self.num_individual_samples + quantile[-1]- 1) // quantile[-1]
+                nas_max = (self.num_individual_samples +
+                           quantile[0] - 1) // quantile[0]
+                nas_min = (self.num_individual_samples + quantile[-1] - 1) // quantile[
+                    -1
+                ]
 
             # reset num_aggregated_samples
             self.num_aggregated_samples = len(self.random_offsets) - 1
 
             # check num_aggregated_samples
-            if self.num_aggregated_samples < nas_min or nas_max < self.num_aggregated_samples:
+            if (
+                self.num_aggregated_samples < nas_min
+                or nas_max < self.num_aggregated_samples
+            ):
                 raise ValueError("Sannity check failed")
 
         # limit number of items to count if needed
         if self.count is not None:
-            self.num_aggregated_samples = min(self.count, self.num_aggregated_samples)
+            self.num_aggregated_samples = min(
+                self.count, self.num_aggregated_samples)
 
         # dump the trace of aggregated samples
         if samples_to_aggregate_trace_file is not None:
-            with open(samples_to_aggregate_trace_file, 'w') as f:
+            with open(samples_to_aggregate_trace_file, "w") as f:
                 for l in range(self.num_aggregated_samples):
                     if self.use_fixed_size:
                         s = l * self.samples_to_aggregate
-                        e = min((l + 1) * self.samples_to_aggregate, self.num_individual_samples)
+                        e = min(
+                            (l + 1) * self.samples_to_aggregate,
+                            self.num_individual_samples,
+                        )
                     else:
                         s = self.random_offsets[l]
-                        e = self.random_offsets[l+1]
-                    f.write(str(s) + ", " + str(e) + ", " + str(e-s) + "\n")
+                        e = self.random_offsets[l + 1]
+                    f.write(str(s) + ", " + str(e) + ", " + str(e - s) + "\n")
 
     def get_item_count(self):
         # get number of items in the dataset
         return self.num_aggregated_samples
 
-    ''' lg compatibilty routine '''
+    """ lg compatibilty routine """
+
     def unload_query_samples(self, sample_list):
         self.items_in_memory = {}
 
-    ''' lg compatibilty routine '''
+    """ lg compatibilty routine """
+
     def load_query_samples(self, sample_list):
         self.items_in_memory = {}
 
@@ -242,16 +285,18 @@ def load_query_samples(self, sample_list):
         # while we can index into the dataset itself.
         for l in sample_list:
             # approach 1: single sample as an item
-            '''
+            """
             self.items_in_memory[l] = self.test_data[l]
-            '''
+            """
             # approach 2: multiple samples as an item
             if self.use_fixed_size:
                 s = l * self.samples_to_aggregate
-                e = min((l + 1) * self.samples_to_aggregate, self.num_individual_samples)
+                e = min(
+                    (l + 1) * self.samples_to_aggregate, self.num_individual_samples
+                )
             else:
                 s = self.random_offsets[l]
-                e = self.random_offsets[l+1]
+                e = self.random_offsets[l + 1]
 
             if self.use_mlperf_bin_loader and self.samples_to_aggregate > 1:
                 ls = [self.test_data[l]]
@@ -262,7 +307,8 @@ def load_query_samples(self, sample_list):
                 ls_t = list(zip(*ls))
                 X = torch.cat(ls_t[0])
                 (num_s, len_ls) = torch.cat(ls_t[1], dim=1).size()
-                lS_o = torch.stack([torch.tensor(range(len_ls)) for _ in range(num_s)])
+                lS_o = torch.stack([torch.tensor(range(len_ls))
+                                   for _ in range(num_s)])
                 lS_i = torch.cat(ls_t[2], dim=1)
                 T = torch.cat(ls_t[3])
                 self.items_in_memory[l] = (X, lS_o, lS_i, T)
@@ -273,7 +319,8 @@ def load_query_samples(self, sample_list):
 
         self.last_loaded = time.time()
 
-    ''' lg compatibilty routine '''
+    """ lg compatibilty routine """
+
     def get_samples(self, id_list):
 
         # build list tuples as need by the batch conversion routine
@@ -289,7 +336,7 @@ def get_samples(self, id_list):
         # print(idx_offsets)
 
         # approach 1: collate a mini-batch of single samples
-        '''
+        """
         if self.use_mlperf_bin_loader:
             # NOTE: in binary dataset the values are transformed
             ls_t = list(zip(*ls))
@@ -302,7 +349,7 @@ def get_samples(self, id_list):
         else:
             # NOTE: in original dataset the values are not transformed and collate besides stacking transforms them
             X, lS_o, lS_i, T = self.test_loader.collate_fn(ls)
-        '''
+        """
         # approach 2: collate a mini-batch of multiple samples
         # NOTE: recall that the samples have already been transformed for both datasets
         # (by earlier calls in load_query_samples), therefore we just need to stack them
@@ -336,7 +383,8 @@ def __call__(self, results, expected=None, result_dict=None):
         n = len(results)
         for idx in range(0, n):
             # NOTE: copy from GPU to CPU while post processing, if needed. Alternatively,
-            # we could do this on the output of predict function in backend_pytorch_native.py
+            # we could do this on the output of predict function in
+            # backend_pytorch_native.py
             result = results[idx].detach().cpu()
             target = expected[idx]
             processed_results.append([result, target])
@@ -359,7 +407,7 @@ def start(self):
         self.roc_auc = 0
         self.results = []
 
-    def finalize(self, result_dict, ds=False,  output_dir=None):
+    def finalize(self, result_dict, ds=False, output_dir=None):
         # AUC metric
         self.results = np.concatenate(self.results, axis=0)
         results, targets = list(zip(*self.results))
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/python/dataset.py b/retired_benchmarks/recommendation/dlrm/pytorch/python/dataset.py
index 597c7519f3..15dd2707b1 100755
--- a/retired_benchmarks/recommendation/dlrm/pytorch/python/dataset.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/python/dataset.py
@@ -15,7 +15,8 @@
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("dataset")
 
-class Item():
+
+class Item:
     def __init__(self, label, img, idx):
         self.label = label
         self.img = img
@@ -24,19 +25,25 @@ def __init__(self, label, img, idx):
 
 
 def usleep(sec):
-    if sys.platform == 'win32':
+    if sys.platform == "win32":
         # on windows time.sleep() doesn't work to well
         import ctypes
+
         kernel32 = ctypes.windll.kernel32
-        timer = kernel32.CreateWaitableTimerA(ctypes.c_void_p(), True, ctypes.c_void_p())
+        timer = kernel32.CreateWaitableTimerA(
+            ctypes.c_void_p(), True, ctypes.c_void_p()
+        )
         delay = ctypes.c_longlong(int(-1 * (10 * 1000000 * sec)))
-        kernel32.SetWaitableTimer(timer, ctypes.byref(delay), 0, ctypes.c_void_p(), ctypes.c_void_p(), False)
-        kernel32.WaitForSingleObject(timer, 0xffffffff)
+        kernel32.SetWaitableTimer(
+            timer, ctypes.byref(
+                delay), 0, ctypes.c_void_p(), ctypes.c_void_p(), False
+        )
+        kernel32.WaitForSingleObject(timer, 0xFFFFFFFF)
     else:
         time.sleep(sec)
 
 
-class Dataset():
+class Dataset:
     def __init__(self):
         self.arrival = None
         self.image_list = []
@@ -62,7 +69,7 @@ def load_query_samples(self, sample_list):
     def unload_query_samples(self, sample_list):
         if sample_list:
             for sample in sample_list:
-                if sample in self.image_list_inmemory :
+                if sample in self.image_list_inmemory:
                     del self.image_list_inmemory[sample]
         else:
             self.image_list_inmemory = {}
@@ -102,7 +109,7 @@ def start(self):
         self.good = 0
         self.total = 0
 
-    def finalize(self, results, ds=False,  output_dir=None):
+    def finalize(self, results, ds=False, output_dir=None):
         results["good"] = self.good
         results["total"] = self.total
 
@@ -141,6 +148,7 @@ def finalize(self, results, ds=False, output_dir=None):
 # pre-processing
 #
 
+
 def center_crop(img, out_height, out_width):
     height, width, _ = img.shape
     left = int((width - out_width) / 2)
@@ -151,10 +159,12 @@ def center_crop(img, out_height, out_width):
     return img
 
 
-def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
+def resize_with_aspectratio(
+    img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR
+):
     height, width, _ = img.shape
-    new_height = int(100. * out_height / scale)
-    new_width = int(100. * out_width / scale)
+    new_height = int(100.0 * out_height / scale)
+    new_width = int(100.0 * out_width / scale)
     if height > width:
         w = new_width
         h = int(new_height * height / width)
@@ -170,9 +180,11 @@ def pre_process_vgg(img, dims=None, need_transpose=False):
 
     output_height, output_width, _ = dims
     cv2_interpol = cv2.INTER_AREA
-    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2_interpol)
+    img = resize_with_aspectratio(
+        img, output_height, output_width, inter_pol=cv2_interpol
+    )
     img = center_crop(img, output_height, output_width)
-    img = np.asarray(img, dtype='float32')
+    img = np.asarray(img, dtype="float32")
 
     # normalize image
     means = np.array([123.68, 116.78, 103.94], dtype=np.float32)
@@ -188,9 +200,11 @@ def pre_process_mobilenet(img, dims=None, need_transpose=False):
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
 
     output_height, output_width, _ = dims
-    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR)
+    img = resize_with_aspectratio(
+        img, output_height, output_width, inter_pol=cv2.INTER_LINEAR
+    )
     img = center_crop(img, output_height, output_width)
-    img = np.asarray(img, dtype='float32')
+    img = np.asarray(img, dtype="float32")
 
     img /= 255.0
     img -= 0.5
@@ -208,9 +222,10 @@ def maybe_resize(img, dims):
         # some images might be grayscale
         img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    if dims != None:
+    if dims is not None:
         im_height, im_width, _ = dims
-        img = cv2.resize(img, (im_width, im_height), interpolation=cv2.INTER_LINEAR)
+        img = cv2.resize(img, (im_width, im_height),
+                         interpolation=cv2.INTER_LINEAR)
     return img
 
 
@@ -238,7 +253,7 @@ def pre_process_coco_resnet34(img, dims=None, need_transpose=False):
     mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
     std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
 
-    img = img / 255. - mean
+    img = img / 255.0 - mean
     img = img / std
 
     if need_transpose:
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/python/main.py b/retired_benchmarks/recommendation/dlrm/pytorch/python/main.py
index fae9138886..68e86b429e 100755
--- a/retired_benchmarks/recommendation/dlrm/pytorch/python/main.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/python/main.py
@@ -25,7 +25,7 @@
 
 # add dlrm code path
 try:
-    dlrm_dir_path = os.environ['DLRM_DIR']
+    dlrm_dir_path = os.environ["DLRM_DIR"]
     sys.path.append(dlrm_dir_path)
 except KeyError:
     print("ERROR: Please set DLRM_DIR environment variable to the dlrm code location")
@@ -41,12 +41,18 @@
 
 # the datasets we support
 SUPPORTED_DATASETS = {
-    "kaggle":
-        (criteo.Criteo, criteo.pre_process_criteo_dlrm, criteo.DlrmPostProcess(),
-         {"randomize": 'total',  "memory_map": True}),
-    "terabyte":
-        (criteo.Criteo, criteo.pre_process_criteo_dlrm, criteo.DlrmPostProcess(),
-         {"randomize": 'total',  "memory_map": True}),
+    "kaggle": (
+        criteo.Criteo,
+        criteo.pre_process_criteo_dlrm,
+        criteo.DlrmPostProcess(),
+        {"randomize": "total", "memory_map": True},
+    ),
+    "terabyte": (
+        criteo.Criteo,
+        criteo.pre_process_criteo_dlrm,
+        criteo.DlrmPostProcess(),
+        {"randomize": "total", "memory_map": True},
+    ),
 }
 
 # pre-defined command line options so simplify things. They are used as defaults and can be
@@ -125,46 +131,129 @@ def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", help="name of the mlperf model, ie. dlrm")
-    parser.add_argument("--model-path", required=True, help="path to the model file")
-    parser.add_argument("--dataset", choices=SUPPORTED_DATASETS.keys(), help="dataset")
-    parser.add_argument("--dataset-path", required=True, help="path to the dataset")
-    parser.add_argument("--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles")
-    parser.add_argument("--scenario", default="SingleStream",
-                        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())))
-    parser.add_argument("--test-num-workers", type=int, default=0, help='# of workers reading the data')
+    parser.add_argument(
+        "--model-path",
+        required=True,
+        help="path to the model file")
+    parser.add_argument(
+        "--dataset",
+        choices=SUPPORTED_DATASETS.keys(),
+        help="dataset")
+    parser.add_argument(
+        "--dataset-path",
+        required=True,
+        help="path to the dataset")
+    parser.add_argument(
+        "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
+    )
+    parser.add_argument(
+        "--scenario",
+        default="SingleStream",
+        help="mlperf benchmark scenario, one of " +
+        str(list(SCENARIO_MAP.keys())),
+    )
+    parser.add_argument(
+        "--test-num-workers", type=int, default=0, help="# of workers reading the data"
+    )
     parser.add_argument("--max-ind-range", type=int, default=-1)
     parser.add_argument("--data-sub-sample-rate", type=float, default=0.0)
-    parser.add_argument("--mlperf-bin-loader", action='store_true', default=False)
-    parser.add_argument("--max-batchsize", type=int, help="max batch size in a single inference")
+    parser.add_argument(
+        "--mlperf-bin-loader",
+        action="store_true",
+        default=False)
+    parser.add_argument(
+        "--max-batchsize", type=int, help="max batch size in a single inference"
+    )
     parser.add_argument("--output", help="test results")
     parser.add_argument("--inputs", help="model inputs (currently not used)")
     parser.add_argument("--outputs", help="model outputs (currently not used)")
     parser.add_argument("--backend", help="runtime to use")
     parser.add_argument("--use-gpu", action="store_true", default=False)
-    parser.add_argument("--threads", default=os.cpu_count(), type=int, help="threads")
-    parser.add_argument("--cache", type=int, default=0, help="use cache (currently not used)")
-    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
-    parser.add_argument("--find-peak-performance", action="store_true", help="enable finding peak performance pass")
+    parser.add_argument(
+        "--threads",
+        default=os.cpu_count(),
+        type=int,
+        help="threads")
+    parser.add_argument(
+        "--cache", type=int, default=0, help="use cache (currently not used)"
+    )
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
+    parser.add_argument(
+        "--find-peak-performance",
+        action="store_true",
+        help="enable finding peak performance pass",
+    )
 
     # file to use mlperf rules compliant parameters
-    parser.add_argument("--mlperf_conf", default="mlperf.conf", help="mlperf rules config")
+    parser.add_argument(
+        "--mlperf_conf", default="mlperf.conf", help="mlperf rules config"
+    )
     # file for user LoadGen settings such as target QPS
-    parser.add_argument("--user_conf", default="user.conf", help="user config for user LoadGen settings such as target QPS")
-
-    # below will override mlperf rules compliant settings - don't use for official submission
-    parser.add_argument("--duration", type=int, help="duration in milliseconds (ms)")
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+
+    # below will override mlperf rules compliant settings - don't use for
+    # official submission
+    parser.add_argument(
+        "--duration",
+        type=int,
+        help="duration in milliseconds (ms)")
     parser.add_argument("--target-qps", type=int, help="target/expected qps")
-    parser.add_argument("--max-latency", type=float, help="mlperf max latency in pct tile")
-    parser.add_argument("--count-samples", type=int, help="dataset items to use")
-    parser.add_argument("--count-queries", type=int, help="number of queries to use")
-    parser.add_argument("--samples-per-query-multistream", default=8, type=int, help="query length for multi-stream scenario (in terms of aggregated samples)")
+    parser.add_argument(
+        "--max-latency", type=float, help="mlperf max latency in pct tile"
+    )
+    parser.add_argument(
+        "--count-samples",
+        type=int,
+        help="dataset items to use")
+    parser.add_argument(
+        "--count-queries",
+        type=int,
+        help="number of queries to use")
+    parser.add_argument(
+        "--samples-per-query-multistream",
+        default=8,
+        type=int,
+        help="query length for multi-stream scenario (in terms of aggregated samples)",
+    )
     # --samples-per-query-offline is equivalent to perf_sample_count
-    parser.add_argument("--samples-per-query-offline", type=int, default=2048, help="query length for offline scenario (in terms of aggregated samples)")
-    parser.add_argument("--samples-to-aggregate-fix", type=int, help="number of samples to be treated as one")
-    parser.add_argument("--samples-to-aggregate-min", type=int, help="min number of samples to be treated as one in random query size")
-    parser.add_argument("--samples-to-aggregate-max", type=int, help="max number of samples to be treated as one in random query size")
-    parser.add_argument("--samples-to-aggregate-quantile-file", type=str, help="distribution quantile used to generate number of samples to be treated as one in random query size")
-    parser.add_argument("--samples-to-aggregate-trace-file", type=str, default="dlrm_trace_of_aggregated_samples.txt")
+    parser.add_argument(
+        "--samples-per-query-offline",
+        type=int,
+        default=2048,
+        help="query length for offline scenario (in terms of aggregated samples)",
+    )
+    parser.add_argument(
+        "--samples-to-aggregate-fix",
+        type=int,
+        help="number of samples to be treated as one",
+    )
+    parser.add_argument(
+        "--samples-to-aggregate-min",
+        type=int,
+        help="min number of samples to be treated as one in random query size",
+    )
+    parser.add_argument(
+        "--samples-to-aggregate-max",
+        type=int,
+        help="max number of samples to be treated as one in random query size",
+    )
+    parser.add_argument(
+        "--samples-to-aggregate-quantile-file",
+        type=str,
+        help="distribution quantile used to generate number of samples to be treated as one in random query size",
+    )
+    parser.add_argument(
+        "--samples-to-aggregate-trace-file",
+        type=str,
+        default="dlrm_trace_of_aggregated_samples.txt",
+    )
     parser.add_argument("--numpy-rand-seed", type=int, default=123)
     args = parser.parse_args()
 
@@ -192,113 +281,390 @@ def get_args():
     return args
 
 
-def get_backend(backend, dataset, max_ind_range, data_sub_sample_rate, use_gpu):
+def get_backend(backend, dataset, max_ind_range,
+                data_sub_sample_rate, use_gpu):
 
     if backend == "pytorch-native":
         from backend_pytorch_native import BackendPytorchNative
+
         # NOTE: pass model parameters here, the following options are available
         if dataset == "kaggle":
-            # 1. Criteo Kaggle Display Advertisement Challenge Dataset (see ./bench/dlrm_s_criteo_kaggle.sh)
+            # 1. Criteo Kaggle Display Advertisement Challenge Dataset (see
+            # ./bench/dlrm_s_criteo_kaggle.sh)
             backend = BackendPytorchNative(
                 m_spa=16,
-                ln_emb=np.array([1460,583,10131227,2202608,305,24,12517,633,3,93145,5683,8351593,3194,27,14992,5461306,10,5652,2173,4,7046547,18,15,286181,105,142572]),
-                ln_bot=np.array([13,512,256,64,16]),
-                ln_top=np.array([367,512,256,1]),
-                use_gpu=use_gpu
+                ln_emb=np.array(
+                    [
+                        1460,
+                        583,
+                        10131227,
+                        2202608,
+                        305,
+                        24,
+                        12517,
+                        633,
+                        3,
+                        93145,
+                        5683,
+                        8351593,
+                        3194,
+                        27,
+                        14992,
+                        5461306,
+                        10,
+                        5652,
+                        2173,
+                        4,
+                        7046547,
+                        18,
+                        15,
+                        286181,
+                        105,
+                        142572,
+                    ]
+                ),
+                ln_bot=np.array([13, 512, 256, 64, 16]),
+                ln_top=np.array([367, 512, 256, 1]),
+                use_gpu=use_gpu,
             )
         elif dataset == "terabyte":
             if max_ind_range == 10000000:
-                # 2. Criteo Terabyte (see ./bench/dlrm_s_criteo_terabyte.sh [--sub-sample=0.875] --max-in-range=10000000)
+                # 2. Criteo Terabyte (see ./bench/dlrm_s_criteo_terabyte.sh
+                # [--sub-sample=0.875] --max-in-range=10000000)
                 backend = BackendPytorchNative(
                     m_spa=64,
-                    ln_emb=np.array([9980333,36084,17217,7378,20134,3,7112,1442,61, 9758201,1333352,313829,10,2208,11156,122,4,970,14, 9994222, 7267859, 9946608,415421,12420,101, 36]),
-                    ln_bot=np.array([13,512,256,64]),
-                    ln_top=np.array([415,512,512,256,1]),
-                    use_gpu=use_gpu
+                    ln_emb=np.array(
+                        [
+                            9980333,
+                            36084,
+                            17217,
+                            7378,
+                            20134,
+                            3,
+                            7112,
+                            1442,
+                            61,
+                            9758201,
+                            1333352,
+                            313829,
+                            10,
+                            2208,
+                            11156,
+                            122,
+                            4,
+                            970,
+                            14,
+                            9994222,
+                            7267859,
+                            9946608,
+                            415421,
+                            12420,
+                            101,
+                            36,
+                        ]
+                    ),
+                    ln_bot=np.array([13, 512, 256, 64]),
+                    ln_top=np.array([415, 512, 512, 256, 1]),
+                    use_gpu=use_gpu,
                 )
             elif max_ind_range == 40000000:
-                # 3. Criteo Terabyte MLPerf training (see ./bench/run_and_time.sh --max-in-range=40000000)
+                # 3. Criteo Terabyte MLPerf training (see
+                # ./bench/run_and_time.sh --max-in-range=40000000)
                 backend = BackendPytorchNative(
                     m_spa=128,
-                    ln_emb=np.array([39884406,39043,17289,7420,20263,3,7120,1543,63,38532951,2953546,403346,10,2208,11938,155,4,976,14,39979771,25641295,39664984,585935,12972,108,36]),
-                    ln_bot=np.array([13,512,256,128]),
-                    ln_top=np.array([479,1024,1024,512,256,1]),
-                    use_gpu=use_gpu
+                    ln_emb=np.array(
+                        [
+                            39884406,
+                            39043,
+                            17289,
+                            7420,
+                            20263,
+                            3,
+                            7120,
+                            1543,
+                            63,
+                            38532951,
+                            2953546,
+                            403346,
+                            10,
+                            2208,
+                            11938,
+                            155,
+                            4,
+                            976,
+                            14,
+                            39979771,
+                            25641295,
+                            39664984,
+                            585935,
+                            12972,
+                            108,
+                            36,
+                        ]
+                    ),
+                    ln_bot=np.array([13, 512, 256, 128]),
+                    ln_top=np.array([479, 1024, 1024, 512, 256, 1]),
+                    use_gpu=use_gpu,
                 )
             else:
-                raise ValueError("only --max-ind-range 10M or 40M is supported")
+                raise ValueError(
+                    "only --max-ind-range 10M or 40M is supported")
         else:
-            raise ValueError("only kaggle|terabyte dataset options are supported")
+            raise ValueError(
+                "only kaggle|terabyte dataset options are supported")
 
     elif backend == "onnxruntime":
         from backend_onnxruntime import BackendOnnxruntime
 
         # NOTE: pass model parameters here, the following options are available
         if dataset == "kaggle":
-            # 1. Criteo Kaggle Display Advertisement Challenge Dataset (see ./bench/dlrm_s_criteo_kaggle.sh)
+            # 1. Criteo Kaggle Display Advertisement Challenge Dataset (see
+            # ./bench/dlrm_s_criteo_kaggle.sh)
             backend = BackendOnnxruntime(
                 m_spa=16,
-                ln_emb=np.array([1460,583,10131227,2202608,305,24,12517,633,3,93145,5683,8351593,3194,27,14992,5461306,10,5652,2173,4,7046547,18,15,286181,105,142572]),
-                ln_bot=np.array([13,512,256,64,16]),
-                ln_top=np.array([367,512,256,1]),
-                use_gpu=use_gpu
+                ln_emb=np.array(
+                    [
+                        1460,
+                        583,
+                        10131227,
+                        2202608,
+                        305,
+                        24,
+                        12517,
+                        633,
+                        3,
+                        93145,
+                        5683,
+                        8351593,
+                        3194,
+                        27,
+                        14992,
+                        5461306,
+                        10,
+                        5652,
+                        2173,
+                        4,
+                        7046547,
+                        18,
+                        15,
+                        286181,
+                        105,
+                        142572,
+                    ]
+                ),
+                ln_bot=np.array([13, 512, 256, 64, 16]),
+                ln_top=np.array([367, 512, 256, 1]),
+                use_gpu=use_gpu,
             )
         elif dataset == "terabyte":
             if max_ind_range == 10000000:
-                # 2. Criteo Terabyte (see ./bench/dlrm_s_criteo_terabyte.sh [--sub-sample=0.875] --max-in-range=10000000)
+                # 2. Criteo Terabyte (see ./bench/dlrm_s_criteo_terabyte.sh
+                # [--sub-sample=0.875] --max-in-range=10000000)
                 backend = BackendOnnxruntime(
                     m_spa=64,
-                    ln_emb=np.array([9980333,36084,17217,7378,20134,3,7112,1442,61, 9758201,1333352,313829,10,2208,11156,122,4,970,14, 9994222, 7267859, 9946608,415421,12420,101, 36]),
-                    ln_bot=np.array([13,512,256,64]),
-                    ln_top=np.array([415,512,512,256,1]),
-                    use_gpu=use_gpu
+                    ln_emb=np.array(
+                        [
+                            9980333,
+                            36084,
+                            17217,
+                            7378,
+                            20134,
+                            3,
+                            7112,
+                            1442,
+                            61,
+                            9758201,
+                            1333352,
+                            313829,
+                            10,
+                            2208,
+                            11156,
+                            122,
+                            4,
+                            970,
+                            14,
+                            9994222,
+                            7267859,
+                            9946608,
+                            415421,
+                            12420,
+                            101,
+                            36,
+                        ]
+                    ),
+                    ln_bot=np.array([13, 512, 256, 64]),
+                    ln_top=np.array([415, 512, 512, 256, 1]),
+                    use_gpu=use_gpu,
                 )
             elif max_ind_range == 40000000:
-                # 3. Criteo Terabyte MLPerf training (see ./bench/run_and_time.sh --max-in-range=40000000)
+                # 3. Criteo Terabyte MLPerf training (see
+                # ./bench/run_and_time.sh --max-in-range=40000000)
                 backend = BackendOnnxruntime(
                     m_spa=128,
-                    ln_emb=np.array([39884406,39043,17289,7420,20263,3,7120,1543,63,38532951,2953546,403346,10,2208,11938,155,4,976,14,39979771,25641295,39664984,585935,12972,108,36]),
-                    ln_bot=np.array([13,512,256,128]),
-                    ln_top=np.array([479,1024,1024,512,256,1]),
-                    use_gpu=use_gpu
+                    ln_emb=np.array(
+                        [
+                            39884406,
+                            39043,
+                            17289,
+                            7420,
+                            20263,
+                            3,
+                            7120,
+                            1543,
+                            63,
+                            38532951,
+                            2953546,
+                            403346,
+                            10,
+                            2208,
+                            11938,
+                            155,
+                            4,
+                            976,
+                            14,
+                            39979771,
+                            25641295,
+                            39664984,
+                            585935,
+                            12972,
+                            108,
+                            36,
+                        ]
+                    ),
+                    ln_bot=np.array([13, 512, 256, 128]),
+                    ln_top=np.array([479, 1024, 1024, 512, 256, 1]),
+                    use_gpu=use_gpu,
                 )
             else:
                 raise ValueError("only --max-in-range 10M or 40M is supported")
         else:
-            raise ValueError("only kaggle|terabyte dataset options are supported")
+            raise ValueError(
+                "only kaggle|terabyte dataset options are supported")
 
     elif backend == "tensorflow":
         from backend_tf import BackendTF
+
         # NOTE: pass model parameters here, the following options are available
         if dataset == "kaggle":
-            # 1. Criteo Kaggle Display Advertisement Challenge Dataset (see ./bench/dlrm_s_criteo_kaggle.sh)
+            # 1. Criteo Kaggle Display Advertisement Challenge Dataset (see
+            # ./bench/dlrm_s_criteo_kaggle.sh)
             backend = BackendTF(
                 dim_embed=16,
-                vocab_sizes=np.array([1460,583,10131227,2202608,305,24,12517,633,3,93145,5683,8351593,3194,27,14992,5461306,10,5652,2173,4,7046547,18,15,286181,105,142572]),
-                mlp_bottom=np.array([13,512,256,64,16]),
-                mlp_top=np.array([367,512,256,1]),
+                vocab_sizes=np.array(
+                    [
+                        1460,
+                        583,
+                        10131227,
+                        2202608,
+                        305,
+                        24,
+                        12517,
+                        633,
+                        3,
+                        93145,
+                        5683,
+                        8351593,
+                        3194,
+                        27,
+                        14992,
+                        5461306,
+                        10,
+                        5652,
+                        2173,
+                        4,
+                        7046547,
+                        18,
+                        15,
+                        286181,
+                        105,
+                        142572,
+                    ]
+                ),
+                mlp_bottom=np.array([13, 512, 256, 64, 16]),
+                mlp_top=np.array([367, 512, 256, 1]),
             )
         elif dataset == "terabyte":
             if max_ind_range == 10000000:
-                # 2. Criteo Terabyte (see ./bench/dlrm_s_criteo_terabyte.sh [--sub-sample=0.875] --max-in-range=10000000)
+                # 2. Criteo Terabyte (see ./bench/dlrm_s_criteo_terabyte.sh
+                # [--sub-sample=0.875] --max-in-range=10000000)
                 backend = BackendTF(
                     dim_embed=64,
-                    vocab_sizes=np.array([9980333,36084,17217,7378,20134,3,7112,1442,61, 9758201,1333352,313829,10,2208,11156,122,4,970,14, 9994222, 7267859, 9946608,415421,12420,101, 36]),
-                    mlp_bottom=np.array([13,512,256,64]),
-                    mlp_top=np.array([415,512,512,256,1]),
+                    vocab_sizes=np.array(
+                        [
+                            9980333,
+                            36084,
+                            17217,
+                            7378,
+                            20134,
+                            3,
+                            7112,
+                            1442,
+                            61,
+                            9758201,
+                            1333352,
+                            313829,
+                            10,
+                            2208,
+                            11156,
+                            122,
+                            4,
+                            970,
+                            14,
+                            9994222,
+                            7267859,
+                            9946608,
+                            415421,
+                            12420,
+                            101,
+                            36,
+                        ]
+                    ),
+                    mlp_bottom=np.array([13, 512, 256, 64]),
+                    mlp_top=np.array([415, 512, 512, 256, 1]),
                 )
             elif max_ind_range == 40000000:
-                # 3. Criteo Terabyte MLPerf training (see ./bench/run_and_time.sh --max-in-range=40000000)
+                # 3. Criteo Terabyte MLPerf training (see
+                # ./bench/run_and_time.sh --max-in-range=40000000)
                 backend = BackendTF(
                     dim_embed=128,
-                    vocab_sizes=np.array([39884406,39043,17289,7420,20263,3,7120,1543,63,38532951,2953546,403346,10,2208,11938,155,4,976,14,39979771,25641295,39664984,585935,12972,108,36]),
-                    mlp_bottom=np.array([13,512,256,128]),
-                    mlp_top=np.array([479,1024,1024,512,256,1]),
+                    vocab_sizes=np.array(
+                        [
+                            39884406,
+                            39043,
+                            17289,
+                            7420,
+                            20263,
+                            3,
+                            7120,
+                            1543,
+                            63,
+                            38532951,
+                            2953546,
+                            403346,
+                            10,
+                            2208,
+                            11938,
+                            155,
+                            4,
+                            976,
+                            14,
+                            39979771,
+                            25641295,
+                            39664984,
+                            585935,
+                            12972,
+                            108,
+                            36,
+                        ]
+                    ),
+                    mlp_bottom=np.array([13, 512, 256, 128]),
+                    mlp_top=np.array([479, 1024, 1024, 512, 256, 1]),
                 )
             else:
                 raise ValueError("only --max-in-range 10M or 40M is supported")
         else:
-            raise ValueError("only kaggle|terabyte dataset options are supported")
+            raise ValueError(
+                "only kaggle|terabyte dataset options are supported")
 
     else:
         raise ValueError("unknown backend: " + backend)
@@ -308,7 +674,16 @@ def get_backend(backend, dataset, max_ind_range, data_sub_sample_rate, use_gpu):
 class Item:
     """An item that we queue for processing by the thread pool."""
 
-    def __init__(self, query_id, content_id, batch_dense_X, batch_lS_o, batch_lS_i, batch_T=None, idx_offsets=None):
+    def __init__(
+        self,
+        query_id,
+        content_id,
+        batch_dense_X,
+        batch_lS_o,
+        batch_lS_i,
+        batch_T=None,
+        idx_offsets=None,
+    ):
         self.query_id = query_id
         self.content_id = content_id
         self.batch_dense_X = batch_dense_X
@@ -318,6 +693,7 @@ def __init__(self, query_id, content_id, batch_dense_X, batch_lS_o, batch_lS_i,
         self.idx_offsets = idx_offsets
         self.start = time.time()
 
+
 class RunnerBase:
     def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
         self.take_accuracy = False
@@ -341,8 +717,12 @@ def run_one_item(self, qitem):
         # run the prediction
         processed_results = []
         try:
-            results = self.model.predict(qitem.batch_dense_X, qitem.batch_lS_o, qitem.batch_lS_i)
-            processed_results = self.post_process(results, qitem.batch_T, self.result_dict)
+            results = self.model.predict(
+                qitem.batch_dense_X, qitem.batch_lS_o, qitem.batch_lS_i
+            )
+            processed_results = self.post_process(
+                results, qitem.batch_T, self.result_dict
+            )
             if self.take_accuracy:
                 self.post_process.add_results(processed_results)
             self.result_timing.append(time.time() - qitem.start)
@@ -362,7 +742,10 @@ def run_one_item(self, qitem):
                 e_idx = qitem.idx_offsets[idx + 1]
                 # debug prints
                 # print("s,e:",s_idx,e_idx, len(processed_results))
-                response_array = array.array("B", np.array(processed_results[s_idx:e_idx], np.float32).tobytes())
+                response_array = array.array(
+                    "B", np.array(
+                        processed_results[s_idx:e_idx], np.float32).tobytes()
+                )
                 response_array_refs.append(response_array)
                 bi = response_array.buffer_info()
                 response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
@@ -374,14 +757,38 @@ def enqueue(self, query_samples):
         query_len = len(query_samples)
 
         if query_len < self.max_batchsize:
-            batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets = self.ds.get_samples(idx)
-            self.run_one_item(Item(query_id, idx, batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets))
+            batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets = (
+                self.ds.get_samples(idx)
+            )
+            self.run_one_item(
+                Item(
+                    query_id,
+                    idx,
+                    batch_dense_X,
+                    batch_lS_o,
+                    batch_lS_i,
+                    batch_T,
+                    idx_offsets,
+                )
+            )
         else:
             bs = self.max_batchsize
             for i in range(0, query_len, bs):
                 ie = min(i + bs, query_len)
-                batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets = self.ds.get_samples(idx[i:ie])
-                self.run_one_item(Item(query_id[i:ie], idx[i:ie], batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets))
+                batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets = (
+                    self.ds.get_samples(idx[i:ie])
+                )
+                self.run_one_item(
+                    Item(
+                        query_id[i:ie],
+                        idx[i:ie],
+                        batch_dense_X,
+                        batch_lS_o,
+                        batch_lS_i,
+                        batch_T,
+                        idx_offsets,
+                    )
+                )
 
     def finish(self):
         pass
@@ -390,13 +797,17 @@ def finish(self):
 class QueueRunner(RunnerBase):
     def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
         super().__init__(model, ds, threads, post_proc, max_batchsize)
-        queue_size_multiplier = 4 #(args.samples_per_query_offline + max_batchsize - 1) // max_batchsize)
+        queue_size_multiplier = (
+            4  # (args.samples_per_query_offline + max_batchsize - 1) // max_batchsize)
+        )
         self.tasks = JoinableQueue(maxsize=threads * queue_size_multiplier)
         self.workers = []
         self.result_dict = {}
 
         for _ in range(self.threads):
-            worker = threading.Thread(target=self.handle_tasks, args=(self.tasks,))
+            worker = threading.Thread(
+                target=self.handle_tasks, args=(
+                    self.tasks,))
             worker.daemon = True
             self.workers.append(worker)
             worker.start()
@@ -418,14 +829,38 @@ def enqueue(self, query_samples):
         query_len = len(query_samples)
 
         if query_len < self.max_batchsize:
-            batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets = self.ds.get_samples(idx)
-            self.tasks.put(Item(query_id, idx, batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets))
+            batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets = (
+                self.ds.get_samples(idx)
+            )
+            self.tasks.put(
+                Item(
+                    query_id,
+                    idx,
+                    batch_dense_X,
+                    batch_lS_o,
+                    batch_lS_i,
+                    batch_T,
+                    idx_offsets,
+                )
+            )
         else:
             bs = self.max_batchsize
             for i in range(0, query_len, bs):
                 ie = min(i + bs, query_len)
-                batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets = self.ds.get_samples(idx[i:ie])
-                self.tasks.put(Item(query_id[i:ie], idx[i:ie], batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets))
+                batch_dense_X, batch_lS_o, batch_lS_i, batch_T, idx_offsets = (
+                    self.ds.get_samples(idx[i:ie])
+                )
+                self.tasks.put(
+                    Item(
+                        query_id[i:ie],
+                        idx[i:ie],
+                        batch_dense_X,
+                        batch_lS_o,
+                        batch_lS_i,
+                        batch_T,
+                        idx_offsets,
+                    )
+                )
 
     def finish(self):
         # exit all threads
@@ -435,11 +870,14 @@ def finish(self):
             worker.join()
 
 
-
-def add_results(final_results, name, result_dict, result_list, took, show_accuracy=False):
-    percentiles = [50., 80., 90., 95., 99., 99.9]
+def add_results(
+    final_results, name, result_dict, result_list, took, show_accuracy=False
+):
+    percentiles = [50.0, 80.0, 90.0, 95.0, 99.0, 99.9]
     buckets = np.percentile(result_list, percentiles).tolist()
-    buckets_str = ",".join(["{}:{:.4f}".format(p, b) for p, b in zip(percentiles, buckets)])
+    buckets_str = ",".join(
+        ["{}:{:.4f}".format(p, b) for p, b in zip(percentiles, buckets)]
+    )
 
     if result_dict["total"] == 0:
         result_dict["total"] = len(result_list)
@@ -456,19 +894,27 @@ def add_results(final_results, name, result_dict, result_list, took, show_accura
     }
     acc_str = ""
     if show_accuracy:
-        result["accuracy"] = 100. * result_dict["good"] / result_dict["total"]
+        result["accuracy"] = 100.0 * result_dict["good"] / result_dict["total"]
         acc_str = ", acc={:.3f}%".format(result["accuracy"])
         if "roc_auc" in result_dict:
-            result["roc_auc"] = 100. * result_dict["roc_auc"]
+            result["roc_auc"] = 100.0 * result_dict["roc_auc"]
             acc_str += ", auc={:.3f}%".format(result["roc_auc"])
 
     # add the result to the result dict
     final_results[name] = result
 
     # to stdout
-    print("{} qps={:.2f}, mean={:.4f}, time={:.3f}{}, queries={}, tiles={}".format(
-        name, result["qps"], result["mean"], took, acc_str,
-        len(result_list), buckets_str))
+    print(
+        "{} qps={:.2f}, mean={:.4f}, time={:.3f}{}, queries={}, tiles={}".format(
+            name,
+            result["qps"],
+            result["mean"],
+            took,
+            acc_str,
+            len(result_list),
+            buckets_str,
+        )
+    )
 
 
 def main():
@@ -478,29 +924,40 @@ def main():
     log.info(args)
 
     # find backend
-    backend = get_backend(args.backend, args.dataset, args.max_ind_range, args.data_sub_sample_rate, args.use_gpu)
+    backend = get_backend(
+        args.backend,
+        args.dataset,
+        args.max_ind_range,
+        args.data_sub_sample_rate,
+        args.use_gpu,
+    )
 
     # dataset to use
     wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
 
     # --count-samples can be used to limit the number of samples used for testing
-    ds = wanted_dataset(data_path=args.dataset_path,
-                        name=args.dataset,
-                        pre_process=pre_proc,  # currently an identity function
-                        use_cache=args.cache,  # currently not used
-                        count=args.count_samples,
-                        samples_to_aggregate_fix=args.samples_to_aggregate_fix,
-                        samples_to_aggregate_min=args.samples_to_aggregate_min,
-                        samples_to_aggregate_max=args.samples_to_aggregate_max,
-                        samples_to_aggregate_quantile_file=args.samples_to_aggregate_quantile_file,
-                        samples_to_aggregate_trace_file=args.samples_to_aggregate_trace_file,
-                        test_num_workers=args.test_num_workers,
-                        max_ind_range=args.max_ind_range,
-                        sub_sample_rate=args.data_sub_sample_rate,
-                        mlperf_bin_loader=args.mlperf_bin_loader,
-                        **kwargs)
+    ds = wanted_dataset(
+        data_path=args.dataset_path,
+        name=args.dataset,
+        pre_process=pre_proc,  # currently an identity function
+        use_cache=args.cache,  # currently not used
+        count=args.count_samples,
+        samples_to_aggregate_fix=args.samples_to_aggregate_fix,
+        samples_to_aggregate_min=args.samples_to_aggregate_min,
+        samples_to_aggregate_max=args.samples_to_aggregate_max,
+        samples_to_aggregate_quantile_file=args.samples_to_aggregate_quantile_file,
+        samples_to_aggregate_trace_file=args.samples_to_aggregate_trace_file,
+        test_num_workers=args.test_num_workers,
+        max_ind_range=args.max_ind_range,
+        sub_sample_rate=args.data_sub_sample_rate,
+        mlperf_bin_loader=args.mlperf_bin_loader,
+        **kwargs
+    )
     # load model to backend
-    model = backend.load(args.model_path, inputs=args.inputs, outputs=args.outputs)
+    model = backend.load(
+        args.model_path,
+        inputs=args.inputs,
+        outputs=args.outputs)
     final_results = {
         "runtime": model.name(),
         "version": model.version(),
@@ -541,10 +998,12 @@ def main():
         lg.TestScenario.SingleStream: RunnerBase,
         lg.TestScenario.MultiStream: QueueRunner,
         lg.TestScenario.Server: QueueRunner,
-        lg.TestScenario.Offline: QueueRunner
+        lg.TestScenario.Offline: QueueRunner,
     }
 
-    runner = runner_map[scenario](model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize)
+    runner = runner_map[scenario](
+        model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize
+    )
 
     def issue_queries(query_samples):
         runner.enqueue(query_samples)
@@ -581,13 +1040,23 @@ def flush_queries():
 
     if args.max_latency:
         settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
-        settings.multi_stream_expected_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_expected_latency_ns = int(
+            args.max_latency * NANO_SEC)
 
     sut = lg.ConstructSUT(issue_queries, flush_queries)
-    qsl = lg.ConstructQSL(count, min(count, args.samples_per_query_offline), ds.load_query_samples, ds.unload_query_samples)
+    qsl = lg.ConstructQSL(
+        count,
+        min(count, args.samples_per_query_offline),
+        ds.load_query_samples,
+        ds.unload_query_samples,
+    )
 
     log.info("starting {}".format(scenario))
-    result_dict = {"good": 0, "total": 0, "roc_auc": 0, "scenario": str(scenario)}
+    result_dict = {
+        "good": 0,
+        "total": 0,
+        "roc_auc": 0,
+        "scenario": str(scenario)}
     runner.start_run(result_dict, args.accuracy)
     lg.StartTest(sut, qsl, settings)
 
@@ -599,9 +1068,14 @@ def flush_queries():
     if args.accuracy:
         post_proc.finalize(result_dict, ds, output_dir=args.output)
 
-    add_results(final_results, "{}".format(scenario),
-                result_dict, last_timeing, time.time() - ds.last_loaded, args.accuracy)
-
+    add_results(
+        final_results,
+        "{}".format(scenario),
+        result_dict,
+        last_timeing,
+        time.time() - ds.last_loaded,
+        args.accuracy,
+    )
 
     runner.finish()
     lg.DestroyQSL(qsl)
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/python/tf_dlrm.py b/retired_benchmarks/recommendation/dlrm/pytorch/python/tf_dlrm.py
index f99117e98d..269d1fd344 100644
--- a/retired_benchmarks/recommendation/dlrm/pytorch/python/tf_dlrm.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/python/tf_dlrm.py
@@ -22,231 +22,251 @@
 import tensorflow as tf
 
 from tensorflow.compiler.tf2xla.python import xla
-from tensorflow.contrib import  layers as contrib_layers
+from tensorflow.contrib import layers as contrib_layers
+
 # import utils  # ti
 
-import sys # ti
+import sys  # ti
+
 
 def rand_features(batch_size):
-  """Emits random input features, used for testing."""
-  features = {}
-  pos_size = batch_size // 2
-  neg_size = batch_size - pos_size
-  features["clicked"] = tf.concat([
-      tf.ones([pos_size, 1], dtype=tf.float32),
-      tf.zeros([neg_size, 1], dtype=tf.float32)
-  ], axis=0)
-  features["int-features"] = tf.random.uniform(
-      shape=(batch_size, 13),
-      maxval=100)
-  features["cat-features"] = tf.random.uniform(
-      shape=(batch_size, 26),
-      maxval=100,
-      dtype=tf.int32)
-  return features
+    """Emits random input features, used for testing."""
+    features = {}
+    pos_size = batch_size // 2
+    neg_size = batch_size - pos_size
+    features["clicked"] = tf.concat(
+        [
+            tf.ones([pos_size, 1], dtype=tf.float32),
+            tf.zeros([neg_size, 1], dtype=tf.float32),
+        ],
+        axis=0,
+    )
+    features["int-features"] = tf.random.uniform(
+        shape=(batch_size, 13), maxval=100)
+    features["cat-features"] = tf.random.uniform(
+        shape=(batch_size, 26), maxval=100, dtype=tf.int32
+    )
+    return features
+
 
 def rand_features_np(batch_size, num_d, num_s, minsize):
-  """Emits random input features, used for testing."""
-  # features = {}
-  # pos_size = batch_size // 2
-  # neg_size = batch_size - pos_size
-  # features["clicked"] = tf.concat([
-  #     tf.ones([pos_size, 1], dtype=tf.float32),
-  #     tf.zeros([neg_size, 1], dtype=tf.float32)
-  # ], axis=0)
-  features_int_np = np.random.randint(100,
-      size=(batch_size, num_d)
-  )
-  features_cat_np = np.random.randint(minsize,
-      size=(batch_size, num_s),
-      dtype=np.int32
-  )
-  return features_int_np, features_cat_np
+    """Emits random input features, used for testing."""
+    # features = {}
+    # pos_size = batch_size // 2
+    # neg_size = batch_size - pos_size
+    # features["clicked"] = tf.concat([
+    #     tf.ones([pos_size, 1], dtype=tf.float32),
+    #     tf.zeros([neg_size, 1], dtype=tf.float32)
+    # ], axis=0)
+    features_int_np = np.random.randint(100, size=(batch_size, num_d))
+    features_cat_np = np.random.randint(
+        minsize, size=(batch_size, num_s), dtype=np.int32
+    )
+    return features_int_np, features_cat_np
 
 
 def dot_interact(concat_features, params=None):
-  """Performs feature interaction operation between dense and sparse.
-
-  Input tensors represent dense and sparse features.
-  Pre-condition: The tensors have been stacked along dimension 1.
-
-  Args:
-    concat_features: Tensor of features with shape [B, n_features, feature_dim].
-    params: Model params.
-
-  Returns:
-    activations: Tensor representing interacted features.
-  """
-  # batch_size = concat_features.shape[0]
-  if not params:
-    params = {}
-
-  # Interact features, select lower-triangular portion, and re-shape.
-  xactions = tf.matmul(concat_features, concat_features, transpose_b=True)
-  tf.logging.info("Model_FN: xactions shape: %s", xactions.get_shape())
-  ones = tf.ones_like(xactions)
-  upper_tri_mask = tf.linalg.band_part(ones, 0, -1)
-  feature_dim = xactions.shape[-1]
-
-  if params["opt_skip_gather"]:
-    upper_tri_bool = tf.cast(upper_tri_mask, tf.bool)
-    activations = tf.where(
-        condition=upper_tri_bool, x=tf.zeros_like(xactions), y=xactions)
-    tf.logging.info("Model_FN: activations shape: %s", activations.get_shape())
-    out_dim = feature_dim * feature_dim
-  else:
-    lower_tri_mask = ones - upper_tri_mask
-    activations = tf.boolean_mask(xactions, lower_tri_mask)
-    tf.logging.info("Model_FN: activations shape: %s", activations.get_shape())
-    out_dim = feature_dim * (feature_dim - 1) // 2
-
-  activations = tf.reshape(activations, (-1, out_dim))
-  return activations
+    """Performs feature interaction operation between dense and sparse.
+
+    Input tensors represent dense and sparse features.
+    Pre-condition: The tensors have been stacked along dimension 1.
+
+    Args:
+      concat_features: Tensor of features with shape [B, n_features, feature_dim].
+      params: Model params.
+
+    Returns:
+      activations: Tensor representing interacted features.
+    """
+    # batch_size = concat_features.shape[0]
+    if not params:
+        params = {}
+
+    # Interact features, select lower-triangular portion, and re-shape.
+    xactions = tf.matmul(concat_features, concat_features, transpose_b=True)
+    tf.logging.info("Model_FN: xactions shape: %s", xactions.get_shape())
+    ones = tf.ones_like(xactions)
+    upper_tri_mask = tf.linalg.band_part(ones, 0, -1)
+    feature_dim = xactions.shape[-1]
+
+    if params["opt_skip_gather"]:
+        upper_tri_bool = tf.cast(upper_tri_mask, tf.bool)
+        activations = tf.where(
+            condition=upper_tri_bool, x=tf.zeros_like(xactions), y=xactions
+        )
+        tf.logging.info(
+            "Model_FN: activations shape: %s",
+            activations.get_shape())
+        out_dim = feature_dim * feature_dim
+    else:
+        lower_tri_mask = ones - upper_tri_mask
+        activations = tf.boolean_mask(xactions, lower_tri_mask)
+        tf.logging.info(
+            "Model_FN: activations shape: %s",
+            activations.get_shape())
+        out_dim = feature_dim * (feature_dim - 1) // 2
+
+    activations = tf.reshape(activations, (-1, out_dim))
+    return activations
 
 
 def logits_fn(features_int, features_cat, params):
-  """Calculate predictions."""
-  # tf.logging.info("Model_FN: Number of input features: %d", len(features))
-  # for ft in sorted(features.keys()):
-  #   tf.logging.info("Model_FN: Feature %s -- shape %s", ft,
-  #                   features[ft].get_shape())
-
-  reuse = False if params["is_training"] else True
-
-  bot_mlp_input = features_int
-  tf.logging.info("Model_FN: Bottom MLP input (int features) shape: %s",
-                  bot_mlp_input.get_shape())
-  mlp_dims_bottom = params["mlp_bottom"]
-
-  for layer_idx in range(len(mlp_dims_bottom)):
-    bot_mlp_input = tf.layers.dense(
-        bot_mlp_input,
-        mlp_dims_bottom[layer_idx],
-        activation="relu",
-        # ti: modules dont exist
-        # kernel_initializer=tf.compat.v2.initializers.GlorotNormal(),
-        kernel_initializer=tf.compat.v1.keras.initializers.glorot_normal(),
-        # bias_initializer=tf.compat.v2.initializers.RandomNormal(
-        #     mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_bottom[layer_idx])),
-        bias_initializer=tf.compat.v1.random_normal_initializer(
-          mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_bottom[layer_idx])),
-        name="bottom_mlp_layer_%d" % layer_idx,
-        reuse=reuse
+    """Calculate predictions."""
+    # tf.logging.info("Model_FN: Number of input features: %d", len(features))
+    # for ft in sorted(features.keys()):
+    #   tf.logging.info("Model_FN: Feature %s -- shape %s", ft,
+    #                   features[ft].get_shape())
+
+    reuse = False if params["is_training"] else True
+
+    bot_mlp_input = features_int
+    tf.logging.info(
+        "Model_FN: Bottom MLP input (int features) shape: %s", bot_mlp_input.get_shape(
+        )
+    )
+    mlp_dims_bottom = params["mlp_bottom"]
+
+    for layer_idx in range(len(mlp_dims_bottom)):
+        bot_mlp_input = tf.layers.dense(
+            bot_mlp_input,
+            mlp_dims_bottom[layer_idx],
+            activation="relu",
+            # ti: modules dont exist
+            # kernel_initializer=tf.compat.v2.initializers.GlorotNormal(),
+            kernel_initializer=tf.compat.v1.keras.initializers.glorot_normal(),
+            # bias_initializer=tf.compat.v2.initializers.RandomNormal(
+            # mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_bottom[layer_idx])),
+            bias_initializer=tf.compat.v1.random_normal_initializer(
+                mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_bottom[layer_idx])
+            ),
+            name="bottom_mlp_layer_%d" % layer_idx,
+            reuse=reuse,
+        )
+    bot_mlp_output = bot_mlp_input
+
+    cat_features = []
+    emb_tables = []
+    # for feature_name, value in sorted(features.items()):
+    #   if "categorical-feature" in feature_name:
+    #     cat_features.append(value)
+    num_s = params["num_sparse_features"]
+    for j in range(num_s):
+        emb_init = tf.random_uniform(
+            [params["vocab_sizes"][j], params["dim_embed"]], -1.0, 1.0
+        )
+        emb_matrix = tf.get_variable(
+            name="embedding_table%02d" % j,
+            dtype=tf.float32,
+            trainable=True,
+            initializer=emb_init,
+        )
+        emb_tables.append(emb_matrix)
+
+    for j in range(num_s):
+        col = tf.slice(features_cat, [0, j], [-1, 1])
+        ecol = tf.nn.embedding_lookup(emb_tables[j], col)
+        ecol = tf.reshape(ecol, [-1, params["dim_embed"]])
+        cat_features.append(ecol)
+
+    # tc_features = []
+    # if "tc-features" in features:
+    #   # Compute offsets for single concatenated table.
+    #   batch_size = features_tc.shape[0]
+    #   num_tc_features = features_tc.shape[1]
+    #   num_tables_in_ec = params["num_tables_in_ec"]
+    #   tc_table_sizes = feature_config.get_sorted_table_size()[num_tables_in_ec:]  # params["vocab_sizes"]
+    #   total_tbl_size = sum(tc_table_sizes)
+    #   idx_offsets = [0] + list(np.cumsum(tc_table_sizes[:-1]))
+    #   idx_offsets = tf.broadcast_to(
+    #       tf.constant(idx_offsets), (batch_size, num_tc_features))
+    #   idxs = idx_offsets + features_tc
+
+    #   def _create_init_table():
+    #     """Table initialization varies depending on the vocab size."""
+    #     full_tbl = np.zeros(
+    #         shape=(total_tbl_size, params["dim_embed"]), dtype=np.float32)
+    #     start_idx = 0
+    #     for idx, tbl_size in enumerate(tc_table_sizes):
+    #       end_idx = start_idx + tc_table_sizes[idx]
+    #       cur_tbl_init = np.random.uniform(
+    #           low=-1 / np.sqrt(tbl_size),
+    #           high=1 / np.sqrt(tbl_size),
+    #           size=(tbl_size, params["dim_embed"])).astype(np.float32)
+    #       full_tbl[start_idx:end_idx, :] = cur_tbl_init
+    #       start_idx += tc_table_sizes[idx]
+    #     return tf.constant(full_tbl)
+
+    #   tc_embedding_table = tf.get_variable(
+    #       name="tc_embedding_table",
+    #       dtype=tf.float32,
+    #       trainable=True,
+    #       # pylint: disable=unnecessary-lambda
+    #       initializer=lambda: _create_init_table())
+    #   tc_features = tf.gather(tc_embedding_table, idxs)
+    #   tf.logging.info("TC features shape: {}".format(tc_features.get_shape()))
+
+    # Dot feature interaction
+    # Concat and reshape, instead of stack. Better for performance.
+    # batch_size = bot_mlp_output.shape[0]
+    feature_stack = tf.concat([bot_mlp_output] + cat_features, axis=-1)
+    feature_stack = tf.reshape(
+        feature_stack, [-1, params["num_sparse_features"] +
+                        1, params["dim_embed"]]
+    )
+
+    # if "tc-features" in features:
+    #   feature_stack = tf.concat([feature_stack, tc_features], axis=1)
+    tf.logging.info(
+        "Model_FN: concated feature shape: %s",
+        feature_stack.get_shape())
+    dot_interact_output = dot_interact(
+        concat_features=feature_stack, params=params)
+    top_mlp_input = tf.concat([bot_mlp_output, dot_interact_output], axis=1)
+    tf.logging.info(
+        "Model_FN: Top MLP input (full features) shape: %s", top_mlp_input.get_shape(
+        )
+    )
+
+    # Capture original MLP fan-in for proper kernel initialization.
+    num_fts = len(cat_features) + 1
+    orig_top_mlp_dim = (num_fts * (num_fts - 1)) / 2 + params["dim_embed"]
+    tf.logging.info(
+        "Model_FN: Original feature len: {}".format(orig_top_mlp_dim))
+
+    # Top MLP
+    # NOTE: For the top MLP, the last layer is a sigmoid. The loss function should
+    # therefore take [0,1] probability values as inputs, instead of logits.
+    mlp_dims_top = params["mlp_top"]
+    num_layers_top = len(mlp_dims_top)
+    sigmoid_layer_top = num_layers_top - 1
+    for layer_idx in range(num_layers_top):
+        fan_in = orig_top_mlp_dim if layer_idx == 0 else mlp_dims_top[layer_idx - 1]
+        fan_out = mlp_dims_top[layer_idx]
+        tf.logging.info(
+            "  layer {}: fan_in={} fan_out={}".format(
+                layer_idx, fan_in, fan_out)
         )
-  bot_mlp_output = bot_mlp_input
-
-  cat_features = []
-  emb_tables = []
-  # for feature_name, value in sorted(features.items()):
-  #   if "categorical-feature" in feature_name:
-  #     cat_features.append(value)
-  num_s = params["num_sparse_features"]
-  for j in range(num_s):
-    emb_init = tf.random_uniform([params["vocab_sizes"][j], params["dim_embed"]], -1.0, 1.0)
-    emb_matrix = tf.get_variable(
-        name="embedding_table%02d" % j,
-        dtype=tf.float32,
-        trainable=True,
-        initializer=emb_init)
-    emb_tables.append(emb_matrix)
-
-  for j in range(num_s):
-    col = tf.slice(features_cat, [0, j], [-1, 1])
-    ecol = tf.nn.embedding_lookup(emb_tables[j], col)
-    ecol = tf.reshape(ecol, [-1, params["dim_embed"]])
-    cat_features.append(ecol)
-
-  # tc_features = []
-  # if "tc-features" in features:
-  #   # Compute offsets for single concatenated table.
-  #   batch_size = features_tc.shape[0]
-  #   num_tc_features = features_tc.shape[1]
-  #   num_tables_in_ec = params["num_tables_in_ec"]
-  #   tc_table_sizes = feature_config.get_sorted_table_size()[num_tables_in_ec:]  # params["vocab_sizes"]
-  #   total_tbl_size = sum(tc_table_sizes)
-  #   idx_offsets = [0] + list(np.cumsum(tc_table_sizes[:-1]))
-  #   idx_offsets = tf.broadcast_to(
-  #       tf.constant(idx_offsets), (batch_size, num_tc_features))
-  #   idxs = idx_offsets + features_tc
-
-  #   def _create_init_table():
-  #     """Table initialization varies depending on the vocab size."""
-  #     full_tbl = np.zeros(
-  #         shape=(total_tbl_size, params["dim_embed"]), dtype=np.float32)
-  #     start_idx = 0
-  #     for idx, tbl_size in enumerate(tc_table_sizes):
-  #       end_idx = start_idx + tc_table_sizes[idx]
-  #       cur_tbl_init = np.random.uniform(
-  #           low=-1 / np.sqrt(tbl_size),
-  #           high=1 / np.sqrt(tbl_size),
-  #           size=(tbl_size, params["dim_embed"])).astype(np.float32)
-  #       full_tbl[start_idx:end_idx, :] = cur_tbl_init
-  #       start_idx += tc_table_sizes[idx]
-  #     return tf.constant(full_tbl)
-
-  #   tc_embedding_table = tf.get_variable(
-  #       name="tc_embedding_table",
-  #       dtype=tf.float32,
-  #       trainable=True,
-  #       # pylint: disable=unnecessary-lambda
-  #       initializer=lambda: _create_init_table())
-  #   tc_features = tf.gather(tc_embedding_table, idxs)
-  #   tf.logging.info("TC features shape: {}".format(tc_features.get_shape()))
-
-  # Dot feature interaction
-  # Concat and reshape, instead of stack. Better for performance.
-  # batch_size = bot_mlp_output.shape[0]
-  feature_stack = tf.concat([bot_mlp_output] + cat_features, axis=-1)
-  feature_stack = tf.reshape(feature_stack,
-                             [-1, params["num_sparse_features"] + 1, params["dim_embed"]])
-
-  # if "tc-features" in features:
-  #   feature_stack = tf.concat([feature_stack, tc_features], axis=1)
-  tf.logging.info("Model_FN: concated feature shape: %s",
-                  feature_stack.get_shape())
-  dot_interact_output = dot_interact(
-      concat_features=feature_stack, params=params)
-  top_mlp_input = tf.concat([bot_mlp_output, dot_interact_output], axis=1)
-  tf.logging.info("Model_FN: Top MLP input (full features) shape: %s",
-                  top_mlp_input.get_shape())
-
-  # Capture original MLP fan-in for proper kernel initialization.
-  num_fts = len(cat_features) + 1
-  orig_top_mlp_dim = (num_fts * (num_fts - 1)) / 2 + params["dim_embed"]
-  tf.logging.info("Model_FN: Original feature len: {}".format(orig_top_mlp_dim))
-
-  # Top MLP
-  # NOTE: For the top MLP, the last layer is a sigmoid. The loss function should
-  #       therefore take [0,1] probability values as inputs, instead of logits.
-  mlp_dims_top = params["mlp_top"]
-  num_layers_top = len(mlp_dims_top)
-  sigmoid_layer_top = num_layers_top - 1
-  for layer_idx in range(num_layers_top):
-    fan_in = orig_top_mlp_dim if layer_idx == 0 else mlp_dims_top[layer_idx - 1]
-    fan_out = mlp_dims_top[layer_idx]
-    tf.logging.info("  layer {}: fan_in={} fan_out={}".format(
-        layer_idx, fan_in, fan_out))
-    top_mlp_input = tf.layers.dense(
-        top_mlp_input,
-        mlp_dims_top[layer_idx],
-        activation="sigmoid" if layer_idx == sigmoid_layer_top else "relu",
-        # NOTE: We would usually use GlorotNormal() initializer here. But due to
-        # the skip_gather optimization, the GlorotNormal would result in a
-        # mathematical error, as Glorot is a function of the fan-in.
-        # The fan-in will be larger for skip-gather activations since we also
-        # pass in the zeros. Therefore we explicitly set the kernel intializer
-        # to RandomNormal(0, sqrt(2/(fan_in+fan_out))
-
-        # ti: see above
-        # kernel_initializer=tf.compat.v2.initializers.RandomNormal(
-        #     mean=0.0, stddev=math.sqrt(2.0 / (fan_in + fan_out))),
-        # bias_initializer=tf.compat.v2.initializers.RandomNormal(
-        #     mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_top[layer_idx])),
-        kernel_initializer=tf.compat.v1.keras.initializers.glorot_normal(),
-        bias_initializer=tf.compat.v1.random_normal_initializer(
-          mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_top[layer_idx])),
-        name="top_mlp_layer_%d" % layer_idx,
-        reuse=reuse
+        top_mlp_input = tf.layers.dense(
+            top_mlp_input,
+            mlp_dims_top[layer_idx],
+            activation="sigmoid" if layer_idx == sigmoid_layer_top else "relu",
+            # NOTE: We would usually use GlorotNormal() initializer here. But due to
+            # the skip_gather optimization, the GlorotNormal would result in a
+            # mathematical error, as Glorot is a function of the fan-in.
+            # The fan-in will be larger for skip-gather activations since we also
+            # pass in the zeros. Therefore we explicitly set the kernel intializer
+            # to RandomNormal(0, sqrt(2/(fan_in+fan_out))
+            # ti: see above
+            # kernel_initializer=tf.compat.v2.initializers.RandomNormal(
+            #     mean=0.0, stddev=math.sqrt(2.0 / (fan_in + fan_out))),
+            # bias_initializer=tf.compat.v2.initializers.RandomNormal(
+            #     mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_top[layer_idx])),
+            kernel_initializer=tf.compat.v1.keras.initializers.glorot_normal(),
+            bias_initializer=tf.compat.v1.random_normal_initializer(
+                mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_top[layer_idx])
+            ),
+            name="top_mlp_layer_%d" % layer_idx,
+            reuse=reuse,
         )
-  predictions = top_mlp_input
-  return predictions
+    predictions = top_mlp_input
+    return predictions
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/python/version.py b/retired_benchmarks/recommendation/dlrm/pytorch/python/version.py
index 1152dbb417..570348596c 100644
--- a/retired_benchmarks/recommendation/dlrm/pytorch/python/version.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/python/version.py
@@ -1,3 +1,2 @@
-
-version = '0.1.0'
-git_version = '05df3bae82ef9fc933277385eb778e3f22cd0c6a'
+version = "0.1.0"
+git_version = "05df3bae82ef9fc933277385eb778e3f22cd0c6a"
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/setup.py b/retired_benchmarks/recommendation/dlrm/pytorch/setup.py
index c1e2fbcf07..758d874fbd 100644
--- a/retired_benchmarks/recommendation/dlrm/pytorch/setup.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/setup.py
@@ -13,17 +13,20 @@
 from setuptools import setup, find_packages, Command
 
 TOP_DIR = os.path.realpath(os.path.dirname(__file__))
-SRC_DIR = os.path.join(TOP_DIR, 'python')
+SRC_DIR = os.path.join(TOP_DIR, "python")
 
 try:
-    git_version = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=TOP_DIR).decode('ascii').strip()
+    git_version = (
+        subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=TOP_DIR)
+        .decode("ascii")
+        .strip()
+    )
 except (OSError, subprocess.CalledProcessError):
     git_version = None
 
-with open(os.path.join(TOP_DIR, 'VERSION_NUMBER')) as version_file:
-    VersionInfo = namedtuple('VersionInfo', ['version', 'git_version'])(
-        version=version_file.read().strip(),
-        git_version=git_version
+with open(os.path.join(TOP_DIR, "VERSION_NUMBER")) as version_file:
+    VersionInfo = namedtuple("VersionInfo", ["version", "git_version"])(
+        version=version_file.read().strip(), git_version=git_version
     )
 
 
@@ -37,49 +40,67 @@ def finalize_options(self):
         pass
 
     def run(self):
-        with open(os.path.join(SRC_DIR, 'version.py'), 'w') as f:
-            f.write(dedent('''
+        with open(os.path.join(SRC_DIR, "version.py"), "w") as f:
+            f.write(
+                dedent(
+                    """
             version = '{version}'
             git_version = '{git_version}'
-            '''.format(**dict(VersionInfo._asdict()))))
+            """.format(
+                        **dict(VersionInfo._asdict())
+                    )
+                )
+            )
 
 
 class build_py(setuptools.command.build_py.build_py):
     def run(self):
-        self.run_command('create_version')
+        self.run_command("create_version")
         setuptools.command.build_py.build_py.run(self)
 
 
 class build(distutils.command.build.build):
     def run(self):
-        self.run_command('build_py')
+        self.run_command("build_py")
 
 
 class develop(setuptools.command.develop.develop):
     def run(self):
-        self.run_command('create_version')
-        self.run_command('build')
+        self.run_command("create_version")
+        self.run_command("build")
         setuptools.command.develop.develop.run(self)
 
 
 cmdclass = {
-    'create_version': create_version,
-    'build_py': build_py,
-    'build': build,
-    'develop': develop,
+    "create_version": create_version,
+    "build_py": build_py,
+    "build": build,
+    "develop": develop,
 }
 
 setup(
     name="mlperf-inference",
     version=VersionInfo.version,
-    description='mlperf inference benchmark',
-    setup_requires=['pytest-runner'],
-    tests_require=['graphviz', 'parameterized', 'pytest', 'pytest-cov', 'pyyaml'],
+    description="mlperf inference benchmark",
+    setup_requires=["pytest-runner"],
+    tests_require=[
+        "graphviz",
+        "parameterized",
+        "pytest",
+        "pytest-cov",
+        "pyyaml"],
     cmdclass=cmdclass,
     packages=find_packages(),
-    author='guschmue@microsoft.com',
-    author_email='guschmue@microsoft.com',
-    url='https://github.com/mlperf/inference',
-    install_requires=['numpy>=1.14.1', 'onnx>=1.5', 'pybind11', 'Cython',
-                        'pycocotools', 'mlperf_loadgen', 'opencv-python-headless']
+    author="guschmue@microsoft.com",
+    author_email="guschmue@microsoft.com",
+    url="https://github.com/mlperf/inference",
+    install_requires=[
+        "numpy>=1.14.1",
+        "onnx>=1.5",
+        "pybind11",
+        "Cython",
+        "pycocotools",
+        "mlperf_loadgen",
+        "opencv-python-headless",
+    ],
 )
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/tools/accuracy-dlrm.py b/retired_benchmarks/recommendation/dlrm/pytorch/tools/accuracy-dlrm.py
index 873f6a0e10..fc8332ac43 100644
--- a/retired_benchmarks/recommendation/dlrm/pytorch/tools/accuracy-dlrm.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/tools/accuracy-dlrm.py
@@ -16,25 +16,39 @@
 
 # pylint: disable=missing-docstring
 
+
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--day-23-file", default=None,
-        help="path to day_23 file. If present, it is assumed that the accuracy log contains only the prediction, not the ground truth label.")
-    parser.add_argument("--aggregation-trace-file", default=None,
-        help="path to dlrm_trace_of_aggregated_samples.txt. Only needed if --day-23-file is specified")
-    parser.add_argument("--verbose", action="store_true", help="verbose messages")
-    parser.add_argument("--dtype", default="float32", choices=["float32", "int32", "int64"], help="data type of the label")
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--day-23-file",
+        default=None,
+        help="path to day_23 file. If present, it is assumed that the accuracy log contains only the prediction, not the ground truth label.",
+    )
+    parser.add_argument(
+        "--aggregation-trace-file",
+        default=None,
+        help="path to dlrm_trace_of_aggregated_samples.txt. Only needed if --day-23-file is specified",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--dtype",
+        default="float32",
+        choices=["float32", "int32", "int64"],
+        help="data type of the label",
+    )
     args = parser.parse_args()
     return args
 
 
-dtype_map = {
-    "float32": np.float32,
-    "int32": np.int32,
-    "int64": np.int64
-}
+dtype_map = {"float32": np.float32, "int32": np.int32, "int64": np.int64}
+
 
 def get_targets(args, qsl_indices):
     # Parse aggregation trace file to know the sample -> user-item pair mapping
@@ -42,8 +56,11 @@ def get_targets(args, qsl_indices):
     sample_boundaries = [0]
     with open(args.aggregation_trace_file) as f:
         for line in f:
-            sample_boundaries.append(sample_boundaries[-1] + int(line.split(", ")[2]))
-    assert len(sample_boundaries) == len(qsl_indices) + 1, "Number of samples in trace file does not match number of samples in loadgen accuracy log!"
+            sample_boundaries.append(
+                sample_boundaries[-1] + int(line.split(", ")[2]))
+    assert (
+        len(sample_boundaries) == len(qsl_indices) + 1
+    ), "Number of samples in trace file does not match number of samples in loadgen accuracy log!"
     # Get all the ground truth labels in the original order in day_23
     print("Parsing ground truth labels from day_23 file...")
     ground_truths = []
@@ -52,18 +69,22 @@ def get_targets(args, qsl_indices):
             if line_idx >= sample_boundaries[-1]:
                 break
             ground_truths.append(int(line.split("\t")[0]))
-    # Re-order the ground truth labels according to the qsl indices in the loadgen log.
+    # Re-order the ground truth labels according to the qsl indices in the
+    # loadgen log.
     print("Re-ordering ground truth labels...")
     targets = []
     for qsl_idx in qsl_indices:
-        for i in range(sample_boundaries[qsl_idx], sample_boundaries[qsl_idx + 1]):
+        for i in range(sample_boundaries[qsl_idx],
+                       sample_boundaries[qsl_idx + 1]):
             targets.append(ground_truths[i])
     return targets
 
+
 def main():
     args = get_args()
 
-    # If "--day-23-file" is specified, assume that the accuracy log contains only the prediction, not the ground truth label.
+    # If "--day-23-file" is specified, assume that the accuracy log contains
+    # only the prediction, not the ground truth label.
     log_contains_gt = args.day_23_file is None
 
     if log_contains_gt:
@@ -77,12 +98,12 @@ def main():
 
     seen = set()
     good = 0
-    total= 0
+    total = 0
     all_results = []
     all_targets = []
     qsl_indices = []
     for j in results:
-        idx = j['qsl_idx']
+        idx = j["qsl_idx"]
 
         # de-dupe in case loadgen sends the same sample multiple times
         if idx in seen:
@@ -91,7 +112,7 @@ def main():
         qsl_indices.append(idx)
 
         # reconstruct label from mlperf accuracy log
-        data = np.frombuffer(bytes.fromhex(j['data']), dtype_map[args.dtype])
+        data = np.frombuffer(bytes.fromhex(j["data"]), dtype_map[args.dtype])
 
         # data stores both predictions and targets
         output_count = 2 if log_contains_gt else 1
@@ -114,7 +135,11 @@ def main():
                     good += 1
                 else:
                     if args.verbose:
-                        print("{}:{}, expected: {}, found {}".format(idx, k, target, result.round()))
+                        print(
+                            "{}:{}, expected: {}, found {}".format(
+                                idx, k, target, result.round()
+                            )
+                        )
 
     if not log_contains_gt:
         all_targets = get_targets(args, qsl_indices)
@@ -129,9 +154,16 @@ def main():
     roc_auc = sklearn.metrics.roc_auc_score(all_targets, all_results)
     # compute accuracy metric
     acc = good / total
-    print("AUC={:.3f}%, accuracy={:.3f}%, good={}, total={}, queries={}".format(100. * roc_auc, 100. * acc, good, total, len(seen)))
+    print(
+        "AUC={:.3f}%, accuracy={:.3f}%, good={}, total={}, queries={}".format(
+            100.0 * roc_auc, 100.0 * acc, good, total, len(seen)
+        )
+    )
     if args.verbose:
-        print("found and ignored {} query dupes".format(len(results) - len(seen)))
+        print(
+            "found and ignored {} query dupes".format(
+                len(results) -
+                len(seen)))
 
 
 if __name__ == "__main__":
diff --git a/retired_benchmarks/recommendation/dlrm/pytorch/tools/quickgen.py b/retired_benchmarks/recommendation/dlrm/pytorch/tools/quickgen.py
index 9a82322715..bed5243481 100644
--- a/retired_benchmarks/recommendation/dlrm/pytorch/tools/quickgen.py
+++ b/retired_benchmarks/recommendation/dlrm/pytorch/tools/quickgen.py
@@ -1,11 +1,12 @@
-'''
+"""
 quick generator of random samples for debugging
-'''
+"""
 
 import sys
 import argparse
 import numpy as np
 
+
 def quickgen(num_samples, num_t, num_d, num_s, ln_emb, text_file=None):
     # generate place holder random array, including dense features
     a = np.random.randint(0, 10, (num_t + num_d + num_s, num_samples))
@@ -23,23 +24,31 @@ def quickgen(num_samples, num_t, num_d, num_s, ln_emb, text_file=None):
     for _ in range(num_s):
         lstr.append("%x")
     if text_file is not None:
-        np.savetxt(text_file, a, fmt=lstr, delimiter='\t',)
+        np.savetxt(
+            text_file,
+            a,
+            fmt=lstr,
+            delimiter="\t",
+        )
 
     return a
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Quick generator of random samples for debugging."
     )
-    parser.add_argument("--num-samples",         type=int, default=4096)
-    parser.add_argument("--num-dense-features",  type=int, default=13)
+    parser.add_argument("--num-samples", type=int, default=4096)
+    parser.add_argument("--num-dense-features", type=int, default=13)
     parser.add_argument("--num-sparse-features", type=str, default="4-3-2")
-    parser.add_argument("--num-targets",         type=int, default=1)
-    parser.add_argument("--profile",             type=str, default="")  # kaggle|terabyte0875|terabyte
-    parser.add_argument("--num-days",            type=int, default=24)
-    parser.add_argument("--numpy-rand-seed",     type=int, default=123)
-    parser.add_argument("--output-name",         type=str, default="day_")
-    parser.add_argument("--output-dir",          type=str, default="./")
+    parser.add_argument("--num-targets", type=int, default=1)
+    parser.add_argument(
+        "--profile", type=str, default=""
+    )  # kaggle|terabyte0875|terabyte
+    parser.add_argument("--num-days", type=int, default=24)
+    parser.add_argument("--numpy-rand-seed", type=int, default=123)
+    parser.add_argument("--output-name", type=str, default="day_")
+    parser.add_argument("--output-dir", type=str, default="./")
     args = parser.parse_args()
 
     np.random.seed(args.numpy_rand_seed)
@@ -48,27 +57,118 @@ def quickgen(num_samples, num_t, num_d, num_s, ln_emb, text_file=None):
     out_name = args.output_name
     ln_emb = np.fromstring(args.num_sparse_features, dtype=int, sep="-")
     if args.profile == "kaggle":
-        # 1. Criteo Kaggle Display Advertisement Challenge Dataset (see ./bench/dlrm_s_criteo_kaggle.sh)
+        # 1. Criteo Kaggle Display Advertisement Challenge Dataset (see
+        # ./bench/dlrm_s_criteo_kaggle.sh)
         num_days = 1
         out_name = "train.txt"
-        ln_emb = np.array([1460,583,10131227,2202608,305,24,12517,633,3,93145,5683,8351593,3194,27,14992,5461306,10,5652,2173,4,7046547,18,15,286181,105,142572])
+        ln_emb = np.array(
+            [
+                1460,
+                583,
+                10131227,
+                2202608,
+                305,
+                24,
+                12517,
+                633,
+                3,
+                93145,
+                5683,
+                8351593,
+                3194,
+                27,
+                14992,
+                5461306,
+                10,
+                5652,
+                2173,
+                4,
+                7046547,
+                18,
+                15,
+                286181,
+                105,
+                142572,
+            ]
+        )
     elif args.profile == "terabyte0875":
-        # 2. Criteo Terabyte (see ./bench/dlrm_s_criteo_terabyte.sh [--sub-sample=0.875] --max-in-range=10000000)
+        # 2. Criteo Terabyte (see ./bench/dlrm_s_criteo_terabyte.sh
+        # [--sub-sample=0.875] --max-in-range=10000000)
         num_days = 24
         out_name = "day_"
-        ln_emb = np.array([9980333,36084,17217,7378,20134,3,7112,1442,61, 9758201,1333352,313829,10,2208,11156,122,4,970,14, 9994222, 7267859, 9946608,415421,12420,101, 36])
+        ln_emb = np.array(
+            [
+                9980333,
+                36084,
+                17217,
+                7378,
+                20134,
+                3,
+                7112,
+                1442,
+                61,
+                9758201,
+                1333352,
+                313829,
+                10,
+                2208,
+                11156,
+                122,
+                4,
+                970,
+                14,
+                9994222,
+                7267859,
+                9946608,
+                415421,
+                12420,
+                101,
+                36,
+            ]
+        )
     elif args.profile == "terabyte":
-        # 3. Criteo Terabyte MLPerf training (see ./bench/run_and_time.sh --max-in-range=40000000)
+        # 3. Criteo Terabyte MLPerf training (see ./bench/run_and_time.sh
+        # --max-in-range=40000000)
         num_days = 24
         out_name = "day_"
-        ln_emb=np.array([39884406,39043,17289,7420,20263,3,7120,1543,63,38532951,2953546,403346,10,2208,11938,155,4,976,14,39979771,25641295,39664984,585935,12972,108,36])
+        ln_emb = np.array(
+            [
+                39884406,
+                39043,
+                17289,
+                7420,
+                20263,
+                3,
+                7120,
+                1543,
+                63,
+                38532951,
+                2953546,
+                403346,
+                10,
+                2208,
+                11938,
+                155,
+                4,
+                976,
+                14,
+                39979771,
+                25641295,
+                39664984,
+                585935,
+                12972,
+                108,
+                36,
+            ]
+        )
 
-    num_d   = args.num_dense_features
-    num_s   = len(ln_emb)
-    num_t   = args.num_targets
+    num_d = args.num_dense_features
+    num_s = len(ln_emb)
+    num_t = args.num_targets
     out_dir = args.output_dir
     for k in range(num_days):
-        text_file =  out_dir + out_name + ("" if args.profile == "kaggle" else str(k))
+        text_file = out_dir + out_name + \
+            ("" if args.profile == "kaggle" else str(k))
         print(text_file)
 
         quickgen(args.num_samples, num_t, num_d, num_s, ln_emb, text_file)
diff --git a/retired_benchmarks/recommendation/dlrm/tf/common.py b/retired_benchmarks/recommendation/dlrm/tf/common.py
index 20e0ce70a5..76b2beacde 100644
--- a/retired_benchmarks/recommendation/dlrm/tf/common.py
+++ b/retired_benchmarks/recommendation/dlrm/tf/common.py
@@ -13,216 +13,240 @@
 # limitations under the License.
 # ==============================================================================
 """Flags and common definitions for all modules in the DLRM module."""
- 
+
 import collections
 from typing import Dict, Any
 from absl import flags
- 
- 
+
+
 FLAGS = flags.FLAGS
- 
+
 PARAMS = collections.defaultdict(
     lambda: None,  # Set default value to None.
     default_batch_size=32,
- 
     # TPU-specific parameters
     use_tpu=True,
 )
- 
+
 FAKE_DATA_VOCAB_SIZE = 1000
 
+
 def get_params() -> Dict[str, Any]:
-  """Provides param dict and sets defaults.
- 
-  Returns:
-    A dict representing the params for the model execution.
-  Raises:
-    ValueError: If parameters violate model architecture constraints.
-  """
- 
-  params = PARAMS.copy()
- 
-  params["data_dir"] = FLAGS.data_dir
-  params["model_dir"] = FLAGS.model_dir
-  params["summary_every_n_steps"] = FLAGS.summary_every_n_steps
-  params["batch_size"] = FLAGS.batch_size
-  params["eval_batch_size"] = FLAGS.eval_batch_size
-  params["dim_embed"] = FLAGS.dim_embed
-  params["vocab_sizes"] = [int(x) for x in FLAGS.vocab_sizes_embed]
-  params["num_dense_features"] = FLAGS.num_dense_features
-  params["num_tables_in_ec"] = FLAGS.num_tables_in_ec
-  params["mlp_bottom"] = [int(x) for x in FLAGS.mlp_bottom]
-  params["mlp_top"] = [int(x) for x in FLAGS.mlp_top]
-  params["learning_rate"] = FLAGS.learning_rate
-  params["lr_warmup_steps"] = FLAGS.lr_warmup_steps
-  params["decay_steps"] = FLAGS.decay_steps
-  params["decay_start_step"] = FLAGS.decay_start_step
-  params["optimizer"] = FLAGS.optimizer
-  params["adagrad_init_accum"] = FLAGS.adagrad_init_accum
-  params["num_shards"] = FLAGS.num_tpu_shards
-  params["eval_steps"] = FLAGS.eval_steps
-  params["replicas_per_host"] = FLAGS.replicas_per_host
-  params["bfloat16_grads_all_reduce"] = FLAGS.bfloat16_grads_all_reduce
-  # Dataset
-  params["terabyte"] = FLAGS.terabyte
-  params["use_synthetic_data"] = FLAGS.use_synthetic_data
-  params["use_cached_data"] = FLAGS.use_cached_data
-  if params["use_synthetic_data"]:
-    params["vocab_sizes"] = [FAKE_DATA_VOCAB_SIZE for _ in FLAGS.vse]
-  # Optimization
-  params["opt_skip_gather"] = True
- 
-  if params["dim_embed"] != params["mlp_bottom"][-1]:
-    raise ValueError("Dimensionality of latent features (embedding dim) " +
-                     "must be equal to size of last layer of the bottom MLP.")
-  if params["batch_size"] % params["num_shards"]:
-    raise ValueError("Training batch size {} must be a multiple of num_cores {}"
-                     .format(params["batch_size"], params["num_shards"]))
-  if params["eval_batch_size"] % params["num_shards"]:
-    raise ValueError("Eval batch size {} must be a multiple of num_cores {}"
-                     .format(params["eval_batch_size"], params["num_shards"]))
-  return params
- 
- 
+    """Provides param dict and sets defaults.
+
+    Returns:
+      A dict representing the params for the model execution.
+    Raises:
+      ValueError: If parameters violate model architecture constraints.
+    """
+
+    params = PARAMS.copy()
+
+    params["data_dir"] = FLAGS.data_dir
+    params["model_dir"] = FLAGS.model_dir
+    params["summary_every_n_steps"] = FLAGS.summary_every_n_steps
+    params["batch_size"] = FLAGS.batch_size
+    params["eval_batch_size"] = FLAGS.eval_batch_size
+    params["dim_embed"] = FLAGS.dim_embed
+    params["vocab_sizes"] = [int(x) for x in FLAGS.vocab_sizes_embed]
+    params["num_dense_features"] = FLAGS.num_dense_features
+    params["num_tables_in_ec"] = FLAGS.num_tables_in_ec
+    params["mlp_bottom"] = [int(x) for x in FLAGS.mlp_bottom]
+    params["mlp_top"] = [int(x) for x in FLAGS.mlp_top]
+    params["learning_rate"] = FLAGS.learning_rate
+    params["lr_warmup_steps"] = FLAGS.lr_warmup_steps
+    params["decay_steps"] = FLAGS.decay_steps
+    params["decay_start_step"] = FLAGS.decay_start_step
+    params["optimizer"] = FLAGS.optimizer
+    params["adagrad_init_accum"] = FLAGS.adagrad_init_accum
+    params["num_shards"] = FLAGS.num_tpu_shards
+    params["eval_steps"] = FLAGS.eval_steps
+    params["replicas_per_host"] = FLAGS.replicas_per_host
+    params["bfloat16_grads_all_reduce"] = FLAGS.bfloat16_grads_all_reduce
+    # Dataset
+    params["terabyte"] = FLAGS.terabyte
+    params["use_synthetic_data"] = FLAGS.use_synthetic_data
+    params["use_cached_data"] = FLAGS.use_cached_data
+    if params["use_synthetic_data"]:
+        params["vocab_sizes"] = [FAKE_DATA_VOCAB_SIZE for _ in FLAGS.vse]
+    # Optimization
+    params["opt_skip_gather"] = True
+
+    if params["dim_embed"] != params["mlp_bottom"][-1]:
+        raise ValueError(
+            "Dimensionality of latent features (embedding dim) "
+            + "must be equal to size of last layer of the bottom MLP."
+        )
+    if params["batch_size"] % params["num_shards"]:
+        raise ValueError(
+            "Training batch size {} must be a multiple of num_cores {}".format(
+                params["batch_size"], params["num_shards"]
+            )
+        )
+    if params["eval_batch_size"] % params["num_shards"]:
+        raise ValueError(
+            "Eval batch size {} must be a multiple of num_cores {}".format(
+                params["eval_batch_size"], params["num_shards"]
+            )
+        )
+    return params
+
+
 def define_dlrm_flags() -> None:
-  """Flags for running dlrm_main."""
- 
-  # TODO(tayo): Merge flags with the low level runner.
-  flags.DEFINE_string(
-      "data_dir",
-      default=None,
-      help="Path to the data directory.")
-  flags.DEFINE_integer(
-      "batch_size",
-      default=32,
-      help="Batch size for training.")
-  flags.DEFINE_bool(
-      "use_synthetic_data",
-      default=False,
-      help="If true, uses synthetic data.")
-  flags.DEFINE_enum(
-      "optimizer",
-      default="sgd",
-      enum_values=["sgd", "adagrad"],
-      help="Optimizer to use for parameter updates.")
-  flags.DEFINE_float(
-      name="adagrad_init_accum",
-      default=0.01,
-      help="Adagrad initial accumulator values.")
-  flags.DEFINE_integer(
-      name="lr_warmup_steps",
-      default=0,
-      help="Number of warmup steps in learning rate.")
-  flags.DEFINE_integer(
-      name="decay_steps",
-      default=0,
-      help="Number of decay steps used in polynomial decay.")
-  flags.DEFINE_integer(
-      name="decay_start_step",
-      default=0,
-      help="Step to begin decay, if decay_steps > 0.")
-  flags.DEFINE_integer(
-      name="num_tpu_shards",
-      default=8,
-      help="Number of shards (cores).")
-  flags.DEFINE_integer(
-      name="eval_batch_size",
-      short_name="ebs",
-      default=16384,
-      help="Global batch size to use during eval.")
-  flags.DEFINE_float(
-      name="learning_rate",
-      short_name="lr",
-      default=0.01,
-      help="The learning rate.")
-  flags.DEFINE_integer(
-      name="train_steps",
-      short_name="ts",
-      default=1000,
-      help="The number of steps used to train.")
-  flags.DEFINE_integer(
-      name="eval_steps",
-      short_name="es",
-      default=5440,
-      help="The number of steps used to eval.")
-  flags.DEFINE_integer(
-      name="steps_between_evals",
-      short_name="sbe",
-      default=100,
-      help="The Number of training steps to run between evaluations. This is "
-           "used if --train_steps is defined.")
-  flags.DEFINE_integer(
-      name="summary_every_n_steps",
-      default=100,
-      help="Number of training steps to run before communicating with host to "
-           "send summaries.")
-  flags.DEFINE_bool(
-      name="use_cached_data",
-      default=False,
-      help="If true, take a few samples and repeat.")
-  flags.DEFINE_string(
-      name="mode",
-      default="train",
-      help="mode: train or eval")
-  flags.DEFINE_bool(
-      name="use_ctl",
-      default=False,
-      help="Whether the model runs with custom training loop.")
-  flags.DEFINE_bool(
-      name="terabyte",
-      default=True,
-      help="If true, data paths use terabyte format. Else kaggle.")
-  # System params.
-  flags.DEFINE_bool(
-      name="pipeline_execution",
-      default=False,
-      help="If true, pipeline embedding execution with TensorCore.")
-  flags.DEFINE_bool(
-      name="use_batched_tfrecords",
-      default=False,
-      help="If true, use dataset of batched TFRecords, instead of csv.")
-  flags.DEFINE_bool(
-      name="bfloat16_grads_all_reduce",
-      default=False,
-      help="If true, use bfloat16 for all-reduce computation.")
-  flags.DEFINE_string(
-      name="data_cell",
-      default="mb",
-      help="Data cell. Path to data directory is determined dynamically.")
-  flags.DEFINE_enum(
-      "partition_strategy",
-      default="div",
-      enum_values=["div", "mod"],
-      help="Partition strategy for the embeddings.")
-  # Model architecture params.
-  flags.DEFINE_integer(
-      name="dim_embed",
-      short_name="de",
-      default=4,
-      help="Embedding dimension.")
-  flags.DEFINE_list(
-      name="mlp_bottom",
-      default="8, 4",
-      help="Hidden layers for the bottom MLP. "
-           "To specify different sizes of MLP layers: --layers=32,16,8,4")
-  flags.DEFINE_list(
-      name="mlp_top",
-      default="128, 64, 1",
-      help="The sizes of hidden layers for MLP. "
-           "To specify different sizes of MLP layers: --layers=32,16,8,4")
-  flags.DEFINE_list(
-      name="vocab_sizes_embed",
-      short_name="vse",
-      default="8, 8, 8, 8",
-      help="Vocab sizes for each of the sparse features. The order agrees with "
-           "the order of the input data.")
-  flags.DEFINE_integer(
-      name="num_dense_features",
-      short_name="ndf",
-      default=3,
-      help="Number of dense features.")
-  flags.DEFINE_integer(
-      name="num_tables_in_ec",
-      default=26,
-      help="Number of embedding tables in the embedding core.")
+    """Flags for running dlrm_main."""
 
+    # TODO(tayo): Merge flags with the low level runner.
+    flags.DEFINE_string(
+        "data_dir",
+        default=None,
+        help="Path to the data directory.")
+    flags.DEFINE_integer(
+        "batch_size",
+        default=32,
+        help="Batch size for training.")
+    flags.DEFINE_bool(
+        "use_synthetic_data", default=False, help="If true, uses synthetic data."
+    )
+    flags.DEFINE_enum(
+        "optimizer",
+        default="sgd",
+        enum_values=["sgd", "adagrad"],
+        help="Optimizer to use for parameter updates.",
+    )
+    flags.DEFINE_float(
+        name="adagrad_init_accum",
+        default=0.01,
+        help="Adagrad initial accumulator values.",
+    )
+    flags.DEFINE_integer(
+        name="lr_warmup_steps",
+        default=0,
+        help="Number of warmup steps in learning rate.",
+    )
+    flags.DEFINE_integer(
+        name="decay_steps",
+        default=0,
+        help="Number of decay steps used in polynomial decay.",
+    )
+    flags.DEFINE_integer(
+        name="decay_start_step",
+        default=0,
+        help="Step to begin decay, if decay_steps > 0.",
+    )
+    flags.DEFINE_integer(
+        name="num_tpu_shards", default=8, help="Number of shards (cores)."
+    )
+    flags.DEFINE_integer(
+        name="eval_batch_size",
+        short_name="ebs",
+        default=16384,
+        help="Global batch size to use during eval.",
+    )
+    flags.DEFINE_float(
+        name="learning_rate", short_name="lr", default=0.01, help="The learning rate."
+    )
+    flags.DEFINE_integer(
+        name="train_steps",
+        short_name="ts",
+        default=1000,
+        help="The number of steps used to train.",
+    )
+    flags.DEFINE_integer(
+        name="eval_steps",
+        short_name="es",
+        default=5440,
+        help="The number of steps used to eval.",
+    )
+    flags.DEFINE_integer(
+        name="steps_between_evals",
+        short_name="sbe",
+        default=100,
+        help="The Number of training steps to run between evaluations. This is "
+        "used if --train_steps is defined.",
+    )
+    flags.DEFINE_integer(
+        name="summary_every_n_steps",
+        default=100,
+        help="Number of training steps to run before communicating with host to "
+        "send summaries.",
+    )
+    flags.DEFINE_bool(
+        name="use_cached_data",
+        default=False,
+        help="If true, take a few samples and repeat.",
+    )
+    flags.DEFINE_string(
+        name="mode",
+        default="train",
+        help="mode: train or eval")
+    flags.DEFINE_bool(
+        name="use_ctl",
+        default=False,
+        help="Whether the model runs with custom training loop.",
+    )
+    flags.DEFINE_bool(
+        name="terabyte",
+        default=True,
+        help="If true, data paths use terabyte format. Else kaggle.",
+    )
+    # System params.
+    flags.DEFINE_bool(
+        name="pipeline_execution",
+        default=False,
+        help="If true, pipeline embedding execution with TensorCore.",
+    )
+    flags.DEFINE_bool(
+        name="use_batched_tfrecords",
+        default=False,
+        help="If true, use dataset of batched TFRecords, instead of csv.",
+    )
+    flags.DEFINE_bool(
+        name="bfloat16_grads_all_reduce",
+        default=False,
+        help="If true, use bfloat16 for all-reduce computation.",
+    )
+    flags.DEFINE_string(
+        name="data_cell",
+        default="mb",
+        help="Data cell. Path to data directory is determined dynamically.",
+    )
+    flags.DEFINE_enum(
+        "partition_strategy",
+        default="div",
+        enum_values=["div", "mod"],
+        help="Partition strategy for the embeddings.",
+    )
+    # Model architecture params.
+    flags.DEFINE_integer(
+        name="dim_embed", short_name="de", default=4, help="Embedding dimension."
+    )
+    flags.DEFINE_list(
+        name="mlp_bottom",
+        default="8, 4",
+        help="Hidden layers for the bottom MLP. "
+        "To specify different sizes of MLP layers: --layers=32,16,8,4",
+    )
+    flags.DEFINE_list(
+        name="mlp_top",
+        default="128, 64, 1",
+        help="The sizes of hidden layers for MLP. "
+        "To specify different sizes of MLP layers: --layers=32,16,8,4",
+    )
+    flags.DEFINE_list(
+        name="vocab_sizes_embed",
+        short_name="vse",
+        default="8, 8, 8, 8",
+        help="Vocab sizes for each of the sparse features. The order agrees with "
+        "the order of the input data.",
+    )
+    flags.DEFINE_integer(
+        name="num_dense_features",
+        short_name="ndf",
+        default=3,
+        help="Number of dense features.",
+    )
+    flags.DEFINE_integer(
+        name="num_tables_in_ec",
+        default=26,
+        help="Number of embedding tables in the embedding core.",
+    )
diff --git a/retired_benchmarks/recommendation/dlrm/tf/dataloader.py b/retired_benchmarks/recommendation/dlrm/tf/dataloader.py
index daa73c0c73..681d9773a0 100644
--- a/retired_benchmarks/recommendation/dlrm/tf/dataloader.py
+++ b/retired_benchmarks/recommendation/dlrm/tf/dataloader.py
@@ -20,244 +20,275 @@
 
 
 def rand_features(batch_size):
-  """Emits random input features, used for testing."""
-  features = {}
-  pos_size = batch_size // 2
-  neg_size = batch_size - pos_size
-  features[fc.LABEL_FEATURE] = tf.concat([
-      tf.ones([pos_size, 1], dtype=tf.float32),
-      tf.zeros([neg_size, 1], dtype=tf.float32)
-  ],
-                                         axis=0)
-  features["int-features"] = tf.random.uniform(
-      shape=(batch_size, len(fc.INT_FEATURES)),
-      maxval=fc.FAKE_DATA_INT_MAX)
-  features["cat-features"] = tf.random.uniform(
-      shape=(batch_size, len(fc.CATEGORICAL_FEATURES)),
-      maxval=fc.FAKE_DATA_VOCAB_SIZE,
-      dtype=tf.int32)
-  return features
+    """Emits random input features, used for testing."""
+    features = {}
+    pos_size = batch_size // 2
+    neg_size = batch_size - pos_size
+    features[fc.LABEL_FEATURE] = tf.concat(
+        [
+            tf.ones([pos_size, 1], dtype=tf.float32),
+            tf.zeros([neg_size, 1], dtype=tf.float32),
+        ],
+        axis=0,
+    )
+    features["int-features"] = tf.random.uniform(
+        shape=(batch_size, len(fc.INT_FEATURES)), maxval=fc.FAKE_DATA_INT_MAX
+    )
+    features["cat-features"] = tf.random.uniform(
+        shape=(batch_size, len(fc.CATEGORICAL_FEATURES)),
+        maxval=fc.FAKE_DATA_VOCAB_SIZE,
+        dtype=tf.int32,
+    )
+    return features
 
 
 class CriteoTFRecordReader(object):
-  """Input reader fn for TFRecords that have been serialized in batched form."""
-
-  def __init__(self,
-               file_path=None,
-               feature_config=None,
-               is_training=True,
-               use_cached_data=False,
-               use_synthetic_data=False,
-               params=None):
-    self._file_path = file_path
-    self._feature_config = feature_config
-    self._is_training = is_training
-    self._use_cached_data = use_cached_data
-    self._use_synthetic_data = use_synthetic_data
-    self._params = params
-
-  def __call__(self, params):
-
-    batch_size = params["batch_size"]
-    if self._use_synthetic_data:
-      ds = tf.data.Dataset.from_tensor_slices(rand_features(batch_size))
-      ds = ds.batch(batch_size, drop_remainder=True)
-      ds = ds.take(1).cache().repeat()
-      return ds
-
-    def _get_feature_spec():
-      feature_spec = {}
-      feature_spec[fc.LABEL_FEATURE] = tf.FixedLenFeature([
-          batch_size,
-      ],
-                                                          dtype=tf.float32)
-      for int_ft in fc.INT_FEATURES:
-        feature_spec[int_ft] = tf.FixedLenFeature([
-            batch_size,
-        ],
-                                                  dtype=tf.float32)
-      for cat_ft in fc.CATEGORICAL_FEATURES:
-        feature_spec[cat_ft] = tf.FixedLenFeature([], dtype=tf.string)
-      return feature_spec
-
-    def _parse_fn(serialized_example):
-      feature_spec = _get_feature_spec()
-      p_features = tf.parse_single_example(serialized_example, feature_spec)
-
-      features = {}
-      features[fc.LABEL_FEATURE] = tf.reshape(p_features[fc.LABEL_FEATURE],
-                                              (batch_size, 1))
-
-      int_features = []
-      for int_ft in fc.INT_FEATURES:
-        cur_feature = tf.reshape(p_features[int_ft], (batch_size, 1))
-        int_features.append(cur_feature)
-      features["int-features"] = tf.concat(int_features, axis=-1)
-      cat_features = []
-      tc_features = []
-
-      tbl_idxs_sorted = self._feature_config.get_table_idx_orderd_by_size()
-      for idx in range(len(fc.CATEGORICAL_FEATURES)):
-        # Add features from largest-vocab to smallest-vocab.
-        raw_tbl_idx = tbl_idxs_sorted[idx]
-        cat_feature_idx = raw_tbl_idx + 14
-        cat_feature = "categorical-feature-%d" % cat_feature_idx
-
-        # Decode from bytes to int32.
-        cat_ft_int32 = tf.io.decode_raw(p_features[cat_feature], tf.int32)
-        cat_ft_int32 = tf.reshape(cat_ft_int32, (batch_size, 1))
-        if idx < self._feature_config.get_num_tables_in_ec():
-          cat_features.append(cat_ft_int32)
-        else:
-          tc_features.append(cat_ft_int32)
-      features["cat-features"] = tf.concat(cat_features, axis=-1)
-      if tc_features:
-        features["tc-features"] = tf.concat(tc_features, axis=-1)
-
-      return features
-
-    ds = tf.data.Dataset.list_files(self._file_path, shuffle=False)
-    ds = ds.shard(params["dataset_num_shards"],
-                  params["dataset_index"])
-
-    if self._is_training:
-      ds = ds.shuffle(
-          tf.to_int64(
-              max(256, params["dataset_num_shards"]) /
-              params["dataset_num_shards"]))
-      ds = ds.repeat()
-
-    ds = tf.data.TFRecordDataset(
-        ds, buffer_size=64 * 1024 * 1024, num_parallel_reads=8)
-    ds = ds.map(_parse_fn, num_parallel_calls=8)
-
-    if not self._is_training:
-      num_dataset_samples = self._params["eval_steps"] * (
-          self._params["eval_batch_size"] // params["dataset_num_shards"])
-      num_dataset_batches = num_dataset_samples // batch_size
-      def _mark_as_padding(features):
-        """Padding will be denoted with a label value of -1."""
-        features[fc.LABEL_FEATURE] = -1 * tf.ones(
-            (batch_size, 1), dtype=tf.float32)
-        return features
-      # 100 steps worth of padding.
-      padding_ds = ds.take(self._params["replicas_per_host"])
-      padding_ds = padding_ds.map(_mark_as_padding).repeat(100)
-      ds = ds.concatenate(padding_ds).take(num_dataset_batches)
-    ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
-
-    if self._use_cached_data:
-      ds = ds.take(100).cache().repeat()
-    return ds
+    """Input reader fn for TFRecords that have been serialized in batched form."""
+
+    def __init__(
+        self,
+        file_path=None,
+        feature_config=None,
+        is_training=True,
+        use_cached_data=False,
+        use_synthetic_data=False,
+        params=None,
+    ):
+        self._file_path = file_path
+        self._feature_config = feature_config
+        self._is_training = is_training
+        self._use_cached_data = use_cached_data
+        self._use_synthetic_data = use_synthetic_data
+        self._params = params
+
+    def __call__(self, params):
+
+        batch_size = params["batch_size"]
+        if self._use_synthetic_data:
+            ds = tf.data.Dataset.from_tensor_slices(rand_features(batch_size))
+            ds = ds.batch(batch_size, drop_remainder=True)
+            ds = ds.take(1).cache().repeat()
+            return ds
+
+        def _get_feature_spec():
+            feature_spec = {}
+            feature_spec[fc.LABEL_FEATURE] = tf.FixedLenFeature(
+                [
+                    batch_size,
+                ],
+                dtype=tf.float32,
+            )
+            for int_ft in fc.INT_FEATURES:
+                feature_spec[int_ft] = tf.FixedLenFeature(
+                    [
+                        batch_size,
+                    ],
+                    dtype=tf.float32,
+                )
+            for cat_ft in fc.CATEGORICAL_FEATURES:
+                feature_spec[cat_ft] = tf.FixedLenFeature([], dtype=tf.string)
+            return feature_spec
+
+        def _parse_fn(serialized_example):
+            feature_spec = _get_feature_spec()
+            p_features = tf.parse_single_example(
+                serialized_example, feature_spec)
+
+            features = {}
+            features[fc.LABEL_FEATURE] = tf.reshape(
+                p_features[fc.LABEL_FEATURE], (batch_size, 1)
+            )
+
+            int_features = []
+            for int_ft in fc.INT_FEATURES:
+                cur_feature = tf.reshape(p_features[int_ft], (batch_size, 1))
+                int_features.append(cur_feature)
+            features["int-features"] = tf.concat(int_features, axis=-1)
+            cat_features = []
+            tc_features = []
+
+            tbl_idxs_sorted = self._feature_config.get_table_idx_orderd_by_size()
+            for idx in range(len(fc.CATEGORICAL_FEATURES)):
+                # Add features from largest-vocab to smallest-vocab.
+                raw_tbl_idx = tbl_idxs_sorted[idx]
+                cat_feature_idx = raw_tbl_idx + 14
+                cat_feature = "categorical-feature-%d" % cat_feature_idx
+
+                # Decode from bytes to int32.
+                cat_ft_int32 = tf.io.decode_raw(
+                    p_features[cat_feature], tf.int32)
+                cat_ft_int32 = tf.reshape(cat_ft_int32, (batch_size, 1))
+                if idx < self._feature_config.get_num_tables_in_ec():
+                    cat_features.append(cat_ft_int32)
+                else:
+                    tc_features.append(cat_ft_int32)
+            features["cat-features"] = tf.concat(cat_features, axis=-1)
+            if tc_features:
+                features["tc-features"] = tf.concat(tc_features, axis=-1)
+
+            return features
+
+        ds = tf.data.Dataset.list_files(self._file_path, shuffle=False)
+        ds = ds.shard(params["dataset_num_shards"], params["dataset_index"])
+
+        if self._is_training:
+            ds = ds.shuffle(
+                tf.to_int64(
+                    max(256, params["dataset_num_shards"])
+                    / params["dataset_num_shards"]
+                )
+            )
+            ds = ds.repeat()
+
+        ds = tf.data.TFRecordDataset(
+            ds, buffer_size=64 * 1024 * 1024, num_parallel_reads=8
+        )
+        ds = ds.map(_parse_fn, num_parallel_calls=8)
+
+        if not self._is_training:
+            num_dataset_samples = self._params["eval_steps"] * (
+                self._params["eval_batch_size"] // params["dataset_num_shards"]
+            )
+            num_dataset_batches = num_dataset_samples // batch_size
+
+            def _mark_as_padding(features):
+                """Padding will be denoted with a label value of -1."""
+                features[fc.LABEL_FEATURE] = -1 * tf.ones(
+                    (batch_size, 1), dtype=tf.float32
+                )
+                return features
+
+            # 100 steps worth of padding.
+            padding_ds = ds.take(self._params["replicas_per_host"])
+            padding_ds = padding_ds.map(_mark_as_padding).repeat(100)
+            ds = ds.concatenate(padding_ds).take(num_dataset_batches)
+        ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
+
+        if self._use_cached_data:
+            ds = ds.take(100).cache().repeat()
+        return ds
 
 
 class CriteoTsvReader(object):
-  """Input reader fn for pre-processed Criteo data.
-
-  Raw Criteo data is assumed to be preprocessed in the following way:
-  1. Missing values are replaced with zeros.
-  2. Negative values are replaced with zeros.
-  3. Integer features are transformed by log(x+1) and are hence tf.float32.
-  4. Categorical data is bucketized and are hence tf.int32.
-  """
-
-  def __init__(self,
-               file_path=None,
-               feature_config=None,
-               is_training=True,
-               distributed_eval=False,
-               parallelism=1,
-               use_cached_data=False,
-               use_synthetic_data=False):
-    self._file_path = file_path
-    self._feature_config = feature_config
-    self._is_training = is_training
-    self._distributed_eval = distributed_eval
-    self._parallelism = parallelism
-    self._use_cached_data = use_cached_data
-    self._use_synthetic_data = use_synthetic_data
-
-  def __call__(self, params):
-    batch_size = params["batch_size"]
-    if self._use_synthetic_data:
-      ds = tf.data.Dataset.from_tensor_slices(rand_features(batch_size))
-      ds = ds.batch(batch_size, drop_remainder=True)
-      ds = ds.take(1).cache().repeat()
-      return ds
-
-    @tf.function
-    def _parse_example_fn(example):
-      """Parser function for pre-processed Criteo TSV records."""
-      label_defaults = [[0.0]]
-      int_defaults = [
-          [0.0] for _ in range(self._feature_config.get_num_dense_features())
-      ]
-      categorical_defaults = [
-          [0] for _ in range(self._feature_config.get_num_sparse_features())
-      ]
-      record_defaults = label_defaults + int_defaults + categorical_defaults
-      fields = tf.decode_csv(
-          example, record_defaults, field_delim="\t", na_value="-1")
-
-      num_labels = 1
-      num_dense = len(int_defaults)
-      features = {}
-      features[fc.LABEL_FEATURE] = tf.reshape(fields[0], [batch_size, 1])
-
-      int_features = []
-      for idx in range(num_dense):
-        int_features.append(fields[idx + num_labels])
-      features["int-features"] = tf.stack(int_features, axis=1)
-
-      cat_features = []
-      tc_features = []
-      # Features for tables in EmbeddingCore is in cat_features; features for
-      # tables in  is in tc_features. The order of the input data
-      # follows the order of FLAG.vocab_sizes_embed, so we reorder the input
-      # data with resepct to the table sizes.
-      for idx, idx_by_size in enumerate(
-          self._feature_config.get_table_idx_orderd_by_size()):
-        if idx < self._feature_config.get_num_tables_in_ec():
-          cat_features.append(
-              tf.cast(
-                  fields[idx_by_size + num_dense + num_labels], dtype=tf.int32))
-        else:
-          tc_features.append(
-              tf.cast(
-                  fields[idx_by_size + num_dense + num_labels], dtype=tf.int32))
-      features["cat-features"] = tf.stack(cat_features, axis=1)
-      if tc_features:
-        features["tc-features"] = tf.stack(tc_features, axis=1)
-
-      return features
-
-    filenames = tf.data.Dataset.list_files(self._file_path, shuffle=False)
-    filenames = filenames.shard(params["dataset_num_shards"],
-                                params["dataset_index"])
-
-    def make_dataset(ds_index):
-      ds = filenames.shard(self._parallelism, ds_index)
-      ds = ds.repeat(2)
-      ds = ds.interleave(
-          tf.data.TextLineDataset,
-          cycle_length=16,
-          block_length=batch_size // 8,
-          num_parallel_calls=8,
-          deterministic=False)
-      ds = ds.batch(batch_size, drop_remainder=True)
-      ds = ds.map(_parse_example_fn, num_parallel_calls=16)
-      return ds
-
-    ds_indices = tf.data.Dataset.range(self._parallelism)
-    ds = ds_indices.interleave(
-        make_dataset,
-        cycle_length=self._parallelism,
-        block_length=1,
-        num_parallel_calls=self._parallelism,
-        deterministic=False)
-    ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
-
-    if self._use_cached_data:
-      ds = ds.take(100).cache().repeat()
-
-    return ds
+    """Input reader fn for pre-processed Criteo data.
+
+    Raw Criteo data is assumed to be preprocessed in the following way:
+    1. Missing values are replaced with zeros.
+    2. Negative values are replaced with zeros.
+    3. Integer features are transformed by log(x+1) and are hence tf.float32.
+    4. Categorical data is bucketized and are hence tf.int32.
+    """
+
+    def __init__(
+        self,
+        file_path=None,
+        feature_config=None,
+        is_training=True,
+        distributed_eval=False,
+        parallelism=1,
+        use_cached_data=False,
+        use_synthetic_data=False,
+    ):
+        self._file_path = file_path
+        self._feature_config = feature_config
+        self._is_training = is_training
+        self._distributed_eval = distributed_eval
+        self._parallelism = parallelism
+        self._use_cached_data = use_cached_data
+        self._use_synthetic_data = use_synthetic_data
+
+    def __call__(self, params):
+        batch_size = params["batch_size"]
+        if self._use_synthetic_data:
+            ds = tf.data.Dataset.from_tensor_slices(rand_features(batch_size))
+            ds = ds.batch(batch_size, drop_remainder=True)
+            ds = ds.take(1).cache().repeat()
+            return ds
+
+        @tf.function
+        def _parse_example_fn(example):
+            """Parser function for pre-processed Criteo TSV records."""
+            label_defaults = [[0.0]]
+            int_defaults = [
+                [0.0] for _ in range(self._feature_config.get_num_dense_features())
+            ]
+            categorical_defaults = [
+                [0] for _ in range(self._feature_config.get_num_sparse_features())
+            ]
+            record_defaults = label_defaults + int_defaults + categorical_defaults
+            fields = tf.decode_csv(
+                example, record_defaults, field_delim="\t", na_value="-1"
+            )
+
+            num_labels = 1
+            num_dense = len(int_defaults)
+            features = {}
+            features[fc.LABEL_FEATURE] = tf.reshape(fields[0], [batch_size, 1])
+
+            int_features = []
+            for idx in range(num_dense):
+                int_features.append(fields[idx + num_labels])
+            features["int-features"] = tf.stack(int_features, axis=1)
+
+            cat_features = []
+            tc_features = []
+            # Features for tables in EmbeddingCore is in cat_features; features for
+            # tables in  is in tc_features. The order of the input data
+            # follows the order of FLAG.vocab_sizes_embed, so we reorder the input
+            # data with resepct to the table sizes.
+            for idx, idx_by_size in enumerate(
+                self._feature_config.get_table_idx_orderd_by_size()
+            ):
+                if idx < self._feature_config.get_num_tables_in_ec():
+                    cat_features.append(
+                        tf.cast(
+                            fields[idx_by_size + num_dense +
+                                   num_labels], dtype=tf.int32
+                        )
+                    )
+                else:
+                    tc_features.append(
+                        tf.cast(
+                            fields[idx_by_size + num_dense +
+                                   num_labels], dtype=tf.int32
+                        )
+                    )
+            features["cat-features"] = tf.stack(cat_features, axis=1)
+            if tc_features:
+                features["tc-features"] = tf.stack(tc_features, axis=1)
+
+            return features
+
+        filenames = tf.data.Dataset.list_files(self._file_path, shuffle=False)
+        filenames = filenames.shard(
+            params["dataset_num_shards"], params["dataset_index"]
+        )
+
+        def make_dataset(ds_index):
+            ds = filenames.shard(self._parallelism, ds_index)
+            ds = ds.repeat(2)
+            ds = ds.interleave(
+                tf.data.TextLineDataset,
+                cycle_length=16,
+                block_length=batch_size // 8,
+                num_parallel_calls=8,
+                deterministic=False,
+            )
+            ds = ds.batch(batch_size, drop_remainder=True)
+            ds = ds.map(_parse_example_fn, num_parallel_calls=16)
+            return ds
+
+        ds_indices = tf.data.Dataset.range(self._parallelism)
+        ds = ds_indices.interleave(
+            make_dataset,
+            cycle_length=self._parallelism,
+            block_length=1,
+            num_parallel_calls=self._parallelism,
+            deterministic=False,
+        )
+        ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
+
+        if self._use_cached_data:
+            ds = ds.take(100).cache().repeat()
+
+        return ds
diff --git a/retired_benchmarks/recommendation/dlrm/tf/dlrm.py b/retired_benchmarks/recommendation/dlrm/tf/dlrm.py
index bbc97e16c6..1e3590db5a 100644
--- a/retired_benchmarks/recommendation/dlrm/tf/dlrm.py
+++ b/retired_benchmarks/recommendation/dlrm/tf/dlrm.py
@@ -21,350 +21,397 @@
 import tensorflow.compat.v1 as tf
 
 from tensorflow.compiler.tf2xla.python import xla
-from tensorflow.contrib import  layers as contrib_layers
+from tensorflow.contrib import layers as contrib_layers
 import utils
 
 
 def dot_interact(concat_features, params=None):
-  """Performs feature interaction operation between dense and sparse.
-
-  Input tensors represent dense and sparse features.
-  Pre-condition: The tensors have been stacked along dimension 1.
-
-  Args:
-    concat_features: Tensor of features with shape [B, n_features, feature_dim].
-    params: Model params.
-
-  Returns:
-    activations: Tensor representing interacted features.
-  """
-  batch_size = concat_features.shape[0]
-  if not params:
-    params = {}
-
-  # Interact features, select lower-triangular portion, and re-shape.
-  xactions = tf.matmul(concat_features, concat_features, transpose_b=True)
-  tf.logging.info("Model_FN: xactions shape: %s", xactions.get_shape())
-  ones = tf.ones_like(xactions)
-  upper_tri_mask = tf.linalg.band_part(ones, 0, -1)
-  feature_dim = xactions.shape[-1]
-
-  if params["opt_skip_gather"]:
-    upper_tri_bool = tf.cast(upper_tri_mask, tf.bool)
-    activations = tf.where(
-        condition=upper_tri_bool, x=tf.zeros_like(xactions), y=xactions)
-    tf.logging.info("Model_FN: activations shape: %s", activations.get_shape())
-    out_dim = feature_dim * feature_dim
-  else:
-    lower_tri_mask = ones - upper_tri_mask
-    activations = tf.boolean_mask(xactions, lower_tri_mask)
-    tf.logging.info("Model_FN: activations shape: %s", activations.get_shape())
-    out_dim = feature_dim * (feature_dim - 1) // 2
-
-  activations = tf.reshape(activations, (batch_size, out_dim))
-  return activations
+    """Performs feature interaction operation between dense and sparse.
+
+    Input tensors represent dense and sparse features.
+    Pre-condition: The tensors have been stacked along dimension 1.
+
+    Args:
+      concat_features: Tensor of features with shape [B, n_features, feature_dim].
+      params: Model params.
+
+    Returns:
+      activations: Tensor representing interacted features.
+    """
+    batch_size = concat_features.shape[0]
+    if not params:
+        params = {}
+
+    # Interact features, select lower-triangular portion, and re-shape.
+    xactions = tf.matmul(concat_features, concat_features, transpose_b=True)
+    tf.logging.info("Model_FN: xactions shape: %s", xactions.get_shape())
+    ones = tf.ones_like(xactions)
+    upper_tri_mask = tf.linalg.band_part(ones, 0, -1)
+    feature_dim = xactions.shape[-1]
+
+    if params["opt_skip_gather"]:
+        upper_tri_bool = tf.cast(upper_tri_mask, tf.bool)
+        activations = tf.where(
+            condition=upper_tri_bool, x=tf.zeros_like(xactions), y=xactions
+        )
+        tf.logging.info(
+            "Model_FN: activations shape: %s",
+            activations.get_shape())
+        out_dim = feature_dim * feature_dim
+    else:
+        lower_tri_mask = ones - upper_tri_mask
+        activations = tf.boolean_mask(xactions, lower_tri_mask)
+        tf.logging.info(
+            "Model_FN: activations shape: %s",
+            activations.get_shape())
+        out_dim = feature_dim * (feature_dim - 1) // 2
+
+    activations = tf.reshape(activations, (batch_size, out_dim))
+    return activations
 
 
 def logits_fn(features, params, feature_config):
-  """Calculate predictions."""
-  tf.logging.info("Model_FN: Number of input features: %d", len(features))
-  for ft in sorted(features.keys()):
-    tf.logging.info("Model_FN: Feature %s -- shape %s", ft,
-                    features[ft].get_shape())
-
-  bot_mlp_input = features["int-features"]
-  tf.logging.info("Model_FN: Bottom MLP input (int features) shape: %s",
-                  bot_mlp_input.get_shape())
-  mlp_dims_bottom = params["mlp_bottom"]
-
-  for layer_idx in range(len(mlp_dims_bottom)):
-    bot_mlp_input = tf.layers.dense(
-        bot_mlp_input,
-        mlp_dims_bottom[layer_idx],
-        activation="relu",
-        kernel_initializer=tf.compat.v2.initializers.GlorotNormal(),
-        bias_initializer=tf.compat.v2.initializers.RandomNormal(
-            mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_bottom[layer_idx])),
-        name="bottom_mlp_layer_%d" % layer_idx)
-  bot_mlp_output = bot_mlp_input
-
-  cat_features = []
-  for feature_name, value in sorted(features.items()):
-    if "categorical-feature" in feature_name:
-      cat_features.append(value)
-
-  tc_features = []
-  if "tc-features" in features:
-    # Compute offsets for single concatenated table.
-    batch_size = features["tc-features"].shape[0]
-    num_tc_features = features["tc-features"].shape[1]
-    num_tables_in_ec = params["num_tables_in_ec"]
-    tc_table_sizes = feature_config.get_sorted_table_size()[num_tables_in_ec:]
-    total_tbl_size = sum(tc_table_sizes)
-    idx_offsets = [0] + list(np.cumsum(tc_table_sizes[:-1]))
-    idx_offsets = tf.broadcast_to(
-        tf.constant(idx_offsets), (batch_size, num_tc_features))
-    idxs = idx_offsets + features["tc-features"]
-
-    def _create_init_table():
-      """Table initialization varies depending on the vocab size."""
-      full_tbl = np.zeros(
-          shape=(total_tbl_size, params["dim_embed"]), dtype=np.float32)
-      start_idx = 0
-      for idx, tbl_size in enumerate(tc_table_sizes):
-        end_idx = start_idx + tc_table_sizes[idx]
-        cur_tbl_init = np.random.uniform(
-            low=-1 / np.sqrt(tbl_size),
-            high=1 / np.sqrt(tbl_size),
-            size=(tbl_size, params["dim_embed"])).astype(np.float32)
-        full_tbl[start_idx:end_idx, :] = cur_tbl_init
-        start_idx += tc_table_sizes[idx]
-      return tf.constant(full_tbl)
-
-    tc_embedding_table = tf.get_variable(
-        name="tc_embedding_table",
-        dtype=tf.float32,
-        trainable=True,
-        # pylint: disable=unnecessary-lambda
-        initializer=lambda: _create_init_table())
-    tc_features = tf.gather(tc_embedding_table, idxs)
-    tf.logging.info("TC features shape: {}".format(tc_features.get_shape()))
-
-  # Dot feature interaction
-  # Concat and reshape, instead of stack. Better for performance.
-  batch_size = bot_mlp_output.shape[0]
-  feature_stack = tf.concat([bot_mlp_output] + cat_features, axis=-1)
-  feature_stack = tf.reshape(feature_stack,
-                             [batch_size, -1, params["dim_embed"]])
-  if "tc-features" in features:
-    feature_stack = tf.concat([feature_stack, tc_features], axis=1)
-  tf.logging.info("Model_FN: concated feature shape: %s",
-                  feature_stack.get_shape())
-  dot_interact_output = dot_interact(
-      concat_features=feature_stack, params=params)
-  top_mlp_input = tf.concat([bot_mlp_output, dot_interact_output], axis=1)
-  tf.logging.info("Model_FN: Top MLP input (full features) shape: %s",
-                  top_mlp_input.get_shape())
-
-  # Capture original MLP fan-in for proper kernel initialization.
-  num_fts = len(cat_features) + 1
-  orig_top_mlp_dim = (num_fts * (num_fts - 1)) / 2 + params["dim_embed"]
-  tf.logging.info("Model_FN: Original feature len: {}".format(orig_top_mlp_dim))
-
-  # Top MLP
-  # NOTE: For the top MLP, the last layer is a sigmoid. The loss function should
-  #       therefore take [0,1] probability values as inputs, instead of logits.
-  mlp_dims_top = params["mlp_top"]
-  num_layers_top = len(mlp_dims_top)
-  sigmoid_layer_top = num_layers_top - 1
-  for layer_idx in range(num_layers_top):
-    fan_in = orig_top_mlp_dim if layer_idx == 0 else mlp_dims_top[layer_idx - 1]
-    fan_out = mlp_dims_top[layer_idx]
-    tf.logging.info("  layer {}: fan_in={} fan_out={}".format(
-        layer_idx, fan_in, fan_out))
-    top_mlp_input = tf.layers.dense(
-        top_mlp_input,
-        mlp_dims_top[layer_idx],
-        activation="sigmoid" if layer_idx == sigmoid_layer_top else "relu",
-        # NOTE: We would usually use GlorotNormal() initializer here. But due to
-        # the skip_gather optimization, the GlorotNormal would result in a
-        # mathematical error, as Glorot is a function of the fan-in.
-        # The fan-in will be larger for skip-gather activations since we also
-        # pass in the zeros. Therefore we explicitly set the kernel intializer
-        # to RandomNormal(0, sqrt(2/(fan_in+fan_out))
-        kernel_initializer=tf.compat.v2.initializers.RandomNormal(
-            mean=0.0, stddev=math.sqrt(2.0 / (fan_in + fan_out))),
-        bias_initializer=tf.compat.v2.initializers.RandomNormal(
-            mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_top[layer_idx])),
-        name="top_mlp_layer_%d" % layer_idx)
-  predictions = top_mlp_input
-  return predictions, None
+    """Calculate predictions."""
+    tf.logging.info("Model_FN: Number of input features: %d", len(features))
+    for ft in sorted(features.keys()):
+        tf.logging.info(
+            "Model_FN: Feature %s -- shape %s", ft, features[ft].get_shape()
+        )
+
+    bot_mlp_input = features["int-features"]
+    tf.logging.info(
+        "Model_FN: Bottom MLP input (int features) shape: %s", bot_mlp_input.get_shape(
+        )
+    )
+    mlp_dims_bottom = params["mlp_bottom"]
+
+    for layer_idx in range(len(mlp_dims_bottom)):
+        bot_mlp_input = tf.layers.dense(
+            bot_mlp_input,
+            mlp_dims_bottom[layer_idx],
+            activation="relu",
+            kernel_initializer=tf.compat.v2.initializers.GlorotNormal(),
+            bias_initializer=tf.compat.v2.initializers.RandomNormal(
+                mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_bottom[layer_idx])
+            ),
+            name="bottom_mlp_layer_%d" % layer_idx,
+        )
+    bot_mlp_output = bot_mlp_input
+
+    cat_features = []
+    for feature_name, value in sorted(features.items()):
+        if "categorical-feature" in feature_name:
+            cat_features.append(value)
+
+    tc_features = []
+    if "tc-features" in features:
+        # Compute offsets for single concatenated table.
+        batch_size = features["tc-features"].shape[0]
+        num_tc_features = features["tc-features"].shape[1]
+        num_tables_in_ec = params["num_tables_in_ec"]
+        tc_table_sizes = feature_config.get_sorted_table_size()[
+            num_tables_in_ec:]
+        total_tbl_size = sum(tc_table_sizes)
+        idx_offsets = [0] + list(np.cumsum(tc_table_sizes[:-1]))
+        idx_offsets = tf.broadcast_to(
+            tf.constant(idx_offsets), (batch_size, num_tc_features)
+        )
+        idxs = idx_offsets + features["tc-features"]
+
+        def _create_init_table():
+            """Table initialization varies depending on the vocab size."""
+            full_tbl = np.zeros(
+                shape=(total_tbl_size, params["dim_embed"]), dtype=np.float32
+            )
+            start_idx = 0
+            for idx, tbl_size in enumerate(tc_table_sizes):
+                end_idx = start_idx + tc_table_sizes[idx]
+                cur_tbl_init = np.random.uniform(
+                    low=-1 / np.sqrt(tbl_size),
+                    high=1 / np.sqrt(tbl_size),
+                    size=(tbl_size, params["dim_embed"]),
+                ).astype(np.float32)
+                full_tbl[start_idx:end_idx, :] = cur_tbl_init
+                start_idx += tc_table_sizes[idx]
+            return tf.constant(full_tbl)
+
+        tc_embedding_table = tf.get_variable(
+            name="tc_embedding_table",
+            dtype=tf.float32,
+            trainable=True,
+            # pylint: disable=unnecessary-lambda
+            initializer=lambda: _create_init_table(),
+        )
+        tc_features = tf.gather(tc_embedding_table, idxs)
+        tf.logging.info(
+            "TC features shape: {}".format(
+                tc_features.get_shape()))
+
+    # Dot feature interaction
+    # Concat and reshape, instead of stack. Better for performance.
+    batch_size = bot_mlp_output.shape[0]
+    feature_stack = tf.concat([bot_mlp_output] + cat_features, axis=-1)
+    feature_stack = tf.reshape(
+        feature_stack, [
+            batch_size, -1, params["dim_embed"]])
+    if "tc-features" in features:
+        feature_stack = tf.concat([feature_stack, tc_features], axis=1)
+    tf.logging.info(
+        "Model_FN: concated feature shape: %s",
+        feature_stack.get_shape())
+    dot_interact_output = dot_interact(
+        concat_features=feature_stack, params=params)
+    top_mlp_input = tf.concat([bot_mlp_output, dot_interact_output], axis=1)
+    tf.logging.info(
+        "Model_FN: Top MLP input (full features) shape: %s", top_mlp_input.get_shape(
+        )
+    )
+
+    # Capture original MLP fan-in for proper kernel initialization.
+    num_fts = len(cat_features) + 1
+    orig_top_mlp_dim = (num_fts * (num_fts - 1)) / 2 + params["dim_embed"]
+    tf.logging.info(
+        "Model_FN: Original feature len: {}".format(orig_top_mlp_dim))
+
+    # Top MLP
+    # NOTE: For the top MLP, the last layer is a sigmoid. The loss function should
+    # therefore take [0,1] probability values as inputs, instead of logits.
+    mlp_dims_top = params["mlp_top"]
+    num_layers_top = len(mlp_dims_top)
+    sigmoid_layer_top = num_layers_top - 1
+    for layer_idx in range(num_layers_top):
+        fan_in = orig_top_mlp_dim if layer_idx == 0 else mlp_dims_top[layer_idx - 1]
+        fan_out = mlp_dims_top[layer_idx]
+        tf.logging.info(
+            "  layer {}: fan_in={} fan_out={}".format(
+                layer_idx, fan_in, fan_out)
+        )
+        top_mlp_input = tf.layers.dense(
+            top_mlp_input,
+            mlp_dims_top[layer_idx],
+            activation="sigmoid" if layer_idx == sigmoid_layer_top else "relu",
+            # NOTE: We would usually use GlorotNormal() initializer here. But due to
+            # the skip_gather optimization, the GlorotNormal would result in a
+            # mathematical error, as Glorot is a function of the fan-in.
+            # The fan-in will be larger for skip-gather activations since we also
+            # pass in the zeros. Therefore we explicitly set the kernel intializer
+            # to RandomNormal(0, sqrt(2/(fan_in+fan_out))
+            kernel_initializer=tf.compat.v2.initializers.RandomNormal(
+                mean=0.0, stddev=math.sqrt(2.0 / (fan_in + fan_out))
+            ),
+            bias_initializer=tf.compat.v2.initializers.RandomNormal(
+                mean=0.0, stddev=math.sqrt(1.0 / mlp_dims_top[layer_idx])
+            ),
+            name="top_mlp_layer_%d" % layer_idx,
+        )
+    predictions = top_mlp_input
+    return predictions, None
 
 
 def create_model_fn():
-  """Creates the model_fn to be used by the TPUEstimator."""
+    """Creates the model_fn to be used by the TPUEstimator."""
+
+    def _dlrm_model_fn(features, mode, params):
+        """Model function definition for DLRM.
+
+        Args:
+          features: List of feature tensors used in model.
+          mode: Usage mode of the model function, e.g. train, eval, etc.
+          params: Hparams for the model.
+
+        Returns:
+          TPUEstimatorSpec providing the train_op and loss operators.
+
+        Raises:
+          NotImplementedError for unsupported execution modes.
+        """
+
+        preds, host_call_fn = logits_fn(features, params, None)
+        tf.logging.info(
+            "Model_FN: Shape of predictions: %s",
+            preds.get_shape())
+        labels = features["clicked"]
+        tf.logging.info("Model_FN: Shape of labels: %s", labels.get_shape())
+
+        if mode == tf.estimator.ModeKeys.EVAL:
+            labels = tf.reshape(labels, [-1])
+            preds = tf.reshape(preds, [-1])
+            bce_func = tf.keras.losses.BinaryCrossentropy(
+                from_logits=False, reduction=tf.compat.v2.keras.losses.Reduction.NONE
+            )
+            eval_loss = tf.reduce_mean(bce_func(labels, preds))
+
+            def metric_fn(labels, predictions):
+                label_weights = tf.ones_like(labels, dtype=tf.float32)
+                prediction_labels = tf.round(predictions)
+
+                return {
+                    utils.ACC_KEY: tf.metrics.accuracy(
+                        labels=labels,
+                        predictions=prediction_labels,
+                        weights=label_weights,
+                    ),
+                    utils.AUC_KEY: tf.metrics.auc(
+                        labels=labels,
+                        predictions=predictions,
+                        weights=label_weights,
+                        num_thresholds=1000,
+                        curve="ROC",
+                    ),
+                }
+
+            eval_metrics = (metric_fn, [labels, preds])
+
+            tf.logging.info(
+                "Model_FN EVAL: Metrics have been set up. Now returning..")
+
+            return tf.estimator.tpu.TPUEstimatorSpec(
+                mode=mode,
+                loss=eval_loss,
+                host_call=host_call_fn,
+                eval_metrics=eval_metrics,
+            )
+
+        elif mode == tf.estimator.ModeKeys.TRAIN:
+
+            bce_func = tf.keras.losses.BinaryCrossentropy(
+                from_logits=False, reduction=tf.compat.v2.keras.losses.Reduction.NONE
+            )
+            loss = tf.reduce_mean(bce_func(labels, preds))
+
+            global_step = tf.train.get_global_step()
+            optimizer = tf.train.GradientDescentOptimizer(
+                learning_rate=params["learning_rate"]
+            )
+            optimizer = tf.tpu.CrossShardOptimizer(optimizer)
+            train_op = optimizer.minimize(loss, global_step=global_step)
+
+            return tf.estimator.tpu.TPUEstimatorSpec(
+                mode=mode,
+                loss=loss,
+                train_op=train_op,
+                host_call=host_call_fn,
+            )
+
+        else:
+            raise NotImplementedError(
+                "Only TRAIN and EVAL modes are supported. Got: %s" % (mode)
+            )
+
+    return _dlrm_model_fn
+
+
+class ConditionalOptimizer(tf.train.Optimizer):
+    """Conditional optimizer."""
+
+    def __init__(self, params, lr, global_step):
+        self._params = params
+        self._global_step = global_step
+        self._bfloat16_grads_all_reduce = params["bfloat16_grads_all_reduce"]
+        self._lr = lr
+        self._opt = tf.train.GradientDescentOptimizer(
+            learning_rate=utils.lr_fn(self._params, self._global_step)
+        )
+        if params["optimizer"] == "adagrad":
+            self._opt = tf.train.AdagradOptimizer(
+                learning_rate=params["learning_rate"],
+                initial_accumulator_value=params["adagrad_init_accum"],
+            )
+
+    def _cast_like(self, x, y):
+        """Cast x to y's dtype, if necessary."""
+        x = tf.convert_to_tensor(x)
+        y = tf.convert_to_tensor(y)
+        if x.dtype.base_dtype == y.dtype.base_dtype:
+            return x
+        cast_x = tf.cast(x, y.dtype)
+        if cast_x.device != x.device:
+            tf.logging.warning(
+                "Cast for %s may induce copy from '%s' to '%s'",
+                x.name,
+                x.device,
+                cast_x.device,
+            )
+        return cast_x
+
+    # pylint: disable=arguments-differ
+    def compute_gradients(self, loss, var_list=None, **kwargs):
+        gradients = self._opt.compute_gradients(loss, var_list, **kwargs)
+
+        def cast_grad(g, v):
+            if v is not None and g is not None:
+                g = self._cast_like(g, v)
+            return (g, v)
+
+        gradients = [cast_grad(g, v) for g, v in gradients]
+        if self._bfloat16_grads_all_reduce:
+            gradients = [(tf.cast(g, tf.bfloat16), v) for g, v in gradients]
+        return gradients
+
+    def apply_gradients(self, grads_and_vars, global_step=None, name=None):
+        # Cast the gradients back to float32 for weight updates.
+        if self._bfloat16_grads_all_reduce:
+            grads_and_vars = [(tf.cast(g, tf.float32), v)
+                              for g, v in grads_and_vars]
+        return self._opt.apply_gradients(
+            grads_and_vars, global_step=global_step, name=name
+        )
 
-  def _dlrm_model_fn(features, mode, params):
-    """Model function definition for DLRM.
+
+# TODO(tayo): Clean this up and merge with estimator invocation.
+def dlrm_llr_model_fn(
+    params,
+    feature_config,
+    features,
+    labels,
+    is_training,
+    eval_step_num=None,
+    predictions=None,
+):
+    """Model fn.
 
     Args:
-      features: List of feature tensors used in model.
-      mode: Usage mode of the model function, e.g. train, eval, etc.
-      params: Hparams for the model.
+      params: Params dict for the model.
+      feature_config: Configuration of features.
+      features: Features dict for the model.
+      labels: Labels tensor. Not used for this model.
+      is_training: Boolean, True if training.
+      eval_step_num: Int tensor, representing the batch number during eval.
+      predictions: [num_batches, batch_size, 2] tensor holding all predictions.
 
     Returns:
-      TPUEstimatorSpec providing the train_op and loss operators.
-
-    Raises:
-      NotImplementedError for unsupported execution modes.
+      [train_op, predictions]
     """
-
-    preds, host_call_fn = logits_fn(features, params, None)
-    tf.logging.info("Model_FN: Shape of predictions: %s", preds.get_shape())
+    assert labels is None, "Labels should be None. Reconfigure."
     labels = features["clicked"]
-    tf.logging.info("Model_FN: Shape of labels: %s", labels.get_shape())
-
-    if mode == tf.estimator.ModeKeys.EVAL:
-      labels = tf.reshape(labels, [-1])
-      preds = tf.reshape(preds, [-1])
-      bce_func = tf.keras.losses.BinaryCrossentropy(
-          from_logits=False, reduction=tf.compat.v2.keras.losses.Reduction.NONE)
-      eval_loss = tf.reduce_mean(bce_func(labels, preds))
-
-      def metric_fn(labels, predictions):
-        label_weights = tf.ones_like(labels, dtype=tf.float32)
-        prediction_labels = tf.round(predictions)
-
-        return {
-            utils.ACC_KEY:
-                tf.metrics.accuracy(
-                    labels=labels,
-                    predictions=prediction_labels,
-                    weights=label_weights),
-            utils.AUC_KEY:
-                tf.metrics.auc(
-                    labels=labels,
-                    predictions=predictions,
-                    weights=label_weights,
-                    num_thresholds=1000,
-                    curve="ROC"),
-        }
-
-      eval_metrics = (metric_fn, [labels, preds])
-
-      tf.logging.info(
-          "Model_FN EVAL: Metrics have been set up. Now returning..")
-
-      return tf.estimator.tpu.TPUEstimatorSpec(
-          mode=mode,
-          loss=eval_loss,
-          host_call=host_call_fn,
-          eval_metrics=eval_metrics)
-
-    elif mode == tf.estimator.ModeKeys.TRAIN:
-
-      bce_func = tf.keras.losses.BinaryCrossentropy(
-          from_logits=False, reduction=tf.compat.v2.keras.losses.Reduction.NONE)
-      loss = tf.reduce_mean(bce_func(labels, preds))
-
-      global_step = tf.train.get_global_step()
-      optimizer = tf.train.GradientDescentOptimizer(
-          learning_rate=params["learning_rate"])
-      optimizer = tf.tpu.CrossShardOptimizer(optimizer)
-      train_op = optimizer.minimize(loss, global_step=global_step)
-
-      return tf.estimator.tpu.TPUEstimatorSpec(
-          mode=mode,
-          loss=loss,
-          train_op=train_op,
-          host_call=host_call_fn,
-      )
-
+    preds, _ = logits_fn(features, params, feature_config)
+    global_step = tf.train.get_or_create_global_step()
+
+    if is_training:
+        bce_func = tf.keras.losses.BinaryCrossentropy(
+            from_logits=False, reduction=tf.compat.v2.keras.losses.Reduction.NONE
+        )
+        loss = tf.reduce_mean(bce_func(labels, preds))
+        learning_rate = utils.lr_fn(params, global_step)
+        optimizer = ConditionalOptimizer(params, learning_rate, global_step)
+        optimizer = tf.tpu.CrossShardOptimizer(optimizer)
+        train_op = contrib_layers.optimize_loss(
+            name="training",
+            loss=loss,
+            global_step=global_step,
+            learning_rate=learning_rate,
+            optimizer=optimizer,
+            colocate_gradients_with_ops=True,
+        )
+        return train_op, None
     else:
-      raise NotImplementedError(
-          "Only TRAIN and EVAL modes are supported. Got: %s" % (mode))
+        # TODO(tayo): Consider adding a local key-value sort.
+        new_preds = tf.concat([preds, tf.cast(labels, tf.float32)], axis=1)
 
-  return _dlrm_model_fn
+        predictions = xla.dynamic_update_slice(
+            predictions,
+            tf.expand_dims(new_preds, axis=0),
+            tf.stack([eval_step_num, tf.constant(0), tf.constant(0)]),
+        )
 
-
-class ConditionalOptimizer(tf.train.Optimizer):
-  """Conditional optimizer."""
-
-  def __init__(self, params, lr, global_step):
-    self._params = params
-    self._global_step = global_step
-    self._bfloat16_grads_all_reduce = params["bfloat16_grads_all_reduce"]
-    self._lr = lr
-    self._opt = tf.train.GradientDescentOptimizer(
-        learning_rate=utils.lr_fn(self._params, self._global_step))
-    if params["optimizer"] == "adagrad":
-      self._opt = tf.train.AdagradOptimizer(
-          learning_rate=params["learning_rate"],
-          initial_accumulator_value=params["adagrad_init_accum"])
-
-  def _cast_like(self, x, y):
-    """Cast x to y's dtype, if necessary."""
-    x = tf.convert_to_tensor(x)
-    y = tf.convert_to_tensor(y)
-    if x.dtype.base_dtype == y.dtype.base_dtype:
-      return x
-    cast_x = tf.cast(x, y.dtype)
-    if cast_x.device != x.device:
-      tf.logging.warning("Cast for %s may induce copy from '%s' to '%s'",
-                         x.name, x.device, cast_x.device)
-    return cast_x
-
-  # pylint: disable=arguments-differ
-  def compute_gradients(self, loss, var_list=None, **kwargs):
-    gradients = self._opt.compute_gradients(loss, var_list, **kwargs)
-
-    def cast_grad(g, v):
-      if v is not None and g is not None:
-        g = self._cast_like(g, v)
-      return (g, v)
-
-    gradients = [cast_grad(g, v) for g, v in gradients]
-    if self._bfloat16_grads_all_reduce:
-      gradients = [(tf.cast(g, tf.bfloat16), v) for g, v in gradients]
-    return gradients
-
-  def apply_gradients(self, grads_and_vars, global_step=None, name=None):
-    # Cast the gradients back to float32 for weight updates.
-    if self._bfloat16_grads_all_reduce:
-      grads_and_vars = [(tf.cast(g, tf.float32), v) for g, v in grads_and_vars]
-    return self._opt.apply_gradients(
-        grads_and_vars, global_step=global_step, name=name)
-
-
-# TODO(tayo): Clean this up and merge with estimator invocation.
-def dlrm_llr_model_fn(params,
-                      feature_config,
-                      features,
-                      labels,
-                      is_training,
-                      eval_step_num=None,
-                      predictions=None):
-  """Model fn.
-
-  Args:
-    params: Params dict for the model.
-    feature_config: Configuration of features.
-    features: Features dict for the model.
-    labels: Labels tensor. Not used for this model.
-    is_training: Boolean, True if training.
-    eval_step_num: Int tensor, representing the batch number during eval.
-    predictions: [num_batches, batch_size, 2] tensor holding all predictions.
-
-  Returns:
-    [train_op, predictions]
-  """
-  assert labels is None, "Labels should be None. Reconfigure."
-  labels = features["clicked"]
-  preds, _ = logits_fn(features, params, feature_config)
-  global_step = tf.train.get_or_create_global_step()
-
-  if is_training:
-    bce_func = tf.keras.losses.BinaryCrossentropy(
-        from_logits=False, reduction=tf.compat.v2.keras.losses.Reduction.NONE)
-    loss = tf.reduce_mean(bce_func(labels, preds))
-    learning_rate = utils.lr_fn(params, global_step)
-    optimizer = ConditionalOptimizer(params, learning_rate, global_step)
-    optimizer = tf.tpu.CrossShardOptimizer(optimizer)
-    train_op = contrib_layers.optimize_loss(
-        name="training",
-        loss=loss,
-        global_step=global_step,
-        learning_rate=learning_rate,
-        optimizer=optimizer,
-        colocate_gradients_with_ops=True)
-    return train_op, None
-  else:
-    # TODO(tayo): Consider adding a local key-value sort.
-    new_preds = tf.concat([preds, tf.cast(labels, tf.float32)], axis=1)
-
-    predictions = xla.dynamic_update_slice(
-        predictions, tf.expand_dims(new_preds, axis=0),
-        tf.stack([eval_step_num, tf.constant(0),
-                  tf.constant(0)]))
-
-    return None, dict(results=predictions)
+        return None, dict(results=predictions)
diff --git a/retired_benchmarks/recommendation/dlrm/tf/dlrm_embedding_runner.py b/retired_benchmarks/recommendation/dlrm/tf/dlrm_embedding_runner.py
index d3d5535fe6..0e11d2442d 100644
--- a/retired_benchmarks/recommendation/dlrm/tf/dlrm_embedding_runner.py
+++ b/retired_benchmarks/recommendation/dlrm/tf/dlrm_embedding_runner.py
@@ -53,148 +53,165 @@
 
 
 class DLRMEmbeddingRunner(tr.TrainAndEvalRunner):
-  """Augmentation of the TrainAndEvalRunner with embedding support.
-
-  This class uses the TPUEmbedding library as an API for organizing embedding
-  metadata for:
-  1. Configuration
-  2. Building infeed ops
-  3. Buidling embedding table load/restore ops
-  4. Building an embedding update/train op.
-
-  Attributes:
-    sparse_features_key: String key used for all embedding features. This class
-      requires all embedding features to be keyed under this string. This is
-      necessary for the runner to properly strip away only those features and
-      enqueue them properly.
-    embedding: TPUEmbedding object representing the table and feature config.
-      This attribute is required.
-    **kwargs: See TrainAndEvalRunner.
-  """
-
-  def __init__(self, sparse_features_key, embedding, **kwargs):
-    """Initializes the runner."""
-    super(DLRMEmbeddingRunner, self).__init__(**kwargs, do_initialize=False)
-    self.embedding = embedding
-    self.embedding_config = embedding.config_proto
-    self.features_key = sparse_features_key
-    self.embed_vars_and_ops = None
-    self.retrieve_ops = None
-    self.enqueue_datas_list = {True: [], False: []}
-    self.dummy_variables = None
-    self.dummy_variables_init = None
-    self.num_outfeeds = 1
-
-    with self.graph.as_default():
-      self.embed_vars_and_ops = self.embedding.create_variables_and_ops()
-      self.dummy_variables, self.dummy_variables_init = (
-          tpu_embedding_gradient.create_dummy_table_variables(self.embedding))
-    self.device_topology = tf.Session(
-        self.master, config=self.config).run(
-            tpu.initialize_system(embedding_config=self.embedding_config))
-
-  def eval_step(self, step_num, preds):
-    """One evaluation step."""
-    inp = self.infeed_op[False].generate_dequeue_op()
-    flatten_structure = tf.nest.flatten(self.feature_structure[False])
-    inp = [
-        tf.slice(i, [0] * i.shape.ndims, j.shape)
-        for i, j in zip(inp, flatten_structure)
-    ]
-    eval_has_labels = False
-    if eval_has_labels:
-      features, labels = tf.nest.pack_sequence_as(self.feature_structure[False],
-                                                  inp)
-    else:
-      features = tf.nest.pack_sequence_as(self.feature_structure[False], inp)
-      labels = None
-    self.maybe_add_embedding_features(features, False)
-    _, self.predict_output = self.model_fn(features, labels, False, step_num,
-                                           preds)
-    for _ in self.predict_output:
-      self.dequeue_ops.append([])
-    with tf.device(tr.device_for_tpu_core(self.get_host(0))):
-      return step_num + 1, self.predict_output["results"]
-
-  @tpu_function.on_device_training_loop
-  def eval_loop(self):
-    per_replica_eval_batch_size = self.eval_batch_size // self.num_replicas
-    tf.get_variable_scope().reuse_variables()
-    predictions = tf.zeros([self.eval_steps, per_replica_eval_batch_size, 2])
-    _, predictions = training_loop.repeat(
-        int(self.eval_steps), self.eval_step, [tf.constant(0), predictions])
-    with tf.control_dependencies([tpu_ops.outfeed_enqueue_tuple([predictions])
-                                 ]):
-      return tf.no_op()
-
-  def maybe_capture_embedding_inputs(self, inputs, is_training):
-    """Removes sparse inputs and stores them.
-
-    Args:
-      inputs: Dict of input features, resulting from iterator.get_next().
-      is_training: Boolean that is True for training and False otherwise.
+    """Augmentation of the TrainAndEvalRunner with embedding support.
+
+    This class uses the TPUEmbedding library as an API for organizing embedding
+    metadata for:
+    1. Configuration
+    2. Building infeed ops
+    3. Buidling embedding table load/restore ops
+    4. Building an embedding update/train op.
+
+    Attributes:
+      sparse_features_key: String key used for all embedding features. This class
+        requires all embedding features to be keyed under this string. This is
+        necessary for the runner to properly strip away only those features and
+        enqueue them properly.
+      embedding: TPUEmbedding object representing the table and feature config.
+        This attribute is required.
+      **kwargs: See TrainAndEvalRunner.
     """
-    sparse_inputs = inputs.pop(self.features_key)
-    sparse_inputs = tf.split(sparse_inputs, sparse_inputs.shape[-1], axis=1)
-    sparse_inputs = [tf.squeeze(x) for x in sparse_inputs]
-    self.enqueue_datas_list[is_training].append(sparse_inputs)
 
-  def maybe_add_embedding_enqueue_ops_int(self, is_training, enqueue_ops):
-    """Adds embedding input enqueue ops.
-
-    Args:
-      is_training: Boolean that is True for training and False otherwise.
-      enqueue_ops: List of existing enqueue ops used by the runner.
-    """
-    sparse_enqueue_ops = []
-    for i, batch_data in enumerate(self.enqueue_datas_list[is_training]):
-      enqueue_op = tpu_ops.enqueue_tpu_embedding_integer_batch(
-          batch=batch_data,
-          device_ordinal=i % FLAGS.replicas_per_host,
-          mode_override="inference" if not is_training else None)
-      sparse_enqueue_ops.append(enqueue_op)
-    enqueue_ops.extend(sparse_enqueue_ops)
-    # Clear sparse input list for this host.
-    del self.enqueue_datas_list[is_training][:]
-
-  def maybe_get_embedding_train_op(self):
-    """Builds embedding table update op.
-
-    Returns:
-      An op which computes gradients and updates tables.
-    """
-    with tf.device(tr.device_for_tpu_core(self.get_host(0))):
-      sparse_grads = (
-          tpu_embedding_gradient.get_gradients_through_dummy_table_variables(
-              self.embedding))
-      embedding_train_op = self.embedding.generate_send_gradients_op(
-          sparse_grads, tf.compat.v1.train.get_global_step())
-      return embedding_train_op
-
-  def maybe_add_embedding_features(self, features, hook_dummy_variables):
-    """Adds sparse activations to feature list.
-
-    Args:
-      features: Dict of features, used by the model_fn.
-      hook_dummy_variables: Boolean telling whether to back-propagate through
-        embedding activations. Set to true when training and desiring backprop
-        to extend to the embedding tables.
-    """
-    if hook_dummy_variables:
-      with tf.device(tr.device_for_tpu_core(self.get_host(0))):
-        embedding_activations = self.embedding.get_activations()
-        new_embedding_activations = tpu_embedding_gradient.hook_dummy_table_variables_to_activations(
-            self.embedding, embedding_activations, self.dummy_variables)
-        features.update(new_embedding_activations)
-    else:
-      embedding_activations = self.embedding.get_activations()
-      features.update(embedding_activations)
-
-  def maybe_load_embedding_vars(self):
-    """Loads tables into accelerator device memory."""
-    self.sess.run(self.dummy_variables_init)
-    self.sess.run(self.embed_vars_and_ops.load_ops())
-    self.retrieve_ops = self.embed_vars_and_ops.retrieve_ops()
-
-  def retrieve_embedding_vars(self):
-    self.sess.run(self.retrieve_ops)
+    def __init__(self, sparse_features_key, embedding, **kwargs):
+        """Initializes the runner."""
+        super(
+            DLRMEmbeddingRunner,
+            self).__init__(
+            **kwargs,
+            do_initialize=False)
+        self.embedding = embedding
+        self.embedding_config = embedding.config_proto
+        self.features_key = sparse_features_key
+        self.embed_vars_and_ops = None
+        self.retrieve_ops = None
+        self.enqueue_datas_list = {True: [], False: []}
+        self.dummy_variables = None
+        self.dummy_variables_init = None
+        self.num_outfeeds = 1
+
+        with self.graph.as_default():
+            self.embed_vars_and_ops = self.embedding.create_variables_and_ops()
+            self.dummy_variables, self.dummy_variables_init = (
+                tpu_embedding_gradient.create_dummy_table_variables(
+                    self.embedding)
+            )
+        self.device_topology = tf.Session(self.master, config=self.config).run(
+            tpu.initialize_system(embedding_config=self.embedding_config)
+        )
+
+    def eval_step(self, step_num, preds):
+        """One evaluation step."""
+        inp = self.infeed_op[False].generate_dequeue_op()
+        flatten_structure = tf.nest.flatten(self.feature_structure[False])
+        inp = [
+            tf.slice(i, [0] * i.shape.ndims, j.shape)
+            for i, j in zip(inp, flatten_structure)
+        ]
+        eval_has_labels = False
+        if eval_has_labels:
+            features, labels = tf.nest.pack_sequence_as(
+                self.feature_structure[False], inp
+            )
+        else:
+            features = tf.nest.pack_sequence_as(
+                self.feature_structure[False], inp)
+            labels = None
+        self.maybe_add_embedding_features(features, False)
+        _, self.predict_output = self.model_fn(
+            features, labels, False, step_num, preds)
+        for _ in self.predict_output:
+            self.dequeue_ops.append([])
+        with tf.device(tr.device_for_tpu_core(self.get_host(0))):
+            return step_num + 1, self.predict_output["results"]
+
+    @tpu_function.on_device_training_loop
+    def eval_loop(self):
+        per_replica_eval_batch_size = self.eval_batch_size // self.num_replicas
+        tf.get_variable_scope().reuse_variables()
+        predictions = tf.zeros(
+            [self.eval_steps, per_replica_eval_batch_size, 2])
+        _, predictions = training_loop.repeat(
+            int(self.eval_steps), self.eval_step, [tf.constant(0), predictions]
+        )
+        with tf.control_dependencies([tpu_ops.outfeed_enqueue_tuple([predictions])]):
+            return tf.no_op()
+
+    def maybe_capture_embedding_inputs(self, inputs, is_training):
+        """Removes sparse inputs and stores them.
+
+        Args:
+          inputs: Dict of input features, resulting from iterator.get_next().
+          is_training: Boolean that is True for training and False otherwise.
+        """
+        sparse_inputs = inputs.pop(self.features_key)
+        sparse_inputs = tf.split(
+            sparse_inputs, sparse_inputs.shape[-1], axis=1)
+        sparse_inputs = [tf.squeeze(x) for x in sparse_inputs]
+        self.enqueue_datas_list[is_training].append(sparse_inputs)
+
+    def maybe_add_embedding_enqueue_ops_int(self, is_training, enqueue_ops):
+        """Adds embedding input enqueue ops.
+
+        Args:
+          is_training: Boolean that is True for training and False otherwise.
+          enqueue_ops: List of existing enqueue ops used by the runner.
+        """
+        sparse_enqueue_ops = []
+        for i, batch_data in enumerate(self.enqueue_datas_list[is_training]):
+            enqueue_op = tpu_ops.enqueue_tpu_embedding_integer_batch(
+                batch=batch_data,
+                device_ordinal=i % FLAGS.replicas_per_host,
+                mode_override="inference" if not is_training else None,
+            )
+            sparse_enqueue_ops.append(enqueue_op)
+        enqueue_ops.extend(sparse_enqueue_ops)
+        # Clear sparse input list for this host.
+        del self.enqueue_datas_list[is_training][:]
+
+    def maybe_get_embedding_train_op(self):
+        """Builds embedding table update op.
+
+        Returns:
+          An op which computes gradients and updates tables.
+        """
+        with tf.device(tr.device_for_tpu_core(self.get_host(0))):
+            sparse_grads = (
+                tpu_embedding_gradient.get_gradients_through_dummy_table_variables(
+                    self.embedding
+                )
+            )
+            embedding_train_op = self.embedding.generate_send_gradients_op(
+                sparse_grads, tf.compat.v1.train.get_global_step()
+            )
+            return embedding_train_op
+
+    def maybe_add_embedding_features(self, features, hook_dummy_variables):
+        """Adds sparse activations to feature list.
+
+        Args:
+          features: Dict of features, used by the model_fn.
+          hook_dummy_variables: Boolean telling whether to back-propagate through
+            embedding activations. Set to true when training and desiring backprop
+            to extend to the embedding tables.
+        """
+        if hook_dummy_variables:
+            with tf.device(tr.device_for_tpu_core(self.get_host(0))):
+                embedding_activations = self.embedding.get_activations()
+                new_embedding_activations = (
+                    tpu_embedding_gradient.hook_dummy_table_variables_to_activations(
+                        self.embedding, embedding_activations, self.dummy_variables
+                    )
+                )
+                features.update(new_embedding_activations)
+        else:
+            embedding_activations = self.embedding.get_activations()
+            features.update(embedding_activations)
+
+    def maybe_load_embedding_vars(self):
+        """Loads tables into accelerator device memory."""
+        self.sess.run(self.dummy_variables_init)
+        self.sess.run(self.embed_vars_and_ops.load_ops())
+        self.retrieve_ops = self.embed_vars_and_ops.retrieve_ops()
+
+    def retrieve_embedding_vars(self):
+        self.sess.run(self.retrieve_ops)
diff --git a/retired_benchmarks/recommendation/dlrm/tf/dlrm_main.py b/retired_benchmarks/recommendation/dlrm/tf/dlrm_main.py
index 8f0381e23f..dc70fecebf 100644
--- a/retired_benchmarks/recommendation/dlrm/tf/dlrm_main.py
+++ b/retired_benchmarks/recommendation/dlrm/tf/dlrm_main.py
@@ -1,6 +1,6 @@
 """Training script for DLRM model."""
-import functools
 
+import functools
 
 
 from absl import app as absl_app
@@ -26,213 +26,234 @@
 
 
 def get_input_fns(params, feature_config):
-  """Returns input function objects."""
-
-  def _csv_record_path(mode):
-    return "{data_dir}/terabyte_processed_golden_shuffled/{mode}/{mode}*".format(
-        data_dir=FLAGS.data_dir, mode=mode)
-
-  def _batched_tfrecord_path(mode):
-    """Pre-generated data: 16 files per task for batch_size 64K."""
-    replica_batch_size = params["batch_size"] // params["num_shards"]
-    file_cnt = (128 * 1024) // replica_batch_size
-    if replica_batch_size > 1024:
-      # Minimum number of files.
-      file_cnt = 64
-    elif replica_batch_size == 432:
-      # Special case for batch 54K hparams.
-      file_cnt = 256
-    return "{data_dir}/terabyte_tfrecords_batched{bs}/{mode}{file_cnt}shards/{mode}*".format(
-        data_dir=FLAGS.data_dir,
-        bs=replica_batch_size,
-        mode=mode,
-        file_cnt=file_cnt)
-
-  if FLAGS.use_batched_tfrecords:
-    train_input_fn = dataloader.CriteoTFRecordReader(
-        file_path=_batched_tfrecord_path("train"),
-        feature_config=feature_config,
-        is_training=True,
-        use_cached_data=params["use_cached_data"],
-        use_synthetic_data=params["use_synthetic_data"],
-        params=params)
-    eval_input_fn = dataloader.CriteoTFRecordReader(
-        file_path=_batched_tfrecord_path("eval"),
-        feature_config=feature_config,
-        is_training=False,
-        use_cached_data=params["use_cached_data"],
-        use_synthetic_data=params["use_synthetic_data"],
-        params=params)
-  else:
-    train_input_fn = dataloader.CriteoTsvReader(
-        file_path=_csv_record_path("train"),
-        feature_config=feature_config,
-        is_training=True,
-        parallelism=16,
-        use_cached_data=params["use_cached_data"],
-        use_synthetic_data=params["use_synthetic_data"])
-
-    eval_input_fn = dataloader.CriteoTsvReader(
-        file_path=_csv_record_path("eval"),
-        feature_config=feature_config,
-        is_training=False,
-        parallelism=16,
-        use_cached_data=params["use_cached_data"],
-        use_synthetic_data=params["use_synthetic_data"])
-
-  return train_input_fn, eval_input_fn
-
-
-def run_model(params,
-              eval_init_fn=None,
-              eval_finish_fn=None,
-              run_finish_fn=None):
-  """Run the DLRM model, using a pre-defined configuration.
-
-  Args:
-    params: HPTuner object that provides new params for the trial.
-    eval_init_fn: Lambda to run at start of eval. None means use the default.
-    eval_finish_fn: Lambda for end of eval. None means use the default.
-    run_finish_fn: Lambda for end of execution. None means use the default.
-
-  Returns:
-    A list of tuples, each entry describing the eval metric for one eval. Each
-    tuple entry is (global_step, metric_value).
-  """
-  mlp_log.mlperf_print(key="cache_clear", value=True)
-  mlp_log.mlperf_print(key="init_start", value=None)
-  mlp_log.mlperf_print("global_batch_size", params["batch_size"])
-  mlp_log.mlperf_print("train_samples", _NUM_TRAIN_EXAMPLES)
-  mlp_log.mlperf_print("eval_samples", _NUM_EVAL_EXAMPLES)
-  adjusted_lr = params["learning_rate"] * (params["batch_size"] / 2048.0)
-  mlp_log.mlperf_print("opt_base_learning_rate", adjusted_lr)
-  mlp_log.mlperf_print("sgd_opt_base_learning_rate", adjusted_lr)
-  mlp_log.mlperf_print("sgd_opt_learning_rate_decay_poly_power", 2)
-  mlp_log.mlperf_print("sgd_opt_learning_rate_decay_steps",
-                       params["decay_steps"])
-  mlp_log.mlperf_print("lr_decay_start_steps", params["decay_start_step"])
-  mlp_log.mlperf_print("opt_learning_rate_warmup_steps",
-                       params["lr_warmup_steps"])
-
-  # Used for vizier. List of tuples. Each entry is (global_step, auc_metric).
-  eval_metrics = [(0, 0.0)]
-
-  feature_config = fc.FeatureConfig(params)
-  (feature_to_config_dict,
-   table_to_config_dict) = feature_config.get_feature_tbl_config()
-  opt_params = {
-      "sgd":
-          tpu_embedding.StochasticGradientDescentParameters(
-              learning_rate=params["learning_rate"]),
-      "adagrad":
-          tpu_embedding.AdagradParameters(
-              learning_rate=params["learning_rate"],
-              initial_accumulator=params["adagrad_init_accum"])
-  }
-  embedding = tpu_embedding.TPUEmbedding(
-      table_to_config_dict,
-      feature_to_config_dict,
-      params["batch_size"],
-      mode=tpu_embedding.TRAINING,
-      optimization_parameters=opt_params[params["optimizer"]],
-      partition_strategy="mod",
-      pipeline_execution_with_tensor_core=FLAGS.pipeline_execution,
-      master=FLAGS.master)
-
-  runner = dlrm_embedding_runner.DLRMEmbeddingRunner(
-      iterations_per_loop=FLAGS.steps_between_evals,
-      train_steps=FLAGS.train_steps,
-      eval_steps=FLAGS.eval_steps,
-      num_replicas=FLAGS.num_tpu_shards,
-      sparse_features_key="cat-features",
-      embedding=embedding)
-
-  train_input_fn, eval_input_fn = get_input_fns(params, feature_config)
-
-  runner.initialize(
-      train_input_fn,
-      eval_input_fn,
-      functools.partial(dlrm.dlrm_llr_model_fn, params, feature_config),
-      params["batch_size"],
-      params["eval_batch_size"],
-      train_has_labels=False,
-      eval_has_labels=False)
-
-  mlp_log.mlperf_print("init_stop", None)
-  mlp_log.mlperf_print("run_start", None)
-
-  def _default_eval_init_fn(cur_step):
-    """Logging statements executed before every eval."""
-    eval_num = 0
-    if FLAGS.steps_between_evals:
-      eval_num = cur_step // FLAGS.steps_between_evals
-    tf.logging.info("== Block {}. Step {} of {}".format(eval_num + 1, cur_step,
-                                                        FLAGS.train_steps))
-    mlp_log.mlperf_print(
-        "block_start",
-        None,
-        metadata={
-            "first_epoch_num": eval_num + 1,
-            "epoch_count": 1
-        })
-    mlp_log.mlperf_print(
-        "eval_start", None, metadata={"epoch_num": eval_num + 1})
-
-  def _default_eval_finish_fn(cur_step, eval_output, summary_writer=None):
-    eval_num = 0
-    if FLAGS.steps_between_evals:
-      eval_num = cur_step // FLAGS.steps_between_evals
-    mlp_log.mlperf_print(
-        "eval_stop", None, metadata={"epoch_num": eval_num + 1})
+    """Returns input function objects."""
+
+    def _csv_record_path(mode):
+        return "{data_dir}/terabyte_processed_golden_shuffled/{mode}/{mode}*".format(
+            data_dir=FLAGS.data_dir, mode=mode
+        )
+
+    def _batched_tfrecord_path(mode):
+        """Pre-generated data: 16 files per task for batch_size 64K."""
+        replica_batch_size = params["batch_size"] // params["num_shards"]
+        file_cnt = (128 * 1024) // replica_batch_size
+        if replica_batch_size > 1024:
+            # Minimum number of files.
+            file_cnt = 64
+        elif replica_batch_size == 432:
+            # Special case for batch 54K hparams.
+            file_cnt = 256
+        return "{data_dir}/terabyte_tfrecords_batched{bs}/{mode}{file_cnt}shards/{mode}*".format(
+            data_dir=FLAGS.data_dir, bs=replica_batch_size, mode=mode, file_cnt=file_cnt
+        )
+
+    if FLAGS.use_batched_tfrecords:
+        train_input_fn = dataloader.CriteoTFRecordReader(
+            file_path=_batched_tfrecord_path("train"),
+            feature_config=feature_config,
+            is_training=True,
+            use_cached_data=params["use_cached_data"],
+            use_synthetic_data=params["use_synthetic_data"],
+            params=params,
+        )
+        eval_input_fn = dataloader.CriteoTFRecordReader(
+            file_path=_batched_tfrecord_path("eval"),
+            feature_config=feature_config,
+            is_training=False,
+            use_cached_data=params["use_cached_data"],
+            use_synthetic_data=params["use_synthetic_data"],
+            params=params,
+        )
+    else:
+        train_input_fn = dataloader.CriteoTsvReader(
+            file_path=_csv_record_path("train"),
+            feature_config=feature_config,
+            is_training=True,
+            parallelism=16,
+            use_cached_data=params["use_cached_data"],
+            use_synthetic_data=params["use_synthetic_data"],
+        )
+
+        eval_input_fn = dataloader.CriteoTsvReader(
+            file_path=_csv_record_path("eval"),
+            feature_config=feature_config,
+            is_training=False,
+            parallelism=16,
+            use_cached_data=params["use_cached_data"],
+            use_synthetic_data=params["use_synthetic_data"],
+        )
+
+    return train_input_fn, eval_input_fn
+
+
+def run_model(params, eval_init_fn=None,
+              eval_finish_fn=None, run_finish_fn=None):
+    """Run the DLRM model, using a pre-defined configuration.
+
+    Args:
+      params: HPTuner object that provides new params for the trial.
+      eval_init_fn: Lambda to run at start of eval. None means use the default.
+      eval_finish_fn: Lambda for end of eval. None means use the default.
+      run_finish_fn: Lambda for end of execution. None means use the default.
+
+    Returns:
+      A list of tuples, each entry describing the eval metric for one eval. Each
+      tuple entry is (global_step, metric_value).
+    """
+    mlp_log.mlperf_print(key="cache_clear", value=True)
+    mlp_log.mlperf_print(key="init_start", value=None)
+    mlp_log.mlperf_print("global_batch_size", params["batch_size"])
+    mlp_log.mlperf_print("train_samples", _NUM_TRAIN_EXAMPLES)
+    mlp_log.mlperf_print("eval_samples", _NUM_EVAL_EXAMPLES)
+    adjusted_lr = params["learning_rate"] * (params["batch_size"] / 2048.0)
+    mlp_log.mlperf_print("opt_base_learning_rate", adjusted_lr)
+    mlp_log.mlperf_print("sgd_opt_base_learning_rate", adjusted_lr)
+    mlp_log.mlperf_print("sgd_opt_learning_rate_decay_poly_power", 2)
     mlp_log.mlperf_print(
-        "block_stop", None, metadata={"first_epoch_num": eval_num + 1})
-    tf.logging.info(
-        "== Eval finished (step {}). Computing metric..".format(cur_step))
-
-    results_np = np.array(eval_output["results"])
-    results_np = np.reshape(results_np, (-1, 2))
-    predictions_np = results_np[:, 0].astype(np.float32)
-    targets_np = results_np[:, 1].astype(np.int32)
-    # TODO: Fix roc clif in cloud.
-    # roc_obj = roc_metrics.RocMetrics(predictions_np, targets_np)
-    # roc_auc = roc_obj.ComputeRocAuc()
-    roc_auc = 0.0
-    tf.logging.info("== Eval shape: {}.  AUC = {:.4f}".format(
-        predictions_np.shape, roc_auc))
-    success = roc_auc >= _ACCURACY_THRESH
+        "sgd_opt_learning_rate_decay_steps",
+        params["decay_steps"])
+    mlp_log.mlperf_print("lr_decay_start_steps", params["decay_start_step"])
     mlp_log.mlperf_print(
-        "eval_accuracy", roc_auc, metadata={"epoch_num": eval_num + 1})
-    if success:
-      mlp_log.mlperf_print("run_stop", None, metadata={"status": "success"})
-    if summary_writer:
-      summary_writer.add_summary(
-          utils.create_scalar_summary("auc", roc_auc),
-          global_step=cur_step + FLAGS.steps_between_evals)
-    eval_metrics.append((cur_step + FLAGS.steps_between_evals, roc_auc))
-    return success
-
-  def _default_run_finish_fn(success_status):
-    if not success_status:
-      mlp_log.mlperf_print("run_stop", None, metadata={"status": "failure"})
-    tf.logging.info("Retrieving embedding vars and writing stats.")
-    runner.retrieve_embedding_vars()
-
-  runner.train_and_eval(
-      eval_init_fn=eval_init_fn or _default_eval_init_fn,
-      eval_finish_fn=eval_finish_fn or _default_eval_finish_fn,
-      run_finish_fn=run_finish_fn or _default_run_finish_fn)
-
-  return eval_metrics
+        "opt_learning_rate_warmup_steps",
+        params["lr_warmup_steps"])
+
+    # Used for vizier. List of tuples. Each entry is (global_step, auc_metric).
+    eval_metrics = [(0, 0.0)]
+
+    feature_config = fc.FeatureConfig(params)
+    (feature_to_config_dict, table_to_config_dict) = (
+        feature_config.get_feature_tbl_config()
+    )
+    opt_params = {
+        "sgd": tpu_embedding.StochasticGradientDescentParameters(
+            learning_rate=params["learning_rate"]
+        ),
+        "adagrad": tpu_embedding.AdagradParameters(
+            learning_rate=params["learning_rate"],
+            initial_accumulator=params["adagrad_init_accum"],
+        ),
+    }
+    embedding = tpu_embedding.TPUEmbedding(
+        table_to_config_dict,
+        feature_to_config_dict,
+        params["batch_size"],
+        mode=tpu_embedding.TRAINING,
+        optimization_parameters=opt_params[params["optimizer"]],
+        partition_strategy="mod",
+        pipeline_execution_with_tensor_core=FLAGS.pipeline_execution,
+        master=FLAGS.master,
+    )
+
+    runner = dlrm_embedding_runner.DLRMEmbeddingRunner(
+        iterations_per_loop=FLAGS.steps_between_evals,
+        train_steps=FLAGS.train_steps,
+        eval_steps=FLAGS.eval_steps,
+        num_replicas=FLAGS.num_tpu_shards,
+        sparse_features_key="cat-features",
+        embedding=embedding,
+    )
+
+    train_input_fn, eval_input_fn = get_input_fns(params, feature_config)
+
+    runner.initialize(
+        train_input_fn,
+        eval_input_fn,
+        functools.partial(dlrm.dlrm_llr_model_fn, params, feature_config),
+        params["batch_size"],
+        params["eval_batch_size"],
+        train_has_labels=False,
+        eval_has_labels=False,
+    )
+
+    mlp_log.mlperf_print("init_stop", None)
+    mlp_log.mlperf_print("run_start", None)
+
+    def _default_eval_init_fn(cur_step):
+        """Logging statements executed before every eval."""
+        eval_num = 0
+        if FLAGS.steps_between_evals:
+            eval_num = cur_step // FLAGS.steps_between_evals
+        tf.logging.info(
+            "== Block {}. Step {} of {}".format(
+                eval_num + 1, cur_step, FLAGS.train_steps
+            )
+        )
+        mlp_log.mlperf_print(
+            "block_start",
+            None,
+            metadata={"first_epoch_num": eval_num + 1, "epoch_count": 1},
+        )
+        mlp_log.mlperf_print(
+            "eval_start", None, metadata={
+                "epoch_num": eval_num + 1})
+
+    def _default_eval_finish_fn(cur_step, eval_output, summary_writer=None):
+        eval_num = 0
+        if FLAGS.steps_between_evals:
+            eval_num = cur_step // FLAGS.steps_between_evals
+        mlp_log.mlperf_print(
+            "eval_stop", None, metadata={
+                "epoch_num": eval_num + 1})
+        mlp_log.mlperf_print(
+            "block_stop", None, metadata={"first_epoch_num": eval_num + 1}
+        )
+        tf.logging.info(
+            "== Eval finished (step {}). Computing metric..".format(cur_step)
+        )
+
+        results_np = np.array(eval_output["results"])
+        results_np = np.reshape(results_np, (-1, 2))
+        predictions_np = results_np[:, 0].astype(np.float32)
+        targets_np = results_np[:, 1].astype(np.int32)
+        # TODO: Fix roc clif in cloud.
+        # roc_obj = roc_metrics.RocMetrics(predictions_np, targets_np)
+        # roc_auc = roc_obj.ComputeRocAuc()
+        roc_auc = 0.0
+        tf.logging.info(
+            "== Eval shape: {}.  AUC = {:.4f}".format(
+                predictions_np.shape, roc_auc)
+        )
+        success = roc_auc >= _ACCURACY_THRESH
+        mlp_log.mlperf_print(
+            "eval_accuracy", roc_auc, metadata={"epoch_num": eval_num + 1}
+        )
+        if success:
+            mlp_log.mlperf_print(
+                "run_stop", None, metadata={
+                    "status": "success"})
+        if summary_writer:
+            summary_writer.add_summary(
+                utils.create_scalar_summary("auc", roc_auc),
+                global_step=cur_step + FLAGS.steps_between_evals,
+            )
+        eval_metrics.append((cur_step + FLAGS.steps_between_evals, roc_auc))
+        return success
+
+    def _default_run_finish_fn(success_status):
+        if not success_status:
+            mlp_log.mlperf_print(
+                "run_stop", None, metadata={
+                    "status": "failure"})
+        tf.logging.info("Retrieving embedding vars and writing stats.")
+        runner.retrieve_embedding_vars()
+
+    runner.train_and_eval(
+        eval_init_fn=eval_init_fn or _default_eval_init_fn,
+        eval_finish_fn=eval_finish_fn or _default_eval_finish_fn,
+        run_finish_fn=run_finish_fn or _default_run_finish_fn,
+    )
+
+    return eval_metrics
 
 
 def main(argv):
-  del argv
-  params = common.get_params()
-  run_model(params)
+    del argv
+    params = common.get_params()
+    run_model(params)
 
 
 if __name__ == "__main__":
-  tf.logging.set_verbosity(tf.logging.INFO)
-  tf.disable_v2_behavior()
-  common.define_dlrm_flags()
-  absl_app.run(main)
+    tf.logging.set_verbosity(tf.logging.INFO)
+    tf.disable_v2_behavior()
+    common.define_dlrm_flags()
+    absl_app.run(main)
diff --git a/retired_benchmarks/recommendation/dlrm/tf/feature_config.py b/retired_benchmarks/recommendation/dlrm/tf/feature_config.py
index 12318ed7d6..ffdd57c5f2 100644
--- a/retired_benchmarks/recommendation/dlrm/tf/feature_config.py
+++ b/retired_benchmarks/recommendation/dlrm/tf/feature_config.py
@@ -24,138 +24,153 @@
 
 LABEL_FEATURE = "clicked"
 INT_FEATURES = ["int-feature-%d" % x for x in range(1, 14)]
-CATEGORICAL_FEATURES = [
-    "categorical-feature-%d" % x for x in range(14, 40)
-]
+CATEGORICAL_FEATURES = ["categorical-feature-%d" % x for x in range(14, 40)]
 FAKE_DATA_VOCAB_SIZE = 1000
 FAKE_DATA_INT_MAX = 10.0
 
 
 class FeatureConfig(object):
-  """Configure dense and sparse features.
-
-  The embedding tables can be placed either in EmbeddingCore or .
-  In practice, large embedding tables are placed in EmbeddingCore while small
-  ones are in . FeatureConfig sorts the embedding table by its size
-  and stores the metadata such that input (dataloader.py) and model (dlrm.py)
-  see a consistent view of the placement of all embedding tables.
-
-  """
-
-  def __init__(self, params):
-    """Init method."""
-    # Hyperparameters
-    self._batch_size = params["batch_size"]
-    self._learning_rate = params["learning_rate"]
-    self._lr_warmup_steps = params["lr_warmup_steps"]
-    self._optimizer = params["optimizer"]
-    self._decay_steps = params["decay_steps"]
-    self._decay_start_steps = params["decay_start_step"]
-
-    self._vse = params["vocab_sizes"]
-    self._de = params["dim_embed"]
-    self._num_dense_features = params["num_dense_features"]
-    self._num_sparse_features = len(self._vse)
-
-    self._num_tables_in_ec = params["num_tables_in_ec"]
-    # Get the sorted table size in a descending order.
-    self._table_size_sorted = sorted(self._vse)[::-1]
-    # The following is equivalent to np.argsort in a descending order.
-    self._table_idx_ordered_by_size = sorted(
-        range(len(self._vse)), key=self._vse.__getitem__)[::-1]
-    tf.logging.info(self._table_size_sorted)
-    tf.logging.info(self._table_idx_ordered_by_size)
-
-  def get_sorted_table_size(self):
-    return self._table_size_sorted
-
-  def get_table_idx_orderd_by_size(self):
-    return self._table_idx_ordered_by_size
-
-  def get_num_tables_in_ec(self):
-    return self._num_tables_in_ec
-
-  def get_num_dense_features(self):
-    return self._num_dense_features
-
-  def get_num_sparse_features(self):
-    return self._num_sparse_features
-
-  def get_feature_tbl_config(self):
-    """Creates table configuration data structures.
-
-    For all tables, vocab size and width are given by params.
-
-    Table setup:
-    tbl0 - categorical-feature-14
-    tbl1 - categorical-feature-15
-    ..
-
-    Feature setup:
-    categorical-feature-14 -- tbl0 (first sparse feature)
-    categorical-feature-15 -- tbl1 (second sparse feature)
-
-    Returns:
-      A tuple of dicts, one for feature_to_config and one for table_to_config.
+    """Configure dense and sparse features.
+
+    The embedding tables can be placed either in EmbeddingCore or .
+    In practice, large embedding tables are placed in EmbeddingCore while small
+    ones are in . FeatureConfig sorts the embedding table by its size
+    and stores the metadata such that input (dataloader.py) and model (dlrm.py)
+    see a consistent view of the placement of all embedding tables.
+
     """
 
-    def lr_fn(global_step):
-      """Learning function for the embeddings. Linear warmup and poly decay."""
-      decay_exp = 2
-      scal = self._batch_size / 2048
-      adj_lr = self._learning_rate * scal
-      if self._lr_warmup_steps == 0:
-        return adj_lr
-      if self._optimizer == "adagrad":
-        return self._learning_rate
-      warmup_lr = tf.cast(
-          global_step, dtype=tf.float32) / self._lr_warmup_steps * adj_lr
-
-      global_step = tf.cast(global_step, tf.float32)
-      decay_steps = tf.cast(self._decay_steps, tf.float32)
-      decay_start_step = tf.cast(self._decay_start_steps, tf.float32)
-      steps_since_decay_start = global_step - decay_start_step
-      already_decayed_steps = tf.minimum(steps_since_decay_start, decay_steps)
-      decay_lr = adj_lr * (
-          (decay_steps - already_decayed_steps) / decay_steps)**decay_exp
-      decay_lr = tf.maximum(0.0000001, decay_lr)
-
-      lr = tf.where(
-          global_step < self._lr_warmup_steps, warmup_lr,
-          tf.where(
-              tf.logical_and(decay_steps > 0, global_step > decay_start_step),
-              decay_lr, adj_lr))
-
-      return lr
-
-    table_to_config_dict = {}
-    for i in range(self._num_tables_in_ec):
-      vocab_size = self._table_size_sorted[i]
-      table_to_config_dict["tbl%02d" % i] = tpu_embedding.TableConfig(
-          vocabulary_size=vocab_size,
-          dimension=self._de,
-          # NOTE: Default weight initializer uses trunc_normal,
-          #       stddv=1/dimension. This is changed to match the mlperf
-          #       reference model.
-          initializer=tf.random_uniform_initializer(
-              minval=-1 / math.sqrt(vocab_size),
-              maxval=1 / math.sqrt(vocab_size)),
-          combiner=None,
-          learning_rate_fn=lr_fn,
-
-          # TODO(tayo): Using the utils lr_fn leads to problems with embedding
-          # table size. The embedding table stops being able to fit.
-          # learning_rate_fn=functools.partial(utils.lr_fn, params)
-      )
-
-    # Use an offset to allow the categorical feature numbering to be subsequent
-    # to the integer feature numbering.
-    offset = 1 + self._num_dense_features
-    feature_to_config_dict = {}
-    feature_to_config_dict.update([
-        ("categorical-feature-%02d" % i,
-         tpu_embedding.FeatureConfig(table_id="tbl%02d" % (i - offset)))
-        for i in range(offset, offset + self._num_tables_in_ec)
-    ])
-
-    return feature_to_config_dict, table_to_config_dict
+    def __init__(self, params):
+        """Init method."""
+        # Hyperparameters
+        self._batch_size = params["batch_size"]
+        self._learning_rate = params["learning_rate"]
+        self._lr_warmup_steps = params["lr_warmup_steps"]
+        self._optimizer = params["optimizer"]
+        self._decay_steps = params["decay_steps"]
+        self._decay_start_steps = params["decay_start_step"]
+
+        self._vse = params["vocab_sizes"]
+        self._de = params["dim_embed"]
+        self._num_dense_features = params["num_dense_features"]
+        self._num_sparse_features = len(self._vse)
+
+        self._num_tables_in_ec = params["num_tables_in_ec"]
+        # Get the sorted table size in a descending order.
+        self._table_size_sorted = sorted(self._vse)[::-1]
+        # The following is equivalent to np.argsort in a descending order.
+        self._table_idx_ordered_by_size = sorted(
+            range(len(self._vse)), key=self._vse.__getitem__
+        )[::-1]
+        tf.logging.info(self._table_size_sorted)
+        tf.logging.info(self._table_idx_ordered_by_size)
+
+    def get_sorted_table_size(self):
+        return self._table_size_sorted
+
+    def get_table_idx_orderd_by_size(self):
+        return self._table_idx_ordered_by_size
+
+    def get_num_tables_in_ec(self):
+        return self._num_tables_in_ec
+
+    def get_num_dense_features(self):
+        return self._num_dense_features
+
+    def get_num_sparse_features(self):
+        return self._num_sparse_features
+
+    def get_feature_tbl_config(self):
+        """Creates table configuration data structures.
+
+        For all tables, vocab size and width are given by params.
+
+        Table setup:
+        tbl0 - categorical-feature-14
+        tbl1 - categorical-feature-15
+        ..
+
+        Feature setup:
+        categorical-feature-14 -- tbl0 (first sparse feature)
+        categorical-feature-15 -- tbl1 (second sparse feature)
+
+        Returns:
+          A tuple of dicts, one for feature_to_config and one for table_to_config.
+        """
+
+        def lr_fn(global_step):
+            """Learning function for the embeddings. Linear warmup and poly decay."""
+            decay_exp = 2
+            scal = self._batch_size / 2048
+            adj_lr = self._learning_rate * scal
+            if self._lr_warmup_steps == 0:
+                return adj_lr
+            if self._optimizer == "adagrad":
+                return self._learning_rate
+            warmup_lr = (
+                tf.cast(global_step, dtype=tf.float32) /
+                self._lr_warmup_steps * adj_lr
+            )
+
+            global_step = tf.cast(global_step, tf.float32)
+            decay_steps = tf.cast(self._decay_steps, tf.float32)
+            decay_start_step = tf.cast(self._decay_start_steps, tf.float32)
+            steps_since_decay_start = global_step - decay_start_step
+            already_decayed_steps = tf.minimum(
+                steps_since_decay_start, decay_steps)
+            decay_lr = (
+                adj_lr
+                * ((decay_steps - already_decayed_steps) / decay_steps) ** decay_exp
+            )
+            decay_lr = tf.maximum(0.0000001, decay_lr)
+
+            lr = tf.where(
+                global_step < self._lr_warmup_steps,
+                warmup_lr,
+                tf.where(
+                    tf.logical_and(
+                        decay_steps > 0,
+                        global_step > decay_start_step),
+                    decay_lr,
+                    adj_lr,
+                ),
+            )
+
+            return lr
+
+        table_to_config_dict = {}
+        for i in range(self._num_tables_in_ec):
+            vocab_size = self._table_size_sorted[i]
+            table_to_config_dict["tbl%02d" % i] = tpu_embedding.TableConfig(
+                vocabulary_size=vocab_size,
+                dimension=self._de,
+                # NOTE: Default weight initializer uses trunc_normal,
+                #       stddv=1/dimension. This is changed to match the mlperf
+                #       reference model.
+                initializer=tf.random_uniform_initializer(
+                    minval=-1 / math.sqrt(vocab_size), maxval=1 / math.sqrt(vocab_size)
+                ),
+                combiner=None,
+                learning_rate_fn=lr_fn,
+                # TODO(tayo): Using the utils lr_fn leads to problems with embedding
+                # table size. The embedding table stops being able to fit.
+                # learning_rate_fn=functools.partial(utils.lr_fn, params)
+            )
+
+        # Use an offset to allow the categorical feature numbering to be subsequent
+        # to the integer feature numbering.
+        offset = 1 + self._num_dense_features
+        feature_to_config_dict = {}
+        feature_to_config_dict.update(
+            [
+                (
+                    "categorical-feature-%02d" % i,
+                    tpu_embedding.FeatureConfig(
+                        table_id="tbl%02d" %
+                        (i - offset)),
+                )
+                for i in range(offset, offset + self._num_tables_in_ec)
+            ]
+        )
+
+        return feature_to_config_dict, table_to_config_dict
diff --git a/retired_benchmarks/recommendation/dlrm/tf/mlp_log.py b/retired_benchmarks/recommendation/dlrm/tf/mlp_log.py
index 48d9f12d78..523f4be282 100644
--- a/retired_benchmarks/recommendation/dlrm/tf/mlp_log.py
+++ b/retired_benchmarks/recommendation/dlrm/tf/mlp_log.py
@@ -28,11 +28,11 @@
 import sys
 import time
 
-PATTERN = re.compile('[a-zA-Z0-9]+')
+PATTERN = re.compile("[a-zA-Z0-9]+")
 
-LOG_FILE = os.getenv('COMPLIANCE_FILE')
+LOG_FILE = os.getenv("COMPLIANCE_FILE")
 # create logger with 'spam_application'
-LOGGER = logging.getLogger('mlperf_compliance')
+LOGGER = logging.getLogger("mlperf_compliance")
 LOGGER.setLevel(logging.DEBUG)
 
 _STREAM_HANDLER = logging.StreamHandler(stream=sys.stdout)
@@ -40,21 +40,22 @@
 LOGGER.addHandler(_STREAM_HANDLER)
 
 if LOG_FILE:
-  _FILE_HANDLER = logging.FileHandler(LOG_FILE)
-  _FILE_HANDLER.setLevel(logging.DEBUG)
-  LOGGER.addHandler(_FILE_HANDLER)
+    _FILE_HANDLER = logging.FileHandler(LOG_FILE)
+    _FILE_HANDLER.setLevel(logging.DEBUG)
+    LOGGER.addHandler(_FILE_HANDLER)
 else:
-  _STREAM_HANDLER.setLevel(logging.DEBUG)
+    _STREAM_HANDLER.setLevel(logging.DEBUG)
 
 
 def get_caller(stack_index=2, root_dir=None):
-  caller = inspect.getframeinfo(inspect.stack()[stack_index][0])
+    caller = inspect.getframeinfo(inspect.stack()[stack_index][0])
+
+    # Trim the filenames for readability.
+    filename = caller.filename
+    if root_dir is not None:
+        filename = re.sub("^" + root_dir + "/", "", filename)
+    return (filename, caller.lineno)
 
-  # Trim the filenames for readability.
-  filename = caller.filename
-  if root_dir is not None:
-    filename = re.sub('^' + root_dir + '/', '', filename)
-  return (filename, caller.lineno)
 
 # :::MLL 1556733699.71 run_start: {"value": null,
 # "metadata": {"lineno": 77, "file": main.py}}
@@ -62,21 +63,29 @@ def get_caller(stack_index=2, root_dir=None):
 
 
 def mlperf_format(key, value, stack_offset=0, metadata=None):
-  """Format a message for MLPerf."""
-  if metadata is None:
-    metadata = {}
+    """Format a message for MLPerf."""
+    if metadata is None:
+        metadata = {}
 
-  if 'lineno' not in metadata:
-    filename, lineno = get_caller(2 + stack_offset, root_dir=None)
-    metadata['lineno'] = lineno
-    metadata['file'] = filename
+    if "lineno" not in metadata:
+        filename, lineno = get_caller(2 + stack_offset, root_dir=None)
+        metadata["lineno"] = lineno
+        metadata["file"] = filename
 
-  now = time.time()
-  msg = LOG_TEMPLATE.format(now, key, json.dumps(value), json.dumps(metadata))
-  return msg
+    now = time.time()
+    msg = LOG_TEMPLATE.format(
+        now,
+        key,
+        json.dumps(value),
+        json.dumps(metadata))
+    return msg
 
 
 def mlperf_print(key, value, stack_offset=0, metadata=None):
-  LOGGER.info(
-      mlperf_format(
-          key, value, stack_offset=stack_offset + 1, metadata=metadata))
+    LOGGER.info(
+        mlperf_format(
+            key,
+            value,
+            stack_offset=stack_offset + 1,
+            metadata=metadata)
+    )
diff --git a/retired_benchmarks/recommendation/dlrm/tf/train_and_eval_runner.py b/retired_benchmarks/recommendation/dlrm/tf/train_and_eval_runner.py
index ca0651b8c9..b2554cfdaa 100644
--- a/retired_benchmarks/recommendation/dlrm/tf/train_and_eval_runner.py
+++ b/retired_benchmarks/recommendation/dlrm/tf/train_and_eval_runner.py
@@ -42,46 +42,55 @@
     "master",
     default=None,
     help="The Cloud TPU to use for training. This should be either the name "
-    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")
+    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.",
+)
 
 flags.DEFINE_string(
     "gcp_project",
     default=None,
     help="Project name for the Cloud TPU-enabled project. If not specified, we "
-    "will attempt to automatically detect the GCE project from metadata.")
+    "will attempt to automatically detect the GCE project from metadata.",
+)
 
 flags.DEFINE_string(
     "tpu_zone",
     default=None,
     help="GCE zone where the Cloud TPU is located in. If not specified, we "
-    "will attempt to automatically detect the GCE project from metadata.")
+    "will attempt to automatically detect the GCE project from metadata.",
+)
 
 flags.DEFINE_integer(
-    "replicas_per_host", default=8, help=("Number of replicas per host."))
+    "replicas_per_host", default=8, help=("Number of replicas per host.")
+)
 
 flags.DEFINE_bool("enable_summary", default=False, help=("Enable summary"))
 
 flags.DEFINE_string(
     "model_dir",
     default=None,
-    help=("The directory where the model and summaries are stored."))
+    help=("The directory where the model and summaries are stored."),
+)
 
 flags.DEFINE_bool("save_checkpoint", default=False, help=("Save checkpoint"))
 
 flags.DEFINE_bool(
-    "restore_checkpoint", default=False, help=("Restore checkpoint"))
+    "restore_checkpoint",
+    default=False,
+    help=("Restore checkpoint"))
 
 flags.DEFINE_integer(
-    "sleep_after_init", default=60, help=("Sleep for N seconds after init."))
+    "sleep_after_init", default=60, help=("Sleep for N seconds after init.")
+)
 
 flags.DEFINE_bool(
-    "enable_mlir_bridge", default=False, help=("Enable TF/XLA MLIR bridge"))
+    "enable_mlir_bridge", default=False, help=("Enable TF/XLA MLIR bridge")
+)
 
 flags.DEFINE_bool(
     "enable_profiling",
     default=False,
-    help=("Get xprof traces at"
-          "the start and middle of the train loops"))
+    help=("Get xprof traces at" "the start and middle of the train loops"),
+)
 
 _NUM_CORES_TO_COMPUTATION_SHAPE = {
     1: [1, 1, 1, 1],
@@ -93,495 +102,586 @@
 
 
 def _profiler_callback(comment, session_id):
-  if session_id is None:
-    tf.logging.info("Profiling failed for %s", comment)
-  else:
-    tf.logging.info("Profiling succeeded for %s. Overview page url:", comment)
+    if session_id is None:
+        tf.logging.info("Profiling failed for %s", comment)
+    else:
+        tf.logging.info(
+            "Profiling succeeded for %s. Overview page url:",
+            comment)
 
 
 # Decorator function for tpu computation func that was passed to tpu.rewrite()
 # if there are embedded train and eval loops in this func, trace tools will
 # generate step markers for each iteration.
 def on_device_train_and_eval_loops(func):
-  # Value for this attribute is from xla.DebugOptions.StepMarkerLocation.
-  setattr(func, "step_marker_location", "STEP_MARK_AT_SECOND_LEVEL_WHILE_LOOP")
-  return func
+    # Value for this attribute is from xla.DebugOptions.StepMarkerLocation.
+    setattr(
+        func,
+        "step_marker_location",
+        "STEP_MARK_AT_SECOND_LEVEL_WHILE_LOOP")
+    return func
 
 
 def device_for_tpu_core(host_name, core=0):
-  return host_name + "/device:TPU_REPLICATED_CORE:%d" % core
+    return host_name + "/device:TPU_REPLICATED_CORE:%d" % core
 
 
 def device_for_host(host_name):
-  return host_name + "/device:CPU:0"
+    return host_name + "/device:CPU:0"
 
 
 class TrainAndEvalRunner(object):
-  """Remove init overheads in TPU Estimator via direct session.run calls."""
-
-  def __init__(self,
-               iterations_per_loop,
-               train_steps,
-               eval_steps,
-               num_replicas,
-               eval_dataset_repeats=True,
-               do_initialize=True):
-    self.feature_structure = {}
-    self.infeed_op = {}
-    self.num_replicas = num_replicas
-    self.eval_dataset_repeats = eval_dataset_repeats
-    # Set number of input graphs to number of hosts up to a maximum of 32.
-    self.num_input_graphs = min(32,
-                                self.num_replicas // FLAGS.replicas_per_host)
-    # Following data has separated copies for training and eval, thus
-    # represented as a map from is_train(boolean) to actual data
-    self.dataset_initializer = {True: [], False: []}
-    self.input_graph = {True: [], False: []}
-    self.input_sess = {True: [], False: []}
-    self.enqueue_ops = {True: [], False: []}
-    for _ in range(self.num_input_graphs):
-      self.input_graph[True].append(tf.Graph())
-      self.input_graph[False].append(tf.Graph())
-      self.dataset_initializer[True].append([])
-      self.dataset_initializer[False].append([])
-      self.enqueue_ops[True].append([])
-      self.enqueue_ops[False].append([])
-      self.input_sess[True].append([])
-      self.input_sess[False].append([])
-    # dequeue_ops is only for eval
-    self.dequeue_ops = []
-    self.iterations_per_loop = iterations_per_loop
-    self.sess = None
-    self.output_sess = None
-    self.train_eval_thread = None
-    self.graph = tf.Graph()
-    if iterations_per_loop != 0 and train_steps % iterations_per_loop != 0:
-      train_steps = iterations_per_loop * int(
-          math.ceil(train_steps / iterations_per_loop))
-    self.train_steps = train_steps
-    if iterations_per_loop == 0:
-      self.max_train_iterations = 1
-    else:
-      self.max_train_iterations = train_steps // iterations_per_loop
-    self.eval_steps = int(eval_steps)
-    self.train_batch_size = 0
-    self.eval_batch_size = 0
-    self.eval_has_labels = 0
-    self.model_fn = None
-    self.num_outfeeds = self.eval_steps
-    self.config = tf.ConfigProto(
-        operation_timeout_in_ms=600 * 60 * 1000,
-        allow_soft_placement=True,
-        graph_options=tf.GraphOptions(
-            rewrite_options=rewriter_config_pb2.RewriterConfig(
-                disable_meta_optimizer=True)),
-        isolate_session_state=True)
-
-    if FLAGS.enable_mlir_bridge:
-      self.config.experimental.enable_mlir_bridge = True
-
-    tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
-        FLAGS.master, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project,
-        job_name="tpu_worker")
-    self.master = tpu_cluster_resolver.get_master()
-    self.job_name = tpu_cluster_resolver.get_job_name() or "tpu_worker"
-    self.embedding_config = None
-    self.device_topology = None
-    if do_initialize:
-      self.device_topology = tf.Session(
-          self.master, config=self.config).run(
-              tpu.initialize_system())
-
-  def maybe_capture_embedding_inputs(self, inputs, is_training):
-    pass
-
-  def maybe_add_embedding_enqueue_ops_int(self, is_training, enqueue_ops):
-    pass
-
-  def maybe_get_embedding_train_op(self):
-    return tf.no_op()
-
-  def maybe_add_embedding_features(self, features, hook_dummy_variables):
-    pass
-
-  def maybe_load_embedding_vars(self):
-    pass
-
-  def get_host(self, host_id):
-    if self.master in ("", "local"):
-      return "/replica:0/task:0"
-    return "/job:%s/task:%d" % (self.job_name, host_id)
-
-  def build_enqueue_ops(self, input_fn, is_training, input_partition_dims,
-                        params):
-    """Build enqueue operations for the input pipeline in a given host.
-
-    Args:
-      input_fn: dataset input graph generation function
-      is_training: boolean indicates if it is training
-      input_partition_dims: list of integers to partition input
-      params: hyper parameters
-    """
-
-    def _tpu_ordinal_fn(shard_index_in_host):
-      replica_id = self.device_assignment.lookup_replicas(
-          host_id, logical_core=0)[shard_index_in_host]
-      return self.device_assignment.tpu_ordinal(
-          replica=replica_id, logical_core=0)
-
-    host_id = params["dataset_index"]
-    gindex = host_id % self.num_input_graphs
-    with self.input_graph[is_training][gindex].as_default():
-      with tf.device(device_for_host(self.get_host(host_id))):
-        dataset = input_fn(params)
-        if not is_training and self.eval_dataset_repeats:
-          dataset = dataset.cache().repeat()
-        iterator = dataset.make_initializable_iterator()
-        self.dataset_initializer[is_training][gindex].append(
-            iterator.initializer)
-
-        def enqueue_ops_fn(idx):
-          """Generate the infeed enqueue ops graph."""
-
-          per_host_sharded_inputs = []
-          control_deps = []
-          for _ in range(FLAGS.replicas_per_host):
-            with tf.control_dependencies(control_deps):
-              self.feature_structure[is_training] = iterator.get_next()
-            self.maybe_capture_embedding_inputs(
-                self.feature_structure[is_training], is_training)
-            flattened_inputs = tf.nest.flatten(
-                self.feature_structure[is_training])
-            control_deps.extend(flattened_inputs)
-            if input_partition_dims:
-              padded_inputs = []
-              for inp in flattened_inputs:
-                if inp.shape.ndims < len(input_partition_dims):
-                  padded_inputs.append(inp)
-                  continue
-                paddings = []
-                for i, j in enumerate(input_partition_dims):
-                  r = inp.shape.as_list()[i] % j
-                  if r > 0:
-                    paddings.append([0, j - r])
-                  else:
-                    paddings.append([0, 0])
-                for i in range(inp.shape.ndims - len(input_partition_dims)):
-                  paddings.append([0, 0])
-                padded_inputs.append(tf.pad(inp, paddings))
-              per_host_sharded_inputs.append(padded_inputs)
-            else:
-              per_host_sharded_inputs.append(flattened_inputs)
-
-          if input_partition_dims:
-            flattened_input_dims = []
-            for i in per_host_sharded_inputs[0]:
-              if i.shape.ndims == len(input_partition_dims):
-                flattened_input_dims.append(input_partition_dims)
-              elif i.shape.ndims > len(input_partition_dims):
-                flattened_input_dims.append(
-                    input_partition_dims + [1] *
-                    (i.shape.ndims - len(input_partition_dims)))
-              else:
-                flattened_input_dims.append([1] * i.shape.ndims)
-            # pylint: disable=protected-access
-            self.infeed_op[is_training] = tpu_feed._PartitionedInfeedQueue(
-                number_of_tuple_elements=len(per_host_sharded_inputs[0]),
-                host_id=host_id,
-                input_partition_dims=flattened_input_dims,
-                device_assignment=self.device_assignment)
-            with tf.control_dependencies(
-                self.infeed_op[is_training].generate_enqueue_ops(
-                    per_host_sharded_inputs)):
-              return idx + 1
-          else:
-            self.infeed_op[is_training] = tpu_feed.InfeedQueue(
-                number_of_tuple_elements=len(per_host_sharded_inputs[0]))
-            per_host_enqueue_ops = (
-                self.infeed_op[is_training].generate_enqueue_ops(
-                    per_host_sharded_inputs,
-                    tpu_ordinal_function=_tpu_ordinal_fn))
-
-          self.maybe_add_embedding_enqueue_ops_int(
-              is_training, per_host_enqueue_ops)
-          with tf.control_dependencies(per_host_enqueue_ops):
-            return idx + 1
-
-        iterations = self.iterations_per_loop if is_training else self.eval_steps
-        self.enqueue_ops[is_training][gindex].append(
-            tf.while_loop(
-                lambda i: tf.less(i, iterations),
-                enqueue_ops_fn, [tf.constant(0)],
-                parallel_iterations=1))
-
-  def launch_profiler(self):
-    """Launches a profiling session to collect a trace from worker-0."""
-    if result == profiler_client.PROFILED_IN_NEW_THREAD:
-      tf.logging.info("A profiler session launched in a new thread.")
-    else:
-      tf.logging.info("profiler.collect() failed.")
-
-  def eval_step(self):
-    """One evaluation step."""
-    inp = self.infeed_op[False].generate_dequeue_op()
-    flatten_structure = tf.nest.flatten(self.feature_structure[False])
-    inp = [
-        tf.slice(i, [0] * i.shape.ndims, j.shape)
-        for i, j in zip(inp, flatten_structure)
-    ]
-    if self.eval_has_labels:
-      features, labels = tf.nest.pack_sequence_as(
-          self.feature_structure[False], inp)
-    else:
-      features = tf.nest.pack_sequence_as(self.feature_structure[False], inp)
-      labels = None
-    self.maybe_add_embedding_features(features, False)
-    _, self.predict_output = self.model_fn(features, labels, False)
-    for _ in self.predict_output:
-      self.dequeue_ops.append([])
-    with tf.device(device_for_tpu_core(self.get_host(0))):
-      return [
-          tpu_ops.outfeed_enqueue_tuple(tf.nest.flatten(self.predict_output))
-      ]
-
-  @tpu_function.on_device_training_loop
-  def eval_loop(self):
-    tf.get_variable_scope().reuse_variables()
-    return training_loop.repeat(int(self.eval_steps), self.eval_step)
-
-  def initialize(self,
-                 train_input_fn,
-                 eval_input_fn,
-                 model_fn,
-                 train_batch_size,
-                 eval_batch_size,
-                 input_partition_dims=None,
-                 init_fn=None,
-                 train_has_labels=True,
-                 eval_has_labels=True,
-                 params=None,
-                 num_partitions=None):
-    """Build graphs for the TPU device and the input pipelines."""
-    num_cores_per_replica = 1
-    num_cores_per_replica = functools.reduce(
-        operator.mul, input_partition_dims
-    ) if input_partition_dims else num_partitions if num_partitions else 1
-
-    self.device_assignment = device_assignment.device_assignment(
-        topology=self.device_topology,
-        computation_shape=_NUM_CORES_TO_COMPUTATION_SHAPE[
-            num_cores_per_replica],
-        num_replicas=self.num_replicas)
-    self.train_batch_size = train_batch_size
-    self.eval_batch_size = eval_batch_size
-    self.eval_has_labels = eval_has_labels
-    self.model_fn = model_fn
-
-    if params is None:
-      params = {}
-    params["dataset_num_shards"] = self.num_replicas // FLAGS.replicas_per_host
-    per_replica_train_batch_size = train_batch_size // self.num_replicas
-    per_replica_eval_batch_size = eval_batch_size // self.num_replicas
-    for i in range(self.num_replicas // FLAGS.replicas_per_host):
-      params["dataset_index"] = i
-      params["batch_size"] = per_replica_train_batch_size
-      self.build_enqueue_ops(train_input_fn, True, input_partition_dims, params)
-      if self.eval_steps > 0:
-        params["batch_size"] = per_replica_eval_batch_size
-        self.build_enqueue_ops(eval_input_fn, False, input_partition_dims,
-                               params)
-
-    def train_step(_):
-      """One train step."""
-      inp = self.infeed_op[True].generate_dequeue_op()
-      flatten_structure = tf.nest.flatten(self.feature_structure[True])
-      inp = [
-          tf.slice(i, [0] * i.shape.ndims, j.shape)
-          for i, j in zip(inp, flatten_structure)
-      ]
-      if train_has_labels:
-        features, labels = tf.nest.pack_sequence_as(
-            self.feature_structure[True], inp)
-      else:
-        features = tf.nest.pack_sequence_as(self.feature_structure[True], inp)
-        labels = None
-      self.maybe_add_embedding_features(features, True)
-      train_op, _ = model_fn(features, labels, True)
-      embedding_train_op = self.maybe_get_embedding_train_op()
-      with tf.device(device_for_tpu_core(self.get_host(0))):
-        with tf.control_dependencies([train_op, embedding_train_op]):
-          return tf.constant(0)
+    """Remove init overheads in TPU Estimator via direct session.run calls."""
+
+    def __init__(
+        self,
+        iterations_per_loop,
+        train_steps,
+        eval_steps,
+        num_replicas,
+        eval_dataset_repeats=True,
+        do_initialize=True,
+    ):
+        self.feature_structure = {}
+        self.infeed_op = {}
+        self.num_replicas = num_replicas
+        self.eval_dataset_repeats = eval_dataset_repeats
+        # Set number of input graphs to number of hosts up to a maximum of 32.
+        self.num_input_graphs = min(
+            32, self.num_replicas // FLAGS.replicas_per_host)
+        # Following data has separated copies for training and eval, thus
+        # represented as a map from is_train(boolean) to actual data
+        self.dataset_initializer = {True: [], False: []}
+        self.input_graph = {True: [], False: []}
+        self.input_sess = {True: [], False: []}
+        self.enqueue_ops = {True: [], False: []}
+        for _ in range(self.num_input_graphs):
+            self.input_graph[True].append(tf.Graph())
+            self.input_graph[False].append(tf.Graph())
+            self.dataset_initializer[True].append([])
+            self.dataset_initializer[False].append([])
+            self.enqueue_ops[True].append([])
+            self.enqueue_ops[False].append([])
+            self.input_sess[True].append([])
+            self.input_sess[False].append([])
+        # dequeue_ops is only for eval
+        self.dequeue_ops = []
+        self.iterations_per_loop = iterations_per_loop
+        self.sess = None
+        self.output_sess = None
+        self.train_eval_thread = None
+        self.graph = tf.Graph()
+        if iterations_per_loop != 0 and train_steps % iterations_per_loop != 0:
+            train_steps = iterations_per_loop * int(
+                math.ceil(train_steps / iterations_per_loop)
+            )
+        self.train_steps = train_steps
+        if iterations_per_loop == 0:
+            self.max_train_iterations = 1
+        else:
+            self.max_train_iterations = train_steps // iterations_per_loop
+        self.eval_steps = int(eval_steps)
+        self.train_batch_size = 0
+        self.eval_batch_size = 0
+        self.eval_has_labels = 0
+        self.model_fn = None
+        self.num_outfeeds = self.eval_steps
+        self.config = tf.ConfigProto(
+            operation_timeout_in_ms=600 * 60 * 1000,
+            allow_soft_placement=True,
+            graph_options=tf.GraphOptions(
+                rewrite_options=rewriter_config_pb2.RewriterConfig(
+                    disable_meta_optimizer=True
+                )
+            ),
+            isolate_session_state=True,
+        )
+
+        if FLAGS.enable_mlir_bridge:
+            self.config.experimental.enable_mlir_bridge = True
+
+        tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
+            FLAGS.master,
+            zone=FLAGS.tpu_zone,
+            project=FLAGS.gcp_project,
+            job_name="tpu_worker",
+        )
+        self.master = tpu_cluster_resolver.get_master()
+        self.job_name = tpu_cluster_resolver.get_job_name() or "tpu_worker"
+        self.embedding_config = None
+        self.device_topology = None
+        if do_initialize:
+            self.device_topology = tf.Session(self.master, config=self.config).run(
+                tpu.initialize_system()
+            )
+
+    def maybe_capture_embedding_inputs(self, inputs, is_training):
+        pass
+
+    def maybe_add_embedding_enqueue_ops_int(self, is_training, enqueue_ops):
+        pass
+
+    def maybe_get_embedding_train_op(self):
+        return tf.no_op()
+
+    def maybe_add_embedding_features(self, features, hook_dummy_variables):
+        pass
+
+    def maybe_load_embedding_vars(self):
+        pass
+
+    def get_host(self, host_id):
+        if self.master in ("", "local"):
+            return "/replica:0/task:0"
+        return "/job:%s/task:%d" % (self.job_name, host_id)
+
+    def build_enqueue_ops(self, input_fn, is_training,
+                          input_partition_dims, params):
+        """Build enqueue operations for the input pipeline in a given host.
+
+        Args:
+          input_fn: dataset input graph generation function
+          is_training: boolean indicates if it is training
+          input_partition_dims: list of integers to partition input
+          params: hyper parameters
+        """
+
+        def _tpu_ordinal_fn(shard_index_in_host):
+            replica_id = self.device_assignment.lookup_replicas(
+                host_id, logical_core=0
+            )[shard_index_in_host]
+            return self.device_assignment.tpu_ordinal(
+                replica=replica_id, logical_core=0
+            )
+
+        host_id = params["dataset_index"]
+        gindex = host_id % self.num_input_graphs
+        with self.input_graph[is_training][gindex].as_default():
+            with tf.device(device_for_host(self.get_host(host_id))):
+                dataset = input_fn(params)
+                if not is_training and self.eval_dataset_repeats:
+                    dataset = dataset.cache().repeat()
+                iterator = dataset.make_initializable_iterator()
+                self.dataset_initializer[is_training][gindex].append(
+                    iterator.initializer
+                )
+
+                def enqueue_ops_fn(idx):
+                    """Generate the infeed enqueue ops graph."""
+
+                    per_host_sharded_inputs = []
+                    control_deps = []
+                    for _ in range(FLAGS.replicas_per_host):
+                        with tf.control_dependencies(control_deps):
+                            self.feature_structure[is_training] = iterator.get_next(
+                            )
+                        self.maybe_capture_embedding_inputs(
+                            self.feature_structure[is_training], is_training
+                        )
+                        flattened_inputs = tf.nest.flatten(
+                            self.feature_structure[is_training]
+                        )
+                        control_deps.extend(flattened_inputs)
+                        if input_partition_dims:
+                            padded_inputs = []
+                            for inp in flattened_inputs:
+                                if inp.shape.ndims < len(input_partition_dims):
+                                    padded_inputs.append(inp)
+                                    continue
+                                paddings = []
+                                for i, j in enumerate(input_partition_dims):
+                                    r = inp.shape.as_list()[i] % j
+                                    if r > 0:
+                                        paddings.append([0, j - r])
+                                    else:
+                                        paddings.append([0, 0])
+                                for i in range(
+                                    inp.shape.ndims - len(input_partition_dims)
+                                ):
+                                    paddings.append([0, 0])
+                                padded_inputs.append(tf.pad(inp, paddings))
+                            per_host_sharded_inputs.append(padded_inputs)
+                        else:
+                            per_host_sharded_inputs.append(flattened_inputs)
+
+                    if input_partition_dims:
+                        flattened_input_dims = []
+                        for i in per_host_sharded_inputs[0]:
+                            if i.shape.ndims == len(input_partition_dims):
+                                flattened_input_dims.append(
+                                    input_partition_dims)
+                            elif i.shape.ndims > len(input_partition_dims):
+                                flattened_input_dims.append(
+                                    input_partition_dims
+                                    + [1] * (i.shape.ndims -
+                                             len(input_partition_dims))
+                                )
+                            else:
+                                flattened_input_dims.append(
+                                    [1] * i.shape.ndims)
+                        # pylint: disable=protected-access
+                        self.infeed_op[is_training] = tpu_feed._PartitionedInfeedQueue(
+                            number_of_tuple_elements=len(
+                                per_host_sharded_inputs[0]),
+                            host_id=host_id,
+                            input_partition_dims=flattened_input_dims,
+                            device_assignment=self.device_assignment,
+                        )
+                        with tf.control_dependencies(
+                            self.infeed_op[is_training].generate_enqueue_ops(
+                                per_host_sharded_inputs
+                            )
+                        ):
+                            return idx + 1
+                    else:
+                        self.infeed_op[is_training] = tpu_feed.InfeedQueue(
+                            number_of_tuple_elements=len(
+                                per_host_sharded_inputs[0])
+                        )
+                        per_host_enqueue_ops = self.infeed_op[
+                            is_training
+                        ].generate_enqueue_ops(
+                            per_host_sharded_inputs,
+                            tpu_ordinal_function=_tpu_ordinal_fn,
+                        )
+
+                    self.maybe_add_embedding_enqueue_ops_int(
+                        is_training, per_host_enqueue_ops
+                    )
+                    with tf.control_dependencies(per_host_enqueue_ops):
+                        return idx + 1
+
+                iterations = (
+                    self.iterations_per_loop if is_training else self.eval_steps
+                )
+                self.enqueue_ops[is_training][gindex].append(
+                    tf.while_loop(
+                        lambda i: tf.less(i, iterations),
+                        enqueue_ops_fn,
+                        [tf.constant(0)],
+                        parallel_iterations=1,
+                    )
+                )
+
+    def launch_profiler(self):
+        """Launches a profiling session to collect a trace from worker-0."""
+        if result == profiler_client.PROFILED_IN_NEW_THREAD:
+            tf.logging.info("A profiler session launched in a new thread.")
+        else:
+            tf.logging.info("profiler.collect() failed.")
+
+    def eval_step(self):
+        """One evaluation step."""
+        inp = self.infeed_op[False].generate_dequeue_op()
+        flatten_structure = tf.nest.flatten(self.feature_structure[False])
+        inp = [
+            tf.slice(i, [0] * i.shape.ndims, j.shape)
+            for i, j in zip(inp, flatten_structure)
+        ]
+        if self.eval_has_labels:
+            features, labels = tf.nest.pack_sequence_as(
+                self.feature_structure[False], inp
+            )
+        else:
+            features = tf.nest.pack_sequence_as(
+                self.feature_structure[False], inp)
+            labels = None
+        self.maybe_add_embedding_features(features, False)
+        _, self.predict_output = self.model_fn(features, labels, False)
+        for _ in self.predict_output:
+            self.dequeue_ops.append([])
+        with tf.device(device_for_tpu_core(self.get_host(0))):
+            return [tpu_ops.outfeed_enqueue_tuple(
+                tf.nest.flatten(self.predict_output))]
 
     @tpu_function.on_device_training_loop
-    def train_loop():
-      return training_loop.repeat(self.iterations_per_loop, train_step,
-                                  tf.constant(0))
-
-    def train_eval_step():
-      with tf.control_dependencies(train_loop()):
+    def eval_loop(self):
+        tf.get_variable_scope().reuse_variables()
+        return training_loop.repeat(int(self.eval_steps), self.eval_step)
+
+    def initialize(
+        self,
+        train_input_fn,
+        eval_input_fn,
+        model_fn,
+        train_batch_size,
+        eval_batch_size,
+        input_partition_dims=None,
+        init_fn=None,
+        train_has_labels=True,
+        eval_has_labels=True,
+        params=None,
+        num_partitions=None,
+    ):
+        """Build graphs for the TPU device and the input pipelines."""
+        num_cores_per_replica = 1
+        num_cores_per_replica = (
+            functools.reduce(operator.mul, input_partition_dims)
+            if input_partition_dims
+            else num_partitions if num_partitions else 1
+        )
+
+        self.device_assignment = device_assignment.device_assignment(
+            topology=self.device_topology,
+            computation_shape=_NUM_CORES_TO_COMPUTATION_SHAPE[num_cores_per_replica],
+            num_replicas=self.num_replicas,
+        )
+        self.train_batch_size = train_batch_size
+        self.eval_batch_size = eval_batch_size
+        self.eval_has_labels = eval_has_labels
+        self.model_fn = model_fn
+
+        if params is None:
+            params = {}
+        params["dataset_num_shards"] = self.num_replicas // FLAGS.replicas_per_host
+        per_replica_train_batch_size = train_batch_size // self.num_replicas
+        per_replica_eval_batch_size = eval_batch_size // self.num_replicas
+        for i in range(self.num_replicas // FLAGS.replicas_per_host):
+            params["dataset_index"] = i
+            params["batch_size"] = per_replica_train_batch_size
+            self.build_enqueue_ops(
+                train_input_fn, True, input_partition_dims, params)
+            if self.eval_steps > 0:
+                params["batch_size"] = per_replica_eval_batch_size
+                self.build_enqueue_ops(
+                    eval_input_fn, False, input_partition_dims, params
+                )
+
+        def train_step(_):
+            """One train step."""
+            inp = self.infeed_op[True].generate_dequeue_op()
+            flatten_structure = tf.nest.flatten(self.feature_structure[True])
+            inp = [
+                tf.slice(i, [0] * i.shape.ndims, j.shape)
+                for i, j in zip(inp, flatten_structure)
+            ]
+            if train_has_labels:
+                features, labels = tf.nest.pack_sequence_as(
+                    self.feature_structure[True], inp
+                )
+            else:
+                features = tf.nest.pack_sequence_as(
+                    self.feature_structure[True], inp)
+                labels = None
+            self.maybe_add_embedding_features(features, True)
+            train_op, _ = model_fn(features, labels, True)
+            embedding_train_op = self.maybe_get_embedding_train_op()
+            with tf.device(device_for_tpu_core(self.get_host(0))):
+                with tf.control_dependencies([train_op, embedding_train_op]):
+                    return tf.constant(0)
+
+        @tpu_function.on_device_training_loop
+        def train_loop():
+            return training_loop.repeat(
+                self.iterations_per_loop, train_step, tf.constant(0)
+            )
+
+        def train_eval_step():
+            with tf.control_dependencies(train_loop()):
+                if self.eval_steps > 0:
+                    return self.eval_loop()
+                else:
+                    return tf.no_op()
+
+        @on_device_train_and_eval_loops
+        def train_eval_loop():
+            return training_loop.repeat(
+                self.max_train_iterations, train_eval_step)
+
+        with self.graph.as_default():
+            (self.train_eval_op,) = tpu.shard(
+                train_eval_loop,
+                inputs=[],
+                num_shards=self.num_replicas,
+                outputs_from_all_shards=False,
+                device_assignment=self.device_assignment,
+            )
+            if FLAGS.model_dir:
+                tf.io.write_graph(self.graph, FLAGS.model_dir, "graph.pbtxt")
+
+        output_graph = tf.Graph()
         if self.eval_steps > 0:
-          return self.eval_loop()
+            with output_graph.as_default():
+                flatten_output = tf.nest.flatten(self.predict_output)
+                self.dequeue_ops = [[] for _ in flatten_output]
+                tensor_dtypes = [v.dtype for v in flatten_output]
+                tensor_shapes = [v.shape for v in flatten_output]
+                is_padded_index = (
+                    flatten_output.index(self.predict_output[_IS_PADDED])
+                    if _IS_PADDED in self.predict_output
+                    else -1
+                )
+                for i in range(self.num_replicas // FLAGS.replicas_per_host):
+                    with tf.device(device_for_host(self.get_host(i))):
+                        host_dequeue_ops = [[] for _ in flatten_output]
+                        for j in range(FLAGS.replicas_per_host):
+                            replica_id = self.device_assignment.lookup_replicas(i, 0)[
+                                j]
+                            ordinal = self.device_assignment.tpu_ordinal(
+                                replica=replica_id, logical_core=0
+                            )
+                            dequeue_ops = tpu_ops.outfeed_dequeue_tuple(
+                                dtypes=tensor_dtypes,
+                                shapes=tensor_shapes,
+                                device_ordinal=ordinal,
+                            )
+                            if is_padded_index >= 0:
+                                num_non_pad = tf.shape(dequeue_ops[is_padded_index])[
+                                    0
+                                ] - tf.reduce_sum(
+                                    tf.cast(
+                                        dequeue_ops[is_padded_index], tf.int32)
+                                )
+                                dequeue_ops = [
+                                    tf.slice(
+                                        k,
+                                        [0] * k.shape.ndims,
+                                        [num_non_pad] + [-1] *
+                                        (k.shape.ndims - 1),
+                                    )
+                                    for k in dequeue_ops
+                                ]
+                            for k, item in enumerate(dequeue_ops):
+                                host_dequeue_ops[k].append(item)
+                        for k in range(len(self.predict_output)):
+                            self.dequeue_ops[k].append(
+                                tf.concat(host_dequeue_ops[k], axis=0)
+                            )
+
+        self.sess = tf.Session(
+            self.master,
+            graph=self.graph,
+            config=self.config)
+        for is_training in [True, False]:
+            if is_training or self.eval_steps > 0:
+                for i in range(self.num_input_graphs):
+                    with self.input_graph[is_training][i].as_default():
+                        self.input_sess[is_training][i] = tf.Session(
+                            self.master,
+                            graph=self.input_graph[is_training][i],
+                            config=self.config,
+                        )
+                        self.input_sess[is_training][i].run(
+                            self.dataset_initializer[is_training][i]
+                        )
+        self.output_sess = tf.Session(
+            self.master, graph=output_graph, config=self.config
+        )
+
+        with self.graph.as_default():
+            _ = tf.train.get_or_create_global_step()
+            if init_fn:
+                init_fn()
+            checkpoint_path = (
+                tf.train.latest_checkpoint(
+                    FLAGS.model_dir) if FLAGS.model_dir else None
+            )
+            if FLAGS.restore_checkpoint and checkpoint_path:
+                tf.train.Saver().restore(self.sess, checkpoint_path)
+            else:
+                self.sess.run(tf.global_variables_initializer())
+                self.sess.run(tf.local_variables_initializer())
+            self.maybe_load_embedding_vars()
+            self.global_step = self.sess.run(
+                tf.train.get_global_step(self.graph))
+
+        def train_eval_thread_fn(sess, train_eval_op):
+            sess.run([train_eval_op])
+
+        # Start the just in time compilation of the model function
+        self.train_eval_thread = threading.Thread(
+            target=train_eval_thread_fn, args=(self.sess, self.train_eval_op)
+        )
+        self.train_eval_thread.start()
+
+        # Sleep for JTC to finish
+        time.sleep(FLAGS.sleep_after_init)
+
+    def train_and_eval(
+        self, eval_init_fn=None, eval_finish_fn=None, run_finish_fn=None
+    ):
+        """Run the Train steps on the TPU device."""
+        if FLAGS.enable_summary:
+            output_dir = os.path.join(FLAGS.model_dir, "eval")
+            tf.gfile.MakeDirs(output_dir)
+            summary_writer = tf.summary.FileWriter(output_dir)
         else:
-          return tf.no_op()
-
-    @on_device_train_and_eval_loops
-    def train_eval_loop():
-      return training_loop.repeat(self.max_train_iterations, train_eval_step)
-
-    with self.graph.as_default():
-      (self.train_eval_op,) = tpu.shard(
-          train_eval_loop,
-          inputs=[],
-          num_shards=self.num_replicas,
-          outputs_from_all_shards=False,
-          device_assignment=self.device_assignment)
-      if FLAGS.model_dir:
-        tf.io.write_graph(self.graph, FLAGS.model_dir, "graph.pbtxt")
-
-    output_graph = tf.Graph()
-    if self.eval_steps > 0:
-      with output_graph.as_default():
-        flatten_output = tf.nest.flatten(self.predict_output)
-        self.dequeue_ops = [[] for _ in flatten_output]
-        tensor_dtypes = [v.dtype for v in flatten_output]
-        tensor_shapes = [v.shape for v in flatten_output]
-        is_padded_index = flatten_output.index(
-            self.predict_output[_IS_PADDED]
-        ) if _IS_PADDED in self.predict_output else -1
-        for i in range(self.num_replicas // FLAGS.replicas_per_host):
-          with tf.device(device_for_host(self.get_host(i))):
-            host_dequeue_ops = [[] for _ in flatten_output]
-            for j in range(FLAGS.replicas_per_host):
-              replica_id = self.device_assignment.lookup_replicas(i, 0)[j]
-              ordinal = self.device_assignment.tpu_ordinal(
-                  replica=replica_id, logical_core=0)
-              dequeue_ops = tpu_ops.outfeed_dequeue_tuple(
-                  dtypes=tensor_dtypes,
-                  shapes=tensor_shapes,
-                  device_ordinal=ordinal)
-              if is_padded_index >= 0:
-                num_non_pad = tf.shape(
-                    dequeue_ops[is_padded_index])[0] - tf.reduce_sum(
-                        tf.cast(dequeue_ops[is_padded_index], tf.int32))
-                dequeue_ops = [
-                    tf.slice(k, [0] * k.shape.ndims,
-                             [num_non_pad] + [-1] * (k.shape.ndims - 1))
-                    for k in dequeue_ops
-                ]
-              for k, item in enumerate(dequeue_ops):
-                host_dequeue_ops[k].append(item)
-            for k in range(len(self.predict_output)):
-              self.dequeue_ops[k].append(tf.concat(host_dequeue_ops[k], axis=0))
-
-    self.sess = tf.Session(self.master, graph=self.graph, config=self.config)
-    for is_training in [True, False]:
-      if is_training or self.eval_steps > 0:
+            summary_writer = None
+
+        def infeed_thread_fn(thread_index):
+            # Wait for condition
+            """Build and infeed session.run calls in a background thread."""
+            for _ in range(self.max_train_iterations):
+                self.input_sess[True][thread_index].run(
+                    [self.enqueue_ops[True][thread_index]]
+                )
+                if self.eval_steps > 0:
+                    if not self.eval_dataset_repeats:
+                        self.input_sess[False][thread_index].run(
+                            self.dataset_initializer[False][thread_index]
+                        )
+                    self.input_sess[False][thread_index].run(
+                        [self.enqueue_ops[False][thread_index]]
+                    )
+
+        infeed_threads = []
         for i in range(self.num_input_graphs):
-          with self.input_graph[is_training][i].as_default():
-            self.input_sess[is_training][i] = tf.Session(
-                self.master,
-                graph=self.input_graph[is_training][i],
-                config=self.config)
-            self.input_sess[is_training][i].run(
-                self.dataset_initializer[is_training][i])
-    self.output_sess = tf.Session(
-        self.master, graph=output_graph, config=self.config)
-
-    with self.graph.as_default():
-      _ = tf.train.get_or_create_global_step()
-      if init_fn:
-        init_fn()
-      checkpoint_path = tf.train.latest_checkpoint(
-          FLAGS.model_dir) if FLAGS.model_dir else None
-      if FLAGS.restore_checkpoint and checkpoint_path:
-        tf.train.Saver().restore(self.sess, checkpoint_path)
-      else:
-        self.sess.run(tf.global_variables_initializer())
-        self.sess.run(tf.local_variables_initializer())
-      self.maybe_load_embedding_vars()
-      self.global_step = self.sess.run(tf.train.get_global_step(self.graph))
-
-    def train_eval_thread_fn(sess, train_eval_op):
-      sess.run([train_eval_op])
-
-    # Start the just in time compilation of the model function
-    self.train_eval_thread = threading.Thread(
-        target=train_eval_thread_fn, args=(self.sess, self.train_eval_op))
-    self.train_eval_thread.start()
-
-    # Sleep for JTC to finish
-    time.sleep(FLAGS.sleep_after_init)
-
-  def train_and_eval(self,
-                     eval_init_fn=None,
-                     eval_finish_fn=None,
-                     run_finish_fn=None):
-    """Run the Train steps on the TPU device."""
-    if FLAGS.enable_summary:
-      output_dir = os.path.join(FLAGS.model_dir, "eval")
-      tf.gfile.MakeDirs(output_dir)
-      summary_writer = tf.summary.FileWriter(output_dir)
-    else:
-      summary_writer = None
-
-    def infeed_thread_fn(thread_index):
-      # Wait for condition
-      """Build and infeed session.run calls in a background thread."""
-      for _ in range(self.max_train_iterations):
-        self.input_sess[True][thread_index].run(
-            [self.enqueue_ops[True][thread_index]])
+            thread = threading.Thread(target=infeed_thread_fn, args=([i]))
+            thread.start()
+            infeed_threads.append(thread)
+
+        global_step = self.global_step
+
         if self.eval_steps > 0:
-          if not self.eval_dataset_repeats:
-            self.input_sess[False][thread_index].run(
-                self.dataset_initializer[False][thread_index])
-          self.input_sess[False][thread_index].run(
-              [self.enqueue_ops[False][thread_index]])
-
-    infeed_threads = []
-    for i in range(self.num_input_graphs):
-      thread = threading.Thread(target=infeed_thread_fn, args=([i]))
-      thread.start()
-      infeed_threads.append(thread)
-
-    global_step = self.global_step
-
-    if self.eval_steps > 0:
-      enable_tracing = FLAGS.enable_profiling
-      if enable_tracing:
-        self.launch_profiler()
-
-      success = False
-      step_range = [global_step] if self.iterations_per_loop == 0 else range(
-          global_step, global_step + self.train_steps, self.iterations_per_loop)
-      for cur_step in step_range:
-        if not success and eval_init_fn:
-          eval_init_fn(cur_step)
-        eval_output = [[] for _ in self.dequeue_ops]
-        for _ in range(self.num_outfeeds):
-          for i, t in enumerate(self.output_sess.run(self.dequeue_ops)):
-            eval_output[i] += list(t)
-        eval_output = tf.nest.pack_sequence_as(self.predict_output, eval_output)
-        if eval_finish_fn and not success and eval_finish_fn(
-            cur_step, eval_output, summary_writer):
-          success = True
-        if enable_tracing and cur_step > self.train_steps // 4:
-          self.launch_profiler()
-          enable_tracing = False
-
-      if run_finish_fn:
-        run_finish_fn(success)
-
-    if FLAGS.save_checkpoint:
-      with self.graph.as_default():
-        self.global_step = self.sess.run(tf.train.get_global_step(self.graph))
-        checkpoint_path = FLAGS.model_dir + "/model.ckpt-%d" % self.global_step
-        tf.train.Saver().save(self.sess, checkpoint_path)
-        tf.logging.info("Checkpoint saved to %s", checkpoint_path)
-
-    if FLAGS.enable_summary:
-      summary_writer.close()
-
-    self.train_eval_thread.join()
-    for i in range(self.num_input_graphs):
-      infeed_threads[i].join()
-    self.sess.close()
+            enable_tracing = FLAGS.enable_profiling
+            if enable_tracing:
+                self.launch_profiler()
+
+            success = False
+            step_range = (
+                [global_step]
+                if self.iterations_per_loop == 0
+                else range(
+                    global_step,
+                    global_step + self.train_steps,
+                    self.iterations_per_loop,
+                )
+            )
+            for cur_step in step_range:
+                if not success and eval_init_fn:
+                    eval_init_fn(cur_step)
+                eval_output = [[] for _ in self.dequeue_ops]
+                for _ in range(self.num_outfeeds):
+                    for i, t in enumerate(
+                            self.output_sess.run(self.dequeue_ops)):
+                        eval_output[i] += list(t)
+                eval_output = tf.nest.pack_sequence_as(
+                    self.predict_output, eval_output)
+                if (
+                    eval_finish_fn
+                    and not success
+                    and eval_finish_fn(cur_step, eval_output, summary_writer)
+                ):
+                    success = True
+                if enable_tracing and cur_step > self.train_steps // 4:
+                    self.launch_profiler()
+                    enable_tracing = False
+
+            if run_finish_fn:
+                run_finish_fn(success)
+
+        if FLAGS.save_checkpoint:
+            with self.graph.as_default():
+                self.global_step = self.sess.run(
+                    tf.train.get_global_step(self.graph))
+                checkpoint_path = FLAGS.model_dir + "/model.ckpt-%d" % self.global_step
+                tf.train.Saver().save(self.sess, checkpoint_path)
+                tf.logging.info("Checkpoint saved to %s", checkpoint_path)
+
+        if FLAGS.enable_summary:
+            summary_writer.close()
+
+        self.train_eval_thread.join()
+        for i in range(self.num_input_graphs):
+            infeed_threads[i].join()
+        self.sess.close()
diff --git a/retired_benchmarks/recommendation/dlrm/tf/utils.py b/retired_benchmarks/recommendation/dlrm/tf/utils.py
index edcf2a79a6..e3b3d8283e 100644
--- a/retired_benchmarks/recommendation/dlrm/tf/utils.py
+++ b/retired_benchmarks/recommendation/dlrm/tf/utils.py
@@ -29,58 +29,68 @@
 
 
 def create_scalar_summary(name, simple_value):
-  return tf.Summary(
-      value=[tf.Summary.Value(tag=name, simple_value=simple_value)])
+    return tf.Summary(value=[tf.Summary.Value(
+        tag=name, simple_value=simple_value)])
 
 
 def train_loop_iters():
 
-  def _ceil(n, d):
-    return (n + d - 1) // d
+    def _ceil(n, d):
+        return (n + d - 1) // d
 
-  return _ceil(FLAGS.train_steps, FLAGS.steps_between_evals)
+    return _ceil(FLAGS.train_steps, FLAGS.steps_between_evals)
 
 
 def lr_fn(params, global_step):
-  """Calculates adjusted LR based on global step.
-
-  Linear warmup and polynomial decay.
-
-  Args:
-    params: Params dict for the model.
-    global_step: Variable representing the current step.
-
-  Returns:
-    New learning rate tensor (float32).
-  """
-  decay_exp = 2
-  base_learning_rate = params["learning_rate"]
-  global_step = tf.cast(global_step, tf.float32)
-  lr_warmup_steps = tf.constant(params["lr_warmup_steps"], tf.float32)
-  decay_steps_float = tf.constant(params["decay_steps"], tf.float32)
-  decay_start_step_float = tf.constant(params["decay_start_step"], tf.float32)
-  global_batch_size = params["batch_size"]
-  scaling_factor = global_batch_size / 2048.0
-  adjusted_lr = base_learning_rate * scaling_factor
-  adjusted_lr = tf.constant(adjusted_lr, tf.float32)
-  if not params["lr_warmup_steps"]:
-    return adjusted_lr
-
-  change_rate = adjusted_lr / lr_warmup_steps
-  warmup_lr = adjusted_lr - (lr_warmup_steps - global_step) * change_rate
-
-  steps_since_decay_start_float = global_step - decay_start_step_float
-  already_decayed_steps = tf.minimum(steps_since_decay_start_float,
-                                     decay_steps_float)
-  decay_lr = adjusted_lr * ((decay_steps_float - already_decayed_steps) /
-                            decay_steps_float)**decay_exp
-  decay_lr = tf.maximum(decay_lr, tf.constant(0.0000001))
-
-  is_warmup_step = tf.cast(global_step < lr_warmup_steps, tf.float32)
-  is_decay_step = tf.cast(global_step > decay_start_step_float, tf.float32)
-  is_middle_step = tf.cast(
-      tf.equal(is_warmup_step + is_decay_step, 0.0), tf.float32)
-
-  lr = (is_warmup_step * warmup_lr + is_middle_step * adjusted_lr +
-        is_decay_step * decay_lr)
-  return lr
+    """Calculates adjusted LR based on global step.
+
+    Linear warmup and polynomial decay.
+
+    Args:
+      params: Params dict for the model.
+      global_step: Variable representing the current step.
+
+    Returns:
+      New learning rate tensor (float32).
+    """
+    decay_exp = 2
+    base_learning_rate = params["learning_rate"]
+    global_step = tf.cast(global_step, tf.float32)
+    lr_warmup_steps = tf.constant(params["lr_warmup_steps"], tf.float32)
+    decay_steps_float = tf.constant(params["decay_steps"], tf.float32)
+    decay_start_step_float = tf.constant(
+        params["decay_start_step"], tf.float32)
+    global_batch_size = params["batch_size"]
+    scaling_factor = global_batch_size / 2048.0
+    adjusted_lr = base_learning_rate * scaling_factor
+    adjusted_lr = tf.constant(adjusted_lr, tf.float32)
+    if not params["lr_warmup_steps"]:
+        return adjusted_lr
+
+    change_rate = adjusted_lr / lr_warmup_steps
+    warmup_lr = adjusted_lr - (lr_warmup_steps - global_step) * change_rate
+
+    steps_since_decay_start_float = global_step - decay_start_step_float
+    already_decayed_steps = tf.minimum(
+        steps_since_decay_start_float, decay_steps_float)
+    decay_lr = (
+        adjusted_lr
+        * ((decay_steps_float - already_decayed_steps) / decay_steps_float) ** decay_exp
+    )
+    decay_lr = tf.maximum(decay_lr, tf.constant(0.0000001))
+
+    is_warmup_step = tf.cast(global_step < lr_warmup_steps, tf.float32)
+    is_decay_step = tf.cast(global_step > decay_start_step_float, tf.float32)
+    is_middle_step = tf.cast(
+        tf.equal(
+            is_warmup_step +
+            is_decay_step,
+            0.0),
+        tf.float32)
+
+    lr = (
+        is_warmup_step * warmup_lr
+        + is_middle_step * adjusted_lr
+        + is_decay_step * decay_lr
+    )
+    return lr
diff --git a/retired_benchmarks/speech_recognition/rnnt/QSL.py b/retired_benchmarks/speech_recognition/rnnt/QSL.py
index 9c0abe4e73..b65ada42a5 100644
--- a/retired_benchmarks/speech_recognition/rnnt/QSL.py
+++ b/retired_benchmarks/speech_recognition/rnnt/QSL.py
@@ -1,33 +1,39 @@
 import sys
 import os
-sys.path.insert(0, os.path.join(os.getcwd(), "pytorch"))
-
-from parts.manifest import Manifest
-from parts.segment import AudioSegment
 
-import numpy as np
+sys.path.insert(0, os.path.join(os.getcwd(), "pytorch"))
 
-import mlperf_loadgen as lg
+try:
+    import mlperf_loadgen as lg
+    import numpy as np
+    from parts.segment import AudioSegment
+    from parts.manifest import Manifest
+except BaseException:
+    raise Exception("Error importing local modules")
 
 
 class AudioQSL:
-    def __init__(self, dataset_dir, manifest_filepath, labels,
-                 sample_rate=16000, perf_count=None):
+    def __init__(
+        self, dataset_dir, manifest_filepath, labels, sample_rate=16000, perf_count=None
+    ):
         m_paths = [manifest_filepath]
-        self.manifest = Manifest(dataset_dir, m_paths, labels, len(labels),
-                                 normalize=True, max_duration=15.0)
+        self.manifest = Manifest(
+            dataset_dir, m_paths, labels, len(labels), normalize=True, max_duration=15.0
+        )
         self.sample_rate = sample_rate
         self.count = len(self.manifest)
         perf_count = self.count if perf_count is None else perf_count
         self.sample_id_to_sample = {}
-        self.qsl = lg.ConstructQSL(self.count, perf_count,
-                                   self.load_query_samples,
-                                   self.unload_query_samples)
+        self.qsl = lg.ConstructQSL(
+            self.count, perf_count, self.load_query_samples, self.unload_query_samples
+        )
         print(
             "Dataset loaded with {0:.2f} hours. Filtered {1:.2f} hours. Number of samples: {2}".format(
                 self.manifest.duration / 3600,
                 self.manifest.filtered_duration / 3600,
-                self.count))
+                self.count,
+            )
+        )
 
     def load_query_samples(self, sample_list):
         for sample_id in sample_list:
@@ -39,10 +45,13 @@ def unload_query_samples(self, sample_list):
 
     def _load_sample(self, index):
         sample = self.manifest[index]
-        segment = AudioSegment.from_file(sample['audio_filepath'][0],
-                                         target_sr=self.sample_rate)
+        segment = AudioSegment.from_file(
+            sample["audio_filepath"][0], target_sr=self.sample_rate
+        )
         waveform = segment.samples
-        assert isinstance(waveform, np.ndarray) and waveform.dtype == np.float32
+        assert isinstance(
+            waveform,
+            np.ndarray) and waveform.dtype == np.float32
         return waveform
 
     def __getitem__(self, index):
@@ -52,13 +61,16 @@ def __del__(self):
         lg.DestroyQSL(self.qsl)
         print("Finished destroying QSL.")
 
+
 # We have no problem fitting all data in memory, so we do that, in
 # order to speed up execution of the benchmark.
 class AudioQSLInMemory(AudioQSL):
-    def __init__(self, dataset_dir, manifest_filepath, labels,
-                 sample_rate=16000, perf_count=None):
-        super().__init__(dataset_dir, manifest_filepath, labels,
-                         sample_rate, perf_count)
+    def __init__(
+        self, dataset_dir, manifest_filepath, labels, sample_rate=16000, perf_count=None
+    ):
+        super().__init__(
+            dataset_dir, manifest_filepath, labels, sample_rate, perf_count
+        )
         super().load_query_samples(range(self.count))
 
     def load_query_samples(self, sample_list):
diff --git a/retired_benchmarks/speech_recognition/rnnt/accuracy_eval.py b/retired_benchmarks/speech_recognition/rnnt/accuracy_eval.py
index 4341900c53..c9bf81dfe6 100644
--- a/retired_benchmarks/speech_recognition/rnnt/accuracy_eval.py
+++ b/retired_benchmarks/speech_recognition/rnnt/accuracy_eval.py
@@ -8,44 +8,98 @@
 
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), "pytorch"))
 
-from helpers import process_evaluation_epoch, __gather_predictions
-from parts.manifest import Manifest
+try:
+    from parts.manifest import Manifest
+    from helpers import process_evaluation_epoch, __gather_predictions
+except ImportError:
+    raise Exception("Error importing local modules")
+
 
 dtype_map = {
-    "int8": 'b',
-    "int16": 'h',
-    "int32": 'l',
-    "int64": 'q',
+    "int8": "b",
+    "int16": "h",
+    "int32": "l",
+    "int64": "q",
 }
 
+
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--log_dir", required=True)
     parser.add_argument("--dataset_dir", required=True)
     parser.add_argument("--manifest", required=True)
-    parser.add_argument("--output_dtype", default="int64", choices=dtype_map.keys(), help="Output data type")
+    parser.add_argument(
+        "--output_dtype",
+        default="int64",
+        choices=dtype_map.keys(),
+        help="Output data type",
+    )
     args = parser.parse_args()
     return args
 
+
 def main():
     args = get_args()
-    labels = [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
-    manifest = Manifest(args.dataset_dir, [args.manifest], labels, len(labels), normalize=True, max_duration=15.0)
+    labels = [
+        " ",
+        "a",
+        "b",
+        "c",
+        "d",
+        "e",
+        "f",
+        "g",
+        "h",
+        "i",
+        "j",
+        "k",
+        "l",
+        "m",
+        "n",
+        "o",
+        "p",
+        "q",
+        "r",
+        "s",
+        "t",
+        "u",
+        "v",
+        "w",
+        "x",
+        "y",
+        "z",
+        "'",
+    ]
+    manifest = Manifest(
+        args.dataset_dir,
+        [args.manifest],
+        labels,
+        len(labels),
+        normalize=True,
+        max_duration=15.0,
+    )
     with open(os.path.join(args.log_dir, "mlperf_log_accuracy.json")) as fh:
         results = json.load(fh)
     hypotheses = []
     references = []
     for result in results:
-        hypotheses.append(array.array(dtype_map[args.output_dtype], bytes.fromhex(result["data"])).tolist())
+        hypotheses.append(
+            array.array(
+                dtype_map[args.output_dtype], bytes.fromhex(result["data"])
+            ).tolist()
+        )
         references.append(manifest[result["qsl_idx"]]["transcript"])
 
     references = __gather_predictions([references], labels=labels)
     hypotheses = __gather_predictions([hypotheses], labels=labels)
 
-    d = dict(predictions=hypotheses,
-             transcripts=references)
+    d = dict(predictions=hypotheses, transcripts=references)
     wer = process_evaluation_epoch(d)
-    print("Word Error Rate: {:}%, accuracy={:}%".format(wer * 100, (1 - wer) * 100))
+    print(
+        "Word Error Rate: {:}%, accuracy={:}%".format(
+            wer * 100,
+         (1 - wer) * 100))
+
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/dataset.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/dataset.py
index 7b9036f1c5..3c89ca6b39 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/dataset.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/dataset.py
@@ -30,62 +30,72 @@ def seq_collate_fn(batch):
     Returns
     batches of tensors
     """
-    audio_lengths = torch.LongTensor([sample.waveform.size(0)
-                                      for sample in batch])
-    transcript_lengths = torch.LongTensor([sample.transcript.size(0)
-                                           for sample in batch])
+    audio_lengths = torch.LongTensor(
+        [sample.waveform.size(0) for sample in batch])
+    transcript_lengths = torch.LongTensor(
+        [sample.transcript.size(0) for sample in batch]
+    )
     permute_indices = torch.argsort(audio_lengths, descending=True)
 
     audio_lengths = audio_lengths[permute_indices]
     transcript_lengths = transcript_lengths[permute_indices]
     padded_audio_signals = torch.nn.utils.rnn.pad_sequence(
-        [batch[i].waveform for i in permute_indices],
-        batch_first=True
+        [batch[i].waveform for i in permute_indices], batch_first=True
+    )
+    transcript_list = [batch[i].transcript for i in permute_indices]
+    packed_transcripts = torch.nn.utils.rnn.pack_sequence(
+        transcript_list, enforce_sorted=False
     )
-    transcript_list = [batch[i].transcript
-                       for i in permute_indices]
-    packed_transcripts = torch.nn.utils.rnn.pack_sequence(transcript_list,
-                                                          enforce_sorted=False)
 
     # TODO: Don't I need to stop grad at some point now?
-    return (padded_audio_signals, audio_lengths, transcript_list,
-            packed_transcripts, transcript_lengths)
+    return (
+        padded_audio_signals,
+        audio_lengths,
+        transcript_list,
+        packed_transcripts,
+        transcript_lengths,
+    )
 
 
 class AudioToTextDataLayer:
-    """Data layer with data loader
-    """
+    """Data layer with data loader"""
 
     def __init__(self, **kwargs):
-        featurizer_config = kwargs['featurizer_config']
-        pad_to_max = kwargs.get('pad_to_max', False)
-        perturb_config = kwargs.get('perturb_config', None)
-        manifest_filepath = kwargs['manifest_filepath']
-        dataset_dir = kwargs['dataset_dir']
-        labels = kwargs['labels']
-        batch_size = kwargs['batch_size']
-        drop_last = kwargs.get('drop_last', False)
-        shuffle = kwargs.get('shuffle', True)
-        min_duration = featurizer_config.get('min_duration', 0.1)
-        max_duration = featurizer_config.get('max_duration', None)
-        normalize_transcripts = kwargs.get('normalize_transcripts', True)
-        trim_silence = kwargs.get('trim_silence', False)
-        sampler_type = kwargs.get('sampler', 'default')
-        speed_perturbation = featurizer_config.get('speed_perturbation', False)
-        sort_by_duration = sampler_type == 'bucket'
+        featurizer_config = kwargs["featurizer_config"]
+        pad_to_max = kwargs.get("pad_to_max", False)
+        perturb_config = kwargs.get("perturb_config", None)
+        manifest_filepath = kwargs["manifest_filepath"]
+        dataset_dir = kwargs["dataset_dir"]
+        labels = kwargs["labels"]
+        batch_size = kwargs["batch_size"]
+        drop_last = kwargs.get("drop_last", False)
+        shuffle = kwargs.get("shuffle", True)
+        min_duration = featurizer_config.get("min_duration", 0.1)
+        max_duration = featurizer_config.get("max_duration", None)
+        normalize_transcripts = kwargs.get("normalize_transcripts", True)
+        trim_silence = kwargs.get("trim_silence", False)
+        sampler_type = kwargs.get("sampler", "default")
+        speed_perturbation = featurizer_config.get("speed_perturbation", False)
+        sort_by_duration = sampler_type == "bucket"
         self._featurizer = WaveformFeaturizer.from_config(
-            featurizer_config, perturbation_configs=perturb_config)
+            featurizer_config, perturbation_configs=perturb_config
+        )
         self._dataset = AudioDataset(
             dataset_dir=dataset_dir,
             manifest_filepath=manifest_filepath,
-            labels=labels, blank_index=len(labels),
+            labels=labels,
+            blank_index=len(labels),
             sort_by_duration=sort_by_duration,
             pad_to_max=pad_to_max,
-            featurizer=self._featurizer, max_duration=max_duration,
-            min_duration=min_duration, normalize=normalize_transcripts,
-            trim=trim_silence, speed_perturbation=speed_perturbation)
+            featurizer=self._featurizer,
+            max_duration=max_duration,
+            min_duration=min_duration,
+            normalize=normalize_transcripts,
+            trim=trim_silence,
+            speed_perturbation=speed_perturbation,
+        )
 
-        print('sort_by_duration', sort_by_duration)
+        print("sort_by_duration", sort_by_duration)
 
         self._dataloader = torch.utils.data.DataLoader(
             dataset=self._dataset,
@@ -95,7 +105,7 @@ def __init__(self, **kwargs):
             shuffle=shuffle,
             num_workers=0,
             pin_memory=True,
-            sampler=None
+            sampler=None,
         )
 
     def __len__(self):
@@ -107,9 +117,22 @@ def data_iterator(self):
 
 
 class AudioDataset(Dataset):
-    def __init__(self, dataset_dir, manifest_filepath, labels, featurizer, max_duration=None, pad_to_max=False,
-                 min_duration=None, blank_index=0, max_utts=0, normalize=True, sort_by_duration=False,
-                 trim=False, speed_perturbation=False):
+    def __init__(
+        self,
+        dataset_dir,
+        manifest_filepath,
+        labels,
+        featurizer,
+        max_duration=None,
+        pad_to_max=False,
+        min_duration=None,
+        blank_index=0,
+        max_utts=0,
+        normalize=True,
+        sort_by_duration=False,
+        trim=False,
+        speed_perturbation=False,
+    ):
         """Dataset that loads tensors via a json file containing paths to audio files, transcripts, and durations
         (in seconds). Each entry is a different audio sample.
         Args:
@@ -128,32 +151,44 @@ def __init__(self, dataset_dir, manifest_filepath, labels, featurizer, max_durat
             speed_perturbation: specify if using data contains speed perburbation
         """
         m_paths = [manifest_filepath]
-        self.manifest = Manifest(dataset_dir, m_paths, labels, blank_index, pad_to_max=pad_to_max,
-                                 max_duration=max_duration,
-                                 sort_by_duration=sort_by_duration,
-                                 min_duration=min_duration, max_utts=max_utts,
-                                 normalize=normalize, speed_perturbation=speed_perturbation)
+        self.manifest = Manifest(
+            dataset_dir,
+            m_paths,
+            labels,
+            blank_index,
+            pad_to_max=pad_to_max,
+            max_duration=max_duration,
+            sort_by_duration=sort_by_duration,
+            min_duration=min_duration,
+            max_utts=max_utts,
+            normalize=normalize,
+            speed_perturbation=speed_perturbation,
+        )
         self.featurizer = featurizer
         self.blank_index = blank_index
         self.trim = trim
         print(
             "Dataset loaded with {0:.2f} hours. Filtered {1:.2f} hours.".format(
-                self.manifest.duration / 3600,
-                self.manifest.filtered_duration / 3600))
+                self.manifest.duration / 3600, self.manifest.filtered_duration / 3600
+            )
+        )
 
     def __getitem__(self, index):
         sample = self.manifest[index]
-        rn_indx = np.random.randint(len(sample['audio_filepath']))
-        duration = sample['audio_duration'][rn_indx] if 'audio_duration' in sample else 0
-        offset = sample['offset'] if 'offset' in sample else 0
-        features = self.featurizer.process(sample['audio_filepath'][rn_indx],
-                                           offset=offset, duration=duration,
-                                           trim=self.trim)
-
-        AudioSample = namedtuple('AudioSample', ['waveform',
-                                                 'transcript'])
-        return AudioSample(features,
-                           torch.LongTensor(sample["transcript"]))
+        rn_indx = np.random.randint(len(sample["audio_filepath"]))
+        duration = (
+            sample["audio_duration"][rn_indx] if "audio_duration" in sample else 0
+        )
+        offset = sample["offset"] if "offset" in sample else 0
+        features = self.featurizer.process(
+            sample["audio_filepath"][rn_indx],
+            offset=offset,
+            duration=duration,
+            trim=self.trim,
+        )
+
+        AudioSample = namedtuple("AudioSample", ["waveform", "transcript"])
+        return AudioSample(features, torch.LongTensor(sample["transcript"]))
 
     def __len__(self):
         return len(self.manifest)
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/decoders.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/decoders.py
index 56745d7ecd..7a6239da5f 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/decoders.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/decoders.py
@@ -42,12 +42,19 @@ def __init__(self, blank_index, model, max_symbols_per_step=30):
         self._model = model
         self._blank_id = blank_index
         self._SOS = -1
-        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+        self.dev = (
+            torch.device("cuda:0")
+            if torch.cuda.is_available()
+            and os.environ.get("USE_GPU", "").lower() not in ["no", "false"]
+            else torch.device("cpu")
+        )
         assert max_symbols_per_step > 0
         self._max_symbols_per_step = max_symbols_per_step
 
     @torch.jit.export
-    def forward(self, x: torch.Tensor, out_lens: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, List[List[int]]]:
+    def forward(
+        self, x: torch.Tensor, out_lens: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[List[int]]]:
         """Returns a list of sentences given an input batch.
 
         Args:
@@ -73,7 +80,8 @@ def forward(self, x: torch.Tensor, out_lens: torch.Tensor) -> Tuple[torch.Tensor
 
         return logits, logits_lens, output
 
-    def _greedy_decode(self, x: torch.Tensor, out_len: torch.Tensor) -> List[int]:
+    def _greedy_decode(self, x: torch.Tensor,
+                       out_len: torch.Tensor) -> List[int]:
         hidden: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
         label: List[int] = []
         for time_idx in range(int(out_len.item())):
@@ -84,9 +92,7 @@ def _greedy_decode(self, x: torch.Tensor, out_len: torch.Tensor) -> List[int]:
 
             while not_blank and symbols_added < self._max_symbols_per_step:
                 g, hidden_prime = self._pred_step(
-                    self._get_last_symb(label),
-                    hidden
-                )
+                    self._get_last_symb(label), hidden)
                 logp = self._joint_step(f, g, log_normalize=False)[0, :]
 
                 # get index k, of max prob
@@ -102,7 +108,9 @@ def _greedy_decode(self, x: torch.Tensor, out_len: torch.Tensor) -> List[int]:
 
         return label
 
-    def _pred_step(self, label: int, hidden: Optional[Tuple[torch.Tensor, torch.Tensor]]) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+    def _pred_step(
+        self, label: int, hidden: Optional[Tuple[torch.Tensor, torch.Tensor]]
+    ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         if label == self._SOS:
             return self._model.prediction(None, hidden)
         if label > self._blank_id:
@@ -110,7 +118,9 @@ def _pred_step(self, label: int, hidden: Optional[Tuple[torch.Tensor, torch.Tens
         label = torch.tensor([[label]], dtype=torch.int64)
         return self._model.prediction(label, hidden)
 
-    def _joint_step(self, enc: torch.Tensor, pred: torch.Tensor, log_normalize: bool=False) -> torch.Tensor:
+    def _joint_step(
+        self, enc: torch.Tensor, pred: torch.Tensor, log_normalize: bool = False
+    ) -> torch.Tensor:
         logits = self._model.joint(enc, pred)[:, 0, 0, :]
         if not log_normalize:
             return logits
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/helpers.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/helpers.py
index cfe3b66f3c..4427ad094d 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/helpers.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/helpers.py
@@ -20,6 +20,7 @@
 class Optimization(Enum):
     """Various levels of Optimization.
     WARNING: This might have effect on model accuracy."""
+
     nothing = 0
     mxprO0 = 1
     mxprO1 = 2
@@ -27,10 +28,12 @@ class Optimization(Enum):
     mxprO3 = 4
 
 
-AmpOptimizations = {Optimization.mxprO0: "O0",
-                    Optimization.mxprO1: "O1",
-                    Optimization.mxprO2: "O2",
-                    Optimization.mxprO3: "O3"}
+AmpOptimizations = {
+    Optimization.mxprO0: "O0",
+    Optimization.mxprO1: "O1",
+    Optimization.mxprO2: "O2",
+    Optimization.mxprO3: "O3",
+}
 
 
 def add_blank_label(labels):
@@ -53,7 +56,7 @@ def __rnnt_decoder_predictions_tensor(tensor, labels):
     labels_map = dict([(i, labels[i]) for i in range(len(labels))])
     # iterate over batch
     for ind in range(len(tensor)):
-        hypothesis = ''.join([labels_map[c] for c in tensor[ind]])
+        hypothesis = "".join([labels_map[c] for c in tensor[ind]])
         hypotheses.append(hypothesis)
     return hypotheses
 
@@ -65,13 +68,14 @@ def __gather_predictions(predictions_list: list, labels: list) -> list:
     return results
 
 
-def __gather_transcripts(transcript_list: list, transcript_len_list: list,
-                         labels: list) -> list:
+def __gather_transcripts(
+    transcript_list: list, transcript_len_list: list, labels: list
+) -> list:
     results = []
     labels_map = dict([(i, labels[i]) for i in range(len(labels))])
     for i, t in enumerate(transcript_list):
         target = t.numpy().tolist()
-        reference = ''.join([labels_map[c] for c in target])
+        reference = "".join([labels_map[c] for c in target])
         results.append(reference)
     return results
 
@@ -85,17 +89,17 @@ def process_evaluation_batch(tensors: dict, global_vars: dict, labels: list):
         labels: A list of labels
     """
     for kv, v in tensors.items():
-        if kv.startswith('predictions'):
-            global_vars['predictions'] += __gather_predictions(
+        if kv.startswith("predictions"):
+            global_vars["predictions"] += __gather_predictions(
                 v, labels=labels)
-        elif kv.startswith('transcript_length'):
+        elif kv.startswith("transcript_length"):
             transcript_len_list = v
-        elif kv.startswith('transcript'):
+        elif kv.startswith("transcript"):
             transcript_list = v
 
-    global_vars['transcripts'] += __gather_transcripts(transcript_list,
-                                                       transcript_len_list,
-                                                       labels=labels)
+    global_vars["transcripts"] += __gather_transcripts(
+        transcript_list, transcript_len_list, labels=labels
+    )
 
 
 def process_evaluation_epoch(global_vars: dict, tag=None):
@@ -107,17 +111,18 @@ def process_evaluation_epoch(global_vars: dict, tag=None):
         wer: final word error rate
         loss: final loss
     """
-    hypotheses = global_vars['predictions']
-    references = global_vars['transcripts']
+    hypotheses = global_vars["predictions"]
+    references = global_vars["transcripts"]
 
     wer, scores, num_words = word_error_rate(
-        hypotheses=hypotheses, references=references)
+        hypotheses=hypotheses, references=references
+    )
     return wer
 
 
 def print_dict(d):
     maxLen = max([len(ii) for ii in d.keys()])
-    fmtString = '\t%' + str(maxLen) + 's : %s'
-    print('Arguments:')
+    fmtString = "\t%" + str(maxLen) + "s : %s"
+    print("Arguments:")
     for keyPair in sorted(d.items()):
         print(fmtString % keyPair)
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/metrics.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/metrics.py
index 5426e37237..2fb1ad1834 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/metrics.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/metrics.py
@@ -16,8 +16,7 @@
 
 
 def __levenshtein(a: List, b: List) -> int:
-    """Calculates the Levenshtein distance between a and b.
-    """
+    """Calculates the Levenshtein distance between a and b."""
     n, m = len(a), len(b)
     if n > m:
         # Make sure n <= m, to use O(min(n,m)) space
@@ -52,9 +51,12 @@ def word_error_rate(hypotheses: List[str], references: List[str]) -> float:
     scores = 0
     words = 0
     if len(hypotheses) != len(references):
-        raise ValueError("In word error rate calculation, hypotheses and reference"
-                         " lists must have the same number of elements. But I got:"
-                         "{0} and {1} correspondingly".format(len(hypotheses), len(references)))
+        raise ValueError(
+            "In word error rate calculation, hypotheses and reference"
+            " lists must have the same number of elements. But I got:"
+            "{0} and {1} correspondingly".format(
+                len(hypotheses), len(references))
+        )
     for h, r in zip(hypotheses, references):
         h_list = h.split()
         r_list = r.split()
@@ -63,5 +65,5 @@ def word_error_rate(hypotheses: List[str], references: List[str]) -> float:
     if words != 0:
         wer = (1.0 * scores) / words
     else:
-        wer = float('inf')
+        wer = float("inf")
     return wer, scores, words
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/model_separable_rnnt.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/model_separable_rnnt.py
index 39e181358a..638d115a22 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/model_separable_rnnt.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/model_separable_rnnt.py
@@ -16,10 +16,11 @@ def __init__(self, rnnt=None, num_classes=1, **kwargs):
             feat_config = kwargs.get("feature_config")
             # This may be useful in the future, for MLPerf
             # configuration.
-            in_features = feat_config['features'] * \
+            in_features = feat_config["features"] * \
                 feat_config.get("frame_splicing", 1)
 
-        self.encoder = Encoder(in_features,
+        self.encoder = Encoder(
+            in_features,
             rnnt["encoder_n_hidden"],
             rnnt["encoder_pre_rnn_layers"],
             rnnt["encoder_post_rnn_layers"],
@@ -48,17 +49,32 @@ def __init__(self, rnnt=None, num_classes=1, **kwargs):
             rnnt["dropout"],
         )
 
-    def forward(self, x_padded: torch.Tensor, x_lens: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    def forward(
+        self, x_padded: torch.Tensor, x_lens: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         return self.encoder(x_padded, x_lens)
-        
+
 
 class Encoder(torch.nn.Module):
-    def __init__(self, in_features, encoder_n_hidden,
-                 encoder_pre_rnn_layers, encoder_post_rnn_layers,
-                 forget_gate_bias, norm, rnn_type, encoder_stack_time_factor,
-                 dropout):
+    def __init__(
+        self,
+        in_features,
+        encoder_n_hidden,
+        encoder_pre_rnn_layers,
+        encoder_post_rnn_layers,
+        forget_gate_bias,
+        norm,
+        rnn_type,
+        encoder_stack_time_factor,
+        dropout,
+    ):
         super().__init__()
-        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+        self.dev = (
+            torch.device("cuda:0")
+            if torch.cuda.is_available()
+            and os.environ.get("USE_GPU", "").lower() not in ["no", "false"]
+            else torch.device("cpu")
+        )
 
         self.pre_rnn = rnn(
             rnn=rnn_type,
@@ -81,7 +97,9 @@ def __init__(self, in_features, encoder_n_hidden,
             dropout=dropout,
         )
 
-    def forward(self, x_padded: torch.Tensor, x_lens: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    def forward(
+        self, x_padded: torch.Tensor, x_lens: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         x_padded, _ = self.pre_rnn(x_padded.to(self.dev), None)
         x_padded, x_lens = self.stack_time(x_padded, x_lens)
         # (T, B, H)
@@ -90,11 +108,25 @@ def forward(self, x_padded: torch.Tensor, x_lens: torch.Tensor) -> Tuple[torch.T
         x_padded = x_padded.transpose(0, 1)
         return x_padded, x_lens
 
+
 class Prediction(torch.nn.Module):
-    def __init__(self, vocab_size, n_hidden, pred_rnn_layers,
-                 forget_gate_bias, norm, rnn_type, dropout):
+    def __init__(
+        self,
+        vocab_size,
+        n_hidden,
+        pred_rnn_layers,
+        forget_gate_bias,
+        norm,
+        rnn_type,
+        dropout,
+    ):
         super().__init__()
-        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+        self.dev = (
+            torch.device("cuda:0")
+            if torch.cuda.is_available()
+            and os.environ.get("USE_GPU", "").lower() not in ["no", "false"]
+            else torch.device("cpu")
+        )
 
         self.embed = torch.nn.Embedding(vocab_size - 1, n_hidden)
         self.n_hidden = n_hidden
@@ -108,8 +140,11 @@ def __init__(self, vocab_size, n_hidden, pred_rnn_layers,
             dropout=dropout,
         )
 
-    def forward(self, y: Optional[torch.Tensor],
-                state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+    def forward(
+        self,
+        y: Optional[torch.Tensor],
+        state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         """
         B - batch size
         U - label length
@@ -133,7 +168,9 @@ def forward(self, y: Optional[torch.Tensor],
             assert state is None
             # Hacky, no way to determine this right now!
             B = 1
-            y = torch.zeros((B, 1, self.n_hidden), dtype=torch.float32).to(self.dev)
+            y = torch.zeros(
+                (B, 1, self.n_hidden), dtype=torch.float32).to(
+                self.dev)
         else:
             y = self.embed(y.to(self.dev)).to(self.dev)
 
@@ -151,22 +188,35 @@ def forward(self, y: Optional[torch.Tensor],
         # del y, state
         return g, hid
 
+
 class Joint(torch.nn.Module):
-    def __init__(self, vocab_size, pred_n_hidden, enc_n_hidden,
-                 joint_n_hidden, dropout):
+    def __init__(
+        self, vocab_size, pred_n_hidden, enc_n_hidden, joint_n_hidden, dropout
+    ):
         super().__init__()
-        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
-
-        layers = [
-            torch.nn.Linear(pred_n_hidden + enc_n_hidden, joint_n_hidden),
-            torch.nn.ReLU(),
-        ] + ([torch.nn.Dropout(p=dropout), ] if dropout else []) + [
-            torch.nn.Linear(joint_n_hidden, vocab_size)
-        ]
-        self.net = torch.nn.Sequential(
-            *layers
+        self.dev = (
+            torch.device("cuda:0")
+            if torch.cuda.is_available()
+            and os.environ.get("USE_GPU", "").lower() not in ["no", "false"]
+            else torch.device("cpu")
         )
 
+        layers = (
+            [
+                torch.nn.Linear(pred_n_hidden + enc_n_hidden, joint_n_hidden),
+                torch.nn.ReLU(),
+            ]
+            + (
+                [
+                    torch.nn.Dropout(p=dropout),
+                ]
+                if dropout
+                else []
+            )
+            + [torch.nn.Linear(joint_n_hidden, vocab_size)]
+        )
+        self.net = torch.nn.Sequential(*layers)
+
     def forward(self, f: torch.Tensor, g: torch.Tensor):
         """
         f should be shape (B, T, H)
@@ -179,17 +229,18 @@ def forward(self, f: torch.Tensor, g: torch.Tensor):
         B, T, H = f.shape
         B, U_, H2 = g.shape
 
-        f = f.unsqueeze(dim=2).to(self.dev)   # (B, T, 1, H)
+        f = f.unsqueeze(dim=2).to(self.dev)  # (B, T, 1, H)
         f = f.expand((B, T, U_, H))
 
-        g = g.unsqueeze(dim=1).to(self.dev)   # (B, 1, U + 1, H)
+        g = g.unsqueeze(dim=1).to(self.dev)  # (B, 1, U + 1, H)
         g = g.expand((B, T, U_, H2))
 
-        inp = torch.cat([f, g], dim=3)   # (B, T, U, 2H)
+        inp = torch.cat([f, g], dim=3)  # (B, T, U, 2H)
         res = self.net(inp)
         # del f, g, inp
         return res
 
+
 def label_collate(labels):
     """Collates the label inputs for the rnn-t prediction network.
 
@@ -206,15 +257,14 @@ def label_collate(labels):
         return labels.type(torch.int64)
     if not isinstance(labels, (list, tuple)):
         raise ValueError(
-            f"`labels` should be a list or tensor not {type(labels)}"
-        )
+            f"`labels` should be a list or tensor not {type(labels)}")
 
     batch_size = len(labels)
     max_len = max(len(l) for l in labels)
 
     cat_labels = np.full((batch_size, max_len), fill_value=0.0, dtype=np.int32)
     for e, l in enumerate(labels):
-        cat_labels[e, :len(l)] = l
+        cat_labels[e, : len(l)] = l
     labels = torch.LongTensor(cat_labels)
 
     return labels
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/features.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/features.py
index 5a4decd615..109509e386 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/features.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/features.py
@@ -27,11 +27,14 @@ def __init__(self, input_cfg):
         self.cfg = input_cfg
 
     def process(self, file_path, offset=0, duration=0, trim=False):
-        audio = AudioSegment.from_file(file_path,
-                                       target_sr=self.cfg['sample_rate'],
-                                       int_values=self.cfg.get(
-                                           'int_values', False),
-                                       offset=offset, duration=duration, trim=trim)
+        audio = AudioSegment.from_file(
+            file_path,
+            target_sr=self.cfg["sample_rate"],
+            int_values=self.cfg.get("int_values", False),
+            offset=offset,
+            duration=duration,
+            trim=trim,
+        )
         return self.process_segment(audio)
 
     def process_segment(self, audio_segment):
@@ -47,13 +50,15 @@ def from_config(cls, input_config, perturbation_configs=None):
 
 def normalize_batch(x, seq_len, normalize_type):
     if normalize_type == "per_feature":
-        x_mean = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype,
-                             device=x.device)
-        x_std = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype,
-                            device=x.device)
+        x_mean = torch.zeros(
+            (seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device
+        )
+        x_std = torch.zeros(
+            (seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device
+        )
         for i in range(x.shape[0]):
-            x_mean[i, :] = x[i, :, :seq_len[i]].mean(dim=1)
-            x_std[i, :] = x[i, :, :seq_len[i]].std(dim=1)
+            x_mean[i, :] = x[i, :, : seq_len[i]].mean(dim=1)
+            x_std[i, :] = x[i, :, : seq_len[i]].std(dim=1)
         # make sure x_std is not zero
         x_std += constant
         return (x - x_mean.unsqueeze(2)) / x_std.unsqueeze(2)
@@ -61,8 +66,8 @@ def normalize_batch(x, seq_len, normalize_type):
         x_mean = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device)
         x_std = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device)
         for i in range(x.shape[0]):
-            x_mean[i] = x[i, :, :seq_len[i].item()].mean()
-            x_std[i] = x[i, :, :seq_len[i].item()].std()
+            x_mean[i] = x[i, :, : seq_len[i].item()].mean()
+            x_std[i] = x[i, :, : seq_len[i].item()].std()
         # make sure x_std is not zero
         x_std += constant
         return (x - x_mean.view(-1, 1, 1)) / x_std.view(-1, 1, 1)
@@ -71,7 +76,7 @@ def normalize_batch(x, seq_len, normalize_type):
 
 
 def splice_frames(x, frame_splicing):
-    """ Stacks frames together across feature dim
+    """Stacks frames together across feature dim
 
     input is batch_size, feature_dim, num_frames
     output is batch_size, feature_dim*frame_splicing, num_frames
@@ -86,23 +91,34 @@ def splice_frames(x, frame_splicing):
 
 
 class FilterbankFeatures(nn.Module):
-    def __init__(self, sample_rate=8000, window_size=0.02, window_stride=0.01,
-                 window="hamming", normalize="per_feature", n_fft=None,
-                 preemph=0.97,
-                 nfilt=64, lowfreq=0, highfreq=None, log=True, dither=constant,
-                 pad_to=8,
-                 max_duration=16.7,
-                 frame_splicing=1):
+    def __init__(
+        self,
+        sample_rate=8000,
+        window_size=0.02,
+        window_stride=0.01,
+        window="hamming",
+        normalize="per_feature",
+        n_fft=None,
+        preemph=0.97,
+        nfilt=64,
+        lowfreq=0,
+        highfreq=None,
+        log=True,
+        dither=constant,
+        pad_to=8,
+        max_duration=16.7,
+        frame_splicing=1,
+    ):
         super(FilterbankFeatures, self).__init__()
-#        print("PADDING: {}".format(pad_to))
+        #        print("PADDING: {}".format(pad_to))
 
         torch_windows = {
-                'hann': torch.hann_window,
-                'hamming': torch.hamming_window,
-                'blackman': torch.blackman_window,
-                'bartlett': torch.bartlett_window,
-                'none': None,
-                }
+            "hann": torch.hann_window,
+            "hamming": torch.hamming_window,
+            "blackman": torch.blackman_window,
+            "bartlett": torch.bartlett_window,
+            "none": None,
+        }
 
         self.win_length = int(sample_rate * window_size)  # frame size
         self.hop_length = int(sample_rate * window_stride)
@@ -116,23 +132,33 @@ def __init__(self, sample_rate=8000, window_size=0.02, window_stride=0.01,
         self.preemph = preemph
         self.pad_to = pad_to
         # For now, always enable this.
-        # See https://docs.google.com/presentation/d/1IVC3J-pHB-ipJpKsJox_SqmDHYdkIaoCXTbKmJmV2-I/edit?usp=sharing for elaboration
+        # See
+        # https://docs.google.com/presentation/d/1IVC3J-pHB-ipJpKsJox_SqmDHYdkIaoCXTbKmJmV2-I/edit?usp=sharing
+        # for elaboration
         self.use_deterministic_dithering = True
         highfreq = highfreq or sample_rate / 2
         window_fn = torch_windows.get(window, None)
-        window_tensor = window_fn(self.win_length,
-                                  periodic=False) if window_fn else None
+        window_tensor = (
+            window_fn(self.win_length, periodic=False) if window_fn else None
+        )
         filterbanks = torch.tensor(
-                librosa.filters.mel(sr=sample_rate, n_fft=self.n_fft, n_mels=nfilt, fmin=lowfreq,
-                                    fmax=highfreq), dtype=torch.float).unsqueeze(0)
+            librosa.filters.mel(
+                sr=sample_rate,
+                n_fft=self.n_fft,
+                n_mels=nfilt,
+                fmin=lowfreq,
+                fmax=highfreq,
+            ),
+            dtype=torch.float,
+        ).unsqueeze(0)
         # self.fb = filterbanks
         # self.window = window_tensor
         self.register_buffer("fb", filterbanks)
         self.register_buffer("window", window_tensor)
         # Calculate maximum sequence length (# frames)
         max_length = 1 + math.ceil(
-                (max_duration * sample_rate - self.win_length) / self.hop_length
-                )
+            (max_duration * sample_rate - self.win_length) / self.hop_length
+        )
         max_pad = 16 - (max_length % 16)
         self.max_length = max_length + max_pad
 
@@ -156,20 +182,27 @@ def forward(self, inp: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
         # do preemphasis
         # Ideally, we would mask immediately after this... Ugh :(
         if self.preemph is not None:
-            x = torch.cat((x[:, 0].unsqueeze(1), x[:, 1:] - self.preemph * x[:, :-1]),
-                          dim=1)
+            x = torch.cat(
+                (x[:, 0].unsqueeze(1), x[:, 1:] - self.preemph * x[:, :-1]), dim=1
+            )
 
         # do stft
-        x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length,
-                       win_length=self.win_length,
-                       center=True, window=self.window.to(dtype=torch.float), return_complex = True)
+        x = torch.stft(
+            x,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            win_length=self.win_length,
+            center=True,
+            window=self.window.to(dtype=torch.float),
+            return_complex=True,
+        )
         x = torch.view_as_real(x)
 
         # get power spectrum
         x = x.pow(2).sum(-1)
 
         if self.dither > 0 and self.use_deterministic_dithering:
-            x = x + self.dither ** 2
+            x = x + self.dither**2
         # dot with filterbank energies
         x = torch.matmul(self.fb.to(x.dtype), x)
 
@@ -184,18 +217,20 @@ def forward(self, inp: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
                 tmp = torch.zeros_like(x)
                 tmp[:, :, :-n] = x[:, :, n:]
                 seq.append(tmp)
-            x = torch.cat(seq, dim=1)[:, :, ::self.frame_splicing]
+            x = torch.cat(seq, dim=1)[:, :, :: self.frame_splicing]
 
         # normalize if required
         constant = 1e-5
         if self.normalize == "per_feature":
-            x_mean = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype,
-                                 device=x.device)
-            x_std = torch.zeros((seq_len.shape[0], x.shape[1]), dtype=x.dtype,
-                                device=x.device)
+            x_mean = torch.zeros(
+                (seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device
+            )
+            x_std = torch.zeros(
+                (seq_len.shape[0], x.shape[1]), dtype=x.dtype, device=x.device
+            )
             for i in range(x.shape[0]):
-                x_mean[i, :] = x[i, :, :seq_len[i]].mean(dim=1)
-                x_std[i, :] = x[i, :, :seq_len[i]].std(dim=1)
+                x_mean[i, :] = x[i, :, : seq_len[i]].mean(dim=1)
+                x_std[i, :] = x[i, :, : seq_len[i]].std(dim=1)
                 # make sure x_std is not zero
                 x_std += constant
             x = (x - x_mean.unsqueeze(2)) / x_std.unsqueeze(2)
@@ -203,8 +238,8 @@ def forward(self, inp: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
             x_mean = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device)
             x_std = torch.zeros(seq_len.shape, dtype=x.dtype, device=x.device)
             for i in range(x.shape[0]):
-                x_mean[i] = x[i, :, :seq_len[i].item()].mean()
-                x_std[i] = x[i, :, :seq_len[i].item()].std()
+                x_mean[i] = x[i, :, : seq_len[i].item()].mean()
+                x_std[i] = x[i, :, : seq_len[i].item()].std()
                 # make sure x_std is not zero
                 x_std += constant
             x = (x - x_mean.view(-1, 1, 1)) / x_std.view(-1, 1, 1)
@@ -215,9 +250,9 @@ def forward(self, inp: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
 
         # mask to zero any values beyond seq_len in batch, pad to multiple of `pad_to` (for efficiency)
         # max_len = x.size(-1)
-        x = x[:, :, :seq_len.max()]   # rnnt loss requires lengths to match
+        x = x[:, :, : seq_len.max()]  # rnnt loss requires lengths to match
         # mask = torch.arange(max_len).to(seq_len.dtype).to(x.device).expand(x.size(0),
-        #                                                                   max_len) >= seq_len.unsqueeze(1)
+        # max_len) >= seq_len.unsqueeze(1)
 
         # x = x.masked_fill(mask.unsqueeze(1).to(device=x.device), 0)
         pad_to = self.pad_to
@@ -234,27 +269,34 @@ def forward(self, inp: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
 
     @classmethod
     def from_config(cls, cfg, log=False):
-        return cls(sample_rate=cfg['sample_rate'], window_size=cfg['window_size'],
-                   window_stride=cfg['window_stride'], n_fft=cfg['n_fft'],
-                   nfilt=cfg['features'], window=cfg['window'],
-                   normalize=cfg['normalize'],
-                   max_duration=cfg.get('max_duration', 16.7),
-                   dither=cfg['dither'], pad_to=cfg.get("pad_to", 0),
-                   frame_splicing=cfg.get("frame_splicing", 1), log=log)
+        return cls(
+            sample_rate=cfg["sample_rate"],
+            window_size=cfg["window_size"],
+            window_stride=cfg["window_stride"],
+            n_fft=cfg["n_fft"],
+            nfilt=cfg["features"],
+            window=cfg["window"],
+            normalize=cfg["normalize"],
+            max_duration=cfg.get("max_duration", 16.7),
+            dither=cfg["dither"],
+            pad_to=cfg.get("pad_to", 0),
+            frame_splicing=cfg.get("frame_splicing", 1),
+            log=log,
+        )
 
 
 class FeatureFactory(object):
     featurizers = {
-            "logfbank": FilterbankFeatures,
-            "fbank": FilterbankFeatures,
-            }
+        "logfbank": FilterbankFeatures,
+        "fbank": FilterbankFeatures,
+    }
 
     def __init__(self):
         pass
 
     @classmethod
     def from_config(cls, cfg):
-        feat_type = cfg.get('feat_type', "logspect")
+        feat_type = cfg.get("feat_type", "logspect")
         featurizer = cls.featurizers[feat_type]
         # return featurizer.from_config(cfg, log="log" in cfg['feat_type'])
         return featurizer.from_config(cfg, log="log" in feat_type)
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/manifest.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/manifest.py
index fb04c5da88..b2233c1ade 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/manifest.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/manifest.py
@@ -41,16 +41,28 @@ def good_token(token, labels):
 
     try:
         text = _clean_text(s, ["english_cleaners"], table).strip()
-        return ''.join([t for t in text if good_token(t, labels=labels)])
-    except:
+        return "".join([t for t in text if good_token(t, labels=labels)])
+    except BaseException:
         print("WARNING: Normalizing {} failed".format(s))
         return None
 
 
 class Manifest(object):
-    def __init__(self, data_dir, manifest_paths, labels, blank_index, max_duration=None, pad_to_max=False,
-                 min_duration=None, sort_by_duration=False, max_utts=0,
-                 normalize=True, speed_perturbation=False, filter_speed=1.0):
+    def __init__(
+        self,
+        data_dir,
+        manifest_paths,
+        labels,
+        blank_index,
+        max_duration=None,
+        pad_to_max=False,
+        min_duration=None,
+        sort_by_duration=False,
+        max_utts=0,
+        normalize=True,
+        speed_perturbation=False,
+        filter_speed=1.0,
+    ):
         self.labels_map = dict([(labels[i], i) for i in range(len(labels))])
         self.blank_index = blank_index
         self.max_duration = max_duration
@@ -71,7 +83,8 @@ def __init__(self, data_dir, manifest_paths, labels, blank_index, max_duration=N
             # ~ -> tilde
             # _ -> underscore
             # % -> percent
-            # If a punctuation symbol is inside our vocab, we do not remove from text
+            # If a punctuation symbol is inside our vocab, we do not remove
+            # from text
             for l in labels:
                 punctuation = punctuation.replace(l, "")
             # Turn all punctuation to whitespace
@@ -80,72 +93,88 @@ def __init__(self, data_dir, manifest_paths, labels, blank_index, max_duration=N
             with open(manifest_path, "r", encoding="utf-8") as fh:
                 a = json.load(fh)
                 for data in a:
-                    files_and_speeds = data['files']
+                    files_and_speeds = data["files"]
 
                     if pad_to_max:
                         if not speed_perturbation:
                             min_speed = filter_speed
                         else:
-                            min_speed = min(x['speed']
+                            min_speed = min(x["speed"]
                                             for x in files_and_speeds)
                         max_duration = self.max_duration * min_speed
 
-                    data['duration'] = data['original_duration']
-                    if min_duration is not None and data['duration'] < min_duration:
-                        filtered_duration += data['duration']
+                    data["duration"] = data["original_duration"]
+                    if min_duration is not None and data["duration"] < min_duration:
+                        filtered_duration += data["duration"]
                         continue
-                    if max_duration is not None and data['duration'] > max_duration:
-                        filtered_duration += data['duration']
+                    if max_duration is not None and data["duration"] > max_duration:
+                        filtered_duration += data["duration"]
                         continue
 
                     # Prune and normalize according to transcript
-                    transcript_text = data[
-                        'transcript'] if "transcript" in data else self.load_transcript(
-                        data['text_filepath'])
+                    transcript_text = (
+                        data["transcript"]
+                        if "transcript" in data
+                        else self.load_transcript(data["text_filepath"])
+                    )
                     if normalize:
-                        transcript_text = normalize_string(transcript_text, labels=labels,
-                                                           table=table)
+                        transcript_text = normalize_string(
+                            transcript_text, labels=labels, table=table
+                        )
                     if not isinstance(transcript_text, str):
                         print(
                             "WARNING: Got transcript: {}. It is not a string. Dropping data point".format(
-                                transcript_text))
-                        filtered_duration += data['duration']
+                                transcript_text
+                            )
+                        )
+                        filtered_duration += data["duration"]
                         continue
                     data["transcript"] = self.parse_transcript(
-                        transcript_text)  # convert to vocab indices
+                        transcript_text
+                    )  # convert to vocab indices
 
                     if speed_perturbation:
-                        audio_paths = [x['fname'] for x in files_and_speeds]
-                        data['audio_duration'] = [x['duration']
-                                                  for x in files_and_speeds]
+                        audio_paths = [x["fname"] for x in files_and_speeds]
+                        data["audio_duration"] = [
+                            x["duration"] for x in files_and_speeds
+                        ]
                     else:
                         audio_paths = [
-                            x['fname'] for x in files_and_speeds if x['speed'] == filter_speed]
-                        data['audio_duration'] = [x['duration']
-                                                  for x in files_and_speeds if x['speed'] == filter_speed]
-                    data['audio_filepath'] = [os.path.join(
-                        data_dir, x) for x in audio_paths]
-                    data.pop('files')
-                    data.pop('original_duration')
+                            x["fname"]
+                            for x in files_and_speeds
+                            if x["speed"] == filter_speed
+                        ]
+                        data["audio_duration"] = [
+                            x["duration"]
+                            for x in files_and_speeds
+                            if x["speed"] == filter_speed
+                        ]
+                    data["audio_filepath"] = [
+                        os.path.join(data_dir, x) for x in audio_paths
+                    ]
+                    data.pop("files")
+                    data.pop("original_duration")
 
                     ids.append(data)
-                    duration += data['duration']
+                    duration += data["duration"]
 
                     if max_utts > 0 and len(ids) >= max_utts:
                         print(
-                            'Stopping parsing %s as max_utts=%d' % (manifest_path, max_utts))
+                            "Stopping parsing %s as max_utts=%d"
+                            % (manifest_path, max_utts)
+                        )
                         break
 
         if sort_by_duration:
-            ids = sorted(ids, key=lambda x: x['duration'])
+            ids = sorted(ids, key=lambda x: x["duration"])
         self._data = ids
         self._size = len(ids)
         self._duration = duration
         self._filtered_duration = filtered_duration
 
     def load_transcript(self, transcript_path):
-        with open(transcript_path, 'r', encoding="utf-8") as transcript_file:
-            transcript = transcript_file.read().replace('\n', '')
+        with open(transcript_path, "r", encoding="utf-8") as transcript_file:
+            transcript = transcript_file.read().replace("\n", "")
         return transcript
 
     def parse_transcript(self, transcript):
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/segment.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/segment.py
index 08aa5c6a49..3c1cc0a1b8 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/segment.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/segment.py
@@ -26,8 +26,8 @@ class AudioSegment(object):
     :raises TypeError: If the sample data type is not float or int.
     """
 
-    def __init__(self, samples, sample_rate, target_sr=None, trim=False,
-                 trim_db=60):
+    def __init__(self, samples, sample_rate,
+                 target_sr=None, trim=False, trim_db=60):
         """Create audio segment from samples.
         Samples are convert float32 internally, with int scaled to [-1, 1].
         """
@@ -44,7 +44,7 @@ def __init__(self, samples, sample_rate, target_sr=None, trim=False,
 
     def __eq__(self, other):
         """Return whether two objects are equal."""
-        if type(other) is not type(self):
+        if not isinstance(other, type(self)):
             return False
         if self._sample_rate != other._sample_rate:
             return False
@@ -60,9 +60,13 @@ def __ne__(self, other):
 
     def __str__(self):
         """Return human-readable representation of segment."""
-        return ("%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, "
-                "rms=%.2fdB" % (type(self), self.num_samples, self.sample_rate,
-                                self.duration, self.rms_db))
+        return "%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, " "rms=%.2fdB" % (
+            type(self),
+            self.num_samples,
+            self.sample_rate,
+            self.duration,
+            self.rms_db,
+        )
 
     @staticmethod
     def _convert_samples_to_float32(samples):
@@ -70,19 +74,26 @@ def _convert_samples_to_float32(samples):
         Audio sample type is usually integer or float-point.
         Integers will be scaled to [-1, 1] in float32.
         """
-        float32_samples = samples.astype('float32')
-        if samples.dtype in np.sctypes['int']:
+        float32_samples = samples.astype("float32")
+        if samples.dtype in np.sctypes["int"]:
             bits = np.iinfo(samples.dtype).bits
-            float32_samples *= (1. / 2 ** (bits - 1))
-        elif samples.dtype in np.sctypes['float']:
+            float32_samples *= 1.0 / 2 ** (bits - 1)
+        elif samples.dtype in np.sctypes["float"]:
             pass
         else:
             raise TypeError("Unsupported sample type: %s." % samples.dtype)
         return float32_samples
 
     @classmethod
-    def from_file(cls, filename, target_sr=None, int_values=False, offset=0,
-                  duration=0, trim=False):
+    def from_file(
+        cls,
+        filename,
+        target_sr=None,
+        int_values=False,
+        offset=0,
+        duration=0,
+        trim=False,
+    ):
         """
         Load a file supported by librosa and return as an AudioSegment.
         :param filename: path of file to load
@@ -92,8 +103,8 @@ def from_file(cls, filename, target_sr=None, int_values=False, offset=0,
         :param duration: duration in seconds when loading audio
         :return: numpy array of samples
         """
-        with sf.SoundFile(filename, 'r') as f:
-            dtype = 'int32' if int_values else 'float32'
+        with sf.SoundFile(filename, "r") as f:
+            dtype = "int32" if int_values else "float32"
             sample_rate = f.samplerate
             if offset > 0:
                 f.seek(int(offset * sample_rate))
@@ -122,20 +133,20 @@ def duration(self):
 
     @property
     def rms_db(self):
-        mean_square = np.mean(self._samples ** 2)
+        mean_square = np.mean(self._samples**2)
         return 10 * np.log10(mean_square)
 
     def gain_db(self, gain):
-        self._samples *= 10. ** (gain / 20.)
+        self._samples *= 10.0 ** (gain / 20.0)
 
     def pad(self, pad_size, symmetric=False):
         """Add zero padding to the sample. The pad size is given in number of samples.
         If symmetric=True, `pad_size` will be added to both sides. If false, `pad_size`
         zeros will be added only to the end.
         """
-        self._samples = np.pad(self._samples,
-                               (pad_size if symmetric else 0, pad_size),
-                               mode='constant')
+        self._samples = np.pad(
+            self._samples, (pad_size if symmetric else 0, pad_size), mode="constant"
+        )
 
     def subsegment(self, start_time=None, end_time=None):
         """Cut the AudioSegment between given boundaries.
@@ -154,17 +165,23 @@ def subsegment(self, start_time=None, end_time=None):
         if end_time < 0.0:
             end_time = self.duration + end_time
         if start_time < 0.0:
-            raise ValueError("The slice start position (%f s) is out of "
-                             "bounds." % start_time)
+            raise ValueError(
+                "The slice start position (%f s) is out of " "bounds." % start_time
+            )
         if end_time < 0.0:
-            raise ValueError("The slice end position (%f s) is out of bounds." %
-                             end_time)
+            raise ValueError(
+                "The slice end position (%f s) is out of bounds." % end_time
+            )
         if start_time > end_time:
-            raise ValueError("The slice start position (%f s) is later than "
-                             "the end position (%f s)." % (start_time, end_time))
+            raise ValueError(
+                "The slice start position (%f s) is later than "
+                "the end position (%f s)." % (start_time, end_time)
+            )
         if end_time > self.duration:
-            raise ValueError("The slice end position (%f s) is out of bounds "
-                             "(> %f s)" % (end_time, self.duration))
+            raise ValueError(
+                "The slice end position (%f s) is out of bounds "
+                "(> %f s)" % (end_time, self.duration)
+            )
         start_sample = int(round(start_time * self._sample_rate))
         end_sample = int(round(end_time * self._sample_rate))
         self._samples = self._samples[start_sample:end_sample]
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/__init__.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/__init__.py
index 61936879a9..ae1c39abd5 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/__init__.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/__init__.py
@@ -7,6 +7,6 @@ def _clean_text(text, cleaner_names, *args):
     for name in cleaner_names:
         cleaner = getattr(cleaners, name)
         if not cleaner:
-            raise Exception('Unknown cleaner: %s' % name)
+            raise Exception("Unknown cleaner: %s" % name)
         text = cleaner(text, *args)
     return text
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/cleaners.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/cleaners.py
index 0187fb4acc..54f3a6b99b 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/cleaners.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/cleaners.py
@@ -17,7 +17,7 @@
 Modified to add puncturation removal
 """
 
-'''
+"""
 Cleaners are transformations that run over the input text at both training and eval time.
 
 Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
@@ -28,36 +28,40 @@
     3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
          the symbols in symbols.py to match your data).
 
-'''
+"""
 
 
 # Regular expression matching whitespace:
+
 import re
 from unidecode import unidecode
 from .numbers import normalize_numbers
-_whitespace_re = re.compile(r'\s+')
+_whitespace_re = re.compile(r"\s+")
 
 # List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('mrs', 'misess'),
-    ('mr', 'mister'),
-    ('dr', 'doctor'),
-    ('st', 'saint'),
-    ('co', 'company'),
-    ('jr', 'junior'),
-    ('maj', 'major'),
-    ('gen', 'general'),
-    ('drs', 'doctors'),
-    ('rev', 'reverend'),
-    ('lt', 'lieutenant'),
-    ('hon', 'honorable'),
-    ('sgt', 'sergeant'),
-    ('capt', 'captain'),
-    ('esq', 'esquire'),
-    ('ltd', 'limited'),
-    ('col', 'colonel'),
-    ('ft', 'fort'),
-]]
+_abbreviations = [
+    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("mrs", "misess"),
+        ("mr", "mister"),
+        ("dr", "doctor"),
+        ("st", "saint"),
+        ("co", "company"),
+        ("jr", "junior"),
+        ("maj", "major"),
+        ("gen", "general"),
+        ("drs", "doctors"),
+        ("rev", "reverend"),
+        ("lt", "lieutenant"),
+        ("hon", "honorable"),
+        ("sgt", "sergeant"),
+        ("capt", "captain"),
+        ("esq", "esquire"),
+        ("ltd", "limited"),
+        ("col", "colonel"),
+        ("ft", "fort"),
+    ]
+]
 
 
 def expand_abbreviations(text):
@@ -75,7 +79,7 @@ def lowercase(text):
 
 
 def collapse_whitespace(text):
-    return re.sub(_whitespace_re, ' ', text)
+    return re.sub(_whitespace_re, " ", text)
 
 
 def convert_to_ascii(text):
@@ -84,20 +88,20 @@ def convert_to_ascii(text):
 
 def remove_punctuation(text, table):
     text = text.translate(table)
-    text = re.sub(r'&', " and ", text)
-    text = re.sub(r'\+', " plus ", text)
+    text = re.sub(r"&", " and ", text)
+    text = re.sub(r"\+", " plus ", text)
     return text
 
 
 def basic_cleaners(text):
-    '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
+    """Basic pipeline that lowercases and collapses whitespace without transliteration."""
     text = lowercase(text)
     text = collapse_whitespace(text)
     return text
 
 
 def transliteration_cleaners(text):
-    '''Pipeline for non-English text that transliterates to ASCII.'''
+    """Pipeline for non-English text that transliterates to ASCII."""
     text = convert_to_ascii(text)
     text = lowercase(text)
     text = collapse_whitespace(text)
@@ -105,7 +109,7 @@ def transliteration_cleaners(text):
 
 
 def english_cleaners(text, table=None):
-    '''Pipeline for English text, including number and abbreviation expansion.'''
+    """Pipeline for English text, including number and abbreviation expansion."""
     text = convert_to_ascii(text)
     text = lowercase(text)
     text = expand_numbers(text)
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/numbers.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/numbers.py
index 3d2f77121c..2f0579f40b 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/numbers.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/parts/text/numbers.py
@@ -21,42 +21,42 @@
 
 
 _inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-_time_re = re.compile(r'([0-9]{1,2}):([0-9]{2})')
+_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
+_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
+_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
+_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
+_number_re = re.compile(r"[0-9]+")
+_time_re = re.compile(r"([0-9]{1,2}):([0-9]{2})")
 
 
 def _remove_commas(m):
-    return m.group(1).replace(',', '')
+    return m.group(1).replace(",", "")
 
 
 def _expand_decimal_point(m):
-    return m.group(1).replace('.', ' point ')
+    return m.group(1).replace(".", " point ")
 
 
 def _expand_dollars(m):
     match = m.group(1)
-    parts = match.split('.')
+    parts = match.split(".")
     if len(parts) > 2:
-        return match + ' dollars'  # Unexpected format
+        return match + " dollars"  # Unexpected format
     dollars = int(parts[0]) if parts[0] else 0
     cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
     if dollars and cents:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
     elif dollars:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        return '%s %s' % (dollars, dollar_unit)
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return "%s %s" % (dollars, dollar_unit)
     elif cents:
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s' % (cents, cent_unit)
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s" % (cents, cent_unit)
     else:
-        return 'zero dollars'
+        return "zero dollars"
 
 
 def _expand_ordinal(m):
@@ -65,34 +65,39 @@ def _expand_ordinal(m):
 
 def _expand_number(m):
     if int(m.group(0)[0]) == 0:
-        return _inflect.number_to_words(m.group(0), andword='', group=1)
+        return _inflect.number_to_words(m.group(0), andword="", group=1)
     num = int(m.group(0))
     if num > 1000 and num < 3000:
         if num == 2000:
-            return 'two thousand'
+            return "two thousand"
         elif num > 2000 and num < 2010:
-            return 'two thousand ' + _inflect.number_to_words(num % 100)
+            return "two thousand " + _inflect.number_to_words(num % 100)
         elif num % 100 == 0:
-            return _inflect.number_to_words(num // 100) + ' hundred'
+            return _inflect.number_to_words(num // 100) + " hundred"
         else:
-            return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
+            return _inflect.number_to_words(
+                num, andword="", zero="oh", group=2
+            ).replace(", ", " ")
     # Add check for number phones and other large numbers
     elif num > 1000000000 and num % 10000 != 0:
-        return _inflect.number_to_words(num, andword='', group=1)
+        return _inflect.number_to_words(num, andword="", group=1)
     else:
-        return _inflect.number_to_words(num, andword='')
+        return _inflect.number_to_words(num, andword="")
 
 
 def _expand_time(m):
     mins = int(m.group(2))
     if mins == 0:
         return _inflect.number_to_words(m.group(1))
-    return " ".join([_inflect.number_to_words(m.group(1)), _inflect.number_to_words(m.group(2))])
+    return " ".join(
+        [_inflect.number_to_words(m.group(1)),
+         _inflect.number_to_words(m.group(2))]
+    )
 
 
 def normalize_numbers(text):
     text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r'\1 pounds', text)
+    text = re.sub(_pounds_re, r"\1 pounds", text)
     text = re.sub(_dollars_re, _expand_dollars, text)
     text = re.sub(_decimal_number_re, _expand_decimal_point, text)
     text = re.sub(_ordinal_re, _expand_ordinal, text)
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/preprocessing.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/preprocessing.py
index 581885466b..2a7c591563 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/preprocessing.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/preprocessing.py
@@ -22,16 +22,17 @@
 
 
 class AudioPreprocessing(nn.Module):
-    """GPU accelerated audio preprocessing
-    """
+    """GPU accelerated audio preprocessing"""
 
     def __init__(self, **kwargs):
-        nn.Module.__init__(self)    # For PyTorch API
+        nn.Module.__init__(self)  # For PyTorch API
         self.optim_level = kwargs.get(
-            'optimization_level', Optimization.nothing)
+            "optimization_level", Optimization.nothing)
         self.featurizer = FeatureFactory.from_config(kwargs)
 
-    def forward(self, x: Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
+    def forward(
+        self, x: Tuple[torch.Tensor, torch.Tensor]
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         input_signal, length = x
         length.requires_grad_(False)
         processed_signal = self.featurizer(x)
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/rnn.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/rnn.py
index 6bc825011a..564eb9324a 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/rnn.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/rnn.py
@@ -18,8 +18,16 @@
 from typing import Optional, Tuple
 
 
-def rnn(rnn, input_size, hidden_size, num_layers, norm=None,
-        forget_gate_bias=1.0, dropout=0.0, **kwargs):
+def rnn(
+    rnn,
+    input_size,
+    hidden_size,
+    num_layers,
+    norm=None,
+    forget_gate_bias=1.0,
+    dropout=0.0,
+    **kwargs,
+):
     """TODO"""
     if rnn != "lstm":
         raise ValueError(f"Unknown rnn={rnn}")
@@ -33,14 +41,15 @@ def rnn(rnn, input_size, hidden_size, num_layers, norm=None,
             num_layers=num_layers,
             dropout=dropout,
             forget_gate_bias=forget_gate_bias,
-            **kwargs
+            **kwargs,
         )
 
 
 class LstmDrop(torch.nn.Module):
 
-    def __init__(self, input_size, hidden_size, num_layers, dropout, forget_gate_bias,
-                 **kwargs):
+    def __init__(
+        self, input_size, hidden_size, num_layers, dropout, forget_gate_bias, **kwargs
+    ):
         """Returns an LSTM with forget gate bias init to `forget_gate_bias`.
 
         Args:
@@ -55,7 +64,12 @@ def __init__(self, input_size, hidden_size, num_layers, dropout, forget_gate_bia
             A `torch.nn.LSTM`.
         """
         super(LstmDrop, self).__init__()
-        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+        self.dev = (
+            torch.device("cuda:0")
+            if torch.cuda.is_available()
+            and os.environ.get("USE_GPU", "").lower() not in ["no", "false"]
+            else torch.device("cpu")
+        )
 
         self.lstm = torch.nn.LSTM(
             input_size=input_size,
@@ -67,18 +81,20 @@ def __init__(self, input_size, hidden_size, num_layers, dropout, forget_gate_bia
             for name, v in self.lstm.named_parameters():
                 if "bias_ih" in name:
                     bias = getattr(self.lstm, name)
-                    bias.data[hidden_size:2 * hidden_size].fill_(forget_gate_bias)
+                    bias.data[hidden_size: 2 *
+                              hidden_size].fill_(forget_gate_bias)
                 if "bias_hh" in name:
                     bias = getattr(self.lstm, name)
-                    bias.data[hidden_size:2 * hidden_size].fill_(0)
+                    bias.data[hidden_size: 2 * hidden_size].fill_(0)
 
         if dropout:
             self.inplace_dropout = torch.nn.Dropout(dropout, inplace=True)
         else:
             self.inplace_droput = None
 
-    def forward(self, x: torch.Tensor,
-                h: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+    def forward(
+        self, x: torch.Tensor, h: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
+    ):
         x, h = self.lstm(x, h)
 
         if self.inplace_dropout is not None:
@@ -94,15 +110,20 @@ class StackTime(torch.nn.Module):
     def __init__(self, factor):
         super().__init__()
         self.factor = int(factor)
-        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
-
+        self.dev = (
+            torch.device("cuda:0")
+            if torch.cuda.is_available()
+            and os.environ.get("USE_GPU", "").lower() not in ["no", "false"]
+            else torch.device("cpu")
+        )
 
     def forward(self, x, x_lens):
         # T, B, U
         r = torch.transpose(x, 0, 1).to(self.dev)
         s = r.shape
         zeros = torch.zeros(
-            s[0], (-s[1]) % self.factor, s[2], dtype=r.dtype, device=r.device)
+            s[0], (-s[1]) % self.factor, s[2], dtype=r.dtype, device=r.device
+        )
         r = torch.cat([r, zeros], 1)
         s = r.shape
         rs = [s[0], s[1] // self.factor, s[2] * self.factor]
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/convert_librispeech.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/convert_librispeech.py
index e90076cb09..54bcc9b3eb 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/convert_librispeech.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/convert_librispeech.py
@@ -24,40 +24,59 @@
 
 from preprocessing_utils import parallel_preprocess
 
-parser = argparse.ArgumentParser(description='Preprocess LibriSpeech.')
-parser.add_argument('--input_dir', type=str, required=True,
-                    help='LibriSpeech collection input dir')
-parser.add_argument('--dest_dir', type=str, required=True,
-                    help='Output dir')
-parser.add_argument('--output_json', type=str, default='./',
-                    help='name of the output json file.')
-parser.add_argument('-s', '--speed', type=float, nargs='*',
-                    help='Speed perturbation ratio')
-parser.add_argument('--target_sr', type=int, default=None,
-                    help='Target sample rate. '
-                         'defaults to the input sample rate')
-parser.add_argument('--overwrite', action='store_true',
-                    help='Overwrite file if exists')
-parser.add_argument('--parallel', type=int, default=multiprocessing.cpu_count(),
-                    help='Number of threads to use when processing audio files')
+parser = argparse.ArgumentParser(description="Preprocess LibriSpeech.")
+parser.add_argument(
+    "--input_dir", type=str, required=True, help="LibriSpeech collection input dir"
+)
+parser.add_argument("--dest_dir", type=str, required=True, help="Output dir")
+parser.add_argument(
+    "--output_json", type=str, default="./", help="name of the output json file."
+)
+parser.add_argument(
+    "-s", "--speed", type=float, nargs="*", help="Speed perturbation ratio"
+)
+parser.add_argument(
+    "--target_sr",
+    type=int,
+    default=None,
+    help="Target sample rate. " "defaults to the input sample rate",
+)
+parser.add_argument(
+    "--overwrite",
+    action="store_true",
+    help="Overwrite file if exists")
+parser.add_argument(
+    "--parallel",
+    type=int,
+    default=multiprocessing.cpu_count(),
+    help="Number of threads to use when processing audio files",
+)
 args = parser.parse_args()
 
-args.input_dir = args.input_dir.rstrip('/')
-args.dest_dir = args.dest_dir.rstrip('/')
+args.input_dir = args.input_dir.rstrip("/")
+args.dest_dir = args.dest_dir.rstrip("/")
 
 
 def build_input_arr(input_dir):
-    txt_files = glob.glob(os.path.join(input_dir, '**', '*.trans.txt'),
-                          recursive=True)
+    txt_files = glob.glob(
+        os.path.join(
+            input_dir,
+            "**",
+            "*.trans.txt"),
+        recursive=True)
     input_data = []
     for txt_file in txt_files:
         rel_path = os.path.relpath(txt_file, input_dir)
         with open(txt_file) as fp:
             for line in fp:
-                fname, _, transcript = line.partition(' ')
-                input_data.append(dict(input_relpath=os.path.dirname(rel_path),
-                                       input_fname=fname + '.flac',
-                                       transcript=transcript))
+                fname, _, transcript = line.partition(" ")
+                input_data.append(
+                    dict(
+                        input_relpath=os.path.dirname(rel_path),
+                        input_fname=fname + ".flac",
+                        transcript=transcript,
+                    )
+                )
     return input_data
 
 
@@ -65,18 +84,20 @@ def build_input_arr(input_dir):
 dataset = build_input_arr(input_dir=args.input_dir)
 
 print("[%s] Converting audio files..." % args.output_json)
-dataset = parallel_preprocess(dataset=dataset,
-                              input_dir=args.input_dir,
-                              dest_dir=args.dest_dir,
-                              target_sr=args.target_sr,
-                              speed=args.speed,
-                              overwrite=args.overwrite,
-                              parallel=args.parallel)
+dataset = parallel_preprocess(
+    dataset=dataset,
+    input_dir=args.input_dir,
+    dest_dir=args.dest_dir,
+    target_sr=args.target_sr,
+    speed=args.speed,
+    overwrite=args.overwrite,
+    parallel=args.parallel,
+)
 
 print("[%s] Generating json..." % args.output_json)
 df = pd.DataFrame(dataset, dtype=object)
 
 # Save json with python. df.to_json() produces back slashed in file paths
-dataset = df.to_dict(orient='records')
-with open(args.output_json, 'w') as fp:
+dataset = df.to_dict(orient="records")
+with open(args.output_json, "w") as fp:
     json.dump(dataset, fp, indent=2)
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/download_librispeech.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/download_librispeech.py
index f7e5eda130..1a08754337 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/download_librispeech.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/download_librispeech.py
@@ -21,29 +21,39 @@
 from download_utils import download_file, md5_checksum, extract
 
 parser = argparse.ArgumentParser(
-    description='Download, verify and extract dataset files')
-parser.add_argument('csv', type=str,
-                    help='CSV file with urls and checksums to download.')
-parser.add_argument('dest', type=str,
-                    help='Download destnation folder.')
-parser.add_argument('-e', type=str, default=None,
-                    help='Extraction destnation folder. Defaults to download folder if not provided')
-parser.add_argument('--skip_download', action='store_true',
-                    help='Skip downloading the files')
-parser.add_argument('--skip_checksum', action='store_true',
-                    help='Skip checksum')
-parser.add_argument('--skip_extract', action='store_true',
-                    help='Skip extracting files')
+    description="Download, verify and extract dataset files"
+)
+parser.add_argument(
+    "csv", type=str, help="CSV file with urls and checksums to download."
+)
+parser.add_argument("dest", type=str, help="Download destnation folder.")
+parser.add_argument(
+    "-e",
+    type=str,
+    default=None,
+    help="Extraction destnation folder. Defaults to download folder if not provided",
+)
+parser.add_argument(
+    "--skip_download", action="store_true", help="Skip downloading the files"
+)
+parser.add_argument(
+    "--skip_checksum",
+    action="store_true",
+    help="Skip checksum")
+parser.add_argument(
+    "--skip_extract",
+    action="store_true",
+    help="Skip extracting files")
 args = parser.parse_args()
 args.e = args.e or args.dest
 
 
-df = pd.read_csv(args.csv, delimiter=',')
+df = pd.read_csv(args.csv, delimiter=",")
 
 
 if not args.skip_download:
     for url in df.url:
-        fname = url.split('/')[-1]
+        fname = url.split("/")[-1]
         print("Downloading %s:" % fname)
         download_file(url=url, dest_folder=args.dest, fname=fname)
 else:
@@ -52,11 +62,11 @@
 
 if not args.skip_checksum:
     for index, row in df.iterrows():
-        url = row['url']
-        md5 = row['md5']
-        fname = url.split('/')[-1]
+        url = row["url"]
+        md5 = row["md5"]
+        fname = url.split("/")[-1]
         fpath = os.path.join(args.dest, fname)
-        print("Verifing %s: " % fname, end='')
+        print("Verifing %s: " % fname, end="")
         ret = md5_checksum(fpath=fpath, target_hash=md5)
         if not ret:
             raise ValueError(f"Checksum for {fname} failed!")
@@ -68,7 +78,7 @@
 
 if not args.skip_extract:
     for url in df.url:
-        fname = url.split('/')[-1]
+        fname = url.split("/")[-1]
         fpath = os.path.join(args.dest, fname)
         print("Decompressing %s:" % fpath)
         extract(fpath=fpath, dest_folder=args.e)
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/download_utils.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/download_utils.py
index bda4193fbb..8b059229fa 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/download_utils.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/download_utils.py
@@ -30,17 +30,18 @@ def download_file(url, dest_folder, fname, overwrite=False):
             print("File exists, skipping download.")
             return
 
-    tmp_fpath = fpath + '.tmp'
+    tmp_fpath = fpath + ".tmp"
 
     r = requests.get(url, stream=True)
-    file_size = int(r.headers['Content-Length'])
+    file_size = int(r.headers["Content-Length"])
     chunk_size = 1024 * 1024  # 1MB
     total_chunks = int(file_size / chunk_size)
 
-    with open(tmp_fpath, 'wb') as fp:
+    with open(tmp_fpath, "wb") as fp:
         content_iterator = r.iter_content(chunk_size=chunk_size)
-        chunks = tqdm.tqdm(content_iterator, total=total_chunks,
-                           unit='MB', desc=fpath, leave=True)
+        chunks = tqdm.tqdm(
+            content_iterator, total=total_chunks, unit="MB", desc=fpath, leave=True
+        )
         for chunk in chunks:
             fp.write(chunk)
 
@@ -56,14 +57,15 @@ def md5_checksum(fpath, target_hash):
 
 
 def extract(fpath, dest_folder):
-    if fpath.endswith('.tar.gz'):
-        mode = 'r:gz'
-    elif fpath.endswith('.tar'):
-        mode = 'r:'
+    if fpath.endswith(".tar.gz"):
+        mode = "r:gz"
+    elif fpath.endswith(".tar"):
+        mode = "r:"
     else:
-        raise IOError('fpath has unknown extention: %s' % fpath)
+        raise IOError("fpath has unknown extention: %s" % fpath)
 
     with tarfile.open(fpath, mode) as tar:
         members = tar.getmembers()
-        for member in tqdm.tqdm(iterable=members, total=len(members), leave=True):
+        for member in tqdm.tqdm(
+                iterable=members, total=len(members), leave=True):
             tar.extract(path=dest_folder, member=member)
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/preprocessing_utils.py b/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/preprocessing_utils.py
index 260e860b80..8d5eba12de 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/preprocessing_utils.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch/utils/preprocessing_utils.py
@@ -23,55 +23,62 @@
 from tqdm import tqdm
 
 
-def preprocess(data, input_dir, dest_dir, target_sr=None, speed=None,
-               overwrite=True):
+def preprocess(data, input_dir, dest_dir, target_sr=None,
+               speed=None, overwrite=True):
     speed = speed or []
     speed.append(1)
     speed = list(set(speed))  # Make uniqe
 
-    input_fname = os.path.join(input_dir,
-                               data['input_relpath'],
-                               data['input_fname'])
+    input_fname = os.path.join(
+        input_dir,
+        data["input_relpath"],
+        data["input_fname"])
     input_sr = sox.file_info.sample_rate(input_fname)
     target_sr = target_sr or input_sr
 
-    os.makedirs(os.path.join(dest_dir, data['input_relpath']), exist_ok=True)
+    os.makedirs(os.path.join(dest_dir, data["input_relpath"]), exist_ok=True)
 
     output_dict = {}
-    output_dict['transcript'] = data['transcript'].lower().strip()
-    output_dict['files'] = []
+    output_dict["transcript"] = data["transcript"].lower().strip()
+    output_dict["files"] = []
 
-    fname = os.path.splitext(data['input_fname'])[0]
+    fname = os.path.splitext(data["input_fname"])[0]
     for s in speed:
         output_fname = fname + \
-            '{}.wav'.format('' if s == 1 else '-{}'.format(s))
-        output_fpath = os.path.join(dest_dir,
-                                    data['input_relpath'],
-                                    output_fname)
+            "{}.wav".format("" if s == 1 else "-{}".format(s))
+        output_fpath = os.path.join(
+            dest_dir, data["input_relpath"], output_fname)
 
         if not os.path.exists(output_fpath) or overwrite:
             cbn = sox.Transformer().speed(factor=s).convert(target_sr)
             cbn.build(input_fname, output_fpath)
 
         file_info = sox.file_info.info(output_fpath)
-        file_info['fname'] = os.path.join(os.path.basename(dest_dir),
-                                          data['input_relpath'],
-                                          output_fname)
-        file_info['speed'] = s
-        output_dict['files'].append(file_info)
+        file_info["fname"] = os.path.join(
+            os.path.basename(dest_dir), data["input_relpath"], output_fname
+        )
+        file_info["speed"] = s
+        output_dict["files"].append(file_info)
 
         if s == 1:
             file_info = sox.file_info.info(output_fpath)
-            output_dict['original_duration'] = file_info['duration']
-            output_dict['original_num_samples'] = file_info['num_samples']
+            output_dict["original_duration"] = file_info["duration"]
+            output_dict["original_num_samples"] = file_info["num_samples"]
 
     return output_dict
 
 
-def parallel_preprocess(dataset, input_dir, dest_dir, target_sr, speed, overwrite, parallel):
+def parallel_preprocess(
+    dataset, input_dir, dest_dir, target_sr, speed, overwrite, parallel
+):
     with multiprocessing.Pool(parallel) as p:
-        func = functools.partial(preprocess,
-                                 input_dir=input_dir, dest_dir=dest_dir,
-                                 target_sr=target_sr, speed=speed, overwrite=overwrite)
+        func = functools.partial(
+            preprocess,
+            input_dir=input_dir,
+            dest_dir=dest_dir,
+            target_sr=target_sr,
+            speed=speed,
+            overwrite=overwrite,
+        )
         dataset = list(tqdm(p.imap(func, dataset), total=len(dataset)))
         return dataset
diff --git a/retired_benchmarks/speech_recognition/rnnt/pytorch_SUT.py b/retired_benchmarks/speech_recognition/rnnt/pytorch_SUT.py
index 429d742342..2dad4c273d 100644
--- a/retired_benchmarks/speech_recognition/rnnt/pytorch_SUT.py
+++ b/retired_benchmarks/speech_recognition/rnnt/pytorch_SUT.py
@@ -14,26 +14,29 @@
 
 import sys
 import os
-sys.path.insert(0, os.path.join(os.getcwd(), "pytorch"))
 
-import array
-import torch
-import numpy as np
-import toml
-import mlperf_loadgen as lg
-from tqdm import tqdm
+sys.path.insert(0, os.path.join(os.getcwd(), "pytorch"))
 
-from QSL import AudioQSL, AudioQSLInMemory
-from decoders import ScriptGreedyDecoder
-from helpers import add_blank_label
-from preprocessing import AudioPreprocessing
-from model_separable_rnnt import RNNT
+try:
+    from model_separable_rnnt import RNNT
+    from preprocessing import AudioPreprocessing
+    from helpers import add_blank_label
+    from decoders import ScriptGreedyDecoder
+    from QSL import AudioQSL, AudioQSLInMemory
+    from tqdm import tqdm
+    import mlperf_loadgen as lg
+    import toml
+    import numpy as np
+    import torch
+    import array
+except ImportError:
+    raise Exception("Error importing local modules")
 
 
 def load_and_migrate_checkpoint(ckpt_path):
     checkpoint = torch.load(ckpt_path, map_location="cpu")
     migrated_state_dict = {}
-    for key, value in checkpoint['state_dict'].items():
+    for key, value in checkpoint["state_dict"].items():
         key = key.replace("joint_net", "joint.net")
         migrated_state_dict[key] = value
     del migrated_state_dict["audio_preprocessor.featurizer.fb"]
@@ -42,46 +45,59 @@ def load_and_migrate_checkpoint(ckpt_path):
 
 
 class PytorchSUT:
-    def __init__(self, config_toml, checkpoint_path, dataset_dir,
-                 manifest_filepath, perf_count):
+    def __init__(
+        self, config_toml, checkpoint_path, dataset_dir, manifest_filepath, perf_count
+    ):
         config = toml.load(config_toml)
 
-        dataset_vocab = config['labels']['labels']
+        dataset_vocab = config["labels"]["labels"]
         rnnt_vocab = add_blank_label(dataset_vocab)
-        featurizer_config = config['input_eval']
+        featurizer_config = config["input_eval"]
 
-        self.dev = torch.device("cuda:0") if torch.cuda.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+        self.dev = (
+            torch.device("cuda:0")
+            if torch.cuda.is_available()
+            and os.environ.get("USE_GPU", "").lower() not in ["no", "false"]
+            else torch.device("cpu")
+        )
 
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
-        self.qsl = AudioQSLInMemory(dataset_dir,
-                                    manifest_filepath,
-                                    dataset_vocab,
-                                    featurizer_config["sample_rate"],
-                                    perf_count)
+        self.qsl = AudioQSLInMemory(
+            dataset_dir,
+            manifest_filepath,
+            dataset_vocab,
+            featurizer_config["sample_rate"],
+            perf_count,
+        )
         self.audio_preprocessor = AudioPreprocessing(**featurizer_config)
         self.audio_preprocessor.eval()
         self.audio_preprocessor = torch.jit.script(self.audio_preprocessor)
         self.audio_preprocessor = torch.jit._recursive.wrap_cpp_module(
-            torch._C._freeze_module(self.audio_preprocessor._c))
+            torch._C._freeze_module(self.audio_preprocessor._c)
+        )
 
         model = RNNT(
             feature_config=featurizer_config,
-            rnnt=config['rnnt'],
-            num_classes=len(rnnt_vocab)
+            rnnt=config["rnnt"],
+            num_classes=len(rnnt_vocab),
         )
-        model.load_state_dict(load_and_migrate_checkpoint(checkpoint_path),
-                              strict=True)
+        model.load_state_dict(
+            load_and_migrate_checkpoint(checkpoint_path),
+            strict=True)
         model.to(self.dev)
         model.eval()
         model.encoder = torch.jit.script(model.encoder)
         model.encoder = torch.jit._recursive.wrap_cpp_module(
-            torch._C._freeze_module(model.encoder._c))
+            torch._C._freeze_module(model.encoder._c)
+        )
         model.prediction = torch.jit.script(model.prediction)
         model.prediction = torch.jit._recursive.wrap_cpp_module(
-            torch._C._freeze_module(model.prediction._c))
+            torch._C._freeze_module(model.prediction._c)
+        )
         model.joint = torch.jit.script(model.joint)
         model.joint = torch.jit._recursive.wrap_cpp_module(
-            torch._C._freeze_module(model.joint._c))
+            torch._C._freeze_module(model.joint._c)
+        )
         model = torch.jit.script(model)
 
         self.greedy_decoder = ScriptGreedyDecoder(len(rnnt_vocab) - 1, model)
@@ -96,18 +112,22 @@ def issue_queries(self, query_samples):
             with torch.no_grad():
                 waveform = torch.from_numpy(waveform)
                 waveform_length = torch.from_numpy(waveform_length)
-                feature, feature_length = self.audio_preprocessor.forward((waveform, waveform_length))
+                feature, feature_length = self.audio_preprocessor.forward(
+                    (waveform, waveform_length)
+                )
                 assert feature.ndim == 3
                 assert feature_length.ndim == 1
                 feature = feature.permute(2, 0, 1)
 
-                _, _, transcript = self.greedy_decoder.forward(feature, feature_length)
+                _, _, transcript = self.greedy_decoder.forward(
+                    feature, feature_length)
 
             assert len(transcript) == 1
-            response_array = array.array('q', transcript[0])
+            response_array = array.array("q", transcript[0])
             bi = response_array.buffer_info()
-            response = lg.QuerySampleResponse(query_sample.id, bi[0],
-                                              bi[1] * response_array.itemsize)
+            response = lg.QuerySampleResponse(
+                query_sample.id, bi[0], bi[1] * response_array.itemsize
+            )
             lg.QuerySamplesComplete([response])
 
     def flush_queries(self):
diff --git a/retired_benchmarks/speech_recognition/rnnt/run.py b/retired_benchmarks/speech_recognition/rnnt/run.py
index 02f12e73b2..1a8e4cef2d 100644
--- a/retired_benchmarks/speech_recognition/rnnt/run.py
+++ b/retired_benchmarks/speech_recognition/rnnt/run.py
@@ -20,20 +20,46 @@
 import os
 from pathlib import Path
 
-MLPERF_CONF = Path(os.path.dirname(os.path.realpath(__file__))) / "../../mlperf.conf"
+MLPERF_CONF = Path(
+    os.path.dirname(
+        os.path.realpath(__file__))) / "../../mlperf.conf"
 MLPERF_CONF = MLPERF_CONF.resolve()
 
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--backend", choices=["pytorch"], default="pytorch", help="Backend")
-    parser.add_argument("--scenario", choices=["SingleStream", "Offline", "Server"], default="Offline", help="Scenario")
-    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
-    parser.add_argument("--mlperf_conf", default=str(MLPERF_CONF), help="mlperf rules config")
-    parser.add_argument("--user_conf", default="user.conf", help="user config for user LoadGen settings such as target QPS")
-    parser.add_argument("--audit_conf", default="audit.conf", help="audit config for LoadGen settings during compliance runs")
-    parser.add_argument("--pytorch_config_toml", default="pytorch/configs/rnnt.toml")
-    parser.add_argument("--pytorch_checkpoint", default="pytorch/work_dir/rnnt.pt")
+    parser.add_argument(
+        "--backend", choices=["pytorch"], default="pytorch", help="Backend"
+    )
+    parser.add_argument(
+        "--scenario",
+        choices=["SingleStream", "Offline", "Server"],
+        default="Offline",
+        help="Scenario",
+    )
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
+    parser.add_argument(
+        "--mlperf_conf", default=str(MLPERF_CONF), help="mlperf rules config"
+    )
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+    parser.add_argument(
+        "--audit_conf",
+        default="audit.conf",
+        help="audit config for LoadGen settings during compliance runs",
+    )
+    parser.add_argument(
+        "--pytorch_config_toml",
+        default="pytorch/configs/rnnt.toml")
+    parser.add_argument(
+        "--pytorch_checkpoint",
+        default="pytorch/work_dir/rnnt.pt")
     parser.add_argument("--dataset_dir", required=True)
     parser.add_argument("--manifest", required=True)
     parser.add_argument("--perf_count", type=int, default=None)
@@ -54,8 +80,14 @@ def main():
 
     if args.backend == "pytorch":
         from pytorch_SUT import PytorchSUT
-        sut = PytorchSUT(args.pytorch_config_toml, args.pytorch_checkpoint,
-                         args.dataset_dir, args.manifest, args.perf_count)
+
+        sut = PytorchSUT(
+            args.pytorch_config_toml,
+            args.pytorch_checkpoint,
+            args.dataset_dir,
+            args.manifest,
+            args.perf_count,
+        )
     else:
         raise ValueError("Unknown backend: {:}".format(args.backend))
 
@@ -78,7 +110,9 @@ def main():
     log_settings.log_output = log_output_settings
 
     print("Running Loadgen test...")
-    lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings, args.audit_conf)
+    lg.StartTestWithLogSettings(
+        sut.sut, sut.qsl.qsl, settings, log_settings, args.audit_conf
+    )
 
     if args.accuracy:
         cmd = f"python3 accuracy_eval.py --log_dir {log_path} --dataset_dir {args.dataset_dir} --manifest {args.manifest}"
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/generic_loadgen.py b/retired_benchmarks/translation/gnmt/tensorflow/generic_loadgen.py
index 3c280bdd70..50c7bd3b06 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/generic_loadgen.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/generic_loadgen.py
@@ -20,14 +20,17 @@
 import mlperf_loadgen
 import array
 
-class ImplementationException (Exception):
+
+class ImplementationException(Exception):
     def __init__(self, msg):
         self.msg = msg
 
     def __repr__(self):
         return "ImplementationException: {}".format(self.msg)
 
-def flush_queries(): pass
+
+def flush_queries():
+    pass
 
 
 class Task:
@@ -35,8 +38,9 @@ def __init__(self, query_id, sample_id):
         self.query_id = query_id
         self.sample_id = sample_id
 
+
 class Runner:
-    
+
     def __init__(self, qSize=5):
         self.tasks = Queue(maxsize=qSize)
 
@@ -59,7 +63,7 @@ def handle_tasks(self):
             # Block until an item becomes available
             qitem = self.tasks.get(block=True)
 
-            # When a "None" item was added, it is a 
+            # When a "None" item was added, it is a
             # signal from the parent to indicate we should stop
             # working (see finish)
             if qitem is None:
@@ -71,7 +75,7 @@ def handle_tasks(self):
             self.post_process(qitem.query_id, results)
 
             self.tasks.task_done()
-    
+
     ##
     # @brief Post process results
     # @note This should serialize the results for query_ids and hand it over to loadgen
@@ -106,7 +110,8 @@ def start_worker(self):
         self.worker.daemon = True
         self.worker.start()
 
-class DummyRunner (Runner):
+
+class DummyRunner(Runner):
     def __init__(self):
         Runner.__init__(self)
         self.count = 0
@@ -118,11 +123,16 @@ def enqueue(self, query_samples):
             self.tasks.put(task)
 
     def process(self, qitem):
-        print("Default dummy process, processing the {}'th query for sample ID {}.".format(self.count, qitem.sample_id[0]))
+        print(
+            "Default dummy process, processing the {}'th query for sample ID {}.".format(
+                self.count, qitem.sample_id[0]
+            )
+        )
         self.count += 1
-        
+
         return self.count
 
+
 if __name__ == "__main__":
     runner = DummyRunner()
 
@@ -135,14 +145,17 @@ def process(self, qitem):
     # Specify exactly how many queries need to be made
     settings.min_query_count = 3003
     settings.max_query_count = 3003
-    
-    total_queries = 256 # Maximum sample ID + 1
-    perf_queries = 8   # TBD: Doesn't seem to have an effect
+
+    total_queries = 256  # Maximum sample ID + 1
+    perf_queries = 8  # TBD: Doesn't seem to have an effect
 
     sut = mlperf_loadgen.ConstructSUT(runner.enqueue, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        total_queries, perf_queries, runner.load_samples_to_ram, runner.unload_samples_from_ram)
+        total_queries,
+        perf_queries,
+        runner.load_samples_to_ram,
+        runner.unload_samples_from_ram,
+    )
     mlperf_loadgen.StartTest(sut, qsl, settings)
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
-
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/loadgen_gnmt.py b/retired_benchmarks/translation/gnmt/tensorflow/loadgen_gnmt.py
index b99debf665..2661bd274f 100755
--- a/retired_benchmarks/translation/gnmt/tensorflow/loadgen_gnmt.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/loadgen_gnmt.py
@@ -28,6 +28,7 @@
 
 NANO_SEC = 1e9
 
+
 ##
 # @brief Translation task that contains 1 sentence ID.
 class TranslationTask:
@@ -37,13 +38,14 @@ def __init__(self, query_id, sentence_id, output_file):
         self.output_file = output_file
         self.start = time.time()
 
+
 ##
 # @brief Translation task that contains an array of sentence IDs
 class BatchTranslationTask:
     def __init__(self, sentence_id_list, query_id_list):
         self.sentence_id_list = sentence_id_list
         self.query_id_list = query_id_list
-        self.query_id = query_id_list   #FIXME generic_loadgen needs this
+        self.query_id = query_id_list  # FIXME generic_loadgen needs this
 
 
 ##
@@ -56,33 +58,46 @@ class GNMTWrapper:
     # @param vocab_prefix: Path to vocabulary file (note: don't add .en or .de suffixes)
     # @param outdir: Output directory to optionally write translations to
     # @param batch_size: batch size to use when processing BatchTranslationTasks
-    def __init__(self, ckpt_path=None, hparams_path=None, vocab_prefix=None, outdir=None, batch_size=32):
-        # If no value is provided for the construtor arguments, set defaults here
+    def __init__(
+        self,
+        ckpt_path=None,
+        hparams_path=None,
+        vocab_prefix=None,
+        outdir=None,
+        batch_size=32,
+    ):
+        # If no value is provided for the construtor arguments, set defaults
+        # here
         if ckpt_path is None:
-            ckpt_path = os.path.join(os.getcwd(), 'ende_gnmt_model_4_layer',
-                        'translate.ckpt')
+            ckpt_path = os.path.join(
+                os.getcwd(), "ende_gnmt_model_4_layer", "translate.ckpt"
+            )
 
         if hparams_path is None:
-            hparams_path= os.path.join(os.getcwd(), 'nmt', 'standard_hparams',
-                             'wmt16_gnmt_4_layer.json')
+            hparams_path = os.path.join(
+                os.getcwd(), "nmt", "standard_hparams", "wmt16_gnmt_4_layer.json"
+            )
 
         if vocab_prefix is None:
-            vocab_prefix = os.path.join(os.getcwd(), 'nmt', 'data', 'vocab.bpe.32000')
+            vocab_prefix = os.path.join(
+                os.getcwd(), "nmt", "data", "vocab.bpe.32000")
 
-
-        flags = self.parse_options(ckpt_path, hparams_path, vocab_prefix, outdir, batch_size)
+        flags = self.parse_options(
+            ckpt_path, hparams_path, vocab_prefix, outdir, batch_size
+        )
 
         self.setup(flags)
 
         self.count = 0
         self.infer_data = []  # This will be filled by load_sentences
 
-
     ##
     # @brief Parse GNMT-specific options before setting up
-    def parse_options(self, ckpt_path, hparams_path, vocab_prefix, outdir, batch_size):
+    def parse_options(self, ckpt_path, hparams_path,
+                      vocab_prefix, outdir, batch_size):
         FLAGS = None
-        # TBD remove argument parsing, and just have it return all default values.
+        # TBD remove argument parsing, and just have it return all default
+        # values.
         nmt_parser = argparse.ArgumentParser()
         add_arguments(nmt_parser)
         FLAGS, unparsed = nmt_parser.parse_known_args()
@@ -93,58 +108,66 @@ def parse_options(self, ckpt_path, hparams_path, vocab_prefix, outdir, batch_siz
         FLAGS.infer_batch_size = batch_size
         FLAGS.num_inter_threads = 1
         FLAGS.num_intra_threads = 1
-        FLAGS.run = "accuracy" # Needs to be set to accuracy to generate output
+        FLAGS.run = "accuracy"  # Needs to be set to accuracy to generate output
 
         # Pass in inference specific flags
         FLAGS.ckpt = ckpt_path
-        FLAGS.src = 'en'
-        FLAGS.tgt = 'de'
+        FLAGS.src = "en"
+        FLAGS.tgt = "de"
         FLAGS.hparams_path = hparams_path
         FLAGS.out_dir = outdir
         FLAGS.vocab_prefix = vocab_prefix
-        
+
         return FLAGS
 
     ##
-    # @brief Configure hparams and setup GNMT graph 
+    # @brief Configure hparams and setup GNMT graph
     # @pre Requires output from parse_options
     def setup(self, flags):
         # Model output directory
         out_dir = flags.out_dir
         if out_dir and not tf.gfile.Exists(out_dir):
-          tf.gfile.MakeDirs(out_dir)
+            tf.gfile.MakeDirs(out_dir)
 
         # Load hparams.
         default_hparams = create_hparams(flags)
         loaded_hparams = False
         if flags.ckpt:  # Try to load hparams from the same directory as ckpt
-          ckpt_dir = os.path.dirname(flags.ckpt)
-          ckpt_hparams_file = os.path.join(ckpt_dir, "hparams")
-          if tf.gfile.Exists(ckpt_hparams_file) or flags.hparams_path:
-                # Note: for some reason this will create an empty "best_bleu" directory and copy vocab files
-                hparams = create_or_load_hparams(ckpt_dir, default_hparams, flags.hparams_path, save_hparams=False)
+            ckpt_dir = os.path.dirname(flags.ckpt)
+            ckpt_hparams_file = os.path.join(ckpt_dir, "hparams")
+            if tf.gfile.Exists(ckpt_hparams_file) or flags.hparams_path:
+                # Note: for some reason this will create an empty "best_bleu"
+                # directory and copy vocab files
+                hparams = create_or_load_hparams(
+                    ckpt_dir, default_hparams, flags.hparams_path, save_hparams=False
+                )
                 loaded_hparams = True
-        
+
         assert loaded_hparams
 
         # GPU device
         config_proto = utils.get_config_proto(
             allow_soft_placement=True,
             num_intra_threads=hparams.num_intra_threads,
-            num_inter_threads=hparams.num_inter_threads)
+            num_inter_threads=hparams.num_inter_threads,
+        )
         utils.print_out(
-            "# Devices visible to TensorFlow: %s" 
-            % repr(tf.Session(config=config_proto).list_devices()))
-
+            "# Devices visible to TensorFlow: %s"
+            % repr(tf.Session(config=config_proto).list_devices())
+        )
 
-        # Inference indices (inference_indices is broken, but without setting it to None we'll crash)
+        # Inference indices (inference_indices is broken, but without setting
+        # it to None we'll crash)
         hparams.inference_indices = None
-        
+
         # Create the graph
         model_creator = get_model_creator(hparams)
-        infer_model = model_helper.create_infer_model(model_creator, hparams, scope=None)
-        sess, loaded_infer_model = start_sess_and_load_model(infer_model, flags.ckpt,
-                                                       hparams)
+        infer_model = model_helper.create_infer_model(
+            model_creator, hparams, scope=None
+        )
+        sess, loaded_infer_model = start_sess_and_load_model(
+            infer_model, flags.ckpt, hparams
+        )
 
         # Parameters needed by TF GNMT
         self.hparams = hparams
@@ -165,28 +188,37 @@ def translate(self, sentence_id_list):
             self.sess.run(
                 self.infer_model.iterator.initializer,
                 feed_dict={
-                    self.infer_model.src_placeholder: [self.infer_data[i] for i in sentence_id_list],
-                    self.infer_model.batch_size_placeholder: min(self.hparams.infer_batch_size, len(sentence_id_list))
-                })
+                    self.infer_model.src_placeholder: [
+                        self.infer_data[i] for i in sentence_id_list
+                    ],
+                    self.infer_model.batch_size_placeholder: min(
+                        self.hparams.infer_batch_size, len(sentence_id_list)
+                    ),
+                },
+            )
 
         # Start the translation
         nmt_outputs, _ = self.loaded_infer_model.decode(self.sess)
         if infer_mode != "beam_search":
-          nmt_outputs = np.expand_dims(nmt_outputs, 0)
+            nmt_outputs = np.expand_dims(nmt_outputs, 0)
 
         batch_size = nmt_outputs.shape[1]
         assert batch_size <= self.hparams.infer_batch_size
 
-        # Whether beam search is being used or not, we only want 1 final translation
+        # Whether beam search is being used or not, we only want 1 final
+        # translation
         assert self.hparams.num_translations_per_input == 1
 
         translation = []
         for decoded_id in range(batch_size):
-            translation += [nmt_utils.get_translation(
-                        nmt_outputs[0],
-                       decoded_id,
-                       tgt_eos=self.hparams.eos,
-                       subword_option=self.hparams.subword_option)]
+            translation += [
+                nmt_utils.get_translation(
+                    nmt_outputs[0],
+                    decoded_id,
+                    tgt_eos=self.hparams.eos,
+                    subword_option=self.hparams.subword_option,
+                )
+            ]
 
         # Keeping track of how many translations happened
         self.count += len(translation)
@@ -205,24 +237,28 @@ def getBatchSize(self):
     def load_sentences(self, input_file):
         self.infer_data = load_data(input_file, self.hparams)
 
+
 ##
 # @brief Basic class in which LoadGen can store queries that will be processed by GNMT
-class GNMTRunner (Runner):
+class GNMTRunner(Runner):
     ##
-    # @brief Constructor 
+    # @brief Constructor
     # @param model: GNMTWrapper object
     # @param input_file: path to the input text
     # @param verbose: provide some information on the progress
     def __init__(self, model, input_file=None, verbose=False):
         Runner.__init__(self)
 
-        # If no value is provided for the construtor arguments, set defaults here
+        # If no value is provided for the construtor arguments, set defaults
+        # here
         if input_file is None:
-            input_file = os.path.join(os.getcwd(), 'nmt', 'data', 'newstest2014.tok.bpe.32000.en')
+            input_file = os.path.join(
+                os.getcwd(), "nmt", "data", "newstest2014.tok.bpe.32000.en"
+            )
 
         self.gnmt = model
         self.input_file = input_file
-        
+
         self.VERBOSE = verbose
 
     ##
@@ -258,12 +294,14 @@ def process(self, qitem):
 
         # Split the samples over batches
         for i in range(0, num_samples, bs):
-            cur_sentid_list = [index for index in qitem.sentence_id_list[i:min(i+bs, num_samples)]] 
+            cur_sentid_list = [
+                index for index in qitem.sentence_id_list[i: min(i + bs, num_samples)]
+            ]
             translation += self.gnmt.translate(cur_sentid_list)
 
         if self.VERBOSE:
             print("Performed {} translations".format(self.gnmt.getCount()))
-        
+
         return translation
 
     ##
@@ -272,40 +310,50 @@ def enqueue(self, query_samples):
         if self.VERBOSE:
             print("Received query")
         query_id_list = [sample.id for sample in query_samples]
-        sentence_id_list = [sample.index for sample in query_samples] 
+        sentence_id_list = [sample.index for sample in query_samples]
         task = BatchTranslationTask(sentence_id_list, query_id_list)
         self.tasks.put(task)
 
     ##
     # @brief Serialize the result and give it to mlperf_loadgen
     # @param query_ids is a list of query ids that generated the samples
-    # @param results is a list of UTF-8 encoded strings 
+    # @param results is a list of UTF-8 encoded strings
     # @note Because of Python's Garbage Collection, we need to call QuerySamplesComplete before returning
     def post_process(self, query_ids, results):
         response = []
         # To prevent the garbage collector from removing serialized data before the call to QuerySamplesComplete
         # we need to keep track of serialized data here.
-        gc_hack = []    
+        gc_hack = []
         for res, q_id in zip(results, query_ids):
-            result_arr = array.array('B', res)
+            result_arr = array.array("B", res)
             gc_hack.append(result_arr)
             r_info = result_arr.buffer_info()
-            response.append(mlperf_loadgen.QuerySampleResponse(q_id, r_info[0], r_info[1]))
+            response.append(
+                mlperf_loadgen.QuerySampleResponse(q_id, r_info[0], r_info[1])
+            )
 
         # Tell loadgen that we're ready with this query
         mlperf_loadgen.QuerySamplesComplete(response)
 
+
 ##
 # @brief Subclass of GNMTRunner, specialized for batch size 1
-class SingleStreamGNMTRunner (GNMTRunner):
+class SingleStreamGNMTRunner(GNMTRunner):
     ##
-    # @brief Constructor 
+    # @brief Constructor
     # @param model: GNMTWrapper object
     # @param input_file: path to the input text
     # @param store_translation: whether output should be stored
     # @param verbose: provide some information on the progress
     # @param outdir: Output directory to optionally write translations to
-    def __init__(self, model, input_file=None, store_translation=False, verbose=False, outdir=None):
+    def __init__(
+        self,
+        model,
+        input_file=None,
+        store_translation=False,
+        verbose=False,
+        outdir=None,
+    ):
         GNMTRunner.__init__(self, model, input_file, verbose=verbose)
 
         # SingleStreamGNMTRunner only handles batch sizes of 1
@@ -319,12 +367,14 @@ def __init__(self, model, input_file=None, store_translation=False, verbose=Fals
     def process(self, qitem):
         if self.store_translation or self.VERBOSE:
             assert len(qitem.query_id) == 1
-            msg = "translate {} (QID {}): Sentence ID {}".format(self.gnmt.getCount(), qitem.query_id[0], qitem.sentence_id)
+            msg = "translate {} (QID {}): Sentence ID {}".format(
+                self.gnmt.getCount(), qitem.query_id[0], qitem.sentence_id
+            )
             if self.store_translation:
                 msg += " --> " + qitem.output_file
-            print (msg)
-       
-        sentence_id = qitem.sentence_id 
+            print(msg)
+
+        sentence_id = qitem.sentence_id
 
         translation = self.gnmt.translate([sentence_id])
 
@@ -337,8 +387,9 @@ def process(self, qitem):
     ##
     # @brief Write translation to file
     def write_output(self, translation, trans_file):
-          with codecs.getwriter("utf-8")(
-              tf.gfile.GFile(trans_file, mode="wb")) as trans_f:
+        with codecs.getwriter("utf-8")(
+            tf.gfile.GFile(trans_file, mode="wb")
+        ) as trans_f:
             trans_f.write((translation + b"\n").decode("utf-8"))
 
     ##
@@ -347,21 +398,25 @@ def enqueue(self, query_samples):
         assert len(query_samples) == 1
         sample = query_samples[0]
         sentence_id = sample.index
-        output_file = os.path.join(self.out_dir, "sentence_{}_de".format(sample.index))
+        output_file = os.path.join(
+            self.out_dir,
+            "sentence_{}_de".format(
+                sample.index))
 
         task = TranslationTask(sample.id, sentence_id, output_file)
         self.tasks.put(task)
 
+
 ##
 # @brief subclass of GNMTRunner, specialized for grouping multiple querries together
 class ServerGNMTRunner(GNMTRunner):
     ##
-    # @brief Constructor 
+    # @brief Constructor
     # @param model: GNMTWrapper object
     # @param input_file: path to the input text
     # @param verbose: provide some information on the progress
     def __init__(self, model, input_file=None, verbose=False):
-            GNMTRunner.__init__(self, model, input_file, verbose)
+        GNMTRunner.__init__(self, model, input_file, verbose)
 
     ##
     # @brief Override the default handle_tasks loop for smart batching
@@ -382,7 +437,7 @@ def handle_tasks(self):
             # or until we aggregated all current qurries
             try:
                 # @note that by definition, Server queries should have no more than 1 element
-                # Therefore we don't need to worry that batched_querries would be come larger than 
+                # Therefore we don't need to worry that batched_querries would be come larger than
                 # the batch size
                 while len(sentence_id_list) < self.gnmt.getBatchSize():
                     qitem = self.tasks.get(block=False)
@@ -396,10 +451,15 @@ def handle_tasks(self):
             except queue.Empty as e:
                 pass
 
-            batched_qitem = BatchTranslationTask(sentence_id_list, query_id_list)
+            batched_qitem = BatchTranslationTask(
+                sentence_id_list, query_id_list)
 
             if self.VERBOSE:
-                print("Aggregated {} single-sample querries.".format(len(batched_qitem.sentence_id_list)))
+                print(
+                    "Aggregated {} single-sample querries.".format(
+                        len(batched_qitem.sentence_id_list)
+                    )
+                )
 
             results = self.process(batched_qitem)
             response = []
@@ -412,43 +472,71 @@ def handle_tasks(self):
 
 if __name__ == "__main__":
     SCENARIO_MAP = {
-    "SingleStream": mlperf_loadgen.TestScenario.SingleStream,
-    "MultiStream": mlperf_loadgen.TestScenario.MultiStream,
-    "Server": mlperf_loadgen.TestScenario.Server,
-    "Offline": mlperf_loadgen.TestScenario.Offline,
+        "SingleStream": mlperf_loadgen.TestScenario.SingleStream,
+        "MultiStream": mlperf_loadgen.TestScenario.MultiStream,
+        "Server": mlperf_loadgen.TestScenario.Server,
+        "Offline": mlperf_loadgen.TestScenario.Offline,
     }
 
     MODE_MAP = {
         "Performance": mlperf_loadgen.TestMode.PerformanceOnly,
-        "Accuracy": mlperf_loadgen.TestMode.AccuracyOnly
+        "Accuracy": mlperf_loadgen.TestMode.AccuracyOnly,
     }
 
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('--scenario', type=str, default='SingleStream',
-                            help="Scenario to be run: can be one of {SingleStream, Offline, MultiStream, Server}")
-
-    parser.add_argument('--batch_size', type=int, default=32,
-                            help="Max batch size to use in Offline and MultiStream scenarios.")
-
-    parser.add_argument('--store_translation', default=False, action='store_true',
-                            help="Store the output of translation? Note: Only valid with SingleStream scenario.")
-
-    parser.add_argument('--verbose', default=False, action='store_true',
-                            help="Verbose output.")
-
-    parser.add_argument("--mode", default="Performance", help="Can be one of {Performance, Accuracy}")
-
-    parser.add_argument("--debug_settings", default=False, action='store_true', 
-        help="For debugging purposes, modify settings to small number of querries.")
-
-    parser.add_argument("--qps", type=int, default=10, help="target qps estimate")
-
-    parser.add_argument("--max-latency", type=str, default="0.100", help="mlperf max latency in 99pct tile")
+    parser.add_argument(
+        "--scenario",
+        type=str,
+        default="SingleStream",
+        help="Scenario to be run: can be one of {SingleStream, Offline, MultiStream, Server}",
+    )
+
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=32,
+        help="Max batch size to use in Offline and MultiStream scenarios.",
+    )
+
+    parser.add_argument(
+        "--store_translation",
+        default=False,
+        action="store_true",
+        help="Store the output of translation? Note: Only valid with SingleStream scenario.",
+    )
+
+    parser.add_argument(
+        "--verbose", default=False, action="store_true", help="Verbose output."
+    )
+
+    parser.add_argument(
+        "--mode", default="Performance", help="Can be one of {Performance, Accuracy}"
+    )
+
+    parser.add_argument(
+        "--debug_settings",
+        default=False,
+        action="store_true",
+        help="For debugging purposes, modify settings to small number of querries.",
+    )
+
+    parser.add_argument(
+        "--qps",
+        type=int,
+        default=10,
+        help="target qps estimate")
+
+    parser.add_argument(
+        "--max-latency",
+        type=str,
+        default="0.100",
+        help="mlperf max latency in 99pct tile",
+    )
 
     args = parser.parse_args()
 
-    outdir = os.path.join(os.getcwd(), 'lg_output')
+    outdir = os.path.join(os.getcwd(), "lg_output")
 
     # Create loadGen settings
     settings = mlperf_loadgen.TestSettings()
@@ -468,9 +556,13 @@ def handle_tasks(self):
 
     # Specify input file
     if args.mode == "Accuracy":
-        input_file = os.path.join(os.getcwd(), 'nmt', 'data', "newstest2014.tok.bpe.32000.en")
+        input_file = os.path.join(
+            os.getcwd(), "nmt", "data", "newstest2014.tok.bpe.32000.en"
+        )
     else:
-        input_file = os.path.join(os.getcwd(), 'nmt', 'data', "newstest2014.tok.bpe.32000.en.large")
+        input_file = os.path.join(
+            os.getcwd(), "nmt", "data", "newstest2014.tok.bpe.32000.en.large"
+        )
 
     # Build the GNMT model
     if args.scenario == "SingleStream":
@@ -478,27 +570,39 @@ def handle_tasks(self):
     else:
         batch_size = args.batch_size
 
-    gnmt_model = GNMTWrapper(batch_size = batch_size, outdir=outdir)
+    gnmt_model = GNMTWrapper(batch_size=batch_size, outdir=outdir)
 
     if args.scenario == "SingleStream":
-        runner = SingleStreamGNMTRunner(gnmt_model, input_file=input_file, store_translation=args.store_translation, verbose=args.verbose, outdir=outdir)
-        
+        runner = SingleStreamGNMTRunner(
+            gnmt_model,
+            input_file=input_file,
+            store_translation=args.store_translation,
+            verbose=args.verbose,
+            outdir=outdir,
+        )
+
         # Specify exactly how many queries need to be made
         if args.debug_settings:
             settings.min_query_count = 80
             settings.max_query_count = 80
 
     elif args.scenario == "Offline":
-        runner = GNMTRunner(gnmt_model, input_file=input_file, verbose=args.verbose)
-        
+        runner = GNMTRunner(
+            gnmt_model,
+            input_file=input_file,
+            verbose=args.verbose)
+
         # Specify exactly how many queries need to be made
         if args.debug_settings:
             settings.min_query_count = 1
             settings.max_query_count = 1
 
     elif args.scenario == "MultiStream":
-        runner = GNMTRunner(gnmt_model, input_file=input_file, verbose=args.verbose)
-        
+        runner = GNMTRunner(
+            gnmt_model,
+            input_file=input_file,
+            verbose=args.verbose)
+
         # Specify exactly how many queries need to be made
         if args.debug_settings:
             settings.min_query_count = 100
@@ -506,8 +610,10 @@ def handle_tasks(self):
             settings.multi_stream_samples_per_query = 8
 
     elif args.scenario == "Server":
-        runner = ServerGNMTRunner(gnmt_model, input_file=input_file, verbose=args.verbose)
-        
+        runner = ServerGNMTRunner(
+            gnmt_model, input_file=input_file, verbose=args.verbose
+        )
+
         # Specify exactly how many queries need to be made
         if args.debug_settings:
             settings.min_query_count = 20
@@ -519,13 +625,18 @@ def handle_tasks(self):
     # Create a thread in the GNMTRunner to start accepting work
     runner.start_worker()
 
-    total_queries = runner.getTotalNumSentences() # Maximum sample ID + 1
-    perf_queries = min(total_queries, 3003)   # Select the same subset of $perf_queries samples
+    total_queries = runner.getTotalNumSentences()  # Maximum sample ID + 1
+    perf_queries = min(
+        total_queries, 3003
+    )  # Select the same subset of $perf_queries samples
 
     sut = mlperf_loadgen.ConstructSUT(runner.enqueue, flush_queries)
     qsl = mlperf_loadgen.ConstructQSL(
-        total_queries, perf_queries, runner.load_samples_to_ram, runner.unload_samples_from_ram)
-
+        total_queries,
+        perf_queries,
+        runner.load_samples_to_ram,
+        runner.unload_samples_from_ram,
+    )
 
     # Start generating queries by starting the test
     # A single test for all non-server scenarios
@@ -536,7 +647,10 @@ def handle_tasks(self):
     # Multiple tests (depending on target latency array) for server scenario
     else:
         for target_latency in max_latency:
-            print("starting {} scenario, latency={}".format(args.scenario, target_latency))
+            print(
+                "starting {} scenario, latency={}".format(
+                    args.scenario, target_latency)
+            )
             settings.server_target_latency_ns = int(target_latency * NANO_SEC)
 
             mlperf_loadgen.StartTest(sut, qsl, settings)
@@ -544,4 +658,3 @@ def handle_tasks(self):
     runner.finish()
     mlperf_loadgen.DestroyQSL(qsl)
     mlperf_loadgen.DestroySUT(sut)
-
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/mlcube.py b/retired_benchmarks/translation/gnmt/tensorflow/mlcube.py
index f24708869b..0aab3c3e17 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/mlcube.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/mlcube.py
@@ -1,4 +1,5 @@
 """MLCube handler file"""
+
 import os
 import yaml
 import typer
@@ -21,7 +22,9 @@ def run(data_dir: str) -> None:
 
         env = os.environ.copy()
         env.update(
-            {"DATA_DIR": data_dir,}
+            {
+                "DATA_DIR": data_dir,
+            }
         )
 
         process = subprocess.Popen("./download_dataset.sh", cwd=".", env=env)
@@ -42,10 +45,13 @@ def run(model_dir: str) -> None:
 
         env = os.environ.copy()
         env.update(
-            {"MODEL_DIR": model_dir,}
+            {
+                "MODEL_DIR": model_dir,
+            }
         )
 
-        process = subprocess.Popen("./download_trained_model.sh", cwd=".", env=env)
+        process = subprocess.Popen(
+            "./download_trained_model.sh", cwd=".", env=env)
         process.wait()
 
 
@@ -59,18 +65,21 @@ class RunPerformanceTask(object):
     Then executes the benchmark script"""
 
     @staticmethod
-    def run(data_dir: str, model_dir: str, output_dir: str, parameters_file: str) -> None:
+    def run(
+        data_dir: str, model_dir: str, output_dir: str, parameters_file: str
+    ) -> None:
         with open(parameters_file, "r") as stream:
             parameters = yaml.safe_load(stream)
 
-        
         env = os.environ.copy()
-        env.update({
-            'DATA_DIR': os.path.join(data_dir, 'nmt', 'data'),
-            'MODEL_DIR': os.path.join(model_dir, 'ende_gnmt_model_4_layer'),
-            'OUTPUT_DIR': output_dir,
-            'TASK': "performance"
-        })
+        env.update(
+            {
+                "DATA_DIR": os.path.join(data_dir, "nmt", "data"),
+                "MODEL_DIR": os.path.join(model_dir, "ende_gnmt_model_4_layer"),
+                "OUTPUT_DIR": output_dir,
+                "TASK": "performance",
+            }
+        )
 
         env.update(parameters)
 
@@ -88,18 +97,21 @@ class RunAccuracyTask(object):
     Then executes the benchmark script"""
 
     @staticmethod
-    def run(data_dir: str, model_dir: str, output_dir: str, parameters_file: str) -> None:
+    def run(
+        data_dir: str, model_dir: str, output_dir: str, parameters_file: str
+    ) -> None:
         with open(parameters_file, "r") as stream:
             parameters = yaml.safe_load(stream)
 
-        
         env = os.environ.copy()
-        env.update({
-            'DATA_DIR': os.path.join(data_dir, 'nmt', 'data'),
-            'MODEL_DIR': os.path.join(model_dir, 'ende_gnmt_model_4_layer'),
-            'OUTPUT_DIR': output_dir,
-            'TASK': "accuracy"
-        })
+        env.update(
+            {
+                "DATA_DIR": os.path.join(data_dir, "nmt", "data"),
+                "MODEL_DIR": os.path.join(model_dir, "ende_gnmt_model_4_layer"),
+                "OUTPUT_DIR": output_dir,
+                "TASK": "accuracy",
+            }
+        )
 
         env.update(parameters)
 
@@ -126,6 +138,7 @@ def run_performance(
 ):
     RunPerformanceTask.run(data_dir, model_dir, output_dir, parameters_file)
 
+
 @app.command("run_accuracy")
 def run_accuracy(
     data_dir: str = typer.Option(..., "--data_dir"),
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/attention_model.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/attention_model.py
index d262b8e8ef..908be0824d 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/attention_model.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/attention_model.py
@@ -26,169 +26,184 @@
 
 
 class AttentionModel(model.Model):
-  """Sequence-to-sequence dynamic model with attention.
-
-  This class implements a multi-layer recurrent neural network as encoder,
-  and an attention-based decoder. This is the same as the model described in
-  (Luong et al., EMNLP'2015) paper: https://arxiv.org/pdf/1508.04025v5.pdf.
-  This class also allows to use GRU cells in addition to LSTM cells with
-  support for dropout.
-  """
-
-  def __init__(self,
-               hparams,
-               mode,
-               iterator,
-               source_vocab_table,
-               target_vocab_table,
-               reverse_target_vocab_table=None,
-               scope=None,
-               extra_args=None):
-    self.has_attention = hparams.attention_architecture and hparams.attention
-
-    # Set attention_mechanism_fn
-    if self.has_attention:
-      if extra_args and extra_args.attention_mechanism_fn:
-        self.attention_mechanism_fn = extra_args.attention_mechanism_fn
-      else:
-        self.attention_mechanism_fn = create_attention_mechanism
-
-    super(AttentionModel, self).__init__(
-        hparams=hparams,
-        mode=mode,
-        iterator=iterator,
-        source_vocab_table=source_vocab_table,
-        target_vocab_table=target_vocab_table,
-        reverse_target_vocab_table=reverse_target_vocab_table,
-        scope=scope,
-        extra_args=extra_args)
-
-  def _prepare_beam_search_decoder_inputs(
-      self, beam_width, memory, source_sequence_length, encoder_state):
-    memory = tf.contrib.seq2seq.tile_batch(
-        memory, multiplier=beam_width)
-    source_sequence_length = tf.contrib.seq2seq.tile_batch(
-        source_sequence_length, multiplier=beam_width)
-    encoder_state = tf.contrib.seq2seq.tile_batch(
-        encoder_state, multiplier=beam_width)
-    batch_size = self.batch_size * beam_width
-    return memory, source_sequence_length, encoder_state, batch_size
-
-  def _build_decoder_cell(self, hparams, encoder_outputs, encoder_state,
-                          source_sequence_length):
-    """Build a RNN cell with attention mechanism that can be used by decoder."""
-    # No Attention
-    if not self.has_attention:
-      return super(AttentionModel, self)._build_decoder_cell(
-          hparams, encoder_outputs, encoder_state, source_sequence_length)
-    elif hparams.attention_architecture != "standard":
-      raise ValueError(
-          "Unknown attention architecture %s" % hparams.attention_architecture)
-
-    num_units = hparams.num_units
-    num_layers = self.num_decoder_layers
-    num_residual_layers = self.num_decoder_residual_layers
-    infer_mode = hparams.infer_mode
-
-    dtype = tf.float32
-
-    # Ensure memory is batch-major
-    if self.time_major:
-      memory = tf.transpose(encoder_outputs, [1, 0, 2])
+    """Sequence-to-sequence dynamic model with attention.
+
+    This class implements a multi-layer recurrent neural network as encoder,
+    and an attention-based decoder. This is the same as the model described in
+    (Luong et al., EMNLP'2015) paper: https://arxiv.org/pdf/1508.04025v5.pdf.
+    This class also allows to use GRU cells in addition to LSTM cells with
+    support for dropout.
+    """
+
+    def __init__(
+        self,
+        hparams,
+        mode,
+        iterator,
+        source_vocab_table,
+        target_vocab_table,
+        reverse_target_vocab_table=None,
+        scope=None,
+        extra_args=None,
+    ):
+        self.has_attention = hparams.attention_architecture and hparams.attention
+
+        # Set attention_mechanism_fn
+        if self.has_attention:
+            if extra_args and extra_args.attention_mechanism_fn:
+                self.attention_mechanism_fn = extra_args.attention_mechanism_fn
+            else:
+                self.attention_mechanism_fn = create_attention_mechanism
+
+        super(AttentionModel, self).__init__(
+            hparams=hparams,
+            mode=mode,
+            iterator=iterator,
+            source_vocab_table=source_vocab_table,
+            target_vocab_table=target_vocab_table,
+            reverse_target_vocab_table=reverse_target_vocab_table,
+            scope=scope,
+            extra_args=extra_args,
+        )
+
+    def _prepare_beam_search_decoder_inputs(
+        self, beam_width, memory, source_sequence_length, encoder_state
+    ):
+        memory = tf.contrib.seq2seq.tile_batch(memory, multiplier=beam_width)
+        source_sequence_length = tf.contrib.seq2seq.tile_batch(
+            source_sequence_length, multiplier=beam_width
+        )
+        encoder_state = tf.contrib.seq2seq.tile_batch(
+            encoder_state, multiplier=beam_width
+        )
+        batch_size = self.batch_size * beam_width
+        return memory, source_sequence_length, encoder_state, batch_size
+
+    def _build_decoder_cell(
+        self, hparams, encoder_outputs, encoder_state, source_sequence_length
+    ):
+        """Build a RNN cell with attention mechanism that can be used by decoder."""
+        # No Attention
+        if not self.has_attention:
+            return super(AttentionModel, self)._build_decoder_cell(
+                hparams, encoder_outputs, encoder_state, source_sequence_length
+            )
+        elif hparams.attention_architecture != "standard":
+            raise ValueError(
+                "Unknown attention architecture %s" % hparams.attention_architecture
+            )
+
+        num_units = hparams.num_units
+        num_layers = self.num_decoder_layers
+        num_residual_layers = self.num_decoder_residual_layers
+        infer_mode = hparams.infer_mode
+
+        dtype = tf.float32
+
+        # Ensure memory is batch-major
+        if self.time_major:
+            memory = tf.transpose(encoder_outputs, [1, 0, 2])
+        else:
+            memory = encoder_outputs
+
+        if self.mode == tf.contrib.learn.ModeKeys.INFER and infer_mode == "beam_search":
+            memory, source_sequence_length, encoder_state, batch_size = (
+                self._prepare_beam_search_decoder_inputs(
+                    hparams.beam_width, memory, source_sequence_length, encoder_state
+                )
+            )
+        else:
+            batch_size = self.batch_size
+
+        # Attention
+        attention_mechanism = self.attention_mechanism_fn(
+            hparams.attention, num_units, memory, source_sequence_length, self.mode
+        )
+
+        cell = model_helper.create_rnn_cell(
+            unit_type=hparams.unit_type,
+            num_units=num_units,
+            num_layers=num_layers,
+            num_residual_layers=num_residual_layers,
+            forget_bias=hparams.forget_bias,
+            dropout=hparams.dropout,
+            num_gpus=self.num_gpus,
+            mode=self.mode,
+            single_cell_fn=self.single_cell_fn,
+        )
+
+        # Only generate alignment in greedy INFER mode.
+        alignment_history = (
+            self.mode == tf.contrib.learn.ModeKeys.INFER and infer_mode != "beam_search"
+        )
+        cell = tf.contrib.seq2seq.AttentionWrapper(
+            cell,
+            attention_mechanism,
+            attention_layer_size=num_units,
+            alignment_history=alignment_history,
+            output_attention=hparams.output_attention,
+            name="attention",
+        )
+
+        # TODO(thangluong): do we need num_layers, num_gpus?
+        cell = tf.contrib.rnn.DeviceWrapper(
+            cell, model_helper.get_device_str(num_layers - 1, self.num_gpus)
+        )
+
+        if hparams.pass_hidden_state:
+            decoder_initial_state = cell.zero_state(batch_size, dtype).clone(
+                cell_state=encoder_state
+            )
+        else:
+            decoder_initial_state = cell.zero_state(batch_size, dtype)
+
+        return cell, decoder_initial_state
+
+    def _get_infer_summary(self, hparams):
+        if not self.has_attention or hparams.infer_mode == "beam_search":
+            return tf.no_op()
+        return _create_attention_images_summary(self.final_context_state)
+
+
+def create_attention_mechanism(
+    attention_option, num_units, memory, source_sequence_length, mode
+):
+    """Create attention mechanism based on the attention_option."""
+    del mode  # unused
+
+    # Mechanism
+    if attention_option == "luong":
+        attention_mechanism = tf.contrib.seq2seq.LuongAttention(
+            num_units, memory, memory_sequence_length=source_sequence_length
+        )
+    elif attention_option == "scaled_luong":
+        attention_mechanism = tf.contrib.seq2seq.LuongAttention(
+            num_units, memory, memory_sequence_length=source_sequence_length, scale=True
+        )
+    elif attention_option == "bahdanau":
+        attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(
+            num_units, memory, memory_sequence_length=source_sequence_length
+        )
+    elif attention_option == "normed_bahdanau":
+        attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(
+            num_units,
+            memory,
+            memory_sequence_length=source_sequence_length,
+            normalize=True,
+        )
     else:
-      memory = encoder_outputs
-
-    if (self.mode == tf.contrib.learn.ModeKeys.INFER and
-        infer_mode == "beam_search"):
-      memory, source_sequence_length, encoder_state, batch_size = (
-          self._prepare_beam_search_decoder_inputs(
-              hparams.beam_width, memory, source_sequence_length,
-              encoder_state))
-    else:
-      batch_size = self.batch_size
-
-    # Attention
-    attention_mechanism = self.attention_mechanism_fn(
-        hparams.attention, num_units, memory, source_sequence_length, self.mode)
-
-    cell = model_helper.create_rnn_cell(
-        unit_type=hparams.unit_type,
-        num_units=num_units,
-        num_layers=num_layers,
-        num_residual_layers=num_residual_layers,
-        forget_bias=hparams.forget_bias,
-        dropout=hparams.dropout,
-        num_gpus=self.num_gpus,
-        mode=self.mode,
-        single_cell_fn=self.single_cell_fn)
-
-    # Only generate alignment in greedy INFER mode.
-    alignment_history = (self.mode == tf.contrib.learn.ModeKeys.INFER and
-                         infer_mode != "beam_search")
-    cell = tf.contrib.seq2seq.AttentionWrapper(
-        cell,
-        attention_mechanism,
-        attention_layer_size=num_units,
-        alignment_history=alignment_history,
-        output_attention=hparams.output_attention,
-        name="attention")
-
-    # TODO(thangluong): do we need num_layers, num_gpus?
-    cell = tf.contrib.rnn.DeviceWrapper(cell,
-                                        model_helper.get_device_str(
-                                            num_layers - 1, self.num_gpus))
-
-    if hparams.pass_hidden_state:
-      decoder_initial_state = cell.zero_state(batch_size, dtype).clone(
-          cell_state=encoder_state)
-    else:
-      decoder_initial_state = cell.zero_state(batch_size, dtype)
-
-    return cell, decoder_initial_state
-
-  def _get_infer_summary(self, hparams):
-    if not self.has_attention or hparams.infer_mode == "beam_search":
-      return tf.no_op()
-    return _create_attention_images_summary(self.final_context_state)
-
-
-def create_attention_mechanism(attention_option, num_units, memory,
-                               source_sequence_length, mode):
-  """Create attention mechanism based on the attention_option."""
-  del mode  # unused
-
-  # Mechanism
-  if attention_option == "luong":
-    attention_mechanism = tf.contrib.seq2seq.LuongAttention(
-        num_units, memory, memory_sequence_length=source_sequence_length)
-  elif attention_option == "scaled_luong":
-    attention_mechanism = tf.contrib.seq2seq.LuongAttention(
-        num_units,
-        memory,
-        memory_sequence_length=source_sequence_length,
-        scale=True)
-  elif attention_option == "bahdanau":
-    attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(
-        num_units, memory, memory_sequence_length=source_sequence_length)
-  elif attention_option == "normed_bahdanau":
-    attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(
-        num_units,
-        memory,
-        memory_sequence_length=source_sequence_length,
-        normalize=True)
-  else:
-    raise ValueError("Unknown attention option %s" % attention_option)
-
-  return attention_mechanism
+        raise ValueError("Unknown attention option %s" % attention_option)
+
+    return attention_mechanism
 
 
 def _create_attention_images_summary(final_context_state):
-  """create attention image and attention summary."""
-  attention_images = (final_context_state.alignment_history.stack())
-  # Reshape to (batch, src_seq_len, tgt_seq_len,1)
-  attention_images = tf.expand_dims(
-      tf.transpose(attention_images, [1, 2, 0]), -1)
-  # Scale to range [0, 255]
-  attention_images *= 255
-  attention_summary = tf.summary.image("attention_images", attention_images)
-  return attention_summary
+    """create attention image and attention summary."""
+    attention_images = final_context_state.alignment_history.stack()
+    # Reshape to (batch, src_seq_len, tgt_seq_len,1)
+    attention_images = tf.expand_dims(
+        tf.transpose(attention_images, [1, 2, 0]), -1)
+    # Scale to range [0, 255]
+    attention_images *= 255
+    attention_summary = tf.summary.image("attention_images", attention_images)
+    return attention_summary
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/gnmt_model.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/gnmt_model.py
index 468a5d00cf..d86e6e23a4 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/gnmt_model.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/gnmt_model.py
@@ -29,305 +29,344 @@
 
 
 class GNMTModel(attention_model.AttentionModel):
-  """Sequence-to-sequence dynamic model with GNMT attention architecture.
-  """
-
-  def __init__(self,
-               hparams,
-               mode,
-               iterator,
-               source_vocab_table,
-               target_vocab_table,
-               reverse_target_vocab_table=None,
-               scope=None,
-               extra_args=None):
-    self.is_gnmt_attention = (
-        hparams.attention_architecture in ["gnmt", "gnmt_v2"])
-
-    super(GNMTModel, self).__init__(
-        hparams=hparams,
-        mode=mode,
-        iterator=iterator,
-        source_vocab_table=source_vocab_table,
-        target_vocab_table=target_vocab_table,
-        reverse_target_vocab_table=reverse_target_vocab_table,
-        scope=scope,
-        extra_args=extra_args)
-
-  def _build_encoder(self, hparams):
-    """Build a GNMT encoder."""
-    if hparams.encoder_type == "uni" or hparams.encoder_type == "bi":
-      return super(GNMTModel, self)._build_encoder(hparams)
-
-    if hparams.encoder_type != "gnmt":
-      raise ValueError("Unknown encoder_type %s" % hparams.encoder_type)
-
-    # Build GNMT encoder.
-    num_bi_layers = 1
-    num_uni_layers = self.num_encoder_layers - num_bi_layers
-    utils.print_out("# Build a GNMT encoder")
-    utils.print_out("  num_bi_layers = %d" % num_bi_layers)
-    utils.print_out("  num_uni_layers = %d" % num_uni_layers)
-
-    iterator = self.iterator
-    source = iterator.source
-    if self.time_major:
-      source = tf.transpose(source)
-
-    with tf.variable_scope("encoder") as scope:
-      dtype = scope.dtype
-
-      self.encoder_emb_inp = self.encoder_emb_lookup_fn(
-          self.embedding_encoder, source)
-
-      # Execute _build_bidirectional_rnn from Model class
-      bi_encoder_outputs, bi_encoder_state = self._build_bidirectional_rnn(
-          inputs=self.encoder_emb_inp,
-          sequence_length=iterator.source_sequence_length,
-          dtype=dtype,
-          hparams=hparams,
-          num_bi_layers=num_bi_layers,
-          num_bi_residual_layers=0,  # no residual connection
-      )
-
-      # Build unidirectional layers
-      if self.extract_encoder_layers:
-        encoder_state, encoder_outputs = self._build_individual_encoder_layers(
-            bi_encoder_outputs, num_uni_layers, dtype, hparams)
-      else:
-        encoder_state, encoder_outputs = self._build_all_encoder_layers(
-            bi_encoder_outputs, num_uni_layers, dtype, hparams)
-
-      # Pass all encoder states to the decoder
-      #   except the first bi-directional layer
-      encoder_state = (bi_encoder_state[1],) + (
-          (encoder_state,) if num_uni_layers == 1 else encoder_state)
-
-    return encoder_outputs, encoder_state
-
-  def _build_all_encoder_layers(self, bi_encoder_outputs,
-                                num_uni_layers, dtype, hparams):
-    """Build encoder layers all at once."""
-    uni_cell = model_helper.create_rnn_cell(
-        unit_type=hparams.unit_type,
-        num_units=hparams.num_units,
-        num_layers=num_uni_layers,
-        num_residual_layers=self.num_encoder_residual_layers,
-        forget_bias=hparams.forget_bias,
-        dropout=hparams.dropout,
-        num_gpus=self.num_gpus,
-        base_gpu=1,
-        mode=self.mode,
-        single_cell_fn=self.single_cell_fn)
-    encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
-        uni_cell,
-        bi_encoder_outputs,
-        dtype=dtype,
-        sequence_length=self.iterator.source_sequence_length,
-        time_major=self.time_major)
-
-    # Use the top layer for now
-    self.encoder_state_list = [encoder_outputs]
-
-    return encoder_state, encoder_outputs
-
-  def _build_individual_encoder_layers(self, bi_encoder_outputs,
-                                       num_uni_layers, dtype, hparams):
-    """Run each of the encoder layer separately, not used in general seq2seq."""
-    uni_cell_lists = model_helper._cell_list(
-        unit_type=hparams.unit_type,
-        num_units=hparams.num_units,
-        num_layers=num_uni_layers,
-        num_residual_layers=self.num_encoder_residual_layers,
-        forget_bias=hparams.forget_bias,
-        dropout=hparams.dropout,
-        num_gpus=self.num_gpus,
-        base_gpu=1,
-        mode=self.mode,
-        single_cell_fn=self.single_cell_fn)
-
-    encoder_inp = bi_encoder_outputs
-    encoder_states = []
-    self.encoder_state_list = [bi_encoder_outputs[:, :, :hparams.num_units],
-                               bi_encoder_outputs[:, :, hparams.num_units:]]
-    with tf.variable_scope("rnn/multi_rnn_cell"):
-      for i, cell in enumerate(uni_cell_lists):
-        with tf.variable_scope("cell_%d" % i) as scope:
-          encoder_inp, encoder_state = tf.nn.dynamic_rnn(
-              cell,
-              encoder_inp,
-              dtype=dtype,
-              sequence_length=self.iterator.source_sequence_length,
-              time_major=self.time_major,
-              scope=scope)
-          encoder_states.append(encoder_state)
-          self.encoder_state_list.append(encoder_inp)
-
-    encoder_state = tuple(encoder_states)
-    encoder_outputs = self.encoder_state_list[-1]
-    return encoder_state, encoder_outputs
-
-  def _build_decoder_cell(self, hparams, encoder_outputs, encoder_state,
-                          source_sequence_length):
-    """Build a RNN cell with GNMT attention architecture."""
-    # Standard attention
-    if not self.is_gnmt_attention:
-      return super(GNMTModel, self)._build_decoder_cell(
-          hparams, encoder_outputs, encoder_state, source_sequence_length)
-
-    # GNMT attention
-    attention_option = hparams.attention
-    attention_architecture = hparams.attention_architecture
-    num_units = hparams.num_units
-    infer_mode = hparams.infer_mode
-
-    dtype = tf.float32
-
-    if self.time_major:
-      memory = tf.transpose(encoder_outputs, [1, 0, 2])
-    else:
-      memory = encoder_outputs
-
-    if (self.mode == tf.contrib.learn.ModeKeys.INFER and
-        infer_mode == "beam_search"):
-      memory, source_sequence_length, encoder_state, batch_size = (
-          self._prepare_beam_search_decoder_inputs(
-              hparams.beam_width, memory, source_sequence_length,
-              encoder_state))
-    else:
-      batch_size = self.batch_size
-
-    attention_mechanism = self.attention_mechanism_fn(
-        attention_option, num_units, memory, source_sequence_length, self.mode)
-
-    cell_list = model_helper._cell_list(  # pylint: disable=protected-access
-        unit_type=hparams.unit_type,
-        num_units=num_units,
-        num_layers=self.num_decoder_layers,
-        num_residual_layers=self.num_decoder_residual_layers,
-        forget_bias=hparams.forget_bias,
-        dropout=hparams.dropout,
-        num_gpus=self.num_gpus,
-        mode=self.mode,
-        single_cell_fn=self.single_cell_fn,
-        residual_fn=gnmt_residual_fn
-    )
-
-    # Only wrap the bottom layer with the attention mechanism.
-    attention_cell = cell_list.pop(0)
-
-    # Only generate alignment in greedy INFER mode.
-    alignment_history = (self.mode == tf.contrib.learn.ModeKeys.INFER and
-                         infer_mode != "beam_search")
-    attention_cell = tf.contrib.seq2seq.AttentionWrapper(
-        attention_cell,
-        attention_mechanism,
-        attention_layer_size=None,  # don't use attention layer.
-        output_attention=False,
-        alignment_history=alignment_history,
-        name="attention")
-
-    if attention_architecture == "gnmt":
-      cell = GNMTAttentionMultiCell(
-          attention_cell, cell_list)
-    elif attention_architecture == "gnmt_v2":
-      cell = GNMTAttentionMultiCell(
-          attention_cell, cell_list, use_new_attention=True)
-    else:
-      raise ValueError(
-          "Unknown attention_architecture %s" % attention_architecture)
-
-    if hparams.pass_hidden_state:
-      decoder_initial_state = tuple(
-          zs.clone(cell_state=es)
-          if isinstance(zs, tf.contrib.seq2seq.AttentionWrapperState) else es
-          for zs, es in zip(
-              cell.zero_state(batch_size, dtype), encoder_state))
-    else:
-      decoder_initial_state = cell.zero_state(batch_size, dtype)
-
-    return cell, decoder_initial_state
-
-  def _get_infer_summary(self, hparams):
-    if hparams.infer_mode == "beam_search":
-      return tf.no_op()
-    elif self.is_gnmt_attention:
-      return attention_model._create_attention_images_summary(
-          self.final_context_state[0])
-    else:
-      return super(GNMTModel, self)._get_infer_summary(hparams)
+    """Sequence-to-sequence dynamic model with GNMT attention architecture."""
+
+    def __init__(
+        self,
+        hparams,
+        mode,
+        iterator,
+        source_vocab_table,
+        target_vocab_table,
+        reverse_target_vocab_table=None,
+        scope=None,
+        extra_args=None,
+    ):
+        self.is_gnmt_attention = hparams.attention_architecture in [
+            "gnmt", "gnmt_v2"]
+
+        super(GNMTModel, self).__init__(
+            hparams=hparams,
+            mode=mode,
+            iterator=iterator,
+            source_vocab_table=source_vocab_table,
+            target_vocab_table=target_vocab_table,
+            reverse_target_vocab_table=reverse_target_vocab_table,
+            scope=scope,
+            extra_args=extra_args,
+        )
+
+    def _build_encoder(self, hparams):
+        """Build a GNMT encoder."""
+        if hparams.encoder_type == "uni" or hparams.encoder_type == "bi":
+            return super(GNMTModel, self)._build_encoder(hparams)
+
+        if hparams.encoder_type != "gnmt":
+            raise ValueError("Unknown encoder_type %s" % hparams.encoder_type)
+
+        # Build GNMT encoder.
+        num_bi_layers = 1
+        num_uni_layers = self.num_encoder_layers - num_bi_layers
+        utils.print_out("# Build a GNMT encoder")
+        utils.print_out("  num_bi_layers = %d" % num_bi_layers)
+        utils.print_out("  num_uni_layers = %d" % num_uni_layers)
+
+        iterator = self.iterator
+        source = iterator.source
+        if self.time_major:
+            source = tf.transpose(source)
+
+        with tf.variable_scope("encoder") as scope:
+            dtype = scope.dtype
+
+            self.encoder_emb_inp = self.encoder_emb_lookup_fn(
+                self.embedding_encoder, source
+            )
+
+            # Execute _build_bidirectional_rnn from Model class
+            bi_encoder_outputs, bi_encoder_state = self._build_bidirectional_rnn(
+                inputs=self.encoder_emb_inp,
+                sequence_length=iterator.source_sequence_length,
+                dtype=dtype,
+                hparams=hparams,
+                num_bi_layers=num_bi_layers,
+                num_bi_residual_layers=0,  # no residual connection
+            )
+
+            # Build unidirectional layers
+            if self.extract_encoder_layers:
+                encoder_state, encoder_outputs = self._build_individual_encoder_layers(
+                    bi_encoder_outputs, num_uni_layers, dtype, hparams
+                )
+            else:
+                encoder_state, encoder_outputs = self._build_all_encoder_layers(
+                    bi_encoder_outputs, num_uni_layers, dtype, hparams
+                )
+
+            # Pass all encoder states to the decoder
+            #   except the first bi-directional layer
+            encoder_state = (bi_encoder_state[1],) + (
+                (encoder_state,) if num_uni_layers == 1 else encoder_state
+            )
+
+        return encoder_outputs, encoder_state
+
+    def _build_all_encoder_layers(
+        self, bi_encoder_outputs, num_uni_layers, dtype, hparams
+    ):
+        """Build encoder layers all at once."""
+        uni_cell = model_helper.create_rnn_cell(
+            unit_type=hparams.unit_type,
+            num_units=hparams.num_units,
+            num_layers=num_uni_layers,
+            num_residual_layers=self.num_encoder_residual_layers,
+            forget_bias=hparams.forget_bias,
+            dropout=hparams.dropout,
+            num_gpus=self.num_gpus,
+            base_gpu=1,
+            mode=self.mode,
+            single_cell_fn=self.single_cell_fn,
+        )
+        encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
+            uni_cell,
+            bi_encoder_outputs,
+            dtype=dtype,
+            sequence_length=self.iterator.source_sequence_length,
+            time_major=self.time_major,
+        )
+
+        # Use the top layer for now
+        self.encoder_state_list = [encoder_outputs]
+
+        return encoder_state, encoder_outputs
+
+    def _build_individual_encoder_layers(
+        self, bi_encoder_outputs, num_uni_layers, dtype, hparams
+    ):
+        """Run each of the encoder layer separately, not used in general seq2seq."""
+        uni_cell_lists = model_helper._cell_list(
+            unit_type=hparams.unit_type,
+            num_units=hparams.num_units,
+            num_layers=num_uni_layers,
+            num_residual_layers=self.num_encoder_residual_layers,
+            forget_bias=hparams.forget_bias,
+            dropout=hparams.dropout,
+            num_gpus=self.num_gpus,
+            base_gpu=1,
+            mode=self.mode,
+            single_cell_fn=self.single_cell_fn,
+        )
+
+        encoder_inp = bi_encoder_outputs
+        encoder_states = []
+        self.encoder_state_list = [
+            bi_encoder_outputs[:, :, : hparams.num_units],
+            bi_encoder_outputs[:, :, hparams.num_units:],
+        ]
+        with tf.variable_scope("rnn/multi_rnn_cell"):
+            for i, cell in enumerate(uni_cell_lists):
+                with tf.variable_scope("cell_%d" % i) as scope:
+                    encoder_inp, encoder_state = tf.nn.dynamic_rnn(
+                        cell,
+                        encoder_inp,
+                        dtype=dtype,
+                        sequence_length=self.iterator.source_sequence_length,
+                        time_major=self.time_major,
+                        scope=scope,
+                    )
+                    encoder_states.append(encoder_state)
+                    self.encoder_state_list.append(encoder_inp)
+
+        encoder_state = tuple(encoder_states)
+        encoder_outputs = self.encoder_state_list[-1]
+        return encoder_state, encoder_outputs
+
+    def _build_decoder_cell(
+        self, hparams, encoder_outputs, encoder_state, source_sequence_length
+    ):
+        """Build a RNN cell with GNMT attention architecture."""
+        # Standard attention
+        if not self.is_gnmt_attention:
+            return super(GNMTModel, self)._build_decoder_cell(
+                hparams, encoder_outputs, encoder_state, source_sequence_length
+            )
+
+        # GNMT attention
+        attention_option = hparams.attention
+        attention_architecture = hparams.attention_architecture
+        num_units = hparams.num_units
+        infer_mode = hparams.infer_mode
+
+        dtype = tf.float32
+
+        if self.time_major:
+            memory = tf.transpose(encoder_outputs, [1, 0, 2])
+        else:
+            memory = encoder_outputs
+
+        if self.mode == tf.contrib.learn.ModeKeys.INFER and infer_mode == "beam_search":
+            memory, source_sequence_length, encoder_state, batch_size = (
+                self._prepare_beam_search_decoder_inputs(
+                    hparams.beam_width, memory, source_sequence_length, encoder_state
+                )
+            )
+        else:
+            batch_size = self.batch_size
+
+        attention_mechanism = self.attention_mechanism_fn(
+            attention_option, num_units, memory, source_sequence_length, self.mode
+        )
+
+        cell_list = model_helper._cell_list(  # pylint: disable=protected-access
+            unit_type=hparams.unit_type,
+            num_units=num_units,
+            num_layers=self.num_decoder_layers,
+            num_residual_layers=self.num_decoder_residual_layers,
+            forget_bias=hparams.forget_bias,
+            dropout=hparams.dropout,
+            num_gpus=self.num_gpus,
+            mode=self.mode,
+            single_cell_fn=self.single_cell_fn,
+            residual_fn=gnmt_residual_fn,
+        )
+
+        # Only wrap the bottom layer with the attention mechanism.
+        attention_cell = cell_list.pop(0)
+
+        # Only generate alignment in greedy INFER mode.
+        alignment_history = (
+            self.mode == tf.contrib.learn.ModeKeys.INFER and infer_mode != "beam_search"
+        )
+        attention_cell = tf.contrib.seq2seq.AttentionWrapper(
+            attention_cell,
+            attention_mechanism,
+            attention_layer_size=None,  # don't use attention layer.
+            output_attention=False,
+            alignment_history=alignment_history,
+            name="attention",
+        )
+
+        if attention_architecture == "gnmt":
+            cell = GNMTAttentionMultiCell(attention_cell, cell_list)
+        elif attention_architecture == "gnmt_v2":
+            cell = GNMTAttentionMultiCell(
+                attention_cell, cell_list, use_new_attention=True
+            )
+        else:
+            raise ValueError(
+                "Unknown attention_architecture %s" % attention_architecture
+            )
+
+        if hparams.pass_hidden_state:
+            decoder_initial_state = tuple(
+                (
+                    zs.clone(cell_state=es)
+                    if isinstance(zs, tf.contrib.seq2seq.AttentionWrapperState)
+                    else es
+                )
+                for zs, es in zip(cell.zero_state(batch_size, dtype), encoder_state)
+            )
+        else:
+            decoder_initial_state = cell.zero_state(batch_size, dtype)
+
+        return cell, decoder_initial_state
+
+    def _get_infer_summary(self, hparams):
+        if hparams.infer_mode == "beam_search":
+            return tf.no_op()
+        elif self.is_gnmt_attention:
+            return attention_model._create_attention_images_summary(
+                self.final_context_state[0]
+            )
+        else:
+            return super(GNMTModel, self)._get_infer_summary(hparams)
 
 
 class GNMTAttentionMultiCell(tf.nn.rnn_cell.MultiRNNCell):
-  """A MultiCell with GNMT attention style."""
-
-  def __init__(self, attention_cell, cells, use_new_attention=False):
-    """Creates a GNMTAttentionMultiCell.
-
-    Args:
-      attention_cell: An instance of AttentionWrapper.
-      cells: A list of RNNCell wrapped with AttentionInputWrapper.
-      use_new_attention: Whether to use the attention generated from current
-        step bottom layer's output. Default is False.
-    """
-    cells = [attention_cell] + cells
-    self.use_new_attention = use_new_attention
-    super(GNMTAttentionMultiCell, self).__init__(cells, state_is_tuple=True)
-
-  def __call__(self, inputs, state, scope=None):
-    """Run the cell with bottom layer's attention copied to all upper layers."""
-    if not tf.contrib.framework.nest.is_sequence(state):
-      raise ValueError(
-          "Expected state to be a tuple of length %d, but received: %s"
-          % (len(self.state_size), state))
+    """A MultiCell with GNMT attention style."""
+
+    def __init__(self, attention_cell, cells, use_new_attention=False):
+        """Creates a GNMTAttentionMultiCell.
+
+        Args:
+          attention_cell: An instance of AttentionWrapper.
+          cells: A list of RNNCell wrapped with AttentionInputWrapper.
+          use_new_attention: Whether to use the attention generated from current
+            step bottom layer's output. Default is False.
+        """
+        cells = [attention_cell] + cells
+        self.use_new_attention = use_new_attention
+        super(
+            GNMTAttentionMultiCell,
+            self).__init__(
+            cells,
+            state_is_tuple=True)
+
+    def __call__(self, inputs, state, scope=None):
+        """Run the cell with bottom layer's attention copied to all upper layers."""
+        if not tf.contrib.framework.nest.is_sequence(state):
+            raise ValueError(
+                "Expected state to be a tuple of length %d, but received: %s"
+                % (len(self.state_size), state)
+            )
+
+        with tf.variable_scope(scope or "multi_rnn_cell"):
+            new_states = []
+
+            with tf.variable_scope("cell_0_attention"):
+                attention_cell = self._cells[0]
+                attention_state = state[0]
+                cur_inp, new_attention_state = attention_cell(
+                    inputs, attention_state)
+                new_states.append(new_attention_state)
+
+            for i in range(1, len(self._cells)):
+                with tf.variable_scope("cell_%d" % i):
+
+                    cell = self._cells[i]
+                    cur_state = state[i]
+
+                    if self.use_new_attention:
+                        cur_inp = tf.concat(
+                            [cur_inp, new_attention_state.attention], -1
+                        )
+                    else:
+                        cur_inp = tf.concat(
+                            [cur_inp, attention_state.attention], -1)
+
+                    cur_inp, new_state = cell(cur_inp, cur_state)
+                    new_states.append(new_state)
+
+        return cur_inp, tuple(new_states)
 
-    with tf.variable_scope(scope or "multi_rnn_cell"):
-      new_states = []
 
-      with tf.variable_scope("cell_0_attention"):
-        attention_cell = self._cells[0]
-        attention_state = state[0]
-        cur_inp, new_attention_state = attention_cell(inputs, attention_state)
-        new_states.append(new_attention_state)
-
-      for i in range(1, len(self._cells)):
-        with tf.variable_scope("cell_%d" % i):
+def gnmt_residual_fn(inputs, outputs):
+    """Residual function that handles different inputs and outputs inner dims.
 
-          cell = self._cells[i]
-          cur_state = state[i]
+    Args:
+      inputs: cell inputs, this is actual inputs concatenated with the attention
+        vector.
+      outputs: cell outputs
 
-          if self.use_new_attention:
-            cur_inp = tf.concat([cur_inp, new_attention_state.attention], -1)
-          else:
-            cur_inp = tf.concat([cur_inp, attention_state.attention], -1)
+    Returns:
+      outputs + actual inputs
+    """
 
-          cur_inp, new_state = cell(cur_inp, cur_state)
-          new_states.append(new_state)
+    def split_input(inp, out):
+        out_dim = out.get_shape().as_list()[-1]
+        inp_dim = inp.get_shape().as_list()[-1]
+        return tf.split(inp, [out_dim, inp_dim - out_dim], axis=-1)
 
-    return cur_inp, tuple(new_states)
+    actual_inputs, _ = tf.contrib.framework.nest.map_structure(
+        split_input, inputs, outputs
+    )
 
+    def assert_shape_match(inp, out):
+        inp.get_shape().assert_is_compatible_with(out.get_shape())
 
-def gnmt_residual_fn(inputs, outputs):
-  """Residual function that handles different inputs and outputs inner dims.
-
-  Args:
-    inputs: cell inputs, this is actual inputs concatenated with the attention
-      vector.
-    outputs: cell outputs
-
-  Returns:
-    outputs + actual inputs
-  """
-  def split_input(inp, out):
-    out_dim = out.get_shape().as_list()[-1]
-    inp_dim = inp.get_shape().as_list()[-1]
-    return tf.split(inp, [out_dim, inp_dim - out_dim], axis=-1)
-  actual_inputs, _ = tf.contrib.framework.nest.map_structure(
-      split_input, inputs, outputs)
-  def assert_shape_match(inp, out):
-    inp.get_shape().assert_is_compatible_with(out.get_shape())
-  tf.contrib.framework.nest.assert_same_structure(actual_inputs, outputs)
-  tf.contrib.framework.nest.map_structure(
-      assert_shape_match, actual_inputs, outputs)
-  return tf.contrib.framework.nest.map_structure(
-      lambda inp, out: inp + out, actual_inputs, outputs)
+    tf.contrib.framework.nest.assert_same_structure(actual_inputs, outputs)
+    tf.contrib.framework.nest.map_structure(
+        assert_shape_match, actual_inputs, outputs)
+    return tf.contrib.framework.nest.map_structure(
+        lambda inp, out: inp + out, actual_inputs, outputs
+    )
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/inference.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/inference.py
index dd425d2d4f..7d7127ecb2 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/inference.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/inference.py
@@ -28,244 +28,284 @@
 from .utils import misc_utils as utils
 from .utils import nmt_utils
 
-__all__ = ["load_data", "inference",
-           "single_worker_inference", "multi_worker_inference"]
-
-
-def _decode_inference_indices(model, sess, output_infer,
-                              output_infer_summary_prefix,
-                              inference_indices,
-                              tgt_eos,
-                              subword_option):
-  """Decoding only a specific set of sentences."""
-  utils.print_out("  decoding to output %s , num sents %d." %
-                  (output_infer, len(inference_indices)))
-  start_time = time.time()
-  with codecs.getwriter("utf-8")(
-      tf.gfile.GFile(output_infer, mode="wb")) as trans_f:
-    trans_f.write("")  # Write empty string to ensure file is created.
-    for decode_id in inference_indices:
-      nmt_outputs, infer_summary = model.decode(sess)
-
-      # get text translation
-      assert nmt_outputs.shape[0] == 1
-      translation = nmt_utils.get_translation(
-          nmt_outputs,
-          sent_id=0,
-          tgt_eos=tgt_eos,
-          subword_option=subword_option)
-
-      if infer_summary is not None:  # Attention models
-        image_file = output_infer_summary_prefix + str(decode_id) + ".png"
-        utils.print_out("  save attention image to %s*" % image_file)
-        image_summ = tf.Summary()
-        image_summ.ParseFromString(infer_summary)
-        with tf.gfile.GFile(image_file, mode="w") as img_f:
-          img_f.write(image_summ.value[0].image.encoded_image_string)
-
-      trans_f.write("%s\n" % translation)
-      utils.print_out(translation + b"\n")
-  utils.print_time("  done", start_time)
+__all__ = [
+    "load_data",
+    "inference",
+    "single_worker_inference",
+    "multi_worker_inference",
+]
+
+
+def _decode_inference_indices(
+    model,
+    sess,
+    output_infer,
+    output_infer_summary_prefix,
+    inference_indices,
+    tgt_eos,
+    subword_option,
+):
+    """Decoding only a specific set of sentences."""
+    utils.print_out(
+        "  decoding to output %s , num sents %d."
+        % (output_infer, len(inference_indices))
+    )
+    start_time = time.time()
+    with codecs.getwriter("utf-8")(tf.gfile.GFile(output_infer, mode="wb")) as trans_f:
+        trans_f.write("")  # Write empty string to ensure file is created.
+        for decode_id in inference_indices:
+            nmt_outputs, infer_summary = model.decode(sess)
+
+            # get text translation
+            assert nmt_outputs.shape[0] == 1
+            translation = nmt_utils.get_translation(
+                nmt_outputs, sent_id=0, tgt_eos=tgt_eos, subword_option=subword_option
+            )
+
+            if infer_summary is not None:  # Attention models
+                image_file = output_infer_summary_prefix + \
+                    str(decode_id) + ".png"
+                utils.print_out("  save attention image to %s*" % image_file)
+                image_summ = tf.Summary()
+                image_summ.ParseFromString(infer_summary)
+                with tf.gfile.GFile(image_file, mode="w") as img_f:
+                    img_f.write(image_summ.value[0].image.encoded_image_string)
+
+            trans_f.write("%s\n" % translation)
+            utils.print_out(translation + b"\n")
+    utils.print_time("  done", start_time)
 
 
 def load_data(inference_input_file, hparams=None):
-  """Load inference data."""
-  with codecs.getreader("utf-8")(
-      tf.gfile.GFile(inference_input_file, mode="rb")) as f:
-    inference_data = f.read().splitlines()
+    """Load inference data."""
+    with codecs.getreader("utf-8")(
+        tf.gfile.GFile(inference_input_file, mode="rb")
+    ) as f:
+        inference_data = f.read().splitlines()
 
-  if hparams and hparams.inference_indices:
-    inference_data = [inference_data[i] for i in hparams.inference_indices]
+    if hparams and hparams.inference_indices:
+        inference_data = [inference_data[i] for i in hparams.inference_indices]
 
-  return inference_data
+    return inference_data
 
 
 def get_model_creator(hparams):
-  """Get the right model class depending on configuration."""
-  if (hparams.encoder_type == "gnmt" or
-      hparams.attention_architecture in ["gnmt", "gnmt_v2"]):
-    model_creator = gnmt_model.GNMTModel
-  elif hparams.attention_architecture == "standard":
-    model_creator = attention_model.AttentionModel
-  elif not hparams.attention:
-    model_creator = nmt_model.Model
-  else:
-    raise ValueError("Unknown attention architecture %s" %
-                     hparams.attention_architecture)
-  return model_creator
+    """Get the right model class depending on configuration."""
+    if hparams.encoder_type == "gnmt" or hparams.attention_architecture in [
+        "gnmt",
+        "gnmt_v2",
+    ]:
+        model_creator = gnmt_model.GNMTModel
+    elif hparams.attention_architecture == "standard":
+        model_creator = attention_model.AttentionModel
+    elif not hparams.attention:
+        model_creator = nmt_model.Model
+    else:
+        raise ValueError(
+            "Unknown attention architecture %s" % hparams.attention_architecture
+        )
+    return model_creator
 
 
 def start_sess_and_load_model(infer_model, ckpt_path, hparams):
-  """Start session and load model."""
-  print("num_intra_threads = %d, num_inter_threads = %d \n"
-        %(hparams.num_intra_threads, hparams.num_inter_threads))
-  sess = tf.Session(graph=infer_model.graph,
-         config=utils.get_config_proto(
-         num_intra_threads=hparams.num_intra_threads,
-         num_inter_threads=hparams.num_inter_threads)
-         )
-  with infer_model.graph.as_default():
-    loaded_infer_model = model_helper.load_model(
-        infer_model.model, ckpt_path, sess, "infer")
-  return sess, loaded_infer_model
-
-
-def inference(run,
-              iterations,
-              ckpt_path,
-              inference_input_file,
-              inference_output_file,
-              hparams,
-              num_workers=1,
-              jobid=0,
-              scope=None):
-  """Perform translation."""
-  if hparams.inference_indices:
-    assert num_workers == 1
-
-  model_creator = get_model_creator(hparams)
-  infer_model = model_helper.create_infer_model(model_creator, hparams, scope)
-  sess, loaded_infer_model = start_sess_and_load_model(infer_model, ckpt_path,
-                                                       hparams)
-
-  if num_workers == 1:
-    single_worker_inference(
-        run,
-        iterations,
-        sess,
-        infer_model,
-        loaded_infer_model,
-        inference_input_file,
-        inference_output_file,
-        hparams)
-  else:
-    multi_worker_inference(
-        sess,
-        infer_model,
-        loaded_infer_model,
-        inference_input_file,
-        inference_output_file,
-        hparams,
-        num_workers=num_workers,
-        jobid=jobid)
-  sess.close()
-
-
-def single_worker_inference(run,
-                            iterations,
-                            sess,
-                            infer_model,
-                            loaded_infer_model,
-                            inference_input_file,
-                            inference_output_file,
-                            hparams):
-  """Inference with a single worker."""
-  output_infer = inference_output_file
-
-  # Read data
-  infer_data = load_data(inference_input_file, hparams)
-
-  with infer_model.graph.as_default():
-    sess.run(
-        infer_model.iterator.initializer,
-        feed_dict={
-            infer_model.src_placeholder: infer_data,
-            infer_model.batch_size_placeholder: hparams.infer_batch_size
-        })
-    # Decode
-    utils.print_out("# Start decoding")
+    """Start session and load model."""
+    print(
+        "num_intra_threads = %d, num_inter_threads = %d \n"
+        % (hparams.num_intra_threads, hparams.num_inter_threads)
+    )
+    sess = tf.Session(
+        graph=infer_model.graph,
+        config=utils.get_config_proto(
+            num_intra_threads=hparams.num_intra_threads,
+            num_inter_threads=hparams.num_inter_threads,
+        ),
+    )
+    with infer_model.graph.as_default():
+        loaded_infer_model = model_helper.load_model(
+            infer_model.model, ckpt_path, sess, "infer"
+        )
+    return sess, loaded_infer_model
+
+
+def inference(
+    run,
+    iterations,
+    ckpt_path,
+    inference_input_file,
+    inference_output_file,
+    hparams,
+    num_workers=1,
+    jobid=0,
+    scope=None,
+):
+    """Perform translation."""
     if hparams.inference_indices:
-      _decode_inference_indices(
-          loaded_infer_model,
-          sess,
-          output_infer=output_infer,
-          output_infer_summary_prefix=output_infer,
-          inference_indices=hparams.inference_indices,
-          tgt_eos=hparams.eos,
-          subword_option=hparams.subword_option)
+        assert num_workers == 1
+
+    model_creator = get_model_creator(hparams)
+    infer_model = model_helper.create_infer_model(
+        model_creator, hparams, scope)
+    sess, loaded_infer_model = start_sess_and_load_model(
+        infer_model, ckpt_path, hparams
+    )
+
+    if num_workers == 1:
+        single_worker_inference(
+            run,
+            iterations,
+            sess,
+            infer_model,
+            loaded_infer_model,
+            inference_input_file,
+            inference_output_file,
+            hparams,
+        )
     else:
-      nmt_utils.decode_and_evaluate(
-          run,
-          iterations,
-          "infer",
-          loaded_infer_model,
-          sess,
-          output_infer,
-          ref_file=None,
-          metrics=hparams.metrics,
-          subword_option=hparams.subword_option,
-          beam_width=hparams.beam_width,
-          tgt_eos=hparams.eos,
-          num_translations_per_input=hparams.num_translations_per_input,
-          infer_mode=hparams.infer_mode)
-
-
-def multi_worker_inference(sess,
-                           infer_model,
-                           loaded_infer_model,
-                           inference_input_file,
-                           inference_output_file,
-                           hparams,
-                           num_workers,
-                           jobid):
-  """Inference using multiple workers."""
-  assert num_workers > 1
-
-  final_output_infer = inference_output_file
-  output_infer = "%s_%d" % (inference_output_file, jobid)
-  output_infer_done = "%s_done_%d" % (inference_output_file, jobid)
-
-  # Read data
-  infer_data = load_data(inference_input_file, hparams)
-
-  # Split data to multiple workers
-  total_load = len(infer_data)
-  load_per_worker = int((total_load - 1) / num_workers) + 1
-  start_position = jobid * load_per_worker
-  end_position = min(start_position + load_per_worker, total_load)
-  infer_data = infer_data[start_position:end_position]
-
-  with infer_model.graph.as_default():
-    sess.run(infer_model.iterator.initializer,
-             {
-                 infer_model.src_placeholder: infer_data,
-                 infer_model.batch_size_placeholder: hparams.infer_batch_size
-             })
-    # Decode
-    utils.print_out("# Start decoding")
-    nmt_utils.decode_and_evaluate(
-        "infer",
-        loaded_infer_model,
-        sess,
-        output_infer,
-        ref_file=None,
-        metrics=hparams.metrics,
-        subword_option=hparams.subword_option,
-        beam_width=hparams.beam_width,
-        tgt_eos=hparams.eos,
-        num_translations_per_input=hparams.num_translations_per_input,
-        infer_mode=hparams.infer_mode)
-
-    # Change file name to indicate the file writing is completed.
-    tf.gfile.Rename(output_infer, output_infer_done, overwrite=True)
-
-    # Job 0 is responsible for the clean up.
-    if jobid != 0: return
-
-    # Now write all translations
-    with codecs.getwriter("utf-8")(
-        tf.gfile.GFile(final_output_infer, mode="wb")) as final_f:
-      for worker_id in range(num_workers):
-        worker_infer_done = "%s_done_%d" % (inference_output_file, worker_id)
-        while not tf.gfile.Exists(worker_infer_done):
-          utils.print_out("  waiting job %d to complete." % worker_id)
-          time.sleep(10)
-
-        with codecs.getreader("utf-8")(
-            tf.gfile.GFile(worker_infer_done, mode="rb")) as f:
-          for translation in f:
-            final_f.write("%s" % translation)
-
-      for worker_id in range(num_workers):
-        worker_infer_done = "%s_done_%d" % (inference_output_file, worker_id)
-        tf.gfile.Remove(worker_infer_done)
+        multi_worker_inference(
+            sess,
+            infer_model,
+            loaded_infer_model,
+            inference_input_file,
+            inference_output_file,
+            hparams,
+            num_workers=num_workers,
+            jobid=jobid,
+        )
+    sess.close()
+
+
+def single_worker_inference(
+    run,
+    iterations,
+    sess,
+    infer_model,
+    loaded_infer_model,
+    inference_input_file,
+    inference_output_file,
+    hparams,
+):
+    """Inference with a single worker."""
+    output_infer = inference_output_file
+
+    # Read data
+    infer_data = load_data(inference_input_file, hparams)
+
+    with infer_model.graph.as_default():
+        sess.run(
+            infer_model.iterator.initializer,
+            feed_dict={
+                infer_model.src_placeholder: infer_data,
+                infer_model.batch_size_placeholder: hparams.infer_batch_size,
+            },
+        )
+        # Decode
+        utils.print_out("# Start decoding")
+        if hparams.inference_indices:
+            _decode_inference_indices(
+                loaded_infer_model,
+                sess,
+                output_infer=output_infer,
+                output_infer_summary_prefix=output_infer,
+                inference_indices=hparams.inference_indices,
+                tgt_eos=hparams.eos,
+                subword_option=hparams.subword_option,
+            )
+        else:
+            nmt_utils.decode_and_evaluate(
+                run,
+                iterations,
+                "infer",
+                loaded_infer_model,
+                sess,
+                output_infer,
+                ref_file=None,
+                metrics=hparams.metrics,
+                subword_option=hparams.subword_option,
+                beam_width=hparams.beam_width,
+                tgt_eos=hparams.eos,
+                num_translations_per_input=hparams.num_translations_per_input,
+                infer_mode=hparams.infer_mode,
+            )
+
+
+def multi_worker_inference(
+    sess,
+    infer_model,
+    loaded_infer_model,
+    inference_input_file,
+    inference_output_file,
+    hparams,
+    num_workers,
+    jobid,
+):
+    """Inference using multiple workers."""
+    assert num_workers > 1
+
+    final_output_infer = inference_output_file
+    output_infer = "%s_%d" % (inference_output_file, jobid)
+    output_infer_done = "%s_done_%d" % (inference_output_file, jobid)
+
+    # Read data
+    infer_data = load_data(inference_input_file, hparams)
+
+    # Split data to multiple workers
+    total_load = len(infer_data)
+    load_per_worker = int((total_load - 1) / num_workers) + 1
+    start_position = jobid * load_per_worker
+    end_position = min(start_position + load_per_worker, total_load)
+    infer_data = infer_data[start_position:end_position]
+
+    with infer_model.graph.as_default():
+        sess.run(
+            infer_model.iterator.initializer,
+            {
+                infer_model.src_placeholder: infer_data,
+                infer_model.batch_size_placeholder: hparams.infer_batch_size,
+            },
+        )
+        # Decode
+        utils.print_out("# Start decoding")
+        nmt_utils.decode_and_evaluate(
+            "infer",
+            loaded_infer_model,
+            sess,
+            output_infer,
+            ref_file=None,
+            metrics=hparams.metrics,
+            subword_option=hparams.subword_option,
+            beam_width=hparams.beam_width,
+            tgt_eos=hparams.eos,
+            num_translations_per_input=hparams.num_translations_per_input,
+            infer_mode=hparams.infer_mode,
+        )
+
+        # Change file name to indicate the file writing is completed.
+        tf.gfile.Rename(output_infer, output_infer_done, overwrite=True)
+
+        # Job 0 is responsible for the clean up.
+        if jobid != 0:
+            return
+
+        # Now write all translations
+        with codecs.getwriter("utf-8")(
+            tf.gfile.GFile(final_output_infer, mode="wb")
+        ) as final_f:
+            for worker_id in range(num_workers):
+                worker_infer_done = "%s_done_%d" % (
+                    inference_output_file, worker_id)
+                while not tf.gfile.Exists(worker_infer_done):
+                    utils.print_out(
+                        "  waiting job %d to complete." %
+                        worker_id)
+                    time.sleep(10)
+
+                with codecs.getreader("utf-8")(
+                    tf.gfile.GFile(worker_infer_done, mode="rb")
+                ) as f:
+                    for translation in f:
+                        final_f.write("%s" % translation)
+
+            for worker_id in range(num_workers):
+                worker_infer_done = "%s_done_%d" % (
+                    inference_output_file, worker_id)
+                tf.gfile.Remove(worker_infer_done)
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/inference_test.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/inference_test.py
index 317024b810..989f9fe33e 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/inference_test.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/inference_test.py
@@ -34,142 +34,153 @@
 
 class InferenceTest(tf.test.TestCase):
 
-  def _createTestInferCheckpoint(self, hparams, name):
-    # Prepare
-    hparams.vocab_prefix = (
-        "nmt/testdata/test_infer_vocab")
-    hparams.src_vocab_file = hparams.vocab_prefix + "." + hparams.src
-    hparams.tgt_vocab_file = hparams.vocab_prefix + "." + hparams.tgt
-    out_dir = os.path.join(tf.test.get_temp_dir(), name)
-    os.makedirs(out_dir)
-    hparams.out_dir = out_dir
-
-    # Create check point
-    model_creator = inference.get_model_creator(hparams)
-    infer_model = model_helper.create_infer_model(model_creator, hparams)
-    with self.test_session(graph=infer_model.graph) as sess:
-      loaded_model, global_step = model_helper.create_or_load_model(
-          infer_model.model, out_dir, sess, "infer_name")
-      ckpt_path = loaded_model.saver.save(
-          sess, os.path.join(out_dir, "translate.ckpt"),
-          global_step=global_step)
-    return ckpt_path
-
-  def testBasicModel(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type="uni",
-        num_layers=1,
-        attention="",
-        attention_architecture="",
-        use_residual=False,)
-    ckpt_path = self._createTestInferCheckpoint(hparams, "basic_infer")
-    infer_file = "nmt/testdata/test_infer_file"
-    output_infer = os.path.join(hparams.out_dir, "output_infer")
-    inference.inference(ckpt_path, infer_file, output_infer, hparams)
-    with open(output_infer) as f:
-      self.assertEqual(5, len(list(f)))
-
-  def testBasicModelWithMultipleTranslations(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type="uni",
-        num_layers=1,
-        attention="",
-        attention_architecture="",
-        use_residual=False,
-        num_translations_per_input=2,
-        beam_width=2,
-    )
-    hparams.infer_mode = "beam_search"
-
-    ckpt_path = self._createTestInferCheckpoint(hparams, "multi_basic_infer")
-    infer_file = "nmt/testdata/test_infer_file"
-    output_infer = os.path.join(hparams.out_dir, "output_infer")
-    inference.inference(ckpt_path, infer_file, output_infer, hparams)
-    with open(output_infer) as f:
-      self.assertEqual(10, len(list(f)))
-
-  def testAttentionModel(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type="uni",
-        num_layers=1,
-        attention="scaled_luong",
-        attention_architecture="standard",
-        use_residual=False,)
-    ckpt_path = self._createTestInferCheckpoint(hparams, "attention_infer")
-    infer_file = "nmt/testdata/test_infer_file"
-    output_infer = os.path.join(hparams.out_dir, "output_infer")
-    inference.inference(ckpt_path, infer_file, output_infer, hparams)
-    with open(output_infer) as f:
-      self.assertEqual(5, len(list(f)))
-
-  def testMultiWorkers(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type="uni",
-        num_layers=2,
-        attention="scaled_luong",
-        attention_architecture="standard",
-        use_residual=False,)
-
-    num_workers = 3
-
-    # There are 5 examples, make batch_size=3 makes job0 has 3 examples, job1
-    # has 2 examples, and job2 has 0 example. This helps testing some edge
-    # cases.
-    hparams.batch_size = 3
-
-    ckpt_path = self._createTestInferCheckpoint(hparams, "multi_worker_infer")
-    infer_file = "nmt/testdata/test_infer_file"
-    output_infer = os.path.join(hparams.out_dir, "output_infer")
-    inference.inference(
-        ckpt_path, infer_file, output_infer, hparams, num_workers, jobid=1)
-
-    inference.inference(
-        ckpt_path, infer_file, output_infer, hparams, num_workers, jobid=2)
-
-    # Note: Need to start job 0 at the end; otherwise, it will block the testing
-    # thread.
-    inference.inference(
-        ckpt_path, infer_file, output_infer, hparams, num_workers, jobid=0)
-
-    with open(output_infer) as f:
-      self.assertEqual(5, len(list(f)))
-
-  def testBasicModelWithInferIndices(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type="uni",
-        num_layers=1,
-        attention="",
-        attention_architecture="",
-        use_residual=False,
-        inference_indices=[0])
-    ckpt_path = self._createTestInferCheckpoint(hparams,
-                                                "basic_infer_with_indices")
-    infer_file = "nmt/testdata/test_infer_file"
-    output_infer = os.path.join(hparams.out_dir, "output_infer")
-    inference.inference(ckpt_path, infer_file, output_infer, hparams)
-    with open(output_infer) as f:
-      self.assertEqual(1, len(list(f)))
-
-  def testAttentionModelWithInferIndices(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type="uni",
-        num_layers=1,
-        attention="scaled_luong",
-        attention_architecture="standard",
-        use_residual=False,
-        inference_indices=[1, 2])
-    # TODO(rzhao): Make infer indices support batch_size > 1.
-    hparams.infer_batch_size = 1
-    ckpt_path = self._createTestInferCheckpoint(hparams,
-                                                "attention_infer_with_indices")
-    infer_file = "nmt/testdata/test_infer_file"
-    output_infer = os.path.join(hparams.out_dir, "output_infer")
-    inference.inference(ckpt_path, infer_file, output_infer, hparams)
-    with open(output_infer) as f:
-      self.assertEqual(2, len(list(f)))
-    self.assertTrue(os.path.exists(output_infer+str(1)+".png"))
-    self.assertTrue(os.path.exists(output_infer+str(2)+".png"))
+    def _createTestInferCheckpoint(self, hparams, name):
+        # Prepare
+        hparams.vocab_prefix = "nmt/testdata/test_infer_vocab"
+        hparams.src_vocab_file = hparams.vocab_prefix + "." + hparams.src
+        hparams.tgt_vocab_file = hparams.vocab_prefix + "." + hparams.tgt
+        out_dir = os.path.join(tf.test.get_temp_dir(), name)
+        os.makedirs(out_dir)
+        hparams.out_dir = out_dir
+
+        # Create check point
+        model_creator = inference.get_model_creator(hparams)
+        infer_model = model_helper.create_infer_model(model_creator, hparams)
+        with self.test_session(graph=infer_model.graph) as sess:
+            loaded_model, global_step = model_helper.create_or_load_model(
+                infer_model.model, out_dir, sess, "infer_name"
+            )
+            ckpt_path = loaded_model.saver.save(
+                sess, os.path.join(out_dir, "translate.ckpt"), global_step=global_step
+            )
+        return ckpt_path
+
+    def testBasicModel(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=1,
+            attention="",
+            attention_architecture="",
+            use_residual=False,
+        )
+        ckpt_path = self._createTestInferCheckpoint(hparams, "basic_infer")
+        infer_file = "nmt/testdata/test_infer_file"
+        output_infer = os.path.join(hparams.out_dir, "output_infer")
+        inference.inference(ckpt_path, infer_file, output_infer, hparams)
+        with open(output_infer) as f:
+            self.assertEqual(5, len(list(f)))
+
+    def testBasicModelWithMultipleTranslations(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=1,
+            attention="",
+            attention_architecture="",
+            use_residual=False,
+            num_translations_per_input=2,
+            beam_width=2,
+        )
+        hparams.infer_mode = "beam_search"
+
+        ckpt_path = self._createTestInferCheckpoint(
+            hparams, "multi_basic_infer")
+        infer_file = "nmt/testdata/test_infer_file"
+        output_infer = os.path.join(hparams.out_dir, "output_infer")
+        inference.inference(ckpt_path, infer_file, output_infer, hparams)
+        with open(output_infer) as f:
+            self.assertEqual(10, len(list(f)))
+
+    def testAttentionModel(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=1,
+            attention="scaled_luong",
+            attention_architecture="standard",
+            use_residual=False,
+        )
+        ckpt_path = self._createTestInferCheckpoint(hparams, "attention_infer")
+        infer_file = "nmt/testdata/test_infer_file"
+        output_infer = os.path.join(hparams.out_dir, "output_infer")
+        inference.inference(ckpt_path, infer_file, output_infer, hparams)
+        with open(output_infer) as f:
+            self.assertEqual(5, len(list(f)))
+
+    def testMultiWorkers(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=2,
+            attention="scaled_luong",
+            attention_architecture="standard",
+            use_residual=False,
+        )
+
+        num_workers = 3
+
+        # There are 5 examples, make batch_size=3 makes job0 has 3 examples, job1
+        # has 2 examples, and job2 has 0 example. This helps testing some edge
+        # cases.
+        hparams.batch_size = 3
+
+        ckpt_path = self._createTestInferCheckpoint(
+            hparams, "multi_worker_infer")
+        infer_file = "nmt/testdata/test_infer_file"
+        output_infer = os.path.join(hparams.out_dir, "output_infer")
+        inference.inference(
+            ckpt_path, infer_file, output_infer, hparams, num_workers, jobid=1
+        )
+
+        inference.inference(
+            ckpt_path, infer_file, output_infer, hparams, num_workers, jobid=2
+        )
+
+        # Note: Need to start job 0 at the end; otherwise, it will block the testing
+        # thread.
+        inference.inference(
+            ckpt_path, infer_file, output_infer, hparams, num_workers, jobid=0
+        )
+
+        with open(output_infer) as f:
+            self.assertEqual(5, len(list(f)))
+
+    def testBasicModelWithInferIndices(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=1,
+            attention="",
+            attention_architecture="",
+            use_residual=False,
+            inference_indices=[0],
+        )
+        ckpt_path = self._createTestInferCheckpoint(
+            hparams, "basic_infer_with_indices")
+        infer_file = "nmt/testdata/test_infer_file"
+        output_infer = os.path.join(hparams.out_dir, "output_infer")
+        inference.inference(ckpt_path, infer_file, output_infer, hparams)
+        with open(output_infer) as f:
+            self.assertEqual(1, len(list(f)))
+
+    def testAttentionModelWithInferIndices(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=1,
+            attention="scaled_luong",
+            attention_architecture="standard",
+            use_residual=False,
+            inference_indices=[1, 2],
+        )
+        # TODO(rzhao): Make infer indices support batch_size > 1.
+        hparams.infer_batch_size = 1
+        ckpt_path = self._createTestInferCheckpoint(
+            hparams, "attention_infer_with_indices"
+        )
+        infer_file = "nmt/testdata/test_infer_file"
+        output_infer = os.path.join(hparams.out_dir, "output_infer")
+        inference.inference(ckpt_path, infer_file, output_infer, hparams)
+        with open(output_infer) as f:
+            self.assertEqual(2, len(list(f)))
+        self.assertTrue(os.path.exists(output_infer + str(1) + ".png"))
+        self.assertTrue(os.path.exists(output_infer + str(2) + ".png"))
 
 
 if __name__ == "__main__":
-  tf.test.main()
+    tf.test.main()
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/model.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/model.py
index e0c4f4e03e..99639668ef 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/model.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/model.py
@@ -34,844 +34,975 @@
 __all__ = ["BaseModel", "Model"]
 
 
-class TrainOutputTuple(collections.namedtuple(
-    "TrainOutputTuple", ("train_summary", "train_loss", "predict_count",
-                         "global_step", "word_count", "batch_size", "grad_norm",
-                         "learning_rate"))):
-  """To allow for flexibily in returing different outputs."""
-  pass
+class TrainOutputTuple(
+    collections.namedtuple(
+        "TrainOutputTuple",
+        (
+            "train_summary",
+            "train_loss",
+            "predict_count",
+            "global_step",
+            "word_count",
+            "batch_size",
+            "grad_norm",
+            "learning_rate",
+        ),
+    )
+):
+    """To allow for flexibily in returing different outputs."""
 
+    pass
 
-class EvalOutputTuple(collections.namedtuple(
-    "EvalOutputTuple", ("eval_loss", "predict_count", "batch_size"))):
-  """To allow for flexibily in returing different outputs."""
-  pass
 
+class EvalOutputTuple(
+    collections.namedtuple(
+        "EvalOutputTuple", ("eval_loss", "predict_count", "batch_size")
+    )
+):
+    """To allow for flexibily in returing different outputs."""
 
-class InferOutputTuple(collections.namedtuple(
-    "InferOutputTuple", ("infer_logits", "infer_summary", "sample_id",
-                         "sample_words"))):
-  """To allow for flexibily in returing different outputs."""
-  pass
+    pass
 
 
-class BaseModel(object):
-  """Sequence-to-sequence base class.
-  """
-
-  def __init__(self,
-               hparams,
-               mode,
-               iterator,
-               source_vocab_table,
-               target_vocab_table,
-               reverse_target_vocab_table=None,
-               scope=None,
-               extra_args=None):
-    """Create the model.
-
-    Args:
-      hparams: Hyperparameter configurations.
-      mode: TRAIN | EVAL | INFER
-      iterator: Dataset Iterator that feeds data.
-      source_vocab_table: Lookup table mapping source words to ids.
-      target_vocab_table: Lookup table mapping target words to ids.
-      reverse_target_vocab_table: Lookup table mapping ids to target words. Only
-        required in INFER mode. Defaults to None.
-      scope: scope of the model.
-      extra_args: model_helper.ExtraArgs, for passing customizable functions.
+class InferOutputTuple(
+    collections.namedtuple(
+        "InferOutputTuple",
+        ("infer_logits", "infer_summary", "sample_id", "sample_words"),
+    )
+):
+    """To allow for flexibily in returing different outputs."""
 
-    """
-    # Set params
-    self._set_params_initializer(hparams, mode, iterator,
-                                 source_vocab_table, target_vocab_table,
-                                 scope, extra_args)
-
-    # Not used in general seq2seq models; when True, ignore decoder & training
-    self.extract_encoder_layers = (hasattr(hparams, "extract_encoder_layers")
-                                   and hparams.extract_encoder_layers)
-
-    # Train graph
-    res = self.build_graph(hparams, scope=scope)
-    if not self.extract_encoder_layers:
-      self._set_train_or_infer(res, reverse_target_vocab_table, hparams)
-
-    # Saver
-    self.saver = tf.train.Saver(
-        tf.global_variables(), max_to_keep=hparams.num_keep_ckpts)
-
-  def _set_params_initializer(self,
-                              hparams,
-                              mode,
-                              iterator,
-                              source_vocab_table,
-                              target_vocab_table,
-                              scope,
-                              extra_args=None):
-    """Set various params for self and initialize."""
-    assert isinstance(iterator, iterator_utils.BatchedInput)
-    self.iterator = iterator
-    self.mode = mode
-    self.src_vocab_table = source_vocab_table
-    self.tgt_vocab_table = target_vocab_table
-
-    self.src_vocab_size = hparams.src_vocab_size
-    self.tgt_vocab_size = hparams.tgt_vocab_size
-    self.num_gpus = hparams.num_gpus
-    self.time_major = hparams.time_major
-
-    if hparams.use_char_encode:
-      assert (not self.time_major), ("Can't use time major for"
-                                     " char-level inputs.")
-
-    self.dtype = tf.float32
-    self.num_sampled_softmax = hparams.num_sampled_softmax
-
-    # extra_args: to make it flexible for adding external customizable code
-    self.single_cell_fn = None
-    if extra_args:
-      self.single_cell_fn = extra_args.single_cell_fn
-
-    # Set num units
-    self.num_units = hparams.num_units
-
-    # Set num layers
-    self.num_encoder_layers = hparams.num_encoder_layers
-    self.num_decoder_layers = hparams.num_decoder_layers
-    assert self.num_encoder_layers
-    assert self.num_decoder_layers
-
-    # Set num residual layers
-    if hasattr(hparams, "num_residual_layers"):  # compatible common_test_utils
-      self.num_encoder_residual_layers = hparams.num_residual_layers
-      self.num_decoder_residual_layers = hparams.num_residual_layers
-    else:
-      self.num_encoder_residual_layers = hparams.num_encoder_residual_layers
-      self.num_decoder_residual_layers = hparams.num_decoder_residual_layers
-
-    # Batch size
-    self.batch_size = tf.size(self.iterator.source_sequence_length)
-
-    # Global step
-    self.global_step = tf.Variable(0, trainable=False)
-
-    # Initializer
-    self.random_seed = hparams.random_seed
-    initializer = model_helper.get_initializer(
-        hparams.init_op, self.random_seed, hparams.init_weight)
-    tf.get_variable_scope().set_initializer(initializer)
-
-    # Embeddings
-    if extra_args and extra_args.encoder_emb_lookup_fn:
-      self.encoder_emb_lookup_fn = extra_args.encoder_emb_lookup_fn
-    else:
-      self.encoder_emb_lookup_fn = tf.nn.embedding_lookup
-    self.init_embeddings(hparams, scope)
-
-  def _set_train_or_infer(self, res, reverse_target_vocab_table, hparams):
-    """Set up training and inference."""
-    if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
-      self.train_loss = res[1]
-      self.word_count = tf.reduce_sum(
-          self.iterator.source_sequence_length) + tf.reduce_sum(
-              self.iterator.target_sequence_length)
-    elif self.mode == tf.contrib.learn.ModeKeys.EVAL:
-      self.eval_loss = res[1]
-    elif self.mode == tf.contrib.learn.ModeKeys.INFER:
-      self.infer_logits, _, self.final_context_state, self.sample_id = res
-      self.sample_words = reverse_target_vocab_table.lookup(
-          tf.to_int64(self.sample_id))
-
-    if self.mode != tf.contrib.learn.ModeKeys.INFER:
-      ## Count the number of predicted words for compute ppl.
-      self.predict_count = tf.reduce_sum(
-          self.iterator.target_sequence_length)
-
-    params = tf.trainable_variables()
-
-    # Gradients and SGD update operation for training the model.
-    # Arrange for the embedding vars to appear at the beginning.
-    if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
-      self.learning_rate = tf.constant(hparams.learning_rate)
-      # warm-up
-      self.learning_rate = self._get_learning_rate_warmup(hparams)
-      # decay
-      self.learning_rate = self._get_learning_rate_decay(hparams)
-
-      # Optimizer
-      if hparams.optimizer == "sgd":
-        opt = tf.train.GradientDescentOptimizer(self.learning_rate)
-      elif hparams.optimizer == "adam":
-        opt = tf.train.AdamOptimizer(self.learning_rate)
-      else:
-        raise ValueError("Unknown optimizer type %s" % hparams.optimizer)
-
-      # Gradients
-      gradients = tf.gradients(
-          self.train_loss,
-          params,
-          colocate_gradients_with_ops=hparams.colocate_gradients_with_ops)
-
-      clipped_grads, grad_norm_summary, grad_norm = model_helper.gradient_clip(
-          gradients, max_gradient_norm=hparams.max_gradient_norm)
-      self.grad_norm_summary = grad_norm_summary
-      self.grad_norm = grad_norm
-
-      self.update = opt.apply_gradients(
-          zip(clipped_grads, params), global_step=self.global_step)
-
-      # Summary
-      self.train_summary = self._get_train_summary()
-    elif self.mode == tf.contrib.learn.ModeKeys.INFER:
-      self.infer_summary = self._get_infer_summary(hparams)
-
-    # Print trainable variables
-    utils.print_out("# Trainable variables")
-    utils.print_out("Format: <name>, <shape>, <(soft) device placement>")
-    for param in params:
-      utils.print_out("  %s, %s, %s" % (param.name, str(param.get_shape()),
-                                        param.op.device))
-
-  def _get_learning_rate_warmup(self, hparams):
-    """Get learning rate warmup."""
-    warmup_steps = hparams.warmup_steps
-    warmup_scheme = hparams.warmup_scheme
-    utils.print_out("  learning_rate=%g, warmup_steps=%d, warmup_scheme=%s" %
-                    (hparams.learning_rate, warmup_steps, warmup_scheme))
-
-    # Apply inverse decay if global steps less than warmup steps.
-    # Inspired by https://arxiv.org/pdf/1706.03762.pdf (Section 5.3)
-    # When step < warmup_steps,
-    #   learing_rate *= warmup_factor ** (warmup_steps - step)
-    if warmup_scheme == "t2t":
-      # 0.01^(1/warmup_steps): we start with a lr, 100 times smaller
-      warmup_factor = tf.exp(tf.log(0.01) / warmup_steps)
-      inv_decay = warmup_factor**(
-          tf.to_float(warmup_steps - self.global_step))
-    else:
-      raise ValueError("Unknown warmup scheme %s" % warmup_scheme)
-
-    return tf.cond(
-        self.global_step < hparams.warmup_steps,
-        lambda: inv_decay * self.learning_rate,
-        lambda: self.learning_rate,
-        name="learning_rate_warump_cond")
-
-  def _get_decay_info(self, hparams):
-    """Return decay info based on decay_scheme."""
-    if hparams.decay_scheme in ["luong5", "luong10", "luong234"]:
-      decay_factor = 0.5
-      if hparams.decay_scheme == "luong5":
-        start_decay_step = int(hparams.num_train_steps / 2)
-        decay_times = 5
-      elif hparams.decay_scheme == "luong10":
-        start_decay_step = int(hparams.num_train_steps / 2)
-        decay_times = 10
-      elif hparams.decay_scheme == "luong234":
-        start_decay_step = int(hparams.num_train_steps * 2 / 3)
-        decay_times = 4
-      remain_steps = hparams.num_train_steps - start_decay_step
-      decay_steps = int(remain_steps / decay_times)
-    elif not hparams.decay_scheme:  # no decay
-      start_decay_step = hparams.num_train_steps
-      decay_steps = 0
-      decay_factor = 1.0
-    elif hparams.decay_scheme:
-      raise ValueError("Unknown decay scheme %s" % hparams.decay_scheme)
-    return start_decay_step, decay_steps, decay_factor
-
-  def _get_learning_rate_decay(self, hparams):
-    """Get learning rate decay."""
-    start_decay_step, decay_steps, decay_factor = self._get_decay_info(hparams)
-    utils.print_out("  decay_scheme=%s, start_decay_step=%d, decay_steps %d, "
-                    "decay_factor %g" % (hparams.decay_scheme,
-                                         start_decay_step,
-                                         decay_steps,
-                                         decay_factor))
-
-    return tf.cond(
-        self.global_step < start_decay_step,
-        lambda: self.learning_rate,
-        lambda: tf.train.exponential_decay(
-            self.learning_rate,
-            (self.global_step - start_decay_step),
-            decay_steps, decay_factor, staircase=True),
-        name="learning_rate_decay_cond")
-
-  def init_embeddings(self, hparams, scope):
-    """Init embeddings."""
-    self.embedding_encoder, self.embedding_decoder = (
-        model_helper.create_emb_for_encoder_and_decoder(
-            share_vocab=hparams.share_vocab,
-            src_vocab_size=self.src_vocab_size,
-            tgt_vocab_size=self.tgt_vocab_size,
-            src_embed_size=self.num_units,
-            tgt_embed_size=self.num_units,
-            num_enc_partitions=hparams.num_enc_emb_partitions,
-            num_dec_partitions=hparams.num_dec_emb_partitions,
-            src_vocab_file=hparams.src_vocab_file,
-            tgt_vocab_file=hparams.tgt_vocab_file,
-            src_embed_file=hparams.src_embed_file,
-            tgt_embed_file=hparams.tgt_embed_file,
-            use_char_encode=hparams.use_char_encode,
-            scope=scope,))
-
-  def _get_train_summary(self):
-    """Get train summary."""
-    train_summary = tf.summary.merge(
-        [tf.summary.scalar("lr", self.learning_rate),
-         tf.summary.scalar("train_loss", self.train_loss)] +
-        self.grad_norm_summary)
-    return train_summary
-
-  def train(self, sess):
-    """Execute train graph."""
-    assert self.mode == tf.contrib.learn.ModeKeys.TRAIN
-    output_tuple = TrainOutputTuple(train_summary=self.train_summary,
-                                    train_loss=self.train_loss,
-                                    predict_count=self.predict_count,
-                                    global_step=self.global_step,
-                                    word_count=self.word_count,
-                                    batch_size=self.batch_size,
-                                    grad_norm=self.grad_norm,
-                                    learning_rate=self.learning_rate)
-    return sess.run([self.update, output_tuple])
-
-  def eval(self, sess):
-    """Execute eval graph."""
-    assert self.mode == tf.contrib.learn.ModeKeys.EVAL
-    output_tuple = EvalOutputTuple(eval_loss=self.eval_loss,
-                                   predict_count=self.predict_count,
-                                   batch_size=self.batch_size)
-    return sess.run(output_tuple)
-
-  def build_graph(self, hparams, scope=None):
-    """Subclass must implement this method.
-
-    Creates a sequence-to-sequence model with dynamic RNN decoder API.
-    Args:
-      hparams: Hyperparameter configurations.
-      scope: VariableScope for the created subgraph; default "dynamic_seq2seq".
-
-    Returns:
-      A tuple of the form (logits, loss_tuple, final_context_state, sample_id),
-      where:
-        logits: float32 Tensor [batch_size x num_decoder_symbols].
-        loss: loss = the total loss / batch_size.
-        final_context_state: the final state of decoder RNN.
-        sample_id: sampling indices.
-
-    Raises:
-      ValueError: if encoder_type differs from mono and bi, or
-        attention_option is not (luong | scaled_luong |
-        bahdanau | normed_bahdanau).
-    """
-    utils.print_out("# Creating %s graph ..." % self.mode)
-
-    # Projection
-    if not self.extract_encoder_layers:
-      with tf.variable_scope(scope or "build_network"):
-        with tf.variable_scope("decoder/output_projection"):
-          self.output_layer = tf.layers.Dense(
-              self.tgt_vocab_size, use_bias=False, name="output_projection")
-
-    with tf.variable_scope(scope or "dynamic_seq2seq", dtype=self.dtype):
-      # Encoder
-      if hparams.language_model:  # no encoder for language modeling
-        utils.print_out("  language modeling: no encoder")
-        self.encoder_outputs = None
-        encoder_state = None
-      else:
-        self.encoder_outputs, encoder_state = self._build_encoder(hparams)
-
-      # Skip decoder if extracting only encoder layers
-      if self.extract_encoder_layers:
-        return
-
-      ## Decoder
-      logits, decoder_cell_outputs, sample_id, final_context_state = (
-          self._build_decoder(self.encoder_outputs, encoder_state, hparams))
-
-      ## Loss
-      if self.mode != tf.contrib.learn.ModeKeys.INFER:
-        with tf.device(model_helper.get_device_str(self.num_encoder_layers - 1,
-                                                   self.num_gpus)):
-          loss = self._compute_loss(logits, decoder_cell_outputs)
-      else:
-        loss = tf.constant(0.0)
-
-      return logits, loss, final_context_state, sample_id
-
-  @abc.abstractmethod
-  def _build_encoder(self, hparams):
-    """Subclass must implement this.
-
-    Build and run an RNN encoder.
-
-    Args:
-      hparams: Hyperparameters configurations.
-
-    Returns:
-      A tuple of encoder_outputs and encoder_state.
-    """
     pass
 
-  def _build_encoder_cell(self, hparams, num_layers, num_residual_layers,
-                          base_gpu=0):
-    """Build a multi-layer RNN cell that can be used by encoder."""
-
-    return model_helper.create_rnn_cell(
-        unit_type=hparams.unit_type,
-        num_units=self.num_units,
-        num_layers=num_layers,
-        num_residual_layers=num_residual_layers,
-        forget_bias=hparams.forget_bias,
-        dropout=hparams.dropout,
-        num_gpus=hparams.num_gpus,
-        mode=self.mode,
-        base_gpu=base_gpu,
-        single_cell_fn=self.single_cell_fn)
-
-  def _get_infer_maximum_iterations(self, hparams, source_sequence_length):
-    """Maximum decoding steps at inference time."""
-    if hparams.tgt_max_len_infer:
-      maximum_iterations = hparams.tgt_max_len_infer
-      utils.print_out("  decoding maximum_iterations %d" % maximum_iterations)
-    else:
-      # TODO(thangluong): add decoding_length_factor flag
-      decoding_length_factor = 2.0
-      max_encoder_length = tf.reduce_max(source_sequence_length)
-      maximum_iterations = tf.to_int32(tf.round(
-          tf.to_float(max_encoder_length) * decoding_length_factor))
-    return maximum_iterations
-
-  def _build_decoder(self, encoder_outputs, encoder_state, hparams):
-    """Build and run a RNN decoder with a final projection layer.
-
-    Args:
-      encoder_outputs: The outputs of encoder for every time step.
-      encoder_state: The final state of the encoder.
-      hparams: The Hyperparameters configurations.
-
-    Returns:
-      A tuple of final logits and final decoder state:
-        logits: size [time, batch_size, vocab_size] when time_major=True.
-    """
-    tgt_sos_id = tf.cast(self.tgt_vocab_table.lookup(tf.constant(hparams.sos)),
-                         tf.int32)
-    tgt_eos_id = tf.cast(self.tgt_vocab_table.lookup(tf.constant(hparams.eos)),
-                         tf.int32)
-    iterator = self.iterator
-
-    # maximum_iteration: The maximum decoding steps.
-    maximum_iterations = self._get_infer_maximum_iterations(
-        hparams, iterator.source_sequence_length)
-
-    ## Decoder.
-    with tf.variable_scope("decoder") as decoder_scope:
-      cell, decoder_initial_state = self._build_decoder_cell(
-          hparams, encoder_outputs, encoder_state,
-          iterator.source_sequence_length)
-
-      # Optional ops depends on which mode we are in and which loss function we
-      # are using.
-      logits = tf.no_op()
-      decoder_cell_outputs = None
-
-      ## Train or eval
-      if self.mode != tf.contrib.learn.ModeKeys.INFER:
-        # decoder_emp_inp: [max_time, batch_size, num_units]
-        target_input = iterator.target_input
-        if self.time_major:
-          target_input = tf.transpose(target_input)
-        decoder_emb_inp = tf.nn.embedding_lookup(
-            self.embedding_decoder, target_input)
-
-        # Helper
-        helper = tf.contrib.seq2seq.TrainingHelper(
-            decoder_emb_inp, iterator.target_sequence_length,
-            time_major=self.time_major)
-
-        # Decoder
-        my_decoder = tf.contrib.seq2seq.BasicDecoder(
-            cell,
-            helper,
-            decoder_initial_state,)
-
-        # Dynamic decoding
-        outputs, final_context_state, _ = tf.contrib.seq2seq.dynamic_decode(
-            my_decoder,
-            output_time_major=self.time_major,
-            swap_memory=True,
-            scope=decoder_scope)
 
-        sample_id = outputs.sample_id
+class BaseModel(object):
+    """Sequence-to-sequence base class."""
+
+    def __init__(
+        self,
+        hparams,
+        mode,
+        iterator,
+        source_vocab_table,
+        target_vocab_table,
+        reverse_target_vocab_table=None,
+        scope=None,
+        extra_args=None,
+    ):
+        """Create the model.
+
+        Args:
+          hparams: Hyperparameter configurations.
+          mode: TRAIN | EVAL | INFER
+          iterator: Dataset Iterator that feeds data.
+          source_vocab_table: Lookup table mapping source words to ids.
+          target_vocab_table: Lookup table mapping target words to ids.
+          reverse_target_vocab_table: Lookup table mapping ids to target words. Only
+            required in INFER mode. Defaults to None.
+          scope: scope of the model.
+          extra_args: model_helper.ExtraArgs, for passing customizable functions.
+
+        """
+        # Set params
+        self._set_params_initializer(
+            hparams,
+            mode,
+            iterator,
+            source_vocab_table,
+            target_vocab_table,
+            scope,
+            extra_args,
+        )
+
+        # Not used in general seq2seq models; when True, ignore decoder &
+        # training
+        self.extract_encoder_layers = (
+            hasattr(hparams, "extract_encoder_layers")
+            and hparams.extract_encoder_layers
+        )
+
+        # Train graph
+        res = self.build_graph(hparams, scope=scope)
+        if not self.extract_encoder_layers:
+            self._set_train_or_infer(res, reverse_target_vocab_table, hparams)
+
+        # Saver
+        self.saver = tf.train.Saver(
+            tf.global_variables(), max_to_keep=hparams.num_keep_ckpts
+        )
+
+    def _set_params_initializer(
+        self,
+        hparams,
+        mode,
+        iterator,
+        source_vocab_table,
+        target_vocab_table,
+        scope,
+        extra_args=None,
+    ):
+        """Set various params for self and initialize."""
+        assert isinstance(iterator, iterator_utils.BatchedInput)
+        self.iterator = iterator
+        self.mode = mode
+        self.src_vocab_table = source_vocab_table
+        self.tgt_vocab_table = target_vocab_table
+
+        self.src_vocab_size = hparams.src_vocab_size
+        self.tgt_vocab_size = hparams.tgt_vocab_size
+        self.num_gpus = hparams.num_gpus
+        self.time_major = hparams.time_major
+
+        if hparams.use_char_encode:
+            assert not self.time_major, "Can't use time major for" " char-level inputs."
+
+        self.dtype = tf.float32
+        self.num_sampled_softmax = hparams.num_sampled_softmax
+
+        # extra_args: to make it flexible for adding external customizable code
+        self.single_cell_fn = None
+        if extra_args:
+            self.single_cell_fn = extra_args.single_cell_fn
+
+        # Set num units
+        self.num_units = hparams.num_units
+
+        # Set num layers
+        self.num_encoder_layers = hparams.num_encoder_layers
+        self.num_decoder_layers = hparams.num_decoder_layers
+        assert self.num_encoder_layers
+        assert self.num_decoder_layers
+
+        # Set num residual layers
+        if hasattr(
+                hparams, "num_residual_layers"):  # compatible common_test_utils
+            self.num_encoder_residual_layers = hparams.num_residual_layers
+            self.num_decoder_residual_layers = hparams.num_residual_layers
+        else:
+            self.num_encoder_residual_layers = hparams.num_encoder_residual_layers
+            self.num_decoder_residual_layers = hparams.num_decoder_residual_layers
 
-        if self.num_sampled_softmax > 0:
-          # Note: this is required when using sampled_softmax_loss.
-          decoder_cell_outputs = outputs.rnn_output
-
-        # Note: there's a subtle difference here between train and inference.
-        # We could have set output_layer when create my_decoder
-        #   and shared more code between train and inference.
-        # We chose to apply the output_layer to all timesteps for speed:
-        #   10% improvements for small models & 20% for larger ones.
-        # If memory is a concern, we should apply output_layer per timestep.
-        num_layers = self.num_decoder_layers
-        num_gpus = self.num_gpus
-        device_id = num_layers if num_layers < num_gpus else (num_layers - 1)
-        # Colocate output layer with the last RNN cell if there is no extra GPU
-        # available. Otherwise, put last layer on a separate GPU.
-        with tf.device(model_helper.get_device_str(device_id, num_gpus)):
-          logits = self.output_layer(outputs.rnn_output)
+        # Batch size
+        self.batch_size = tf.size(self.iterator.source_sequence_length)
 
-        if self.num_sampled_softmax > 0:
-          logits = tf.no_op()  # unused when using sampled softmax loss.
+        # Global step
+        self.global_step = tf.Variable(0, trainable=False)
 
-      ## Inference
-      else:
-        infer_mode = hparams.infer_mode
-        start_tokens = tf.fill([self.batch_size], tgt_sos_id)
-        end_token = tgt_eos_id
+        # Initializer
+        self.random_seed = hparams.random_seed
+        initializer = model_helper.get_initializer(
+            hparams.init_op, self.random_seed, hparams.init_weight
+        )
+        tf.get_variable_scope().set_initializer(initializer)
+
+        # Embeddings
+        if extra_args and extra_args.encoder_emb_lookup_fn:
+            self.encoder_emb_lookup_fn = extra_args.encoder_emb_lookup_fn
+        else:
+            self.encoder_emb_lookup_fn = tf.nn.embedding_lookup
+        self.init_embeddings(hparams, scope)
+
+    def _set_train_or_infer(self, res, reverse_target_vocab_table, hparams):
+        """Set up training and inference."""
+        if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
+            self.train_loss = res[1]
+            self.word_count = tf.reduce_sum(
+                self.iterator.source_sequence_length
+            ) + tf.reduce_sum(self.iterator.target_sequence_length)
+        elif self.mode == tf.contrib.learn.ModeKeys.EVAL:
+            self.eval_loss = res[1]
+        elif self.mode == tf.contrib.learn.ModeKeys.INFER:
+            self.infer_logits, _, self.final_context_state, self.sample_id = res
+            self.sample_words = reverse_target_vocab_table.lookup(
+                tf.to_int64(self.sample_id)
+            )
+
+        if self.mode != tf.contrib.learn.ModeKeys.INFER:
+            # Count the number of predicted words for compute ppl.
+            self.predict_count = tf.reduce_sum(
+                self.iterator.target_sequence_length)
+
+        params = tf.trainable_variables()
+
+        # Gradients and SGD update operation for training the model.
+        # Arrange for the embedding vars to appear at the beginning.
+        if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
+            self.learning_rate = tf.constant(hparams.learning_rate)
+            # warm-up
+            self.learning_rate = self._get_learning_rate_warmup(hparams)
+            # decay
+            self.learning_rate = self._get_learning_rate_decay(hparams)
+
+            # Optimizer
+            if hparams.optimizer == "sgd":
+                opt = tf.train.GradientDescentOptimizer(self.learning_rate)
+            elif hparams.optimizer == "adam":
+                opt = tf.train.AdamOptimizer(self.learning_rate)
+            else:
+                raise ValueError(
+                    "Unknown optimizer type %s" %
+                    hparams.optimizer)
+
+            # Gradients
+            gradients = tf.gradients(
+                self.train_loss,
+                params,
+                colocate_gradients_with_ops=hparams.colocate_gradients_with_ops,
+            )
+
+            clipped_grads, grad_norm_summary, grad_norm = model_helper.gradient_clip(
+                gradients, max_gradient_norm=hparams.max_gradient_norm
+            )
+            self.grad_norm_summary = grad_norm_summary
+            self.grad_norm = grad_norm
+
+            self.update = opt.apply_gradients(
+                zip(clipped_grads, params), global_step=self.global_step
+            )
+
+            # Summary
+            self.train_summary = self._get_train_summary()
+        elif self.mode == tf.contrib.learn.ModeKeys.INFER:
+            self.infer_summary = self._get_infer_summary(hparams)
+
+        # Print trainable variables
+        utils.print_out("# Trainable variables")
+        utils.print_out("Format: <name>, <shape>, <(soft) device placement>")
+        for param in params:
+            utils.print_out(
+                "  %s, %s, %s" % (param.name, str(
+                    param.get_shape()), param.op.device)
+            )
+
+    def _get_learning_rate_warmup(self, hparams):
+        """Get learning rate warmup."""
+        warmup_steps = hparams.warmup_steps
+        warmup_scheme = hparams.warmup_scheme
         utils.print_out(
-            "  decoder: infer_mode=%sbeam_width=%d, length_penalty=%f" % (
-                infer_mode, hparams.beam_width, hparams.length_penalty_weight))
-
-        if infer_mode == "beam_search":
-          beam_width = hparams.beam_width
-          length_penalty_weight = hparams.length_penalty_weight
-
-          my_decoder = tf.contrib.seq2seq.BeamSearchDecoder(
-              cell=cell,
-              embedding=self.embedding_decoder,
-              start_tokens=start_tokens,
-              end_token=end_token,
-              initial_state=decoder_initial_state,
-              beam_width=beam_width,
-              output_layer=self.output_layer,
-              length_penalty_weight=length_penalty_weight)
-        elif infer_mode == "sample":
-          # Helper
-          sampling_temperature = hparams.sampling_temperature
-          assert sampling_temperature > 0.0, (
-              "sampling_temperature must greater than 0.0 when using sample"
-              " decoder.")
-          helper = tf.contrib.seq2seq.SampleEmbeddingHelper(
-              self.embedding_decoder, start_tokens, end_token,
-              softmax_temperature=sampling_temperature,
-              seed=self.random_seed)
-        elif infer_mode == "greedy":
-          helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
-              self.embedding_decoder, start_tokens, end_token)
+            "  learning_rate=%g, warmup_steps=%d, warmup_scheme=%s"
+            % (hparams.learning_rate, warmup_steps, warmup_scheme)
+        )
+
+        # Apply inverse decay if global steps less than warmup steps.
+        # Inspired by https://arxiv.org/pdf/1706.03762.pdf (Section 5.3)
+        # When step < warmup_steps,
+        #   learing_rate *= warmup_factor ** (warmup_steps - step)
+        if warmup_scheme == "t2t":
+            # 0.01^(1/warmup_steps): we start with a lr, 100 times smaller
+            warmup_factor = tf.exp(tf.log(0.01) / warmup_steps)
+            inv_decay = warmup_factor ** (
+                tf.to_float(warmup_steps - self.global_step))
         else:
-          raise ValueError("Unknown infer_mode '%s'", infer_mode)
-
-        if infer_mode != "beam_search":
-          my_decoder = tf.contrib.seq2seq.BasicDecoder(
-              cell,
-              helper,
-              decoder_initial_state,
-              output_layer=self.output_layer  # applied per timestep
-          )
-
-        # Dynamic decoding
-        outputs, final_context_state, _ = tf.contrib.seq2seq.dynamic_decode(
-            my_decoder,
-            maximum_iterations=maximum_iterations,
-            output_time_major=self.time_major,
-            swap_memory=True,
-            scope=decoder_scope)
-
-        if infer_mode == "beam_search":
-          sample_id = outputs.predicted_ids
+            raise ValueError("Unknown warmup scheme %s" % warmup_scheme)
+
+        return tf.cond(
+            self.global_step < hparams.warmup_steps,
+            lambda: inv_decay * self.learning_rate,
+            lambda: self.learning_rate,
+            name="learning_rate_warump_cond",
+        )
+
+    def _get_decay_info(self, hparams):
+        """Return decay info based on decay_scheme."""
+        if hparams.decay_scheme in ["luong5", "luong10", "luong234"]:
+            decay_factor = 0.5
+            if hparams.decay_scheme == "luong5":
+                start_decay_step = int(hparams.num_train_steps / 2)
+                decay_times = 5
+            elif hparams.decay_scheme == "luong10":
+                start_decay_step = int(hparams.num_train_steps / 2)
+                decay_times = 10
+            elif hparams.decay_scheme == "luong234":
+                start_decay_step = int(hparams.num_train_steps * 2 / 3)
+                decay_times = 4
+            remain_steps = hparams.num_train_steps - start_decay_step
+            decay_steps = int(remain_steps / decay_times)
+        elif not hparams.decay_scheme:  # no decay
+            start_decay_step = hparams.num_train_steps
+            decay_steps = 0
+            decay_factor = 1.0
+        elif hparams.decay_scheme:
+            raise ValueError("Unknown decay scheme %s" % hparams.decay_scheme)
+        return start_decay_step, decay_steps, decay_factor
+
+    def _get_learning_rate_decay(self, hparams):
+        """Get learning rate decay."""
+        start_decay_step, decay_steps, decay_factor = self._get_decay_info(
+            hparams)
+        utils.print_out(
+            "  decay_scheme=%s, start_decay_step=%d, decay_steps %d, "
+            "decay_factor %g"
+            % (hparams.decay_scheme, start_decay_step, decay_steps, decay_factor)
+        )
+
+        return tf.cond(
+            self.global_step < start_decay_step,
+            lambda: self.learning_rate,
+            lambda: tf.train.exponential_decay(
+                self.learning_rate,
+                (self.global_step - start_decay_step),
+                decay_steps,
+                decay_factor,
+                staircase=True,
+            ),
+            name="learning_rate_decay_cond",
+        )
+
+    def init_embeddings(self, hparams, scope):
+        """Init embeddings."""
+        self.embedding_encoder, self.embedding_decoder = (
+            model_helper.create_emb_for_encoder_and_decoder(
+                share_vocab=hparams.share_vocab,
+                src_vocab_size=self.src_vocab_size,
+                tgt_vocab_size=self.tgt_vocab_size,
+                src_embed_size=self.num_units,
+                tgt_embed_size=self.num_units,
+                num_enc_partitions=hparams.num_enc_emb_partitions,
+                num_dec_partitions=hparams.num_dec_emb_partitions,
+                src_vocab_file=hparams.src_vocab_file,
+                tgt_vocab_file=hparams.tgt_vocab_file,
+                src_embed_file=hparams.src_embed_file,
+                tgt_embed_file=hparams.tgt_embed_file,
+                use_char_encode=hparams.use_char_encode,
+                scope=scope,
+            )
+        )
+
+    def _get_train_summary(self):
+        """Get train summary."""
+        train_summary = tf.summary.merge(
+            [
+                tf.summary.scalar("lr", self.learning_rate),
+                tf.summary.scalar("train_loss", self.train_loss),
+            ]
+            + self.grad_norm_summary
+        )
+        return train_summary
+
+    def train(self, sess):
+        """Execute train graph."""
+        assert self.mode == tf.contrib.learn.ModeKeys.TRAIN
+        output_tuple = TrainOutputTuple(
+            train_summary=self.train_summary,
+            train_loss=self.train_loss,
+            predict_count=self.predict_count,
+            global_step=self.global_step,
+            word_count=self.word_count,
+            batch_size=self.batch_size,
+            grad_norm=self.grad_norm,
+            learning_rate=self.learning_rate,
+        )
+        return sess.run([self.update, output_tuple])
+
+    def eval(self, sess):
+        """Execute eval graph."""
+        assert self.mode == tf.contrib.learn.ModeKeys.EVAL
+        output_tuple = EvalOutputTuple(
+            eval_loss=self.eval_loss,
+            predict_count=self.predict_count,
+            batch_size=self.batch_size,
+        )
+        return sess.run(output_tuple)
+
+    def build_graph(self, hparams, scope=None):
+        """Subclass must implement this method.
+
+        Creates a sequence-to-sequence model with dynamic RNN decoder API.
+        Args:
+          hparams: Hyperparameter configurations.
+          scope: VariableScope for the created subgraph; default "dynamic_seq2seq".
+
+        Returns:
+          A tuple of the form (logits, loss_tuple, final_context_state, sample_id),
+          where:
+            logits: float32 Tensor [batch_size x num_decoder_symbols].
+            loss: loss = the total loss / batch_size.
+            final_context_state: the final state of decoder RNN.
+            sample_id: sampling indices.
+
+        Raises:
+          ValueError: if encoder_type differs from mono and bi, or
+            attention_option is not (luong | scaled_luong |
+            bahdanau | normed_bahdanau).
+        """
+        utils.print_out("# Creating %s graph ..." % self.mode)
+
+        # Projection
+        if not self.extract_encoder_layers:
+            with tf.variable_scope(scope or "build_network"):
+                with tf.variable_scope("decoder/output_projection"):
+                    self.output_layer = tf.layers.Dense(
+                        self.tgt_vocab_size, use_bias=False, name="output_projection"
+                    )
+
+        with tf.variable_scope(scope or "dynamic_seq2seq", dtype=self.dtype):
+            # Encoder
+            if hparams.language_model:  # no encoder for language modeling
+                utils.print_out("  language modeling: no encoder")
+                self.encoder_outputs = None
+                encoder_state = None
+            else:
+                self.encoder_outputs, encoder_state = self._build_encoder(
+                    hparams)
+
+            # Skip decoder if extracting only encoder layers
+            if self.extract_encoder_layers:
+                return
+
+            # Decoder
+            logits, decoder_cell_outputs, sample_id, final_context_state = (
+                self._build_decoder(
+                    self.encoder_outputs, encoder_state, hparams)
+            )
+
+            # Loss
+            if self.mode != tf.contrib.learn.ModeKeys.INFER:
+                with tf.device(
+                    model_helper.get_device_str(
+                        self.num_encoder_layers - 1, self.num_gpus
+                    )
+                ):
+                    loss = self._compute_loss(logits, decoder_cell_outputs)
+            else:
+                loss = tf.constant(0.0)
+
+            return logits, loss, final_context_state, sample_id
+
+    @abc.abstractmethod
+    def _build_encoder(self, hparams):
+        """Subclass must implement this.
+
+        Build and run an RNN encoder.
+
+        Args:
+          hparams: Hyperparameters configurations.
+
+        Returns:
+          A tuple of encoder_outputs and encoder_state.
+        """
+        pass
+
+    def _build_encoder_cell(self, hparams, num_layers,
+                            num_residual_layers, base_gpu=0):
+        """Build a multi-layer RNN cell that can be used by encoder."""
+
+        return model_helper.create_rnn_cell(
+            unit_type=hparams.unit_type,
+            num_units=self.num_units,
+            num_layers=num_layers,
+            num_residual_layers=num_residual_layers,
+            forget_bias=hparams.forget_bias,
+            dropout=hparams.dropout,
+            num_gpus=hparams.num_gpus,
+            mode=self.mode,
+            base_gpu=base_gpu,
+            single_cell_fn=self.single_cell_fn,
+        )
+
+    def _get_infer_maximum_iterations(self, hparams, source_sequence_length):
+        """Maximum decoding steps at inference time."""
+        if hparams.tgt_max_len_infer:
+            maximum_iterations = hparams.tgt_max_len_infer
+            utils.print_out(
+                "  decoding maximum_iterations %d" %
+                maximum_iterations)
         else:
-          logits = outputs.rnn_output
-          sample_id = outputs.sample_id
-
-    return logits, decoder_cell_outputs, sample_id, final_context_state
-
-  def get_max_time(self, tensor):
-    time_axis = 0 if self.time_major else 1
-    return tensor.shape[time_axis].value or tf.shape(tensor)[time_axis]
-
-  @abc.abstractmethod
-  def _build_decoder_cell(self, hparams, encoder_outputs, encoder_state,
-                          source_sequence_length):
-    """Subclass must implement this.
+            # TODO(thangluong): add decoding_length_factor flag
+            decoding_length_factor = 2.0
+            max_encoder_length = tf.reduce_max(source_sequence_length)
+            maximum_iterations = tf.to_int32(
+                tf.round(
+                    tf.to_float(max_encoder_length) *
+                    decoding_length_factor)
+            )
+        return maximum_iterations
+
+    def _build_decoder(self, encoder_outputs, encoder_state, hparams):
+        """Build and run a RNN decoder with a final projection layer.
+
+        Args:
+          encoder_outputs: The outputs of encoder for every time step.
+          encoder_state: The final state of the encoder.
+          hparams: The Hyperparameters configurations.
+
+        Returns:
+          A tuple of final logits and final decoder state:
+            logits: size [time, batch_size, vocab_size] when time_major=True.
+        """
+        tgt_sos_id = tf.cast(
+            self.tgt_vocab_table.lookup(tf.constant(hparams.sos)), tf.int32
+        )
+        tgt_eos_id = tf.cast(
+            self.tgt_vocab_table.lookup(tf.constant(hparams.eos)), tf.int32
+        )
+        iterator = self.iterator
+
+        # maximum_iteration: The maximum decoding steps.
+        maximum_iterations = self._get_infer_maximum_iterations(
+            hparams, iterator.source_sequence_length
+        )
+
+        # Decoder.
+        with tf.variable_scope("decoder") as decoder_scope:
+            cell, decoder_initial_state = self._build_decoder_cell(
+                hparams, encoder_outputs, encoder_state, iterator.source_sequence_length
+            )
+
+            # Optional ops depends on which mode we are in and which loss function we
+            # are using.
+            logits = tf.no_op()
+            decoder_cell_outputs = None
+
+            # Train or eval
+            if self.mode != tf.contrib.learn.ModeKeys.INFER:
+                # decoder_emp_inp: [max_time, batch_size, num_units]
+                target_input = iterator.target_input
+                if self.time_major:
+                    target_input = tf.transpose(target_input)
+                decoder_emb_inp = tf.nn.embedding_lookup(
+                    self.embedding_decoder, target_input
+                )
+
+                # Helper
+                helper = tf.contrib.seq2seq.TrainingHelper(
+                    decoder_emb_inp,
+                    iterator.target_sequence_length,
+                    time_major=self.time_major,
+                )
+
+                # Decoder
+                my_decoder = tf.contrib.seq2seq.BasicDecoder(
+                    cell,
+                    helper,
+                    decoder_initial_state,
+                )
+
+                # Dynamic decoding
+                outputs, final_context_state, _ = tf.contrib.seq2seq.dynamic_decode(
+                    my_decoder,
+                    output_time_major=self.time_major,
+                    swap_memory=True,
+                    scope=decoder_scope,
+                )
+
+                sample_id = outputs.sample_id
+
+                if self.num_sampled_softmax > 0:
+                    # Note: this is required when using sampled_softmax_loss.
+                    decoder_cell_outputs = outputs.rnn_output
+
+                # Note: there's a subtle difference here between train and inference.
+                # We could have set output_layer when create my_decoder
+                #   and shared more code between train and inference.
+                # We chose to apply the output_layer to all timesteps for speed:
+                #   10% improvements for small models & 20% for larger ones.
+                # If memory is a concern, we should apply output_layer per
+                # timestep.
+                num_layers = self.num_decoder_layers
+                num_gpus = self.num_gpus
+                device_id = num_layers if num_layers < num_gpus else (
+                    num_layers - 1)
+                # Colocate output layer with the last RNN cell if there is no extra GPU
+                # available. Otherwise, put last layer on a separate GPU.
+                with tf.device(model_helper.get_device_str(device_id, num_gpus)):
+                    logits = self.output_layer(outputs.rnn_output)
+
+                if self.num_sampled_softmax > 0:
+                    # unused when using sampled softmax loss.
+                    logits = tf.no_op()
+
+            # Inference
+            else:
+                infer_mode = hparams.infer_mode
+                start_tokens = tf.fill([self.batch_size], tgt_sos_id)
+                end_token = tgt_eos_id
+                utils.print_out(
+                    "  decoder: infer_mode=%sbeam_width=%d, length_penalty=%f"
+                    % (infer_mode, hparams.beam_width, hparams.length_penalty_weight)
+                )
+
+                if infer_mode == "beam_search":
+                    beam_width = hparams.beam_width
+                    length_penalty_weight = hparams.length_penalty_weight
+
+                    my_decoder = tf.contrib.seq2seq.BeamSearchDecoder(
+                        cell=cell,
+                        embedding=self.embedding_decoder,
+                        start_tokens=start_tokens,
+                        end_token=end_token,
+                        initial_state=decoder_initial_state,
+                        beam_width=beam_width,
+                        output_layer=self.output_layer,
+                        length_penalty_weight=length_penalty_weight,
+                    )
+                elif infer_mode == "sample":
+                    # Helper
+                    sampling_temperature = hparams.sampling_temperature
+                    assert sampling_temperature > 0.0, (
+                        "sampling_temperature must greater than 0.0 when using sample"
+                        " decoder."
+                    )
+                    helper = tf.contrib.seq2seq.SampleEmbeddingHelper(
+                        self.embedding_decoder,
+                        start_tokens,
+                        end_token,
+                        softmax_temperature=sampling_temperature,
+                        seed=self.random_seed,
+                    )
+                elif infer_mode == "greedy":
+                    helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
+                        self.embedding_decoder, start_tokens, end_token
+                    )
+                else:
+                    raise ValueError("Unknown infer_mode '%s'", infer_mode)
+
+                if infer_mode != "beam_search":
+                    my_decoder = tf.contrib.seq2seq.BasicDecoder(
+                        cell,
+                        helper,
+                        decoder_initial_state,
+                        output_layer=self.output_layer,  # applied per timestep
+                    )
+
+                # Dynamic decoding
+                outputs, final_context_state, _ = tf.contrib.seq2seq.dynamic_decode(
+                    my_decoder,
+                    maximum_iterations=maximum_iterations,
+                    output_time_major=self.time_major,
+                    swap_memory=True,
+                    scope=decoder_scope,
+                )
+
+                if infer_mode == "beam_search":
+                    sample_id = outputs.predicted_ids
+                else:
+                    logits = outputs.rnn_output
+                    sample_id = outputs.sample_id
+
+        return logits, decoder_cell_outputs, sample_id, final_context_state
+
+    def get_max_time(self, tensor):
+        time_axis = 0 if self.time_major else 1
+        return tensor.shape[time_axis].value or tf.shape(tensor)[time_axis]
+
+    @abc.abstractmethod
+    def _build_decoder_cell(
+        self, hparams, encoder_outputs, encoder_state, source_sequence_length
+    ):
+        """Subclass must implement this.
+
+        Args:
+          hparams: Hyperparameters configurations.
+          encoder_outputs: The outputs of encoder for every time step.
+          encoder_state: The final state of the encoder.
+          source_sequence_length: sequence length of encoder_outputs.
+
+        Returns:
+          A tuple of a multi-layer RNN cell used by decoder and the intial state of
+          the decoder RNN.
+        """
+        pass
+
+    def _softmax_cross_entropy_loss(
+            self, logits, decoder_cell_outputs, labels):
+        """Compute softmax loss or sampled softmax loss."""
+        if self.num_sampled_softmax > 0:
 
-    Args:
-      hparams: Hyperparameters configurations.
-      encoder_outputs: The outputs of encoder for every time step.
-      encoder_state: The final state of the encoder.
-      source_sequence_length: sequence length of encoder_outputs.
+            is_sequence = decoder_cell_outputs.shape.ndims == 3
+
+            if is_sequence:
+                labels = tf.reshape(labels, [-1, 1])
+                inputs = tf.reshape(decoder_cell_outputs, [-1, self.num_units])
+
+            crossent = tf.nn.sampled_softmax_loss(
+                weights=tf.transpose(self.output_layer.kernel),
+                biases=self.output_layer.bias or tf.zeros(
+                    [self.tgt_vocab_size]),
+                labels=labels,
+                inputs=inputs,
+                num_sampled=self.num_sampled_softmax,
+                num_classes=self.tgt_vocab_size,
+                partition_strategy="div",
+                seed=self.random_seed,
+            )
+
+            if is_sequence:
+                if self.time_major:
+                    crossent = tf.reshape(crossent, [-1, self.batch_size])
+                else:
+                    crossent = tf.reshape(crossent, [self.batch_size, -1])
 
-    Returns:
-      A tuple of a multi-layer RNN cell used by decoder and the intial state of
-      the decoder RNN.
-    """
-    pass
-
-  def _softmax_cross_entropy_loss(
-      self, logits, decoder_cell_outputs, labels):
-    """Compute softmax loss or sampled softmax loss."""
-    if self.num_sampled_softmax > 0:
+        else:
+            crossent = tf.nn.sparse_softmax_cross_entropy_with_logits(
+                labels=labels, logits=logits
+            )
 
-      is_sequence = (decoder_cell_outputs.shape.ndims == 3)
+        return crossent
 
-      if is_sequence:
-        labels = tf.reshape(labels, [-1, 1])
-        inputs = tf.reshape(decoder_cell_outputs, [-1, self.num_units])
+    def _compute_loss(self, logits, decoder_cell_outputs):
+        """Compute optimization loss."""
+        target_output = self.iterator.target_output
+        if self.time_major:
+            target_output = tf.transpose(target_output)
+        max_time = self.get_max_time(target_output)
 
-      crossent = tf.nn.sampled_softmax_loss(
-          weights=tf.transpose(self.output_layer.kernel),
-          biases=self.output_layer.bias or tf.zeros([self.tgt_vocab_size]),
-          labels=labels,
-          inputs=inputs,
-          num_sampled=self.num_sampled_softmax,
-          num_classes=self.tgt_vocab_size,
-          partition_strategy="div",
-          seed=self.random_seed)
+        crossent = self._softmax_cross_entropy_loss(
+            logits, decoder_cell_outputs, target_output
+        )
 
-      if is_sequence:
+        target_weights = tf.sequence_mask(
+            self.iterator.target_sequence_length, max_time, dtype=self.dtype
+        )
         if self.time_major:
-          crossent = tf.reshape(crossent, [-1, self.batch_size])
+            target_weights = tf.transpose(target_weights)
+
+        loss = tf.reduce_sum(crossent * target_weights) / \
+            tf.to_float(self.batch_size)
+        return loss
+
+    def _get_infer_summary(self, hparams):
+        del hparams
+        return tf.no_op()
+
+    def infer(self, sess):
+        assert self.mode == tf.contrib.learn.ModeKeys.INFER
+        output_tuple = InferOutputTuple(
+            infer_logits=self.infer_logits,
+            infer_summary=self.infer_summary,
+            sample_id=self.sample_id,
+            sample_words=self.sample_words,
+        )
+        return sess.run(output_tuple)
+
+    def decode(self, sess):
+        """Decode a batch.
+
+        Args:
+          sess: tensorflow session to use.
+
+        Returns:
+          A tuple consiting of outputs, infer_summary.
+            outputs: of size [batch_size, time]
+        """
+        output_tuple = self.infer(sess)
+        sample_words = output_tuple.sample_words
+        infer_summary = output_tuple.infer_summary
+
+        # make sure outputs is of shape [batch_size, time] or [beam_width,
+        # batch_size, time] when using beam search.
+        if self.time_major:
+            sample_words = sample_words.transpose()
+        elif sample_words.ndim == 3:
+            # beam search output in [batch_size, time, beam_width] shape.
+            sample_words = sample_words.transpose([2, 0, 1])
+        return sample_words, infer_summary
+
+    def build_encoder_states(self, include_embeddings=False):
+        """Stack encoder states and return tensor [batch, length, layer, size]."""
+        assert self.mode == tf.contrib.learn.ModeKeys.INFER
+        if include_embeddings:
+            stack_state_list = tf.stack(
+                [self.encoder_emb_inp] + self.encoder_state_list, 2
+            )
         else:
-          crossent = tf.reshape(crossent, [self.batch_size, -1])
-
-    else:
-      crossent = tf.nn.sparse_softmax_cross_entropy_with_logits(
-          labels=labels, logits=logits)
-
-    return crossent
-
-  def _compute_loss(self, logits, decoder_cell_outputs):
-    """Compute optimization loss."""
-    target_output = self.iterator.target_output
-    if self.time_major:
-      target_output = tf.transpose(target_output)
-    max_time = self.get_max_time(target_output)
-
-    crossent = self._softmax_cross_entropy_loss(
-        logits, decoder_cell_outputs, target_output)
-
-    target_weights = tf.sequence_mask(
-        self.iterator.target_sequence_length, max_time, dtype=self.dtype)
-    if self.time_major:
-      target_weights = tf.transpose(target_weights)
-
-    loss = tf.reduce_sum(
-        crossent * target_weights) / tf.to_float(self.batch_size)
-    return loss
-
-  def _get_infer_summary(self, hparams):
-    del hparams
-    return tf.no_op()
-
-  def infer(self, sess):
-    assert self.mode == tf.contrib.learn.ModeKeys.INFER
-    output_tuple = InferOutputTuple(infer_logits=self.infer_logits,
-                                    infer_summary=self.infer_summary,
-                                    sample_id=self.sample_id,
-                                    sample_words=self.sample_words)
-    return sess.run(output_tuple)
-
-  def decode(self, sess):
-    """Decode a batch.
-
-    Args:
-      sess: tensorflow session to use.
-
-    Returns:
-      A tuple consiting of outputs, infer_summary.
-        outputs: of size [batch_size, time]
-    """
-    output_tuple = self.infer(sess)
-    sample_words = output_tuple.sample_words
-    infer_summary = output_tuple.infer_summary
-
-    # make sure outputs is of shape [batch_size, time] or [beam_width,
-    # batch_size, time] when using beam search.
-    if self.time_major:
-      sample_words = sample_words.transpose()
-    elif sample_words.ndim == 3:
-      # beam search output in [batch_size, time, beam_width] shape.
-      sample_words = sample_words.transpose([2, 0, 1])
-    return sample_words, infer_summary
-
-  def build_encoder_states(self, include_embeddings=False):
-    """Stack encoder states and return tensor [batch, length, layer, size]."""
-    assert self.mode == tf.contrib.learn.ModeKeys.INFER
-    if include_embeddings:
-      stack_state_list = tf.stack(
-          [self.encoder_emb_inp] + self.encoder_state_list, 2)
-    else:
-      stack_state_list = tf.stack(self.encoder_state_list, 2)
-
-    # transform from [length, batch, ...] -> [batch, length, ...]
-    if self.time_major:
-      stack_state_list = tf.transpose(stack_state_list, [1, 0, 2, 3])
-
-    return stack_state_list
+            stack_state_list = tf.stack(self.encoder_state_list, 2)
 
+        # transform from [length, batch, ...] -> [batch, length, ...]
+        if self.time_major:
+            stack_state_list = tf.transpose(stack_state_list, [1, 0, 2, 3])
 
-class Model(BaseModel):
-  """Sequence-to-sequence dynamic model.
-
-  This class implements a multi-layer recurrent neural network as encoder,
-  and a multi-layer recurrent neural network decoder.
-  """
-  def _build_encoder_from_sequence(self, hparams, sequence, sequence_length):
-    """Build an encoder from a sequence.
+        return stack_state_list
 
-    Args:
-      hparams: hyperparameters.
-      sequence: tensor with input sequence data.
-      sequence_length: tensor with length of the input sequence.
 
-    Returns:
-      encoder_outputs: RNN encoder outputs.
-      encoder_state: RNN encoder state.
+class Model(BaseModel):
+    """Sequence-to-sequence dynamic model.
 
-    Raises:
-      ValueError: if encoder_type is neither "uni" nor "bi".
+    This class implements a multi-layer recurrent neural network as encoder,
+    and a multi-layer recurrent neural network decoder.
     """
-    num_layers = self.num_encoder_layers
-    num_residual_layers = self.num_encoder_residual_layers
 
-    if self.time_major:
-      sequence = tf.transpose(sequence)
+    def _build_encoder_from_sequence(self, hparams, sequence, sequence_length):
+        """Build an encoder from a sequence.
 
-    with tf.variable_scope("encoder") as scope:
-      dtype = scope.dtype
+        Args:
+          hparams: hyperparameters.
+          sequence: tensor with input sequence data.
+          sequence_length: tensor with length of the input sequence.
 
-      self.encoder_emb_inp = self.encoder_emb_lookup_fn(
-          self.embedding_encoder, sequence)
+        Returns:
+          encoder_outputs: RNN encoder outputs.
+          encoder_state: RNN encoder state.
 
-      # Encoder_outputs: [max_time, batch_size, num_units]
-      if hparams.encoder_type == "uni":
-        utils.print_out("  num_layers = %d, num_residual_layers=%d" %
-                        (num_layers, num_residual_layers))
-        cell = self._build_encoder_cell(hparams, num_layers,
-                                        num_residual_layers)
+        Raises:
+          ValueError: if encoder_type is neither "uni" nor "bi".
+        """
+        num_layers = self.num_encoder_layers
+        num_residual_layers = self.num_encoder_residual_layers
 
-        encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
-            cell,
-            self.encoder_emb_inp,
+        if self.time_major:
+            sequence = tf.transpose(sequence)
+
+        with tf.variable_scope("encoder") as scope:
+            dtype = scope.dtype
+
+            self.encoder_emb_inp = self.encoder_emb_lookup_fn(
+                self.embedding_encoder, sequence
+            )
+
+            # Encoder_outputs: [max_time, batch_size, num_units]
+            if hparams.encoder_type == "uni":
+                utils.print_out(
+                    "  num_layers = %d, num_residual_layers=%d"
+                    % (num_layers, num_residual_layers)
+                )
+                cell = self._build_encoder_cell(
+                    hparams, num_layers, num_residual_layers
+                )
+
+                encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
+                    cell,
+                    self.encoder_emb_inp,
+                    dtype=dtype,
+                    sequence_length=sequence_length,
+                    time_major=self.time_major,
+                    swap_memory=True,
+                )
+            elif hparams.encoder_type == "bi":
+                num_bi_layers = int(num_layers / 2)
+                num_bi_residual_layers = int(num_residual_layers / 2)
+                utils.print_out(
+                    "  num_bi_layers = %d, num_bi_residual_layers=%d"
+                    % (num_bi_layers, num_bi_residual_layers)
+                )
+
+                encoder_outputs, bi_encoder_state = self._build_bidirectional_rnn(
+                    inputs=self.encoder_emb_inp,
+                    sequence_length=sequence_length,
+                    dtype=dtype,
+                    hparams=hparams,
+                    num_bi_layers=num_bi_layers,
+                    num_bi_residual_layers=num_bi_residual_layers,
+                )
+
+                if num_bi_layers == 1:
+                    encoder_state = bi_encoder_state
+                else:
+                    # alternatively concat forward and backward states
+                    encoder_state = []
+                    for layer_id in range(num_bi_layers):
+                        encoder_state.append(
+                            bi_encoder_state[0][layer_id])  # forward
+                        encoder_state.append(
+                            bi_encoder_state[1][layer_id])  # backward
+                    encoder_state = tuple(encoder_state)
+            else:
+                raise ValueError(
+                    "Unknown encoder_type %s" %
+                    hparams.encoder_type)
+
+        # Use the top layer for now
+        self.encoder_state_list = [encoder_outputs]
+
+        return encoder_outputs, encoder_state
+
+    def _build_encoder(self, hparams):
+        """Build encoder from source."""
+        utils.print_out("# Build a basic encoder")
+        return self._build_encoder_from_sequence(
+            hparams, self.iterator.source, self.iterator.source_sequence_length
+        )
+
+    def _build_bidirectional_rnn(
+        self,
+        inputs,
+        sequence_length,
+        dtype,
+        hparams,
+        num_bi_layers,
+        num_bi_residual_layers,
+        base_gpu=0,
+    ):
+        """Create and call biddirectional RNN cells.
+
+        Args:
+          num_residual_layers: Number of residual layers from top to bottom. For
+            example, if `num_bi_layers=4` and `num_residual_layers=2`, the last 2 RNN
+            layers in each RNN cell will be wrapped with `ResidualWrapper`.
+          base_gpu: The gpu device id to use for the first forward RNN layer. The
+            i-th forward RNN layer will use `(base_gpu + i) % num_gpus` as its
+            device id. The `base_gpu` for backward RNN cell is `(base_gpu +
+            num_bi_layers)`.
+
+        Returns:
+          The concatenated bidirectional output and the bidirectional RNN cell"s
+          state.
+        """
+        # Construct forward and backward cells
+        fw_cell = self._build_encoder_cell(
+            hparams, num_bi_layers, num_bi_residual_layers, base_gpu=base_gpu
+        )
+        bw_cell = self._build_encoder_cell(
+            hparams,
+            num_bi_layers,
+            num_bi_residual_layers,
+            base_gpu=(base_gpu + num_bi_layers),
+        )
+
+        bi_outputs, bi_state = tf.nn.bidirectional_dynamic_rnn(
+            fw_cell,
+            bw_cell,
+            inputs,
             dtype=dtype,
             sequence_length=sequence_length,
             time_major=self.time_major,
-            swap_memory=True)
-      elif hparams.encoder_type == "bi":
-        num_bi_layers = int(num_layers / 2)
-        num_bi_residual_layers = int(num_residual_layers / 2)
-        utils.print_out("  num_bi_layers = %d, num_bi_residual_layers=%d" %
-                        (num_bi_layers, num_bi_residual_layers))
-
-        encoder_outputs, bi_encoder_state = (
-            self._build_bidirectional_rnn(
-                inputs=self.encoder_emb_inp,
-                sequence_length=sequence_length,
-                dtype=dtype,
-                hparams=hparams,
-                num_bi_layers=num_bi_layers,
-                num_bi_residual_layers=num_bi_residual_layers))
-
-        if num_bi_layers == 1:
-          encoder_state = bi_encoder_state
+            swap_memory=True,
+        )
+
+        return tf.concat(bi_outputs, -1), bi_state
+
+    def _build_decoder_cell(
+        self,
+        hparams,
+        encoder_outputs,
+        encoder_state,
+        source_sequence_length,
+        base_gpu=0,
+    ):
+        """Build an RNN cell that can be used by decoder."""
+        # We only make use of encoder_outputs in attention-based models
+        if hparams.attention:
+            raise ValueError("BasicModel doesn't support attention.")
+
+        cell = model_helper.create_rnn_cell(
+            unit_type=hparams.unit_type,
+            num_units=self.num_units,
+            num_layers=self.num_decoder_layers,
+            num_residual_layers=self.num_decoder_residual_layers,
+            forget_bias=hparams.forget_bias,
+            dropout=hparams.dropout,
+            num_gpus=self.num_gpus,
+            mode=self.mode,
+            single_cell_fn=self.single_cell_fn,
+            base_gpu=base_gpu,
+        )
+
+        if hparams.language_model:
+            encoder_state = cell.zero_state(self.batch_size, self.dtype)
+        elif not hparams.pass_hidden_state:
+            raise ValueError(
+                "For non-attentional model, "
+                "pass_hidden_state needs to be set to True"
+            )
+
+        # For beam search, we need to replicate encoder infos beam_width times
+        if (
+            self.mode == tf.contrib.learn.ModeKeys.INFER
+            and hparams.infer_mode == "beam_search"
+        ):
+            decoder_initial_state = tf.contrib.seq2seq.tile_batch(
+                encoder_state, multiplier=hparams.beam_width
+            )
         else:
-          # alternatively concat forward and backward states
-          encoder_state = []
-          for layer_id in range(num_bi_layers):
-            encoder_state.append(bi_encoder_state[0][layer_id])  # forward
-            encoder_state.append(bi_encoder_state[1][layer_id])  # backward
-          encoder_state = tuple(encoder_state)
-      else:
-        raise ValueError("Unknown encoder_type %s" % hparams.encoder_type)
-
-    # Use the top layer for now
-    self.encoder_state_list = [encoder_outputs]
-
-    return encoder_outputs, encoder_state
-
-  def _build_encoder(self, hparams):
-    """Build encoder from source."""
-    utils.print_out("# Build a basic encoder")
-    return self._build_encoder_from_sequence(
-        hparams, self.iterator.source, self.iterator.source_sequence_length)
-
-  def _build_bidirectional_rnn(self, inputs, sequence_length,
-                               dtype, hparams,
-                               num_bi_layers,
-                               num_bi_residual_layers,
-                               base_gpu=0):
-    """Create and call biddirectional RNN cells.
-
-    Args:
-      num_residual_layers: Number of residual layers from top to bottom. For
-        example, if `num_bi_layers=4` and `num_residual_layers=2`, the last 2 RNN
-        layers in each RNN cell will be wrapped with `ResidualWrapper`.
-      base_gpu: The gpu device id to use for the first forward RNN layer. The
-        i-th forward RNN layer will use `(base_gpu + i) % num_gpus` as its
-        device id. The `base_gpu` for backward RNN cell is `(base_gpu +
-        num_bi_layers)`.
-
-    Returns:
-      The concatenated bidirectional output and the bidirectional RNN cell"s
-      state.
-    """
-    # Construct forward and backward cells
-    fw_cell = self._build_encoder_cell(hparams,
-                                       num_bi_layers,
-                                       num_bi_residual_layers,
-                                       base_gpu=base_gpu)
-    bw_cell = self._build_encoder_cell(hparams,
-                                       num_bi_layers,
-                                       num_bi_residual_layers,
-                                       base_gpu=(base_gpu + num_bi_layers))
-
-    bi_outputs, bi_state = tf.nn.bidirectional_dynamic_rnn(
-        fw_cell,
-        bw_cell,
-        inputs,
-        dtype=dtype,
-        sequence_length=sequence_length,
-        time_major=self.time_major,
-        swap_memory=True)
-
-    return tf.concat(bi_outputs, -1), bi_state
-
-  def _build_decoder_cell(self, hparams, encoder_outputs, encoder_state,
-                          source_sequence_length, base_gpu=0):
-    """Build an RNN cell that can be used by decoder."""
-    # We only make use of encoder_outputs in attention-based models
-    if hparams.attention:
-      raise ValueError("BasicModel doesn't support attention.")
-
-    cell = model_helper.create_rnn_cell(
-        unit_type=hparams.unit_type,
-        num_units=self.num_units,
-        num_layers=self.num_decoder_layers,
-        num_residual_layers=self.num_decoder_residual_layers,
-        forget_bias=hparams.forget_bias,
-        dropout=hparams.dropout,
-        num_gpus=self.num_gpus,
-        mode=self.mode,
-        single_cell_fn=self.single_cell_fn,
-        base_gpu=base_gpu
-    )
+            decoder_initial_state = encoder_state
 
-    if hparams.language_model:
-      encoder_state = cell.zero_state(self.batch_size, self.dtype)
-    elif not hparams.pass_hidden_state:
-      raise ValueError("For non-attentional model, "
-                       "pass_hidden_state needs to be set to True")
-
-    # For beam search, we need to replicate encoder infos beam_width times
-    if (self.mode == tf.contrib.learn.ModeKeys.INFER and
-        hparams.infer_mode == "beam_search"):
-      decoder_initial_state = tf.contrib.seq2seq.tile_batch(
-          encoder_state, multiplier=hparams.beam_width)
-    else:
-      decoder_initial_state = encoder_state
-
-    return cell, decoder_initial_state
+        return cell, decoder_initial_state
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/model_helper.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/model_helper.py
index 65e1114145..5a2ab2ac8a 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/model_helper.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/model_helper.py
@@ -29,11 +29,18 @@
 from .utils import vocab_utils
 
 __all__ = [
-    "get_initializer", "get_device_str", "create_train_model",
-    "create_eval_model", "create_infer_model",
-    "create_emb_for_encoder_and_decoder", "create_rnn_cell", "gradient_clip",
-    "create_or_load_model", "load_model", "avg_checkpoints",
-    "compute_perplexity"
+    "get_initializer",
+    "get_device_str",
+    "create_train_model",
+    "create_eval_model",
+    "create_infer_model",
+    "create_emb_for_encoder_and_decoder",
+    "create_rnn_cell",
+    "gradient_clip",
+    "create_or_load_model",
+    "load_model",
+    "avg_checkpoints",
+    "compute_perplexity",
 ]
 
 # If a vocab size is greater than this value, put the embedding on cpu instead
@@ -41,623 +48,725 @@
 
 
 def get_initializer(init_op, seed=None, init_weight=None):
-  """Create an initializer. init_weight is only for uniform."""
-  if init_op == "uniform":
-    assert init_weight
-    return tf.random_uniform_initializer(
-        -init_weight, init_weight, seed=seed)
-  elif init_op == "glorot_normal":
-    return tf.keras.initializers.glorot_normal(
-        seed=seed)
-  elif init_op == "glorot_uniform":
-    return tf.keras.initializers.glorot_uniform(
-        seed=seed)
-  else:
-    raise ValueError("Unknown init_op %s" % init_op)
+    """Create an initializer. init_weight is only for uniform."""
+    if init_op == "uniform":
+        assert init_weight
+        return tf.random_uniform_initializer(-init_weight,
+                                             init_weight, seed=seed)
+    elif init_op == "glorot_normal":
+        return tf.keras.initializers.glorot_normal(seed=seed)
+    elif init_op == "glorot_uniform":
+        return tf.keras.initializers.glorot_uniform(seed=seed)
+    else:
+        raise ValueError("Unknown init_op %s" % init_op)
 
 
 def get_device_str(device_id, num_gpus):
-  """Return a device string for multi-GPU setup."""
-  if num_gpus == 0:
-    return "/cpu:0"
-  device_str_output = "/gpu:%d" % (device_id % num_gpus)
-  return device_str_output
-
-
-class ExtraArgs(collections.namedtuple(
-    "ExtraArgs", ("single_cell_fn", "model_device_fn",
-                  "attention_mechanism_fn", "encoder_emb_lookup_fn"))):
-  pass
+    """Return a device string for multi-GPU setup."""
+    if num_gpus == 0:
+        return "/cpu:0"
+    device_str_output = "/gpu:%d" % (device_id % num_gpus)
+    return device_str_output
+
+
+class ExtraArgs(
+    collections.namedtuple(
+        "ExtraArgs",
+        (
+            "single_cell_fn",
+            "model_device_fn",
+            "attention_mechanism_fn",
+            "encoder_emb_lookup_fn",
+        ),
+    )
+):
+    pass
 
 
 class TrainModel(
-    collections.namedtuple("TrainModel", ("graph", "model", "iterator",
-                                          "skip_count_placeholder"))):
-  pass
+    collections.namedtuple(
+        "TrainModel", ("graph", "model", "iterator", "skip_count_placeholder")
+    )
+):
+    pass
 
 
 def create_train_model(
-    model_creator, hparams, scope=None, num_workers=1, jobid=0,
-    extra_args=None):
-  """Create train graph, model, and iterator."""
-  src_file = "%s.%s" % (hparams.train_prefix, hparams.src)
-  tgt_file = "%s.%s" % (hparams.train_prefix, hparams.tgt)
-  src_vocab_file = hparams.src_vocab_file
-  tgt_vocab_file = hparams.tgt_vocab_file
-
-  graph = tf.Graph()
-
-  with graph.as_default(), tf.container(scope or "train"):
-    src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
-        src_vocab_file, tgt_vocab_file, hparams.share_vocab)
-
-    src_dataset = tf.data.TextLineDataset(tf.gfile.Glob(src_file))
-    tgt_dataset = tf.data.TextLineDataset(tf.gfile.Glob(tgt_file))
-    skip_count_placeholder = tf.placeholder(shape=(), dtype=tf.int64)
-
-    iterator = iterator_utils.get_iterator(
-        src_dataset,
-        tgt_dataset,
-        src_vocab_table,
-        tgt_vocab_table,
-        batch_size=hparams.batch_size,
-        sos=hparams.sos,
-        eos=hparams.eos,
-        random_seed=hparams.random_seed,
-        num_buckets=hparams.num_buckets,
-        src_max_len=hparams.src_max_len,
-        tgt_max_len=hparams.tgt_max_len,
-        skip_count=skip_count_placeholder,
-        num_shards=num_workers,
-        shard_index=jobid,
-        use_char_encode=hparams.use_char_encode)
-
-    # Note: One can set model_device_fn to
-    # `tf.train.replica_device_setter(ps_tasks)` for distributed training.
-    model_device_fn = None
-    if extra_args: model_device_fn = extra_args.model_device_fn
-    with tf.device(model_device_fn):
-      model = model_creator(
-          hparams,
-          iterator=iterator,
-          mode=tf.contrib.learn.ModeKeys.TRAIN,
-          source_vocab_table=src_vocab_table,
-          target_vocab_table=tgt_vocab_table,
-          scope=scope,
-          extra_args=extra_args)
-
-  return TrainModel(
-      graph=graph,
-      model=model,
-      iterator=iterator,
-      skip_count_placeholder=skip_count_placeholder)
+    model_creator, hparams, scope=None, num_workers=1, jobid=0, extra_args=None
+):
+    """Create train graph, model, and iterator."""
+    src_file = "%s.%s" % (hparams.train_prefix, hparams.src)
+    tgt_file = "%s.%s" % (hparams.train_prefix, hparams.tgt)
+    src_vocab_file = hparams.src_vocab_file
+    tgt_vocab_file = hparams.tgt_vocab_file
+
+    graph = tf.Graph()
+
+    with graph.as_default(), tf.container(scope or "train"):
+        src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
+            src_vocab_file, tgt_vocab_file, hparams.share_vocab
+        )
+
+        src_dataset = tf.data.TextLineDataset(tf.gfile.Glob(src_file))
+        tgt_dataset = tf.data.TextLineDataset(tf.gfile.Glob(tgt_file))
+        skip_count_placeholder = tf.placeholder(shape=(), dtype=tf.int64)
+
+        iterator = iterator_utils.get_iterator(
+            src_dataset,
+            tgt_dataset,
+            src_vocab_table,
+            tgt_vocab_table,
+            batch_size=hparams.batch_size,
+            sos=hparams.sos,
+            eos=hparams.eos,
+            random_seed=hparams.random_seed,
+            num_buckets=hparams.num_buckets,
+            src_max_len=hparams.src_max_len,
+            tgt_max_len=hparams.tgt_max_len,
+            skip_count=skip_count_placeholder,
+            num_shards=num_workers,
+            shard_index=jobid,
+            use_char_encode=hparams.use_char_encode,
+        )
+
+        # Note: One can set model_device_fn to
+        # `tf.train.replica_device_setter(ps_tasks)` for distributed training.
+        model_device_fn = None
+        if extra_args:
+            model_device_fn = extra_args.model_device_fn
+        with tf.device(model_device_fn):
+            model = model_creator(
+                hparams,
+                iterator=iterator,
+                mode=tf.contrib.learn.ModeKeys.TRAIN,
+                source_vocab_table=src_vocab_table,
+                target_vocab_table=tgt_vocab_table,
+                scope=scope,
+                extra_args=extra_args,
+            )
+
+    return TrainModel(
+        graph=graph,
+        model=model,
+        iterator=iterator,
+        skip_count_placeholder=skip_count_placeholder,
+    )
 
 
 class EvalModel(
-    collections.namedtuple("EvalModel",
-                           ("graph", "model", "src_file_placeholder",
-                            "tgt_file_placeholder", "iterator"))):
-  pass
+    collections.namedtuple(
+        "EvalModel",
+        ("graph", "model", "src_file_placeholder",
+         "tgt_file_placeholder", "iterator"),
+    )
+):
+    pass
 
 
 def create_eval_model(model_creator, hparams, scope=None, extra_args=None):
-  """Create train graph, model, src/tgt file holders, and iterator."""
-  src_vocab_file = hparams.src_vocab_file
-  tgt_vocab_file = hparams.tgt_vocab_file
-  graph = tf.Graph()
-
-  with graph.as_default(), tf.container(scope or "eval"):
-    src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
-        src_vocab_file, tgt_vocab_file, hparams.share_vocab)
-    reverse_tgt_vocab_table = lookup_ops.index_to_string_table_from_file(
-        tgt_vocab_file, default_value=vocab_utils.UNK)
-
-    src_file_placeholder = tf.placeholder(shape=(), dtype=tf.string)
-    tgt_file_placeholder = tf.placeholder(shape=(), dtype=tf.string)
-    src_dataset = tf.data.TextLineDataset(src_file_placeholder)
-    tgt_dataset = tf.data.TextLineDataset(tgt_file_placeholder)
-    iterator = iterator_utils.get_iterator(
-        src_dataset,
-        tgt_dataset,
-        src_vocab_table,
-        tgt_vocab_table,
-        hparams.batch_size,
-        sos=hparams.sos,
-        eos=hparams.eos,
-        random_seed=hparams.random_seed,
-        num_buckets=hparams.num_buckets,
-        src_max_len=hparams.src_max_len_infer,
-        tgt_max_len=hparams.tgt_max_len_infer,
-        use_char_encode=hparams.use_char_encode)
-    model = model_creator(
-        hparams,
+    """Create train graph, model, src/tgt file holders, and iterator."""
+    src_vocab_file = hparams.src_vocab_file
+    tgt_vocab_file = hparams.tgt_vocab_file
+    graph = tf.Graph()
+
+    with graph.as_default(), tf.container(scope or "eval"):
+        src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
+            src_vocab_file, tgt_vocab_file, hparams.share_vocab
+        )
+        reverse_tgt_vocab_table = lookup_ops.index_to_string_table_from_file(
+            tgt_vocab_file, default_value=vocab_utils.UNK
+        )
+
+        src_file_placeholder = tf.placeholder(shape=(), dtype=tf.string)
+        tgt_file_placeholder = tf.placeholder(shape=(), dtype=tf.string)
+        src_dataset = tf.data.TextLineDataset(src_file_placeholder)
+        tgt_dataset = tf.data.TextLineDataset(tgt_file_placeholder)
+        iterator = iterator_utils.get_iterator(
+            src_dataset,
+            tgt_dataset,
+            src_vocab_table,
+            tgt_vocab_table,
+            hparams.batch_size,
+            sos=hparams.sos,
+            eos=hparams.eos,
+            random_seed=hparams.random_seed,
+            num_buckets=hparams.num_buckets,
+            src_max_len=hparams.src_max_len_infer,
+            tgt_max_len=hparams.tgt_max_len_infer,
+            use_char_encode=hparams.use_char_encode,
+        )
+        model = model_creator(
+            hparams,
+            iterator=iterator,
+            mode=tf.contrib.learn.ModeKeys.EVAL,
+            source_vocab_table=src_vocab_table,
+            target_vocab_table=tgt_vocab_table,
+            reverse_target_vocab_table=reverse_tgt_vocab_table,
+            scope=scope,
+            extra_args=extra_args,
+        )
+    return EvalModel(
+        graph=graph,
+        model=model,
+        src_file_placeholder=src_file_placeholder,
+        tgt_file_placeholder=tgt_file_placeholder,
         iterator=iterator,
-        mode=tf.contrib.learn.ModeKeys.EVAL,
-        source_vocab_table=src_vocab_table,
-        target_vocab_table=tgt_vocab_table,
-        reverse_target_vocab_table=reverse_tgt_vocab_table,
-        scope=scope,
-        extra_args=extra_args)
-  return EvalModel(
-      graph=graph,
-      model=model,
-      src_file_placeholder=src_file_placeholder,
-      tgt_file_placeholder=tgt_file_placeholder,
-      iterator=iterator)
+    )
 
 
 class InferModel(
-    collections.namedtuple("InferModel",
-                           ("graph", "model", "src_placeholder",
-                            "batch_size_placeholder", "iterator"))):
-  pass
+    collections.namedtuple(
+        "InferModel",
+        ("graph", "model", "src_placeholder",
+         "batch_size_placeholder", "iterator"),
+    )
+):
+    pass
 
 
 def create_infer_model(model_creator, hparams, scope=None, extra_args=None):
-  """Create inference model."""
-  graph = tf.Graph()
-  src_vocab_file = hparams.src_vocab_file
-  tgt_vocab_file = hparams.tgt_vocab_file
-
-  with graph.as_default(), tf.container(scope or "infer"):
-    src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
-        src_vocab_file, tgt_vocab_file, hparams.share_vocab)
-    reverse_tgt_vocab_table = lookup_ops.index_to_string_table_from_file(
-        tgt_vocab_file, default_value=vocab_utils.UNK)
-
-    src_placeholder = tf.placeholder(shape=[None], dtype=tf.string)
-    batch_size_placeholder = tf.placeholder(shape=[], dtype=tf.int64)
-
-    src_dataset = tf.data.Dataset.from_tensor_slices(
-        src_placeholder)
-    iterator = iterator_utils.get_infer_iterator(
-        src_dataset,
-        src_vocab_table,
-        batch_size=batch_size_placeholder,
-        eos=hparams.eos,
-        src_max_len=hparams.src_max_len_infer,
-        use_char_encode=hparams.use_char_encode)
-    model = model_creator(
-        hparams,
+    """Create inference model."""
+    graph = tf.Graph()
+    src_vocab_file = hparams.src_vocab_file
+    tgt_vocab_file = hparams.tgt_vocab_file
+
+    with graph.as_default(), tf.container(scope or "infer"):
+        src_vocab_table, tgt_vocab_table = vocab_utils.create_vocab_tables(
+            src_vocab_file, tgt_vocab_file, hparams.share_vocab
+        )
+        reverse_tgt_vocab_table = lookup_ops.index_to_string_table_from_file(
+            tgt_vocab_file, default_value=vocab_utils.UNK
+        )
+
+        src_placeholder = tf.placeholder(shape=[None], dtype=tf.string)
+        batch_size_placeholder = tf.placeholder(shape=[], dtype=tf.int64)
+
+        src_dataset = tf.data.Dataset.from_tensor_slices(src_placeholder)
+        iterator = iterator_utils.get_infer_iterator(
+            src_dataset,
+            src_vocab_table,
+            batch_size=batch_size_placeholder,
+            eos=hparams.eos,
+            src_max_len=hparams.src_max_len_infer,
+            use_char_encode=hparams.use_char_encode,
+        )
+        model = model_creator(
+            hparams,
+            iterator=iterator,
+            mode=tf.contrib.learn.ModeKeys.INFER,
+            source_vocab_table=src_vocab_table,
+            target_vocab_table=tgt_vocab_table,
+            reverse_target_vocab_table=reverse_tgt_vocab_table,
+            scope=scope,
+            extra_args=extra_args,
+        )
+    return InferModel(
+        graph=graph,
+        model=model,
+        src_placeholder=src_placeholder,
+        batch_size_placeholder=batch_size_placeholder,
         iterator=iterator,
-        mode=tf.contrib.learn.ModeKeys.INFER,
-        source_vocab_table=src_vocab_table,
-        target_vocab_table=tgt_vocab_table,
-        reverse_target_vocab_table=reverse_tgt_vocab_table,
-        scope=scope,
-        extra_args=extra_args)
-  return InferModel(
-      graph=graph,
-      model=model,
-      src_placeholder=src_placeholder,
-      batch_size_placeholder=batch_size_placeholder,
-      iterator=iterator)
+    )
 
 
 def _get_embed_device(vocab_size):
-  """Decide on which device to place an embed matrix given its vocab size."""
-  if vocab_size > VOCAB_SIZE_THRESHOLD_CPU:
-    return "/cpu:0"
-  else:
-    return "/gpu:0"
+    """Decide on which device to place an embed matrix given its vocab size."""
+    if vocab_size > VOCAB_SIZE_THRESHOLD_CPU:
+        return "/cpu:0"
+    else:
+        return "/gpu:0"
 
 
 def _create_pretrained_emb_from_txt(
-    vocab_file, embed_file, num_trainable_tokens=3, dtype=tf.float32,
-    scope=None):
-  """Load pretrain embeding from embed_file, and return an embedding matrix.
-
-  Args:
-    embed_file: Path to a Glove formated embedding txt file.
-    num_trainable_tokens: Make the first n tokens in the vocab file as trainable
-      variables. Default is 3, which is "<unk>", "<s>" and "</s>".
-  """
-  vocab, _ = vocab_utils.load_vocab(vocab_file)
-  trainable_tokens = vocab[:num_trainable_tokens]
-
-  utils.print_out("# Using pretrained embedding: %s." % embed_file)
-  utils.print_out("  with trainable tokens: ")
-
-  emb_dict, emb_size = vocab_utils.load_embed_txt(embed_file)
-  for token in trainable_tokens:
-    utils.print_out("    %s" % token)
-    if token not in emb_dict:
-      emb_dict[token] = [0.0] * emb_size
-
-  emb_mat = np.array(
-      [emb_dict[token] for token in vocab], dtype=dtype.as_numpy_dtype())
-  emb_mat = tf.constant(emb_mat)
-  emb_mat_const = tf.slice(emb_mat, [num_trainable_tokens, 0], [-1, -1])
-  with tf.variable_scope(scope or "pretrain_embeddings", dtype=dtype) as scope:
-    with tf.device(_get_embed_device(num_trainable_tokens)):
-      emb_mat_var = tf.get_variable(
-          "emb_mat_var", [num_trainable_tokens, emb_size])
-  return tf.concat([emb_mat_var, emb_mat_const], 0)
-
-
-def _create_or_load_embed(embed_name, vocab_file, embed_file,
-                          vocab_size, embed_size, dtype):
-  """Create a new or load an existing embedding matrix."""
-  if vocab_file and embed_file:
-    embedding = _create_pretrained_emb_from_txt(vocab_file, embed_file)
-  else:
-    with tf.device(_get_embed_device(vocab_size)):
-      embedding = tf.get_variable(
-          embed_name, [vocab_size, embed_size], dtype)
-  return embedding
-
-
-def create_emb_for_encoder_and_decoder(share_vocab,
-                                       src_vocab_size,
-                                       tgt_vocab_size,
-                                       src_embed_size,
-                                       tgt_embed_size,
-                                       dtype=tf.float32,
-                                       num_enc_partitions=0,
-                                       num_dec_partitions=0,
-                                       src_vocab_file=None,
-                                       tgt_vocab_file=None,
-                                       src_embed_file=None,
-                                       tgt_embed_file=None,
-                                       use_char_encode=False,
-                                       scope=None):
-  """Create embedding matrix for both encoder and decoder.
-
-  Args:
-    share_vocab: A boolean. Whether to share embedding matrix for both
-      encoder and decoder.
-    src_vocab_size: An integer. The source vocab size.
-    tgt_vocab_size: An integer. The target vocab size.
-    src_embed_size: An integer. The embedding dimension for the encoder's
-      embedding.
-    tgt_embed_size: An integer. The embedding dimension for the decoder's
-      embedding.
-    dtype: dtype of the embedding matrix. Default to float32.
-    num_enc_partitions: number of partitions used for the encoder's embedding
-      vars.
-    num_dec_partitions: number of partitions used for the decoder's embedding
-      vars.
-    scope: VariableScope for the created subgraph. Default to "embedding".
-
-  Returns:
-    embedding_encoder: Encoder's embedding matrix.
-    embedding_decoder: Decoder's embedding matrix.
-
-  Raises:
-    ValueError: if use share_vocab but source and target have different vocab
-      size.
-  """
-  if num_enc_partitions <= 1:
-    enc_partitioner = None
-  else:
-    # Note: num_partitions > 1 is required for distributed training due to
-    # embedding_lookup tries to colocate single partition-ed embedding variable
-    # with lookup ops. This may cause embedding variables being placed on worker
-    # jobs.
-    enc_partitioner = tf.fixed_size_partitioner(num_enc_partitions)
-
-  if num_dec_partitions <= 1:
-    dec_partitioner = None
-  else:
-    # Note: num_partitions > 1 is required for distributed training due to
-    # embedding_lookup tries to colocate single partition-ed embedding variable
-    # with lookup ops. This may cause embedding variables being placed on worker
-    # jobs.
-    dec_partitioner = tf.fixed_size_partitioner(num_dec_partitions)
-
-  if src_embed_file and enc_partitioner:
-    raise ValueError(
-        "Can't set num_enc_partitions > 1 when using pretrained encoder "
-        "embedding")
-
-  if tgt_embed_file and dec_partitioner:
-    raise ValueError(
-        "Can't set num_dec_partitions > 1 when using pretrained decdoer "
-        "embedding")
-
-  with tf.variable_scope(
-      scope or "embeddings", dtype=dtype, partitioner=enc_partitioner) as scope:
-    # Share embedding
-    if share_vocab:
-      if src_vocab_size != tgt_vocab_size:
-        raise ValueError("Share embedding but different src/tgt vocab sizes"
-                         " %d vs. %d" % (src_vocab_size, tgt_vocab_size))
-      assert src_embed_size == tgt_embed_size
-      utils.print_out("# Use the same embedding for source and target")
-      vocab_file = src_vocab_file or tgt_vocab_file
-      embed_file = src_embed_file or tgt_embed_file
-
-      embedding_encoder = _create_or_load_embed(
-          "embedding_share", vocab_file, embed_file,
-          src_vocab_size, src_embed_size, dtype)
-      embedding_decoder = embedding_encoder
+    vocab_file, embed_file, num_trainable_tokens=3, dtype=tf.float32, scope=None
+):
+    """Load pretrain embeding from embed_file, and return an embedding matrix.
+
+    Args:
+      embed_file: Path to a Glove formated embedding txt file.
+      num_trainable_tokens: Make the first n tokens in the vocab file as trainable
+        variables. Default is 3, which is "<unk>", "<s>" and "</s>".
+    """
+    vocab, _ = vocab_utils.load_vocab(vocab_file)
+    trainable_tokens = vocab[:num_trainable_tokens]
+
+    utils.print_out("# Using pretrained embedding: %s." % embed_file)
+    utils.print_out("  with trainable tokens: ")
+
+    emb_dict, emb_size = vocab_utils.load_embed_txt(embed_file)
+    for token in trainable_tokens:
+        utils.print_out("    %s" % token)
+        if token not in emb_dict:
+            emb_dict[token] = [0.0] * emb_size
+
+    emb_mat = np.array(
+        [emb_dict[token] for token in vocab], dtype=dtype.as_numpy_dtype()
+    )
+    emb_mat = tf.constant(emb_mat)
+    emb_mat_const = tf.slice(emb_mat, [num_trainable_tokens, 0], [-1, -1])
+    with tf.variable_scope(scope or "pretrain_embeddings", dtype=dtype) as scope:
+        with tf.device(_get_embed_device(num_trainable_tokens)):
+            emb_mat_var = tf.get_variable(
+                "emb_mat_var", [num_trainable_tokens, emb_size]
+            )
+    return tf.concat([emb_mat_var, emb_mat_const], 0)
+
+
+def _create_or_load_embed(
+    embed_name, vocab_file, embed_file, vocab_size, embed_size, dtype
+):
+    """Create a new or load an existing embedding matrix."""
+    if vocab_file and embed_file:
+        embedding = _create_pretrained_emb_from_txt(vocab_file, embed_file)
     else:
-      if not use_char_encode:
-        with tf.variable_scope("encoder", partitioner=enc_partitioner):
-          embedding_encoder = _create_or_load_embed(
-              "embedding_encoder", src_vocab_file, src_embed_file,
-              src_vocab_size, src_embed_size, dtype)
-      else:
-        embedding_encoder = None
-
-      with tf.variable_scope("decoder", partitioner=dec_partitioner):
-        embedding_decoder = _create_or_load_embed(
-            "embedding_decoder", tgt_vocab_file, tgt_embed_file,
-            tgt_vocab_size, tgt_embed_size, dtype)
-
-  return embedding_encoder, embedding_decoder
-
-
-def _single_cell(unit_type, num_units, forget_bias, dropout, mode,
-                 residual_connection=False, device_str=None, residual_fn=None):
-  """Create an instance of a single RNN cell."""
-  # dropout (= 1 - keep_prob) is set to 0 during eval and infer
-  dropout = dropout if mode == tf.contrib.learn.ModeKeys.TRAIN else 0.0
-
-  # Cell Type
-  if unit_type == "lstm":
-    utils.print_out("  LSTM, forget_bias=%g" % forget_bias, new_line=False)
-    single_cell = tf.contrib.rnn.BasicLSTMCell(
-        num_units,
-        forget_bias=forget_bias)
-  elif unit_type == "gru":
-    utils.print_out("  GRU", new_line=False)
-    single_cell = tf.contrib.rnn.GRUCell(num_units)
-  elif unit_type == "layer_norm_lstm":
-    utils.print_out("  Layer Normalized LSTM, forget_bias=%g" % forget_bias,
-                    new_line=False)
-    single_cell = tf.contrib.rnn.LayerNormBasicLSTMCell(
-        num_units,
-        forget_bias=forget_bias,
-        layer_norm=True)
-  elif unit_type == "nas":
-    utils.print_out("  NASCell", new_line=False)
-    single_cell = tf.contrib.rnn.NASCell(num_units)
-  else:
-    raise ValueError("Unknown unit type %s!" % unit_type)
-
-  # Dropout (= 1 - keep_prob)
-  if dropout > 0.0:
-    single_cell = tf.contrib.rnn.DropoutWrapper(
-        cell=single_cell, input_keep_prob=(1.0 - dropout))
-    utils.print_out("  %s, dropout=%g " %(type(single_cell).__name__, dropout),
-                    new_line=False)
-
-  # Residual
-  if residual_connection:
-    single_cell = tf.contrib.rnn.ResidualWrapper(
-        single_cell, residual_fn=residual_fn)
-    utils.print_out("  %s" % type(single_cell).__name__, new_line=False)
-
-  # Device Wrapper
-  if device_str:
-    single_cell = tf.contrib.rnn.DeviceWrapper(single_cell, device_str)
-    utils.print_out("  %s, device=%s" %
-                    (type(single_cell).__name__, device_str), new_line=False)
-
-  return single_cell
-
-
-def _cell_list(unit_type, num_units, num_layers, num_residual_layers,
-               forget_bias, dropout, mode, num_gpus, base_gpu=0,
-               single_cell_fn=None, residual_fn=None):
-  """Create a list of RNN cells."""
-  if not single_cell_fn:
-    single_cell_fn = _single_cell
-
-  # Multi-GPU
-  cell_list = []
-  for i in range(num_layers):
-    utils.print_out("  cell %d" % i, new_line=False)
-    single_cell = single_cell_fn(
+        with tf.device(_get_embed_device(vocab_size)):
+            embedding = tf.get_variable(
+                embed_name, [vocab_size, embed_size], dtype)
+    return embedding
+
+
+def create_emb_for_encoder_and_decoder(
+    share_vocab,
+    src_vocab_size,
+    tgt_vocab_size,
+    src_embed_size,
+    tgt_embed_size,
+    dtype=tf.float32,
+    num_enc_partitions=0,
+    num_dec_partitions=0,
+    src_vocab_file=None,
+    tgt_vocab_file=None,
+    src_embed_file=None,
+    tgt_embed_file=None,
+    use_char_encode=False,
+    scope=None,
+):
+    """Create embedding matrix for both encoder and decoder.
+
+    Args:
+      share_vocab: A boolean. Whether to share embedding matrix for both
+        encoder and decoder.
+      src_vocab_size: An integer. The source vocab size.
+      tgt_vocab_size: An integer. The target vocab size.
+      src_embed_size: An integer. The embedding dimension for the encoder's
+        embedding.
+      tgt_embed_size: An integer. The embedding dimension for the decoder's
+        embedding.
+      dtype: dtype of the embedding matrix. Default to float32.
+      num_enc_partitions: number of partitions used for the encoder's embedding
+        vars.
+      num_dec_partitions: number of partitions used for the decoder's embedding
+        vars.
+      scope: VariableScope for the created subgraph. Default to "embedding".
+
+    Returns:
+      embedding_encoder: Encoder's embedding matrix.
+      embedding_decoder: Decoder's embedding matrix.
+
+    Raises:
+      ValueError: if use share_vocab but source and target have different vocab
+        size.
+    """
+    if num_enc_partitions <= 1:
+        enc_partitioner = None
+    else:
+        # Note: num_partitions > 1 is required for distributed training due to
+        # embedding_lookup tries to colocate single partition-ed embedding variable
+        # with lookup ops. This may cause embedding variables being placed on worker
+        # jobs.
+        enc_partitioner = tf.fixed_size_partitioner(num_enc_partitions)
+
+    if num_dec_partitions <= 1:
+        dec_partitioner = None
+    else:
+        # Note: num_partitions > 1 is required for distributed training due to
+        # embedding_lookup tries to colocate single partition-ed embedding variable
+        # with lookup ops. This may cause embedding variables being placed on worker
+        # jobs.
+        dec_partitioner = tf.fixed_size_partitioner(num_dec_partitions)
+
+    if src_embed_file and enc_partitioner:
+        raise ValueError(
+            "Can't set num_enc_partitions > 1 when using pretrained encoder "
+            "embedding"
+        )
+
+    if tgt_embed_file and dec_partitioner:
+        raise ValueError(
+            "Can't set num_dec_partitions > 1 when using pretrained decdoer "
+            "embedding"
+        )
+
+    with tf.variable_scope(
+        scope or "embeddings", dtype=dtype, partitioner=enc_partitioner
+    ) as scope:
+        # Share embedding
+        if share_vocab:
+            if src_vocab_size != tgt_vocab_size:
+                raise ValueError(
+                    "Share embedding but different src/tgt vocab sizes"
+                    " %d vs. %d" % (src_vocab_size, tgt_vocab_size)
+                )
+            assert src_embed_size == tgt_embed_size
+            utils.print_out("# Use the same embedding for source and target")
+            vocab_file = src_vocab_file or tgt_vocab_file
+            embed_file = src_embed_file or tgt_embed_file
+
+            embedding_encoder = _create_or_load_embed(
+                "embedding_share",
+                vocab_file,
+                embed_file,
+                src_vocab_size,
+                src_embed_size,
+                dtype,
+            )
+            embedding_decoder = embedding_encoder
+        else:
+            if not use_char_encode:
+                with tf.variable_scope("encoder", partitioner=enc_partitioner):
+                    embedding_encoder = _create_or_load_embed(
+                        "embedding_encoder",
+                        src_vocab_file,
+                        src_embed_file,
+                        src_vocab_size,
+                        src_embed_size,
+                        dtype,
+                    )
+            else:
+                embedding_encoder = None
+
+            with tf.variable_scope("decoder", partitioner=dec_partitioner):
+                embedding_decoder = _create_or_load_embed(
+                    "embedding_decoder",
+                    tgt_vocab_file,
+                    tgt_embed_file,
+                    tgt_vocab_size,
+                    tgt_embed_size,
+                    dtype,
+                )
+
+    return embedding_encoder, embedding_decoder
+
+
+def _single_cell(
+    unit_type,
+    num_units,
+    forget_bias,
+    dropout,
+    mode,
+    residual_connection=False,
+    device_str=None,
+    residual_fn=None,
+):
+    """Create an instance of a single RNN cell."""
+    # dropout (= 1 - keep_prob) is set to 0 during eval and infer
+    dropout = dropout if mode == tf.contrib.learn.ModeKeys.TRAIN else 0.0
+
+    # Cell Type
+    if unit_type == "lstm":
+        utils.print_out("  LSTM, forget_bias=%g" % forget_bias, new_line=False)
+        single_cell = tf.contrib.rnn.BasicLSTMCell(
+            num_units, forget_bias=forget_bias)
+    elif unit_type == "gru":
+        utils.print_out("  GRU", new_line=False)
+        single_cell = tf.contrib.rnn.GRUCell(num_units)
+    elif unit_type == "layer_norm_lstm":
+        utils.print_out(
+            "  Layer Normalized LSTM, forget_bias=%g" % forget_bias, new_line=False
+        )
+        single_cell = tf.contrib.rnn.LayerNormBasicLSTMCell(
+            num_units, forget_bias=forget_bias, layer_norm=True
+        )
+    elif unit_type == "nas":
+        utils.print_out("  NASCell", new_line=False)
+        single_cell = tf.contrib.rnn.NASCell(num_units)
+    else:
+        raise ValueError("Unknown unit type %s!" % unit_type)
+
+    # Dropout (= 1 - keep_prob)
+    if dropout > 0.0:
+        single_cell = tf.contrib.rnn.DropoutWrapper(
+            cell=single_cell, input_keep_prob=(1.0 - dropout)
+        )
+        utils.print_out(
+            "  %s, dropout=%g " % (type(single_cell).__name__, dropout), new_line=False
+        )
+
+    # Residual
+    if residual_connection:
+        single_cell = tf.contrib.rnn.ResidualWrapper(
+            single_cell, residual_fn=residual_fn
+        )
+        utils.print_out("  %s" % type(single_cell).__name__, new_line=False)
+
+    # Device Wrapper
+    if device_str:
+        single_cell = tf.contrib.rnn.DeviceWrapper(single_cell, device_str)
+        utils.print_out(
+            "  %s, device=%s" % (type(single_cell).__name__, device_str), new_line=False
+        )
+
+    return single_cell
+
+
+def _cell_list(
+    unit_type,
+    num_units,
+    num_layers,
+    num_residual_layers,
+    forget_bias,
+    dropout,
+    mode,
+    num_gpus,
+    base_gpu=0,
+    single_cell_fn=None,
+    residual_fn=None,
+):
+    """Create a list of RNN cells."""
+    if not single_cell_fn:
+        single_cell_fn = _single_cell
+
+    # Multi-GPU
+    cell_list = []
+    for i in range(num_layers):
+        utils.print_out("  cell %d" % i, new_line=False)
+        single_cell = single_cell_fn(
+            unit_type=unit_type,
+            num_units=num_units,
+            forget_bias=forget_bias,
+            dropout=dropout,
+            mode=mode,
+            residual_connection=(i >= num_layers - num_residual_layers),
+            device_str=get_device_str(i + base_gpu, num_gpus),
+            residual_fn=residual_fn,
+        )
+        utils.print_out("")
+        cell_list.append(single_cell)
+
+    return cell_list
+
+
+def create_rnn_cell(
+    unit_type,
+    num_units,
+    num_layers,
+    num_residual_layers,
+    forget_bias,
+    dropout,
+    mode,
+    num_gpus,
+    base_gpu=0,
+    single_cell_fn=None,
+):
+    """Create multi-layer RNN cell.
+
+    Args:
+      unit_type: string representing the unit type, i.e. "lstm".
+      num_units: the depth of each unit.
+      num_layers: number of cells.
+      num_residual_layers: Number of residual layers from top to bottom. For
+        example, if `num_layers=4` and `num_residual_layers=2`, the last 2 RNN
+        cells in the returned list will be wrapped with `ResidualWrapper`.
+      forget_bias: the initial forget bias of the RNNCell(s).
+      dropout: floating point value between 0.0 and 1.0:
+        the probability of dropout.  this is ignored if `mode != TRAIN`.
+      mode: either tf.contrib.learn.TRAIN/EVAL/INFER
+      num_gpus: The number of gpus to use when performing round-robin
+        placement of layers.
+      base_gpu: The gpu device id to use for the first RNN cell in the
+        returned list. The i-th RNN cell will use `(base_gpu + i) % num_gpus`
+        as its device id.
+      single_cell_fn: allow for adding customized cell.
+        When not specified, we default to model_helper._single_cell
+    Returns:
+      An `RNNCell` instance.
+    """
+    cell_list = _cell_list(
         unit_type=unit_type,
         num_units=num_units,
+        num_layers=num_layers,
+        num_residual_layers=num_residual_layers,
         forget_bias=forget_bias,
         dropout=dropout,
         mode=mode,
-        residual_connection=(i >= num_layers - num_residual_layers),
-        device_str=get_device_str(i + base_gpu, num_gpus),
-        residual_fn=residual_fn
+        num_gpus=num_gpus,
+        base_gpu=base_gpu,
+        single_cell_fn=single_cell_fn,
     )
-    utils.print_out("")
-    cell_list.append(single_cell)
-
-  return cell_list
-
-
-def create_rnn_cell(unit_type, num_units, num_layers, num_residual_layers,
-                    forget_bias, dropout, mode, num_gpus, base_gpu=0,
-                    single_cell_fn=None):
-  """Create multi-layer RNN cell.
-
-  Args:
-    unit_type: string representing the unit type, i.e. "lstm".
-    num_units: the depth of each unit.
-    num_layers: number of cells.
-    num_residual_layers: Number of residual layers from top to bottom. For
-      example, if `num_layers=4` and `num_residual_layers=2`, the last 2 RNN
-      cells in the returned list will be wrapped with `ResidualWrapper`.
-    forget_bias: the initial forget bias of the RNNCell(s).
-    dropout: floating point value between 0.0 and 1.0:
-      the probability of dropout.  this is ignored if `mode != TRAIN`.
-    mode: either tf.contrib.learn.TRAIN/EVAL/INFER
-    num_gpus: The number of gpus to use when performing round-robin
-      placement of layers.
-    base_gpu: The gpu device id to use for the first RNN cell in the
-      returned list. The i-th RNN cell will use `(base_gpu + i) % num_gpus`
-      as its device id.
-    single_cell_fn: allow for adding customized cell.
-      When not specified, we default to model_helper._single_cell
-  Returns:
-    An `RNNCell` instance.
-  """
-  cell_list = _cell_list(unit_type=unit_type,
-                         num_units=num_units,
-                         num_layers=num_layers,
-                         num_residual_layers=num_residual_layers,
-                         forget_bias=forget_bias,
-                         dropout=dropout,
-                         mode=mode,
-                         num_gpus=num_gpus,
-                         base_gpu=base_gpu,
-                         single_cell_fn=single_cell_fn)
-
-  if len(cell_list) == 1:  # Single layer.
-    return cell_list[0]
-  else:  # Multi layers
-    return tf.contrib.rnn.MultiRNNCell(cell_list)
+
+    if len(cell_list) == 1:  # Single layer.
+        return cell_list[0]
+    else:  # Multi layers
+        return tf.contrib.rnn.MultiRNNCell(cell_list)
 
 
 def gradient_clip(gradients, max_gradient_norm):
-  """Clipping gradients of a model."""
-  clipped_gradients, gradient_norm = tf.clip_by_global_norm(
-      gradients, max_gradient_norm)
-  gradient_norm_summary = [tf.summary.scalar("grad_norm", gradient_norm)]
-  gradient_norm_summary.append(
-      tf.summary.scalar("clipped_gradient", tf.global_norm(clipped_gradients)))
+    """Clipping gradients of a model."""
+    clipped_gradients, gradient_norm = tf.clip_by_global_norm(
+        gradients, max_gradient_norm
+    )
+    gradient_norm_summary = [tf.summary.scalar("grad_norm", gradient_norm)]
+    gradient_norm_summary.append(
+        tf.summary.scalar(
+            "clipped_gradient",
+            tf.global_norm(clipped_gradients))
+    )
 
-  return clipped_gradients, gradient_norm_summary, gradient_norm
+    return clipped_gradients, gradient_norm_summary, gradient_norm
 
 
 def print_variables_in_ckpt(ckpt_path):
-  """Print a list of variables in a checkpoint together with their shapes."""
-  utils.print_out("# Variables in ckpt %s" % ckpt_path)
-  reader = tf.train.NewCheckpointReader(ckpt_path)
-  variable_map = reader.get_variable_to_shape_map()
-  for key in sorted(variable_map.keys()):
-    utils.print_out("  %s: %s" % (key, variable_map[key]))
+    """Print a list of variables in a checkpoint together with their shapes."""
+    utils.print_out("# Variables in ckpt %s" % ckpt_path)
+    reader = tf.train.NewCheckpointReader(ckpt_path)
+    variable_map = reader.get_variable_to_shape_map()
+    for key in sorted(variable_map.keys()):
+        utils.print_out("  %s: %s" % (key, variable_map[key]))
 
 
 def load_model(model, ckpt_path, session, name):
-  """Load model from a checkpoint."""
-  start_time = time.time()
-  try:
-    model.saver.restore(session, ckpt_path)
-  except tf.errors.NotFoundError as e:
-    utils.print_out("Can't load checkpoint")
-    print_variables_in_ckpt(ckpt_path)
-    utils.print_out("%s" % str(e))
-
-  session.run(tf.tables_initializer())
-  utils.print_out(
-      "  loaded %s model parameters from %s, time %.2fs" %
-      (name, ckpt_path, time.time() - start_time))
-  return model
-
-
-def avg_checkpoints(model_dir, num_last_checkpoints, global_step,
-                    global_step_name):
-  """Average the last N checkpoints in the model_dir."""
-  checkpoint_state = tf.train.get_checkpoint_state(model_dir)
-  if not checkpoint_state:
-    utils.print_out("# No checkpoint file found in directory: %s" % model_dir)
-    return None
-
-  # Checkpoints are ordered from oldest to newest.
-  checkpoints = (
-      checkpoint_state.all_model_checkpoint_paths[-num_last_checkpoints:])
-
-  if len(checkpoints) < num_last_checkpoints:
+    """Load model from a checkpoint."""
+    start_time = time.time()
+    try:
+        model.saver.restore(session, ckpt_path)
+    except tf.errors.NotFoundError as e:
+        utils.print_out("Can't load checkpoint")
+        print_variables_in_ckpt(ckpt_path)
+        utils.print_out("%s" % str(e))
+
+    session.run(tf.tables_initializer())
     utils.print_out(
-        "# Skipping averaging checkpoints because not enough checkpoints is "
-        "avaliable."
+        "  loaded %s model parameters from %s, time %.2fs"
+        % (name, ckpt_path, time.time() - start_time)
     )
-    return None
+    return model
+
+
+def avg_checkpoints(model_dir, num_last_checkpoints,
+                    global_step, global_step_name):
+    """Average the last N checkpoints in the model_dir."""
+    checkpoint_state = tf.train.get_checkpoint_state(model_dir)
+    if not checkpoint_state:
+        utils.print_out(
+            "# No checkpoint file found in directory: %s" %
+            model_dir)
+        return None
+
+    # Checkpoints are ordered from oldest to newest.
+    checkpoints = checkpoint_state.all_model_checkpoint_paths[-num_last_checkpoints:]
+
+    if len(checkpoints) < num_last_checkpoints:
+        utils.print_out(
+            "# Skipping averaging checkpoints because not enough checkpoints is "
+            "avaliable."
+        )
+        return None
+
+    avg_model_dir = os.path.join(model_dir, "avg_checkpoints")
+    if not tf.gfile.Exists(avg_model_dir):
+        utils.print_out(
+            "# Creating new directory %s for saving averaged checkpoints."
+            % avg_model_dir
+        )
+        tf.gfile.MakeDirs(avg_model_dir)
+
+    utils.print_out("# Reading and averaging variables in checkpoints:")
+    var_list = tf.contrib.framework.list_variables(checkpoints[0])
+    var_values, var_dtypes = {}, {}
+    for name, shape in var_list:
+        if name != global_step_name:
+            var_values[name] = np.zeros(shape)
+
+    for checkpoint in checkpoints:
+        utils.print_out("    %s" % checkpoint)
+        reader = tf.contrib.framework.load_checkpoint(checkpoint)
+        for name in var_values:
+            tensor = reader.get_tensor(name)
+            var_dtypes[name] = tensor.dtype
+            var_values[name] += tensor
 
-  avg_model_dir = os.path.join(model_dir, "avg_checkpoints")
-  if not tf.gfile.Exists(avg_model_dir):
-    utils.print_out(
-        "# Creating new directory %s for saving averaged checkpoints." %
-        avg_model_dir)
-    tf.gfile.MakeDirs(avg_model_dir)
-
-  utils.print_out("# Reading and averaging variables in checkpoints:")
-  var_list = tf.contrib.framework.list_variables(checkpoints[0])
-  var_values, var_dtypes = {}, {}
-  for (name, shape) in var_list:
-    if name != global_step_name:
-      var_values[name] = np.zeros(shape)
-
-  for checkpoint in checkpoints:
-    utils.print_out("    %s" % checkpoint)
-    reader = tf.contrib.framework.load_checkpoint(checkpoint)
     for name in var_values:
-      tensor = reader.get_tensor(name)
-      var_dtypes[name] = tensor.dtype
-      var_values[name] += tensor
-
-  for name in var_values:
-    var_values[name] /= len(checkpoints)
-
-  # Build a graph with same variables in the checkpoints, and save the averaged
-  # variables into the avg_model_dir.
-  with tf.Graph().as_default():
-    tf_vars = [
-        tf.get_variable(v, shape=var_values[v].shape, dtype=var_dtypes[name])
-        for v in var_values
-    ]
-
-    placeholders = [tf.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
-    assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
-    global_step_var = tf.Variable(
-        global_step, name=global_step_name, trainable=False)
-    saver = tf.train.Saver(tf.all_variables())
-
-    with tf.Session() as sess:
-      sess.run(tf.initialize_all_variables())
-      for p, assign_op, (name, value) in zip(placeholders, assign_ops,
-                                             six.iteritems(var_values)):
-        sess.run(assign_op, {p: value})
-
-      # Use the built saver to save the averaged checkpoint. Only keep 1
-      # checkpoint and the best checkpoint will be moved to avg_best_metric_dir.
-      saver.save(
-          sess,
-          os.path.join(avg_model_dir, "translate.ckpt"))
-
-  return avg_model_dir
+        var_values[name] /= len(checkpoints)
+
+    # Build a graph with same variables in the checkpoints, and save the averaged
+    # variables into the avg_model_dir.
+    with tf.Graph().as_default():
+        tf_vars = [
+            tf.get_variable(
+                v,
+                shape=var_values[v].shape,
+                dtype=var_dtypes[name])
+            for v in var_values
+        ]
+
+        placeholders = [
+            tf.placeholder(
+                v.dtype,
+                shape=v.shape) for v in tf_vars]
+        assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
+        global_step_var = tf.Variable(
+            global_step, name=global_step_name, trainable=False
+        )
+        saver = tf.train.Saver(tf.all_variables())
+
+        with tf.Session() as sess:
+            sess.run(tf.initialize_all_variables())
+            for p, assign_op, (name, value) in zip(
+                placeholders, assign_ops, six.iteritems(var_values)
+            ):
+                sess.run(assign_op, {p: value})
+
+            # Use the built saver to save the averaged checkpoint. Only keep 1
+            # checkpoint and the best checkpoint will be moved to
+            # avg_best_metric_dir.
+            saver.save(sess, os.path.join(avg_model_dir, "translate.ckpt"))
+
+    return avg_model_dir
 
 
 def create_or_load_model(model, model_dir, session, name):
-  """Create translation model and initialize or load parameters in session."""
-  latest_ckpt = tf.train.latest_checkpoint(model_dir)
-  if latest_ckpt:
-    model = load_model(model, latest_ckpt, session, name)
-  else:
-    start_time = time.time()
-    session.run(tf.global_variables_initializer())
-    session.run(tf.tables_initializer())
-    utils.print_out("  created %s model with fresh parameters, time %.2fs" %
-                    (name, time.time() - start_time))
+    """Create translation model and initialize or load parameters in session."""
+    latest_ckpt = tf.train.latest_checkpoint(model_dir)
+    if latest_ckpt:
+        model = load_model(model, latest_ckpt, session, name)
+    else:
+        start_time = time.time()
+        session.run(tf.global_variables_initializer())
+        session.run(tf.tables_initializer())
+        utils.print_out(
+            "  created %s model with fresh parameters, time %.2fs"
+            % (name, time.time() - start_time)
+        )
 
-  global_step = model.global_step.eval(session=session)
-  return model, global_step
+    global_step = model.global_step.eval(session=session)
+    return model, global_step
 
 
 def compute_perplexity(model, sess, name):
-  """Compute perplexity of the output of the model.
-
-  Args:
-    model: model for compute perplexity.
-    sess: tensorflow session to use.
-    name: name of the batch.
-
-  Returns:
-    The perplexity of the eval outputs.
-  """
-  total_loss = 0
-  total_predict_count = 0
-  start_time = time.time()
+    """Compute perplexity of the output of the model.
+
+    Args:
+      model: model for compute perplexity.
+      sess: tensorflow session to use.
+      name: name of the batch.
+
+    Returns:
+      The perplexity of the eval outputs.
+    """
+    total_loss = 0
+    total_predict_count = 0
+    start_time = time.time()
 
-  while True:
-    try:
-      output_tuple = model.eval(sess)
-      total_loss += output_tuple.eval_loss * output_tuple.batch_size
-      total_predict_count += output_tuple.predict_count
-    except tf.errors.OutOfRangeError:
-      break
-
-  perplexity = utils.safe_exp(total_loss / total_predict_count)
-  utils.print_time("  eval %s: perplexity %.2f" % (name, perplexity),
-                   start_time)
-  return perplexity
+    while True:
+        try:
+            output_tuple = model.eval(sess)
+            total_loss += output_tuple.eval_loss * output_tuple.batch_size
+            total_predict_count += output_tuple.predict_count
+        except tf.errors.OutOfRangeError:
+            break
+
+    perplexity = utils.safe_exp(total_loss / total_predict_count)
+    utils.print_time(
+        "  eval %s: perplexity %.2f" %
+        (name, perplexity), start_time)
+    return perplexity
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/model_test.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/model_test.py
index 168895844c..b753981f31 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/model_test.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/model_test.py
@@ -34,1001 +34,1113 @@
 int32 = np.int32
 array = np.array
 
-SOS = '<s>'
-EOS = '</s>'
+SOS = "<s>"
+EOS = "</s>"
 
 
 class ModelTest(tf.test.TestCase):
 
-  @classmethod
-  def setUpClass(cls):
-    cls.actual_vars_values = {}
-    cls.expected_vars_values = {
-        'AttentionMechanismBahdanau/att_layer_weight/shape': (10, 5),
-        'AttentionMechanismBahdanau/att_layer_weight/sum':
-            -0.64981574,
-        'AttentionMechanismBahdanau/last_dec_weight/shape': (10, 20),
-        'AttentionMechanismBahdanau/last_dec_weight/sum':
-            0.058069646,
-        'AttentionMechanismBahdanau/last_enc_weight/shape': (10, 20),
-        'AttentionMechanismBahdanau/last_enc_weight/sum':
-            0.058028102,
-        'AttentionMechanismLuong/att_layer_weight/shape': (10, 5),
-        'AttentionMechanismLuong/att_layer_weight/sum':
-            -0.64981574,
-        'AttentionMechanismLuong/last_dec_weight/shape': (10, 20),
-        'AttentionMechanismLuong/last_dec_weight/sum':
-            0.058069646,
-        'AttentionMechanismLuong/last_enc_weight/shape': (10, 20),
-        'AttentionMechanismLuong/last_enc_weight/sum':
-            0.058028102,
-        'AttentionMechanismNormedBahdanau/att_layer_weight/shape': (10, 5),
-        'AttentionMechanismNormedBahdanau/att_layer_weight/sum':
-            -0.64981973,
-        'AttentionMechanismNormedBahdanau/last_dec_weight/shape': (10, 20),
-        'AttentionMechanismNormedBahdanau/last_dec_weight/sum':
-            0.058067322,
-        'AttentionMechanismNormedBahdanau/last_enc_weight/shape': (10, 20),
-        'AttentionMechanismNormedBahdanau/last_enc_weight/sum':
-            0.058022559,
-        'AttentionMechanismScaledLuong/att_layer_weight/shape': (10, 5),
-        'AttentionMechanismScaledLuong/att_layer_weight/sum':
-            -0.64981574,
-        'AttentionMechanismScaledLuong/last_dec_weight/shape': (10, 20),
-        'AttentionMechanismScaledLuong/last_dec_weight/sum':
-            0.058069646,
-        'AttentionMechanismScaledLuong/last_enc_weight/shape': (10, 20),
-        'AttentionMechanismScaledLuong/last_enc_weight/sum':
-            0.058028102,
-        'GNMTModel_gnmt/last_dec_weight/shape': (15, 20),
-        'GNMTModel_gnmt/last_dec_weight/sum':
-            -0.48634407,
-        'GNMTModel_gnmt/last_enc_weight/shape': (10, 20),
-        'GNMTModel_gnmt/last_enc_weight/sum':
-            0.058025002,
-        'GNMTModel_gnmt/mem_layer_weight/shape': (5, 5),
-        'GNMTModel_gnmt/mem_layer_weight/sum':
-            -0.44815454,
-        'GNMTModel_gnmt_v2/last_dec_weight/shape': (15, 20),
-        'GNMTModel_gnmt_v2/last_dec_weight/sum':
-            -0.48634392,
-        'GNMTModel_gnmt_v2/last_enc_weight/shape': (10, 20),
-        'GNMTModel_gnmt_v2/last_enc_weight/sum':
-            0.058024824,
-        'GNMTModel_gnmt_v2/mem_layer_weight/shape': (5, 5),
-        'GNMTModel_gnmt_v2/mem_layer_weight/sum':
-            -0.44815454,
-        'NoAttentionNoResidualUniEncoder/last_dec_weight/shape': (10, 20),
-        'NoAttentionNoResidualUniEncoder/last_dec_weight/sum':
-            0.057424068,
-        'NoAttentionNoResidualUniEncoder/last_enc_weight/shape': (10, 20),
-        'NoAttentionNoResidualUniEncoder/last_enc_weight/sum':
-            0.058453858,
-        'NoAttentionResidualBiEncoder/last_dec_weight/shape': (10, 20),
-        'NoAttentionResidualBiEncoder/last_dec_weight/sum':
-            0.058025062,
-        'NoAttentionResidualBiEncoder/last_enc_weight/shape': (10, 20),
-        'NoAttentionResidualBiEncoder/last_enc_weight/sum':
-            0.058053195,
-        'UniEncoderBottomAttentionArchitecture/last_dec_weight/shape': (10, 20),
-        'UniEncoderBottomAttentionArchitecture/last_dec_weight/sum':
-            0.058024943,
-        'UniEncoderBottomAttentionArchitecture/last_enc_weight/shape': (10, 20),
-        'UniEncoderBottomAttentionArchitecture/last_enc_weight/sum':
-            0.058025122,
-        'UniEncoderBottomAttentionArchitecture/mem_layer_weight/shape': (5, 5),
-        'UniEncoderBottomAttentionArchitecture/mem_layer_weight/sum':
-            -0.44815454,
-        'UniEncoderStandardAttentionArchitecture/last_dec_weight/shape': (10,
-                                                                          20),
-        'UniEncoderStandardAttentionArchitecture/last_dec_weight/sum':
-            0.058025002,
-        'UniEncoderStandardAttentionArchitecture/last_enc_weight/shape': (10,
-                                                                          20),
-        'UniEncoderStandardAttentionArchitecture/last_enc_weight/sum':
-            0.058024883,
-        'UniEncoderStandardAttentionArchitecture/mem_layer_weight/shape': (5,
-                                                                           5),
-        'UniEncoderStandardAttentionArchitecture/mem_layer_weight/sum':
-            -0.44815454,
-    }
-
-    cls.actual_train_values = {}
-    cls.expected_train_values = {
-        'AttentionMechanismBahdanau/loss': 8.8519039,
-        'AttentionMechanismLuong/loss': 8.8519039,
-        'AttentionMechanismNormedBahdanau/loss': 8.851902,
-        'AttentionMechanismScaledLuong/loss': 8.8519039,
-        'GNMTModel_gnmt/loss': 8.8519087,
-        'GNMTModel_gnmt_v2/loss': 8.8519087,
-        'NoAttentionNoResidualUniEncoder/loss': 8.8516064,
-        'NoAttentionResidualBiEncoder/loss': 8.851984,
-        'UniEncoderStandardAttentionArchitecture/loss': 8.8519087,
-        'InitializerGlorotNormal/loss': 8.9779415,
-        'InitializerGlorotUniform/loss': 8.7643699,
-        'SampledSoftmaxLoss/loss': 5.83928,
-    }
-
-    cls.actual_eval_values = {}
-    cls.expected_eval_values = {
-        'AttentionMechanismBahdanau/loss': 8.8517132,
-        'AttentionMechanismBahdanau/predict_count': 11.0,
-        'AttentionMechanismLuong/loss': 8.8517132,
-        'AttentionMechanismLuong/predict_count': 11.0,
-        'AttentionMechanismNormedBahdanau/loss': 8.8517132,
-        'AttentionMechanismNormedBahdanau/predict_count': 11.0,
-        'AttentionMechanismScaledLuong/loss': 8.8517132,
-        'AttentionMechanismScaledLuong/predict_count': 11.0,
-        'GNMTModel_gnmt/loss': 8.8443403,
-        'GNMTModel_gnmt/predict_count': 11.0,
-        'GNMTModel_gnmt_v2/loss': 8.8443756,
-        'GNMTModel_gnmt_v2/predict_count': 11.0,
-        'NoAttentionNoResidualUniEncoder/loss': 8.8440113,
-        'NoAttentionNoResidualUniEncoder/predict_count': 11.0,
-        'NoAttentionResidualBiEncoder/loss': 8.8291245,
-        'NoAttentionResidualBiEncoder/predict_count': 11.0,
-        'UniEncoderBottomAttentionArchitecture/loss': 8.844492,
-        'UniEncoderBottomAttentionArchitecture/predict_count': 11.0,
-        'UniEncoderStandardAttentionArchitecture/loss': 8.8517151,
-        'UniEncoderStandardAttentionArchitecture/predict_count': 11.0
-    }
-
-    cls.actual_infer_values = {}
-    cls.expected_infer_values = {
-        'AttentionMechanismBahdanau/logits_sum': -0.026374687,
-        'AttentionMechanismLuong/logits_sum': -0.026374735,
-        'AttentionMechanismNormedBahdanau/logits_sum': -0.026376063,
-        'AttentionMechanismScaledLuong/logits_sum': -0.026374735,
-        'GNMTModel_gnmt/logits_sum': -1.10848486,
-        'GNMTModel_gnmt_v2/logits_sum': -1.10950875,
-        'NoAttentionNoResidualUniEncoder/logits_sum': -1.0808625,
-        'NoAttentionResidualBiEncoder/logits_sum': -2.8147559,
-        'UniEncoderBottomAttentionArchitecture/logits_sum': -0.97026241,
-        'UniEncoderStandardAttentionArchitecture/logits_sum': -0.02665353
-    }
-
-    cls.actual_beam_sentences = {}
-    cls.expected_beam_sentences = {
-        'BeamSearchAttentionModel: batch 0 of beam 0': '',
-        'BeamSearchAttentionModel: batch 0 of beam 1': '%s a %s a' % (SOS, SOS),
-        'BeamSearchAttentionModel: batch 1 of beam 0': '',
-        'BeamSearchAttentionModel: batch 1 of beam 1': 'b',
-        'BeamSearchBasicModel: batch 0 of beam 0': 'b b b b',
-        'BeamSearchBasicModel: batch 0 of beam 1': 'b b b %s' % SOS,
-        'BeamSearchBasicModel: batch 0 of beam 2': 'b b b c',
-        'BeamSearchBasicModel: batch 1 of beam 0': 'b b b b',
-        'BeamSearchBasicModel: batch 1 of beam 1': 'a b b b',
-        'BeamSearchBasicModel: batch 1 of beam 2': 'b b b %s' % SOS,
-        'BeamSearchGNMTModel: batch 0 of beam 0': '',
-        'BeamSearchGNMTModel: batch 1 of beam 0': '',
-    }
-
-  @classmethod
-  def tearDownClass(cls):
-    print('ModelTest - actual_vars_values: ')
-    pprint.pprint(cls.actual_vars_values)
-    sys.stdout.flush()
-
-    print('ModelTest - actual_train_values: ')
-    pprint.pprint(cls.actual_train_values)
-    sys.stdout.flush()
-
-    print('ModelTest - actual_eval_values: ')
-    pprint.pprint(cls.actual_eval_values)
-    sys.stdout.flush()
-
-    print('ModelTest - actual_infer_values: ')
-    pprint.pprint(cls.actual_infer_values)
-    sys.stdout.flush()
-
-    print('ModelTest - actual_beam_sentences: ')
-    pprint.pprint(cls.actual_beam_sentences)
-    sys.stdout.flush()
-
-  def assertAllClose(self, *args, **kwargs):
-    kwargs['atol'] = 5e-2
-    kwargs['rtol'] = 5e-2
-    return super(ModelTest, self).assertAllClose(*args, **kwargs)
-
-  def _assertModelVariableNames(self, expected_var_names, model_var_names,
-                                name):
-
-    print('{} variable names are: '.format(name), model_var_names)
-
-    self.assertEqual(len(expected_var_names), len(model_var_names))
-    self.assertEqual(sorted(expected_var_names), sorted(model_var_names))
-
-  def _assertModelVariable(self, variable, sess, name):
-    var_shape = tuple(variable.get_shape().as_list())
-    var_res = sess.run(variable)
-    var_weight_sum = np.sum(var_res)
-
-    print('{} weight sum is: '.format(name), var_weight_sum)
-    expected_sum = self.expected_vars_values[name + '/sum']
-    expected_shape = self.expected_vars_values[name + '/shape']
-    self.actual_vars_values[name + '/sum'] = var_weight_sum
-    self.actual_vars_values[name + '/shape'] = var_shape
-
-    self.assertEqual(expected_shape, var_shape)
-    self.assertAllClose(expected_sum, var_weight_sum)
-
-  def _assertTrainStepsLoss(self, m, sess, name, num_steps=1):
-    for _ in range(num_steps):
-      _, output_tuple = m.train(sess)
-    loss = output_tuple.train_loss
-    print('{} {}-th step loss is: '.format(name, num_steps), loss)
-    expected_loss = self.expected_train_values[name + '/loss']
-    self.actual_train_values[name + '/loss'] = loss
-
-    self.assertAllClose(expected_loss, loss)
-
-  def _assertEvalLossAndPredictCount(self, m, sess, name):
-    output_tuple = m.eval(sess)
-    loss = output_tuple.eval_loss
-    predict_count = output_tuple.predict_count
-    print('{} eval loss is: '.format(name), loss)
-    print('{} predict count is: '.format(name), predict_count)
-    expected_loss = self.expected_eval_values[name + '/loss']
-    expected_predict_count = self.expected_eval_values[name + '/predict_count']
-    self.actual_eval_values[name + '/loss'] = loss
-    self.actual_eval_values[name + '/predict_count'] = predict_count
-
-    self.assertAllClose(expected_loss, loss)
-    self.assertAllClose(expected_predict_count, predict_count)
-
-  def _assertInferLogits(self, m, sess, name):
-    output_tuple = m.infer(sess)
-    logits_sum = np.sum(output_tuple.infer_logits)
-
-    print('{} infer logits sum is: '.format(name), logits_sum)
-    expected_logits_sum = self.expected_infer_values[name + '/logits_sum']
-    self.actual_infer_values[name + '/logits_sum'] = logits_sum
-
-    self.assertAllClose(expected_logits_sum, logits_sum)
-
-  def _assertBeamSearchOutputs(self, m, sess, assert_top_k_sentence, name):
-    nmt_outputs, _ = m.decode(sess)
-
-    for i in range(assert_top_k_sentence):
-      output_words = nmt_outputs[i]
-      for j in range(output_words.shape[0]):
-        sentence = nmt_utils.get_translation(
-            output_words, j, tgt_eos=EOS, subword_option='')
-        sentence_key = ('%s: batch %d of beam %d' % (name, j, i))
-        self.actual_beam_sentences[sentence_key] = sentence
-        expected_sentence = self.expected_beam_sentences[sentence_key]
-        self.assertEqual(expected_sentence, sentence)
-
-  def _createTestTrainModel(self, m_creator, hparams, sess):
-    train_mode = tf.contrib.learn.ModeKeys.TRAIN
-    train_iterator, src_vocab_table, tgt_vocab_table = (
-        common_test_utils.create_test_iterator(hparams, train_mode))
-    train_m = m_creator(
-        hparams,
-        train_mode,
-        train_iterator,
-        src_vocab_table,
-        tgt_vocab_table,
-        scope='dynamic_seq2seq')
-    sess.run(tf.global_variables_initializer())
-    sess.run(tf.tables_initializer())
-    sess.run(train_iterator.initializer)
-    return train_m
-
-  def _createTestEvalModel(self, m_creator, hparams, sess):
-    eval_mode = tf.contrib.learn.ModeKeys.EVAL
-    eval_iterator, src_vocab_table, tgt_vocab_table = (
-        common_test_utils.create_test_iterator(hparams, eval_mode))
-    eval_m = m_creator(
-        hparams,
-        eval_mode,
-        eval_iterator,
-        src_vocab_table,
-        tgt_vocab_table,
-        scope='dynamic_seq2seq')
-    sess.run(tf.tables_initializer())
-    sess.run(eval_iterator.initializer)
-    return eval_m
-
-  def _createTestInferModel(
-      self, m_creator, hparams, sess, init_global_vars=False):
-    infer_mode = tf.contrib.learn.ModeKeys.INFER
-    (infer_iterator, src_vocab_table,
-     tgt_vocab_table, reverse_tgt_vocab_table) = (
-         common_test_utils.create_test_iterator(hparams, infer_mode))
-    infer_m = m_creator(
-        hparams,
-        infer_mode,
-        infer_iterator,
-        src_vocab_table,
-        tgt_vocab_table,
-        reverse_tgt_vocab_table,
-        scope='dynamic_seq2seq')
-    if init_global_vars:
-      sess.run(tf.global_variables_initializer())
-    sess.run(tf.tables_initializer())
-    sess.run(infer_iterator.initializer)
-    return infer_m
-
-  def _get_session_config(self):
-    config = tf.ConfigProto()
-    config.allow_soft_placement = True
-    return config
-
-  ## Testing 3 encoders:
-  # uni: no attention, no residual, 1 layers
-  # bi: no attention, with residual, 4 layers
-  def testNoAttentionNoResidualUniEncoder(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        num_layers=1,
-        attention='',
-        attention_architecture='',
-        use_residual=False,)
-
-    workers, _ = tf.test.create_local_cluster(1, 0)
-    worker = workers[0]
-
-    # pylint: disable=line-too-long
-    expected_var_names = [
-        'dynamic_seq2seq/encoder/embedding_encoder:0',
-        'dynamic_seq2seq/decoder/embedding_decoder:0',
-        'dynamic_seq2seq/encoder/rnn/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/output_projection/kernel:0'
-    ]
-    # pylint: enable=line-too-long
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        train_m = self._createTestTrainModel(model.Model, hparams, sess)
-
-        m_vars = tf.trainable_variables()
-        self._assertModelVariableNames(expected_var_names,
-                                       [v.name for v in m_vars],
-                                       'NoAttentionNoResidualUniEncoder')
-
-        with tf.variable_scope('dynamic_seq2seq', reuse=True):
-          last_enc_weight = tf.get_variable(
-              'encoder/rnn/basic_lstm_cell/kernel')
-          last_dec_weight = tf.get_variable('decoder/basic_lstm_cell/kernel')
-        self._assertTrainStepsLoss(train_m, sess,
-                                   'NoAttentionNoResidualUniEncoder')
-        self._assertModelVariable(
-            last_enc_weight, sess,
-            'NoAttentionNoResidualUniEncoder/last_enc_weight')
-        self._assertModelVariable(
-            last_dec_weight, sess,
-            'NoAttentionNoResidualUniEncoder/last_dec_weight')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        eval_m = self._createTestEvalModel(model.Model, hparams, sess)
-        self._assertEvalLossAndPredictCount(eval_m, sess,
-                                            'NoAttentionNoResidualUniEncoder')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        infer_m = self._createTestInferModel(model.Model, hparams, sess)
-        self._assertInferLogits(infer_m, sess,
-                                'NoAttentionNoResidualUniEncoder')
-
-  def testNoAttentionResidualBiEncoder(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='bi',
-        num_layers=4,
-        attention='',
-        attention_architecture='',
-        use_residual=True,)
-
-    workers, _ = tf.test.create_local_cluster(1, 0)
-    worker = workers[0]
-
-    # pylint: disable=line-too-long
-    expected_var_names = [
-        'dynamic_seq2seq/encoder/embedding_encoder:0',
-        'dynamic_seq2seq/decoder/embedding_decoder:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/output_projection/kernel:0'
-    ]
-    # pylint: enable=line-too-long
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        train_m = self._createTestTrainModel(model.Model, hparams, sess)
-
-        m_vars = tf.trainable_variables()
-        self._assertModelVariableNames(expected_var_names,
-                                       [v.name for v in m_vars],
-                                       'NoAttentionResidualBiEncoder')
-        with tf.variable_scope('dynamic_seq2seq', reuse=True):
-          last_enc_weight = tf.get_variable(
-              'encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
-          )
-          last_dec_weight = tf.get_variable(
-              'decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel')
-        self._assertTrainStepsLoss(train_m, sess,
-                                   'NoAttentionResidualBiEncoder')
-        self._assertModelVariable(
-            last_enc_weight, sess,
-            'NoAttentionResidualBiEncoder/last_enc_weight')
-        self._assertModelVariable(
-            last_dec_weight, sess,
-            'NoAttentionResidualBiEncoder/last_dec_weight')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        eval_m = self._createTestEvalModel(model.Model, hparams, sess)
-        self._assertEvalLossAndPredictCount(eval_m, sess,
-                                            'NoAttentionResidualBiEncoder')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        infer_m = self._createTestInferModel(model.Model, hparams, sess)
-        self._assertInferLogits(infer_m, sess, 'NoAttentionResidualBiEncoder')
-
-  ## Test attention mechanisms: luong, scaled_luong, bahdanau, normed_bahdanau
-  def testAttentionMechanismLuong(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        attention='luong',
-        attention_architecture='standard',
-        num_layers=2,
-        use_residual=False,)
-
-    workers, _ = tf.test.create_local_cluster(1, 0)
-    worker = workers[0]
-
-    # pylint: disable=line-too-long
-    expected_var_names = [
-        'dynamic_seq2seq/encoder/embedding_encoder:0',
-        'dynamic_seq2seq/decoder/embedding_decoder:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/memory_layer/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/attention_layer/kernel:0',
-        'dynamic_seq2seq/decoder/output_projection/kernel:0'
-    ]
-    # pylint: enable=line-too-long
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        train_m = self._createTestTrainModel(attention_model.AttentionModel,
-                                             hparams, sess)
-
-        m_vars = tf.trainable_variables()
-        self._assertModelVariableNames(
-            expected_var_names, [v.name
-                                 for v in m_vars], 'AttentionMechanismLuong')
-
-        with tf.variable_scope('dynamic_seq2seq', reuse=True):
-          # pylint: disable=line-too-long
-          last_enc_weight = tf.get_variable(
-              'encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel')
-          last_dec_weight = tf.get_variable(
-              'decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel')
-          att_layer_weight = tf.get_variable(
-              'decoder/attention/attention_layer/kernel')
-          # pylint: enable=line-too-long
-        self._assertTrainStepsLoss(train_m, sess, 'AttentionMechanismLuong')
-        self._assertModelVariable(last_enc_weight, sess,
-                                  'AttentionMechanismLuong/last_enc_weight')
-        self._assertModelVariable(last_dec_weight, sess,
-                                  'AttentionMechanismLuong/last_dec_weight')
-        self._assertModelVariable(att_layer_weight, sess,
-                                  'AttentionMechanismLuong/att_layer_weight')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        eval_m = self._createTestEvalModel(attention_model.AttentionModel,
-                                           hparams, sess)
-        self._assertEvalLossAndPredictCount(eval_m, sess,
-                                            'AttentionMechanismLuong')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        infer_m = self._createTestInferModel(attention_model.AttentionModel,
-                                             hparams, sess)
-        self._assertInferLogits(infer_m, sess, 'AttentionMechanismLuong')
-
-  def testAttentionMechanismScaledLuong(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        attention='scaled_luong',
-        attention_architecture='standard',
-        num_layers=2,
-        use_residual=False,)
-
-    workers, _ = tf.test.create_local_cluster(1, 0)
-    worker = workers[0]
-
-    # pylint: disable=line-too-long
-    expected_var_names = [
-        'dynamic_seq2seq/encoder/embedding_encoder:0',
-        'dynamic_seq2seq/decoder/embedding_decoder:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/memory_layer/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0',
-        'dynamic_seq2seq/decoder/attention/attention_layer/kernel:0',
-        'dynamic_seq2seq/decoder/output_projection/kernel:0'
-    ]
-    # pylint: enable=line-too-long
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        train_m = self._createTestTrainModel(attention_model.AttentionModel,
-                                             hparams, sess)
-
-        m_vars = tf.trainable_variables()
-        self._assertModelVariableNames(expected_var_names,
-                                       [v.name for v in m_vars],
-                                       'AttentionMechanismScaledLuong')
-
-        with tf.variable_scope('dynamic_seq2seq', reuse=True):
-          # pylint: disable=line-too-long
-          last_enc_weight = tf.get_variable(
-              'encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel')
-          last_dec_weight = tf.get_variable(
-              'decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel')
-          att_layer_weight = tf.get_variable(
-              'decoder/attention/attention_layer/kernel')
-          # pylint: enable=line-too-long
-
-        self._assertTrainStepsLoss(train_m, sess,
-                                   'AttentionMechanismScaledLuong')
-        self._assertModelVariable(
-            last_enc_weight, sess,
-            'AttentionMechanismScaledLuong/last_enc_weight')
-        self._assertModelVariable(
-            last_dec_weight, sess,
-            'AttentionMechanismScaledLuong/last_dec_weight')
-        self._assertModelVariable(
-            att_layer_weight, sess,
-            'AttentionMechanismScaledLuong/att_layer_weight')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        eval_m = self._createTestEvalModel(attention_model.AttentionModel,
-                                           hparams, sess)
-        self._assertEvalLossAndPredictCount(eval_m, sess,
-                                            'AttentionMechanismScaledLuong')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        infer_m = self._createTestInferModel(attention_model.AttentionModel,
-                                             hparams, sess)
-        self._assertInferLogits(infer_m, sess, 'AttentionMechanismScaledLuong')
-
-  def testAttentionMechanismBahdanau(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        attention='bahdanau',
-        attention_architecture='standard',
-        num_layers=2,
-        use_residual=False,)
-
-    workers, _ = tf.test.create_local_cluster(1, 0)
-    worker = workers[0]
-
-    # pylint: disable=line-too-long
-    expected_var_names = [
-        'dynamic_seq2seq/encoder/embedding_encoder:0',
-        'dynamic_seq2seq/decoder/embedding_decoder:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/memory_layer/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/bahdanau_attention/query_layer/kernel:0',
-        'dynamic_seq2seq/decoder/attention/bahdanau_attention/attention_v:0',
-        'dynamic_seq2seq/decoder/attention/attention_layer/kernel:0',
-        'dynamic_seq2seq/decoder/output_projection/kernel:0'
-    ]
-    # pylint: enable=line-too-long
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        train_m = self._createTestTrainModel(attention_model.AttentionModel,
-                                             hparams, sess)
-
-        m_vars = tf.trainable_variables()
-        self._assertModelVariableNames(
-            expected_var_names, [v.name
-                                 for v in m_vars], 'AttentionMechanismBahdanau')
-
-        with tf.variable_scope('dynamic_seq2seq', reuse=True):
-          # pylint: disable=line-too-long
-          last_enc_weight = tf.get_variable(
-              'encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel')
-          last_dec_weight = tf.get_variable(
-              'decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel')
-          att_layer_weight = tf.get_variable(
-              'decoder/attention/attention_layer/kernel')
-          # pylint: enable=line-too-long
-        self._assertTrainStepsLoss(train_m, sess, 'AttentionMechanismBahdanau')
-        self._assertModelVariable(last_enc_weight, sess,
-                                  'AttentionMechanismBahdanau/last_enc_weight')
-        self._assertModelVariable(last_dec_weight, sess,
-                                  'AttentionMechanismBahdanau/last_dec_weight')
-        self._assertModelVariable(att_layer_weight, sess,
-                                  'AttentionMechanismBahdanau/att_layer_weight')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        eval_m = self._createTestEvalModel(attention_model.AttentionModel,
-                                           hparams, sess)
-        self._assertEvalLossAndPredictCount(eval_m, sess,
-                                            'AttentionMechanismBahdanau')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        infer_m = self._createTestInferModel(attention_model.AttentionModel,
-                                             hparams, sess)
-        self._assertInferLogits(infer_m, sess, 'AttentionMechanismBahdanau')
-
-  def testAttentionMechanismNormedBahdanau(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        attention='normed_bahdanau',
-        attention_architecture='standard',
-        num_layers=2,
-        use_residual=False,)
-
-    workers, _ = tf.test.create_local_cluster(1, 0)
-    worker = workers[0]
-
-    # pylint: disable=line-too-long
-    expected_var_names = [
-        'dynamic_seq2seq/encoder/embedding_encoder:0',
-        'dynamic_seq2seq/decoder/embedding_decoder:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/memory_layer/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/bahdanau_attention/query_layer/kernel:0',
-        'dynamic_seq2seq/decoder/attention/bahdanau_attention/attention_v:0',
-        'dynamic_seq2seq/decoder/attention/bahdanau_attention/attention_g:0',
-        'dynamic_seq2seq/decoder/attention/bahdanau_attention/attention_b:0',
-        'dynamic_seq2seq/decoder/attention/attention_layer/kernel:0',
-        'dynamic_seq2seq/decoder/output_projection/kernel:0'
-    ]
-    # pylint: enable=line-too-long
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        train_m = self._createTestTrainModel(attention_model.AttentionModel,
-                                             hparams, sess)
-
-        m_vars = tf.trainable_variables()
-        self._assertModelVariableNames(expected_var_names,
-                                       [v.name for v in m_vars],
-                                       'AttentionMechanismNormedBahdanau')
-
-        with tf.variable_scope('dynamic_seq2seq', reuse=True):
-          # pylint: disable=line-too-long
-          last_enc_weight = tf.get_variable(
-              'encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel')
-          last_dec_weight = tf.get_variable(
-              'decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel')
-          att_layer_weight = tf.get_variable(
-              'decoder/attention/attention_layer/kernel')
-          # pylint: enable=line-too-long
-        self._assertTrainStepsLoss(train_m, sess,
-                                   'AttentionMechanismNormedBahdanau')
-        self._assertModelVariable(
-            last_enc_weight, sess,
-            'AttentionMechanismNormedBahdanau/last_enc_weight')
-        self._assertModelVariable(
-            last_dec_weight, sess,
-            'AttentionMechanismNormedBahdanau/last_dec_weight')
-        self._assertModelVariable(
-            att_layer_weight, sess,
-            'AttentionMechanismNormedBahdanau/att_layer_weight')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        eval_m = self._createTestEvalModel(attention_model.AttentionModel,
-                                           hparams, sess)
-        self._assertEvalLossAndPredictCount(eval_m, sess,
-                                            'AttentionMechanismNormedBahdanau')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        infer_m = self._createTestInferModel(attention_model.AttentionModel,
-                                             hparams, sess)
-        self._assertInferLogits(infer_m, sess,
-                                'AttentionMechanismNormedBahdanau')
-
-  ## Test encoder vs. attention (all use residual):
-  # uni encoder, standard attention
-  def testUniEncoderStandardAttentionArchitecture(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        num_layers=4,
-        attention='scaled_luong',
-        attention_architecture='standard',)
-
-    workers, _ = tf.test.create_local_cluster(1, 0)
-    worker = workers[0]
-
-    # pylint: disable=line-too-long
-    expected_var_names = [
-        'dynamic_seq2seq/encoder/embedding_encoder:0',
-        'dynamic_seq2seq/decoder/embedding_decoder:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/memory_layer/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0',
-        'dynamic_seq2seq/decoder/attention/attention_layer/kernel:0',
-        'dynamic_seq2seq/decoder/output_projection/kernel:0'
-    ]
-    # pylint: enable=line-too-long
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        train_m = self._createTestTrainModel(attention_model.AttentionModel,
-                                             hparams, sess)
-
-        m_vars = tf.trainable_variables()
-        self._assertModelVariableNames(expected_var_names, [
-            v.name for v in m_vars
-        ], 'UniEncoderStandardAttentionArchitecture')
-        with tf.variable_scope('dynamic_seq2seq', reuse=True):
-          last_enc_weight = tf.get_variable(
-              'encoder/rnn/multi_rnn_cell/cell_3/basic_lstm_cell/kernel')
-          last_dec_weight = tf.get_variable(
-              'decoder/attention/multi_rnn_cell/cell_3/basic_lstm_cell/kernel')
-          mem_layer_weight = tf.get_variable('decoder/memory_layer/kernel')
-        self._assertTrainStepsLoss(train_m, sess,
-                                   'UniEncoderStandardAttentionArchitecture')
-        self._assertModelVariable(
-            last_enc_weight, sess,
-            'UniEncoderStandardAttentionArchitecture/last_enc_weight')
-        self._assertModelVariable(
-            last_dec_weight, sess,
-            'UniEncoderStandardAttentionArchitecture/last_dec_weight')
-        self._assertModelVariable(
-            mem_layer_weight, sess,
-            'UniEncoderStandardAttentionArchitecture/mem_layer_weight')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        eval_m = self._createTestEvalModel(attention_model.AttentionModel,
-                                           hparams, sess)
-        self._assertEvalLossAndPredictCount(
-            eval_m, sess, 'UniEncoderStandardAttentionArchitecture')
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        infer_m = self._createTestInferModel(attention_model.AttentionModel,
-                                             hparams, sess)
-        self._assertInferLogits(infer_m, sess,
-                                'UniEncoderStandardAttentionArchitecture')
-
-  # Test gnmt model.
-  def _testGNMTModel(self, architecture):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='gnmt',
-        num_layers=4,
-        attention='scaled_luong',
-        attention_architecture=architecture)
-
-    workers, _ = tf.test.create_local_cluster(1, 0)
-    worker = workers[0]
-
-    # pylint: disable=line-too-long
-    expected_var_names = [
-        'dynamic_seq2seq/encoder/embedding_encoder:0',
-        'dynamic_seq2seq/decoder/embedding_decoder:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/memory_layer/kernel:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_0_attention/attention/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_0_attention/attention/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_0_attention/attention/luong_attention/attention_g:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel:0',
-        'dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/bias:0',
-        'dynamic_seq2seq/decoder/output_projection/kernel:0'
-    ]
-    # pylint: enable=line-too-long
-
-    test_prefix = 'GNMTModel_%s' % architecture
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        train_m = self._createTestTrainModel(gnmt_model.GNMTModel, hparams,
-                                             sess)
-
-        m_vars = tf.trainable_variables()
-        self._assertModelVariableNames(expected_var_names,
-                                       [v.name for v in m_vars], test_prefix)
-        with tf.variable_scope('dynamic_seq2seq', reuse=True):
-          last_enc_weight = tf.get_variable(
-              'encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel')
-          last_dec_weight = tf.get_variable(
-              'decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel')
-          mem_layer_weight = tf.get_variable('decoder/memory_layer/kernel')
-        self._assertTrainStepsLoss(train_m, sess, test_prefix)
-
-        self._assertModelVariable(last_enc_weight, sess,
-                                  '%s/last_enc_weight' % test_prefix)
-        self._assertModelVariable(last_dec_weight, sess,
-                                  '%s/last_dec_weight' % test_prefix)
-        self._assertModelVariable(mem_layer_weight, sess,
-                                  '%s/mem_layer_weight' % test_prefix)
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        eval_m = self._createTestEvalModel(gnmt_model.GNMTModel, hparams, sess)
-        self._assertEvalLossAndPredictCount(eval_m, sess, test_prefix)
-
-    with tf.Graph().as_default():
-      with tf.Session(worker.target, config=self._get_session_config()) as sess:
-        infer_m = self._createTestInferModel(gnmt_model.GNMTModel, hparams,
-                                             sess)
-        self._assertInferLogits(infer_m, sess, test_prefix)
-
-  def testGNMTModel(self):
-    self._testGNMTModel('gnmt')
-
-  def testGNMTModelV2(self):
-    self._testGNMTModel('gnmt_v2')
-
-  # Test beam search.
-  def testBeamSearchBasicModel(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        num_layers=1,
-        attention='',
-        attention_architecture='',
-        use_residual=False,)
-    hparams.beam_width = 3
-    hparams.infer_mode = "beam_search"
-    hparams.tgt_max_len_infer = 4
-    assert_top_k_sentence = 3
-
-    with self.test_session() as sess:
-      infer_m = self._createTestInferModel(
-          model.Model, hparams, sess, True)
-      self._assertBeamSearchOutputs(
-          infer_m, sess, assert_top_k_sentence, 'BeamSearchBasicModel')
-
-  def testBeamSearchAttentionModel(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        attention='scaled_luong',
-        attention_architecture='standard',
-        num_layers=2,
-        use_residual=False,)
-    hparams.beam_width = 3
-    hparams.infer_mode = "beam_search"
-    hparams.tgt_max_len_infer = 4
-    assert_top_k_sentence = 2
-
-    with self.test_session() as sess:
-      infer_m = self._createTestInferModel(
-          attention_model.AttentionModel, hparams, sess, True)
-      self._assertBeamSearchOutputs(
-          infer_m, sess, assert_top_k_sentence, 'BeamSearchAttentionModel')
-
-  def testBeamSearchGNMTModel(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='gnmt',
-        num_layers=4,
-        attention='scaled_luong',
-        attention_architecture='gnmt')
-    hparams.beam_width = 3
-    hparams.infer_mode = "beam_search"
-    hparams.tgt_max_len_infer = 4
-    assert_top_k_sentence = 1
-
-    with self.test_session() as sess:
-      infer_m = self._createTestInferModel(
-          gnmt_model.GNMTModel, hparams, sess, True)
-      self._assertBeamSearchOutputs(
-          infer_m, sess, assert_top_k_sentence, 'BeamSearchGNMTModel')
-
-  def testInitializerGlorotNormal(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        num_layers=1,
-        attention='',
-        attention_architecture='',
-        use_residual=False,
-        init_op='glorot_normal')
-
-    with self.test_session() as sess:
-      train_m = self._createTestTrainModel(model.Model, hparams, sess)
-      self._assertTrainStepsLoss(train_m, sess,
-                                 'InitializerGlorotNormal')
-
-  def testInitializerGlorotUniform(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='uni',
-        num_layers=1,
-        attention='',
-        attention_architecture='',
-        use_residual=False,
-        init_op='glorot_uniform')
-
-    with self.test_session() as sess:
-      train_m = self._createTestTrainModel(model.Model, hparams, sess)
-      self._assertTrainStepsLoss(train_m, sess,
-                                 'InitializerGlorotUniform')
-
-  def testSampledSoftmaxLoss(self):
-    hparams = common_test_utils.create_test_hparams(
-        encoder_type='gnmt',
-        num_layers=4,
-        attention='scaled_luong',
-        attention_architecture='gnmt')
-    hparams.num_sampled_softmax = 3
-
-    with self.test_session() as sess:
-      train_m = self._createTestTrainModel(gnmt_model.GNMTModel, hparams, sess)
-      self._assertTrainStepsLoss(train_m, sess,
-                                 'SampledSoftmaxLoss')
-
-if __name__ == '__main__':
-  tf.test.main()
+    @classmethod
+    def setUpClass(cls):
+        cls.actual_vars_values = {}
+        cls.expected_vars_values = {
+            "AttentionMechanismBahdanau/att_layer_weight/shape": (10, 5),
+            "AttentionMechanismBahdanau/att_layer_weight/sum": -0.64981574,
+            "AttentionMechanismBahdanau/last_dec_weight/shape": (10, 20),
+            "AttentionMechanismBahdanau/last_dec_weight/sum": 0.058069646,
+            "AttentionMechanismBahdanau/last_enc_weight/shape": (10, 20),
+            "AttentionMechanismBahdanau/last_enc_weight/sum": 0.058028102,
+            "AttentionMechanismLuong/att_layer_weight/shape": (10, 5),
+            "AttentionMechanismLuong/att_layer_weight/sum": -0.64981574,
+            "AttentionMechanismLuong/last_dec_weight/shape": (10, 20),
+            "AttentionMechanismLuong/last_dec_weight/sum": 0.058069646,
+            "AttentionMechanismLuong/last_enc_weight/shape": (10, 20),
+            "AttentionMechanismLuong/last_enc_weight/sum": 0.058028102,
+            "AttentionMechanismNormedBahdanau/att_layer_weight/shape": (10, 5),
+            "AttentionMechanismNormedBahdanau/att_layer_weight/sum": -0.64981973,
+            "AttentionMechanismNormedBahdanau/last_dec_weight/shape": (10, 20),
+            "AttentionMechanismNormedBahdanau/last_dec_weight/sum": 0.058067322,
+            "AttentionMechanismNormedBahdanau/last_enc_weight/shape": (10, 20),
+            "AttentionMechanismNormedBahdanau/last_enc_weight/sum": 0.058022559,
+            "AttentionMechanismScaledLuong/att_layer_weight/shape": (10, 5),
+            "AttentionMechanismScaledLuong/att_layer_weight/sum": -0.64981574,
+            "AttentionMechanismScaledLuong/last_dec_weight/shape": (10, 20),
+            "AttentionMechanismScaledLuong/last_dec_weight/sum": 0.058069646,
+            "AttentionMechanismScaledLuong/last_enc_weight/shape": (10, 20),
+            "AttentionMechanismScaledLuong/last_enc_weight/sum": 0.058028102,
+            "GNMTModel_gnmt/last_dec_weight/shape": (15, 20),
+            "GNMTModel_gnmt/last_dec_weight/sum": -0.48634407,
+            "GNMTModel_gnmt/last_enc_weight/shape": (10, 20),
+            "GNMTModel_gnmt/last_enc_weight/sum": 0.058025002,
+            "GNMTModel_gnmt/mem_layer_weight/shape": (5, 5),
+            "GNMTModel_gnmt/mem_layer_weight/sum": -0.44815454,
+            "GNMTModel_gnmt_v2/last_dec_weight/shape": (15, 20),
+            "GNMTModel_gnmt_v2/last_dec_weight/sum": -0.48634392,
+            "GNMTModel_gnmt_v2/last_enc_weight/shape": (10, 20),
+            "GNMTModel_gnmt_v2/last_enc_weight/sum": 0.058024824,
+            "GNMTModel_gnmt_v2/mem_layer_weight/shape": (5, 5),
+            "GNMTModel_gnmt_v2/mem_layer_weight/sum": -0.44815454,
+            "NoAttentionNoResidualUniEncoder/last_dec_weight/shape": (10, 20),
+            "NoAttentionNoResidualUniEncoder/last_dec_weight/sum": 0.057424068,
+            "NoAttentionNoResidualUniEncoder/last_enc_weight/shape": (10, 20),
+            "NoAttentionNoResidualUniEncoder/last_enc_weight/sum": 0.058453858,
+            "NoAttentionResidualBiEncoder/last_dec_weight/shape": (10, 20),
+            "NoAttentionResidualBiEncoder/last_dec_weight/sum": 0.058025062,
+            "NoAttentionResidualBiEncoder/last_enc_weight/shape": (10, 20),
+            "NoAttentionResidualBiEncoder/last_enc_weight/sum": 0.058053195,
+            "UniEncoderBottomAttentionArchitecture/last_dec_weight/shape": (10, 20),
+            "UniEncoderBottomAttentionArchitecture/last_dec_weight/sum": 0.058024943,
+            "UniEncoderBottomAttentionArchitecture/last_enc_weight/shape": (10, 20),
+            "UniEncoderBottomAttentionArchitecture/last_enc_weight/sum": 0.058025122,
+            "UniEncoderBottomAttentionArchitecture/mem_layer_weight/shape": (5, 5),
+            "UniEncoderBottomAttentionArchitecture/mem_layer_weight/sum": -0.44815454,
+            "UniEncoderStandardAttentionArchitecture/last_dec_weight/shape": (10, 20),
+            "UniEncoderStandardAttentionArchitecture/last_dec_weight/sum": 0.058025002,
+            "UniEncoderStandardAttentionArchitecture/last_enc_weight/shape": (10, 20),
+            "UniEncoderStandardAttentionArchitecture/last_enc_weight/sum": 0.058024883,
+            "UniEncoderStandardAttentionArchitecture/mem_layer_weight/shape": (5, 5),
+            "UniEncoderStandardAttentionArchitecture/mem_layer_weight/sum": -0.44815454,
+        }
+
+        cls.actual_train_values = {}
+        cls.expected_train_values = {
+            "AttentionMechanismBahdanau/loss": 8.8519039,
+            "AttentionMechanismLuong/loss": 8.8519039,
+            "AttentionMechanismNormedBahdanau/loss": 8.851902,
+            "AttentionMechanismScaledLuong/loss": 8.8519039,
+            "GNMTModel_gnmt/loss": 8.8519087,
+            "GNMTModel_gnmt_v2/loss": 8.8519087,
+            "NoAttentionNoResidualUniEncoder/loss": 8.8516064,
+            "NoAttentionResidualBiEncoder/loss": 8.851984,
+            "UniEncoderStandardAttentionArchitecture/loss": 8.8519087,
+            "InitializerGlorotNormal/loss": 8.9779415,
+            "InitializerGlorotUniform/loss": 8.7643699,
+            "SampledSoftmaxLoss/loss": 5.83928,
+        }
+
+        cls.actual_eval_values = {}
+        cls.expected_eval_values = {
+            "AttentionMechanismBahdanau/loss": 8.8517132,
+            "AttentionMechanismBahdanau/predict_count": 11.0,
+            "AttentionMechanismLuong/loss": 8.8517132,
+            "AttentionMechanismLuong/predict_count": 11.0,
+            "AttentionMechanismNormedBahdanau/loss": 8.8517132,
+            "AttentionMechanismNormedBahdanau/predict_count": 11.0,
+            "AttentionMechanismScaledLuong/loss": 8.8517132,
+            "AttentionMechanismScaledLuong/predict_count": 11.0,
+            "GNMTModel_gnmt/loss": 8.8443403,
+            "GNMTModel_gnmt/predict_count": 11.0,
+            "GNMTModel_gnmt_v2/loss": 8.8443756,
+            "GNMTModel_gnmt_v2/predict_count": 11.0,
+            "NoAttentionNoResidualUniEncoder/loss": 8.8440113,
+            "NoAttentionNoResidualUniEncoder/predict_count": 11.0,
+            "NoAttentionResidualBiEncoder/loss": 8.8291245,
+            "NoAttentionResidualBiEncoder/predict_count": 11.0,
+            "UniEncoderBottomAttentionArchitecture/loss": 8.844492,
+            "UniEncoderBottomAttentionArchitecture/predict_count": 11.0,
+            "UniEncoderStandardAttentionArchitecture/loss": 8.8517151,
+            "UniEncoderStandardAttentionArchitecture/predict_count": 11.0,
+        }
+
+        cls.actual_infer_values = {}
+        cls.expected_infer_values = {
+            "AttentionMechanismBahdanau/logits_sum": -0.026374687,
+            "AttentionMechanismLuong/logits_sum": -0.026374735,
+            "AttentionMechanismNormedBahdanau/logits_sum": -0.026376063,
+            "AttentionMechanismScaledLuong/logits_sum": -0.026374735,
+            "GNMTModel_gnmt/logits_sum": -1.10848486,
+            "GNMTModel_gnmt_v2/logits_sum": -1.10950875,
+            "NoAttentionNoResidualUniEncoder/logits_sum": -1.0808625,
+            "NoAttentionResidualBiEncoder/logits_sum": -2.8147559,
+            "UniEncoderBottomAttentionArchitecture/logits_sum": -0.97026241,
+            "UniEncoderStandardAttentionArchitecture/logits_sum": -0.02665353,
+        }
+
+        cls.actual_beam_sentences = {}
+        cls.expected_beam_sentences = {
+            "BeamSearchAttentionModel: batch 0 of beam 0": "",
+            "BeamSearchAttentionModel: batch 0 of beam 1": "%s a %s a" % (SOS, SOS),
+            "BeamSearchAttentionModel: batch 1 of beam 0": "",
+            "BeamSearchAttentionModel: batch 1 of beam 1": "b",
+            "BeamSearchBasicModel: batch 0 of beam 0": "b b b b",
+            "BeamSearchBasicModel: batch 0 of beam 1": "b b b %s" % SOS,
+            "BeamSearchBasicModel: batch 0 of beam 2": "b b b c",
+            "BeamSearchBasicModel: batch 1 of beam 0": "b b b b",
+            "BeamSearchBasicModel: batch 1 of beam 1": "a b b b",
+            "BeamSearchBasicModel: batch 1 of beam 2": "b b b %s" % SOS,
+            "BeamSearchGNMTModel: batch 0 of beam 0": "",
+            "BeamSearchGNMTModel: batch 1 of beam 0": "",
+        }
+
+    @classmethod
+    def tearDownClass(cls):
+        print("ModelTest - actual_vars_values: ")
+        pprint.pprint(cls.actual_vars_values)
+        sys.stdout.flush()
+
+        print("ModelTest - actual_train_values: ")
+        pprint.pprint(cls.actual_train_values)
+        sys.stdout.flush()
+
+        print("ModelTest - actual_eval_values: ")
+        pprint.pprint(cls.actual_eval_values)
+        sys.stdout.flush()
+
+        print("ModelTest - actual_infer_values: ")
+        pprint.pprint(cls.actual_infer_values)
+        sys.stdout.flush()
+
+        print("ModelTest - actual_beam_sentences: ")
+        pprint.pprint(cls.actual_beam_sentences)
+        sys.stdout.flush()
+
+    def assertAllClose(self, *args, **kwargs):
+        kwargs["atol"] = 5e-2
+        kwargs["rtol"] = 5e-2
+        return super(ModelTest, self).assertAllClose(*args, **kwargs)
+
+    def _assertModelVariableNames(
+            self, expected_var_names, model_var_names, name):
+
+        print("{} variable names are: ".format(name), model_var_names)
+
+        self.assertEqual(len(expected_var_names), len(model_var_names))
+        self.assertEqual(sorted(expected_var_names), sorted(model_var_names))
+
+    def _assertModelVariable(self, variable, sess, name):
+        var_shape = tuple(variable.get_shape().as_list())
+        var_res = sess.run(variable)
+        var_weight_sum = np.sum(var_res)
+
+        print("{} weight sum is: ".format(name), var_weight_sum)
+        expected_sum = self.expected_vars_values[name + "/sum"]
+        expected_shape = self.expected_vars_values[name + "/shape"]
+        self.actual_vars_values[name + "/sum"] = var_weight_sum
+        self.actual_vars_values[name + "/shape"] = var_shape
+
+        self.assertEqual(expected_shape, var_shape)
+        self.assertAllClose(expected_sum, var_weight_sum)
+
+    def _assertTrainStepsLoss(self, m, sess, name, num_steps=1):
+        for _ in range(num_steps):
+            _, output_tuple = m.train(sess)
+        loss = output_tuple.train_loss
+        print("{} {}-th step loss is: ".format(name, num_steps), loss)
+        expected_loss = self.expected_train_values[name + "/loss"]
+        self.actual_train_values[name + "/loss"] = loss
+
+        self.assertAllClose(expected_loss, loss)
+
+    def _assertEvalLossAndPredictCount(self, m, sess, name):
+        output_tuple = m.eval(sess)
+        loss = output_tuple.eval_loss
+        predict_count = output_tuple.predict_count
+        print("{} eval loss is: ".format(name), loss)
+        print("{} predict count is: ".format(name), predict_count)
+        expected_loss = self.expected_eval_values[name + "/loss"]
+        expected_predict_count = self.expected_eval_values[name +
+                                                           "/predict_count"]
+        self.actual_eval_values[name + "/loss"] = loss
+        self.actual_eval_values[name + "/predict_count"] = predict_count
+
+        self.assertAllClose(expected_loss, loss)
+        self.assertAllClose(expected_predict_count, predict_count)
+
+    def _assertInferLogits(self, m, sess, name):
+        output_tuple = m.infer(sess)
+        logits_sum = np.sum(output_tuple.infer_logits)
+
+        print("{} infer logits sum is: ".format(name), logits_sum)
+        expected_logits_sum = self.expected_infer_values[name + "/logits_sum"]
+        self.actual_infer_values[name + "/logits_sum"] = logits_sum
+
+        self.assertAllClose(expected_logits_sum, logits_sum)
+
+    def _assertBeamSearchOutputs(self, m, sess, assert_top_k_sentence, name):
+        nmt_outputs, _ = m.decode(sess)
+
+        for i in range(assert_top_k_sentence):
+            output_words = nmt_outputs[i]
+            for j in range(output_words.shape[0]):
+                sentence = nmt_utils.get_translation(
+                    output_words, j, tgt_eos=EOS, subword_option=""
+                )
+                sentence_key = "%s: batch %d of beam %d" % (name, j, i)
+                self.actual_beam_sentences[sentence_key] = sentence
+                expected_sentence = self.expected_beam_sentences[sentence_key]
+                self.assertEqual(expected_sentence, sentence)
+
+    def _createTestTrainModel(self, m_creator, hparams, sess):
+        train_mode = tf.contrib.learn.ModeKeys.TRAIN
+        train_iterator, src_vocab_table, tgt_vocab_table = (
+            common_test_utils.create_test_iterator(hparams, train_mode)
+        )
+        train_m = m_creator(
+            hparams,
+            train_mode,
+            train_iterator,
+            src_vocab_table,
+            tgt_vocab_table,
+            scope="dynamic_seq2seq",
+        )
+        sess.run(tf.global_variables_initializer())
+        sess.run(tf.tables_initializer())
+        sess.run(train_iterator.initializer)
+        return train_m
+
+    def _createTestEvalModel(self, m_creator, hparams, sess):
+        eval_mode = tf.contrib.learn.ModeKeys.EVAL
+        eval_iterator, src_vocab_table, tgt_vocab_table = (
+            common_test_utils.create_test_iterator(hparams, eval_mode)
+        )
+        eval_m = m_creator(
+            hparams,
+            eval_mode,
+            eval_iterator,
+            src_vocab_table,
+            tgt_vocab_table,
+            scope="dynamic_seq2seq",
+        )
+        sess.run(tf.tables_initializer())
+        sess.run(eval_iterator.initializer)
+        return eval_m
+
+    def _createTestInferModel(self, m_creator, hparams,
+                              sess, init_global_vars=False):
+        infer_mode = tf.contrib.learn.ModeKeys.INFER
+        (infer_iterator, src_vocab_table, tgt_vocab_table, reverse_tgt_vocab_table) = (
+            common_test_utils.create_test_iterator(hparams, infer_mode)
+        )
+        infer_m = m_creator(
+            hparams,
+            infer_mode,
+            infer_iterator,
+            src_vocab_table,
+            tgt_vocab_table,
+            reverse_tgt_vocab_table,
+            scope="dynamic_seq2seq",
+        )
+        if init_global_vars:
+            sess.run(tf.global_variables_initializer())
+        sess.run(tf.tables_initializer())
+        sess.run(infer_iterator.initializer)
+        return infer_m
+
+    def _get_session_config(self):
+        config = tf.ConfigProto()
+        config.allow_soft_placement = True
+        return config
+
+    # Testing 3 encoders:
+    # uni: no attention, no residual, 1 layers
+    # bi: no attention, with residual, 4 layers
+    def testNoAttentionNoResidualUniEncoder(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=1,
+            attention="",
+            attention_architecture="",
+            use_residual=False,
+        )
+
+        workers, _ = tf.test.create_local_cluster(1, 0)
+        worker = workers[0]
+
+        # pylint: disable=line-too-long
+        expected_var_names = [
+            "dynamic_seq2seq/encoder/embedding_encoder:0",
+            "dynamic_seq2seq/decoder/embedding_decoder:0",
+            "dynamic_seq2seq/encoder/rnn/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/output_projection/kernel:0",
+        ]
+        # pylint: enable=line-too-long
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                train_m = self._createTestTrainModel(
+                    model.Model, hparams, sess)
+
+                m_vars = tf.trainable_variables()
+                self._assertModelVariableNames(
+                    expected_var_names,
+                    [v.name for v in m_vars],
+                    "NoAttentionNoResidualUniEncoder",
+                )
+
+                with tf.variable_scope("dynamic_seq2seq", reuse=True):
+                    last_enc_weight = tf.get_variable(
+                        "encoder/rnn/basic_lstm_cell/kernel"
+                    )
+                    last_dec_weight = tf.get_variable(
+                        "decoder/basic_lstm_cell/kernel")
+                self._assertTrainStepsLoss(
+                    train_m, sess, "NoAttentionNoResidualUniEncoder"
+                )
+                self._assertModelVariable(
+                    last_enc_weight,
+                    sess,
+                    "NoAttentionNoResidualUniEncoder/last_enc_weight",
+                )
+                self._assertModelVariable(
+                    last_dec_weight,
+                    sess,
+                    "NoAttentionNoResidualUniEncoder/last_dec_weight",
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                eval_m = self._createTestEvalModel(model.Model, hparams, sess)
+                self._assertEvalLossAndPredictCount(
+                    eval_m, sess, "NoAttentionNoResidualUniEncoder"
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                infer_m = self._createTestInferModel(
+                    model.Model, hparams, sess)
+                self._assertInferLogits(
+                    infer_m, sess, "NoAttentionNoResidualUniEncoder"
+                )
+
+    def testNoAttentionResidualBiEncoder(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="bi",
+            num_layers=4,
+            attention="",
+            attention_architecture="",
+            use_residual=True,
+        )
+
+        workers, _ = tf.test.create_local_cluster(1, 0)
+        worker = workers[0]
+
+        # pylint: disable=line-too-long
+        expected_var_names = [
+            "dynamic_seq2seq/encoder/embedding_encoder:0",
+            "dynamic_seq2seq/decoder/embedding_decoder:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/output_projection/kernel:0",
+        ]
+        # pylint: enable=line-too-long
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                train_m = self._createTestTrainModel(
+                    model.Model, hparams, sess)
+
+                m_vars = tf.trainable_variables()
+                self._assertModelVariableNames(
+                    expected_var_names,
+                    [v.name for v in m_vars],
+                    "NoAttentionResidualBiEncoder",
+                )
+                with tf.variable_scope("dynamic_seq2seq", reuse=True):
+                    last_enc_weight = tf.get_variable(
+                        "encoder/bidirectional_rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel"
+                    )
+                    last_dec_weight = tf.get_variable(
+                        "decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel"
+                    )
+                self._assertTrainStepsLoss(
+                    train_m, sess, "NoAttentionResidualBiEncoder"
+                )
+                self._assertModelVariable(
+                    last_enc_weight,
+                    sess,
+                    "NoAttentionResidualBiEncoder/last_enc_weight",
+                )
+                self._assertModelVariable(
+                    last_dec_weight,
+                    sess,
+                    "NoAttentionResidualBiEncoder/last_dec_weight",
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                eval_m = self._createTestEvalModel(model.Model, hparams, sess)
+                self._assertEvalLossAndPredictCount(
+                    eval_m, sess, "NoAttentionResidualBiEncoder"
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                infer_m = self._createTestInferModel(
+                    model.Model, hparams, sess)
+                self._assertInferLogits(
+                    infer_m, sess, "NoAttentionResidualBiEncoder")
+
+    # Test attention mechanisms: luong, scaled_luong, bahdanau, normed_bahdanau
+    def testAttentionMechanismLuong(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            attention="luong",
+            attention_architecture="standard",
+            num_layers=2,
+            use_residual=False,
+        )
+
+        workers, _ = tf.test.create_local_cluster(1, 0)
+        worker = workers[0]
+
+        # pylint: disable=line-too-long
+        expected_var_names = [
+            "dynamic_seq2seq/encoder/embedding_encoder:0",
+            "dynamic_seq2seq/decoder/embedding_decoder:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/memory_layer/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/attention_layer/kernel:0",
+            "dynamic_seq2seq/decoder/output_projection/kernel:0",
+        ]
+        # pylint: enable=line-too-long
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                train_m = self._createTestTrainModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+
+                m_vars = tf.trainable_variables()
+                self._assertModelVariableNames(
+                    expected_var_names,
+                    [v.name for v in m_vars],
+                    "AttentionMechanismLuong",
+                )
+
+                with tf.variable_scope("dynamic_seq2seq", reuse=True):
+                    # pylint: disable=line-too-long
+                    last_enc_weight = tf.get_variable(
+                        "encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel"
+                    )
+                    last_dec_weight = tf.get_variable(
+                        "decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel"
+                    )
+                    att_layer_weight = tf.get_variable(
+                        "decoder/attention/attention_layer/kernel"
+                    )
+                    # pylint: enable=line-too-long
+                self._assertTrainStepsLoss(
+                    train_m, sess, "AttentionMechanismLuong")
+                self._assertModelVariable(
+                    last_enc_weight, sess, "AttentionMechanismLuong/last_enc_weight"
+                )
+                self._assertModelVariable(
+                    last_dec_weight, sess, "AttentionMechanismLuong/last_dec_weight"
+                )
+                self._assertModelVariable(
+                    att_layer_weight, sess, "AttentionMechanismLuong/att_layer_weight"
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                eval_m = self._createTestEvalModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertEvalLossAndPredictCount(
+                    eval_m, sess, "AttentionMechanismLuong"
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                infer_m = self._createTestInferModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertInferLogits(
+                    infer_m, sess, "AttentionMechanismLuong")
+
+    def testAttentionMechanismScaledLuong(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            attention="scaled_luong",
+            attention_architecture="standard",
+            num_layers=2,
+            use_residual=False,
+        )
+
+        workers, _ = tf.test.create_local_cluster(1, 0)
+        worker = workers[0]
+
+        # pylint: disable=line-too-long
+        expected_var_names = [
+            "dynamic_seq2seq/encoder/embedding_encoder:0",
+            "dynamic_seq2seq/decoder/embedding_decoder:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/memory_layer/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0",
+            "dynamic_seq2seq/decoder/attention/attention_layer/kernel:0",
+            "dynamic_seq2seq/decoder/output_projection/kernel:0",
+        ]
+        # pylint: enable=line-too-long
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                train_m = self._createTestTrainModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+
+                m_vars = tf.trainable_variables()
+                self._assertModelVariableNames(
+                    expected_var_names,
+                    [v.name for v in m_vars],
+                    "AttentionMechanismScaledLuong",
+                )
+
+                with tf.variable_scope("dynamic_seq2seq", reuse=True):
+                    # pylint: disable=line-too-long
+                    last_enc_weight = tf.get_variable(
+                        "encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel"
+                    )
+                    last_dec_weight = tf.get_variable(
+                        "decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel"
+                    )
+                    att_layer_weight = tf.get_variable(
+                        "decoder/attention/attention_layer/kernel"
+                    )
+                    # pylint: enable=line-too-long
+
+                self._assertTrainStepsLoss(
+                    train_m, sess, "AttentionMechanismScaledLuong"
+                )
+                self._assertModelVariable(
+                    last_enc_weight,
+                    sess,
+                    "AttentionMechanismScaledLuong/last_enc_weight",
+                )
+                self._assertModelVariable(
+                    last_dec_weight,
+                    sess,
+                    "AttentionMechanismScaledLuong/last_dec_weight",
+                )
+                self._assertModelVariable(
+                    att_layer_weight,
+                    sess,
+                    "AttentionMechanismScaledLuong/att_layer_weight",
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                eval_m = self._createTestEvalModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertEvalLossAndPredictCount(
+                    eval_m, sess, "AttentionMechanismScaledLuong"
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                infer_m = self._createTestInferModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertInferLogits(
+                    infer_m, sess, "AttentionMechanismScaledLuong")
+
+    def testAttentionMechanismBahdanau(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            attention="bahdanau",
+            attention_architecture="standard",
+            num_layers=2,
+            use_residual=False,
+        )
+
+        workers, _ = tf.test.create_local_cluster(1, 0)
+        worker = workers[0]
+
+        # pylint: disable=line-too-long
+        expected_var_names = [
+            "dynamic_seq2seq/encoder/embedding_encoder:0",
+            "dynamic_seq2seq/decoder/embedding_decoder:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/memory_layer/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/bahdanau_attention/query_layer/kernel:0",
+            "dynamic_seq2seq/decoder/attention/bahdanau_attention/attention_v:0",
+            "dynamic_seq2seq/decoder/attention/attention_layer/kernel:0",
+            "dynamic_seq2seq/decoder/output_projection/kernel:0",
+        ]
+        # pylint: enable=line-too-long
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                train_m = self._createTestTrainModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+
+                m_vars = tf.trainable_variables()
+                self._assertModelVariableNames(
+                    expected_var_names,
+                    [v.name for v in m_vars],
+                    "AttentionMechanismBahdanau",
+                )
+
+                with tf.variable_scope("dynamic_seq2seq", reuse=True):
+                    # pylint: disable=line-too-long
+                    last_enc_weight = tf.get_variable(
+                        "encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel"
+                    )
+                    last_dec_weight = tf.get_variable(
+                        "decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel"
+                    )
+                    att_layer_weight = tf.get_variable(
+                        "decoder/attention/attention_layer/kernel"
+                    )
+                    # pylint: enable=line-too-long
+                self._assertTrainStepsLoss(
+                    train_m, sess, "AttentionMechanismBahdanau")
+                self._assertModelVariable(
+                    last_enc_weight, sess, "AttentionMechanismBahdanau/last_enc_weight"
+                )
+                self._assertModelVariable(
+                    last_dec_weight, sess, "AttentionMechanismBahdanau/last_dec_weight"
+                )
+                self._assertModelVariable(
+                    att_layer_weight,
+                    sess,
+                    "AttentionMechanismBahdanau/att_layer_weight",
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                eval_m = self._createTestEvalModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertEvalLossAndPredictCount(
+                    eval_m, sess, "AttentionMechanismBahdanau"
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                infer_m = self._createTestInferModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertInferLogits(
+                    infer_m, sess, "AttentionMechanismBahdanau")
+
+    def testAttentionMechanismNormedBahdanau(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            attention="normed_bahdanau",
+            attention_architecture="standard",
+            num_layers=2,
+            use_residual=False,
+        )
+
+        workers, _ = tf.test.create_local_cluster(1, 0)
+        worker = workers[0]
+
+        # pylint: disable=line-too-long
+        expected_var_names = [
+            "dynamic_seq2seq/encoder/embedding_encoder:0",
+            "dynamic_seq2seq/decoder/embedding_decoder:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/memory_layer/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/bahdanau_attention/query_layer/kernel:0",
+            "dynamic_seq2seq/decoder/attention/bahdanau_attention/attention_v:0",
+            "dynamic_seq2seq/decoder/attention/bahdanau_attention/attention_g:0",
+            "dynamic_seq2seq/decoder/attention/bahdanau_attention/attention_b:0",
+            "dynamic_seq2seq/decoder/attention/attention_layer/kernel:0",
+            "dynamic_seq2seq/decoder/output_projection/kernel:0",
+        ]
+        # pylint: enable=line-too-long
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                train_m = self._createTestTrainModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+
+                m_vars = tf.trainable_variables()
+                self._assertModelVariableNames(
+                    expected_var_names,
+                    [v.name for v in m_vars],
+                    "AttentionMechanismNormedBahdanau",
+                )
+
+                with tf.variable_scope("dynamic_seq2seq", reuse=True):
+                    # pylint: disable=line-too-long
+                    last_enc_weight = tf.get_variable(
+                        "encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel"
+                    )
+                    last_dec_weight = tf.get_variable(
+                        "decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel"
+                    )
+                    att_layer_weight = tf.get_variable(
+                        "decoder/attention/attention_layer/kernel"
+                    )
+                    # pylint: enable=line-too-long
+                self._assertTrainStepsLoss(
+                    train_m, sess, "AttentionMechanismNormedBahdanau"
+                )
+                self._assertModelVariable(
+                    last_enc_weight,
+                    sess,
+                    "AttentionMechanismNormedBahdanau/last_enc_weight",
+                )
+                self._assertModelVariable(
+                    last_dec_weight,
+                    sess,
+                    "AttentionMechanismNormedBahdanau/last_dec_weight",
+                )
+                self._assertModelVariable(
+                    att_layer_weight,
+                    sess,
+                    "AttentionMechanismNormedBahdanau/att_layer_weight",
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                eval_m = self._createTestEvalModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertEvalLossAndPredictCount(
+                    eval_m, sess, "AttentionMechanismNormedBahdanau"
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                infer_m = self._createTestInferModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertInferLogits(
+                    infer_m, sess, "AttentionMechanismNormedBahdanau"
+                )
+
+    # Test encoder vs. attention (all use residual):
+    # uni encoder, standard attention
+    def testUniEncoderStandardAttentionArchitecture(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=4,
+            attention="scaled_luong",
+            attention_architecture="standard",
+        )
+
+        workers, _ = tf.test.create_local_cluster(1, 0)
+        worker = workers[0]
+
+        # pylint: disable=line-too-long
+        expected_var_names = [
+            "dynamic_seq2seq/encoder/embedding_encoder:0",
+            "dynamic_seq2seq/decoder/embedding_decoder:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_3/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/memory_layer/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/attention/multi_rnn_cell/cell_3/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/attention/luong_attention/attention_g:0",
+            "dynamic_seq2seq/decoder/attention/attention_layer/kernel:0",
+            "dynamic_seq2seq/decoder/output_projection/kernel:0",
+        ]
+        # pylint: enable=line-too-long
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                train_m = self._createTestTrainModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+
+                m_vars = tf.trainable_variables()
+                self._assertModelVariableNames(
+                    expected_var_names,
+                    [v.name for v in m_vars],
+                    "UniEncoderStandardAttentionArchitecture",
+                )
+                with tf.variable_scope("dynamic_seq2seq", reuse=True):
+                    last_enc_weight = tf.get_variable(
+                        "encoder/rnn/multi_rnn_cell/cell_3/basic_lstm_cell/kernel"
+                    )
+                    last_dec_weight = tf.get_variable(
+                        "decoder/attention/multi_rnn_cell/cell_3/basic_lstm_cell/kernel"
+                    )
+                    mem_layer_weight = tf.get_variable(
+                        "decoder/memory_layer/kernel")
+                self._assertTrainStepsLoss(
+                    train_m, sess, "UniEncoderStandardAttentionArchitecture"
+                )
+                self._assertModelVariable(
+                    last_enc_weight,
+                    sess,
+                    "UniEncoderStandardAttentionArchitecture/last_enc_weight",
+                )
+                self._assertModelVariable(
+                    last_dec_weight,
+                    sess,
+                    "UniEncoderStandardAttentionArchitecture/last_dec_weight",
+                )
+                self._assertModelVariable(
+                    mem_layer_weight,
+                    sess,
+                    "UniEncoderStandardAttentionArchitecture/mem_layer_weight",
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                eval_m = self._createTestEvalModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertEvalLossAndPredictCount(
+                    eval_m, sess, "UniEncoderStandardAttentionArchitecture"
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                infer_m = self._createTestInferModel(
+                    attention_model.AttentionModel, hparams, sess
+                )
+                self._assertInferLogits(
+                    infer_m, sess, "UniEncoderStandardAttentionArchitecture"
+                )
+
+    # Test gnmt model.
+    def _testGNMTModel(self, architecture):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="gnmt",
+            num_layers=4,
+            attention="scaled_luong",
+            attention_architecture=architecture,
+        )
+
+        workers, _ = tf.test.create_local_cluster(1, 0)
+        worker = workers[0]
+
+        # pylint: disable=line-too-long
+        expected_var_names = [
+            "dynamic_seq2seq/encoder/embedding_encoder:0",
+            "dynamic_seq2seq/decoder/embedding_decoder:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/fw/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/bidirectional_rnn/bw/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/memory_layer/kernel:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_0_attention/attention/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_0_attention/attention/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_0_attention/attention/luong_attention/attention_g:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_1/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_2/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel:0",
+            "dynamic_seq2seq/decoder/multi_rnn_cell/cell_3/basic_lstm_cell/bias:0",
+            "dynamic_seq2seq/decoder/output_projection/kernel:0",
+        ]
+        # pylint: enable=line-too-long
+
+        test_prefix = "GNMTModel_%s" % architecture
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                train_m = self._createTestTrainModel(
+                    gnmt_model.GNMTModel, hparams, sess
+                )
+
+                m_vars = tf.trainable_variables()
+                self._assertModelVariableNames(
+                    expected_var_names, [v.name for v in m_vars], test_prefix
+                )
+                with tf.variable_scope("dynamic_seq2seq", reuse=True):
+                    last_enc_weight = tf.get_variable(
+                        "encoder/rnn/multi_rnn_cell/cell_2/basic_lstm_cell/kernel"
+                    )
+                    last_dec_weight = tf.get_variable(
+                        "decoder/multi_rnn_cell/cell_3/basic_lstm_cell/kernel"
+                    )
+                    mem_layer_weight = tf.get_variable(
+                        "decoder/memory_layer/kernel")
+                self._assertTrainStepsLoss(train_m, sess, test_prefix)
+
+                self._assertModelVariable(
+                    last_enc_weight, sess, "%s/last_enc_weight" % test_prefix
+                )
+                self._assertModelVariable(
+                    last_dec_weight, sess, "%s/last_dec_weight" % test_prefix
+                )
+                self._assertModelVariable(
+                    mem_layer_weight, sess, "%s/mem_layer_weight" % test_prefix
+                )
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                eval_m = self._createTestEvalModel(
+                    gnmt_model.GNMTModel, hparams, sess)
+                self._assertEvalLossAndPredictCount(eval_m, sess, test_prefix)
+
+        with tf.Graph().as_default():
+            with tf.Session(worker.target, config=self._get_session_config()) as sess:
+                infer_m = self._createTestInferModel(
+                    gnmt_model.GNMTModel, hparams, sess
+                )
+                self._assertInferLogits(infer_m, sess, test_prefix)
+
+    def testGNMTModel(self):
+        self._testGNMTModel("gnmt")
+
+    def testGNMTModelV2(self):
+        self._testGNMTModel("gnmt_v2")
+
+    # Test beam search.
+    def testBeamSearchBasicModel(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=1,
+            attention="",
+            attention_architecture="",
+            use_residual=False,
+        )
+        hparams.beam_width = 3
+        hparams.infer_mode = "beam_search"
+        hparams.tgt_max_len_infer = 4
+        assert_top_k_sentence = 3
+
+        with self.test_session() as sess:
+            infer_m = self._createTestInferModel(
+                model.Model, hparams, sess, True)
+            self._assertBeamSearchOutputs(
+                infer_m, sess, assert_top_k_sentence, "BeamSearchBasicModel"
+            )
+
+    def testBeamSearchAttentionModel(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            attention="scaled_luong",
+            attention_architecture="standard",
+            num_layers=2,
+            use_residual=False,
+        )
+        hparams.beam_width = 3
+        hparams.infer_mode = "beam_search"
+        hparams.tgt_max_len_infer = 4
+        assert_top_k_sentence = 2
+
+        with self.test_session() as sess:
+            infer_m = self._createTestInferModel(
+                attention_model.AttentionModel, hparams, sess, True
+            )
+            self._assertBeamSearchOutputs(
+                infer_m, sess, assert_top_k_sentence, "BeamSearchAttentionModel"
+            )
+
+    def testBeamSearchGNMTModel(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="gnmt",
+            num_layers=4,
+            attention="scaled_luong",
+            attention_architecture="gnmt",
+        )
+        hparams.beam_width = 3
+        hparams.infer_mode = "beam_search"
+        hparams.tgt_max_len_infer = 4
+        assert_top_k_sentence = 1
+
+        with self.test_session() as sess:
+            infer_m = self._createTestInferModel(
+                gnmt_model.GNMTModel, hparams, sess, True
+            )
+            self._assertBeamSearchOutputs(
+                infer_m, sess, assert_top_k_sentence, "BeamSearchGNMTModel"
+            )
+
+    def testInitializerGlorotNormal(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=1,
+            attention="",
+            attention_architecture="",
+            use_residual=False,
+            init_op="glorot_normal",
+        )
+
+        with self.test_session() as sess:
+            train_m = self._createTestTrainModel(model.Model, hparams, sess)
+            self._assertTrainStepsLoss(
+                train_m, sess, "InitializerGlorotNormal")
+
+    def testInitializerGlorotUniform(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="uni",
+            num_layers=1,
+            attention="",
+            attention_architecture="",
+            use_residual=False,
+            init_op="glorot_uniform",
+        )
+
+        with self.test_session() as sess:
+            train_m = self._createTestTrainModel(model.Model, hparams, sess)
+            self._assertTrainStepsLoss(
+                train_m, sess, "InitializerGlorotUniform")
+
+    def testSampledSoftmaxLoss(self):
+        hparams = common_test_utils.create_test_hparams(
+            encoder_type="gnmt",
+            num_layers=4,
+            attention="scaled_luong",
+            attention_architecture="gnmt",
+        )
+        hparams.num_sampled_softmax = 3
+
+        with self.test_session() as sess:
+            train_m = self._createTestTrainModel(
+                gnmt_model.GNMTModel, hparams, sess)
+            self._assertTrainStepsLoss(train_m, sess, "SampledSoftmaxLoss")
+
+
+if __name__ == "__main__":
+    tf.test.main()
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/nmt.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/nmt.py
index ce9e683e14..d5c05bd64f 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/nmt.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/nmt.py
@@ -35,85 +35,160 @@
 
 FLAGS = None
 
-INFERENCE_KEYS = ["src_max_len_infer", "tgt_max_len_infer", "subword_option",
-                  "infer_batch_size", "beam_width",
-                  "length_penalty_weight", "sampling_temperature",
-                  "num_translations_per_input", "infer_mode"]
+INFERENCE_KEYS = [
+    "src_max_len_infer",
+    "tgt_max_len_infer",
+    "subword_option",
+    "infer_batch_size",
+    "beam_width",
+    "length_penalty_weight",
+    "sampling_temperature",
+    "num_translations_per_input",
+    "infer_mode",
+]
 
 
 def add_arguments(parser):
-  """Build ArgumentParser."""
-  parser.register("type", "bool", lambda v: v.lower() == "true")
-
-  # network
-  parser.add_argument("--num_units", type=int, default=32, help="Network size.")
-  parser.add_argument("--num_layers", type=int, default=2,
-                      help="Network depth.")
-  parser.add_argument("--num_encoder_layers", type=int, default=None,
-                      help="Encoder depth, equal to num_layers if None.")
-  parser.add_argument("--num_decoder_layers", type=int, default=None,
-                      help="Decoder depth, equal to num_layers if None.")
-  parser.add_argument("--encoder_type", type=str, default="uni", help="""\
+    """Build ArgumentParser."""
+    parser.register("type", "bool", lambda v: v.lower() == "true")
+
+    # network
+    parser.add_argument(
+        "--num_units",
+        type=int,
+        default=32,
+        help="Network size.")
+    parser.add_argument(
+        "--num_layers",
+        type=int,
+        default=2,
+        help="Network depth.")
+    parser.add_argument(
+        "--num_encoder_layers",
+        type=int,
+        default=None,
+        help="Encoder depth, equal to num_layers if None.",
+    )
+    parser.add_argument(
+        "--num_decoder_layers",
+        type=int,
+        default=None,
+        help="Decoder depth, equal to num_layers if None.",
+    )
+    parser.add_argument(
+        "--encoder_type",
+        type=str,
+        default="uni",
+        help="""\
       uni | bi | gnmt.
       For bi, we build num_encoder_layers/2 bi-directional layers.
       For gnmt, we build 1 bi-directional layer, and (num_encoder_layers - 1)
         uni-directional layers.\
-      """)
-  parser.add_argument("--residual", type="bool", nargs="?", const=True,
-                      default=False,
-                      help="Whether to add residual connections.")
-  parser.add_argument("--time_major", type="bool", nargs="?", const=True,
-                      default=True,
-                      help="Whether to use time-major mode for dynamic RNN.")
-  parser.add_argument("--num_embeddings_partitions", type=int, default=0,
-                      help="Number of partitions for embedding vars.")
-
-  # attention mechanisms
-  parser.add_argument("--attention", type=str, default="", help="""\
+      """,
+    )
+    parser.add_argument(
+        "--residual",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=False,
+        help="Whether to add residual connections.",
+    )
+    parser.add_argument(
+        "--time_major",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=True,
+        help="Whether to use time-major mode for dynamic RNN.",
+    )
+    parser.add_argument(
+        "--num_embeddings_partitions",
+        type=int,
+        default=0,
+        help="Number of partitions for embedding vars.",
+    )
+
+    # attention mechanisms
+    parser.add_argument(
+        "--attention",
+        type=str,
+        default="",
+        help="""\
       luong | scaled_luong | bahdanau | normed_bahdanau or set to "" for no
       attention\
-      """)
-  parser.add_argument(
-      "--attention_architecture",
-      type=str,
-      default="standard",
-      help="""\
+      """,
+    )
+    parser.add_argument(
+        "--attention_architecture",
+        type=str,
+        default="standard",
+        help="""\
       standard | gnmt | gnmt_v2.
       standard: use top layer to compute attention.
       gnmt: GNMT style of computing attention, use previous bottom layer to
           compute attention.
       gnmt_v2: similar to gnmt, but use current bottom layer to compute
           attention.\
-      """)
-  parser.add_argument(
-      "--output_attention", type="bool", nargs="?", const=True,
-      default=True,
-      help="""\
+      """,
+    )
+    parser.add_argument(
+        "--output_attention",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=True,
+        help="""\
       Only used in standard attention_architecture. Whether use attention as
       the cell output at each timestep.
       .\
-      """)
-  parser.add_argument(
-      "--pass_hidden_state", type="bool", nargs="?", const=True,
-      default=True,
-      help="""\
+      """,
+    )
+    parser.add_argument(
+        "--pass_hidden_state",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=True,
+        help="""\
       Whether to pass encoder's hidden state to decoder when using an attention
       based model.\
-      """)
-
-  # optimizer
-  parser.add_argument("--optimizer", type=str, default="sgd", help="sgd | adam")
-  parser.add_argument("--learning_rate", type=float, default=1.0,
-                      help="Learning rate. Adam: 0.001 | 0.0001")
-  parser.add_argument("--warmup_steps", type=int, default=0,
-                      help="How many steps we inverse-decay learning.")
-  parser.add_argument("--warmup_scheme", type=str, default="t2t", help="""\
+      """,
+    )
+
+    # optimizer
+    parser.add_argument(
+        "--optimizer",
+        type=str,
+        default="sgd",
+        help="sgd | adam")
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1.0,
+        help="Learning rate. Adam: 0.001 | 0.0001",
+    )
+    parser.add_argument(
+        "--warmup_steps",
+        type=int,
+        default=0,
+        help="How many steps we inverse-decay learning.",
+    )
+    parser.add_argument(
+        "--warmup_scheme",
+        type=str,
+        default="t2t",
+        help="""\
       How to warmup learning rates. Options include:
         t2t: Tensor2Tensor's way, start with lr 100 times smaller, then
              exponentiate until the specified lr.\
-      """)
-  parser.add_argument(
-      "--decay_scheme", type=str, default="", help="""\
+      """,
+    )
+    parser.add_argument(
+        "--decay_scheme",
+        type=str,
+        default="",
+        help="""\
       How we decay learning rate. Options include:
         luong234: after 2/3 num train steps, we start halving the learning rate
           for 4 times before finishing.
@@ -121,597 +196,862 @@ def add_arguments(parser):
           for 5 times before finishing.\
         luong10: after 1/2 num train steps, we start halving the learning rate
           for 10 times before finishing.\
-      """)
-
-  parser.add_argument(
-      "--num_train_steps", type=int, default=12000, help="Num steps to train.")
-  parser.add_argument("--colocate_gradients_with_ops", type="bool", nargs="?",
-                      const=True,
-                      default=True,
-                      help=("Whether try colocating gradients with "
-                            "corresponding op"))
-
-  # initializer
-  parser.add_argument("--init_op", type=str, default="uniform",
-                      help="uniform | glorot_normal | glorot_uniform")
-  parser.add_argument("--init_weight", type=float, default=0.1,
-                      help=("for uniform init_op, initialize weights "
-                            "between [-this, this]."))
-
-  # data
-  parser.add_argument("--src", type=str, default=None,
-                      help="Source suffix, e.g., en.")
-  parser.add_argument("--tgt", type=str, default=None,
-                      help="Target suffix, e.g., de.")
-  parser.add_argument("--train_prefix", type=str, default=None,
-                      help="Train prefix, expect files with src/tgt suffixes.")
-  parser.add_argument("--dev_prefix", type=str, default=None,
-                      help="Dev prefix, expect files with src/tgt suffixes.")
-  parser.add_argument("--test_prefix", type=str, default=None,
-                      help="Test prefix, expect files with src/tgt suffixes.")
-  parser.add_argument("--out_dir", type=str, default=None,
-                      help="Store log/model files.")
-
-  # Vocab
-  parser.add_argument("--vocab_prefix", type=str, default=None, help="""\
+      """,
+    )
+
+    parser.add_argument(
+        "--num_train_steps", type=int, default=12000, help="Num steps to train."
+    )
+    parser.add_argument(
+        "--colocate_gradients_with_ops",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=True,
+        help=("Whether try colocating gradients with " "corresponding op"),
+    )
+
+    # initializer
+    parser.add_argument(
+        "--init_op",
+        type=str,
+        default="uniform",
+        help="uniform | glorot_normal | glorot_uniform",
+    )
+    parser.add_argument(
+        "--init_weight",
+        type=float,
+        default=0.1,
+        help=(
+            "for uniform init_op, initialize weights "
+            "between [-this, this]."),
+    )
+
+    # data
+    parser.add_argument(
+        "--src", type=str, default=None, help="Source suffix, e.g., en."
+    )
+    parser.add_argument(
+        "--tgt", type=str, default=None, help="Target suffix, e.g., de."
+    )
+    parser.add_argument(
+        "--train_prefix",
+        type=str,
+        default=None,
+        help="Train prefix, expect files with src/tgt suffixes.",
+    )
+    parser.add_argument(
+        "--dev_prefix",
+        type=str,
+        default=None,
+        help="Dev prefix, expect files with src/tgt suffixes.",
+    )
+    parser.add_argument(
+        "--test_prefix",
+        type=str,
+        default=None,
+        help="Test prefix, expect files with src/tgt suffixes.",
+    )
+    parser.add_argument(
+        "--out_dir", type=str, default=None, help="Store log/model files."
+    )
+
+    # Vocab
+    parser.add_argument(
+        "--vocab_prefix",
+        type=str,
+        default=None,
+        help="""\
       Vocab prefix, expect files with src/tgt suffixes.\
-      """)
-  parser.add_argument("--embed_prefix", type=str, default=None, help="""\
+      """,
+    )
+    parser.add_argument(
+        "--embed_prefix",
+        type=str,
+        default=None,
+        help="""\
       Pretrained embedding prefix, expect files with src/tgt suffixes.
       The embedding files should be Glove formated txt files.\
-      """)
-  parser.add_argument("--sos", type=str, default="<s>",
-                      help="Start-of-sentence symbol.")
-  parser.add_argument("--eos", type=str, default="</s>",
-                      help="End-of-sentence symbol.")
-  parser.add_argument("--share_vocab", type="bool", nargs="?", const=True,
-                      default=False,
-                      help="""\
+      """,
+    )
+    parser.add_argument(
+        "--sos", type=str, default="<s>", help="Start-of-sentence symbol."
+    )
+    parser.add_argument(
+        "--eos", type=str, default="</s>", help="End-of-sentence symbol."
+    )
+    parser.add_argument(
+        "--share_vocab",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=False,
+        help="""\
       Whether to use the source vocab and embeddings for both source and
       target.\
-      """)
-  parser.add_argument("--check_special_token", type="bool", default=True,
-                      help="""\
+      """,
+    )
+    parser.add_argument(
+        "--check_special_token",
+        type="bool",
+        default=True,
+        help="""\
                       Whether check special sos, eos, unk tokens exist in the
                       vocab files.\
-                      """)
-
-  # Sequence lengths
-  parser.add_argument("--src_max_len", type=int, default=50,
-                      help="Max length of src sequences during training.")
-  parser.add_argument("--tgt_max_len", type=int, default=50,
-                      help="Max length of tgt sequences during training.")
-  parser.add_argument("--src_max_len_infer", type=int, default=None,
-                      help="Max length of src sequences during inference.")
-  parser.add_argument("--tgt_max_len_infer", type=int, default=None,
-                      help="""\
+                      """,
+    )
+
+    # Sequence lengths
+    parser.add_argument(
+        "--src_max_len",
+        type=int,
+        default=50,
+        help="Max length of src sequences during training.",
+    )
+    parser.add_argument(
+        "--tgt_max_len",
+        type=int,
+        default=50,
+        help="Max length of tgt sequences during training.",
+    )
+    parser.add_argument(
+        "--src_max_len_infer",
+        type=int,
+        default=None,
+        help="Max length of src sequences during inference.",
+    )
+    parser.add_argument(
+        "--tgt_max_len_infer",
+        type=int,
+        default=None,
+        help="""\
       Max length of tgt sequences during inference.  Also use to restrict the
       maximum decoding length.\
-      """)
-
-  # Default settings works well (rarely need to change)
-  parser.add_argument("--unit_type", type=str, default="lstm",
-                      help="lstm | gru | layer_norm_lstm | nas")
-  parser.add_argument("--forget_bias", type=float, default=1.0,
-                      help="Forget bias for BasicLSTMCell.")
-  parser.add_argument("--dropout", type=float, default=0.2,
-                      help="Dropout rate (not keep_prob)")
-  parser.add_argument("--max_gradient_norm", type=float, default=5.0,
-                      help="Clip gradients to this norm.")
-  parser.add_argument("--batch_size", type=int, default=128, help="Batch size.")
-
-  parser.add_argument("--steps_per_stats", type=int, default=100,
-                      help=("How many training steps to do per stats logging."
-                            "Save checkpoint every 10x steps_per_stats"))
-  parser.add_argument("--max_train", type=int, default=0,
-                      help="Limit on the size of training data (0: no limit).")
-  parser.add_argument("--num_buckets", type=int, default=5,
-                      help="Put data into similar-length buckets.")
-  parser.add_argument("--num_sampled_softmax", type=int, default=0,
-                      help=("Use sampled_softmax_loss if > 0."
-                            "Otherwise, use full softmax loss."))
-
-  # SPM
-  parser.add_argument("--subword_option", type=str, default="",
-                      choices=["", "bpe", "spm"],
-                      help="""\
+      """,
+    )
+
+    # Default settings works well (rarely need to change)
+    parser.add_argument(
+        "--unit_type",
+        type=str,
+        default="lstm",
+        help="lstm | gru | layer_norm_lstm | nas",
+    )
+    parser.add_argument(
+        "--forget_bias", type=float, default=1.0, help="Forget bias for BasicLSTMCell."
+    )
+    parser.add_argument(
+        "--dropout", type=float, default=0.2, help="Dropout rate (not keep_prob)"
+    )
+    parser.add_argument(
+        "--max_gradient_norm",
+        type=float,
+        default=5.0,
+        help="Clip gradients to this norm.",
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=128,
+        help="Batch size.")
+
+    parser.add_argument(
+        "--steps_per_stats",
+        type=int,
+        default=100,
+        help=(
+            "How many training steps to do per stats logging."
+            "Save checkpoint every 10x steps_per_stats"
+        ),
+    )
+    parser.add_argument(
+        "--max_train",
+        type=int,
+        default=0,
+        help="Limit on the size of training data (0: no limit).",
+    )
+    parser.add_argument(
+        "--num_buckets",
+        type=int,
+        default=5,
+        help="Put data into similar-length buckets.",
+    )
+    parser.add_argument(
+        "--num_sampled_softmax",
+        type=int,
+        default=0,
+        help=(
+            "Use sampled_softmax_loss if > 0."
+            "Otherwise, use full softmax loss."),
+    )
+
+    # SPM
+    parser.add_argument(
+        "--subword_option",
+        type=str,
+        default="",
+        choices=["", "bpe", "spm"],
+        help="""\
                       Set to bpe or spm to activate subword desegmentation.\
-                      """)
-
-  # Experimental encoding feature.
-  parser.add_argument("--use_char_encode", type="bool", default=False,
-                      help="""\
+                      """,
+    )
+
+    # Experimental encoding feature.
+    parser.add_argument(
+        "--use_char_encode",
+        type="bool",
+        default=False,
+        help="""\
                       Whether to split each word or bpe into character, and then
                       generate the word-level representation from the character
                       reprentation.
-                      """)
-
-  # Misc
-  parser.add_argument("--num_gpus", type=int, default=1,
-                      help="Number of gpus in each worker.")
-  parser.add_argument("--log_device_placement", type="bool", nargs="?",
-                      const=True, default=False, help="Debug GPU allocation.")
-  parser.add_argument("--metrics", type=str, default="bleu",
-                      help=("Comma-separated list of evaluations "
-                            "metrics (bleu,rouge,accuracy)"))
-  parser.add_argument("--steps_per_external_eval", type=int, default=None,
-                      help="""\
+                      """,
+    )
+
+    # Misc
+    parser.add_argument(
+        "--num_gpus", type=int, default=1, help="Number of gpus in each worker."
+    )
+    parser.add_argument(
+        "--log_device_placement",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=False,
+        help="Debug GPU allocation.",
+    )
+    parser.add_argument(
+        "--metrics",
+        type=str,
+        default="bleu",
+        help=(
+            "Comma-separated list of evaluations "
+            "metrics (bleu,rouge,accuracy)"),
+    )
+    parser.add_argument(
+        "--steps_per_external_eval",
+        type=int,
+        default=None,
+        help="""\
       How many training steps to do per external evaluation.  Automatically set
       based on data if None.\
-      """)
-  parser.add_argument("--scope", type=str, default=None,
-                      help="scope to put variables under")
-  parser.add_argument("--hparams_path", type=str, default=None,
-                      help=("Path to standard hparams json file that overrides"
-                            "hparams values from FLAGS."))
-  parser.add_argument("--random_seed", type=int, default=None,
-                      help="Random seed (>0, set a specific seed).")
-  parser.add_argument("--override_loaded_hparams", type="bool", nargs="?",
-                      const=True, default=False,
-                      help="Override loaded hparams with values specified")
-  parser.add_argument("--num_keep_ckpts", type=int, default=5,
-                      help="Max number of checkpoints to keep.")
-  parser.add_argument("--avg_ckpts", type="bool", nargs="?",
-                      const=True, default=False, help=("""\
+      """,
+    )
+    parser.add_argument(
+        "--scope", type=str, default=None, help="scope to put variables under"
+    )
+    parser.add_argument(
+        "--hparams_path",
+        type=str,
+        default=None,
+        help=(
+            "Path to standard hparams json file that overrides"
+            "hparams values from FLAGS."
+        ),
+    )
+    parser.add_argument(
+        "--random_seed",
+        type=int,
+        default=None,
+        help="Random seed (>0, set a specific seed).",
+    )
+    parser.add_argument(
+        "--override_loaded_hparams",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=False,
+        help="Override loaded hparams with values specified",
+    )
+    parser.add_argument(
+        "--num_keep_ckpts",
+        type=int,
+        default=5,
+        help="Max number of checkpoints to keep.",
+    )
+    parser.add_argument(
+        "--avg_ckpts",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=False,
+        help=(
+            """\
                       Average the last N checkpoints for external evaluation.
                       N can be controlled by setting --num_keep_ckpts.\
-                      """))
-  parser.add_argument("--language_model", type="bool", nargs="?",
-                      const=True, default=False,
-                      help="True to train a language model, ignoring encoder")
-
-  # Inference
-  parser.add_argument("--ckpt", type=str, default="",
-                      help="Checkpoint file to load a model for inference.")
-  parser.add_argument("--inference_input_file", type=str, default=None,
-                      help="Set to the text to decode.")
-  parser.add_argument("--inference_list", type=str, default=None,
-                      help=("A comma-separated list of sentence indices "
-                            "(0-based) to decode."))
-  parser.add_argument("--infer_batch_size", type=int, default=None,
-                      help="Batch size for inference mode.")
-  parser.add_argument("--inference_output_file", type=str, default=None,
-                      help="Output file to store decoding results.")
-  parser.add_argument("--inference_ref_file", type=str, default=None,
-                      help=("""\
+                      """
+        ),
+    )
+    parser.add_argument(
+        "--language_model",
+        type="bool",
+        nargs="?",
+        const=True,
+        default=False,
+        help="True to train a language model, ignoring encoder",
+    )
+
+    # Inference
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="",
+        help="Checkpoint file to load a model for inference.",
+    )
+    parser.add_argument(
+        "--inference_input_file",
+        type=str,
+        default=None,
+        help="Set to the text to decode.",
+    )
+    parser.add_argument(
+        "--inference_list",
+        type=str,
+        default=None,
+        help=(
+            "A comma-separated list of sentence indices "
+            "(0-based) to decode."),
+    )
+    parser.add_argument(
+        "--infer_batch_size",
+        type=int,
+        default=None,
+        help="Batch size for inference mode.",
+    )
+    parser.add_argument(
+        "--inference_output_file",
+        type=str,
+        default=None,
+        help="Output file to store decoding results.",
+    )
+    parser.add_argument(
+        "--inference_ref_file",
+        type=str,
+        default=None,
+        help=(
+            """\
       Reference file to compute evaluation scores (if provided).\
-      """))
-
-  # Advanced inference arguments
-  parser.add_argument("--infer_mode", type=str, default="greedy",
-                      choices=["greedy", "sample", "beam_search"],
-                      help="Which type of decoder to use during inference.")
-  parser.add_argument("--beam_width", type=int, default=0,
-                      help=("""\
+      """
+        ),
+    )
+
+    # Advanced inference arguments
+    parser.add_argument(
+        "--infer_mode",
+        type=str,
+        default="greedy",
+        choices=["greedy", "sample", "beam_search"],
+        help="Which type of decoder to use during inference.",
+    )
+    parser.add_argument(
+        "--beam_width",
+        type=int,
+        default=0,
+        help=(
+            """\
       beam width when using beam search decoder. If 0 (default), use standard
       decoder with greedy helper.\
-      """))
-  parser.add_argument("--length_penalty_weight", type=float, default=0.0,
-                      help="Length penalty for beam search.")
-  parser.add_argument("--sampling_temperature", type=float,
-                      default=0.0,
-                      help=("""\
+      """
+        ),
+    )
+    parser.add_argument(
+        "--length_penalty_weight",
+        type=float,
+        default=0.0,
+        help="Length penalty for beam search.",
+    )
+    parser.add_argument(
+        "--sampling_temperature",
+        type=float,
+        default=0.0,
+        help=(
+            """\
       Softmax sampling temperature for inference decoding, 0.0 means greedy
       decoding. This option is ignored when using beam search.\
-      """))
-  parser.add_argument("--num_translations_per_input", type=int, default=1,
-                      help=("""\
+      """
+        ),
+    )
+    parser.add_argument(
+        "--num_translations_per_input",
+        type=int,
+        default=1,
+        help=(
+            """\
       Number of translations generated for each sentence. This is only used for
       inference.\
-      """))
-
-  # Job info
-  parser.add_argument("--jobid", type=int, default=0,
-                      help="Task id of the worker.")
-  parser.add_argument("--num_workers", type=int, default=1,
-                      help="Number of workers (inference only).")
-  parser.add_argument("--num_inter_threads", type=int, default=0,
-                      help="number of inter_op_parallelism_threads")
-  parser.add_argument("--num_intra_threads", type=int, default=0,
-                      help="number of intra_op_parallelism_threads")
-  parser.add_argument("--iterations", type=int, default=1,
-                      help="number of iterations")
-  parser.add_argument("--workloadName", type=str, default="",
-                       help="name of workload")
-  parser.add_argument("--run", type=str, default='performance',
-                       help="Determine criteria run for infernece")
+      """
+        ),
+    )
+
+    # Job info
+    parser.add_argument(
+        "--jobid",
+        type=int,
+        default=0,
+        help="Task id of the worker.")
+    parser.add_argument(
+        "--num_workers", type=int, default=1, help="Number of workers (inference only)."
+    )
+    parser.add_argument(
+        "--num_inter_threads",
+        type=int,
+        default=0,
+        help="number of inter_op_parallelism_threads",
+    )
+    parser.add_argument(
+        "--num_intra_threads",
+        type=int,
+        default=0,
+        help="number of intra_op_parallelism_threads",
+    )
+    parser.add_argument(
+        "--iterations", type=int, default=1, help="number of iterations"
+    )
+    parser.add_argument(
+        "--workloadName",
+        type=str,
+        default="",
+        help="name of workload")
+    parser.add_argument(
+        "--run",
+        type=str,
+        default="performance",
+        help="Determine criteria run for infernece",
+    )
 
 
 def create_hparams(flags):
-  """Create training hparams."""
-  return tf.contrib.training.HParams(
-      # Data
-      src=flags.src,
-      tgt=flags.tgt,
-      train_prefix=flags.train_prefix,
-      dev_prefix=flags.dev_prefix,
-      test_prefix=flags.test_prefix,
-      vocab_prefix=flags.vocab_prefix,
-      embed_prefix=flags.embed_prefix,
-      out_dir=flags.out_dir,
-
-      # Networks
-      num_units=flags.num_units,
-      num_encoder_layers=(flags.num_encoder_layers or flags.num_layers),
-      num_decoder_layers=(flags.num_decoder_layers or flags.num_layers),
-      dropout=flags.dropout,
-      unit_type=flags.unit_type,
-      encoder_type=flags.encoder_type,
-      residual=flags.residual,
-      time_major=flags.time_major,
-      num_embeddings_partitions=flags.num_embeddings_partitions,
-
-      # Attention mechanisms
-      attention=flags.attention,
-      attention_architecture=flags.attention_architecture,
-      output_attention=flags.output_attention,
-      pass_hidden_state=flags.pass_hidden_state,
-
-      # Train
-      optimizer=flags.optimizer,
-      num_train_steps=flags.num_train_steps,
-      batch_size=flags.batch_size,
-      init_op=flags.init_op,
-      init_weight=flags.init_weight,
-      max_gradient_norm=flags.max_gradient_norm,
-      learning_rate=flags.learning_rate,
-      warmup_steps=flags.warmup_steps,
-      warmup_scheme=flags.warmup_scheme,
-      decay_scheme=flags.decay_scheme,
-      colocate_gradients_with_ops=flags.colocate_gradients_with_ops,
-      num_sampled_softmax=flags.num_sampled_softmax,
-
-      # Data constraints
-      num_buckets=flags.num_buckets,
-      max_train=flags.max_train,
-      src_max_len=flags.src_max_len,
-      tgt_max_len=flags.tgt_max_len,
-
-      # Inference
-      src_max_len_infer=flags.src_max_len_infer,
-      tgt_max_len_infer=flags.tgt_max_len_infer,
-      infer_batch_size=flags.infer_batch_size,
-
-      # Advanced inference arguments
-      infer_mode=flags.infer_mode,
-      beam_width=flags.beam_width,
-      length_penalty_weight=flags.length_penalty_weight,
-      sampling_temperature=flags.sampling_temperature,
-      num_translations_per_input=flags.num_translations_per_input,
-
-      # Vocab
-      sos=flags.sos if flags.sos else vocab_utils.SOS,
-      eos=flags.eos if flags.eos else vocab_utils.EOS,
-      subword_option=flags.subword_option,
-      check_special_token=flags.check_special_token,
-      use_char_encode=flags.use_char_encode,
-
-      # Misc
-      forget_bias=flags.forget_bias,
-      num_gpus=flags.num_gpus,
-      epoch_step=0,  # record where we were within an epoch.
-      steps_per_stats=flags.steps_per_stats,
-      steps_per_external_eval=flags.steps_per_external_eval,
-      share_vocab=flags.share_vocab,
-      metrics=flags.metrics.split(","),
-      log_device_placement=flags.log_device_placement,
-      random_seed=flags.random_seed,
-      override_loaded_hparams=flags.override_loaded_hparams,
-      num_keep_ckpts=flags.num_keep_ckpts,
-      avg_ckpts=flags.avg_ckpts,
-      language_model=flags.language_model,
-      num_intra_threads=flags.num_intra_threads,
-      num_inter_threads=flags.num_inter_threads,
-      iterations=flags.iterations,
-      run = flags.run,
-  )
+    """Create training hparams."""
+    return tf.contrib.training.HParams(
+        # Data
+        src=flags.src,
+        tgt=flags.tgt,
+        train_prefix=flags.train_prefix,
+        dev_prefix=flags.dev_prefix,
+        test_prefix=flags.test_prefix,
+        vocab_prefix=flags.vocab_prefix,
+        embed_prefix=flags.embed_prefix,
+        out_dir=flags.out_dir,
+        # Networks
+        num_units=flags.num_units,
+        num_encoder_layers=(flags.num_encoder_layers or flags.num_layers),
+        num_decoder_layers=(flags.num_decoder_layers or flags.num_layers),
+        dropout=flags.dropout,
+        unit_type=flags.unit_type,
+        encoder_type=flags.encoder_type,
+        residual=flags.residual,
+        time_major=flags.time_major,
+        num_embeddings_partitions=flags.num_embeddings_partitions,
+        # Attention mechanisms
+        attention=flags.attention,
+        attention_architecture=flags.attention_architecture,
+        output_attention=flags.output_attention,
+        pass_hidden_state=flags.pass_hidden_state,
+        # Train
+        optimizer=flags.optimizer,
+        num_train_steps=flags.num_train_steps,
+        batch_size=flags.batch_size,
+        init_op=flags.init_op,
+        init_weight=flags.init_weight,
+        max_gradient_norm=flags.max_gradient_norm,
+        learning_rate=flags.learning_rate,
+        warmup_steps=flags.warmup_steps,
+        warmup_scheme=flags.warmup_scheme,
+        decay_scheme=flags.decay_scheme,
+        colocate_gradients_with_ops=flags.colocate_gradients_with_ops,
+        num_sampled_softmax=flags.num_sampled_softmax,
+        # Data constraints
+        num_buckets=flags.num_buckets,
+        max_train=flags.max_train,
+        src_max_len=flags.src_max_len,
+        tgt_max_len=flags.tgt_max_len,
+        # Inference
+        src_max_len_infer=flags.src_max_len_infer,
+        tgt_max_len_infer=flags.tgt_max_len_infer,
+        infer_batch_size=flags.infer_batch_size,
+        # Advanced inference arguments
+        infer_mode=flags.infer_mode,
+        beam_width=flags.beam_width,
+        length_penalty_weight=flags.length_penalty_weight,
+        sampling_temperature=flags.sampling_temperature,
+        num_translations_per_input=flags.num_translations_per_input,
+        # Vocab
+        sos=flags.sos if flags.sos else vocab_utils.SOS,
+        eos=flags.eos if flags.eos else vocab_utils.EOS,
+        subword_option=flags.subword_option,
+        check_special_token=flags.check_special_token,
+        use_char_encode=flags.use_char_encode,
+        # Misc
+        forget_bias=flags.forget_bias,
+        num_gpus=flags.num_gpus,
+        epoch_step=0,  # record where we were within an epoch.
+        steps_per_stats=flags.steps_per_stats,
+        steps_per_external_eval=flags.steps_per_external_eval,
+        share_vocab=flags.share_vocab,
+        metrics=flags.metrics.split(","),
+        log_device_placement=flags.log_device_placement,
+        random_seed=flags.random_seed,
+        override_loaded_hparams=flags.override_loaded_hparams,
+        num_keep_ckpts=flags.num_keep_ckpts,
+        avg_ckpts=flags.avg_ckpts,
+        language_model=flags.language_model,
+        num_intra_threads=flags.num_intra_threads,
+        num_inter_threads=flags.num_inter_threads,
+        iterations=flags.iterations,
+        run=flags.run,
+    )
 
 
 def _add_argument(hparams, key, value, update=True):
-  """Add an argument to hparams; if exists, change the value if update==True."""
-  if hasattr(hparams, key):
-    if update:
-      setattr(hparams, key, value)
-  else:
-    hparams.add_hparam(key, value)
+    """Add an argument to hparams; if exists, change the value if update==True."""
+    if hasattr(hparams, key):
+        if update:
+            setattr(hparams, key, value)
+    else:
+        hparams.add_hparam(key, value)
 
 
 def extend_hparams(hparams):
-  """Add new arguments to hparams."""
-  # Sanity checks
-  if hparams.encoder_type == "bi" and hparams.num_encoder_layers % 2 != 0:
-    raise ValueError("For bi, num_encoder_layers %d should be even" %
-                     hparams.num_encoder_layers)
-  if (hparams.attention_architecture in ["gnmt"] and
-      hparams.num_encoder_layers < 2):
-    raise ValueError("For gnmt attention architecture, "
-                     "num_encoder_layers %d should be >= 2" %
-                     hparams.num_encoder_layers)
-  if hparams.subword_option and hparams.subword_option not in ["spm", "bpe"]:
-    raise ValueError("subword option must be either spm, or bpe")
-  if hparams.infer_mode == "beam_search" and hparams.beam_width <= 0:
-    raise ValueError("beam_width must greater than 0 when using beam_search"
-                     "decoder.")
-  if hparams.infer_mode == "sample" and hparams.sampling_temperature <= 0.0:
-    raise ValueError("sampling_temperature must greater than 0.0 when using"
-                     "sample decoder.")
-
-  # Different number of encoder / decoder layers
-  assert hparams.num_encoder_layers and hparams.num_decoder_layers
-  if hparams.num_encoder_layers != hparams.num_decoder_layers:
-    hparams.pass_hidden_state = False
-    utils.print_out("Num encoder layer %d is different from num decoder layer"
-                    " %d, so set pass_hidden_state to False" % (
-                        hparams.num_encoder_layers,
-                        hparams.num_decoder_layers))
-
-  # Set residual layers
-  num_encoder_residual_layers = 0
-  num_decoder_residual_layers = 0
-  if hparams.residual:
-    if hparams.num_encoder_layers > 1:
-      num_encoder_residual_layers = hparams.num_encoder_layers - 1
-    if hparams.num_decoder_layers > 1:
-      num_decoder_residual_layers = hparams.num_decoder_layers - 1
-
-    if hparams.encoder_type == "gnmt":
-      # The first unidirectional layer (after the bi-directional layer) in
-      # the GNMT encoder can't have residual connection due to the input is
-      # the concatenation of fw_cell and bw_cell's outputs.
-      num_encoder_residual_layers = hparams.num_encoder_layers - 2
-
-      # Compatible for GNMT models
-      if hparams.num_encoder_layers == hparams.num_decoder_layers:
-        num_decoder_residual_layers = num_encoder_residual_layers
-  _add_argument(hparams, "num_encoder_residual_layers",
-                num_encoder_residual_layers)
-  _add_argument(hparams, "num_decoder_residual_layers",
-                num_decoder_residual_layers)
-
-  # Language modeling
-  if getattr(hparams, "language_model", None):
-    hparams.attention = "normed_bahdanau"
-    hparams.attention_architecture = "gnmt_v2"
-    hparams.pass_hidden_state = False
-    hparams.share_vocab = True
-    hparams.src = hparams.tgt
-    utils.print_out("For language modeling, we turn off attention and "
-                    "pass_hidden_state; turn on share_vocab; set src to tgt.")
-
-  ## Vocab
-  # Get vocab file names first
-  if hparams.vocab_prefix:
-    src_vocab_file = hparams.vocab_prefix + "." + hparams.src
-    tgt_vocab_file = hparams.vocab_prefix + "." + hparams.tgt
-  else:
-    raise ValueError("hparams.vocab_prefix must be provided.")
-
-  # Source vocab
-  check_special_token = getattr(hparams, "check_special_token", True)
-  src_vocab_size, src_vocab_file = vocab_utils.check_vocab(
-      src_vocab_file,
-      hparams.out_dir,
-      check_special_token=check_special_token,
-      sos=hparams.sos,
-      eos=hparams.eos,
-      unk=vocab_utils.UNK)
-
-  # Target vocab
-  if hparams.share_vocab:
-    utils.print_out("  using source vocab for target")
-    tgt_vocab_file = src_vocab_file
-    tgt_vocab_size = src_vocab_size
-  else:
-    tgt_vocab_size, tgt_vocab_file = vocab_utils.check_vocab(
-        tgt_vocab_file,
+    """Add new arguments to hparams."""
+    # Sanity checks
+    if hparams.encoder_type == "bi" and hparams.num_encoder_layers % 2 != 0:
+        raise ValueError(
+            "For bi, num_encoder_layers %d should be even" % hparams.num_encoder_layers
+        )
+    if hparams.attention_architecture in [
+            "gnmt"] and hparams.num_encoder_layers < 2:
+        raise ValueError(
+            "For gnmt attention architecture, "
+            "num_encoder_layers %d should be >= 2" % hparams.num_encoder_layers
+        )
+    if hparams.subword_option and hparams.subword_option not in ["spm", "bpe"]:
+        raise ValueError("subword option must be either spm, or bpe")
+    if hparams.infer_mode == "beam_search" and hparams.beam_width <= 0:
+        raise ValueError(
+            "beam_width must greater than 0 when using beam_search" "decoder."
+        )
+    if hparams.infer_mode == "sample" and hparams.sampling_temperature <= 0.0:
+        raise ValueError(
+            "sampling_temperature must greater than 0.0 when using" "sample decoder."
+        )
+
+    # Different number of encoder / decoder layers
+    assert hparams.num_encoder_layers and hparams.num_decoder_layers
+    if hparams.num_encoder_layers != hparams.num_decoder_layers:
+        hparams.pass_hidden_state = False
+        utils.print_out(
+            "Num encoder layer %d is different from num decoder layer"
+            " %d, so set pass_hidden_state to False"
+            % (hparams.num_encoder_layers, hparams.num_decoder_layers)
+        )
+
+    # Set residual layers
+    num_encoder_residual_layers = 0
+    num_decoder_residual_layers = 0
+    if hparams.residual:
+        if hparams.num_encoder_layers > 1:
+            num_encoder_residual_layers = hparams.num_encoder_layers - 1
+        if hparams.num_decoder_layers > 1:
+            num_decoder_residual_layers = hparams.num_decoder_layers - 1
+
+        if hparams.encoder_type == "gnmt":
+            # The first unidirectional layer (after the bi-directional layer) in
+            # the GNMT encoder can't have residual connection due to the input is
+            # the concatenation of fw_cell and bw_cell's outputs.
+            num_encoder_residual_layers = hparams.num_encoder_layers - 2
+
+            # Compatible for GNMT models
+            if hparams.num_encoder_layers == hparams.num_decoder_layers:
+                num_decoder_residual_layers = num_encoder_residual_layers
+    _add_argument(
+        hparams,
+        "num_encoder_residual_layers",
+        num_encoder_residual_layers)
+    _add_argument(
+        hparams,
+        "num_decoder_residual_layers",
+        num_decoder_residual_layers)
+
+    # Language modeling
+    if getattr(hparams, "language_model", None):
+        hparams.attention = "normed_bahdanau"
+        hparams.attention_architecture = "gnmt_v2"
+        hparams.pass_hidden_state = False
+        hparams.share_vocab = True
+        hparams.src = hparams.tgt
+        utils.print_out(
+            "For language modeling, we turn off attention and "
+            "pass_hidden_state; turn on share_vocab; set src to tgt."
+        )
+
+    # Vocab
+    # Get vocab file names first
+    if hparams.vocab_prefix:
+        src_vocab_file = hparams.vocab_prefix + "." + hparams.src
+        tgt_vocab_file = hparams.vocab_prefix + "." + hparams.tgt
+    else:
+        raise ValueError("hparams.vocab_prefix must be provided.")
+
+    # Source vocab
+    check_special_token = getattr(hparams, "check_special_token", True)
+    src_vocab_size, src_vocab_file = vocab_utils.check_vocab(
+        src_vocab_file,
         hparams.out_dir,
         check_special_token=check_special_token,
         sos=hparams.sos,
         eos=hparams.eos,
-        unk=vocab_utils.UNK)
-  _add_argument(hparams, "src_vocab_size", src_vocab_size)
-  _add_argument(hparams, "tgt_vocab_size", tgt_vocab_size)
-  _add_argument(hparams, "src_vocab_file", src_vocab_file)
-  _add_argument(hparams, "tgt_vocab_file", tgt_vocab_file)
-
-  # Num embedding partitions
-  num_embeddings_partitions = getattr(hparams, "num_embeddings_partitions", 0)
-  _add_argument(hparams, "num_enc_emb_partitions", num_embeddings_partitions)
-  _add_argument(hparams, "num_dec_emb_partitions", num_embeddings_partitions)
-
-  # Pretrained Embeddings
-  _add_argument(hparams, "src_embed_file", "")
-  _add_argument(hparams, "tgt_embed_file", "")
-  if getattr(hparams, "embed_prefix", None):
-    src_embed_file = hparams.embed_prefix + "." + hparams.src
-    tgt_embed_file = hparams.embed_prefix + "." + hparams.tgt
-
-    if tf.gfile.Exists(src_embed_file):
-      utils.print_out("  src_embed_file %s exist" % src_embed_file)
-      hparams.src_embed_file = src_embed_file
-
-      utils.print_out(
-          "For pretrained embeddings, set num_enc_emb_partitions to 1")
-      hparams.num_enc_emb_partitions = 1
+        unk=vocab_utils.UNK,
+    )
+
+    # Target vocab
+    if hparams.share_vocab:
+        utils.print_out("  using source vocab for target")
+        tgt_vocab_file = src_vocab_file
+        tgt_vocab_size = src_vocab_size
     else:
-      utils.print_out("  src_embed_file %s doesn't exist" % src_embed_file)
-
-    if tf.gfile.Exists(tgt_embed_file):
-      utils.print_out("  tgt_embed_file %s exist" % tgt_embed_file)
-      hparams.tgt_embed_file = tgt_embed_file
-
-      utils.print_out(
-          "For pretrained embeddings, set num_dec_emb_partitions to 1")
-      hparams.num_dec_emb_partitions = 1
-    else:
-      utils.print_out("  tgt_embed_file %s doesn't exist" % tgt_embed_file)
-
-  # Evaluation
-  for metric in hparams.metrics:
-    best_metric_dir = os.path.join(hparams.out_dir, "best_" + metric)
-    tf.gfile.MakeDirs(best_metric_dir)
-    _add_argument(hparams, "best_" + metric, 0, update=False)
-    _add_argument(hparams, "best_" + metric + "_dir", best_metric_dir)
-
-    if getattr(hparams, "avg_ckpts", None):
-      best_metric_dir = os.path.join(hparams.out_dir, "avg_best_" + metric)
-      tf.gfile.MakeDirs(best_metric_dir)
-      _add_argument(hparams, "avg_best_" + metric, 0, update=False)
-      _add_argument(hparams, "avg_best_" + metric + "_dir", best_metric_dir)
-
-  return hparams
-
+        tgt_vocab_size, tgt_vocab_file = vocab_utils.check_vocab(
+            tgt_vocab_file,
+            hparams.out_dir,
+            check_special_token=check_special_token,
+            sos=hparams.sos,
+            eos=hparams.eos,
+            unk=vocab_utils.UNK,
+        )
+    _add_argument(hparams, "src_vocab_size", src_vocab_size)
+    _add_argument(hparams, "tgt_vocab_size", tgt_vocab_size)
+    _add_argument(hparams, "src_vocab_file", src_vocab_file)
+    _add_argument(hparams, "tgt_vocab_file", tgt_vocab_file)
+
+    # Num embedding partitions
+    num_embeddings_partitions = getattr(
+        hparams, "num_embeddings_partitions", 0)
+    _add_argument(hparams, "num_enc_emb_partitions", num_embeddings_partitions)
+    _add_argument(hparams, "num_dec_emb_partitions", num_embeddings_partitions)
+
+    # Pretrained Embeddings
+    _add_argument(hparams, "src_embed_file", "")
+    _add_argument(hparams, "tgt_embed_file", "")
+    if getattr(hparams, "embed_prefix", None):
+        src_embed_file = hparams.embed_prefix + "." + hparams.src
+        tgt_embed_file = hparams.embed_prefix + "." + hparams.tgt
+
+        if tf.gfile.Exists(src_embed_file):
+            utils.print_out("  src_embed_file %s exist" % src_embed_file)
+            hparams.src_embed_file = src_embed_file
+
+            utils.print_out(
+                "For pretrained embeddings, set num_enc_emb_partitions to 1"
+            )
+            hparams.num_enc_emb_partitions = 1
+        else:
+            utils.print_out(
+                "  src_embed_file %s doesn't exist" %
+                src_embed_file)
+
+        if tf.gfile.Exists(tgt_embed_file):
+            utils.print_out("  tgt_embed_file %s exist" % tgt_embed_file)
+            hparams.tgt_embed_file = tgt_embed_file
+
+            utils.print_out(
+                "For pretrained embeddings, set num_dec_emb_partitions to 1"
+            )
+            hparams.num_dec_emb_partitions = 1
+        else:
+            utils.print_out(
+                "  tgt_embed_file %s doesn't exist" %
+                tgt_embed_file)
 
-def ensure_compatible_hparams(hparams, default_hparams, hparams_path=""):
-  """Make sure the loaded hparams is compatible with new changes."""
-  default_hparams = utils.maybe_parse_standard_hparams(
-      default_hparams, hparams_path)
-
-  # Set num encoder/decoder layers (for old checkpoints)
-  if hasattr(hparams, "num_layers"):
-    if not hasattr(hparams, "num_encoder_layers"):
-      hparams.add_hparam("num_encoder_layers", hparams.num_layers)
-    if not hasattr(hparams, "num_decoder_layers"):
-      hparams.add_hparam("num_decoder_layers", hparams.num_layers)
-
-  # For compatible reason, if there are new fields in default_hparams,
-  #   we add them to the current hparams
-  default_config = default_hparams.values()
-  config = hparams.values()
-  for key in default_config:
-    if key not in config:
-      hparams.add_hparam(key, default_config[key])
-
-  # Update all hparams' keys if override_loaded_hparams=True
-  if getattr(default_hparams, "override_loaded_hparams", None):
-    overwritten_keys = default_config.keys()
-  else:
-    # For inference
-    overwritten_keys = INFERENCE_KEYS
-
-  for key in overwritten_keys:
-    if getattr(hparams, key) != default_config[key]:
-      utils.print_out("# Updating hparams.%s: %s -> %s" %
-                      (key, str(getattr(hparams, key)),
-                       str(default_config[key])))
-      setattr(hparams, key, default_config[key])
-  return hparams
-
-
-def create_or_load_hparams(
-    out_dir, default_hparams, hparams_path, save_hparams=True):
-  """Create hparams or load hparams from out_dir."""
-  hparams = utils.load_hparams(out_dir)
-  if not hparams:
-    hparams = default_hparams
-    hparams = utils.maybe_parse_standard_hparams(
-        hparams, hparams_path)
-  else:
-    hparams = ensure_compatible_hparams(hparams, default_hparams, hparams_path)
-  hparams = extend_hparams(hparams)
-
-  # Save HParams
-  if save_hparams:
-    utils.save_hparams(out_dir, hparams)
+    # Evaluation
     for metric in hparams.metrics:
-      utils.save_hparams(getattr(hparams, "best_" + metric + "_dir"), hparams)
-
-  # Print HParams
-  utils.print_hparams(hparams)
-  return hparams
-
-
-def run_main(flags, default_hparams, train_fn, inference_fn, target_session=""):
-  """Run main."""
-  # Job
-  jobid = flags.jobid
-  num_workers = flags.num_workers
-  utils.print_out("# Job id %d" % jobid)
-
-  # Random
-  random_seed = flags.random_seed
-  if random_seed is not None and random_seed > 0:
-    utils.print_out("# Set random seed to %d" % random_seed)
-    random.seed(random_seed + jobid)
-    np.random.seed(random_seed + jobid)
-
-  # Model output directory
-  out_dir = flags.out_dir
-  if out_dir and not tf.gfile.Exists(out_dir):
-    utils.print_out("# Creating output directory %s ..." % out_dir)
-    tf.gfile.MakeDirs(out_dir)
-
-  # Load hparams.
-  loaded_hparams = False
-  if flags.ckpt:  # Try to load hparams from the same directory as ckpt
-    ckpt_dir = os.path.dirname(flags.ckpt)
-    ckpt_hparams_file = os.path.join(ckpt_dir, "hparams")
-    if tf.gfile.Exists(ckpt_hparams_file) or flags.hparams_path:
-      hparams = create_or_load_hparams(
-          ckpt_dir, default_hparams, flags.hparams_path,
-          save_hparams=False)
-      loaded_hparams = True
-  if not loaded_hparams:  # Try to load from out_dir
-    assert out_dir
-    hparams = create_or_load_hparams(
-        out_dir, default_hparams, flags.hparams_path,
-        save_hparams=(jobid == 0))
-
- # GPU device
-  config_proto = utils.get_config_proto(
-      allow_soft_placement=True,
-      num_intra_threads=hparams.num_intra_threads,
-      num_inter_threads=hparams.num_inter_threads)
-  utils.print_out(
-      "# Devices visible to TensorFlow: %s" 
-      % repr(tf.Session(config=config_proto).list_devices()))
-
-  ## Train / Decode
-  if flags.inference_input_file:
-    # Inference output directory
-    trans_file = flags.inference_output_file
-    assert trans_file
-    trans_dir = os.path.dirname(trans_file)
-    if not tf.gfile.Exists(trans_dir): tf.gfile.MakeDirs(trans_dir)
-
-    # Inference indices
-    hparams.inference_indices = None
-    if flags.inference_list:
-      (hparams.inference_indices) = (
-          [int(token)  for token in flags.inference_list.split(",")])
+        best_metric_dir = os.path.join(hparams.out_dir, "best_" + metric)
+        tf.gfile.MakeDirs(best_metric_dir)
+        _add_argument(hparams, "best_" + metric, 0, update=False)
+        _add_argument(hparams, "best_" + metric + "_dir", best_metric_dir)
+
+        if getattr(hparams, "avg_ckpts", None):
+            best_metric_dir = os.path.join(
+                hparams.out_dir, "avg_best_" + metric)
+            tf.gfile.MakeDirs(best_metric_dir)
+            _add_argument(hparams, "avg_best_" + metric, 0, update=False)
+            _add_argument(
+                hparams,
+                "avg_best_" +
+                metric +
+                "_dir",
+                best_metric_dir)
+
+    return hparams
 
-    # Inference
-    ckpt = flags.ckpt
-    if not ckpt:
-      ckpt = tf.train.latest_checkpoint(out_dir)
-    inference_fn(flags.run,flags.iterations,ckpt, flags.inference_input_file,
-                 trans_file, hparams, num_workers, jobid)
 
-    # Evaluation
-    if flags.run == 'accuracy':
-        ref_file = flags.inference_ref_file
-        if ref_file and tf.gfile.Exists(trans_file):
-          for metric in hparams.metrics:
-            score = evaluation_utils.evaluate(
-                ref_file,
-                trans_file,
-                metric,
-                hparams.subword_option)
-            utils.print_out("  %s: %.1f" % (metric, score))
-        else:
-        # Train
-            train_fn(hparams, target_session=target_session)
+def ensure_compatible_hparams(hparams, default_hparams, hparams_path=""):
+    """Make sure the loaded hparams is compatible with new changes."""
+    default_hparams = utils.maybe_parse_standard_hparams(
+        default_hparams, hparams_path)
+
+    # Set num encoder/decoder layers (for old checkpoints)
+    if hasattr(hparams, "num_layers"):
+        if not hasattr(hparams, "num_encoder_layers"):
+            hparams.add_hparam("num_encoder_layers", hparams.num_layers)
+        if not hasattr(hparams, "num_decoder_layers"):
+            hparams.add_hparam("num_decoder_layers", hparams.num_layers)
+
+    # For compatible reason, if there are new fields in default_hparams,
+    #   we add them to the current hparams
+    default_config = default_hparams.values()
+    config = hparams.values()
+    for key in default_config:
+        if key not in config:
+            hparams.add_hparam(key, default_config[key])
+
+    # Update all hparams' keys if override_loaded_hparams=True
+    if getattr(default_hparams, "override_loaded_hparams", None):
+        overwritten_keys = default_config.keys()
+    else:
+        # For inference
+        overwritten_keys = INFERENCE_KEYS
+
+    for key in overwritten_keys:
+        if getattr(hparams, key) != default_config[key]:
+            utils.print_out(
+                "# Updating hparams.%s: %s -> %s"
+                % (key, str(getattr(hparams, key)), str(default_config[key]))
+            )
+            setattr(hparams, key, default_config[key])
+    return hparams
+
+
+def create_or_load_hparams(out_dir, default_hparams,
+                           hparams_path, save_hparams=True):
+    """Create hparams or load hparams from out_dir."""
+    hparams = utils.load_hparams(out_dir)
+    if not hparams:
+        hparams = default_hparams
+        hparams = utils.maybe_parse_standard_hparams(hparams, hparams_path)
+    else:
+        hparams = ensure_compatible_hparams(
+            hparams, default_hparams, hparams_path)
+    hparams = extend_hparams(hparams)
+
+    # Save HParams
+    if save_hparams:
+        utils.save_hparams(out_dir, hparams)
+        for metric in hparams.metrics:
+            utils.save_hparams(
+                getattr(
+                    hparams,
+                    "best_" +
+                    metric +
+                    "_dir"),
+                hparams)
+
+    # Print HParams
+    utils.print_hparams(hparams)
+    return hparams
+
+
+def run_main(flags, default_hparams, train_fn,
+             inference_fn, target_session=""):
+    """Run main."""
+    # Job
+    jobid = flags.jobid
+    num_workers = flags.num_workers
+    utils.print_out("# Job id %d" % jobid)
+
+    # Random
+    random_seed = flags.random_seed
+    if random_seed is not None and random_seed > 0:
+        utils.print_out("# Set random seed to %d" % random_seed)
+        random.seed(random_seed + jobid)
+        np.random.seed(random_seed + jobid)
+
+    # Model output directory
+    out_dir = flags.out_dir
+    if out_dir and not tf.gfile.Exists(out_dir):
+        utils.print_out("# Creating output directory %s ..." % out_dir)
+        tf.gfile.MakeDirs(out_dir)
+
+    # Load hparams.
+    loaded_hparams = False
+    if flags.ckpt:  # Try to load hparams from the same directory as ckpt
+        ckpt_dir = os.path.dirname(flags.ckpt)
+        ckpt_hparams_file = os.path.join(ckpt_dir, "hparams")
+        if tf.gfile.Exists(ckpt_hparams_file) or flags.hparams_path:
+            hparams = create_or_load_hparams(
+                ckpt_dir, default_hparams, flags.hparams_path, save_hparams=False
+            )
+            loaded_hparams = True
+    if not loaded_hparams:  # Try to load from out_dir
+        assert out_dir
+        hparams = create_or_load_hparams(
+            out_dir, default_hparams, flags.hparams_path, save_hparams=(
+                jobid == 0)
+        )
+
+    # GPU device
+    config_proto = utils.get_config_proto(
+        allow_soft_placement=True,
+        num_intra_threads=hparams.num_intra_threads,
+        num_inter_threads=hparams.num_inter_threads,
+    )
+    utils.print_out(
+        "# Devices visible to TensorFlow: %s"
+        % repr(tf.Session(config=config_proto).list_devices())
+    )
+
+    # Train / Decode
+    if flags.inference_input_file:
+        # Inference output directory
+        trans_file = flags.inference_output_file
+        assert trans_file
+        trans_dir = os.path.dirname(trans_file)
+        if not tf.gfile.Exists(trans_dir):
+            tf.gfile.MakeDirs(trans_dir)
+
+        # Inference indices
+        hparams.inference_indices = None
+        if flags.inference_list:
+            (hparams.inference_indices) = [
+                int(token) for token in flags.inference_list.split(",")
+            ]
+
+        # Inference
+        ckpt = flags.ckpt
+        if not ckpt:
+            ckpt = tf.train.latest_checkpoint(out_dir)
+        inference_fn(
+            flags.run,
+            flags.iterations,
+            ckpt,
+            flags.inference_input_file,
+            trans_file,
+            hparams,
+            num_workers,
+            jobid,
+        )
+
+        # Evaluation
+        if flags.run == "accuracy":
+            ref_file = flags.inference_ref_file
+            if ref_file and tf.gfile.Exists(trans_file):
+                for metric in hparams.metrics:
+                    score = evaluation_utils.evaluate(
+                        ref_file, trans_file, metric, hparams.subword_option
+                    )
+                    utils.print_out("  %s: %.1f" % (metric, score))
+            else:
+                # Train
+                train_fn(hparams, target_session=target_session)
 
 
 def main(unused_argv):
-  default_hparams = create_hparams(FLAGS)
-  train_fn = train.train
-  inference_fn = inference.inference
-  run_main(FLAGS, default_hparams, train_fn, inference_fn)
+    default_hparams = create_hparams(FLAGS)
+    train_fn = train.train
+    inference_fn = inference.inference
+    run_main(FLAGS, default_hparams, train_fn, inference_fn)
 
 
 if __name__ == "__main__":
-  nmt_parser = argparse.ArgumentParser()
-  add_arguments(nmt_parser)
-  FLAGS, unparsed = nmt_parser.parse_known_args()
-  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
+    nmt_parser = argparse.ArgumentParser()
+    add_arguments(nmt_parser)
+    FLAGS, unparsed = nmt_parser.parse_known_args()
+    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/nmt_test.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/nmt_test.py
index c12179d5aa..45328f151e 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/nmt_test.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/nmt_test.py
@@ -29,79 +29,71 @@
 
 
 def _update_flags(flags, test_name):
-  """Update flags for basic training."""
-  flags.num_train_steps = 100
-  flags.steps_per_stats = 5
-  flags.src = "en"
-  flags.tgt = "vi"
-  flags.train_prefix = ("nmt/testdata/"
-                        "iwslt15.tst2013.100")
-  flags.vocab_prefix = ("nmt/testdata/"
-                        "iwslt15.vocab.100")
-  flags.dev_prefix = ("nmt/testdata/"
-                      "iwslt15.tst2013.100")
-  flags.test_prefix = ("nmt/testdata/"
-                       "iwslt15.tst2013.100")
-  flags.out_dir = os.path.join(tf.test.get_temp_dir(), test_name)
+    """Update flags for basic training."""
+    flags.num_train_steps = 100
+    flags.steps_per_stats = 5
+    flags.src = "en"
+    flags.tgt = "vi"
+    flags.train_prefix = "nmt/testdata/" "iwslt15.tst2013.100"
+    flags.vocab_prefix = "nmt/testdata/" "iwslt15.vocab.100"
+    flags.dev_prefix = "nmt/testdata/" "iwslt15.tst2013.100"
+    flags.test_prefix = "nmt/testdata/" "iwslt15.tst2013.100"
+    flags.out_dir = os.path.join(tf.test.get_temp_dir(), test_name)
 
 
 class NMTTest(tf.test.TestCase):
 
-  def testTrain(self):
-    """Test the training loop is functional with basic hparams."""
-    nmt_parser = argparse.ArgumentParser()
-    nmt.add_arguments(nmt_parser)
-    FLAGS, unparsed = nmt_parser.parse_known_args()
+    def testTrain(self):
+        """Test the training loop is functional with basic hparams."""
+        nmt_parser = argparse.ArgumentParser()
+        nmt.add_arguments(nmt_parser)
+        FLAGS, unparsed = nmt_parser.parse_known_args()
 
-    _update_flags(FLAGS, "nmt_train_test")
+        _update_flags(FLAGS, "nmt_train_test")
 
-    default_hparams = nmt.create_hparams(FLAGS)
+        default_hparams = nmt.create_hparams(FLAGS)
 
-    train_fn = train.train
-    nmt.run_main(FLAGS, default_hparams, train_fn, None)
+        train_fn = train.train
+        nmt.run_main(FLAGS, default_hparams, train_fn, None)
 
+    def testTrainWithAvgCkpts(self):
+        """Test the training loop is functional with basic hparams."""
+        nmt_parser = argparse.ArgumentParser()
+        nmt.add_arguments(nmt_parser)
+        FLAGS, unparsed = nmt_parser.parse_known_args()
 
-  def testTrainWithAvgCkpts(self):
-    """Test the training loop is functional with basic hparams."""
-    nmt_parser = argparse.ArgumentParser()
-    nmt.add_arguments(nmt_parser)
-    FLAGS, unparsed = nmt_parser.parse_known_args()
+        _update_flags(FLAGS, "nmt_train_test_avg_ckpts")
+        FLAGS.avg_ckpts = True
 
-    _update_flags(FLAGS, "nmt_train_test_avg_ckpts")
-    FLAGS.avg_ckpts = True
+        default_hparams = nmt.create_hparams(FLAGS)
 
-    default_hparams = nmt.create_hparams(FLAGS)
+        train_fn = train.train
+        nmt.run_main(FLAGS, default_hparams, train_fn, None)
 
-    train_fn = train.train
-    nmt.run_main(FLAGS, default_hparams, train_fn, None)
+    def testInference(self):
+        """Test inference is function with basic hparams."""
+        nmt_parser = argparse.ArgumentParser()
+        nmt.add_arguments(nmt_parser)
+        FLAGS, unparsed = nmt_parser.parse_known_args()
 
+        _update_flags(FLAGS, "nmt_train_infer")
 
-  def testInference(self):
-    """Test inference is function with basic hparams."""
-    nmt_parser = argparse.ArgumentParser()
-    nmt.add_arguments(nmt_parser)
-    FLAGS, unparsed = nmt_parser.parse_known_args()
+        # Train one step so we have a checkpoint.
+        FLAGS.num_train_steps = 1
+        default_hparams = nmt.create_hparams(FLAGS)
+        train_fn = train.train
+        nmt.run_main(FLAGS, default_hparams, train_fn, None)
 
-    _update_flags(FLAGS, "nmt_train_infer")
+        # Update FLAGS for inference.
+        FLAGS.inference_input_file = "nmt/testdata/" "iwslt15.tst2013.100.en"
+        FLAGS.inference_output_file = os.path.join(FLAGS.out_dir, "output")
+        FLAGS.inference_ref_file = "nmt/testdata/" "iwslt15.tst2013.100.vi"
 
-    # Train one step so we have a checkpoint.
-    FLAGS.num_train_steps = 1
-    default_hparams = nmt.create_hparams(FLAGS)
-    train_fn = train.train
-    nmt.run_main(FLAGS, default_hparams, train_fn, None)
+        default_hparams = nmt.create_hparams(FLAGS)
 
-    # Update FLAGS for inference.
-    FLAGS.inference_input_file = ("nmt/testdata/"
-                                  "iwslt15.tst2013.100.en")
-    FLAGS.inference_output_file = os.path.join(FLAGS.out_dir, "output")
-    FLAGS.inference_ref_file = ("nmt/testdata/"
-                                "iwslt15.tst2013.100.vi")
-
-    default_hparams = nmt.create_hparams(FLAGS)
-
-    inference_fn = inference.inference
-    nmt.run_main(FLAGS, default_hparams, None, inference_fn)
+        inference_fn = inference.inference
+        nmt.run_main(FLAGS, default_hparams, None, inference_fn)
 
 
 if __name__ == "__main__":
-  tf.test.main()
+    tf.test.main()
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/scripts/bleu.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/scripts/bleu.py
index 494e39c6c5..c677b9db64 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/scripts/bleu.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/scripts/bleu.py
@@ -27,146 +27,164 @@
 import collections
 import math
 
+
 ##
 # @brief Class to compute running BLEU scores
 # @detail BLEU scores can be computed in a non-linear way,
 # or without having access to the full translated corpus in time.
 class RunningBLEUScorer:
 
-  def __init__(self, max_order=4, smooth=False):
-    self.max_order = max_order
-    self.smooth = smooth
-    self.reset()
-
-  ##
-  # @brief Reset all variables (none of the previus sentences will be taken into account)
-  def reset(self):
-    self.matches_by_order = [0] * self.max_order
-    self.possible_matches_by_order = [0] * self.max_order
-    self.reference_length = 0
-    self.translation_length = 0
-
-  ##
-  # @brief Add a single sentence
-  # @param reference list of words for a reference sentence
-  # @param translation list of words for its corresponding translated sentence
-  # @post Updates internal structures to take this sentence's translation 
-  # result into account in final BLEU score
-  def add_sentence(self, reference, translation):
-    self.add_sentence_with_multiple_refs([reference], translation)
-
-  ##
-  # @brief Add a single reference, with potentially multiple references
-  # @param reference list of list of words for a reference sentence
-  # @note That we could have multiple sentences serving as a reference
-  # @param translation (single) list of words for its corresponding translated sentence
-  # @post Updates internal structures to take this sentence's translation 
-  # result into account in final BLEU score
-  def add_sentence_with_multiple_refs(self, references, translation):
-    self.reference_length += min(len(r) for r in references)
-    self.translation_length += len(translation)
-
-    merged_ref_ngram_counts = collections.Counter()
-    for reference in references:
-      merged_ref_ngram_counts |= self._get_ngrams(reference)
-
-    translation_ngram_counts = self._get_ngrams(translation)
-    
-    new_matches_by_order, new_possible_matches_by_order = self._get_ngram_match_values(merged_ref_ngram_counts, translation_ngram_counts, len(translation))
-
-    for i in range(self.max_order):
-      self.matches_by_order[i] += new_matches_by_order[i]
-      self.possible_matches_by_order[i] += new_possible_matches_by_order[i]
-
-  ##
-  # @brief Calculate final BLEU score
-  def calc_BLEU_score(self):
-    precisions = [0] * self.max_order
-    for i in range(0, self.max_order):
-      if self.smooth:
-        precisions[i] = ((self.matches_by_order[i] + 1.) /
-                         (self.possible_matches_by_order[i] + 1.))
-      else:
-        if self.possible_matches_by_order[i] > 0:
-          precisions[i] = (float(self.matches_by_order[i]) /
-                           self.possible_matches_by_order[i])
+    def __init__(self, max_order=4, smooth=False):
+        self.max_order = max_order
+        self.smooth = smooth
+        self.reset()
+
+    ##
+    # @brief Reset all variables (none of the previus sentences will be taken into account)
+    def reset(self):
+        self.matches_by_order = [0] * self.max_order
+        self.possible_matches_by_order = [0] * self.max_order
+        self.reference_length = 0
+        self.translation_length = 0
+
+    ##
+    # @brief Add a single sentence
+    # @param reference list of words for a reference sentence
+    # @param translation list of words for its corresponding translated sentence
+    # @post Updates internal structures to take this sentence's translation
+    # result into account in final BLEU score
+    def add_sentence(self, reference, translation):
+        self.add_sentence_with_multiple_refs([reference], translation)
+
+    ##
+    # @brief Add a single reference, with potentially multiple references
+    # @param reference list of list of words for a reference sentence
+    # @note That we could have multiple sentences serving as a reference
+    # @param translation (single) list of words for its corresponding translated sentence
+    # @post Updates internal structures to take this sentence's translation
+    # result into account in final BLEU score
+    def add_sentence_with_multiple_refs(self, references, translation):
+        self.reference_length += min(len(r) for r in references)
+        self.translation_length += len(translation)
+
+        merged_ref_ngram_counts = collections.Counter()
+        for reference in references:
+            merged_ref_ngram_counts |= self._get_ngrams(reference)
+
+        translation_ngram_counts = self._get_ngrams(translation)
+
+        new_matches_by_order, new_possible_matches_by_order = (
+            self._get_ngram_match_values(
+                merged_ref_ngram_counts, translation_ngram_counts, len(
+                    translation)
+            )
+        )
+
+        for i in range(self.max_order):
+            self.matches_by_order[i] += new_matches_by_order[i]
+            self.possible_matches_by_order[i] += new_possible_matches_by_order[i]
+
+    ##
+    # @brief Calculate final BLEU score
+    def calc_BLEU_score(self):
+        precisions = [0] * self.max_order
+        for i in range(0, self.max_order):
+            if self.smooth:
+                precisions[i] = (self.matches_by_order[i] + 1.0) / (
+                    self.possible_matches_by_order[i] + 1.0
+                )
+            else:
+                if self.possible_matches_by_order[i] > 0:
+                    precisions[i] = (
+                        float(self.matches_by_order[i])
+                        / self.possible_matches_by_order[i]
+                    )
+                else:
+                    precisions[i] = 0.0
+
+        if min(precisions) > 0:
+            p_log_sum = sum((1.0 / self.max_order) * math.log(p)
+                            for p in precisions)
+            geo_mean = math.exp(p_log_sum)
         else:
-          precisions[i] = 0.0
-
-    if min(precisions) > 0:
-      p_log_sum = sum((1. / self.max_order) * math.log(p) for p in precisions)
-      geo_mean = math.exp(p_log_sum)
-    else:
-      geo_mean = 0
-
-    ratio = float(self.translation_length) / self.reference_length
-
-    if ratio > 1.0:
-      bp = 1.
-    else:
-      bp = math.exp(1 - 1. / ratio)
-
-    bleu = geo_mean * bp
-
-    return (bleu, precisions, bp, ratio, self.translation_length, self.reference_length)
+            geo_mean = 0
 
-  ##
-  # @brief Internal function to compute matching percentages for different order ngrams
-  def _get_ngram_match_values(self, ref_ngram_counts, translation_ngram_counts, translation_length):
-    new_matches_by_order = [0] * self.max_order
-    new_possible_matches_by_order = [0] * self.max_order
+        ratio = float(self.translation_length) / self.reference_length
 
-    overlap = translation_ngram_counts & ref_ngram_counts
-    for ngram in overlap:
-      new_matches_by_order[len(ngram)-1] += overlap[ngram]
-    for order in range(1, self.max_order+1):
-      possible_matches = translation_length - order + 1
-      new_possible_matches_by_order[order-1] = max(0, possible_matches)
-
-    return (new_matches_by_order, new_possible_matches_by_order)
-
-  def _get_ngrams(self, segment):
-    """Internal function to extract all n-grams upto a given maximum order from an input segment.
+        if ratio > 1.0:
+            bp = 1.0
+        else:
+            bp = math.exp(1 - 1.0 / ratio)
+
+        bleu = geo_mean * bp
+
+        return (
+            bleu,
+            precisions,
+            bp,
+            ratio,
+            self.translation_length,
+            self.reference_length,
+        )
+
+    ##
+    # @brief Internal function to compute matching percentages for different order ngrams
+    def _get_ngram_match_values(
+        self, ref_ngram_counts, translation_ngram_counts, translation_length
+    ):
+        new_matches_by_order = [0] * self.max_order
+        new_possible_matches_by_order = [0] * self.max_order
+
+        overlap = translation_ngram_counts & ref_ngram_counts
+        for ngram in overlap:
+            new_matches_by_order[len(ngram) - 1] += overlap[ngram]
+        for order in range(1, self.max_order + 1):
+            possible_matches = translation_length - order + 1
+            new_possible_matches_by_order[order - 1] = max(0, possible_matches)
+
+        return (new_matches_by_order, new_possible_matches_by_order)
+
+    def _get_ngrams(self, segment):
+        """Internal function to extract all n-grams upto a given maximum order from an input segment.
+
+        Args:
+          segment: text segment from which n-grams will be extracted.
+
+        Returns:
+          The Counter containing all n-grams upto max_order in segment
+          with a count of how many times each n-gram occurred.
+        """
+        ngram_counts = collections.Counter()
+        for order in range(1, self.max_order + 1):
+            for i in range(0, len(segment) - order + 1):
+                ngram = tuple(segment[i: i + order])
+                ngram_counts[ngram] += 1
+        return ngram_counts
+
+
+def compute_bleu(reference_corpus, translation_corpus,
+                 max_order=4, smooth=False):
+    """Computes BLEU score of translated segments against one or more references.
+      This is the most common usage when calculating BLEU scores.
 
     Args:
-      segment: text segment from which n-grams will be extracted.
+      reference_corpus: list of lists of references for each translation. Each
+          reference should be tokenized into a list of tokens.
+          reference_corpus[i][j][k] represents the k'th word of the i'th sentence
+          for the j'th reference text
+      translation_corpus: list of translated sentences to score. Each sentence
+          should be tokenized into a list of tokens.
+          translation_corpus[i][j] represents the j'th word for the i'th sentence
+      max_order: Maximum n-gram order to use when computing BLEU score.
+      smooth: Whether or not to apply Lin et al. 2004 smoothing.
 
     Returns:
-      The Counter containing all n-grams upto max_order in segment
-      with a count of how many times each n-gram occurred.
+      3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram
+      precisions and brevity penalty.
     """
-    ngram_counts = collections.Counter()
-    for order in range(1, self.max_order + 1):
-      for i in range(0, len(segment) - order + 1):
-        ngram = tuple(segment[i:i+order])
-        ngram_counts[ngram] += 1
-    return ngram_counts
-
-def compute_bleu(reference_corpus, translation_corpus, max_order=4,
-                 smooth=False):
-  """Computes BLEU score of translated segments against one or more references.
-    This is the most common usage when calculating BLEU scores.
-
-  Args:
-    reference_corpus: list of lists of references for each translation. Each
-        reference should be tokenized into a list of tokens.
-        reference_corpus[i][j][k] represents the k'th word of the i'th sentence
-        for the j'th reference text
-    translation_corpus: list of translated sentences to score. Each sentence
-        should be tokenized into a list of tokens.
-        translation_corpus[i][j] represents the j'th word for the i'th sentence
-    max_order: Maximum n-gram order to use when computing BLEU score.
-    smooth: Whether or not to apply Lin et al. 2004 smoothing.
-
-  Returns:
-    3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram
-    precisions and brevity penalty.
-  """
-  runningBLEU = RunningBLEUScorer(max_order=max_order, smooth=smooth)
-
-
-  for (references, translation) in zip(reference_corpus,
-                                       translation_corpus):
-    runningBLEU.add_sentence_with_multiple_refs(references, translation)
- 
-  return runningBLEU.calc_BLEU_score()
\ No newline at end of file
+    runningBLEU = RunningBLEUScorer(max_order=max_order, smooth=smooth)
+
+    for references, translation in zip(reference_corpus, translation_corpus):
+        runningBLEU.add_sentence_with_multiple_refs(references, translation)
+
+    return runningBLEU.calc_BLEU_score()
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/scripts/rouge.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/scripts/rouge.py
index e0269b9c13..505ab7f650 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/scripts/rouge.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/scripts/rouge.py
@@ -13,340 +13,336 @@
 import itertools
 import numpy as np
 
-#pylint: disable=C0103
+# pylint: disable=C0103
 
 
 def _get_ngrams(n, text):
-  """Calcualtes n-grams.
+    """Calcualtes n-grams.
 
-  Args:
-    n: which n-grams to calculate
-    text: An array of tokens
+    Args:
+      n: which n-grams to calculate
+      text: An array of tokens
 
-  Returns:
-    A set of n-grams
-  """
-  ngram_set = set()
-  text_length = len(text)
-  max_index_ngram_start = text_length - n
-  for i in range(max_index_ngram_start + 1):
-    ngram_set.add(tuple(text[i:i + n]))
-  return ngram_set
+    Returns:
+      A set of n-grams
+    """
+    ngram_set = set()
+    text_length = len(text)
+    max_index_ngram_start = text_length - n
+    for i in range(max_index_ngram_start + 1):
+        ngram_set.add(tuple(text[i: i + n]))
+    return ngram_set
 
 
 def _split_into_words(sentences):
-  """Splits multiple sentences into words and flattens the result"""
-  return list(itertools.chain(*[_.split(" ") for _ in sentences]))
+    """Splits multiple sentences into words and flattens the result"""
+    return list(itertools.chain(*[_.split(" ") for _ in sentences]))
 
 
 def _get_word_ngrams(n, sentences):
-  """Calculates word n-grams for multiple sentences.
-  """
-  assert len(sentences) > 0
-  assert n > 0
+    """Calculates word n-grams for multiple sentences."""
+    assert len(sentences) > 0
+    assert n > 0
 
-  words = _split_into_words(sentences)
-  return _get_ngrams(n, words)
+    words = _split_into_words(sentences)
+    return _get_ngrams(n, words)
 
 
 def _len_lcs(x, y):
-  """
-  Returns the length of the Longest Common Subsequence between sequences x
-  and y.
-  Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
+    """
+    Returns the length of the Longest Common Subsequence between sequences x
+    and y.
+    Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
 
-  Args:
-    x: sequence of words
-    y: sequence of words
+    Args:
+      x: sequence of words
+      y: sequence of words
 
-  Returns
-    integer: Length of LCS between x and y
-  """
-  table = _lcs(x, y)
-  n, m = len(x), len(y)
-  return table[n, m]
+    Returns
+      integer: Length of LCS between x and y
+    """
+    table = _lcs(x, y)
+    n, m = len(x), len(y)
+    return table[n, m]
 
 
 def _lcs(x, y):
-  """
-  Computes the length of the longest common subsequence (lcs) between two
-  strings. The implementation below uses a DP programming algorithm and runs
-  in O(nm) time where n = len(x) and m = len(y).
-  Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
-
-  Args:
-    x: collection of words
-    y: collection of words
-
-  Returns:
-    Table of dictionary of coord and len lcs
-  """
-  n, m = len(x), len(y)
-  table = dict()
-  for i in range(n + 1):
-    for j in range(m + 1):
-      if i == 0 or j == 0:
-        table[i, j] = 0
-      elif x[i - 1] == y[j - 1]:
-        table[i, j] = table[i - 1, j - 1] + 1
-      else:
-        table[i, j] = max(table[i - 1, j], table[i, j - 1])
-  return table
+    """
+    Computes the length of the longest common subsequence (lcs) between two
+    strings. The implementation below uses a DP programming algorithm and runs
+    in O(nm) time where n = len(x) and m = len(y).
+    Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
+
+    Args:
+      x: collection of words
+      y: collection of words
+
+    Returns:
+      Table of dictionary of coord and len lcs
+    """
+    n, m = len(x), len(y)
+    table = dict()
+    for i in range(n + 1):
+        for j in range(m + 1):
+            if i == 0 or j == 0:
+                table[i, j] = 0
+            elif x[i - 1] == y[j - 1]:
+                table[i, j] = table[i - 1, j - 1] + 1
+            else:
+                table[i, j] = max(table[i - 1, j], table[i, j - 1])
+    return table
 
 
 def _recon_lcs(x, y):
-  """
-  Returns the Longest Subsequence between x and y.
-  Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
-
-  Args:
-    x: sequence of words
-    y: sequence of words
-
-  Returns:
-    sequence: LCS of x and y
-  """
-  i, j = len(x), len(y)
-  table = _lcs(x, y)
-
-  def _recon(i, j):
-    """private recon calculation"""
-    if i == 0 or j == 0:
-      return []
-    elif x[i - 1] == y[j - 1]:
-      return _recon(i - 1, j - 1) + [(x[i - 1], i)]
-    elif table[i - 1, j] > table[i, j - 1]:
-      return _recon(i - 1, j)
-    else:
-      return _recon(i, j - 1)
-
-  recon_tuple = tuple(map(lambda x: x[0], _recon(i, j)))
-  return recon_tuple
+    """
+    Returns the Longest Subsequence between x and y.
+    Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
+
+    Args:
+      x: sequence of words
+      y: sequence of words
+
+    Returns:
+      sequence: LCS of x and y
+    """
+    i, j = len(x), len(y)
+    table = _lcs(x, y)
+
+    def _recon(i, j):
+        """private recon calculation"""
+        if i == 0 or j == 0:
+            return []
+        elif x[i - 1] == y[j - 1]:
+            return _recon(i - 1, j - 1) + [(x[i - 1], i)]
+        elif table[i - 1, j] > table[i, j - 1]:
+            return _recon(i - 1, j)
+        else:
+            return _recon(i, j - 1)
+
+    recon_tuple = tuple(map(lambda x: x[0], _recon(i, j)))
+    return recon_tuple
 
 
 def rouge_n(evaluated_sentences, reference_sentences, n=2):
-  """
-  Computes ROUGE-N of two text collections of sentences.
-  Sourece: http://research.microsoft.com/en-us/um/people/cyl/download/
-  papers/rouge-working-note-v1.3.1.pdf
-
-  Args:
-    evaluated_sentences: The sentences that have been picked by the summarizer
-    reference_sentences: The sentences from the referene set
-    n: Size of ngram.  Defaults to 2.
-
-  Returns:
-    A tuple (f1, precision, recall) for ROUGE-N
-
-  Raises:
-    ValueError: raises exception if a param has len <= 0
-  """
-  if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
-    raise ValueError("Collections must contain at least 1 sentence.")
-
-  evaluated_ngrams = _get_word_ngrams(n, evaluated_sentences)
-  reference_ngrams = _get_word_ngrams(n, reference_sentences)
-  reference_count = len(reference_ngrams)
-  evaluated_count = len(evaluated_ngrams)
-
-  # Gets the overlapping ngrams between evaluated and reference
-  overlapping_ngrams = evaluated_ngrams.intersection(reference_ngrams)
-  overlapping_count = len(overlapping_ngrams)
-
-  # Handle edge case. This isn't mathematically correct, but it's good enough
-  if evaluated_count == 0:
-    precision = 0.0
-  else:
-    precision = overlapping_count / evaluated_count
+    """
+    Computes ROUGE-N of two text collections of sentences.
+    Sourece: http://research.microsoft.com/en-us/um/people/cyl/download/
+    papers/rouge-working-note-v1.3.1.pdf
+
+    Args:
+      evaluated_sentences: The sentences that have been picked by the summarizer
+      reference_sentences: The sentences from the referene set
+      n: Size of ngram.  Defaults to 2.
+
+    Returns:
+      A tuple (f1, precision, recall) for ROUGE-N
+
+    Raises:
+      ValueError: raises exception if a param has len <= 0
+    """
+    if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
+        raise ValueError("Collections must contain at least 1 sentence.")
+
+    evaluated_ngrams = _get_word_ngrams(n, evaluated_sentences)
+    reference_ngrams = _get_word_ngrams(n, reference_sentences)
+    reference_count = len(reference_ngrams)
+    evaluated_count = len(evaluated_ngrams)
+
+    # Gets the overlapping ngrams between evaluated and reference
+    overlapping_ngrams = evaluated_ngrams.intersection(reference_ngrams)
+    overlapping_count = len(overlapping_ngrams)
+
+    # Handle edge case. This isn't mathematically correct, but it's good enough
+    if evaluated_count == 0:
+        precision = 0.0
+    else:
+        precision = overlapping_count / evaluated_count
 
-  if reference_count == 0:
-    recall = 0.0
-  else:
-    recall = overlapping_count / reference_count
+    if reference_count == 0:
+        recall = 0.0
+    else:
+        recall = overlapping_count / reference_count
 
-  f1_score = 2.0 * ((precision * recall) / (precision + recall + 1e-8))
+    f1_score = 2.0 * ((precision * recall) / (precision + recall + 1e-8))
 
-  # return overlapping_count / reference_count
-  return f1_score, precision, recall
+    # return overlapping_count / reference_count
+    return f1_score, precision, recall
 
 
 def _f_p_r_lcs(llcs, m, n):
-  """
-  Computes the LCS-based F-measure score
-  Source: http://research.microsoft.com/en-us/um/people/cyl/download/papers/
-  rouge-working-note-v1.3.1.pdf
-
-  Args:
-    llcs: Length of LCS
-    m: number of words in reference summary
-    n: number of words in candidate summary
-
-  Returns:
-    Float. LCS-based F-measure score
-  """
-  r_lcs = llcs / m
-  p_lcs = llcs / n
-  beta = p_lcs / (r_lcs + 1e-12)
-  num = (1 + (beta**2)) * r_lcs * p_lcs
-  denom = r_lcs + ((beta**2) * p_lcs)
-  f_lcs = num / (denom + 1e-12)
-  return f_lcs, p_lcs, r_lcs
+    """
+    Computes the LCS-based F-measure score
+    Source: http://research.microsoft.com/en-us/um/people/cyl/download/papers/
+    rouge-working-note-v1.3.1.pdf
+
+    Args:
+      llcs: Length of LCS
+      m: number of words in reference summary
+      n: number of words in candidate summary
+
+    Returns:
+      Float. LCS-based F-measure score
+    """
+    r_lcs = llcs / m
+    p_lcs = llcs / n
+    beta = p_lcs / (r_lcs + 1e-12)
+    num = (1 + (beta**2)) * r_lcs * p_lcs
+    denom = r_lcs + ((beta**2) * p_lcs)
+    f_lcs = num / (denom + 1e-12)
+    return f_lcs, p_lcs, r_lcs
 
 
 def rouge_l_sentence_level(evaluated_sentences, reference_sentences):
-  """
-  Computes ROUGE-L (sentence level) of two text collections of sentences.
-  http://research.microsoft.com/en-us/um/people/cyl/download/papers/
-  rouge-working-note-v1.3.1.pdf
-
-  Calculated according to:
-  R_lcs = LCS(X,Y)/m
-  P_lcs = LCS(X,Y)/n
-  F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)
-
-  where:
-  X = reference summary
-  Y = Candidate summary
-  m = length of reference summary
-  n = length of candidate summary
-
-  Args:
-    evaluated_sentences: The sentences that have been picked by the summarizer
-    reference_sentences: The sentences from the referene set
-
-  Returns:
-    A float: F_lcs
-
-  Raises:
-    ValueError: raises exception if a param has len <= 0
-  """
-  if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
-    raise ValueError("Collections must contain at least 1 sentence.")
-  reference_words = _split_into_words(reference_sentences)
-  evaluated_words = _split_into_words(evaluated_sentences)
-  m = len(reference_words)
-  n = len(evaluated_words)
-  lcs = _len_lcs(evaluated_words, reference_words)
-  return _f_p_r_lcs(lcs, m, n)
+    """
+    Computes ROUGE-L (sentence level) of two text collections of sentences.
+    http://research.microsoft.com/en-us/um/people/cyl/download/papers/
+    rouge-working-note-v1.3.1.pdf
+
+    Calculated according to:
+    R_lcs = LCS(X,Y)/m
+    P_lcs = LCS(X,Y)/n
+    F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)
+
+    where:
+    X = reference summary
+    Y = Candidate summary
+    m = length of reference summary
+    n = length of candidate summary
+
+    Args:
+      evaluated_sentences: The sentences that have been picked by the summarizer
+      reference_sentences: The sentences from the referene set
+
+    Returns:
+      A float: F_lcs
+
+    Raises:
+      ValueError: raises exception if a param has len <= 0
+    """
+    if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
+        raise ValueError("Collections must contain at least 1 sentence.")
+    reference_words = _split_into_words(reference_sentences)
+    evaluated_words = _split_into_words(evaluated_sentences)
+    m = len(reference_words)
+    n = len(evaluated_words)
+    lcs = _len_lcs(evaluated_words, reference_words)
+    return _f_p_r_lcs(lcs, m, n)
 
 
 def _union_lcs(evaluated_sentences, reference_sentence):
-  """
-  Returns LCS_u(r_i, C) which is the LCS score of the union longest common
-  subsequence between reference sentence ri and candidate summary C. For example
-  if r_i= w1 w2 w3 w4 w5, and C contains two sentences: c1 = w1 w2 w6 w7 w8 and
-  c2 = w1 w3 w8 w9 w5, then the longest common subsequence of r_i and c1 is
-  "w1 w2" and the longest common subsequence of r_i and c2 is "w1 w3 w5". The
-  union longest common subsequence of r_i, c1, and c2 is "w1 w2 w3 w5" and
-  LCS_u(r_i, C) = 4/5.
-
-  Args:
-    evaluated_sentences: The sentences that have been picked by the summarizer
-    reference_sentence: One of the sentences in the reference summaries
-
-  Returns:
-    float: LCS_u(r_i, C)
-
-  ValueError:
-    Raises exception if a param has len <= 0
-  """
-  if len(evaluated_sentences) <= 0:
-    raise ValueError("Collections must contain at least 1 sentence.")
-
-  lcs_union = set()
-  reference_words = _split_into_words([reference_sentence])
-  combined_lcs_length = 0
-  for eval_s in evaluated_sentences:
-    evaluated_words = _split_into_words([eval_s])
-    lcs = set(_recon_lcs(reference_words, evaluated_words))
-    combined_lcs_length += len(lcs)
-    lcs_union = lcs_union.union(lcs)
-
-  union_lcs_count = len(lcs_union)
-  union_lcs_value = union_lcs_count / combined_lcs_length
-  return union_lcs_value
+    """
+    Returns LCS_u(r_i, C) which is the LCS score of the union longest common
+    subsequence between reference sentence ri and candidate summary C. For example
+    if r_i= w1 w2 w3 w4 w5, and C contains two sentences: c1 = w1 w2 w6 w7 w8 and
+    c2 = w1 w3 w8 w9 w5, then the longest common subsequence of r_i and c1 is
+    "w1 w2" and the longest common subsequence of r_i and c2 is "w1 w3 w5". The
+    union longest common subsequence of r_i, c1, and c2 is "w1 w2 w3 w5" and
+    LCS_u(r_i, C) = 4/5.
+
+    Args:
+      evaluated_sentences: The sentences that have been picked by the summarizer
+      reference_sentence: One of the sentences in the reference summaries
+
+    Returns:
+      float: LCS_u(r_i, C)
+
+    ValueError:
+      Raises exception if a param has len <= 0
+    """
+    if len(evaluated_sentences) <= 0:
+        raise ValueError("Collections must contain at least 1 sentence.")
+
+    lcs_union = set()
+    reference_words = _split_into_words([reference_sentence])
+    combined_lcs_length = 0
+    for eval_s in evaluated_sentences:
+        evaluated_words = _split_into_words([eval_s])
+        lcs = set(_recon_lcs(reference_words, evaluated_words))
+        combined_lcs_length += len(lcs)
+        lcs_union = lcs_union.union(lcs)
+
+    union_lcs_count = len(lcs_union)
+    union_lcs_value = union_lcs_count / combined_lcs_length
+    return union_lcs_value
 
 
 def rouge_l_summary_level(evaluated_sentences, reference_sentences):
-  """
-  Computes ROUGE-L (summary level) of two text collections of sentences.
-  http://research.microsoft.com/en-us/um/people/cyl/download/papers/
-  rouge-working-note-v1.3.1.pdf
+    """
+    Computes ROUGE-L (summary level) of two text collections of sentences.
+    http://research.microsoft.com/en-us/um/people/cyl/download/papers/
+    rouge-working-note-v1.3.1.pdf
 
-  Calculated according to:
-  R_lcs = SUM(1, u)[LCS<union>(r_i,C)]/m
-  P_lcs = SUM(1, u)[LCS<union>(r_i,C)]/n
-  F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)
+    Calculated according to:
+    R_lcs = SUM(1, u)[LCS<union>(r_i,C)]/m
+    P_lcs = SUM(1, u)[LCS<union>(r_i,C)]/n
+    F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)
 
-  where:
-  SUM(i,u) = SUM from i through u
-  u = number of sentences in reference summary
-  C = Candidate summary made up of v sentences
-  m = number of words in reference summary
-  n = number of words in candidate summary
+    where:
+    SUM(i,u) = SUM from i through u
+    u = number of sentences in reference summary
+    C = Candidate summary made up of v sentences
+    m = number of words in reference summary
+    n = number of words in candidate summary
 
-  Args:
-    evaluated_sentences: The sentences that have been picked by the summarizer
-    reference_sentence: One of the sentences in the reference summaries
+    Args:
+      evaluated_sentences: The sentences that have been picked by the summarizer
+      reference_sentence: One of the sentences in the reference summaries
 
-  Returns:
-    A float: F_lcs
+    Returns:
+      A float: F_lcs
 
-  Raises:
-    ValueError: raises exception if a param has len <= 0
-  """
-  if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
-    raise ValueError("Collections must contain at least 1 sentence.")
+    Raises:
+      ValueError: raises exception if a param has len <= 0
+    """
+    if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
+        raise ValueError("Collections must contain at least 1 sentence.")
 
-  # total number of words in reference sentences
-  m = len(_split_into_words(reference_sentences))
+    # total number of words in reference sentences
+    m = len(_split_into_words(reference_sentences))
 
-  # total number of words in evaluated sentences
-  n = len(_split_into_words(evaluated_sentences))
+    # total number of words in evaluated sentences
+    n = len(_split_into_words(evaluated_sentences))
 
-  union_lcs_sum_across_all_references = 0
-  for ref_s in reference_sentences:
-    union_lcs_sum_across_all_references += _union_lcs(evaluated_sentences,
-                                                      ref_s)
-  return _f_p_r_lcs(union_lcs_sum_across_all_references, m, n)
+    union_lcs_sum_across_all_references = 0
+    for ref_s in reference_sentences:
+        union_lcs_sum_across_all_references += _union_lcs(
+            evaluated_sentences, ref_s)
+    return _f_p_r_lcs(union_lcs_sum_across_all_references, m, n)
 
 
 def rouge(hypotheses, references):
-  """Calculates average rouge scores for a list of hypotheses and
-  references"""
-
-  # Filter out hyps that are of 0 length
-  # hyps_and_refs = zip(hypotheses, references)
-  # hyps_and_refs = [_ for _ in hyps_and_refs if len(_[0]) > 0]
-  # hypotheses, references = zip(*hyps_and_refs)
-
-  # Calculate ROUGE-1 F1, precision, recall scores
-  rouge_1 = [
-      rouge_n([hyp], [ref], 1) for hyp, ref in zip(hypotheses, references)
-  ]
-  rouge_1_f, rouge_1_p, rouge_1_r = map(np.mean, zip(*rouge_1))
-
-  # Calculate ROUGE-2 F1, precision, recall scores
-  rouge_2 = [
-      rouge_n([hyp], [ref], 2) for hyp, ref in zip(hypotheses, references)
-  ]
-  rouge_2_f, rouge_2_p, rouge_2_r = map(np.mean, zip(*rouge_2))
-
-  # Calculate ROUGE-L F1, precision, recall scores
-  rouge_l = [
-      rouge_l_sentence_level([hyp], [ref])
-      for hyp, ref in zip(hypotheses, references)
-  ]
-  rouge_l_f, rouge_l_p, rouge_l_r = map(np.mean, zip(*rouge_l))
-
-  return {
-      "rouge_1/f_score": rouge_1_f,
-      "rouge_1/r_score": rouge_1_r,
-      "rouge_1/p_score": rouge_1_p,
-      "rouge_2/f_score": rouge_2_f,
-      "rouge_2/r_score": rouge_2_r,
-      "rouge_2/p_score": rouge_2_p,
-      "rouge_l/f_score": rouge_l_f,
-      "rouge_l/r_score": rouge_l_r,
-      "rouge_l/p_score": rouge_l_p,
-  }
+    """Calculates average rouge scores for a list of hypotheses and
+    references"""
+
+    # Filter out hyps that are of 0 length
+    # hyps_and_refs = zip(hypotheses, references)
+    # hyps_and_refs = [_ for _ in hyps_and_refs if len(_[0]) > 0]
+    # hypotheses, references = zip(*hyps_and_refs)
+
+    # Calculate ROUGE-1 F1, precision, recall scores
+    rouge_1 = [rouge_n([hyp], [ref], 1)
+               for hyp, ref in zip(hypotheses, references)]
+    rouge_1_f, rouge_1_p, rouge_1_r = map(np.mean, zip(*rouge_1))
+
+    # Calculate ROUGE-2 F1, precision, recall scores
+    rouge_2 = [rouge_n([hyp], [ref], 2)
+               for hyp, ref in zip(hypotheses, references)]
+    rouge_2_f, rouge_2_p, rouge_2_r = map(np.mean, zip(*rouge_2))
+
+    # Calculate ROUGE-L F1, precision, recall scores
+    rouge_l = [
+        rouge_l_sentence_level([hyp], [ref]) for hyp, ref in zip(hypotheses, references)
+    ]
+    rouge_l_f, rouge_l_p, rouge_l_r = map(np.mean, zip(*rouge_l))
+
+    return {
+        "rouge_1/f_score": rouge_1_f,
+        "rouge_1/r_score": rouge_1_r,
+        "rouge_1/p_score": rouge_1_p,
+        "rouge_2/f_score": rouge_2_f,
+        "rouge_2/r_score": rouge_2_r,
+        "rouge_2/p_score": rouge_2_p,
+        "rouge_l/f_score": rouge_l_f,
+        "rouge_l/r_score": rouge_l_r,
+        "rouge_l/p_score": rouge_l_p,
+    }
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/train.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/train.py
index 1f061486bb..a440b1a128 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/train.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/train.py
@@ -33,718 +33,931 @@
 utils.check_tensorflow_version()
 
 __all__ = [
-    "run_sample_decode", "run_internal_eval", "run_external_eval",
-    "run_avg_external_eval", "run_full_eval", "init_stats", "update_stats",
-    "print_step_info", "process_stats", "train", "get_model_creator",
-    "add_info_summaries", "get_best_results"
+    "run_sample_decode",
+    "run_internal_eval",
+    "run_external_eval",
+    "run_avg_external_eval",
+    "run_full_eval",
+    "init_stats",
+    "update_stats",
+    "print_step_info",
+    "process_stats",
+    "train",
+    "get_model_creator",
+    "add_info_summaries",
+    "get_best_results",
 ]
 
 
-def run_sample_decode(infer_model, infer_sess, model_dir, hparams,
-                      summary_writer, src_data, tgt_data):
-  """Sample decode a random sentence from src_data."""
-  with infer_model.graph.as_default():
-    loaded_infer_model, global_step = model_helper.create_or_load_model(
-        infer_model.model, model_dir, infer_sess, "infer")
-
-  _sample_decode(loaded_infer_model, global_step, infer_sess, hparams,
-                 infer_model.iterator, src_data, tgt_data,
-                 infer_model.src_placeholder,
-                 infer_model.batch_size_placeholder, summary_writer)
-
-
-def run_internal_eval(eval_model,
-                      eval_sess,
-                      model_dir,
-                      hparams,
-                      summary_writer,
-                      use_test_set=True,
-                      dev_eval_iterator_feed_dict=None,
-                      test_eval_iterator_feed_dict=None):
-  """Compute internal evaluation (perplexity) for both dev / test.
-
-  Computes development and testing perplexities for given model.
-
-  Args:
-    eval_model: Evaluation model for which to compute perplexities.
-    eval_sess: Evaluation TensorFlow session.
-    model_dir: Directory from which to load evaluation model from.
-    hparams: Model hyper-parameters.
-    summary_writer: Summary writer for logging metrics to TensorBoard.
-    use_test_set: Computes testing perplexity if true; does not otherwise.
-      Note that the development perplexity is always computed regardless of
-      value of this parameter.
-    dev_eval_iterator_feed_dict: Feed dictionary for a TensorFlow session.
-      Can be used to pass in additional inputs necessary for running the
-      development evaluation.
-    test_eval_iterator_feed_dict: Feed dictionary for a TensorFlow session.
-      Can be used to pass in additional inputs necessary for running the
-      testing evaluation.
-  Returns:
-    Pair containing development perplexity and testing perplexity, in this
-    order.
-  """
-  if dev_eval_iterator_feed_dict is None:
-    dev_eval_iterator_feed_dict = {}
-  if test_eval_iterator_feed_dict is None:
-    test_eval_iterator_feed_dict = {}
-  with eval_model.graph.as_default():
-    loaded_eval_model, global_step = model_helper.create_or_load_model(
-        eval_model.model, model_dir, eval_sess, "eval")
-
-  dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
-  dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
-  dev_eval_iterator_feed_dict[eval_model.src_file_placeholder] = dev_src_file
-  dev_eval_iterator_feed_dict[eval_model.tgt_file_placeholder] = dev_tgt_file
-
-  dev_ppl = _internal_eval(loaded_eval_model, global_step, eval_sess,
-                           eval_model.iterator, dev_eval_iterator_feed_dict,
-                           summary_writer, "dev")
-  test_ppl = None
-  if use_test_set and hparams.test_prefix:
-    test_src_file = "%s.%s" % (hparams.test_prefix, hparams.src)
-    test_tgt_file = "%s.%s" % (hparams.test_prefix, hparams.tgt)
-    test_eval_iterator_feed_dict[
-        eval_model.src_file_placeholder] = test_src_file
-    test_eval_iterator_feed_dict[
-        eval_model.tgt_file_placeholder] = test_tgt_file
-    test_ppl = _internal_eval(loaded_eval_model, global_step, eval_sess,
-                              eval_model.iterator, test_eval_iterator_feed_dict,
-                              summary_writer, "test")
-  return dev_ppl, test_ppl
-
-
-def run_external_eval(infer_model,
-                      infer_sess,
-                      model_dir,
-                      hparams,
-                      summary_writer,
-                      save_best_dev=True,
-                      use_test_set=True,
-                      avg_ckpts=False,
-                      dev_infer_iterator_feed_dict=None,
-                      test_infer_iterator_feed_dict=None):
-  """Compute external evaluation for both dev / test.
-
-  Computes development and testing external evaluation (e.g. bleu, rouge) for
-  given model.
-
-  Args:
-    infer_model: Inference model for which to compute perplexities.
-    infer_sess: Inference TensorFlow session.
-    model_dir: Directory from which to load inference model from.
-    hparams: Model hyper-parameters.
-    summary_writer: Summary writer for logging metrics to TensorBoard.
-    use_test_set: Computes testing external evaluation if true; does not
-      otherwise. Note that the development external evaluation is always
-      computed regardless of value of this parameter.
-    dev_infer_iterator_feed_dict: Feed dictionary for a TensorFlow session.
-      Can be used to pass in additional inputs necessary for running the
-      development external evaluation.
-    test_infer_iterator_feed_dict: Feed dictionary for a TensorFlow session.
-      Can be used to pass in additional inputs necessary for running the
-      testing external evaluation.
-  Returns:
-    Triple containing development scores, testing scores and the TensorFlow
-    Variable for the global step number, in this order.
-  """
-  if dev_infer_iterator_feed_dict is None:
-    dev_infer_iterator_feed_dict = {}
-  if test_infer_iterator_feed_dict is None:
-    test_infer_iterator_feed_dict = {}
-  with infer_model.graph.as_default():
-    loaded_infer_model, global_step = model_helper.create_or_load_model(
-        infer_model.model, model_dir, infer_sess, "infer")
-
-  dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
-  dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
-  dev_infer_iterator_feed_dict[
-      infer_model.src_placeholder] = inference.load_data(dev_src_file)
-  dev_infer_iterator_feed_dict[
-      infer_model.batch_size_placeholder] = hparams.infer_batch_size
-  dev_scores = _external_eval(
-      loaded_infer_model,
-      global_step,
-      infer_sess,
-      hparams,
-      infer_model.iterator,
-      dev_infer_iterator_feed_dict,
-      dev_tgt_file,
-      "dev",
-      summary_writer,
-      save_on_best=save_best_dev,
-      avg_ckpts=avg_ckpts)
-
-  test_scores = None
-  if use_test_set and hparams.test_prefix:
-    test_src_file = "%s.%s" % (hparams.test_prefix, hparams.src)
-    test_tgt_file = "%s.%s" % (hparams.test_prefix, hparams.tgt)
-    test_infer_iterator_feed_dict[
-        infer_model.src_placeholder] = inference.load_data(test_src_file)
-    test_infer_iterator_feed_dict[
-        infer_model.batch_size_placeholder] = hparams.infer_batch_size
-    test_scores = _external_eval(
+def run_sample_decode(
+    infer_model, infer_sess, model_dir, hparams, summary_writer, src_data, tgt_data
+):
+    """Sample decode a random sentence from src_data."""
+    with infer_model.graph.as_default():
+        loaded_infer_model, global_step = model_helper.create_or_load_model(
+            infer_model.model, model_dir, infer_sess, "infer"
+        )
+
+    _sample_decode(
+        loaded_infer_model,
+        global_step,
+        infer_sess,
+        hparams,
+        infer_model.iterator,
+        src_data,
+        tgt_data,
+        infer_model.src_placeholder,
+        infer_model.batch_size_placeholder,
+        summary_writer,
+    )
+
+
+def run_internal_eval(
+    eval_model,
+    eval_sess,
+    model_dir,
+    hparams,
+    summary_writer,
+    use_test_set=True,
+    dev_eval_iterator_feed_dict=None,
+    test_eval_iterator_feed_dict=None,
+):
+    """Compute internal evaluation (perplexity) for both dev / test.
+
+    Computes development and testing perplexities for given model.
+
+    Args:
+      eval_model: Evaluation model for which to compute perplexities.
+      eval_sess: Evaluation TensorFlow session.
+      model_dir: Directory from which to load evaluation model from.
+      hparams: Model hyper-parameters.
+      summary_writer: Summary writer for logging metrics to TensorBoard.
+      use_test_set: Computes testing perplexity if true; does not otherwise.
+        Note that the development perplexity is always computed regardless of
+        value of this parameter.
+      dev_eval_iterator_feed_dict: Feed dictionary for a TensorFlow session.
+        Can be used to pass in additional inputs necessary for running the
+        development evaluation.
+      test_eval_iterator_feed_dict: Feed dictionary for a TensorFlow session.
+        Can be used to pass in additional inputs necessary for running the
+        testing evaluation.
+    Returns:
+      Pair containing development perplexity and testing perplexity, in this
+      order.
+    """
+    if dev_eval_iterator_feed_dict is None:
+        dev_eval_iterator_feed_dict = {}
+    if test_eval_iterator_feed_dict is None:
+        test_eval_iterator_feed_dict = {}
+    with eval_model.graph.as_default():
+        loaded_eval_model, global_step = model_helper.create_or_load_model(
+            eval_model.model, model_dir, eval_sess, "eval"
+        )
+
+    dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
+    dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
+    dev_eval_iterator_feed_dict[eval_model.src_file_placeholder] = dev_src_file
+    dev_eval_iterator_feed_dict[eval_model.tgt_file_placeholder] = dev_tgt_file
+
+    dev_ppl = _internal_eval(
+        loaded_eval_model,
+        global_step,
+        eval_sess,
+        eval_model.iterator,
+        dev_eval_iterator_feed_dict,
+        summary_writer,
+        "dev",
+    )
+    test_ppl = None
+    if use_test_set and hparams.test_prefix:
+        test_src_file = "%s.%s" % (hparams.test_prefix, hparams.src)
+        test_tgt_file = "%s.%s" % (hparams.test_prefix, hparams.tgt)
+        test_eval_iterator_feed_dict[eval_model.src_file_placeholder] = test_src_file
+        test_eval_iterator_feed_dict[eval_model.tgt_file_placeholder] = test_tgt_file
+        test_ppl = _internal_eval(
+            loaded_eval_model,
+            global_step,
+            eval_sess,
+            eval_model.iterator,
+            test_eval_iterator_feed_dict,
+            summary_writer,
+            "test",
+        )
+    return dev_ppl, test_ppl
+
+
+def run_external_eval(
+    infer_model,
+    infer_sess,
+    model_dir,
+    hparams,
+    summary_writer,
+    save_best_dev=True,
+    use_test_set=True,
+    avg_ckpts=False,
+    dev_infer_iterator_feed_dict=None,
+    test_infer_iterator_feed_dict=None,
+):
+    """Compute external evaluation for both dev / test.
+
+    Computes development and testing external evaluation (e.g. bleu, rouge) for
+    given model.
+
+    Args:
+      infer_model: Inference model for which to compute perplexities.
+      infer_sess: Inference TensorFlow session.
+      model_dir: Directory from which to load inference model from.
+      hparams: Model hyper-parameters.
+      summary_writer: Summary writer for logging metrics to TensorBoard.
+      use_test_set: Computes testing external evaluation if true; does not
+        otherwise. Note that the development external evaluation is always
+        computed regardless of value of this parameter.
+      dev_infer_iterator_feed_dict: Feed dictionary for a TensorFlow session.
+        Can be used to pass in additional inputs necessary for running the
+        development external evaluation.
+      test_infer_iterator_feed_dict: Feed dictionary for a TensorFlow session.
+        Can be used to pass in additional inputs necessary for running the
+        testing external evaluation.
+    Returns:
+      Triple containing development scores, testing scores and the TensorFlow
+      Variable for the global step number, in this order.
+    """
+    if dev_infer_iterator_feed_dict is None:
+        dev_infer_iterator_feed_dict = {}
+    if test_infer_iterator_feed_dict is None:
+        test_infer_iterator_feed_dict = {}
+    with infer_model.graph.as_default():
+        loaded_infer_model, global_step = model_helper.create_or_load_model(
+            infer_model.model, model_dir, infer_sess, "infer"
+        )
+
+    dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
+    dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
+    dev_infer_iterator_feed_dict[infer_model.src_placeholder] = inference.load_data(
+        dev_src_file
+    )
+    dev_infer_iterator_feed_dict[infer_model.batch_size_placeholder] = (
+        hparams.infer_batch_size
+    )
+    dev_scores = _external_eval(
         loaded_infer_model,
         global_step,
         infer_sess,
         hparams,
         infer_model.iterator,
-        test_infer_iterator_feed_dict,
-        test_tgt_file,
-        "test",
+        dev_infer_iterator_feed_dict,
+        dev_tgt_file,
+        "dev",
+        summary_writer,
+        save_on_best=save_best_dev,
+        avg_ckpts=avg_ckpts,
+    )
+
+    test_scores = None
+    if use_test_set and hparams.test_prefix:
+        test_src_file = "%s.%s" % (hparams.test_prefix, hparams.src)
+        test_tgt_file = "%s.%s" % (hparams.test_prefix, hparams.tgt)
+        test_infer_iterator_feed_dict[infer_model.src_placeholder] = (
+            inference.load_data(test_src_file)
+        )
+        test_infer_iterator_feed_dict[infer_model.batch_size_placeholder] = (
+            hparams.infer_batch_size
+        )
+        test_scores = _external_eval(
+            loaded_infer_model,
+            global_step,
+            infer_sess,
+            hparams,
+            infer_model.iterator,
+            test_infer_iterator_feed_dict,
+            test_tgt_file,
+            "test",
+            summary_writer,
+            save_on_best=False,
+            avg_ckpts=avg_ckpts,
+        )
+    return dev_scores, test_scores, global_step
+
+
+def run_avg_external_eval(
+    infer_model, infer_sess, model_dir, hparams, summary_writer, global_step
+):
+    """Creates an averaged checkpoint and run external eval with it."""
+    avg_dev_scores, avg_test_scores = None, None
+    if hparams.avg_ckpts:
+        # Convert VariableName:0 to VariableName.
+        global_step_name = infer_model.model.global_step.name.split(":")[0]
+        avg_model_dir = model_helper.avg_checkpoints(
+            model_dir, hparams.num_keep_ckpts, global_step, global_step_name
+        )
+
+        if avg_model_dir:
+            avg_dev_scores, avg_test_scores, _ = run_external_eval(
+                infer_model,
+                infer_sess,
+                avg_model_dir,
+                hparams,
+                summary_writer,
+                avg_ckpts=True,
+            )
+
+    return avg_dev_scores, avg_test_scores
+
+
+def run_internal_and_external_eval(
+    model_dir,
+    infer_model,
+    infer_sess,
+    eval_model,
+    eval_sess,
+    hparams,
+    summary_writer,
+    avg_ckpts=False,
+    dev_eval_iterator_feed_dict=None,
+    test_eval_iterator_feed_dict=None,
+    dev_infer_iterator_feed_dict=None,
+    test_infer_iterator_feed_dict=None,
+):
+    """Compute internal evaluation (perplexity) for both dev / test.
+
+    Computes development and testing perplexities for given model.
+
+    Args:
+      model_dir: Directory from which to load models from.
+      infer_model: Inference model for which to compute perplexities.
+      infer_sess: Inference TensorFlow session.
+      eval_model: Evaluation model for which to compute perplexities.
+      eval_sess: Evaluation TensorFlow session.
+      hparams: Model hyper-parameters.
+      summary_writer: Summary writer for logging metrics to TensorBoard.
+      avg_ckpts: Whether to compute average external evaluation scores.
+      dev_eval_iterator_feed_dict: Feed dictionary for a TensorFlow session.
+        Can be used to pass in additional inputs necessary for running the
+        internal development evaluation.
+      test_eval_iterator_feed_dict: Feed dictionary for a TensorFlow session.
+        Can be used to pass in additional inputs necessary for running the
+        internal testing evaluation.
+      dev_infer_iterator_feed_dict: Feed dictionary for a TensorFlow session.
+        Can be used to pass in additional inputs necessary for running the
+        external development evaluation.
+      test_infer_iterator_feed_dict: Feed dictionary for a TensorFlow session.
+        Can be used to pass in additional inputs necessary for running the
+        external testing evaluation.
+    Returns:
+      Triple containing results summary, global step Tensorflow Variable and
+      metrics in this order.
+    """
+    dev_ppl, test_ppl = run_internal_eval(
+        eval_model,
+        eval_sess,
+        model_dir,
+        hparams,
+        summary_writer,
+        dev_eval_iterator_feed_dict=dev_eval_iterator_feed_dict,
+        test_eval_iterator_feed_dict=test_eval_iterator_feed_dict,
+    )
+    dev_scores, test_scores, global_step = run_external_eval(
+        infer_model,
+        infer_sess,
+        model_dir,
+        hparams,
+        summary_writer,
+        dev_infer_iterator_feed_dict=dev_infer_iterator_feed_dict,
+        test_infer_iterator_feed_dict=test_infer_iterator_feed_dict,
+    )
+
+    metrics = {
+        "dev_ppl": dev_ppl,
+        "test_ppl": test_ppl,
+        "dev_scores": dev_scores,
+        "test_scores": test_scores,
+    }
+
+    avg_dev_scores, avg_test_scores = None, None
+    if avg_ckpts:
+        avg_dev_scores, avg_test_scores = run_avg_external_eval(
+            infer_model, infer_sess, model_dir, hparams, summary_writer, global_step
+        )
+        metrics["avg_dev_scores"] = avg_dev_scores
+        metrics["avg_test_scores"] = avg_test_scores
+
+    result_summary = _format_results(
+        "dev", dev_ppl, dev_scores, hparams.metrics)
+    if avg_dev_scores:
+        result_summary += ", " + _format_results(
+            "avg_dev", None, avg_dev_scores, hparams.metrics
+        )
+    if hparams.test_prefix:
+        result_summary += ", " + _format_results(
+            "test", test_ppl, test_scores, hparams.metrics
+        )
+        if avg_test_scores:
+            result_summary += ", " + _format_results(
+                "avg_test", None, avg_test_scores, hparams.metrics
+            )
+
+    return result_summary, global_step, metrics
+
+
+def run_full_eval(
+    model_dir,
+    infer_model,
+    infer_sess,
+    eval_model,
+    eval_sess,
+    hparams,
+    summary_writer,
+    sample_src_data,
+    sample_tgt_data,
+    avg_ckpts=False,
+):
+    """Wrapper for running sample_decode, internal_eval and external_eval.
+
+    Args:
+      model_dir: Directory from which to load models from.
+      infer_model: Inference model for which to compute perplexities.
+      infer_sess: Inference TensorFlow session.
+      eval_model: Evaluation model for which to compute perplexities.
+      eval_sess: Evaluation TensorFlow session.
+      hparams: Model hyper-parameters.
+      summary_writer: Summary writer for logging metrics to TensorBoard.
+      sample_src_data: sample of source data for sample decoding.
+      sample_tgt_data: sample of target data for sample decoding.
+      avg_ckpts: Whether to compute average external evaluation scores.
+    Returns:
+      Triple containing results summary, global step Tensorflow Variable and
+      metrics in this order.
+    """
+    run_sample_decode(
+        infer_model,
+        infer_sess,
+        model_dir,
+        hparams,
         summary_writer,
-        save_on_best=False,
-        avg_ckpts=avg_ckpts)
-  return dev_scores, test_scores, global_step
-
-
-def run_avg_external_eval(infer_model, infer_sess, model_dir, hparams,
-                          summary_writer, global_step):
-  """Creates an averaged checkpoint and run external eval with it."""
-  avg_dev_scores, avg_test_scores = None, None
-  if hparams.avg_ckpts:
-    # Convert VariableName:0 to VariableName.
-    global_step_name = infer_model.model.global_step.name.split(":")[0]
-    avg_model_dir = model_helper.avg_checkpoints(
-        model_dir, hparams.num_keep_ckpts, global_step, global_step_name)
-
-    if avg_model_dir:
-      avg_dev_scores, avg_test_scores, _ = run_external_eval(
-          infer_model,
-          infer_sess,
-          avg_model_dir,
-          hparams,
-          summary_writer,
-          avg_ckpts=True)
-
-  return avg_dev_scores, avg_test_scores
-
-
-def run_internal_and_external_eval(model_dir,
-                                   infer_model,
-                                   infer_sess,
-                                   eval_model,
-                                   eval_sess,
-                                   hparams,
-                                   summary_writer,
-                                   avg_ckpts=False,
-                                   dev_eval_iterator_feed_dict=None,
-                                   test_eval_iterator_feed_dict=None,
-                                   dev_infer_iterator_feed_dict=None,
-                                   test_infer_iterator_feed_dict=None):
-  """Compute internal evaluation (perplexity) for both dev / test.
-
-  Computes development and testing perplexities for given model.
-
-  Args:
-    model_dir: Directory from which to load models from.
-    infer_model: Inference model for which to compute perplexities.
-    infer_sess: Inference TensorFlow session.
-    eval_model: Evaluation model for which to compute perplexities.
-    eval_sess: Evaluation TensorFlow session.
-    hparams: Model hyper-parameters.
-    summary_writer: Summary writer for logging metrics to TensorBoard.
-    avg_ckpts: Whether to compute average external evaluation scores.
-    dev_eval_iterator_feed_dict: Feed dictionary for a TensorFlow session.
-      Can be used to pass in additional inputs necessary for running the
-      internal development evaluation.
-    test_eval_iterator_feed_dict: Feed dictionary for a TensorFlow session.
-      Can be used to pass in additional inputs necessary for running the
-      internal testing evaluation.
-    dev_infer_iterator_feed_dict: Feed dictionary for a TensorFlow session.
-      Can be used to pass in additional inputs necessary for running the
-      external development evaluation.
-    test_infer_iterator_feed_dict: Feed dictionary for a TensorFlow session.
-      Can be used to pass in additional inputs necessary for running the
-      external testing evaluation.
-  Returns:
-    Triple containing results summary, global step Tensorflow Variable and
-    metrics in this order.
-  """
-  dev_ppl, test_ppl = run_internal_eval(
-      eval_model,
-      eval_sess,
-      model_dir,
-      hparams,
-      summary_writer,
-      dev_eval_iterator_feed_dict=dev_eval_iterator_feed_dict,
-      test_eval_iterator_feed_dict=test_eval_iterator_feed_dict)
-  dev_scores, test_scores, global_step = run_external_eval(
-      infer_model,
-      infer_sess,
-      model_dir,
-      hparams,
-      summary_writer,
-      dev_infer_iterator_feed_dict=dev_infer_iterator_feed_dict,
-      test_infer_iterator_feed_dict=test_infer_iterator_feed_dict)
-
-  metrics = {
-      "dev_ppl": dev_ppl,
-      "test_ppl": test_ppl,
-      "dev_scores": dev_scores,
-      "test_scores": test_scores,
-  }
-
-  avg_dev_scores, avg_test_scores = None, None
-  if avg_ckpts:
-    avg_dev_scores, avg_test_scores = run_avg_external_eval(
-        infer_model, infer_sess, model_dir, hparams, summary_writer,
-        global_step)
-    metrics["avg_dev_scores"] = avg_dev_scores
-    metrics["avg_test_scores"] = avg_test_scores
-
-  result_summary = _format_results("dev", dev_ppl, dev_scores, hparams.metrics)
-  if avg_dev_scores:
-    result_summary += ", " + _format_results("avg_dev", None, avg_dev_scores,
-                                             hparams.metrics)
-  if hparams.test_prefix:
-    result_summary += ", " + _format_results("test", test_ppl, test_scores,
-                                             hparams.metrics)
-    if avg_test_scores:
-      result_summary += ", " + _format_results("avg_test", None,
-                                               avg_test_scores, hparams.metrics)
-
-  return result_summary, global_step, metrics
-
-
-def run_full_eval(model_dir,
-                  infer_model,
-                  infer_sess,
-                  eval_model,
-                  eval_sess,
-                  hparams,
-                  summary_writer,
-                  sample_src_data,
-                  sample_tgt_data,
-                  avg_ckpts=False):
-  """Wrapper for running sample_decode, internal_eval and external_eval.
-
-  Args:
-    model_dir: Directory from which to load models from.
-    infer_model: Inference model for which to compute perplexities.
-    infer_sess: Inference TensorFlow session.
-    eval_model: Evaluation model for which to compute perplexities.
-    eval_sess: Evaluation TensorFlow session.
-    hparams: Model hyper-parameters.
-    summary_writer: Summary writer for logging metrics to TensorBoard.
-    sample_src_data: sample of source data for sample decoding.
-    sample_tgt_data: sample of target data for sample decoding.
-    avg_ckpts: Whether to compute average external evaluation scores.
-  Returns:
-    Triple containing results summary, global step Tensorflow Variable and
-    metrics in this order.
-  """
-  run_sample_decode(infer_model, infer_sess, model_dir, hparams, summary_writer,
-                    sample_src_data, sample_tgt_data)
-  return run_internal_and_external_eval(model_dir, infer_model, infer_sess,
-                                        eval_model, eval_sess, hparams,
-                                        summary_writer, avg_ckpts)
+        sample_src_data,
+        sample_tgt_data,
+    )
+    return run_internal_and_external_eval(
+        model_dir,
+        infer_model,
+        infer_sess,
+        eval_model,
+        eval_sess,
+        hparams,
+        summary_writer,
+        avg_ckpts,
+    )
 
 
 def init_stats():
-  """Initialize statistics that we want to accumulate."""
-  return {"step_time": 0.0, "train_loss": 0.0,
-          "predict_count": 0.0,  # word count on the target side
-          "word_count": 0.0,  # word counts for both source and target
-          "sequence_count": 0.0,  # number of training examples processed
-          "grad_norm": 0.0}
+    """Initialize statistics that we want to accumulate."""
+    return {
+        "step_time": 0.0,
+        "train_loss": 0.0,
+        "predict_count": 0.0,  # word count on the target side
+        "word_count": 0.0,  # word counts for both source and target
+        "sequence_count": 0.0,  # number of training examples processed
+        "grad_norm": 0.0,
+    }
 
 
 def update_stats(stats, start_time, step_result):
-  """Update stats: write summary and accumulate statistics."""
-  _, output_tuple = step_result
+    """Update stats: write summary and accumulate statistics."""
+    _, output_tuple = step_result
 
-  # Update statistics
-  batch_size = output_tuple.batch_size
-  stats["step_time"] += time.time() - start_time
-  stats["train_loss"] += output_tuple.train_loss * batch_size
-  stats["grad_norm"] += output_tuple.grad_norm
-  stats["predict_count"] += output_tuple.predict_count
-  stats["word_count"] += output_tuple.word_count
-  stats["sequence_count"] += batch_size
+    # Update statistics
+    batch_size = output_tuple.batch_size
+    stats["step_time"] += time.time() - start_time
+    stats["train_loss"] += output_tuple.train_loss * batch_size
+    stats["grad_norm"] += output_tuple.grad_norm
+    stats["predict_count"] += output_tuple.predict_count
+    stats["word_count"] += output_tuple.word_count
+    stats["sequence_count"] += batch_size
 
-  return (output_tuple.global_step, output_tuple.learning_rate,
-          output_tuple.train_summary)
+    return (
+        output_tuple.global_step,
+        output_tuple.learning_rate,
+        output_tuple.train_summary,
+    )
 
 
 def print_step_info(prefix, global_step, info, result_summary, log_f):
-  """Print all info at the current global step."""
-  utils.print_out(
-      "%sstep %d lr %g step-time %.2fs wps %.2fK ppl %.2f gN %.2f %s, %s" %
-      (prefix, global_step, info["learning_rate"], info["avg_step_time"],
-       info["speed"], info["train_ppl"], info["avg_grad_norm"], result_summary,
-       time.ctime()),
-      log_f)
+    """Print all info at the current global step."""
+    utils.print_out(
+        "%sstep %d lr %g step-time %.2fs wps %.2fK ppl %.2f gN %.2f %s, %s"
+        % (
+            prefix,
+            global_step,
+            info["learning_rate"],
+            info["avg_step_time"],
+            info["speed"],
+            info["train_ppl"],
+            info["avg_grad_norm"],
+            result_summary,
+            time.ctime(),
+        ),
+        log_f,
+    )
 
 
 def add_info_summaries(summary_writer, global_step, info):
-  """Add stuffs in info to summaries."""
-  excluded_list = ["learning_rate"]
-  for key in info:
-    if key not in excluded_list:
-      utils.add_summary(summary_writer, global_step, key, info[key])
+    """Add stuffs in info to summaries."""
+    excluded_list = ["learning_rate"]
+    for key in info:
+        if key not in excluded_list:
+            utils.add_summary(summary_writer, global_step, key, info[key])
 
 
 def process_stats(stats, info, global_step, steps_per_stats, log_f):
-  """Update info and check for overflow."""
-  # Per-step info
-  info["avg_step_time"] = stats["step_time"] / steps_per_stats
-  info["avg_grad_norm"] = stats["grad_norm"] / steps_per_stats
-  info["avg_sequence_count"] = stats["sequence_count"] / steps_per_stats
-  info["speed"] = stats["word_count"] / (1000 * stats["step_time"])
-
-  # Per-predict info
-  info["train_ppl"] = (
-      utils.safe_exp(stats["train_loss"] / stats["predict_count"]))
-
-  # Check for overflow
-  is_overflow = False
-  train_ppl = info["train_ppl"]
-  if math.isnan(train_ppl) or math.isinf(train_ppl) or train_ppl > 1e20:
-    utils.print_out("  step %d overflow, stop early" % global_step,
-                    log_f)
-    is_overflow = True
-
-  return is_overflow
-
-
-def before_train(loaded_train_model, train_model, train_sess, global_step,
-                 hparams, log_f):
-  """Misc tasks to do before training."""
-  stats = init_stats()
-  info = {"train_ppl": 0.0, "speed": 0.0,
-          "avg_step_time": 0.0,
-          "avg_grad_norm": 0.0,
-          "avg_sequence_count": 0.0,
-          "learning_rate": loaded_train_model.learning_rate.eval(
-              session=train_sess)}
-  start_train_time = time.time()
-  utils.print_out("# Start step %d, lr %g, %s" %
-                  (global_step, info["learning_rate"], time.ctime()), log_f)
-
-  # Initialize all of the iterators
-  skip_count = hparams.batch_size * hparams.epoch_step
-  utils.print_out("# Init train iterator, skipping %d elements" % skip_count)
-  train_sess.run(
-      train_model.iterator.initializer,
-      feed_dict={train_model.skip_count_placeholder: skip_count})
-
-  return stats, info, start_train_time
+    """Update info and check for overflow."""
+    # Per-step info
+    info["avg_step_time"] = stats["step_time"] / steps_per_stats
+    info["avg_grad_norm"] = stats["grad_norm"] / steps_per_stats
+    info["avg_sequence_count"] = stats["sequence_count"] / steps_per_stats
+    info["speed"] = stats["word_count"] / (1000 * stats["step_time"])
+
+    # Per-predict info
+    info["train_ppl"] = utils.safe_exp(
+        stats["train_loss"] / stats["predict_count"])
+
+    # Check for overflow
+    is_overflow = False
+    train_ppl = info["train_ppl"]
+    if math.isnan(train_ppl) or math.isinf(train_ppl) or train_ppl > 1e20:
+        utils.print_out("  step %d overflow, stop early" % global_step, log_f)
+        is_overflow = True
+
+    return is_overflow
+
+
+def before_train(
+    loaded_train_model, train_model, train_sess, global_step, hparams, log_f
+):
+    """Misc tasks to do before training."""
+    stats = init_stats()
+    info = {
+        "train_ppl": 0.0,
+        "speed": 0.0,
+        "avg_step_time": 0.0,
+        "avg_grad_norm": 0.0,
+        "avg_sequence_count": 0.0,
+        "learning_rate": loaded_train_model.learning_rate.eval(session=train_sess),
+    }
+    start_train_time = time.time()
+    utils.print_out(
+        "# Start step %d, lr %g, %s"
+        % (global_step, info["learning_rate"], time.ctime()),
+        log_f,
+    )
+
+    # Initialize all of the iterators
+    skip_count = hparams.batch_size * hparams.epoch_step
+    utils.print_out("# Init train iterator, skipping %d elements" % skip_count)
+    train_sess.run(
+        train_model.iterator.initializer,
+        feed_dict={train_model.skip_count_placeholder: skip_count},
+    )
+
+    return stats, info, start_train_time
 
 
 def get_model_creator(hparams):
-  """Get the right model class depending on configuration."""
-  if (hparams.encoder_type == "gnmt" or
-      hparams.attention_architecture in ["gnmt", "gnmt_v2"]):
-    model_creator = gnmt_model.GNMTModel
-  elif hparams.attention_architecture == "standard":
-    model_creator = attention_model.AttentionModel
-  elif not hparams.attention:
-    model_creator = nmt_model.Model
-  else:
-    raise ValueError("Unknown attention architecture %s" %
-                     hparams.attention_architecture)
-  return model_creator
+    """Get the right model class depending on configuration."""
+    if hparams.encoder_type == "gnmt" or hparams.attention_architecture in [
+        "gnmt",
+        "gnmt_v2",
+    ]:
+        model_creator = gnmt_model.GNMTModel
+    elif hparams.attention_architecture == "standard":
+        model_creator = attention_model.AttentionModel
+    elif not hparams.attention:
+        model_creator = nmt_model.Model
+    else:
+        raise ValueError(
+            "Unknown attention architecture %s" % hparams.attention_architecture
+        )
+    return model_creator
 
 
 def train(hparams, scope=None, target_session=""):
-  """Train a translation model."""
-  log_device_placement = hparams.log_device_placement
-  out_dir = hparams.out_dir
-  num_train_steps = hparams.num_train_steps
-  steps_per_stats = hparams.steps_per_stats
-  steps_per_external_eval = hparams.steps_per_external_eval
-  steps_per_eval = 10 * steps_per_stats
-  avg_ckpts = hparams.avg_ckpts
-
-  if not steps_per_external_eval:
-    steps_per_external_eval = 5 * steps_per_eval
-
-  # Create model
-  model_creator = get_model_creator(hparams)
-  train_model = model_helper.create_train_model(model_creator, hparams, scope)
-  eval_model = model_helper.create_eval_model(model_creator, hparams, scope)
-  infer_model = model_helper.create_infer_model(model_creator, hparams, scope)
-
-  # Preload data for sample decoding.
-  dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
-  dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
-  sample_src_data = inference.load_data(dev_src_file)
-  sample_tgt_data = inference.load_data(dev_tgt_file)
-
-  summary_name = "train_log"
-  model_dir = hparams.out_dir
-
-  # Log and output files
-  log_file = os.path.join(out_dir, "log_%d" % time.time())
-  log_f = tf.gfile.GFile(log_file, mode="a")
-  utils.print_out("# log_file=%s" % log_file, log_f)
-
-  # TensorFlow model
-  config_proto = utils.get_config_proto(
-      log_device_placement=log_device_placement,
-      num_intra_threads=hparams.num_intra_threads,
-      num_inter_threads=hparams.num_inter_threads)
-  train_sess = tf.Session(
-      target=target_session, config=config_proto, graph=train_model.graph)
-  eval_sess = tf.Session(
-      target=target_session, config=config_proto, graph=eval_model.graph)
-  infer_sess = tf.Session(
-      target=target_session, config=config_proto, graph=infer_model.graph)
-
-  with train_model.graph.as_default():
-    loaded_train_model, global_step = model_helper.create_or_load_model(
-        train_model.model, model_dir, train_sess, "train")
-
-  # Summary writer
-  summary_writer = tf.summary.FileWriter(
-      os.path.join(out_dir, summary_name), train_model.graph)
-
-  # First evaluation
-  run_full_eval(
-      model_dir, infer_model, infer_sess,
-      eval_model, eval_sess, hparams,
-      summary_writer, sample_src_data,
-      sample_tgt_data, avg_ckpts)
-
-  last_stats_step = global_step
-  last_eval_step = global_step
-  last_external_eval_step = global_step
-
-  # This is the training loop.
-  stats, info, start_train_time = before_train(
-      loaded_train_model, train_model, train_sess, global_step, hparams, log_f)
-  while global_step < num_train_steps:
-    ### Run a step ###
-    start_time = time.time()
-    try:
-      step_result = loaded_train_model.train(train_sess)
-      hparams.epoch_step += 1
-    except tf.errors.OutOfRangeError:
-      # Finished going through the training dataset.  Go to next epoch.
-      hparams.epoch_step = 0
-      utils.print_out(
-          "# Finished an epoch, step %d. Perform external evaluation" %
-          global_step)
-      run_sample_decode(infer_model, infer_sess, model_dir, hparams,
-                        summary_writer, sample_src_data, sample_tgt_data)
-      run_external_eval(infer_model, infer_sess, model_dir, hparams,
-                        summary_writer)
-
-      if avg_ckpts:
-        run_avg_external_eval(infer_model, infer_sess, model_dir, hparams,
-                              summary_writer, global_step)
-
-      train_sess.run(
-          train_model.iterator.initializer,
-          feed_dict={train_model.skip_count_placeholder: 0})
-      continue
-
-    # Process step_result, accumulate stats, and write summary
-    global_step, info["learning_rate"], step_summary = update_stats(
-        stats, start_time, step_result)
-    summary_writer.add_summary(step_summary, global_step)
-
-    # Once in a while, we print statistics.
-    if global_step - last_stats_step >= steps_per_stats:
-      last_stats_step = global_step
-      is_overflow = process_stats(
-          stats, info, global_step, steps_per_stats, log_f)
-      print_step_info("  ", global_step, info, get_best_results(hparams),
-                      log_f)
-      if is_overflow:
-        break
-
-      # Reset statistics
-      stats = init_stats()
-
-    if global_step - last_eval_step >= steps_per_eval:
-      last_eval_step = global_step
-      utils.print_out("# Save eval, global step %d" % global_step)
-      add_info_summaries(summary_writer, global_step, info)
-
-      # Save checkpoint
-      loaded_train_model.saver.save(
-          train_sess,
-          os.path.join(out_dir, "translate.ckpt"),
-          global_step=global_step)
-
-      # Evaluate on dev/test
-      run_sample_decode(infer_model, infer_sess,
-                        model_dir, hparams, summary_writer, sample_src_data,
-                        sample_tgt_data)
-      run_internal_eval(
-          eval_model, eval_sess, model_dir, hparams, summary_writer)
-
-    if global_step - last_external_eval_step >= steps_per_external_eval:
-      last_external_eval_step = global_step
-
-      # Save checkpoint
-      loaded_train_model.saver.save(
-          train_sess,
-          os.path.join(out_dir, "translate.ckpt"),
-          global_step=global_step)
-      run_sample_decode(infer_model, infer_sess,
-                        model_dir, hparams, summary_writer, sample_src_data,
-                        sample_tgt_data)
-      run_external_eval(
-          infer_model, infer_sess, model_dir,
-          hparams, summary_writer)
-
-      if avg_ckpts:
-        run_avg_external_eval(infer_model, infer_sess, model_dir, hparams,
-                              summary_writer, global_step)
-
-  # Done training
-  loaded_train_model.saver.save(
-      train_sess,
-      os.path.join(out_dir, "translate.ckpt"),
-      global_step=global_step)
-
-  (result_summary, _, final_eval_metrics) = (
-      run_full_eval(
-          model_dir, infer_model, infer_sess, eval_model, eval_sess, hparams,
-          summary_writer, sample_src_data, sample_tgt_data, avg_ckpts))
-  print_step_info("# Final, ", global_step, info, result_summary, log_f)
-  utils.print_time("# Done training!", start_train_time)
-
-  summary_writer.close()
-
-  utils.print_out("# Start evaluating saved best models.")
-  for metric in hparams.metrics:
-    best_model_dir = getattr(hparams, "best_" + metric + "_dir")
+    """Train a translation model."""
+    log_device_placement = hparams.log_device_placement
+    out_dir = hparams.out_dir
+    num_train_steps = hparams.num_train_steps
+    steps_per_stats = hparams.steps_per_stats
+    steps_per_external_eval = hparams.steps_per_external_eval
+    steps_per_eval = 10 * steps_per_stats
+    avg_ckpts = hparams.avg_ckpts
+
+    if not steps_per_external_eval:
+        steps_per_external_eval = 5 * steps_per_eval
+
+    # Create model
+    model_creator = get_model_creator(hparams)
+    train_model = model_helper.create_train_model(
+        model_creator, hparams, scope)
+    eval_model = model_helper.create_eval_model(model_creator, hparams, scope)
+    infer_model = model_helper.create_infer_model(
+        model_creator, hparams, scope)
+
+    # Preload data for sample decoding.
+    dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
+    dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
+    sample_src_data = inference.load_data(dev_src_file)
+    sample_tgt_data = inference.load_data(dev_tgt_file)
+
+    summary_name = "train_log"
+    model_dir = hparams.out_dir
+
+    # Log and output files
+    log_file = os.path.join(out_dir, "log_%d" % time.time())
+    log_f = tf.gfile.GFile(log_file, mode="a")
+    utils.print_out("# log_file=%s" % log_file, log_f)
+
+    # TensorFlow model
+    config_proto = utils.get_config_proto(
+        log_device_placement=log_device_placement,
+        num_intra_threads=hparams.num_intra_threads,
+        num_inter_threads=hparams.num_inter_threads,
+    )
+    train_sess = tf.Session(
+        target=target_session, config=config_proto, graph=train_model.graph
+    )
+    eval_sess = tf.Session(
+        target=target_session, config=config_proto, graph=eval_model.graph
+    )
+    infer_sess = tf.Session(
+        target=target_session, config=config_proto, graph=infer_model.graph
+    )
+
+    with train_model.graph.as_default():
+        loaded_train_model, global_step = model_helper.create_or_load_model(
+            train_model.model, model_dir, train_sess, "train"
+        )
+
+    # Summary writer
     summary_writer = tf.summary.FileWriter(
-        os.path.join(best_model_dir, summary_name), infer_model.graph)
-    result_summary, best_global_step, _ = run_full_eval(
-        best_model_dir, infer_model, infer_sess, eval_model, eval_sess, hparams,
-        summary_writer, sample_src_data, sample_tgt_data)
-    print_step_info("# Best %s, " % metric, best_global_step, info,
-                    result_summary, log_f)
-    summary_writer.close()
+        os.path.join(out_dir, summary_name), train_model.graph
+    )
 
-    if avg_ckpts:
-      best_model_dir = getattr(hparams, "avg_best_" + metric + "_dir")
-      summary_writer = tf.summary.FileWriter(
-          os.path.join(best_model_dir, summary_name), infer_model.graph)
-      result_summary, best_global_step, _ = run_full_eval(
-          best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
-          hparams, summary_writer, sample_src_data, sample_tgt_data)
-      print_step_info("# Averaged Best %s, " % metric, best_global_step, info,
-                      result_summary, log_f)
-      summary_writer.close()
+    # First evaluation
+    run_full_eval(
+        model_dir,
+        infer_model,
+        infer_sess,
+        eval_model,
+        eval_sess,
+        hparams,
+        summary_writer,
+        sample_src_data,
+        sample_tgt_data,
+        avg_ckpts,
+    )
+
+    last_stats_step = global_step
+    last_eval_step = global_step
+    last_external_eval_step = global_step
+
+    # This is the training loop.
+    stats, info, start_train_time = before_train(
+        loaded_train_model, train_model, train_sess, global_step, hparams, log_f
+    )
+    while global_step < num_train_steps:
+        ### Run a step ###
+        start_time = time.time()
+        try:
+            step_result = loaded_train_model.train(train_sess)
+            hparams.epoch_step += 1
+        except tf.errors.OutOfRangeError:
+            # Finished going through the training dataset.  Go to next epoch.
+            hparams.epoch_step = 0
+            utils.print_out(
+                "# Finished an epoch, step %d. Perform external evaluation"
+                % global_step
+            )
+            run_sample_decode(
+                infer_model,
+                infer_sess,
+                model_dir,
+                hparams,
+                summary_writer,
+                sample_src_data,
+                sample_tgt_data,
+            )
+            run_external_eval(
+                infer_model, infer_sess, model_dir, hparams, summary_writer
+            )
+
+            if avg_ckpts:
+                run_avg_external_eval(
+                    infer_model,
+                    infer_sess,
+                    model_dir,
+                    hparams,
+                    summary_writer,
+                    global_step,
+                )
+
+            train_sess.run(
+                train_model.iterator.initializer,
+                feed_dict={train_model.skip_count_placeholder: 0},
+            )
+            continue
+
+        # Process step_result, accumulate stats, and write summary
+        global_step, info["learning_rate"], step_summary = update_stats(
+            stats, start_time, step_result
+        )
+        summary_writer.add_summary(step_summary, global_step)
+
+        # Once in a while, we print statistics.
+        if global_step - last_stats_step >= steps_per_stats:
+            last_stats_step = global_step
+            is_overflow = process_stats(
+                stats, info, global_step, steps_per_stats, log_f
+            )
+            print_step_info(
+                "  ",
+                global_step,
+                info,
+                get_best_results(hparams),
+                log_f)
+            if is_overflow:
+                break
+
+            # Reset statistics
+            stats = init_stats()
+
+        if global_step - last_eval_step >= steps_per_eval:
+            last_eval_step = global_step
+            utils.print_out("# Save eval, global step %d" % global_step)
+            add_info_summaries(summary_writer, global_step, info)
+
+            # Save checkpoint
+            loaded_train_model.saver.save(
+                train_sess,
+                os.path.join(out_dir, "translate.ckpt"),
+                global_step=global_step,
+            )
+
+            # Evaluate on dev/test
+            run_sample_decode(
+                infer_model,
+                infer_sess,
+                model_dir,
+                hparams,
+                summary_writer,
+                sample_src_data,
+                sample_tgt_data,
+            )
+            run_internal_eval(
+                eval_model,
+                eval_sess,
+                model_dir,
+                hparams,
+                summary_writer)
+
+        if global_step - last_external_eval_step >= steps_per_external_eval:
+            last_external_eval_step = global_step
+
+            # Save checkpoint
+            loaded_train_model.saver.save(
+                train_sess,
+                os.path.join(out_dir, "translate.ckpt"),
+                global_step=global_step,
+            )
+            run_sample_decode(
+                infer_model,
+                infer_sess,
+                model_dir,
+                hparams,
+                summary_writer,
+                sample_src_data,
+                sample_tgt_data,
+            )
+            run_external_eval(
+                infer_model, infer_sess, model_dir, hparams, summary_writer
+            )
+
+            if avg_ckpts:
+                run_avg_external_eval(
+                    infer_model,
+                    infer_sess,
+                    model_dir,
+                    hparams,
+                    summary_writer,
+                    global_step,
+                )
+
+    # Done training
+    loaded_train_model.saver.save(
+        train_sess, os.path.join(out_dir, "translate.ckpt"), global_step=global_step
+    )
+
+    (result_summary, _, final_eval_metrics) = run_full_eval(
+        model_dir,
+        infer_model,
+        infer_sess,
+        eval_model,
+        eval_sess,
+        hparams,
+        summary_writer,
+        sample_src_data,
+        sample_tgt_data,
+        avg_ckpts,
+    )
+    print_step_info("# Final, ", global_step, info, result_summary, log_f)
+    utils.print_time("# Done training!", start_train_time)
+
+    summary_writer.close()
 
-  return final_eval_metrics, global_step
+    utils.print_out("# Start evaluating saved best models.")
+    for metric in hparams.metrics:
+        best_model_dir = getattr(hparams, "best_" + metric + "_dir")
+        summary_writer = tf.summary.FileWriter(
+            os.path.join(best_model_dir, summary_name), infer_model.graph
+        )
+        result_summary, best_global_step, _ = run_full_eval(
+            best_model_dir,
+            infer_model,
+            infer_sess,
+            eval_model,
+            eval_sess,
+            hparams,
+            summary_writer,
+            sample_src_data,
+            sample_tgt_data,
+        )
+        print_step_info(
+            "# Best %s, " % metric, best_global_step, info, result_summary, log_f
+        )
+        summary_writer.close()
+
+        if avg_ckpts:
+            best_model_dir = getattr(hparams, "avg_best_" + metric + "_dir")
+            summary_writer = tf.summary.FileWriter(
+                os.path.join(best_model_dir, summary_name), infer_model.graph
+            )
+            result_summary, best_global_step, _ = run_full_eval(
+                best_model_dir,
+                infer_model,
+                infer_sess,
+                eval_model,
+                eval_sess,
+                hparams,
+                summary_writer,
+                sample_src_data,
+                sample_tgt_data,
+            )
+            print_step_info(
+                "# Averaged Best %s, " % metric,
+                best_global_step,
+                info,
+                result_summary,
+                log_f,
+            )
+            summary_writer.close()
+
+    return final_eval_metrics, global_step
 
 
 def _format_results(name, ppl, scores, metrics):
-  """Format results."""
-  result_str = ""
-  if ppl:
-    result_str = "%s ppl %.2f" % (name, ppl)
-  if scores:
-    for metric in metrics:
-      if result_str:
-        result_str += ", %s %s %.1f" % (name, metric, scores[metric])
-      else:
-        result_str = "%s %s %.1f" % (name, metric, scores[metric])
-  return result_str
+    """Format results."""
+    result_str = ""
+    if ppl:
+        result_str = "%s ppl %.2f" % (name, ppl)
+    if scores:
+        for metric in metrics:
+            if result_str:
+                result_str += ", %s %s %.1f" % (name, metric, scores[metric])
+            else:
+                result_str = "%s %s %.1f" % (name, metric, scores[metric])
+    return result_str
 
 
 def get_best_results(hparams):
-  """Summary of the current best results."""
-  tokens = []
-  for metric in hparams.metrics:
-    tokens.append("%s %.2f" % (metric, getattr(hparams, "best_" + metric)))
-  return ", ".join(tokens)
-
-
-def _internal_eval(model, global_step, sess, iterator, iterator_feed_dict,
-                   summary_writer, label):
-  """Computing perplexity."""
-  sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
-  ppl = model_helper.compute_perplexity(model, sess, label)
-  utils.add_summary(summary_writer, global_step, "%s_ppl" % label, ppl)
-  return ppl
-
-
-def _sample_decode(model, global_step, sess, hparams, iterator, src_data,
-                   tgt_data, iterator_src_placeholder,
-                   iterator_batch_size_placeholder, summary_writer):
-  """Pick a sentence and decode."""
-  decode_id = random.randint(0, len(src_data) - 1)
-  utils.print_out("  # %d" % decode_id)
-
-  iterator_feed_dict = {
-      iterator_src_placeholder: [src_data[decode_id]],
-      iterator_batch_size_placeholder: 1,
-  }
-  sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
-
-  nmt_outputs, attention_summary = model.decode(sess)
-
-  if hparams.infer_mode == "beam_search":
-    # get the top translation.
-    nmt_outputs = nmt_outputs[0]
-
-  translation = nmt_utils.get_translation(
-      nmt_outputs,
-      sent_id=0,
-      tgt_eos=hparams.eos,
-      subword_option=hparams.subword_option)
-  utils.print_out("    src: %s" % src_data[decode_id])
-  utils.print_out("    ref: %s" % tgt_data[decode_id])
-  utils.print_out(b"    nmt: " + translation)
-
-  # Summary
-  if attention_summary is not None:
-    summary_writer.add_summary(attention_summary, global_step)
-
-
-def _external_eval(model, global_step, sess, hparams, iterator,
-                   iterator_feed_dict, tgt_file, label, summary_writer,
-                   save_on_best, avg_ckpts=False):
-  """External evaluation such as BLEU and ROUGE scores."""
-  out_dir = hparams.out_dir
-  decode = global_step > 0
-
-  if avg_ckpts:
-    label = "avg_" + label
-
-  if decode:
-    utils.print_out("# External evaluation, global step %d" % global_step)
-
-  sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
-
-  output = os.path.join(out_dir, "output_%s" % label)
-  scores = nmt_utils.decode_and_evaluate(
-      label,
-      model,
-      sess,
-      output,
-      ref_file=tgt_file,
-      metrics=hparams.metrics,
-      subword_option=hparams.subword_option,
-      beam_width=hparams.beam_width,
-      tgt_eos=hparams.eos,
-      decode=decode,
-      infer_mode=hparams.infer_mode)
-  # Save on best metrics
-  if decode:
+    """Summary of the current best results."""
+    tokens = []
     for metric in hparams.metrics:
-      if avg_ckpts:
-        best_metric_label = "avg_best_" + metric
-      else:
-        best_metric_label = "best_" + metric
-
-      utils.add_summary(summary_writer, global_step, "%s_%s" % (label, metric),
-                        scores[metric])
-      # metric: larger is better
-      if save_on_best and scores[metric] > getattr(hparams, best_metric_label):
-        setattr(hparams, best_metric_label, scores[metric])
-        model.saver.save(
-            sess,
-            os.path.join(
-                getattr(hparams, best_metric_label + "_dir"), "translate.ckpt"),
-            global_step=model.global_step)
-    utils.save_hparams(out_dir, hparams)
-  return scores
+        tokens.append("%s %.2f" % (metric, getattr(hparams, "best_" + metric)))
+    return ", ".join(tokens)
+
+
+def _internal_eval(
+    model, global_step, sess, iterator, iterator_feed_dict, summary_writer, label
+):
+    """Computing perplexity."""
+    sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
+    ppl = model_helper.compute_perplexity(model, sess, label)
+    utils.add_summary(summary_writer, global_step, "%s_ppl" % label, ppl)
+    return ppl
+
+
+def _sample_decode(
+    model,
+    global_step,
+    sess,
+    hparams,
+    iterator,
+    src_data,
+    tgt_data,
+    iterator_src_placeholder,
+    iterator_batch_size_placeholder,
+    summary_writer,
+):
+    """Pick a sentence and decode."""
+    decode_id = random.randint(0, len(src_data) - 1)
+    utils.print_out("  # %d" % decode_id)
+
+    iterator_feed_dict = {
+        iterator_src_placeholder: [src_data[decode_id]],
+        iterator_batch_size_placeholder: 1,
+    }
+    sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
+
+    nmt_outputs, attention_summary = model.decode(sess)
+
+    if hparams.infer_mode == "beam_search":
+        # get the top translation.
+        nmt_outputs = nmt_outputs[0]
+
+    translation = nmt_utils.get_translation(
+        nmt_outputs,
+        sent_id=0,
+        tgt_eos=hparams.eos,
+        subword_option=hparams.subword_option,
+    )
+    utils.print_out("    src: %s" % src_data[decode_id])
+    utils.print_out("    ref: %s" % tgt_data[decode_id])
+    utils.print_out(b"    nmt: " + translation)
+
+    # Summary
+    if attention_summary is not None:
+        summary_writer.add_summary(attention_summary, global_step)
+
+
+def _external_eval(
+    model,
+    global_step,
+    sess,
+    hparams,
+    iterator,
+    iterator_feed_dict,
+    tgt_file,
+    label,
+    summary_writer,
+    save_on_best,
+    avg_ckpts=False,
+):
+    """External evaluation such as BLEU and ROUGE scores."""
+    out_dir = hparams.out_dir
+    decode = global_step > 0
+
+    if avg_ckpts:
+        label = "avg_" + label
+
+    if decode:
+        utils.print_out("# External evaluation, global step %d" % global_step)
+
+    sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
+
+    output = os.path.join(out_dir, "output_%s" % label)
+    scores = nmt_utils.decode_and_evaluate(
+        label,
+        model,
+        sess,
+        output,
+        ref_file=tgt_file,
+        metrics=hparams.metrics,
+        subword_option=hparams.subword_option,
+        beam_width=hparams.beam_width,
+        tgt_eos=hparams.eos,
+        decode=decode,
+        infer_mode=hparams.infer_mode,
+    )
+    # Save on best metrics
+    if decode:
+        for metric in hparams.metrics:
+            if avg_ckpts:
+                best_metric_label = "avg_best_" + metric
+            else:
+                best_metric_label = "best_" + metric
+
+            utils.add_summary(
+                summary_writer, global_step, "%s_%s" % (
+                    label, metric), scores[metric]
+            )
+            # metric: larger is better
+            if save_on_best and scores[metric] > getattr(
+                    hparams, best_metric_label):
+                setattr(hparams, best_metric_label, scores[metric])
+                model.saver.save(
+                    sess,
+                    os.path.join(
+                        getattr(
+                            hparams, best_metric_label + "_dir"), "translate.ckpt"
+                    ),
+                    global_step=model.global_step,
+                )
+        utils.save_hparams(out_dir, hparams)
+    return scores
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/common_test_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/common_test_utils.py
index 68ff209f9f..57918a1fc6 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/common_test_utils.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/common_test_utils.py
@@ -27,110 +27,120 @@
 from ..utils import standard_hparams_utils
 
 
-def create_test_hparams(unit_type="lstm",
-                        encoder_type="uni",
-                        num_layers=4,
-                        attention="",
-                        attention_architecture=None,
-                        use_residual=False,
-                        inference_indices=None,
-                        num_translations_per_input=1,
-                        beam_width=0,
-                        init_op="uniform"):
-  """Create training and inference test hparams."""
-  num_residual_layers = 0
-  if use_residual:
-    # TODO(rzhao): Put num_residual_layers computation logic into
-    # `model_utils.py`, so we can also test it here.
-    num_residual_layers = 2
-
-  standard_hparams = standard_hparams_utils.create_standard_hparams()
-
-  # Networks
-  standard_hparams.num_units = 5
-  standard_hparams.num_encoder_layers = num_layers
-  standard_hparams.num_decoder_layers = num_layers
-  standard_hparams.dropout = 0.5
-  standard_hparams.unit_type = unit_type
-  standard_hparams.encoder_type = encoder_type
-  standard_hparams.residual = use_residual
-  standard_hparams.num_residual_layers = num_residual_layers
-
-  # Attention mechanisms
-  standard_hparams.attention = attention
-  standard_hparams.attention_architecture = attention_architecture
-
-  # Train
-  standard_hparams.init_op = init_op
-  standard_hparams.num_train_steps = 1
-  standard_hparams.decay_scheme = ""
-
-  # Infer
-  standard_hparams.tgt_max_len_infer = 100
-  standard_hparams.beam_width = beam_width
-  standard_hparams.num_translations_per_input = num_translations_per_input
-
-  # Misc
-  standard_hparams.forget_bias = 0.0
-  standard_hparams.random_seed = 3
-  standard_hparams.language_model = False
-
-  # Vocab
-  standard_hparams.src_vocab_size = 5
-  standard_hparams.tgt_vocab_size = 5
-  standard_hparams.eos = "</s>"
-  standard_hparams.sos = "<s>"
-  standard_hparams.src_vocab_file = ""
-  standard_hparams.tgt_vocab_file = ""
-  standard_hparams.src_embed_file = ""
-  standard_hparams.tgt_embed_file = ""
-
-  # For inference.py test
-  standard_hparams.subword_option = "bpe"
-  standard_hparams.src = "src"
-  standard_hparams.tgt = "tgt"
-  standard_hparams.src_max_len = 400
-  standard_hparams.tgt_eos_id = 0
-  standard_hparams.inference_indices = inference_indices
-  return standard_hparams
+def create_test_hparams(
+    unit_type="lstm",
+    encoder_type="uni",
+    num_layers=4,
+    attention="",
+    attention_architecture=None,
+    use_residual=False,
+    inference_indices=None,
+    num_translations_per_input=1,
+    beam_width=0,
+    init_op="uniform",
+):
+    """Create training and inference test hparams."""
+    num_residual_layers = 0
+    if use_residual:
+        # TODO(rzhao): Put num_residual_layers computation logic into
+        # `model_utils.py`, so we can also test it here.
+        num_residual_layers = 2
+
+    standard_hparams = standard_hparams_utils.create_standard_hparams()
+
+    # Networks
+    standard_hparams.num_units = 5
+    standard_hparams.num_encoder_layers = num_layers
+    standard_hparams.num_decoder_layers = num_layers
+    standard_hparams.dropout = 0.5
+    standard_hparams.unit_type = unit_type
+    standard_hparams.encoder_type = encoder_type
+    standard_hparams.residual = use_residual
+    standard_hparams.num_residual_layers = num_residual_layers
+
+    # Attention mechanisms
+    standard_hparams.attention = attention
+    standard_hparams.attention_architecture = attention_architecture
+
+    # Train
+    standard_hparams.init_op = init_op
+    standard_hparams.num_train_steps = 1
+    standard_hparams.decay_scheme = ""
+
+    # Infer
+    standard_hparams.tgt_max_len_infer = 100
+    standard_hparams.beam_width = beam_width
+    standard_hparams.num_translations_per_input = num_translations_per_input
+
+    # Misc
+    standard_hparams.forget_bias = 0.0
+    standard_hparams.random_seed = 3
+    standard_hparams.language_model = False
+
+    # Vocab
+    standard_hparams.src_vocab_size = 5
+    standard_hparams.tgt_vocab_size = 5
+    standard_hparams.eos = "</s>"
+    standard_hparams.sos = "<s>"
+    standard_hparams.src_vocab_file = ""
+    standard_hparams.tgt_vocab_file = ""
+    standard_hparams.src_embed_file = ""
+    standard_hparams.tgt_embed_file = ""
+
+    # For inference.py test
+    standard_hparams.subword_option = "bpe"
+    standard_hparams.src = "src"
+    standard_hparams.tgt = "tgt"
+    standard_hparams.src_max_len = 400
+    standard_hparams.tgt_eos_id = 0
+    standard_hparams.inference_indices = inference_indices
+    return standard_hparams
 
 
 def create_test_iterator(hparams, mode):
-  """Create test iterator."""
-  src_vocab_table = lookup_ops.index_table_from_tensor(
-      tf.constant([hparams.eos, "a", "b", "c", "d"]))
-  tgt_vocab_mapping = tf.constant([hparams.sos, hparams.eos, "a", "b", "c"])
-  tgt_vocab_table = lookup_ops.index_table_from_tensor(tgt_vocab_mapping)
-  if mode == tf.contrib.learn.ModeKeys.INFER:
-    reverse_tgt_vocab_table = lookup_ops.index_to_string_table_from_tensor(
-        tgt_vocab_mapping)
-
-  src_dataset = tf.data.Dataset.from_tensor_slices(
-      tf.constant(["a a b b c", "a b b"]))
-
-  if mode != tf.contrib.learn.ModeKeys.INFER:
-    tgt_dataset = tf.data.Dataset.from_tensor_slices(
-        tf.constant(["a b c b c", "a b c b"]))
-    return (
-        iterator_utils.get_iterator(
-            src_dataset=src_dataset,
-            tgt_dataset=tgt_dataset,
-            src_vocab_table=src_vocab_table,
-            tgt_vocab_table=tgt_vocab_table,
-            batch_size=hparams.batch_size,
-            sos=hparams.sos,
-            eos=hparams.eos,
-            random_seed=hparams.random_seed,
-            num_buckets=hparams.num_buckets),
-        src_vocab_table,
-        tgt_vocab_table)
-  else:
-    return (
-        iterator_utils.get_infer_iterator(
-            src_dataset=src_dataset,
-            src_vocab_table=src_vocab_table,
-            eos=hparams.eos,
-            batch_size=hparams.batch_size),
-        src_vocab_table,
-        tgt_vocab_table,
-        reverse_tgt_vocab_table)
+    """Create test iterator."""
+    src_vocab_table = lookup_ops.index_table_from_tensor(
+        tf.constant([hparams.eos, "a", "b", "c", "d"])
+    )
+    tgt_vocab_mapping = tf.constant([hparams.sos, hparams.eos, "a", "b", "c"])
+    tgt_vocab_table = lookup_ops.index_table_from_tensor(tgt_vocab_mapping)
+    if mode == tf.contrib.learn.ModeKeys.INFER:
+        reverse_tgt_vocab_table = lookup_ops.index_to_string_table_from_tensor(
+            tgt_vocab_mapping
+        )
+
+    src_dataset = tf.data.Dataset.from_tensor_slices(
+        tf.constant(["a a b b c", "a b b"])
+    )
+
+    if mode != tf.contrib.learn.ModeKeys.INFER:
+        tgt_dataset = tf.data.Dataset.from_tensor_slices(
+            tf.constant(["a b c b c", "a b c b"])
+        )
+        return (
+            iterator_utils.get_iterator(
+                src_dataset=src_dataset,
+                tgt_dataset=tgt_dataset,
+                src_vocab_table=src_vocab_table,
+                tgt_vocab_table=tgt_vocab_table,
+                batch_size=hparams.batch_size,
+                sos=hparams.sos,
+                eos=hparams.eos,
+                random_seed=hparams.random_seed,
+                num_buckets=hparams.num_buckets,
+            ),
+            src_vocab_table,
+            tgt_vocab_table,
+        )
+    else:
+        return (
+            iterator_utils.get_infer_iterator(
+                src_dataset=src_dataset,
+                src_vocab_table=src_vocab_table,
+                eos=hparams.eos,
+                batch_size=hparams.batch_size,
+            ),
+            src_vocab_table,
+            tgt_vocab_table,
+            reverse_tgt_vocab_table,
+        )
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/evaluation_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/evaluation_utils.py
index e567e119fc..5d563bec7a 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/evaluation_utils.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/evaluation_utils.py
@@ -29,156 +29,158 @@
 
 
 def evaluate(ref_file, trans_file, metric, subword_option=None):
-  """Pick a metric and evaluate depending on task."""
-  # BLEU scores for translation task
-  if metric.lower() == "bleu":
-    evaluation_score = _bleu(ref_file, trans_file,
-                             subword_option=subword_option)
-  # ROUGE scores for summarization tasks
-  elif metric.lower() == "rouge":
-    evaluation_score = _rouge(ref_file, trans_file,
-                              subword_option=subword_option)
-  elif metric.lower() == "accuracy":
-    evaluation_score = _accuracy(ref_file, trans_file)
-  elif metric.lower() == "word_accuracy":
-    evaluation_score = _word_accuracy(ref_file, trans_file)
-  else:
-    raise ValueError("Unknown metric %s" % metric)
-
-  return evaluation_score
+    """Pick a metric and evaluate depending on task."""
+    # BLEU scores for translation task
+    if metric.lower() == "bleu":
+        evaluation_score = _bleu(
+            ref_file,
+            trans_file,
+            subword_option=subword_option)
+    # ROUGE scores for summarization tasks
+    elif metric.lower() == "rouge":
+        evaluation_score = _rouge(
+            ref_file, trans_file, subword_option=subword_option)
+    elif metric.lower() == "accuracy":
+        evaluation_score = _accuracy(ref_file, trans_file)
+    elif metric.lower() == "word_accuracy":
+        evaluation_score = _word_accuracy(ref_file, trans_file)
+    else:
+        raise ValueError("Unknown metric %s" % metric)
+
+    return evaluation_score
 
 
 def _clean(sentence, subword_option):
-  """Clean and handle BPE or SPM outputs."""
-  sentence = sentence.strip()
+    """Clean and handle BPE or SPM outputs."""
+    sentence = sentence.strip()
 
-  # BPE
-  if subword_option == "bpe":
-    sentence = re.sub("@@ ", "", sentence)
+    # BPE
+    if subword_option == "bpe":
+        sentence = re.sub("@@ ", "", sentence)
 
-  # SPM
-  elif subword_option == "spm":
-    sentence = u"".join(sentence.split()).replace(u"\u2581", u" ").lstrip()
+    # SPM
+    elif subword_option == "spm":
+        sentence = "".join(sentence.split()).replace("\u2581", " ").lstrip()
 
-  return sentence
+    return sentence
 
 
 # Follow //transconsole/localization/machine_translation/metrics/bleu_calc.py
 def _bleu(ref_file, trans_file, subword_option=None):
-  """Compute BLEU scores and handling BPE."""
-  max_order = 4
-  smooth = False
-
-  ref_files = [ref_file]
-  reference_text = []
-  for reference_filename in ref_files:
-    with codecs.getreader("utf-8")(
-        tf.gfile.GFile(reference_filename, "rb")) as fh:
-      reference_text.append(fh.readlines())
-
-  per_segment_references = []
-  for references in zip(*reference_text):
-    reference_list = []
-    for reference in references:
-      reference = _clean(reference, subword_option)
-      reference_list.append(reference.split(" "))
-    per_segment_references.append(reference_list)
-
-  translations = []
-  with codecs.getreader("utf-8")(tf.gfile.GFile(trans_file, "rb")) as fh:
-    for line in fh:
-      line = _clean(line, subword_option=None)
-      translations.append(line.split(" "))
-
-  # bleu_score, precisions, bp, ratio, translation_length, reference_length
-  bleu_score, _, _, _, _, _ = bleu.compute_bleu(
-      per_segment_references, translations, max_order, smooth)
-  return 100 * bleu_score
+    """Compute BLEU scores and handling BPE."""
+    max_order = 4
+    smooth = False
+
+    ref_files = [ref_file]
+    reference_text = []
+    for reference_filename in ref_files:
+        with codecs.getreader("utf-8")(tf.gfile.GFile(reference_filename, "rb")) as fh:
+            reference_text.append(fh.readlines())
+
+    per_segment_references = []
+    for references in zip(*reference_text):
+        reference_list = []
+        for reference in references:
+            reference = _clean(reference, subword_option)
+            reference_list.append(reference.split(" "))
+        per_segment_references.append(reference_list)
+
+    translations = []
+    with codecs.getreader("utf-8")(tf.gfile.GFile(trans_file, "rb")) as fh:
+        for line in fh:
+            line = _clean(line, subword_option=None)
+            translations.append(line.split(" "))
+
+    # bleu_score, precisions, bp, ratio, translation_length, reference_length
+    bleu_score, _, _, _, _, _ = bleu.compute_bleu(
+        per_segment_references, translations, max_order, smooth
+    )
+    return 100 * bleu_score
 
 
 def _rouge(ref_file, summarization_file, subword_option=None):
-  """Compute ROUGE scores and handling BPE."""
+    """Compute ROUGE scores and handling BPE."""
 
-  references = []
-  with codecs.getreader("utf-8")(tf.gfile.GFile(ref_file, "rb")) as fh:
-    for line in fh:
-      references.append(_clean(line, subword_option))
+    references = []
+    with codecs.getreader("utf-8")(tf.gfile.GFile(ref_file, "rb")) as fh:
+        for line in fh:
+            references.append(_clean(line, subword_option))
 
-  hypotheses = []
-  with codecs.getreader("utf-8")(
-      tf.gfile.GFile(summarization_file, "rb")) as fh:
-    for line in fh:
-      hypotheses.append(_clean(line, subword_option=None))
+    hypotheses = []
+    with codecs.getreader("utf-8")(tf.gfile.GFile(summarization_file, "rb")) as fh:
+        for line in fh:
+            hypotheses.append(_clean(line, subword_option=None))
 
-  rouge_score_map = rouge.rouge(hypotheses, references)
-  return 100 * rouge_score_map["rouge_l/f_score"]
+    rouge_score_map = rouge.rouge(hypotheses, references)
+    return 100 * rouge_score_map["rouge_l/f_score"]
 
 
 def _accuracy(label_file, pred_file):
-  """Compute accuracy, each line contains a label."""
+    """Compute accuracy, each line contains a label."""
 
-  with codecs.getreader("utf-8")(tf.gfile.GFile(label_file, "rb")) as label_fh:
-    with codecs.getreader("utf-8")(tf.gfile.GFile(pred_file, "rb")) as pred_fh:
-      count = 0.0
-      match = 0.0
-      for label in label_fh:
-        label = label.strip()
-        pred = pred_fh.readline().strip()
-        if label == pred:
-          match += 1
-        count += 1
-  return 100 * match / count
+    with codecs.getreader("utf-8")(tf.gfile.GFile(label_file, "rb")) as label_fh:
+        with codecs.getreader("utf-8")(tf.gfile.GFile(pred_file, "rb")) as pred_fh:
+            count = 0.0
+            match = 0.0
+            for label in label_fh:
+                label = label.strip()
+                pred = pred_fh.readline().strip()
+                if label == pred:
+                    match += 1
+                count += 1
+    return 100 * match / count
 
 
 def _word_accuracy(label_file, pred_file):
-  """Compute accuracy on per word basis."""
-
-  with codecs.getreader("utf-8")(tf.gfile.GFile(label_file, "r")) as label_fh:
-    with codecs.getreader("utf-8")(tf.gfile.GFile(pred_file, "r")) as pred_fh:
-      total_acc, total_count = 0., 0.
-      for sentence in label_fh:
-        labels = sentence.strip().split(" ")
-        preds = pred_fh.readline().strip().split(" ")
-        match = 0.0
-        for pos in range(min(len(labels), len(preds))):
-          label = labels[pos]
-          pred = preds[pos]
-          if label == pred:
-            match += 1
-        total_acc += 100 * match / max(len(labels), len(preds))
-        total_count += 1
-  return total_acc / total_count
+    """Compute accuracy on per word basis."""
+
+    with codecs.getreader("utf-8")(tf.gfile.GFile(label_file, "r")) as label_fh:
+        with codecs.getreader("utf-8")(tf.gfile.GFile(pred_file, "r")) as pred_fh:
+            total_acc, total_count = 0.0, 0.0
+            for sentence in label_fh:
+                labels = sentence.strip().split(" ")
+                preds = pred_fh.readline().strip().split(" ")
+                match = 0.0
+                for pos in range(min(len(labels), len(preds))):
+                    label = labels[pos]
+                    pred = preds[pos]
+                    if label == pred:
+                        match += 1
+                total_acc += 100 * match / max(len(labels), len(preds))
+                total_count += 1
+    return total_acc / total_count
 
 
 def _moses_bleu(multi_bleu_script, tgt_test, trans_file, subword_option=None):
-  """Compute BLEU scores using Moses multi-bleu.perl script."""
-
-  # TODO(thangluong): perform rewrite using python
-  # BPE
-  if subword_option == "bpe":
-    debpe_tgt_test = tgt_test + ".debpe"
-    if not os.path.exists(debpe_tgt_test):
-      # TODO(thangluong): not use shell=True, can be a security hazard
-      subprocess.call("cp %s %s" % (tgt_test, debpe_tgt_test), shell=True)
-      subprocess.call("sed s/@@ //g %s" % (debpe_tgt_test),
-                      shell=True)
-    tgt_test = debpe_tgt_test
-  elif subword_option == "spm":
-    despm_tgt_test = tgt_test + ".despm"
-    if not os.path.exists(despm_tgt_test):
-      subprocess.call("cp %s %s" % (tgt_test, despm_tgt_test))
-      subprocess.call("sed s/ //g %s" % (despm_tgt_test))
-      subprocess.call(u"sed s/^\u2581/g %s" % (despm_tgt_test))
-      subprocess.call(u"sed s/\u2581/ /g %s" % (despm_tgt_test))
-    tgt_test = despm_tgt_test
-  cmd = "%s %s < %s" % (multi_bleu_script, tgt_test, trans_file)
-
-  # subprocess
-  # TODO(thangluong): not use shell=True, can be a security hazard
-  bleu_output = subprocess.check_output(cmd, shell=True)
-
-  # extract BLEU score
-  m = re.search("BLEU = (.+?),", bleu_output)
-  bleu_score = float(m.group(1))
-
-  return bleu_score
+    """Compute BLEU scores using Moses multi-bleu.perl script."""
+
+    # TODO(thangluong): perform rewrite using python
+    # BPE
+    if subword_option == "bpe":
+        debpe_tgt_test = tgt_test + ".debpe"
+        if not os.path.exists(debpe_tgt_test):
+            # TODO(thangluong): not use shell=True, can be a security hazard
+            subprocess.call(
+                "cp %s %s" %
+                (tgt_test, debpe_tgt_test), shell=True)
+            subprocess.call("sed s/@@ //g %s" % (debpe_tgt_test), shell=True)
+        tgt_test = debpe_tgt_test
+    elif subword_option == "spm":
+        despm_tgt_test = tgt_test + ".despm"
+        if not os.path.exists(despm_tgt_test):
+            subprocess.call("cp %s %s" % (tgt_test, despm_tgt_test))
+            subprocess.call("sed s/ //g %s" % (despm_tgt_test))
+            subprocess.call("sed s/^\u2581/g %s" % (despm_tgt_test))
+            subprocess.call("sed s/\u2581/ /g %s" % (despm_tgt_test))
+        tgt_test = despm_tgt_test
+    cmd = "%s %s < %s" % (multi_bleu_script, tgt_test, trans_file)
+
+    # subprocess
+    # TODO(thangluong): not use shell=True, can be a security hazard
+    bleu_output = subprocess.check_output(cmd, shell=True)
+
+    # extract BLEU score
+    m = re.search("BLEU = (.+?),", bleu_output)
+    bleu_score = float(m.group(1))
+
+    return bleu_score
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/evaluation_utils_test.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/evaluation_utils_test.py
index f4a5e10ec4..9512d673ac 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/evaluation_utils_test.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/evaluation_utils_test.py
@@ -26,50 +26,53 @@
 
 class EvaluationUtilsTest(tf.test.TestCase):
 
-  def testEvaluate(self):
-    output = "nmt/testdata/deen_output"
-    ref_bpe = "nmt/testdata/deen_ref_bpe"
-    ref_spm = "nmt/testdata/deen_ref_spm"
+    def testEvaluate(self):
+        output = "nmt/testdata/deen_output"
+        ref_bpe = "nmt/testdata/deen_ref_bpe"
+        ref_spm = "nmt/testdata/deen_ref_spm"
 
-    expected_bleu_score = 22.5855084573
-    expected_rouge_score = 50.8429782599
+        expected_bleu_score = 22.5855084573
+        expected_rouge_score = 50.8429782599
 
-    bpe_bleu_score = evaluation_utils.evaluate(
-        ref_bpe, output, "bleu", "bpe")
-    bpe_rouge_score = evaluation_utils.evaluate(
-        ref_bpe, output, "rouge", "bpe")
+        bpe_bleu_score = evaluation_utils.evaluate(
+            ref_bpe, output, "bleu", "bpe")
+        bpe_rouge_score = evaluation_utils.evaluate(
+            ref_bpe, output, "rouge", "bpe")
 
-    self.assertAlmostEqual(expected_bleu_score, bpe_bleu_score)
-    self.assertAlmostEqual(expected_rouge_score, bpe_rouge_score)
+        self.assertAlmostEqual(expected_bleu_score, bpe_bleu_score)
+        self.assertAlmostEqual(expected_rouge_score, bpe_rouge_score)
 
-    spm_bleu_score = evaluation_utils.evaluate(
-        ref_spm, output, "bleu", "spm")
-    spm_rouge_score = evaluation_utils.evaluate(
-        ref_spm, output, "rouge", "spm")
+        spm_bleu_score = evaluation_utils.evaluate(
+            ref_spm, output, "bleu", "spm")
+        spm_rouge_score = evaluation_utils.evaluate(
+            ref_spm, output, "rouge", "spm")
 
-    self.assertAlmostEqual(expected_rouge_score, spm_rouge_score)
-    self.assertAlmostEqual(expected_bleu_score, spm_bleu_score)
+        self.assertAlmostEqual(expected_rouge_score, spm_rouge_score)
+        self.assertAlmostEqual(expected_bleu_score, spm_bleu_score)
 
-  def testAccuracy(self):
-    pred_output = "nmt/testdata/pred_output"
-    label_ref = "nmt/testdata/label_ref"
+    def testAccuracy(self):
+        pred_output = "nmt/testdata/pred_output"
+        label_ref = "nmt/testdata/label_ref"
 
-    expected_accuracy_score = 60.00
+        expected_accuracy_score = 60.00
 
-    accuracy_score = evaluation_utils.evaluate(
-        label_ref, pred_output, "accuracy")
-    self.assertAlmostEqual(expected_accuracy_score, accuracy_score)
+        accuracy_score = evaluation_utils.evaluate(
+            label_ref, pred_output, "accuracy")
+        self.assertAlmostEqual(expected_accuracy_score, accuracy_score)
 
-  def testWordAccuracy(self):
-    pred_output = "nmt/testdata/pred_output"
-    label_ref = "nmt/testdata/label_ref"
+    def testWordAccuracy(self):
+        pred_output = "nmt/testdata/pred_output"
+        label_ref = "nmt/testdata/label_ref"
 
-    expected_word_accuracy_score = 60.00
+        expected_word_accuracy_score = 60.00
 
-    word_accuracy_score = evaluation_utils.evaluate(
-        label_ref, pred_output, "word_accuracy")
-    self.assertAlmostEqual(expected_word_accuracy_score, word_accuracy_score)
+        word_accuracy_score = evaluation_utils.evaluate(
+            label_ref, pred_output, "word_accuracy"
+        )
+        self.assertAlmostEqual(
+            expected_word_accuracy_score,
+            word_accuracy_score)
 
 
 if __name__ == "__main__":
-  tf.test.main()
+    tf.test.main()
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/iterator_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/iterator_utils.py
index 31efb11ff6..c6f57a3c07 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/iterator_utils.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/iterator_utils.py
@@ -27,222 +27,282 @@
 
 # NOTE(ebrevdo): When we subclass this, instances' __dict__ becomes empty.
 class BatchedInput(
-    collections.namedtuple("BatchedInput",
-                           ("initializer", "source", "target_input",
-                            "target_output", "source_sequence_length",
-                            "target_sequence_length"))):
-  pass
-
-
-def get_infer_iterator(src_dataset,
-                       src_vocab_table,
-                       batch_size,
-                       eos,
-                       src_max_len=None,
-                       use_char_encode=False):
-  if use_char_encode:
-    src_eos_id = vocab_utils.EOS_CHAR_ID
-  else:
-    src_eos_id = tf.cast(src_vocab_table.lookup(tf.constant(eos)), tf.int32)
-  src_dataset = src_dataset.map(lambda src: tf.string_split([src]).values)
-
-  if src_max_len:
-    src_dataset = src_dataset.map(lambda src: src[:src_max_len])
-
-  if use_char_encode:
-    # Convert the word strings to character ids
-    src_dataset = src_dataset.map(
-        lambda src: tf.reshape(vocab_utils.tokens_to_bytes(src), [-1]))
-  else:
-    # Convert the word strings to ids
-    src_dataset = src_dataset.map(
-        lambda src: tf.cast(src_vocab_table.lookup(src), tf.int32))
-
-  # Add in the word counts.
-  if use_char_encode:
-    src_dataset = src_dataset.map(
-        lambda src: (src,
-                     tf.to_int32(
-                         tf.size(src) / vocab_utils.DEFAULT_CHAR_MAXLEN)))
-  else:
-    src_dataset = src_dataset.map(lambda src: (src, tf.size(src)))
-
-  def batching_func(x):
-    return x.padded_batch(
-        batch_size,
-        # The entry is the source line rows;
-        # this has unknown-length vectors.  The last entry is
-        # the source row size; this is a scalar.
-        padded_shapes=(
-            tf.TensorShape([None]),  # src
-            tf.TensorShape([])),  # src_len
-        # Pad the source sequences with eos tokens.
-        # (Though notice we don't generally need to do this since
-        # later on we will be masking out calculations past the true sequence.
-        padding_values=(
-            src_eos_id,  # src
-            0))  # src_len -- unused
-
-  batched_dataset = batching_func(src_dataset)
-  batched_iter = batched_dataset.make_initializable_iterator()
-  (src_ids, src_seq_len) = batched_iter.get_next()
-  return BatchedInput(
-      initializer=batched_iter.initializer,
-      source=src_ids,
-      target_input=None,
-      target_output=None,
-      source_sequence_length=src_seq_len,
-      target_sequence_length=None)
-
-
-def get_iterator(src_dataset,
-                 tgt_dataset,
-                 src_vocab_table,
-                 tgt_vocab_table,
-                 batch_size,
-                 sos,
-                 eos,
-                 random_seed,
-                 num_buckets,
-                 src_max_len=None,
-                 tgt_max_len=None,
-                 num_parallel_calls=4,
-                 output_buffer_size=None,
-                 skip_count=None,
-                 num_shards=1,
-                 shard_index=0,
-                 reshuffle_each_iteration=True,
-                 use_char_encode=False):
-  if not output_buffer_size:
-    output_buffer_size = batch_size * 1000
-
-  if use_char_encode:
-    src_eos_id = vocab_utils.EOS_CHAR_ID
-  else:
-    src_eos_id = tf.cast(src_vocab_table.lookup(tf.constant(eos)), tf.int32)
-
-  tgt_sos_id = tf.cast(tgt_vocab_table.lookup(tf.constant(sos)), tf.int32)
-  tgt_eos_id = tf.cast(tgt_vocab_table.lookup(tf.constant(eos)), tf.int32)
-
-  src_tgt_dataset = tf.data.Dataset.zip((src_dataset, tgt_dataset))
-
-  src_tgt_dataset = src_tgt_dataset.shard(num_shards, shard_index)
-  if skip_count is not None:
-    src_tgt_dataset = src_tgt_dataset.skip(skip_count)
-
-  src_tgt_dataset = src_tgt_dataset.shuffle(
-      output_buffer_size, random_seed, reshuffle_each_iteration)
-
-  src_tgt_dataset = src_tgt_dataset.map(
-      lambda src, tgt: (
-          tf.string_split([src]).values, tf.string_split([tgt]).values),
-      num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
-
-  # Filter zero length input sequences.
-  src_tgt_dataset = src_tgt_dataset.filter(
-      lambda src, tgt: tf.logical_and(tf.size(src) > 0, tf.size(tgt) > 0))
-
-  if src_max_len:
-    src_tgt_dataset = src_tgt_dataset.map(
-        lambda src, tgt: (src[:src_max_len], tgt),
-        num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
-  if tgt_max_len:
-    src_tgt_dataset = src_tgt_dataset.map(
-        lambda src, tgt: (src, tgt[:tgt_max_len]),
-        num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
+    collections.namedtuple(
+        "BatchedInput",
+        (
+            "initializer",
+            "source",
+            "target_input",
+            "target_output",
+            "source_sequence_length",
+            "target_sequence_length",
+        ),
+    )
+):
+    pass
+
+
+def get_infer_iterator(
+    src_dataset,
+    src_vocab_table,
+    batch_size,
+    eos,
+    src_max_len=None,
+    use_char_encode=False,
+):
+    if use_char_encode:
+        src_eos_id = vocab_utils.EOS_CHAR_ID
+    else:
+        src_eos_id = tf.cast(
+            src_vocab_table.lookup(
+                tf.constant(eos)), tf.int32)
+    src_dataset = src_dataset.map(lambda src: tf.string_split([src]).values)
+
+    if src_max_len:
+        src_dataset = src_dataset.map(lambda src: src[:src_max_len])
+
+    if use_char_encode:
+        # Convert the word strings to character ids
+        src_dataset = src_dataset.map(
+            lambda src: tf.reshape(vocab_utils.tokens_to_bytes(src), [-1])
+        )
+    else:
+        # Convert the word strings to ids
+        src_dataset = src_dataset.map(
+            lambda src: tf.cast(src_vocab_table.lookup(src), tf.int32)
+        )
+
+    # Add in the word counts.
+    if use_char_encode:
+        src_dataset = src_dataset.map(
+            lambda src: (
+                src,
+                tf.to_int32(tf.size(src) / vocab_utils.DEFAULT_CHAR_MAXLEN),
+            )
+        )
+    else:
+        src_dataset = src_dataset.map(lambda src: (src, tf.size(src)))
+
+    def batching_func(x):
+        return x.padded_batch(
+            batch_size,
+            # The entry is the source line rows;
+            # this has unknown-length vectors.  The last entry is
+            # the source row size; this is a scalar.
+            padded_shapes=(
+                tf.TensorShape([None]),  # src
+                tf.TensorShape([]),
+            ),  # src_len
+            # Pad the source sequences with eos tokens.
+            # (Though notice we don't generally need to do this since
+            # later on we will be masking out calculations past the true
+            # sequence.
+            padding_values=(src_eos_id, 0),  # src
+        )  # src_len -- unused
+
+    batched_dataset = batching_func(src_dataset)
+    batched_iter = batched_dataset.make_initializable_iterator()
+    (src_ids, src_seq_len) = batched_iter.get_next()
+    return BatchedInput(
+        initializer=batched_iter.initializer,
+        source=src_ids,
+        target_input=None,
+        target_output=None,
+        source_sequence_length=src_seq_len,
+        target_sequence_length=None,
+    )
+
+
+def get_iterator(
+    src_dataset,
+    tgt_dataset,
+    src_vocab_table,
+    tgt_vocab_table,
+    batch_size,
+    sos,
+    eos,
+    random_seed,
+    num_buckets,
+    src_max_len=None,
+    tgt_max_len=None,
+    num_parallel_calls=4,
+    output_buffer_size=None,
+    skip_count=None,
+    num_shards=1,
+    shard_index=0,
+    reshuffle_each_iteration=True,
+    use_char_encode=False,
+):
+    if not output_buffer_size:
+        output_buffer_size = batch_size * 1000
+
+    if use_char_encode:
+        src_eos_id = vocab_utils.EOS_CHAR_ID
+    else:
+        src_eos_id = tf.cast(
+            src_vocab_table.lookup(
+                tf.constant(eos)), tf.int32)
+
+    tgt_sos_id = tf.cast(tgt_vocab_table.lookup(tf.constant(sos)), tf.int32)
+    tgt_eos_id = tf.cast(tgt_vocab_table.lookup(tf.constant(eos)), tf.int32)
+
+    src_tgt_dataset = tf.data.Dataset.zip((src_dataset, tgt_dataset))
+
+    src_tgt_dataset = src_tgt_dataset.shard(num_shards, shard_index)
+    if skip_count is not None:
+        src_tgt_dataset = src_tgt_dataset.skip(skip_count)
+
+    src_tgt_dataset = src_tgt_dataset.shuffle(
+        output_buffer_size, random_seed, reshuffle_each_iteration
+    )
 
-  # Convert the word strings to ids.  Word strings that are not in the
-  # vocab get the lookup table's default_value integer.
-  if use_char_encode:
-    src_tgt_dataset = src_tgt_dataset.map(
-        lambda src, tgt: (tf.reshape(vocab_utils.tokens_to_bytes(src), [-1]),
-                          tf.cast(tgt_vocab_table.lookup(tgt), tf.int32)),
-        num_parallel_calls=num_parallel_calls)
-  else:
-    src_tgt_dataset = src_tgt_dataset.map(
-        lambda src, tgt: (tf.cast(src_vocab_table.lookup(src), tf.int32),
-                          tf.cast(tgt_vocab_table.lookup(tgt), tf.int32)),
-        num_parallel_calls=num_parallel_calls)
-
-  src_tgt_dataset = src_tgt_dataset.prefetch(output_buffer_size)
-  # Create a tgt_input prefixed with <sos> and a tgt_output suffixed with <eos>.
-  src_tgt_dataset = src_tgt_dataset.map(
-      lambda src, tgt: (src,
-                        tf.concat(([tgt_sos_id], tgt), 0),
-                        tf.concat((tgt, [tgt_eos_id]), 0)),
-      num_parallel_calls=num_parallel_calls).prefetch(output_buffer_size)
-  # Add in sequence lengths.
-  if use_char_encode:
     src_tgt_dataset = src_tgt_dataset.map(
-        lambda src, tgt_in, tgt_out: (
-            src, tgt_in, tgt_out,
-            tf.to_int32(tf.size(src) / vocab_utils.DEFAULT_CHAR_MAXLEN),
-            tf.size(tgt_in)),
-        num_parallel_calls=num_parallel_calls)
-  else:
+        lambda src, tgt: (
+            tf.string_split(
+                [src]).values, tf.string_split(
+                [tgt]).values),
+        num_parallel_calls=num_parallel_calls,
+    ).prefetch(output_buffer_size)
+
+    # Filter zero length input sequences.
+    src_tgt_dataset = src_tgt_dataset.filter(
+        lambda src, tgt: tf.logical_and(tf.size(src) > 0, tf.size(tgt) > 0)
+    )
+
+    if src_max_len:
+        src_tgt_dataset = src_tgt_dataset.map(
+            lambda src, tgt: (src[:src_max_len], tgt),
+            num_parallel_calls=num_parallel_calls,
+        ).prefetch(output_buffer_size)
+    if tgt_max_len:
+        src_tgt_dataset = src_tgt_dataset.map(
+            lambda src, tgt: (src, tgt[:tgt_max_len]),
+            num_parallel_calls=num_parallel_calls,
+        ).prefetch(output_buffer_size)
+
+    # Convert the word strings to ids.  Word strings that are not in the
+    # vocab get the lookup table's default_value integer.
+    if use_char_encode:
+        src_tgt_dataset = src_tgt_dataset.map(
+            lambda src, tgt: (
+                tf.reshape(vocab_utils.tokens_to_bytes(src), [-1]),
+                tf.cast(tgt_vocab_table.lookup(tgt), tf.int32),
+            ),
+            num_parallel_calls=num_parallel_calls,
+        )
+    else:
+        src_tgt_dataset = src_tgt_dataset.map(
+            lambda src, tgt: (
+                tf.cast(src_vocab_table.lookup(src), tf.int32),
+                tf.cast(tgt_vocab_table.lookup(tgt), tf.int32),
+            ),
+            num_parallel_calls=num_parallel_calls,
+        )
+
+    src_tgt_dataset = src_tgt_dataset.prefetch(output_buffer_size)
+    # Create a tgt_input prefixed with <sos> and a tgt_output suffixed with
+    # <eos>.
     src_tgt_dataset = src_tgt_dataset.map(
-        lambda src, tgt_in, tgt_out: (
-            src, tgt_in, tgt_out, tf.size(src), tf.size(tgt_in)),
-        num_parallel_calls=num_parallel_calls)
-
-  src_tgt_dataset = src_tgt_dataset.prefetch(output_buffer_size)
-
-  # Bucket by source sequence length (buckets for lengths 0-9, 10-19, ...)
-  def batching_func(x):
-    return x.padded_batch(
-        batch_size,
-        # The first three entries are the source and target line rows;
-        # these have unknown-length vectors.  The last two entries are
-        # the source and target row sizes; these are scalars.
-        padded_shapes=(
-            tf.TensorShape([None]),  # src
-            tf.TensorShape([None]),  # tgt_input
-            tf.TensorShape([None]),  # tgt_output
-            tf.TensorShape([]),  # src_len
-            tf.TensorShape([])),  # tgt_len
-        # Pad the source and target sequences with eos tokens.
-        # (Though notice we don't generally need to do this since
-        # later on we will be masking out calculations past the true sequence.
-        padding_values=(
-            src_eos_id,  # src
-            tgt_eos_id,  # tgt_input
-            tgt_eos_id,  # tgt_output
-            0,  # src_len -- unused
-            0))  # tgt_len -- unused
-
-  if num_buckets > 1:
-
-    def key_func(unused_1, unused_2, unused_3, src_len, tgt_len):
-      # Calculate bucket_width by maximum source sequence length.
-      # Pairs with length [0, bucket_width) go to bucket 0, length
-      # [bucket_width, 2 * bucket_width) go to bucket 1, etc.  Pairs with length
-      # over ((num_bucket-1) * bucket_width) words all go into the last bucket.
-      if src_max_len:
-        bucket_width = (src_max_len + num_buckets - 1) // num_buckets
-      else:
-        bucket_width = 10
-
-      # Bucket sentence pairs by the length of their source sentence and target
-      # sentence.
-      bucket_id = tf.maximum(src_len // bucket_width, tgt_len // bucket_width)
-      return tf.to_int64(tf.minimum(num_buckets, bucket_id))
-
-    def reduce_func(unused_key, windowed_data):
-      return batching_func(windowed_data)
-
-    batched_dataset = src_tgt_dataset.apply(
-        tf.contrib.data.group_by_window(
-            key_func=key_func, reduce_func=reduce_func, window_size=batch_size))
-
-  else:
-    batched_dataset = batching_func(src_tgt_dataset)
-  batched_iter = batched_dataset.make_initializable_iterator()
-  (src_ids, tgt_input_ids, tgt_output_ids, src_seq_len,
-   tgt_seq_len) = (batched_iter.get_next())
-  return BatchedInput(
-      initializer=batched_iter.initializer,
-      source=src_ids,
-      target_input=tgt_input_ids,
-      target_output=tgt_output_ids,
-      source_sequence_length=src_seq_len,
-      target_sequence_length=tgt_seq_len)
+        lambda src, tgt: (
+            src,
+            tf.concat(([tgt_sos_id], tgt), 0),
+            tf.concat((tgt, [tgt_eos_id]), 0),
+        ),
+        num_parallel_calls=num_parallel_calls,
+    ).prefetch(output_buffer_size)
+    # Add in sequence lengths.
+    if use_char_encode:
+        src_tgt_dataset = src_tgt_dataset.map(
+            lambda src, tgt_in, tgt_out: (
+                src,
+                tgt_in,
+                tgt_out,
+                tf.to_int32(tf.size(src) / vocab_utils.DEFAULT_CHAR_MAXLEN),
+                tf.size(tgt_in),
+            ),
+            num_parallel_calls=num_parallel_calls,
+        )
+    else:
+        src_tgt_dataset = src_tgt_dataset.map(
+            lambda src, tgt_in, tgt_out: (
+                src,
+                tgt_in,
+                tgt_out,
+                tf.size(src),
+                tf.size(tgt_in),
+            ),
+            num_parallel_calls=num_parallel_calls,
+        )
+
+    src_tgt_dataset = src_tgt_dataset.prefetch(output_buffer_size)
+
+    # Bucket by source sequence length (buckets for lengths 0-9, 10-19, ...)
+    def batching_func(x):
+        return x.padded_batch(
+            batch_size,
+            # The first three entries are the source and target line rows;
+            # these have unknown-length vectors.  The last two entries are
+            # the source and target row sizes; these are scalars.
+            padded_shapes=(
+                tf.TensorShape([None]),  # src
+                tf.TensorShape([None]),  # tgt_input
+                tf.TensorShape([None]),  # tgt_output
+                tf.TensorShape([]),  # src_len
+                tf.TensorShape([]),
+            ),  # tgt_len
+            # Pad the source and target sequences with eos tokens.
+            # (Though notice we don't generally need to do this since
+            # later on we will be masking out calculations past the true
+            # sequence.
+            padding_values=(
+                src_eos_id,  # src
+                tgt_eos_id,  # tgt_input
+                tgt_eos_id,  # tgt_output
+                0,  # src_len -- unused
+                0,
+            ),
+        )  # tgt_len -- unused
+
+    if num_buckets > 1:
+
+        def key_func(unused_1, unused_2, unused_3, src_len, tgt_len):
+            # Calculate bucket_width by maximum source sequence length.
+            # Pairs with length [0, bucket_width) go to bucket 0, length
+            # [bucket_width, 2 * bucket_width) go to bucket 1, etc.  Pairs with length
+            # over ((num_bucket-1) * bucket_width) words all go into the last
+            # bucket.
+            if src_max_len:
+                bucket_width = (src_max_len + num_buckets - 1) // num_buckets
+            else:
+                bucket_width = 10
+
+            # Bucket sentence pairs by the length of their source sentence and target
+            # sentence.
+            bucket_id = tf.maximum(
+                src_len // bucket_width,
+                tgt_len // bucket_width)
+            return tf.to_int64(tf.minimum(num_buckets, bucket_id))
+
+        def reduce_func(unused_key, windowed_data):
+            return batching_func(windowed_data)
+
+        batched_dataset = src_tgt_dataset.apply(
+            tf.contrib.data.group_by_window(
+                key_func=key_func, reduce_func=reduce_func, window_size=batch_size
+            )
+        )
+
+    else:
+        batched_dataset = batching_func(src_tgt_dataset)
+    batched_iter = batched_dataset.make_initializable_iterator()
+    (src_ids, tgt_input_ids, tgt_output_ids, src_seq_len, tgt_seq_len) = (
+        batched_iter.get_next()
+    )
+    return BatchedInput(
+        initializer=batched_iter.initializer,
+        source=src_ids,
+        target_input=tgt_input_ids,
+        target_output=tgt_output_ids,
+        source_sequence_length=src_seq_len,
+        target_sequence_length=tgt_seq_len,
+    )
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/iterator_utils_test.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/iterator_utils_test.py
index 3ef1dc8d93..7ba426c7e0 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/iterator_utils_test.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/iterator_utils_test.py
@@ -28,294 +28,300 @@
 
 class IteratorUtilsTest(tf.test.TestCase):
 
-  def testGetIterator(self):
-    tf.set_random_seed(1)
-    tgt_vocab_table = src_vocab_table = lookup_ops.index_table_from_tensor(
-        tf.constant(["a", "b", "c", "eos", "sos"]))
-    src_dataset = tf.data.Dataset.from_tensor_slices(
-        tf.constant(["f e a g", "c c a", "d", "c a"]))
-    tgt_dataset = tf.data.Dataset.from_tensor_slices(
-        tf.constant(["c c", "a b", "", "b c"]))
-    hparams = tf.contrib.training.HParams(
-        random_seed=3,
-        num_buckets=5,
-        eos="eos",
-        sos="sos")
-    batch_size = 2
-    src_max_len = 3
-    iterator = iterator_utils.get_iterator(
-        src_dataset=src_dataset,
-        tgt_dataset=tgt_dataset,
-        src_vocab_table=src_vocab_table,
-        tgt_vocab_table=tgt_vocab_table,
-        batch_size=batch_size,
-        sos=hparams.sos,
-        eos=hparams.eos,
-        random_seed=hparams.random_seed,
-        num_buckets=hparams.num_buckets,
-        src_max_len=src_max_len,
-        reshuffle_each_iteration=False)
-    table_initializer = tf.tables_initializer()
-    source = iterator.source
-    target_input = iterator.target_input
-    target_output = iterator.target_output
-    src_seq_len = iterator.source_sequence_length
-    tgt_seq_len = iterator.target_sequence_length
-    self.assertEqual([None, None], source.shape.as_list())
-    self.assertEqual([None, None], target_input.shape.as_list())
-    self.assertEqual([None, None], target_output.shape.as_list())
-    self.assertEqual([None], src_seq_len.shape.as_list())
-    self.assertEqual([None], tgt_seq_len.shape.as_list())
-    with self.test_session() as sess:
-      sess.run(table_initializer)
-      sess.run(iterator.initializer)
+    def testGetIterator(self):
+        tf.set_random_seed(1)
+        tgt_vocab_table = src_vocab_table = lookup_ops.index_table_from_tensor(
+            tf.constant(["a", "b", "c", "eos", "sos"])
+        )
+        src_dataset = tf.data.Dataset.from_tensor_slices(
+            tf.constant(["f e a g", "c c a", "d", "c a"])
+        )
+        tgt_dataset = tf.data.Dataset.from_tensor_slices(
+            tf.constant(["c c", "a b", "", "b c"])
+        )
+        hparams = tf.contrib.training.HParams(
+            random_seed=3, num_buckets=5, eos="eos", sos="sos"
+        )
+        batch_size = 2
+        src_max_len = 3
+        iterator = iterator_utils.get_iterator(
+            src_dataset=src_dataset,
+            tgt_dataset=tgt_dataset,
+            src_vocab_table=src_vocab_table,
+            tgt_vocab_table=tgt_vocab_table,
+            batch_size=batch_size,
+            sos=hparams.sos,
+            eos=hparams.eos,
+            random_seed=hparams.random_seed,
+            num_buckets=hparams.num_buckets,
+            src_max_len=src_max_len,
+            reshuffle_each_iteration=False,
+        )
+        table_initializer = tf.tables_initializer()
+        source = iterator.source
+        target_input = iterator.target_input
+        target_output = iterator.target_output
+        src_seq_len = iterator.source_sequence_length
+        tgt_seq_len = iterator.target_sequence_length
+        self.assertEqual([None, None], source.shape.as_list())
+        self.assertEqual([None, None], target_input.shape.as_list())
+        self.assertEqual([None, None], target_output.shape.as_list())
+        self.assertEqual([None], src_seq_len.shape.as_list())
+        self.assertEqual([None], tgt_seq_len.shape.as_list())
+        with self.test_session() as sess:
+            sess.run(table_initializer)
+            sess.run(iterator.initializer)
 
-      (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
-          sess.run((source, src_seq_len, target_input, target_output,
-                    tgt_seq_len)))
-      self.assertAllEqual(
-          [[-1, -1, 0], # "f" == unknown, "e" == unknown, a
-           [2, 0, 3]],  # c a eos -- eos is padding
-          source_v)
-      self.assertAllEqual([3, 2], src_len_v)
-      self.assertAllEqual(
-          [[4, 2, 2],   # sos c c
-           [4, 1, 2]],  # sos b c
-          target_input_v)
-      self.assertAllEqual(
-          [[2, 2, 3],   # c c eos
-           [1, 2, 3]],  # b c eos
-          target_output_v)
-      self.assertAllEqual([3, 3], tgt_len_v)
+            (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
+                sess.run(
+                    (source, src_seq_len, target_input, target_output, tgt_seq_len)
+                )
+            )
+            self.assertAllEqual(
+                [
+                    [-1, -1, 0],  # "f" == unknown, "e" == unknown, a
+                    [2, 0, 3],
+                ],  # c a eos -- eos is padding
+                source_v,
+            )
+            self.assertAllEqual([3, 2], src_len_v)
+            self.assertAllEqual(
+                [[4, 2, 2], [4, 1, 2]], target_input_v  # sos c c  # sos b c
+            )
+            self.assertAllEqual(
+                [[2, 2, 3], [1, 2, 3]], target_output_v  # c c eos  # b c eos
+            )
+            self.assertAllEqual([3, 3], tgt_len_v)
 
-      (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
-          sess.run((source, src_seq_len, target_input, target_output,
-                    tgt_seq_len)))
-      self.assertAllEqual(
-          [[2, 2, 0]],  # c c a
-          source_v)
-      self.assertAllEqual([3], src_len_v)
-      self.assertAllEqual(
-          [[4, 0, 1]],  # sos a b
-          target_input_v)
-      self.assertAllEqual(
-          [[0, 1, 3]],  # a b eos
-          target_output_v)
-      self.assertAllEqual([3], tgt_len_v)
+            (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
+                sess.run(
+                    (source, src_seq_len, target_input, target_output, tgt_seq_len)
+                )
+            )
+            self.assertAllEqual([[2, 2, 0]], source_v)  # c c a
+            self.assertAllEqual([3], src_len_v)
+            self.assertAllEqual([[4, 0, 1]], target_input_v)  # sos a b
+            self.assertAllEqual([[0, 1, 3]], target_output_v)  # a b eos
+            self.assertAllEqual([3], tgt_len_v)
 
-      with self.assertRaisesOpError("End of sequence"):
-        sess.run(source)
+            with self.assertRaisesOpError("End of sequence"):
+                sess.run(source)
 
-  def testGetIteratorWithShard(self):
-    tf.set_random_seed(1)
-    tgt_vocab_table = src_vocab_table = lookup_ops.index_table_from_tensor(
-        tf.constant(["a", "b", "c", "eos", "sos"]))
-    src_dataset = tf.data.Dataset.from_tensor_slices(
-        tf.constant(["c c a", "f e a g", "d", "c a"]))
-    tgt_dataset = tf.data.Dataset.from_tensor_slices(
-        tf.constant(["a b", "c c", "", "b c"]))
-    hparams = tf.contrib.training.HParams(
-        random_seed=3,
-        num_buckets=5,
-        eos="eos",
-        sos="sos")
-    batch_size = 2
-    src_max_len = 3
-    iterator = iterator_utils.get_iterator(
-        src_dataset=src_dataset,
-        tgt_dataset=tgt_dataset,
-        src_vocab_table=src_vocab_table,
-        tgt_vocab_table=tgt_vocab_table,
-        batch_size=batch_size,
-        sos=hparams.sos,
-        eos=hparams.eos,
-        random_seed=hparams.random_seed,
-        num_buckets=hparams.num_buckets,
-        src_max_len=src_max_len,
-        num_shards=2,
-        shard_index=1,
-        reshuffle_each_iteration=False)
-    table_initializer = tf.tables_initializer()
-    source = iterator.source
-    target_input = iterator.target_input
-    target_output = iterator.target_output
-    src_seq_len = iterator.source_sequence_length
-    tgt_seq_len = iterator.target_sequence_length
-    self.assertEqual([None, None], source.shape.as_list())
-    self.assertEqual([None, None], target_input.shape.as_list())
-    self.assertEqual([None, None], target_output.shape.as_list())
-    self.assertEqual([None], src_seq_len.shape.as_list())
-    self.assertEqual([None], tgt_seq_len.shape.as_list())
-    with self.test_session() as sess:
-      sess.run(table_initializer)
-      sess.run(iterator.initializer)
+    def testGetIteratorWithShard(self):
+        tf.set_random_seed(1)
+        tgt_vocab_table = src_vocab_table = lookup_ops.index_table_from_tensor(
+            tf.constant(["a", "b", "c", "eos", "sos"])
+        )
+        src_dataset = tf.data.Dataset.from_tensor_slices(
+            tf.constant(["c c a", "f e a g", "d", "c a"])
+        )
+        tgt_dataset = tf.data.Dataset.from_tensor_slices(
+            tf.constant(["a b", "c c", "", "b c"])
+        )
+        hparams = tf.contrib.training.HParams(
+            random_seed=3, num_buckets=5, eos="eos", sos="sos"
+        )
+        batch_size = 2
+        src_max_len = 3
+        iterator = iterator_utils.get_iterator(
+            src_dataset=src_dataset,
+            tgt_dataset=tgt_dataset,
+            src_vocab_table=src_vocab_table,
+            tgt_vocab_table=tgt_vocab_table,
+            batch_size=batch_size,
+            sos=hparams.sos,
+            eos=hparams.eos,
+            random_seed=hparams.random_seed,
+            num_buckets=hparams.num_buckets,
+            src_max_len=src_max_len,
+            num_shards=2,
+            shard_index=1,
+            reshuffle_each_iteration=False,
+        )
+        table_initializer = tf.tables_initializer()
+        source = iterator.source
+        target_input = iterator.target_input
+        target_output = iterator.target_output
+        src_seq_len = iterator.source_sequence_length
+        tgt_seq_len = iterator.target_sequence_length
+        self.assertEqual([None, None], source.shape.as_list())
+        self.assertEqual([None, None], target_input.shape.as_list())
+        self.assertEqual([None, None], target_output.shape.as_list())
+        self.assertEqual([None], src_seq_len.shape.as_list())
+        self.assertEqual([None], tgt_seq_len.shape.as_list())
+        with self.test_session() as sess:
+            sess.run(table_initializer)
+            sess.run(iterator.initializer)
 
-      (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
-          sess.run((source, src_seq_len, target_input, target_output,
-                    tgt_seq_len)))
-      self.assertAllEqual(
-          [[-1, -1, 0], # "f" == unknown, "e" == unknown, a
-           [2, 0, 3]],  # c a eos -- eos is padding
-          source_v)
-      self.assertAllEqual([3, 2], src_len_v)
-      self.assertAllEqual(
-          [[4, 2, 2],   # sos c c
-           [4, 1, 2]],  # sos b c
-          target_input_v)
-      self.assertAllEqual(
-          [[2, 2, 3],   # c c eos
-           [1, 2, 3]],  # b c eos
-          target_output_v)
-      self.assertAllEqual([3, 3], tgt_len_v)
+            (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
+                sess.run(
+                    (source, src_seq_len, target_input, target_output, tgt_seq_len)
+                )
+            )
+            self.assertAllEqual(
+                [
+                    [-1, -1, 0],  # "f" == unknown, "e" == unknown, a
+                    [2, 0, 3],
+                ],  # c a eos -- eos is padding
+                source_v,
+            )
+            self.assertAllEqual([3, 2], src_len_v)
+            self.assertAllEqual(
+                [[4, 2, 2], [4, 1, 2]], target_input_v  # sos c c  # sos b c
+            )
+            self.assertAllEqual(
+                [[2, 2, 3], [1, 2, 3]], target_output_v  # c c eos  # b c eos
+            )
+            self.assertAllEqual([3, 3], tgt_len_v)
 
-      with self.assertRaisesOpError("End of sequence"):
-        sess.run(source)
+            with self.assertRaisesOpError("End of sequence"):
+                sess.run(source)
 
-  def testGetIteratorWithSkipCount(self):
-    tf.set_random_seed(1)
-    tgt_vocab_table = src_vocab_table = lookup_ops.index_table_from_tensor(
-        tf.constant(["a", "b", "c", "eos", "sos"]))
-    src_dataset = tf.data.Dataset.from_tensor_slices(
-        tf.constant(["c a", "c c a", "d", "f e a g"]))
-    tgt_dataset = tf.data.Dataset.from_tensor_slices(
-        tf.constant(["b c", "a b", "", "c c"]))
-    hparams = tf.contrib.training.HParams(
-        random_seed=3,
-        num_buckets=5,
-        eos="eos",
-        sos="sos")
-    batch_size = 2
-    src_max_len = 3
-    skip_count = tf.placeholder(shape=(), dtype=tf.int64)
-    iterator = iterator_utils.get_iterator(
-        src_dataset=src_dataset,
-        tgt_dataset=tgt_dataset,
-        src_vocab_table=src_vocab_table,
-        tgt_vocab_table=tgt_vocab_table,
-        batch_size=batch_size,
-        sos=hparams.sos,
-        eos=hparams.eos,
-        random_seed=hparams.random_seed,
-        num_buckets=hparams.num_buckets,
-        src_max_len=src_max_len,
-        skip_count=skip_count,
-        reshuffle_each_iteration=False)
-    table_initializer = tf.tables_initializer()
-    source = iterator.source
-    target_input = iterator.target_input
-    target_output = iterator.target_output
-    src_seq_len = iterator.source_sequence_length
-    tgt_seq_len = iterator.target_sequence_length
-    self.assertEqual([None, None], source.shape.as_list())
-    self.assertEqual([None, None], target_input.shape.as_list())
-    self.assertEqual([None, None], target_output.shape.as_list())
-    self.assertEqual([None], src_seq_len.shape.as_list())
-    self.assertEqual([None], tgt_seq_len.shape.as_list())
-    with self.test_session() as sess:
-      sess.run(table_initializer)
-      sess.run(iterator.initializer, feed_dict={skip_count: 3})
+    def testGetIteratorWithSkipCount(self):
+        tf.set_random_seed(1)
+        tgt_vocab_table = src_vocab_table = lookup_ops.index_table_from_tensor(
+            tf.constant(["a", "b", "c", "eos", "sos"])
+        )
+        src_dataset = tf.data.Dataset.from_tensor_slices(
+            tf.constant(["c a", "c c a", "d", "f e a g"])
+        )
+        tgt_dataset = tf.data.Dataset.from_tensor_slices(
+            tf.constant(["b c", "a b", "", "c c"])
+        )
+        hparams = tf.contrib.training.HParams(
+            random_seed=3, num_buckets=5, eos="eos", sos="sos"
+        )
+        batch_size = 2
+        src_max_len = 3
+        skip_count = tf.placeholder(shape=(), dtype=tf.int64)
+        iterator = iterator_utils.get_iterator(
+            src_dataset=src_dataset,
+            tgt_dataset=tgt_dataset,
+            src_vocab_table=src_vocab_table,
+            tgt_vocab_table=tgt_vocab_table,
+            batch_size=batch_size,
+            sos=hparams.sos,
+            eos=hparams.eos,
+            random_seed=hparams.random_seed,
+            num_buckets=hparams.num_buckets,
+            src_max_len=src_max_len,
+            skip_count=skip_count,
+            reshuffle_each_iteration=False,
+        )
+        table_initializer = tf.tables_initializer()
+        source = iterator.source
+        target_input = iterator.target_input
+        target_output = iterator.target_output
+        src_seq_len = iterator.source_sequence_length
+        tgt_seq_len = iterator.target_sequence_length
+        self.assertEqual([None, None], source.shape.as_list())
+        self.assertEqual([None, None], target_input.shape.as_list())
+        self.assertEqual([None, None], target_output.shape.as_list())
+        self.assertEqual([None], src_seq_len.shape.as_list())
+        self.assertEqual([None], tgt_seq_len.shape.as_list())
+        with self.test_session() as sess:
+            sess.run(table_initializer)
+            sess.run(iterator.initializer, feed_dict={skip_count: 3})
 
-      (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
-          sess.run((source, src_seq_len, target_input, target_output,
-                    tgt_seq_len)))
-      self.assertAllEqual(
-          [[-1, -1, 0]], # "f" == unknown, "e" == unknown, a
-          source_v)
-      self.assertAllEqual([3], src_len_v)
-      self.assertAllEqual(
-          [[4, 2, 2]],   # sos c c
-          target_input_v)
-      self.assertAllEqual(
-          [[2, 2, 3]],   # c c eos
-          target_output_v)
-      self.assertAllEqual([3], tgt_len_v)
+            (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
+                sess.run(
+                    (source, src_seq_len, target_input, target_output, tgt_seq_len)
+                )
+            )
+            self.assertAllEqual(
+                [[-1, -1, 0]], source_v  # "f" == unknown, "e" == unknown, a
+            )
+            self.assertAllEqual([3], src_len_v)
+            self.assertAllEqual([[4, 2, 2]], target_input_v)  # sos c c
+            self.assertAllEqual([[2, 2, 3]], target_output_v)  # c c eos
+            self.assertAllEqual([3], tgt_len_v)
 
-      with self.assertRaisesOpError("End of sequence"):
-        sess.run(source)
+            with self.assertRaisesOpError("End of sequence"):
+                sess.run(source)
 
-      # Re-init iterator with skip_count=0.
-      sess.run(iterator.initializer, feed_dict={skip_count: 0})
+            # Re-init iterator with skip_count=0.
+            sess.run(iterator.initializer, feed_dict={skip_count: 0})
 
-      (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
-          sess.run((source, src_seq_len, target_input, target_output,
-                    tgt_seq_len)))
-      self.assertAllEqual(
-          [[2, 0, 3],    # c a eos -- eos is padding
-           [-1, -1, 0]], # "f" == unknown, "e" == unknown, a
-          source_v)
-      self.assertAllEqual([2, 3], src_len_v)
-      self.assertAllEqual(
-          [[4, 1, 2],   # sos b c
-           [4, 2, 2]],  # sos c c
-          target_input_v)
-      self.assertAllEqual(
-          [[1, 2, 3],   # b c eos
-           [2, 2, 3]],  # c c eos
-          target_output_v)
-      self.assertAllEqual([3, 3], tgt_len_v)
+            (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
+                sess.run(
+                    (source, src_seq_len, target_input, target_output, tgt_seq_len)
+                )
+            )
+            self.assertAllEqual(
+                [
+                    [2, 0, 3],  # c a eos -- eos is padding
+                    [-1, -1, 0],
+                ],  # "f" == unknown, "e" == unknown, a
+                source_v,
+            )
+            self.assertAllEqual([2, 3], src_len_v)
+            self.assertAllEqual(
+                [[4, 1, 2], [4, 2, 2]], target_input_v  # sos b c  # sos c c
+            )
+            self.assertAllEqual(
+                [[1, 2, 3], [2, 2, 3]], target_output_v  # b c eos  # c c eos
+            )
+            self.assertAllEqual([3, 3], tgt_len_v)
 
-      (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
-          sess.run((source, src_seq_len, target_input, target_output,
-                    tgt_seq_len)))
-      self.assertAllEqual(
-          [[2, 2, 0]],  # c c a
-          source_v)
-      self.assertAllEqual([3], src_len_v)
-      self.assertAllEqual(
-          [[4, 0, 1]],  # sos a b
-          target_input_v)
-      self.assertAllEqual(
-          [[0, 1, 3]],  # a b eos
-          target_output_v)
-      self.assertAllEqual([3], tgt_len_v)
+            (source_v, src_len_v, target_input_v, target_output_v, tgt_len_v) = (
+                sess.run(
+                    (source, src_seq_len, target_input, target_output, tgt_seq_len)
+                )
+            )
+            self.assertAllEqual([[2, 2, 0]], source_v)  # c c a
+            self.assertAllEqual([3], src_len_v)
+            self.assertAllEqual([[4, 0, 1]], target_input_v)  # sos a b
+            self.assertAllEqual([[0, 1, 3]], target_output_v)  # a b eos
+            self.assertAllEqual([3], tgt_len_v)
 
-      with self.assertRaisesOpError("End of sequence"):
-        sess.run(source)
+            with self.assertRaisesOpError("End of sequence"):
+                sess.run(source)
 
+    def testGetInferIterator(self):
+        src_vocab_table = lookup_ops.index_table_from_tensor(
+            tf.constant(["a", "b", "c", "eos", "sos"])
+        )
+        src_dataset = tf.data.Dataset.from_tensor_slices(
+            tf.constant(["c c a", "c a", "d", "f e a g"])
+        )
+        hparams = tf.contrib.training.HParams(
+            random_seed=3, eos="eos", sos="sos")
+        batch_size = 2
+        src_max_len = 3
+        iterator = iterator_utils.get_infer_iterator(
+            src_dataset=src_dataset,
+            src_vocab_table=src_vocab_table,
+            batch_size=batch_size,
+            eos=hparams.eos,
+            src_max_len=src_max_len,
+        )
+        table_initializer = tf.tables_initializer()
+        source = iterator.source
+        seq_len = iterator.source_sequence_length
+        self.assertEqual([None, None], source.shape.as_list())
+        self.assertEqual([None], seq_len.shape.as_list())
+        with self.test_session() as sess:
+            sess.run(table_initializer)
+            sess.run(iterator.initializer)
 
-  def testGetInferIterator(self):
-    src_vocab_table = lookup_ops.index_table_from_tensor(
-        tf.constant(["a", "b", "c", "eos", "sos"]))
-    src_dataset = tf.data.Dataset.from_tensor_slices(
-        tf.constant(["c c a", "c a", "d", "f e a g"]))
-    hparams = tf.contrib.training.HParams(
-        random_seed=3,
-        eos="eos",
-        sos="sos")
-    batch_size = 2
-    src_max_len = 3
-    iterator = iterator_utils.get_infer_iterator(
-        src_dataset=src_dataset,
-        src_vocab_table=src_vocab_table,
-        batch_size=batch_size,
-        eos=hparams.eos,
-        src_max_len=src_max_len)
-    table_initializer = tf.tables_initializer()
-    source = iterator.source
-    seq_len = iterator.source_sequence_length
-    self.assertEqual([None, None], source.shape.as_list())
-    self.assertEqual([None], seq_len.shape.as_list())
-    with self.test_session() as sess:
-      sess.run(table_initializer)
-      sess.run(iterator.initializer)
+            (source_v, seq_len_v) = sess.run((source, seq_len))
+            self.assertAllEqual([[2, 2, 0], [2, 0, 3]],
+                                source_v)  # c c a  # c a eos
+            self.assertAllEqual([3, 2], seq_len_v)
 
-      (source_v, seq_len_v) = sess.run((source, seq_len))
-      self.assertAllEqual(
-          [[2, 2, 0],   # c c a
-           [2, 0, 3]],  # c a eos
-          source_v)
-      self.assertAllEqual([3, 2], seq_len_v)
+            (source_v, seq_len_v) = sess.run((source, seq_len))
+            self.assertAllEqual(
+                [
+                    [-1, 3, 3],  # "d" == unknown, eos eos
+                    [-1, -1, 0],
+                ],  # "f" == unknown, "e" == unknown, a
+                source_v,
+            )
+            self.assertAllEqual([1, 3], seq_len_v)
 
-      (source_v, seq_len_v) = sess.run((source, seq_len))
-      self.assertAllEqual(
-          [[-1, 3, 3],    # "d" == unknown, eos eos
-           [-1, -1, 0]],  # "f" == unknown, "e" == unknown, a
-          source_v)
-      self.assertAllEqual([1, 3], seq_len_v)
-
-      with self.assertRaisesOpError("End of sequence"):
-        sess.run((source, seq_len))
+            with self.assertRaisesOpError("End of sequence"):
+                sess.run((source, seq_len))
 
 
 if __name__ == "__main__":
-  tf.test.main()
+    tf.test.main()
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils.py
index 63dc5a69c0..508211c9fc 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils.py
@@ -30,152 +30,167 @@
 
 
 def check_tensorflow_version():
-  min_tf_version = "1.4.0-dev20171024"
-  if (version.LooseVersion(tf.__version__) <
-      version.LooseVersion(min_tf_version)):
-    raise EnvironmentError("Tensorflow version must >= %s" % min_tf_version)
+    min_tf_version = "1.4.0-dev20171024"
+    if version.LooseVersion(
+            tf.__version__) < version.LooseVersion(min_tf_version):
+        raise EnvironmentError(
+            "Tensorflow version must >= %s" %
+            min_tf_version)
 
 
 def safe_exp(value):
-  """Exponentiation with catching of overflow error."""
-  try:
-    ans = math.exp(value)
-  except OverflowError:
-    ans = float("inf")
-  return ans
+    """Exponentiation with catching of overflow error."""
+    try:
+        ans = math.exp(value)
+    except OverflowError:
+        ans = float("inf")
+    return ans
 
 
 def print_time(s, start_time):
-  """Take a start time, print elapsed duration, and return a new time."""
-  print("%s, time %ds, %s." % (s, (time.time() - start_time), time.ctime()))
-  sys.stdout.flush()
-  return time.time()
+    """Take a start time, print elapsed duration, and return a new time."""
+    print("%s, time %ds, %s." % (s, (time.time() - start_time), time.ctime()))
+    sys.stdout.flush()
+    return time.time()
 
 
 def print_out(s, f=None, new_line=True):
-  """Similar to print but with support to flush and output to a file."""
-  if isinstance(s, bytes):
-    s = s.decode("utf-8")
+    """Similar to print but with support to flush and output to a file."""
+    if isinstance(s, bytes):
+        s = s.decode("utf-8")
 
-  if f:
-    f.write(s.encode("utf-8"))
-    if new_line:
-      f.write(b"\n")
+    if f:
+        f.write(s.encode("utf-8"))
+        if new_line:
+            f.write(b"\n")
 
-  # stdout
-  out_s = s.encode("utf-8")
-  if not isinstance(out_s, str):
-    out_s = out_s.decode("utf-8")
-  print(out_s, end="", file=sys.stdout)
+    # stdout
+    out_s = s.encode("utf-8")
+    if not isinstance(out_s, str):
+        out_s = out_s.decode("utf-8")
+    print(out_s, end="", file=sys.stdout)
 
-  if new_line:
-    sys.stdout.write("\n")
-  sys.stdout.flush()
+    if new_line:
+        sys.stdout.write("\n")
+    sys.stdout.flush()
 
 
 def print_hparams(hparams, skip_patterns=None, header=None):
-  """Print hparams, can skip keys based on pattern."""
-  if header: print_out("%s" % header)
-  values = hparams.values()
-  for key in sorted(values.keys()):
-    if not skip_patterns or all(
-        [skip_pattern not in key for skip_pattern in skip_patterns]):
-      print_out("  %s=%s" % (key, str(values[key])))
+    """Print hparams, can skip keys based on pattern."""
+    if header:
+        print_out("%s" % header)
+    values = hparams.values()
+    for key in sorted(values.keys()):
+        if not skip_patterns or all(
+            [skip_pattern not in key for skip_pattern in skip_patterns]
+        ):
+            print_out("  %s=%s" % (key, str(values[key])))
 
 
 def load_hparams(model_dir):
-  """Load hparams from an existing model directory."""
-  hparams_file = os.path.join(model_dir, "hparams")
-  if tf.gfile.Exists(hparams_file):
-    print_out("# Loading hparams from %s" % hparams_file)
-    with codecs.getreader("utf-8")(tf.gfile.GFile(hparams_file, "rb")) as f:
-      try:
-        hparams_values = json.load(f)
-        hparams = tf.contrib.training.HParams(**hparams_values)
-      except ValueError:
-        print_out("  can't load hparams file")
+    """Load hparams from an existing model directory."""
+    hparams_file = os.path.join(model_dir, "hparams")
+    if tf.gfile.Exists(hparams_file):
+        print_out("# Loading hparams from %s" % hparams_file)
+        with codecs.getreader("utf-8")(tf.gfile.GFile(hparams_file, "rb")) as f:
+            try:
+                hparams_values = json.load(f)
+                hparams = tf.contrib.training.HParams(**hparams_values)
+            except ValueError:
+                print_out("  can't load hparams file")
+                return None
+        return hparams
+    else:
         return None
-    return hparams
-  else:
-    return None
 
 
 def maybe_parse_standard_hparams(hparams, hparams_path):
-  """Override hparams values with existing standard hparams config."""
-  if hparams_path and tf.gfile.Exists(hparams_path):
-    print_out("# Loading standard hparams from %s" % hparams_path)
-    with codecs.getreader("utf-8")(tf.gfile.GFile(hparams_path, "rb")) as f:
-      hparams.parse_json(f.read())
-  return hparams
+    """Override hparams values with existing standard hparams config."""
+    if hparams_path and tf.gfile.Exists(hparams_path):
+        print_out("# Loading standard hparams from %s" % hparams_path)
+        with codecs.getreader("utf-8")(tf.gfile.GFile(hparams_path, "rb")) as f:
+            hparams.parse_json(f.read())
+    return hparams
 
 
 def save_hparams(out_dir, hparams):
-  """Save hparams."""
-  hparams_file = os.path.join(out_dir, "hparams")
-  print_out("  saving hparams to %s" % hparams_file)
-  with codecs.getwriter("utf-8")(tf.gfile.GFile(hparams_file, "wb")) as f:
-    f.write(hparams.to_json(indent=4, sort_keys=True))
+    """Save hparams."""
+    hparams_file = os.path.join(out_dir, "hparams")
+    print_out("  saving hparams to %s" % hparams_file)
+    with codecs.getwriter("utf-8")(tf.gfile.GFile(hparams_file, "wb")) as f:
+        f.write(hparams.to_json(indent=4, sort_keys=True))
 
 
 def debug_tensor(s, msg=None, summarize=10):
-  """Print the shape and value of a tensor at test time. Return a new tensor."""
-  if not msg:
-    msg = s.name
-  return tf.Print(s, [tf.shape(s), s], msg + " ", summarize=summarize)
+    """Print the shape and value of a tensor at test time. Return a new tensor."""
+    if not msg:
+        msg = s.name
+    return tf.Print(s, [tf.shape(s), s], msg + " ", summarize=summarize)
 
 
 def add_summary(summary_writer, global_step, tag, value):
-  """Add a new summary to the current summary_writer.
-  Useful to log things that are not part of the training graph, e.g., tag=BLEU.
-  """
-  summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
-  summary_writer.add_summary(summary, global_step)
-
-
-def get_config_proto(log_device_placement=False, allow_soft_placement=True,
-                     num_intra_threads=0, num_inter_threads=0):
-  # GPU options:
-  # https://www.tensorflow.org/versions/r0.10/how_tos/using_gpu/index.html
-  config_proto = tf.ConfigProto(
-      log_device_placement=log_device_placement,
-      allow_soft_placement=allow_soft_placement)
-  config_proto.gpu_options.allow_growth = True
-
-  # CPU threads options
-  if num_intra_threads:
-    config_proto.intra_op_parallelism_threads = num_intra_threads
-  if num_inter_threads:
-    config_proto.inter_op_parallelism_threads = num_inter_threads
-
-  return config_proto
+    """Add a new summary to the current summary_writer.
+    Useful to log things that are not part of the training graph, e.g., tag=BLEU.
+    """
+    summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
+    summary_writer.add_summary(summary, global_step)
+
+
+def get_config_proto(
+    log_device_placement=False,
+    allow_soft_placement=True,
+    num_intra_threads=0,
+    num_inter_threads=0,
+):
+    # GPU options:
+    # https://www.tensorflow.org/versions/r0.10/how_tos/using_gpu/index.html
+    config_proto = tf.ConfigProto(
+        log_device_placement=log_device_placement,
+        allow_soft_placement=allow_soft_placement,
+    )
+    config_proto.gpu_options.allow_growth = True
+
+    # CPU threads options
+    if num_intra_threads:
+        config_proto.intra_op_parallelism_threads = num_intra_threads
+    if num_inter_threads:
+        config_proto.inter_op_parallelism_threads = num_inter_threads
+
+    return config_proto
 
 
 def format_text(words):
-  """Convert a sequence words into sentence."""
-  if (not hasattr(words, "__len__") and  # for numpy array
-      not isinstance(words, collections.Iterable)):
-    words = [words]
-  return b" ".join(words)
+    """Convert a sequence words into sentence."""
+    if not hasattr(words, "__len__") and not isinstance(  # for numpy array
+        words, collections.Iterable
+    ):
+        words = [words]
+    return b" ".join(words)
 
 
 def format_bpe_text(symbols, delimiter=b"@@"):
-  """Convert a sequence of bpe words into sentence."""
-  words = []
-  word = b""
-  if isinstance(symbols, str):
-    symbols = symbols.encode()
-  delimiter_len = len(delimiter)
-  for symbol in symbols:
-    if len(symbol) >= delimiter_len and symbol[-delimiter_len:] == delimiter:
-      word += symbol[:-delimiter_len]
-    else:  # end of a word
-      word += symbol
-      words.append(word)
-      word = b""
-  return b" ".join(words)
+    """Convert a sequence of bpe words into sentence."""
+    words = []
+    word = b""
+    if isinstance(symbols, str):
+        symbols = symbols.encode()
+    delimiter_len = len(delimiter)
+    for symbol in symbols:
+        if len(
+                symbol) >= delimiter_len and symbol[-delimiter_len:] == delimiter:
+            word += symbol[:-delimiter_len]
+        else:  # end of a word
+            word += symbol
+            words.append(word)
+            word = b""
+    return b" ".join(words)
 
 
 def format_spm_text(symbols):
-  """Decode a text in SPM (https://github.com/google/sentencepiece) format."""
-  return u"".join(format_text(symbols).decode("utf-8").split()).replace(
-      u"\u2581", u" ").strip().encode("utf-8")
+    """Decode a text in SPM (https://github.com/google/sentencepiece) format."""
+    return (
+        "".join(format_text(symbols).decode("utf-8").split())
+        .replace("\u2581", " ")
+        .strip()
+        .encode("utf-8")
+    )
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils_test.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils_test.py
index a15551d8c0..5bc1779262 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils_test.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/misc_utils_test.py
@@ -26,24 +26,26 @@
 
 class MiscUtilsTest(tf.test.TestCase):
 
-  def testFormatBpeText(self):
-    bpe_line = (
-        b"En@@ ough to make already reluc@@ tant men hesitate to take screening"
-        b" tests ."
-    )
-    expected_result = (
-        b"Enough to make already reluctant men hesitate to take screening tests"
-        b" ."
-    )
-    self.assertEqual(expected_result,
-                     misc_utils.format_bpe_text(bpe_line.split(b" ")))
-
-  def testFormatSPMText(self):
-    spm_line = u"\u2581This \u2581is \u2581a \u2581 te st .".encode("utf-8")
-    expected_result = "This is a test."
-    self.assertEqual(expected_result,
-                     misc_utils.format_spm_text(spm_line.split(b" ")))
+    def testFormatBpeText(self):
+        bpe_line = (
+            b"En@@ ough to make already reluc@@ tant men hesitate to take screening"
+            b" tests ."
+        )
+        expected_result = (
+            b"Enough to make already reluctant men hesitate to take screening tests"
+            b" ."
+        )
+        self.assertEqual(
+            expected_result, misc_utils.format_bpe_text(bpe_line.split(b" "))
+        )
+
+    def testFormatSPMText(self):
+        spm_line = "\u2581This \u2581is \u2581a \u2581 te st .".encode("utf-8")
+        expected_result = "This is a test."
+        self.assertEqual(
+            expected_result, misc_utils.format_spm_text(spm_line.split(b" "))
+        )
 
 
 if __name__ == "__main__":
-  tf.test.main()
+    tf.test.main()
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/nmt_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/nmt_utils.py
index ff417a6682..857b74e6a6 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/nmt_utils.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/nmt_utils.py
@@ -27,113 +27,131 @@
 __all__ = ["decode_and_evaluate", "get_translation"]
 
 
-def decode_and_evaluate(run,
-                        iterations,
-                        name,
-                        model,
-                        sess,
-                        trans_file,
-                        ref_file,
-                        metrics,
-                        subword_option,
-                        beam_width,
-                        tgt_eos,
-                        num_translations_per_input=1,
-                        decode=True,
-                        infer_mode="greedy"):
-  """Decode a test set and compute a score according to the evaluation task."""
-  # Decode
-  if decode:
-    utils.print_out("  decoding to output %s" % trans_file)
-
-    num_sentences = 0
-    with codecs.getwriter("utf-8")(
-        tf.gfile.GFile(trans_file, mode="wb")) as trans_f:
-      trans_f.write("")  # Write empty string to ensure file is created.
-
-      if infer_mode == "greedy":
-        num_translations_per_input = 1
-      elif infer_mode == "beam_search":
-        num_translations_per_input = min(num_translations_per_input, beam_width)
-
-      print("  infer_mode %s, beam_width %g, num translations per input %d. " \
-            % (infer_mode, beam_width, num_translations_per_input))
-      print("  total iterations count %d." % iterations)
-
-      # prediction time is the time for the model prediction only
-      # overall time is the time for data pre-processing and data post-processing
-      prediction_times = list()
-      overall_start = time.time()
-
-      n = 0
-      while n < iterations:
-        n += 1
-        while True:
-          try:
-            start = time.time()
-            nmt_outputs, _ = model.decode(sess)
-            prediction_times.append(time.time() - start)
-            if infer_mode != "beam_search":
-              nmt_outputs = np.expand_dims(nmt_outputs, 0)
-
-            batch_size = nmt_outputs.shape[1]
-
-            num_sentences += batch_size
-            for sent_id in range(batch_size):
-              for beam_id in range(num_translations_per_input):
-                translation = get_translation(
-                          nmt_outputs[beam_id],
-                         sent_id,
-                         tgt_eos=tgt_eos,
-                         subword_option=subword_option)
-                if run == 'accuracy':
-                  trans_f.write((translation + b"\n").decode("utf-8"))
-
-          except tf.errors.OutOfRangeError:
-            utils.print_time(
-                "  done, num sentences %d, num translations per input %d" %
-                (num_sentences, num_translations_per_input), overall_start)
-            break
-
-    overall_time = (time.time() - overall_start)
-    if run == 'performance':
-      print("\nAverage Prediction Latency: {:.5f} sec per batch.".format(
-        sum(prediction_times)/float(len(prediction_times))))
-      print("Overall Latency: {:.5f} sec for the entire test "
-            "dataset.".format(overall_time/float(iterations)))
-      print("Overall Throughput : {:.3f} sentences per sec.".format(
-            num_sentences/float(overall_time)))
-
-  # Evaluation
-  evaluation_scores = {}
-  if ref_file and tf.gfile.Exists(trans_file):
-    for metric in metrics:
-      score = evaluation_utils.evaluate(
-             ref_file,
-             trans_file,
-             metric,
-             subword_option=subword_option)
-      evaluation_scores[metric] = score
-      utils.print_out("  %s %s: %.1f" % (metric, name, score))
-
-  return evaluation_scores
+def decode_and_evaluate(
+    run,
+    iterations,
+    name,
+    model,
+    sess,
+    trans_file,
+    ref_file,
+    metrics,
+    subword_option,
+    beam_width,
+    tgt_eos,
+    num_translations_per_input=1,
+    decode=True,
+    infer_mode="greedy",
+):
+    """Decode a test set and compute a score according to the evaluation task."""
+    # Decode
+    if decode:
+        utils.print_out("  decoding to output %s" % trans_file)
+
+        num_sentences = 0
+        with codecs.getwriter("utf-8")(
+            tf.gfile.GFile(trans_file, mode="wb")
+        ) as trans_f:
+            trans_f.write("")  # Write empty string to ensure file is created.
+
+            if infer_mode == "greedy":
+                num_translations_per_input = 1
+            elif infer_mode == "beam_search":
+                num_translations_per_input = min(
+                    num_translations_per_input, beam_width)
+
+            print(
+                "  infer_mode %s, beam_width %g, num translations per input %d. "
+                % (infer_mode, beam_width, num_translations_per_input)
+            )
+            print("  total iterations count %d." % iterations)
+
+            # prediction time is the time for the model prediction only
+            # overall time is the time for data pre-processing and data
+            # post-processing
+            prediction_times = list()
+            overall_start = time.time()
+
+            n = 0
+            while n < iterations:
+                n += 1
+                while True:
+                    try:
+                        start = time.time()
+                        nmt_outputs, _ = model.decode(sess)
+                        prediction_times.append(time.time() - start)
+                        if infer_mode != "beam_search":
+                            nmt_outputs = np.expand_dims(nmt_outputs, 0)
+
+                        batch_size = nmt_outputs.shape[1]
+
+                        num_sentences += batch_size
+                        for sent_id in range(batch_size):
+                            for beam_id in range(num_translations_per_input):
+                                translation = get_translation(
+                                    nmt_outputs[beam_id],
+                                    sent_id,
+                                    tgt_eos=tgt_eos,
+                                    subword_option=subword_option,
+                                )
+                                if run == "accuracy":
+                                    trans_f.write(
+                                        (translation + b"\n").decode("utf-8"))
+
+                    except tf.errors.OutOfRangeError:
+                        utils.print_time(
+                            "  done, num sentences %d, num translations per input %d"
+                            % (num_sentences, num_translations_per_input),
+                            overall_start,
+                        )
+                        break
+
+        overall_time = time.time() - overall_start
+        if run == "performance":
+            print(
+                "\nAverage Prediction Latency: {:.5f} sec per batch.".format(
+                    sum(prediction_times) / float(len(prediction_times))
+                )
+            )
+            print(
+                "Overall Latency: {:.5f} sec for the entire test "
+                "dataset.".format(overall_time / float(iterations))
+            )
+            print(
+                "Overall Throughput : {:.3f} sentences per sec.".format(
+                    num_sentences / float(overall_time)
+                )
+            )
+
+    # Evaluation
+    evaluation_scores = {}
+    if ref_file and tf.gfile.Exists(trans_file):
+        for metric in metrics:
+            score = evaluation_utils.evaluate(
+                ref_file, trans_file, metric, subword_option=subword_option
+            )
+            evaluation_scores[metric] = score
+            utils.print_out("  %s %s: %.1f" % (metric, name, score))
+
+    return evaluation_scores
 
 
 def get_translation(nmt_outputs, sent_id, tgt_eos, subword_option):
-  """Given batch decoding outputs, select a sentence and turn to text."""
-  if tgt_eos: tgt_eos = tgt_eos.encode("utf-8")
-  # Select a sentence
-  output = nmt_outputs[sent_id, :].tolist()
-
-  # If there is an eos symbol in outputs, cut them at that point.
-  if tgt_eos and tgt_eos in output:
-    output = output[:output.index(tgt_eos)]
-
-  if subword_option == "bpe":  # BPE
-    translation = utils.format_bpe_text(output)
-  elif subword_option == "spm":  # SPM
-    translation = utils.format_spm_text(output)
-  else:
-    translation = utils.format_text(output)
-
-  return translation
+    """Given batch decoding outputs, select a sentence and turn to text."""
+    if tgt_eos:
+        tgt_eos = tgt_eos.encode("utf-8")
+    # Select a sentence
+    output = nmt_outputs[sent_id, :].tolist()
+
+    # If there is an eos symbol in outputs, cut them at that point.
+    if tgt_eos and tgt_eos in output:
+        output = output[: output.index(tgt_eos)]
+
+    if subword_option == "bpe":  # BPE
+        translation = utils.format_bpe_text(output)
+    elif subword_option == "spm":  # SPM
+        translation = utils.format_spm_text(output)
+    else:
+        translation = utils.format_text(output)
+
+    return translation
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/standard_hparams_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/standard_hparams_utils.py
index c47a6f6b32..1643ff0e70 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/standard_hparams_utils.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/standard_hparams_utils.py
@@ -23,89 +23,81 @@
 
 
 def create_standard_hparams():
-  return tf.contrib.training.HParams(
-      # Data
-      src="",
-      tgt="",
-      train_prefix="",
-      dev_prefix="",
-      test_prefix="",
-      vocab_prefix="",
-      embed_prefix="",
-      out_dir="",
-
-      # Networks
-      num_units=512,
-      num_encoder_layers=2,
-      num_decoder_layers=2,
-      dropout=0.2,
-      unit_type="lstm",
-      encoder_type="bi",
-      residual=False,
-      time_major=True,
-      num_embeddings_partitions=0,
-      num_enc_emb_partitions=0,
-      num_dec_emb_partitions=0,
-
-      # Attention mechanisms
-      attention="scaled_luong",
-      attention_architecture="standard",
-      output_attention=True,
-      pass_hidden_state=True,
-
-      # Train
-      optimizer="sgd",
-      batch_size=128,
-      init_op="uniform",
-      init_weight=0.1,
-      max_gradient_norm=5.0,
-      learning_rate=1.0,
-      warmup_steps=0,
-      warmup_scheme="t2t",
-      decay_scheme="luong234",
-      colocate_gradients_with_ops=True,
-      num_train_steps=12000,
-      num_sampled_softmax=0,
-
-      # Data constraints
-      num_buckets=5,
-      max_train=0,
-      src_max_len=50,
-      tgt_max_len=50,
-      src_max_len_infer=0,
-      tgt_max_len_infer=0,
-
-      # Data format
-      sos="<s>",
-      eos="</s>",
-      subword_option="",
-      use_char_encode=False,
-      check_special_token=True,
-
-      # Misc
-      forget_bias=1.0,
-      num_gpus=1,
-      epoch_step=0,  # record where we were within an epoch.
-      steps_per_stats=100,
-      steps_per_external_eval=0,
-      share_vocab=False,
-      metrics=["bleu"],
-      log_device_placement=False,
-      random_seed=None,
-      # only enable beam search during inference when beam_width > 0.
-      beam_width=0,
-      length_penalty_weight=0.0,
-      override_loaded_hparams=True,
-      num_keep_ckpts=5,
-      avg_ckpts=False,
-
-      # For inference
-      inference_indices=None,
-      infer_batch_size=32,
-      sampling_temperature=0.0,
-      num_translations_per_input=1,
-      infer_mode="greedy",
-
-      # Language model
-      language_model=False,
-  )
+    return tf.contrib.training.HParams(
+        # Data
+        src="",
+        tgt="",
+        train_prefix="",
+        dev_prefix="",
+        test_prefix="",
+        vocab_prefix="",
+        embed_prefix="",
+        out_dir="",
+        # Networks
+        num_units=512,
+        num_encoder_layers=2,
+        num_decoder_layers=2,
+        dropout=0.2,
+        unit_type="lstm",
+        encoder_type="bi",
+        residual=False,
+        time_major=True,
+        num_embeddings_partitions=0,
+        num_enc_emb_partitions=0,
+        num_dec_emb_partitions=0,
+        # Attention mechanisms
+        attention="scaled_luong",
+        attention_architecture="standard",
+        output_attention=True,
+        pass_hidden_state=True,
+        # Train
+        optimizer="sgd",
+        batch_size=128,
+        init_op="uniform",
+        init_weight=0.1,
+        max_gradient_norm=5.0,
+        learning_rate=1.0,
+        warmup_steps=0,
+        warmup_scheme="t2t",
+        decay_scheme="luong234",
+        colocate_gradients_with_ops=True,
+        num_train_steps=12000,
+        num_sampled_softmax=0,
+        # Data constraints
+        num_buckets=5,
+        max_train=0,
+        src_max_len=50,
+        tgt_max_len=50,
+        src_max_len_infer=0,
+        tgt_max_len_infer=0,
+        # Data format
+        sos="<s>",
+        eos="</s>",
+        subword_option="",
+        use_char_encode=False,
+        check_special_token=True,
+        # Misc
+        forget_bias=1.0,
+        num_gpus=1,
+        epoch_step=0,  # record where we were within an epoch.
+        steps_per_stats=100,
+        steps_per_external_eval=0,
+        share_vocab=False,
+        metrics=["bleu"],
+        log_device_placement=False,
+        random_seed=None,
+        # only enable beam search during inference when beam_width > 0.
+        beam_width=0,
+        length_penalty_weight=0.0,
+        override_loaded_hparams=True,
+        num_keep_ckpts=5,
+        avg_ckpts=False,
+        # For inference
+        inference_indices=None,
+        infer_batch_size=32,
+        sampling_temperature=0.0,
+        num_translations_per_input=1,
+        infer_mode="greedy",
+        # Language model
+        language_model=False,
+    )
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils.py
index 5063bf2ef3..9a3103bf29 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils.py
@@ -45,150 +45,162 @@
 
 
 def _string_to_bytes(text, max_length):
-  """Given string and length, convert to byte seq of at most max_length.
+    """Given string and length, convert to byte seq of at most max_length.
 
-  This process mimics docqa/elmo's preprocessing:
-  https://github.com/allenai/document-qa/blob/master/docqa/elmo/data.py
+    This process mimics docqa/elmo's preprocessing:
+    https://github.com/allenai/document-qa/blob/master/docqa/elmo/data.py
 
-  Note that we make use of BOS_CHAR_ID and EOS_CHAR_ID in iterator_utils.py & 
-  our usage differs from docqa/elmo.
+    Note that we make use of BOS_CHAR_ID and EOS_CHAR_ID in iterator_utils.py &
+    our usage differs from docqa/elmo.
 
-  Args:
-    text: tf.string tensor of shape []
-    max_length: max number of chars for each word.
+    Args:
+      text: tf.string tensor of shape []
+      max_length: max number of chars for each word.
 
-  Returns:
-    A tf.int32 tensor of the byte encoded text.
-  """
-  byte_ids = tf.to_int32(tf.decode_raw(text, tf.uint8))
-  byte_ids = byte_ids[:max_length - 2]
-  padding = tf.fill([max_length - tf.shape(byte_ids)[0] - 2], PAD_CHAR_ID)
-  byte_ids = tf.concat(
-      [[BOW_CHAR_ID], byte_ids, [EOW_CHAR_ID], padding], axis=0)
-  tf.logging.info(byte_ids)
+    Returns:
+      A tf.int32 tensor of the byte encoded text.
+    """
+    byte_ids = tf.to_int32(tf.decode_raw(text, tf.uint8))
+    byte_ids = byte_ids[: max_length - 2]
+    padding = tf.fill([max_length - tf.shape(byte_ids)[0] - 2], PAD_CHAR_ID)
+    byte_ids = tf.concat(
+        [[BOW_CHAR_ID], byte_ids, [EOW_CHAR_ID], padding], axis=0)
+    tf.logging.info(byte_ids)
 
-  byte_ids = tf.reshape(byte_ids, [max_length])
-  tf.logging.info(byte_ids.get_shape().as_list())
-  return byte_ids + 1
+    byte_ids = tf.reshape(byte_ids, [max_length])
+    tf.logging.info(byte_ids.get_shape().as_list())
+    return byte_ids + 1
 
 
 def tokens_to_bytes(tokens):
-  """Given a sequence of strings, map to sequence of bytes.
-
-  Args:
-    tokens: A tf.string tensor
-
-  Returns:
-    A tensor of shape words.shape + [bytes_per_word] containing byte versions
-    of each word.
-  """
-  bytes_per_word = DEFAULT_CHAR_MAXLEN
-  with tf.device("/cpu:0"):
-    tf.assert_rank(tokens, 1)
-    shape = tf.shape(tokens)
-    tf.logging.info(tokens)
-    tokens_flat = tf.reshape(tokens, [-1])
-    as_bytes_flat = tf.map_fn(
-        fn=lambda x: _string_to_bytes(x, max_length=bytes_per_word),
-        elems=tokens_flat,
-        dtype=tf.int32,
-        back_prop=False)
-    tf.logging.info(as_bytes_flat)
-    as_bytes = tf.reshape(as_bytes_flat, [shape[0], bytes_per_word])
-  return as_bytes
+    """Given a sequence of strings, map to sequence of bytes.
+
+    Args:
+      tokens: A tf.string tensor
+
+    Returns:
+      A tensor of shape words.shape + [bytes_per_word] containing byte versions
+      of each word.
+    """
+    bytes_per_word = DEFAULT_CHAR_MAXLEN
+    with tf.device("/cpu:0"):
+        tf.assert_rank(tokens, 1)
+        shape = tf.shape(tokens)
+        tf.logging.info(tokens)
+        tokens_flat = tf.reshape(tokens, [-1])
+        as_bytes_flat = tf.map_fn(
+            fn=lambda x: _string_to_bytes(x, max_length=bytes_per_word),
+            elems=tokens_flat,
+            dtype=tf.int32,
+            back_prop=False,
+        )
+        tf.logging.info(as_bytes_flat)
+        as_bytes = tf.reshape(as_bytes_flat, [shape[0], bytes_per_word])
+    return as_bytes
 
 
 def load_vocab(vocab_file):
-  vocab = []
-  with codecs.getreader("utf-8")(tf.gfile.GFile(vocab_file, "rb")) as f:
-    vocab_size = 0
-    for word in f:
-      vocab_size += 1
-      vocab.append(word.strip())
-  return vocab, vocab_size
-
-
-def check_vocab(vocab_file, out_dir, check_special_token=True, sos=None,
-                eos=None, unk=None):
-  """Check if vocab_file doesn't exist, create from corpus_file."""
-  if tf.gfile.Exists(vocab_file):
-    utils.print_out("# Vocab file %s exists" % vocab_file)
-    vocab, vocab_size = load_vocab(vocab_file)
-    if check_special_token:
-      # Verify if the vocab starts with unk, sos, eos
-      # If not, prepend those tokens & generate a new vocab file
-      if not unk: unk = UNK
-      if not sos: sos = SOS
-      if not eos: eos = EOS
-      assert len(vocab) >= 3
-      if vocab[0] != unk or vocab[1] != sos or vocab[2] != eos:
-        utils.print_out("The first 3 vocab words [%s, %s, %s]"
-                        " are not [%s, %s, %s]" %
-                        (vocab[0], vocab[1], vocab[2], unk, sos, eos))
-        vocab = [unk, sos, eos] + vocab
-        vocab_size += 3
-        new_vocab_file = os.path.join(out_dir, os.path.basename(vocab_file))
-        with codecs.getwriter("utf-8")(
-            tf.gfile.GFile(new_vocab_file, "wb")) as f:
-          for word in vocab:
-            f.write("%s\n" % word)
-        vocab_file = new_vocab_file
-  else:
-    raise ValueError("vocab_file '%s' does not exist." % vocab_file)
-
-  vocab_size = len(vocab)
-  return vocab_size, vocab_file
+    vocab = []
+    with codecs.getreader("utf-8")(tf.gfile.GFile(vocab_file, "rb")) as f:
+        vocab_size = 0
+        for word in f:
+            vocab_size += 1
+            vocab.append(word.strip())
+    return vocab, vocab_size
+
+
+def check_vocab(
+    vocab_file, out_dir, check_special_token=True, sos=None, eos=None, unk=None
+):
+    """Check if vocab_file doesn't exist, create from corpus_file."""
+    if tf.gfile.Exists(vocab_file):
+        utils.print_out("# Vocab file %s exists" % vocab_file)
+        vocab, vocab_size = load_vocab(vocab_file)
+        if check_special_token:
+            # Verify if the vocab starts with unk, sos, eos
+            # If not, prepend those tokens & generate a new vocab file
+            if not unk:
+                unk = UNK
+            if not sos:
+                sos = SOS
+            if not eos:
+                eos = EOS
+            assert len(vocab) >= 3
+            if vocab[0] != unk or vocab[1] != sos or vocab[2] != eos:
+                utils.print_out(
+                    "The first 3 vocab words [%s, %s, %s]"
+                    " are not [%s, %s, %s]"
+                    % (vocab[0], vocab[1], vocab[2], unk, sos, eos)
+                )
+                vocab = [unk, sos, eos] + vocab
+                vocab_size += 3
+                new_vocab_file = os.path.join(
+                    out_dir, os.path.basename(vocab_file))
+                with codecs.getwriter("utf-8")(
+                    tf.gfile.GFile(new_vocab_file, "wb")
+                ) as f:
+                    for word in vocab:
+                        f.write("%s\n" % word)
+                vocab_file = new_vocab_file
+    else:
+        raise ValueError("vocab_file '%s' does not exist." % vocab_file)
+
+    vocab_size = len(vocab)
+    return vocab_size, vocab_file
 
 
 def create_vocab_tables(src_vocab_file, tgt_vocab_file, share_vocab):
-  """Creates vocab tables for src_vocab_file and tgt_vocab_file."""
-  src_vocab_table = lookup_ops.index_table_from_file(
-      src_vocab_file, default_value=UNK_ID)
-  if share_vocab:
-    tgt_vocab_table = src_vocab_table
-  else:
-    tgt_vocab_table = lookup_ops.index_table_from_file(
-        tgt_vocab_file, default_value=UNK_ID)
-  return src_vocab_table, tgt_vocab_table
+    """Creates vocab tables for src_vocab_file and tgt_vocab_file."""
+    src_vocab_table = lookup_ops.index_table_from_file(
+        src_vocab_file, default_value=UNK_ID
+    )
+    if share_vocab:
+        tgt_vocab_table = src_vocab_table
+    else:
+        tgt_vocab_table = lookup_ops.index_table_from_file(
+            tgt_vocab_file, default_value=UNK_ID
+        )
+    return src_vocab_table, tgt_vocab_table
 
 
 def load_embed_txt(embed_file):
-  """Load embed_file into a python dictionary.
-
-  Note: the embed_file should be a Glove/word2vec formatted txt file. Assuming
-  Here is an exampe assuming embed_size=5:
-
-  the -0.071549 0.093459 0.023738 -0.090339 0.056123
-  to 0.57346 0.5417 -0.23477 -0.3624 0.4037
-  and 0.20327 0.47348 0.050877 0.002103 0.060547
-
-  For word2vec format, the first line will be: <num_words> <emb_size>.
-
-  Args:
-    embed_file: file path to the embedding file.
-  Returns:
-    a dictionary that maps word to vector, and the size of embedding dimensions.
-  """
-  emb_dict = dict()
-  emb_size = None
-
-  is_first_line = True
-  with codecs.getreader("utf-8")(tf.gfile.GFile(embed_file, "rb")) as f:
-    for line in f:
-      tokens = line.rstrip().split(" ")
-      if is_first_line:
-        is_first_line = False
-        if len(tokens) == 2:  # header line
-          emb_size = int(tokens[1])
-          continue
-      word = tokens[0]
-      vec = list(map(float, tokens[1:]))
-      emb_dict[word] = vec
-      if emb_size:
-        if emb_size != len(vec):
-          utils.print_out(
-              "Ignoring %s since embeding size is inconsistent." % word)
-          del emb_dict[word]
-      else:
-        emb_size = len(vec)
-  return emb_dict, emb_size
+    """Load embed_file into a python dictionary.
+
+    Note: the embed_file should be a Glove/word2vec formatted txt file. Assuming
+    Here is an exampe assuming embed_size=5:
+
+    the -0.071549 0.093459 0.023738 -0.090339 0.056123
+    to 0.57346 0.5417 -0.23477 -0.3624 0.4037
+    and 0.20327 0.47348 0.050877 0.002103 0.060547
+
+    For word2vec format, the first line will be: <num_words> <emb_size>.
+
+    Args:
+      embed_file: file path to the embedding file.
+    Returns:
+      a dictionary that maps word to vector, and the size of embedding dimensions.
+    """
+    emb_dict = dict()
+    emb_size = None
+
+    is_first_line = True
+    with codecs.getreader("utf-8")(tf.gfile.GFile(embed_file, "rb")) as f:
+        for line in f:
+            tokens = line.rstrip().split(" ")
+            if is_first_line:
+                is_first_line = False
+                if len(tokens) == 2:  # header line
+                    emb_size = int(tokens[1])
+                    continue
+            word = tokens[0]
+            vec = list(map(float, tokens[1:]))
+            emb_dict[word] = vec
+            if emb_size:
+                if emb_size != len(vec):
+                    utils.print_out(
+                        "Ignoring %s since embeding size is inconsistent." % word
+                    )
+                    del emb_dict[word]
+            else:
+                emb_size = len(vec)
+    return emb_dict, emb_size
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils_test.py b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils_test.py
index 391400f5cd..8dfd041f4f 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils_test.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/nmt/utils/vocab_utils_test.py
@@ -28,30 +28,31 @@
 
 class VocabUtilsTest(tf.test.TestCase):
 
-  def testCheckVocab(self):
-    # Create a vocab file
-    vocab_dir = os.path.join(tf.test.get_temp_dir(), "vocab_dir")
-    os.makedirs(vocab_dir)
-    vocab_file = os.path.join(vocab_dir, "vocab_file")
-    vocab = ["a", "b", "c"]
-    with codecs.getwriter("utf-8")(tf.gfile.GFile(vocab_file, "wb")) as f:
-      for word in vocab:
-        f.write("%s\n" % word)
-
-    # Call vocab_utils
-    out_dir = os.path.join(tf.test.get_temp_dir(), "out_dir")
-    os.makedirs(out_dir)
-    vocab_size, new_vocab_file = vocab_utils.check_vocab(
-        vocab_file, out_dir)
-
-    # Assert: we expect the code to add  <unk>, <s>, </s> and
-    # create a new vocab file
-    self.assertEqual(len(vocab) + 3, vocab_size)
-    self.assertEqual(os.path.join(out_dir, "vocab_file"), new_vocab_file)
-    new_vocab, _ = vocab_utils.load_vocab(new_vocab_file)
-    self.assertEqual(
-        [vocab_utils.UNK, vocab_utils.SOS, vocab_utils.EOS] + vocab, new_vocab)
+    def testCheckVocab(self):
+        # Create a vocab file
+        vocab_dir = os.path.join(tf.test.get_temp_dir(), "vocab_dir")
+        os.makedirs(vocab_dir)
+        vocab_file = os.path.join(vocab_dir, "vocab_file")
+        vocab = ["a", "b", "c"]
+        with codecs.getwriter("utf-8")(tf.gfile.GFile(vocab_file, "wb")) as f:
+            for word in vocab:
+                f.write("%s\n" % word)
+
+        # Call vocab_utils
+        out_dir = os.path.join(tf.test.get_temp_dir(), "out_dir")
+        os.makedirs(out_dir)
+        vocab_size, new_vocab_file = vocab_utils.check_vocab(
+            vocab_file, out_dir)
+
+        # Assert: we expect the code to add  <unk>, <s>, </s> and
+        # create a new vocab file
+        self.assertEqual(len(vocab) + 3, vocab_size)
+        self.assertEqual(os.path.join(out_dir, "vocab_file"), new_vocab_file)
+        new_vocab, _ = vocab_utils.load_vocab(new_vocab_file)
+        self.assertEqual(
+            [vocab_utils.UNK, vocab_utils.SOS, vocab_utils.EOS] + vocab, new_vocab
+        )
 
 
 if __name__ == "__main__":
-  tf.test.main()
+    tf.test.main()
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/process_accuracy.py b/retired_benchmarks/translation/gnmt/tensorflow/process_accuracy.py
index 77343f39ad..53f3c7cf18 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/process_accuracy.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/process_accuracy.py
@@ -24,29 +24,44 @@
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser()
-    parser.add_argument('--reference', type=str, default=os.path.join(os.getcwd(), 'nmt', 'data', 'newstest2014.tok.bpe.32000.de'),
-                            help="Reference text to compare accuracy against.")
-    parser.add_argument('--accuracy_log', type=str, default = 'mlperf_log_accuracy.json',
-                            help="Accuracy log file")
+    parser.add_argument(
+        "--reference",
+        type=str,
+        default=os.path.join(
+            os.getcwd(), "nmt", "data", "newstest2014.tok.bpe.32000.de"
+        ),
+        help="Reference text to compare accuracy against.",
+    )
+    parser.add_argument(
+        "--accuracy_log",
+        type=str,
+        default="mlperf_log_accuracy.json",
+        help="Accuracy log file",
+    )
     args = parser.parse_args()
 
-
     # Check whether reference and log files exist
     if not os.path.exists(args.reference):
-        print("Could not find reference file {}. Please specify its location".format(args.reference))
+        print(
+            "Could not find reference file {}. Please specify its location".format(
+                args.reference
+            )
+        )
         sys.exit(0)
 
     if not os.path.exists(args.accuracy_log):
-        print("Could not find accuracy log file {}. Please specify its location".format(args.accuracy_log))
+        print(
+            "Could not find accuracy log file {}. Please specify its location".format(
+                args.accuracy_log
+            )
+        )
         sys.exit(0)
 
-    
     ##
     # @note: List of lists of words from the reference
     # @note: ref[i][j] refers to the j'th word of sentence i
     ref = []
-    with codecs.getreader("utf-8")(
-        tf.gfile.GFile(args.reference, "rb")) as ifh:
+    with codecs.getreader("utf-8")(tf.gfile.GFile(args.reference, "rb")) as ifh:
         ref_sentences = ifh.readlines()
         # Sanitize each sentence and convert to array of words
         ref = [e_utils._clean(s, "bpe").split(" ") for s in ref_sentences]
@@ -81,7 +96,6 @@
             # Update the Running BLEU Scorer for this sentence
             runningBLUE.add_sentence(ref[sent_id], trans)
 
-
     (bleu, _, _, _, _, _) = runningBLUE.calc_BLEU_score()
 
-    print("BLEU: %.1f" % (bleu * 100))
\ No newline at end of file
+    print("BLEU: %.1f" % (bleu * 100))
diff --git a/retired_benchmarks/translation/gnmt/tensorflow/run_task.py b/retired_benchmarks/translation/gnmt/tensorflow/run_task.py
index 365ce76ec8..b2c0be68fb 100644
--- a/retired_benchmarks/translation/gnmt/tensorflow/run_task.py
+++ b/retired_benchmarks/translation/gnmt/tensorflow/run_task.py
@@ -15,73 +15,117 @@
 
 parser = argparse.ArgumentParser()
 
-parser.add_argument('--run', type=str, default='performance',
-                    help="Specify either 'accuracy' for BLEU metric or "
-                    "'performance' (default) for prediction latency and throughput"
+parser.add_argument(
+    "--run",
+    type=str,
+    default="performance",
+    help="Specify either 'accuracy' for BLEU metric or "
+    "'performance' (default) for prediction latency and throughput",
 )
 
 
-parser.add_argument('--batch_size', type=str, default='32',
-                    help="Specify inference batch size"
+parser.add_argument(
+    "--batch_size", type=str, default="32", help="Specify inference batch size"
 )
 
-parser.add_argument('--num_inter_threads', type=str, default='0',
-                    help="Specify inference num_inter_threads"
+parser.add_argument(
+    "--num_inter_threads",
+    type=str,
+    default="0",
+    help="Specify inference num_inter_threads",
 )
 
-parser.add_argument('--num_intra_threads', type=str, default='0',
-                    help="Specify inference num_intra_threads"
+parser.add_argument(
+    "--num_intra_threads",
+    type=str,
+    default="0",
+    help="Specify inference num_intra_threads",
 )
 
-parser.add_argument('--dataset_path', type=str, 
-                    default=os.path.join(os.getcwd(), 'nmt', 'data'),
-                    help="Specify dataset directory path"
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    default=os.path.join(os.getcwd(), "nmt", "data"),
+    help="Specify dataset directory path",
 )
 
-parser.add_argument('--model_path', type=str, 
-                    default=os.path.join(os.getcwd(), 'ende_gnmt_model_4_layer'),
-                    help="Specify model directory path"
+parser.add_argument(
+    "--model_path",
+    type=str,
+    default=os.path.join(os.getcwd(), "ende_gnmt_model_4_layer"),
+    help="Specify model directory path",
 )
 
-parser.add_argument('--output_path', type=str,
-                    default=os.path.join(os.getcwd(), 'nmt', 'data'),
-                    help="Specify output directory path"
+parser.add_argument(
+    "--output_path",
+    type=str,
+    default=os.path.join(os.getcwd(), "nmt", "data"),
+    help="Specify output directory path",
 )
 
 
-
 args = parser.parse_args()
 
-cpk_path = os.path.join(args.model_path, 'translate.ckpt')
+cpk_path = os.path.join(args.model_path, "translate.ckpt")
 
-haparams_path = os.path.join(os.getcwd(), 'nmt', 'standard_hparams',
-                             'wmt16_gnmt_4_layer.json')
+haparams_path = os.path.join(
+    os.getcwd(), "nmt", "standard_hparams", "wmt16_gnmt_4_layer.json"
+)
 
-vocab_prefix = os.path.join(args.dataset_path, 'vocab.bpe.32000')
+vocab_prefix = os.path.join(args.dataset_path, "vocab.bpe.32000")
 
-inference_ref_file = os.path.join(args.dataset_path, 'newstest2014.tok.bpe.32000.de')
+inference_ref_file = os.path.join(
+    args.dataset_path,
+    "newstest2014.tok.bpe.32000.de")
 
-inference_input_file = os.path.join(args.dataset_path, 'newstest2014.tok.bpe.32000.en')
+inference_input_file = os.path.join(
+    args.dataset_path,
+    "newstest2014.tok.bpe.32000.en")
 
-out_dir = os.path.join(args.output_path, 'result', 'output')
+out_dir = os.path.join(args.output_path, "result", "output")
 
-inference_output_file = os.path.join(args.output_path, 'output', 'g_nmt-out')
+inference_output_file = os.path.join(args.output_path, "output", "g_nmt-out")
 
-outpath = os.path.join(args.output_path, 'output', 'console_out_gnmt.txt')
+outpath = os.path.join(args.output_path, "output", "console_out_gnmt.txt")
 
-cmd = "python -m nmt.nmt \
+cmd = (
+    "python -m nmt.nmt \
     --src=en --tgt=de \
-    --ckpt="+cpk_path+" \
-    --hparams_path="+haparams_path+" \
-    --out_dir="+out_dir+" \
-    --vocab_prefix="+vocab_prefix+" \
-    --inference_input_file="+inference_input_file+" \
-    --inference_output_file="+inference_output_file+" \
-    --inference_ref_file="+inference_ref_file+" \
-    --infer_batch_size="+args.batch_size+" \
-    --num_inter_threads="+args.num_inter_threads+" \
-    --num_intra_threads="+args.num_intra_threads+" \
-    --iterations="+str(iterations)+" \
-    --run="+args.run
+    --ckpt="
+    + cpk_path
+    + " \
+    --hparams_path="
+    + haparams_path
+    + " \
+    --out_dir="
+    + out_dir
+    + " \
+    --vocab_prefix="
+    + vocab_prefix
+    + " \
+    --inference_input_file="
+    + inference_input_file
+    + " \
+    --inference_output_file="
+    + inference_output_file
+    + " \
+    --inference_ref_file="
+    + inference_ref_file
+    + " \
+    --infer_batch_size="
+    + args.batch_size
+    + " \
+    --num_inter_threads="
+    + args.num_inter_threads
+    + " \
+    --num_intra_threads="
+    + args.num_intra_threads
+    + " \
+    --iterations="
+    + str(iterations)
+    + " \
+    --run="
+    + args.run
+)
 
 return_code = subprocess.call(cmd, shell=True)
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend.py b/retired_benchmarks/vision/classification_and_detection/python/backend.py
index 955eddb888..6fc13454a8 100755
--- a/retired_benchmarks/vision/classification_and_detection/python/backend.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend.py
@@ -2,10 +2,10 @@
 abstract backend class
 """
 
-
 # pylint: disable=unused-argument,missing-docstring
 
-class Backend():
+
+class Backend:
     def __init__(self):
         self.inputs = []
         self.outputs = []
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch_native.py b/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch_native.py
index f631ac5d3d..ac79dee3a3 100755
--- a/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch_native.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend_pytorch_native.py
@@ -1,18 +1,19 @@
 """
-pytoch native backend 
+pytoch native backend
 """
+
 # pylint: disable=unused-argument,missing-docstring
 import torch  # currently supports pytorch1.0
 import backend
 
 
-
 class BackendPytorchNative(backend.Backend):
     def __init__(self):
         super(BackendPytorchNative, self).__init__()
         self.sess = None
         self.model = None
         self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
     def version(self):
         return torch.__version__
 
@@ -23,7 +24,10 @@ def image_format(self):
         return "NCHW"
 
     def load(self, model_path, inputs=None, outputs=None):
-        self.model = torch.load(model_path,map_location=lambda storage, loc: storage)
+        self.model = torch.load(
+            model_path,
+            map_location=lambda storage,
+            loc: storage)
         self.model.eval()
         # find inputs from the model if not passed in by config
         if inputs:
@@ -48,10 +52,9 @@ def load(self, model_path, inputs=None, outputs=None):
         self.model = self.model.to(self.device)
         return self
 
-        
     def predict(self, feed):
-        key=[key for key in feed.keys()][0]    
+        key = [key for key in feed.keys()][0]
         feed[key] = torch.tensor(feed[key]).float().to(self.device)
         with torch.no_grad():
-            output = self.model(feed[key])    
+            output = self.model(feed[key])
         return output
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend_tf.py b/retired_benchmarks/vision/classification_and_detection/python/backend_tf.py
index 05245a4547..7cf2e14e4c 100755
--- a/retired_benchmarks/vision/classification_and_detection/python/backend_tf.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend_tf.py
@@ -23,11 +23,13 @@ def name(self):
         return "tensorflow"
 
     def image_format(self):
-        # By default tensorflow uses NHWC (and the cpu implementation only does NHWC)
+        # By default tensorflow uses NHWC (and the cpu implementation only does
+        # NHWC)
         return "NHWC"
 
     def load(self, model_path, inputs=None, outputs=None):
-        # there is no input/output meta data i the graph so it need to come from config.
+        # there is no input/output meta data i the graph so it need to come
+        # from config.
         if not inputs:
             raise ValueError("BackendTensorflow needs inputs")
         if not outputs:
@@ -36,26 +38,40 @@ def load(self, model_path, inputs=None, outputs=None):
         self.inputs = inputs
 
         infer_config = tf.compat.v1.ConfigProto()
-        infer_config.intra_op_parallelism_threads = int(os.environ['TF_INTRA_OP_PARALLELISM_THREADS']) \
-                if 'TF_INTRA_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
-        infer_config.inter_op_parallelism_threads = int(os.environ['TF_INTER_OP_PARALLELISM_THREADS']) \
-                if 'TF_INTER_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
+        infer_config.intra_op_parallelism_threads = (
+            int(os.environ["TF_INTRA_OP_PARALLELISM_THREADS"])
+            if "TF_INTRA_OP_PARALLELISM_THREADS" in os.environ
+            else os.cpu_count()
+        )
+        infer_config.inter_op_parallelism_threads = (
+            int(os.environ["TF_INTER_OP_PARALLELISM_THREADS"])
+            if "TF_INTER_OP_PARALLELISM_THREADS" in os.environ
+            else os.cpu_count()
+        )
         infer_config.use_per_session_threads = 1
 
         # TODO: support checkpoint and saved_model formats?
         graph_def = tf.compat.v1.GraphDef()
         with tf.compat.v1.gfile.FastGFile(model_path, "rb") as f:
             graph_def.ParseFromString(f.read())
-        for as_datatype_enum in [dtypes.float32.as_datatype_enum, dtypes.uint8.as_datatype_enum]:
+        for as_datatype_enum in [
+            dtypes.float32.as_datatype_enum,
+            dtypes.uint8.as_datatype_enum,
+        ]:
             try:
-                optimized_graph_def = optimize_for_inference(graph_def, [item.split(':')[0] for item in inputs],
-                        [item.split(':')[0] for item in outputs], as_datatype_enum, False)
+                optimized_graph_def = optimize_for_inference(
+                    graph_def,
+                    [item.split(":")[0] for item in inputs],
+                    [item.split(":")[0] for item in outputs],
+                    as_datatype_enum,
+                    False,
+                )
                 graph_def = optimized_graph_def
                 break
             except ValueError:
                 pass
 
-        g = tf.compat.v1.import_graph_def(graph_def, name='')
+        g = tf.compat.v1.import_graph_def(graph_def, name="")
         self.sess = tf.compat.v1.Session(graph=g, config=infer_config)
         return self
 
diff --git a/retired_benchmarks/vision/classification_and_detection/python/backend_tflite.py b/retired_benchmarks/vision/classification_and_detection/python/backend_tflite.py
index 7c8c78c139..fa6cc5ba25 100755
--- a/retired_benchmarks/vision/classification_and_detection/python/backend_tflite.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/backend_tflite.py
@@ -10,12 +10,14 @@
     # try dedicated tflite package first
     import tflite_runtime
     import tflite_runtime.interpreter as tflite
+
     _version = tflite_runtime.__version__
     _git_version = tflite_runtime.__git_version__
-except:
+except BaseException:
     # fall back to tflite bundled in tensorflow
     import tensorflow as tf
     from tensorflow.lite.python import interpreter as tflite
+
     _version = tf.__version__
     _git_version = tf.__git_version__
 
@@ -43,8 +45,12 @@ def load(self, model_path, inputs=None, outputs=None):
         self.sess = tflite.Interpreter(model_path=model_path)
         self.sess.allocate_tensors()
         # keep input/output name to index mapping
-        self.input2index = {i["name"]: i["index"] for i in self.sess.get_input_details()}
-        self.output2index = {i["name"]: i["index"] for i in self.sess.get_output_details()}
+        self.input2index = {
+            i["name"]: i["index"] for i in self.sess.get_input_details()
+        }
+        self.output2index = {
+            i["name"]: i["index"] for i in self.sess.get_output_details()
+        }
         # keep input/output names
         self.inputs = list(self.input2index.keys())
         self.outputs = list(self.output2index.keys())
diff --git a/retired_benchmarks/vision/classification_and_detection/python/coco.py b/retired_benchmarks/vision/classification_and_detection/python/coco.py
index 4080158744..d11ac2c5f0 100644
--- a/retired_benchmarks/vision/classification_and_detection/python/coco.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/coco.py
@@ -20,8 +20,19 @@
 
 
 class Coco(dataset.Dataset):
-    def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
-                 image_format="NHWC", pre_process=None, count=None, cache_dir=None,use_label_map=False):
+    def __init__(
+        self,
+        data_path,
+        image_list,
+        name,
+        use_cache=0,
+        image_size=None,
+        image_format="NHWC",
+        pre_process=None,
+        count=None,
+        cache_dir=None,
+        use_label_map=False,
+    ):
         super().__init__()
         self.image_size = image_size
         self.image_list = []
@@ -32,17 +43,19 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         self.use_cache = use_cache
         self.data_path = data_path
         self.pre_process = pre_process
-        self.use_label_map=use_label_map
+        self.use_label_map = use_label_map
         if not cache_dir:
             cache_dir = os.getcwd()
-        self.cache_dir = os.path.join(cache_dir, "preprocessed", name, image_format)
+        self.cache_dir = os.path.join(
+            cache_dir, "preprocessed", name, image_format)
         # input images are in HWC
         self.need_transpose = True if image_format == "NCHW" else False
-        not_found = 0 
+        not_found = 0
         empty_80catageories = 0
         if image_list is None:
             # by default look for val_map.txt
-            image_list = os.path.join(data_path, "annotations/instances_val2017.json")
+            image_list = os.path.join(
+                data_path, "annotations/instances_val2017.json")
         self.annotation_file = image_list
         if self.use_label_map:
             # for pytorch
@@ -58,16 +71,22 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         with open(image_list, "r") as f:
             coco = json.load(f)
         for i in coco["images"]:
-            images[i["id"]] = {"file_name": i["file_name"],
-                               "height": i["height"],
-                               "width": i["width"],
-                               "bbox": [],
-                               "category": []}
+            images[i["id"]] = {
+                "file_name": i["file_name"],
+                "height": i["height"],
+                "width": i["width"],
+                "bbox": [],
+                "category": [],
+            }
         for a in coco["annotations"]:
             i = images.get(a["image_id"])
             if i is None:
                 continue
-            catagory_ids = label_map[a.get("category_id")] if self.use_label_map else a.get("category_id")
+            catagory_ids = (
+                label_map[a.get("category_id")]
+                if self.use_label_map
+                else a.get("category_id")
+            )
             i["category"].append(catagory_ids)
             i["bbox"].append(a.get("bbox"))
 
@@ -78,17 +97,23 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
                 # if the image does not exists ignore it
                 not_found += 1
                 continue
-            if len(img["category"])==0 and self.use_label_map: 
-                #if an image doesn't have any of the 81 categories in it    
-                empty_80catageories += 1 #should be 48 images - thus the validation sert has 4952 images
-                continue 
+            if len(img["category"]) == 0 and self.use_label_map:
+                # if an image doesn't have any of the 81 categories in it
+                empty_80catageories += (
+                    1  # should be 48 images - thus the validation sert has 4952 images
+                )
+                continue
 
-            os.makedirs(os.path.dirname(os.path.join(self.cache_dir, image_name)), exist_ok=True)
+            os.makedirs(
+                os.path.dirname(os.path.join(self.cache_dir, image_name)), exist_ok=True
+            )
             dst = os.path.join(self.cache_dir, image_name)
             if not os.path.exists(dst + ".npy"):
                 # cache a preprocessed version of the image
                 img_org = cv2.imread(src)
-                processed = self.pre_process(img_org, need_transpose=self.need_transpose, dims=self.image_size)
+                processed = self.pre_process(
+                    img_org, need_transpose=self.need_transpose, dims=self.image_size
+                )
                 np.save(dst, processed)
 
             self.image_ids.append(image_id)
@@ -107,10 +132,16 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         if not_found > 0:
             log.info("reduced image list, %d images not found", not_found)
         if empty_80catageories > 0:
-            log.info("reduced image list, %d images without any of the 80 categories", empty_80catageories)
+            log.info(
+                "reduced image list, %d images without any of the 80 categories",
+                empty_80catageories,
+            )
 
-        log.info("loaded {} images, cache={}, took={:.1f}sec".format(
-            len(self.image_list), use_cache, time_taken))
+        log.info(
+            "loaded {} images, cache={}, took={:.1f}sec".format(
+                len(self.image_list), use_cache, time_taken
+            )
+        )
 
         self.label_list = np.array(self.label_list)
 
@@ -129,6 +160,7 @@ class PostProcessCoco:
     """
     Post processing for tensorflow ssd-mobilenet style models
     """
+
     def __init__(self):
         self.results = []
         self.good = 0
@@ -139,14 +171,22 @@ def __init__(self):
     def add_results(self, results):
         self.results.extend(results)
 
-    def __call__(self, results, ids, expected=None, result_dict=None, ):
+    def __call__(
+        self,
+        results,
+        ids,
+        expected=None,
+        result_dict=None,
+    ):
         # results come as:
-        #   tensorflow, ssd-mobilenet: num_detections,detection_boxes,detection_scores,detection_classes
+        # tensorflow, ssd-mobilenet:
+        # num_detections,detection_boxes,detection_scores,detection_classes
         processed_results = []
         # batch size
         bs = len(results[0])
         for idx in range(0, bs):
-            # keep the content_id from loadgen to handle content_id's without results
+            # keep the content_id from loadgen to handle content_id's without
+            # results
             self.content_ids.append(ids[idx])
             processed_results.append([])
             detection_num = int(results[0][idx])
@@ -158,10 +198,17 @@ def __call__(self, results, ids, expected=None, result_dict=None, ):
                 if detection_class in expected_classes:
                     self.good += 1
                 box = detection_boxes[detection]
-                processed_results[idx].append([float(ids[idx]),
-                                              box[0], box[1], box[2], box[3],
-                                              results[2][idx][detection],
-                                              float(detection_class)])
+                processed_results[idx].append(
+                    [
+                        float(ids[idx]),
+                        box[0],
+                        box[1],
+                        box[2],
+                        box[3],
+                        results[2][idx][detection],
+                        float(detection_class),
+                    ]
+                )
                 self.total += 1
         return processed_results
 
@@ -181,7 +228,7 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                 annotations = json.load(fin)
             for cnt, cat in enumerate(annotations["categories"]):
                 label_map[cat["id"]] = cnt + 1
-            inv_map = {v:k for k,v in label_map.items()}
+            inv_map = {v: k for k, v in label_map.items()}
 
         detections = []
         image_indices = []
@@ -192,8 +239,13 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                 # this is the index of the coco image
                 image_idx = int(detection[0])
                 if image_idx != self.content_ids[batch]:
-                    # working with the coco index/id is error prone - extra check to make sure it is consistent
-                    log.error("image_idx missmatch, lg={} / result={}".format(image_idx, self.content_ids[batch]))
+                    # working with the coco index/id is error prone - extra
+                    # check to make sure it is consistent
+                    log.error(
+                        "image_idx missmatch, lg={} / result={}".format(
+                            image_idx, self.content_ids[batch]
+                        )
+                    )
                 # map the index to the coco image id
                 detection[0] = ds.image_ids[image_idx]
                 height, width = ds.image_sizes[image_idx]
@@ -211,16 +263,19 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                     cat_id = inv_map.get(int(detection[6]), -1)
                     if cat_id == -1:
                         # FIXME:
-                        log.info("finalize can't map category {}".format(int(detection[6])))
-                    detection[6] =  cat_id
+                        log.info(
+                            "finalize can't map category {}".format(
+                                int(detection[6]))
+                        )
+                    detection[6] = cat_id
                 detections.append(np.array(detection))
 
         # map indices to coco image id's
-        image_ids = [ds.image_ids[i]  for i in image_indices]
+        image_ids = [ds.image_ids[i] for i in image_indices]
         self.results = []
         cocoGt = pycoco.COCO(ds.annotation_file)
         cocoDt = cocoGt.loadRes(np.array(detections))
-        cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+        cocoEval = COCOeval(cocoGt, cocoDt, iouType="bbox")
         cocoEval.params.imgIds = image_ids
         cocoEval.evaluate()
         cocoEval.accumulate()
@@ -232,11 +287,12 @@ class PostProcessCocoPt(PostProcessCoco):
     """
     Post processing required by ssd-resnet34 / pytorch
     """
-    def __init__(self,use_inv_map,score_threshold):
+
+    def __init__(self, use_inv_map, score_threshold):
         super().__init__()
         self.use_inv_map = use_inv_map
         self.score_threshold = score_threshold
-        
+
     def __call__(self, results, ids, expected=None, result_dict=None):
         # results come as:
         #   detection_boxes,detection_classes,detection_scores
@@ -251,7 +307,7 @@ def __call__(self, results, ids, expected=None, result_dict=None):
             detection_classes = results[1][idx]
             expected_classes = expected[idx][0]
             scores = results[2][idx]
-            #for detection in range(0, len(expected_classes)):
+            # for detection in range(0, len(expected_classes)):
             for detection in range(0, len(scores)):
                 if scores[detection] < self.score_threshold:
                     break
@@ -260,10 +316,17 @@ def __call__(self, results, ids, expected=None, result_dict=None):
                     self.good += 1
                 box = detection_boxes[detection]
                 # comes from model as:  0=xmax 1=ymax 2=xmin 3=ymin
-                processed_results[idx].append([float(ids[idx]),
-                                              box[1], box[0], box[3], box[2],
-                                              scores[detection],
-                                              float(detection_class)])
+                processed_results[idx].append(
+                    [
+                        float(ids[idx]),
+                        box[1],
+                        box[0],
+                        box[3],
+                        box[2],
+                        scores[detection],
+                        float(detection_class),
+                    ]
+                )
                 self.total += 1
         return processed_results
 
@@ -272,12 +335,14 @@ class PostProcessCocoOnnx(PostProcessCoco):
     """
     Post processing required by ssd-resnet34 / onnx
     """
+
     def __init__(self):
         super().__init__()
 
     def __call__(self, results, ids, expected=None, result_dict=None):
         # results come as:
-        #   onnx (from pytorch ssd-resnet34): detection_boxes,detection_classes,detection_scores
+        # onnx (from pytorch ssd-resnet34):
+        # detection_boxes,detection_classes,detection_scores
 
         processed_results = []
 
@@ -298,17 +363,26 @@ def __call__(self, results, ids, expected=None, result_dict=None):
                     self.good += 1
                 box = detection_boxes[detection]
                 # comes from model as:  0=xmax 1=ymax 2=xmin 3=ymin
-                processed_results[idx].append([float(ids[idx]),
-                                              box[1], box[0], box[3], box[2],
-                                              scores[detection],
-                                              float(detection_class)])
+                processed_results[idx].append(
+                    [
+                        float(ids[idx]),
+                        box[1],
+                        box[0],
+                        box[3],
+                        box[2],
+                        scores[detection],
+                        float(detection_class),
+                    ]
+                )
                 self.total += 1
         return processed_results
 
+
 class PostProcessCocoTf(PostProcessCoco):
     """
     Post processing required by ssd-resnet34 / pytorch
     """
+
     def __init__(self):
         super().__init__()
         self.use_inv_map = True
@@ -335,9 +409,16 @@ def __call__(self, results, ids, expected=None, result_dict=None):
                     self.good += 1
                 box = detection_boxes[detection]
                 # comes from model as:  0=xmax 1=ymax 2=xmin 3=ymin
-                processed_results[idx].append([float(ids[idx]),
-                                              box[0], box[1], box[2], box[3],
-                                              scores[detection],
-                                              float(detection_class)])
+                processed_results[idx].append(
+                    [
+                        float(ids[idx]),
+                        box[0],
+                        box[1],
+                        box[2],
+                        box[3],
+                        scores[detection],
+                        float(detection_class),
+                    ]
+                )
                 self.total += 1
         return processed_results
diff --git a/retired_benchmarks/vision/classification_and_detection/python/dataset.py b/retired_benchmarks/vision/classification_and_detection/python/dataset.py
index dce968a3de..34ffa54b53 100755
--- a/retired_benchmarks/vision/classification_and_detection/python/dataset.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/dataset.py
@@ -15,7 +15,8 @@
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("dataset")
 
-class Item():
+
+class Item:
     def __init__(self, label, img, idx):
         self.label = label
         self.img = img
@@ -24,19 +25,25 @@ def __init__(self, label, img, idx):
 
 
 def usleep(sec):
-    if sys.platform == 'win32':
+    if sys.platform == "win32":
         # on windows time.sleep() doesn't work to well
         import ctypes
+
         kernel32 = ctypes.windll.kernel32
-        timer = kernel32.CreateWaitableTimerA(ctypes.c_void_p(), True, ctypes.c_void_p())
+        timer = kernel32.CreateWaitableTimerA(
+            ctypes.c_void_p(), True, ctypes.c_void_p()
+        )
         delay = ctypes.c_longlong(int(-1 * (10 * 1000000 * sec)))
-        kernel32.SetWaitableTimer(timer, ctypes.byref(delay), 0, ctypes.c_void_p(), ctypes.c_void_p(), False)
-        kernel32.WaitForSingleObject(timer, 0xffffffff)
+        kernel32.SetWaitableTimer(
+            timer, ctypes.byref(
+                delay), 0, ctypes.c_void_p(), ctypes.c_void_p(), False
+        )
+        kernel32.WaitForSingleObject(timer, 0xFFFFFFFF)
     else:
         time.sleep(sec)
 
 
-class Dataset():
+class Dataset:
     def __init__(self):
         self.arrival = None
         self.image_list = []
@@ -62,7 +69,7 @@ def load_query_samples(self, sample_list):
     def unload_query_samples(self, sample_list):
         if sample_list:
             for sample in sample_list:
-                if sample in self.image_list_inmemory :
+                if sample in self.image_list_inmemory:
                     del self.image_list_inmemory[sample]
         else:
             self.image_list_inmemory = {}
@@ -102,7 +109,7 @@ def start(self):
         self.good = 0
         self.total = 0
 
-    def finalize(self, results, ds=False,  output_dir=None):
+    def finalize(self, results, ds=False, output_dir=None):
         results["good"] = self.good
         results["total"] = self.total
 
@@ -141,6 +148,7 @@ def finalize(self, results, ds=False, output_dir=None):
 # pre-processing
 #
 
+
 def center_crop(img, out_height, out_width):
     height, width, _ = img.shape
     left = int((width - out_width) / 2)
@@ -151,10 +159,12 @@ def center_crop(img, out_height, out_width):
     return img
 
 
-def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
+def resize_with_aspectratio(
+    img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR
+):
     height, width, _ = img.shape
-    new_height = int(100. * out_height / scale)
-    new_width = int(100. * out_width / scale)
+    new_height = int(100.0 * out_height / scale)
+    new_width = int(100.0 * out_width / scale)
     if height > width:
         w = new_width
         h = int(new_height * height / width)
@@ -170,9 +180,11 @@ def pre_process_vgg(img, dims=None, need_transpose=False):
 
     output_height, output_width, _ = dims
     cv2_interpol = cv2.INTER_AREA
-    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2_interpol)
+    img = resize_with_aspectratio(
+        img, output_height, output_width, inter_pol=cv2_interpol
+    )
     img = center_crop(img, output_height, output_width)
-    img = np.asarray(img, dtype='float32')
+    img = np.asarray(img, dtype="float32")
 
     # normalize image
     means = np.array([123.68, 116.78, 103.94], dtype=np.float32)
@@ -188,9 +200,11 @@ def pre_process_mobilenet(img, dims=None, need_transpose=False):
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
 
     output_height, output_width, _ = dims
-    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR)
+    img = resize_with_aspectratio(
+        img, output_height, output_width, inter_pol=cv2.INTER_LINEAR
+    )
     img = center_crop(img, output_height, output_width)
-    img = np.asarray(img, dtype='float32')
+    img = np.asarray(img, dtype="float32")
 
     img /= 255.0
     img -= 0.5
@@ -211,10 +225,12 @@ def pre_process_imagenet_pytorch(img, dims=None, need_transpose=False):
     img = F.resize(img, 256, Image.BILINEAR)
     img = F.center_crop(img, 224)
     img = F.to_tensor(img)
-    img = F.normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], inplace=False)
+    img = F.normalize(
+        img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], inplace=False
+    )
     if not need_transpose:
-        img = img.permute(1, 2, 0) # NHWC
-    img = np.asarray(img, dtype='float32')
+        img = img.permute(1, 2, 0)  # NHWC
+    img = np.asarray(img, dtype="float32")
     return img
 
 
@@ -224,9 +240,10 @@ def maybe_resize(img, dims):
         # some images might be grayscale
         img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    if dims != None:
+    if dims is not None:
         im_height, im_width, _ = dims
-        img = cv2.resize(img, (im_width, im_height), interpolation=cv2.INTER_LINEAR)
+        img = cv2.resize(img, (im_width, im_height),
+                         interpolation=cv2.INTER_LINEAR)
     return img
 
 
@@ -254,7 +271,7 @@ def pre_process_coco_resnet34(img, dims=None, need_transpose=False):
     mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
     std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
 
-    img = img / 255. - mean
+    img = img / 255.0 - mean
     img = img / std
 
     if need_transpose:
diff --git a/retired_benchmarks/vision/classification_and_detection/python/imagenet.py b/retired_benchmarks/vision/classification_and_detection/python/imagenet.py
index 57865c6b7c..d48845ba9b 100755
--- a/retired_benchmarks/vision/classification_and_detection/python/imagenet.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/imagenet.py
@@ -20,8 +20,18 @@
 
 class Imagenet(dataset.Dataset):
 
-    def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
-                 image_format="NHWC", pre_process=None, count=None, cache_dir=None):
+    def __init__(
+        self,
+        data_path,
+        image_list,
+        name,
+        use_cache=0,
+        image_size=None,
+        image_format="NHWC",
+        pre_process=None,
+        count=None,
+        cache_dir=None,
+    ):
         super(Imagenet, self).__init__()
         if image_size is None:
             self.image_size = [224, 224, 3]
@@ -33,7 +43,8 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         self.label_list = []
         self.count = count
         self.use_cache = use_cache
-        self.cache_dir = os.path.join(cache_dir, "preprocessed", name, image_format)
+        self.cache_dir = os.path.join(
+            cache_dir, "preprocessed", name, image_format)
         self.data_path = data_path
         self.pre_process = pre_process
         # input images are in HWC
@@ -47,7 +58,7 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         os.makedirs(self.cache_dir, exist_ok=True)
 
         start = time.time()
-        with open(image_list, 'r') as f:
+        with open(image_list, "r") as f:
             for s in f:
                 image_name, label = re.split(r"\s+", s.strip())
                 src = os.path.join(data_path, image_name)
@@ -55,15 +66,22 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
                     # if the image does not exists ignore it
                     not_found += 1
                     continue
-                os.makedirs(os.path.dirname(os.path.join(self.cache_dir, image_name)), exist_ok=True)
+                os.makedirs(
+                    os.path.dirname(os.path.join(self.cache_dir, image_name)),
+                    exist_ok=True,
+                )
                 dst = os.path.join(self.cache_dir, image_name)
                 if not os.path.exists(dst + ".npy"):
                     # cache a preprocessed version of the image
                     # TODO: make this multi threaded ?
                     img_org = cv2.imread(src)
-                    processed = self.pre_process(img_org, need_transpose=self.need_transpose, dims=self.image_size)
+                    processed = self.pre_process(
+                        img_org,
+                        need_transpose=self.need_transpose,
+                        dims=self.image_size,
+                    )
                     np.save(dst, processed)
-                
+
                 self.image_list.append(image_name)
                 self.label_list.append(int(label))
 
@@ -78,8 +96,11 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         if not_found > 0:
             log.info("reduced image list, %d images not found", not_found)
 
-        log.info("loaded {} images, cache={}, took={:.1f}sec".format(
-            len(self.image_list), use_cache, time_taken))
+        log.info(
+            "loaded {} images, cache={}, took={:.1f}sec".format(
+                len(self.image_list), use_cache, time_taken
+            )
+        )
 
         self.label_list = np.array(self.label_list)
 
@@ -92,4 +113,3 @@ def get_item(self, nr):
     def get_item_loc(self, nr):
         src = os.path.join(self.data_path, self.image_list[nr])
         return src
-
diff --git a/retired_benchmarks/vision/classification_and_detection/python/main.py b/retired_benchmarks/vision/classification_and_detection/python/main.py
index 904a8b74a8..23eaea9fa6 100755
--- a/retired_benchmarks/vision/classification_and_detection/python/main.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/main.py
@@ -34,33 +34,60 @@
 
 # the datasets we support
 SUPPORTED_DATASETS = {
-    "imagenet":
-        (imagenet.Imagenet, dataset.pre_process_vgg, dataset.PostProcessCommon(offset=-1),
-         {"image_size": [224, 224, 3]}),
-    "imagenet_mobilenet":
-        (imagenet.Imagenet, dataset.pre_process_mobilenet, dataset.PostProcessArgMax(offset=-1),
-         {"image_size": [224, 224, 3]}),
-    "imagenet_pytorch":
-        (imagenet.Imagenet, dataset.pre_process_imagenet_pytorch, dataset.PostProcessArgMax(offset=0),
-         {"image_size": [224, 224, 3]}),
-    "coco-300":
-        (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(),
-         {"image_size": [300, 300, 3]}),
-    "coco-300-pt":
-        (coco.Coco, dataset.pre_process_coco_pt_mobilenet, coco.PostProcessCocoPt(False,0.3),
-         {"image_size": [300, 300, 3]}),         
-    "coco-1200":
-        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCoco(),
-         {"image_size": [1200, 1200, 3]}),
-    "coco-1200-onnx":
-        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoOnnx(),
-         {"image_size": [1200, 1200, 3]}),
-    "coco-1200-pt":
-        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoPt(True,0.05),
-         {"image_size": [1200, 1200, 3],"use_label_map": True}),
-    "coco-1200-tf":
-        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoTf(),
-         {"image_size": [1200, 1200, 3],"use_label_map": False}),
+    "imagenet": (
+        imagenet.Imagenet,
+        dataset.pre_process_vgg,
+        dataset.PostProcessCommon(offset=-1),
+        {"image_size": [224, 224, 3]},
+    ),
+    "imagenet_mobilenet": (
+        imagenet.Imagenet,
+        dataset.pre_process_mobilenet,
+        dataset.PostProcessArgMax(offset=-1),
+        {"image_size": [224, 224, 3]},
+    ),
+    "imagenet_pytorch": (
+        imagenet.Imagenet,
+        dataset.pre_process_imagenet_pytorch,
+        dataset.PostProcessArgMax(offset=0),
+        {"image_size": [224, 224, 3]},
+    ),
+    "coco-300": (
+        coco.Coco,
+        dataset.pre_process_coco_mobilenet,
+        coco.PostProcessCoco(),
+        {"image_size": [300, 300, 3]},
+    ),
+    "coco-300-pt": (
+        coco.Coco,
+        dataset.pre_process_coco_pt_mobilenet,
+        coco.PostProcessCocoPt(False, 0.3),
+        {"image_size": [300, 300, 3]},
+    ),
+    "coco-1200": (
+        coco.Coco,
+        dataset.pre_process_coco_resnet34,
+        coco.PostProcessCoco(),
+        {"image_size": [1200, 1200, 3]},
+    ),
+    "coco-1200-onnx": (
+        coco.Coco,
+        dataset.pre_process_coco_resnet34,
+        coco.PostProcessCocoOnnx(),
+        {"image_size": [1200, 1200, 3]},
+    ),
+    "coco-1200-pt": (
+        coco.Coco,
+        dataset.pre_process_coco_resnet34,
+        coco.PostProcessCocoPt(True, 0.05),
+        {"image_size": [1200, 1200, 3], "use_label_map": True},
+    ),
+    "coco-1200-tf": (
+        coco.Coco,
+        dataset.pre_process_coco_resnet34,
+        coco.PostProcessCocoTf(),
+        {"image_size": [1200, 1200, 3], "use_label_map": False},
+    ),
 }
 
 # pre-defined command line options so simplify things. They are used as defaults and can be
@@ -73,7 +100,6 @@
         "cache": 0,
         "max-batchsize": 32,
     },
-
     # resnet
     "resnet50-tf": {
         "inputs": "input_tensor:0",
@@ -95,7 +121,6 @@
         "backend": "onnxruntime",
         "model-name": "resnet50",
     },
-
     # mobilenet
     "mobilenet-tf": {
         "inputs": "input:0",
@@ -110,7 +135,6 @@
         "backend": "onnxruntime",
         "model-name": "mobilenet",
     },
-
     # ssd-mobilenet
     "ssd-mobilenet-tf": {
         "inputs": "image_tensor:0",
@@ -133,7 +157,6 @@
         "data-format": "NHWC",
         "model-name": "ssd-mobilenet",
     },
-
     # ssd-resnet34
     "ssd-resnet34-tf": {
         "inputs": "image:0",
@@ -182,37 +205,83 @@
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--dataset", choices=SUPPORTED_DATASETS.keys(), help="dataset")
-    parser.add_argument("--dataset-path", required=True, help="path to the dataset")
+    parser.add_argument(
+        "--dataset",
+        choices=SUPPORTED_DATASETS.keys(),
+        help="dataset")
+    parser.add_argument(
+        "--dataset-path",
+        required=True,
+        help="path to the dataset")
     parser.add_argument("--dataset-list", help="path to the dataset list")
-    parser.add_argument("--data-format", choices=["NCHW", "NHWC"], help="data format")
-    parser.add_argument("--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles")
-    parser.add_argument("--scenario", default="SingleStream",
-                        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())))
-    parser.add_argument("--max-batchsize", type=int, help="max batch size in a single inference")
+    parser.add_argument(
+        "--data-format",
+        choices=[
+            "NCHW",
+            "NHWC"],
+        help="data format")
+    parser.add_argument(
+        "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
+    )
+    parser.add_argument(
+        "--scenario",
+        default="SingleStream",
+        help="mlperf benchmark scenario, one of " +
+        str(list(SCENARIO_MAP.keys())),
+    )
+    parser.add_argument(
+        "--max-batchsize", type=int, help="max batch size in a single inference"
+    )
     parser.add_argument("--model", required=True, help="model file")
     parser.add_argument("--output", default="output", help="test results")
     parser.add_argument("--inputs", help="model inputs")
     parser.add_argument("--outputs", help="model outputs")
     parser.add_argument("--backend", help="runtime to use")
-    parser.add_argument("--model-name", help="name of the mlperf model, ie. resnet50")
-    parser.add_argument("--threads", default=os.cpu_count(), type=int, help="threads")
+    parser.add_argument(
+        "--model-name",
+        help="name of the mlperf model, ie. resnet50")
+    parser.add_argument(
+        "--threads",
+        default=os.cpu_count(),
+        type=int,
+        help="threads")
     parser.add_argument("--qps", type=int, help="target qps")
     parser.add_argument("--cache", type=int, default=0, help="use cache")
-    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
-    parser.add_argument("--find-peak-performance", action="store_true", help="enable finding peak performance pass")
-    parser.add_argument("--debug", action="store_true", help="debug, turn traces on")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
+    parser.add_argument(
+        "--find-peak-performance",
+        action="store_true",
+        help="enable finding peak performance pass",
+    )
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="debug, turn traces on")
 
     # file to use mlperf rules compliant parameters
-    parser.add_argument("--mlperf_conf", default="../../mlperf.conf", help="mlperf rules config")
+    parser.add_argument(
+        "--mlperf_conf", default="../../mlperf.conf", help="mlperf rules config"
+    )
     # file for user LoadGen settings such as target QPS
-    parser.add_argument("--user_conf", default="user.conf", help="user config for user LoadGen settings such as target QPS")
-
-    # below will override mlperf rules compliant settings - don't use for official submission
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+
+    # below will override mlperf rules compliant settings - don't use for
+    # official submission
     parser.add_argument("--time", type=int, help="time to scan in seconds")
     parser.add_argument("--count", type=int, help="dataset items to use")
-    parser.add_argument("--max-latency", type=float, help="mlperf max latency in pct tile")
-    parser.add_argument("--samples-per-query", type=int, help="mlperf multi-stream sample per query")
+    parser.add_argument(
+        "--max-latency", type=float, help="mlperf max latency in pct tile"
+    )
+    parser.add_argument(
+        "--samples-per-query", type=int, help="mlperf multi-stream sample per query"
+    )
     args = parser.parse_args()
 
     # don't use defaults in argparser. Instead we default to a dict, override that with a profile
@@ -239,21 +308,27 @@ def get_args():
 def get_backend(backend):
     if backend == "tensorflow":
         from backend_tf import BackendTensorflow
+
         backend = BackendTensorflow()
     elif backend == "onnxruntime":
         from backend_onnxruntime import BackendOnnxruntime
+
         backend = BackendOnnxruntime()
     elif backend == "null":
         from backend_null import BackendNull
+
         backend = BackendNull()
     elif backend == "pytorch":
         from backend_pytorch import BackendPytorch
+
         backend = BackendPytorch()
     elif backend == "pytorch-native":
         from backend_pytorch_native import BackendPytorchNative
-        backend = BackendPytorchNative()      
+
+        backend = BackendPytorchNative()
     elif backend == "tflite":
         from backend_tflite import BackendTflite
+
         backend = BackendTflite()
     else:
         raise ValueError("unknown backend: " + backend)
@@ -296,7 +371,9 @@ def run_one_item(self, qitem):
         processed_results = []
         try:
             results = self.model.predict({self.model.inputs[0]: qitem.img})
-            processed_results = self.post_process(results, qitem.content_id, qitem.label, self.result_dict)
+            processed_results = self.post_process(
+                results, qitem.content_id, qitem.label, self.result_dict
+            )
             if self.take_accuracy:
                 self.post_process.add_results(processed_results)
                 self.result_timing.append(time.time() - qitem.start)
@@ -309,7 +386,9 @@ def run_one_item(self, qitem):
             response_array_refs = []
             response = []
             for idx, query_id in enumerate(qitem.query_id):
-                response_array = array.array("B", np.array(processed_results[idx], np.float32).tobytes())
+                response_array = array.array(
+                    "B", np.array(processed_results[idx], np.float32).tobytes()
+                )
                 response_array_refs.append(response_array)
                 bi = response_array.buffer_info()
                 response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
@@ -324,8 +403,10 @@ def enqueue(self, query_samples):
         else:
             bs = self.max_batchsize
             for i in range(0, len(idx), bs):
-                data, label = self.ds.get_samples(idx[i:i+bs])
-                self.run_one_item(Item(query_id[i:i+bs], idx[i:i+bs], data, label))
+                data, label = self.ds.get_samples(idx[i: i + bs])
+                self.run_one_item(
+                    Item(query_id[i: i + bs], idx[i: i + bs], data, label)
+                )
 
     def finish(self):
         pass
@@ -339,7 +420,9 @@ def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
         self.result_dict = {}
 
         for _ in range(self.threads):
-            worker = threading.Thread(target=self.handle_tasks, args=(self.tasks,))
+            worker = threading.Thread(
+                target=self.handle_tasks, args=(
+                    self.tasks,))
             worker.daemon = True
             self.workers.append(worker)
             worker.start()
@@ -376,10 +459,14 @@ def finish(self):
             worker.join()
 
 
-def add_results(final_results, name, result_dict, result_list, took, show_accuracy=False):
-    percentiles = [50., 80., 90., 95., 99., 99.9]
+def add_results(
+    final_results, name, result_dict, result_list, took, show_accuracy=False
+):
+    percentiles = [50.0, 80.0, 90.0, 95.0, 99.0, 99.9]
     buckets = np.percentile(result_list, percentiles).tolist()
-    buckets_str = ",".join(["{}:{:.4f}".format(p, b) for p, b in zip(percentiles, buckets)])
+    buckets_str = ",".join(
+        ["{}:{:.4f}".format(p, b) for p, b in zip(percentiles, buckets)]
+    )
 
     if result_dict["total"] == 0:
         result_dict["total"] = len(result_list)
@@ -396,19 +483,27 @@ def add_results(final_results, name, result_dict, result_list, took, show_accura
     }
     acc_str = ""
     if show_accuracy:
-        result["accuracy"] = 100. * result_dict["good"] / result_dict["total"]
+        result["accuracy"] = 100.0 * result_dict["good"] / result_dict["total"]
         acc_str = ", acc={:.3f}%".format(result["accuracy"])
         if "mAP" in result_dict:
-            result["mAP"] = 100. * result_dict["mAP"]
+            result["mAP"] = 100.0 * result_dict["mAP"]
             acc_str += ", mAP={:.3f}%".format(result["mAP"])
 
     # add the result to the result dict
     final_results[name] = result
 
     # to stdout
-    print("{} qps={:.2f}, mean={:.4f}, time={:.3f}{}, queries={}, tiles={}".format(
-        name, result["qps"], result["mean"], took, acc_str,
-        len(result_list), buckets_str))
+    print(
+        "{} qps={:.2f}, mean={:.4f}, time={:.3f}{}, queries={}, tiles={}".format(
+            name,
+            result["qps"],
+            result["mean"],
+            took,
+            acc_str,
+            len(result_list),
+            buckets_str,
+        )
+    )
 
 
 def main():
@@ -432,13 +527,16 @@ def main():
 
     # dataset to use
     wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
-    ds = wanted_dataset(data_path=args.dataset_path,
-                        image_list=args.dataset_list,
-                        name=args.dataset,
-                        image_format=image_format,
-                        pre_process=pre_proc,
-                        use_cache=args.cache,
-                        count=count, **kwargs)
+    ds = wanted_dataset(
+        data_path=args.dataset_path,
+        image_list=args.dataset_list,
+        name=args.dataset,
+        image_format=image_format,
+        pre_process=pre_proc,
+        use_cache=args.cache,
+        count=count,
+        **kwargs
+    )
     # load model to backend
     model = backend.load(args.model, inputs=args.inputs, outputs=args.outputs)
     final_results = {
@@ -480,9 +578,11 @@ def main():
         lg.TestScenario.SingleStream: RunnerBase,
         lg.TestScenario.MultiStream: QueueRunner,
         lg.TestScenario.Server: QueueRunner,
-        lg.TestScenario.Offline: QueueRunner
+        lg.TestScenario.Offline: QueueRunner,
     }
-    runner = runner_map[scenario](model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize)
+    runner = runner_map[scenario](
+        model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize
+    )
 
     def issue_queries(query_samples):
         runner.enqueue(query_samples)
@@ -530,10 +630,13 @@ def process_latencies(latencies_ns):
         settings.multi_stream_samples_per_query = args.samples_per_query
     if args.max_latency:
         settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
-        settings.multi_stream_target_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_target_latency_ns = int(
+            args.max_latency * NANO_SEC)
 
     sut = lg.ConstructSUT(issue_queries, flush_queries, process_latencies)
-    qsl = lg.ConstructQSL(count, min(count, 500), ds.load_query_samples, ds.unload_query_samples)
+    qsl = lg.ConstructQSL(
+        count, min(count, 500), ds.load_query_samples, ds.unload_query_samples
+    )
 
     log.info("starting {}".format(scenario))
     result_dict = {"good": 0, "total": 0, "scenario": str(scenario)}
@@ -546,8 +649,14 @@ def process_latencies(latencies_ns):
     if args.accuracy:
         post_proc.finalize(result_dict, ds, output_dir=args.output)
 
-    add_results(final_results, "{}".format(scenario),
-                result_dict, last_timeing, time.time() - ds.last_loaded, args.accuracy)
+    add_results(
+        final_results,
+        "{}".format(scenario),
+        result_dict,
+        last_timeing,
+        time.time() - ds.last_loaded,
+        args.accuracy,
+    )
 
     runner.finish()
     lg.DestroyQSL(qsl)
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/anchor_generator.py b/retired_benchmarks/vision/classification_and_detection/python/models/anchor_generator.py
index 9a2d9d4904..be04012684 100644
--- a/retired_benchmarks/vision/classification_and_detection/python/models/anchor_generator.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/anchor_generator.py
@@ -2,11 +2,12 @@
 import numpy as np
 
 
-# The following functions were taken from 
+# The following functions were taken from
 # https://github.com/tensorflow/models/tree/master/research/object_detection
 # with minor modifications so that they use
 # torch operations instead
 
+
 def expanded_shape(orig_shape, start_dim, num_dims):
     s = (1,) * num_dims
     return orig_shape[:start_dim] + s + orig_shape[start_dim:]
@@ -45,350 +46,407 @@ def meshgrid(x, y):
     return xgrid, ygrid
 
 
-def tile_anchors(grid_height,
-                 grid_width,
-                 scales,
-                 aspect_ratios,
-                 base_anchor_size,
-                 anchor_stride,
-                 anchor_offset):
-  """Create a tiled set of anchors strided along a grid in image space.
-  This op creates a set of anchor boxes by placing a "basis" collection of
-  boxes with user-specified scales and aspect ratios centered at evenly
-  distributed points along a grid.  The basis collection is specified via the
-  scale and aspect_ratios arguments.  For example, setting scales=[.1, .2, .2]
-  and aspect ratios = [2,2,1/2] means that we create three boxes: one with scale
-  .1, aspect ratio 2, one with scale .2, aspect ratio 2, and one with scale .2
-  and aspect ratio 1/2.  Each box is multiplied by "base_anchor_size" before
-  placing it over its respective center.
-  Grid points are specified via grid_height, grid_width parameters as well as
-  the anchor_stride and anchor_offset parameters.
-  Args:
-    grid_height: size of the grid in the y direction (int or int scalar tensor)
-    grid_width: size of the grid in the x direction (int or int scalar tensor)
-    scales: a 1-d  (float) tensor representing the scale of each box in the
-      basis set.
-    aspect_ratios: a 1-d (float) tensor representing the aspect ratio of each
-      box in the basis set.  The length of the scales and aspect_ratios tensors
-      must be equal.
-    base_anchor_size: base anchor size as [height, width]
-      (float tensor of shape [2])
-    anchor_stride: difference in centers between base anchors for adjacent grid
-                   positions (float tensor of shape [2])
-    anchor_offset: center of the anchor with scale and aspect ratio 1 for the
-                   upper left element of the grid, this should be zero for
-                   feature networks with only VALID padding and even receptive
-                   field size, but may need some additional calculation if other
-                   padding is used (float tensor of shape [2])
-  Returns:
-    a BoxList holding a collection of N anchor boxes
-  """
-  aspect_ratios = torch.as_tensor(aspect_ratios, dtype=torch.float32)
-  scales = torch.as_tensor(scales, dtype=torch.float32)
+def tile_anchors(
+    grid_height,
+    grid_width,
+    scales,
+    aspect_ratios,
+    base_anchor_size,
+    anchor_stride,
+    anchor_offset,
+):
+    """Create a tiled set of anchors strided along a grid in image space.
+    This op creates a set of anchor boxes by placing a "basis" collection of
+    boxes with user-specified scales and aspect ratios centered at evenly
+    distributed points along a grid.  The basis collection is specified via the
+    scale and aspect_ratios arguments.  For example, setting scales=[.1, .2, .2]
+    and aspect ratios = [2,2,1/2] means that we create three boxes: one with scale
+    .1, aspect ratio 2, one with scale .2, aspect ratio 2, and one with scale .2
+    and aspect ratio 1/2.  Each box is multiplied by "base_anchor_size" before
+    placing it over its respective center.
+    Grid points are specified via grid_height, grid_width parameters as well as
+    the anchor_stride and anchor_offset parameters.
+    Args:
+      grid_height: size of the grid in the y direction (int or int scalar tensor)
+      grid_width: size of the grid in the x direction (int or int scalar tensor)
+      scales: a 1-d  (float) tensor representing the scale of each box in the
+        basis set.
+      aspect_ratios: a 1-d (float) tensor representing the aspect ratio of each
+        box in the basis set.  The length of the scales and aspect_ratios tensors
+        must be equal.
+      base_anchor_size: base anchor size as [height, width]
+        (float tensor of shape [2])
+      anchor_stride: difference in centers between base anchors for adjacent grid
+                     positions (float tensor of shape [2])
+      anchor_offset: center of the anchor with scale and aspect ratio 1 for the
+                     upper left element of the grid, this should be zero for
+                     feature networks with only VALID padding and even receptive
+                     field size, but may need some additional calculation if other
+                     padding is used (float tensor of shape [2])
+    Returns:
+      a BoxList holding a collection of N anchor boxes
+    """
+    aspect_ratios = torch.as_tensor(aspect_ratios, dtype=torch.float32)
+    scales = torch.as_tensor(scales, dtype=torch.float32)
 
-  ratio_sqrts = torch.sqrt(aspect_ratios)
-  heights = scales / ratio_sqrts * base_anchor_size[0]
-  widths = scales * ratio_sqrts * base_anchor_size[1]
+    ratio_sqrts = torch.sqrt(aspect_ratios)
+    heights = scales / ratio_sqrts * base_anchor_size[0]
+    widths = scales * ratio_sqrts * base_anchor_size[1]
 
-  # Get a grid of box centers
-  y_centers = torch.arange(grid_height, dtype=torch.float32)
-  y_centers = y_centers * anchor_stride[0] + anchor_offset[0]
-  x_centers = torch.arange(grid_width, dtype=torch.float32)
-  x_centers = x_centers * anchor_stride[1] + anchor_offset[1]
+    # Get a grid of box centers
+    y_centers = torch.arange(grid_height, dtype=torch.float32)
+    y_centers = y_centers * anchor_stride[0] + anchor_offset[0]
+    x_centers = torch.arange(grid_width, dtype=torch.float32)
+    x_centers = x_centers * anchor_stride[1] + anchor_offset[1]
 
-  x_centers, y_centers = meshgrid(x_centers, y_centers)
+    x_centers, y_centers = meshgrid(x_centers, y_centers)
 
-  widths_grid, x_centers_grid = meshgrid(widths, x_centers)
-  heights_grid, y_centers_grid = meshgrid(heights, y_centers)
+    widths_grid, x_centers_grid = meshgrid(widths, x_centers)
+    heights_grid, y_centers_grid = meshgrid(heights, y_centers)
 
-  bbox_centers = torch.stack([y_centers_grid, x_centers_grid], dim=3)
-  bbox_sizes = torch.stack([heights_grid, widths_grid], dim=3)
-  bbox_centers = torch.reshape(bbox_centers, [-1, 2])
-  bbox_sizes = torch.reshape(bbox_sizes, [-1, 2])
-  bbox_corners = _center_size_bbox_to_corners_bbox(bbox_centers, bbox_sizes)
-  return bbox_corners
+    bbox_centers = torch.stack([y_centers_grid, x_centers_grid], dim=3)
+    bbox_sizes = torch.stack([heights_grid, widths_grid], dim=3)
+    bbox_centers = torch.reshape(bbox_centers, [-1, 2])
+    bbox_sizes = torch.reshape(bbox_sizes, [-1, 2])
+    bbox_corners = _center_size_bbox_to_corners_bbox(bbox_centers, bbox_sizes)
+    return bbox_corners
 
 
 def _center_size_bbox_to_corners_bbox(centers, sizes):
-  """Converts bbox center-size representation to corners representation.
-  Args:
-    centers: a tensor with shape [N, 2] representing bounding box centers
-    sizes: a tensor with shape [N, 2] representing bounding boxes
-  Returns:
-    corners: tensor with shape [N, 4] representing bounding boxes in corners
-      representation
-  """
-  return torch.cat([centers - .5 * sizes, centers + .5 * sizes], 1)
-
-
-def create_ssd_anchors(num_layers=6,
-                       min_scale=0.2,
-                       max_scale=0.95,
-                       scales=None,
-                       aspect_ratios=(1.0, 2.0, 1.0 / 2, 3.0, 1.0 / 3),
-                       interpolated_scale_aspect_ratio=1.0,
-                       base_anchor_size=None,
-                       anchor_strides=None,
-                       anchor_offsets=None,
-                       reduce_boxes_in_lowest_layer=True):
-  """Creates MultipleGridAnchorGenerator for SSD anchors.
-  This function instantiates a MultipleGridAnchorGenerator that reproduces
-  ``default box`` construction proposed by Liu et al in the SSD paper.
-  See Section 2.2 for details. Grid sizes are assumed to be passed in
-  at generation time from finest resolution to coarsest resolution --- this is
-  used to (linearly) interpolate scales of anchor boxes corresponding to the
-  intermediate grid sizes.
-  Anchors that are returned by calling the `generate` method on the returned
-  MultipleGridAnchorGenerator object are always in normalized coordinates
-  and clipped to the unit square: (i.e. all coordinates lie in [0, 1]x[0, 1]).
-  Args:
-    num_layers: integer number of grid layers to create anchors for (actual
-      grid sizes passed in at generation time)
-    min_scale: scale of anchors corresponding to finest resolution (float)
-    max_scale: scale of anchors corresponding to coarsest resolution (float)
-    scales: As list of anchor scales to use. When not None and not empty,
-      min_scale and max_scale are not used.
-    aspect_ratios: list or tuple of (float) aspect ratios to place on each
-      grid point.
-    interpolated_scale_aspect_ratio: An additional anchor is added with this
-      aspect ratio and a scale interpolated between the scale for a layer
-      and the scale for the next layer (1.0 for the last layer).
-      This anchor is not included if this value is 0.
-    base_anchor_size: base anchor size as [height, width].
-      The height and width values are normalized to the minimum dimension of the
-      input height and width, so that when the base anchor height equals the
-      base anchor width, the resulting anchor is square even if the input image
-      is not square.
-    anchor_strides: list of pairs of strides in pixels (in y and x directions
-      respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
-      means that we want the anchors corresponding to the first layer to be
-      strided by 25 pixels and those in the second layer to be strided by 50
-      pixels in both y and x directions. If anchor_strides=None, they are set to
-      be the reciprocal of the corresponding feature map shapes.
-    anchor_offsets: list of pairs of offsets in pixels (in y and x directions
-      respectively). The offset specifies where we want the center of the
-      (0, 0)-th anchor to lie for each layer. For example, setting
-      anchor_offsets=[(10, 10), (20, 20)]) means that we want the
-      (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
-      and likewise that we want the (0, 0)-th anchor of the second layer to lie
-      at (25, 25) in pixel space. If anchor_offsets=None, then they are set to
-      be half of the corresponding anchor stride.
-    reduce_boxes_in_lowest_layer: a boolean to indicate whether the fixed 3
-      boxes per location is used in the lowest layer.
-  Returns:
-    a MultipleGridAnchorGenerator
-  """
-  if base_anchor_size is None:
-    base_anchor_size = [1.0, 1.0]
-  base_anchor_size = torch.tensor(base_anchor_size, dtype=torch.float32)
-  box_specs_list = []
-  if scales is None or not scales:
-    scales = [min_scale + (max_scale - min_scale) * i / (num_layers - 1)
-              for i in range(num_layers)] + [1.0]
-  else:
-    # Add 1.0 to the end, which will only be used in scale_next below and used
-    # for computing an interpolated scale for the largest scale in the list.
-    scales += [1.0]
-
-  for layer, scale, scale_next in zip(
-      range(num_layers), scales[:-1], scales[1:]):
-    layer_box_specs = []
-    if layer == 0 and reduce_boxes_in_lowest_layer:
-      layer_box_specs = [(0.1, 1.0), (scale, 2.0), (scale, 0.5)]
-    else:
-      for aspect_ratio in aspect_ratios:
-        layer_box_specs.append((scale, aspect_ratio))
-      # Add one more anchor, with a scale between the current scale, and the
-      # scale for the next layer, with a specified aspect ratio (1.0 by
-      # default).
-      if interpolated_scale_aspect_ratio > 0.0:
-        layer_box_specs.append((np.sqrt(scale*scale_next),
-                                interpolated_scale_aspect_ratio))
-    box_specs_list.append(layer_box_specs)
-
-  return MultipleGridAnchorGenerator(box_specs_list, base_anchor_size,
-                                     anchor_strides, anchor_offsets)
+    """Converts bbox center-size representation to corners representation.
+    Args:
+      centers: a tensor with shape [N, 2] representing bounding box centers
+      sizes: a tensor with shape [N, 2] representing bounding boxes
+    Returns:
+      corners: tensor with shape [N, 4] representing bounding boxes in corners
+        representation
+    """
+    return torch.cat([centers - 0.5 * sizes, centers + 0.5 * sizes], 1)
 
-class MultipleGridAnchorGenerator(object):
-  """Generate a grid of anchors for multiple CNN layers."""
 
-  def __init__(self,
-               box_specs_list,
-               base_anchor_size=None,
-               anchor_strides=None,
-               anchor_offsets=None,
-               clip_window=None):
-    """Constructs a MultipleGridAnchorGenerator.
-    To construct anchors, at multiple grid resolutions, one must provide a
-    list of feature_map_shape_list (e.g., [(8, 8), (4, 4)]), and for each grid
-    size, a corresponding list of (scale, aspect ratio) box specifications.
-    For example:
-    box_specs_list = [[(.1, 1.0), (.1, 2.0)],  # for 8x8 grid
-                      [(.2, 1.0), (.3, 1.0), (.2, 2.0)]]  # for 4x4 grid
-    To support the fully convolutional setting, we pass grid sizes in at
-    generation time, while scale and aspect ratios are fixed at construction
-    time.
+def create_ssd_anchors(
+    num_layers=6,
+    min_scale=0.2,
+    max_scale=0.95,
+    scales=None,
+    aspect_ratios=(1.0, 2.0, 1.0 / 2, 3.0, 1.0 / 3),
+    interpolated_scale_aspect_ratio=1.0,
+    base_anchor_size=None,
+    anchor_strides=None,
+    anchor_offsets=None,
+    reduce_boxes_in_lowest_layer=True,
+):
+    """Creates MultipleGridAnchorGenerator for SSD anchors.
+    This function instantiates a MultipleGridAnchorGenerator that reproduces
+    ``default box`` construction proposed by Liu et al in the SSD paper.
+    See Section 2.2 for details. Grid sizes are assumed to be passed in
+    at generation time from finest resolution to coarsest resolution --- this is
+    used to (linearly) interpolate scales of anchor boxes corresponding to the
+    intermediate grid sizes.
+    Anchors that are returned by calling the `generate` method on the returned
+    MultipleGridAnchorGenerator object are always in normalized coordinates
+    and clipped to the unit square: (i.e. all coordinates lie in [0, 1]x[0, 1]).
     Args:
-      box_specs_list: list of list of (scale, aspect ratio) pairs with the
-        outside list having the same number of entries as feature_map_shape_list
-        (which is passed in at generation time).
-      base_anchor_size: base anchor size as [height, width]
-                        (length-2 float tensor, default=[1.0, 1.0]).
-                        The height and width values are normalized to the
-                        minimum dimension of the input height and width, so that
-                        when the base anchor height equals the base anchor
-                        width, the resulting anchor is square even if the input
-                        image is not square.
+      num_layers: integer number of grid layers to create anchors for (actual
+        grid sizes passed in at generation time)
+      min_scale: scale of anchors corresponding to finest resolution (float)
+      max_scale: scale of anchors corresponding to coarsest resolution (float)
+      scales: As list of anchor scales to use. When not None and not empty,
+        min_scale and max_scale are not used.
+      aspect_ratios: list or tuple of (float) aspect ratios to place on each
+        grid point.
+      interpolated_scale_aspect_ratio: An additional anchor is added with this
+        aspect ratio and a scale interpolated between the scale for a layer
+        and the scale for the next layer (1.0 for the last layer).
+        This anchor is not included if this value is 0.
+      base_anchor_size: base anchor size as [height, width].
+        The height and width values are normalized to the minimum dimension of the
+        input height and width, so that when the base anchor height equals the
+        base anchor width, the resulting anchor is square even if the input image
+        is not square.
       anchor_strides: list of pairs of strides in pixels (in y and x directions
         respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
         means that we want the anchors corresponding to the first layer to be
         strided by 25 pixels and those in the second layer to be strided by 50
-        pixels in both y and x directions. If anchor_strides=None, they are set
-        to be the reciprocal of the corresponding feature map shapes.
+        pixels in both y and x directions. If anchor_strides=None, they are set to
+        be the reciprocal of the corresponding feature map shapes.
       anchor_offsets: list of pairs of offsets in pixels (in y and x directions
         respectively). The offset specifies where we want the center of the
         (0, 0)-th anchor to lie for each layer. For example, setting
         anchor_offsets=[(10, 10), (20, 20)]) means that we want the
         (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
-        and likewise that we want the (0, 0)-th anchor of the second layer to
-        lie at (25, 25) in pixel space. If anchor_offsets=None, then they are
-        set to be half of the corresponding anchor stride.
-      clip_window: a tensor of shape [4] specifying a window to which all
-        anchors should be clipped. If clip_window is None, then no clipping
-        is performed.
-    Raises:
-      ValueError: if box_specs_list is not a list of list of pairs
-      ValueError: if clip_window is not either None or a tensor of shape [4]
+        and likewise that we want the (0, 0)-th anchor of the second layer to lie
+        at (25, 25) in pixel space. If anchor_offsets=None, then they are set to
+        be half of the corresponding anchor stride.
+      reduce_boxes_in_lowest_layer: a boolean to indicate whether the fixed 3
+        boxes per location is used in the lowest layer.
+    Returns:
+      a MultipleGridAnchorGenerator
     """
-    if isinstance(box_specs_list, list) and all(
-        [isinstance(list_item, list) for list_item in box_specs_list]):
-      self._box_specs = box_specs_list
-    else:
-      raise ValueError('box_specs_list is expected to be a '
-                       'list of lists of pairs')
     if base_anchor_size is None:
-      base_anchor_size = torch.tensor([256, 256], dtype=torch.float32)
-    self._base_anchor_size = base_anchor_size
-    self._anchor_strides = anchor_strides
-    self._anchor_offsets = anchor_offsets
-    if clip_window is not None and list(clip_window.shape) != [4]:
-      raise ValueError('clip_window must either be None or a shape [4] tensor')
-    self._clip_window = clip_window
-    self._scales = []
-    self._aspect_ratios = []
-    for box_spec in self._box_specs:
-      if not all([isinstance(entry, tuple) and len(entry) == 2
-                  for entry in box_spec]):
-        raise ValueError('box_specs_list is expected to be a '
-                         'list of lists of pairs')
-      scales, aspect_ratios = zip(*box_spec)
-      self._scales.append(scales)
-      self._aspect_ratios.append(aspect_ratios)
+        base_anchor_size = [1.0, 1.0]
+    base_anchor_size = torch.tensor(base_anchor_size, dtype=torch.float32)
+    box_specs_list = []
+    if scales is None or not scales:
+        scales = [
+            min_scale + (max_scale - min_scale) * i / (num_layers - 1)
+            for i in range(num_layers)
+        ] + [1.0]
+    else:
+        # Add 1.0 to the end, which will only be used in scale_next below and used
+        # for computing an interpolated scale for the largest scale in the
+        # list.
+        scales += [1.0]
 
-    for arg, arg_name in zip([self._anchor_strides, self._anchor_offsets],
-                             ['anchor_strides', 'anchor_offsets']):
-      if arg and not (isinstance(arg, list) and
-                      len(arg) == len(self._box_specs)):
-        raise ValueError('%s must be a list with the same length '
-                         'as self._box_specs' % arg_name)
-      if arg and not all([
-          isinstance(list_item, tuple) and len(list_item) == 2
-          for list_item in arg
-      ]):
-        raise ValueError('%s must be a list of pairs.' % arg_name)
+    for layer, scale, scale_next in zip(
+            range(num_layers), scales[:-1], scales[1:]):
+        layer_box_specs = []
+        if layer == 0 and reduce_boxes_in_lowest_layer:
+            layer_box_specs = [(0.1, 1.0), (scale, 2.0), (scale, 0.5)]
+        else:
+            for aspect_ratio in aspect_ratios:
+                layer_box_specs.append((scale, aspect_ratio))
+            # Add one more anchor, with a scale between the current scale, and the
+            # scale for the next layer, with a specified aspect ratio (1.0 by
+            # default).
+            if interpolated_scale_aspect_ratio > 0.0:
+                layer_box_specs.append(
+                    (np.sqrt(scale * scale_next), interpolated_scale_aspect_ratio)
+                )
+        box_specs_list.append(layer_box_specs)
 
+    return MultipleGridAnchorGenerator(
+        box_specs_list, base_anchor_size, anchor_strides, anchor_offsets
+    )
 
-  def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
-    """Generates a collection of bounding boxes to be used as anchors.
-    The number of anchors generated for a single grid with shape MxM where we
-    place k boxes over each grid center is k*M^2 and thus the total number of
-    anchors is the sum over all grids. In our box_specs_list example
-    (see the constructor docstring), we would place two boxes over each grid
-    point on an 8x8 grid and three boxes over each grid point on a 4x4 grid and
-    thus end up with 2*8^2 + 3*4^2 = 176 anchors in total. The layout of the
-    output anchors follows the order of how the grid sizes and box_specs are
-    specified (with box_spec index varying the fastest, followed by width
-    index, then height index, then grid index).
-    Args:
-      feature_map_shape_list: list of pairs of convnet layer resolutions in the
-        format [(height_0, width_0), (height_1, width_1), ...]. For example,
-        setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
-        correspond to an 8x8 layer followed by a 7x7 layer.
-      im_height: the height of the image to generate the grid for. If both
-        im_height and im_width are 1, the generated anchors default to
-        absolute coordinates, otherwise normalized coordinates are produced.
-      im_width: the width of the image to generate the grid for. If both
-        im_height and im_width are 1, the generated anchors default to
-        absolute coordinates, otherwise normalized coordinates are produced.
-    Returns:
-      boxes_list: a list of BoxLists each holding anchor boxes corresponding to
-        the input feature map shapes.
-    Raises:
-      ValueError: if feature_map_shape_list, box_specs_list do not have the same
-        length.
-      ValueError: if feature_map_shape_list does not consist of pairs of
-        integers
-    """
-    if not (isinstance(feature_map_shape_list, list)
-            and len(feature_map_shape_list) == len(self._box_specs)):
-      raise ValueError('feature_map_shape_list must be a list with the same '
-                       'length as self._box_specs')
-    if not all([isinstance(list_item, tuple) and len(list_item) == 2
-                for list_item in feature_map_shape_list]):
-      raise ValueError('feature_map_shape_list must be a list of pairs.')
 
-    im_height = float(im_height)
-    im_width = float(im_width)
+class MultipleGridAnchorGenerator(object):
+    """Generate a grid of anchors for multiple CNN layers."""
 
-    if not self._anchor_strides:
-      anchor_strides = [(1.0 / float(pair[0]), 1.0 / float(pair[1]))
-                        for pair in feature_map_shape_list]
-    else:
-      anchor_strides = [(float(stride[0]) / im_height,
-                         float(stride[1]) / im_width)
-                        for stride in self._anchor_strides]
-    if not self._anchor_offsets:
-      anchor_offsets = [(0.5 * stride[0], 0.5 * stride[1])
-                        for stride in anchor_strides]
-    else:
-      anchor_offsets = [(float(offset[0]) / im_height,
-                         float(offset[1]) / im_width)
-                        for offset in self._anchor_offsets]
+    def __init__(
+        self,
+        box_specs_list,
+        base_anchor_size=None,
+        anchor_strides=None,
+        anchor_offsets=None,
+        clip_window=None,
+    ):
+        """Constructs a MultipleGridAnchorGenerator.
+        To construct anchors, at multiple grid resolutions, one must provide a
+        list of feature_map_shape_list (e.g., [(8, 8), (4, 4)]), and for each grid
+        size, a corresponding list of (scale, aspect ratio) box specifications.
+        For example:
+        box_specs_list = [[(.1, 1.0), (.1, 2.0)],  # for 8x8 grid
+                          [(.2, 1.0), (.3, 1.0), (.2, 2.0)]]  # for 4x4 grid
+        To support the fully convolutional setting, we pass grid sizes in at
+        generation time, while scale and aspect ratios are fixed at construction
+        time.
+        Args:
+          box_specs_list: list of list of (scale, aspect ratio) pairs with the
+            outside list having the same number of entries as feature_map_shape_list
+            (which is passed in at generation time).
+          base_anchor_size: base anchor size as [height, width]
+                            (length-2 float tensor, default=[1.0, 1.0]).
+                            The height and width values are normalized to the
+                            minimum dimension of the input height and width, so that
+                            when the base anchor height equals the base anchor
+                            width, the resulting anchor is square even if the input
+                            image is not square.
+          anchor_strides: list of pairs of strides in pixels (in y and x directions
+            respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
+            means that we want the anchors corresponding to the first layer to be
+            strided by 25 pixels and those in the second layer to be strided by 50
+            pixels in both y and x directions. If anchor_strides=None, they are set
+            to be the reciprocal of the corresponding feature map shapes.
+          anchor_offsets: list of pairs of offsets in pixels (in y and x directions
+            respectively). The offset specifies where we want the center of the
+            (0, 0)-th anchor to lie for each layer. For example, setting
+            anchor_offsets=[(10, 10), (20, 20)]) means that we want the
+            (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
+            and likewise that we want the (0, 0)-th anchor of the second layer to
+            lie at (25, 25) in pixel space. If anchor_offsets=None, then they are
+            set to be half of the corresponding anchor stride.
+          clip_window: a tensor of shape [4] specifying a window to which all
+            anchors should be clipped. If clip_window is None, then no clipping
+            is performed.
+        Raises:
+          ValueError: if box_specs_list is not a list of list of pairs
+          ValueError: if clip_window is not either None or a tensor of shape [4]
+        """
+        if isinstance(box_specs_list, list) and all(
+            [isinstance(list_item, list) for list_item in box_specs_list]
+        ):
+            self._box_specs = box_specs_list
+        else:
+            raise ValueError(
+                "box_specs_list is expected to be a " "list of lists of pairs"
+            )
+        if base_anchor_size is None:
+            base_anchor_size = torch.tensor([256, 256], dtype=torch.float32)
+        self._base_anchor_size = base_anchor_size
+        self._anchor_strides = anchor_strides
+        self._anchor_offsets = anchor_offsets
+        if clip_window is not None and list(clip_window.shape) != [4]:
+            raise ValueError(
+                "clip_window must either be None or a shape [4] tensor")
+        self._clip_window = clip_window
+        self._scales = []
+        self._aspect_ratios = []
+        for box_spec in self._box_specs:
+            if not all(
+                [isinstance(entry, tuple) and len(entry)
+                 == 2 for entry in box_spec]
+            ):
+                raise ValueError(
+                    "box_specs_list is expected to be a " "list of lists of pairs"
+                )
+            scales, aspect_ratios = zip(*box_spec)
+            self._scales.append(scales)
+            self._aspect_ratios.append(aspect_ratios)
+
+        for arg, arg_name in zip(
+            [self._anchor_strides, self._anchor_offsets],
+            ["anchor_strides", "anchor_offsets"],
+        ):
+            if arg and not (isinstance(arg, list) and len(arg)
+                            == len(self._box_specs)):
+                raise ValueError(
+                    "%s must be a list with the same length "
+                    "as self._box_specs" % arg_name
+                )
+            if arg and not all(
+                [
+                    isinstance(list_item, tuple) and len(list_item) == 2
+                    for list_item in arg
+                ]
+            ):
+                raise ValueError("%s must be a list of pairs." % arg_name)
+
+    def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
+        """Generates a collection of bounding boxes to be used as anchors.
+        The number of anchors generated for a single grid with shape MxM where we
+        place k boxes over each grid center is k*M^2 and thus the total number of
+        anchors is the sum over all grids. In our box_specs_list example
+        (see the constructor docstring), we would place two boxes over each grid
+        point on an 8x8 grid and three boxes over each grid point on a 4x4 grid and
+        thus end up with 2*8^2 + 3*4^2 = 176 anchors in total. The layout of the
+        output anchors follows the order of how the grid sizes and box_specs are
+        specified (with box_spec index varying the fastest, followed by width
+        index, then height index, then grid index).
+        Args:
+          feature_map_shape_list: list of pairs of convnet layer resolutions in the
+            format [(height_0, width_0), (height_1, width_1), ...]. For example,
+            setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
+            correspond to an 8x8 layer followed by a 7x7 layer.
+          im_height: the height of the image to generate the grid for. If both
+            im_height and im_width are 1, the generated anchors default to
+            absolute coordinates, otherwise normalized coordinates are produced.
+          im_width: the width of the image to generate the grid for. If both
+            im_height and im_width are 1, the generated anchors default to
+            absolute coordinates, otherwise normalized coordinates are produced.
+        Returns:
+          boxes_list: a list of BoxLists each holding anchor boxes corresponding to
+            the input feature map shapes.
+        Raises:
+          ValueError: if feature_map_shape_list, box_specs_list do not have the same
+            length.
+          ValueError: if feature_map_shape_list does not consist of pairs of
+            integers
+        """
+        if not (
+            isinstance(feature_map_shape_list, list)
+            and len(feature_map_shape_list) == len(self._box_specs)
+        ):
+            raise ValueError(
+                "feature_map_shape_list must be a list with the same "
+                "length as self._box_specs"
+            )
+        if not all(
+            [
+                isinstance(list_item, tuple) and len(list_item) == 2
+                for list_item in feature_map_shape_list
+            ]
+        ):
+            raise ValueError("feature_map_shape_list must be a list of pairs.")
+
+        im_height = float(im_height)
+        im_width = float(im_width)
+
+        if not self._anchor_strides:
+            anchor_strides = [
+                (1.0 / float(pair[0]), 1.0 / float(pair[1]))
+                for pair in feature_map_shape_list
+            ]
+        else:
+            anchor_strides = [
+                (float(stride[0]) / im_height, float(stride[1]) / im_width)
+                for stride in self._anchor_strides
+            ]
+        if not self._anchor_offsets:
+            anchor_offsets = [
+                (0.5 * stride[0], 0.5 * stride[1]) for stride in anchor_strides
+            ]
+        else:
+            anchor_offsets = [
+                (float(offset[0]) / im_height, float(offset[1]) / im_width)
+                for offset in self._anchor_offsets
+            ]
 
-    for arg, arg_name in zip([anchor_strides, anchor_offsets],
-                             ['anchor_strides', 'anchor_offsets']):
-      if not (isinstance(arg, list) and len(arg) == len(self._box_specs)):
-        raise ValueError('%s must be a list with the same length '
-                         'as self._box_specs' % arg_name)
-      if not all([isinstance(list_item, tuple) and len(list_item) == 2
-                  for list_item in arg]):
-        raise ValueError('%s must be a list of pairs.' % arg_name)
+        for arg, arg_name in zip(
+            [anchor_strides, anchor_offsets], [
+                "anchor_strides", "anchor_offsets"]
+        ):
+            if not (isinstance(arg, list) and len(
+                    arg) == len(self._box_specs)):
+                raise ValueError(
+                    "%s must be a list with the same length "
+                    "as self._box_specs" % arg_name
+                )
+            if not all(
+                [
+                    isinstance(list_item, tuple) and len(list_item) == 2
+                    for list_item in arg
+                ]
+            ):
+                raise ValueError("%s must be a list of pairs." % arg_name)
 
-    anchor_grid_list = []
-    min_im_shape = min(im_height, im_width)
-    scale_height = min_im_shape / im_height
-    scale_width = min_im_shape / im_width
-    base_anchor_size = [
-        scale_height * self._base_anchor_size[0],
-        scale_width * self._base_anchor_size[1]
-    ]
-    for feature_map_index, (grid_size, scales, aspect_ratios, stride,
-                            offset) in enumerate(
-                                zip(feature_map_shape_list, self._scales,
-                                    self._aspect_ratios, anchor_strides,
-                                    anchor_offsets)):
-      tiled_anchors = tile_anchors(
-          grid_height=grid_size[0],
-          grid_width=grid_size[1],
-          scales=scales,
-          aspect_ratios=aspect_ratios,
-          base_anchor_size=base_anchor_size,
-          anchor_stride=stride,
-          anchor_offset=offset)
-      if self._clip_window is not None:
-        raise NotImplementedError("Oups!")
-      num_anchors_in_layer = len(tiled_anchors)
-      anchor_indices = feature_map_index * torch.ones(num_anchors_in_layer)
-      anchor_grid_list.append(tiled_anchors)
+        anchor_grid_list = []
+        min_im_shape = min(im_height, im_width)
+        scale_height = min_im_shape / im_height
+        scale_width = min_im_shape / im_width
+        base_anchor_size = [
+            scale_height * self._base_anchor_size[0],
+            scale_width * self._base_anchor_size[1],
+        ]
+        for feature_map_index, (
+            grid_size,
+            scales,
+            aspect_ratios,
+            stride,
+            offset,
+        ) in enumerate(
+            zip(
+                feature_map_shape_list,
+                self._scales,
+                self._aspect_ratios,
+                anchor_strides,
+                anchor_offsets,
+            )
+        ):
+            tiled_anchors = tile_anchors(
+                grid_height=grid_size[0],
+                grid_width=grid_size[1],
+                scales=scales,
+                aspect_ratios=aspect_ratios,
+                base_anchor_size=base_anchor_size,
+                anchor_stride=stride,
+                anchor_offset=offset,
+            )
+            if self._clip_window is not None:
+                raise NotImplementedError("Oups!")
+            num_anchors_in_layer = len(tiled_anchors)
+            anchor_indices = feature_map_index * \
+                torch.ones(num_anchors_in_layer)
+            anchor_grid_list.append(tiled_anchors)
 
-    return anchor_grid_list
+        return anchor_grid_list
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/base_model_r34.py b/retired_benchmarks/vision/classification_and_detection/python/models/base_model_r34.py
index ea224a7caa..9e8a2da618 100644
--- a/retired_benchmarks/vision/classification_and_detection/python/models/base_model_r34.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/base_model_r34.py
@@ -2,7 +2,7 @@
     Load the vgg16 weight and save it to special file
 """
 
-#from torchvision.models.vgg import vgg16
+# from torchvision.models.vgg import vgg16
 import torch.nn as nn
 import torch.nn.functional as F
 import torch
@@ -11,29 +11,32 @@
 
 from torchvision.models.resnet import resnet18, resnet34, resnet50
 
+
 def _ModifyConvStrideDilation(conv, stride=(1, 1), padding=None):
     conv.stride = stride
 
     if padding is not None:
         conv.padding = padding
 
+
 def _ModifyBlock(block, bottleneck=False, **kwargs):
     for m in list(block.children()):
         if bottleneck:
-           _ModifyConvStrideDilation(m.conv2, **kwargs)
+            _ModifyConvStrideDilation(m.conv2, **kwargs)
         else:
-           _ModifyConvStrideDilation(m.conv1, **kwargs)
+            _ModifyConvStrideDilation(m.conv1, **kwargs)
 
         if m.downsample is not None:
             # need to make sure no padding for the 1x1 residual connection
-            _ModifyConvStrideDilation(list(m.downsample.children())[0], **kwargs)
+            _ModifyConvStrideDilation(
+                list(m.downsample.children())[0], **kwargs)
+
 
 class ResNet18(nn.Module):
     def __init__(self):
         super().__init__()
         rn18 = resnet18(pretrained=True)
 
-
         # discard last Resnet block, avrpooling and classification FC
         # layer1 = up to and including conv3 block
         self.layer1 = nn.Sequential(*list(rn18.children())[:6])
@@ -43,7 +46,7 @@ def __init__(self):
         # modify conv4 if necessary
         # Always deal with stride in first block
         modulelist = list(self.layer2.children())
-        _ModifyBlock(modulelist[0], stride=(1,1))
+        _ModifyBlock(modulelist[0], stride=(1, 1))
 
     def forward(self, data):
         layer1_activation = self.layer1(data)
@@ -53,6 +56,7 @@ def forward(self, data):
         # Only need the output of conv4
         return [layer2_activation]
 
+
 class ResNet34(nn.Module):
     def __init__(self):
         super().__init__()
@@ -64,8 +68,7 @@ def __init__(self):
         # modify conv4 if necessary
         # Always deal with stride in first block
         modulelist = list(self.layer2.children())
-        _ModifyBlock(modulelist[0], stride=(1,1))
-
+        _ModifyBlock(modulelist[0], stride=(1, 1))
 
     def forward(self, data):
         layer1_activation = self.layer1(data)
@@ -74,22 +77,28 @@ def forward(self, data):
 
         return [layer2_activation]
 
+
 class L2Norm(nn.Module):
     """
-       Scale shall be learnable according to original paper
-       scale: initial scale number
-       chan_num: L2Norm channel number (norm over all channels)
+    Scale shall be learnable according to original paper
+    scale: initial scale number
+    chan_num: L2Norm channel number (norm over all channels)
     """
+
     def __init__(self, scale=20, chan_num=512):
         super(L2Norm, self).__init__()
         # Scale across channels
-        self.scale = \
-            nn.Parameter(torch.Tensor([scale]*chan_num).view(1, chan_num, 1, 1))
+        self.scale = nn.Parameter(
+            torch.Tensor([scale] * chan_num).view(1, chan_num, 1, 1)
+        )
 
     def forward(self, data):
         # normalize accross channel
-        return self.scale*data*data.pow(2).sum(dim=1, keepdim=True).clamp(min=1e-12).rsqrt()
-
+        return (
+            self.scale
+            * data
+            * data.pow(2).sum(dim=1, keepdim=True).clamp(min=1e-12).rsqrt()
+        )
 
 
 def tailor_module(src_model, src_dir, tgt_model, tgt_dir):
@@ -107,22 +116,23 @@ def tailor_module(src_model, src_dir, tgt_model, tgt_dir):
     for k1, k2 in zip(keys1, keys2):
         # print(k1, k2)
         state[k2] = src_state[k1]
-    #diff_keys = state.keys() - target_model.state_dict().keys()
-    #print("Different Keys:", diff_keys)
+    # diff_keys = state.keys() - target_model.state_dict().keys()
+    # print("Different Keys:", diff_keys)
     # Remove unecessary keys
-    #for k in diff_keys:
+    # for k in diff_keys:
     #    state.pop(k)
     tgt_model.load_state_dict(state)
     torch.save(tgt_model.state_dict(), tgt_dir)
 
+
 # Default vgg16 in pytorch seems different from ssd
 def make_layers(cfg, batch_norm=False):
     layers = []
     in_channels = 3
     for v in cfg:
-        if v == 'M':
+        if v == "M":
             layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
-        elif v == 'C':
+        elif v == "C":
             # Notice ceil_mode is true
             layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
         else:
@@ -134,42 +144,51 @@ def make_layers(cfg, batch_norm=False):
             in_channels = v
     return layers
 
+
 class Loss(nn.Module):
     """
-        Implements the loss as the sum of the followings:
-        1. Confidence Loss: All labels, with hard negative mining
-        2. Localization Loss: Only on positive labels
-        Suppose input dboxes has the shape 8732x4
+    Implements the loss as the sum of the followings:
+    1. Confidence Loss: All labels, with hard negative mining
+    2. Localization Loss: Only on positive labels
+    Suppose input dboxes has the shape 8732x4
     """
 
     def __init__(self, dboxes):
         super(Loss, self).__init__()
-        self.scale_xy = 1.0/dboxes.scale_xy
-        self.scale_wh = 1.0/dboxes.scale_wh
+        self.scale_xy = 1.0 / dboxes.scale_xy
+        self.scale_wh = 1.0 / dboxes.scale_wh
 
         self.sl1_loss = nn.SmoothL1Loss(reduce=False)
-        self.dboxes = nn.Parameter(dboxes(order="xywh").transpose(0, 1).unsqueeze(dim = 0),
-            requires_grad=False)
+        self.dboxes = nn.Parameter(
+            dboxes(order="xywh").transpose(0, 1).unsqueeze(dim=0), requires_grad=False
+        )
         # Two factor are from following links
         # http://jany.st/post/2017-11-05-single-shot-detector-ssd-from-scratch-in-tensorflow.html
         self.con_loss = nn.CrossEntropyLoss(reduce=False)
 
     def _loc_vec(self, loc):
         """
-            Generate Location Vectors
+        Generate Location Vectors
         """
-        gxy = self.scale_xy*(loc[:, :2, :] - self.dboxes[:, :2, :])/self.dboxes[:, 2:, ]
-        gwh = self.scale_wh*(loc[:, 2:, :]/self.dboxes[:, 2:, :]).log()
+        gxy = (
+            self.scale_xy
+            * (loc[:, :2, :] - self.dboxes[:, :2, :])
+            / self.dboxes[
+                :,
+                2:,
+            ]
+        )
+        gwh = self.scale_wh * (loc[:, 2:, :] / self.dboxes[:, 2:, :]).log()
 
         return torch.cat((gxy, gwh), dim=1).contiguous()
 
     def forward(self, ploc, plabel, gloc, glabel):
         """
-            ploc, plabel: Nx4x8732, Nxlabel_numx8732
-                predicted location and labels
+        ploc, plabel: Nx4x8732, Nxlabel_numx8732
+            predicted location and labels
 
-            gloc, glabel: Nx4x8732, Nx8732
-                ground truth location and labels
+        gloc, glabel: Nx4x8732, Nx8732
+            ground truth location and labels
         """
 
         mask = glabel > 0
@@ -177,7 +196,7 @@ def forward(self, ploc, plabel, gloc, glabel):
         vec_gd = self._loc_vec(gloc)
         # sum on four coordinates, and mask
         sl1 = self.sl1_loss(ploc, vec_gd).sum(dim=1)
-        sl1 = (mask.float()*sl1).sum(dim=1)
+        sl1 = (mask.float() * sl1).sum(dim=1)
 
         # hard negative mining
         con = self.con_loss(plabel, glabel)
@@ -189,16 +208,15 @@ def forward(self, ploc, plabel, gloc, glabel):
         _, con_rank = con_idx.sort(dim=1)
 
         # number of negative three times positive
-        neg_num = torch.clamp(3*pos_num, max=mask.size(1)).unsqueeze(-1)
+        neg_num = torch.clamp(3 * pos_num, max=mask.size(1)).unsqueeze(-1)
         neg_mask = con_rank < neg_num
 
-        closs = (con*(mask.float() + neg_mask.float())).sum(dim=1)
+        closs = (con * (mask.float() + neg_mask.float())).sum(dim=1)
 
         # avoid no object detected
         total_loss = sl1 + closs
         num_mask = (pos_num > 0).float()
         pos_num = pos_num.float().clamp(min=1e-6)
 
-        ret = (total_loss*num_mask/pos_num).mean(dim=0)
+        ret = (total_loss * num_mask / pos_num).mean(dim=0)
         return ret
-
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/convert_tf_weights.py b/retired_benchmarks/vision/classification_and_detection/python/models/convert_tf_weights.py
index 02c51bf1bb..4d063caef4 100644
--- a/retired_benchmarks/vision/classification_and_detection/python/models/convert_tf_weights.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/convert_tf_weights.py
@@ -17,7 +17,7 @@ def remap_tf_base_names(orig_weights):
         if "batchnorm" not in k and "pointwise_" not in k
     }
 
-    matcher = re.compile("(.*)Conv2d_(\d+)")
+    matcher = re.compile("(.*)Conv2d_(\\d+)")
     mapping = {}
     for k in convs.keys():
         l = matcher.match(k).group(2)
@@ -52,7 +52,7 @@ def remap_tf_extras(orig_weights):
     }
     weights = {k: v for k, v in weights.items() if "pointwise_" in k}
 
-    matcher = re.compile("(.*)Conv2d_(\d+)_(\d)x(\d)")
+    matcher = re.compile("(.*)Conv2d_(\\d+)_(\\d)x(\\d)")
     mapping = {}
     for k in weights.keys():
         m = matcher.match(k)
@@ -75,7 +75,7 @@ def remap_tf_predictors(orig_weights):
     weights = {k: v for k, v in orig_weights.items() if "BoxPredictor" in k}
     weights = {k: v for k, v in weights.items() if "BoxEncodingPredictor" in k}
 
-    matcher = re.compile("BoxPredictor_(\d+)")
+    matcher = re.compile("BoxPredictor_(\\d+)")
     for k in weights.keys():
         pos = matcher.match(k).group(1)
         wtype = "weight" if "weights" in k else "bias"
@@ -125,13 +125,15 @@ def get_state_dict(weights):
 def read_tf_weights(frozen_model):
     import tensorflow as tf
     from tensorflow.python.framework import tensor_util
+
     weights = {}
     with tf.Session() as sess:
-        with tf.gfile.GFile(frozen_model, 'rb') as f:
+        with tf.gfile.GFile(frozen_model, "rb") as f:
             graph_def = tf.GraphDef()
             graph_def.ParseFromString(f.read())
             tf.import_graph_def(graph_def)
         for n in graph_def.node:
-            if n.op == 'Const':
-                weights[n.name] = tensor_util.MakeNdarray(n.attr['value'].tensor)
+            if n.op == "Const":
+                weights[n.name] = tensor_util.MakeNdarray(
+                    n.attr["value"].tensor)
     return weights
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py b/retired_benchmarks/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py
index dc77808c77..9d7b271917 100644
--- a/retired_benchmarks/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py
@@ -81,7 +81,8 @@ def __init__(self, in_channels, num_classes, num_anchors):
         self.classification = nn.Conv2d(
             in_channels, num_classes * num_anchors, kernel_size=1
         )
-        self.regression = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1)
+        self.regression = nn.Conv2d(
+            in_channels, num_anchors * 4, kernel_size=1)
 
         self.num_classes = num_classes
         self.num_anchors = num_anchors
@@ -161,13 +162,12 @@ def ssd_model(self, x):
             self._feature_map_shapes = shapes
 
         self.coder_weights = self.coder_weights.to(scores)
-        if box_regression.dim()==2:
+        if box_regression.dim() == 2:
             box_regression = box_regression[None]
         boxes = decode_boxes(box_regression, self.priors, self.coder_weights)
         # add a batch dimension
         return scores, boxes
 
-
     def forward(self, images):
         """
         Arguments:
@@ -175,13 +175,15 @@ def forward(self, images):
         """
 
         scores, boxes = self.ssd_model(images)
-        list_boxes=[]; list_labels=[]; list_scores=[]
+        list_boxes = []
+        list_labels = []
+        list_scores = []
         for b in range(len(scores)):
             bboxes, blabels, bscores = self.filter_results(scores[b], boxes[b])
             list_boxes.append(bboxes)
             list_labels.append(blabels.long())
             list_scores.append(bscores)
-        #boxes = self.rescale_boxes(boxes, height, width)
+        # boxes = self.rescale_boxes(boxes, height, width)
         return [list_boxes, list_labels, list_scores]
 
     def filter_results(self, scores, boxes):
@@ -190,8 +192,8 @@ def filter_results(self, scores, boxes):
         # on python. This implementation is faster on the
         # CPU, which is why we run this part on the CPU
         cpu_device = torch.device("cpu")
-        #boxes = boxes[0]
-        #scores = scores[0]
+        # boxes = boxes[0]
+        # scores = scores[0]
         boxes = boxes.to(cpu_device)
         scores = scores.to(cpu_device)
         selected_box_probs = []
@@ -205,7 +207,8 @@ def filter_results(self, scores, boxes):
             box_probs = nms(box_probs, self.nms_threshold)
             selected_box_probs.append(box_probs)
             labels.append(
-                torch.full((box_probs.size(0),), class_index, dtype=torch.int64)
+                torch.full(
+                    (box_probs.size(0),), class_index, dtype=torch.int64)
             )
         selected_box_probs = torch.cat(selected_box_probs)
         labels = torch.cat(labels)
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/ssd_r34.py b/retired_benchmarks/vision/classification_and_detection/python/models/ssd_r34.py
index 63e596b4a5..e8138e3a19 100644
--- a/retired_benchmarks/vision/classification_and_detection/python/models/ssd_r34.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/ssd_r34.py
@@ -6,102 +6,114 @@
 import itertools
 import torch.nn.functional as F
 
-##Inspired by https://github.com/kuangliu/pytorch-ssd
+# Inspired by https://github.com/kuangliu/pytorch-ssd
+
 
 class Encoder(object):
     """
-        Transform between (bboxes, lables) <-> SSD output
-        
-        dboxes: default boxes in size 8732 x 4, 
-            encoder: input ltrb format, output xywh format
-            decoder: input xywh format, output ltrb format 
-
-        decode:
-            input  : bboxes_in (Tensor 8732 x 4), scores_in (Tensor 8732 x nitems)
-            output : bboxes_out (Tensor nboxes x 4), labels_out (Tensor nboxes)
-            criteria : IoU threshold of bboexes
-            max_output : maximum number of output bboxes
+    Transform between (bboxes, lables) <-> SSD output
+
+    dboxes: default boxes in size 8732 x 4,
+        encoder: input ltrb format, output xywh format
+        decoder: input xywh format, output ltrb format
+
+    decode:
+        input  : bboxes_in (Tensor 8732 x 4), scores_in (Tensor 8732 x nitems)
+        output : bboxes_out (Tensor nboxes x 4), labels_out (Tensor nboxes)
+        criteria : IoU threshold of bboexes
+        max_output : maximum number of output bboxes
     """
 
     def __init__(self, dboxes):
         self.dboxes = dboxes(order="ltrb")
         self.dboxes_xywh = dboxes(order="xywh").unsqueeze(dim=0)
         self.nboxes = self.dboxes.size(0)
-        #print("# Bounding boxes: {}".format(self.nboxes))
+        # print("# Bounding boxes: {}".format(self.nboxes))
         self.scale_xy = torch.tensor(dboxes.scale_xy)
         self.scale_wh = torch.tensor(dboxes.scale_wh)
-    
-    
-    def decode_batch(self, bboxes_in, scores_in,  criteria = 0.45, max_output=200):
+
+    def decode_batch(self, bboxes_in, scores_in,
+                     criteria=0.45, max_output=200):
         self.dboxes = self.dboxes.to(bboxes_in)
         self.dboxes_xywh = self.dboxes_xywh.to(bboxes_in)
-        bboxes, probs = scale_back_batch(bboxes_in, scores_in,self.scale_xy,self.scale_wh,self.dboxes_xywh)
-        boxes = []; labels=[]; scores=[]
+        bboxes, probs = scale_back_batch(
+            bboxes_in, scores_in, self.scale_xy, self.scale_wh, self.dboxes_xywh
+        )
+        boxes = []
+        labels = []
+        scores = []
         for bbox, prob in zip(bboxes.split(1, 0), probs.split(1, 0)):
             bbox = bbox.squeeze(0)
             prob = prob.squeeze(0)
-            dbox,dlabel,dscore=self.decode_single(bbox, prob, criteria, max_output)
+            dbox, dlabel, dscore = self.decode_single(
+                bbox, prob, criteria, max_output)
             boxes.append(dbox)
             labels.append(dlabel)
             scores.append(dscore)
-           
-        return [boxes,labels,scores]
+
+        return [boxes, labels, scores]
 
     # perform non-maximum suppression
-    def decode_single(self, bboxes_in, scores_in, criteria, max_output, max_num=200):
+    def decode_single(self, bboxes_in, scores_in,
+                      criteria, max_output, max_num=200):
         # Reference to https://github.com/amdegroot/ssd.pytorch
-       
-        bboxes_out = []        
+
+        bboxes_out = []
         scores_out = []
         labels_out = []
 
         for i, score in enumerate(scores_in.split(1, 1)):
             # skip background
-            if i == 0: continue
-            
+            if i == 0:
+                continue
+
             score = score.squeeze(1)
             mask = score > 0.05
 
             bboxes, score = bboxes_in[mask, :], score[mask]
-            if score.size(0) == 0: continue
+            if score.size(0) == 0:
+                continue
 
             score_sorted, score_idx_sorted = score.sort(dim=0)
-        
+
             # select max_output indices
             score_idx_sorted = score_idx_sorted[-max_num:]
             candidates = []
-        
+
             while score_idx_sorted.numel() > 0:
                 idx = score_idx_sorted[-1].item()
                 bboxes_sorted = bboxes[score_idx_sorted, :]
                 bboxes_idx = bboxes[idx, :].unsqueeze(dim=0)
-                iou_sorted = calc_iou_tensor(bboxes_sorted, bboxes_idx).squeeze()
-                # we only need iou < criteria 
+                iou_sorted = calc_iou_tensor(
+                    bboxes_sorted, bboxes_idx).squeeze()
+                # we only need iou < criteria
                 score_idx_sorted = score_idx_sorted[iou_sorted < criteria]
                 candidates.append(idx)
 
             bboxes_out.append(bboxes[candidates, :])
             scores_out.append(score[candidates])
-            labels_out.extend([i]*len(candidates))
-
-        bboxes_out, labels_out, scores_out = torch.cat(bboxes_out, dim=0), \
-               torch.tensor(labels_out, dtype=torch.long), \
-               torch.cat(scores_out, dim=0)
+            labels_out.extend([i] * len(candidates))
 
+        bboxes_out, labels_out, scores_out = (
+            torch.cat(bboxes_out, dim=0),
+            torch.tensor(labels_out, dtype=torch.long),
+            torch.cat(scores_out, dim=0),
+        )
 
         _, max_ids = scores_out.sort(dim=0)
         max_ids = max_ids[-max_output:]
         return bboxes_out[max_ids, :], labels_out[max_ids], scores_out[max_ids]
 
+
 @torch.jit.script
 def calc_iou_tensor(box1, box2):
-    """ Calculation of IoU based on two boxes tensor,
-        Reference to https://github.com/kuangliu/pytorch-ssd
-        input:
-            box1 (N, 4) 
-            box2 (M, 4)
-        output:
-            IoU (N, M)
+    """Calculation of IoU based on two boxes tensor,
+    Reference to https://github.com/kuangliu/pytorch-ssd
+    input:
+        box1 (N, 4)
+        box2 (M, 4)
+    output:
+        IoU (N, M)
     """
     N = box1.size(0)
     M = box2.size(0)
@@ -110,37 +122,42 @@ def calc_iou_tensor(box1, box2):
     be2 = box2.unsqueeze(0).expand(N, -1, -1)
 
     # Left Top & Right Bottom
-    lt = torch.max(be1[:,:,:2], be2[:,:,:2])
-    rb = torch.min(be1[:,:,2:], be2[:,:,2:])
+    lt = torch.max(be1[:, :, :2], be2[:, :, :2])
+    rb = torch.min(be1[:, :, 2:], be2[:, :, 2:])
     delta = rb - lt
-    delta.clone().masked_fill_(delta < 0,0)
-    intersect = delta[:,:,0]*delta[:,:,1]
-    delta1 = be1[:,:,2:] - be1[:,:,:2]
-    area1 = delta1[:,:,0]*delta1[:,:,1]
-    delta2 = be2[:,:,2:] - be2[:,:,:2]
-    area2 = delta2[:,:,0]*delta2[:,:,1]
-
-    iou = intersect/(area1 + area2 - intersect)
+    delta.clone().masked_fill_(delta < 0, 0)
+    intersect = delta[:, :, 0] * delta[:, :, 1]
+    delta1 = be1[:, :, 2:] - be1[:, :, :2]
+    area1 = delta1[:, :, 0] * delta1[:, :, 1]
+    delta2 = be2[:, :, 2:] - be2[:, :, :2]
+    area2 = delta2[:, :, 0] * delta2[:, :, 1]
+
+    iou = intersect / (area1 + area2 - intersect)
     return iou
 
+
 @torch.jit.script
-def scale_back_batch(bboxes_in, scores_in,scale_xy,scale_wh,dboxes_xywh):
+def scale_back_batch(bboxes_in, scores_in, scale_xy, scale_wh, dboxes_xywh):
+    """
+    Do scale and transform from xywh to ltrb
+    suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox
     """
-        Do scale and transform from xywh to ltrb
-        suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox
-    """     
     bboxes_in = bboxes_in.permute(0, 2, 1)
     scores_in = scores_in.permute(0, 2, 1)
 
-    bboxes_in[:, :, :2] = scale_xy*bboxes_in[:, :, :2]
-    bboxes_in[:, :, 2:] = scale_wh*bboxes_in[:, :, 2:]
-    bboxes_in[:, :, :2] = bboxes_in[:, :, :2]*dboxes_xywh[:, :, 2:] + dboxes_xywh[:, :, :2]
-    bboxes_in[:, :, 2:] = bboxes_in[:, :, 2:].exp()*dboxes_xywh[:, :, 2:]
-    # Transform format to ltrb 
-    l, t, r, b = bboxes_in[:, :, 0] - 0.5*bboxes_in[:, :, 2],\
-                 bboxes_in[:, :, 1] - 0.5*bboxes_in[:, :, 3],\
-                 bboxes_in[:, :, 0] + 0.5*bboxes_in[:, :, 2],\
-                 bboxes_in[:, :, 1] + 0.5*bboxes_in[:, :, 3]
+    bboxes_in[:, :, :2] = scale_xy * bboxes_in[:, :, :2]
+    bboxes_in[:, :, 2:] = scale_wh * bboxes_in[:, :, 2:]
+    bboxes_in[:, :, :2] = (
+        bboxes_in[:, :, :2] * dboxes_xywh[:, :, 2:] + dboxes_xywh[:, :, :2]
+    )
+    bboxes_in[:, :, 2:] = bboxes_in[:, :, 2:].exp() * dboxes_xywh[:, :, 2:]
+    # Transform format to ltrb
+    l, t, r, b = (
+        bboxes_in[:, :, 0] - 0.5 * bboxes_in[:, :, 2],
+        bboxes_in[:, :, 1] - 0.5 * bboxes_in[:, :, 3],
+        bboxes_in[:, :, 0] + 0.5 * bboxes_in[:, :, 2],
+        bboxes_in[:, :, 1] + 0.5 * bboxes_in[:, :, 3],
+    )
     bboxes_in[:, :, 0] = l
     bboxes_in[:, :, 1] = t
     bboxes_in[:, :, 2] = r
@@ -149,92 +166,116 @@ def scale_back_batch(bboxes_in, scores_in,scale_xy,scale_wh,dboxes_xywh):
 
 
 class DefaultBoxes(object):
-    def __init__(self, fig_size, feat_size, steps, scales, aspect_ratios, \
-                       scale_xy=0.1, scale_wh=0.2):
+    def __init__(
+        self,
+        fig_size,
+        feat_size,
+        steps,
+        scales,
+        aspect_ratios,
+        scale_xy=0.1,
+        scale_wh=0.2,
+    ):
 
         self.feat_size = feat_size
-        self.fig_size_w,self.fig_size_h = fig_size
+        self.fig_size_w, self.fig_size_h = fig_size
 
         self.scale_xy_ = scale_xy
         self.scale_wh_ = scale_wh
-        
+
         # According to https://github.com/weiliu89/caffe
         # Calculation method slightly different from paper
         self.steps_w = [st[0] for st in steps]
         self.steps_h = [st[1] for st in steps]
         self.scales = scales
-        fkw = self.fig_size_w//np.array(self.steps_w)
-        fkh = self.fig_size_h//np.array(self.steps_h)
+        fkw = self.fig_size_w // np.array(self.steps_w)
+        fkh = self.fig_size_h // np.array(self.steps_h)
         self.aspect_ratios = aspect_ratios
 
         self.default_boxes = []
         # size of feature and number of feature
         for idx, sfeat in enumerate(self.feat_size):
-            sfeat_w,sfeat_h=sfeat
-            sk1 = scales[idx][0]/self.fig_size_w
-            sk2 = scales[idx+1][1]/self.fig_size_h
-            sk3 = sqrt(sk1*sk2)
+            sfeat_w, sfeat_h = sfeat
+            sk1 = scales[idx][0] / self.fig_size_w
+            sk2 = scales[idx + 1][1] / self.fig_size_h
+            sk3 = sqrt(sk1 * sk2)
             all_sizes = [(sk1, sk1), (sk3, sk3)]
             for alpha in aspect_ratios[idx]:
-                w, h = sk1*sqrt(alpha), sk1/sqrt(alpha)
+                w, h = sk1 * sqrt(alpha), sk1 / sqrt(alpha)
                 all_sizes.append((w, h))
                 all_sizes.append((h, w))
             for w, h in all_sizes:
                 for i, j in itertools.product(range(sfeat_w), range(sfeat_h)):
-                    cx, cy = (j+0.5)/fkh[idx], (i+0.5)/fkw[idx]
-                    self.default_boxes.append((cx, cy, w, h)) 
+                    cx, cy = (j + 0.5) / fkh[idx], (i + 0.5) / fkw[idx]
+                    self.default_boxes.append((cx, cy, w, h))
         self.dboxes = torch.tensor(self.default_boxes)
         self.dboxes.clamp_(min=0, max=1)
         # For IoU calculation
         self.dboxes_ltrb = self.dboxes.clone()
-        self.dboxes_ltrb[:, 0] = self.dboxes[:, 0] - 0.5*self.dboxes[:, 2]
-        self.dboxes_ltrb[:, 1] = self.dboxes[:, 1] - 0.5*self.dboxes[:, 3]
-        self.dboxes_ltrb[:, 2] = self.dboxes[:, 0] + 0.5*self.dboxes[:, 2]
-        self.dboxes_ltrb[:, 3] = self.dboxes[:, 1] + 0.5*self.dboxes[:, 3]
-    
+        self.dboxes_ltrb[:, 0] = self.dboxes[:, 0] - 0.5 * self.dboxes[:, 2]
+        self.dboxes_ltrb[:, 1] = self.dboxes[:, 1] - 0.5 * self.dboxes[:, 3]
+        self.dboxes_ltrb[:, 2] = self.dboxes[:, 0] + 0.5 * self.dboxes[:, 2]
+        self.dboxes_ltrb[:, 3] = self.dboxes[:, 1] + 0.5 * self.dboxes[:, 3]
+
     @property
     def scale_xy(self):
         return self.scale_xy_
-    
-    @property    
+
+    @property
     def scale_wh(self):
         return self.scale_wh_
 
     def __call__(self, order="ltrb"):
-        if order == "ltrb": return self.dboxes_ltrb
-        if order == "xywh": return self.dboxes
+        if order == "ltrb":
+            return self.dboxes_ltrb
+        if order == "xywh":
+            return self.dboxes
+
 
-def dboxes_R34_coco(figsize,strides):
+def dboxes_R34_coco(figsize, strides):
     feat_size = [[50, 50], [25, 25], [13, 13], [7, 7], [3, 3], [3, 3]]
-    steps=[(int(figsize[0]/fs[0]),int(figsize[1]/fs[1])) for fs in feat_size]
-    scales = [(int(s*figsize[0]/300),int(s*figsize[1]/300)) for s in [21, 45, 99, 153, 207, 261, 315]] 
-    aspect_ratios =  [[2], [2, 3], [2, 3], [2, 3], [2], [2]] 
+    steps = [(int(figsize[0] / fs[0]), int(figsize[1] / fs[1]))
+             for fs in feat_size]
+    scales = [
+        (int(s * figsize[0] / 300), int(s * figsize[1] / 300))
+        for s in [21, 45, 99, 153, 207, 261, 315]
+    ]
+    aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
     dboxes = DefaultBoxes(figsize, feat_size, steps, scales, aspect_ratios)
     return dboxes
 
+
 class SSD_R34(nn.Module):
     """
-        Build a SSD module to take 300x300 image input,
-        and output 8732 per class bounding boxes
+    Build a SSD module to take 300x300 image input,
+    and output 8732 per class bounding boxes
 
-        vggt: pretrained vgg16 (partial) model
-        label_num: number of classes (including background 0)
+    vggt: pretrained vgg16 (partial) model
+    label_num: number of classes (including background 0)
     """
-    def __init__(self, label_num=81, backbone='resnet34', model_path="./resnet34-333f7ec4.pth",strides=[3,3 ,2 ,2 ,2 ,2],extract_shapes=False):
+
+    def __init__(
+        self,
+        label_num=81,
+        backbone="resnet34",
+        model_path="./resnet34-333f7ec4.pth",
+        strides=[3, 3, 2, 2, 2, 2],
+        extract_shapes=False,
+    ):
 
         super(SSD_R34, self).__init__()
 
         self.label_num = label_num
         self.strides = strides
-        if backbone == 'resnet34':
+        if backbone == "resnet34":
             self.model = ResNet34()
             out_channels = 256
             self.out_chan = [out_channels, 512, 512, 256, 256, 256]
         else:
-            raise ValueError('Invalid backbone chosen')
+            raise ValueError("Invalid backbone chosen")
 
         self._build_additional_features(self.out_chan)
-        self.extract_shapes=extract_shapes
+        self.extract_shapes = extract_shapes
         # after l2norm, conv7, conv8_2, conv9_2, conv10_2, conv11_2
         # classifer 1, 2, 3, 4, 5 ,6
 
@@ -242,110 +283,159 @@ def __init__(self, label_num=81, backbone='resnet34', model_path="./resnet34-333
         self.loc = []
         self.conf = []
         for nd, oc in zip(self.num_defaults, self.out_chan):
-            self.loc.append(nn.Conv2d(oc, nd*4, kernel_size=3, padding=1,stride=self.strides[0]))
-            self.conf.append(nn.Conv2d(oc, nd*label_num, kernel_size=3, padding=1,stride=self.strides[1]))
+            self.loc.append(
+                nn.Conv2d(
+                    oc,
+                    nd * 4,
+                    kernel_size=3,
+                    padding=1,
+                    stride=self.strides[0])
+            )
+            self.conf.append(
+                nn.Conv2d(
+                    oc, nd * label_num, kernel_size=3, padding=1, stride=self.strides[1]
+                )
+            )
 
         self.loc = nn.ModuleList(self.loc)
         self.conf = nn.ModuleList(self.conf)
         if not extract_shapes:
-            self.size=(1200,1200)
-            dboxes = dboxes_R34_coco(list(self.size),[3,3,2,2,2,2])
+            self.size = (1200, 1200)
+            dboxes = dboxes_R34_coco(list(self.size), [3, 3, 2, 2, 2, 2])
             self.encoder = Encoder(dboxes)
         # intitalize all weights
         self._init_weights()
         self.device = 1
+
     def _build_additional_features(self, input_channels):
         idx = 0
         self.additional_blocks = []
-        
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 256, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(256, input_channels[idx+1], kernel_size=3, padding=1,stride=self.strides[2]),
-            nn.ReLU(inplace=True),
-        ))
+
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 256, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(
+                    256,
+                    input_channels[idx + 1],
+                    kernel_size=3,
+                    padding=1,
+                    stride=self.strides[2],
+                ),
+                nn.ReLU(inplace=True),
+            )
+        )
         idx += 1
 
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 256, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(256, input_channels[idx+1], kernel_size=3, padding=1, stride=self.strides[3]),
-            nn.ReLU(inplace=True),
-        ))
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 256, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(
+                    256,
+                    input_channels[idx + 1],
+                    kernel_size=3,
+                    padding=1,
+                    stride=self.strides[3],
+                ),
+                nn.ReLU(inplace=True),
+            )
+        )
         idx += 1
 
         # conv9_1, conv9_2
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 128, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(128, input_channels[idx+1], kernel_size=3, padding=1, stride=self.strides[4]),
-            nn.ReLU(inplace=True),
-        ))
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 128, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(
+                    128,
+                    input_channels[idx + 1],
+                    kernel_size=3,
+                    padding=1,
+                    stride=self.strides[4],
+                ),
+                nn.ReLU(inplace=True),
+            )
+        )
         idx += 1
 
         # conv10_1, conv10_2
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 128, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(128, input_channels[idx+1], kernel_size=3,stride=self.strides[5]),
-            nn.ReLU(inplace=True),
-        ))
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 128, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(
+                    128, input_channels[idx + 1], kernel_size=3, stride=self.strides[5]
+                ),
+                nn.ReLU(inplace=True),
+            )
+        )
         idx += 1
 
-
-
         # conv11_1, conv11_2
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 128, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(128, input_channels[idx+1], kernel_size=3),
-            nn.ReLU(inplace=True),
-        ))
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 128, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(128, input_channels[idx + 1], kernel_size=3),
+                nn.ReLU(inplace=True),
+            )
+        )
 
         self.additional_blocks = nn.ModuleList(self.additional_blocks)
 
     def _init_weights(self):
 
-        layers = [
-            *self.additional_blocks,
-            *self.loc, *self.conf]
+        layers = [*self.additional_blocks, *self.loc, *self.conf]
 
         for layer in layers:
             for param in layer.parameters():
-                if param.dim() > 1: nn.init.xavier_uniform_(param)
+                if param.dim() > 1:
+                    nn.init.xavier_uniform_(param)
 
     # Shape the classifier to the view of bboxes
-    def bbox_view(self, src, loc, conf,extract_shapes=False):
+    def bbox_view(self, src, loc, conf, extract_shapes=False):
         ret = []
         features_shapes = []
         for s, l, c in zip(src, loc, conf):
-            ret.append((l(s).view(s.size(0), 4, -1), c(s).view(s.size(0), self.label_num, -1)))
-            # extract shapes for prior box initliziation 
+            ret.append(
+                (l(s).view(s.size(0), 4, -1),
+                 c(s).view(s.size(0), self.label_num, -1))
+            )
+            # extract shapes for prior box initliziation
             if extract_shapes:
-                ls=l(s)
-                features_shapes.append([ls.shape[2],ls.shape[3]])
+                ls = l(s)
+                features_shapes.append([ls.shape[2], ls.shape[3]])
         locs, confs = list(zip(*ret))
-        locs, confs = torch.cat(locs, 2).contiguous(), torch.cat(confs, 2).contiguous()
-        return locs, confs,features_shapes
+        locs, confs = torch.cat(
+            locs, 2).contiguous(), torch.cat(
+            confs, 2).contiguous()
+        return locs, confs, features_shapes
 
     def forward(self, data):
         layers = self.model(data)
 
         # last result from network goes into additional blocks
         x = layers[-1]
-        
+
         additional_results = []
         for i, l in enumerate(self.additional_blocks):
-            
+
             x = l(x)
             additional_results.append(x)
 
         src = [*layers, *additional_results]
-        # Feature maps sizes depend on the image size. For 300x300 with strides=[1,1,2,2,2,1] it is 38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4, 1x1x4 
-        locs, confs,features_shapes = self.bbox_view(src, self.loc, self.conf,extract_shapes=self.extract_shapes)
+        # Feature maps sizes depend on the image size. For 300x300 with
+        # strides=[1,1,2,2,2,1] it is 38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4,
+        # 1x1x4
+        locs, confs, features_shapes = self.bbox_view(
+            src, self.loc, self.conf, extract_shapes=self.extract_shapes
+        )
         if self.extract_shapes:
-            return locs, confs,features_shapes
-        else:    
-            # For SSD 300 with strides=[1,1,2,2,2,1] , shall return nbatch x 8732 x {nlabels, nlocs} results 
-            results=self.encoder.decode_batch(locs, confs, 0.50, 200) #[0]
-            return results #locs, confs,features_shapes
+            return locs, confs, features_shapes
+        else:
+            # For SSD 300 with strides=[1,1,2,2,2,1] , shall return nbatch x
+            # 8732 x {nlabels, nlocs} results
+            results = self.encoder.decode_batch(locs, confs, 0.50, 200)  # [0]
+            return results  # locs, confs,features_shapes
diff --git a/retired_benchmarks/vision/classification_and_detection/python/models/utils.py b/retired_benchmarks/vision/classification_and_detection/python/models/utils.py
index 940722075a..49299f584c 100644
--- a/retired_benchmarks/vision/classification_and_detection/python/models/utils.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/models/utils.py
@@ -15,7 +15,7 @@ def __init__(self, out):
         self.register_buffer("scale", torch.ones(out))
         self.register_buffer("bias", torch.zeros(out))
 
-    #@torch.jit.script_method
+    # @torch.jit.script_method
     def forward(self, x):
         scale = self.scale.view(1, -1, 1, 1)
         bias = self.bias.view(1, -1, 1, 1)
@@ -31,7 +31,7 @@ def __init__(self, out):
         super(BiasAdd, self).__init__()
         self.register_buffer("bias", torch.zeros(out))
 
-    #@torch.jit.script_method
+    # @torch.jit.script_method
     def forward(self, x):
         bias = self.bias.view(1, -1, 1, 1)
         return x + bias
@@ -52,14 +52,15 @@ def _compute_padding(self, input, dim):
         effective_filter_size = (filter_size - 1) * self.dilation[dim] + 1
         out_size = (input_size + self.stride[dim] - 1) // self.stride[dim]
         total_padding = max(
-            0, (out_size - 1) * self.stride[dim] + effective_filter_size - input_size
+            0, (out_size - 1) * self.stride[dim] +
+            effective_filter_size - input_size
         )
         additional_padding = int(total_padding % 2 != 0)
 
         return additional_padding, total_padding
 
     def forward(self, input):
-        #import pdb; pdb.set_trace()
+        # import pdb; pdb.set_trace()
         if self.padding == "VALID":
             return F.conv2d(
                 input,
@@ -151,8 +152,8 @@ def decode_boxes(rel_codes, boxes, weights):
     # type: (torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor
 
     # perform some unpacking to make it JIT-fusion friendly
-    
-    #rel_codes=rel_codes[0][None]
+
+    # rel_codes=rel_codes[0][None]
     wx = weights[1]
     wy = weights[0]
     ww = weights[3]
@@ -163,10 +164,10 @@ def decode_boxes(rel_codes, boxes, weights):
     boxes_x2 = boxes[:, 3].unsqueeze(1).unsqueeze(0)
     boxes_y2 = boxes[:, 2].unsqueeze(1).unsqueeze(0)
 
-    dx = rel_codes[:,:, 1].unsqueeze(2)
-    dy = rel_codes[:,:, 0].unsqueeze(2)
-    dw = rel_codes[:,:, 3].unsqueeze(2)
-    dh = rel_codes[:,:, 2].unsqueeze(2)
+    dx = rel_codes[:, :, 1].unsqueeze(2)
+    dy = rel_codes[:, :, 0].unsqueeze(2)
+    dw = rel_codes[:, :, 3].unsqueeze(2)
+    dh = rel_codes[:, :, 2].unsqueeze(2)
 
     # implementation starts here
     widths = boxes_x2 - boxes_x1
@@ -180,7 +181,7 @@ def decode_boxes(rel_codes, boxes, weights):
     dh = dh / wh
 
     pred_ctr_x = dx * widths + ctr_x
-    #import pdb; pdb.set_trace()
+    # import pdb; pdb.set_trace()
     pred_ctr_y = dy * heights + ctr_y
     pred_w = torch.exp(dw) * widths
     pred_h = torch.exp(dh) * heights
@@ -194,5 +195,5 @@ def decode_boxes(rel_codes, boxes, weights):
         ],
         dim=2,
     )
-    #import pdb; pdb.set_trace()
+    # import pdb; pdb.set_trace()
     return pred_boxes
diff --git a/retired_benchmarks/vision/classification_and_detection/python/pycoco.py b/retired_benchmarks/vision/classification_and_detection/python/pycoco.py
index 9318635696..f9d5f2e873 100644
--- a/retired_benchmarks/vision/classification_and_detection/python/pycoco.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/pycoco.py
@@ -1,5 +1,5 @@
-__author__ = 'tylin'
-__version__ = '2.0'
+__author__ = "tylin"
+__version__ = "2.0"
 # Interface for accessing the Microsoft COCO dataset.
 
 # Microsoft COCO is a large image dataset designed for object detection,
@@ -46,9 +46,10 @@
 
 import json
 import time
-#import matplotlib.pyplot as plt
-#from matplotlib.collections import PatchCollection
-#from matplotlib.patches import Polygon
+
+# import matplotlib.pyplot as plt
+# from matplotlib.collections import PatchCollection
+# from matplotlib.patches import Polygon
 import numpy as np
 import copy
 import itertools
@@ -56,6 +57,7 @@
 import os
 from collections import defaultdict
 import sys
+
 PYTHON_VERSION = sys.version_info[0]
 if PYTHON_VERSION == 2:
     from urllib import urlretrieve
@@ -64,7 +66,7 @@
 
 
 def _isArrayLike(obj):
-    return hasattr(obj, '__iter__') and hasattr(obj, '__len__')
+    return hasattr(obj, "__iter__") and hasattr(obj, "__len__")
 
 
 class COCO:
@@ -76,40 +78,42 @@ def __init__(self, annotation_file=None):
         :return:
         """
         # load dataset
-        self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()
+        self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict()
         self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)
-        if not annotation_file == None:
-            print('loading annotations into memory...')
+        if not annotation_file is None:
+            print("loading annotations into memory...")
             tic = time.time()
-            dataset = json.load(open(annotation_file, 'r'))
-            assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))
-            print('Done (t={:0.2f}s)'.format(time.time()- tic))
+            dataset = json.load(open(annotation_file, "r"))
+            assert (
+                isinstance(dataset, dict)
+            ), "annotation file format {} not supported".format(type(dataset))
+            print("Done (t={:0.2f}s)".format(time.time() - tic))
             self.dataset = dataset
             self.createIndex()
 
     def createIndex(self):
         # create index
-        print('creating index...')
+        print("creating index...")
         anns, cats, imgs = {}, {}, {}
-        imgToAnns,catToImgs = defaultdict(list),defaultdict(list)
-        if 'annotations' in self.dataset:
-            for ann in self.dataset['annotations']:
-                imgToAnns[ann['image_id']].append(ann)
-                anns[ann['id']] = ann
+        imgToAnns, catToImgs = defaultdict(list), defaultdict(list)
+        if "annotations" in self.dataset:
+            for ann in self.dataset["annotations"]:
+                imgToAnns[ann["image_id"]].append(ann)
+                anns[ann["id"]] = ann
 
-        if 'images' in self.dataset:
-            for img in self.dataset['images']:
-                imgs[img['id']] = img
+        if "images" in self.dataset:
+            for img in self.dataset["images"]:
+                imgs[img["id"]] = img
 
-        if 'categories' in self.dataset:
-            for cat in self.dataset['categories']:
-                cats[cat['id']] = cat
+        if "categories" in self.dataset:
+            for cat in self.dataset["categories"]:
+                cats[cat["id"]] = cat
 
-        if 'annotations' in self.dataset and 'categories' in self.dataset:
-            for ann in self.dataset['annotations']:
-                catToImgs[ann['category_id']].append(ann['image_id'])
+        if "annotations" in self.dataset and "categories" in self.dataset:
+            for ann in self.dataset["annotations"]:
+                catToImgs[ann["category_id"]].append(ann["image_id"])
 
-        print('index created!')
+        print("index created!")
 
         # create class members
         self.anns = anns
@@ -123,8 +127,8 @@ def info(self):
         Print information about the annotation file.
         :return:
         """
-        for key, value in self.dataset['info'].items():
-            print('{}: {}'.format(key, value))
+        for key, value in self.dataset["info"].items():
+            print("{}: {}".format(key, value))
 
     def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
         """
@@ -139,19 +143,33 @@ def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
         catIds = catIds if _isArrayLike(catIds) else [catIds]
 
         if len(imgIds) == len(catIds) == len(areaRng) == 0:
-            anns = self.dataset['annotations']
+            anns = self.dataset["annotations"]
         else:
             if not len(imgIds) == 0:
-                lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]
+                lists = [
+                    self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns
+                ]
                 anns = list(itertools.chain.from_iterable(lists))
             else:
-                anns = self.dataset['annotations']
-            anns = anns if len(catIds)  == 0 else [ann for ann in anns if ann['category_id'] in catIds]
-            anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
-        if not iscrowd == None:
-            ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
+                anns = self.dataset["annotations"]
+            anns = (
+                anns
+                if len(catIds) == 0
+                else [ann for ann in anns if ann["category_id"] in catIds]
+            )
+            anns = (
+                anns
+                if len(areaRng) == 0
+                else [
+                    ann
+                    for ann in anns
+                    if ann["area"] > areaRng[0] and ann["area"] < areaRng[1]
+                ]
+            )
+        if not iscrowd is None:
+            ids = [ann["id"] for ann in anns if ann["iscrowd"] == iscrowd]
         else:
-            ids = [ann['id'] for ann in anns]
+            ids = [ann["id"] for ann in anns]
         return ids
 
     def getCatIds(self, catNms=[], supNms=[], catIds=[]):
@@ -167,22 +185,34 @@ def getCatIds(self, catNms=[], supNms=[], catIds=[]):
         catIds = catIds if _isArrayLike(catIds) else [catIds]
 
         if len(catNms) == len(supNms) == len(catIds) == 0:
-            cats = self.dataset['categories']
+            cats = self.dataset["categories"]
         else:
-            cats = self.dataset['categories']
-            cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name']          in catNms]
-            cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
-            cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id']            in catIds]
-        ids = [cat['id'] for cat in cats]
+            cats = self.dataset["categories"]
+            cats = (
+                cats
+                if len(catNms) == 0
+                else [cat for cat in cats if cat["name"] in catNms]
+            )
+            cats = (
+                cats
+                if len(supNms) == 0
+                else [cat for cat in cats if cat["supercategory"] in supNms]
+            )
+            cats = (
+                cats
+                if len(catIds) == 0
+                else [cat for cat in cats if cat["id"] in catIds]
+            )
+        ids = [cat["id"] for cat in cats]
         return ids
 
     def getImgIds(self, imgIds=[], catIds=[]):
-        '''
+        """
         Get img ids that satisfy given filter conditions.
         :param imgIds (int array) : get imgs for given ids
         :param catIds (int array) : get imgs with all given cats
         :return: ids (int array)  : integer array of img ids
-        '''
+        """
         imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
         catIds = catIds if _isArrayLike(catIds) else [catIds]
 
@@ -205,7 +235,7 @@ def loadAnns(self, ids=[]):
         """
         if _isArrayLike(ids):
             return [self.anns[id] for id in ids]
-        elif type(ids) == int:
+        elif isinstance(ids, int):
             return [self.anns[ids]]
 
     def loadCats(self, ids=[]):
@@ -216,7 +246,7 @@ def loadCats(self, ids=[]):
         """
         if _isArrayLike(ids):
             return [self.cats[id] for id in ids]
-        elif type(ids) == int:
+        elif isinstance(ids, int):
             return [self.cats[ids]]
 
     def loadImgs(self, ids=[]):
@@ -227,7 +257,7 @@ def loadImgs(self, ids=[]):
         """
         if _isArrayLike(ids):
             return [self.imgs[id] for id in ids]
-        elif type(ids) == int:
+        elif isinstance(ids, int):
             return [self.imgs[ids]]
 
     def showAnns(self, anns):
@@ -238,61 +268,88 @@ def showAnns(self, anns):
         """
         if len(anns) == 0:
             return 0
-        if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
-            datasetType = 'instances'
-        elif 'caption' in anns[0]:
-            datasetType = 'captions'
+        if "segmentation" in anns[0] or "keypoints" in anns[0]:
+            datasetType = "instances"
+        elif "caption" in anns[0]:
+            datasetType = "captions"
         else:
-            raise Exception('datasetType not supported')
-        if datasetType == 'instances':
+            raise Exception("datasetType not supported")
+        if datasetType == "instances":
             ax = plt.gca()
             ax.set_autoscale_on(False)
             polygons = []
             color = []
             for ann in anns:
-                c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
-                if 'segmentation' in ann:
-                    if type(ann['segmentation']) == list:
+                c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+                if "segmentation" in ann:
+                    if isinstance(ann["segmentation"], list):
                         # polygon
-                        for seg in ann['segmentation']:
-                            poly = np.array(seg).reshape((int(len(seg)/2), 2))
+                        for seg in ann["segmentation"]:
+                            poly = np.array(seg).reshape(
+                                (int(len(seg) / 2), 2))
                             polygons.append(Polygon(poly))
                             color.append(c)
                     else:
                         # mask
-                        t = self.imgs[ann['image_id']]
-                        if type(ann['segmentation']['counts']) == list:
-                            rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
+                        t = self.imgs[ann["image_id"]]
+                        if isinstance(ann["segmentation"]["counts"], list):
+                            rle = maskUtils.frPyObjects(
+                                [ann["segmentation"]], t["height"], t["width"]
+                            )
                         else:
-                            rle = [ann['segmentation']]
+                            rle = [ann["segmentation"]]
                         m = maskUtils.decode(rle)
-                        img = np.ones( (m.shape[0], m.shape[1], 3) )
-                        if ann['iscrowd'] == 1:
-                            color_mask = np.array([2.0,166.0,101.0])/255
-                        if ann['iscrowd'] == 0:
+                        img = np.ones((m.shape[0], m.shape[1], 3))
+                        if ann["iscrowd"] == 1:
+                            color_mask = np.array([2.0, 166.0, 101.0]) / 255
+                        if ann["iscrowd"] == 0:
                             color_mask = np.random.random((1, 3)).tolist()[0]
                         for i in range(3):
-                            img[:,:,i] = color_mask[i]
-                        ax.imshow(np.dstack( (img, m*0.5) ))
-                if 'keypoints' in ann and type(ann['keypoints']) == list:
+                            img[:, :, i] = color_mask[i]
+                        ax.imshow(np.dstack((img, m * 0.5)))
+                if "keypoints" in ann and isinstance(ann["keypoints"], list):
                     # turn skeleton into zero-based index
-                    sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
-                    kp = np.array(ann['keypoints'])
+                    sks = np.array(
+                        self.loadCats(
+                            ann["category_id"])[0]["skeleton"]) - 1
+                    kp = np.array(ann["keypoints"])
                     x = kp[0::3]
                     y = kp[1::3]
                     v = kp[2::3]
                     for sk in sks:
-                        if np.all(v[sk]>0):
-                            plt.plot(x[sk],y[sk], linewidth=3, color=c)
-                    plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
-                    plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)
-            p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
+                        if np.all(v[sk] > 0):
+                            plt.plot(x[sk], y[sk], linewidth=3, color=c)
+                    plt.plot(
+                        x[v > 0],
+                        y[v > 0],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor="k",
+                        markeredgewidth=2,
+                    )
+                    plt.plot(
+                        x[v > 1],
+                        y[v > 1],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor=c,
+                        markeredgewidth=2,
+                    )
+            p = PatchCollection(
+                polygons,
+                facecolor=color,
+                linewidths=0,
+                alpha=0.4)
             ax.add_collection(p)
-            p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)
+            p = PatchCollection(
+                polygons, facecolor="none", edgecolors=color, linewidths=2
+            )
             ax.add_collection(p)
-        elif datasetType == 'captions':
+        elif datasetType == "captions":
             for ann in anns:
-                print(ann['caption'])
+                print(ann["caption"])
 
     def loadRes(self, resFile):
         """
@@ -301,69 +358,78 @@ def loadRes(self, resFile):
         :return: res (obj)         : result api object
         """
         res = COCO()
-        res.dataset['images'] = [img for img in self.dataset['images']]
+        res.dataset["images"] = [img for img in self.dataset["images"]]
 
-        print('Loading and preparing results...')
+        print("Loading and preparing results...")
         tic = time.time()
-        if type(resFile) == str: #or type(resFile) == unicode:
+        if isinstance(resFile, str):  # or type(resFile) == unicode:
             anns = json.load(open(resFile))
-        elif type(resFile) == np.ndarray:
+        elif isinstance(resFile, np.ndarray):
             anns = self.loadNumpyAnnotations(resFile)
         else:
             anns = resFile
-        assert type(anns) == list, 'results in not an array of objects'
-        annsImgIds = [ann['image_id'] for ann in anns]
-        assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
-               'Results do not correspond to current coco set'
-        if 'caption' in anns[0]:
-            imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
-            res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
+        assert isinstance(anns, list), "results in not an array of objects"
+        annsImgIds = [ann["image_id"] for ann in anns]
+        assert set(annsImgIds) == (
+            set(annsImgIds) & set(self.getImgIds())
+        ), "Results do not correspond to current coco set"
+        if "caption" in anns[0]:
+            imgIds = set([img["id"] for img in res.dataset["images"]]) & set(
+                [ann["image_id"] for ann in anns]
+            )
+            res.dataset["images"] = [
+                img for img in res.dataset["images"] if img["id"] in imgIds
+            ]
             for id, ann in enumerate(anns):
-                ann['id'] = id+1
-        elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+                ann["id"] = id + 1
+        elif "bbox" in anns[0] and not anns[0]["bbox"] == []:
+            res.dataset["categories"] = copy.deepcopy(
+                self.dataset["categories"])
             for id, ann in enumerate(anns):
-                bb = ann['bbox']
-                x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
-                if not 'segmentation' in ann:
-                    ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
-                ann['area'] = bb[2]*bb[3]
-                ann['id'] = id+1
-                ann['iscrowd'] = 0
-        elif 'segmentation' in anns[0]:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+                bb = ann["bbox"]
+                x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
+                if not "segmentation" in ann:
+                    ann["segmentation"] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
+                ann["area"] = bb[2] * bb[3]
+                ann["id"] = id + 1
+                ann["iscrowd"] = 0
+        elif "segmentation" in anns[0]:
+            res.dataset["categories"] = copy.deepcopy(
+                self.dataset["categories"])
             for id, ann in enumerate(anns):
-                # now only support compressed RLE format as segmentation results
-                ann['area'] = maskUtils.area(ann['segmentation'])
-                if not 'bbox' in ann:
-                    ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
-                ann['id'] = id+1
-                ann['iscrowd'] = 0
-        elif 'keypoints' in anns[0]:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+                # now only support compressed RLE format as segmentation
+                # results
+                ann["area"] = maskUtils.area(ann["segmentation"])
+                if not "bbox" in ann:
+                    ann["bbox"] = maskUtils.toBbox(ann["segmentation"])
+                ann["id"] = id + 1
+                ann["iscrowd"] = 0
+        elif "keypoints" in anns[0]:
+            res.dataset["categories"] = copy.deepcopy(
+                self.dataset["categories"])
             for id, ann in enumerate(anns):
-                s = ann['keypoints']
+                s = ann["keypoints"]
                 x = s[0::3]
                 y = s[1::3]
-                x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y)
-                ann['area'] = (x1-x0)*(y1-y0)
-                ann['id'] = id + 1
-                ann['bbox'] = [x0,y0,x1-x0,y1-y0]
-        print('DONE (t={:0.2f}s)'.format(time.time()- tic))
+                x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y)
+                ann["area"] = (x1 - x0) * (y1 - y0)
+                ann["id"] = id + 1
+                ann["bbox"] = [x0, y0, x1 - x0, y1 - y0]
+        print("DONE (t={:0.2f}s)".format(time.time() - tic))
 
-        res.dataset['annotations'] = anns
+        res.dataset["annotations"] = anns
         res.createIndex()
         return res
 
-    def download(self, tarDir = None, imgIds = [] ):
-        '''
+    def download(self, tarDir=None, imgIds=[]):
+        """
         Download COCO images from mscoco.org server.
         :param tarDir (str): COCO results directory name
                imgIds (list): images to be downloaded
         :return:
-        '''
+        """
         if tarDir is None:
-            print('Please specify target directory')
+            print("Please specify target directory")
             return -1
         if len(imgIds) == 0:
             imgs = self.imgs.values()
@@ -374,10 +440,13 @@ def download(self, tarDir = None, imgIds = [] ):
             os.makedirs(tarDir)
         for i, img in enumerate(imgs):
             tic = time.time()
-            fname = os.path.join(tarDir, img['file_name'])
+            fname = os.path.join(tarDir, img["file_name"])
             if not os.path.exists(fname):
-                urlretrieve(img['coco_url'], fname)
-            print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic))
+                urlretrieve(img["coco_url"], fname)
+            print(
+                "downloaded {}/{} images (t={:0.1f}s)".format(i,
+                                                              N, time.time() - tic)
+            )
 
     def loadNumpyAnnotations(self, data):
         """
@@ -385,21 +454,23 @@ def loadNumpyAnnotations(self, data):
         :param  data (numpy.ndarray)
         :return: annotations (python nested list)
         """
-        print('Converting ndarray to lists...')
-        assert(type(data) == np.ndarray)
+        print("Converting ndarray to lists...")
+        assert isinstance(data, np.ndarray)
         print(data.shape)
-        assert(data.shape[1] == 7)
+        assert data.shape[1] == 7
         N = data.shape[0]
         ann = []
         for i in range(N):
             if i % 1000000 == 0:
-                print('{}/{}'.format(i,N))
-            ann += [{
-                'image_id'  : int(data[i, 0]),
-                'bbox'  : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ],
-                'score' : data[i, 5],
-                'category_id': int(data[i, 6]),
-                }]
+                print("{}/{}".format(i, N))
+            ann += [
+                {
+                    "image_id": int(data[i, 0]),
+                    "bbox": [data[i, 1], data[i, 2], data[i, 3], data[i, 4]],
+                    "score": data[i, 5],
+                    "category_id": int(data[i, 6]),
+                }
+            ]
         return ann
 
     def annToRLE(self, ann):
@@ -407,20 +478,20 @@ def annToRLE(self, ann):
         Convert annotation which can be polygons, uncompressed RLE to RLE.
         :return: binary mask (numpy 2D array)
         """
-        t = self.imgs[ann['image_id']]
-        h, w = t['height'], t['width']
-        segm = ann['segmentation']
-        if type(segm) == list:
+        t = self.imgs[ann["image_id"]]
+        h, w = t["height"], t["width"]
+        segm = ann["segmentation"]
+        if isinstance(segm, list):
             # polygon -- a single object might consist of multiple parts
             # we merge all parts into one mask rle code
             rles = maskUtils.frPyObjects(segm, h, w)
             rle = maskUtils.merge(rles)
-        elif type(segm['counts']) == list:
+        elif isinstance(segm["counts"], list):
             # uncompressed RLE
             rle = maskUtils.frPyObjects(segm, h, w)
         else:
             # rle
-            rle = ann['segmentation']
+            rle = ann["segmentation"]
         return rle
 
     def annToMask(self, ann):
diff --git a/retired_benchmarks/vision/classification_and_detection/python/version.py b/retired_benchmarks/vision/classification_and_detection/python/version.py
index 1152dbb417..570348596c 100644
--- a/retired_benchmarks/vision/classification_and_detection/python/version.py
+++ b/retired_benchmarks/vision/classification_and_detection/python/version.py
@@ -1,3 +1,2 @@
-
-version = '0.1.0'
-git_version = '05df3bae82ef9fc933277385eb778e3f22cd0c6a'
+version = "0.1.0"
+git_version = "05df3bae82ef9fc933277385eb778e3f22cd0c6a"
diff --git a/retired_benchmarks/vision/classification_and_detection/setup.py b/retired_benchmarks/vision/classification_and_detection/setup.py
index c1e2fbcf07..758d874fbd 100644
--- a/retired_benchmarks/vision/classification_and_detection/setup.py
+++ b/retired_benchmarks/vision/classification_and_detection/setup.py
@@ -13,17 +13,20 @@
 from setuptools import setup, find_packages, Command
 
 TOP_DIR = os.path.realpath(os.path.dirname(__file__))
-SRC_DIR = os.path.join(TOP_DIR, 'python')
+SRC_DIR = os.path.join(TOP_DIR, "python")
 
 try:
-    git_version = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=TOP_DIR).decode('ascii').strip()
+    git_version = (
+        subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=TOP_DIR)
+        .decode("ascii")
+        .strip()
+    )
 except (OSError, subprocess.CalledProcessError):
     git_version = None
 
-with open(os.path.join(TOP_DIR, 'VERSION_NUMBER')) as version_file:
-    VersionInfo = namedtuple('VersionInfo', ['version', 'git_version'])(
-        version=version_file.read().strip(),
-        git_version=git_version
+with open(os.path.join(TOP_DIR, "VERSION_NUMBER")) as version_file:
+    VersionInfo = namedtuple("VersionInfo", ["version", "git_version"])(
+        version=version_file.read().strip(), git_version=git_version
     )
 
 
@@ -37,49 +40,67 @@ def finalize_options(self):
         pass
 
     def run(self):
-        with open(os.path.join(SRC_DIR, 'version.py'), 'w') as f:
-            f.write(dedent('''
+        with open(os.path.join(SRC_DIR, "version.py"), "w") as f:
+            f.write(
+                dedent(
+                    """
             version = '{version}'
             git_version = '{git_version}'
-            '''.format(**dict(VersionInfo._asdict()))))
+            """.format(
+                        **dict(VersionInfo._asdict())
+                    )
+                )
+            )
 
 
 class build_py(setuptools.command.build_py.build_py):
     def run(self):
-        self.run_command('create_version')
+        self.run_command("create_version")
         setuptools.command.build_py.build_py.run(self)
 
 
 class build(distutils.command.build.build):
     def run(self):
-        self.run_command('build_py')
+        self.run_command("build_py")
 
 
 class develop(setuptools.command.develop.develop):
     def run(self):
-        self.run_command('create_version')
-        self.run_command('build')
+        self.run_command("create_version")
+        self.run_command("build")
         setuptools.command.develop.develop.run(self)
 
 
 cmdclass = {
-    'create_version': create_version,
-    'build_py': build_py,
-    'build': build,
-    'develop': develop,
+    "create_version": create_version,
+    "build_py": build_py,
+    "build": build,
+    "develop": develop,
 }
 
 setup(
     name="mlperf-inference",
     version=VersionInfo.version,
-    description='mlperf inference benchmark',
-    setup_requires=['pytest-runner'],
-    tests_require=['graphviz', 'parameterized', 'pytest', 'pytest-cov', 'pyyaml'],
+    description="mlperf inference benchmark",
+    setup_requires=["pytest-runner"],
+    tests_require=[
+        "graphviz",
+        "parameterized",
+        "pytest",
+        "pytest-cov",
+        "pyyaml"],
     cmdclass=cmdclass,
     packages=find_packages(),
-    author='guschmue@microsoft.com',
-    author_email='guschmue@microsoft.com',
-    url='https://github.com/mlperf/inference',
-    install_requires=['numpy>=1.14.1', 'onnx>=1.5', 'pybind11', 'Cython',
-                        'pycocotools', 'mlperf_loadgen', 'opencv-python-headless']
+    author="guschmue@microsoft.com",
+    author_email="guschmue@microsoft.com",
+    url="https://github.com/mlperf/inference",
+    install_requires=[
+        "numpy>=1.14.1",
+        "onnx>=1.5",
+        "pybind11",
+        "Cython",
+        "pycocotools",
+        "mlperf_loadgen",
+        "opencv-python-headless",
+    ],
 )
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/accuracy-coco.py b/retired_benchmarks/vision/classification_and_detection/tools/accuracy-coco.py
index 1e15999f1d..95e04193ba 100644
--- a/retired_benchmarks/vision/classification_and_detection/tools/accuracy-coco.py
+++ b/retired_benchmarks/vision/classification_and_detection/tools/accuracy-coco.py
@@ -19,15 +19,29 @@
 
 # pylint: disable=missing-docstring
 
+
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
     parser.add_argument("--coco-dir", required=True, help="coco directory")
-    parser.add_argument("--verbose", action="store_true", help="verbose messages")
-    parser.add_argument("--output-file", default="coco-results.json", help="path to output file")
-    parser.add_argument("--use-inv-map", action="store_true", help="use inverse label map")
-    parser.add_argument("--remove-48-empty-images", action="store_true", help="used in case you removed 48 empty images while preprocessing the dataset")
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--output-file", default="coco-results.json", help="path to output file"
+    )
+    parser.add_argument(
+        "--use-inv-map", action="store_true", help="use inverse label map"
+    )
+    parser.add_argument(
+        "--remove-48-empty-images",
+        action="store_true",
+        help="used in case you removed 48 empty images while preprocessing the dataset",
+    )
     args = parser.parse_args()
     return args
 
@@ -35,10 +49,14 @@ def get_args():
 def main():
     args = get_args()
 
-    cocoGt = COCO(os.path.join(args.coco_dir, "annotations/instances_val2017.json"))
+    cocoGt = COCO(
+        os.path.join(
+            args.coco_dir,
+            "annotations/instances_val2017.json"))
 
     if args.use_inv_map:
-        inv_map = [0] + cocoGt.getCatIds() # First label in inv_map is not used
+        # First label in inv_map is not used
+        inv_map = [0] + cocoGt.getCatIds()
 
     with open(args.mlperf_accuracy_file, "r") as f:
         results = json.load(f)
@@ -47,7 +65,7 @@ def main():
     image_ids = set()
     seen = set()
     no_results = 0
-    if args.remove_48_empty_images:        
+    if args.remove_48_empty_images:
         im_ids = []
         for i in cocoGt.getCatIds():
             im_ids += cocoGt.catToImgs[i]
@@ -57,7 +75,7 @@ def main():
         image_map = cocoGt.dataset["images"]
 
     for j in results:
-        idx = j['qsl_idx']
+        idx = j["qsl_idx"]
         # de-dupe in case loadgen sends the same image multiple times
         if idx in seen:
             continue
@@ -66,12 +84,14 @@ def main():
         # reconstruct from mlperf accuracy log
         # what is written by the benchmark is an array of float32's:
         # id, box[0], box[1], box[2], box[3], score, detection_class
-        # note that id is a index into instances_val2017.json, not the actual image_id
-        data = np.frombuffer(bytes.fromhex(j['data']), np.float32)
+        # note that id is a index into instances_val2017.json, not the actual
+        # image_id
+        data = np.frombuffer(bytes.fromhex(j["data"]), np.float32)
         if len(data) < 7:
             # handle images that had no results
             image = image_map[idx]
-            # by adding the id to image_ids we make pycoco aware of the no-result image
+            # by adding the id to image_ids we make pycoco aware of the
+            # no-result image
             image_ids.add(image["id"])
             no_results += 1
             if args.verbose:
@@ -79,11 +99,15 @@ def main():
             continue
 
         for i in range(0, len(data), 7):
-            image_idx, ymin, xmin, ymax, xmax, score, label = data[i:i + 7]
+            image_idx, ymin, xmin, ymax, xmax, score, label = data[i: i + 7]
             image = image_map[idx]
             image_idx = int(image_idx)
             if image_idx != idx:
-                print("ERROR: loadgen({}) and payload({}) disagree on image_idx".format(idx, image_idx))
+                print(
+                    "ERROR: loadgen({}) and payload({}) disagree on image_idx".format(
+                        idx, image_idx
+                    )
+                )
             image_id = image["id"]
             height, width = image["height"], image["width"]
             ymin *= height
@@ -95,25 +119,35 @@ def main():
             if args.use_inv_map:
                 label = inv_map[label]
             # pycoco wants {imageID,x1,y1,w,h,score,class}
-            detections.append({
-                "image_id": image_id,
-                "image_loc": loc,
-                "category_id": label,
-                "bbox": [float(xmin), float(ymin), float(xmax - xmin), float(ymax - ymin)],
-                "score": float(score)})
+            detections.append(
+                {
+                    "image_id": image_id,
+                    "image_loc": loc,
+                    "category_id": label,
+                    "bbox": [
+                        float(xmin),
+                        float(ymin),
+                        float(xmax - xmin),
+                        float(ymax - ymin),
+                    ],
+                    "score": float(score),
+                }
+            )
             image_ids.add(image_id)
 
     with open(args.output_file, "w") as fp:
         json.dump(detections, fp, sort_keys=True, indent=4)
 
-    cocoDt = cocoGt.loadRes(args.output_file) # Load from file to bypass error with Python3
-    cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+    cocoDt = cocoGt.loadRes(
+        args.output_file
+    )  # Load from file to bypass error with Python3
+    cocoEval = COCOeval(cocoGt, cocoDt, iouType="bbox")
     cocoEval.params.imgIds = list(image_ids)
     cocoEval.evaluate()
     cocoEval.accumulate()
     cocoEval.summarize()
 
-    print("mAP={:.3f}%".format(100. * cocoEval.stats[0]))
+    print("mAP={:.3f}%".format(100.0 * cocoEval.stats[0]))
     if args.verbose:
         print("found {} results".format(len(results)))
         print("found {} images".format(len(image_ids)))
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/accuracy-imagenet.py b/retired_benchmarks/vision/classification_and_detection/tools/accuracy-imagenet.py
index 1879e0f09a..a57810891d 100644
--- a/retired_benchmarks/vision/classification_and_detection/tools/accuracy-imagenet.py
+++ b/retired_benchmarks/vision/classification_and_detection/tools/accuracy-imagenet.py
@@ -15,21 +15,32 @@
 
 # pylint: disable=missing-docstring
 
+
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--imagenet-val-file", required=True, help="path to imagenet val_map.txt")
-    parser.add_argument("--verbose", action="store_true", help="verbose messages")
-    parser.add_argument("--dtype", default="float32", choices=["float32", "int32", "int64"], help="data type of the label")
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--imagenet-val-file", required=True, help="path to imagenet val_map.txt"
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--dtype",
+        default="float32",
+        choices=["float32", "int32", "int64"],
+        help="data type of the label",
+    )
     args = parser.parse_args()
     return args
 
-dtype_map = {
-    "float32": np.float32,
-    "int32": np.int32,
-    "int64": np.int64
-}
+
+dtype_map = {"float32": np.float32, "int32": np.int32, "int64": np.int64}
+
 
 def main():
     args = get_args()
@@ -46,7 +57,7 @@ def main():
     seen = set()
     good = 0
     for j in results:
-        idx = j['qsl_idx']
+        idx = j["qsl_idx"]
 
         # de-dupe in case loadgen sends the same image multiple times
         if idx in seen:
@@ -57,7 +68,7 @@ def main():
         img, label = imagenet[idx]
 
         # reconstruct label from mlperf accuracy log
-        data = np.frombuffer(bytes.fromhex(j['data']), dtype_map[args.dtype])
+        data = np.frombuffer(bytes.fromhex(j["data"]), dtype_map[args.dtype])
         found = int(data[0])
         if label == found:
             good += 1
@@ -65,7 +76,11 @@ def main():
             if args.verbose:
                 print("{}, expected: {}, found {}".format(img, label, found))
 
-    print("accuracy={:.3f}%, good={}, total={}".format(100. * good / len(seen), good, len(seen)))
+    print(
+        "accuracy={:.3f}%, good={}, total={}".format(
+            100.0 * good / len(seen), good, len(seen)
+        )
+    )
     if args.verbose:
         print("found and ignored {} dupes".format(len(results) - len(seen)))
 
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/calibrate_torchvision_model.py b/retired_benchmarks/vision/classification_and_detection/tools/calibrate_torchvision_model.py
index 815e5fe206..3b002003ab 100644
--- a/retired_benchmarks/vision/classification_and_detection/tools/calibrate_torchvision_model.py
+++ b/retired_benchmarks/vision/classification_and_detection/tools/calibrate_torchvision_model.py
@@ -12,12 +12,13 @@
 
 class CalibrationDataset(Dataset):
     def __init__(self, root, files, transform):
-        with open(files, 'r') as f:
-            self.files = [os.path.join(root, fn.strip()) for fn in f.readlines()]
+        with open(files, "r") as f:
+            self.files = [os.path.join(root, fn.strip())
+                          for fn in f.readlines()]
         self.transform = transform
 
     def __getitem__(self, idx):
-        image = Image.open(self.files[idx]).convert('RGB')
+        image = Image.open(self.files[idx]).convert("RGB")
         image = self.transform(image)
         return image
 
@@ -25,7 +26,7 @@ def __len__(self):
         return len(self.files)
 
 
-def quantize_model(model, dataloader, backend='fbgemm'):
+def quantize_model(model, dataloader, backend="fbgemm"):
     if backend not in torch.backends.quantized.supported_engines:
         raise RuntimeError("Quantized backend not supported ")
     torch.backends.quantized.engine = backend
@@ -36,29 +37,40 @@ def quantize_model(model, dataloader, backend='fbgemm'):
     # Make sure that weight qconfig matches that of the serialized models
     model.qconfig = torch.quantization.get_default_qconfig(backend)
     torch.quantization.prepare(model, inplace=True)
-    print('calibrating...')
+    print("calibrating...")
     for x in tqdm(dataloader):
         model(x)
-    print('calibration DONE!')
+    print("calibration DONE!")
     torch.quantization.convert(model, inplace=True)
 
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--model', type=str, default='resnet50')
-    parser.add_argument('--image-dir', type=str, default='imagenet/val')
-    parser.add_argument('--image-list', type=str, default='../../calibration/ImageNet/cal_image_list_option_1.txt')
+    parser.add_argument("--model", type=str, default="resnet50")
+    parser.add_argument("--image-dir", type=str, default="imagenet/val")
+    parser.add_argument(
+        "--image-list",
+        type=str,
+        default="../../calibration/ImageNet/cal_image_list_option_1.txt",
+    )
     args = parser.parse_args()
     print(args)
 
-    transform = transforms.Compose([                                                   
-        transforms.Resize(256),                                                        
-        transforms.CenterCrop(224),                                                    
-        transforms.ToTensor(),                                                         
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),   
-    ])                                                                                 
-
-    dataset = CalibrationDataset(root=args.image_dir, files=args.image_list, transform=transform)
+    transform = transforms.Compose(
+        [
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=[
+                    0.485, 0.456, 0.406], std=[
+                    0.229, 0.224, 0.225]),
+        ]
+    )
+
+    dataset = CalibrationDataset(
+        root=args.image_dir, files=args.image_list, transform=transform
+    )
     dataloader = DataLoader(dataset, batch_size=1)
 
     model = eval(args.model)(pretrained=True, progress=True, quantize=False)
@@ -67,11 +79,10 @@ def main():
 
     inp = torch.rand(1, 3, 224, 224)
     script_module = torch.jit.trace(model, inp)
-    save_path = f'{args.model}.pt'
+    save_path = f"{args.model}.pt"
     torch.jit.save(script_module, save_path)
-    print(f'saved: {save_path}')
+    print(f"saved: {save_path}")
 
 
-if __name__=='__main__':
+if __name__ == "__main__":
     main()
-
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/coco-analyze.py b/retired_benchmarks/vision/classification_and_detection/tools/coco-analyze.py
index 7f22daa84e..f58677f9d4 100755
--- a/retired_benchmarks/vision/classification_and_detection/tools/coco-analyze.py
+++ b/retired_benchmarks/vision/classification_and_detection/tools/coco-analyze.py
@@ -36,7 +36,7 @@ def annotate_image(results, cocoGt, output):
 
     new_results = collections.defaultdict(list)
     for result in results:
-        new_results[result['image_id']].append(result)
+        new_results[result["image_id"]].append(result)
     print("Unique images = {}".format(len(new_results)))
     results = new_results
 
@@ -44,32 +44,40 @@ def annotate_image(results, cocoGt, output):
         draw = None
         image = None
         for v in result:
-            box = v['bbox']
-            score = v['score']
+            box = v["bbox"]
+            score = v["score"]
             predicted_class = v["category_id"]
             try:
                 predicted_class = cocoGt.loadCats(predicted_class)[0]["name"]
             except Exception as ex:
-                print("category {} not found, image {}".format(predicted_class, v["image_loc"]))
+                print(
+                    "category {} not found, image {}".format(
+                        predicted_class, v["image_loc"]
+                    )
+                )
             # predicted_class = self.class_names[c]
             # "image_loc": "/home/gs/data/coco300/val2017/000000397133.jpg",
             if not draw:
-                image = Image.open(v['image_loc'])
-                if image.mode != 'RGB':
-                    image = image.convert('RGB')
+                image = Image.open(v["image_loc"])
+                if image.mode != "RGB":
+                    image = image.convert("RGB")
 
                 draw = ImageDraw.Draw(image)
             # font = ImageFont.truetype(font='FreeMono.ttf',
-            #            size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
+            # size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
             try:
                 left, top, w, h = box
                 bottom = top + h
                 right = left + w
-                top = max(0, np.floor(top + 0.5).astype('int32'))
-                left = max(0, np.floor(left + 0.5).astype('int32'))
-                bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
-                right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
-                label = '{} {:.2f}'.format(predicted_class, score)
+                top = max(0, np.floor(top + 0.5).astype("int32"))
+                left = max(0, np.floor(left + 0.5).astype("int32"))
+                bottom = min(
+                    image.size[1], np.floor(
+                        bottom + 0.5).astype("int32"))
+                right = min(
+                    image.size[0], np.floor(
+                        right + 0.5).astype("int32"))
+                label = "{} {:.2f}".format(predicted_class, score)
                 # label_size = draw.textsize(label, font)
                 label_size = draw.textsize(label)
 
@@ -80,11 +88,19 @@ def annotate_image(results, cocoGt, output):
 
                 color = ImageColor.getrgb("red")
                 thickness = 0
-                draw.rectangle([left + thickness, top + thickness, right - thickness, bottom - thickness], outline=color)
+                draw.rectangle(
+                    [
+                        left + thickness,
+                        top + thickness,
+                        right - thickness,
+                        bottom - thickness,
+                    ],
+                    outline=color,
+                )
                 draw.text(text_origin, label, fill=color)  # , font=font)
             except Exception as ex:
-                print("{} failed, ex {}".format(v['image_loc'], ex))
-        image.save(os.path.join(output, os.path.basename(v['image_loc'])))
+                print("{} failed, ex {}".format(v["image_loc"], ex))
+        image.save(os.path.join(output, os.path.basename(v["image_loc"])))
         del draw
 
 
@@ -93,7 +109,7 @@ def calculate_map(results, cocoGt, output):
     # x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
 
     cocoDt = cocoGt.loadRes(results)
-    cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+    cocoEval = COCOeval(cocoGt, cocoDt, iouType="bbox")
     cocoEval.evaluate()
     cocoEval.accumulate()
     cocoEval.summarize()
@@ -110,11 +126,11 @@ def calculate_map(results, cocoGt, output):
         "DetectionBoxes_Recall/AR@100": cocoEval.stats[8],
         "DetectionBoxes_Recall/AR@100 (small)": cocoEval.stats[9],
         "DetectionBoxes_Recall/AR@100 (medium)": cocoEval.stats[10],
-        "DetectionBoxes_Recall/AR@100 (large)": cocoEval.stats[11]
+        "DetectionBoxes_Recall/AR@100 (large)": cocoEval.stats[11],
     }
 
-    mAP = all_metrics['DetectionBoxes_Precision/mAP']
-    recall = all_metrics['DetectionBoxes_Recall/AR@100']
+    mAP = all_metrics["DetectionBoxes_Precision/mAP"]
+    recall = all_metrics["DetectionBoxes_Recall/AR@100"]
     print("mAP={}, recall={}".format(mAP, recall))
 
 
@@ -124,7 +140,8 @@ def main():
     with open(args.input, "r") as f:
         results = json.load(f)
 
-    annotation_file = os.path.join(args.coco, "annotations/instances_val2017.json")
+    annotation_file = os.path.join(
+        args.coco, "annotations/instances_val2017.json")
     cocoGt = COCO(annotation_file)
     annotate_image(results, cocoGt, args.output)
     calculate_map(args.input, cocoGt, args.output)
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/lglog2csv.py b/retired_benchmarks/vision/classification_and_detection/tools/lglog2csv.py
index 901bd795de..6f32433f2d 100644
--- a/retired_benchmarks/vision/classification_and_detection/tools/lglog2csv.py
+++ b/retired_benchmarks/vision/classification_and_detection/tools/lglog2csv.py
@@ -36,32 +36,43 @@ def main():
     with open(args.input, "r") as fp:
         mode, mean, latency_90, latency_99, qps = None, 0, 0, 0, 0
         for line in fp:
-            m = re.match("^Scenario\s*:\s*(\w+).*", line)
+            m = re.match("^Scenario\\s*:\\s*(\\w+).*", line)
             if m:
                 mode = m.group(1)
-            m = re.match("^90.00 percentile latency.*:\s*(\d+).*", line)
+            m = re.match("^90.00 percentile latency.*:\\s*(\\d+).*", line)
             if m:
                 latency_90 = m.group(1)
-            m = re.match("^99.00 percentile latency.*:\s*(\d+).*", line)
+            m = re.match("^99.00 percentile latency.*:\\s*(\\d+).*", line)
             if m:
                 latency_99 = m.group(1)
-            m = re.match("^Mean latency.*:\s*(\d+).*", line)
+            m = re.match("^Mean latency.*:\\s*(\\d+).*", line)
             if m:
                 mean = m.group(1)
-            m = re.match("^Completed samples per second.*:\s*(\d+).*", line)
+            m = re.match("^Completed samples per second.*:\\s*(\\d+).*", line)
             if m:
                 qps = m.group(1)
-            m = re.match("^QPS w/ loadgen overhead.*:\s*(\d+).*", line)
+            m = re.match("^QPS w/ loadgen overhead.*:\\s*(\\d+).*", line)
             if m:
                 qps = m.group(1)
-            m = re.match("^Samples per second.*:\s*(\d+).*", line)
+            m = re.match("^Samples per second.*:\\s*(\\d+).*", line)
             if m:
                 qps = m.group(1)
             m = re.match("Test Parameters Used.*", line)
             if m:
-                print("{},{},{},{},{},{},{},{},{},{}".format(
-                    args.name, now, args.machine, args.runtime, args.model,
-                    mode, qps, mean, latency_90, latency_99))
+                print(
+                    "{},{},{},{},{},{},{},{},{},{}".format(
+                        args.name,
+                        now,
+                        args.machine,
+                        args.runtime,
+                        args.model,
+                        mode,
+                        qps,
+                        mean,
+                        latency_90,
+                        latency_99,
+                    )
+                )
                 mode, mean, latency_90, latency_99, qps = None, 0, 0, 0, 0
 
 
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/resnet_save.py b/retired_benchmarks/vision/classification_and_detection/tools/resnet_save.py
index fca66ea269..0eaa0ef58a 100755
--- a/retired_benchmarks/vision/classification_and_detection/tools/resnet_save.py
+++ b/retired_benchmarks/vision/classification_and_detection/tools/resnet_save.py
@@ -43,256 +43,308 @@
 from official.utils.misc import model_helpers
 
 
-
-
 def image_bytes_serving_input_fn(image_shape, dtype=tf.float32):
-  """Serving input fn for raw jpeg images."""
-
-  def _preprocess_image(image_bytes):
-    """Preprocess a single raw image."""
-    # Bounding box around the whole image.
-    bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=dtype, shape=[1, 1, 4])
-    height, width, num_channels = image_shape
-    image = imagenet_preprocessing.preprocess_image(
-        image_bytes, bbox, height, width, num_channels, is_training=False)
-    return image
-
-  image_bytes_list = tf.placeholder(
-      shape=[None], dtype=tf.string, name='input_tensor')
-  images = tf.map_fn(
-      _preprocess_image, image_bytes_list, back_prop=False, dtype=dtype)
-  return tf.estimator.export.TensorServingInputReceiver(
-      images, {'image_bytes': image_bytes_list})
-
-
-
-
-def resnet_model_fn(features, labels, mode, model_class,
-                    resnet_size, weight_decay, learning_rate_fn, momentum,
-                    data_format, resnet_version, loss_scale,
-                    loss_filter_fn=None, dtype=resnet_model.DEFAULT_DTYPE,
-                    fine_tune=False):
-  """Shared functionality for different resnet model_fns.
-
-  Initializes the ResnetModel representing the model layers
-  and uses that model to build the necessary EstimatorSpecs for
-  the `mode` in question. For training, this means building losses,
-  the optimizer, and the train op that get passed into the EstimatorSpec.
-  For evaluation and prediction, the EstimatorSpec is returned without
-  a train op, but with the necessary parameters for the given mode.
-
-  Args:
-    features: tensor representing input images
-    labels: tensor representing class labels for all input images
-    mode: current estimator mode; should be one of
-      `tf.estimator.ModeKeys.TRAIN`, `EVALUATE`, `PREDICT`
-    model_class: a class representing a TensorFlow model that has a __call__
-      function. We assume here that this is a subclass of ResnetModel.
-    resnet_size: A single integer for the size of the ResNet model.
-    weight_decay: weight decay loss rate used to regularize learned variables.
-    learning_rate_fn: function that returns the current learning rate given
-      the current global_step
-    momentum: momentum term used for optimization
-    data_format: Input format ('channels_last', 'channels_first', or None).
-      If set to None, the format is dependent on whether a GPU is available.
-    resnet_version: Integer representing which version of the ResNet network to
-      use. See README for details. Valid values: [1, 2]
-    loss_scale: The factor to scale the loss for numerical stability. A detailed
-      summary is present in the arg parser help text.
-    loss_filter_fn: function that takes a string variable name and returns
-      True if the var should be included in loss calculation, and False
-      otherwise. If None, batch_normalization variables will be excluded
-      from the loss.
-    dtype: the TensorFlow dtype to use for calculations.
-    fine_tune: If True only train the dense layers(final layers).
-
-  Returns:
-    EstimatorSpec parameterized according to the input params and the
-    current mode.
-  """
-
-  model = model_class(resnet_size, data_format, resnet_version=resnet_version,
-                      dtype=dtype)
-
-  logits = model(features, mode == tf.estimator.ModeKeys.TRAIN)
-
-  # This acts as a no-op if the logits are already in fp32 (provided logits are
-  # not a SparseTensor). If dtype is is low precision, logits must be cast to
-  # fp32 for numerical stability.
-  logits = tf.cast(logits, tf.float32)
-
-  predictions = {
-      'classes': tf.argmax(logits, axis=1),
-      'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
-  }
-
-  if mode == tf.estimator.ModeKeys.PREDICT:
-    # Return the predictions and the specification for serving a SavedModel
+    """Serving input fn for raw jpeg images."""
+
+    def _preprocess_image(image_bytes):
+        """Preprocess a single raw image."""
+        # Bounding box around the whole image.
+        bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=dtype, shape=[1, 1, 4])
+        height, width, num_channels = image_shape
+        image = imagenet_preprocessing.preprocess_image(
+            image_bytes, bbox, height, width, num_channels, is_training=False
+        )
+        return image
+
+    image_bytes_list = tf.placeholder(
+        shape=[None], dtype=tf.string, name="input_tensor"
+    )
+    images = tf.map_fn(
+        _preprocess_image, image_bytes_list, back_prop=False, dtype=dtype
+    )
+    return tf.estimator.export.TensorServingInputReceiver(
+        images, {"image_bytes": image_bytes_list}
+    )
+
+
+def resnet_model_fn(
+    features,
+    labels,
+    mode,
+    model_class,
+    resnet_size,
+    weight_decay,
+    learning_rate_fn,
+    momentum,
+    data_format,
+    resnet_version,
+    loss_scale,
+    loss_filter_fn=None,
+    dtype=resnet_model.DEFAULT_DTYPE,
+    fine_tune=False,
+):
+    """Shared functionality for different resnet model_fns.
+
+    Initializes the ResnetModel representing the model layers
+    and uses that model to build the necessary EstimatorSpecs for
+    the `mode` in question. For training, this means building losses,
+    the optimizer, and the train op that get passed into the EstimatorSpec.
+    For evaluation and prediction, the EstimatorSpec is returned without
+    a train op, but with the necessary parameters for the given mode.
+
+    Args:
+      features: tensor representing input images
+      labels: tensor representing class labels for all input images
+      mode: current estimator mode; should be one of
+        `tf.estimator.ModeKeys.TRAIN`, `EVALUATE`, `PREDICT`
+      model_class: a class representing a TensorFlow model that has a __call__
+        function. We assume here that this is a subclass of ResnetModel.
+      resnet_size: A single integer for the size of the ResNet model.
+      weight_decay: weight decay loss rate used to regularize learned variables.
+      learning_rate_fn: function that returns the current learning rate given
+        the current global_step
+      momentum: momentum term used for optimization
+      data_format: Input format ('channels_last', 'channels_first', or None).
+        If set to None, the format is dependent on whether a GPU is available.
+      resnet_version: Integer representing which version of the ResNet network to
+        use. See README for details. Valid values: [1, 2]
+      loss_scale: The factor to scale the loss for numerical stability. A detailed
+        summary is present in the arg parser help text.
+      loss_filter_fn: function that takes a string variable name and returns
+        True if the var should be included in loss calculation, and False
+        otherwise. If None, batch_normalization variables will be excluded
+        from the loss.
+      dtype: the TensorFlow dtype to use for calculations.
+      fine_tune: If True only train the dense layers(final layers).
+
+    Returns:
+      EstimatorSpec parameterized according to the input params and the
+      current mode.
+    """
+
+    model = model_class(
+        resnet_size, data_format, resnet_version=resnet_version, dtype=dtype
+    )
+
+    logits = model(features, mode == tf.estimator.ModeKeys.TRAIN)
+
+    # This acts as a no-op if the logits are already in fp32 (provided logits are
+    # not a SparseTensor). If dtype is is low precision, logits must be cast to
+    # fp32 for numerical stability.
+    logits = tf.cast(logits, tf.float32)
+
+    predictions = {
+        "classes": tf.argmax(logits, axis=1),
+        "probabilities": tf.nn.softmax(logits, name="softmax_tensor"),
+    }
+
+    if mode == tf.estimator.ModeKeys.PREDICT:
+        # Return the predictions and the specification for serving a SavedModel
+        return tf.estimator.EstimatorSpec(
+            mode=mode,
+            predictions=predictions,
+            export_outputs={
+                "predict": tf.estimator.export.PredictOutput(predictions)},
+        )
+
+    # Calculate loss, which includes softmax cross entropy and L2
+    # regularization.
+    cross_entropy = tf.losses.sparse_softmax_cross_entropy(
+        logits=logits, labels=labels)
+
+    # Create a tensor named cross_entropy for logging purposes.
+    tf.identity(cross_entropy, name="cross_entropy")
+
+    # If no loss_filter_fn is passed, assume we want the default behavior,
+    # which is that batch_normalization variables are excluded from loss.
+    def exclude_batch_norm(name):
+        return "batch_normalization" not in name
+
+    loss_filter_fn = loss_filter_fn or exclude_batch_norm
+
+    # Add weight decay to the loss.
+    l2_loss = weight_decay * tf.add_n(
+        # loss is computed using fp32 for numerical stability.
+        [
+            tf.nn.l2_loss(tf.cast(v, tf.float32))
+            for v in tf.trainable_variables()
+            if loss_filter_fn(v.name)
+        ]
+    )
+    tf.summary.scalar("l2_loss", l2_loss)
+    loss = cross_entropy + l2_loss
+
     return tf.estimator.EstimatorSpec(
         mode=mode,
         predictions=predictions,
-        export_outputs={
-            'predict': tf.estimator.export.PredictOutput(predictions)
-        })
-
-  # Calculate loss, which includes softmax cross entropy and L2 regularization.
-  cross_entropy = tf.losses.sparse_softmax_cross_entropy(
-      logits=logits, labels=labels)
-
-  # Create a tensor named cross_entropy for logging purposes.
-  tf.identity(cross_entropy, name='cross_entropy')
-
-  # If no loss_filter_fn is passed, assume we want the default behavior,
-  # which is that batch_normalization variables are excluded from loss.
-  def exclude_batch_norm(name):
-    return 'batch_normalization' not in name
-  loss_filter_fn = loss_filter_fn or exclude_batch_norm
-
-  # Add weight decay to the loss.
-  l2_loss = weight_decay * tf.add_n(
-      # loss is computed using fp32 for numerical stability.
-      [tf.nn.l2_loss(tf.cast(v, tf.float32)) for v in tf.trainable_variables()
-       if loss_filter_fn(v.name)])
-  tf.summary.scalar('l2_loss', l2_loss)
-  loss = cross_entropy + l2_loss
-
-  return tf.estimator.EstimatorSpec(
-      mode=mode,
-      predictions=predictions,
-      loss=loss,
-      train_op=train_op,
-      eval_metric_ops=metrics)
-
-
-def resnet_main(
-    flags_obj, model_function, input_function, dataset_name, shape=None):
-  """Shared main loop for ResNet Models.
-
-  Args:
-    flags_obj: An object containing parsed flags. See define_resnet_flags()
-      for details.
-    model_function: the function that instantiates the Model and builds the
-      ops for train/eval. This will be passed directly into the estimator.
-    input_function: the function that processes the dataset and returns a
-      dataset that the estimator can train on. This will be wrapped with
-      all the relevant flags for running and passed to estimator.
-    dataset_name: the name of the dataset for training and evaluation. This is
-      used for logging purpose.
-    shape: list of ints representing the shape of the images used for training.
-      This is only used if flags_obj.export_dir is passed.
-  """
-
-  print("RESNET MAIN")
-  model_helpers.apply_clean(flags.FLAGS)
-
-  # Ensures flag override logic is only executed if explicitly triggered.
-  if flags_obj.tf_gpu_thread_mode:
-    override_flags_and_set_envars_for_gpu_thread_pool(flags_obj)
-
-  # Creates session config. allow_soft_placement = True, is required for
-  # multi-GPU and is not harmful for other modes.
-  session_config = tf.ConfigProto(allow_soft_placement=True)
-
-  run_config = tf.estimator.RunConfig(
-      session_config=session_config,
-      save_checkpoints_secs=60*60*24)
-
-  # Initializes model with all but the dense layer from pretrained ResNet.
-  if flags_obj.pretrained_model_checkpoint_path is not None:
-    warm_start_settings = tf.estimator.WarmStartSettings(
-        flags_obj.pretrained_model_checkpoint_path,
-        vars_to_warm_start='^(?!.*dense)')
-  else:
-    warm_start_settings = None
-
-  classifier = tf.estimator.Estimator(
-      model_fn=model_function, model_dir=flags_obj.model_dir, config=run_config,
-      warm_start_from=warm_start_settings, params={
-          'resnet_size': int(flags_obj.resnet_size),
-          'data_format': flags_obj.data_format,
-          'batch_size': flags_obj.batch_size,
-          'resnet_version': int(flags_obj.resnet_version),
-          'loss_scale': flags_core.get_loss_scale(flags_obj),
-          'dtype': flags_core.get_tf_dtype(flags_obj),
-          'fine_tune': flags_obj.fine_tune
-      })
-
-  run_params = {
-      'batch_size': flags_obj.batch_size,
-      'dtype': flags_core.get_tf_dtype(flags_obj),
-      'resnet_size': flags_obj.resnet_size,
-      'resnet_version': flags_obj.resnet_version,
-      'synthetic_data': flags_obj.use_synthetic_data,
-      'train_epochs': flags_obj.train_epochs,
-  }
-
-  def input_fn_eval():
-    return input_function(
-        is_training=False,
-        data_dir=flags_obj.data_dir,
-        batch_size=distribution_utils.per_device_batch_size(
-            flags_obj.batch_size, flags_core.get_num_gpus(flags_obj)),
-        num_epochs=1,
-        dtype=flags_core.get_tf_dtype(flags_obj))
-
-  schedule, n_loops = [0], 1
-  if flags_obj.export_dir is not None:
-    # Exports a saved model for the given classifier.
-    export_dtype = flags_core.get_tf_dtype(flags_obj)
-    if flags_obj.image_bytes_as_serving_input:
-      input_receiver_fn = functools.partial(
-          image_bytes_serving_input_fn, shape, dtype=export_dtype)
+        loss=loss,
+        train_op=train_op,
+        eval_metric_ops=metrics,
+    )
+
+
+def resnet_main(flags_obj, model_function,
+                input_function, dataset_name, shape=None):
+    """Shared main loop for ResNet Models.
+
+    Args:
+      flags_obj: An object containing parsed flags. See define_resnet_flags()
+        for details.
+      model_function: the function that instantiates the Model and builds the
+        ops for train/eval. This will be passed directly into the estimator.
+      input_function: the function that processes the dataset and returns a
+        dataset that the estimator can train on. This will be wrapped with
+        all the relevant flags for running and passed to estimator.
+      dataset_name: the name of the dataset for training and evaluation. This is
+        used for logging purpose.
+      shape: list of ints representing the shape of the images used for training.
+        This is only used if flags_obj.export_dir is passed.
+    """
+
+    print("RESNET MAIN")
+    model_helpers.apply_clean(flags.FLAGS)
+
+    # Ensures flag override logic is only executed if explicitly triggered.
+    if flags_obj.tf_gpu_thread_mode:
+        override_flags_and_set_envars_for_gpu_thread_pool(flags_obj)
+
+    # Creates session config. allow_soft_placement = True, is required for
+    # multi-GPU and is not harmful for other modes.
+    session_config = tf.ConfigProto(allow_soft_placement=True)
+
+    run_config = tf.estimator.RunConfig(
+        session_config=session_config, save_checkpoints_secs=60 * 60 * 24
+    )
+
+    # Initializes model with all but the dense layer from pretrained ResNet.
+    if flags_obj.pretrained_model_checkpoint_path is not None:
+        warm_start_settings = tf.estimator.WarmStartSettings(
+            flags_obj.pretrained_model_checkpoint_path,
+            vars_to_warm_start="^(?!.*dense)",
+        )
     else:
-      input_receiver_fn = export.build_tensor_serving_input_receiver_fn(
-          shape, batch_size=flags_obj.batch_size, dtype=export_dtype)
-    classifier.export_savedmodel(flags_obj.export_dir, input_receiver_fn,
-                                 strip_default_attrs=True)
+        warm_start_settings = None
+
+    classifier = tf.estimator.Estimator(
+        model_fn=model_function,
+        model_dir=flags_obj.model_dir,
+        config=run_config,
+        warm_start_from=warm_start_settings,
+        params={
+            "resnet_size": int(flags_obj.resnet_size),
+            "data_format": flags_obj.data_format,
+            "batch_size": flags_obj.batch_size,
+            "resnet_version": int(flags_obj.resnet_version),
+            "loss_scale": flags_core.get_loss_scale(flags_obj),
+            "dtype": flags_core.get_tf_dtype(flags_obj),
+            "fine_tune": flags_obj.fine_tune,
+        },
+    )
+
+    run_params = {
+        "batch_size": flags_obj.batch_size,
+        "dtype": flags_core.get_tf_dtype(flags_obj),
+        "resnet_size": flags_obj.resnet_size,
+        "resnet_version": flags_obj.resnet_version,
+        "synthetic_data": flags_obj.use_synthetic_data,
+        "train_epochs": flags_obj.train_epochs,
+    }
+
+    def input_fn_eval():
+        return input_function(
+            is_training=False,
+            data_dir=flags_obj.data_dir,
+            batch_size=distribution_utils.per_device_batch_size(
+                flags_obj.batch_size, flags_core.get_num_gpus(flags_obj)
+            ),
+            num_epochs=1,
+            dtype=flags_core.get_tf_dtype(flags_obj),
+        )
+
+    schedule, n_loops = [0], 1
+    if flags_obj.export_dir is not None:
+        # Exports a saved model for the given classifier.
+        export_dtype = flags_core.get_tf_dtype(flags_obj)
+        if flags_obj.image_bytes_as_serving_input:
+            input_receiver_fn = functools.partial(
+                image_bytes_serving_input_fn, shape, dtype=export_dtype
+            )
+        else:
+            input_receiver_fn = export.build_tensor_serving_input_receiver_fn(
+                shape, batch_size=flags_obj.batch_size, dtype=export_dtype
+            )
+        classifier.export_savedmodel(
+            flags_obj.export_dir, input_receiver_fn, strip_default_attrs=True
+        )
 
 
 def define_resnet_flags(resnet_size_choices=None):
-  """Add flags and validators for ResNet."""
-  flags_core.define_base()
-  flags_core.define_performance(num_parallel_calls=False,
-                                tf_gpu_thread_mode=True,
-                                datasets_num_private_threads=True,
-                                datasets_num_parallel_batches=True)
-  flags_core.define_image()
-  flags_core.define_benchmark()
-  flags.adopt_module_key_flags(flags_core)
-
-  flags.DEFINE_enum(
-      name='resnet_version', short_name='rv', default='1',
-      enum_values=['1', '2'],
-      help=flags_core.help_wrap(
-          'Version of ResNet. (1 or 2) See README.md for details.'))
-  flags.DEFINE_bool(
-      name='fine_tune', short_name='ft', default=False,
-      help=flags_core.help_wrap(
-          'If True do not train any parameters except for the final layer.'))
-  flags.DEFINE_string(
-      name='pretrained_model_checkpoint_path', short_name='pmcp', default=None,
-      help=flags_core.help_wrap(
-          'If not None initialize all the network except the final layer with '
-          'these values'))
-  flags.DEFINE_boolean(
-      name='eval_only', default=False,
-      help=flags_core.help_wrap('Skip training and only perform evaluation on '
-                                'the latest checkpoint.'))
-  flags.DEFINE_boolean(
-      name='image_bytes_as_serving_input', default=False,
-      help=flags_core.help_wrap(
-          'If True exports savedmodel with serving signature that accepts '
-          'JPEG image bytes instead of a fixed size [HxWxC] tensor that '
-          'represents the image. The former is easier to use for serving at '
-          'the expense of image resize/cropping being done as part of model '
-          'inference. Note, this flag only applies to ImageNet and cannot '
-          'be used for CIFAR.'))
-
-  choice_kwargs = dict(
-      name='resnet_size', short_name='rs', default='50',
-      help=flags_core.help_wrap('The size of the ResNet model to use.'))
-
-  if resnet_size_choices is None:
-    flags.DEFINE_string(**choice_kwargs)
-  else:
-    flags.DEFINE_enum(enum_values=resnet_size_choices, **choice_kwargs)
+    """Add flags and validators for ResNet."""
+    flags_core.define_base()
+    flags_core.define_performance(
+        num_parallel_calls=False,
+        tf_gpu_thread_mode=True,
+        datasets_num_private_threads=True,
+        datasets_num_parallel_batches=True,
+    )
+    flags_core.define_image()
+    flags_core.define_benchmark()
+    flags.adopt_module_key_flags(flags_core)
+
+    flags.DEFINE_enum(
+        name="resnet_version",
+        short_name="rv",
+        default="1",
+        enum_values=["1", "2"],
+        help=flags_core.help_wrap(
+            "Version of ResNet. (1 or 2) See README.md for details."
+        ),
+    )
+    flags.DEFINE_bool(
+        name="fine_tune",
+        short_name="ft",
+        default=False,
+        help=flags_core.help_wrap(
+            "If True do not train any parameters except for the final layer."
+        ),
+    )
+    flags.DEFINE_string(
+        name="pretrained_model_checkpoint_path",
+        short_name="pmcp",
+        default=None,
+        help=flags_core.help_wrap(
+            "If not None initialize all the network except the final layer with "
+            "these values"
+        ),
+    )
+    flags.DEFINE_boolean(
+        name="eval_only",
+        default=False,
+        help=flags_core.help_wrap(
+            "Skip training and only perform evaluation on " "the latest checkpoint."
+        ),
+    )
+    flags.DEFINE_boolean(
+        name="image_bytes_as_serving_input",
+        default=False,
+        help=flags_core.help_wrap(
+            "If True exports savedmodel with serving signature that accepts "
+            "JPEG image bytes instead of a fixed size [HxWxC] tensor that "
+            "represents the image. The former is easier to use for serving at "
+            "the expense of image resize/cropping being done as part of model "
+            "inference. Note, this flag only applies to ImageNet and cannot "
+            "be used for CIFAR."
+        ),
+    )
+
+    choice_kwargs = dict(
+        name="resnet_size",
+        short_name="rs",
+        default="50",
+        help=flags_core.help_wrap("The size of the ResNet model to use."),
+    )
+
+    if resnet_size_choices is None:
+        flags.DEFINE_string(**choice_kwargs)
+    else:
+        flags.DEFINE_enum(enum_values=resnet_size_choices, **choice_kwargs)
diff --git a/retired_benchmarks/vision/classification_and_detection/tools/ssd-nhwc.py b/retired_benchmarks/vision/classification_and_detection/tools/ssd-nhwc.py
index f255fd965f..d7ef9599e5 100644
--- a/retired_benchmarks/vision/classification_and_detection/tools/ssd-nhwc.py
+++ b/retired_benchmarks/vision/classification_and_detection/tools/ssd-nhwc.py
@@ -4,33 +4,39 @@
 import tensorflow as tf
 from tensorflow.core.framework import graph_pb2
 
+
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument('pbfile')
+    parser.add_argument("pbfile")
     return parser.parse_args()
 
+
 def insert_transpose(graph, a, b, to_nchw):
     if not isinstance(b, list):
         b = [b]
     trans_perm = graph.node.add()
-    trans_perm.name = a.name + '/transpose/perm'
-    trans_perm.op = 'Const'
-    trans_perm.attr['dtype'].type = 3 # DT_INT32
-    trans_perm.attr['value'].tensor.dtype = 3 # DT_INT32
-    trans_perm.attr['value'].tensor.tensor_shape.dim.add()
-    trans_perm.attr['value'].tensor.tensor_shape.dim[0].size = 4
+    trans_perm.name = a.name + "/transpose/perm"
+    trans_perm.op = "Const"
+    trans_perm.attr["dtype"].type = 3  # DT_INT32
+    trans_perm.attr["value"].tensor.dtype = 3  # DT_INT32
+    trans_perm.attr["value"].tensor.tensor_shape.dim.add()
+    trans_perm.attr["value"].tensor.tensor_shape.dim[0].size = 4
     if to_nchw:
-        trans_perm.attr['value'].tensor.tensor_content = b'\000\000\000\000\003\000\000\000\001\000\000\000\002\000\000\000'
+        trans_perm.attr["value"].tensor.tensor_content = (
+            b"\000\000\000\000\003\000\000\000\001\000\000\000\002\000\000\000"
+        )
     else:
-        trans_perm.attr['value'].tensor.tensor_content = b'\000\000\000\000\002\000\000\000\003\000\000\000\001\000\000\000'
-    
+        trans_perm.attr["value"].tensor.tensor_content = (
+            b"\000\000\000\000\002\000\000\000\003\000\000\000\001\000\000\000"
+        )
+
     trans = graph.node.add()
-    trans.name = a.name + '/transpose'
-    trans.op = 'Transpose'
+    trans.name = a.name + "/transpose"
+    trans.op = "Transpose"
     trans.input.append(a.name)
     trans.input.append(trans_perm.name)
-    trans.attr['T'].type = 1
-    trans.attr['Tperm'].type = 3
+    trans.attr["T"].type = 1
+    trans.attr["Tperm"].type = 3
 
     for n in b:
         inputs = []
@@ -45,6 +51,7 @@ def insert_transpose(graph, a, b, to_nchw):
         for i in range(0, cnt):
             n.input.append(inputs[i])
 
+
 def convert_list_nhwc(l):
     c = l.i[1]
     h = l.i[2]
@@ -52,102 +59,116 @@ def convert_list_nhwc(l):
     l.i[1] = h
     l.i[2] = w
     l.i[3] = c
-    
+
+
 def convert_conv_nhwc(node_conv):
-    node_conv.attr['data_format'].s = b'NHWC'
-    convert_list_nhwc(node_conv.attr['dilations'].list)
-    convert_list_nhwc(node_conv.attr['strides'].list)
+    node_conv.attr["data_format"].s = b"NHWC"
+    convert_list_nhwc(node_conv.attr["dilations"].list)
+    convert_list_nhwc(node_conv.attr["strides"].list)
+
 
 def convert_general_nhwc(node):
-    node.attr['data_format'].s = b'NHWC'
+    node.attr["data_format"].s = b"NHWC"
+
 
 def convert_mp_nhwc(node_mp):
-    node_mp.attr['data_format'].s = b'NHWC'
-    convert_list_nhwc(node_mp.attr['ksize'].list)
-    convert_list_nhwc(node_mp.attr['strides'].list)
+    node_mp.attr["data_format"].s = b"NHWC"
+    convert_list_nhwc(node_mp.attr["ksize"].list)
+    convert_list_nhwc(node_mp.attr["strides"].list)
+
 
 def convert_image_nhwc(node_image):
-    c = node_image.attr['shape'].shape.dim[1].size
-    del node_image.attr['shape'].shape.dim[1]
-    d = node_image.attr['shape'].shape.dim.add()
+    c = node_image.attr["shape"].shape.dim[1].size
+    del node_image.attr["shape"].shape.dim[1]
+    d = node_image.attr["shape"].shape.dim.add()
     d.size = c
 
+
 def init_node(n):
     node = {}
-    node['node'] = n
-    node['inputs'] = []
-    node['outputs'] = []
+    node["node"] = n
+    node["inputs"] = []
+    node["outputs"] = []
     return node
 
+
 def connect_nodes(n1, n2):
-    if n2['node'].name not in n1['outputs']:
-        n1['outputs'].append(n2['node'].name)
-        n2['inputs'].append(n1['node'].name)
+    if n2["node"].name not in n1["outputs"]:
+        n1["outputs"].append(n2["node"].name)
+        n2["inputs"].append(n1["node"].name)
     else:
-        print('{} -> {} already connected'.format(n1['node'].name, n2['node'].name))
+        print(
+            "{} -> {} already connected".format(n1["node"].name, n2["node"].name))
+
 
 def disconnect_nodes(n1, n2):
-    if n1['node'].name not in n2['inputs'] or n2['node'].name not in n1['outputs']:
-        print('{} -> {} not connected'.format(n1['node'].name, n2['node'].name))
-    for i in range(0, len(n1['outputs'])):
-        if n1['outputs'][i] == n2['node'].name:
-            del n1['outputs'][i]
+    if n1["node"].name not in n2["inputs"] or n2["node"].name not in n1["outputs"]:
+        print(
+            "{} -> {} not connected".format(n1["node"].name, n2["node"].name))
+    for i in range(0, len(n1["outputs"])):
+        if n1["outputs"][i] == n2["node"].name:
+            del n1["outputs"][i]
             break
-    for i in range(0, len(n2['inputs'])):
-        if n2['inputs'][i] == n1['node'].name:
-            del n2['inputs'][i]
+    for i in range(0, len(n2["inputs"])):
+        if n2["inputs"][i] == n1["node"].name:
+            del n2["inputs"][i]
             break
-            
+
+
 def build_graph(graph):
     node_map = {}
     for n in graph.node:
         node = init_node(n)
         node_map[n.name] = node
     for n in node_map:
-        for i in node_map[n]['node'].input:
-            if ':' in i:
-                i = i[:i.find(':')]
-            i = i.lstrip('^')
+        for i in node_map[n]["node"].input:
+            if ":" in i:
+                i = i[: i.find(":")]
+            i = i.lstrip("^")
             if i not in node_map:
-                print('node {} not found'.format(i))
+                print("node {} not found".format(i))
             else:
                 connect_nodes(node_map[i], node_map[n])
     return node_map
 
+
 def trim_const_from_graph(node_map):
     trim_list = []
     for n in node_map:
-        if node_map[n]['node'].op == 'Const':
+        if node_map[n]["node"].op == "Const":
             trim_list.append(n)
     for n in trim_list:
-        print('trimming {}'.format(n))
-        for o in node_map[n]['outputs']:
+        print("trimming {}".format(n))
+        for o in node_map[n]["outputs"]:
             disconnect_nodes(node_map[n], node_map[o])
         del node_map[n]
 
     trim_list = []
     for n in node_map:
-        if node_map[n]['node'].op == 'Identity' and len(node_map[n]['inputs']) == 0:
+        if node_map[n]["node"].op == "Identity" and len(
+                node_map[n]["inputs"]) == 0:
             trim_list.append(n)
     for n in trim_list:
-        print('trimming {}'.format(n))
-        for o in node_map[n]['outputs']:
+        print("trimming {}".format(n))
+        for o in node_map[n]["outputs"]:
             disconnect_nodes(node_map[n], node_map[o])
         del node_map[n]
 
 
 def all_input_in_nhwc(n, node_map, nhwc_nodes):
-    for i in node_map[n]['inputs']:
+    for i in node_map[n]["inputs"]:
         if i not in nhwc_nodes:
             return False
     return True
 
+
 def all_output_in_nhwc(n, node_map, nhwc_nodes):
-    for o in node_map[n]['outputs']:
+    for o in node_map[n]["outputs"]:
         if o not in nhwc_nodes:
             return False
     return True
 
+
 def find_nhwc_region(node_map):
     transpose_nhwc_nodes = {}
     transpose_nchw_nodes = {}
@@ -156,7 +177,7 @@ def find_nhwc_region(node_map):
     transpose_nhwc_nodes_append_list = []
     transpose_nchw_nodes_append_list = []
     for n in node_map:
-        if node_map[n]['node'].op == 'Conv2D':
+        if node_map[n]["node"].op == "Conv2D":
             transpose_nhwc_nodes_append_list.append(n)
             transpose_nchw_nodes_append_list.append(n)
             nhwc_nodes.append(n)
@@ -168,12 +189,18 @@ def find_nhwc_region(node_map):
             transpose_nchw_nodes[n] = 1
 
     prev_cnt_nhwc_nodes = len(nhwc_nodes)
-    nhwc_op_list = ['Conv2D', 'Relu', 'FusedBatchNorm', 'MaxPool', 'BiasAdd', 'Add']
+    nhwc_op_list = [
+        "Conv2D",
+        "Relu",
+        "FusedBatchNorm",
+        "MaxPool",
+        "BiasAdd",
+        "Add"]
     while True:
         transpose_nchw_nodes_append_list = []
         for n in transpose_nchw_nodes:
-            for o in node_map[n]['outputs']:
-                if o not in nhwc_nodes and node_map[o]['node'].op in nhwc_op_list:
+            for o in node_map[n]["outputs"]:
+                if o not in nhwc_nodes and node_map[o]["node"].op in nhwc_op_list:
                     if all_input_in_nhwc(o, node_map, nhwc_nodes):
                         nhwc_nodes.append(o)
                         if o not in transpose_nchw_nodes_append_list:
@@ -182,15 +209,19 @@ def find_nhwc_region(node_map):
         transpose_nhwc_nodes_remove_list = []
         transpose_nchw_nodes_remove_list = []
         for n in transpose_nhwc_nodes:
-            if (all_input_in_nhwc(n, node_map, nhwc_nodes) and
-                n not in transpose_nhwc_nodes_remove_list):
+            if (
+                all_input_in_nhwc(n, node_map, nhwc_nodes)
+                and n not in transpose_nhwc_nodes_remove_list
+            ):
                 transpose_nhwc_nodes_remove_list.append(n)
         for n in transpose_nhwc_nodes_remove_list:
             del transpose_nhwc_nodes[n]
 
         for n in transpose_nchw_nodes:
-            if (all_output_in_nhwc(n, node_map, nhwc_nodes) and
-                n not in transpose_nchw_nodes_remove_list):
+            if (
+                all_output_in_nhwc(n, node_map, nhwc_nodes)
+                and n not in transpose_nchw_nodes_remove_list
+            ):
                 transpose_nchw_nodes_remove_list.append(n)
         for n in transpose_nchw_nodes_remove_list:
             del transpose_nchw_nodes[n]
@@ -202,66 +233,81 @@ def find_nhwc_region(node_map):
         if len(nhwc_nodes) == prev_cnt_nhwc_nodes:
             break
         prev_cnt_nhwc_nodes = len(nhwc_nodes)
-                        
-    print('\n\nTranspose to NHWC at nodes:')
+
+    print("\n\nTranspose to NHWC at nodes:")
     for n in transpose_nhwc_nodes:
-        print('    {}'.format(n))
-    
-    print('\n\nTranspose to NCHW at nodes:')
+        print("    {}".format(n))
+
+    print("\n\nTranspose to NCHW at nodes:")
     for n in transpose_nchw_nodes:
-        print('    {}'.format(n))
-    
+        print("    {}".format(n))
+
     return nhwc_nodes, transpose_nhwc_nodes, transpose_nchw_nodes
 
+
 def main():
     args = get_args()
 
     graph = graph_pb2.GraphDef()
-    with open(args.pbfile, 'rb') as f:
+    with open(args.pbfile, "rb") as f:
         graph.ParseFromString(f.read())
 
     node_map = build_graph(graph)
     trim_const_from_graph(node_map)
 
-    nhwc_nodes, transpose_nhwc_nodes, transpose_nchw_nodes = find_nhwc_region(node_map)
+    nhwc_nodes, transpose_nhwc_nodes, transpose_nchw_nodes = find_nhwc_region(
+        node_map)
 
-    nhwc_op_list = ['Conv2D', 'Relu', 'FusedBatchNorm', 'MaxPool', 'BiasAdd', 'Add']
+    nhwc_op_list = [
+        "Conv2D",
+        "Relu",
+        "FusedBatchNorm",
+        "MaxPool",
+        "BiasAdd",
+        "Add"]
     for n in nhwc_nodes:
-        if node_map[n]['node'].op == 'Conv2D':
-            convert_conv_nhwc(node_map[n]['node'])
-        elif node_map[n]['node'].op in ['FusedBatchNorm', 'BiasAdd']:
-            convert_general_nhwc(node_map[n]['node'])
-        elif node_map[n]['node'].op == 'MaxPool':
-            convert_mp_nhwc(node_map[n]['node'])
-      
+        if node_map[n]["node"].op == "Conv2D":
+            convert_conv_nhwc(node_map[n]["node"])
+        elif node_map[n]["node"].op in ["FusedBatchNorm", "BiasAdd"]:
+            convert_general_nhwc(node_map[n]["node"])
+        elif node_map[n]["node"].op == "MaxPool":
+            convert_mp_nhwc(node_map[n]["node"])
+
     done_nhwc = False
     if len(transpose_nhwc_nodes) == 1:
         for n in transpose_nhwc_nodes:
-            if len(node_map[n]['inputs']) == 1 and node_map[n]['inputs'][0] == 'image':
+            if len(node_map[n]["inputs"]
+                   ) == 1 and node_map[n]["inputs"][0] == "image":
                 image_outputs = []
-                for o in node_map['image']['outputs']:
+                for o in node_map["image"]["outputs"]:
                     if o != n:
-                        image_outputs.append(node_map[o]['node'])
-                insert_transpose(graph, node_map['image']['node'], image_outputs, True)
-                convert_image_nhwc(node_map['image']['node'])
+                        image_outputs.append(node_map[o]["node"])
+                insert_transpose(
+                    graph,
+                    node_map["image"]["node"],
+                    image_outputs,
+                    True)
+                convert_image_nhwc(node_map["image"]["node"])
                 done_nhwc = True
 
     if not done_nhwc:
         for n in transpose_nhwc_nodes:
-            for i in node_map[n]['inputs']:
+            for i in node_map[n]["inputs"]:
                 if i not in nhwc_nodes:
-                    insert_transpose(graph, node_map[i]['node'], node_map[n]['node'], False)
+                    insert_transpose(
+                        graph, node_map[i]["node"], node_map[n]["node"], False
+                    )
 
     for n in transpose_nchw_nodes:
         node_outputs = []
-        for o in node_map[n]['outputs']:
+        for o in node_map[n]["outputs"]:
             if o not in nhwc_nodes:
-                node_outputs.append(node_map[o]['node'])
-        insert_transpose(graph, node_map[n]['node'], node_outputs, True)
+                node_outputs.append(node_map[o]["node"])
+        insert_transpose(graph, node_map[n]["node"], node_outputs, True)
 
-    with open(args.pbfile+'.patch', 'wb') as f:
+    with open(args.pbfile + ".patch", "wb") as f:
         f.write(graph.SerializeToString())
 
-if __name__ == '__main__':
-    main()
 
+if __name__ == "__main__":
+    main()
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Task043_BraTS_2019.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Task043_BraTS_2019.py
index 6c196077bd..d75a1e2794 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Task043_BraTS_2019.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/Task043_BraTS_2019.py
@@ -14,7 +14,8 @@
 #    limitations under the License.
 
 # This file is copied from nnUnet/nnunet/dataset_conversion/Task043_BraTS_2019.py, except that
-# the validation/test set part is removed and downloaded_data_dir is now configurable.
+# the validation/test set part is removed and downloaded_data_dir is now
+# configurable.
 
 import argparse
 import numpy as np
@@ -24,27 +25,37 @@
 
 sys.path.insert(0, os.path.join(os.getcwd(), "nnUnet"))
 
-from batchgenerators.utilities.file_and_folder_operations import *
-from nnunet.paths import nnUNet_raw_data
-import SimpleITK as sitk
-import shutil
+try:
+    import shutil
+    import SimpleITK as sitk
+    from nnunet.paths import nnUNet_raw_data
+    from batchgenerators.utilities.file_and_folder_operations import *
+except BaseException:
+    raise Exception("Error importing local modules")
+
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--downloaded_data_dir", default="build/MICCAI_BraTS_2019_Data_Training", help="path to MICCAI_BraTS_2019_Data_Training")
+    parser.add_argument(
+        "--downloaded_data_dir",
+        default="build/MICCAI_BraTS_2019_Data_Training",
+        help="path to MICCAI_BraTS_2019_Data_Training",
+    )
     args = parser.parse_args()
     return args
 
+
 def copy_BraTS_segmentation_and_convert_labels(in_file, out_file):
     # use this for segmentation only!!!
-    # nnUNet wants the labels to be continuous. BraTS is 0, 1, 2, 4 -> we make that into 0, 1, 2, 3
+    # nnUNet wants the labels to be continuous. BraTS is 0, 1, 2, 4 -> we make
+    # that into 0, 1, 2, 3
     img = sitk.ReadImage(in_file)
     img_npy = sitk.GetArrayFromImage(img)
 
     uniques = np.unique(img_npy)
     for u in uniques:
         if u not in [0, 1, 2, 4]:
-            raise RuntimeError('unexpected label')
+            raise RuntimeError("unexpected label")
 
     seg_new = np.zeros_like(img_npy)
     seg_new[img_npy == 4] = 3
@@ -54,6 +65,7 @@ def copy_BraTS_segmentation_and_convert_labels(in_file, out_file):
     img_corr.CopyInformation(img)
     sitk.WriteImage(img_corr, out_file)
 
+
 def main():
 
     args = get_args()
@@ -89,47 +101,65 @@ def main():
             flair = join(patdir, p + "_flair.nii.gz")
             seg = join(patdir, p + "_seg.nii.gz")
 
-            assert all([
-                isfile(t1),
-                isfile(t1c),
-                isfile(t2),
-                isfile(flair),
-                isfile(seg)
-            ]), "%s" % patient_name
-
-            shutil.copy(t1, join(target_imagesTr, patient_name + "_0000.nii.gz"))
-            shutil.copy(t1c, join(target_imagesTr, patient_name + "_0001.nii.gz"))
-            shutil.copy(t2, join(target_imagesTr, patient_name + "_0002.nii.gz"))
-            shutil.copy(flair, join(target_imagesTr, patient_name + "_0003.nii.gz"))
-
-            copy_BraTS_segmentation_and_convert_labels(seg, join(target_labelsTr, patient_name + ".nii.gz"))
+            assert all(
+                [isfile(t1), isfile(t1c), isfile(t2),
+                 isfile(flair), isfile(seg)]
+            ), ("%s" % patient_name)
+
+            shutil.copy(
+                t1,
+                join(
+                    target_imagesTr,
+                    patient_name +
+                    "_0000.nii.gz"))
+            shutil.copy(
+                t1c,
+                join(
+                    target_imagesTr,
+                    patient_name +
+                    "_0001.nii.gz"))
+            shutil.copy(
+                t2,
+                join(
+                    target_imagesTr,
+                    patient_name +
+                    "_0002.nii.gz"))
+            shutil.copy(
+                flair,
+                join(
+                    target_imagesTr,
+                    patient_name +
+                    "_0003.nii.gz"))
+
+            copy_BraTS_segmentation_and_convert_labels(
+                seg, join(target_labelsTr, patient_name + ".nii.gz")
+            )
 
     json_dict = OrderedDict()
-    json_dict['name'] = "BraTS2019"
-    json_dict['description'] = "nothing"
-    json_dict['tensorImageSize'] = "4D"
-    json_dict['reference'] = "see BraTS2019"
-    json_dict['licence'] = "see BraTS2019 license"
-    json_dict['release'] = "0.0"
-    json_dict['modality'] = {
-        "0": "T1",
-        "1": "T1ce",
-        "2": "T2",
-        "3": "FLAIR"
-    }
-    json_dict['labels'] = {
+    json_dict["name"] = "BraTS2019"
+    json_dict["description"] = "nothing"
+    json_dict["tensorImageSize"] = "4D"
+    json_dict["reference"] = "see BraTS2019"
+    json_dict["licence"] = "see BraTS2019 license"
+    json_dict["release"] = "0.0"
+    json_dict["modality"] = {"0": "T1", "1": "T1ce", "2": "T2", "3": "FLAIR"}
+    json_dict["labels"] = {
         "0": "background",
         "1": "edema",
         "2": "non-enhancing",
         "3": "enhancing",
     }
-    json_dict['numTraining'] = len(patient_names)
-    json_dict['numTest'] = 0
-    json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in
-                             patient_names]
-    json_dict['test'] = []
+    json_dict["numTraining"] = len(patient_names)
+    json_dict["numTest"] = 0
+    json_dict["training"] = [
+        {"image": "./imagesTr/%s.nii.gz" %
+            i, "label": "./labelsTr/%s.nii.gz" % i}
+        for i in patient_names
+    ]
+    json_dict["test"] = []
 
     save_json(json_dict, join(target_base, "dataset.json"))
 
+
 if __name__ == "__main__":
     main()
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/accuracy-brats.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/accuracy-brats.py
index 82c9ce6c7e..722a3273f3 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/accuracy-brats.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/accuracy-brats.py
@@ -23,9 +23,18 @@
 
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), "nnUnet"))
 
-from multiprocessing import Pool
-from nnunet.evaluation.region_based_evaluation import evaluate_regions, get_brats_regions
-from nnunet.inference.segmentation_export import save_segmentation_nifti_from_softmax
+try:
+    from nnunet.inference.segmentation_export import (
+        save_segmentation_nifti_from_softmax,
+    )
+    from nnunet.evaluation.region_based_evaluation import (
+        evaluate_regions,
+        get_brats_regions,
+    )
+    from multiprocessing import Pool
+except BaseException:
+    raise Exception("Error importing local modules")
+
 
 dtype_map = {
     "int8": np.int8,
@@ -34,43 +43,101 @@
     "int64": np.int64,
     "float16": np.float16,
     "float32": np.float32,
-    "float64": np.float64
+    "float64": np.float64,
 }
 
+
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--log_file", default="build/logs/mlperf_log_accuracy.json", help="Path to accuracy log json file")
-    parser.add_argument("--output_dtype", default="float16", choices=dtype_map.keys(), help="Output data type")
-    parser.add_argument("--preprocessed_data_dir", default="build/preprocessed_data", help="Path to the directory containing preprocessed data")
-    parser.add_argument("--postprocessed_data_dir", default="build/postprocessed_data", help="Path to the directory containing postprocessed data")
-    parser.add_argument("--label_data_dir", default="build/raw_data/nnUNet_raw_data/Task043_BraTS2019/labelsTr",
-        help="Path to the directory containing ground truth labels")
-    parser.add_argument("--num_threads_nifti_save", type=int, default=12, help="Number of threads to run the postprocessing with")
+    parser.add_argument(
+        "--log_file",
+        default="build/logs/mlperf_log_accuracy.json",
+        help="Path to accuracy log json file",
+    )
+    parser.add_argument(
+        "--output_dtype",
+        default="float16",
+        choices=dtype_map.keys(),
+        help="Output data type",
+    )
+    parser.add_argument(
+        "--preprocessed_data_dir",
+        default="build/preprocessed_data",
+        help="Path to the directory containing preprocessed data",
+    )
+    parser.add_argument(
+        "--postprocessed_data_dir",
+        default="build/postprocessed_data",
+        help="Path to the directory containing postprocessed data",
+    )
+    parser.add_argument(
+        "--label_data_dir",
+        default="build/raw_data/nnUNet_raw_data/Task043_BraTS2019/labelsTr",
+        help="Path to the directory containing ground truth labels",
+    )
+    parser.add_argument(
+        "--num_threads_nifti_save",
+        type=int,
+        default=12,
+        help="Number of threads to run the postprocessing with",
+    )
     args = parser.parse_args()
     return args
 
-def save_predictions_MLPerf(predictions, output_folder, output_files, dictionaries, num_threads_nifti_save, all_in_gpu, force_separate_z=None, interp_order=3, interp_order_z=0):
+
+def save_predictions_MLPerf(
+    predictions,
+    output_folder,
+    output_files,
+    dictionaries,
+    num_threads_nifti_save,
+    all_in_gpu,
+    force_separate_z=None,
+    interp_order=3,
+    interp_order_z=0,
+):
     print("Saving predictions...")
     pool = Pool(num_threads_nifti_save)
     results = []
     for i, output_filename in enumerate(output_files):
         print(i, "/", len(output_files))
-        output_filename = os.path.join(output_folder, output_filename + ".nii.gz")
+        output_filename = os.path.join(
+            output_folder, output_filename + ".nii.gz")
         softmax_mean = predictions[i]
         dct = dictionaries[i]
         bytes_per_voxel = 4
         if all_in_gpu:
-            bytes_per_voxel = 2  # if all_in_gpu then the return value is half (float16)
-        if np.prod(softmax_mean.shape) > (2e9 / bytes_per_voxel * 0.85):  # * 0.85 just to be save
+            # if all_in_gpu then the return value is half (float16)
+            bytes_per_voxel = 2
+        if np.prod(softmax_mean.shape) > (
+            2e9 / bytes_per_voxel * 0.85
+        ):  # * 0.85 just to be save
             print(
-                "This output is too large for python process-process communication. Saving output temporarily to disk")
+                "This output is too large for python process-process communication. Saving output temporarily to disk"
+            )
             np.save(output_filename[:-7] + ".npy", softmax_mean)
             softmax_mean = output_filename[:-7] + ".npy"
 
-        results.append(pool.starmap_async(save_segmentation_nifti_from_softmax,
-                                          ((softmax_mean, output_filename, dct, interp_order, None, None, None,
-                                            None, None, force_separate_z, interp_order_z),)
-                                          ))
+        results.append(
+            pool.starmap_async(
+                save_segmentation_nifti_from_softmax,
+                (
+                    (
+                        softmax_mean,
+                        output_filename,
+                        dct,
+                        interp_order,
+                        None,
+                        None,
+                        None,
+                        None,
+                        None,
+                        force_separate_z,
+                        interp_order_z,
+                    ),
+                ),
+            )
+        )
     _ = [i.get() for i in results]
 
     pool.close()
@@ -78,11 +145,14 @@ def save_predictions_MLPerf(predictions, output_folder, output_files, dictionari
 
     del predictions
 
+
 def load_loadgen_log(log_file, result_dtype, dictionaries):
     with open(log_file) as f:
         predictions = json.load(f)
 
-    assert len(predictions) == len(dictionaries), "Number of predictions does not match number of samples in validation set!"
+    assert len(predictions) == len(
+        dictionaries
+    ), "Number of predictions does not match number of samples in validation set!"
 
     padded_shape = [224, 224, 160]
     results = [None for i in range(len(predictions))]
@@ -92,15 +162,27 @@ def load_loadgen_log(log_file, result_dtype, dictionaries):
         raw_shape = list(dictionaries[qsl_idx]["size_after_cropping"])
         # Remove the padded part
         pad_before = [(p - r) // 2 for p, r in zip(padded_shape, raw_shape)]
-        pad_after = [-(p - r - b) for p, r, b in zip(padded_shape, raw_shape, pad_before)]
+        pad_after = [
+            -(p - r - b) for p, r, b in zip(padded_shape, raw_shape, pad_before)
+        ]
         result_shape = (4,) + tuple(padded_shape)
-        result = np.frombuffer(bytes.fromhex(prediction["data"]), result_dtype).reshape(result_shape).astype(np.float16)
-        results[qsl_idx] = result[:, pad_before[0]:pad_after[0], pad_before[1]:pad_after[1], pad_before[2]:pad_after[2]]
+        result = (
+            np.frombuffer(bytes.fromhex(prediction["data"]), result_dtype)
+            .reshape(result_shape)
+            .astype(np.float16)
+        )
+        results[qsl_idx] = result[
+            :,
+            pad_before[0]: pad_after[0],
+            pad_before[1]: pad_after[1],
+            pad_before[2]: pad_after[2],
+        ]
 
     assert all([i is not None for i in results]), "Missing some results!"
 
     return results
 
+
 def main():
     args = get_args()
     log_file = args.log_file
@@ -120,7 +202,10 @@ def main():
         preprocessed_files = pickle.load(f)
     dictionaries = []
     for preprocessed_file in preprocessed_files:
-        with open(os.path.join(preprocessed_data_dir, preprocessed_file + ".pkl"), "rb") as f:
+        with open(
+            os.path.join(preprocessed_data_dir,
+                         preprocessed_file + ".pkl"), "rb"
+        ) as f:
             dct = pickle.load(f)[1]
             dictionaries.append(dct)
 
@@ -131,7 +216,17 @@ def main():
     # Save predictions
     # This runs in multiprocess
     print("Running postprocessing with multiple threads...")
-    save_predictions_MLPerf(predictions, output_folder, preprocessed_files, dictionaries, num_threads_nifti_save, all_in_gpu, force_separate_z, interp_order, interp_order_z)
+    save_predictions_MLPerf(
+        predictions,
+        output_folder,
+        preprocessed_files,
+        dictionaries,
+        num_threads_nifti_save,
+        all_in_gpu,
+        force_separate_z,
+        interp_order,
+        interp_order_z,
+    )
 
     # Run evaluation
     print("Running evaluation...")
@@ -147,10 +242,15 @@ def main():
                 core = float(words[2])
                 enhancing = float(words[3])
                 mean = (whole + core + enhancing) / 3
-                print("Accuracy: mean = {:.5f}, whole tumor = {:.4f}, tumor core = {:.4f}, enhancing tumor = {:.4f}".format(mean, whole, core, enhancing))
+                print(
+                    "Accuracy: mean = {:.5f}, whole tumor = {:.4f}, tumor core = {:.4f}, enhancing tumor = {:.4f}".format(
+                        mean, whole, core, enhancing
+                    )
+                )
                 break
 
     print("Done!")
 
+
 if __name__ == "__main__":
     main()
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/brats_QSL.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/brats_QSL.py
index 7be8e1e3cd..b4c10e811c 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/brats_QSL.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/brats_QSL.py
@@ -17,18 +17,27 @@
 import os
 import pickle
 import sys
+
 sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
 
 sys.path.insert(0, os.path.join(os.getcwd(), "nnUnet"))
-from nnUnet.nnunet.inference.predict import preprocess_multithreaded
 
-class BraTS_2019_QSL():
+try:
+    from nnUnet.nnunet.inference.predict import preprocess_multithreaded
+    import mlperf_loadgen as lg
+except BaseException:
+    raise Exception("Error importing local modules")
+
+
+class BraTS_2019_QSL:
     def __init__(self, preprocessed_data_dir, perf_count):
         print("Constructing QSL...")
         self.preprocessed_data_dir = preprocessed_data_dir
-        with open(os.path.join(self.preprocessed_data_dir, "preprocessed_files.pkl"), "rb") as f:
+        with open(
+            os.path.join(self.preprocessed_data_dir,
+                         "preprocessed_files.pkl"), "rb"
+        ) as f:
             self.preprocess_files = pickle.load(f)
 
         self.count = len(self.preprocess_files)
@@ -37,14 +46,24 @@ def __init__(self, preprocessed_data_dir, perf_count):
         print("Using performance count = {:d}".format(self.perf_count))
 
         self.loaded_files = {}
-        self.qsl = lg.ConstructQSL(self.count, self.perf_count, self.load_query_samples, self.unload_query_samples)
+        self.qsl = lg.ConstructQSL(
+            self.count,
+            self.perf_count,
+            self.load_query_samples,
+            self.unload_query_samples,
+        )
         print("Finished constructing QSL.")
 
     def load_query_samples(self, sample_list):
         for sample_id in sample_list:
             file_name = self.preprocess_files[sample_id]
             print("Loading file {:}".format(file_name))
-            with open(os.path.join(self.preprocessed_data_dir, "{:}.pkl".format(file_name)), "rb") as f:
+            with open(
+                os.path.join(
+                    self.preprocessed_data_dir,
+                    "{:}.pkl".format(file_name)),
+                "rb",
+            ) as f:
                 self.loaded_files[sample_id] = pickle.load(f)[0]
 
     def unload_query_samples(self, sample_list):
@@ -54,5 +73,7 @@ def unload_query_samples(self, sample_list):
     def get_features(self, sample_id):
         return self.loaded_files[sample_id]
 
-def get_brats_QSL(preprocessed_data_dir="build/preprocessed_data", perf_count=None):
+
+def get_brats_QSL(
+        preprocessed_data_dir="build/preprocessed_data", perf_count=None):
     return BraTS_2019_QSL(preprocessed_data_dir, perf_count)
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/onnxruntime_SUT.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/onnxruntime_SUT.py
index ba5cce4115..9398b36406 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/onnxruntime_SUT.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/onnxruntime_SUT.py
@@ -14,19 +14,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from brats_QSL import get_brats_QSL
+import onnxruntime
+import numpy as np
+import mlperf_loadgen as lg
 import array
 import json
 import os
 import sys
-sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
-import numpy as np
-import onnxruntime
+sys.path.insert(0, os.getcwd())
 
-from brats_QSL import get_brats_QSL
 
-class _3DUNET_ONNXRuntime_SUT():
+class _3DUNET_ONNXRuntime_SUT:
     def __init__(self, model_path, preprocessed_data_dir, performance_count):
         print("Loading ONNX model...")
         self.sess = onnxruntime.InferenceSession(model_path)
@@ -40,19 +40,31 @@ def issue_queries(self, query_samples):
         for i in range(len(query_samples)):
             data = self.qsl.get_features(query_samples[i].index)
 
-            print("Processing sample id {:d} with shape = {:}".format(query_samples[i].index, data.shape))
+            print(
+                "Processing sample id {:d} with shape = {:}".format(
+                    query_samples[i].index, data.shape
+                )
+            )
 
             # Follow the PyTorch implementation.
-            # The ONNX file has five outputs, but we only care about the one named "output".
-            output = self.sess.run(["output"], {"input": data[np.newaxis, ...]})[0].squeeze(0).astype(np.float16)
+            # The ONNX file has five outputs, but we only care about the one
+            # named "output".
+            output = (
+                self.sess.run(["output"], {"input": data[np.newaxis, ...]})[0]
+                .squeeze(0)
+                .astype(np.float16)
+            )
 
             response_array = array.array("B", output.tobytes())
             bi = response_array.buffer_info()
-            response = lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])
+            response = lg.QuerySampleResponse(
+                query_samples[i].id, bi[0], bi[1])
             lg.QuerySamplesComplete([response])
 
     def flush_queries(self):
         pass
 
+
 def get_onnxruntime_sut(model_path, preprocessed_data_dir, performance_count):
-    return _3DUNET_ONNXRuntime_SUT(model_path, preprocessed_data_dir, performance_count)
\ No newline at end of file
+    return _3DUNET_ONNXRuntime_SUT(
+        model_path, preprocessed_data_dir, performance_count)
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/ov_SUT.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/ov_SUT.py
index cd42546733..4ed732c602 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/ov_SUT.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/ov_SUT.py
@@ -14,6 +14,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from scipy.special import softmax
+from openvino.inference_engine import IECore
+from brats_QSL import get_brats_QSL
+import numpy as np
+import mlperf_loadgen as lg
 import array
 import json
 import os
@@ -21,21 +26,13 @@
 
 sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
-import numpy as np
-
-from brats_QSL import get_brats_QSL
-
-from openvino.inference_engine import IECore
-from scipy.special import softmax
-
 
-class _3DUNET_OV_SUT():
+class _3DUNET_OV_SUT:
     def __init__(self, model_path, preprocessed_data_dir, performance_count):
         print("Loading OV model...")
 
         model_xml = model_path
-        model_bin = os.path.splitext(model_xml)[0] + '.bin'
+        model_bin = os.path.splitext(model_xml)[0] + ".bin"
 
         ie = IECore()
         net = ie.read_network(model=model_xml, weights=model_bin)
@@ -49,7 +46,7 @@ def __init__(self, model_path, preprocessed_data_dir, performance_count):
             if max_channels < net.outputs[output].shape[-1]:
                 _3DUNET_OV_SUT.output_name = output
 
-        self.exec_net = ie.load_network(network=net, device_name='CPU')
+        self.exec_net = ie.load_network(network=net, device_name="CPU")
 
         print("Constructing SUT...")
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
@@ -60,17 +57,20 @@ def issue_queries(self, query_samples):
         for i in range(len(query_samples)):
             data = self.qsl.get_features(query_samples[i].index)
 
-            print("Processing sample id {:d} with shape = {:}".format(
-                query_samples[i].index, data.shape))
+            print(
+                "Processing sample id {:d} with shape = {:}".format(
+                    query_samples[i].index, data.shape
+                )
+            )
 
             output = self.exec_net.infer(
-                inputs={self.input_name: data[np.newaxis, ...]})[
-                _3DUNET_OV_SUT.output_name].astype(np.float16)
+                inputs={self.input_name: data[np.newaxis, ...]}
+            )[_3DUNET_OV_SUT.output_name].astype(np.float16)
 
             response_array = array.array("B", output.tobytes())
             bi = response_array.buffer_info()
-            response = lg.QuerySampleResponse(query_samples[i].id, bi[0],
-                                              bi[1])
+            response = lg.QuerySampleResponse(
+                query_samples[i].id, bi[0], bi[1])
             lg.QuerySamplesComplete([response])
 
     def flush_queries(self):
@@ -78,4 +78,4 @@ def flush_queries(self):
 
 
 def get_ov_sut(model_path, preprocessed_data_dir, performance_count):
-    return _3DUNET_OV_SUT(model_path, preprocessed_data_dir, performance_count)
\ No newline at end of file
+    return _3DUNET_OV_SUT(model_path, preprocessed_data_dir, performance_count)
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/preprocess.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/preprocess.py
index 758d92488e..d4749550dd 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/preprocess.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/preprocess.py
@@ -23,31 +23,68 @@
 
 sys.path.insert(0, os.path.join(os.getcwd(), "nnUnet"))
 
-from batchgenerators.augmentations.utils import pad_nd_image
-from batchgenerators.utilities.file_and_folder_operations import subfiles
-from nnunet.training.model_restore import load_model_and_checkpoint_files
-from nnunet.inference.predict import preprocess_multithreaded
+try:
+    from nnunet.inference.predict import preprocess_multithreaded
+    from nnunet.training.model_restore import load_model_and_checkpoint_files
+    from batchgenerators.utilities.file_and_folder_operations import subfiles
+    from batchgenerators.augmentations.utils import pad_nd_image
+except BaseException:
+    raise Exception("Error importing local modules")
+
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model_dir", default="build/result/nnUNet/3d_fullres/Task043_BraTS2019/nnUNetTrainerV2__nnUNetPlansv2.mlperf.1",
-        help="Path to the directory containing plans.pkl")
-    parser.add_argument("--raw_data_dir", default="build/raw_data/nnUNet_raw_data/Task043_BraTS2019/imagesTr",
-        help="Path to the directory containing raw nii.gz files")
-    parser.add_argument("--preprocessed_data_dir", default="build/preprocessed_data", help="Path to the directory containing preprocessed data")
-    parser.add_argument("--validation_fold_file", default="folds/fold1_validation.txt", help="Path to the txt file storing all the sample names for the validation fold")
-    parser.add_argument("--num_threads_preprocessing", type=int, default=12, help="Number of threads to run the preprocessing with")
+    parser.add_argument(
+        "--model_dir",
+        default="build/result/nnUNet/3d_fullres/Task043_BraTS2019/nnUNetTrainerV2__nnUNetPlansv2.mlperf.1",
+        help="Path to the directory containing plans.pkl",
+    )
+    parser.add_argument(
+        "--raw_data_dir",
+        default="build/raw_data/nnUNet_raw_data/Task043_BraTS2019/imagesTr",
+        help="Path to the directory containing raw nii.gz files",
+    )
+    parser.add_argument(
+        "--preprocessed_data_dir",
+        default="build/preprocessed_data",
+        help="Path to the directory containing preprocessed data",
+    )
+    parser.add_argument(
+        "--validation_fold_file",
+        default="folds/fold1_validation.txt",
+        help="Path to the txt file storing all the sample names for the validation fold",
+    )
+    parser.add_argument(
+        "--num_threads_preprocessing",
+        type=int,
+        default=12,
+        help="Number of threads to run the preprocessing with",
+    )
     args = parser.parse_args()
     return args
 
-def preprocess_MLPerf(model, checkpoint_name, folds, fp16, list_of_lists, output_filenames, preprocessing_folder, num_threads_preprocessing):
+
+def preprocess_MLPerf(
+    model,
+    checkpoint_name,
+    folds,
+    fp16,
+    list_of_lists,
+    output_filenames,
+    preprocessing_folder,
+    num_threads_preprocessing,
+):
     assert len(list_of_lists) == len(output_filenames)
 
     print("loading parameters for folds", folds)
-    trainer, params = load_model_and_checkpoint_files(model, folds, fp16=fp16, checkpoint_name=checkpoint_name)
+    trainer, params = load_model_and_checkpoint_files(
+        model, folds, fp16=fp16, checkpoint_name=checkpoint_name
+    )
 
     print("starting preprocessing generator")
-    preprocessing = preprocess_multithreaded(trainer, list_of_lists, output_filenames, num_threads_preprocessing, None)
+    preprocessing = preprocess_multithreaded(
+        trainer, list_of_lists, output_filenames, num_threads_preprocessing, None
+    )
     print("Preprocessing images...")
     all_output_files = []
 
@@ -63,11 +100,13 @@ def preprocess_MLPerf(model, checkpoint_name, folds, fp16, list_of_lists, output
         # Pad to the desired full volume
         d = pad_nd_image(d, trainer.patch_size, "constant", None, False, None)
 
-        with open(os.path.join(preprocessing_folder, output_filename+ ".pkl"), "wb") as f:
+        with open(
+            os.path.join(preprocessing_folder, output_filename + ".pkl"), "wb"
+        ) as f:
             pickle.dump([d, dct], f)
         f.close()
 
-    return  all_output_files
+    return all_output_files
 
 
 def main():
@@ -82,7 +121,9 @@ def main():
     # Make sure the model exists
     model_dir = args.model_dir
     model_path = os.path.join(model_dir, "plans.pkl")
-    assert os.path.isfile(model_path), "Cannot find the model file {:}!".format(model_path)
+    assert os.path.isfile(model_path), "Cannot find the model file {:}!".format(
+        model_path
+    )
     checkpoint_name = "model_final_checkpoint"
 
     # Other settings
@@ -103,14 +144,28 @@ def main():
 
     # Create list of images locations (i.e. 4 images per case => 4 modalities)
     all_files = subfiles(raw_data_dir, suffix=".nii.gz", join=False, sort=True)
-    list_of_lists = [[os.path.join(raw_data_dir, i) for i in all_files if i[:len(j)].startswith(j) and
-                      len(i) == (len(j) + 12)] for j in validation_files]
+    list_of_lists = [
+        [
+            os.path.join(raw_data_dir, i)
+            for i in all_files
+            if i[: len(j)].startswith(j) and len(i) == (len(j) + 12)
+        ]
+        for j in validation_files
+    ]
 
     # Preprocess images, returns filenames list
     # This runs in multiprocess
     print("Acually preprocessing data...")
-    preprocessed_files = preprocess_MLPerf(model_dir, checkpoint_name, fold, fp16, list_of_lists,
-        validation_files, preprocessed_data_dir, num_threads_preprocessing)
+    preprocessed_files = preprocess_MLPerf(
+        model_dir,
+        checkpoint_name,
+        fold,
+        fp16,
+        list_of_lists,
+        validation_files,
+        preprocessed_data_dir,
+        num_threads_preprocessing,
+    )
 
     print("Saving metadata of the preprocessed data...")
     with open(os.path.join(preprocessed_data_dir, "preprocessed_files.pkl"), "wb") as f:
@@ -119,5 +174,6 @@ def main():
     print("Preprocessed data saved to {:}".format(preprocessed_data_dir))
     print("Done!")
 
+
 if __name__ == "__main__":
     main()
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/pytorch_SUT.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/pytorch_SUT.py
index 7a9acae725..e27ecea62d 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/pytorch_SUT.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/pytorch_SUT.py
@@ -18,26 +18,44 @@
 import json
 import os
 import sys
+
 sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
-import numpy as np
-import torch
-import torch.nn.functional as F
-from brats_QSL import get_brats_QSL
 
 sys.path.insert(0, os.path.join(os.getcwd(), "nnUnet"))
-from nnunet.training.model_restore import load_model_and_checkpoint_files
 
-class _3DUNET_PyTorch_SUT():
-    def __init__(self, model_dir, preprocessed_data_dir, performance_count, folds, checkpoint_name):
+try:
+    from nnunet.training.model_restore import load_model_and_checkpoint_files
+    from brats_QSL import get_brats_QSL
+    import torch.nn.functional as F
+    import torch
+    import numpy as np
+    import mlperf_loadgen as lg
+except BaseException:
+    raise Exception("Error importing local modules")
+
+
+class _3DUNET_PyTorch_SUT:
+    def __init__(
+        self,
+        model_dir,
+        preprocessed_data_dir,
+        performance_count,
+        folds,
+        checkpoint_name,
+    ):
 
         print("Loading PyTorch model...")
         model_path = os.path.join(model_dir, "plans.pkl")
-        assert os.path.isfile(model_path), "Cannot find the model file {:}!".format(model_path)
-        self.trainer, params = load_model_and_checkpoint_files(model_dir, folds, fp16=False, checkpoint_name=checkpoint_name)
+        assert os.path.isfile(model_path), "Cannot find the model file {:}!".format(
+            model_path
+        )
+        self.trainer, params = load_model_and_checkpoint_files(
+            model_dir, folds, fp16=False, checkpoint_name=checkpoint_name
+        )
         self.trainer.load_checkpoint_ram(params[0], False)
-        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.device = torch.device(
+            "cuda:0" if torch.cuda.is_available() else "cpu")
 
         print("Constructing SUT...")
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
@@ -49,23 +67,48 @@ def issue_queries(self, query_samples):
             for i in range(len(query_samples)):
                 data = self.qsl.get_features(query_samples[i].index)
 
-                print("Processing sample id {:d} with shape = {:}".format(query_samples[i].index, data.shape))
+                print(
+                    "Processing sample id {:d} with shape = {:}".format(
+                        query_samples[i].index, data.shape
+                    )
+                )
 
-                image = torch.from_numpy(data[np.newaxis,...]).float().to(self.device)
-                output = self.trainer.network(image)[0].cpu().numpy().astype(np.float16)
+                image = torch.from_numpy(
+                    data[np.newaxis, ...]).float().to(self.device)
+                output = self.trainer.network(
+                    image)[0].cpu().numpy().astype(np.float16)
 
                 transpose_forward = self.trainer.plans.get("transpose_forward")
-                transpose_backward = self.trainer.plans.get("transpose_backward")
-                assert transpose_forward == [0, 1, 2], "Unexpected transpose_forward {:}".format(transpose_forward)
-                assert transpose_backward == [0, 1, 2], "Unexpected transpose_backward {:}".format(transpose_backward)
+                transpose_backward = self.trainer.plans.get(
+                    "transpose_backward")
+                assert transpose_forward == [
+                    0,
+                    1,
+                    2,
+                ], "Unexpected transpose_forward {:}".format(transpose_forward)
+                assert transpose_backward == [
+                    0,
+                    1,
+                    2,
+                ], "Unexpected transpose_backward {:}".format(transpose_backward)
 
                 response_array = array.array("B", output.tobytes())
                 bi = response_array.buffer_info()
-                response = lg.QuerySampleResponse(query_samples[i].id, bi[0], bi[1])
+                response = lg.QuerySampleResponse(
+                    query_samples[i].id, bi[0], bi[1])
                 lg.QuerySamplesComplete([response])
 
     def flush_queries(self):
         pass
 
-def get_pytorch_sut(model_dir, preprocessed_data_dir, performance_count, folds=1, checkpoint_name="model_final_checkpoint"):
-    return _3DUNET_PyTorch_SUT(model_dir, preprocessed_data_dir, performance_count, folds, checkpoint_name)
\ No newline at end of file
+
+def get_pytorch_sut(
+    model_dir,
+    preprocessed_data_dir,
+    performance_count,
+    folds=1,
+    checkpoint_name="model_final_checkpoint",
+):
+    return _3DUNET_PyTorch_SUT(
+        model_dir, preprocessed_data_dir, performance_count, folds, checkpoint_name
+    )
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/run.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/run.py
index e69f2b6e7f..23f6e9bca6 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/run.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/run.py
@@ -15,48 +15,57 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import subprocess
+import mlperf_loadgen as lg
+import argparse
 import os
 import sys
-sys.path.insert(0, os.getcwd())
 
-import argparse
-import mlperf_loadgen as lg
-import subprocess
+sys.path.insert(0, os.getcwd())
 
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--backend",
-                        choices=["pytorch", "onnxruntime", "tf", "ov"],
-                        default="pytorch",
-                        help="Backend")
+    parser.add_argument(
+        "--backend",
+        choices=["pytorch", "onnxruntime", "tf", "ov"],
+        default="pytorch",
+        help="Backend",
+    )
     parser.add_argument(
         "--scenario",
         choices=["SingleStream", "Offline", "Server", "MultiStream"],
         default="Offline",
-        help="Scenario")
-    parser.add_argument("--accuracy",
-                        action="store_true",
-                        help="enable accuracy pass")
-    parser.add_argument("--mlperf_conf",
-                        default="build/mlperf.conf",
-                        help="mlperf rules config")
-    parser.add_argument("--user_conf",
-                        default="user.conf",
-                        help="user config for user LoadGen settings such as target QPS")
+        help="Scenario",
+    )
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
+    parser.add_argument(
+        "--mlperf_conf", default="build/mlperf.conf", help="mlperf rules config"
+    )
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
     parser.add_argument(
         "--model_dir",
-        default=
-        "build/result/nnUNet/3d_fullres/Task043_BraTS2019/nnUNetTrainerV2__nnUNetPlansv2.mlperf.1",
-        help="Path to the directory containing plans.pkl")
-    parser.add_argument("--model", help="Path to the ONNX, OpenVINO, or TF model")
-    parser.add_argument("--preprocessed_data_dir",
-                        default="build/preprocessed_data",
-                        help="path to preprocessed data")
-    parser.add_argument("--performance_count",
-                        type=int,
-                        default=16,
-                        help="performance count")
+        default="build/result/nnUNet/3d_fullres/Task043_BraTS2019/nnUNetTrainerV2__nnUNetPlansv2.mlperf.1",
+        help="Path to the directory containing plans.pkl",
+    )
+    parser.add_argument(
+        "--model",
+        help="Path to the ONNX, OpenVINO, or TF model")
+    parser.add_argument(
+        "--preprocessed_data_dir",
+        default="build/preprocessed_data",
+        help="path to preprocessed data",
+    )
+    parser.add_argument(
+        "--performance_count", type=int, default=16, help="performance count"
+    )
     args = parser.parse_args()
     return args
 
@@ -65,7 +74,7 @@ def get_args():
     "SingleStream": lg.TestScenario.SingleStream,
     "Offline": lg.TestScenario.Offline,
     "Server": lg.TestScenario.Server,
-    "MultiStream": lg.TestScenario.MultiStream
+    "MultiStream": lg.TestScenario.MultiStream,
 }
 
 
@@ -74,20 +83,30 @@ def main():
 
     if args.backend == "pytorch":
         from pytorch_SUT import get_pytorch_sut
-        sut = get_pytorch_sut(args.model_dir, args.preprocessed_data_dir,
-                              args.performance_count)
+
+        sut = get_pytorch_sut(
+            args.model_dir, args.preprocessed_data_dir, args.performance_count
+        )
     elif args.backend == "onnxruntime":
         from onnxruntime_SUT import get_onnxruntime_sut
-        sut = get_onnxruntime_sut(args.model, args.preprocessed_data_dir,
-                                  args.performance_count)
+
+        sut = get_onnxruntime_sut(
+            args.model, args.preprocessed_data_dir, args.performance_count
+        )
     elif args.backend == "tf":
         from tf_SUT import get_tf_sut
-        sut = get_tf_sut(args.model, args.preprocessed_data_dir,
-                         args.performance_count)
+
+        sut = get_tf_sut(
+            args.model,
+            args.preprocessed_data_dir,
+            args.performance_count)
     elif args.backend == "ov":
         from ov_SUT import get_ov_sut
-        sut = get_ov_sut(args.model, args.preprocessed_data_dir,
-                         args.performance_count)
+
+        sut = get_ov_sut(
+            args.model,
+            args.preprocessed_data_dir,
+            args.performance_count)
     else:
         raise ValueError("Unknown backend: {:}".format(args.backend))
 
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/tf_SUT.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/tf_SUT.py
index 6a38a2ee7f..9ceb0a3349 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/tf_SUT.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/tf_SUT.py
@@ -14,21 +14,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from brats_QSL import get_brats_QSL
+from tensorflow.core.framework import graph_pb2
+import tensorflow as tf
+import numpy as np
+import mlperf_loadgen as lg
 import array
 import json
 import os
 import sys
-sys.path.insert(0, os.getcwd())
 
-import mlperf_loadgen as lg
-import numpy as np
-import tensorflow as tf
-from tensorflow.core.framework import graph_pb2
-
-from brats_QSL import get_brats_QSL
+sys.path.insert(0, os.getcwd())
 
 
-class _3DUNET_TF_SUT():
+class _3DUNET_TF_SUT:
     def __init__(self, model_path, preprocessed_data_dir, performance_count):
         print("Loading TF model...")
         graph_def = graph_pb2.GraphDef()
@@ -50,15 +49,20 @@ def issue_queries(self, query_samples):
         for i in range(len(query_samples)):
             data = self.qsl.get_features(query_samples[i].index)
 
-            print("Processing sample id {:d} with shape = {:}".format(
-                query_samples[i].index, data.shape))
+            print(
+                "Processing sample id {:d} with shape = {:}".format(
+                    query_samples[i].index, data.shape
+                )
+            )
 
-            output = self.sess.run(self.output, feed_dict={self.input: data[np.newaxis, ...]})[0].astype(np.float16)
+            output = self.sess.run(
+                self.output, feed_dict={self.input: data[np.newaxis, ...]}
+            )[0].astype(np.float16)
 
             response_array = array.array("B", output.tobytes())
             bi = response_array.buffer_info()
-            response = lg.QuerySampleResponse(query_samples[i].id, bi[0],
-                                              bi[1])
+            response = lg.QuerySampleResponse(
+                query_samples[i].id, bi[0], bi[1])
             lg.QuerySamplesComplete([response])
 
     def flush_queries(self):
@@ -66,4 +70,4 @@ def flush_queries(self):
 
 
 def get_tf_sut(model_path, preprocessed_data_dir, performance_count):
-    return _3DUNET_TF_SUT(model_path, preprocessed_data_dir, performance_count)
\ No newline at end of file
+    return _3DUNET_TF_SUT(model_path, preprocessed_data_dir, performance_count)
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_onnx_to_tf.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_onnx_to_tf.py
index 472bcd61b1..3c2768dfa8 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_onnx_to_tf.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_onnx_to_tf.py
@@ -14,28 +14,32 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import onnx_tf
+import onnx
+import argparse
 import os
 import sys
+
 sys.path.insert(0, os.getcwd())
 
-import argparse
-import onnx
-import onnx_tf
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--onnx_model",
-                        default="build/model/224_224_160.onnx",
-                        help="Path to the ONNX model")
-    parser.add_argument("--output_name",
-                        default="224_224_160.pb",
-                        help="Name of output model")
-    parser.add_argument("--output_dir",
-                        default="build/model",
-                        help="Directory to save output model")
+    parser.add_argument(
+        "--onnx_model",
+        default="build/model/224_224_160.onnx",
+        help="Path to the ONNX model",
+    )
+    parser.add_argument(
+        "--output_name", default="224_224_160.pb", help="Name of output model"
+    )
+    parser.add_argument(
+        "--output_dir", default="build/model", help="Directory to save output model"
+    )
     args = parser.parse_args()
     return args
 
+
 def main():
     args = get_args()
 
diff --git a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_pytorch_to_onnx.py b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_pytorch_to_onnx.py
index 9652d1a6df..0bf896fa73 100644
--- a/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_pytorch_to_onnx.py
+++ b/retired_benchmarks/vision/medical_imaging/3d-unet-brats19/unet_pytorch_to_onnx.py
@@ -14,34 +14,45 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import torch
+import onnx
+import argparse
 import os
 import sys
+
 sys.path.insert(0, os.getcwd())
 
-import argparse
-import onnx
-import torch
 
 sys.path.insert(0, os.path.join(os.getcwd(), "nnUnet"))
-from nnunet.training.model_restore import load_model_and_checkpoint_files
+
+try:
+    from nnunet.training.model_restore import load_model_and_checkpoint_files
+except BaseException:
+    raise Exception("Error importing local modules")
+
 
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model_dir",
-                        default="build/result/nnUNet/3d_fullres/Task043_BraTS2019/nnUNetTrainerV2__nnUNetPlansv2.mlperf.1",
-                        help="Path to the PyTorch model")
-    parser.add_argument("--output_name",
-                        default="224_224_160.onnx",
-                        help="Name of output model")
-    parser.add_argument("--dynamic_bs_output_name",
-                        default="224_224_160_dyanmic_bs.onnx",
-                        help="Name of output model")
-    parser.add_argument("--output_dir",
-                        default="build/model",
-                        help="Directory to save output model")
+    parser.add_argument(
+        "--model_dir",
+        default="build/result/nnUNet/3d_fullres/Task043_BraTS2019/nnUNetTrainerV2__nnUNetPlansv2.mlperf.1",
+        help="Path to the PyTorch model",
+    )
+    parser.add_argument(
+        "--output_name", default="224_224_160.onnx", help="Name of output model"
+    )
+    parser.add_argument(
+        "--dynamic_bs_output_name",
+        default="224_224_160_dyanmic_bs.onnx",
+        help="Name of output model",
+    )
+    parser.add_argument(
+        "--output_dir", default="build/model", help="Directory to save output model"
+    )
     args = parser.parse_args()
     return args
 
+
 def main():
     args = get_args()
 
@@ -51,26 +62,49 @@ def main():
         os.makedirs(args.output_dir)
 
     output_path = "./{}/{}".format(args.output_dir, args.output_name)
-    dynamic_bs_output_path = "./{}/{}".format(args.output_dir, args.dynamic_bs_output_name)
+    dynamic_bs_output_path = "./{}/{}".format(
+        args.output_dir, args.dynamic_bs_output_name
+    )
 
     print("Loading Pytorch model...")
     checkpoint_name = "model_final_checkpoint"
     folds = 1
-    trainer, params = load_model_and_checkpoint_files(args.model_dir, folds, fp16=False, checkpoint_name=checkpoint_name)
+    trainer, params = load_model_and_checkpoint_files(
+        args.model_dir, folds, fp16=False, checkpoint_name=checkpoint_name
+    )
     trainer.load_checkpoint_ram(params[0], False)
     height = 224
     width = 224
     depth = 160
     channels = 4
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    dummy_input = torch.rand([1, channels, height, width, depth]).float().to(device)
-    torch.onnx.export(trainer.network, dummy_input, output_path, opset_version=11,
-                      input_names=['input'], output_names=['output'])
-    torch.onnx.export(trainer.network, dummy_input, dynamic_bs_output_path, opset_version=11,
-                      input_names=['input'], output_names=['output'],
-                      dynamic_axes=({"input": {0: "batch_size"}, "output": {0: "batch_size"}}))
-
-    print("Successfully exported model {} and {}".format(output_path, dynamic_bs_output_path))
+    dummy_input = torch.rand(
+        [1, channels, height, width, depth]).float().to(device)
+    torch.onnx.export(
+        trainer.network,
+        dummy_input,
+        output_path,
+        opset_version=11,
+        input_names=["input"],
+        output_names=["output"],
+    )
+    torch.onnx.export(
+        trainer.network,
+        dummy_input,
+        dynamic_bs_output_path,
+        opset_version=11,
+        input_names=["input"],
+        output_names=["output"],
+        dynamic_axes=({"input": {0: "batch_size"},
+                      "output": {0: "batch_size"}}),
+    )
+
+    print(
+        "Successfully exported model {} and {}".format(
+            output_path, dynamic_bs_output_path
+        )
+    )
+
 
 if __name__ == "__main__":
     main()
diff --git a/text_to_image/backend_pytorch.py b/text_to_image/backend_pytorch.py
index 300d7e58b5..1a45c1ca5b 100644
--- a/text_to_image/backend_pytorch.py
+++ b/text_to_image/backend_pytorch.py
@@ -90,7 +90,7 @@ def load(self):
             # self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)
 
         self.pipe.to(self.device)
-        #self.pipe.set_progress_bar_config(disable=True)
+        # self.pipe.set_progress_bar_config(disable=True)
 
         self.negative_prompt_tokens = self.pipe.tokenizer(
             self.convert_prompt(self.negative_prompt, self.pipe.tokenizer),
@@ -211,13 +211,15 @@ def encode_tokens(
                     text_input_ids.to(device), output_hidden_states=True
                 )
 
-                # We are only ALWAYS interested in the pooled output of the final text encoder
+                # We are only ALWAYS interested in the pooled output of the
+                # final text encoder
                 pooled_prompt_embeds = prompt_embeds[0]
                 if clip_skip is None:
                     prompt_embeds = prompt_embeds.hidden_states[-2]
                 else:
                     # "2" because SDXL always indexes from the penultimate layer.
-                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+                    prompt_embeds = prompt_embeds.hidden_states[-(
+                        clip_skip + 2)]
 
                 prompt_embeds_list.append(prompt_embeds)
 
@@ -233,7 +235,8 @@ def encode_tokens(
             and zero_out_negative_prompt
         ):
             negative_prompt_embeds = torch.zeros_like(prompt_embeds)
-            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(
+                pooled_prompt_embeds)
         elif do_classifier_free_guidance and negative_prompt_embeds is None:
             negative_prompt = negative_prompt or ""
             negative_prompt_2 = negative_prompt_2 or negative_prompt
@@ -260,30 +263,35 @@ def encode_tokens(
                     uncond_input.to(device),
                     output_hidden_states=True,
                 )
-                # We are only ALWAYS interested in the pooled output of the final text encoder
+                # We are only ALWAYS interested in the pooled output of the
+                # final text encoder
                 negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
 
                 negative_prompt_embeds_list.append(negative_prompt_embeds)
 
-            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+            negative_prompt_embeds = torch.concat(
+                negative_prompt_embeds_list, dim=-1)
 
         if pipe.text_encoder_2 is not None:
             prompt_embeds = prompt_embeds.to(
                 dtype=pipe.text_encoder_2.dtype, device=device
             )
         else:
-            prompt_embeds = prompt_embeds.to(dtype=pipe.unet.dtype, device=device)
+            prompt_embeds = prompt_embeds.to(
+                dtype=pipe.unet.dtype, device=device)
 
         bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        # duplicate text embeddings for each generation per prompt, using mps
+        # friendly method
         prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
         prompt_embeds = prompt_embeds.view(
             bs_embed * num_images_per_prompt, seq_len, -1
         )
 
         if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            # duplicate unconditional embeddings for each generation per
+            # prompt, using mps friendly method
             seq_len = negative_prompt_embeds.shape[1]
 
             if pipe.text_encoder_2 is not None:
@@ -315,7 +323,7 @@ def encode_tokens(
             pooled_prompt_embeds,
             negative_pooled_prompt_embeds,
         )
-    
+
     def prepare_inputs(self, inputs, i):
         if self.batch_size == 1:
             return self.encode_tokens(
@@ -330,7 +338,7 @@ def prepare_inputs(self, inputs, i):
             negative_prompt_embeds = []
             pooled_prompt_embeds = []
             negative_pooled_prompt_embeds = []
-            for prompt in inputs[i:min(i+self.batch_size, len(inputs))]:
+            for prompt in inputs[i: min(i + self.batch_size, len(inputs))]:
                 assert isinstance(prompt, dict)
                 text_input = prompt["input_tokens"]
                 text_input_2 = prompt["input_tokens_2"]
@@ -351,18 +359,26 @@ def prepare_inputs(self, inputs, i):
                 pooled_prompt_embeds.append(p_p_e)
                 negative_pooled_prompt_embeds.append(n_p_p_e)
 
-
             prompt_embeds = torch.cat(prompt_embeds)
             negative_prompt_embeds = torch.cat(negative_prompt_embeds)
             pooled_prompt_embeds = torch.cat(pooled_prompt_embeds)
-            negative_pooled_prompt_embeds = torch.cat(negative_pooled_prompt_embeds)
-            return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+            negative_pooled_prompt_embeds = torch.cat(
+                negative_pooled_prompt_embeds)
+            return (
+                prompt_embeds,
+                negative_prompt_embeds,
+                pooled_prompt_embeds,
+                negative_pooled_prompt_embeds,
+            )
 
     def predict(self, inputs):
         images = []
         with torch.no_grad():
             for i in range(0, len(inputs), self.batch_size):
-                latents_input = [inputs[idx]["latents"] for idx in range(i, min(i+self.batch_size, len(inputs)))]
+                latents_input = [
+                    inputs[idx]["latents"]
+                    for idx in range(i, min(i + self.batch_size, len(inputs)))
+                ]
                 latents_input = torch.cat(latents_input).to(self.device)
                 (
                     prompt_embeds,
@@ -382,4 +398,3 @@ def predict(self, inputs):
                 ).images
                 images.extend(generated)
         return images
-
diff --git a/text_to_image/coco.py b/text_to_image/coco.py
index b2c9d6dfc2..cb3956a014 100644
--- a/text_to_image/coco.py
+++ b/text_to_image/coco.py
@@ -38,7 +38,8 @@ def __init__(
         **kwargs,
     ):
         super().__init__()
-        self.captions_df = pd.read_csv(f"{data_path}/captions/captions.tsv", sep="\t")
+        self.captions_df = pd.read_csv(
+            f"{data_path}/captions/captions.tsv", sep="\t")
         self.image_size = image_size
         self.preprocessed_dir = os.path.abspath(f"{data_path}/preprocessed/")
         self.img_dir = os.path.abspath(f"{data_path}/validation/data/")
@@ -116,7 +117,10 @@ def get_item_count(self):
         return len(self.captions_df)
 
     def get_img(self, id):
-        img = Image.open(self.img_dir + "/" + self.captions_df.loc[id]["file_name"])
+        img = Image.open(
+            self.img_dir +
+            "/" +
+            self.captions_df.loc[id]["file_name"])
         return self.image_to_tensor(img)
 
     def get_imgs(self, id_list):
@@ -137,7 +141,11 @@ def get_item_loc(self, id):
 
 class PostProcessCoco:
     def __init__(
-        self, device="cpu", dtype="uint8", statistics_path=os.path.join(os.path.dirname(__file__), "tools", "val2014.npz")
+        self,
+        device="cpu",
+        dtype="uint8",
+        statistics_path=os.path.join(
+            os.path.dirname(__file__), "tools", "val2014.npz"),
     ):
         self.results = []
         self.good = 0
@@ -159,10 +167,12 @@ def add_results(self, results):
     def __call__(self, results, ids, expected=None, result_dict=None):
         self.content_ids.extend(ids)
         return [
-            (t.cpu().permute(1, 2, 0).float().numpy() * 255).round().astype(self.numpy_dtype)
+            (t.cpu().permute(1, 2, 0).float().numpy() * 255)
+            .round()
+            .astype(self.numpy_dtype)
             for t in results
         ]
-    
+
     def save_images(self, ids, ds):
         info = []
         idx = {}
@@ -195,7 +205,10 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                 100 * clip.get_clip_score(caption, generated).item()
             )
 
-        fid_score = compute_fid(self.results, self.statistics_path, self.device)
+        fid_score = compute_fid(
+            self.results,
+            self.statistics_path,
+            self.device)
         result_dict["FID_SCORE"] = fid_score
         result_dict["CLIP_SCORE"] = np.mean(self.clip_scores)
 
diff --git a/text_to_image/main.py b/text_to_image/main.py
index 57bfb56dd4..6aa7c15e75 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -146,9 +146,8 @@ def get_args():
     # parser.add_argument("--output-images", action="store_true", help="Store a subset of the generated images")
     # do not modify this argument for official submission
     parser.add_argument(
-        "--ids-path",
-        help="Path to caption ids",
-        default="tools/sample_ids.txt")
+        "--ids-path", help="Path to caption ids", default="tools/sample_ids.txt"
+    )
 
     # below will override mlperf rules compliant settings - don't use for
     # official submission
@@ -156,7 +155,10 @@ def get_args():
     parser.add_argument("--count", type=int, help="dataset items to use")
     parser.add_argument("--debug", action="store_true", help="debug")
     parser.add_argument(
-        "--performance-sample-count", type=int, help="performance sample count", default=5000
+        "--performance-sample-count",
+        type=int,
+        help="performance sample count",
+        default=5000,
     )
     parser.add_argument(
         "--max-latency", type=float, help="mlperf max latency in pct tile"
@@ -336,7 +338,7 @@ def main():
         precision=args.dtype,
         device=args.device,
         model_path=args.model_path,
-        batch_size=args.max_batchsize
+        batch_size=args.max_batchsize,
     )
     if args.dtype == "fp16":
         dtype = torch.float16
diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
index 027efdafb4..2d7c36506d 100644
--- a/text_to_image/tools/accuracy_coco.py
+++ b/text_to_image/tools/accuracy_coco.py
@@ -4,7 +4,6 @@
 the images in coco's captions/captions.tsv.
 """
 
-
 import argparse
 import json
 import os
@@ -15,23 +14,49 @@
 import torch
 from clip.clip_encoder import CLIPEncoder
 from fid.inception import InceptionV3
-from fid.fid_score import compute_fid, compute_statistics_of_path, get_activations, calculate_frechet_distance
+from fid.fid_score import (
+    compute_fid,
+    compute_statistics_of_path,
+    get_activations,
+    calculate_frechet_distance,
+)
 from tqdm import tqdm
 import ijson
 
 
-
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--caption-path", default="coco2014/captions/captions_source.tsv", help="path to coco captions")
-    parser.add_argument("--statistics-path", default=None, help="path to statistics")
-    parser.add_argument("--verbose", action="store_true", help="verbose messages")
-    parser.add_argument("--output-file", default="coco-results.json", help="path to output file")
-    parser.add_argument("--compliance-images-path", required=False, help="path to dump 10 stable diffusion xl compliance images")
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--caption-path",
+        default="coco2014/captions/captions_source.tsv",
+        help="path to coco captions",
+    )
+    parser.add_argument(
+        "--statistics-path",
+        default=None,
+        help="path to statistics")
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--output-file", default="coco-results.json", help="path to output file"
+    )
+    parser.add_argument(
+        "--compliance-images-path",
+        required=False,
+        help="path to dump 10 stable diffusion xl compliance images",
+    )
     parser.add_argument("--device", default="cpu", choices=["gpu", "cpu"])
-    parser.add_argument("--low_memory", action="store_true", help="If device is has limited memory (<70G), use the memory saving path.")
+    parser.add_argument(
+        "--low_memory",
+        action="store_true",
+        help="If device is has limited memory (<70G), use the memory saving path.",
+    )
     args = parser.parse_args()
     return args
 
@@ -41,11 +66,12 @@ def preprocess_image(img_dir, file_name):
     img = np.asarray(img)
     if len(img.shape) == 2:
         img = np.expand_dims(img, axis=-1)
-    tensor = torch.Tensor(np.asarray(img).transpose([2,0,1])).to(torch.uint8)
+    tensor = torch.Tensor(np.asarray(img).transpose([2, 0, 1])).to(torch.uint8)
     if tensor.shape[0] == 1:
-        tensor = tensor.repeat(3,1,1)
+        tensor = tensor.repeat(3, 1, 1)
     return tensor.unsqueeze(0)
 
+
 def main():
     args = get_args()
 
@@ -60,7 +86,8 @@ def main():
     # set statistics path
     statistics_path = args.statistics_path
     if args.statistics_path is None:
-        statistics_path = os.path.join(os.path.dirname(__file__), "val2014.npz")
+        statistics_path = os.path.join(
+            os.path.dirname(__file__), "val2014.npz")
 
     # Set compliance images path
     dump_compliance_images = False
@@ -70,14 +97,19 @@ def main():
             os.makedirs(args.compliance_images_path)
         dump_compliance_images = True
         compliance_images_idx_list = []
-        with open(os.path.join(os.path.dirname(__file__), "sample_ids.txt"), 'r') as compliance_id_file:
+        with open(
+            os.path.join(os.path.dirname(__file__), "sample_ids.txt"), "r"
+        ) as compliance_id_file:
             for line in compliance_id_file:
                 idx = int(line.strip())
                 compliance_images_idx_list.append(idx)
         # Dump caption.txt
-        with open(os.path.join(args.compliance_images_path, "captions.txt"), "w+") as caption_file:
+        with open(
+            os.path.join(args.compliance_images_path, "captions.txt"), "w+"
+        ) as caption_file:
             for idx in compliance_images_idx_list:
-                caption_file.write(f"{idx}  {df_captions.iloc[idx]['caption']}\n")
+                caption_file.write(
+                    f"{idx}  {df_captions.iloc[idx]['caption']}\n")
 
     # Compute accuracy
     if args.low_memory:
@@ -102,18 +134,18 @@ def main():
             args.compliance_images_path,
             df_captions,
             statistics_path,
-        )        
+        )
 
 
 def compute_accuracy(
-    mlperf_accuracy_file, 
+    mlperf_accuracy_file,
     output_file,
     device,
     dump_compliance_images,
     compliance_images_idx_list,
     compliance_images_path,
     df_captions,
-    statistics_path,    
+    statistics_path,
 ):
     # Load torchmetrics modules
     clip = CLIPEncoder(device=device)
@@ -127,26 +159,33 @@ def compute_accuracy(
         results = json.load(f)
 
     for j in tqdm(results):
-        idx = j['qsl_idx']
+        idx = j["qsl_idx"]
         if idx in seen:
             continue
         seen.add(idx)
 
         # Load generated image
-        generated_img = np.frombuffer(bytes.fromhex(j['data']), np.uint8).reshape(1024, 1024, 3)
+        generated_img = np.frombuffer(bytes.fromhex(j["data"]), np.uint8).reshape(
+            1024, 1024, 3
+        )
         result_list.append(generated_img)
         generated_img = Image.fromarray(generated_img)
 
         # Dump compliance images
         if dump_compliance_images and idx in compliance_images_idx_list:
-            generated_img.save(os.path.join(compliance_images_path, f"{idx}.png"))
+            generated_img.save(
+                os.path.join(
+                    compliance_images_path,
+                    f"{idx}.png"))
 
         # generated_img = torch.Tensor(generated_img).to(torch.uint8).to(device)
         # Load Ground Truth
         caption = df_captions.iloc[idx]["caption"]
         clip_scores.append(
-            100 * clip.get_clip_score(caption, generated_img).item()
-        )
+            100 *
+            clip.get_clip_score(
+                caption,
+                generated_img).item())
     fid_score = compute_fid(result_list, statistics_path, device)
 
     result_dict["FID_SCORE"] = fid_score
@@ -158,7 +197,7 @@ def compute_accuracy(
 
 
 def compute_accuracy_low_memory(
-    mlperf_accuracy_file, 
+    mlperf_accuracy_file,
     output_file,
     device,
     dump_compliance_images,
@@ -169,7 +208,7 @@ def compute_accuracy_low_memory(
     batch_size=256,
     inception_dims=2048,
     num_workers=1,
-):    
+):
     if num_workers is None:
         try:
             num_cpus = len(os.sched_getaffinity(0))
@@ -181,36 +220,41 @@ def compute_accuracy_low_memory(
 
         num_workers = min(num_cpus, 8) if num_cpus is not None else 0
     else:
-        num_workers = num_workers    
+        num_workers = num_workers
 
     # Load torchmetrics modules
     block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[inception_dims]
     inception_model = InceptionV3([block_idx]).to(device)
     clip_model = CLIPEncoder(device=device)
-    
+
     clip_scores = []
     seen = set()
     result_batch = []
     result_dict = {}
     activations = np.empty((0, inception_dims))
-    
+
     # Load model outputs
     with open(mlperf_accuracy_file, "r") as f:
         results = ijson.items(f, "item")
 
         for j in tqdm(results):
-            idx = j['qsl_idx']
+            idx = j["qsl_idx"]
             if idx in seen:
                 continue
             seen.add(idx)
 
             # Load generated image
-            generated_img = np.frombuffer(bytes.fromhex(j['data']), np.uint8).reshape(1024, 1024, 3)
+            generated_img = np.frombuffer(bytes.fromhex(j["data"]), np.uint8).reshape(
+                1024, 1024, 3
+            )
             generated_img = Image.fromarray(generated_img)
 
             # Dump compliance images
             if dump_compliance_images and idx in compliance_images_idx_list:
-                generated_img.save(os.path.join(compliance_images_path, f"{idx}.png"))
+                generated_img.save(
+                    os.path.join(
+                        compliance_images_path,
+                        f"{idx}.png"))
 
             # Load Ground Truth
             caption = df_captions.iloc[idx]["caption"]
@@ -221,15 +265,29 @@ def compute_accuracy_low_memory(
             result_batch.append(generated_img.convert("RGB"))
 
             if len(result_batch) == batch_size:
-                act = get_activations(result_batch, inception_model, batch_size, inception_dims, device, num_workers)
+                act = get_activations(
+                    result_batch,
+                    inception_model,
+                    batch_size,
+                    inception_dims,
+                    device,
+                    num_workers,
+                )
                 activations = np.append(activations, act, axis=0)
                 result_batch.clear()
-        
+
         # Remaining data for last batch
         if len(result_batch) > 0:
-            act = get_activations(result_batch, inception_model, len(result_batch), inception_dims, device, num_workers)
+            act = get_activations(
+                result_batch,
+                inception_model,
+                len(result_batch),
+                inception_dims,
+                device,
+                num_workers,
+            )
             activations = np.append(activations, act, axis=0)
-            
+
     m1, s1 = compute_statistics_of_path(
         statistics_path,
         inception_model,
@@ -253,5 +311,6 @@ def compute_accuracy_low_memory(
     with open(output_file, "w") as fp:
         json.dump(result_dict, fp, sort_keys=True, indent=4)
 
+
 if __name__ == "__main__":
     main()
diff --git a/text_to_image/tools/clip/clip_encoder.py b/text_to_image/tools/clip/clip_encoder.py
index da24ada271..8f83170b2a 100644
--- a/text_to_image/tools/clip/clip_encoder.py
+++ b/text_to_image/tools/clip/clip_encoder.py
@@ -9,7 +9,7 @@
 class CLIPEncoder(nn.Module):
     """
     A class for encoding images and texts using a specified CLIP model and computing the similarity between them.
-    
+
     Attributes:
     -----------
     clip_version: str
@@ -23,14 +23,17 @@ class CLIPEncoder(nn.Module):
     device: str
         The device to which the model is moved.
     """
-    def __init__(self, 
-                 clip_version: str = 'ViT-B/32',
-                 pretrained: Optional[str] = '',
-                 cache_dir: Optional[str] = None,
-                 device: str = 'cpu'):
+
+    def __init__(
+        self,
+        clip_version: str = "ViT-B/32",
+        pretrained: Optional[str] = "",
+        cache_dir: Optional[str] = None,
+        device: str = "cpu",
+    ):
         """
         Initializes the CLIPEncoder with the specified CLIP model version and pre-trained weights.
-        
+
         Parameters:
         -----------
         clip_version: str, optional
@@ -46,10 +49,10 @@ def __init__(self,
 
         self.clip_version = clip_version
         self.pretrained = pretrained if pretrained else self._get_default_pretrained()
-        
-        self.model, _, self.preprocess = open_clip.create_model_and_transforms(self.clip_version,
-                                                                               pretrained=self.pretrained,
-                                                                               cache_dir=cache_dir)
+
+        self.model, _, self.preprocess = open_clip.create_model_and_transforms(
+            self.clip_version, pretrained=self.pretrained, cache_dir=cache_dir
+        )
 
         self.model.eval()
         self.model.to(device)
@@ -57,25 +60,27 @@ def __init__(self,
 
     def _get_default_pretrained(self) -> str:
         """Returns the default pretrained weights based on the clip_version."""
-        if self.clip_version == 'ViT-H-14':
-            return 'laion2b_s32b_b79k'
-        elif self.clip_version == 'ViT-g-14':
-            return 'laion2b_s12b_b42k'
+        if self.clip_version == "ViT-H-14":
+            return "laion2b_s32b_b79k"
+        elif self.clip_version == "ViT-g-14":
+            return "laion2b_s12b_b42k"
         else:
-            return 'openai'
+            return "openai"
 
     @torch.no_grad()
-    def get_clip_score(self, text: Union[str, List[str]], image: Union[Image.Image, torch.Tensor]) -> torch.Tensor:
+    def get_clip_score(
+        self, text: Union[str, List[str]], image: Union[Image.Image, torch.Tensor]
+    ) -> torch.Tensor:
         """
         Computes the similarity score between the given text(s) and image using the CLIP model.
-        
+
         Parameters:
         -----------
         text: Union[str, List[str]]
             The text or list of texts to compare with the image.
         image: Image.Image
             The input image.
-            
+
         Returns:
         --------
         torch.Tensor
@@ -83,7 +88,7 @@ def get_clip_score(self, text: Union[str, List[str]], image: Union[Image.Image,
         """
         # Preprocess the image and move it to the specified device
         image = self.preprocess(image).unsqueeze(0).to(self.device)
-        
+
         # Normalize the image features
         image_features = self.model.encode_image(image).float()
         image_features /= image_features.norm(dim=-1, keepdim=True)
@@ -91,15 +96,15 @@ def get_clip_score(self, text: Union[str, List[str]], image: Union[Image.Image,
         # If a single text string is provided, convert it to a list
         if not isinstance(text, (list, tuple)):
             text = [text]
-        
+
         # Tokenize the text and move it to the specified device
         text = open_clip.tokenize(text).to(self.device)
-        
+
         # Normalize the text features
         text_features = self.model.encode_text(text).float()
         text_features /= text_features.norm(dim=-1, keepdim=True)
-        
+
         # Compute the similarity between the image and text features
         similarity = image_features @ text_features.T
 
-        return similarity
\ No newline at end of file
+        return similarity
diff --git a/text_to_image/tools/coco.py b/text_to_image/tools/coco.py
index c11f2225ce..77b2a5da2f 100644
--- a/text_to_image/tools/coco.py
+++ b/text_to_image/tools/coco.py
@@ -27,24 +27,36 @@ def get_args():
         type=int,
         help="Maximun number of images to download",
     )
-    parser.add_argument("--num-workers", default=1, type=int, help="Number of processes to download images")
+    parser.add_argument(
+        "--num-workers",
+        default=1,
+        type=int,
+        help="Number of processes to download images",
+    )
     parser.add_argument(
         "--allow-duplicate-images",
         action="store_true",
-        help="Allow mulple captions per image"
+        help="Allow mulple captions per image",
     )
     parser.add_argument(
-        "--latents-path-torch", default="latents.pt", type=str, help="Path to pytorch latents"
+        "--latents-path-torch",
+        default="latents.pt",
+        type=str,
+        help="Path to pytorch latents",
     )
     parser.add_argument(
-        "--latents-path-numpy", default="latents.npy", type=str, help="Path to numpy latents"
+        "--latents-path-numpy",
+        default="latents.npy",
+        type=str,
+        help="Path to numpy latents",
     )
     parser.add_argument(
         "--seed", type=int, default=2023, help="Seed to choose the dataset"
     )
     parser.add_argument(
-        "--keep-raw", action="store_true", help="Keep raw folder"
-    )
+        "--keep-raw",
+        action="store_true",
+        help="Keep raw folder")
     parser.add_argument(
         "--download-images", action="store_true", help="Download the calibration set"
     )
@@ -66,22 +78,29 @@ def download_img(args):
     dataset_dir = os.path.abspath(args.dataset_dir)
     # Check if the annotation dataframe is there
     if os.path.exists(f"{dataset_dir}/captions/captions_source.tsv"):
-        df_annotations = pd.read_csv(f"{dataset_dir}/captions/captions_source.tsv", sep="\t")
+        df_annotations = pd.read_csv(
+            f"{dataset_dir}/captions/captions_source.tsv", sep="\t"
+        )
         df_annotations = df_annotations.iloc[: args.max_images]
     elif os.path.exists(f"{dataset_dir}/../captions_source.tsv"):
         os.makedirs(f"{dataset_dir}/captions/", exist_ok=True)
-        os.system(f"cp {dataset_dir}/../captions_source.tsv {dataset_dir}/captions/")
-        df_annotations = pd.read_csv(f"{dataset_dir}/captions/captions_source.tsv", sep="\t")
+        os.system(
+            f"cp {dataset_dir}/../captions_source.tsv {dataset_dir}/captions/")
+        df_annotations = pd.read_csv(
+            f"{dataset_dir}/captions/captions_source.tsv", sep="\t"
+        )
         df_annotations = df_annotations.iloc[: args.max_images]
     elif args.tsv_path is not None and os.path.exists(f"{args.tsv_path}"):
         file_name = args.tsv_path.split("/")[-1]
         os.makedirs(f"{dataset_dir}/captions/", exist_ok=True)
         os.system(f"cp {args.tsv_path} {dataset_dir}/captions/")
-        df_annotations = pd.read_csv(f"{dataset_dir}/captions/{file_name}", sep="\t")
+        df_annotations = pd.read_csv(
+            f"{dataset_dir}/captions/{file_name}", sep="\t")
         df_annotations = df_annotations.iloc[: args.max_images]
     else:
         # Check if raw annotations file already exist
-        if not os.path.exists(f"{dataset_dir}/raw/annotations/captions_val2014.json"):
+        if not os.path.exists(
+                f"{dataset_dir}/raw/annotations/captions_val2014.json"):
             # Download annotations
             os.makedirs(f"{dataset_dir}/raw/", exist_ok=True)
             os.makedirs(f"{dataset_dir}/download_aux/", exist_ok=True)
@@ -121,7 +140,9 @@ def download_img(args):
             frac=1, random_state=args.seed
         ).reset_index(drop=True)
         df_annotations = df_annotations.iloc[: args.max_images]
-        df_annotations['caption'] = df_annotations['caption'].apply(lambda x: x.replace('\n', '').strip())
+        df_annotations["caption"] = df_annotations["caption"].apply(
+            lambda x: x.replace("\n", "").strip()
+        )
         df_annotations = (
             df_annotations.merge(
                 df_images, how="inner", left_on="image_id", right_on="id"
@@ -135,11 +156,18 @@ def download_img(args):
     if args.download_images:
         os.makedirs(f"{dataset_dir}/validation/data/", exist_ok=True)
         tasks = [
-            (row["coco_url"], f"{dataset_dir}/validation/data/", row["file_name"])
+            (row["coco_url"],
+             f"{dataset_dir}/validation/data/",
+             row["file_name"])
             for i, row in df_annotations.iterrows()
         ]
         pool = Pool(processes=args.num_workers)
-        [_ for _ in tqdm.tqdm(pool.imap_unordered(download_img, tasks), total=len(tasks))]
+        [
+            _
+            for _ in tqdm.tqdm(
+                pool.imap_unordered(download_img, tasks), total=len(tasks)
+            )
+        ]
     # Finalize annotations
     df_annotations[
         ["id", "image_id", "caption", "height", "width", "file_name", "coco_url"]
diff --git a/text_to_image/tools/coco_calibration.py b/text_to_image/tools/coco_calibration.py
index c8417f52a3..dc4f49009d 100644
--- a/text_to_image/tools/coco_calibration.py
+++ b/text_to_image/tools/coco_calibration.py
@@ -22,13 +22,19 @@ def get_args():
     parser.add_argument(
         "--tsv-path", default=None, help="Precomputed tsv file location"
     )
-    parser.add_argument("--num-workers", default=1, type=int, help="Number of processes to download images")
     parser.add_argument(
-        "--calibration-dir", default=None, help="Calibration ids location"
+        "--num-workers",
+        default=1,
+        type=int,
+        help="Number of processes to download images",
     )
     parser.add_argument(
-        "--keep-raw", action="store_true", help="Keep the raw dataset"
+        "--calibration-dir", default=None, help="Calibration ids location"
     )
+    parser.add_argument(
+        "--keep-raw",
+        action="store_true",
+        help="Keep the raw dataset")
     parser.add_argument(
         "--download-images", action="store_true", help="Download the calibration set"
     )
@@ -47,17 +53,28 @@ def download_img(args):
 if __name__ == "__main__":
     args = get_args()
     dataset_dir = os.path.abspath(args.dataset_dir)
-    calibration_dir = args.calibration_dir if args.calibration_dir is not None else os.path.join(os.path.dirname(__file__), "..", "..", "calibration", "COCO-2014")
+    calibration_dir = (
+        args.calibration_dir
+        if args.calibration_dir is not None
+        else os.path.join(
+            os.path.dirname(__file__), "..", "..", "calibration", "COCO-2014"
+        )
+    )
     # Check if the annotation dataframe is there
     if os.path.exists(f"{dataset_dir}/calibration/captions.tsv"):
-        df_annotations = pd.read_csv(f"{dataset_dir}/calibration/captions.tsv", sep="\t")
+        df_annotations = pd.read_csv(
+            f"{dataset_dir}/calibration/captions.tsv", sep="\t"
+        )
     elif args.tsv_path is not None and os.path.exists(f"{args.tsv_path}"):
         os.makedirs(f"{dataset_dir}/calibration/", exist_ok=True)
         os.system(f"cp {args.tsv_path} {dataset_dir}/calibration/")
-        df_annotations = pd.read_csv(f"{dataset_dir}/calibration/captions.tsv", sep="\t")
+        df_annotations = pd.read_csv(
+            f"{dataset_dir}/calibration/captions.tsv", sep="\t"
+        )
     else:
         # Check if raw annotations file already exist
-        if not os.path.exists(f"{dataset_dir}/raw/annotations/captions_train2014.json"):
+        if not os.path.exists(
+                f"{dataset_dir}/raw/annotations/captions_train2014.json"):
             # Download annotations
             os.makedirs(f"{dataset_dir}/raw/", exist_ok=True)
             os.makedirs(f"{dataset_dir}/download_aux/", exist_ok=True)
@@ -88,15 +105,19 @@ def download_img(args):
         df_annotations = pd.DataFrame(annotations)
         df_images = pd.DataFrame(images)
 
-        # Calibration images 
+        # Calibration images
         with open(f"{calibration_dir}/coco_cal_captions_list.txt") as f:
             calibration_ids = f.readlines()
-            calibration_ids = [int(id.replace('\n', '')) for id in calibration_ids]
+            calibration_ids = [int(id.replace("\n", ""))
+                               for id in calibration_ids]
             calibration_ids = calibration_ids
 
-        df_annotations = df_annotations[np.isin(df_annotations["id"], calibration_ids)]
+        df_annotations = df_annotations[np.isin(
+            df_annotations["id"], calibration_ids)]
         df_annotations = df_annotations.sort_values(by=["id"])
-        df_annotations['caption'] = df_annotations['caption'].apply(lambda x: x.replace('\n', '').strip())
+        df_annotations["caption"] = df_annotations["caption"].apply(
+            lambda x: x.replace("\n", "").strip()
+        )
         df_annotations = (
             df_annotations.merge(
                 df_images, how="inner", left_on="image_id", right_on="id"
@@ -111,11 +132,18 @@ def download_img(args):
     if args.download_images:
         os.makedirs(f"{dataset_dir}/calibration/data/", exist_ok=True)
         tasks = [
-            (row["coco_url"], f"{dataset_dir}/calibration/data/", row["file_name"])
+            (row["coco_url"],
+             f"{dataset_dir}/calibration/data/",
+             row["file_name"])
             for i, row in df_annotations.iterrows()
         ]
         pool = Pool(processes=args.num_workers)
-        [_ for _ in tqdm.tqdm(pool.imap_unordered(download_img, tasks), total=len(tasks))]
+        [
+            _
+            for _ in tqdm.tqdm(
+                pool.imap_unordered(download_img, tasks), total=len(tasks)
+            )
+        ]
     # Finalize annotations
     df_annotations[
         ["id", "image_id", "caption", "height", "width", "file_name", "coco_url"]
diff --git a/text_to_image/tools/coco_generate_calibration.py b/text_to_image/tools/coco_generate_calibration.py
index a2a7254c0c..4c83489af7 100644
--- a/text_to_image/tools/coco_generate_calibration.py
+++ b/text_to_image/tools/coco_generate_calibration.py
@@ -32,8 +32,9 @@ def get_args():
         "--seed", type=int, default=2023, help="Seed to choose the dataset"
     )
     parser.add_argument(
-        "--keep-raw", action="store_true", help="Keep raw folder"
-    )
+        "--keep-raw",
+        action="store_true",
+        help="Keep raw folder")
 
     args = parser.parse_args()
     return args
@@ -42,10 +43,17 @@ def get_args():
 if __name__ == "__main__":
     args = get_args()
     dataset_dir = os.path.abspath(args.dataset_dir)
-    calibration_dir = args.calibration_dir if args.calibration_dir is not None else os.path.join(os.path.dirname(__file__), "..", "..", "calibration", "COCO-2014")
-    
+    calibration_dir = (
+        args.calibration_dir
+        if args.calibration_dir is not None
+        else os.path.join(
+            os.path.dirname(__file__), "..", "..", "calibration", "COCO-2014"
+        )
+    )
+
     # Check if raw annotations file already exist
-    if not os.path.exists(f"{dataset_dir}/raw/annotations/captions_train2014.json"):
+    if not os.path.exists(
+            f"{dataset_dir}/raw/annotations/captions_train2014.json"):
         # Download annotations
         os.makedirs(f"{dataset_dir}/raw/", exist_ok=True)
         os.makedirs(f"{dataset_dir}/download_aux/", exist_ok=True)
@@ -68,19 +76,24 @@ def get_args():
     df_annotations = pd.DataFrame(annotations)
     df_images = pd.DataFrame(images)
 
-    # Calibration images 
+    # Calibration images
     df_annotations = df_annotations.drop_duplicates(
-        subset=["image_id"], keep="first"
-    )
+        subset=["image_id"], keep="first")
     # Sort, shuffle and choose the final dataset
     df_annotations = df_annotations.sort_values(by=["id"])
-    df_annotations = df_annotations.sample(frac=1, random_state=args.seed).reset_index(drop=True)
+    df_annotations = df_annotations.sample(frac=1, random_state=args.seed).reset_index(
+        drop=True
+    )
     df_annotations = df_annotations.iloc[: args.max_images]
-    df_annotations['caption'] = df_annotations['caption'].apply(lambda x: x.replace('\n', '').strip())
+    df_annotations["caption"] = df_annotations["caption"].apply(
+        lambda x: x.replace("\n", "").strip()
+    )
     df_annotations = (
         df_annotations.merge(
-            df_images, how="inner", left_on="image_id", right_on="id"
-        )
+            df_images,
+            how="inner",
+            left_on="image_id",
+            right_on="id")
         .drop(["id_y"], axis=1)
         .rename(columns={"id_x": "id"})
         .sort_values(by=["id"])
@@ -92,4 +105,3 @@ def get_args():
         f.write(s)
     # Remove Folder
     os.system(f"rm -rf {dataset_dir}")
-
diff --git a/text_to_image/tools/fid/fid_score.py b/text_to_image/tools/fid/fid_score.py
index d9f84d98da..2c3f1aab79 100644
--- a/text_to_image/tools/fid/fid_score.py
+++ b/text_to_image/tools/fid/fid_score.py
@@ -31,20 +31,22 @@
 See the License for the specific language governing permissions and
 limitations under the License.
 """
+
+from inception import InceptionV3
+from torch.nn.functional import adaptive_avg_pool2d
+from scipy import linalg
+from PIL import Image
+import torchvision.transforms as TF
+import torch
+import random
+import numpy as np
+from typing import Any
+from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
+import pathlib
 import os
 import sys
-sys.path.append(os.path.dirname(__file__))
-import pathlib
-from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
-from typing import Any
 
-import numpy as np
-import random
-import torch
-import torchvision.transforms as TF
-from PIL import Image
-from scipy import linalg
-from torch.nn.functional import adaptive_avg_pool2d
+sys.path.append(os.path.dirname(__file__))
 
 
 try:
@@ -55,9 +57,6 @@ def tqdm(x):
         return x
 
 
-from inception import InceptionV3
-
-
 class ImagesDataset(torch.utils.data.Dataset):
     def __init__(self, imgs, transforms=None):
         self.imgs = imgs
@@ -71,7 +70,7 @@ def __getitem__(self, i):
         if self.transforms is not None:
             img = self.transforms(img)
         return img
-    
+
 
 def get_activations(
     files, model, batch_size=50, dims=2048, device="cpu", num_workers=1
@@ -132,7 +131,7 @@ def get_activations(
 
         pred = pred.squeeze(3).squeeze(2).cpu().numpy()
 
-        pred_arr[start_idx : start_idx + pred.shape[0]] = pred
+        pred_arr[start_idx: start_idx + pred.shape[0]] = pred
 
         start_idx = start_idx + pred.shape[0]
 
@@ -196,7 +195,8 @@ def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
 
     tr_covmean = np.trace(covmean)
 
-    return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
+    return diff.dot(diff) + np.trace(sigma1) + \
+        np.trace(sigma2) - 2 * tr_covmean
 
 
 def calculate_activation_statistics(
@@ -241,7 +241,8 @@ def compute_statistics_of_path(
     else:
         path = pathlib.Path(path)
         files = sorted(
-            [file for ext in IMAGE_EXTENSIONS for file in path.glob("*.{}".format(ext))]
+            [file for ext in IMAGE_EXTENSIONS for file in path.glob(
+                "*.{}".format(ext))]
         )
         if subset_size is not None:
             random.seed(shuffle_seed)
@@ -319,7 +320,7 @@ def compute_fid(
     num_workers=1,
     batch_size=1,
     subset_size=None,
-    shuffle_seed=None
+    shuffle_seed=None,
 ):
     imgs = [Image.fromarray(e).convert("RGB") for e in results]
     device = torch.device(device if torch.cuda.is_available() else "cpu")
@@ -359,4 +360,3 @@ def compute_fid(
     fid_value = calculate_frechet_distance(m1, s1, m2, s2)
 
     return fid_value
-
diff --git a/text_to_image/tools/fid/inception.py b/text_to_image/tools/fid/inception.py
index cc56870522..7854487cac 100644
--- a/text_to_image/tools/fid/inception.py
+++ b/text_to_image/tools/fid/inception.py
@@ -10,7 +10,7 @@
 
 # Inception weights ported to Pytorch from
 # http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'  # noqa: E501
+FID_WEIGHTS_URL = "https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth"  # noqa: E501
 
 
 class InceptionV3(nn.Module):
@@ -22,18 +22,20 @@ class InceptionV3(nn.Module):
 
     # Maps feature dimensionality to their output blocks indices
     BLOCK_INDEX_BY_DIM = {
-        64: 0,   # First max pooling features
+        64: 0,  # First max pooling features
         192: 1,  # Second max pooling featurs
         768: 2,  # Pre-aux classifier features
-        2048: 3  # Final average pooling features
+        2048: 3,  # Final average pooling features
     }
 
-    def __init__(self,
-                 output_blocks=(DEFAULT_BLOCK_INDEX,),
-                 resize_input=True,
-                 normalize_input=True,
-                 requires_grad=False,
-                 use_fid_inception=True):
+    def __init__(
+        self,
+        output_blocks=(DEFAULT_BLOCK_INDEX,),
+        resize_input=True,
+        normalize_input=True,
+        requires_grad=False,
+        use_fid_inception=True,
+    ):
         """Build pretrained InceptionV3
 
         Parameters
@@ -71,22 +73,21 @@ def __init__(self,
         self.output_blocks = sorted(output_blocks)
         self.last_needed_block = max(output_blocks)
 
-        assert self.last_needed_block <= 3, \
-            'Last possible output block index is 3'
+        assert self.last_needed_block <= 3, "Last possible output block index is 3"
 
         self.blocks = nn.ModuleList()
 
         if use_fid_inception:
             inception = fid_inception_v3()
         else:
-            inception = _inception_v3(weights='DEFAULT')
+            inception = _inception_v3(weights="DEFAULT")
 
         # Block 0: input to maxpool1
         block0 = [
             inception.Conv2d_1a_3x3,
             inception.Conv2d_2a_3x3,
             inception.Conv2d_2b_3x3,
-            nn.MaxPool2d(kernel_size=3, stride=2)
+            nn.MaxPool2d(kernel_size=3, stride=2),
         ]
         self.blocks.append(nn.Sequential(*block0))
 
@@ -95,7 +96,7 @@ def __init__(self,
             block1 = [
                 inception.Conv2d_3b_1x1,
                 inception.Conv2d_4a_3x3,
-                nn.MaxPool2d(kernel_size=3, stride=2)
+                nn.MaxPool2d(kernel_size=3, stride=2),
             ]
             self.blocks.append(nn.Sequential(*block1))
 
@@ -119,7 +120,7 @@ def __init__(self,
                 inception.Mixed_7a,
                 inception.Mixed_7b,
                 inception.Mixed_7c,
-                nn.AdaptiveAvgPool2d(output_size=(1, 1))
+                nn.AdaptiveAvgPool2d(output_size=(1, 1)),
             ]
             self.blocks.append(nn.Sequential(*block3))
 
@@ -144,10 +145,13 @@ def forward(self, inp):
         x = inp
 
         if self.resize_input:
-            x = F.interpolate(x,
-                              size=(299, 299),
-                              mode='bilinear',
-                              align_corners=False)
+            x = F.interpolate(
+                x,
+                size=(
+                    299,
+                    299),
+                mode="bilinear",
+                align_corners=False)
 
         if self.normalize_input:
             x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
@@ -166,7 +170,7 @@ def forward(self, inp):
 def _inception_v3(*args, **kwargs):
     """Wraps `torchvision.models.inception_v3`"""
     try:
-        version = tuple(map(int, torchvision.__version__.split('.')[:2]))
+        version = tuple(map(int, torchvision.__version__.split(".")[:2]))
     except ValueError:
         # Just a caution against weird version strings
         version = (0,)
@@ -174,22 +178,22 @@ def _inception_v3(*args, **kwargs):
     # Skips default weight inititialization if supported by torchvision
     # version. See https://github.com/mseitzer/pytorch-fid/issues/28.
     if version >= (0, 6):
-        kwargs['init_weights'] = False
+        kwargs["init_weights"] = False
 
     # Backwards compatibility: `weights` argument was handled by `pretrained`
     # argument prior to version 0.13.
-    if version < (0, 13) and 'weights' in kwargs:
-        if kwargs['weights'] == 'DEFAULT':
-            kwargs['pretrained'] = True
-        elif kwargs['weights'] is None:
-            kwargs['pretrained'] = False
+    if version < (0, 13) and "weights" in kwargs:
+        if kwargs["weights"] == "DEFAULT":
+            kwargs["pretrained"] = True
+        elif kwargs["weights"] is None:
+            kwargs["pretrained"] = False
         else:
             raise ValueError(
-                'weights=={} not supported in torchvision {}'.format(
-                    kwargs['weights'], torchvision.__version__
+                "weights=={} not supported in torchvision {}".format(
+                    kwargs["weights"], torchvision.__version__
                 )
             )
-        del kwargs['weights']
+        del kwargs["weights"]
 
     return torchvision.models.inception_v3(*args, **kwargs)
 
@@ -203,9 +207,7 @@ def fid_inception_v3():
     This method first constructs torchvision's Inception and then patches the
     necessary parts that are different in the FID Inception model.
     """
-    inception = _inception_v3(num_classes=1008,
-                              aux_logits=False,
-                              weights=None)
+    inception = _inception_v3(num_classes=1008, aux_logits=False, weights=None)
     inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
     inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
     inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
@@ -223,6 +225,7 @@ def fid_inception_v3():
 
 class FIDInceptionA(torchvision.models.inception.InceptionA):
     """InceptionA block patched for FID computation"""
+
     def __init__(self, in_channels, pool_features):
         super(FIDInceptionA, self).__init__(in_channels, pool_features)
 
@@ -238,8 +241,9 @@ def forward(self, x):
 
         # Patch: Tensorflow's average pool does not use the padded zero's in
         # its average calculation
-        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
-                                   count_include_pad=False)
+        branch_pool = F.avg_pool2d(
+            x, kernel_size=3, stride=1, padding=1, count_include_pad=False
+        )
         branch_pool = self.branch_pool(branch_pool)
 
         outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
@@ -248,6 +252,7 @@ def forward(self, x):
 
 class FIDInceptionC(torchvision.models.inception.InceptionC):
     """InceptionC block patched for FID computation"""
+
     def __init__(self, in_channels, channels_7x7):
         super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
 
@@ -266,8 +271,9 @@ def forward(self, x):
 
         # Patch: Tensorflow's average pool does not use the padded zero's in
         # its average calculation
-        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
-                                   count_include_pad=False)
+        branch_pool = F.avg_pool2d(
+            x, kernel_size=3, stride=1, padding=1, count_include_pad=False
+        )
         branch_pool = self.branch_pool(branch_pool)
 
         outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
@@ -276,6 +282,7 @@ def forward(self, x):
 
 class FIDInceptionE_1(torchvision.models.inception.InceptionE):
     """First InceptionE block patched for FID computation"""
+
     def __init__(self, in_channels):
         super(FIDInceptionE_1, self).__init__(in_channels)
 
@@ -299,8 +306,9 @@ def forward(self, x):
 
         # Patch: Tensorflow's average pool does not use the padded zero's in
         # its average calculation
-        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
-                                   count_include_pad=False)
+        branch_pool = F.avg_pool2d(
+            x, kernel_size=3, stride=1, padding=1, count_include_pad=False
+        )
         branch_pool = self.branch_pool(branch_pool)
 
         outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
@@ -309,6 +317,7 @@ def forward(self, x):
 
 class FIDInceptionE_2(torchvision.models.inception.InceptionE):
     """Second InceptionE block patched for FID computation"""
+
     def __init__(self, in_channels):
         super(FIDInceptionE_2, self).__init__(in_channels)
 
@@ -338,4 +347,4 @@ def forward(self, x):
         branch_pool = self.branch_pool(branch_pool)
 
         outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)
\ No newline at end of file
+        return torch.cat(outputs, 1)
diff --git a/text_to_image/tools/latent.py b/text_to_image/tools/latent.py
index dcbfc97613..d8c3e4efed 100644
--- a/text_to_image/tools/latent.py
+++ b/text_to_image/tools/latent.py
@@ -12,8 +12,16 @@ def get_args():
     parser.add_argument(
         "--num-channels-latents", type=int, default=4, help="Batch size of the latent"
     )
-    parser.add_argument("--height", type=int, default=1024, help="Height of the image")
-    parser.add_argument("--width", type=int, default=1024, help="Width of the image")
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=1024,
+        help="Height of the image")
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=1024,
+        help="Width of the image")
     parser.add_argument(
         "--dtype",
         type=str,
@@ -33,7 +41,13 @@ def get_args():
         default=8,
         help="Variational Autoencoder scale factor, obtainded from model inspection",
     )
-    parser.add_argument("--output-type", type=str, default="pt", choices=["pt", "np"])
+    parser.add_argument(
+        "--output-type",
+        type=str,
+        default="pt",
+        choices=[
+            "pt",
+            "np"])
     args = parser.parse_args()
     return args
 
diff --git a/text_to_image/tools/sample_ids.py b/text_to_image/tools/sample_ids.py
index 8c440ec5b3..dee3a5793d 100644
--- a/text_to_image/tools/sample_ids.py
+++ b/text_to_image/tools/sample_ids.py
@@ -1,20 +1,24 @@
-
 import argparse
 import numpy as np
 import pandas as pd
 
+
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--tsv-path", default="../coco2014/captions/captions_source.tsv", help="Dataset download location"
+        "--tsv-path",
+        default="../coco2014/captions/captions_source.tsv",
+        help="Dataset download location",
     )
     parser.add_argument(
         "--output-path", default="sample_ids.txt", help="Dataset download location"
     )
     parser.add_argument(
-        "--n", type=int, default=10, help="Dataset download location"
-    )
+        "--n",
+        type=int,
+        default=10,
+        help="Dataset download location")
     parser.add_argument(
         "--seed", "-s", type=int, default=633994880, help="Dataset download location"
     )
@@ -29,8 +33,7 @@ def get_args():
     sample_ids = list(np.random.choice(df_annotations.shape[0], args.n))
     with open(args.output_path, "w+") as f:
         for i, sample in enumerate(sample_ids):
-            if i != (len(sample_ids)-1):
+            if i != (len(sample_ids) - 1):
                 f.write(str(sample) + "\n")
             else:
                 f.write(str(sample))
-    
\ No newline at end of file
diff --git a/tools/submission/filter_errors.py b/tools/submission/filter_errors.py
index 95fe7a2944..2da9dbe95b 100644
--- a/tools/submission/filter_errors.py
+++ b/tools/submission/filter_errors.py
@@ -43,8 +43,10 @@
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--input", required=True,
-                        help="orignal submission directory")
+    parser.add_argument(
+        "--input",
+        required=True,
+        help="orignal submission directory")
     parser.add_argument("--output", help="new submission directory")
     args = parser.parse_args()
     return args
diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 160d274245..a590cacc61 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -2,6 +2,7 @@
 
 The resulting excel files can be imported into google sheets.
 """
+
 import argparse
 import os
 import sys
@@ -10,315 +11,364 @@
 import pandas as pd
 import json
 
+
 def get_args():
-  """Parse commandline."""
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--input', required=True, help='results csv from checker')
-  parser.add_argument('--version', default='4.0', help='mlperf version')
-  parser.add_argument('--repository', default='submissions_inference_4.0', help='mlperf repository')
-  args = parser.parse_args()
-  return args
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input",
+        required=True,
+        help="results csv from checker")
+    parser.add_argument("--version", default="4.0", help="mlperf version")
+    parser.add_argument(
+        "--repository", default="submissions_inference_4.0", help="mlperf repository"
+    )
+    args = parser.parse_args()
+    return args
 
 
 def main():
-  args = get_args()
-
-  df = pd.read_csv(args.input).fillna('')
-
-  if df.empty:
-    return
-
-  # rename some fields
-  df.rename(
-      columns={
-          'Organization': 'Submitter',
-          'Division': 'Category',
-          'SystemType': 'Suite',
-          'SystemName': 'System',
-          'number_of_nodes': 'Nodes',
-          'host_processor_model_name': 'Processor',
-          'accelerator_model_name': 'Accelerator',
-          'accelerators_per_node': 'a#',
-          'notes': 'Notes',
-          'framework': 'Software',
-      },
-      inplace=True)
-  df.rename(columns={'Model': 'UsedModel'}, inplace=True)
-  df.rename(columns={'MlperfModel': 'Model'}, inplace=True)
-
-  # fix issues with raw data
-  df['host_processor_core_count'] = df['host_processor_core_count'].apply(
-      lambda x: 2 if x == '2 (big); 4 (LITTLE)' else x)
-  df['Availability'] = df['Availability'].apply(lambda x: 'available'
-                                                if x == 'on-premise' else x)
-
-  # cleanup counts
-  df['Accelerator'] = df['Accelerator'].apply(lambda x: x if x != '-' else '')
-  df['a#'] = df['a#'].apply(lambda x: int(x) if str(x).isnumeric() else x)
-  df['a#'] = df['a#'].apply(lambda x: x if x != 0 else '')
-  df['p#'] = df.apply(lambda x: int(x['host_processors_per_node']), axis=1)
-
-  # details url
-  base_url = f'https://github.com/mlcommons/{args.repository}/tree/main'
-  df['Details'] = df.apply(
-      lambda x: '=HYPERLINK("{}","details")'.format('/'.join(
-          [base_url, x['Category'], x['Submitter'], 'results', x['Platform']])),
-      axis=1)
-
-  # code url
-  df['Code'] = df.apply(
-      lambda x: '=HYPERLINK("{}","code")'.format('/'.join(
-          [base_url, x['Category'], x['Submitter'], 'code'])),
-      axis=1)
-
-  output = args.input[:-4]
-  writer = pd.ExcelWriter(output + '.xlsx', engine='xlsxwriter')
-  outjsondata = []
-
-  indices = {}
-  indices['closed'] = [
-      'ID',
-      'Unique ID (e.g. for Audit)',
-      'ColorKey',
-      'Submitter',
-      'Availability',
-      'System',
-      'Nodes',
-      'Processor',
-      'p#',
-      'Accelerator',
-      'a#',
-      'Software',
-      'Notes',
-  ]
-  indices['open'] = indices['closed'].copy()
-  indices['closed'].append('Details')
-  indices['closed'].append('Code')
-  indices['network'] = indices['closed'].copy()
-  indices['open'].append('UsedModel')
-  indices['open'].append('Accuracy')
-  indices['open'].append('Details')
-  indices['open'].append('Code')
-  columns = [
-      'Model',
-      'Scenario',
-      'Units',
-  ]
-  columns_order = [['Result'],
-                   [
-                       'resnet', 'retinanet', '3d-unet-99', '3d-unet-99.9',
-                       'rnnt', 'bert-99', 'bert-99.9', 'dlrm-v2-99', 'dlrm-v2-99.9',
-                       'gptj-99', 'gptj-99.9', 'stable-diffusion-xl', 'llama2-70b-99', 'llama2-70b-99.9',
-                       'mixtral-8x7b',
-                   ], ['SingleStream', 'MultiStream', 'Server', 'Offline'],
-                   [
-                       'Latency (ms)',
-                       'Samples/s',
-                       'Queries/s',
-                       'Tokens/s',
-                       'millijoules',
-                       'Watts',
-                   ]]
-
-  filter_scenarios = {
-      'datacenter': {
-          'resnet': ['Server', 'Offline'],
-          'retinanet': ['Server', 'Offline'],
-          'rnnt': ['Server', 'Offline'],
-          'bert-99': ['Server', 'Offline'],
-          'bert-99.9': ['Server', 'Offline'],
-          'dlrm-v2-99': ['Server', 'Offline'],
-          'dlrm-v2-99.9': ['Server', 'Offline'],
-          '3d-unet-99': ['Offline'],
-          '3d-unet-99.9': ['Offline'],
-          'gptj-99': ['Server', 'Offline'],
-          'gptj-99.9': ['Server', 'Offline'],
-          'stable-diffusion-xl': ['Server', 'Offline'],
-          'llama2-70b-99': ['Server', 'Offline'],
-          'llama2-70b-99.9': ['Server', 'Offline'],
-          'mixtral-8x7b': ['Server', 'Offline'],
-      },
-      'edge': {
-          'resnet': ['SingleStream', 'MultiStream', 'Offline'],
-          'retinanet': ['SingleStream', 'MultiStream', 'Offline'],
-          'rnnt': ['SingleStream', 'Offline'],
-          'bert-99': ['SingleStream', 'Offline'],
-          'bert-99.9': [],
-          'dlrm-v2-99': [],
-          'dlrm-v2-99.9': [],
-          '3d-unet-99': ['SingleStream', 'Offline'],
-          '3d-unet-99.9': ['SingleStream', 'Offline'],
-          'gptj-99': ['SingleStream', 'Offline'],
-          'gptj-99.9': ['SingleStream', 'Offline'],
-          'stable-diffusion-xl': ['SingleStream', 'Offline'],
-      }
-  }
-
-  def MakeWorksheet(df, index, filter_dict, sheet_name, outjsondata=[]):
-    for key, value in filter_dict.items():
-      if type(key) == tuple:
-        key = list(key)
-      df = df[value(df[key])]
-    if df.size == 0:
-      return
-    json_df = df.to_json(orient='records')
-    outjsondata += json.loads(json_df)
-
-    df = df.pivot_table(index=index, columns=columns, values=['Result'])
-    df = df.fillna('')
-    for i, order in enumerate(columns_order):
-      df = df.reindex(columns=order, level=i)
-    df.to_excel(writer, sheet_name=sheet_name)
-
-  def Equal(x):
-    return lambda y: y == x
-
-  def NotEqual(x):
-    return lambda y: y != x
-
-  def Contain(x):
-    return lambda y: y.str.find(x) != -1
-
-  def And(x, y):
-    return lambda z: x(z) & y(z)
-
-  def Apply(f, *args):
-    return lambda x: f(x, *args)
-
-  def FilterScenario(x, suite):
-    return x.apply(
-        lambda y: y['Scenario'] in filter_scenarios[suite][y['Model']], axis=1)
-
-  def MakeUniqueID(x):
-    key_list = ['Suite', 'Category', 'Submitter', 'Platform']
-    if x['Category'] == 'open':
-      key_list.append('UsedModel')
-    return '/'.join(x[key_list])
-
-  df['Unique ID (e.g. for Audit)'] = df.apply(MakeUniqueID, axis=1)
-  df['ColorKey'] = df.apply(
-      lambda x: ''.join(x[['Availability', 'Submitter']]), axis=1)
-  df.sort_values(
-      by=[
-          'Category', 'Availability', 'Submitter', 'Unique ID (e.g. for Audit)'
-      ],
-      inplace=True)
-  id_dict = {
-      key: 1 + value
-      for (value,
-           key) in enumerate(pd.unique(df['Unique ID (e.g. for Audit)']))
-  }
-  df['ID'] = df.apply(
-      lambda x: '{}-{:04}'.format(args.version, id_dict[x['Unique ID (e.g. for Audit)']]),
-      axis=1)
-
-  for category in ['closed', 'open', 'network']:
-    for suite in ['datacenter', 'edge']:
-      MakeWorksheet(
-          df, indices[category], {
-              'Category':
-                  Equal(category),
-              'Suite':
-                  Contain(suite),
-              'Units':
-                  And(
-                      And(NotEqual('Watts'), NotEqual('millijoules')),
-                      NotEqual('millijoules/Stream')),
-              ('Scenario', 'Model'):
-                  Apply(FilterScenario, suite)
-          }, suite + ' - ' + category, outjsondata)
-
-      MakeWorksheet(
-          df, indices[category], {
-              'Category': Equal(category),
-              'Suite': Contain(suite),
-              'has_power': Equal(True),
-              ('Scenario', 'Model'): Apply(FilterScenario, suite)
-          }, suite + ' - ' + category + ' - power', outjsondata)
-
-  def reformatlink(data, key):
-    details = data[key]
-    details = details[details.find("(")+2:details.find(",")-1]
-    return details
-
-  for i,result in enumerate(outjsondata):
-    result['Details'] = reformatlink(result, "Details")
-    result['Code'] = reformatlink(result, "Code")
-    result_id = result.pop('ID')
-    outjsondata[i] = {'ID': result_id, **result}
-
-  outjsondata.sort(key=lambda x:x["Units"])
-  outjsondata.sort(key=lambda x:x["Scenario"])
-  outjsondata.sort(key=lambda x:x["UsedModel"])
-  outjsondata.sort(key=lambda x:x["ID"])
-
-  #remove duplicate perf results
-  keystomatch = ['ID', 'UsedModel', 'Scenario', 'Units']
-  i = 0
-  n = len(outjsondata)
-  while i < n:
-    result = outjsondata[i]
-    while i < n - 1 and all(result[key] == outjsondata[i+1][key] for key in keystomatch):
-      del(outjsondata[i+1])
-      n -= 1
-    i += 1
-
-  #merge perf and power results
-  keystomatch.pop()
-
-  for i in range(len(outjsondata)):
-    result = outjsondata[i]
-    if not result:
-      continue
-    if i < len(outjsondata) - 1:
-      if all(result[key] == outjsondata[i+1][key] for key in keystomatch):
-        #print(result)
-        #print(outjsondata[i+1])
-        if "Watts" in result['Units'] or "joules" in result['Units']:
-          result['Performance_Result'] = outjsondata[i+1]['Result']
-          result['Performance_Units'] = outjsondata[i+1]['Units']
-          result['Power_Result'] = result['Result']
-          result['Power_Units'] = result['Units']
-        else:
-          result['Power_Result'] = outjsondata[i+1]['Result']
-          result['Power_Units'] = outjsondata[i+1]['Units']
-          result['Performance_Result'] = result['Result']
-          result['Performance_Units'] = result['Units']
-        outjsondata[i+1] = {}
-        del(result['Result'])
-        del(result['Units'])
-
-  for i,result in enumerate(outjsondata):
-    if result.get('Result'):
-      result['Performance_Result'] = result['Result']
-      result['Performance_Units'] = result['Units']
-      del(result['Result'])
-      del(result['Units'])
-
-  outjsondata = [ i for i in outjsondata if i != {}]
-  with open(f"{output}_results.json", "w") as f:
-    f.write(json.dumps(outjsondata, indent=2))
-  score_format = writer.book.add_format({'num_format': '#,##0.00'})
-  bg_format = writer.book.add_format({'bg_color': '#efefef'})
-  for ws in writer.book.worksheets():
-    ws.set_column(1, 1, None, None, {'hidden': 1})
-    ws.set_column(2, 2, None, None, {'hidden': 1})
-    ws.set_column(len(indices['closed']), 100, None, score_format)
-    ws.conditional_format(
-        2 + len(columns), 0, 200, 100, {
-            'type':
-                'formula',
-            'criteria':
-                '=mod(countunique($c$' + str(len(columns) + 3) + ':$c' +
-                str(len(columns) + 3) + '), 2) = 0',
-            'format':
-                bg_format,
-        })
-
-  writer.close()
-
-  return 0
-
-
-if __name__ == '__main__':
-  sys.exit(main())
+    args = get_args()
+
+    df = pd.read_csv(args.input).fillna("")
+
+    if df.empty:
+        return
+
+    # rename some fields
+    df.rename(
+        columns={
+            "Organization": "Submitter",
+            "Division": "Category",
+            "SystemType": "Suite",
+            "SystemName": "System",
+            "number_of_nodes": "Nodes",
+            "host_processor_model_name": "Processor",
+            "accelerator_model_name": "Accelerator",
+            "accelerators_per_node": "a#",
+            "notes": "Notes",
+            "framework": "Software",
+        },
+        inplace=True,
+    )
+    df.rename(columns={"Model": "UsedModel"}, inplace=True)
+    df.rename(columns={"MlperfModel": "Model"}, inplace=True)
+
+    # fix issues with raw data
+    df["host_processor_core_count"] = df["host_processor_core_count"].apply(
+        lambda x: 2 if x == "2 (big); 4 (LITTLE)" else x
+    )
+    df["Availability"] = df["Availability"].apply(
+        lambda x: "available" if x == "on-premise" else x
+    )
+
+    # cleanup counts
+    df["Accelerator"] = df["Accelerator"].apply(
+        lambda x: x if x != "-" else "")
+    df["a#"] = df["a#"].apply(lambda x: int(x) if str(x).isnumeric() else x)
+    df["a#"] = df["a#"].apply(lambda x: x if x != 0 else "")
+    df["p#"] = df.apply(lambda x: int(x["host_processors_per_node"]), axis=1)
+
+    # details url
+    base_url = f"https://github.com/mlcommons/{args.repository}/tree/main"
+    df["Details"] = df.apply(
+        lambda x: '=HYPERLINK("{}","details")'.format(
+            "/".join(
+                [base_url, x["Category"], x["Submitter"], "results", x["Platform"]]
+            )
+        ),
+        axis=1,
+    )
+
+    # code url
+    df["Code"] = df.apply(
+        lambda x: '=HYPERLINK("{}","code")'.format(
+            "/".join([base_url, x["Category"], x["Submitter"], "code"])
+        ),
+        axis=1,
+    )
+
+    output = args.input[:-4]
+    writer = pd.ExcelWriter(output + ".xlsx", engine="xlsxwriter")
+    outjsondata = []
+
+    indices = {}
+    indices["closed"] = [
+        "ID",
+        "Unique ID (e.g. for Audit)",
+        "ColorKey",
+        "Submitter",
+        "Availability",
+        "System",
+        "Nodes",
+        "Processor",
+        "p#",
+        "Accelerator",
+        "a#",
+        "Software",
+        "Notes",
+    ]
+    indices["open"] = indices["closed"].copy()
+    indices["closed"].append("Details")
+    indices["closed"].append("Code")
+    indices["network"] = indices["closed"].copy()
+    indices["open"].append("UsedModel")
+    indices["open"].append("Accuracy")
+    indices["open"].append("Details")
+    indices["open"].append("Code")
+    columns = [
+        "Model",
+        "Scenario",
+        "Units",
+    ]
+    columns_order = [
+        ["Result"],
+        [
+            "resnet",
+            "retinanet",
+            "3d-unet-99",
+            "3d-unet-99.9",
+            "rnnt",
+            "bert-99",
+            "bert-99.9",
+            "dlrm-v2-99",
+            "dlrm-v2-99.9",
+            "gptj-99",
+            "gptj-99.9",
+            "stable-diffusion-xl",
+            "llama2-70b-99",
+            "llama2-70b-99.9",
+            "mixtral-8x7b",
+        ],
+        ["SingleStream", "MultiStream", "Server", "Offline"],
+        [
+            "Latency (ms)",
+            "Samples/s",
+            "Queries/s",
+            "Tokens/s",
+            "millijoules",
+            "Watts",
+        ],
+    ]
+
+    filter_scenarios = {
+        "datacenter": {
+            "resnet": ["Server", "Offline"],
+            "retinanet": ["Server", "Offline"],
+            "rnnt": ["Server", "Offline"],
+            "bert-99": ["Server", "Offline"],
+            "bert-99.9": ["Server", "Offline"],
+            "dlrm-v2-99": ["Server", "Offline"],
+            "dlrm-v2-99.9": ["Server", "Offline"],
+            "3d-unet-99": ["Offline"],
+            "3d-unet-99.9": ["Offline"],
+            "gptj-99": ["Server", "Offline"],
+            "gptj-99.9": ["Server", "Offline"],
+            "stable-diffusion-xl": ["Server", "Offline"],
+            "llama2-70b-99": ["Server", "Offline"],
+            "llama2-70b-99.9": ["Server", "Offline"],
+            "mixtral-8x7b": ["Server", "Offline"],
+        },
+        "edge": {
+            "resnet": ["SingleStream", "MultiStream", "Offline"],
+            "retinanet": ["SingleStream", "MultiStream", "Offline"],
+            "rnnt": ["SingleStream", "Offline"],
+            "bert-99": ["SingleStream", "Offline"],
+            "bert-99.9": [],
+            "dlrm-v2-99": [],
+            "dlrm-v2-99.9": [],
+            "3d-unet-99": ["SingleStream", "Offline"],
+            "3d-unet-99.9": ["SingleStream", "Offline"],
+            "gptj-99": ["SingleStream", "Offline"],
+            "gptj-99.9": ["SingleStream", "Offline"],
+            "stable-diffusion-xl": ["SingleStream", "Offline"],
+        },
+    }
+
+    def MakeWorksheet(df, index, filter_dict, sheet_name, outjsondata=[]):
+        for key, value in filter_dict.items():
+            if isinstance(key, tuple):
+                key = list(key)
+            df = df[value(df[key])]
+        if df.size == 0:
+            return
+        json_df = df.to_json(orient="records")
+        outjsondata += json.loads(json_df)
+
+        df = df.pivot_table(index=index, columns=columns, values=["Result"])
+        df = df.fillna("")
+        for i, order in enumerate(columns_order):
+            df = df.reindex(columns=order, level=i)
+        df.to_excel(writer, sheet_name=sheet_name)
+
+    def Equal(x):
+        return lambda y: y == x
+
+    def NotEqual(x):
+        return lambda y: y != x
+
+    def Contain(x):
+        return lambda y: y.str.find(x) != -1
+
+    def And(x, y):
+        return lambda z: x(z) & y(z)
+
+    def Apply(f, *args):
+        return lambda x: f(x, *args)
+
+    def FilterScenario(x, suite):
+        return x.apply(
+            lambda y: y["Scenario"] in filter_scenarios[suite][y["Model"]], axis=1
+        )
+
+    def MakeUniqueID(x):
+        key_list = ["Suite", "Category", "Submitter", "Platform"]
+        if x["Category"] == "open":
+            key_list.append("UsedModel")
+        return "/".join(x[key_list])
+
+    df["Unique ID (e.g. for Audit)"] = df.apply(MakeUniqueID, axis=1)
+    df["ColorKey"] = df.apply(
+        lambda x: "".join(x[["Availability", "Submitter"]]), axis=1
+    )
+    df.sort_values(
+        by=["Category", "Availability", "Submitter",
+            "Unique ID (e.g. for Audit)"],
+        inplace=True,
+    )
+    id_dict = {
+        key: 1 + value
+        for (value, key) in enumerate(pd.unique(df["Unique ID (e.g. for Audit)"]))
+    }
+    df["ID"] = df.apply(
+        lambda x: "{}-{:04}".format(
+            args.version, id_dict[x["Unique ID (e.g. for Audit)"]]
+        ),
+        axis=1,
+    )
+
+    for category in ["closed", "open", "network"]:
+        for suite in ["datacenter", "edge"]:
+            MakeWorksheet(
+                df,
+                indices[category],
+                {
+                    "Category": Equal(category),
+                    "Suite": Contain(suite),
+                    "Units": And(
+                        And(NotEqual("Watts"), NotEqual("millijoules")),
+                        NotEqual("millijoules/Stream"),
+                    ),
+                    ("Scenario", "Model"): Apply(FilterScenario, suite),
+                },
+                suite + " - " + category,
+                outjsondata,
+            )
+
+            MakeWorksheet(
+                df,
+                indices[category],
+                {
+                    "Category": Equal(category),
+                    "Suite": Contain(suite),
+                    "has_power": Equal(True),
+                    ("Scenario", "Model"): Apply(FilterScenario, suite),
+                },
+                suite + " - " + category + " - power",
+                outjsondata,
+            )
+
+    def reformatlink(data, key):
+        details = data[key]
+        details = details[details.find("(") + 2: details.find(",") - 1]
+        return details
+
+    for i, result in enumerate(outjsondata):
+        result["Details"] = reformatlink(result, "Details")
+        result["Code"] = reformatlink(result, "Code")
+        result_id = result.pop("ID")
+        outjsondata[i] = {"ID": result_id, **result}
+
+    outjsondata.sort(key=lambda x: x["Units"])
+    outjsondata.sort(key=lambda x: x["Scenario"])
+    outjsondata.sort(key=lambda x: x["UsedModel"])
+    outjsondata.sort(key=lambda x: x["ID"])
+
+    # remove duplicate perf results
+    keystomatch = ["ID", "UsedModel", "Scenario", "Units"]
+    i = 0
+    n = len(outjsondata)
+    while i < n:
+        result = outjsondata[i]
+        while i < n - 1 and all(
+            result[key] == outjsondata[i + 1][key] for key in keystomatch
+        ):
+            del outjsondata[i + 1]
+            n -= 1
+        i += 1
+
+    # merge perf and power results
+    keystomatch.pop()
+
+    for i in range(len(outjsondata)):
+        result = outjsondata[i]
+        if not result:
+            continue
+        if i < len(outjsondata) - 1:
+            if all(result[key] == outjsondata[i + 1][key]
+                   for key in keystomatch):
+                # print(result)
+                # print(outjsondata[i+1])
+                if "Watts" in result["Units"] or "joules" in result["Units"]:
+                    result["Performance_Result"] = outjsondata[i + 1]["Result"]
+                    result["Performance_Units"] = outjsondata[i + 1]["Units"]
+                    result["Power_Result"] = result["Result"]
+                    result["Power_Units"] = result["Units"]
+                else:
+                    result["Power_Result"] = outjsondata[i + 1]["Result"]
+                    result["Power_Units"] = outjsondata[i + 1]["Units"]
+                    result["Performance_Result"] = result["Result"]
+                    result["Performance_Units"] = result["Units"]
+                outjsondata[i + 1] = {}
+                del result["Result"]
+                del result["Units"]
+
+    for i, result in enumerate(outjsondata):
+        if result.get("Result"):
+            result["Performance_Result"] = result["Result"]
+            result["Performance_Units"] = result["Units"]
+            del result["Result"]
+            del result["Units"]
+
+    outjsondata = [i for i in outjsondata if i != {}]
+    with open(f"{output}_results.json", "w") as f:
+        f.write(json.dumps(outjsondata, indent=2))
+    score_format = writer.book.add_format({"num_format": "#,##0.00"})
+    bg_format = writer.book.add_format({"bg_color": "#efefef"})
+    for ws in writer.book.worksheets():
+        ws.set_column(1, 1, None, None, {"hidden": 1})
+        ws.set_column(2, 2, None, None, {"hidden": 1})
+        ws.set_column(len(indices["closed"]), 100, None, score_format)
+        ws.conditional_format(
+            2 + len(columns),
+            0,
+            200,
+            100,
+            {
+                "type": "formula",
+                "criteria": "=mod(countunique($c$"
+                + str(len(columns) + 3)
+                + ":$c"
+                + str(len(columns) + 3)
+                + "), 2) = 0",
+                "format": bg_format,
+            },
+        )
+
+    writer.close()
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/tools/submission/log_parser.py b/tools/submission/log_parser.py
index 178495e320..df9d82ad15 100755
--- a/tools/submission/log_parser.py
+++ b/tools/submission/log_parser.py
@@ -21,9 +21,13 @@
 
 # pylint: disable=missing-docstring
 
-logging.basicConfig(level=logging.INFO, format="[%(asctime)s %(filename)s:%(lineno)d %(levelname)s] %(message)s")
+logging.basicConfig(
+    level=logging.INFO,
+    format="[%(asctime)s %(filename)s:%(lineno)d %(levelname)s] %(message)s",
+)
 
-class MLPerfLog():
+
+class MLPerfLog:
     def __init__(self, log_path, strict=True):
         """
         Helper class to parse the detail logs.
@@ -38,16 +42,22 @@ def __init__(self, log_path, strict=True):
                 line = line.rstrip()
                 if line.find(self.marker) == 0:
                     try:
-                        self.messages.append(json.loads(line[len(self.marker):]))
-                    except:
+                        self.messages.append(
+                            json.loads(line[len(self.marker):]))
+                    except BaseException:
                         if strict:
-                            raise RuntimeError("Encountered invalid line: {:}".format(line))
+                            raise RuntimeError(
+                                "Encountered invalid line: {:}".format(line)
+                            )
                         else:
-                            self.logger.warning("Skipping invalid line: {:}".format(line))
+                            self.logger.warning(
+                                "Skipping invalid line: {:}".format(line)
+                            )
         self.keys = set()
         for message in self.messages:
             self.keys.add(message["key"])
-        self.logger.info("Sucessfully loaded MLPerf log from {:}.".format(log_path))
+        self.logger.info(
+            "Sucessfully loaded MLPerf log from {:}.".format(log_path))
 
     def __getitem__(self, key):
         """
@@ -60,7 +70,11 @@ def __getitem__(self, key):
             if message["key"] == key:
                 results.append(message)
         if len(results) != 1:
-            self.logger.warning("There are multiple messages with key {:} in the log. Emprically choosing the first one.".format(key))
+            self.logger.warning(
+                "There are multiple messages with key {:} in the log. Emprically choosing the first one.".format(
+                    key
+                )
+            )
         return results[0]["value"]
 
     def get(self, key):
@@ -95,7 +109,11 @@ def get_dict(self):
             if message["key"] not in result:
                 result[message["key"]] = message["value"]
             else:
-                self.logger.warning("There are multiple messages with key {:} in the log. Emprically choosing the first one.".format(key))
+                self.logger.warning(
+                    "There are multiple messages with key {:} in the log. Emprically choosing the first one.".format(
+                        key
+                    )
+                )
 
     def dump(self, output_path):
         """
@@ -105,11 +123,11 @@ def dump(self, output_path):
             json.dump(self.messages, f, indent=4)
 
     def num_messages(self):
-        """ Get number of messages (including errors and warnings) in the log. """
+        """Get number of messages (including errors and warnings) in the log."""
         return len(self.messages)
 
     def num_errors(self):
-        """ Get number of errors in the log. """
+        """Get number of errors in the log."""
         count = 0
         for message in self.messages:
             if message["metadata"]["is_error"]:
@@ -117,7 +135,7 @@ def num_errors(self):
         return count
 
     def num_warnings(self):
-        """ Get number of warning in the log. """
+        """Get number of warning in the log."""
         count = 0
         for message in self.messages:
             if message["metadata"]["is_warning"]:
@@ -125,11 +143,11 @@ def num_warnings(self):
         return count
 
     def has_error(self):
-        """ Check if the log contains any errors. """
+        """Check if the log contains any errors."""
         return self.num_errors() != 0
 
     def has_warning(self):
-        """ Check if the log contains any warnings. """
+        """Check if the log contains any warnings."""
         return self.num_warnings() != 0
 
     def get_errors(self):
@@ -152,14 +170,23 @@ def get_warnings(self):
                 results.append(message)
         return results
 
+
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--input", required=True, help="path to the detail log")
-    parser.add_argument("--ignore_invalid_lines", action="store_true", help="whether to stop if there are lines with invalid formats")
+    parser.add_argument(
+        "--input",
+        required=True,
+        help="path to the detail log")
+    parser.add_argument(
+        "--ignore_invalid_lines",
+        action="store_true",
+        help="whether to stop if there are lines with invalid formats",
+    )
     args = parser.parse_args()
     return args
 
+
 def main():
     """
     Inspect a detailed log.
@@ -174,8 +201,9 @@ def main():
     logger.info("- Contents:")
     messages = mlperf_log.get_messages()
     for message in messages:
-        logger.info("\"{:}\": {:}".format(message["key"], message["value"]))
+        logger.info('"{:}": {:}'.format(message["key"], message["value"]))
     logger.info("Done!")
 
+
 if __name__ == "__main__":
     sys.exit(main())
diff --git a/tools/submission/power/power_checker.py b/tools/submission/power/power_checker.py
index 93d9d6fb98..834cd59ddf 100755
--- a/tools/submission/power/power_checker.py
+++ b/tools/submission/power/power_checker.py
@@ -81,7 +81,8 @@ class CheckerWarning(Exception):
 ]
 COMMON_ERROR_TESTING = ["USB."]
 WARNING_NEEDS_TO_BE_ERROR_TESTING_RE = [
-    re.compile(r"Uncertainty \d+.\d+%, which is above 1.00% limit for the last sample!")
+    re.compile(
+        r"Uncertainty \d+.\d+%, which is above 1.00% limit for the last sample!")
 ]
 
 TIME_DELTA_TOLERANCE = 800  # in milliseconds
@@ -89,7 +90,7 @@ class CheckerWarning(Exception):
 
 def _normalize(path: str) -> str:
     allparts: List[str] = []
-    while 1:
+    while True:
         parts = os.path.split(path)
         if parts[0] == path:  # sentinel for absolute paths
             allparts.insert(0, parts[0])
@@ -126,8 +127,10 @@ def get_time_from_line(
 ) -> float:
     log_time_str = re.search(data_regexp, line)
     if log_time_str and log_time_str.group(0):
-        log_datetime = datetime.strptime(log_time_str.group(0), "%m-%d-%Y %H:%M:%S.%f")
-        return log_datetime.replace(tzinfo=timezone.utc).timestamp() + timezone_offset
+        log_datetime = datetime.strptime(
+            log_time_str.group(0), "%m-%d-%Y %H:%M:%S.%f")
+        return log_datetime.replace(
+            tzinfo=timezone.utc).timestamp() + timezone_offset
     raise LineWithoutTimeStamp(f"{line.strip()!r} in {file}.")
 
 
@@ -156,8 +159,10 @@ def required_fields_check(self) -> None:
         ), f"Required fields {', '.join(absent_keys)!r} does not exist in {self.path!r}"
 
 
-def compare_dicts_values(d1: Dict[str, str], d2: Dict[str, str], comment: str) -> None:
-    files_with_diff_check_sum = {k: d1[k] for k in d1 if k in d2 and d1[k] != d2[k]}
+def compare_dicts_values(
+        d1: Dict[str, str], d2: Dict[str, str], comment: str) -> None:
+    files_with_diff_check_sum = {k: d1[k]
+                                 for k in d1 if k in d2 and d1[k] != d2[k]}
     assert len(files_with_diff_check_sum) == 0, f"{comment}" + "".join(
         [
             f"Expected {d1[i]}, but got {d2[i]} for {i}\n"
@@ -166,7 +171,8 @@ def compare_dicts_values(d1: Dict[str, str], d2: Dict[str, str], comment: str) -
     )
 
 
-def compare_dicts(s1: Dict[str, str], s2: Dict[str, str], comment: str) -> None:
+def compare_dicts(s1: Dict[str, str],
+                  s2: Dict[str, str], comment: str) -> None:
     assert (
         not s1.keys() - s2.keys()
     ), f"{comment} Missing {', '.join(sorted(s1.keys() - s2.keys()))!r}"
@@ -224,7 +230,8 @@ def check_reply(cmd: str, reply: str) -> None:
         for msg in msgs:
             if msg["cmd"].startswith(cmd):
                 if msg["cmd"] == "Stop":
-                    # In normal flow the third answer to stop command is `Error: no measurement to stop`
+                    # In normal flow the third answer to stop command is
+                    # `Error: no measurement to stop`
                     if stop_counter == 2:
                         reply = "Error: no measurement to stop"
                     stop_counter += 1
@@ -243,13 +250,15 @@ def check_reply(cmd: str, reply: str) -> None:
     def get_initial_range(param_num: int, reply: str) -> str:
         reply_list = reply.split(",")
         try:
-            if reply_list[param_num] == "0" and float(reply_list[param_num + 1]) > 0:
+            if reply_list[param_num] == "0" and float(
+                    reply_list[param_num + 1]) > 0:
                 return reply_list[param_num + 1]
         except (ValueError, IndexError):
             assert False, f"Can not get power meters initial values from {reply!r}"
         return "Auto"
 
-    def get_command_by_value_and_number(cmd: str, number: int) -> Optional[str]:
+    def get_command_by_value_and_number(
+            cmd: str, number: int) -> Optional[str]:
         command_counter = 0
         for msg in msgs:
             if msg["cmd"].startswith(cmd):
@@ -273,7 +282,8 @@ def get_command_by_value_and_number(cmd: str, number: int) -> Optional[str]:
     ), f"Do not set Volts range as initial. Expected 'SR,V,{initial_volts}', got {initial_volts_command!r}."
 
 
-def uuid_check(client_sd: SessionDescriptor, server_sd: SessionDescriptor) -> None:
+def uuid_check(client_sd: SessionDescriptor,
+               server_sd: SessionDescriptor) -> None:
     """Compare UUIDs from client.json and server.json. They should be the same."""
     uuid_c = client_sd.json_object["uuid"]
     uuid_s = server_sd.json_object["uuid"]
@@ -362,7 +372,8 @@ def compare_duration(range_duration: float, test_duration: float) -> None:
     def compare_time_boundaries(
         begin: float, end: float, phases: List[Any], mode: str
     ) -> None:
-        # TODO: temporary workaround, remove when proper DST handling is implemented!
+        # TODO: temporary workaround, remove when proper DST handling is
+        # implemented!
         assert (
             phases[1][0] < begin < phases[2][0]
             or phases[1][0] < begin - 3600 < phases[2][0]
@@ -380,8 +391,16 @@ def compare_time_boundaries(
         os.path.join(path, "run_1"), client_sd
     )
 
-    compare_time_boundaries(system_begin_r, system_end_r, phases_ranging_c, "ranging")
-    compare_time_boundaries(system_begin_t, system_end_t, phases_testing_c, "testing")
+    compare_time_boundaries(
+        system_begin_r,
+        system_end_r,
+        phases_ranging_c,
+        "ranging")
+    compare_time_boundaries(
+        system_begin_t,
+        system_end_t,
+        phases_testing_c,
+        "testing")
 
     ranging_duration_d = system_end_r - system_begin_r
     testing_duration_d = system_end_t - system_begin_t
@@ -464,7 +483,8 @@ def session_name_check(
     ), f"Session name is not equal. Client session name is {session_name_c!r}. Server session name is {session_name_s!r}"
 
 
-def messages_check(client_sd: SessionDescriptor, server_sd: SessionDescriptor) -> None:
+def messages_check(client_sd: SessionDescriptor,
+                   server_sd: SessionDescriptor) -> None:
     """Compare client and server messages list length.
     Compare messages values and replies from client.json and server.json.
     Compare client and server version.
@@ -488,14 +508,19 @@ def messages_check(client_sd: SessionDescriptor, server_sd: SessionDescriptor) -
             )
 
     # Check client and server version from server.json.
-    # Server.json contains all client.json messages and replies. Checked earlier.
+    # Server.json contains all client.json messages and replies. Checked
+    # earlier.
     def get_version(regexp: str, line: str) -> str:
         version_o = re.search(regexp, line)
         assert version_o is not None, f"Server version is not defined in:'{line}'"
         return version_o.group(1)
 
-    client_version = get_version(r"mlcommons\/power client v(\d+)$", ms[0]["cmd"])
-    server_version = get_version(r"mlcommons\/power server v(\d+)$", ms[0]["reply"])
+    client_version = get_version(
+        r"mlcommons\/power client v(\d+)$",
+        ms[0]["cmd"])
+    server_version = get_version(
+        r"mlcommons\/power server v(\d+)$",
+        ms[0]["reply"])
 
     assert (
         client_version == server_version
@@ -550,7 +575,8 @@ def remove_optional_path(res: Dict[str, str]) -> None:
         f"{client_sd.path} and {server_sd.path} results checksum comparison",
     )
 
-    # Check if the hashes of the files in results directory match the ones recorded in server.json/client.json.
+    # Check if the hashes of the files in results directory match the ones
+    # recorded in server.json/client.json.
     result_c_s = {**results_c, **results_s}
 
     compare_dicts(
@@ -616,7 +642,8 @@ def find_error_or_warning(reg_exp: str, line: str, error: bool) -> None:
 
                 # Treat uncommon errors in ranging phase as warnings
                 if all(
-                    not problem_line.group(0).strip().startswith(common_ranging_error)
+                    not problem_line.group(0).strip().startswith(
+                        common_ranging_error)
                     for common_ranging_error in COMMON_ERROR_RANGING
                 ):
                     raise CheckerWarning(
@@ -674,7 +701,8 @@ def get_msg_without_time(line: str) -> Optional[str]:
     is_uncertainty_check_activated = False
 
     for line in ptd_log_lines:
-        msg_o = re.search(r"Uncertainty checking for Yokogawa\S+ is activated", line)
+        msg_o = re.search(
+            r"Uncertainty checking for Yokogawa\S+ is activated", line)
         if msg_o is not None:
             try:
                 log_time = None
@@ -742,7 +770,8 @@ def debug_check(server_sd: SessionDescriptor) -> None:
     ), "Server was running in debug mode"
 
 
-def check_with_logging(check_name: str, check: Callable[[], None]) -> Tuple[bool, bool]:
+def check_with_logging(
+        check_name: str, check: Callable[[], None]) -> Tuple[bool, bool]:
     try:
         check()
     except AssertionError as e:
@@ -811,7 +840,9 @@ def check(path: str) -> int:
     parser = argparse.ArgumentParser(
         description="Check PTD client-server session results"
     )
-    parser.add_argument("session_directory", help="directory with session results data")
+    parser.add_argument(
+        "session_directory",
+        help="directory with session results data")
 
     args = parser.parse_args()
 
diff --git a/tools/submission/preprocess_submission.py b/tools/submission/preprocess_submission.py
index fee2aadde8..d49d0f1ea7 100644
--- a/tools/submission/preprocess_submission.py
+++ b/tools/submission/preprocess_submission.py
@@ -30,29 +30,44 @@
 
 """
 
+
 def get_args():
     """Parse commandline."""
-    parser = argparse.ArgumentParser(description="Infer scenario results",
-                    formatter_class=argparse.RawDescriptionHelpFormatter, epilog=HELP_TEXT)
-    parser.add_argument("--input", required=True, help="orignal submission directory")
+    parser = argparse.ArgumentParser(
+        description="Infer scenario results",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=HELP_TEXT,
+    )
+    parser.add_argument(
+        "--input",
+        required=True,
+        help="orignal submission directory")
     parser.add_argument("--output", help="new submission directory")
-    parser.add_argument("--noinfer_low_accuracy_results",
+    parser.add_argument(
+        "--noinfer_low_accuracy_results",
         help="do not infer low accuracy results if a high accuracy result is present",
-        default=False, action="store_true")
-    parser.add_argument("--nodelete_empty_dirs",
+        default=False,
+        action="store_true",
+    )
+    parser.add_argument(
+        "--nodelete_empty_dirs",
         help="do not delete empty dirs in submission tree",
-        default=False, action="store_true")
+        default=False,
+        action="store_true",
+    )
     parser.add_argument(
         "--version",
         default="v4.0",
         choices=list(checker.MODEL_CONFIG.keys()),
-        help="mlperf version")
+        help="mlperf version",
+    )
     parser.add_argument("--submitter", help="filter to submitter")
     parser.add_argument(
         "--extra-model-benchmark-map",
         help="File containing extra custom model mapping.\
         It is assumed to be inside the folder open/<submitter>",
-        default="model_mapping.json")
+        default="model_mapping.json",
+    )
 
     args = parser.parse_args()
     if not args.output:
@@ -69,6 +84,7 @@ def list_dir(*path):
     path = os.path.join(*path)
     return next(os.walk(path))[1]
 
+
 def delete_empty_dirs(src):
     """
     Deletes any empty directory in the src tree
@@ -76,13 +92,15 @@ def delete_empty_dirs(src):
     if not os.path.isdir(src):
         return False
 
-    if all ([delete_empty_dirs(os.path.join(src, file)) for file in os.listdir(src)]):
+    if all([delete_empty_dirs(os.path.join(src, file))
+           for file in os.listdir(src)]):
         log.info("Removing empty dir: (%s)", src)
         os.rmdir(src)
         return True
 
     return False
 
+
 def copy_submission_dir(src, dst, filter_submitter):
     """
     Copies the submission tree to output directory for processing
@@ -93,11 +111,14 @@ def copy_submission_dir(src, dst, filter_submitter):
         for submitter in next(os.walk(os.path.join(src, division)))[1]:
             if filter_submitter and submitter != filter_submitter:
                 continue
-            shutil.copytree(os.path.join(src, division, submitter),
-                            os.path.join(dst, division, submitter))
+            shutil.copytree(
+                os.path.join(src, division, submitter),
+                os.path.join(dst, division, submitter),
+            )
 
 
-def infer_scenario_results(filter_submitter, noinfer_low_accuracy_results, config):
+def infer_scenario_results(
+        filter_submitter, noinfer_low_accuracy_results, config):
     """Walk result dir and check for singlestream (SS) folders and \
                corresponding offline and multistream (MS) ones.
        If SS exists and offline and MS are not existing, \
@@ -116,8 +137,9 @@ def infer_scenario_results(filter_submitter, noinfer_low_accuracy_results, confi
                 continue
 
             # process results
-            for directory in ["results", "measurements"] + \
-                (["compliance"] if division == "closed" else []):
+            for directory in ["results", "measurements"] + (
+                ["compliance"] if division == "closed" else []
+            ):
 
                 log_path = os.path.join(division, submitter, directory)
                 if not os.path.exists(log_path):
@@ -125,136 +147,216 @@ def infer_scenario_results(filter_submitter, noinfer_low_accuracy_results, confi
                     continue
 
                 for system_desc in list_dir(log_path):
-                    system_id_json = os.path.join(division, submitter, "systems",
-                                      system_desc + ".json")
+                    system_id_json = os.path.join(
+                        division, submitter, "systems", system_desc + ".json"
+                    )
                     if not os.path.exists(system_id_json):
-                        log.error("no system_desc for %s/%s/%s", division, submitter,
-                            system_desc)
+                        log.error(
+                            "no system_desc for %s/%s/%s",
+                            division,
+                            submitter,
+                            system_desc,
+                        )
                         continue
 
                     with open(system_id_json) as system_info:
                         system_json = json.load(system_info)
                     system_type = system_json.get("system_type")
-                    valid_system_types = ["datacenter", "edge", \
-                            "datacenter,edge", "edge,datacenter"]
+                    valid_system_types = [
+                        "datacenter",
+                        "edge",
+                        "datacenter,edge",
+                        "edge,datacenter",
+                    ]
                     if system_type not in valid_system_types:
-                        log.error("Division %s, submitter %s, "\
-                                "system %s has invalid system type (%s)", \
-                                division, submitter, system_id_json, system_type)
+                        log.error(
+                            "Division %s, submitter %s, "
+                            "system %s has invalid system type (%s)",
+                            division,
+                            submitter,
+                            system_id_json,
+                            system_type,
+                        )
                     config.set_type(system_type)
 
                     for model in list_dir(log_path, system_desc):
                         extra_model_mapping = None
                         if division == "open":
-                            model_mapping_path = (
-                                f"{division}/{submitter}/{config.extra_model_benchmark_map}"
-                            )
+                            model_mapping_path = f"{division}/{submitter}/{config.extra_model_benchmark_map}"
                             if os.path.exists(model_mapping_path):
                                 with open(model_mapping_path) as fp:
                                     extra_model_mapping = json.load(fp)
 
-                        mlperf_model = config.get_mlperf_model(model, extra_model_mapping)
+                        mlperf_model = config.get_mlperf_model(
+                            model, extra_model_mapping
+                        )
                         if not mlperf_model:
-                            log.error("Division %s, submitter %s, system %s has "\
-                                    "invalid model (%s)", division, submitter, \
-                                    system_id_json, model)
+                            log.error(
+                                "Division %s, submitter %s, system %s has "
+                                "invalid model (%s)",
+                                division,
+                                submitter,
+                                system_id_json,
+                                model,
+                            )
                             continue
 
                         if mlperf_model not in config.required:
-                            log.error("Division %s, submitter %s, system %s has invalid "\
-                                    "MLPerf model (%s) corresponding to given model (%s). "\
-                                    "Valid ones for MLPerf inference version (%s) in (%s) "\
-                                    "category are [%s]", division, submitter, system_id_json,\
-                                    mlperf_model, model, config.version, system_type, \
-                                    config.required.keys())
+                            log.error(
+                                "Division %s, submitter %s, system %s has invalid "
+                                "MLPerf model (%s) corresponding to given model (%s). "
+                                "Valid ones for MLPerf inference version (%s) in (%s) "
+                                "category are [%s]",
+                                division,
+                                submitter,
+                                system_id_json,
+                                mlperf_model,
+                                model,
+                                config.version,
+                                system_type,
+                                config.required.keys(),
+                            )
                             continue
 
-
                         required_scenarios = config.get_required(model)
                         all_scenarios = set(
-                            list(required_scenarios) +
-                            list(config.get_optional(mlperf_model)))
+                            list(required_scenarios)
+                            + list(config.get_optional(mlperf_model))
+                        )
 
                         for scenario in list_dir(log_path, system_desc, model):
 
-                            scenario_path = os.path.join(log_path, system_desc, model, scenario)
+                            scenario_path = os.path.join(
+                                log_path, system_desc, model, scenario
+                            )
 
                             if scenario.lower() == "singlestream":
                                 tobeinferredpaths = []
-                                offline_scenario_path =  os.path.join(log_path, system_desc, \
-                                        model, "offline")
-                                multistream_scenario_path =  os.path.join(log_path, system_desc, \
-                                        model, "multistream")
-                                if not os.path.exists(multistream_scenario_path) and \
-                                        not os.path.exists(offline_scenario_path):
-                                    #infer both the scenarios from SS
-                                    tobeinferredpaths = [ offline_scenario_path ]
+                                offline_scenario_path = os.path.join(
+                                    log_path, system_desc, model, "offline"
+                                )
+                                multistream_scenario_path = os.path.join(
+                                    log_path, system_desc, model, "multistream"
+                                )
+                                if not os.path.exists(
+                                    multistream_scenario_path
+                                ) and not os.path.exists(offline_scenario_path):
+                                    # infer both the scenarios from SS
+                                    tobeinferredpaths = [offline_scenario_path]
                                     if "MultiStream" in all_scenarios:
-                                        tobeinferredpaths.append(multistream_scenario_path)
+                                        tobeinferredpaths.append(
+                                            multistream_scenario_path
+                                        )
 
                                     for tobeinferredpath in tobeinferredpaths:
-                                        inferred_scenario = os.path.basename(tobeinferredpath)
-                                        log.info("Division %s, submitter %s, system %s, " \
+                                        inferred_scenario = os.path.basename(
+                                            tobeinferredpath
+                                        )
+                                        log.info(
+                                            "Division %s, submitter %s, system %s, "
                                             "model %s: \
-                                            inferring %s results from %s", \
-                                            division, submitter, system_desc, model, \
-                                            inferred_scenario, "singlestream")
-                                        shutil.copytree(scenario_path, tobeinferredpath)
-
-                                elif not os.path.exists(multistream_scenario_path) and \
-                                        "MultiStream" in all_scenarios:
-                                    #infer MS from SS
-                                    for tobeinferredpath in [ multistream_scenario_path ]:
-                                        log.info("Division %s, submitter %s, system %s, model %s: \
-                                            inferring %s results from %s", division, submitter, \
-                                            system_desc, model, "multistream", "singlestream")
-                                        shutil.copytree(scenario_path, multistream_scenario_path)
+                                            inferring %s results from %s",
+                                            division,
+                                            submitter,
+                                            system_desc,
+                                            model,
+                                            inferred_scenario,
+                                            "singlestream",
+                                        )
+                                        shutil.copytree(
+                                            scenario_path, tobeinferredpath)
+
+                                elif (
+                                    not os.path.exists(
+                                        multistream_scenario_path)
+                                    and "MultiStream" in all_scenarios
+                                ):
+                                    # infer MS from SS
+                                    for tobeinferredpath in [
+                                            multistream_scenario_path]:
+                                        log.info(
+                                            "Division %s, submitter %s, system %s, model %s: \
+                                            inferring %s results from %s",
+                                            division,
+                                            submitter,
+                                            system_desc,
+                                            model,
+                                            "multistream",
+                                            "singlestream",
+                                        )
+                                        shutil.copytree(
+                                            scenario_path, multistream_scenario_path
+                                        )
                                 elif not os.path.exists(offline_scenario_path):
-                                    '''we have both MS and SS results. Inferring from MS is \
+                                    """we have both MS and SS results. Inferring from MS is \
                                         expected to be better \
-                                    '''
+                                    """
                                     pass
 
                             elif scenario.lower() == "multistream":
-                                offline_scenario_path =  os.path.join(log_path, system_desc, \
-                                        model, "offline")
-                                '''Need to check if MS is indeed a measured result and not infeered.\
+                                offline_scenario_path = os.path.join(
+                                    log_path, system_desc, model, "offline"
+                                )
+                                """Need to check if MS is indeed a measured result and not infeered.\
                                 But if MS is indeed inferred from SS, offline scenario will also be \
                                 inferred already by the inferring code above \
-                                '''
-                                for tobeinferredpath in [offline_scenario_path]:
+                                """
+                                for tobeinferredpath in [
+                                        offline_scenario_path]:
                                     if not os.path.exists(tobeinferredpath):
-                                        log.info("Division %s, submitter %s, system %s, model %s: \
-                                                inferring %s results from %s", division, submitter,\
-                                                system_desc, model, "offline", "multistream")
-                                        shutil.copytree(scenario_path, tobeinferredpath)
+                                        log.info(
+                                            "Division %s, submitter %s, system %s, model %s: \
+                                                inferring %s results from %s",
+                                            division,
+                                            submitter,
+                                            system_desc,
+                                            model,
+                                            "offline",
+                                            "multistream",
+                                        )
+                                        shutil.copytree(
+                                            scenario_path, tobeinferredpath)
 
                 if not noinfer_low_accuracy_results:
                     for system_desc in list_dir(log_path):
                         for model in list_dir(log_path, system_desc):
                             if model.endswith("-99.9"):
-                                low_accuracy_model =model[:-2]
+                                low_accuracy_model = model[:-2]
                                 if low_accuracy_model not in config.required:
                                     continue
-                                high_accuracy_model_path = os.path.join(log_path, \
-                                        system_desc, model)
-                                low_accuracy_model_path = os.path.join(log_path, system_desc, \
-                                        low_accuracy_model)
+                                high_accuracy_model_path = os.path.join(
+                                    log_path, system_desc, model
+                                )
+                                low_accuracy_model_path = os.path.join(
+                                    log_path, system_desc, low_accuracy_model
+                                )
                                 if not os.path.exists(low_accuracy_model_path):
-                                    log.info("Division %s, submitter %s, system %s: \
-                                            copying %s results to %s", division, submitter, \
-                                            system_desc, model, low_accuracy_model)
-
-                                    shutil.copytree(high_accuracy_model_path, \
-                                            low_accuracy_model_path)
-                                high_accuracy_model_code_path = os.path.join(log_path, "..", \
-                                        "code", model)
-                                low_accuracy_model_code_path = os.path.join(log_path, "..", \
-                                        "code", low_accuracy_model)
-                                if not os.path.exists(low_accuracy_model_code_path):
-                                    shutil.copytree(high_accuracy_model_code_path, \
-                                            low_accuracy_model_code_path)
-
+                                    log.info(
+                                        "Division %s, submitter %s, system %s: \
+                                            copying %s results to %s",
+                                        division,
+                                        submitter,
+                                        system_desc,
+                                        model,
+                                        low_accuracy_model,
+                                    )
+
+                                    shutil.copytree(
+                                        high_accuracy_model_path,
+                                        low_accuracy_model_path,
+                                    )
+                                high_accuracy_model_code_path = os.path.join(
+                                    log_path, "..", "code", model
+                                )
+                                low_accuracy_model_code_path = os.path.join(
+                                    log_path, "..", "code", low_accuracy_model
+                                )
+                                if not os.path.exists(
+                                        low_accuracy_model_code_path):
+                                    shutil.copytree(
+                                        high_accuracy_model_code_path,
+                                        low_accuracy_model_code_path,
+                                    )
 
 
 def main():
@@ -272,16 +374,17 @@ def main():
     copy_submission_dir(args.input, args.output, args.submitter)
     src_dir = args.output
 
-    config = checker.Config(
-      args.version,
-      args.extra_model_benchmark_map)
+    config = checker.Config(args.version, args.extra_model_benchmark_map)
 
     if not args.nodelete_empty_dirs:
         delete_empty_dirs(os.path.join(src_dir))
 
     os.chdir(src_dir)
 
-    infer_scenario_results(args.submitter, args.noinfer_low_accuracy_results, config)
+    infer_scenario_results(
+        args.submitter,
+        args.noinfer_low_accuracy_results,
+        config)
 
     return 0
 
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 52c85be7e5..e6218aa2e5 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -98,22 +98,65 @@
             "dlrm-v2-99.9": ("AUC", 80.31 * 0.999),
             "3d-unet-99": ("DICE", 0.86170 * 0.99),
             "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
-            "gptj-99" : ("ROUGE1", 42.9865 * 0.99, "ROUGE2", 20.1235 * 0.99, "ROUGEL", 29.9881 * 0.99, "GEN_LEN", 4016878*0.9),
-            "gptj-99.9" : ("ROUGE1", 42.9865 * 0.999, "ROUGE2", 20.1235 * 0.999, "ROUGEL", 29.9881 * 0.999, "GEN_LEN", 4016878*0.9),
-            "llama2-70b-99" : ("ROUGE1", 44.4312 * 0.99, "ROUGE2", 22.0352 * 0.99, "ROUGEL", 28.6162 * 0.99, "TOKENS_PER_SAMPLE", 294.45*0.9),
-            "llama2-70b-99.9" : ("ROUGE1", 44.4312 * 0.999, "ROUGE2", 22.0352 * 0.999, "ROUGEL", 28.6162 * 0.999, "TOKENS_PER_SAMPLE", 294.45*0.9),
-            "stable-diffusion-xl": ("CLIP_SCORE", 31.68631873, "FID_SCORE", 23.01085758)
+            "gptj-99": (
+                "ROUGE1",
+                42.9865 * 0.99,
+                "ROUGE2",
+                20.1235 * 0.99,
+                "ROUGEL",
+                29.9881 * 0.99,
+                "GEN_LEN",
+                4016878 * 0.9,
+            ),
+            "gptj-99.9": (
+                "ROUGE1",
+                42.9865 * 0.999,
+                "ROUGE2",
+                20.1235 * 0.999,
+                "ROUGEL",
+                29.9881 * 0.999,
+                "GEN_LEN",
+                4016878 * 0.9,
+            ),
+            "llama2-70b-99": (
+                "ROUGE1",
+                44.4312 * 0.99,
+                "ROUGE2",
+                22.0352 * 0.99,
+                "ROUGEL",
+                28.6162 * 0.99,
+                "TOKENS_PER_SAMPLE",
+                294.45 * 0.9,
+            ),
+            "llama2-70b-99.9": (
+                "ROUGE1",
+                44.4312 * 0.999,
+                "ROUGE2",
+                22.0352 * 0.999,
+                "ROUGEL",
+                28.6162 * 0.999,
+                "TOKENS_PER_SAMPLE",
+                294.45 * 0.9,
+            ),
+            "stable-diffusion-xl": (
+                "CLIP_SCORE",
+                31.68631873,
+                "FID_SCORE",
+                23.01085758,
+            ),
         },
         "accuracy-upper-limit": {
-            "stable-diffusion-xl": ("CLIP_SCORE", 31.81331801, "FID_SCORE", 23.95007626),
-            "llama2-70b-99" : ("TOKENS_PER_SAMPLE", 294.45*1.1),
-            "llama2-70b-99.9" : ("TOKENS_PER_SAMPLE", 294.45*1.1)
+            "stable-diffusion-xl": (
+                "CLIP_SCORE",
+                31.81331801,
+                "FID_SCORE",
+                23.95007626,
+            ),
+            "llama2-70b-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
+            "llama2-70b-99.9": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
         },
         "accuracy-delta-perc": {
-            "stable-diffusion-xl": {
-                "CLIP_SCORE": 1,
-                "FID_SCORE": 2
-            }
+            "stable-diffusion-xl": {"CLIP_SCORE": 1, "FID_SCORE": 2}
         },
         "performance-sample-count": {
             "resnet": 1024,
@@ -129,14 +172,14 @@
             "gptj-99.9": 13368,
             "llama2-70b-99": 24576,
             "llama2-70b-99.9": 24576,
-            "stable-diffusion-xl": 5000
+            "stable-diffusion-xl": 5000,
         },
         # TODO: Update this list.
         "model_mapping": {
             # map model names to the official mlperf model class
             "ssd-resnet34": "retinanet",
             "mobilenet": "resnet",
-            "resnet50": "resnet"
+            "resnet50": "resnet",
         },
         "seeds": {
             # TODO: Update random seeds
@@ -163,7 +206,7 @@
             "gptj-99.9": {"Server": 20000000000},
             "llama2-70b-99": {"Server": 20000000000},
             "llama2-70b-99.9": {"Server": 20000000000},
-            "stable-diffusion-xl" : {"Server": 20000000000}
+            "stable-diffusion-xl": {"Server": 20000000000},
         },
         "min-queries": {
             "resnet": {
@@ -189,7 +232,11 @@
             "gptj-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "llama2-70b-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "llama2-70b-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "stable-diffusion-xl": {"SingleStream": 1024, "Server": 270336, "Offline": 1}
+            "stable-diffusion-xl": {
+                "SingleStream": 1024,
+                "Server": 270336,
+                "Offline": 1,
+            },
         },
     },
     "v4.1": {
@@ -207,7 +254,7 @@
             "llama2-70b-99",
             "llama2-70b-99.9",
             "stable-diffusion-xl",
-            "mixtral-8x7b"
+            "mixtral-8x7b",
         ],
         "required-scenarios-datacenter": {
             "resnet": ["Server", "Offline"],
@@ -223,7 +270,7 @@
             "llama2-70b-99": ["Server", "Offline"],
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["Server", "Offline"],
-            "mixtral-8x7b": ["Server", "Offline"]
+            "mixtral-8x7b": ["Server", "Offline"],
         },
         "optional-scenarios-datacenter": {},
         "required-scenarios-edge": {
@@ -251,7 +298,7 @@
             "llama2-70b-99": ["Server", "Offline"],
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
-            "mixtral-8x7b": ["SingleStream", "Server", "Offline"]
+            "mixtral-8x7b": ["SingleStream", "Server", "Offline"],
         },
         "optional-scenarios-datacenter-edge": {},
         "accuracy-target": {
@@ -263,24 +310,80 @@
             "dlrm-v2-99.9": ("AUC", 80.31 * 0.999),
             "3d-unet-99": ("DICE", 0.86170 * 0.99),
             "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
-            "gptj-99" : ("ROUGE1", 42.9865 * 0.99, "ROUGE2", 20.1235 * 0.99, "ROUGEL", 29.9881 * 0.99, "GEN_LEN", 4016878*0.9),
-            "gptj-99.9" : ("ROUGE1", 42.9865 * 0.999, "ROUGE2", 20.1235 * 0.999, "ROUGEL", 29.9881 * 0.999, "GEN_LEN", 4016878*0.9),
-            "llama2-70b-99" : ("ROUGE1", 44.4312 * 0.99, "ROUGE2", 22.0352 * 0.99, "ROUGEL", 28.6162 * 0.99, "TOKENS_PER_SAMPLE", 294.45*0.9),
-            "llama2-70b-99.9" : ("ROUGE1", 44.4312 * 0.999, "ROUGE2", 22.0352 * 0.999, "ROUGEL", 28.6162 * 0.999, "TOKENS_PER_SAMPLE", 294.45*0.9),
-            "stable-diffusion-xl": ("CLIP_SCORE", 31.68631873, "FID_SCORE", 23.01085758),
-            "mixtral-8x7b" : ("ROUGE1", 45.4911 * 0.99, "ROUGE2", 23.2829 * 0.99, "ROUGEL", 30.3615 * 0.99, "TOKENS_PER_SAMPLE", 145.9 * 0.9, "gsm8k_accuracy", 73.78*0.99, "mbxp_accuracy", 60.12 * 0.99),
+            "gptj-99": (
+                "ROUGE1",
+                42.9865 * 0.99,
+                "ROUGE2",
+                20.1235 * 0.99,
+                "ROUGEL",
+                29.9881 * 0.99,
+                "GEN_LEN",
+                4016878 * 0.9,
+            ),
+            "gptj-99.9": (
+                "ROUGE1",
+                42.9865 * 0.999,
+                "ROUGE2",
+                20.1235 * 0.999,
+                "ROUGEL",
+                29.9881 * 0.999,
+                "GEN_LEN",
+                4016878 * 0.9,
+            ),
+            "llama2-70b-99": (
+                "ROUGE1",
+                44.4312 * 0.99,
+                "ROUGE2",
+                22.0352 * 0.99,
+                "ROUGEL",
+                28.6162 * 0.99,
+                "TOKENS_PER_SAMPLE",
+                294.45 * 0.9,
+            ),
+            "llama2-70b-99.9": (
+                "ROUGE1",
+                44.4312 * 0.999,
+                "ROUGE2",
+                22.0352 * 0.999,
+                "ROUGEL",
+                28.6162 * 0.999,
+                "TOKENS_PER_SAMPLE",
+                294.45 * 0.9,
+            ),
+            "stable-diffusion-xl": (
+                "CLIP_SCORE",
+                31.68631873,
+                "FID_SCORE",
+                23.01085758,
+            ),
+            "mixtral-8x7b": (
+                "ROUGE1",
+                45.4911 * 0.99,
+                "ROUGE2",
+                23.2829 * 0.99,
+                "ROUGEL",
+                30.3615 * 0.99,
+                "TOKENS_PER_SAMPLE",
+                145.9 * 0.9,
+                "gsm8k_accuracy",
+                73.78 * 0.99,
+                "mbxp_accuracy",
+                60.12 * 0.99,
+            ),
         },
         "accuracy-upper-limit": {
-            "stable-diffusion-xl": ("CLIP_SCORE", 31.81331801, "FID_SCORE", 23.95007626),
-            "llama2-70b-99" : ("TOKENS_PER_SAMPLE", 294.45*1.1),
-            "llama2-70b-99.9" : ("TOKENS_PER_SAMPLE", 294.45*1.1),
-            "mixtral-8x7b" : ("TOKENS_PER_SAMPLE", 145.9 * 1.1)
+            "stable-diffusion-xl": (
+                "CLIP_SCORE",
+                31.81331801,
+                "FID_SCORE",
+                23.95007626,
+            ),
+            "llama2-70b-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
+            "llama2-70b-99.9": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
+            "mixtral-8x7b": ("TOKENS_PER_SAMPLE", 145.9 * 1.1),
         },
         "accuracy-delta-perc": {
-            "stable-diffusion-xl": {
-                "CLIP_SCORE": 1,
-                "FID_SCORE": 2
-            }
+            "stable-diffusion-xl": {"CLIP_SCORE": 1, "FID_SCORE": 2}
         },
         "performance-sample-count": {
             "resnet": 1024,
@@ -303,7 +406,7 @@
             # map model names to the official mlperf model class
             "ssd-resnet34": "retinanet",
             "mobilenet": "resnet",
-            "resnet50": "resnet"
+            "resnet50": "resnet",
         },
         "seeds": {
             # TODO: Update random seeds
@@ -329,7 +432,7 @@
             "gptj-99.9": {"Server": 20000000000},
             "llama2-70b-99": {"Server": 20000000000},
             "llama2-70b-99.9": {"Server": 20000000000},
-            "stable-diffusion-xl" : {"Server": 20000000000}
+            "stable-diffusion-xl": {"Server": 20000000000},
             # TODO: Mixtral metrics
             # "mixtral-8x7b" : {"Server": 20000000000}
         },
@@ -356,7 +459,11 @@
             "gptj-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "llama2-70b-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "llama2-70b-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "stable-diffusion-xl": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "stable-diffusion-xl": {
+                "SingleStream": 1024,
+                "Server": 270336,
+                "Offline": 1,
+            },
             "mixtral-8x7b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
         },
     },
@@ -393,7 +500,7 @@
                 "3757",
                 "1578",
                 "3319",
-                "95"
+                "95",
             ]
         },
         "v4.1": {
@@ -407,9 +514,9 @@
                 "2179",
                 "1826",
                 "2094",
-                "3340"
+                "3340",
             ]
-        }
+        },
     }
 }
 REQUIRED_MEASURE_FILES = ["mlperf.conf", "user.conf", "README.md"]
@@ -444,7 +551,7 @@
     "llama2-70b-99": 24576,
     "llama2-70b-99.9": 24576,
     "stable-diffusion-xl": 5000,
-    "mixtral-8x7b": 15000
+    "mixtral-8x7b": 15000,
 }
 
 SCENARIO_MAPPING = {
@@ -475,7 +582,7 @@
         "MultiStreamLegacy": "effective_samples_per_query",
         "MultiStream": "early_stopping_latency_ms",
         "Server": "result_completed_samples_per_sec",
-    }
+    },
 }
 
 RESULT_FIELD_BENCHMARK_OVERWRITE = {
@@ -509,29 +616,18 @@
         "mixtral-8x7b": {
             "Offline": "result_tokens_per_second",
             "Server": "result_completed_tokens_per_second",
-        }
-    }
+        },
+    },
 }
 
 LLM_LATENCY_LIMITS = {
-    "llama2-70b-99":{
-        "conversational": {
-            "ttft": 2000 * 1000000,
-            "tpot": 200 * 1000000
-        }
+    "llama2-70b-99": {
+        "conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}
     },
-    "llama2-70b-99.9":{
-        "conversational": {
-            "ttft": 2000 * 1000000,
-            "tpot": 200 * 1000000
-        }
+    "llama2-70b-99.9": {
+        "conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}
     },
-    "mixtral-8x7b":{
-        "conversational": {
-            "ttft": 2000 * 1000000,
-            "tpot": 200 * 1000000
-        }
-    }
+    "mixtral-8x7b": {"conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}},
 }
 
 ACC_PATTERN = {
@@ -589,7 +685,7 @@
     "hw_notes",
     "sw_notes",
     "host_network_card_count",
-    "system_type_detail"
+    "system_type_detail",
 ]
 
 SYSTEM_DESC_MEANINGFUL_RESPONSE_REQUIRED_FIELDS = [
@@ -755,7 +851,7 @@ def get_accuracy_target(self, model):
         if model not in self.accuracy_target:
             raise ValueError("model not known: " + model)
         return self.accuracy_target[model]
-    
+
     def get_accuracy_upper_limit(self, model):
         return self.accuracy_upper_limit.get(model, None)
 
@@ -781,12 +877,12 @@ def get_min_query_count(self, model, scenario):
         if model not in self.min_queries:
             raise ValueError("model not known: " + model)
         return self.min_queries[model].get(scenario)
-    
+
     def get_delta_perc(self, model, metric):
         if model in self.accuracy_delta_perc:
             if metric in self.accuracy_delta_perc[model]:
                 return self.accuracy_delta_perc[model][metric]
-        
+
         more_accurate = model.find("99.9")
         if more_accurate == -1:
             required_delta_perc = 1
@@ -797,23 +893,21 @@ def get_delta_perc(self, model, metric):
     def has_new_logging_format(self):
         return True
 
-
     def uses_early_stopping(self, scenario):
-        return (
-            scenario in ["Server", "SingleStream", "MultiStream"]
-        )
-    
+        return scenario in ["Server", "SingleStream", "MultiStream"]
+
     def requires_equal_issue(self, model, division):
         return (
-            division in ["closed", "network"] and
-            model in [
+            division in ["closed", "network"]
+            and model
+            in [
                 "3d-unet-99",
                 "3d-unet-99.9",
                 "gptj-99",
                 "gptj-99.9",
                 "llama2-70b-99",
-                "llama2-70b-99.9", 
-                "mixtral-8x7b"
+                "llama2-70b-99.9",
+                "mixtral-8x7b",
             ]
             and self.version in ["v4.1"]
         )
@@ -830,7 +924,10 @@ def get_args():
         help="mlperf version",
     )
     parser.add_argument("--submitter", help="filter to submitter")
-    parser.add_argument("--csv", default="summary.csv", help="csv file with results")
+    parser.add_argument(
+        "--csv",
+        default="summary.csv",
+        help="csv file with results")
     parser.add_argument(
         "--skip_compliance",
         action="store_true",
@@ -841,7 +938,10 @@ def get_args():
         help="File containing extra custom model mapping. It is assumed to be inside the folder open/<submitter>",
         default="model_mapping.json",
     )
-    parser.add_argument("--debug", action="store_true", help="extra debug output")
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="extra debug output")
     parser.add_argument(
         "--submission-exceptions",
         action="store_true",
@@ -875,7 +975,7 @@ def get_args():
     parser.add_argument(
         "--scenarios-to-skip",
         help="Delimited list input of scenarios to skip. i.e. if you only have Offline results, pass in 'Server'",
-        type=str
+        type=str,
     )
     args = parser.parse_args()
     return args
@@ -883,17 +983,20 @@ def get_args():
 
 def list_dir(*path):
     path = os.path.join(*path)
-    return [f for f in os.listdir(path) if os.path.isdir(os.path.join(path, f))]
+    return [f for f in os.listdir(
+        path) if os.path.isdir(os.path.join(path, f))]
 
 
 def list_files(*path):
     path = os.path.join(*path)
-    return [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
+    return [f for f in os.listdir(
+        path) if os.path.isfile(os.path.join(path, f))]
 
 
 def list_empty_dirs_recursively(*path):
     path = os.path.join(*path)
-    return [dirpath for dirpath, dirs, files in os.walk(path) if not dirs and not files]
+    return [dirpath for dirpath, dirs, files in os.walk(
+        path) if not dirs and not files]
 
 
 def list_dirs_recursively(*path):
@@ -919,30 +1022,36 @@ def check_extra_files(path, target_files):
             check_pass = False
             missing_files.append(os.path.join(path, dir))
         else:
-            files = [f.split(".")[0] for f in list_files(os.path.join(path, dir))]
+            files = [f.split(".")[0]
+                     for f in list_files(os.path.join(path, dir))]
             for target_file in target_files[dir]:
                 if target_file not in files:
                     check_pass = False
-                    missing_files.append(f"{os.path.join(path, dir, target_file)}.png")
+                    missing_files.append(
+                        f"{os.path.join(path, dir, target_file)}.png")
             if "captions" not in files:
-                missing_files.append(f"{os.path.join(path, dir, 'captions.txt')}")
+                missing_files.append(
+                    f"{os.path.join(path, dir, 'captions.txt')}")
     return check_pass, missing_files
 
 
 def split_path(m):
     return m.replace("\\", "/").split("/")
 
+
 def get_boolean(s):
     if s is None:
         return False
     elif isinstance(s, bool):
         return s
     elif isinstance(s, str):
-        return (s.lower() == "true")
+        return s.lower() == "true"
     elif isinstance(s, int):
         return bool(s)
     else:
-        raise TypeError(f"Variable should be bool, string or int, got {type(s)} instead")
+        raise TypeError(
+            f"Variable should be bool, string or int, got {type(s)} instead"
+        )
 
 
 def find_error_in_detail_log(config, fname):
@@ -956,10 +1065,7 @@ def find_error_in_detail_log(config, fname):
             if config.ignore_uncommited:
                 has_other_errors = False
                 for error in mlperf_log.get_errors():
-                    if (
-                        "Loadgen built with uncommitted changes!"
-                        not in error["value"]
-                    ):
+                    if "Loadgen built with uncommitted changes!" not in error["value"]:
                         has_other_errors = True
 
             log.error("%s contains errors:", fname)
@@ -987,19 +1093,21 @@ def check_accuracy_dir(config, model, path, verbose):
         up_patterns = []
         acc_limit_check = True
         for i in range(0, len(acc_upper_limit), 2):
-            acc_type, acc_target = acc_upper_limit[i:i+2]
+            acc_type, acc_target = acc_upper_limit[i: i + 2]
             acc_limits.append(acc_target)
             up_patterns.append(ACC_PATTERN[acc_type])
 
     for i in range(0, len(target), 2):
-        acc_type, acc_target = target[i:i+2]
+        acc_type, acc_target = target[i: i + 2]
         patterns.append(ACC_PATTERN[acc_type])
         acc_targets.append(acc_target)
         acc_types.append(acc_type)
     acc_seen = [False for _ in acc_targets]
     with open(os.path.join(path, "accuracy.txt"), "r", encoding="utf-8") as f:
         for line in f:
-            for i, (pattern, acc_target, acc_type) in enumerate(zip(patterns, acc_targets, acc_types)):
+            for i, (pattern, acc_target, acc_type) in enumerate(
+                zip(patterns, acc_targets, acc_types)
+            ):
                 m = re.match(pattern, line)
                 if m:
                     acc = m.group(1)
@@ -1011,24 +1119,39 @@ def check_accuracy_dir(config, model, path, verbose):
                     acc_seen[i] = True
                 elif acc is not None:
                     all_accuracy_valid = False
-                    log.warning("%s accuracy not met: expected=%f, found=%s", path, acc_target, acc)
+                    log.warning(
+                        "%s accuracy not met: expected=%f, found=%s",
+                        path,
+                        acc_target,
+                        acc,
+                    )
                 if acc:
                     result_acc[acc_type] = acc
                 acc = None
             if acc_upper_limit is not None:
-                for i, (pattern, acc_limit) in enumerate(zip(up_patterns, acc_limits)):
+                for i, (pattern, acc_limit) in enumerate(
+                        zip(up_patterns, acc_limits)):
                     m = re.match(pattern, line)
                     if m:
                         acc = m.group(1)
                     m = re.match(r"^hash=([\w\d]+)$", line)
                     if m:
                         hash_val = m.group(1)
-                    if acc is not None and acc_upper_limit is not None and float(acc) > acc_limit:
+                    if (
+                        acc is not None
+                        and acc_upper_limit is not None
+                        and float(acc) > acc_limit
+                    ):
                         acc_limit_check = False
-                        log.warning("%s accuracy not met: upper limit=%f, found=%s", path, acc_limit, acc)
+                        log.warning(
+                            "%s accuracy not met: upper limit=%f, found=%s",
+                            path,
+                            acc_limit,
+                            acc,
+                        )
                     acc = None
             if all(acc_seen) and hash_val:
-                break;
+                break
         is_valid = all_accuracy_valid & all(acc_seen)
         if acc_upper_limit is not None:
             is_valid &= acc_limit_check
@@ -1056,26 +1179,34 @@ def check_accuracy_dir(config, model, path, verbose):
 
 
 def extra_check_llm(mlperf_log, scenario, model):
-    if (mlperf_log["requested_use_token_latencies"]):
+    if mlperf_log["requested_use_token_latencies"]:
         if scenario == "Offline":
             # For offline no further checks are necessary
             return None, True
         else:
             for constraint, limits in LLM_LATENCY_LIMITS[model].items():
-                if mlperf_log["result_first_token_99.00_percentile_latency_ns"] < limits["ttft"] and mlperf_log["result_time_per_output_token_99.00_percentile_ns"] < limits["tpot"]:
+                if (
+                    mlperf_log["result_first_token_99.00_percentile_latency_ns"]
+                    < limits["ttft"]
+                    and mlperf_log["result_time_per_output_token_99.00_percentile_ns"]
+                    < limits["tpot"]
+                ):
                     return constraint, True
     else:
-        log.error(f'use_token_latencies flag needs to be enabled for Llama2 benchmark')
+        log.error(
+            f"use_token_latencies flag needs to be enabled for Llama2 benchmark")
         return None, False
 
-    log.error(f'Failed Llama2 extra check for TTFT and TPOT. TTFT 99-tile: {mlperf_log["result_first_token_99.00_percentile_latency_ns"]}, TPOT 99-tile: {mlperf_log["result_time_per_output_token_99.00_percentile_ns"]}')
+    log.error(
+        f'Failed Llama2 extra check for TTFT and TPOT. TTFT 99-tile: {mlperf_log["result_first_token_99.00_percentile_latency_ns"]}, TPOT 99-tile: {mlperf_log["result_time_per_output_token_99.00_percentile_ns"]}'
+    )
     return None, False
-            
+
 
 def get_performance_metric(
     config, model, path, scenario_fixed, division, system_json, has_power=False
 ):
-    #Assumes new logging format
+    # Assumes new logging format
     version = config.version
 
     fname = os.path.join(path, "mlperf_log_detail.txt")
@@ -1088,15 +1219,25 @@ def get_performance_metric(
     scenario = mlperf_log["effective_scenario"]
 
     res = float(mlperf_log[RESULT_FIELD_NEW[version][scenario]])
-    if version in RESULT_FIELD_BENCHMARK_OVERWRITE and model in RESULT_FIELD_BENCHMARK_OVERWRITE[version] and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[version][model]:
-        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[version][model][scenario]])
+    if (
+        version in RESULT_FIELD_BENCHMARK_OVERWRITE
+        and model in RESULT_FIELD_BENCHMARK_OVERWRITE[version]
+        and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[version][model]
+    ):
+        res = float(
+            mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[version]
+                       [model][scenario]]
+        )
 
     inferred = False
     if scenario_fixed != scenario:
-        inferred, res =  get_inferred_result(scenario_fixed, scenario, res, mlperf_log, config, False)
+        inferred, res = get_inferred_result(
+            scenario_fixed, scenario, res, mlperf_log, config, False
+        )
 
     return res
 
+
 def check_performance_dir(
     config, model, path, scenario_fixed, division, system_json, has_power=False
 ):
@@ -1119,12 +1260,19 @@ def check_performance_dir(
     scenario = mlperf_log["effective_scenario"]
 
     res = float(mlperf_log[RESULT_FIELD_NEW[version][scenario]])
-    if version in RESULT_FIELD_BENCHMARK_OVERWRITE and model in RESULT_FIELD_BENCHMARK_OVERWRITE[version] and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[version][model]:
-        res = float(mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[version][model][scenario]])
+    if (
+        version in RESULT_FIELD_BENCHMARK_OVERWRITE
+        and model in RESULT_FIELD_BENCHMARK_OVERWRITE[version]
+        and scenario in RESULT_FIELD_BENCHMARK_OVERWRITE[version][model]
+    ):
+        res = float(
+            mlperf_log[RESULT_FIELD_BENCHMARK_OVERWRITE[version]
+                       [model][scenario]]
+        )
 
-        
     if model in ["llama2-70b-99", "llama2-70b-99.9", "mixtral-8x7b"]:
-        llama_constraint, is_valid = extra_check_llm(mlperf_log, scenario_fixed, model)
+        llama_constraint, is_valid = extra_check_llm(
+            mlperf_log, scenario_fixed, model)
 
     latency_99_percentile = mlperf_log["result_99.00_percentile_latency_ns"]
     latency_mean = mlperf_log["result_mean_latency_ns"]
@@ -1136,7 +1284,9 @@ def check_performance_dir(
     min_query_count = mlperf_log["effective_min_query_count"]
     samples_per_query = mlperf_log["effective_samples_per_query"]
     min_duration = mlperf_log["effective_min_duration_ms"]
-    equal_issue_used_check = (mlperf_log["effective_sample_concatenate_permutation"] == "true")
+    equal_issue_used_check = (
+        mlperf_log["effective_sample_concatenate_permutation"] == "true"
+    )
     if not config.requires_equal_issue(model, division):
         equal_issue_used_check = True
     sut_name = mlperf_log["sut_name"]
@@ -1146,7 +1296,8 @@ def check_performance_dir(
     if not find_error_in_detail_log(config, fname):
         is_valid = False
 
-    required_performance_sample_count = config.get_performance_sample_count(model)
+    required_performance_sample_count = config.get_performance_sample_count(
+        model)
     if performance_sample_count < required_performance_sample_count:
         log.error(
             "%s performance_sample_count, found %d, needs to be >= %d",
@@ -1184,7 +1335,6 @@ def check_performance_dir(
 
     if scenario == "SingleStream" or scenario == "MultiStream":
         res /= MS_TO_NS
-    
 
     # Check if the current scenario uses early stopping
     uses_early_stopping = config.uses_early_stopping(scenario)
@@ -1201,7 +1351,8 @@ def check_performance_dir(
         # If the scenario has a target latency (Server scenario), check
         # that the target latency that was passed to the early stopping
         # is less than the target latency.
-        target_latency = config.latency_constraint.get(model, dict()).get(scenario)
+        target_latency = config.latency_constraint.get(
+            model, dict()).get(scenario)
         if target_latency:
             early_stopping_latency_ns = mlperf_log["effective_target_latency_ns"]
             log.info(
@@ -1221,7 +1372,8 @@ def check_performance_dir(
 
     else:
         # check if the benchmark meets latency constraint
-        target_latency = config.latency_constraint.get(model, dict()).get(scenario)
+        target_latency = config.latency_constraint.get(
+            model, dict()).get(scenario)
         log.info(
             "Target latency: %s, Latency: %s, Scenario: %s",
             target_latency,
@@ -1252,7 +1404,8 @@ def check_performance_dir(
             )
             is_valid = False
 
-    if scenario == "Offline" and (samples_per_query < OFFLINE_MIN_SPQ_SINCE_V4[model]):
+    if scenario == "Offline" and (
+            samples_per_query < OFFLINE_MIN_SPQ_SINCE_V4[model]):
         log.error(
             "%s Required minimum samples per query not met by user config, Expected=%s, Found=%s",
             fname,
@@ -1275,14 +1428,17 @@ def check_performance_dir(
 
     inferred = False
     if scenario_fixed != scenario:
-        inferred, res =  get_inferred_result(scenario_fixed, scenario, res, mlperf_log, config, True)
+        inferred, res = get_inferred_result(
+            scenario_fixed, scenario, res, mlperf_log, config, True
+        )
 
     is_network_system, is_network_mode_valid = is_system_over_network(
         division, system_json, path
     )
     is_valid &= is_network_mode_valid
     if is_network_system:
-        # for network mode verify the SUT name is valid, according to the rules (must include "Network SUT" in name)
+        # for network mode verify the SUT name is valid, according to the rules
+        # (must include "Network SUT" in name)
         if NETWORK_MODE_REQUIRED_SUBSTRING_IN_SUT_NAME not in sut_name:
             log.error(
                 f"{fname} invalid sut name for network mode. expecting the substring '{NETWORK_MODE_REQUIRED_SUBSTRING_IN_SUT_NAME}' got '{sut_name}'"
@@ -1291,7 +1447,10 @@ def check_performance_dir(
 
     return is_valid, res, inferred, equal_issue_used_check
 
-def get_inferred_result(scenario_fixed, scenario, res, mlperf_log, config, log_error=False):
+
+def get_inferred_result(
+    scenario_fixed, scenario, res, mlperf_log, config, log_error=False
+):
 
     inferred = False
     # Check if current scenario (and version) uses early stopping
@@ -1305,7 +1464,8 @@ def get_inferred_result(scenario_fixed, scenario, res, mlperf_log, config, log_e
         latency_mean = mlperf_log["result_mean_query_latency_ns"]
     samples_per_query = mlperf_log["effective_samples_per_query"]
     if scenario == "SingleStream":
-        # qps_wo_loadgen_overhead is only used for inferring Offline from SingleStream; only for old submissions
+        # qps_wo_loadgen_overhead is only used for inferring Offline from
+        # SingleStream; only for old submissions
         qps_wo_loadgen_overhead = mlperf_log["result_qps_without_loadgen_overhead"]
 
     # special case for results inferred from different scenario
@@ -1313,15 +1473,11 @@ def get_inferred_result(scenario_fixed, scenario, res, mlperf_log, config, log_e
         inferred = True
         res = qps_wo_loadgen_overhead
 
-    if (
-        scenario_fixed in ["Offline"]
-    ) and scenario in ["MultiStream"]:
+    if (scenario_fixed in ["Offline"]) and scenario in ["MultiStream"]:
         inferred = True
         res = samples_per_query * S_TO_MS / (latency_mean / MS_TO_NS)
 
-    if (
-        scenario_fixed in ["MultiStream"]
-    ) and scenario in ["SingleStream"]:
+    if (scenario_fixed in ["MultiStream"]) and scenario in ["SingleStream"]:
         inferred = True
         # samples_per_query does not match with the one reported in the logs
         # when inferring MultiStream from SingleStream
@@ -1338,11 +1494,12 @@ def get_inferred_result(scenario_fixed, scenario, res, mlperf_log, config, log_e
             res = (latency_99_percentile * samples_per_query) / MS_TO_NS
     return inferred, res
 
+
 def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
     # parse the power logs
     server_timezone = datetime.timedelta(0)
     client_timezone = datetime.timedelta(0)
-    
+
     detail_log_fname = os.path.join(log_path, "mlperf_log_detail.txt")
     mlperf_log = MLPerfLog(detail_log_fname)
     datetime_format = "%m-%d-%Y %H:%M:%S.%f"
@@ -1356,7 +1513,7 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
     )
     # Obtain the scenario also from logs to check if power is inferred
     scenario = mlperf_log["effective_scenario"]
-    
+
     spl_fname = os.path.join(log_path, "spl.txt")
     power_list = []
     with open(spl_fname) as f:
@@ -1389,7 +1546,8 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
             avg_power_efficiency = res / avg_power
 
         else:
-            # In SingleStream and MultiStream scenarios, the power metric is in mJ/query.
+            # In SingleStream and MultiStream scenarios, the power metric is in
+            # mJ/query.
             assert scenario_fixed in [
                 "MultiStream",
                 "SingleStream",
@@ -1404,9 +1562,8 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
             elif scenario_fixed in ["MultiStream"]:
                 samples_per_query = 8
 
-            if (
-                scenario_fixed in ["MultiStream"]
-            ) and scenario in ["SingleStream"]:
+            if (scenario_fixed in ["MultiStream"]
+                    ) and scenario in ["SingleStream"]:
                 power_metric = (
                     avg_power * power_duration * samples_per_query * 1000 / num_queries
                 )
@@ -1432,11 +1589,17 @@ def check_power_dir(
 
     # check if all the required files are present
     required_files = REQUIRED_PERF_FILES + REQUIRED_PERF_POWER_FILES
-    diff = files_diff(list_files(testing_path), required_files, OPTIONAL_PERF_FILES)
+    diff = files_diff(
+        list_files(testing_path),
+        required_files,
+        OPTIONAL_PERF_FILES)
     if diff:
         log.error("%s has file list mismatch (%s)", testing_path, diff)
         is_valid = False
-    diff = files_diff(list_files(ranging_path), required_files, OPTIONAL_PERF_FILES)
+    diff = files_diff(
+        list_files(ranging_path),
+        required_files,
+        OPTIONAL_PERF_FILES)
     if diff:
         log.error("%s has file list mismatch (%s)", ranging_path, diff)
         is_valid = False
@@ -1446,7 +1609,7 @@ def check_power_dir(
         is_valid = False
 
     # uncomment to measure ranging mode power
-    '''
+    """
     (
         is_valid,
         power_metric_ranging,
@@ -1455,7 +1618,7 @@ def check_power_dir(
     ) = get_power_metric(
         config, scenario_fixed, ranging_path, is_valid, power_res_ranging
     )
-    '''
+    """
     is_valid, power_metric, scenario, power_efficiency_testing = get_power_metric(
         config, scenario_fixed, testing_path, is_valid, power_res_testing
     )
@@ -1474,7 +1637,9 @@ def check_power_dir(
         sys.stdout.flush()
         sys.stderr.flush()
         if check_power_result != 0:
-            log.error("Power WG power_checker.py did not pass for: %s", perf_path)
+            log.error(
+                "Power WG power_checker.py did not pass for: %s",
+                perf_path)
             is_valid = False
 
     return is_valid, power_metric, power_efficiency_testing
@@ -1524,7 +1689,7 @@ def check_results_dir(
     skip_empty_files_check=False,
     skip_check_power_measure_files=False,
     skip_extra_files_in_root_check=False,
-    scenarios_to_skip=[]
+    scenarios_to_skip=[],
 ):
     """
     Walk the results directory and do the checking.
@@ -1575,7 +1740,7 @@ def check_results_dir(
         "inferred",
         "has_power",
         "Units",
-        "weight_data_types"
+        "weight_data_types",
     ]
     fmt = ",".join(["{}"] * len(head)) + "\n"
     csv.write(",".join(head) + "\n")
@@ -1601,7 +1766,7 @@ def log_result(
         config,
         inferred=0,
         power_metric=0,
-        weight_data_types="fp32"
+        weight_data_types="fp32",
     ):
         notes = system_json.get("hw_notes", "")
         if system_json.get("sw_notes"):
@@ -1620,24 +1785,24 @@ def log_result(
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
-            "llama2-70b-99" : {
+            "llama2-70b-99": {
                 "SingleStream": "Latency (ms)",
                 "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
-            "llama2-70b-99.9" : {
+            "llama2-70b-99.9": {
                 "SingleStream": "Latency (ms)",
                 "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
-            "mixtral-8x7b" : {
+            "mixtral-8x7b": {
                 "SingleStream": "Latency (ms)",
                 "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
-            }
+            },
         }
         unit_dict = {
             "SingleStream": "Latency (ms)",
@@ -1657,8 +1822,9 @@ def log_result(
             unit = special_unit_dict.get(model_name, unit_dict)[scenario_fixed]
         power_unit = power_unit_dict[scenario_fixed]
 
-
-        if (power_metric <= 0) or (not get_boolean(system_json.get("system_power_only"))):
+        if (power_metric <= 0) or (
+            not get_boolean(system_json.get("system_power_only"))
+        ):
             csv.write(
                 fmt.format(
                     submitter,
@@ -1690,7 +1856,7 @@ def log_result(
                     unit,
                     '"' + weight_data_types + '"',
                 )
-        )
+            )
 
         if power_metric > 0:
             csv.write(
@@ -1770,7 +1936,7 @@ def log_result(
             if not os.path.exists(results_path):
                 continue
 
-            ## Apply folder checks
+            # Apply folder checks
             dirs = list_dirs_recursively(division, submitter)
             files = list_files_recursively(division, submitter)
 
@@ -1871,7 +2037,9 @@ def log_result(
                         extra_model_mapping = json.load(fp)
 
             for system_desc in list_dir(results_path):
-                # we are looking at ./$division/$submitter/results/$system_desc, ie ./closed/mlperf_org/results/t4-ort
+                # we are looking at
+                # ./$division/$submitter/results/$system_desc, ie
+                # ./closed/mlperf_org/results/t4-ort
 
                 #
                 # check if system_id is good.
@@ -1900,7 +2068,8 @@ def log_result(
                     if config.version not in ["v0.5"]:
                         valid_system_types = ["datacenter", "edge"]
                         if config.version not in ["v0.7"]:
-                            valid_system_types += ["datacenter,edge", "edge,datacenter"]
+                            valid_system_types += ["datacenter,edge",
+                                                   "edge,datacenter"]
                         if system_type not in valid_system_types:
                             log.error(
                                 "%s has invalid system type (%s)",
@@ -1934,7 +2103,8 @@ def log_result(
 
                     if is_closed_or_network and mlperf_model not in config.models:
                         # for closed/network divisions we want the model name to match.
-                        # for open division the model_name might be different than the task
+                        # for open division the model_name might be different
+                        # than the task
                         log.error(
                             "%s has an invalid model %s for closed/network division",
                             name,
@@ -1962,9 +2132,12 @@ def log_result(
                         list(required_scenarios)
                         + list(config.get_optional(mlperf_model))
                     )
-                    for scenario in list_dir(results_path, system_desc, model_name):
-                        # some submissions in v0.5 use lower case scenarios - map them for now
-                        scenario_fixed = SCENARIO_MAPPING.get(scenario, scenario)
+                    for scenario in list_dir(
+                            results_path, system_desc, model_name):
+                        # some submissions in v0.5 use lower case scenarios -
+                        # map them for now
+                        scenario_fixed = SCENARIO_MAPPING.get(
+                            scenario, scenario)
 
                         # Skip scenario for debug purposes
                         if scenario in scenarios_to_skip:
@@ -2016,12 +2189,17 @@ def log_result(
                             scenario,
                         )
                         if not os.path.exists(measurement_dir):
-                            log.error("no measurement_dir for %s", measurement_dir)
+                            log.error(
+                                "no measurement_dir for %s", measurement_dir)
                             results[measurement_dir] = None
                             errors += 1
                             continue
                         else:
-                            measurement_check, conf_equal_issue_check, weight_data_types = check_measurement_dir(
+                            (
+                                measurement_check,
+                                conf_equal_issue_check,
+                                weight_data_types,
+                            ) = check_measurement_dir(
                                 config,
                                 measurement_dir,
                                 name,
@@ -2046,7 +2224,8 @@ def log_result(
                         # check accuracy
                         accuracy_is_valid = False
                         acc_path = os.path.join(name, "accuracy")
-                        if not os.path.exists(os.path.join(acc_path, "accuracy.txt")):
+                        if not os.path.exists(
+                                os.path.join(acc_path, "accuracy.txt")):
                             log.error(
                                 "%s has no accuracy.txt. Generate it with accuracy-imagenet.py or accuracy-coco.py or "
                                 "process_accuracy.py",
@@ -2055,7 +2234,8 @@ def log_result(
                             errors += 1
                             continue
                         elif scenario not in scenarios_to_skip:
-                            diff = files_diff(list_files(acc_path), REQUIRED_ACC_FILES)
+                            diff = files_diff(
+                                list_files(acc_path), REQUIRED_ACC_FILES)
                             if diff:
                                 log.error(
                                     "%s has file list mismatch (%s)", acc_path, diff
@@ -2068,13 +2248,29 @@ def log_result(
                                 acc_path,
                                 debug or is_closed_or_network,
                             )
-                            acc = json.dumps(acc).replace(",", " ").replace('"', "").replace("{", "").replace("}", "")
+                            acc = (
+                                json.dumps(acc)
+                                .replace(",", " ")
+                                .replace('"', "")
+                                .replace("{", "")
+                                .replace("}", "")
+                            )
                             if mlperf_model in REQUIRED_ACC_BENCHMARK:
-                                if config.version in REQUIRED_ACC_BENCHMARK[mlperf_model]:
-                                    extra_files_pass, missing_files = check_extra_files(acc_path, REQUIRED_ACC_BENCHMARK[mlperf_model][config.version])
+                                if (
+                                    config.version
+                                    in REQUIRED_ACC_BENCHMARK[mlperf_model]
+                                ):
+                                    extra_files_pass, missing_files = check_extra_files(
+                                        acc_path,
+                                        REQUIRED_ACC_BENCHMARK[mlperf_model][
+                                            config.version
+                                        ],
+                                    )
                                     if not extra_files_pass:
                                         log.error(
-                                            "%s expected to have the following extra files (%s)", acc_path, missing_files
+                                            "%s expected to have the following extra files (%s)",
+                                            acc_path,
+                                            missing_files,
                                         )
                                         accuracy_is_valid = False
                             if not accuracy_is_valid and not is_closed_or_network:
@@ -2085,7 +2281,8 @@ def log_result(
                                     )
                                 accuracy_is_valid = True
                             if not accuracy_is_valid:
-                                # a little below we'll not copy this into the results csv
+                                # a little below we'll not copy this into the
+                                # results csv
                                 errors += 1
                                 log.error("%s, accuracy not valid", acc_path)
 
@@ -2118,7 +2315,12 @@ def log_result(
                                 continue
 
                             try:
-                                is_valid, r, is_inferred, performance_equal_issue_check = check_performance_dir(
+                                (
+                                    is_valid,
+                                    r,
+                                    is_inferred,
+                                    performance_equal_issue_check,
+                                ) = check_performance_dir(
                                     config,
                                     mlperf_model,
                                     perf_path,
@@ -2134,12 +2336,18 @@ def log_result(
                                     )
 
                                 # Check equal issue mode
-                                if not (conf_equal_issue_check or performance_equal_issue_check):
+                                if not (
+                                    conf_equal_issue_check
+                                    or performance_equal_issue_check
+                                ):
                                     log.error(
                                         "%s %s requires equal issue mode (sample_concatenate_permutation), expected=true, found=%s",
                                         perf_path,
                                         measurement_dir,
-                                        not (conf_equal_issue_check or performance_equal_issue_check),
+                                        not (
+                                            conf_equal_issue_check
+                                            or performance_equal_issue_check
+                                        ),
                                     )
                                     is_valid, r = False, None
                             except Exception as e:
@@ -2156,9 +2364,7 @@ def log_result(
                                     ranging_path = os.path.join(
                                         name, "performance", "ranging"
                                     )
-                                    (
-                                        ranging_r
-                                    ) = get_performance_metric(
+                                    (ranging_r) = get_performance_metric(
                                         config,
                                         mlperf_model,
                                         ranging_path,
@@ -2246,7 +2452,7 @@ def log_result(
                                 config,
                                 division,
                                 system_json,
-                                name
+                                name,
                             ):
                                 log.error(
                                     "compliance dir %s has issues", compliance_dir
@@ -2276,18 +2482,20 @@ def log_result(
                                     config,
                                     inferred=inferred,
                                     power_metric=power_metric,
-                                    weight_data_types=weight_data_types
+                                    weight_data_types=weight_data_types,
                                 )
                             else:
                                 results[name] = None
-                                log.error("%s is OK but accuracy has issues", name)
+                                log.error(
+                                    "%s is OK but accuracy has issues", name)
 
                     # Discard scenarios that we want to skip
                     for scenario in scenarios_to_skip:
                         required_scenarios.discard(scenario)
 
                     if required_scenarios:
-                        name = os.path.join(results_path, system_desc, model_name)
+                        name = os.path.join(
+                            results_path, system_desc, model_name)
                         if is_closed_or_network:
                             results[name] = None
                             log.error(
@@ -2315,9 +2523,8 @@ def check_system_desc_id(
 ):
     is_valid = True
     # check all required fields
-   
-    required_fields = SYSTEM_DESC_REQUIRED_FIELDS.copy()
 
+    required_fields = SYSTEM_DESC_REQUIRED_FIELDS.copy()
 
     is_network_system, is_network_mode_valid = is_system_over_network(
         division, systems_json, fname
@@ -2342,7 +2549,6 @@ def check_system_desc_id(
                 "%s, field %s requires a meaningful response but is empty", fname, k
             )
 
-   
     # SYSTEM_DESC_REQUIRED_FIELDS_POWER should be mandatory when a submission has power logs, but since we
     # check power submission in check_results_dir, the information is not available yet at this stage and we do
     # this check later
@@ -2430,21 +2636,37 @@ def check_measurement_dir(
 
     if has_power and not skip_check_power_measure_files:
         path = measurement_dir
-        all_files_1 = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
+        all_files_1 = [
+            os.path.join(path, f)
+            for f in os.listdir(path)
+            if os.path.isfile(os.path.join(path, f))
+        ]
         path = os.path.join(path, "..")
-        all_files_2 = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
+        all_files_2 = [
+            os.path.join(path, f)
+            for f in os.listdir(path)
+            if os.path.isfile(os.path.join(path, f))
+        ]
         path = os.path.join(path, "..")
-        all_files_3 = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
+        all_files_3 = [
+            os.path.join(path, f)
+            for f in os.listdir(path)
+            if os.path.isfile(os.path.join(path, f))
+        ]
         path = os.path.join(path, "..")
-        all_files_4 = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
+        all_files_4 = [
+            os.path.join(path, f)
+            for f in os.listdir(path)
+            if os.path.isfile(os.path.join(path, f))
+        ]
         all_files = all_files_1 + all_files_2 + all_files_3 + all_files_4
 
         for i in REQUIRED_POWER_MEASURE_FILES:
             found = False
             for file in all_files:
                 if re.match(i, os.path.basename(file)):
-                   found = True
-                   file_path = file
+                    found = True
+                    file_path = file
             if not found:
                 log.error("%s is missing %s", measurement_dir, i)
                 is_valid = False
@@ -2477,16 +2699,17 @@ def check_measurement_dir(
     if system_file:
         with open(os.path.join(measurement_dir, system_file), "r") as f:
             j = json.load(f)
-            weight_data_types = j['weight_data_types']
+            weight_data_types = j["weight_data_types"]
             for k in SYSTEM_IMP_REQUIRED_FILES:
                 if k not in j:
                     is_valid = False
                     log.error("%s, field %s is missing", fname, k)
                 elif check_empty_fields and not j[k]:
                     is_valid = False
-                    log.error("%s, field %s is missing meaningful value", fname, k)
+                    log.error(
+                        "%s, field %s is missing meaningful value", fname, k)
 
-        impl = system_file[len(system_desc) + 1 : -end]
+        impl = system_file[len(system_desc) + 1: -end]
         code_dir = os.path.join(root, "code", model)
         if os.path.isfile(code_dir):
             with open(code_dir, "r") as f:
@@ -2500,10 +2723,11 @@ def check_measurement_dir(
             if not os.path.exists(os.path.dirname(code_dir)):
                 log.error("%s is missing code_dir %s", fname, code_dir)
                 is_valid = False
-  
-        # Check equal issue mode 
-        equal_issue_used = False       
-        if "mlperf.conf" in files and config.requires_equal_issue(model, division):
+
+        # Check equal issue mode
+        equal_issue_used = False
+        if "mlperf.conf" in files and config.requires_equal_issue(
+                model, division):
             with open(f"{measurement_dir}/mlperf.conf") as f:
                 lines = f.readlines()
                 conf_ref_model = model.replace("-99.9", "").replace("-99", "")
@@ -2519,16 +2743,17 @@ def check_measurement_dir(
                         val.replace(" ", "")
                         conf_model, conf_scenario, conf_key = key.split(".")
                         if (
-                            (conf_key == "sample_concatenate_permutation") and 
-                            ((conf_model == conf_ref_model) or conf_model == "*") and
-                            ((conf_scenario == scenario) or conf_scenario == "*")
+                            (conf_key == "sample_concatenate_permutation")
+                            and ((conf_model == conf_ref_model) or conf_model == "*")
+                            and ((conf_scenario == scenario) or conf_scenario == "*")
                         ):
                             if val.isnumeric():
                                 val = int(val)
-                                equal_issue_used = (val == 1)
+                                equal_issue_used = val == 1
                                 break
 
-        if "user.conf" in files and config.requires_equal_issue(model, division):
+        if "user.conf" in files and config.requires_equal_issue(
+                model, division):
             with open(f"{measurement_dir}/user.conf") as f:
                 lines = f.readlines()
                 conf_ref_model = model.replace("-99.9", "").replace("-99", "")
@@ -2544,13 +2769,13 @@ def check_measurement_dir(
                         val.replace(" ", "")
                         conf_model, conf_scenario, conf_key = key.split(".")
                         if (
-                            (conf_key == "sample_concatenate_permutation") and 
-                            ((conf_model == conf_ref_model) or conf_model == "*") and
-                            ((conf_scenario == scenario) or conf_scenario == "*")
+                            (conf_key == "sample_concatenate_permutation")
+                            and ((conf_model == conf_ref_model) or conf_model == "*")
+                            and ((conf_scenario == scenario) or conf_scenario == "*")
                         ):
                             if val.isnumeric():
                                 val = int(val)
-                                equal_issue_used = (val == 1)
+                                equal_issue_used = val == 1
                                 break
     else:
         log.error("%s is missing %s*.json", fname, system_desc)
@@ -2574,7 +2799,9 @@ def check_compliance_perf_dir(test_dir):
                     is_valid = True
                     break
         if is_valid == False:
-            log.error("Compliance test performance check in %s failed", test_dir)
+            log.error(
+                "Compliance test performance check in %s failed",
+                test_dir)
 
         # Check performance dir
         test_perf_path = os.path.join(test_dir, "performance", "run_1")
@@ -2588,7 +2815,10 @@ def check_compliance_perf_dir(test_dir):
                 ["mlperf_log_accuracy.json"],
             )
             if diff:
-                log.error("%s has file list mismatch (%s)", test_perf_path, diff)
+                log.error(
+                    "%s has file list mismatch (%s)",
+                    test_perf_path,
+                    diff)
                 is_valid = False
 
     return is_valid
@@ -2625,28 +2855,29 @@ def check_compliance_acc_dir(test_dir, model, config):
             else:
                 diff = files_diff(
                     list_files(test_acc_path),
-                    REQUIRED_TEST01_ACC_FILES_1
-                    if acc_passed
-                    else REQUIRED_TEST01_ACC_FILES,
+                    (
+                        REQUIRED_TEST01_ACC_FILES_1
+                        if acc_passed
+                        else REQUIRED_TEST01_ACC_FILES
+                    ),
                 )
                 if diff:
-                    log.error("%s has file list mismatch (%s)", test_acc_path, diff)
+                    log.error(
+                        "%s has file list mismatch (%s)",
+                        test_acc_path,
+                        diff)
                     is_valid = False
                 elif not acc_passed:
                     target = config.get_accuracy_target(model)
                     patterns = []
                     acc_types = []
                     for i in range(0, len(target), 2):
-                        acc_type = target[i:i+2]
+                        acc_type = target[i: i + 2]
                         acc_types.append(acc_type)
                         patterns.append(ACC_PATTERN[acc_type[0]])
                     acc_seen = [False for _ in acc_type]
-                    acc_baseline = {
-                        acc_type: 0 for acc_type in acc_types
-                    }
-                    acc_compliance = {
-                        acc_type: 0 for acc_type in acc_types
-                    }
+                    acc_baseline = {acc_type: 0 for acc_type in acc_types}
+                    acc_compliance = {acc_type: 0 for acc_type in acc_types}
                     with open(
                         os.path.join(test_acc_path, "baseline_accuracy.txt"),
                         "r",
@@ -2666,14 +2897,24 @@ def check_compliance_acc_dir(test_dir, model, config):
                             for acc_type, pattern in zip(acc_types, patterns):
                                 m = re.match(pattern, line)
                                 if m:
-                                    acc_compliance[acc_type] = float(m.group(1))
+                                    acc_compliance[acc_type] = float(
+                                        m.group(1))
                     for acc_type in acc_types:
                         if acc_baseline[acc_type] == 0 or acc_compliance[acc_type] == 0:
                             is_valid = False
                             break
                         else:
-                            required_delta_perc = config.get_delta_perc(model, acc_type[0])
-                            delta_perc = abs(1 - acc_baseline[acc_type] / acc_compliance[acc_type]) * 100
+                            required_delta_perc = config.get_delta_perc(
+                                model, acc_type[0]
+                            )
+                            delta_perc = (
+                                abs(
+                                    1
+                                    - acc_baseline[acc_type] /
+                                    acc_compliance[acc_type]
+                                )
+                                * 100
+                            )
                             if delta_perc <= required_delta_perc:
                                 is_valid = True
                             else:
@@ -2689,14 +2930,21 @@ def check_compliance_acc_dir(test_dir, model, config):
             with open(fname, "r") as f:
                 lines = f.readlines()
             lines = [line.strip() for line in lines]
-            first_token_pass = "First token check pass: True" in lines or "First token check pass: Skipped" in lines
+            first_token_pass = (
+                "First token check pass: True" in lines
+                or "First token check pass: Skipped" in lines
+            )
             eos_pass = "EOS check pass: True" in lines
             length_check_pass = "Sample length check pass: True" in lines
             is_valid = first_token_pass and eos_pass and length_check_pass
             if not is_valid:
-                log.error(f"TEST06 accuracy check failed. first_token_check: {first_token_pass} eos_check: {eos_pass} length_check: {length_check_pass}.")
+                log.error(
+                    f"TEST06 accuracy check failed. first_token_check: {first_token_pass} eos_check: {eos_pass} length_check: {length_check_pass}."
+                )
         else:
-            raise NotImplemented(f"{test_dir} is neither TEST01 and TEST06, which doesn't require accuracy check")
+            raise NotImplemented(
+                f"{test_dir} is neither TEST01 and TEST06, which doesn't require accuracy check"
+            )
 
     return is_valid
 
@@ -2722,7 +2970,7 @@ def check_compliance_dir(
         "gptj-99.9",
         "llama2-70b-99",
         "llama2-70b-99.9",
-        "mixtral-8x7b"
+        "mixtral-8x7b",
     ]:
         test_list.remove("TEST04")
 
@@ -2732,7 +2980,7 @@ def check_compliance_dir(
         "llama2-70b-99",
         "llama2-70b-99.9",
         "stable-diffusion-xl",
-        "mixtral-8x7b"
+        "mixtral-8x7b",
     ]:
         test_list.remove("TEST05")
 
@@ -2741,23 +2989,16 @@ def check_compliance_dir(
         "gptj-99.9",
         "llama2-70b-99",
         "llama2-70b-99.9",
-        "mixtral-8x7b"
+        "mixtral-8x7b",
     ]:
         test_list.remove("TEST01")
 
-    if model in [
-        "stable-diffusion-xl"
-    ] and config.version in [ "v4.0" ]:
+    if model in ["stable-diffusion-xl"] and config.version in ["v4.0"]:
         test_list.remove("TEST01")
         test_list.remove("TEST04")
 
-
-    if model in [
-        "llama2-70b-99",
-        "llama2-70b-99.9",
-        "mixtral-8x7b"
-    ]:
-        test_list.append("TEST06") 
+    if model in ["llama2-70b-99", "llama2-70b-99.9", "mixtral-8x7b"]:
+        test_list.append("TEST06")
 
     if test_list and not os.path.exists(compliance_dir):
         log.error("no compliance dir for %s: %s", name, compliance_dir)
@@ -2781,7 +3022,10 @@ def check_compliance_dir(
                     config, model, compliance_perf_dir, scenario, division, system_json
                 )
                 if is_inferred:
-                    log.info("%s has inferred results, qps=%s", compliance_perf_dir, r)
+                    log.info(
+                        "%s has inferred results, qps=%s",
+                        compliance_perf_dir,
+                        r)
             except Exception as e:
                 log.error(
                     "%s caused exception in check_performance_dir: %s",
@@ -2795,7 +3039,7 @@ def check_compliance_dir(
                 and compliance_perf_valid
             )
 
-    compliance_acc_pass= True
+    compliance_acc_pass = True
     for test in ["TEST01", "TEST06"]:
         if test in test_list:
             # Check accuracy for TEST01
@@ -2803,7 +3047,6 @@ def check_compliance_dir(
                 os.path.join(compliance_dir, test), model, config
             )
 
-
     return compliance_perf_pass and compliance_acc_pass and compliance_perf_dir_pass
 
 
@@ -2819,8 +3062,7 @@ def main():
 
     if args.scenarios_to_skip:
         scenarios_to_skip = [
-            scenario for scenario in args.scenarios_to_skip.split(',')
-        ]
+            scenario for scenario in args.scenarios_to_skip.split(",")]
     else:
         scenarios_to_skip = []
 
@@ -2837,7 +3079,7 @@ def main():
             args.skip_empty_files_check,
             args.skip_check_power_measure_files,
             args.skip_extra_files_in_root_check,
-            scenarios_to_skip
+            scenarios_to_skip,
         )
 
     # log results
@@ -2889,7 +3131,8 @@ def merge_two_dict(x, y):
     unique_closed_systems = merge_two_dict(
         closed_power_systems, closed_non_power_systems
     )
-    unique_open_systems = merge_two_dict(open_power_systems, open_non_power_systems)
+    unique_open_systems = merge_two_dict(
+        open_power_systems, open_non_power_systems)
     unique_network_systems = merge_two_dict(
         network_power_systems, network_non_power_systems
     )
@@ -2898,8 +3141,10 @@ def merge_two_dict(x, y):
     unique_systems = merge_two_dict(unique_systems, unique_network_systems)
 
     # power systems can be repeating in open, closed and network
-    unique_power_systems = merge_two_dict(closed_power_systems, open_power_systems)
-    unique_power_systems = merge_two_dict(unique_power_systems, network_power_systems)
+    unique_power_systems = merge_two_dict(
+        closed_power_systems, open_power_systems)
+    unique_power_systems = merge_two_dict(
+        unique_power_systems, network_power_systems)
 
     number_systems = len(unique_systems)
     number_power_systems = len(unique_power_systems)
@@ -2920,7 +3165,8 @@ def sum_dict_values(x):
     count_open_results = count_open_power_results + count_open_non_power_results
 
     count_network_power_results = sum_dict_values(network_power_systems)
-    count_network_non_power_results = sum_dict_values(network_non_power_systems)
+    count_network_non_power_results = sum_dict_values(
+        network_non_power_systems)
     count_network_results = (
         count_network_power_results + count_network_non_power_results
     )
@@ -2958,7 +3204,10 @@ def sum_dict_values(x):
     )
     log.info("---")
 
-    log.info("Systems=%d, Power Systems=%d", number_systems, number_power_systems)
+    log.info(
+        "Systems=%d, Power Systems=%d",
+        number_systems,
+        number_power_systems)
     log.info(
         "Closed Systems=%d, Closed Power Systems=%d",
         number_closed_systems,
diff --git a/tools/submission/truncate_accuracy_log.py b/tools/submission/truncate_accuracy_log.py
index 8cfe1414df..b7a9509aeb 100755
--- a/tools/submission/truncate_accuracy_log.py
+++ b/tools/submission/truncate_accuracy_log.py
@@ -35,24 +35,35 @@
     submission directory.
 
     python tools/submission/truncate_accuracy_log.py --input ROOT_OF_SUBMISSION_DIRECTORY --submitter MY_ORG \\
-        --backup MY_SUPER_SAFE_STORAGE 
+        --backup MY_SUPER_SAFE_STORAGE
 """
 
+
 def get_args():
     """Parse commandline."""
-    parser = argparse.ArgumentParser(description="Truncate mlperf_log_accuracy.json files.",
-                                     formatter_class=argparse.RawDescriptionHelpFormatter, epilog=HELP_TEXT)
-    parser.add_argument("--input", required=True, help="orignal submission directory")
+    parser = argparse.ArgumentParser(
+        description="Truncate mlperf_log_accuracy.json files.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=HELP_TEXT,
+    )
+    parser.add_argument(
+        "--input",
+        required=True,
+        help="orignal submission directory")
     parser.add_argument("--output", help="new submission directory")
-    parser.add_argument("--submitter", required=True, help="filter to submitter")
-    parser.add_argument("--backup", help="directory to store the original accuacy log")
+    parser.add_argument(
+        "--submitter",
+        required=True,
+        help="filter to submitter")
+    parser.add_argument(
+        "--backup",
+        help="directory to store the original accuacy log")
     parser.add_argument(
         "--scenarios-to-skip",
         help="Delimited list input of scenarios to skip. i.e. if you only have Offline results, pass in 'Server'",
-        type=str
+        type=str,
     )
 
-
     args = parser.parse_args()
     if not args.output and not args.backup:
         parser.print_help()
@@ -63,12 +74,14 @@ def get_args():
 
 def list_dir(*path):
     path = os.path.join(*path)
-    return [f for f in os.listdir(path) if os.path.isdir(os.path.join(path, f))]
+    return [f for f in os.listdir(
+        path) if os.path.isdir(os.path.join(path, f))]
 
 
 def list_files(*path):
     path = os.path.join(*path)
-    return [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
+    return [f for f in os.listdir(
+        path) if os.path.isfile(os.path.join(path, f))]
 
 
 def split_path(m):
@@ -88,7 +101,7 @@ def truncate_file(fname):
     """Truncate file to 4K from start and 4K from end."""
     size = os.stat(fname).st_size
     if size < VIEWABLE_SIZE:
-       return
+        return
     with open(fname, "r") as src:
         start = src.read(VIEWABLE_SIZE)
         src.seek(size - VIEWABLE_SIZE, 0)
@@ -106,15 +119,17 @@ def copy_submission_dir(src, dst, filter_submitter):
         for submitter in list_dir(os.path.join(src, division)):
             if filter_submitter and submitter != filter_submitter:
                 continue
-            shutil.copytree(os.path.join(src, division, submitter),
-                            os.path.join(dst, division, submitter))
+            shutil.copytree(
+                os.path.join(src, division, submitter),
+                os.path.join(dst, division, submitter),
+            )
 
 
 def truncate_results_dir(filter_submitter, backup, scenarios_to_skip):
-    """Walk result dir and 
-       write a hash of mlperf_log_accuracy.json to accuracy.txt
-       copy mlperf_log_accuracy.json to a backup location
-       truncate mlperf_log_accuracy.
+    """Walk result dir and
+    write a hash of mlperf_log_accuracy.json to accuracy.txt
+    copy mlperf_log_accuracy.json to a backup location
+    truncate mlperf_log_accuracy.
     """
     for division in list_dir("."):
         # we are looking at ./$division, ie ./closed
@@ -139,16 +154,25 @@ def truncate_results_dir(filter_submitter, backup, scenarios_to_skip):
                         for scenario in list_dir(log_path, system_desc, model):
                             if scenario in scenarios_to_skip:
                                 continue
-                            for test in list_dir(log_path, system_desc, model, scenario):
+                            for test in list_dir(
+                                log_path, system_desc, model, scenario
+                            ):
 
-                                name = os.path.join(log_path, system_desc, model, scenario)
+                                name = os.path.join(
+                                    log_path, system_desc, model, scenario
+                                )
                                 if directory == "compliance":
-                                    name = os.path.join(log_path, system_desc, model, scenario, test)
+                                    name = os.path.join(
+                                        log_path, system_desc, model, scenario, test
+                                    )
 
                                 hash_val = None
                                 acc_path = os.path.join(name, "accuracy")
-                                acc_log = os.path.join(acc_path, "mlperf_log_accuracy.json")
-                                acc_txt = os.path.join(acc_path, "accuracy.txt")
+                                acc_log = os.path.join(
+                                    acc_path, "mlperf_log_accuracy.json"
+                                )
+                                acc_txt = os.path.join(
+                                    acc_path, "accuracy.txt")
 
                                 # only TEST01 has an accuracy log
                                 if directory == "compliance" and test != "TEST01":
@@ -156,30 +180,53 @@ def truncate_results_dir(filter_submitter, backup, scenarios_to_skip):
                                 if not os.path.exists(acc_log):
                                     log.error("%s missing", acc_log)
                                     continue
-                                if not os.path.exists(acc_txt) and directory == "compliance":
-                                    # compliance test directory will not have an accuracy.txt file by default
-                                    log.info("no accuracy.txt in compliance directory %s", acc_path)
+                                if (
+                                    not os.path.exists(acc_txt)
+                                    and directory == "compliance"
+                                ):
+                                    # compliance test directory will not have
+                                    # an accuracy.txt file by default
+                                    log.info(
+                                        "no accuracy.txt in compliance directory %s",
+                                        acc_path,
+                                    )
                                 else:
                                     if not os.path.exists(acc_txt):
-                                        log.error("%s missing, generate to continue", acc_txt)
+                                        log.error(
+                                            "%s missing, generate to continue", acc_txt
+                                        )
                                         continue
                                     with open(acc_txt, "r", encoding="utf-8") as f:
                                         for line in f:
-                                            m = re.match(r"^hash=([\w\d]+)$", line)
+                                            m = re.match(
+                                                r"^hash=([\w\d]+)$", line)
                                             if m:
                                                 hash_val = m.group(1)
                                                 break
                                 size = os.stat(acc_log).st_size
                                 if hash_val and size < MAX_ACCURACY_LOG_SIZE:
-                                    log.info("%s already has hash and size seems truncated", acc_path)
+                                    log.info(
+                                        "%s already has hash and size seems truncated",
+                                        acc_path,
+                                    )
                                     continue
 
                                 if backup:
-                                    backup_dir = os.path.join(backup, name, "accuracy")
+                                    backup_dir = os.path.join(
+                                        backup, name, "accuracy")
                                     os.makedirs(backup_dir, exist_ok=True)
-                                    dst = os.path.join(backup, name, "accuracy", "mlperf_log_accuracy.json")
+                                    dst = os.path.join(
+                                        backup,
+                                        name,
+                                        "accuracy",
+                                        "mlperf_log_accuracy.json",
+                                    )
                                     if os.path.exists(dst):
-                                        log.error("not processing %s because %s already exist", acc_log, dst)
+                                        log.error(
+                                            "not processing %s because %s already exist",
+                                            acc_log,
+                                            dst,
+                                        )
                                         continue
                                     shutil.copy(acc_log, dst)
 
@@ -190,7 +237,8 @@ def truncate_results_dir(filter_submitter, backup, scenarios_to_skip):
                                 truncate_file(acc_log)
                                 log.info("%s truncated", acc_log)
 
-                                # No need to iterate on compliance test subdirectories in the results folder
+                                # No need to iterate on compliance test
+                                # subdirectories in the results folder
                                 if directory == "results":
                                     break
 
@@ -211,8 +259,7 @@ def main():
 
     if args.scenarios_to_skip:
         scenarios_to_skip = [
-            scenario for scenario in args.scenarios_to_skip.split(',')
-        ]
+            scenario for scenario in args.scenarios_to_skip.split(",")]
     else:
         scenarios_to_skip = []
 
@@ -220,7 +267,10 @@ def main():
     truncate_results_dir(args.submitter, args.backup, scenarios_to_skip)
 
     backup_location = args.output or args.backup
-    log.info("Make sure you keep a backup of %s in case mlperf wants to see the original accuracy logs", backup_location)
+    log.info(
+        "Make sure you keep a backup of %s in case mlperf wants to see the original accuracy logs",
+        backup_location,
+    )
 
     return 0
 
diff --git a/tools/upscale_coco/coco.py b/tools/upscale_coco/coco.py
index dd0e880be1..11813957ee 100755
--- a/tools/upscale_coco/coco.py
+++ b/tools/upscale_coco/coco.py
@@ -1,5 +1,5 @@
-__author__ = 'tylin'
-__version__ = '2.0'
+__author__ = "tylin"
+__version__ = "2.0"
 # Interface for accessing the Microsoft COCO dataset.
 
 # Microsoft COCO is a large image dataset designed for object detection,
@@ -56,6 +56,7 @@
 import os
 from collections import defaultdict
 import sys
+
 PYTHON_VERSION = sys.version_info[0]
 if PYTHON_VERSION == 2:
     from urllib import urlretrieve
@@ -64,7 +65,7 @@
 
 
 def _isArrayLike(obj):
-    return hasattr(obj, '__iter__') and hasattr(obj, '__len__')
+    return hasattr(obj, "__iter__") and hasattr(obj, "__len__")
 
 
 class COCO:
@@ -76,40 +77,42 @@ def __init__(self, annotation_file=None):
         :return:
         """
         # load dataset
-        self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()
+        self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict()
         self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)
-        if not annotation_file == None:
-            print('loading annotations into memory...')
+        if not annotation_file is None:
+            print("loading annotations into memory...")
             tic = time.time()
-            dataset = json.load(open(annotation_file, 'r'))
-            assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))
-            print('Done (t={:0.2f}s)'.format(time.time()- tic))
+            dataset = json.load(open(annotation_file, "r"))
+            assert (
+                isinstance(dataset, dict)
+            ), "annotation file format {} not supported".format(type(dataset))
+            print("Done (t={:0.2f}s)".format(time.time() - tic))
             self.dataset = dataset
             self.createIndex()
 
     def createIndex(self):
         # create index
-        print('creating index...')
+        print("creating index...")
         anns, cats, imgs = {}, {}, {}
-        imgToAnns,catToImgs = defaultdict(list),defaultdict(list)
-        if 'annotations' in self.dataset:
-            for ann in self.dataset['annotations']:
-                imgToAnns[ann['image_id']].append(ann)
-                anns[ann['id']] = ann
+        imgToAnns, catToImgs = defaultdict(list), defaultdict(list)
+        if "annotations" in self.dataset:
+            for ann in self.dataset["annotations"]:
+                imgToAnns[ann["image_id"]].append(ann)
+                anns[ann["id"]] = ann
 
-        if 'images' in self.dataset:
-            for img in self.dataset['images']:
-                imgs[img['id']] = img
+        if "images" in self.dataset:
+            for img in self.dataset["images"]:
+                imgs[img["id"]] = img
 
-        if 'categories' in self.dataset:
-            for cat in self.dataset['categories']:
-                cats[cat['id']] = cat
+        if "categories" in self.dataset:
+            for cat in self.dataset["categories"]:
+                cats[cat["id"]] = cat
 
-        if 'annotations' in self.dataset and 'categories' in self.dataset:
-            for ann in self.dataset['annotations']:
-                catToImgs[ann['category_id']].append(ann['image_id'])
+        if "annotations" in self.dataset and "categories" in self.dataset:
+            for ann in self.dataset["annotations"]:
+                catToImgs[ann["category_id"]].append(ann["image_id"])
 
-        print('index created!')
+        print("index created!")
 
         # create class members
         self.anns = anns
@@ -123,8 +126,8 @@ def info(self):
         Print information about the annotation file.
         :return:
         """
-        for key, value in self.dataset['info'].items():
-            print('{}: {}'.format(key, value))
+        for key, value in self.dataset["info"].items():
+            print("{}: {}".format(key, value))
 
     def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
         """
@@ -139,19 +142,33 @@ def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
         catIds = catIds if _isArrayLike(catIds) else [catIds]
 
         if len(imgIds) == len(catIds) == len(areaRng) == 0:
-            anns = self.dataset['annotations']
+            anns = self.dataset["annotations"]
         else:
             if not len(imgIds) == 0:
-                lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]
+                lists = [
+                    self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns
+                ]
                 anns = list(itertools.chain.from_iterable(lists))
             else:
-                anns = self.dataset['annotations']
-            anns = anns if len(catIds)  == 0 else [ann for ann in anns if ann['category_id'] in catIds]
-            anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
-        if not iscrowd == None:
-            ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
+                anns = self.dataset["annotations"]
+            anns = (
+                anns
+                if len(catIds) == 0
+                else [ann for ann in anns if ann["category_id"] in catIds]
+            )
+            anns = (
+                anns
+                if len(areaRng) == 0
+                else [
+                    ann
+                    for ann in anns
+                    if ann["area"] > areaRng[0] and ann["area"] < areaRng[1]
+                ]
+            )
+        if not iscrowd is None:
+            ids = [ann["id"] for ann in anns if ann["iscrowd"] == iscrowd]
         else:
-            ids = [ann['id'] for ann in anns]
+            ids = [ann["id"] for ann in anns]
         return ids
 
     def getCatIds(self, catNms=[], supNms=[], catIds=[]):
@@ -167,22 +184,34 @@ def getCatIds(self, catNms=[], supNms=[], catIds=[]):
         catIds = catIds if _isArrayLike(catIds) else [catIds]
 
         if len(catNms) == len(supNms) == len(catIds) == 0:
-            cats = self.dataset['categories']
+            cats = self.dataset["categories"]
         else:
-            cats = self.dataset['categories']
-            cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name']          in catNms]
-            cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
-            cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id']            in catIds]
-        ids = [cat['id'] for cat in cats]
+            cats = self.dataset["categories"]
+            cats = (
+                cats
+                if len(catNms) == 0
+                else [cat for cat in cats if cat["name"] in catNms]
+            )
+            cats = (
+                cats
+                if len(supNms) == 0
+                else [cat for cat in cats if cat["supercategory"] in supNms]
+            )
+            cats = (
+                cats
+                if len(catIds) == 0
+                else [cat for cat in cats if cat["id"] in catIds]
+            )
+        ids = [cat["id"] for cat in cats]
         return ids
 
     def getImgIds(self, imgIds=[], catIds=[]):
-        '''
+        """
         Get img ids that satisfy given filter conditions.
         :param imgIds (int array) : get imgs for given ids
         :param catIds (int array) : get imgs with all given cats
         :return: ids (int array)  : integer array of img ids
-        '''
+        """
         imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
         catIds = catIds if _isArrayLike(catIds) else [catIds]
 
@@ -205,7 +234,7 @@ def loadAnns(self, ids=[]):
         """
         if _isArrayLike(ids):
             return [self.anns[id] for id in ids]
-        elif type(ids) == int:
+        elif isinstance(ids, int):
             return [self.anns[ids]]
 
     def loadCats(self, ids=[]):
@@ -216,7 +245,7 @@ def loadCats(self, ids=[]):
         """
         if _isArrayLike(ids):
             return [self.cats[id] for id in ids]
-        elif type(ids) == int:
+        elif isinstance(ids, int):
             return [self.cats[ids]]
 
     def loadImgs(self, ids=[]):
@@ -227,7 +256,7 @@ def loadImgs(self, ids=[]):
         """
         if _isArrayLike(ids):
             return [self.imgs[id] for id in ids]
-        elif type(ids) == int:
+        elif isinstance(ids, int):
             return [self.imgs[ids]]
 
     def showAnns(self, anns):
@@ -238,61 +267,88 @@ def showAnns(self, anns):
         """
         if len(anns) == 0:
             return 0
-        if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
-            datasetType = 'instances'
-        elif 'caption' in anns[0]:
-            datasetType = 'captions'
+        if "segmentation" in anns[0] or "keypoints" in anns[0]:
+            datasetType = "instances"
+        elif "caption" in anns[0]:
+            datasetType = "captions"
         else:
-            raise Exception('datasetType not supported')
-        if datasetType == 'instances':
+            raise Exception("datasetType not supported")
+        if datasetType == "instances":
             ax = plt.gca()
             ax.set_autoscale_on(False)
             polygons = []
             color = []
             for ann in anns:
-                c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
-                if 'segmentation' in ann:
-                    if type(ann['segmentation']) == list:
+                c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+                if "segmentation" in ann:
+                    if isinstance(ann["segmentation"], list):
                         # polygon
-                        for seg in ann['segmentation']:
-                            poly = np.array(seg).reshape((int(len(seg)/2), 2))
+                        for seg in ann["segmentation"]:
+                            poly = np.array(seg).reshape(
+                                (int(len(seg) / 2), 2))
                             polygons.append(Polygon(poly))
                             color.append(c)
                     else:
                         # mask
-                        t = self.imgs[ann['image_id']]
-                        if type(ann['segmentation']['counts']) == list:
-                            rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
+                        t = self.imgs[ann["image_id"]]
+                        if isinstance(ann["segmentation"]["counts"], list):
+                            rle = maskUtils.frPyObjects(
+                                [ann["segmentation"]], t["height"], t["width"]
+                            )
                         else:
-                            rle = [ann['segmentation']]
+                            rle = [ann["segmentation"]]
                         m = maskUtils.decode(rle)
-                        img = np.ones( (m.shape[0], m.shape[1], 3) )
-                        if ann['iscrowd'] == 1:
-                            color_mask = np.array([2.0,166.0,101.0])/255
-                        if ann['iscrowd'] == 0:
+                        img = np.ones((m.shape[0], m.shape[1], 3))
+                        if ann["iscrowd"] == 1:
+                            color_mask = np.array([2.0, 166.0, 101.0]) / 255
+                        if ann["iscrowd"] == 0:
                             color_mask = np.random.random((1, 3)).tolist()[0]
                         for i in range(3):
-                            img[:,:,i] = color_mask[i]
-                        ax.imshow(np.dstack( (img, m*0.5) ))
-                if 'keypoints' in ann and type(ann['keypoints']) == list:
+                            img[:, :, i] = color_mask[i]
+                        ax.imshow(np.dstack((img, m * 0.5)))
+                if "keypoints" in ann and isinstance(ann["keypoints"], list):
                     # turn skeleton into zero-based index
-                    sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
-                    kp = np.array(ann['keypoints'])
+                    sks = np.array(
+                        self.loadCats(
+                            ann["category_id"])[0]["skeleton"]) - 1
+                    kp = np.array(ann["keypoints"])
                     x = kp[0::3]
                     y = kp[1::3]
                     v = kp[2::3]
                     for sk in sks:
-                        if np.all(v[sk]>0):
-                            plt.plot(x[sk],y[sk], linewidth=3, color=c)
-                    plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
-                    plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)
-            p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
+                        if np.all(v[sk] > 0):
+                            plt.plot(x[sk], y[sk], linewidth=3, color=c)
+                    plt.plot(
+                        x[v > 0],
+                        y[v > 0],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor="k",
+                        markeredgewidth=2,
+                    )
+                    plt.plot(
+                        x[v > 1],
+                        y[v > 1],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor=c,
+                        markeredgewidth=2,
+                    )
+            p = PatchCollection(
+                polygons,
+                facecolor=color,
+                linewidths=0,
+                alpha=0.4)
             ax.add_collection(p)
-            p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)
+            p = PatchCollection(
+                polygons, facecolor="none", edgecolors=color, linewidths=2
+            )
             ax.add_collection(p)
-        elif datasetType == 'captions':
+        elif datasetType == "captions":
             for ann in anns:
-                print(ann['caption'])
+                print(ann["caption"])
 
     def loadRes(self, resFile):
         """
@@ -301,69 +357,78 @@ def loadRes(self, resFile):
         :return: res (obj)         : result api object
         """
         res = COCO()
-        res.dataset['images'] = [img for img in self.dataset['images']]
+        res.dataset["images"] = [img for img in self.dataset["images"]]
 
-        print('Loading and preparing results...')
+        print("Loading and preparing results...")
         tic = time.time()
-        if type(resFile) == str: #or type(resFile) == unicode:
+        if isinstance(resFile, str):  # or type(resFile) == unicode:
             anns = json.load(open(resFile))
-        elif type(resFile) == np.ndarray:
+        elif isinstance(resFile, np.ndarray):
             anns = self.loadNumpyAnnotations(resFile)
         else:
             anns = resFile
-        assert type(anns) == list, 'results in not an array of objects'
-        annsImgIds = [ann['image_id'] for ann in anns]
-        assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
-               'Results do not correspond to current coco set'
-        if 'caption' in anns[0]:
-            imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
-            res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
+        assert isinstance(anns, list), "results in not an array of objects"
+        annsImgIds = [ann["image_id"] for ann in anns]
+        assert set(annsImgIds) == (
+            set(annsImgIds) & set(self.getImgIds())
+        ), "Results do not correspond to current coco set"
+        if "caption" in anns[0]:
+            imgIds = set([img["id"] for img in res.dataset["images"]]) & set(
+                [ann["image_id"] for ann in anns]
+            )
+            res.dataset["images"] = [
+                img for img in res.dataset["images"] if img["id"] in imgIds
+            ]
             for id, ann in enumerate(anns):
-                ann['id'] = id+1
-        elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+                ann["id"] = id + 1
+        elif "bbox" in anns[0] and not anns[0]["bbox"] == []:
+            res.dataset["categories"] = copy.deepcopy(
+                self.dataset["categories"])
             for id, ann in enumerate(anns):
-                bb = ann['bbox']
-                x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
-                if not 'segmentation' in ann:
-                    ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
-                ann['area'] = bb[2]*bb[3]
-                ann['id'] = id+1
-                ann['iscrowd'] = 0
-        elif 'segmentation' in anns[0]:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+                bb = ann["bbox"]
+                x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
+                if not "segmentation" in ann:
+                    ann["segmentation"] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
+                ann["area"] = bb[2] * bb[3]
+                ann["id"] = id + 1
+                ann["iscrowd"] = 0
+        elif "segmentation" in anns[0]:
+            res.dataset["categories"] = copy.deepcopy(
+                self.dataset["categories"])
             for id, ann in enumerate(anns):
-                # now only support compressed RLE format as segmentation results
-                ann['area'] = maskUtils.area(ann['segmentation'])
-                if not 'bbox' in ann:
-                    ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
-                ann['id'] = id+1
-                ann['iscrowd'] = 0
-        elif 'keypoints' in anns[0]:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+                # now only support compressed RLE format as segmentation
+                # results
+                ann["area"] = maskUtils.area(ann["segmentation"])
+                if not "bbox" in ann:
+                    ann["bbox"] = maskUtils.toBbox(ann["segmentation"])
+                ann["id"] = id + 1
+                ann["iscrowd"] = 0
+        elif "keypoints" in anns[0]:
+            res.dataset["categories"] = copy.deepcopy(
+                self.dataset["categories"])
             for id, ann in enumerate(anns):
-                s = ann['keypoints']
+                s = ann["keypoints"]
                 x = s[0::3]
                 y = s[1::3]
-                x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y)
-                ann['area'] = (x1-x0)*(y1-y0)
-                ann['id'] = id + 1
-                ann['bbox'] = [x0,y0,x1-x0,y1-y0]
-        print('DONE (t={:0.2f}s)'.format(time.time()- tic))
+                x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y)
+                ann["area"] = (x1 - x0) * (y1 - y0)
+                ann["id"] = id + 1
+                ann["bbox"] = [x0, y0, x1 - x0, y1 - y0]
+        print("DONE (t={:0.2f}s)".format(time.time() - tic))
 
-        res.dataset['annotations'] = anns
+        res.dataset["annotations"] = anns
         res.createIndex()
         return res
 
-    def download(self, tarDir = None, imgIds = [] ):
-        '''
+    def download(self, tarDir=None, imgIds=[]):
+        """
         Download COCO images from mscoco.org server.
         :param tarDir (str): COCO results directory name
                imgIds (list): images to be downloaded
         :return:
-        '''
+        """
         if tarDir is None:
-            print('Please specify target directory')
+            print("Please specify target directory")
             return -1
         if len(imgIds) == 0:
             imgs = self.imgs.values()
@@ -374,10 +439,13 @@ def download(self, tarDir = None, imgIds = [] ):
             os.makedirs(tarDir)
         for i, img in enumerate(imgs):
             tic = time.time()
-            fname = os.path.join(tarDir, img['file_name'])
+            fname = os.path.join(tarDir, img["file_name"])
             if not os.path.exists(fname):
-                urlretrieve(img['coco_url'], fname)
-            print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic))
+                urlretrieve(img["coco_url"], fname)
+            print(
+                "downloaded {}/{} images (t={:0.1f}s)".format(i,
+                                                              N, time.time() - tic)
+            )
 
     def loadNumpyAnnotations(self, data):
         """
@@ -385,21 +453,23 @@ def loadNumpyAnnotations(self, data):
         :param  data (numpy.ndarray)
         :return: annotations (python nested list)
         """
-        print('Converting ndarray to lists...')
-        assert(type(data) == np.ndarray)
+        print("Converting ndarray to lists...")
+        assert isinstance(data, np.ndarray)
         print(data.shape)
-        assert(data.shape[1] == 7)
+        assert data.shape[1] == 7
         N = data.shape[0]
         ann = []
         for i in range(N):
             if i % 1000000 == 0:
-                print('{}/{}'.format(i,N))
-            ann += [{
-                'image_id'  : int(data[i, 0]),
-                'bbox'  : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ],
-                'score' : data[i, 5],
-                'category_id': int(data[i, 6]),
-                }]
+                print("{}/{}".format(i, N))
+            ann += [
+                {
+                    "image_id": int(data[i, 0]),
+                    "bbox": [data[i, 1], data[i, 2], data[i, 3], data[i, 4]],
+                    "score": data[i, 5],
+                    "category_id": int(data[i, 6]),
+                }
+            ]
         return ann
 
     def annToRLE(self, ann):
@@ -407,20 +477,20 @@ def annToRLE(self, ann):
         Convert annotation which can be polygons, uncompressed RLE to RLE.
         :return: binary mask (numpy 2D array)
         """
-        t = self.imgs[ann['image_id']]
-        h, w = t['height'], t['width']
-        segm = ann['segmentation']
-        if type(segm) == list:
+        t = self.imgs[ann["image_id"]]
+        h, w = t["height"], t["width"]
+        segm = ann["segmentation"]
+        if isinstance(segm, list):
             # polygon -- a single object might consist of multiple parts
             # we merge all parts into one mask rle code
             rles = maskUtils.frPyObjects(segm, h, w)
             rle = maskUtils.merge(rles)
-        elif type(segm['counts']) == list:
+        elif isinstance(segm["counts"], list):
             # uncompressed RLE
             rle = maskUtils.frPyObjects(segm, h, w)
         else:
             # rle
-            rle = ann['segmentation']
+            rle = ann["segmentation"]
         return rle
 
     def annToMask(self, ann):
diff --git a/tools/upscale_coco/upscale_coco.py b/tools/upscale_coco/upscale_coco.py
index 19c9624c11..6a97e32146 100755
--- a/tools/upscale_coco/upscale_coco.py
+++ b/tools/upscale_coco/upscale_coco.py
@@ -10,24 +10,49 @@
 
 def parse_args():
     parser = argparse.ArgumentParser(description="Upscale COCO dataset")
-    parser.add_argument('--inputs', '-i', type=str, default='/coco',
-                        help='input directory for coco dataset')
-    parser.add_argument('--outputs', '-o', type=str, default='/cocoup',
-                        help='output directory for upscaled coco dataset')
-    parser.add_argument('--images', '-im', type=str, default='val2017',
-                        help='image directory')
-    parser.add_argument('--annotations', '-a', type=str, default='annotations/instances_val2017.json',
-                        help='annotations directory')
-    parser.add_argument('--size', required=True, type=int, nargs='+',
-                        help='upscaled image sizes (e.g 300 300, 1200 1200')
-    parser.add_argument('--format', '-f', type=str, default='jpg',
-                        help='image format')
+    parser.add_argument(
+        "--inputs",
+        "-i",
+        type=str,
+        default="/coco",
+        help="input directory for coco dataset",
+    )
+    parser.add_argument(
+        "--outputs",
+        "-o",
+        type=str,
+        default="/cocoup",
+        help="output directory for upscaled coco dataset",
+    )
+    parser.add_argument(
+        "--images", "-im", type=str, default="val2017", help="image directory"
+    )
+    parser.add_argument(
+        "--annotations",
+        "-a",
+        type=str,
+        default="annotations/instances_val2017.json",
+        help="annotations directory",
+    )
+    parser.add_argument(
+        "--size",
+        required=True,
+        type=int,
+        nargs="+",
+        help="upscaled image sizes (e.g 300 300, 1200 1200",
+    )
+    parser.add_argument(
+        "--format",
+        "-f",
+        type=str,
+        default="jpg",
+        help="image format")
     return parser.parse_args()
 
 
 def upscale_coco(indir, outdir, image_dir, annotate_file, size, fmt):
     # Build directories.
-    print('Building directories...')
+    print("Building directories...")
     size = tuple(size)
     image_in_path = os.path.join(indir, image_dir)
     image_out_path = os.path.join(outdir, image_dir)
@@ -41,68 +66,71 @@ def upscale_coco(indir, outdir, image_dir, annotate_file, size, fmt):
         os.makedirs(annotate_out_path)
 
     # Read annotations.
-    print('Reading COCO dataset...')
+    print("Reading COCO dataset...")
     coco = COCO(annotate_in_file)
-    print(len(coco.imgs), 'images')
-    print(len(coco.anns), 'annotations')
+    print(len(coco.imgs), "images")
+    print(len(coco.anns), "annotations")
 
     # Upscale annotations.
-    print('Upscaling annotations...')
+    print("Upscaling annotations...")
     annotations = []
     for idx in coco.anns:
         ann = coco.anns[idx]
         # Scaling factors
-        img = coco.imgs[ann['image_id']]
-        sx = size[0] / img['width']
-        sy = size[1] / img['height']
+        img = coco.imgs[ann["image_id"]]
+        sx = size[0] / img["width"]
+        sy = size[1] / img["height"]
         # Bounding boxes
-        bb = ann['bbox']
+        bb = ann["bbox"]
         bb[0] = bb[0] * sx
         bb[1] = bb[1] * sy
         bb[2] = bb[2] * sx
         bb[3] = bb[3] * sy
         # Area
-        ann['area'] = ann['area'] * sx * sy
+        ann["area"] = ann["area"] * sx * sy
         annotations.append(ann)
 
     # Upscale images.
-    print('Upscaling images...')
+    print("Upscaling images...")
     count = 0
     images = []
     for idx in coco.imgs:
         img = coco.imgs[idx]
         # Load, upscale, and save image.
-        image = cv2.imread(os.path.join(image_in_path, img['file_name']))
+        image = cv2.imread(os.path.join(image_in_path, img["file_name"]))
         if len(image.shape) < 3 or image.shape[2] != 3:
             # some images might be grayscale
             image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
         image = cv2.resize(image, size, interpolation=cv2.INTER_LINEAR)
-        cv2.imwrite(os.path.join(image_out_path, img['file_name'][0:-3] + fmt), image)
+        cv2.imwrite(os.path.join(image_out_path,
+                    img["file_name"][0:-3] + fmt), image)
         # Update image file extension
-        img['file_name'] = img['file_name'][0:-3] + fmt
+        img["file_name"] = img["file_name"][0:-3] + fmt
         # Image dimensions
-        img['width'] = size[0]
-        img['height'] = size[1]
+        img["width"] = size[0]
+        img["height"] = size[1]
         count = count + 1
         # print(count, end=' ', flush=True)
         images.append(img)
 
     # Save annotations.
-    print('Saving annotations...')
+    print("Saving annotations...")
     with open(annotate_in_file) as f:
         dataset = json.load(f)
-    dataset['images'] = images
-    dataset['annotations'] = annotations
-    with open(annotate_out_file, 'w') as outfile:
+    dataset["images"] = images
+    dataset["annotations"] = annotations
+    with open(annotate_out_file, "w") as outfile:
         json.dump(dataset, outfile)
-    print('Done.')
+    print("Done.")
 
 
 def main():
     # Get arguments.
     args = parse_args()
     # Upscale coco.
-    upscale_coco(args.inputs, args.outputs, args.images, args.annotations, args.size, args.format)
+    upscale_coco(
+        args.inputs, args.outputs, args.images, args.annotations, args.size, args.format
+    )
 
 
 main()
diff --git a/upcomming_benchmarks/graph/R-GAT/backend_pytorch.py b/upcomming_benchmarks/graph/R-GAT/backend_pytorch.py
index 05b899c085..70777cef17 100644
--- a/upcomming_benchmarks/graph/R-GAT/backend_pytorch.py
+++ b/upcomming_benchmarks/graph/R-GAT/backend_pytorch.py
@@ -1,3 +1,7 @@
+from graphlearn_torch.typing import InputNodes, NumNeighbors
+from graphlearn_torch.sampler import NeighborSampler, NodeSamplerInput
+from graphlearn_torch.data import Dataset
+from graphlearn_torch.loader import NodeLoader
 from typing import Optional, List, Union
 import os
 import torch
@@ -11,12 +15,6 @@
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("backend-pytorch")
 
-from graphlearn_torch.loader import NodeLoader
-
-from graphlearn_torch.data import Dataset
-from graphlearn_torch.sampler import NeighborSampler, NodeSamplerInput
-from graphlearn_torch.typing import InputNodes, NumNeighbors
-
 
 class CustomNeighborLoader(NodeLoader):
     # Copyright 2022 Alibaba Group Holding Limited. All Rights Reserved.
@@ -143,14 +141,17 @@ def __init__(
         # Create Node and neighbor loade
         self.glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
         self.glt_dataset.init_node_features(
-            node_feature_data=igbh_dataset.feat_dict, with_gpu=(device == "gpu"), dtype=self.type
+            node_feature_data=igbh_dataset.feat_dict,
+            with_gpu=(device == "gpu"),
+            dtype=self.type,
         )
         self.glt_dataset.init_graph(
             edge_index=igbh_dataset.edge_dict,
             layout=layout,
             graph_mode="ZERO_COPY" if (device == "gpu") else "CPU",
         )
-        self.glt_dataset.init_node_labels(node_label_data={"paper": igbh_dataset.label})
+        self.glt_dataset.init_node_labels(
+            node_label_data={"paper": igbh_dataset.label})
         self.neighbor_loader = CustomNeighborLoader(
             self.glt_dataset,
             [15, 10, 5],
@@ -161,17 +162,21 @@ def __init__(
             seed=42,
         )
 
-        self.model = RGNN(
-            self.glt_dataset.get_edge_types(),
-            self.glt_dataset.node_features["paper"].shape[1],
-            512,
-            2983,
-            num_layers=3,
-            dropout=0.2,
-            model=model_type,
-            heads=4,
-            node_type="paper",
-        ).to(self.type).to(self.device)
+        self.model = (
+            RGNN(
+                self.glt_dataset.get_edge_types(),
+                self.glt_dataset.node_features["paper"].shape[1],
+                512,
+                2983,
+                num_layers=3,
+                dropout=0.2,
+                model=model_type,
+                heads=4,
+                node_type="paper",
+            )
+            .to(self.type)
+            .to(self.device)
+        )
         self.model.eval()
         ckpt = None
         if ckpt_path is not None:
@@ -207,4 +212,3 @@ def predict(self, inputs: torch.Tensor):
                 batch.edge_index_dict,
             )[:input_size]
         return out
-
diff --git a/upcomming_benchmarks/graph/R-GAT/igbh.py b/upcomming_benchmarks/graph/R-GAT/igbh.py
index 70c6688801..e23a816e12 100644
--- a/upcomming_benchmarks/graph/R-GAT/igbh.py
+++ b/upcomming_benchmarks/graph/R-GAT/igbh.py
@@ -5,9 +5,12 @@
 # pylint: disable=unused-argument,missing-docstring
 # Parts of this script were taken from:
 # https://github.com/mlcommons/training/blob/master/graph_neural_network/dataset.py
-# Specifically the float2half function and the IGBH class are 
+# Specifically the float2half function and the IGBH class are
 # slightly modified copies.
 
+from typing import Literal
+from torch_geometric.utils import add_self_loops, remove_self_loops
+import torch
 import os
 import logging
 import argparse
@@ -21,13 +24,6 @@
 log = logging.getLogger("coco")
 
 
-import numpy as np
-import torch
-
-from torch_geometric.utils import add_self_loops, remove_self_loops
-from typing import Literal
-
-
 def float2half(base_path, dataset_size):
     paper_nodes_num = {
         "tiny": 100000,
@@ -45,7 +41,8 @@ def float2half(base_path, dataset_size):
     }
     # paper node
     paper_feat_path = os.path.join(base_path, "paper", "node_feat.npy")
-    paper_fp16_feat_path = os.path.join(base_path, "paper", "node_feat_fp16.pt")
+    paper_fp16_feat_path = os.path.join(
+        base_path, "paper", "node_feat_fp16.pt")
     if not os.path.exists(paper_fp16_feat_path):
         if dataset_size in ["large", "full"]:
             num_paper_nodes = paper_nodes_num[dataset_size]
@@ -66,7 +63,8 @@ def float2half(base_path, dataset_size):
 
     # author node
     author_feat_path = os.path.join(base_path, "author", "node_feat.npy")
-    author_fp16_feat_path = os.path.join(base_path, "author", "node_feat_fp16.pt")
+    author_fp16_feat_path = os.path.join(
+        base_path, "author", "node_feat_fp16.pt")
     if not os.path.exists(author_fp16_feat_path):
         if dataset_size in ["large", "full"]:
             num_author_nodes = author_nodes_num[dataset_size]
@@ -87,7 +85,8 @@ def float2half(base_path, dataset_size):
 
     # institute node
     institute_feat_path = os.path.join(base_path, "institute", "node_feat.npy")
-    institute_fp16_feat_path = os.path.join(base_path, "institute", "node_feat_fp16.pt")
+    institute_fp16_feat_path = os.path.join(
+        base_path, "institute", "node_feat_fp16.pt")
     if not os.path.exists(institute_fp16_feat_path):
         institute_node_features = torch.from_numpy(
             np.load(institute_feat_path, mmap_mode="r")
@@ -99,12 +98,14 @@ def float2half(base_path, dataset_size):
     fos_feat_path = os.path.join(base_path, "fos", "node_feat.npy")
     fos_fp16_feat_path = os.path.join(base_path, "fos", "node_feat_fp16.pt")
     if not os.path.exists(fos_fp16_feat_path):
-        fos_node_features = torch.from_numpy(np.load(fos_feat_path, mmap_mode="r"))
+        fos_node_features = torch.from_numpy(
+            np.load(fos_feat_path, mmap_mode="r"))
         fos_node_features = fos_node_features.half()
         torch.save(fos_node_features, fos_fp16_feat_path)
 
     # conference node
-    conference_feat_path = os.path.join(base_path, "conference", "node_feat.npy")
+    conference_feat_path = os.path.join(
+        base_path, "conference", "node_feat.npy")
     conference_fp16_feat_path = os.path.join(
         base_path, "conference", "node_feat_fp16.pt"
     )
@@ -117,7 +118,8 @@ def float2half(base_path, dataset_size):
 
     # journal node
     journal_feat_path = os.path.join(base_path, "journal", "node_feat.npy")
-    journal_fp16_feat_path = os.path.join(base_path, "journal", "node_feat_fp16.pt")
+    journal_fp16_feat_path = os.path.join(
+        base_path, "journal", "node_feat_fp16.pt")
     if not os.path.exists(journal_fp16_feat_path):
         journal_node_features = torch.from_numpy(
             np.load(journal_feat_path, mmap_mode="r")
@@ -145,7 +147,13 @@ def __init__(
         self.layout = layout
         self.use_fp16 = use_fp16
 
-        self.ntypes = ["paper", "author", "institute", "fos", "journal", "conference"]
+        self.ntypes = [
+            "paper",
+            "author",
+            "institute",
+            "fos",
+            "journal",
+            "conference"]
         self.etypes = None
         self.edge_dict = {}
         self.feat_dict = {}
@@ -286,7 +294,8 @@ def process(self):
                         )
                     ).t()
 
-                cites_edge = add_self_loops(remove_self_loops(paper_paper_edges)[0])[0]
+                cites_edge = add_self_loops(
+                    remove_self_loops(paper_paper_edges)[0])[0]
                 self.edge_dict = {
                     ("paper", "cites", "paper"): (
                         torch.cat([cites_edge[1, :], cites_edge[0, :]]),
@@ -308,7 +317,7 @@ def process(self):
                         paper_fos_edges[0, :],
                     ),
                 }
-            
+
                 self.edge_dict[("paper", "published", "journal")] = (
                     paper_published_journal
                 )
@@ -324,17 +333,20 @@ def process(self):
                     paper_venue_conference[0, :],
                 )
 
-            # directly load from CSC or CSC files, which can be generated using compress_graph.py
+            # directly load from CSC or CSC files, which can be generated using
+            # compress_graph.py
             else:
                 compress_edge_dict = {}
-                compress_edge_dict[("paper", "cites", "paper")] = "paper__cites__paper"
+                compress_edge_dict[("paper", "cites", "paper")
+                                   ] = "paper__cites__paper"
                 compress_edge_dict[("paper", "written_by", "author")] = (
                     "paper__written_by__author"
                 )
                 compress_edge_dict[("author", "affiliated_to", "institute")] = (
                     "author__affiliated_to__institute"
                 )
-                compress_edge_dict[("paper", "topic", "fos")] = "paper__topic__fos"
+                compress_edge_dict[("paper", "topic", "fos")
+                                   ] = "paper__topic__fos"
                 compress_edge_dict[("author", "rev_written_by", "paper")] = (
                     "author__rev_written_by__paper"
                 )
@@ -365,8 +377,10 @@ def process(self):
                         edge_path = os.path.join(
                             self.base_path, self.layout, compress_edge_dict[etype]
                         )
-                        indptr = torch.load(os.path.join(edge_path, "indptr.pt"))
-                        indices = torch.load(os.path.join(edge_path, "indices.pt"))
+                        indptr = torch.load(
+                            os.path.join(edge_path, "indptr.pt"))
+                        indices = torch.load(
+                            os.path.join(edge_path, "indices.pt"))
                         if self.layout == "CSC":
                             self.edge_dict[etype] = (indices, indptr)
                         else:
@@ -383,7 +397,8 @@ def process(self):
         label_file = (
             "node_label_19.npy" if not self.use_label_2K else "node_label_2K.npy"
         )
-        paper_feat_path = os.path.join(self.base_path, "paper", "node_feat.npy")
+        paper_feat_path = os.path.join(
+            self.base_path, "paper", "node_feat.npy")
         paper_lbl_path = os.path.join(self.base_path, "paper", label_file)
         num_paper_nodes = self.paper_nodes_num[self.dataset_size]
         if self.in_memory:
@@ -392,7 +407,8 @@ def process(self):
                     os.path.join(self.base_path, "paper", "node_feat_fp16.pt")
                 )
             else:
-                paper_node_features = torch.from_numpy(np.load(paper_feat_path))
+                paper_node_features = torch.from_numpy(
+                    np.load(paper_feat_path))
         else:
             if self.dataset_size in ["large", "full"]:
                 paper_node_features = torch.from_numpy(
@@ -414,19 +430,23 @@ def process(self):
                 )
             ).to(torch.long)
         else:
-            paper_node_labels = torch.from_numpy(np.load(paper_lbl_path)).to(torch.long)
+            paper_node_labels = torch.from_numpy(
+                np.load(paper_lbl_path)).to(
+                torch.long)
         self.feat_dict["paper"] = paper_node_features
         self.label = paper_node_labels
 
         num_author_nodes = self.author_nodes_num[self.dataset_size]
-        author_feat_path = os.path.join(self.base_path, "author", "node_feat.npy")
+        author_feat_path = os.path.join(
+            self.base_path, "author", "node_feat.npy")
         if self.in_memory:
             if self.use_fp16:
                 author_node_features = torch.load(
                     os.path.join(self.base_path, "author", "node_feat_fp16.pt")
                 )
             else:
-                author_node_features = torch.from_numpy(np.load(author_feat_path))
+                author_node_features = torch.from_numpy(
+                    np.load(author_feat_path))
         else:
             if self.dataset_size in ["large", "full"]:
                 author_node_features = torch.from_numpy(
@@ -446,11 +466,18 @@ def process(self):
         if self.in_memory:
             if self.use_fp16:
                 institute_node_features = torch.load(
-                    os.path.join(self.base_path, "institute", "node_feat_fp16.pt")
+                    os.path.join(
+                        self.base_path,
+                        "institute",
+                        "node_feat_fp16.pt")
                 )
             else:
                 institute_node_features = torch.from_numpy(
-                    np.load(os.path.join(self.base_path, "institute", "node_feat.npy"))
+                    np.load(
+                        os.path.join(
+                            self.base_path,
+                            "institute",
+                            "node_feat.npy"))
                 )
         else:
             institute_node_features = torch.from_numpy(
@@ -468,7 +495,11 @@ def process(self):
                 )
             else:
                 fos_node_features = torch.from_numpy(
-                    np.load(os.path.join(self.base_path, "fos", "node_feat.npy"))
+                    np.load(
+                        os.path.join(
+                            self.base_path,
+                            "fos",
+                            "node_feat.npy"))
                 )
         else:
             fos_node_features = torch.from_numpy(
@@ -481,18 +512,26 @@ def process(self):
         if self.in_memory:
             if self.use_fp16:
                 conference_node_features = torch.load(
-                    os.path.join(self.base_path, "conference", "node_feat_fp16.pt")
+                    os.path.join(
+                        self.base_path,
+                        "conference",
+                        "node_feat_fp16.pt")
                 )
             else:
                 conference_node_features = torch.from_numpy(
                     np.load(
-                        os.path.join(self.base_path, "conference", "node_feat.npy")
-                    )
+                        os.path.join(
+                            self.base_path,
+                            "conference",
+                            "node_feat.npy"))
                 )
         else:
             conference_node_features = torch.from_numpy(
                 np.load(
-                    os.path.join(self.base_path, "conference", "node_feat.npy"),
+                    os.path.join(
+                        self.base_path,
+                        "conference",
+                        "node_feat.npy"),
                     mmap_mode="r",
                 )
             )
@@ -501,13 +540,18 @@ def process(self):
         if self.in_memory:
             if self.use_fp16:
                 journal_node_features = torch.load(
-                    os.path.join(self.base_path, "journal", "node_feat_fp16.pt")
+                    os.path.join(
+                        self.base_path,
+                        "journal",
+                        "node_feat_fp16.pt")
                 )
             else:
                 journal_node_features = torch.from_numpy(
                     np.load(
-                        os.path.join(self.base_path, "journal", "node_feat.npy")
-                    )
+                        os.path.join(
+                            self.base_path,
+                            "journal",
+                            "node_feat.npy"))
                 )
         else:
             journal_node_features = torch.from_numpy(
@@ -518,10 +562,17 @@ def process(self):
             )
         self.feat_dict["journal"] = journal_node_features
 
-        # Please ensure that train_idx and val_idx have been generated using split_seeds.py
+        # Please ensure that train_idx and val_idx have been generated using
+        # split_seeds.py
         try:
-            self.train_idx = torch.load(os.path.join(self.base_path, "train_idx.pt"))
-            self.val_idx = torch.load(os.path.join(self.base_path, "val_idx.pt"))
+            self.train_idx = torch.load(
+                os.path.join(
+                    self.base_path,
+                    "train_idx.pt"))
+            self.val_idx = torch.load(
+                os.path.join(
+                    self.base_path,
+                    "val_idx.pt"))
         except FileNotFoundError as e:
             print(
                 f"FileNotFound: {e}, please ensure that train_idx and val_idx have been generated using split_seeds.py"
@@ -565,10 +616,10 @@ def get_samples(self, id_list):
 
     def get_labels(self, id_list):
         return self.igbh_dataset.label[self.get_samples(id_list)]
-    
+
     def get_item_count(self):
         return len(self.igbh_dataset.val_idx)
-    
+
     def load_query_samples(self, id):
         pass
 
@@ -581,7 +632,8 @@ def __init__(
         self,
         device="cpu",
         dtype="uint8",
-        statistics_path=os.path.join(os.path.dirname(__file__), "tools", "val2014.npz"),
+        statistics_path=os.path.join(
+            os.path.dirname(__file__), "tools", "val2014.npz"),
     ):
         self.results = []
         self.content_ids = []
@@ -603,8 +655,7 @@ def finalize(self, result_dict, ds=None, output_dir=None):
         total = len(self.results)
         good = 0
         for l, r in zip(labels, self.results):
-            if (l == r):
+            if l == r:
                 good += 1
         result_dict["accuracy"] = good / total
         return result_dict
-
diff --git a/upcomming_benchmarks/graph/R-GAT/main.py b/upcomming_benchmarks/graph/R-GAT/main.py
index 145c3931b3..a7697481a9 100644
--- a/upcomming_benchmarks/graph/R-GAT/main.py
+++ b/upcomming_benchmarks/graph/R-GAT/main.py
@@ -60,7 +60,7 @@
         dataset.preprocess,
         igbh.PostProcessIGBH(),
         {"dataset_size": "full", "use_label_2K": True},
-    )
+    ),
 }
 
 
@@ -108,17 +108,32 @@
 def get_args():
     parser = argparse.ArgumentParser()
     # Dataset arguments
-    parser.add_argument("--dataset", choices=SUPPORTED_DATASETS.keys(), help="dataset")
-    parser.add_argument("--dataset-path", required=True, help="path to the dataset")
-    parser.add_argument("--in-memory", action="store_true", help="path to the dataset")
-    parser.add_argument("--layout", default="COO", choices=["CSC", "CSR", "COO"], help="path to the dataset")
+    parser.add_argument(
+        "--dataset",
+        choices=SUPPORTED_DATASETS.keys(),
+        help="dataset")
+    parser.add_argument(
+        "--dataset-path",
+        required=True,
+        help="path to the dataset")
+    parser.add_argument(
+        "--in-memory",
+        action="store_true",
+        help="path to the dataset")
+    parser.add_argument(
+        "--layout",
+        default="COO",
+        choices=["CSC", "CSR", "COO"],
+        help="path to the dataset",
+    )
     parser.add_argument(
         "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
     )
     parser.add_argument(
         "--scenario",
         default="SingleStream",
-        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())),
+        help="mlperf benchmark scenario, one of " +
+        str(list(SCENARIO_MAP.keys())),
     )
     parser.add_argument(
         "--max-batchsize",
@@ -127,7 +142,10 @@ def get_args():
         help="max batch size in a single inference",
     )
     parser.add_argument("--threads", default=1, type=int, help="threads")
-    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
     parser.add_argument(
         "--find-peak-performance",
         action="store_true",
@@ -168,12 +186,16 @@ def get_args():
         "--audit_conf", default="audit.config", help="config for LoadGen audit settings"
     )
 
-    # below will override mlperf rules compliant settings - don't use for official submission
+    # below will override mlperf rules compliant settings - don't use for
+    # official submission
     parser.add_argument("--time", type=int, help="time to scan in seconds")
     parser.add_argument("--count", type=int, help="dataset items to use")
     parser.add_argument("--debug", action="store_true", help="debug")
     parser.add_argument(
-        "--performance-sample-count", type=int, help="performance sample count", default=5000
+        "--performance-sample-count",
+        type=int,
+        help="performance sample count",
+        default=5000,
     )
     parser.add_argument(
         "--max-latency", type=float, help="mlperf max latency in pct tile"
@@ -206,6 +228,7 @@ def get_args():
 def get_backend(backend, **kwargs):
     if backend == "pytorch":
         from backend_pytorch import BackendPytorch
+
         backend = BackendPytorch(**kwargs)
     else:
         raise ValueError("unknown backend: " + backend)
@@ -279,10 +302,9 @@ def enqueue(self, query_samples):
         else:
             bs = self.max_batchsize
             for i in range(0, len(idx), bs):
-                samples = self.ds.get_samples(idx[i : i + bs])
+                samples = self.ds.get_samples(idx[i: i + bs])
                 self.run_one_item(
-                    Item(query_id[i : i + bs], idx[i : i + bs], samples)
-                )
+                    Item(query_id[i: i + bs], idx[i: i + bs], samples))
 
     def finish(self):
         pass
@@ -296,7 +318,9 @@ def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
         self.result_dict = {}
 
         for _ in range(self.threads):
-            worker = threading.Thread(target=self.handle_tasks, args=(self.tasks,))
+            worker = threading.Thread(
+                target=self.handle_tasks, args=(
+                    self.tasks,))
             worker.daemon = True
             self.workers.append(worker)
             worker.start()
@@ -357,7 +381,7 @@ def main():
         ckpt_path=args.model_path,
         batch_size=args.max_batchsize,
         igbh_dataset=ds.igbh_dataset,
-        layout=args.layout
+        layout=args.layout,
     )
 
     # --count applies to accuracy mode only and can be used to limit the number of images
@@ -389,7 +413,6 @@ def main():
         sys.exit(1)
 
     audit_config = os.path.abspath(args.audit_conf)
-    
 
     if args.output:
         output_dir = os.path.abspath(args.output)
@@ -458,7 +481,8 @@ def flush_queries():
         settings.multi_stream_samples_per_query = args.samples_per_query
     if args.max_latency:
         settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
-        settings.multi_stream_expected_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_expected_latency_ns = int(
+            args.max_latency * NANO_SEC)
 
     performance_sample_count = (
         args.performance_sample_count
diff --git a/upcomming_benchmarks/graph/R-GAT/rgnn.py b/upcomming_benchmarks/graph/R-GAT/rgnn.py
index 8743fe3097..11d43eac91 100644
--- a/upcomming_benchmarks/graph/R-GAT/rgnn.py
+++ b/upcomming_benchmarks/graph/R-GAT/rgnn.py
@@ -7,63 +7,99 @@
 from torch_geometric.nn import HeteroConv, GATConv, GCNConv, SAGEConv
 from torch_geometric.utils import trim_to_layer
 
+
 class RGNN(torch.nn.Module):
-  r""" [Relational GNN model](https://arxiv.org/abs/1703.06103).
+    r"""[Relational GNN model](https://arxiv.org/abs/1703.06103).
+
+    Args:
+      etypes: edge types.
+      in_dim: input size.
+      h_dim: Dimension of hidden layer.
+      out_dim: Output dimension.
+      num_layers: Number of conv layers.
+      dropout: Dropout probability for hidden layers.
+      model: "rsage" or "rgat".
+      heads: Number of multi-head-attentions for GAT.
+      node_type: The predict node type for node classification.
+
+    """
 
-  Args:
-    etypes: edge types.
-    in_dim: input size.
-    h_dim: Dimension of hidden layer.
-    out_dim: Output dimension.
-    num_layers: Number of conv layers.
-    dropout: Dropout probability for hidden layers.
-    model: "rsage" or "rgat".
-    heads: Number of multi-head-attentions for GAT.
-    node_type: The predict node type for node classification.
+    def __init__(
+        self,
+        etypes,
+        in_dim,
+        h_dim,
+        out_dim,
+        num_layers=2,
+        dropout=0.2,
+        model="rgat",
+        heads=4,
+        node_type=None,
+        with_trim=False,
+    ):
+        super().__init__()
+        self.node_type = node_type
+        if node_type is not None:
+            self.lin = torch.nn.Linear(h_dim, out_dim)
 
-  """
-  def __init__(self, etypes, in_dim, h_dim, out_dim, num_layers=2,
-               dropout=0.2, model='rgat', heads=4, node_type=None, with_trim=False):
-    super().__init__()
-    self.node_type = node_type
-    if node_type is not None:
-      self.lin = torch.nn.Linear(h_dim, out_dim)
+        self.convs = torch.nn.ModuleList()
+        for i in range(num_layers):
+            in_dim = in_dim if i == 0 else h_dim
+            h_dim = out_dim if (
+                i == (
+                    num_layers -
+                    1) and node_type is None) else h_dim
+            if model == "rsage":
+                self.convs.append(
+                    HeteroConv(
+                        {
+                            etype: SAGEConv(in_dim, h_dim, root_weight=False)
+                            for etype in etypes
+                        }
+                    )
+                )
+            elif model == "rgat":
+                self.convs.append(
+                    HeteroConv(
+                        {
+                            etype: GATConv(
+                                in_dim,
+                                h_dim // heads,
+                                heads=heads,
+                                add_self_loops=False,
+                            )
+                            for etype in etypes
+                        }
+                    )
+                )
+        self.dropout = torch.nn.Dropout(dropout)
+        self.with_trim = with_trim
 
-    self.convs = torch.nn.ModuleList()
-    for i in range(num_layers):
-      in_dim = in_dim if i == 0 else h_dim
-      h_dim = out_dim if (i == (num_layers - 1) and node_type is None) else h_dim
-      if model == 'rsage':
-        self.convs.append(HeteroConv({
-            etype: SAGEConv(in_dim, h_dim, root_weight=False)
-            for etype in etypes}))
-      elif model == 'rgat':
-        self.convs.append(HeteroConv({
-            etype: GATConv(in_dim, h_dim // heads, heads=heads, add_self_loops=False)
-            for etype in etypes}))
-    self.dropout = torch.nn.Dropout(dropout)
-    self.with_trim = with_trim
+    def forward(
+        self,
+        x_dict,
+        edge_index_dict,
+        num_sampled_edges_dict=None,
+        num_sampled_nodes_dict=None,
+    ):
+        for i, conv in enumerate(self.convs):
+            if self.with_trim:
+                x_dict, edge_index_dict, _ = trim_to_layer(
+                    layer=i,
+                    num_sampled_nodes_per_hop=num_sampled_nodes_dict,
+                    num_sampled_edges_per_hop=num_sampled_edges_dict,
+                    x=x_dict,
+                    edge_index=edge_index_dict,
+                )
+            for key in list(edge_index_dict.keys()):
+                if key[0] not in x_dict or key[-1] not in x_dict:
+                    del edge_index_dict[key]
 
-  def forward(self, x_dict, edge_index_dict, num_sampled_edges_dict=None,
-              num_sampled_nodes_dict=None):
-    for i, conv in enumerate(self.convs):
-      if self.with_trim:
-        x_dict, edge_index_dict, _ = trim_to_layer(
-          layer=i,
-          num_sampled_nodes_per_hop=num_sampled_nodes_dict,
-          num_sampled_edges_per_hop=num_sampled_edges_dict,
-          x=x_dict,
-          edge_index=edge_index_dict
-        )
-      for key in list(edge_index_dict.keys()):
-        if key[0] not in x_dict or key[-1] not in x_dict:
-          del edge_index_dict[key]
-          
-      x_dict = conv(x_dict, edge_index_dict)
-      if i != len(self.convs) - 1:
-        x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()}
-        x_dict = {key: self.dropout(x) for key, x in x_dict.items()}
-    if hasattr(self, 'lin'): # for node classification
-      return self.lin(x_dict[self.node_type])
-    else:
-      return x_dict
\ No newline at end of file
+            x_dict = conv(x_dict, edge_index_dict)
+            if i != len(self.convs) - 1:
+                x_dict = {key: F.leaky_relu(x) for key, x in x_dict.items()}
+                x_dict = {key: self.dropout(x) for key, x in x_dict.items()}
+        if hasattr(self, "lin"):  # for node classification
+            return self.lin(x_dict[self.node_type])
+        else:
+            return x_dict
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py b/upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py
index 252d8458bf..6fa2c2989b 100644
--- a/upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py
+++ b/upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py
@@ -1,6 +1,13 @@
-# This is a modified version of a script taken from: https://github.com/mlcommons/training/blob/master/graph_neural_network/compress_graph.py
+# This is a modified version of a script taken from:
+# https://github.com/mlcommons/training/blob/master/graph_neural_network/compress_graph.py
 
-import argparse, datetime, os
+from typing import Literal
+from torch_geometric.utils import add_self_loops, remove_self_loops
+from igb.download import download_dataset
+from igbh import float2half
+import argparse
+import datetime
+import os
 import numpy as np
 import torch
 import os.path as osp
@@ -8,113 +15,237 @@
 import graphlearn_torch as glt
 
 import sys
+
 sys.path.append(os.path.dirname(__file__))
 sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir))
-from igbh import float2half
-from igb.download import download_dataset
-from torch_geometric.utils import add_self_loops, remove_self_loops
-from typing import Literal
 
 
 class IGBHeteroDatasetCompress(object):
-  def __init__(self,
-               path,
-               dataset_size,
-               layout: Literal['CSC', 'CSR'] = 'CSC',):
-    self.dir = path
-    self.dataset_size = dataset_size
-    self.layout = layout
-
-    self.ntypes = ['paper', 'author', 'institute', 'fos']
-    self.etypes = None
-    self.edge_dict = {}
-    self.paper_nodes_num = {'tiny':100000, 'small':1000000, 'medium':10000000, 'large':100000000, 'full':269346174}
-    self.author_nodes_num = {'tiny':357041, 'small':1926066, 'medium':15544654, 'large':116959896, 'full':277220883}
-    if not osp.exists(osp.join(path, self.dataset_size, 'processed')):
-      download_dataset(path, 'heterogeneous', dataset_size)
-    self.process()
+    def __init__(
+        self,
+        path,
+        dataset_size,
+        layout: Literal["CSC", "CSR"] = "CSC",
+    ):
+        self.dir = path
+        self.dataset_size = dataset_size
+        self.layout = layout
 
-  def process(self):
-    paper_paper_edges = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
-    'paper__cites__paper', 'edge_index.npy'))).t()
-    author_paper_edges = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
-    'paper__written_by__author', 'edge_index.npy'))).t()
-    affiliation_author_edges = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
-    'author__affiliated_to__institute', 'edge_index.npy'))).t()
-    paper_fos_edges = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
-    'paper__topic__fos', 'edge_index.npy'))).t()
-    paper_published_journal = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
-    'paper__published__journal', 'edge_index.npy'))).t()
-    paper_venue_conference = torch.from_numpy(np.load(osp.join(self.dir, self.dataset_size, 'processed',
-    'paper__venue__conference', 'edge_index.npy'))).t()
+        self.ntypes = ["paper", "author", "institute", "fos"]
+        self.etypes = None
+        self.edge_dict = {}
+        self.paper_nodes_num = {
+            "tiny": 100000,
+            "small": 1000000,
+            "medium": 10000000,
+            "large": 100000000,
+            "full": 269346174,
+        }
+        self.author_nodes_num = {
+            "tiny": 357041,
+            "small": 1926066,
+            "medium": 15544654,
+            "large": 116959896,
+            "full": 277220883,
+        }
+        if not osp.exists(osp.join(path, self.dataset_size, "processed")):
+            download_dataset(path, "heterogeneous", dataset_size)
+        self.process()
 
-    cites_edge = add_self_loops(remove_self_loops(paper_paper_edges)[0])[0]
-    self.edge_dict = {
-        ('paper', 'cites', 'paper'): (torch.cat([cites_edge[1, :], cites_edge[0, :]]), torch.cat([cites_edge[0, :], cites_edge[1, :]])),
-        ('paper', 'written_by', 'author'): author_paper_edges,
-        ('author', 'affiliated_to', 'institute'): affiliation_author_edges,
-        ('paper', 'topic', 'fos'): paper_fos_edges,
-        ('author', 'rev_written_by', 'paper'): (author_paper_edges[1, :], author_paper_edges[0, :]),
-        ('institute', 'rev_affiliated_to', 'author'): (affiliation_author_edges[1, :], affiliation_author_edges[0, :]),
-        ('fos', 'rev_topic', 'paper'): (paper_fos_edges[1, :], paper_fos_edges[0, :])
-    }
-    self.edge_dict[('paper', 'published', 'journal')] = paper_published_journal
-    self.edge_dict[('paper', 'venue', 'conference')] = paper_venue_conference
-    self.edge_dict[('journal', 'rev_published', 'paper')] = (paper_published_journal[1, :], paper_published_journal[0, :])
-    self.edge_dict[('conference', 'rev_venue', 'paper')] = (paper_venue_conference[1, :], paper_venue_conference[0, :])
-    self.etypes = list(self.edge_dict.keys())
+    def process(self):
+        paper_paper_edges = torch.from_numpy(
+            np.load(
+                osp.join(
+                    self.dir,
+                    self.dataset_size,
+                    "processed",
+                    "paper__cites__paper",
+                    "edge_index.npy",
+                )
+            )
+        ).t()
+        author_paper_edges = torch.from_numpy(
+            np.load(
+                osp.join(
+                    self.dir,
+                    self.dataset_size,
+                    "processed",
+                    "paper__written_by__author",
+                    "edge_index.npy",
+                )
+            )
+        ).t()
+        affiliation_author_edges = torch.from_numpy(
+            np.load(
+                osp.join(
+                    self.dir,
+                    self.dataset_size,
+                    "processed",
+                    "author__affiliated_to__institute",
+                    "edge_index.npy",
+                )
+            )
+        ).t()
+        paper_fos_edges = torch.from_numpy(
+            np.load(
+                osp.join(
+                    self.dir,
+                    self.dataset_size,
+                    "processed",
+                    "paper__topic__fos",
+                    "edge_index.npy",
+                )
+            )
+        ).t()
+        paper_published_journal = torch.from_numpy(
+            np.load(
+                osp.join(
+                    self.dir,
+                    self.dataset_size,
+                    "processed",
+                    "paper__published__journal",
+                    "edge_index.npy",
+                )
+            )
+        ).t()
+        paper_venue_conference = torch.from_numpy(
+            np.load(
+                osp.join(
+                    self.dir,
+                    self.dataset_size,
+                    "processed",
+                    "paper__venue__conference",
+                    "edge_index.npy",
+                )
+            )
+        ).t()
 
-    # init graphlearn_torch Dataset.
-    edge_dir = 'out' if self.layout == 'CSR' else 'in'
-    glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
-    glt_dataset.init_graph(
-      edge_index=self.edge_dict,
-      graph_mode='CPU',
-    )
+        cites_edge = add_self_loops(remove_self_loops(paper_paper_edges)[0])[0]
+        self.edge_dict = {
+            ("paper", "cites", "paper"): (
+                torch.cat([cites_edge[1, :], cites_edge[0, :]]),
+                torch.cat([cites_edge[0, :], cites_edge[1, :]]),
+            ),
+            ("paper", "written_by", "author"): author_paper_edges,
+            ("author", "affiliated_to", "institute"): affiliation_author_edges,
+            ("paper", "topic", "fos"): paper_fos_edges,
+            ("author", "rev_written_by", "paper"): (
+                author_paper_edges[1, :],
+                author_paper_edges[0, :],
+            ),
+            ("institute", "rev_affiliated_to", "author"): (
+                affiliation_author_edges[1, :],
+                affiliation_author_edges[0, :],
+            ),
+            ("fos", "rev_topic", "paper"): (
+                paper_fos_edges[1, :],
+                paper_fos_edges[0, :],
+            ),
+        }
+        self.edge_dict[("paper", "published", "journal")
+                       ] = paper_published_journal
+        self.edge_dict[("paper", "venue", "conference")
+                       ] = paper_venue_conference
+        self.edge_dict[("journal", "rev_published", "paper")] = (
+            paper_published_journal[1, :],
+            paper_published_journal[0, :],
+        )
+        self.edge_dict[("conference", "rev_venue", "paper")] = (
+            paper_venue_conference[1, :],
+            paper_venue_conference[0, :],
+        )
+        self.etypes = list(self.edge_dict.keys())
 
-    # save the corresponding csr or csc file
-    compress_edge_dict = {}
-    compress_edge_dict[('paper', 'cites', 'paper')] = 'paper__cites__paper'
-    compress_edge_dict[('paper', 'written_by', 'author')] = 'paper__written_by__author'
-    compress_edge_dict[('author', 'affiliated_to', 'institute')] = 'author__affiliated_to__institute'
-    compress_edge_dict[('paper', 'topic', 'fos')] = 'paper__topic__fos'
-    compress_edge_dict[('author', 'rev_written_by', 'paper')] = 'author__rev_written_by__paper'
-    compress_edge_dict[('institute', 'rev_affiliated_to', 'author')] = 'institute__rev_affiliated_to__author'
-    compress_edge_dict[('fos', 'rev_topic', 'paper')] = 'fos__rev_topic__paper'
-    compress_edge_dict[('paper', 'published', 'journal')] = 'paper__published__journal'
-    compress_edge_dict[('paper', 'venue', 'conference')] = 'paper__venue__conference'
-    compress_edge_dict[('journal', 'rev_published', 'paper')] = 'journal__rev_published__paper'
-    compress_edge_dict[('conference', 'rev_venue', 'paper')] = 'conference__rev_venue__paper'
+        # init graphlearn_torch Dataset.
+        edge_dir = "out" if self.layout == "CSR" else "in"
+        glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
+        glt_dataset.init_graph(
+            edge_index=self.edge_dict,
+            graph_mode="CPU",
+        )
 
-    for etype in self.etypes:
-      graph = glt_dataset.get_graph(etype)
-      indptr, indices, _ = graph.export_topology()
-      path = os.path.join(self.dir, self.dataset_size, 'processed', self.layout, compress_edge_dict[etype])
-      if not os.path.exists(path):
-        os.makedirs(path)
-      torch.save(indptr, os.path.join(path, 'indptr.pt'))
-      torch.save(indices, os.path.join(path, 'indices.pt'))
-    path = os.path.join(self.dir, self.dataset_size, 'processed', self.layout)
-    print(f"The {self.layout} graph has been persisted in path: {path}")
+        # save the corresponding csr or csc file
+        compress_edge_dict = {}
+        compress_edge_dict[("paper", "cites", "paper")] = "paper__cites__paper"
+        compress_edge_dict[("paper", "written_by", "author")] = (
+            "paper__written_by__author"
+        )
+        compress_edge_dict[("author", "affiliated_to", "institute")] = (
+            "author__affiliated_to__institute"
+        )
+        compress_edge_dict[("paper", "topic", "fos")] = "paper__topic__fos"
+        compress_edge_dict[("author", "rev_written_by", "paper")] = (
+            "author__rev_written_by__paper"
+        )
+        compress_edge_dict[("institute", "rev_affiliated_to", "author")] = (
+            "institute__rev_affiliated_to__author"
+        )
+        compress_edge_dict[("fos", "rev_topic", "paper")
+                           ] = "fos__rev_topic__paper"
+        compress_edge_dict[("paper", "published", "journal")] = (
+            "paper__published__journal"
+        )
+        compress_edge_dict[("paper", "venue", "conference")] = (
+            "paper__venue__conference"
+        )
+        compress_edge_dict[("journal", "rev_published", "paper")] = (
+            "journal__rev_published__paper"
+        )
+        compress_edge_dict[("conference", "rev_venue", "paper")] = (
+            "conference__rev_venue__paper"
+        )
 
+        for etype in self.etypes:
+            graph = glt_dataset.get_graph(etype)
+            indptr, indices, _ = graph.export_topology()
+            path = os.path.join(
+                self.dir,
+                self.dataset_size,
+                "processed",
+                self.layout,
+                compress_edge_dict[etype],
+            )
+            if not os.path.exists(path):
+                os.makedirs(path)
+            torch.save(indptr, os.path.join(path, "indptr.pt"))
+            torch.save(indices, os.path.join(path, "indices.pt"))
+        path = os.path.join(
+            self.dir,
+            self.dataset_size,
+            "processed",
+            self.layout)
+        print(f"The {self.layout} graph has been persisted in path: {path}")
 
 
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  root = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.realpath(__file__)))), 'data', 'igbh')
-  glt.utils.ensure_dir(root)
-  parser.add_argument('--path', type=str, default=root,
-      help='path containing the datasets')
-  parser.add_argument('--dataset_size', type=str, default='full',
-      choices=['tiny', 'small', 'medium', 'large', 'full'],
-      help='size of the datasets')
-  parser.add_argument("--layout", type=str, default='CSC')
-  parser.add_argument('--use_fp16', action="store_true",
-    help="convert the node/edge feature into fp16 format")
-  args = parser.parse_args()
-  print(f"Start constructing the {args.layout} graph...")
-  igbh_dataset = IGBHeteroDatasetCompress(args.path, args.dataset_size, args.layout)
-  if args.use_fp16:
-    base_path = osp.join(args.path, args.dataset_size, 'processed')
-    float2half(base_path, args.dataset_size)
-  
\ No newline at end of file
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    root = osp.join(
+        osp.dirname(
+            osp.dirname(
+                osp.dirname(
+                    osp.realpath(__file__)))), "data", "igbh"
+    )
+    glt.utils.ensure_dir(root)
+    parser.add_argument(
+        "--path", type=str, default=root, help="path containing the datasets"
+    )
+    parser.add_argument(
+        "--dataset_size",
+        type=str,
+        default="full",
+        choices=["tiny", "small", "medium", "large", "full"],
+        help="size of the datasets",
+    )
+    parser.add_argument("--layout", type=str, default="CSC")
+    parser.add_argument(
+        "--use_fp16",
+        action="store_true",
+        help="convert the node/edge feature into fp16 format",
+    )
+    args = parser.parse_args()
+    print(f"Start constructing the {args.layout} graph...")
+    igbh_dataset = IGBHeteroDatasetCompress(
+        args.path, args.dataset_size, args.layout)
+    if args.use_fp16:
+        base_path = osp.join(args.path, args.dataset_size, "processed")
+        float2half(base_path, args.dataset_size)
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/format_model.py b/upcomming_benchmarks/graph/R-GAT/tools/format_model.py
index b7cd50507f..04d3267223 100644
--- a/upcomming_benchmarks/graph/R-GAT/tools/format_model.py
+++ b/upcomming_benchmarks/graph/R-GAT/tools/format_model.py
@@ -1,33 +1,32 @@
+from collections import OrderedDict
+from graphlearn_torch.typing import InputNodes, NumNeighbors
+from graphlearn_torch.sampler import NeighborSampler, NodeSamplerInput
+from graphlearn_torch.data import Dataset
+from graphlearn_torch.loader import NodeLoader
+import graphlearn_torch as glt
+from igbh import IGBHeteroDataset, IGBH
+from rgnn import RGNN
 from typing import Literal
 import torch
-import os, sys
+import os
+import sys
+
 sys.path.append(os.path.dirname(__file__))
 sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir))
-from rgnn import RGNN
-from igbh import IGBHeteroDataset, IGBH
-import graphlearn_torch as glt
-
-from graphlearn_torch.loader import NodeLoader
-
-from graphlearn_torch.data import Dataset
-from graphlearn_torch.sampler import NeighborSampler, NodeSamplerInput
-from graphlearn_torch.typing import InputNodes, NumNeighbors
-
-from collections import OrderedDict
-
 
 
 class Formatter:
-    def __init__(self,
-            model_type="rgat",
-            type: Literal["fp16", "fp32"] = "fp16",
-            device: Literal["cpu", "gpu"] = "gpu",
-            ckpt_path: str = None,
-            igbh_dataset: IGBHeteroDataset = None,
-            batch_size: int = 1,
-            layout: Literal["CSC", "CSR", "COO"] = "COO",
-            edge_dir: str = "in",
-        ) -> None:
+    def __init__(
+        self,
+        model_type="rgat",
+        type: Literal["fp16", "fp32"] = "fp16",
+        device: Literal["cpu", "gpu"] = "gpu",
+        ckpt_path: str = None,
+        igbh_dataset: IGBHeteroDataset = None,
+        batch_size: int = 1,
+        layout: Literal["CSC", "CSR", "COO"] = "COO",
+        edge_dir: str = "in",
+    ) -> None:
         if device == "gpu":
             self.device = torch.device("cuda")
         else:
@@ -40,26 +39,33 @@ def __init__(self,
         # Create Node and neighbor loade
         self.glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
         self.glt_dataset.init_node_features(
-            node_feature_data=igbh_dataset.feat_dict, with_gpu=(device == "gpu"), dtype=self.type
+            node_feature_data=igbh_dataset.feat_dict,
+            with_gpu=(device == "gpu"),
+            dtype=self.type,
         )
         self.glt_dataset.init_graph(
             edge_index=igbh_dataset.edge_dict,
             layout=layout,
             graph_mode="ZERO_COPY" if (device == "gpu") else "CPU",
         )
-        self.glt_dataset.init_node_labels(node_label_data={"paper": igbh_dataset.label})
+        self.glt_dataset.init_node_labels(
+            node_label_data={"paper": igbh_dataset.label})
 
-        self.model = RGNN(
-            self.glt_dataset.get_edge_types(),
-            self.glt_dataset.node_features["paper"].shape[1],
-            512,
-            2983,
-            num_layers=3,
-            dropout=0.2,
-            model=model_type,
-            heads=4,
-            node_type="paper",
-        ).to(self.type).to(self.device)
+        self.model = (
+            RGNN(
+                self.glt_dataset.get_edge_types(),
+                self.glt_dataset.node_features["paper"].shape[1],
+                512,
+                2983,
+                num_layers=3,
+                dropout=0.2,
+                model=model_type,
+                heads=4,
+                node_type="paper",
+            )
+            .to(self.type)
+            .to(self.device)
+        )
         self.model.eval()
         ckpt = None
         if ckpt_path is not None:
@@ -75,12 +81,20 @@ def __init__(self,
                 if "lin_dst" in k:
                     pass
                 elif "lin_src" in k:
-                    formatted_ckpt["model_state_dict"][str(k).replace("lin_src", "lin")] = ckpt["model_state_dict"][k]
+                    formatted_ckpt["model_state_dict"][
+                        str(k).replace("lin_src", "lin")
+                    ] = ckpt["model_state_dict"][k]
                 else:
                     formatted_ckpt["model_state_dict"][k] = ckpt["model_state_dict"][k]
             self.model.load_state_dict(formatted_ckpt["model_state_dict"])
-            torch.save({'model_state_dict': self.model.state_dict()}, "model/FULL_model_seq_69294_formatted.ckpt")
+            torch.save(
+                {"model_state_dict": self.model.state_dict()},
+                "model/FULL_model_seq_69294_formatted.ckpt",
+            )
+
 
 if __name__ == "__main__":
     igbh = IGBHeteroDataset("igbh", use_label_2K=True)
-    f = Formatter(igbh_dataset=igbh, ckpt_path="model/FULL_model_seq_69294.ckpt", device="cpu")
+    f = Formatter(
+        igbh_dataset=igbh, ckpt_path="model/FULL_model_seq_69294.ckpt", device="cpu"
+    )
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py b/upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py
index 9dcfe71687..5bbed6d0a7 100644
--- a/upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py
+++ b/upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py
@@ -1,61 +1,93 @@
-# Script taken from https://github.com/mlcommons/training/blob/master/graph_neural_network/split_seeds.py
+# Script taken from
+# https://github.com/mlcommons/training/blob/master/graph_neural_network/split_seeds.py
 
 import argparse
 import os.path as osp
 import torch
 
+
 class SeedSplitter(object):
-  def __init__(self,
-               path,
-               dataset_size='tiny',
-               use_label_2K=True,
-               random_seed=42,
-               validation_frac=0.01):
-    self.path = path
-    self.dataset_size = dataset_size
-    self.use_label_2K = use_label_2K
-    self.random_seed = random_seed
-    self.validation_frac = validation_frac
-    self.paper_nodes_num = {'tiny':100000, 'small':1000000, 'medium':10000000, 'large':100000000, 'full':269346174}
-    self.process()
-  
-  def process(self):
-    torch.manual_seed(self.random_seed)
-    n_labeled_idx = self.paper_nodes_num[self.dataset_size]
-    if self.dataset_size == 'full':
-      if self.use_label_2K:
-          n_labeled_idx = 157675969
-      else:
-          n_labeled_idx = 227130858
-
-    shuffled_index = torch.randperm(n_labeled_idx)
-    n_train = int(n_labeled_idx * 0.6)
-    n_val = int(n_labeled_idx * self.validation_frac)
-
-    train_idx = shuffled_index[:n_train]
-    val_idx = shuffled_index[n_train : n_train + n_val]
-
-    path = osp.join(self.path, self.dataset_size, 'processed')
-    torch.save(train_idx, osp.join(path, 'train_idx.pt'))
-    torch.save(val_idx, osp.join(path, 'val_idx.pt'))
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  root = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.realpath(__file__)))), 'data', 'igbh')
-  parser.add_argument('--path', type=str, default=root,
-      help='path containing the datasets')
-  parser.add_argument('--dataset_size', type=str, default='full',
-      choices=['tiny', 'small', 'medium', 'large', 'full'],
-      help='size of the datasets')
-  parser.add_argument("--random_seed", type=int, default='42')
-  parser.add_argument('--num_classes', type=int, default=2983,
-      choices=[19, 2983], help='number of classes')
-  parser.add_argument("--validation_frac", type=float, default=0.005,
-      help="Fraction of labeled vertices to be used for validation.")
-  
-  args = parser.parse_args()
-  splitter = SeedSplitter(path=args.path,
-                          dataset_size=args.dataset_size,
-                          use_label_2K=(args.num_classes==2983),
-                          random_seed=args.random_seed,
-                          validation_frac=args.validation_frac)
\ No newline at end of file
+    def __init__(
+        self,
+        path,
+        dataset_size="tiny",
+        use_label_2K=True,
+        random_seed=42,
+        validation_frac=0.01,
+    ):
+        self.path = path
+        self.dataset_size = dataset_size
+        self.use_label_2K = use_label_2K
+        self.random_seed = random_seed
+        self.validation_frac = validation_frac
+        self.paper_nodes_num = {
+            "tiny": 100000,
+            "small": 1000000,
+            "medium": 10000000,
+            "large": 100000000,
+            "full": 269346174,
+        }
+        self.process()
+
+    def process(self):
+        torch.manual_seed(self.random_seed)
+        n_labeled_idx = self.paper_nodes_num[self.dataset_size]
+        if self.dataset_size == "full":
+            if self.use_label_2K:
+                n_labeled_idx = 157675969
+            else:
+                n_labeled_idx = 227130858
+
+        shuffled_index = torch.randperm(n_labeled_idx)
+        n_train = int(n_labeled_idx * 0.6)
+        n_val = int(n_labeled_idx * self.validation_frac)
+
+        train_idx = shuffled_index[:n_train]
+        val_idx = shuffled_index[n_train: n_train + n_val]
+
+        path = osp.join(self.path, self.dataset_size, "processed")
+        torch.save(train_idx, osp.join(path, "train_idx.pt"))
+        torch.save(val_idx, osp.join(path, "val_idx.pt"))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    root = osp.join(
+        osp.dirname(
+            osp.dirname(
+                osp.dirname(
+                    osp.realpath(__file__)))), "data", "igbh"
+    )
+    parser.add_argument(
+        "--path", type=str, default=root, help="path containing the datasets"
+    )
+    parser.add_argument(
+        "--dataset_size",
+        type=str,
+        default="full",
+        choices=["tiny", "small", "medium", "large", "full"],
+        help="size of the datasets",
+    )
+    parser.add_argument("--random_seed", type=int, default="42")
+    parser.add_argument(
+        "--num_classes",
+        type=int,
+        default=2983,
+        choices=[19, 2983],
+        help="number of classes",
+    )
+    parser.add_argument(
+        "--validation_frac",
+        type=float,
+        default=0.005,
+        help="Fraction of labeled vertices to be used for validation.",
+    )
+
+    args = parser.parse_args()
+    splitter = SeedSplitter(
+        path=args.path,
+        dataset_size=args.dataset_size,
+        use_label_2K=(args.num_classes == 2983),
+        random_seed=args.random_seed,
+        validation_frac=args.validation_frac,
+    )
diff --git a/vision/classification_and_detection/python/backend.py b/vision/classification_and_detection/python/backend.py
index 955eddb888..6fc13454a8 100755
--- a/vision/classification_and_detection/python/backend.py
+++ b/vision/classification_and_detection/python/backend.py
@@ -2,10 +2,10 @@
 abstract backend class
 """
 
-
 # pylint: disable=unused-argument,missing-docstring
 
-class Backend():
+
+class Backend:
     def __init__(self):
         self.inputs = []
         self.outputs = []
diff --git a/vision/classification_and_detection/python/backend_ncnn.py b/vision/classification_and_detection/python/backend_ncnn.py
index 3cc8ceabf8..16d44a02fa 100644
--- a/vision/classification_and_detection/python/backend_ncnn.py
+++ b/vision/classification_and_detection/python/backend_ncnn.py
@@ -3,6 +3,7 @@
 import backend
 from ncnn_models import *
 
+
 class BackendNCNN(backend.Backend):
     def __init__(self):
         super(BackendNCNN, self).__init__()
@@ -24,9 +25,12 @@ def load(self, model_path, inputs=None, outputs=None):
             self.net = Resnet50(param_file, bin_file)
         else:
             import sys
-            print("please add your ncnn model .param and .bin files to dir named 'resnet'")
+
+            print(
+                "please add your ncnn model .param and .bin files to dir named 'resnet'"
+            )
             sys.exit()
-        
+
         if not inputs:
             self.inputs = [self.net.input_name]
         else:
diff --git a/vision/classification_and_detection/python/backend_onnxruntime.py b/vision/classification_and_detection/python/backend_onnxruntime.py
index 2b25003c35..787e721f22 100755
--- a/vision/classification_and_detection/python/backend_onnxruntime.py
+++ b/vision/classification_and_detection/python/backend_onnxruntime.py
@@ -30,16 +30,23 @@ def load(self, model_path, inputs=None, outputs=None):
 
         # By default all optimizations are enabled
         # https://onnxruntime.ai/docs/performance/graph-optimizations.html
-        # Enable only upto extended optimizations on aarch64 due to an accuracy issue
+        # Enable only upto extended optimizations on aarch64 due to an accuracy
+        # issue
         if os.environ.get("HOST_PLATFORM_FLAVOR", "") == "aarch64":
             opt.graph_optimization_level = rt.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
 
         # self.sess = rt.InferenceSession(model_path, opt)
-        if len(rt.get_all_providers()) > 1 and os.environ.get("USE_GPU", "yes").lower() not in [ "0", "false", "off", "no" ]:
-            self.sess = rt.InferenceSession(model_path, opt, providers=["CUDAExecutionProvider"])
+        if len(rt.get_all_providers()) > 1 and os.environ.get(
+            "USE_GPU", "yes"
+        ).lower() not in ["0", "false", "off", "no"]:
+            self.sess = rt.InferenceSession(
+                model_path, opt, providers=["CUDAExecutionProvider"]
+            )
         else:
-            self.sess = rt.InferenceSession(model_path, opt, providers=["CPUExecutionProvider"])
-            
+            self.sess = rt.InferenceSession(
+                model_path, opt, providers=["CPUExecutionProvider"]
+            )
+
         # get input and output names
         if not inputs:
             self.inputs = [meta.name for meta in self.sess.get_inputs()]
diff --git a/vision/classification_and_detection/python/backend_pytorch_native.py b/vision/classification_and_detection/python/backend_pytorch_native.py
index 219e4159af..db152f8e00 100755
--- a/vision/classification_and_detection/python/backend_pytorch_native.py
+++ b/vision/classification_and_detection/python/backend_pytorch_native.py
@@ -1,6 +1,7 @@
 """
-pytoch native backend 
+pytoch native backend
 """
+
 # pylint: disable=unused-argument,missing-docstring
 import torch  # currently supports pytorch1.0
 import torchvision
diff --git a/vision/classification_and_detection/python/backend_tf.py b/vision/classification_and_detection/python/backend_tf.py
index ab32b56fc3..b53bf0b784 100755
--- a/vision/classification_and_detection/python/backend_tf.py
+++ b/vision/classification_and_detection/python/backend_tf.py
@@ -23,11 +23,13 @@ def name(self):
         return "tensorflow"
 
     def image_format(self):
-        # By default tensorflow uses NHWC (and the cpu implementation only does NHWC)
+        # By default tensorflow uses NHWC (and the cpu implementation only does
+        # NHWC)
         return "NHWC"
 
     def load(self, model_path, inputs=None, outputs=None):
-        # there is no input/output meta data i the graph so it need to come from config.
+        # there is no input/output meta data i the graph so it need to come
+        # from config.
         if not inputs:
             raise ValueError("BackendTensorflow needs inputs")
         if not outputs:
@@ -36,10 +38,16 @@ def load(self, model_path, inputs=None, outputs=None):
         self.inputs = inputs
 
         infer_config = tf.compat.v1.ConfigProto()
-        infer_config.intra_op_parallelism_threads = int(os.environ['TF_INTRA_OP_PARALLELISM_THREADS']) \
-                if 'TF_INTRA_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
-        infer_config.inter_op_parallelism_threads = int(os.environ['TF_INTER_OP_PARALLELISM_THREADS']) \
-                if 'TF_INTER_OP_PARALLELISM_THREADS' in os.environ else os.cpu_count()
+        infer_config.intra_op_parallelism_threads = (
+            int(os.environ["TF_INTRA_OP_PARALLELISM_THREADS"])
+            if "TF_INTRA_OP_PARALLELISM_THREADS" in os.environ
+            else os.cpu_count()
+        )
+        infer_config.inter_op_parallelism_threads = (
+            int(os.environ["TF_INTER_OP_PARALLELISM_THREADS"])
+            if "TF_INTER_OP_PARALLELISM_THREADS" in os.environ
+            else os.cpu_count()
+        )
         infer_config.use_per_session_threads = 1
 
         # TODO: support checkpoint and saved_model formats?
@@ -47,16 +55,26 @@ def load(self, model_path, inputs=None, outputs=None):
         with tf.compat.v1.gfile.FastGFile(model_path, "rb") as f:
             graph_def.ParseFromString(f.read())
         try:
-            optimized_graph_def = optimize_for_inference(graph_def, [item.split(':')[0] for item in inputs],
-                    [item.split(':')[0] for item in outputs], dtypes.float32.as_datatype_enum, False)
-            g = tf.compat.v1.import_graph_def(optimized_graph_def, name='')
+            optimized_graph_def = optimize_for_inference(
+                graph_def,
+                [item.split(":")[0] for item in inputs],
+                [item.split(":")[0] for item in outputs],
+                dtypes.float32.as_datatype_enum,
+                False,
+            )
+            g = tf.compat.v1.import_graph_def(optimized_graph_def, name="")
         except ValueError:
             try:
-                optimized_graph_def = optimize_for_inference(graph_def, [item.split(':')[0] for item in inputs],
-                        [item.split(':')[0] for item in outputs], dtypes.uint8.as_datatype_enum, False)
-                g = tf.compat.v1.import_graph_def(optimized_graph_def, name='')
+                optimized_graph_def = optimize_for_inference(
+                    graph_def,
+                    [item.split(":")[0] for item in inputs],
+                    [item.split(":")[0] for item in outputs],
+                    dtypes.uint8.as_datatype_enum,
+                    False,
+                )
+                g = tf.compat.v1.import_graph_def(optimized_graph_def, name="")
             except ValueError:
-                g = tf.compat.v1.import_graph_def(graph_def, name='')
+                g = tf.compat.v1.import_graph_def(graph_def, name="")
         self.sess = tf.compat.v1.Session(graph=g, config=infer_config)
         return self
 
diff --git a/vision/classification_and_detection/python/backend_tflite.py b/vision/classification_and_detection/python/backend_tflite.py
index bb8ed7d16e..c1a32251e0 100755
--- a/vision/classification_and_detection/python/backend_tflite.py
+++ b/vision/classification_and_detection/python/backend_tflite.py
@@ -10,12 +10,14 @@
     # try dedicated tflite package first
     import tflite_runtime
     import tflite_runtime.interpreter as tflite
+
     _version = tflite_runtime.__version__
     _git_version = tflite_runtime.__git_version__
-except:
+except BaseException:
     # fall back to tflite bundled in tensorflow
     import tensorflow as tf
     from tensorflow.lite.python import interpreter as tflite
+
     _version = tf.__version__
     _git_version = tf.__git_version__
 
@@ -43,13 +45,18 @@ def load(self, model_path, inputs=None, outputs=None, use_tpu=False):
         self.use_tpu = use_tpu
         if use_tpu:
             from pycoral.utils.edgetpu import make_interpreter
+
             self.sess = make_interpreter(model_path)
         else:
             self.sess = tflite.Interpreter(model_path=model_path)
         self.sess.allocate_tensors()
         # keep input/output name to index mapping
-        self.input2index = {i["name"]: i["index"] for i in self.sess.get_input_details()}
-        self.output2index = {i["name"]: i["index"] for i in self.sess.get_output_details()}
+        self.input2index = {
+            i["name"]: i["index"] for i in self.sess.get_input_details()
+        }
+        self.output2index = {
+            i["name"]: i["index"] for i in self.sess.get_output_details()
+        }
         # keep input/output names
         self.inputs = list(self.input2index.keys())
         self.outputs = list(self.output2index.keys())
@@ -59,8 +66,11 @@ def predict(self, feed):
         self.lock.acquire()
         # set inputs
         for k, v in self.input2index.items():
-            if self.use_tpu and self.sess.get_input_details()[v]['dtype'] == np.uint8:
-                input_scale, input_zero_point = self.sess.get_input_details()[v]["quantization"]
+            if self.use_tpu and self.sess.get_input_details()[
+                    v]["dtype"] == np.uint8:
+                input_scale, input_zero_point = self.sess.get_input_details()[v][
+                    "quantization"
+                ]
                 feed[k] = feed[k] / input_scale + input_zero_point
                 feed[k] = feed[k].astype(np.uint8)
             self.sess.set_tensor(v, feed[k])
diff --git a/vision/classification_and_detection/python/backend_tvm.py b/vision/classification_and_detection/python/backend_tvm.py
index 7e5a97787e..86223b60e5 100644
--- a/vision/classification_and_detection/python/backend_tvm.py
+++ b/vision/classification_and_detection/python/backend_tvm.py
@@ -41,8 +41,10 @@ def image_format(self):
 
     def create_omp_args(self, arena_idx):
         idx_start = self.arena_size * arena_idx
-        cur_arena_size = min(multiprocessing.cpu_count() -
-                             idx_start, self.arena_size)
+        cur_arena_size = min(
+            multiprocessing.cpu_count() -
+            idx_start,
+            self.arena_size)
         # idx_end = idx_start + cur_arena_size
 
         # OMP_PLACES="{N},{N+1},{N+2},...,{N+SZ}"
@@ -59,7 +61,7 @@ def create_omp_args(self, arena_idx):
     def set_omp_envs(omp_args):
         for env_arg in omp_args:
             os.environ[env_arg[0]] = env_arg[1]
-                
+
     @staticmethod
     def vmobj_to_list(o):
         if isinstance(o, tvm.nd.NDArray):
@@ -81,7 +83,8 @@ def vmobj_to_list(o):
             elif "tensor" in o.constructor.name_hint:
                 result = [o.fields[0].numpy()]
             else:
-                raise RuntimeError(f"Unknown object type: {o.constructor.name_hint}")
+                raise RuntimeError(
+                    f"Unknown object type: {o.constructor.name_hint}")
         else:
             raise RuntimeError(f"Unknown object type: {type(o)}")
         return result
@@ -90,25 +93,24 @@ def load_impl(self, model_path, inputs, outputs, max_batchsize):
 
         self.max_batchsize = max_batchsize
         _, self.model_format = os.path.splitext(model_path)
-        
+
         work_dir = os.path.dirname(model_path)
-        compiled_model = os.path.join(work_dir, 'model-tvm.so')
-        
-        with open(os.path.join(work_dir, "input_layer_name"), 'r') as file:
+        compiled_model = os.path.join(work_dir, "model-tvm.so")
+
+        with open(os.path.join(work_dir, "input_layer_name"), "r") as file:
             self.input_layer_name = file.read().strip()
 
-        if compiled_model.endswith('.so') or compiled_model.endswith('.dylib'):
+        if compiled_model.endswith(".so") or compiled_model.endswith(".dylib"):
             if not os.path.isfile(compiled_model):
                 print()
                 raise RuntimeError(
-                    f"Error: Model file {compiled_model} not found!"
-                )
+                    f"Error: Model file {compiled_model} not found!")
         else:
             raise RuntimeError(
                 f"Error: The specified path ({model_path}) does not match path to the compiled model!"
             )
 
-        print('TVM: loading model ' + compiled_model)
+        print("TVM: loading model " + compiled_model)
 
         mod = tvm.runtime.load_module(compiled_model)
         device = tvm.device("llvm", 0)
@@ -123,8 +125,7 @@ def load_impl(self, model_path, inputs, outputs, max_batchsize):
 
             for sub_dir in next(os.walk(work_dir))[1]:
                 if sub_dir.endswith("-tvm-tmp"):
-                    path_consts = os.path.join(
-                        work_dir, sub_dir + "/consts")
+                    path_consts = os.path.join(work_dir, sub_dir + "/consts")
                     break
 
             vm_exec.mod["load_late_bound_consts"](path_consts)
@@ -132,23 +133,26 @@ def load_impl(self, model_path, inputs, outputs, max_batchsize):
             self.executor = runtime_vm.VirtualMachine(vm_exec, device)
         else:
             self.executor_type = "graph_executor"
-            self.executor = graph_executor.GraphModule(
-                mod['default'](device))
+            self.executor = graph_executor.GraphModule(mod["default"](device))
 
         if not inputs:
             if self.executor_type == "virtual_machine":
-                inputs = [str(idx) for idx in range(
-                    self.executor.module["get_num_outputs"]())]
+                inputs = [
+                    str(idx) for idx in range(self.executor.module["get_num_outputs"]())
+                ]
             else:
-                inputs = [str(idx) for idx in range(
-                    self.executor.get_num_outputs())]
+                inputs = [
+                    str(idx) for idx in range(
+                        self.executor.get_num_outputs())]
         if not outputs:
             if self.executor_type == "virtual_machine":
-                outputs = [str(idx) for idx in range(
-                    self.executor.module["get_num_outputs"]())]
+                outputs = [
+                    str(idx) for idx in range(self.executor.module["get_num_outputs"]())
+                ]
             else:
-                outputs = [str(idx) for idx in range(
-                    self.executor.get_num_outputs())]
+                outputs = [
+                    str(idx) for idx in range(
+                        self.executor.get_num_outputs())]
 
         self.inputs = inputs
         self.outputs = outputs
@@ -163,7 +167,8 @@ def predict_impl(self, feed):
             item = np.vstack((item, item_extra))
         elif batch_size > self.max_batchsize:
             raise ValueError(
-                "Internal MLPerf error: dynamic batch size > max batch size")
+                "Internal MLPerf error: dynamic batch size > max batch size"
+            )
         input_idx = self.inputs.index(iname)
         if self.executor_type == "virtual_machine":
             self.executor.set_input(
@@ -176,11 +181,14 @@ def predict_impl(self, feed):
         else:
             self.executor.set_input(input_idx, tvm.nd.array(item))
             self.executor.run()
-            return [self.executor.get_output(0).asnumpy()[:batch_size],
-                    self.executor.get_output(1).asnumpy()[:batch_size]]
+            return [
+                self.executor.get_output(0).asnumpy()[:batch_size],
+                self.executor.get_output(1).asnumpy()[:batch_size],
+            ]
 
     @staticmethod
-    def _worker_initializer(model_path, inputs, outputs, max_batchsize, omp_envs):
+    def _worker_initializer(model_path, inputs, outputs,
+                            max_batchsize, omp_envs):
         BackendTVM.set_omp_envs(omp_envs)
         global global_executor
         global_executor = BackendTVM()
@@ -197,12 +205,20 @@ def load(self, model_path, inputs=None, outputs=None):
         self.load_impl(model_path, inputs, outputs, self.max_batchsize)
 
         if self.arena_num > 1:
-            multiprocessing.set_start_method(os.getenv("PYTHON_MP_START_METHOD", "fork"))
-            self.pool = multiprocessing.Pool(self.arena_num,
-                                             initializer=self._worker_initializer,
-                                             initargs=(model_path, inputs, outputs, self.max_batchsize,
-                                                       self.create_omp_args(0))
-                                             )
+            multiprocessing.set_start_method(
+                os.getenv("PYTHON_MP_START_METHOD", "fork")
+            )
+            self.pool = multiprocessing.Pool(
+                self.arena_num,
+                initializer=self._worker_initializer,
+                initargs=(
+                    model_path,
+                    inputs,
+                    outputs,
+                    self.max_batchsize,
+                    self.create_omp_args(0),
+                ),
+            )
 
         return self
 
diff --git a/vision/classification_and_detection/python/coco.py b/vision/classification_and_detection/python/coco.py
index 8e88bbfc75..a3e7472767 100644
--- a/vision/classification_and_detection/python/coco.py
+++ b/vision/classification_and_detection/python/coco.py
@@ -20,8 +20,21 @@
 
 
 class Coco(dataset.Dataset):
-    def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
-                 image_format="NHWC", pre_process=None, count=None, cache_dir=None, preprocessed_dir=None, use_label_map=False, threads=os.cpu_count()):
+    def __init__(
+        self,
+        data_path,
+        image_list,
+        name,
+        use_cache=0,
+        image_size=None,
+        image_format="NHWC",
+        pre_process=None,
+        count=None,
+        cache_dir=None,
+        preprocessed_dir=None,
+        use_label_map=False,
+        threads=os.cpu_count(),
+    ):
         super().__init__()
         self.image_size = image_size
         self.image_list = []
@@ -32,17 +45,19 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         self.use_cache = use_cache
         self.data_path = data_path
         self.pre_process = pre_process
-        self.use_label_map=use_label_map
+        self.use_label_map = use_label_map
         if not cache_dir:
             cache_dir = os.getcwd()
-        self.cache_dir = os.path.join(cache_dir, "preprocessed", name, image_format)
+        self.cache_dir = os.path.join(
+            cache_dir, "preprocessed", name, image_format)
         # input images are in HWC
         self.need_transpose = True if image_format == "NCHW" else False
-        not_found = 0 
+        not_found = 0
         empty_80catageories = 0
         if image_list is None:
             # by default look for val_map.txt
-            image_list = os.path.join(data_path, "annotations/instances_val2017.json")
+            image_list = os.path.join(
+                data_path, "annotations/instances_val2017.json")
         self.annotation_file = image_list
         if self.use_label_map:
             # for pytorch
@@ -58,16 +73,22 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         with open(image_list, "r") as f:
             coco = json.load(f)
         for i in coco["images"]:
-            images[i["id"]] = {"file_name": i["file_name"],
-                               "height": i["height"],
-                               "width": i["width"],
-                               "bbox": [],
-                               "category": []}
+            images[i["id"]] = {
+                "file_name": i["file_name"],
+                "height": i["height"],
+                "width": i["width"],
+                "bbox": [],
+                "category": [],
+            }
         for a in coco["annotations"]:
             i = images.get(a["image_id"])
             if i is None:
                 continue
-            catagory_ids = label_map[a.get("category_id")] if self.use_label_map else a.get("category_id")
+            catagory_ids = (
+                label_map[a.get("category_id")]
+                if self.use_label_map
+                else a.get("category_id")
+            )
             i["category"].append(catagory_ids)
             i["bbox"].append(a.get("bbox"))
 
@@ -78,17 +99,23 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
                 # if the image does not exists ignore it
                 not_found += 1
                 continue
-            if len(img["category"])==0 and self.use_label_map: 
-                #if an image doesn't have any of the 81 categories in it    
-                empty_80catageories += 1 #should be 48 images - thus the validation sert has 4952 images
-                continue 
+            if len(img["category"]) == 0 and self.use_label_map:
+                # if an image doesn't have any of the 81 categories in it
+                empty_80catageories += (
+                    1  # should be 48 images - thus the validation sert has 4952 images
+                )
+                continue
 
-            os.makedirs(os.path.dirname(os.path.join(self.cache_dir, image_name)), exist_ok=True)
+            os.makedirs(
+                os.path.dirname(os.path.join(self.cache_dir, image_name)), exist_ok=True
+            )
             dst = os.path.join(self.cache_dir, image_name)
             if not os.path.exists(dst + ".npy"):
                 # cache a preprocessed version of the image
                 img_org = cv2.imread(src)
-                processed = self.pre_process(img_org, need_transpose=self.need_transpose, dims=self.image_size)
+                processed = self.pre_process(
+                    img_org, need_transpose=self.need_transpose, dims=self.image_size
+                )
                 np.save(dst, processed)
 
             self.image_ids.append(image_id)
@@ -107,10 +134,16 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         if not_found > 0:
             log.info("reduced image list, %d images not found", not_found)
         if empty_80catageories > 0:
-            log.info("reduced image list, %d images without any of the 80 categories", empty_80catageories)
+            log.info(
+                "reduced image list, %d images without any of the 80 categories",
+                empty_80catageories,
+            )
 
-        log.info("loaded {} images, cache={}, took={:.1f}sec".format(
-            len(self.image_list), use_cache, time_taken))
+        log.info(
+            "loaded {} images, cache={}, took={:.1f}sec".format(
+                len(self.image_list), use_cache, time_taken
+            )
+        )
 
         self.label_list = np.array(self.label_list)
 
@@ -129,6 +162,7 @@ class PostProcessCoco:
     """
     Post processing for tensorflow ssd-mobilenet style models
     """
+
     def __init__(self):
         self.results = []
         self.good = 0
@@ -139,14 +173,22 @@ def __init__(self):
     def add_results(self, results):
         self.results.extend(results)
 
-    def __call__(self, results, ids, expected=None, result_dict=None, ):
+    def __call__(
+        self,
+        results,
+        ids,
+        expected=None,
+        result_dict=None,
+    ):
         # results come as:
-        #   tensorflow, ssd-mobilenet: num_detections,detection_boxes,detection_scores,detection_classes
+        # tensorflow, ssd-mobilenet:
+        # num_detections,detection_boxes,detection_scores,detection_classes
         processed_results = []
         # batch size
         bs = len(results[0])
         for idx in range(0, bs):
-            # keep the content_id from loadgen to handle content_id's without results
+            # keep the content_id from loadgen to handle content_id's without
+            # results
             self.content_ids.append(ids[idx])
             processed_results.append([])
             detection_num = int(results[0][idx])
@@ -158,10 +200,17 @@ def __call__(self, results, ids, expected=None, result_dict=None, ):
                 if detection_class in expected_classes:
                     self.good += 1
                 box = detection_boxes[detection]
-                processed_results[idx].append([float(ids[idx]),
-                                              box[0], box[1], box[2], box[3],
-                                              results[2][idx][detection],
-                                              float(detection_class)])
+                processed_results[idx].append(
+                    [
+                        float(ids[idx]),
+                        box[0],
+                        box[1],
+                        box[2],
+                        box[3],
+                        results[2][idx][detection],
+                        float(detection_class),
+                    ]
+                )
                 self.total += 1
         return processed_results
 
@@ -181,7 +230,7 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                 annotations = json.load(fin)
             for cnt, cat in enumerate(annotations["categories"]):
                 label_map[cat["id"]] = cnt + 1
-            inv_map = {v:k for k,v in label_map.items()}
+            inv_map = {v: k for k, v in label_map.items()}
 
         detections = []
         image_indices = []
@@ -192,8 +241,13 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                 # this is the index of the coco image
                 image_idx = int(detection[0])
                 if image_idx != self.content_ids[batch]:
-                    # working with the coco index/id is error prone - extra check to make sure it is consistent
-                    log.error("image_idx missmatch, lg={} / result={}".format(image_idx, self.content_ids[batch]))
+                    # working with the coco index/id is error prone - extra
+                    # check to make sure it is consistent
+                    log.error(
+                        "image_idx missmatch, lg={} / result={}".format(
+                            image_idx, self.content_ids[batch]
+                        )
+                    )
                 # map the index to the coco image id
                 detection[0] = ds.image_ids[image_idx]
                 height, width = ds.image_sizes[image_idx]
@@ -211,16 +265,19 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                     cat_id = inv_map.get(int(detection[6]), -1)
                     if cat_id == -1:
                         # FIXME:
-                        log.info("finalize can't map category {}".format(int(detection[6])))
-                    detection[6] =  cat_id
+                        log.info(
+                            "finalize can't map category {}".format(
+                                int(detection[6]))
+                        )
+                    detection[6] = cat_id
                 detections.append(np.array(detection))
 
         # map indices to coco image id's
-        image_ids = [ds.image_ids[i]  for i in image_indices]
+        image_ids = [ds.image_ids[i] for i in image_indices]
         self.results = []
         cocoGt = pycoco.COCO(ds.annotation_file)
         cocoDt = cocoGt.loadRes(np.array(detections))
-        cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+        cocoEval = COCOeval(cocoGt, cocoDt, iouType="bbox")
         cocoEval.params.imgIds = image_ids
         cocoEval.evaluate()
         cocoEval.accumulate()
@@ -232,11 +289,12 @@ class PostProcessCocoPt(PostProcessCoco):
     """
     Post processing required by ssd-resnet34 / pytorch
     """
-    def __init__(self,use_inv_map,score_threshold):
+
+    def __init__(self, use_inv_map, score_threshold):
         super().__init__()
         self.use_inv_map = use_inv_map
         self.score_threshold = score_threshold
-        
+
     def __call__(self, results, ids, expected=None, result_dict=None):
         # results come as:
         #   detection_boxes,detection_classes,detection_scores
@@ -251,7 +309,7 @@ def __call__(self, results, ids, expected=None, result_dict=None):
             detection_classes = results[1][idx]
             expected_classes = expected[idx][0]
             scores = results[2][idx]
-            #for detection in range(0, len(expected_classes)):
+            # for detection in range(0, len(expected_classes)):
             for detection in range(0, len(scores)):
                 if scores[detection] < self.score_threshold:
                     break
@@ -260,10 +318,17 @@ def __call__(self, results, ids, expected=None, result_dict=None):
                     self.good += 1
                 box = detection_boxes[detection]
                 # comes from model as:  0=xmax 1=ymax 2=xmin 3=ymin
-                processed_results[idx].append([float(ids[idx]),
-                                              box[1], box[0], box[3], box[2],
-                                              scores[detection],
-                                              float(detection_class)])
+                processed_results[idx].append(
+                    [
+                        float(ids[idx]),
+                        box[1],
+                        box[0],
+                        box[3],
+                        box[2],
+                        scores[detection],
+                        float(detection_class),
+                    ]
+                )
                 self.total += 1
         return processed_results
 
@@ -272,12 +337,14 @@ class PostProcessCocoOnnx(PostProcessCoco):
     """
     Post processing required by ssd-resnet34 / onnx
     """
+
     def __init__(self):
         super().__init__()
 
     def __call__(self, results, ids, expected=None, result_dict=None):
         # results come as:
-        #   onnx (from pytorch ssd-resnet34): detection_boxes,detection_classes,detection_scores
+        # onnx (from pytorch ssd-resnet34):
+        # detection_boxes,detection_classes,detection_scores
 
         processed_results = []
 
@@ -298,17 +365,26 @@ def __call__(self, results, ids, expected=None, result_dict=None):
                     self.good += 1
                 box = detection_boxes[detection]
                 # comes from model as:  0=xmax 1=ymax 2=xmin 3=ymin
-                processed_results[idx].append([float(ids[idx]),
-                                              box[1], box[0], box[3], box[2],
-                                              scores[detection],
-                                              float(detection_class)])
+                processed_results[idx].append(
+                    [
+                        float(ids[idx]),
+                        box[1],
+                        box[0],
+                        box[3],
+                        box[2],
+                        scores[detection],
+                        float(detection_class),
+                    ]
+                )
                 self.total += 1
         return processed_results
 
+
 class PostProcessCocoTf(PostProcessCoco):
     """
     Post processing required by ssd-resnet34 / pytorch
     """
+
     def __init__(self):
         super().__init__()
         self.use_inv_map = True
@@ -335,10 +411,16 @@ def __call__(self, results, ids, expected=None, result_dict=None):
                     self.good += 1
                 box = detection_boxes[detection]
                 # comes from model as:  0=xmax 1=ymax 2=xmin 3=ymin
-                processed_results[idx].append([float(ids[idx]),
-                                              box[0], box[1], box[2], box[3],
-                                              scores[detection],
-                                              float(detection_class)])
+                processed_results[idx].append(
+                    [
+                        float(ids[idx]),
+                        box[0],
+                        box[1],
+                        box[2],
+                        box[3],
+                        scores[detection],
+                        float(detection_class),
+                    ]
+                )
                 self.total += 1
         return processed_results
-
diff --git a/vision/classification_and_detection/python/dataset.py b/vision/classification_and_detection/python/dataset.py
index 0d82a99a3c..07e6b26ac0 100755
--- a/vision/classification_and_detection/python/dataset.py
+++ b/vision/classification_and_detection/python/dataset.py
@@ -15,7 +15,8 @@
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("dataset")
 
-class Item():
+
+class Item:
     def __init__(self, label, img, idx):
         self.label = label
         self.img = img
@@ -24,19 +25,25 @@ def __init__(self, label, img, idx):
 
 
 def usleep(sec):
-    if sys.platform == 'win32':
+    if sys.platform == "win32":
         # on windows time.sleep() doesn't work to well
         import ctypes
+
         kernel32 = ctypes.windll.kernel32
-        timer = kernel32.CreateWaitableTimerA(ctypes.c_void_p(), True, ctypes.c_void_p())
+        timer = kernel32.CreateWaitableTimerA(
+            ctypes.c_void_p(), True, ctypes.c_void_p()
+        )
         delay = ctypes.c_longlong(int(-1 * (10 * 1000000 * sec)))
-        kernel32.SetWaitableTimer(timer, ctypes.byref(delay), 0, ctypes.c_void_p(), ctypes.c_void_p(), False)
-        kernel32.WaitForSingleObject(timer, 0xffffffff)
+        kernel32.SetWaitableTimer(
+            timer, ctypes.byref(
+                delay), 0, ctypes.c_void_p(), ctypes.c_void_p(), False
+        )
+        kernel32.WaitForSingleObject(timer, 0xFFFFFFFF)
     else:
         time.sleep(sec)
 
 
-class Dataset():
+class Dataset:
     def __init__(self):
         self.arrival = None
         self.image_list = []
@@ -62,7 +69,7 @@ def load_query_samples(self, sample_list):
     def unload_query_samples(self, sample_list):
         if sample_list:
             for sample in sample_list:
-                if sample in self.image_list_inmemory :
+                if sample in self.image_list_inmemory:
                     del self.image_list_inmemory[sample]
         else:
             self.image_list_inmemory = {}
@@ -102,7 +109,7 @@ def start(self):
         self.good = 0
         self.total = 0
 
-    def finalize(self, results, ds=False,  output_dir=None):
+    def finalize(self, results, ds=False, output_dir=None):
         results["good"] = self.good
         results["total"] = self.total
 
@@ -141,6 +148,7 @@ def finalize(self, results, ds=False, output_dir=None):
 # pre-processing
 #
 
+
 def center_crop(img, out_height, out_width):
     height, width, _ = img.shape
     left = int((width - out_width) / 2)
@@ -151,10 +159,12 @@ def center_crop(img, out_height, out_width):
     return img
 
 
-def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
+def resize_with_aspectratio(
+    img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR
+):
     height, width, _ = img.shape
-    new_height = int(100. * out_height / scale)
-    new_width = int(100. * out_width / scale)
+    new_height = int(100.0 * out_height / scale)
+    new_width = int(100.0 * out_width / scale)
     if height > width:
         w = new_width
         h = int(new_height * height / width)
@@ -170,9 +180,11 @@ def pre_process_vgg(img, dims=None, need_transpose=False):
 
     output_height, output_width, _ = dims
     cv2_interpol = cv2.INTER_AREA
-    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2_interpol)
+    img = resize_with_aspectratio(
+        img, output_height, output_width, inter_pol=cv2_interpol
+    )
     img = center_crop(img, output_height, output_width)
-    img = np.asarray(img, dtype='float32')
+    img = np.asarray(img, dtype="float32")
 
     # normalize image
     means = np.array([123.68, 116.78, 103.94], dtype=np.float32)
@@ -188,9 +200,11 @@ def pre_process_mobilenet(img, dims=None, need_transpose=False):
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
 
     output_height, output_width, _ = dims
-    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR)
+    img = resize_with_aspectratio(
+        img, output_height, output_width, inter_pol=cv2.INTER_LINEAR
+    )
     img = center_crop(img, output_height, output_width)
-    img = np.asarray(img, dtype='float32')
+    img = np.asarray(img, dtype="float32")
 
     img /= 255.0
     img -= 0.5
@@ -206,13 +220,13 @@ def pre_process_imagenet_tflite_tpu(img, dims=None, need_transpose=False):
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     img = resize_with_aspectratio(img, 224, 224, inter_pol=cv2.INTER_LINEAR)
     img = center_crop(img, 224, 224)
-    img = np.asarray(img, dtype='float32')
-   
+    img = np.asarray(img, dtype="float32")
+
     img = img[..., ::-1]
     means = np.array([103.939, 116.779, 123.68], dtype=np.float32)
     img -= means
     return img
-    
+
 
 def pre_process_imagenet_pytorch(img, dims=None, need_transpose=False):
     from PIL import Image
@@ -223,10 +237,12 @@ def pre_process_imagenet_pytorch(img, dims=None, need_transpose=False):
     img = F.resize(img, 256, Image.BILINEAR)
     img = F.center_crop(img, 224)
     img = F.to_tensor(img)
-    img = F.normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], inplace=False)
+    img = F.normalize(
+        img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], inplace=False
+    )
     if not need_transpose:
-        img = img.permute(1, 2, 0) # NHWC
-    img = np.asarray(img, dtype='float32')
+        img = img.permute(1, 2, 0)  # NHWC
+    img = np.asarray(img, dtype="float32")
     return img
 
 
@@ -236,9 +252,10 @@ def maybe_resize(img, dims):
         # some images might be grayscale
         img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    if dims != None:
+    if dims is not None:
         im_height, im_width, _ = dims
-        img = cv2.resize(img, (im_width, im_height), interpolation=cv2.INTER_LINEAR)
+        img = cv2.resize(img, (im_width, im_height),
+                         interpolation=cv2.INTER_LINEAR)
     return img
 
 
@@ -266,7 +283,7 @@ def pre_process_coco_resnet34(img, dims=None, need_transpose=False):
     mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
     std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
 
-    img = img / 255. - mean
+    img = img / 255.0 - mean
     img = img / std
 
     if need_transpose:
@@ -287,8 +304,8 @@ def pre_process_coco_resnet34_tf(img, dims=None, need_transpose=False):
 
 def pre_process_openimages_retinanet(img, dims=None, need_transpose=False):
     img = maybe_resize(img, dims)
-    img /= 255.
+    img /= 255.0
     # transpose if needed
     if need_transpose:
         img = img.transpose([2, 0, 1])
-    return img
\ No newline at end of file
+    return img
diff --git a/vision/classification_and_detection/python/imagenet.py b/vision/classification_and_detection/python/imagenet.py
index 14734ab5f5..31144befb2 100644
--- a/vision/classification_and_detection/python/imagenet.py
+++ b/vision/classification_and_detection/python/imagenet.py
@@ -20,8 +20,20 @@
 
 class Imagenet(dataset.Dataset):
 
-    def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
-            image_format="NHWC", pre_process=None, count=None, cache_dir=None, preprocessed_dir=None, threads=os.cpu_count()):
+    def __init__(
+        self,
+        data_path,
+        image_list,
+        name,
+        use_cache=0,
+        image_size=None,
+        image_format="NHWC",
+        pre_process=None,
+        count=None,
+        cache_dir=None,
+        preprocessed_dir=None,
+        threads=os.cpu_count(),
+    ):
         super(Imagenet, self).__init__()
         if image_size is None:
             self.image_size = [224, 224, 3]
@@ -33,13 +45,16 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         self.label_list = []
         self.count = count
         self.data_path = data_path
-        self.pre_process = pre_process # if None we assume data_path is having preprocessed dataset
+        self.pre_process = (
+            pre_process  # if None we assume data_path is having preprocessed dataset
+        )
         self.use_cache = use_cache
 
         if preprocessed_dir:
             self.cache_dir = preprocessed_dir
         elif pre_process:
-            self.cache_dir = os.path.join(cache_dir, "preprocessed", name, image_format)
+            self.cache_dir = os.path.join(
+                cache_dir, "preprocessed", name, image_format)
         else:
             self.cache_dir = cache_dir
 
@@ -50,7 +65,7 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         if image_list is None:
             # by default look for val_map.txt
             image_list = os.path.join(data_path, "val_map.txt")
-        with open(image_list, 'r') as fp:
+        with open(image_list, "r") as fp:
             for count, line in enumerate(fp):
                 pass
         count = count + 1
@@ -64,31 +79,42 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         start = time.time()
         N = threads
         import concurrent.futures
+
         if N > CNT:
             N = CNT
 
         if not pre_process:
-            log.info("Loading {} preprocessed images using {} threads".format(CNT, N))
+            log.info(
+                "Loading {} preprocessed images using {} threads".format(
+                    CNT, N))
         else:
             log.info("Preprocessing {} images using {} threads".format(CNT, N))
 
-        with open(image_list, 'r') as f:
+        with open(image_list, "r") as f:
             lists = []
             image_lists = []
             label_lists = []
             for i in range(N):
-                lists.append([ next(f) for x in range(int(CNT/N)) ])
+                lists.append([next(f) for x in range(int(CNT / N))])
                 image_lists.append([])
                 label_lists.append([])
-            if int(CNT%N) > 0:
-                lists.append([ next(f) for x in range(int(CNT%N)) ])
+            if int(CNT % N) > 0:
+                lists.append([next(f) for x in range(int(CNT % N))])
                 image_lists.append([])
                 label_lists.append([])
         executor = concurrent.futures.ThreadPoolExecutor(N)
-        futures = [executor.submit(self.process, data_path, item, image_lists[lists.index(item)],
-            label_lists[lists.index(item)]) for item in lists]
+        futures = [
+            executor.submit(
+                self.process,
+                data_path,
+                item,
+                image_lists[lists.index(item)],
+                label_lists[lists.index(item)],
+            )
+            for item in lists
+        ]
         concurrent.futures.wait(futures)
-        for i in range (len(image_lists)):
+        for i in range(len(image_lists)):
             self.image_list += image_lists[i]
             self.label_list += label_lists[i]
         time_taken = time.time() - start
@@ -98,8 +124,11 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         if self.not_found > 0:
             log.info("reduced image list, %d images not found", self.not_found)
 
-        log.info("loaded {} images, cache={}, already_preprocessed={}, took={:.1f}sec".format(
-            len(self.image_list), use_cache, pre_process is None, time_taken))
+        log.info(
+            "loaded {} images, cache={}, already_preprocessed={}, took={:.1f}sec".format(
+                len(self.image_list), use_cache, pre_process is None, time_taken
+            )
+        )
         self.label_list = np.array(self.label_list)
 
     def process(self, data_path, files, image_list, label_list):
@@ -107,7 +136,8 @@ def process(self, data_path, files, image_list, label_list):
             image_name, label = re.split(r"\s+", s.strip())
             src = os.path.join(data_path, image_name)
             if not self.pre_process:
-                if not os.path.exists(os.path.join(data_path, image_name) + ".npy"):
+                if not os.path.exists(os.path.join(
+                        data_path, image_name) + ".npy"):
                     # if the image does not exists ignore it
                     self.not_found += 1
                     continue
@@ -116,12 +146,19 @@ def process(self, data_path, files, image_list, label_list):
                     # if the image does not exists ignore it
                     self.not_found += 1
                     continue
-                os.makedirs(os.path.dirname(os.path.join(self.cache_dir, image_name)), exist_ok=True)
+                os.makedirs(
+                    os.path.dirname(os.path.join(self.cache_dir, image_name)),
+                    exist_ok=True,
+                )
                 dst = os.path.join(self.cache_dir, image_name)
                 if not os.path.exists(dst + ".npy"):
                     # cache a preprocessed version of the image
                     img_org = cv2.imread(src)
-                    processed = self.pre_process(img_org, need_transpose=self.need_transpose, dims=self.image_size)
+                    processed = self.pre_process(
+                        img_org,
+                        need_transpose=self.need_transpose,
+                        dims=self.image_size,
+                    )
                     np.save(dst, processed)
             image_list.append(image_name)
             label_list.append(int(label))
@@ -139,4 +176,3 @@ def get_item(self, nr):
     def get_item_loc(self, nr):
         src = os.path.join(self.data_path, self.image_list[nr])
         return src
-
diff --git a/vision/classification_and_detection/python/main.py b/vision/classification_and_detection/python/main.py
index a41e0c1dff..44a7b6bf7f 100755
--- a/vision/classification_and_detection/python/main.py
+++ b/vision/classification_and_detection/python/main.py
@@ -35,48 +35,91 @@
 
 # the datasets we support
 SUPPORTED_DATASETS = {
-    "imagenet":
-        (imagenet.Imagenet, dataset.pre_process_vgg, dataset.PostProcessCommon(offset=-1),
-         {"image_size": [224, 224, 3]}),
-    "imagenet_mobilenet":
-        (imagenet.Imagenet, dataset.pre_process_mobilenet, dataset.PostProcessArgMax(offset=-1),
-         {"image_size": [224, 224, 3]}),
-    "imagenet_tflite_tpu":
-        (imagenet.Imagenet, dataset.pre_process_imagenet_tflite_tpu, dataset.PostProcessArgMax(offset=0),
-         {"image_size": [224, 224, 3]}),
-    "imagenet_pytorch":
-        (imagenet.Imagenet, dataset.pre_process_imagenet_pytorch, dataset.PostProcessArgMax(offset=0),
-         {"image_size": [224, 224, 3]}),
-    "coco-300":
-        (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(),
-         {"image_size": [300, 300, 3]}),
-    "coco-300-pt":
-        (coco.Coco, dataset.pre_process_coco_pt_mobilenet, coco.PostProcessCocoPt(False, 0.3),
-         {"image_size": [300, 300, 3]}),
-    "openimages-300-retinanet":
-        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False, 0.05, 300, 300),
-         {"image_size": [300, 300, 3]}),
-    "openimages-800-retinanet":
-        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False, 0.05, 800, 800),
-         {"image_size": [800, 800, 3]}),
-    "openimages-1200-retinanet":
-        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False, 0.05, 1200, 1200),
-         {"image_size": [1200, 1200, 3]}),
-    "openimages-800-retinanet-onnx":
-        (openimages.OpenImages, dataset.pre_process_openimages_retinanet, openimages.PostProcessOpenImagesRetinanet(False, 0.05, 800, 800, False),
-         {"image_size": [800, 800, 3]}),
-    "coco-1200":
-        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCoco(),
-         {"image_size": [1200, 1200, 3]}),
-    "coco-1200-onnx":
-        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoOnnx(),
-         {"image_size": [1200, 1200, 3]}),
-    "coco-1200-pt":
-        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoPt(True, 0.05),
-         {"image_size": [1200, 1200, 3], "use_label_map": True}),
-    "coco-1200-tf":
-        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoTf(),
-         {"image_size": [1200, 1200, 3], "use_label_map": False}),
+    "imagenet": (
+        imagenet.Imagenet,
+        dataset.pre_process_vgg,
+        dataset.PostProcessCommon(offset=-1),
+        {"image_size": [224, 224, 3]},
+    ),
+    "imagenet_mobilenet": (
+        imagenet.Imagenet,
+        dataset.pre_process_mobilenet,
+        dataset.PostProcessArgMax(offset=-1),
+        {"image_size": [224, 224, 3]},
+    ),
+    "imagenet_tflite_tpu": (
+        imagenet.Imagenet,
+        dataset.pre_process_imagenet_tflite_tpu,
+        dataset.PostProcessArgMax(offset=0),
+        {"image_size": [224, 224, 3]},
+    ),
+    "imagenet_pytorch": (
+        imagenet.Imagenet,
+        dataset.pre_process_imagenet_pytorch,
+        dataset.PostProcessArgMax(offset=0),
+        {"image_size": [224, 224, 3]},
+    ),
+    "coco-300": (
+        coco.Coco,
+        dataset.pre_process_coco_mobilenet,
+        coco.PostProcessCoco(),
+        {"image_size": [300, 300, 3]},
+    ),
+    "coco-300-pt": (
+        coco.Coco,
+        dataset.pre_process_coco_pt_mobilenet,
+        coco.PostProcessCocoPt(False, 0.3),
+        {"image_size": [300, 300, 3]},
+    ),
+    "openimages-300-retinanet": (
+        openimages.OpenImages,
+        dataset.pre_process_openimages_retinanet,
+        openimages.PostProcessOpenImagesRetinanet(False, 0.05, 300, 300),
+        {"image_size": [300, 300, 3]},
+    ),
+    "openimages-800-retinanet": (
+        openimages.OpenImages,
+        dataset.pre_process_openimages_retinanet,
+        openimages.PostProcessOpenImagesRetinanet(False, 0.05, 800, 800),
+        {"image_size": [800, 800, 3]},
+    ),
+    "openimages-1200-retinanet": (
+        openimages.OpenImages,
+        dataset.pre_process_openimages_retinanet,
+        openimages.PostProcessOpenImagesRetinanet(False, 0.05, 1200, 1200),
+        {"image_size": [1200, 1200, 3]},
+    ),
+    "openimages-800-retinanet-onnx": (
+        openimages.OpenImages,
+        dataset.pre_process_openimages_retinanet,
+        openimages.PostProcessOpenImagesRetinanet(
+            False, 0.05, 800, 800, False),
+        {"image_size": [800, 800, 3]},
+    ),
+    "coco-1200": (
+        coco.Coco,
+        dataset.pre_process_coco_resnet34,
+        coco.PostProcessCoco(),
+        {"image_size": [1200, 1200, 3]},
+    ),
+    "coco-1200-onnx": (
+        coco.Coco,
+        dataset.pre_process_coco_resnet34,
+        coco.PostProcessCocoOnnx(),
+        {"image_size": [1200, 1200, 3]},
+    ),
+    "coco-1200-pt": (
+        coco.Coco,
+        dataset.pre_process_coco_resnet34,
+        coco.PostProcessCocoPt(True, 0.05),
+        {"image_size": [1200, 1200, 3], "use_label_map": True},
+    ),
+    "coco-1200-tf": (
+        coco.Coco,
+        dataset.pre_process_coco_resnet34,
+        coco.PostProcessCocoTf(),
+        {"image_size": [1200, 1200, 3], "use_label_map": False},
+    ),
 }
 
 # pre-defined command line options so simplify things. They are used as defaults and can be
@@ -89,7 +132,6 @@
         "cache": 0,
         "max-batchsize": 32,
     },
-
     # resnet
     "resnet50-tf": {
         "inputs": "input_tensor:0",
@@ -123,7 +165,6 @@
         "backend": "tflite",
         "model-name": "resnet50",
     },
-
     # mobilenet
     "mobilenet-tf": {
         "inputs": "input:0",
@@ -138,7 +179,6 @@
         "backend": "onnxruntime",
         "model-name": "mobilenet",
     },
-
     # ssd-mobilenet
     "ssd-mobilenet-tf": {
         "inputs": "image_tensor:0",
@@ -161,7 +201,6 @@
         "data-format": "NHWC",
         "model-name": "ssd-mobilenet",
     },
-
     # ssd-resnet34
     "ssd-resnet34-tf": {
         "inputs": "image:0",
@@ -195,7 +234,6 @@
         "data-format": "NHWC",
         "model-name": "ssd-resnet34",
     },
-
     # retinanet
     "retinanet-pytorch": {
         "inputs": "image",
@@ -210,7 +248,7 @@
         "dataset": "openimages-800-retinanet-onnx",
         "backend": "onnxruntime",
         "model-name": "retinanet",
-        "max-batchsize": 1
+        "max-batchsize": 1,
     },
 }
 
@@ -243,15 +281,17 @@ def get_args():
             "NHWC"],
         help="data format")
     parser.add_argument(
-        "--profile",
-        choices=SUPPORTED_PROFILES.keys(),
-        help="standard profiles")
-    parser.add_argument("--scenario", default="SingleStream",
-                        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())))
+        "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
+    )
     parser.add_argument(
-        "--max-batchsize",
-        type=int,
-        help="max batch size in a single inference")
+        "--scenario",
+        default="SingleStream",
+        help="mlperf benchmark scenario, one of " +
+        str(list(SCENARIO_MAP.keys())),
+    )
+    parser.add_argument(
+        "--max-batchsize", type=int, help="max batch size in a single inference"
+    )
     parser.add_argument("--model", required=True, help="model file")
     parser.add_argument("--output", default="output", help="test results")
     parser.add_argument("--inputs", help="model inputs")
@@ -269,19 +309,19 @@ def get_args():
     parser.add_argument("--qps", type=int, help="target qps")
     parser.add_argument("--cache", type=int, default=0, help="use cache")
     parser.add_argument(
-        "--cache_dir",
-        type=str,
-        default=None,
-        help="dir path for caching")
+        "--cache_dir", type=str, default=None, help="dir path for caching"
+    )
     parser.add_argument(
         "--preprocessed_dir",
         type=str,
         default=None,
-        help="dir path for storing preprocessed images (overrides cache_dir)")
+        help="dir path for storing preprocessed images (overrides cache_dir)",
+    )
     parser.add_argument(
         "--use_preprocessed_dataset",
         action="store_true",
-        help="use preprocessed dataset instead of the original")
+        help="use preprocessed dataset instead of the original",
+    )
     parser.add_argument(
         "--accuracy",
         action="store_true",
@@ -289,7 +329,8 @@ def get_args():
     parser.add_argument(
         "--find-peak-performance",
         action="store_true",
-        help="enable finding peak performance pass")
+        help="enable finding peak performance pass",
+    )
     parser.add_argument(
         "--debug",
         action="store_true",
@@ -299,30 +340,29 @@ def get_args():
     parser.add_argument(
         "--user_conf",
         default="user.conf",
-        help="user config for user LoadGen settings such as target QPS")
+        help="user config for user LoadGen settings such as target QPS",
+    )
     # file for LoadGen audit settings
     parser.add_argument(
-        "--audit_conf",
-        default="audit.config",
-        help="config for LoadGen audit settings")
+        "--audit_conf", default="audit.config", help="config for LoadGen audit settings"
+    )
 
     # below will override mlperf rules compliant settings - don't use for
     # official submission
     parser.add_argument("--time", type=int, help="time to scan in seconds")
     parser.add_argument("--count", type=int, help="dataset items to use")
     parser.add_argument(
-        "--performance-sample-count",
-        type=int,
-        help="performance sample count")
+        "--performance-sample-count", type=int, help="performance sample count"
+    )
     parser.add_argument(
-        "--max-latency",
-        type=float,
-        help="mlperf max latency in pct tile")
+        "--max-latency", type=float, help="mlperf max latency in pct tile"
+    )
     parser.add_argument(
         "--samples-per-query",
         default=8,
         type=int,
-        help="mlperf multi-stream samples per query")
+        help="mlperf multi-stream samples per query",
+    )
     args = parser.parse_args()
 
     # don't use defaults in argparser. Instead we default to a dict, override that with a profile
@@ -349,27 +389,35 @@ def get_args():
 def get_backend(backend):
     if backend == "tensorflow":
         from backend_tf import BackendTensorflow
+
         backend = BackendTensorflow()
     elif backend == "onnxruntime":
         from backend_onnxruntime import BackendOnnxruntime
+
         backend = BackendOnnxruntime()
     elif backend == "tvm":
         from backend_tvm import BackendTVM
+
         backend = BackendTVM()
     elif backend == "null":
         from backend_null import BackendNull
+
         backend = BackendNull()
     elif backend == "pytorch":
         from backend_pytorch import BackendPytorch
+
         backend = BackendPytorch()
     elif backend == "pytorch-native":
         from backend_pytorch_native import BackendPytorchNative
+
         backend = BackendPytorchNative()
     elif backend == "tflite":
         from backend_tflite import BackendTflite
+
         backend = BackendTflite()
     elif backend == "ncnn":
         from backend_ncnn import BackendNCNN
+
         backend = BackendNCNN()
     else:
         raise ValueError("unknown backend: " + backend)
@@ -413,7 +461,8 @@ def run_one_item(self, qitem):
         try:
             results = self.model.predict({self.model.inputs[0]: qitem.img})
             processed_results = self.post_process(
-                results, qitem.content_id, qitem.label, self.result_dict)
+                results, qitem.content_id, qitem.label, self.result_dict
+            )
             if self.take_accuracy:
                 self.post_process.add_results(processed_results)
             self.result_timing.append(time.time() - qitem.start)
@@ -426,8 +475,9 @@ def run_one_item(self, qitem):
             response_array_refs = []
             response = []
             for idx, query_id in enumerate(qitem.query_id):
-                response_array = array.array("B", np.array(
-                    processed_results[idx], np.float32).tobytes())
+                response_array = array.array(
+                    "B", np.array(processed_results[idx], np.float32).tobytes()
+                )
                 response_array_refs.append(response_array)
                 bi = response_array.buffer_info()
                 response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
@@ -442,9 +492,10 @@ def enqueue(self, query_samples):
         else:
             bs = self.max_batchsize
             for i in range(0, len(idx), bs):
-                data, label = self.ds.get_samples(idx[i:i + bs])
+                data, label = self.ds.get_samples(idx[i: i + bs])
                 self.run_one_item(
-                    Item(query_id[i:i + bs], idx[i:i + bs], data, label))
+                    Item(query_id[i: i + bs], idx[i: i + bs], data, label)
+                )
 
     def finish(self):
         pass
@@ -497,12 +548,14 @@ def finish(self):
             worker.join()
 
 
-def add_results(final_results, name, result_dict,
-                result_list, took, show_accuracy=False):
-    percentiles = [50., 80., 90., 95., 99., 99.9]
+def add_results(
+    final_results, name, result_dict, result_list, took, show_accuracy=False
+):
+    percentiles = [50.0, 80.0, 90.0, 95.0, 99.0, 99.9]
     buckets = np.percentile(result_list, percentiles).tolist()
-    buckets_str = ",".join(["{}:{:.4f}".format(p, b)
-                           for p, b in zip(percentiles, buckets)])
+    buckets_str = ",".join(
+        ["{}:{:.4f}".format(p, b) for p, b in zip(percentiles, buckets)]
+    )
 
     if result_dict["total"] == 0:
         result_dict["total"] = len(result_list)
@@ -519,19 +572,27 @@ def add_results(final_results, name, result_dict,
     }
     acc_str = ""
     if show_accuracy:
-        result["accuracy"] = 100. * result_dict["good"] / result_dict["total"]
+        result["accuracy"] = 100.0 * result_dict["good"] / result_dict["total"]
         acc_str = ", acc={:.3f}%".format(result["accuracy"])
         if "mAP" in result_dict:
-            result["mAP"] = 100. * result_dict["mAP"]
+            result["mAP"] = 100.0 * result_dict["mAP"]
             acc_str += ", mAP={:.3f}%".format(result["mAP"])
 
     # add the result to the result dict
     final_results[name] = result
 
     # to stdout
-    print("{} qps={:.2f}, mean={:.4f}, time={:.3f}{}, queries={}, tiles={}".format(
-        name, result["qps"], result["mean"], took, acc_str,
-        len(result_list), buckets_str))
+    print(
+        "{} qps={:.2f}, mean={:.4f}, time={:.3f}{}, queries={}, tiles={}".format(
+            name,
+            result["qps"],
+            result["mean"],
+            took,
+            acc_str,
+            len(result_list),
+            buckets_str,
+        )
+    )
 
 
 def main():
@@ -544,7 +605,7 @@ def main():
     backend = get_backend(args.backend)
 
     # If TVM, pass max_batchsize to the backend
-    if args.backend.startswith('tvm'):
+    if args.backend.startswith("tvm"):
         backend.max_batchsize = args.max_batchsize
         backend.arena_num = args.threads
         backend.arena_size = 4
@@ -563,24 +624,24 @@ def main():
     wanted_dataset, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
     if args.use_preprocessed_dataset:
         pre_proc = None
-    ds = wanted_dataset(data_path=args.dataset_path,
-                        image_list=args.dataset_list,
-                        name=args.dataset,
-                        image_format=image_format,
-                        pre_process=pre_proc,
-                        use_cache=args.cache,
-                        count=count,
-                        cache_dir=args.cache_dir,
-                        preprocessed_dir=args.preprocessed_dir,
-                        threads=args.threads,
-                        **kwargs)
+    ds = wanted_dataset(
+        data_path=args.dataset_path,
+        image_list=args.dataset_list,
+        name=args.dataset,
+        image_format=image_format,
+        pre_process=pre_proc,
+        use_cache=args.cache,
+        count=count,
+        cache_dir=args.cache_dir,
+        preprocessed_dir=args.preprocessed_dir,
+        threads=args.threads,
+        **kwargs
+    )
     # load model to backend
     if args.device == "tpu":
         model = backend.load(
-            args.model,
-            inputs=args.inputs,
-            outputs=args.outputs,
-            use_tpu=True)
+            args.model, inputs=args.inputs, outputs=args.outputs, use_tpu=True
+        )
     else:
         model = backend.load(
             args.model,
@@ -623,14 +684,11 @@ def main():
         lg.TestScenario.SingleStream: RunnerBase,
         lg.TestScenario.MultiStream: QueueRunner,
         lg.TestScenario.Server: QueueRunner,
-        lg.TestScenario.Offline: QueueRunner
+        lg.TestScenario.Offline: QueueRunner,
     }
     runner = runner_map[scenario](
-        model,
-        ds,
-        args.threads,
-        post_proc=post_proc,
-        max_batchsize=args.max_batchsize)
+        model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize
+    )
 
     def issue_queries(query_samples):
         runner.enqueue(query_samples)
@@ -677,14 +735,15 @@ def flush_queries():
         settings.multi_stream_expected_latency_ns = int(
             args.max_latency * NANO_SEC)
 
-    performance_sample_count = args.performance_sample_count if args.performance_sample_count else min(
-        count, 500)
+    performance_sample_count = (
+        args.performance_sample_count
+        if args.performance_sample_count
+        else min(count, 500)
+    )
     sut = lg.ConstructSUT(issue_queries, flush_queries)
     qsl = lg.ConstructQSL(
-        count,
-        performance_sample_count,
-        ds.load_query_samples,
-        ds.unload_query_samples)
+        count, performance_sample_count, ds.load_query_samples, ds.unload_query_samples
+    )
 
     log.info("starting {}".format(scenario))
     result_dict = {"good": 0, "total": 0, "scenario": str(scenario)}
@@ -697,8 +756,14 @@ def flush_queries():
     if args.accuracy:
         post_proc.finalize(result_dict, ds, output_dir=args.output)
 
-    add_results(final_results, "{}".format(scenario),
-                result_dict, last_timeing, time.time() - ds.last_loaded, args.accuracy)
+    add_results(
+        final_results,
+        "{}".format(scenario),
+        result_dict,
+        last_timeing,
+        time.time() - ds.last_loaded,
+        args.accuracy,
+    )
 
     runner.finish()
     lg.DestroyQSL(qsl)
diff --git a/vision/classification_and_detection/python/models/anchor_generator.py b/vision/classification_and_detection/python/models/anchor_generator.py
index 9a2d9d4904..be04012684 100644
--- a/vision/classification_and_detection/python/models/anchor_generator.py
+++ b/vision/classification_and_detection/python/models/anchor_generator.py
@@ -2,11 +2,12 @@
 import numpy as np
 
 
-# The following functions were taken from 
+# The following functions were taken from
 # https://github.com/tensorflow/models/tree/master/research/object_detection
 # with minor modifications so that they use
 # torch operations instead
 
+
 def expanded_shape(orig_shape, start_dim, num_dims):
     s = (1,) * num_dims
     return orig_shape[:start_dim] + s + orig_shape[start_dim:]
@@ -45,350 +46,407 @@ def meshgrid(x, y):
     return xgrid, ygrid
 
 
-def tile_anchors(grid_height,
-                 grid_width,
-                 scales,
-                 aspect_ratios,
-                 base_anchor_size,
-                 anchor_stride,
-                 anchor_offset):
-  """Create a tiled set of anchors strided along a grid in image space.
-  This op creates a set of anchor boxes by placing a "basis" collection of
-  boxes with user-specified scales and aspect ratios centered at evenly
-  distributed points along a grid.  The basis collection is specified via the
-  scale and aspect_ratios arguments.  For example, setting scales=[.1, .2, .2]
-  and aspect ratios = [2,2,1/2] means that we create three boxes: one with scale
-  .1, aspect ratio 2, one with scale .2, aspect ratio 2, and one with scale .2
-  and aspect ratio 1/2.  Each box is multiplied by "base_anchor_size" before
-  placing it over its respective center.
-  Grid points are specified via grid_height, grid_width parameters as well as
-  the anchor_stride and anchor_offset parameters.
-  Args:
-    grid_height: size of the grid in the y direction (int or int scalar tensor)
-    grid_width: size of the grid in the x direction (int or int scalar tensor)
-    scales: a 1-d  (float) tensor representing the scale of each box in the
-      basis set.
-    aspect_ratios: a 1-d (float) tensor representing the aspect ratio of each
-      box in the basis set.  The length of the scales and aspect_ratios tensors
-      must be equal.
-    base_anchor_size: base anchor size as [height, width]
-      (float tensor of shape [2])
-    anchor_stride: difference in centers between base anchors for adjacent grid
-                   positions (float tensor of shape [2])
-    anchor_offset: center of the anchor with scale and aspect ratio 1 for the
-                   upper left element of the grid, this should be zero for
-                   feature networks with only VALID padding and even receptive
-                   field size, but may need some additional calculation if other
-                   padding is used (float tensor of shape [2])
-  Returns:
-    a BoxList holding a collection of N anchor boxes
-  """
-  aspect_ratios = torch.as_tensor(aspect_ratios, dtype=torch.float32)
-  scales = torch.as_tensor(scales, dtype=torch.float32)
+def tile_anchors(
+    grid_height,
+    grid_width,
+    scales,
+    aspect_ratios,
+    base_anchor_size,
+    anchor_stride,
+    anchor_offset,
+):
+    """Create a tiled set of anchors strided along a grid in image space.
+    This op creates a set of anchor boxes by placing a "basis" collection of
+    boxes with user-specified scales and aspect ratios centered at evenly
+    distributed points along a grid.  The basis collection is specified via the
+    scale and aspect_ratios arguments.  For example, setting scales=[.1, .2, .2]
+    and aspect ratios = [2,2,1/2] means that we create three boxes: one with scale
+    .1, aspect ratio 2, one with scale .2, aspect ratio 2, and one with scale .2
+    and aspect ratio 1/2.  Each box is multiplied by "base_anchor_size" before
+    placing it over its respective center.
+    Grid points are specified via grid_height, grid_width parameters as well as
+    the anchor_stride and anchor_offset parameters.
+    Args:
+      grid_height: size of the grid in the y direction (int or int scalar tensor)
+      grid_width: size of the grid in the x direction (int or int scalar tensor)
+      scales: a 1-d  (float) tensor representing the scale of each box in the
+        basis set.
+      aspect_ratios: a 1-d (float) tensor representing the aspect ratio of each
+        box in the basis set.  The length of the scales and aspect_ratios tensors
+        must be equal.
+      base_anchor_size: base anchor size as [height, width]
+        (float tensor of shape [2])
+      anchor_stride: difference in centers between base anchors for adjacent grid
+                     positions (float tensor of shape [2])
+      anchor_offset: center of the anchor with scale and aspect ratio 1 for the
+                     upper left element of the grid, this should be zero for
+                     feature networks with only VALID padding and even receptive
+                     field size, but may need some additional calculation if other
+                     padding is used (float tensor of shape [2])
+    Returns:
+      a BoxList holding a collection of N anchor boxes
+    """
+    aspect_ratios = torch.as_tensor(aspect_ratios, dtype=torch.float32)
+    scales = torch.as_tensor(scales, dtype=torch.float32)
 
-  ratio_sqrts = torch.sqrt(aspect_ratios)
-  heights = scales / ratio_sqrts * base_anchor_size[0]
-  widths = scales * ratio_sqrts * base_anchor_size[1]
+    ratio_sqrts = torch.sqrt(aspect_ratios)
+    heights = scales / ratio_sqrts * base_anchor_size[0]
+    widths = scales * ratio_sqrts * base_anchor_size[1]
 
-  # Get a grid of box centers
-  y_centers = torch.arange(grid_height, dtype=torch.float32)
-  y_centers = y_centers * anchor_stride[0] + anchor_offset[0]
-  x_centers = torch.arange(grid_width, dtype=torch.float32)
-  x_centers = x_centers * anchor_stride[1] + anchor_offset[1]
+    # Get a grid of box centers
+    y_centers = torch.arange(grid_height, dtype=torch.float32)
+    y_centers = y_centers * anchor_stride[0] + anchor_offset[0]
+    x_centers = torch.arange(grid_width, dtype=torch.float32)
+    x_centers = x_centers * anchor_stride[1] + anchor_offset[1]
 
-  x_centers, y_centers = meshgrid(x_centers, y_centers)
+    x_centers, y_centers = meshgrid(x_centers, y_centers)
 
-  widths_grid, x_centers_grid = meshgrid(widths, x_centers)
-  heights_grid, y_centers_grid = meshgrid(heights, y_centers)
+    widths_grid, x_centers_grid = meshgrid(widths, x_centers)
+    heights_grid, y_centers_grid = meshgrid(heights, y_centers)
 
-  bbox_centers = torch.stack([y_centers_grid, x_centers_grid], dim=3)
-  bbox_sizes = torch.stack([heights_grid, widths_grid], dim=3)
-  bbox_centers = torch.reshape(bbox_centers, [-1, 2])
-  bbox_sizes = torch.reshape(bbox_sizes, [-1, 2])
-  bbox_corners = _center_size_bbox_to_corners_bbox(bbox_centers, bbox_sizes)
-  return bbox_corners
+    bbox_centers = torch.stack([y_centers_grid, x_centers_grid], dim=3)
+    bbox_sizes = torch.stack([heights_grid, widths_grid], dim=3)
+    bbox_centers = torch.reshape(bbox_centers, [-1, 2])
+    bbox_sizes = torch.reshape(bbox_sizes, [-1, 2])
+    bbox_corners = _center_size_bbox_to_corners_bbox(bbox_centers, bbox_sizes)
+    return bbox_corners
 
 
 def _center_size_bbox_to_corners_bbox(centers, sizes):
-  """Converts bbox center-size representation to corners representation.
-  Args:
-    centers: a tensor with shape [N, 2] representing bounding box centers
-    sizes: a tensor with shape [N, 2] representing bounding boxes
-  Returns:
-    corners: tensor with shape [N, 4] representing bounding boxes in corners
-      representation
-  """
-  return torch.cat([centers - .5 * sizes, centers + .5 * sizes], 1)
-
-
-def create_ssd_anchors(num_layers=6,
-                       min_scale=0.2,
-                       max_scale=0.95,
-                       scales=None,
-                       aspect_ratios=(1.0, 2.0, 1.0 / 2, 3.0, 1.0 / 3),
-                       interpolated_scale_aspect_ratio=1.0,
-                       base_anchor_size=None,
-                       anchor_strides=None,
-                       anchor_offsets=None,
-                       reduce_boxes_in_lowest_layer=True):
-  """Creates MultipleGridAnchorGenerator for SSD anchors.
-  This function instantiates a MultipleGridAnchorGenerator that reproduces
-  ``default box`` construction proposed by Liu et al in the SSD paper.
-  See Section 2.2 for details. Grid sizes are assumed to be passed in
-  at generation time from finest resolution to coarsest resolution --- this is
-  used to (linearly) interpolate scales of anchor boxes corresponding to the
-  intermediate grid sizes.
-  Anchors that are returned by calling the `generate` method on the returned
-  MultipleGridAnchorGenerator object are always in normalized coordinates
-  and clipped to the unit square: (i.e. all coordinates lie in [0, 1]x[0, 1]).
-  Args:
-    num_layers: integer number of grid layers to create anchors for (actual
-      grid sizes passed in at generation time)
-    min_scale: scale of anchors corresponding to finest resolution (float)
-    max_scale: scale of anchors corresponding to coarsest resolution (float)
-    scales: As list of anchor scales to use. When not None and not empty,
-      min_scale and max_scale are not used.
-    aspect_ratios: list or tuple of (float) aspect ratios to place on each
-      grid point.
-    interpolated_scale_aspect_ratio: An additional anchor is added with this
-      aspect ratio and a scale interpolated between the scale for a layer
-      and the scale for the next layer (1.0 for the last layer).
-      This anchor is not included if this value is 0.
-    base_anchor_size: base anchor size as [height, width].
-      The height and width values are normalized to the minimum dimension of the
-      input height and width, so that when the base anchor height equals the
-      base anchor width, the resulting anchor is square even if the input image
-      is not square.
-    anchor_strides: list of pairs of strides in pixels (in y and x directions
-      respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
-      means that we want the anchors corresponding to the first layer to be
-      strided by 25 pixels and those in the second layer to be strided by 50
-      pixels in both y and x directions. If anchor_strides=None, they are set to
-      be the reciprocal of the corresponding feature map shapes.
-    anchor_offsets: list of pairs of offsets in pixels (in y and x directions
-      respectively). The offset specifies where we want the center of the
-      (0, 0)-th anchor to lie for each layer. For example, setting
-      anchor_offsets=[(10, 10), (20, 20)]) means that we want the
-      (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
-      and likewise that we want the (0, 0)-th anchor of the second layer to lie
-      at (25, 25) in pixel space. If anchor_offsets=None, then they are set to
-      be half of the corresponding anchor stride.
-    reduce_boxes_in_lowest_layer: a boolean to indicate whether the fixed 3
-      boxes per location is used in the lowest layer.
-  Returns:
-    a MultipleGridAnchorGenerator
-  """
-  if base_anchor_size is None:
-    base_anchor_size = [1.0, 1.0]
-  base_anchor_size = torch.tensor(base_anchor_size, dtype=torch.float32)
-  box_specs_list = []
-  if scales is None or not scales:
-    scales = [min_scale + (max_scale - min_scale) * i / (num_layers - 1)
-              for i in range(num_layers)] + [1.0]
-  else:
-    # Add 1.0 to the end, which will only be used in scale_next below and used
-    # for computing an interpolated scale for the largest scale in the list.
-    scales += [1.0]
-
-  for layer, scale, scale_next in zip(
-      range(num_layers), scales[:-1], scales[1:]):
-    layer_box_specs = []
-    if layer == 0 and reduce_boxes_in_lowest_layer:
-      layer_box_specs = [(0.1, 1.0), (scale, 2.0), (scale, 0.5)]
-    else:
-      for aspect_ratio in aspect_ratios:
-        layer_box_specs.append((scale, aspect_ratio))
-      # Add one more anchor, with a scale between the current scale, and the
-      # scale for the next layer, with a specified aspect ratio (1.0 by
-      # default).
-      if interpolated_scale_aspect_ratio > 0.0:
-        layer_box_specs.append((np.sqrt(scale*scale_next),
-                                interpolated_scale_aspect_ratio))
-    box_specs_list.append(layer_box_specs)
-
-  return MultipleGridAnchorGenerator(box_specs_list, base_anchor_size,
-                                     anchor_strides, anchor_offsets)
+    """Converts bbox center-size representation to corners representation.
+    Args:
+      centers: a tensor with shape [N, 2] representing bounding box centers
+      sizes: a tensor with shape [N, 2] representing bounding boxes
+    Returns:
+      corners: tensor with shape [N, 4] representing bounding boxes in corners
+        representation
+    """
+    return torch.cat([centers - 0.5 * sizes, centers + 0.5 * sizes], 1)
 
-class MultipleGridAnchorGenerator(object):
-  """Generate a grid of anchors for multiple CNN layers."""
 
-  def __init__(self,
-               box_specs_list,
-               base_anchor_size=None,
-               anchor_strides=None,
-               anchor_offsets=None,
-               clip_window=None):
-    """Constructs a MultipleGridAnchorGenerator.
-    To construct anchors, at multiple grid resolutions, one must provide a
-    list of feature_map_shape_list (e.g., [(8, 8), (4, 4)]), and for each grid
-    size, a corresponding list of (scale, aspect ratio) box specifications.
-    For example:
-    box_specs_list = [[(.1, 1.0), (.1, 2.0)],  # for 8x8 grid
-                      [(.2, 1.0), (.3, 1.0), (.2, 2.0)]]  # for 4x4 grid
-    To support the fully convolutional setting, we pass grid sizes in at
-    generation time, while scale and aspect ratios are fixed at construction
-    time.
+def create_ssd_anchors(
+    num_layers=6,
+    min_scale=0.2,
+    max_scale=0.95,
+    scales=None,
+    aspect_ratios=(1.0, 2.0, 1.0 / 2, 3.0, 1.0 / 3),
+    interpolated_scale_aspect_ratio=1.0,
+    base_anchor_size=None,
+    anchor_strides=None,
+    anchor_offsets=None,
+    reduce_boxes_in_lowest_layer=True,
+):
+    """Creates MultipleGridAnchorGenerator for SSD anchors.
+    This function instantiates a MultipleGridAnchorGenerator that reproduces
+    ``default box`` construction proposed by Liu et al in the SSD paper.
+    See Section 2.2 for details. Grid sizes are assumed to be passed in
+    at generation time from finest resolution to coarsest resolution --- this is
+    used to (linearly) interpolate scales of anchor boxes corresponding to the
+    intermediate grid sizes.
+    Anchors that are returned by calling the `generate` method on the returned
+    MultipleGridAnchorGenerator object are always in normalized coordinates
+    and clipped to the unit square: (i.e. all coordinates lie in [0, 1]x[0, 1]).
     Args:
-      box_specs_list: list of list of (scale, aspect ratio) pairs with the
-        outside list having the same number of entries as feature_map_shape_list
-        (which is passed in at generation time).
-      base_anchor_size: base anchor size as [height, width]
-                        (length-2 float tensor, default=[1.0, 1.0]).
-                        The height and width values are normalized to the
-                        minimum dimension of the input height and width, so that
-                        when the base anchor height equals the base anchor
-                        width, the resulting anchor is square even if the input
-                        image is not square.
+      num_layers: integer number of grid layers to create anchors for (actual
+        grid sizes passed in at generation time)
+      min_scale: scale of anchors corresponding to finest resolution (float)
+      max_scale: scale of anchors corresponding to coarsest resolution (float)
+      scales: As list of anchor scales to use. When not None and not empty,
+        min_scale and max_scale are not used.
+      aspect_ratios: list or tuple of (float) aspect ratios to place on each
+        grid point.
+      interpolated_scale_aspect_ratio: An additional anchor is added with this
+        aspect ratio and a scale interpolated between the scale for a layer
+        and the scale for the next layer (1.0 for the last layer).
+        This anchor is not included if this value is 0.
+      base_anchor_size: base anchor size as [height, width].
+        The height and width values are normalized to the minimum dimension of the
+        input height and width, so that when the base anchor height equals the
+        base anchor width, the resulting anchor is square even if the input image
+        is not square.
       anchor_strides: list of pairs of strides in pixels (in y and x directions
         respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
         means that we want the anchors corresponding to the first layer to be
         strided by 25 pixels and those in the second layer to be strided by 50
-        pixels in both y and x directions. If anchor_strides=None, they are set
-        to be the reciprocal of the corresponding feature map shapes.
+        pixels in both y and x directions. If anchor_strides=None, they are set to
+        be the reciprocal of the corresponding feature map shapes.
       anchor_offsets: list of pairs of offsets in pixels (in y and x directions
         respectively). The offset specifies where we want the center of the
         (0, 0)-th anchor to lie for each layer. For example, setting
         anchor_offsets=[(10, 10), (20, 20)]) means that we want the
         (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
-        and likewise that we want the (0, 0)-th anchor of the second layer to
-        lie at (25, 25) in pixel space. If anchor_offsets=None, then they are
-        set to be half of the corresponding anchor stride.
-      clip_window: a tensor of shape [4] specifying a window to which all
-        anchors should be clipped. If clip_window is None, then no clipping
-        is performed.
-    Raises:
-      ValueError: if box_specs_list is not a list of list of pairs
-      ValueError: if clip_window is not either None or a tensor of shape [4]
+        and likewise that we want the (0, 0)-th anchor of the second layer to lie
+        at (25, 25) in pixel space. If anchor_offsets=None, then they are set to
+        be half of the corresponding anchor stride.
+      reduce_boxes_in_lowest_layer: a boolean to indicate whether the fixed 3
+        boxes per location is used in the lowest layer.
+    Returns:
+      a MultipleGridAnchorGenerator
     """
-    if isinstance(box_specs_list, list) and all(
-        [isinstance(list_item, list) for list_item in box_specs_list]):
-      self._box_specs = box_specs_list
-    else:
-      raise ValueError('box_specs_list is expected to be a '
-                       'list of lists of pairs')
     if base_anchor_size is None:
-      base_anchor_size = torch.tensor([256, 256], dtype=torch.float32)
-    self._base_anchor_size = base_anchor_size
-    self._anchor_strides = anchor_strides
-    self._anchor_offsets = anchor_offsets
-    if clip_window is not None and list(clip_window.shape) != [4]:
-      raise ValueError('clip_window must either be None or a shape [4] tensor')
-    self._clip_window = clip_window
-    self._scales = []
-    self._aspect_ratios = []
-    for box_spec in self._box_specs:
-      if not all([isinstance(entry, tuple) and len(entry) == 2
-                  for entry in box_spec]):
-        raise ValueError('box_specs_list is expected to be a '
-                         'list of lists of pairs')
-      scales, aspect_ratios = zip(*box_spec)
-      self._scales.append(scales)
-      self._aspect_ratios.append(aspect_ratios)
+        base_anchor_size = [1.0, 1.0]
+    base_anchor_size = torch.tensor(base_anchor_size, dtype=torch.float32)
+    box_specs_list = []
+    if scales is None or not scales:
+        scales = [
+            min_scale + (max_scale - min_scale) * i / (num_layers - 1)
+            for i in range(num_layers)
+        ] + [1.0]
+    else:
+        # Add 1.0 to the end, which will only be used in scale_next below and used
+        # for computing an interpolated scale for the largest scale in the
+        # list.
+        scales += [1.0]
 
-    for arg, arg_name in zip([self._anchor_strides, self._anchor_offsets],
-                             ['anchor_strides', 'anchor_offsets']):
-      if arg and not (isinstance(arg, list) and
-                      len(arg) == len(self._box_specs)):
-        raise ValueError('%s must be a list with the same length '
-                         'as self._box_specs' % arg_name)
-      if arg and not all([
-          isinstance(list_item, tuple) and len(list_item) == 2
-          for list_item in arg
-      ]):
-        raise ValueError('%s must be a list of pairs.' % arg_name)
+    for layer, scale, scale_next in zip(
+            range(num_layers), scales[:-1], scales[1:]):
+        layer_box_specs = []
+        if layer == 0 and reduce_boxes_in_lowest_layer:
+            layer_box_specs = [(0.1, 1.0), (scale, 2.0), (scale, 0.5)]
+        else:
+            for aspect_ratio in aspect_ratios:
+                layer_box_specs.append((scale, aspect_ratio))
+            # Add one more anchor, with a scale between the current scale, and the
+            # scale for the next layer, with a specified aspect ratio (1.0 by
+            # default).
+            if interpolated_scale_aspect_ratio > 0.0:
+                layer_box_specs.append(
+                    (np.sqrt(scale * scale_next), interpolated_scale_aspect_ratio)
+                )
+        box_specs_list.append(layer_box_specs)
 
+    return MultipleGridAnchorGenerator(
+        box_specs_list, base_anchor_size, anchor_strides, anchor_offsets
+    )
 
-  def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
-    """Generates a collection of bounding boxes to be used as anchors.
-    The number of anchors generated for a single grid with shape MxM where we
-    place k boxes over each grid center is k*M^2 and thus the total number of
-    anchors is the sum over all grids. In our box_specs_list example
-    (see the constructor docstring), we would place two boxes over each grid
-    point on an 8x8 grid and three boxes over each grid point on a 4x4 grid and
-    thus end up with 2*8^2 + 3*4^2 = 176 anchors in total. The layout of the
-    output anchors follows the order of how the grid sizes and box_specs are
-    specified (with box_spec index varying the fastest, followed by width
-    index, then height index, then grid index).
-    Args:
-      feature_map_shape_list: list of pairs of convnet layer resolutions in the
-        format [(height_0, width_0), (height_1, width_1), ...]. For example,
-        setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
-        correspond to an 8x8 layer followed by a 7x7 layer.
-      im_height: the height of the image to generate the grid for. If both
-        im_height and im_width are 1, the generated anchors default to
-        absolute coordinates, otherwise normalized coordinates are produced.
-      im_width: the width of the image to generate the grid for. If both
-        im_height and im_width are 1, the generated anchors default to
-        absolute coordinates, otherwise normalized coordinates are produced.
-    Returns:
-      boxes_list: a list of BoxLists each holding anchor boxes corresponding to
-        the input feature map shapes.
-    Raises:
-      ValueError: if feature_map_shape_list, box_specs_list do not have the same
-        length.
-      ValueError: if feature_map_shape_list does not consist of pairs of
-        integers
-    """
-    if not (isinstance(feature_map_shape_list, list)
-            and len(feature_map_shape_list) == len(self._box_specs)):
-      raise ValueError('feature_map_shape_list must be a list with the same '
-                       'length as self._box_specs')
-    if not all([isinstance(list_item, tuple) and len(list_item) == 2
-                for list_item in feature_map_shape_list]):
-      raise ValueError('feature_map_shape_list must be a list of pairs.')
 
-    im_height = float(im_height)
-    im_width = float(im_width)
+class MultipleGridAnchorGenerator(object):
+    """Generate a grid of anchors for multiple CNN layers."""
 
-    if not self._anchor_strides:
-      anchor_strides = [(1.0 / float(pair[0]), 1.0 / float(pair[1]))
-                        for pair in feature_map_shape_list]
-    else:
-      anchor_strides = [(float(stride[0]) / im_height,
-                         float(stride[1]) / im_width)
-                        for stride in self._anchor_strides]
-    if not self._anchor_offsets:
-      anchor_offsets = [(0.5 * stride[0], 0.5 * stride[1])
-                        for stride in anchor_strides]
-    else:
-      anchor_offsets = [(float(offset[0]) / im_height,
-                         float(offset[1]) / im_width)
-                        for offset in self._anchor_offsets]
+    def __init__(
+        self,
+        box_specs_list,
+        base_anchor_size=None,
+        anchor_strides=None,
+        anchor_offsets=None,
+        clip_window=None,
+    ):
+        """Constructs a MultipleGridAnchorGenerator.
+        To construct anchors, at multiple grid resolutions, one must provide a
+        list of feature_map_shape_list (e.g., [(8, 8), (4, 4)]), and for each grid
+        size, a corresponding list of (scale, aspect ratio) box specifications.
+        For example:
+        box_specs_list = [[(.1, 1.0), (.1, 2.0)],  # for 8x8 grid
+                          [(.2, 1.0), (.3, 1.0), (.2, 2.0)]]  # for 4x4 grid
+        To support the fully convolutional setting, we pass grid sizes in at
+        generation time, while scale and aspect ratios are fixed at construction
+        time.
+        Args:
+          box_specs_list: list of list of (scale, aspect ratio) pairs with the
+            outside list having the same number of entries as feature_map_shape_list
+            (which is passed in at generation time).
+          base_anchor_size: base anchor size as [height, width]
+                            (length-2 float tensor, default=[1.0, 1.0]).
+                            The height and width values are normalized to the
+                            minimum dimension of the input height and width, so that
+                            when the base anchor height equals the base anchor
+                            width, the resulting anchor is square even if the input
+                            image is not square.
+          anchor_strides: list of pairs of strides in pixels (in y and x directions
+            respectively). For example, setting anchor_strides=[(25, 25), (50, 50)]
+            means that we want the anchors corresponding to the first layer to be
+            strided by 25 pixels and those in the second layer to be strided by 50
+            pixels in both y and x directions. If anchor_strides=None, they are set
+            to be the reciprocal of the corresponding feature map shapes.
+          anchor_offsets: list of pairs of offsets in pixels (in y and x directions
+            respectively). The offset specifies where we want the center of the
+            (0, 0)-th anchor to lie for each layer. For example, setting
+            anchor_offsets=[(10, 10), (20, 20)]) means that we want the
+            (0, 0)-th anchor of the first layer to lie at (10, 10) in pixel space
+            and likewise that we want the (0, 0)-th anchor of the second layer to
+            lie at (25, 25) in pixel space. If anchor_offsets=None, then they are
+            set to be half of the corresponding anchor stride.
+          clip_window: a tensor of shape [4] specifying a window to which all
+            anchors should be clipped. If clip_window is None, then no clipping
+            is performed.
+        Raises:
+          ValueError: if box_specs_list is not a list of list of pairs
+          ValueError: if clip_window is not either None or a tensor of shape [4]
+        """
+        if isinstance(box_specs_list, list) and all(
+            [isinstance(list_item, list) for list_item in box_specs_list]
+        ):
+            self._box_specs = box_specs_list
+        else:
+            raise ValueError(
+                "box_specs_list is expected to be a " "list of lists of pairs"
+            )
+        if base_anchor_size is None:
+            base_anchor_size = torch.tensor([256, 256], dtype=torch.float32)
+        self._base_anchor_size = base_anchor_size
+        self._anchor_strides = anchor_strides
+        self._anchor_offsets = anchor_offsets
+        if clip_window is not None and list(clip_window.shape) != [4]:
+            raise ValueError(
+                "clip_window must either be None or a shape [4] tensor")
+        self._clip_window = clip_window
+        self._scales = []
+        self._aspect_ratios = []
+        for box_spec in self._box_specs:
+            if not all(
+                [isinstance(entry, tuple) and len(entry)
+                 == 2 for entry in box_spec]
+            ):
+                raise ValueError(
+                    "box_specs_list is expected to be a " "list of lists of pairs"
+                )
+            scales, aspect_ratios = zip(*box_spec)
+            self._scales.append(scales)
+            self._aspect_ratios.append(aspect_ratios)
+
+        for arg, arg_name in zip(
+            [self._anchor_strides, self._anchor_offsets],
+            ["anchor_strides", "anchor_offsets"],
+        ):
+            if arg and not (isinstance(arg, list) and len(arg)
+                            == len(self._box_specs)):
+                raise ValueError(
+                    "%s must be a list with the same length "
+                    "as self._box_specs" % arg_name
+                )
+            if arg and not all(
+                [
+                    isinstance(list_item, tuple) and len(list_item) == 2
+                    for list_item in arg
+                ]
+            ):
+                raise ValueError("%s must be a list of pairs." % arg_name)
+
+    def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
+        """Generates a collection of bounding boxes to be used as anchors.
+        The number of anchors generated for a single grid with shape MxM where we
+        place k boxes over each grid center is k*M^2 and thus the total number of
+        anchors is the sum over all grids. In our box_specs_list example
+        (see the constructor docstring), we would place two boxes over each grid
+        point on an 8x8 grid and three boxes over each grid point on a 4x4 grid and
+        thus end up with 2*8^2 + 3*4^2 = 176 anchors in total. The layout of the
+        output anchors follows the order of how the grid sizes and box_specs are
+        specified (with box_spec index varying the fastest, followed by width
+        index, then height index, then grid index).
+        Args:
+          feature_map_shape_list: list of pairs of convnet layer resolutions in the
+            format [(height_0, width_0), (height_1, width_1), ...]. For example,
+            setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
+            correspond to an 8x8 layer followed by a 7x7 layer.
+          im_height: the height of the image to generate the grid for. If both
+            im_height and im_width are 1, the generated anchors default to
+            absolute coordinates, otherwise normalized coordinates are produced.
+          im_width: the width of the image to generate the grid for. If both
+            im_height and im_width are 1, the generated anchors default to
+            absolute coordinates, otherwise normalized coordinates are produced.
+        Returns:
+          boxes_list: a list of BoxLists each holding anchor boxes corresponding to
+            the input feature map shapes.
+        Raises:
+          ValueError: if feature_map_shape_list, box_specs_list do not have the same
+            length.
+          ValueError: if feature_map_shape_list does not consist of pairs of
+            integers
+        """
+        if not (
+            isinstance(feature_map_shape_list, list)
+            and len(feature_map_shape_list) == len(self._box_specs)
+        ):
+            raise ValueError(
+                "feature_map_shape_list must be a list with the same "
+                "length as self._box_specs"
+            )
+        if not all(
+            [
+                isinstance(list_item, tuple) and len(list_item) == 2
+                for list_item in feature_map_shape_list
+            ]
+        ):
+            raise ValueError("feature_map_shape_list must be a list of pairs.")
+
+        im_height = float(im_height)
+        im_width = float(im_width)
+
+        if not self._anchor_strides:
+            anchor_strides = [
+                (1.0 / float(pair[0]), 1.0 / float(pair[1]))
+                for pair in feature_map_shape_list
+            ]
+        else:
+            anchor_strides = [
+                (float(stride[0]) / im_height, float(stride[1]) / im_width)
+                for stride in self._anchor_strides
+            ]
+        if not self._anchor_offsets:
+            anchor_offsets = [
+                (0.5 * stride[0], 0.5 * stride[1]) for stride in anchor_strides
+            ]
+        else:
+            anchor_offsets = [
+                (float(offset[0]) / im_height, float(offset[1]) / im_width)
+                for offset in self._anchor_offsets
+            ]
 
-    for arg, arg_name in zip([anchor_strides, anchor_offsets],
-                             ['anchor_strides', 'anchor_offsets']):
-      if not (isinstance(arg, list) and len(arg) == len(self._box_specs)):
-        raise ValueError('%s must be a list with the same length '
-                         'as self._box_specs' % arg_name)
-      if not all([isinstance(list_item, tuple) and len(list_item) == 2
-                  for list_item in arg]):
-        raise ValueError('%s must be a list of pairs.' % arg_name)
+        for arg, arg_name in zip(
+            [anchor_strides, anchor_offsets], [
+                "anchor_strides", "anchor_offsets"]
+        ):
+            if not (isinstance(arg, list) and len(
+                    arg) == len(self._box_specs)):
+                raise ValueError(
+                    "%s must be a list with the same length "
+                    "as self._box_specs" % arg_name
+                )
+            if not all(
+                [
+                    isinstance(list_item, tuple) and len(list_item) == 2
+                    for list_item in arg
+                ]
+            ):
+                raise ValueError("%s must be a list of pairs." % arg_name)
 
-    anchor_grid_list = []
-    min_im_shape = min(im_height, im_width)
-    scale_height = min_im_shape / im_height
-    scale_width = min_im_shape / im_width
-    base_anchor_size = [
-        scale_height * self._base_anchor_size[0],
-        scale_width * self._base_anchor_size[1]
-    ]
-    for feature_map_index, (grid_size, scales, aspect_ratios, stride,
-                            offset) in enumerate(
-                                zip(feature_map_shape_list, self._scales,
-                                    self._aspect_ratios, anchor_strides,
-                                    anchor_offsets)):
-      tiled_anchors = tile_anchors(
-          grid_height=grid_size[0],
-          grid_width=grid_size[1],
-          scales=scales,
-          aspect_ratios=aspect_ratios,
-          base_anchor_size=base_anchor_size,
-          anchor_stride=stride,
-          anchor_offset=offset)
-      if self._clip_window is not None:
-        raise NotImplementedError("Oups!")
-      num_anchors_in_layer = len(tiled_anchors)
-      anchor_indices = feature_map_index * torch.ones(num_anchors_in_layer)
-      anchor_grid_list.append(tiled_anchors)
+        anchor_grid_list = []
+        min_im_shape = min(im_height, im_width)
+        scale_height = min_im_shape / im_height
+        scale_width = min_im_shape / im_width
+        base_anchor_size = [
+            scale_height * self._base_anchor_size[0],
+            scale_width * self._base_anchor_size[1],
+        ]
+        for feature_map_index, (
+            grid_size,
+            scales,
+            aspect_ratios,
+            stride,
+            offset,
+        ) in enumerate(
+            zip(
+                feature_map_shape_list,
+                self._scales,
+                self._aspect_ratios,
+                anchor_strides,
+                anchor_offsets,
+            )
+        ):
+            tiled_anchors = tile_anchors(
+                grid_height=grid_size[0],
+                grid_width=grid_size[1],
+                scales=scales,
+                aspect_ratios=aspect_ratios,
+                base_anchor_size=base_anchor_size,
+                anchor_stride=stride,
+                anchor_offset=offset,
+            )
+            if self._clip_window is not None:
+                raise NotImplementedError("Oups!")
+            num_anchors_in_layer = len(tiled_anchors)
+            anchor_indices = feature_map_index * \
+                torch.ones(num_anchors_in_layer)
+            anchor_grid_list.append(tiled_anchors)
 
-    return anchor_grid_list
+        return anchor_grid_list
diff --git a/vision/classification_and_detection/python/models/base_model_r34.py b/vision/classification_and_detection/python/models/base_model_r34.py
index ea224a7caa..9e8a2da618 100644
--- a/vision/classification_and_detection/python/models/base_model_r34.py
+++ b/vision/classification_and_detection/python/models/base_model_r34.py
@@ -2,7 +2,7 @@
     Load the vgg16 weight and save it to special file
 """
 
-#from torchvision.models.vgg import vgg16
+# from torchvision.models.vgg import vgg16
 import torch.nn as nn
 import torch.nn.functional as F
 import torch
@@ -11,29 +11,32 @@
 
 from torchvision.models.resnet import resnet18, resnet34, resnet50
 
+
 def _ModifyConvStrideDilation(conv, stride=(1, 1), padding=None):
     conv.stride = stride
 
     if padding is not None:
         conv.padding = padding
 
+
 def _ModifyBlock(block, bottleneck=False, **kwargs):
     for m in list(block.children()):
         if bottleneck:
-           _ModifyConvStrideDilation(m.conv2, **kwargs)
+            _ModifyConvStrideDilation(m.conv2, **kwargs)
         else:
-           _ModifyConvStrideDilation(m.conv1, **kwargs)
+            _ModifyConvStrideDilation(m.conv1, **kwargs)
 
         if m.downsample is not None:
             # need to make sure no padding for the 1x1 residual connection
-            _ModifyConvStrideDilation(list(m.downsample.children())[0], **kwargs)
+            _ModifyConvStrideDilation(
+                list(m.downsample.children())[0], **kwargs)
+
 
 class ResNet18(nn.Module):
     def __init__(self):
         super().__init__()
         rn18 = resnet18(pretrained=True)
 
-
         # discard last Resnet block, avrpooling and classification FC
         # layer1 = up to and including conv3 block
         self.layer1 = nn.Sequential(*list(rn18.children())[:6])
@@ -43,7 +46,7 @@ def __init__(self):
         # modify conv4 if necessary
         # Always deal with stride in first block
         modulelist = list(self.layer2.children())
-        _ModifyBlock(modulelist[0], stride=(1,1))
+        _ModifyBlock(modulelist[0], stride=(1, 1))
 
     def forward(self, data):
         layer1_activation = self.layer1(data)
@@ -53,6 +56,7 @@ def forward(self, data):
         # Only need the output of conv4
         return [layer2_activation]
 
+
 class ResNet34(nn.Module):
     def __init__(self):
         super().__init__()
@@ -64,8 +68,7 @@ def __init__(self):
         # modify conv4 if necessary
         # Always deal with stride in first block
         modulelist = list(self.layer2.children())
-        _ModifyBlock(modulelist[0], stride=(1,1))
-
+        _ModifyBlock(modulelist[0], stride=(1, 1))
 
     def forward(self, data):
         layer1_activation = self.layer1(data)
@@ -74,22 +77,28 @@ def forward(self, data):
 
         return [layer2_activation]
 
+
 class L2Norm(nn.Module):
     """
-       Scale shall be learnable according to original paper
-       scale: initial scale number
-       chan_num: L2Norm channel number (norm over all channels)
+    Scale shall be learnable according to original paper
+    scale: initial scale number
+    chan_num: L2Norm channel number (norm over all channels)
     """
+
     def __init__(self, scale=20, chan_num=512):
         super(L2Norm, self).__init__()
         # Scale across channels
-        self.scale = \
-            nn.Parameter(torch.Tensor([scale]*chan_num).view(1, chan_num, 1, 1))
+        self.scale = nn.Parameter(
+            torch.Tensor([scale] * chan_num).view(1, chan_num, 1, 1)
+        )
 
     def forward(self, data):
         # normalize accross channel
-        return self.scale*data*data.pow(2).sum(dim=1, keepdim=True).clamp(min=1e-12).rsqrt()
-
+        return (
+            self.scale
+            * data
+            * data.pow(2).sum(dim=1, keepdim=True).clamp(min=1e-12).rsqrt()
+        )
 
 
 def tailor_module(src_model, src_dir, tgt_model, tgt_dir):
@@ -107,22 +116,23 @@ def tailor_module(src_model, src_dir, tgt_model, tgt_dir):
     for k1, k2 in zip(keys1, keys2):
         # print(k1, k2)
         state[k2] = src_state[k1]
-    #diff_keys = state.keys() - target_model.state_dict().keys()
-    #print("Different Keys:", diff_keys)
+    # diff_keys = state.keys() - target_model.state_dict().keys()
+    # print("Different Keys:", diff_keys)
     # Remove unecessary keys
-    #for k in diff_keys:
+    # for k in diff_keys:
     #    state.pop(k)
     tgt_model.load_state_dict(state)
     torch.save(tgt_model.state_dict(), tgt_dir)
 
+
 # Default vgg16 in pytorch seems different from ssd
 def make_layers(cfg, batch_norm=False):
     layers = []
     in_channels = 3
     for v in cfg:
-        if v == 'M':
+        if v == "M":
             layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
-        elif v == 'C':
+        elif v == "C":
             # Notice ceil_mode is true
             layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
         else:
@@ -134,42 +144,51 @@ def make_layers(cfg, batch_norm=False):
             in_channels = v
     return layers
 
+
 class Loss(nn.Module):
     """
-        Implements the loss as the sum of the followings:
-        1. Confidence Loss: All labels, with hard negative mining
-        2. Localization Loss: Only on positive labels
-        Suppose input dboxes has the shape 8732x4
+    Implements the loss as the sum of the followings:
+    1. Confidence Loss: All labels, with hard negative mining
+    2. Localization Loss: Only on positive labels
+    Suppose input dboxes has the shape 8732x4
     """
 
     def __init__(self, dboxes):
         super(Loss, self).__init__()
-        self.scale_xy = 1.0/dboxes.scale_xy
-        self.scale_wh = 1.0/dboxes.scale_wh
+        self.scale_xy = 1.0 / dboxes.scale_xy
+        self.scale_wh = 1.0 / dboxes.scale_wh
 
         self.sl1_loss = nn.SmoothL1Loss(reduce=False)
-        self.dboxes = nn.Parameter(dboxes(order="xywh").transpose(0, 1).unsqueeze(dim = 0),
-            requires_grad=False)
+        self.dboxes = nn.Parameter(
+            dboxes(order="xywh").transpose(0, 1).unsqueeze(dim=0), requires_grad=False
+        )
         # Two factor are from following links
         # http://jany.st/post/2017-11-05-single-shot-detector-ssd-from-scratch-in-tensorflow.html
         self.con_loss = nn.CrossEntropyLoss(reduce=False)
 
     def _loc_vec(self, loc):
         """
-            Generate Location Vectors
+        Generate Location Vectors
         """
-        gxy = self.scale_xy*(loc[:, :2, :] - self.dboxes[:, :2, :])/self.dboxes[:, 2:, ]
-        gwh = self.scale_wh*(loc[:, 2:, :]/self.dboxes[:, 2:, :]).log()
+        gxy = (
+            self.scale_xy
+            * (loc[:, :2, :] - self.dboxes[:, :2, :])
+            / self.dboxes[
+                :,
+                2:,
+            ]
+        )
+        gwh = self.scale_wh * (loc[:, 2:, :] / self.dboxes[:, 2:, :]).log()
 
         return torch.cat((gxy, gwh), dim=1).contiguous()
 
     def forward(self, ploc, plabel, gloc, glabel):
         """
-            ploc, plabel: Nx4x8732, Nxlabel_numx8732
-                predicted location and labels
+        ploc, plabel: Nx4x8732, Nxlabel_numx8732
+            predicted location and labels
 
-            gloc, glabel: Nx4x8732, Nx8732
-                ground truth location and labels
+        gloc, glabel: Nx4x8732, Nx8732
+            ground truth location and labels
         """
 
         mask = glabel > 0
@@ -177,7 +196,7 @@ def forward(self, ploc, plabel, gloc, glabel):
         vec_gd = self._loc_vec(gloc)
         # sum on four coordinates, and mask
         sl1 = self.sl1_loss(ploc, vec_gd).sum(dim=1)
-        sl1 = (mask.float()*sl1).sum(dim=1)
+        sl1 = (mask.float() * sl1).sum(dim=1)
 
         # hard negative mining
         con = self.con_loss(plabel, glabel)
@@ -189,16 +208,15 @@ def forward(self, ploc, plabel, gloc, glabel):
         _, con_rank = con_idx.sort(dim=1)
 
         # number of negative three times positive
-        neg_num = torch.clamp(3*pos_num, max=mask.size(1)).unsqueeze(-1)
+        neg_num = torch.clamp(3 * pos_num, max=mask.size(1)).unsqueeze(-1)
         neg_mask = con_rank < neg_num
 
-        closs = (con*(mask.float() + neg_mask.float())).sum(dim=1)
+        closs = (con * (mask.float() + neg_mask.float())).sum(dim=1)
 
         # avoid no object detected
         total_loss = sl1 + closs
         num_mask = (pos_num > 0).float()
         pos_num = pos_num.float().clamp(min=1e-6)
 
-        ret = (total_loss*num_mask/pos_num).mean(dim=0)
+        ret = (total_loss * num_mask / pos_num).mean(dim=0)
         return ret
-
diff --git a/vision/classification_and_detection/python/models/convert_tf_weights.py b/vision/classification_and_detection/python/models/convert_tf_weights.py
index 02c51bf1bb..4d063caef4 100644
--- a/vision/classification_and_detection/python/models/convert_tf_weights.py
+++ b/vision/classification_and_detection/python/models/convert_tf_weights.py
@@ -17,7 +17,7 @@ def remap_tf_base_names(orig_weights):
         if "batchnorm" not in k and "pointwise_" not in k
     }
 
-    matcher = re.compile("(.*)Conv2d_(\d+)")
+    matcher = re.compile("(.*)Conv2d_(\\d+)")
     mapping = {}
     for k in convs.keys():
         l = matcher.match(k).group(2)
@@ -52,7 +52,7 @@ def remap_tf_extras(orig_weights):
     }
     weights = {k: v for k, v in weights.items() if "pointwise_" in k}
 
-    matcher = re.compile("(.*)Conv2d_(\d+)_(\d)x(\d)")
+    matcher = re.compile("(.*)Conv2d_(\\d+)_(\\d)x(\\d)")
     mapping = {}
     for k in weights.keys():
         m = matcher.match(k)
@@ -75,7 +75,7 @@ def remap_tf_predictors(orig_weights):
     weights = {k: v for k, v in orig_weights.items() if "BoxPredictor" in k}
     weights = {k: v for k, v in weights.items() if "BoxEncodingPredictor" in k}
 
-    matcher = re.compile("BoxPredictor_(\d+)")
+    matcher = re.compile("BoxPredictor_(\\d+)")
     for k in weights.keys():
         pos = matcher.match(k).group(1)
         wtype = "weight" if "weights" in k else "bias"
@@ -125,13 +125,15 @@ def get_state_dict(weights):
 def read_tf_weights(frozen_model):
     import tensorflow as tf
     from tensorflow.python.framework import tensor_util
+
     weights = {}
     with tf.Session() as sess:
-        with tf.gfile.GFile(frozen_model, 'rb') as f:
+        with tf.gfile.GFile(frozen_model, "rb") as f:
             graph_def = tf.GraphDef()
             graph_def.ParseFromString(f.read())
             tf.import_graph_def(graph_def)
         for n in graph_def.node:
-            if n.op == 'Const':
-                weights[n.name] = tensor_util.MakeNdarray(n.attr['value'].tensor)
+            if n.op == "Const":
+                weights[n.name] = tensor_util.MakeNdarray(
+                    n.attr["value"].tensor)
     return weights
diff --git a/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py b/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py
index eead72ae71..97be219824 100644
--- a/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py
+++ b/vision/classification_and_detection/python/models/ssd_mobilenet_v1.py
@@ -81,7 +81,8 @@ def __init__(self, in_channels, num_classes, num_anchors):
         self.classification = nn.Conv2d(
             in_channels, num_classes * num_anchors, kernel_size=1
         )
-        self.regression = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1)
+        self.regression = nn.Conv2d(
+            in_channels, num_anchors * 4, kernel_size=1)
 
         self.num_classes = num_classes
         self.num_anchors = num_anchors
@@ -161,13 +162,12 @@ def ssd_model(self, x):
             self._feature_map_shapes = shapes
 
         self.coder_weights = self.coder_weights.to(scores)
-        if box_regression.dim()==2:
+        if box_regression.dim() == 2:
             box_regression = box_regression[None]
         boxes = decode_boxes(box_regression, self.priors, self.coder_weights)
         # add a batch dimension
         return scores, boxes
 
-
     def forward(self, images):
         """
         Arguments:
@@ -175,13 +175,15 @@ def forward(self, images):
         """
 
         scores, boxes = self.ssd_model(images)
-        list_boxes=[]; list_labels=[]; list_scores=[]
+        list_boxes = []
+        list_labels = []
+        list_scores = []
         for b in range(len(scores)):
             bboxes, blabels, bscores = self.filter_results(scores[b], boxes[b])
             list_boxes.append(bboxes)
             list_labels.append(blabels.long())
             list_scores.append(bscores)
-        #boxes = self.rescale_boxes(boxes, height, width)
+        # boxes = self.rescale_boxes(boxes, height, width)
         return [list_boxes, list_labels, list_scores]
 
     def filter_results(self, scores, boxes):
@@ -190,8 +192,8 @@ def filter_results(self, scores, boxes):
         # on python. This implementation is faster on the
         # CPU, which is why we run this part on the CPU
         cpu_device = torch.device("cpu")
-        #boxes = boxes[0]
-        #scores = scores[0]
+        # boxes = boxes[0]
+        # scores = scores[0]
         boxes = boxes.to(cpu_device)
         scores = scores.to(cpu_device)
         selected_box_probs = []
@@ -205,7 +207,8 @@ def filter_results(self, scores, boxes):
             box_probs = nms(box_probs, self.nms_threshold)
             selected_box_probs.append(box_probs)
             labels.append(
-                torch.full((box_probs.size(0),), class_index, dtype=torch.int64)
+                torch.full(
+                    (box_probs.size(0),), class_index, dtype=torch.int64)
             )
         selected_box_probs = torch.cat(selected_box_probs)
         labels = torch.cat(labels)
diff --git a/vision/classification_and_detection/python/models/ssd_r34.py b/vision/classification_and_detection/python/models/ssd_r34.py
index 63e596b4a5..e8138e3a19 100644
--- a/vision/classification_and_detection/python/models/ssd_r34.py
+++ b/vision/classification_and_detection/python/models/ssd_r34.py
@@ -6,102 +6,114 @@
 import itertools
 import torch.nn.functional as F
 
-##Inspired by https://github.com/kuangliu/pytorch-ssd
+# Inspired by https://github.com/kuangliu/pytorch-ssd
+
 
 class Encoder(object):
     """
-        Transform between (bboxes, lables) <-> SSD output
-        
-        dboxes: default boxes in size 8732 x 4, 
-            encoder: input ltrb format, output xywh format
-            decoder: input xywh format, output ltrb format 
-
-        decode:
-            input  : bboxes_in (Tensor 8732 x 4), scores_in (Tensor 8732 x nitems)
-            output : bboxes_out (Tensor nboxes x 4), labels_out (Tensor nboxes)
-            criteria : IoU threshold of bboexes
-            max_output : maximum number of output bboxes
+    Transform between (bboxes, lables) <-> SSD output
+
+    dboxes: default boxes in size 8732 x 4,
+        encoder: input ltrb format, output xywh format
+        decoder: input xywh format, output ltrb format
+
+    decode:
+        input  : bboxes_in (Tensor 8732 x 4), scores_in (Tensor 8732 x nitems)
+        output : bboxes_out (Tensor nboxes x 4), labels_out (Tensor nboxes)
+        criteria : IoU threshold of bboexes
+        max_output : maximum number of output bboxes
     """
 
     def __init__(self, dboxes):
         self.dboxes = dboxes(order="ltrb")
         self.dboxes_xywh = dboxes(order="xywh").unsqueeze(dim=0)
         self.nboxes = self.dboxes.size(0)
-        #print("# Bounding boxes: {}".format(self.nboxes))
+        # print("# Bounding boxes: {}".format(self.nboxes))
         self.scale_xy = torch.tensor(dboxes.scale_xy)
         self.scale_wh = torch.tensor(dboxes.scale_wh)
-    
-    
-    def decode_batch(self, bboxes_in, scores_in,  criteria = 0.45, max_output=200):
+
+    def decode_batch(self, bboxes_in, scores_in,
+                     criteria=0.45, max_output=200):
         self.dboxes = self.dboxes.to(bboxes_in)
         self.dboxes_xywh = self.dboxes_xywh.to(bboxes_in)
-        bboxes, probs = scale_back_batch(bboxes_in, scores_in,self.scale_xy,self.scale_wh,self.dboxes_xywh)
-        boxes = []; labels=[]; scores=[]
+        bboxes, probs = scale_back_batch(
+            bboxes_in, scores_in, self.scale_xy, self.scale_wh, self.dboxes_xywh
+        )
+        boxes = []
+        labels = []
+        scores = []
         for bbox, prob in zip(bboxes.split(1, 0), probs.split(1, 0)):
             bbox = bbox.squeeze(0)
             prob = prob.squeeze(0)
-            dbox,dlabel,dscore=self.decode_single(bbox, prob, criteria, max_output)
+            dbox, dlabel, dscore = self.decode_single(
+                bbox, prob, criteria, max_output)
             boxes.append(dbox)
             labels.append(dlabel)
             scores.append(dscore)
-           
-        return [boxes,labels,scores]
+
+        return [boxes, labels, scores]
 
     # perform non-maximum suppression
-    def decode_single(self, bboxes_in, scores_in, criteria, max_output, max_num=200):
+    def decode_single(self, bboxes_in, scores_in,
+                      criteria, max_output, max_num=200):
         # Reference to https://github.com/amdegroot/ssd.pytorch
-       
-        bboxes_out = []        
+
+        bboxes_out = []
         scores_out = []
         labels_out = []
 
         for i, score in enumerate(scores_in.split(1, 1)):
             # skip background
-            if i == 0: continue
-            
+            if i == 0:
+                continue
+
             score = score.squeeze(1)
             mask = score > 0.05
 
             bboxes, score = bboxes_in[mask, :], score[mask]
-            if score.size(0) == 0: continue
+            if score.size(0) == 0:
+                continue
 
             score_sorted, score_idx_sorted = score.sort(dim=0)
-        
+
             # select max_output indices
             score_idx_sorted = score_idx_sorted[-max_num:]
             candidates = []
-        
+
             while score_idx_sorted.numel() > 0:
                 idx = score_idx_sorted[-1].item()
                 bboxes_sorted = bboxes[score_idx_sorted, :]
                 bboxes_idx = bboxes[idx, :].unsqueeze(dim=0)
-                iou_sorted = calc_iou_tensor(bboxes_sorted, bboxes_idx).squeeze()
-                # we only need iou < criteria 
+                iou_sorted = calc_iou_tensor(
+                    bboxes_sorted, bboxes_idx).squeeze()
+                # we only need iou < criteria
                 score_idx_sorted = score_idx_sorted[iou_sorted < criteria]
                 candidates.append(idx)
 
             bboxes_out.append(bboxes[candidates, :])
             scores_out.append(score[candidates])
-            labels_out.extend([i]*len(candidates))
-
-        bboxes_out, labels_out, scores_out = torch.cat(bboxes_out, dim=0), \
-               torch.tensor(labels_out, dtype=torch.long), \
-               torch.cat(scores_out, dim=0)
+            labels_out.extend([i] * len(candidates))
 
+        bboxes_out, labels_out, scores_out = (
+            torch.cat(bboxes_out, dim=0),
+            torch.tensor(labels_out, dtype=torch.long),
+            torch.cat(scores_out, dim=0),
+        )
 
         _, max_ids = scores_out.sort(dim=0)
         max_ids = max_ids[-max_output:]
         return bboxes_out[max_ids, :], labels_out[max_ids], scores_out[max_ids]
 
+
 @torch.jit.script
 def calc_iou_tensor(box1, box2):
-    """ Calculation of IoU based on two boxes tensor,
-        Reference to https://github.com/kuangliu/pytorch-ssd
-        input:
-            box1 (N, 4) 
-            box2 (M, 4)
-        output:
-            IoU (N, M)
+    """Calculation of IoU based on two boxes tensor,
+    Reference to https://github.com/kuangliu/pytorch-ssd
+    input:
+        box1 (N, 4)
+        box2 (M, 4)
+    output:
+        IoU (N, M)
     """
     N = box1.size(0)
     M = box2.size(0)
@@ -110,37 +122,42 @@ def calc_iou_tensor(box1, box2):
     be2 = box2.unsqueeze(0).expand(N, -1, -1)
 
     # Left Top & Right Bottom
-    lt = torch.max(be1[:,:,:2], be2[:,:,:2])
-    rb = torch.min(be1[:,:,2:], be2[:,:,2:])
+    lt = torch.max(be1[:, :, :2], be2[:, :, :2])
+    rb = torch.min(be1[:, :, 2:], be2[:, :, 2:])
     delta = rb - lt
-    delta.clone().masked_fill_(delta < 0,0)
-    intersect = delta[:,:,0]*delta[:,:,1]
-    delta1 = be1[:,:,2:] - be1[:,:,:2]
-    area1 = delta1[:,:,0]*delta1[:,:,1]
-    delta2 = be2[:,:,2:] - be2[:,:,:2]
-    area2 = delta2[:,:,0]*delta2[:,:,1]
-
-    iou = intersect/(area1 + area2 - intersect)
+    delta.clone().masked_fill_(delta < 0, 0)
+    intersect = delta[:, :, 0] * delta[:, :, 1]
+    delta1 = be1[:, :, 2:] - be1[:, :, :2]
+    area1 = delta1[:, :, 0] * delta1[:, :, 1]
+    delta2 = be2[:, :, 2:] - be2[:, :, :2]
+    area2 = delta2[:, :, 0] * delta2[:, :, 1]
+
+    iou = intersect / (area1 + area2 - intersect)
     return iou
 
+
 @torch.jit.script
-def scale_back_batch(bboxes_in, scores_in,scale_xy,scale_wh,dboxes_xywh):
+def scale_back_batch(bboxes_in, scores_in, scale_xy, scale_wh, dboxes_xywh):
+    """
+    Do scale and transform from xywh to ltrb
+    suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox
     """
-        Do scale and transform from xywh to ltrb
-        suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox
-    """     
     bboxes_in = bboxes_in.permute(0, 2, 1)
     scores_in = scores_in.permute(0, 2, 1)
 
-    bboxes_in[:, :, :2] = scale_xy*bboxes_in[:, :, :2]
-    bboxes_in[:, :, 2:] = scale_wh*bboxes_in[:, :, 2:]
-    bboxes_in[:, :, :2] = bboxes_in[:, :, :2]*dboxes_xywh[:, :, 2:] + dboxes_xywh[:, :, :2]
-    bboxes_in[:, :, 2:] = bboxes_in[:, :, 2:].exp()*dboxes_xywh[:, :, 2:]
-    # Transform format to ltrb 
-    l, t, r, b = bboxes_in[:, :, 0] - 0.5*bboxes_in[:, :, 2],\
-                 bboxes_in[:, :, 1] - 0.5*bboxes_in[:, :, 3],\
-                 bboxes_in[:, :, 0] + 0.5*bboxes_in[:, :, 2],\
-                 bboxes_in[:, :, 1] + 0.5*bboxes_in[:, :, 3]
+    bboxes_in[:, :, :2] = scale_xy * bboxes_in[:, :, :2]
+    bboxes_in[:, :, 2:] = scale_wh * bboxes_in[:, :, 2:]
+    bboxes_in[:, :, :2] = (
+        bboxes_in[:, :, :2] * dboxes_xywh[:, :, 2:] + dboxes_xywh[:, :, :2]
+    )
+    bboxes_in[:, :, 2:] = bboxes_in[:, :, 2:].exp() * dboxes_xywh[:, :, 2:]
+    # Transform format to ltrb
+    l, t, r, b = (
+        bboxes_in[:, :, 0] - 0.5 * bboxes_in[:, :, 2],
+        bboxes_in[:, :, 1] - 0.5 * bboxes_in[:, :, 3],
+        bboxes_in[:, :, 0] + 0.5 * bboxes_in[:, :, 2],
+        bboxes_in[:, :, 1] + 0.5 * bboxes_in[:, :, 3],
+    )
     bboxes_in[:, :, 0] = l
     bboxes_in[:, :, 1] = t
     bboxes_in[:, :, 2] = r
@@ -149,92 +166,116 @@ def scale_back_batch(bboxes_in, scores_in,scale_xy,scale_wh,dboxes_xywh):
 
 
 class DefaultBoxes(object):
-    def __init__(self, fig_size, feat_size, steps, scales, aspect_ratios, \
-                       scale_xy=0.1, scale_wh=0.2):
+    def __init__(
+        self,
+        fig_size,
+        feat_size,
+        steps,
+        scales,
+        aspect_ratios,
+        scale_xy=0.1,
+        scale_wh=0.2,
+    ):
 
         self.feat_size = feat_size
-        self.fig_size_w,self.fig_size_h = fig_size
+        self.fig_size_w, self.fig_size_h = fig_size
 
         self.scale_xy_ = scale_xy
         self.scale_wh_ = scale_wh
-        
+
         # According to https://github.com/weiliu89/caffe
         # Calculation method slightly different from paper
         self.steps_w = [st[0] for st in steps]
         self.steps_h = [st[1] for st in steps]
         self.scales = scales
-        fkw = self.fig_size_w//np.array(self.steps_w)
-        fkh = self.fig_size_h//np.array(self.steps_h)
+        fkw = self.fig_size_w // np.array(self.steps_w)
+        fkh = self.fig_size_h // np.array(self.steps_h)
         self.aspect_ratios = aspect_ratios
 
         self.default_boxes = []
         # size of feature and number of feature
         for idx, sfeat in enumerate(self.feat_size):
-            sfeat_w,sfeat_h=sfeat
-            sk1 = scales[idx][0]/self.fig_size_w
-            sk2 = scales[idx+1][1]/self.fig_size_h
-            sk3 = sqrt(sk1*sk2)
+            sfeat_w, sfeat_h = sfeat
+            sk1 = scales[idx][0] / self.fig_size_w
+            sk2 = scales[idx + 1][1] / self.fig_size_h
+            sk3 = sqrt(sk1 * sk2)
             all_sizes = [(sk1, sk1), (sk3, sk3)]
             for alpha in aspect_ratios[idx]:
-                w, h = sk1*sqrt(alpha), sk1/sqrt(alpha)
+                w, h = sk1 * sqrt(alpha), sk1 / sqrt(alpha)
                 all_sizes.append((w, h))
                 all_sizes.append((h, w))
             for w, h in all_sizes:
                 for i, j in itertools.product(range(sfeat_w), range(sfeat_h)):
-                    cx, cy = (j+0.5)/fkh[idx], (i+0.5)/fkw[idx]
-                    self.default_boxes.append((cx, cy, w, h)) 
+                    cx, cy = (j + 0.5) / fkh[idx], (i + 0.5) / fkw[idx]
+                    self.default_boxes.append((cx, cy, w, h))
         self.dboxes = torch.tensor(self.default_boxes)
         self.dboxes.clamp_(min=0, max=1)
         # For IoU calculation
         self.dboxes_ltrb = self.dboxes.clone()
-        self.dboxes_ltrb[:, 0] = self.dboxes[:, 0] - 0.5*self.dboxes[:, 2]
-        self.dboxes_ltrb[:, 1] = self.dboxes[:, 1] - 0.5*self.dboxes[:, 3]
-        self.dboxes_ltrb[:, 2] = self.dboxes[:, 0] + 0.5*self.dboxes[:, 2]
-        self.dboxes_ltrb[:, 3] = self.dboxes[:, 1] + 0.5*self.dboxes[:, 3]
-    
+        self.dboxes_ltrb[:, 0] = self.dboxes[:, 0] - 0.5 * self.dboxes[:, 2]
+        self.dboxes_ltrb[:, 1] = self.dboxes[:, 1] - 0.5 * self.dboxes[:, 3]
+        self.dboxes_ltrb[:, 2] = self.dboxes[:, 0] + 0.5 * self.dboxes[:, 2]
+        self.dboxes_ltrb[:, 3] = self.dboxes[:, 1] + 0.5 * self.dboxes[:, 3]
+
     @property
     def scale_xy(self):
         return self.scale_xy_
-    
-    @property    
+
+    @property
     def scale_wh(self):
         return self.scale_wh_
 
     def __call__(self, order="ltrb"):
-        if order == "ltrb": return self.dboxes_ltrb
-        if order == "xywh": return self.dboxes
+        if order == "ltrb":
+            return self.dboxes_ltrb
+        if order == "xywh":
+            return self.dboxes
+
 
-def dboxes_R34_coco(figsize,strides):
+def dboxes_R34_coco(figsize, strides):
     feat_size = [[50, 50], [25, 25], [13, 13], [7, 7], [3, 3], [3, 3]]
-    steps=[(int(figsize[0]/fs[0]),int(figsize[1]/fs[1])) for fs in feat_size]
-    scales = [(int(s*figsize[0]/300),int(s*figsize[1]/300)) for s in [21, 45, 99, 153, 207, 261, 315]] 
-    aspect_ratios =  [[2], [2, 3], [2, 3], [2, 3], [2], [2]] 
+    steps = [(int(figsize[0] / fs[0]), int(figsize[1] / fs[1]))
+             for fs in feat_size]
+    scales = [
+        (int(s * figsize[0] / 300), int(s * figsize[1] / 300))
+        for s in [21, 45, 99, 153, 207, 261, 315]
+    ]
+    aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
     dboxes = DefaultBoxes(figsize, feat_size, steps, scales, aspect_ratios)
     return dboxes
 
+
 class SSD_R34(nn.Module):
     """
-        Build a SSD module to take 300x300 image input,
-        and output 8732 per class bounding boxes
+    Build a SSD module to take 300x300 image input,
+    and output 8732 per class bounding boxes
 
-        vggt: pretrained vgg16 (partial) model
-        label_num: number of classes (including background 0)
+    vggt: pretrained vgg16 (partial) model
+    label_num: number of classes (including background 0)
     """
-    def __init__(self, label_num=81, backbone='resnet34', model_path="./resnet34-333f7ec4.pth",strides=[3,3 ,2 ,2 ,2 ,2],extract_shapes=False):
+
+    def __init__(
+        self,
+        label_num=81,
+        backbone="resnet34",
+        model_path="./resnet34-333f7ec4.pth",
+        strides=[3, 3, 2, 2, 2, 2],
+        extract_shapes=False,
+    ):
 
         super(SSD_R34, self).__init__()
 
         self.label_num = label_num
         self.strides = strides
-        if backbone == 'resnet34':
+        if backbone == "resnet34":
             self.model = ResNet34()
             out_channels = 256
             self.out_chan = [out_channels, 512, 512, 256, 256, 256]
         else:
-            raise ValueError('Invalid backbone chosen')
+            raise ValueError("Invalid backbone chosen")
 
         self._build_additional_features(self.out_chan)
-        self.extract_shapes=extract_shapes
+        self.extract_shapes = extract_shapes
         # after l2norm, conv7, conv8_2, conv9_2, conv10_2, conv11_2
         # classifer 1, 2, 3, 4, 5 ,6
 
@@ -242,110 +283,159 @@ def __init__(self, label_num=81, backbone='resnet34', model_path="./resnet34-333
         self.loc = []
         self.conf = []
         for nd, oc in zip(self.num_defaults, self.out_chan):
-            self.loc.append(nn.Conv2d(oc, nd*4, kernel_size=3, padding=1,stride=self.strides[0]))
-            self.conf.append(nn.Conv2d(oc, nd*label_num, kernel_size=3, padding=1,stride=self.strides[1]))
+            self.loc.append(
+                nn.Conv2d(
+                    oc,
+                    nd * 4,
+                    kernel_size=3,
+                    padding=1,
+                    stride=self.strides[0])
+            )
+            self.conf.append(
+                nn.Conv2d(
+                    oc, nd * label_num, kernel_size=3, padding=1, stride=self.strides[1]
+                )
+            )
 
         self.loc = nn.ModuleList(self.loc)
         self.conf = nn.ModuleList(self.conf)
         if not extract_shapes:
-            self.size=(1200,1200)
-            dboxes = dboxes_R34_coco(list(self.size),[3,3,2,2,2,2])
+            self.size = (1200, 1200)
+            dboxes = dboxes_R34_coco(list(self.size), [3, 3, 2, 2, 2, 2])
             self.encoder = Encoder(dboxes)
         # intitalize all weights
         self._init_weights()
         self.device = 1
+
     def _build_additional_features(self, input_channels):
         idx = 0
         self.additional_blocks = []
-        
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 256, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(256, input_channels[idx+1], kernel_size=3, padding=1,stride=self.strides[2]),
-            nn.ReLU(inplace=True),
-        ))
+
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 256, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(
+                    256,
+                    input_channels[idx + 1],
+                    kernel_size=3,
+                    padding=1,
+                    stride=self.strides[2],
+                ),
+                nn.ReLU(inplace=True),
+            )
+        )
         idx += 1
 
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 256, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(256, input_channels[idx+1], kernel_size=3, padding=1, stride=self.strides[3]),
-            nn.ReLU(inplace=True),
-        ))
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 256, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(
+                    256,
+                    input_channels[idx + 1],
+                    kernel_size=3,
+                    padding=1,
+                    stride=self.strides[3],
+                ),
+                nn.ReLU(inplace=True),
+            )
+        )
         idx += 1
 
         # conv9_1, conv9_2
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 128, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(128, input_channels[idx+1], kernel_size=3, padding=1, stride=self.strides[4]),
-            nn.ReLU(inplace=True),
-        ))
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 128, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(
+                    128,
+                    input_channels[idx + 1],
+                    kernel_size=3,
+                    padding=1,
+                    stride=self.strides[4],
+                ),
+                nn.ReLU(inplace=True),
+            )
+        )
         idx += 1
 
         # conv10_1, conv10_2
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 128, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(128, input_channels[idx+1], kernel_size=3,stride=self.strides[5]),
-            nn.ReLU(inplace=True),
-        ))
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 128, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(
+                    128, input_channels[idx + 1], kernel_size=3, stride=self.strides[5]
+                ),
+                nn.ReLU(inplace=True),
+            )
+        )
         idx += 1
 
-
-
         # conv11_1, conv11_2
-        self.additional_blocks.append(nn.Sequential(
-            nn.Conv2d(input_channels[idx], 128, kernel_size=1),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(128, input_channels[idx+1], kernel_size=3),
-            nn.ReLU(inplace=True),
-        ))
+        self.additional_blocks.append(
+            nn.Sequential(
+                nn.Conv2d(input_channels[idx], 128, kernel_size=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(128, input_channels[idx + 1], kernel_size=3),
+                nn.ReLU(inplace=True),
+            )
+        )
 
         self.additional_blocks = nn.ModuleList(self.additional_blocks)
 
     def _init_weights(self):
 
-        layers = [
-            *self.additional_blocks,
-            *self.loc, *self.conf]
+        layers = [*self.additional_blocks, *self.loc, *self.conf]
 
         for layer in layers:
             for param in layer.parameters():
-                if param.dim() > 1: nn.init.xavier_uniform_(param)
+                if param.dim() > 1:
+                    nn.init.xavier_uniform_(param)
 
     # Shape the classifier to the view of bboxes
-    def bbox_view(self, src, loc, conf,extract_shapes=False):
+    def bbox_view(self, src, loc, conf, extract_shapes=False):
         ret = []
         features_shapes = []
         for s, l, c in zip(src, loc, conf):
-            ret.append((l(s).view(s.size(0), 4, -1), c(s).view(s.size(0), self.label_num, -1)))
-            # extract shapes for prior box initliziation 
+            ret.append(
+                (l(s).view(s.size(0), 4, -1),
+                 c(s).view(s.size(0), self.label_num, -1))
+            )
+            # extract shapes for prior box initliziation
             if extract_shapes:
-                ls=l(s)
-                features_shapes.append([ls.shape[2],ls.shape[3]])
+                ls = l(s)
+                features_shapes.append([ls.shape[2], ls.shape[3]])
         locs, confs = list(zip(*ret))
-        locs, confs = torch.cat(locs, 2).contiguous(), torch.cat(confs, 2).contiguous()
-        return locs, confs,features_shapes
+        locs, confs = torch.cat(
+            locs, 2).contiguous(), torch.cat(
+            confs, 2).contiguous()
+        return locs, confs, features_shapes
 
     def forward(self, data):
         layers = self.model(data)
 
         # last result from network goes into additional blocks
         x = layers[-1]
-        
+
         additional_results = []
         for i, l in enumerate(self.additional_blocks):
-            
+
             x = l(x)
             additional_results.append(x)
 
         src = [*layers, *additional_results]
-        # Feature maps sizes depend on the image size. For 300x300 with strides=[1,1,2,2,2,1] it is 38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4, 1x1x4 
-        locs, confs,features_shapes = self.bbox_view(src, self.loc, self.conf,extract_shapes=self.extract_shapes)
+        # Feature maps sizes depend on the image size. For 300x300 with
+        # strides=[1,1,2,2,2,1] it is 38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4,
+        # 1x1x4
+        locs, confs, features_shapes = self.bbox_view(
+            src, self.loc, self.conf, extract_shapes=self.extract_shapes
+        )
         if self.extract_shapes:
-            return locs, confs,features_shapes
-        else:    
-            # For SSD 300 with strides=[1,1,2,2,2,1] , shall return nbatch x 8732 x {nlabels, nlocs} results 
-            results=self.encoder.decode_batch(locs, confs, 0.50, 200) #[0]
-            return results #locs, confs,features_shapes
+            return locs, confs, features_shapes
+        else:
+            # For SSD 300 with strides=[1,1,2,2,2,1] , shall return nbatch x
+            # 8732 x {nlabels, nlocs} results
+            results = self.encoder.decode_batch(locs, confs, 0.50, 200)  # [0]
+            return results  # locs, confs,features_shapes
diff --git a/vision/classification_and_detection/python/models/utils.py b/vision/classification_and_detection/python/models/utils.py
index 332d19c17a..232838ef0c 100644
--- a/vision/classification_and_detection/python/models/utils.py
+++ b/vision/classification_and_detection/python/models/utils.py
@@ -15,7 +15,7 @@ def __init__(self, out):
         self.register_buffer("scale", torch.ones(out))
         self.register_buffer("bias", torch.zeros(out))
 
-    #@torch.jit.script_method
+    # @torch.jit.script_method
     def forward(self, x):
         scale = self.scale.view(1, -1, 1, 1)
         bias = self.bias.view(1, -1, 1, 1)
@@ -31,7 +31,7 @@ def __init__(self, out):
         super(BiasAdd, self).__init__()
         self.register_buffer("bias", torch.zeros(out))
 
-    #@torch.jit.script_method
+    # @torch.jit.script_method
     def forward(self, x):
         bias = self.bias.view(1, -1, 1, 1)
         return x + bias
@@ -52,14 +52,15 @@ def _compute_padding(self, input, dim):
         effective_filter_size = (filter_size - 1) * self.dilation[dim] + 1
         out_size = (input_size + self.stride[dim] - 1) // self.stride[dim]
         total_padding = max(
-            0, (out_size - 1) * self.stride[dim] + effective_filter_size - input_size
+            0, (out_size - 1) * self.stride[dim] +
+            effective_filter_size - input_size
         )
         additional_padding = int(total_padding % 2 != 0)
 
         return additional_padding, total_padding
 
     def forward(self, input):
-        #import pdb; pdb.set_trace()
+        # import pdb; pdb.set_trace()
         if self.padding == "valid":
             return F.conv2d(
                 input,
@@ -151,8 +152,8 @@ def decode_boxes(rel_codes, boxes, weights):
     # type: (torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor
 
     # perform some unpacking to make it JIT-fusion friendly
-    
-    #rel_codes=rel_codes[0][None]
+
+    # rel_codes=rel_codes[0][None]
     wx = weights[1]
     wy = weights[0]
     ww = weights[3]
@@ -163,10 +164,10 @@ def decode_boxes(rel_codes, boxes, weights):
     boxes_x2 = boxes[:, 3].unsqueeze(1).unsqueeze(0)
     boxes_y2 = boxes[:, 2].unsqueeze(1).unsqueeze(0)
 
-    dx = rel_codes[:,:, 1].unsqueeze(2)
-    dy = rel_codes[:,:, 0].unsqueeze(2)
-    dw = rel_codes[:,:, 3].unsqueeze(2)
-    dh = rel_codes[:,:, 2].unsqueeze(2)
+    dx = rel_codes[:, :, 1].unsqueeze(2)
+    dy = rel_codes[:, :, 0].unsqueeze(2)
+    dw = rel_codes[:, :, 3].unsqueeze(2)
+    dh = rel_codes[:, :, 2].unsqueeze(2)
 
     # implementation starts here
     widths = boxes_x2 - boxes_x1
@@ -180,7 +181,7 @@ def decode_boxes(rel_codes, boxes, weights):
     dh = dh / wh
 
     pred_ctr_x = dx * widths + ctr_x
-    #import pdb; pdb.set_trace()
+    # import pdb; pdb.set_trace()
     pred_ctr_y = dy * heights + ctr_y
     pred_w = torch.exp(dw) * widths
     pred_h = torch.exp(dh) * heights
@@ -194,5 +195,5 @@ def decode_boxes(rel_codes, boxes, weights):
         ],
         dim=2,
     )
-    #import pdb; pdb.set_trace()
+    # import pdb; pdb.set_trace()
     return pred_boxes
diff --git a/vision/classification_and_detection/python/ncnn_models/__init__.py b/vision/classification_and_detection/python/ncnn_models/__init__.py
index 5059696350..99211f4bf3 100644
--- a/vision/classification_and_detection/python/ncnn_models/__init__.py
+++ b/vision/classification_and_detection/python/ncnn_models/__init__.py
@@ -1 +1 @@
-from .resnet50 import Resnet50
\ No newline at end of file
+from .resnet50 import Resnet50
diff --git a/vision/classification_and_detection/python/ncnn_models/resnet50.py b/vision/classification_and_detection/python/ncnn_models/resnet50.py
index a62e4a8abd..d92a074748 100644
--- a/vision/classification_and_detection/python/ncnn_models/resnet50.py
+++ b/vision/classification_and_detection/python/ncnn_models/resnet50.py
@@ -3,7 +3,9 @@
 
 
 class Resnet50:
-    def __init__(self, model_param, model_bin, target_size=224, num_threads=1, use_gpu=False):
+    def __init__(
+        self, model_param, model_bin, target_size=224, num_threads=1, use_gpu=False
+    ):
         self.target_size = target_size
         self.num_threads = num_threads
 
@@ -14,11 +16,11 @@ def __init__(self, model_param, model_bin, target_size=224, num_threads=1, use_g
 
     def __del__(self):
         self.net = None
-    
+
     @property
     def input_name(self):
         return "in0"
-    
+
     @property
     def output_name(self):
         return "out0"
diff --git a/vision/classification_and_detection/python/openimages.py b/vision/classification_and_detection/python/openimages.py
index 449f408242..e593f59f4b 100644
--- a/vision/classification_and_detection/python/openimages.py
+++ b/vision/classification_and_detection/python/openimages.py
@@ -14,8 +14,21 @@
 
 
 class OpenImages(dataset.Dataset):
-    def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
-                 image_format="NHWC", pre_process=None, count=None, cache_dir=None, preprocessed_dir=None, use_label_map=False, threads=os.cpu_count()):
+    def __init__(
+        self,
+        data_path,
+        image_list,
+        name,
+        use_cache=0,
+        image_size=None,
+        image_format="NHWC",
+        pre_process=None,
+        count=None,
+        cache_dir=None,
+        preprocessed_dir=None,
+        use_label_map=False,
+        threads=os.cpu_count(),
+    ):
         super().__init__()
         self.image_size = image_size
         self.image_list = []
@@ -26,7 +39,7 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         self.use_cache = use_cache
         self.data_path = data_path
         self.pre_process = pre_process
-        self.use_label_map=use_label_map
+        self.use_label_map = use_label_map
 
         if not cache_dir:
             cache_dir = os.getcwd()
@@ -34,16 +47,19 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
             if preprocessed_dir:
                 self.cache_dir = preprocessed_dir
             else:
-                self.cache_dir = os.path.join(cache_dir, "preprocessed", name, image_format)
+                self.cache_dir = os.path.join(
+                    cache_dir, "preprocessed", name, image_format
+                )
         else:
             self.cache_dir = cache_dir
         # input images are in HWC
         self.need_transpose = True if image_format == "NCHW" else False
-        not_found = 0 
+        not_found = 0
         empty_80catageories = 0
         if image_list is None:
             # by default look for val_map.txt
-            image_list = os.path.join(data_path, "annotations/openimages-mlperf.json")
+            image_list = os.path.join(
+                data_path, "annotations/openimages-mlperf.json")
         self.annotation_file = image_list
         if self.use_label_map:
             # for pytorch
@@ -59,28 +75,37 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         with open(image_list, "r") as f:
             openimages = json.load(f)
         for i in openimages["images"]:
-            images[i["id"]] = {"file_name": i["file_name"],
-                               "height": i["height"],
-                               "width": i["width"],
-                               "bbox": [],
-                               "category": []}
+            images[i["id"]] = {
+                "file_name": i["file_name"],
+                "height": i["height"],
+                "width": i["width"],
+                "bbox": [],
+                "category": [],
+            }
         for a in openimages["annotations"]:
             i = images.get(a["image_id"])
             if i is None:
                 continue
-            catagory_ids = label_map[a.get("category_id")] if self.use_label_map else a.get("category_id")
+            catagory_ids = (
+                label_map[a.get("category_id")]
+                if self.use_label_map
+                else a.get("category_id")
+            )
             i["category"].append(catagory_ids)
             i["bbox"].append(a.get("bbox"))
 
         for image_id, img in images.items():
             image_name = img["file_name"]
-            if len(img["category"])==0 and self.use_label_map: 
-                #if an image doesn't have any of the 81 categories in it    
-                empty_80catageories += 1 #should be 48 images - thus the validation sert has 4952 images
-                continue 
+            if len(img["category"]) == 0 and self.use_label_map:
+                # if an image doesn't have any of the 81 categories in it
+                empty_80catageories += (
+                    1  # should be 48 images - thus the validation sert has 4952 images
+                )
+                continue
 
             if not self.pre_process:
-                if not os.path.exists(os.path.join(data_path, image_name) + ".npy"):
+                if not os.path.exists(os.path.join(
+                        data_path, image_name) + ".npy"):
                     # if the image does not exists ignore it
                     not_found += 1
                     continue
@@ -90,12 +115,19 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
                     # if the image does not exists ignore it
                     not_found += 1
                     continue
-                os.makedirs(os.path.dirname(os.path.join(self.cache_dir, image_name)), exist_ok=True)
+                os.makedirs(
+                    os.path.dirname(os.path.join(self.cache_dir, image_name)),
+                    exist_ok=True,
+                )
                 dst = os.path.join(self.cache_dir, image_name)
                 if not os.path.exists(dst + ".npy"):
                     # cache a preprocessed version of the image
                     img_org = cv2.imread(src)
-                    processed = self.pre_process(img_org, need_transpose=self.need_transpose, dims=self.image_size)
+                    processed = self.pre_process(
+                        img_org,
+                        need_transpose=self.need_transpose,
+                        dims=self.image_size,
+                    )
                     np.save(dst, processed)
 
             self.image_ids.append(image_id)
@@ -114,10 +146,16 @@ def __init__(self, data_path, image_list, name, use_cache=0, image_size=None,
         if not_found > 0:
             log.info("reduced image list, %d images not found", not_found)
         if empty_80catageories > 0:
-            log.info("reduced image list, %d images without any of the 80 categories", empty_80catageories)
+            log.info(
+                "reduced image list, %d images without any of the 80 categories",
+                empty_80catageories,
+            )
 
-        log.info("loaded {} images, cache={}, already_preprocessed={}, took={:.1f}sec".format(
-            len(self.image_list), use_cache, pre_process is None, time_taken))
+        log.info(
+            "loaded {} images, cache={}, already_preprocessed={}, took={:.1f}sec".format(
+                len(self.image_list), use_cache, pre_process is None, time_taken
+            )
+        )
 
         self.label_list = np.array(self.label_list, dtype=list)
 
@@ -137,6 +175,7 @@ class PostProcessOpenImages:
     Post processing for open images dataset. Annotations should
     be exported into coco format.
     """
+
     def __init__(self):
         self.results = []
         self.good = 0
@@ -147,14 +186,22 @@ def __init__(self):
     def add_results(self, results):
         self.results.extend(results)
 
-    def __call__(self, results, ids, expected=None, result_dict=None, ):
+    def __call__(
+        self,
+        results,
+        ids,
+        expected=None,
+        result_dict=None,
+    ):
         # results come as:
-        #   tensorflow, ssd-mobilenet: num_detections,detection_boxes,detection_scores,detection_classes
+        # tensorflow, ssd-mobilenet:
+        # num_detections,detection_boxes,detection_scores,detection_classes
         processed_results = []
         # batch size
         bs = len(results[0])
         for idx in range(0, bs):
-            # keep the content_id from loadgen to handle content_id's without results
+            # keep the content_id from loadgen to handle content_id's without
+            # results
             self.content_ids.append(ids[idx])
             processed_results.append([])
             detection_num = int(results[0][idx])
@@ -166,10 +213,17 @@ def __call__(self, results, ids, expected=None, result_dict=None, ):
                 if detection_class in expected_classes:
                     self.good += 1
                 box = detection_boxes[detection]
-                processed_results[idx].append([float(ids[idx]),
-                                              box[0], box[1], box[2], box[3],
-                                              results[2][idx][detection],
-                                              float(detection_class)])
+                processed_results[idx].append(
+                    [
+                        float(ids[idx]),
+                        box[0],
+                        box[1],
+                        box[2],
+                        box[3],
+                        results[2][idx][detection],
+                        float(detection_class),
+                    ]
+                )
                 self.total += 1
         return processed_results
 
@@ -189,7 +243,7 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                 annotations = json.load(fin)
             for cnt, cat in enumerate(annotations["categories"]):
                 label_map[cat["id"]] = cnt + 1
-            inv_map = {v:k for k,v in label_map.items()}
+            inv_map = {v: k for k, v in label_map.items()}
 
         detections = []
         image_indices = []
@@ -200,8 +254,13 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                 # this is the index of the coco image
                 image_idx = int(detection[0])
                 if image_idx != self.content_ids[batch]:
-                    # working with the coco index/id is error prone - extra check to make sure it is consistent
-                    log.error("image_idx missmatch, lg={} / result={}".format(image_idx, self.content_ids[batch]))
+                    # working with the coco index/id is error prone - extra
+                    # check to make sure it is consistent
+                    log.error(
+                        "image_idx missmatch, lg={} / result={}".format(
+                            image_idx, self.content_ids[batch]
+                        )
+                    )
                 # map the index to the coco image id
                 detection[0] = ds.image_ids[image_idx]
                 height, width = ds.image_sizes[image_idx]
@@ -219,16 +278,19 @@ def finalize(self, result_dict, ds=None, output_dir=None):
                     cat_id = inv_map.get(int(detection[6]), -1)
                     if cat_id == -1:
                         # FIXME:
-                        log.info("finalize can't map category {}".format(int(detection[6])))
-                    detection[6] =  cat_id
+                        log.info(
+                            "finalize can't map category {}".format(
+                                int(detection[6]))
+                        )
+                    detection[6] = cat_id
                 detections.append(np.array(detection))
 
         # map indices to coco image id's
-        image_ids = [ds.image_ids[i]  for i in image_indices]
+        image_ids = [ds.image_ids[i] for i in image_indices]
         self.results = []
         cocoGt = pycoco.COCO(ds.annotation_file)
         cocoDt = cocoGt.loadRes(np.array(detections))
-        cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+        cocoEval = COCOeval(cocoGt, cocoDt, iouType="bbox")
         cocoEval.params.imgIds = image_ids
         cocoEval.evaluate()
         cocoEval.accumulate()
@@ -240,7 +302,9 @@ class PostProcessOpenImagesRetinanet(PostProcessOpenImages):
     """
     Post processing required by retinanet / pytorch & onnx
     """
-    def __init__(self, use_inv_map, score_threshold, height, width, dict_format=True):
+
+    def __init__(self, use_inv_map, score_threshold,
+                 height, width, dict_format=True):
         """
         Args:
             height (int): Height of the input image
@@ -259,9 +323,9 @@ def __call__(self, results, ids, expected=None, result_dict=None):
         if self.dict_format:
             # If the output of the model is in dictionary format. This happens
             # for the model retinanet-pytorch
-            bboxes_ = [e['boxes'].cpu() for e in results]
-            labels_ = [e['labels'].cpu() for e in results]
-            scores_ = [e['scores'].cpu() for e in results]
+            bboxes_ = [e["boxes"].cpu() for e in results]
+            labels_ = [e["labels"].cpu() for e in results]
+            scores_ = [e["scores"].cpu() for e in results]
             results = [bboxes_, labels_, scores_]
         else:
             bboxes_ = [results[0]]
@@ -288,8 +352,8 @@ def __call__(self, results, ids, expected=None, result_dict=None):
                     self.good += 1
                 box = detection_boxes[detection]
                 # box comes from model as: xmin, ymin, xmax, ymax
-                # box comes with dimentions in the range of [0, height] 
-                # and [0, width] respectively. It is necesary to scale 
+                # box comes with dimentions in the range of [0, height]
+                # and [0, width] respectively. It is necesary to scale
                 # them in the range [0, 1]
                 processed_results[idx].append(
                     [
diff --git a/vision/classification_and_detection/python/pycoco.py b/vision/classification_and_detection/python/pycoco.py
index 9318635696..f9d5f2e873 100644
--- a/vision/classification_and_detection/python/pycoco.py
+++ b/vision/classification_and_detection/python/pycoco.py
@@ -1,5 +1,5 @@
-__author__ = 'tylin'
-__version__ = '2.0'
+__author__ = "tylin"
+__version__ = "2.0"
 # Interface for accessing the Microsoft COCO dataset.
 
 # Microsoft COCO is a large image dataset designed for object detection,
@@ -46,9 +46,10 @@
 
 import json
 import time
-#import matplotlib.pyplot as plt
-#from matplotlib.collections import PatchCollection
-#from matplotlib.patches import Polygon
+
+# import matplotlib.pyplot as plt
+# from matplotlib.collections import PatchCollection
+# from matplotlib.patches import Polygon
 import numpy as np
 import copy
 import itertools
@@ -56,6 +57,7 @@
 import os
 from collections import defaultdict
 import sys
+
 PYTHON_VERSION = sys.version_info[0]
 if PYTHON_VERSION == 2:
     from urllib import urlretrieve
@@ -64,7 +66,7 @@
 
 
 def _isArrayLike(obj):
-    return hasattr(obj, '__iter__') and hasattr(obj, '__len__')
+    return hasattr(obj, "__iter__") and hasattr(obj, "__len__")
 
 
 class COCO:
@@ -76,40 +78,42 @@ def __init__(self, annotation_file=None):
         :return:
         """
         # load dataset
-        self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()
+        self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict()
         self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)
-        if not annotation_file == None:
-            print('loading annotations into memory...')
+        if not annotation_file is None:
+            print("loading annotations into memory...")
             tic = time.time()
-            dataset = json.load(open(annotation_file, 'r'))
-            assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))
-            print('Done (t={:0.2f}s)'.format(time.time()- tic))
+            dataset = json.load(open(annotation_file, "r"))
+            assert (
+                isinstance(dataset, dict)
+            ), "annotation file format {} not supported".format(type(dataset))
+            print("Done (t={:0.2f}s)".format(time.time() - tic))
             self.dataset = dataset
             self.createIndex()
 
     def createIndex(self):
         # create index
-        print('creating index...')
+        print("creating index...")
         anns, cats, imgs = {}, {}, {}
-        imgToAnns,catToImgs = defaultdict(list),defaultdict(list)
-        if 'annotations' in self.dataset:
-            for ann in self.dataset['annotations']:
-                imgToAnns[ann['image_id']].append(ann)
-                anns[ann['id']] = ann
+        imgToAnns, catToImgs = defaultdict(list), defaultdict(list)
+        if "annotations" in self.dataset:
+            for ann in self.dataset["annotations"]:
+                imgToAnns[ann["image_id"]].append(ann)
+                anns[ann["id"]] = ann
 
-        if 'images' in self.dataset:
-            for img in self.dataset['images']:
-                imgs[img['id']] = img
+        if "images" in self.dataset:
+            for img in self.dataset["images"]:
+                imgs[img["id"]] = img
 
-        if 'categories' in self.dataset:
-            for cat in self.dataset['categories']:
-                cats[cat['id']] = cat
+        if "categories" in self.dataset:
+            for cat in self.dataset["categories"]:
+                cats[cat["id"]] = cat
 
-        if 'annotations' in self.dataset and 'categories' in self.dataset:
-            for ann in self.dataset['annotations']:
-                catToImgs[ann['category_id']].append(ann['image_id'])
+        if "annotations" in self.dataset and "categories" in self.dataset:
+            for ann in self.dataset["annotations"]:
+                catToImgs[ann["category_id"]].append(ann["image_id"])
 
-        print('index created!')
+        print("index created!")
 
         # create class members
         self.anns = anns
@@ -123,8 +127,8 @@ def info(self):
         Print information about the annotation file.
         :return:
         """
-        for key, value in self.dataset['info'].items():
-            print('{}: {}'.format(key, value))
+        for key, value in self.dataset["info"].items():
+            print("{}: {}".format(key, value))
 
     def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
         """
@@ -139,19 +143,33 @@ def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
         catIds = catIds if _isArrayLike(catIds) else [catIds]
 
         if len(imgIds) == len(catIds) == len(areaRng) == 0:
-            anns = self.dataset['annotations']
+            anns = self.dataset["annotations"]
         else:
             if not len(imgIds) == 0:
-                lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]
+                lists = [
+                    self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns
+                ]
                 anns = list(itertools.chain.from_iterable(lists))
             else:
-                anns = self.dataset['annotations']
-            anns = anns if len(catIds)  == 0 else [ann for ann in anns if ann['category_id'] in catIds]
-            anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
-        if not iscrowd == None:
-            ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
+                anns = self.dataset["annotations"]
+            anns = (
+                anns
+                if len(catIds) == 0
+                else [ann for ann in anns if ann["category_id"] in catIds]
+            )
+            anns = (
+                anns
+                if len(areaRng) == 0
+                else [
+                    ann
+                    for ann in anns
+                    if ann["area"] > areaRng[0] and ann["area"] < areaRng[1]
+                ]
+            )
+        if not iscrowd is None:
+            ids = [ann["id"] for ann in anns if ann["iscrowd"] == iscrowd]
         else:
-            ids = [ann['id'] for ann in anns]
+            ids = [ann["id"] for ann in anns]
         return ids
 
     def getCatIds(self, catNms=[], supNms=[], catIds=[]):
@@ -167,22 +185,34 @@ def getCatIds(self, catNms=[], supNms=[], catIds=[]):
         catIds = catIds if _isArrayLike(catIds) else [catIds]
 
         if len(catNms) == len(supNms) == len(catIds) == 0:
-            cats = self.dataset['categories']
+            cats = self.dataset["categories"]
         else:
-            cats = self.dataset['categories']
-            cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name']          in catNms]
-            cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
-            cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id']            in catIds]
-        ids = [cat['id'] for cat in cats]
+            cats = self.dataset["categories"]
+            cats = (
+                cats
+                if len(catNms) == 0
+                else [cat for cat in cats if cat["name"] in catNms]
+            )
+            cats = (
+                cats
+                if len(supNms) == 0
+                else [cat for cat in cats if cat["supercategory"] in supNms]
+            )
+            cats = (
+                cats
+                if len(catIds) == 0
+                else [cat for cat in cats if cat["id"] in catIds]
+            )
+        ids = [cat["id"] for cat in cats]
         return ids
 
     def getImgIds(self, imgIds=[], catIds=[]):
-        '''
+        """
         Get img ids that satisfy given filter conditions.
         :param imgIds (int array) : get imgs for given ids
         :param catIds (int array) : get imgs with all given cats
         :return: ids (int array)  : integer array of img ids
-        '''
+        """
         imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
         catIds = catIds if _isArrayLike(catIds) else [catIds]
 
@@ -205,7 +235,7 @@ def loadAnns(self, ids=[]):
         """
         if _isArrayLike(ids):
             return [self.anns[id] for id in ids]
-        elif type(ids) == int:
+        elif isinstance(ids, int):
             return [self.anns[ids]]
 
     def loadCats(self, ids=[]):
@@ -216,7 +246,7 @@ def loadCats(self, ids=[]):
         """
         if _isArrayLike(ids):
             return [self.cats[id] for id in ids]
-        elif type(ids) == int:
+        elif isinstance(ids, int):
             return [self.cats[ids]]
 
     def loadImgs(self, ids=[]):
@@ -227,7 +257,7 @@ def loadImgs(self, ids=[]):
         """
         if _isArrayLike(ids):
             return [self.imgs[id] for id in ids]
-        elif type(ids) == int:
+        elif isinstance(ids, int):
             return [self.imgs[ids]]
 
     def showAnns(self, anns):
@@ -238,61 +268,88 @@ def showAnns(self, anns):
         """
         if len(anns) == 0:
             return 0
-        if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
-            datasetType = 'instances'
-        elif 'caption' in anns[0]:
-            datasetType = 'captions'
+        if "segmentation" in anns[0] or "keypoints" in anns[0]:
+            datasetType = "instances"
+        elif "caption" in anns[0]:
+            datasetType = "captions"
         else:
-            raise Exception('datasetType not supported')
-        if datasetType == 'instances':
+            raise Exception("datasetType not supported")
+        if datasetType == "instances":
             ax = plt.gca()
             ax.set_autoscale_on(False)
             polygons = []
             color = []
             for ann in anns:
-                c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
-                if 'segmentation' in ann:
-                    if type(ann['segmentation']) == list:
+                c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+                if "segmentation" in ann:
+                    if isinstance(ann["segmentation"], list):
                         # polygon
-                        for seg in ann['segmentation']:
-                            poly = np.array(seg).reshape((int(len(seg)/2), 2))
+                        for seg in ann["segmentation"]:
+                            poly = np.array(seg).reshape(
+                                (int(len(seg) / 2), 2))
                             polygons.append(Polygon(poly))
                             color.append(c)
                     else:
                         # mask
-                        t = self.imgs[ann['image_id']]
-                        if type(ann['segmentation']['counts']) == list:
-                            rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
+                        t = self.imgs[ann["image_id"]]
+                        if isinstance(ann["segmentation"]["counts"], list):
+                            rle = maskUtils.frPyObjects(
+                                [ann["segmentation"]], t["height"], t["width"]
+                            )
                         else:
-                            rle = [ann['segmentation']]
+                            rle = [ann["segmentation"]]
                         m = maskUtils.decode(rle)
-                        img = np.ones( (m.shape[0], m.shape[1], 3) )
-                        if ann['iscrowd'] == 1:
-                            color_mask = np.array([2.0,166.0,101.0])/255
-                        if ann['iscrowd'] == 0:
+                        img = np.ones((m.shape[0], m.shape[1], 3))
+                        if ann["iscrowd"] == 1:
+                            color_mask = np.array([2.0, 166.0, 101.0]) / 255
+                        if ann["iscrowd"] == 0:
                             color_mask = np.random.random((1, 3)).tolist()[0]
                         for i in range(3):
-                            img[:,:,i] = color_mask[i]
-                        ax.imshow(np.dstack( (img, m*0.5) ))
-                if 'keypoints' in ann and type(ann['keypoints']) == list:
+                            img[:, :, i] = color_mask[i]
+                        ax.imshow(np.dstack((img, m * 0.5)))
+                if "keypoints" in ann and isinstance(ann["keypoints"], list):
                     # turn skeleton into zero-based index
-                    sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
-                    kp = np.array(ann['keypoints'])
+                    sks = np.array(
+                        self.loadCats(
+                            ann["category_id"])[0]["skeleton"]) - 1
+                    kp = np.array(ann["keypoints"])
                     x = kp[0::3]
                     y = kp[1::3]
                     v = kp[2::3]
                     for sk in sks:
-                        if np.all(v[sk]>0):
-                            plt.plot(x[sk],y[sk], linewidth=3, color=c)
-                    plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
-                    plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)
-            p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
+                        if np.all(v[sk] > 0):
+                            plt.plot(x[sk], y[sk], linewidth=3, color=c)
+                    plt.plot(
+                        x[v > 0],
+                        y[v > 0],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor="k",
+                        markeredgewidth=2,
+                    )
+                    plt.plot(
+                        x[v > 1],
+                        y[v > 1],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor=c,
+                        markeredgewidth=2,
+                    )
+            p = PatchCollection(
+                polygons,
+                facecolor=color,
+                linewidths=0,
+                alpha=0.4)
             ax.add_collection(p)
-            p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)
+            p = PatchCollection(
+                polygons, facecolor="none", edgecolors=color, linewidths=2
+            )
             ax.add_collection(p)
-        elif datasetType == 'captions':
+        elif datasetType == "captions":
             for ann in anns:
-                print(ann['caption'])
+                print(ann["caption"])
 
     def loadRes(self, resFile):
         """
@@ -301,69 +358,78 @@ def loadRes(self, resFile):
         :return: res (obj)         : result api object
         """
         res = COCO()
-        res.dataset['images'] = [img for img in self.dataset['images']]
+        res.dataset["images"] = [img for img in self.dataset["images"]]
 
-        print('Loading and preparing results...')
+        print("Loading and preparing results...")
         tic = time.time()
-        if type(resFile) == str: #or type(resFile) == unicode:
+        if isinstance(resFile, str):  # or type(resFile) == unicode:
             anns = json.load(open(resFile))
-        elif type(resFile) == np.ndarray:
+        elif isinstance(resFile, np.ndarray):
             anns = self.loadNumpyAnnotations(resFile)
         else:
             anns = resFile
-        assert type(anns) == list, 'results in not an array of objects'
-        annsImgIds = [ann['image_id'] for ann in anns]
-        assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
-               'Results do not correspond to current coco set'
-        if 'caption' in anns[0]:
-            imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
-            res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
+        assert isinstance(anns, list), "results in not an array of objects"
+        annsImgIds = [ann["image_id"] for ann in anns]
+        assert set(annsImgIds) == (
+            set(annsImgIds) & set(self.getImgIds())
+        ), "Results do not correspond to current coco set"
+        if "caption" in anns[0]:
+            imgIds = set([img["id"] for img in res.dataset["images"]]) & set(
+                [ann["image_id"] for ann in anns]
+            )
+            res.dataset["images"] = [
+                img for img in res.dataset["images"] if img["id"] in imgIds
+            ]
             for id, ann in enumerate(anns):
-                ann['id'] = id+1
-        elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+                ann["id"] = id + 1
+        elif "bbox" in anns[0] and not anns[0]["bbox"] == []:
+            res.dataset["categories"] = copy.deepcopy(
+                self.dataset["categories"])
             for id, ann in enumerate(anns):
-                bb = ann['bbox']
-                x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
-                if not 'segmentation' in ann:
-                    ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
-                ann['area'] = bb[2]*bb[3]
-                ann['id'] = id+1
-                ann['iscrowd'] = 0
-        elif 'segmentation' in anns[0]:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+                bb = ann["bbox"]
+                x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
+                if not "segmentation" in ann:
+                    ann["segmentation"] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
+                ann["area"] = bb[2] * bb[3]
+                ann["id"] = id + 1
+                ann["iscrowd"] = 0
+        elif "segmentation" in anns[0]:
+            res.dataset["categories"] = copy.deepcopy(
+                self.dataset["categories"])
             for id, ann in enumerate(anns):
-                # now only support compressed RLE format as segmentation results
-                ann['area'] = maskUtils.area(ann['segmentation'])
-                if not 'bbox' in ann:
-                    ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
-                ann['id'] = id+1
-                ann['iscrowd'] = 0
-        elif 'keypoints' in anns[0]:
-            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
+                # now only support compressed RLE format as segmentation
+                # results
+                ann["area"] = maskUtils.area(ann["segmentation"])
+                if not "bbox" in ann:
+                    ann["bbox"] = maskUtils.toBbox(ann["segmentation"])
+                ann["id"] = id + 1
+                ann["iscrowd"] = 0
+        elif "keypoints" in anns[0]:
+            res.dataset["categories"] = copy.deepcopy(
+                self.dataset["categories"])
             for id, ann in enumerate(anns):
-                s = ann['keypoints']
+                s = ann["keypoints"]
                 x = s[0::3]
                 y = s[1::3]
-                x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y)
-                ann['area'] = (x1-x0)*(y1-y0)
-                ann['id'] = id + 1
-                ann['bbox'] = [x0,y0,x1-x0,y1-y0]
-        print('DONE (t={:0.2f}s)'.format(time.time()- tic))
+                x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y)
+                ann["area"] = (x1 - x0) * (y1 - y0)
+                ann["id"] = id + 1
+                ann["bbox"] = [x0, y0, x1 - x0, y1 - y0]
+        print("DONE (t={:0.2f}s)".format(time.time() - tic))
 
-        res.dataset['annotations'] = anns
+        res.dataset["annotations"] = anns
         res.createIndex()
         return res
 
-    def download(self, tarDir = None, imgIds = [] ):
-        '''
+    def download(self, tarDir=None, imgIds=[]):
+        """
         Download COCO images from mscoco.org server.
         :param tarDir (str): COCO results directory name
                imgIds (list): images to be downloaded
         :return:
-        '''
+        """
         if tarDir is None:
-            print('Please specify target directory')
+            print("Please specify target directory")
             return -1
         if len(imgIds) == 0:
             imgs = self.imgs.values()
@@ -374,10 +440,13 @@ def download(self, tarDir = None, imgIds = [] ):
             os.makedirs(tarDir)
         for i, img in enumerate(imgs):
             tic = time.time()
-            fname = os.path.join(tarDir, img['file_name'])
+            fname = os.path.join(tarDir, img["file_name"])
             if not os.path.exists(fname):
-                urlretrieve(img['coco_url'], fname)
-            print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic))
+                urlretrieve(img["coco_url"], fname)
+            print(
+                "downloaded {}/{} images (t={:0.1f}s)".format(i,
+                                                              N, time.time() - tic)
+            )
 
     def loadNumpyAnnotations(self, data):
         """
@@ -385,21 +454,23 @@ def loadNumpyAnnotations(self, data):
         :param  data (numpy.ndarray)
         :return: annotations (python nested list)
         """
-        print('Converting ndarray to lists...')
-        assert(type(data) == np.ndarray)
+        print("Converting ndarray to lists...")
+        assert isinstance(data, np.ndarray)
         print(data.shape)
-        assert(data.shape[1] == 7)
+        assert data.shape[1] == 7
         N = data.shape[0]
         ann = []
         for i in range(N):
             if i % 1000000 == 0:
-                print('{}/{}'.format(i,N))
-            ann += [{
-                'image_id'  : int(data[i, 0]),
-                'bbox'  : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ],
-                'score' : data[i, 5],
-                'category_id': int(data[i, 6]),
-                }]
+                print("{}/{}".format(i, N))
+            ann += [
+                {
+                    "image_id": int(data[i, 0]),
+                    "bbox": [data[i, 1], data[i, 2], data[i, 3], data[i, 4]],
+                    "score": data[i, 5],
+                    "category_id": int(data[i, 6]),
+                }
+            ]
         return ann
 
     def annToRLE(self, ann):
@@ -407,20 +478,20 @@ def annToRLE(self, ann):
         Convert annotation which can be polygons, uncompressed RLE to RLE.
         :return: binary mask (numpy 2D array)
         """
-        t = self.imgs[ann['image_id']]
-        h, w = t['height'], t['width']
-        segm = ann['segmentation']
-        if type(segm) == list:
+        t = self.imgs[ann["image_id"]]
+        h, w = t["height"], t["width"]
+        segm = ann["segmentation"]
+        if isinstance(segm, list):
             # polygon -- a single object might consist of multiple parts
             # we merge all parts into one mask rle code
             rles = maskUtils.frPyObjects(segm, h, w)
             rle = maskUtils.merge(rles)
-        elif type(segm['counts']) == list:
+        elif isinstance(segm["counts"], list):
             # uncompressed RLE
             rle = maskUtils.frPyObjects(segm, h, w)
         else:
             # rle
-            rle = ann['segmentation']
+            rle = ann["segmentation"]
         return rle
 
     def annToMask(self, ann):
diff --git a/vision/classification_and_detection/python/version.py b/vision/classification_and_detection/python/version.py
index 1152dbb417..570348596c 100644
--- a/vision/classification_and_detection/python/version.py
+++ b/vision/classification_and_detection/python/version.py
@@ -1,3 +1,2 @@
-
-version = '0.1.0'
-git_version = '05df3bae82ef9fc933277385eb778e3f22cd0c6a'
+version = "0.1.0"
+git_version = "05df3bae82ef9fc933277385eb778e3f22cd0c6a"
diff --git a/vision/classification_and_detection/setup.py b/vision/classification_and_detection/setup.py
index c1e2fbcf07..758d874fbd 100644
--- a/vision/classification_and_detection/setup.py
+++ b/vision/classification_and_detection/setup.py
@@ -13,17 +13,20 @@
 from setuptools import setup, find_packages, Command
 
 TOP_DIR = os.path.realpath(os.path.dirname(__file__))
-SRC_DIR = os.path.join(TOP_DIR, 'python')
+SRC_DIR = os.path.join(TOP_DIR, "python")
 
 try:
-    git_version = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=TOP_DIR).decode('ascii').strip()
+    git_version = (
+        subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=TOP_DIR)
+        .decode("ascii")
+        .strip()
+    )
 except (OSError, subprocess.CalledProcessError):
     git_version = None
 
-with open(os.path.join(TOP_DIR, 'VERSION_NUMBER')) as version_file:
-    VersionInfo = namedtuple('VersionInfo', ['version', 'git_version'])(
-        version=version_file.read().strip(),
-        git_version=git_version
+with open(os.path.join(TOP_DIR, "VERSION_NUMBER")) as version_file:
+    VersionInfo = namedtuple("VersionInfo", ["version", "git_version"])(
+        version=version_file.read().strip(), git_version=git_version
     )
 
 
@@ -37,49 +40,67 @@ def finalize_options(self):
         pass
 
     def run(self):
-        with open(os.path.join(SRC_DIR, 'version.py'), 'w') as f:
-            f.write(dedent('''
+        with open(os.path.join(SRC_DIR, "version.py"), "w") as f:
+            f.write(
+                dedent(
+                    """
             version = '{version}'
             git_version = '{git_version}'
-            '''.format(**dict(VersionInfo._asdict()))))
+            """.format(
+                        **dict(VersionInfo._asdict())
+                    )
+                )
+            )
 
 
 class build_py(setuptools.command.build_py.build_py):
     def run(self):
-        self.run_command('create_version')
+        self.run_command("create_version")
         setuptools.command.build_py.build_py.run(self)
 
 
 class build(distutils.command.build.build):
     def run(self):
-        self.run_command('build_py')
+        self.run_command("build_py")
 
 
 class develop(setuptools.command.develop.develop):
     def run(self):
-        self.run_command('create_version')
-        self.run_command('build')
+        self.run_command("create_version")
+        self.run_command("build")
         setuptools.command.develop.develop.run(self)
 
 
 cmdclass = {
-    'create_version': create_version,
-    'build_py': build_py,
-    'build': build,
-    'develop': develop,
+    "create_version": create_version,
+    "build_py": build_py,
+    "build": build,
+    "develop": develop,
 }
 
 setup(
     name="mlperf-inference",
     version=VersionInfo.version,
-    description='mlperf inference benchmark',
-    setup_requires=['pytest-runner'],
-    tests_require=['graphviz', 'parameterized', 'pytest', 'pytest-cov', 'pyyaml'],
+    description="mlperf inference benchmark",
+    setup_requires=["pytest-runner"],
+    tests_require=[
+        "graphviz",
+        "parameterized",
+        "pytest",
+        "pytest-cov",
+        "pyyaml"],
     cmdclass=cmdclass,
     packages=find_packages(),
-    author='guschmue@microsoft.com',
-    author_email='guschmue@microsoft.com',
-    url='https://github.com/mlperf/inference',
-    install_requires=['numpy>=1.14.1', 'onnx>=1.5', 'pybind11', 'Cython',
-                        'pycocotools', 'mlperf_loadgen', 'opencv-python-headless']
+    author="guschmue@microsoft.com",
+    author_email="guschmue@microsoft.com",
+    url="https://github.com/mlperf/inference",
+    install_requires=[
+        "numpy>=1.14.1",
+        "onnx>=1.5",
+        "pybind11",
+        "Cython",
+        "pycocotools",
+        "mlperf_loadgen",
+        "opencv-python-headless",
+    ],
 )
diff --git a/vision/classification_and_detection/tools/accuracy-coco.py b/vision/classification_and_detection/tools/accuracy-coco.py
index 1e15999f1d..95e04193ba 100644
--- a/vision/classification_and_detection/tools/accuracy-coco.py
+++ b/vision/classification_and_detection/tools/accuracy-coco.py
@@ -19,15 +19,29 @@
 
 # pylint: disable=missing-docstring
 
+
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
     parser.add_argument("--coco-dir", required=True, help="coco directory")
-    parser.add_argument("--verbose", action="store_true", help="verbose messages")
-    parser.add_argument("--output-file", default="coco-results.json", help="path to output file")
-    parser.add_argument("--use-inv-map", action="store_true", help="use inverse label map")
-    parser.add_argument("--remove-48-empty-images", action="store_true", help="used in case you removed 48 empty images while preprocessing the dataset")
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--output-file", default="coco-results.json", help="path to output file"
+    )
+    parser.add_argument(
+        "--use-inv-map", action="store_true", help="use inverse label map"
+    )
+    parser.add_argument(
+        "--remove-48-empty-images",
+        action="store_true",
+        help="used in case you removed 48 empty images while preprocessing the dataset",
+    )
     args = parser.parse_args()
     return args
 
@@ -35,10 +49,14 @@ def get_args():
 def main():
     args = get_args()
 
-    cocoGt = COCO(os.path.join(args.coco_dir, "annotations/instances_val2017.json"))
+    cocoGt = COCO(
+        os.path.join(
+            args.coco_dir,
+            "annotations/instances_val2017.json"))
 
     if args.use_inv_map:
-        inv_map = [0] + cocoGt.getCatIds() # First label in inv_map is not used
+        # First label in inv_map is not used
+        inv_map = [0] + cocoGt.getCatIds()
 
     with open(args.mlperf_accuracy_file, "r") as f:
         results = json.load(f)
@@ -47,7 +65,7 @@ def main():
     image_ids = set()
     seen = set()
     no_results = 0
-    if args.remove_48_empty_images:        
+    if args.remove_48_empty_images:
         im_ids = []
         for i in cocoGt.getCatIds():
             im_ids += cocoGt.catToImgs[i]
@@ -57,7 +75,7 @@ def main():
         image_map = cocoGt.dataset["images"]
 
     for j in results:
-        idx = j['qsl_idx']
+        idx = j["qsl_idx"]
         # de-dupe in case loadgen sends the same image multiple times
         if idx in seen:
             continue
@@ -66,12 +84,14 @@ def main():
         # reconstruct from mlperf accuracy log
         # what is written by the benchmark is an array of float32's:
         # id, box[0], box[1], box[2], box[3], score, detection_class
-        # note that id is a index into instances_val2017.json, not the actual image_id
-        data = np.frombuffer(bytes.fromhex(j['data']), np.float32)
+        # note that id is a index into instances_val2017.json, not the actual
+        # image_id
+        data = np.frombuffer(bytes.fromhex(j["data"]), np.float32)
         if len(data) < 7:
             # handle images that had no results
             image = image_map[idx]
-            # by adding the id to image_ids we make pycoco aware of the no-result image
+            # by adding the id to image_ids we make pycoco aware of the
+            # no-result image
             image_ids.add(image["id"])
             no_results += 1
             if args.verbose:
@@ -79,11 +99,15 @@ def main():
             continue
 
         for i in range(0, len(data), 7):
-            image_idx, ymin, xmin, ymax, xmax, score, label = data[i:i + 7]
+            image_idx, ymin, xmin, ymax, xmax, score, label = data[i: i + 7]
             image = image_map[idx]
             image_idx = int(image_idx)
             if image_idx != idx:
-                print("ERROR: loadgen({}) and payload({}) disagree on image_idx".format(idx, image_idx))
+                print(
+                    "ERROR: loadgen({}) and payload({}) disagree on image_idx".format(
+                        idx, image_idx
+                    )
+                )
             image_id = image["id"]
             height, width = image["height"], image["width"]
             ymin *= height
@@ -95,25 +119,35 @@ def main():
             if args.use_inv_map:
                 label = inv_map[label]
             # pycoco wants {imageID,x1,y1,w,h,score,class}
-            detections.append({
-                "image_id": image_id,
-                "image_loc": loc,
-                "category_id": label,
-                "bbox": [float(xmin), float(ymin), float(xmax - xmin), float(ymax - ymin)],
-                "score": float(score)})
+            detections.append(
+                {
+                    "image_id": image_id,
+                    "image_loc": loc,
+                    "category_id": label,
+                    "bbox": [
+                        float(xmin),
+                        float(ymin),
+                        float(xmax - xmin),
+                        float(ymax - ymin),
+                    ],
+                    "score": float(score),
+                }
+            )
             image_ids.add(image_id)
 
     with open(args.output_file, "w") as fp:
         json.dump(detections, fp, sort_keys=True, indent=4)
 
-    cocoDt = cocoGt.loadRes(args.output_file) # Load from file to bypass error with Python3
-    cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+    cocoDt = cocoGt.loadRes(
+        args.output_file
+    )  # Load from file to bypass error with Python3
+    cocoEval = COCOeval(cocoGt, cocoDt, iouType="bbox")
     cocoEval.params.imgIds = list(image_ids)
     cocoEval.evaluate()
     cocoEval.accumulate()
     cocoEval.summarize()
 
-    print("mAP={:.3f}%".format(100. * cocoEval.stats[0]))
+    print("mAP={:.3f}%".format(100.0 * cocoEval.stats[0]))
     if args.verbose:
         print("found {} results".format(len(results)))
         print("found {} images".format(len(image_ids)))
diff --git a/vision/classification_and_detection/tools/accuracy-imagenet.py b/vision/classification_and_detection/tools/accuracy-imagenet.py
index 1879e0f09a..a57810891d 100644
--- a/vision/classification_and_detection/tools/accuracy-imagenet.py
+++ b/vision/classification_and_detection/tools/accuracy-imagenet.py
@@ -15,21 +15,32 @@
 
 # pylint: disable=missing-docstring
 
+
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--imagenet-val-file", required=True, help="path to imagenet val_map.txt")
-    parser.add_argument("--verbose", action="store_true", help="verbose messages")
-    parser.add_argument("--dtype", default="float32", choices=["float32", "int32", "int64"], help="data type of the label")
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--imagenet-val-file", required=True, help="path to imagenet val_map.txt"
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--dtype",
+        default="float32",
+        choices=["float32", "int32", "int64"],
+        help="data type of the label",
+    )
     args = parser.parse_args()
     return args
 
-dtype_map = {
-    "float32": np.float32,
-    "int32": np.int32,
-    "int64": np.int64
-}
+
+dtype_map = {"float32": np.float32, "int32": np.int32, "int64": np.int64}
+
 
 def main():
     args = get_args()
@@ -46,7 +57,7 @@ def main():
     seen = set()
     good = 0
     for j in results:
-        idx = j['qsl_idx']
+        idx = j["qsl_idx"]
 
         # de-dupe in case loadgen sends the same image multiple times
         if idx in seen:
@@ -57,7 +68,7 @@ def main():
         img, label = imagenet[idx]
 
         # reconstruct label from mlperf accuracy log
-        data = np.frombuffer(bytes.fromhex(j['data']), dtype_map[args.dtype])
+        data = np.frombuffer(bytes.fromhex(j["data"]), dtype_map[args.dtype])
         found = int(data[0])
         if label == found:
             good += 1
@@ -65,7 +76,11 @@ def main():
             if args.verbose:
                 print("{}, expected: {}, found {}".format(img, label, found))
 
-    print("accuracy={:.3f}%, good={}, total={}".format(100. * good / len(seen), good, len(seen)))
+    print(
+        "accuracy={:.3f}%, good={}, total={}".format(
+            100.0 * good / len(seen), good, len(seen)
+        )
+    )
     if args.verbose:
         print("found and ignored {} dupes".format(len(results) - len(seen)))
 
diff --git a/vision/classification_and_detection/tools/accuracy-openimages.py b/vision/classification_and_detection/tools/accuracy-openimages.py
index 655ae5c168..0a0831d24c 100644
--- a/vision/classification_and_detection/tools/accuracy-openimages.py
+++ b/vision/classification_and_detection/tools/accuracy-openimages.py
@@ -19,27 +19,43 @@
 
 # pylint: disable=missing-docstring
 
+
 def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json")
-    parser.add_argument("--openimages-dir", required=True, help="openimages directory")
-    parser.add_argument("--verbose", action="store_true", help="verbose messages")
-    parser.add_argument("--output-file", default="openimages-results.json", help="path to output file")
-    parser.add_argument("--use-inv-map", action="store_true", help="use inverse label map")
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--openimages-dir",
+        required=True,
+        help="openimages directory")
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--output-file", default="openimages-results.json", help="path to output file"
+    )
+    parser.add_argument(
+        "--use-inv-map", action="store_true", help="use inverse label map"
+    )
     args = parser.parse_args()
     return args
 
 
 def main():
     args = get_args()
-    annotations_file = os.environ.get('DATASET_ANNOTATIONS_FILE_PATH')
+    annotations_file = os.environ.get("DATASET_ANNOTATIONS_FILE_PATH")
     if not annotations_file:
-        annotations_file = os.path.join(args.openimages_dir, "annotations/openimages-mlperf.json")
+        annotations_file = os.path.join(
+            args.openimages_dir, "annotations/openimages-mlperf.json"
+        )
     cocoGt = COCO(annotations_file)
 
     if args.use_inv_map:
-        inv_map = [0] + cocoGt.getCatIds() # First label in inv_map is not used
+        # First label in inv_map is not used
+        inv_map = [0] + cocoGt.getCatIds()
 
     with open(args.mlperf_accuracy_file, "r") as f:
         results = json.load(f)
@@ -51,7 +67,7 @@ def main():
     image_map = cocoGt.dataset["images"]
 
     for j in results:
-        idx = j['qsl_idx']
+        idx = j["qsl_idx"]
         # de-dupe in case loadgen sends the same image multiple times
         if idx in seen:
             continue
@@ -60,12 +76,14 @@ def main():
         # reconstruct from mlperf accuracy log
         # what is written by the benchmark is an array of float32's:
         # id, box[0], box[1], box[2], box[3], score, detection_class
-        # note that id is a index into instances_val2017.json, not the actual image_id
-        data = np.frombuffer(bytes.fromhex(j['data']), np.float32)
+        # note that id is a index into instances_val2017.json, not the actual
+        # image_id
+        data = np.frombuffer(bytes.fromhex(j["data"]), np.float32)
         if len(data) < 7:
             # handle images that had no results
             image = image_map[idx]
-            # by adding the id to image_ids we make pycoco aware of the no-result image
+            # by adding the id to image_ids we make pycoco aware of the
+            # no-result image
             image_ids.add(image["id"])
             no_results += 1
             if args.verbose:
@@ -73,41 +91,57 @@ def main():
             continue
 
         for i in range(0, len(data), 7):
-            image_idx, ymin, xmin, ymax, xmax, score, label = data[i:i + 7]
+            image_idx, ymin, xmin, ymax, xmax, score, label = data[i: i + 7]
             image = image_map[idx]
             image_idx = int(image_idx)
             if image_idx != idx:
-                print("ERROR: loadgen({}) and payload({}) disagree on image_idx".format(idx, image_idx))
+                print(
+                    "ERROR: loadgen({}) and payload({}) disagree on image_idx".format(
+                        idx, image_idx
+                    )
+                )
             image_id = image["id"]
             height, width = image["height"], image["width"]
             ymin *= height
             xmin *= width
             ymax *= height
             xmax *= width
-            loc = os.path.join(args.openimages_dir, "validation/data", image["file_name"])
+            loc = os.path.join(
+                args.openimages_dir, "validation/data", image["file_name"]
+            )
             label = int(label)
             if args.use_inv_map:
                 label = inv_map[label]
             # pycoco wants {imageID,x1,y1,w,h,score,class}
-            detections.append({
-                "image_id": image_id,
-                "image_loc": loc,
-                "category_id": label,
-                "bbox": [float(xmin), float(ymin), float(xmax - xmin), float(ymax - ymin)],
-                "score": float(score)})
+            detections.append(
+                {
+                    "image_id": image_id,
+                    "image_loc": loc,
+                    "category_id": label,
+                    "bbox": [
+                        float(xmin),
+                        float(ymin),
+                        float(xmax - xmin),
+                        float(ymax - ymin),
+                    ],
+                    "score": float(score),
+                }
+            )
             image_ids.add(image_id)
 
     with open(args.output_file, "w") as fp:
         json.dump(detections, fp, sort_keys=True, indent=4)
 
-    cocoDt = cocoGt.loadRes(args.output_file) # Load from file to bypass error with Python3
-    cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+    cocoDt = cocoGt.loadRes(
+        args.output_file
+    )  # Load from file to bypass error with Python3
+    cocoEval = COCOeval(cocoGt, cocoDt, iouType="bbox")
     cocoEval.params.imgIds = list(image_ids)
     cocoEval.evaluate()
     cocoEval.accumulate()
     cocoEval.summarize()
 
-    print("mAP={:.3f}%".format(100. * cocoEval.stats[0]))
+    print("mAP={:.3f}%".format(100.0 * cocoEval.stats[0]))
     if args.verbose:
         print("found {} results".format(len(results)))
         print("found {} images".format(len(image_ids)))
diff --git a/vision/classification_and_detection/tools/calibrate_torchvision_model.py b/vision/classification_and_detection/tools/calibrate_torchvision_model.py
index 815e5fe206..3b002003ab 100644
--- a/vision/classification_and_detection/tools/calibrate_torchvision_model.py
+++ b/vision/classification_and_detection/tools/calibrate_torchvision_model.py
@@ -12,12 +12,13 @@
 
 class CalibrationDataset(Dataset):
     def __init__(self, root, files, transform):
-        with open(files, 'r') as f:
-            self.files = [os.path.join(root, fn.strip()) for fn in f.readlines()]
+        with open(files, "r") as f:
+            self.files = [os.path.join(root, fn.strip())
+                          for fn in f.readlines()]
         self.transform = transform
 
     def __getitem__(self, idx):
-        image = Image.open(self.files[idx]).convert('RGB')
+        image = Image.open(self.files[idx]).convert("RGB")
         image = self.transform(image)
         return image
 
@@ -25,7 +26,7 @@ def __len__(self):
         return len(self.files)
 
 
-def quantize_model(model, dataloader, backend='fbgemm'):
+def quantize_model(model, dataloader, backend="fbgemm"):
     if backend not in torch.backends.quantized.supported_engines:
         raise RuntimeError("Quantized backend not supported ")
     torch.backends.quantized.engine = backend
@@ -36,29 +37,40 @@ def quantize_model(model, dataloader, backend='fbgemm'):
     # Make sure that weight qconfig matches that of the serialized models
     model.qconfig = torch.quantization.get_default_qconfig(backend)
     torch.quantization.prepare(model, inplace=True)
-    print('calibrating...')
+    print("calibrating...")
     for x in tqdm(dataloader):
         model(x)
-    print('calibration DONE!')
+    print("calibration DONE!")
     torch.quantization.convert(model, inplace=True)
 
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--model', type=str, default='resnet50')
-    parser.add_argument('--image-dir', type=str, default='imagenet/val')
-    parser.add_argument('--image-list', type=str, default='../../calibration/ImageNet/cal_image_list_option_1.txt')
+    parser.add_argument("--model", type=str, default="resnet50")
+    parser.add_argument("--image-dir", type=str, default="imagenet/val")
+    parser.add_argument(
+        "--image-list",
+        type=str,
+        default="../../calibration/ImageNet/cal_image_list_option_1.txt",
+    )
     args = parser.parse_args()
     print(args)
 
-    transform = transforms.Compose([                                                   
-        transforms.Resize(256),                                                        
-        transforms.CenterCrop(224),                                                    
-        transforms.ToTensor(),                                                         
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),   
-    ])                                                                                 
-
-    dataset = CalibrationDataset(root=args.image_dir, files=args.image_list, transform=transform)
+    transform = transforms.Compose(
+        [
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=[
+                    0.485, 0.456, 0.406], std=[
+                    0.229, 0.224, 0.225]),
+        ]
+    )
+
+    dataset = CalibrationDataset(
+        root=args.image_dir, files=args.image_list, transform=transform
+    )
     dataloader = DataLoader(dataset, batch_size=1)
 
     model = eval(args.model)(pretrained=True, progress=True, quantize=False)
@@ -67,11 +79,10 @@ def main():
 
     inp = torch.rand(1, 3, 224, 224)
     script_module = torch.jit.trace(model, inp)
-    save_path = f'{args.model}.pt'
+    save_path = f"{args.model}.pt"
     torch.jit.save(script_module, save_path)
-    print(f'saved: {save_path}')
+    print(f"saved: {save_path}")
 
 
-if __name__=='__main__':
+if __name__ == "__main__":
     main()
-
diff --git a/vision/classification_and_detection/tools/coco-analyze.py b/vision/classification_and_detection/tools/coco-analyze.py
index 7f22daa84e..f58677f9d4 100755
--- a/vision/classification_and_detection/tools/coco-analyze.py
+++ b/vision/classification_and_detection/tools/coco-analyze.py
@@ -36,7 +36,7 @@ def annotate_image(results, cocoGt, output):
 
     new_results = collections.defaultdict(list)
     for result in results:
-        new_results[result['image_id']].append(result)
+        new_results[result["image_id"]].append(result)
     print("Unique images = {}".format(len(new_results)))
     results = new_results
 
@@ -44,32 +44,40 @@ def annotate_image(results, cocoGt, output):
         draw = None
         image = None
         for v in result:
-            box = v['bbox']
-            score = v['score']
+            box = v["bbox"]
+            score = v["score"]
             predicted_class = v["category_id"]
             try:
                 predicted_class = cocoGt.loadCats(predicted_class)[0]["name"]
             except Exception as ex:
-                print("category {} not found, image {}".format(predicted_class, v["image_loc"]))
+                print(
+                    "category {} not found, image {}".format(
+                        predicted_class, v["image_loc"]
+                    )
+                )
             # predicted_class = self.class_names[c]
             # "image_loc": "/home/gs/data/coco300/val2017/000000397133.jpg",
             if not draw:
-                image = Image.open(v['image_loc'])
-                if image.mode != 'RGB':
-                    image = image.convert('RGB')
+                image = Image.open(v["image_loc"])
+                if image.mode != "RGB":
+                    image = image.convert("RGB")
 
                 draw = ImageDraw.Draw(image)
             # font = ImageFont.truetype(font='FreeMono.ttf',
-            #            size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
+            # size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
             try:
                 left, top, w, h = box
                 bottom = top + h
                 right = left + w
-                top = max(0, np.floor(top + 0.5).astype('int32'))
-                left = max(0, np.floor(left + 0.5).astype('int32'))
-                bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
-                right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
-                label = '{} {:.2f}'.format(predicted_class, score)
+                top = max(0, np.floor(top + 0.5).astype("int32"))
+                left = max(0, np.floor(left + 0.5).astype("int32"))
+                bottom = min(
+                    image.size[1], np.floor(
+                        bottom + 0.5).astype("int32"))
+                right = min(
+                    image.size[0], np.floor(
+                        right + 0.5).astype("int32"))
+                label = "{} {:.2f}".format(predicted_class, score)
                 # label_size = draw.textsize(label, font)
                 label_size = draw.textsize(label)
 
@@ -80,11 +88,19 @@ def annotate_image(results, cocoGt, output):
 
                 color = ImageColor.getrgb("red")
                 thickness = 0
-                draw.rectangle([left + thickness, top + thickness, right - thickness, bottom - thickness], outline=color)
+                draw.rectangle(
+                    [
+                        left + thickness,
+                        top + thickness,
+                        right - thickness,
+                        bottom - thickness,
+                    ],
+                    outline=color,
+                )
                 draw.text(text_origin, label, fill=color)  # , font=font)
             except Exception as ex:
-                print("{} failed, ex {}".format(v['image_loc'], ex))
-        image.save(os.path.join(output, os.path.basename(v['image_loc'])))
+                print("{} failed, ex {}".format(v["image_loc"], ex))
+        image.save(os.path.join(output, os.path.basename(v["image_loc"])))
         del draw
 
 
@@ -93,7 +109,7 @@ def calculate_map(results, cocoGt, output):
     # x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
 
     cocoDt = cocoGt.loadRes(results)
-    cocoEval = COCOeval(cocoGt, cocoDt, iouType='bbox')
+    cocoEval = COCOeval(cocoGt, cocoDt, iouType="bbox")
     cocoEval.evaluate()
     cocoEval.accumulate()
     cocoEval.summarize()
@@ -110,11 +126,11 @@ def calculate_map(results, cocoGt, output):
         "DetectionBoxes_Recall/AR@100": cocoEval.stats[8],
         "DetectionBoxes_Recall/AR@100 (small)": cocoEval.stats[9],
         "DetectionBoxes_Recall/AR@100 (medium)": cocoEval.stats[10],
-        "DetectionBoxes_Recall/AR@100 (large)": cocoEval.stats[11]
+        "DetectionBoxes_Recall/AR@100 (large)": cocoEval.stats[11],
     }
 
-    mAP = all_metrics['DetectionBoxes_Precision/mAP']
-    recall = all_metrics['DetectionBoxes_Recall/AR@100']
+    mAP = all_metrics["DetectionBoxes_Precision/mAP"]
+    recall = all_metrics["DetectionBoxes_Recall/AR@100"]
     print("mAP={}, recall={}".format(mAP, recall))
 
 
@@ -124,7 +140,8 @@ def main():
     with open(args.input, "r") as f:
         results = json.load(f)
 
-    annotation_file = os.path.join(args.coco, "annotations/instances_val2017.json")
+    annotation_file = os.path.join(
+        args.coco, "annotations/instances_val2017.json")
     cocoGt = COCO(annotation_file)
     annotate_image(results, cocoGt, args.output)
     calculate_map(args.input, cocoGt, args.output)
diff --git a/vision/classification_and_detection/tools/lglog2csv.py b/vision/classification_and_detection/tools/lglog2csv.py
index 901bd795de..6f32433f2d 100644
--- a/vision/classification_and_detection/tools/lglog2csv.py
+++ b/vision/classification_and_detection/tools/lglog2csv.py
@@ -36,32 +36,43 @@ def main():
     with open(args.input, "r") as fp:
         mode, mean, latency_90, latency_99, qps = None, 0, 0, 0, 0
         for line in fp:
-            m = re.match("^Scenario\s*:\s*(\w+).*", line)
+            m = re.match("^Scenario\\s*:\\s*(\\w+).*", line)
             if m:
                 mode = m.group(1)
-            m = re.match("^90.00 percentile latency.*:\s*(\d+).*", line)
+            m = re.match("^90.00 percentile latency.*:\\s*(\\d+).*", line)
             if m:
                 latency_90 = m.group(1)
-            m = re.match("^99.00 percentile latency.*:\s*(\d+).*", line)
+            m = re.match("^99.00 percentile latency.*:\\s*(\\d+).*", line)
             if m:
                 latency_99 = m.group(1)
-            m = re.match("^Mean latency.*:\s*(\d+).*", line)
+            m = re.match("^Mean latency.*:\\s*(\\d+).*", line)
             if m:
                 mean = m.group(1)
-            m = re.match("^Completed samples per second.*:\s*(\d+).*", line)
+            m = re.match("^Completed samples per second.*:\\s*(\\d+).*", line)
             if m:
                 qps = m.group(1)
-            m = re.match("^QPS w/ loadgen overhead.*:\s*(\d+).*", line)
+            m = re.match("^QPS w/ loadgen overhead.*:\\s*(\\d+).*", line)
             if m:
                 qps = m.group(1)
-            m = re.match("^Samples per second.*:\s*(\d+).*", line)
+            m = re.match("^Samples per second.*:\\s*(\\d+).*", line)
             if m:
                 qps = m.group(1)
             m = re.match("Test Parameters Used.*", line)
             if m:
-                print("{},{},{},{},{},{},{},{},{},{}".format(
-                    args.name, now, args.machine, args.runtime, args.model,
-                    mode, qps, mean, latency_90, latency_99))
+                print(
+                    "{},{},{},{},{},{},{},{},{},{}".format(
+                        args.name,
+                        now,
+                        args.machine,
+                        args.runtime,
+                        args.model,
+                        mode,
+                        qps,
+                        mean,
+                        latency_90,
+                        latency_99,
+                    )
+                )
                 mode, mean, latency_90, latency_99, qps = None, 0, 0, 0, 0
 
 
diff --git a/vision/classification_and_detection/tools/openimages.py b/vision/classification_and_detection/tools/openimages.py
index 2d14bdd226..b6e22f3ab2 100644
--- a/vision/classification_and_detection/tools/openimages.py
+++ b/vision/classification_and_detection/tools/openimages.py
@@ -52,17 +52,20 @@
 
 
 BUCKET_NAME = "open-images-dataset"
-BBOX_ANNOTATIONS_URL = "https://storage.googleapis.com/openimages/v5/validation-annotations-bbox.csv"
+BBOX_ANNOTATIONS_URL = (
+    "https://storage.googleapis.com/openimages/v5/validation-annotations-bbox.csv"
+)
 ANNOTATIONS_FILE = "validation-annotations-bbox.csv"
-MAP_CLASSES_URL = "https://storage.googleapis.com/openimages/v5/class-descriptions-boxable.csv"
+MAP_CLASSES_URL = (
+    "https://storage.googleapis.com/openimages/v5/class-descriptions-boxable.csv"
+)
 MAP_CLASSES_FILE = "class-descriptions-boxable.csv"
 CHUNK_SIZE = 1024 * 8
 
 
 def get_args():
     parser = argparse.ArgumentParser(
-        description="Download OpenImages", add_help=True
-    )
+        description="Download OpenImages", add_help=True)
     parser.add_argument(
         "--dataset-dir",
         default="/open-images-v6",
@@ -138,20 +141,26 @@ def export_to_coco(
     image_list_df.columns = ["image_list"]
     image_list_df[["height", "width"]] = image_list_df.apply(
         lambda x: extract_dims(
-            os.path.join(dataset_path, f"{x['image_list']}.jpg")
-        ),
+            os.path.join(
+                dataset_path,
+                f"{x['image_list']}.jpg")),
         axis=1,
         result_type="expand",
     )
-    annotations = pd.merge(annotations, image_list_df, how="inner", left_on="ImageID", right_on="image_list")
+    annotations = pd.merge(
+        annotations,
+        image_list_df,
+        how="inner",
+        left_on="ImageID",
+        right_on="image_list",
+    )
     annotations = annotations.merge(class_map, on="LabelName", how="inner")
     annotations = annotations.sort_values(by=["ImageID"])
     annotations["image_id"] = pd.factorize(annotations["ImageID"].tolist())[0]
     # Images
     images_ = []
-    for i, row in (
-        annotations.groupby(["image_id", "ImageID"]).first().iterrows()
-    ):
+    for i, row in annotations.groupby(
+            ["image_id", "ImageID"]).first().iterrows():
         id, ImageID = i
         images_.append(
             {
@@ -237,8 +246,7 @@ def download_one_image(bucket, split, image_id, download_folder):
         )
     except botocore.exceptions.ClientError as exception:
         sys.exit(
-            f"ERROR when downloading image `{split}/{image_id}`: {str(exception)}"
-        )
+            f"ERROR when downloading image `{split}/{image_id}`: {str(exception)}")
 
 
 def download_all_images(args):
@@ -285,8 +293,7 @@ def download_all_images(args):
         if args.max_images is not None:
             np.random.seed(args.seed)
             selected_index = np.random.choice(
-                len(image_list), size=args.max_images
-            )
+                len(image_list), size=args.max_images)
             image_list = [image_list[i] for i in selected_index]
     except ValueError as exception:
         sys.exit(exception)
@@ -294,9 +301,7 @@ def download_all_images(args):
     progress_bar = tqdm.tqdm(
         total=len(image_list), desc="Downloading images", leave=True
     )
-    with futures.ThreadPoolExecutor(
-        max_workers=args.num_processes
-    ) as executor:
+    with futures.ThreadPoolExecutor(max_workers=args.num_processes) as executor:
         all_futures = [
             executor.submit(
                 download_one_image,
diff --git a/vision/classification_and_detection/tools/openimages_calibration.py b/vision/classification_and_detection/tools/openimages_calibration.py
index 468e488b1a..a2a4aa4be8 100644
--- a/vision/classification_and_detection/tools/openimages_calibration.py
+++ b/vision/classification_and_detection/tools/openimages_calibration.py
@@ -35,9 +35,13 @@
 
 
 BUCKET_NAME = "open-images-dataset"
-BBOX_ANNOTATIONS_URL = "https://storage.googleapis.com/openimages/v6/oidv6-train-annotations-bbox.csv"
+BBOX_ANNOTATIONS_URL = (
+    "https://storage.googleapis.com/openimages/v6/oidv6-train-annotations-bbox.csv"
+)
 ANNOTATIONS_FILE = "oidv6-train-annotations-bbox.csv"
-MAP_CLASSES_URL = "https://storage.googleapis.com/openimages/v5/class-descriptions-boxable.csv"
+MAP_CLASSES_URL = (
+    "https://storage.googleapis.com/openimages/v5/class-descriptions-boxable.csv"
+)
 MAP_CLASSES_FILE = "class-descriptions-boxable.csv"
 CHUNK_SIZE = 1024 * 8
 
@@ -116,16 +120,16 @@ def export_to_coco(
     annotations["image_id"] = pd.factorize(annotations["ImageID"].tolist())[0]
     annotations[["height", "width"]] = annotations.apply(
         lambda x: extract_dims(
-            os.path.join(dataset_path, f"{x['ImageID']}.jpg")
-        ),
+            os.path.join(
+                dataset_path,
+                f"{x['ImageID']}.jpg")),
         axis=1,
         result_type="expand",
     )
     # Images
     images_ = []
-    for i, row in (
-        annotations.groupby(["image_id", "ImageID"]).first().iterrows()
-    ):
+    for i, row in annotations.groupby(
+            ["image_id", "ImageID"]).first().iterrows():
         id, ImageID = i
         images_.append(
             {
@@ -197,8 +201,7 @@ def download_one_image(bucket, split, image_id, download_folder):
         )
     except botocore.exceptions.ClientError as exception:
         sys.exit(
-            f"ERROR when downloading image `{split}/{image_id}`: {str(exception)}"
-        )
+            f"ERROR when downloading image `{split}/{image_id}`: {str(exception)}")
 
 
 def download_all_images(args, image_list):
@@ -216,9 +219,8 @@ def download_all_images(args, image_list):
     if not os.path.exists(os.path.join(download_folder, "annotations")):
         os.makedirs(os.path.join(download_folder, "annotations"))
 
-    if not os.path.exists(
-        os.path.join(download_folder, "calibration", "data")
-    ):
+    if not os.path.exists(os.path.join(
+            download_folder, "calibration", "data")):
         os.makedirs(os.path.join(download_folder, "calibration", "data"))
 
     try:
@@ -246,9 +248,7 @@ def download_all_images(args, image_list):
     progress_bar = tqdm.tqdm(
         total=len(image_list), desc="Downloading images", leave=True
     )
-    with futures.ThreadPoolExecutor(
-        max_workers=args.num_processes
-    ) as executor:
+    with futures.ThreadPoolExecutor(max_workers=args.num_processes) as executor:
         all_futures = [
             executor.submit(
                 download_one_image,
@@ -280,8 +280,9 @@ def download_all_images(args, image_list):
         # Try to find the calibration file in case it was not provided
         repo_root = os.path.dirname(
             os.path.dirname(
-                os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-            )
+                os.path.dirname(
+                    os.path.dirname(
+                        os.path.abspath(__file__))))
         )
         calibration_file = os.path.join(
             repo_root,
diff --git a/vision/classification_and_detection/tools/resnet50_tflite_edgetpu.py b/vision/classification_and_detection/tools/resnet50_tflite_edgetpu.py
index b99c989a23..5a828bd4c3 100644
--- a/vision/classification_and_detection/tools/resnet50_tflite_edgetpu.py
+++ b/vision/classification_and_detection/tools/resnet50_tflite_edgetpu.py
@@ -5,19 +5,26 @@
 
 import tensorflow as tf
 from tensorflow.keras.preprocessing import image
-from tensorflow.keras.applications.resnet50 import ResNet50, decode_predictions, preprocess_input
+from tensorflow.keras.applications.resnet50 import (
+    ResNet50,
+    decode_predictions,
+    preprocess_input,
+)
 
 
 def main(path_to_mlperf_calib_dataset):
     # load tf model
-    model = ResNet50(weights='imagenet')
+    model = ResNet50(weights="imagenet")
 
     # read jpeg files from mlperf calib dataset
     jpeg_files = []
     for filename in os.listdir(path_to_mlperf_calib_dataset):
-        if filename.lower().endswith('.jpg') or filename.lower().endswith('.jpeg'):
-            jpeg_files.append(os.path.join(path_to_mlperf_calib_dataset, filename))
-    
+        if filename.lower().endswith(".jpg") or filename.lower().endswith(".jpeg"):
+            jpeg_files.append(
+                os.path.join(
+                    path_to_mlperf_calib_dataset,
+                    filename))
+
     # qunatization
     def representative_data_gen():
         for img_path in jpeg_files:
@@ -38,13 +45,13 @@ def representative_data_gen():
     converter.inference_output_type = tf.uint8
 
     tflite_model_quant = converter.convert()
-    with open('resnet50_quant_full_mlperf.tflite', 'wb') as f:
+    with open("resnet50_quant_full_mlperf.tflite", "wb") as f:
         f.write(tflite_model_quant)
 
 
-if __name__=="__main__":
+if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--image-dir', type=str, default=None)
+    parser.add_argument("--image-dir", type=str, default=None)
     args = parser.parse_args()
     print(args)
     if args.image_dir is None or not os.path.exists(args.image_dir):
@@ -52,4 +59,4 @@ def representative_data_gen():
     main(args.image_dir)
 
     # compile model for edge tpu
-    os.system("edgetpu_compiler resnet50_quant_full_mlperf.tflite")
\ No newline at end of file
+    os.system("edgetpu_compiler resnet50_quant_full_mlperf.tflite")
diff --git a/vision/classification_and_detection/tools/resnet50_v1_to_ncnn.py b/vision/classification_and_detection/tools/resnet50_v1_to_ncnn.py
index 01d7401e56..6a13e90436 100644
--- a/vision/classification_and_detection/tools/resnet50_v1_to_ncnn.py
+++ b/vision/classification_and_detection/tools/resnet50_v1_to_ncnn.py
@@ -1,3 +1,6 @@
+import ncnn
+import numpy as np
+import resnet50_v1_pnnx
 import os
 import torch
 import torchvision.models as models
@@ -22,12 +25,11 @@
 os.system("pnnx resnet50_v1.pt inputshape=[1,3,224,224] fp16=0")
 
 # pnnx inference
-import resnet50_v1_pnnx
+
 b = resnet50_v1_pnnx.test_inference()
 
 # ncnn inference
-import numpy as np
-import ncnn
+
 with ncnn.Net() as net:
     net.opt.use_fp16_packed = False
     net.opt.use_fp16_storage = False
diff --git a/vision/classification_and_detection/tools/resnet_save.py b/vision/classification_and_detection/tools/resnet_save.py
index fca66ea269..0eaa0ef58a 100755
--- a/vision/classification_and_detection/tools/resnet_save.py
+++ b/vision/classification_and_detection/tools/resnet_save.py
@@ -43,256 +43,308 @@
 from official.utils.misc import model_helpers
 
 
-
-
 def image_bytes_serving_input_fn(image_shape, dtype=tf.float32):
-  """Serving input fn for raw jpeg images."""
-
-  def _preprocess_image(image_bytes):
-    """Preprocess a single raw image."""
-    # Bounding box around the whole image.
-    bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=dtype, shape=[1, 1, 4])
-    height, width, num_channels = image_shape
-    image = imagenet_preprocessing.preprocess_image(
-        image_bytes, bbox, height, width, num_channels, is_training=False)
-    return image
-
-  image_bytes_list = tf.placeholder(
-      shape=[None], dtype=tf.string, name='input_tensor')
-  images = tf.map_fn(
-      _preprocess_image, image_bytes_list, back_prop=False, dtype=dtype)
-  return tf.estimator.export.TensorServingInputReceiver(
-      images, {'image_bytes': image_bytes_list})
-
-
-
-
-def resnet_model_fn(features, labels, mode, model_class,
-                    resnet_size, weight_decay, learning_rate_fn, momentum,
-                    data_format, resnet_version, loss_scale,
-                    loss_filter_fn=None, dtype=resnet_model.DEFAULT_DTYPE,
-                    fine_tune=False):
-  """Shared functionality for different resnet model_fns.
-
-  Initializes the ResnetModel representing the model layers
-  and uses that model to build the necessary EstimatorSpecs for
-  the `mode` in question. For training, this means building losses,
-  the optimizer, and the train op that get passed into the EstimatorSpec.
-  For evaluation and prediction, the EstimatorSpec is returned without
-  a train op, but with the necessary parameters for the given mode.
-
-  Args:
-    features: tensor representing input images
-    labels: tensor representing class labels for all input images
-    mode: current estimator mode; should be one of
-      `tf.estimator.ModeKeys.TRAIN`, `EVALUATE`, `PREDICT`
-    model_class: a class representing a TensorFlow model that has a __call__
-      function. We assume here that this is a subclass of ResnetModel.
-    resnet_size: A single integer for the size of the ResNet model.
-    weight_decay: weight decay loss rate used to regularize learned variables.
-    learning_rate_fn: function that returns the current learning rate given
-      the current global_step
-    momentum: momentum term used for optimization
-    data_format: Input format ('channels_last', 'channels_first', or None).
-      If set to None, the format is dependent on whether a GPU is available.
-    resnet_version: Integer representing which version of the ResNet network to
-      use. See README for details. Valid values: [1, 2]
-    loss_scale: The factor to scale the loss for numerical stability. A detailed
-      summary is present in the arg parser help text.
-    loss_filter_fn: function that takes a string variable name and returns
-      True if the var should be included in loss calculation, and False
-      otherwise. If None, batch_normalization variables will be excluded
-      from the loss.
-    dtype: the TensorFlow dtype to use for calculations.
-    fine_tune: If True only train the dense layers(final layers).
-
-  Returns:
-    EstimatorSpec parameterized according to the input params and the
-    current mode.
-  """
-
-  model = model_class(resnet_size, data_format, resnet_version=resnet_version,
-                      dtype=dtype)
-
-  logits = model(features, mode == tf.estimator.ModeKeys.TRAIN)
-
-  # This acts as a no-op if the logits are already in fp32 (provided logits are
-  # not a SparseTensor). If dtype is is low precision, logits must be cast to
-  # fp32 for numerical stability.
-  logits = tf.cast(logits, tf.float32)
-
-  predictions = {
-      'classes': tf.argmax(logits, axis=1),
-      'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
-  }
-
-  if mode == tf.estimator.ModeKeys.PREDICT:
-    # Return the predictions and the specification for serving a SavedModel
+    """Serving input fn for raw jpeg images."""
+
+    def _preprocess_image(image_bytes):
+        """Preprocess a single raw image."""
+        # Bounding box around the whole image.
+        bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=dtype, shape=[1, 1, 4])
+        height, width, num_channels = image_shape
+        image = imagenet_preprocessing.preprocess_image(
+            image_bytes, bbox, height, width, num_channels, is_training=False
+        )
+        return image
+
+    image_bytes_list = tf.placeholder(
+        shape=[None], dtype=tf.string, name="input_tensor"
+    )
+    images = tf.map_fn(
+        _preprocess_image, image_bytes_list, back_prop=False, dtype=dtype
+    )
+    return tf.estimator.export.TensorServingInputReceiver(
+        images, {"image_bytes": image_bytes_list}
+    )
+
+
+def resnet_model_fn(
+    features,
+    labels,
+    mode,
+    model_class,
+    resnet_size,
+    weight_decay,
+    learning_rate_fn,
+    momentum,
+    data_format,
+    resnet_version,
+    loss_scale,
+    loss_filter_fn=None,
+    dtype=resnet_model.DEFAULT_DTYPE,
+    fine_tune=False,
+):
+    """Shared functionality for different resnet model_fns.
+
+    Initializes the ResnetModel representing the model layers
+    and uses that model to build the necessary EstimatorSpecs for
+    the `mode` in question. For training, this means building losses,
+    the optimizer, and the train op that get passed into the EstimatorSpec.
+    For evaluation and prediction, the EstimatorSpec is returned without
+    a train op, but with the necessary parameters for the given mode.
+
+    Args:
+      features: tensor representing input images
+      labels: tensor representing class labels for all input images
+      mode: current estimator mode; should be one of
+        `tf.estimator.ModeKeys.TRAIN`, `EVALUATE`, `PREDICT`
+      model_class: a class representing a TensorFlow model that has a __call__
+        function. We assume here that this is a subclass of ResnetModel.
+      resnet_size: A single integer for the size of the ResNet model.
+      weight_decay: weight decay loss rate used to regularize learned variables.
+      learning_rate_fn: function that returns the current learning rate given
+        the current global_step
+      momentum: momentum term used for optimization
+      data_format: Input format ('channels_last', 'channels_first', or None).
+        If set to None, the format is dependent on whether a GPU is available.
+      resnet_version: Integer representing which version of the ResNet network to
+        use. See README for details. Valid values: [1, 2]
+      loss_scale: The factor to scale the loss for numerical stability. A detailed
+        summary is present in the arg parser help text.
+      loss_filter_fn: function that takes a string variable name and returns
+        True if the var should be included in loss calculation, and False
+        otherwise. If None, batch_normalization variables will be excluded
+        from the loss.
+      dtype: the TensorFlow dtype to use for calculations.
+      fine_tune: If True only train the dense layers(final layers).
+
+    Returns:
+      EstimatorSpec parameterized according to the input params and the
+      current mode.
+    """
+
+    model = model_class(
+        resnet_size, data_format, resnet_version=resnet_version, dtype=dtype
+    )
+
+    logits = model(features, mode == tf.estimator.ModeKeys.TRAIN)
+
+    # This acts as a no-op if the logits are already in fp32 (provided logits are
+    # not a SparseTensor). If dtype is is low precision, logits must be cast to
+    # fp32 for numerical stability.
+    logits = tf.cast(logits, tf.float32)
+
+    predictions = {
+        "classes": tf.argmax(logits, axis=1),
+        "probabilities": tf.nn.softmax(logits, name="softmax_tensor"),
+    }
+
+    if mode == tf.estimator.ModeKeys.PREDICT:
+        # Return the predictions and the specification for serving a SavedModel
+        return tf.estimator.EstimatorSpec(
+            mode=mode,
+            predictions=predictions,
+            export_outputs={
+                "predict": tf.estimator.export.PredictOutput(predictions)},
+        )
+
+    # Calculate loss, which includes softmax cross entropy and L2
+    # regularization.
+    cross_entropy = tf.losses.sparse_softmax_cross_entropy(
+        logits=logits, labels=labels)
+
+    # Create a tensor named cross_entropy for logging purposes.
+    tf.identity(cross_entropy, name="cross_entropy")
+
+    # If no loss_filter_fn is passed, assume we want the default behavior,
+    # which is that batch_normalization variables are excluded from loss.
+    def exclude_batch_norm(name):
+        return "batch_normalization" not in name
+
+    loss_filter_fn = loss_filter_fn or exclude_batch_norm
+
+    # Add weight decay to the loss.
+    l2_loss = weight_decay * tf.add_n(
+        # loss is computed using fp32 for numerical stability.
+        [
+            tf.nn.l2_loss(tf.cast(v, tf.float32))
+            for v in tf.trainable_variables()
+            if loss_filter_fn(v.name)
+        ]
+    )
+    tf.summary.scalar("l2_loss", l2_loss)
+    loss = cross_entropy + l2_loss
+
     return tf.estimator.EstimatorSpec(
         mode=mode,
         predictions=predictions,
-        export_outputs={
-            'predict': tf.estimator.export.PredictOutput(predictions)
-        })
-
-  # Calculate loss, which includes softmax cross entropy and L2 regularization.
-  cross_entropy = tf.losses.sparse_softmax_cross_entropy(
-      logits=logits, labels=labels)
-
-  # Create a tensor named cross_entropy for logging purposes.
-  tf.identity(cross_entropy, name='cross_entropy')
-
-  # If no loss_filter_fn is passed, assume we want the default behavior,
-  # which is that batch_normalization variables are excluded from loss.
-  def exclude_batch_norm(name):
-    return 'batch_normalization' not in name
-  loss_filter_fn = loss_filter_fn or exclude_batch_norm
-
-  # Add weight decay to the loss.
-  l2_loss = weight_decay * tf.add_n(
-      # loss is computed using fp32 for numerical stability.
-      [tf.nn.l2_loss(tf.cast(v, tf.float32)) for v in tf.trainable_variables()
-       if loss_filter_fn(v.name)])
-  tf.summary.scalar('l2_loss', l2_loss)
-  loss = cross_entropy + l2_loss
-
-  return tf.estimator.EstimatorSpec(
-      mode=mode,
-      predictions=predictions,
-      loss=loss,
-      train_op=train_op,
-      eval_metric_ops=metrics)
-
-
-def resnet_main(
-    flags_obj, model_function, input_function, dataset_name, shape=None):
-  """Shared main loop for ResNet Models.
-
-  Args:
-    flags_obj: An object containing parsed flags. See define_resnet_flags()
-      for details.
-    model_function: the function that instantiates the Model and builds the
-      ops for train/eval. This will be passed directly into the estimator.
-    input_function: the function that processes the dataset and returns a
-      dataset that the estimator can train on. This will be wrapped with
-      all the relevant flags for running and passed to estimator.
-    dataset_name: the name of the dataset for training and evaluation. This is
-      used for logging purpose.
-    shape: list of ints representing the shape of the images used for training.
-      This is only used if flags_obj.export_dir is passed.
-  """
-
-  print("RESNET MAIN")
-  model_helpers.apply_clean(flags.FLAGS)
-
-  # Ensures flag override logic is only executed if explicitly triggered.
-  if flags_obj.tf_gpu_thread_mode:
-    override_flags_and_set_envars_for_gpu_thread_pool(flags_obj)
-
-  # Creates session config. allow_soft_placement = True, is required for
-  # multi-GPU and is not harmful for other modes.
-  session_config = tf.ConfigProto(allow_soft_placement=True)
-
-  run_config = tf.estimator.RunConfig(
-      session_config=session_config,
-      save_checkpoints_secs=60*60*24)
-
-  # Initializes model with all but the dense layer from pretrained ResNet.
-  if flags_obj.pretrained_model_checkpoint_path is not None:
-    warm_start_settings = tf.estimator.WarmStartSettings(
-        flags_obj.pretrained_model_checkpoint_path,
-        vars_to_warm_start='^(?!.*dense)')
-  else:
-    warm_start_settings = None
-
-  classifier = tf.estimator.Estimator(
-      model_fn=model_function, model_dir=flags_obj.model_dir, config=run_config,
-      warm_start_from=warm_start_settings, params={
-          'resnet_size': int(flags_obj.resnet_size),
-          'data_format': flags_obj.data_format,
-          'batch_size': flags_obj.batch_size,
-          'resnet_version': int(flags_obj.resnet_version),
-          'loss_scale': flags_core.get_loss_scale(flags_obj),
-          'dtype': flags_core.get_tf_dtype(flags_obj),
-          'fine_tune': flags_obj.fine_tune
-      })
-
-  run_params = {
-      'batch_size': flags_obj.batch_size,
-      'dtype': flags_core.get_tf_dtype(flags_obj),
-      'resnet_size': flags_obj.resnet_size,
-      'resnet_version': flags_obj.resnet_version,
-      'synthetic_data': flags_obj.use_synthetic_data,
-      'train_epochs': flags_obj.train_epochs,
-  }
-
-  def input_fn_eval():
-    return input_function(
-        is_training=False,
-        data_dir=flags_obj.data_dir,
-        batch_size=distribution_utils.per_device_batch_size(
-            flags_obj.batch_size, flags_core.get_num_gpus(flags_obj)),
-        num_epochs=1,
-        dtype=flags_core.get_tf_dtype(flags_obj))
-
-  schedule, n_loops = [0], 1
-  if flags_obj.export_dir is not None:
-    # Exports a saved model for the given classifier.
-    export_dtype = flags_core.get_tf_dtype(flags_obj)
-    if flags_obj.image_bytes_as_serving_input:
-      input_receiver_fn = functools.partial(
-          image_bytes_serving_input_fn, shape, dtype=export_dtype)
+        loss=loss,
+        train_op=train_op,
+        eval_metric_ops=metrics,
+    )
+
+
+def resnet_main(flags_obj, model_function,
+                input_function, dataset_name, shape=None):
+    """Shared main loop for ResNet Models.
+
+    Args:
+      flags_obj: An object containing parsed flags. See define_resnet_flags()
+        for details.
+      model_function: the function that instantiates the Model and builds the
+        ops for train/eval. This will be passed directly into the estimator.
+      input_function: the function that processes the dataset and returns a
+        dataset that the estimator can train on. This will be wrapped with
+        all the relevant flags for running and passed to estimator.
+      dataset_name: the name of the dataset for training and evaluation. This is
+        used for logging purpose.
+      shape: list of ints representing the shape of the images used for training.
+        This is only used if flags_obj.export_dir is passed.
+    """
+
+    print("RESNET MAIN")
+    model_helpers.apply_clean(flags.FLAGS)
+
+    # Ensures flag override logic is only executed if explicitly triggered.
+    if flags_obj.tf_gpu_thread_mode:
+        override_flags_and_set_envars_for_gpu_thread_pool(flags_obj)
+
+    # Creates session config. allow_soft_placement = True, is required for
+    # multi-GPU and is not harmful for other modes.
+    session_config = tf.ConfigProto(allow_soft_placement=True)
+
+    run_config = tf.estimator.RunConfig(
+        session_config=session_config, save_checkpoints_secs=60 * 60 * 24
+    )
+
+    # Initializes model with all but the dense layer from pretrained ResNet.
+    if flags_obj.pretrained_model_checkpoint_path is not None:
+        warm_start_settings = tf.estimator.WarmStartSettings(
+            flags_obj.pretrained_model_checkpoint_path,
+            vars_to_warm_start="^(?!.*dense)",
+        )
     else:
-      input_receiver_fn = export.build_tensor_serving_input_receiver_fn(
-          shape, batch_size=flags_obj.batch_size, dtype=export_dtype)
-    classifier.export_savedmodel(flags_obj.export_dir, input_receiver_fn,
-                                 strip_default_attrs=True)
+        warm_start_settings = None
+
+    classifier = tf.estimator.Estimator(
+        model_fn=model_function,
+        model_dir=flags_obj.model_dir,
+        config=run_config,
+        warm_start_from=warm_start_settings,
+        params={
+            "resnet_size": int(flags_obj.resnet_size),
+            "data_format": flags_obj.data_format,
+            "batch_size": flags_obj.batch_size,
+            "resnet_version": int(flags_obj.resnet_version),
+            "loss_scale": flags_core.get_loss_scale(flags_obj),
+            "dtype": flags_core.get_tf_dtype(flags_obj),
+            "fine_tune": flags_obj.fine_tune,
+        },
+    )
+
+    run_params = {
+        "batch_size": flags_obj.batch_size,
+        "dtype": flags_core.get_tf_dtype(flags_obj),
+        "resnet_size": flags_obj.resnet_size,
+        "resnet_version": flags_obj.resnet_version,
+        "synthetic_data": flags_obj.use_synthetic_data,
+        "train_epochs": flags_obj.train_epochs,
+    }
+
+    def input_fn_eval():
+        return input_function(
+            is_training=False,
+            data_dir=flags_obj.data_dir,
+            batch_size=distribution_utils.per_device_batch_size(
+                flags_obj.batch_size, flags_core.get_num_gpus(flags_obj)
+            ),
+            num_epochs=1,
+            dtype=flags_core.get_tf_dtype(flags_obj),
+        )
+
+    schedule, n_loops = [0], 1
+    if flags_obj.export_dir is not None:
+        # Exports a saved model for the given classifier.
+        export_dtype = flags_core.get_tf_dtype(flags_obj)
+        if flags_obj.image_bytes_as_serving_input:
+            input_receiver_fn = functools.partial(
+                image_bytes_serving_input_fn, shape, dtype=export_dtype
+            )
+        else:
+            input_receiver_fn = export.build_tensor_serving_input_receiver_fn(
+                shape, batch_size=flags_obj.batch_size, dtype=export_dtype
+            )
+        classifier.export_savedmodel(
+            flags_obj.export_dir, input_receiver_fn, strip_default_attrs=True
+        )
 
 
 def define_resnet_flags(resnet_size_choices=None):
-  """Add flags and validators for ResNet."""
-  flags_core.define_base()
-  flags_core.define_performance(num_parallel_calls=False,
-                                tf_gpu_thread_mode=True,
-                                datasets_num_private_threads=True,
-                                datasets_num_parallel_batches=True)
-  flags_core.define_image()
-  flags_core.define_benchmark()
-  flags.adopt_module_key_flags(flags_core)
-
-  flags.DEFINE_enum(
-      name='resnet_version', short_name='rv', default='1',
-      enum_values=['1', '2'],
-      help=flags_core.help_wrap(
-          'Version of ResNet. (1 or 2) See README.md for details.'))
-  flags.DEFINE_bool(
-      name='fine_tune', short_name='ft', default=False,
-      help=flags_core.help_wrap(
-          'If True do not train any parameters except for the final layer.'))
-  flags.DEFINE_string(
-      name='pretrained_model_checkpoint_path', short_name='pmcp', default=None,
-      help=flags_core.help_wrap(
-          'If not None initialize all the network except the final layer with '
-          'these values'))
-  flags.DEFINE_boolean(
-      name='eval_only', default=False,
-      help=flags_core.help_wrap('Skip training and only perform evaluation on '
-                                'the latest checkpoint.'))
-  flags.DEFINE_boolean(
-      name='image_bytes_as_serving_input', default=False,
-      help=flags_core.help_wrap(
-          'If True exports savedmodel with serving signature that accepts '
-          'JPEG image bytes instead of a fixed size [HxWxC] tensor that '
-          'represents the image. The former is easier to use for serving at '
-          'the expense of image resize/cropping being done as part of model '
-          'inference. Note, this flag only applies to ImageNet and cannot '
-          'be used for CIFAR.'))
-
-  choice_kwargs = dict(
-      name='resnet_size', short_name='rs', default='50',
-      help=flags_core.help_wrap('The size of the ResNet model to use.'))
-
-  if resnet_size_choices is None:
-    flags.DEFINE_string(**choice_kwargs)
-  else:
-    flags.DEFINE_enum(enum_values=resnet_size_choices, **choice_kwargs)
+    """Add flags and validators for ResNet."""
+    flags_core.define_base()
+    flags_core.define_performance(
+        num_parallel_calls=False,
+        tf_gpu_thread_mode=True,
+        datasets_num_private_threads=True,
+        datasets_num_parallel_batches=True,
+    )
+    flags_core.define_image()
+    flags_core.define_benchmark()
+    flags.adopt_module_key_flags(flags_core)
+
+    flags.DEFINE_enum(
+        name="resnet_version",
+        short_name="rv",
+        default="1",
+        enum_values=["1", "2"],
+        help=flags_core.help_wrap(
+            "Version of ResNet. (1 or 2) See README.md for details."
+        ),
+    )
+    flags.DEFINE_bool(
+        name="fine_tune",
+        short_name="ft",
+        default=False,
+        help=flags_core.help_wrap(
+            "If True do not train any parameters except for the final layer."
+        ),
+    )
+    flags.DEFINE_string(
+        name="pretrained_model_checkpoint_path",
+        short_name="pmcp",
+        default=None,
+        help=flags_core.help_wrap(
+            "If not None initialize all the network except the final layer with "
+            "these values"
+        ),
+    )
+    flags.DEFINE_boolean(
+        name="eval_only",
+        default=False,
+        help=flags_core.help_wrap(
+            "Skip training and only perform evaluation on " "the latest checkpoint."
+        ),
+    )
+    flags.DEFINE_boolean(
+        name="image_bytes_as_serving_input",
+        default=False,
+        help=flags_core.help_wrap(
+            "If True exports savedmodel with serving signature that accepts "
+            "JPEG image bytes instead of a fixed size [HxWxC] tensor that "
+            "represents the image. The former is easier to use for serving at "
+            "the expense of image resize/cropping being done as part of model "
+            "inference. Note, this flag only applies to ImageNet and cannot "
+            "be used for CIFAR."
+        ),
+    )
+
+    choice_kwargs = dict(
+        name="resnet_size",
+        short_name="rs",
+        default="50",
+        help=flags_core.help_wrap("The size of the ResNet model to use."),
+    )
+
+    if resnet_size_choices is None:
+        flags.DEFINE_string(**choice_kwargs)
+    else:
+        flags.DEFINE_enum(enum_values=resnet_size_choices, **choice_kwargs)
diff --git a/vision/classification_and_detection/tools/retinanet_pytorch_to_onnx.py b/vision/classification_and_detection/tools/retinanet_pytorch_to_onnx.py
index cee4af6c6c..3a0fb2d998 100644
--- a/vision/classification_and_detection/tools/retinanet_pytorch_to_onnx.py
+++ b/vision/classification_and_detection/tools/retinanet_pytorch_to_onnx.py
@@ -35,7 +35,8 @@ def get_args():
     num_classes = 264
     image_size = [800, 800]
 
-    model = retinanet_from_backbone(backbone, num_classes, image_size=image_size)
+    model = retinanet_from_backbone(
+        backbone, num_classes, image_size=image_size)
     checkpoint = torch.load(args.weights, map_location="cpu")
     model.load_state_dict(checkpoint["model"])
     model.eval()
diff --git a/vision/classification_and_detection/tools/ssd-nhwc.py b/vision/classification_and_detection/tools/ssd-nhwc.py
index f255fd965f..d7ef9599e5 100644
--- a/vision/classification_and_detection/tools/ssd-nhwc.py
+++ b/vision/classification_and_detection/tools/ssd-nhwc.py
@@ -4,33 +4,39 @@
 import tensorflow as tf
 from tensorflow.core.framework import graph_pb2
 
+
 def get_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument('pbfile')
+    parser.add_argument("pbfile")
     return parser.parse_args()
 
+
 def insert_transpose(graph, a, b, to_nchw):
     if not isinstance(b, list):
         b = [b]
     trans_perm = graph.node.add()
-    trans_perm.name = a.name + '/transpose/perm'
-    trans_perm.op = 'Const'
-    trans_perm.attr['dtype'].type = 3 # DT_INT32
-    trans_perm.attr['value'].tensor.dtype = 3 # DT_INT32
-    trans_perm.attr['value'].tensor.tensor_shape.dim.add()
-    trans_perm.attr['value'].tensor.tensor_shape.dim[0].size = 4
+    trans_perm.name = a.name + "/transpose/perm"
+    trans_perm.op = "Const"
+    trans_perm.attr["dtype"].type = 3  # DT_INT32
+    trans_perm.attr["value"].tensor.dtype = 3  # DT_INT32
+    trans_perm.attr["value"].tensor.tensor_shape.dim.add()
+    trans_perm.attr["value"].tensor.tensor_shape.dim[0].size = 4
     if to_nchw:
-        trans_perm.attr['value'].tensor.tensor_content = b'\000\000\000\000\003\000\000\000\001\000\000\000\002\000\000\000'
+        trans_perm.attr["value"].tensor.tensor_content = (
+            b"\000\000\000\000\003\000\000\000\001\000\000\000\002\000\000\000"
+        )
     else:
-        trans_perm.attr['value'].tensor.tensor_content = b'\000\000\000\000\002\000\000\000\003\000\000\000\001\000\000\000'
-    
+        trans_perm.attr["value"].tensor.tensor_content = (
+            b"\000\000\000\000\002\000\000\000\003\000\000\000\001\000\000\000"
+        )
+
     trans = graph.node.add()
-    trans.name = a.name + '/transpose'
-    trans.op = 'Transpose'
+    trans.name = a.name + "/transpose"
+    trans.op = "Transpose"
     trans.input.append(a.name)
     trans.input.append(trans_perm.name)
-    trans.attr['T'].type = 1
-    trans.attr['Tperm'].type = 3
+    trans.attr["T"].type = 1
+    trans.attr["Tperm"].type = 3
 
     for n in b:
         inputs = []
@@ -45,6 +51,7 @@ def insert_transpose(graph, a, b, to_nchw):
         for i in range(0, cnt):
             n.input.append(inputs[i])
 
+
 def convert_list_nhwc(l):
     c = l.i[1]
     h = l.i[2]
@@ -52,102 +59,116 @@ def convert_list_nhwc(l):
     l.i[1] = h
     l.i[2] = w
     l.i[3] = c
-    
+
+
 def convert_conv_nhwc(node_conv):
-    node_conv.attr['data_format'].s = b'NHWC'
-    convert_list_nhwc(node_conv.attr['dilations'].list)
-    convert_list_nhwc(node_conv.attr['strides'].list)
+    node_conv.attr["data_format"].s = b"NHWC"
+    convert_list_nhwc(node_conv.attr["dilations"].list)
+    convert_list_nhwc(node_conv.attr["strides"].list)
+
 
 def convert_general_nhwc(node):
-    node.attr['data_format'].s = b'NHWC'
+    node.attr["data_format"].s = b"NHWC"
+
 
 def convert_mp_nhwc(node_mp):
-    node_mp.attr['data_format'].s = b'NHWC'
-    convert_list_nhwc(node_mp.attr['ksize'].list)
-    convert_list_nhwc(node_mp.attr['strides'].list)
+    node_mp.attr["data_format"].s = b"NHWC"
+    convert_list_nhwc(node_mp.attr["ksize"].list)
+    convert_list_nhwc(node_mp.attr["strides"].list)
+
 
 def convert_image_nhwc(node_image):
-    c = node_image.attr['shape'].shape.dim[1].size
-    del node_image.attr['shape'].shape.dim[1]
-    d = node_image.attr['shape'].shape.dim.add()
+    c = node_image.attr["shape"].shape.dim[1].size
+    del node_image.attr["shape"].shape.dim[1]
+    d = node_image.attr["shape"].shape.dim.add()
     d.size = c
 
+
 def init_node(n):
     node = {}
-    node['node'] = n
-    node['inputs'] = []
-    node['outputs'] = []
+    node["node"] = n
+    node["inputs"] = []
+    node["outputs"] = []
     return node
 
+
 def connect_nodes(n1, n2):
-    if n2['node'].name not in n1['outputs']:
-        n1['outputs'].append(n2['node'].name)
-        n2['inputs'].append(n1['node'].name)
+    if n2["node"].name not in n1["outputs"]:
+        n1["outputs"].append(n2["node"].name)
+        n2["inputs"].append(n1["node"].name)
     else:
-        print('{} -> {} already connected'.format(n1['node'].name, n2['node'].name))
+        print(
+            "{} -> {} already connected".format(n1["node"].name, n2["node"].name))
+
 
 def disconnect_nodes(n1, n2):
-    if n1['node'].name not in n2['inputs'] or n2['node'].name not in n1['outputs']:
-        print('{} -> {} not connected'.format(n1['node'].name, n2['node'].name))
-    for i in range(0, len(n1['outputs'])):
-        if n1['outputs'][i] == n2['node'].name:
-            del n1['outputs'][i]
+    if n1["node"].name not in n2["inputs"] or n2["node"].name not in n1["outputs"]:
+        print(
+            "{} -> {} not connected".format(n1["node"].name, n2["node"].name))
+    for i in range(0, len(n1["outputs"])):
+        if n1["outputs"][i] == n2["node"].name:
+            del n1["outputs"][i]
             break
-    for i in range(0, len(n2['inputs'])):
-        if n2['inputs'][i] == n1['node'].name:
-            del n2['inputs'][i]
+    for i in range(0, len(n2["inputs"])):
+        if n2["inputs"][i] == n1["node"].name:
+            del n2["inputs"][i]
             break
-            
+
+
 def build_graph(graph):
     node_map = {}
     for n in graph.node:
         node = init_node(n)
         node_map[n.name] = node
     for n in node_map:
-        for i in node_map[n]['node'].input:
-            if ':' in i:
-                i = i[:i.find(':')]
-            i = i.lstrip('^')
+        for i in node_map[n]["node"].input:
+            if ":" in i:
+                i = i[: i.find(":")]
+            i = i.lstrip("^")
             if i not in node_map:
-                print('node {} not found'.format(i))
+                print("node {} not found".format(i))
             else:
                 connect_nodes(node_map[i], node_map[n])
     return node_map
 
+
 def trim_const_from_graph(node_map):
     trim_list = []
     for n in node_map:
-        if node_map[n]['node'].op == 'Const':
+        if node_map[n]["node"].op == "Const":
             trim_list.append(n)
     for n in trim_list:
-        print('trimming {}'.format(n))
-        for o in node_map[n]['outputs']:
+        print("trimming {}".format(n))
+        for o in node_map[n]["outputs"]:
             disconnect_nodes(node_map[n], node_map[o])
         del node_map[n]
 
     trim_list = []
     for n in node_map:
-        if node_map[n]['node'].op == 'Identity' and len(node_map[n]['inputs']) == 0:
+        if node_map[n]["node"].op == "Identity" and len(
+                node_map[n]["inputs"]) == 0:
             trim_list.append(n)
     for n in trim_list:
-        print('trimming {}'.format(n))
-        for o in node_map[n]['outputs']:
+        print("trimming {}".format(n))
+        for o in node_map[n]["outputs"]:
             disconnect_nodes(node_map[n], node_map[o])
         del node_map[n]
 
 
 def all_input_in_nhwc(n, node_map, nhwc_nodes):
-    for i in node_map[n]['inputs']:
+    for i in node_map[n]["inputs"]:
         if i not in nhwc_nodes:
             return False
     return True
 
+
 def all_output_in_nhwc(n, node_map, nhwc_nodes):
-    for o in node_map[n]['outputs']:
+    for o in node_map[n]["outputs"]:
         if o not in nhwc_nodes:
             return False
     return True
 
+
 def find_nhwc_region(node_map):
     transpose_nhwc_nodes = {}
     transpose_nchw_nodes = {}
@@ -156,7 +177,7 @@ def find_nhwc_region(node_map):
     transpose_nhwc_nodes_append_list = []
     transpose_nchw_nodes_append_list = []
     for n in node_map:
-        if node_map[n]['node'].op == 'Conv2D':
+        if node_map[n]["node"].op == "Conv2D":
             transpose_nhwc_nodes_append_list.append(n)
             transpose_nchw_nodes_append_list.append(n)
             nhwc_nodes.append(n)
@@ -168,12 +189,18 @@ def find_nhwc_region(node_map):
             transpose_nchw_nodes[n] = 1
 
     prev_cnt_nhwc_nodes = len(nhwc_nodes)
-    nhwc_op_list = ['Conv2D', 'Relu', 'FusedBatchNorm', 'MaxPool', 'BiasAdd', 'Add']
+    nhwc_op_list = [
+        "Conv2D",
+        "Relu",
+        "FusedBatchNorm",
+        "MaxPool",
+        "BiasAdd",
+        "Add"]
     while True:
         transpose_nchw_nodes_append_list = []
         for n in transpose_nchw_nodes:
-            for o in node_map[n]['outputs']:
-                if o not in nhwc_nodes and node_map[o]['node'].op in nhwc_op_list:
+            for o in node_map[n]["outputs"]:
+                if o not in nhwc_nodes and node_map[o]["node"].op in nhwc_op_list:
                     if all_input_in_nhwc(o, node_map, nhwc_nodes):
                         nhwc_nodes.append(o)
                         if o not in transpose_nchw_nodes_append_list:
@@ -182,15 +209,19 @@ def find_nhwc_region(node_map):
         transpose_nhwc_nodes_remove_list = []
         transpose_nchw_nodes_remove_list = []
         for n in transpose_nhwc_nodes:
-            if (all_input_in_nhwc(n, node_map, nhwc_nodes) and
-                n not in transpose_nhwc_nodes_remove_list):
+            if (
+                all_input_in_nhwc(n, node_map, nhwc_nodes)
+                and n not in transpose_nhwc_nodes_remove_list
+            ):
                 transpose_nhwc_nodes_remove_list.append(n)
         for n in transpose_nhwc_nodes_remove_list:
             del transpose_nhwc_nodes[n]
 
         for n in transpose_nchw_nodes:
-            if (all_output_in_nhwc(n, node_map, nhwc_nodes) and
-                n not in transpose_nchw_nodes_remove_list):
+            if (
+                all_output_in_nhwc(n, node_map, nhwc_nodes)
+                and n not in transpose_nchw_nodes_remove_list
+            ):
                 transpose_nchw_nodes_remove_list.append(n)
         for n in transpose_nchw_nodes_remove_list:
             del transpose_nchw_nodes[n]
@@ -202,66 +233,81 @@ def find_nhwc_region(node_map):
         if len(nhwc_nodes) == prev_cnt_nhwc_nodes:
             break
         prev_cnt_nhwc_nodes = len(nhwc_nodes)
-                        
-    print('\n\nTranspose to NHWC at nodes:')
+
+    print("\n\nTranspose to NHWC at nodes:")
     for n in transpose_nhwc_nodes:
-        print('    {}'.format(n))
-    
-    print('\n\nTranspose to NCHW at nodes:')
+        print("    {}".format(n))
+
+    print("\n\nTranspose to NCHW at nodes:")
     for n in transpose_nchw_nodes:
-        print('    {}'.format(n))
-    
+        print("    {}".format(n))
+
     return nhwc_nodes, transpose_nhwc_nodes, transpose_nchw_nodes
 
+
 def main():
     args = get_args()
 
     graph = graph_pb2.GraphDef()
-    with open(args.pbfile, 'rb') as f:
+    with open(args.pbfile, "rb") as f:
         graph.ParseFromString(f.read())
 
     node_map = build_graph(graph)
     trim_const_from_graph(node_map)
 
-    nhwc_nodes, transpose_nhwc_nodes, transpose_nchw_nodes = find_nhwc_region(node_map)
+    nhwc_nodes, transpose_nhwc_nodes, transpose_nchw_nodes = find_nhwc_region(
+        node_map)
 
-    nhwc_op_list = ['Conv2D', 'Relu', 'FusedBatchNorm', 'MaxPool', 'BiasAdd', 'Add']
+    nhwc_op_list = [
+        "Conv2D",
+        "Relu",
+        "FusedBatchNorm",
+        "MaxPool",
+        "BiasAdd",
+        "Add"]
     for n in nhwc_nodes:
-        if node_map[n]['node'].op == 'Conv2D':
-            convert_conv_nhwc(node_map[n]['node'])
-        elif node_map[n]['node'].op in ['FusedBatchNorm', 'BiasAdd']:
-            convert_general_nhwc(node_map[n]['node'])
-        elif node_map[n]['node'].op == 'MaxPool':
-            convert_mp_nhwc(node_map[n]['node'])
-      
+        if node_map[n]["node"].op == "Conv2D":
+            convert_conv_nhwc(node_map[n]["node"])
+        elif node_map[n]["node"].op in ["FusedBatchNorm", "BiasAdd"]:
+            convert_general_nhwc(node_map[n]["node"])
+        elif node_map[n]["node"].op == "MaxPool":
+            convert_mp_nhwc(node_map[n]["node"])
+
     done_nhwc = False
     if len(transpose_nhwc_nodes) == 1:
         for n in transpose_nhwc_nodes:
-            if len(node_map[n]['inputs']) == 1 and node_map[n]['inputs'][0] == 'image':
+            if len(node_map[n]["inputs"]
+                   ) == 1 and node_map[n]["inputs"][0] == "image":
                 image_outputs = []
-                for o in node_map['image']['outputs']:
+                for o in node_map["image"]["outputs"]:
                     if o != n:
-                        image_outputs.append(node_map[o]['node'])
-                insert_transpose(graph, node_map['image']['node'], image_outputs, True)
-                convert_image_nhwc(node_map['image']['node'])
+                        image_outputs.append(node_map[o]["node"])
+                insert_transpose(
+                    graph,
+                    node_map["image"]["node"],
+                    image_outputs,
+                    True)
+                convert_image_nhwc(node_map["image"]["node"])
                 done_nhwc = True
 
     if not done_nhwc:
         for n in transpose_nhwc_nodes:
-            for i in node_map[n]['inputs']:
+            for i in node_map[n]["inputs"]:
                 if i not in nhwc_nodes:
-                    insert_transpose(graph, node_map[i]['node'], node_map[n]['node'], False)
+                    insert_transpose(
+                        graph, node_map[i]["node"], node_map[n]["node"], False
+                    )
 
     for n in transpose_nchw_nodes:
         node_outputs = []
-        for o in node_map[n]['outputs']:
+        for o in node_map[n]["outputs"]:
             if o not in nhwc_nodes:
-                node_outputs.append(node_map[o]['node'])
-        insert_transpose(graph, node_map[n]['node'], node_outputs, True)
+                node_outputs.append(node_map[o]["node"])
+        insert_transpose(graph, node_map[n]["node"], node_outputs, True)
 
-    with open(args.pbfile+'.patch', 'wb') as f:
+    with open(args.pbfile + ".patch", "wb") as f:
         f.write(graph.SerializeToString())
 
-if __name__ == '__main__':
-    main()
 
+if __name__ == "__main__":
+    main()
diff --git a/vision/medical_imaging/3d-unet-kits19/accuracy_kits.py b/vision/medical_imaging/3d-unet-kits19/accuracy_kits.py
index 93ab11adcb..1beb601558 100644
--- a/vision/medical_imaging/3d-unet-kits19/accuracy_kits.py
+++ b/vision/medical_imaging/3d-unet-kits19/accuracy_kits.py
@@ -41,11 +41,11 @@
 Accuracy check from MLPerf-Inference accuracy log file:
     python3 accuracy_kits.py
     or
-    python3 accuracy_kits.py --log_file $(LOG_DIR)/$(ACCURACY_LOG_FILENAME) 
+    python3 accuracy_kits.py --log_file $(LOG_DIR)/$(ACCURACY_LOG_FILENAME)
                              --output_dtype $(DTYPE)
-                             --preprocessed_data_dir $(PREPROCESSED_DATA_DIR) 
+                             --preprocessed_data_dir $(PREPROCESSED_DATA_DIR)
                              --postprocessed_data_dir $(POSTPROCESSED_DATA_DIR)
-                             --num_proc $(NUMBER_PROCESSES) 
+                             --num_proc $(NUMBER_PROCESSES)
 """
 
 # $(DTYPE) mapping to numpy dtype
@@ -57,7 +57,7 @@
     "uint8": np.uint8,
     "float16": np.float16,
     "float32": np.float32,
-    "float64": np.float64
+    "float64": np.float64,
 }
 
 
@@ -65,25 +65,36 @@ def get_args():
     """
     Args used for postprocessing
     """
-    parser = argparse.ArgumentParser(description=__doc__,
-                                     formatter_class=argparse.RawTextHelpFormatter)
-    parser.add_argument("--log_file",
-                        default="build/logs/mlperf_log_accuracy.json",
-                        help="Path to accuracy log json file")
-    parser.add_argument("--output_dtype",
-                        default="uint8",
-                        choices=dtype_map.keys(),
-                        help="Output data type")
-    parser.add_argument("--preprocessed_data_dir",
-                        default="build/preprocessed_data",
-                        help="Path to the directory containing preprocessed data")
-    parser.add_argument("--postprocessed_data_dir",
-                        default="build/postprocessed_data",
-                        help="Path to the directory containing postprocessed data")
-    parser.add_argument("--num_proc",
-                        type=int,
-                        default=4,
-                        help="Number of processors running postprocessing")
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawTextHelpFormatter
+    )
+    parser.add_argument(
+        "--log_file",
+        default="build/logs/mlperf_log_accuracy.json",
+        help="Path to accuracy log json file",
+    )
+    parser.add_argument(
+        "--output_dtype",
+        default="uint8",
+        choices=dtype_map.keys(),
+        help="Output data type",
+    )
+    parser.add_argument(
+        "--preprocessed_data_dir",
+        default="build/preprocessed_data",
+        help="Path to the directory containing preprocessed data",
+    )
+    parser.add_argument(
+        "--postprocessed_data_dir",
+        default="build/postprocessed_data",
+        help="Path to the directory containing postprocessed data",
+    )
+    parser.add_argument(
+        "--num_proc",
+        type=int,
+        default=4,
+        help="Number of processors running postprocessing",
+    )
     args = parser.parse_args()
     return args
 
@@ -104,7 +115,7 @@ def prepare_one_hot(my_array, num_classes):
     Reinterprets my_array into one-hot encoded, for classes as many as num_classes
     """
     res = np.eye(num_classes)[np.array(my_array).reshape(-1)]
-    return res.reshape(list(my_array.shape)+[num_classes])
+    return res.reshape(list(my_array.shape) + [num_classes])
 
 
 def get_dice_score(case, prediction, target):
@@ -128,10 +139,12 @@ def get_dice_score(case, prediction, target):
     prediction = prediction[:, 1:]
 
     # calculate dice score
-    assert target.shape == prediction.shape, \
-        f"Different shape -- target: {target.shape}, prediction: {prediction.shape}"
-    assert target.dtype == np.float64 and prediction.dtype == np.float64, \
-        f"Unexpected dtype -- target: {target.dtype}, prediction: {prediction.dtype}"
+    assert (
+        target.shape == prediction.shape
+    ), f"Different shape -- target: {target.shape}, prediction: {prediction.shape}"
+    assert (
+        target.dtype == np.float64 and prediction.dtype == np.float64
+    ), f"Unexpected dtype -- target: {target.dtype}, prediction: {prediction.dtype}"
 
     # intersection for numerator; target/prediction sum for denominator
     # easy b/c one-hot encoded format
@@ -140,24 +153,28 @@ def get_dice_score(case, prediction, target):
     prediction_sum = np.sum(prediction, axis=reduce_axis)
 
     # get DICE score for each class
-    dice_val = (2.0 * intersection + smooth_nr) / \
-        (target_sum + prediction_sum + smooth_dr)
+    dice_val = (2.0 * intersection + smooth_nr) / (
+        target_sum + prediction_sum + smooth_dr
+    )
 
     # return after removing batch dim
     return (case, dice_val[0])
 
 
-def evaluate(target_files, preprocessed_data_dir, postprocessed_data_dir, num_proc):
+def evaluate(target_files, preprocessed_data_dir,
+             postprocessed_data_dir, num_proc):
     """
     Collects and summarizes DICE scores of all the predicted files using multi-processes
     """
     bundle = list()
 
     for case in target_files:
-        groundtruth_path = Path(preprocessed_data_dir,
-                                "nifti", case, "segmentation.nii.gz").absolute()
-        prediction_path = Path(postprocessed_data_dir,
-                               case, "prediction.nii.gz").absolute()
+        groundtruth_path = Path(
+            preprocessed_data_dir, "nifti", case, "segmentation.nii.gz"
+        ).absolute()
+        prediction_path = Path(
+            postprocessed_data_dir, case, "prediction.nii.gz"
+        ).absolute()
 
         groundtruth = nib.load(groundtruth_path).get_fdata().astype(np.uint8)
         prediction = nib.load(prediction_path).get_fdata().astype(np.uint8)
@@ -165,9 +182,11 @@ def evaluate(target_files, preprocessed_data_dir, postprocessed_data_dir, num_pr
         groundtruth = np.expand_dims(groundtruth, 0)
         prediction = np.expand_dims(prediction, 0)
 
-        assert groundtruth.shape == prediction.shape,\
-            "{} -- groundtruth: {} and prediction: {} have different shapes".format(
-                case, groundtruth.shape, prediction.shape)
+        assert (
+            groundtruth.shape == prediction.shape
+        ), "{} -- groundtruth: {} and prediction: {} have different shapes".format(
+            case, groundtruth.shape, prediction.shape
+        )
 
         bundle.append((case, groundtruth, prediction))
 
@@ -190,12 +209,9 @@ def save_evaluation_summary(postprocessed_data_dir, dice_scores):
         tumor = arr[1]
         composite = np.mean(arr)
         df = df.append(
-            {
-                "case": case,
-                "kidney": kidney,
-                "tumor": tumor,
-                "composite": composite
-            }, ignore_index=True)
+            {"case": case, "kidney": kidney, "tumor": tumor, "composite": composite},
+            ignore_index=True,
+        )
 
     df.set_index("case", inplace=True)
     # consider NaN as a crash hence zero
@@ -211,16 +227,18 @@ def save_nifti(bundle):
     # Note that affine has to be valid, otherwise NIFTI image will look weird
     image, affine, path_to_file = bundle
     if len(image.shape) != 3:
-        assert len(image.shape) == 4 and image.shape[0] == 1,\
-            "Unexpected image: {}".format(image.shape)
+        assert (
+            len(image.shape) == 4 and image.shape[0] == 1
+        ), "Unexpected image: {}".format(image.shape)
         image = np.squeeze(image, 0)
     nifti_image = nib.Nifti1Image(image, affine=affine)
     path_to_file.parent.mkdir(parents=True, exist_ok=True)
     nib.save(nifti_image, path_to_file)
 
 
-def save_predictions(predictions, output_dir, preprocessed_data_dir,
-                     preprocessed_files, aux, num_proc):
+def save_predictions(
+    predictions, output_dir, preprocessed_data_dir, preprocessed_files, aux, num_proc
+):
     """
     Saves all the segmentation result from inference into NIFTI files using affine matrices
     Affine matrices were stored for input images during preprocessing
@@ -230,8 +248,10 @@ def save_predictions(predictions, output_dir, preprocessed_data_dir,
     bundle = list()
     for case, case_d in predictions.items():
         pred_file_path = Path(output_dir, case, "prediction.nii.gz")
-        bundle.append((case_d['prediction'], aux[case]
-                       ['reshaped_affine'], pred_file_path))
+        bundle.append(
+            (case_d["prediction"], aux[case]
+             ["reshaped_affine"], pred_file_path)
+        )
 
     with Pool(num_proc) as p:
         p.map(save_nifti, bundle)
@@ -249,8 +269,9 @@ def load_loadgen_log(log_file, result_dtype, file_list, aux):
     with open(log_file) as f:
         predictions = json.load(f)
 
-    assert len(predictions) == len(aux.keys()),\
-        "Number of predictions does not match number of samples in validation set!"
+    assert len(predictions) == len(
+        aux.keys()
+    ), "Number of predictions does not match number of samples in validation set!"
 
     results = dict()
     for prediction in predictions:
@@ -258,12 +279,10 @@ def load_loadgen_log(log_file, result_dtype, file_list, aux):
         case = file_list[qsl_idx]
         assert qsl_idx >= 0 and qsl_idx < len(predictions), "Invalid qsl_idx!"
         result_shape = np.array(list(aux[case]["image_shape"]))
-        result = np.frombuffer(bytes.fromhex(
-            prediction["data"]), result_dtype).reshape(result_shape)
-        results[case] = {
-            'qsl_idx': qsl_idx,
-            'prediction': result
-        }
+        result = np.frombuffer(bytes.fromhex(prediction["data"]), result_dtype).reshape(
+            result_shape
+        )
+        results[case] = {"qsl_idx": qsl_idx, "prediction": result}
 
     assert len(results) == len(predictions), "Missing some results!"
 
@@ -287,8 +306,8 @@ def main():
     print("Loading necessary metadata...")
     with open(Path(preprocessed_data_dir, "preprocessed_files.pkl"), "rb") as f:
         preprocessed_files_content = pickle.load(f)
-    target_files = preprocessed_files_content['file_list']
-    aux = preprocessed_files_content['cases']
+    target_files = preprocessed_files_content["file_list"]
+    aux = preprocessed_files_content["cases"]
 
     # Load predictions from loadgen accuracy log.
     print("Loading loadgen accuracy log...")
@@ -296,23 +315,35 @@ def main():
 
     # Save predictions
     print("Running postprocessing...")
-    save_predictions(predictions, postprocessed_data_dir, preprocessed_data_dir,
-                     target_files, aux, num_proc)
+    save_predictions(
+        predictions,
+        postprocessed_data_dir,
+        preprocessed_data_dir,
+        target_files,
+        aux,
+        num_proc,
+    )
 
     # Run evaluation
     print("Running evaluation...")
-    evaluate(target_files, preprocessed_data_dir,
-             postprocessed_data_dir, num_proc)
+    evaluate(
+        target_files,
+        preprocessed_data_dir,
+        postprocessed_data_dir,
+        num_proc)
 
     # Finalize evaluation from evaluation summary
     print("Processing evaluation summary...")
     df = pd.read_csv(Path(postprocessed_data_dir, "summary.csv"))
-    final = df.loc[df['case'] == 'mean']
-    composite = float(final['composite'])
-    kidney = float(final['kidney'])
-    tumor = float(final['tumor'])
-    print("Accuracy: mean = {:.5f}, kidney = {:.4f}, tumor = {:.4f}".format(
-          composite, kidney, tumor))
+    final = df.loc[df["case"] == "mean"]
+    composite = float(final["composite"])
+    kidney = float(final["kidney"])
+    tumor = float(final["tumor"])
+    print(
+        "Accuracy: mean = {:.5f}, kidney = {:.4f}, tumor = {:.4f}".format(
+            composite, kidney, tumor
+        )
+    )
     print("Done!")
 
 
diff --git a/vision/medical_imaging/3d-unet-kits19/base_SUT.py b/vision/medical_imaging/3d-unet-kits19/base_SUT.py
index 2208fbb63d..d94319b773 100644
--- a/vision/medical_imaging/3d-unet-kits19/base_SUT.py
+++ b/vision/medical_imaging/3d-unet-kits19/base_SUT.py
@@ -118,31 +118,39 @@ def infer_single_query(self, query, mystr):
         # prepare arrays
         image = query[np.newaxis, ...]
         result, norm_map, norm_patch = infu.prepare_arrays(image, ROI_SHAPE)
-        t_image, t_result, t_norm_map, t_norm_patch =\
-            self.to_tensor(image), self.to_tensor(result), self.to_tensor(
-                norm_map), self.to_tensor(norm_patch)
+        t_image, t_result, t_norm_map, t_norm_patch = (
+            self.to_tensor(image),
+            self.to_tensor(result),
+            self.to_tensor(norm_map),
+            self.to_tensor(norm_patch),
+        )
 
         # sliding window inference
         subvol_cnt = 0
-        for i, j, k in infu.get_slice_for_sliding_window(t_image, ROI_SHAPE, SLIDE_OVERLAP_FACTOR):
+        for i, j, k in infu.get_slice_for_sliding_window(
+            t_image, ROI_SHAPE, SLIDE_OVERLAP_FACTOR
+        ):
             subvol_cnt += 1
             result_slice = t_result[
                 ...,
-                i:(ROI_SHAPE[0] + i),
-                j:(ROI_SHAPE[1] + j),
-                k:(ROI_SHAPE[2] + k)]
+                i: (ROI_SHAPE[0] + i),
+                j: (ROI_SHAPE[1] + j),
+                k: (ROI_SHAPE[2] + k),
+            ]
 
             input_slice = t_image[
                 ...,
-                i:(ROI_SHAPE[0] + i),
-                j:(ROI_SHAPE[1] + j),
-                k:(ROI_SHAPE[2] + k)]
+                i: (ROI_SHAPE[0] + i),
+                j: (ROI_SHAPE[1] + j),
+                k: (ROI_SHAPE[2] + k),
+            ]
 
             norm_map_slice = t_norm_map[
                 ...,
-                i:(ROI_SHAPE[0] + i),
-                j:(ROI_SHAPE[1] + j),
-                k:(ROI_SHAPE[2] + k)]
+                i: (ROI_SHAPE[0] + i),
+                j: (ROI_SHAPE[1] + j),
+                k: (ROI_SHAPE[2] + k),
+            ]
 
             result_slice += self.do_infer(input_slice) * t_norm_patch
 
@@ -169,7 +177,8 @@ def issue_queries(self, query_samples):
         for qsi in range(total):
             query = self.qsl.get_features(query_samples[qsi].index)
             mystr = "{:5d}/{:5d} -- Processing sample id {:2d} with shape = {:}".format(
-                    qsi+1, total, query_samples[qsi].index, query.shape)
+                qsi + 1, total, query_samples[qsi].index, query.shape
+            )
             final_result, mystr = self.infer_single_query(query, mystr)
             if mystr:
                 print(mystr)
diff --git a/vision/medical_imaging/3d-unet-kits19/global_vars.py b/vision/medical_imaging/3d-unet-kits19/global_vars.py
index 883d2f778a..78c5e12355 100644
--- a/vision/medical_imaging/3d-unet-kits19/global_vars.py
+++ b/vision/medical_imaging/3d-unet-kits19/global_vars.py
@@ -24,33 +24,41 @@
 """
 
 __all__ = [
-    'CHECKSUM_INFER_FILE',
-    'CHECKSUM_CALIB_FILE',
-    'TARGET_CASES',
-    'CALIB_CASES',
-    'MEAN_VAL',
-    'STDDEV_VAL',
-    'MIN_CLIP_VAL',
-    'MAX_CLIP_VAL',
-    'PADDING_VAL',
-    'TARGET_SPACING',
-    'ROI_SHAPE',
-    'SLIDE_OVERLAP_FACTOR',
+    "CHECKSUM_INFER_FILE",
+    "CHECKSUM_CALIB_FILE",
+    "TARGET_CASES",
+    "CALIB_CASES",
+    "MEAN_VAL",
+    "STDDEV_VAL",
+    "MIN_CLIP_VAL",
+    "MAX_CLIP_VAL",
+    "PADDING_VAL",
+    "TARGET_SPACING",
+    "ROI_SHAPE",
+    "SLIDE_OVERLAP_FACTOR",
 ]
 
 # file pointers and sanity checks
 INFERENCE_CASE_FILE = Path(
-    Path.cwd(), 'meta', 'inference_cases.json').absolute()
+    Path.cwd(),
+    "meta",
+    "inference_cases.json").absolute()
 CALIBRATION_CASE_FILE = Path(
-    Path.cwd(), 'meta', 'calibration_cases.json').absolute()
+    Path.cwd(),
+    "meta",
+    "calibration_cases.json").absolute()
 CHECKSUM_INFER_FILE = Path(
-    Path.cwd(), 'meta', 'checksum_inference.json').absolute()
+    Path.cwd(),
+    "meta",
+    "checksum_inference.json").absolute()
 CHECKSUM_CALIB_FILE = Path(
-    Path.cwd(), 'meta', 'checksum_calibration.json').absolute()
-assert INFERENCE_CASE_FILE.is_file(), 'inference_cases.json is not found'
-assert CALIBRATION_CASE_FILE.is_file(), 'calibration_cases.json is not found'
-assert CHECKSUM_INFER_FILE.is_file(), 'checksum_inference.json is not found'
-assert CHECKSUM_CALIB_FILE.is_file(), 'checksum_calibration.json is not found'
+    Path.cwd(),
+    "meta",
+    "checksum_calibration.json").absolute()
+assert INFERENCE_CASE_FILE.is_file(), "inference_cases.json is not found"
+assert CALIBRATION_CASE_FILE.is_file(), "calibration_cases.json is not found"
+assert CHECKSUM_INFER_FILE.is_file(), "checksum_inference.json is not found"
+assert CHECKSUM_CALIB_FILE.is_file(), "checksum_calibration.json is not found"
 
 # cases used for inference and calibration
 TARGET_CASES = json.load(open(INFERENCE_CASE_FILE))
@@ -65,11 +73,16 @@
 TARGET_SPACING = [1.6, 1.2, 1.2]
 ROI_SHAPE = [128, 128, 128]
 SLIDE_OVERLAP_FACTOR = 0.5
-assert isinstance(TARGET_SPACING, list) and \
-    len(TARGET_SPACING) == 3 and any(TARGET_SPACING), \
-    f"Need proper target spacing: {TARGET_SPACING}"
-assert isinstance(ROI_SHAPE, list) and len(ROI_SHAPE) == 3 and any(ROI_SHAPE), \
-    f"Need proper ROI shape: {ROI_SHAPE}"
-assert isinstance(SLIDE_OVERLAP_FACTOR, float) and \
-    SLIDE_OVERLAP_FACTOR > 0 and SLIDE_OVERLAP_FACTOR < 1, \
-    f"Need sliding window overlap factor in (0,1): {SLIDE_OVERLAP_FACTOR}"
+assert (
+    isinstance(TARGET_SPACING, list)
+    and len(TARGET_SPACING) == 3
+    and any(TARGET_SPACING)
+), f"Need proper target spacing: {TARGET_SPACING}"
+assert (
+    isinstance(ROI_SHAPE, list) and len(ROI_SHAPE) == 3 and any(ROI_SHAPE)
+), f"Need proper ROI shape: {ROI_SHAPE}"
+assert (
+    isinstance(SLIDE_OVERLAP_FACTOR, float)
+    and SLIDE_OVERLAP_FACTOR > 0
+    and SLIDE_OVERLAP_FACTOR < 1
+), f"Need sliding window overlap factor in (0,1): {SLIDE_OVERLAP_FACTOR}"
diff --git a/vision/medical_imaging/3d-unet-kits19/inference_utils.py b/vision/medical_imaging/3d-unet-kits19/inference_utils.py
index 5f7b66cd93..333f9846cf 100644
--- a/vision/medical_imaging/3d-unet-kits19/inference_utils.py
+++ b/vision/medical_imaging/3d-unet-kits19/inference_utils.py
@@ -103,28 +103,34 @@ def prepare_arrays(image, roi_shape=ROI_SHAPE):
     - norm_map where normal map is constructed upon
     - norm_patch, a gaussian kernel that is applied to each sub-volume inference result
     """
-    assert isinstance(roi_shape, list) and len(roi_shape) == 3 and any(roi_shape),\
-        f"Need proper ROI shape: {roi_shape}"
+    assert (
+        isinstance(roi_shape, list) and len(roi_shape) == 3 and any(roi_shape)
+    ), f"Need proper ROI shape: {roi_shape}"
 
     image_shape = list(image.shape[2:])
 
     result = np.zeros(shape=(1, 3, *image_shape), dtype=image.dtype)
     norm_map = np.zeros_like(result)
-    norm_patch = gaussian_kernel(
-        roi_shape[0], 0.125*roi_shape[0]).astype(norm_map.dtype)
+    norm_patch = gaussian_kernel(roi_shape[0], 0.125 * roi_shape[0]).astype(
+        norm_map.dtype
+    )
 
     return result, norm_map, norm_patch
 
 
-def get_slice_for_sliding_window(image, roi_shape=ROI_SHAPE, overlap=SLIDE_OVERLAP_FACTOR):
+def get_slice_for_sliding_window(
+    image, roi_shape=ROI_SHAPE, overlap=SLIDE_OVERLAP_FACTOR
+):
     """
     Returns indices for image stride, to fulfill sliding window inference
     Stride is determined by roi_shape and overlap
     """
-    assert isinstance(roi_shape, list) and len(roi_shape) == 3 and any(roi_shape),\
-        f"Need proper ROI shape: {roi_shape}"
-    assert isinstance(overlap, float) and overlap > 0 and overlap < 1,\
-        f"Need sliding window overlap factor in (0,1): {overlap}"
+    assert (
+        isinstance(roi_shape, list) and len(roi_shape) == 3 and any(roi_shape)
+    ), f"Need proper ROI shape: {roi_shape}"
+    assert (
+        isinstance(overlap, float) and overlap > 0 and overlap < 1
+    ), f"Need sliding window overlap factor in (0,1): {overlap}"
 
     image_shape = list(image.shape[2:])
     dim = len(image_shape)
@@ -150,7 +156,7 @@ def get_latency(*args, **kw):
         ts = time.time()
         result, mystr = function(*args, **kw)
         te = time.time()
-        print('{:86} took {:>10.5f} sec'.format(mystr, te - ts))
+        print("{:86} took {:>10.5f} sec".format(mystr, te - ts))
         return result, ""
 
     return get_latency
diff --git a/vision/medical_imaging/3d-unet-kits19/kits_QSL.py b/vision/medical_imaging/3d-unet-kits19/kits_QSL.py
index 59a7b31d97..85ffb80f46 100644
--- a/vision/medical_imaging/3d-unet-kits19/kits_QSL.py
+++ b/vision/medical_imaging/3d-unet-kits19/kits_QSL.py
@@ -26,12 +26,12 @@ class KiTS_2019_QSL:
     """
     A class to represent QSL (Query Sample Library) for MLPerf.
 
-    This populates preprocessed KiTS19 inference data set into LoadGen compatible QSL 
+    This populates preprocessed KiTS19 inference data set into LoadGen compatible QSL
 
     Attributes
     ----------
     preprocessed_data_dir: str
-        path to directory containing preprocessed data 
+        path to directory containing preprocessed data
     preprocessed_files: list of str
         list of KiTS19 cases that are used in inference
     count: int
@@ -62,8 +62,10 @@ def __init__(self, preprocessed_data_dir, perf_count):
         """
         print("Constructing QSL...")
         self.preprocessed_data_dir = preprocessed_data_dir
-        with open(Path(self.preprocessed_data_dir, "preprocessed_files.pkl"), "rb") as f:
-            self.preprocess_files = pickle.load(f)['file_list']
+        with open(
+            Path(self.preprocessed_data_dir, "preprocessed_files.pkl"), "rb"
+        ) as f:
+            self.preprocess_files = pickle.load(f)["file_list"]
 
         self.count = len(self.preprocess_files)
         self.perf_count = perf_count if perf_count is not None else self.count
@@ -72,7 +74,11 @@ def __init__(self, preprocessed_data_dir, perf_count):
 
         self.loaded_files = {}
         self.qsl = lg.ConstructQSL(
-            self.count, self.perf_count, self.load_query_samples, self.unload_query_samples)
+            self.count,
+            self.perf_count,
+            self.load_query_samples,
+            self.unload_query_samples,
+        )
         print("Finished constructing QSL.")
 
     def load_query_samples(self, sample_list):
@@ -82,7 +88,10 @@ def load_query_samples(self, sample_list):
         for sample_id in sample_list:
             file_name = self.preprocess_files[sample_id]
             print("Loading file {:}".format(file_name))
-            with open(Path(self.preprocessed_data_dir, "{:}.pkl".format(file_name)), "rb") as f:
+            with open(
+                Path(self.preprocessed_data_dir,
+                     "{:}.pkl".format(file_name)), "rb"
+            ) as f:
                 self.loaded_files[sample_id] = pickle.load(f)[0]
 
     def unload_query_samples(self, sample_list):
@@ -99,5 +108,6 @@ def get_features(self, sample_id):
         return self.loaded_files[sample_id]
 
 
-def get_kits_QSL(preprocessed_data_dir="build/preprocessed_data", perf_count=None):
+def get_kits_QSL(
+        preprocessed_data_dir="build/preprocessed_data", perf_count=None):
     return KiTS_2019_QSL(preprocessed_data_dir, perf_count)
diff --git a/vision/medical_imaging/3d-unet-kits19/onnxruntime_SUT.py b/vision/medical_imaging/3d-unet-kits19/onnxruntime_SUT.py
index 37fe5fd141..4c64185444 100644
--- a/vision/medical_imaging/3d-unet-kits19/onnxruntime_SUT.py
+++ b/vision/medical_imaging/3d-unet-kits19/onnxruntime_SUT.py
@@ -23,6 +23,7 @@
 import onnxruntime
 import os
 
+
 class _3DUNET_ONNXRuntime_SUT(BASE_3DUNET_SUT):
     """
     A class to represent SUT (System Under Test) for MLPerf.
@@ -56,17 +57,24 @@ def __init__(self, model_path, preprocessed_data_dir, performance_count):
             preprocessed_data_dir: str or PosixPath
                 path to directory containing preprocessed data
             performance_count: int
-                number of query samples guaranteed to fit in memory            
+                number of query samples guaranteed to fit in memory
         """
         super().__init__(preprocessed_data_dir, performance_count)
         print("Loading ONNX model...")
-        assert Path(model_path).is_file(
-        ), "Cannot find the model file {:}!".format(model_path)
+        assert Path(model_path).is_file(), "Cannot find the model file {:}!".format(
+            model_path
+        )
         opt = onnxruntime.SessionOptions()
-        if len(onnxruntime.get_all_providers()) > 1 and os.environ.get("USE_GPU", "yes").lower() not in [ "0", "false", "off", "no" ]:
-            self.sess = onnxruntime.InferenceSession(model_path, opt, providers=["CUDAExecutionProvider"])
+        if len(onnxruntime.get_all_providers()) > 1 and os.environ.get(
+            "USE_GPU", "yes"
+        ).lower() not in ["0", "false", "off", "no"]:
+            self.sess = onnxruntime.InferenceSession(
+                model_path, opt, providers=["CUDAExecutionProvider"]
+            )
         else:
-            self.sess = onnxruntime.InferenceSession(model_path, opt, providers=["CPUExecutionProvider"])
+            self.sess = onnxruntime.InferenceSession(
+                model_path, opt, providers=["CPUExecutionProvider"]
+            )
 
     def do_infer(self, input_tensor):
         """
@@ -79,4 +87,5 @@ def get_sut(model_path, preprocessed_data_dir, performance_count):
     """
     Redirect the call for instantiating SUT to ONNX Runtime specific SUT
     """
-    return _3DUNET_ONNXRuntime_SUT(model_path, preprocessed_data_dir, performance_count)
+    return _3DUNET_ONNXRuntime_SUT(
+        model_path, preprocessed_data_dir, performance_count)
diff --git a/vision/medical_imaging/3d-unet-kits19/preprocess.py b/vision/medical_imaging/3d-unet-kits19/preprocess.py
index e71c96da1b..ce7ea33c49 100644
--- a/vision/medical_imaging/3d-unet-kits19/preprocess.py
+++ b/vision/medical_imaging/3d-unet-kits19/preprocess.py
@@ -67,11 +67,11 @@ class Stats:
         collects average values in the preprocessed images
     std: list
         collects standard deviation of values in the preprocessed images
-    d: 
+    d:
         collects depths of the preprocessed images
-    h: 
+    h:
         collects heights of the preprocessed images
-    w: 
+    w:
         collects widths of the preprocessed images
 
     Methods
@@ -94,11 +94,11 @@ def __init__(self):
             collects average values in the preprocessed images
         std: list
             collects standard deviation of values in the preprocessed images
-        d: 
+        d:
             collects depths of the preprocessed images
-        h: 
+        h:
             collects heights of the preprocessed images
-        w: 
+        w:
             collects widths of the preprocessed images
         """
         self.mean = []
@@ -182,7 +182,7 @@ class Preprocessor:
     pad_to_min_shape(image, label):
         pads image/label so that the shape is equal or larger than ROI shape
     load_and_resample(case):
-        gets a pair of CT-imaging/segmentation data for the case, then, 
+        gets a pair of CT-imaging/segmentation data for the case, then,
         resample to the same, predetermined common voxel spacing
     normalize_intensity(image):
         normalize intensity for a given target stats
@@ -245,9 +245,11 @@ def collect_cases(self):
         all_set = set([f for f in os.listdir(self.data_dir) if "case" in f])
         target_set = set(self.target_cases)
         collected_set = all_set & target_set
-        assert collected_set == target_set,\
-            "Some of the target inference cases were found: {}".format(
-                target_set - collected_set)
+        assert (
+            collected_set == target_set
+        ), "Some of the target inference cases were found: {}".format(
+            target_set - collected_set
+        )
         return sorted(list(collected_set))
 
     def print_stats(self):
@@ -279,7 +281,7 @@ def preprocess_case(self, case):
         image, label = self.pad_to_min_shape(image, label)
         image, label = self.adjust_shape_for_sliding_window(image, label)
         self.save(image, label, aux)
-        aux['image_shape'] = image.shape
+        aux["image_shape"] = image.shape
         return aux
 
     @staticmethod
@@ -290,8 +292,9 @@ def pad_to_min_shape(image, label):
         current_shape = image.shape[1:]
         bounds = [max(0, ROI_SHAPE[i] - current_shape[i]) for i in range(3)]
         paddings = [(0, 0)]
-        paddings.extend([(bounds[i] // 2, bounds[i] - bounds[i] // 2)
-                         for i in range(3)])
+        paddings.extend(
+            [(bounds[i] // 2, bounds[i] - bounds[i] // 2) for i in range(3)]
+        )
 
         image = np.pad(image, paddings, mode="edge")
         label = np.pad(label, paddings, mode="edge")
@@ -307,9 +310,13 @@ def load_and_resample(self, case: str):
         aux = dict()
 
         image = nibabel.load(
-            Path(self.data_dir, case, "imaging.nii.gz").absolute())
+            Path(
+                self.data_dir,
+                case,
+                "imaging.nii.gz").absolute())
         label = nibabel.load(
-            Path(self.data_dir, case, "segmentation.nii.gz").absolute())
+            Path(self.data_dir, case, "segmentation.nii.gz").absolute()
+        )
 
         image_spacings = image.header["pixdim"][1:4].tolist()
         original_affine = image.affine
@@ -329,15 +336,27 @@ def load_and_resample(self, case: str):
             reshaped_affine[i][idx] = targ_arr[idx] * sign
 
         if image_spacings != self.target_spacing:
-            image = zoom(image, zoom_factor, order=1,
-                         mode='constant', cval=image.min(), grid_mode=False)
-            label = zoom(label, zoom_factor, order=0,
-                         mode='constant', cval=label.min(), grid_mode=False)
-
-        aux['original_affine'] = original_affine
-        aux['reshaped_affine'] = reshaped_affine
-        aux['zoom_factor'] = zoom_factor
-        aux['case'] = case
+            image = zoom(
+                image,
+                zoom_factor,
+                order=1,
+                mode="constant",
+                cval=image.min(),
+                grid_mode=False,
+            )
+            label = zoom(
+                label,
+                zoom_factor,
+                order=0,
+                mode="constant",
+                cval=label.min(),
+                grid_mode=False,
+            )
+
+        aux["original_affine"] = original_affine
+        aux["reshaped_affine"] = reshaped_affine
+        aux["zoom_factor"] = zoom_factor
+        aux["case"] = case
 
         image = np.expand_dims(image, 0)
         label = np.expand_dims(label, 0)
@@ -369,40 +388,57 @@ def adjust_shape_for_sliding_window(self, image, label):
         strides = [int(roi_shape[i] * (1 - overlap)) for i in range(dim)]
 
         bounds = [image_shape[i] % strides[i] for i in range(dim)]
-        bounds = [bounds[i] if bounds[i] <
-                  strides[i] // 2 else 0 for i in range(dim)]
-        image = image[...,
-                      bounds[0] // 2: image_shape[0] - (bounds[0] - bounds[0] // 2),
-                      bounds[1] // 2: image_shape[1] - (bounds[1] - bounds[1] // 2),
-                      bounds[2] // 2: image_shape[2] - (bounds[2] - bounds[2] // 2)]
-        label = label[...,
-                      bounds[0] // 2: image_shape[0] - (bounds[0] - bounds[0] // 2),
-                      bounds[1] // 2: image_shape[1] - (bounds[1] - bounds[1] // 2),
-                      bounds[2] // 2: image_shape[2] - (bounds[2] - bounds[2] // 2)]
+        bounds = [
+            bounds[i] if bounds[i] < strides[i] //
+            2 else 0 for i in range(dim)]
+        image = image[
+            ...,
+            bounds[0] // 2: image_shape[0] - (bounds[0] - bounds[0] // 2),
+            bounds[1] // 2: image_shape[1] - (bounds[1] - bounds[1] // 2),
+            bounds[2] // 2: image_shape[2] - (bounds[2] - bounds[2] // 2),
+        ]
+        label = label[
+            ...,
+            bounds[0] // 2: image_shape[0] - (bounds[0] - bounds[0] // 2),
+            bounds[1] // 2: image_shape[1] - (bounds[1] - bounds[1] // 2),
+            bounds[2] // 2: image_shape[2] - (bounds[2] - bounds[2] // 2),
+        ]
         image, paddings = self.constant_pad_volume(
-            image, roi_shape, strides, self.padding_val)
+            image, roi_shape, strides, self.padding_val
+        )
         label, paddings = self.constant_pad_volume(
             label, roi_shape, strides, 0)
 
         return image, label
 
-    def constant_pad_volume(self, volume, roi_shape, strides, padding_val, dim=3):
+    def constant_pad_volume(self, volume, roi_shape,
+                            strides, padding_val, dim=3):
         """
         Helper padding volume symmetrically with value of padding_val
         Padded volume becomes ROI shape friendly
         """
-        bounds = [(strides[i] - volume.shape[1:][i] % strides[i]) %
-                  strides[i] for i in range(dim)]
-        bounds = [bounds[i] if (volume.shape[1:][i] + bounds[i]) >= roi_shape[i] else
-                  bounds[i] + strides[i]
-                  for i in range(dim)]
-        paddings = [(0, 0),
-                    (bounds[0] // 2, bounds[0] - bounds[0] // 2),
-                    (bounds[1] // 2, bounds[1] - bounds[1] // 2),
-                    (bounds[2] // 2, bounds[2] - bounds[2] // 2)]
+        bounds = [
+            (strides[i] - volume.shape[1:][i] % strides[i]) % strides[i]
+            for i in range(dim)
+        ]
+        bounds = [
+            (
+                bounds[i]
+                if (volume.shape[1:][i] + bounds[i]) >= roi_shape[i]
+                else bounds[i] + strides[i]
+            )
+            for i in range(dim)
+        ]
+        paddings = [
+            (0, 0),
+            (bounds[0] // 2, bounds[0] - bounds[0] // 2),
+            (bounds[1] // 2, bounds[1] - bounds[1] // 2),
+            (bounds[2] // 2, bounds[2] - bounds[2] // 2),
+        ]
 
         padded_volume = np.pad(
-            volume, paddings, mode='constant', constant_values=[padding_val])
+            volume, paddings, mode="constant", constant_values=[padding_val]
+        )
         return padded_volume, paddings
 
     def save(self, image, label, aux):
@@ -416,35 +452,51 @@ def save(self, image, label, aux):
             - case name
         Preprocessed imaging/segmentation data saved as NIFTI
         """
-        case = aux['case']
-        reshaped_affine = aux['reshaped_affine']
+        case = aux["case"]
+        reshaped_affine = aux["reshaped_affine"]
         image = image.astype(np.float32)
         label = label.astype(np.uint8)
         mean, std = np.round(np.mean(image, (1, 2, 3)), 2), np.round(
-            np.std(image, (1, 2, 3)), 2)
+            np.std(image, (1, 2, 3)), 2
+        )
         self.stats.append(
-            mean, std, image.shape[1], image.shape[2], image.shape[3])
+            mean,
+            std,
+            image.shape[1],
+            image.shape[2],
+            image.shape[3])
         pickle_file_path = Path(self.results_dir, f"{case}.pkl").absolute()
         with open(pickle_file_path, "wb") as f:
             pickle.dump([image, label], f)
         f.close()
         print(
-            f"Saved {str(pickle_file_path)} -- shape {image.shape} mean {mean} std {std}")
+            f"Saved {str(pickle_file_path)} -- shape {image.shape} mean {mean} std {std}"
+        )
         if not self.calibration:
             path_to_nifti_dir = Path(
                 self.results_dir, "nifti", case).absolute()
             path_to_nifti_dir.mkdir(parents=True, exist_ok=True)
             nifti_image = nibabel.Nifti1Image(
-                np.squeeze(image, 0), affine=reshaped_affine)
+                np.squeeze(image, 0), affine=reshaped_affine
+            )
             nifti_label = nibabel.Nifti1Image(
-                np.squeeze(label, 0), affine=reshaped_affine)
-            nibabel.save(nifti_image, Path(
-                path_to_nifti_dir / "imaging.nii.gz"))
-            nibabel.save(nifti_label, Path(
-                path_to_nifti_dir / "segmentation.nii.gz"))
-            assert nifti_image.shape == nifti_label.shape, \
-                "While saving NIfTI files to {}, image: {} and label: {} have different shape".format(
-                    path_to_nifti_dir, nifti_image.shape, nifti_label.shape)
+                np.squeeze(label, 0), affine=reshaped_affine
+            )
+            nibabel.save(
+                nifti_image,
+                Path(
+                    path_to_nifti_dir /
+                    "imaging.nii.gz"))
+            nibabel.save(
+                nifti_label,
+                Path(
+                    path_to_nifti_dir /
+                    "segmentation.nii.gz"))
+            assert (
+                nifti_image.shape == nifti_label.shape
+            ), "While saving NIfTI files to {}, image: {} and label: {} have different shape".format(
+                path_to_nifti_dir, nifti_image.shape, nifti_label.shape
+            )
 
 
 def preprocess_multiproc_helper(preproc, case):
@@ -459,10 +511,12 @@ def save_preprocessed_info(preproc_dir, aux, targets):
     """
     Saves list of preprocessed files and the associated aux info into preprocessed_files.pkl
     """
-    assert len(targets) == len(aux['cases']),\
-        "Error in number of preprocessed files:\nExpected:{}\nProcessed:{}".format(
-            targets, list(aux['cases'].keys()))
-    with open(os.path.join(preproc_dir, 'preprocessed_files.pkl'), 'wb') as f:
+    assert len(targets) == len(
+        aux["cases"]
+    ), "Error in number of preprocessed files:\nExpected:{}\nProcessed:{}".format(
+        targets, list(aux["cases"].keys())
+    )
+    with open(os.path.join(preproc_dir, "preprocessed_files.pkl"), "wb") as f:
         pickle.dump(aux, f)
     f.close()
 
@@ -473,16 +527,14 @@ def preprocess_with_multiproc(args):
     """
     preproc = Preprocessor(args)
     cases = preproc.collect_cases()
-    aux = {
-        'file_list': preproc.target_cases,
-        'cases': dict()
-    }
+    aux = {"file_list": preproc.target_cases, "cases": dict()}
     with Pool(args.num_proc) as p:
-        pool_out = p.starmap(preprocess_multiproc_helper,
-                             zip([preproc]*len(cases), cases))
+        pool_out = p.starmap(
+            preprocess_multiproc_helper, zip([preproc] * len(cases), cases)
+        )
 
     for _d in pool_out:
-        aux['cases'][_d['case']] = _d
+        aux["cases"][_d["case"]] = _d
     save_preprocessed_info(preproc.results_dir, aux, preproc.target_cases)
     p.join()
     p.close()
@@ -492,7 +544,7 @@ def generate_hash_from_volume(vol_path):
     """
     Generates MD5 hash from a single preprocessed file
     """
-    with open(vol_path, 'rb') as f:
+    with open(vol_path, "rb") as f:
         data = f.read()
         md5_hash = hashlib.md5(data).hexdigest()
     f.close()
@@ -507,19 +559,21 @@ def generate_hash_from_dataset(args):
     num_proc = args.num_proc
     checksum = dict()
     CHECKSUM_FILE = CHECKSUM_CALIB_FILE if args.calibration else CHECKSUM_INFER_FILE
-    results = [f for f in os.listdir(results_dir) if f.startswith(
-        'case') and f.endswith('pkl')]
+    results = [
+        f for f in os.listdir(results_dir) if f.startswith("case") and f.endswith("pkl")
+    ]
     vol_path = [os.path.join(results_dir, v) for v in results]
 
     print(
-        f"Generating checksum file checksum.json from preprocessed data in {results_dir}...")
+        f"Generating checksum file checksum.json from preprocessed data in {results_dir}..."
+    )
     with Pool(num_proc) as p:
         pool_out = p.map(generate_hash_from_volume, vol_path)
 
     for vol, md5 in pool_out:
         checksum[vol] = md5
 
-    with open(CHECKSUM_FILE, 'w') as f:
+    with open(CHECKSUM_FILE, "w") as f:
         json.dump(dict(sorted(checksum.items())), f, indent=4, sort_keys=True)
     f.close()
 
@@ -535,8 +589,9 @@ def verify_dataset(args):
     results_dir = args.results_dir
     num_proc = args.num_proc
     CHECKSUM_FILE = CHECKSUM_CALIB_FILE if args.calibration else CHECKSUM_INFER_FILE
-    results = [f for f in os.listdir(results_dir) if f.startswith(
-        'case') and f.endswith('pkl')]
+    results = [
+        f for f in os.listdir(results_dir) if f.startswith("case") and f.endswith("pkl")
+    ]
     vol_path = [os.path.join(results_dir, v) for v in results]
     violations = dict()
 
@@ -544,9 +599,11 @@ def verify_dataset(args):
     with open(CHECKSUM_FILE) as f:
         source = json.load(f)
     f.close()
-    assert len(source) == len(results),\
-        "checksum.json has {} entries while {} volumes found".format(
-            len(source), len(results))
+    assert len(source) == len(
+        results
+    ), "checksum.json has {} entries while {} volumes found".format(
+        len(source), len(results)
+    )
 
     with Pool(num_proc) as p:
         pool_out = p.map(generate_hash_from_volume, vol_path)
@@ -558,9 +615,9 @@ def verify_dataset(args):
     if any(violations):
         for vol, (res, ref) in violations.items():
             print(f"{vol} -- Invalid hash, got {res} while expecting {ref}")
-        assert False,\
-            "Verification failed, {}/{} mismatch(es) found".format(
-                len(violations), len(results))
+        assert False, "Verification failed, {}/{} mismatch(es) found".format(
+            len(violations), len(results)
+        )
 
     p.join()
     p.close()
@@ -571,34 +628,45 @@ def parse_args():
     """
     Args used for preprocessing
     """
-    PARSER = argparse.ArgumentParser(description=__doc__,
-                                     formatter_class=argparse.RawTextHelpFormatter)
-
-    PARSER.add_argument('--raw_data_dir',
-                        dest='data_dir',
-                        required=True,
-                        help="Dir where KiTS19 GIT repo is cloned")
-    PARSER.add_argument('--results_dir',
-                        dest='results_dir',
-                        required=True,
-                        help="Dir to store preprocessed data")
-    PARSER.add_argument('--mode',
-                        dest='mode',
-                        choices=["preprocess", "verify", "gen_hash"],
-                        default="preprocess",
-                        help="""preprocess for generating inference dataset, 
-                                gen_hash for generating new checksum file, 
-                                verify for verifying the checksums against stored checksum file""")
-    PARSER.add_argument('--calibration',
-                        dest='calibration',
-                        action='store_true',
-                        help="Preprocess calibration dataset instead of inference dataset")
-    PARSER.add_argument('--num_proc',
-                        dest='num_proc',
-                        type=int,
-                        choices=list(range(1, 17)),
-                        default=4,
-                        help="Number of processes to be used")
+    PARSER = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawTextHelpFormatter
+    )
+
+    PARSER.add_argument(
+        "--raw_data_dir",
+        dest="data_dir",
+        required=True,
+        help="Dir where KiTS19 GIT repo is cloned",
+    )
+    PARSER.add_argument(
+        "--results_dir",
+        dest="results_dir",
+        required=True,
+        help="Dir to store preprocessed data",
+    )
+    PARSER.add_argument(
+        "--mode",
+        dest="mode",
+        choices=["preprocess", "verify", "gen_hash"],
+        default="preprocess",
+        help="""preprocess for generating inference dataset,
+                                gen_hash for generating new checksum file,
+                                verify for verifying the checksums against stored checksum file""",
+    )
+    PARSER.add_argument(
+        "--calibration",
+        dest="calibration",
+        action="store_true",
+        help="Preprocess calibration dataset instead of inference dataset",
+    )
+    PARSER.add_argument(
+        "--num_proc",
+        dest="num_proc",
+        type=int,
+        choices=list(range(1, 17)),
+        default=4,
+        help="Number of processes to be used",
+    )
 
     args = PARSER.parse_args()
 
@@ -622,5 +690,5 @@ def main():
         verify_dataset(args)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/vision/medical_imaging/3d-unet-kits19/pytorch_SUT.py b/vision/medical_imaging/3d-unet-kits19/pytorch_SUT.py
index af3051ada4..cd136adf19 100644
--- a/vision/medical_imaging/3d-unet-kits19/pytorch_SUT.py
+++ b/vision/medical_imaging/3d-unet-kits19/pytorch_SUT.py
@@ -63,12 +63,13 @@ def __init__(self, model_path, preprocessed_data_dir, performance_count):
             preprocessed_data_dir: str or PosixPath
                 path to directory containing preprocessed data
             performance_count: int
-                number of query samples guaranteed to fit in memory                
+                number of query samples guaranteed to fit in memory
         """
         super().__init__(preprocessed_data_dir, performance_count)
         print("Loading PyTorch model...")
-        assert Path(model_path).is_file(
-        ), "Cannot find the model file {:}!".format(model_path)
+        assert Path(model_path).is_file(), "Cannot find the model file {:}!".format(
+            model_path
+        )
         self.device = torch.device(
             "cuda:0" if torch.cuda.is_available() else "cpu")
         self.model = torch.jit.load(model_path, map_location=self.device)
@@ -98,4 +99,5 @@ def get_sut(model_path, preprocessed_data_dir, performance_count):
     """
     Redirect the call for instantiating SUT to PyTorch/TorchScript specific SUT
     """
-    return _3DUNET_PyTorch_SUT(model_path, preprocessed_data_dir, performance_count)
+    return _3DUNET_PyTorch_SUT(
+        model_path, preprocessed_data_dir, performance_count)
diff --git a/vision/medical_imaging/3d-unet-kits19/pytorch_checkpoint_SUT.py b/vision/medical_imaging/3d-unet-kits19/pytorch_checkpoint_SUT.py
index a4a047510e..1453d5cf59 100644
--- a/vision/medical_imaging/3d-unet-kits19/pytorch_checkpoint_SUT.py
+++ b/vision/medical_imaging/3d-unet-kits19/pytorch_checkpoint_SUT.py
@@ -38,6 +38,7 @@
     "none": lambda _, __: nn.Identity(),
 }
 
+
 def _normalization(norm_type, num_features, num_groups=16):
     """
     A helper redirecting normalization function used in 3D-UNet
@@ -46,6 +47,7 @@ def _normalization(norm_type, num_features, num_groups=16):
         return normalizations[norm_type](num_features, num_groups)
     raise ValueError(f"Unknown normalization {norm_type}")
 
+
 def _activation(activation):
     """
     A helper redirecting activation function used in 3D-UNet
@@ -54,33 +56,53 @@ def _activation(activation):
         return activations[activation]
     raise ValueError(f"Unknown activation {activation}")
 
-def conv_block_factory(in_channels, out_channels,
-                       kernel_size=3, stride=1, padding=1,
-                       conv_type="regular",
-                       normalization="instancenorm", activation="relu"):
+
+def conv_block_factory(
+    in_channels,
+    out_channels,
+    kernel_size=3,
+    stride=1,
+    padding=1,
+    conv_type="regular",
+    normalization="instancenorm",
+    activation="relu",
+):
     """
     A method used for building basic 3D Convolution block of 3D-UNet
     """
     conv = convolutions[conv_type]
-    conv = conv(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
-                padding=padding, bias=normalization=="none")
+    conv = conv(
+        in_channels,
+        out_channels,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=padding,
+        bias=normalization == "none",
+    )
 
     normalization = _normalization(normalization, out_channels)
     activation = _activation(activation)
 
-    return nn.Sequential(conv, normalization, activation)    
+    return nn.Sequential(conv, normalization, activation)
 
 
 class DownsampleBlock(nn.Module):
     """
     A class building encoder block of 3D-UNet
     """
+
     def __init__(self, in_channels, out_channels):
         super(DownsampleBlock, self).__init__()
-        self.conv1 = conv_block_factory(in_channels, out_channels, stride=2,
-                                        normalization="instancenorm", activation="relu")
-        self.conv2 = conv_block_factory(out_channels, out_channels, 
-                                        normalization="instancenorm", activation="relu")
+        self.conv1 = conv_block_factory(
+            in_channels,
+            out_channels,
+            stride=2,
+            normalization="instancenorm",
+            activation="relu",
+        )
+        self.conv2 = conv_block_factory(
+            out_channels, out_channels, normalization="instancenorm", activation="relu"
+        )
 
     def forward(self, x):
         x = self.conv1(x)
@@ -92,20 +114,32 @@ class UpsampleBlock(nn.Module):
     """
     A class building decoder block of 3D-UNet
     """
+
     def __init__(self, in_channels, out_channels):
         super(UpsampleBlock, self).__init__()
 
         self.in_channels = in_channels
         self.out_channels = out_channels
-        self.upsample_conv = conv_block_factory(in_channels, out_channels,
-                                                kernel_size=2, stride=2, padding=0,
-                                                conv_type="transpose", 
-                                                normalization="none", activation="none")
+        self.upsample_conv = conv_block_factory(
+            in_channels,
+            out_channels,
+            kernel_size=2,
+            stride=2,
+            padding=0,
+            conv_type="transpose",
+            normalization="none",
+            activation="none",
+        )
 
-        self.conv1 = conv_block_factory(2 * out_channels, out_channels,
-                                        normalization="instancenorm", activation="relu")
-        self.conv2 = conv_block_factory(out_channels, out_channels,
-                                        normalization="instancenorm", activation="relu")
+        self.conv1 = conv_block_factory(
+            2 * out_channels,
+            out_channels,
+            normalization="instancenorm",
+            activation="relu",
+        )
+        self.conv2 = conv_block_factory(
+            out_channels, out_channels, normalization="instancenorm", activation="relu"
+        )
 
     def forward(self, x, skip):
         x = self.upsample_conv(x)
@@ -119,12 +153,15 @@ class InputBlock(nn.Module):
     """
     A class building the very first input block of 3D-UNet
     """
+
     def __init__(self, in_channels, out_channels):
         super(InputBlock, self).__init__()
-        self.conv1 = conv_block_factory(in_channels, out_channels, 
-                                        normalization="instancenorm", activation="relu")
-        self.conv2 = conv_block_factory(out_channels, out_channels,
-                                        normalization="instancenorm", activation="relu")
+        self.conv1 = conv_block_factory(
+            in_channels, out_channels, normalization="instancenorm", activation="relu"
+        )
+        self.conv2 = conv_block_factory(
+            out_channels, out_channels, normalization="instancenorm", activation="relu"
+        )
 
     def forward(self, x):
         x = self.conv1(x)
@@ -136,10 +173,17 @@ class OutputLayer(nn.Module):
     """
     A class building final output block of 3D-UNet
     """
+
     def __init__(self, in_channels, n_class):
         super(OutputLayer, self).__init__()
-        self.conv = conv_block_factory(in_channels, n_class, kernel_size=1, padding=0,
-                                       activation="none", normalization="none")
+        self.conv = conv_block_factory(
+            in_channels,
+            n_class,
+            kernel_size=1,
+            padding=0,
+            activation="none",
+            normalization="none",
+        )
 
     def forward(self, x):
         return self.conv(x)
@@ -157,12 +201,13 @@ class Unet3D(nn.Module):
     n_class: int
         number of classes the segmentation ends up for
     """
+
     def __init__(self, in_channels=1, n_class=3):
         """
         Constructs 3D-UNet as in MLPerf-Training 3D-UNet:
         https://github.com/mlcommons/training/blob/master/image_segmentation/pytorch
 
-        """        
+        """
         super(Unet3D, self).__init__()
 
         filters = [32, 64, 128, 256, 320]
@@ -175,18 +220,21 @@ def __init__(self, in_channels=1, n_class=3):
         self.input_block = InputBlock(in_channels, input_dim)
 
         self.downsample = nn.ModuleList(
-            [DownsampleBlock(i, o)
-             for (i, o) in zip(self.inp, self.out)]
+            [DownsampleBlock(i, o) for (i, o) in zip(self.inp, self.out)]
         )
         self.bottleneck = DownsampleBlock(filters[-1], filters[-1])
         upsample = [UpsampleBlock(filters[-1], filters[-1])]
-        upsample.extend([UpsampleBlock(i, o)
-                         for (i, o) in zip(reversed(self.out), reversed(self.inp))])
+        upsample.extend(
+            [
+                UpsampleBlock(i, o)
+                for (i, o) in zip(reversed(self.out), reversed(self.inp))
+            ]
+        )
         self.upsample = nn.ModuleList(upsample)
         self.output = OutputLayer(input_dim, n_class)
 
         for name, v in self.named_parameters():
-            if 'weight' in name or 'bias' in name:
+            if "weight" in name or "bias" in name:
                 v.data *= 1.0
 
     def forward(self, x):
@@ -246,18 +294,19 @@ def __init__(self, model_path, preprocessed_data_dir, performance_count):
             preprocessed_data_dir: str or PosixPath
                 path to directory containing preprocessed data
             performance_count: int
-                number of query samples guaranteed to fit in memory                
+                number of query samples guaranteed to fit in memory
         """
         super().__init__(preprocessed_data_dir, performance_count)
         print("Loading PyTorch model...")
-        assert Path(model_path).is_file(
-        ), "Cannot find the model file {:}!".format(model_path)
+        assert Path(model_path).is_file(), "Cannot find the model file {:}!".format(
+            model_path
+        )
         self.device = torch.device(
             "cuda:0" if torch.cuda.is_available() else "cpu")
         self.model = Unet3D()
         self.model.to(self.device)
         checkpoint = torch.load(model_path, map_location=self.device)
-        self.model.load_state_dict(checkpoint['best_model_state_dict'])
+        self.model.load_state_dict(checkpoint["best_model_state_dict"])
         self.model.eval()
 
     def do_infer(self, input_tensor):
@@ -284,4 +333,6 @@ def get_sut(model_path, preprocessed_data_dir, performance_count):
     """
     Redirect the call for instantiating SUT to PyTorch/TorchScript specific SUT
     """
-    return _3DUNET_PyTorch_CHECKPOINT_SUT(model_path, preprocessed_data_dir, performance_count)
+    return _3DUNET_PyTorch_CHECKPOINT_SUT(
+        model_path, preprocessed_data_dir, performance_count
+    )
diff --git a/vision/medical_imaging/3d-unet-kits19/run.py b/vision/medical_imaging/3d-unet-kits19/run.py
index c4517e85c8..9d48f99283 100644
--- a/vision/medical_imaging/3d-unet-kits19/run.py
+++ b/vision/medical_imaging/3d-unet-kits19/run.py
@@ -16,15 +16,14 @@
 # limitations under the License.
 
 
+import mlperf_loadgen as lg
+from pathlib import Path
+import subprocess
+import argparse
 import os
 import sys
-sys.path.insert(0, os.getcwd())
-import argparse
-import subprocess
-
-from pathlib import Path
 
-import mlperf_loadgen as lg
+sys.path.insert(0, os.getcwd())
 
 
 __doc__ = """
@@ -41,7 +40,7 @@
 $(SCENARIO): Offline, SingleStream, MultiStream, or Server (Note: MultiStream may be deprecated)
 $(MODEL) should point to correct model for the chosen backend
 
-If run for the accuracy, DICE scores will be summarized and printed at the end of the test, and 
+If run for the accuracy, DICE scores will be summarized and printed at the end of the test, and
 inference results will be stored as NIFTI files.
 
 Performance run can be more specific as:
@@ -65,39 +64,52 @@ def get_args():
     """
     Args used for running 3D UNet KITS19
     """
-    parser = argparse.ArgumentParser(description=__doc__,
-                                     formatter_class=argparse.RawTextHelpFormatter)
-
-    parser.add_argument("--backend",
-                        choices=["pytorch", "pytorch_checkpoint", "onnxruntime", "tensorflow"],
-                        default="pytorch",
-                        help="Backend")
-    parser.add_argument("--scenario",
-                        choices=["SingleStream", "Offline"],
-                        default="Offline",
-                        help="Scenario")
-    parser.add_argument("--accuracy",
-                        action="store_true",
-                        help="enable accuracy pass")
-    parser.add_argument("--mlperf_conf",
-                        default="build/mlperf.conf",
-                        help="mlperf rules config")
-    parser.add_argument("--user_conf",
-                        default="user.conf",
-                        help="user config for user LoadGen settings such as target QPS")
-    parser.add_argument("--audit_conf",
-                        default="audit.conf",
-                        help="audit config for LoadGen settings during compliance runs")
-    parser.add_argument("--model",
-                        default="build/model/3dunet_kits19_pytorch.ptc",
-                        help="Path to PyTorch, ONNX, or TF model")
-    parser.add_argument("--preprocessed_data_dir",
-                        default="build/preprocessed_data",
-                        help="path to preprocessed data")
-    parser.add_argument("--performance_count",
-                        type=int,
-                        default=None,
-                        help="performance count")
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawTextHelpFormatter
+    )
+
+    parser.add_argument(
+        "--backend",
+        choices=["pytorch", "pytorch_checkpoint", "onnxruntime", "tensorflow"],
+        default="pytorch",
+        help="Backend",
+    )
+    parser.add_argument(
+        "--scenario",
+        choices=["SingleStream", "Offline"],
+        default="Offline",
+        help="Scenario",
+    )
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
+    parser.add_argument(
+        "--mlperf_conf", default="build/mlperf.conf", help="mlperf rules config"
+    )
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+    parser.add_argument(
+        "--audit_conf",
+        default="audit.conf",
+        help="audit config for LoadGen settings during compliance runs",
+    )
+    parser.add_argument(
+        "--model",
+        default="build/model/3dunet_kits19_pytorch.ptc",
+        help="Path to PyTorch, ONNX, or TF model",
+    )
+    parser.add_argument(
+        "--preprocessed_data_dir",
+        default="build/preprocessed_data",
+        help="path to preprocessed data",
+    )
+    parser.add_argument(
+        "--performance_count", type=int, default=None, help="performance count"
+    )
     args = parser.parse_args()
     return args
 
@@ -118,7 +130,7 @@ def main():
         "SingleStream": lg.TestScenario.SingleStream,
         "Offline": lg.TestScenario.Offline,
         "Server": lg.TestScenario.Server,
-        "MultiStream": lg.TestScenario.MultiStream
+        "MultiStream": lg.TestScenario.MultiStream,
     }
 
     args = get_args()
@@ -134,8 +146,10 @@ def main():
         from tensorflow_SUT import get_sut
     else:
         raise ValueError("Unknown backend: {:}".format(args.backend))
-    sut = get_sut(args.model, args.preprocessed_data_dir,
-                  args.performance_count)
+    sut = get_sut(
+        args.model,
+        args.preprocessed_data_dir,
+        args.performance_count)
 
     # setup LoadGen
     settings = lg.TestSettings()
@@ -148,7 +162,9 @@ def main():
         settings.mode = lg.TestMode.PerformanceOnly
 
     # set up mlperf logger
-    log_path = Path(os.environ.get("LOG_PATH", os.path.join("build", "logs"))).absolute()
+    log_path = Path(
+        os.environ.get("LOG_PATH", os.path.join("build", "logs"))
+    ).absolute()
     log_path.mkdir(parents=True, exist_ok=True)
     log_output_settings = lg.LogOutputSettings()
     log_output_settings.outdir = str(log_path)
@@ -158,10 +174,12 @@ def main():
 
     # start running test, from LoadGen
     print("Running Loadgen test...")
-    lg.StartTestWithLogSettings(sut.sut, sut.qsl.qsl, settings, log_settings, args.audit_conf)
+    lg.StartTestWithLogSettings(
+        sut.sut, sut.qsl.qsl, settings, log_settings, args.audit_conf
+    )
 
     # if needed check accuracy
-    if args.accuracy and not os.environ.get('SKIP_VERIFY_ACCURACY', False):
+    if args.accuracy and not os.environ.get("SKIP_VERIFY_ACCURACY", False):
         print("Checking accuracy...")
         cmd = f"python3 accuracy_kits.py --preprocessed_data_dir={args.preprocessed_data_dir} --log_file={os.path.join(log_path, 'mlperf_log_accuracy.json')}"
         subprocess.check_call(cmd, shell=True)
diff --git a/vision/medical_imaging/3d-unet-kits19/tensorflow_SUT.py b/vision/medical_imaging/3d-unet-kits19/tensorflow_SUT.py
index 4e6a843ba7..d2ba1bc528 100644
--- a/vision/medical_imaging/3d-unet-kits19/tensorflow_SUT.py
+++ b/vision/medical_imaging/3d-unet-kits19/tensorflow_SUT.py
@@ -61,8 +61,9 @@ def __init__(self, model_path, preprocessed_data_dir, performance_count):
         """
         super().__init__(preprocessed_data_dir, performance_count)
         print("Loading TensorFlow model...")
-        assert Path(model_path, "saved_model.pb").is_file(),\
-            "Cannot find the model file {:}!".format(model_path)
+        assert Path(
+            model_path, "saved_model.pb"
+        ).is_file(), "Cannot find the model file {:}!".format(model_path)
         loaded_model = tf.saved_model.load(model_path)
         self.model = loaded_model.signatures["serving_default"]
         self.output_name = list(self.model.structured_outputs)[0]
@@ -78,4 +79,5 @@ def get_sut(model_path, preprocessed_data_dir, performance_count):
     """
     Redirect the call for instantiating SUT to TensorFlow specific SUT
     """
-    return _3DUNET_TensorFlow_SUT(model_path, preprocessed_data_dir, performance_count)
+    return _3DUNET_TensorFlow_SUT(
+        model_path, preprocessed_data_dir, performance_count)
diff --git a/vision/medical_imaging/3d-unet-kits19/unet_onnx_to_tensorflow.py b/vision/medical_imaging/3d-unet-kits19/unet_onnx_to_tensorflow.py
index 72d8fa3dda..4539e0b986 100644
--- a/vision/medical_imaging/3d-unet-kits19/unet_onnx_to_tensorflow.py
+++ b/vision/medical_imaging/3d-unet-kits19/unet_onnx_to_tensorflow.py
@@ -22,6 +22,7 @@
 import argparse
 import os
 import sys
+
 sys.path.insert(0, os.getcwd())
 
 
@@ -49,18 +50,23 @@ def get_args():
     """
     Args used for converting ONNX to TensorFlow model
     """
-    parser = argparse.ArgumentParser(description=__doc__,
-                                     formatter_class=argparse.RawTextHelpFormatter)
-
-    parser.add_argument("--model",
-                        default="build/model/3dunet_kits19_128x128x128.onnx",
-                        help="Path to the ONNX model")
-    parser.add_argument("--output_name",
-                        default="3dunet_kits19_128x128x128.tf",
-                        help="Name of output model")
-    parser.add_argument("--output_dir",
-                        default="build/model",
-                        help="Directory to save output model")
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawTextHelpFormatter
+    )
+
+    parser.add_argument(
+        "--model",
+        default="build/model/3dunet_kits19_128x128x128.onnx",
+        help="Path to the ONNX model",
+    )
+    parser.add_argument(
+        "--output_name",
+        default="3dunet_kits19_128x128x128.tf",
+        help="Name of output model",
+    )
+    parser.add_argument(
+        "--output_dir", default="build/model", help="Directory to save output model"
+    )
 
     args = parser.parse_args()
 
diff --git a/vision/medical_imaging/3d-unet-kits19/unet_pytorch_to_onnx.py b/vision/medical_imaging/3d-unet-kits19/unet_pytorch_to_onnx.py
index 3c9e2ad1f1..8164d7d05c 100644
--- a/vision/medical_imaging/3d-unet-kits19/unet_pytorch_to_onnx.py
+++ b/vision/medical_imaging/3d-unet-kits19/unet_pytorch_to_onnx.py
@@ -16,17 +16,14 @@
 # limitations under the License.
 
 
+from global_vars import *
+from pathlib import Path
+import torch
+import argparse
 import os
 import sys
-sys.path.insert(0, os.getcwd())
-
-import argparse
-import torch
-
-from pathlib import Path
-
-from global_vars import *
 
+sys.path.insert(0, os.getcwd())
 
 
 __doc__ = """
@@ -50,21 +47,28 @@ def get_args():
     """
     Args used for converting PyTorch/TorchScript to ONNX model
     """
-    parser = argparse.ArgumentParser(description=__doc__,
-                                     formatter_class=argparse.RawTextHelpFormatter)
-
-    parser.add_argument("--model",
-                        default="build/model/3dunet_kits19_pytorch.ptc",
-                        help="Path to the PyTorch model")
-    parser.add_argument("--output_name",
-                        default="3dunet_kits19_128x128x128.onnx",
-                        help="Name of output model")
-    parser.add_argument("--dynamic_bs_output_name",
-                        default="3dunet_kits19_128x128x128_dynbatch.onnx",
-                        help="Name of output model")
-    parser.add_argument("--output_dir",
-                        default="build/model",
-                        help="Directory to save output model")
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawTextHelpFormatter
+    )
+
+    parser.add_argument(
+        "--model",
+        default="build/model/3dunet_kits19_pytorch.ptc",
+        help="Path to the PyTorch model",
+    )
+    parser.add_argument(
+        "--output_name",
+        default="3dunet_kits19_128x128x128.onnx",
+        help="Name of output model",
+    )
+    parser.add_argument(
+        "--dynamic_bs_output_name",
+        default="3dunet_kits19_128x128x128_dynbatch.onnx",
+        help="Name of output model",
+    )
+    parser.add_argument(
+        "--output_dir", default="build/model", help="Directory to save output model"
+    )
 
     args = parser.parse_args()
 
@@ -85,11 +89,13 @@ def main():
 
     output_path = Path(args.output_dir, args.output_name).absolute()
     dynamic_bs_output_path = Path(
-        args.output_dir, args.dynamic_bs_output_name).absolute()
+        args.output_dir, args.dynamic_bs_output_name
+    ).absolute()
 
     print("Loading PyTorch model...")
-    assert Path(model_path).is_file(
-    ), "Cannot find the model file {:}!".format(model_path)
+    assert Path(model_path).is_file(), "Cannot find the model file {:}!".format(
+        model_path
+    )
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
     model = torch.jit.load(model_path, map_location=device)
@@ -100,24 +106,45 @@ def main():
     output_channels = 3
     depth, height, width = ROI_SHAPE
 
-    dummy_input = torch.rand(
-        [batchsize, input_channels, height, width, depth]).float().to(device)
-    dummy_output = torch.rand(
-        [batchsize, output_channels, height, width, depth]).float().to(device)
+    dummy_input = (
+        torch.rand([batchsize, input_channels, height,
+                   width, depth]).float().to(device)
+    )
+    dummy_output = (
+        torch.rand([batchsize, output_channels, height, width, depth])
+        .float()
+        .to(device)
+    )
 
     # using opset version 12
-    torch.onnx.export(model, dummy_input, output_path, opset_version=12,
-                      do_constant_folding=False, input_names=['input'], output_names=['output'],
-                      example_outputs=dummy_output)
-
-    torch.onnx.export(model, dummy_input, dynamic_bs_output_path, opset_version=12,
-                      do_constant_folding=False, input_names=['input'], output_names=['output'],
-                      dynamic_axes={"input": {0: "batch_size"},
-                                    "output": {0: "batch_size"}},
-                      example_outputs=dummy_output)
-
-    print("Successfully exported model:\n  {}\nand\n  {}".format(
-        output_path, dynamic_bs_output_path))
+    torch.onnx.export(
+        model,
+        dummy_input,
+        output_path,
+        opset_version=12,
+        do_constant_folding=False,
+        input_names=["input"],
+        output_names=["output"],
+        example_outputs=dummy_output,
+    )
+
+    torch.onnx.export(
+        model,
+        dummy_input,
+        dynamic_bs_output_path,
+        opset_version=12,
+        do_constant_folding=False,
+        input_names=["input"],
+        output_names=["output"],
+        dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}},
+        example_outputs=dummy_output,
+    )
+
+    print(
+        "Successfully exported model:\n  {}\nand\n  {}".format(
+            output_path, dynamic_bs_output_path
+        )
+    )
 
 
 if __name__ == "__main__":

From 82101bbf15b83a1b6e6d369245dd719b9256effe Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 29 Oct 2024 11:43:00 -0500
Subject: [PATCH 161/305] Update Moe dataset download instructions (#1888)

---
 language/mixtral-8x7b/README.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 7b20b6ba0b..853bf1e758 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -104,16 +104,15 @@ sudo -v ; curl https://rclone.org/install.sh | sudo bash
 ```
 Once Rclone is installed, cd into the folder where you want to place the dataset and run:
 ```bash
-rclone copyurl https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_15k_v4.pkl ./ -a -P
+rclone copyurl https://inference.mlcommons-storage.org/mixtral_8x7b/09292024_mixtral_15k_mintoken2_v1.pkl ./ -a -P
 ```
 #### Using wget
 
 Alternatively, you can simply cd into the folder where you want to place the dataset and run
 
-TBD: The dataset is being replaced in v5.0 due to https://github.com/mlcommons/inference/issues/1777
 
 ```bash
-wget https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_15k_v4.pkl
+wget https://inference.mlcommons-storage.org/mixtral_8x7b/09292024_mixtral_15k_mintoken2_v1.pkl
 ```
 
 ### Calibration dataset

From c72a59f37815429f47d831d1dba90f969771c37a Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 29 Oct 2024 16:45:34 +0000
Subject: [PATCH 162/305] Update evaluation.py | Output GPTJ accuracy metric as
 string (#1886)

* Update evaluation.py | Output GPTJ accuracy metric as string

Fixes https://github.com/mlcommons/inference/issues/1885

* [Automated Commit] Format Codebase
---
 language/gpt-j/evaluation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/gpt-j/evaluation.py b/language/gpt-j/evaluation.py
index 3203359d0f..a95c8cd5cd 100644
--- a/language/gpt-j/evaluation.py
+++ b/language/gpt-j/evaluation.py
@@ -108,7 +108,7 @@ def main():
     result = metric.compute(
         predictions=preds, references=targets, use_stemmer=True, use_aggregator=False
     )
-    result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
+    result = {k: f"{round(np.mean(v) * 100, 4)}" for k, v in result.items()}
     prediction_lens = [len(pred) for pred in preds]
     result["gen_len"] = np.sum(prediction_lens)
     result["gen_num"] = len(preds)

From de6c550acc783bbead4ce255ebc5c9b3ab283d29 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 29 Oct 2024 16:47:27 +0000
Subject: [PATCH 163/305] Update format.yml | Improve the github action (#1875)

* Update format.yml | Improve the github action

* Update format-py.sh

* Update format-cpp.sh

* Update format.yml

* Update accuracy-squad.py | testing

* Update format.yml

* [Automated Commit] Format Codebase

---------

Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
---
 .github/scripts/format-cpp.sh   |  4 ++--
 .github/scripts/format-py.sh    |  4 ++--
 .github/workflows/format.yml    | 25 +++++++++++--------------
 language/bert/accuracy-squad.py |  3 +--
 4 files changed, 16 insertions(+), 20 deletions(-)

diff --git a/.github/scripts/format-cpp.sh b/.github/scripts/format-cpp.sh
index aa36c75647..c0a2374890 100755
--- a/.github/scripts/format-cpp.sh
+++ b/.github/scripts/format-cpp.sh
@@ -19,8 +19,8 @@
 
 # Checks all the modified c/c++ files, format them and adds them
 # to the commit.
-for FILE in $(git diff upstream/$1 --name-only | grep -E '.*\.(cc|cpp|h|hpp)$')
+for FILE in $(git diff $1/$2 --name-only | grep -E '.*\.(cc|cpp|h|hpp)$')
 do
     clang-format -i -style=file $FILE
     git add $FILE
-done
\ No newline at end of file
+done
diff --git a/.github/scripts/format-py.sh b/.github/scripts/format-py.sh
index 0a85078b70..ca3ca5671f 100755
--- a/.github/scripts/format-py.sh
+++ b/.github/scripts/format-py.sh
@@ -19,8 +19,8 @@
 
 # Checks all the modified c/c++ files, format them and adds them
 # to the commit.
-for FILE in $(git diff upstream/$1 --name-only | grep -E '.*\.py$')
+for FILE in $(git diff $1/$2 --name-only | grep -E '.*\.py$')
 do
     autopep8 --in-place -a $FILE
     git add $FILE
-done
\ No newline at end of file
+done
diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index 9e4675d2c8..b21674c4c8 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -1,4 +1,4 @@
-
+# Automatic code formatting
 name: "format"
 on:
   pull_request:
@@ -8,7 +8,6 @@ on:
 
 env:
   python_version: "3.9"
-  repo: "mlcommons/inference"
 
 jobs:
   format-code:
@@ -16,8 +15,7 @@ jobs:
     steps:
       - uses: actions/checkout@v3
         with:
-          ref: ${{ github.head_ref || github.ref_name }}
-          repository: ${{ github.event.pull_request.head.repo.full_name }}
+          ref: ${{ github.event.pull_request.head.ref }}
       - name: Set up Python ${{ env.python_version }}
         uses: actions/setup-python@v3
         with:
@@ -29,27 +27,26 @@ jobs:
 
       - name: Grant permissions
         run: |
-          chmod 777 "${GITHUB_WORKSPACE}/.github/scripts/format-cpp.sh"
-          chmod 777 "${GITHUB_WORKSPACE}/.github/scripts/format-py.sh"
+          chmod +x "${GITHUB_WORKSPACE}/.github/scripts/format-cpp.sh"
+          chmod +x "${GITHUB_WORKSPACE}/.github/scripts/format-py.sh"
       
       - name: Format Codebase
-        if: ${{ github.event.pull_request.base.repo.full_name == env.repo }}
         run: |
           git remote add upstream ${{ github.event.pull_request.base.repo.clone_url }}
-          git fetch upstream ${{ github.event.pull_request.base.ref }}
-          ".github/scripts/format-cpp.sh" "${{ github.event.pull_request.base.ref }}"
-          ".github/scripts/format-py.sh" "${{ github.event.pull_request.base.ref }}"
+          git fetch upstream
+          ".github/scripts/format-cpp.sh" "upstream" "${{ github.event.pull_request.base.ref }}"
+          ".github/scripts/format-py.sh" "upstream" "${{ github.event.pull_request.base.ref }}"
 
       - name: Commit
         run: |
           cd ${GITHUB_WORKSPACE}
-          HAS_CHANGES=$(git diff --cached --name-only)
+          HAS_CHANGES=$(git diff --staged --name-only)
           if [ ${#HAS_CHANGES} -gt 0 ]; then
             git log
-            git config --global user.email "${GITHUB_ACTOR_ID}+${GITHUB_ACTOR}@users.noreply.github.com"
-            git config --global user.name "${GITHUB_ACTOR}"
+            git config --global user.email "${{ github.actor }}@users.noreply.github.com"
+            git config --global user.name "${{ github.actor }}"
             git commit -m '[Automated Commit] Format Codebase'
-            git push upstream ${{ github.head_ref || github.ref_name }}
+            git push 
           fi
 
       
diff --git a/language/bert/accuracy-squad.py b/language/bert/accuracy-squad.py
index 557f360fcb..514258de38 100644
--- a/language/bert/accuracy-squad.py
+++ b/language/bert/accuracy-squad.py
@@ -82,8 +82,7 @@
     "int64": np.int64,
     "float16": np.float16,
     "float32": np.float32,
-    "float64": np.float64,
-}
+    "float64": np.float64}
 
 
 def get_final_text(pred_text, orig_text, do_lower_case):

From 064c7fd0fb864a56014a41d1ff007acff92843c0 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 29 Oct 2024 16:49:24 +0000
Subject: [PATCH 164/305] =?UTF-8?q?Update=20submission=5Fchecker.py=20|=20?=
 =?UTF-8?q?mixtral=20is=20not=20having=20singlestream=20sce=E2=80=A6=20(#1?=
 =?UTF-8?q?881)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Update submission_checker.py | mixtral is not having singlestream scenario

* [Automated Commit] Format Codebase
---
 tools/submission/submission_checker.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index e6218aa2e5..bd06eeec86 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -298,7 +298,7 @@
             "llama2-70b-99": ["Server", "Offline"],
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
-            "mixtral-8x7b": ["SingleStream", "Server", "Offline"],
+            "mixtral-8x7b": ["Server", "Offline"]
         },
         "optional-scenarios-datacenter-edge": {},
         "accuracy-target": {
@@ -2743,6 +2743,7 @@ def check_measurement_dir(
                         val.replace(" ", "")
                         conf_model, conf_scenario, conf_key = key.split(".")
                         if (
+
                             (conf_key == "sample_concatenate_permutation")
                             and ((conf_model == conf_ref_model) or conf_model == "*")
                             and ((conf_scenario == scenario) or conf_scenario == "*")

From ec8cbe54ac2fd1a8b3585ca6b9b7493a99cd023f Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 29 Oct 2024 16:51:29 +0000
Subject: [PATCH 165/305] Docs update for IndySCC24, Improve the final report
 generation, Make some GH actions run only on MLCommons (#1876)

* Support batch-size in llama2 run

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-Cloudflare download instructiosn to README.md

* Minor wording edit to README.md

* Add Rclone-Cloudflare download instructions to README.md

* Add Rclone-GDrive download instructions to README.md

* Add new and old instructions to README.md

* Tweak language in README.md

* Language tweak in README.md

* Minor language tweak in README.md

* Fix typo in README.md

* Count error when logging errors: submission_checker.py

* Fixes #1648, restrict loadgen uncommitted error message to within the loadgen directory

* Update test-rnnt.yml (#1688)

Stopping the github action for rnnt

* Added docs init

Added github action for website publish

Update benchmark documentation

Update publish.yaml

Update publish.yaml

Update benchmark documentation

Improved the submission documentation

Fix taskname

Removed unused images

* Fix benchmark URLs

* Fix links

* Add _full variation to run commands

* Added script flow diagram

* Added docker setup command for CM, extra run options

* Added support for docker options in the docs

* Added --quiet to the CM run_cmds in docs

* Fix the test query count for cm commands

* Support ctuning-cpp implementation

* Added commands for mobilenet models

* Docs cleanup

* Docs cleanup

* Added separate files for dataset and models in the docs

* Remove redundant tab in the docs

* Fixes some WIP models in the docs

* Use the official docs page for CM installation

* Fix the deadlink in docs

* Fix indendation issue in docs

* Added dockerinfo for nvidia implementation

* Added run options for gptj

* Added execution environment tabs

* Cleanup of the docs

* Cleanup of the docs

* Reordered the sections of the docs page

* Removed an unnecessary heading in the docs

* Fixes the commands for datacenter

* Fix the build --sdist for loadgen

* Fixes #1761, llama2 and mixtral runtime error on CPU systems

* Added mixtral to the benchmark list, improved benchmark docs

* Update docs for MLPerf inference v4.1

* Update docs for MLPerf inference v4.1

* Fix typo

* Gave direct link to implementation readmes

* Added tables detailing implementations

* Update vision README.md, split the frameworks into separate rows

* Update README.md

* pointed links to specific frameworks

* pointed links to specific frameworks

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* api support llama2

* Added request module and reduced max token len

* Fix for llama2 api server

* Update SUT_API offline to work for OpenAI

* Update SUT_API.py

* Minor fixes

* Fix json import in SUT_API.py

* Fix llama2 token length

* Added model name verification with server

* clean temp files

* support num_workers in LLAMA2 SUTs

* Remove batching from Offline SUT_API.py

* Update SUT_API.py

* Minor fixes for llama2 API

* Fix for llama2 API

* removed table of contents

* enabled llama2-nvidia + vllm-NM : WIP

* enabled dlrm for intel

* lower cased implementation

* added raw data input

* corrected data download commands

* renamed filename

* changes for bert and vllm

* documentation to work on custom repo and branch

* benchmark index page update

* enabled sdxl for nvidia and intel

* updated vllm server run cmd

* benchmark page information addition

* fix indendation issue

* Added submission categories

* update submission page - generate submission with or w/o using CM for benchmarking

* Updated kits dataset documentation

* Updated model parameters

* updation of information

* updated non cm based benchmark

* added info about hf password

* added links to model and access tokens

* Updated reference results structuree tree

* submission docs cleanup

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* added generic stubs deepsparse

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info (FID and CLIP data added)

* typo fix for bert deepsparse framework

* added min system requirements for models

* fixed code version

* changes for displaying reference and intel implementation tip

* added reference to installation page

* updated neural magic documentation

* Added links to the install page, redirect benchmarks page

* added tips about batch size and dataset for nvidia llama2

* fix conditions logic

* modified tips and additional run cmds

* sentence corrections

* Minor fix for the documentation

* fixed bug in deepsparse generic model stubs + styling

* added more information to stubs

* Added SCC24 readme, support reproducibility in the docs

* Made clear the custom CM repo URL format

* Support conditional implementation, setup and run tips

* Support rocm for sdxl

* Fix _short tag support

* Fix install URL

* Expose bfloat16 and float16 options for sdxl

* Expose download model to host option for sdxl

* IndySCC24 documentation added

* Improve the SCC24 docs

* Improve the support of short variation

* Improved the indyscc24 documentation

* Updated scc run commands

* removed test_query_count option for scc

* Remove scc24 in the main docs

* Remove scc24 in the main docs

* Fix docs: indendation issue on the submission page

* generalised code for skipping test query count

* Fixes for SCC24 docs

* Fix scenario text in main.py

* Fix links for scc24

* Fix links for scc24

* Improve the general docs

* Fix links for scc24

* Use float16 in scc24 doc

* Improve scc24 docs

* Improve scc24 docs

* Use float16 in scc24 doc

* fixed command bug

* Fix typo in docs

* Fix typo in docs

* Remove unnecessary indendation in docs

* initial commit for tip - native run CUDA

* Updated tip

* added docker_cm_repo_branch to more run option - docker

* Update docs for IndySCC24

* Support custom repo branch and owner for final report generation

* enabled amd implementation for llama2

* updations for amd - docs

* Fix scenarios in docs page

* formatted the files to pass the gh action

* scenarios -> fixed_scenarios in docs

* Update docs for specifying custom results dir

* Increment version to 4.1.8

* Increment version to 4.1.9

* Merge

* Increment version to 4.1.8

* Let the CLA and build wheels test run only on the mlcommons repo

* Update index.md

---------

Co-authored-by: Nathan Wasson <nathanw@mlcommons.org>
Co-authored-by: anandhu-eng <anandhukicks@gmail.com>
Co-authored-by: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>
Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
---
 .github/workflows/build_wheels.yml            |  1 +
 .github/workflows/cla.yml                     |  1 +
 docs/benchmarks/language/llama2-70b.md        |  7 +++
 .../reproducibility/indyscc24-bert.md         | 62 +++++++++++++------
 .../text_to_image/reproducibility/scc24.md    | 10 +--
 docs/submission/index.md                      |  3 +
 main.py                                       | 37 ++++++-----
 tools/submission/generate_final_report.py     | 20 ++++--
 8 files changed, 96 insertions(+), 45 deletions(-)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index d7f98764ea..c35870b42c 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -13,6 +13,7 @@ on:
 jobs:
   update_version:
     name: Update version only on ubuntu but used by windows and macos
+    if: github.repository_owner == 'mlcommons'
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
diff --git a/.github/workflows/cla.yml b/.github/workflows/cla.yml
index c0e1544d43..5793f13c2e 100644
--- a/.github/workflows/cla.yml
+++ b/.github/workflows/cla.yml
@@ -8,6 +8,7 @@ on:
 
 jobs:
   cla-check:
+    if: github.repository_owner == 'mlcommons'
     runs-on: ubuntu-latest
     steps:
       - name: "MLCommons CLA bot check"
diff --git a/docs/benchmarks/language/llama2-70b.md b/docs/benchmarks/language/llama2-70b.md
index e686937166..40c62cf714 100644
--- a/docs/benchmarks/language/llama2-70b.md
+++ b/docs/benchmarks/language/llama2-70b.md
@@ -25,3 +25,10 @@ hide:
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "neuralmagic") }}
 
 {{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "neuralmagic") }}
+
+=== "AMD"
+    ## AMD MLPerf Implementation
+    
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99", "amd") }}
+
+{{ mlperf_inference_implementation_readme (4, "llama2-70b-99.9", "amd") }}
diff --git a/docs/benchmarks/language/reproducibility/indyscc24-bert.md b/docs/benchmarks/language/reproducibility/indyscc24-bert.md
index 68215c5e14..86f4f285d0 100644
--- a/docs/benchmarks/language/reproducibility/indyscc24-bert.md
+++ b/docs/benchmarks/language/reproducibility/indyscc24-bert.md
@@ -9,15 +9,11 @@ hide:
 
 This guide is designed for the [IndySCC 2024](https://sc24.supercomputing.org/students/indyscc/) to walk participants through running and optimizing the [MLPerf Inference Benchmark](https://arxiv.org/abs/1911.02549) using [Bert Large](https://github.com/mlcommons/inference/tree/master/language/bert#supported-models) across various software and hardware configurations. The goal is to maximize system throughput (measured in samples per second) without compromising accuracy.
 
-For a valid MLPerf inference submission, two types of runs are required: a performance run and an accuracy run. In this competition, we focus on the `Offline` scenario, where throughput is the key metric—higher values are better. The official MLPerf inference benchmark for Bert Large requires processing a minimum of 10833 samples in both performance and accuracy modes using the Squad v1.1 dataset. Setting up for Nvidia GPUs may take 2-3 hours but can be done offline. Your final output will be a tarball (`mlperf_submission.tar.gz`) containing MLPerf-compatible results, which you will submit to the SCC organizers for scoring.
+For a valid MLPerf inference submission, two types of runs are required: a performance run and an accuracy run. In this competition, we focus on the `Offline` scenario, where throughput is the key metric—higher values are better. The official MLPerf inference benchmark for Bert Large requires processing a minimum of 10833 samples in both performance and accuracy modes using the Squad v1.1 dataset.
 
 ## Scoring
 
-In the SCC, your first objective will be to run a reference (unoptimized) Python implementation or a vendor-provided version (such as Nvidia's) of the MLPerf inference benchmark to secure a baseline score.
-
-Once the initial run is successful, you'll have the opportunity to optimize the benchmark further by maximizing system utilization, applying quantization techniques, adjusting ML frameworks, experimenting with batch sizes, and more, all of which can earn you additional points.
-
-Since vendor implementations of the MLPerf inference benchmark vary and are often limited to single-node benchmarking, teams will compete within their respective hardware categories (e.g., Nvidia GPUs, AMD GPUs). Points will be awarded based on the throughput achieved on your system.
+In the IndySCC 2024, your objective will be to run a reference (unoptimized) Python implementation of the MLPerf inference benchmark to complete a successful submission passing the submission checker. Only one of the available framework needs to be submitted.
 
 
 !!! info
@@ -25,24 +21,50 @@ Since vendor implementations of the MLPerf inference benchmark vary and are ofte
     If you encounter issues or have questions, please submit them [here](https://github.com/mlcommons/cm4mlops/issues)
 
 ## Artifacts to submit to the SCC committee
-
-You will need to submit the following files:
-
-* `mlperf_submission_short.tar.gz` - automatically generated file with validated MLPerf results.
-* `mlperf_submission_short_summary.json` - automatically generated summary of MLPerf results.
-* `mlperf_submission_short.run` - CM commands to run MLPerf BERT inference benchmark saved to this file.
-* `mlperf_submission_short.tstamps` - execution timestamps before and after CM command saved to this file.
-* `mlperf_submission_short.md` - description of your platform and some highlights of the MLPerf benchmark execution.
-
+All the needed files are automatically pushed to the GitHub repository if you manage to complete the given commands. No additional files need to be submitted.
 
 
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
     
-{{ mlperf_inference_implementation_readme (4, "bert-99", "reference", extra_variation_tags=",_short", scenarios=["Offline"],categories=["Edge"], setup_tips=False) }}
+{{ mlperf_inference_implementation_readme (4, "bert-99", "reference", extra_variation_tags="", fixed_scenarios=["Offline"],categories=["Edge"], setup_tips=False) }}
 
-=== "Nvidia"
-    ## Nvidia MLPerf Implementation
-{{ mlperf_inference_implementation_readme (4, "bert-99", "nvidia", extra_variation_tags=",_short", scenarios=["Offline"],categories=["Edge"], setup_tips=False, implementation_tips=False) }}
-    
 
+## Submission Commands
+
+### Generate actual submission tree
+
+```bash
+cm run script --tags=generate,inference,submission \
+   --clean \
+   --preprocess_submission=yes \
+   --run-checker \
+   --tar=yes \
+   --env.CM_TAR_OUTFILE=submission.tar.gz \
+   --division=open \
+   --category=edge \
+   --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+   --run_style=test \
+   --quiet \
+   --submitter=<Team Name>
+```
+
+* Use `--hw_name="My system name"` to give a meaningful system name.
+
+
+### Push Results to GitHub
+
+Fork the `mlperf-inference-results-scc24` branch of the repository URL at [https://github.com/mlcommons/cm4mlperf-inference](https://github.com/mlcommons/cm4mlperf-inference).
+
+Run the following command after **replacing `--repo_url` with your GitHub fork URL**.
+
+```bash
+cm run script --tags=push,github,mlperf,inference,submission \
+   --repo_url=https://github.com/<myfork>/cm4mlperf-inference \
+   --repo_branch=mlperf-inference-results-scc24 \
+   --commit_message="Results on system <HW Name>" \
+   --quiet
+```
+
+Once uploaded give a Pull Request to the origin repository. Github action will be running there and once
+finished you can see your submitted results at [https://docs.mlcommons.org/cm4mlperf-inference](https://docs.mlcommons.org/cm4mlperf-inference).
diff --git a/docs/benchmarks/text_to_image/reproducibility/scc24.md b/docs/benchmarks/text_to_image/reproducibility/scc24.md
index bae4eceb39..6fe119b533 100644
--- a/docs/benchmarks/text_to_image/reproducibility/scc24.md
+++ b/docs/benchmarks/text_to_image/reproducibility/scc24.md
@@ -46,11 +46,11 @@ or supporting multi-node execution) useful for the community and [MLCommons](htt
 === "MLCommons-Python"
     ## MLPerf Reference Implementation in Python
     
-{{ mlperf_inference_implementation_readme (4, "sdxl", "reference", extra_variation_tags=",_short,_scc24-base", devices=["ROCm", "CUDA"],scenarios=["Offline"],categories=["Datacenter"], setup_tips=False, skip_test_query_count=True, extra_input_string="--precision=float16") }}
+{{ mlperf_inference_implementation_readme (4, "sdxl", "reference", extra_variation_tags=",_short,_scc24-base", devices=["ROCm", "CUDA"],fixed_scenarios=["Offline"],categories=["Datacenter"], setup_tips=False, skip_test_query_count=True, extra_input_string="--precision=float16") }}
 
 === "Nvidia"
     ## Nvidia MLPerf Implementation
-{{ mlperf_inference_implementation_readme (4, "sdxl", "nvidia", extra_variation_tags=",_short,_scc24-base", scenarios=["Offline"],categories=["Datacenter"], setup_tips=False, implementation_tips=False, skip_test_query_count=True) }}
+{{ mlperf_inference_implementation_readme (4, "sdxl", "nvidia", extra_variation_tags=",_short,_scc24-base", fixed_scenarios=["Offline"],categories=["Datacenter"], setup_tips=False, implementation_tips=False, skip_test_query_count=True) }}
 
 !!! info
     Once the above run is successful, you can change `_scc24-base` to `_scc24-main` to run the main variant.
@@ -80,17 +80,17 @@ cm run script --tags=generate,inference,submission \
 
 ### Push Results to GitHub
 
-Fork the repository URL at [https://github.com/gateoverflow/cm4mlperf-inference](https://github.com/gateoverflow/cm4mlperf-inference). 
+Fork the `mlperf-inference-results-scc24` branch of the repository URL at [https://github.com/mlcommons/cm4mlperf-inference](https://github.com/mlcommons/cm4mlperf-inference). 
 
 Run the following command after **replacing `--repo_url` with your GitHub fork URL**.
 
 ```bash
 cm run script --tags=push,github,mlperf,inference,submission \
-   --repo_url=https://github.com/gateoverflow/cm4mlperf-inference \
+   --repo_url=https://github.com/<myfork>/cm4mlperf-inference \
    --repo_branch=mlperf-inference-results-scc24 \
    --commit_message="Results on system <HW Name>" \
    --quiet
 ```
 
 Once uploaded give a Pull Request to the origin repository. Github action will be running there and once 
-finished you can see your submitted results at [https://gateoverflow.github.io/cm4mlperf-inference](https://gateoverflow.github.io/cm4mlperf-inference).
+finished you can see your submitted results at [https://docs.mlcommons.org/cm4mlperf-inference](https://docs.mlcommons.org/cm4mlperf-inference).
diff --git a/docs/submission/index.md b/docs/submission/index.md
index 4f6e05c255..adcd3df53e 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -2,6 +2,7 @@
 hide:
   - toc
 ---
+[![Streamline your MLPerf results using CM Framework](https://img.youtube.com/vi/eI1Hoecc3ho/0.jpg)](https://youtu.be/eI1Hoecc3ho)
 
 === "CM based benchmark"
     If you have followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, all the valid results will get aggregated to the `cm cache` folder. The following command could be used to browse the structure of inference results folder generated by CM.
@@ -125,6 +126,8 @@ Once all the results across all the models are ready you can use the following c
 
 * Use `--hw_notes_extra` option to add additional notes like `--hw_notes_extra="Result taken by NAME" `
 
+* Use `--results_dir` option to specify the results folder for Non CM based benchmarks
+
 The above command should generate "submission.tar.gz" if there are no submission checker issues and you can upload it to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission).
 
 ## Aggregate Results in GitHub
diff --git a/main.py b/main.py
index 8fc8f202b7..a0a0ec99e0 100755
--- a/main.py
+++ b/main.py
@@ -10,7 +10,7 @@ def mlperf_inference_implementation_readme(
         setup_tips=True,
         run_tips=True,
         skip_test_query_count=False,
-        scenarios=[],
+        fixed_scenarios=[],
         devices=[],
         frameworks=[],
         categories=[],
@@ -59,6 +59,11 @@ def mlperf_inference_implementation_readme(
             devices = ["CUDA"]
             frameworks = ["TensorRT"]
 
+        elif implementation == "amd":
+            devices = ["cuda"]
+            frameworks = ["pytorch"]
+            execution_envs.remove("Docker")
+
         elif implementation == "neuralmagic":
             devices = ["CUDA"]
             frameworks = ["pytorch"]
@@ -107,7 +112,7 @@ def mlperf_inference_implementation_readme(
             frameworks = ["Onnxruntime"]
 
         elif implementation == "ctuning-cpp":
-            scenarios = ["SingleStream"]
+            fixed_scenarios = ["SingleStream"]
             devices = ["CPU"]
             if model.lower() == "resnet50":
                 frameworks = ["TFLite"]
@@ -132,16 +137,16 @@ def mlperf_inference_implementation_readme(
         final_run_mode = "valid" if "short" not in extra_variation_tags else "test"
 
         for category in categories:
-            if not scenarios:
-                if category == "Edge" and not scenarios:
-                    scenarios = ["Offline", "SingleStream"]
-                    if (
-                        model.lower() in ["resnet50", "retinanet"]
-                        and not "MultiStream" in scenarios
-                    ):  # MultiStream was duplicating
-                        scenarios.append("MultiStream")
-                elif category == "Datacenter":
-                    scenarios = ["Offline", "Server"]
+            if category == "Edge":
+                scenarios = ["Offline", "SingleStream"]
+                if model.lower() in [
+                        "resnet50", "retinanet"] and not "MultiStream" in scenarios:  # MultiStream was duplicating
+                    scenarios.append("MultiStream")
+            elif category == "Datacenter":
+                scenarios = ["Offline", "Server"]
+            if fixed_scenarios:
+                scenarios = [
+                    scenario for scenario in scenarios if scenario in fixed_scenarios]
 
             content += f'{pre_space}=== "{category.lower()}"\n\n'
 
@@ -163,7 +168,7 @@ def mlperf_inference_implementation_readme(
                     cur_space3 = cur_space2 + "    "
                     cur_space4 = cur_space3 + "    "
 
-                    content += f'{cur_space1}=== "{device}"\n'
+                    content += f"{cur_space1}=== \"{device}\"\n"
                     content += f"{cur_space2}##### {device} device\n\n"
 
                     # minimum system requirements
@@ -185,6 +190,7 @@ def mlperf_inference_implementation_readme(
                         # ref to cm installation
                         content += f"{cur_space3}Please refer to the [installation page](site:inference/install/) to install CM for running the automated benchmark commands.\n\n"
                         test_query_count = get_test_query_count(
+
                             model, implementation, device.lower()
                         )
                         if (
@@ -262,6 +268,7 @@ def mlperf_inference_implementation_readme(
                                 content += f"{cur_space3}<details>\n"
                                 content += f"{cur_space3}<summary> Please click here to see more options for the docker launch </summary>\n\n"
                                 content += f"{cur_space3}* `--docker_cm_repo=<Custom CM GitHub repo URL in username@repo format>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
+                                content += f"{cur_space3}* `--docker_cm_repo_branch=<Custom CM GitHub repo Branch>`: to checkout a custom branch of the cloned cm4mlops repository inside the docker image\n\n"
                                 content += f"{cur_space3}* `--docker_cache=no`: to not use docker cache during the image build\n"
 
                                 if implementation.lower() == "nvidia":
@@ -325,7 +332,7 @@ def mlperf_inference_implementation_readme(
                             run_suffix += f"{cur_space3}</details>\n"
 
                         for scenario in scenarios:
-                            content += f'{cur_space3}=== "{scenario}"\n{cur_space4}###### {scenario}\n\n'
+                            content += f"{cur_space3}=== \"{scenario}\"\n{cur_space4}###### {scenario}\n\n"
                             run_cmd = mlperf_inference_run_command(
                                 spaces + 21,
                                 model,
@@ -347,7 +354,7 @@ def mlperf_inference_implementation_readme(
                             # content += run_suffix
 
                         if len(scenarios) > 1:
-                            content += f'{cur_space3}=== "All Scenarios"\n{cur_space4}###### All Scenarios\n\n'
+                            content += f"{cur_space3}=== \"All Scenarios\"\n{cur_space4}###### All Scenarios\n\n"
                             run_cmd = mlperf_inference_run_command(
                                 spaces + 21,
                                 model,
diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index a590cacc61..ba2c368cdf 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -16,13 +16,22 @@ def get_args():
     """Parse commandline."""
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--input",
+        '--input',
         required=True,
-        help="results csv from checker")
-    parser.add_argument("--version", default="4.0", help="mlperf version")
+        help='results csv from checker')
+    parser.add_argument('--version', default='4.1', help='mlperf version')
     parser.add_argument(
-        "--repository", default="submissions_inference_4.0", help="mlperf repository"
-    )
+        '--repository',
+        default='submissions_inference_4.1',
+        help='mlperf repository')
+    parser.add_argument(
+        '--repository-owner',
+        default='mlcommons',
+        help='mlperf repository owner in GitHub')
+    parser.add_argument(
+        '--repository-branch',
+        default='main',
+        help='mlperf repository branch')
     args = parser.parse_args()
     return args
 
@@ -343,6 +352,7 @@ def reformatlink(data, key):
     outjsondata = [i for i in outjsondata if i != {}]
     with open(f"{output}_results.json", "w") as f:
         f.write(json.dumps(outjsondata, indent=2))
+
     score_format = writer.book.add_format({"num_format": "#,##0.00"})
     bg_format = writer.book.add_format({"bg_color": "#efefef"})
     for ws in writer.book.worksheets():

From 106f44fc41cc26a87491d1aca7c57ab08842bf60 Mon Sep 17 00:00:00 2001
From: Ryan <104543985+rysc3@users.noreply.github.com>
Date: Tue, 29 Oct 2024 11:10:28 -0600
Subject: [PATCH 166/305] Correcting README typos (#1869)

* Correcting README typos

* Update format.yml | Fix the gh action

---------

Co-authored-by: Arjun Suresh <arjunsuresh1987@gmail.com>
Co-authored-by: Miro <mirhodak@amd.com>
Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
---
 .github/workflows/format.yml | 2 --
 text_to_image/README.md      | 6 +++---
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index b21674c4c8..1f24dc38bc 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -14,8 +14,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
-        with:
-          ref: ${{ github.event.pull_request.head.ref }}
       - name: Set up Python ${{ env.python_version }}
         uses: actions/setup-python@v3
         with:
diff --git a/text_to_image/README.md b/text_to_image/README.md
index bdd1901295..05701452d9 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -29,7 +29,7 @@ export MODEL_PATH=$PWD/inference/text_to_image/model/
 ```
 ### Clone the repository
 ```bash
-git clone --recurse-submodules https://github.com/mlcommmons/inference.git --depth 1
+git clone --recurse-submodules https://github.com/mlcommons/inference --depth 1
 ```
 Finally copy the `mlperf.conf` file to the stable diffusion folder
 ```bash
@@ -39,12 +39,12 @@ cp $ROOT/mlperf.conf $SD_FOLDER
 ### Install requirements (only for running without using docker)
 Install requirements:
 ```bash
-cd SD_FOLDER
+cd $SD_FOLDER
 pip install -r requirements.txt
 ```
 Install loadgen:
 ```bash
-cd LOADGEN_FOLDER
+cd $LOADGEN_FOLDER
 CFLAGS="-std=c++14" python setup.py install
 ```
 

From c8c1e61f1a2164b5797504f1991d5c7aeea2364f Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 31 Oct 2024 00:05:23 +0000
Subject: [PATCH 167/305] =?UTF-8?q?Removed=20mlperf.conf=20check=20in=20su?=
 =?UTF-8?q?bmission=20checker,=20removed=20equal=20issue=20=E2=80=A6=20(#1?=
 =?UTF-8?q?887)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Removed mlperf.conf check in submission checker, removed equal issue mode check in conf files

* Embed mlperf.conf as a binary string in the distribution

* Added package_dir for loadgen, added pypi loadgen test in github action

* Support impl in model-info.json

* Submission checker cleanup, support move/removal of invalid results

* Added compliance checks to preprocess submission, use logging for debug output

* Update test-submission-checker.yml to check inference_results_v4.1 from v4.0

* Added skip-accuarcy-files-check option to submission checker


---------

Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
---
 .github/workflows/build_wheels.yml            |  28 +-
 .github/workflows/test-resnet50.yml           |   3 +-
 .github/workflows/test-submission-checker.yml |   4 +-
 loadgen/CMakeLists.txt                        |  46 +-
 loadgen/MANIFEST.in                           |   1 -
 loadgen/VERSION.txt                           |   2 +-
 loadgen/__init__.py                           |   4 +
 loadgen/setup.py                              |  57 ++-
 loadgen/test_settings_internal.cc             |  36 +-
 loadgen/version_generator.py                  |  23 +-
 tools/submission/preprocess_submission.py     | 483 +++++++++++-------
 tools/submission/submission_checker.py        | 177 ++-----
 12 files changed, 492 insertions(+), 372 deletions(-)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index c35870b42c..df19102117 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -7,6 +7,7 @@ on:
     branches:
       - master
       - loadgen-release
+      - dev
     paths:
       - loadgen/**
 
@@ -17,25 +18,28 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
 
-      # Step 3: Check if VERSION.txt file has changed in this push
+      # Check if VERSION.txt file has changed in this push
       - name: Check if VERSION.txt file has changed
         id: version_changed
         run: |
+          echo "version_changed=false" >> $GITHUB_ENV
+          echo "new_version=" >> $GITHUB_ENV  # Initialize with empty value
           if git diff --name-only HEAD~1 | grep -q "VERSION.txt"; then
             echo "VERSION.txt file has been modified"
-            echo "::set-output name=version_changed::true"
-            new_version=$(cat VERSION.txt)
+            echo "version_changed=true" >> $GITHUB_ENV
+            new_version=$(cat loadgen/VERSION.txt)
+            echo "new_version=$new_version" >> $GITHUB_ENV
           else
-            echo "VERSION file has NOT been modified"
-            echo "::set-output name=version_changed::false"
+            echo "VERSION.txt file has NOT been modified"
           fi
-          echo "::set-output name=new_version::$new_version"
 
-      # Step 4: Increment version if VERSION was not changed
+      # Step 4: Increment version if VERSION.txt was not changed
       - name: Increment version if necessary
         id: do_version_increment
-        if: steps.version_changed.outputs.version_changed == 'false'
+        if: env.version_changed == 'false'
         run: |
           cd loadgen
           # Check if VERSION file exists, else initialize it
@@ -49,17 +53,17 @@ jobs:
           new_version="$major.$minor.$patch"
           echo $new_version > VERSION.txt
           echo "New version: $new_version"
-          echo "::set-output name=new_version::$new_version"
+          echo "new_version=$new_version" >> $GITHUB_ENV
 
       # Step 5: Commit the updated version to the repository
       - name: Commit updated version
-        if: steps.version_changed.outputs.version_changed == 'false'
+        if: env.version_changed == 'false'
         run: |
           cd loadgen
           git config --global user.name "${{ github.actor }}"
           git config --global user.email "${{ github.actor }}@users.noreply.github.com"
           git add VERSION.txt
-          git commit -m "Increment version to ${{ steps.do_version_increment.outputs.new_version }}"
+          git commit -m "Increment version to $new_version"
           git push
   
   build_wheels:
@@ -80,7 +84,7 @@ jobs:
         run: python -m pip install cibuildwheel twine
 
       - name: Build wheels
-        run: python -m cibuildwheel loadgen/ --output-dir wheels
+        run: git pull && python -m cibuildwheel loadgen/ --output-dir wheels
 
        # Save wheels as artifacts
       - name: Upload built wheels
diff --git a/.github/workflows/test-resnet50.yml b/.github/workflows/test-resnet50.yml
index f1e3e6c394..64636a3438 100755
--- a/.github/workflows/test-resnet50.yml
+++ b/.github/workflows/test-resnet50.yml
@@ -22,6 +22,7 @@ jobs:
       matrix:
         python-version: [ "3.9" ]
         backend: [ "onnxruntime", "tf" ]
+        loadgen-flag: [ "", "--adr.loadgen.tags=_from-pip --pip_loadgen=yes" ]
 
     steps:
     - uses: actions/checkout@v3
@@ -34,4 +35,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Resnet50 and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --adr.loadgen.version=custom
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}
diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index da4192bddc..29a06248e2 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -29,7 +29,7 @@ jobs:
     - name: Install dependencies
       run: |
         python3 -m pip install cm4mlops
-        git clone https://github.com/mlcommons/inference_results_v4.0 --depth 1
+        git clone https://github.com/mlcommons/inference_results_v4.1 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
-        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.0 --src_version=v4.0 --quiet 
+        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.1 --src_version=v4.1 --extra_args=" --skip-extra-accuracy-files-check" --quiet 
diff --git a/loadgen/CMakeLists.txt b/loadgen/CMakeLists.txt
index 66a68f6abe..37270ac241 100644
--- a/loadgen/CMakeLists.txt
+++ b/loadgen/CMakeLists.txt
@@ -1,18 +1,25 @@
-cmake_minimum_required(VERSION 3.5)
+cmake_minimum_required(VERSION 3.12)
 
 project(mlperf_loadgen)
 
 # Read the version file
 file(READ "${CMAKE_SOURCE_DIR}/VERSION.txt" VERSION_CONTENTS)
 
-# Extract the major and minor versions from the VERSION file (assuming "MAJOR.MINOR.PATCH" format)
-string(REGEX MATCH "^([0-9]+)\\.([0-9]+)" VERSION_MATCH ${VERSION_CONTENTS})
+# Extract the major, minor, and patch versions from the VERSION file (assuming "MAJOR.MINOR.PATCH" format)
+string(REGEX MATCH "^([0-9]+)\\.([0-9]+)\\.([0-9]+)" VERSION_MATCH ${VERSION_CONTENTS})
 
-# Set the variables for the major and minor versions
+# Set the variables for the major, minor, and patch versions
 set(mlperf_loadgen_VERSION_MAJOR "${CMAKE_MATCH_1}")
 set(mlperf_loadgen_VERSION_MINOR "${CMAKE_MATCH_2}")
+set(mlperf_loadgen_VERSION_PATCH "${CMAKE_MATCH_3}")
 
-message("mlperf_loadgen v${mlperf_loadgen_VERSION_MAJOR}.${mlperf_loadgen_VERSION_MINOR}")
+# Check if the version format was parsed correctly
+if(NOT mlperf_loadgen_VERSION_MAJOR OR NOT mlperf_loadgen_VERSION_MINOR OR NOT mlperf_loadgen_VERSION_PATCH)
+    message(FATAL_ERROR "Version format in VERSION.txt is incorrect. Expected format: MAJOR.MINOR.PATCH")
+endif()
+
+# Print out the version
+message("mlperf_loadgen v${mlperf_loadgen_VERSION_MAJOR}.${mlperf_loadgen_VERSION_MINOR}.${mlperf_loadgen_VERSION_PATCH}")
 
 # Set build options. NB: CXX_STANDARD is supported since CMake 3.1.
 if (NOT MSVC)
@@ -38,6 +45,28 @@ message(STATUS "Using output path: ${LIBRARY_OUTPUT_PATH}")
 find_package(PythonInterp)
 message(STATUS "Using Python interpreter: ${PYTHON_EXECUTABLE}")
 
+# Specify the source and destination files
+set(CONF_FILE "mlperf.conf")
+set(HEADER_FILE "mlperf_conf.h")
+
+# Read the content of the configuration file
+file(READ ${CONF_FILE} CONF_CONTENTS)
+
+# Escape all double quotes and backslashes
+string(REPLACE "\\" "\\\\" CONF_CONTENTS "${CONF_CONTENTS}")
+string(REPLACE "\"" "\\\"" CONF_CONTENTS "${CONF_CONTENTS}")
+
+# Handle new lines
+string(REPLACE "\n" "\\n\"\n\"" CONF_CONTENTS "${CONF_CONTENTS}")
+
+# Wrap the content in a C++ string declaration
+set(FORMATTED_CONTENT "const char* mlperf_conf =\n\"${CONF_CONTENTS}\";\n")
+
+# Write the formatted content to the header file
+file(WRITE ${HEADER_FILE} "${FORMATTED_CONTENT}")
+
+message(STATUS "Output config:  ${CMAKE_BINARY_DIR}/mlperf_conf.h")
+
 # Generate source file with version info.
 execute_process(COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/version_generator.py ${CMAKE_BINARY_DIR}/version_generated.cc ${CMAKE_CURRENT_SOURCE_DIR})
 
@@ -58,6 +87,8 @@ set(SOURCE
   ${CMAKE_CURRENT_SOURCE_DIR}/results.cc
   ${CMAKE_CURRENT_SOURCE_DIR}/version.cc
   ${CMAKE_CURRENT_SOURCE_DIR}/version.h
+  ${CMAKE_CURRENT_SOURCE_DIR}/mlperf_conf.h
+  ${CMAKE_CURRENT_SOURCE_DIR}/VERSION.txt
   ${CMAKE_BINARY_DIR}/version_generated.cc
 )
 
@@ -80,8 +111,3 @@ install(TARGETS mlperf_loadgen
 	DESTINATION ${CMAKE_INSTALL_PREFIX}/lib)
 install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/
 	DESTINATION ${CMAKE_INSTALL_PREFIX}/include FILES_MATCHING PATTERN "*.h")
-install(FILES ${CMAKE_CURRENT_SOURCE_DIR}/mlperf.conf
-        DESTINATION ${CMAKE_INSTALL_PREFIX}/include)
-
-# Define preprocessor macro with the path to mlperf.conf
-add_definitions(-DMLPERF_CONF_PATH=\"${CMAKE_INSTALL_PREFIX}/include/mlperf.conf\")
diff --git a/loadgen/MANIFEST.in b/loadgen/MANIFEST.in
index 1cdd95c6d6..74282fceee 100644
--- a/loadgen/MANIFEST.in
+++ b/loadgen/MANIFEST.in
@@ -1,2 +1 @@
-include mlperf.conf
 include VERSION.txt
diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 9edf2a44f4..59fe035d9c 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-4.1.7
+4.1.23
diff --git a/loadgen/__init__.py b/loadgen/__init__.py
index e69de29bb2..4551a307d2 100644
--- a/loadgen/__init__.py
+++ b/loadgen/__init__.py
@@ -0,0 +1,4 @@
+import sys
+
+# Aliasing mlcommons_loadgen as mlperf_loadgen
+sys.modules['mlperf_loadgen'] = sys.modules[__name__]
diff --git a/loadgen/setup.py b/loadgen/setup.py
index 662d47bc9c..6254eea17d 100644
--- a/loadgen/setup.py
+++ b/loadgen/setup.py
@@ -29,6 +29,7 @@
 from pybind11 import get_include
 from pybind11.setup_helpers import Pybind11Extension, build_ext
 from version_generator import generate_loadgen_version_definitions
+import subprocess
 
 generated_version_source_filename = "generated/version_generated.cc"
 generate_loadgen_version_definitions(generated_version_source_filename, ".")
@@ -41,7 +42,7 @@
     "test_settings.h",
     "issue_query_controller.h",
     "early_stopping.h",
-    "query_dispatch_library.h",
+    "query_dispatch_library.h"
 ]
 
 lib_headers = [
@@ -53,6 +54,7 @@
     "results.h",
     "bindings/c_api.h",
     "version_generator.py",
+    "mlperf_conf.h"
 ]
 
 lib_sources = [
@@ -82,38 +84,53 @@
     "README.md").read_text(
         encoding="utf-8")
 
-config_file_path = Path(__file__).parent / "mlperf.conf"
-
 with open("VERSION.txt", "r") as f:
     version = f.read()
 version_split = version.split(".")
 
 if len(version_split) < 2:
-    print("Version is incomplete. Needs a format like 4.1 in VERSION file")
+    print("Version is incomplete. Needs a format like 4.1.1 in VERSION file")
+
+
+try:
+    with open("mlperf.conf", 'r') as file:
+        conf_contents = file.read()
+
+    # Escape backslashes and double quotes
+    conf_contents = conf_contents.replace('\\', '\\\\').replace('"', '\\"')
 
+    # Convert newlines
+    conf_contents = conf_contents.replace('\n', '\\n"\n"')
+
+    formatted_content = f'const char* mlperf_conf =\n"{conf_contents}";\n'
+
+    with open("mlperf_conf.h", 'w') as header_file:
+        header_file.write(formatted_content)
+
+except IOError as e:
+    raise RuntimeError(f"Failed to generate header file: {e}")
 
 mlperf_loadgen_module = Pybind11Extension(
     "mlperf_loadgen",
     define_macros=[
-        ("MAJOR_VERSION", version_split[0]),
-        ("MINOR_VERSION", version_split[1]),
-        ("MLPERF_CONF_PATH", f'"{config_file_path}"'),
+        ("MAJOR_VERSION",
+         version_split[0]),
+        ("MINOR_VERSION",
+         version_split[1])
     ],
     include_dirs=[".", get_include()],
     sources=mlperf_loadgen_sources,
     depends=mlperf_loadgen_headers,
 )
 
-setup(
-    name="mlcommons_loadgen",
-    version=version,
-    description="MLPerf Inference LoadGen python bindings",
-    url="https://mlcommons.org/",
-    cmdclass={"build_ext": build_ext},
-    ext_modules=[mlperf_loadgen_module],
-    packages=[""],
-    package_data={"": ["mlperf.conf"]},
-    include_package_data=True,
-    long_description=mlperf_long_description,
-    long_description_content_type="text/markdown",
-)
+setup(name="mlcommons_loadgen",
+      version=version,
+      description="MLPerf Inference LoadGen python bindings",
+      url="https://mlcommons.org/",
+      cmdclass={"build_ext": build_ext},
+      ext_modules=[mlperf_loadgen_module],
+      packages=['mlcommons_loadgen'],
+      package_dir={'mlcommons_loadgen': '.'},
+      include_package_data=True,
+      long_description=mlperf_long_description,
+      long_description_content_type='text/markdown')
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 3dd49c25d4..1a7387f595 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <string>
 
 #include "logging.h"
+#include "mlperf_conf.h"
 #include "utils.h"
 
 namespace mlperf {
@@ -527,7 +528,7 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
     if (configCount == 0) {
       // Only allow userConf as the single configFile and loadgen loads the
       // mlperfConf automatically
-      FromConfig(MLPERF_CONF_PATH, model, scenario, true);
+      FromConfig("", model, scenario, true);
     }
 
     else {
@@ -579,24 +580,33 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
     return true;
   };
 
-  // dirt simple config parser
-  std::ifstream fss(path);
-  std::string line;
   int line_nr = 0;
   int errors = 0;
-  if (!fss.is_open()) {
-    LogDetail([p = path](AsyncDetail &detail) {
+  // Declare the input stream before the if-else block
+  std::unique_ptr<std::istream> fss;
+  std::string line;
+
+  if (!is_mlperf_conf) {
+    // dirt simple config parser
+    fss = std::make_unique<std::ifstream>(path);
+    if (!static_cast<std::ifstream *>(fss.get())->is_open()) {
+      LogDetail([p = path](AsyncDetail &detail) {
 #if USE_NEW_LOGGING_FORMAT
-      std::stringstream ss;
-      ss << "can't open file " << p;
-      MLPERF_LOG_ERROR(detail, "error_invalid_config", ss.str());
+        std::stringstream ss;
+        ss << "can't open file " << p;
+        MLPERF_LOG_ERROR(detail, "error_invalid_config", ss.str());
 #else
-      detail.Error("can't open file ", p);
+        detail.Error("can't open file ", p);
 #endif
-    });
-    return -ENOENT;
+      });
+      return -ENOENT;
+    }
+  } else {
+    // Convert unsigned char array to std::string
+    std::string config_str(mlperf_conf);
+    fss = std::make_unique<std::istringstream>(config_str);
   }
-  while (std::getline(fss, line)) {
+  while (std::getline(*fss, line)) {
     line_nr++;
     std::istringstream iss(line);
     std::string s, k;
diff --git a/loadgen/version_generator.py b/loadgen/version_generator.py
index 49340408a3..2e7524330d 100644
--- a/loadgen/version_generator.py
+++ b/loadgen/version_generator.py
@@ -49,17 +49,14 @@ def func_def(name, string):
 # any modifications.
 def generate_loadgen_version_definitions_git(ofile, git_command):
     git_rev = os.popen(git_command + "rev-parse --short=10 HEAD").read()
-    git_commit_date = os.popen(git_command + 'log --format="%cI" -n 1').read()
+    git_commit_date = os.popen(
+        git_command +
+        "log --format=\"%cI\" -n 1").read()
     git_status = os.popen(git_command + "status -s -uno .").read()
     git_log = subprocess.Popen(
-        git_command + "log --pretty=oneline -n 16 --no-decorate",
-        stdout=subprocess.PIPE,
-        shell=True,
-        encoding="ascii",
-        errors="ignore",
-    ).stdout.read()
-    ofile.write(func_def("GitRevision", '"' + git_rev[0:-1] + '"'))
-    ofile.write(func_def("GitCommitDate", '"' + git_commit_date[0:-1] + '"'))
+        git_command + "log --pretty=oneline -n 16 --no-decorate", stdout=subprocess.PIPE, shell=True, encoding='ascii', errors="ignore").stdout.read()
+    ofile.write(func_def("GitRevision", "\"" + git_rev[0:-1] + "\""))
+    ofile.write(func_def("GitCommitDate", "\"" + git_commit_date[0:-1] + "\""))
     ofile.write(func_def("GitStatus", make_raw_string(git_status[0:-1])))
     ofile.write(func_def("GitLog", make_raw_string(git_log[0:-1])))
 
@@ -105,7 +102,13 @@ def generate_loadgen_version_definitions(cc_filename, loadgen_root):
     ofile.write("// DO NOT EDIT: Autogenerated by version_generator.py.\n\n")
     ofile.write("#include <string>\n\n")
     ofile.write("namespace mlperf {\n\n")
-    ofile.write(func_def("Version", '"4.1"'))
+    # Open and read the VERSION.txt file
+    with open(os.path.join(loadgen_root, "VERSION.txt"), "r") as version_file:
+        # Read and strip any extra whitespace/newlines
+        version_contents = version_file.read().strip()
+
+    # Write the version into the function definition
+    ofile.write(func_def("Version", f"\"{version_contents}\""))
 
     date_time_now_local = datetime.datetime.now().isoformat()
     date_time_now_utc = datetime.datetime.utcnow().isoformat()
diff --git a/tools/submission/preprocess_submission.py b/tools/submission/preprocess_submission.py
index d49d0f1ea7..1e26b81ca4 100644
--- a/tools/submission/preprocess_submission.py
+++ b/tools/submission/preprocess_submission.py
@@ -26,38 +26,36 @@
 
     cd tools/submission
     python3 preprocess_submission.py --input ORIGINAL_SUBMISSION_DIRECTORY --submitter MY_ORG \\
-        --output NEW_SUBMISSION_DIRECTORY
+            --output NEW_SUBMISSION_DIRECTORY
 
 """
 
 
 def get_args():
     """Parse commandline."""
-    parser = argparse.ArgumentParser(
-        description="Infer scenario results",
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-        epilog=HELP_TEXT,
-    )
+    parser = argparse.ArgumentParser(description="Infer scenario results",
+                                     formatter_class=argparse.RawDescriptionHelpFormatter, epilog=HELP_TEXT)
     parser.add_argument(
         "--input",
         required=True,
         help="orignal submission directory")
     parser.add_argument("--output", help="new submission directory")
-    parser.add_argument(
-        "--noinfer_low_accuracy_results",
-        help="do not infer low accuracy results if a high accuracy result is present",
-        default=False,
-        action="store_true",
-    )
-    parser.add_argument(
-        "--nodelete_empty_dirs",
-        help="do not delete empty dirs in submission tree",
-        default=False,
-        action="store_true",
-    )
+    parser.add_argument("--noinfer-low-accuracy-results",
+                        help="do not infer low accuracy results if a high accuracy result is present",
+                        default=False, action="store_true")
+    parser.add_argument("--nodelete-empty-dirs",
+                        help="do not delete empty dirs in submission tree",
+                        default=False, action="store_true")
+    parser.add_argument("--nomove-failed-to-open",
+                        help="do not move failed results to open division",
+                        default=False, action="store_true")
+    parser.add_argument("--nodelete-failed",
+                        help="do not delete failed results (submission checker will fail)",
+                        default=False, action="store_true")
+
     parser.add_argument(
         "--version",
-        default="v4.0",
+        default="v4.1",
         choices=list(checker.MODEL_CONFIG.keys()),
         help="mlperf version",
     )
@@ -65,9 +63,8 @@ def get_args():
     parser.add_argument(
         "--extra-model-benchmark-map",
         help="File containing extra custom model mapping.\
-        It is assumed to be inside the folder open/<submitter>",
-        default="model_mapping.json",
-    )
+              It is assumed to be inside the folder open/<submitter>",
+        default="model_mapping.json")
 
     args = parser.parse_args()
     if not args.output:
@@ -117,19 +114,209 @@ def copy_submission_dir(src, dst, filter_submitter):
             )
 
 
-def infer_scenario_results(
-        filter_submitter, noinfer_low_accuracy_results, config):
+def change_first_directory_to_open(path):
+    # Split the path into components
+    parts = path.split(os.path.sep)
+
+    # Modify the first directory in the path
+    for i, part in enumerate(parts):
+        if part:  # Skip empty parts to handle initial slashes in absolute paths
+            parts[i] = "open"
+            break
+
+    # Join the parts back into a modified path
+    modified_path = os.path.join(*parts)
+    return modified_path
+
+
+def change_folder_name_in_path(path, old_folder_name, new_folder_name):
+    # Split the path into components
+    path_parts = path.split(os.path.sep)
+
+    # Replace the old folder name with the new one
+    path_parts = [new_folder_name if part ==
+                  old_folder_name else part for part in path_parts]
+
+    # Reassemble the path
+    new_path = os.path.join(*path_parts)
+    return new_path
+
+
+def clean_invalid_results(args, log_path, config, system_desc, system_json,
+                          model, mlperf_model, division, system_id_json, is_closed_or_network):
+    # cleanup invalid results
+    for scenario in list_dir(log_path, system_desc, model):
+        scenario_fixed = checker.SCENARIO_MAPPING.get(
+            scenario, scenario)
+        scenario_path = os.path.join(log_path, system_desc, model, scenario)
+        if not os.path.exists(
+                scenario_path):  # can happen since scenario results may be moved to open division on failure
+            continue
+        acc_path = os.path.join(scenario_path, "accuracy")
+        try:
+            accuracy_is_valid, acc = checker.check_accuracy_dir(
+                config,
+                mlperf_model,
+                acc_path,
+                is_closed_or_network,
+            )
+        except Exception as e:
+            log.warning(e)
+            accuracy_is_valid = False
+        perf_path = os.path.join(scenario_path, "performance", "run_1")
+        try:
+            perf_is_valid, r, is_inferred = checker.check_performance_dir(
+                config,
+                mlperf_model,
+                perf_path,
+                scenario_fixed,
+                division,
+                system_json,
+            )
+        except Exception as e:
+            log.warning(e)
+            perf_is_valid = False
+        if perf_is_valid:
+            power_path = os.path.join(scenario_path, "performance", "power")
+            has_power = os.path.exists(power_path)
+            if has_power:
+                ranging_path = os.path.join(
+                    scenario_path, "performance", "ranging")
+                try:
+                    ranging_r = get_performance_metric(
+                        config,
+                        mlperf_model,
+                        ranging_path,
+                        scenario_fixed,
+                    )
+                    (
+                        power_is_valid,
+                        power_metric,
+                        power_efficiency,
+                    ) = check_power_dir(
+                        power_path,
+                        ranging_path,
+                        perf_path,
+                        scenario_fixed,
+                        ranging_r,
+                        r,
+                        config,
+                    )
+                except Exception as e:
+                    power_is_valid = False
+                if not power_is_valid:
+                    log.warning(
+                        f"Power result is invalid for {system_desc}: {model} {scenario} scenario in {division} division. Removing...")
+                    shutil.rmtree(power_path)
+                    shutil.rmtree(ranging_path)
+                    shutil.rmtree(os.path.join(perf_path, "spl.txt"))
+
+            compliance_is_valid = True
+            if is_closed_or_network:
+                compliance_dir = change_folder_name_in_path(
+                    scenario_path, "results", "compliance")
+                if not checker.check_compliance_dir(
+                    compliance_dir,
+                    mlperf_model,
+                    scenario_fixed,
+                    config,
+                    division,
+                    system_json,
+                    os.path.dirname(scenario_path)
+                ):
+                    compliance_is_valid = False
+
+        is_valid = accuracy_is_valid and perf_is_valid and compliance_is_valid
+        if not is_valid:  # Remove the scenario result
+            scenario_measurements_path = change_folder_name_in_path(
+                scenario_path, "results", "measurements")
+            if scenario in [
+                    "Offline", "MultiStream"] and (not accuracy_is_valid or not perf_is_valid) or division == "open":  # they can be inferred
+                scenario_compliance_path = change_folder_name_in_path(
+                    scenario_path, "results", "compliance")
+                log.warning(
+                    f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} division. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Removing...")
+                shutil.rmtree(scenario_path)
+                shutil.rmtree(scenario_measurements_path)
+                shutil.rmtree(scenario_compliance_path)
+            elif division in ["closed", "network"]:
+                model_results_path = os.path.dirname(scenario_path)
+                model_measurements_path = change_folder_name_in_path(
+                    model_results_path, "results", "measurements")
+                model_compliance_path = change_folder_name_in_path(
+                    model_results_path, "results", "compliance")
+                model_code_path = os.path.join(
+                    change_folder_name_in_path(
+                        log_path, "results", "code"), model)
+                if not args.nomove_failed_to_open:
+                    target_code_path = change_first_directory_to_open(
+                        model_code_path)
+                    target_results_path = change_first_directory_to_open(
+                        model_results_path)
+                    target_measurements_path = change_first_directory_to_open(
+                        model_measurements_path)
+                    target_system_json = change_first_directory_to_open(
+                        system_id_json)
+                    # if only accuracy or compliance failed, result is valid
+                    # for open
+                    if not perf_is_valid:
+                        shutil.rmtree(scenario_path)
+                        log.warning(
+                            f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} and open divisions. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Removing it...")
+                    if not os.path.exists(target_results_path):
+                        shutil.copytree(
+                            model_results_path, target_results_path)
+                    if not os.path.exists(target_measurements_path):
+                        shutil.copytree(
+                            model_measurements_path,
+                            target_measurements_path)
+                    if not os.path.exists(target_code_path):
+                        shutil.copytree(model_code_path, target_code_path)
+                    if not os.path.exists(target_system_json):
+                        dst_dir = os.path.dirname(target_system_json)
+                        if not os.path.exists(dst_dir):
+                            os.makedirs(dst_dir, exist_ok=True)
+                        import copy
+                        target_system_json_contents = copy.deepcopy(
+                            system_json)
+                        target_system_json_contents['division'] = 'open'
+                        with open(target_system_json, 'w') as f:
+                            json.dump(target_system_json_contents, f, indent=2)
+                    if perf_is_valid:
+                        log.warning(f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} division. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Compliance: {compliance_is_valid}. Moving {model} results to open...")
+                    else:
+                        log.warning(f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} division. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Compliance: {compliance_is_valid}. Moving other scenario results of {model} to open...")
+                else:
+                    log.warning(f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} division. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Removing all dependent scenario results...")
+                shutil.rmtree(model_results_path)
+                shutil.rmtree(model_measurements_path)
+                shutil.rmtree(model_compliance_path)
+            else:  # delete this result
+                # delete other scenario results too
+                shutil.rmtree(scenario_path)
+                # delete other scenario results too
+                shutil.rmtree(scenario_measurements_path)
+                log.warning(
+                    f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} division. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Removing it...")
+
+
+def infer_scenario_results(args, config):
     """Walk result dir and check for singlestream (SS) folders and \
-               corresponding offline and multistream (MS) ones.
+            corresponding offline and multistream (MS) ones.
        If SS exists and offline and MS are not existing, \
                SS folder is copied to MS and offline folders.
        If SS and offline exists and MS is not existing, MS is inferred from SS.
        If SS and MS exists and offline is not existing, offline is inferred from MS.
     """
-    for division in list_dir("."):
+    filter_submitter = args.submitter
+    noinfer_low_accuracy_results = args.noinfer_low_accuracy_results
+
+    for division in sorted(
+            list_dir(".")):  # process closed and network before open
         # we are looking at ./$division, ie ./closed
         if division not in ["closed", "open", "network"]:
             continue
+        is_closed_or_network = division in ["closed", "network"]
 
         for submitter in list_dir(division):
             # we are looking at ./$division/$submitter, ie ./closed/mlperf_org
@@ -137,9 +324,8 @@ def infer_scenario_results(
                 continue
 
             # process results
-            for directory in ["results", "measurements"] + (
-                ["compliance"] if division == "closed" else []
-            ):
+            for directory in ["results", "measurements"] + \
+                    (["compliance"] if division == "closed" else []):
 
                 log_path = os.path.join(division, submitter, directory)
                 if not os.path.exists(log_path):
@@ -147,36 +333,23 @@ def infer_scenario_results(
                     continue
 
                 for system_desc in list_dir(log_path):
-                    system_id_json = os.path.join(
-                        division, submitter, "systems", system_desc + ".json"
-                    )
+                    system_id_json = os.path.join(division, submitter, "systems",
+                                                  system_desc + ".json")
                     if not os.path.exists(system_id_json):
-                        log.error(
-                            "no system_desc for %s/%s/%s",
-                            division,
-                            submitter,
-                            system_desc,
-                        )
+                        log.error("no system_desc for %s/%s/%s", division, submitter,
+                                  system_desc)
                         continue
 
                     with open(system_id_json) as system_info:
                         system_json = json.load(system_info)
                     system_type = system_json.get("system_type")
-                    valid_system_types = [
-                        "datacenter",
-                        "edge",
-                        "datacenter,edge",
-                        "edge,datacenter",
-                    ]
+                    valid_system_types = ["datacenter", "edge",
+                                          "datacenter,edge", "edge,datacenter"]
                     if system_type not in valid_system_types:
-                        log.error(
-                            "Division %s, submitter %s, "
-                            "system %s has invalid system type (%s)",
-                            division,
-                            submitter,
-                            system_id_json,
-                            system_type,
-                        )
+                        log.error("Division %s, submitter %s, "
+                                  "system %s has invalid system type (%s)",
+                                  division, submitter, system_id_json, system_type)
+
                     config.set_type(system_type)
 
                     for model in list_dir(log_path, system_desc):
@@ -188,132 +361,105 @@ def infer_scenario_results(
                                     extra_model_mapping = json.load(fp)
 
                         mlperf_model = config.get_mlperf_model(
-                            model, extra_model_mapping
-                        )
+                            model, extra_model_mapping)
                         if not mlperf_model:
-                            log.error(
-                                "Division %s, submitter %s, system %s has "
-                                "invalid model (%s)",
-                                division,
-                                submitter,
-                                system_id_json,
-                                model,
-                            )
+                            log.error("Division %s, submitter %s, system %s has "
+                                      "invalid model (%s)", division, submitter,
+                                      system_id_json, model)
                             continue
 
                         if mlperf_model not in config.required:
-                            log.error(
-                                "Division %s, submitter %s, system %s has invalid "
-                                "MLPerf model (%s) corresponding to given model (%s). "
-                                "Valid ones for MLPerf inference version (%s) in (%s) "
-                                "category are [%s]",
-                                division,
-                                submitter,
-                                system_id_json,
-                                mlperf_model,
-                                model,
-                                config.version,
-                                system_type,
-                                config.required.keys(),
-                            )
+                            log.error("Division %s, submitter %s, system %s has invalid "
+                                      "MLPerf model (%s) corresponding to given model (%s). "
+                                      "Valid ones for MLPerf inference version (%s) in (%s) "
+                                      "category are [%s]", division, submitter, system_id_json,
+                                      mlperf_model, model, config.version, system_type,
+                                      config.required.keys())
                             continue
 
-                        required_scenarios = config.get_required(model)
+                        required_scenarios = config.get_required(mlperf_model)
                         all_scenarios = set(
                             list(required_scenarios)
                             + list(config.get_optional(mlperf_model))
                         )
 
+                        if directory == "results":
+                            clean_invalid_results(
+                                args,
+                                log_path,
+                                config,
+                                system_desc,
+                                system_json,
+                                model,
+                                mlperf_model,
+                                division,
+                                system_id_json,
+                                is_closed_or_network)
+                            if not os.path.exists(os.path.join(
+                                    log_path, system_desc, model)):
+                                continue
+
                         for scenario in list_dir(log_path, system_desc, model):
 
                             scenario_path = os.path.join(
-                                log_path, system_desc, model, scenario
-                            )
+                                log_path, system_desc, model, scenario)
 
                             if scenario.lower() == "singlestream":
                                 tobeinferredpaths = []
-                                offline_scenario_path = os.path.join(
-                                    log_path, system_desc, model, "offline"
-                                )
-                                multistream_scenario_path = os.path.join(
-                                    log_path, system_desc, model, "multistream"
-                                )
-                                if not os.path.exists(
-                                    multistream_scenario_path
-                                ) and not os.path.exists(offline_scenario_path):
+                                offline_scenario_path = os.path.join(log_path, system_desc,
+                                                                     model, "offline")
+                                multistream_scenario_path = os.path.join(log_path, system_desc,
+                                                                         model, "multistream")
+                                if not os.path.exists(multistream_scenario_path) and \
+                                        not os.path.exists(offline_scenario_path):
+
                                     # infer both the scenarios from SS
                                     tobeinferredpaths = [offline_scenario_path]
                                     if "MultiStream" in all_scenarios:
                                         tobeinferredpaths.append(
-                                            multistream_scenario_path
-                                        )
+                                            multistream_scenario_path)
 
                                     for tobeinferredpath in tobeinferredpaths:
                                         inferred_scenario = os.path.basename(
-                                            tobeinferredpath
-                                        )
-                                        log.info(
-                                            "Division %s, submitter %s, system %s, "
-                                            "model %s: \
-                                            inferring %s results from %s",
-                                            division,
-                                            submitter,
-                                            system_desc,
-                                            model,
-                                            inferred_scenario,
-                                            "singlestream",
-                                        )
+                                            tobeinferredpath)
+                                        log.info("Division %s, submitter %s, system %s, "
+                                                 "model %s: \
+                                                inferring %s results from %s",
+                                                 division, submitter, system_desc, model,
+                                                 inferred_scenario, "singlestream")
                                         shutil.copytree(
                                             scenario_path, tobeinferredpath)
 
-                                elif (
-                                    not os.path.exists(
-                                        multistream_scenario_path)
-                                    and "MultiStream" in all_scenarios
-                                ):
+                                elif not os.path.exists(multistream_scenario_path) and \
+                                        "MultiStream" in all_scenarios:
                                     # infer MS from SS
                                     for tobeinferredpath in [
                                             multistream_scenario_path]:
-                                        log.info(
-                                            "Division %s, submitter %s, system %s, model %s: \
-                                            inferring %s results from %s",
-                                            division,
-                                            submitter,
-                                            system_desc,
-                                            model,
-                                            "multistream",
-                                            "singlestream",
-                                        )
+                                        log.info("Division %s, submitter %s, system %s, model %s: \
+                                                inferring %s results from %s", division, submitter,
+                                                 system_desc, model, "multistream", "singlestream")
                                         shutil.copytree(
-                                            scenario_path, multistream_scenario_path
-                                        )
+                                            scenario_path, multistream_scenario_path)
                                 elif not os.path.exists(offline_scenario_path):
-                                    """we have both MS and SS results. Inferring from MS is \
-                                        expected to be better \
-                                    """
+                                    '''we have both MS and SS results. Inferring from MS is \
+                                            expected to be better \
+                                            '''
                                     pass
 
                             elif scenario.lower() == "multistream":
-                                offline_scenario_path = os.path.join(
-                                    log_path, system_desc, model, "offline"
-                                )
-                                """Need to check if MS is indeed a measured result and not infeered.\
-                                But if MS is indeed inferred from SS, offline scenario will also be \
-                                inferred already by the inferring code above \
-                                """
+                                offline_scenario_path = os.path.join(log_path, system_desc,
+                                                                     model, "offline")
+                                '''Need to check if MS is indeed a measured result and not infeered.\
+                                        But if MS is indeed inferred from SS, offline scenario will also be \
+                                        inferred already by the inferring code above \
+                                        '''
                                 for tobeinferredpath in [
                                         offline_scenario_path]:
                                     if not os.path.exists(tobeinferredpath):
-                                        log.info(
-                                            "Division %s, submitter %s, system %s, model %s: \
-                                                inferring %s results from %s",
-                                            division,
-                                            submitter,
-                                            system_desc,
-                                            model,
-                                            "offline",
-                                            "multistream",
-                                        )
+                                        log.info("Division %s, submitter %s, system %s, model %s: \
+                                                inferring %s results from %s", division, submitter,
+                                                 system_desc, model, "offline", "multistream")
+
                                         shutil.copytree(
                                             scenario_path, tobeinferredpath)
 
@@ -324,39 +470,25 @@ def infer_scenario_results(
                                 low_accuracy_model = model[:-2]
                                 if low_accuracy_model not in config.required:
                                     continue
-                                high_accuracy_model_path = os.path.join(
-                                    log_path, system_desc, model
-                                )
-                                low_accuracy_model_path = os.path.join(
-                                    log_path, system_desc, low_accuracy_model
-                                )
+                                high_accuracy_model_path = os.path.join(log_path,
+                                                                        system_desc, model)
+                                low_accuracy_model_path = os.path.join(log_path, system_desc,
+                                                                       low_accuracy_model)
                                 if not os.path.exists(low_accuracy_model_path):
-                                    log.info(
-                                        "Division %s, submitter %s, system %s: \
-                                            copying %s results to %s",
-                                        division,
-                                        submitter,
-                                        system_desc,
-                                        model,
-                                        low_accuracy_model,
-                                    )
-
-                                    shutil.copytree(
-                                        high_accuracy_model_path,
-                                        low_accuracy_model_path,
-                                    )
-                                high_accuracy_model_code_path = os.path.join(
-                                    log_path, "..", "code", model
-                                )
-                                low_accuracy_model_code_path = os.path.join(
-                                    log_path, "..", "code", low_accuracy_model
-                                )
+                                    log.info("Division %s, submitter %s, system %s: \
+                                            copying %s results to %s", division, submitter,
+                                             system_desc, model, low_accuracy_model)
+
+                                    shutil.copytree(high_accuracy_model_path,
+                                                    low_accuracy_model_path)
+                                high_accuracy_model_code_path = os.path.join(log_path, "..",
+                                                                             "code", model)
+                                low_accuracy_model_code_path = os.path.join(log_path, "..",
+                                                                            "code", low_accuracy_model)
                                 if not os.path.exists(
                                         low_accuracy_model_code_path):
-                                    shutil.copytree(
-                                        high_accuracy_model_code_path,
-                                        low_accuracy_model_code_path,
-                                    )
+                                    shutil.copytree(high_accuracy_model_code_path,
+                                                    low_accuracy_model_code_path)
 
 
 def main():
@@ -368,23 +500,22 @@ def main():
     src_dir = args.input
 
     if os.path.exists(args.output):
-        print("output directory already exists")
+        log.error(f"output directory {args.output} already exists")
         sys.exit(1)
     os.makedirs(args.output)
     copy_submission_dir(args.input, args.output, args.submitter)
     src_dir = args.output
 
-    config = checker.Config(args.version, args.extra_model_benchmark_map)
+    config = checker.Config(
+        args.version,
+        args.extra_model_benchmark_map)
 
     if not args.nodelete_empty_dirs:
         delete_empty_dirs(os.path.join(src_dir))
 
     os.chdir(src_dir)
 
-    infer_scenario_results(
-        args.submitter,
-        args.noinfer_low_accuracy_results,
-        config)
+    infer_scenario_results(args, config)
 
     return 0
 
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index bd06eeec86..5f2e272673 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1,4 +1,4 @@
-"""A checker for MLPerf Inference submissions
+"""A checker for MLPerf Inference submissions from v4.0 onwards (for checking older submissions please use the submission checker from the respective release)
 """
 
 from __future__ import division
@@ -98,6 +98,7 @@
             "dlrm-v2-99.9": ("AUC", 80.31 * 0.999),
             "3d-unet-99": ("DICE", 0.86170 * 0.99),
             "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
+
             "gptj-99": (
                 "ROUGE1",
                 42.9865 * 0.99,
@@ -206,6 +207,7 @@
             "gptj-99.9": {"Server": 20000000000},
             "llama2-70b-99": {"Server": 20000000000},
             "llama2-70b-99.9": {"Server": 20000000000},
+
             "stable-diffusion-xl": {"Server": 20000000000},
         },
         "min-queries": {
@@ -310,6 +312,7 @@
             "dlrm-v2-99.9": ("AUC", 80.31 * 0.999),
             "3d-unet-99": ("DICE", 0.86170 * 0.99),
             "3d-unet-99.9": ("DICE", 0.86170 * 0.999),
+
             "gptj-99": (
                 "ROUGE1",
                 42.9865 * 0.99,
@@ -519,7 +522,7 @@
         },
     }
 }
-REQUIRED_MEASURE_FILES = ["mlperf.conf", "user.conf", "README.md"]
+REQUIRED_MEASURE_FILES = ["user.conf", "README.md"]
 REQUIRED_POWER_MEASURE_FILES = ["analyzer_table.*", "power_settings.*"]
 MS_TO_NS = 1000 * 1000
 S_TO_MS = 1000
@@ -909,7 +912,7 @@ def requires_equal_issue(self, model, division):
                 "llama2-70b-99.9",
                 "mixtral-8x7b",
             ]
-            and self.version in ["v4.1"]
+            and self.version not in ["v4.0", "v4.1"]
         )
 
 
@@ -972,6 +975,11 @@ def get_args():
         action="store_true",
         help="skips the check of extra files inside the root submission dir",
     )
+    parser.add_argument(
+        "--skip-extra-accuracy-files-check",
+        action="store_true",
+        help="skips the check of extra accuracy files like the images folder of SDXL",
+    )
     parser.add_argument(
         "--scenarios-to-skip",
         help="Delimited list input of scenarios to skip. i.e. if you only have Offline results, pass in 'Server'",
@@ -1103,6 +1111,7 @@ def check_accuracy_dir(config, model, path, verbose):
         acc_targets.append(acc_target)
         acc_types.append(acc_type)
     acc_seen = [False for _ in acc_targets]
+
     with open(os.path.join(path, "accuracy.txt"), "r", encoding="utf-8") as f:
         for line in f:
             for i, (pattern, acc_target, acc_type) in enumerate(
@@ -1204,8 +1213,7 @@ def extra_check_llm(mlperf_log, scenario, model):
 
 
 def get_performance_metric(
-    config, model, path, scenario_fixed, division, system_json, has_power=False
-):
+        config, model, path, scenario_fixed):
     # Assumes new logging format
     version = config.version
 
@@ -1239,8 +1247,7 @@ def get_performance_metric(
 
 
 def check_performance_dir(
-    config, model, path, scenario_fixed, division, system_json, has_power=False
-):
+        config, model, path, scenario_fixed, division, system_json):
     is_valid = False
     rt = {}
 
@@ -1289,6 +1296,12 @@ def check_performance_dir(
     )
     if not config.requires_equal_issue(model, division):
         equal_issue_used_check = True
+    if not equal_issue_used_check:
+        log.error(
+            "%s requires equal issue mode (sample_concatenate_permutation), expected=true, found=false", path
+        )
+        is_valid = False
+
     sut_name = mlperf_log["sut_name"]
 
     # check if there are any errors in the detailed log
@@ -1393,7 +1406,7 @@ def check_performance_dir(
     # Check if this run uses early stopping. If it does, get the
     # min_queries from the detail log, otherwise get this value
     # from the config
-    if not (uses_early_stopping or config.requires_equal_issue(model, division)):
+    if not uses_early_stopping:
         required_min_query_count = config.get_min_query_count(model, scenario)
         if required_min_query_count and min_query_count < required_min_query_count:
             log.error(
@@ -1445,7 +1458,7 @@ def check_performance_dir(
             )
             is_valid = False
 
-    return is_valid, res, inferred, equal_issue_used_check
+    return is_valid, res, inferred
 
 
 def get_inferred_result(
@@ -1689,6 +1702,7 @@ def check_results_dir(
     skip_empty_files_check=False,
     skip_check_power_measure_files=False,
     skip_extra_files_in_root_check=False,
+    skip_extra_accuracy_files_check=False,
     scenarios_to_skip=[],
 ):
     """
@@ -2065,19 +2079,18 @@ def log_result(
                         results[name] = None
                         continue
                     system_type = system_json.get("system_type")
-                    if config.version not in ["v0.5"]:
-                        valid_system_types = ["datacenter", "edge"]
-                        if config.version not in ["v0.7"]:
-                            valid_system_types += ["datacenter,edge",
-                                                   "edge,datacenter"]
-                        if system_type not in valid_system_types:
-                            log.error(
-                                "%s has invalid system type (%s)",
-                                system_id_json,
-                                system_type,
-                            )
-                            results[name] = None
-                            continue
+                    valid_system_types = [
+                        "datacenter", "edge", "datacenter,edge", "edge,datacenter"]
+
+                    if system_type not in valid_system_types:
+                        log.error(
+                            "%s has invalid system type (%s)",
+                            system_id_json,
+                            system_type,
+                        )
+                        results[name] = None
+                        continue
+
                     config.set_type(system_type)
                     if not check_system_desc_id(
                         name,
@@ -2195,11 +2208,7 @@ def log_result(
                             errors += 1
                             continue
                         else:
-                            (
-                                measurement_check,
-                                conf_equal_issue_check,
-                                weight_data_types,
-                            ) = check_measurement_dir(
+                            measurement_check, weight_data_types = check_measurement_dir(
                                 config,
                                 measurement_dir,
                                 name,
@@ -2257,9 +2266,8 @@ def log_result(
                             )
                             if mlperf_model in REQUIRED_ACC_BENCHMARK:
                                 if (
-                                    config.version
-                                    in REQUIRED_ACC_BENCHMARK[mlperf_model]
-                                ):
+                                        config.version
+                                        in REQUIRED_ACC_BENCHMARK[mlperf_model] and not skip_extra_accuracy_files_check):
                                     extra_files_pass, missing_files = check_extra_files(
                                         acc_path,
                                         REQUIRED_ACC_BENCHMARK[mlperf_model][
@@ -2315,19 +2323,13 @@ def log_result(
                                 continue
 
                             try:
-                                (
-                                    is_valid,
-                                    r,
-                                    is_inferred,
-                                    performance_equal_issue_check,
-                                ) = check_performance_dir(
+                                is_valid, r, is_inferred = check_performance_dir(
                                     config,
                                     mlperf_model,
                                     perf_path,
                                     scenario_fixed,
                                     division,
                                     system_json,
-                                    has_power,
                                 )
                                 if is_inferred:
                                     inferred = 1
@@ -2335,21 +2337,6 @@ def log_result(
                                         "%s has inferred results, qps=%s", perf_path, r
                                     )
 
-                                # Check equal issue mode
-                                if not (
-                                    conf_equal_issue_check
-                                    or performance_equal_issue_check
-                                ):
-                                    log.error(
-                                        "%s %s requires equal issue mode (sample_concatenate_permutation), expected=true, found=%s",
-                                        perf_path,
-                                        measurement_dir,
-                                        not (
-                                            conf_equal_issue_check
-                                            or performance_equal_issue_check
-                                        ),
-                                    )
-                                    is_valid, r = False, None
                             except Exception as e:
                                 log.error(
                                     "%s caused exception in check_performance_dir: %s",
@@ -2364,14 +2351,11 @@ def log_result(
                                     ranging_path = os.path.join(
                                         name, "performance", "ranging"
                                     )
-                                    (ranging_r) = get_performance_metric(
+                                    ranging_r = get_performance_metric(
                                         config,
                                         mlperf_model,
                                         ranging_path,
                                         scenario_fixed,
-                                        division,
-                                        system_json,
-                                        has_power,
                                     )
                                 except Exception as e:
                                     log.error(
@@ -2379,7 +2363,7 @@ def log_result(
                                         ranging_path,
                                         e,
                                     )
-                                    is_valid, r = False, None
+                                    is_valid, ranging_r = False, None
 
                                 try:
                                     (
@@ -2674,8 +2658,13 @@ def check_measurement_dir(
                 log.error("%s is having empty %s", measurement_dir, i)
                 is_valid = False
 
+    if config.version in ["v4.0", "v4.1"]:
+        system_file_prefix = system_desc
+    else:
+        system_file_prefix = "model-info"
     for i in files:
-        if i.startswith(system_desc) and i.endswith("_" + scenario + ".json"):
+        if i.startswith(system_desc) and i.endswith(
+                "_" + scenario + ".json"):
             system_file = i
             end = len("_" + scenario + ".json")
             break
@@ -2684,17 +2673,6 @@ def check_measurement_dir(
             end = len(".json")
             break
 
-    if not system_file and os.environ.get("INFER_SYSTEM_FILE", "") == "yes":
-        for i in files:
-            if i.endswith(".json"):
-                system_file = system_desc + ".json"
-                os.rename(
-                    os.path.join(measurement_dir, i),
-                    os.path.join(measurement_dir, system_file),
-                )
-                end = len(".json")
-                break
-
     weight_data_types = None
     if system_file:
         with open(os.path.join(measurement_dir, system_file), "r") as f:
@@ -2724,65 +2702,11 @@ def check_measurement_dir(
                 log.error("%s is missing code_dir %s", fname, code_dir)
                 is_valid = False
 
-        # Check equal issue mode
-        equal_issue_used = False
-        if "mlperf.conf" in files and config.requires_equal_issue(
-                model, division):
-            with open(f"{measurement_dir}/mlperf.conf") as f:
-                lines = f.readlines()
-                conf_ref_model = model.replace("-99.9", "").replace("-99", "")
-                for line in lines:
-                    line = line.replace(" ", "").replace("\n", "")
-                    if line.startswith("#"):
-                        continue
-                    elif line == "":
-                        continue
-                    else:
-                        key, val = line.split("=")
-                        key.replace(" ", "")
-                        val.replace(" ", "")
-                        conf_model, conf_scenario, conf_key = key.split(".")
-                        if (
-
-                            (conf_key == "sample_concatenate_permutation")
-                            and ((conf_model == conf_ref_model) or conf_model == "*")
-                            and ((conf_scenario == scenario) or conf_scenario == "*")
-                        ):
-                            if val.isnumeric():
-                                val = int(val)
-                                equal_issue_used = val == 1
-                                break
-
-        if "user.conf" in files and config.requires_equal_issue(
-                model, division):
-            with open(f"{measurement_dir}/user.conf") as f:
-                lines = f.readlines()
-                conf_ref_model = model.replace("-99.9", "").replace("-99", "")
-                for line in lines:
-                    line = line.replace(" ", "").replace("\n", "")
-                    if line.startswith("#"):
-                        continue
-                    elif line == "":
-                        continue
-                    else:
-                        key, val = line.split("=")
-                        key.replace(" ", "")
-                        val.replace(" ", "")
-                        conf_model, conf_scenario, conf_key = key.split(".")
-                        if (
-                            (conf_key == "sample_concatenate_permutation")
-                            and ((conf_model == conf_ref_model) or conf_model == "*")
-                            and ((conf_scenario == scenario) or conf_scenario == "*")
-                        ):
-                            if val.isnumeric():
-                                val = int(val)
-                                equal_issue_used = val == 1
-                                break
     else:
         log.error("%s is missing %s*.json", fname, system_desc)
         is_valid = False
 
-    return is_valid, equal_issue_used, weight_data_types
+    return is_valid, weight_data_types
 
 
 def check_compliance_perf_dir(test_dir):
@@ -2959,7 +2883,6 @@ def check_compliance_dir(
     test_list = ["TEST01", "TEST04", "TEST05"]
 
     if model in [
-        "rnnt",
         "bert-99",
         "bert-99.9",
         "dlrm-v2-99",
@@ -2967,6 +2890,7 @@ def check_compliance_dir(
         "3d-unet-99",
         "3d-unet-99.9",
         "retinanet",
+        "rnnt",
         "gptj-99",
         "gptj-99.9",
         "llama2-70b-99",
@@ -3019,7 +2943,7 @@ def check_compliance_dir(
                 compliance_perf_dir = os.path.join(
                     compliance_dir, test, "performance", "run_1"
                 )
-                compliance_perf_valid, r, is_inferred, _ = check_performance_dir(
+                compliance_perf_valid, r, is_inferred = check_performance_dir(
                     config, model, compliance_perf_dir, scenario, division, system_json
                 )
                 if is_inferred:
@@ -3080,6 +3004,7 @@ def main():
             args.skip_empty_files_check,
             args.skip_check_power_measure_files,
             args.skip_extra_files_in_root_check,
+            args.skip_extra_accuracy_files_check,
             scenarios_to_skip,
         )
 

From 39109730ab162e2fc88ba599cf676ea51376c776 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 6 Nov 2024 22:30:13 +0000
Subject: [PATCH 168/305] Update build_wheels.yml | Recommendation from
 MLCommons Admins (#1895)

---
 .github/workflows/build_wheels.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index df19102117..7763f800f3 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -17,9 +17,10 @@ jobs:
     if: github.repository_owner == 'mlcommons'
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 2
+          ssh-key: ${{ secrets.DEPLOY_KEY }}
 
       # Check if VERSION.txt file has changed in this push
       - name: Check if VERSION.txt file has changed

From 6dcaae866b8ed86265c58d67791438023d23e23d Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 6 Nov 2024 22:33:15 +0000
Subject: [PATCH 169/305] Update fid_score.py | Move the sys path update before
 the import. (#1897)

* Update fid_score.py | Move the sys path update before the import.

Currently the sys path is meaningless and this change was done by the automated code formatting

* Update format.yml



---------

Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
---
 .github/workflows/format.yml         | 11 +++++------
 text_to_image/tools/fid/fid_score.py |  5 ++---
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index 1f24dc38bc..b98a3c37bc 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -14,6 +14,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
       - name: Set up Python ${{ env.python_version }}
         uses: actions/setup-python@v3
         with:
@@ -31,20 +33,17 @@ jobs:
       - name: Format Codebase
         run: |
           git remote add upstream ${{ github.event.pull_request.base.repo.clone_url }}
-          git fetch upstream
+          git fetch upstream ${{ github.event.pull_request.base.ref }}
           ".github/scripts/format-cpp.sh" "upstream" "${{ github.event.pull_request.base.ref }}"
           ".github/scripts/format-py.sh" "upstream" "${{ github.event.pull_request.base.ref }}"
 
       - name: Commit
         run: |
-          cd ${GITHUB_WORKSPACE}
           HAS_CHANGES=$(git diff --staged --name-only)
           if [ ${#HAS_CHANGES} -gt 0 ]; then
-            git log
+            git checkout -B "${{ github.head_ref }}"
             git config --global user.email "${{ github.actor }}@users.noreply.github.com"
             git config --global user.name "${{ github.actor }}"
             git commit -m '[Automated Commit] Format Codebase'
-            git push 
+            git push origin "${{ github.head_ref }}"
           fi
-
-      
diff --git a/text_to_image/tools/fid/fid_score.py b/text_to_image/tools/fid/fid_score.py
index 2c3f1aab79..febc12ff5d 100644
--- a/text_to_image/tools/fid/fid_score.py
+++ b/text_to_image/tools/fid/fid_score.py
@@ -32,7 +32,6 @@
 limitations under the License.
 """
 
-from inception import InceptionV3
 from torch.nn.functional import adaptive_avg_pool2d
 from scipy import linalg
 from PIL import Image
@@ -45,8 +44,8 @@
 import pathlib
 import os
 import sys
-
-sys.path.append(os.path.dirname(__file__))
+sys.path.insert("..", 0)
+from inception import InceptionV3  # noqa: E402
 
 
 try:

From b0686db3b66d4a0ebb0f69584b1501fc53e6e804 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Wed, 6 Nov 2024 22:34:14 +0000
Subject: [PATCH 170/305] Fix SDXL bug with diffuser 0.30.0 while giving
 downloaded model path (#1905)

---
 text_to_image/backend_pytorch.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/text_to_image/backend_pytorch.py b/text_to_image/backend_pytorch.py
index 1a45c1ca5b..36e2b80090 100644
--- a/text_to_image/backend_pytorch.py
+++ b/text_to_image/backend_pytorch.py
@@ -84,7 +84,6 @@ def load(self):
                 scheduler=self.scheduler,
                 safety_checker=None,
                 add_watermarker=False,
-                variant="fp16" if (self.dtype == torch.float16) else None,
                 torch_dtype=self.dtype,
             )
             # self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)

From d3c01ed3de6618a8d39dc8a2a82871e598ea801f Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 6 Nov 2024 17:46:52 -0500
Subject: [PATCH 171/305] Update GNN reference implementation: add DGL backend
 (#1903)

* Update GNN reference implementation: add DGL backend

* [Automated Commit] Format Codebase

* Update README.md
---
 .../graph => graph}/R-GAT/README.md           |  31 +-
 .../graph => graph}/R-GAT/backend.py          |   0
 graph/R-GAT/backend_dgl.py                    |  96 ++++
 .../R-GAT/backend_glt.py                      |  10 +-
 .../graph => graph}/R-GAT/dataset.py          |   0
 graph/R-GAT/dgl_utilities/components.py       | 202 ++++++++
 graph/R-GAT/dgl_utilities/feature_fetching.py | 434 ++++++++++++++++++
 graph/R-GAT/dgl_utilities/pyg_sampler.py      |  90 ++++
 .../graph => graph}/R-GAT/igbh.py             |   2 -
 graph/R-GAT/igbh/tiny/models/dataloader.py    |  82 ++++
 graph/R-GAT/igbh/tiny/models/gnn.py           | 296 ++++++++++++
 graph/R-GAT/igbh/tiny/models/main.py          |  79 ++++
 graph/R-GAT/igbh/tiny/models/utils.py         | 224 +++++++++
 .../graph => graph}/R-GAT/main.py             | 115 +++--
 .../graph => graph}/R-GAT/requirements.txt    |   2 +-
 .../graph => graph}/R-GAT/rgnn.py             |   0
 .../R-GAT/tools/compress_graph.py             |   0
 .../R-GAT/tools/download_igbh_full.sh         |   0
 .../R-GAT/tools/download_igbh_test.py         |   0
 .../R-GAT/tools/format_model.py               |   0
 .../R-GAT/tools/split_seeds.py                |   0
 .../graph => graph}/R-GAT/user.conf           |   0
 22 files changed, 1618 insertions(+), 45 deletions(-)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/README.md (69%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/backend.py (100%)
 create mode 100644 graph/R-GAT/backend_dgl.py
 rename upcomming_benchmarks/graph/R-GAT/backend_pytorch.py => graph/R-GAT/backend_glt.py (97%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/dataset.py (100%)
 create mode 100644 graph/R-GAT/dgl_utilities/components.py
 create mode 100644 graph/R-GAT/dgl_utilities/feature_fetching.py
 create mode 100644 graph/R-GAT/dgl_utilities/pyg_sampler.py
 rename {upcomming_benchmarks/graph => graph}/R-GAT/igbh.py (99%)
 create mode 100644 graph/R-GAT/igbh/tiny/models/dataloader.py
 create mode 100644 graph/R-GAT/igbh/tiny/models/gnn.py
 create mode 100644 graph/R-GAT/igbh/tiny/models/main.py
 create mode 100644 graph/R-GAT/igbh/tiny/models/utils.py
 rename {upcomming_benchmarks/graph => graph}/R-GAT/main.py (86%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/requirements.txt (90%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/rgnn.py (100%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/tools/compress_graph.py (100%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/tools/download_igbh_full.sh (100%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/tools/download_igbh_test.py (100%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/tools/format_model.py (100%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/tools/split_seeds.py (100%)
 rename {upcomming_benchmarks/graph => graph}/R-GAT/user.conf (100%)

diff --git a/upcomming_benchmarks/graph/R-GAT/README.md b/graph/R-GAT/README.md
similarity index 69%
rename from upcomming_benchmarks/graph/R-GAT/README.md
rename to graph/R-GAT/README.md
index b8d230f5fe..5a2b3ae6f0 100644
--- a/upcomming_benchmarks/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -1,6 +1,6 @@
 # MLPerf™ Inference Benchmarks for Text to Image
 
-This is the reference implementation for MLPerf Inference text to image
+This is the reference implementation for MLPerf Inference text to image. Two implementations are currently supported, Graphlearn for Pytorch (GLT) and Deep Graph Library (DGL), both using pytorch as the backbone of the model.
 
 ## Supported Models
 
@@ -47,14 +47,21 @@ Install loadgen:
 cd $LOADGEN_FOLDER
 CFLAGS="-std=c++14" python setup.py install
 ```
-### Install graphlearn for pytorch
 
-Install pytorch geometric:
+### Install pytorch geometric
+
 ```bash
 export TORCH_VERSION=$(python -c "import torch; print(torch.__version__)")
 pip install torch-geometric torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-${TORCH_VERSION}.html
 ```
 
+### Install DGL
+```bash
+pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/cu121/repo.html
+```
+
+### Install graphlearn for pytorch (Only for GLT implementation)
+
 Follow instalation instructions at: https://github.com/alibaba/graphlearn-for-pytorch.git
 
 ### Download model
@@ -80,7 +87,7 @@ cd $GRAPH_FOLDER
 python3 tools/split_seeds.py --path igbh --dataset_size tiny
 ```
 
-**Compress graph (optional)**
+**Compress graph (optional, only for GLT implementation)**
 ```bash
 cd $GRAPH_FOLDER
 python3 tools/compress_graph.py --path igbh --dataset_size tiny --layout <CSC or CSR>
@@ -99,7 +106,7 @@ cd $GRAPH_FOLDER
 python3 tools/split_seeds.py --path igbh --dataset_size full
 ```
 
-**Compress graph (optional)**
+**Compress graph (optional, only for GLT implementation)**
 ```bash
 cd $GRAPH_FOLDER
 python3 tools/compress_graph.py --path igbh --dataset_size tiny --layout <CSC or CSR>
@@ -114,16 +121,22 @@ TODO
 ```bash
 # Go to the benchmark folder
 cd $GRAPH_FOLDER
-# Run the benchmark
-python3 main.py --dataset igbh-tiny --dataset-path igbh/ --profile debug [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
+# Run the benchmark GLT
+python3 main.py --dataset igbh-glt-tiny --dataset-path igbh/ --profile debug-glt [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
+
+# Run the benchmark DGL
+python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
 #### Local run
 ```bash
 # Go to the benchmark folder
 cd $GRAPH_FOLDER
-# Run the benchmark
-python3 main.py --dataset igbh --dataset-path igbh/ [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
+# Run the benchmark GLT
+python3 main.py --dataset igbh-glt --dataset-path igbh/ --profile rgat-glt-full [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
+
+# Run the benchmark DGL
+python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 #### Run using docker
 
diff --git a/upcomming_benchmarks/graph/R-GAT/backend.py b/graph/R-GAT/backend.py
similarity index 100%
rename from upcomming_benchmarks/graph/R-GAT/backend.py
rename to graph/R-GAT/backend.py
diff --git a/graph/R-GAT/backend_dgl.py b/graph/R-GAT/backend_dgl.py
new file mode 100644
index 0000000000..b0e1362bea
--- /dev/null
+++ b/graph/R-GAT/backend_dgl.py
@@ -0,0 +1,96 @@
+
+from typing import Optional, List, Union, Any
+from dgl_utilities.feature_fetching import IGBHeteroGraphStructure, Features, IGBH
+from dgl_utilities.components import build_graph, get_loader, RGAT
+from dgl_utilities.pyg_sampler import PyGSampler
+import os
+import torch
+import logging
+import backend
+from typing import Literal
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("backend-dgl")
+
+
+class BackendDGL(backend.Backend):
+    def __init__(
+        self,
+        model_type="rgat",
+        type: Literal["fp16", "fp32"] = "fp16",
+        device: Literal["cpu", "gpu"] = "gpu",
+        ckpt_path: str = None,
+        igbh: IGBH = None,
+        batch_size: int = 1,
+        layout: Literal["CSC", "CSR", "COO"] = "COO",
+        edge_dir: str = "in",
+    ):
+        super(BackendDGL, self).__init__()
+        # Set device and type
+        if device == "gpu":
+            self.device = torch.device("cuda")
+        else:
+            self.device = torch.device("cpu")
+
+        if type == "fp32":
+            self.type = torch.float32
+        else:
+            self.type = torch.float16
+        # Create Node and neighbor loader
+        self.fan_out = [5, 10, 15]
+        self.igbh_graph_structure = igbh.igbh_dataset
+        self.feature_store = Features(
+            self.igbh_graph_structure.dir,
+            self.igbh_graph_structure.dataset_size,
+            self.igbh_graph_structure.in_memory,
+            use_fp16=self.igbh_graph_structure.use_fp16,
+        )
+        self.feature_store.build_features(use_journal_conference=True)
+        self.graph = build_graph(
+            self.igbh_graph_structure,
+            "dgl",
+            features=self.feature_store)
+        self.neighbor_loader = PyGSampler([5, 10, 15])
+        # Load model Architechture
+        self.model = RGAT(
+            backend="dgl",
+            device=device,
+            graph=self.graph,
+            in_feats=1024,
+            h_feats=512,
+            num_classes=2983,
+            num_layers=len(self.fan_out),
+            n_heads=4
+        ).to(self.type).to(self.device)
+        self.model.eval()
+        # Load model checkpoint
+        ckpt = None
+        if ckpt_path is not None:
+            try:
+                ckpt = torch.load(ckpt_path, map_location=self.device)
+            except FileNotFoundError as e:
+                print(f"Checkpoint file not found: {e}")
+                return -1
+        if ckpt is not None:
+            self.model.load_state_dict(ckpt["model_state_dict"])
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "pytorch-SUT"
+
+    def image_format(self):
+        return "NCHW"
+
+    def load(self):
+        return self
+
+    def predict(self, inputs: torch.Tensor):
+        with torch.no_grad():
+            input_size = inputs.shape[0]
+            # Get batch
+            batch = self.neighbor_loader.sample(self.graph, {"paper": inputs})
+            batch_preds, batch_labels = self.model(
+                batch, self.device, self.feature_store)
+        return batch_preds
diff --git a/upcomming_benchmarks/graph/R-GAT/backend_pytorch.py b/graph/R-GAT/backend_glt.py
similarity index 97%
rename from upcomming_benchmarks/graph/R-GAT/backend_pytorch.py
rename to graph/R-GAT/backend_glt.py
index 70777cef17..f721ed0fc2 100644
--- a/upcomming_benchmarks/graph/R-GAT/backend_pytorch.py
+++ b/graph/R-GAT/backend_glt.py
@@ -13,7 +13,7 @@
 import graphlearn_torch as glt
 
 logging.basicConfig(level=logging.INFO)
-log = logging.getLogger("backend-pytorch")
+log = logging.getLogger("backend-glt")
 
 
 class CustomNeighborLoader(NodeLoader):
@@ -114,20 +114,19 @@ def get_neighbors(self, seeds: torch.Tensor):
         return result
 
 
-class BackendPytorch(backend.Backend):
+class BackendGLT(backend.Backend):
     def __init__(
         self,
         model_type="rgat",
         type: Literal["fp16", "fp32"] = "fp16",
         device: Literal["cpu", "gpu"] = "gpu",
         ckpt_path: str = None,
-        igbh_dataset: IGBHeteroDataset = None,
+        igbh: IGBH = None,
         batch_size: int = 1,
         layout: Literal["CSC", "CSR", "COO"] = "COO",
         edge_dir: str = "in",
     ):
-        super(BackendPytorch, self).__init__()
-        self.i = 0
+        super(BackendGLT, self).__init__()
         # Set device and type
         if device == "gpu":
             self.device = torch.device("cuda")
@@ -140,6 +139,7 @@ def __init__(
             self.type = torch.float16
         # Create Node and neighbor loade
         self.glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
+        igbh_dataset = igbh.igbh_dataset
         self.glt_dataset.init_node_features(
             node_feature_data=igbh_dataset.feat_dict,
             with_gpu=(device == "gpu"),
diff --git a/upcomming_benchmarks/graph/R-GAT/dataset.py b/graph/R-GAT/dataset.py
similarity index 100%
rename from upcomming_benchmarks/graph/R-GAT/dataset.py
rename to graph/R-GAT/dataset.py
diff --git a/graph/R-GAT/dgl_utilities/components.py b/graph/R-GAT/dgl_utilities/components.py
new file mode 100644
index 0000000000..d7b8f245af
--- /dev/null
+++ b/graph/R-GAT/dgl_utilities/components.py
@@ -0,0 +1,202 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+from dgl_utilities.pyg_sampler import PyGSampler
+
+DGL_AVAILABLE = True
+
+try:
+    import dgl
+except ModuleNotFoundError:
+    DGL_AVAILABLE = False
+    dgl = None
+
+
+def check_dgl_available():
+    assert DGL_AVAILABLE, "DGL Not available in the container"
+
+
+def build_graph(graph_structure, backend, features=None):
+    assert graph_structure.separate_sampling_aggregation or (features is not None), \
+        "Either we need a feature to build the graph, or \
+            we should specify to separate sampling from aggregation"
+
+    if backend.lower() == "dgl":
+        check_dgl_available()
+
+        graph = dgl.heterograph(graph_structure.edge_dict)
+        graph.predict = "paper"
+
+        if features is not None:
+            for node, node_feature in features.feature.items():
+                if graph.num_nodes(ntype=node) < node_feature.shape[0]:
+                    graph.add_nodes(
+                        node_feature.shape[0] -
+                        graph.num_nodes(
+                            ntype=node),
+                        ntype=node)
+                else:
+                    assert graph.num_nodes(ntype=node) == node_feature.shape[0], f"\
+                    Graph has more {node} nodes ({graph.num_nodes(ntype=node)}) \
+                        than feature shape ({node_feature.shape[0]})"
+
+                if not graph_structure.separate_sampling_aggregation:
+                    for node, node_feature in features.feature.items():
+                        graph.nodes[node].data['feat'] = node_feature
+                        setattr(
+                            graph,
+                            f"num_{node}_nodes",
+                            node_feature.shape[0])
+
+        graph = dgl.remove_self_loop(graph, etype="cites")
+        graph = dgl.add_self_loop(graph, etype="cites")
+
+        graph.nodes['paper'].data['label'] = graph_structure.label
+
+        return graph
+    else:
+        assert False, "Unrecognized backend " + backend
+
+
+def get_sampler(use_pyg_sampler=False):
+    if use_pyg_sampler:
+        return PyGSampler
+    else:
+        return dgl.dataloading.MultiLayerNeighborSampler
+
+
+def get_loader(graph, index, fanouts, backend, use_pyg_sampler=True, **kwargs):
+    if backend.lower() == "dgl":
+        check_dgl_available()
+        fanouts = [int(fanout) for fanout in fanouts.split(",")]
+        return dgl.dataloading.DataLoader(
+            graph, {"paper": index},
+            get_sampler(use_pyg_sampler=use_pyg_sampler)(fanouts),
+            **kwargs
+        )
+    else:
+        assert False, "Unrecognized backend " + backend
+
+
+def glorot(value):
+    if isinstance(value, torch.Tensor):
+        stdv = math.sqrt(6.0 / (value.size(-2) + value.size(-1)))
+        value.data.uniform_(-stdv, stdv)
+    else:
+        for v in value.parameters() if hasattr(value, 'parameters') else []:
+            glorot(v)
+        for v in value.buffers() if hasattr(value, 'buffers') else []:
+            glorot(v)
+
+
+class GATPatched(dgl.nn.pytorch.GATConv):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def reset_parameters(self):
+        if hasattr(self, 'fc'):
+            glorot(self.fc.weight)
+        else:
+            glorot(self.fc_src.weight)
+            glorot(self.fc_dst.weight)
+        glorot(self.attn_l)
+        glorot(self.attn_r)
+        if self.bias is not None:
+            nn.init.constant_(self.bias, 0)
+        if isinstance(self.res_fc, nn.Linear):
+            glorot(self.res_fc.weight)
+
+
+class RGAT_DGL(nn.Module):
+    def __init__(
+            self,
+            etypes,
+            in_feats, h_feats, num_classes,
+            num_layers=2, n_heads=4, dropout=0.2,
+            with_trim=None):
+        super().__init__()
+        self.layers = nn.ModuleList()
+
+        # does not support other models since they are not used
+        self.layers.append(dgl.nn.pytorch.HeteroGraphConv({
+            etype: GATPatched(in_feats, h_feats // n_heads, n_heads)
+            for etype in etypes}))
+
+        for _ in range(num_layers - 2):
+            self.layers.append(dgl.nn.pytorch.HeteroGraphConv({
+                etype: GATPatched(h_feats, h_feats // n_heads, n_heads)
+                for etype in etypes}))
+
+        self.layers.append(dgl.nn.pytorch.HeteroGraphConv({
+            etype: GATPatched(h_feats, h_feats // n_heads, n_heads)
+            for etype in etypes}))
+        self.dropout = nn.Dropout(dropout)
+        self.linear = nn.Linear(h_feats, num_classes)
+
+    def forward(self, blocks, x):
+        h = x
+        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
+            h = layer(block, h)
+            h = dgl.apply_each(
+                h, lambda x: x.view(
+                    x.shape[0], x.shape[1] * x.shape[2]))
+            if l != len(self.layers) - 1:
+                h = dgl.apply_each(h, F.leaky_relu)
+                h = dgl.apply_each(h, self.dropout)
+        return self.linear(h['paper'])
+
+    def extract_graph_structure(self, batch, device):
+        # moves all blocks to device
+        return [block.to(device) for block in batch[-1]]
+
+    def extract_inputs_and_outputs(self, sampled_subgraph, device, features):
+        # input to the batch argument would be a list of blocks
+        # the sampled sbgraph is already moved to device in
+        # extract_graph_structure
+
+        # in case if the input feature is not stored on the graph,
+        # but rather in shared memory: (separate_sampling_aggregation)
+        # we use this method to extract them based on the blocks
+        if features is None or features.feature == {}:
+            batch_inputs = {
+                key: value.to(torch.float32)
+                for key, value in sampled_subgraph[0].srcdata['feat'].items()
+            }
+        else:
+            batch_inputs = features.get_input_features(
+                sampled_subgraph[0].srcdata[dgl.NID],
+                device
+            )
+        batch_labels = sampled_subgraph[-1].dstdata['label']['paper']
+        return batch_inputs, batch_labels
+
+
+class RGAT(torch.nn.Module):
+    def __init__(self, backend, device, graph, **model_kwargs):
+        super().__init__()
+        self.backend = backend.lower()
+        if backend.lower() == "dgl":
+            check_dgl_available()
+            etypes = graph.etypes
+            self.model = RGAT_DGL(etypes=etypes, **model_kwargs)
+        else:
+            assert False, "Unrecognized backend " + backend
+
+        self.device = device
+        self.layers = self.model.layers
+
+    def forward(self, batch, device, features):
+        # a general method to get the batches and move them to the
+        # corresponding device
+        batch = self.model.extract_graph_structure(batch, device)
+
+        # a general method to fetch the features given the sampled blocks
+        # and move them to corresponding device
+        batch_inputs, batch_labels = self.model.extract_inputs_and_outputs(
+            sampled_subgraph=batch,
+            device=device,
+            features=features,
+        )
+        return self.model.forward(batch, batch_inputs), batch_labels
diff --git a/graph/R-GAT/dgl_utilities/feature_fetching.py b/graph/R-GAT/dgl_utilities/feature_fetching.py
new file mode 100644
index 0000000000..6e1b6cfffa
--- /dev/null
+++ b/graph/R-GAT/dgl_utilities/feature_fetching.py
@@ -0,0 +1,434 @@
+import torch
+import os
+import concurrent.futures
+import os.path as osp
+import numpy as np
+from typing import Literal
+
+
+def float2half(base_path, dataset_size):
+    paper_nodes_num = {
+        "tiny": 100000,
+        "small": 1000000,
+        "medium": 10000000,
+        "large": 100000000,
+        "full": 269346174,
+    }
+    author_nodes_num = {
+        "tiny": 357041,
+        "small": 1926066,
+        "medium": 15544654,
+        "large": 116959896,
+        "full": 277220883,
+    }
+    # paper node
+    paper_feat_path = os.path.join(base_path, "paper", "node_feat.npy")
+    paper_fp16_feat_path = os.path.join(
+        base_path, "paper", "node_feat_fp16.pt")
+    if not os.path.exists(paper_fp16_feat_path):
+        if dataset_size in ["large", "full"]:
+            num_paper_nodes = paper_nodes_num[dataset_size]
+            paper_node_features = torch.from_numpy(
+                np.memmap(
+                    paper_feat_path,
+                    dtype="float32",
+                    mode="r",
+                    shape=(num_paper_nodes, 1024),
+                )
+            )
+        else:
+            paper_node_features = torch.from_numpy(
+                np.load(paper_feat_path, mmap_mode="r")
+            )
+        paper_node_features = paper_node_features.half()
+        torch.save(paper_node_features, paper_fp16_feat_path)
+
+    # author node
+    author_feat_path = os.path.join(base_path, "author", "node_feat.npy")
+    author_fp16_feat_path = os.path.join(
+        base_path, "author", "node_feat_fp16.pt")
+    if not os.path.exists(author_fp16_feat_path):
+        if dataset_size in ["large", "full"]:
+            num_author_nodes = author_nodes_num[dataset_size]
+            author_node_features = torch.from_numpy(
+                np.memmap(
+                    author_feat_path,
+                    dtype="float32",
+                    mode="r",
+                    shape=(num_author_nodes, 1024),
+                )
+            )
+        else:
+            author_node_features = torch.from_numpy(
+                np.load(author_feat_path, mmap_mode="r")
+            )
+        author_node_features = author_node_features.half()
+        torch.save(author_node_features, author_fp16_feat_path)
+
+    # institute node
+    institute_feat_path = os.path.join(base_path, "institute", "node_feat.npy")
+    institute_fp16_feat_path = os.path.join(
+        base_path, "institute", "node_feat_fp16.pt")
+    if not os.path.exists(institute_fp16_feat_path):
+        institute_node_features = torch.from_numpy(
+            np.load(institute_feat_path, mmap_mode="r")
+        )
+        institute_node_features = institute_node_features.half()
+        torch.save(institute_node_features, institute_fp16_feat_path)
+
+    # fos node
+    fos_feat_path = os.path.join(base_path, "fos", "node_feat.npy")
+    fos_fp16_feat_path = os.path.join(base_path, "fos", "node_feat_fp16.pt")
+    if not os.path.exists(fos_fp16_feat_path):
+        fos_node_features = torch.from_numpy(
+            np.load(fos_feat_path, mmap_mode="r"))
+        fos_node_features = fos_node_features.half()
+        torch.save(fos_node_features, fos_fp16_feat_path)
+
+    # conference node
+    conference_feat_path = os.path.join(
+        base_path, "conference", "node_feat.npy")
+    conference_fp16_feat_path = os.path.join(
+        base_path, "conference", "node_feat_fp16.pt"
+    )
+    if not os.path.exists(conference_fp16_feat_path):
+        conference_node_features = torch.from_numpy(
+            np.load(conference_feat_path, mmap_mode="r")
+        )
+        conference_node_features = conference_node_features.half()
+        torch.save(conference_node_features, conference_fp16_feat_path)
+
+    # journal node
+    journal_feat_path = os.path.join(base_path, "journal", "node_feat.npy")
+    journal_fp16_feat_path = os.path.join(
+        base_path, "journal", "node_feat_fp16.pt")
+    if not os.path.exists(journal_fp16_feat_path):
+        journal_node_features = torch.from_numpy(
+            np.load(journal_feat_path, mmap_mode="r")
+        )
+        journal_node_features = journal_node_features.half()
+        torch.save(journal_node_features, journal_fp16_feat_path)
+
+
+class IGBH:
+    def __init__(
+        self,
+        data_path,
+        name="igbh",
+        dataset_size="full",
+        use_label_2K=True,
+        in_memory=False,
+        layout: Literal["CSC", "CSR", "COO"] = "COO",
+        type: Literal["fp16", "fp32"] = "fp16",
+        device="cpu",
+        edge_dir="in",
+        **kwargs,
+    ):
+        super().__init__()
+        self.data_path = data_path
+        self.name = name
+        self.size = dataset_size
+        self.igbh_dataset = IGBHeteroGraphStructure(
+            data_path,
+            dataset_size=dataset_size,
+            in_memory=in_memory,
+            use_label_2K=use_label_2K,
+            layout=layout,
+            use_fp16=(type == "fp16")
+        )
+        self.num_samples = len(self.igbh_dataset.val_idx)
+
+    def get_samples(self, id_list):
+        return self.igbh_dataset.val_idx[id_list]
+
+    def get_labels(self, id_list):
+        return self.igbh_dataset.label[self.get_samples(id_list)]
+
+    def get_item_count(self):
+        return len(self.igbh_dataset.val_idx)
+
+    def load_query_samples(self, id):
+        pass
+
+    def unload_query_samples(self, sample_list):
+        pass
+
+
+class IGBHeteroGraphStructure:
+    """
+    Synchronously (optionally parallelly) loads the edge relations for IGBH.
+    Current IGBH edge relations are not yet converted to torch tensor.
+    """
+
+    def __init__(
+        self,
+        data_path,
+        dataset_size="full",
+        use_label_2K=True,
+        in_memory=False,
+        use_fp16=True,
+        # in-memory and memory-related optimizations
+        separate_sampling_aggregation=False,
+        # perf related
+        multithreading=True,
+        **kwargs,
+    ):
+
+        self.dir = data_path
+        self.dataset_size = dataset_size
+        self.use_fp16 = use_fp16
+        self.in_memory = in_memory
+        self.use_label_2K = use_label_2K
+        self.num_classes = 2983 if not self.use_label_2K else 19
+        self.label_file = "node_label_19.npy" if not self.use_label_2K else "node_label_2K.npy"
+
+        self.num_nodes = {
+            "full": {'paper': 269346174, 'author': 277220883, 'institute': 26918, 'fos': 712960, 'journal': 49052, 'conference': 4547},
+            "small": {'paper': 1000000, 'author': 1926066, 'institute': 14751, 'fos': 190449, 'journal': 15277, 'conference': 1215},
+            "medium": {'paper': 10000000, 'author': 15544654, 'institute': 23256, 'fos': 415054, 'journal': 37565, 'conference': 4189},
+            "large": {'paper': 100000000, 'author': 116959896, 'institute': 26524, 'fos': 649707, 'journal': 48820, 'conference': 4490},
+            "tiny": {'paper': 100000, 'author': 357041, 'institute': 8738, 'fos': 84220, 'journal': 8101, 'conference': 398}
+        }[self.dataset_size]
+
+        self.use_journal_conference = True
+        self.separate_sampling_aggregation = separate_sampling_aggregation
+
+        self.torch_tensor_input_dir = data_path
+        self.torch_tensor_input = self.torch_tensor_input_dir != ""
+
+        self.multithreading = multithreading
+
+        # This class only stores the edge data, labels, and the train/val
+        # indices
+        self.edge_dict = self.load_edge_dict()
+        self.label = self.load_labels()
+        self.full_num_trainable_nodes = (
+            227130858 if self.num_classes != 2983 else 157675969)
+        self.train_idx, self.val_idx = self.get_train_val_test_indices()
+        if self.use_fp16:
+            float2half(
+                os.path.join(
+                    self.dir,
+                    self.dataset_size,
+                    "processed"),
+                self.dataset_size)
+
+    def load_edge_dict(self):
+        mmap_mode = None if self.in_memory else "r"
+
+        edges = [
+            "paper__cites__paper",
+            "paper__written_by__author",
+            "author__affiliated_to__institute",
+            "paper__topic__fos"]
+        if self.use_journal_conference:
+            edges += ["paper__published__journal", "paper__venue__conference"]
+
+        loaded_edges = None
+
+        def load_edge(edge, mmap=mmap_mode, parent_path=osp.join(
+                self.dir, self.dataset_size, "processed")):
+            return edge, torch.from_numpy(
+                np.load(osp.join(parent_path, edge, "edge_index.npy"), mmap_mode=mmap))
+
+        if self.multithreading:
+            with concurrent.futures.ThreadPoolExecutor() as executor:
+                loaded_edges = executor.map(load_edge, edges)
+            loaded_edges = {
+                tuple(edge.split("__")): (edge_index[:, 0], edge_index[:, 1]) for edge, edge_index in loaded_edges
+            }
+        else:
+            loaded_edges = {
+                tuple(edge.split("__")): (edge_index[:, 0], edge_index[:, 1])
+                for edge, edge_index in map(load_edge, edges)
+            }
+
+        return self.augment_edges(loaded_edges)
+
+    def load_labels(self):
+        if self.dataset_size not in ['full', 'large']:
+            return torch.from_numpy(
+                np.load(
+                    osp.join(
+                        self.dir,
+                        self.dataset_size,
+                        'processed',
+                        'paper',
+                        self.label_file)
+                )
+            ).to(torch.long)
+        else:
+            return torch.from_numpy(
+                np.memmap(
+                    osp.join(
+                        self.dir,
+                        self.dataset_size,
+                        'processed',
+                        'paper',
+                        self.label_file
+                    ),
+                    dtype='float32',
+                    mode='r',
+                    shape=(
+                        (269346174 if self.dataset_size == "full" else 100000000)
+                    )
+                )
+            ).to(torch.long)
+
+    def augment_edges(self, edge_dict):
+        # Adds reverse edge connections to the graph
+        # add rev_{edge} to every edge except paper-cites-paper
+        edge_dict.update(
+            {
+                (dst, f"rev_{edge}", src): (dst_idx, src_idx)
+                for (src, edge, dst), (src_idx, dst_idx) in edge_dict.items()
+                if src != dst
+            }
+        )
+
+        paper_cites_paper = edge_dict[("paper", 'cites', 'paper')]
+
+        self_loop = torch.arange(self.num_nodes['paper'])
+        mask = paper_cites_paper[0] != paper_cites_paper[1]
+
+        paper_cites_paper = (
+            torch.cat((paper_cites_paper[0][mask], self_loop.clone())),
+            torch.cat((paper_cites_paper[1][mask], self_loop.clone()))
+        )
+
+        edge_dict[("paper", 'cites', 'paper')] = (
+            torch.cat((paper_cites_paper[0], paper_cites_paper[1])),
+            torch.cat((paper_cites_paper[1], paper_cites_paper[0]))
+        )
+
+        return edge_dict
+
+    def get_train_val_test_indices(self):
+        base_dir = osp.join(self.dir, self.dataset_size, "processed")
+        assert osp.exists(osp.join(base_dir, "train_idx.pt")) and osp.exists(osp.join(base_dir, "val_idx.pt")), \
+            "Train and validation indices not found. Please run GLT's split_seeds.py first."
+
+        return (
+            torch.load(
+                osp.join(
+                    self.dir,
+                    self.dataset_size,
+                    "processed",
+                    "train_idx.pt")),
+            torch.load(
+                osp.join(
+                    self.dir,
+                    self.dataset_size,
+                    "processed",
+                    "val_idx.pt"))
+        )
+
+
+class Features:
+    """
+    Lazily initializes the features for IGBH.
+
+    Features will be initialized only when *build_features* is called.
+
+    Features will be placed into shared memory when *share_features* is called
+    or if the features are built (either mmap-ed or loaded in memory)
+    and *torch.multiprocessing.spawn* is called
+    """
+
+    def __init__(self, path, dataset_size, in_memory=True, use_fp16=True):
+        self.path = path
+        self.dataset_size = dataset_size
+        self.in_memory = in_memory
+        self.use_fp16 = use_fp16
+        if self.use_fp16:
+            self.dtype = torch.float16
+        else:
+            self.dtype = torch.float32
+        self.feature = {}
+
+    def build_features(self, use_journal_conference=False,
+                       multithreading=False):
+        node_types = ['paper', 'author', 'institute', 'fos']
+        if use_journal_conference or self.dataset_size in ['large', 'full']:
+            node_types += ['conference', 'journal']
+
+        if multithreading:
+            def load_feature(feature_store, feature_name):
+                return feature_store.load(feature_name), feature_name
+
+            with concurrent.futures.ThreadPoolExecutor() as executor:
+                loaded_features = executor.map(
+                    load_feature, [(self, ntype) for ntype in node_types])
+                self.feature = {
+                    node_type: feature_value for feature_value, node_type in loaded_features
+                }
+        else:
+            for node_type in node_types:
+                self.feature[node_type] = self.load(node_type)
+
+    def share_features(self):
+        for node_type in self.feature:
+            self.feature[node_type] = self.feature[node_type].share_memory_()
+
+    def load_from_tensor(self, node):
+        return torch.load(osp.join(self.path, self.dataset_size,
+                          "processed", node, "node_feat_fp16.pt"))
+
+    def load_in_memory_numpy(self, node):
+        return torch.from_numpy(np.load(
+            osp.join(self.path, self.dataset_size, 'processed', node, 'node_feat.npy')))
+
+    def load_mmap_numpy(self, node):
+        """
+        Loads a given numpy array through mmap_mode="r"
+        """
+        return torch.from_numpy(np.load(osp.join(
+            self.path, self.dataset_size, "processed", node, "node_feat.npy"), mmap_mode="r"))
+
+    def memmap_mmap_numpy(self, node):
+        """
+        Loads a given NumPy array through memory-mapping np.memmap.
+
+        This is the same code as the one provided in IGB codebase.
+        """
+        shape = [None, 1024]
+        if self.dataset_size == "full":
+            if node == "paper":
+                shape[0] = 269346174
+            elif node == "author":
+                shape[0] = 277220883
+        elif self.dataset_size == "large":
+            if node == "paper":
+                shape[0] = 100000000
+            elif node == "author":
+                shape[0] = 116959896
+
+        assert shape[0] is not None
+        return torch.from_numpy(np.memmap(osp.join(self.path, self.dataset_size,
+                                "processed", node, "node_feat.npy"), dtype="float32", mode='r', shape=shape))
+
+    def load(self, node):
+        if self.in_memory:
+            if self.use_fp16:
+                return self.load_from_tensor(node)
+            else:
+                if self.dataset_size in [
+                        'large', 'full'] and node in ['paper', 'author']:
+                    return self.memmap_mmap_numpy(node)
+                else:
+                    return self.load_in_memory_numpy(node)
+        else:
+            if self.dataset_size in [
+                    'large', 'full'] and node in ['paper', 'author']:
+                return self.memmap_mmap_numpy(node)
+            else:
+                return self.load_mmap_numpy(node)
+
+    def get_input_features(self, input_dict, device):
+        # fetches the batch inputs
+        # moving it here so so that future modifications could be easier
+        return {
+            key: self.feature[key][value.to(torch.device("cpu")), :].to(
+                device).to(self.dtype)
+            for key, value in input_dict.items()
+        }
diff --git a/graph/R-GAT/dgl_utilities/pyg_sampler.py b/graph/R-GAT/dgl_utilities/pyg_sampler.py
new file mode 100644
index 0000000000..ed75a59846
--- /dev/null
+++ b/graph/R-GAT/dgl_utilities/pyg_sampler.py
@@ -0,0 +1,90 @@
+import dgl
+import torch
+
+
+class PyGSampler(dgl.dataloading.Sampler):
+    r"""
+    An example DGL sampler implementation that matches PyG/GLT sampler behavior.
+    The following differences need to be addressed:
+    1.  PyG/GLT applies conv_i to edges in layer_i, and all subsequent layers, while DGL only applies conv_i to edges in layer_i.
+        For instance, consider a path a->b->c. At layer 0,
+        DGL updates only node b's embedding with a->b, but
+        PyG/GLT updates both node b and c's embeddings.
+        Therefore, if we use h_i(x) to denote the hidden representation of node x at layer i, then the output h_2(c) is:
+            DGL:     h_2(c) = conv_2(h_1(c), h_1(b)) = conv_2(h_0(c), conv_1(h_0(b), h_0(a)))
+            PyG/GLT: h_2(c) = conv_2(h_1(c), h_1(b)) = conv_2(conv_1(h_0(c), h_0(b)), conv_1(h_0(b), h_0(a)))
+    2.  When creating blocks for layer i-1, DGL not only uses the destination nodes from layer i,
+        but also includes all subsequent i+1 ... n layers' destination nodes as seed nodes.
+    More discussions and examples can be found here: https://github.com/alibaba/graphlearn-for-pytorch/issues/79.
+    """
+
+    def __init__(self, fanouts, num_threads=1):
+        super().__init__()
+        self.fanouts = fanouts
+        self.num_threads = num_threads
+
+    def sample(self, g, seed_nodes):
+        if self.num_threads != 1:
+            old_num_threads = torch.get_num_threads()
+            torch.set_num_threads(self.num_threads)
+        output_nodes = seed_nodes
+        subgs = []
+        previous_edges = {}
+        previous_seed_nodes = seed_nodes
+        input_nodes = seed_nodes
+
+        device = None
+        for key in seed_nodes:
+            device = seed_nodes[key].device
+
+        not_sampled = {
+            ntype: torch.ones([g.num_nodes(ntype)], dtype=torch.bool, device=device) for ntype in g.ntypes
+        }
+
+        for fanout in reversed(self.fanouts):
+            for node_type in seed_nodes:
+                not_sampled[node_type][seed_nodes[node_type]] = 0
+
+            # Sample a fixed number of neighbors of the current seed nodes.
+            sg = g.sample_neighbors(seed_nodes, fanout)
+
+            # Before we add the edges, we need to first record the source nodes (of the current seed nodes)
+            # so that other edges' source nodes will not be included as next
+            # layer's seed nodes.
+            temp = dgl.to_block(sg, previous_seed_nodes,
+                                include_dst_in_src=False)
+            seed_nodes = temp.srcdata[dgl.NID]
+
+            # GLT/PyG does not sample again on previously-sampled nodes
+            # we mimic this behavior here
+            for node_type in g.ntypes:
+                seed_nodes[node_type] = seed_nodes[node_type][not_sampled[node_type]
+                                                              [seed_nodes[node_type]]]
+
+            # We add all previously accumulated edges to this subgraph
+            for etype in previous_edges:
+                sg.add_edges(*previous_edges[etype], etype=etype)
+
+            # This subgraph now contains all its new edges
+            # and previously accumulated edges
+            # so we add them
+            previous_edges = {}
+            for etype in sg.etypes:
+                previous_edges[etype] = sg.edges(etype=etype)
+
+            # Convert this subgraph to a message flow graph.
+            # we need to turn on the include_dst_in_src
+            # so that we get compatibility with DGL's OOTB GATConv.
+            sg = dgl.to_block(sg, previous_seed_nodes, include_dst_in_src=True)
+
+            # for this layers seed nodes -
+            # they will be our next layers' destination nodes
+            # so we add them to the collection of previous seed nodes.
+            previous_seed_nodes = sg.srcdata[dgl.NID]
+
+            # we insert the block to our list of blocks
+            subgs.insert(0, sg)
+            input_nodes = seed_nodes
+        if self.num_threads != 1:
+            torch.set_num_threads(old_num_threads)
+        return input_nodes, output_nodes, subgs
diff --git a/upcomming_benchmarks/graph/R-GAT/igbh.py b/graph/R-GAT/igbh.py
similarity index 99%
rename from upcomming_benchmarks/graph/R-GAT/igbh.py
rename to graph/R-GAT/igbh.py
index e23a816e12..cdab173da3 100644
--- a/upcomming_benchmarks/graph/R-GAT/igbh.py
+++ b/graph/R-GAT/igbh.py
@@ -16,8 +16,6 @@
 import argparse
 import dataset
 import numpy as np
-import graphlearn_torch as glt
-from igb.dataloader import IGB260MDGLDataset
 
 
 logging.basicConfig(level=logging.INFO)
diff --git a/graph/R-GAT/igbh/tiny/models/dataloader.py b/graph/R-GAT/igbh/tiny/models/dataloader.py
new file mode 100644
index 0000000000..cc64d14668
--- /dev/null
+++ b/graph/R-GAT/igbh/tiny/models/dataloader.py
@@ -0,0 +1,82 @@
+import torch
+from torch_geometric.data import InMemoryDataset, Data
+from dgl.data import DGLDataset
+
+from utils import IGL260MDataset
+
+# TODO: Make a PyG dataloader for large datasets
+
+
+class IGL260M_PyG(InMemoryDataset):
+    def __init__(self, args):
+        super().__init__(root, transform, pre_transform, pre_filter)
+
+    def process(self):
+        dataset = IGL260MDataset(root=self.dir, size=args.dataset_size,
+                                 in_memory=args.in_memory, classes=args.type_classes, synthetic=args.synthetic)
+        node_features = torch.from_numpy(dataset.paper_feat)
+        node_edges = torch.from_numpy(dataset.paper_edge).T
+        node_labels = torch.from_numpy(dataset.paper_label).to(torch.long)
+        data = Data(x=node_features, edge_index=node_edges, y=node_labels)
+
+        n_nodes = node_features.shape[0]
+
+        n_train = int(n_nodes * 0.6)
+        n_val = int(n_nodes * 0.2)
+
+        train_mask = torch.zeros(n_nodes, dtype=torch.bool)
+        val_mask = torch.zeros(n_nodes, dtype=torch.bool)
+        test_mask = torch.zeros(n_nodes, dtype=torch.bool)
+
+        train_mask[:n_train] = True
+        val_mask[n_train:n_train + n_val] = True
+        test_mask[n_train + n_val:] = True
+
+        data.train_mask = train_mask
+        data.val_mask = val_mask
+        data.test_mask = test_mask
+
+
+class IGL260M_DGL(DGLDataset):
+    def __init__(self, args):
+        self.dir = args.path
+        super().__init__(name='IGB260M')
+
+    def process(self):
+        dataset = IGL260MDataset(root=self.dir, size=args.dataset_size,
+                                 in_memory=args.in_memory, classes=args.type_classes, synthetic=args.synthetic)
+        node_features = torch.from_numpy(dataset.paper_feat)
+        node_edges = torch.from_numpy(dataset.paper_edge)
+        node_labels = torch.from_numpy(dataset.paper_label).to(torch.long)
+
+        self.graph = dgl.graph(
+            (node_edges[:, 0], node_edges[:, 1]), num_nodes=node_features.shape[0])
+
+        self.graph.ndata['feat'] = node_features
+        self.graph.ndata['label'] = node_labels
+
+        self.graph = dgl.remove_self_loop(self.graph)
+        self.graph = dgl.add_self_loop(self.graph)
+
+        n_nodes = node_features.shape[0]
+
+        n_train = int(n_nodes * 0.6)
+        n_val = int(n_nodes * 0.2)
+
+        train_mask = torch.zeros(n_nodes, dtype=torch.bool)
+        val_mask = torch.zeros(n_nodes, dtype=torch.bool)
+        test_mask = torch.zeros(n_nodes, dtype=torch.bool)
+
+        train_mask[:n_train] = True
+        val_mask[n_train:n_train + n_val] = True
+        test_mask[n_train + n_val:] = True
+
+        self.graph.ndata['train_mask'] = train_mask
+        self.graph.ndata['val_mask'] = val_mask
+        self.graph.ndata['test_mask'] = test_mask
+
+    def __getitem__(self, i):
+        return self.graph
+
+    def __len__(self):
+        return 1
diff --git a/graph/R-GAT/igbh/tiny/models/gnn.py b/graph/R-GAT/igbh/tiny/models/gnn.py
new file mode 100644
index 0000000000..20d5ecd72a
--- /dev/null
+++ b/graph/R-GAT/igbh/tiny/models/gnn.py
@@ -0,0 +1,296 @@
+from utils import IGL260MDataset
+import warnings
+from tqdm import tqdm
+import numpy as np
+import time
+import torch.nn.functional as F
+import torch.optim as optim
+import torch.nn as nn
+import dgl
+from dgl.data import DGLDataset
+import dgl.nn.pytorch as dglnn
+from dgl.nn.pytorch import GATConv, GraphConv, SAGEConv
+import os.path as osp
+from sys import getsizeof
+
+
+import torch
+torch.manual_seed(0)
+dgl.seed(0)
+warnings.filterwarnings("ignore")
+
+
+class GCN(nn.Module):
+    def __init__(self,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation,
+                 dropout):
+        super(GCN, self).__init__()
+        self.layers = nn.ModuleList()
+        self.n_layers = n_layers
+        self.n_hidden = n_hidden
+        self.n_classes = n_classes
+        # input layer
+        self.layers.append(
+            GraphConv(
+                in_feats,
+                n_hidden,
+                activation=activation))
+        # hidden layers
+        for i in range(n_layers - 1):
+            self.layers.append(
+                GraphConv(
+                    n_hidden,
+                    n_hidden,
+                    activation=activation))
+        # output layer
+        self.layers.append(GraphConv(n_hidden, n_classes))
+        self.dropout = nn.Dropout(p=dropout)
+        self.activation = activation
+
+    def forward(self, blocks, x):
+        h = x
+        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
+            if l != len(self.layers) - 1:
+                # h = self.activation(h)
+                h = self.dropout(h)
+            h = layer(block, h)
+        return h
+
+    def inference(self, g, x, batch_size, device):
+        """
+        Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling).
+        g : the entire graph.
+        x : the input of entire node set.
+        The inference code is written in a fashion that it could handle any number of nodes and
+        layers.
+        """
+        # During inference with sampling, multi-layer blocks are very inefficient because
+        # lots of computations in the first few layers are repeated.
+        # Therefore, we compute the representation of all nodes layer by layer.  The nodes
+        # on each layer are of course splitted in batches.
+        # TODO: can we standardize this?
+        for l, layer in enumerate(self.layers):
+            y = torch.zeros(g.number_of_nodes(), self.n_hidden if l !=
+                            len(self.layers) - 1 else self.n_classes)
+
+            sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
+            dataloader = dgl.dataloading.NodeDataLoader(
+                g,
+                torch.arange(g.number_of_nodes()),
+                sampler,
+                batch_size=batch_size,
+                shuffle=True,
+                drop_last=False,
+                num_workers=4)
+
+            for input_nodes, output_nodes, blocks in dataloader:
+                block = blocks[0]
+
+                block = block.int().to(device)
+                h = x[input_nodes].to(device)
+                h = layer(block, h)
+                if l != len(self.layers) - 1:
+                    h = self.activation(h)
+                    h = self.dropout(h)
+
+                y[output_nodes] = h.cpu()
+
+            x = y
+        return y
+
+
+class GAT(nn.Module):
+    def __init__(
+        self, in_feats, n_hidden, n_classes, n_layers, num_heads, activation
+    ):
+        super().__init__()
+        self.n_layers = n_layers
+        self.n_hidden = n_hidden
+        self.n_classes = n_classes
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            dglnn.GATConv(
+                (in_feats, in_feats),
+                n_hidden,
+                num_heads=num_heads,
+                activation=activation,
+            )
+        )
+        for i in range(1, n_layers - 1):
+            self.layers.append(
+                dglnn.GATConv(
+                    (n_hidden * num_heads, n_hidden * num_heads),
+                    n_hidden,
+                    num_heads=num_heads,
+                    activation=activation,
+                )
+            )
+        self.layers.append(
+            dglnn.GATConv(
+                (n_hidden * num_heads, n_hidden * num_heads),
+                n_classes,
+                num_heads=num_heads,
+                activation=None,
+            )
+        )
+
+    def forward(self, blocks, x):
+        h = x
+        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
+            # We need to first copy the representation of nodes on the RHS from the
+            # appropriate nodes on the LHS.
+            # Note that the shape of h is (num_nodes_LHS, D) and the shape of h_dst
+            # would be (num_nodes_RHS, D)
+            h_dst = h[: block.num_dst_nodes()]
+            # Then we compute the updated representation on the RHS.
+            # The shape of h now becomes (num_nodes_RHS, D)
+            if l < self.n_layers - 1:
+                h = layer(block, (h, h_dst)).flatten(1)
+            else:
+                h = layer(block, (h, h_dst))
+        h = h.mean(1)
+        return h.log_softmax(dim=-1)
+
+    def inference(self, g, x, batch_size, device):
+        """
+        Inference with the GAT model on full neighbors (i.e. without neighbor sampling).
+        g : the entire graph.
+        x : the input of entire node set.
+        The inference code is written in a fashion that it could handle any number of nodes and
+        layers.
+        """
+        # During inference with sampling, multi-layer blocks are very inefficient because
+        # lots of computations in the first few layers are repeated.
+        # Therefore, we compute the representation of all nodes layer by layer.  The nodes
+        # on each layer are of course splitted in batches.
+        # TODO: can we standardize this?
+        # TODO: make thiw into a variable
+        num_heads = 2
+        for l, layer in enumerate(self.layers):
+            if l < self.n_layers - 1:
+                y = torch.zeros(
+                    g.num_nodes(),
+                    self.n_hidden * num_heads
+                    if l != len(self.layers) - 1
+                    else self.n_classes,
+                )
+            else:
+                y = torch.zeros(
+                    g.num_nodes(),
+                    self.n_hidden
+                    if l != len(self.layers) - 1
+                    else self.n_classes,
+                )
+
+            sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
+            dataloader = dgl.dataloading.DataLoader(
+                g,
+                torch.arange(g.num_nodes()),
+                sampler,
+                batch_size=batch_size,
+                shuffle=True,
+                drop_last=False,
+                num_workers=4,
+            )
+
+            for input_nodes, output_nodes, blocks in dataloader:
+                block = blocks[0].int().to(device)
+
+                h = x[input_nodes].to(device)
+                h_dst = h[: block.num_dst_nodes()]
+                if l < self.n_layers - 1:
+                    h = layer(block, (h, h_dst)).flatten(1)
+                else:
+                    h = layer(block, (h, h_dst))
+                    h = h.mean(1)
+                    h = h.log_softmax(dim=-1)
+
+                y[output_nodes] = h.cpu()
+
+            x = y
+        return y
+
+
+class SAGE(nn.Module):
+    def __init__(self,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation,
+                 dropout,
+                 aggregator_type):
+        super().__init__()
+        self.n_layers = n_layers
+        self.n_hidden = n_hidden
+        self.n_classes = n_classes
+        self.layers = nn.ModuleList()
+        self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, aggregator_type))
+        for i in range(1, n_layers - 1):
+            self.layers.append(
+                dglnn.SAGEConv(
+                    n_hidden,
+                    n_hidden,
+                    aggregator_type))
+        self.layers.append(
+            dglnn.SAGEConv(
+                n_hidden,
+                n_classes,
+                aggregator_type))
+        self.dropout = nn.Dropout(dropout)
+        self.activation = activation
+
+    def forward(self, blocks, x):
+        h = x
+        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
+            h = layer(block, h)
+            if l != len(self.layers) - 1:
+                h = self.activation(h)
+                h = self.dropout(h)
+        return h
+
+    def inference(self, g, x, batch_size, device):
+        """
+        Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling).
+        g : the entire graph.
+        x : the input of entire node set.
+        The inference code is written in a fashion that it could handle any number of nodes and
+        layers.
+        """
+        # During inference with sampling, multi-layer blocks are very inefficient because
+        # lots of computations in the first few layers are repeated.
+        # Therefore, we compute the representation of all nodes layer by layer.  The nodes
+        # on each layer are of course splitted in batches.
+        # TODO: can we standardize this?
+        for l, layer in enumerate(self.layers):
+            y = torch.zeros(g.number_of_nodes(), self.n_hidden if l !=
+                            len(self.layers) - 1 else self.n_classes)
+
+            sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
+            dataloader = dgl.dataloading.NodeDataLoader(
+                g,
+                torch.arange(g.number_of_nodes()),
+                sampler,
+                batch_size=batch_size,
+                shuffle=True,
+                drop_last=False,
+                num_workers=4)
+
+            for input_nodes, output_nodes, blocks in dataloader:
+                block = blocks[0]
+
+                block = block.int().to(device)
+                h = x[input_nodes].to(device)
+                h = layer(block, h)
+                if l != len(self.layers) - 1:
+                    h = self.activation(h)
+                    h = self.dropout(h)
+
+                y[output_nodes] = h.cpu()
+
+            x = y
+        return y
diff --git a/graph/R-GAT/igbh/tiny/models/main.py b/graph/R-GAT/igbh/tiny/models/main.py
new file mode 100644
index 0000000000..4ab22eb753
--- /dev/null
+++ b/graph/R-GAT/igbh/tiny/models/main.py
@@ -0,0 +1,79 @@
+import argparse
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    # Input/output paths
+    parser.add_argument('--path', type=str, default='/gnndataset/')
+    parser.add_argument('--modelpath', type=str, default='gcn_19.pt')
+
+    # Dataset selection
+    parser.add_argument(
+        '--dataset_size',
+        type=str,
+        default='experimental',
+        choices=[
+            'experimental',
+            'small',
+            'medium',
+            'large',
+            'full'])
+    parser.add_argument(
+        '--type_classes',
+        type=int,
+        default=19,
+        choices=[
+            19,
+            292,
+            2983])
+
+    # Hyperparameters
+    parser.add_argument('--hidden_channels', type=int, default=16)
+    parser.add_argument('--fan_out', type=str, default='5,10')
+    parser.add_argument('--num_layers', type=int, default=2)
+    parser.add_argument('--learning_rate', type=int, default=0.01)
+    parser.add_argument('--decay', type=int, default=0.001)
+    parser.add_argument('--num_workers', type=int, default=4)
+    parser.add_argument('--batch_size', type=int, default=2048 * 16)
+    parser.add_argument('--dropout', type=float, default=0.2)
+    parser.add_argument('--epochs', type=int, default=20)
+    parser.add_argument(
+        '--model_type',
+        type=str,
+        default='gcn',
+        choices=[
+            'gat',
+            'sage',
+            'gcn'])
+    parser.add_argument('--in_memory', type=int, default=0)
+    parser.add_argument('--synthetic', type=int, default=0)
+    parser.add_argument('--device', type=str, default='1')
+    args = parser.parse_args()
+
+    print("Dataset_size: " + args.dataset_size)
+    print("Model       : " + args.model)
+    print("Num_classes : " + str(args.num_classes))
+    print()
+
+    device = f'cuda:' + args.device if torch.cuda.is_available() else 'cpu'
+
+    dataset = IGL260M_DGL(args)
+    g = dataset[0]
+
+    best_test_acc, train_acc, test_acc = track_acc(g, args)
+
+    print(
+        f"Train accuracy: {np.mean(train_acc):.2f} \u00B1 {np.std(train_acc):.2f} \t Best: {np.max(train_acc) * 100:.4f}%")
+    print(
+        f"Test accuracy: {np.mean(test_acc):.2f} \u00B1 {np.std(test_acc):.2f} \t Best: {np.max(test_acc) * 100:.4f}%")
+    print()
+    print(" -------- For debugging --------- ")
+    print("Parameters: ", args)
+    print(g)
+    print("Train accuracy: ", train_acc)
+    print("Test accuracy: ", test_acc)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/graph/R-GAT/igbh/tiny/models/utils.py b/graph/R-GAT/igbh/tiny/models/utils.py
new file mode 100644
index 0000000000..5e9e1a25d9
--- /dev/null
+++ b/graph/R-GAT/igbh/tiny/models/utils.py
@@ -0,0 +1,224 @@
+import numpy as np
+import torch
+
+
+class IGL260MDataset(object):
+    def __init__(self, root: str, size: str, in_memory: int,
+                 classes: int, synthetic: int):
+        self.dir = root
+        self.size = size
+        self.synthetic = synthetic
+        self.in_memory = in_memory
+        self.num_classes = classes
+        self.__meta__ = torch.load(osp.join(self.dir, self.size, 'meta.pt'))
+
+        self.num_features = self.__meta__['paper']['emb_dim']
+        self.num_nodes = self.__meta__['paper']['num_node']
+        self.num_edges = self.__meta__['cites']['num_edge']
+
+    @property
+    def paper_feat(self) -> np.ndarray:
+        if self.synthetic:
+            return np.random((self.num_nodes, self.num_edges))
+
+        path = osp.join(
+            self.dir,
+            self.size,
+            'processed',
+            'paper',
+            'node_feat.npy')
+        if self.in_memory:
+            return np.load(path)
+        else:
+            return np.load(path, mmap_mode='r')
+
+    @property
+    def paper_label(self) -> np.ndarray:
+        if self.num_classes == 19:
+            path = osp.join(
+                self.dir,
+                self.size,
+                'processed',
+                'paper',
+                'node_label_19.npy')
+        else:
+            path = osp.join(
+                self.dir,
+                self.size,
+                'processed',
+                'paper',
+                'node_label_2K.npy')
+        if self.in_memory:
+            return np.load(path)
+        else:
+            return np.load(path, mmap_mode='r')
+
+    @property
+    def paper_edge(self) -> np.ndarray:
+        path = osp.join(
+            self.dir,
+            self.size,
+            'processed',
+            'paper__cites__paper',
+            'edge_index.npy')
+        if self.in_memory:
+            return np.load(path)
+        else:
+            return np.load(path, mmap_mode='r')
+
+
+def compute_acc(pred, labels):
+    """
+    Compute the accuracy of prediction given the labels.
+    """
+    labels = labels.long()
+    return (torch.argmax(pred, dim=1) == labels).float().sum() / len(pred)
+
+
+def evaluate(model, g, inputs, labels, val_nid, batch_size, device):
+    """
+    Evaluate the model on the validation set specified by ``val_nid``.
+    g : The entire graph.
+    inputs : The features of all the nodes.
+    labels : The labels of all the nodes.
+    val_nid : the node Ids for validation.
+    batch_size : Number of nodes to compute at the same time.
+    device : The GPU device to evaluate on.
+    """
+    model.eval()
+    with torch.no_grad():
+        pred = model.inference(g, inputs, batch_size, device)
+    model.train()
+    return compute_acc(pred[val_nid], labels[val_nid])
+
+
+def load_subtensor(g, seeds, input_nodes, device):
+    """
+    Copys features and labels of a set of nodes onto GPU.
+    """
+    batch_inputs = g.ndata['features'][input_nodes].to(device)
+    batch_labels = g.ndata['labels'][seeds].to(device)
+    return batch_inputs, batch_labels
+
+
+def track_acc(g, args):
+    train_accuracy = []
+    test_accuracy = []
+    g.ndata['features'] = g.ndata['feat']
+    g.ndata['labels'] = g.ndata['label']
+    in_feats = g.ndata['features'].shape[1]
+    n_classes = args.num_classes
+
+    # Create csr/coo/csc formats before launching training processes with multi-gpu.
+    # This avoids creating certain formats in each sub-process, which saves
+    # momory and CPU.
+    g.create_formats_()
+
+    num_epochs = args.epochs
+    num_hidden = args.hidden_channels
+    num_layers = args.num_layers
+    fan_out = args.fan_out
+    batch_size = args.batch_size
+    lr = args.learning_rate
+    dropout = args.dropout
+    num_workers = args.num_workers
+
+    train_nid = torch.nonzero(g.ndata['train_mask'], as_tuple=True)[0]
+
+    # Create PyTorch DataLoader for constructing blocks
+    sampler = dgl.dataloading.MultiLayerNeighborSampler(
+        [int(fanout) for fanout in fan_out.split(',')])
+
+    dataloader = dgl.dataloading.NodeDataLoader(
+        g,
+        train_nid,
+        sampler,
+        batch_size=batch_size,
+        shuffle=True,
+        drop_last=False,
+        num_workers=num_workers)
+
+    # Define model and optimizer
+    if args.model_type == 'gcn':
+        model = GCN(in_feats, num_hidden, n_classes, 1, F.relu, dropout)
+    if args.model_type == 'sage':
+        model = SAGE(
+            in_feats,
+            num_hidden,
+            n_classes,
+            num_layers,
+            F.relu,
+            dropout,
+            'gcn')
+    if args.model_type == 'gat':
+        model = GAT(in_feats, num_hidden, n_classes, num_layers, 2, F.relu)
+
+    model = model.to(device)
+    loss_fcn = nn.CrossEntropyLoss()
+    loss_fcn = loss_fcn.to(device)
+    optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=args.decay)
+
+    # Training loop
+    avg = 0
+    best_test_acc = 0
+    log_every = 1
+    training_start = time.time()
+    for epoch in (range(num_epochs)):
+        # Loop over the dataloader to sample the computation dependency graph as a list of
+        # blocks.
+        epoch_loss = 0
+        gpu_mem_alloc = 0
+        epoch_start = time.time()
+        for step, (input_nodes, seeds, blocks) in (enumerate(dataloader)):
+            # Load the input features as well as output labels
+            # batch_inputs, batch_labels = load_subtensor(g, seeds, input_nodes, device)
+            blocks = [block.int().to(device) for block in blocks]
+            batch_inputs = blocks[0].srcdata['features']
+            batch_labels = blocks[-1].dstdata['labels']
+
+            # Compute loss and prediction
+            batch_pred = model(blocks, batch_inputs)
+            loss = loss_fcn(batch_pred, batch_labels)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            epoch_loss += loss.detach()
+
+            gpu_mem_alloc += (
+                torch.cuda.max_memory_allocated() / 1000000
+                if torch.cuda.is_available()
+                else 0
+            )
+
+        train_g = g
+        train_nid = torch.nonzero(
+            train_g.ndata['train_mask'], as_tuple=True)[0]
+        train_acc = evaluate(
+            model, train_g, train_g.ndata['features'], train_g.ndata['labels'], train_nid, batch_size, device)
+
+        test_g = g
+        test_nid = torch.nonzero(
+            test_g.ndata['test_mask'], as_tuple=True)[0]
+        test_acc = evaluate(
+            model, test_g, test_g.ndata['features'], test_g.ndata['labels'], test_nid, batch_size, device)
+
+        if test_acc.item() > best_test_acc:
+            best_test_acc = test_acc.item()
+        tqdm.write(
+            "Epoch {:05d} | Loss {:.4f} | Train Acc {:.4f} | Test Acc {:.4f} | Time {:.2f}s | GPU {:.1f} MB".format(
+                epoch,
+                epoch_loss,
+                train_acc.item(),
+                test_acc.item(),
+                time.time() - epoch_start,
+                gpu_mem_alloc
+            )
+        )
+        test_accuracy.append(test_acc.item())
+        train_accuracy.append(train_acc.item())
+        torch.save(model.state_dict(), args.modelpath)
+    print()
+    print("Total time taken: ", time.time() - training_start)
+
+    return best_test_acc, train_accuracy, test_accuracy
diff --git a/upcomming_benchmarks/graph/R-GAT/main.py b/graph/R-GAT/main.py
similarity index 86%
rename from upcomming_benchmarks/graph/R-GAT/main.py
rename to graph/R-GAT/main.py
index a7697481a9..d76ea090d2 100644
--- a/upcomming_benchmarks/graph/R-GAT/main.py
+++ b/graph/R-GAT/main.py
@@ -23,6 +23,8 @@
 
 import dataset
 import igbh
+import dgl_utilities.feature_fetching as dgl_igbh
+
 
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("main")
@@ -31,68 +33,123 @@
 MILLI_SEC = 1000
 
 SUPPORTED_DATASETS = {
-    "igbh-tiny": (
+    "igbh-glt-tiny": (
         igbh.IGBH,
         dataset.preprocess,
         igbh.PostProcessIGBH(),
         {"dataset_size": "tiny", "use_label_2K": True},
     ),
-    "igbh-small": (
+    "igbh-glt-small": (
         igbh.IGBH,
         dataset.preprocess,
         igbh.PostProcessIGBH(),
         {"dataset_size": "small", "use_label_2K": True},
     ),
-    "igbh-medium": (
+    "igbh-glt-medium": (
         igbh.IGBH,
         dataset.preprocess,
         igbh.PostProcessIGBH(),
         {"dataset_size": "medium", "use_label_2K": True},
     ),
-    "igbh-large": (
+    "igbh-glt-large": (
         igbh.IGBH,
         dataset.preprocess,
         igbh.PostProcessIGBH(),
         {"dataset_size": "large", "use_label_2K": True},
     ),
-    "igbh": (
+    "igbh-glt": (
         igbh.IGBH,
         dataset.preprocess,
         igbh.PostProcessIGBH(),
         {"dataset_size": "full", "use_label_2K": True},
     ),
+    "igbh-dgl-tiny": (
+        dgl_igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "tiny", "use_label_2K": True},
+    ),
+    "igbh-dgl-small": (
+        dgl_igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "small", "use_label_2K": True},
+    ),
+    "igbh-dgl-medium": (
+        dgl_igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "medium", "use_label_2K": True},
+    ),
+    "igbh-dgl-large": (
+        dgl_igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "large", "use_label_2K": True},
+    ),
+    "igbh-dgl": (
+        dgl_igbh.IGBH,
+        dataset.preprocess,
+        igbh.PostProcessIGBH(),
+        {"dataset_size": "full", "use_label_2K": True},
+    ),
 }
 
 
 SUPPORTED_PROFILES = {
     "defaults": {
-        "dataset": "igbh-tiny",
-        "backend": "pytorch",
+        "dataset": "igbh-glt-tiny",
+        "backend": "glt",
+        "model-name": "rgat",
+    },
+    "debug-glt": {
+        "dataset": "igbh-glt-tiny",
+        "backend": "glt",
+        "model-name": "rgat",
+    },
+    "rgat-glt-small": {
+        "dataset": "igbh-glt-small",
+        "backend": "glt",
+        "model-name": "rgat",
+    },
+    "rgat-glt-medium": {
+        "dataset": "igbh-glt-medium",
+        "backend": "glt",
+        "model-name": "rgat",
+    },
+    "rgat-glt-large": {
+        "dataset": "igbh-glt-large",
+        "backend": "glt",
+        "model-name": "rgat",
+    },
+    "rgat-glt-full": {
+        "dataset": "igbh-glt",
+        "backend": "glt",
         "model-name": "rgat",
     },
-    "debug": {
-        "dataset": "igbh-tiny",
-        "backend": "pytorch",
+    "debug-dgl": {
+        "dataset": "igbh-dgl-tiny",
+        "backend": "dgl",
         "model-name": "rgat",
     },
-    "rgat-pytorch-small": {
-        "dataset": "igbh-small",
-        "backend": "pytorch",
+    "rgat-dgl-small": {
+        "dataset": "igbh-dgl-small",
+        "backend": "dgl",
         "model-name": "rgat",
     },
-    "rgat-pytorch-medium": {
-        "dataset": "igbh-medium",
-        "backend": "pytorch",
+    "rgat-dgl-medium": {
+        "dataset": "igbh-dgl-medium",
+        "backend": "dgl",
         "model-name": "rgat",
     },
-    "rgat-pytorch-large": {
-        "dataset": "igbh-large",
-        "backend": "pytorch",
+    "rgat-dgl-large": {
+        "dataset": "igbh-dgl-large",
+        "backend": "dgl",
         "model-name": "rgat",
     },
-    "rgat-pytorch-full": {
-        "dataset": "igbh",
-        "backend": "pytorch",
+    "rgat-dgl-full": {
+        "dataset": "igbh-dgl",
+        "backend": "dgl",
         "model-name": "rgat",
     },
 }
@@ -226,10 +283,12 @@ def get_args():
 
 
 def get_backend(backend, **kwargs):
-    if backend == "pytorch":
-        from backend_pytorch import BackendPytorch
-
-        backend = BackendPytorch(**kwargs)
+    if backend == "glt":
+        from backend_glt import BackendGLT
+        backend = BackendGLT(**kwargs)
+    elif backend == "dgl":
+        from backend_dgl import BackendDGL
+        backend = BackendDGL(**kwargs)
     else:
         raise ValueError("unknown backend: " + backend)
     return backend
@@ -380,7 +439,7 @@ def main():
         device=args.device,
         ckpt_path=args.model_path,
         batch_size=args.max_batchsize,
-        igbh_dataset=ds.igbh_dataset,
+        igbh=ds,
         layout=args.layout,
     )
 
@@ -425,7 +484,7 @@ def main():
     count = ds.get_item_count()
 
     # warmup
-    warmup_samples = torch.Tensor([0]).to(torch.int64).to(backend.device)
+    warmup_samples = torch.Tensor([0]).to(torch.int64)
     for i in range(5):
         _ = backend.predict(warmup_samples)
 
diff --git a/upcomming_benchmarks/graph/R-GAT/requirements.txt b/graph/R-GAT/requirements.txt
similarity index 90%
rename from upcomming_benchmarks/graph/R-GAT/requirements.txt
rename to graph/R-GAT/requirements.txt
index 062b710cd8..8e6e7276f9 100644
--- a/upcomming_benchmarks/graph/R-GAT/requirements.txt
+++ b/graph/R-GAT/requirements.txt
@@ -2,7 +2,7 @@ colorama==0.4.6
 tqdm==4.66.4
 requests==2.32.2
 torch==2.1.0
-dgl==2.1.0
+torchdata==0.7.0
 pybind11==2.12.0
 PyYAML==6.0.1
 pydantic==2.7.1
diff --git a/upcomming_benchmarks/graph/R-GAT/rgnn.py b/graph/R-GAT/rgnn.py
similarity index 100%
rename from upcomming_benchmarks/graph/R-GAT/rgnn.py
rename to graph/R-GAT/rgnn.py
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py b/graph/R-GAT/tools/compress_graph.py
similarity index 100%
rename from upcomming_benchmarks/graph/R-GAT/tools/compress_graph.py
rename to graph/R-GAT/tools/compress_graph.py
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/download_igbh_full.sh b/graph/R-GAT/tools/download_igbh_full.sh
similarity index 100%
rename from upcomming_benchmarks/graph/R-GAT/tools/download_igbh_full.sh
rename to graph/R-GAT/tools/download_igbh_full.sh
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/download_igbh_test.py b/graph/R-GAT/tools/download_igbh_test.py
similarity index 100%
rename from upcomming_benchmarks/graph/R-GAT/tools/download_igbh_test.py
rename to graph/R-GAT/tools/download_igbh_test.py
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/format_model.py b/graph/R-GAT/tools/format_model.py
similarity index 100%
rename from upcomming_benchmarks/graph/R-GAT/tools/format_model.py
rename to graph/R-GAT/tools/format_model.py
diff --git a/upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py b/graph/R-GAT/tools/split_seeds.py
similarity index 100%
rename from upcomming_benchmarks/graph/R-GAT/tools/split_seeds.py
rename to graph/R-GAT/tools/split_seeds.py
diff --git a/upcomming_benchmarks/graph/R-GAT/user.conf b/graph/R-GAT/user.conf
similarity index 100%
rename from upcomming_benchmarks/graph/R-GAT/user.conf
rename to graph/R-GAT/user.conf

From dffd29273e98ffcbbfad0e66648c5d390d814509 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Fri, 8 Nov 2024 16:24:36 -0600
Subject: [PATCH 172/305] Update artifact actions from v3 to v4 in
 build_wheels.yml (#1906)

---
 .github/workflows/build_wheels.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 7763f800f3..e62f010dfa 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -89,7 +89,7 @@ jobs:
 
        # Save wheels as artifacts
       - name: Upload built wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: wheels-${{ matrix.os }}
           path: wheels
@@ -106,19 +106,19 @@ jobs:
 
       # Download the built wheels from ubuntu
       - name: Download Ubuntu wheels
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: wheels-ubuntu-latest
           path: wheels
       # Download the built wheels from macOS
       - name: Download macOS wheels
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: wheels-macos-latest
           path: wheels
       # Download the built wheels from Windows
       - name: Download Windows wheels
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: wheels-windows-latest
           path: wheels

From 97122a2aa017eb737fa9ab2f2508683073e8d247 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 14 Nov 2024 22:55:08 +0000
Subject: [PATCH 173/305] Fix typo in fid_score.py, fail_safe for SDXL short
 runs, fix argument error in DLRMv2 implementation #1909, fixes
 preprocess_submission (#1910)

* Update generate_final_report.py

* Fix sdxl (#1911)

* Fix typo in fid_score.py, fail_safe for SDXL short runs

* [Automated Commit] Format Codebase

* Fix typo in fid_score.py, fail_safe for SDXL short runs

* Fix dlrmv2 reference implementation | Update run_local.sh

* Fixes for filtering invalid results

* [Automated Commit] Format Codebase

* Update preprocess_submission.py

* Added an option to pass in sample_ids.txt for SDXL accuracy check

* [Automated Commit] Format Codebase

* Update accuracy_coco.py

* [Automated Commit] Format Codebase

* Fix typo

* Not use default for sample_ids.txt

---------

Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
---
 recommendation/dlrm_v2/pytorch/run_local.sh |  4 ++-
 text_to_image/coco.py                       | 24 +++++++------
 text_to_image/tools/accuracy_coco.py        |  9 ++++-
 text_to_image/tools/fid/fid_score.py        |  2 +-
 tools/submission/generate_final_report.py   |  2 +-
 tools/submission/preprocess_submission.py   | 38 ++++++++++++++++-----
 6 files changed, 57 insertions(+), 22 deletions(-)

diff --git a/recommendation/dlrm_v2/pytorch/run_local.sh b/recommendation/dlrm_v2/pytorch/run_local.sh
index 0d054c6c45..3bc8ec667c 100755
--- a/recommendation/dlrm_v2/pytorch/run_local.sh
+++ b/recommendation/dlrm_v2/pytorch/run_local.sh
@@ -2,7 +2,9 @@
 
 source ./run_common.sh
 
-common_opt="--mlperf_conf ../../../mlperf.conf"
+#mlperf.conf is now automatically loaded by loadgen
+#common_opt="--mlperf_conf ../../../mlperf.conf"
+
 OUTPUT_DIR=`pwd`/output/$name
 if [ ! -d $OUTPUT_DIR ]; then
     mkdir -p $OUTPUT_DIR
diff --git a/text_to_image/coco.py b/text_to_image/coco.py
index cb3956a014..e9499b0e6c 100644
--- a/text_to_image/coco.py
+++ b/text_to_image/coco.py
@@ -176,20 +176,24 @@ def __call__(self, results, ids, expected=None, result_dict=None):
     def save_images(self, ids, ds):
         info = []
         idx = {}
-        for i, id in enumerate(self.content_ids):
-            if id in ids:
-                idx[id] = i
+        for i, image_id in enumerate(self.content_ids):
+            if image_id in ids:
+                idx[image_id] = i
         if not os.path.exists("images/"):
             os.makedirs("images/", exist_ok=True)
-        for id in ids:
-            caption = ds.get_caption(id)
-            generated = Image.fromarray(self.results[idx[id]])
-            image_path_tmp = f"images/{self.content_ids[idx[id]]}.png"
+        for image_id in ids:
+            if not idx.get(image_id):
+                print(
+                    f"image id {image_id} is missing in the results. Hence not saved.")
+                continue
+            caption = ds.get_caption(image_id)
+            generated = Image.fromarray(self.results[idx[image_id]])
+            image_path_tmp = f"images/{self.content_ids[idx[image_id]]}.png"
             generated.save(image_path_tmp)
-            info.append((self.content_ids[idx[id]], caption))
+            info.append((self.content_ids[idx[image_id]], caption))
         with open("images/captions.txt", "w+") as f:
-            for id, caption in info:
-                f.write(f"{id}  {caption}\n")
+            for image_id, caption in info:
+                f.write(f"{image_id}  {caption}\n")
 
     def start(self):
         self.results = []
diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
index 2d7c36506d..b5f1be3783 100644
--- a/text_to_image/tools/accuracy_coco.py
+++ b/text_to_image/tools/accuracy_coco.py
@@ -51,6 +51,11 @@ def get_args():
         required=False,
         help="path to dump 10 stable diffusion xl compliance images",
     )
+    # Do not use for official MLPerf inference submissions as only the default
+    # one is valid
+    parser.add_argument(
+        "--ids-path", help="Path to 10 caption ids to dump as compliance images"
+    )
     parser.add_argument("--device", default="cpu", choices=["gpu", "cpu"])
     parser.add_argument(
         "--low_memory",
@@ -97,8 +102,10 @@ def main():
             os.makedirs(args.compliance_images_path)
         dump_compliance_images = True
         compliance_images_idx_list = []
+        sample_ids_file_path = args.ids_path if args.ids_path else os.path.join(
+            os.path.dirname(__file__), "sample_ids.txt")
         with open(
-            os.path.join(os.path.dirname(__file__), "sample_ids.txt"), "r"
+            sample_ids_file_path, "r"
         ) as compliance_id_file:
             for line in compliance_id_file:
                 idx = int(line.strip())
diff --git a/text_to_image/tools/fid/fid_score.py b/text_to_image/tools/fid/fid_score.py
index febc12ff5d..8e486c8b7a 100644
--- a/text_to_image/tools/fid/fid_score.py
+++ b/text_to_image/tools/fid/fid_score.py
@@ -44,7 +44,7 @@
 import pathlib
 import os
 import sys
-sys.path.insert("..", 0)
+sys.path.insert(0, "..")
 from inception import InceptionV3  # noqa: E402
 
 
diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index ba2c368cdf..79d9fe0767 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -79,7 +79,7 @@ def main():
     df["p#"] = df.apply(lambda x: int(x["host_processors_per_node"]), axis=1)
 
     # details url
-    base_url = f"https://github.com/mlcommons/{args.repository}/tree/main"
+    base_url = f"https://github.com/{args.repository_owner}/{args.repository}/tree/{args.repository_branch}"
     df["Details"] = df.apply(
         lambda x: '=HYPERLINK("{}","details")'.format(
             "/".join(
diff --git a/tools/submission/preprocess_submission.py b/tools/submission/preprocess_submission.py
index 1e26b81ca4..a1678c79d0 100644
--- a/tools/submission/preprocess_submission.py
+++ b/tools/submission/preprocess_submission.py
@@ -2,10 +2,6 @@
 Tool to infer scenario results and cleanup submission tree
 """
 
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 import argparse
 import logging
 import os
@@ -142,6 +138,27 @@ def change_folder_name_in_path(path, old_folder_name, new_folder_name):
     return new_path
 
 
+def clean_model_dir(model_results_dir):
+    model_measurements_dir = change_folder_name_in_path(
+        model_results_dir, "results", "measurements")
+    model_compliance_dir = change_folder_name_in_path(
+        model_results_dir, "results", "compliance")
+
+    print(f"rmtree {model_results_dir}")
+    shutil.rmtree(model_results_dir)
+    shutil.rmtree(model_measurements_dir)
+    shutil.rmtree(model_compliance_dir)
+    sut_results_dir = os.path.dirname(model_results_dir)
+    if not os.listdir(sut_results_dir):
+        # clean sut dir
+        sut = os.path.basename(sut_results_dir)
+        log.info(
+            f"No benchmark results remaining for {sut}. rmtree {sut_results_dir}")
+        shutil.rmtree(sut_results_dir)
+        shutil.rmtree(os.path.dirname(model_measurements_dir))
+        shutil.rmtree(os.path.dirname(model_compliance_dir))
+
+
 def clean_invalid_results(args, log_path, config, system_desc, system_json,
                           model, mlperf_model, division, system_id_json, is_closed_or_network):
     # cleanup invalid results
@@ -176,6 +193,7 @@ def clean_invalid_results(args, log_path, config, system_desc, system_json,
         except Exception as e:
             log.warning(e)
             perf_is_valid = False
+        compliance_is_valid = False
         if perf_is_valid:
             power_path = os.path.join(scenario_path, "performance", "power")
             has_power = os.path.exists(power_path)
@@ -260,9 +278,12 @@ def clean_invalid_results(args, log_path, config, system_desc, system_json,
                     # if only accuracy or compliance failed, result is valid
                     # for open
                     if not perf_is_valid:
-                        shutil.rmtree(scenario_path)
                         log.warning(
                             f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} and open divisions. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Removing it...")
+                        shutil.rmtree(scenario_path)
+                        scenario_measurements_path = change_folder_name_in_path(
+                            scenario_path, "results", "measurements")
+                        shutil.rmtree(scenario_measurements_path)
                     if not os.path.exists(target_results_path):
                         shutil.copytree(
                             model_results_path, target_results_path)
@@ -288,9 +309,7 @@ def clean_invalid_results(args, log_path, config, system_desc, system_json,
                         log.warning(f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} division. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Compliance: {compliance_is_valid}. Moving other scenario results of {model} to open...")
                 else:
                     log.warning(f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} division. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Removing all dependent scenario results...")
-                shutil.rmtree(model_results_path)
-                shutil.rmtree(model_measurements_path)
-                shutil.rmtree(model_compliance_path)
+                clean_model_dir(model_results_path)
             else:  # delete this result
                 # delete other scenario results too
                 shutil.rmtree(scenario_path)
@@ -517,6 +536,9 @@ def main():
 
     infer_scenario_results(args, config)
 
+    if not args.nodelete_empty_dirs:
+        delete_empty_dirs(os.path.join(src_dir))
+
     return 0
 
 

From e398b90e85f9e64d34d6c67fe4b04d6cb7145c98 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Thu, 14 Nov 2024 23:12:35 +0000
Subject: [PATCH 174/305] Update requirements.txt (#1907)

Updating the pip packages
---
 text_to_image/requirements.txt | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/text_to_image/requirements.txt b/text_to_image/requirements.txt
index a0a8507731..857de950e8 100644
--- a/text_to_image/requirements.txt
+++ b/text_to_image/requirements.txt
@@ -1,8 +1,8 @@
-diffusers==0.21.2
-transformers==4.33.2
-accelerate==0.23.0
-open-clip-torch==2.7.0
-opencv-python==4.8.1.78
+diffusers==0.30.3
+transformers==4.45.2
+accelerate==1.0.1
+open-clip-torch==2.26.1
+opencv-python==4.10.0.84
 pycocotools==2.0.7
-torchmetrics[image]==1.2.0
-scipy==1.9.1
+torchmetrics[image]==1.4.3
+scipy==1.10.1

From 4763c6b5e7c3ad904c461bc1053674c448771ec4 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Fri, 15 Nov 2024 21:09:48 +0000
Subject: [PATCH 175/305] Removed TEST05 compliance requirement (#1925)

---
 compliance/nvidia/README.md                   |  11 +-
 compliance/nvidia/TEST05/README.md            |  41 -----
 compliance/nvidia/TEST05/audit.config         |   6 -
 compliance/nvidia/TEST05/run_verification.py  | 127 --------------
 .../nvidia/TEST05/verify_performance.py       | 157 ------------------
 tools/submission/submission_checker.py        |   2 +-
 6 files changed, 6 insertions(+), 338 deletions(-)
 delete mode 100755 compliance/nvidia/TEST05/README.md
 delete mode 100644 compliance/nvidia/TEST05/audit.config
 delete mode 100644 compliance/nvidia/TEST05/run_verification.py
 delete mode 100644 compliance/nvidia/TEST05/verify_performance.py

diff --git a/compliance/nvidia/README.md b/compliance/nvidia/README.md
index 3abb256c18..91c8d8df7e 100755
--- a/compliance/nvidia/README.md
+++ b/compliance/nvidia/README.md
@@ -30,12 +30,11 @@ The `run_verification.py` found in each test directory will copy the test files
 
 | model | Required Compliance Tests
 | ---- | ---- |
-| resnet50-v1.5 | [TEST01](./TEST01/), [TEST04](./TEST04/), [TEST05](./TEST05/) |
-| retinanet 800x800 | [TEST01](./TEST01/), [TEST05](./TEST05/) |
-| bert | [TEST01](./TEST01/), [TEST05](./TEST05/) |
-| dlrm-v2 | [TEST01](./TEST01/), [TEST05](./TEST05/) |
-| 3d-unet | [TEST01](./TEST01/), [TEST05](./TEST05/) |
-| rnnt | [TEST01](./TEST01/), [TEST05](./TEST05/) |
+| resnet50-v1.5 | [TEST01](./TEST01/), [TEST04](./TEST04/) |
+| retinanet 800x800 | [TEST01](./TEST01/) |
+| bert | [TEST01](./TEST01/) |
+| dlrm-v2 | [TEST01](./TEST01/) |
+| 3d-unet | [TEST01](./TEST01/) |
 | gpt-j | - |
 | stable-diffusion-xl | [TEST01](./TEST01/), [TEST04](./TEST04/) |
 | Llama2-70b | [TEST06](./TEST06/) |
diff --git a/compliance/nvidia/TEST05/README.md b/compliance/nvidia/TEST05/README.md
deleted file mode 100755
index ca381cbee6..0000000000
--- a/compliance/nvidia/TEST05/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
-
-# Test 05 - Vary RNG seeds
-## Introduction
-The purpose of this test is to ensure that the SUT does not favor a particular set of Loadgen RNG seed values. The pass condition is that performance with non-default RNG seed values should be similar to the submitted performance.
-
-The seeds that are changed are listed below:
- - qsl_rng_seed - determines order of samples in QSL
- - sample_index_rng_seed - determines subset of samples in each loadable set
-  - schedule_rng_seed - determines scheduling of samples in server mode
-
-## Prerequisites
-This script works best with Python 3.3 or later.
-This script also assumes that the submission runs have already been run and that results comply with the submission directory structure as described in [https://github.com/mlperf/policies/blob/master/submission_rules.adoc#562-inference](https://github.com/mlperf/policies/blob/master/submission_rules.adoc#562-inference)
-## Pass Criteria
-Performance must be within 5% of the submission performance. In single stream mode, latencies can be very short for high performance systems and run-to-run variation due to external disturbances (OS) can be significant. In such cases and when submission latencies are less or equal to 0.2ms, the pass threshold is relaxed to 20%.
-
-## Instructions
-
-### Part I
-Run the benchmark with the provided audit.config in the corresponding benchmark subdirectory. 
-
-The audit.config file must be copied to the directory where the benchmark is being run from. Verification that audit.config was properly read can be done by checking that loadgen has found audit.config in mlperf_log_detail.txt 
-
-Alternatively, you can alter the mlperf.conf (or the configuration file's copy your benchmark is using) by setting the value `*.*.test05 = 1`. For this option make sure you have the right values for `*.*.test05_qsl_rng_seed`, `*.*.test05_sample_index_rng_seed` and `*.*.test05_schedule_rng_seed` included in your configuration file.
-
-### Part II
-Run the verification script:
-  `python3 run_verification.py -r RESULTS_DIR -c COMPLIANCE_DIR -o OUTPUT_DIR [--dtype {byte,float32,int32,int64}]`
-  
-- RESULTS_DIR: Specifies the path to the corresponding results directory that contains the accuracy and performance subdirectories containing the submission logs, i.e. `inference_results_v0.7/closed/NVIDIA/results/GPU/resnet/Offline`
-- COMPLIANCE_DIR: Specifies the path to the directory containing the logs from the compliance test run.
-- OUTPUT_DIR: Specifies the path to the output directory where compliance logs will be uploaded from, i.e. `inference_results_v0.7/closed/NVIDIA/compliance/GPU/resnet/Offline`
-
-Expected outcome:
-              
-    Performance check pass: True             
-    TEST05 verification complete        
-
-     
-
-
diff --git a/compliance/nvidia/TEST05/audit.config b/compliance/nvidia/TEST05/audit.config
deleted file mode 100644
index f6074948c7..0000000000
--- a/compliance/nvidia/TEST05/audit.config
+++ /dev/null
@@ -1,6 +0,0 @@
-# The format of this config file is 'key = value'.
-# The key has the format 'model.scenario.key'. Value is mostly int64_t.
-# Model maybe '*' as wildcard. In that case the value applies to all models.
-# All times are in milli seconds
-
-*.*.test05 = 1
diff --git a/compliance/nvidia/TEST05/run_verification.py b/compliance/nvidia/TEST05/run_verification.py
deleted file mode 100644
index 8041551879..0000000000
--- a/compliance/nvidia/TEST05/run_verification.py
+++ /dev/null
@@ -1,127 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2018-2022 The MLPerf Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-import os
-import sys
-import shutil
-import subprocess
-import argparse
-import json
-
-import numpy as np
-
-sys.path.append(os.getcwd())
-
-
-def main():
-
-    py3 = sys.version_info >= (3, 0)
-    # Parse arguments to identify the path to the logs from the performance
-    # runs
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--results_dir",
-        "-r",
-        help="Specifies the path to the corresponding results directory that contains the performance subdirectories containing the submission logs, i.e. inference_results_v0.7/closed/NVIDIA/results/T4x8/resnet/Offline.",
-        required=True,
-    )
-    parser.add_argument(
-        "--compliance_dir",
-        "-c",
-        help="Specifies the path to the directory containing the logs from the compliance test run.",
-        required=True,
-    )
-    parser.add_argument(
-        "--output_dir",
-        "-o",
-        help="Specifies the path to the output directory where compliance logs will be uploaded from, i.e. inference_results_v0.7/closed/NVIDIA/compliance/T4x8/resnet/Offline.",
-        required=True,
-    )
-
-    args = parser.parse_args()
-
-    print("Parsing arguments.")
-    results_dir = args.results_dir
-    compliance_dir = args.compliance_dir
-    output_dir = os.path.join(args.output_dir, "TEST05")
-
-    # run verify performance
-    verify_performance_binary = os.path.join(
-        os.path.dirname(__file__), "verify_performance.py"
-    )
-    verify_performance_command = (
-        "python3 "
-        + verify_performance_binary
-        + " -r "
-        + results_dir
-        + "/performance/run_1/mlperf_log_summary.txt"
-        + " -t "
-        + compliance_dir
-        + "/mlperf_log_summary.txt | tee verify_performance.txt"
-    )
-    try:
-        os.system(verify_performance_command)
-    except Exception:
-        print(
-            "Exception occurred trying to execute:\n  " +
-            verify_performance_command)
-
-    # check if verify performance script passes
-    performance_pass_command = "grep PASS verify_performance.txt"
-    try:
-        performance_pass = "TEST PASS" in subprocess.check_output(
-            performance_pass_command, shell=True
-        ).decode("utf-8")
-    except Exception:
-        performance_pass = False
-
-    # setup output compliance directory structure
-    output_performance_dir = os.path.join(output_dir, "performance", "run_1")
-    try:
-        if not os.path.isdir(output_performance_dir):
-            os.makedirs(output_performance_dir)
-    except Exception:
-        print("Exception occurred trying to create " + output_performance_dir)
-
-    # copy compliance logs to output compliance directory
-    shutil.copy2("verify_performance.txt", output_dir)
-    summary_file = os.path.join(compliance_dir, "mlperf_log_summary.txt")
-    detail_file = os.path.join(compliance_dir, "mlperf_log_detail.txt")
-
-    try:
-        shutil.copy2(summary_file, output_performance_dir)
-    except Exception:
-        print(
-            "Exception occured trying to copy "
-            + summary_file
-            + " to "
-            + output_performance_dir
-        )
-    try:
-        shutil.copy2(detail_file, output_performance_dir)
-    except Exception:
-        print(
-            "Exception occured trying to copy "
-            + detail_file
-            + " to "
-            + output_performance_dir
-        )
-
-    print("Performance check pass: {:}".format(performance_pass))
-    print("TEST05 verification complete")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/compliance/nvidia/TEST05/verify_performance.py b/compliance/nvidia/TEST05/verify_performance.py
deleted file mode 100644
index 864a8af446..0000000000
--- a/compliance/nvidia/TEST05/verify_performance.py
+++ /dev/null
@@ -1,157 +0,0 @@
-#! /usr/bin/env python3
-# Copyright 2018-2022 The MLPerf Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-import json
-import argparse
-import os
-import sys
-import re
-
-sys.path.append(os.getcwd())
-
-
-def main():
-    # Parse arguments to identify the path to the accuracy logs from
-    #   the accuracy and performance runs
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--reference_summary",
-        "-r",
-        help="Specifies the path to the summary log for the performance run.",
-        default="",
-    )
-    parser.add_argument(
-        "--test_summary",
-        "-t",
-        help="Specifies the path to the summary log for this test.",
-        default="",
-    )
-    args = parser.parse_args()
-
-    print("Verifying performance.")
-    ref_file = open(args.reference_summary, "r")
-    test_file = open(args.test_summary, "r")
-    ref_score = 0
-    test_score = 0
-    ref_mode = ""
-    test_mode = ""
-
-    for line in ref_file:
-        if re.match("Scenario", line):
-            ref_mode = line.split(": ", 1)[1].strip()
-            continue
-
-        if ref_mode == "SingleStream":
-            if re.match(".*Early stopping 90th percentile estimate", line):
-                ref_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if ref_mode == "MultiStream":
-            if re.match(".*Early stopping 99th percentile estimate", line):
-                ref_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if ref_mode == "Server":
-            if re.match("Completed samples per second", line):
-                ref_score = line.split(": ", 1)[1].strip()
-                continue
-            if re.match("target_latency (ns)", line):
-                ref_target_latency = line.split(": ", 1)[1].strip()
-                continue
-
-        if ref_mode == "Offline":
-            if re.match("Samples per second", line):
-                ref_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if re.match("Result is", line):
-            valid = line.split(": ", 1)[1].strip()
-            if valid == "INVALID":
-                sys.exit("TEST FAIL: Reference results are invalid")
-
-        if re.match("\\d+ ERROR", line):
-            error = line.split(" ", 1)[0].strip()
-            print("WARNING: " + error + " ERROR reported in reference results")
-
-    for line in test_file:
-        if re.match("Scenario", line):
-            test_mode = line.split(": ", 1)[1].strip()
-            continue
-
-        if test_mode == "SingleStream":
-            if re.match(".*Early stopping 90th percentile estimate", line):
-                test_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if test_mode == "MultiStream":
-            if re.match(".*Early stopping 99th percentile estimate", line):
-                test_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if test_mode == "Server":
-            if re.match("Completed samples per second", line):
-                test_score = line.split(": ", 1)[1].strip()
-                continue
-            if re.match("target_latency (ns)", line):
-                test_target_latency = line.split(": ", 1)[1].strip()
-                if test_target_latency != ref_target_latency:
-                    print("TEST FAIL: Server target latency mismatch")
-                    sys.exit()
-                continue
-
-        if test_mode == "Offline":
-            if re.match("Samples per second", line):
-                test_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if re.match("Result is", line):
-            valid = line.split(": ", 1)[1].strip()
-            if valid == "INVALID":
-                sys.exit("TEST FAIL: Test results are invalid")
-
-        if re.match("\\d+ ERROR", line):
-            error = line.split(" ", 1)[0].strip()
-            print("WARNING: " + error + " ERROR reported in test results")
-
-    if test_mode != ref_mode:
-        sys.exit("Test and reference scenarios do not match!")
-
-    print("reference score = {}".format(ref_score))
-    print("test score = {}".format(test_score))
-
-    threshold = 0.05
-
-    # In single-/multi-stream mode, latencies can be very short for high performance systems
-    # and run-to-run variation due to external disturbances (OS) can be significant.
-    # In this case we relax pass threshold to 20%
-    if (ref_mode == "SingleStream" and float(ref_score) <= 200000) or (
-        ref_mode == "MultiStream" and float(ref_score) <= 1600000
-    ):
-        threshold = 0.20
-
-    if (
-        ref_mode in ["Offline", "Server"]
-        and float(test_score) > float(ref_score) * (1 - threshold)
-    ) or (
-        "Stream" in ref_mode and float(test_score) < float(
-            ref_score) * (1 + threshold)
-    ):
-        print("TEST PASS")
-    else:
-        print("TEST FAIL: Test score invalid")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 5f2e272673..a31a6fede3 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -2906,7 +2906,7 @@ def check_compliance_dir(
         "llama2-70b-99.9",
         "stable-diffusion-xl",
         "mixtral-8x7b",
-    ]:
+    ] or config.version not in ["v4.0", "v4.1"]:
         test_list.remove("TEST05")
 
     if model in [

From c570ff4fab75bd9ace20e07d15206fcceff7c9e9 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjunsuresh1987@gmail.com>
Date: Tue, 19 Nov 2024 17:17:33 +0000
Subject: [PATCH 176/305] Fix a minor bug in preprocess_submission (#1926)

* Update generate_final_report.py

* Fix sdxl (#1911)

* Fix typo in fid_score.py, fail_safe for SDXL short runs

* [Automated Commit] Format Codebase

* Fix typo in fid_score.py, fail_safe for SDXL short runs

* Fix dlrmv2 reference implementation | Update run_local.sh

* Fixes for filtering invalid results

* [Automated Commit] Format Codebase

* Update preprocess_submission.py

* Added an option to pass in sample_ids.txt for SDXL accuracy check

* [Automated Commit] Format Codebase

* Update accuracy_coco.py

* [Automated Commit] Format Codebase

* Fix typo

* Not use default for sample_ids.txt

* Update requirements.txt (#1907)

Updating the pip packages

* Fix preprocess_sudbmission for a bug

* Update submission_checker.py | Removed TEST05

---------

Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
---
 tools/submission/preprocess_submission.py | 3 ++-
 tools/submission/submission_checker.py    | 9 +++++----
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/tools/submission/preprocess_submission.py b/tools/submission/preprocess_submission.py
index a1678c79d0..a737c1f16c 100644
--- a/tools/submission/preprocess_submission.py
+++ b/tools/submission/preprocess_submission.py
@@ -84,7 +84,6 @@ def delete_empty_dirs(src):
     """
     if not os.path.isdir(src):
         return False
-
     if all([delete_empty_dirs(os.path.join(src, file))
            for file in os.listdir(src)]):
         log.info("Removing empty dir: (%s)", src)
@@ -532,9 +531,11 @@ def main():
     if not args.nodelete_empty_dirs:
         delete_empty_dirs(os.path.join(src_dir))
 
+    run_dir = os.getcwd()
     os.chdir(src_dir)
 
     infer_scenario_results(args, config)
+    os.chdir(run_dir)
 
     if not args.nodelete_empty_dirs:
         delete_empty_dirs(os.path.join(src_dir))
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index a31a6fede3..deff9eb8c4 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -188,6 +188,7 @@
             "sample_index_rng_seed": 198141574272810017,
             "schedule_rng_seed": 7575108116881280410,
         },
+        # not required for v5.0+
         "test05_seeds": {
             # TODO: Update random seeds
             "qsl_rng_seed": 2376919268182438552,
@@ -2880,7 +2881,7 @@ def check_compliance_dir(
     compliance_perf_pass = True
     compliance_perf_dir_pass = True
     compliance_acc_pass = True
-    test_list = ["TEST01", "TEST04", "TEST05"]
+    test_list = ["TEST01", "TEST04"]
 
     if model in [
         "bert-99",
@@ -2899,15 +2900,15 @@ def check_compliance_dir(
     ]:
         test_list.remove("TEST04")
 
-    if model in [
+    if config.version in ["v4.0", "v4.1"] and model not in [
         "gptj-99",
         "gptj-99.9",
         "llama2-70b-99",
         "llama2-70b-99.9",
         "stable-diffusion-xl",
         "mixtral-8x7b",
-    ] or config.version not in ["v4.0", "v4.1"]:
-        test_list.remove("TEST05")
+    ]:
+        test_list.append("TEST05")
 
     if model in [
         "gptj-99",

From b2b346c5211a0d0e9078eca0f6e7189fe352167f Mon Sep 17 00:00:00 2001
From: Joyce <joycebrum@google.com>
Date: Tue, 19 Nov 2024 14:18:30 -0300
Subject: [PATCH 177/305] Fix: GitHub workflow script injection (#1936)

Signed-off-by: Joyce Brum <joycebrum@google.com>
Co-authored-by: Mitchelle Rasquinha <80070689+mrasquinha-g@users.noreply.github.com>
---
 .github/workflows/format.yml                  | 5 +++--
 .github/workflows/test-bert.yml               | 5 ++++-
 .github/workflows/test-loadgen.yml            | 5 ++++-
 .github/workflows/test-resnet50.yml           | 5 ++++-
 .github/workflows/test-retinanet.yml          | 5 ++++-
 .github/workflows/test-rnnt.yml               | 5 ++++-
 .github/workflows/test-submission-checker.yml | 5 ++++-
 .github/workflows/test-tvm.yml                | 5 ++++-
 8 files changed, 31 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index b98a3c37bc..829a3fcb7a 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -8,6 +8,7 @@ on:
 
 env:
   python_version: "3.9"
+  HEAD_REF: ${{ github.head_ref }}
 
 jobs:
   format-code:
@@ -41,9 +42,9 @@ jobs:
         run: |
           HAS_CHANGES=$(git diff --staged --name-only)
           if [ ${#HAS_CHANGES} -gt 0 ]; then
-            git checkout -B "${{ github.head_ref }}"
+            git checkout -B "$HEAD_REF"
             git config --global user.email "${{ github.actor }}@users.noreply.github.com"
             git config --global user.name "${{ github.actor }}"
             git commit -m '[Automated Commit] Format Codebase'
-            git push origin "${{ github.head_ref }}"
+            git push origin "$HEAD_REF"
           fi
diff --git a/.github/workflows/test-bert.yml b/.github/workflows/test-bert.yml
index 52ec1dd4d9..2ce156c452 100755
--- a/.github/workflows/test-bert.yml
+++ b/.github/workflows/test-bert.yml
@@ -12,6 +12,9 @@ on:
       - .github/workflows/test-bert.yml
       - '!**.md'
 
+env:
+  PR_HEAD_REF: ${{ github.event.pull_request.head.ref }}
+
 jobs:
   build:
 
@@ -33,4 +36,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test BERT and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }} --adr.loadgen.version=custom
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.loadgen.version=custom
diff --git a/.github/workflows/test-loadgen.yml b/.github/workflows/test-loadgen.yml
index 09436cc020..8e42f625ec 100755
--- a/.github/workflows/test-loadgen.yml
+++ b/.github/workflows/test-loadgen.yml
@@ -11,6 +11,9 @@ on:
       - .github/workflows/test-loadgen.yml
       - '!**.md'
 
+env:
+  PR_HEAD_REF: ${{ github.event.pull_request.head.ref }}
+
 jobs:
   build:
 
@@ -31,4 +34,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Loadgen
       run: |
-        cm run script --tags=get,mlperf,inference,loadgen --quiet --version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }} --adr.loadgen.tags=_no-compilation-warnings
+        cm run script --tags=get,mlperf,inference,loadgen --quiet --version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.loadgen.tags=_no-compilation-warnings
diff --git a/.github/workflows/test-resnet50.yml b/.github/workflows/test-resnet50.yml
index 64636a3438..c942258b24 100755
--- a/.github/workflows/test-resnet50.yml
+++ b/.github/workflows/test-resnet50.yml
@@ -13,6 +13,9 @@ on:
       - .github/workflows/test-resnet50.yml
       - '!**.md'
 
+env:
+  PR_HEAD_REF: ${{ github.event.pull_request.head.ref }}
+
 jobs:
   build:
 
@@ -35,4 +38,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Resnet50 and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}
diff --git a/.github/workflows/test-retinanet.yml b/.github/workflows/test-retinanet.yml
index a2f5bf8ec7..6482666260 100755
--- a/.github/workflows/test-retinanet.yml
+++ b/.github/workflows/test-retinanet.yml
@@ -12,6 +12,9 @@ on:
       - .github/workflows/test-retinanet.yml
       - '!**.md'
 
+env:
+  PR_HEAD_REF: ${{ github.event.pull_request.head.ref }}
+
 jobs:
   build:
 
@@ -33,4 +36,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Retinanet and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }} --adr.loadgen.version=custom
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.loadgen.version=custom
diff --git a/.github/workflows/test-rnnt.yml b/.github/workflows/test-rnnt.yml
index a765739009..a08803741a 100644
--- a/.github/workflows/test-rnnt.yml
+++ b/.github/workflows/test-rnnt.yml
@@ -12,6 +12,9 @@ on:
       - .github/workflows/test-rnnt.yml
       - '!**.md'
 
+env:
+  PR_HEAD_REF: ${{ github.event.pull_request.head.ref }}
+
 jobs:
   build:
 
@@ -36,4 +39,4 @@ jobs:
         cm run script --quiet --tags=get,sys-utils-cm
     - name: Test RNNT and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_performance-only --quiet --submitter="MLCommons" --hw_name=default --model=rnnt --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --precision=${{ matrix.precision }} --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.env.CM_GIT_CHECKOUT=${{ github.event.pull_request.head.ref }} --adr.inference-src.env.CM_GIT_URL=${{ github.event.pull_request.head.repo.html_url }} --adr.ml-engine-pytorch.version=1.13.0 --adr.ml-engine-torchvision.version=0.14.1 --adr.librosa.version_max=0.9.1
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_performance-only --quiet --submitter="MLCommons" --hw_name=default --model=rnnt --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --precision=${{ matrix.precision }} --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.env.CM_GIT_CHECKOUT=$PR_HEAD_REF --adr.inference-src.env.CM_GIT_URL=${{ github.event.pull_request.head.repo.html_url }} --adr.ml-engine-pytorch.version=1.13.0 --adr.ml-engine-torchvision.version=0.14.1 --adr.librosa.version_max=0.9.1
diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index 29a06248e2..b7ab88ddb8 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -11,6 +11,9 @@ on:
       - .github/workflows/test-submission-checker.yml
       - '!**.md'
 
+env:
+  PR_HEAD_REF: ${{ github.event.pull_request.head.ref }}
+
 jobs:
   build:
 
@@ -32,4 +35,4 @@ jobs:
         git clone https://github.com/mlcommons/inference_results_v4.1 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
-        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.${{ github.event.pull_request.head.ref }},_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.1 --src_version=v4.1 --extra_args=" --skip-extra-accuracy-files-check" --quiet 
+        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.1 --src_version=v4.1 --extra_args=" --skip-extra-accuracy-files-check" --quiet 
diff --git a/.github/workflows/test-tvm.yml b/.github/workflows/test-tvm.yml
index b132fc0025..6741c36106 100755
--- a/.github/workflows/test-tvm.yml
+++ b/.github/workflows/test-tvm.yml
@@ -12,6 +12,9 @@ on:
       - .github/workflows/test-tvm.yml
       - '!**.md'
 
+env:
+  PR_HEAD_REF: ${{ github.event.pull_request.head.ref }}
+
 jobs:
   build:
 
@@ -33,4 +36,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Resnet50 TVM backend
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=5 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.${{ github.event.pull_request.head.ref }}
+        cm run script --tags=run,mlperf,inference,generate-run-cmds --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=5 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF

From 4d7a5c820249d3cbc0432ebb298316c0496f1f6d Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 19 Nov 2024 12:21:57 -0500
Subject: [PATCH 178/305] Add GNN calibration ids (#1916)

Co-authored-by: Mitchelle Rasquinha <80070689+mrasquinha-g@users.noreply.github.com>
---
 calibration/IGBH/calibration.txt | 5000 ++++++++++++++++++++++++++++++
 graph/R-GAT/tools/split_seeds.py |   14 +
 2 files changed, 5014 insertions(+)
 create mode 100644 calibration/IGBH/calibration.txt

diff --git a/calibration/IGBH/calibration.txt b/calibration/IGBH/calibration.txt
new file mode 100644
index 0000000000..ab678797ef
--- /dev/null
+++ b/calibration/IGBH/calibration.txt
@@ -0,0 +1,5000 @@
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diff --git a/graph/R-GAT/tools/split_seeds.py b/graph/R-GAT/tools/split_seeds.py
index 5bbed6d0a7..7431bcea4d 100644
--- a/graph/R-GAT/tools/split_seeds.py
+++ b/graph/R-GAT/tools/split_seeds.py
@@ -14,12 +14,14 @@ def __init__(
         use_label_2K=True,
         random_seed=42,
         validation_frac=0.01,
+        calibration=False
     ):
         self.path = path
         self.dataset_size = dataset_size
         self.use_label_2K = use_label_2K
         self.random_seed = random_seed
         self.validation_frac = validation_frac
+        self.calibration = calibration
         self.paper_nodes_num = {
             "tiny": 100000,
             "small": 1000000,
@@ -49,6 +51,12 @@ def process(self):
         torch.save(train_idx, osp.join(path, "train_idx.pt"))
         torch.save(val_idx, osp.join(path, "val_idx.pt"))
 
+        if self.calibration and self.dataset_size == "full":
+            n_calibration = 5000
+            calibration_idx = shuffled_index[:n_calibration].numpy().tolist()
+            with open(osp.join(path, "calibration.txt"), "w+") as f:
+                f.writelines([f"{idx}\n" for idx in calibration_idx])
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -82,6 +90,11 @@ def process(self):
         default=0.005,
         help="Fraction of labeled vertices to be used for validation.",
     )
+    parser.add_argument(
+        "--calibration",
+        action="store_true",
+        help="Save calibration dataset",
+    )
 
     args = parser.parse_args()
     splitter = SeedSplitter(
@@ -90,4 +103,5 @@ def process(self):
         use_label_2K=(args.num_classes == 2983),
         random_seed=args.random_seed,
         validation_frac=args.validation_frac,
+        calibration=args.calibration,
     )

From acc3eb4c69f753fd3d35a23fb930b78cac6129c3 Mon Sep 17 00:00:00 2001
From: Bryan Shan <58582368+Oseltamivir@users.noreply.github.com>
Date: Wed, 20 Nov 2024 01:41:05 +0800
Subject: [PATCH 179/305] - Fixed syntax error on list .insert method (#1921)

* - Fixed syntax error on list .insert method

* Update fid_score.py

---------

Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
Co-authored-by: Arjun Suresh <arjunsuresh1987@gmail.com>
---
 text_to_image/tools/fid/fid_score.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/text_to_image/tools/fid/fid_score.py b/text_to_image/tools/fid/fid_score.py
index 8e486c8b7a..b2de332ce5 100644
--- a/text_to_image/tools/fid/fid_score.py
+++ b/text_to_image/tools/fid/fid_score.py
@@ -44,8 +44,7 @@
 import pathlib
 import os
 import sys
-sys.path.insert(0, "..")
-from inception import InceptionV3  # noqa: E402
+from .inception import InceptionV3
 
 
 try:

From 61a8a6af7d60697f6fe714a37ced7bb02495e288 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 19 Nov 2024 17:44:59 +0000
Subject: [PATCH 180/305] Sync Docs (#1908)

Docs update
---
 .../reproducibility/indyscc24-bert.md         |   1 -
 .../text_to_image/reproducibility/scc24.md    |   2 +-
 docs/img/submission-flow.png                  | Bin 0 -> 10048 bytes
 docs/submission/index.md                      |  11 ++++-
 main.py                                       |  40 ++++++++++++++++--
 tools/submission/generate_final_report.py     |   2 +
 6 files changed, 50 insertions(+), 6 deletions(-)
 create mode 100644 docs/img/submission-flow.png

diff --git a/docs/benchmarks/language/reproducibility/indyscc24-bert.md b/docs/benchmarks/language/reproducibility/indyscc24-bert.md
index 86f4f285d0..463d1a299c 100644
--- a/docs/benchmarks/language/reproducibility/indyscc24-bert.md
+++ b/docs/benchmarks/language/reproducibility/indyscc24-bert.md
@@ -37,7 +37,6 @@ All the needed files are automatically pushed to the GitHub repository if you ma
 ```bash
 cm run script --tags=generate,inference,submission \
    --clean \
-   --preprocess_submission=yes \
    --run-checker \
    --tar=yes \
    --env.CM_TAR_OUTFILE=submission.tar.gz \
diff --git a/docs/benchmarks/text_to_image/reproducibility/scc24.md b/docs/benchmarks/text_to_image/reproducibility/scc24.md
index 6fe119b533..82a231b4d3 100644
--- a/docs/benchmarks/text_to_image/reproducibility/scc24.md
+++ b/docs/benchmarks/text_to_image/reproducibility/scc24.md
@@ -59,10 +59,10 @@ or supporting multi-node execution) useful for the community and [MLCommons](htt
 
 ### Generate actual submission tree
 
+
 ```bash
 cm run script --tags=generate,inference,submission \
    --clean \
-   --preprocess_submission=yes \
    --run-checker \
    --tar=yes \
    --env.CM_TAR_OUTFILE=submission.tar.gz \
diff --git a/docs/img/submission-flow.png b/docs/img/submission-flow.png
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/docs/submission/index.md b/docs/submission/index.md
index adcd3df53e..c998024206 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -2,7 +2,16 @@
 hide:
   - toc
 ---
-[![Streamline your MLPerf results using CM Framework](https://img.youtube.com/vi/eI1Hoecc3ho/0.jpg)](https://youtu.be/eI1Hoecc3ho)
+
+<p align="center">
+  <img src="../img/submission-flow.png" alt="Submission Generation Flow">
+</p>
+
+<p align="center"><em>Figure: MLPerf Inference Submission Generation Flow</em></p>
+
+<!--![Submission Generation Flow](../img/submission-flow.png)-->
+
+Click [here](https://youtu.be/eI1Hoecc3ho) to view the recording of the workshop: Streamlining your MLPerf Inference results using CM.
 
 === "CM based benchmark"
     If you have followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, all the valid results will get aggregated to the `cm cache` folder. The following command could be used to browse the structure of inference results folder generated by CM.
diff --git a/main.py b/main.py
index a0a0ec99e0..c8c64b8c31 100755
--- a/main.py
+++ b/main.py
@@ -122,6 +122,7 @@ def mlperf_inference_implementation_readme(
         if not categories:
             if model.lower() == "bert-99.9":
                 categories = ["Datacenter"]
+
             elif (
                 "dlrm" in model.lower()
                 or "llama2" in model.lower()
@@ -148,7 +149,7 @@ def mlperf_inference_implementation_readme(
                 scenarios = [
                     scenario for scenario in scenarios if scenario in fixed_scenarios]
 
-            content += f'{pre_space}=== "{category.lower()}"\n\n'
+            content += f"{pre_space}=== \"{category.lower()}\"\n\n"
 
             cur_space = pre_space + "    "
             scenarios_string = ", ".join(scenarios)
@@ -173,6 +174,7 @@ def mlperf_inference_implementation_readme(
 
                     # minimum system requirements
                     content += get_min_system_requirements(
+
                         cur_space2, model, implementation, device
                     )
 
@@ -235,6 +237,11 @@ def mlperf_inference_implementation_readme(
                                 extra_docker_input_string,
                             )
 
+                            common_info = get_common_info(
+                                spaces + 16,
+                                implementation
+                            )
+
                             if (
                                 execution_env == "Native"
                             ):  # Native implementation steps through virtual environment
@@ -242,6 +249,8 @@ def mlperf_inference_implementation_readme(
                                 content += get_venv_command(spaces + 16)
                                 content += f"{cur_space3}####### Performance Estimation for Offline Scenario\n"
 
+                                content += common_info
+
                                 content += setup_run_cmd.replace(
                                     "--docker ", "")
 
@@ -256,6 +265,9 @@ def mlperf_inference_implementation_readme(
                                     device,
                                     setup_tips,
                                 )
+
+                                content += common_info
+
                                 content += docker_info
 
                                 content += setup_run_cmd
@@ -373,7 +385,8 @@ def mlperf_inference_implementation_readme(
                                 extra_input_string,
                             )
                             content += run_cmd
-                            content += run_suffix
+
+                        content += run_suffix
 
         readme_prefix = get_readme_prefix(
             spaces, model, implementation, extra_variation_tags
@@ -473,6 +486,24 @@ def get_venv_command(spaces):
 {pre_space}export CM_SCRIPT_EXTRA_CMD=\"--adr.python.name=mlperf\"
 {pre_space}```\n"""
 
+    # contains run command information which is common to both docker and
+    # native runs
+    def get_common_info(spaces, implementation):
+        info = ""
+        pre_space = ""
+        for i in range(1, spaces):
+            pre_space = pre_space + " "
+        pre_space += " "
+        # pre_space = "                "
+        info += f"\n{pre_space}!!! tip\n\n"
+        info += f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
+        if implementation.lower() == "reference":
+            info += f"{pre_space}    - Add `--adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK>` if you are modifying the official MLPerf Inference implementation in a custom fork.\n\n"
+            info += f"{pre_space}    - Add `--adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK>` if you are modifying the model config accuracy script in the submission checker within a custom fork.\n\n"
+            info += f"{pre_space}    - Add `--adr.inference-src.version=custom` if you are using the modified MLPerf Inference code or accuracy script on submission checker within a custom fork.\n\n"
+
+        return info
+
     def get_docker_info(spaces, model, implementation,
                         device, setup_tips=True):
         info = ""
@@ -487,7 +518,6 @@ def get_docker_info(spaces, model, implementation,
             if model == "sdxl":
                 info += f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
 
-            info += f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
             if implementation.lower() == "nvidia":
                 info += f"{pre_space}    - Default batch size is assigned based on [GPU memory](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1129) or the [specified GPU](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1370). Please click more option for *docker launch* or *run command* to see how to specify the GPU name.\n\n"
                 info += f"{pre_space}    - When run with `--all_models=yes`, all the benchmark models of NVIDIA implementation can be executed within the same container.\n\n"
@@ -499,6 +529,10 @@ def get_docker_info(spaces, model, implementation,
                 info += f"\n{pre_space}!!! tip\n\n"
                 info += f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
 
+        # return empty string if nothing is filled inside the tip
+        if info == f"\n{pre_space}!!! tip\n\n":
+            return ""
+
         return info
 
     def get_readme_prefix(spaces, model, implementation, extra_variation_tags):
diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 79d9fe0767..5e5d22c45c 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -201,6 +201,7 @@ def MakeWorksheet(df, index, filter_dict, sheet_name, outjsondata=[]):
             df = df[value(df[key])]
         if df.size == 0:
             return
+
         json_df = df.to_json(orient="records")
         outjsondata += json.loads(json_df)
 
@@ -328,6 +329,7 @@ def reformatlink(data, key):
                    for key in keystomatch):
                 # print(result)
                 # print(outjsondata[i+1])
+
                 if "Watts" in result["Units"] or "joules" in result["Units"]:
                     result["Performance_Result"] = outjsondata[i + 1]["Result"]
                     result["Performance_Units"] = outjsondata[i + 1]["Units"]

From 35c7e1f0dbbd1a63b608ea06ae80d69f231d6c05 Mon Sep 17 00:00:00 2001
From: mlcommons-bot <74634038+mlcommons-bot@users.noreply.github.com>
Date: Tue, 19 Nov 2024 11:47:42 -0600
Subject: [PATCH 181/305] =?UTF-8?q?=F0=9F=94=84=20synced=20file(s)=20with?=
 =?UTF-8?q?=20mlcommons/power-dev=20(#1918)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* 🔄 synced local 'tools/submission/power/power_checker.py' with remote 'compliance/check.py'

* [Automated Commit] Format Codebase

---------

Co-authored-by: mlcommons-bot <null>
Co-authored-by: mlcommons-bot <mlcommons-bot@users.noreply.github.com>
Co-authored-by: Mitchelle Rasquinha <80070689+mrasquinha-g@users.noreply.github.com>
Co-authored-by: Arjun Suresh <arjunsuresh1987@gmail.com>
Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
---
 tools/submission/power/power_checker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/submission/power/power_checker.py b/tools/submission/power/power_checker.py
index 834cd59ddf..5a0eb0953e 100755
--- a/tools/submission/power/power_checker.py
+++ b/tools/submission/power/power_checker.py
@@ -90,7 +90,7 @@ class CheckerWarning(Exception):
 
 def _normalize(path: str) -> str:
     allparts: List[str] = []
-    while True:
+    while 1:
         parts = os.path.split(path)
         if parts[0] == path:  # sentinel for absolute paths
             allparts.insert(0, parts[0])

From d28a53009c4390cc43ccde00fba160b9fbf4c30e Mon Sep 17 00:00:00 2001
From: qihqi <qihan.dev@gmail.com>
Date: Tue, 26 Nov 2024 09:21:40 -0800
Subject: [PATCH 182/305] Fix Dockerfile.eval: huggingface-cli doesn't exist
 (#1805)

https://huggingface.co/docs/huggingface_hub/main/en/guides/cli

Co-authored-by: Arjun Suresh <arjunsuresh1987@gmail.com>
Co-authored-by: Miro <mirhodak@amd.com>
---
 language/mixtral-8x7b/Dockerfile.eval | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/language/mixtral-8x7b/Dockerfile.eval b/language/mixtral-8x7b/Dockerfile.eval
index 9fc13772b8..c0829ae1da 100644
--- a/language/mixtral-8x7b/Dockerfile.eval
+++ b/language/mixtral-8x7b/Dockerfile.eval
@@ -17,7 +17,7 @@ RUN pip --version
 RUN git --version
 
 # Install requirements
-RUN pip install transformers nltk==3.8.1 evaluate==0.4.0 absl-py==1.4.0 rouge-score==0.1.2 sentencepiece==0.1.99 accelerate==0.21.0 huggingface-cli
+RUN pip install transformers nltk==3.8.1 evaluate==0.4.0 absl-py==1.4.0 rouge-score==0.1.2 sentencepiece==0.1.99 accelerate==0.21.0 huggingface_hub[cli]
 
 # Clone and install mxeval
 RUN git clone https://github.com/amazon-science/mxeval.git

From 45544f3b7af4cf61c71d55bfc279c9c35c73ab71 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 3 Dec 2024 12:53:25 -0500
Subject: [PATCH 183/305] Llm large reference (#1951)

* Initial codebase llama3-405b reference implementation

* Add VLLM backend

* Prune llama2 files & Update commands

* Update evaluate accuracy script

* Fix minor issues

* Add Llama3 configuration

* [Automated Commit] Format Codebase

* Set tensor_parallel_size to 8

* Add requirements.txt

* Remove consolidate_results.py

* Host dataset + add instructions to download it

* [Automated Commit] Format Codebase

* Fix dockerfile + update README

---------

Co-authored-by: pgmpablo157321 <pgmpablo157321@users.noreply.github.com>
---
 language/llama3-405b/Dockerfile           |  48 ++++
 language/llama3-405b/README.md            | 197 +++++++++++++++
 language/llama3-405b/SUT_VLLM.py          | 292 ++++++++++++++++++++++
 language/llama3-405b/build.sh             |   8 +
 language/llama3-405b/dataset.py           |  92 +++++++
 language/llama3-405b/evaluate-accuracy.py | 203 +++++++++++++++
 language/llama3-405b/launch.sh            |  37 +++
 language/llama3-405b/main.py              | 208 +++++++++++++++
 language/llama3-405b/requirements.txt     |   9 +
 language/llama3-405b/run_accuracy.sh      |  21 ++
 language/llama3-405b/run_offline.sh       |  10 +
 language/llama3-405b/run_server.sh        |  12 +
 language/llama3-405b/user.conf            |  13 +
 loadgen/mlperf.conf                       |  12 +-
 14 files changed, 1160 insertions(+), 2 deletions(-)
 create mode 100644 language/llama3-405b/Dockerfile
 create mode 100644 language/llama3-405b/README.md
 create mode 100644 language/llama3-405b/SUT_VLLM.py
 create mode 100644 language/llama3-405b/build.sh
 create mode 100644 language/llama3-405b/dataset.py
 create mode 100644 language/llama3-405b/evaluate-accuracy.py
 create mode 100644 language/llama3-405b/launch.sh
 create mode 100644 language/llama3-405b/main.py
 create mode 100644 language/llama3-405b/requirements.txt
 create mode 100644 language/llama3-405b/run_accuracy.sh
 create mode 100644 language/llama3-405b/run_offline.sh
 create mode 100644 language/llama3-405b/run_server.sh
 create mode 100644 language/llama3-405b/user.conf

diff --git a/language/llama3-405b/Dockerfile b/language/llama3-405b/Dockerfile
new file mode 100644
index 0000000000..7987d4851d
--- /dev/null
+++ b/language/llama3-405b/Dockerfile
@@ -0,0 +1,48 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+FROM nvcr.io/nvidia/pytorch:24.07-py3
+SHELL ["/bin/bash", "-c"]
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+
+ENV TZ=US/Pacific
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
+RUN rm -rf /var/lib/apt/lists/* && rm /etc/apt/sources.list.d/* \
+ && apt update \
+ && apt install -y --no-install-recommends build-essential autoconf \
+        libtool git ccache curl wget pkg-config sudo ca-certificates \
+        automake libssl-dev bc python3-dev python3-pip google-perftools \
+        gdb libglib2.0-dev clang sshfs libre2-dev libboost-dev \
+        libnuma-dev numactl sysstat sshpass ntpdate less iputils-ping \
+ && apt -y autoremove \
+ && apt remove -y cmake \
+ && apt install -y --no-install-recommends pkg-config zip g++ zlib1g-dev \
+        unzip libarchive-dev
+RUN apt install -y --no-install-recommends rsync
+
+# Install setuptools
+RUN python3 -m pip install --upgrade pip \
+    && python3 -m pip install --upgrade setuptools wheel virtualenv
+
+# Install conda
+WORKDIR /tmp
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.5.2-0-Linux-x86_64.sh \
+    && bash Miniconda3-* -b -p /opt/miniconda3
+ENV PATH="$PATH:/opt/miniconda3/bin"
+RUN conda create -n llama3-405b python=3.10
+RUN chmod -R 777 /opt/miniconda3
diff --git a/language/llama3-405b/README.md b/language/llama3-405b/README.md
new file mode 100644
index 0000000000..9f2eff3cab
--- /dev/null
+++ b/language/llama3-405b/README.md
@@ -0,0 +1,197 @@
+# Reference Implementation for llama3-405b
+
+**Basic implementation for llama3-405b. Few noteworthy items:**
+
++ Streamer for communicating with loadgen has quite some overhead. This is only meant to provide functional implementation
++ For custom/optimized implementations of this benchmark it is important to include the :
+        - For server scenario, it is necessary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
+        - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama3-405b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
+ 
+## Prepare environment
+
+Copy the mlperf.conf file to this folder.
+```
+cp ../../mlperf.conf .
+```
+
+For a CPU-only run:
+
+```
+conda create -n llama3-405b python=3.9
+conda activate llama3-405b
+
+# Install packages
+pip install -r requirements.txt
+
+export CUR_DIR=${PWD}
+cd <inference-repo-root>/loadgen
+
+
+python -m pip install .
+```
+
+For a GPU-based run:
+
+A dockerfile is provided, along with scripts to help launch it. First, add any docker volume mounts you want in
+`launch.sh`. There is a section at the top of the file that looks like:
+```
+# Add any volume mounts here with the following syntax
+# /path/to/src:/path/to/dir/in/container
+MOUNTS=(
+    $MLCOMMONS_REPO_PATH:$MLCOMMONS_REPO_PATH
+)
+```
+
+For example if you have a raid space located at `/raid/data` on your local machine, you can add it to the same path in the container like so:
+```
+# Add any volume mounts here with the following syntax
+# /path/to/src:/path/to/dir/in/container
+MOUNTS=(
+    $MLCOMMONS_REPO_PATH:$MLCOMMONS_REPO_PATH
+    /raid/data:/raid/data
+)
+```
+Once you have added all your mounts, launch the container with `bash launch.sh`.
+
+Inside the container, set up the environment with `bash build.sh`. This will install all the dependencies from the
+CPU-only setup, as well as any GPU versions for applicable libraries like PyTorch.
+
+
+## Get Model
+### MLCommons Members Download
+
+TODO: Host model and grant access to submitters
+
+
+### External Download
++ First go to [llama3-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloning below.
++ Requires Git Large Files Storage
+```
+export CHECKPOINT_PATH=${PWD}/Llama-2-70b-chat-hf
+git lfs install
+git clone https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct ${CHECKPOINT_PATH}
+
+```
+
+## Get Dataset
+
+### Preprocessed
+
+You can use Rclone to download the preprocessed dataset from a Cloudflare R2 bucket.
+
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
+```
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
+```
+Once Rclone is installed, run the following command to authenticate with the bucket:
+```
+rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+```
+You can then navigate in the terminal to your desired download directory and run the following command to download the dataset:
+
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/llama3_405b/mlperf_llama3.1_405b_dataset_8313_processed_fp16_eval.pkl ./ -P
+```
+
+You can also download the calibration dataset from the Cloudflare R2 bucket by running the following command:
+
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/llama3_405b/mlperf_llama3.1_405b_calibration_dataset_512_processed_fp16_eval.pkl ./ -P
+```
+
+## Run Performance Benchmarks
+
+### Offline
+```
+python -u main.py --scenario Offline \
+                --model-path ${CHECKPOINT_PATH} \
+                --dtype float16 \
+                --user-conf user.conf \
+                --total-sample-count 8312 \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir output \
+                --tensor-parallel-size ${GPU_COUNT} \
+                --vllm
+
+```
+
+### Server
+```
+python -u main.py --scenario Server \
+                --model-path ${CHECKPOINT_PATH} \
+                --dtype float16 \
+                --user-conf user.conf \
+                --total-sample-count 8312 \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir output \
+                --tensor-parallel-size ${GPU_COUNT} \
+                --vllm
+```
+
+The ServerSUT was not tested for GPU runs.
+
+
+## Run Accuracy Benchmarks
+
+### Offline
+```
+OUTPUT_LOG_DIR=offline-accuracy-logs
+
+mkdir -p "run_outputs"  # The script will dump all the outputs to 'run_outputs'.
+
+python -u main.py --scenario Offline \
+                --model-path ${CHECKPOINT_PATH} \
+                --accuracy \
+                --dtype float16 \
+                --user-conf user.conf \
+                --total-sample-count 8312 \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir output \
+                --tensor-parallel-size ${GPU_COUNT} \
+                --vllm
+
+
+ACCURACY_LOG_FILE=${OUTPUT_LOG_DIR}/mlperf_log_accuracy.json
+if [ -e ${ACCURACY_LOG_FILE} ]; then
+        python evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
+                --mlperf-accuracy-file ${ACCURACY_LOG_FILE} --dataset-file ${DATASET_PATH} --dtype int32
+fi
+```
+
+For the GPU run - The above steps have been automated in `run_accuracy.sh`. You can also modify this script to use
+`--device cpu` to adapt it to a CPU-only run.
+
+
+### Server
+```
+OUTPUT_LOG_DIR=server-accuracy-logs
+
+python -u main.py --scenario Server \
+                --model-path ${CHECKPOINT_PATH} \
+                --accuracy \
+                --dtype float16 \
+                --user-conf user.conf \
+                --total-sample-count 8312 \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir output \
+                --tensor-parallel-size ${GPU_COUNT} \
+                --vllm
+
+
+ACCURACY_LOG_FILE=${OUTPUT_LOG_DIR}/mlperf_log_accuracy.json
+if [ -e ${ACCURACY_LOG_FILE} ]; then
+        python evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
+                --mlperf-accuracy-file ${ACCURACY_LOG_FILE} --dataset-file ${DATASET_PATH} --dtype int32
+fi
+```
+
+The ServerSUT was not tested for GPU runs.
+
+
+## Accuracy Target
+Running the GPU implementation in FP16 precision resulted in the following FP16 accuracy targets (normalized to a 0-100
+scale from a 0.0-1.0 scale):
diff --git a/language/llama3-405b/SUT_VLLM.py b/language/llama3-405b/SUT_VLLM.py
new file mode 100644
index 0000000000..9656b09714
--- /dev/null
+++ b/language/llama3-405b/SUT_VLLM.py
@@ -0,0 +1,292 @@
+import asyncio
+import os
+import time
+import numpy as np
+import array
+import torch
+from torch.nn.functional import pad
+from vllm import LLM, AsyncLLMEngine, AsyncEngineArgs, SamplingParams
+from vllm.inputs import TokensPrompt
+
+import pickle
+import time
+import threading
+import tqdm
+import queue
+
+import logging
+from typing import TYPE_CHECKING, Optional, List
+from pathlib import Path
+
+import mlperf_loadgen as lg
+from dataset import Dataset
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Llama-405B-SUT")
+
+
+class SUT:
+    def __init__(
+        self,
+        model_path=None,
+        dtype="bfloat16",
+        batch_size=None,
+        total_sample_count=8312,
+        dataset_path=None,
+        use_cached_outputs=False,
+        # Set this to True *only for test accuracy runs* in case your prior
+        # session was killed partway through
+        workers=1,
+        tensor_parallel_size=8
+    ):
+
+        self.model_path = model_path or f"Meta-Llama-3.1-405B-Instruct{'-FP8' if dtype == 'float8' else ''}"
+
+        if not batch_size:
+            batch_size = 1
+        self.batch_size = batch_size
+
+        self.dtype = dtype
+        self.tensor_parallel_size = tensor_parallel_size
+
+        if not torch.cuda.is_available():
+            assert False, "torch gpu is not available, exiting..."
+
+        self.dataset_path = dataset_path
+        self.data_object = Dataset(
+            self.model_path,
+            dataset_path=self.dataset_path,
+            total_sample_count=total_sample_count,
+            dtype=dtype
+        )
+        self.qsl = lg.ConstructQSL(
+            self.data_object.total_sample_count,
+            self.data_object.perf_count,
+            self.data_object.LoadSamplesToRam,
+            self.data_object.UnloadSamplesFromRam,
+        )
+
+        self.load_model()
+        gen_kwargs = {
+            "temperature": 1,
+            "top_p": 1,
+            "top_k": 1,
+            "seed": 42,
+            "max_tokens": 20000,
+            "min_tokens": 2
+        }
+        self.sampling_params = SamplingParams(**gen_kwargs)
+        # self.sampling_params.all_stop_token_ids.add(self.model.get_tokenizer().eos_token_id)
+
+        self.num_workers = workers
+        self.worker_threads = [None] * self.num_workers
+        self.query_queue = queue.Queue()
+
+        self.use_cached_outputs = use_cached_outputs
+        self.sample_counter = 0
+        self.sample_counter_lock = threading.Lock()
+
+    def start(self):
+        # Create worker threads
+        for j in range(self.num_workers):
+            worker = threading.Thread(target=self.process_queries)
+            worker.start()
+            self.worker_threads[j] = worker
+
+    def stop(self):
+        for _ in range(self.num_workers):
+            self.query_queue.put(None)
+
+        for worker in self.worker_threads:
+            worker.join()
+
+    def process_queries(self):
+        """Processor of the queued queries. User may choose to add batching logic"""
+        while True:
+            qitem = self.query_queue.get()
+            if qitem is None:
+                break
+
+            query_ids = [q.index for q in qitem]
+
+            tik1 = time.time()
+
+            input_ids_tensor = [
+                self.data_object.input_ids[q.index] for q in qitem]
+
+            tik2 = time.time()
+            outputs = self.model.generate(
+                prompt_token_ids=input_ids_tensor, sampling_params=self.sampling_params
+            )
+            pred_output_tokens = []
+            for output in outputs:
+                pred_output_tokens.append(list(output.outputs[0].token_ids))
+            tik3 = time.time()
+
+            processed_output = self.data_object.postProcess(
+                pred_output_tokens,
+                query_id_list=query_ids,
+            )
+
+        for i in range(len(qitem)):
+            n_tokens = processed_output[i].shape[0]
+            response_array = array.array(
+                "B", processed_output[i].tobytes())
+            bi = response_array.buffer_info()
+            response = [
+                lg.QuerySampleResponse(
+                    qitem[i].id,
+                    bi[0],
+                    bi[1],
+                    n_tokens)]
+            lg.QuerySamplesComplete(response)
+
+        tok = time.time()
+
+        with self.sample_counter_lock:
+            self.sample_counter += len(qitem)
+            log.info(f"Samples run: {self.sample_counter}")
+            if tik1:
+                log.info(f"\tBatchMaker time: {tik2 - tik1}")
+                log.info(f"\tInference time: {tik3 - tik2}")
+                log.info(f"\tPostprocess time: {tok - tik3}")
+                log.info(f"\t==== Total time: {tok - tik1}")
+
+    def load_model(self):
+        log.info("Loading model...")
+        self.model = LLM(
+            self.model_path,
+            dtype=self.dtype,
+            tensor_parallel_size=self.tensor_parallel_size,
+        )
+        log.info("Loaded model")
+
+    def get_sut(self):
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        return self.sut
+
+    def get_qsl(self):
+        return self.qsl
+
+    def predict(self, **kwargs):
+        raise NotImplementedError
+
+    def issue_queries(self, query_samples):
+        """Receives samples from loadgen and adds them to queue. Users may choose to batch here"""
+
+        list_prompts_tokens = []
+        list_prompts_attn_masks = []
+
+        log.info(f"IssueQuery started with {len(query_samples)} samples")
+        while len(query_samples) > 0:
+            self.query_queue.put(query_samples[: self.batch_size])
+            query_samples = query_samples[self.batch_size:]
+        log.info(f"IssueQuery done")
+
+    def flush_queries(self):
+        pass
+
+    def __del__(self):
+        pass
+
+
+class SUTServer(SUT):
+    def __init__(
+        self,
+        model_path=None,
+        dtype="bfloat16",
+        total_sample_count=8312,
+        dataset_path=None,
+        batch_size=None,
+        workers=1,
+        tensor_parallel_size=8
+    ):
+
+        super().__init__(
+            model_path=model_path,
+            dtype=dtype,
+            total_sample_count=total_sample_count,
+            dataset_path=dataset_path,
+            workers=workers,
+            tensor_parallel_size=tensor_parallel_size,
+        )
+        self.request_id = 0
+
+        self.first_token_queue = queue.Queue()
+
+    def start(self):
+        # Create worker threads
+        for j in range(self.num_workers):
+            worker = threading.Thread(target=self.process_queries)
+            worker.start()
+            self.worker_threads[j] = worker
+
+    async def stream_output(self, qitem, results_generator):
+        first = True
+        async for request_output in results_generator:
+            output_response = request_output
+            if first:
+                first_tokens = list(output_response.outputs[0].token_ids)
+                response_data = array.array(
+                    "B", np.array(first_tokens, np.int32).tobytes())
+                bi = response_data.buffer_info()
+                response = [lg.QuerySampleResponse(qitem.id, bi[0], bi[1])]
+                lg.FirstTokenComplete(response)
+                first = False
+
+        outputs = output_response
+        pred_output_tokens = list(output_response.outputs[0].token_ids)
+        n_tokens = len(pred_output_tokens)
+        response_array = array.array(
+            "B", np.array(pred_output_tokens, np.int32).tobytes()
+        )
+        bi = response_array.buffer_info()
+        response = [
+            lg.QuerySampleResponse(
+                qitem.id,
+                bi[0],
+                bi[1],
+                n_tokens)]
+        lg.QuerySamplesComplete(response)
+
+    def process_queries(self):
+        """Processor of the queued queries. User may choose to add batching logic"""
+        while True:
+
+            qitem = self.query_queue.get()
+            if qitem is None:
+                break
+
+            input_ids_tensor = TokensPrompt(
+                prompt_token_ids=self.data_object.input_ids[qitem.index])
+
+            # TODO: This PoC is super slow with significant overhead. Best to
+            # create a patch to `generate`
+            results_generator = self.model.generate(
+                prompt=input_ids_tensor, sampling_params=self.sampling_params, request_id=str(
+                    self.request_id)
+            )
+            self.request_id += 1
+            asyncio.run(self.stream_output(qitem, results_generator))
+
+    def issue_queries(self, query_samples):
+        self.query_queue.put(query_samples[0])
+
+    def stop(self):
+        for _ in range(self.num_workers):
+            self.query_queue.put(None)
+
+        for worker in self.worker_threads:
+            worker.join()
+
+        self.first_token_queue.put(None)
+        self.ft_response_thread.join()
+
+    def load_model(self):
+        log.info("Loading model")
+        self.engine_args = AsyncEngineArgs(
+            self.model_path,
+            dtype=self.dtype,
+            tensor_parallel_size=self.tensor_parallel_size)
+        self.model = AsyncLLMEngine.from_engine_args(self.engine_args)
+        log.info("Loaded model")
diff --git a/language/llama3-405b/build.sh b/language/llama3-405b/build.sh
new file mode 100644
index 0000000000..87afb992fa
--- /dev/null
+++ b/language/llama3-405b/build.sh
@@ -0,0 +1,8 @@
+set -e
+
+conda install pybind11==2.10.4 -c conda-forge -y
+conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch-nightly -c nvidia
+python -m pip install transformers==4.31.0 nltk==3.8.1 evaluate==0.4.0 absl-py==1.4.0 rouge-score==0.1.2 sentencepiece==0.1.99 accelerate==0.21.0
+
+
+cd ../../loadgen && python3 -m pip install .
diff --git a/language/llama3-405b/dataset.py b/language/llama3-405b/dataset.py
new file mode 100644
index 0000000000..ff95557c7b
--- /dev/null
+++ b/language/llama3-405b/dataset.py
@@ -0,0 +1,92 @@
+import random
+import os
+import time
+import numpy as np
+import torch
+from datasets import load_dataset, load_from_disk
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from torch.nn.functional import pad
+from torch.utils.data import DataLoader
+from typing import Optional, Dict, Sequence
+import io
+
+# import utils
+import copy
+import pickle
+
+import logging
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Llama-405B-Dataset")
+
+
+class Dataset:
+    def __init__(
+        self,
+        model_name=None,
+        total_sample_count=8312,
+        perf_count_override=None,
+        dataset_path=None,
+        dtype="bfloat16"
+    ):
+        self.model_name = model_name or f"Meta-Llama-3.1-405B-Instruct{'-FP8' if dtype == 'float8' else ''}"
+        self.dataset_path = dataset_path
+
+        # self.total_sample_count = total_sample_count
+        self.load_processed_dataset()
+
+        self.total_sample_count = min(len(self.input_ids), total_sample_count)
+        self.perf_count = perf_count_override or self.total_sample_count
+
+    def load_processed_dataset(self):
+        if not os.path.isfile(self.dataset_path):
+            log.warn(
+                "Processed pickle file {} not found. Please check that the path is correct".format(
+                    self.dataset_path
+                )
+            )
+
+        log.info("Loading dataset...")
+        import pandas as pd
+
+        self.processed_data = pd.read_pickle(self.dataset_path)
+
+        self.input = self.processed_data.input.tolist()
+        self.input_ids = self.processed_data.tok_input.tolist()
+        self.input_lens = self.processed_data.tok_input_len.tolist()
+
+        log.info("Finished loading dataset.")
+
+    def postProcess(
+        self,
+        out_tokens,
+        query_id_list=None,
+        sample_index_list=None,
+    ):
+        """Postprocesses output prediction"""
+
+        # TODO: Create response object in postProcess(?)
+        """
+        preds = []
+        for i in range(out_tokens.shape[0]):
+            #pred = out_tokens[i].reshape(-1).cpu().numpy() # Slice up to original input length as below?
+
+            input_len = input_seq_lens[i] if input_seq_lens else 0
+            pred = out_tokens[i, input_len:].reshape(-1).cpu().numpy()
+            preds.append(pred)
+        """
+        # Everything is padded to max_len (1024), so prune the input and parse
+        # to numpy
+        output_seq = out_tokens
+        assert len(query_id_list) == len(output_seq)
+
+        return np.asarray(output_seq, dtype=np.int32)
+
+    def LoadSamplesToRam(self, sample_list):
+        pass
+
+    def UnloadSamplesFromRam(self, sample_list):
+        pass
+
+    def __del__(self):
+        pass
diff --git a/language/llama3-405b/evaluate-accuracy.py b/language/llama3-405b/evaluate-accuracy.py
new file mode 100644
index 0000000000..5faaaa0289
--- /dev/null
+++ b/language/llama3-405b/evaluate-accuracy.py
@@ -0,0 +1,203 @@
+import argparse
+from transformers import AutoTokenizer
+import nltk
+from multiprocessing import Pool, cpu_count
+from tqdm import tqdm
+import numpy as np
+import pandas as pd
+import json
+import re
+from rouge_score import rouge_scorer
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--checkpoint-path",
+        default="meta-llama/Meta-Llama-3-8B",
+        help="Path to Llama3-405b-hf-chat checkpoint"
+    )
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--dataset-file",
+        required=True,
+        help="path to processed dataset set",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--dtype",
+        default="int64",
+        help="dtype of the accuracy log",
+        choices=["int32", "int64", "float"],
+    )
+    args = parser.parse_args()
+    return args
+
+
+scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)
+
+
+def rouge(label, pred):
+    score = scorer.score(label, pred)
+    return {
+        'rougeL': 100 * score['rougeL'].fmeasure,
+    }
+
+
+def niah_em(label, pred):
+    label_uuids = re.findall(
+        r'[\w]{8}-[\w]{4}-[\w]{4}-[\w]{4}-[\w]{12}', label)
+    pred_uuids = re.findall(r'[\w]{8}-[\w]{4}-[\w]{4}-[\w]{4}-[\w]{12}', pred)
+
+    if len(pred_uuids) == 0:
+        return {'exact_match': 0.0}
+
+    # https://github.com/hsiehjackson/RULER/blob/main/scripts/eval/synthetic/constants.py#L28
+    score = sum([
+        sum([1.0 if r.lower() in pred.lower() else 0.0 for r in ref]) / len(ref)
+        for pred, ref in zip(pred_uuids, label_uuids)
+    ]) / len(pred_uuids) * 100
+
+    return {'exact_match': round(score, 2)}
+
+
+def qa_em(label, pred):
+    answer_substring = pred
+
+    if 'Answer: ' in pred:
+        last_answer_index = pred.rfind("Answer: ")
+        if last_answer_index == -1:
+            return {'exact_match': 0.0}
+
+        answer_substring = pred[last_answer_index + len("Answer: "):]
+
+    if answer_substring in label:
+        return {'exact_match': 100.0}
+
+    normalized_answer = re.sub(r'\s+', '', answer_substring).lower()
+    label_entries = [re.sub(r'\s+', '', entry).lower()
+                     for entry in label.split('|')]
+
+    match_found = any(entry in normalized_answer for entry in label_entries)
+    return {'exact_match': 100.0 if match_found else 0.0}
+
+
+metrics = {
+    fn.__name__: fn
+    for fn in [rouge, niah_em, qa_em]
+}
+
+
+def get_groundtruth(processed_dataset_file, return_metrics=True):
+    data = pd.read_pickle(processed_dataset_file)
+    ground_truths = data["gt_output"]
+    if return_metrics:
+        metrics = data["metric"]
+        return ground_truths, metrics
+    return ground_truths
+
+
+def postprocess_text(preds, targets):
+    preds = [pred.strip() for pred in preds]
+    targets = [target.strip() for target in targets]
+
+    # rougeLSum expects newline after each sentence
+    preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
+    targets = ["\n".join(nltk.sent_tokenize(target)) for target in targets]
+
+    return preds, targets
+
+
+def process_item(item):
+    pred, target, metric = item
+    metric_fn = metrics[metric]
+    metric_eval = metric_fn(target, pred)
+    return metric_eval
+
+
+def run_evaluation(preds, targets, metrics, n_process=None):
+    n_process = cpu_count() if n_process is None else n_process
+    with Pool(n_process) as pool:
+        accuracies = list(
+            tqdm(
+                pool.imap(
+                    process_item, zip(
+                        preds, targets, metrics)), total=len(preds)))
+    df = pd.DataFrame({"accuracy": accuracies, "metric": metrics})
+    return df.accuracy.apply(pd.Series).describe().loc["mean"].to_dict()
+
+
+def main():
+
+    args = get_args()
+    dataset_path = args.dataset_file
+    checkpoint_path = args.checkpoint_path
+    nltk.download("punkt")
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        checkpoint_path,
+        model_max_length=2048,
+        padding_side="left",
+        use_fast=False,
+    )
+
+    targets, metrics = get_groundtruth(args.dataset_file)
+
+    target_required = []
+    metrics_required = []
+    preds_token_ids = []
+
+    eval_dtype = np.int64
+    if args.dtype == "int32":
+        eval_dtype = np.int32
+    elif args.dtype == "float":
+        eval_dtype = np.float32
+
+    with open(args.mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+    seen = set()
+    gen_tok_len = 0
+    for pred in results:
+        qsl_idx = pred["qsl_idx"]
+        if qsl_idx in seen:
+            continue
+
+        seen.add(qsl_idx)
+        target_required.append(targets[qsl_idx])
+        metrics_required.append(metrics[qsl_idx])
+        pred = np.frombuffer(bytes.fromhex(pred["data"]), eval_dtype)
+
+        gen_tok_len += len(pred)
+        preds_token_ids.append(pred)
+
+    preds_decoded_text = tokenizer.batch_decode(
+        preds_token_ids, skip_special_tokens=True
+    )
+
+    preds, targets = postprocess_text(preds_decoded_text, target_required)
+
+    result = run_evaluation(preds, targets, metrics)
+    result = dict(result)
+    prediction_lens = [len(pred) for pred in preds]
+    gen_num = len(preds)
+
+    result = {
+        **result,
+        "gen_len": np.sum(prediction_lens),
+        "gen_num": gen_num,
+        "gen_tok_len": gen_tok_len,
+        "tokens_per_sample": round(gen_tok_len / gen_num, 1),
+    }
+
+    print("\nResults\n")
+    print(result)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/language/llama3-405b/launch.sh b/language/llama3-405b/launch.sh
new file mode 100644
index 0000000000..c3389c5162
--- /dev/null
+++ b/language/llama3-405b/launch.sh
@@ -0,0 +1,37 @@
+#!/bin/bash
+
+MLCOMMONS_REPO_PATH="$(dirname "$(dirname "$PWD")")"
+
+# Add any volume mounts here with the following syntax
+# /path/to/src:/path/to/dir/in/container
+MOUNTS=(
+    $MLCOMMONS_REPO_PATH:$MLCOMMONS_REPO_PATH
+)
+
+# Set up docker environment file for current user
+rm -f .docker_env
+echo "CI_BUILD_USER=`id -u -n`" >> .docker_env
+echo "CI_BUILD_UID=`id -u`" >> .docker_env
+echo "CI_BUILD_GROUP=`id -g -n`" >> .docker_env
+echo "CI_BUILD_GID=`id -g`" >> .docker_env
+cat .docker_env
+
+# Build container
+docker build . -t llm/gpubringup
+
+# Build mount flags
+declare -a MOUNT_FLAGS
+for _mount in ${MOUNTS[@]}; do
+    _split=($(echo $_mount | tr ':' '\n'));
+    MOUNT_FLAGS+=("--mount type=bind,source=${_split[0]},target=${_split[1]}");
+done
+
+set -x
+nvidia-docker run -it --rm --net=host --runtime=nvidia --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
+  --cap-add=SYS_PTRACE --cap-add=SYS_ADMIN --cap-add=DAC_READ_SEARCH \
+  --security-opt seccomp=unconfined \
+  -w $PWD \
+  --env-file `pwd`/.docker_env \
+  ${MOUNT_FLAGS[*]} \
+  llm/gpubringup \
+  bash ./with_the_same_user
diff --git a/language/llama3-405b/main.py b/language/llama3-405b/main.py
new file mode 100644
index 0000000000..86049658cd
--- /dev/null
+++ b/language/llama3-405b/main.py
@@ -0,0 +1,208 @@
+import subprocess
+import mlperf_loadgen as lg
+import argparse
+import os
+import logging
+import sys
+import requests
+import json
+
+sys.path.insert(0, os.getcwd())
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("Llama-405B-MAIN")
+
+# function to check the model name in server matches the user specified one
+
+
+def verify_model_name(user_specified_name, url):
+    response = requests.get(url)
+    if response.status_code == 200:
+        response_dict = response.json()
+        server_model_name = response_dict["data"][0]["id"]
+        if user_specified_name == server_model_name:
+            return {"matched": True, "error": False}
+        else:
+            return {
+                "matched": False,
+                "error": f"User specified {user_specified_name} and server model name {server_model_name} mismatch!",
+            }
+    else:
+        return {
+            "matched": False,
+            "error": f"Failed to get a valid response. Status code: {response.status_code}",
+        }
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--scenario",
+        type=str,
+        choices=["Offline", "Server"],
+        default="Offline",
+        help="Scenario",
+    )
+    parser.add_argument(
+        "--model-path",
+        type=str,
+        default="meta-llama/Llama-2-70b-chat-hf",
+        help="Model name",
+    )
+    parser.add_argument("--dataset-path", type=str, default=None, help="")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="Run accuracy mode")
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        default="float32",
+        help="data type of the model, choose from float16, bfloat16 and float32",
+    )
+    parser.add_argument(
+        "--audit-conf",
+        type=str,
+        default="audit.conf",
+        help="audit config for LoadGen settings during compliance runs",
+    )
+    parser.add_argument(
+        "--user-conf",
+        type=str,
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+    # TODO: This interpretation of 'total-sample-count' is a little
+    # misleading. Fix it
+    parser.add_argument(
+        "--total-sample-count",
+        type=int,
+        default=8312,
+        help="Number of samples to use in benchmark.",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=1,
+        help="Model batch-size to use in benchmark.",
+    )
+    parser.add_argument(
+        "--output-log-dir", type=str, default="output-logs", help="Where logs are saved"
+    )
+    parser.add_argument(
+        "--enable-log-trace",
+        action="store_true",
+        help="Enable log tracing. This file can become quite large",
+    )
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        default=1,
+        help="Number of workers to process queries",
+    )
+    parser.add_argument(
+        "--tensor-parallel-size",
+        type=int,
+        default=8,
+        help="Number of workers to process queries",
+    )
+    parser.add_argument("--vllm", action="store_true", help="vllm mode")
+    parser.add_argument(
+        "--api-model-name",
+        type=str,
+        default="meta-llama/Llama-2-70b-chat-hf",
+        help="Model name(specified in llm server)",
+    )
+    parser.add_argument(
+        "--api-server",
+        type=str,
+        default=None,
+        help="Specify an api endpoint call to use api mode",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+scenario_map = {
+    "offline": lg.TestScenario.Offline,
+    "server": lg.TestScenario.Server,
+}
+
+
+def main():
+    args = get_args()
+
+    settings = lg.TestSettings()
+    settings.scenario = scenario_map[args.scenario.lower()]
+    # mlperf.conf is automatically loaded by the loadgen
+    # settings.FromConfig(args.mlperf_conf, "llama3-405b", args.scenario)
+    settings.FromConfig(args.user_conf, "llama3-405b", args.scenario)
+
+    if args.accuracy:
+        settings.mode = lg.TestMode.AccuracyOnly
+    else:
+        settings.mode = lg.TestMode.PerformanceOnly
+
+    os.makedirs(args.output_log_dir, exist_ok=True)
+    log_output_settings = lg.LogOutputSettings()
+    log_output_settings.outdir = args.output_log_dir
+    log_output_settings.copy_summary_to_stdout = True
+    log_settings = lg.LogSettings()
+    log_settings.log_output = log_output_settings
+    log_settings.enable_trace = args.enable_log_trace
+
+    if args.vllm:
+        from SUT_VLLM import SUT, SUTServer
+    else:
+        raise NotImplementedError
+
+    sut_map = {"offline": SUT, "server": SUTServer}
+
+    sut_cls = sut_map[args.scenario.lower()]
+
+    if args.vllm:
+        sut = sut_cls(
+            model_path=args.model_path,
+            dtype=args.dtype,
+            batch_size=args.batch_size,
+            dataset_path=args.dataset_path,
+            total_sample_count=args.total_sample_count,
+            workers=args.num_workers,
+            tensor_parallel_size=args.tensor_parallel_size
+        )
+    else:
+        sut = sut_cls(
+            model_path=args.model_path,
+            dtype=args.dtype,
+            batch_size=args.batch_size,
+            dataset_path=args.dataset_path,
+            total_sample_count=args.total_sample_count,
+            workers=args.num_workers,
+        )
+
+    # Start sut before loadgen starts
+    sut.start()
+    lgSUT = lg.ConstructSUT(sut.issue_queries, sut.flush_queries)
+    log.info("Starting Benchmark run")
+    lg.StartTestWithLogSettings(
+        lgSUT,
+        sut.qsl,
+        settings,
+        log_settings,
+        args.audit_conf)
+
+    # Stop sut after completion
+    sut.stop()
+
+    log.info("Run Completed!")
+
+    log.info("Destroying SUT...")
+    lg.DestroySUT(lgSUT)
+
+    log.info("Destroying QSL...")
+    lg.DestroyQSL(sut.qsl)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/language/llama3-405b/requirements.txt b/language/llama3-405b/requirements.txt
new file mode 100644
index 0000000000..d8f5fbd12f
--- /dev/null
+++ b/language/llama3-405b/requirements.txt
@@ -0,0 +1,9 @@
+transformers==4.46.2
+nltk==3.8.1
+evaluate==0.4.0
+absl-py==1.4.0
+rouge-score==0.1.2
+sentencepiece==0.1.99
+accelerate==0.21.0
+vllm==0.6.3
+pybind11==2.10.4
\ No newline at end of file
diff --git a/language/llama3-405b/run_accuracy.sh b/language/llama3-405b/run_accuracy.sh
new file mode 100644
index 0000000000..0752459131
--- /dev/null
+++ b/language/llama3-405b/run_accuracy.sh
@@ -0,0 +1,21 @@
+CHECKPOINT_PATH="${CHECKPOINT_PATH:Meta-Llama-3.1-405B-Instruct}"
+DATASET_PATH="${DATASET_PATH:mlperf_llama3.1_405b_dataset_8318.pkl}"
+
+mkdir -p "run_outputs"
+
+python3 -u main.py --scenario Offline \
+        --model-path ${CHECKPOINT_PATH} \
+        --accuracy \
+        --mlperf-conf mlperf.conf \
+        --user-conf user.conf \
+        --total-sample-count 8312 \
+        --dataset-path ${DATASET_PATH} \
+        --output-log-dir offline_accuracy_loadgen_logs \
+        --dtype float32 | tee offline_accuracy_log.log
+
+python3 evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
+        --mlperf-accuracy-file offline_accuracy_loadgen_logs/mlperf_log_accuracy.json \
+        --dataset-file ${DATASET_PATH} \
+        --dtype int32
+
+python3 consolidate_results.py --dataset-path ${DATASET_PATH} --model-dir ${CHECKPOINT_PATH}
diff --git a/language/llama3-405b/run_offline.sh b/language/llama3-405b/run_offline.sh
new file mode 100644
index 0000000000..471aa1ce7d
--- /dev/null
+++ b/language/llama3-405b/run_offline.sh
@@ -0,0 +1,10 @@
+CHECKPOINT_PATH="${CHECKPOINT_PATH:-meta-llama/Llama-2-70b-chat-hf}"
+DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
+
+python -u main.py --scenario Offline \
+		--model-path ${CHECKPOINT_PATH} \
+		--mlperf-conf mlperf.conf \
+		--user-conf user.conf \
+		--total-sample-count 8312 \
+		--dataset-path ${DATASET_PATH} \
+		--device cpu 2>&1 | tee server_log.log
diff --git a/language/llama3-405b/run_server.sh b/language/llama3-405b/run_server.sh
new file mode 100644
index 0000000000..b570f2e9c4
--- /dev/null
+++ b/language/llama3-405b/run_server.sh
@@ -0,0 +1,12 @@
+
+
+CHECKPOINT_PATH="${CHECKPOINT_PATH:-meta-llama/Llama-2-70b-chat-hf}"
+DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
+
+python -u main.py --scenario Server \
+		--model-path ${CHECKPOINT_PATH} \
+		--mlperf-conf mlperf.conf \
+		--user-conf user.conf \
+		--total-sample-count 8312 \
+		--dataset-path ${DATASET_PATH} \
+		--device cpu 2>&1 | tee server_log.log
diff --git a/language/llama3-405b/user.conf b/language/llama3-405b/user.conf
new file mode 100644
index 0000000000..9f4eb5f9a6
--- /dev/null
+++ b/language/llama3-405b/user.conf
@@ -0,0 +1,13 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+#
+*.Offline.min_duration = 600000
+*.Offline.min_query_count = 2000
+
+*.Server.target_qps = 0.5
+*.Server.min_duration = 120000
+*.Server.min_query_count = 100
+
+llama3-405b.Server.sample_concatenate_permutation = 1
\ No newline at end of file
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 10f7ae78f6..d261318a16 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -14,6 +14,7 @@ dlrm-v2.*.performance_sample_count_override = 204800
 rnnt.*.performance_sample_count_override = 2513
 gptj.*.performance_sample_count_override = 13368
 llama2-70b.*.performance_sample_count_override = 24576
+llama3-405b.*.performance_sample_count_override = 8312
 stable-diffusion-xl.*.performance_sample_count_override = 5000
 # set to 0 to let entire sample set to be performance sample
 3d-unet.*.performance_sample_count_override = 0
@@ -44,6 +45,7 @@ retinanet.MultiStream.target_latency = 528
 gptj.*.sample_concatenate_permutation = 1
 llama2-70b.*.sample_concatenate_permutation = 1
 mixtral-8x7b.*.sample_concatenate_permutation = 1
+llama3-405b.*.sample_concatenate_permutation = 1
 
 *.Server.target_latency = 10
 *.Server.target_latency_percentile = 99
@@ -57,13 +59,14 @@ dlrm-v2.Server.target_latency = 60
 rnnt.Server.target_latency = 1000
 gptj.Server.target_latency = 20000
 stable-diffusion-xl.Server.target_latency = 20000
-# Llama2-70b benchmarks measures token latencies
+# Benchmarks that measure token latencies
 llama2-70b.*.use_token_latencies = 1
 mixtral-8x7b.*.use_token_latencies = 1
+llama3-405b.*.use_token_latencies = 1
 # gptj benchmark infers token latencies
 gptj.*.infer_token_latencies = 1
 gptj.*.token_latency_scaling_factor = 69
-# Only ttft and tpot are tracked for the llama2-70b & mixtral-8x7B benchmark therefore target_latency = 0
+# Only ttft and tpot are tracked for the llama2-70b, mixtral-8x7B & llama3-405b benchmark therefore target_latency = 0
 llama2-70b.Server.target_latency = 0
 llama2-70b.Server.ttft_latency = 2000
 llama2-70b.Server.tpot_latency = 200
@@ -72,6 +75,10 @@ mixtral-8x7b.Server.target_latency = 0
 mixtral-8x7b.Server.ttft_latency = 2000
 mixtral-8x7b.Server.tpot_latency = 200
 
+llama3-405b.Server.target_latency = 0
+llama3-405b.Server.ttft_latency = 6000
+llama3-405b.Server.tpot_latency = 175
+
 *.Offline.target_latency_percentile = 90
 *.Offline.min_duration = 600000
 
@@ -89,6 +96,7 @@ rnnt.Offline.min_query_count = 2513
 3d-unet.Offline.min_query_count = 43
 stable-diffusion-xl.Offline.min_query_count = 5000
 llama2-70b.Offline.min_query_count = 24576
+llama3-405b.Offline.min_query_count = 8312
 mixtral-8x7b.Offline.min_query_count = 15000
 
 # These fields should be defined and overridden by user.conf.

From feca9f610bc55cfe269404438b6fd4f7d2295b11 Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Tue, 3 Dec 2024 17:53:37 +0000
Subject: [PATCH 184/305] Increment version to 4.1.24

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 59fe035d9c..2afe9065c1 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-4.1.23
+4.1.24

From b6f5a343ba5afa29af2ad9ae596d0545c025683b Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Wed, 4 Dec 2024 20:30:47 +0530
Subject: [PATCH 185/305] Fixes to SDXL accuracy output, submission
 preprocessor, fixes #1938 (#1948)

* Update generate_final_report.py

* Fix sdxl (#1911)

* Fix typo in fid_score.py, fail_safe for SDXL short runs

* [Automated Commit] Format Codebase

* Fix typo in fid_score.py, fail_safe for SDXL short runs

* Fix dlrmv2 reference implementation | Update run_local.sh

* Fixes for filtering invalid results

* [Automated Commit] Format Codebase

* Update preprocess_submission.py

* Added an option to pass in sample_ids.txt for SDXL accuracy check

* [Automated Commit] Format Codebase

* Update accuracy_coco.py

* [Automated Commit] Format Codebase

* Fix typo

* Not use default for sample_ids.txt

* Update requirements.txt (#1907)

Updating the pip packages

* Fix preprocess_sudbmission for a bug

* Update submission_checker.py | Removed TEST05

* Fix to SDXL accuracy output

* Added exists checks for rmtree in preprocess_submission script

* [Automated Commit] Format Codebase

* Delete .github/workflows/format.yml

* Delete .github/scripts directory

* Update build_wheels.yml | Added src distribution

* Update VERSION.txt

* Update build_wheels.yml

* Update VERSION.txt

* Update pyproject.toml

* Increment version to 4.1.26

* Update MANIFEST.in

* Increment version to 4.1.27

* Update pyproject.toml

* Increment version to 4.1.28

* Update build_wheels.yml

* Update VERSION.txt

* Update accuracy_coco.py

* Making sdxl run thread safe

* Create format.yml | Run format on push instead of PR

---------

Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
---
 .github/scripts/format-cpp.sh             | 26 ----------
 .github/scripts/format-py.sh              | 26 ----------
 .github/workflows/build_wheels.yml        | 19 ++++++--
 .github/workflows/format.yml              | 58 +++++++++++++----------
 loadgen/MANIFEST.in                       |  1 +
 loadgen/VERSION.txt                       |  2 +-
 text_to_image/backend_pytorch.py          | 23 +++++----
 text_to_image/main.py                     | 28 ++++++-----
 text_to_image/tools/accuracy_coco.py      |  8 ++--
 text_to_image/tools/fid/fid_score.py      |  4 ++
 tools/submission/preprocess_submission.py | 54 ++++++++++++++-------
 11 files changed, 123 insertions(+), 126 deletions(-)
 delete mode 100755 .github/scripts/format-cpp.sh
 delete mode 100755 .github/scripts/format-py.sh

diff --git a/.github/scripts/format-cpp.sh b/.github/scripts/format-cpp.sh
deleted file mode 100755
index c0a2374890..0000000000
--- a/.github/scripts/format-cpp.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#================================================================
-# HEADER
-#================================================================
-# DESCRIPTION
-#    This is a script containing some commands to automatically 
-#    format the c/c++ code contained in one folder. 
-#    This will help to maintain good quality code in the github 
-#    repository. 
-# SET UP
-#    You need to allow the correct permissions for this file.
-#    This can be done by running:
-#    chmod u+x <path to file>
-# REQUIREMENTS
-#    clang-format
-#================================================================
-# END_OF_HEADER
-#================================================================
-
-
-# Checks all the modified c/c++ files, format them and adds them
-# to the commit.
-for FILE in $(git diff $1/$2 --name-only | grep -E '.*\.(cc|cpp|h|hpp)$')
-do
-    clang-format -i -style=file $FILE
-    git add $FILE
-done
diff --git a/.github/scripts/format-py.sh b/.github/scripts/format-py.sh
deleted file mode 100755
index ca3ca5671f..0000000000
--- a/.github/scripts/format-py.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#================================================================
-# HEADER
-#================================================================
-# DESCRIPTION
-#    This is a script containing some commands to automatically 
-#    format the c/c++ code contained in one folder. 
-#    This will help to maintain good quality code in the github 
-#    repository. 
-# SET UP
-#    You need to allow the correct permissions for this file.
-#    This can be done by running:
-#    chmod u+x <path to file>
-# REQUIREMENTS
-#    clang-format
-#================================================================
-# END_OF_HEADER
-#================================================================
-
-
-# Checks all the modified c/c++ files, format them and adds them
-# to the commit.
-for FILE in $(git diff $1/$2 --name-only | grep -E '.*\.py$')
-do
-    autopep8 --in-place -a $FILE
-    git add $FILE
-done
diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index e62f010dfa..6f67f56de3 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -74,7 +74,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
+        os: [ubuntu-latest, windows-latest, macos-latest, macos-13]
 
     steps:
       - uses: actions/checkout@v3
@@ -82,7 +82,12 @@ jobs:
       - uses: actions/setup-python@v3
 
       - name: Install requirements
-        run: python -m pip install cibuildwheel twine
+        run: python -m pip install cibuildwheel twine build
+
+      - name: Build src dist
+        if: ${{ matrix.os == 'ubuntu-latest' }}
+        run: |
+          python -m build --sdist --outdir wheels loadgen
 
       - name: Build wheels
         run: git pull && python -m cibuildwheel loadgen/ --output-dir wheels
@@ -110,12 +115,18 @@ jobs:
         with:
           name: wheels-ubuntu-latest
           path: wheels
-      # Download the built wheels from macOS
-      - name: Download macOS wheels
+      # Download the built wheels from macOS-latest
+      - name: Download macOS-latest wheels
         uses: actions/download-artifact@v4
         with:
           name: wheels-macos-latest
           path: wheels
+      # Download the built wheels from macOS-13 (x86)
+      - name: Download macOS-13 (x86) wheels
+        uses: actions/download-artifact@v4
+        with:
+          name: wheels-macos-13
+          path: wheels
       # Download the built wheels from Windows
       - name: Download Windows wheels
         uses: actions/download-artifact@v4
diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index 829a3fcb7a..45ebb521b6 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -1,50 +1,60 @@
 # Automatic code formatting
-name: "format"
+name: "Code formatting"
 on:
-  pull_request:
-    branches: [ master ]
-    types: [opened, closed, synchronize]
-
+  push:
+    branches:
+    - "**"
 
 env:
   python_version: "3.9"
-  HEAD_REF: ${{ github.head_ref }}
 
 jobs:
   format-code:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
+          ssh-key: ${{ secrets.DEPLOY_KEY }}
       - name: Set up Python ${{ env.python_version }}
         uses: actions/setup-python@v3
         with:
           python-version: ${{ env.python_version }}
 
-      - name: Install dependencies
+      - name: Format modified python files
+        env:
+          filter: ${{ github.event.before }}
         run: |
           python3 -m pip install autopep8
+          for FILE in $(git diff --name-only $filter | grep -E '.*\.py$')
+          do
+            # Check if the file still exists in the working tree
+            if [ -f "$FILE" ]; then
+              autopep8 --in-place -a "$FILE"
+              git add "$FILE"
+            fi
+          done
 
-      - name: Grant permissions
-        run: |
-          chmod +x "${GITHUB_WORKSPACE}/.github/scripts/format-cpp.sh"
-          chmod +x "${GITHUB_WORKSPACE}/.github/scripts/format-py.sh"
-      
-      - name: Format Codebase
+      - name: Format modified C++ files
+        env:
+          filter: ${{ github.event.before }}
         run: |
-          git remote add upstream ${{ github.event.pull_request.base.repo.clone_url }}
-          git fetch upstream ${{ github.event.pull_request.base.ref }}
-          ".github/scripts/format-cpp.sh" "upstream" "${{ github.event.pull_request.base.ref }}"
-          ".github/scripts/format-py.sh" "upstream" "${{ github.event.pull_request.base.ref }}"
+          for FILE in $(git diff --name-only $filter | grep -E '.*\.(cc|cpp|h|hpp)$')
+          do
+            # Check if the file still exists in the working tree
+            if [ -f "$FILE" ]; then
+              clang-format -i -style=file $FILE
+              git add $FILE
+            fi
+          done
 
-      - name: Commit
+      - name: Commit and push changes
         run: |
           HAS_CHANGES=$(git diff --staged --name-only)
           if [ ${#HAS_CHANGES} -gt 0 ]; then
-            git checkout -B "$HEAD_REF"
-            git config --global user.email "${{ github.actor }}@users.noreply.github.com"
-            git config --global user.name "${{ github.actor }}"
+            git config --global user.name mlcommons-bot
+            git config --global user.email "mlcommons-bot@users.noreply.github.com"
+            # Commit changes
             git commit -m '[Automated Commit] Format Codebase'
-            git push origin "$HEAD_REF"
-          fi
+            git push
+          fi 
diff --git a/loadgen/MANIFEST.in b/loadgen/MANIFEST.in
index 74282fceee..152b53111a 100644
--- a/loadgen/MANIFEST.in
+++ b/loadgen/MANIFEST.in
@@ -1 +1,2 @@
 include VERSION.txt
+include mlperf.conf
diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 2afe9065c1..4d3eac880f 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-4.1.24
+4.1.29
\ No newline at end of file
diff --git a/text_to_image/backend_pytorch.py b/text_to_image/backend_pytorch.py
index 36e2b80090..027e155655 100644
--- a/text_to_image/backend_pytorch.py
+++ b/text_to_image/backend_pytorch.py
@@ -5,6 +5,7 @@
 import backend
 from diffusers import StableDiffusionXLPipeline
 from diffusers import EulerDiscreteScheduler
+import threading
 
 logging.basicConfig(level=logging.INFO)
 log = logging.getLogger("backend-pytorch")
@@ -24,6 +25,7 @@ def __init__(
     ):
         super(BackendPytorch, self).__init__()
         self.model_path = model_path
+        self.lock = threading.Lock()
         if model_id == "xl":
             self.model_id = "stabilityai/stable-diffusion-xl-base-1.0"
         else:
@@ -385,15 +387,16 @@ def predict(self, inputs):
                     pooled_prompt_embeds,
                     negative_pooled_prompt_embeds,
                 ) = self.prepare_inputs(inputs, i)
-                generated = self.pipe(
-                    prompt_embeds=prompt_embeds,
-                    negative_prompt_embeds=negative_prompt_embeds,
-                    pooled_prompt_embeds=pooled_prompt_embeds,
-                    negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-                    guidance_scale=self.guidance,
-                    num_inference_steps=self.steps,
-                    output_type="pt",
-                    latents=latents_input,
-                ).images
+                with lock:
+                    generated = self.pipe(
+                        prompt_embeds=prompt_embeds,
+                        negative_prompt_embeds=negative_prompt_embeds,
+                        pooled_prompt_embeds=pooled_prompt_embeds,
+                        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+                        guidance_scale=self.guidance,
+                        num_inference_steps=self.steps,
+                        output_type="pt",
+                        latents=latents_input,
+                    ).images
                 images.extend(generated)
         return images
diff --git a/text_to_image/main.py b/text_to_image/main.py
index 6aa7c15e75..b8c98dc9c0 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -250,6 +250,7 @@ def run_one_item(self, qitem: Item):
             log.error("thread: failed on contentid=%s, %s", src, ex)
             # since post_process will not run, fake empty responses
             processed_results = [[]] * len(qitem.query_id)
+            raise
         finally:
             response_array_refs = []
             response = []
@@ -402,19 +403,20 @@ def main():
     #
     count = ds.get_item_count()
 
-    # warmup
-    syntetic_str = "Lorem ipsum dolor sit amet, consectetur adipiscing elit"
-    latents_pt = torch.rand(ds.latents.shape, dtype=dtype).to(args.device)
-    warmup_samples = [
-        {
-            "input_tokens": ds.preprocess(syntetic_str, model.pipe.tokenizer),
-            "input_tokens_2": ds.preprocess(syntetic_str, model.pipe.tokenizer_2),
-            "latents": latents_pt,
-        }
-        for _ in range(args.max_batchsize)
-    ]
-    for i in range(5):
-        _ = backend.predict(warmup_samples)
+    if os.environ.get('FORCE_NO_WARMUP', '').lower() not in [ "yes", "true", "1" ]:
+        # warmup
+        syntetic_str = "Lorem ipsum dolor sit amet, consectetur adipiscing elit"
+        latents_pt = torch.rand(ds.latents.shape, dtype=dtype).to(args.device)
+        warmup_samples = [
+            {
+                "input_tokens": ds.preprocess(syntetic_str, model.pipe.tokenizer),
+                "input_tokens_2": ds.preprocess(syntetic_str, model.pipe.tokenizer_2),
+                "latents": latents_pt,
+            }
+            for _ in range(args.max_batchsize)
+        ]
+        for i in range(5):
+            _ = backend.predict(warmup_samples)
 
     scenario = SCENARIO_MAP[args.scenario]
     runner_map = {
diff --git a/text_to_image/tools/accuracy_coco.py b/text_to_image/tools/accuracy_coco.py
index b5f1be3783..0fedd278b4 100644
--- a/text_to_image/tools/accuracy_coco.py
+++ b/text_to_image/tools/accuracy_coco.py
@@ -195,8 +195,8 @@ def compute_accuracy(
                 generated_img).item())
     fid_score = compute_fid(result_list, statistics_path, device)
 
-    result_dict["FID_SCORE"] = fid_score
-    result_dict["CLIP_SCORE"] = np.mean(clip_scores)
+    result_dict["FID_SCORE"] = f"{fid_score}"
+    result_dict["CLIP_SCORE"] = f"{np.mean(clip_scores)}"
     print(f"Accuracy Results: {result_dict}")
 
     with open(output_file, "w") as fp:
@@ -311,8 +311,8 @@ def compute_accuracy_low_memory(
 
     fid_score = calculate_frechet_distance(m1, s1, m2, s2)
 
-    result_dict["FID_SCORE"] = fid_score
-    result_dict["CLIP_SCORE"] = np.mean(clip_scores)
+    result_dict["FID_SCORE"] = f"{fid_score}"
+    result_dict["CLIP_SCORE"] = f"{np.mean(clip_scores)}"
     print(f"Accuracy Results: {result_dict}")
 
     with open(output_file, "w") as fp:
diff --git a/text_to_image/tools/fid/fid_score.py b/text_to_image/tools/fid/fid_score.py
index b2de332ce5..1df208dd84 100644
--- a/text_to_image/tools/fid/fid_score.py
+++ b/text_to_image/tools/fid/fid_score.py
@@ -355,6 +355,10 @@ def compute_fid(
         imgs, model, batch_size, dims, device, num_workers
     )
 
+    # Ensure dimensions match before calculating FID
+    assert s1.shape == s2.shape, f"Covariance shapes mismatch: {s1.shape} vs {s2.shape}"
+    assert m1.shape == m2.shape, f"Mean shapes mismatch: {m1.shape} vs {m2.shape}"
+
     fid_value = calculate_frechet_distance(m1, s1, m2, s2)
 
     return fid_value
diff --git a/tools/submission/preprocess_submission.py b/tools/submission/preprocess_submission.py
index a737c1f16c..977af4d476 100644
--- a/tools/submission/preprocess_submission.py
+++ b/tools/submission/preprocess_submission.py
@@ -144,18 +144,25 @@ def clean_model_dir(model_results_dir):
         model_results_dir, "results", "compliance")
 
     print(f"rmtree {model_results_dir}")
-    shutil.rmtree(model_results_dir)
-    shutil.rmtree(model_measurements_dir)
-    shutil.rmtree(model_compliance_dir)
+    if os.path.exists(model_results_dir):
+        shutil.rmtree(model_results_dir)
+    if os.path.exists(model_measurements_dir):
+        shutil.rmtree(model_measurements_dir)
+    if os.path.exists(model_compliance_dir):
+        shutil.rmtree(model_compliance_dir)
+
     sut_results_dir = os.path.dirname(model_results_dir)
-    if not os.listdir(sut_results_dir):
+    if os.path.exists(sut_results_dir) and not os.listdir(sut_results_dir):
         # clean sut dir
         sut = os.path.basename(sut_results_dir)
         log.info(
             f"No benchmark results remaining for {sut}. rmtree {sut_results_dir}")
-        shutil.rmtree(sut_results_dir)
-        shutil.rmtree(os.path.dirname(model_measurements_dir))
-        shutil.rmtree(os.path.dirname(model_compliance_dir))
+        if os.path.exists(sut_results_dir):
+            shutil.rmtree(sut_results_dir)
+        if os.path.exists(os.path.dirname(model_measurements_dir)):
+            shutil.rmtree(os.path.dirname(model_measurements_dir))
+        if os.path.exists(os.path.dirname(model_compliance_dir)):
+            shutil.rmtree(os.path.dirname(model_compliance_dir))
 
 
 def clean_invalid_results(args, log_path, config, system_desc, system_json,
@@ -200,7 +207,7 @@ def clean_invalid_results(args, log_path, config, system_desc, system_json,
                 ranging_path = os.path.join(
                     scenario_path, "performance", "ranging")
                 try:
-                    ranging_r = get_performance_metric(
+                    ranging_r = checker.get_performance_metric(
                         config,
                         mlperf_model,
                         ranging_path,
@@ -224,9 +231,13 @@ def clean_invalid_results(args, log_path, config, system_desc, system_json,
                 if not power_is_valid:
                     log.warning(
                         f"Power result is invalid for {system_desc}: {model} {scenario} scenario in {division} division. Removing...")
-                    shutil.rmtree(power_path)
-                    shutil.rmtree(ranging_path)
-                    shutil.rmtree(os.path.join(perf_path, "spl.txt"))
+                    if os.path.exists(power_path):
+                        shutil.rmtree(power_path)
+                    if os.path.exists(ranging_path):
+                        shutil.rmtree(ranging_path)
+                    spl_path = os.path.join(perf_path, "spl.txt")
+                    if os.path.exists(spl_path):
+                        os.remove(spl_path)
 
             compliance_is_valid = True
             if is_closed_or_network:
@@ -253,9 +264,12 @@ def clean_invalid_results(args, log_path, config, system_desc, system_json,
                     scenario_path, "results", "compliance")
                 log.warning(
                     f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} division. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Removing...")
-                shutil.rmtree(scenario_path)
-                shutil.rmtree(scenario_measurements_path)
-                shutil.rmtree(scenario_compliance_path)
+                if os.path.exists(scenario_path):
+                    shutil.rmtree(scenario_path)
+                if os.path.exists(scenario_measurements_path):
+                    shutil.rmtree(scenario_measurements_path)
+                if os.path.exists(scenario_compliance_path):
+                    shutil.rmtree(scenario_compliance_path)
             elif division in ["closed", "network"]:
                 model_results_path = os.path.dirname(scenario_path)
                 model_measurements_path = change_folder_name_in_path(
@@ -279,10 +293,12 @@ def clean_invalid_results(args, log_path, config, system_desc, system_json,
                     if not perf_is_valid:
                         log.warning(
                             f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} and open divisions. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Removing it...")
-                        shutil.rmtree(scenario_path)
+                        if os.path.exists(scenario_path):
+                            shutil.rmtree(scenario_path)
                         scenario_measurements_path = change_folder_name_in_path(
                             scenario_path, "results", "measurements")
-                        shutil.rmtree(scenario_measurements_path)
+                        if os.path.exists(scenario_measurements_path):
+                            shutil.rmtree(scenario_measurements_path)
                     if not os.path.exists(target_results_path):
                         shutil.copytree(
                             model_results_path, target_results_path)
@@ -311,9 +327,11 @@ def clean_invalid_results(args, log_path, config, system_desc, system_json,
                 clean_model_dir(model_results_path)
             else:  # delete this result
                 # delete other scenario results too
-                shutil.rmtree(scenario_path)
+                if os.path.exists(scenario_path):
+                    shutil.rmtree(scenario_path)
                 # delete other scenario results too
-                shutil.rmtree(scenario_measurements_path)
+                if os.path.exists(scenario_measurements_path):
+                    shutil.rmtree(scenario_measurements_path)
                 log.warning(
                     f"{scenario} scenario result is invalid for {system_desc}: {model} in {division} division. Accuracy: {accuracy_is_valid}, Performance: {perf_is_valid}. Removing it...")
 

From 6ac00b14f35f7b0f0c10dc04841926103c190b80 Mon Sep 17 00:00:00 2001
From: mlcommons-bot <mlcommons-bot@users.noreply.github.com>
Date: Wed, 4 Dec 2024 15:01:11 +0000
Subject: [PATCH 186/305] [Automated Commit] Format Codebase

---
 text_to_image/main.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/text_to_image/main.py b/text_to_image/main.py
index b8c98dc9c0..5c6bad8f93 100644
--- a/text_to_image/main.py
+++ b/text_to_image/main.py
@@ -403,7 +403,8 @@ def main():
     #
     count = ds.get_item_count()
 
-    if os.environ.get('FORCE_NO_WARMUP', '').lower() not in [ "yes", "true", "1" ]:
+    if os.environ.get('FORCE_NO_WARMUP', '').lower() not in [
+            "yes", "true", "1"]:
         # warmup
         syntetic_str = "Lorem ipsum dolor sit amet, consectetur adipiscing elit"
         latents_pt = torch.rand(ds.latents.shape, dtype=dtype).to(args.device)

From a2baf120f8d0b54e4160eaa3d600f4d2bdc90a3c Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 4 Dec 2024 11:44:32 -0500
Subject: [PATCH 187/305] Add R-GAT calibration + remove old reference
 implementation (#1943)

* Remove GLT implementation for GNN

* Update Readme: remove GLT and add checkpoint

* Update mlperf.conf file

* Dockerize rgat benchmark

* Update README

* Add gnn accuracy script

* Remove mlperf.conf from GNN reference implementation

* Add minimun system requirements to README

* Fix paths in accuracy script

* Fix typing in accuracy script
---
 graph/R-GAT/README.md                         |  94 +++++--
 graph/R-GAT/backend_glt.py                    | 214 ---------------
 graph/R-GAT/dgl_utilities/feature_fetching.py |   2 +-
 graph/R-GAT/dockerfile.cpu                    |  67 +++++
 graph/R-GAT/dockerfile.gpu                    |  44 +++
 graph/R-GAT/main.py                           |  75 +-----
 graph/R-GAT/requirements.txt                  |   1 -
 graph/R-GAT/tools/accuracy_igbh.py            | 112 ++++++++
 graph/R-GAT/tools/compress_graph.py           | 251 ------------------
 graph/R-GAT/tools/format_model.py             | 100 -------
 loadgen/mlperf.conf                           |   2 +
 text_to_image/README.md                       |   4 -
 12 files changed, 296 insertions(+), 670 deletions(-)
 delete mode 100644 graph/R-GAT/backend_glt.py
 create mode 100644 graph/R-GAT/dockerfile.cpu
 create mode 100644 graph/R-GAT/dockerfile.gpu
 create mode 100644 graph/R-GAT/tools/accuracy_igbh.py
 delete mode 100644 graph/R-GAT/tools/compress_graph.py
 delete mode 100644 graph/R-GAT/tools/format_model.py

diff --git a/graph/R-GAT/README.md b/graph/R-GAT/README.md
index 5a2b3ae6f0..569233ac67 100644
--- a/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -1,6 +1,8 @@
-# MLPerf™ Inference Benchmarks for Text to Image
+# MLPerf™ Inference Benchmark for Graph Neural Network
 
-This is the reference implementation for MLPerf Inference text to image. Two implementations are currently supported, Graphlearn for Pytorch (GLT) and Deep Graph Library (DGL), both using pytorch as the backbone of the model.
+This is the reference implementation for MLPerf Inference Graph Neural Network. The reference implementation currently uses Deep Graph Library (DGL), and pytorch as the backbone of the model.
+
+**Hardware requirements:** The minimun requirements to run this benchmark are ~600GB of RAM and ~2.3TB of disk. This requires to create a memory map for the graph features and not load them to memory all at once. If you want to load all the features to ram, you will need ~3TB and can be done by using the flag `--in-memory`
 
 ## Supported Models
 
@@ -13,7 +15,7 @@ This is the reference implementation for MLPerf Inference text to image. Two imp
 | Data | Description | Task |
 | ---- | ---- | ---- |
 | IGBH | Illinois Graph Benchmark Heterogeneous is a graph dataset consisting of one heterogeneous graph with 547,306,935 nodes and 5,812,005,639 edges. Node types: Author, Conference, FoS, Institute, Journal, Paper. A subset of 1% of the paper nodes are randomly choosen as the validation dataset using the [split seeds script](tools/split_seeds.py). The validation dataset will be used as the input queries for the SUT, however the whole dataset is needed to run the benchmarks, since all the graph connections are needed to achieve the quality target. | Node Classification |
-| IGBH (calibration) | We sampled x nodes from the training paper nodes of the IGBH for the calibration dataset | Node Classification |
+| IGBH (calibration) | We sampled 5000 nodes from the training paper nodes of the IGBH for the calibration dataset. We provide the [Node ids](../../calibration/IGBH/calibration.txt) and the [script](tools/split_seeds.py) to generate them (using the `--calibration` flag). | Node Classification |
 
 ## Automated command to run the benchmark via MLCommons CM
 
@@ -29,13 +31,23 @@ export MODEL_PATH=$PWD/inference/graph/R-GAT/model/
 ```
 ### Clone the repository
 ```bash
-git clone --recurse-submodules https://github.com/mlcommmons/inference.git --depth 1
+git clone --recurse-submodules https://github.com/mlcommons/inference.git --depth 1
 ```
 Finally copy the `mlperf.conf` file to the stable diffusion folder
 ```bash
 cp $ROOT_INFERENCE/mlperf.conf $GRAPH_FOLDER
 ```
 
+### Install pytorch
+**For NVIDIA GPU based runs:**
+```bash
+pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
+```
+**For CPU based runs:**
+```bash
+pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cpu
+```
+
 ### Install requirements (only for running without using docker)
 Install requirements:
 ```bash
@@ -56,20 +68,31 @@ pip install torch-geometric torch-scatter torch-sparse -f https://data.pyg.org/w
 ```
 
 ### Install DGL
+**For NVIDIA GPU based runs:**
 ```bash
 pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/cu121/repo.html
 ```
+**For CPU based runs:**
+```bash
+pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html
+```
 
-### Install graphlearn for pytorch (Only for GLT implementation)
-
-Follow instalation instructions at: https://github.com/alibaba/graphlearn-for-pytorch.git
+### Download model using Rclone
 
-### Download model
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
+```
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
+```
+Once Rclone is installed, run the following command to authenticate with the bucket:
+```
+rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+```
+You can then navigate in the terminal to your desired download directory and run the following commands to download the checkpoints:
 
-TODO: fix a ckpt url
-```bash
-mkdir -p $MODEL_PATH
-wget <checkpoint url>
+**`fp32`**
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/R-GAT/RGAT.pt $MODEL_PATH -P
 ```
 
 ### Download and setup dataset
@@ -87,11 +110,6 @@ cd $GRAPH_FOLDER
 python3 tools/split_seeds.py --path igbh --dataset_size tiny
 ```
 
-**Compress graph (optional, only for GLT implementation)**
-```bash
-cd $GRAPH_FOLDER
-python3 tools/compress_graph.py --path igbh --dataset_size tiny --layout <CSC or CSR>
-```
 
 #### Full Dataset
 **Warning:** This script will download 2.2TB of data 
@@ -106,14 +124,10 @@ cd $GRAPH_FOLDER
 python3 tools/split_seeds.py --path igbh --dataset_size full
 ```
 
-**Compress graph (optional, only for GLT implementation)**
-```bash
-cd $GRAPH_FOLDER
-python3 tools/compress_graph.py --path igbh --dataset_size tiny --layout <CSC or CSR>
-```
 
 #### Calibration dataset
-TODO
+
+The calibration dataset contains 5000 nodes from the training paper nodes of the IGBH dataset. We provide the [Node ids](../../calibration/IGBH/calibration.txt) and the [script](tools/split_seeds.py) to generate them (using the `--calibration` flag). 
 
 
 ### Run the benchmark
@@ -121,8 +135,6 @@ TODO
 ```bash
 # Go to the benchmark folder
 cd $GRAPH_FOLDER
-# Run the benchmark GLT
-python3 main.py --dataset igbh-glt-tiny --dataset-path igbh/ --profile debug-glt [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
 
 # Run the benchmark DGL
 python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
@@ -132,8 +144,6 @@ python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl
 ```bash
 # Go to the benchmark folder
 cd $GRAPH_FOLDER
-# Run the benchmark GLT
-python3 main.py --dataset igbh-glt --dataset-path igbh/ --profile rgat-glt-full [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
 
 # Run the benchmark DGL
 python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
@@ -147,3 +157,33 @@ Add the `--accuracy` to the command to run the benchmark
 ```bash
 python3 main.py --dataset igbh --dataset-path igbh/ --accuracy --model-path model/ [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
 ```
+
+### Docker run
+**CPU:**
+Build docker image
+```bash
+docker build . -f dockerfile.cpu -t rgat-cpu
+```
+Run docker container:
+```bash
+docker run --rm -it -v $(pwd):/root rgat-cpu
+```
+Run benchmark inside the docker container:
+```bash
+python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device cpu [--model-path <path_to_ckpt>] [--in-memory] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+```
+
+
+**GPU:**
+Build docker image
+```bash
+docker build . -f dockerfile.gpu -t rgat-gpu
+```
+Run docker container:
+```bash
+docker run --rm -it -v $(pwd):/root --gpus all rgat-gpu
+```
+Run benchmark inside the docker container:
+```bash
+python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device gpu [--model-path <path_to_ckpt>] [--in-memory] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+```
diff --git a/graph/R-GAT/backend_glt.py b/graph/R-GAT/backend_glt.py
deleted file mode 100644
index f721ed0fc2..0000000000
--- a/graph/R-GAT/backend_glt.py
+++ /dev/null
@@ -1,214 +0,0 @@
-from graphlearn_torch.typing import InputNodes, NumNeighbors
-from graphlearn_torch.sampler import NeighborSampler, NodeSamplerInput
-from graphlearn_torch.data import Dataset
-from graphlearn_torch.loader import NodeLoader
-from typing import Optional, List, Union
-import os
-import torch
-import logging
-import backend
-from typing import Literal
-from rgnn import RGNN
-from igbh import IGBHeteroDataset, IGBH
-import graphlearn_torch as glt
-
-logging.basicConfig(level=logging.INFO)
-log = logging.getLogger("backend-glt")
-
-
-class CustomNeighborLoader(NodeLoader):
-    # Copyright 2022 Alibaba Group Holding Limited. All Rights Reserved.
-    #
-    # Licensed under the Apache License, Version 2.0 (the "License");
-    # you may not use this file except in compliance with the License.
-    # You may obtain a copy of the License at
-    #
-    #     http://www.apache.org/licenses/LICENSE-2.0
-    #
-    # Unless required by applicable law or agreed to in writing, software
-    # distributed under the License is distributed on an "AS IS" BASIS,
-    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-    # See the License for the specific language governing permissions and
-    # limitations under the License.
-    # ==============================================================================
-    r"""
-    This class is a modified version of the NeighborLoader found in this link:
-    https://github.com/alibaba/graphlearn-for-pytorch/blob/main/graphlearn_torch/python/loader/neighbor_loader.py
-
-    A data loader that performs node neighbor sampling for mini-batch training
-    of GNNs on large-scale graphs.
-
-    Args:
-      data (Dataset): The `graphlearn_torch.data.Dataset` object.
-      num_neighbors (List[int] or Dict[Tuple[str, str, str], List[int]]): The
-        number of neighbors to sample for each node in each iteration.
-        In heterogeneous graphs, may also take in a dictionary denoting
-        the amount of neighbors to sample for each individual edge type.
-        If an entry is set to :obj:`-1`, all neighbors will be included.
-      input_nodes (torch.Tensor or str or Tuple[str, torch.Tensor]): The
-        indices of nodes for which neighbors are sampled to create
-        mini-batches.
-        Needs to be either given as a :obj:`torch.LongTensor` or
-        :obj:`torch.BoolTensor`.
-        In heterogeneous graphs, needs to be passed as a tuple that holds
-        the node type and node indices.
-      batch_size (int): How many samples per batch to load (default: ``1``).
-      shuffle (bool): Set to ``True`` to have the data reshuffled at every
-        epoch (default: ``False``).
-      drop_last (bool): Set to ``True`` to drop the last incomplete batch, if
-        the dataset size is not divisible by the batch size. If ``False`` and
-        the size of dataset is not divisible by the batch size, then the last
-        batch will be smaller. (default: ``False``).
-      with_edge (bool): Set to ``True`` to sample with edge ids and also include
-        them in the sampled results. (default: ``False``).
-      strategy: (str): Set sampling strategy for the default neighbor sampler
-        provided by graphlearn-torch. (default: ``"random"``).
-      as_pyg_v1 (bool): Set to ``True`` to return result as the NeighborSampler
-        in PyG v1. (default: ``False``).
-    """
-
-    def __init__(
-        self,
-        data: Dataset,
-        num_neighbors: NumNeighbors,
-        input_nodes: InputNodes,
-        neighbor_sampler: Optional[NeighborSampler] = None,
-        batch_size: int = 1,
-        shuffle: bool = False,
-        drop_last: bool = False,
-        with_edge: bool = False,
-        with_weight: bool = False,
-        strategy: str = "random",
-        device: torch.device = torch.device(0),
-        seed: Optional[int] = None,
-        **kwargs,
-    ):
-        if neighbor_sampler is None:
-            neighbor_sampler = NeighborSampler(
-                data.graph,
-                num_neighbors=num_neighbors,
-                strategy=strategy,
-                with_edge=with_edge,
-                with_weight=with_weight,
-                device=device,
-                edge_dir=data.edge_dir,
-                seed=seed,
-            )
-        self.edge_dir = data.edge_dir
-        super().__init__(
-            data=data,
-            node_sampler=neighbor_sampler,
-            input_nodes=input_nodes,
-            device=device,
-            batch_size=batch_size,
-            shuffle=shuffle,
-            drop_last=drop_last,
-            **kwargs,
-        )
-
-    def get_neighbors(self, seeds: torch.Tensor):
-        inputs = NodeSamplerInput(node=seeds, input_type=self._input_type)
-        out = self.sampler.sample_from_nodes(inputs)
-        result = self._collate_fn(out)
-
-        return result
-
-
-class BackendGLT(backend.Backend):
-    def __init__(
-        self,
-        model_type="rgat",
-        type: Literal["fp16", "fp32"] = "fp16",
-        device: Literal["cpu", "gpu"] = "gpu",
-        ckpt_path: str = None,
-        igbh: IGBH = None,
-        batch_size: int = 1,
-        layout: Literal["CSC", "CSR", "COO"] = "COO",
-        edge_dir: str = "in",
-    ):
-        super(BackendGLT, self).__init__()
-        # Set device and type
-        if device == "gpu":
-            self.device = torch.device("cuda")
-        else:
-            self.device = torch.device("cpu")
-
-        if type == "fp32":
-            self.type = torch.float32
-        else:
-            self.type = torch.float16
-        # Create Node and neighbor loade
-        self.glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
-        igbh_dataset = igbh.igbh_dataset
-        self.glt_dataset.init_node_features(
-            node_feature_data=igbh_dataset.feat_dict,
-            with_gpu=(device == "gpu"),
-            dtype=self.type,
-        )
-        self.glt_dataset.init_graph(
-            edge_index=igbh_dataset.edge_dict,
-            layout=layout,
-            graph_mode="ZERO_COPY" if (device == "gpu") else "CPU",
-        )
-        self.glt_dataset.init_node_labels(
-            node_label_data={"paper": igbh_dataset.label})
-        self.neighbor_loader = CustomNeighborLoader(
-            self.glt_dataset,
-            [15, 10, 5],
-            input_nodes=("paper", igbh_dataset.val_idx),
-            shuffle=False,
-            drop_last=False,
-            device=self.device,
-            seed=42,
-        )
-
-        self.model = (
-            RGNN(
-                self.glt_dataset.get_edge_types(),
-                self.glt_dataset.node_features["paper"].shape[1],
-                512,
-                2983,
-                num_layers=3,
-                dropout=0.2,
-                model=model_type,
-                heads=4,
-                node_type="paper",
-            )
-            .to(self.type)
-            .to(self.device)
-        )
-        self.model.eval()
-        ckpt = None
-        if ckpt_path is not None:
-            try:
-                ckpt = torch.load(ckpt_path, map_location=self.device)
-            except FileNotFoundError as e:
-                print(f"Checkpoint file not found: {e}")
-                return -1
-        if ckpt is not None:
-            self.model.load_state_dict(ckpt["model_state_dict"])
-
-    def version(self):
-        return torch.__version__
-
-    def name(self):
-        return "pytorch-SUT"
-
-    def image_format(self):
-        return "NCHW"
-
-    def load(self):
-        return self
-
-    def predict(self, inputs: torch.Tensor):
-        with torch.no_grad():
-            input_size = inputs.shape[0]
-            batch = self.neighbor_loader.get_neighbors(inputs)
-            out = self.model(
-                {
-                    node_name: node_feat.to(self.device)
-                    for node_name, node_feat in batch.x_dict.items()
-                },
-                batch.edge_index_dict,
-            )[:input_size]
-        return out
diff --git a/graph/R-GAT/dgl_utilities/feature_fetching.py b/graph/R-GAT/dgl_utilities/feature_fetching.py
index 6e1b6cfffa..62951c103f 100644
--- a/graph/R-GAT/dgl_utilities/feature_fetching.py
+++ b/graph/R-GAT/dgl_utilities/feature_fetching.py
@@ -405,7 +405,7 @@ def memmap_mmap_numpy(self, node):
 
         assert shape[0] is not None
         return torch.from_numpy(np.memmap(osp.join(self.path, self.dataset_size,
-                                "processed", node, "node_feat.npy"), dtype="float32", mode='r', shape=shape))
+                                "processed", node, "node_feat.npy"), dtype="float32", mode='r', shape=tuple(shape)))
 
     def load(self, node):
         if self.in_memory:
diff --git a/graph/R-GAT/dockerfile.cpu b/graph/R-GAT/dockerfile.cpu
new file mode 100644
index 0000000000..6da91eed9e
--- /dev/null
+++ b/graph/R-GAT/dockerfile.cpu
@@ -0,0 +1,67 @@
+FROM ubuntu:22.04
+
+ENV PYTHON_VERSION=3.10
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PATH /opt/anaconda3/bin:$PATH
+
+WORKDIR /root
+ENV HOME /root
+
+RUN apt-get update
+
+RUN apt-get install -y --no-install-recommends \
+      git \
+      build-essential \
+      software-properties-common \
+      ca-certificates \
+      wget \
+      curl \
+      htop \
+      zip \
+      unzip
+
+
+
+# Install conda
+RUN arch=$(uname -m) && \
+    if [ "$arch" = "x86_64" ]; then \
+    MINICONDA_URL="https://repo.anaconda.com/miniconda/Miniconda3-py310_24.9.2-0-Linux-x86_64.sh"; \
+    elif [ "$arch" = "aarch64" ]; then \
+    MINICONDA_URL="https://repo.anaconda.com/miniconda/Miniconda3-py310_24.9.2-0-Linux-aarch64.sh"; \
+    else \
+    echo "Unsupported architecture: $arch"; \
+    exit 1; \
+    fi && \
+    cd /opt && \
+    wget --quiet $MINICONDA_URL -O miniconda.sh && \
+    bash ./miniconda.sh -b -p /opt/anaconda3 && \
+    rm miniconda.sh && \
+    /opt/anaconda3/bin/conda clean -a && \
+    ln -s /opt/anaconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
+    echo ". /opt/anaconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \
+    echo "conda activate base" >> ~/.bashrc && \
+    conda config --set always_yes yes --set changeps1 no
+
+
+# Install requirements
+RUN conda install -c conda-forge libstdcxx-ng
+RUN pip install --upgrade pip
+RUN pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cpu
+COPY requirements.txt requirements.txt
+RUN pip install -r requirements.txt
+RUN cd /tmp && \
+    git clone --recursive https://github.com/mlcommons/inference && \
+    cd inference/loadgen && \
+    pip install pybind11 && \
+    CFLAGS="-std=c++14" python3 setup.py install
+
+RUN export TORCH_VERSION=$(python -c "import torch; print(torch.__version__)")
+RUN pip install torch-geometric torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-${TORCH_VERSION}.html
+RUN pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html
+# Clean up
+RUN rm -rf mlperf \
+    rm requirements.txt
+
+
+ENTRYPOINT ["/bin/bash"]
diff --git a/graph/R-GAT/dockerfile.gpu b/graph/R-GAT/dockerfile.gpu
new file mode 100644
index 0000000000..fae65081f0
--- /dev/null
+++ b/graph/R-GAT/dockerfile.gpu
@@ -0,0 +1,44 @@
+ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:23.04-py3
+FROM ${FROM_IMAGE_NAME}
+
+SHELL ["/bin/bash", "-c"]
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+
+ENV TZ=US/Pacific
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
+RUN rm -rf /var/lib/apt/lists/* && rm -rf /etc/apt/sources.list.d/* \
+ && apt update \
+ && apt install -y --no-install-recommends build-essential autoconf \
+        libtool git ccache curl wget pkg-config sudo ca-certificates \
+        automake libssl-dev bc python3-dev python3-pip google-perftools \
+        gdb libglib2.0-dev clang sshfs libre2-dev libboost-dev \
+        libnuma-dev numactl sysstat sshpass ntpdate less iputils-ping \
+ && apt -y autoremove \
+ && apt remove -y cmake \
+ && apt install -y --no-install-recommends pkg-config zip g++ zlib1g-dev \
+        unzip libarchive-dev
+RUN apt install -y --no-install-recommends rsync
+
+# Upgrade pip
+RUN python3 -m pip install --upgrade pip
+
+
+COPY requirements.txt requirements.txt
+RUN pip install -r requirements.txt
+RUN cd /tmp && \
+    git clone --recursive https://github.com/mlcommons/inference && \
+    cd inference/loadgen && \
+    pip install pybind11 && \
+    CFLAGS="-std=c++14" python3 setup.py install
+
+RUN export TORCH_VERSION=$(python -c "import torch; print(torch.__version__)")
+RUN pip install torch-geometric torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-${TORCH_VERSION}.html
+RUN pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/cu121/repo.html
+
+# Clean up
+RUN rm -rf mlperf \
+    rm requirements.txt
\ No newline at end of file
diff --git a/graph/R-GAT/main.py b/graph/R-GAT/main.py
index d76ea090d2..4fa714f37f 100644
--- a/graph/R-GAT/main.py
+++ b/graph/R-GAT/main.py
@@ -33,36 +33,6 @@
 MILLI_SEC = 1000
 
 SUPPORTED_DATASETS = {
-    "igbh-glt-tiny": (
-        igbh.IGBH,
-        dataset.preprocess,
-        igbh.PostProcessIGBH(),
-        {"dataset_size": "tiny", "use_label_2K": True},
-    ),
-    "igbh-glt-small": (
-        igbh.IGBH,
-        dataset.preprocess,
-        igbh.PostProcessIGBH(),
-        {"dataset_size": "small", "use_label_2K": True},
-    ),
-    "igbh-glt-medium": (
-        igbh.IGBH,
-        dataset.preprocess,
-        igbh.PostProcessIGBH(),
-        {"dataset_size": "medium", "use_label_2K": True},
-    ),
-    "igbh-glt-large": (
-        igbh.IGBH,
-        dataset.preprocess,
-        igbh.PostProcessIGBH(),
-        {"dataset_size": "large", "use_label_2K": True},
-    ),
-    "igbh-glt": (
-        igbh.IGBH,
-        dataset.preprocess,
-        igbh.PostProcessIGBH(),
-        {"dataset_size": "full", "use_label_2K": True},
-    ),
     "igbh-dgl-tiny": (
         dgl_igbh.IGBH,
         dataset.preprocess,
@@ -98,33 +68,8 @@
 
 SUPPORTED_PROFILES = {
     "defaults": {
-        "dataset": "igbh-glt-tiny",
-        "backend": "glt",
-        "model-name": "rgat",
-    },
-    "debug-glt": {
-        "dataset": "igbh-glt-tiny",
-        "backend": "glt",
-        "model-name": "rgat",
-    },
-    "rgat-glt-small": {
-        "dataset": "igbh-glt-small",
-        "backend": "glt",
-        "model-name": "rgat",
-    },
-    "rgat-glt-medium": {
-        "dataset": "igbh-glt-medium",
-        "backend": "glt",
-        "model-name": "rgat",
-    },
-    "rgat-glt-large": {
-        "dataset": "igbh-glt-large",
-        "backend": "glt",
-        "model-name": "rgat",
-    },
-    "rgat-glt-full": {
-        "dataset": "igbh-glt",
-        "backend": "glt",
+        "dataset": "igbh-dgl-tiny",
+        "backend": "dgl",
         "model-name": "rgat",
     },
     "debug-dgl": {
@@ -227,11 +172,6 @@ def get_args():
         choices=["gpu", "cpu"],
         help="device to run the benchmark",
     )
-
-    # file to use mlperf rules compliant parameters
-    parser.add_argument(
-        "--mlperf_conf", default="mlperf.conf", help="mlperf rules config"
-    )
     # file for user LoadGen settings such as target QPS
     parser.add_argument(
         "--user_conf",
@@ -283,10 +223,7 @@ def get_args():
 
 
 def get_backend(backend, **kwargs):
-    if backend == "glt":
-        from backend_glt import BackendGLT
-        backend = BackendGLT(**kwargs)
-    elif backend == "dgl":
+    if backend == "dgl":
         from backend_dgl import BackendDGL
         backend = BackendDGL(**kwargs)
     else:
@@ -461,11 +398,6 @@ def main():
         "cmdline": str(args),
     }
 
-    mlperf_conf = os.path.abspath(args.mlperf_conf)
-    if not os.path.exists(mlperf_conf):
-        log.error("{} not found".format(mlperf_conf))
-        sys.exit(1)
-
     user_conf = os.path.abspath(args.user_conf)
     if not os.path.exists(user_conf):
         log.error("{} not found".format(user_conf))
@@ -513,7 +445,6 @@ def flush_queries():
     log_settings.log_output = log_output_settings
 
     settings = lg.TestSettings()
-    settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
     settings.FromConfig(user_conf, args.model_name, args.scenario)
     settings.scenario = scenario
     settings.mode = lg.TestMode.PerformanceOnly
diff --git a/graph/R-GAT/requirements.txt b/graph/R-GAT/requirements.txt
index 8e6e7276f9..f9af916527 100644
--- a/graph/R-GAT/requirements.txt
+++ b/graph/R-GAT/requirements.txt
@@ -1,7 +1,6 @@
 colorama==0.4.6
 tqdm==4.66.4
 requests==2.32.2
-torch==2.1.0
 torchdata==0.7.0
 pybind11==2.12.0
 PyYAML==6.0.1
diff --git a/graph/R-GAT/tools/accuracy_igbh.py b/graph/R-GAT/tools/accuracy_igbh.py
new file mode 100644
index 0000000000..1095debe08
--- /dev/null
+++ b/graph/R-GAT/tools/accuracy_igbh.py
@@ -0,0 +1,112 @@
+import argparse
+import numpy as np
+import torch
+import json
+import os
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
+    )
+    parser.add_argument(
+        "--dataset-path",
+        default="igbh",
+        help="Path to IHGB dataset",
+    )
+    parser.add_argument(
+        "--dataset-size",
+        default="full",
+        choices=["tiny", "small", "medium", "large", "full"]
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--output-file", default="results.json", help="path to output file"
+    )
+    parser.add_argument(
+        "--dtype",
+        default="uint8",
+        choices=["uint8","float32", "int32", "int64"],
+        help="data type of the label",
+    )
+    args = parser.parse_args()
+    return args
+
+def load_labels(base_path, dataset_size, use_label_2K = True):
+    # load labels
+    paper_nodes_num = {
+        "tiny": 100000,
+        "small": 1000000,
+        "medium": 10000000,
+        "large": 100000000,
+        "full": 269346174,
+    }
+    label_file = (
+        "node_label_19.npy" if not use_label_2K else "node_label_2K.npy"
+    )
+    paper_lbl_path = os.path.join(base_path, dataset_size, "processed", "paper", label_file)
+
+    if dataset_size in ["large", "full"]:
+        paper_node_labels = torch.from_numpy(
+            np.memmap(
+                paper_lbl_path, dtype="float32", mode="r", shape=(paper_nodes_num[dataset_size])
+            )
+        ).to(torch.long)
+    else:
+        paper_node_labels = torch.from_numpy(
+            np.load(paper_lbl_path)).to(
+            torch.long)
+    labels = paper_node_labels
+    val_idx = torch.load(
+                os.path.join(
+                    base_path,
+                    dataset_size,
+                    "processed",
+                "val_idx.pt"))
+    return labels, val_idx
+
+
+def get_labels(labels, val_idx, id_list):
+    return labels[val_idx[id_list]]
+
+
+if __name__ == "__main__":
+    args = get_args()
+    dtype_map = {"uint8": np.uint8,"float32": np.float32, "int32": np.int32, "int64": np.int64}
+
+    with open(args.mlperf_accuracy_file, "r") as f:
+        mlperf_results = json.load(f)
+
+    labels, val_idx = load_labels(args.dataset_path, args.dataset_size)
+    results = {}
+
+    seen = set()
+    good = 0
+    total = 0
+    for result in mlperf_results:
+        idx = result["qsl_idx"]
+        if idx in seen:
+            continue
+        seen.add(idx)
+
+        # get ground truth
+        label = get_labels(labels, val_idx, idx)
+        # get prediction
+        data = int(np.frombuffer(bytes.fromhex(result["data"]), dtype_map[args.dtype])[0])
+        if label == data:
+            good += 1
+        total += 1
+    results["accuracy"] = good / total
+    results["model"] = "rgat"
+    results["number_correct_samples"] = good
+    results["performance_sample_count"] = total
+
+
+    with open(args.output_file, "w") as fp:
+        json.dump(results, fp)
+    
\ No newline at end of file
diff --git a/graph/R-GAT/tools/compress_graph.py b/graph/R-GAT/tools/compress_graph.py
deleted file mode 100644
index 6fa2c2989b..0000000000
--- a/graph/R-GAT/tools/compress_graph.py
+++ /dev/null
@@ -1,251 +0,0 @@
-# This is a modified version of a script taken from:
-# https://github.com/mlcommons/training/blob/master/graph_neural_network/compress_graph.py
-
-from typing import Literal
-from torch_geometric.utils import add_self_loops, remove_self_loops
-from igb.download import download_dataset
-from igbh import float2half
-import argparse
-import datetime
-import os
-import numpy as np
-import torch
-import os.path as osp
-
-import graphlearn_torch as glt
-
-import sys
-
-sys.path.append(os.path.dirname(__file__))
-sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir))
-
-
-class IGBHeteroDatasetCompress(object):
-    def __init__(
-        self,
-        path,
-        dataset_size,
-        layout: Literal["CSC", "CSR"] = "CSC",
-    ):
-        self.dir = path
-        self.dataset_size = dataset_size
-        self.layout = layout
-
-        self.ntypes = ["paper", "author", "institute", "fos"]
-        self.etypes = None
-        self.edge_dict = {}
-        self.paper_nodes_num = {
-            "tiny": 100000,
-            "small": 1000000,
-            "medium": 10000000,
-            "large": 100000000,
-            "full": 269346174,
-        }
-        self.author_nodes_num = {
-            "tiny": 357041,
-            "small": 1926066,
-            "medium": 15544654,
-            "large": 116959896,
-            "full": 277220883,
-        }
-        if not osp.exists(osp.join(path, self.dataset_size, "processed")):
-            download_dataset(path, "heterogeneous", dataset_size)
-        self.process()
-
-    def process(self):
-        paper_paper_edges = torch.from_numpy(
-            np.load(
-                osp.join(
-                    self.dir,
-                    self.dataset_size,
-                    "processed",
-                    "paper__cites__paper",
-                    "edge_index.npy",
-                )
-            )
-        ).t()
-        author_paper_edges = torch.from_numpy(
-            np.load(
-                osp.join(
-                    self.dir,
-                    self.dataset_size,
-                    "processed",
-                    "paper__written_by__author",
-                    "edge_index.npy",
-                )
-            )
-        ).t()
-        affiliation_author_edges = torch.from_numpy(
-            np.load(
-                osp.join(
-                    self.dir,
-                    self.dataset_size,
-                    "processed",
-                    "author__affiliated_to__institute",
-                    "edge_index.npy",
-                )
-            )
-        ).t()
-        paper_fos_edges = torch.from_numpy(
-            np.load(
-                osp.join(
-                    self.dir,
-                    self.dataset_size,
-                    "processed",
-                    "paper__topic__fos",
-                    "edge_index.npy",
-                )
-            )
-        ).t()
-        paper_published_journal = torch.from_numpy(
-            np.load(
-                osp.join(
-                    self.dir,
-                    self.dataset_size,
-                    "processed",
-                    "paper__published__journal",
-                    "edge_index.npy",
-                )
-            )
-        ).t()
-        paper_venue_conference = torch.from_numpy(
-            np.load(
-                osp.join(
-                    self.dir,
-                    self.dataset_size,
-                    "processed",
-                    "paper__venue__conference",
-                    "edge_index.npy",
-                )
-            )
-        ).t()
-
-        cites_edge = add_self_loops(remove_self_loops(paper_paper_edges)[0])[0]
-        self.edge_dict = {
-            ("paper", "cites", "paper"): (
-                torch.cat([cites_edge[1, :], cites_edge[0, :]]),
-                torch.cat([cites_edge[0, :], cites_edge[1, :]]),
-            ),
-            ("paper", "written_by", "author"): author_paper_edges,
-            ("author", "affiliated_to", "institute"): affiliation_author_edges,
-            ("paper", "topic", "fos"): paper_fos_edges,
-            ("author", "rev_written_by", "paper"): (
-                author_paper_edges[1, :],
-                author_paper_edges[0, :],
-            ),
-            ("institute", "rev_affiliated_to", "author"): (
-                affiliation_author_edges[1, :],
-                affiliation_author_edges[0, :],
-            ),
-            ("fos", "rev_topic", "paper"): (
-                paper_fos_edges[1, :],
-                paper_fos_edges[0, :],
-            ),
-        }
-        self.edge_dict[("paper", "published", "journal")
-                       ] = paper_published_journal
-        self.edge_dict[("paper", "venue", "conference")
-                       ] = paper_venue_conference
-        self.edge_dict[("journal", "rev_published", "paper")] = (
-            paper_published_journal[1, :],
-            paper_published_journal[0, :],
-        )
-        self.edge_dict[("conference", "rev_venue", "paper")] = (
-            paper_venue_conference[1, :],
-            paper_venue_conference[0, :],
-        )
-        self.etypes = list(self.edge_dict.keys())
-
-        # init graphlearn_torch Dataset.
-        edge_dir = "out" if self.layout == "CSR" else "in"
-        glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
-        glt_dataset.init_graph(
-            edge_index=self.edge_dict,
-            graph_mode="CPU",
-        )
-
-        # save the corresponding csr or csc file
-        compress_edge_dict = {}
-        compress_edge_dict[("paper", "cites", "paper")] = "paper__cites__paper"
-        compress_edge_dict[("paper", "written_by", "author")] = (
-            "paper__written_by__author"
-        )
-        compress_edge_dict[("author", "affiliated_to", "institute")] = (
-            "author__affiliated_to__institute"
-        )
-        compress_edge_dict[("paper", "topic", "fos")] = "paper__topic__fos"
-        compress_edge_dict[("author", "rev_written_by", "paper")] = (
-            "author__rev_written_by__paper"
-        )
-        compress_edge_dict[("institute", "rev_affiliated_to", "author")] = (
-            "institute__rev_affiliated_to__author"
-        )
-        compress_edge_dict[("fos", "rev_topic", "paper")
-                           ] = "fos__rev_topic__paper"
-        compress_edge_dict[("paper", "published", "journal")] = (
-            "paper__published__journal"
-        )
-        compress_edge_dict[("paper", "venue", "conference")] = (
-            "paper__venue__conference"
-        )
-        compress_edge_dict[("journal", "rev_published", "paper")] = (
-            "journal__rev_published__paper"
-        )
-        compress_edge_dict[("conference", "rev_venue", "paper")] = (
-            "conference__rev_venue__paper"
-        )
-
-        for etype in self.etypes:
-            graph = glt_dataset.get_graph(etype)
-            indptr, indices, _ = graph.export_topology()
-            path = os.path.join(
-                self.dir,
-                self.dataset_size,
-                "processed",
-                self.layout,
-                compress_edge_dict[etype],
-            )
-            if not os.path.exists(path):
-                os.makedirs(path)
-            torch.save(indptr, os.path.join(path, "indptr.pt"))
-            torch.save(indices, os.path.join(path, "indices.pt"))
-        path = os.path.join(
-            self.dir,
-            self.dataset_size,
-            "processed",
-            self.layout)
-        print(f"The {self.layout} graph has been persisted in path: {path}")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    root = osp.join(
-        osp.dirname(
-            osp.dirname(
-                osp.dirname(
-                    osp.realpath(__file__)))), "data", "igbh"
-    )
-    glt.utils.ensure_dir(root)
-    parser.add_argument(
-        "--path", type=str, default=root, help="path containing the datasets"
-    )
-    parser.add_argument(
-        "--dataset_size",
-        type=str,
-        default="full",
-        choices=["tiny", "small", "medium", "large", "full"],
-        help="size of the datasets",
-    )
-    parser.add_argument("--layout", type=str, default="CSC")
-    parser.add_argument(
-        "--use_fp16",
-        action="store_true",
-        help="convert the node/edge feature into fp16 format",
-    )
-    args = parser.parse_args()
-    print(f"Start constructing the {args.layout} graph...")
-    igbh_dataset = IGBHeteroDatasetCompress(
-        args.path, args.dataset_size, args.layout)
-    if args.use_fp16:
-        base_path = osp.join(args.path, args.dataset_size, "processed")
-        float2half(base_path, args.dataset_size)
diff --git a/graph/R-GAT/tools/format_model.py b/graph/R-GAT/tools/format_model.py
deleted file mode 100644
index 04d3267223..0000000000
--- a/graph/R-GAT/tools/format_model.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from collections import OrderedDict
-from graphlearn_torch.typing import InputNodes, NumNeighbors
-from graphlearn_torch.sampler import NeighborSampler, NodeSamplerInput
-from graphlearn_torch.data import Dataset
-from graphlearn_torch.loader import NodeLoader
-import graphlearn_torch as glt
-from igbh import IGBHeteroDataset, IGBH
-from rgnn import RGNN
-from typing import Literal
-import torch
-import os
-import sys
-
-sys.path.append(os.path.dirname(__file__))
-sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir))
-
-
-class Formatter:
-    def __init__(
-        self,
-        model_type="rgat",
-        type: Literal["fp16", "fp32"] = "fp16",
-        device: Literal["cpu", "gpu"] = "gpu",
-        ckpt_path: str = None,
-        igbh_dataset: IGBHeteroDataset = None,
-        batch_size: int = 1,
-        layout: Literal["CSC", "CSR", "COO"] = "COO",
-        edge_dir: str = "in",
-    ) -> None:
-        if device == "gpu":
-            self.device = torch.device("cuda")
-        else:
-            self.device = torch.device("cpu")
-
-        if type == "fp32":
-            self.type = torch.float32
-        else:
-            self.type = torch.float16
-        # Create Node and neighbor loade
-        self.glt_dataset = glt.data.Dataset(edge_dir=edge_dir)
-        self.glt_dataset.init_node_features(
-            node_feature_data=igbh_dataset.feat_dict,
-            with_gpu=(device == "gpu"),
-            dtype=self.type,
-        )
-        self.glt_dataset.init_graph(
-            edge_index=igbh_dataset.edge_dict,
-            layout=layout,
-            graph_mode="ZERO_COPY" if (device == "gpu") else "CPU",
-        )
-        self.glt_dataset.init_node_labels(
-            node_label_data={"paper": igbh_dataset.label})
-
-        self.model = (
-            RGNN(
-                self.glt_dataset.get_edge_types(),
-                self.glt_dataset.node_features["paper"].shape[1],
-                512,
-                2983,
-                num_layers=3,
-                dropout=0.2,
-                model=model_type,
-                heads=4,
-                node_type="paper",
-            )
-            .to(self.type)
-            .to(self.device)
-        )
-        self.model.eval()
-        ckpt = None
-        if ckpt_path is not None:
-            try:
-                ckpt = torch.load(ckpt_path, map_location=self.device)
-            except FileNotFoundError as e:
-                print(f"Checkpoint file not found: {e}")
-                return -1
-        if ckpt is not None:
-            formatted_ckpt = OrderedDict()
-            formatted_ckpt["model_state_dict"] = OrderedDict()
-            for k in ckpt["model_state_dict"]:
-                if "lin_dst" in k:
-                    pass
-                elif "lin_src" in k:
-                    formatted_ckpt["model_state_dict"][
-                        str(k).replace("lin_src", "lin")
-                    ] = ckpt["model_state_dict"][k]
-                else:
-                    formatted_ckpt["model_state_dict"][k] = ckpt["model_state_dict"][k]
-            self.model.load_state_dict(formatted_ckpt["model_state_dict"])
-            torch.save(
-                {"model_state_dict": self.model.state_dict()},
-                "model/FULL_model_seq_69294_formatted.ckpt",
-            )
-
-
-if __name__ == "__main__":
-    igbh = IGBHeteroDataset("igbh", use_label_2K=True)
-    f = Formatter(
-        igbh_dataset=igbh, ckpt_path="model/FULL_model_seq_69294.ckpt", device="cpu"
-    )
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index d261318a16..31ad5ef62c 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -16,6 +16,7 @@ gptj.*.performance_sample_count_override = 13368
 llama2-70b.*.performance_sample_count_override = 24576
 llama3-405b.*.performance_sample_count_override = 8312
 stable-diffusion-xl.*.performance_sample_count_override = 5000
+rgat.*.performance_sample_count_override = 788379
 # set to 0 to let entire sample set to be performance sample
 3d-unet.*.performance_sample_count_override = 0
 
@@ -98,6 +99,7 @@ stable-diffusion-xl.Offline.min_query_count = 5000
 llama2-70b.Offline.min_query_count = 24576
 llama3-405b.Offline.min_query_count = 8312
 mixtral-8x7b.Offline.min_query_count = 15000
+rgat.Offline.min_query_count = 788379
 
 # These fields should be defined and overridden by user.conf.
 *.SingleStream.target_latency = 10
diff --git a/text_to_image/README.md b/text_to_image/README.md
index 05701452d9..57c4343b18 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -31,10 +31,6 @@ export MODEL_PATH=$PWD/inference/text_to_image/model/
 ```bash
 git clone --recurse-submodules https://github.com/mlcommons/inference --depth 1
 ```
-Finally copy the `mlperf.conf` file to the stable diffusion folder
-```bash
-cp $ROOT/mlperf.conf $SD_FOLDER
-```
 
 ### Install requirements (only for running without using docker)
 Install requirements:

From e7c301cc70480ba1c34cc1587b54726aa8d494d5 Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Wed, 4 Dec 2024 16:44:45 +0000
Subject: [PATCH 188/305] Increment version to 4.1.30

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 4d3eac880f..d9b366bb7a 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-4.1.29
\ No newline at end of file
+4.1.30

From 21d845e67124a9a42b2361425f2b44406670cf0c Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 4 Dec 2024 13:04:46 -0500
Subject: [PATCH 189/305] Llm large fixes (#1959)

* Fix llama3 multi-gpu issue

* Fix identation

* Remove llama2 outdated references

* Fix run_evaluation argument
---
 language/llama3-405b/README.md            |  2 +-
 language/llama3-405b/SUT_VLLM.py          | 25 +++++++++++------------
 language/llama3-405b/dataset.py           |  2 +-
 language/llama3-405b/evaluate-accuracy.py |  2 +-
 language/llama3-405b/main.py              |  4 ++--
 language/llama3-405b/run_offline.sh       |  2 +-
 language/llama3-405b/run_server.sh        |  2 +-
 7 files changed, 19 insertions(+), 20 deletions(-)

diff --git a/language/llama3-405b/README.md b/language/llama3-405b/README.md
index 9f2eff3cab..8c04c202ea 100644
--- a/language/llama3-405b/README.md
+++ b/language/llama3-405b/README.md
@@ -70,7 +70,7 @@ TODO: Host model and grant access to submitters
 + First go to [llama3-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloning below.
 + Requires Git Large Files Storage
 ```
-export CHECKPOINT_PATH=${PWD}/Llama-2-70b-chat-hf
+export CHECKPOINT_PATH=Meta-Llama-3.1-405B-Instruct
 git lfs install
 git clone https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct ${CHECKPOINT_PATH}
 
diff --git a/language/llama3-405b/SUT_VLLM.py b/language/llama3-405b/SUT_VLLM.py
index 9656b09714..e64999d09c 100644
--- a/language/llama3-405b/SUT_VLLM.py
+++ b/language/llama3-405b/SUT_VLLM.py
@@ -127,19 +127,18 @@ def process_queries(self):
                 pred_output_tokens,
                 query_id_list=query_ids,
             )
-
-        for i in range(len(qitem)):
-            n_tokens = processed_output[i].shape[0]
-            response_array = array.array(
-                "B", processed_output[i].tobytes())
-            bi = response_array.buffer_info()
-            response = [
-                lg.QuerySampleResponse(
-                    qitem[i].id,
-                    bi[0],
-                    bi[1],
-                    n_tokens)]
-            lg.QuerySamplesComplete(response)
+            for i in range(len(qitem)):
+                n_tokens = processed_output[i].shape[0]
+                response_array = array.array(
+                    "B", processed_output[i].tobytes())
+                bi = response_array.buffer_info()
+                response = [
+                    lg.QuerySampleResponse(
+                        qitem[i].id,
+                        bi[0],
+                        bi[1],
+                        n_tokens)]
+                lg.QuerySamplesComplete(response)
 
         tok = time.time()
 
diff --git a/language/llama3-405b/dataset.py b/language/llama3-405b/dataset.py
index ff95557c7b..04fe9c4b2d 100644
--- a/language/llama3-405b/dataset.py
+++ b/language/llama3-405b/dataset.py
@@ -80,7 +80,7 @@ def postProcess(
         output_seq = out_tokens
         assert len(query_id_list) == len(output_seq)
 
-        return np.asarray(output_seq, dtype=np.int32)
+        return [np.asarray(out, dtype=np.int32) for out in output_seq]
 
     def LoadSamplesToRam(self, sample_list):
         pass
diff --git a/language/llama3-405b/evaluate-accuracy.py b/language/llama3-405b/evaluate-accuracy.py
index 5faaaa0289..ccc87f71fd 100644
--- a/language/llama3-405b/evaluate-accuracy.py
+++ b/language/llama3-405b/evaluate-accuracy.py
@@ -182,7 +182,7 @@ def main():
 
     preds, targets = postprocess_text(preds_decoded_text, target_required)
 
-    result = run_evaluation(preds, targets, metrics)
+    result = run_evaluation(preds, targets, metrics_required)
     result = dict(result)
     prediction_lens = [len(pred) for pred in preds]
     gen_num = len(preds)
diff --git a/language/llama3-405b/main.py b/language/llama3-405b/main.py
index 86049658cd..26d5726b34 100644
--- a/language/llama3-405b/main.py
+++ b/language/llama3-405b/main.py
@@ -46,7 +46,7 @@ def get_args():
     parser.add_argument(
         "--model-path",
         type=str,
-        default="meta-llama/Llama-2-70b-chat-hf",
+        default="Meta-Llama-3.1-405B-Instruct",
         help="Model name",
     )
     parser.add_argument("--dataset-path", type=str, default=None, help="")
@@ -110,7 +110,7 @@ def get_args():
     parser.add_argument(
         "--api-model-name",
         type=str,
-        default="meta-llama/Llama-2-70b-chat-hf",
+        default="Meta-Llama-3.1-405B-Instruct",
         help="Model name(specified in llm server)",
     )
     parser.add_argument(
diff --git a/language/llama3-405b/run_offline.sh b/language/llama3-405b/run_offline.sh
index 471aa1ce7d..89fa9e45f6 100644
--- a/language/llama3-405b/run_offline.sh
+++ b/language/llama3-405b/run_offline.sh
@@ -1,4 +1,4 @@
-CHECKPOINT_PATH="${CHECKPOINT_PATH:-meta-llama/Llama-2-70b-chat-hf}"
+CHECKPOINT_PATH="${CHECKPOINT_PATH:Meta-Llama-3.1-405B-Instruct}"
 DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
 
 python -u main.py --scenario Offline \
diff --git a/language/llama3-405b/run_server.sh b/language/llama3-405b/run_server.sh
index b570f2e9c4..fe2a31c435 100644
--- a/language/llama3-405b/run_server.sh
+++ b/language/llama3-405b/run_server.sh
@@ -1,6 +1,6 @@
 
 
-CHECKPOINT_PATH="${CHECKPOINT_PATH:-meta-llama/Llama-2-70b-chat-hf}"
+CHECKPOINT_PATH="${CHECKPOINT_PATH:Meta-Llama-3.1-405B-Instruct}"
 DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
 
 python -u main.py --scenario Server \

From dd9be9635fe1623528e3a3ba17a3670860562073 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 4 Dec 2024 14:32:58 -0500
Subject: [PATCH 190/305] Submission checker v5.0 (#1955)

* Submission checker v5.0

* [Automated Commit] Format Codebase

---------

Co-authored-by: pgmpablo157321 <pgmpablo157321@users.noreply.github.com>
Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
---
 graph/R-GAT/tools/accuracy_igbh.py     |  33 ++++--
 tools/submission/submission_checker.py | 157 ++++++++++++++++++++-----
 2 files changed, 147 insertions(+), 43 deletions(-)

diff --git a/graph/R-GAT/tools/accuracy_igbh.py b/graph/R-GAT/tools/accuracy_igbh.py
index 1095debe08..c9c56a91d7 100644
--- a/graph/R-GAT/tools/accuracy_igbh.py
+++ b/graph/R-GAT/tools/accuracy_igbh.py
@@ -31,13 +31,14 @@ def get_args():
     parser.add_argument(
         "--dtype",
         default="uint8",
-        choices=["uint8","float32", "int32", "int64"],
+        choices=["uint8", "float32", "int32", "int64"],
         help="data type of the label",
     )
     args = parser.parse_args()
     return args
 
-def load_labels(base_path, dataset_size, use_label_2K = True):
+
+def load_labels(base_path, dataset_size, use_label_2K=True):
     # load labels
     paper_nodes_num = {
         "tiny": 100000,
@@ -49,7 +50,12 @@ def load_labels(base_path, dataset_size, use_label_2K = True):
     label_file = (
         "node_label_19.npy" if not use_label_2K else "node_label_2K.npy"
     )
-    paper_lbl_path = os.path.join(base_path, dataset_size, "processed", "paper", label_file)
+    paper_lbl_path = os.path.join(
+        base_path,
+        dataset_size,
+        "processed",
+        "paper",
+        label_file)
 
     if dataset_size in ["large", "full"]:
         paper_node_labels = torch.from_numpy(
@@ -63,11 +69,11 @@ def load_labels(base_path, dataset_size, use_label_2K = True):
             torch.long)
     labels = paper_node_labels
     val_idx = torch.load(
-                os.path.join(
-                    base_path,
-                    dataset_size,
-                    "processed",
-                "val_idx.pt"))
+        os.path.join(
+            base_path,
+            dataset_size,
+            "processed",
+            "val_idx.pt"))
     return labels, val_idx
 
 
@@ -77,7 +83,11 @@ def get_labels(labels, val_idx, id_list):
 
 if __name__ == "__main__":
     args = get_args()
-    dtype_map = {"uint8": np.uint8,"float32": np.float32, "int32": np.int32, "int64": np.int64}
+    dtype_map = {
+        "uint8": np.uint8,
+        "float32": np.float32,
+        "int32": np.int32,
+        "int64": np.int64}
 
     with open(args.mlperf_accuracy_file, "r") as f:
         mlperf_results = json.load(f)
@@ -97,7 +107,8 @@ def get_labels(labels, val_idx, id_list):
         # get ground truth
         label = get_labels(labels, val_idx, idx)
         # get prediction
-        data = int(np.frombuffer(bytes.fromhex(result["data"]), dtype_map[args.dtype])[0])
+        data = int(np.frombuffer(bytes.fromhex(
+            result["data"]), dtype_map[args.dtype])[0])
         if label == data:
             good += 1
         total += 1
@@ -106,7 +117,5 @@ def get_labels(labels, val_idx, id_list):
     results["number_correct_samples"] = good
     results["performance_sample_count"] = total
 
-
     with open(args.output_file, "w") as fp:
         json.dump(results, fp)
-    
\ No newline at end of file
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index deff9eb8c4..51f8c7aaba 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -25,11 +25,10 @@
 submission_checker_dir = os.path.dirname(os.path.realpath(__file__))
 
 MODEL_CONFIG = {
-    "v4.0": {
+    "v4.1": {
         "models": [
             "resnet",
             "retinanet",
-            "rnnt",
             "bert-99",
             "bert-99.9",
             "dlrm-v2-99",
@@ -41,11 +40,11 @@
             "llama2-70b-99",
             "llama2-70b-99.9",
             "stable-diffusion-xl",
+            "mixtral-8x7b",
         ],
         "required-scenarios-datacenter": {
             "resnet": ["Server", "Offline"],
             "retinanet": ["Server", "Offline"],
-            "rnnt": ["Server", "Offline"],
             "bert-99": ["Server", "Offline"],
             "bert-99.9": ["Server", "Offline"],
             "dlrm-v2-99": ["Server", "Offline"],
@@ -57,12 +56,12 @@
             "llama2-70b-99": ["Server", "Offline"],
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["Server", "Offline"],
+            "mixtral-8x7b": ["Server", "Offline"],
         },
         "optional-scenarios-datacenter": {},
         "required-scenarios-edge": {
             "resnet": ["SingleStream", "MultiStream", "Offline"],
             "retinanet": ["SingleStream", "MultiStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
             "bert-99": ["SingleStream", "Offline"],
             "3d-unet-99": ["SingleStream", "Offline"],
             "3d-unet-99.9": ["SingleStream", "Offline"],
@@ -74,7 +73,6 @@
         "required-scenarios-datacenter-edge": {
             "resnet": ["SingleStream", "Offline", "MultiStream", "Server"],
             "retinanet": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "rnnt": ["SingleStream", "Offline", "Server"],
             "bert-99": ["SingleStream", "Offline", "Server"],
             "bert-99.9": ["Offline", "Server"],
             "dlrm-v2-99": ["Offline", "Server"],
@@ -86,12 +84,12 @@
             "llama2-70b-99": ["Server", "Offline"],
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
+            "mixtral-8x7b": ["Server", "Offline"]
         },
         "optional-scenarios-datacenter-edge": {},
         "accuracy-target": {
             "resnet": ("acc", 76.46 * 0.99),
             "retinanet": ("mAP", 37.55 * 0.99),
-            "rnnt": ("WER", (100 - 7.452) * 0.99),
             "bert-99": ("F1", 90.874 * 0.99),
             "bert-99.9": ("F1", 90.874 * 0.999),
             "dlrm-v2-99": ("AUC", 80.31 * 0.99),
@@ -145,6 +143,20 @@
                 "FID_SCORE",
                 23.01085758,
             ),
+            "mixtral-8x7b": (
+                "ROUGE1",
+                45.4911 * 0.99,
+                "ROUGE2",
+                23.2829 * 0.99,
+                "ROUGEL",
+                30.3615 * 0.99,
+                "TOKENS_PER_SAMPLE",
+                145.9 * 0.9,
+                "gsm8k_accuracy",
+                73.78 * 0.99,
+                "mbxp_accuracy",
+                60.12 * 0.99,
+            ),
         },
         "accuracy-upper-limit": {
             "stable-diffusion-xl": (
@@ -155,6 +167,7 @@
             ),
             "llama2-70b-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
             "llama2-70b-99.9": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
+            "mixtral-8x7b": ("TOKENS_PER_SAMPLE", 145.9 * 1.1),
         },
         "accuracy-delta-perc": {
             "stable-diffusion-xl": {"CLIP_SCORE": 1, "FID_SCORE": 2}
@@ -162,7 +175,6 @@
         "performance-sample-count": {
             "resnet": 1024,
             "retinanet": 64,
-            "rnnt": 2513,
             "bert-99": 10833,
             "bert-99.9": 10833,
             "dlrm-v2-99": 204800,
@@ -174,6 +186,7 @@
             "llama2-70b-99": 24576,
             "llama2-70b-99.9": 24576,
             "stable-diffusion-xl": 5000,
+            "mixtral-8x7b": 15000,
         },
         # TODO: Update this list.
         "model_mapping": {
@@ -184,22 +197,20 @@
         },
         "seeds": {
             # TODO: Update random seeds
-            "qsl_rng_seed": 13281865557512327830,
-            "sample_index_rng_seed": 198141574272810017,
-            "schedule_rng_seed": 7575108116881280410,
+            "qsl_rng_seed": 3066443479025735752,
+            "sample_index_rng_seed": 10688027786191513374,
+            "schedule_rng_seed": 14962580496156340209,
         },
-        # not required for v5.0+
         "test05_seeds": {
             # TODO: Update random seeds
-            "qsl_rng_seed": 2376919268182438552,
-            "sample_index_rng_seed": 11176391829184272374,
-            "schedule_rng_seed": 3911940905271271337,
+            "qsl_rng_seed": 16799458546791641818,
+            "sample_index_rng_seed": 5453809927556429288,
+            "schedule_rng_seed": 5435552105434836064,
         },
         "ignore_errors": [],
         "latency-constraint": {
             "resnet": {"Server": 15000000},
             "retinanet": {"Server": 100000000},
-            "rnnt": {"Server": 1000000000},
             "bert-99": {"Server": 130000000},
             "bert-99.9": {"Server": 130000000},
             "dlrm-v2-99": {"Server": 60000000},
@@ -208,8 +219,9 @@
             "gptj-99.9": {"Server": 20000000000},
             "llama2-70b-99": {"Server": 20000000000},
             "llama2-70b-99.9": {"Server": 20000000000},
-
             "stable-diffusion-xl": {"Server": 20000000000},
+            # TODO: Mixtral metrics
+            # "mixtral-8x7b" : {"Server": 20000000000}
         },
         "min-queries": {
             "resnet": {
@@ -224,7 +236,6 @@
                 "Server": 270336,
                 "Offline": 1,
             },
-            "rnnt": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "dlrm-v2-99": {"Server": 270336, "Offline": 1},
@@ -240,14 +251,16 @@
                 "Server": 270336,
                 "Offline": 1,
             },
+            "mixtral-8x7b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
         },
     },
-    "v4.1": {
+    "v5.0": {
         "models": [
             "resnet",
             "retinanet",
             "bert-99",
             "bert-99.9",
+            # TODO: remove dlrm?
             "dlrm-v2-99",
             "dlrm-v2-99.9",
             "3d-unet-99",
@@ -258,12 +271,14 @@
             "llama2-70b-99.9",
             "stable-diffusion-xl",
             "mixtral-8x7b",
+            "llama3-405b-99",
+            "llama3-405b-99.9",
+            "rgat",
+            # TODO: add automotive
         ],
         "required-scenarios-datacenter": {
             "resnet": ["Server", "Offline"],
             "retinanet": ["Server", "Offline"],
-            "bert-99": ["Server", "Offline"],
-            "bert-99.9": ["Server", "Offline"],
             "dlrm-v2-99": ["Server", "Offline"],
             "dlrm-v2-99.9": ["Server", "Offline"],
             "3d-unet-99": ["Offline"],
@@ -274,6 +289,9 @@
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["Server", "Offline"],
             "mixtral-8x7b": ["Server", "Offline"],
+            "llama3-405b-99": ["Server", "Offline"],
+            "llama3-405b-99.9": ["Server", "Offline"],
+            "rgat": ["Offline"],
         },
         "optional-scenarios-datacenter": {},
         "required-scenarios-edge": {
@@ -301,7 +319,10 @@
             "llama2-70b-99": ["Server", "Offline"],
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
-            "mixtral-8x7b": ["Server", "Offline"]
+            "mixtral-8x7b": ["Server", "Offline"],
+            "llama3-405b-99": ["Server", "Offline"],
+            "llama3-405b-99.9": ["Server", "Offline"],
+            "rgat": ["Offline"],
         },
         "optional-scenarios-datacenter-edge": {},
         "accuracy-target": {
@@ -374,6 +395,24 @@
                 "mbxp_accuracy",
                 60.12 * 0.99,
             ),
+            # TODO: Get llama3 metrics
+            "llama3-405b-99": (
+                "ROUGEL",
+                1 * 0.99,
+                "exact_match",
+                1 * 0.99,
+                "TOKENS_PER_SAMPLE",
+                1000 * 0.9,
+            ),
+            "llama3-405b-99.9": (
+                "ROUGEL",
+                1 * 0.99,
+                "exact_match",
+                1 * 0.99,
+                "TOKENS_PER_SAMPLE",
+                20000 * 0.9,
+            ),
+            "rgat": ("acc", 0.7286 * 0.99),
         },
         "accuracy-upper-limit": {
             "stable-diffusion-xl": (
@@ -385,6 +424,8 @@
             "llama2-70b-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
             "llama2-70b-99.9": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
             "mixtral-8x7b": ("TOKENS_PER_SAMPLE", 145.9 * 1.1),
+            "llama3-405b-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
+            "llama3-405b-99.9": ("TOKENS_PER_SAMPLE", 20000 * 1.1),
         },
         "accuracy-delta-perc": {
             "stable-diffusion-xl": {"CLIP_SCORE": 1, "FID_SCORE": 2}
@@ -404,6 +445,10 @@
             "llama2-70b-99.9": 24576,
             "stable-diffusion-xl": 5000,
             "mixtral-8x7b": 15000,
+            "llama3-405b-99": 8312,
+            "llama3-405b-99.9": 8312,
+            "rgat": 788379
+
         },
         # TODO: Update this list.
         "model_mapping": {
@@ -434,11 +479,7 @@
             "dlrm-v2-99.9": {"Server": 60000000},
             "gptj-99": {"Server": 20000000000},
             "gptj-99.9": {"Server": 20000000000},
-            "llama2-70b-99": {"Server": 20000000000},
-            "llama2-70b-99.9": {"Server": 20000000000},
             "stable-diffusion-xl": {"Server": 20000000000},
-            # TODO: Mixtral metrics
-            # "mixtral-8x7b" : {"Server": 20000000000}
         },
         "min-queries": {
             "resnet": {
@@ -469,6 +510,9 @@
                 "Offline": 1,
             },
             "mixtral-8x7b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama3-405b-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama3-405b-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "rgat": {"SingleStream": 1024, "Server": 270336, "Offline": 1}
         },
     },
 }
@@ -556,6 +600,9 @@
     "llama2-70b-99.9": 24576,
     "stable-diffusion-xl": 5000,
     "mixtral-8x7b": 15000,
+    "llama3-405b-99": 8312,
+    "llama2-405b-99.9": 8312,
+    "rgat": 788379,
 }
 
 SCENARIO_MAPPING = {
@@ -590,17 +637,29 @@
 }
 
 RESULT_FIELD_BENCHMARK_OVERWRITE = {
-    "v4.0": {
+    "v4.1": {
         "llama2-70b-99": {
             "Offline": "result_tokens_per_second",
-            "Server": "result_completed_samples_per_sec",
+            "Server": "result_completed_tokens_per_second",
         },
         "llama2-70b-99.9": {
             "Offline": "result_tokens_per_second",
-            "Server": "result_completed_samples_per_sec",
+            "Server": "result_completed_tokens_per_second",
+        },
+        "gptj-99": {
+            "Offline": "result_inferred_tokens_per_second",
+            "Server": "result_inferred_completed_tokens_per_second",
+        },
+        "gptj-99.9": {
+            "Offline": "result_inferred_tokens_per_second",
+            "Server": "result_inferred_completed_tokens_per_second",
+        },
+        "mixtral-8x7b": {
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_tokens_per_second",
         },
     },
-    "v4.1": {
+    "v5.0": {
         "llama2-70b-99": {
             "Offline": "result_tokens_per_second",
             "Server": "result_completed_tokens_per_second",
@@ -621,6 +680,14 @@
             "Offline": "result_tokens_per_second",
             "Server": "result_completed_tokens_per_second",
         },
+        "llama3-405b-99": {
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_tokens_per_second",
+        },
+        "llama3-405b-99.9": {
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_tokens_per_second",
+        },
     },
 }
 
@@ -632,6 +699,12 @@
         "conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}
     },
     "mixtral-8x7b": {"conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}},
+    "llama3-405b-99": {
+        "conversational": {"ttft": 6000 * 1000000, "tpot": 175 * 1000000}
+    },
+    "llama3-405b-99.9": {
+        "conversational": {"ttft": 6000 * 1000000, "tpot": 175 * 1000000}
+    },
 }
 
 ACC_PATTERN = {
@@ -652,6 +725,7 @@
     "FID_SCORE": r".*'FID_SCORE':\s([\d.]+).*",
     "gsm8k_accuracy": r".*'gsm8k':\s([\d.]+).*",
     "mbxp_accuracy": r".*'mbxp':\s([\d.]+).*",
+    "exact_match": r".*'exact_match':\s([\d.]+).*"
 }
 
 SYSTEM_DESC_REQUIRED_FIELDS = [
@@ -912,6 +986,8 @@ def requires_equal_issue(self, model, division):
                 "llama2-70b-99",
                 "llama2-70b-99.9",
                 "mixtral-8x7b",
+                "llama3-405b-99",
+                "llama3-405b-99.9",
             ]
             and self.version not in ["v4.0", "v4.1"]
         )
@@ -1278,7 +1354,8 @@ def check_performance_dir(
                        [model][scenario]]
         )
 
-    if model in ["llama2-70b-99", "llama2-70b-99.9", "mixtral-8x7b"]:
+    if model in ["llama2-70b-99", "llama2-70b-99.9",
+                 "mixtral-8x7b", "llama3-405b-99", "llama3-405b-99.9"]:
         llama_constraint, is_valid = extra_check_llm(
             mlperf_log, scenario_fixed, model)
 
@@ -1818,6 +1895,18 @@ def log_result(
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
+            "llama3-405b-99": {
+                "SingleStream": "Latency (ms)",
+                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            },
+            "llama3-405b-99.9": {
+                "SingleStream": "Latency (ms)",
+                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            },
         }
         unit_dict = {
             "SingleStream": "Latency (ms)",
@@ -2897,6 +2986,9 @@ def check_compliance_dir(
         "llama2-70b-99",
         "llama2-70b-99.9",
         "mixtral-8x7b",
+        "llama3-405b-99",
+        "llama3-405b-99.9",
+        "rgat",
     ]:
         test_list.remove("TEST04")
 
@@ -2916,6 +3008,8 @@ def check_compliance_dir(
         "llama2-70b-99",
         "llama2-70b-99.9",
         "mixtral-8x7b",
+        "llama3-405b-99",
+        "llama3-405b-99.9",
     ]:
         test_list.remove("TEST01")
 
@@ -2923,7 +3017,8 @@ def check_compliance_dir(
         test_list.remove("TEST01")
         test_list.remove("TEST04")
 
-    if model in ["llama2-70b-99", "llama2-70b-99.9", "mixtral-8x7b"]:
+    if model in ["llama2-70b-99", "llama2-70b-99.9",
+                 "mixtral-8x7b", "llama3-405b-99", "llama3-405b-99.9"]:
         test_list.append("TEST06")
 
     if test_list and not os.path.exists(compliance_dir):

From 8ce46d5fd792e05981b0df6389c641426ec0cf13 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Mon, 9 Dec 2024 11:32:32 -0800
Subject: [PATCH 191/305] Upgrade loadgen version to 5.0 (#1962)

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index d9b366bb7a..0062ac9718 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-4.1.30
+5.0.0

From 4968ba0ef914390ba292bb85ac4fe4800c98a7d1 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 10 Dec 2024 04:35:20 +0530
Subject: [PATCH 192/305] Fix loadgen build for version numbers having "0"
 (#1967)

* Fix loadgen build for version numbers having "0"

* Update test-resnet50.yml

* Update test-retinanet.yml

* Update test-bert.yml
---
 .github/workflows/test-bert.yml      | 2 +-
 .github/workflows/test-resnet50.yml  | 2 +-
 .github/workflows/test-retinanet.yml | 2 +-
 loadgen/CMakeLists.txt               | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test-bert.yml b/.github/workflows/test-bert.yml
index 2ce156c452..b7012a6e8d 100755
--- a/.github/workflows/test-bert.yml
+++ b/.github/workflows/test-bert.yml
@@ -36,4 +36,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test BERT and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.loadgen.version=custom
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom
diff --git a/.github/workflows/test-resnet50.yml b/.github/workflows/test-resnet50.yml
index c942258b24..50404b0d99 100755
--- a/.github/workflows/test-resnet50.yml
+++ b/.github/workflows/test-resnet50.yml
@@ -38,4 +38,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Resnet50 and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}
diff --git a/.github/workflows/test-retinanet.yml b/.github/workflows/test-retinanet.yml
index 6482666260..5b18619de9 100755
--- a/.github/workflows/test-retinanet.yml
+++ b/.github/workflows/test-retinanet.yml
@@ -36,4 +36,4 @@ jobs:
         python3 -m pip install cm4mlops
     - name: Test Retinanet and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.loadgen.version=custom
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom
diff --git a/loadgen/CMakeLists.txt b/loadgen/CMakeLists.txt
index 37270ac241..4fec0e44f7 100644
--- a/loadgen/CMakeLists.txt
+++ b/loadgen/CMakeLists.txt
@@ -14,7 +14,7 @@ set(mlperf_loadgen_VERSION_MINOR "${CMAKE_MATCH_2}")
 set(mlperf_loadgen_VERSION_PATCH "${CMAKE_MATCH_3}")
 
 # Check if the version format was parsed correctly
-if(NOT mlperf_loadgen_VERSION_MAJOR OR NOT mlperf_loadgen_VERSION_MINOR OR NOT mlperf_loadgen_VERSION_PATCH)
+if(NOT DEFINED mlperf_loadgen_VERSION_MAJOR OR NOT DEFINED mlperf_loadgen_VERSION_MINOR OR NOT DEFINED mlperf_loadgen_VERSION_PATCH)
     message(FATAL_ERROR "Version format in VERSION.txt is incorrect. Expected format: MAJOR.MINOR.PATCH")
 endif()
 

From 6afe9962a8b8a2f12ef8f8c88dc4e977f3cddd60 Mon Sep 17 00:00:00 2001
From: pgmpablo157321 <pgmpablo157321@users.noreply.github.com>
Date: Mon, 9 Dec 2024 23:05:32 +0000
Subject: [PATCH 193/305] Increment version to 5.0.1

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 0062ac9718..6b244dcd69 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.0
+5.0.1

From e69b504b5c38f7ac30a60c6c25a369e2744a7c44 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Mon, 9 Dec 2024 21:48:24 -0800
Subject: [PATCH 194/305] Fix Dockerfile for 405B (#1960)

Co-authored-by: Miro <mirhodak@amd.com>
---
 language/llama3-405b/Dockerfile       | 5 ++++-
 language/llama3-405b/requirements.txt | 4 ++--
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/language/llama3-405b/Dockerfile b/language/llama3-405b/Dockerfile
index 7987d4851d..67edcc46b9 100644
--- a/language/llama3-405b/Dockerfile
+++ b/language/llama3-405b/Dockerfile
@@ -22,7 +22,7 @@ ENV TZ=US/Pacific
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
-RUN rm -rf /var/lib/apt/lists/* && rm /etc/apt/sources.list.d/* \
+RUN rm -rf /var/lib/apt/lists/* && rm -rf /etc/apt/sources.list.d/* \
  && apt update \
  && apt install -y --no-install-recommends build-essential autoconf \
         libtool git ccache curl wget pkg-config sudo ca-certificates \
@@ -46,3 +46,6 @@ RUN wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.5.2-0-Linux-x86
 ENV PATH="$PATH:/opt/miniconda3/bin"
 RUN conda create -n llama3-405b python=3.10
 RUN chmod -R 777 /opt/miniconda3
+
+# Set the env variable for vLLM
+ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
diff --git a/language/llama3-405b/requirements.txt b/language/llama3-405b/requirements.txt
index d8f5fbd12f..a62f68e7ba 100644
--- a/language/llama3-405b/requirements.txt
+++ b/language/llama3-405b/requirements.txt
@@ -3,7 +3,7 @@ nltk==3.8.1
 evaluate==0.4.0
 absl-py==1.4.0
 rouge-score==0.1.2
-sentencepiece==0.1.99
+sentencepiece==0.2.0
 accelerate==0.21.0
 vllm==0.6.3
-pybind11==2.10.4
\ No newline at end of file
+pybind11==2.10.4

From cec62ff6107f4e63b12cdeb900f3cd9c522594c9 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 10 Dec 2024 23:01:57 +0530
Subject: [PATCH 195/305] Update backend_pytorch.py | Fix lock usage (#1964)

Co-authored-by: Miro <mirhodak@amd.com>
---
 text_to_image/backend_pytorch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/text_to_image/backend_pytorch.py b/text_to_image/backend_pytorch.py
index 027e155655..8e52e0a616 100644
--- a/text_to_image/backend_pytorch.py
+++ b/text_to_image/backend_pytorch.py
@@ -387,7 +387,7 @@ def predict(self, inputs):
                     pooled_prompt_embeds,
                     negative_pooled_prompt_embeds,
                 ) = self.prepare_inputs(inputs, i)
-                with lock:
+                with self.lock:
                     generated = self.pipe(
                         prompt_embeds=prompt_embeds,
                         negative_prompt_embeds=negative_prompt_embeds,

From bc5e6fbeb9a47210cd47039ec12462140525ccb4 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 11 Dec 2024 17:23:47 -0500
Subject: [PATCH 196/305] Add llama3 metrics + remove llama3-99.9 (#1973)

---
 language/llama3-405b/README.md         | 11 +++-
 tools/submission/submission_checker.py | 69 +++++++-------------------
 2 files changed, 28 insertions(+), 52 deletions(-)

diff --git a/language/llama3-405b/README.md b/language/llama3-405b/README.md
index 8c04c202ea..dcc5344c42 100644
--- a/language/llama3-405b/README.md
+++ b/language/llama3-405b/README.md
@@ -193,5 +193,12 @@ The ServerSUT was not tested for GPU runs.
 
 
 ## Accuracy Target
-Running the GPU implementation in FP16 precision resulted in the following FP16 accuracy targets (normalized to a 0-100
-scale from a 0.0-1.0 scale):
+Running the GPU implementation in FP16 precision resulted in the following FP16 accuracy targets:
+```
+{
+        'rougeL': 21.6666,
+        'exact_match': 90.1335,
+        'tokens_per_sample': 684.68,
+}
+```
+
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 51f8c7aaba..37cd3a0f84 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -271,8 +271,7 @@
             "llama2-70b-99.9",
             "stable-diffusion-xl",
             "mixtral-8x7b",
-            "llama3-405b-99",
-            "llama3-405b-99.9",
+            "llama3-405b",
             "rgat",
             # TODO: add automotive
         ],
@@ -289,8 +288,7 @@
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["Server", "Offline"],
             "mixtral-8x7b": ["Server", "Offline"],
-            "llama3-405b-99": ["Server", "Offline"],
-            "llama3-405b-99.9": ["Server", "Offline"],
+            "llama3-405b": ["Server", "Offline"],
             "rgat": ["Offline"],
         },
         "optional-scenarios-datacenter": {},
@@ -320,8 +318,7 @@
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
             "mixtral-8x7b": ["Server", "Offline"],
-            "llama3-405b-99": ["Server", "Offline"],
-            "llama3-405b-99.9": ["Server", "Offline"],
+            "llama3-405b": ["Server", "Offline"],
             "rgat": ["Offline"],
         },
         "optional-scenarios-datacenter-edge": {},
@@ -395,22 +392,13 @@
                 "mbxp_accuracy",
                 60.12 * 0.99,
             ),
-            # TODO: Get llama3 metrics
-            "llama3-405b-99": (
+            "llama3-405b": (
                 "ROUGEL",
-                1 * 0.99,
+                21.6666 * 0.99,
                 "exact_match",
-                1 * 0.99,
+                90.1335 * 0.99,
                 "TOKENS_PER_SAMPLE",
-                1000 * 0.9,
-            ),
-            "llama3-405b-99.9": (
-                "ROUGEL",
-                1 * 0.99,
-                "exact_match",
-                1 * 0.99,
-                "TOKENS_PER_SAMPLE",
-                20000 * 0.9,
+                684.68 * 0.9,
             ),
             "rgat": ("acc", 0.7286 * 0.99),
         },
@@ -424,8 +412,7 @@
             "llama2-70b-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
             "llama2-70b-99.9": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
             "mixtral-8x7b": ("TOKENS_PER_SAMPLE", 145.9 * 1.1),
-            "llama3-405b-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
-            "llama3-405b-99.9": ("TOKENS_PER_SAMPLE", 20000 * 1.1),
+            "llama3-405b": ("TOKENS_PER_SAMPLE", 684.68 * 1.1),
         },
         "accuracy-delta-perc": {
             "stable-diffusion-xl": {"CLIP_SCORE": 1, "FID_SCORE": 2}
@@ -445,8 +432,7 @@
             "llama2-70b-99.9": 24576,
             "stable-diffusion-xl": 5000,
             "mixtral-8x7b": 15000,
-            "llama3-405b-99": 8312,
-            "llama3-405b-99.9": 8312,
+            "llama3-405b": 8312,
             "rgat": 788379
 
         },
@@ -510,8 +496,7 @@
                 "Offline": 1,
             },
             "mixtral-8x7b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "llama3-405b-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "llama3-405b-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama3-405b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "rgat": {"SingleStream": 1024, "Server": 270336, "Offline": 1}
         },
     },
@@ -600,7 +585,7 @@
     "llama2-70b-99.9": 24576,
     "stable-diffusion-xl": 5000,
     "mixtral-8x7b": 15000,
-    "llama3-405b-99": 8312,
+    "llama3-405b": 8312,
     "llama2-405b-99.9": 8312,
     "rgat": 788379,
 }
@@ -680,11 +665,7 @@
             "Offline": "result_tokens_per_second",
             "Server": "result_completed_tokens_per_second",
         },
-        "llama3-405b-99": {
-            "Offline": "result_tokens_per_second",
-            "Server": "result_completed_tokens_per_second",
-        },
-        "llama3-405b-99.9": {
+        "llama3-405b": {
             "Offline": "result_tokens_per_second",
             "Server": "result_completed_tokens_per_second",
         },
@@ -699,10 +680,7 @@
         "conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}
     },
     "mixtral-8x7b": {"conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}},
-    "llama3-405b-99": {
-        "conversational": {"ttft": 6000 * 1000000, "tpot": 175 * 1000000}
-    },
-    "llama3-405b-99.9": {
+    "llama3-405b": {
         "conversational": {"ttft": 6000 * 1000000, "tpot": 175 * 1000000}
     },
 }
@@ -986,8 +964,7 @@ def requires_equal_issue(self, model, division):
                 "llama2-70b-99",
                 "llama2-70b-99.9",
                 "mixtral-8x7b",
-                "llama3-405b-99",
-                "llama3-405b-99.9",
+                "llama3-405b",
             ]
             and self.version not in ["v4.0", "v4.1"]
         )
@@ -1355,7 +1332,7 @@ def check_performance_dir(
         )
 
     if model in ["llama2-70b-99", "llama2-70b-99.9",
-                 "mixtral-8x7b", "llama3-405b-99", "llama3-405b-99.9"]:
+                 "mixtral-8x7b", "llama3-405b"]:
         llama_constraint, is_valid = extra_check_llm(
             mlperf_log, scenario_fixed, model)
 
@@ -1895,13 +1872,7 @@ def log_result(
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
-            "llama3-405b-99": {
-                "SingleStream": "Latency (ms)",
-                "MultiStream": "Latency (ms)",
-                "Offline": "Tokens/s",
-                "Server": "Tokens/s",
-            },
-            "llama3-405b-99.9": {
+            "llama3-405b": {
                 "SingleStream": "Latency (ms)",
                 "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
@@ -2986,8 +2957,7 @@ def check_compliance_dir(
         "llama2-70b-99",
         "llama2-70b-99.9",
         "mixtral-8x7b",
-        "llama3-405b-99",
-        "llama3-405b-99.9",
+        "llama3-405b",
         "rgat",
     ]:
         test_list.remove("TEST04")
@@ -3008,8 +2978,7 @@ def check_compliance_dir(
         "llama2-70b-99",
         "llama2-70b-99.9",
         "mixtral-8x7b",
-        "llama3-405b-99",
-        "llama3-405b-99.9",
+        "llama3-405b",
     ]:
         test_list.remove("TEST01")
 
@@ -3018,7 +2987,7 @@ def check_compliance_dir(
         test_list.remove("TEST04")
 
     if model in ["llama2-70b-99", "llama2-70b-99.9",
-                 "mixtral-8x7b", "llama3-405b-99", "llama3-405b-99.9"]:
+                 "mixtral-8x7b", "llama3-405b"]:
         test_list.append("TEST06")
 
     if test_list and not os.path.exists(compliance_dir):

From 8948934e2485e4f82d1712661167bcce64eadcec Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Thu, 12 Dec 2024 20:15:56 +0530
Subject: [PATCH 197/305] Fix submission checker for v5.0 rgat (#1974)

* Fix submission checker for v5.0 rgat

* Update submission_checker.py | Updates for v5.0

* [Automated Commit] Format Codebase

* Update submission_checker.py | Fixes latency_constraints for v5.0

* [Automated Commit] Format Codebase

---------

Co-authored-by: mlcommons-bot <mlcommons-bot@users.noreply.github.com>
---
 tools/submission/submission_checker.py | 46 +++++++++++---------------
 1 file changed, 19 insertions(+), 27 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 37cd3a0f84..35b70a4e7b 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1,4 +1,4 @@
-"""A checker for MLPerf Inference submissions from v4.0 onwards (for checking older submissions please use the submission checker from the respective release)
+"""A checker for MLPerf Inference submissions from v4.1 onwards (for checking older submissions please use the submission checker from the respective release)
 """
 
 from __future__ import division
@@ -196,13 +196,11 @@
             "resnet50": "resnet",
         },
         "seeds": {
-            # TODO: Update random seeds
             "qsl_rng_seed": 3066443479025735752,
             "sample_index_rng_seed": 10688027786191513374,
             "schedule_rng_seed": 14962580496156340209,
         },
         "test05_seeds": {
-            # TODO: Update random seeds
             "qsl_rng_seed": 16799458546791641818,
             "sample_index_rng_seed": 5453809927556429288,
             "schedule_rng_seed": 5435552105434836064,
@@ -220,8 +218,7 @@
             "llama2-70b-99": {"Server": 20000000000},
             "llama2-70b-99.9": {"Server": 20000000000},
             "stable-diffusion-xl": {"Server": 20000000000},
-            # TODO: Mixtral metrics
-            # "mixtral-8x7b" : {"Server": 20000000000}
+            "mixtral-8x7b": {"Server": 20000000000}
         },
         "min-queries": {
             "resnet": {
@@ -260,7 +257,6 @@
             "retinanet",
             "bert-99",
             "bert-99.9",
-            # TODO: remove dlrm?
             "dlrm-v2-99",
             "dlrm-v2-99.9",
             "3d-unet-99",
@@ -273,7 +269,7 @@
             "mixtral-8x7b",
             "llama3-405b",
             "rgat",
-            # TODO: add automotive
+            # TODO: add automotive?
         ],
         "required-scenarios-datacenter": {
             "resnet": ["Server", "Offline"],
@@ -296,6 +292,7 @@
             "resnet": ["SingleStream", "MultiStream", "Offline"],
             "retinanet": ["SingleStream", "MultiStream", "Offline"],
             "bert-99": ["SingleStream", "Offline"],
+            "bert-99.9": ["SingleStream", "Offline"],
             "3d-unet-99": ["SingleStream", "Offline"],
             "3d-unet-99.9": ["SingleStream", "Offline"],
             "gptj-99": ["SingleStream", "Offline"],
@@ -306,8 +303,8 @@
         "required-scenarios-datacenter-edge": {
             "resnet": ["SingleStream", "Offline", "MultiStream", "Server"],
             "retinanet": ["SingleStream", "Offline", "MultiStream", "Server"],
-            "bert-99": ["SingleStream", "Offline", "Server"],
-            "bert-99.9": ["Offline", "Server"],
+            "bert-99": ["SingleStream", "Offline"],
+            "bert-99.9": ["SingleStream", "Offline"],
             "dlrm-v2-99": ["Offline", "Server"],
             "dlrm-v2-99.9": ["Offline", "Server"],
             "3d-unet-99": ["SingleStream", "Offline"],
@@ -436,10 +433,11 @@
             "rgat": 788379
 
         },
-        # TODO: Update this list.
+        # model_mapping.json is expected in the root directory of the
+        # submission folder for open submissions and so the below dictionary is
+        # not really needed
         "model_mapping": {
             # map model names to the official mlperf model class
-            "ssd-resnet34": "retinanet",
             "mobilenet": "resnet",
             "resnet50": "resnet",
         },
@@ -449,23 +447,19 @@
             "sample_index_rng_seed": 10688027786191513374,
             "schedule_rng_seed": 14962580496156340209,
         },
-        "test05_seeds": {
-            # TODO: Update random seeds
-            "qsl_rng_seed": 16799458546791641818,
-            "sample_index_rng_seed": 5453809927556429288,
-            "schedule_rng_seed": 5435552105434836064,
-        },
         "ignore_errors": [],
         "latency-constraint": {
             "resnet": {"Server": 15000000},
             "retinanet": {"Server": 100000000},
-            "bert-99": {"Server": 130000000},
-            "bert-99.9": {"Server": 130000000},
             "dlrm-v2-99": {"Server": 60000000},
             "dlrm-v2-99.9": {"Server": 60000000},
             "gptj-99": {"Server": 20000000000},
             "gptj-99.9": {"Server": 20000000000},
             "stable-diffusion-xl": {"Server": 20000000000},
+            "llama2-70b-99": {"Server": 20000000000},
+            "llama2-70b-99.9": {"Server": 20000000000},
+            "mixtral-8x7b": {"Server": 20000000000},
+            "llama3-405b": {"Server": 60000000000}
         },
         "min-queries": {
             "resnet": {
@@ -480,8 +474,8 @@
                 "Server": 270336,
                 "Offline": 1,
             },
-            "bert-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "bert-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "bert-99": {"SingleStream": 1024, "Offline": 1},
+            "bert-99.9": {"SingleStream": 1024, "Offline": 1},
             "dlrm-v2-99": {"Server": 270336, "Offline": 1},
             "dlrm-v2-99.9": {"Server": 270336, "Offline": 1},
             "3d-unet-99": {"SingleStream": 1024, "Offline": 1},
@@ -497,7 +491,7 @@
             },
             "mixtral-8x7b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "llama3-405b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "rgat": {"SingleStream": 1024, "Server": 270336, "Offline": 1}
+            "rgat": {"SingleStream": 1024, "Offline": 1}
         },
     },
 }
@@ -605,17 +599,15 @@
 }
 
 RESULT_FIELD_NEW = {
-    "v4.0": {
+    "v4.1": {
         "Offline": "result_samples_per_second",
         "SingleStream": "early_stopping_latency_ss",
-        "MultiStreamLegacy": "effective_samples_per_query",
         "MultiStream": "early_stopping_latency_ms",
-        "Server": "result_scheduled_samples_per_sec",
+        "Server": "result_completed_samples_per_sec",
     },
-    "v4.1": {
+    "v5.0": {
         "Offline": "result_samples_per_second",
         "SingleStream": "early_stopping_latency_ss",
-        "MultiStreamLegacy": "effective_samples_per_query",
         "MultiStream": "early_stopping_latency_ms",
         "Server": "result_completed_samples_per_sec",
     },

From 2d2eb3081dcda64e766a28dc1b6fb9c40fb9eefa Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Fri, 13 Dec 2024 00:43:35 +0530
Subject: [PATCH 198/305] Fix test05 seeds missing error for v5.0 submission
 checker (#1976)

---
 tools/submission/submission_checker.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 35b70a4e7b..a5165b3810 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -827,7 +827,8 @@ def __init__(
         self.version = version
         self.models = self.base["models"]
         self.seeds = self.base["seeds"]
-        self.test05_seeds = self.base["test05_seeds"]
+        if self.base.get("test05_seeds"):
+            self.test05_seeds = self.base["test05_seeds"]
         self.accuracy_target = self.base["accuracy-target"]
         self.accuracy_delta_perc = self.base["accuracy-delta-perc"]
         self.accuracy_upper_limit = self.base.get("accuracy-upper-limit", {})
@@ -968,7 +969,7 @@ def get_args():
     parser.add_argument("--input", required=True, help="submission directory")
     parser.add_argument(
         "--version",
-        default="v4.1",
+        default="v5.0",
         choices=list(MODEL_CONFIG.keys()),
         help="mlperf version",
     )

From fa274563d74b555f0fb8cd6fe5d11a24e83684ce Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Thu, 12 Dec 2024 20:38:37 -0800
Subject: [PATCH 199/305] Fix llama3-405B docker workflow and performance
 sample count (#1978)

* Fix llama3-405B docker workflow

* Fix the performance sample count from 8312 to 8313

* More fixes
---
 .gitignore                                    |  1 +
 language/llama3-405b/README.md                | 81 ++++++++++++++-----
 .../{launch.sh => launch_docker.sh}           |  0
 language/llama3-405b/main.py                  |  2 +-
 language/llama3-405b/run_accuracy.sh          |  5 +-
 language/llama3-405b/run_offline.sh           | 17 ++--
 language/llama3-405b/run_server.sh            | 17 ++--
 language/llama3-405b/with_the_same_user       | 27 +++++++
 loadgen/mlperf.conf                           |  8 +-
 tools/submission/submission_checker.py        |  5 +-
 10 files changed, 116 insertions(+), 47 deletions(-)
 rename language/llama3-405b/{launch.sh => launch_docker.sh} (100%)
 create mode 100755 language/llama3-405b/with_the_same_user

diff --git a/.gitignore b/.gitignore
index 9545a79772..eba8bb3413 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@ loadgen/build/
 libmlperf_loadgen.a
 __pycache__/
 generated/
+*.swp
diff --git a/language/llama3-405b/README.md b/language/llama3-405b/README.md
index dcc5344c42..8df2a81f10 100644
--- a/language/llama3-405b/README.md
+++ b/language/llama3-405b/README.md
@@ -9,34 +9,64 @@
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama3-405b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
- 
+
 ## Prepare environment
 
-Copy the mlperf.conf file to this folder.
-```
-cp ../../mlperf.conf .
+### Local Environment Run
+
+The following steps were tested in Ubuntu 22.04 with python 3.10
+
+- **Prerrequisite for GPU runs:** Install Nvidia Driver and cuda 12.1.
+
+The following links contain the commands for installing the [NVIDIA Driver](https://developer.nvidia.com/datacenter-driver-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local) and [Cuda](https://developer.nvidia.com/cuda-12-1-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local)
+
+- **Prerrequisite:** Install conda.
+
+```bash
+mkdir -p ~/miniconda3
+wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.5.2-0-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
+bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
+rm ~/miniconda3/miniconda.sh
+~/miniconda3/bin/conda init
 ```
 
-For a CPU-only run:
+- Set the following helper variables
+```bash
+export ROOT=$PWD/inference
+export LLAMA_FOLDER=$PWD/inference/language/llama3-405b
+export LOADGEN_FOLDER=$PWD/inference/loadgen
+export DATASET_FOLDER=$PWD/inference/language/llama3-405b/dataset
+```
 
+- Clone the inference repository:
+```bash
+git clone --recurse-submodules https://github.com/mlcommons/inference.git \
+ --depth 1
 ```
-conda create -n llama3-405b python=3.9
+
+- Create a conda environment:
+```bash
+conda create -y -n llama3-405b python=3.10
 conda activate llama3-405b
+conda install -y -c conda-forge libstdcxx-ng=12
+```
 
+- Install requirements and loadgen:
+```bash
+cd $LLAMA_FOLDER
 # Install packages
 pip install -r requirements.txt
+```
 
-export CUR_DIR=${PWD}
-cd <inference-repo-root>/loadgen
-
-
-python -m pip install .
+```bash
+cd $LOADGEN_FOLDER
+pip install -e .
 ```
 
-For a GPU-based run:
+### Docker Run
 
 A dockerfile is provided, along with scripts to help launch it. First, add any docker volume mounts you want in
-`launch.sh`. There is a section at the top of the file that looks like:
+`launch_docker.sh`. There is a section at the top of the file that looks like:
 ```
 # Add any volume mounts here with the following syntax
 # /path/to/src:/path/to/dir/in/container
@@ -54,10 +84,13 @@ MOUNTS=(
     /raid/data:/raid/data
 )
 ```
-Once you have added all your mounts, launch the container with `bash launch.sh`.
+Once you have added all your mounts, build and launch the container with `bash launch.sh`.
 
-Inside the container, set up the environment with `bash build.sh`. This will install all the dependencies from the
-CPU-only setup, as well as any GPU versions for applicable libraries like PyTorch.
+Now install all the dependencies:
+```
+pip install -r requirements.txt
+pip install -e ../../loadgen
+```
 
 
 ## Get Model
@@ -73,7 +106,7 @@ TODO: Host model and grant access to submitters
 export CHECKPOINT_PATH=Meta-Llama-3.1-405B-Instruct
 git lfs install
 git clone https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct ${CHECKPOINT_PATH}
-
+cd ${CHECKPOINT_PATH} && git checkout be673f326cab4cd22ccfef76109faf68e41aa5f1
 ```
 
 ## Get Dataset
@@ -109,9 +142,10 @@ rclone copy mlc-inference:mlcommons-inference-wg-public/llama3_405b/mlperf_llama
 ```
 python -u main.py --scenario Offline \
                 --model-path ${CHECKPOINT_PATH} \
+                --batch-size 16 \
                 --dtype float16 \
                 --user-conf user.conf \
-                --total-sample-count 8312 \
+                --total-sample-count 8313 \
                 --dataset-path ${DATASET_PATH} \
                 --output-log-dir output \
                 --tensor-parallel-size ${GPU_COUNT} \
@@ -123,9 +157,10 @@ python -u main.py --scenario Offline \
 ```
 python -u main.py --scenario Server \
                 --model-path ${CHECKPOINT_PATH} \
+                --batch-size 16 \
                 --dtype float16 \
                 --user-conf user.conf \
-                --total-sample-count 8312 \
+                --total-sample-count 8313 \
                 --dataset-path ${DATASET_PATH} \
                 --output-log-dir output \
                 --tensor-parallel-size ${GPU_COUNT} \
@@ -145,10 +180,11 @@ mkdir -p "run_outputs"  # The script will dump all the outputs to 'run_outputs'.
 
 python -u main.py --scenario Offline \
                 --model-path ${CHECKPOINT_PATH} \
+                --batch-size 16 \
                 --accuracy \
                 --dtype float16 \
                 --user-conf user.conf \
-                --total-sample-count 8312 \
+                --total-sample-count 8313 \
                 --dataset-path ${DATASET_PATH} \
                 --output-log-dir output \
                 --tensor-parallel-size ${GPU_COUNT} \
@@ -172,10 +208,11 @@ OUTPUT_LOG_DIR=server-accuracy-logs
 
 python -u main.py --scenario Server \
                 --model-path ${CHECKPOINT_PATH} \
+                --batch-size 16 \
                 --accuracy \
                 --dtype float16 \
                 --user-conf user.conf \
-                --total-sample-count 8312 \
+                --total-sample-count 8313 \
                 --dataset-path ${DATASET_PATH} \
                 --output-log-dir output \
                 --tensor-parallel-size ${GPU_COUNT} \
@@ -201,4 +238,4 @@ Running the GPU implementation in FP16 precision resulted in the following FP16
         'tokens_per_sample': 684.68,
 }
 ```
-
+The accuracy target is 99% for rougeL and exact_match, and 90% for tokens_per_sample
diff --git a/language/llama3-405b/launch.sh b/language/llama3-405b/launch_docker.sh
similarity index 100%
rename from language/llama3-405b/launch.sh
rename to language/llama3-405b/launch_docker.sh
diff --git a/language/llama3-405b/main.py b/language/llama3-405b/main.py
index 26d5726b34..f7802687ed 100644
--- a/language/llama3-405b/main.py
+++ b/language/llama3-405b/main.py
@@ -77,7 +77,7 @@ def get_args():
     parser.add_argument(
         "--total-sample-count",
         type=int,
-        default=8312,
+        default=8313,
         help="Number of samples to use in benchmark.",
     )
     parser.add_argument(
diff --git a/language/llama3-405b/run_accuracy.sh b/language/llama3-405b/run_accuracy.sh
index 0752459131..9a54d8f131 100644
--- a/language/llama3-405b/run_accuracy.sh
+++ b/language/llama3-405b/run_accuracy.sh
@@ -5,10 +5,11 @@ mkdir -p "run_outputs"
 
 python3 -u main.py --scenario Offline \
         --model-path ${CHECKPOINT_PATH} \
+        --batch-size 16 \
         --accuracy \
         --mlperf-conf mlperf.conf \
         --user-conf user.conf \
-        --total-sample-count 8312 \
+        --total-sample-count 8313 \
         --dataset-path ${DATASET_PATH} \
         --output-log-dir offline_accuracy_loadgen_logs \
         --dtype float32 | tee offline_accuracy_log.log
@@ -17,5 +18,3 @@ python3 evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
         --mlperf-accuracy-file offline_accuracy_loadgen_logs/mlperf_log_accuracy.json \
         --dataset-file ${DATASET_PATH} \
         --dtype int32
-
-python3 consolidate_results.py --dataset-path ${DATASET_PATH} --model-dir ${CHECKPOINT_PATH}
diff --git a/language/llama3-405b/run_offline.sh b/language/llama3-405b/run_offline.sh
index 89fa9e45f6..6b3a56e012 100644
--- a/language/llama3-405b/run_offline.sh
+++ b/language/llama3-405b/run_offline.sh
@@ -1,10 +1,13 @@
 CHECKPOINT_PATH="${CHECKPOINT_PATH:Meta-Llama-3.1-405B-Instruct}"
-DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
+DATASET_PATH="${DATASET_PATH:mlperf_llama3.1_405b_dataset_8318.pkl}"
 
 python -u main.py --scenario Offline \
-		--model-path ${CHECKPOINT_PATH} \
-		--mlperf-conf mlperf.conf \
-		--user-conf user.conf \
-		--total-sample-count 8312 \
-		--dataset-path ${DATASET_PATH} \
-		--device cpu 2>&1 | tee server_log.log
+	--model-path ${CHECKPOINT_PATH} \
+	--batch-size 16 \
+	--dtype float16 \
+	--user-conf user.conf \
+	--total-sample-count 8313 \
+	--dataset-path ${DATASET_PATH} \
+	--output-log-dir output \
+	--tensor-parallel-size ${GPU_COUNT} \
+	--vllm 2>&1 | tee offline.log
diff --git a/language/llama3-405b/run_server.sh b/language/llama3-405b/run_server.sh
index fe2a31c435..010a359de1 100644
--- a/language/llama3-405b/run_server.sh
+++ b/language/llama3-405b/run_server.sh
@@ -1,12 +1,15 @@
 
 
 CHECKPOINT_PATH="${CHECKPOINT_PATH:Meta-Llama-3.1-405B-Instruct}"
-DATASET_PATH="${DATASET_PATH:-open-orca-val-set.pkl}"
+DATASET_PATH="${DATASET_PATH:mlperf_llama3.1_405b_dataset_8318.pkl}"
 
 python -u main.py --scenario Server \
-		--model-path ${CHECKPOINT_PATH} \
-		--mlperf-conf mlperf.conf \
-		--user-conf user.conf \
-		--total-sample-count 8312 \
-		--dataset-path ${DATASET_PATH} \
-		--device cpu 2>&1 | tee server_log.log
+	--model-path ${CHECKPOINT_PATH} \
+	--batch-size 16 \
+	--dtype float16 \
+	--user-conf user.conf \
+	--total-sample-count 8313 \
+	--dataset-path ${DATASET_PATH} \
+	--output-log-dir output \
+	--tensor-parallel-size ${GPU_COUNT} \
+	--vllm 2>&1 | tee server.log
diff --git a/language/llama3-405b/with_the_same_user b/language/llama3-405b/with_the_same_user
new file mode 100755
index 0000000000..cfa57902fd
--- /dev/null
+++ b/language/llama3-405b/with_the_same_user
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+# wkong: manually set the user info in env first
+
+set -ex
+
+if [ -z "$@" ]; then
+  COMMAND=(bash)
+else
+  COMMAND=("$@")
+fi
+
+apt-get update && apt-get install -y sudo
+
+getent group "${CI_BUILD_GID}" || addgroup --gid "${CI_BUILD_GID}" "${CI_BUILD_GROUP}"
+getent passwd "${CI_BUILD_UID}" || adduser --gid "${CI_BUILD_GID}" --uid "${CI_BUILD_UID}" --gecos "${CI_BUILD_USER} (generated by with_the_same_user script)" --disabled-password --quiet "${CI_BUILD_USER}"
+
+usermod -a -G dip "${CI_BUILD_USER}"
+usermod -a -G sudo "${CI_BUILD_USER}"
+usermod -a -G root "${CI_BUILD_USER}"
+
+echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+
+sudo -H -u "#${CI_BUILD_UID}" --preserve-env \
+  PATH="${PATH}" \
+  LD_LIBRARY_PATH="${LD_LIBRARY_PATH}" \
+  PYTHONPATH="${PYTHONPATH}" \
+  ${COMMAND[@]}
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 31ad5ef62c..1fe2022539 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -14,7 +14,7 @@ dlrm-v2.*.performance_sample_count_override = 204800
 rnnt.*.performance_sample_count_override = 2513
 gptj.*.performance_sample_count_override = 13368
 llama2-70b.*.performance_sample_count_override = 24576
-llama3-405b.*.performance_sample_count_override = 8312
+llama3-405b.*.performance_sample_count_override = 8313
 stable-diffusion-xl.*.performance_sample_count_override = 5000
 rgat.*.performance_sample_count_override = 788379
 # set to 0 to let entire sample set to be performance sample
@@ -84,8 +84,8 @@ llama3-405b.Server.tpot_latency = 175
 *.Offline.min_duration = 600000
 
 # In Offline scenario, we always have one query. But LoadGen maps this to
-# min_sample_count internally in Offline scenario. If the dataset size is larger 
-# than 24576 we limit the min_query_count to 24576 and otherwise we use 
+# min_sample_count internally in Offline scenario. If the dataset size is larger
+# than 24576 we limit the min_query_count to 24576 and otherwise we use
 # the dataset size as the limit
 
 resnet50.Offline.min_query_count = 24576
@@ -97,7 +97,7 @@ rnnt.Offline.min_query_count = 2513
 3d-unet.Offline.min_query_count = 43
 stable-diffusion-xl.Offline.min_query_count = 5000
 llama2-70b.Offline.min_query_count = 24576
-llama3-405b.Offline.min_query_count = 8312
+llama3-405b.Offline.min_query_count = 8313
 mixtral-8x7b.Offline.min_query_count = 15000
 rgat.Offline.min_query_count = 788379
 
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index a5165b3810..43fa1350c8 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -429,7 +429,7 @@
             "llama2-70b-99.9": 24576,
             "stable-diffusion-xl": 5000,
             "mixtral-8x7b": 15000,
-            "llama3-405b": 8312,
+            "llama3-405b": 8313,
             "rgat": 788379
 
         },
@@ -579,8 +579,7 @@
     "llama2-70b-99.9": 24576,
     "stable-diffusion-xl": 5000,
     "mixtral-8x7b": 15000,
-    "llama3-405b": 8312,
-    "llama2-405b-99.9": 8312,
+    "llama3-405b": 8313,
     "rgat": 788379,
 }
 

From 1e48c0dce32ab98b8c930b50e919e13f1fc8f57a Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Fri, 13 Dec 2024 04:38:49 +0000
Subject: [PATCH 200/305] Increment version to 5.0.2

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 6b244dcd69..a1ef0cae18 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.1
+5.0.2

From c71b83f92f0cd2c3b2f07ffec9d3108bb227b712 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Fri, 13 Dec 2024 20:57:22 +0530
Subject: [PATCH 201/305] Fix submission generation for v5.0 (#1981)

* Fix submission checker for v5.0 rgat

* Fix accuracy pattern for rgat, report-generator for v5.0
---
 tools/submission/generate_final_report.py | 99 +++++++++++++++--------
 tools/submission/submission_checker.py    |  2 +-
 2 files changed, 67 insertions(+), 34 deletions(-)

diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 5e5d22c45c..34ae82fb15 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -160,39 +160,72 @@ def main():
         ],
     ]
 
-    filter_scenarios = {
-        "datacenter": {
-            "resnet": ["Server", "Offline"],
-            "retinanet": ["Server", "Offline"],
-            "rnnt": ["Server", "Offline"],
-            "bert-99": ["Server", "Offline"],
-            "bert-99.9": ["Server", "Offline"],
-            "dlrm-v2-99": ["Server", "Offline"],
-            "dlrm-v2-99.9": ["Server", "Offline"],
-            "3d-unet-99": ["Offline"],
-            "3d-unet-99.9": ["Offline"],
-            "gptj-99": ["Server", "Offline"],
-            "gptj-99.9": ["Server", "Offline"],
-            "stable-diffusion-xl": ["Server", "Offline"],
-            "llama2-70b-99": ["Server", "Offline"],
-            "llama2-70b-99.9": ["Server", "Offline"],
-            "mixtral-8x7b": ["Server", "Offline"],
-        },
-        "edge": {
-            "resnet": ["SingleStream", "MultiStream", "Offline"],
-            "retinanet": ["SingleStream", "MultiStream", "Offline"],
-            "rnnt": ["SingleStream", "Offline"],
-            "bert-99": ["SingleStream", "Offline"],
-            "bert-99.9": [],
-            "dlrm-v2-99": [],
-            "dlrm-v2-99.9": [],
-            "3d-unet-99": ["SingleStream", "Offline"],
-            "3d-unet-99.9": ["SingleStream", "Offline"],
-            "gptj-99": ["SingleStream", "Offline"],
-            "gptj-99.9": ["SingleStream", "Offline"],
-            "stable-diffusion-xl": ["SingleStream", "Offline"],
-        },
-    }
+    if args.version == "4.1":
+        filter_scenarios = {
+            "datacenter": {
+                "resnet": ["Server", "Offline"],
+                "retinanet": ["Server", "Offline"],
+                "rnnt": ["Server", "Offline"],
+                "bert-99": ["Server", "Offline"],
+                "bert-99.9": ["Server", "Offline"],
+                "dlrm-v2-99": ["Server", "Offline"],
+                "dlrm-v2-99.9": ["Server", "Offline"],
+                "3d-unet-99": ["Offline"],
+                "3d-unet-99.9": ["Offline"],
+                "gptj-99": ["Server", "Offline"],
+                "gptj-99.9": ["Server", "Offline"],
+                "stable-diffusion-xl": ["Server", "Offline"],
+                "llama2-70b-99": ["Server", "Offline"],
+                "llama2-70b-99.9": ["Server", "Offline"],
+                "mixtral-8x7b": ["Server", "Offline"],
+            },
+            "edge": {
+                "resnet": ["SingleStream", "MultiStream", "Offline"],
+                "retinanet": ["SingleStream", "MultiStream", "Offline"],
+                "rnnt": ["SingleStream", "Offline"],
+                "bert-99": ["SingleStream", "Offline"],
+                "bert-99.9": [],
+                "dlrm-v2-99": [],
+                "dlrm-v2-99.9": [],
+                "3d-unet-99": ["SingleStream", "Offline"],
+                "3d-unet-99.9": ["SingleStream", "Offline"],
+                "gptj-99": ["SingleStream", "Offline"],
+                "gptj-99.9": ["SingleStream", "Offline"],
+                "stable-diffusion-xl": ["SingleStream", "Offline"],
+            },
+        }
+    else:
+        filter_scenarios = {
+            "datacenter": {
+                "resnet": ["Server", "Offline"],
+                "retinanet": ["Server", "Offline"],
+                "rnnt": ["Server", "Offline"],
+                "dlrm-v2-99": ["Server", "Offline"],
+                "dlrm-v2-99.9": ["Server", "Offline"],
+                "3d-unet-99": ["Offline"],
+                "3d-unet-99.9": ["Offline"],
+                "gptj-99": ["Server", "Offline"],
+                "gptj-99.9": ["Server", "Offline"],
+                "stable-diffusion-xl": ["Server", "Offline"],
+                "llama2-70b-99": ["Server", "Offline"],
+                "llama2-70b-99.9": ["Server", "Offline"],
+                "mixtral-8x7b": ["Server", "Offline"],
+                "rgat": ["Offline"],
+                "llama3-405b": ["Offline", "Server"]
+            },
+            "edge": {
+                "resnet": ["SingleStream", "MultiStream", "Offline"],
+                "retinanet": ["SingleStream", "MultiStream", "Offline"],
+                "rnnt": ["SingleStream", "Offline"],
+                "bert-99": ["SingleStream", "Offline"],
+                "bert-99.9": ["SingleStream", "Offline"],
+                "3d-unet-99": ["SingleStream", "Offline"],
+                "3d-unet-99.9": ["SingleStream", "Offline"],
+                "gptj-99": ["SingleStream", "Offline"],
+                "gptj-99.9": ["SingleStream", "Offline"],
+                "stable-diffusion-xl": ["SingleStream", "Offline"],
+            },
+        }
 
     def MakeWorksheet(df, index, filter_dict, sheet_name, outjsondata=[]):
         for key, value in filter_dict.items():
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 43fa1350c8..4a463f3040 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -677,7 +677,7 @@
 }
 
 ACC_PATTERN = {
-    "acc": r"^accuracy=([\d\.]+).*",
+    "acc": r"^(?:\{\"accuracy|accuracy)[\": ]*=?\s*([\d\.]+).*",
     "AUC": r"^AUC=([\d\.]+).*",
     "mAP": r"^mAP=([\d\.]+).*",
     "bleu": r"^BLEU\:\s*([\d\.]+).*",

From aebc0183310fa7f8dd4598d2d19985734c5113d3 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Tue, 17 Dec 2024 12:27:19 -0800
Subject: [PATCH 202/305] More minor fixes for llama3.1-405b (#1983)

* More minor fixes

* Fix indentation for stats report
---
 language/llama3-405b/SUT_VLLM.py          | 22 +++++++++++-----------
 language/llama3-405b/dataset.py           |  2 +-
 language/llama3-405b/evaluate-accuracy.py |  2 +-
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/language/llama3-405b/SUT_VLLM.py b/language/llama3-405b/SUT_VLLM.py
index e64999d09c..f5a8020210 100644
--- a/language/llama3-405b/SUT_VLLM.py
+++ b/language/llama3-405b/SUT_VLLM.py
@@ -31,7 +31,7 @@ def __init__(
         model_path=None,
         dtype="bfloat16",
         batch_size=None,
-        total_sample_count=8312,
+        total_sample_count=8313,
         dataset_path=None,
         use_cached_outputs=False,
         # Set this to True *only for test accuracy runs* in case your prior
@@ -140,16 +140,16 @@ def process_queries(self):
                         n_tokens)]
                 lg.QuerySamplesComplete(response)
 
-        tok = time.time()
+            tok = time.time()
 
-        with self.sample_counter_lock:
-            self.sample_counter += len(qitem)
-            log.info(f"Samples run: {self.sample_counter}")
-            if tik1:
-                log.info(f"\tBatchMaker time: {tik2 - tik1}")
-                log.info(f"\tInference time: {tik3 - tik2}")
-                log.info(f"\tPostprocess time: {tok - tik3}")
-                log.info(f"\t==== Total time: {tok - tik1}")
+            with self.sample_counter_lock:
+                self.sample_counter += len(qitem)
+                log.info(f"Samples run: {self.sample_counter}")
+                if tik1:
+                    log.info(f"\tBatchMaker time: {tik2 - tik1}")
+                    log.info(f"\tInference time: {tik3 - tik2}")
+                    log.info(f"\tPostprocess time: {tok - tik3}")
+                    log.info(f"\t==== Total time: {tok - tik1}")
 
     def load_model(self):
         log.info("Loading model...")
@@ -194,7 +194,7 @@ def __init__(
         self,
         model_path=None,
         dtype="bfloat16",
-        total_sample_count=8312,
+        total_sample_count=8313,
         dataset_path=None,
         batch_size=None,
         workers=1,
diff --git a/language/llama3-405b/dataset.py b/language/llama3-405b/dataset.py
index 04fe9c4b2d..084f132083 100644
--- a/language/llama3-405b/dataset.py
+++ b/language/llama3-405b/dataset.py
@@ -24,7 +24,7 @@ class Dataset:
     def __init__(
         self,
         model_name=None,
-        total_sample_count=8312,
+        total_sample_count=8313,
         perf_count_override=None,
         dataset_path=None,
         dtype="bfloat16"
diff --git a/language/llama3-405b/evaluate-accuracy.py b/language/llama3-405b/evaluate-accuracy.py
index ccc87f71fd..f5677820e7 100644
--- a/language/llama3-405b/evaluate-accuracy.py
+++ b/language/llama3-405b/evaluate-accuracy.py
@@ -141,7 +141,7 @@ def main():
 
     tokenizer = AutoTokenizer.from_pretrained(
         checkpoint_path,
-        model_max_length=2048,
+        model_max_length=22000,
         padding_side="left",
         use_fast=False,
     )

From 3ae2b2a20b01fda1236e6950d089f2bc6eac91aa Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 18 Dec 2024 12:28:57 -0500
Subject: [PATCH 203/305] Remove unused rgat files (#1961)

Co-authored-by: Miro <mirhodak@amd.com>
---
 graph/R-GAT/igbh/tiny/models/dataloader.py |  82 ------
 graph/R-GAT/igbh/tiny/models/gnn.py        | 296 ---------------------
 graph/R-GAT/igbh/tiny/models/main.py       |  79 ------
 graph/R-GAT/igbh/tiny/models/utils.py      | 224 ----------------
 4 files changed, 681 deletions(-)
 delete mode 100644 graph/R-GAT/igbh/tiny/models/dataloader.py
 delete mode 100644 graph/R-GAT/igbh/tiny/models/gnn.py
 delete mode 100644 graph/R-GAT/igbh/tiny/models/main.py
 delete mode 100644 graph/R-GAT/igbh/tiny/models/utils.py

diff --git a/graph/R-GAT/igbh/tiny/models/dataloader.py b/graph/R-GAT/igbh/tiny/models/dataloader.py
deleted file mode 100644
index cc64d14668..0000000000
--- a/graph/R-GAT/igbh/tiny/models/dataloader.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import torch
-from torch_geometric.data import InMemoryDataset, Data
-from dgl.data import DGLDataset
-
-from utils import IGL260MDataset
-
-# TODO: Make a PyG dataloader for large datasets
-
-
-class IGL260M_PyG(InMemoryDataset):
-    def __init__(self, args):
-        super().__init__(root, transform, pre_transform, pre_filter)
-
-    def process(self):
-        dataset = IGL260MDataset(root=self.dir, size=args.dataset_size,
-                                 in_memory=args.in_memory, classes=args.type_classes, synthetic=args.synthetic)
-        node_features = torch.from_numpy(dataset.paper_feat)
-        node_edges = torch.from_numpy(dataset.paper_edge).T
-        node_labels = torch.from_numpy(dataset.paper_label).to(torch.long)
-        data = Data(x=node_features, edge_index=node_edges, y=node_labels)
-
-        n_nodes = node_features.shape[0]
-
-        n_train = int(n_nodes * 0.6)
-        n_val = int(n_nodes * 0.2)
-
-        train_mask = torch.zeros(n_nodes, dtype=torch.bool)
-        val_mask = torch.zeros(n_nodes, dtype=torch.bool)
-        test_mask = torch.zeros(n_nodes, dtype=torch.bool)
-
-        train_mask[:n_train] = True
-        val_mask[n_train:n_train + n_val] = True
-        test_mask[n_train + n_val:] = True
-
-        data.train_mask = train_mask
-        data.val_mask = val_mask
-        data.test_mask = test_mask
-
-
-class IGL260M_DGL(DGLDataset):
-    def __init__(self, args):
-        self.dir = args.path
-        super().__init__(name='IGB260M')
-
-    def process(self):
-        dataset = IGL260MDataset(root=self.dir, size=args.dataset_size,
-                                 in_memory=args.in_memory, classes=args.type_classes, synthetic=args.synthetic)
-        node_features = torch.from_numpy(dataset.paper_feat)
-        node_edges = torch.from_numpy(dataset.paper_edge)
-        node_labels = torch.from_numpy(dataset.paper_label).to(torch.long)
-
-        self.graph = dgl.graph(
-            (node_edges[:, 0], node_edges[:, 1]), num_nodes=node_features.shape[0])
-
-        self.graph.ndata['feat'] = node_features
-        self.graph.ndata['label'] = node_labels
-
-        self.graph = dgl.remove_self_loop(self.graph)
-        self.graph = dgl.add_self_loop(self.graph)
-
-        n_nodes = node_features.shape[0]
-
-        n_train = int(n_nodes * 0.6)
-        n_val = int(n_nodes * 0.2)
-
-        train_mask = torch.zeros(n_nodes, dtype=torch.bool)
-        val_mask = torch.zeros(n_nodes, dtype=torch.bool)
-        test_mask = torch.zeros(n_nodes, dtype=torch.bool)
-
-        train_mask[:n_train] = True
-        val_mask[n_train:n_train + n_val] = True
-        test_mask[n_train + n_val:] = True
-
-        self.graph.ndata['train_mask'] = train_mask
-        self.graph.ndata['val_mask'] = val_mask
-        self.graph.ndata['test_mask'] = test_mask
-
-    def __getitem__(self, i):
-        return self.graph
-
-    def __len__(self):
-        return 1
diff --git a/graph/R-GAT/igbh/tiny/models/gnn.py b/graph/R-GAT/igbh/tiny/models/gnn.py
deleted file mode 100644
index 20d5ecd72a..0000000000
--- a/graph/R-GAT/igbh/tiny/models/gnn.py
+++ /dev/null
@@ -1,296 +0,0 @@
-from utils import IGL260MDataset
-import warnings
-from tqdm import tqdm
-import numpy as np
-import time
-import torch.nn.functional as F
-import torch.optim as optim
-import torch.nn as nn
-import dgl
-from dgl.data import DGLDataset
-import dgl.nn.pytorch as dglnn
-from dgl.nn.pytorch import GATConv, GraphConv, SAGEConv
-import os.path as osp
-from sys import getsizeof
-
-
-import torch
-torch.manual_seed(0)
-dgl.seed(0)
-warnings.filterwarnings("ignore")
-
-
-class GCN(nn.Module):
-    def __init__(self,
-                 in_feats,
-                 n_hidden,
-                 n_classes,
-                 n_layers,
-                 activation,
-                 dropout):
-        super(GCN, self).__init__()
-        self.layers = nn.ModuleList()
-        self.n_layers = n_layers
-        self.n_hidden = n_hidden
-        self.n_classes = n_classes
-        # input layer
-        self.layers.append(
-            GraphConv(
-                in_feats,
-                n_hidden,
-                activation=activation))
-        # hidden layers
-        for i in range(n_layers - 1):
-            self.layers.append(
-                GraphConv(
-                    n_hidden,
-                    n_hidden,
-                    activation=activation))
-        # output layer
-        self.layers.append(GraphConv(n_hidden, n_classes))
-        self.dropout = nn.Dropout(p=dropout)
-        self.activation = activation
-
-    def forward(self, blocks, x):
-        h = x
-        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
-            if l != len(self.layers) - 1:
-                # h = self.activation(h)
-                h = self.dropout(h)
-            h = layer(block, h)
-        return h
-
-    def inference(self, g, x, batch_size, device):
-        """
-        Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling).
-        g : the entire graph.
-        x : the input of entire node set.
-        The inference code is written in a fashion that it could handle any number of nodes and
-        layers.
-        """
-        # During inference with sampling, multi-layer blocks are very inefficient because
-        # lots of computations in the first few layers are repeated.
-        # Therefore, we compute the representation of all nodes layer by layer.  The nodes
-        # on each layer are of course splitted in batches.
-        # TODO: can we standardize this?
-        for l, layer in enumerate(self.layers):
-            y = torch.zeros(g.number_of_nodes(), self.n_hidden if l !=
-                            len(self.layers) - 1 else self.n_classes)
-
-            sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
-            dataloader = dgl.dataloading.NodeDataLoader(
-                g,
-                torch.arange(g.number_of_nodes()),
-                sampler,
-                batch_size=batch_size,
-                shuffle=True,
-                drop_last=False,
-                num_workers=4)
-
-            for input_nodes, output_nodes, blocks in dataloader:
-                block = blocks[0]
-
-                block = block.int().to(device)
-                h = x[input_nodes].to(device)
-                h = layer(block, h)
-                if l != len(self.layers) - 1:
-                    h = self.activation(h)
-                    h = self.dropout(h)
-
-                y[output_nodes] = h.cpu()
-
-            x = y
-        return y
-
-
-class GAT(nn.Module):
-    def __init__(
-        self, in_feats, n_hidden, n_classes, n_layers, num_heads, activation
-    ):
-        super().__init__()
-        self.n_layers = n_layers
-        self.n_hidden = n_hidden
-        self.n_classes = n_classes
-        self.layers = nn.ModuleList()
-        self.layers.append(
-            dglnn.GATConv(
-                (in_feats, in_feats),
-                n_hidden,
-                num_heads=num_heads,
-                activation=activation,
-            )
-        )
-        for i in range(1, n_layers - 1):
-            self.layers.append(
-                dglnn.GATConv(
-                    (n_hidden * num_heads, n_hidden * num_heads),
-                    n_hidden,
-                    num_heads=num_heads,
-                    activation=activation,
-                )
-            )
-        self.layers.append(
-            dglnn.GATConv(
-                (n_hidden * num_heads, n_hidden * num_heads),
-                n_classes,
-                num_heads=num_heads,
-                activation=None,
-            )
-        )
-
-    def forward(self, blocks, x):
-        h = x
-        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
-            # We need to first copy the representation of nodes on the RHS from the
-            # appropriate nodes on the LHS.
-            # Note that the shape of h is (num_nodes_LHS, D) and the shape of h_dst
-            # would be (num_nodes_RHS, D)
-            h_dst = h[: block.num_dst_nodes()]
-            # Then we compute the updated representation on the RHS.
-            # The shape of h now becomes (num_nodes_RHS, D)
-            if l < self.n_layers - 1:
-                h = layer(block, (h, h_dst)).flatten(1)
-            else:
-                h = layer(block, (h, h_dst))
-        h = h.mean(1)
-        return h.log_softmax(dim=-1)
-
-    def inference(self, g, x, batch_size, device):
-        """
-        Inference with the GAT model on full neighbors (i.e. without neighbor sampling).
-        g : the entire graph.
-        x : the input of entire node set.
-        The inference code is written in a fashion that it could handle any number of nodes and
-        layers.
-        """
-        # During inference with sampling, multi-layer blocks are very inefficient because
-        # lots of computations in the first few layers are repeated.
-        # Therefore, we compute the representation of all nodes layer by layer.  The nodes
-        # on each layer are of course splitted in batches.
-        # TODO: can we standardize this?
-        # TODO: make thiw into a variable
-        num_heads = 2
-        for l, layer in enumerate(self.layers):
-            if l < self.n_layers - 1:
-                y = torch.zeros(
-                    g.num_nodes(),
-                    self.n_hidden * num_heads
-                    if l != len(self.layers) - 1
-                    else self.n_classes,
-                )
-            else:
-                y = torch.zeros(
-                    g.num_nodes(),
-                    self.n_hidden
-                    if l != len(self.layers) - 1
-                    else self.n_classes,
-                )
-
-            sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
-            dataloader = dgl.dataloading.DataLoader(
-                g,
-                torch.arange(g.num_nodes()),
-                sampler,
-                batch_size=batch_size,
-                shuffle=True,
-                drop_last=False,
-                num_workers=4,
-            )
-
-            for input_nodes, output_nodes, blocks in dataloader:
-                block = blocks[0].int().to(device)
-
-                h = x[input_nodes].to(device)
-                h_dst = h[: block.num_dst_nodes()]
-                if l < self.n_layers - 1:
-                    h = layer(block, (h, h_dst)).flatten(1)
-                else:
-                    h = layer(block, (h, h_dst))
-                    h = h.mean(1)
-                    h = h.log_softmax(dim=-1)
-
-                y[output_nodes] = h.cpu()
-
-            x = y
-        return y
-
-
-class SAGE(nn.Module):
-    def __init__(self,
-                 in_feats,
-                 n_hidden,
-                 n_classes,
-                 n_layers,
-                 activation,
-                 dropout,
-                 aggregator_type):
-        super().__init__()
-        self.n_layers = n_layers
-        self.n_hidden = n_hidden
-        self.n_classes = n_classes
-        self.layers = nn.ModuleList()
-        self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, aggregator_type))
-        for i in range(1, n_layers - 1):
-            self.layers.append(
-                dglnn.SAGEConv(
-                    n_hidden,
-                    n_hidden,
-                    aggregator_type))
-        self.layers.append(
-            dglnn.SAGEConv(
-                n_hidden,
-                n_classes,
-                aggregator_type))
-        self.dropout = nn.Dropout(dropout)
-        self.activation = activation
-
-    def forward(self, blocks, x):
-        h = x
-        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
-            h = layer(block, h)
-            if l != len(self.layers) - 1:
-                h = self.activation(h)
-                h = self.dropout(h)
-        return h
-
-    def inference(self, g, x, batch_size, device):
-        """
-        Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling).
-        g : the entire graph.
-        x : the input of entire node set.
-        The inference code is written in a fashion that it could handle any number of nodes and
-        layers.
-        """
-        # During inference with sampling, multi-layer blocks are very inefficient because
-        # lots of computations in the first few layers are repeated.
-        # Therefore, we compute the representation of all nodes layer by layer.  The nodes
-        # on each layer are of course splitted in batches.
-        # TODO: can we standardize this?
-        for l, layer in enumerate(self.layers):
-            y = torch.zeros(g.number_of_nodes(), self.n_hidden if l !=
-                            len(self.layers) - 1 else self.n_classes)
-
-            sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
-            dataloader = dgl.dataloading.NodeDataLoader(
-                g,
-                torch.arange(g.number_of_nodes()),
-                sampler,
-                batch_size=batch_size,
-                shuffle=True,
-                drop_last=False,
-                num_workers=4)
-
-            for input_nodes, output_nodes, blocks in dataloader:
-                block = blocks[0]
-
-                block = block.int().to(device)
-                h = x[input_nodes].to(device)
-                h = layer(block, h)
-                if l != len(self.layers) - 1:
-                    h = self.activation(h)
-                    h = self.dropout(h)
-
-                y[output_nodes] = h.cpu()
-
-            x = y
-        return y
diff --git a/graph/R-GAT/igbh/tiny/models/main.py b/graph/R-GAT/igbh/tiny/models/main.py
deleted file mode 100644
index 4ab22eb753..0000000000
--- a/graph/R-GAT/igbh/tiny/models/main.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import argparse
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    # Input/output paths
-    parser.add_argument('--path', type=str, default='/gnndataset/')
-    parser.add_argument('--modelpath', type=str, default='gcn_19.pt')
-
-    # Dataset selection
-    parser.add_argument(
-        '--dataset_size',
-        type=str,
-        default='experimental',
-        choices=[
-            'experimental',
-            'small',
-            'medium',
-            'large',
-            'full'])
-    parser.add_argument(
-        '--type_classes',
-        type=int,
-        default=19,
-        choices=[
-            19,
-            292,
-            2983])
-
-    # Hyperparameters
-    parser.add_argument('--hidden_channels', type=int, default=16)
-    parser.add_argument('--fan_out', type=str, default='5,10')
-    parser.add_argument('--num_layers', type=int, default=2)
-    parser.add_argument('--learning_rate', type=int, default=0.01)
-    parser.add_argument('--decay', type=int, default=0.001)
-    parser.add_argument('--num_workers', type=int, default=4)
-    parser.add_argument('--batch_size', type=int, default=2048 * 16)
-    parser.add_argument('--dropout', type=float, default=0.2)
-    parser.add_argument('--epochs', type=int, default=20)
-    parser.add_argument(
-        '--model_type',
-        type=str,
-        default='gcn',
-        choices=[
-            'gat',
-            'sage',
-            'gcn'])
-    parser.add_argument('--in_memory', type=int, default=0)
-    parser.add_argument('--synthetic', type=int, default=0)
-    parser.add_argument('--device', type=str, default='1')
-    args = parser.parse_args()
-
-    print("Dataset_size: " + args.dataset_size)
-    print("Model       : " + args.model)
-    print("Num_classes : " + str(args.num_classes))
-    print()
-
-    device = f'cuda:' + args.device if torch.cuda.is_available() else 'cpu'
-
-    dataset = IGL260M_DGL(args)
-    g = dataset[0]
-
-    best_test_acc, train_acc, test_acc = track_acc(g, args)
-
-    print(
-        f"Train accuracy: {np.mean(train_acc):.2f} \u00B1 {np.std(train_acc):.2f} \t Best: {np.max(train_acc) * 100:.4f}%")
-    print(
-        f"Test accuracy: {np.mean(test_acc):.2f} \u00B1 {np.std(test_acc):.2f} \t Best: {np.max(test_acc) * 100:.4f}%")
-    print()
-    print(" -------- For debugging --------- ")
-    print("Parameters: ", args)
-    print(g)
-    print("Train accuracy: ", train_acc)
-    print("Test accuracy: ", test_acc)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/graph/R-GAT/igbh/tiny/models/utils.py b/graph/R-GAT/igbh/tiny/models/utils.py
deleted file mode 100644
index 5e9e1a25d9..0000000000
--- a/graph/R-GAT/igbh/tiny/models/utils.py
+++ /dev/null
@@ -1,224 +0,0 @@
-import numpy as np
-import torch
-
-
-class IGL260MDataset(object):
-    def __init__(self, root: str, size: str, in_memory: int,
-                 classes: int, synthetic: int):
-        self.dir = root
-        self.size = size
-        self.synthetic = synthetic
-        self.in_memory = in_memory
-        self.num_classes = classes
-        self.__meta__ = torch.load(osp.join(self.dir, self.size, 'meta.pt'))
-
-        self.num_features = self.__meta__['paper']['emb_dim']
-        self.num_nodes = self.__meta__['paper']['num_node']
-        self.num_edges = self.__meta__['cites']['num_edge']
-
-    @property
-    def paper_feat(self) -> np.ndarray:
-        if self.synthetic:
-            return np.random((self.num_nodes, self.num_edges))
-
-        path = osp.join(
-            self.dir,
-            self.size,
-            'processed',
-            'paper',
-            'node_feat.npy')
-        if self.in_memory:
-            return np.load(path)
-        else:
-            return np.load(path, mmap_mode='r')
-
-    @property
-    def paper_label(self) -> np.ndarray:
-        if self.num_classes == 19:
-            path = osp.join(
-                self.dir,
-                self.size,
-                'processed',
-                'paper',
-                'node_label_19.npy')
-        else:
-            path = osp.join(
-                self.dir,
-                self.size,
-                'processed',
-                'paper',
-                'node_label_2K.npy')
-        if self.in_memory:
-            return np.load(path)
-        else:
-            return np.load(path, mmap_mode='r')
-
-    @property
-    def paper_edge(self) -> np.ndarray:
-        path = osp.join(
-            self.dir,
-            self.size,
-            'processed',
-            'paper__cites__paper',
-            'edge_index.npy')
-        if self.in_memory:
-            return np.load(path)
-        else:
-            return np.load(path, mmap_mode='r')
-
-
-def compute_acc(pred, labels):
-    """
-    Compute the accuracy of prediction given the labels.
-    """
-    labels = labels.long()
-    return (torch.argmax(pred, dim=1) == labels).float().sum() / len(pred)
-
-
-def evaluate(model, g, inputs, labels, val_nid, batch_size, device):
-    """
-    Evaluate the model on the validation set specified by ``val_nid``.
-    g : The entire graph.
-    inputs : The features of all the nodes.
-    labels : The labels of all the nodes.
-    val_nid : the node Ids for validation.
-    batch_size : Number of nodes to compute at the same time.
-    device : The GPU device to evaluate on.
-    """
-    model.eval()
-    with torch.no_grad():
-        pred = model.inference(g, inputs, batch_size, device)
-    model.train()
-    return compute_acc(pred[val_nid], labels[val_nid])
-
-
-def load_subtensor(g, seeds, input_nodes, device):
-    """
-    Copys features and labels of a set of nodes onto GPU.
-    """
-    batch_inputs = g.ndata['features'][input_nodes].to(device)
-    batch_labels = g.ndata['labels'][seeds].to(device)
-    return batch_inputs, batch_labels
-
-
-def track_acc(g, args):
-    train_accuracy = []
-    test_accuracy = []
-    g.ndata['features'] = g.ndata['feat']
-    g.ndata['labels'] = g.ndata['label']
-    in_feats = g.ndata['features'].shape[1]
-    n_classes = args.num_classes
-
-    # Create csr/coo/csc formats before launching training processes with multi-gpu.
-    # This avoids creating certain formats in each sub-process, which saves
-    # momory and CPU.
-    g.create_formats_()
-
-    num_epochs = args.epochs
-    num_hidden = args.hidden_channels
-    num_layers = args.num_layers
-    fan_out = args.fan_out
-    batch_size = args.batch_size
-    lr = args.learning_rate
-    dropout = args.dropout
-    num_workers = args.num_workers
-
-    train_nid = torch.nonzero(g.ndata['train_mask'], as_tuple=True)[0]
-
-    # Create PyTorch DataLoader for constructing blocks
-    sampler = dgl.dataloading.MultiLayerNeighborSampler(
-        [int(fanout) for fanout in fan_out.split(',')])
-
-    dataloader = dgl.dataloading.NodeDataLoader(
-        g,
-        train_nid,
-        sampler,
-        batch_size=batch_size,
-        shuffle=True,
-        drop_last=False,
-        num_workers=num_workers)
-
-    # Define model and optimizer
-    if args.model_type == 'gcn':
-        model = GCN(in_feats, num_hidden, n_classes, 1, F.relu, dropout)
-    if args.model_type == 'sage':
-        model = SAGE(
-            in_feats,
-            num_hidden,
-            n_classes,
-            num_layers,
-            F.relu,
-            dropout,
-            'gcn')
-    if args.model_type == 'gat':
-        model = GAT(in_feats, num_hidden, n_classes, num_layers, 2, F.relu)
-
-    model = model.to(device)
-    loss_fcn = nn.CrossEntropyLoss()
-    loss_fcn = loss_fcn.to(device)
-    optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=args.decay)
-
-    # Training loop
-    avg = 0
-    best_test_acc = 0
-    log_every = 1
-    training_start = time.time()
-    for epoch in (range(num_epochs)):
-        # Loop over the dataloader to sample the computation dependency graph as a list of
-        # blocks.
-        epoch_loss = 0
-        gpu_mem_alloc = 0
-        epoch_start = time.time()
-        for step, (input_nodes, seeds, blocks) in (enumerate(dataloader)):
-            # Load the input features as well as output labels
-            # batch_inputs, batch_labels = load_subtensor(g, seeds, input_nodes, device)
-            blocks = [block.int().to(device) for block in blocks]
-            batch_inputs = blocks[0].srcdata['features']
-            batch_labels = blocks[-1].dstdata['labels']
-
-            # Compute loss and prediction
-            batch_pred = model(blocks, batch_inputs)
-            loss = loss_fcn(batch_pred, batch_labels)
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-            epoch_loss += loss.detach()
-
-            gpu_mem_alloc += (
-                torch.cuda.max_memory_allocated() / 1000000
-                if torch.cuda.is_available()
-                else 0
-            )
-
-        train_g = g
-        train_nid = torch.nonzero(
-            train_g.ndata['train_mask'], as_tuple=True)[0]
-        train_acc = evaluate(
-            model, train_g, train_g.ndata['features'], train_g.ndata['labels'], train_nid, batch_size, device)
-
-        test_g = g
-        test_nid = torch.nonzero(
-            test_g.ndata['test_mask'], as_tuple=True)[0]
-        test_acc = evaluate(
-            model, test_g, test_g.ndata['features'], test_g.ndata['labels'], test_nid, batch_size, device)
-
-        if test_acc.item() > best_test_acc:
-            best_test_acc = test_acc.item()
-        tqdm.write(
-            "Epoch {:05d} | Loss {:.4f} | Train Acc {:.4f} | Test Acc {:.4f} | Time {:.2f}s | GPU {:.1f} MB".format(
-                epoch,
-                epoch_loss,
-                train_acc.item(),
-                test_acc.item(),
-                time.time() - epoch_start,
-                gpu_mem_alloc
-            )
-        )
-        test_accuracy.append(test_acc.item())
-        train_accuracy.append(train_acc.item())
-        torch.save(model.state_dict(), args.modelpath)
-    print()
-    print("Total time taken: ", time.time() - training_start)
-
-    return best_test_acc, train_accuracy, test_accuracy

From 03c96663dc2bd47cc5e8f5fbb0fc4079ae2c784d Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 18 Dec 2024 12:31:23 -0500
Subject: [PATCH 204/305] Update docker GPU, avoid long build time (#1966)

Co-authored-by: Miro <mirhodak@amd.com>
---
 graph/R-GAT/README.md      | 5 +++--
 graph/R-GAT/dockerfile.gpu | 6 ++----
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/graph/R-GAT/README.md b/graph/R-GAT/README.md
index 569233ac67..aecf7ffe9e 100644
--- a/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -181,9 +181,10 @@ docker build . -f dockerfile.gpu -t rgat-gpu
 ```
 Run docker container:
 ```bash
-docker run --rm -it -v $(pwd):/root --gpus all rgat-gpu
+docker run --rm -it -v $(pwd):/workspace/root --gpus all rgat-gpu
 ```
-Run benchmark inside the docker container:
+Go inside the root folder and run benchmark inside the docker container:
 ```bash
+cd root
 python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device gpu [--model-path <path_to_ckpt>] [--in-memory] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
diff --git a/graph/R-GAT/dockerfile.gpu b/graph/R-GAT/dockerfile.gpu
index fae65081f0..f600028fe2 100644
--- a/graph/R-GAT/dockerfile.gpu
+++ b/graph/R-GAT/dockerfile.gpu
@@ -26,6 +26,8 @@ RUN apt install -y --no-install-recommends rsync
 # Upgrade pip
 RUN python3 -m pip install --upgrade pip
 
+RUN pip install torch-geometric torch-scatter torch-sparse -f https://pytorch-geometric.com/whl/torch-2.1.0+cu121.html
+RUN pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/cu121/repo.html
 
 COPY requirements.txt requirements.txt
 RUN pip install -r requirements.txt
@@ -35,10 +37,6 @@ RUN cd /tmp && \
     pip install pybind11 && \
     CFLAGS="-std=c++14" python3 setup.py install
 
-RUN export TORCH_VERSION=$(python -c "import torch; print(torch.__version__)")
-RUN pip install torch-geometric torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-${TORCH_VERSION}.html
-RUN pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/cu121/repo.html
-
 # Clean up
 RUN rm -rf mlperf \
     rm requirements.txt
\ No newline at end of file

From 867def46417627eaa9de8f926721bf88167009ba Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 18 Dec 2024 13:15:33 -0500
Subject: [PATCH 205/305] Require equal issue mode for R-GAT (#1968)

* Require equal issue mode for R-GAT

* Add equal issue note in readme

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 graph/R-GAT/README.md                  | 2 ++
 loadgen/mlperf.conf                    | 3 +++
 tools/submission/submission_checker.py | 1 +
 3 files changed, 6 insertions(+)

diff --git a/graph/R-GAT/README.md b/graph/R-GAT/README.md
index aecf7ffe9e..69883c0d13 100644
--- a/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -188,3 +188,5 @@ Go inside the root folder and run benchmark inside the docker container:
 cd root
 python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device gpu [--model-path <path_to_ckpt>] [--in-memory] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
+
+**NOTE:** For official submissions, this benchmark is required to run in equal issue mode. Please make sure that the flag `rgat.*.sample_concatenate_permutation` is set to one in the [mlperf.conf](../../loadgen/mlperf.conf) file when loadgen is built.
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 1fe2022539..95cc083512 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -42,6 +42,9 @@ retinanet.MultiStream.target_latency = 528
 # 3D-UNet uses equal issue mode because it has non-uniform inputs
 3d-unet.*.sample_concatenate_permutation = 1
 
+# R-GAT uses equal issue mode because it may have non-uniform inputs
+rgat.*.sample_concatenate_permutation = 1
+
 # LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario
 gptj.*.sample_concatenate_permutation = 1
 llama2-70b.*.sample_concatenate_permutation = 1
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 4a463f3040..dcdad1180f 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -957,6 +957,7 @@ def requires_equal_issue(self, model, division):
                 "llama2-70b-99.9",
                 "mixtral-8x7b",
                 "llama3-405b",
+                "rgat",
             ]
             and self.version not in ["v4.0", "v4.1"]
         )

From b3e1e8e636908a6989a3d04c4c09c21b756f3a4a Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Wed, 18 Dec 2024 18:15:47 +0000
Subject: [PATCH 206/305] Increment version to 5.0.3

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index a1ef0cae18..50e2274e6d 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.2
+5.0.3

From 8397bec7447afc2eba5a0b630594981cc4dfed27 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Wed, 18 Dec 2024 18:17:07 +0000
Subject: [PATCH 207/305] Docs update for r-gat (#1969)

* Fixes #1648, restrict loadgen uncommitted error message to within the loadgen directory

* Update test-rnnt.yml (#1688)

Stopping the github action for rnnt

* Added docs init

Added github action for website publish

Update benchmark documentation

Update publish.yaml

Update publish.yaml

Update benchmark documentation

Improved the submission documentation

Fix taskname

Removed unused images

* Fix benchmark URLs

* Fix links

* Add _full variation to run commands

* Added script flow diagram

* Added docker setup command for CM, extra run options

* Added support for docker options in the docs

* Added --quiet to the CM run_cmds in docs

* Fix the test query count for cm commands

* Support ctuning-cpp implementation

* Added commands for mobilenet models

* Docs cleanup

* Docs cleanup

* Added separate files for dataset and models in the docs

* Remove redundant tab in the docs

* Fixes some WIP models in the docs

* Use the official docs page for CM installation

* Fix the deadlink in docs

* Fix indendation issue in docs

* Added dockerinfo for nvidia implementation

* Added run options for gptj

* Added execution environment tabs

* Cleanup of the docs

* Cleanup of the docs

* Reordered the sections of the docs page

* Removed an unnecessary heading in the docs

* Fixes the commands for datacenter

* Fix the build --sdist for loadgen

* Fixes #1761, llama2 and mixtral runtime error on CPU systems

* Added mixtral to the benchmark list, improved benchmark docs

* Update docs for MLPerf inference v4.1

* Update docs for MLPerf inference v4.1

* Fix typo

* Gave direct link to implementation readmes

* Added tables detailing implementations

* Update vision README.md, split the frameworks into separate rows

* Update README.md

* pointed links to specific frameworks

* pointed links to specific frameworks

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* api support llama2

* Added request module and reduced max token len

* Fix for llama2 api server

* Update SUT_API offline to work for OpenAI

* Update SUT_API.py

* Minor fixes

* Fix json import in SUT_API.py

* Fix llama2 token length

* Added model name verification with server

* clean temp files

* support num_workers in LLAMA2 SUTs

* Remove batching from Offline SUT_API.py

* Update SUT_API.py

* Minor fixes for llama2 API

* Fix for llama2 API

* removed table of contents

* enabled llama2-nvidia + vllm-NM : WIP

* enabled dlrm for intel

* lower cased implementation

* added raw data input

* corrected data download commands

* renamed filename

* changes for bert and vllm

* documentation to work on custom repo and branch

* benchmark index page update

* enabled sdxl for nvidia and intel

* updated vllm server run cmd

* benchmark page information addition

* fix indendation issue

* Added submission categories

* update submission page - generate submission with or w/o using CM for benchmarking

* Updated kits dataset documentation

* Updated model parameters

* updation of information

* updated non cm based benchmark

* added info about hf password

* added links to model and access tokens

* Updated reference results structuree tree

* submission docs cleanup

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* added generic stubs deepsparse

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info (FID and CLIP data added)

* typo fix for bert deepsparse framework

* added min system requirements for models

* fixed code version

* changes for displaying reference and intel implementation tip

* added reference to installation page

* updated neural magic documentation

* Added links to the install page, redirect benchmarks page

* added tips about batch size and dataset for nvidia llama2

* fix conditions logic

* modified tips and additional run cmds

* sentence corrections

* Minor fix for the documentation

* fixed bug in deepsparse generic model stubs + styling

* added more information to stubs

* Added SCC24 readme, support reproducibility in the docs

* Made clear the custom CM repo URL format

* Support conditional implementation, setup and run tips

* Support rocm for sdxl

* Fix _short tag support

* Fix install URL

* Expose bfloat16 and float16 options for sdxl

* Expose download model to host option for sdxl

* IndySCC24 documentation added

* Improve the SCC24 docs

* Improve the support of short variation

* Improved the indyscc24 documentation

* Updated scc run commands

* removed test_query_count option for scc

* Remove scc24 in the main docs

* Remove scc24 in the main docs

* Fix docs: indendation issue on the submission page

* generalised code for skipping test query count

* Fixes for SCC24 docs

* Fix scenario text in main.py

* Fix links for scc24

* Fix links for scc24

* Improve the general docs

* Fix links for scc24

* Use float16 in scc24 doc

* Improve scc24 docs

* Improve scc24 docs

* Use float16 in scc24 doc

* fixed command bug

* Fix typo in docs

* Fix typo in docs

* Remove unnecessary indendation in docs

* initial commit for tip - native run CUDA

* Updated tip

* added docker_cm_repo_branch to more run option - docker

* Update docs for IndySCC24

* Support custom repo branch and owner for final report generation

* enabled amd implementation for llama2

* updations for amd - docs

* Fix scenarios in docs page

* formatted the files to pass the gh action

* scenarios -> fixed_scenarios in docs

* [Automated Commit] Format Codebase

* Update indyscc24-bert.md

* Update scc24.md

* updated tip for reference implementation (#1912)

* [Automated Commit] Format Codebase

* fix for run suffix (#1913)

* [Automated Commit] Format Codebase

* Updation for adding submission flow diagram

* Added submission flow diagram

* Update scc24.md

* changes in submission documentation (#1946)

* update results category (#1947)

* changes for adding rgat to docs (#1965)

* Update index.md | Added R-GAT details (WIP)

* Update index.md

* Create system_requirements.yml

* Update system_requirements.yml

* Update system_requirements.yml

* Update system_requirements.yml

---------

Co-authored-by: anandhu-eng <anandhukicks@gmail.com>
Co-authored-by: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>
Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Co-authored-by: Mitchelle Rasquinha <80070689+mrasquinha-g@users.noreply.github.com>
Co-authored-by: Miro <mirhodak@amd.com>
---
 docs/benchmarks/graph/get-rgat-data.md |  39 ++++++
 docs/benchmarks/graph/rgat.md          |  13 ++
 docs/index.md                          |  19 ++-
 docs/submission/index.md               | 160 +++++++++++++------------
 docs/system_requirements.yml           |  50 ++++++++
 main.py                                |   9 +-
 mkdocs.yml                             |   2 +
 7 files changed, 211 insertions(+), 81 deletions(-)
 create mode 100644 docs/benchmarks/graph/get-rgat-data.md
 create mode 100644 docs/benchmarks/graph/rgat.md
 create mode 100644 docs/system_requirements.yml

diff --git a/docs/benchmarks/graph/get-rgat-data.md b/docs/benchmarks/graph/get-rgat-data.md
new file mode 100644
index 0000000000..189c25b874
--- /dev/null
+++ b/docs/benchmarks/graph/get-rgat-data.md
@@ -0,0 +1,39 @@
+---
+hide:
+  - toc
+---
+
+# Graph Neural Network using R-GAT 
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Full Dataset"
+    R-GAT validation run uses the IGBH dataset consisting of 547,306,935 nodes and 5,812,005,639 edges.
+
+    ### Get Full Dataset
+    ```
+    cm run script --tags=get,dataset,igbh,_full -j
+    ```
+
+=== "Debug Dataset"
+    R-GAT debug run uses the IGBH debug dataset(tiny).
+
+    ### Get Full Dataset
+    ```
+    cm run script --tags=get,dataset,igbh,_debug -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf R-GAT Model
+
+=== "PyTorch"
+
+    ### PyTorch
+    ```
+    cm run script --tags=get,ml-model,rgat -j
+    ```
+
diff --git a/docs/benchmarks/graph/rgat.md b/docs/benchmarks/graph/rgat.md
new file mode 100644
index 0000000000..ffff467a49
--- /dev/null
+++ b/docs/benchmarks/graph/rgat.md
@@ -0,0 +1,13 @@
+---
+hide:
+  - toc
+---
+
+
+# Graph Neural Network using R-GAT 
+
+
+=== "MLCommons-Python"
+    ## MLPerf Reference Implementation in Python
+    
+{{ mlperf_inference_implementation_readme (4, "rgat", "reference", devices = ["CPU", "CUDA"]) }}
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
index 11f2a52c21..b46d4c2749 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,7 +1,7 @@
 # MLPerf Inference Benchmarks
 
 ## Overview
-The currently valid [MLPerf Inference Benchmarks](index_gh.md) as of MLPerf inference v4.0 round are listed below, categorized by tasks. Under each model you can find its details like the dataset used, reference accuracy, server latency constraints etc.
+The currently valid [MLPerf Inference Benchmarks](index_gh.md) as of MLPerf inference v5.0 round are listed below, categorized by tasks. Under each model you can find its details like the dataset used, reference accuracy, server latency constraints etc.
 
 ---
 
@@ -80,7 +80,7 @@ The currently valid [MLPerf Inference Benchmarks](index_gh.md) as of MLPerf infe
 - **Server Scenario Latency Constraint**: 130ms
 - **Equal Issue mode**: False
 - **High accuracy variant**: yes
-- **Submission Category**: Datacenter, Edge
+- **Submission Category**: Edge
 
 #### [LLAMA2-70B](benchmarks/language/llama2-70b.md)
 - **Dataset**: OpenORCA (GPT-4 split, max_seq_len=1024)
@@ -157,11 +157,22 @@ The currently valid [MLPerf Inference Benchmarks](index_gh.md) as of MLPerf infe
 - **High accuracy variant**: Yes
 - **Submission Category**: Datacenter
 
+## Graph Neural Networks
+### [R-GAT](benchmarks/graph/rgat.md)
+- **Dataset**: Illinois Graph Benchmark Heterogeneous validation dataset
+    - **Dataset Size**: 788,379
+    - **QSL Size**: 788,379
+- **Number of Parameters**: 
+- **Reference Model Accuracy**: ACC = ?
+- **Server Scenario Latency Constraint**: N/A
+- **Equal Issue mode**: True
+- **High accuracy variant**: No
+- **Submission Category**: Datacenter
 ---
 
 ## Submission Categories
-- **Datacenter Category**: All the current inference benchmarks are applicable to the datacenter category.
-- **Edge Category**: All benchmarks except DLRMv2, LLAMA2-70B, and Mixtral-8x7B are applicable to the edge category.
+- **Datacenter Category**: All benchmarks except bert are applicable to the datacenter category for inference v5.0.
+- **Edge Category**: All benchmarks except DLRMv2, LLAMA2-70B, Mixtral-8x7B and R-GAT are applicable to the edge category for v5.0.
 
 ## High Accuracy Variants
 - **Benchmarks**: `bert`, `llama2-70b`, `gpt-j`,  `dlrm_v2`, and `3d-unet` have a normal accuracy variant as well as a high accuracy variant.
diff --git a/docs/submission/index.md b/docs/submission/index.md
index c998024206..1050f5fb0e 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -13,13 +13,15 @@ hide:
 
 Click [here](https://youtu.be/eI1Hoecc3ho) to view the recording of the workshop: Streamlining your MLPerf Inference results using CM.
 
-=== "CM based benchmark"
+Click [here](https://docs.google.com/presentation/d/1cmbpZUpVr78EIrhzyMBnnWnjJrD-mZ2vmSb-yETkTA8/edit?usp=sharing) to view the prposal slide for Common Automation for MLPerf Inference Submission Generation through CM.
+
+=== "CM based results"
     If you have followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, all the valid results will get aggregated to the `cm cache` folder. The following command could be used to browse the structure of inference results folder generated by CM.
     ### Get results folder structure
     ```bash
     cm find cache --tags=get,mlperf,inference,results,dir | xargs tree
     ```
-=== "Non CM based benchmark"
+=== "Non CM based results"
     If you have not followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, please make sure that the result directory is structured in the following way. 
     ```
     └── System description ID(SUT Name)
@@ -35,18 +37,20 @@ Click [here](https://youtu.be/eI1Hoecc3ho) to view the recording of the workshop
                 |   ├── mlperf_log_detail.txt
                 |   ├── mlperf_log_accuracy.json
                 |   └── accuracy.txt
-                └── Compliance_Test_ID
-                    ├── Performance
-                    |   └── run_x/#1 run for all scenarios
-                    |       ├── mlperf_log_summary.txt
-                    |       └── mlperf_log_detail.txt
-                    ├── Accuracy
-                    |   ├── baseline_accuracy.txt
-                    |   ├── compliance_accuracy.txt
-                    |   ├── mlperf_log_accuracy.json
-                    |   └── accuracy.txt
-                    ├── verify_performance.txt
-                    └── verify_accuracy.txt #for TEST01 only
+                |── Compliance_Test_ID
+                |   ├── Performance
+                |   |   └── run_x/#1 run for all scenarios
+                |   |       ├── mlperf_log_summary.txt
+                |   |       └── mlperf_log_detail.txt
+                |   ├── Accuracy
+                |   |   ├── baseline_accuracy.txt
+                |   |   ├── compliance_accuracy.txt
+                |   |   ├── mlperf_log_accuracy.json
+                |   |   └── accuracy.txt
+                |   ├── verify_performance.txt
+                |   └── verify_accuracy.txt #for TEST01 only
+                |── user.conf
+                └── measurements.json
     ```
     
     <details>
@@ -67,67 +71,69 @@ Once all the results across all the models are ready you can use the following c
 
 ## Generate actual submission tree
 
-=== "Closed Edge"
-    ### Closed Edge Submission
-    ```bash
-    cm run script --tags=generate,inference,submission \
-       --clean \
-       --preprocess_submission=yes \
-       --run-checker \
-       --submitter=MLCommons \
-       --tar=yes \
-       --env.CM_TAR_OUTFILE=submission.tar.gz \
-       --division=closed \
-       --category=edge \
-       --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-       --quiet
-    ```
-
-=== "Closed Datacenter"
-    ### Closed Datacenter Submission
-    ```bash
-    cm run script --tags=generate,inference,submission \
-       --clean \
-       --preprocess_submission=yes \
-       --run-checker \
-       --submitter=MLCommons \
-       --tar=yes \
-       --env.CM_TAR_OUTFILE=submission.tar.gz \
-       --division=closed \
-       --category=datacenter \
-       --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-       --quiet
-    ```
-=== "Open Edge"
-    ### Open Edge Submission
-    ```bash
-    cm run script --tags=generate,inference,submission \
-       --clean \
-       --preprocess_submission=yes \
-       --run-checker \
-       --submitter=MLCommons \
-       --tar=yes \
-       --env.CM_TAR_OUTFILE=submission.tar.gz \
-       --division=open \
-       --category=edge \
-       --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-       --quiet
-    ```
-=== "Open Datacenter"
-    ### Closed Datacenter Submission
-    ```bash
-    cm run script --tags=generate,inference,submission \
-       --clean \
-       --preprocess_submission=yes \
-       --run-checker \
-       --submitter=MLCommons \
-       --tar=yes \
-       --env.CM_TAR_OUTFILE=submission.tar.gz \
-       --division=open \
-       --category=datacenter \
-       --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-       --quiet
-    ```
+=== "Docker run"
+    ### Docker run
+    === "Closed"
+        ### Closed Submission
+        ```bash
+        cm docker script --tags=generate,inference,submission \
+            --clean \
+            --preprocess_submission=yes \
+            --run-checker \
+            --submitter=MLCommons \
+            --tar=yes \
+            --env.CM_TAR_OUTFILE=submission.tar.gz \
+            --division=closed \
+            --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+            --quiet
+        ```
+
+    === "Open"
+        ### Open Submission
+        ```bash
+        cm docker script --tags=generate,inference,submission \
+            --clean \
+            --preprocess_submission=yes \
+            --run-checker \
+            --submitter=MLCommons \
+            --tar=yes \
+            --env.CM_TAR_OUTFILE=submission.tar.gz \
+            --division=open \
+            --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+            --quiet
+        ```
+
+=== "Native run"
+    ### Native run
+    === "Closed"
+        ### Closed Submission
+        ```bash
+        cm run script --tags=generate,inference,submission \
+            --clean \
+            --preprocess_submission=yes \
+            --run-checker \
+            --submitter=MLCommons \
+            --tar=yes \
+            --env.CM_TAR_OUTFILE=submission.tar.gz \
+            --division=closed \
+            --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+            --quiet
+        ```
+
+    === "Open"
+        ### Open Submission
+        ```bash
+        cm run script --tags=generate,inference,submission \
+            --clean \
+            --preprocess_submission=yes \
+            --run-checker \
+            --submitter=MLCommons \
+            --tar=yes \
+            --env.CM_TAR_OUTFILE=submission.tar.gz \
+            --division=open \
+            --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+            --quiet
+        ```
 
 * Use `--hw_name="My system name"` to give a meaningful system name. Examples can be seen [here](https://github.com/mlcommons/inference_results_v3.0/tree/main/open/cTuning/systems)
 
@@ -137,6 +143,10 @@ Once all the results across all the models are ready you can use the following c
 
 * Use `--results_dir` option to specify the results folder for Non CM based benchmarks
 
+* Use `--category` option to specify the category for which submission is generated(datacenter/edge). By default, the category is taken from `system_meta.json` file located in the SUT root directory.
+
+* Use `--submission_base_dir` to specify the directory to which outputs from preprocess submission script and final submission is to be dumped. No need to provide `--submission_dir` along with this. For `docker run`, use `--submission_base_dir` instead of `--submission_dir`.
+
 The above command should generate "submission.tar.gz" if there are no submission checker issues and you can upload it to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission).
 
 ## Aggregate Results in GitHub
diff --git a/docs/system_requirements.yml b/docs/system_requirements.yml
new file mode 100644
index 0000000000..5dfec202a5
--- /dev/null
+++ b/docs/system_requirements.yml
@@ -0,0 +1,50 @@
+# All memory requirements in GB
+resnet:
+  reference:
+    fp32:
+      system_memory: 8
+      accelerator_memory: 4
+      disk_storage: 25
+  nvidia:
+    int8:
+      system_memory: 8
+      accelerator_memory: 4
+      disk_storage: 100
+  intel:
+    int8:
+      system_memory: 8
+      accelerator_memory: 0
+      disk_storage: 50
+  qualcomm:
+    int8:
+      system_memory: 8
+      accelerator_memory: 8
+      disk_storage: 50
+retinanet:
+  reference:
+    fp32:
+      system_memory: 8
+      accelerator_memory: 8
+      disk_storage: 200
+  nvidia:
+    int8:
+      system_memory: 8
+      accelerator_memory: 8
+      disk_storage: 200
+  intel:
+    int8:
+      system_memory: 8
+      accelerator_memory: 0
+      disk_storage: 200
+  qualcomm:
+    int8:
+      system_memory: 8
+      accelerator_memory: 8
+      disk_storage: 200
+rgat:
+  reference:
+    fp32:
+      system_memory: 768
+      accelerator_memory: 8
+      disk_storage: 2300
+
diff --git a/main.py b/main.py
index c8c64b8c31..c5b22a7055 100755
--- a/main.py
+++ b/main.py
@@ -239,7 +239,8 @@ def mlperf_inference_implementation_readme(
 
                             common_info = get_common_info(
                                 spaces + 16,
-                                implementation
+                                implementation,
+                                model.lower()
                             )
 
                             if (
@@ -488,7 +489,7 @@ def get_venv_command(spaces):
 
     # contains run command information which is common to both docker and
     # native runs
-    def get_common_info(spaces, implementation):
+    def get_common_info(spaces, implementation, model):
         info = ""
         pre_space = ""
         for i in range(1, spaces):
@@ -496,7 +497,11 @@ def get_common_info(spaces, implementation):
         pre_space += " "
         # pre_space = "                "
         info += f"\n{pre_space}!!! tip\n\n"
+        info += f"{pre_space}    - Number of threads could be adjusted using `--threads=#`, where `#` is the desired number of threads. This option works only if the implementation in use supports threading.\n\n"
         info += f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
+        if model == "rgat":
+            info += f"{pre_space}    - Add `--env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset>` if you have already downloaded the dataset. The path will be automatically mounted when using docker run.\n\n"
+            info += f"{pre_space}    - Add `--env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint>` if you have already downloaded the model. The path will be automatically mounted when using docker run.\n\n"  
         if implementation.lower() == "reference":
             info += f"{pre_space}    - Add `--adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK>` if you are modifying the official MLPerf Inference implementation in a custom fork.\n\n"
             info += f"{pre_space}    - Add `--adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK>` if you are modifying the model config accuracy script in the submission checker within a custom fork.\n\n"
diff --git a/mkdocs.yml b/mkdocs.yml
index 95dfb6e861..96bcfb7583 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -42,6 +42,8 @@ nav:
       - MIXTRAL-8x7B: benchmarks/language/mixtral-8x7b.md
     - Recommendation:
       - DLRM-v2: benchmarks/recommendation/dlrm-v2.md
+    - Graph Neural Networks:
+      - R-GAT: benchmarks/graph/rgat.md
   - Install CM:
     - install/index.md
   - Submission:

From 647f9f84ff91394eb865ed9eaf5de688a1d37448 Mon Sep 17 00:00:00 2001
From: mlcommons-bot <mlcommons-bot@users.noreply.github.com>
Date: Wed, 18 Dec 2024 18:17:31 +0000
Subject: [PATCH 208/305] [Automated Commit] Format Codebase

---
 main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main.py b/main.py
index c5b22a7055..6a34587dd6 100755
--- a/main.py
+++ b/main.py
@@ -501,7 +501,7 @@ def get_common_info(spaces, implementation, model):
         info += f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
         if model == "rgat":
             info += f"{pre_space}    - Add `--env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset>` if you have already downloaded the dataset. The path will be automatically mounted when using docker run.\n\n"
-            info += f"{pre_space}    - Add `--env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint>` if you have already downloaded the model. The path will be automatically mounted when using docker run.\n\n"  
+            info += f"{pre_space}    - Add `--env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint>` if you have already downloaded the model. The path will be automatically mounted when using docker run.\n\n"
         if implementation.lower() == "reference":
             info += f"{pre_space}    - Add `--adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK>` if you are modifying the official MLPerf Inference implementation in a custom fork.\n\n"
             info += f"{pre_space}    - Add `--adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK>` if you are modifying the model config accuracy script in the submission checker within a custom fork.\n\n"

From e6069aa91c2d8e75f62b00fbdd566b399e77f3f8 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Wed, 18 Dec 2024 23:48:11 +0530
Subject: [PATCH 209/305] Update automated run command section - R-GAT (#1970)

* Update automated run command section

* add cm commands for model and dataset downloads

* Update README.md

* Update cm run cmds

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 graph/R-GAT/README.md | 53 ++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 52 insertions(+), 1 deletion(-)

diff --git a/graph/R-GAT/README.md b/graph/R-GAT/README.md
index 69883c0d13..fbfca47092 100644
--- a/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -19,7 +19,7 @@ This is the reference implementation for MLPerf Inference Graph Neural Network.
 
 ## Automated command to run the benchmark via MLCommons CM
 
-TODO
+Please check the official inference documentation [here](https://docs.mlcommons.org/inference/benchmarks/graph/rgat/)
  
 ## Setup
 Set the following helper variables
@@ -95,6 +95,12 @@ You can then navigate in the terminal to your desired download directory and run
 rclone copy mlc-inference:mlcommons-inference-wg-public/R-GAT/RGAT.pt $MODEL_PATH -P
 ```
 
+### Download model through CM (Collective Minds)
+
+```
+cm run script --tags=get,ml-model,rgat -j
+```
+
 ### Download and setup dataset
 #### Debug Dataset
 
@@ -110,6 +116,10 @@ cd $GRAPH_FOLDER
 python3 tools/split_seeds.py --path igbh --dataset_size tiny
 ```
 
+**CM Command**
+```
+cm run script --tags=get,dataset,igbh,_debug -j
+```
 
 #### Full Dataset
 **Warning:** This script will download 2.2TB of data 
@@ -124,6 +134,11 @@ cd $GRAPH_FOLDER
 python3 tools/split_seeds.py --path igbh --dataset_size full
 ```
 
+**CM Command**
+```
+cm run script --tags=get,dataset,igbh,_full -j
+```
+
 
 #### Calibration dataset
 
@@ -140,6 +155,21 @@ cd $GRAPH_FOLDER
 python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
+##### Debug Run using CM
+```
+cm run script --tags=run-mlperf,inference,_submission,_short,_r5.0-dev \
+   --model=rgat \
+   --implementation=reference \
+   --framework=pytorch \
+   --category=edge \
+   --scenario=Offline \
+   --execution_mode=test \
+   --device=<cpu or cuda> \
+   --quiet \
+   --test_query_count=10 \
+   --docker
+```
+
 #### Local run
 ```bash
 # Go to the benchmark folder
@@ -148,6 +178,27 @@ cd $GRAPH_FOLDER
 # Run the benchmark DGL
 python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
+
+##### Local Run using CM
+```
+cm run script --tags=run-mlperf,inference,_submission,_full,_r5.0-dev \
+   --model=rgat \
+   --implementation=reference \
+   --framework=pytorch \
+   --category=edge \
+   --scenario=Offline \
+   --execution_mode=test \
+   --device=<>cpu or cuda> \
+   --quiet \
+   --test_query_count=10 \
+   --docker
+```
+
+- Number of threads could be adjusted using `--threads=#`, where # is the desired number of threads. This option works only if the implementation in use supports threading.
+- Batch size could be adjusted using `--batch_size=#`, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.
+- Add `--env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset>` if you have already downloaded the dataset. The path will be automatically mounted when using docker run.
+- Add `--env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint>` if you have already downloaded the model. The path will be automatically mounted when using docker run.
+
 #### Run using docker
 
 Not implemented yet

From 00945c3b0378a48fc385a0fd2851f02f40f49863 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 19 Dec 2024 01:36:42 -0500
Subject: [PATCH 210/305] Unify llama3 names to llama3.1-405b (#1982)

* Unify llama3 names to llama3.1-405b

* Set mlperf.conf name to llama3_1-405b
---
 .../{llama3-405b => llama3.1-405b}/Dockerfile |  2 +-
 .../{llama3-405b => llama3.1-405b}/README.md  | 20 +++++------
 .../SUT_VLLM.py                               |  0
 .../{llama3-405b => llama3.1-405b}/build.sh   |  0
 .../{llama3-405b => llama3.1-405b}/dataset.py |  0
 .../evaluate-accuracy.py                      |  2 +-
 .../launch_docker.sh                          |  0
 .../{llama3-405b => llama3.1-405b}/main.py    |  4 +--
 .../requirements.txt                          |  0
 .../run_accuracy.sh                           |  0
 .../run_offline.sh                            |  0
 .../run_server.sh                             |  0
 .../{llama3-405b => llama3.1-405b}/user.conf  |  2 +-
 .../with_the_same_user                        |  0
 loadgen/mlperf.conf                           | 16 ++++-----
 tools/submission/generate_final_report.py     |  2 +-
 tools/submission/submission_checker.py        | 35 ++++++++++---------
 17 files changed, 42 insertions(+), 41 deletions(-)
 rename language/{llama3-405b => llama3.1-405b}/Dockerfile (97%)
 rename language/{llama3-405b => llama3.1-405b}/README.md (87%)
 rename language/{llama3-405b => llama3.1-405b}/SUT_VLLM.py (100%)
 rename language/{llama3-405b => llama3.1-405b}/build.sh (100%)
 rename language/{llama3-405b => llama3.1-405b}/dataset.py (100%)
 rename language/{llama3-405b => llama3.1-405b}/evaluate-accuracy.py (98%)
 rename language/{llama3-405b => llama3.1-405b}/launch_docker.sh (100%)
 rename language/{llama3-405b => llama3.1-405b}/main.py (97%)
 rename language/{llama3-405b => llama3.1-405b}/requirements.txt (100%)
 rename language/{llama3-405b => llama3.1-405b}/run_accuracy.sh (100%)
 rename language/{llama3-405b => llama3.1-405b}/run_offline.sh (100%)
 rename language/{llama3-405b => llama3.1-405b}/run_server.sh (100%)
 rename language/{llama3-405b => llama3.1-405b}/user.conf (87%)
 rename language/{llama3-405b => llama3.1-405b}/with_the_same_user (100%)

diff --git a/language/llama3-405b/Dockerfile b/language/llama3.1-405b/Dockerfile
similarity index 97%
rename from language/llama3-405b/Dockerfile
rename to language/llama3.1-405b/Dockerfile
index 67edcc46b9..14d0a202d9 100644
--- a/language/llama3-405b/Dockerfile
+++ b/language/llama3.1-405b/Dockerfile
@@ -44,7 +44,7 @@ WORKDIR /tmp
 RUN wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.5.2-0-Linux-x86_64.sh \
     && bash Miniconda3-* -b -p /opt/miniconda3
 ENV PATH="$PATH:/opt/miniconda3/bin"
-RUN conda create -n llama3-405b python=3.10
+RUN conda create -n llama3.1-405b python=3.10
 RUN chmod -R 777 /opt/miniconda3
 
 # Set the env variable for vLLM
diff --git a/language/llama3-405b/README.md b/language/llama3.1-405b/README.md
similarity index 87%
rename from language/llama3-405b/README.md
rename to language/llama3.1-405b/README.md
index 8df2a81f10..d1dd5ad4f7 100644
--- a/language/llama3-405b/README.md
+++ b/language/llama3.1-405b/README.md
@@ -1,13 +1,13 @@
-# Reference Implementation for llama3-405b
+# Reference Implementation for llama3.1-405b
 
-**Basic implementation for llama3-405b. Few noteworthy items:**
+**Basic implementation for llama3.1-405b. Few noteworthy items:**
 
 + Streamer for communicating with loadgen has quite some overhead. This is only meant to provide functional implementation
 + For custom/optimized implementations of this benchmark it is important to include the :
         - For server scenario, it is necessary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
         - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
 
-Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama3-405b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama3.1-405b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
 
 ## Prepare environment
@@ -33,9 +33,9 @@ rm ~/miniconda3/miniconda.sh
 - Set the following helper variables
 ```bash
 export ROOT=$PWD/inference
-export LLAMA_FOLDER=$PWD/inference/language/llama3-405b
+export LLAMA_FOLDER=$PWD/inference/language/llama3.1-405b
 export LOADGEN_FOLDER=$PWD/inference/loadgen
-export DATASET_FOLDER=$PWD/inference/language/llama3-405b/dataset
+export DATASET_FOLDER=$PWD/inference/language/llama3.1-405b/dataset
 ```
 
 - Clone the inference repository:
@@ -46,8 +46,8 @@ git clone --recurse-submodules https://github.com/mlcommons/inference.git \
 
 - Create a conda environment:
 ```bash
-conda create -y -n llama3-405b python=3.10
-conda activate llama3-405b
+conda create -y -n llama3.1-405b python=3.10
+conda activate llama3.1-405b
 conda install -y -c conda-forge libstdcxx-ng=12
 ```
 
@@ -100,7 +100,7 @@ TODO: Host model and grant access to submitters
 
 
 ### External Download
-+ First go to [llama3-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloning below.
++ First go to [llama3.1-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloning below.
 + Requires Git Large Files Storage
 ```
 export CHECKPOINT_PATH=Meta-Llama-3.1-405B-Instruct
@@ -127,13 +127,13 @@ rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5ee
 You can then navigate in the terminal to your desired download directory and run the following command to download the dataset:
 
 ```
-rclone copy mlc-inference:mlcommons-inference-wg-public/llama3_405b/mlperf_llama3.1_405b_dataset_8313_processed_fp16_eval.pkl ./ -P
+rclone copy mlc-inference:mlcommons-inference-wg-public/llama3.1_405b/mlperf_llama3.1_405b_dataset_8313_processed_fp16_eval.pkl ./ -P
 ```
 
 You can also download the calibration dataset from the Cloudflare R2 bucket by running the following command:
 
 ```
-rclone copy mlc-inference:mlcommons-inference-wg-public/llama3_405b/mlperf_llama3.1_405b_calibration_dataset_512_processed_fp16_eval.pkl ./ -P
+rclone copy mlc-inference:mlcommons-inference-wg-public/llama3.1_405b/mlperf_llama3.1_405b_calibration_dataset_512_processed_fp16_eval.pkl ./ -P
 ```
 
 ## Run Performance Benchmarks
diff --git a/language/llama3-405b/SUT_VLLM.py b/language/llama3.1-405b/SUT_VLLM.py
similarity index 100%
rename from language/llama3-405b/SUT_VLLM.py
rename to language/llama3.1-405b/SUT_VLLM.py
diff --git a/language/llama3-405b/build.sh b/language/llama3.1-405b/build.sh
similarity index 100%
rename from language/llama3-405b/build.sh
rename to language/llama3.1-405b/build.sh
diff --git a/language/llama3-405b/dataset.py b/language/llama3.1-405b/dataset.py
similarity index 100%
rename from language/llama3-405b/dataset.py
rename to language/llama3.1-405b/dataset.py
diff --git a/language/llama3-405b/evaluate-accuracy.py b/language/llama3.1-405b/evaluate-accuracy.py
similarity index 98%
rename from language/llama3-405b/evaluate-accuracy.py
rename to language/llama3.1-405b/evaluate-accuracy.py
index f5677820e7..7c803e1ca2 100644
--- a/language/llama3-405b/evaluate-accuracy.py
+++ b/language/llama3.1-405b/evaluate-accuracy.py
@@ -15,7 +15,7 @@ def get_args():
     parser.add_argument(
         "--checkpoint-path",
         default="meta-llama/Meta-Llama-3-8B",
-        help="Path to Llama3-405b-hf-chat checkpoint"
+        help="Path to Llama3.1-405b-hf-chat checkpoint"
     )
     parser.add_argument(
         "--mlperf-accuracy-file", required=True, help="path to mlperf_log_accuracy.json"
diff --git a/language/llama3-405b/launch_docker.sh b/language/llama3.1-405b/launch_docker.sh
similarity index 100%
rename from language/llama3-405b/launch_docker.sh
rename to language/llama3.1-405b/launch_docker.sh
diff --git a/language/llama3-405b/main.py b/language/llama3.1-405b/main.py
similarity index 97%
rename from language/llama3-405b/main.py
rename to language/llama3.1-405b/main.py
index f7802687ed..32f80060c2 100644
--- a/language/llama3-405b/main.py
+++ b/language/llama3.1-405b/main.py
@@ -136,8 +136,8 @@ def main():
     settings = lg.TestSettings()
     settings.scenario = scenario_map[args.scenario.lower()]
     # mlperf.conf is automatically loaded by the loadgen
-    # settings.FromConfig(args.mlperf_conf, "llama3-405b", args.scenario)
-    settings.FromConfig(args.user_conf, "llama3-405b", args.scenario)
+    # settings.FromConfig(args.mlperf_conf, "llama3_1-405b", args.scenario)
+    settings.FromConfig(args.user_conf, "llama3_1-405b", args.scenario)
 
     if args.accuracy:
         settings.mode = lg.TestMode.AccuracyOnly
diff --git a/language/llama3-405b/requirements.txt b/language/llama3.1-405b/requirements.txt
similarity index 100%
rename from language/llama3-405b/requirements.txt
rename to language/llama3.1-405b/requirements.txt
diff --git a/language/llama3-405b/run_accuracy.sh b/language/llama3.1-405b/run_accuracy.sh
similarity index 100%
rename from language/llama3-405b/run_accuracy.sh
rename to language/llama3.1-405b/run_accuracy.sh
diff --git a/language/llama3-405b/run_offline.sh b/language/llama3.1-405b/run_offline.sh
similarity index 100%
rename from language/llama3-405b/run_offline.sh
rename to language/llama3.1-405b/run_offline.sh
diff --git a/language/llama3-405b/run_server.sh b/language/llama3.1-405b/run_server.sh
similarity index 100%
rename from language/llama3-405b/run_server.sh
rename to language/llama3.1-405b/run_server.sh
diff --git a/language/llama3-405b/user.conf b/language/llama3.1-405b/user.conf
similarity index 87%
rename from language/llama3-405b/user.conf
rename to language/llama3.1-405b/user.conf
index 9f4eb5f9a6..30681302c4 100644
--- a/language/llama3-405b/user.conf
+++ b/language/llama3.1-405b/user.conf
@@ -10,4 +10,4 @@
 *.Server.min_duration = 120000
 *.Server.min_query_count = 100
 
-llama3-405b.Server.sample_concatenate_permutation = 1
\ No newline at end of file
+llama3_1-405b.Server.sample_concatenate_permutation = 1
\ No newline at end of file
diff --git a/language/llama3-405b/with_the_same_user b/language/llama3.1-405b/with_the_same_user
similarity index 100%
rename from language/llama3-405b/with_the_same_user
rename to language/llama3.1-405b/with_the_same_user
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 95cc083512..1d036f4b48 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -14,7 +14,7 @@ dlrm-v2.*.performance_sample_count_override = 204800
 rnnt.*.performance_sample_count_override = 2513
 gptj.*.performance_sample_count_override = 13368
 llama2-70b.*.performance_sample_count_override = 24576
-llama3-405b.*.performance_sample_count_override = 8313
+llama3_1-405b.*.performance_sample_count_override = 8313
 stable-diffusion-xl.*.performance_sample_count_override = 5000
 rgat.*.performance_sample_count_override = 788379
 # set to 0 to let entire sample set to be performance sample
@@ -49,7 +49,7 @@ rgat.*.sample_concatenate_permutation = 1
 gptj.*.sample_concatenate_permutation = 1
 llama2-70b.*.sample_concatenate_permutation = 1
 mixtral-8x7b.*.sample_concatenate_permutation = 1
-llama3-405b.*.sample_concatenate_permutation = 1
+llama3_1-405b.*.sample_concatenate_permutation = 1
 
 *.Server.target_latency = 10
 *.Server.target_latency_percentile = 99
@@ -66,11 +66,11 @@ stable-diffusion-xl.Server.target_latency = 20000
 # Benchmarks that measure token latencies
 llama2-70b.*.use_token_latencies = 1
 mixtral-8x7b.*.use_token_latencies = 1
-llama3-405b.*.use_token_latencies = 1
+llama3_1-405b.*.use_token_latencies = 1
 # gptj benchmark infers token latencies
 gptj.*.infer_token_latencies = 1
 gptj.*.token_latency_scaling_factor = 69
-# Only ttft and tpot are tracked for the llama2-70b, mixtral-8x7B & llama3-405b benchmark therefore target_latency = 0
+# Only ttft and tpot are tracked for the llama2-70b, mixtral-8x7B & llama3_1-405b benchmark therefore target_latency = 0
 llama2-70b.Server.target_latency = 0
 llama2-70b.Server.ttft_latency = 2000
 llama2-70b.Server.tpot_latency = 200
@@ -79,9 +79,9 @@ mixtral-8x7b.Server.target_latency = 0
 mixtral-8x7b.Server.ttft_latency = 2000
 mixtral-8x7b.Server.tpot_latency = 200
 
-llama3-405b.Server.target_latency = 0
-llama3-405b.Server.ttft_latency = 6000
-llama3-405b.Server.tpot_latency = 175
+llama3_1-405b.Server.target_latency = 0
+llama3_1-405b.Server.ttft_latency = 6000
+llama3_1-405b.Server.tpot_latency = 175
 
 *.Offline.target_latency_percentile = 90
 *.Offline.min_duration = 600000
@@ -100,7 +100,7 @@ rnnt.Offline.min_query_count = 2513
 3d-unet.Offline.min_query_count = 43
 stable-diffusion-xl.Offline.min_query_count = 5000
 llama2-70b.Offline.min_query_count = 24576
-llama3-405b.Offline.min_query_count = 8313
+llama3_1-405b.Offline.min_query_count = 8313
 mixtral-8x7b.Offline.min_query_count = 15000
 rgat.Offline.min_query_count = 788379
 
diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 34ae82fb15..aa5b369835 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -211,7 +211,7 @@ def main():
                 "llama2-70b-99.9": ["Server", "Offline"],
                 "mixtral-8x7b": ["Server", "Offline"],
                 "rgat": ["Offline"],
-                "llama3-405b": ["Offline", "Server"]
+                "llama3.1-405b": ["Offline", "Server"]
             },
             "edge": {
                 "resnet": ["SingleStream", "MultiStream", "Offline"],
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index dcdad1180f..26d5212f90 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -194,6 +194,7 @@
             "ssd-resnet34": "retinanet",
             "mobilenet": "resnet",
             "resnet50": "resnet",
+            "llama3_1-405b": "llama3.1-405b"
         },
         "seeds": {
             "qsl_rng_seed": 3066443479025735752,
@@ -267,7 +268,7 @@
             "llama2-70b-99.9",
             "stable-diffusion-xl",
             "mixtral-8x7b",
-            "llama3-405b",
+            "llama3.1-405b",
             "rgat",
             # TODO: add automotive?
         ],
@@ -284,7 +285,7 @@
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["Server", "Offline"],
             "mixtral-8x7b": ["Server", "Offline"],
-            "llama3-405b": ["Server", "Offline"],
+            "llama3.1-405b": ["Server", "Offline"],
             "rgat": ["Offline"],
         },
         "optional-scenarios-datacenter": {},
@@ -315,7 +316,7 @@
             "llama2-70b-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
             "mixtral-8x7b": ["Server", "Offline"],
-            "llama3-405b": ["Server", "Offline"],
+            "llama3.1-405b": ["Server", "Offline"],
             "rgat": ["Offline"],
         },
         "optional-scenarios-datacenter-edge": {},
@@ -389,7 +390,7 @@
                 "mbxp_accuracy",
                 60.12 * 0.99,
             ),
-            "llama3-405b": (
+            "llama3.1-405b": (
                 "ROUGEL",
                 21.6666 * 0.99,
                 "exact_match",
@@ -409,7 +410,7 @@
             "llama2-70b-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
             "llama2-70b-99.9": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
             "mixtral-8x7b": ("TOKENS_PER_SAMPLE", 145.9 * 1.1),
-            "llama3-405b": ("TOKENS_PER_SAMPLE", 684.68 * 1.1),
+            "llama3.1-405b": ("TOKENS_PER_SAMPLE", 684.68 * 1.1),
         },
         "accuracy-delta-perc": {
             "stable-diffusion-xl": {"CLIP_SCORE": 1, "FID_SCORE": 2}
@@ -429,7 +430,7 @@
             "llama2-70b-99.9": 24576,
             "stable-diffusion-xl": 5000,
             "mixtral-8x7b": 15000,
-            "llama3-405b": 8313,
+            "llama3.1-405b": 8313,
             "rgat": 788379
 
         },
@@ -459,7 +460,7 @@
             "llama2-70b-99": {"Server": 20000000000},
             "llama2-70b-99.9": {"Server": 20000000000},
             "mixtral-8x7b": {"Server": 20000000000},
-            "llama3-405b": {"Server": 60000000000}
+            "llama3.1-405b": {"Server": 60000000000}
         },
         "min-queries": {
             "resnet": {
@@ -490,7 +491,7 @@
                 "Offline": 1,
             },
             "mixtral-8x7b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "llama3-405b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama3.1-405b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "rgat": {"SingleStream": 1024, "Offline": 1}
         },
     },
@@ -579,7 +580,7 @@
     "llama2-70b-99.9": 24576,
     "stable-diffusion-xl": 5000,
     "mixtral-8x7b": 15000,
-    "llama3-405b": 8313,
+    "llama3.1-405b": 8313,
     "rgat": 788379,
 }
 
@@ -656,7 +657,7 @@
             "Offline": "result_tokens_per_second",
             "Server": "result_completed_tokens_per_second",
         },
-        "llama3-405b": {
+        "llama3.1-405b": {
             "Offline": "result_tokens_per_second",
             "Server": "result_completed_tokens_per_second",
         },
@@ -671,7 +672,7 @@
         "conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}
     },
     "mixtral-8x7b": {"conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}},
-    "llama3-405b": {
+    "llama3.1-405b": {
         "conversational": {"ttft": 6000 * 1000000, "tpot": 175 * 1000000}
     },
 }
@@ -956,7 +957,7 @@ def requires_equal_issue(self, model, division):
                 "llama2-70b-99",
                 "llama2-70b-99.9",
                 "mixtral-8x7b",
-                "llama3-405b",
+                "llama3.1-405b",
                 "rgat",
             ]
             and self.version not in ["v4.0", "v4.1"]
@@ -1325,7 +1326,7 @@ def check_performance_dir(
         )
 
     if model in ["llama2-70b-99", "llama2-70b-99.9",
-                 "mixtral-8x7b", "llama3-405b"]:
+                 "mixtral-8x7b", "llama3.1-405b"]:
         llama_constraint, is_valid = extra_check_llm(
             mlperf_log, scenario_fixed, model)
 
@@ -1865,7 +1866,7 @@ def log_result(
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
-            "llama3-405b": {
+            "llama3.1-405b": {
                 "SingleStream": "Latency (ms)",
                 "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
@@ -2950,7 +2951,7 @@ def check_compliance_dir(
         "llama2-70b-99",
         "llama2-70b-99.9",
         "mixtral-8x7b",
-        "llama3-405b",
+        "llama3.1-405b",
         "rgat",
     ]:
         test_list.remove("TEST04")
@@ -2971,7 +2972,7 @@ def check_compliance_dir(
         "llama2-70b-99",
         "llama2-70b-99.9",
         "mixtral-8x7b",
-        "llama3-405b",
+        "llama3.1-405b",
     ]:
         test_list.remove("TEST01")
 
@@ -2980,7 +2981,7 @@ def check_compliance_dir(
         test_list.remove("TEST04")
 
     if model in ["llama2-70b-99", "llama2-70b-99.9",
-                 "mixtral-8x7b", "llama3-405b"]:
+                 "mixtral-8x7b", "llama3.1-405b"]:
         test_list.append("TEST06")
 
     if test_list and not os.path.exists(compliance_dir):

From 6af0288cd09c3abac9a1ba397c5b35df0d46045c Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Thu, 19 Dec 2024 06:36:54 +0000
Subject: [PATCH 211/305] Increment version to 5.0.4

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 50e2274e6d..2d6c0bcf19 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.3
+5.0.4

From f5382b76d85e5da8b333f057fcd7e6926e79dcb1 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Thu, 19 Dec 2024 06:37:49 +0000
Subject: [PATCH 212/305] Create test-rgat.yml (#1984)

* Create test-rgat.yml

* Update test-rgat.yml

* Update test-rgat.yml

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 .github/workflows/test-rgat.yml | 38 +++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)
 create mode 100644 .github/workflows/test-rgat.yml

diff --git a/.github/workflows/test-rgat.yml b/.github/workflows/test-rgat.yml
new file mode 100644
index 0000000000..990c58a669
--- /dev/null
+++ b/.github/workflows/test-rgat.yml
@@ -0,0 +1,38 @@
+name: Test for MLPerf inference rgat submission generation using CM script automation
+
+on:
+  pull_request:
+    branches: [ "master", "dev" ]
+    paths:
+      - graph/R-GAT/**
+      - loadgen/**
+      - tools/submission/**
+      - .github/workflows/test-rgat.yml
+      - '!**.md'
+
+env:
+  PR_HEAD_REF: ${{ github.event.pull_request.head.ref }}
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [ "3.11" ]
+        backend: [ "pytorch" ]
+        loadgen-flag: [ "" ]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python3 -m pip install cm4mlops
+    - name: Test R-GAT and end to end submission generation
+      run: |
+        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --category=datacenter --hw_name=default --model=rgat --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}

From 939b2fe5c4e041e3aaa541835b467735375d8937 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 19 Dec 2024 01:39:06 -0500
Subject: [PATCH 213/305] Update compliance test table (#1987)

Co-authored-by: Miro <mirhodak@amd.com>
---
 compliance/nvidia/README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/compliance/nvidia/README.md b/compliance/nvidia/README.md
index 91c8d8df7e..bcf050a99b 100755
--- a/compliance/nvidia/README.md
+++ b/compliance/nvidia/README.md
@@ -38,4 +38,6 @@ The `run_verification.py` found in each test directory will copy the test files
 | gpt-j | - |
 | stable-diffusion-xl | [TEST01](./TEST01/), [TEST04](./TEST04/) |
 | Llama2-70b | [TEST06](./TEST06/) |
+| Llama3.1-405b | [TEST06](./TEST06/) |
 | mixtral-8x7b | [TEST06](./TEST06/) |
+| R-GAT | [TEST01](./TEST01/) |

From ae23dfc72c2ce713a279c240e13b61f35769f957 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Thu, 19 Dec 2024 06:43:29 +0000
Subject: [PATCH 214/305] Create benchmark-checklist.md for r-gat (#1985)

* Create benchmark-checklist.md for r-gat

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 graph/R-GAT/benchmark-checklist.md | 86 ++++++++++++++++++++++++++++++
 1 file changed, 86 insertions(+)
 create mode 100644 graph/R-GAT/benchmark-checklist.md

diff --git a/graph/R-GAT/benchmark-checklist.md b/graph/R-GAT/benchmark-checklist.md
new file mode 100644
index 0000000000..2e76acb992
--- /dev/null
+++ b/graph/R-GAT/benchmark-checklist.md
@@ -0,0 +1,86 @@
+
+#### **1. Applicable Categories**
+- Datacenter
+
+---
+
+#### **2. Applicable Scenarios for Each Category**
+- Offline
+
+---
+
+#### **3. Applicable Compliance Tests**
+- TEST01
+
+---
+
+#### **4. Latency Threshold for Server Scenarios**
+- Not applicable
+
+---
+
+#### **5. Validation Dataset: Unique Samples**
+Number of **unique samples** in the validation dataset and the QSL size specified in 
+- [ ] [inference policies benchmark section](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc#41-benchmarks)
+- [X] [mlperf.conf](https://github.com/mlcommons/inference/blob/master/loadgen/mlperf.conf)
+- [X] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
+  *(Ensure QSL size overflows the system cache if possible.)*
+
+---
+
+#### **6. Equal Issue Mode Applicability**
+Documented whether **Equal Issue Mode** is applicable in 
+- [X] [mlperf.conf](https://github.com/mlcommons/inference/blob/master/loadgen/mlperf.conf#L42)
+- [X] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
+  *(Relevant if sample processing times are inconsistent across inputs.)*
+
+---
+
+#### **7. Expected Accuracy and `accuracy.txt` Contents**
+- [ ] Detailed expected accuracy and the required contents of the `accuracy.txt` file.
+
+---
+
+#### **8. Reference Model Details**
+- [ ] Reference model details updated in [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)  
+
+---
+
+#### **9. Reference Implementation Dataset Coverage**
+- [ ] Reference implementation successfully processes the entire validation dataset during:
+  - [ ] Performance runs
+  - [ ] Accuracy runs
+  - [ ] Compliance runs  
+- [ ] Valid log files passing the submission checker are generated for all runs.
+
+---
+
+#### **10. Test Runs with Smaller Input Sets**
+- [X] Verified the reference implementation can perform test runs with a smaller subset of inputs for:
+  - [X] Performance runs
+  - [X] Accuracy runs
+
+---
+
+#### **11. Dataset and Reference Model Instructions**
+- [X] Clear instructions provided for:
+  - [X] Downloading the dataset and reference model.
+  - [X] Using the dataset and model for the benchmark.
+
+---
+
+#### **12. Documentation of Recommended System Requirements to run the reference implementation**
+- [X] Added [here](https://github.com/mlcommons/inference/blob/docs/docs/system_requirements.yml#L44)
+
+---
+
+#### **13. Submission Checker Modifications**
+- [X] All necessary changes made to the **submission checker** to validate the benchmark.
+
+---
+
+#### **14. Sample Log Files**
+- [ ] Include sample logs for all the applicable scenario runs:
+  - [ ] `mlperf_log_summary.txt`
+  - [ ] `mlperf_log_detail.txt`  
+- [ ] Ensure sample logs successfully pass the submission checker and applicable compliance runs.

From 9abda7c4759f99faca06954268d2f56b5bd4682b Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Mon, 16 Dec 2024 18:14:17 -0500
Subject: [PATCH 215/305] Add rgat accuracy format

---
 graph/R-GAT/README.md | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/graph/R-GAT/README.md b/graph/R-GAT/README.md
index fbfca47092..4c31b42aa4 100644
--- a/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -8,7 +8,7 @@ This is the reference implementation for MLPerf Inference Graph Neural Network.
 
 | model | accuracy | dataset | model source | precision | notes |
 | ---- | ---- | ---- | ---- | ---- | ---- |
-| RGAT | - | IGBH | [Illiois Graph Benchmark](https://github.com/IllinoisGraphBenchmark/IGB-Datasets/) | fp32 | - |
+| RGAT | 0.7286 | IGBH | [Illiois Graph Benchmark](https://github.com/IllinoisGraphBenchmark/IGB-Datasets/) | fp32 | - |
 
 ## Dataset
 
@@ -209,6 +209,12 @@ Add the `--accuracy` to the command to run the benchmark
 python3 main.py --dataset igbh --dataset-path igbh/ --accuracy --model-path model/ [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
 ```
 
+**NOTE:** For official submissions you should submit the results of the accuracy run in a file called `accuracy.txt` with the following format:
+```
+accuracy=<accuracy>%, good=<number_of_good_samples>, total=<number_of_total_samples>
+hash=<hash>
+```
+
 ### Docker run
 **CPU:**
 Build docker image

From c2cd92e9ffdb2c8c92d8bb446108a6e45e23a45e Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 19 Dec 2024 16:53:15 -0500
Subject: [PATCH 216/305] Output accuracy in expected submission format

---
 graph/R-GAT/tools/accuracy_igbh.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/graph/R-GAT/tools/accuracy_igbh.py b/graph/R-GAT/tools/accuracy_igbh.py
index c9c56a91d7..eba9e19857 100644
--- a/graph/R-GAT/tools/accuracy_igbh.py
+++ b/graph/R-GAT/tools/accuracy_igbh.py
@@ -118,4 +118,6 @@ def get_labels(labels, val_idx, id_list):
     results["performance_sample_count"] = total
 
     with open(args.output_file, "w") as fp:
-        json.dump(results, fp)
+        fp.write("accuracy={:.3f}%, good={}, total={}".format(
+            100.0 * results["accuracy"], results["number_correct_samples"], results["performance_sample_count"]
+        ))

From be6ff52235b74a2f4ef85bf86fa3785045229fa8 Mon Sep 17 00:00:00 2001
From: mlcommons-bot <mlcommons-bot@users.noreply.github.com>
Date: Fri, 20 Dec 2024 20:08:40 +0000
Subject: [PATCH 217/305] [Automated Commit] Format Codebase

---
 graph/R-GAT/tools/accuracy_igbh.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/graph/R-GAT/tools/accuracy_igbh.py b/graph/R-GAT/tools/accuracy_igbh.py
index eba9e19857..da63a69777 100644
--- a/graph/R-GAT/tools/accuracy_igbh.py
+++ b/graph/R-GAT/tools/accuracy_igbh.py
@@ -119,5 +119,6 @@ def get_labels(labels, val_idx, id_list):
 
     with open(args.output_file, "w") as fp:
         fp.write("accuracy={:.3f}%, good={}, total={}".format(
-            100.0 * results["accuracy"], results["number_correct_samples"], results["performance_sample_count"]
+            100.0 *
+            results["accuracy"], results["number_correct_samples"], results["performance_sample_count"]
         ))

From 2fdb814e6a765d994872fb99ee38f8c43418d5b0 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Sat, 21 Dec 2024 05:00:47 +0000
Subject: [PATCH 218/305] Automotive reference implementation (#1954)

* Automotive reference implementation sketch

* WIP: automotive reference implementation

* WIP add segmentation, dataset, and util functions to reference

* WIP: reference implementation with issues during post processing

* WIP update dockerfile remove pdb breaks

* WIP: reference implementation that runs samples

* Update README.md with initial docker runs

* WIP: add accuracy checker to reference

* Fix: set lidar detector to evaluation mode

* [Automated Commit] Format Codebase

* Update README.md

---------

Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Co-authored-by: Radoyeh Shojaei <radoyeh@gmail.com>
Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
Co-authored-by: Miro <mirhodak@amd.com>
---
 automotive/3d-object-detection/README.md      |  14 +
 .../3d-object-detection/accuracy_waymo.py     | 128 +++++
 automotive/3d-object-detection/backend.py     |  21 +
 .../3d-object-detection/backend_debug.py      |  24 +
 .../3d-object-detection/backend_deploy.py     | 155 ++++++
 automotive/3d-object-detection/dataset.py     |  76 +++
 automotive/3d-object-detection/dockerfile cpu |  21 +
 automotive/3d-object-detection/dockerfile.gpu |  31 ++
 automotive/3d-object-detection/main.py        | 469 ++++++++++++++++
 .../3d-object-detection/model/__init__.py     |   2 +
 .../3d-object-detection/model/anchors.py      | 290 ++++++++++
 .../3d-object-detection/model/painter.py      | 267 +++++++++
 .../3d-object-detection/model/pointpillars.py | 515 ++++++++++++++++++
 .../model/segmentation/__init__.py            |   2 +
 .../model/segmentation/_deeplab.py            | 210 +++++++
 .../model/segmentation/backbone/__init__.py   |   1 +
 .../model/segmentation/backbone/resnet.py     | 353 ++++++++++++
 .../model/segmentation/modeling.py            | 103 ++++
 .../model/segmentation/utils.py               | 121 ++++
 .../3d-object-detection/ops/__init__.py       |   1 +
 .../3d-object-detection/ops/iou3d/iou3d.cpp   | 213 ++++++++
 .../ops/iou3d/iou3d_kernel.cu                 | 439 +++++++++++++++
 .../3d-object-detection/ops/voxel_module.py   | 253 +++++++++
 .../output/mlperf_log_accuracy.json           |   1 +
 .../output/mlperf_log_detail.txt              |  64 +++
 .../output/mlperf_log_summary.txt             |   0
 .../output/mlperf_log_trace.json              |   0
 .../3d-object-detection/requirements.txt      |   1 +
 .../tools/download_dataset.py                 |   1 +
 .../3d-object-detection/tools/evaluate.py     | 261 +++++++++
 .../3d-object-detection/tools/process.py      | 341 ++++++++++++
 automotive/3d-object-detection/user.conf      |   4 +
 automotive/3d-object-detection/waymo.py       | 247 +++++++++
 33 files changed, 4629 insertions(+)
 create mode 100644 automotive/3d-object-detection/README.md
 create mode 100644 automotive/3d-object-detection/accuracy_waymo.py
 create mode 100644 automotive/3d-object-detection/backend.py
 create mode 100644 automotive/3d-object-detection/backend_debug.py
 create mode 100644 automotive/3d-object-detection/backend_deploy.py
 create mode 100644 automotive/3d-object-detection/dataset.py
 create mode 100644 automotive/3d-object-detection/dockerfile cpu
 create mode 100644 automotive/3d-object-detection/dockerfile.gpu
 create mode 100644 automotive/3d-object-detection/main.py
 create mode 100644 automotive/3d-object-detection/model/__init__.py
 create mode 100644 automotive/3d-object-detection/model/anchors.py
 create mode 100644 automotive/3d-object-detection/model/painter.py
 create mode 100644 automotive/3d-object-detection/model/pointpillars.py
 create mode 100644 automotive/3d-object-detection/model/segmentation/__init__.py
 create mode 100644 automotive/3d-object-detection/model/segmentation/_deeplab.py
 create mode 100644 automotive/3d-object-detection/model/segmentation/backbone/__init__.py
 create mode 100644 automotive/3d-object-detection/model/segmentation/backbone/resnet.py
 create mode 100644 automotive/3d-object-detection/model/segmentation/modeling.py
 create mode 100644 automotive/3d-object-detection/model/segmentation/utils.py
 create mode 100644 automotive/3d-object-detection/ops/__init__.py
 create mode 100644 automotive/3d-object-detection/ops/iou3d/iou3d.cpp
 create mode 100644 automotive/3d-object-detection/ops/iou3d/iou3d_kernel.cu
 create mode 100644 automotive/3d-object-detection/ops/voxel_module.py
 create mode 100644 automotive/3d-object-detection/output/mlperf_log_accuracy.json
 create mode 100644 automotive/3d-object-detection/output/mlperf_log_detail.txt
 create mode 100644 automotive/3d-object-detection/output/mlperf_log_summary.txt
 create mode 100644 automotive/3d-object-detection/output/mlperf_log_trace.json
 create mode 100644 automotive/3d-object-detection/requirements.txt
 create mode 100644 automotive/3d-object-detection/tools/download_dataset.py
 create mode 100644 automotive/3d-object-detection/tools/evaluate.py
 create mode 100644 automotive/3d-object-detection/tools/process.py
 create mode 100644 automotive/3d-object-detection/user.conf
 create mode 100644 automotive/3d-object-detection/waymo.py

diff --git a/automotive/3d-object-detection/README.md b/automotive/3d-object-detection/README.md
new file mode 100644
index 0000000000..f4848647f4
--- /dev/null
+++ b/automotive/3d-object-detection/README.md
@@ -0,0 +1,14 @@
+## Reference implementation fo automotive 3D detection benchmark
+
+## TODO: Instructions for dataset download after it is uploaded somewhere appropriate
+
+## TODO: Instructions for checkpoints downloads after it is uploaded somewhere appropriate
+
+## Running with docker
+```
+docker build -t auto_inference -f dockerfile.gpu .
+
+docker run --gpus=all -it -v <directory to inference repo>/inference/:/inference -v <directory to waymo dataset>/waymo:/waymo --rm auto_inference
+
+cd /inference/automotive/3d-object-detection
+python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp_ep36.pth --segmentor-path <checkpoint_path>/best_deeplabv3plus_resnet50_waymo_os16.pth --mlperf_conf /inference/mlperf.conf
diff --git a/automotive/3d-object-detection/accuracy_waymo.py b/automotive/3d-object-detection/accuracy_waymo.py
new file mode 100644
index 0000000000..c8b5cb72c8
--- /dev/null
+++ b/automotive/3d-object-detection/accuracy_waymo.py
@@ -0,0 +1,128 @@
+"""
+Tool to calculate accuracy for loadgen accuracy output found in mlperf_log_accuracy.json
+We assume that loadgen's query index is in the same order as
+the images in coco's annotations/instances_val2017.json.
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import json
+import os
+
+import numpy as np
+from waymo import Waymo
+from tools.evaluate import do_eval
+# pylint: disable=missing-docstring
+CLASSES = Waymo.CLASSES
+LABEL2CLASSES = {v: k for k, v in CLASSES.items()}
+
+
+def get_args():
+    """Parse commandline."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--mlperf-accuracy-file",
+        required=True,
+        help="path to mlperf_log_accuracy.json")
+    parser.add_argument(
+        "--waymo-dir",
+        required=True,
+        help="waymo dataset directory")
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="verbose messages")
+    parser.add_argument(
+        "--output-file",
+        default="openimages-results.json",
+        help="path to output file")
+    parser.add_argument(
+        "--use-inv-map",
+        action="store_true",
+        help="use inverse label map")
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    with open(args.mlperf_accuracy_file, "r") as f:
+        results = json.load(f)
+
+    detections = {}
+    image_ids = set()
+    seen = set()
+    no_results = 0
+
+    val_dataset = Waymo(
+        data_root=args.waymo_dir,
+        split='val',
+        painted=True,
+        cam_sync=False)
+
+    for j in results:
+        idx = j['qsl_idx']
+        # de-dupe in case loadgen sends the same image multiple times
+        if idx in seen:
+            continue
+        seen.add(idx)
+
+        # reconstruct from mlperf accuracy log
+        # what is written by the benchmark is an array of float32's:
+        # id, box[0], box[1], box[2], box[3], score, detection_class
+        # note that id is a index into instances_val2017.json, not the actual
+        # image_id
+        data = np.frombuffer(bytes.fromhex(j['data']), np.float32)
+
+        for i in range(0, len(data), 14):
+            dimension = [float(x) for x in data[i:i + 3]]
+            location = [float(x) for x in data[i + 3:i + 6]]
+            rotation_y = float(data[i + 6])
+            bbox = [float(x) for x in data[i + 7:i + 11]]
+            label = int(data[i + 11])
+            score = float(data[i + 12])
+            image_idx = int(data[i + 13])
+            if image_idx not in detections:
+                detections[image_idx] = {
+                    'name': [],
+                    'dimensions': [],
+                    'location': [],
+                    'rotation_y': [],
+                    'bbox': [],
+                    'score': []
+                }
+
+            detections[image_idx]['name'].append(LABEL2CLASSES[label])
+            detections[image_idx]['dimensions'].append(dimension)
+            detections[image_idx]['location'].append(location)
+            detections[image_idx]['rotation_y'].append(rotation_y)
+            detections[image_idx]['bbox'].append(bbox)
+            detections[image_idx]['score'].append(score)
+            image_ids.add(image_idx)
+
+    with open(args.output_file, "w") as fp:
+        json.dump(detections, fp, sort_keys=True, indent=4)
+    format_results = {}
+    for key in detections.keys():
+        format_results[key] = {k: np.array(v)
+                               for k, v in detections[key].items()}
+    map_stats = do_eval(
+        format_results,
+        val_dataset.data_infos,
+        CLASSES,
+        cam_sync=False)
+
+    print(map_stats)
+    if args.verbose:
+        print("found {} results".format(len(results)))
+        print("found {} images".format(len(image_ids)))
+        print("found {} images with no results".format(no_results))
+        print("ignored {} dupes".format(len(results) - len(seen)))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/automotive/3d-object-detection/backend.py b/automotive/3d-object-detection/backend.py
new file mode 100644
index 0000000000..58e4f9fa6c
--- /dev/null
+++ b/automotive/3d-object-detection/backend.py
@@ -0,0 +1,21 @@
+"""
+abstract backend class
+"""
+
+
+class Backend:
+    def __init__(self):
+        self.inputs = []
+        self.outputs = []
+
+    def version(self):
+        raise NotImplementedError("Backend:version")
+
+    def name(self):
+        raise NotImplementedError("Backend:name")
+
+    def load(self, model_path, inputs=None, outputs=None):
+        raise NotImplementedError("Backend:load")
+
+    def predict(self, feed):
+        raise NotImplementedError("Backend:predict")
diff --git a/automotive/3d-object-detection/backend_debug.py b/automotive/3d-object-detection/backend_debug.py
new file mode 100644
index 0000000000..086a84a7a4
--- /dev/null
+++ b/automotive/3d-object-detection/backend_debug.py
@@ -0,0 +1,24 @@
+import torch
+import backend
+
+
+class BackendDebug(backend.Backend):
+    def __init__(self, image_size=[3, 1024, 1024], **kwargs):
+        super(BackendDebug, self).__init__()
+        self.image_size = image_size
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "debug-SUT"
+
+    def image_format(self):
+        return "NCHW"
+
+    def load(self):
+        return self
+
+    def predict(self, prompts):
+        images = []
+        return images
diff --git a/automotive/3d-object-detection/backend_deploy.py b/automotive/3d-object-detection/backend_deploy.py
new file mode 100644
index 0000000000..1a2f3dee4f
--- /dev/null
+++ b/automotive/3d-object-detection/backend_deploy.py
@@ -0,0 +1,155 @@
+from typing import Optional, List, Union
+import os
+import torch
+import logging
+import backend
+from collections import namedtuple
+from model.painter import Painter
+from model.pointpillars import PointPillars
+import numpy as np
+from tools.process import keep_bbox_from_image_range
+from waymo import Waymo
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("backend-pytorch")
+
+
+def change_calib_device(calib, cuda):
+    result = {}
+    if cuda:
+        device = 'cuda'
+    else:
+        device = 'cpu'
+    result['R0_rect'] = calib['R0_rect'].to(device=device, dtype=torch.float)
+    for i in range(5):
+        result['P' + str(i)] = calib['P' + str(i)
+                                     ].to(device=device, dtype=torch.float)
+        result['Tr_velo_to_cam_' +
+               str(i)] = calib['Tr_velo_to_cam_' +
+                               str(i)].to(device=device, dtype=torch.float)
+    return result
+
+
+class BackendDeploy(backend.Backend):
+    def __init__(
+        self,
+        segmentor_path,
+        lidar_detector_path,
+        data_path
+    ):
+        super(BackendDeploy, self).__init__()
+        self.segmentor_path = segmentor_path
+        self.lidar_detector_path = lidar_detector_path
+        # self.segmentation_classes = 18
+        self.detection_classes = 3
+        self.data_root = data_path
+        CLASSES = Waymo.CLASSES
+        self.LABEL2CLASSES = {v: k for k, v in CLASSES.items()}
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "python-SUT"
+
+    def load(self):
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        PaintArgs = namedtuple(
+            'PaintArgs', [
+                'training_path', 'model_path', 'cam_sync'])
+        painting_args = PaintArgs(
+            os.path.join(
+                self.data_root,
+                'training'),
+            self.segmentor_path,
+            False)
+        self.painter = Painter(painting_args)
+        self.segmentor = self.painter.model
+        model = PointPillars(
+            nclasses=self.detection_classes,
+            painted=True).to(
+            device=device)
+        model.eval()
+        checkpoint = torch.load(self.lidar_detector_path)
+        model.load_state_dict(checkpoint["model_state_dict"])
+        self.lidar_detector = model
+
+        return self
+
+    def predict(self, inputs):
+        # TODO: implement predict
+        dimensions, locations, rotation_y, box2d, class_labels, class_scores, ids = [
+        ], [], [], [], [], [], []
+        with torch.inference_mode():
+            device = torch.device(
+                "cuda:0" if torch.cuda.is_available() else "cpu")
+            format_results = {}
+            model_input = inputs[0]
+            batched_pts = model_input['pts']
+            scores_from_cam = []
+            for i in range(len(model_input['images'])):
+                segmentation_score = self.segmentor(
+                    model_input['images'][i].to(device))[0]
+                scores_from_cam.append(
+                    self.painter.get_score(segmentation_score).cpu())
+            points = self.painter.augment_lidar_class_scores_both(
+                scores_from_cam, batched_pts, model_input['calib_info'])
+            batch_results = self.lidar_detector(
+                batched_pts=[points.to(device=device)], mode='val')
+            for j, result in enumerate(batch_results):
+                format_result = {
+                    'class': [],
+                    'truncated': [],
+                    'occluded': [],
+                    'alpha': [],
+                    'bbox': [],
+                    'dimensions': [],
+                    'location': [],
+                    'rotation_y': [],
+                    'score': [],
+                    'idx': -1
+                }
+
+                calib_info = model_input['calib_info']
+                image_info = model_input['image_info']
+                idx = model_input['image_info']['image_idx']
+
+                calib_info = change_calib_device(calib_info, False)
+                result_filter = keep_bbox_from_image_range(
+                    result, calib_info, 5, image_info, False)
+
+                lidar_bboxes = result_filter['lidar_bboxes']
+                labels, scores = result_filter['labels'], result_filter['scores']
+                bboxes2d, camera_bboxes = result_filter['bboxes2d'], result_filter['camera_bboxes']
+                for lidar_bbox, label, score, bbox2d, camera_bbox in \
+                        zip(lidar_bboxes, labels, scores, bboxes2d, camera_bboxes):
+                    format_result['class'].append(label.item())
+                    format_result['truncated'].append(0.0)
+                    format_result['occluded'].append(0)
+                    alpha = camera_bbox[6] - \
+                        np.arctan2(camera_bbox[0], camera_bbox[2])
+                    format_result['alpha'].append(alpha.item())
+                    format_result['bbox'].append(bbox2d.tolist())
+                    format_result['dimensions'].append(camera_bbox[3:6])
+                    format_result['location'].append(camera_bbox[:3])
+                    format_result['rotation_y'].append(camera_bbox[6].item())
+                    format_result['score'].append(score.item())
+                    format_results['idx'] = idx
+
+                # write_label(format_result, os.path.join(saved_submit_path, f'{idx:06d}.txt'))
+
+                if len(format_result['dimensions']) > 0:
+                    format_result['dimensions'] = torch.stack(
+                        format_result['dimensions'])
+                    format_result['location'] = torch.stack(
+                        format_result['location'])
+                dimensions.append(format_result['dimensions'])
+                locations.append(format_result['location'])
+                rotation_y.append(format_result['rotation_y'])
+                class_labels.append(format_result['class'])
+                class_scores.append(format_result['score'])
+                box2d.append(format_result['bbox'])
+                ids.append(format_results['idx'])
+            # return Boxes, Classes, Scores # Change to desired output
+        return dimensions, locations, rotation_y, box2d, class_labels, class_scores, ids
diff --git a/automotive/3d-object-detection/dataset.py b/automotive/3d-object-detection/dataset.py
new file mode 100644
index 0000000000..04748dd948
--- /dev/null
+++ b/automotive/3d-object-detection/dataset.py
@@ -0,0 +1,76 @@
+"""
+dataset related classes and methods
+"""
+
+# pylint: disable=unused-argument,missing-docstring
+
+import logging
+import sys
+import time
+
+import numpy as np
+import torch
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("dataset")
+
+
+class Dataset:
+    def __init__(self):
+        self.items_inmemory = {}
+
+    def preprocess(self, use_cache=True):
+        raise NotImplementedError("Dataset:preprocess")
+
+    def get_item_count(self):
+        raise NotImplementedError("Dataset:get_item_count")
+
+    def get_list(self):
+        raise NotImplementedError("Dataset:get_list")
+
+    def load_query_samples(self, sample_list):
+        raise NotImplementedError("Dataset:load_query_samples")
+
+    def unload_query_samples(self, sample_list):
+        raise NotImplementedError("Dataset:unload_query_samples")
+
+    def get_samples(self, id_list):
+        raise NotImplementedError("Dataset:get_samples")
+
+    def get_item(self, id):
+        raise NotImplementedError("Dataset:get_item")
+
+
+def preprocess(list_data):
+    batched_pts_list, batched_gt_bboxes_list = [], []
+    batched_labels_list, batched_names_list = [], []
+    batched_difficulty_list = []
+    batched_img_list, batched_calib_list = [], []
+    batched_images = []
+    for data_dict in list_data:
+        pts, gt_bboxes_3d = data_dict['pts'], data_dict['gt_bboxes_3d']
+        gt_labels, gt_names = data_dict['gt_labels'], data_dict['gt_names']
+        difficulty = data_dict['difficulty']
+        image_info, calib_info = data_dict['image_info'], data_dict['calib_info']
+
+        batched_pts_list.append(torch.from_numpy(pts))
+        batched_gt_bboxes_list.append(torch.from_numpy(gt_bboxes_3d))
+        batched_labels_list.append(torch.from_numpy(gt_labels))
+        batched_names_list.append(gt_names)  # List(str)
+        batched_difficulty_list.append(torch.from_numpy(difficulty))
+        batched_img_list.append(image_info)
+        batched_calib_list.append(calib_info)
+        batched_images.append(data_dict['images'])
+    rt_data_dict = dict(
+        batched_pts=batched_pts_list,
+        batched_gt_bboxes=batched_gt_bboxes_list,
+        batched_labels=batched_labels_list,
+        batched_names=batched_names_list,
+        batched_difficulty=batched_difficulty_list,
+        batched_img_info=batched_img_list,
+        batched_calib_info=batched_calib_list,
+        batched_images=batched_images
+    )
+
+    return rt_data_dict
diff --git a/automotive/3d-object-detection/dockerfile cpu b/automotive/3d-object-detection/dockerfile cpu
new file mode 100644
index 0000000000..875cdf76c5
--- /dev/null
+++ b/automotive/3d-object-detection/dockerfile cpu	
@@ -0,0 +1,21 @@
+ARG FROM_IMAGE_NAME=nvcr.io/nvidia/pytorch:23.08-py3
+FROM ${FROM_IMAGE_NAME}
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# apt dependencies
+RUN apt-get update
+RUN apt-get install -y ffmpeg libsm6 libxext6
+
+# install LDM
+COPY . /diffusion
+RUN cd /diffusion && \
+    pip install --no-cache-dir -r requirements.txt
+
+# install loadgen
+RUN cd /tmp && \
+    git clone --recursive https://github.com/mlcommons/inference && \
+    cd inference/loadgen && \
+    pip install pybind11 && \
+    CFLAGS="-std=c++14" python setup.py install && \
+    rm -rf mlperf
\ No newline at end of file
diff --git a/automotive/3d-object-detection/dockerfile.gpu b/automotive/3d-object-detection/dockerfile.gpu
new file mode 100644
index 0000000000..02acca7a36
--- /dev/null
+++ b/automotive/3d-object-detection/dockerfile.gpu
@@ -0,0 +1,31 @@
+ARG FROM_IMAGE_NAME=pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel
+FROM ${FROM_IMAGE_NAME}
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# apt dependencies
+RUN apt-get update
+RUN apt-get install -y ffmpeg libsm6 libxext6 git
+
+# install LDM
+COPY . /diffusion
+RUN cd /diffusion && \
+    pip install --no-cache-dir -r requirements.txt
+
+# install loadgen
+RUN cd /tmp && \
+    git clone --recursive https://github.com/mlcommons/inference && \
+    cd inference/loadgen && \
+    pip install pybind11 && \
+    CFLAGS="-std=c++14" python setup.py install && \
+    rm -rf mlperf
+
+RUN pip install tqdm
+RUN pip install numba
+RUN pip install opencv-python
+RUN pip install open3d
+RUN pip install tensorboard
+RUN pip install scikit-image
+RUN pip install ninja
+RUN pip install visdom
+RUN pip install shapely
\ No newline at end of file
diff --git a/automotive/3d-object-detection/main.py b/automotive/3d-object-detection/main.py
new file mode 100644
index 0000000000..04269e428f
--- /dev/null
+++ b/automotive/3d-object-detection/main.py
@@ -0,0 +1,469 @@
+"""
+mlperf inference benchmarking tool
+"""
+
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import array
+import collections
+import json
+import logging
+import os
+import sys
+import threading
+import time
+from queue import Queue
+
+import mlperf_loadgen as lg
+import numpy as np
+import torch
+
+import dataset
+import waymo
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("main")
+
+NANO_SEC = 1e9
+MILLI_SEC = 1000
+
+SUPPORTED_DATASETS = {
+    "waymo": (
+        waymo.Waymo,
+        dataset.preprocess,
+        waymo.PostProcessWaymo(),
+        {}  # "image_size": [3, 1024, 1024]},
+    )
+}
+
+
+SUPPORTED_PROFILES = {
+    "defaults": {
+        "dataset": "waymo",
+        "backend": "pytorch",
+        "model-name": "pointpainting",
+    },
+}
+
+SCENARIO_MAP = {
+    "SingleStream": lg.TestScenario.SingleStream,
+    "MultiStream": lg.TestScenario.MultiStream,
+    "Server": lg.TestScenario.Server,
+    "Offline": lg.TestScenario.Offline,
+}
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset",
+        choices=SUPPORTED_DATASETS.keys(),
+        help="dataset")
+    parser.add_argument(
+        "--dataset-path",
+        required=True,
+        help="path to the dataset")
+    parser.add_argument(
+        "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
+    )
+    parser.add_argument(
+        "--scenario",
+        default="SingleStream",
+        help="mlperf benchmark scenario, one of " +
+        str(list(SCENARIO_MAP.keys())),
+    )
+    parser.add_argument(
+        "--max-batchsize",
+        type=int,
+        default=1,
+        help="max batch size in a single inference",
+    )
+    parser.add_argument("--threads", default=1, type=int, help="threads")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
+    parser.add_argument(
+        "--find-peak-performance",
+        action="store_true",
+        help="enable finding peak performance pass",
+    )
+    parser.add_argument("--backend", help="Name of the backend")
+    parser.add_argument("--model-name", help="Name of the model")
+    parser.add_argument("--output", default="output", help="test results")
+    parser.add_argument("--qps", type=int, help="target qps")
+    parser.add_argument("--lidar-path", help="Path to model weights")
+    parser.add_argument("--segmentor-path", help="Path to model weights")
+
+    parser.add_argument(
+        "--dtype",
+        default="fp32",
+        choices=["fp32", "fp16", "bf16"],
+        help="dtype of the model",
+    )
+    parser.add_argument(
+        "--device",
+        default="cuda",
+        choices=["cuda", "cpu"],
+        help="device to run the benchmark",
+    )
+
+    # file to use mlperf rules compliant parameters
+    parser.add_argument(
+        "--mlperf_conf", default="mlperf.conf", help="mlperf rules config"
+    )
+    # file for user LoadGen settings such as target QPS
+    parser.add_argument(
+        "--user_conf",
+        default="user.conf",
+        help="user config for user LoadGen settings such as target QPS",
+    )
+    # file for LoadGen audit settings
+    parser.add_argument(
+        "--audit_conf", default="audit.config", help="config for LoadGen audit settings"
+    )
+
+    # below will override mlperf rules compliant settings - don't use for
+    # official submission
+    parser.add_argument("--time", type=int, help="time to scan in seconds")
+    parser.add_argument("--count", type=int, help="dataset items to use")
+    parser.add_argument("--debug", action="store_true", help="debug")
+    parser.add_argument(
+        "--performance-sample-count", type=int, help="performance sample count", default=5000
+    )
+    parser.add_argument(
+        "--max-latency", type=float, help="mlperf max latency in pct tile"
+    )
+    parser.add_argument(
+        "--samples-per-query",
+        default=8,
+        type=int,
+        help="mlperf multi-stream samples per query",
+    )
+    args = parser.parse_args()
+
+    # don't use defaults in argparser. Instead we default to a dict, override that with a profile
+    # and take this as default unless command line give
+    defaults = SUPPORTED_PROFILES["defaults"]
+
+    if args.profile:
+        profile = SUPPORTED_PROFILES[args.profile]
+        defaults.update(profile)
+    for k, v in defaults.items():
+        kc = k.replace("-", "_")
+        if getattr(args, kc) is None:
+            setattr(args, kc, v)
+
+    if args.scenario not in SCENARIO_MAP:
+        parser.error("valid scanarios:" + str(list(SCENARIO_MAP.keys())))
+    return args
+
+
+def get_backend(backend, **kwargs):
+    if backend == "pytorch":
+        from backend_deploy import BackendDeploy
+
+        backend = BackendDeploy(**kwargs)
+
+    elif backend == "debug":
+        from backend_debug import BackendDebug
+
+        backend = BackendDebug()
+    else:
+        raise ValueError("unknown backend: " + backend)
+    return backend
+
+
+class Item:
+    """An item that we queue for processing by the thread pool."""
+
+    def __init__(self, query_id, content_id, inputs, img=None):
+        self.query_id = query_id
+        self.content_id = content_id
+        self.img = img
+        self.inputs = inputs
+        self.start = time.time()
+
+
+class RunnerBase:
+    def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
+        self.take_accuracy = False
+        self.ds = ds
+        self.model = model
+        self.post_process = post_proc
+        self.threads = threads
+        self.take_accuracy = False
+        self.max_batchsize = max_batchsize
+        self.result_timing = []
+
+    def handle_tasks(self, tasks_queue):
+        pass
+
+    def start_run(self, result_dict, take_accuracy):
+        self.result_dict = result_dict
+        self.result_timing = []
+        self.take_accuracy = take_accuracy
+        self.post_process.start()
+
+    def run_one_item(self, qitem: Item):
+        # run the prediction
+        processed_results = []
+        try:
+            results = self.model.predict(qitem.inputs)
+            processed_results = self.post_process(
+                results, qitem.content_id, qitem.inputs, self.result_dict)
+
+            if self.take_accuracy:
+                self.post_process.add_results(processed_results)
+            self.result_timing.append(time.time() - qitem.start)
+        except Exception as ex:  # pylint: disable=broad-except
+            src = [self.ds.get_item_loc(i) for i in qitem.content_id]
+            log.error("thread: failed on contentid=%s, %s", src, ex)
+            # since post_process will not run, fake empty responses
+            processed_results = [[]] * len(qitem.query_id)
+        finally:
+            response_array_refs = []
+            response = []
+            for idx, query_id in enumerate(qitem.query_id):
+                response_array = array.array("B", np.array(
+                    processed_results[idx], np.float32).tobytes())
+
+                response_array_refs.append(response_array)
+                bi = response_array.buffer_info()
+                response.append(lg.QuerySampleResponse(query_id, bi[0], bi[1]))
+            lg.QuerySamplesComplete(response)
+
+    def enqueue(self, query_samples):
+        idx = [q.index for q in query_samples]
+        query_id = [q.id for q in query_samples]
+        if len(query_samples) < self.max_batchsize:
+            data, label = self.ds.get_samples(idx)
+            self.run_one_item(Item(query_id, idx, data, label))
+        else:
+            bs = self.max_batchsize
+            for i in range(0, len(idx), bs):
+                data, label = self.ds.get_samples(idx[i: i + bs])
+                self.run_one_item(
+                    Item(query_id[i: i + bs], idx[i: i + bs], data, label)
+                )
+
+    def finish(self):
+        pass
+
+
+class QueueRunner(RunnerBase):
+    def __init__(self, model, ds, threads, post_proc=None, max_batchsize=128):
+        super().__init__(model, ds, threads, post_proc, max_batchsize)
+        self.tasks = Queue(maxsize=threads * 4)
+        self.workers = []
+        self.result_dict = {}
+
+        for _ in range(self.threads):
+            worker = threading.Thread(
+                target=self.handle_tasks, args=(
+                    self.tasks,))
+            worker.daemon = True
+            self.workers.append(worker)
+            worker.start()
+
+    def handle_tasks(self, tasks_queue):
+        """Worker thread."""
+        while True:
+            qitem = tasks_queue.get()
+            if qitem is None:
+                # None in the queue indicates the parent want us to exit
+                tasks_queue.task_done()
+                break
+            self.run_one_item(qitem)
+            tasks_queue.task_done()
+
+    def enqueue(self, query_samples):
+        idx = [q.index for q in query_samples]
+        query_id = [q.id for q in query_samples]
+        if len(query_samples) < self.max_batchsize:
+            data, label = self.ds.get_samples(idx)
+            self.tasks.put(Item(query_id, idx, data, label))
+        else:
+            bs = self.max_batchsize
+            for i in range(0, len(idx), bs):
+                ie = i + bs
+                data, label = self.ds.get_samples(idx[i:ie])
+                self.tasks.put(Item(query_id[i:ie], idx[i:ie], data, label))
+
+    def finish(self):
+        # exit all threads
+        for _ in self.workers:
+            self.tasks.put(None)
+        for worker in self.workers:
+            worker.join()
+
+
+def main():
+    args = get_args()
+
+    log.info(args)
+
+    # find backend
+    backend = get_backend(
+        # TODO: pass model, inference and backend arguments
+        args.backend,
+        lidar_detector_path=args.lidar_path,
+        segmentor_path=args.segmentor_path,
+        data_path=args.dataset_path
+
+    )
+    if args.dtype == "fp16":
+        dtype = torch.float16
+    elif args.dtype == "bf16":
+        dtype = torch.bfloat16
+    else:
+        dtype = torch.float32
+
+    # --count applies to accuracy mode only and can be used to limit the number of images
+    # for testing.
+    count_override = False
+    count = args.count
+    if count:
+        count_override = True
+
+    # load model to backend
+    model = backend.load()
+
+    # dataset to use
+    dataset_class, pre_proc, post_proc, kwargs = SUPPORTED_DATASETS[args.dataset]
+    ds = dataset_class(
+        data_root=args.dataset_path,
+        split='val',
+        painted=True,
+        cam_sync=False)
+
+    final_results = {
+        "runtime": model.name(),
+        "version": model.version(),
+        "time": int(time.time()),
+        "args": vars(args),
+        "cmdline": str(args),
+    }
+
+    mlperf_conf = os.path.abspath(args.mlperf_conf)
+    if not os.path.exists(mlperf_conf):
+        log.error("{} not found".format(mlperf_conf))
+        sys.exit(1)
+
+    user_conf = os.path.abspath(args.user_conf)
+    if not os.path.exists(user_conf):
+        log.error("{} not found".format(user_conf))
+        sys.exit(1)
+
+    audit_config = os.path.abspath(args.audit_conf)
+
+    if args.output:
+        output_dir = os.path.abspath(args.output)
+        os.makedirs(output_dir, exist_ok=True)
+        os.chdir(output_dir)
+
+    #
+    # make one pass over the dataset to validate accuracy
+    #
+    count = ds.get_item_count()
+
+    # warmup
+    # TODO: Load warmup samples, the following code is a general
+    # way of doing this, but might need some fixing
+    ds.load_query_samples([0])
+    for i in range(5):
+        input = ds.get_samples([0])
+        _ = backend.predict(input[0])
+
+    scenario = SCENARIO_MAP[args.scenario]
+    runner_map = {
+        lg.TestScenario.SingleStream: RunnerBase,
+        lg.TestScenario.MultiStream: QueueRunner,
+        lg.TestScenario.Server: QueueRunner,
+        lg.TestScenario.Offline: QueueRunner,
+    }
+    runner = runner_map[scenario](
+        model, ds, args.threads, post_proc=post_proc, max_batchsize=args.max_batchsize
+    )
+
+    def issue_queries(query_samples):
+        runner.enqueue(query_samples)
+
+    def flush_queries():
+        pass
+
+    log_output_settings = lg.LogOutputSettings()
+    log_output_settings.outdir = output_dir
+    log_output_settings.copy_summary_to_stdout = False
+    log_settings = lg.LogSettings()
+    log_settings.enable_trace = args.debug
+    log_settings.log_output = log_output_settings
+
+    settings = lg.TestSettings()
+    settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
+    settings.FromConfig(user_conf, args.model_name, args.scenario)
+    settings.scenario = scenario
+    settings.mode = lg.TestMode.PerformanceOnly
+    if args.accuracy:
+        settings.mode = lg.TestMode.AccuracyOnly
+    if args.find_peak_performance:
+        settings.mode = lg.TestMode.FindPeakPerformance
+
+    if args.time:
+        # override the time we want to run
+        settings.min_duration_ms = args.time * MILLI_SEC
+        settings.max_duration_ms = args.time * MILLI_SEC
+
+    if args.qps:
+        qps = float(args.qps)
+        settings.server_target_qps = qps
+        settings.offline_expected_qps = qps
+
+    if count_override:
+        settings.min_query_count = count
+        settings.max_query_count = count
+
+    if args.samples_per_query:
+        settings.multi_stream_samples_per_query = args.samples_per_query
+    if args.max_latency:
+        settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
+        settings.multi_stream_expected_latency_ns = int(
+            args.max_latency * NANO_SEC)
+
+    performance_sample_count = (
+        args.performance_sample_count
+        if args.performance_sample_count
+        else min(count, 500)
+    )
+    sut = lg.ConstructSUT(issue_queries, flush_queries)
+    qsl = lg.ConstructQSL(
+        count, performance_sample_count, ds.load_query_samples, ds.unload_query_samples
+    )
+
+    log.info("starting {}".format(scenario))
+    result_dict = {"scenario": str(scenario)}
+    runner.start_run(result_dict, args.accuracy)
+
+    lg.StartTestWithLogSettings(sut, qsl, settings, log_settings, audit_config)
+
+    if args.accuracy:
+        post_proc.finalize(result_dict, ds)
+        final_results["accuracy_results"] = result_dict
+
+    runner.finish()
+    lg.DestroyQSL(qsl)
+    lg.DestroySUT(sut)
+
+    #
+    # write final results
+    #
+    if args.output:
+        with open("results.json", "w") as f:
+            json.dump(final_results, f, sort_keys=True, indent=4)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/automotive/3d-object-detection/model/__init__.py b/automotive/3d-object-detection/model/__init__.py
new file mode 100644
index 0000000000..340ddce010
--- /dev/null
+++ b/automotive/3d-object-detection/model/__init__.py
@@ -0,0 +1,2 @@
+from .anchors import Anchors, anchors2bboxes, bboxes2deltas
+from .pointpillars import PointPillars
diff --git a/automotive/3d-object-detection/model/anchors.py b/automotive/3d-object-detection/model/anchors.py
new file mode 100644
index 0000000000..7e104ece31
--- /dev/null
+++ b/automotive/3d-object-detection/model/anchors.py
@@ -0,0 +1,290 @@
+import torch
+import math
+from tools.process import limit_period, iou2d_nearest
+
+
+class Anchors():
+    def __init__(self, ranges, sizes, rotations):
+        assert len(ranges) == len(sizes)
+        self.ranges = ranges
+        self.sizes = sizes
+        self.rotations = rotations
+
+    def get_anchors(self, feature_map_size, anchor_range,
+                    anchor_size, rotations):
+        '''
+        feature_map_size: (y_l, x_l)
+        anchor_range: [x1, y1, z1, x2, y2, z2]
+        anchor_size: [w, l, h]
+        rotations: [0, 1.57]
+        return: shape=(y_l, x_l, 2, 7)
+        '''
+        device = feature_map_size.device
+        x_centers = torch.linspace(
+            anchor_range[0],
+            anchor_range[3],
+            feature_map_size[1] + 1,
+            device=device)
+        y_centers = torch.linspace(
+            anchor_range[1],
+            anchor_range[4],
+            feature_map_size[0] + 1,
+            device=device)
+        z_centers = torch.linspace(
+            anchor_range[2],
+            anchor_range[5],
+            1 + 1,
+            device=device)
+
+        x_shift = (x_centers[1] - x_centers[0]) / 2
+        y_shift = (y_centers[1] - y_centers[0]) / 2
+        z_shift = (z_centers[1] - z_centers[0]) / 2
+        x_centers = x_centers[:feature_map_size[1]] + \
+            x_shift  # (feature_map_size[1], )
+        y_centers = y_centers[:feature_map_size[0]] + \
+            y_shift  # (feature_map_size[0], )
+        z_centers = z_centers[:1] + z_shift  # (1, )
+
+        # [feature_map_size[1], feature_map_size[0], 1, 2] * 4
+        meshgrids = torch.meshgrid(x_centers, y_centers, z_centers, rotations)
+        meshgrids = list(meshgrids)
+        for i in range(len(meshgrids)):
+            # [feature_map_size[1], feature_map_size[0], 1, 2, 1]
+            meshgrids[i] = meshgrids[i][..., None]
+
+        anchor_size = anchor_size[None, None, None, None, :]
+        repeat_shape = [
+            feature_map_size[1],
+            feature_map_size[0],
+            1,
+            len(rotations),
+            1]
+        # [feature_map_size[1], feature_map_size[0], 1, 2, 3]
+        anchor_size = anchor_size.repeat(repeat_shape)
+        meshgrids.insert(3, anchor_size)
+        # [1, feature_map_size[0], feature_map_size[1], 2, 7]
+        anchors = torch.cat(
+            meshgrids,
+            dim=-
+            1).permute(
+            2,
+            1,
+            0,
+            3,
+            4).contiguous()
+        return anchors.squeeze(0)
+
+    def get_multi_anchors(self, feature_map_size):
+        '''
+        feature_map_size: (y_l, x_l)
+        ranges: [[x1, y1, z1, x2, y2, z2], [x1, y1, z1, x2, y2, z2], [x1, y1, z1, x2, y2, z2]]
+        sizes: [[w, l, h], [w, l, h], [w, l, h]]
+        rotations: [0, 1.57]
+        return: shape=(y_l, x_l, 3, 2, 7)
+        '''
+        device = feature_map_size.device
+        ranges = torch.tensor(self.ranges, device=device)
+        sizes = torch.tensor(self.sizes, device=device)
+        rotations = torch.tensor(self.rotations, device=device)
+        multi_anchors = []
+        for i in range(len(ranges)):
+            anchors = self.get_anchors(feature_map_size=feature_map_size,
+                                       anchor_range=ranges[i],
+                                       anchor_size=sizes[i],
+                                       rotations=rotations)
+            multi_anchors.append(anchors[:, :, None, :, :])
+        multi_anchors = torch.cat(multi_anchors, dim=2)
+
+        return multi_anchors
+
+
+def anchors2bboxes(anchors, deltas):
+    '''
+    anchors: (M, 7),  (x, y, z, w, l, h, theta)
+    deltas: (M, 7)
+    return: (M, 7)
+    '''
+    da = torch.sqrt(anchors[:, 3] ** 2 + anchors[:, 4] ** 2)
+    x = deltas[:, 0] * da + anchors[:, 0]
+    y = deltas[:, 1] * da + anchors[:, 1]
+    z = deltas[:, 2] * anchors[:, 5] + anchors[:, 2] + anchors[:, 5] / 2
+
+    w = anchors[:, 3] * torch.exp(deltas[:, 3])
+    l = anchors[:, 4] * torch.exp(deltas[:, 4])
+    h = anchors[:, 5] * torch.exp(deltas[:, 5])
+
+    z = z - h / 2
+
+    theta = anchors[:, 6] + deltas[:, 6]
+
+    bboxes = torch.stack([x, y, z, w, l, h, theta], dim=1)
+    return bboxes
+
+
+def bboxes2deltas(bboxes, anchors):
+    '''
+    bboxes: (M, 7), (x, y, z, w, l, h, theta)
+    anchors: (M, 7)
+    return: (M, 7)
+    '''
+    da = torch.sqrt(anchors[:, 3] ** 2 + anchors[:, 4] ** 2)
+
+    dx = (bboxes[:, 0] - anchors[:, 0]) / da
+    dy = (bboxes[:, 1] - anchors[:, 1]) / da
+
+    zb = bboxes[:, 2] + bboxes[:, 5] / 2  # bottom center
+    za = anchors[:, 2] + anchors[:, 5] / 2  # bottom center
+    dz = (zb - za) / anchors[:, 5]  # bottom center
+
+    dw = torch.log(bboxes[:, 3] / anchors[:, 3])
+    dl = torch.log(bboxes[:, 4] / anchors[:, 4])
+    dh = torch.log(bboxes[:, 5] / anchors[:, 5])
+    dtheta = bboxes[:, 6] - anchors[:, 6]
+
+    deltas = torch.stack([dx, dy, dz, dw, dl, dh, dtheta], dim=1)
+    return deltas
+
+
+def anchor_target(batched_anchors, batched_gt_bboxes,
+                  batched_gt_labels, assigners, nclasses):
+    '''
+    batched_anchors: [(y_l, x_l, 3, 2, 7), (y_l, x_l, 3, 2, 7), ... ]
+    batched_gt_bboxes: [(n1, 7), (n2, 7), ...]
+    batched_gt_labels: [(n1, ), (n2, ), ...]
+    return:
+           dict = {batched_anchors_labels: (bs, n_anchors),
+                   batched_labels_weights: (bs, n_anchors),
+                   batched_anchors_reg: (bs, n_anchors, 7),
+                   batched_reg_weights: (bs, n_anchors),
+                   batched_anchors_dir: (bs, n_anchors),
+                   batched_dir_weights: (bs, n_anchors)}
+    '''
+    assert len(batched_anchors) == len(
+        batched_gt_bboxes) == len(batched_gt_labels)
+    batch_size = len(batched_anchors)
+    n_assigners = len(assigners)
+    batched_labels, batched_label_weights = [], []
+    batched_bbox_reg, batched_bbox_reg_weights = [], []
+    batched_dir_labels, batched_dir_labels_weights = [], []
+    for i in range(batch_size):
+        anchors = batched_anchors[i]
+        gt_bboxes, gt_labels = batched_gt_bboxes[i], batched_gt_labels[i]
+        # what we want to get next ?
+        # 1. identify positive anchors and negative anchors  -> cls
+        # 2. identify the regresstion values  -> reg
+        # 3. indentify the direction  -> dir_cls
+        multi_labels, multi_label_weights = [], []
+        multi_bbox_reg, multi_bbox_reg_weights = [], []
+        multi_dir_labels, multi_dir_labels_weights = [], []
+        d1, d2, d3, d4, d5 = anchors.size()
+        for j in range(n_assigners):  # multi anchors
+            assigner = assigners[j]
+            pos_iou_thr, neg_iou_thr, min_iou_thr = \
+                assigner['pos_iou_thr'], assigner['neg_iou_thr'], assigner['min_iou_thr']
+            cur_anchors = anchors[:, :, j, :, :].reshape(-1, 7)
+            overlaps = iou2d_nearest(gt_bboxes, cur_anchors)
+            if overlaps.shape[0] == 0:
+                max_overlaps = torch.zeros_like(
+                    cur_anchors[:, 0], dtype=cur_anchors.dtype)
+                max_overlaps_idx = torch.zeros_like(
+                    cur_anchors[:, 0], dtype=torch.long)
+            else:
+                max_overlaps, max_overlaps_idx = torch.max(overlaps, dim=0)
+                gt_max_overlaps, _ = torch.max(overlaps, dim=1)
+
+            assigned_gt_inds = - \
+                torch.ones_like(cur_anchors[:, 0], dtype=torch.long)
+            # a. negative anchors
+            assigned_gt_inds[max_overlaps < neg_iou_thr] = 0
+
+            # b. positive anchors
+            # rule 1
+            assigned_gt_inds[max_overlaps >=
+                             pos_iou_thr] = max_overlaps_idx[max_overlaps >= pos_iou_thr] + 1
+
+            # rule 2
+            # support one bbox to multi anchors, only if the anchors are with the highest iou.
+            # rule2 may modify the labels generated by rule 1
+            for i in range(len(gt_bboxes)):
+                if gt_max_overlaps[i] >= min_iou_thr:
+                    assigned_gt_inds[overlaps[i] == gt_max_overlaps[i]] = i + 1
+
+            pos_flag = assigned_gt_inds > 0
+            neg_flag = assigned_gt_inds == 0
+            # 1. anchor labels
+            # -1 is not optimal, for some bboxes are with labels -1
+            assigned_gt_labels = torch.zeros_like(
+                cur_anchors[:, 0], dtype=torch.long) + nclasses
+            assigned_gt_labels[pos_flag] = gt_labels[assigned_gt_inds[pos_flag] - 1].long()
+            assigned_gt_labels_weights = torch.zeros_like(cur_anchors[:, 0])
+            assigned_gt_labels_weights[pos_flag] = 1
+            assigned_gt_labels_weights[neg_flag] = 1
+
+            # 2. anchor regression
+            assigned_gt_reg_weights = torch.zeros_like(cur_anchors[:, 0])
+            assigned_gt_reg_weights[pos_flag] = 1
+
+            assigned_gt_reg = torch.zeros_like(cur_anchors)
+            positive_anchors = cur_anchors[pos_flag]
+            corr_gt_bboxes = gt_bboxes[assigned_gt_inds[pos_flag] - 1]
+            assigned_gt_reg[pos_flag] = bboxes2deltas(
+                corr_gt_bboxes, positive_anchors)
+
+            # 3. anchor direction
+            assigned_gt_dir_weights = torch.zeros_like(cur_anchors[:, 0])
+            assigned_gt_dir_weights[pos_flag] = 1
+
+            assigned_gt_dir = torch.zeros_like(
+                cur_anchors[:, 0], dtype=torch.long)
+            dir_cls_targets = limit_period(
+                corr_gt_bboxes[:, 6].cpu(), 0, 2 * math.pi).to(corr_gt_bboxes)
+            dir_cls_targets = torch.floor(dir_cls_targets / math.pi).long()
+            assigned_gt_dir[pos_flag] = torch.clamp(
+                dir_cls_targets, min=0, max=1)
+
+            multi_labels.append(assigned_gt_labels.reshape(d1, d2, 1, d4))
+            multi_label_weights.append(
+                assigned_gt_labels_weights.reshape(
+                    d1, d2, 1, d4))
+            multi_bbox_reg.append(assigned_gt_reg.reshape(d1, d2, 1, d4, -1))
+            multi_bbox_reg_weights.append(
+                assigned_gt_reg_weights.reshape(
+                    d1, d2, 1, d4))
+            multi_dir_labels.append(assigned_gt_dir.reshape(d1, d2, 1, d4))
+            multi_dir_labels_weights.append(
+                assigned_gt_dir_weights.reshape(
+                    d1, d2, 1, d4))
+
+        multi_labels = torch.cat(multi_labels, dim=-2).reshape(-1)
+        multi_label_weights = torch.cat(
+            multi_label_weights, dim=-2).reshape(-1)
+        multi_bbox_reg = torch.cat(multi_bbox_reg, dim=-3).reshape(-1, d5)
+        multi_bbox_reg_weights = torch.cat(
+            multi_bbox_reg_weights, dim=-2).reshape(-1)
+        multi_dir_labels = torch.cat(multi_dir_labels, dim=-2).reshape(-1)
+        multi_dir_labels_weights = torch.cat(
+            multi_dir_labels_weights, dim=-2).reshape(-1)
+
+        batched_labels.append(multi_labels)
+        batched_label_weights.append(multi_label_weights)
+        batched_bbox_reg.append(multi_bbox_reg)
+        batched_bbox_reg_weights.append(multi_bbox_reg_weights)
+        batched_dir_labels.append(multi_dir_labels)
+        batched_dir_labels_weights.append(multi_dir_labels_weights)
+
+    rt_dict = dict(
+        batched_labels=torch.stack(
+            batched_labels, 0),  # (bs, y_l * x_l * 3 * 2)
+        batched_label_weights=torch.stack(
+            batched_label_weights, 0),  # (bs, y_l * x_l * 3 * 2)
+        batched_bbox_reg=torch.stack(
+            batched_bbox_reg, 0),  # (bs, y_l * x_l * 3 * 2, 7)
+        batched_bbox_reg_weights=torch.stack(
+            batched_bbox_reg_weights, 0),  # (bs, y_l * x_l * 3 * 2)
+        batched_dir_labels=torch.stack(
+            batched_dir_labels, 0),  # (bs, y_l * x_l * 3 * 2)
+        batched_dir_labels_weights=torch.stack(
+            batched_dir_labels_weights, 0)  # (bs, y_l * x_l * 3 * 2)
+    )
+
+    return rt_dict
diff --git a/automotive/3d-object-detection/model/painter.py b/automotive/3d-object-detection/model/painter.py
new file mode 100644
index 0000000000..f0680931f5
--- /dev/null
+++ b/automotive/3d-object-detection/model/painter.py
@@ -0,0 +1,267 @@
+import argparse
+import model.segmentation as network
+import os
+import numpy as np
+import torch
+from torchvision import transforms
+from PIL import Image
+import copy
+import sys
+from tqdm import tqdm
+sys.path.append('..')
+
+
+def get_calib_from_file(calib_file):
+    """Read in a calibration file and parse into a dictionary."""
+    data = {}
+
+    with open(calib_file, 'r') as f:
+        lines = [line for line in f.readlines() if line.strip()]
+    for line in lines:
+        key, value = line.split(':', 1)
+        # The only non-float values in these files are dates, which
+        # we don't care about anyway
+        try:
+            if key == 'R0_rect':
+                data['R0'] = torch.tensor([float(x)
+                                          for x in value.split()]).reshape(3, 3)
+            else:
+                data[key] = torch.tensor([float(x)
+                                         for x in value.split()]).reshape(3, 4)
+        except ValueError:
+            pass
+
+    return data
+
+
+class Painter:
+    def __init__(self, args):
+        self.root_split_path = args.training_path
+        self.save_path = os.path.join(args.training_path, "painted_lidar/")
+        if not os.path.exists(self.save_path):
+            os.mkdir(self.save_path)
+
+        self.seg_net_index = 0
+        self.model = None
+        print(f'Using Segmentation Network -- deeplabv3plus')
+        checkpoint_file = args.model_path
+        model = network.modeling.__dict__['deeplabv3plus_resnet50'](
+            num_classes=19, output_stride=16)
+        checkpoint = torch.load(checkpoint_file)
+        model.load_state_dict(checkpoint["model_state"])
+        model.eval()
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        model.to(device)
+        self.model = model
+        self.cam_sync = args.cam_sync
+
+    def get_lidar(self, idx):
+        lidar_file = os.path.join(
+            self.root_split_path, 'velodyne/' + ('%s.bin' % idx))
+        return torch.from_numpy(np.fromfile(
+            str(lidar_file), dtype=np.float32).reshape(-1, 6))
+
+    def get_image(self, idx, camera):
+        filename = os.path.join(self.root_split_path,
+                                camera + ('%s.jpg' % idx))
+        input_image = Image.open(filename)
+        preprocess = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=[
+                    0.485, 0.456, 0.406], std=[
+                    0.229, 0.224, 0.225]),
+        ])
+
+        input_tensor = preprocess(input_image)
+        # create a mini-batch as expected by the model
+        input_batch = input_tensor.unsqueeze(0)
+        if torch.cuda.is_available():
+            input_batch = input_batch.to('cuda')
+        # move the input and model to GPU for speed if available
+        if torch.cuda.is_available():
+            input_batch = input_batch.to('cuda')
+        return input_batch
+
+    def get_model_output(self, input_batch):
+        with torch.no_grad():
+            output = self.model(input_batch)[0]
+        return output
+
+    def get_score(self, model_output):
+        sf = torch.nn.Softmax(dim=2)
+        output_permute = model_output.permute(1, 2, 0)
+        output_permute = sf(output_permute)
+        output_reassign = torch.zeros(
+            output_permute.size(0), output_permute.size(1), 6).to(
+            device=model_output.device)
+        output_reassign[:, :, 0] = torch.sum(
+            output_permute[:, :, :11], dim=2)  # background
+        output_reassign[:, :, 1] = output_permute[:, :, 18]  # bicycle
+        output_reassign[:, :, 2] = torch.sum(
+            output_permute[:, :, [13, 14, 15, 16]], dim=2)  # vehicles
+        output_reassign[:, :, 3] = output_permute[:, :, 11]  # person
+        output_reassign[:, :, 4] = output_permute[:, :, 12]  # rider
+        output_reassign[:, :, 5] = output_permute[:, :, 17]  # motorcycle
+
+        return output_reassign
+
+    def get_calib_fromfile(self, idx, device):
+        calib_file = os.path.join(
+            self.root_split_path, 'calib/' + ('%s.txt' % idx))
+        calib = get_calib_from_file(calib_file)
+        calib['P0'] = torch.cat([calib['P0'], torch.tensor(
+            [[0., 0., 0., 1.]])], axis=0).to(device=device)
+        calib['P1'] = torch.cat([calib['P1'], torch.tensor(
+            [[0., 0., 0., 1.]])], axis=0).to(device=device)
+        calib['P2'] = torch.cat([calib['P2'], torch.tensor(
+            [[0., 0., 0., 1.]])], axis=0).to(device=device)
+        calib['P3'] = torch.cat([calib['P3'], torch.tensor(
+            [[0., 0., 0., 1.]])], axis=0).to(device=device)
+        calib['P4'] = torch.cat([calib['P4'], torch.tensor(
+            [[0., 0., 0., 1.]])], axis=0).to(device=device)
+        calib['R0_rect'] = torch.zeros(
+            [4, 4], dtype=calib['R0'].dtype, device=device)
+        calib['R0_rect'][3, 3] = 1.
+        calib['R0_rect'][:3, :3] = calib['R0'].to(device=device)
+        calib['Tr_velo_to_cam_0'] = torch.cat([calib['Tr_velo_to_cam_0'], torch.tensor(
+            [[0., 0., 0., 1.]], )], axis=0).to(device=device)
+        calib['Tr_velo_to_cam_1'] = torch.cat([calib['Tr_velo_to_cam_1'], torch.tensor(
+            [[0., 0., 0., 1.]], )], axis=0).to(device=device)
+        calib['Tr_velo_to_cam_2'] = torch.cat([calib['Tr_velo_to_cam_2'], torch.tensor(
+            [[0., 0., 0., 1.]], )], axis=0).to(device=device)
+        calib['Tr_velo_to_cam_3'] = torch.cat([calib['Tr_velo_to_cam_3'], torch.tensor(
+            [[0., 0., 0., 1.]], )], axis=0).to(device=device)
+        calib['Tr_velo_to_cam_4'] = torch.cat([calib['Tr_velo_to_cam_4'], torch.tensor(
+            [[0., 0., 0., 1.]], )], axis=0).to(device=device)
+        return calib
+
+    def cam_to_lidar(self, pointcloud, projection_mats, camera_num):
+        """
+        Takes in lidar in velo coords, returns lidar points in camera coords
+
+        :param pointcloud: (n_points, 4) np.array (x,y,z,r) in velodyne coordinates
+        :return lidar_cam_coords: (n_points, 4) np.array (x,y,z,r) in camera coordinates
+        """
+
+        lidar_velo_coords = copy.deepcopy(pointcloud)
+        # copy reflectances column
+        reflectances = copy.deepcopy(lidar_velo_coords[:, -1])
+        lidar_velo_coords[:, -1] = 1  # for multiplying with homogeneous matrix
+        lidar_cam_coords = projection_mats['Tr_velo_to_cam_' +
+                                           str(camera_num)].matmul(lidar_velo_coords.transpose(0, 1))
+        lidar_cam_coords = lidar_cam_coords.transpose(0, 1)
+        lidar_cam_coords[:, -1] = reflectances
+
+        return lidar_cam_coords
+
+    def project_points_mask(self, lidar_cam_points,
+                            projection_mats, class_scores, camera_num):
+        points_projected_on_mask = projection_mats['P' + str(camera_num)].matmul(
+            projection_mats['R0_rect'].matmul(lidar_cam_points.transpose(0, 1)))
+        points_projected_on_mask = points_projected_on_mask.transpose(0, 1)
+        points_projected_on_mask = points_projected_on_mask / \
+            (points_projected_on_mask[:, 2].reshape(-1, 1))
+
+        true_where_x_on_img = (0 < points_projected_on_mask[:, 0]) & (
+            points_projected_on_mask[:, 0] < class_scores[camera_num].shape[1])  # x in img coords is cols of img
+        true_where_y_on_img = (0 < points_projected_on_mask[:, 1]) & (
+            points_projected_on_mask[:, 1] < class_scores[camera_num].shape[0])
+        true_where_point_on_img = true_where_x_on_img & true_where_y_on_img & (
+            lidar_cam_points[:, 2] > 0)
+
+        # filter out points that don't project to image
+        points_projected_on_mask = points_projected_on_mask[true_where_point_on_img]
+        # using floor so you don't end up indexing num_rows+1th row or col
+        points_projected_on_mask = torch.floor(points_projected_on_mask).int()
+        # drops homogenous coord 1 from every point, giving (N_pts, 2) int
+        # array
+        points_projected_on_mask = points_projected_on_mask[:, :2]
+        return (points_projected_on_mask, true_where_point_on_img)
+
+    def augment_lidar_class_scores_both(
+            self, class_scores, lidar_raw, projection_mats):
+        """
+        Projects lidar points onto segmentation map, appends class score each point projects onto.
+        """
+        # lidar_cam_coords = self.cam_to_lidar(lidar_raw, projection_mats)
+
+        ################################
+        lidar_cam_coords = self.cam_to_lidar(
+            lidar_raw[:, :4], projection_mats, 0)
+
+        lidar_cam_coords[:, -1] = 1  # homogenous coords for projection
+
+        points_projected_on_mask_0, true_where_point_on_img_0 = self.project_points_mask(
+            lidar_cam_coords, projection_mats, class_scores, 0)
+
+        lidar_cam_coords = self.cam_to_lidar(
+            lidar_raw[:, :4], projection_mats, 1)
+        lidar_cam_coords[:, -1] = 1  # homogenous coords for projection
+
+        points_projected_on_mask_1, true_where_point_on_img_1 = self.project_points_mask(
+            lidar_cam_coords, projection_mats, class_scores, 1)
+
+        lidar_cam_coords = self.cam_to_lidar(
+            lidar_raw[:, :4], projection_mats, 2)
+        lidar_cam_coords[:, -1] = 1
+        points_projected_on_mask_2, true_where_point_on_img_2 = self.project_points_mask(
+            lidar_cam_coords, projection_mats, class_scores, 2)
+
+        lidar_cam_coords = self.cam_to_lidar(
+            lidar_raw[:, :4], projection_mats, 3)
+        lidar_cam_coords[:, -1] = 1
+        points_projected_on_mask_3, true_where_point_on_img_3 = self.project_points_mask(
+            lidar_cam_coords, projection_mats, class_scores, 3)
+
+        lidar_cam_coords = self.cam_to_lidar(
+            lidar_raw[:, :4], projection_mats, 4)
+        lidar_cam_coords[:, -1] = 1
+        points_projected_on_mask_4, true_where_point_on_img_4 = self.project_points_mask(
+            lidar_cam_coords, projection_mats, class_scores, 4)
+
+        true_where_point_on_both_0_1 = true_where_point_on_img_0 & true_where_point_on_img_1
+        true_where_point_on_both_0_2 = true_where_point_on_img_0 & true_where_point_on_img_2
+        true_where_point_on_both_1_3 = true_where_point_on_img_1 & true_where_point_on_img_3
+        true_where_point_on_both_2_4 = true_where_point_on_img_2 & true_where_point_on_img_4
+        true_where_point_on_img = true_where_point_on_img_1 | true_where_point_on_img_0 | true_where_point_on_img_2 | true_where_point_on_img_3 | true_where_point_on_img_4
+
+        point_scores_0 = class_scores[0][points_projected_on_mask_0[:, 1],
+                                         points_projected_on_mask_0[:, 0]].reshape(-1, class_scores[0].shape[2])
+        point_scores_1 = class_scores[1][points_projected_on_mask_1[:, 1],
+                                         points_projected_on_mask_1[:, 0]].reshape(-1, class_scores[1].shape[2])
+        point_scores_2 = class_scores[2][points_projected_on_mask_2[:, 1],
+                                         points_projected_on_mask_2[:, 0]].reshape(-1, class_scores[2].shape[2])
+        point_scores_3 = class_scores[3][points_projected_on_mask_3[:, 1],
+                                         points_projected_on_mask_3[:, 0]].reshape(-1, class_scores[3].shape[2])
+        point_scores_4 = class_scores[4][points_projected_on_mask_4[:, 1],
+                                         points_projected_on_mask_4[:, 0]].reshape(-1, class_scores[4].shape[2])
+
+        augmented_lidar = torch.cat((lidar_raw[:, :5], torch.zeros(
+            (lidar_raw.shape[0], class_scores[1].shape[2])).to(device=lidar_raw.device)), axis=1)
+        augmented_lidar[true_where_point_on_img_0, -
+                        class_scores[0].shape[2]:] += point_scores_0
+        augmented_lidar[true_where_point_on_img_1, -
+                        class_scores[1].shape[2]:] += point_scores_1
+        augmented_lidar[true_where_point_on_img_2, -
+                        class_scores[2].shape[2]:] += point_scores_2
+        augmented_lidar[true_where_point_on_img_3, -
+                        class_scores[3].shape[2]:] += point_scores_3
+        augmented_lidar[true_where_point_on_img_4, -
+                        class_scores[4].shape[2]:] += point_scores_4
+        augmented_lidar[true_where_point_on_both_0_1, -class_scores[0].shape[2]:] = 0.5 * \
+            augmented_lidar[true_where_point_on_both_0_1, -
+                            class_scores[0].shape[2]:]
+        augmented_lidar[true_where_point_on_both_0_2, -class_scores[0].shape[2]:] = 0.5 * \
+            augmented_lidar[true_where_point_on_both_0_2, -
+                            class_scores[0].shape[2]:]
+        augmented_lidar[true_where_point_on_both_1_3, -class_scores[1].shape[2]:] = 0.5 * \
+            augmented_lidar[true_where_point_on_both_1_3, -
+                            class_scores[1].shape[2]:]
+        augmented_lidar[true_where_point_on_both_2_4, -class_scores[2].shape[2]:] = 0.5 * \
+            augmented_lidar[true_where_point_on_both_2_4, -
+                            class_scores[2].shape[2]:]
+        if self.cam_sync:
+            augmented_lidar = augmented_lidar[true_where_point_on_img]
+
+        return augmented_lidar
diff --git a/automotive/3d-object-detection/model/pointpillars.py b/automotive/3d-object-detection/model/pointpillars.py
new file mode 100644
index 0000000000..49257fe5a9
--- /dev/null
+++ b/automotive/3d-object-detection/model/pointpillars.py
@@ -0,0 +1,515 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from model.anchors import Anchors, anchor_target, anchors2bboxes
+from ops import Voxelization
+import open3d.ml.torch as ml3d
+from tools.process import limit_period
+import math
+
+
+class PillarLayer(nn.Module):
+    def __init__(self, voxel_size, point_cloud_range,
+                 max_num_points, max_voxels):
+        super().__init__()
+        self.voxel_layer = Voxelization(voxel_size=voxel_size,
+                                        point_cloud_range=point_cloud_range,
+                                        max_num_points=max_num_points,
+                                        max_voxels=max_voxels)
+
+    @torch.no_grad()
+    def forward(self, batched_pts):
+        '''
+        batched_pts: list[tensor], len(batched_pts) = bs
+        return:
+               pillars: (p1 + p2 + ... + pb, num_points, c),
+               coors_batch: (p1 + p2 + ... + pb, 1 + 3),
+               num_points_per_pillar: (p1 + p2 + ... + pb, ), (b: batch size)
+        '''
+        pillars, coors, npoints_per_pillar = [], [], []
+        for i, pts in enumerate(batched_pts):
+            voxels_out, coors_out, num_points_per_voxel_out = self.voxel_layer(
+                pts)
+            # voxels_out: (max_voxel, num_points, c), coors_out: (max_voxel, 3)
+            # num_points_per_voxel_out: (max_voxel, )
+            pillars.append(voxels_out)
+            coors.append(coors_out.long())
+            npoints_per_pillar.append(num_points_per_voxel_out)
+
+        # (p1 + p2 + ... + pb, num_points, c)
+        pillars = torch.cat(pillars, dim=0)
+        npoints_per_pillar = torch.cat(
+            npoints_per_pillar,
+            dim=0)  # (p1 + p2 + ... + pb, )
+        coors_batch = []
+        for i, cur_coors in enumerate(coors):
+            coors_batch.append(F.pad(cur_coors, (1, 0), value=i))
+        # (p1 + p2 + ... + pb, 1 + 3)
+        coors_batch = torch.cat(coors_batch, dim=0)
+
+        return pillars, coors_batch, npoints_per_pillar
+
+
+class PillarEncoder(nn.Module):
+    def __init__(self, voxel_size, point_cloud_range, in_channel, out_channel):
+        super().__init__()
+        self.out_channel = out_channel
+        self.vx, self.vy = voxel_size[0], voxel_size[1]
+        self.x_offset = voxel_size[0] / 2 + point_cloud_range[0]
+        self.y_offset = voxel_size[1] / 2 + point_cloud_range[1]
+        self.x_l = math.ceil(
+            (point_cloud_range[3] -
+             point_cloud_range[0]) /
+            voxel_size[0])
+        self.y_l = math.ceil(
+            (point_cloud_range[4] -
+             point_cloud_range[1]) /
+            voxel_size[1])
+
+        self.conv = nn.Conv1d(in_channel, out_channel, 1, bias=False)
+        self.bn = nn.BatchNorm1d(out_channel, eps=1e-3, momentum=0.01)
+
+    def forward(self, pillars, coors_batch, npoints_per_pillar):
+        '''
+        pillars: (p1 + p2 + ... + pb, num_points, c), c = 4
+        coors_batch: (p1 + p2 + ... + pb, 1 + 3)
+        npoints_per_pillar: (p1 + p2 + ... + pb, )
+        return:  (bs, out_channel, y_l, x_l)
+        '''
+        device = pillars.device
+        # 1. calculate offset to the points center (in each pillar)
+        offset_pt_center = pillars[:,
+                                   :,
+                                   :3] - torch.sum(pillars[:,
+                                                           :,
+                                                           :3],
+                                                   dim=1,
+                                                   keepdim=True) / npoints_per_pillar[:,
+                                                                                      None,
+                                                                                      None]  # (p1 + p2 + ... + pb, num_points, 3)
+
+        # 2. calculate offset to the pillar center
+        # (p1 + p2 + ... + pb, num_points, 1)
+        x_offset_pi_center = pillars[:, :, :1] - \
+            (coors_batch[:, None, 1:2] * self.vx + self.x_offset)
+        # (p1 + p2 + ... + pb, num_points, 1)
+        y_offset_pi_center = pillars[:, :, 1:2] - \
+            (coors_batch[:, None, 2:3] * self.vy + self.y_offset)
+
+        # 3. encoder
+        features = torch.cat([pillars,
+                              offset_pt_center,
+                              x_offset_pi_center,
+                              y_offset_pi_center],
+                             dim=-1)  # (p1 + p2 + ... + pb, num_points, 9)
+        features[:, :, 0:1] = x_offset_pi_center  # tmp
+        features[:, :, 1:2] = y_offset_pi_center  # tmp
+        # In consitent with mmdet3d.
+        # The reason can be referenced to
+        # https://github.com/open-mmlab/mmdetection3d/issues/1150
+
+        # 4. find mask for (0, 0, 0) and update the encoded features
+        # a very beautiful implementation
+        voxel_ids = torch.arange(
+            0, pillars.size(1)).to(device)  # (num_points, )
+        # (num_points, p1 + p2 + ... + pb)
+        mask = voxel_ids[:, None] < npoints_per_pillar[None, :]
+        # (p1 + p2 + ... + pb, num_points)
+        mask = mask.permute(1, 0).contiguous()
+        features *= mask[:, :, None]
+
+        # 5. embedding
+        # (p1 + p2 + ... + pb, 9, num_points)
+        features = features.permute(0, 2, 1).contiguous()
+        # (p1 + p2 + ... + pb, out_channels, num_points)
+        features = F.relu(self.bn(self.conv(features)))
+        # (p1 + p2 + ... + pb, out_channels)
+        pooling_features = torch.max(features, dim=-1)[0]
+
+        # 6. pillar scatter
+        batched_canvas = []
+        bs = coors_batch[-1, 0] + 1
+        for i in range(bs):
+            cur_coors_idx = coors_batch[:, 0] == i
+            cur_coors = coors_batch[cur_coors_idx, :]
+            cur_features = pooling_features[cur_coors_idx]
+
+            canvas = torch.zeros(
+                (self.x_l,
+                 self.y_l,
+                 self.out_channel),
+                dtype=torch.float32,
+                device=device)
+            canvas[cur_coors[:, 1], cur_coors[:, 2]] = cur_features
+            canvas = canvas.permute(2, 1, 0).contiguous()
+            batched_canvas.append(canvas)
+        # (bs, in_channel, self.y_l, self.x_l)
+        batched_canvas = torch.stack(batched_canvas, dim=0)
+        return batched_canvas
+
+
+class Backbone(nn.Module):
+    def __init__(self, in_channel, out_channels,
+                 layer_nums, layer_strides=[2, 2, 2]):
+        super().__init__()
+        assert len(out_channels) == len(layer_nums)
+        assert len(out_channels) == len(layer_strides)
+
+        self.multi_blocks = nn.ModuleList()
+        for i in range(len(layer_strides)):
+            blocks = []
+            blocks.append(
+                nn.Conv2d(
+                    in_channel,
+                    out_channels[i],
+                    3,
+                    stride=layer_strides[i],
+                    bias=False,
+                    padding=1))
+            blocks.append(
+                nn.BatchNorm2d(
+                    out_channels[i],
+                    eps=1e-3,
+                    momentum=0.01))
+            blocks.append(nn.ReLU(inplace=True))
+
+            for _ in range(layer_nums[i]):
+                blocks.append(
+                    nn.Conv2d(
+                        out_channels[i],
+                        out_channels[i],
+                        3,
+                        bias=False,
+                        padding=1))
+                blocks.append(
+                    nn.BatchNorm2d(
+                        out_channels[i],
+                        eps=1e-3,
+                        momentum=0.01))
+                blocks.append(nn.ReLU(inplace=True))
+
+            in_channel = out_channels[i]
+            self.multi_blocks.append(nn.Sequential(*blocks))
+
+        # in consitent with mmdet3d
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu')
+
+    def forward(self, x):
+        '''
+        x: (b, c, y_l, x_l). Default: (6, 64, 496, 432)
+        return: list[]. Default: [(6, 64, 248, 216), (6, 128, 124, 108), (6, 256, 62, 54)]
+        '''
+        outs = []
+        for i in range(len(self.multi_blocks)):
+            x = self.multi_blocks[i](x)
+            outs.append(x)
+        return outs
+
+
+class Neck(nn.Module):
+    def __init__(self, in_channels, upsample_strides, out_channels):
+        super().__init__()
+        assert len(in_channels) == len(upsample_strides)
+        assert len(upsample_strides) == len(out_channels)
+
+        self.decoder_blocks = nn.ModuleList()
+        for i in range(len(in_channels)):
+            decoder_block = []
+            decoder_block.append(nn.ConvTranspose2d(in_channels[i],
+                                                    out_channels[i],
+                                                    upsample_strides[i],
+                                                    stride=upsample_strides[i],
+                                                    bias=False))
+            decoder_block.append(
+                nn.BatchNorm2d(
+                    out_channels[i],
+                    eps=1e-3,
+                    momentum=0.01))
+            decoder_block.append(nn.ReLU(inplace=True))
+
+            self.decoder_blocks.append(nn.Sequential(*decoder_block))
+
+        # in consitent with mmdet3d
+        for m in self.modules():
+            if isinstance(m, nn.ConvTranspose2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu')
+
+    def forward(self, x):
+        '''
+        x: [(bs, 64, 248, 216), (bs, 128, 124, 108), (bs, 256, 62, 54)]
+        return: (bs, 384, 248, 216)
+        '''
+        outs = []
+        for i in range(len(self.decoder_blocks)):
+            xi = self.decoder_blocks[i](x[i])  # (bs, 128, 248, 216)
+            outs.append(xi)
+        out = torch.cat(outs, dim=1)
+        return out
+
+
+class Head(nn.Module):
+    def __init__(self, in_channel, n_anchors, n_classes):
+        super().__init__()
+
+        self.conv_cls = nn.Conv2d(in_channel, n_anchors * n_classes, 1)
+        self.conv_reg = nn.Conv2d(in_channel, n_anchors * 7, 1)
+        self.conv_dir_cls = nn.Conv2d(in_channel, n_anchors * 2, 1)
+
+        # in consitent with mmdet3d
+        conv_layer_id = 0
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.normal_(m.weight, mean=0, std=0.01)
+                if conv_layer_id == 0:
+                    prior_prob = 0.01
+                    bias_init = float(-math.log((1 - prior_prob) / prior_prob))
+                    nn.init.constant_(m.bias, bias_init)
+                else:
+                    nn.init.constant_(m.bias, 0)
+                conv_layer_id += 1
+
+    def forward(self, x):
+        '''
+        x: (bs, 384, 248, 216)
+        return:
+              bbox_cls_pred: (bs, n_anchors*3, 248, 216)
+              bbox_pred: (bs, n_anchors*7, 248, 216)
+              bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216)
+        '''
+        bbox_cls_pred = self.conv_cls(x)
+        bbox_pred = self.conv_reg(x)
+        bbox_dir_cls_pred = self.conv_dir_cls(x)
+        return bbox_cls_pred, bbox_pred, bbox_dir_cls_pred
+
+
+class PointPillars(nn.Module):
+    def __init__(self,
+                 nclasses=3,
+                 voxel_size=[0.32, 0.32, 6],
+                 point_cloud_range=[-74.88, -74.88, -2, 74.88, 74.88, 4],
+                 max_num_points=20,
+                 max_voxels=(32000, 32000),
+                 painted=False):
+        super().__init__()
+        self.nclasses = nclasses
+        self.pillar_layer = PillarLayer(voxel_size=voxel_size,
+                                        point_cloud_range=point_cloud_range,
+                                        max_num_points=max_num_points,
+                                        max_voxels=max_voxels)
+        if painted:
+            pillar_channel = 16
+        else:
+            pillar_channel = 10
+        self.pillar_encoder = PillarEncoder(voxel_size=voxel_size,
+                                            point_cloud_range=point_cloud_range,
+                                            in_channel=pillar_channel,
+                                            out_channel=64)
+        self.backbone = Backbone(in_channel=64,
+                                 out_channels=[64, 128, 256],
+                                 layer_nums=[3, 5, 5],
+                                 layer_strides=[1, 2, 2])
+        self.neck = Neck(in_channels=[64, 128, 256],
+                         upsample_strides=[1, 2, 4],
+                         out_channels=[128, 128, 128])
+        self.head = Head(
+            in_channel=384,
+            n_anchors=2 * nclasses,
+            n_classes=nclasses)
+
+        # anchors
+        ranges = [[-74.88, -74.88, -0.0345, 74.88, 74.88, -0.0345],
+                  [-74.88, -74.88, 0, 74.88, 74.88, 0],
+                  [-74.88, -74.88, -0.1188, 74.88, 74.88, -0.1188]]
+        sizes = [[0.84, .91, 1.74], [.84, 1.81, 1.77], [2.08, 4.73, 1.77]]
+        rotations = [0, 1.57]
+        self.anchors_generator = Anchors(ranges=ranges,
+                                         sizes=sizes,
+                                         rotations=rotations)
+
+        # train
+        self.assigners = [
+            {'pos_iou_thr': 0.5, 'neg_iou_thr': 0.3, 'min_iou_thr': 0.3},
+            {'pos_iou_thr': 0.5, 'neg_iou_thr': 0.3, 'min_iou_thr': 0.3},
+            {'pos_iou_thr': 0.55, 'neg_iou_thr': 0.4, 'min_iou_thr': 0.4},
+        ]
+
+        # val and test
+        self.nms_pre = 4096
+        self.nms_thr = 0.25
+        self.score_thr = 0.1
+        self.max_num = 500
+
+    def get_predicted_bboxes_single(
+            self, bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchors):
+        '''
+        bbox_cls_pred: (n_anchors*3, 248, 216)
+        bbox_pred: (n_anchors*7, 248, 216)
+        bbox_dir_cls_pred: (n_anchors*2, 248, 216)
+        anchors: (y_l, x_l, 3, 2, 7)
+        return:
+            bboxes: (k, 7)
+            labels: (k, )
+            scores: (k, )
+        '''
+        # 0. pre-process
+        bbox_cls_pred = bbox_cls_pred.permute(
+            1, 2, 0).reshape(-1, self.nclasses)
+        bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 7)
+        bbox_dir_cls_pred = bbox_dir_cls_pred.permute(1, 2, 0).reshape(-1, 2)
+        anchors = anchors.reshape(-1, 7)
+
+        bbox_cls_pred = torch.sigmoid(bbox_cls_pred)
+        bbox_dir_cls_pred = torch.max(bbox_dir_cls_pred, dim=1)[1]
+
+        # 1. obtain self.nms_pre bboxes based on scores
+        inds = bbox_cls_pred.max(1)[0].topk(self.nms_pre)[1]
+        bbox_cls_pred = bbox_cls_pred[inds]
+        bbox_pred = bbox_pred[inds]
+        bbox_dir_cls_pred = bbox_dir_cls_pred[inds]
+        anchors = anchors[inds]
+
+        # 2. decode predicted offsets to bboxes
+        bbox_pred = anchors2bboxes(anchors, bbox_pred)
+
+        # 3. nms
+        bbox_pred2d_xy = bbox_pred[:, [0, 1]]
+        bbox_pred2d_lw = bbox_pred[:, [3, 4]]
+        bbox_pred2d = torch.cat([bbox_pred2d_xy - bbox_pred2d_lw / 2,
+                                 bbox_pred2d_xy + bbox_pred2d_lw / 2,
+                                 bbox_pred[:, 6:]], dim=-1)  # (n_anchors, 5)
+
+        ret_bboxes, ret_labels, ret_scores = [], [], []
+        for i in range(self.nclasses):
+            # 3.1 filter bboxes with scores below self.score_thr
+            cur_bbox_cls_pred = bbox_cls_pred[:, i]
+            score_inds = cur_bbox_cls_pred > self.score_thr
+            if score_inds.sum() == 0:
+                continue
+
+            cur_bbox_cls_pred = cur_bbox_cls_pred[score_inds]
+            cur_bbox_pred2d = bbox_pred2d[score_inds]
+            cur_bbox_pred = bbox_pred[score_inds]
+            cur_bbox_dir_cls_pred = bbox_dir_cls_pred[score_inds]
+
+            # 3.2 nms core
+            keep_inds = ml3d.ops.nms(
+                cur_bbox_pred2d, cur_bbox_cls_pred, self.nms_thr)
+
+            cur_bbox_cls_pred = cur_bbox_cls_pred[keep_inds]
+            cur_bbox_pred = cur_bbox_pred[keep_inds]
+            cur_bbox_dir_cls_pred = cur_bbox_dir_cls_pred[keep_inds]
+            cur_bbox_pred[:, -
+                          1] = limit_period(cur_bbox_pred[:, -
+                                                          1].detach().cpu(), 1, math.pi).to(cur_bbox_pred)  # [-pi, 0]
+            cur_bbox_pred[:, -1] += (1 - cur_bbox_dir_cls_pred) * math.pi
+
+            ret_bboxes.append(cur_bbox_pred)
+            ret_labels.append(torch.zeros_like(
+                cur_bbox_pred[:, 0], dtype=torch.long) + i)
+            ret_scores.append(cur_bbox_cls_pred)
+
+        # 4. filter some bboxes if bboxes number is above self.max_num
+        if len(ret_bboxes) == 0:
+            return {
+                'lidar_bboxes': torch.empty((0, 7)).detach().cpu(),
+                'labels': torch.empty(0).detach().cpu(),
+                'scores': torch.empty(0).detach().cpu()
+            }
+        ret_bboxes = torch.cat(ret_bboxes, 0)
+        ret_labels = torch.cat(ret_labels, 0)
+        ret_scores = torch.cat(ret_scores, 0)
+        if ret_bboxes.size(0) > self.max_num:
+            final_inds = ret_scores.topk(self.max_num)[1]
+            ret_bboxes = ret_bboxes[final_inds]
+            ret_labels = ret_labels[final_inds]
+            ret_scores = ret_scores[final_inds]
+        result = {
+            'lidar_bboxes': ret_bboxes.detach().cpu(),
+            'labels': ret_labels.detach().cpu(),
+            'scores': ret_scores.detach().cpu()
+        }
+        return result
+
+    def get_predicted_bboxes(
+            self, bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, batched_anchors):
+        '''
+        bbox_cls_pred: (bs, n_anchors*3, 248, 216)
+        bbox_pred: (bs, n_anchors*7, 248, 216)
+        bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216)
+        batched_anchors: (bs, y_l, x_l, 3, 2, 7)
+        return:
+            bboxes: [(k1, 7), (k2, 7), ... ]
+            labels: [(k1, ), (k2, ), ... ]
+            scores: [(k1, ), (k2, ), ... ]
+        '''
+        results = []
+        bs = bbox_cls_pred.size(0)
+        for i in range(bs):
+            result = self.get_predicted_bboxes_single(bbox_cls_pred=bbox_cls_pred[i],
+                                                      bbox_pred=bbox_pred[i],
+                                                      bbox_dir_cls_pred=bbox_dir_cls_pred[i],
+                                                      anchors=batched_anchors[i])
+            results.append(result)
+        return results
+
+    def forward(self, batched_pts, mode='test',
+                batched_gt_bboxes=None, batched_gt_labels=None):
+        batch_size = len(batched_pts)
+        # batched_pts: list[tensor] -> pillars: (p1 + p2 + ... + pb, num_points, c),
+        #                              coors_batch: (p1 + p2 + ... + pb, 1 + 3),
+        # num_points_per_pillar: (p1 + p2 + ... + pb, ), (b: batch size)
+        pillars, coors_batch, npoints_per_pillar = self.pillar_layer(
+            batched_pts)
+
+        # pillars: (p1 + p2 + ... + pb, num_points, c), c = 4
+        # coors_batch: (p1 + p2 + ... + pb, 1 + 3)
+        # npoints_per_pillar: (p1 + p2 + ... + pb, )
+        #                     -> pillar_features: (bs, out_channel, y_l, x_l)
+        pillar_features = self.pillar_encoder(
+            pillars, coors_batch, npoints_per_pillar)
+
+        # xs:  [(bs, 64, 248, 216), (bs, 128, 124, 108), (bs, 256, 62, 54)]
+        xs = self.backbone(pillar_features)
+
+        # x: (bs, 384, 248, 216)
+        x = self.neck(xs)
+
+        # bbox_cls_pred: (bs, n_anchors*3, 248, 216)
+        # bbox_pred: (bs, n_anchors*7, 248, 216)
+        # bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216)
+        bbox_cls_pred, bbox_pred, bbox_dir_cls_pred = self.head(x)
+
+        # anchors
+        device = bbox_cls_pred.device
+        feature_map_size = torch.tensor(
+            list(bbox_cls_pred.size()[-2:]), device=device)
+        anchors = self.anchors_generator.get_multi_anchors(feature_map_size)
+        batched_anchors = [anchors for _ in range(batch_size)]
+
+        if mode == 'train':
+            anchor_target_dict = anchor_target(batched_anchors=batched_anchors,
+                                               batched_gt_bboxes=batched_gt_bboxes,
+                                               batched_gt_labels=batched_gt_labels,
+                                               assigners=self.assigners,
+                                               nclasses=self.nclasses)
+
+            return bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchor_target_dict
+        elif mode == 'val':
+            results = self.get_predicted_bboxes(bbox_cls_pred=bbox_cls_pred,
+                                                bbox_pred=bbox_pred,
+                                                bbox_dir_cls_pred=bbox_dir_cls_pred,
+                                                batched_anchors=batched_anchors)
+            return results
+
+        elif mode == 'test':
+            results = self.get_predicted_bboxes(bbox_cls_pred=bbox_cls_pred,
+                                                bbox_pred=bbox_pred,
+                                                bbox_dir_cls_pred=bbox_dir_cls_pred,
+                                                batched_anchors=batched_anchors)
+            return results
+        else:
+            raise ValueError
diff --git a/automotive/3d-object-detection/model/segmentation/__init__.py b/automotive/3d-object-detection/model/segmentation/__init__.py
new file mode 100644
index 0000000000..f6d9c2307b
--- /dev/null
+++ b/automotive/3d-object-detection/model/segmentation/__init__.py
@@ -0,0 +1,2 @@
+from .modeling import *
+from ._deeplab import convert_to_separable_conv
diff --git a/automotive/3d-object-detection/model/segmentation/_deeplab.py b/automotive/3d-object-detection/model/segmentation/_deeplab.py
new file mode 100644
index 0000000000..4c01a651ce
--- /dev/null
+++ b/automotive/3d-object-detection/model/segmentation/_deeplab.py
@@ -0,0 +1,210 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .utils import _SimpleSegmentationModel
+
+
+__all__ = ["DeepLabV3"]
+
+
+class DeepLabV3(_SimpleSegmentationModel):
+    """
+    Implements DeepLabV3 model from
+    `"Rethinking Atrous Convolution for Semantic Image Segmentation"
+    <https://arxiv.org/abs/1706.05587>`_.
+
+    Arguments:
+        backbone (nn.Module): the network used to compute the features for the model.
+            The backbone should return an OrderedDict[Tensor], with the key being
+            "out" for the last feature map used, and "aux" if an auxiliary classifier
+            is used.
+        classifier (nn.Module): module that takes the "out" element returned from
+            the backbone and returns a dense prediction.
+        aux_classifier (nn.Module, optional): auxiliary classifier used during training
+    """
+    pass
+
+
+class DeepLabHeadV3Plus(nn.Module):
+    def __init__(self, in_channels, low_level_channels,
+                 num_classes, aspp_dilate=[12, 24, 36]):
+        super(DeepLabHeadV3Plus, self).__init__()
+        self.project = nn.Sequential(
+            nn.Conv2d(low_level_channels, 48, 1, bias=False),
+            nn.BatchNorm2d(48),
+            nn.ReLU(inplace=True),
+        )
+
+        self.aspp = ASPP(in_channels, aspp_dilate)
+
+        self.classifier = nn.Sequential(
+            nn.Conv2d(304, 256, 3, padding=1, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, num_classes, 1)
+        )
+        self._init_weight()
+
+    def forward(self, feature):
+        low_level_feature = self.project(feature['low_level'])
+        output_feature = self.aspp(feature['out'])
+        output_feature = F.interpolate(output_feature,
+                                       size=low_level_feature.shape[2:],
+                                       mode='bilinear',
+                                       align_corners=False)
+        return self.classifier(
+            torch.cat([low_level_feature, output_feature], dim=1))
+
+    def _init_weight(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight)
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+
+class DeepLabHead(nn.Module):
+    def __init__(self, in_channels, num_classes, aspp_dilate=[12, 24, 36]):
+        super(DeepLabHead, self).__init__()
+
+        self.classifier = nn.Sequential(
+            ASPP(in_channels, aspp_dilate),
+            nn.Conv2d(256, 256, 3, padding=1, bias=False),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, num_classes, 1)
+        )
+        self._init_weight()
+
+    def forward(self, feature):
+        return self.classifier(feature['out'])
+
+    def _init_weight(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight)
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+
+class AtrousSeparableConvolution(nn.Module):
+    """ Atrous Separable Convolution
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size,
+                 stride=1, padding=0, dilation=1, bias=True):
+        super(AtrousSeparableConvolution, self).__init__()
+        self.body = nn.Sequential(
+            # Separable Conv
+            nn.Conv2d(
+                in_channels,
+                in_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                bias=bias,
+                groups=in_channels),
+            # PointWise Conv
+            nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=bias),
+        )
+
+        self._init_weight()
+
+    def forward(self, x):
+        return self.body(x)
+
+    def _init_weight(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight)
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+
+class ASPPConv(nn.Sequential):
+    def __init__(self, in_channels, out_channels, dilation):
+        modules = [
+            nn.Conv2d(
+                in_channels,
+                out_channels,
+                3,
+                padding=dilation,
+                dilation=dilation,
+                bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        ]
+        super(ASPPConv, self).__init__(*modules)
+
+
+class ASPPPooling(nn.Sequential):
+    def __init__(self, in_channels, out_channels):
+        super(ASPPPooling, self).__init__(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(in_channels, out_channels, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True))
+
+    def forward(self, x):
+        size = x.shape[-2:]
+        x = super(ASPPPooling, self).forward(x)
+        return F.interpolate(
+            x, size=size, mode='bilinear', align_corners=False)
+
+
+class ASPP(nn.Module):
+    def __init__(self, in_channels, atrous_rates):
+        super(ASPP, self).__init__()
+        out_channels = 256
+        modules = []
+        modules.append(nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)))
+
+        rate1, rate2, rate3 = tuple(atrous_rates)
+        modules.append(ASPPConv(in_channels, out_channels, rate1))
+        modules.append(ASPPConv(in_channels, out_channels, rate2))
+        modules.append(ASPPConv(in_channels, out_channels, rate3))
+        modules.append(ASPPPooling(in_channels, out_channels))
+
+        self.convs = nn.ModuleList(modules)
+
+        self.project = nn.Sequential(
+            nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+            nn.Dropout(0.1),)
+
+    def forward(self, x):
+        res = []
+        for conv in self.convs:
+            res.append(conv(x))
+        res = torch.cat(res, dim=1)
+        return self.project(res)
+
+
+def convert_to_separable_conv(module):
+    new_module = module
+    if isinstance(module, nn.Conv2d) and module.kernel_size[0] > 1:
+        new_module = AtrousSeparableConvolution(module.in_channels,
+                                                module.out_channels,
+                                                module.kernel_size,
+                                                module.stride,
+                                                module.padding,
+                                                module.dilation,
+                                                module.bias)
+    for name, child in module.named_children():
+        new_module.add_module(name, convert_to_separable_conv(child))
+    return new_module
diff --git a/automotive/3d-object-detection/model/segmentation/backbone/__init__.py b/automotive/3d-object-detection/model/segmentation/backbone/__init__.py
new file mode 100644
index 0000000000..c2cbcf4d23
--- /dev/null
+++ b/automotive/3d-object-detection/model/segmentation/backbone/__init__.py
@@ -0,0 +1 @@
+from . import resnet
diff --git a/automotive/3d-object-detection/model/segmentation/backbone/resnet.py b/automotive/3d-object-detection/model/segmentation/backbone/resnet.py
new file mode 100644
index 0000000000..70809b3baf
--- /dev/null
+++ b/automotive/3d-object-detection/model/segmentation/backbone/resnet.py
@@ -0,0 +1,353 @@
+import torch
+import torch.nn as nn
+try:  # for torchvision<0.4
+    from torchvision.models.utils import load_state_dict_from_url
+except BaseException:  # for torchvision>=0.4
+    from torch.hub import load_state_dict_from_url
+
+
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
+           'wide_resnet50_2', 'wide_resnet101_2']
+
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
+    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
+    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
+    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=dilation, groups=groups, bias=False, dilation=dilation)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1,
+                     stride=stride, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError(
+                'BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError(
+                "Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when
+        # stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None):
+        super(Bottleneck, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        width = int(planes * (base_width / 64.)) * groups
+        # Both self.conv2 and self.downsample layers downsample the input when
+        # stride != 1
+        self.conv1 = conv1x1(inplanes, width)
+        self.bn1 = norm_layer(width)
+        self.conv2 = conv3x3(width, width, stride, groups, dilation)
+        self.bn2 = norm_layer(width)
+        self.conv3 = conv1x1(width, planes * self.expansion)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
+                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
+                 norm_layer=None):
+        super(ResNet, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer
+
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            # each element in the tuple indicates if we should replace
+            # the 2x2 stride with a dilated convolution instead
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError("replace_stride_with_dilation should be None "
+                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+        self.groups = groups
+        self.base_width = width_per_group
+        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = norm_layer(self.inplanes)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
+                                       dilate=replace_stride_with_dilation[0])
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
+                                       dilate=replace_stride_with_dilation[1])
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
+                                       dilate=replace_stride_with_dilation[2])
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to
+        # https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
+                            self.base_width, previous_dilation, norm_layer))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups=self.groups,
+                                base_width=self.base_width, dilation=self.dilation,
+                                norm_layer=norm_layer))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.fc(x)
+
+        return x
+
+
+def _resnet(arch, block, layers, pretrained, progress, **kwargs):
+    model = ResNet(block, layers, **kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(model_urls[arch],
+                                              progress=progress)
+        model.load_state_dict(state_dict)
+    return model
+
+
+def resnet18(pretrained=False, progress=True, **kwargs):
+    r"""ResNet-18 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
+                   **kwargs)
+
+
+def resnet34(pretrained=False, progress=True, **kwargs):
+    r"""ResNet-34 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
+                   **kwargs)
+
+
+def resnet50(pretrained=False, progress=True, **kwargs):
+    r"""ResNet-50 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
+                   **kwargs)
+
+
+def resnet101(pretrained=False, progress=True, **kwargs):
+    r"""ResNet-101 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
+                   **kwargs)
+
+
+def resnet152(pretrained=False, progress=True, **kwargs):
+    r"""ResNet-152 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
+                   **kwargs)
+
+
+def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
+    r"""ResNeXt-50 32x4d model from
+    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['groups'] = 32
+    kwargs['width_per_group'] = 4
+    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
+                   pretrained, progress, **kwargs)
+
+
+def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
+    r"""ResNeXt-101 32x8d model from
+    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['groups'] = 32
+    kwargs['width_per_group'] = 8
+    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
+                   pretrained, progress, **kwargs)
+
+
+def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
+    r"""Wide ResNet-50-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+
+    The model is the same as ResNet except for the bottleneck number of channels
+    which is twice larger in every block. The number of channels in outer 1x1
+    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['width_per_group'] = 64 * 2
+    return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
+                   pretrained, progress, **kwargs)
+
+
+def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
+    r"""Wide ResNet-101-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+
+    The model is the same as ResNet except for the bottleneck number of channels
+    which is twice larger in every block. The number of channels in outer 1x1
+    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['width_per_group'] = 64 * 2
+    return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
+                   pretrained, progress, **kwargs)
diff --git a/automotive/3d-object-detection/model/segmentation/modeling.py b/automotive/3d-object-detection/model/segmentation/modeling.py
new file mode 100644
index 0000000000..06c3757b6d
--- /dev/null
+++ b/automotive/3d-object-detection/model/segmentation/modeling.py
@@ -0,0 +1,103 @@
+from .utils import IntermediateLayerGetter
+from ._deeplab import DeepLabHead, DeepLabHeadV3Plus, DeepLabV3
+from .backbone import (resnet)
+
+
+def _segm_resnet(name, backbone_name, num_classes,
+                 output_stride, pretrained_backbone):
+
+    if output_stride == 8:
+        replace_stride_with_dilation = [False, True, True]
+        aspp_dilate = [12, 24, 36]
+    else:
+        replace_stride_with_dilation = [False, False, True]
+        aspp_dilate = [6, 12, 18]
+
+    backbone = resnet.__dict__[backbone_name](
+        pretrained=pretrained_backbone,
+        replace_stride_with_dilation=replace_stride_with_dilation)
+
+    inplanes = 2048
+    low_level_planes = 256
+
+    if name == 'deeplabv3plus':
+        return_layers = {'layer4': 'out', 'layer1': 'low_level'}
+        classifier = DeepLabHeadV3Plus(
+            inplanes, low_level_planes, num_classes, aspp_dilate)
+    elif name == 'deeplabv3':
+        return_layers = {'layer4': 'out'}
+        classifier = DeepLabHead(inplanes, num_classes, aspp_dilate)
+    backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
+
+    model = DeepLabV3(backbone, classifier)
+    return model
+
+
+def _load_model(arch_type, backbone, num_classes,
+                output_stride, pretrained_backbone):
+
+    if backbone.startswith('resnet'):
+        model = _segm_resnet(
+            arch_type,
+            backbone,
+            num_classes,
+            output_stride=output_stride,
+            pretrained_backbone=pretrained_backbone)
+    else:
+        raise NotImplementedError
+    return model
+
+
+# Deeplab v3
+def deeplabv3_resnet50(num_classes=21, output_stride=8,
+                       pretrained_backbone=True):
+    """Constructs a DeepLabV3 model with a ResNet-50 backbone.
+
+    Args:
+        num_classes (int): number of classes.
+        output_stride (int): output stride for deeplab.
+        pretrained_backbone (bool): If True, use the pretrained backbone.
+    """
+    return _load_model('deeplabv3', 'resnet50', num_classes,
+                       output_stride=output_stride, pretrained_backbone=pretrained_backbone)
+
+
+def deeplabv3_resnet101(num_classes=21, output_stride=8,
+                        pretrained_backbone=True):
+    """Constructs a DeepLabV3 model with a ResNet-101 backbone.
+
+    Args:
+        num_classes (int): number of classes.
+        output_stride (int): output stride for deeplab.
+        pretrained_backbone (bool): If True, use the pretrained backbone.
+    """
+    return _load_model('deeplabv3', 'resnet101', num_classes,
+                       output_stride=output_stride, pretrained_backbone=pretrained_backbone)
+
+# Deeplab v3+
+
+
+def deeplabv3plus_resnet50(
+        num_classes=21, output_stride=8, pretrained_backbone=True):
+    """Constructs a DeepLabV3 model with a ResNet-50 backbone.
+
+    Args:
+        num_classes (int): number of classes.
+        output_stride (int): output stride for deeplab.
+        pretrained_backbone (bool): If True, use the pretrained backbone.
+    """
+    return _load_model('deeplabv3plus', 'resnet50', num_classes,
+                       output_stride=output_stride, pretrained_backbone=pretrained_backbone)
+
+
+def deeplabv3plus_resnet101(
+        num_classes=21, output_stride=8, pretrained_backbone=True):
+    """Constructs a DeepLabV3+ model with a ResNet-101 backbone.
+
+    Args:
+        num_classes (int): number of classes.
+        output_stride (int): output stride for deeplab.
+        pretrained_backbone (bool): If True, use the pretrained backbone.
+    """
+    return _load_model('deeplabv3plus', 'resnet101', num_classes,
+                       output_stride=output_stride, pretrained_backbone=pretrained_backbone)
diff --git a/automotive/3d-object-detection/model/segmentation/utils.py b/automotive/3d-object-detection/model/segmentation/utils.py
new file mode 100644
index 0000000000..bfc7e90e32
--- /dev/null
+++ b/automotive/3d-object-detection/model/segmentation/utils.py
@@ -0,0 +1,121 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+from collections import OrderedDict
+
+
+class _SimpleSegmentationModel(nn.Module):
+    def __init__(self, backbone, classifier):
+        super(_SimpleSegmentationModel, self).__init__()
+        self.backbone = backbone
+        self.classifier = classifier
+
+    def forward(self, x):
+        input_shape = x.shape[-2:]
+        features = self.backbone(x)
+        x = self.classifier(features)
+        x = F.interpolate(
+            x,
+            size=input_shape,
+            mode='bilinear',
+            align_corners=False)
+        return x
+
+
+class IntermediateLayerGetter(nn.ModuleDict):
+    """
+    Module wrapper that returns intermediate layers from a model
+
+    It has a strong assumption that the modules have been registered
+    into the model in the same order as they are used.
+    This means that one should **not** reuse the same nn.Module
+    twice in the forward if you want this to work.
+
+    Additionally, it is only able to query submodules that are directly
+    assigned to the model. So if `model` is passed, `model.feature1` can
+    be returned, but not `model.feature1.layer2`.
+
+    Arguments:
+        model (nn.Module): model on which we will extract the features
+        return_layers (Dict[name, new_name]): a dict containing the names
+            of the modules for which the activations will be returned as
+            the key of the dict, and the value of the dict is the name
+            of the returned activation (which the user can specify).
+
+    Examples::
+
+        >>> m = torchvision.models.resnet18(pretrained=True)
+        >>> # extract layer1 and layer3, giving as names `feat1` and feat2`
+        >>> new_m = torchvision.models._utils.IntermediateLayerGetter(m,
+        >>>     {'layer1': 'feat1', 'layer3': 'feat2'})
+        >>> out = new_m(torch.rand(1, 3, 224, 224))
+        >>> print([(k, v.shape) for k, v in out.items()])
+        >>>     [('feat1', torch.Size([1, 64, 56, 56])),
+        >>>      ('feat2', torch.Size([1, 256, 14, 14]))]
+    """
+
+    def __init__(self, model, return_layers, hrnet_flag=False):
+        if not set(return_layers).issubset(
+                [name for name, _ in model.named_children()]):
+            raise ValueError("return_layers are not present in model")
+
+        self.hrnet_flag = hrnet_flag
+
+        orig_return_layers = return_layers
+        return_layers = {k: v for k, v in return_layers.items()}
+        layers = OrderedDict()
+        for name, module in model.named_children():
+            layers[name] = module
+            if name in return_layers:
+                del return_layers[name]
+            if not return_layers:
+                break
+
+        super(IntermediateLayerGetter, self).__init__(layers)
+        self.return_layers = orig_return_layers
+
+    def forward(self, x):
+        out = OrderedDict()
+        for name, module in self.named_children():
+            if self.hrnet_flag and name.startswith(
+                    'transition'):  # if using hrnet, you need to take care of transition
+                if name == 'transition1':  # in transition1, you need to split the module to two streams first
+                    x = [trans(x) for trans in module]
+                else:  # all other transition is just an extra one stream split
+                    x.append(module(x[-1]))
+            # other models (ex:resnet,mobilenet) are convolutions in series.
+            else:
+                x = module(x)
+
+            if name in self.return_layers:
+                out_name = self.return_layers[name]
+                if name == 'stage4' and self.hrnet_flag:  # In HRNetV2, we upsample and concat all outputs streams together
+                    # Upsample to size of highest resolution stream
+                    output_h, output_w = x[0].size(2), x[0].size(3)
+                    x1 = F.interpolate(
+                        x[1],
+                        size=(
+                            output_h,
+                            output_w),
+                        mode='bilinear',
+                        align_corners=False)
+                    x2 = F.interpolate(
+                        x[2],
+                        size=(
+                            output_h,
+                            output_w),
+                        mode='bilinear',
+                        align_corners=False)
+                    x3 = F.interpolate(
+                        x[3],
+                        size=(
+                            output_h,
+                            output_w),
+                        mode='bilinear',
+                        align_corners=False)
+                    x = torch.cat([x[0], x1, x2, x3], dim=1)
+                    out[out_name] = x
+                else:
+                    out[out_name] = x
+        return out
diff --git a/automotive/3d-object-detection/ops/__init__.py b/automotive/3d-object-detection/ops/__init__.py
new file mode 100644
index 0000000000..66b7399379
--- /dev/null
+++ b/automotive/3d-object-detection/ops/__init__.py
@@ -0,0 +1 @@
+from .voxel_module import Voxelization
diff --git a/automotive/3d-object-detection/ops/iou3d/iou3d.cpp b/automotive/3d-object-detection/ops/iou3d/iou3d.cpp
new file mode 100644
index 0000000000..a337a93c75
--- /dev/null
+++ b/automotive/3d-object-detection/ops/iou3d/iou3d.cpp
@@ -0,0 +1,213 @@
+// Modified from
+// https://github.com/open-mmlab/OpenPCDet/blob/master/pcdet/ops/iou3d_nms/src/iou3d_nms.cpp
+
+/*
+3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others)
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+#include <cuda.h>
+#include <cuda_runtime_api.h>
+#include <torch/extension.h>
+#include <torch/serialize/tensor.h>
+
+#include <cstdint>
+#include <vector>
+
+#define CHECK_CUDA(x)                                                          \
+  TORCH_CHECK(x.device().is_cuda(), #x, " must be a CUDAtensor ")
+#define CHECK_CONTIGUOUS(x)                                                    \
+  TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ")
+#define CHECK_INPUT(x)                                                         \
+  CHECK_CUDA(x);                                                               \
+  CHECK_CONTIGUOUS(x)
+
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
+
+#define CHECK_ERROR(ans)                                                       \
+  { gpuAssert((ans), __FILE__, __LINE__); }
+inline void gpuAssert(cudaError_t code, const char *file, int line,
+                      bool abort = true) {
+  if (code != cudaSuccess) {
+    fprintf(stderr, "GPUassert: %s %s %d\n", cudaGetErrorString(code), file,
+            line);
+    if (abort)
+      exit(code);
+  }
+}
+
+const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8;
+
+void boxesoverlapLauncher(const int num_a, const float *boxes_a,
+                          const int num_b, const float *boxes_b,
+                          float *ans_overlap);
+void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b,
+                         const float *boxes_b, float *ans_iou);
+void nmsLauncher(const float *boxes, unsigned long long *mask, int boxes_num,
+                 float nms_overlap_thresh);
+void nmsNormalLauncher(const float *boxes, unsigned long long *mask,
+                       int boxes_num, float nms_overlap_thresh);
+
+int boxes_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b,
+                          at::Tensor ans_overlap) {
+  // params boxes_a: (N, 5) [x1, y1, x2, y2, ry]
+  // params boxes_b: (M, 5)
+  // params ans_overlap: (N, M)
+
+  CHECK_INPUT(boxes_a);
+  CHECK_INPUT(boxes_b);
+  CHECK_INPUT(ans_overlap);
+
+  int num_a = boxes_a.size(0);
+  int num_b = boxes_b.size(0);
+
+  const float *boxes_a_data = boxes_a.data_ptr<float>();
+  const float *boxes_b_data = boxes_b.data_ptr<float>();
+  float *ans_overlap_data = ans_overlap.data_ptr<float>();
+
+  boxesoverlapLauncher(num_a, boxes_a_data, num_b, boxes_b_data,
+                       ans_overlap_data);
+
+  return 1;
+}
+
+int boxes_iou_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b,
+                      at::Tensor ans_iou) {
+  // params boxes_a: (N, 5) [x1, y1, x2, y2, ry]
+  // params boxes_b: (M, 5)
+  // params ans_overlap: (N, M)
+
+  CHECK_INPUT(boxes_a);
+  CHECK_INPUT(boxes_b);
+  CHECK_INPUT(ans_iou);
+
+  int num_a = boxes_a.size(0);
+  int num_b = boxes_b.size(0);
+
+  const float *boxes_a_data = boxes_a.data_ptr<float>();
+  const float *boxes_b_data = boxes_b.data_ptr<float>();
+  float *ans_iou_data = ans_iou.data_ptr<float>();
+
+  boxesioubevLauncher(num_a, boxes_a_data, num_b, boxes_b_data, ans_iou_data);
+
+  return 1;
+}
+
+int nms_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh,
+            int device_id) {
+  // params boxes: (N, 5) [x1, y1, x2, y2, ry]
+  // params keep: (N)
+
+  CHECK_INPUT(boxes);
+  CHECK_CONTIGUOUS(keep);
+  cudaSetDevice(device_id);
+
+  int boxes_num = boxes.size(0);
+  const float *boxes_data = boxes.data_ptr<float>();
+  int64_t *keep_data = keep.data_ptr<int64_t>();
+
+  const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+
+  unsigned long long *mask_data = NULL;
+  CHECK_ERROR(cudaMalloc((void **)&mask_data,
+                         boxes_num * col_blocks * sizeof(unsigned long long)));
+  nmsLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh);
+
+  // unsigned long long mask_cpu[boxes_num * col_blocks];
+  // unsigned long long *mask_cpu = new unsigned long long [boxes_num *
+  // col_blocks];
+  std::vector<unsigned long long> mask_cpu(boxes_num * col_blocks);
+
+  //    printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks);
+  CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data,
+                         boxes_num * col_blocks * sizeof(unsigned long long),
+                         cudaMemcpyDeviceToHost));
+
+  cudaFree(mask_data);
+
+  unsigned long long *remv_cpu = new unsigned long long[col_blocks]();
+
+  int num_to_keep = 0;
+
+  for (int i = 0; i < boxes_num; i++) {
+    int nblock = i / THREADS_PER_BLOCK_NMS;
+    int inblock = i % THREADS_PER_BLOCK_NMS;
+
+    if (!(remv_cpu[nblock] & (1ULL << inblock))) {
+      keep_data[num_to_keep++] = i;
+      unsigned long long *p = &mask_cpu[0] + i * col_blocks;
+      for (int j = nblock; j < col_blocks; j++) {
+        remv_cpu[j] |= p[j];
+      }
+    }
+  }
+  delete[] remv_cpu;
+  if (cudaSuccess != cudaGetLastError())
+    printf("Error!\n");
+
+  return num_to_keep;
+}
+
+int nms_normal_gpu(at::Tensor boxes, at::Tensor keep, float nms_overlap_thresh,
+                   int device_id) {
+  // params boxes: (N, 5) [x1, y1, x2, y2, ry]
+  // params keep: (N)
+
+  CHECK_INPUT(boxes);
+  CHECK_CONTIGUOUS(keep);
+  cudaSetDevice(device_id);
+
+  int boxes_num = boxes.size(0);
+  const float *boxes_data = boxes.data_ptr<float>();
+  int64_t *keep_data = keep.data_ptr<int64_t>();
+
+  const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+
+  unsigned long long *mask_data = NULL;
+  CHECK_ERROR(cudaMalloc((void **)&mask_data,
+                         boxes_num * col_blocks * sizeof(unsigned long long)));
+  nmsNormalLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh);
+
+  // unsigned long long mask_cpu[boxes_num * col_blocks];
+  // unsigned long long *mask_cpu = new unsigned long long [boxes_num *
+  // col_blocks];
+  std::vector<unsigned long long> mask_cpu(boxes_num * col_blocks);
+
+  //    printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks);
+  CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data,
+                         boxes_num * col_blocks * sizeof(unsigned long long),
+                         cudaMemcpyDeviceToHost));
+
+  cudaFree(mask_data);
+
+  unsigned long long *remv_cpu = new unsigned long long[col_blocks]();
+
+  int num_to_keep = 0;
+
+  for (int i = 0; i < boxes_num; i++) {
+    int nblock = i / THREADS_PER_BLOCK_NMS;
+    int inblock = i % THREADS_PER_BLOCK_NMS;
+
+    if (!(remv_cpu[nblock] & (1ULL << inblock))) {
+      keep_data[num_to_keep++] = i;
+      unsigned long long *p = &mask_cpu[0] + i * col_blocks;
+      for (int j = nblock; j < col_blocks; j++) {
+        remv_cpu[j] |= p[j];
+      }
+    }
+  }
+  delete[] remv_cpu;
+  if (cudaSuccess != cudaGetLastError())
+    printf("Error!\n");
+
+  return num_to_keep;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("boxes_overlap_bev_gpu", &boxes_overlap_bev_gpu,
+        "oriented boxes overlap");
+  m.def("boxes_iou_bev_gpu", &boxes_iou_bev_gpu, "oriented boxes iou");
+  m.def("nms_gpu", &nms_gpu, "oriented nms gpu");
+  m.def("nms_normal_gpu", &nms_normal_gpu, "nms gpu");
+}
diff --git a/automotive/3d-object-detection/ops/iou3d/iou3d_kernel.cu b/automotive/3d-object-detection/ops/iou3d/iou3d_kernel.cu
new file mode 100644
index 0000000000..861aea3c5a
--- /dev/null
+++ b/automotive/3d-object-detection/ops/iou3d/iou3d_kernel.cu
@@ -0,0 +1,439 @@
+// Modified from
+// https://github.com/open-mmlab/OpenPCDet/blob/master/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.cu
+
+/*
+3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others)
+Written by Shaoshuai Shi
+All Rights Reserved 2019-2020.
+*/
+
+#include <stdio.h>
+#define THREADS_PER_BLOCK 16
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
+
+//#define DEBUG
+const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8;
+__device__ const float EPS = 1e-8;
+struct Point {
+  float x, y;
+  __device__ Point() {}
+  __device__ Point(double _x, double _y) { x = _x, y = _y; }
+
+  __device__ void set(float _x, float _y) {
+    x = _x;
+    y = _y;
+  }
+
+  __device__ Point operator+(const Point &b) const {
+    return Point(x + b.x, y + b.y);
+  }
+
+  __device__ Point operator-(const Point &b) const {
+    return Point(x - b.x, y - b.y);
+  }
+};
+
+__device__ inline float cross(const Point &a, const Point &b) {
+  return a.x * b.y - a.y * b.x;
+}
+
+__device__ inline float cross(const Point &p1, const Point &p2,
+                              const Point &p0) {
+  return (p1.x - p0.x) * (p2.y - p0.y) - (p2.x - p0.x) * (p1.y - p0.y);
+}
+
+__device__ int check_rect_cross(const Point &p1, const Point &p2,
+                                const Point &q1, const Point &q2) {
+  int ret = min(p1.x, p2.x) <= max(q1.x, q2.x) &&
+            min(q1.x, q2.x) <= max(p1.x, p2.x) &&
+            min(p1.y, p2.y) <= max(q1.y, q2.y) &&
+            min(q1.y, q2.y) <= max(p1.y, p2.y);
+  return ret;
+}
+
+__device__ inline int check_in_box2d(const float *box, const Point &p) {
+  // params: box (5) [x1, y1, x2, y2, angle]
+  const float MARGIN = 1e-5;
+
+  float center_x = (box[0] + box[2]) / 2;
+  float center_y = (box[1] + box[3]) / 2;
+  float angle_cos = cos(-box[4]),
+        angle_sin =
+            sin(-box[4]);  // rotate the point in the opposite direction of box
+  float rot_x =
+      (p.x - center_x) * angle_cos + (p.y - center_y) * angle_sin + center_x;
+  float rot_y =
+      -(p.x - center_x) * angle_sin + (p.y - center_y) * angle_cos + center_y;
+#ifdef DEBUG
+  printf("box: (%.3f, %.3f, %.3f, %.3f, %.3f)\n", box[0], box[1], box[2],
+         box[3], box[4]);
+  printf(
+      "center: (%.3f, %.3f), cossin(%.3f, %.3f), src(%.3f, %.3f), rot(%.3f, "
+      "%.3f)\n",
+      center_x, center_y, angle_cos, angle_sin, p.x, p.y, rot_x, rot_y);
+#endif
+  return (rot_x > box[0] - MARGIN && rot_x < box[2] + MARGIN &&
+          rot_y > box[1] - MARGIN && rot_y < box[3] + MARGIN);
+}
+
+__device__ inline int intersection(const Point &p1, const Point &p0,
+                                   const Point &q1, const Point &q0,
+                                   Point &ans) {
+  // fast exclusion
+  if (check_rect_cross(p0, p1, q0, q1) == 0) return 0;
+
+  // check cross standing
+  float s1 = cross(q0, p1, p0);
+  float s2 = cross(p1, q1, p0);
+  float s3 = cross(p0, q1, q0);
+  float s4 = cross(q1, p1, q0);
+
+  if (!(s1 * s2 > 0 && s3 * s4 > 0)) return 0;
+
+  // calculate intersection of two lines
+  float s5 = cross(q1, p1, p0);
+  if (fabs(s5 - s1) > EPS) {
+    ans.x = (s5 * q0.x - s1 * q1.x) / (s5 - s1);
+    ans.y = (s5 * q0.y - s1 * q1.y) / (s5 - s1);
+
+  } else {
+    float a0 = p0.y - p1.y, b0 = p1.x - p0.x, c0 = p0.x * p1.y - p1.x * p0.y;
+    float a1 = q0.y - q1.y, b1 = q1.x - q0.x, c1 = q0.x * q1.y - q1.x * q0.y;
+    float D = a0 * b1 - a1 * b0;
+
+    ans.x = (b0 * c1 - b1 * c0) / D;
+    ans.y = (a1 * c0 - a0 * c1) / D;
+  }
+
+  return 1;
+}
+
+__device__ inline void rotate_around_center(const Point &center,
+                                            const float angle_cos,
+                                            const float angle_sin, Point &p) {
+  float new_x =
+      (p.x - center.x) * angle_cos + (p.y - center.y) * angle_sin + center.x;
+  float new_y =
+      -(p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos + center.y;
+  p.set(new_x, new_y);
+}
+
+__device__ inline int point_cmp(const Point &a, const Point &b,
+                                const Point &center) {
+  return atan2(a.y - center.y, a.x - center.x) >
+         atan2(b.y - center.y, b.x - center.x);
+}
+
+__device__ inline float box_overlap(const float *box_a, const float *box_b) {
+  // params: box_a (5) [x1, y1, x2, y2, angle]
+  // params: box_b (5) [x1, y1, x2, y2, angle]
+
+  float a_x1 = box_a[0], a_y1 = box_a[1], a_x2 = box_a[2], a_y2 = box_a[3],
+        a_angle = box_a[4];
+  float b_x1 = box_b[0], b_y1 = box_b[1], b_x2 = box_b[2], b_y2 = box_b[3],
+        b_angle = box_b[4];
+
+  Point center_a((a_x1 + a_x2) / 2, (a_y1 + a_y2) / 2);
+  Point center_b((b_x1 + b_x2) / 2, (b_y1 + b_y2) / 2);
+#ifdef DEBUG
+  printf(
+      "a: (%.3f, %.3f, %.3f, %.3f, %.3f), b: (%.3f, %.3f, %.3f, %.3f, %.3f)\n",
+      a_x1, a_y1, a_x2, a_y2, a_angle, b_x1, b_y1, b_x2, b_y2, b_angle);
+  printf("center a: (%.3f, %.3f), b: (%.3f, %.3f)\n", center_a.x, center_a.y,
+         center_b.x, center_b.y);
+#endif
+
+  Point box_a_corners[5];
+  box_a_corners[0].set(a_x1, a_y1);
+  box_a_corners[1].set(a_x2, a_y1);
+  box_a_corners[2].set(a_x2, a_y2);
+  box_a_corners[3].set(a_x1, a_y2);
+
+  Point box_b_corners[5];
+  box_b_corners[0].set(b_x1, b_y1);
+  box_b_corners[1].set(b_x2, b_y1);
+  box_b_corners[2].set(b_x2, b_y2);
+  box_b_corners[3].set(b_x1, b_y2);
+
+  // get oriented corners
+  float a_angle_cos = cos(a_angle), a_angle_sin = sin(a_angle);
+  float b_angle_cos = cos(b_angle), b_angle_sin = sin(b_angle);
+
+  for (int k = 0; k < 4; k++) {
+#ifdef DEBUG
+    printf("before corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k,
+           box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x,
+           box_b_corners[k].y);
+#endif
+    rotate_around_center(center_a, a_angle_cos, a_angle_sin, box_a_corners[k]);
+    rotate_around_center(center_b, b_angle_cos, b_angle_sin, box_b_corners[k]);
+#ifdef DEBUG
+    printf("corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x,
+           box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y);
+#endif
+  }
+
+  box_a_corners[4] = box_a_corners[0];
+  box_b_corners[4] = box_b_corners[0];
+
+  // get intersection of lines
+  Point cross_points[16];
+  Point poly_center;
+  int cnt = 0, flag = 0;
+
+  poly_center.set(0, 0);
+  for (int i = 0; i < 4; i++) {
+    for (int j = 0; j < 4; j++) {
+      flag = intersection(box_a_corners[i + 1], box_a_corners[i],
+                          box_b_corners[j + 1], box_b_corners[j],
+                          cross_points[cnt]);
+      if (flag) {
+        poly_center = poly_center + cross_points[cnt];
+        cnt++;
+      }
+    }
+  }
+
+  // check corners
+  for (int k = 0; k < 4; k++) {
+    if (check_in_box2d(box_a, box_b_corners[k])) {
+      poly_center = poly_center + box_b_corners[k];
+      cross_points[cnt] = box_b_corners[k];
+      cnt++;
+    }
+    if (check_in_box2d(box_b, box_a_corners[k])) {
+      poly_center = poly_center + box_a_corners[k];
+      cross_points[cnt] = box_a_corners[k];
+      cnt++;
+    }
+  }
+
+  poly_center.x /= cnt;
+  poly_center.y /= cnt;
+
+  // sort the points of polygon
+  Point temp;
+  for (int j = 0; j < cnt - 1; j++) {
+    for (int i = 0; i < cnt - j - 1; i++) {
+      if (point_cmp(cross_points[i], cross_points[i + 1], poly_center)) {
+        temp = cross_points[i];
+        cross_points[i] = cross_points[i + 1];
+        cross_points[i + 1] = temp;
+      }
+    }
+  }
+
+#ifdef DEBUG
+  printf("cnt=%d\n", cnt);
+  for (int i = 0; i < cnt; i++) {
+    printf("All cross point %d: (%.3f, %.3f)\n", i, cross_points[i].x,
+           cross_points[i].y);
+  }
+#endif
+
+  // get the overlap areas
+  float area = 0;
+  for (int k = 0; k < cnt - 1; k++) {
+    area += cross(cross_points[k] - cross_points[0],
+                  cross_points[k + 1] - cross_points[0]);
+  }
+
+  return fabs(area) / 2.0;
+}
+
+__device__ inline float iou_bev(const float *box_a, const float *box_b) {
+  // params: box_a (5) [x1, y1, x2, y2, angle]
+  // params: box_b (5) [x1, y1, x2, y2, angle]
+  float sa = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1]);
+  float sb = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1]);
+  float s_overlap = box_overlap(box_a, box_b);
+  return s_overlap / fmaxf(sa + sb - s_overlap, EPS);
+}
+
+__global__ void boxes_overlap_kernel(const int num_a, const float *boxes_a,
+                                     const int num_b, const float *boxes_b,
+                                     float *ans_overlap) {
+  const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
+  const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+
+  if (a_idx >= num_a || b_idx >= num_b) {
+    return;
+  }
+  const float *cur_box_a = boxes_a + a_idx * 5;
+  const float *cur_box_b = boxes_b + b_idx * 5;
+  float s_overlap = box_overlap(cur_box_a, cur_box_b);
+  ans_overlap[a_idx * num_b + b_idx] = s_overlap;
+}
+
+__global__ void boxes_iou_bev_kernel(const int num_a, const float *boxes_a,
+                                     const int num_b, const float *boxes_b,
+                                     float *ans_iou) {
+  const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
+  const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+
+  if (a_idx >= num_a || b_idx >= num_b) {
+    return;
+  }
+
+  const float *cur_box_a = boxes_a + a_idx * 5;
+  const float *cur_box_b = boxes_b + b_idx * 5;
+  float cur_iou_bev = iou_bev(cur_box_a, cur_box_b);
+  ans_iou[a_idx * num_b + b_idx] = cur_iou_bev;
+}
+
+__global__ void nms_kernel(const int boxes_num, const float nms_overlap_thresh,
+                           const float *boxes, unsigned long long *mask) {
+  // params: boxes (N, 5) [x1, y1, x2, y2, ry]
+  // params: mask (N, N/THREADS_PER_BLOCK_NMS)
+
+  const int row_start = blockIdx.y;
+  const int col_start = blockIdx.x;
+
+  // if (row_start > col_start) return;
+
+  const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS,
+                             THREADS_PER_BLOCK_NMS);
+  const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS,
+                             THREADS_PER_BLOCK_NMS);
+
+  __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5];
+
+  if (threadIdx.x < col_size) {
+    block_boxes[threadIdx.x * 5 + 0] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0];
+    block_boxes[threadIdx.x * 5 + 1] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1];
+    block_boxes[threadIdx.x * 5 + 2] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2];
+    block_boxes[threadIdx.x * 5 + 3] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3];
+    block_boxes[threadIdx.x * 5 + 4] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4];
+  }
+  __syncthreads();
+
+  if (threadIdx.x < row_size) {
+    const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
+    const float *cur_box = boxes + cur_box_idx * 5;
+
+    int i = 0;
+    unsigned long long t = 0;
+    int start = 0;
+    if (row_start == col_start) {
+      start = threadIdx.x + 1;
+    }
+    for (i = start; i < col_size; i++) {
+      if (iou_bev(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
+        t |= 1ULL << i;
+      }
+    }
+    const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+    mask[cur_box_idx * col_blocks + col_start] = t;
+  }
+}
+
+__device__ inline float iou_normal(float const *const a, float const *const b) {
+  float left = fmaxf(a[0], b[0]), right = fminf(a[2], b[2]);
+  float top = fmaxf(a[1], b[1]), bottom = fminf(a[3], b[3]);
+  float width = fmaxf(right - left, 0.f), height = fmaxf(bottom - top, 0.f);
+  float interS = width * height;
+  float Sa = (a[2] - a[0]) * (a[3] - a[1]);
+  float Sb = (b[2] - b[0]) * (b[3] - b[1]);
+  return interS / fmaxf(Sa + Sb - interS, EPS);
+}
+
+__global__ void nms_normal_kernel(const int boxes_num,
+                                  const float nms_overlap_thresh,
+                                  const float *boxes,
+                                  unsigned long long *mask) {
+  // params: boxes (N, 5) [x1, y1, x2, y2, ry]
+  // params: mask (N, N/THREADS_PER_BLOCK_NMS)
+
+  const int row_start = blockIdx.y;
+  const int col_start = blockIdx.x;
+
+  // if (row_start > col_start) return;
+
+  const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS,
+                             THREADS_PER_BLOCK_NMS);
+  const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS,
+                             THREADS_PER_BLOCK_NMS);
+
+  __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5];
+
+  if (threadIdx.x < col_size) {
+    block_boxes[threadIdx.x * 5 + 0] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0];
+    block_boxes[threadIdx.x * 5 + 1] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1];
+    block_boxes[threadIdx.x * 5 + 2] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2];
+    block_boxes[threadIdx.x * 5 + 3] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3];
+    block_boxes[threadIdx.x * 5 + 4] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4];
+  }
+  __syncthreads();
+
+  if (threadIdx.x < row_size) {
+    const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
+    const float *cur_box = boxes + cur_box_idx * 5;
+
+    int i = 0;
+    unsigned long long t = 0;
+    int start = 0;
+    if (row_start == col_start) {
+      start = threadIdx.x + 1;
+    }
+    for (i = start; i < col_size; i++) {
+      if (iou_normal(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
+        t |= 1ULL << i;
+      }
+    }
+    const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+    mask[cur_box_idx * col_blocks + col_start] = t;
+  }
+}
+
+void boxesoverlapLauncher(const int num_a, const float *boxes_a,
+                          const int num_b, const float *boxes_b,
+                          float *ans_overlap) {
+  dim3 blocks(
+      DIVUP(num_b, THREADS_PER_BLOCK),
+      DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
+
+  boxes_overlap_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b,
+                                            ans_overlap);
+#ifdef DEBUG
+  cudaDeviceSynchronize();  // for using printf in kernel function
+#endif
+}
+
+void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b,
+                         const float *boxes_b, float *ans_iou) {
+  dim3 blocks(
+      DIVUP(num_b, THREADS_PER_BLOCK),
+      DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
+
+  boxes_iou_bev_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b,
+                                            ans_iou);
+}
+
+void nmsLauncher(const float *boxes, unsigned long long *mask, int boxes_num,
+                 float nms_overlap_thresh) {
+  dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
+              DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
+  dim3 threads(THREADS_PER_BLOCK_NMS);
+  nms_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes, mask);
+}
+
+void nmsNormalLauncher(const float *boxes, unsigned long long *mask,
+                       int boxes_num, float nms_overlap_thresh) {
+  dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
+              DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
+  dim3 threads(THREADS_PER_BLOCK_NMS);
+  nms_normal_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes,
+                                         mask);
+}
diff --git a/automotive/3d-object-detection/ops/voxel_module.py b/automotive/3d-object-detection/ops/voxel_module.py
new file mode 100644
index 0000000000..9850dd289e
--- /dev/null
+++ b/automotive/3d-object-detection/ops/voxel_module.py
@@ -0,0 +1,253 @@
+# This file is modified from
+# https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/ops/voxel/voxelize.py
+
+import torch
+import torch.nn as nn
+import numpy as np
+import numba
+
+
+@numba.jit(nopython=True)
+def _points_to_voxel_reverse_kernel(points,
+                                    voxel_size,
+                                    coors_range,
+                                    num_points_per_voxel,
+                                    coor_to_voxelidx,
+                                    voxels,
+                                    coors,
+                                    max_points=35,
+                                    max_voxels=20000):
+    # put all computations to one loop.
+    # we shouldn't create large array in main jit code, otherwise
+    # reduce performance
+    N = points.shape[0]
+    # ndim = points.shape[1] - 1
+    ndim = 3
+    ndim_minus_1 = ndim - 1
+    grid_size = (coors_range[3:] - coors_range[:3]) / voxel_size
+    # np.round(grid_size)
+    # grid_size = np.round(grid_size).astype(np.int64)(np.int32)
+    grid_size = np.round(grid_size, 0, grid_size).astype(np.int32)
+    coor = np.zeros(shape=(3, ), dtype=np.int32)
+    voxel_num = 0
+    failed = False
+    for i in range(N):
+        failed = False
+        for j in range(ndim):
+            c = np.floor((points[i, j] - coors_range[j]) / voxel_size[j])
+            if c < 0 or c >= grid_size[j]:
+                failed = True
+                break
+            coor[ndim_minus_1 - j] = c
+        if failed:
+            continue
+        voxelidx = coor_to_voxelidx[coor[0], coor[1], coor[2]]
+        if voxelidx == -1:
+            voxelidx = voxel_num
+            if voxel_num >= max_voxels:
+                break
+            voxel_num += 1
+            coor_to_voxelidx[coor[0], coor[1], coor[2]] = voxelidx
+            coors[voxelidx] = coor
+        num = num_points_per_voxel[voxelidx]
+        if num < max_points:
+            voxels[voxelidx, num] = points[i]
+            num_points_per_voxel[voxelidx] += 1
+    return voxel_num
+
+
+@numba.jit(nopython=True)
+def _points_to_voxel_kernel(points,
+                            voxel_size,
+                            coors_range,
+                            num_points_per_voxel,
+                            coor_to_voxelidx,
+                            voxels,
+                            coors,
+                            max_points=35,
+                            max_voxels=20000):
+    # need mutex if write in cuda, but numba.cuda don't support mutex.
+    # in addition, pytorch don't support cuda in dataloader(tensorflow support this).
+    # put all computations to one loop.
+    # we shouldn't create large array in main jit code, otherwise
+    # decrease performance
+    N = points.shape[0]
+    # ndim = points.shape[1] - 1
+    ndim = 3
+    grid_size = (coors_range[3:] - coors_range[:3]) / voxel_size
+    # grid_size = np.round(grid_size).astype(np.int64)(np.int32)
+    grid_size = np.round(grid_size, 0, grid_size).astype(np.int32)
+
+    lower_bound = coors_range[:3]
+    upper_bound = coors_range[3:]
+    coor = np.zeros(shape=(3, ), dtype=np.int32)
+    voxel_num = 0
+    failed = False
+    for i in range(N):
+        failed = False
+        for j in range(ndim):
+            c = np.floor((points[i, j] - coors_range[j]) / voxel_size[j])
+            if c < 0 or c >= grid_size[j]:
+                failed = True
+                break
+            coor[j] = c
+        if failed:
+            continue
+        voxelidx = coor_to_voxelidx[coor[0], coor[1], coor[2]]
+        if voxelidx == -1:
+            voxelidx = voxel_num
+            if voxel_num >= max_voxels:
+                break
+            voxel_num += 1
+            coor_to_voxelidx[coor[0], coor[1], coor[2]] = voxelidx
+            coors[voxelidx] = coor
+        num = num_points_per_voxel[voxelidx]
+        if num < max_points:
+            voxels[voxelidx, num] = points[i]
+            num_points_per_voxel[voxelidx] += 1
+    return voxel_num
+
+
+def points_to_voxel(points,
+                    voxel_size,
+                    coors_range,
+                    max_points=35,
+                    reverse_index=True,
+                    max_voxels=20000):
+    """convert kitti points(N, >=3) to voxels. This version calculate
+    everything in one loop. now it takes only 4.2ms(complete point cloud)
+    with jit and 3.2ghz cpu.(don't calculate other features)
+    Note: this function in ubuntu seems faster than windows 10.
+
+    Args:
+        points: [N, ndim] float tensor. points[:, :3] contain xyz points and
+            points[:, 3:] contain other information such as reflectivity.
+        voxel_size: [3] list/tuple or array, float. xyz, indicate voxel size
+        coors_range: [6] list/tuple or array, float. indicate voxel range.
+            format: xyzxyz, minmax
+        max_points: int. indicate maximum points contained in a voxel.
+        reverse_index: boolean. indicate whether return reversed coordinates.
+            if points has xyz format and reverse_index is True, output
+            coordinates will be zyx format, but points in features always
+            xyz format.
+        max_voxels: int. indicate maximum voxels this function create.
+            for second, 20000 is a good choice. you should shuffle points
+            before call this function because max_voxels may drop some points.
+
+    Returns:
+        voxels: [M, max_points, ndim] float tensor. only contain points.
+        coordinates: [M, 3] int32 tensor.
+        num_points_per_voxel: [M] int32 tensor.
+    """
+    if not isinstance(voxel_size, np.ndarray):
+        voxel_size = np.array(voxel_size, dtype=points.dtype)
+    if not isinstance(coors_range, np.ndarray):
+        coors_range = np.array(coors_range, dtype=points.dtype)
+    voxelmap_shape = (coors_range[3:] - coors_range[:3]) / voxel_size
+    voxelmap_shape = tuple(np.round(voxelmap_shape).astype(np.int32).tolist())
+    if reverse_index:
+        voxelmap_shape = voxelmap_shape[::-1]
+    # don't create large array in jit(nopython=True) code.
+    num_points_per_voxel = np.zeros(shape=(max_voxels, ), dtype=np.int32)
+    coor_to_voxelidx = -np.ones(shape=voxelmap_shape, dtype=np.int32)
+    voxels = np.zeros(
+        shape=(max_voxels, max_points, points.shape[-1]), dtype=points.dtype)
+    coors = np.zeros(shape=(max_voxels, 3), dtype=np.int32)
+    if reverse_index:
+        voxel_num = _points_to_voxel_reverse_kernel(
+            points, voxel_size, coors_range, num_points_per_voxel,
+            coor_to_voxelidx, voxels, coors, max_points, max_voxels)
+
+    else:
+        voxel_num = _points_to_voxel_kernel(
+            points, voxel_size, coors_range, num_points_per_voxel,
+            coor_to_voxelidx, voxels, coors, max_points, max_voxels)
+
+    coors = coors[:voxel_num]
+    voxels = voxels[:voxel_num]
+    num_points_per_voxel = num_points_per_voxel[:voxel_num]
+    # voxels[:, :, -3:] = voxels[:, :, :3] - \
+    #     voxels[:, :, :3].sum(axis=1, keepdims=True)/num_points_per_voxel.reshape(-1, 1, 1)
+    return voxels, coors, num_points_per_voxel
+
+
+class Voxelization(nn.Module):
+
+    def __init__(self,
+                 voxel_size,
+                 point_cloud_range,
+                 max_num_points,
+                 max_voxels,
+                 deterministic=True):
+        super(Voxelization, self).__init__()
+        """
+        Args:
+            voxel_size (list): list [x, y, z] size of three dimension
+            point_cloud_range (list):
+                [x_min, y_min, z_min, x_max, y_max, z_max]
+            max_num_points (int): max number of points per voxel
+            max_voxels (tuple): max number of voxels in
+                (training, testing) time
+            deterministic: bool. whether to invoke the non-deterministic
+                version of hard-voxelization implementations. non-deterministic
+                version is considerablly fast but is not deterministic. only
+                affects hard voxelization. default True. for more information
+                of this argument and the implementation insights, please refer
+                to the following links:
+                https://github.com/open-mmlab/mmdetection3d/issues/894
+                https://github.com/open-mmlab/mmdetection3d/pull/904
+                it is an experimental feature and we will appreciate it if
+                you could share with us the failing cases.
+        """
+        self.voxel_size = voxel_size
+        self.point_cloud_range = point_cloud_range
+        self.max_num_points = max_num_points
+        self.max_voxels = max_voxels
+        self.deterministic = deterministic
+
+        point_cloud_range = torch.tensor(
+            point_cloud_range, dtype=torch.float32)
+
+        voxel_size = torch.tensor(voxel_size, dtype=torch.float32)
+        grid_size = (point_cloud_range[3:] -
+                     point_cloud_range[:3]) / voxel_size
+        grid_size = torch.round(grid_size).long()
+        input_feat_shape = grid_size[:2]
+        self.grid_size = grid_size
+        # the origin shape is as [x-len, y-len, z-len]
+        # [w, h, d] -> [d, h, w]
+        self.pcd_shape = [*input_feat_shape, 1][::-1]
+
+    def forward(self, input):
+        """
+        input: shape=(N, c)
+        """
+        if self.training:
+            max_voxels = self.max_voxels[0]
+        else:
+            max_voxels = self.max_voxels[1]
+        voxel_parts = points_to_voxel(input.detach().cpu().numpy(),
+                                      self.voxel_size,
+                                      self.point_cloud_range,
+                                      self.max_num_points,
+                                      reverse_index=False,
+                                      max_voxels=max_voxels)
+        # return _Voxelization.apply(input, self.voxel_size, self.point_cloud_range,
+        #                           self.max_num_points, max_voxels,
+        #                          self.deterministic)
+        voxels = torch.from_numpy(voxel_parts[0]).to(device=input.device)
+        coors = torch.from_numpy(voxel_parts[1]).to(device=input.device)
+        num_points_per_voxel = torch.from_numpy(
+            voxel_parts[2]).to(
+            device=input.device)
+        return voxels, coors, num_points_per_voxel
+
+    def __repr__(self):
+        tmpstr = self.__class__.__name__ + '('
+        tmpstr += 'voxel_size=' + str(self.voxel_size)
+        tmpstr += ', point_cloud_range=' + str(self.point_cloud_range)
+        tmpstr += ', max_num_points=' + str(self.max_num_points)
+        tmpstr += ', max_voxels=' + str(self.max_voxels)
+        tmpstr += ', deterministic=' + str(self.deterministic)
+        tmpstr += ')'
+        return tmpstr
diff --git a/automotive/3d-object-detection/output/mlperf_log_accuracy.json b/automotive/3d-object-detection/output/mlperf_log_accuracy.json
new file mode 100644
index 0000000000..8e2f0bef13
--- /dev/null
+++ b/automotive/3d-object-detection/output/mlperf_log_accuracy.json
@@ -0,0 +1 @@
+[
\ No newline at end of file
diff --git a/automotive/3d-object-detection/output/mlperf_log_detail.txt b/automotive/3d-object-detection/output/mlperf_log_detail.txt
new file mode 100644
index 0000000000..bfe9a78127
--- /dev/null
+++ b/automotive/3d-object-detection/output/mlperf_log_detail.txt
@@ -0,0 +1,64 @@
+:::MLLOG {"key": "error_invalid_config", "value": "Multiple conf files are used. This is not valid for official submission.", "time_ms": 18446744073709.254158, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": true, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 538, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "loadgen_version", "value": "4.1 @ f74d16f541", "time_ms": 0.002363, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 53, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "loadgen_build_date_local", "value": "2024-10-28T22:57:16.905751", "time_ms": 0.002363, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 55, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "loadgen_build_date_utc", "value": "2024-10-28T22:57:16.905756", "time_ms": 0.002363, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 56, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "loadgen_git_commit_date", "value": "2024-10-22T15:38:02-05:00", "time_ms": 0.002363, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 57, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "loadgen_git_log_message", "value": "f74d16f54131d9080b9e45f234cc23e0ebaaf20c Apply min_new_tokens=2 to mixtral-8x7b, address #1777 (#1884)\necb880167756cb4b36ad70766b8d3254bfb06d26 [Postmortem 4.1] Make mlperf.conf static in loadgen, enable automatic Pypi release (#1882)\nf5c8f1758374aeaba26b2e84d31690111cfdf054 Fix bug: Loadgen ignoring token latency targets in user conf (#1874)\n976bb1ad9c7946be79507f3ff67955c27426af52 Set correct remote repo (#1871)\n41fa8aadd1ba0ecc97f6a519d8b42b04278e5f24 Add format files github action (#1682)\n518b454fd8647bfbd23a074e875e87353f33393e Tflite tpu (#1449)\ne0fdec1c7a75c98cfc194f13d62ac4388d419c8a Fix link in GettingStarted.ipynb (#1512)\n92bd8198d15411d7fb7d7c27f8904bc5a0bcfe7a Fix warning in the submission checker (#1808)\n224cfbf5c0e82cae6d48620025b7e1258ae3666a Fix typo in reference datatype (#1851)\n3ef1249b7f50a250c02c568342e0aea6638fc5a7 Fix docs (#1853)\na0874c100c54cbc54fb743ac8bf9fb5fadc64135 Update build_wheels.yml (#1758)\n6eff09986e337ccf03f675c9f244d8ee93644e16 Extend the final report generation script to output a json file of results (#1825)\n54f3f93a73cc8ca5e3319ad87fb325e510574f56 Add binding for server_num_issue_query_threads parameter (#1862)\nc4d0b3ea98e6fe7252e50cb573f0d523da7979df Update docs: SCC24,  fix broken redirect  (#1843)\n7d2f0c41e5cd79c9178702867392e38f57953338 Update DLRM readme (#1811)\ncf5fddc5d0746bf3820eb0ab7294bbf709d788ab Enable systems to be marked as power only (#1850)", "time_ms": 0.002363, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 58, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "loadgen_git_status_message", "value": "", "time_ms": 0.002363, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 60, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "loadgen_file_sha1", "value": {"/.clang-format":"012aad77e5206c89d50718c46c119d1f3cb056b2","/CMakeLists.txt":"b73434348f7860471606aaa395b570e81113cb6d","/MANIFEST.in":"8d3c4ac6c325b7b9a0fd4cf4a4108cbeff8d5025","/README.md":"20a55bb946c2c0bbb564ced2af1e48efd096b3a8","/README_BUILD.md":"5f6c6a784e9cd6995db47f9b9f70b1769909c9d8","/README_FAQ.md":"01f9ae9887f50bc030dc6107e740f40c43ca388f","/VERSION.txt":"cb67dcc41adcbb7849a0a808a501ee9ccd951d92","/__init__.py":"da39a3ee5e6b4b0d3255bfef95601890afd80709","/bindings/c_api.cc":"32181da9e161c285f8fe46ddaa49e6cba2f9f918","/bindings/c_api.h":"91f58bd79b83b278f3240174a9af747fc38aff74","/bindings/python_api.cc":"9f538d2a5390c77ae0bc3f8a351bcdb2587bc66c","/diagram_network_submission.png":"53dba8ad4272190ceb6335c12fd25e53dc02a8cb","/diagram_submission.png":"84c2f79309b237cef652aef6a187ba8e875a3952","/early_stopping.cc":"0cd7b546a389deac73f7955cd39255ed76557d62","/early_stopping.h":"158fcae6a5f47e82150d6416fa1f7bcef37e77fe","/issue_query_controller.cc":"126e952d00f4ea9efd12405fb209aa3ed585e4b2","/issue_query_controller.h":"923d9d5cdf598e3ec33d7a1110a31f7e11527ec7","/loadgen.cc":"6650091ba7a918f343b06eb7a5aa540eae87275f","/loadgen.h":"e00fdc6dbc85a8c9a8485dbcbfe2944f81251c4e","/loadgen_integration_diagram.svg":"47f748307536f80cfc606947b440dd732afc2637","/logging.cc":"197efc96d178e5d33a750d07fa7b2966417506ea","/logging.h":"ddb961df7bcc145bcd7cce8c21f7cf075350dcbe","/mlperf.conf":"0a4daef277bb3151139980e484dd5e644bf36e18","/pyproject.toml":"712fab87b72ba67ef2a068d0f9f47da65130342f","/query_dispatch_library.h":"13ad6d842200cb161d6927eb74a3fafd79c46c75","/query_sample.h":"e9187c8612bbdc972305b789feb6e15c26e96cfe","/query_sample_library.h":"8323a2225be1dff31f08ecc86b76eb3de06568bc","/requirements.txt":"a5ff7e77caa6e9e22ada90f0de0c865c987bf167","/results.cc":"34e2d2a44324cb07c884f92146ecbb8ef9d704e2","/results.h":"fce22d5a588d91fd968a6b25c27896dba87bc276","/setup.py":"a722046e05858c6d9f38f0e2b3fe425334beef28","/system_under_test.h":"18d4809589dae33317d88d9beeb5491a6e1ccdec","/test_settings.h":"476ecd4032f3bafe6f201df25d68aca4e177f659","/test_settings_internal.cc":"ce4322c849d24ffafc28a37b5e528a4cb4df227d","/test_settings_internal.h":"f1d5335b53ca610c30e0edc5d07999a27b5b4b9a","/utils.cc":"3df8fdabf6eaea4697cf25d1dcb89cae88e36efd","/utils.h":"40775e32d619ea6356826ae5ea4174c7911f6894","/version.cc":"cbec2a5f98f9786c8c3d8b06b3d12df0b6550fa0","/version.h":"9d574baa64424e9c708fcfedd3dbb0b518a65fcc","/version_generator.py":"eea9b9cb1a06cd1abe1bbdaee82f9af31527fedb"}, "time_ms": 0.002363, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 67, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "test_datetime", "value": "2024-10-29T02:08:01Z", "time_ms": 0.002578, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1198, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "sut_name", "value": "PySUT", "time_ms": 0.002578, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1199, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "get_sut_name_duration_ns", "value": 182, "time_ms": 0.002578, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1200, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "qsl_name", "value": "PyQSL", "time_ms": 0.002578, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1201, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "qsl_reported_total_count", "value": 198, "time_ms": 0.002578, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1202, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "qsl_reported_performance_count", "value": 5000, "time_ms": 0.002578, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1203, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_scenario", "value": "SingleStream", "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 271, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_test_mode", "value": "PerformanceOnly", "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 272, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_single_stream_expected_latency_ns", "value": 1e+07, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 277, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_single_stream_target_latency_percentile", "value": 0.9, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 279, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_min_duration_ms", "value": 600000, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 315, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_max_duration_ms", "value": 0, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 316, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_min_query_count", "value": 100, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 317, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_max_query_count", "value": 0, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 318, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_qsl_rng_seed", "value": 3066443479025735752, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 319, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_sample_index_rng_seed", "value": 10688027786191513374, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 320, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_schedule_rng_seed", "value": 14962580496156340209, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 322, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_accuracy_log_rng_seed", "value": 0, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 323, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_accuracy_log_probability", "value": 0, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 325, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_accuracy_log_sampling_target", "value": 0, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 327, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_print_timestamps", "value": false, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 329, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_performance_issue_unique", "value": false, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 330, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_performance_issue_same", "value": false, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 332, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_performance_issue_same_index", "value": 0, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 334, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_performance_sample_count_override", "value": 0, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 336, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "requested_sample_concatenate_permutation", "value": false, "time_ms": 0.005484, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 338, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_scenario", "value": "SingleStream", "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 417, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_test_mode", "value": "PerformanceOnly", "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 418, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_samples_per_query", "value": 1, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 420, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_target_qps", "value": 100, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 421, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_target_latency_ns", "value": 0, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 422, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_target_latency_percentile", "value": 0.9, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 423, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_max_async_queries", "value": 1, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 425, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_target_duration_ms", "value": 600000, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 426, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_min_duration_ms", "value": 600000, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 428, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_max_duration_ms", "value": 0, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 429, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_min_query_count", "value": 100, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 430, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_max_query_count", "value": 0, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 431, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_min_sample_count", "value": 100, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 432, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_qsl_rng_seed", "value": 3066443479025735752, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 433, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_sample_index_rng_seed", "value": 10688027786191513374, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 434, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_schedule_rng_seed", "value": 14962580496156340209, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 436, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_accuracy_log_rng_seed", "value": 0, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 437, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_accuracy_log_probability", "value": 0, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 439, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_accuracy_log_sampling_target", "value": 0, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 441, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_print_timestamps", "value": false, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 443, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_performance_issue_unique", "value": false, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 444, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_performance_issue_same", "value": false, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 446, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_performance_issue_same_index", "value": 0, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 448, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_performance_sample_count", "value": 5000, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 450, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "effective_sample_concatenate_permutation", "value": false, "time_ms": 0.005668, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 452, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "generic_message", "value": "Starting performance mode", "time_ms": 0.007752, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 841, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "loaded_qsl_set", "value": [166,119,189,146,100,142,64,87,84,70,135,40,195,77,168,185,85,163,193,29,190,30,53,50,99,164,157,167,156,165,49,27,187,79,125,170,123,182,88,9,101,6,128,62,151,124,92,38,42,56,66,90,51,181,48,12,148,35,178,15,82,106,67,176,143,153,147,97,102,61,83,109,158,118,52,107,131,114,113,69,0,39,55,127,173,155,160,75,162,5,21,24,121,186,18,59,145,7,25,26,74,14,91,2,76,141,86,63,196,134,110,137,20,180,23,34,105,188,117,68,133,108,129,154,194,116,44,197,57,126,41,80,58,111,43,32,78,10,98,28,89,152,22,179,47,73,159,149,130,144,11,60,17,3,112,115,103,174,171,36,140,37,72,96,31,104,138,8,120,81,19,175,13,184,16,71,172,65,136,169,93,1,132,191,45,192,94,54,95,46,183,177,122,139,161,33,150,4], "time_ms": 0.015220, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 613, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "generated_query_count", "value": 120001, "time_ms": 25.346542, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 428, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "generated_samples_per_query", "value": 1, "time_ms": 25.346542, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 429, "pid": 3279, "tid": 3279}}
+:::MLLOG {"key": "generated_query_duration", "value": 1200010000000, "time_ms": 25.346542, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 430, "pid": 3279, "tid": 3279}}
diff --git a/automotive/3d-object-detection/output/mlperf_log_summary.txt b/automotive/3d-object-detection/output/mlperf_log_summary.txt
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/automotive/3d-object-detection/output/mlperf_log_trace.json b/automotive/3d-object-detection/output/mlperf_log_trace.json
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/automotive/3d-object-detection/requirements.txt b/automotive/3d-object-detection/requirements.txt
new file mode 100644
index 0000000000..a5bb20b23e
--- /dev/null
+++ b/automotive/3d-object-detection/requirements.txt
@@ -0,0 +1 @@
+# TODO: Add requirements
diff --git a/automotive/3d-object-detection/tools/download_dataset.py b/automotive/3d-object-detection/tools/download_dataset.py
new file mode 100644
index 0000000000..8a567ccf50
--- /dev/null
+++ b/automotive/3d-object-detection/tools/download_dataset.py
@@ -0,0 +1 @@
+# TODO: script to download dataset
diff --git a/automotive/3d-object-detection/tools/evaluate.py b/automotive/3d-object-detection/tools/evaluate.py
new file mode 100644
index 0000000000..3bc56ee4f9
--- /dev/null
+++ b/automotive/3d-object-detection/tools/evaluate.py
@@ -0,0 +1,261 @@
+import argparse
+import numpy as np
+import os
+import torch
+from tqdm import tqdm
+
+from tools.process import iou2d, iou3d_camera
+
+
+def get_score_thresholds(tp_scores, total_num_valid_gt, num_sample_pts=41):
+    score_thresholds = []
+    tp_scores = sorted(tp_scores)[::-1]
+    cur_recall, pts_ind = 0, 0
+    for i, score in enumerate(tp_scores):
+        lrecall = (i + 1) / total_num_valid_gt
+        rrecall = (i + 2) / total_num_valid_gt
+
+        if i == len(tp_scores) - 1:
+            score_thresholds.append(score)
+            break
+
+        if (lrecall + rrecall) / 2 < cur_recall:
+            continue
+
+        score_thresholds.append(score)
+        pts_ind += 1
+        cur_recall = pts_ind / (num_sample_pts - 1)
+    return score_thresholds
+
+
+def convert_calib(calib, cuda):
+    result = {}
+    if cuda:
+        device = 'cuda'
+    else:
+        device = 'cpu'
+    result['R0_rect'] = torch.from_numpy(
+        calib['R0_rect']).to(
+        device=device,
+        dtype=torch.float)
+    for i in range(5):
+        result['P' + str(i)] = torch.from_numpy(calib['P' + str(i)]
+                                                ).to(device=device, dtype=torch.float)
+        result['Tr_velo_to_cam_' +
+               str(i)] = torch.from_numpy(calib['Tr_velo_to_cam_' +
+                                                str(i)]).to(device=device, dtype=torch.float)
+    return result
+
+
+def do_eval(det_results, gt_results, CLASSES, cam_sync=False):
+    '''
+    det_results: list,
+    gt_results: dict(id -> det_results)
+    CLASSES: dict
+    '''
+    assert len(det_results) == len(gt_results)
+
+    # 1. calculate iou
+    ious = {
+        'bbox_3d': []
+    }
+    # ids = list(sorted([g['image']['image_idx'] for g in gt_results]))
+    if cam_sync:
+        annos_label = 'cam_sync_annos'
+    else:
+        annos_label = 'annos'
+    for id in range(len(gt_results)):
+        gt_result = gt_results[id][annos_label]
+        det_result = det_results[gt_results[id]['image']['image_idx']]
+
+        # 1.2, bev iou
+        gt_location = gt_result['location'].astype(np.float32)
+        gt_dimensions = gt_result['dimensions'].astype(np.float32)
+        gt_rotation_y = gt_result['rotation_y'].astype(np.float32)
+        det_location = det_result['location'].astype(np.float32).reshape(-1, 3)
+        det_dimensions = det_result['dimensions'].astype(
+            np.float32).reshape(-1, 3)
+        det_rotation_y = det_result['rotation_y'].astype(np.float32)
+
+        # 1.3, 3dbboxes iou
+        gt_bboxes3d = np.concatenate(
+            [gt_location, gt_dimensions, gt_rotation_y[:, None]], axis=-1)
+        det_bboxes3d = np.concatenate(
+            [det_location, det_dimensions, det_rotation_y[:, None]], axis=-1)
+        iou3d_v = iou3d_camera(
+            torch.from_numpy(gt_bboxes3d).cuda(),
+            torch.from_numpy(det_bboxes3d).cuda())
+        ious['bbox_3d'].append(iou3d_v.cpu().numpy())
+
+    MIN_IOUS = {
+        'Pedestrian': [0.5],
+        'Cyclist': [0.5],
+        'Car': [0.7]
+    }
+    MIN_HEIGHT = [-1]
+
+    overall_results = {}
+    for e_ind, eval_type in enumerate(['bbox_3d']):
+        eval_ious = ious[eval_type]
+        eval_ap_results, eval_aos_results = {}, {}
+        for cls in CLASSES:
+            eval_ap_results[cls] = []
+            eval_aos_results[cls] = []
+            CLS_MIN_IOU = MIN_IOUS[cls][e_ind]
+            for difficulty in [0]:
+                # 1. bbox property
+                total_gt_ignores, total_det_ignores, total_dc_bboxes, total_scores = [], [], [], []
+                for id in range(len(gt_results)):
+                    gt_result = gt_results[id][annos_label]
+                    det_result = det_results[gt_results[id]
+                                             ['image']['image_idx']]
+
+                    # 1.1 gt bbox property
+                    cur_gt_names = gt_result['name']
+                    cur_difficulty = gt_result['difficulty']
+                    gt_ignores, dc_bboxes = [], []
+                    for j, cur_gt_name in enumerate(cur_gt_names):
+                        ignore = cur_difficulty[j] < 0 or cur_difficulty[j] > difficulty
+                        if cur_gt_name == cls:
+                            valid_class = 1
+                        elif cls == 'Pedestrian' and cur_gt_name == 'Person_sitting':
+                            valid_class = 0
+                        elif cls == 'Car' and cur_gt_name == 'Van':
+                            valid_class = 0
+                        else:
+                            valid_class = -1
+
+                        if valid_class == 1 and not ignore:
+                            gt_ignores.append(0)
+                        elif valid_class == 0 or (valid_class == 1 and ignore):
+                            gt_ignores.append(1)
+                        else:
+                            gt_ignores.append(-1)
+
+                        if cur_gt_name == 'DontCare':
+                            dc_bboxes.append(gt_result['bbox'][j])
+                    total_gt_ignores.append(gt_ignores)
+                    total_dc_bboxes.append(np.array(dc_bboxes))
+
+                    # 1.2 det bbox property
+                    cur_det_names = det_result['name']
+                    if len(cur_det_names) == 0:
+                        cur_det_heights = np.empty_like(det_result['bbox'])
+                    else:
+                        cur_det_heights = det_result['bbox'][:,
+                                                             3] - det_result['bbox'][:, 1]
+                    det_ignores = []
+                    for j, cur_det_name in enumerate(cur_det_names):
+                        if cur_det_heights[j] < MIN_HEIGHT[difficulty]:
+                            det_ignores.append(1)
+                        elif cur_det_name == cls:
+                            det_ignores.append(0)
+                        else:
+                            det_ignores.append(-1)
+                    total_det_ignores.append(det_ignores)
+                    total_scores.append(det_result['score'])
+
+                # 2. calculate scores thresholds for PR curve
+                tp_scores = []
+                for i in range(len(gt_results)):
+                    cur_eval_ious = eval_ious[i]
+                    gt_ignores, det_ignores = total_gt_ignores[i], total_det_ignores[i]
+                    scores = total_scores[i]
+
+                    nn, mm = cur_eval_ious.shape
+                    assigned = np.zeros((mm, ), dtype=np.bool_)
+                    for j in range(nn):
+                        if gt_ignores[j] == -1:
+                            continue
+                        match_id, match_score = -1, -1
+                        for k in range(mm):
+                            if not assigned[k] and det_ignores[k] >= 0 and cur_eval_ious[j,
+                                                                                         k] > CLS_MIN_IOU and scores[k] > match_score:
+                                match_id = k
+                                match_score = scores[k]
+                        if match_id != -1:
+                            assigned[match_id] = True
+                            if det_ignores[match_id] == 0 and gt_ignores[j] == 0:
+                                tp_scores.append(match_score)
+                total_num_valid_gt = np.sum(
+                    [np.sum(np.array(gt_ignores) == 0) for gt_ignores in total_gt_ignores])
+                score_thresholds = get_score_thresholds(
+                    tp_scores, total_num_valid_gt)
+
+                # 3. draw PR curve and calculate mAP
+                tps, fns, fps, total_aos = [], [], [], []
+
+                for score_threshold in score_thresholds:
+                    tp, fn, fp = 0, 0, 0
+                    aos = 0
+                    for i in range(len(gt_results)):
+                        cur_eval_ious = eval_ious[i]
+                        gt_ignores, det_ignores = total_gt_ignores[i], total_det_ignores[i]
+                        scores = total_scores[i]
+
+                        nn, mm = cur_eval_ious.shape
+                        assigned = np.zeros((mm, ), dtype=np.bool_)
+                        for j in range(nn):
+                            if gt_ignores[j] == -1:
+                                continue
+                            match_id, match_iou = -1, -1
+                            for k in range(mm):
+                                if not assigned[k] and det_ignores[k] >= 0 and scores[
+                                        k] >= score_threshold and cur_eval_ious[j, k] > CLS_MIN_IOU:
+
+                                    if det_ignores[k] == 0 and cur_eval_ious[j,
+                                                                             k] > match_iou:
+                                        match_iou = cur_eval_ious[j, k]
+                                        match_id = k
+                                    elif det_ignores[k] == 1 and match_iou == -1:
+                                        match_id = k
+
+                            if match_id != -1:
+                                assigned[match_id] = True
+                                if det_ignores[match_id] == 0 and gt_ignores[j] == 0:
+                                    tp += 1
+                            else:
+                                if gt_ignores[j] == 0:
+                                    fn += 1
+
+                        for k in range(mm):
+                            if det_ignores[k] == 0 and scores[k] >= score_threshold and not assigned[k]:
+                                fp += 1
+
+                        # In case 2d bbox evaluation, we should consider
+                        # dontcare bboxes
+                        if eval_type == 'bbox_2d':
+                            dc_bboxes = total_dc_bboxes[i]
+                            det_bboxes = det_results[gt_results[i]
+                                                     ['image']['image_idx']]['bbox']
+                            if len(dc_bboxes) > 0:
+                                ious_dc_det = iou2d(
+                                    torch.from_numpy(det_bboxes),
+                                    torch.from_numpy(dc_bboxes),
+                                    metric=1).numpy().T
+                                for j in range(len(dc_bboxes)):
+                                    for k in range(len(det_bboxes)):
+                                        if det_ignores[k] == 0 and scores[k] >= score_threshold and not assigned[k]:
+                                            if ious_dc_det[j, k] > CLS_MIN_IOU:
+                                                fp -= 1
+                                                assigned[k] = True
+
+                    tps.append(tp)
+                    fns.append(fn)
+                    fps.append(fp)
+                    if eval_type == 'bbox_2d':
+                        total_aos.append(aos)
+
+                tps, fns, fps = np.array(tps), np.array(fns), np.array(fps)
+
+                recalls = tps / (tps + fns)
+                precisions = tps / (tps + fps)
+                for i in range(len(score_thresholds)):
+                    precisions[i] = np.max(precisions[i:])
+
+                sums_AP = 0
+                for i in range(0, len(score_thresholds), 4):
+                    sums_AP += precisions[i]
+                mAP = sums_AP / 11 * 100
+                eval_ap_results[cls].append(mAP)
+    return eval_ap_results
diff --git a/automotive/3d-object-detection/tools/process.py b/automotive/3d-object-detection/tools/process.py
new file mode 100644
index 0000000000..59705d533b
--- /dev/null
+++ b/automotive/3d-object-detection/tools/process.py
@@ -0,0 +1,341 @@
+import shapely.geometry
+import numpy as np
+import torch
+import copy
+
+
+def bbox_camera2lidar(bboxes, tr_velo_to_cam, r0_rect):
+    '''
+    bboxes: shape=(N, 7)
+    tr_velo_to_cam: shape=(4, 4)
+    r0_rect: shape=(4, 4)
+    return: shape=(N, 7)
+    '''
+    x_size, y_size, z_size = bboxes[:, 3:4], bboxes[:, 4:5], bboxes[:, 5:6]
+    xyz_size = np.concatenate([z_size, x_size, y_size], axis=1)
+    extended_xyz = np.pad(
+        bboxes[:, :3], ((0, 0), (0, 1)), 'constant', constant_values=1.0)
+    rt_mat = np.linalg.inv(r0_rect @ tr_velo_to_cam)
+    xyz = extended_xyz @ rt_mat.T
+    bboxes_lidar = np.concatenate(
+        [xyz[:, :3], xyz_size, bboxes[:, 6:]], axis=1)
+    return np.array(bboxes_lidar, dtype=np.float32)
+
+
+def bbox_lidar2camera(bboxes, tr_velo_to_cam, r0_rect):
+    '''
+    bboxes: shape=(N, 7)
+    tr_velo_to_cam: shape=(4, 4)
+    r0_rect: shape=(4, 4)
+    return: shape=(N, 7)
+    '''
+    x_size, y_size, z_size = bboxes[:, 3:4], bboxes[:, 4:5], bboxes[:, 5:6]
+    xyz_size = torch.cat([y_size, z_size, x_size], axis=1)
+    extended_xyz = torch.nn.functional.pad(
+        bboxes[:, :3], (0, 1), 'constant', value=1.0)
+    rt_mat = r0_rect @ tr_velo_to_cam
+    xyz = extended_xyz @ rt_mat.T
+    bboxes_camera = torch.cat([xyz[:, :3], xyz_size, bboxes[:, 6:]], axis=1)
+    return bboxes_camera
+
+
+def bbox3d2corners_camera(bboxes):
+    '''
+    bboxes: shape=(n, 7)
+    return: shape=(n, 8, 3)
+        z (front)            6 ------ 5
+        /                  / |     / |
+       /                  2 -|---- 1 |
+      /                   |  |     | |
+    |o ------> x(right)   | 7 -----| 4
+    |                     |/   o   |/
+    |                     3 ------ 0
+    |
+    v y(down)
+    '''
+    centers, dims, angles = bboxes[:, :3], bboxes[:, 3:6], bboxes[:, 6]
+
+    # 1.generate bbox corner coordinates, clockwise from minimal point
+    bboxes_corners = torch.tensor([[0.5, 0.0, -0.5], [0.5, -1.0, -0.5], [-0.5, -1.0, -0.5], [-0.5, 0.0, -0.5],
+                                   [0.5, 0.0, 0.5], [0.5, -1.0, 0.5], [-0.5, -1.0, 0.5], [-0.5, 0.0, 0.5]])
+    # (1, 8, 3) * (n, 1, 3) -> (n, 8, 3)
+    bboxes_corners = bboxes_corners[None, :, :] * dims[:, None, :]
+
+    # 2. rotate around y axis
+    rot_sin, rot_cos = torch.sin(angles), torch.cos(angles)
+    # in fact, angle
+    rot_mat = torch.stack([torch.stack([rot_cos, torch.zeros_like(rot_cos), rot_sin]),
+                           torch.stack([torch.zeros_like(rot_cos), torch.ones_like(
+                               rot_cos), torch.zeros_like(rot_cos)]),
+                           torch.stack([-rot_sin, torch.zeros_like(rot_cos), rot_cos])])  # (3, 3, n)
+    rot_mat = torch.permute(rot_mat, (2, 1, 0))  # (n, 3, 3)
+    bboxes_corners = bboxes_corners @ rot_mat  # (n, 8, 3)
+
+    # 3. translate to centers
+    bboxes_corners += centers[:, None, :]
+    return bboxes_corners.clone().detach()
+
+
+def points_camera2image(points, P2):
+    '''
+    points: shape=(N, 8, 3)
+    P2: shape=(4, 4)
+    return: shape=(N, 8, 2)
+    '''
+    extended_points = torch.nn.functional.pad(
+        points, (0, 1), 'constant', value=1.0)  # (n, 8, 4)
+    image_points = extended_points @ P2.T  # (N, 8, 4)
+    image_points = image_points[:, :, :2] / image_points[:, :, 2:3]
+    return image_points.clone().detach()
+
+
+def keep_bbox_from_image_range(
+        result, calib_info, num_images, image_info, cam_sync=False):
+    r0_rect = calib_info['R0_rect']
+    lidar_bboxes = result['lidar_bboxes']
+    labels = result['labels']
+    scores = result['scores']
+    total_keep_flag = torch.zeros(lidar_bboxes.size(dim=0)).bool()
+    for i in range(num_images):
+        h, w = image_info['camera'][i]['image_shape']
+        tr_velo_to_cam = calib_info['Tr_velo_to_cam_' + str(i)]
+        P = calib_info['P' + str(i)]
+        camera_bboxes = bbox_lidar2camera(
+            lidar_bboxes, tr_velo_to_cam, r0_rect)  # (n, 7)
+        if i == 0:
+            main_camera_bboxes = camera_bboxes.clone()
+        bboxes_points = bbox3d2corners_camera(camera_bboxes)  # (n, 8, 3)
+        image_points = points_camera2image(bboxes_points, P)  # (n, 8, 2)
+        image_x1y1 = torch.min(image_points, axis=1)[0]  # (n, 2)
+        image_x1y1 = torch.maximum(image_x1y1, torch.tensor(0))
+        image_x2y2 = torch.max(image_points, axis=1)[0]  # (n, 2)
+        image_x2y2 = torch.minimum(image_x2y2, torch.tensor([w, h]))
+        bboxes2d = torch.cat([image_x1y1, image_x2y2], axis=-1)
+
+        keep_flag = (image_x1y1[:, 0] < w) & (image_x1y1[:, 1] < h) & (
+            image_x2y2[:, 0] > 0) & (image_x2y2[:, 1] > 0) & (camera_bboxes[:, 2] > 0)
+        total_keep_flag = total_keep_flag | keep_flag
+    if cam_sync:
+        result = {
+            'lidar_bboxes': lidar_bboxes[total_keep_flag],
+            'labels': labels[total_keep_flag],
+            'scores': scores[total_keep_flag],
+            'bboxes2d': bboxes2d[total_keep_flag],
+            'camera_bboxes': main_camera_bboxes[total_keep_flag]
+        }
+    else:
+        result = {
+            'lidar_bboxes': lidar_bboxes,
+            'labels': labels,
+            'scores': scores,
+            'bboxes2d': bboxes2d,
+            'camera_bboxes': main_camera_bboxes
+        }
+    return result
+
+
+def limit_period(val, offset=0.5, period=np.pi):
+    """
+    val: array or float
+    offset: float
+    period: float
+    return: Value in the range of [-offset * period, (1-offset) * period]
+    """
+    limited_val = val - np.floor(val / period + offset) * period
+    return limited_val
+
+
+def iou2d(bboxes1, bboxes2, metric=0):
+    '''
+    bboxes1: (n, 4), (x1, y1, x2, y2)
+    bboxes2: (m, 4), (x1, y1, x2, y2)
+    return: (n, m)
+    '''
+    rows = len(bboxes1)
+    cols = len(bboxes2)
+    if rows * cols == 0:
+        return torch.empty((rows, cols))
+    bboxes_x1 = torch.maximum(
+        bboxes1[:, 0][:, None], bboxes2[:, 0][None, :])  # (n, m)
+    bboxes_y1 = torch.maximum(
+        bboxes1[:, 1][:, None], bboxes2[:, 1][None, :])  # (n, m)
+    bboxes_x2 = torch.minimum(bboxes1[:, 2][:, None], bboxes2[:, 2][None, :])
+    bboxes_y2 = torch.minimum(bboxes1[:, 3][:, None], bboxes2[:, 3][None, :])
+
+    bboxes_w = torch.clamp(bboxes_x2 - bboxes_x1, min=0)
+    bboxes_h = torch.clamp(bboxes_y2 - bboxes_y1, min=0)
+
+    iou_area = bboxes_w * bboxes_h  # (n, m)
+
+    bboxes1_wh = bboxes1[:, 2:] - bboxes1[:, :2]
+    area1 = bboxes1_wh[:, 0] * bboxes1_wh[:, 1]  # (n, )
+    bboxes2_wh = bboxes2[:, 2:] - bboxes2[:, :2]
+    area2 = bboxes2_wh[:, 0] * bboxes2_wh[:, 1]  # (m, )
+    if metric == 0:
+        iou = iou_area / (area1[:, None] + area2[None, :] - iou_area + 1e-8)
+    elif metric == 1:
+        iou = iou_area / (area1[:, None] + 1e-8)
+    return iou
+
+
+def nearest_bev(bboxes):
+    '''
+    bboxes: (n, 7), (x, y, z, w, l, h, theta)
+    return: (n, 4), (x1, y1, x2, y2)
+    '''
+    bboxes_bev = copy.deepcopy(bboxes[:, [0, 1, 3, 4]])
+    bboxes_angle = limit_period(
+        bboxes[:, 6].cpu(), offset=0.5, period=np.pi).to(bboxes_bev)
+    bboxes_bev = torch.where(torch.abs(
+        bboxes_angle[:, None]) > np.pi / 4, bboxes_bev[:, [0, 1, 3, 2]], bboxes_bev)
+
+    bboxes_xy = bboxes_bev[:, :2]
+    bboxes_wl = bboxes_bev[:, 2:]
+    bboxes_bev_x1y1x2y2 = torch.cat(
+        [bboxes_xy - bboxes_wl / 2, bboxes_xy + bboxes_wl / 2], dim=-1)
+    return bboxes_bev_x1y1x2y2
+
+
+def iou2d_nearest(bboxes1, bboxes2):
+    '''
+    bboxes1: (n, 7), (x, y, z, w, l, h, theta)
+    bboxes2: (m, 7),
+    return: (n, m)
+    '''
+    bboxes1_bev = nearest_bev(bboxes1)
+    bboxes2_bev = nearest_bev(bboxes2)
+    iou = iou2d(bboxes1_bev, bboxes2_bev)
+    return iou
+
+
+def limit_period(val, offset=0.5, period=np.pi):
+    """
+    val: array or float
+    offset: float
+    period: float
+    return: Value in the range of [-offset * period, (1-offset) * period]
+    """
+    limited_val = val - np.floor(val / period + offset) * period
+    return limited_val
+
+
+def iou3d_camera(bboxes1, bboxes2):
+    '''
+    bboxes1: (n, 7), (x, y, z, w, l, h, theta)
+    bboxes2: (m, 7)
+    return: (n, m)
+    '''
+    rows = len(bboxes1)
+    cols = len(bboxes2)
+    if rows * cols == 0:
+        return torch.empty((rows, cols))
+    # 1. height overlap
+    bboxes1_bottom, bboxes2_bottom = bboxes1[:, 1] - \
+        bboxes1[:, 4], bboxes2[:, 1] - bboxes2[:, 4]  # (n, ), (m, )
+    bboxes1_top, bboxes2_top = bboxes1[:, 1], bboxes2[:, 1]  # (n, ), (m, )
+    bboxes_bottom = torch.maximum(
+        bboxes1_bottom[:, None], bboxes2_bottom[None, :])  # (n, m)
+    bboxes_top = torch.minimum(bboxes1_top[:, None], bboxes2_top[None, :])
+    height_overlap = torch.clamp(bboxes_top - bboxes_bottom, min=0)
+
+    # 2. bev overlap
+    bboxes1_x1y1 = bboxes1[:, [0, 2]] - bboxes1[:, [3, 5]] / 2
+    bboxes1_x2y2 = bboxes1[:, [0, 2]] + bboxes1[:, [3, 5]] / 2
+    bboxes2_x1y1 = bboxes2[:, [0, 2]] - bboxes2[:, [3, 5]] / 2
+    bboxes2_x2y2 = bboxes2[:, [0, 2]] + bboxes2[:, [3, 5]] / 2
+    bboxes1_bev = torch.cat(
+        [bboxes1_x1y1, bboxes1_x2y2, bboxes1[:, 6:]], dim=-1)
+    bboxes2_bev = torch.cat(
+        [bboxes2_x1y1, bboxes2_x2y2, bboxes2[:, 6:]], dim=-1)
+    bev_overlap = (
+        rotated_box_iou(
+            bboxes1_bev,
+            bboxes2_bev)).to(
+        device=height_overlap.device)  # (n, m)
+
+    # 3. overlap and volume
+    overlap = height_overlap * bev_overlap
+    volume1 = bboxes1[:, 3] * bboxes1[:, 4] * bboxes1[:, 5]
+    volume2 = bboxes2[:, 3] * bboxes2[:, 4] * bboxes2[:, 5]
+    volume = volume1[:, None] + volume2[None, :]  # (n, m)
+
+    # 4. iou
+    iou = overlap / (volume - overlap + 1e-8)
+
+    return iou
+
+
+def boxes_overlap_bev(boxes_a, boxes_b):
+    """Calculate boxes Overlap in the bird view.
+
+    Args:
+        boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
+        boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
+
+    Returns:
+        ans_overlap (torch.Tensor): Overlap result with shape (M, N).
+    """
+    ans_overlap = boxes_a.new_zeros(
+        torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
+    if ans_overlap.size(0) * ans_overlap.size(1) == 0:
+        return ans_overlap
+    boxes_overlap_bev_gpu(
+        boxes_a.contiguous(),
+        boxes_b.contiguous(),
+        ans_overlap)
+
+    return ans_overlap
+
+
+def rotated_box_iou(boxes1, boxes2):
+    """
+    Calculates IoU for rotated bounding boxes.
+
+    Args:
+        boxes1 (torch.Tensor): Tensor of shape (N, 5) representing rotated boxes in format (x_center, y_center, width, height, angle).
+        boxes2 (torch.Tensor): Tensor of shape (M, 5) representing rotated boxes in the same format.
+
+    Returns:
+        torch.Tensor: IoU matrix of shape (N, M).
+    """
+
+    # Convert boxes to polygons
+    polygons1 = boxes_to_polygons(boxes1)
+    polygons2 = boxes_to_polygons(boxes2)
+
+    # Calculate IoU for each pair of polygons
+    ious = torch.zeros((boxes1.shape[0], boxes2.shape[0]))
+    overlaps = torch.zeros((boxes1.shape[0], boxes2.shape[0]))
+    for i in range(boxes1.shape[0]):
+        for j in range(boxes2.shape[0]):
+            intersection = polygon_intersection(polygons1[i], polygons2[j])
+            union = polygon_union(polygons1[i], polygons2[j])
+            ious[i, j] = intersection / union
+            overlaps[i, j] = intersection
+
+    return overlaps
+
+
+def boxes_to_polygons(boxes):
+    # Implementation to convert boxes to polygons
+    polygons = []
+    for box in boxes:
+        x_min = box[0]
+        y_min = box[1]
+        x_max = box[2]
+        y_max = box[3]
+        polygon = shapely.geometry.Polygon(
+            [(x_min, y_min), (x_max, y_min), (x_max, y_max), (x_min, y_max)])
+        polygon = shapely.affinity.rotate(
+            polygon, -1 * box[4], use_radians=True)
+        polygons.append(polygon)
+    return polygons
+
+
+def polygon_intersection(polygon1, polygon2):
+    return shapely.intersection(polygon1, polygon2).area
+
+
+def polygon_union(polygon1, polygon2):
+    # Implementation to calculate union area of polygons
+    return shapely.union(polygon1, polygon2).area
diff --git a/automotive/3d-object-detection/user.conf b/automotive/3d-object-detection/user.conf
new file mode 100644
index 0000000000..07a10bbe21
--- /dev/null
+++ b/automotive/3d-object-detection/user.conf
@@ -0,0 +1,4 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
diff --git a/automotive/3d-object-detection/waymo.py b/automotive/3d-object-detection/waymo.py
new file mode 100644
index 0000000000..5818ed01e0
--- /dev/null
+++ b/automotive/3d-object-detection/waymo.py
@@ -0,0 +1,247 @@
+"""
+implementation of waymo dataset
+"""
+
+# pylint: disable=unused-argument,missing-docstring
+
+import json
+import logging
+import os
+import time
+
+from PIL import Image
+import numpy as np
+import pandas as pd
+import dataset
+import os
+import pickle
+import torch
+from torchvision import transforms
+import tools.process as process
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("waymo")
+
+
+def read_pickle(file_path, suffix='.pkl'):
+    assert os.path.splitext(file_path)[1] == suffix
+    with open(file_path, 'rb') as f:
+        data = pickle.load(f)
+    return data
+
+
+def point_range_filter(data_dict, point_range):
+    '''
+    data_dict: dict(pts, gt_bboxes_3d, gt_labels, gt_names, difficulty)
+    point_range: [x1, y1, z1, x2, y2, z2]
+    '''
+    pts = data_dict['pts']
+    flag_x_low = pts[:, 0] > point_range[0]
+    flag_y_low = pts[:, 1] > point_range[1]
+    flag_z_low = pts[:, 2] > point_range[2]
+    flag_x_high = pts[:, 0] < point_range[3]
+    flag_y_high = pts[:, 1] < point_range[4]
+    flag_z_high = pts[:, 2] < point_range[5]
+    keep_mask = flag_x_low & flag_y_low & flag_z_low & flag_x_high & flag_y_high & flag_z_high
+    pts = pts[keep_mask]
+    data_dict.update({'pts': pts})
+    return data_dict
+
+
+def read_points(file_path, dim=4):
+    suffix = os.path.splitext(file_path)[1]
+    assert suffix in ['.bin', '.ply', '.npy']
+    if suffix == '.bin':
+        return np.fromfile(file_path, dtype=np.float32).reshape(-1, dim)
+    elif suffix == '.npy':
+        return np.load(file_path).astype(np.float32)
+    else:
+        raise NotImplementedError
+
+
+class Waymo(dataset.Dataset):
+    CLASSES = {
+        'Pedestrian': 0,
+        'Cyclist': 1,
+        'Car': 2
+    }
+
+    def __init__(self, data_root, split,
+                 pts_prefix='velodyne_reduced', painted=True, cam_sync=False):
+        super().__init__()
+        assert split in ['train', 'val', 'trainval', 'test']
+        self.data_root = data_root
+        self.split = split
+        self.pts_prefix = pts_prefix
+        info_file = f'waymo_infos_val.pkl'
+        self.painted = painted
+        self.cam_sync = cam_sync
+        self.point_range_filter = [-74.88, -74.88, -2, 74.88, 74.88, 4]
+        if painted or cam_sync:
+            info_file = f'painted_waymo_infos_{split}.pkl'
+        else:
+            info_file = f'waymo_infos_{split}.pkl'
+        self.data_infos = read_pickle(os.path.join(data_root, info_file))
+        self.sorted_ids = range(len(self.data_infos))
+
+    def preprocess(self, input):
+        image_transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=[
+                    0.485, 0.456, 0.406], std=[
+                    0.229, 0.224, 0.225]),
+        ])
+        input_images = input['images']
+        for i in range(len(input_images)):
+            input['images'][i] = image_transform(
+                input['images'][i]).unsqueeze(0)
+        return input
+
+    def get_list(self):
+        raise NotImplementedError("Dataset:get_list")
+
+    def load_query_samples(self, sample_list):
+        # TODO: Load queries into memory, if needed
+        pass
+
+    def unload_query_samples(self, sample_list):
+        # TODO: Unload queries from memory, if needed
+        pass
+
+    def get_samples(self, id_list):
+        data = []
+        labels = []
+        for id in id_list:
+            item = self.get_item(id)
+            data.append({'pts': item['pts'],
+                         'images': item['images'],
+                         'calib_info': item['calib_info'],
+                         'image_info': item['image_info']})
+            labels.append({'gt_labels': item['gt_labels'],
+                           'calib_info': item['calib_info'],
+                           'gt_names': item['gt_names'],
+                           })
+        return data, labels
+
+    def get_item(self, id):
+        data_info = self.data_infos[self.sorted_ids[id]]
+        image_info, calib_info, annos_info = \
+            data_info['image'], data_info['calib'].copy(), data_info['annos']
+        # point cloud input
+        velodyne_path = data_info['point_cloud']['velodyne_path']
+        pts_path = os.path.join(self.data_root, velodyne_path)
+        if self.cam_sync:
+            annos_info = data_info['cam_sync_annos']
+        pts = read_points(pts_path, 6)
+        pts = pts[:, :5]
+
+        # calib input: for bbox coordinates transformation between Camera and Lidar.
+        # because
+        tr_velo_to_cam = calib_info['Tr_velo_to_cam_0'].astype(np.float32)
+        r0_rect = calib_info['R0_rect'].astype(np.float32)
+        for key in calib_info.keys():
+            calib_info[key] = torch.from_numpy(
+                calib_info[key]).type(
+                torch.float32)
+
+        # annotations input
+        annos_info = self.remove_dont_care(annos_info)
+        annos_name = annos_info['name']
+        annos_location = annos_info['location']
+        annos_dimension = annos_info['dimensions']
+        rotation_y = annos_info['rotation_y']
+        gt_bboxes = np.concatenate(
+            [annos_location, annos_dimension, rotation_y[:, None]], axis=1).astype(np.float32)
+        gt_bboxes_3d = process.bbox_camera2lidar(
+            gt_bboxes, tr_velo_to_cam, r0_rect)
+        gt_labels = [self.CLASSES.get(name, -1) for name in annos_name]
+        data_dict = {
+            'pts': torch.from_numpy(pts),
+            'gt_bboxes_3d': torch.from_numpy(gt_bboxes_3d),
+            'gt_labels': torch.from_numpy(np.array(gt_labels)),
+            'gt_names': annos_name,
+            'difficulty': annos_info['difficulty'],
+            'image_info': image_info,
+            'calib_info': calib_info
+        }
+        data_dict = point_range_filter(
+            data_dict, point_range=self.point_range_filter)
+        images = []
+        for i in range(5):
+            image = self.get_image(
+                image_info['image_idx'],
+                'image_' + str(i) + '/')
+            images.append(image)
+        data_dict['images'] = images
+
+        return data_dict
+
+    def remove_dont_care(self, annos_info):
+        keep_ids = [
+            i for i, name in enumerate(
+                annos_info['name']) if name != 'DontCare']
+        for k, v in annos_info.items():
+            annos_info[k] = v[keep_ids]
+        return annos_info
+
+    def get_image(self, idx, camera):
+        filename = os.path.join(
+            self.data_root, 'training', camera + ('%s.jpg' % idx))
+        input_image = Image.open(filename)
+        preprocess = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=[
+                    0.485, 0.456, 0.406], std=[
+                    0.229, 0.224, 0.225]),
+        ])
+
+        input_tensor = preprocess(input_image)
+        input_batch = input_tensor.unsqueeze(0)
+        return input_batch
+
+
+class PostProcessWaymo:
+    def __init__(
+        self,  # Postprocess parameters
+    ):
+        self.content_ids = []
+        # TODO: Init Postprocess parameters
+        self.results = []
+
+    def add_results(self, results):
+        self.results.extend(results)
+
+    def __call__(self, results, content_id, inputs, result_dict):
+        self.content_ids.extend(content_id)
+        processed_results = []
+        for idx in range(len(content_id)):
+            processed_results.append([])
+            detection_num = len(results[0][idx])
+            for detection in range(0, detection_num):
+                processed_results[idx].append([
+                    results[0][idx][detection][0],
+                    results[0][idx][detection][1],
+                    results[0][idx][detection][2],
+                    results[1][idx][detection][0],
+                    results[1][idx][detection][1],
+                    results[1][idx][detection][2],
+                    results[2][idx][detection],
+                    results[3][idx][detection][0],
+                    results[3][idx][detection][1],
+                    results[3][idx][detection][2],
+                    results[3][idx][detection][3],
+                    results[4][idx][detection],
+                    results[5][idx][detection],
+                    results[6][idx]
+                ])
+        return processed_results
+
+    def start(self):
+        self.results = []
+
+    def finalize(self, result_dict, ds=None):
+
+        return result_dict

From b9f22d6c37fa9024616538516e1d3bb59dd7c019 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Thu, 2 Jan 2025 16:00:19 -0600
Subject: [PATCH 219/305] Implement KSM to pull a PAT for commits in format.yml
 (#2011)

---
 .github/workflows/format.yml | 24 ++++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index 45ebb521b6..dbf9a78bbe 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -3,7 +3,7 @@ name: "Code formatting"
 on:
   push:
     branches:
-    - "**"
+      - "**"
 
 env:
   python_version: "3.9"
@@ -12,16 +12,25 @@ jobs:
   format-code:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - name: Retrieve secrets from Keeper
+        id: ksecrets
+        uses: Keeper-Security/ksm-action@master
+        with:
+          keeper-secret-config: ${{ secrets.KSM_CONFIG }}
+          secrets: |-
+            v2h4jKiZlJywDSoKzRMnRw/field/Access Token > env:PAT  # Fetch PAT and store in environment variable
+
+      - name: Checkout code
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
-          ssh-key: ${{ secrets.DEPLOY_KEY }}
+
       - name: Set up Python ${{ env.python_version }}
         uses: actions/setup-python@v3
         with:
           python-version: ${{ env.python_version }}
 
-      - name: Format modified python files
+      - name: Format modified Python files
         env:
           filter: ${{ github.event.before }}
         run: |
@@ -49,6 +58,8 @@ jobs:
           done
 
       - name: Commit and push changes
+        env:
+          PAT: ${{ env.PAT }}  # Use PAT fetched from Keeper
         run: |
           HAS_CHANGES=$(git diff --staged --name-only)
           if [ ${#HAS_CHANGES} -gt 0 ]; then
@@ -56,5 +67,6 @@ jobs:
             git config --global user.email "mlcommons-bot@users.noreply.github.com"
             # Commit changes
             git commit -m '[Automated Commit] Format Codebase'
-            git push
-          fi 
+            # Use the PAT to push changes
+            git push https://x-access-token:${PAT}@github.com/${{ github.repository }} HEAD:${{ github.ref_name }}
+          fi

From d6c3a8d9e0c1dfb570733b957e643b5cebd2340e Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 7 Jan 2025 23:22:16 +0000
Subject: [PATCH 220/305] Don't do a loadgen release from dev branch, add
 python3.12,13 to loadgen test, exclude power-checker from auto format (#1994)

* Update generate_final_report.py

* Fix sdxl (#1911)

* Fix typo in fid_score.py, fail_safe for SDXL short runs

* [Automated Commit] Format Codebase

* Fix typo in fid_score.py, fail_safe for SDXL short runs

* Fix dlrmv2 reference implementation | Update run_local.sh

* Fixes for filtering invalid results

* [Automated Commit] Format Codebase

* Update preprocess_submission.py

* Added an option to pass in sample_ids.txt for SDXL accuracy check

* [Automated Commit] Format Codebase

* Update accuracy_coco.py

* [Automated Commit] Format Codebase

* Fix typo

* Not use default for sample_ids.txt

* Update requirements.txt (#1907)

Updating the pip packages

* Fix preprocess_sudbmission for a bug

* Update submission_checker.py | Removed TEST05

* Fix to SDXL accuracy output

* Added exists checks for rmtree in preprocess_submission script

* [Automated Commit] Format Codebase

* Delete .github/workflows/format.yml

* Delete .github/scripts directory

* Update build_wheels.yml | Added src distribution

* Update VERSION.txt

* Update build_wheels.yml

* Update VERSION.txt

* Update pyproject.toml

* Increment version to 4.1.26

* Update MANIFEST.in

* Increment version to 4.1.27

* Update pyproject.toml

* Increment version to 4.1.28

* Update build_wheels.yml

* Update VERSION.txt

* Update accuracy_coco.py

* Making sdxl run thread safe

* Create format.yml | Run format on push instead of PR

* Update backend_pytorch.py | Fix lock usage

* Upgrade loadgen version to 5.0 (#1962)

* Fix loadgen build for version numbers having "0" (#1967)

* Fix loadgen build for version numbers having "0"

* Update test-resnet50.yml

* Update test-retinanet.yml

* Update test-bert.yml

* Increment version to 5.0.1

* Fix Dockerfile for 405B (#1960)

Co-authored-by: Miro <mirhodak@amd.com>

* Add llama3 metrics + remove llama3-99.9 (#1973)

* Fix submission checker for v5.0 rgat (#1974)

* Fix submission checker for v5.0 rgat

* Update submission_checker.py | Updates for v5.0

* [Automated Commit] Format Codebase

* Update submission_checker.py | Fixes latency_constraints for v5.0

* [Automated Commit] Format Codebase

---------

Co-authored-by: mlcommons-bot <mlcommons-bot@users.noreply.github.com>

* Fix test05 seeds missing error for v5.0 submission checker (#1976)

* Fix llama3-405B docker workflow and performance sample count (#1978)

* Fix llama3-405B docker workflow

* Fix the performance sample count from 8312 to 8313

* More fixes

* Increment version to 5.0.2

* Fix submission generation for v5.0 (#1981)

* Fix submission checker for v5.0 rgat

* Fix accuracy pattern for rgat, report-generator for v5.0

* More minor fixes for llama3.1-405b (#1983)

* More minor fixes

* Fix indentation for stats report

* Remove unused rgat files (#1961)

Co-authored-by: Miro <mirhodak@amd.com>

* Update docker GPU, avoid long build time (#1966)

Co-authored-by: Miro <mirhodak@amd.com>

* Require equal issue mode for R-GAT (#1968)

* Require equal issue mode for R-GAT

* Add equal issue note in readme

---------

Co-authored-by: Miro <mirhodak@amd.com>

* Increment version to 5.0.3

* Docs update for r-gat (#1969)

* Fixes #1648, restrict loadgen uncommitted error message to within the loadgen directory

* Update test-rnnt.yml (#1688)

Stopping the github action for rnnt

* Added docs init

Added github action for website publish

Update benchmark documentation

Update publish.yaml

Update publish.yaml

Update benchmark documentation

Improved the submission documentation

Fix taskname

Removed unused images

* Fix benchmark URLs

* Fix links

* Add _full variation to run commands

* Added script flow diagram

* Added docker setup command for CM, extra run options

* Added support for docker options in the docs

* Added --quiet to the CM run_cmds in docs

* Fix the test query count for cm commands

* Support ctuning-cpp implementation

* Added commands for mobilenet models

* Docs cleanup

* Docs cleanup

* Added separate files for dataset and models in the docs

* Remove redundant tab in the docs

* Fixes some WIP models in the docs

* Use the official docs page for CM installation

* Fix the deadlink in docs

* Fix indendation issue in docs

* Added dockerinfo for nvidia implementation

* Added run options for gptj

* Added execution environment tabs

* Cleanup of the docs

* Cleanup of the docs

* Reordered the sections of the docs page

* Removed an unnecessary heading in the docs

* Fixes the commands for datacenter

* Fix the build --sdist for loadgen

* Fixes #1761, llama2 and mixtral runtime error on CPU systems

* Added mixtral to the benchmark list, improved benchmark docs

* Update docs for MLPerf inference v4.1

* Update docs for MLPerf inference v4.1

* Fix typo

* Gave direct link to implementation readmes

* Added tables detailing implementations

* Update vision README.md, split the frameworks into separate rows

* Update README.md

* pointed links to specific frameworks

* pointed links to specific frameworks

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* Update Submission_Guidelines.md

* api support llama2

* Added request module and reduced max token len

* Fix for llama2 api server

* Update SUT_API offline to work for OpenAI

* Update SUT_API.py

* Minor fixes

* Fix json import in SUT_API.py

* Fix llama2 token length

* Added model name verification with server

* clean temp files

* support num_workers in LLAMA2 SUTs

* Remove batching from Offline SUT_API.py

* Update SUT_API.py

* Minor fixes for llama2 API

* Fix for llama2 API

* removed table of contents

* enabled llama2-nvidia + vllm-NM : WIP

* enabled dlrm for intel

* lower cased implementation

* added raw data input

* corrected data download commands

* renamed filename

* changes for bert and vllm

* documentation to work on custom repo and branch

* benchmark index page update

* enabled sdxl for nvidia and intel

* updated vllm server run cmd

* benchmark page information addition

* fix indendation issue

* Added submission categories

* update submission page - generate submission with or w/o using CM for benchmarking

* Updated kits dataset documentation

* Updated model parameters

* updation of information

* updated non cm based benchmark

* added info about hf password

* added links to model and access tokens

* Updated reference results structuree tree

* submission docs cleanup

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* added generic stubs deepsparse

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info

* Some cleanups for benchmark info (FID and CLIP data added)

* typo fix for bert deepsparse framework

* added min system requirements for models

* fixed code version

* changes for displaying reference and intel implementation tip

* added reference to installation page

* updated neural magic documentation

* Added links to the install page, redirect benchmarks page

* added tips about batch size and dataset for nvidia llama2

* fix conditions logic

* modified tips and additional run cmds

* sentence corrections

* Minor fix for the documentation

* fixed bug in deepsparse generic model stubs + styling

* added more information to stubs

* Added SCC24 readme, support reproducibility in the docs

* Made clear the custom CM repo URL format

* Support conditional implementation, setup and run tips

* Support rocm for sdxl

* Fix _short tag support

* Fix install URL

* Expose bfloat16 and float16 options for sdxl

* Expose download model to host option for sdxl

* IndySCC24 documentation added

* Improve the SCC24 docs

* Improve the support of short variation

* Improved the indyscc24 documentation

* Updated scc run commands

* removed test_query_count option for scc

* Remove scc24 in the main docs

* Remove scc24 in the main docs

* Fix docs: indendation issue on the submission page

* generalised code for skipping test query count

* Fixes for SCC24 docs

* Fix scenario text in main.py

* Fix links for scc24

* Fix links for scc24

* Improve the general docs

* Fix links for scc24

* Use float16 in scc24 doc

* Improve scc24 docs

* Improve scc24 docs

* Use float16 in scc24 doc

* fixed command bug

* Fix typo in docs

* Fix typo in docs

* Remove unnecessary indendation in docs

* initial commit for tip - native run CUDA

* Updated tip

* added docker_cm_repo_branch to more run option - docker

* Update docs for IndySCC24

* Support custom repo branch and owner for final report generation

* enabled amd implementation for llama2

* updations for amd - docs

* Fix scenarios in docs page

* formatted the files to pass the gh action

* scenarios -> fixed_scenarios in docs

* [Automated Commit] Format Codebase

* Update indyscc24-bert.md

* Update scc24.md

* updated tip for reference implementation (#1912)

* [Automated Commit] Format Codebase

* fix for run suffix (#1913)

* [Automated Commit] Format Codebase

* Updation for adding submission flow diagram

* Added submission flow diagram

* Update scc24.md

* changes in submission documentation (#1946)

* update results category (#1947)

* changes for adding rgat to docs (#1965)

* Update index.md | Added R-GAT details (WIP)

* Update index.md

* Create system_requirements.yml

* Update system_requirements.yml

* Update system_requirements.yml

* Update system_requirements.yml

---------

Co-authored-by: anandhu-eng <anandhukicks@gmail.com>
Co-authored-by: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>
Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Co-authored-by: Mitchelle Rasquinha <80070689+mrasquinha-g@users.noreply.github.com>
Co-authored-by: Miro <mirhodak@amd.com>

* [Automated Commit] Format Codebase

* Update automated run command section - R-GAT (#1970)

* Update automated run command section

* add cm commands for model and dataset downloads

* Update README.md

* Update cm run cmds

---------

Co-authored-by: Miro <mirhodak@amd.com>

* Unify llama3 names to llama3.1-405b (#1982)

* Unify llama3 names to llama3.1-405b

* Set mlperf.conf name to llama3_1-405b

* Increment version to 5.0.4

* Create test-rgat.yml (#1984)

* Create test-rgat.yml

* Update test-rgat.yml

* Update test-rgat.yml

---------

Co-authored-by: Miro <mirhodak@amd.com>

* Update compliance test table (#1987)

Co-authored-by: Miro <mirhodak@amd.com>

* Create benchmark-checklist.md for r-gat (#1985)

* Create benchmark-checklist.md for r-gat

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

* Update benchmark-checklist.md

---------

Co-authored-by: Miro <mirhodak@amd.com>

* Increment version to 5.0.5

* Added python3.12, 3.13 to loadgen test

* Update format.yml | Don't format power_checker being synced from power-dev repo

* Update index.md | Update accuracy for r-gat

* Update benchmark-checklist.md for r-gat

* Update CM commands in R-GAT README.md

* Update README.md

* Create reset-branch.yml

* Create auto-update-dev.yml

* Tested and fixed SDXL README (#1997)

* Update SDXL README.md, improved CM commands

* Update README.md | Fix SDXL model download path

* Update README.md | Added cm command for downloading coco2014 size.50

* Update README.md | Fix SDXL calibration download command

* Update SDXL README.md

* Update README.md

* Update preprocess_submission.py

* Update README.md

* Update README.md | added the outdirname in the CM command

* Update README.md | added the outdirname in the CM Command

* include cm commands - accuracy and calibration

* Update README.md

* Update README.md | added the outdirname in the CM command

* Update README.md| added outdirname in the CM command

* Support audit.conf with static mlperf.conf

* Support audit.conf with static mlperf.conf

* [Automated Commit] Format Codebase

* Update test_settings_internal.cc | Fix conf_type usage

* Update test_settings_internal.cc

* Fixes to submission checker

* [Automated Commit] Format Codebase

* [Automated Commit] Format Codebase

* Update submission_checker.py | Fix rgat performance_sample_count

* Update evaluate-accuracy.py | Fixes #2008

* Update index.md

* Update index.md

* Update index.md

* Update submission generation steps (WIP)

* add submission generation graphs for local sync and through github repo (#2016)

* add graphs for local sync and through github repo

* Update index.md

* Update index.md

* Update index.md

* Update index.md

* Update index.md

* Update index.md

* Fixes to submission generation docs

* Fixes to submission generation docs

* Added link to the expected results folder structure

* add docs for llama3 + inference version upgrade (#2020)

* add docs for llama3 + inference version upgrade

* add output path and hf token

* Update CM run commands for llama3_1-405b (#2019)

* Update CM run commands for llama3_1-405b

* Update cm commands for llama3

* add information about hf tokens

* Fixes the submission README

* Update README.md

* Create test-submission-generation.yml

* Update test-submission-generation.yml

* Clean invalid model results in preprocess_submission script

* [Automated Commit] Format Codebase

* Fixes the submission README

* Update README.md

* Update README.md

* Update test-submission-generation.yml

---------

Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
Co-authored-by: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Co-authored-by: pgmpablo157321 <pgmpablo157321@users.noreply.github.com>
Co-authored-by: Miro <mirhodak@amd.com>
Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Co-authored-by: mlcommons-bot <mlcommons-bot@users.noreply.github.com>
Co-authored-by: mrmhodak <mrmhodak@users.noreply.github.com>
Co-authored-by: anandhu-eng <anandhukicks@gmail.com>
Co-authored-by: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>
Co-authored-by: Mitchelle Rasquinha <80070689+mrasquinha-g@users.noreply.github.com>
Co-authored-by: sahilavaran <139779393+sahilavaran@users.noreply.github.com>
---
 .github/workflows/auto-update-dev.yml         |  34 +++
 .github/workflows/build_wheels.yml            |   1 -
 .github/workflows/format.yml                  |   2 +-
 .github/workflows/reset-branch.yml            |  42 +++
 .github/workflows/test-loadgen.yml            |   2 +-
 .../workflows/test-submission-generation.yml  |  52 ++++
 .../language/get-llama3_1-405b-data.md        |  41 +++
 docs/benchmarks/language/llama3_1-405b.md     |  13 +
 docs/index.md                                 |   2 +-
 docs/submission/index.md                      | 277 ++++++++++--------
 graph/R-GAT/README.md                         |  78 ++---
 graph/R-GAT/benchmark-checklist.md            |   7 +-
 language/gpt-j/README.md                      |   7 +-
 language/llama3.1-405b/README.md              |  33 ++-
 language/mixtral-8x7b/evaluate-accuracy.py    |   1 +
 loadgen/VERSION.txt                           |   2 +-
 loadgen/bindings/python_api.cc                |   7 +-
 loadgen/loadgen.cc                            |   2 +-
 loadgen/test_settings.h                       |   2 +-
 loadgen/test_settings_internal.cc             |  32 +-
 main.py                                       |  10 +-
 mkdocs.yml                                    |   1 +
 text_to_image/README.md                       |  60 ++--
 tools/submission/README.md                    |   2 +
 tools/submission/preprocess_submission.py     |  14 +-
 tools/submission/submission_checker.py        |  56 ++--
 tools/submission/truncate_accuracy_log.py     |   2 +-
 27 files changed, 535 insertions(+), 247 deletions(-)
 create mode 100644 .github/workflows/auto-update-dev.yml
 create mode 100644 .github/workflows/reset-branch.yml
 create mode 100644 .github/workflows/test-submission-generation.yml
 create mode 100644 docs/benchmarks/language/get-llama3_1-405b-data.md
 create mode 100644 docs/benchmarks/language/llama3_1-405b.md

diff --git a/.github/workflows/auto-update-dev.yml b/.github/workflows/auto-update-dev.yml
new file mode 100644
index 0000000000..69cfdb2813
--- /dev/null
+++ b/.github/workflows/auto-update-dev.yml
@@ -0,0 +1,34 @@
+name: Auto-Update Dev Branch from Master
+
+on:
+  push:
+    branches:
+      - master  # Trigger workflow on commits to 'dev' branch
+
+jobs:
+  update-main:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write  # Required to push to protected branches
+
+    steps:
+      - name: Checkout Main Branch
+        uses: actions/checkout@v4
+        with:
+          ref: dev
+          fetch-depth: 0
+          ssh-key: ${{ secrets.DEPLOY_KEY }}
+
+      - name: Configure Git User
+        run: |
+          git config user.name "github-actions"
+          git config user.email "github-actions@github.com"
+
+      - name: Merge auto-update into dev
+        run: |
+          git fetch origin master:master
+          git merge --no-ff master -m "Auto-merge updates from master branch"
+
+      - name: Push Changes to Main
+        run: |
+          git push origin dev
diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 6f67f56de3..91282ec9c7 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -7,7 +7,6 @@ on:
     branches:
       - master
       - loadgen-release
-      - dev
     paths:
       - loadgen/**
 
diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index dbf9a78bbe..2ed014d04c 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -38,7 +38,7 @@ jobs:
           for FILE in $(git diff --name-only $filter | grep -E '.*\.py$')
           do
             # Check if the file still exists in the working tree
-            if [ -f "$FILE" ]; then
+            if [ -f "$FILE" ] && [ "$FILE" != "tools/submission/power/power_checker.py" ]; then
               autopep8 --in-place -a "$FILE"
               git add "$FILE"
             fi
diff --git a/.github/workflows/reset-branch.yml b/.github/workflows/reset-branch.yml
new file mode 100644
index 0000000000..76cf0b97eb
--- /dev/null
+++ b/.github/workflows/reset-branch.yml
@@ -0,0 +1,42 @@
+name: Reset Current Branch to Upstream After Squash Merge
+
+on:
+  workflow_dispatch:
+    inputs:
+      branch:
+        description: 'Branch to reset (leave blank for current branch)'
+        required: false
+        default: 'dev'
+
+jobs:
+  reset-branch:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Detect Current Branch
+        if: ${{ inputs.branch == '' }}
+        run: echo "branch=$(git rev-parse --abbrev-ref HEAD)" >> $GITHUB_ENV
+
+      - name: Use Input Branch
+        if: ${{ inputs.branch != '' }}
+        run: echo "branch=${{ inputs.branch }}" >> $GITHUB_ENV
+
+      - name: Add Upstream Remote
+        run: |
+          git remote add upstream https://github.com/mlcommons/inference.git
+          git fetch upstream
+      - name: Reset Branch to Upstream
+        run: |
+          git checkout ${{ env.branch }}
+          git reset --hard upstream/${{ env.branch }}
+        if: success()
+
+      - name: Force Push to Origin
+        run: |
+          git push origin ${{ env.branch }} --force-with-lease
+        if: success()
diff --git a/.github/workflows/test-loadgen.yml b/.github/workflows/test-loadgen.yml
index 8e42f625ec..d73d5913bd 100755
--- a/.github/workflows/test-loadgen.yml
+++ b/.github/workflows/test-loadgen.yml
@@ -21,7 +21,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
 
     steps:
     - uses: actions/checkout@v3
diff --git a/.github/workflows/test-submission-generation.yml b/.github/workflows/test-submission-generation.yml
new file mode 100644
index 0000000000..97afc58cdd
--- /dev/null
+++ b/.github/workflows/test-submission-generation.yml
@@ -0,0 +1,52 @@
+# This workflow will test the submission generation using MLPerf Automation 
+
+name: CM based Submission Generation
+
+on:
+  pull_request:
+    branches: [ "master", "dev" ]
+    paths:
+      - '.github/workflows/test-submission-generation.yml'
+      - '**'  
+      - '!**.md'
+jobs:
+  submission_generation:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: [ "3.12" ]
+        division: ["closed", "open", "closed-open"]
+        category: ["datacenter", "edge"]
+        case: ["closed"]
+        action: ["run", "docker"]
+        exclude:
+          - os: macos-latest
+          - os: windows-latest
+          - category: "edge"
+            
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        pip install cm4mlops
+    - name: Pull repo where test cases are uploaded
+      run: |
+        git clone -b submission-generation-examples https://github.com/mlcommons/inference.git submission_generation_examples
+    - name: Run Submission Generation - ${{ matrix.case }} ${{ matrix.action }} ${{ matrix.category }} ${{ matrix.division }} 
+      continue-on-error: true
+      run: |
+        if [ "${{ matrix.case }}" == "closed" ]; then
+          description="Test submission - contains closed edge and datacenter"
+        elif [ "${{ matrix.case }}" == "closed-power" ]; then
+          description="Test submission - contains closed-power edge and datacenter results"
+        fi
+        # Dynamically set the log group to simulate a dynamic step name
+        echo "::group::$description"
+        cm ${{ matrix.action }} script --tags=generate,inference,submission  --adr.compiler.tags=gcc --version=v5.0 --clean --preprocess_submission=yes --submission_base_dir=mysubmissions --results_dir=$PWD/submission_generation_tests/${{ matrix.case }}/ --run-checker --submitter=MLCommons --tar=yes --division=${{ matrix.division }} --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes --quiet
+        cm ${{ matrix.action }} script --tags=run,submission,checker --submitter_id_off=mysubmitter_id --tar=yes --submission_dir=mysubmissions/submissions --submission_tar_file=mysubmission.tar.gz
diff --git a/docs/benchmarks/language/get-llama3_1-405b-data.md b/docs/benchmarks/language/get-llama3_1-405b-data.md
new file mode 100644
index 0000000000..7333be64dd
--- /dev/null
+++ b/docs/benchmarks/language/get-llama3_1-405b-data.md
@@ -0,0 +1,41 @@
+---
+hide:
+  - toc
+---
+
+# Text Summarization using LLAMA3.1-405b
+
+## Dataset
+
+The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
+
+=== "Validation"
+
+    ### Get Validation Dataset
+    ```
+    cm run script --tags=get,dataset,mlperf,inference,llama3,_validation --outdirname=<path to download> -j
+    ```
+    
+=== "Calibration"
+
+    ### Get Calibration Dataset
+    ```
+    cm run script --tags=get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+Get the Official MLPerf LLAMA3.1-405b Model
+
+=== "Pytorch"
+
+    ### Pytorch
+    ```
+    cm run script --tags=get,ml-model,llama3 --outdirname=<path to download> --hf_token=<huggingface access token> -j
+    ```
+  
+!!! tip
+
+    Downloading llama3.1-405B model from Hugging Face will require an [**access token**](https://huggingface.co/settings/tokens) which could be generated for your account. Additionally, ensure that your account has access to the [llama3.1-405B](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) model. 
+
diff --git a/docs/benchmarks/language/llama3_1-405b.md b/docs/benchmarks/language/llama3_1-405b.md
new file mode 100644
index 0000000000..8163bb1e8e
--- /dev/null
+++ b/docs/benchmarks/language/llama3_1-405b.md
@@ -0,0 +1,13 @@
+---
+hide:
+  - toc
+---
+
+# Text Summarization using LLAMA3_1-405b
+
+=== "MLCommons-Python"
+    ## MLPerf Reference Implementation in Python
+    
+{{ mlperf_inference_implementation_readme (4, "llama3_1-405b-99", "reference", devices=["CPU","CUDA"]) }}
+
+{{ mlperf_inference_implementation_readme (4, "llama3_1-405b-99.9", "reference", devices=["CPU","CUDA"]) }}
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
index b46d4c2749..db9e3e4404 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -163,7 +163,7 @@ The currently valid [MLPerf Inference Benchmarks](index_gh.md) as of MLPerf infe
     - **Dataset Size**: 788,379
     - **QSL Size**: 788,379
 - **Number of Parameters**: 
-- **Reference Model Accuracy**: ACC = ?
+- **Reference Model Accuracy**: ACC = 72.86%
 - **Server Scenario Latency Constraint**: N/A
 - **Equal Issue mode**: True
 - **High accuracy variant**: No
diff --git a/docs/submission/index.md b/docs/submission/index.md
index 1050f5fb0e..64ef5afa79 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -3,33 +3,17 @@ hide:
   - toc
 ---
 
-<p align="center">
-  <img src="../img/submission-flow.png" alt="Submission Generation Flow">
-</p>
+Click [here](https://docs.google.com/presentation/d/1cmbpZUpVr78EIrhzyMBnnWnjJrD-mZ2vmSb-yETkTA8/edit?usp=sharing) to view the proposal slide for Common Automation for MLPerf Inference Submission Generation through CM.
 
-<p align="center"><em>Figure: MLPerf Inference Submission Generation Flow</em></p>
-
-<!--![Submission Generation Flow](../img/submission-flow.png)-->
-
-Click [here](https://youtu.be/eI1Hoecc3ho) to view the recording of the workshop: Streamlining your MLPerf Inference results using CM.
-
-Click [here](https://docs.google.com/presentation/d/1cmbpZUpVr78EIrhzyMBnnWnjJrD-mZ2vmSb-yETkTA8/edit?usp=sharing) to view the prposal slide for Common Automation for MLPerf Inference Submission Generation through CM.
-
-=== "CM based results"
-    If you have followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, all the valid results will get aggregated to the `cm cache` folder. The following command could be used to browse the structure of inference results folder generated by CM.
-    ### Get results folder structure
-    ```bash
-    cm find cache --tags=get,mlperf,inference,results,dir | xargs tree
-    ```
-=== "Non CM based results"
-    If you have not followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, please make sure that the result directory is structured in the following way. 
+=== "Custom automation based MLPerf results"
+    If you have not followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, please make sure that the result directory is structured in the following way. You can see the real examples for the expected folder structure [here](https://github.com/mlcommons/inference/tree/submission-generation-examples).
     ```
     └── System description ID(SUT Name)
         ├── system_meta.json
         └── Benchmark
             └── Scenario
                 ├── Performance
-                |   └── run_x/#1 run for all scenarios
+                |   └── run_1 run for all scenarios
                 |       ├── mlperf_log_summary.txt
                 |       └── mlperf_log_detail.txt
                 ├── Accuracy
@@ -42,13 +26,13 @@ Click [here](https://docs.google.com/presentation/d/1cmbpZUpVr78EIrhzyMBnnWnjJrD
                 |   |   └── run_x/#1 run for all scenarios
                 |   |       ├── mlperf_log_summary.txt
                 |   |       └── mlperf_log_detail.txt
-                |   ├── Accuracy
-                |   |   ├── baseline_accuracy.txt
-                |   |   ├── compliance_accuracy.txt
+                |   ├── Accuracy # for TEST01 only
+                |   |   ├── baseline_accuracy.txt (if test fails in deterministic mode)
+                |   |   ├── compliance_accuracy.txt (if test fails in deterministic mode)
                 |   |   ├── mlperf_log_accuracy.json
                 |   |   └── accuracy.txt
                 |   ├── verify_performance.txt
-                |   └── verify_accuracy.txt #for TEST01 only
+                |   └── verify_accuracy.txt # for TEST01 only
                 |── user.conf
                 └── measurements.json
     ```
@@ -67,99 +51,160 @@ Click [here](https://docs.google.com/presentation/d/1cmbpZUpVr78EIrhzyMBnnWnjJrD
     ```
     </details>
 
-Once all the results across all the models are ready you can use the following command to generate a valid submission tree compliant with the [MLPerf requirements](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc#inference-1).
-
-## Generate actual submission tree
-
-=== "Docker run"
-    ### Docker run
-    === "Closed"
-        ### Closed Submission
-        ```bash
-        cm docker script --tags=generate,inference,submission \
-            --clean \
-            --preprocess_submission=yes \
-            --run-checker \
-            --submitter=MLCommons \
-            --tar=yes \
-            --env.CM_TAR_OUTFILE=submission.tar.gz \
-            --division=closed \
-            --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-            --quiet
-        ```
-
-    === "Open"
-        ### Open Submission
-        ```bash
-        cm docker script --tags=generate,inference,submission \
-            --clean \
-            --preprocess_submission=yes \
-            --run-checker \
-            --submitter=MLCommons \
-            --tar=yes \
-            --env.CM_TAR_OUTFILE=submission.tar.gz \
-            --division=open \
-            --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-            --quiet
-        ```
-
-=== "Native run"
-    ### Native run
-    === "Closed"
-        ### Closed Submission
-        ```bash
-        cm run script --tags=generate,inference,submission \
-            --clean \
-            --preprocess_submission=yes \
-            --run-checker \
-            --submitter=MLCommons \
-            --tar=yes \
-            --env.CM_TAR_OUTFILE=submission.tar.gz \
-            --division=closed \
-            --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-            --quiet
-        ```
-
-    === "Open"
-        ### Open Submission
-        ```bash
-        cm run script --tags=generate,inference,submission \
-            --clean \
-            --preprocess_submission=yes \
-            --run-checker \
-            --submitter=MLCommons \
-            --tar=yes \
-            --env.CM_TAR_OUTFILE=submission.tar.gz \
-            --division=open \
-            --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
-            --quiet
-        ```
-
-* Use `--hw_name="My system name"` to give a meaningful system name. Examples can be seen [here](https://github.com/mlcommons/inference_results_v3.0/tree/main/open/cTuning/systems)
-
-* Use `--submitter=<Your name>` if your organization is an official MLCommons member and would like to submit under your organization
-
-* Use `--hw_notes_extra` option to add additional notes like `--hw_notes_extra="Result taken by NAME" `
-
-* Use `--results_dir` option to specify the results folder for Non CM based benchmarks
-
-* Use `--category` option to specify the category for which submission is generated(datacenter/edge). By default, the category is taken from `system_meta.json` file located in the SUT root directory.
-
-* Use `--submission_base_dir` to specify the directory to which outputs from preprocess submission script and final submission is to be dumped. No need to provide `--submission_dir` along with this. For `docker run`, use `--submission_base_dir` instead of `--submission_dir`.
-
-The above command should generate "submission.tar.gz" if there are no submission checker issues and you can upload it to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission).
-
-## Aggregate Results in GitHub
-
-If you are collecting results across multiple systems you can generate different submissions and aggregate all of them to a GitHub repository (can be private) and use it to generate a single tar ball which can be uploaded to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission). 
-
-Run the following command after **replacing `--repo_url` with your GitHub repository URL**.
+=== "CM automation based results"
+    If you have followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, all the valid results will get aggregated to the `cm cache` folder. The following command could be used to browse the structure of inference results folder generated by CM.
+    ### Get results folder structure
+    ```bash
+    cm find cache --tags=get,mlperf,inference,results,dir | xargs tree
+    ```
+
+
+Once all the results across all the models are ready you can use the following the below section to generate a valid submission tree compliant with the [MLPerf requirements](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc#inference-1).
+
+## Generate submission folder
+
+The submission generation flow is explained in the below diagram
+
+```mermaid
+flowchart LR
+    subgraph Generation [Submission Generation SUT1]
+      direction TB
+      A[populate system details] --> B[generate submission structure]
+      B --> C[truncate-accuracy-logs]
+      C --> D{Infer low talency results <br>and/or<br> filter out invalid results}
+      D --> yes --> E[preprocess-mlperf-inference-submission]
+      D --> no --> F[run-mlperf-inference-submission-checker]
+      E --> F
+    end
+    Input((Results SUT1)) --> Generation
+    Generation --> Output((Submission Folder <br> SUT1))
+```
+
+### Command to generate submission folder
 
 ```bash
-cm run script --tags=push,github,mlperf,inference,submission \
-   --repo_url=https://github.com/GATEOverflow/mlperf_inference_submissions_v4.1 \
-   --commit_message="Results on <HW name> added by <Name>" \
-   --quiet
+cm run script --tags=generate,inference,submission \
+  --clean \
+  --preprocess_submission=yes \
+  --run-checker=yes \
+  --submitter=MLCommons \
+  --division=closed \
+  --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+  --quiet
 ```
+!!! tip
+    * Use `--hw_name="My system name"` to give a meaningful system name. Examples can be seen [here](https://github.com/mlcommons/inference_results_v3.0/tree/main/open/cTuning/systems)
+
+    * Use `--submitter=<Your name>` if your organization is an official MLCommons member and would like to submit under your organization
+
+    * Use `--hw_notes_extra` option to add additional notes like `--hw_notes_extra="Result taken by NAME" `
+
+    * Use `--results_dir` option to specify the results folder.  It is automatically taken from CM cache for MLPerf automation based runs
+
+    * Use `--submission_dir` option to specify the submission folder. (You can avoid this if you're pushing to github or only running a single SUT and CM will use its cache folder)
+
+    * Use `--division=open` for open division submission 
+
+    * Use `--category` option to specify the category for which submission is generated(datacenter/edge). By default, the category is taken from `system_meta.json` file located in the SUT root directory.
+
+    * Use `--submission_base_dir` to specify the directory to which the outputs from preprocess submission script and final submission is added. No need to provide `--submission_dir` along with this. For `docker run`, use `--submission_base_dir` instead of `--submission_dir`.
+
+
+If there are multiple systems where MLPerf results are collected, the same process needs to be repeated on each of them. One we have submission folders on all the SUTs, we need to sync them to make a single submission folder
+
+=== "Sync Locally"
+    If you are having results in multiple systems, you need to merge them to one system. You can use `rsync` for this. For example, the below command will sync the submission folder from SUT2 to the one in SUT1. 
+    ```
+    rsync -avz username@host1:<path_to_submission_folder2>/ <path_to_submission_folder1>/
+    ```
+    Same needs to be repeated for all other SUTs so that we have the full submissions in SUT1.
+
+    ```mermaid
+        flowchart LR
+            subgraph SUT1 [Submission Generation SUT1]
+              A[Submission Folder SUT1]
+            end
+            subgraph SUT2 [Submission Generation SUT2]
+              B[Submission Folder SUT2]
+            end
+            subgraph SUT3 [Submission Generation SUT3]
+              C[Submission Folder SUT3]
+            end
+            subgraph SUTN [Submission Generation SUTN]
+              D[Submission Folder SUTN]
+            end
+            SUT2 --> SUT1
+            SUT3 --> SUT1
+            SUTN --> SUT1
+           
+    ```
+
+=== "Sync via a Github repo"
+    If you are collecting results across multiple systems you can generate different submissions and aggregate all of them to a GitHub repository (can be private) and use it to generate a single tar ball which can be uploaded to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission). 
+
+    Run the following command after **replacing `--repo_url` with your GitHub repository URL**.
+
+    ```bash
+    cm run script --tags=push,github,mlperf,inference,submission \
+       --repo_url=https://github.com/mlcommons/mlperf_inference_submissions_v5.0 \
+       --commit_message="Results on <HW name> added by <Name>" \
+       --quiet
+    ```
+    
+    ```mermaid
+        flowchart LR
+            subgraph SUT1 [Submission Generation SUT1]
+              A[Submission Folder SUT1]
+            end
+            subgraph SUT2 [Submission Generation SUT2]
+              B[Submission Folder SUT2]
+            end
+            subgraph SUT3 [Submission Generation SUT3]
+              C[Submission Folder SUT3]
+            end
+            subgraph SUTN [Submission Generation SUTN]
+              D[Submission Folder SUTN]
+            end
+	    SUT2 -- git sync and push --> G[Github Repo]
+	    SUT3 -- git sync and push --> G[Github Repo]
+	    SUTN -- git sync and push --> G[Github Repo]
+	    SUT1 -- git sync and push --> G[Github Repo]
+           
+    ```
+
+## Upload the final submission
+    
+!!! warning
+    If you are using GitHub for consolidating your results, make sure that you have run the [`push-to-github` command](#__tabbed_2_2) on the same system to ensure results are synced as is on the GitHub repository.
+
+Once you have all the results on the system, you can upload them to the MLCommons submission server as follows:
+
+=== "via CLI"
+    You can do the following command which will run the submission checker and upload the results to the MLCommons submission server
+    ```
+    cm run script --tags=run,submission,checker \
+    --submitter_id=<> \
+    --submission_dir=<Path to the submission folder>
+    ```
+=== "via Browser"
+    You can do the following command to generate the final submission tar file and then upload to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission). 
+    ```
+    cm run script --tags=run,submission,checker \
+    --submission_dir=<Path to the submission folder> \
+    --tar=yes \
+    --submission_tar_file=mysubmission.tar.gz
+    ```
+    
+```mermaid
+        flowchart LR
+            subgraph SUT [Combined Submissions]
+              A[Combined Submission Folder in SUT1]
+            end
+	    SUT --> B[Run submission checker]
+	    B --> C[Upload to MLC Submission server]
+	    C --> D[Receive validation email]
+```
+
+
 
-At the end, you can download the github repo and upload to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission).
+<!--Click [here](https://youtu.be/eI1Hoecc3ho) to view the recording of the workshop: Streamlining your MLPerf Inference results using CM.-->
diff --git a/graph/R-GAT/README.md b/graph/R-GAT/README.md
index 4c31b42aa4..561a65e6f7 100644
--- a/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -19,7 +19,9 @@ This is the reference implementation for MLPerf Inference Graph Neural Network.
 
 ## Automated command to run the benchmark via MLCommons CM
 
-Please check the official inference documentation [here](https://docs.mlcommons.org/inference/benchmarks/graph/rgat/)
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/graph/rgat/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
+You can also do `pip install cm4mlops` and then use `cm` commands for downloading the model and datasets using the commands given in the later sections.
  
 ## Setup
 Set the following helper variables
@@ -33,10 +35,7 @@ export MODEL_PATH=$PWD/inference/graph/R-GAT/model/
 ```bash
 git clone --recurse-submodules https://github.com/mlcommons/inference.git --depth 1
 ```
-Finally copy the `mlperf.conf` file to the stable diffusion folder
-```bash
-cp $ROOT_INFERENCE/mlperf.conf $GRAPH_FOLDER
-```
+
 
 ### Install pytorch
 **For NVIDIA GPU based runs:**
@@ -77,6 +76,13 @@ pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/cu121/repo.html
 pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html
 ```
 
+
+### Download model through CM (Collective Minds)
+
+```
+cm run script --tags=get,ml-model,rgat --outdirname=<path_to_download>
+```
+
 ### Download model using Rclone
 
 To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
@@ -95,15 +101,16 @@ You can then navigate in the terminal to your desired download directory and run
 rclone copy mlc-inference:mlcommons-inference-wg-public/R-GAT/RGAT.pt $MODEL_PATH -P
 ```
 
-### Download model through CM (Collective Minds)
 
-```
-cm run script --tags=get,ml-model,rgat -j
-```
 
 ### Download and setup dataset
 #### Debug Dataset
 
+**CM Command**
+```
+cm run script --tags=get,dataset,igbh,_debug --outdirname=<path to download>
+```
+
 **Download Dataset**
 ```bash
 cd $GRAPH_FOLDER
@@ -116,13 +123,16 @@ cd $GRAPH_FOLDER
 python3 tools/split_seeds.py --path igbh --dataset_size tiny
 ```
 
+
+
+#### Full Dataset
+**Warning:** This script will download 2.2TB of data
+
 **CM Command**
 ```
-cm run script --tags=get,dataset,igbh,_debug -j
+cm run script --tags=get,dataset,igbh,_full --outdirname=<path to download>
 ```
 
-#### Full Dataset
-**Warning:** This script will download 2.2TB of data 
 ```bash
 cd $GRAPH_FOLDER
 ./tools/download_igbh_full.sh igbh/
@@ -134,16 +144,15 @@ cd $GRAPH_FOLDER
 python3 tools/split_seeds.py --path igbh --dataset_size full
 ```
 
-**CM Command**
-```
-cm run script --tags=get,dataset,igbh,_full -j
-```
-
 
 #### Calibration dataset
 
 The calibration dataset contains 5000 nodes from the training paper nodes of the IGBH dataset. We provide the [Node ids](../../calibration/IGBH/calibration.txt) and the [script](tools/split_seeds.py) to generate them (using the `--calibration` flag). 
 
+**CM Command**
+```
+cm run script --tags=get,dataset,igbh,_full,_calibration --outdirname=<path to download>
+```
 
 ### Run the benchmark
 #### Debug Run
@@ -155,20 +164,6 @@ cd $GRAPH_FOLDER
 python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
-##### Debug Run using CM
-```
-cm run script --tags=run-mlperf,inference,_submission,_short,_r5.0-dev \
-   --model=rgat \
-   --implementation=reference \
-   --framework=pytorch \
-   --category=edge \
-   --scenario=Offline \
-   --execution_mode=test \
-   --device=<cpu or cuda> \
-   --quiet \
-   --test_query_count=10 \
-   --docker
-```
 
 #### Local run
 ```bash
@@ -179,25 +174,12 @@ cd $GRAPH_FOLDER
 python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
-##### Local Run using CM
-```
-cm run script --tags=run-mlperf,inference,_submission,_full,_r5.0-dev \
-   --model=rgat \
-   --implementation=reference \
-   --framework=pytorch \
-   --category=edge \
-   --scenario=Offline \
-   --execution_mode=test \
-   --device=<>cpu or cuda> \
-   --quiet \
-   --test_query_count=10 \
-   --docker
+### Evaluate the accuracy
+```bash
+cm run script --tags=process,mlperf,accuracy,_igbh --result_dir=<Path to directory where files are generated after the benchmark run>
 ```
 
-- Number of threads could be adjusted using `--threads=#`, where # is the desired number of threads. This option works only if the implementation in use supports threading.
-- Batch size could be adjusted using `--batch_size=#`, where # is the desired batch size. This option works only if the implementation in use is supporting the given batch size.
-- Add `--env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset>` if you have already downloaded the dataset. The path will be automatically mounted when using docker run.
-- Add `--env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint>` if you have already downloaded the model. The path will be automatically mounted when using docker run.
+Please click [here](https://github.com/mlcommons/inference/blob/dev/graph/R-GAT/tools/accuracy_igbh.py) to view the Python script for evaluating accuracy for the IGBH dataset.
 
 #### Run using docker
 
diff --git a/graph/R-GAT/benchmark-checklist.md b/graph/R-GAT/benchmark-checklist.md
index 2e76acb992..f83c816cb6 100644
--- a/graph/R-GAT/benchmark-checklist.md
+++ b/graph/R-GAT/benchmark-checklist.md
@@ -21,7 +21,7 @@
 
 #### **5. Validation Dataset: Unique Samples**
 Number of **unique samples** in the validation dataset and the QSL size specified in 
-- [ ] [inference policies benchmark section](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc#41-benchmarks)
+- [X] [inference policies benchmark section](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc#41-benchmarks)
 - [X] [mlperf.conf](https://github.com/mlcommons/inference/blob/master/loadgen/mlperf.conf)
 - [X] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
   *(Ensure QSL size overflows the system cache if possible.)*
@@ -37,12 +37,13 @@ Documented whether **Equal Issue Mode** is applicable in
 ---
 
 #### **7. Expected Accuracy and `accuracy.txt` Contents**
-- [ ] Detailed expected accuracy and the required contents of the `accuracy.txt` file.
+- [X] Expected accuracy updated in the [inference policies](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc#41-benchmarks)
+- [X] `accuracy.txt` file generated by the reference accuracy script from the MLPerf accuracy log and is validated by the submission checker.
 
 ---
 
 #### **8. Reference Model Details**
-- [ ] Reference model details updated in [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)  
+- [X] Reference model details updated in [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)  
 
 ---
 
diff --git a/language/gpt-j/README.md b/language/gpt-j/README.md
index 9dc024a8e1..7653176357 100644
--- a/language/gpt-j/README.md
+++ b/language/gpt-j/README.md
@@ -2,6 +2,10 @@
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/gpt-j) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/gpt-j/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
+
+
 ### Setup Instructions
 
 ```bash
@@ -75,8 +79,7 @@ Please download the fine-tuned GPT-J checkpoint using the instructions below. Th
 The following MLCommons CM commands can be used to programmatically download the model checkpoint. 
 
 ```
-pip install cm4mlops
-cm run script --tags=get,ml-model,gptj,_pytorch,_rclone -j
+cm run script --tags=get,ml-model,gptj,_pytorch,_rclone ---outdirname =./model -P
 ```
 
 #### Manual method
diff --git a/language/llama3.1-405b/README.md b/language/llama3.1-405b/README.md
index d1dd5ad4f7..ea358bb981 100644
--- a/language/llama3.1-405b/README.md
+++ b/language/llama3.1-405b/README.md
@@ -9,6 +9,11 @@
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama3.1-405b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
+## Automated command to run the benchmark via MLCommons CM
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama3_1-405b/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
+You can also do pip install cm4mlops and then use cm commands for downloading the model and datasets using the commands given in the later sections.
 
 ## Prepare environment
 
@@ -109,6 +114,15 @@ git clone https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct ${CHECKPOINT
 cd ${CHECKPOINT_PATH} && git checkout be673f326cab4cd22ccfef76109faf68e41aa5f1
 ```
 
+### Download model through CM (Collective Mind)
+
+```
+cm run script --tags=get,ml-model,llama3 --outdirname=${CHECKPOINT_PATH} --hf_token=<huggingface access token> -j
+```
+
+**Note:**
+Downloading llama3.1-405B model from Hugging Face will require an [**access token**](https://huggingface.co/settings/tokens) which could be generated for your account. Additionally, ensure that your account has access to the [llama3.1-405B](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) model. 
+
 ## Get Dataset
 
 ### Preprocessed
@@ -129,6 +143,11 @@ You can then navigate in the terminal to your desired download directory and run
 ```
 rclone copy mlc-inference:mlcommons-inference-wg-public/llama3.1_405b/mlperf_llama3.1_405b_dataset_8313_processed_fp16_eval.pkl ./ -P
 ```
+**CM Command**
+
+```
+cm run script --tags=get,dataset,mlperf,inference,llama3,_validation --outdirname=<path to download> -j
+```
 
 You can also download the calibration dataset from the Cloudflare R2 bucket by running the following command:
 
@@ -136,6 +155,12 @@ You can also download the calibration dataset from the Cloudflare R2 bucket by r
 rclone copy mlc-inference:mlcommons-inference-wg-public/llama3.1_405b/mlperf_llama3.1_405b_calibration_dataset_512_processed_fp16_eval.pkl ./ -P
 ```
 
+**CM Command**
+```
+cm run script --tags=get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
+```
+
+
 ## Run Performance Benchmarks
 
 ### Offline
@@ -169,7 +194,6 @@ python -u main.py --scenario Server \
 
 The ServerSUT was not tested for GPU runs.
 
-
 ## Run Accuracy Benchmarks
 
 ### Offline
@@ -201,7 +225,6 @@ fi
 For the GPU run - The above steps have been automated in `run_accuracy.sh`. You can also modify this script to use
 `--device cpu` to adapt it to a CPU-only run.
 
-
 ### Server
 ```
 OUTPUT_LOG_DIR=server-accuracy-logs
@@ -218,7 +241,6 @@ python -u main.py --scenario Server \
                 --tensor-parallel-size ${GPU_COUNT} \
                 --vllm
 
-
 ACCURACY_LOG_FILE=${OUTPUT_LOG_DIR}/mlperf_log_accuracy.json
 if [ -e ${ACCURACY_LOG_FILE} ]; then
         python evaluate-accuracy.py --checkpoint-path ${CHECKPOINT_PATH} \
@@ -228,6 +250,11 @@ fi
 
 The ServerSUT was not tested for GPU runs.
 
+### Evaluate the accuracy using CM
+You can also evaulate the accuracy from the generated accuracy log by using the following CM command
+```
+cm run script --tags=process,mlperf,accuracy,_dataset_llama3 --result_dir=<Path to accuracy log directory>
+```
 
 ## Accuracy Target
 Running the GPU implementation in FP16 precision resulted in the following FP16 accuracy targets:
diff --git a/language/mixtral-8x7b/evaluate-accuracy.py b/language/mixtral-8x7b/evaluate-accuracy.py
index 3ea79cea89..74485d569b 100644
--- a/language/mixtral-8x7b/evaluate-accuracy.py
+++ b/language/mixtral-8x7b/evaluate-accuracy.py
@@ -121,6 +121,7 @@ def main():
     checkpoint_path = args.checkpoint_path
     metric = evaluate.load("rouge")
     nltk.download("punkt")
+    nltk.download("punkt_tab")
 
     tokenizer = AutoTokenizer.from_pretrained(
         checkpoint_path,
diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 2d6c0bcf19..ab0fa336dd 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.4
+5.0.5
diff --git a/loadgen/bindings/python_api.cc b/loadgen/bindings/python_api.cc
index 7f50f5f567..96396dab92 100644
--- a/loadgen/bindings/python_api.cc
+++ b/loadgen/bindings/python_api.cc
@@ -348,10 +348,11 @@ PYBIND11_MODULE(mlperf_loadgen, m) {
                      &TestSettings::token_latency_scaling_factor)
       .def("FromConfig", &TestSettings::FromConfig, pybind11::arg("path"),
            pybind11::arg("model"), pybind11::arg("scenario"),
-           pybind11::arg("is_mlperf_conf") = false,
+           pybind11::arg("conf_type") = 1,
            "This function configures settings from the given user "
-           "configuration file, model, and scenario. The is_mlperf_conf flag "
-           "should be set to false or else only the default mlperf_conf file "
+           "configuration file, model, and scenario. The conf_type flag "
+           "should be set to 1 for loading user.conf or else only the default "
+           "mlperf_conf file "
            "will be loaded by the loadgen.");
 
   pybind11::enum_<LoggingMode>(m, "LoggingMode")
diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index beda3a6c40..c731f1a8da 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -1228,7 +1228,7 @@ void StartTest(SystemUnderTest* sut, QuerySampleLibrary* qsl,
     RemoveValue(&audit_scenario, ' ');
     const std::string generic_model = "*";
     test_settings.FromConfig(audit_config_filename, generic_model,
-                             audit_scenario);
+                             audit_scenario, 2);
   }
   if (test_settings.test05) {
     // If the configuration indicates we are running test05,
diff --git a/loadgen/test_settings.h b/loadgen/test_settings.h
index 739b2947f9..584d073bb8 100644
--- a/loadgen/test_settings.h
+++ b/loadgen/test_settings.h
@@ -237,7 +237,7 @@ struct TestSettings {
 
   /// \brief Load mlperf parameter config from file.
   int FromConfig(const std::string &path, const std::string &model,
-                 const std::string &scenario, bool is_mlperf_conf = false);
+                 const std::string &scenario, int conf_type = 1);
   /**@}*/
 
   // ==================================
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 1a7387f595..f654948f3f 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -520,15 +520,15 @@ void TestSettingsInternal::LogSummary(AsyncSummary &summary) const {
 }  // namespace loadgen
 
 int TestSettings::FromConfig(const std::string &path, const std::string &model,
-                             const std::string &scenario, bool is_mlperf_conf) {
+                             const std::string &scenario, int conf_type) {
   std::map<std::string, std::string> kv;
   static int configCount = 0;
 
-  if (!is_mlperf_conf) {
+  if (conf_type == 1) {
     if (configCount == 0) {
       // Only allow userConf as the single configFile and loadgen loads the
-      // mlperfConf automatically
-      FromConfig("", model, scenario, true);
+      // mlperfConf automatically for perf and accuracy runs
+      FromConfig("", model, scenario, 0);
     }
 
     else {
@@ -586,7 +586,7 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
   std::unique_ptr<std::istream> fss;
   std::string line;
 
-  if (!is_mlperf_conf) {
+  if (conf_type != 0) {
     // dirt simple config parser
     fss = std::make_unique<std::ifstream>(path);
     if (!static_cast<std::ifstream *>(fss.get())->is_open()) {
@@ -691,20 +691,17 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
         break;
     }
   }
-  if (is_mlperf_conf) {
+
+  if (conf_type == 0) {
     lookupkv(model, scenario, "qsl_rng_seed", &qsl_rng_seed, nullptr);
     lookupkv(model, scenario, "sample_index_rng_seed", &sample_index_rng_seed,
              nullptr);
     lookupkv(model, scenario, "schedule_rng_seed", &schedule_rng_seed, nullptr);
-    lookupkv(model, scenario, "accuracy_log_rng_seed", &accuracy_log_rng_seed,
-             nullptr);
-    lookupkv(model, scenario, "accuracy_log_probability", nullptr,
-             &accuracy_log_probability, 0.01);
-    lookupkv(model, scenario, "accuracy_log_sampling_target",
-             &accuracy_log_sampling_target, nullptr);
     if (lookupkv(model, scenario, "sample_concatenate_permutation", &val,
                  nullptr))
       sample_concatenate_permutation = (val == 1) ? true : false;
+    lookupkv(model, scenario, "accuracy_log_probability", nullptr,
+             &accuracy_log_probability, 0.01);
     if (lookupkv(model, scenario, "test05", &val, nullptr))
       test05 = (val == 1) ? true : false;
     lookupkv(model, scenario, "test05_qsl_rng_seed", &test05_qsl_rng_seed,
@@ -715,8 +712,10 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
              &test05_schedule_rng_seed, nullptr);
   }
 
-  // keys that can be overriden in user.conf but will make the results eligibale
-  // only for open submission keys to measure token metrics
+  // keys that can be overriden in user.conf but will make the results eligible
+  // only for open submissions
+
+  // keys to measure token metrics
   if (lookupkv(model, scenario, "use_token_latencies", &val, nullptr)) {
     use_token_latencies = (val == 1) ? true : false;
   }
@@ -781,6 +780,11 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
   if (lookupkv(model, scenario, "print_timestamps", &val, nullptr))
     print_timestamps = (val == 0) ? false : true;
 
+  // keys that are used in audit.conf
+  lookupkv(model, scenario, "accuracy_log_rng_seed", &accuracy_log_rng_seed,
+           nullptr);
+  lookupkv(model, scenario, "accuracy_log_sampling_target",
+           &accuracy_log_sampling_target, nullptr);
   return 0;
 }
 
diff --git a/main.py b/main.py
index 6a34587dd6..1e561175ff 100755
--- a/main.py
+++ b/main.py
@@ -28,7 +28,7 @@ def mlperf_inference_implementation_readme(
         content = ""
 
         execution_envs = ["Docker", "Native"]
-        code_version = "r4.1-dev"
+        code_version = "r5.0-dev"
         implementation_run_options = []
 
         if model == "rnnt":
@@ -50,6 +50,8 @@ def mlperf_inference_implementation_readme(
                     frameworks = ["Onnxruntime", "Pytorch"]
                 elif "bert" in model.lower():
                     frameworks = ["Pytorch", "Deepsparse"]
+                elif "llama3" in model.lower():
+                    frameworks = ["Pytorch"]
                 else:
                     frameworks = ["Pytorch"]
 
@@ -127,6 +129,7 @@ def mlperf_inference_implementation_readme(
                 "dlrm" in model.lower()
                 or "llama2" in model.lower()
                 or "mixtral" in model.lower()
+                or "llama3" in model.lower()
             ):
                 categories = ["Datacenter"]
             else:
@@ -499,6 +502,7 @@ def get_common_info(spaces, implementation, model):
         info += f"\n{pre_space}!!! tip\n\n"
         info += f"{pre_space}    - Number of threads could be adjusted using `--threads=#`, where `#` is the desired number of threads. This option works only if the implementation in use supports threading.\n\n"
         info += f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
+        info += f"{pre_space}    - `_r4.1-dev` could also be given instead of `_r5.0-dev` if you want to run the benchmark with the MLPerf version being 4.1.\n\n"
         if model == "rgat":
             info += f"{pre_space}    - Add `--env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset>` if you have already downloaded the dataset. The path will be automatically mounted when using docker run.\n\n"
             info += f"{pre_space}    - Add `--env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint>` if you have already downloaded the model. The path will be automatically mounted when using docker run.\n\n"
@@ -522,7 +526,9 @@ def get_docker_info(spaces, model, implementation,
 
             if model == "sdxl":
                 info += f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
-
+            elif "llama3" in model.lower():
+                info += f"{pre_space}    - `--env.CM_MLPERF_MODEL_LLAMA3_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
+                info += f"{pre_space}    - `--env.CM_MLPERF_DATASET_LLAMA3_DOWNLOAD_TO_HOST=yes` option can be used to download the dataset on the host so that it can be reused across different container lanuches. \n\n"
             if implementation.lower() == "nvidia":
                 info += f"{pre_space}    - Default batch size is assigned based on [GPU memory](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1129) or the [specified GPU](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1370). Please click more option for *docker launch* or *run command* to see how to specify the GPU name.\n\n"
                 info += f"{pre_space}    - When run with `--all_models=yes`, all the benchmark models of NVIDIA implementation can be executed within the same container.\n\n"
diff --git a/mkdocs.yml b/mkdocs.yml
index 96bcfb7583..9178191a3d 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -39,6 +39,7 @@ nav:
           - IndySCC24: benchmarks/language/reproducibility/indyscc24-bert.md
       - GPT-J: benchmarks/language/gpt-j.md
       - LLAMA2-70B: benchmarks/language/llama2-70b.md
+      - LLAMA3-405B: benchmarks/language/llama3_1-405b.md
       - MIXTRAL-8x7B: benchmarks/language/mixtral-8x7b.md
     - Recommendation:
       - DLRM-v2: benchmarks/recommendation/dlrm-v2.md
diff --git a/text_to_image/README.md b/text_to_image/README.md
index 57c4343b18..84c8c72457 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -1,9 +1,11 @@
 # MLPerf™ Inference Benchmarks for Text to Image
 
-This is the reference implementation for MLPerf Inference text to image.
+## Automated command to run the benchmark via MLCommons CM
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/text_to_image/sdxl) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
+You can also do `pip install cm4mlops` and then use `cm` commands for downloading the model and datasets using the commands given in the later sections.
+ 
 ## Supported Models
 
 | model | accuracy | dataset | model source | precision | notes |
@@ -53,10 +55,10 @@ We host two checkpoints (fp32 and fp16) that are a snapshot of the [Hugging Face
 The following MLCommons CM commands can be used to programmatically download the model checkpoints.
 
 ```
-pip install cmind
-cm pull repo mlcommons@ck
-cm run script --tags=get,ml-model,sdxl,_fp16,_rclone -j
-cm run script --tags=get,ml-model,sdxl,_fp32,_rclone -j
+cm run script --tags=get,ml-model,sdxl,_fp16,_rclone --outdirname=$MODEL_PATH
+```
+```
+cm run script --tags=get,ml-model,sdxl,_fp32,_rclone --outdirname-$MODEL_PATH
 ```
 #### Manual method
 
@@ -72,30 +74,35 @@ Once Rclone is installed, run the following command to authenticate with the buc
 rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
 ```
 You can then navigate in the terminal to your desired download directory and run the following commands to download the checkpoints:
+```
+cd $MODEL_PATH
+```
 
 **`fp32`**
 ```
-rclone copy mlc-inference:mlcommons-inference-wg-public/stable_diffusion_fp32 ./stable_diffusion_fp32 -P
+rclone copy mlc-inference:mlcommons-inference-wg-public/stable_diffusion_fp32 $MODEL_PATH -P
 ```
 **`fp16`**
 ```
-rclone copy mlc-inference:mlcommons-inference-wg-public/stable_diffusion_fp16 ./stable_diffusion_fp16 -P
+rclone copy mlc-inference:mlcommons-inference-wg-public/stable_diffusion_fp16 $MODEL_PATH -P
 ```
 
-#### Move to model path
+### Download validation dataset
 
-```bash
-mkdir $MODEL_PATH
-cd $MODEL_PATH
-# For fp32
-mv <path_to_download>/stable_diffusion_fp32.zip .
-unzip stable_diffusion_fp32.zip
-# For fp16
-mv <path_to_download>/stable_diffusion_fp16.zip .
-unzip stable_diffusion_fp16.zip
+#### CM METHOD
+The following MLCommons CM commands can be used to programmatically download the validation dataset.
+
+```
+cm run script --tags=get,dataset,coco2014,_validation,_full --outdirname=coco2014
+```
+
+For debugging you can download only a part of all the images in the dataset
+```
+cm run script --tags=get,dataset,coco2014,_validation,_size.50 --outdirname=coco2014
 ```
 
-### Download dataset
+
+#### MANUAL METHOD
 ```bash
 cd $SD_FOLDER/tools
 ./download-coco-2014.sh -n <number_of_workers>
@@ -107,14 +114,25 @@ cd $SD_FOLDER/tools
 ```
 If the file [captions.tsv](coco2014/captions/captions.tsv) can be found in the script, it will be used to download the target dataset subset, otherwise it will be generated. We recommend you to have this file for consistency.
 
-#### Calibration dataset
+### Download Calibration dataset (only if you are doing quantization)
+
+#### CM METHOD
+The following MLCommons CM commands can be used to programmatically download the calibration dataset.
+
+```
+cm run script --tags=get,dataset,coco2014,_calibration --outdirname=coco2014
+```
+
+
+#### MANUAL METHOD
 
 We provide a script to download the calibration captions and images. To download only the captions:
 ```bash
 cd $SD_FOLDER/tools
-./download-coco-2014-calibration.sh
+./download-coco-2014-calibration.sh -n <number_of_workers>
 ```
-To download only the captions and images:
+
+To download both the captions and images:
 ```bash
 cd $SD_FOLDER/tools
 ./download-coco-2014-calibration.sh -i -n <number_of_workers>
diff --git a/tools/submission/README.md b/tools/submission/README.md
index 2459ab3634..5a31a304a2 100644
--- a/tools/submission/README.md
+++ b/tools/submission/README.md
@@ -1,5 +1,7 @@
 # Tools to check Submissions
 
+Please follow the [official submission automation page](https://docs.mlcommons.org/inference/submission/) for doing a submission. It wraps all the submission related files listed below. 
+
 ## `truncate_accuracy_log.py` (Mandatory)
 
 ### Inputs
diff --git a/tools/submission/preprocess_submission.py b/tools/submission/preprocess_submission.py
index 977af4d476..7eaa7f8f74 100644
--- a/tools/submission/preprocess_submission.py
+++ b/tools/submission/preprocess_submission.py
@@ -51,7 +51,7 @@ def get_args():
 
     parser.add_argument(
         "--version",
-        default="v4.1",
+        default="v5.0",
         choices=list(checker.MODEL_CONFIG.keys()),
         help="mlperf version",
     )
@@ -405,12 +405,12 @@ def infer_scenario_results(args, config):
                             continue
 
                         if mlperf_model not in config.required:
-                            log.error("Division %s, submitter %s, system %s has invalid "
-                                      "MLPerf model (%s) corresponding to given model (%s). "
-                                      "Valid ones for MLPerf inference version (%s) in (%s) "
-                                      "category are [%s]", division, submitter, system_id_json,
-                                      mlperf_model, model, config.version, system_type,
-                                      config.required.keys())
+                            log.warning(f"""Division {division}, submitter {submitter}, system {system_id_json} has invalid """
+                                        f"""MLPerf model ({mlperf_model}) corresponding to given model ({model}). """
+                                        f"""Valid ones for MLPerf inference version ({config.version}) in ({system_type}) """
+                                        f"""category are [{config.required.keys()}]. Removing...""")
+                            clean_model_dir(os.path.join(
+                                log_path, system_desc, model))
                             continue
 
                         required_scenarios = config.get_required(mlperf_model)
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 26d5212f90..fb1f1bd49c 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1133,21 +1133,18 @@ def find_error_in_detail_log(config, fname):
     return is_valid
 
 
-def check_accuracy_dir(config, model, path, verbose):
-    is_valid = False
-    all_accuracy_valid = True
-    acc = None
-    result_acc = {}
-    hash_val = None
-    target = config.get_accuracy_target(model)
-    acc_upper_limit = config.get_accuracy_upper_limit(model)
+def get_accuracy_values(config, model):
+
     patterns = []
     acc_targets = []
     acc_types = []
+    acc_limits = []
+    up_patterns = []
+    acc_limit_check = False
+
+    target = config.get_accuracy_target(model)
+    acc_upper_limit = config.get_accuracy_upper_limit(model)
     if acc_upper_limit is not None:
-        acc_limits = []
-        up_patterns = []
-        acc_limit_check = True
         for i in range(0, len(acc_upper_limit), 2):
             acc_type, acc_target = acc_upper_limit[i: i + 2]
             acc_limits.append(acc_target)
@@ -1158,6 +1155,22 @@ def check_accuracy_dir(config, model, path, verbose):
         patterns.append(ACC_PATTERN[acc_type])
         acc_targets.append(acc_target)
         acc_types.append(acc_type)
+
+    return patterns, acc_targets, acc_types, acc_limits, up_patterns, acc_upper_limit
+
+
+def check_accuracy_dir(config, model, path, verbose):
+    is_valid = False
+    all_accuracy_valid = True
+    acc = None
+    result_acc = {}
+    hash_val = None
+    target = config.get_accuracy_target(model)
+    # acc_upper_limit = config.get_accuracy_upper_limit(model)
+    patterns, acc_targets, acc_types, acc_limits, up_patterns, acc_upper_limit = get_accuracy_values(
+        config, model)
+    acc_limit_check = True
+
     acc_seen = [False for _ in acc_targets]
 
     with open(os.path.join(path, "accuracy.txt"), "r", encoding="utf-8") as f:
@@ -1185,6 +1198,7 @@ def check_accuracy_dir(config, model, path, verbose):
                 if acc:
                     result_acc[acc_type] = acc
                 acc = None
+
             if acc_upper_limit is not None:
                 for i, (pattern, acc_limit) in enumerate(
                         zip(up_patterns, acc_limits)):
@@ -1341,7 +1355,7 @@ def check_performance_dir(
     samples_per_query = mlperf_log["effective_samples_per_query"]
     min_duration = mlperf_log["effective_min_duration_ms"]
     equal_issue_used_check = (
-        mlperf_log["effective_sample_concatenate_permutation"] == "true"
+        mlperf_log["effective_sample_concatenate_permutation"] == True
     )
     if not config.requires_equal_issue(model, division):
         equal_issue_used_check = True
@@ -1625,7 +1639,7 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
                 samples_per_query = 8
 
             if (scenario_fixed in ["MultiStream"]
-                    ) and scenario in ["SingleStream"]:
+                ) and scenario in ["SingleStream"]:
                 power_metric = (
                     avg_power * power_duration * samples_per_query * 1000 / num_queries
                 )
@@ -2849,13 +2863,11 @@ def check_compliance_acc_dir(test_dir, model, config):
                     is_valid = False
                 elif not acc_passed:
                     target = config.get_accuracy_target(model)
-                    patterns = []
-                    acc_types = []
-                    for i in range(0, len(target), 2):
-                        acc_type = target[i: i + 2]
-                        acc_types.append(acc_type)
-                        patterns.append(ACC_PATTERN[acc_type[0]])
-                    acc_seen = [False for _ in acc_type]
+                    patterns, acc_targets, acc_types, acc_limits, up_patterns, acc_upper_limit = get_accuracy_values(
+                        config, model)
+                    acc_limit_check = True
+
+                    acc_seen = [False for _ in acc_targets]
                     acc_baseline = {acc_type: 0 for acc_type in acc_types}
                     acc_compliance = {acc_type: 0 for acc_type in acc_types}
                     with open(
@@ -2898,6 +2910,10 @@ def check_compliance_acc_dir(test_dir, model, config):
                             if delta_perc <= required_delta_perc:
                                 is_valid = True
                             else:
+                                log.error(
+                                    "Compliance test accuracy check (non-deterministic mode) in %s failed",
+                                    test_dir,
+                                )
                                 is_valid = False
                                 break
         elif "TEST06" in test_dir:
diff --git a/tools/submission/truncate_accuracy_log.py b/tools/submission/truncate_accuracy_log.py
index b7a9509aeb..e0e1973ec6 100755
--- a/tools/submission/truncate_accuracy_log.py
+++ b/tools/submission/truncate_accuracy_log.py
@@ -233,7 +233,7 @@ def truncate_results_dir(filter_submitter, backup, scenarios_to_skip):
                                 # get to work
                                 hash_val = get_hash(acc_log)
                                 with open(acc_txt, "a", encoding="utf-8") as f:
-                                    f.write("hash={0}\n".format(hash_val))
+                                    f.write("\nhash={0}\n".format(hash_val))
                                 truncate_file(acc_log)
                                 log.info("%s truncated", acc_log)
 

From e0d093d6c5ebc28fd55377077077b570a11ea1f2 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Tue, 7 Jan 2025 15:35:51 -0800
Subject: [PATCH 221/305] #1777: Update mixtral reference score in the
 submission checker (#2021)

* #1777: Update mixtral reference score in the submission checker

* More fixes to the mixtral readme and scripts

* One more small fix
---
 language/llama3.1-405b/evaluate-accuracy.py |  1 +
 language/mixtral-8x7b/README.md             | 10 ----------
 language/mixtral-8x7b/run_accuracy.sh       |  1 -
 language/mixtral-8x7b/run_offline.sh        |  1 -
 language/mixtral-8x7b/run_server.sh         |  1 -
 tools/submission/submission_checker.py      | 12 ++++++------
 6 files changed, 7 insertions(+), 19 deletions(-)

diff --git a/language/llama3.1-405b/evaluate-accuracy.py b/language/llama3.1-405b/evaluate-accuracy.py
index 7c803e1ca2..40a9137c50 100644
--- a/language/llama3.1-405b/evaluate-accuracy.py
+++ b/language/llama3.1-405b/evaluate-accuracy.py
@@ -138,6 +138,7 @@ def main():
     dataset_path = args.dataset_file
     checkpoint_path = args.checkpoint_path
     nltk.download("punkt")
+    nltk.download('punkt_tab')
 
     tokenizer = AutoTokenizer.from_pretrained(
         checkpoint_path,
diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 853bf1e758..7c1d4fbad9 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -13,11 +13,6 @@ Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/l
  
 ## Prepare environment
 
-Copy the mlperf.conf file to this folder.
-```
-cp ../../mlperf.conf .
-```
-
 For a CPU-only run:
 
 ```
@@ -136,7 +131,6 @@ wget https://inference.mlcommons-storage.org/mixtral_8x7b%2F2024.06.06_mixtral_1
 ```
 python -u main.py --scenario Offline \
                 --model-path ${CHECKPOINT_PATH} \
-                --mlperf-conf mlperf.conf \
                 --user-conf user.conf \
                 --total-sample-count 15000 \
                 --device cpu \
@@ -149,7 +143,6 @@ For a GPU-based run:
 ```
 python3 -u main.py --scenario Offline \
         --model-path ${CHECKPOINT_PATH} \
-        --mlperf-conf mlperf.conf \
         --user-conf user.conf \
         --total-sample-count 15000 \
         --dataset-path ${DATASET_PATH} \
@@ -162,7 +155,6 @@ python3 -u main.py --scenario Offline \
 ```
 python -u main.py --scenario Server \
                 --model-path ${CHECKPOINT_PATH} \
-                --mlperf-conf mlperf.conf \
                 --user-conf user.conf \
                 --total-sample-count 15000 \
                 --device cpu \
@@ -184,7 +176,6 @@ mkdir -p "run_outputs"  # The script will dump all the outputs to 'run_outputs'.
 python -u main.py --scenario Offline \
                 --model-path ${CHECKPOINT_PATH} \
                 --accuracy \
-                --mlperf-conf mlperf.conf \
                 --user-conf user.conf \
                 --total-sample-count 15000 \
                 --dataset-path ${DATASET_PATH} \
@@ -221,7 +212,6 @@ OUTPUT_LOG_DIR=server-accuracy-logs
 python -u main.py --scenario Server \
                 --model-path ${CHECKPOINT_PATH} \
                 --accuracy \
-                --mlperf-conf mlperf.conf \
                 --user-conf user.conf \
                 --total-sample-count 15000 \
                 --dataset-path ${DATASET_PATH} \
diff --git a/language/mixtral-8x7b/run_accuracy.sh b/language/mixtral-8x7b/run_accuracy.sh
index 80db6b604d..13da4e5daa 100644
--- a/language/mixtral-8x7b/run_accuracy.sh
+++ b/language/mixtral-8x7b/run_accuracy.sh
@@ -6,7 +6,6 @@ mkdir -p "run_outputs"
 python3 -u main.py --scenario Offline \
         --model-path ${CHECKPOINT_PATH} \
         --accuracy \
-        --mlperf-conf mlperf.conf \
         --user-conf user.conf \
         --total-sample-count 15000 \
         --dataset-path ${DATASET_PATH} \
diff --git a/language/mixtral-8x7b/run_offline.sh b/language/mixtral-8x7b/run_offline.sh
index f451fdc243..48afb50bd1 100644
--- a/language/mixtral-8x7b/run_offline.sh
+++ b/language/mixtral-8x7b/run_offline.sh
@@ -3,7 +3,6 @@ DATASET_PATH="${DATASET_PATH:dataset/2024_06_06_mixtral_15k_v4.pkl}"
 
 python -u main.py --scenario Offline \
 		--model-path ${CHECKPOINT_PATH} \
-		--mlperf-conf mlperf.conf \
 		--user-conf user.conf \
 		--total-sample-count 15000 \
 		--dataset-path ${DATASET_PATH} \
diff --git a/language/mixtral-8x7b/run_server.sh b/language/mixtral-8x7b/run_server.sh
index 7c4e4a05e2..ced7ea4432 100644
--- a/language/mixtral-8x7b/run_server.sh
+++ b/language/mixtral-8x7b/run_server.sh
@@ -5,7 +5,6 @@ DATASET_PATH="${DATASET_PATH:dataset/2024_06_06_mixtral_15k_v4.pkl}"
 
 python -u main.py --scenario Server \
 		--model-path ${CHECKPOINT_PATH} \
-		--mlperf-conf mlperf.conf \
 		--user-conf user.conf \
 		--total-sample-count 15000 \
 		--dataset-path ${DATASET_PATH} \
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index fb1f1bd49c..a8520bd25c 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -378,17 +378,17 @@
             ),
             "mixtral-8x7b": (
                 "ROUGE1",
-                45.4911 * 0.99,
+                45.5989 * 0.99,
                 "ROUGE2",
-                23.2829 * 0.99,
+                23.3526 * 0.99,
                 "ROUGEL",
-                30.3615 * 0.99,
+                30.4608 * 0.99,
                 "TOKENS_PER_SAMPLE",
-                145.9 * 0.9,
+                144.84 * 0.9,
                 "gsm8k_accuracy",
-                73.78 * 0.99,
+                73.66 * 0.99,
                 "mbxp_accuracy",
-                60.12 * 0.99,
+                60.16 * 0.99,
             ),
             "llama3.1-405b": (
                 "ROUGEL",

From 1f73d4c28b71e94aa10962fad78f98f46cb04cf8 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 14 Jan 2025 11:59:45 -0500
Subject: [PATCH 222/305] Avoid creating a lot of queries for equal issue mode
 (#2031)

---
 loadgen/loadgen.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index c731f1a8da..f421d88055 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -323,7 +323,7 @@ std::vector<QueryMetadata> GenerateQueries(
       size_t pad_size =
           (loaded_samples.size() - samples_per_query % loaded_samples.size());
       samples_per_query += pad_size;
-    } else if (min_queries % loaded_samples.size() != 0) {
+    } else if ((scenario != TestScenario::Offline) && (min_queries % loaded_samples.size() != 0)) {
       // In Server, SingleStream, MultiStream mode, the min_queries should be
       // padded
       size_t pad_size =

From c40d6e1ef63693ce286b6c9833d950b40d1742e4 Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Tue, 14 Jan 2025 17:00:01 +0000
Subject: [PATCH 223/305] Increment version to 5.0.6

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index ab0fa336dd..c20c645d7e 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.5
+5.0.6

From 88f4d236819f947a943964a6f0ebef2a1ac41041 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Tue, 14 Jan 2025 09:13:37 -0800
Subject: [PATCH 224/305] Add mixtral dockerfile and standalone inference
 script (#2029)

* Add dockerfile and standalone accuracy evaluation scripts

* Minor fixes

---------

Co-authored-by: Miro <mirhodak@amd.com>
---
 language/llama2-70b/evaluate-accuracy.py      |   1 +
 language/mixtral-8x7b/Dockerfile              |   8 +-
 language/mixtral-8x7b/README.md               |   8 +-
 .../mixtral-8x7b/standalone_infer/README.md   |  34 ++
 .../standalone_infer/hf_eval_all.py           | 194 +++++++++++
 .../standalone_infer/run_accuracy.py          | 315 ++++++++++++++++++
 6 files changed, 552 insertions(+), 8 deletions(-)
 create mode 100755 language/mixtral-8x7b/standalone_infer/README.md
 create mode 100755 language/mixtral-8x7b/standalone_infer/hf_eval_all.py
 create mode 100755 language/mixtral-8x7b/standalone_infer/run_accuracy.py

diff --git a/language/llama2-70b/evaluate-accuracy.py b/language/llama2-70b/evaluate-accuracy.py
index 593ea95019..37df2657cc 100644
--- a/language/llama2-70b/evaluate-accuracy.py
+++ b/language/llama2-70b/evaluate-accuracy.py
@@ -59,6 +59,7 @@ def main():
     checkpoint_path = args.checkpoint_path
     metric = evaluate.load("rouge")
     nltk.download("punkt")
+    nltk.download("punkt_tab")
 
     tokenizer = AutoTokenizer.from_pretrained(
         checkpoint_path,
diff --git a/language/mixtral-8x7b/Dockerfile b/language/mixtral-8x7b/Dockerfile
index b04910d732..d25ef1ba04 100644
--- a/language/mixtral-8x7b/Dockerfile
+++ b/language/mixtral-8x7b/Dockerfile
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
+FROM nvcr.io/nvidia/pytorch:24.07-py3
 SHELL ["/bin/bash", "-c"]
 
 ENV LC_ALL=C.UTF-8
@@ -22,7 +22,7 @@ ENV TZ=US/Pacific
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
-RUN rm -rf /var/lib/apt/lists/* && rm /etc/apt/sources.list.d/* \
+RUN rm -rf /var/lib/apt/lists/* && rm -rf /etc/apt/sources.list.d/* \
  && apt update \
  && apt install -y --no-install-recommends build-essential autoconf \
         libtool git ccache curl wget pkg-config sudo ca-certificates \
@@ -44,5 +44,5 @@ WORKDIR /tmp
 RUN wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.5.2-0-Linux-x86_64.sh \
     && bash Miniconda3-* -b -p /opt/miniconda3
 ENV PATH="$PATH:/opt/miniconda3/bin"
-RUN conda create -n llama2-70b python=3.10
+RUN conda create -n llm python=3.10
 RUN chmod -R 777 /opt/miniconda3
diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 7c1d4fbad9..928ca723d4 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -10,7 +10,7 @@
 
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/mixtral-8x7b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
- 
+
 ## Prepare environment
 
 For a CPU-only run:
@@ -234,11 +234,11 @@ Recreating the enviroment for evaluating the quality metrics can be quite tediou
 ```bash
 docker build . -f Dockerfile.eval -t evaluation
 ```
-2. Run the docker in interactive mode and with 
+2. Run the docker in interactive mode and with
 ```bash
-sudo docker run -it -v $(pwd):/eval -t evaluation
+docker run -it --rm --net=host --runtime=nvidia --ipc=host -v $PWD:$PWD -w $PWD evaluation
 ```
-3. 
+3.
 ```bash
 cd eval
 python -u evaluate-accuracy.py --checkpoint-path [path_to_model_checkpoint] \
diff --git a/language/mixtral-8x7b/standalone_infer/README.md b/language/mixtral-8x7b/standalone_infer/README.md
new file mode 100755
index 0000000000..4a0bd6cd91
--- /dev/null
+++ b/language/mixtral-8x7b/standalone_infer/README.md
@@ -0,0 +1,34 @@
+# Mixtral reference standalone inference script
+
+The reference output and accuracy can be checked using the standalone hugginface inference script following the instructions below:
+
+```
+cd language/mixtral-8x7b
+docker build -t mlc-ngc .
+nvidia-docker run -it --rm --net=host --runtime=nvidia --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --cap-add=SYS_PTRACE --cap-add=SYS_ADMIN --cap-add=DAC_READ_SEARCH --security-opt seccomp=unconfined -w $PWD -v $PWD:$PWD -t mlc-ngc
+
+pip install -r requirements.txt
+cd standalone_infer
+# Make sure the checkpoint and reference pickle file is already downloaded
+python3 hf_eval_all.py --input_pkl=09292024_mixtral_15k_mintoken2_v1.pkl --checkpoint_path=/raid/data/mlperf-llm/Mixtral-8x7B-Instruct-v0.1 --output_pkl=mixtral_8x7b_15000_greedy_reference_fp16_mintoken2.pkl --batch_size=64
+
+# Exit the container and enter the evaluation container
+exit
+docker build . -f Dockerfile.eval -t evaluation
+docker run -it --rm --net=host --runtime=nvidia --ipc=host -v $PWD:$PWD -w $PWD evaluation
+cd standalone_infer
+python3 run_accuracy.py --results_path=mixtral_8x7b_15000_greedy_reference_fp16_mintoken2.pkl
+```
+
+Expected output:
+```
+EM: 0.7366, correct: 3683 / 5000, gen_token_per_sample: 129.9604
+Evaluating OpenOrca score...
+OpenOrca score: {'rouge1': np.float64(45.5989), 'rouge2': np.float64(23.3526), 'rougeL': np.float64(30.4608), 'rougeLsum': np.float64(42.5396)}, gen_token_per_sample: 205.8656
+Evaluating MBXP score...
+100%|| 5000/5000 [02:33<00:00, 32.50it/s]
+Processed 5000 in 153.89411109898356s
+ 60.16% pass@1
+{'cpp': 381, 'typescript': 438, 'ruby': 419, 'python': 492, 'php': 809, 'javascript': 469}  out of  {'cpp': 743, 'typescript': 868, 'ruby': 846, 'python': 863, 'php': 846, 'javascript': 834}
+gen_tokens_per_sample: 98.7026
+```
diff --git a/language/mixtral-8x7b/standalone_infer/hf_eval_all.py b/language/mixtral-8x7b/standalone_infer/hf_eval_all.py
new file mode 100755
index 0000000000..1a60cd19f5
--- /dev/null
+++ b/language/mixtral-8x7b/standalone_infer/hf_eval_all.py
@@ -0,0 +1,194 @@
+#!/usr/bin/env python3
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, LogitsProcessor, LogitsProcessorList
+import torch
+import pandas as pd
+import time
+from pathlib import Path
+import argparse
+
+
+def run_infer(df, ckpt_path, bs):
+    """
+    dataset                                                           GSM8K
+    id                                                            train.548
+    question              Gary manages two Amazon distribution centers. ...
+    input                 <s> [INST] As an expert problem solver solve s...
+    ref_output            The first center processes 10000 packages per ...
+    gt_output                                                         14000
+    tok_input             [1, 1, 28705, 733, 16289, 28793, 1136, 396, 75...
+    tok_ref_output        [415, 907, 4982, 9537, 28705, 28740, 28734, 28...
+    stop_sequence                                                      </s>
+    tok_stop_sequence                                                   [2]
+    tok_input_len                                                       662
+    tok_ref_output_len                                                  174
+    Name: 0, dtype: object
+    """
+    device = "cuda"  # the device to load the model onto
+
+    # Load the model from local if possible.
+    model_path = Path(ckpt_path)
+    if not model_path.exists():
+        raise RuntimeError(f"{ckpt_path} not existed. Please download the checkpoint from mlcommon")
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_path, padding_side="left", trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path, device_map="auto", trust_remote_code=True)
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    # gen parameter. We stop at 1024. Starting from v5.0, min_token is set to 2 to avoid 0-output issue
+    gen_kwargs = {
+        # "min_new_tokens": 1,
+        "min_new_tokens": 2,
+        "max_new_tokens": 1024,
+        "do_sample": False,
+        "temperature": None,
+        "top_p": None,
+    }
+
+    # Start inference
+    BS = bs
+    bidx = 0
+    model.eval()
+
+    input_tokens = []
+    input_tokens_lens = []
+    output_tokens = []
+    output_tokens_lens = []
+    output_texts = []
+
+    tic = time.time()
+    for idx in range(0, len(df), BS):
+        tac = time.time()
+        print(f"Processing {idx}/{len(df)}, time: {tac - tic}s")
+        sidx = idx
+        eidx = min(sidx + BS, len(df))
+
+        # We use batch_encode_plus for batch inference.
+        # Note 9/29/2024: Mixtral changed its tokenizer in Jun. Using the Feb 29 2024 version.
+        batch_texts = df['input'][sidx:eidx].tolist()
+        batch_ids = tokenizer.batch_encode_plus(batch_texts, return_tensors="pt", padding=True)
+        # tok_input_length = batch_ids['attention_mask'].sum(
+        #     axis=1).to(torch.int32).tolist()
+        # input_tokens_lens += tok_input_length
+        tok_input_id = batch_ids['input_ids'].to(torch.int32).tolist()
+        # Remove eos from the input id
+        tok_input_id = [[element for element in sublist if element !=
+                        tokenizer.eos_token_id] for sublist in tok_input_id]
+        input_tokens += tok_input_id
+        tok_input_length = [len(seq) for seq in tok_input_id]
+        input_tokens_lens += tok_input_length
+
+        batch_ids = batch_ids.to(device)
+        _, length = batch_ids.input_ids.shape
+        outputs = model.generate(**batch_ids, num_return_sequences=1,
+                                **gen_kwargs)
+
+        output_ids = outputs[:, length:].cpu().tolist()
+        output_tokens += output_ids
+
+        # Filter out EOS
+        id_filtered = [[num for num in sublist if num !=
+                        tokenizer.eos_token_id] for sublist in output_ids]
+        output_id_len = [len(out) for out in id_filtered]
+        output_tokens_lens += output_id_len
+
+        # Detokenizer
+        output_msgs = tokenizer.batch_decode(
+            output_ids, skip_special_tokens=True)
+        output_texts += output_msgs
+        bidx += 1
+
+    # Assemble the output
+    output_df = df[:len(output_tokens)].copy()
+    output_df["infer_tok_input"] = input_tokens
+    output_df["infer_tok_input_length"] = input_tokens_lens
+    output_df["infer_ref_output"] = output_texts
+    output_df["infer_tok_ref_output"] = output_tokens
+    output_df["infer_tok_ref_output_length"] = output_tokens_lens
+
+    # output_df.to_pickle(f"mixtral_8x7b_all15k_{len(output_tokens)}_BS{BS}_greedy_reference_fp16_mintoken1.pkl")
+
+    return output_df
+
+def trim_twos(df):
+    # Remove all trailing 2s except for 1
+    def remove_trailing_twos(lst):
+        count = 0
+        for num in reversed(lst):
+            if num == 2:
+                count += 1
+            else:
+                break
+        return lst[:-count] if count > 0 else lst
+
+    df['infer_tok_ref_output'] = df['infer_tok_ref_output'].apply(remove_trailing_twos)
+    df['trim_lengths'] = df['infer_tok_ref_output'].apply(len)
+    df['tok_ref_output'] = df['tok_ref_output'].apply(remove_trailing_twos)
+    df['tok_ref_output_len'] = df['tok_ref_output'].apply(len)
+    return df
+
+def mbxp_stop(df):
+    stop_tokens = [13, 13940, 28832, 13]
+    def modify_list(lst):
+        for i in range(len(lst) - len(stop_tokens) + 1):
+            if lst[i:i+len(stop_tokens)] == stop_tokens:
+                return lst[:i+len(stop_tokens)]
+        return lst
+
+    df.loc[df['dataset'] == 'MBXP', 'infer_tok_ref_output'] = df[df['dataset'] == 'MBXP']['infer_tok_ref_output'].apply(modify_list)
+    df['trim_lengths'] = df['infer_tok_ref_output'].apply(len)
+    return df
+
+
+def fix_name(df):
+    df.drop(columns=['ref_output'], inplace=True)
+    df.drop(columns=['tok_ref_output'], inplace=True)
+    df.drop(columns=['tok_ref_output_len'], inplace=True)
+    df.drop(columns=['infer_tok_ref_output_length'], inplace=True)
+    df.drop(columns=['infer_tok_input'], inplace=True)
+    df.drop(columns=['infer_tok_input_length'], inplace=True)
+    df.rename(columns={'infer_ref_output': 'ref_output'}, inplace=True)
+    df.rename(columns={'infer_tok_ref_output': 'tok_ref_output'}, inplace=True)
+    df.rename(columns={'trim_lengths': 'tok_ref_output_len'}, inplace=True)
+
+    return df
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_pkl", type=str, default="09292024_mixtral_15k_mintoken2_v1.pkl",
+                        help="The path to the input pkl file")
+    parser.add_argument("--output_pkl", type=str, default="mixtral_8x7b_15000_greedy_reference_fp16_mintoken2.pkl",
+                        help="The path to the output pickle.")
+    parser.add_argument("--checkpoint_path", type=str, default="/raid/data/mlperf-llm/Mixtral-8x7B-Instruct-v0.1",
+                        help="The path to the mixtral checkpoint")
+    parser.add_argument("--batch_size", type=int, default=64,
+                        help="Batch size of the refernece inference")
+    args = parser.parse_args()
+
+    df = pd.read_pickle(args.input_pkl)
+    df = run_infer(df, args.checkpoint_path, args.batch_size)
+
+    df = trim_twos(df)
+    df = mbxp_stop(df)
+    df = fix_name(df)
+
+    df.to_pickle(args.output_pkl)
+
+
diff --git a/language/mixtral-8x7b/standalone_infer/run_accuracy.py b/language/mixtral-8x7b/standalone_infer/run_accuracy.py
new file mode 100755
index 0000000000..1b7c91e676
--- /dev/null
+++ b/language/mixtral-8x7b/standalone_infer/run_accuracy.py
@@ -0,0 +1,315 @@
+#!/usr/bin/env python3
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pandas as pd
+import re
+import numpy as np
+import argparse
+import evaluate
+import nltk
+from tqdm import tqdm
+
+import timeit
+import multiprocessing
+import json
+import pickle
+import queue
+
+from mxeval.execution import check_correctness as check_correctness_python
+from mxeval.execution import (
+    check_correctness_cpp,
+    check_correctness_csharp,
+    check_correctness_go,
+    check_correctness_java,
+    check_correctness_javascript,
+    check_correctness_kotlin,
+    check_correctness_perl,
+    check_correctness_php,
+    check_correctness_ruby,
+    check_correctness_scala,
+    check_correctness_swift,
+    check_correctness_typescript,
+)
+
+nltk.download("punkt")
+nltk.download("punkt_tab")
+metric = evaluate.load("rouge")
+
+
+def calculate_rouge_score(model_outputs, ref_outputs):
+    metric = evaluate.load("rouge")
+    m_preds = [pred.strip() for pred in model_outputs]
+    m_targets = [target.strip() for target in ref_outputs]
+
+    # rougeLSum expects newline after each sentence
+    m_preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in m_preds]
+    m_targets = ["\n".join(nltk.sent_tokenize(target)) for target in m_targets]
+    m_result = metric.compute(
+        predictions=m_preds, references=m_targets, use_stemmer=True, use_aggregator=False
+    )
+    m_rouge_result = {k: round(np.mean(v) * 100, 4) for k, v in m_result.items()}
+
+    return m_rouge_result
+
+
+def find_numbers(x: str) -> list[str]:
+    """Finds all numbers in a string."""
+    # Search for number, possibly negative (hyphen), with thousand separators
+    # (comma), and with a decimal point (period inbetween digits).
+    numbers = re.compile(
+        r'-?[\d,]*\.?\d+',
+        re.MULTILINE | re.DOTALL | re.IGNORECASE,
+    ).findall(x)
+    return numbers
+
+
+def find_number(x: str,
+                answer_delimiter: str = 'The answer is') -> str:
+    """Finds the most relevant number in a string."""
+    # If model uses the answer delimiter, then select the first number following
+    # that format.
+    if answer_delimiter in x:
+        answer = x.split(answer_delimiter)[-1]
+        numbers = find_numbers(answer)
+        if numbers:
+            return numbers[0]
+
+    # In general, select the last number in the string.
+    numbers = find_numbers(x)
+    if numbers:
+        return numbers[-1]
+    return ''
+
+
+def maybe_remove_comma(x: str) -> str:
+    # Example: 5,600 -> 5600
+    return x.replace(',', '')
+
+
+def try_float(x: str):
+    try:
+        ret = float(x)
+    except:
+        ret = None
+    return ret
+
+def postprocess_golang(code: str) -> str:
+    multi_line_imports = re.compile(r"^import \(\n(.+)((?:\n.+)+)\n\)", re.MULTILINE)
+    line_imports = re.compile(r"^import \".*\"")
+    func_main = re.compile(r"^func main.*^}", re.MULTILINE | re.DOTALL)
+
+    code = code.replace("package main", "") # Remove package main
+    code = multi_line_imports.sub("", code)
+    code = line_imports.sub("", code)
+    code = func_main.sub("", code)
+
+    return code
+
+def postprocess_scala(code: str) -> str:
+    code = code.replace("object Main extends App {", "")
+    code = "".join(code.splitlines(True)[:-1])
+    return code
+
+def postprocess_python(code: str) -> str:
+    return code.lstrip()
+
+def worker(inp_queue, out_queue):
+    while True:
+        try:
+            problem = inp_queue.get(timeout=5)
+        except queue.Empty:
+            break
+
+        key = f"{problem['lang']}_{problem['entry_point']}"
+        checker = eval(f"check_correctness_{problem['lang']}")
+
+        problem["task_id"] = key
+        problem["test"] = problem["test_code"]
+
+        solution = problem["response"]
+
+        try:
+            solution = solution[:solution.index("```")]
+        except ValueError:
+            #Happens when a code block isn't closed properly
+            pass
+
+        if problem["lang"] == "go":
+            solution = postprocess_golang(solution)
+        elif problem["lang"] == "python":
+            solution = postprocess_python(solution)
+        elif problem["lang"] == "scala":
+            solution = postprocess_scala(solution)
+
+        # Mixtral likes escaping underscores for some reason, so let's remove these
+        solution = solution.replace("\_", "_")
+
+        # The evaluation script evaluates `code = prompt + solution + tests`
+        # But Mixtral regenerates the prompt in its output, so we should remove this
+        problem["prompt"] = ""
+
+        result = checker(problem, solution, timeout=20.0)
+        out_queue.put((key, problem["lang"], result["passed"], result["result"], problem["response"]))
+
+
+def convert_pickle(df: pd.DataFrame, result_keys: dict):
+    problems = []
+    for _, row in df.iterrows():
+        lang, entry_point = row["id"].split("_", 1)
+        problems.append({
+            "lang": lang,
+            "prompt": row["input"],
+            "test_code": row["gt_output"],
+            "entry_point": entry_point,
+            "response": row[f"{result_keys['result']}"]
+        })
+    return problems
+
+
+def evaluate_mbxp(n_works: int, df: pd.DataFrame, result_keys: dict):
+    print(f"Evaluating MBXP score...")
+    # Convert pickle file into dictionary
+    results = convert_pickle(df, result_keys)
+
+    by_lang = {}
+    for problem in results:
+        by_lang.setdefault(problem["lang"], []).append(problem)
+
+    inp_queue = multiprocessing.Queue()
+    out_queue = multiprocessing.Queue()
+
+    n_problems = 0
+
+    for lang, problems in by_lang.items():
+        if lang not in ["cpp", "python", "php", "javascript", "ruby", "typescript"]:
+            raise RuntimeError(f"{lang} not in supported list.")
+
+        n_problems += len(problems)
+        for problem in problems:
+            inp_queue.put(problem)
+
+    start = timeit.default_timer()
+    workers = []
+    for _ in range(args.n_workers):
+        w = multiprocessing.Process(target=worker, args=(inp_queue, out_queue))
+        w.start()
+        workers.append(w)
+
+    passes = {}
+    n_passed = 0
+    lang_passed = {}
+    lang_counts = {}
+    for i in tqdm(range(n_problems)):
+        key, lang, passed, result, response = out_queue.get()
+        passes[key] = {"passed": passed, "result": result, "response": response}
+        n_passed += passed
+
+        lang_passed.setdefault(lang, 0)
+        lang_passed[lang] += passed
+
+        lang_counts.setdefault(lang, 0)
+        lang_counts[lang] += 1
+
+    end = timeit.default_timer()
+    print(f"Processed {n_problems} in {end - start}s")
+    print(f"{100 * n_passed / n_problems : .02f}% pass@1")
+    print(lang_passed, " out of ", lang_counts)
+
+    gen_token_len = df[result_keys['length']].tolist()
+    gen_token_per_sample = sum(gen_token_len) / len(gen_token_len)
+    print(f"gen_tokens_per_sample: {gen_token_per_sample}")
+
+    # with open("evaluated_test.json", "w") as f:
+    #    json.dump(passes, f, indent=2)
+
+    return n_passed / n_problems
+
+
+def evaluate_openorca(df: pd.DataFrame, result_keys: dict):
+    print(f"Evaluating OpenOrca score...")
+    gen_output = df[f"{result_keys['result']}"].tolist()
+    gt_output = df.gt_output.tolist()
+    score = calculate_rouge_score(gen_output, gt_output)
+    gen_token_len = df[result_keys['length']].tolist()
+    gen_token_per_sample = sum(gen_token_len) / len(gen_token_len)
+    print(f"OpenOrca score: {score}, gen_token_per_sample: {gen_token_per_sample}")
+    return score
+
+
+def evaluate_gsm8k(df: pd.DataFrame, result_keys: dict):
+    print(f"Evaluating GSM8K score...")
+    gen_output = df[f"{result_keys['result']}"].tolist()
+    gt_numbers = df.gt_output.tolist()
+    gen_nums = [maybe_remove_comma(find_number(msg.split("\nQ:")[0]))
+                for msg in gen_output]
+    correct = 0
+    total = len(gt_numbers)
+    for idx in range(len(gt_numbers)):
+        ref = try_float(gt_numbers[idx])
+        tgt = try_float(gen_nums[idx])
+        if tgt is None:
+            continue
+        correct += (ref == tgt)
+
+    em = correct / total
+    gen_token_len = df[result_keys['length']].tolist()
+    gen_token_per_sample = sum(gen_token_len) / len(gen_token_len)
+    print(f"EM: {em}, correct: {correct} / {total}, gen_token_per_sample: {gen_token_per_sample}")
+    return em
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--n_workers", type=int, default=10, help="The number of processes to use")
+    parser.add_argument("--results_path", type=str, default="mixtral_8x7b_15000_greedy_reference_fp16_mintoken2.pkl",
+                        help="The path to the results file pickle file")
+    parser.add_argument("--result_key", type=str, default="ref_output",
+                        help="ref output dict key")
+    parser.add_argument("--length_key", type=str, default="tok_ref_output_len",
+                        help="ref output dict key")
+    args = parser.parse_args()
+
+    """
+    Sample command:
+    python3 nv_accuracy.py --results_path=trtllm_fp16_mixtral_8x7b_all15k_15000_BS128_greedy_06102024.pkl --result_key=nv_tllm_ref_output --length_key=nv_tllm_tok_ref_output_length
+    """
+
+    result_keys = {
+        "result": args.result_key,
+        "length": args.length_key
+    }
+
+    """
+    dataset                                                            MBXP (OpenOrca/GSM8K)
+    id                                            typescript_minimum_Length
+    question              /**\n * Write a typescript function to minimiz...
+    input                 <s> [INST] Complete the following code. Be con...
+    ref_output            \nconst minimumLength = (s: string): number =>...
+    gt_output             \nimport * as assert from 'assert'\n\nlet actu...
+    tok_input             [1, 1, 28705, 733, 16289, 28793, 21929, 272, 2...
+    tok_ref_output        [13, 1978, 7968, 4645, 327, 325, 28713, 28747,...
+    stop_sequence                                                   \n```\n
+    tok_stop_sequence                                [13, 13940, 28832, 13]
+    tok_input_len                                                       139
+    tok_ref_output_len                                                  123
+    """
+
+    df = pd.read_pickle(args.results_path)
+    df_gsm8k = df[df['dataset']=="GSM8K"].copy()
+    evaluate_gsm8k(df_gsm8k, result_keys)
+    df_openorca = df[df['dataset']=="OpenOrca"].copy()
+    evaluate_openorca(df_openorca, result_keys)
+    df_mbxp = df[df['dataset']=="MBXP"].copy()
+    evaluate_mbxp(args.n_workers, df_mbxp, result_keys)

From af05f0ddcc9d30b12342fbd21c6a990b7b84df39 Mon Sep 17 00:00:00 2001
From: v-shobhit <161510941+v-shobhit@users.noreply.github.com>
Date: Tue, 14 Jan 2025 09:18:45 -0800
Subject: [PATCH 225/305] Parallelize evaluation of llama2 rouge scores (#1995)

* Parallelize evaluation of rouge scores

* use variable for repeated val

* [Automated Commit] Format Codebase

---------

Co-authored-by: mlcommons-bot <mlcommons-bot@users.noreply.github.com>
Co-authored-by: Miro <mirhodak@amd.com>
---
 language/llama2-70b/evaluate-accuracy.py | 48 +++++++++++++++++++-----
 1 file changed, 39 insertions(+), 9 deletions(-)

diff --git a/language/llama2-70b/evaluate-accuracy.py b/language/llama2-70b/evaluate-accuracy.py
index 37df2657cc..cf42b294cc 100644
--- a/language/llama2-70b/evaluate-accuracy.py
+++ b/language/llama2-70b/evaluate-accuracy.py
@@ -4,6 +4,7 @@
 import evaluate
 import numpy as np
 import json
+from multiprocessing import Pool, cpu_count
 
 
 def get_args():
@@ -52,12 +53,21 @@ def postprocess_text(preds, targets):
     return preds, targets
 
 
+def compute_rouge_chunk(chunk):
+    """Compute ROUGE scores for a chunk of predictions and references."""
+    metric = evaluate.load("rouge")
+    preds, targets = chunk
+    result = metric.compute(
+        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False
+    )
+    return result
+
+
 def main():
 
     args = get_args()
     dataset_path = args.dataset_file
     checkpoint_path = args.checkpoint_path
-    metric = evaluate.load("rouge")
     nltk.download("punkt")
     nltk.download("punkt_tab")
 
@@ -103,23 +113,43 @@ def main():
 
     preds, targets = postprocess_text(preds_decoded_text, target_required)
 
-    result = metric.compute(
-        predictions=preds, references=targets, use_stemmer=True, use_aggregator=False
-    )
-    result = {k: round(np.mean(v) * 100, 4) for k, v in result.items()}
+    # Split data into chunks for parallel processing
+    num_chunks = cpu_count()  # Number of parallel processes
+    chunk_size = len(preds) // num_chunks + (len(preds) % num_chunks > 0)
+
+    chunks = [
+        (preds[i:i + chunk_size], targets[i:i + chunk_size])
+        for i in range(0, len(preds), chunk_size)
+    ]
+
+    # Use multiprocessing Pool to compute ROUGE scores in parallel
+    with Pool(num_chunks) as pool:
+        results_list = pool.map(compute_rouge_chunk, chunks)
+
+    # Aggregate results from all chunks
+    aggregated_results = {}
+
+    for result in results_list:
+        for k, v in result.items():
+            if k not in aggregated_results:
+                aggregated_results[k] = []
+            aggregated_results[k].extend(v)
+
+    final_result = {k: round(np.mean(v) * 100, 4)
+                    for k, v in aggregated_results.items()}
+
     prediction_lens = [len(pred) for pred in preds]
     gen_num = len(preds)
 
-    result = {
-        **result,
+    final_result.update({
         "gen_len": np.sum(prediction_lens),
         "gen_num": gen_num,
         "gen_tok_len": gen_tok_len,
         "tokens_per_sample": round(gen_tok_len / gen_num, 1),
-    }
+    })
 
     print("\nResults\n")
-    print(result)
+    print(final_result)
 
 
 if __name__ == "__main__":

From be96b28b630eff3bcf1abd13e64ecb55bbdda1ac Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 14 Jan 2025 19:19:35 +0000
Subject: [PATCH 226/305] Update test_settings_internal.cc | Allow
 sample_concatenate_permutation in user.conf (#2035)

---
 loadgen/test_settings_internal.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index f654948f3f..67fb91d2d8 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -697,9 +697,6 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
     lookupkv(model, scenario, "sample_index_rng_seed", &sample_index_rng_seed,
              nullptr);
     lookupkv(model, scenario, "schedule_rng_seed", &schedule_rng_seed, nullptr);
-    if (lookupkv(model, scenario, "sample_concatenate_permutation", &val,
-                 nullptr))
-      sample_concatenate_permutation = (val == 1) ? true : false;
     lookupkv(model, scenario, "accuracy_log_probability", nullptr,
              &accuracy_log_probability, 0.01);
     if (lookupkv(model, scenario, "test05", &val, nullptr))
@@ -759,6 +756,9 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
   lookupkv(model, scenario, "performance_issue_same_index",
            &performance_issue_same_index, nullptr);
 
+  if (lookupkv(model, scenario, "sample_concatenate_permutation", &val,
+            nullptr))
+      sample_concatenate_permutation = (val == 1) ? true : false;
   if (lookupkv(model, "Server", "coalesce_queries", &val, nullptr))
     server_coalesce_queries = (val == 0) ? false : true;
   if (lookupkv(model, "Server", "max_async_queries", &val, nullptr))

From 1618142d400bc7cf00bc9ea3f6501e339ba4ff5b Mon Sep 17 00:00:00 2001
From: pgmpablo157321 <pgmpablo157321@users.noreply.github.com>
Date: Tue, 14 Jan 2025 19:19:49 +0000
Subject: [PATCH 227/305] Increment version to 5.0.7

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index c20c645d7e..00433367c8 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.6
+5.0.7

From 5d83ed5de438ffb55bca4cdb2966fba90a9dbca6 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 14 Jan 2025 18:17:43 -0500
Subject: [PATCH 228/305] 5.0 Random seeds (#2034)

---
 loadgen/mlperf.conf                    | 16 ++++++++--------
 text_to_image/tools/sample_ids.py      |  2 +-
 text_to_image/tools/sample_ids.txt     | 20 ++++++++++----------
 tools/submission/submission_checker.py |  6 +++---
 4 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 1d036f4b48..20ae7cb567 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -20,14 +20,14 @@ rgat.*.performance_sample_count_override = 788379
 # set to 0 to let entire sample set to be performance sample
 3d-unet.*.performance_sample_count_override = 0
 
-# Set seeds. The seeds will be distributed two weeks before the submission.
-*.*.qsl_rng_seed = 3066443479025735752
-*.*.sample_index_rng_seed = 10688027786191513374
-*.*.schedule_rng_seed = 14962580496156340209
-# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission.
-*.*.test05_qsl_rng_seed = 16799458546791641818
-*.*.test05_sample_index_rng_seed = 5453809927556429288
-*.*.test05_schedule_rng_seed = 5435552105434836064
+# Set seeds.
+*.*.qsl_rng_seed = 3587615595324810408
+*.*.sample_index_rng_seed = 16581065910008722638
+*.*.schedule_rng_seed = 11695134899033643297
+# Set seeds for TEST_05.
+*.*.test05_qsl_rng_seed = 7975553102935885558
+*.*.test05_sample_index_rng_seed = 11403566307062068064
+*.*.test05_schedule_rng_seed = 15816800565822761601
 
 
 *.SingleStream.target_latency_percentile = 90
diff --git a/text_to_image/tools/sample_ids.py b/text_to_image/tools/sample_ids.py
index dee3a5793d..01cbcd9c16 100644
--- a/text_to_image/tools/sample_ids.py
+++ b/text_to_image/tools/sample_ids.py
@@ -20,7 +20,7 @@ def get_args():
         default=10,
         help="Dataset download location")
     parser.add_argument(
-        "--seed", "-s", type=int, default=633994880, help="Dataset download location"
+        "--seed", "-s", type=int, default=265673710, help="Dataset download location"
     )
     args = parser.parse_args()
     return args
diff --git a/text_to_image/tools/sample_ids.txt b/text_to_image/tools/sample_ids.txt
index 8bf3d2be8d..03eed50249 100644
--- a/text_to_image/tools/sample_ids.txt
+++ b/text_to_image/tools/sample_ids.txt
@@ -1,10 +1,10 @@
-4655
-2569
-1303
-109
-4509
-3009
-2179
-1826
-2094
-3340
\ No newline at end of file
+716
+2387
+3694
+273
+1221
+2942
+2047
+1691
+1510
+4399
\ No newline at end of file
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index a8520bd25c..91cba0f31c 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -444,9 +444,9 @@
         },
         "seeds": {
             # TODO: Update random seeds
-            "qsl_rng_seed": 3066443479025735752,
-            "sample_index_rng_seed": 10688027786191513374,
-            "schedule_rng_seed": 14962580496156340209,
+            "qsl_rng_seed": 6023615788873153749,
+            "sample_index_rng_seed": 15036839855038426416,
+            "schedule_rng_seed": 9933818062894767841,
         },
         "ignore_errors": [],
         "latency-constraint": {

From ded1712ff4c30a78f3e822f8292835f7dd3f9dc0 Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Tue, 14 Jan 2025 23:17:59 +0000
Subject: [PATCH 229/305] Increment version to 5.0.8

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 00433367c8..51e67ba4d0 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.7
+5.0.8

From 1fa600df13547d674602fee6065b129e72bccc7c Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 14 Jan 2025 18:21:27 -0500
Subject: [PATCH 230/305] RGAT remove in-memory flag (#2033)

Co-authored-by: Miro <mirhodak@amd.com>
---
 graph/R-GAT/README.md | 12 ++++++------
 graph/R-GAT/igbh.py   |  2 +-
 graph/R-GAT/main.py   |  7 +------
 3 files changed, 8 insertions(+), 13 deletions(-)

diff --git a/graph/R-GAT/README.md b/graph/R-GAT/README.md
index 561a65e6f7..4638d526f0 100644
--- a/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -2,7 +2,7 @@
 
 This is the reference implementation for MLPerf Inference Graph Neural Network. The reference implementation currently uses Deep Graph Library (DGL), and pytorch as the backbone of the model.
 
-**Hardware requirements:** The minimun requirements to run this benchmark are ~600GB of RAM and ~2.3TB of disk. This requires to create a memory map for the graph features and not load them to memory all at once. If you want to load all the features to ram, you will need ~3TB and can be done by using the flag `--in-memory`
+**Hardware requirements:** The minimun requirements to run this benchmark are ~600GB of RAM and ~2.3TB of disk. This requires to create a memory map for the graph features and not load them to memory all at once.
 
 ## Supported Models
 
@@ -161,7 +161,7 @@ cm run script --tags=get,dataset,igbh,_full,_calibration --outdirname=<path to d
 cd $GRAPH_FOLDER
 
 # Run the benchmark DGL
-python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl [--model-path <path_to_ckpt>] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
 
@@ -171,7 +171,7 @@ python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl
 cd $GRAPH_FOLDER
 
 # Run the benchmark DGL
-python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full [--model-path <path_to_ckpt>] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
 ### Evaluate the accuracy
@@ -188,7 +188,7 @@ Not implemented yet
 #### Accuracy run
 Add the `--accuracy` to the command to run the benchmark
 ```bash
-python3 main.py --dataset igbh --dataset-path igbh/ --accuracy --model-path model/ [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
+python3 main.py --dataset igbh --dataset-path igbh/ --accuracy --model-path model/ [--model-path <path_to_ckpt>] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
 ```
 
 **NOTE:** For official submissions you should submit the results of the accuracy run in a file called `accuracy.txt` with the following format:
@@ -209,7 +209,7 @@ docker run --rm -it -v $(pwd):/root rgat-cpu
 ```
 Run benchmark inside the docker container:
 ```bash
-python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device cpu [--model-path <path_to_ckpt>] [--in-memory] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device cpu [--model-path <path_to_ckpt>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
 
@@ -225,7 +225,7 @@ docker run --rm -it -v $(pwd):/workspace/root --gpus all rgat-gpu
 Go inside the root folder and run benchmark inside the docker container:
 ```bash
 cd root
-python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device gpu [--model-path <path_to_ckpt>] [--in-memory] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device gpu [--model-path <path_to_ckpt>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
 **NOTE:** For official submissions, this benchmark is required to run in equal issue mode. Please make sure that the flag `rgat.*.sample_concatenate_permutation` is set to one in the [mlperf.conf](../../loadgen/mlperf.conf) file when loadgen is built.
diff --git a/graph/R-GAT/igbh.py b/graph/R-GAT/igbh.py
index cdab173da3..775b42ffb6 100644
--- a/graph/R-GAT/igbh.py
+++ b/graph/R-GAT/igbh.py
@@ -131,7 +131,7 @@ def __init__(
         self,
         path,
         dataset_size="tiny",
-        in_memory=True,
+        in_memory=False,
         use_label_2K=False,
         with_edges=True,
         layout: Literal["CSC", "CSR", "COO"] = "COO",
diff --git a/graph/R-GAT/main.py b/graph/R-GAT/main.py
index 4fa714f37f..ef28a5553b 100644
--- a/graph/R-GAT/main.py
+++ b/graph/R-GAT/main.py
@@ -118,15 +118,11 @@ def get_args():
         "--dataset-path",
         required=True,
         help="path to the dataset")
-    parser.add_argument(
-        "--in-memory",
-        action="store_true",
-        help="path to the dataset")
     parser.add_argument(
         "--layout",
         default="COO",
         choices=["CSC", "CSR", "COO"],
-        help="path to the dataset",
+        help="layout of the dataset",
     )
     parser.add_argument(
         "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
@@ -363,7 +359,6 @@ def main():
     ds = dataset_class(
         data_path=args.dataset_path,
         name=args.dataset,
-        in_memory=args.in_memory,
         layout=args.layout,
         type=args.dtype,
         **kwargs,

From 4fc54b5f4f43095646e87eea9d2f4a28a9923876 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 14 Jan 2025 18:43:38 -0500
Subject: [PATCH 231/305] Fix seeds: correct seeds in mlperf.conf (#2036)

---
 loadgen/mlperf.conf | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 20ae7cb567..9ef1188208 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -21,9 +21,9 @@ rgat.*.performance_sample_count_override = 788379
 3d-unet.*.performance_sample_count_override = 0
 
 # Set seeds.
-*.*.qsl_rng_seed = 3587615595324810408
-*.*.sample_index_rng_seed = 16581065910008722638
-*.*.schedule_rng_seed = 11695134899033643297
+*.*.qsl_rng_seed = 6023615788873153749
+*.*.sample_index_rng_seed = 15036839855038426416
+*.*.schedule_rng_seed = 9933818062894767841
 # Set seeds for TEST_05.
 *.*.test05_qsl_rng_seed = 7975553102935885558
 *.*.test05_sample_index_rng_seed = 11403566307062068064

From 7fe474165b557eaa66f6b06b201e3cd66fbd5864 Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Tue, 14 Jan 2025 23:43:51 +0000
Subject: [PATCH 232/305] Increment version to 5.0.9

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 51e67ba4d0..bb09d45b57 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.8
+5.0.9

From fc2557dbd34b5db3fc1e6555ba779b8e7629b250 Mon Sep 17 00:00:00 2001
From: mlcommons-bot <74634038+mlcommons-bot@users.noreply.github.com>
Date: Tue, 14 Jan 2025 18:30:35 -0600
Subject: [PATCH 233/305] =?UTF-8?q?=F0=9F=94=84=20synced=20file(s)=20with?=
 =?UTF-8?q?=20mlcommons/power-dev=20(#1990)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* 🔄 synced local 'tools/submission/power/power_checker.py' with remote 'compliance/check.py'
---
 README.md                                     |  1 +
 .../standalone_infer/hf_eval_all.py           | 29 ++++---
 .../standalone_infer/run_accuracy.py          | 57 +++++++++----
 loadgen/loadgen.cc                            |  3 +-
 loadgen/test_settings_internal.cc             |  4 +-
 tools/submission/power/power_checker.py       | 79 ++++++-------------
 tools/submission/power/sources_checksums.json |  6 +-
 7 files changed, 90 insertions(+), 89 deletions(-)

diff --git a/README.md b/README.md
index e2f0fa8c0f..58d2b7c14a 100644
--- a/README.md
+++ b/README.md
@@ -13,6 +13,7 @@ Please see the [MLPerf Inference benchmark paper](https://arxiv.org/abs/1911.025
     primaryClass={cs.LG}
 }
 ```
+
 Please see [here](https://docs.mlcommons.org/inference/benchmarks/) for the MLPerf inference documentation website which includes automated commands to run MLPerf inference benchmarks using different implementations.
 
 ## MLPerf Inference v4.1 (submission deadline July 26, 2024)
diff --git a/language/mixtral-8x7b/standalone_infer/hf_eval_all.py b/language/mixtral-8x7b/standalone_infer/hf_eval_all.py
index 1a60cd19f5..8d29d63e35 100755
--- a/language/mixtral-8x7b/standalone_infer/hf_eval_all.py
+++ b/language/mixtral-8x7b/standalone_infer/hf_eval_all.py
@@ -42,7 +42,8 @@ def run_infer(df, ckpt_path, bs):
     # Load the model from local if possible.
     model_path = Path(ckpt_path)
     if not model_path.exists():
-        raise RuntimeError(f"{ckpt_path} not existed. Please download the checkpoint from mlcommon")
+        raise RuntimeError(
+            f"{ckpt_path} not existed. Please download the checkpoint from mlcommon")
 
     tokenizer = AutoTokenizer.from_pretrained(
         model_path, padding_side="left", trust_remote_code=True)
@@ -51,7 +52,8 @@ def run_infer(df, ckpt_path, bs):
     tokenizer.pad_token = tokenizer.eos_token
     tokenizer.pad_token_id = tokenizer.eos_token_id
 
-    # gen parameter. We stop at 1024. Starting from v5.0, min_token is set to 2 to avoid 0-output issue
+    # gen parameter. We stop at 1024. Starting from v5.0, min_token is set to
+    # 2 to avoid 0-output issue
     gen_kwargs = {
         # "min_new_tokens": 1,
         "min_new_tokens": 2,
@@ -80,9 +82,11 @@ def run_infer(df, ckpt_path, bs):
         eidx = min(sidx + BS, len(df))
 
         # We use batch_encode_plus for batch inference.
-        # Note 9/29/2024: Mixtral changed its tokenizer in Jun. Using the Feb 29 2024 version.
+        # Note 9/29/2024: Mixtral changed its tokenizer in Jun. Using the Feb
+        # 29 2024 version.
         batch_texts = df['input'][sidx:eidx].tolist()
-        batch_ids = tokenizer.batch_encode_plus(batch_texts, return_tensors="pt", padding=True)
+        batch_ids = tokenizer.batch_encode_plus(
+            batch_texts, return_tensors="pt", padding=True)
         # tok_input_length = batch_ids['attention_mask'].sum(
         #     axis=1).to(torch.int32).tolist()
         # input_tokens_lens += tok_input_length
@@ -97,7 +101,7 @@ def run_infer(df, ckpt_path, bs):
         batch_ids = batch_ids.to(device)
         _, length = batch_ids.input_ids.shape
         outputs = model.generate(**batch_ids, num_return_sequences=1,
-                                **gen_kwargs)
+                                 **gen_kwargs)
 
         output_ids = outputs[:, length:].cpu().tolist()
         output_tokens += output_ids
@@ -126,6 +130,7 @@ def run_infer(df, ckpt_path, bs):
 
     return output_df
 
+
 def trim_twos(df):
     # Remove all trailing 2s except for 1
     def remove_trailing_twos(lst):
@@ -137,21 +142,25 @@ def remove_trailing_twos(lst):
                 break
         return lst[:-count] if count > 0 else lst
 
-    df['infer_tok_ref_output'] = df['infer_tok_ref_output'].apply(remove_trailing_twos)
+    df['infer_tok_ref_output'] = df['infer_tok_ref_output'].apply(
+        remove_trailing_twos)
     df['trim_lengths'] = df['infer_tok_ref_output'].apply(len)
     df['tok_ref_output'] = df['tok_ref_output'].apply(remove_trailing_twos)
     df['tok_ref_output_len'] = df['tok_ref_output'].apply(len)
     return df
 
+
 def mbxp_stop(df):
     stop_tokens = [13, 13940, 28832, 13]
+
     def modify_list(lst):
         for i in range(len(lst) - len(stop_tokens) + 1):
-            if lst[i:i+len(stop_tokens)] == stop_tokens:
-                return lst[:i+len(stop_tokens)]
+            if lst[i:i + len(stop_tokens)] == stop_tokens:
+                return lst[:i + len(stop_tokens)]
         return lst
 
-    df.loc[df['dataset'] == 'MBXP', 'infer_tok_ref_output'] = df[df['dataset'] == 'MBXP']['infer_tok_ref_output'].apply(modify_list)
+    df.loc[df['dataset'] == 'MBXP', 'infer_tok_ref_output'] = df[df['dataset']
+                                                                 == 'MBXP']['infer_tok_ref_output'].apply(modify_list)
     df['trim_lengths'] = df['infer_tok_ref_output'].apply(len)
     return df
 
@@ -190,5 +199,3 @@ def fix_name(df):
     df = fix_name(df)
 
     df.to_pickle(args.output_pkl)
-
-
diff --git a/language/mixtral-8x7b/standalone_infer/run_accuracy.py b/language/mixtral-8x7b/standalone_infer/run_accuracy.py
index 1b7c91e676..47297df073 100755
--- a/language/mixtral-8x7b/standalone_infer/run_accuracy.py
+++ b/language/mixtral-8x7b/standalone_infer/run_accuracy.py
@@ -59,7 +59,8 @@ def calculate_rouge_score(model_outputs, ref_outputs):
     m_result = metric.compute(
         predictions=m_preds, references=m_targets, use_stemmer=True, use_aggregator=False
     )
-    m_rouge_result = {k: round(np.mean(v) * 100, 4) for k, v in m_result.items()}
+    m_rouge_result = {k: round(np.mean(v) * 100, 4)
+                      for k, v in m_result.items()}
 
     return m_rouge_result
 
@@ -101,30 +102,35 @@ def maybe_remove_comma(x: str) -> str:
 def try_float(x: str):
     try:
         ret = float(x)
-    except:
+    except BaseException:
         ret = None
     return ret
 
+
 def postprocess_golang(code: str) -> str:
-    multi_line_imports = re.compile(r"^import \(\n(.+)((?:\n.+)+)\n\)", re.MULTILINE)
+    multi_line_imports = re.compile(
+        r"^import \(\n(.+)((?:\n.+)+)\n\)", re.MULTILINE)
     line_imports = re.compile(r"^import \".*\"")
     func_main = re.compile(r"^func main.*^}", re.MULTILINE | re.DOTALL)
 
-    code = code.replace("package main", "") # Remove package main
+    code = code.replace("package main", "")  # Remove package main
     code = multi_line_imports.sub("", code)
     code = line_imports.sub("", code)
     code = func_main.sub("", code)
 
     return code
 
+
 def postprocess_scala(code: str) -> str:
     code = code.replace("object Main extends App {", "")
     code = "".join(code.splitlines(True)[:-1])
     return code
 
+
 def postprocess_python(code: str) -> str:
     return code.lstrip()
 
+
 def worker(inp_queue, out_queue):
     while True:
         try:
@@ -143,7 +149,7 @@ def worker(inp_queue, out_queue):
         try:
             solution = solution[:solution.index("```")]
         except ValueError:
-            #Happens when a code block isn't closed properly
+            # Happens when a code block isn't closed properly
             pass
 
         if problem["lang"] == "go":
@@ -153,15 +159,22 @@ def worker(inp_queue, out_queue):
         elif problem["lang"] == "scala":
             solution = postprocess_scala(solution)
 
-        # Mixtral likes escaping underscores for some reason, so let's remove these
-        solution = solution.replace("\_", "_")
+        # Mixtral likes escaping underscores for some reason, so let's remove
+        # these
+        solution = solution.replace("\\_", "_")
 
         # The evaluation script evaluates `code = prompt + solution + tests`
-        # But Mixtral regenerates the prompt in its output, so we should remove this
+        # But Mixtral regenerates the prompt in its output, so we should remove
+        # this
         problem["prompt"] = ""
 
         result = checker(problem, solution, timeout=20.0)
-        out_queue.put((key, problem["lang"], result["passed"], result["result"], problem["response"]))
+        out_queue.put(
+            (key,
+             problem["lang"],
+             result["passed"],
+                result["result"],
+                problem["response"]))
 
 
 def convert_pickle(df: pd.DataFrame, result_keys: dict):
@@ -193,7 +206,8 @@ def evaluate_mbxp(n_works: int, df: pd.DataFrame, result_keys: dict):
     n_problems = 0
 
     for lang, problems in by_lang.items():
-        if lang not in ["cpp", "python", "php", "javascript", "ruby", "typescript"]:
+        if lang not in ["cpp", "python", "php",
+                        "javascript", "ruby", "typescript"]:
             raise RuntimeError(f"{lang} not in supported list.")
 
         n_problems += len(problems)
@@ -213,7 +227,10 @@ def evaluate_mbxp(n_works: int, df: pd.DataFrame, result_keys: dict):
     lang_counts = {}
     for i in tqdm(range(n_problems)):
         key, lang, passed, result, response = out_queue.get()
-        passes[key] = {"passed": passed, "result": result, "response": response}
+        passes[key] = {
+            "passed": passed,
+            "result": result,
+            "response": response}
         n_passed += passed
 
         lang_passed.setdefault(lang, 0)
@@ -244,7 +261,8 @@ def evaluate_openorca(df: pd.DataFrame, result_keys: dict):
     score = calculate_rouge_score(gen_output, gt_output)
     gen_token_len = df[result_keys['length']].tolist()
     gen_token_per_sample = sum(gen_token_len) / len(gen_token_len)
-    print(f"OpenOrca score: {score}, gen_token_per_sample: {gen_token_per_sample}")
+    print(
+        f"OpenOrca score: {score}, gen_token_per_sample: {gen_token_per_sample}")
     return score
 
 
@@ -266,13 +284,18 @@ def evaluate_gsm8k(df: pd.DataFrame, result_keys: dict):
     em = correct / total
     gen_token_len = df[result_keys['length']].tolist()
     gen_token_per_sample = sum(gen_token_len) / len(gen_token_len)
-    print(f"EM: {em}, correct: {correct} / {total}, gen_token_per_sample: {gen_token_per_sample}")
+    print(
+        f"EM: {em}, correct: {correct} / {total}, gen_token_per_sample: {gen_token_per_sample}")
     return em
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--n_workers", type=int, default=10, help="The number of processes to use")
+    parser.add_argument(
+        "--n_workers",
+        type=int,
+        default=10,
+        help="The number of processes to use")
     parser.add_argument("--results_path", type=str, default="mixtral_8x7b_15000_greedy_reference_fp16_mintoken2.pkl",
                         help="The path to the results file pickle file")
     parser.add_argument("--result_key", type=str, default="ref_output",
@@ -307,9 +330,9 @@ def evaluate_gsm8k(df: pd.DataFrame, result_keys: dict):
     """
 
     df = pd.read_pickle(args.results_path)
-    df_gsm8k = df[df['dataset']=="GSM8K"].copy()
+    df_gsm8k = df[df['dataset'] == "GSM8K"].copy()
     evaluate_gsm8k(df_gsm8k, result_keys)
-    df_openorca = df[df['dataset']=="OpenOrca"].copy()
+    df_openorca = df[df['dataset'] == "OpenOrca"].copy()
     evaluate_openorca(df_openorca, result_keys)
-    df_mbxp = df[df['dataset']=="MBXP"].copy()
+    df_mbxp = df[df['dataset'] == "MBXP"].copy()
     evaluate_mbxp(args.n_workers, df_mbxp, result_keys)
diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index f421d88055..85b69894ec 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -323,7 +323,8 @@ std::vector<QueryMetadata> GenerateQueries(
       size_t pad_size =
           (loaded_samples.size() - samples_per_query % loaded_samples.size());
       samples_per_query += pad_size;
-    } else if ((scenario != TestScenario::Offline) && (min_queries % loaded_samples.size() != 0)) {
+    } else if ((scenario != TestScenario::Offline) &&
+               (min_queries % loaded_samples.size() != 0)) {
       // In Server, SingleStream, MultiStream mode, the min_queries should be
       // padded
       size_t pad_size =
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 67fb91d2d8..ddc5cebff2 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -757,8 +757,8 @@ int TestSettings::FromConfig(const std::string &path, const std::string &model,
            &performance_issue_same_index, nullptr);
 
   if (lookupkv(model, scenario, "sample_concatenate_permutation", &val,
-            nullptr))
-      sample_concatenate_permutation = (val == 1) ? true : false;
+               nullptr))
+    sample_concatenate_permutation = (val == 1) ? true : false;
   if (lookupkv(model, "Server", "coalesce_queries", &val, nullptr))
     server_coalesce_queries = (val == 0) ? false : true;
   if (lookupkv(model, "Server", "max_async_queries", &val, nullptr))
diff --git a/tools/submission/power/power_checker.py b/tools/submission/power/power_checker.py
index 5a0eb0953e..06b058da9a 100755
--- a/tools/submission/power/power_checker.py
+++ b/tools/submission/power/power_checker.py
@@ -39,7 +39,7 @@ class CheckerWarning(Exception):
     pass
 
 
-SUPPORTED_VERSION = ["1.10.0"]
+SUPPORTED_VERSION = ["1.11.1"]
 SUPPORTED_MODEL = {
     "YokogawaWT210": 8,
     "YokogawaWT500": 35,
@@ -81,8 +81,7 @@ class CheckerWarning(Exception):
 ]
 COMMON_ERROR_TESTING = ["USB."]
 WARNING_NEEDS_TO_BE_ERROR_TESTING_RE = [
-    re.compile(
-        r"Uncertainty \d+.\d+%, which is above 1.00% limit for the last sample!")
+    re.compile(r"Uncertainty \d+.\d+%, which is above 1.00% limit for the last sample!")
 ]
 
 TIME_DELTA_TOLERANCE = 800  # in milliseconds
@@ -127,10 +126,8 @@ def get_time_from_line(
 ) -> float:
     log_time_str = re.search(data_regexp, line)
     if log_time_str and log_time_str.group(0):
-        log_datetime = datetime.strptime(
-            log_time_str.group(0), "%m-%d-%Y %H:%M:%S.%f")
-        return log_datetime.replace(
-            tzinfo=timezone.utc).timestamp() + timezone_offset
+        log_datetime = datetime.strptime(log_time_str.group(0), "%m-%d-%Y %H:%M:%S.%f")
+        return log_datetime.replace(tzinfo=timezone.utc).timestamp() + timezone_offset
     raise LineWithoutTimeStamp(f"{line.strip()!r} in {file}.")
 
 
@@ -159,10 +156,8 @@ def required_fields_check(self) -> None:
         ), f"Required fields {', '.join(absent_keys)!r} does not exist in {self.path!r}"
 
 
-def compare_dicts_values(
-        d1: Dict[str, str], d2: Dict[str, str], comment: str) -> None:
-    files_with_diff_check_sum = {k: d1[k]
-                                 for k in d1 if k in d2 and d1[k] != d2[k]}
+def compare_dicts_values(d1: Dict[str, str], d2: Dict[str, str], comment: str) -> None:
+    files_with_diff_check_sum = {k: d1[k] for k in d1 if k in d2 and d1[k] != d2[k]}
     assert len(files_with_diff_check_sum) == 0, f"{comment}" + "".join(
         [
             f"Expected {d1[i]}, but got {d2[i]} for {i}\n"
@@ -171,8 +166,7 @@ def compare_dicts_values(
     )
 
 
-def compare_dicts(s1: Dict[str, str],
-                  s2: Dict[str, str], comment: str) -> None:
+def compare_dicts(s1: Dict[str, str], s2: Dict[str, str], comment: str) -> None:
     assert (
         not s1.keys() - s2.keys()
     ), f"{comment} Missing {', '.join(sorted(s1.keys() - s2.keys()))!r}"
@@ -220,7 +214,7 @@ def get_ptd_answer(command: str) -> str:
 
     assert (
         version in SUPPORTED_VERSION
-    ), f"PTD version {version!r} is not supported. Supported versions are 1.10.0"
+    ), f"PTD version {version!r} is not supported. Supported versions are {SUPPORTED_VERSION}"
     assert (
         power_meter_model in SUPPORTED_MODEL.keys()
     ), f"Power meter {power_meter_model!r} is not supported. Only {', '.join(SUPPORTED_MODEL.keys())} are supported."
@@ -230,8 +224,7 @@ def check_reply(cmd: str, reply: str) -> None:
         for msg in msgs:
             if msg["cmd"].startswith(cmd):
                 if msg["cmd"] == "Stop":
-                    # In normal flow the third answer to stop command is
-                    # `Error: no measurement to stop`
+                    # In normal flow the third answer to stop command is `Error: no measurement to stop`
                     if stop_counter == 2:
                         reply = "Error: no measurement to stop"
                     stop_counter += 1
@@ -250,15 +243,13 @@ def check_reply(cmd: str, reply: str) -> None:
     def get_initial_range(param_num: int, reply: str) -> str:
         reply_list = reply.split(",")
         try:
-            if reply_list[param_num] == "0" and float(
-                    reply_list[param_num + 1]) > 0:
+            if reply_list[param_num] == "0" and float(reply_list[param_num + 1]) > 0:
                 return reply_list[param_num + 1]
         except (ValueError, IndexError):
             assert False, f"Can not get power meters initial values from {reply!r}"
         return "Auto"
 
-    def get_command_by_value_and_number(
-            cmd: str, number: int) -> Optional[str]:
+    def get_command_by_value_and_number(cmd: str, number: int) -> Optional[str]:
         command_counter = 0
         for msg in msgs:
             if msg["cmd"].startswith(cmd):
@@ -282,8 +273,7 @@ def get_command_by_value_and_number(
     ), f"Do not set Volts range as initial. Expected 'SR,V,{initial_volts}', got {initial_volts_command!r}."
 
 
-def uuid_check(client_sd: SessionDescriptor,
-               server_sd: SessionDescriptor) -> None:
+def uuid_check(client_sd: SessionDescriptor, server_sd: SessionDescriptor) -> None:
     """Compare UUIDs from client.json and server.json. They should be the same."""
     uuid_c = client_sd.json_object["uuid"]
     uuid_s = server_sd.json_object["uuid"]
@@ -372,8 +362,7 @@ def compare_duration(range_duration: float, test_duration: float) -> None:
     def compare_time_boundaries(
         begin: float, end: float, phases: List[Any], mode: str
     ) -> None:
-        # TODO: temporary workaround, remove when proper DST handling is
-        # implemented!
+        # TODO: temporary workaround, remove when proper DST handling is implemented!
         assert (
             phases[1][0] < begin < phases[2][0]
             or phases[1][0] < begin - 3600 < phases[2][0]
@@ -391,16 +380,8 @@ def compare_time_boundaries(
         os.path.join(path, "run_1"), client_sd
     )
 
-    compare_time_boundaries(
-        system_begin_r,
-        system_end_r,
-        phases_ranging_c,
-        "ranging")
-    compare_time_boundaries(
-        system_begin_t,
-        system_end_t,
-        phases_testing_c,
-        "testing")
+    compare_time_boundaries(system_begin_r, system_end_r, phases_ranging_c, "ranging")
+    compare_time_boundaries(system_begin_t, system_end_t, phases_testing_c, "testing")
 
     ranging_duration_d = system_end_r - system_begin_r
     testing_duration_d = system_end_t - system_begin_t
@@ -483,8 +464,7 @@ def session_name_check(
     ), f"Session name is not equal. Client session name is {session_name_c!r}. Server session name is {session_name_s!r}"
 
 
-def messages_check(client_sd: SessionDescriptor,
-                   server_sd: SessionDescriptor) -> None:
+def messages_check(client_sd: SessionDescriptor, server_sd: SessionDescriptor) -> None:
     """Compare client and server messages list length.
     Compare messages values and replies from client.json and server.json.
     Compare client and server version.
@@ -508,19 +488,14 @@ def messages_check(client_sd: SessionDescriptor,
             )
 
     # Check client and server version from server.json.
-    # Server.json contains all client.json messages and replies. Checked
-    # earlier.
+    # Server.json contains all client.json messages and replies. Checked earlier.
     def get_version(regexp: str, line: str) -> str:
         version_o = re.search(regexp, line)
         assert version_o is not None, f"Server version is not defined in:'{line}'"
         return version_o.group(1)
 
-    client_version = get_version(
-        r"mlcommons\/power client v(\d+)$",
-        ms[0]["cmd"])
-    server_version = get_version(
-        r"mlcommons\/power server v(\d+)$",
-        ms[0]["reply"])
+    client_version = get_version(r"mlcommons\/power client v(\d+)$", ms[0]["cmd"])
+    server_version = get_version(r"mlcommons\/power server v(\d+)$", ms[0]["reply"])
 
     assert (
         client_version == server_version
@@ -575,8 +550,7 @@ def remove_optional_path(res: Dict[str, str]) -> None:
         f"{client_sd.path} and {server_sd.path} results checksum comparison",
     )
 
-    # Check if the hashes of the files in results directory match the ones
-    # recorded in server.json/client.json.
+    # Check if the hashes of the files in results directory match the ones recorded in server.json/client.json.
     result_c_s = {**results_c, **results_s}
 
     compare_dicts(
@@ -642,8 +616,7 @@ def find_error_or_warning(reg_exp: str, line: str, error: bool) -> None:
 
                 # Treat uncommon errors in ranging phase as warnings
                 if all(
-                    not problem_line.group(0).strip().startswith(
-                        common_ranging_error)
+                    not problem_line.group(0).strip().startswith(common_ranging_error)
                     for common_ranging_error in COMMON_ERROR_RANGING
                 ):
                     raise CheckerWarning(
@@ -701,8 +674,7 @@ def get_msg_without_time(line: str) -> Optional[str]:
     is_uncertainty_check_activated = False
 
     for line in ptd_log_lines:
-        msg_o = re.search(
-            r"Uncertainty checking for Yokogawa\S+ is activated", line)
+        msg_o = re.search(r"Uncertainty checking for Yokogawa\S+ is activated", line)
         if msg_o is not None:
             try:
                 log_time = None
@@ -770,8 +742,7 @@ def debug_check(server_sd: SessionDescriptor) -> None:
     ), "Server was running in debug mode"
 
 
-def check_with_logging(
-        check_name: str, check: Callable[[], None]) -> Tuple[bool, bool]:
+def check_with_logging(check_name: str, check: Callable[[], None]) -> Tuple[bool, bool]:
     try:
         check()
     except AssertionError as e:
@@ -840,9 +811,7 @@ def check(path: str) -> int:
     parser = argparse.ArgumentParser(
         description="Check PTD client-server session results"
     )
-    parser.add_argument(
-        "session_directory",
-        help="directory with session results data")
+    parser.add_argument("session_directory", help="directory with session results data")
 
     args = parser.parse_args()
 
diff --git a/tools/submission/power/sources_checksums.json b/tools/submission/power/sources_checksums.json
index 78a240f1ad..6862abcbd2 100644
--- a/tools/submission/power/sources_checksums.json
+++ b/tools/submission/power/sources_checksums.json
@@ -7,13 +7,13 @@
         "lib/common.py": "611d8b29633d331eb19c9455ea3b5fa3284ed6df",
         "lib/external/__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
         "lib/external/ntplib.py": "4da8f970656505a40483206ef2b5d3dd5e81711d",
-        "lib/server.py": "99303c836c683aa9017ec565104e636161d02acb",
+        "lib/server.py": "9bc9a3b6b9b716520658ac175913bcbbdb354336",
         "lib/source_hashes.py": "60a2e02193209e8d392803326208d5466342da18",
-        "lib/summary.py": "aa92f0a3f975eecd44d3c0cd0236342ccc9f941d",
+        "lib/summary.py": "602a9fedfa503e5544d27ab3b0067f235047efc3",
         "lib/time_sync.py": "80894ef2389e540781ff78de94db16aa4203a14e",
         "server.py": "c3f90f2f7eeb4db30727556d0c815ebc89b3d28b",
         "tests/unit/__init__.py": "da39a3ee5e6b4b0d3255bfef95601890afd80709",
         "tests/unit/test_server.py": "948c1995d4008bc2aa6c4046a34ffa3858d6d671",
         "tests/unit/test_source_hashes.py": "00468a2907583c593e6574a1f6b404e4651c221a"
     }
-]
\ No newline at end of file
+]

From ab7f700043f9824683d173057f668a4f83cfed84 Mon Sep 17 00:00:00 2001
From: arjunsuresh <arjunsuresh@users.noreply.github.com>
Date: Wed, 15 Jan 2025 00:30:47 +0000
Subject: [PATCH 234/305] Increment version to 5.0.10

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index bb09d45b57..b07ada5862 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.9
+5.0.10

From ef1bd3b95e17df85c4cf71fa298018773d83a04a Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 16 Jan 2025 00:51:49 -0500
Subject: [PATCH 235/305] Fix reporting of samples per queries with equal issue
 mode (#2039)

* Fix reporting of samples per queries with equal issue mode

* Update VERSION.txt

---------

Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
---
 loadgen/VERSION.txt               | 2 +-
 loadgen/loadgen.cc                | 2 +-
 loadgen/mlperf.conf               | 1 +
 loadgen/test_settings_internal.cc | 6 ++++++
 4 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index b07ada5862..175775885b 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.10
+5.0.11
diff --git a/loadgen/loadgen.cc b/loadgen/loadgen.cc
index 85b69894ec..42b2140de2 100644
--- a/loadgen/loadgen.cc
+++ b/loadgen/loadgen.cc
@@ -421,7 +421,7 @@ std::vector<QueryMetadata> GenerateQueries(
     }
   }
 
-  LogDetail([count = queries.size(), spq = settings.samples_per_query,
+  LogDetail([count = queries.size(), spq = samples_per_query,
              duration = timestamp.count()](AsyncDetail& detail) {
 #if USE_NEW_LOGGING_FORMAT
     MLPERF_LOG(detail, "generated_query_count", count);
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 9ef1188208..836b42564b 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -13,6 +13,7 @@ dlrm.*.performance_sample_count_override = 204800
 dlrm-v2.*.performance_sample_count_override = 204800
 rnnt.*.performance_sample_count_override = 2513
 gptj.*.performance_sample_count_override = 13368
+mixtral-8x7b.*.performance_sample_count_override = 15000
 llama2-70b.*.performance_sample_count_override = 24576
 llama3_1-405b.*.performance_sample_count_override = 8313
 stable-diffusion-xl.*.performance_sample_count_override = 5000
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index ddc5cebff2..5da0952f88 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -515,6 +515,12 @@ void TestSettingsInternal::LogSummary(AsyncSummary &summary) const {
   summary("performance_issue_same : ", performance_issue_same);
   summary("performance_issue_same_index : ", performance_issue_same_index);
   summary("performance_sample_count : ", performance_sample_count);
+  if (sample_concatenate_permutation){
+    summary("WARNING: sample_concatenate_permutation was set to true. \n"
+            "Generated samples per query might be different as the one in the setting.\n"
+            "Check the generated_samples_per_query line in the detailed log for the real\n"
+            "samples_per_query value");
+  }
 }
 
 }  // namespace loadgen

From 277cf38f5e90c8722c6e7fdb425700f778ca7422 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Thu, 16 Jan 2025 10:24:47 -0800
Subject: [PATCH 236/305] Add missing mixtral requirements file (#2042)

---
 language/mixtral-8x7b/requirements.txt | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 language/mixtral-8x7b/requirements.txt

diff --git a/language/mixtral-8x7b/requirements.txt b/language/mixtral-8x7b/requirements.txt
new file mode 100644
index 0000000000..c95404a2c0
--- /dev/null
+++ b/language/mixtral-8x7b/requirements.txt
@@ -0,0 +1,8 @@
+transformers==4.46.2
+nltk==3.8.1
+evaluate==0.4.0
+absl-py==1.4.0
+rouge-score==0.1.2
+sentencepiece==0.2.0
+accelerate==1.2.1
+pybind11==2.10.4

From dcd0c3eadb6189051ba0efe0a1f251a995fe8895 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Thu, 16 Jan 2025 16:59:28 -0600
Subject: [PATCH 237/305] Update format.yml to us GITHUB_TOKEN & GitHub Actions
 bot (#2044)

---
 .github/workflows/format.yml | 20 ++++++--------------
 1 file changed, 6 insertions(+), 14 deletions(-)

diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index 2ed014d04c..7a75ad2032 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -11,15 +11,9 @@ env:
 jobs:
   format-code:
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
     steps:
-      - name: Retrieve secrets from Keeper
-        id: ksecrets
-        uses: Keeper-Security/ksm-action@master
-        with:
-          keeper-secret-config: ${{ secrets.KSM_CONFIG }}
-          secrets: |-
-            v2h4jKiZlJywDSoKzRMnRw/field/Access Token > env:PAT  # Fetch PAT and store in environment variable
-
       - name: Checkout code
         uses: actions/checkout@v4
         with:
@@ -58,15 +52,13 @@ jobs:
           done
 
       - name: Commit and push changes
-        env:
-          PAT: ${{ env.PAT }}  # Use PAT fetched from Keeper
         run: |
           HAS_CHANGES=$(git diff --staged --name-only)
           if [ ${#HAS_CHANGES} -gt 0 ]; then
-            git config --global user.name mlcommons-bot
-            git config --global user.email "mlcommons-bot@users.noreply.github.com"
+            git config --global user.name github-actions[bot]
+            git config --global user.email "github-actions[bot]@users.noreply.github.com"
             # Commit changes
             git commit -m '[Automated Commit] Format Codebase'
-            # Use the PAT to push changes
-            git push https://x-access-token:${PAT}@github.com/${{ github.repository }} HEAD:${{ github.ref_name }}
+            # Use the GITHUB_TOKEN to push changes
+            git push
           fi

From ba71b21c3eeb461ed82b4c92709c654a06eb00f7 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 21 Jan 2025 12:05:12 -0500
Subject: [PATCH 238/305] Low latency checks to submission checker and report
 low latency (#2017)

* Add low latency checks to submission checker

* Set llama2-70b-low-latency as a different model to avoid uncommenting config

* Replace: low-latency->interactive

* Include interactive mode in llama2 reference + README

* Update README to have interactive server scenario + remove unnecessary changes

* Update test-submission-checker.yml

* Update test-submission-checker.yml

---------

Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
---
 .github/workflows/test-submission-checker.yml |   4 +-
 language/llama2-70b/README.md                 |  18 +++
 language/llama2-70b/main.py                   |  11 +-
 language/llama2-70b/user.conf                 |   2 -
 loadgen/mlperf.conf                           |   8 ++
 tools/submission/generate_final_report.py     |   4 +
 tools/submission/submission_checker.py        | 114 +++++++++++++++---
 7 files changed, 135 insertions(+), 26 deletions(-)

diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index b7ab88ddb8..5b7637d7da 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -32,7 +32,7 @@ jobs:
     - name: Install dependencies
       run: |
         python3 -m pip install cm4mlops
-        git clone https://github.com/mlcommons/inference_results_v4.1 --depth 1
+        git clone https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
-        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/inference_results_v4.1 --src_version=v4.1 --extra_args=" --skip-extra-accuracy-files-check" --quiet 
+        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/mlperf_inference_unofficial_submissions_v5.0 --src_version=v5.0 --extra_args=" --skip-extra-files-in-root-check --skip-extra-accuracy-files-check" --quiet 
diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 07d3553445..5268863ac6 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -245,3 +245,21 @@ scale from a 0.0-1.0 scale):
 - Tokens per sample: 294.45
 
 This was run on a DGX-H100 node. Total runtime was ~4.5 days.
+
+# Run llama2-70b-interactive benchmark
+
+For official, Llama2-70b submissions it is also possible to submit in the interactive category. This sets a more strict latency requirements for Time to First Token (ttft) and Time per Output Token (tpot). Specifically, the interactive category requires loadgen to enforce `ttft <= 450ms` and `ttft <= 40ms`
+
+In order to run interactive category, it is sufficient to set the flag `--lg-model-name` as `llama2-70b-interactive` when calling the `main.py` to run the benchmark. For example, to run the server scenario in interactive mode:
+
+```
+python -u main.py --scenario Server \
+                --model-path ${CHECKPOINT_PATH} \
+                --mlperf-conf mlperf.conf \
+                --user-conf user.conf \
+                --total-sample-count 24576 \
+                --device cpu \
+                --dataset-path ${DATASET_PATH} \
+                --output-log-dir server-logs \
+                --lg-model-name llama2-70b-interactive
+```
diff --git a/language/llama2-70b/main.py b/language/llama2-70b/main.py
index 1d795685f1..84ccf849ad 100644
--- a/language/llama2-70b/main.py
+++ b/language/llama2-70b/main.py
@@ -120,7 +120,13 @@ def get_args():
         default=None,
         help="Specify an api endpoint call to use api mode",
     )
-
+    parser.add_argument(
+        "--lg-model-name",
+        type=str,
+        default="llama2-70b",
+        choices=["llama2-70b", "llama2-70b-interactive"],
+        help="Model name(specified in llm server)",
+    )
     args = parser.parse_args()
     return args
 
@@ -146,8 +152,7 @@ def main():
     settings = lg.TestSettings()
     settings.scenario = scenario_map[args.scenario.lower()]
     # mlperf.conf is automatically loaded by the loadgen
-    # settings.FromConfig(args.mlperf_conf, "llama2-70b", args.scenario)
-    settings.FromConfig(args.user_conf, "llama2-70b", args.scenario)
+    settings.FromConfig(args.user_conf, args.lg_model_name, args.scenario)
 
     if args.accuracy:
         settings.mode = lg.TestMode.AccuracyOnly
diff --git a/language/llama2-70b/user.conf b/language/llama2-70b/user.conf
index 945082fe98..bb97c437a9 100644
--- a/language/llama2-70b/user.conf
+++ b/language/llama2-70b/user.conf
@@ -9,5 +9,3 @@
 *.Server.target_qps = 0.5
 *.Server.min_duration = 120000
 *.Server.min_query_count = 100
-
-llama2-70b.Server.sample_concatenate_permutation = 1
\ No newline at end of file
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 836b42564b..582381bbd9 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -15,6 +15,7 @@ rnnt.*.performance_sample_count_override = 2513
 gptj.*.performance_sample_count_override = 13368
 mixtral-8x7b.*.performance_sample_count_override = 15000
 llama2-70b.*.performance_sample_count_override = 24576
+llama2-70b-interactive.*.performance_sample_count_override = 24576
 llama3_1-405b.*.performance_sample_count_override = 8313
 stable-diffusion-xl.*.performance_sample_count_override = 5000
 rgat.*.performance_sample_count_override = 788379
@@ -49,6 +50,7 @@ rgat.*.sample_concatenate_permutation = 1
 # LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario
 gptj.*.sample_concatenate_permutation = 1
 llama2-70b.*.sample_concatenate_permutation = 1
+llama2-70b-interactive.*.sample_concatenate_permutation = 1
 mixtral-8x7b.*.sample_concatenate_permutation = 1
 llama3_1-405b.*.sample_concatenate_permutation = 1
 
@@ -66,6 +68,7 @@ gptj.Server.target_latency = 20000
 stable-diffusion-xl.Server.target_latency = 20000
 # Benchmarks that measure token latencies
 llama2-70b.*.use_token_latencies = 1
+llama2-70b-interactive.*.use_token_latencies = 1
 mixtral-8x7b.*.use_token_latencies = 1
 llama3_1-405b.*.use_token_latencies = 1
 # gptj benchmark infers token latencies
@@ -76,6 +79,11 @@ llama2-70b.Server.target_latency = 0
 llama2-70b.Server.ttft_latency = 2000
 llama2-70b.Server.tpot_latency = 200
 
+# Target Latencies for low latency setting
+llama2-70b-interactive.Server.target_latency = 0
+llama2-70b-interactive.Server.ttft_latency = 450
+llama2-70b-interactive.Server.tpot_latency = 40
+
 mixtral-8x7b.Server.target_latency = 0
 mixtral-8x7b.Server.ttft_latency = 2000
 mixtral-8x7b.Server.tpot_latency = 200
diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index aa5b369835..da965ef3e9 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -147,6 +147,8 @@ def main():
             "stable-diffusion-xl",
             "llama2-70b-99",
             "llama2-70b-99.9",
+            "llama2-70b-interactive-99",
+            "llama2-70b-interactive-99.9",
             "mixtral-8x7b",
         ],
         ["SingleStream", "MultiStream", "Server", "Offline"],
@@ -209,6 +211,8 @@ def main():
                 "stable-diffusion-xl": ["Server", "Offline"],
                 "llama2-70b-99": ["Server", "Offline"],
                 "llama2-70b-99.9": ["Server", "Offline"],
+                "llama2-70b-interactive-99": ["Server", "Offline"],
+                "llama2-70b-interactive-99.9": ["Server", "Offline"],
                 "mixtral-8x7b": ["Server", "Offline"],
                 "rgat": ["Offline"],
                 "llama3.1-405b": ["Offline", "Server"]
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 91cba0f31c..264c9b3738 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -194,7 +194,6 @@
             "ssd-resnet34": "retinanet",
             "mobilenet": "resnet",
             "resnet50": "resnet",
-            "llama3_1-405b": "llama3.1-405b"
         },
         "seeds": {
             "qsl_rng_seed": 3066443479025735752,
@@ -266,6 +265,8 @@
             "gptj-99.9",
             "llama2-70b-99",
             "llama2-70b-99.9",
+            "llama2-70b-interactive-99",
+            "llama2-70b-interactive-99.9",
             "stable-diffusion-xl",
             "mixtral-8x7b",
             "llama3.1-405b",
@@ -283,6 +284,8 @@
             "gptj-99.9": ["Server", "Offline"],
             "llama2-70b-99": ["Server", "Offline"],
             "llama2-70b-99.9": ["Server", "Offline"],
+            "llama2-70b-interactive-99": ["Server", "Offline"],
+            "llama2-70b-interactive-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["Server", "Offline"],
             "mixtral-8x7b": ["Server", "Offline"],
             "llama3.1-405b": ["Server", "Offline"],
@@ -314,6 +317,8 @@
             "gptj-99.9": ["SingleStream", "Offline", "Server"],
             "llama2-70b-99": ["Server", "Offline"],
             "llama2-70b-99.9": ["Server", "Offline"],
+            "llama2-70b-interactive-99": ["Server", "Offline"],
+            "llama2-70b-interactive-99.9": ["Server", "Offline"],
             "stable-diffusion-xl": ["SingleStream", "Offline", "Server"],
             "mixtral-8x7b": ["Server", "Offline"],
             "llama3.1-405b": ["Server", "Offline"],
@@ -370,6 +375,26 @@
                 "TOKENS_PER_SAMPLE",
                 294.45 * 0.9,
             ),
+            "llama2-70b-interactive-99": (
+                "ROUGE1",
+                44.4312 * 0.99,
+                "ROUGE2",
+                22.0352 * 0.99,
+                "ROUGEL",
+                28.6162 * 0.99,
+                "TOKENS_PER_SAMPLE",
+                294.45 * 0.9,
+            ),
+            "llama2-70b-interactive-99.9": (
+                "ROUGE1",
+                44.4312 * 0.999,
+                "ROUGE2",
+                22.0352 * 0.999,
+                "ROUGEL",
+                28.6162 * 0.999,
+                "TOKENS_PER_SAMPLE",
+                294.45 * 0.9,
+            ),
             "stable-diffusion-xl": (
                 "CLIP_SCORE",
                 31.68631873,
@@ -409,6 +434,8 @@
             ),
             "llama2-70b-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
             "llama2-70b-99.9": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
+            "llama2-70b-interactive-99": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
+            "llama2-70b-interactive-99.9": ("TOKENS_PER_SAMPLE", 294.45 * 1.1),
             "mixtral-8x7b": ("TOKENS_PER_SAMPLE", 145.9 * 1.1),
             "llama3.1-405b": ("TOKENS_PER_SAMPLE", 684.68 * 1.1),
         },
@@ -428,6 +455,8 @@
             "gptj-99.9": 13368,
             "llama2-70b-99": 24576,
             "llama2-70b-99.9": 24576,
+            "llama2-70b-interactive-99": 24576,
+            "llama2-70b-interactive-99.9": 24576,
             "stable-diffusion-xl": 5000,
             "mixtral-8x7b": 15000,
             "llama3.1-405b": 8313,
@@ -439,8 +468,10 @@
         # not really needed
         "model_mapping": {
             # map model names to the official mlperf model class
+            "ssd-resnet34": "retinanet",
             "mobilenet": "resnet",
             "resnet50": "resnet",
+            "llama3_1-405b": "llama3.1-405b",
         },
         "seeds": {
             # TODO: Update random seeds
@@ -459,6 +490,8 @@
             "stable-diffusion-xl": {"Server": 20000000000},
             "llama2-70b-99": {"Server": 20000000000},
             "llama2-70b-99.9": {"Server": 20000000000},
+            "llama2-70b-interactive-99": {"Server": 20000000000},
+            "llama2-70b-interactive-99.9": {"Server": 20000000000},
             "mixtral-8x7b": {"Server": 20000000000},
             "llama3.1-405b": {"Server": 60000000000}
         },
@@ -485,6 +518,8 @@
             "gptj-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "llama2-70b-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "llama2-70b-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama2-70b-interactive-99": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
+            "llama2-70b-interactive-99.9": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "stable-diffusion-xl": {
                 "SingleStream": 1024,
                 "Server": 270336,
@@ -578,6 +613,8 @@
     "gptj-99.9": 13368,
     "llama2-70b-99": 24576,
     "llama2-70b-99.9": 24576,
+    "llama2-70b-interactive-99": 24576,
+    "llama2-70b-interactive-99.9": 24576,
     "stable-diffusion-xl": 5000,
     "mixtral-8x7b": 15000,
     "llama3.1-405b": 8313,
@@ -645,6 +682,14 @@
             "Offline": "result_tokens_per_second",
             "Server": "result_completed_tokens_per_second",
         },
+        "llama2-70b-interactive-99": {
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_tokens_per_second",
+        },
+        "llama2-70b-interactive-99.9": {
+            "Offline": "result_tokens_per_second",
+            "Server": "result_completed_tokens_per_second",
+        },
         "gptj-99": {
             "Offline": "result_inferred_tokens_per_second",
             "Server": "result_inferred_completed_tokens_per_second",
@@ -666,14 +711,20 @@
 
 LLM_LATENCY_LIMITS = {
     "llama2-70b-99": {
-        "conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}
+        "ttft": 2000 * 1000000, "tpot": 200 * 1000000
     },
     "llama2-70b-99.9": {
-        "conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}
+        "ttft": 2000 * 1000000, "tpot": 200 * 1000000
+    },
+    "llama2-70b-interactive-99": {
+        "ttft": 450 * 1000000, "tpot": 40 * 1000000
+    },
+    "llama2-70b-interactive-99.9": {
+        "ttft": 450 * 1000000, "tpot": 40 * 1000000
     },
-    "mixtral-8x7b": {"conversational": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000}},
+    "mixtral-8x7b": {"ttft": 2000 * 1000000, "tpot": 200 * 1000000},
     "llama3.1-405b": {
-        "conversational": {"ttft": 6000 * 1000000, "tpot": 175 * 1000000}
+        "ttft": 6000 * 1000000, "tpot": 175 * 1000000
     },
 }
 
@@ -956,6 +1007,8 @@ def requires_equal_issue(self, model, division):
                 "gptj-99.9",
                 "llama2-70b-99",
                 "llama2-70b-99.9",
+                "llama2-70b-interactive-99",
+                "llama2-70b-interactive-99.9",
                 "mixtral-8x7b",
                 "llama3.1-405b",
                 "rgat",
@@ -1253,25 +1306,29 @@ def extra_check_llm(mlperf_log, scenario, model):
     if mlperf_log["requested_use_token_latencies"]:
         if scenario == "Offline":
             # For offline no further checks are necessary
-            return None, True
+            return True
         else:
-            for constraint, limits in LLM_LATENCY_LIMITS[model].items():
-                if (
-                    mlperf_log["result_first_token_99.00_percentile_latency_ns"]
-                    < limits["ttft"]
-                    and mlperf_log["result_time_per_output_token_99.00_percentile_ns"]
-                    < limits["tpot"]
-                ):
-                    return constraint, True
+            limits = LLM_LATENCY_LIMITS[model]
+            if (
+                mlperf_log["result_first_token_99.00_percentile_latency_ns"]
+                < limits["ttft"]
+                and mlperf_log["result_time_per_output_token_99.00_percentile_ns"]
+                < limits["tpot"]
+            ):
+                return True
     else:
         log.error(
             f"use_token_latencies flag needs to be enabled for Llama2 benchmark")
-        return None, False
+        return False
 
     log.error(
-        f'Failed Llama2 extra check for TTFT and TPOT. TTFT 99-tile: {mlperf_log["result_first_token_99.00_percentile_latency_ns"]}, TPOT 99-tile: {mlperf_log["result_time_per_output_token_99.00_percentile_ns"]}'
+        'Failed extra check for TTFT and TPOT. Obtained: TTFT 99-tile: %.4f, TPOT 99-tile: %.4f. Required: TTFT 99-tile: %.4f, TPOT 99-tile: %.4f',
+        mlperf_log["result_first_token_99.00_percentile_latency_ns"],
+        mlperf_log["result_time_per_output_token_99.00_percentile_ns"],
+        limits["ttft"],
+        limits["tpot"]
     )
-    return None, False
+    return False
 
 
 def get_performance_metric(
@@ -1340,9 +1397,11 @@ def check_performance_dir(
         )
 
     if model in ["llama2-70b-99", "llama2-70b-99.9",
+                 "llama2-70b-interactive-99", "llama2-70b-interactive-99.9",
                  "mixtral-8x7b", "llama3.1-405b"]:
-        llama_constraint, is_valid = extra_check_llm(
+        llm_is_valid = extra_check_llm(
             mlperf_log, scenario_fixed, model)
+        is_valid = (llm_is_valid and is_valid)
 
     latency_99_percentile = mlperf_log["result_99.00_percentile_latency_ns"]
     latency_mean = mlperf_log["result_mean_latency_ns"]
@@ -1874,6 +1933,18 @@ def log_result(
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
+            "llama2-70b-interactive-99": {
+                "SingleStream": "Latency (ms)",
+                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            },
+            "llama2-70b-interactive-99.9": {
+                "SingleStream": "Latency (ms)",
+                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            },
             "mixtral-8x7b": {
                 "SingleStream": "Latency (ms)",
                 "MultiStream": "Latency (ms)",
@@ -2398,7 +2469,7 @@ def log_result(
                                     perf_path,
                                     scenario_fixed,
                                     division,
-                                    system_json,
+                                    system_json
                                 )
                                 if is_inferred:
                                     inferred = 1
@@ -2966,6 +3037,8 @@ def check_compliance_dir(
         "gptj-99.9",
         "llama2-70b-99",
         "llama2-70b-99.9",
+        "llama2-70b-interactive-99",
+        "llama2-70b-interactive-99.9",
         "mixtral-8x7b",
         "llama3.1-405b",
         "rgat",
@@ -2987,6 +3060,8 @@ def check_compliance_dir(
         "gptj-99.9",
         "llama2-70b-99",
         "llama2-70b-99.9",
+        "llama2-70b-interactive-99",
+        "llama2-70b-interactive-99.9",
         "mixtral-8x7b",
         "llama3.1-405b",
     ]:
@@ -2997,6 +3072,7 @@ def check_compliance_dir(
         test_list.remove("TEST04")
 
     if model in ["llama2-70b-99", "llama2-70b-99.9",
+                 "llama2-70b-interactive-99", "llama2-70b-interactive-99.9",
                  "mixtral-8x7b", "llama3.1-405b"]:
         test_list.append("TEST06")
 

From 2ed0a3e0c8a973ad4e9cb43e4c419d1ac35f4ff7 Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Tue, 21 Jan 2025 17:05:26 +0000
Subject: [PATCH 239/305] Increment version to 5.0.12

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 175775885b..718db1c46f 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.11
+5.0.12

From 6315397def1f8a723614d22fc84a59d83453fb78 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 21 Jan 2025 17:06:11 +0000
Subject: [PATCH 240/305] Create TEST01 audit.conf for rgat (#2037)

* Create TEST01 audit.conf for rgat

* Update benchmark-checklist.md | RGAT checklist completed
---
 compliance/nvidia/TEST01/rgat/audit.conf |  9 +++++++++
 graph/R-GAT/benchmark-checklist.md       | 19 ++++++++++---------
 loadgen/test_settings_internal.cc        | 13 ++++++++-----
 3 files changed, 27 insertions(+), 14 deletions(-)
 create mode 100644 compliance/nvidia/TEST01/rgat/audit.conf

diff --git a/compliance/nvidia/TEST01/rgat/audit.conf b/compliance/nvidia/TEST01/rgat/audit.conf
new file mode 100644
index 0000000000..c861e2a3d6
--- /dev/null
+++ b/compliance/nvidia/TEST01/rgat/audit.conf
@@ -0,0 +1,9 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+
+# mode dictionary (0 = submission, 1 = accuracy, 2 = performance, 3 = find peak perf)
+*.*.mode = 2
+*.*.accuracy_log_rng_seed = 720381539243781796
+*.*.accuracy_log_sampling_target = 4096
diff --git a/graph/R-GAT/benchmark-checklist.md b/graph/R-GAT/benchmark-checklist.md
index f83c816cb6..1d0a8d379b 100644
--- a/graph/R-GAT/benchmark-checklist.md
+++ b/graph/R-GAT/benchmark-checklist.md
@@ -48,11 +48,11 @@ Documented whether **Equal Issue Mode** is applicable in
 ---
 
 #### **9. Reference Implementation Dataset Coverage**
-- [ ] Reference implementation successfully processes the entire validation dataset during:
-  - [ ] Performance runs
-  - [ ] Accuracy runs
-  - [ ] Compliance runs  
-- [ ] Valid log files passing the submission checker are generated for all runs.
+- [X] Reference implementation successfully processes the entire validation dataset during:
+  - [X] Performance runs
+  - [X] Accuracy runs
+  - [X] Compliance runs  
+- [X] Valid log files passing the submission checker are generated for all runs - [link](https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0/tree/main/closed/MLCommons/results/mlc-server-reference-gpu-pytorch_v2.4.0-cu124/rgat/offline/performance/run_1).
 
 ---
 
@@ -81,7 +81,8 @@ Documented whether **Equal Issue Mode** is applicable in
 ---
 
 #### **14. Sample Log Files**
-- [ ] Include sample logs for all the applicable scenario runs:
-  - [ ] `mlperf_log_summary.txt`
-  - [ ] `mlperf_log_detail.txt`  
-- [ ] Ensure sample logs successfully pass the submission checker and applicable compliance runs.
+- [X] Include sample logs for all the applicable scenario runs:
+  - [X] Offline 
+    - [X] [`mlperf_log_summary.txt`](https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0/blob/main/closed/MLCommons/results/mlc-server-reference-gpu-pytorch_v2.4.0-cu124/rgat/offline/performance/run_1/mlperf_log_summary.txt)
+    - [X] [`mlperf_log_detail.txt`](https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0/blob/main/closed/MLCommons/results/mlc-server-reference-gpu-pytorch_v2.4.0-cu124/rgat/offline/performance/run_1/mlperf_log_detail.txt)  
+- [X] Ensure sample logs successfully pass the submission checker and applicable compliance runs. [Link](https://htmlpreview.github.io/?https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0/blob/refs/heads/auto-update/closed/MLCommons/results/mlc-server-reference-gpu-pytorch_v2.4.0-cu124/summary.html)
diff --git a/loadgen/test_settings_internal.cc b/loadgen/test_settings_internal.cc
index 5da0952f88..3f2cd88473 100644
--- a/loadgen/test_settings_internal.cc
+++ b/loadgen/test_settings_internal.cc
@@ -515,11 +515,14 @@ void TestSettingsInternal::LogSummary(AsyncSummary &summary) const {
   summary("performance_issue_same : ", performance_issue_same);
   summary("performance_issue_same_index : ", performance_issue_same_index);
   summary("performance_sample_count : ", performance_sample_count);
-  if (sample_concatenate_permutation){
-    summary("WARNING: sample_concatenate_permutation was set to true. \n"
-            "Generated samples per query might be different as the one in the setting.\n"
-            "Check the generated_samples_per_query line in the detailed log for the real\n"
-            "samples_per_query value");
+  if (sample_concatenate_permutation) {
+    summary(
+        "WARNING: sample_concatenate_permutation was set to true. \n"
+        "Generated samples per query might be different as the one in the "
+        "setting.\n"
+        "Check the generated_samples_per_query line in the detailed log for "
+        "the real\n"
+        "samples_per_query value");
   }
 }
 

From 25bba8f6c66faee3f13372914c542e9c4bdf781b Mon Sep 17 00:00:00 2001
From: arjunsuresh <arjunsuresh@users.noreply.github.com>
Date: Tue, 21 Jan 2025 17:06:23 +0000
Subject: [PATCH 241/305] Increment version to 5.0.13

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 718db1c46f..2713f1415e 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.12
+5.0.13

From e618c314fc3c323e9b3e82578ce3774e72d9ac2b Mon Sep 17 00:00:00 2001
From: rod409 <radoyeh@gmail.com>
Date: Tue, 21 Jan 2025 14:07:09 -0800
Subject: [PATCH 242/305] Merge automotive reference into main branch (#2047)

* Automotive reference implementation sketch

* WIP: automotive reference implementation

* WIP add segmentation, dataset, and util functions to reference

* WIP: reference implementation with issues during post processing

* WIP update dockerfile remove pdb breaks

* WIP: reference implementation that runs samples

* Update README.md with initial docker runs

* WIP: add accuracy checker to reference

* Fix: set lidar detector to evaluation mode

* [Automated Commit] Format Codebase

* Update README.md

* Remove unnecessary mlperf conf load and implement dataset get_item_count

* add cpu only version of reference

* update dockerfile and display overal mAP

* code cleanup

* Update README.md

* fix packages versions in dockerfile

* handle frames if model predicts no objects

* Allow accuracy check on subset of datasets

* [Automated Commit] Format Codebase

* Updates automotive reference implementation (#2045)

* Remove unnecessary mlperf conf load and implement dataset get_item_count

* add cpu only version of reference

* update dockerfile and display overal mAP

* code cleanup

* Update README.md

* fix packages versions in dockerfile

* handle frames if model predicts no objects

* Allow accuracy check on subset of datasets

* [Automated Commit] Format Codebase

---------

Co-authored-by: mlcommons-bot <mlcommons-bot@users.noreply.github.com>

* [Automated Commit] Format Codebase

* fix whitespace merge errors

* [Automated Commit] Format Codebase

* [Automated Commit] Format Codebase

* add removed code during merge

* [Automated Commit] Format Codebase

* change nms thresholds on cpu only code

---------

Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
Co-authored-by: Miro <mirhodak@amd.com>
Co-authored-by: mlcommons-bot <mlcommons-bot@users.noreply.github.com>
---
 automotive/3d-object-detection/README.md      |  23 +-
 .../3d-object-detection/accuracy_waymo.py     |  15 +-
 .../3d-object-detection/backend_deploy.py     |  10 +-
 .../3d-object-detection/backend_onnx.py       | 168 ++++++
 automotive/3d-object-detection/dockerfile.gpu |  20 +-
 automotive/3d-object-detection/main.py        |   5 +-
 .../3d-object-detection/model/painter.py      |  27 +-
 .../3d-object-detection/model/pointpillars.py |   4 +-
 .../model/pointpillars_core.py                | 547 ++++++++++++++++++
 .../3d-object-detection/tools/evaluate.py     |   3 +-
 automotive/3d-object-detection/waymo.py       |  24 +-
 11 files changed, 800 insertions(+), 46 deletions(-)
 create mode 100644 automotive/3d-object-detection/backend_onnx.py
 create mode 100644 automotive/3d-object-detection/model/pointpillars_core.py

diff --git a/automotive/3d-object-detection/README.md b/automotive/3d-object-detection/README.md
index f4848647f4..80e58a5db1 100644
--- a/automotive/3d-object-detection/README.md
+++ b/automotive/3d-object-detection/README.md
@@ -1,14 +1,27 @@
 ## Reference implementation fo automotive 3D detection benchmark
 
-## TODO: Instructions for dataset download after it is uploaded somewhere appropriate
-
-## TODO: Instructions for checkpoints downloads after it is uploaded somewhere appropriate
+## Dataset and model checkpoints
+Contact MLCommons support for accessing the Waymo Open Dataset along with the model checkpoints for the reference implementation. You will need to accept a license agreement and will be given directions to download the data. You will need to place the kitti_format folder under a directory named waymo. There are four total checkpoints 2 for pytorch and 2 for onnx.
 
 ## Running with docker
+Build the container and mount the inference repo and Waymo dataset directory.
 ```
 docker build -t auto_inference -f dockerfile.gpu .
 
-docker run --gpus=all -it -v <directory to inference repo>/inference/:/inference -v <directory to waymo dataset>/waymo:/waymo --rm auto_inference
-
+docker run --gpus=all -it -v <directory to inference repo>/inference/:/inference -v <path to waymo dataset>/waymo:/waymo --rm auto_inference
+```
+### Run with GPU
+```
 cd /inference/automotive/3d-object-detection
 python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp_ep36.pth --segmentor-path <checkpoint_path>/best_deeplabv3plus_resnet50_waymo_os16.pth --mlperf_conf /inference/mlperf.conf
+```
+
+### Run with CPU and ONNX
+```
+python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp.onnx --segmentor-path <checkpoint_path>/deeplabv3+.onnx --mlperf_conf /inference/mlperf.conf
+```
+
+### Run the accuracy checker
+```
+python accuracy_waymo.py --mlperf-accuracy-file <path to accuracy file>/mlperf_log_accuracy.json --waymo-dir /waymo/kitti_format/
+```
diff --git a/automotive/3d-object-detection/accuracy_waymo.py b/automotive/3d-object-detection/accuracy_waymo.py
index c8b5cb72c8..6e4b2f91a6 100644
--- a/automotive/3d-object-detection/accuracy_waymo.py
+++ b/automotive/3d-object-detection/accuracy_waymo.py
@@ -95,13 +95,13 @@ def main():
                     'bbox': [],
                     'score': []
                 }
-
-            detections[image_idx]['name'].append(LABEL2CLASSES[label])
-            detections[image_idx]['dimensions'].append(dimension)
-            detections[image_idx]['location'].append(location)
-            detections[image_idx]['rotation_y'].append(rotation_y)
-            detections[image_idx]['bbox'].append(bbox)
-            detections[image_idx]['score'].append(score)
+            if dimension[0] > 0:
+                detections[image_idx]['name'].append(LABEL2CLASSES[label])
+                detections[image_idx]['dimensions'].append(dimension)
+                detections[image_idx]['location'].append(location)
+                detections[image_idx]['rotation_y'].append(rotation_y)
+                detections[image_idx]['bbox'].append(bbox)
+                detections[image_idx]['score'].append(score)
             image_ids.add(image_idx)
 
     with open(args.output_file, "w") as fp:
@@ -115,6 +115,7 @@ def main():
         val_dataset.data_infos,
         CLASSES,
         cam_sync=False)
+    map_stats['Total'] = np.mean(list(map_stats.values()))
 
     print(map_stats)
     if args.verbose:
diff --git a/automotive/3d-object-detection/backend_deploy.py b/automotive/3d-object-detection/backend_deploy.py
index 1a2f3dee4f..2207ea279b 100644
--- a/automotive/3d-object-detection/backend_deploy.py
+++ b/automotive/3d-object-detection/backend_deploy.py
@@ -78,13 +78,11 @@ def load(self):
         return self
 
     def predict(self, inputs):
-        # TODO: implement predict
         dimensions, locations, rotation_y, box2d, class_labels, class_scores, ids = [
         ], [], [], [], [], [], []
         with torch.inference_mode():
             device = torch.device(
                 "cuda:0" if torch.cuda.is_available() else "cpu")
-            format_results = {}
             model_input = inputs[0]
             batched_pts = model_input['pts']
             scores_from_cam = []
@@ -114,7 +112,7 @@ def predict(self, inputs):
                 calib_info = model_input['calib_info']
                 image_info = model_input['image_info']
                 idx = model_input['image_info']['image_idx']
-
+                format_result['idx'] = idx
                 calib_info = change_calib_device(calib_info, False)
                 result_filter = keep_bbox_from_image_range(
                     result, calib_info, 5, image_info, False)
@@ -135,9 +133,6 @@ def predict(self, inputs):
                     format_result['location'].append(camera_bbox[:3])
                     format_result['rotation_y'].append(camera_bbox[6].item())
                     format_result['score'].append(score.item())
-                    format_results['idx'] = idx
-
-                # write_label(format_result, os.path.join(saved_submit_path, f'{idx:06d}.txt'))
 
                 if len(format_result['dimensions']) > 0:
                     format_result['dimensions'] = torch.stack(
@@ -150,6 +145,5 @@ def predict(self, inputs):
                 class_labels.append(format_result['class'])
                 class_scores.append(format_result['score'])
                 box2d.append(format_result['bbox'])
-                ids.append(format_results['idx'])
-            # return Boxes, Classes, Scores # Change to desired output
+                ids.append(format_result['idx'])
         return dimensions, locations, rotation_y, box2d, class_labels, class_scores, ids
diff --git a/automotive/3d-object-detection/backend_onnx.py b/automotive/3d-object-detection/backend_onnx.py
new file mode 100644
index 0000000000..f26f9ad99e
--- /dev/null
+++ b/automotive/3d-object-detection/backend_onnx.py
@@ -0,0 +1,168 @@
+from typing import Optional, List, Union
+import os
+import torch
+import logging
+import backend
+from collections import namedtuple
+from model.painter import Painter
+from model.pointpillars_core import PointPillarsPre, PointPillarsPos
+import numpy as np
+from tools.process import keep_bbox_from_image_range
+from waymo import Waymo
+import onnxruntime as ort
+
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("backend-onnx")
+
+
+def change_calib_device(calib, cuda):
+    result = {}
+    if cuda:
+        device = 'cuda'
+    else:
+        device = 'cpu'
+    result['R0_rect'] = calib['R0_rect'].to(device=device, dtype=torch.float)
+    for i in range(5):
+        result['P' + str(i)] = calib['P' + str(i)
+                                     ].to(device=device, dtype=torch.float)
+        result['Tr_velo_to_cam_' +
+               str(i)] = calib['Tr_velo_to_cam_' +
+                               str(i)].to(device=device, dtype=torch.float)
+    return result
+
+
+def to_numpy(tensor):
+    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
+
+
+class BackendOnnx(backend.Backend):
+    def __init__(
+        self,
+        segmentor_path,
+        lidar_detector_path,
+        data_path
+    ):
+        super(BackendOnnx, self).__init__()
+        self.segmentor_path = segmentor_path
+        self.lidar_detector_path = lidar_detector_path
+        # self.segmentation_classes = 18
+        self.detection_classes = 3
+        self.data_root = data_path
+        CLASSES = Waymo.CLASSES
+        self.LABEL2CLASSES = {v: k for k, v in CLASSES.items()}
+
+    def version(self):
+        return torch.__version__
+
+    def name(self):
+        return "python-SUT"
+
+    def load(self):
+        device = torch.device("cpu")
+        PaintArgs = namedtuple(
+            'PaintArgs', [
+                'training_path', 'model_path', 'cam_sync'])
+        painting_args = PaintArgs(
+            os.path.join(
+                self.data_root,
+                'training'),
+            self.segmentor_path,
+            False)
+        self.painter = Painter(painting_args, onnx=True)
+        self.segmentor = self.painter.model
+        model_pre = PointPillarsPre()
+        model_post = PointPillarsPos(self.detection_classes)
+        model_pre.eval()
+        model_post.eval()
+        ort_sess = ort.InferenceSession(self.lidar_detector_path)
+        self.lidar_detector = ort_sess
+        self.model_pre = model_pre
+        self.model_post = model_post
+        return self
+
+    def predict(self, inputs):
+        dimensions, locations, rotation_y, box2d, class_labels, class_scores, ids = [
+        ], [], [], [], [], [], []
+        with torch.inference_mode():
+            model_input = inputs[0]
+            batched_pts = model_input['pts']
+            scores_from_cam = []
+            for i in range(len(model_input['images'])):
+                input_image_name = self.segmentor.get_inputs()[0].name
+                input_data = {
+                    input_image_name: to_numpy(
+                        model_input['images'][i])}
+                segmentation_score = self.segmentor.run(None, input_data)
+                segmentation_score = [
+                    torch.from_numpy(item) for item in segmentation_score]
+                scores_from_cam.append(
+                    self.painter.get_score(
+                        segmentation_score[0].squeeze(0)).cpu())
+            points = self.painter.augment_lidar_class_scores_both(
+                scores_from_cam, batched_pts, model_input['calib_info'])
+            pillars, coors_batch, npoints_per_pillar = self.model_pre(batched_pts=[
+                                                                      points])
+            input_pillars_name = self.lidar_detector.get_inputs()[0].name
+            input_coors_batch_name = self.lidar_detector.get_inputs()[1].name
+            input_npoints_per_pillar_name = self.lidar_detector.get_inputs()[
+                2].name
+            input_data = {input_pillars_name: to_numpy(pillars),
+                          input_coors_batch_name: to_numpy(coors_batch),
+                          input_npoints_per_pillar_name: to_numpy(npoints_per_pillar)}
+            result = self.lidar_detector.run(None, input_data)
+            result = [torch.from_numpy(item) for item in result]
+            batch_results = self.model_post(result)
+            for j, result in enumerate(batch_results):
+                format_result = {
+                    'class': [],
+                    'truncated': [],
+                    'occluded': [],
+                    'alpha': [],
+                    'bbox': [],
+                    'dimensions': [],
+                    'location': [],
+                    'rotation_y': [],
+                    'score': [],
+                    'idx': -1
+                }
+
+                calib_info = model_input['calib_info']
+                image_info = model_input['image_info']
+                idx = model_input['image_info']['image_idx']
+                format_result['idx'] = idx
+                calib_info = change_calib_device(calib_info, False)
+                result_filter = keep_bbox_from_image_range(
+                    result, calib_info, 5, image_info, False)
+
+                lidar_bboxes = result_filter['lidar_bboxes']
+                labels, scores = result_filter['labels'], result_filter['scores']
+                bboxes2d, camera_bboxes = result_filter['bboxes2d'], result_filter['camera_bboxes']
+                for lidar_bbox, label, score, bbox2d, camera_bbox in \
+                        zip(lidar_bboxes, labels, scores, bboxes2d, camera_bboxes):
+                    format_result['class'].append(label.item())
+                    format_result['truncated'].append(0.0)
+                    format_result['occluded'].append(0)
+                    alpha = camera_bbox[6] - \
+                        np.arctan2(camera_bbox[0], camera_bbox[2])
+                    format_result['alpha'].append(alpha.item())
+                    format_result['bbox'].append(bbox2d.tolist())
+                    format_result['dimensions'].append(camera_bbox[3:6])
+                    format_result['location'].append(camera_bbox[:3])
+                    format_result['rotation_y'].append(camera_bbox[6].item())
+                    format_result['score'].append(score.item())
+
+                if len(format_result['dimensions']) > 0:
+                    format_result['dimensions'] = torch.stack(
+                        format_result['dimensions'])
+                    format_result['location'] = torch.stack(
+                        format_result['location'])
+                dimensions.append(format_result['dimensions'])
+                locations.append(format_result['location'])
+                rotation_y.append(format_result['rotation_y'])
+                class_labels.append(format_result['class'])
+                class_scores.append(format_result['score'])
+                box2d.append(format_result['bbox'])
+                ids.append(format_result['idx'])
+
+        return dimensions, locations, rotation_y, box2d, class_labels, class_scores, ids
diff --git a/automotive/3d-object-detection/dockerfile.gpu b/automotive/3d-object-detection/dockerfile.gpu
index 02acca7a36..ad4237cd0c 100644
--- a/automotive/3d-object-detection/dockerfile.gpu
+++ b/automotive/3d-object-detection/dockerfile.gpu
@@ -1,4 +1,4 @@
-ARG FROM_IMAGE_NAME=pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel
+ARG FROM_IMAGE_NAME=pytorch/pytorch:2.2.2-cuda11.8-cudnn8-devel
 FROM ${FROM_IMAGE_NAME}
 
 ENV DEBIAN_FRONTEND=noninteractive
@@ -20,12 +20,12 @@ RUN cd /tmp && \
     CFLAGS="-std=c++14" python setup.py install && \
     rm -rf mlperf
 
-RUN pip install tqdm
-RUN pip install numba
-RUN pip install opencv-python
-RUN pip install open3d
-RUN pip install tensorboard
-RUN pip install scikit-image
-RUN pip install ninja
-RUN pip install visdom
-RUN pip install shapely
\ No newline at end of file
+RUN pip install tqdm==4.65.0
+RUN pip install numba==0.60.0
+RUN pip install opencv-python==4.11.0.86
+RUN pip install open3d==0.19.0
+RUN pip install scikit-image==0.25.0
+RUN pip install ninja==1.11.1
+RUN pip install shapely==2.0.6
+RUN pip install tensorboard==2.18.0
+RUN pip install onnxruntime==1.20.1
\ No newline at end of file
diff --git a/automotive/3d-object-detection/main.py b/automotive/3d-object-detection/main.py
index 04269e428f..caafd9e47c 100644
--- a/automotive/3d-object-detection/main.py
+++ b/automotive/3d-object-detection/main.py
@@ -167,7 +167,9 @@ def get_backend(backend, **kwargs):
         from backend_deploy import BackendDeploy
 
         backend = BackendDeploy(**kwargs)
-
+    elif backend == 'onnx':
+        from backend_onnx import BackendOnnx
+        backend = BackendOnnx(**kwargs)
     elif backend == "debug":
         from backend_debug import BackendDebug
 
@@ -403,7 +405,6 @@ def flush_queries():
     log_settings.log_output = log_output_settings
 
     settings = lg.TestSettings()
-    settings.FromConfig(mlperf_conf, args.model_name, args.scenario)
     settings.FromConfig(user_conf, args.model_name, args.scenario)
     settings.scenario = scenario
     settings.mode = lg.TestMode.PerformanceOnly
diff --git a/automotive/3d-object-detection/model/painter.py b/automotive/3d-object-detection/model/painter.py
index f0680931f5..36bd2aa0b8 100644
--- a/automotive/3d-object-detection/model/painter.py
+++ b/automotive/3d-object-detection/model/painter.py
@@ -1,3 +1,4 @@
+import onnxruntime as ort
 import argparse
 import model.segmentation as network
 import os
@@ -34,10 +35,15 @@ def get_calib_from_file(calib_file):
     return data
 
 
+def to_numpy(tensor):
+    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
+
+
 class Painter:
-    def __init__(self, args):
+    def __init__(self, args, onnx=False):
         self.root_split_path = args.training_path
         self.save_path = os.path.join(args.training_path, "painted_lidar/")
+        self.onnx = onnx
         if not os.path.exists(self.save_path):
             os.mkdir(self.save_path)
 
@@ -45,13 +51,18 @@ def __init__(self, args):
         self.model = None
         print(f'Using Segmentation Network -- deeplabv3plus')
         checkpoint_file = args.model_path
-        model = network.modeling.__dict__['deeplabv3plus_resnet50'](
-            num_classes=19, output_stride=16)
-        checkpoint = torch.load(checkpoint_file)
-        model.load_state_dict(checkpoint["model_state"])
-        model.eval()
-        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        model.to(device)
+        if self.onnx:
+            model = ort.InferenceSession(checkpoint_file)
+            self.input_image_name = model.get_inputs()[0].name
+        else:
+            model = network.modeling.__dict__['deeplabv3plus_resnet50'](
+                num_classes=19, output_stride=16)
+            checkpoint = torch.load(checkpoint_file)
+            model.load_state_dict(checkpoint["model_state"])
+            model.eval()
+            device = torch.device(
+                'cuda' if torch.cuda.is_available() else 'cpu')
+            model.to(device)
         self.model = model
         self.cam_sync = args.cam_sync
 
diff --git a/automotive/3d-object-detection/model/pointpillars.py b/automotive/3d-object-detection/model/pointpillars.py
index 49257fe5a9..ded0d16851 100644
--- a/automotive/3d-object-detection/model/pointpillars.py
+++ b/automotive/3d-object-detection/model/pointpillars.py
@@ -397,7 +397,9 @@ def get_predicted_bboxes_single(
 
             # 3.2 nms core
             keep_inds = ml3d.ops.nms(
-                cur_bbox_pred2d, cur_bbox_cls_pred, self.nms_thr)
+                cur_bbox_pred2d.cpu(),
+                cur_bbox_cls_pred.cpu(),
+                self.nms_thr)
 
             cur_bbox_cls_pred = cur_bbox_cls_pred[keep_inds]
             cur_bbox_pred = cur_bbox_pred[keep_inds]
diff --git a/automotive/3d-object-detection/model/pointpillars_core.py b/automotive/3d-object-detection/model/pointpillars_core.py
new file mode 100644
index 0000000000..a92f46be18
--- /dev/null
+++ b/automotive/3d-object-detection/model/pointpillars_core.py
@@ -0,0 +1,547 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from model.anchors import Anchors, anchor_target, anchors2bboxes
+from ops import Voxelization
+from tools.process import limit_period
+import math
+import open3d.ml.torch as ml3d
+
+
+class PillarLayer(nn.Module):
+    def __init__(self, voxel_size, point_cloud_range,
+                 max_num_points, max_voxels):
+        super().__init__()
+        self.voxel_layer = Voxelization(voxel_size=voxel_size,
+                                        point_cloud_range=point_cloud_range,
+                                        max_num_points=max_num_points,
+                                        max_voxels=max_voxels)
+        self.point_cloud_range = point_cloud_range
+        self.voxel_size = voxel_size
+
+    @torch.no_grad()
+    def forward(self, batched_pts):
+        '''
+        batched_pts: list[tensor], len(batched_pts) = bs
+        return:
+               pillars: (p1 + p2 + ... + pb, num_points, c),
+               coors_batch: (p1 + p2 + ... + pb, 1 + 3),
+               num_points_per_pillar: (p1 + p2 + ... + pb, ), (b: batch size)
+        '''
+        pillars, coors, npoints_per_pillar = [], [], []
+        for i, pts in enumerate(batched_pts):
+            voxels_out, coors_out, num_points_per_voxel_out = self.voxel_layer(
+                pts)
+            # voxels_out: (max_voxel, num_points, c), coors_out: (max_voxel, 3)
+            # num_points_per_voxel_out: (max_voxel, )
+            pillars.append(voxels_out)
+            coors.append(coors_out.long())
+            npoints_per_pillar.append(num_points_per_voxel_out)
+
+        # (p1 + p2 + ... + pb, num_points, c)
+        pillars = torch.cat(pillars, dim=0)
+        npoints_per_pillar = torch.cat(
+            npoints_per_pillar,
+            dim=0)  # (p1 + p2 + ... + pb, )
+        coors_batch = []
+        for i, cur_coors in enumerate(coors):
+            coors_batch.append(F.pad(cur_coors, (1, 0), value=i))
+        # (p1 + p2 + ... + pb, 1 + 3)
+        coors_batch = torch.cat(coors_batch, dim=0)
+
+        return pillars, coors_batch, npoints_per_pillar
+
+
+class PillarEncoder(nn.Module):
+    def __init__(self, voxel_size, point_cloud_range, in_channel, out_channel):
+        super().__init__()
+        self.out_channel = out_channel
+        self.vx, self.vy = voxel_size[0], voxel_size[1]
+        self.x_offset = voxel_size[0] / 2 + point_cloud_range[0]
+        self.y_offset = voxel_size[1] / 2 + point_cloud_range[1]
+        self.x_l = math.ceil(
+            (point_cloud_range[3] -
+             point_cloud_range[0]) /
+            voxel_size[0])
+        self.y_l = math.ceil(
+            (point_cloud_range[4] -
+             point_cloud_range[1]) /
+            voxel_size[1])
+
+        self.conv = nn.Conv1d(in_channel, out_channel, 1, bias=False)
+        self.bn = nn.BatchNorm1d(out_channel, eps=1e-3, momentum=0.01)
+
+    def forward(self, pillars, coors_batch, npoints_per_pillar):
+        '''
+        pillars: (p1 + p2 + ... + pb, num_points, c), c = 4
+        coors_batch: (p1 + p2 + ... + pb, 1 + 3)
+        npoints_per_pillar: (p1 + p2 + ... + pb, )
+        return:  (bs, out_channel, y_l, x_l)
+        '''
+        device = pillars.device
+        # 1. calculate offset to the points center (in each pillar)
+        offset_pt_center = pillars[:,
+                                   :,
+                                   :3] - torch.sum(pillars[:,
+                                                           :,
+                                                           :3],
+                                                   dim=1,
+                                                   keepdim=True) / npoints_per_pillar[:,
+                                                                                      None,
+                                                                                      None]  # (p1 + p2 + ... + pb, num_points, 3)
+
+        # 2. calculate offset to the pillar center
+        # (p1 + p2 + ... + pb, num_points, 1)
+        x_offset_pi_center = pillars[:, :, :1] - \
+            (coors_batch[:, None, 1:2] * self.vx + self.x_offset)
+        # (p1 + p2 + ... + pb, num_points, 1)
+        y_offset_pi_center = pillars[:, :, 1:2] - \
+            (coors_batch[:, None, 2:3] * self.vy + self.y_offset)
+
+        # 3. encoder
+        features = torch.cat([pillars,
+                              offset_pt_center,
+                              x_offset_pi_center,
+                              y_offset_pi_center],
+                             dim=-1)  # (p1 + p2 + ... + pb, num_points, 9)
+        features[:, :, 0:1] = x_offset_pi_center  # tmp
+        features[:, :, 1:2] = y_offset_pi_center  # tmp
+        # In consitent with mmdet3d.
+        # The reason can be referenced to
+        # https://github.com/open-mmlab/mmdetection3d/issues/1150
+
+        # 4. find mask for (0, 0, 0) and update the encoded features
+        # a very beautiful implementation
+        voxel_ids = torch.arange(
+            0, pillars.size(1)).to(device)  # (num_points, )
+        # (num_points, p1 + p2 + ... + pb)
+        mask = voxel_ids[:, None] < npoints_per_pillar[None, :]
+        # (p1 + p2 + ... + pb, num_points)
+        mask = mask.permute(1, 0).contiguous()
+        features *= mask[:, :, None]
+
+        # 5. embedding
+        # (p1 + p2 + ... + pb, 9, num_points)
+        features = features.permute(0, 2, 1).contiguous()
+        # (p1 + p2 + ... + pb, out_channels, num_points)
+        features = F.relu(self.bn(self.conv(features)))
+        # (p1 + p2 + ... + pb, out_channels)
+        pooling_features = torch.max(features, dim=-1)[0]
+
+        # 6. pillar scatter
+        batched_canvas = []
+        bs = coors_batch[-1, 0] + 1
+        for i in range(bs):
+            cur_coors_idx = coors_batch[:, 0] == i
+            cur_coors = coors_batch[cur_coors_idx, :]
+            cur_features = pooling_features[cur_coors_idx]
+
+            canvas = torch.zeros(
+                (self.x_l,
+                 self.y_l,
+                 self.out_channel),
+                dtype=torch.float32,
+                device=device)
+            canvas[cur_coors[:, 1], cur_coors[:, 2]] = cur_features
+            canvas = canvas.permute(2, 1, 0).contiguous()
+            batched_canvas.append(canvas)
+        # (bs, in_channel, self.y_l, self.x_l)
+        batched_canvas = torch.stack(batched_canvas, dim=0)
+        return batched_canvas
+
+
+class Backbone(nn.Module):
+    def __init__(self, in_channel, out_channels,
+                 layer_nums, layer_strides=[2, 2, 2]):
+        super().__init__()
+        assert len(out_channels) == len(layer_nums)
+        assert len(out_channels) == len(layer_strides)
+
+        self.multi_blocks = nn.ModuleList()
+        for i in range(len(layer_strides)):
+            blocks = []
+            blocks.append(
+                nn.Conv2d(
+                    in_channel,
+                    out_channels[i],
+                    3,
+                    stride=layer_strides[i],
+                    bias=False,
+                    padding=1))
+            blocks.append(
+                nn.BatchNorm2d(
+                    out_channels[i],
+                    eps=1e-3,
+                    momentum=0.01))
+            blocks.append(nn.ReLU(inplace=True))
+
+            for _ in range(layer_nums[i]):
+                blocks.append(
+                    nn.Conv2d(
+                        out_channels[i],
+                        out_channels[i],
+                        3,
+                        bias=False,
+                        padding=1))
+                blocks.append(
+                    nn.BatchNorm2d(
+                        out_channels[i],
+                        eps=1e-3,
+                        momentum=0.01))
+                blocks.append(nn.ReLU(inplace=True))
+
+            in_channel = out_channels[i]
+            self.multi_blocks.append(nn.Sequential(*blocks))
+
+        # in consitent with mmdet3d
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu')
+
+    def forward(self, x):
+        '''
+        x: (b, c, y_l, x_l). Default: (6, 64, 496, 432)
+        return: list[]. Default: [(6, 64, 248, 216), (6, 128, 124, 108), (6, 256, 62, 54)]
+        '''
+        outs = []
+        for i in range(len(self.multi_blocks)):
+            x = self.multi_blocks[i](x)
+            outs.append(x)
+        return outs
+
+
+class Neck(nn.Module):
+    def __init__(self, in_channels, upsample_strides, out_channels):
+        super().__init__()
+        assert len(in_channels) == len(upsample_strides)
+        assert len(upsample_strides) == len(out_channels)
+
+        self.decoder_blocks = nn.ModuleList()
+        for i in range(len(in_channels)):
+            decoder_block = []
+            decoder_block.append(nn.ConvTranspose2d(in_channels[i],
+                                                    out_channels[i],
+                                                    upsample_strides[i],
+                                                    stride=upsample_strides[i],
+                                                    bias=False))
+            decoder_block.append(
+                nn.BatchNorm2d(
+                    out_channels[i],
+                    eps=1e-3,
+                    momentum=0.01))
+            decoder_block.append(nn.ReLU(inplace=True))
+
+            self.decoder_blocks.append(nn.Sequential(*decoder_block))
+
+        # in consitent with mmdet3d
+        for m in self.modules():
+            if isinstance(m, nn.ConvTranspose2d):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu')
+
+    def forward(self, x):
+        '''
+        x: [(bs, 64, 248, 216), (bs, 128, 124, 108), (bs, 256, 62, 54)]
+        return: (bs, 384, 248, 216)
+        '''
+        outs = []
+        for i in range(len(self.decoder_blocks)):
+            xi = self.decoder_blocks[i](x[i])  # (bs, 128, 248, 216)
+            outs.append(xi)
+        out = torch.cat(outs, dim=1)
+        return out
+
+
+class Head(nn.Module):
+    def __init__(self, in_channel, n_anchors, n_classes):
+        super().__init__()
+
+        self.conv_cls = nn.Conv2d(in_channel, n_anchors * n_classes, 1)
+        self.conv_reg = nn.Conv2d(in_channel, n_anchors * 7, 1)
+        self.conv_dir_cls = nn.Conv2d(in_channel, n_anchors * 2, 1)
+
+        # in consitent with mmdet3d
+        conv_layer_id = 0
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.normal_(m.weight, mean=0, std=0.01)
+                if conv_layer_id == 0:
+                    prior_prob = 0.01
+                    bias_init = float(-math.log((1 - prior_prob) / prior_prob))
+                    nn.init.constant_(m.bias, bias_init)
+                else:
+                    nn.init.constant_(m.bias, 0)
+                conv_layer_id += 1
+
+    def forward(self, x):
+        '''
+        x: (bs, 384, 248, 216)
+        return:
+              bbox_cls_pred: (bs, n_anchors*3, 248, 216)
+              bbox_pred: (bs, n_anchors*7, 248, 216)
+              bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216)
+        '''
+        bbox_cls_pred = self.conv_cls(x)
+        bbox_pred = self.conv_reg(x)
+        bbox_dir_cls_pred = self.conv_dir_cls(x)
+        return bbox_cls_pred, bbox_pred, bbox_dir_cls_pred
+
+
+class PointPillarsPre(nn.Module):
+    def __init__(self,
+                 nclasses=3,
+                 voxel_size=[0.32, 0.32, 6],
+                 point_cloud_range=[-74.88, -74.88, -2, 74.88, 74.88, 4],
+                 max_num_points=20,
+                 max_voxels=(32000, 32000),
+                 painted=False):
+        super().__init__()
+        self.pillar_layer = PillarLayer(voxel_size=voxel_size,
+                                        point_cloud_range=point_cloud_range,
+                                        max_num_points=max_num_points,
+                                        max_voxels=max_voxels)
+
+    def forward(self, batched_pts):
+        pillars, coors_batch, npoints_per_pillar = self.pillar_layer(
+            batched_pts)
+        return pillars, coors_batch, npoints_per_pillar
+
+
+class PointPillarsCore(nn.Module):
+    def __init__(self,
+                 nclasses=3,
+                 voxel_size=[0.32, 0.32, 6],
+                 point_cloud_range=[-74.88, -74.88, -2, 74.88, 74.88, 4],
+                 max_num_points=20,
+                 max_voxels=(32000, 32000),
+                 painted=False):
+        super().__init__()
+        self.nclasses = nclasses
+        if painted:
+            pillar_channel = 16
+        else:
+            pillar_channel = 10
+        self.pillar_encoder = PillarEncoder(voxel_size=voxel_size,
+                                            point_cloud_range=point_cloud_range,
+                                            in_channel=pillar_channel,
+                                            out_channel=64)
+        self.backbone = Backbone(in_channel=64,
+                                 out_channels=[64, 128, 256],
+                                 layer_nums=[3, 5, 5],
+                                 layer_strides=[1, 2, 2])
+        self.neck = Neck(in_channels=[64, 128, 256],
+                         upsample_strides=[1, 2, 4],
+                         out_channels=[128, 128, 128])
+        self.head = Head(
+            in_channel=384,
+            n_anchors=2 * nclasses,
+            n_classes=nclasses)
+
+        # anchors
+        ranges = [[-74.88, -74.88, -0.0345, 74.88, 74.88, -0.0345],
+                  [-74.88, -74.88, 0, 74.88, 74.88, 0],
+                  [-74.88, -74.88, -0.1188, 74.88, 74.88, -0.1188]]
+        sizes = [[0.84, .91, 1.74], [.84, 1.81, 1.77], [2.08, 4.73, 1.77]]
+        rotations = [0, 1.57]
+        self.anchors_generator = Anchors(ranges=ranges,
+                                         sizes=sizes,
+                                         rotations=rotations)
+
+        # train
+        self.assigners = [
+            {'pos_iou_thr': 0.5, 'neg_iou_thr': 0.3, 'min_iou_thr': 0.3},
+            {'pos_iou_thr': 0.5, 'neg_iou_thr': 0.3, 'min_iou_thr': 0.3},
+            {'pos_iou_thr': 0.55, 'neg_iou_thr': 0.4, 'min_iou_thr': 0.4},
+        ]
+
+        # val and test
+        self.nms_pre = 4096
+        self.nms_thr = 0.25
+        self.score_thr = 0.1
+        self.max_num = 500
+
+    def get_predicted_bboxes_single(
+            self, bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchors):
+        '''
+        bbox_cls_pred: (n_anchors*3, 248, 216)
+        bbox_pred: (n_anchors*7, 248, 216)
+        bbox_dir_cls_pred: (n_anchors*2, 248, 216)
+        anchors: (y_l, x_l, 3, 2, 7)
+        return:
+            bboxes: (k, 7)
+            labels: (k, )
+            scores: (k, )
+        '''
+        # 0. pre-process
+        bbox_cls_pred = bbox_cls_pred.permute(
+            1, 2, 0).reshape(-1, self.nclasses)
+        bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 7)
+        bbox_dir_cls_pred = bbox_dir_cls_pred.permute(1, 2, 0).reshape(-1, 2)
+        anchors = anchors.reshape(-1, 7)
+
+        bbox_cls_pred = torch.sigmoid(bbox_cls_pred)
+        bbox_dir_cls_pred = torch.max(bbox_dir_cls_pred, dim=1)[1]
+
+        # 1. obtain self.nms_pre bboxes based on scores
+        inds = bbox_cls_pred.max(1)[0].topk(self.nms_pre)[1]
+        bbox_cls_pred = bbox_cls_pred[inds]
+        bbox_pred = bbox_pred[inds]
+        bbox_dir_cls_pred = bbox_dir_cls_pred[inds]
+        anchors = anchors[inds]
+
+        # 2. decode predicted offsets to bboxes
+        bbox_pred = anchors2bboxes(anchors, bbox_pred)
+        return torch.cat(
+            [bbox_pred, bbox_cls_pred, bbox_dir_cls_pred[:, None]], 1)
+
+    def get_predicted_bboxes(
+            self, bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, batched_anchors):
+        '''
+        bbox_cls_pred: (bs, n_anchors*3, 248, 216)
+        bbox_pred: (bs, n_anchors*7, 248, 216)
+        bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216)
+        batched_anchors: (bs, y_l, x_l, 3, 2, 7)
+        return:
+            bboxes: [(k1, 7), (k2, 7), ... ]
+            labels: [(k1, ), (k2, ), ... ]
+            scores: [(k1, ), (k2, ), ... ]
+        '''
+        results = []
+        bs = bbox_cls_pred.size(0)
+        for i in range(bs):
+            result = self.get_predicted_bboxes_single(bbox_cls_pred=bbox_cls_pred[i],
+                                                      bbox_pred=bbox_pred[i],
+                                                      bbox_dir_cls_pred=bbox_dir_cls_pred[i],
+                                                      anchors=batched_anchors[i])
+            results.append(result)
+        return results
+
+    def forward(self, pillars, coors_batch, npoints_per_pillar,
+                mode='test', batched_gt_bboxes=None, batched_gt_labels=None):
+        pillar_features = self.pillar_encoder(
+            pillars, coors_batch, npoints_per_pillar)
+
+        # xs:  [(bs, 64, 248, 216), (bs, 128, 124, 108), (bs, 256, 62, 54)]
+        xs = self.backbone(pillar_features)
+
+        # x: (bs, 384, 248, 216)
+        x = self.neck(xs)
+
+        # bbox_cls_pred: (bs, n_anchors*3, 248, 216)
+        # bbox_pred: (bs, n_anchors*7, 248, 216)
+        # bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216)
+        bbox_cls_pred, bbox_pred, bbox_dir_cls_pred = self.head(x)
+
+        # anchors
+        device = bbox_cls_pred.device
+        feature_map_size = torch.tensor(
+            list(bbox_cls_pred.size()[-2:]), device=device)
+        anchors = self.anchors_generator.get_multi_anchors(feature_map_size)
+        batch_size = pillar_features.shape[0]
+        batched_anchors = [anchors for _ in range(batch_size)]
+
+        if mode == 'train':
+            anchor_target_dict = anchor_target(batched_anchors=batched_anchors,
+                                               batched_gt_bboxes=batched_gt_bboxes,
+                                               batched_gt_labels=batched_gt_labels,
+                                               assigners=self.assigners,
+                                               nclasses=self.nclasses)
+
+            return bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchor_target_dict
+        elif mode == 'val':
+            results = self.get_predicted_bboxes(bbox_cls_pred=bbox_cls_pred,
+                                                bbox_pred=bbox_pred,
+                                                bbox_dir_cls_pred=bbox_dir_cls_pred,
+                                                batched_anchors=batched_anchors)
+            return results
+
+        elif mode == 'test':
+            results = self.get_predicted_bboxes(bbox_cls_pred=bbox_cls_pred,
+                                                bbox_pred=bbox_pred,
+                                                bbox_dir_cls_pred=bbox_dir_cls_pred,
+                                                batched_anchors=batched_anchors)
+            return results
+        else:
+            raise ValueError
+
+
+class PointPillarsPos(nn.Module):
+    def __init__(self, nclasses=3):
+        super().__init__()
+        self.nclasses = nclasses
+        self.nms_thr = 0.25
+        self.score_thr = 0.1
+        self.max_num = 500
+
+    def nms_filter(self, bbox_pred, bbox_cls_pred, bbox_dir_cls_pred):
+        # 3. nms
+        bbox_pred2d_xy = bbox_pred[:, [0, 1]]
+        bbox_pred2d_lw = bbox_pred[:, [3, 4]]
+        bbox_pred2d = torch.cat([bbox_pred2d_xy - bbox_pred2d_lw / 2,
+                                 bbox_pred2d_xy + bbox_pred2d_lw / 2,
+                                 bbox_pred[:, 6:]], dim=-1)  # (n_anchors, 5)
+
+        ret_bboxes, ret_labels, ret_scores = [], [], []
+        for i in range(self.nclasses):
+            # 3.1 filter bboxes with scores below self.score_thr
+            cur_bbox_cls_pred = bbox_cls_pred[:, i]
+            score_inds = cur_bbox_cls_pred > self.score_thr
+            if score_inds.sum() == 0:
+                continue
+
+            cur_bbox_cls_pred = cur_bbox_cls_pred[score_inds]
+            cur_bbox_pred2d = bbox_pred2d[score_inds]
+            cur_bbox_pred = bbox_pred[score_inds]
+            cur_bbox_dir_cls_pred = bbox_dir_cls_pred[score_inds]
+
+            # 3.2 nms core
+            keep_inds = ml3d.ops.nms(
+                cur_bbox_pred2d.detach().cpu(),
+                cur_bbox_cls_pred.detach().cpu(),
+                self.nms_thr)
+            cur_bbox_cls_pred = cur_bbox_cls_pred[keep_inds]
+            cur_bbox_pred = cur_bbox_pred[keep_inds]
+            cur_bbox_dir_cls_pred = cur_bbox_dir_cls_pred[keep_inds]
+            cur_bbox_pred[:, -
+                          1] = limit_period(cur_bbox_pred[:, -
+                                                          1].detach().cpu(), 1, math.pi).to(cur_bbox_pred)  # [-pi, 0]
+            cur_bbox_pred[:, -1] += (1 - cur_bbox_dir_cls_pred) * math.pi
+
+            ret_bboxes.append(cur_bbox_pred)
+            ret_labels.append(torch.zeros_like(
+                cur_bbox_pred[:, 0], dtype=torch.long) + i)
+            ret_scores.append(cur_bbox_cls_pred)
+
+        # 4. filter some bboxes if bboxes number is above self.max_num
+        if len(ret_bboxes) == 0:
+            return {
+                'lidar_bboxes': torch.empty((0, 7)).detach().cpu(),
+                'labels': torch.empty(0).detach().cpu(),
+                'scores': torch.empty(0).detach().cpu()
+            }
+        ret_bboxes = torch.cat(ret_bboxes, 0)
+        ret_labels = torch.cat(ret_labels, 0)
+        ret_scores = torch.cat(ret_scores, 0)
+        if ret_bboxes.size(0) > self.max_num:
+            final_inds = ret_scores.topk(self.max_num)[1]
+            ret_bboxes = ret_bboxes[final_inds]
+            ret_labels = ret_labels[final_inds]
+            ret_scores = ret_scores[final_inds]
+        result = {
+            'lidar_bboxes': ret_bboxes.detach().cpu(),
+            'labels': ret_labels.detach().cpu(),
+            'scores': ret_scores.detach().cpu()
+        }
+        return result
+
+    def forward(self, results):
+        pos_results = []
+        for result in results:
+            bbox_pred, bbox_cls_pred, bbox_dir_cls_pred = result[:,
+                                                                 :7], result[:, 7:10], result[:, 10]
+            pos_result = self.nms_filter(
+                bbox_pred, bbox_cls_pred, bbox_dir_cls_pred)
+            if pos_result is not None:
+                pos_results.append(pos_result)
+        return pos_results
diff --git a/automotive/3d-object-detection/tools/evaluate.py b/automotive/3d-object-detection/tools/evaluate.py
index 3bc56ee4f9..eb3c5d32ab 100644
--- a/automotive/3d-object-detection/tools/evaluate.py
+++ b/automotive/3d-object-detection/tools/evaluate.py
@@ -53,7 +53,6 @@ def do_eval(det_results, gt_results, CLASSES, cam_sync=False):
     gt_results: dict(id -> det_results)
     CLASSES: dict
     '''
-    assert len(det_results) == len(gt_results)
 
     # 1. calculate iou
     ious = {
@@ -66,6 +65,8 @@ def do_eval(det_results, gt_results, CLASSES, cam_sync=False):
         annos_label = 'annos'
     for id in range(len(gt_results)):
         gt_result = gt_results[id][annos_label]
+        if gt_results[id]['image']['image_idx'] not in det_results:
+            continue
         det_result = det_results[gt_results[id]['image']['image_idx']]
 
         # 1.2, bev iou
diff --git a/automotive/3d-object-detection/waymo.py b/automotive/3d-object-detection/waymo.py
index 5818ed01e0..71a870f2f1 100644
--- a/automotive/3d-object-detection/waymo.py
+++ b/automotive/3d-object-detection/waymo.py
@@ -78,10 +78,6 @@ def __init__(self, data_root, split,
         self.painted = painted
         self.cam_sync = cam_sync
         self.point_range_filter = [-74.88, -74.88, -2, 74.88, 74.88, 4]
-        if painted or cam_sync:
-            info_file = f'painted_waymo_infos_{split}.pkl'
-        else:
-            info_file = f'waymo_infos_{split}.pkl'
         self.data_infos = read_pickle(os.path.join(data_root, info_file))
         self.sorted_ids = range(len(self.data_infos))
 
@@ -202,6 +198,9 @@ def get_image(self, idx, camera):
         input_batch = input_tensor.unsqueeze(0)
         return input_batch
 
+    def get_item_count(self):
+        return len(self.data_infos)
+
 
 class PostProcessWaymo:
     def __init__(
@@ -220,6 +219,23 @@ def __call__(self, results, content_id, inputs, result_dict):
         for idx in range(len(content_id)):
             processed_results.append([])
             detection_num = len(results[0][idx])
+            if detection_num == 0:
+                processed_results[idx].append([
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    -1,
+                    results[6][idx]
+                ])
             for detection in range(0, detection_num):
                 processed_results[idx].append([
                     results[0][idx][detection][0],

From 138b794ad898eb07cbfce69c9b7f187f47e3cdeb Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 28 Jan 2025 11:55:01 -0500
Subject: [PATCH 243/305] Mixtral fix: match reference with standalone script
 (#2054)

---
 language/mixtral-8x7b/SUT.py     | 87 ++++++--------------------------
 language/mixtral-8x7b/dataset.py | 29 +++++++++--
 2 files changed, 41 insertions(+), 75 deletions(-)

diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
index e12a4318fa..600d9e9d25 100644
--- a/language/mixtral-8x7b/SUT.py
+++ b/language/mixtral-8x7b/SUT.py
@@ -30,11 +30,12 @@
 log = logging.getLogger("Mixtral-8x7B-Instruct-v0.1")
 
 gen_kwargs = {
-    "early_stopping": True,
-    "max_new_tokens": 1024,
+    # "min_new_tokens": 1,
     "min_new_tokens": 2,
-    "num_beams": 1,
+    "max_new_tokens": 1024,
     "do_sample": False,
+    "temperature": None,
+    "top_p": None,
 }
 
 
@@ -238,80 +239,30 @@ def process_queries(self):
                 input_masks_tensor = []
                 input_len = []
                 input_dataset = []
+                batch_texts = []
+                datasets = []
                 for q in qitem:
-                    input_ids_tensor.append(
-                        pad(
-                            self.data_object.input_ids[q.index],
-                            (
-                                max_seq_len -
-                                self.data_object.input_lens[q.index],
-                                0,
-                                0,
-                                0,
-                            ),
-                            value=self.tokenizer.pad_token_id,
-                        )
-                    )
-                    input_masks_tensor.append(
-                        pad(
-                            self.data_object.attention_masks[q.index],
-                            (
-                                max_seq_len -
-                                self.data_object.input_lens[q.index],
-                                0,
-                                0,
-                                0,
-                            ),
-                            value=0,
-                        )
-                    )
+                    batch_texts.append(self.data_object.input_texts[q.index])
                     input_len.append(self.data_object.input_lens[q.index])
-
                     # In case we predict code generation, we can specify an
                     # additional stop sequence
                     input_dataset.append(
                         self.data_object.dataset_names[q.index])
-                input_ids_tensor = torch.cat(input_ids_tensor)
-                input_masks_tensor = torch.cat(input_masks_tensor)
 
-                assert input_ids_tensor.shape == input_masks_tensor.shape
-                assert input_ids_tensor.shape[0] <= self.batch_size
+                batch_ids = self.tokenizer.batch_encode_plus(batch_texts, return_tensors="pt", padding=True)
+                batch_ids = batch_ids.to(self.device)
 
                 tik2 = time.time()
-                logits_processor = LogitsProcessorList(
-                    [StopAfterSequence(
-                        self.tokenizer.eos_token_id, device=self.device)]
-                )
-                for i in range(len(input_ids_tensor)):
-                    ids, masks, dataset = (
-                        input_ids_tensor[i: i + 1],
-                        input_masks_tensor[i: i + 1],
-                        input_dataset[i],
-                    )
-                    pred_output_tokens = []
-                    if dataset == "MBXP":
-                        out = self.model.generate(
-                            input_ids=ids,
-                            attention_mask=masks,
-                            pad_token_id=self.tokenizer.pad_token_id,
-                            logits_processor=logits_processor,
-                            **gen_kwargs,
-                        )
-                    else:
-                        out = self.model.generate(
-                            input_ids=ids,
-                            attention_mask=masks,
-                            pad_token_id=self.tokenizer.pad_token_id,
-                            **gen_kwargs,
-                        )
-                    pred_output_tokens.append(out)
-                pred_output_tokens = torch.cat(pred_output_tokens)
+                _, length = batch_ids.input_ids.shape
+                out = self.model.generate(**batch_ids, num_return_sequences=1, **gen_kwargs)
+                pred_output_tokens = out
                 tik3 = time.time()
 
                 processed_output = self.data_object.postProcess(
                     pred_output_tokens,
-                    input_seq_lens=input_len,
+                    length=length,
                     query_id_list=query_ids,
+                    dataset_list=input_dataset,
                 )
 
             for i in range(len(qitem)):
@@ -342,10 +293,7 @@ def process_queries(self):
 
     def load_model(self):
         self.model = AutoModelForCausalLM.from_pretrained(
-            self.model_path,
-            device_map="auto",
-            low_cpu_mem_usage=True,
-            torch_dtype=self.amp_dtype,
+            self.model_path, device_map="auto", trust_remote_code=True
         )
         print("Loaded model")
 
@@ -362,10 +310,7 @@ def load_model(self):
             pass
 
         self.tokenizer = AutoTokenizer.from_pretrained(
-            self.model_path,
-            model_max_length=1024,
-            padding_side="left",
-            use_fast=False,
+            self.model_path, padding_side="left", trust_remote_code=True
         )
 
         self.tokenizer.pad_token = self.tokenizer.eos_token
diff --git a/language/mixtral-8x7b/dataset.py b/language/mixtral-8x7b/dataset.py
index c8268d8d1d..0c757ba7d9 100644
--- a/language/mixtral-8x7b/dataset.py
+++ b/language/mixtral-8x7b/dataset.py
@@ -67,6 +67,7 @@ def load_processed_dataset(self):
         processed_data = pd.read_pickle(self.dataset_path)
 
         input_tokens = processed_data["tok_input"]
+        self.input_texts = processed_data["input"].to_list()
 
         self.input_ids = []
         self.input_lens = []
@@ -85,12 +86,31 @@ def load_processed_dataset(self):
             self.dataset_names.append(dataset)
         print("Finished loading dataset.")
 
+    
+    def remove_trailing_twos(self, lst, eos = 2):
+        count = 0
+        for num in reversed(lst):
+            if num == eos or num == 0:
+                count += 1
+            else:
+                break
+        return lst[:-count] if count > 0 else lst
+
+    
+    def mbxp_stop(self, lst, stop_tokens = [13, 13940, 28832, 13]):
+        for i in range(len(lst) - len(stop_tokens) + 1):
+            if (lst[i:i+len(stop_tokens)] == stop_tokens).all():
+                return lst[:i+len(stop_tokens)]
+        return lst
+
+
     def postProcess(
         self,
         out_tokens,
-        input_seq_lens=None,
+        length=None,
         query_id_list=None,
         sample_index_list=None,
+        dataset_list=None,
     ):
         """Postprocesses output prediction"""
 
@@ -106,13 +126,14 @@ def postProcess(
         """
         # Everything is padded to max_len (1024), so prune the input and parse
         # to numpy
-        output_seq = out_tokens[:, 1024:].cpu().numpy()
+        output_seq = out_tokens[:, length:].cpu().numpy()
         aux_seq = []
         assert len(query_id_list) == output_seq.shape[0]
         for i in range(len(output_seq)):
             aux = output_seq[i]
-            while len(output_seq[i]) <= 1:
-                aux = np.append(aux, self.tokenizer.eos_token_id)
+            aux = self.remove_trailing_twos(aux)
+            if (dataset_list[i] == "MBXP"):
+                aux = self.mbxp_stop(aux)
             aux_seq.append(aux)
         output_seq = np.stack(aux_seq)
 

From 5091994fe97621068968c57a373b550486052e6e Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 28 Jan 2025 12:06:00 -0500
Subject: [PATCH 244/305] Add automotive submission checker (#2051)

* Add automotive submission checker
---
 .github/workflows/reset-branch.yml     |  1 -
 compliance/nvidia/README.md            |  1 +
 language/mixtral-8x7b/SUT.py           |  6 ++++--
 language/mixtral-8x7b/dataset.py       | 11 ++++-------
 tools/submission/submission_checker.py | 12 ++++++++----
 5 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/.github/workflows/reset-branch.yml b/.github/workflows/reset-branch.yml
index 76cf0b97eb..9eaf944c6e 100644
--- a/.github/workflows/reset-branch.yml
+++ b/.github/workflows/reset-branch.yml
@@ -1,5 +1,4 @@
 name: Reset Current Branch to Upstream After Squash Merge
-
 on:
   workflow_dispatch:
     inputs:
diff --git a/compliance/nvidia/README.md b/compliance/nvidia/README.md
index bcf050a99b..ed90893704 100755
--- a/compliance/nvidia/README.md
+++ b/compliance/nvidia/README.md
@@ -41,3 +41,4 @@ The `run_verification.py` found in each test directory will copy the test files
 | Llama3.1-405b | [TEST06](./TEST06/) |
 | mixtral-8x7b | [TEST06](./TEST06/) |
 | R-GAT | [TEST01](./TEST01/) |
+| PointPainting | [TEST01](./TEST01/) [TEST04](./TEST04/) |
diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
index 600d9e9d25..36cf7e3c01 100644
--- a/language/mixtral-8x7b/SUT.py
+++ b/language/mixtral-8x7b/SUT.py
@@ -249,12 +249,14 @@ def process_queries(self):
                     input_dataset.append(
                         self.data_object.dataset_names[q.index])
 
-                batch_ids = self.tokenizer.batch_encode_plus(batch_texts, return_tensors="pt", padding=True)
+                batch_ids = self.tokenizer.batch_encode_plus(
+                    batch_texts, return_tensors="pt", padding=True)
                 batch_ids = batch_ids.to(self.device)
 
                 tik2 = time.time()
                 _, length = batch_ids.input_ids.shape
-                out = self.model.generate(**batch_ids, num_return_sequences=1, **gen_kwargs)
+                out = self.model.generate(
+                    **batch_ids, num_return_sequences=1, **gen_kwargs)
                 pred_output_tokens = out
                 tik3 = time.time()
 
diff --git a/language/mixtral-8x7b/dataset.py b/language/mixtral-8x7b/dataset.py
index 0c757ba7d9..34b965e68d 100644
--- a/language/mixtral-8x7b/dataset.py
+++ b/language/mixtral-8x7b/dataset.py
@@ -86,8 +86,7 @@ def load_processed_dataset(self):
             self.dataset_names.append(dataset)
         print("Finished loading dataset.")
 
-    
-    def remove_trailing_twos(self, lst, eos = 2):
+    def remove_trailing_twos(self, lst, eos=2):
         count = 0
         for num in reversed(lst):
             if num == eos or num == 0:
@@ -96,14 +95,12 @@ def remove_trailing_twos(self, lst, eos = 2):
                 break
         return lst[:-count] if count > 0 else lst
 
-    
-    def mbxp_stop(self, lst, stop_tokens = [13, 13940, 28832, 13]):
+    def mbxp_stop(self, lst, stop_tokens=[13, 13940, 28832, 13]):
         for i in range(len(lst) - len(stop_tokens) + 1):
-            if (lst[i:i+len(stop_tokens)] == stop_tokens).all():
-                return lst[:i+len(stop_tokens)]
+            if (lst[i:i + len(stop_tokens)] == stop_tokens).all():
+                return lst[:i + len(stop_tokens)]
         return lst
 
-
     def postProcess(
         self,
         out_tokens,
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 264c9b3738..81619168f7 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -271,7 +271,7 @@
             "mixtral-8x7b",
             "llama3.1-405b",
             "rgat",
-            # TODO: add automotive?
+            "pointpainting",
         ],
         "required-scenarios-datacenter": {
             "resnet": ["Server", "Offline"],
@@ -302,6 +302,7 @@
             "gptj-99": ["SingleStream", "Offline"],
             "gptj-99.9": ["SingleStream", "Offline"],
             "stable-diffusion-xl": ["SingleStream", "Offline"],
+            "pointpainting": ["SingleStream"],
         },
         "optional-scenarios-edge": {},
         "required-scenarios-datacenter-edge": {
@@ -323,6 +324,7 @@
             "mixtral-8x7b": ["Server", "Offline"],
             "llama3.1-405b": ["Server", "Offline"],
             "rgat": ["Offline"],
+            "pointpainting": ["SingleStream"],
         },
         "optional-scenarios-datacenter-edge": {},
         "accuracy-target": {
@@ -424,6 +426,7 @@
                 684.68 * 0.9,
             ),
             "rgat": ("acc", 0.7286 * 0.99),
+            "pointpainting":  ("acc", 0.5425 * 0.999),
         },
         "accuracy-upper-limit": {
             "stable-diffusion-xl": (
@@ -460,8 +463,8 @@
             "stable-diffusion-xl": 5000,
             "mixtral-8x7b": 15000,
             "llama3.1-405b": 8313,
-            "rgat": 788379
-
+            "rgat": 788379,
+            "pointpainting": 1024,
         },
         # model_mapping.json is expected in the root directory of the
         # submission folder for open submissions and so the below dictionary is
@@ -527,7 +530,8 @@
             },
             "mixtral-8x7b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
             "llama3.1-405b": {"SingleStream": 1024, "Server": 270336, "Offline": 1},
-            "rgat": {"SingleStream": 1024, "Offline": 1}
+            "rgat": {"SingleStream": 1024, "Offline": 1},
+            "pointpainting": {"SingleStream": 1024},
         },
     },
 }

From be4314b08f88218872251d298e6c4241f5456603 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Tue, 28 Jan 2025 22:37:36 +0530
Subject: [PATCH 245/305] Edit mlperf.conf for pointpainting - automotive
 (#2052)

* Add perf sample count override - pointpainting

* remove mlperf.conf

* [Automated Commit] Format Codebase

* Update mlperf.conf

* add compliance and accuracy target

* updated accuracy pattern for pointpainting
---
 automotive/3d-object-detection/main.py              | 9 ---------
 compliance/nvidia/TEST01/pointpainting/audit.config | 9 +++++++++
 loadgen/mlperf.conf                                 | 6 ++++--
 tools/submission/submission_checker.py              | 4 ++--
 4 files changed, 15 insertions(+), 13 deletions(-)
 create mode 100644 compliance/nvidia/TEST01/pointpainting/audit.config

diff --git a/automotive/3d-object-detection/main.py b/automotive/3d-object-detection/main.py
index caafd9e47c..b66a1878d1 100644
--- a/automotive/3d-object-detection/main.py
+++ b/automotive/3d-object-detection/main.py
@@ -111,10 +111,6 @@ def get_args():
         help="device to run the benchmark",
     )
 
-    # file to use mlperf rules compliant parameters
-    parser.add_argument(
-        "--mlperf_conf", default="mlperf.conf", help="mlperf rules config"
-    )
     # file for user LoadGen settings such as target QPS
     parser.add_argument(
         "--user_conf",
@@ -350,11 +346,6 @@ def main():
         "cmdline": str(args),
     }
 
-    mlperf_conf = os.path.abspath(args.mlperf_conf)
-    if not os.path.exists(mlperf_conf):
-        log.error("{} not found".format(mlperf_conf))
-        sys.exit(1)
-
     user_conf = os.path.abspath(args.user_conf)
     if not os.path.exists(user_conf):
         log.error("{} not found".format(user_conf))
diff --git a/compliance/nvidia/TEST01/pointpainting/audit.config b/compliance/nvidia/TEST01/pointpainting/audit.config
new file mode 100644
index 0000000000..03e70a4c7a
--- /dev/null
+++ b/compliance/nvidia/TEST01/pointpainting/audit.config
@@ -0,0 +1,9 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+
+# mode dictionary (0 = submission, 1 = accuracy, 2 = performance, 3 = find peak perf)
+*.*.mode = 2
+*.*.accuracy_log_rng_seed = 720381539243781796
+*.*.accuracy_log_sampling_target = 256
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 582381bbd9..57190c6c37 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -19,6 +19,7 @@ llama2-70b-interactive.*.performance_sample_count_override = 24576
 llama3_1-405b.*.performance_sample_count_override = 8313
 stable-diffusion-xl.*.performance_sample_count_override = 5000
 rgat.*.performance_sample_count_override = 788379
+pointpainting.*.performance_sample_count_override = 1024
 # set to 0 to let entire sample set to be performance sample
 3d-unet.*.performance_sample_count_override = 0
 
@@ -26,13 +27,14 @@ rgat.*.performance_sample_count_override = 788379
 *.*.qsl_rng_seed = 6023615788873153749
 *.*.sample_index_rng_seed = 15036839855038426416
 *.*.schedule_rng_seed = 9933818062894767841
-# Set seeds for TEST_05.
+
+# Set seeds for TEST_05 (not needed from v5.0 onwards)
 *.*.test05_qsl_rng_seed = 7975553102935885558
 *.*.test05_sample_index_rng_seed = 11403566307062068064
 *.*.test05_schedule_rng_seed = 15816800565822761601
 
-
 *.SingleStream.target_latency_percentile = 90
+pointpainting.SingleStream.target_latency_percentile = 99.9
 *.SingleStream.min_duration = 600000
 
 *.MultiStream.target_latency_percentile = 99
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 81619168f7..793c534714 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -426,7 +426,7 @@
                 684.68 * 0.9,
             ),
             "rgat": ("acc", 0.7286 * 0.99),
-            "pointpainting":  ("acc", 0.5425 * 0.999),
+            "pointpainting": ("mAP", 0.5425 * 0.999),
         },
         "accuracy-upper-limit": {
             "stable-diffusion-xl": (
@@ -735,7 +735,7 @@
 ACC_PATTERN = {
     "acc": r"^(?:\{\"accuracy|accuracy)[\": ]*=?\s*([\d\.]+).*",
     "AUC": r"^AUC=([\d\.]+).*",
-    "mAP": r"^mAP=([\d\.]+).*",
+    "mAP": r".*'(?:mAP|Total)':\s*([\d\.]+)",
     "bleu": r"^BLEU\:\s*([\d\.]+).*",
     "F1": r"^{[\"\']exact_match[\"\']\:\s*[\d\.]+,\s*[\"\']f1[\"\']\:\s*([\d\.]+)}",
     "WER": r"Word Error Rate\:.*, accuracy=([0-9\.]+)%",

From 35030b8ce8cfe18051a484d0464bc067e1bb617c Mon Sep 17 00:00:00 2001
From: arjunsuresh <arjunsuresh@users.noreply.github.com>
Date: Tue, 28 Jan 2025 17:07:47 +0000
Subject: [PATCH 246/305] Increment version to 5.0.14

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 2713f1415e..4b57201ae1 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.13
+5.0.14

From 9c3c34c998315469d4f142d71efe6090754e73ad Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 28 Jan 2025 12:08:30 -0500
Subject: [PATCH 247/305] Automotive benchmark table (#2050)

* Add v5.0 benchmarks table

* Add automotive to benchmark table
---
 README.md | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/README.md b/README.md
index 58d2b7c14a..161da09a9e 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,31 @@ Please see the [MLPerf Inference benchmark paper](https://arxiv.org/abs/1911.025
 
 Please see [here](https://docs.mlcommons.org/inference/benchmarks/) for the MLPerf inference documentation website which includes automated commands to run MLPerf inference benchmarks using different implementations.
 
+## MLPerf Inference v5.0 (submission deadline February 28, 2025)
+
+For submissions, please use the master branch and any commit since the [5.0 seed release](https://github.com/mlcommons/inference/commit/5d83ed5de438ffb55bca4cdb2966fba90a9dbca6) although it is best to use the latest commit in the [master branch](https://github.com/mlcommons/inference).
+
+For power submissions please use [SPEC PTD 1.11.1](https://github.com/mlcommons/power) (needs special access) and any commit of the power-dev repository after the [code-freeze](https://github.com/mlcommons/power-dev/commit/65eedd4a60b5c50ac44cbae061d2a428e9fb190a)
+
+
+| model | reference app | framework | dataset | category
+| ---- | ---- | ---- | ---- | ---- |
+| resnet50-v1.5 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | tensorflow, onnx, tvm, ncnn | imagenet2012 | edge,datacenter |
+| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge |
+| dlrm-v2 | [recommendation/dlrm_v2](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm_v2/pytorch) | pytorch | Multihot Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 | edge,datacenter |
+| gpt-j | [language/gpt-j](https://github.com/mlcommons/inference/tree/master/language/gpt-j)| pytorch | CNN-Daily Mail | edge,datacenter |
+| stable-diffusion-xl | [text_to_image](https://github.com/mlcommons/inference/tree/master/text_to_image) | pytorch | COCO 2014| edge,datacenter |
+| llama2-70b | [language/llama2-70b](https://github.com/mlcommons/inference/tree/master/language/llama2-70b) | pytorch | OpenOrca | datacenter |
+| llama3.1-405b | [language/llama3-405b](https://github.com/mlcommons/inference/tree/master/language/llama3.1-405b) | pytorch | LongBench, LongDataCollections, Ruler, GovReport | datacenter |
+| mixtral-8x7b | [language/mixtral-8x7b](https://github.com/mlcommons/inference/tree/master/language/mixtral-8x7b) | pytorch | OpenOrca, MBXP, GSM8K | datacenter |
+| rgat | [graph/rgat](https://github.com/mlcommons/inference/tree/master/graph/R-GAT) | pytorch | IGBH | datacenter |
+| pointpainting | [automotive/3d-object-detection](https://github.com/mlcommons/inference/tree/master/automotive/3d-object-detection) | pytorch, onnx | Waymo Open Dataset | edge |
+
+* Framework here is given for the reference implementation. Submitters are free to use their own frameworks to run the benchmark.
+
+
 ## MLPerf Inference v4.1 (submission deadline July 26, 2024)
 
 For submissions, please use the master branch and any commit since the [4.1 seed release](https://github.com/mlcommons/inference/pull/1736/files) although it is best to use the latest commit. v4.1 tag will be created from the master branch after the result publication.

From 9dad99d4d7202f25c2ea2fd4b8cb220372de13fe Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 28 Jan 2025 21:49:29 +0000
Subject: [PATCH 248/305] Update backend_pytorch_native.py | Fixes #2056
 (#2057)

* Update backend_pytorch_native.py | Fixes #2056

* Update backend_pytorch_native.py
---
 .../python/backend_pytorch_native.py                          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vision/classification_and_detection/python/backend_pytorch_native.py b/vision/classification_and_detection/python/backend_pytorch_native.py
index db152f8e00..23a1a7f9ca 100755
--- a/vision/classification_and_detection/python/backend_pytorch_native.py
+++ b/vision/classification_and_detection/python/backend_pytorch_native.py
@@ -3,7 +3,7 @@
 """
 
 # pylint: disable=unused-argument,missing-docstring
-import torch  # currently supports pytorch1.0
+import torch
 import torchvision
 import backend
 
@@ -25,7 +25,7 @@ def image_format(self):
         return "NCHW"
 
     def load(self, model_path, inputs=None, outputs=None):
-        self.model = torch.load(model_path)
+        self.model = torch.load(model_path, weights_only=False)
         self.model.eval()
         # find inputs from the model if not passed in by config
         if inputs:

From 115dd5bfee40d97787d254516af2f99ebba4d883 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Wed, 29 Jan 2025 19:31:52 +0000
Subject: [PATCH 249/305] Update truncate_accuracy_log.py | Remove a wrong
 ERROR message in logs (#2061)

---
 tools/submission/truncate_accuracy_log.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/submission/truncate_accuracy_log.py b/tools/submission/truncate_accuracy_log.py
index e0e1973ec6..368f7e8318 100755
--- a/tools/submission/truncate_accuracy_log.py
+++ b/tools/submission/truncate_accuracy_log.py
@@ -142,7 +142,8 @@ def truncate_results_dir(filter_submitter, backup, scenarios_to_skip):
                 continue
 
             # process results
-            for directory in ["results", "compliance"]:
+            required_dirs = ["results", "compliance"] if division in ["closed", "network"] else ["results"]
+            for directory in required_dirs:
 
                 log_path = os.path.join(division, submitter, directory)
                 if not os.path.exists(log_path):

From 9b32a74c53fcfa6a7f6b529dec6bba534e10cc29 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Thu, 30 Jan 2025 01:04:03 +0530
Subject: [PATCH 250/305] Changes for final report generation - PointPainting
 (#2063)

* Update perf sample count default value

* Updation for pointpainting

* Add default value for model name argument
---
 automotive/3d-object-detection/main.py    | 7 +++++--
 tools/submission/generate_final_report.py | 2 ++
 tools/submission/truncate_accuracy_log.py | 4 +++-
 3 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/automotive/3d-object-detection/main.py b/automotive/3d-object-detection/main.py
index b66a1878d1..8ddafcf2ea 100644
--- a/automotive/3d-object-detection/main.py
+++ b/automotive/3d-object-detection/main.py
@@ -92,7 +92,10 @@ def get_args():
         help="enable finding peak performance pass",
     )
     parser.add_argument("--backend", help="Name of the backend")
-    parser.add_argument("--model-name", help="Name of the model")
+    parser.add_argument(
+        "--model-name",
+        help="Name of the model",
+        default="pointpainting")
     parser.add_argument("--output", default="output", help="test results")
     parser.add_argument("--qps", type=int, help="target qps")
     parser.add_argument("--lidar-path", help="Path to model weights")
@@ -128,7 +131,7 @@ def get_args():
     parser.add_argument("--count", type=int, help="dataset items to use")
     parser.add_argument("--debug", action="store_true", help="debug")
     parser.add_argument(
-        "--performance-sample-count", type=int, help="performance sample count", default=5000
+        "--performance-sample-count", type=int, help="performance sample count", default=1024
     )
     parser.add_argument(
         "--max-latency", type=float, help="mlperf max latency in pct tile"
diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index da965ef3e9..4785c94bff 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -150,6 +150,7 @@ def main():
             "llama2-70b-interactive-99",
             "llama2-70b-interactive-99.9",
             "mixtral-8x7b",
+            "pointpainting",
         ],
         ["SingleStream", "MultiStream", "Server", "Offline"],
         [
@@ -228,6 +229,7 @@ def main():
                 "gptj-99": ["SingleStream", "Offline"],
                 "gptj-99.9": ["SingleStream", "Offline"],
                 "stable-diffusion-xl": ["SingleStream", "Offline"],
+                "pointpainting": ["SingleStream"],
             },
         }
 
diff --git a/tools/submission/truncate_accuracy_log.py b/tools/submission/truncate_accuracy_log.py
index 368f7e8318..b0eadaad38 100755
--- a/tools/submission/truncate_accuracy_log.py
+++ b/tools/submission/truncate_accuracy_log.py
@@ -142,7 +142,9 @@ def truncate_results_dir(filter_submitter, backup, scenarios_to_skip):
                 continue
 
             # process results
-            required_dirs = ["results", "compliance"] if division in ["closed", "network"] else ["results"]
+            required_dirs = [
+                "results", "compliance"] if division in [
+                "closed", "network"] else ["results"]
             for directory in required_dirs:
 
                 log_path = os.path.join(division, submitter, directory)

From 10d123c982be145de8e818309440486ee1706439 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Sat, 1 Feb 2025 01:53:20 +0530
Subject: [PATCH 251/305] Fix mlperf.conf link for equal issue mode (#2069)

---
 graph/R-GAT/benchmark-checklist.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graph/R-GAT/benchmark-checklist.md b/graph/R-GAT/benchmark-checklist.md
index 1d0a8d379b..42a4088063 100644
--- a/graph/R-GAT/benchmark-checklist.md
+++ b/graph/R-GAT/benchmark-checklist.md
@@ -30,7 +30,7 @@ Number of **unique samples** in the validation dataset and the QSL size specifie
 
 #### **6. Equal Issue Mode Applicability**
 Documented whether **Equal Issue Mode** is applicable in 
-- [X] [mlperf.conf](https://github.com/mlcommons/inference/blob/master/loadgen/mlperf.conf#L42)
+- [X] [mlperf.conf](https://github.com/mlcommons/inference/blob/master/loadgen/mlperf.conf#L50)
 - [X] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
   *(Relevant if sample processing times are inconsistent across inputs.)*
 

From d7bbf881ca0b69c71cbbee18fa26cf13c7a67be8 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Sat, 1 Feb 2025 00:09:47 +0000
Subject: [PATCH 252/305] Update results.cc | Add another significant digit to
 percentile latency display (#2066)

---
 loadgen/results.cc | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/loadgen/results.cc b/loadgen/results.cc
index 817c5a379b..5e940793e3 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -189,7 +189,7 @@ bool PerformanceSummary::EarlyStopping(
           std::to_string(queries_issued) + ").\n" + " * Would discard " +
           std::to_string(t - 1) + " highest latency queries.\n" +
           " * Early stopping " +
-          DoubleToString(target_latency_percentile.percentile * 100, 0) +
+          DoubleToString(target_latency_percentile.percentile * 100, 1) +
           "th percentile estimate: " + std::to_string(percentile_estimate);
       early_stopping_latency_ss = percentile_estimate;
 
@@ -202,7 +202,7 @@ bool PerformanceSummary::EarlyStopping(
       if (queries_issued < h_min + 1) {
         *recommendation +=
             "\n * Not enough queries processed for " +
-            DoubleToString(multi_stream_percentile * 100, 0) +
+            DoubleToString(multi_stream_percentile * 100, 1) +
             "th percentile\n" +
             " early stopping estimate (would need to process at\n least " +
             std::to_string(h_min + 1) + " total queries).";
@@ -218,7 +218,7 @@ bool PerformanceSummary::EarlyStopping(
         percentile_estimate = (*sample_latencies)[queries_issued - t];
         *recommendation +=
             "\n * Early stopping " +
-            DoubleToString(multi_stream_percentile * 100, 0) +
+            DoubleToString(multi_stream_percentile * 100, 1) +
             "th percentile estimate: " + std::to_string(percentile_estimate);
         early_stopping_latency_ms = percentile_estimate;
       }
@@ -273,7 +273,7 @@ bool PerformanceSummary::EarlyStopping(
           std::to_string(queries_issued) + ").\n" + " * Would discard " +
           std::to_string(t - 1) + " highest latency queries.\n" +
           " * Early stopping " +
-          DoubleToString(target_latency_percentile.percentile * 100, 0) +
+          DoubleToString(target_latency_percentile.percentile * 100, 1) +
           "th percentile estimate: " + std::to_string(percentile_estimate);
       early_stopping_latency_ms = percentile_estimate;
       break;
@@ -392,13 +392,13 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
 
   switch (settings.scenario) {
     case TestScenario::SingleStream: {
-      summary(DoubleToString(target_latency_percentile.percentile * 100, 0) +
+      summary(DoubleToString(target_latency_percentile.percentile * 100, 1) +
                   "th percentile latency (ns) : ",
               target_latency_percentile.sample_latency);
       break;
     }
     case TestScenario::MultiStream: {
-      summary(DoubleToString(target_latency_percentile.percentile * 100, 0) +
+      summary(DoubleToString(target_latency_percentile.percentile * 100, 1) +
                   "th percentile latency (ns) : ",
               target_latency_percentile.query_latency);
       break;
@@ -430,14 +430,14 @@ void PerformanceSummary::LogSummary(AsyncSummary& summary) {
     switch (settings.scenario) {
       case TestScenario::SingleStream: {
         summary(DoubleToString(token_target_latency_percentile.percentile * 100,
-                               0) +
+                               1) +
                     "th first token percentile latency (ns) : ",
                 token_target_latency_percentile.sample_latency);
         break;
       }
       case TestScenario::MultiStream: {
         summary(DoubleToString(token_target_latency_percentile.percentile * 100,
-                               0) +
+                               1) +
                     "th first token percentile latency (ns) : ",
                 token_target_latency_percentile.sample_latency);
         break;

From d67b62657a63c7d64bacf92d5ec3328c03ef0d90 Mon Sep 17 00:00:00 2001
From: arjunsuresh <arjunsuresh@users.noreply.github.com>
Date: Sat, 1 Feb 2025 00:09:59 +0000
Subject: [PATCH 253/305] Increment version to 5.0.15

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 4b57201ae1..1ed9c641c4 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.14
+5.0.15

From aea55edb04b10928b75d1cc318d6bed2f2e4a629 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Sat, 1 Feb 2025 05:40:56 +0530
Subject: [PATCH 254/305] Create benchmark checklist for pointpainting (#2068)

* Create benchmark checklist for pointpainting
---
 .../benchmark-checklist.md                    | 89 +++++++++++++++++++
 1 file changed, 89 insertions(+)
 create mode 100644 automotive/3d-object-detection/benchmark-checklist.md

diff --git a/automotive/3d-object-detection/benchmark-checklist.md b/automotive/3d-object-detection/benchmark-checklist.md
new file mode 100644
index 0000000000..9760a9b86e
--- /dev/null
+++ b/automotive/3d-object-detection/benchmark-checklist.md
@@ -0,0 +1,89 @@
+
+#### **1. Applicable Categories**
+- Edge
+
+---
+
+#### **2. Applicable Scenarios for Each Category**
+- SingleStream
+
+---
+
+#### **3. Applicable Compliance Tests**
+- TEST01
+- TEST04
+
+---
+
+#### **4. Latency Threshold for Server Scenarios**
+- Not applicable
+
+---
+
+#### **5. Validation Dataset: Unique Samples**
+Number of **unique samples** in the validation dataset and the QSL size specified in 
+- [X] [inference policies benchmark section](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc#41-benchmarks)
+- [X] [mlperf.conf](https://github.com/mlcommons/inference/blob/master/loadgen/mlperf.conf)
+- [ ] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
+  *(Ensure QSL size overflows the system cache if possible.)*
+
+---
+
+#### **6. Equal Issue Mode Applicability**
+Documented whether **Equal Issue Mode** is applicable in 
+- [X] [mlperf.conf](https://github.com/mlcommons/inference/blob/master/loadgen/mlperf.conf)
+- [ ] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
+  *(Relevant if sample processing times are inconsistent across inputs.)*
+
+---
+
+#### **7. Expected Accuracy and `accuracy.txt` Contents**
+- [X] Detailed expected accuracy and the required contents of the `accuracy.txt` file [here](https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0/blob/auto-update/open/MLCommons/results/mlc-server-reference-gpu-pytorch_v2.2.2-cu124/pointpainting/singlestream/accuracy/accuracy.txt).
+
+---
+
+#### **8. Reference Model Details**
+- [ ] Reference model details updated in [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)  
+
+---
+
+#### **9. Reference Implementation Test Coverage**
+- [X] Reference implementation successfully does:
+  - [X] Performance runs
+  - [X] Accuracy runs
+  - [X] Compliance runs  
+
+
+---
+
+#### **10. Test Runs with Smaller Input Sets**
+- [X] Verified the reference implementation can perform test runs with a smaller subset of inputs for:
+  - [X] Performance runs
+  - [X] Accuracy runs
+
+---
+
+#### **11. Dataset and Reference Model Instructions**
+- [X] Clear instructions provided for:
+  - [X] Downloading the dataset and reference model.
+  - [X] Using the dataset and model for the benchmark.
+
+---
+
+#### **12. Documentation of Recommended System Requirements to run the reference implementation**
+- [ ] Added [here](https://github.com/mlcommons/inference/blob/docs/docs/system_requirements.yml)
+
+---
+
+#### **13. Submission Checker Modifications**
+- [ ] All necessary changes made to the **submission checker** to validate the benchmark.
+
+---
+
+#### **14. Sample Log Files**
+- [ ] Include sample logs for all the applicable scenario runs:
+  - [ ] `mlperf_log_summary.txt`
+  - [ ] `mlperf_log_detail.txt`  
+- [X] Log files passing the submission checker are generated for all Divisions.
+  - [X] Closed
+  - [X] Open  

From 9a2c0a1c4f07174534485efa96b44ef1dc41d62c Mon Sep 17 00:00:00 2001
From: nv-alicheng <107440104+nv-alicheng@users.noreply.github.com>
Date: Mon, 3 Feb 2025 08:16:03 -0800
Subject: [PATCH 255/305] Fix np.memmap usage, add flag to force not using
 memmap (#2081)

---
 graph/R-GAT/tools/accuracy_igbh.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/graph/R-GAT/tools/accuracy_igbh.py b/graph/R-GAT/tools/accuracy_igbh.py
index da63a69777..9329266749 100644
--- a/graph/R-GAT/tools/accuracy_igbh.py
+++ b/graph/R-GAT/tools/accuracy_igbh.py
@@ -21,6 +21,10 @@ def get_args():
         default="full",
         choices=["tiny", "small", "medium", "large", "full"]
     )
+    parser.add_argument(
+        "--no-memmap",
+        action="store_true",
+        help="do not use memmap even for large/full size variants")
     parser.add_argument(
         "--verbose",
         action="store_true",
@@ -38,7 +42,7 @@ def get_args():
     return args
 
 
-def load_labels(base_path, dataset_size, use_label_2K=True):
+def load_labels(base_path, dataset_size, use_label_2K=True, no_memmap=False):
     # load labels
     paper_nodes_num = {
         "tiny": 100000,
@@ -57,16 +61,12 @@ def load_labels(base_path, dataset_size, use_label_2K=True):
         "paper",
         label_file)
 
-    if dataset_size in ["large", "full"]:
-        paper_node_labels = torch.from_numpy(
-            np.memmap(
-                paper_lbl_path, dtype="float32", mode="r", shape=(paper_nodes_num[dataset_size])
-            )
-        ).to(torch.long)
+    if dataset_size in ["large", "full"] and not no_memmap:
+        mmap_mode = 'r'
     else:
-        paper_node_labels = torch.from_numpy(
-            np.load(paper_lbl_path)).to(
-            torch.long)
+        mmap_mode = None
+
+    paper_node_labels = torch.from_numpy(np.load(paper_lbl_path, mmap_mode=mmap_mode)).to(torch.long)
     labels = paper_node_labels
     val_idx = torch.load(
         os.path.join(
@@ -92,7 +92,7 @@ def get_labels(labels, val_idx, id_list):
     with open(args.mlperf_accuracy_file, "r") as f:
         mlperf_results = json.load(f)
 
-    labels, val_idx = load_labels(args.dataset_path, args.dataset_size)
+    labels, val_idx = load_labels(args.dataset_path, args.dataset_size, no_memmap=args.no_memmap)
     results = {}
 
     seen = set()

From 7f5025f4696e366005b4f7a5dcd5454b2fea2435 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Tue, 4 Feb 2025 22:56:48 +0530
Subject: [PATCH 256/305] Add device map - pointpainting automotive (#2087)

* Added map location

* [Automated Commit] Format Codebase

* Update map location

* [Automated Commit] Format Codebase

* Update evaluate.py

* [Automated Commit] Format Codebase

* Update test-resnet50.yml

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 .github/workflows/test-resnet50.yml              | 2 +-
 automotive/3d-object-detection/backend_deploy.py | 4 +++-
 automotive/3d-object-detection/model/painter.py  | 4 +++-
 automotive/3d-object-detection/tools/evaluate.py | 5 +++--
 4 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/test-resnet50.yml b/.github/workflows/test-resnet50.yml
index 50404b0d99..2c6fddf8f5 100755
--- a/.github/workflows/test-resnet50.yml
+++ b/.github/workflows/test-resnet50.yml
@@ -26,7 +26,7 @@ jobs:
         python-version: [ "3.9" ]
         backend: [ "onnxruntime", "tf" ]
         loadgen-flag: [ "", "--adr.loadgen.tags=_from-pip --pip_loadgen=yes" ]
-
+ 
     steps:
     - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}
diff --git a/automotive/3d-object-detection/backend_deploy.py b/automotive/3d-object-detection/backend_deploy.py
index 2207ea279b..b145b62195 100644
--- a/automotive/3d-object-detection/backend_deploy.py
+++ b/automotive/3d-object-detection/backend_deploy.py
@@ -71,7 +71,9 @@ def load(self):
             painted=True).to(
             device=device)
         model.eval()
-        checkpoint = torch.load(self.lidar_detector_path)
+        checkpoint = torch.load(
+            self.lidar_detector_path, map_location=torch.device(
+                'cuda' if torch.cuda.is_available() else 'cpu'))
         model.load_state_dict(checkpoint["model_state_dict"])
         self.lidar_detector = model
 
diff --git a/automotive/3d-object-detection/model/painter.py b/automotive/3d-object-detection/model/painter.py
index 36bd2aa0b8..545840ecf9 100644
--- a/automotive/3d-object-detection/model/painter.py
+++ b/automotive/3d-object-detection/model/painter.py
@@ -57,7 +57,9 @@ def __init__(self, args, onnx=False):
         else:
             model = network.modeling.__dict__['deeplabv3plus_resnet50'](
                 num_classes=19, output_stride=16)
-            checkpoint = torch.load(checkpoint_file)
+            checkpoint = torch.load(
+                checkpoint_file, map_location=torch.device(
+                    'cuda' if torch.cuda.is_available() else 'cpu'))
             model.load_state_dict(checkpoint["model_state"])
             model.eval()
             device = torch.device(
diff --git a/automotive/3d-object-detection/tools/evaluate.py b/automotive/3d-object-detection/tools/evaluate.py
index eb3c5d32ab..93fae93c61 100644
--- a/automotive/3d-object-detection/tools/evaluate.py
+++ b/automotive/3d-object-detection/tools/evaluate.py
@@ -84,8 +84,9 @@ def do_eval(det_results, gt_results, CLASSES, cam_sync=False):
         det_bboxes3d = np.concatenate(
             [det_location, det_dimensions, det_rotation_y[:, None]], axis=-1)
         iou3d_v = iou3d_camera(
-            torch.from_numpy(gt_bboxes3d).cuda(),
-            torch.from_numpy(det_bboxes3d).cuda())
+            torch.from_numpy(gt_bboxes3d).to(
+                "cuda" if torch.cuda.is_available() else "cpu"),
+            torch.from_numpy(det_bboxes3d).to("cuda" if torch.cuda.is_available() else "cpu"))
         ious['bbox_3d'].append(iou3d_v.cpu().numpy())
 
     MIN_IOUS = {

From 54cc04d06ea7533d0dfb65b5a411614ff2094d11 Mon Sep 17 00:00:00 2001
From: rod409 <radoyeh@gmail.com>
Date: Tue, 4 Feb 2025 09:30:25 -0800
Subject: [PATCH 257/305] PointPainting Documentation update (#2089)

* Automotive reference implementation sketch

* WIP: automotive reference implementation

* WIP add segmentation, dataset, and util functions to reference

* WIP: reference implementation with issues during post processing

* WIP update dockerfile remove pdb breaks

* WIP: reference implementation that runs samples

* Update README.md with initial docker runs

* WIP: add accuracy checker to reference

* Fix: set lidar detector to evaluation mode

* [Automated Commit] Format Codebase

* Update README.md

* Remove unnecessary mlperf conf load and implement dataset get_item_count

* add cpu only version of reference

* update dockerfile and display overal mAP

* code cleanup

* Update README.md

* fix packages versions in dockerfile

* handle frames if model predicts no objects

* Allow accuracy check on subset of datasets

* [Automated Commit] Format Codebase

* Updates automotive reference implementation (#2045)

* Remove unnecessary mlperf conf load and implement dataset get_item_count

* add cpu only version of reference

* update dockerfile and display overal mAP

* code cleanup

* Update README.md

* fix packages versions in dockerfile

* handle frames if model predicts no objects

* Allow accuracy check on subset of datasets

* [Automated Commit] Format Codebase

---------

Co-authored-by: mlcommons-bot <mlcommons-bot@users.noreply.github.com>

* [Automated Commit] Format Codebase

* fix whitespace merge errors

* [Automated Commit] Format Codebase

* [Automated Commit] Format Codebase

* add removed code during merge

* [Automated Commit] Format Codebase

* change nms thresholds on cpu only code

* add more details on dataset download

* Update README with more details on running benchmark.

* Update README with link to dataset access

* [Automated Commit] Format Codebase

* Revert "[Automated Commit] Format Codebase"

This reverts commit 11f3a4de1287110c6c2191a1b80832a81fffb40d.

* [Automated Commit] Format Codebase

* Fix type in readme

---------

Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
Co-authored-by: arjunsuresh <arjunsuresh@users.noreply.github.com>
Co-authored-by: Miro <mirhodak@amd.com>
Co-authored-by: mlcommons-bot <mlcommons-bot@users.noreply.github.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 automotive/3d-object-detection/README.md | 62 +++++++++++++++++++++---
 graph/R-GAT/tools/accuracy_igbh.py       |  9 +++-
 2 files changed, 61 insertions(+), 10 deletions(-)

diff --git a/automotive/3d-object-detection/README.md b/automotive/3d-object-detection/README.md
index 80e58a5db1..9066d0b429 100644
--- a/automotive/3d-object-detection/README.md
+++ b/automotive/3d-object-detection/README.md
@@ -1,27 +1,73 @@
-## Reference implementation fo automotive 3D detection benchmark
+# MLPerf™ Inference Benchmark for Automotive 3D Object Detection
 
-## Dataset and model checkpoints
-Contact MLCommons support for accessing the Waymo Open Dataset along with the model checkpoints for the reference implementation. You will need to accept a license agreement and will be given directions to download the data. You will need to place the kitti_format folder under a directory named waymo. There are four total checkpoints 2 for pytorch and 2 for onnx.
+This is the reference implementation for the MLPerf automotive 3D detection benchmark. The reference uses Pytorch as a backend. Additionally we provide an implementation using ONNX.
+
+| model | accuracy | dataset | model source | precision | notes |
+| ---- | ---- | ---- | ---- | ---- | ---- |
+| PointPainting | 0.5425 mAP | Waymo Open Dataset | https://github.com/rod409/pp | fp32 | Single-Stream 99.9 percentile |
+
+## Downloading the dataset and model checkpoints
+Contact [MLCommons](https://waymo.mlcommons.org/) support for accessing the Waymo Open Dataset along with the model checkpoints for the reference implementation. You will need to accept a license agreement and will be given directions to download the data with rclone. You will need to place the kitti_format folder under a directory named waymo. There are four total checkpoints 2 for pytorch and 2 for onnx.
+
+After downloading, the structure of the data should look like below:
+
+```bash
+├── waymo
+│   ├── best_deeplabv3plus_resnet50_waymo_os16.pth
+│   ├── deeplabv3+.onnx
+│   ├── kitti_format
+│   │   ├── ImageSets
+│   │   ├── painted_waymo_infos_test.pkl
+│   │   ├── painted_waymo_infos_train.pkl
+│   │   ├── painted_waymo_infos_trainval.pkl
+│   │   ├── painted_waymo_infos_val.pkl
+│   │   ├── testing
+│   │   ├── testing_3d_camera_only_detection
+│   │   ├── training
+│   │   ├── waymo_infos_test.pkl
+│   │   ├── waymo_infos_train.pkl
+│   │   ├── waymo_infos_trainval.pkl
+│   │   └── waymo_infos_val.pkl
+│   ├── pp_ep36.pth
+│   ├── pp.onnx
+```
+Within the training folder is the validation data used for the dataset. Extract all the compressed files in the training folder.
+
+```
+cd <your path to waymo>/waymo/kitti_format/training
+for f in *.tar.gz; do tar -xzvf "$f"; done
+```
 
 ## Running with docker
 Build the container and mount the inference repo and Waymo dataset directory.
+Build the container and mount the inference repo and Waymo dataset directory.
 ```
 docker build -t auto_inference -f dockerfile.gpu .
 
 docker run --gpus=all -it -v <directory to inference repo>/inference/:/inference -v <path to waymo dataset>/waymo:/waymo --rm auto_inference
 ```
-### Run with GPU
+### Run performance mode with Pytorch
 ```
 cd /inference/automotive/3d-object-detection
-python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp_ep36.pth --segmentor-path <checkpoint_path>/best_deeplabv3plus_resnet50_waymo_os16.pth --mlperf_conf /inference/mlperf.conf
+python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp_ep36.pth --segmentor-path <checkpoint_path>/best_deeplabv3plus_resnet50_waymo_os16.pth
 ```
 
-### Run with CPU and ONNX
+### Run performance mode with ONNX
 ```
-python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp.onnx --segmentor-path <checkpoint_path>/deeplabv3+.onnx --mlperf_conf /inference/mlperf.conf
+python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp.onnx --segmentor-path <checkpoint_path>/deeplabv3+.onnx --mlperf_conf /inference/mlperf.conf --backend onnx
+```
+> [!Note]
+> The minimum number of queries needed to be run is 6636. To get the best latency, users are encouraged to run a larger number of samples.
+
+### Accuracy run
+
+Add the accuracy flag
+```
+cd /inference/automotive/3d-object-detection
+python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp_ep36.pth --segmentor-path <checkpoint_path>/best_deeplabv3plus_resnet50_waymo_os16.pth --accuracy
 ```
 
-### Run the accuracy checker
+### Run the accuracy checker on the accuracy run log
 ```
 python accuracy_waymo.py --mlperf-accuracy-file <path to accuracy file>/mlperf_log_accuracy.json --waymo-dir /waymo/kitti_format/
 ```
diff --git a/graph/R-GAT/tools/accuracy_igbh.py b/graph/R-GAT/tools/accuracy_igbh.py
index 9329266749..a198dda667 100644
--- a/graph/R-GAT/tools/accuracy_igbh.py
+++ b/graph/R-GAT/tools/accuracy_igbh.py
@@ -66,7 +66,11 @@ def load_labels(base_path, dataset_size, use_label_2K=True, no_memmap=False):
     else:
         mmap_mode = None
 
-    paper_node_labels = torch.from_numpy(np.load(paper_lbl_path, mmap_mode=mmap_mode)).to(torch.long)
+    paper_node_labels = torch.from_numpy(
+        np.load(
+            paper_lbl_path,
+            mmap_mode=mmap_mode)).to(
+        torch.long)
     labels = paper_node_labels
     val_idx = torch.load(
         os.path.join(
@@ -92,7 +96,8 @@ def get_labels(labels, val_idx, id_list):
     with open(args.mlperf_accuracy_file, "r") as f:
         mlperf_results = json.load(f)
 
-    labels, val_idx = load_labels(args.dataset_path, args.dataset_size, no_memmap=args.no_memmap)
+    labels, val_idx = load_labels(
+        args.dataset_path, args.dataset_size, no_memmap=args.no_memmap)
     results = {}
 
     seen = set()

From c2a0117a87273490870c3655b93ab8184c46ca39 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 4 Feb 2025 17:39:32 +0000
Subject: [PATCH 258/305] Update verify_performance.py (#2071)

---
 compliance/nvidia/TEST01/verify_performance.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/compliance/nvidia/TEST01/verify_performance.py b/compliance/nvidia/TEST01/verify_performance.py
index 02ee8753b6..73e1bc3b52 100644
--- a/compliance/nvidia/TEST01/verify_performance.py
+++ b/compliance/nvidia/TEST01/verify_performance.py
@@ -89,9 +89,8 @@ def main():
         if re.match("Scenario", line):
             test_mode = line.split(": ", 1)[1].strip()
             continue
-
         if test_mode == "SingleStream":
-            if re.match(".*Early stopping 90th percentile estimate", line):
+            if re.match(".*Early stopping (90th|99.9th) percentile estimate", line):
                 test_score = line.split(": ", 1)[1].strip()
                 continue
 

From 4b7475bb022918c59278d2c63afa3e90349da037 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 4 Feb 2025 13:19:13 -0500
Subject: [PATCH 259/305] Match server scenario to standalone implementation
 (#2086)

* Match server scenario to standalone implementation
---
 .github/workflows/build_wheels.yml            |  1 +
 .../nvidia/TEST01/verify_performance.py       |  3 +-
 language/mixtral-8x7b/README.md               |  2 +
 language/mixtral-8x7b/SUT.py                  | 49 +++++++++----------
 4 files changed, 28 insertions(+), 27 deletions(-)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 91282ec9c7..be30e89358 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -3,6 +3,7 @@ name: Build loadgen wheels and release them into PYPI
 on:
   release:
     types: [published]
+  
   push:
     branches:
       - master
diff --git a/compliance/nvidia/TEST01/verify_performance.py b/compliance/nvidia/TEST01/verify_performance.py
index 73e1bc3b52..7c5aa897fc 100644
--- a/compliance/nvidia/TEST01/verify_performance.py
+++ b/compliance/nvidia/TEST01/verify_performance.py
@@ -90,7 +90,8 @@ def main():
             test_mode = line.split(": ", 1)[1].strip()
             continue
         if test_mode == "SingleStream":
-            if re.match(".*Early stopping (90th|99.9th) percentile estimate", line):
+            if re.match(
+                    ".*Early stopping (90th|99.9th) percentile estimate", line):
                 test_score = line.split(": ", 1)[1].strip()
                 continue
 
diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 928ca723d4..74935a4dc2 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -250,6 +250,8 @@ python -u evaluate-accuracy.py --checkpoint-path [path_to_model_checkpoint] \
 
 ## Accuracy Target
 
+**WARNING:** The full accuracy target was only verified with the standalone script. The reference implementation matches in a subset of the dataset, but hasn't been fully confirm.
+
 Reference scores:
 Open Orca:
 ```json
diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
index 36cf7e3c01..ec090b06fd 100644
--- a/language/mixtral-8x7b/SUT.py
+++ b/language/mixtral-8x7b/SUT.py
@@ -119,7 +119,7 @@ def put(self, value):
             self.first_token.put((value, self.response_ids[0]))
 
             self.is_first_token = False
-            return
+        
 
         self.tokens_cache.append(value)
 
@@ -356,6 +356,7 @@ def __init__(
         total_sample_count=24576,
         dataset_path=None,
         workers=1,
+        **kwargs,
     ):
 
         super().__init__(
@@ -408,9 +409,13 @@ def process_queries(self):
             if qitem is None:
                 break
 
-            input_ids_tensor = self.data_object.input_ids[qitem.index]
-            input_masks_tensor = self.data_object.attention_masks[qitem.index]
-            dataset = self.data_object.dataset_names[qitem.index]
+            input_dataset = [self.data_object.dataset_names[qitem.index]]
+
+            batch_texts = [self.data_object.input_texts[qitem.index]]
+            batch_ids = self.tokenizer.batch_encode_plus(
+                    batch_texts, return_tensors="pt", padding=True)
+            batch_ids = batch_ids.to(self.device)
+            _, length = batch_ids.input_ids.shape
 
             # TODO: This PoC is super slow with significant overhead. Best to
             # create a patch to `generate`
@@ -422,32 +427,24 @@ def process_queries(self):
                 response_ids=[qitem.id],
             )
 
-            logits_processor = LogitsProcessorList(
-                [StopAfterSequence(
-                    self.tokenizer.eos_token_id, device=self.device)]
+            
+            _ = self.model.generate(
+                **batch_ids,
+                num_return_sequences=1,
+                streamer=tokens_streamer,
+                **gen_kwargs,
             )
-            if dataset == "MBXP":
-                _ = self.model.generate(
-                    input_ids=input_ids_tensor,
-                    attention_mask=input_masks_tensor,
-                    pad_token_id=self.tokenizer.pad_token_id,
-                    streamer=tokens_streamer,
-                    logits_processor=logits_processor,
-                    **gen_kwargs,
-                )
-            else:
-                _ = self.model.generate(
-                    input_ids=input_ids_tensor,
-                    attention_mask=input_masks_tensor,
-                    pad_token_id=self.tokenizer.pad_token_id,
-                    streamer=tokens_streamer,
-                    **gen_kwargs,
-                )
 
             output_tokens = tokens_streamer.get_out_tokens()
-            n_tokens = len(output_tokens)
+            processed_output = self.data_object.postProcess(
+                torch.tensor([output_tokens], dtype=torch.int64),
+                length=0,
+                query_id_list=[qitem.index],
+                dataset_list=input_dataset,
+            )
+            n_tokens = len(processed_output[0])
             response_array = array.array(
-                "B", np.array(output_tokens, np.int32).tobytes()
+                "B", np.array(processed_output[0], np.int32).tobytes()
             )
             bi = response_array.buffer_info()
             response = [

From 421d20bd25650dec1521d7dca774e005c5708fa9 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Tue, 4 Feb 2025 23:50:02 +0530
Subject: [PATCH 260/305] Add reference model details (#2084)

* Add reference model details
---
 docs/index.md | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/docs/index.md b/docs/index.md
index db9e3e4404..84e4cf0aec 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -170,6 +170,23 @@ The currently valid [MLPerf Inference Benchmarks](index_gh.md) as of MLPerf infe
 - **Submission Category**: Datacenter
 ---
 
+## Automotive
+
+### 3D Object Detection
+#### [PointPainting](benchmarks/automotive/3d_object_detection/pointpainting.md)
+- **Dataset**: Waymo
+    - **Dataset Size**: 39,986
+    - **QSL Size**: 1,024
+- **Number of Parameters**: 
+- **FLOPs**: 
+- **Reference Model Accuracy (fp32)**:  mAP: 54.25%
+- **Required Accuracy (Closed Division)**:
+    - mAP: 54.25%
+- **Equal Issue mode**: False
+- **High accuracy variant**: Yes
+- **Submission Category**: Edge
+---
+
 ## Submission Categories
 - **Datacenter Category**: All benchmarks except bert are applicable to the datacenter category for inference v5.0.
 - **Edge Category**: All benchmarks except DLRMv2, LLAMA2-70B, Mixtral-8x7B and R-GAT are applicable to the edge category for v5.0.

From 4d0b3589fb1e9d36d1abe17b930ee3a9554ab0e7 Mon Sep 17 00:00:00 2001
From: Thomas Atta-Fosu <thomas.atta-fosu@intel.com>
Date: Tue, 4 Feb 2025 10:39:25 -0800
Subject: [PATCH 261/305] [405B] Set max_tokens to 2k (#2088)

* [405B] Set max_tokens to 2k

Maximum reference output tokens is under 2k, so we have to limit generation outputs as well.
---
 .github/workflows/build_wheels.yml | 2 +-
 language/llama3.1-405b/SUT_VLLM.py | 2 +-
 language/mixtral-8x7b/SUT.py       | 4 +---
 3 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index be30e89358..24c7e81906 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -3,7 +3,7 @@ name: Build loadgen wheels and release them into PYPI
 on:
   release:
     types: [published]
-  
+
   push:
     branches:
       - master
diff --git a/language/llama3.1-405b/SUT_VLLM.py b/language/llama3.1-405b/SUT_VLLM.py
index f5a8020210..8b0f96de33 100644
--- a/language/llama3.1-405b/SUT_VLLM.py
+++ b/language/llama3.1-405b/SUT_VLLM.py
@@ -72,7 +72,7 @@ def __init__(
             "top_p": 1,
             "top_k": 1,
             "seed": 42,
-            "max_tokens": 20000,
+            "max_tokens": 2000,
             "min_tokens": 2
         }
         self.sampling_params = SamplingParams(**gen_kwargs)
diff --git a/language/mixtral-8x7b/SUT.py b/language/mixtral-8x7b/SUT.py
index ec090b06fd..b29489a322 100644
--- a/language/mixtral-8x7b/SUT.py
+++ b/language/mixtral-8x7b/SUT.py
@@ -119,7 +119,6 @@ def put(self, value):
             self.first_token.put((value, self.response_ids[0]))
 
             self.is_first_token = False
-        
 
         self.tokens_cache.append(value)
 
@@ -413,7 +412,7 @@ def process_queries(self):
 
             batch_texts = [self.data_object.input_texts[qitem.index]]
             batch_ids = self.tokenizer.batch_encode_plus(
-                    batch_texts, return_tensors="pt", padding=True)
+                batch_texts, return_tensors="pt", padding=True)
             batch_ids = batch_ids.to(self.device)
             _, length = batch_ids.input_ids.shape
 
@@ -427,7 +426,6 @@ def process_queries(self):
                 response_ids=[qitem.id],
             )
 
-            
             _ = self.model.generate(
                 **batch_ids,
                 num_return_sequences=1,

From 67a33a445adb4c00ae0717835be6e8a47b542c06 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Wed, 5 Feb 2025 03:04:52 +0530
Subject: [PATCH 262/305] Add parameter and flops values (#2090)

---
 docs/index.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index 84e4cf0aec..0455f722c7 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -177,8 +177,8 @@ The currently valid [MLPerf Inference Benchmarks](index_gh.md) as of MLPerf infe
 - **Dataset**: Waymo
     - **Dataset Size**: 39,986
     - **QSL Size**: 1,024
-- **Number of Parameters**: 
-- **FLOPs**: 
+- **Number of Parameters**: 44 million
+- **FLOPs**: 3 trillion
 - **Reference Model Accuracy (fp32)**:  mAP: 54.25%
 - **Required Accuracy (Closed Division)**:
     - mAP: 54.25%

From f000467984f1571b90647cb9d6877854f9b56722 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Wed, 5 Feb 2025 03:05:48 +0530
Subject: [PATCH 263/305] Update benchmark-checklist.md - PointPainting (#2083)

* Update benchmark-checklist.md- pointpainting
---
 automotive/3d-object-detection/benchmark-checklist.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/automotive/3d-object-detection/benchmark-checklist.md b/automotive/3d-object-detection/benchmark-checklist.md
index 9760a9b86e..667fe9b300 100644
--- a/automotive/3d-object-detection/benchmark-checklist.md
+++ b/automotive/3d-object-detection/benchmark-checklist.md
@@ -24,7 +24,7 @@
 Number of **unique samples** in the validation dataset and the QSL size specified in 
 - [X] [inference policies benchmark section](https://github.com/mlcommons/inference_policies/blob/master/inference_rules.adoc#41-benchmarks)
 - [X] [mlperf.conf](https://github.com/mlcommons/inference/blob/master/loadgen/mlperf.conf)
-- [ ] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
+- [X] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
   *(Ensure QSL size overflows the system cache if possible.)*
 
 ---
@@ -32,7 +32,7 @@ Number of **unique samples** in the validation dataset and the QSL size specifie
 #### **6. Equal Issue Mode Applicability**
 Documented whether **Equal Issue Mode** is applicable in 
 - [X] [mlperf.conf](https://github.com/mlcommons/inference/blob/master/loadgen/mlperf.conf)
-- [ ] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
+- [X] [Inference benchmark docs](https://github.com/mlcommons/inference/blob/docs/docs/index.md)
   *(Relevant if sample processing times are inconsistent across inputs.)*
 
 ---

From 32f9e842187b97b8c2b08f7805f5c0dac39bcf65 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Wed, 5 Feb 2025 00:12:25 +0000
Subject: [PATCH 264/305] Report improvement - support output of IDs to a json
 file (#2059)

* Updated final report generation script to output the list of ids being used
* Update generate_final_report.py
---
 main.py                                   |  3 +-
 tools/submission/generate_final_report.py | 39 +++++++++++++++++++----
 2 files changed, 35 insertions(+), 7 deletions(-)

diff --git a/main.py b/main.py
index 1e561175ff..fc5a1ef5b9 100755
--- a/main.py
+++ b/main.py
@@ -502,7 +502,8 @@ def get_common_info(spaces, implementation, model):
         info += f"\n{pre_space}!!! tip\n\n"
         info += f"{pre_space}    - Number of threads could be adjusted using `--threads=#`, where `#` is the desired number of threads. This option works only if the implementation in use supports threading.\n\n"
         info += f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
-        info += f"{pre_space}    - `_r4.1-dev` could also be given instead of `_r5.0-dev` if you want to run the benchmark with the MLPerf version being 4.1.\n\n"
+        if implementation.lower() == "reference":
+            info += f"{pre_space}    - `_r4.1-dev` could also be given instead of `_r5.0-dev` if you want to run the benchmark with the MLPerf version being 4.1.\n\n"
         if model == "rgat":
             info += f"{pre_space}    - Add `--env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset>` if you have already downloaded the dataset. The path will be automatically mounted when using docker run.\n\n"
             info += f"{pre_space}    - Add `--env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint>` if you have already downloaded the model. The path will be automatically mounted when using docker run.\n\n"
diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 4785c94bff..303a727cc8 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -202,7 +202,8 @@ def main():
             "datacenter": {
                 "resnet": ["Server", "Offline"],
                 "retinanet": ["Server", "Offline"],
-                "rnnt": ["Server", "Offline"],
+                "bert-99": [],
+                "bert-99.9": [],
                 "dlrm-v2-99": ["Server", "Offline"],
                 "dlrm-v2-99.9": ["Server", "Offline"],
                 "3d-unet-99": ["Offline"],
@@ -216,18 +217,26 @@ def main():
                 "llama2-70b-interactive-99.9": ["Server", "Offline"],
                 "mixtral-8x7b": ["Server", "Offline"],
                 "rgat": ["Offline"],
-                "llama3.1-405b": ["Offline", "Server"]
+                "llama3.1-405b": ["Offline", "Server"],
+                "pointpainting": []
             },
             "edge": {
                 "resnet": ["SingleStream", "MultiStream", "Offline"],
                 "retinanet": ["SingleStream", "MultiStream", "Offline"],
-                "rnnt": ["SingleStream", "Offline"],
                 "bert-99": ["SingleStream", "Offline"],
                 "bert-99.9": ["SingleStream", "Offline"],
+                "dlrm-v2-99": [],
+                "dlrm-v2-99.9": [],
                 "3d-unet-99": ["SingleStream", "Offline"],
                 "3d-unet-99.9": ["SingleStream", "Offline"],
+                "llama2-70b-99": [],
+                "llama2-70b-99.9": [],
+                "llama2-70b-interactive-99": [],
+                "llama2-70b-interactive-99.9": [],
+                "llama3.1-405b": [],
                 "gptj-99": ["SingleStream", "Offline"],
                 "gptj-99.9": ["SingleStream", "Offline"],
+                "rgat": [],
                 "stable-diffusion-xl": ["SingleStream", "Offline"],
                 "pointpainting": ["SingleStream"],
             },
@@ -285,16 +294,34 @@ def MakeUniqueID(x):
             "Unique ID (e.g. for Audit)"],
         inplace=True,
     )
-    id_dict = {
-        key: 1 + value
-        for (value, key) in enumerate(pd.unique(df["Unique ID (e.g. for Audit)"]))
+    if os.path.exists("ids.json"):
+        with open("ids.json", "r") as f:
+            id_dict = json.load(f)
+    else:
+        id_dict = {}
+    cur_keys = id_dict.keys()
+    cur_ids = id_dict.values()
+    new_keys = [
+        a for a in pd.unique(
+            df["Unique ID (e.g. for Audit)"]) if a not in cur_keys]
+    if cur_ids:
+        max_cur_id = max(cur_ids)
+    else:
+        max_cur_id = 0
+    id_dict_new = {
+        key: 1 + value + max_cur_id
+        for (value, key) in enumerate(new_keys)
     }
+    id_dict.update(id_dict_new)
+
     df["ID"] = df.apply(
         lambda x: "{}-{:04}".format(
             args.version, id_dict[x["Unique ID (e.g. for Audit)"]]
         ),
         axis=1,
     )
+    with open("ids.json", "w") as f:
+        f.write(json.dumps(id_dict, indent=4))
 
     for category in ["closed", "open", "network"]:
         for suite in ["datacenter", "edge"]:

From 2e996ad6b5d469cb854e2d6baa5f318327e86370 Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Wed, 5 Feb 2025 23:06:23 -0800
Subject: [PATCH 265/305] Fixed GPTJ accuracy checker (#2093)

---
 language/gpt-j/evaluation.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/language/gpt-j/evaluation.py b/language/gpt-j/evaluation.py
index a95c8cd5cd..ab16b83d11 100644
--- a/language/gpt-j/evaluation.py
+++ b/language/gpt-j/evaluation.py
@@ -56,6 +56,7 @@ def main():
     dataset_path = args.dataset_file
     metric = evaluate.load("rouge")
     nltk.download("punkt")
+    nltk.download('punkt_tab')
 
     tokenizer = AutoTokenizer.from_pretrained(
         model_name,

From 2a41918e36f4c5b103c3f6cbdd46b5671745539e Mon Sep 17 00:00:00 2001
From: Zhihan Jiang <68881590+nvzhihanj@users.noreply.github.com>
Date: Thu, 6 Feb 2025 13:31:03 -0800
Subject: [PATCH 266/305] Fix SDXL, Retinanet and GPTJ accuracy checker (#2094)

* Fix SDXL, Retinanet and GPTJ accuracy checker
---
 tools/submission/submission_checker.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 793c534714..2418872fe5 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -735,19 +735,19 @@
 ACC_PATTERN = {
     "acc": r"^(?:\{\"accuracy|accuracy)[\": ]*=?\s*([\d\.]+).*",
     "AUC": r"^AUC=([\d\.]+).*",
-    "mAP": r".*'(?:mAP|Total)':\s*([\d\.]+)",
+    "mAP": r".*(?:mAP=|Total:)\s*([\d.]+)",
     "bleu": r"^BLEU\:\s*([\d\.]+).*",
     "F1": r"^{[\"\']exact_match[\"\']\:\s*[\d\.]+,\s*[\"\']f1[\"\']\:\s*([\d\.]+)}",
     "WER": r"Word Error Rate\:.*, accuracy=([0-9\.]+)%",
     "DICE": r"Accuracy\:\s*mean\s*=\s*([\d\.]+).*",
-    "ROUGE1": r".*'rouge1':\s([\d.]+).*",
-    "ROUGE2": r".*'rouge2':\s([\d.]+).*",
-    "ROUGEL": r".*'rougeL':\s([\d.]+).*",
+    "ROUGE1": r".*'rouge1':\s+'?([\d.]+)'?.*",
+    "ROUGE2": r".*'rouge2':\s+'?([\d.]+)'?.*",
+    "ROUGEL": r".*'rougeL':\s+'?([\d.]+)'?.*",
     "ROUGELSUM": r".*'rougeLsum':\s([\d.]+).*",
     "GEN_LEN": r".*'gen_len':\s([\d.]+).*",
     "TOKENS_PER_SAMPLE": r".*'tokens_per_sample':\s([\d.]+).*",
-    "CLIP_SCORE": r".*'CLIP_SCORE':\s([\d.]+).*",
-    "FID_SCORE": r".*'FID_SCORE':\s([\d.]+).*",
+    "CLIP_SCORE": r".*'CLIP_SCORE':\s.([\d.]+).*",
+    "FID_SCORE": r".*'FID_SCORE':\s.([\d.]+).*",
     "gsm8k_accuracy": r".*'gsm8k':\s([\d.]+).*",
     "mbxp_accuracy": r".*'mbxp':\s([\d.]+).*",
     "exact_match": r".*'exact_match':\s([\d.]+).*"

From 9668b86ab8ec6cf5245997ff0bf7553fc08f6ef4 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Thu, 6 Feb 2025 22:19:42 +0000
Subject: [PATCH 267/305] Update auto-update-dev.yml (#2096)

---
 .github/workflows/auto-update-dev.yml | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/auto-update-dev.yml b/.github/workflows/auto-update-dev.yml
index 69cfdb2813..9c6b17b5e8 100644
--- a/.github/workflows/auto-update-dev.yml
+++ b/.github/workflows/auto-update-dev.yml
@@ -1,4 +1,4 @@
-name: Auto-Update Dev Branch from Master
+name: Auto-Update Dev Branches from Master
 
 on:
   push:
@@ -6,8 +6,12 @@ on:
       - master  # Trigger workflow on commits to 'dev' branch
 
 jobs:
-  update-main:
+  update-dev:
+    strategy:
+      matrix: 
+        branch: [ "dev", "docs" ]
     runs-on: ubuntu-latest
+    
     permissions:
       contents: write  # Required to push to protected branches
 
@@ -15,7 +19,7 @@ jobs:
       - name: Checkout Main Branch
         uses: actions/checkout@v4
         with:
-          ref: dev
+          ref: ${{ matrix.branch }}
           fetch-depth: 0
           ssh-key: ${{ secrets.DEPLOY_KEY }}
 
@@ -24,11 +28,12 @@ jobs:
           git config user.name "github-actions"
           git config user.email "github-actions@github.com"
 
-      - name: Merge auto-update into dev
+      - name: Merge auto-update into ${{ matrix.branch }}
         run: |
           git fetch origin master:master
           git merge --no-ff master -m "Auto-merge updates from master branch"
 
-      - name: Push Changes to Main
+      - name: Push Changes to ${{ matrix.branch }}
         run: |
-          git push origin dev
+          git push origin ${{ matrix.branch }}
+     

From e1d1a4f39b364ce6f1f8d5a75da25e4a85ed8ccd Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Thu, 6 Feb 2025 22:42:08 +0000
Subject: [PATCH 268/305] Update submission_checker.py | Prevent empty accuracy
 in open division (#2097)

* Update submission_checker.py | Prevent empty accuracy in open division

* Update test-submission-checker.yml

* Fix accuracy RE for pointpainting

* Support v4.1 accuracy RE for SDXL
---
 .github/workflows/test-submission-checker.yml |  4 ++--
 tools/submission/submission_checker.py        | 10 +++++-----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index 5b7637d7da..4ad4be4c82 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -31,8 +31,8 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cm4mlops
+        python3 -m pip install mlc-scripts
         git clone https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
-        cm run script --tags=run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/mlperf_inference_unofficial_submissions_v5.0 --src_version=v5.0 --extra_args=" --skip-extra-files-in-root-check --skip-extra-accuracy-files-check" --quiet 
+        mlcr run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/mlperf_inference_unofficial_submissions_v5.0 --src_version=v5.0 --extra_args=" --skip-extra-files-in-root-check --skip-extra-accuracy-files-check" --quiet 
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 2418872fe5..8e131d4a31 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -735,7 +735,7 @@
 ACC_PATTERN = {
     "acc": r"^(?:\{\"accuracy|accuracy)[\": ]*=?\s*([\d\.]+).*",
     "AUC": r"^AUC=([\d\.]+).*",
-    "mAP": r".*(?:mAP=|Total:)\s*([\d.]+)",
+    "mAP": r".*(?:mAP=|'Total':)\s*([\d.]+)",
     "bleu": r"^BLEU\:\s*([\d\.]+).*",
     "F1": r"^{[\"\']exact_match[\"\']\:\s*[\d\.]+,\s*[\"\']f1[\"\']\:\s*([\d\.]+)}",
     "WER": r"Word Error Rate\:.*, accuracy=([0-9\.]+)%",
@@ -746,8 +746,8 @@
     "ROUGELSUM": r".*'rougeLsum':\s([\d.]+).*",
     "GEN_LEN": r".*'gen_len':\s([\d.]+).*",
     "TOKENS_PER_SAMPLE": r".*'tokens_per_sample':\s([\d.]+).*",
-    "CLIP_SCORE": r".*'CLIP_SCORE':\s.([\d.]+).*",
-    "FID_SCORE": r".*'FID_SCORE':\s.([\d.]+).*",
+    "CLIP_SCORE": r".*'CLIP_SCORE':\s+'?([\d.]+).*",
+    "FID_SCORE": r".*'FID_SCORE':\s+'?([\d.]+).*",
     "gsm8k_accuracy": r".*'gsm8k':\s([\d.]+).*",
     "mbxp_accuracy": r".*'mbxp':\s([\d.]+).*",
     "exact_match": r".*'exact_match':\s([\d.]+).*"
@@ -2407,7 +2407,7 @@ def log_result(
                                 .replace('"', "")
                                 .replace("{", "")
                                 .replace("}", "")
-                            )
+                            ).strip()
                             if mlperf_model in REQUIRED_ACC_BENCHMARK:
                                 if (
                                         config.version
@@ -2425,7 +2425,7 @@ def log_result(
                                             missing_files,
                                         )
                                         accuracy_is_valid = False
-                            if not accuracy_is_valid and not is_closed_or_network:
+                            if not accuracy_is_valid and acc and not is_closed_or_network:
                                 if debug:
                                     log.warning(
                                         "%s, accuracy not valid but taken for open",

From fa5d4f74abd2ef277bddead8773e26214fddb39b Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Fri, 7 Feb 2025 23:02:34 +0530
Subject: [PATCH 269/305] Added information about GitHub tests currently live
 (#2091)

Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
---
 tests.md | 61 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 tests.md

diff --git a/tests.md b/tests.md
new file mode 100644
index 0000000000..5a43f33639
--- /dev/null
+++ b/tests.md
@@ -0,0 +1,61 @@
+The below tables lists the actively tests being carried out by MLCommons for the currently active MLPerf inference benchmarks. Please contact [Support](support@mlcommons.org) if you would like to add any new tests here. The tests are run using GitHub actions and the test results go to the below two repositories.
+1. Short Runs: https://github.com/mlcommons/mlperf_inference_test_submissions_v5.0/
+2. Full Runs: https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0/
+
+These reporitories are per inference round.
+
+## Nvidia implementation - full runs
+[![MLPerf Inference Nvidia implementations](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-nvidia-mlperf-inference-implementations.yml/badge.svg)](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-nvidia-mlperf-inference-implementations.yml)
+
+## Reference implementation - short runs
+[![MLPerf inference ResNet50](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-resnet50.yml/badge.svg)](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-resnet50.yml)
+
+[![MLPerf inference retinanet](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-retinanet.yml/badge.svg)](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-retinanet.yml)
+
+[![MLPerf inference bert (deepsparse, tf, onnxruntime, pytorch)](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-bert-deepsparse-tf-onnxruntime-pytorch.yml/badge.svg)](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-bert-deepsparse-tf-onnxruntime-pytorch.yml)
+
+[![MLPerf inference R-GAT](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-rgat.yml/badge.svg)](https://github.com/mlcommons/mlperf-automations/actions/workflows/test-mlperf-inference-rgat.yml)
+
+[![MLPerf inference DLRM-v2](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-dlrm.yml/badge.svg)](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-dlrm.yml)
+
+[![MLPerf inference GPT-J](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-gptj.yml/badge.svg)](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-gptj.yml)
+
+[![MLPerf inference LLAMA2-70B](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-llama2.yml/badge.svg)](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-llama2.yml)
+
+[![MLPerf inference MIXTRAL-8x7B](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-mixtral.yml/badge.svg)](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-mixtral.yml)
+
+[![MLPerf inference SDXL](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-sdxl.yaml/badge.svg)](https://github.com/GATEOverflow/mlperf-automations/actions/workflows/test-mlperf-inference-sdxl.yaml)
+
+
+
+## Reference Implementation
+| Benchmark            | CPU/GPU | Test Type | 
+|----------------------|---------|-----------|
+| resnet50            |   CPU | Short   |     
+| retinanet           |   CPU | Short        |
+| 3d-unet             |    -  |   TBD        |
+| bert               |   CPU  |  Short       |
+| gptj               |   Both | Short        |               
+| llama2-70b         |   CPU  |  Short       |
+| mixtral-8x7b       |   CPU  |  Short       |
+| llama3.1-405b      |   -    |      -       |
+| rgat               |   CPU      |   Short  |
+| pointpainting      |   GPU      |    -     |
+| stable-diffusion-xl |  GPU      |   Short  |
+| dlrm_v2            | CPU        | Short    |
+
+## Nvidia Implementation
+| Benchmark            |  Test Type | 
+|----------------------|-----------|
+| resnet50            | Full       |     
+| retinanet           | Full       |
+| 3d-unet             | Full       |
+| bert               |  Full       |
+| gptj               |  Full       |               
+| llama2-70b         |   TBD       |
+| mixtral-8x7b       |   TBD       |
+| llama3.1-405b      |     -      |
+| rgat               |   NA       |
+| pointpainting      |   NA       |
+| stable-diffusion-xl | Full      |
+| dlrm_v2            |  TBD       |

From 7bf59976b5f4eb7c5b8f30a88af832e028028446 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Fri, 14 Feb 2025 19:28:29 +0000
Subject: [PATCH 270/305] Docs update, fix download links for llama models 
 (#2055)

* add docs for llama3 + inference

* Update llama2-70b README.md

* Update main.py
---
 .github/workflows/build_wheels.yml            |   2 +
 .github/workflows/publish.yaml                |   1 -
 .github/workflows/test-bert.yml               |   6 +-
 .../workflows/test-calibration-downloads.yml  |  94 ++++++++++++++++
 .github/workflows/test-loadgen.yml            |   6 +-
 .github/workflows/test-resnet50.yml           |   6 +-
 .github/workflows/test-retinanet.yml          |   6 +-
 .github/workflows/test-rgat.yml               |   6 +-
 .github/workflows/test-rnnt.yml               |   4 +-
 .github/workflows/test-submission-checker.yml |   2 +-
 .../workflows/test-submission-generation.yml  |   8 +-
 .github/workflows/test-tvm.yml                |   6 +-
 README.md                                     |   1 +
 docs/README.md                                |   2 +-
 .../get-pointpainting-data.md                 |   9 ++
 .../3d_object_detection/pointpainting.md      |  13 +++
 docs/benchmarks/graph/get-rgat-data.md        |   6 +-
 .../image_classification/get-resnet50-data.md |  10 +-
 .../image_classification/mobilenets.md        |  22 ++--
 docs/benchmarks/language/get-bert-data.md     |   8 +-
 docs/benchmarks/language/get-gptj-data.md     |   4 +-
 .../language/get-llama2-70b-data.md           |   4 +-
 .../language/get-llama3_1-405b-data.md        |   6 +-
 .../language/get-mixtral-8x7b-data.md         |   4 +-
 .../reproducibility/indyscc24-bert.md         |   6 +-
 .../medical_imaging/get-3d-unet-data.md       |  10 +-
 .../object_detection/get-retinanet-data.md    |   8 +-
 .../recommendation/get-dlrm-v2-data.md        |   4 +-
 .../benchmarks/text_to_image/get-sdxl-data.md |   4 +-
 .../text_to_image/reproducibility/scc24.md    |   8 +-
 docs/install/index.md                         |  24 ++---
 docs/submission/index.md                      |  28 ++---
 docs/usage/index.md                           |   2 +-
 graph/R-GAT/README.md                         |  22 ++--
 language/bert/README.md                       |  12 +--
 language/bert/bert_QDL.py                     |   2 +-
 language/bert/onnxruntime_SUT.py              |   2 +-
 language/bert/ray_SUT.py                      |   2 +-
 language/gpt-j/GPTJ_QDL.py                    |   2 +-
 language/gpt-j/README.md                      |  14 +--
 language/gpt-j/main.py                        |   2 +-
 language/llama2-70b/README.md                 |  15 ++-
 language/llama3.1-405b/README.md              |  33 +++---
 main.py                                       | 100 +++++++++++++-----
 mkdocs.yml                                    |   7 +-
 recommendation/dlrm_v2/pytorch/README.md      |   8 +-
 text_to_image/README.md                       |  26 ++---
 tools/submission/submission_checker.py        |   2 +-
 48 files changed, 374 insertions(+), 205 deletions(-)
 create mode 100644 .github/workflows/test-calibration-downloads.yml
 create mode 100644 docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md
 create mode 100644 docs/benchmarks/automotive/3d_object_detection/pointpainting.md

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 24c7e81906..5f069c33e3 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -11,6 +11,7 @@ on:
     paths:
       - loadgen/**
 
+
 jobs:
   update_version:
     name: Update version only on ubuntu but used by windows and macos
@@ -133,6 +134,7 @@ jobs:
         with:
           name: wheels-windows-latest
           path: wheels
+      # Publish
       - name: Publish
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml
index 214ac3611b..1fbb9cb886 100644
--- a/.github/workflows/publish.yaml
+++ b/.github/workflows/publish.yaml
@@ -2,7 +2,6 @@
 
 name: Publish site
 
-
 on:
   release:
     types: [published]
diff --git a/.github/workflows/test-bert.yml b/.github/workflows/test-bert.yml
index b7012a6e8d..27fcee0d3f 100755
--- a/.github/workflows/test-bert.yml
+++ b/.github/workflows/test-bert.yml
@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Test for MLPerf inference bert submission generation using CM script automation
+name: Test for MLPerf inference bert submission generation using MLC script automation
 
 on:
   pull_request:
@@ -33,7 +33,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cm4mlops
+        python3 -m pip install mlc-scripts
     - name: Test BERT and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom
+        mlcr run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=bert-99 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom
diff --git a/.github/workflows/test-calibration-downloads.yml b/.github/workflows/test-calibration-downloads.yml
new file mode 100644
index 0000000000..48078804fd
--- /dev/null
+++ b/.github/workflows/test-calibration-downloads.yml
@@ -0,0 +1,94 @@
+name: Test for the downloads of calibration datasets
+on:
+  pull_request:
+    branches: [ "master", "dev" ]
+    paths:
+      - vision/classification_and_detection/**
+      - language/**
+      - recommendation/**
+      - .github/workflows/test-calibration-downloads.yml
+      - '!**.md'
+
+env:
+  PR_HEAD_REF: ${{ github.event.pull_request.head.ref }}
+
+jobs:
+  download-imagenet:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [ "3.9" ]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python3 -m pip install mlc-scripts
+    - name: Download Imagenet Calibration
+      run: |
+        mlcr get,dataset,imagenet,_calibration --outdirname=. --quiet
+
+  download-openimages:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [ "3.9" ]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python3 -m pip install mlc-scripts
+    - name: Download Openimages Calibration
+      run: |
+        mlcr get,dataset,openimages,_calibration --outdirname=. --quiet
+
+  download-igbh:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [ "3.9" ]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python3 -m pip install mlc-scripts
+    # - name: Download IGBH Calibration
+    #   run: |
+    #     mlcr get,dataset,igbh,_calibration --outdirname=. --quiet
+  
+  download-squad:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: [ "3.9" ]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python3 -m pip install mlc-scripts
+    - name: Download SQUAD Calibration
+      run: |
+        mlcr get,dataset,squad,_calib1 --outdirname=. --quiet
diff --git a/.github/workflows/test-loadgen.yml b/.github/workflows/test-loadgen.yml
index d73d5913bd..c90167f566 100755
--- a/.github/workflows/test-loadgen.yml
+++ b/.github/workflows/test-loadgen.yml
@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Test for MLPerf inference loadgen using CM script automation
+name: Test for MLPerf inference loadgen using MLC script automation
 
 on:
   pull_request:
@@ -31,7 +31,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cm4mlops
+        python3 -m pip install mlc-scripts
     - name: Test Loadgen
       run: |
-        cm run script --tags=get,mlperf,inference,loadgen --quiet --version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.loadgen.tags=_no-compilation-warnings
+        mlcr get,mlperf,inference,loadgen --quiet --version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.loadgen.tags=_no-compilation-warnings
diff --git a/.github/workflows/test-resnet50.yml b/.github/workflows/test-resnet50.yml
index 2c6fddf8f5..957d3b0b56 100755
--- a/.github/workflows/test-resnet50.yml
+++ b/.github/workflows/test-resnet50.yml
@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Test for MLPerf inference resnet50 submission generation using CM script automation
+name: Test for MLPerf inference resnet50 submission generation using MLC script automation
 
 on:
   pull_request:
@@ -35,7 +35,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cm4mlops
+        python3 -m pip install mlc-scripts
     - name: Test Resnet50 and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}
+        mlcr run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}
diff --git a/.github/workflows/test-retinanet.yml b/.github/workflows/test-retinanet.yml
index 5b18619de9..2a99e9d0c2 100755
--- a/.github/workflows/test-retinanet.yml
+++ b/.github/workflows/test-retinanet.yml
@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Test for MLPerf inference retinanet submission generation using CM script automation
+name: Test for MLPerf inference retinanet submission generation using MLC script automation
 
 on:
   pull_request:
@@ -33,7 +33,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cm4mlops
+        python3 -m pip install mlc-scripts
     - name: Test Retinanet and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom
+        mlcr run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --hw_name=default --model=retinanet --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=10 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom
diff --git a/.github/workflows/test-rgat.yml b/.github/workflows/test-rgat.yml
index 990c58a669..f334dda450 100644
--- a/.github/workflows/test-rgat.yml
+++ b/.github/workflows/test-rgat.yml
@@ -1,4 +1,4 @@
-name: Test for MLPerf inference rgat submission generation using CM script automation
+name: Test for MLPerf inference rgat submission generation using MLC script automation
 
 on:
   pull_request:
@@ -32,7 +32,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cm4mlops
+        python3 -m pip install mlc-scripts
     - name: Test R-GAT and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --category=datacenter --hw_name=default --model=rgat --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}
+        mlcr run,mlperf,inference,generate-run-cmds,_submission,_short --quiet --submitter="MLCommons" --category=datacenter --hw_name=default --model=rgat --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=500 --adr.compiler.tags=gcc --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src-loadgen.version=custom --adr.loadgen.version=custom ${{ matrix.loadgen-flag }}
diff --git a/.github/workflows/test-rnnt.yml b/.github/workflows/test-rnnt.yml
index a08803741a..415a012d6d 100644
--- a/.github/workflows/test-rnnt.yml
+++ b/.github/workflows/test-rnnt.yml
@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Test for MLPerf inference rnnt submission generation using CM script automation
+name: Test for MLPerf inference rnnt submission generation using MLC script automation
 
 on:
   pull_request:
@@ -39,4 +39,4 @@ jobs:
         cm run script --quiet --tags=get,sys-utils-cm
     - name: Test RNNT and end to end submission generation
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds,_performance-only --quiet --submitter="MLCommons" --hw_name=default --model=rnnt --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --precision=${{ matrix.precision }} --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.env.CM_GIT_CHECKOUT=$PR_HEAD_REF --adr.inference-src.env.CM_GIT_URL=${{ github.event.pull_request.head.repo.html_url }} --adr.ml-engine-pytorch.version=1.13.0 --adr.ml-engine-torchvision.version=0.14.1 --adr.librosa.version_max=0.9.1
+        mlcr run,mlperf,inference,generate-run-cmds,_performance-only --quiet --submitter="MLCommons" --hw_name=default --model=rnnt --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --precision=${{ matrix.precision }} --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.env.CM_GIT_CHECKOUT=$PR_HEAD_REF --adr.inference-src.env.CM_GIT_URL=${{ github.event.pull_request.head.repo.html_url }} --adr.ml-engine-pytorch.version=1.13.0 --adr.ml-engine-torchvision.version=0.14.1 --adr.librosa.version_max=0.9.1
diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index 4ad4be4c82..7dd653ec8f 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Test for MLPerf inference submission checker using CM script automation
+name: Test for MLPerf inference submission checker using MLC script automation
 
 on:
   pull_request:
diff --git a/.github/workflows/test-submission-generation.yml b/.github/workflows/test-submission-generation.yml
index 97afc58cdd..0bb2ad353b 100644
--- a/.github/workflows/test-submission-generation.yml
+++ b/.github/workflows/test-submission-generation.yml
@@ -1,6 +1,6 @@
 # This workflow will test the submission generation using MLPerf Automation 
 
-name: CM based Submission Generation
+name: MLC based Submission Generation
 
 on:
   pull_request:
@@ -34,7 +34,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        pip install cm4mlops
+        pip install mlc-scripts
     - name: Pull repo where test cases are uploaded
       run: |
         git clone -b submission-generation-examples https://github.com/mlcommons/inference.git submission_generation_examples
@@ -48,5 +48,5 @@ jobs:
         fi
         # Dynamically set the log group to simulate a dynamic step name
         echo "::group::$description"
-        cm ${{ matrix.action }} script --tags=generate,inference,submission  --adr.compiler.tags=gcc --version=v5.0 --clean --preprocess_submission=yes --submission_base_dir=mysubmissions --results_dir=$PWD/submission_generation_tests/${{ matrix.case }}/ --run-checker --submitter=MLCommons --tar=yes --division=${{ matrix.division }} --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes --quiet
-        cm ${{ matrix.action }} script --tags=run,submission,checker --submitter_id_off=mysubmitter_id --tar=yes --submission_dir=mysubmissions/submissions --submission_tar_file=mysubmission.tar.gz
+        mlc ${{ matrix.action }} script --tags=generate,inference,submission  --adr.compiler.tags=gcc --version=v5.0 --clean --preprocess_submission=yes --submission_base_dir=mysubmissions --results_dir=$PWD/submission_generation_tests/${{ matrix.case }}/ --run-checker --submitter=MLCommons --tar=yes --division=${{ matrix.division }} --env.MLC_DETERMINE_MEMORY_CONFIGURATION=yes --quiet
+        mlc ${{ matrix.action }} script --tags=run,submission,checker --submitter_id_off=mysubmitter_id --tar=yes --submission_dir=mysubmissions/submissions --submission_tar_file=mysubmission.tar.gz
diff --git a/.github/workflows/test-tvm.yml b/.github/workflows/test-tvm.yml
index 6741c36106..1856ff4bd2 100755
--- a/.github/workflows/test-tvm.yml
+++ b/.github/workflows/test-tvm.yml
@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Test for MLPerf inference resnet50 TVM backend using CM script automation
+name: Test for MLPerf inference resnet50 TVM backend using MLC script automation
 
 on:
   pull_request:
@@ -33,7 +33,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python3 -m pip install cm4mlops
+        python3 -m pip install mlc-scripts
     - name: Test Resnet50 TVM backend
       run: |
-        cm run script --tags=run,mlperf,inference,generate-run-cmds --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=5 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF
+        mlcr run,mlperf,inference,generate-run-cmds --quiet --submitter="MLCommons" --hw_name=default --model=resnet50 --implementation=reference --backend=${{ matrix.backend }} --device=cpu --scenario=Offline --test_query_count=5 --adr.compiler.tags=gcc  --adr.inference-src.version=custom --adr.inference-src.tags=_repo.${{ github.event.pull_request.head.repo.html_url }},_branch.$PR_HEAD_REF
diff --git a/README.md b/README.md
index 161da09a9e..6cab3e2f63 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,7 @@ MLPerf Inference is a benchmark suite for measuring how fast systems can run mod
 
 Please see the [MLPerf Inference benchmark paper](https://arxiv.org/abs/1911.02549) for a detailed description of the benchmarks along with the motivation and guiding principles behind the benchmark suite. If you use any part of this benchmark (e.g., reference implementations, submissions, etc.), please cite the following:
 
+
 ```
 @misc{reddi2019mlperf,
     title={MLPerf Inference Benchmark},
diff --git a/docs/README.md b/docs/README.md
index 92d66225ce..496a93718a 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,4 +1,4 @@
-# Documentation Website for MLPerf Inference using the unified CM interface
+# Documentation Website for MLPerf Inference using the MLC interface
 
 ## Commands to get the website running locally
 ```
diff --git a/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md b/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md
new file mode 100644
index 0000000000..2e5ef33375
--- /dev/null
+++ b/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md
@@ -0,0 +1,9 @@
+---
+hide:
+  - toc
+---
+
+# 3D Object Detection using PointPainting
+
+TBD
+
diff --git a/docs/benchmarks/automotive/3d_object_detection/pointpainting.md b/docs/benchmarks/automotive/3d_object_detection/pointpainting.md
new file mode 100644
index 0000000000..efeb6f2156
--- /dev/null
+++ b/docs/benchmarks/automotive/3d_object_detection/pointpainting.md
@@ -0,0 +1,13 @@
+---
+hide:
+  - toc
+---
+
+
+# 3D Object Detection using PointPainting
+
+
+=== "MLCommons-Python"
+    ## MLPerf Reference Implementation in Python
+    
+{{ mlperf_inference_implementation_readme (4, "pointpainting", "reference", devices = ["CPU", "CUDA"]) }}
\ No newline at end of file
diff --git a/docs/benchmarks/graph/get-rgat-data.md b/docs/benchmarks/graph/get-rgat-data.md
index 189c25b874..6ab9515e59 100644
--- a/docs/benchmarks/graph/get-rgat-data.md
+++ b/docs/benchmarks/graph/get-rgat-data.md
@@ -14,7 +14,7 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Full Dataset
     ```
-    cm run script --tags=get,dataset,igbh,_full -j
+    mlcr get,dataset,igbh,_full -j
     ```
 
 === "Debug Dataset"
@@ -22,7 +22,7 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Full Dataset
     ```
-    cm run script --tags=get,dataset,igbh,_debug -j
+    mlcr get,dataset,igbh,_debug -j
     ```
 
 ## Model
@@ -34,6 +34,6 @@ Get the Official MLPerf R-GAT Model
 
     ### PyTorch
     ```
-    cm run script --tags=get,ml-model,rgat -j
+    mlcr get,ml-model,rgat -j
     ```
 
diff --git a/docs/benchmarks/image_classification/get-resnet50-data.md b/docs/benchmarks/image_classification/get-resnet50-data.md
index 900379d5ae..771571d5c7 100644
--- a/docs/benchmarks/image_classification/get-resnet50-data.md
+++ b/docs/benchmarks/image_classification/get-resnet50-data.md
@@ -14,18 +14,18 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset,imagenet,validation -j
+    mlcr get,dataset,imagenet,validation -j
     ```
 === "Calibration"
     ResNet50 calibration dataset consist of 500 images selected from the Imagenet 2012 validation dataset. There are 2 alternative options for the calibration dataset.
 
     ### Get Calibration Dataset Using Option 1
     ```
-    cm run script --tags=get,dataset,imagenet,calibration,_mlperf.option1 -j
+    mlcr get,dataset,imagenet,calibration,_mlperf.option1 -j
     ```
     ### Get Calibration Dataset Using Option 2
     ```
-    cm run script --tags=get,dataset,imagenet,calibration,_mlperf.option2 -j
+    mlcr get,dataset,imagenet,calibration,_mlperf.option2 -j
     ```
 
 ## Model
@@ -37,12 +37,12 @@ Get the Official MLPerf ResNet50 Model
 
     ### Tensorflow
     ```
-    cm run script --tags=get,ml-model,resnet50,_tensorflow -j
+    mlcr get,ml-model,resnet50,_tensorflow -j
     ```
 === "Onnx"
 
     ### Onnx
     ```
-    cm run script --tags=get,ml-model,resnet50,_onnx -j
+    mlcr get,ml-model,resnet50,_onnx -j
     ```
 
diff --git a/docs/benchmarks/image_classification/mobilenets.md b/docs/benchmarks/image_classification/mobilenets.md
index 09840ef1d4..7e4605b4b0 100644
--- a/docs/benchmarks/image_classification/mobilenets.md
+++ b/docs/benchmarks/image_classification/mobilenets.md
@@ -5,7 +5,7 @@ hide:
 
 # Image Classification using Mobilenet models
 
-Install CM following the [installation page](site:install).
+Install MLC following the [installation page](site:install).
 
 Mobilenet models are not official MLPerf models and so cannot be used for a Closed division MLPerf inference submission. But since they can be run with Imagenet dataset, we are allowed to use them for Open division submission. Only CPU runs are supported now. 
 
@@ -14,31 +14,31 @@ Mobilenet models are not official MLPerf models and so cannot be used for a Clos
 === "Mobilenet-V1"
     ### Mobilenet V1
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v1 \
+    mlcr run,mobilenet-models,_tflite,_mobilenet-v1 \
      --adr.compiler.tags=gcc
     ```
 === "Mobilenet-V2"
     ### Mobilenet V2
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v2 \
+    mlcr run,mobilenet-models,_tflite,_mobilenet-v2 \
      --adr.compiler.tags=gcc
     ```
 === "Mobilenet-V2"
     ### Mobilenet V2
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_mobilenet-v2 \
+    mlcr run,mobilenet-models,_tflite,_mobilenet-v2 \
      --adr.compiler.tags=gcc
     ```
 === "Mobilenets"
     ### Mobilenet V1,V2,V3
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_mobilenet \
+    mlcr run,mobilenet-models,_tflite,_mobilenet \
      --adr.compiler.tags=gcc
     ```
 === "Efficientnet"
     ### Efficientnet
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_efficientnet \
+    mlcr run,mobilenet-models,_tflite,_efficientnet \
      --adr.compiler.tags=gcc
     ```
 
@@ -46,31 +46,31 @@ Mobilenet models are not official MLPerf models and so cannot be used for a Clos
 === "Mobilenet-V1"
     ### Mobilenet V1
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v1 \
+    mlcr run,mobilenet-models,_tflite,_armnn,_mobilenet-v1 \
      --adr.compiler.tags=gcc
     ```
 === "Mobilenet-V2"
     ### Mobilenet V2
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 \
+    mlcr run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 \
      --adr.compiler.tags=gcc
     ```
 === "Mobilenet-V2"
     ### Mobilenet V2
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 \
+    mlcr run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 \
      --adr.compiler.tags=gcc
     ```
 === "Mobilenets"
     ### Mobilenet V1,V2,V3
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_mobilenet \
+    mlcr run,mobilenet-models,_tflite,_armnn,_mobilenet \
      --adr.compiler.tags=gcc
     ```
 === "Efficientnet"
     ### Efficientnet
     ```bash
-    cm run script --tags=run,mobilenet-models,_tflite,_armnn,_efficientnet \
+    mlcr run,mobilenet-models,_tflite,_armnn,_efficientnet \
      --adr.compiler.tags=gcc
     ```
 
diff --git a/docs/benchmarks/language/get-bert-data.md b/docs/benchmarks/language/get-bert-data.md
index fed6375720..430031f319 100644
--- a/docs/benchmarks/language/get-bert-data.md
+++ b/docs/benchmarks/language/get-bert-data.md
@@ -14,7 +14,7 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset,squad,validation -j
+    mlcr get,dataset,squad,validation -j
     ```
 
 ## Model
@@ -26,18 +26,18 @@ Get the Official MLPerf Bert-Large Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,bert-large,_pytorch -j
+    mlcr get,ml-model,bert-large,_pytorch -j
     ```
 === "Onnx"
 
     ### Onnx
     ```
-    cm run script --tags=get,ml-model,bert-large,_onnx -j
+    mlcr get,ml-model,bert-large,_onnx -j
     ```
 === "Tensorflow"
 
     ### Tensorflow
     ```
-    cm run script --tags=get,ml-model,bert-large,_tensorflow -j
+    mlcr get,ml-model,bert-large,_tensorflow -j
     ```
 
diff --git a/docs/benchmarks/language/get-gptj-data.md b/docs/benchmarks/language/get-gptj-data.md
index 90591fb76e..34140598e9 100644
--- a/docs/benchmarks/language/get-gptj-data.md
+++ b/docs/benchmarks/language/get-gptj-data.md
@@ -14,7 +14,7 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset,cnndm,validation -j
+    mlcr get,dataset,cnndm,validation -j
     ```
 
 ## Model
@@ -26,5 +26,5 @@ Get the Official MLPerf GPT-J Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,gptj,_pytorch -j
+    mlcr get,ml-model,gptj,_pytorch -j
     ```
diff --git a/docs/benchmarks/language/get-llama2-70b-data.md b/docs/benchmarks/language/get-llama2-70b-data.md
index 0214d95a5a..d75540b5a9 100644
--- a/docs/benchmarks/language/get-llama2-70b-data.md
+++ b/docs/benchmarks/language/get-llama2-70b-data.md
@@ -14,7 +14,7 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset,openorca,validation -j
+    mlcr get,dataset,openorca,validation -j
     ```
 
 ## Model
@@ -26,7 +26,7 @@ Get the Official MLPerf LLAMA2-70b Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,llama2-70b,_pytorch -j
+    mlcr get,ml-model,llama2-70b,_pytorch -j
     ```
   
 !!! tip
diff --git a/docs/benchmarks/language/get-llama3_1-405b-data.md b/docs/benchmarks/language/get-llama3_1-405b-data.md
index 7333be64dd..e1a5e1c3f6 100644
--- a/docs/benchmarks/language/get-llama3_1-405b-data.md
+++ b/docs/benchmarks/language/get-llama3_1-405b-data.md
@@ -13,14 +13,14 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset,mlperf,inference,llama3,_validation --outdirname=<path to download> -j
+    mlcr get,dataset,mlperf,inference,llama3,_validation --outdirname=<path to download> -j
     ```
     
 === "Calibration"
 
     ### Get Calibration Dataset
     ```
-    cm run script --tags=get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
+    mlcr get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
     ```
 
 ## Model
@@ -32,7 +32,7 @@ Get the Official MLPerf LLAMA3.1-405b Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,llama3 --outdirname=<path to download> --hf_token=<huggingface access token> -j
+    mlcr get,ml-model,llama3 --outdirname=<path to download> --hf_token=<huggingface access token> -j
     ```
   
 !!! tip
diff --git a/docs/benchmarks/language/get-mixtral-8x7b-data.md b/docs/benchmarks/language/get-mixtral-8x7b-data.md
index 1b2df1b9ea..81b90cdb57 100644
--- a/docs/benchmarks/language/get-mixtral-8x7b-data.md
+++ b/docs/benchmarks/language/get-mixtral-8x7b-data.md
@@ -12,7 +12,7 @@ The benchmark implementation run command will automatically download the preproc
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset-mixtral,openorca-mbxp-gsm8k-combined -j
+    mlcr get,dataset-mixtral,openorca-mbxp-gsm8k-combined -j
     ```
 
 ## Model
@@ -24,5 +24,5 @@ Get the Official MLPerf MIXTRAL-8x7b Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,mixtral -j
+    mlcr get,ml-model,mixtral -j
     ```
\ No newline at end of file
diff --git a/docs/benchmarks/language/reproducibility/indyscc24-bert.md b/docs/benchmarks/language/reproducibility/indyscc24-bert.md
index 463d1a299c..a9df271178 100644
--- a/docs/benchmarks/language/reproducibility/indyscc24-bert.md
+++ b/docs/benchmarks/language/reproducibility/indyscc24-bert.md
@@ -17,7 +17,7 @@ In the IndySCC 2024, your objective will be to run a reference (unoptimized) Pyt
 
 
 !!! info
-    Both MLPerf and CM automation are evolving projects.
+    Both MLPerf and MLC automation are evolving projects.
     If you encounter issues or have questions, please submit them [here](https://github.com/mlcommons/cm4mlops/issues)
 
 ## Artifacts to submit to the SCC committee
@@ -35,7 +35,7 @@ All the needed files are automatically pushed to the GitHub repository if you ma
 ### Generate actual submission tree
 
 ```bash
-cm run script --tags=generate,inference,submission \
+mlcr generate,inference,submission \
    --clean \
    --run-checker \
    --tar=yes \
@@ -58,7 +58,7 @@ Fork the `mlperf-inference-results-scc24` branch of the repository URL at [https
 Run the following command after **replacing `--repo_url` with your GitHub fork URL**.
 
 ```bash
-cm run script --tags=push,github,mlperf,inference,submission \
+mlcr push,github,mlperf,inference,submission \
    --repo_url=https://github.com/<myfork>/cm4mlperf-inference \
    --repo_branch=mlperf-inference-results-scc24 \
    --commit_message="Results on system <HW Name>" \
diff --git a/docs/benchmarks/medical_imaging/get-3d-unet-data.md b/docs/benchmarks/medical_imaging/get-3d-unet-data.md
index 6c361f6f14..d68b769209 100644
--- a/docs/benchmarks/medical_imaging/get-3d-unet-data.md
+++ b/docs/benchmarks/medical_imaging/get-3d-unet-data.md
@@ -14,12 +14,12 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset(Original)
     ```
-    cm run script --tags=get,dataset,kits19,_validation -j
+    mlcr get,dataset,kits19,_validation -j
     ```
 
     ### Get Validation Dataset(Preprocessed)
     ```
-    cm run script --tags=get,dataset,kits19,preprocessed -j
+    mlcr get,dataset,kits19,preprocessed -j
     ```
 
 ## Model
@@ -31,18 +31,18 @@ Get the Official MLPerf 3d-unet Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,3d-unet,_pytorch -j
+    mlcr get,ml-model,3d-unet,_pytorch -j
     ```
 === "Onnx"
 
     ### Onnx
     ```
-    cm run script --tags=get,ml-model,3d-unet,_onnx -j
+    mlcr get,ml-model,3d-unet,_onnx -j
     ```
 === "Tensorflow"
 
     ### Tensorflow
     ```
-    cm run script --tags=get,ml-model,3d-unet,_tensorflow -j
+    mlcr get,ml-model,3d-unet,_tensorflow -j
     ```
 
diff --git a/docs/benchmarks/object_detection/get-retinanet-data.md b/docs/benchmarks/object_detection/get-retinanet-data.md
index 900fd572a7..6cd677b4e1 100644
--- a/docs/benchmarks/object_detection/get-retinanet-data.md
+++ b/docs/benchmarks/object_detection/get-retinanet-data.md
@@ -14,13 +14,13 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset,openimages,_validation -j
+    mlcr get,dataset,openimages,_validation -j
     ```
 === "Calibration"
     Retinanet calibration dataset consist of 500 images selected from the OpenImages v6 dataset.
 
     ```
-    cm run script --tags=get,dataset,openimages,_calibration -j
+    mlcr get,dataset,openimages,_calibration -j
     ```
 
 ## Model
@@ -32,12 +32,12 @@ Get the Official MLPerf Retinanet Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,retinanet,_pytorch -j
+    mlcr get,ml-model,retinanet,_pytorch -j
     ```
 === "Onnx"
 
     ### Onnx
     ```
-    cm run script --tags=get,ml-model,retinanet,_onnx -j
+    mlcr get,ml-model,retinanet,_onnx -j
     ```
 
diff --git a/docs/benchmarks/recommendation/get-dlrm-v2-data.md b/docs/benchmarks/recommendation/get-dlrm-v2-data.md
index 1c44ec471c..bb35660b68 100644
--- a/docs/benchmarks/recommendation/get-dlrm-v2-data.md
+++ b/docs/benchmarks/recommendation/get-dlrm-v2-data.md
@@ -14,7 +14,7 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset,criteo,_validation -j
+    mlcr get,dataset,criteo,_validation -j
     ```
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
@@ -25,6 +25,6 @@ Get the Official MLPerf DLRM v2 Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,dlrm,_pytorch -j
+    mlcr get,ml-model,dlrm,_pytorch -j
     ```
 
diff --git a/docs/benchmarks/text_to_image/get-sdxl-data.md b/docs/benchmarks/text_to_image/get-sdxl-data.md
index f0d1376bd8..6d79e331d1 100644
--- a/docs/benchmarks/text_to_image/get-sdxl-data.md
+++ b/docs/benchmarks/text_to_image/get-sdxl-data.md
@@ -14,7 +14,7 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    cm run script --tags=get,dataset,coco2014,_validation -j
+    mlcr get,dataset,coco2014,_validation -j
     ```
 
 ## Model
@@ -26,6 +26,6 @@ Get the Official MLPerf Stable Diffusion Model
 
     ### Pytorch
     ```
-    cm run script --tags=get,ml-model,sdxl,_pytorch -j
+    mlcr get,ml-model,sdxl,_pytorch -j
     ```
 
diff --git a/docs/benchmarks/text_to_image/reproducibility/scc24.md b/docs/benchmarks/text_to_image/reproducibility/scc24.md
index 82a231b4d3..6a989273e7 100644
--- a/docs/benchmarks/text_to_image/reproducibility/scc24.md
+++ b/docs/benchmarks/text_to_image/reproducibility/scc24.md
@@ -23,14 +23,14 @@ Additionally, significant bonus points will be awarded if your team enhances an
 
 
 !!! info
-    Both MLPerf and CM automation are evolving projects.
+    Both MLPerf and MLC automation are evolving projects.
     If you encounter issues or have questions, please submit them [here](https://github.com/mlcommons/cm4mlops/issues)
 
 ## Artifacts to submit to the SCC committee
 
 You will need to submit the following files:
 
-* `mlperf_submission.run` - CM commands to run MLPerf inference benchmark saved to this file.
+* `mlperf_submission.run` - MLC commands to run MLPerf inference benchmark saved to this file.
 * `mlperf_submission.md` - description of your platform and some highlights of the MLPerf benchmark execution.
 * `<Team Name>` under which results are pushed to the github repository. 
 
@@ -61,7 +61,7 @@ or supporting multi-node execution) useful for the community and [MLCommons](htt
 
 
 ```bash
-cm run script --tags=generate,inference,submission \
+mlcr generate,inference,submission \
    --clean \
    --run-checker \
    --tar=yes \
@@ -85,7 +85,7 @@ Fork the `mlperf-inference-results-scc24` branch of the repository URL at [https
 Run the following command after **replacing `--repo_url` with your GitHub fork URL**.
 
 ```bash
-cm run script --tags=push,github,mlperf,inference,submission \
+mlcr push,github,mlperf,inference,submission \
    --repo_url=https://github.com/<myfork>/cm4mlperf-inference \
    --repo_branch=mlperf-inference-results-scc24 \
    --commit_message="Results on system <HW Name>" \
diff --git a/docs/install/index.md b/docs/install/index.md
index 1750d86e43..a3bf002e38 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -4,28 +4,28 @@ hide:
 ---
 
 # Installation
-We use MLCommons CM Automation framework to run MLPerf inference benchmarks.
+We use MLCommons MLC Automation framework to run MLPerf inference benchmarks.
 
-CM needs `git`, `python3-pip` and `python3-venv` installed on your system. If any of these are absent, please follow the [official CM installation page](https://docs.mlcommons.org/ck/install) to install them. Once the dependencies are installed, do the following
+MLC needs `git`, `python3-pip` and `python3-venv` installed on your system. Once the dependencies are installed, do the following
 
-## Activate a Virtual ENV for CM
-This step is not mandatory as CM can use separate virtual environment for MLPerf inference. But the latest `pip` install requires this or else will need the `--break-system-packages` flag while installing `cm4mlops`.
+## Activate a Virtual ENV for MLCFlow
+This step is not mandatory as MLC can use separate virtual environment for MLPerf inference. But the latest `pip` install requires this or else will need the `--break-system-packages` flag while installing `mlc-scripts`.
 
 ```bash
-python3 -m venv cm
-source cm/bin/activate
+python3 -m venv mlc
+source mlc/bin/activate
 ```
 
-## Install CM and pulls any needed repositories
-=== "Use the default fork of CM MLOps repository"
+## Install MLC and pulls any needed repositories
+=== "Use the default fork of MLC-Scripts repository"
     ```bash
-     pip install cm4mlops
+     pip install mlc-scripts
     ```
 
-=== "Use custom fork/branch of the CM MLOps repository"
+=== "Use custom fork/branch of the MLC-Scripts repository"
     ```bash
-     pip install cmind && cm init --quiet --repo=mlcommons@cm4mlops --branch=mlperf-inference
+     pip install mlcflow && mlc pull repo --url=mlcommons@cm4mlops --branch=mlperf-inference
     ```
     Here, `repo` is in the format `githubUsername@githubRepo`.
 
-Now, you are ready to use the `cm` commands to run MLPerf inference as given in the [benchmarks](../index.md) page
+Now, you are ready to use the `mlcr` commands to run MLPerf inference as given in the [benchmarks](../index.md) page
diff --git a/docs/submission/index.md b/docs/submission/index.md
index 64ef5afa79..97414e8bb0 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -3,10 +3,12 @@ hide:
   - toc
 ---
 
-Click [here](https://docs.google.com/presentation/d/1cmbpZUpVr78EIrhzyMBnnWnjJrD-mZ2vmSb-yETkTA8/edit?usp=sharing) to view the proposal slide for Common Automation for MLPerf Inference Submission Generation through CM.
+Click [here](https://docs.google.com/presentation/d/1cmbpZUpVr78EIrhzyMBnnWnjJrD-mZ2vmSb-yETkTA8/edit?usp=sharing) to view the proposal slide for Common Automation for MLPerf Inference Submission Generation through MLCFlow.
+
+Please refer to the [installation page](site:inference/install/) to install MLCFlow for automating the submission generation. In a typical development environment `pip install mlc-scripts` should be enough.
 
 === "Custom automation based MLPerf results"
-    If you have not followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, please make sure that the result directory is structured in the following way. You can see the real examples for the expected folder structure [here](https://github.com/mlcommons/inference/tree/submission-generation-examples).
+    If you have not followed the `mlcr` commands under the individual model pages in the [benchmarks](../index.md) directory, please make sure that the result directory is structured in the following way. You can see the real examples for the expected folder structure [here](https://github.com/mlcommons/inference/tree/submission-generation-examples).
     ```
     └── System description ID(SUT Name)
         ├── system_meta.json
@@ -51,11 +53,11 @@ Click [here](https://docs.google.com/presentation/d/1cmbpZUpVr78EIrhzyMBnnWnjJrD
     ```
     </details>
 
-=== "CM automation based results"
-    If you have followed the `cm run` commands under the individual model pages in the [benchmarks](../index.md) directory, all the valid results will get aggregated to the `cm cache` folder. The following command could be used to browse the structure of inference results folder generated by CM.
+=== "MLC automation based results"
+    If you have followed the `mlcr` commands under the individual model pages in the [benchmarks](../index.md) directory, all the valid results will get aggregated to the `mlc cache` folder. The following command could be used to browse the structure of inference results folder generated by MLCFlow.
     ### Get results folder structure
     ```bash
-    cm find cache --tags=get,mlperf,inference,results,dir | xargs tree
+    mlc find cache --tags=get,mlperf,inference,results,dir | xargs tree
     ```
 
 
@@ -83,13 +85,13 @@ flowchart LR
 ### Command to generate submission folder
 
 ```bash
-cm run script --tags=generate,inference,submission \
+mlcr generate,inference,submission \
   --clean \
   --preprocess_submission=yes \
-  --run-checker=yes \
+  --run_checker=yes \
   --submitter=MLCommons \
   --division=closed \
-  --env.CM_DETERMINE_MEMORY_CONFIGURATION=yes \
+  --env.MLC_DETERMINE_MEMORY_CONFIGURATION=yes \
   --quiet
 ```
 !!! tip
@@ -99,9 +101,9 @@ cm run script --tags=generate,inference,submission \
 
     * Use `--hw_notes_extra` option to add additional notes like `--hw_notes_extra="Result taken by NAME" `
 
-    * Use `--results_dir` option to specify the results folder.  It is automatically taken from CM cache for MLPerf automation based runs
+    * Use `--results_dir` option to specify the results folder.  It is automatically taken from MLC cache for MLPerf automation based runs
 
-    * Use `--submission_dir` option to specify the submission folder. (You can avoid this if you're pushing to github or only running a single SUT and CM will use its cache folder)
+    * Use `--submission_dir` option to specify the submission folder. (You can avoid this if you're pushing to github or only running a single SUT and MLC will use its cache folder)
 
     * Use `--division=open` for open division submission 
 
@@ -145,7 +147,7 @@ If there are multiple systems where MLPerf results are collected, the same proce
     Run the following command after **replacing `--repo_url` with your GitHub repository URL**.
 
     ```bash
-    cm run script --tags=push,github,mlperf,inference,submission \
+    mlcr push,github,mlperf,inference,submission \
        --repo_url=https://github.com/mlcommons/mlperf_inference_submissions_v5.0 \
        --commit_message="Results on <HW name> added by <Name>" \
        --quiet
@@ -182,14 +184,14 @@ Once you have all the results on the system, you can upload them to the MLCommon
 === "via CLI"
     You can do the following command which will run the submission checker and upload the results to the MLCommons submission server
     ```
-    cm run script --tags=run,submission,checker \
+    mlcr run,submission,checker \
     --submitter_id=<> \
     --submission_dir=<Path to the submission folder>
     ```
 === "via Browser"
     You can do the following command to generate the final submission tar file and then upload to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission). 
     ```
-    cm run script --tags=run,submission,checker \
+    mlcr run,submission,checker \
     --submission_dir=<Path to the submission folder> \
     --tar=yes \
     --submission_tar_file=mysubmission.tar.gz
diff --git a/docs/usage/index.md b/docs/usage/index.md
index 01d54c3aa6..ad322582c5 100644
--- a/docs/usage/index.md
+++ b/docs/usage/index.md
@@ -3,4 +3,4 @@ hide:
   - toc
 ---
 
-# Using CM for MLPerf Inference
+# Using MLC for MLPerf Inference
diff --git a/graph/R-GAT/README.md b/graph/R-GAT/README.md
index 4638d526f0..1380cb047d 100644
--- a/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -17,11 +17,11 @@ This is the reference implementation for MLPerf Inference Graph Neural Network.
 | IGBH | Illinois Graph Benchmark Heterogeneous is a graph dataset consisting of one heterogeneous graph with 547,306,935 nodes and 5,812,005,639 edges. Node types: Author, Conference, FoS, Institute, Journal, Paper. A subset of 1% of the paper nodes are randomly choosen as the validation dataset using the [split seeds script](tools/split_seeds.py). The validation dataset will be used as the input queries for the SUT, however the whole dataset is needed to run the benchmarks, since all the graph connections are needed to achieve the quality target. | Node Classification |
 | IGBH (calibration) | We sampled 5000 nodes from the training paper nodes of the IGBH for the calibration dataset. We provide the [Node ids](../../calibration/IGBH/calibration.txt) and the [script](tools/split_seeds.py) to generate them (using the `--calibration` flag). | Node Classification |
 
-## Automated command to run the benchmark via MLCommons CM
+## Automated command to run the benchmark via MLCFlow
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/graph/rgat/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
-You can also do `pip install cm4mlops` and then use `cm` commands for downloading the model and datasets using the commands given in the later sections.
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
  
 ## Setup
 Set the following helper variables
@@ -77,10 +77,10 @@ pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/repo.html
 ```
 
 
-### Download model through CM (Collective Minds)
+### Download model through MLCFlow Automation
 
 ```
-cm run script --tags=get,ml-model,rgat --outdirname=<path_to_download>
+mlcr get,ml-model,rgat --outdirname=<path_to_download>
 ```
 
 ### Download model using Rclone
@@ -106,9 +106,9 @@ rclone copy mlc-inference:mlcommons-inference-wg-public/R-GAT/RGAT.pt $MODEL_PAT
 ### Download and setup dataset
 #### Debug Dataset
 
-**CM Command**
+**MLC Command**
 ```
-cm run script --tags=get,dataset,igbh,_debug --outdirname=<path to download>
+mlcr get,dataset,igbh,_debug --outdirname=<path to download>
 ```
 
 **Download Dataset**
@@ -128,9 +128,9 @@ python3 tools/split_seeds.py --path igbh --dataset_size tiny
 #### Full Dataset
 **Warning:** This script will download 2.2TB of data
 
-**CM Command**
+**MLC Command**
 ```
-cm run script --tags=get,dataset,igbh,_full --outdirname=<path to download>
+mlcr get,dataset,igbh,_full --outdirname=<path to download>
 ```
 
 ```bash
@@ -149,9 +149,9 @@ python3 tools/split_seeds.py --path igbh --dataset_size full
 
 The calibration dataset contains 5000 nodes from the training paper nodes of the IGBH dataset. We provide the [Node ids](../../calibration/IGBH/calibration.txt) and the [script](tools/split_seeds.py) to generate them (using the `--calibration` flag). 
 
-**CM Command**
+**MLC Command**
 ```
-cm run script --tags=get,dataset,igbh,_full,_calibration --outdirname=<path to download>
+mlcr get,dataset,igbh,_full,_calibration --outdirname=<path to download>
 ```
 
 ### Run the benchmark
@@ -176,7 +176,7 @@ python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full
 
 ### Evaluate the accuracy
 ```bash
-cm run script --tags=process,mlperf,accuracy,_igbh --result_dir=<Path to directory where files are generated after the benchmark run>
+mlcr process,mlperf,accuracy,_igbh --result_dir=<Path to directory where files are generated after the benchmark run>
 ```
 
 Please click [here](https://github.com/mlcommons/inference/blob/dev/graph/R-GAT/tools/accuracy_igbh.py) to view the Python script for evaluating accuracy for the IGBH dataset.
diff --git a/language/bert/README.md b/language/bert/README.md
index 6e04686665..e60745d45e 100644
--- a/language/bert/README.md
+++ b/language/bert/README.md
@@ -33,7 +33,7 @@ Please run the following commands:
 - `make launch_docker`: launch docker container with an interaction session.
 - `python3 run.py --backend=[tf|pytorch|onnxruntime|tf_estimator] --scenario=[Offline|SingleStream|MultiStream|Server] [--accuracy] [--quantized]`: run the harness inside the docker container. Performance or Accuracy results will be printed in console.
 
-* ENV variable `CM_MAX_NUM_THREADS` can be used to control the number of parallel threads issuing queries.
+* ENV variable `MLC_MAX_NUM_THREADS` can be used to control the number of parallel threads issuing queries.
 
 ## Details
 
@@ -48,13 +48,13 @@ Please run the following commands:
 ## Loadgen over the Network 
 
 ```
-pip install cm4mlops
+pip install mlc-scripts
 ```
 
-The below CM command will launch the SUT server
+The below MLC command will launch the SUT server
 
 ```
-cm run script --tags=generate-run-cmds,inference --model=bert-99 --backend=pytorch  \
+mlcr generate-run-cmds,inference --model=bert-99 --backend=pytorch  \
 --mode=performance --device=cuda --quiet --test_query_count=1000 --network=sut
 ```
 
@@ -62,12 +62,12 @@ Once the SUT server is launched, the below command can be run on the loadgen nod
 
 
 ```
-cm run script --tags=generate-run-cmds,inference --model=bert-99 --backend=pytorch  --rerun \
+mlcr generate-run-cmds,inference --model=bert-99 --backend=pytorch  --rerun \
 --mode=performance --device=cuda --quiet --test_query_count=1000  \
 --sut_servers,=http://localhost:8000 --network=lon
 ```
 
-If you are not using CM, just add `--network=lon` along with your normal run command on the SUT side.
+If you are not using MLC, just add `--network=lon` along with your normal run command on the SUT side.
 On the loadgen node, add `--network=lon` option and `--sut_server <IP1> <IP2>` to the normal command to connect to SUT nodes at IP addresses IP1, IP2 etc. 
 
 Loadgen over the network works for `onnxruntime` and `pytorch` backends.
diff --git a/language/bert/bert_QDL.py b/language/bert/bert_QDL.py
index 6e804a7998..f18c9f5713 100644
--- a/language/bert/bert_QDL.py
+++ b/language/bert/bert_QDL.py
@@ -82,7 +82,7 @@ def process_query_async(self, query_samples):
 
         max_num_threads = int(
             os.environ.get(
-                "CM_MAX_NUM_THREADS",
+                "MLC_MAX_NUM_THREADS",
                 os.cpu_count()))
 
         for i in range(len(query_samples)):
diff --git a/language/bert/onnxruntime_SUT.py b/language/bert/onnxruntime_SUT.py
index 598192509e..ee4882654a 100644
--- a/language/bert/onnxruntime_SUT.py
+++ b/language/bert/onnxruntime_SUT.py
@@ -69,7 +69,7 @@ def __init__(self, args):
     def issue_queries(self, query_samples):
         max_num_threads = int(
             os.environ.get(
-                "CM_MAX_NUM_THREADS",
+                "MLC_MAX_NUM_THREADS",
                 os.cpu_count()))
 
         for i in range(len(query_samples)):
diff --git a/language/bert/ray_SUT.py b/language/bert/ray_SUT.py
index 6280d0effb..d947d15ed1 100644
--- a/language/bert/ray_SUT.py
+++ b/language/bert/ray_SUT.py
@@ -42,7 +42,7 @@
     raise Exception("Error importing local modules")
 
 # Adjustable Parameters
-# Note. num_samples (called "test_query_count" in CM) must be a multiple
+# Note. num_samples (called "test_query_count" in MLC) must be a multiple
 # of batch_size
 BATCH_SIZE = 16
 
diff --git a/language/gpt-j/GPTJ_QDL.py b/language/gpt-j/GPTJ_QDL.py
index f75ece48d2..e450beae69 100644
--- a/language/gpt-j/GPTJ_QDL.py
+++ b/language/gpt-j/GPTJ_QDL.py
@@ -58,7 +58,7 @@ def process_query_async(self, query_samples):
 
         max_num_threads = int(
             os.environ.get(
-                "CM_MAX_NUM_THREADS",
+                "MLC_MAX_NUM_THREADS",
                 os.cpu_count()))
         if self.scenario == "Offline":
             # Client sends multiple requests using threads
diff --git a/language/gpt-j/README.md b/language/gpt-j/README.md
index 7653176357..cfcf068791 100644
--- a/language/gpt-j/README.md
+++ b/language/gpt-j/README.md
@@ -74,12 +74,12 @@ python prepare-calibration.py --calibration-list-file calibration-list.txt --out
 ### Download GPT-J model
 Please download the fine-tuned GPT-J checkpoint using the instructions below. The download_gptj.py only downloads the default huggingface model which is not fine-tuned on CNN-Daily mail dataset. 
 
-#### CM method
+#### MLC method
 
-The following MLCommons CM commands can be used to programmatically download the model checkpoint. 
+The following MLCommons MLC commands can be used to programmatically download the model checkpoint. 
 
 ```
-cm run script --tags=get,ml-model,gptj,_pytorch,_rclone ---outdirname =./model -P
+mlcr get,ml-model,gptj,_pytorch,_rclone ---outdirname =./model -P
 ```
 
 #### Manual method
@@ -142,13 +142,13 @@ Intel expressly disclaims the accuracy, adequacy, or completeness of any data, d
 ## Loadgen over the Network 
 
 ```
-pip install cm4mlops
+pip install mlc-scripts
 ```
 
-The below CM command will launch the SUT server
+The below MLC command will launch the SUT server
 
 ```
-cm run script --tags=run-mlperf,inference,_performance-only --model=gptj-99  \
+mlcr run-mlperf,inference,_performance-only --model=gptj-99  \
 --backend=pytorch   --device=cuda --beam_size=1 --precision=bfloat16 \
 --network=sut --rerun --quiet --adr.compiler.tags=gcc 
 ```
@@ -159,7 +159,7 @@ In our experimentation, we found out that in addition to memory occupied by the
 Once the SUT server is launched, the below command can be run on the loadgen node to do issue queries to the SUT nodes. In this command `-sut_servers` has just the localhost address - it can be changed to a comma-separated list of any hostname/IP in the network. 
 
 ```
-cm run script --tags=run-mlperf,inference,_performance-only --model=gptj-99 \
+mlcr run-mlperf,inference,_performance-only --model=gptj-99 \
 --backend=pytorch  --test_query_count=30  \
 --network=lon  --rerun --quiet --scenario=Offline \
 --sut_servers,=http://localhost:8000 --adr.compiler.tags=gcc
diff --git a/language/gpt-j/main.py b/language/gpt-j/main.py
index 438c0854b3..981d2cd9d5 100644
--- a/language/gpt-j/main.py
+++ b/language/gpt-j/main.py
@@ -189,7 +189,7 @@ def main():
         if args.gpu:
             free_mem = int(
                 os.environ.get(
-                    "CM_CUDA_DEVICE_PROP_GLOBAL_MEMORY", get_gpu_memory_info()
+                    "MLC_CUDA_DEVICE_PROP_GLOBAL_MEMORY", get_gpu_memory_info()
                 )
             ) / (1024**3)
         else:
diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 5268863ac6..506423cc2c 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -9,14 +9,10 @@
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama2-70b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
  
 ## Prepare environment
 
-Copy the mlperf.conf file to this folder.
-```
-cp ../../mlperf.conf .
-```
-
 For a CPU-only run:
 
 ```
@@ -69,8 +65,12 @@ CPU-only setup, as well as any GPU versions for applicable libraries like PyTorc
 ### MLCommons Members Download
 MLCommons hosts the model and preprocessed dataset for download **exclusively by MLCommons Members**. You must first agree to the [confidentiality notice](https://llama2.mlcommons.org) using your organizational email address, then you will receive a link to a directory containing Rclone download instructions. _If you cannot access the form but you are part of a MLCommons Member organization, submit the [MLCommons subscription form](https://mlcommons.org/community/subscribe/) with your organizational email address and [associate a Google account](https://accounts.google.com/SignUpWithoutGmail) with your organizational email address._
 
+Once you have the access, you can download the model automatically via the below command
+```
+mlcr get,ml-model,llama2 --outdirname=${CHECKPOINT_PATH} -j
+```
 
-### External Download
+### External Download (Not recommended for official submission)
 + First go to [llama2-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloning below.
 + Requires Git Large Files Storage
 ```
@@ -181,7 +181,6 @@ mkdir -p "run_outputs"  # The script will dump all the outputs to 'run_outputs'.
 python -u main.py --scenario Offline \
                 --model-path ${CHECKPOINT_PATH} \
                 --accuracy \
-                --mlperf-conf mlperf.conf \
                 --user-conf user.conf \
                 --total-sample-count 24576 \
                 --dataset-path ${DATASET_PATH} \
@@ -218,7 +217,6 @@ OUTPUT_LOG_DIR=server-accuracy-logs
 python -u main.py --scenario Server \
                 --model-path ${CHECKPOINT_PATH} \
                 --accuracy \
-                --mlperf-conf mlperf.conf \
                 --user-conf user.conf \
                 --total-sample-count 24576 \
                 --dataset-path ${DATASET_PATH} \
@@ -255,7 +253,6 @@ In order to run interactive category, it is sufficient to set the flag `--lg-mod
 ```
 python -u main.py --scenario Server \
                 --model-path ${CHECKPOINT_PATH} \
-                --mlperf-conf mlperf.conf \
                 --user-conf user.conf \
                 --total-sample-count 24576 \
                 --device cpu \
diff --git a/language/llama3.1-405b/README.md b/language/llama3.1-405b/README.md
index ea358bb981..50668263c4 100644
--- a/language/llama3.1-405b/README.md
+++ b/language/llama3.1-405b/README.md
@@ -7,13 +7,11 @@
         - For server scenario, it is necessary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
         - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
 
-Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama3.1-405b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
-
-## Automated command to run the benchmark via MLCommons CM
+## Automated command to run the benchmark via MLFlow
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama3_1-405b/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
-You can also do pip install cm4mlops and then use cm commands for downloading the model and datasets using the commands given in the later sections.
+You can also do pip install mlc-scripts and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
 
 ## Prepare environment
 
@@ -99,12 +97,15 @@ pip install -e ../../loadgen
 
 
 ## Get Model
-### MLCommons Members Download
+### MLCommons Members Download (Recommended for official submission)
 
-TODO: Host model and grant access to submitters
+You need to request for access to [MLcommons](http://llama3-1.mlcommons.org/) and you'll receive an email with the download instructions. You can download the model automatically via the below command
+```
+mlcr get,ml-model,llama3 --outdirname=${CHECKPOINT_PATH} -j
+```
 
 
-### External Download
+### External Download (Not recommended for official submission)
 + First go to [llama3.1-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloning below.
 + Requires Git Large Files Storage
 ```
@@ -114,10 +115,10 @@ git clone https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct ${CHECKPOINT
 cd ${CHECKPOINT_PATH} && git checkout be673f326cab4cd22ccfef76109faf68e41aa5f1
 ```
 
-### Download model through CM (Collective Mind)
+### Download huggingface model through MLC
 
 ```
-cm run script --tags=get,ml-model,llama3 --outdirname=${CHECKPOINT_PATH} --hf_token=<huggingface access token> -j
+mlcr get,ml-model,llama3,_hf --outdirname=${CHECKPOINT_PATH} --hf_token=<huggingface access token> -j
 ```
 
 **Note:**
@@ -143,10 +144,10 @@ You can then navigate in the terminal to your desired download directory and run
 ```
 rclone copy mlc-inference:mlcommons-inference-wg-public/llama3.1_405b/mlperf_llama3.1_405b_dataset_8313_processed_fp16_eval.pkl ./ -P
 ```
-**CM Command**
+**MLC Command**
 
 ```
-cm run script --tags=get,dataset,mlperf,inference,llama3,_validation --outdirname=<path to download> -j
+mlcr get,dataset,mlperf,inference,llama3,_validation --outdirname=<path to download> -j
 ```
 
 You can also download the calibration dataset from the Cloudflare R2 bucket by running the following command:
@@ -155,9 +156,9 @@ You can also download the calibration dataset from the Cloudflare R2 bucket by r
 rclone copy mlc-inference:mlcommons-inference-wg-public/llama3.1_405b/mlperf_llama3.1_405b_calibration_dataset_512_processed_fp16_eval.pkl ./ -P
 ```
 
-**CM Command**
+**MLC Command**
 ```
-cm run script --tags=get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
+mlcr get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
 ```
 
 
@@ -250,10 +251,10 @@ fi
 
 The ServerSUT was not tested for GPU runs.
 
-### Evaluate the accuracy using CM
-You can also evaulate the accuracy from the generated accuracy log by using the following CM command
+### Evaluate the accuracy using MLCFlow
+You can also evaulate the accuracy from the generated accuracy log by using the following MLC command
 ```
-cm run script --tags=process,mlperf,accuracy,_dataset_llama3 --result_dir=<Path to accuracy log directory>
+mlcr process,mlperf,accuracy,_dataset_llama3 --result_dir=<Path to accuracy log directory>
 ```
 
 ## Accuracy Target
diff --git a/main.py b/main.py
index fc5a1ef5b9..48923e2909 100755
--- a/main.py
+++ b/main.py
@@ -28,7 +28,7 @@ def mlperf_inference_implementation_readme(
         content = ""
 
         execution_envs = ["Docker", "Native"]
-        code_version = "r5.0-dev"
+        code_version = "r4.1-dev"
         implementation_run_options = []
 
         if model == "rnnt":
@@ -36,6 +36,8 @@ def mlperf_inference_implementation_readme(
 
         if implementation == "reference":
             # Tip
+            if model != "rnnt":
+                code_version = "r5.0-dev"
             if "99.9" not in model and implementation_tips:
                 content += f"\n{pre_space}!!! tip\n\n"
                 content += f"{pre_space}    - MLCommons reference implementations are only meant to provide a rules compliant reference implementation for the submitters and in most cases are not best performing. If you want to benchmark any system, it is advisable to use the vendor MLPerf implementation for that system like Nvidia, Intel etc.\n\n"
@@ -124,7 +126,8 @@ def mlperf_inference_implementation_readme(
         if not categories:
             if model.lower() == "bert-99.9":
                 categories = ["Datacenter"]
-
+            elif model.lower() in ["pointpainting"]:
+                categories = ["Edge"]
             elif (
                 "dlrm" in model.lower()
                 or "llama2" in model.lower()
@@ -146,6 +149,8 @@ def mlperf_inference_implementation_readme(
                 if model.lower() in [
                         "resnet50", "retinanet"] and not "MultiStream" in scenarios:  # MultiStream was duplicating
                     scenarios.append("MultiStream")
+                if model.lower() in ["pointpainting"]:
+                    scenarios.remove("Offline")
             elif category == "Datacenter":
                 scenarios = ["Offline", "Server"]
             if fixed_scenarios:
@@ -192,8 +197,8 @@ def mlperf_inference_implementation_readme(
                             continue  # Nvidia implementation only supports execution through docker
                         content += f'{cur_space2}=== "{execution_env}"\n'
                         content += f"{cur_space3}###### {execution_env} Environment\n\n"
-                        # ref to cm installation
-                        content += f"{cur_space3}Please refer to the [installation page](site:inference/install/) to install CM for running the automated benchmark commands.\n\n"
+                        # ref to MLCFlow installation
+                        content += f"{cur_space3}Please refer to the [installation page](site:inference/install/) to install MLCFlow for running the automated benchmark commands.\n\n"
                         test_query_count = get_test_query_count(
 
                             model, implementation, device.lower()
@@ -227,7 +232,8 @@ def mlperf_inference_implementation_readme(
                                 implementation,
                                 framework.lower(),
                                 category.lower(),
-                                "Offline",
+                                "SingleStream" if model.lower() in [
+                                    "pointpainting"] else "Offline",
                                 device.lower(),
                                 "test",
                                 test_query_count,
@@ -283,12 +289,12 @@ def mlperf_inference_implementation_readme(
                                 content += f"{cur_space3}The above command should get you to an interactive shell inside the docker container and do a quick test run for the Offline scenario. Once inside the docker container please do the below commands to do the accuracy + performance runs for {scenario_text}.\n\n"
                                 content += f"{cur_space3}<details>\n"
                                 content += f"{cur_space3}<summary> Please click here to see more options for the docker launch </summary>\n\n"
-                                content += f"{cur_space3}* `--docker_cm_repo=<Custom CM GitHub repo URL in username@repo format>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
-                                content += f"{cur_space3}* `--docker_cm_repo_branch=<Custom CM GitHub repo Branch>`: to checkout a custom branch of the cloned cm4mlops repository inside the docker image\n\n"
+                                content += f"{cur_space3}* `--docker_mlc_repo=<Custom MLC GitHub repo URL in username@repo format>`: to use a custom fork of cm4mlops repository inside the docker image\n\n"
+                                content += f"{cur_space3}* `--docker_mlc_repo_branch=<Custom MLC GitHub repo Branch>`: to checkout a custom branch of the cloned cm4mlops repository inside the docker image\n\n"
                                 content += f"{cur_space3}* `--docker_cache=no`: to not use docker cache during the image build\n"
 
                                 if implementation.lower() == "nvidia":
-                                    content += f"{cur_space3}* `--gpu_name=<Name of the GPU>` : The GPUs with supported configs in CM are `orin`, `rtx_4090`, `rtx_a6000`, `rtx_6000_ada`, `l4`, `t4`and `a100`. For other GPUs, default configuration as per the GPU memory will be used.\n"
+                                    content += f"{cur_space3}* `--gpu_name=<Name of the GPU>` : The GPUs with supported configs in MLC are `orin`, `rtx_4090`, `rtx_a6000`, `rtx_6000_ada`, `l4`, `t4`and `a100`. For other GPUs, default configuration as per the GPU memory will be used.\n"
 
                                 if device.lower() not in ["cuda"]:
                                     content += f"{cur_space3}* `--docker_os=ubuntu`: ubuntu and rhel are supported. \n"
@@ -305,7 +311,8 @@ def mlperf_inference_implementation_readme(
                                 implementation,
                                 framework.lower(),
                                 category.lower(),
-                                "Offline",
+                                "SingleStream" if model.lower() in [
+                                    "pointpainting"] else "Offline",
                                 device.lower(),
                                 "test",
                                 test_query_count,
@@ -321,10 +328,17 @@ def mlperf_inference_implementation_readme(
                         run_suffix += f"{cur_space3}<summary> Please click here to see more options for the RUN command</summary>\n\n"
                         run_suffix += f"{cur_space3}* Use `--division=closed` to do a closed division submission which includes compliance runs\n\n"
                         run_suffix += f"{cur_space3}* Use `--rerun` to do a rerun even when a valid run exists\n"
+                        run_suffix += f"{cur_space3}* Use `--compliance` to do the compliance runs (only applicable for closed division) once the valid runs are successful\n"
+
                         if implementation.lower() == "nvidia":
-                            run_suffix += f"{cur_space3}* `--gpu_name=<Name of the GPU>` : The GPUs with supported configs in CM are `orin`, `rtx_4090`, `rtx_a6000`, `rtx_6000_ada`, `l4`, `t4`and `a100`. For other GPUs, default configuration as per the GPU memory will be used.\n"
+                            run_suffix += f"{cur_space3}* `--gpu_name=<Name of the GPU>` : The GPUs with supported configs in MLC are `orin`, `rtx_4090`, `rtx_a6000`, `rtx_6000_ada`, `l4`, `t4`and `a100`. For other GPUs, default configuration as per the GPU memory will be used.\n"
                         run_suffix += f"{cur_space3}</details>\n\n"
 
+                        if (
+                            "bert" in model.lower()
+                            and framework.lower() == "deepsparse"
+                        ):
+                            run_suffix += "You can use any model from [NeuralMagic sparse zoo](https://sparsezoo.neuralmagic.com/?modelSet=computer_vision&architectures=resnet_v1) (trained on Imagenet dataset) as --nm_model_zoo_stub"
                         if (
                             "bert" in model.lower()
                             and framework.lower() == "deepsparse"
@@ -446,6 +460,8 @@ def get_min_system_requirements(spaces, model, implementation, device):
                     device_memory = "24GB(fp32), 16GB(fp16)"
                 elif "gptj" in model:
                     device_memory = "80GB(fp32). 40GB(fp16)"
+                elif "pointpainting" in model:
+                    device_memory = "To be updated"
                 else:
                     device_memory = "8GB"
             min_sys_req_content += f"{spaces}* **Device Memory**: {device_memory}\n\n"
@@ -458,6 +474,8 @@ def get_min_system_requirements(spaces, model, implementation, device):
             disk_space = "100GB"
         elif "retinanet" in model:
             disk_space = "200GB"
+        elif "pointpainting" in model:
+            disk_space = "To be updated"
         else:
             disk_space = "50GB"
         min_sys_req_content += f"{spaces}* **Disk Space**: {disk_space}\n\n"
@@ -476,7 +494,7 @@ def get_inference_server_run_cmd(spaces, implementation):
             pre_space = " " * spaces
             return f"""\n
 {pre_space}```bash
-{pre_space}cm run script --tags=run,vllm-server \\
+{pre_space}mlcr run,vllm-server \\
 {indent}--model=nm-testing/Llama-2-70b-chat-hf-FP8 \\
 {indent}--vllm_model_name=nm-testing/Llama-2-70b-chat-hf-FP8 \\
 {indent}--quiet
@@ -486,8 +504,8 @@ def get_venv_command(spaces):
         pre_space = " " * spaces
         return f"""\n
 {pre_space}```bash
-{pre_space}cm run script --tags=install,python-venv --name=mlperf
-{pre_space}export CM_SCRIPT_EXTRA_CMD=\"--adr.python.name=mlperf\"
+{pre_space}mlcr install,python-venv --name=mlperf
+{pre_space}export MLC_SCRIPT_EXTRA_CMD=\"--adr.python.name=mlperf\"
 {pre_space}```\n"""
 
     # contains run command information which is common to both docker and
@@ -500,13 +518,21 @@ def get_common_info(spaces, implementation, model):
         pre_space += " "
         # pre_space = "                "
         info += f"\n{pre_space}!!! tip\n\n"
-        info += f"{pre_space}    - Number of threads could be adjusted using `--threads=#`, where `#` is the desired number of threads. This option works only if the implementation in use supports threading.\n\n"
-        info += f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
-        if implementation.lower() == "reference":
+        info += f"{pre_space}    - Compliance runs can be enabled by adding `--compliance=yes`.\n\n"
+        if model.lower() not in ["pointpainting"]:
+            info += f"{pre_space}    - Number of threads could be adjusted using `--threads=#`, where `#` is the desired number of threads. This option works only if the implementation in use supports threading.\n\n"
+            info += f"{pre_space}    - Batch size could be adjusted using `--batch_size=#`, where `#` is the desired batch size. This option works only if the implementation in use is supporting the given batch size.\n\n"
+        elif model.lower() in ["pointpainting"]:
+            info += f"{pre_space}    - The maximum duration for a performance run can be disabled by using `--env.MLC_MLPERF_USE_MAX_DURATION=no`.\n\n"
+            info += f"{pre_space}    - In valid execution mode, the query count for performance mode can be adjusted using `--env.MLC_MLPERF_LOADGEN_QUERY_COUNT=<query_count>`.\n\n"
+
+        if implementation.lower() == "reference" and model.lower() not in [
+                "pointpainting"]:
+
             info += f"{pre_space}    - `_r4.1-dev` could also be given instead of `_r5.0-dev` if you want to run the benchmark with the MLPerf version being 4.1.\n\n"
         if model == "rgat":
-            info += f"{pre_space}    - Add `--env.CM_DATASET_IGBH_PATH=<Path to IGBH dataset>` if you have already downloaded the dataset. The path will be automatically mounted when using docker run.\n\n"
-            info += f"{pre_space}    - Add `--env.CM_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint>` if you have already downloaded the model. The path will be automatically mounted when using docker run.\n\n"
+            info += f"{pre_space}    - Add `--env.MLC_DATASET_IGBH_PATH=<Path to IGBH dataset>` if you have already downloaded the dataset. The path will be automatically mounted when using docker run.\n\n"
+            info += f"{pre_space}    - Add `--env.MLC_ML_MODEL_RGAT_CHECKPOINT_PATH=<Path to R-GAT model checkpoint>` if you have already downloaded the model. The path will be automatically mounted when using docker run.\n\n"
         if implementation.lower() == "reference":
             info += f"{pre_space}    - Add `--adr.mlperf-implementation.tags=_branch.master,_repo.<CUSTOM_INFERENCE_REPO_LINK>` if you are modifying the official MLPerf Inference implementation in a custom fork.\n\n"
             info += f"{pre_space}    - Add `--adr.inference-src.tags=_repo.<CUSTOM_INFERENCE_REPO_LINK>` if you are modifying the model config accuracy script in the submission checker within a custom fork.\n\n"
@@ -526,10 +552,10 @@ def get_docker_info(spaces, model, implementation,
             info += f"\n{pre_space}!!! tip\n\n"
 
             if model == "sdxl":
-                info += f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
+                info += f"{pre_space}    - `--env.MLC_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
             elif "llama3" in model.lower():
-                info += f"{pre_space}    - `--env.CM_MLPERF_MODEL_LLAMA3_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
-                info += f"{pre_space}    - `--env.CM_MLPERF_DATASET_LLAMA3_DOWNLOAD_TO_HOST=yes` option can be used to download the dataset on the host so that it can be reused across different container lanuches. \n\n"
+                info += f"{pre_space}    - `--env.MLC_MLPERF_MODEL_LLAMA3_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
+                info += f"{pre_space}    - `--env.MLC_MLPERF_DATASET_LLAMA3_DOWNLOAD_TO_HOST=yes` option can be used to download the dataset on the host so that it can be reused across different container lanuches. \n\n"
             if implementation.lower() == "nvidia":
                 info += f"{pre_space}    - Default batch size is assigned based on [GPU memory](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1129) or the [specified GPU](https://github.com/mlcommons/cm4mlops/blob/dd0c35856969c68945524d5c80414c615f5fe42c/script/app-mlperf-inference-nvidia/_cm.yaml#L1370). Please click more option for *docker launch* or *run command* to see how to specify the GPU name.\n\n"
                 info += f"{pre_space}    - When run with `--all_models=yes`, all the benchmark models of NVIDIA implementation can be executed within the same container.\n\n"
@@ -539,7 +565,7 @@ def get_docker_info(spaces, model, implementation,
         else:
             if model == "sdxl":
                 info += f"\n{pre_space}!!! tip\n\n"
-                info += f"{pre_space}    - `--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
+                info += f"{pre_space}    - `--env.MLC_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes` option can be used to download the model on the host so that it can be reused across different container lanuches. \n\n"
 
         # return empty string if nothing is filled inside the tip
         if info == f"\n{pre_space}!!! tip\n\n":
@@ -603,6 +629,8 @@ def get_run_cmd_extra(
             extra_content += f"{f_pre_space}    * `--precision=bfloat16` can give better performance \n"
         if "gptj" in model and implementation == "reference":
             extra_content += f"{f_pre_space}    * `--beam-size=1` Beam size of 4 is mandatory for a closed division submission but reducing the beam size can help in running the model on GPUs with lower device memory\n"
+        if "pointpainting" in model and implementation == "reference":
+            extra_content += f"{f_pre_space}    * Tjhe `pointpillars_checkpoint_path`, `deeplab_resnet50_path` and `waymo_path` do not need to be provided inside the Docker container as they are already registered in the MLC cache.\n"
         if extra_content:
             extra_content = f"{f_pre_space}!!! tip\n\n" + extra_content
 
@@ -671,8 +699,10 @@ def mlperf_inference_run_command(
                 docker_cmd_suffix += (
                     f" \\\n{pre_space} {extra_docker_input_string} {extra_input_string}"
                 )
+            if "resnet50" in model.lower() and framework == "deepsparse":
+                docker_cmd_suffix += f"\\\n{pre_space} --nm_model_zoo_stub=zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned85_quant-none-vnni"
             if "bert" in model.lower() and framework == "deepsparse":
-                docker_cmd_suffix += f"\\\n{pre_space} --env.CM_MLPERF_NEURALMAGIC_MODEL_ZOO_STUB=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none"
+                docker_cmd_suffix += f"\\\n{pre_space} --nm_model_zoo_stub=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none"
             if "llama2-70b" in model.lower():
                 if implementation == "nvidia":
                     docker_cmd_suffix += f" \\\n{pre_space} --tp_size=2"
@@ -687,6 +717,11 @@ def mlperf_inference_run_command(
             if "dlrm-v2" in model.lower() and implementation == "nvidia":
                 docker_cmd_suffix += f" \\\n{pre_space} --criteo_day23_raw_data_path=<PATH_TO_CRITEO_DAY23_RAW_DATA>"
 
+            if "pointpainting" in model.lower() and implementation == "reference":
+                docker_cmd_suffix += f" \\\n{pre_space} --pointpillars_checkpoint_path=<PATH_TO_POINTPILLAR_MODEL>"
+                docker_cmd_suffix += f" \\\n{pre_space} --deeplab_resnet50_path=<PATH_TO_SEGMENTOR MODEL>"
+                docker_cmd_suffix += f" \\\n{pre_space} --waymo_path=<PATH_TO_WAYMO_DATASET_FOLDER>"
+
             if "short" in extra_variation_tags:
                 full_ds_needed_tag = ""
             else:
@@ -694,7 +729,7 @@ def mlperf_inference_run_command(
 
             docker_setup_cmd = f"""\n
 {f_pre_space}```bash
-{f_pre_space}cm run script --tags=run-mlperf,inference,_find-performance,{full_ds_needed_tag}_{code_version}{scenario_variation_tag}{extra_variation_tags} \\
+{f_pre_space}mlcr run-mlperf,inference,_find-performance,{full_ds_needed_tag}_{code_version}{scenario_variation_tag}{extra_variation_tags} \\
 {pre_space} --model={model} \\
 {pre_space} --implementation={implementation} \\
 {pre_space} --framework={framework} \\
@@ -711,8 +746,11 @@ def mlperf_inference_run_command(
             if execution_mode == "test" and not skip_test_query_count:
                 cmd_suffix += f" \\\n {pre_space} --test_query_count={test_query_count}"
 
+            if "resnet50" in model.lower() and framework == "deepsparse":
+                cmd_suffix += f"\\\n{pre_space} --nm_model_zoo_stub=zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned85_quant-none-vnni"
             if "bert" in model.lower() and framework == "deepsparse":
-                cmd_suffix += f"\\\n{pre_space} --env.CM_MLPERF_NEURALMAGIC_MODEL_ZOO_STUB=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none"
+                cmd_suffix += f"\\\n{pre_space} --nm_model_zoo_stub=zoo:nlp/question_answering/mobilebert-none/pytorch/huggingface/squad/base_quant-none"
+
             if "llama2-70b" in model.lower():
                 if implementation == "nvidia":
                     cmd_suffix += f" \\\n{pre_space} --tp_size=<TP_SIZE>"
@@ -722,12 +760,22 @@ def mlperf_inference_run_command(
                     cmd_suffix += f" \\\n{pre_space} --vllm_model_name=nm-testing/Llama-2-70b-chat-hf-FP8"
                     cmd_suffix += f" \\\n{pre_space} --adr.mlperf-implementation.tags=_repo.https://github.com/neuralmagic/inference,_branch.vllm"
 
+            if "pointpainting" in model.lower() and implementation == "reference":
+                cmd_suffix += f" \\\n{pre_space} --pointpillars_checkpoint_path=<PATH_TO_POINTPILLAR_MODEL>"
+                cmd_suffix += f" \\\n{pre_space} --deeplab_resnet50_path=<PATH_TO_SEGMENTOR MODEL>"
+                cmd_suffix += f" \\\n{pre_space} --waymo_path=<PATH_TO_WAYMO_DATASET_FOLDER>"
+
             if "dlrm-v2" in model and implementation == "nvidia":
                 cmd_suffix += f" \\\n{pre_space} --criteo_day23_raw_data_path=<PATH_TO_CRITEO_DAY23_RAW_DATA>"
 
+            if "short" in extra_variation_tags:
+                full_ds_needed_tag = ""
+            else:
+                full_ds_needed_tag = "_full,"
+
             run_cmd = f"""\n
 {f_pre_space}```bash
-{f_pre_space}cm run script --tags=run-mlperf,inference,_{code_version}{scenario_variation_tag}{extra_variation_tags} \\
+{f_pre_space}mlcr run-mlperf,inference,{full_ds_needed_tag}_{code_version}{scenario_variation_tag}{extra_variation_tags} \\
 {pre_space} --model={model} \\
 {pre_space} --implementation={implementation} \\
 {pre_space} --framework={framework} \\
diff --git a/mkdocs.yml b/mkdocs.yml
index 9178191a3d..9723075ff3 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -28,8 +28,11 @@ nav:
         - Run Commands: benchmarks/text_to_image/sdxl.md
         - Reproducibility:
           - SCC24: benchmarks/text_to_image/reproducibility/scc24.md
-    - Object Detection:
+    - 2D Object Detection:
       - RetinaNet: benchmarks/object_detection/retinanet.md
+    - Automotive:
+      - 3D Object Detection:
+        - PointPainting: benchmarks/automotive/3d_object_detection/pointpainting.md
     - Medical Imaging:
       - 3d-unet: benchmarks/medical_imaging/3d-unet.md
     - Language Processing:
@@ -45,7 +48,7 @@ nav:
       - DLRM-v2: benchmarks/recommendation/dlrm-v2.md
     - Graph Neural Networks:
       - R-GAT: benchmarks/graph/rgat.md
-  - Install CM:
+  - Install MLCFlow:
     - install/index.md
   - Submission:
     - Submission Generation: submission/index.md
diff --git a/recommendation/dlrm_v2/pytorch/README.md b/recommendation/dlrm_v2/pytorch/README.md
index d93e15ac35..6f09e26ded 100755
--- a/recommendation/dlrm_v2/pytorch/README.md
+++ b/recommendation/dlrm_v2/pytorch/README.md
@@ -102,13 +102,13 @@ framework | Size in bytes (`du *`) | MD5 hash (`md5sum *`)
 N/A | pytorch | <2GB | -
  pytorch | 97.31GB | -
 
-#### CM method
+#### MLC method
 
-The following MLCommons CM commands can be used to programmatically download the model checkpoint. 
+The following MLCommons MLC commands can be used to programmatically download the model checkpoint. 
 
 ```
-pip install cm4mlops
-cm run script --tags=get,ml-model,dlrm,_pytorch,_weight_sharded,_rclone -j
+pip install mlc-scripts
+mlcr get,ml-model,dlrm,_pytorch,_weight_sharded,_rclone -j
 ```
 
 #### Manual method
diff --git a/text_to_image/README.md b/text_to_image/README.md
index 84c8c72457..b00595785b 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -1,10 +1,10 @@
 # MLPerf™ Inference Benchmarks for Text to Image
 
-## Automated command to run the benchmark via MLCommons CM
+## Automated command to run the benchmark via MLCFlow
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/text_to_image/sdxl) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
-You can also do `pip install cm4mlops` and then use `cm` commands for downloading the model and datasets using the commands given in the later sections.
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
  
 ## Supported Models
 
@@ -50,15 +50,15 @@ CFLAGS="-std=c++14" python setup.py install
 
 We host two checkpoints (fp32 and fp16) that are a snapshot of the [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) pipeline at the time of the release of the benchmark. Download them and move them to your model path.
 
-#### CM method
+#### MLC method
 
-The following MLCommons CM commands can be used to programmatically download the model checkpoints.
+The following MLCommons MLC commands can be used to programmatically download the model checkpoints.
 
 ```
-cm run script --tags=get,ml-model,sdxl,_fp16,_rclone --outdirname=$MODEL_PATH
+mlcr get,ml-model,sdxl,_fp16,_rclone --outdirname=$MODEL_PATH
 ```
 ```
-cm run script --tags=get,ml-model,sdxl,_fp32,_rclone --outdirname-$MODEL_PATH
+mlcr get,ml-model,sdxl,_fp32,_rclone --outdirname-$MODEL_PATH
 ```
 #### Manual method
 
@@ -89,16 +89,16 @@ rclone copy mlc-inference:mlcommons-inference-wg-public/stable_diffusion_fp16 $M
 
 ### Download validation dataset
 
-#### CM METHOD
-The following MLCommons CM commands can be used to programmatically download the validation dataset.
+#### MLC METHOD
+The following MLCommons MLC commands can be used to programmatically download the validation dataset.
 
 ```
-cm run script --tags=get,dataset,coco2014,_validation,_full --outdirname=coco2014
+mlcr get,dataset,coco2014,_validation,_full --outdirname=coco2014
 ```
 
 For debugging you can download only a part of all the images in the dataset
 ```
-cm run script --tags=get,dataset,coco2014,_validation,_size.50 --outdirname=coco2014
+mlcr get,dataset,coco2014,_validation,_size.50 --outdirname=coco2014
 ```
 
 
@@ -116,11 +116,11 @@ If the file [captions.tsv](coco2014/captions/captions.tsv) can be found in the s
 
 ### Download Calibration dataset (only if you are doing quantization)
 
-#### CM METHOD
-The following MLCommons CM commands can be used to programmatically download the calibration dataset.
+#### MLC METHOD
+The following MLCommons MLC commands can be used to programmatically download the calibration dataset.
 
 ```
-cm run script --tags=get,dataset,coco2014,_calibration --outdirname=coco2014
+mlcr get,dataset,coco2014,_calibration --outdirname=coco2014
 ```
 
 
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 8e131d4a31..c3c30c14d7 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1702,7 +1702,7 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
                 samples_per_query = 8
 
             if (scenario_fixed in ["MultiStream"]
-                ) and scenario in ["SingleStream"]:
+                    ) and scenario in ["SingleStream"]:
                 power_metric = (
                     avg_power * power_duration * samples_per_query * 1000 / num_queries
                 )

From a09ef1116056becc5794009d8ddebc30c55e79c9 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Tue, 18 Feb 2025 23:06:18 +0530
Subject: [PATCH 271/305] Add MLC Automation commands (#2115)

---
 automotive/3d-object-detection/README.md | 30 ++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/automotive/3d-object-detection/README.md b/automotive/3d-object-detection/README.md
index 9066d0b429..e1190e8132 100644
--- a/automotive/3d-object-detection/README.md
+++ b/automotive/3d-object-detection/README.md
@@ -6,6 +6,30 @@ This is the reference implementation for the MLPerf automotive 3D detection benc
 | ---- | ---- | ---- | ---- | ---- | ---- |
 | PointPainting | 0.5425 mAP | Waymo Open Dataset | https://github.com/rod409/pp | fp32 | Single-Stream 99.9 percentile |
 
+## Automated command to run the benchmark via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/automotive/3d_object_detection/pointpainting/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
+
+### Download model through MLCFlow Automation
+
+> [!Note]
+> By default, the waymo dataset is downloaded from the mlcommons official drive. One has to accept the [MLCommons Waymo Open Dataset EULA](https://waymo.mlcommons.org/) to access the dataset files.
+
+```
+mlcr get,ml-model,pointpainting --outdirname=<path_to_download> -j
+```
+
+### Download dataset through MLCFlow Automation
+
+> [!Note]
+> By default, the waymo dataset is downloaded from the mlcommons official drive. One has to accept the [MLCommons Waymo Open Dataset EULA](https://waymo.mlcommons.org/) to access the dataset files.
+
+```
+mlcr get,dataset,waymo --outdirname=<path_to_download> -j
+```
+
 ## Downloading the dataset and model checkpoints
 Contact [MLCommons](https://waymo.mlcommons.org/) support for accessing the Waymo Open Dataset along with the model checkpoints for the reference implementation. You will need to accept a license agreement and will be given directions to download the data with rclone. You will need to place the kitti_format folder under a directory named waymo. There are four total checkpoints 2 for pytorch and 2 for onnx.
 
@@ -66,6 +90,12 @@ Add the accuracy flag
 cd /inference/automotive/3d-object-detection
 python main.py --dataset waymo --dataset-path /waymo/kitti_format/ --lidar-path <checkpoint_path>/pp_ep36.pth --segmentor-path <checkpoint_path>/best_deeplabv3plus_resnet50_waymo_os16.pth --accuracy
 ```
+### Evaluate the accuracy through MLCFlow Automation
+```bash
+mlcr process,mlperf,accuracy,_waymo --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+Please click [here](https://github.com/mlcommons/inference/blob/master/automotive/3d-object-detection/accuracy_waymo.py) to view the Python script for evaluating accuracy for the Waymo dataset.
 
 ### Run the accuracy checker on the accuracy run log
 ```

From eaace3f41be939e723b5e6f3b4c59e8cf403666f Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 18 Feb 2025 14:53:02 -0500
Subject: [PATCH 272/305] Remove llama3.1 user conf unnecessary and misleading
 lines (#2114)

Co-authored-by: Miro <mirhodak@amd.com>
---
 language/llama3.1-405b/user.conf | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/language/llama3.1-405b/user.conf b/language/llama3.1-405b/user.conf
index 30681302c4..ebae62d774 100644
--- a/language/llama3.1-405b/user.conf
+++ b/language/llama3.1-405b/user.conf
@@ -4,10 +4,6 @@
 # All times are in milli seconds
 #
 *.Offline.min_duration = 600000
-*.Offline.min_query_count = 2000
 
 *.Server.target_qps = 0.5
 *.Server.min_duration = 120000
-*.Server.min_query_count = 100
-
-llama3_1-405b.Server.sample_concatenate_permutation = 1
\ No newline at end of file

From df13693e080de5ac369fed1c5db96dd5438b4199 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 18 Feb 2025 23:48:13 +0000
Subject: [PATCH 273/305] Update docs (#2118)

* Update mlcflow commands
---
 .github/workflows/auto-update-dev.yml         |  1 +
 .github/workflows/build_wheels.yml            |  2 +
 .github/workflows/publish.yaml                |  1 +
 .../get-pointpainting-data.md                 | 24 ++++-
 .../language/get-llama2-70b-data.md           |  4 +-
 .../language/get-llama3_1-405b-data.md        |  5 +-
 docs/changelog/changelog.md                   | 19 +++-
 docs/changelog/index.md                       |  7 +-
 docs/power/index.md                           | 93 +++++++++++++++++++
 docs/submission/index.md                      |  4 +-
 main.py                                       | 11 ++-
 mkdocs.yml                                    | 63 +++++++------
 tools/submission/preprocess_submission.py     | 22 +++--
 13 files changed, 207 insertions(+), 49 deletions(-)
 create mode 100644 docs/power/index.md

diff --git a/.github/workflows/auto-update-dev.yml b/.github/workflows/auto-update-dev.yml
index 9c6b17b5e8..58298ec86a 100644
--- a/.github/workflows/auto-update-dev.yml
+++ b/.github/workflows/auto-update-dev.yml
@@ -7,6 +7,7 @@ on:
 
 jobs:
   update-dev:
+    if: github.repository_owner == 'mlcommons'
     strategy:
       matrix: 
         branch: [ "dev", "docs" ]
diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 5f069c33e3..c3970275e7 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -70,6 +70,7 @@ jobs:
   
   build_wheels:
     name: Build wheels on ${{ matrix.os }}
+    if: github.repository_owner == 'mlcommons'
     needs: update_version
     runs-on: ${{ matrix.os }}
     strategy:
@@ -101,6 +102,7 @@ jobs:
           path: wheels
 
   publish_wheels:
+    if: github.repository_owner == 'mlcommons'
     needs: build_wheels  # Wait for the build_wheels job to complete
     runs-on: ubuntu-latest  # Only run this job on Linux
     environment: release
diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml
index 1fbb9cb886..b07e08c5ae 100644
--- a/.github/workflows/publish.yaml
+++ b/.github/workflows/publish.yaml
@@ -9,6 +9,7 @@ on:
     branches:
       - master
       - docs
+      - dev
 
 jobs:
 
diff --git a/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md b/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md
index 2e5ef33375..0a1e65c8ea 100644
--- a/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md
+++ b/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md
@@ -3,7 +3,27 @@ hide:
   - toc
 ---
 
-# 3D Object Detection using PointPainting
+# 3-D Object Detection using PointPainting
 
-TBD
+## Dataset
 
+> **Note:** By default, the waymo dataset is downloaded from the mlcommons official drive. One has to accept the [MLCommons Waymo Open Dataset EULA](https://waymo.mlcommons.org/) to access the dataset files. 
+
+The benchmark implementation run command will automatically download the preprocessed dataset. In case you want to download only the datasets, you can use the below command.
+
+```bash
+mlcr get,dataset,waymo -j
+```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_WAYMO_DATASET>` could be provided to download the dataset to a specific location.
+
+## Model
+> **Note:** By default, the PointPainting is downloaded from the mlcommons official drive. One has to accept the [MLCommons Waymo Open Dataset EULA](https://waymo.mlcommons.org/) to access the model files. 
+
+The benchmark implementation run command will automatically download the model. In case you want to download only the PointPainting model, you can use the below command.
+
+```bash
+mlcr get,ml-model,pointpainting -j
+```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_POINTPAINTING_MODEL>` could be provided to download the model files to a specific location.
diff --git a/docs/benchmarks/language/get-llama2-70b-data.md b/docs/benchmarks/language/get-llama2-70b-data.md
index d75540b5a9..2a31370574 100644
--- a/docs/benchmarks/language/get-llama2-70b-data.md
+++ b/docs/benchmarks/language/get-llama2-70b-data.md
@@ -26,10 +26,10 @@ Get the Official MLPerf LLAMA2-70b Model
 
     ### Pytorch
     ```
-    mlcr get,ml-model,llama2-70b,_pytorch -j
+    mlcr get,ml-model,llama2-70b,_pytorch -j --outdirname=<My download path>
     ```
   
 !!! tip
 
-    Downloading llama2-70B model from Hugging Face will prompt you to enter the Hugging Face username and password. Please note that the password required is the [**access token**](https://huggingface.co/settings/tokens) generated for your account. Additionally, ensure that your account has access to the [llama2-70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) model.
+    [Access Request Link](https://llama2.mlcommons.org/) for MLCommons members
 
diff --git a/docs/benchmarks/language/get-llama3_1-405b-data.md b/docs/benchmarks/language/get-llama3_1-405b-data.md
index e1a5e1c3f6..62b7bd088a 100644
--- a/docs/benchmarks/language/get-llama3_1-405b-data.md
+++ b/docs/benchmarks/language/get-llama3_1-405b-data.md
@@ -32,10 +32,9 @@ Get the Official MLPerf LLAMA3.1-405b Model
 
     ### Pytorch
     ```
-    mlcr get,ml-model,llama3 --outdirname=<path to download> --hf_token=<huggingface access token> -j
+    mlcr get,ml-model,llama3 --outdirname=<path to download>  -j
     ```
   
 !!! tip
 
-    Downloading llama3.1-405B model from Hugging Face will require an [**access token**](https://huggingface.co/settings/tokens) which could be generated for your account. Additionally, ensure that your account has access to the [llama3.1-405B](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) model. 
-
+    [Access Request Link](https://llama3-1.mlcommons.org/) for MLCommons members
diff --git a/docs/changelog/changelog.md b/docs/changelog/changelog.md
index 8b9abcc4b8..6a1f70fbff 100644
--- a/docs/changelog/changelog.md
+++ b/docs/changelog/changelog.md
@@ -1,2 +1,19 @@
-# Release Notes
+# **Release Notes**  
 
+🚀 **mlc-scripts 1.0.0** was released on **February 10, 2025**, introducing full support for **MLPerf Inference v5.0** using the first stable release of **MLCFlow 1.0.1**.  
+
+🔹 All previous **CM scripts** used in MLPerf have been successfully **ported to the MLC interface**, ensuring seamless integration. Additionally, all **GitHub Actions** are now passing, confirming a stable and reliable implementation.  
+
+## **Key Updates in MLCFlow**  
+
+✅ **Simplified Interface**  
+- A redesigned approach using **Actions and Targets**, making the CLI more intuitive for users.  
+
+✅ **Unified Automation Model**  
+- Consolidated into a **single automation entity**: **Script**, which is seamlessly extended by **Cache, Docker, and Tests**.  
+
+✅ **Improved Docker Integration**  
+- A **cleaner, more efficient Docker extension**, streamlining containerized execution.  
+
+✅ **Enhanced Script Management**  
+- Tighter integration between the interface and script automation, making script **creation and management easier than ever**.  
diff --git a/docs/changelog/index.md b/docs/changelog/index.md
index 6033a48260..291aa12ff2 100644
--- a/docs/changelog/index.md
+++ b/docs/changelog/index.md
@@ -3,5 +3,10 @@ hide:
   - toc
 ---
 
-# What's New, What's Coming
+# What's New & What's Coming 🚀  
 
+!!! info  
+    **Inference v5.0 Submission** is approaching! The submission deadline is **February 28, 2025, at 1 PM PST**.  
+
+!!! tip  
+    Starting **January 2025**, MLPerf Inference automations are powered by **[MLCFlow](https://docs.mlcommons.org/mlcflow)**—a newly developed Python package that replaces the previously used **CMind** package. This transition enhances automation, streamlines workflows, and makes MLPerf scripts more independent and simpler.  
diff --git a/docs/power/index.md b/docs/power/index.md
new file mode 100644
index 0000000000..c6673fb6d2
--- /dev/null
+++ b/docs/power/index.md
@@ -0,0 +1,93 @@
+# Power Measurement
+
+*Originally Prepared by the MLCommons taskforce on automation and reproducibility and [OctoML](https://octoml.ai)*.
+
+## Requirements
+
+1. Power analyzer (anyone [certified by SPEC PTDaemon](https://www.spec.org/power/docs/SPECpower-Device_List.html)). Yokogawa is the one that most submitters have submitted with and a new single-channel model like 310E can cost around 3000$. 
+
+2. SPEC PTDaemon (can be downloaded from [here](https://github.com/mlcommons/power) after signing the EULA which can be requested by sending an email to `support@mlcommons.org`). Once you have GitHub access to the MLCommons power repository then the MLC workflow will automatically download and configure the SPEC PTDaemon tool.
+
+3. Access to the [MLCommons power-dev](https://github.com/mlcommons/power-dev) repository which has the `server.py` to be run on the director node and `client.py` to be run on the SUT node. This repository being public will be automatically pulled by the MLC workflow.
+
+## Connecting power analyzer to the computer
+
+We need to connect the power analyzer to a director machine via USB if the director machine is running Linux. Ethernet and serial modes are supported only on Windows. The power supply to the SUT is done through the power analyzer (current in series and voltage in parallel). An adapter like [this](https://amzn.to/3Cl2TV5) can help avoid cutting the electrical wires. 
+
+![pages (14)](https://user-images.githubusercontent.com/4791823/210117283-82375460-5b3a-4e8a-bd85-9d33675a5843.png).
+
+The director machine runs the `server.py` script and loads a server process that communicates with the SPEC PTDaemon. When a client connects to it (using `client.py`), it in turn connects to the PTDaemon and initiates a measurement run. Once the measurement ends, the power log files are transferred to the client. 
+
+## Ranging mode and Testing mode
+
+Power analyzers usually have different current and voltage ranges it supports and the exact ranges to be used depends on a given SUT and this needs some empirical data. We can do a ranging run where the current and voltage ranges are set to `Auto` and the power analyzer automatically figures out the correct ranges needed. These determined ranges are then used for a proper testing mode run. Using the 'auto' mode in a testing run is not allowed as it can mess up the measurements.
+
+
+## Start Power Server (Power analyzer should be connected to this computer and PTDaemon runs here)
+
+If you are having GitHub access to [MLCommons power](https://github.com/mlcommons/power-dev) repository, PTDaemon should be automatically installed using the below MLC command:
+
+PS: The below command will ask for `sudo` permission on Linux and should be run with administrator privilege on Windows (to do NTP time sync).
+```bash
+mlcr mlperf,power,server --device_type=49 --device_port=/dev/usbtmc0
+```
+* ``--interface_flag="-U" and `--device_port=1` (can change as per the USB slot used for connecting) can be used on Windows for USB connection
+* `--device_type=49` corresponds to Yokogawa 310E and `ptd -h` should list the device_type for all supported devices. The location of `ptd` can be found using the below command
+* `--device_port=20` and `--interface_flag="-g" can be used to connect to GPIB interface (currently supported only on Windows) with the serial address set to 20
+```bash
+cat `mlc find cache --tags=get,spec,ptdaemon`/mlc-cached-state.json
+```
+
+An example analyzer configuration file
+```
+[server]
+ntpserver = time.google.com
+listen = 0.0.0.0 4950
+
+[ptd]
+ptd = C:\Users\arjun\CM\repos\local\cache\5a0a52d578724774\repo\PTD\binaries\ptd-windows-x86.exe
+analyzerCount = 2
+[analyzer2]
+interfaceflag = -g
+devicetype = 8
+deviceport = 20
+networkport = 8888
+
+[analyzer1]
+interfaceflag = -y
+devicetype = 49
+deviceport = C3YD21068E
+networkport = 8889
+```
+
+### Running the power server inside a docker container
+
+```bash
+mlc docker script --tags=run,mlperf,power,server --docker_gh_token=<GITHUB AUTH_TOKEN> \
+--device=/dev/usbtmc0
+```
+* Device address may need to be changed depending on the USB port being used
+* The above command uses a host-container port mapping 4950:4950 which can be changed by using `--docker_port_maps,=4950:4950`
+
+## Running a dummy workload with power (on host machine)
+
+```bash
+mlcr mlperf,power,client --power_server=<POWER_SERVER_IP> 
+```
+
+### Run a dummy workload with power inside a docker container
+
+```bash
+mlc docker script --tags==mlperf,power,client --power_server=<POWER_SERVER_IP>"
+```
+
+## Running MLPerf Image Classification with power
+
+```bash
+mlcr app,mlperf,inference,_reference,_power,_resnet50,_onnxruntime,_cpu --mode=performance --power_server=<POWER_SERVER_IP>
+```
+
+### Running MLPerf Image Classification with power inside a docker container
+```bash
+mlcr app,mlperf,inference,_reference,_power,_resnet50,_onnxruntime,_cpu --mode=performance --power_server=<POWER_SERVER_IP> --docker
+```
diff --git a/docs/submission/index.md b/docs/submission/index.md
index 97414e8bb0..da30c18350 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -73,7 +73,7 @@ flowchart LR
       direction TB
       A[populate system details] --> B[generate submission structure]
       B --> C[truncate-accuracy-logs]
-      C --> D{Infer low talency results <br>and/or<br> filter out invalid results}
+      C --> D{Infer low latency results <br>and/or<br> filter out invalid results}
       D --> yes --> E[preprocess-mlperf-inference-submission]
       D --> no --> F[run-mlperf-inference-submission-checker]
       E --> F
@@ -184,7 +184,7 @@ Once you have all the results on the system, you can upload them to the MLCommon
 === "via CLI"
     You can do the following command which will run the submission checker and upload the results to the MLCommons submission server
     ```
-    mlcr run,submission,checker \
+    mlcr run,submission,checker,inference \
     --submitter_id=<> \
     --submission_dir=<Path to the submission folder>
     ```
diff --git a/main.py b/main.py
index 48923e2909..2ccfab2958 100755
--- a/main.py
+++ b/main.py
@@ -334,6 +334,11 @@ def mlperf_inference_implementation_readme(
                             run_suffix += f"{cur_space3}* `--gpu_name=<Name of the GPU>` : The GPUs with supported configs in MLC are `orin`, `rtx_4090`, `rtx_a6000`, `rtx_6000_ada`, `l4`, `t4`and `a100`. For other GPUs, default configuration as per the GPU memory will be used.\n"
                         run_suffix += f"{cur_space3}</details>\n\n"
 
+                        if (
+                            "resnet50" in model.lower()
+                            and framework.lower() == "deepsparse"
+                        ):
+                            run_suffix += f"{cur_space3}You can use any model from [NeuralMagic sparse zoo](https://sparsezoo.neuralmagic.com/?modelSet=computer_vision&architectures=resnet_v1) (trained on Imagenet dataset) as --nm_model_zoo_stub"
                         if (
                             "bert" in model.lower()
                             and framework.lower() == "deepsparse"
@@ -630,7 +635,7 @@ def get_run_cmd_extra(
         if "gptj" in model and implementation == "reference":
             extra_content += f"{f_pre_space}    * `--beam-size=1` Beam size of 4 is mandatory for a closed division submission but reducing the beam size can help in running the model on GPUs with lower device memory\n"
         if "pointpainting" in model and implementation == "reference":
-            extra_content += f"{f_pre_space}    * Tjhe `pointpillars_checkpoint_path`, `deeplab_resnet50_path` and `waymo_path` do not need to be provided inside the Docker container as they are already registered in the MLC cache.\n"
+            extra_content += f"{f_pre_space}    * The `pointpainting_checkpoint_path`, `deeplab_resnet50_path` and `waymo_path` do not need to be provided inside the Docker container as they are already registered in the MLC cache.\n"
         if extra_content:
             extra_content = f"{f_pre_space}!!! tip\n\n" + extra_content
 
@@ -718,7 +723,7 @@ def mlperf_inference_run_command(
                 docker_cmd_suffix += f" \\\n{pre_space} --criteo_day23_raw_data_path=<PATH_TO_CRITEO_DAY23_RAW_DATA>"
 
             if "pointpainting" in model.lower() and implementation == "reference":
-                docker_cmd_suffix += f" \\\n{pre_space} --pointpillars_checkpoint_path=<PATH_TO_POINTPILLAR_MODEL>"
+                docker_cmd_suffix += f" \\\n{pre_space} --pointpainting_checkpoint_path=<PATH_TO_POINTPAINTING_MODEL>"
                 docker_cmd_suffix += f" \\\n{pre_space} --deeplab_resnet50_path=<PATH_TO_SEGMENTOR MODEL>"
                 docker_cmd_suffix += f" \\\n{pre_space} --waymo_path=<PATH_TO_WAYMO_DATASET_FOLDER>"
 
@@ -761,7 +766,7 @@ def mlperf_inference_run_command(
                     cmd_suffix += f" \\\n{pre_space} --adr.mlperf-implementation.tags=_repo.https://github.com/neuralmagic/inference,_branch.vllm"
 
             if "pointpainting" in model.lower() and implementation == "reference":
-                cmd_suffix += f" \\\n{pre_space} --pointpillars_checkpoint_path=<PATH_TO_POINTPILLAR_MODEL>"
+                cmd_suffix += f" \\\n{pre_space} --pointpainting_checkpoint_path=<PATH_TO_POINTPAINTING_MODEL>"
                 cmd_suffix += f" \\\n{pre_space} --deeplab_resnet50_path=<PATH_TO_SEGMENTOR MODEL>"
                 cmd_suffix += f" \\\n{pre_space} --waymo_path=<PATH_TO_WAYMO_DATASET_FOLDER>"
 
diff --git a/mkdocs.yml b/mkdocs.yml
index 9723075ff3..a0ac88ef98 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -11,47 +11,52 @@ theme:
     - content.tabs.link
     - content.code.copy
     - navigation.expand
-    - navigation.sections
     - navigation.indexes
+    - navigation.sections
     - navigation.instant
     - navigation.tabs
+    - navigation.path
     - navigation.tabs.sticky
     - navigation.top
+    - navigation.prune
     - toc.follow
+   
 nav:
   - Home:
-    - index.md
-    - Image Classification:
-      - ResNet50: benchmarks/image_classification/resnet50.md
-    - Text to Image:
-      - Stable Diffusion:
-        - Run Commands: benchmarks/text_to_image/sdxl.md
-        - Reproducibility:
-          - SCC24: benchmarks/text_to_image/reproducibility/scc24.md
-    - 2D Object Detection:
-      - RetinaNet: benchmarks/object_detection/retinanet.md
-    - Automotive:
-      - 3D Object Detection:
-        - PointPainting: benchmarks/automotive/3d_object_detection/pointpainting.md
-    - Medical Imaging:
-      - 3d-unet: benchmarks/medical_imaging/3d-unet.md
-    - Language Processing:
-      - Bert-Large:
-        - Run Commands: benchmarks/language/bert.md
-        - Reproducibility:
-          - IndySCC24: benchmarks/language/reproducibility/indyscc24-bert.md
-      - GPT-J: benchmarks/language/gpt-j.md
-      - LLAMA2-70B: benchmarks/language/llama2-70b.md
-      - LLAMA3-405B: benchmarks/language/llama3_1-405b.md
-      - MIXTRAL-8x7B: benchmarks/language/mixtral-8x7b.md
-    - Recommendation:
-      - DLRM-v2: benchmarks/recommendation/dlrm-v2.md
-    - Graph Neural Networks:
-      - R-GAT: benchmarks/graph/rgat.md
+      - index.md
+      - Image Classification:
+          - ResNet50: benchmarks/image_classification/resnet50.md
+      - Text to Image:
+          - Stable Diffusion:
+              - Run Commands: benchmarks/text_to_image/sdxl.md
+              - Reproducibility:
+                  - SCC24: benchmarks/text_to_image/reproducibility/scc24.md
+      - 2D Object Detection:
+          - RetinaNet: benchmarks/object_detection/retinanet.md
+      - Automotive:
+          - 3D Object Detection:
+              - PointPainting: benchmarks/automotive/3d_object_detection/pointpainting.md
+      - Medical Imaging:
+          - 3d-unet: benchmarks/medical_imaging/3d-unet.md
+      - Language Processing:
+        - Bert-Large:
+          - Run Commands: benchmarks/language/bert.md
+          - Reproducibility:
+            - IndySCC24: benchmarks/language/reproducibility/indyscc24-bert.md
+        - GPT-J: benchmarks/language/gpt-j.md
+        - LLAMA2-70B: benchmarks/language/llama2-70b.md
+        - LLAMA3-405B: benchmarks/language/llama3_1-405b.md
+        - MIXTRAL-8x7B: benchmarks/language/mixtral-8x7b.md
+      - Recommendation:
+        - DLRM-v2: benchmarks/recommendation/dlrm-v2.md
+      - Graph Neural Networks:
+        - R-GAT: benchmarks/graph/rgat.md
   - Install MLCFlow:
     - install/index.md
   - Submission:
     - Submission Generation: submission/index.md
+  - Power:
+    - power/index.md
   - Release Notes:
     - What's New: changelog/index.md
     - Changelog: changelog/changelog.md
diff --git a/tools/submission/preprocess_submission.py b/tools/submission/preprocess_submission.py
index 7eaa7f8f74..59bb3b92b2 100644
--- a/tools/submission/preprocess_submission.py
+++ b/tools/submission/preprocess_submission.py
@@ -39,6 +39,9 @@ def get_args():
     parser.add_argument("--noinfer-low-accuracy-results",
                         help="do not infer low accuracy results if a high accuracy result is present",
                         default=False, action="store_true")
+    parser.add_argument("--noinfer-scenario-results",
+                        help="do not infer offline/multistream results from singlestream/multistream",
+                        default=False, action="store_true")
     parser.add_argument("--nodelete-empty-dirs",
                         help="do not delete empty dirs in submission tree",
                         default=False, action="store_true")
@@ -186,6 +189,10 @@ def clean_invalid_results(args, log_path, config, system_desc, system_json,
         except Exception as e:
             log.warning(e)
             accuracy_is_valid = False
+
+        if not is_closed_or_network:  # open division
+            accuracy_is_valid = True
+
         perf_path = os.path.join(scenario_path, "performance", "run_1")
         try:
             perf_is_valid, r, is_inferred = checker.check_performance_dir(
@@ -345,7 +352,8 @@ def infer_scenario_results(args, config):
        If SS and MS exists and offline is not existing, offline is inferred from MS.
     """
     filter_submitter = args.submitter
-    noinfer_low_accuracy_results = args.noinfer_low_accuracy_results
+    infer_low_accuracy_results = not args.noinfer_low_accuracy_results
+    infer_scenario_results = not args.noinfer_scenario_results
 
     for division in sorted(
             list_dir(".")):  # process closed and network before open
@@ -450,10 +458,12 @@ def infer_scenario_results(args, config):
                                         not os.path.exists(offline_scenario_path):
 
                                     # infer both the scenarios from SS
-                                    tobeinferredpaths = [offline_scenario_path]
-                                    if "MultiStream" in all_scenarios:
-                                        tobeinferredpaths.append(
-                                            multistream_scenario_path)
+                                    if infer_scenario_results:
+                                        tobeinferredpaths = [
+                                            offline_scenario_path]
+                                        if "MultiStream" in all_scenarios:
+                                            tobeinferredpaths.append(
+                                                multistream_scenario_path)
 
                                     for tobeinferredpath in tobeinferredpaths:
                                         inferred_scenario = os.path.basename(
@@ -499,7 +509,7 @@ def infer_scenario_results(args, config):
                                         shutil.copytree(
                                             scenario_path, tobeinferredpath)
 
-                if not noinfer_low_accuracy_results:
+                if infer_low_accuracy_results:
                     for system_desc in list_dir(log_path):
                         for model in list_dir(log_path, system_desc):
                             if model.endswith("-99.9"):

From 2ab7e39e7eaef7a3d85738b7176d176e9566b997 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Wed, 19 Feb 2025 22:42:36 +0000
Subject: [PATCH 274/305] =?UTF-8?q?Update=20verify=5Fperformance.py=20|=20?=
 =?UTF-8?q?Fix=20compliance=20test=20for=20extra=20percenti=E2=80=A6=20(#2?=
 =?UTF-8?q?120)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Update verify_performance.py | Fix compliance test for extra percentile digit

* Update verify_performance.py | Fix TEST04 for extra percentile digit

* [Automated Commit] Format Codebase

* Update build_wheels.yml

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 .github/workflows/build_wheels.yml             |  1 -
 compliance/nvidia/TEST01/verify_performance.py |  8 ++++----
 compliance/nvidia/TEST04/verify_performance.py | 12 ++++++++----
 3 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index c3970275e7..3004ecc2d5 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -11,7 +11,6 @@ on:
     paths:
       - loadgen/**
 
-
 jobs:
   update_version:
     name: Update version only on ubuntu but used by windows and macos
diff --git a/compliance/nvidia/TEST01/verify_performance.py b/compliance/nvidia/TEST01/verify_performance.py
index 7c5aa897fc..cc400c8ed7 100644
--- a/compliance/nvidia/TEST01/verify_performance.py
+++ b/compliance/nvidia/TEST01/verify_performance.py
@@ -54,12 +54,12 @@ def main():
             continue
 
         if ref_mode == "SingleStream":
-            if re.match(".*Early stopping 90th percentile estimate", line):
+            if re.match(".*Early stopping (90th|90.0th|99.9th) percentile estimate", line):
                 ref_score = line.split(": ", 1)[1].strip()
                 continue
 
         if ref_mode == "MultiStream":
-            if re.match(".*Early stopping 99th percentile estimate", line):
+            if re.match(".*Early stopping (99th|99.0th) percentile estimate", line):
                 ref_score = line.split(": ", 1)[1].strip()
                 continue
 
@@ -91,12 +91,12 @@ def main():
             continue
         if test_mode == "SingleStream":
             if re.match(
-                    ".*Early stopping (90th|99.9th) percentile estimate", line):
+                    ".*Early stopping (90th|90.0th|99.9th) percentile estimate", line):
                 test_score = line.split(": ", 1)[1].strip()
                 continue
 
         if test_mode == "MultiStream":
-            if re.match(".*Early stopping 99th percentile estimate", line):
+            if re.match(".*Early stopping (99th|99.0th) percentile estimate", line):
                 test_score = line.split(": ", 1)[1].strip()
                 continue
 
diff --git a/compliance/nvidia/TEST04/verify_performance.py b/compliance/nvidia/TEST04/verify_performance.py
index 861ec609cc..c3b4514c40 100644
--- a/compliance/nvidia/TEST04/verify_performance.py
+++ b/compliance/nvidia/TEST04/verify_performance.py
@@ -53,13 +53,15 @@ def main():
             continue
 
         if ref_mode == "SingleStream":
-            if re.match(".*Early stopping 90th percentile estimate", line):
+            if re.match(
+                    ".*Early stopping (90th|90.0th|99.9th) percentile estimate", line):
                 ref_score = line.split(": ", 1)[1].strip()
                 ref_score = 1e9 / float(ref_score)
                 continue
 
         if ref_mode == "MultiStream":
-            if re.match(".*Early stopping 99th percentile estimate", line):
+            if re.match(
+                    ".*Early stopping (99th|99.0th) percentile estimate", line):
                 ref_score = line.split(": ", 1)[1].strip()
                 ref_score = 1e9 / float(ref_score)
                 continue
@@ -92,13 +94,15 @@ def main():
             continue
 
         if test_mode == "SingleStream":
-            if re.match(".*Early stopping 90th percentile estimate", line):
+            if re.match(
+                    ".*Early stopping (90th|90.0th|99.9th) percentile estimate", line):
                 test_score = line.split(": ", 1)[1].strip()
                 test_score = 1e9 / float(test_score)
                 continue
 
         if test_mode == "MultiStream":
-            if re.match(".*Early stopping 99th percentile estimate", line):
+            if re.match(
+                    ".*Early stopping (99th|99.0th) percentile estimate", line):
                 test_score = line.split(": ", 1)[1].strip()
                 test_score = 1e9 / float(test_score)
                 continue

From cc9a89e94553e0a071fb4a5980e76aa7d0cd2615 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 20 Feb 2025 11:55:42 -0500
Subject: [PATCH 275/305] Update default version of final report script (#2124)

---
 tools/submission/generate_final_report.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 303a727cc8..82bf2ca016 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -19,10 +19,10 @@ def get_args():
         '--input',
         required=True,
         help='results csv from checker')
-    parser.add_argument('--version', default='4.1', help='mlperf version')
+    parser.add_argument('--version', default='5.0', help='mlperf version')
     parser.add_argument(
         '--repository',
-        default='submissions_inference_4.1',
+        default='submissions_inference_5.0',
         help='mlperf repository')
     parser.add_argument(
         '--repository-owner',

From f4a195e79b3be6f1e179bb1a3c63340b3e577e66 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Fri, 21 Feb 2025 04:40:39 +0000
Subject: [PATCH 276/305] Update accuracy_igbh.py (#2123)

---
 graph/R-GAT/tools/accuracy_igbh.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/graph/R-GAT/tools/accuracy_igbh.py b/graph/R-GAT/tools/accuracy_igbh.py
index a198dda667..1c86fbb6fa 100644
--- a/graph/R-GAT/tools/accuracy_igbh.py
+++ b/graph/R-GAT/tools/accuracy_igbh.py
@@ -63,14 +63,18 @@ def load_labels(base_path, dataset_size, use_label_2K=True, no_memmap=False):
 
     if dataset_size in ["large", "full"] and not no_memmap:
         mmap_mode = 'r'
+        paper_node_labels = torch.from_numpy(
+            np.memmap(
+                paper_lbl_path, dtype="float32", mode=mmap_mode, shape=(paper_nodes_num[dataset_size])
+            )
+        ).to(torch.long)
     else:
         mmap_mode = None
-
-    paper_node_labels = torch.from_numpy(
-        np.load(
-            paper_lbl_path,
-            mmap_mode=mmap_mode)).to(
-        torch.long)
+        paper_node_labels = torch.from_numpy(
+            np.load(
+                paper_lbl_path,
+                mmap_mode=mmap_mode)).to(
+            torch.long)
     labels = paper_node_labels
     val_idx = torch.load(
         os.path.join(

From 4bf9fc683b0d52fc046bb227ac210741ec7118ca Mon Sep 17 00:00:00 2001
From: mlcommons-bot <74634038+mlcommons-bot@users.noreply.github.com>
Date: Fri, 21 Feb 2025 09:45:10 -0600
Subject: [PATCH 277/305] =?UTF-8?q?=F0=9F=94=84=20synced=20file(s)=20with?=
 =?UTF-8?q?=20mlcommons/power-dev=20(#2125)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* 🔄 synced local 'tools/submission/power/power_checker.py' with remote 'compliance/check.py'

* 🔄 synced local 'tools/submission/power/sources_checksums.json' with remote 'compliance/sources_checksums.json'

---------

Co-authored-by: mlcommons-bot <null>
---
 tools/submission/power/power_checker.py       | 57 ++++++++++---------
 tools/submission/power/sources_checksums.json |  2 +-
 2 files changed, 30 insertions(+), 29 deletions(-)

diff --git a/tools/submission/power/power_checker.py b/tools/submission/power/power_checker.py
index 06b058da9a..c723adbfd9 100755
--- a/tools/submission/power/power_checker.py
+++ b/tools/submission/power/power_checker.py
@@ -262,12 +262,13 @@ def get_command_by_value_and_number(cmd: str, number: int) -> Optional[str]:
     initial_amps = get_initial_range(1, msgs[2]["reply"])
     initial_volts = get_initial_range(3, msgs[2]["reply"])
 
-    initial_amps_command = get_command_by_value_and_number("SR,A", 3)
+    # sometimes the SR,A,Auto comes from 3rd or 4th response
+    initial_amps_command_3 = get_command_by_value_and_number("SR,A", 3)
+    initial_amps_command_4 = get_command_by_value_and_number("SR,A", 4)
     initial_volts_command = get_command_by_value_and_number("SR,V", 3)
-
-    assert (
-        initial_amps_command == f"SR,A,{initial_amps}"
-    ), f"Do not set Amps range as initial. Expected 'SR,A,{initial_amps}', got {initial_amps_command!r}."
+    assert (initial_amps_command_3 == f"SR,A,{initial_amps}") or (
+        initial_amps_command_4 == f"SR,A,{initial_amps}"
+    ), f"Do not set Amps range as initial. Expected 'SR,A,{initial_amps}', got {initial_amps_command_3!r} and {initial_amps_command_4!r}."
     assert (
         initial_volts_command == f"SR,V,{initial_volts}"
     ), f"Do not set Volts range as initial. Expected 'SR,V,{initial_volts}', got {initial_volts_command!r}."
@@ -706,33 +707,33 @@ def check_ptd_config(server_sd: SessionDescriptor) -> None:
     """
     ptd_config = server_sd.json_object["ptd_config"]
 
-    dev_num = ptd_config["device_type"]
-    assert (
-        dev_num in SUPPORTED_MODEL.values()
-    ), f"Device number {dev_num} is not supported. Supported numbers are " + ", ".join(
-        [str(i) for i in set(SUPPORTED_MODEL.values())]
-    )
+    for analyzer in ptd_config:
+        dev_num = analyzer["device_type"]
+        assert dev_num in SUPPORTED_MODEL.values(), (
+            f"Device number {dev_num} is not supported. Supported numbers are "
+            + ", ".join([str(i) for i in set(SUPPORTED_MODEL.values())])
+        )
 
-    if dev_num == 77:
-        channels = ""
-        command = ptd_config["command"]
+        if dev_num == 77:
+            channels = ""
+            command = analyzer["command"]
 
-        for i in range(len(command)):
-            if command[i] == "-c":
-                channels = command[i + 1]
-                break
+            for i in range(len(command)):
+                if command[i] == "-c":
+                    channels = command[i + 1]
+                    break
 
-        dev_name = ""
-        for name, num in SUPPORTED_MODEL.items():
-            if num == dev_num:
-                dev_name = name
-                break
+            dev_name = ""
+            for name, num in SUPPORTED_MODEL.items():
+                if num == dev_num:
+                    dev_name = name
+                    break
 
-        assert (
-            len(channels.split(",")) == 2
-            and ptd_config["channel"]
-            and len(ptd_config["channel"]) == 2
-        ), f"Expected multichannel mode for {dev_name}, but got 1-channel."
+            assert (
+                len(channels.split(",")) == 2
+                and analyzer["channel"]
+                and len(analyzer["channel"]) == 2
+            ), f"Expected multichannel mode for {dev_name}, but got 1-channel."
 
 
 def debug_check(server_sd: SessionDescriptor) -> None:
diff --git a/tools/submission/power/sources_checksums.json b/tools/submission/power/sources_checksums.json
index 6862abcbd2..ec8e7bc8f2 100644
--- a/tools/submission/power/sources_checksums.json
+++ b/tools/submission/power/sources_checksums.json
@@ -16,4 +16,4 @@
         "tests/unit/test_server.py": "948c1995d4008bc2aa6c4046a34ffa3858d6d671",
         "tests/unit/test_source_hashes.py": "00468a2907583c593e6574a1f6b404e4651c221a"
     }
-]
+]
\ No newline at end of file

From 1873bd76aeedfe589f6260dc29760771ab99f95e Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 25 Feb 2025 12:03:29 -0500
Subject: [PATCH 278/305] Final report cosmetic fix (#2134)

---
 tools/submission/generate_final_report.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 82bf2ca016..1782cf8919 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -149,6 +149,7 @@ def main():
             "llama2-70b-99.9",
             "llama2-70b-interactive-99",
             "llama2-70b-interactive-99.9",
+            "llama3.1-405b",
             "mixtral-8x7b",
             "pointpainting",
         ],

From 9926a70244a91fb5e192b66f4313cf9416f9b1a1 Mon Sep 17 00:00:00 2001
From: Annie <36964858+annietllnd@users.noreply.github.com>
Date: Tue, 25 Feb 2025 18:09:41 +0100
Subject: [PATCH 279/305] Update loadgen package name in
 classification_and_detection setup (#2131)

Co-authored-by: Miro <mirhodak@amd.com>
---
 vision/classification_and_detection/setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vision/classification_and_detection/setup.py b/vision/classification_and_detection/setup.py
index 758d874fbd..a84e1bc829 100644
--- a/vision/classification_and_detection/setup.py
+++ b/vision/classification_and_detection/setup.py
@@ -100,7 +100,7 @@ def run(self):
         "pybind11",
         "Cython",
         "pycocotools",
-        "mlperf_loadgen",
+        "mlcommons_loadgen",
         "opencv-python-headless",
     ],
 )

From 0e72613ad0b8449921b9e65a09491a0be266c7e6 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Tue, 25 Feb 2025 12:21:20 -0700
Subject: [PATCH 280/305] Add exception for github-actions[bot] to cla.yml
 (#2135)

---
 .github/workflows/cla.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/cla.yml b/.github/workflows/cla.yml
index 5793f13c2e..a6b050a509 100644
--- a/.github/workflows/cla.yml
+++ b/.github/workflows/cla.yml
@@ -23,7 +23,7 @@ jobs:
           path-to-signatures: 'cla-bot/v1/cla.json'
           # branch should not be protected
           branch: 'main'
-          allowlist: user1,bot*
+          allowlist: user1,bot*,github-actions[bot]
           remote-organization-name: mlcommons
           remote-repository-name: systems
           

From bec571495d2001097080549955ad71802889b223 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Tue, 25 Feb 2025 13:21:39 -0600
Subject: [PATCH 281/305] Update CODEOWNERS

---
 .github/CODEOWNERS | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 2bebc6f917..f2482b44d7 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -2,4 +2,8 @@
 # Unless a later match takes precedence,they will be requested for review when someone opens a pull request.
 * @mlcommons/wg-inference
 
-/CODEOWNERS @mlcommons/staff
+/.github/CODEOWNERS @mlcommons/systems
+
+/.github/workflows/cla.yml @mlcommons/systems
+
+/LICENSE.md @mlcommons/systems

From 0602e21e0a1732926a66c0a952547cb04c4ff38e Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Tue, 25 Feb 2025 13:26:07 -0600
Subject: [PATCH 282/305] Update GitHub Actions bot exception in cla.yml

---
 .github/workflows/cla.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/cla.yml b/.github/workflows/cla.yml
index a6b050a509..7e0f80a11c 100644
--- a/.github/workflows/cla.yml
+++ b/.github/workflows/cla.yml
@@ -23,7 +23,7 @@ jobs:
           path-to-signatures: 'cla-bot/v1/cla.json'
           # branch should not be protected
           branch: 'main'
-          allowlist: user1,bot*,github-actions[bot]
+          allowlist: user1,bot*,github-actions,github-actions[bot]
           remote-organization-name: mlcommons
           remote-repository-name: systems
           

From c0b60cdbdaa389c5ff88b12e659fdab650b95948 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Thu, 27 Feb 2025 13:27:53 -0500
Subject: [PATCH 283/305] Final report cosmetic fix (#2141)

---
 tools/submission/generate_final_report.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tools/submission/generate_final_report.py b/tools/submission/generate_final_report.py
index 1782cf8919..ee57dfdfbb 100644
--- a/tools/submission/generate_final_report.py
+++ b/tools/submission/generate_final_report.py
@@ -152,6 +152,7 @@ def main():
             "llama3.1-405b",
             "mixtral-8x7b",
             "pointpainting",
+            "rgat",
         ],
         ["SingleStream", "MultiStream", "Server", "Offline"],
         [

From e216af0f8a6e725c798c8847d288c4766da9b267 Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Tue, 4 Mar 2025 18:31:54 +0000
Subject: [PATCH 284/305] Update submission_checker.py | Fix open model unit in
 Results (#2144)

---
 tools/submission/submission_checker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index c3c30c14d7..7a59ab2b10 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1977,7 +1977,7 @@ def log_result(
         if config.version == "v4.0":
             unit = unit_dict[scenario_fixed]
         else:
-            unit = special_unit_dict.get(model_name, unit_dict)[scenario_fixed]
+            unit = special_unit_dict.get(mlperf_model, unit_dict)[scenario_fixed]
         power_unit = power_unit_dict[scenario_fixed]
 
         if (power_metric <= 0) or (

From 5e9039561c8f3e3851b0a98c3e4035e2a7469cca Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Mon, 10 Mar 2025 16:04:48 -0500
Subject: [PATCH 285/305] Add Llama 3.1 to special unit dict (#2150)

---
 tools/submission/submission_checker.py | 22 +++++-----------------
 1 file changed, 5 insertions(+), 17 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 7a59ab2b10..7ac74be40d 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1914,50 +1914,38 @@ def log_result(
             notes = notes + system_json.get("sw_notes")
         special_unit_dict = {
             "gptj-99": {
-                "SingleStream": "Latency (ms)",
-                "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
             "gptj-99.9": {
-                "SingleStream": "Latency (ms)",
-                "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
             "llama2-70b-99": {
-                "SingleStream": "Latency (ms)",
-                "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
             "llama2-70b-99.9": {
-                "SingleStream": "Latency (ms)",
-                "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
             "llama2-70b-interactive-99": {
-                "SingleStream": "Latency (ms)",
-                "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
             "llama2-70b-interactive-99.9": {
-                "SingleStream": "Latency (ms)",
-                "MultiStream": "Latency (ms)",
+                "Offline": "Tokens/s",
+                "Server": "Tokens/s",
+            },
+            "llama3.1-405b": {
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
             "mixtral-8x7b": {
-                "SingleStream": "Latency (ms)",
-                "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
             "llama3.1-405b": {
-                "SingleStream": "Latency (ms)",
-                "MultiStream": "Latency (ms)",
                 "Offline": "Tokens/s",
                 "Server": "Tokens/s",
             },
@@ -1977,7 +1965,7 @@ def log_result(
         if config.version == "v4.0":
             unit = unit_dict[scenario_fixed]
         else:
-            unit = special_unit_dict.get(mlperf_model, unit_dict)[scenario_fixed]
+            unit = special_unit_dict.get(mlperf_model, unit_dict).get(scenario_fixed, unit_dict[scenario_fixed])
         power_unit = power_unit_dict[scenario_fixed]
 
         if (power_metric <= 0) or (

From fd021be1b7f005c5911fe71481b01a512abaa3d4 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 15 Apr 2025 11:33:10 -0500
Subject: [PATCH 286/305] [Post Mortem] Log number of errors in detail log
 (#2164)

* Log number of errors in detail log

* [Automated Commit] Format Codebase

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 loadgen/logging.h                      |  1 +
 loadgen/results.cc                     |  3 ++-
 tools/submission/log_parser.py         |  2 ++
 tools/submission/submission_checker.py | 12 ++++++++++--
 4 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/loadgen/logging.h b/loadgen/logging.h
index c5514562a5..8f1a398e9d 100644
--- a/loadgen/logging.h
+++ b/loadgen/logging.h
@@ -314,6 +314,7 @@ class AsyncLog {
   QuerySampleLatency GetMaxLatencySoFar();
   void SetUseTokens(bool use_tokens);
   void SetNeedsFirstToken(bool needs_first_token);
+  size_t GetErrorCount() { return log_error_count_; };
 
  private:
   void WriteAccuracyHeaderLocked();
diff --git a/loadgen/results.cc b/loadgen/results.cc
index 5e940793e3..0b25ad7761 100644
--- a/loadgen/results.cc
+++ b/loadgen/results.cc
@@ -848,8 +848,9 @@ void PerformanceSummary::LogDetail(AsyncDetail& detail) {
         break;
       }
     }
-#endif
   }
+  MLPERF_LOG(detail, "num_errors", detail.async_log().GetErrorCount());
+#endif
 }
 }  // namespace loadgen
 }  // namespace mlperf
diff --git a/tools/submission/log_parser.py b/tools/submission/log_parser.py
index df9d82ad15..11bd8aaca5 100755
--- a/tools/submission/log_parser.py
+++ b/tools/submission/log_parser.py
@@ -128,6 +128,8 @@ def num_messages(self):
 
     def num_errors(self):
         """Get number of errors in the log."""
+        if "num_errors" in self.keys:
+            return self.__getitem__("num_errors")
         count = 0
         for message in self.messages:
             if message["metadata"]["is_error"]:
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 7ac74be40d..4df77bbed1 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -1302,6 +1302,9 @@ def check_accuracy_dir(config, model, path, verbose):
     fname = os.path.join(path, "mlperf_log_detail.txt")
     if not find_error_in_detail_log(config, fname):
         is_valid = False
+        log.error(
+            "%s has loadgen errors, number of errors: %s", path, mlperf_log.num_errors()
+        )
 
     return is_valid, result_acc
 
@@ -1434,6 +1437,9 @@ def check_performance_dir(
     fname = os.path.join(path, "mlperf_log_detail.txt")
     if not find_error_in_detail_log(config, fname):
         is_valid = False
+        log.error(
+            "%s has loadgen errors, number of errors: %s", path, mlperf_log.num_errors()
+        )
 
     required_performance_sample_count = config.get_performance_sample_count(
         model)
@@ -1702,7 +1708,7 @@ def get_power_metric(config, scenario_fixed, log_path, is_valid, res):
                 samples_per_query = 8
 
             if (scenario_fixed in ["MultiStream"]
-                    ) and scenario in ["SingleStream"]:
+                ) and scenario in ["SingleStream"]:
                 power_metric = (
                     avg_power * power_duration * samples_per_query * 1000 / num_queries
                 )
@@ -1965,7 +1971,9 @@ def log_result(
         if config.version == "v4.0":
             unit = unit_dict[scenario_fixed]
         else:
-            unit = special_unit_dict.get(mlperf_model, unit_dict).get(scenario_fixed, unit_dict[scenario_fixed])
+            unit = special_unit_dict.get(
+                mlperf_model, unit_dict).get(
+                scenario_fixed, unit_dict[scenario_fixed])
         power_unit = power_unit_dict[scenario_fixed]
 
         if (power_metric <= 0) or (

From c6d4f3658c67a011f47705955fde764af1634ef5 Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Tue, 15 Apr 2025 16:33:23 +0000
Subject: [PATCH 287/305] Increment version to 5.0.16

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 1ed9c641c4..db5a3c5c71 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.15
+5.0.16

From 9a5867f54a1e9f23b9bba6fbff2e86756f0ead03 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Tue, 22 Apr 2025 08:28:03 -0500
Subject: [PATCH 288/305] Docs update (#2137)

* [Automated Commit] Format Codebase

* Updated tags for submission checker command in docs

* Update mobilenets docs

* Update main.py

* update dataset download commands - waymo calib (#2130)

* Merge from Master (#2155)

* Update submission_checker.py | Fix open model unit in Results (#2144)

* Add Llama 3.1 to special unit dict (#2150)

---------

Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>

* [Automated Commit] Format Codebase

* Inference docs - Update model and dataset download commands (#2153)

* Update llama2 70b model download docs

* changes in model and dataset download commands

* add powershell command to get result folder structure (#2156)

---------


Co-authored-by: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
---
 .../nvidia/TEST01/verify_performance.py       |  9 ++-
 .../get-pointpainting-data.md                 | 18 +++--
 docs/benchmarks/graph/get-rgat-data.md        | 33 +++++++---
 .../image_classification/get-resnet50-data.md | 38 +++++++----
 .../image_classification/mobilenets.md        | 21 ++++--
 docs/benchmarks/language/get-bert-data.md     | 19 ++++++
 docs/benchmarks/language/get-gptj-data.md     | 14 +++-
 .../language/get-llama2-70b-data.md           | 66 ++++++++++++++-----
 .../language/get-llama3_1-405b-data.md        | 29 +++++---
 .../language/get-mixtral-8x7b-data.md         | 13 +++-
 .../medical_imaging/get-3d-unet-data.md       | 30 +++++++--
 .../object_detection/get-retinanet-data.md    | 33 +++++++---
 .../recommendation/get-dlrm-v2-data.md        |  7 +-
 .../benchmarks/text_to_image/get-sdxl-data.md | 27 ++++++--
 docs/submission/index.md                      | 16 +++--
 main.py                                       | 11 ++--
 16 files changed, 288 insertions(+), 96 deletions(-)

diff --git a/compliance/nvidia/TEST01/verify_performance.py b/compliance/nvidia/TEST01/verify_performance.py
index cc400c8ed7..4b527730b1 100644
--- a/compliance/nvidia/TEST01/verify_performance.py
+++ b/compliance/nvidia/TEST01/verify_performance.py
@@ -54,12 +54,14 @@ def main():
             continue
 
         if ref_mode == "SingleStream":
-            if re.match(".*Early stopping (90th|90.0th|99.9th) percentile estimate", line):
+            if re.match(
+                    ".*Early stopping (90th|90.0th|99.9th) percentile estimate", line):
                 ref_score = line.split(": ", 1)[1].strip()
                 continue
 
         if ref_mode == "MultiStream":
-            if re.match(".*Early stopping (99th|99.0th) percentile estimate", line):
+            if re.match(
+                    ".*Early stopping (99th|99.0th) percentile estimate", line):
                 ref_score = line.split(": ", 1)[1].strip()
                 continue
 
@@ -96,7 +98,8 @@ def main():
                 continue
 
         if test_mode == "MultiStream":
-            if re.match(".*Early stopping (99th|99.0th) percentile estimate", line):
+            if re.match(
+                    ".*Early stopping (99th|99.0th) percentile estimate", line):
                 test_score = line.split(": ", 1)[1].strip()
                 continue
 
diff --git a/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md b/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md
index 0a1e65c8ea..6331b3535b 100644
--- a/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md
+++ b/docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md
@@ -9,11 +9,21 @@ hide:
 
 > **Note:** By default, the waymo dataset is downloaded from the mlcommons official drive. One has to accept the [MLCommons Waymo Open Dataset EULA](https://waymo.mlcommons.org/) to access the dataset files. 
 
-The benchmark implementation run command will automatically download the preprocessed dataset. In case you want to download only the datasets, you can use the below command.
+The benchmark implementation run command will automatically download the preprocessed dataset. In case you want to download only the datasets, you can use the below commands.
 
-```bash
-mlcr get,dataset,waymo -j
-```
+=== "Validation"
+
+    ### Get Validation Dataset
+    ```
+    mlcr get,dataset,waymo -j
+    ```
+    
+=== "Calibration"
+
+    ### Get Calibration Dataset
+    ```
+    mlcr get,dataset,waymo,calibration -j
+    ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_WAYMO_DATASET>` could be provided to download the dataset to a specific location.
 
diff --git a/docs/benchmarks/graph/get-rgat-data.md b/docs/benchmarks/graph/get-rgat-data.md
index 6ab9515e59..bb719fea2e 100644
--- a/docs/benchmarks/graph/get-rgat-data.md
+++ b/docs/benchmarks/graph/get-rgat-data.md
@@ -9,22 +9,34 @@ hide:
 
 The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
 
-=== "Full Dataset"
-    R-GAT validation run uses the IGBH dataset consisting of 547,306,935 nodes and 5,812,005,639 edges.
+=== "Validation"
 
-    ### Get Full Dataset
-    ```
-    mlcr get,dataset,igbh,_full -j
-    ```
+    === "Full Dataset"
+        R-GAT validation run uses the IGBH dataset consisting of 547,306,935 nodes and 5,812,005,639 edges.
+
+        ### Get Full Dataset
+        ```
+        mlcr get,dataset,igbh,_full -j
+        ```
 
-=== "Debug Dataset"
-    R-GAT debug run uses the IGBH debug dataset(tiny).
+    === "Debug Dataset"
+        R-GAT debug run uses the IGBH debug dataset(tiny).
 
-    ### Get Full Dataset
+        ### Get Full Dataset
+        ```
+        mlcr get,dataset,igbh,_debug -j
+        ```
+
+=== "Calibration"
+    The calibration dataset contains 5000 nodes from the training paper nodes of the IGBH dataset. IGBH `full` dataset would be downloaded for creating calibration dataset. 
+
+    ### Get Calibration Dataset
     ```
-    mlcr get,dataset,igbh,_debug -j
+    mlcr get,dataset,igbh,_full,_calibration -j
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_IGBH_DATASET>` could be provided to download the dataset to a specific location.
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
@@ -37,3 +49,4 @@ Get the Official MLPerf R-GAT Model
     mlcr get,ml-model,rgat -j
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_RGAT_MODEL>` could be provided to download the model to a specific location.
\ No newline at end of file
diff --git a/docs/benchmarks/image_classification/get-resnet50-data.md b/docs/benchmarks/image_classification/get-resnet50-data.md
index 771571d5c7..9ecd25c1a5 100644
--- a/docs/benchmarks/image_classification/get-resnet50-data.md
+++ b/docs/benchmarks/image_classification/get-resnet50-data.md
@@ -9,25 +9,34 @@ hide:
 
 The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
 
-=== "Validation"
-    ResNet50 validation run uses the Imagenet 2012 validation dataset consisting of 50,000 images.
+=== "Unprocessed"
+    === "Validation"
+        ResNet50 validation run uses the Imagenet 2012 validation dataset consisting of 50,000 images.
 
-    ### Get Validation Dataset
-    ```
-    mlcr get,dataset,imagenet,validation -j
-    ```
-=== "Calibration"
-    ResNet50 calibration dataset consist of 500 images selected from the Imagenet 2012 validation dataset. There are 2 alternative options for the calibration dataset.
+        ### Get Validation Dataset
+        ```
+        mlcr get,dataset,imagenet,validation -j
+        ```
+    === "Calibration"
+        ResNet50 calibration dataset consist of 500 images selected from the Imagenet 2012 validation dataset. There are 2 alternative options for the calibration dataset.
+
+        ### Get Calibration Dataset Using Option 1
+        ```
+        mlcr get,dataset,imagenet,calibration,_mlperf.option1 -j
+        ```
+        ### Get Calibration Dataset Using Option 2
+        ```
+        mlcr get,dataset,imagenet,calibration,_mlperf.option2 -j
+        ```
+=== "Preprocessed"
+    ### Get ResNet50 preprocessed dataset
 
-    ### Get Calibration Dataset Using Option 1
-    ```
-    mlcr get,dataset,imagenet,calibration,_mlperf.option1 -j
-    ```
-    ### Get Calibration Dataset Using Option 2
     ```
-    mlcr get,dataset,imagenet,calibration,_mlperf.option2 -j
+    mlcr get,dataset,image-classification,imagenet,preprocessed,_pytorch -j
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_IMAGENET_DATASET>` could be provided to download the dataset to a specific location.
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
@@ -46,3 +55,4 @@ Get the Official MLPerf ResNet50 Model
     mlcr get,ml-model,resnet50,_onnx -j
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_RESNET50_MODEL>` could be provided to download the model to a specific location.
\ No newline at end of file
diff --git a/docs/benchmarks/image_classification/mobilenets.md b/docs/benchmarks/image_classification/mobilenets.md
index 7e4605b4b0..9501521b32 100644
--- a/docs/benchmarks/image_classification/mobilenets.md
+++ b/docs/benchmarks/image_classification/mobilenets.md
@@ -23,10 +23,10 @@ Mobilenet models are not official MLPerf models and so cannot be used for a Clos
     mlcr run,mobilenet-models,_tflite,_mobilenet-v2 \
      --adr.compiler.tags=gcc
     ```
-=== "Mobilenet-V2"
-    ### Mobilenet V2
+=== "Mobilenet-V3"
+    ### Mobilenet V3
     ```bash
-    mlcr run,mobilenet-models,_tflite,_mobilenet-v2 \
+    mlcr run,mobilenet-models,_tflite,_mobilenet-v3 \
      --adr.compiler.tags=gcc
     ```
 === "Mobilenets"
@@ -41,6 +41,12 @@ Mobilenet models are not official MLPerf models and so cannot be used for a Clos
     mlcr run,mobilenet-models,_tflite,_efficientnet \
      --adr.compiler.tags=gcc
     ```
+=== "Mobilenets and Efficientnet"
+    ### Mobilenets and Efficientnet
+    ```bash
+    mlcr run,mobilenet-models,_tflite \
+     --adr.compiler.tags=gcc
+    ```
 
 ## ARMNN Backend
 === "Mobilenet-V1"
@@ -55,7 +61,7 @@ Mobilenet models are not official MLPerf models and so cannot be used for a Clos
     mlcr run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 \
      --adr.compiler.tags=gcc
     ```
-=== "Mobilenet-V2"
+=== "Mobilenet-V3"
     ### Mobilenet V2
     ```bash
     mlcr run,mobilenet-models,_tflite,_armnn,_mobilenet-v2 \
@@ -73,4 +79,11 @@ Mobilenet models are not official MLPerf models and so cannot be used for a Clos
     mlcr run,mobilenet-models,_tflite,_armnn,_efficientnet \
      --adr.compiler.tags=gcc
     ```
+=== "Mobilenets and Efficientnet"
+    ### Mobilenets and Efficientnet
+    ```bash
+    mlcr run,mobilenet-models,_tflite,_armnn \
+     --adr.compiler.tags=gcc
+    ```
+
 
diff --git a/docs/benchmarks/language/get-bert-data.md b/docs/benchmarks/language/get-bert-data.md
index 430031f319..ab3ba9b537 100644
--- a/docs/benchmarks/language/get-bert-data.md
+++ b/docs/benchmarks/language/get-bert-data.md
@@ -17,6 +17,24 @@ The benchmark implementation run command will automatically download the validat
     mlcr get,dataset,squad,validation -j
     ```
 
+=== "Calibration"
+
+    === "Calibration Set 1"
+
+        ### Get Calibration Dataset
+        ```
+        mlcr get,dataset,squad,_calib1 -j
+        ```
+    
+    === "Calibration Set 2"
+
+        ### Get Calibration Dataset
+        ```
+        mlcr get,dataset,squad,_calib2 -j
+        ```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_SQUAD_DATASET>` could be provided to download the dataset to a specific location.
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
@@ -41,3 +59,4 @@ Get the Official MLPerf Bert-Large Model
     mlcr get,ml-model,bert-large,_tensorflow -j
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_BERT_MODEL>` could be provided to download the model to a specific location.
diff --git a/docs/benchmarks/language/get-gptj-data.md b/docs/benchmarks/language/get-gptj-data.md
index 34140598e9..60e2568b6e 100644
--- a/docs/benchmarks/language/get-gptj-data.md
+++ b/docs/benchmarks/language/get-gptj-data.md
@@ -14,9 +14,19 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    mlcr get,dataset,cnndm,validation -j
+    mlcr get,dataset,cnndm,_validation -j
     ```
 
+=== "Calibration"
+    GPT-J calibration dataset is extracted from the CNNDM dataset.
+
+    ### Get Validation Dataset
+    ```
+    mlcr get,dataset,cnndm,_calibration -j
+    ```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_CNNDM_DATASET>` could be provided to download the dataset to a specific location.
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
@@ -28,3 +38,5 @@ Get the Official MLPerf GPT-J Model
     ```
     mlcr get,ml-model,gptj,_pytorch -j
     ```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_GPTJ_MODEL>` could be provided to download the model to a specific location.
\ No newline at end of file
diff --git a/docs/benchmarks/language/get-llama2-70b-data.md b/docs/benchmarks/language/get-llama2-70b-data.md
index 2a31370574..ce7cd996eb 100644
--- a/docs/benchmarks/language/get-llama2-70b-data.md
+++ b/docs/benchmarks/language/get-llama2-70b-data.md
@@ -9,27 +9,63 @@ hide:
 
 The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
 
-=== "Validation"
-    LLAMA2-70b validation run uses the Open ORCA dataset.
+=== "Preprocessed Dataset"
 
-    ### Get Validation Dataset
-    ```
-    mlcr get,dataset,openorca,validation -j
-    ```
+    === "Validation"
+        LLAMA2-70b validation run uses the Open ORCA dataset.
+    
+        ### Get Preprocessed Validation Dataset
+        ```
+        mlcr get,dataset,preprocessed,openorca,_validation -j
+        ```
+
+    === "Calibration"
+
+        ### Get Preprocessed Calibration dataset
+        ```
+        mlcr get,dataset,preprocessed,openorca,_calibration -j
+        ```
+
+=== "Unprocessed Dataset"
+
+    === "Validation"
+        LLAMA2-70b validation run uses the Open ORCA dataset.
+
+        ### Get Unprocessed Validation Dataset
+        ```
+        mlcr get,dataset,openorca,_validation -j
+        ```
+
+    === "Calibration"
+
+        ### Get Unprocessed Validation Dataset
+        ```
+        mlcr get,dataset,openorca,_validation -j
+        ```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_OPENORCA_DATASET>` could be provided to download the dataset to a specific location.
 
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
-Get the Official MLPerf LLAMA2-70b Model
-
 === "Pytorch"
 
-    ### Pytorch
-    ```
-    mlcr get,ml-model,llama2-70b,_pytorch -j --outdirname=<My download path>
-    ```
-  
-!!! tip
+    === "From MLCOMMONS Google Drive"
+
+        > **Note:**  One has to accept the [MLCommons Llama 2 License Confidentiality Notice](https://llama2.mlcommons.org/) to access the model files in MLCOMMONS Google Drive. 
+
+        ### Get the Official MLPerf LLAMA2-70B model from MLCOMMONS Google Drive
+        ```
+        mlcr get,ml-model,llama2-70b,_pytorch -j
+        ```
+
+    === "From Hugging Face repo"
+
+        > **Note:** Access to the HuggingFace model could be requested [here](https://ai.meta.com/resources/models-and-libraries/llama-downloads/).
 
-    [Access Request Link](https://llama2.mlcommons.org/) for MLCommons members
+        ### Get model from HuggingFace repo
+        ```
+        mlcr get,ml-model,llama2-70b,_hf --hf_token=<huggingface access token> -j
+        ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_LLAMA2_70B_MODEL>` could be provided to download the model to a specific location.
\ No newline at end of file
diff --git a/docs/benchmarks/language/get-llama3_1-405b-data.md b/docs/benchmarks/language/get-llama3_1-405b-data.md
index 62b7bd088a..ad05ca8610 100644
--- a/docs/benchmarks/language/get-llama3_1-405b-data.md
+++ b/docs/benchmarks/language/get-llama3_1-405b-data.md
@@ -23,18 +23,29 @@ The benchmark implementation run command will automatically download the validat
     mlcr get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_LLAMA3_405B_DATASET>` could be provided to download the dataset to a specific location.
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
-Get the Official MLPerf LLAMA3.1-405b Model
-
 === "Pytorch"
 
-    ### Pytorch
-    ```
-    mlcr get,ml-model,llama3 --outdirname=<path to download>  -j
-    ```
-  
-!!! tip
+    === "From MLCOMMONS Google Drive"
+
+        > **Note:**  One has to accept the [MLCommons Llama 3.1 License Confidentiality Notice](http://llama3-1.mlcommons.org/) to access the model files in MLCOMMONS Google Drive. 
+
+        ### Get the Official MLPerf LLAMA3.1-405B model from MLCOMMONS Google Drive
+        ```
+        mlcr get,ml-model,llama3 -j
+        ```
+
+    === "From Hugging Face repo"
+
+        > **Note:** Access to the HuggingFace model could be requested [here](https://ai.meta.com/resources/models-and-libraries/llama-downloads/).
+
+        ### Get model from HuggingFace repo
+        ```
+        mlcr get,ml-model,llama3,_hf --hf_token=<huggingface access token> -j
+        ```
 
-    [Access Request Link](https://llama3-1.mlcommons.org/) for MLCommons members
+- `--outdirname=<PATH_TO_DOWNLOAD_LLAMA3_405B_MODEL>` could be provided to download the model to a specific location.
\ No newline at end of file
diff --git a/docs/benchmarks/language/get-mixtral-8x7b-data.md b/docs/benchmarks/language/get-mixtral-8x7b-data.md
index 81b90cdb57..cf5225843d 100644
--- a/docs/benchmarks/language/get-mixtral-8x7b-data.md
+++ b/docs/benchmarks/language/get-mixtral-8x7b-data.md
@@ -15,6 +15,15 @@ The benchmark implementation run command will automatically download the preproc
     mlcr get,dataset-mixtral,openorca-mbxp-gsm8k-combined -j
     ```
 
+=== "Calibration"
+    
+    ### Get Calibration Dataset
+    ```
+    mlcr get,dataset-mixtral,openorca-mbxp-gsm8k-combined,_calibration -j
+    ```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_MIXTRAL_DATASET>` could be provided to download the dataset to a specific location.
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
@@ -25,4 +34,6 @@ Get the Official MLPerf MIXTRAL-8x7b Model
     ### Pytorch
     ```
     mlcr get,ml-model,mixtral -j
-    ```
\ No newline at end of file
+    ```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_MIXTRAL_MODEL>` could be provided to download the model to a specific location.
\ No newline at end of file
diff --git a/docs/benchmarks/medical_imaging/get-3d-unet-data.md b/docs/benchmarks/medical_imaging/get-3d-unet-data.md
index d68b769209..9c77fdeaa2 100644
--- a/docs/benchmarks/medical_imaging/get-3d-unet-data.md
+++ b/docs/benchmarks/medical_imaging/get-3d-unet-data.md
@@ -9,19 +9,34 @@ hide:
 
 The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
 
-=== "Validation"
-    3d-unet validation run uses the KiTS19 dataset performing [KiTS 2019](https://kits19.grand-challenge.org/) kidney tumor segmentation task
 
-    ### Get Validation Dataset(Original)
-    ```
-    mlcr get,dataset,kits19,_validation -j
-    ```
+=== "Unprocessed Dataset"
+
+    === "Validation"
+        3d-unet validation run uses the KiTS19 dataset performing [KiTS 2019](https://kits19.grand-challenge.org/) kidney tumor segmentation task
+
+        ### Get Validation Dataset
+        ```
+        mlcr get,dataset,kits19,_validation -j
+        ```
+
+    === "Calibration"
 
-    ### Get Validation Dataset(Preprocessed)
+        ### Get Calibration Dataset
+        ```
+        mlcr get,dataset,kits19,_calibration -j
+        ```
+
+=== "Preprocessed Dataset"
+
+    ### Get Preprocessed Validation Dataset
     ```
     mlcr get,dataset,kits19,preprocessed -j
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_KITS19_DATASET>` could be provided to download the dataset to a specific location.
+
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
@@ -46,3 +61,4 @@ Get the Official MLPerf 3d-unet Model
     mlcr get,ml-model,3d-unet,_tensorflow -j
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_3DUNET_MODEL>` could be provided to download the model to a specific location.
diff --git a/docs/benchmarks/object_detection/get-retinanet-data.md b/docs/benchmarks/object_detection/get-retinanet-data.md
index 6cd677b4e1..6127eed541 100644
--- a/docs/benchmarks/object_detection/get-retinanet-data.md
+++ b/docs/benchmarks/object_detection/get-retinanet-data.md
@@ -9,20 +9,34 @@ hide:
 
 The benchmark implementation run command will automatically download the validation and calibration datasets and do the necessary preprocessing. In case you want to download only the datasets, you can use the below commands.
 
-=== "Validation"
-    Retinanet validation run uses the OpenImages v6 MLPerf validation dataset resized to 800x800 and consisting of 24,576 images.
+=== "Unprocessed"
 
-    ### Get Validation Dataset
-    ```
-    mlcr get,dataset,openimages,_validation -j
-    ```
-=== "Calibration"
-    Retinanet calibration dataset consist of 500 images selected from the OpenImages v6 dataset.
+    === "Validation"
+        Retinanet validation run uses the OpenImages v6 MLPerf validation dataset resized to 800x800 and consisting of 24,576 images.
+
+        ### Get Validation Dataset
+        ```
+        mlcr get,dataset,openimages,_validation -j
+        ```
+
+    === "Calibration"
+        Retinanet calibration dataset consist of 500 images selected from the OpenImages v6 dataset.
 
+        ### Get OpenImages Calibration dataset
+        ```
+        mlcr get,dataset,openimages,_calibration -j
+        ```
+
+=== "Preprocessed"
+
+    ### Get Preprocessed OpenImages dataset
     ```
-    mlcr get,dataset,openimages,_calibration -j
+    get,dataset,object-detection,open-images,openimages,preprocessed,_validation -j 
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_OPENIMAGES_DATASET>` could be provided to download the dataset to a specific location.
+
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
@@ -41,3 +55,4 @@ Get the Official MLPerf Retinanet Model
     mlcr get,ml-model,retinanet,_onnx -j
     ```
 
+- `--outdirname=<PATH_TO_DOWNLOAD_RETINANET_MODEL>` could be provided to download the model to a specific location.
diff --git a/docs/benchmarks/recommendation/get-dlrm-v2-data.md b/docs/benchmarks/recommendation/get-dlrm-v2-data.md
index bb35660b68..8505b31bf4 100644
--- a/docs/benchmarks/recommendation/get-dlrm-v2-data.md
+++ b/docs/benchmarks/recommendation/get-dlrm-v2-data.md
@@ -16,6 +16,9 @@ The benchmark implementation run command will automatically download the validat
     ```
     mlcr get,dataset,criteo,_validation -j
     ```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_CRITEO_DATASET>` could be provided to download the dataset to a specific location.
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
@@ -25,6 +28,8 @@ Get the Official MLPerf DLRM v2 Model
 
     ### Pytorch
     ```
-    mlcr get,ml-model,dlrm,_pytorch -j
+    mlcr get,ml-model,dlrm,_pytorch,_weight_sharded,_rclone -j
     ```
 
+
+- `--outdirname=<PATH_TO_DOWNLOAD_DLRM_V2_MODEL>` could be provided to download the model to a specific location.
\ No newline at end of file
diff --git a/docs/benchmarks/text_to_image/get-sdxl-data.md b/docs/benchmarks/text_to_image/get-sdxl-data.md
index 6d79e331d1..7c5363415c 100644
--- a/docs/benchmarks/text_to_image/get-sdxl-data.md
+++ b/docs/benchmarks/text_to_image/get-sdxl-data.md
@@ -17,15 +17,30 @@ The benchmark implementation run command will automatically download the validat
     mlcr get,dataset,coco2014,_validation -j
     ```
 
+=== "Calibration"
+
+    ### Get COCO2014 Calibration Dataset
+    ```
+    mlcr get,dataset,coco2014,_calibration -j
+    ```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_COCO2014_DATASET>` could be provided to download the dataset to a specific location.
+
 ## Model
 The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
 
 Get the Official MLPerf Stable Diffusion Model
 
 === "Pytorch"
-
-    ### Pytorch
-    ```
-    mlcr get,ml-model,sdxl,_pytorch -j
-    ```
-
+    === "FP 16"
+        ### Pytorch
+        ```
+        mlcr get,ml-model,sdxl,_pytorch,_fp16 -j
+        ```
+    === "FP 32"
+        ### Pytorch
+        ```
+        mlcr get,ml-model,sdxl,_pytorch,_fp32 -j
+        ```
+
+- `--outdirname=<PATH_TO_DOWNLOAD_SDXL_MODEL>` could be provided to download the model to a specific location.
diff --git a/docs/submission/index.md b/docs/submission/index.md
index da30c18350..079a513854 100644
--- a/docs/submission/index.md
+++ b/docs/submission/index.md
@@ -56,9 +56,15 @@ Please refer to the [installation page](site:inference/install/) to install MLCF
 === "MLC automation based results"
     If you have followed the `mlcr` commands under the individual model pages in the [benchmarks](../index.md) directory, all the valid results will get aggregated to the `mlc cache` folder. The following command could be used to browse the structure of inference results folder generated by MLCFlow.
     ### Get results folder structure
-    ```bash
-    mlc find cache --tags=get,mlperf,inference,results,dir | xargs tree
-    ```
+
+    === "Unix Terminal"
+        ```bash
+        mlc find cache --tags=get,mlperf,inference,results,dir | xargs tree
+        ```
+    === "Windows PowerShell"
+        ```
+        mlc find cache --tags=get,mlperf,inference,results,dir |  ForEach-Object { Get-ChildItem -Recurse $_ }
+        ```
 
 
 Once all the results across all the models are ready you can use the following the below section to generate a valid submission tree compliant with the [MLPerf requirements](https://github.com/mlcommons/policies/blob/master/submission_rules.adoc#inference-1).
@@ -184,14 +190,14 @@ Once you have all the results on the system, you can upload them to the MLCommon
 === "via CLI"
     You can do the following command which will run the submission checker and upload the results to the MLCommons submission server
     ```
-    mlcr run,submission,checker,inference \
+    mlcr run,mlperf,submission,checker,inference \
     --submitter_id=<> \
     --submission_dir=<Path to the submission folder>
     ```
 === "via Browser"
     You can do the following command to generate the final submission tar file and then upload to the [MLCommons Submission UI](https://submissions-ui.mlcommons.org/submission). 
     ```
-    mlcr run,submission,checker \
+    mlcr run,mlperf,submission,checker,inference \
     --submission_dir=<Path to the submission folder> \
     --tar=yes \
     --submission_tar_file=mysubmission.tar.gz
diff --git a/main.py b/main.py
index 2ccfab2958..0020d8c4ec 100755
--- a/main.py
+++ b/main.py
@@ -28,11 +28,13 @@ def mlperf_inference_implementation_readme(
         content = ""
 
         execution_envs = ["Docker", "Native"]
-        code_version = "r4.1-dev"
+        code_version = "r5.0-dev"
         implementation_run_options = []
 
         if model == "rnnt":
             code_version = "r4.0"
+        elif implementation == "intel":
+            code_version = "r4.1-dev"
 
         if implementation == "reference":
             # Tip
@@ -339,12 +341,7 @@ def mlperf_inference_implementation_readme(
                             and framework.lower() == "deepsparse"
                         ):
                             run_suffix += f"{cur_space3}You can use any model from [NeuralMagic sparse zoo](https://sparsezoo.neuralmagic.com/?modelSet=computer_vision&architectures=resnet_v1) (trained on Imagenet dataset) as --nm_model_zoo_stub"
-                        if (
-                            "bert" in model.lower()
-                            and framework.lower() == "deepsparse"
-                        ):
-                            run_suffix += "You can use any model from [NeuralMagic sparse zoo](https://sparsezoo.neuralmagic.com/?modelSet=computer_vision&architectures=resnet_v1) (trained on Imagenet dataset) as --nm_model_zoo_stub"
-                        if (
+                        elif (
                             "bert" in model.lower()
                             and framework.lower() == "deepsparse"
                         ):

From 2b465fcc080386a2d8d8cbf38c7852cc79309210 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 22 Apr 2025 11:59:22 -0500
Subject: [PATCH 289/305] [Post Mortem] Check all systems and measurements
 folders have results (#2166)

* Check all systems and measurements folders have results

* Add flag to skip check all systems contain results

* Update test-submission-checker.yml

---------

Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
---
 .github/workflows/test-submission-checker.yml |  2 +-
 tools/submission/submission_checker.py        | 40 +++++++++++++++++++
 2 files changed, 41 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index 7dd653ec8f..289f3e4f56 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -35,4 +35,4 @@ jobs:
         git clone https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
-        mlcr run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/mlperf_inference_unofficial_submissions_v5.0 --src_version=v5.0 --extra_args=" --skip-extra-files-in-root-check --skip-extra-accuracy-files-check" --quiet 
+        mlcr run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/mlperf_inference_unofficial_submissions_v5.0 --src_version=v5.0 --extra_args=" --skip-extra-files-in-root-check --skip-extra-accuracy-files-check --skip-all-systems-have-results-check" --quiet 
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 4df77bbed1..41116e2620 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -876,6 +876,7 @@ def __init__(
         extra_model_benchmark_map,
         ignore_uncommited=False,
         skip_power_check=False,
+        skip_all_systems_with_results=False,
     ):
         self.base = MODEL_CONFIG.get(version)
         self.extra_model_benchmark_map = extra_model_benchmark_map
@@ -894,6 +895,7 @@ def __init__(
         self.optional = None
         self.ignore_uncommited = ignore_uncommited
         self.skip_power_check = skip_power_check
+        self.skip_all_systems_with_results = skip_all_systems_with_results
 
     def set_type(self, submission_type):
         if submission_type == "datacenter":
@@ -1085,6 +1087,11 @@ def get_args():
         action="store_true",
         help="skips the check of extra accuracy files like the images folder of SDXL",
     )
+    parser.add_argument(
+        "--skip-all-systems-have-results-check",
+        action="store_true",
+        help="skips the check that all the systems in the systems and measurements folder should have results",
+    )
     parser.add_argument(
         "--scenarios-to-skip",
         help="Delimited list input of scenarios to skip. i.e. if you only have Offline results, pass in 'Server'",
@@ -2087,6 +2094,8 @@ def log_result(
             if filter_submitter and submitter != filter_submitter:
                 continue
             results_path = os.path.join(division, submitter, "results")
+            measurements_path = os.path.join(division, submitter, "measurements")
+            systems_path = os.path.join(division, submitter, "systems")
             if not os.path.exists(results_path):
                 continue
 
@@ -2190,6 +2199,36 @@ def log_result(
                     with open(model_mapping_path) as fp:
                         extra_model_mapping = json.load(fp)
 
+            if not config.skip_all_systems_with_results:
+                measurement_diff = list(set(list_dir(measurements_path)) - set(list_dir(results_path)))
+                systems_diff = list(
+                    set(
+                        [
+                            system_file.replace(".json", "")
+                            for system_file in list_files(systems_path)
+                            if system_file.endswith(".json")
+                        ]
+                    )
+                    - set(list_dir(results_path))
+                )
+                if len(measurement_diff) > 0:
+                    log.error(
+                        "%s/%s/measurements has the following directories with no results: %s",
+                        division,
+                        submitter,
+                        measurement_diff,
+                    )
+                    results[os.path.join(results_path)] = None
+
+                if len(systems_diff) > 0:
+                    log.error(
+                        "%s/%s/systems has the following files with no results: %s",
+                        division,
+                        submitter,
+                        [(s + ".json") for s in systems_diff],
+                    )
+                    results[os.path.join(results_path)] = None
+
             for system_desc in list_dir(results_path):
                 # we are looking at
                 # ./$division/$submitter/results/$system_desc, ie
@@ -3134,6 +3173,7 @@ def main():
         args.extra_model_benchmark_map,
         ignore_uncommited=args.submission_exceptions,
         skip_power_check=args.skip_power_check,
+        skip_all_systems_with_results = args.skip_all_systems_have_results_check
     )
 
     if args.scenarios_to_skip:

From 836f22c77d3a6c4d925e74ab98cc99c4fc9c1517 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 29 Apr 2025 12:01:32 -0500
Subject: [PATCH 290/305] [Post Mortem] Add calibration check to submission
 checker (#2185)

* Add calibration check to submission checker

* Update test-submission-checker.yml

---------

Co-authored-by: Arjun Suresh <arjun@gateoverflow.com>
---
 .github/workflows/test-submission-checker.yml |  2 +-
 tools/submission/submission_checker.py        | 24 ++++++++++++++++++-
 2 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test-submission-checker.yml b/.github/workflows/test-submission-checker.yml
index 289f3e4f56..65336944ed 100644
--- a/.github/workflows/test-submission-checker.yml
+++ b/.github/workflows/test-submission-checker.yml
@@ -35,4 +35,4 @@ jobs:
         git clone https://github.com/mlcommons/mlperf_inference_unofficial_submissions_v5.0 --depth 1
     - name: Test MLPerf inference submission checker
       run: |
-        mlcr run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/mlperf_inference_unofficial_submissions_v5.0 --src_version=v5.0 --extra_args=" --skip-extra-files-in-root-check --skip-extra-accuracy-files-check --skip-all-systems-have-results-check" --quiet 
+        mlcr run,mlperf,inference,submission,checker --adr.inference-src.tags=_branch.$PR_HEAD_REF,_repo.${{ github.event.pull_request.head.repo.html_url }} --adr.inference-src.version=custom --input=`pwd`/mlperf_inference_unofficial_submissions_v5.0 --src_version=v5.0 --extra_args=" --skip-extra-files-in-root-check --skip-extra-accuracy-files-check --skip-all-systems-have-results-check --skip-calibration-check" --quiet 
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 41116e2620..82e710edc9 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -877,6 +877,7 @@ def __init__(
         ignore_uncommited=False,
         skip_power_check=False,
         skip_all_systems_with_results=False,
+        skip_calibration_check=False
     ):
         self.base = MODEL_CONFIG.get(version)
         self.extra_model_benchmark_map = extra_model_benchmark_map
@@ -896,6 +897,7 @@ def __init__(
         self.ignore_uncommited = ignore_uncommited
         self.skip_power_check = skip_power_check
         self.skip_all_systems_with_results = skip_all_systems_with_results
+        self.skip_calibration_check = skip_calibration_check
 
     def set_type(self, submission_type):
         if submission_type == "datacenter":
@@ -1092,6 +1094,11 @@ def get_args():
         action="store_true",
         help="skips the check that all the systems in the systems and measurements folder should have results",
     )
+    parser.add_argument(
+        "--skip-calibration-check",
+        action="store_true",
+        help="skips the check that the calibration documentation should exist",
+    )
     parser.add_argument(
         "--scenarios-to-skip",
         help="Delimited list input of scenarios to skip. i.e. if you only have Offline results, pass in 'Server'",
@@ -2229,6 +2236,20 @@ def log_result(
                     )
                     results[os.path.join(results_path)] = None
 
+            #  Check for calibration documentation
+            if not config.skip_calibration_check and division not in ["open"]:
+                calibration_path_root = os.path.join(division, submitter, "calibration.md")
+                calibration_path_doc = os.path.join(division, submitter, "documentation", "calibration.md")
+                if not (os.path.exists(calibration_path_root)) and (not os.path.exists(calibration_path_doc)):
+                    log.error(
+                        "%s/%s: has not calibration file. One of %s or %s is required",
+                        division,
+                        submitter,
+                        calibration_path_root,
+                        calibration_path_doc
+                    )
+                    results[os.path.join(results_path)] = None
+
             for system_desc in list_dir(results_path):
                 # we are looking at
                 # ./$division/$submitter/results/$system_desc, ie
@@ -3173,7 +3194,8 @@ def main():
         args.extra_model_benchmark_map,
         ignore_uncommited=args.submission_exceptions,
         skip_power_check=args.skip_power_check,
-        skip_all_systems_with_results = args.skip_all_systems_have_results_check
+        skip_all_systems_with_results = args.skip_all_systems_have_results_check,
+        skip_calibration_check = args.skip_calibration_check
     )
 
     if args.scenarios_to_skip:

From 09715a30649246629a6cd91c4f26aa0d873d28da Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 20 May 2025 11:58:19 -0500
Subject: [PATCH 291/305] Add find peak performance documentation (#2186)

---
 loadgen/README.md | 53 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/loadgen/README.md b/loadgen/README.md
index 483768a65e..212c8a53c0 100644
--- a/loadgen/README.md
+++ b/loadgen/README.md
@@ -168,3 +168,56 @@ It is not specified here how the QDL would query and configure the SUT to execut
 ### Example
 
 Refer to [LON demo](demos/lon) for a reference example illustrating usage of Loadgen over the network.
+
+## Find Peak Performance Mode
+
+The Find Peak Performance mode can be used to find the optimal queries per second (QPS) for the server scenario.
+
+### Setup
+
+You can setup loadgen to run this mode by setting the `mode` variable in the `test_settings` used to run the test. Using the Python API:
+
+```python
+settings = mlperf_loadgen.TestSettings()
+settings.server_target_qps = 100
+settings.scenario = mlperf_loadgen.TestScenario.Server
+settings.mode = mlperf_loadgen.TestMode.FindPeakPerformance
+...
+
+mlperf_loadgen.StartTest(sut, qsl, settings)
+```
+
+Using the C/C++ API:
+```CPP
+mlperf::TestSettings settings;
+setting.server_target_qps = 100;
+settings.scenario = mlperf::TestScenario::Server;
+settings.mode = mlperf::TestMode::FindPeakPerformance;
+mlperf::LogSettings log_settings;
+/*
+Construct QSL and SUT
+*/
+mlperf::StartTest(&sut, &qsl, settings, log_settings);
+```
+
+**Note:** Make sure you are setting the TestScenario to server and you are providing an initial target QPS.
+
+### Description
+
+The Find Peak Performance mode works by finding a lower and upper boundary for the optimal QPS. Then performing a binary search between the lower and upper bound to find the optimal QPS.
+
+#### Finding lower and upper boundary
+
+LoadGen begins by running performance mode at the specified target QPS. If the test passes, this value is used as the lower bound; otherwise, an error is raised. The algorithm then guesses the upper bound as twice the target QPS. 
+
+Then LoadGen will run performance mode using the upper bound guess. If the test is successful, both the lower bound and upper bound will be doubled. This repeats until the upper bound guess fails the test.
+
+```
+[initial_target_qps, 2*initial_target_qps] -> [2*initial_target_qps, 4*initial_target_qps] -> [4*initial_target_qps, 8*initial_target_qps]...
+```
+
+Finally, the final lower bound and upper bound are set to their current values. This process assures that the lower bound passes the performance mode, but the upper bound doesn’t.
+
+#### Binary Search
+
+Once the lower and upper bounds are set, binary search can be performed over the range `[lower, upper]`` to find the optimal QPS. If a given QPS fails in performance mode, the optimal value lies below it; if it passes, the optimal is higher.

From 654b6ec3542c3002082091af3faefd8f5cb6cd13 Mon Sep 17 00:00:00 2001
From: mrmhodak <mrmhodak@users.noreply.github.com>
Date: Tue, 20 May 2025 16:58:33 +0000
Subject: [PATCH 292/305] Increment version to 5.0.17

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index db5a3c5c71..d9150afa6e 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.16
+5.0.17

From cc7d6d8eabc5cc60f8b2e540f25f1edd2a9f6cd3 Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 20 May 2025 14:47:10 -0500
Subject: [PATCH 293/305] Check equal issue for open division + check accuracy
 run covers all the dataset (#2170)

Co-authored-by: Miro <mirhodak@amd.com>
---
 tools/submission/submission_checker.py | 60 ++++++++++++++++++++++++--
 1 file changed, 56 insertions(+), 4 deletions(-)

diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 82e710edc9..74dbf89baa 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -188,6 +188,22 @@
             "stable-diffusion-xl": 5000,
             "mixtral-8x7b": 15000,
         },
+        "dataset-size": {
+            "resnet": 50000,
+            "retinanet": 24781,
+            "bert-99": 10833,
+            "bert-99.9": 10833,
+            "dlrm-v2-99": 204800,
+            "dlrm-v2-99.9": 204800,
+            "3d-unet-99": 43,
+            "3d-unet-99.9": 43,
+            "gptj-99": 13368,
+            "gptj-99.9": 13368,
+            "llama2-70b-99": 24576,
+            "llama2-70b-99.9": 24576,
+            "stable-diffusion-xl": 5000,
+            "mixtral-8x7b": 15000,
+        },
         # TODO: Update this list.
         "model_mapping": {
             # map model names to the official mlperf model class
@@ -466,6 +482,27 @@
             "rgat": 788379,
             "pointpainting": 1024,
         },
+        "dataset-size" : {
+            "resnet": 50000,
+            "retinanet": 24781,
+            "bert-99": 10833,
+            "bert-99.9": 10833,
+            "dlrm-v2-99": 204800,
+            "dlrm-v2-99.9": 204800,
+            "3d-unet-99": 43,
+            "3d-unet-99.9": 43,
+            "gptj-99": 13368,
+            "gptj-99.9": 13368,
+            "llama2-70b-99": 24576,
+            "llama2-70b-99.9": 24576,
+            "llama2-70b-interactive-99": 24576,
+            "llama2-70b-interactive-99.9": 24576,
+            "stable-diffusion-xl": 5000,
+            "mixtral-8x7b": 15000,
+            "llama3.1-405b": 8313,
+            "rgat": 788379,
+            "pointpainting": 39987,
+        },
         # model_mapping.json is expected in the root directory of the
         # submission folder for open submissions and so the below dictionary is
         # not really needed
@@ -890,6 +927,7 @@ def __init__(
         self.accuracy_delta_perc = self.base["accuracy-delta-perc"]
         self.accuracy_upper_limit = self.base.get("accuracy-upper-limit", {})
         self.performance_sample_count = self.base["performance-sample-count"]
+        self.dataset_size = self.base["dataset-size"]
         self.latency_constraint = self.base.get("latency-constraint", {})
         self.min_queries = self.base.get("min-queries", {})
         self.required = None
@@ -985,6 +1023,12 @@ def get_min_query_count(self, model, scenario):
         if model not in self.min_queries:
             raise ValueError("model not known: " + model)
         return self.min_queries[model].get(scenario)
+    
+    def get_dataset_size(self, model):
+        model = self.get_mlperf_model(model)
+        if model not in self.dataset_size:
+            raise ValueError("model not known: " + model)
+        return self.dataset_size[model]
 
     def get_delta_perc(self, model, metric):
         if model in self.accuracy_delta_perc:
@@ -1004,10 +1048,9 @@ def has_new_logging_format(self):
     def uses_early_stopping(self, scenario):
         return scenario in ["Server", "SingleStream", "MultiStream"]
 
-    def requires_equal_issue(self, model, division):
+    def requires_equal_issue(self, model):
         return (
-            division in ["closed", "network"]
-            and model
+            model
             in [
                 "3d-unet-99",
                 "3d-unet-99.9",
@@ -1320,6 +1363,15 @@ def check_accuracy_dir(config, model, path, verbose):
             "%s has loadgen errors, number of errors: %s", path, mlperf_log.num_errors()
         )
 
+    # check the whole dataset was used in the accuracy run
+    mlperf_log = MLPerfLog(fname)
+    qsl_total_count = mlperf_log["qsl_reported_total_count"]
+    expected_qsl_total_count = config.get_dataset_size(model)
+    if qsl_total_count != expected_qsl_total_count:
+        log.error(
+            "%s accurcy run does not cover all dataset, accuracy samples: %s, dataset size: %s", path, qsl_total_count, expected_qsl_total_count
+        )
+
     return is_valid, result_acc
 
 
@@ -1437,7 +1489,7 @@ def check_performance_dir(
     equal_issue_used_check = (
         mlperf_log["effective_sample_concatenate_permutation"] == True
     )
-    if not config.requires_equal_issue(model, division):
+    if not config.requires_equal_issue(model):
         equal_issue_used_check = True
     if not equal_issue_used_check:
         log.error(

From 06b370752d9f229571600b716ad29378e1ab56fa Mon Sep 17 00:00:00 2001
From: Viraat Chandra <viraatc@nvidia.com>
Date: Fri, 6 Jun 2025 17:37:09 -0700
Subject: [PATCH 294/305] add deepseek-r1 (#2198)

---
 .gitmodules                                   |   6 +
 language/deepseek-r1/.gitignore               |  10 +
 language/deepseek-r1/README.md                | 191 ++++
 language/deepseek-r1/backends/__init__.py     |  14 +
 language/deepseek-r1/backends/base_backend.py | 127 +++
 .../deepseek-r1/backends/pytorch_backend.py   | 375 ++++++++
 .../deepseek-r1/backends/sglang_backend.py    | 668 ++++++++++++++
 language/deepseek-r1/backends/utils.py        | 381 ++++++++
 language/deepseek-r1/backends/vllm_backend.py | 308 +++++++
 .../deepseek-r1/docker/Dockerfile.pytorch     |  68 ++
 language/deepseek-r1/docker/Dockerfile.sglang |  71 ++
 language/deepseek-r1/docker/Dockerfile.vllm   |  74 ++
 .../docker/evaluation_requirements.txt        |  10 +
 .../docker/launch_scripts/launch_pytorch.sh   | 223 +++++
 .../docker/launch_scripts/launch_sglang.sh    | 223 +++++
 .../docker/launch_scripts/launch_vllm.sh      | 223 +++++
 .../docker/setup_scripts/common.sh            | 348 ++++++++
 .../docker/setup_scripts/setup_pytorch.sh     | 131 +++
 .../docker/setup_scripts/setup_sglang.sh      | 162 ++++
 .../docker/setup_scripts/setup_vllm.sh        | 105 +++
 language/deepseek-r1/eval_accuracy.py         | 830 ++++++++++++++++++
 language/deepseek-r1/launch_docker.sh         | 136 +++
 language/deepseek-r1/mlperf/__init__.py       |  29 +
 language/deepseek-r1/mlperf/base_sut.py       |  76 ++
 language/deepseek-r1/mlperf/offline_sut.py    | 310 +++++++
 language/deepseek-r1/mlperf/qsl.py            |  87 ++
 language/deepseek-r1/mlperf/server_sut.py     | 427 +++++++++
 language/deepseek-r1/mlperf/user.conf         |  12 +
 language/deepseek-r1/mlperf/utils.py          | 133 +++
 language/deepseek-r1/run_eval.py              | 248 ++++++
 language/deepseek-r1/run_eval_mpi.py          | 237 +++++
 language/deepseek-r1/run_mlperf.py            | 400 +++++++++
 language/deepseek-r1/run_mlperf_mpi.py        | 642 ++++++++++++++
 language/deepseek-r1/setup.sh                 | 278 ++++++
 language/deepseek-r1/submodules/LiveCodeBench |   1 +
 language/deepseek-r1/submodules/prm800k       |   1 +
 language/deepseek-r1/utils/__init__.py        | 104 +++
 .../deepseek-r1/utils/backend_registry.py     | 464 ++++++++++
 language/deepseek-r1/utils/data_utils.py      | 196 +++++
 language/deepseek-r1/utils/error_handling.py  |  48 +
 language/deepseek-r1/utils/runner_utils.py    | 106 +++
 language/deepseek-r1/utils/tokenization.py    | 143 +++
 language/deepseek-r1/utils/validation.py      | 145 +++
 43 files changed, 8771 insertions(+)
 create mode 100644 language/deepseek-r1/.gitignore
 create mode 100644 language/deepseek-r1/README.md
 create mode 100644 language/deepseek-r1/backends/__init__.py
 create mode 100644 language/deepseek-r1/backends/base_backend.py
 create mode 100644 language/deepseek-r1/backends/pytorch_backend.py
 create mode 100644 language/deepseek-r1/backends/sglang_backend.py
 create mode 100644 language/deepseek-r1/backends/utils.py
 create mode 100644 language/deepseek-r1/backends/vllm_backend.py
 create mode 100644 language/deepseek-r1/docker/Dockerfile.pytorch
 create mode 100644 language/deepseek-r1/docker/Dockerfile.sglang
 create mode 100644 language/deepseek-r1/docker/Dockerfile.vllm
 create mode 100644 language/deepseek-r1/docker/evaluation_requirements.txt
 create mode 100755 language/deepseek-r1/docker/launch_scripts/launch_pytorch.sh
 create mode 100755 language/deepseek-r1/docker/launch_scripts/launch_sglang.sh
 create mode 100755 language/deepseek-r1/docker/launch_scripts/launch_vllm.sh
 create mode 100755 language/deepseek-r1/docker/setup_scripts/common.sh
 create mode 100755 language/deepseek-r1/docker/setup_scripts/setup_pytorch.sh
 create mode 100755 language/deepseek-r1/docker/setup_scripts/setup_sglang.sh
 create mode 100755 language/deepseek-r1/docker/setup_scripts/setup_vllm.sh
 create mode 100644 language/deepseek-r1/eval_accuracy.py
 create mode 100755 language/deepseek-r1/launch_docker.sh
 create mode 100644 language/deepseek-r1/mlperf/__init__.py
 create mode 100644 language/deepseek-r1/mlperf/base_sut.py
 create mode 100644 language/deepseek-r1/mlperf/offline_sut.py
 create mode 100644 language/deepseek-r1/mlperf/qsl.py
 create mode 100644 language/deepseek-r1/mlperf/server_sut.py
 create mode 100644 language/deepseek-r1/mlperf/user.conf
 create mode 100644 language/deepseek-r1/mlperf/utils.py
 create mode 100755 language/deepseek-r1/run_eval.py
 create mode 100644 language/deepseek-r1/run_eval_mpi.py
 create mode 100755 language/deepseek-r1/run_mlperf.py
 create mode 100755 language/deepseek-r1/run_mlperf_mpi.py
 create mode 100755 language/deepseek-r1/setup.sh
 create mode 160000 language/deepseek-r1/submodules/LiveCodeBench
 create mode 160000 language/deepseek-r1/submodules/prm800k
 create mode 100644 language/deepseek-r1/utils/__init__.py
 create mode 100644 language/deepseek-r1/utils/backend_registry.py
 create mode 100644 language/deepseek-r1/utils/data_utils.py
 create mode 100644 language/deepseek-r1/utils/error_handling.py
 create mode 100644 language/deepseek-r1/utils/runner_utils.py
 create mode 100644 language/deepseek-r1/utils/tokenization.py
 create mode 100644 language/deepseek-r1/utils/validation.py

diff --git a/.gitmodules b/.gitmodules
index db27fd3ae5..7f0d5d302a 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -4,3 +4,9 @@
 [submodule "vision/medical_imaging/3d-unet-brats19/nnUnet"]
 	path = vision/medical_imaging/3d-unet-brats19/nnUnet
 	url = https://github.com/MIC-DKFZ/nnUNet.git
+[submodule "language/deepseek-r1/submodules/prm800k"]
+	path = language/deepseek-r1/submodules/prm800k
+	url = https://github.com/openai/prm800k
+[submodule "language/deepseek-r1/submodules/LiveCodeBench"]
+	path = language/deepseek-r1/submodules/LiveCodeBench
+	url = https://github.com/LiveCodeBench/LiveCodeBench
diff --git a/language/deepseek-r1/.gitignore b/language/deepseek-r1/.gitignore
new file mode 100644
index 0000000000..b4d055d200
--- /dev/null
+++ b/language/deepseek-r1/.gitignore
@@ -0,0 +1,10 @@
+*.pyc
+.nvimrc.lua
+__pycache__/
+.venv/
+build/
+data/
+mlperf_results/
+remove_dev_files.sh
+.cursor
+.venv_*
diff --git a/language/deepseek-r1/README.md b/language/deepseek-r1/README.md
new file mode 100644
index 0000000000..7c2722c7a6
--- /dev/null
+++ b/language/deepseek-r1/README.md
@@ -0,0 +1,191 @@
+# Mlperf Inference DeepSeek Reference Implementation
+
+## Model & Dataset Download
+
+> **Model**: [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) (revision: `56d4cbbb4d29f4355bab4b9a39ccb717a14ad5ad`)
+
+- DeepSeek-R1 model is automatically downloaded as part of setup
+- Checkpoint conversion is done transparently when needed.
+
+## Dataset Download
+
+### Preprocessed
+
+You can use Rclone to download the preprocessed dataset from a Cloudflare R2 bucket.
+
+To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
+To install Rclone on Linux/macOS/BSD systems, run:
+```
+sudo -v ; curl https://rclone.org/install.sh | sudo bash
+```
+Once Rclone is installed, run the following command to authenticate with the bucket:
+```
+rclone config create mlc-inference s3 provider=Cloudflare access_key_id=f65ba5eef400db161ea49967de89f47b secret_access_key=fbea333914c292b854f14d3fe232bad6c5407bf0ab1bebf78833c2b359bdfd2b endpoint=https://c2686074cb2caf5cbaf6d134bdba8b47.r2.cloudflarestorage.com
+```
+You can then navigate in the terminal to your desired download directory and run the following command to download the dataset:
+
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/deepseek_r1/mlperf_deepseek_r1_dataset_4388_fp8_eval.pkl ./ -P
+```
+
+### Calibration
+
+Download and install Rclone as described in the previous section.
+
+Then navigate in the terminal to your desired download directory and run the following command to download the dataset:
+
+```
+rclone copy mlc-inference:mlcommons-inference-wg-public/deepseek_r1/mlperf_deepseek_r1_calibration_dataset_500_fp8_eval.pkl ./ -P
+```
+
+## Docker
+
+The MLPerf DeepSeek reference implementation includes a comprehensive Docker launch system that supports multiple backends and provides advanced features like user management, persistent storage, and flexible configuration.
+
+### Launch Backend Specific Container
+
+Launch a Docker container with your preferred backend:
+
+```bash
+# Launch PyTorch backend
+./launch_docker.sh --backend pytorch
+
+# Launch vLLM backend
+./launch_docker.sh --backend vllm
+
+# Launch SGLang backend
+./launch_docker.sh --backend sglang
+
+# See launch_docker.sh for full list of args
+./launch_docker.sh --backend vllm --gpu-count 2 --extra-mounts "/data:/data,/models:/models" --local-user 0
+```
+
+### Available Backends
+
+- **pytorch**: via [DeepSeek-Ai/DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) (reference implementation by DeepSeek-Ai)
+- **vllm**: vLLM's LLM api-based inference
+- **sglang**: sglang's OpenAI endpoint-based inference
+
+## Backend-Specific Setup
+
+After launching any Docker container, run the setup script which automatically detects your backend:
+
+```bash
+# Automatic backend detection and setup
+setup.sh
+```
+
+The setup script creates a virtual environment and configures it differently based on the backend:
+
+#### All Backends
+- Virtual environment is **activated** after `setup.sh`
+- Activate backend-specific venv using `source .venv_[pytorch|vllm|sglang]/bin/activate`
+- All commands are to be run using the virtual environment
+
+## Running Evaluations
+
+### PyTorch Backend (Distributed)
+
+> ⚠️ **IMPORTANT NOTE**: The PyTorch reference implementation takes approximately 8 days to run on an H200x8 system. This is because large max-OSL (32K) limits concurrency (max-BS=16), and unoptimized pytorch forward and decode logics.
+
+PyTorch backend uses distributed execution with `torchrun` and `run_eval_mpi.py`:
+
+```bash
+# Regular inference evaluation
+(.venv_pytorch) $ torchrun --nproc_per_node=8 run_eval_mpi.py --input-file <input_dataset>.pkl --output-file pytorch_output.pkl --num-samples 32
+
+# MLPerf performance benchmarks
+(.venv_pytorch) $ torchrun --nproc_per_node=8 run_mlperf_mpi.py --mode offline --input-file <input_dataset>.pkl --output-dir mlperf_results
+
+# MLPerf accuracy mode
+(.venv_pytorch) $ torchrun --nproc_per_node=8 run_mlperf_mpi.py --mode offline --accuracy --input-file <input_dataset>.pkl --output-dir mlperf_results
+```
+
+### vLLM and SGLang Backends
+
+For vLLM and SGLang, use single-process execution in `run_eval.py`:
+
+```bash
+# Regular inference evaluation
+(.venv_vllm) $ python run_eval.py --input-file <input_dataset>.pkl
+(.venv_sglang) $ python run_eval.py --input-file <input_dataset>.pkl
+
+# MLPerf performance benchmarks
+(.venv_vllm) $ python run_mlperf.py --mode offline --input-file <input_dataset>.pkl --output-dir mlperf_results
+(.venv_sglang) $ python run_mlperf.py --mode server --input-file <input_dataset>.pkl --output-dir mlperf_results
+```
+
+## MLPerf Inference Support
+
+The reference implementation includes full support for MLPerf inference benchmarks through a System Under Test (SUT) wrapper that integrates with MLPerf LoadGen.
+
+### Running MLPerf Benchmarks
+
+#### Offline Scenario
+```bash
+(.venv_BACKEND) $ python run_mlperf.py \
+    --mode offline \
+    --input-file <input_dataset>.pkl \
+    --output-dir mlperf_results
+```
+
+#### Server Scenario
+```bash
+(.venv_BACKEND) $ python run_mlperf.py \
+    --mode server \
+    --input-file <input_dataset>.pkl \
+    --output-dir mlperf_results
+```
+
+#### Pytorch Backend for Mlperf
+
+PyTorch backend uses distributed execution with `torchrun` and `run_mlperf_mpi.py`:
+
+```bash
+# PyTorch MLPerf offline scenario
+(.venv_BACKEND) $ torchrun --nproc_per_node=8 run_mlperf_mpi.py \
+    --mode offline \
+    --input-file <input_dataset>.pkl \
+    --output-dir mlperf_results
+```
+
+### MLPerf Command Line Options
+
+| Option         | Description                    | Default          |
+| -------------- | ------------------------------ | ---------------- |
+| `--mode`       | Scenario mode (offline/server) | `offline`        |
+| `--accuracy`   | Run accuracy test              | `False`          |
+| `--output-dir` | Output directory for results   | `mlperf_results` |
+
+### Backend Support Matrix
+
+The following table shows which backends support different evaluation and MLPerf operations:
+
+| Backend     | `run_eval.py` | `run_mlperf.py --mode=offline` | `run_mlperf.py --mode=server` |
+| ----------- | ------------- | ------------------------------ | ----------------------------- |
+| pytorch-fp8 | x             | x                              |                               |
+| vllm-fp8    | x             | x                              |                               |
+| sglang-fp8  | x             | x                              | x                             |
+
+> **Note**: For PyTorch backend, use the `_mpi` versions with `torchrun`. For vLLM and SGLang backends, use the single-process versions without `_mpi`.
+
+## Accuracy Evaluation
+
+Accuracy evaluation is handled uniformly across all backends:
+
+```bash
+# within container, with virtualenv activated
+(.venv_BACKEND) $ python3 eval_accuracy.py --input-file <input_file>.pkl
+```
+
+### Reference Evals
+
+Pytorch reference scores:
+
+```bash
+Evaluation Results: {
+  "mean-accuracy": 81.67730173199635,
+  "mean-output-tok-len": 4043.449863263446,
+  "num-samples": 4388
+}
+```
diff --git a/language/deepseek-r1/backends/__init__.py b/language/deepseek-r1/backends/__init__.py
new file mode 100644
index 0000000000..61ad96a3f2
--- /dev/null
+++ b/language/deepseek-r1/backends/__init__.py
@@ -0,0 +1,14 @@
+"""
+Modular backend system for MLPerf DeepSeek reference implementation.
+
+Supports TensorRT-LLM, SGLang, vLLM, and PyTorch backends with shared API arguments
+but independent execution implementations.
+"""
+
+from .base_backend import BaseBackend
+
+# Note: Specific backend implementations are imported dynamically as needed
+# to avoid dependency issues when only using certain backends
+__all__ = [
+    'BaseBackend',
+]
\ No newline at end of file
diff --git a/language/deepseek-r1/backends/base_backend.py b/language/deepseek-r1/backends/base_backend.py
new file mode 100644
index 0000000000..8daa920aa5
--- /dev/null
+++ b/language/deepseek-r1/backends/base_backend.py
@@ -0,0 +1,127 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Union, AsyncIterator
+import asyncio
+from dataclasses import dataclass
+
+
+@dataclass
+class StreamingChunk:
+    """Standardized streaming response chunk."""
+    token: str
+    token_ids: List[int]
+    is_finished: bool
+    finish_reason: Optional[str] = None
+
+
+class BaseBackend(ABC):
+    """Abstract base class for all inference backends."""
+
+    def __init__(self):
+        """Initialize base backend attributes."""
+        self.is_initialized = False
+
+    @property
+    def backend_name(self) -> str:
+        """Get backend name from class name or registry.
+
+        This property provides a consistent way to get the backend name.
+        Subclasses don't need to set self.backend_name anymore.
+        """
+        # Extract backend name from class name (e.g., VLLMBackend -> vllm)
+        class_name = self.__class__.__name__
+        if class_name.endswith('Backend'):
+            name = class_name[:-7].lower()  # Remove 'Backend' suffix
+        else:
+            name = class_name.lower()
+
+        # Validate against registry
+        from utils.backend_registry import BACKEND_REGISTRY
+        if name not in BACKEND_REGISTRY:
+            # Fallback to class name if not in registry
+            return class_name.lower()
+        return name
+
+    @abstractmethod
+    def initialize(self) -> None:
+        """Initialize the backend (load model, tokenizer, etc.).
+
+        Subclasses should call super().initialize() at the end to set is_initialized=True,
+        or set it manually if they don't call super().
+        """
+        pass
+
+    @abstractmethod
+    def generate(self,
+                 tokenized_prompts: Optional[List[List[int]]] = None,
+                 text_prompts: Optional[List[str]] = None,
+                 **kwargs) -> List[Dict[str, Any]]:
+        """
+        Generate responses for a list of prompts synchronously.
+
+        Args:
+            tokenized_prompts: List of pre-tokenized prompts (token IDs) - used by most backends
+            text_prompts: List of text prompts - used by vLLM and SGLang backends
+
+        Returns:
+            List of dictionaries with standardized output format
+        """
+        pass
+
+    @abstractmethod
+    def generate_async(self,
+                       tokenized_prompts: Optional[List[List[int]]] = None,
+                       text_prompts: Optional[List[str]] = None,
+                       **kwargs) -> List[asyncio.Future]:
+        """
+        Generate responses for a list of prompts asynchronously.
+
+        This method returns immediately with a list of futures that will resolve
+        to the generation results.
+
+        Args:
+            tokenized_prompts: List of pre-tokenized prompts (token IDs) - used by most backends
+            text_prompts: List of text prompts - used by vLLM and SGLang backends
+
+        Returns:
+            List of futures that will resolve to dictionaries with standardized output format
+        """
+        pass
+
+    async def generate_stream(self,
+                              tokenized_prompts: Optional[List[List[int]]] = None,
+                              text_prompts: Optional[List[str]] = None,
+                              **kwargs) -> List[AsyncIterator[StreamingChunk]]:
+        """
+        Generate responses for a list of prompts with streaming.
+
+        Args:
+            tokenized_prompts: List of tokenized prompts
+            text_prompts: List of text prompts
+            **kwargs: Additional generation parameters
+
+        Returns:
+            List of async iterators, one per prompt, yielding StreamingChunk objects
+
+        Raises:
+            NotImplementedError: If backend doesn't support streaming
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not support streaming generation")
+
+    @abstractmethod
+    def shutdown(self) -> None:
+        """Clean up resources and shut down the backend.
+
+        Subclasses should call super().shutdown() at the end to set is_initialized=False,
+        or set it manually if they don't call super().
+        """
+        pass
+
+    def __enter__(self):
+        """Context manager entry."""
+        self.initialize()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit."""
+        self.shutdown()
diff --git a/language/deepseek-r1/backends/pytorch_backend.py b/language/deepseek-r1/backends/pytorch_backend.py
new file mode 100644
index 0000000000..c1e426185d
--- /dev/null
+++ b/language/deepseek-r1/backends/pytorch_backend.py
@@ -0,0 +1,375 @@
+import os
+import sys
+
+# Add ref_dsinfer to path BEFORE importing from ref_dsinfer
+ref_dsinfer_path = os.environ.get(
+    'REF_DSINFER_PATH', '/opt/ref_dsinfer/inference')
+sys.path.append(ref_dsinfer_path)
+
+from safetensors.torch import load_model
+from ref_dsinfer.inference.model import Transformer, ModelArgs
+import json
+import logging
+from typing import Any, Dict, List, Optional
+import asyncio
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+from transformers import AutoTokenizer
+
+from .base_backend import BaseBackend
+from .utils import get_cache_directory
+from utils.backend_registry import get_backend_config
+from utils.validation import require_initialized, BackendNotInitializedError
+
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchBackend(BaseBackend):
+    """PyTorch backend for DeepSeek model inference with MPI support."""
+
+    def __init__(self, config: Dict[str, Any] = None):
+        """
+        Initialize PyTorch backend for MPI/distributed inference.
+
+        Args:
+            config: Optional configuration dict (ignored, uses registry config)
+        """
+        super().__init__()  # Initialize base class
+        # Use config from registry
+        self.config = get_backend_config('pytorch')
+
+        # Get distributed setup from environment
+        self.world_size = int(os.getenv("WORLD_SIZE", "1"))
+        self.rank = int(os.getenv("RANK", "0"))
+        self.local_rank = int(os.getenv("LOCAL_RANK", "0"))
+
+        # Get cache directory and model paths
+        cache_base = get_cache_directory()
+        self.cache_dir = cache_base.parent / 'models'
+        model_dir_name = self.config['model_name'].replace('/', '_')
+        self.model_path = self.cache_dir / f"{model_dir_name}-Demo"
+        self.config_path = Path(
+            '/opt/ref_dsinfer/inference/configs/config_671B.json')
+
+        # Model components
+        self.model = None
+        self.tokenizer = None
+        self.model_args = None
+
+        # Convert dtype string to torch dtype
+        dtype_map = {
+            'bfloat16': torch.bfloat16,
+            'float16': torch.float16,
+            'float32': torch.float32,
+        }
+        self.dtype = dtype_map.get(self.config['dtype'], torch.bfloat16)
+
+        # Check if model exists
+        if not self.model_path.exists():
+            raise RuntimeError(f"Model not found at {self.model_path}")
+
+        # Check if checkpoint exists for this rank
+        checkpoint_file = self.model_path / \
+            f"model{self.rank}-mp{self.world_size}.safetensors"
+        if not checkpoint_file.exists():
+            raise RuntimeError(f"Checkpoint not found: {checkpoint_file}")
+
+    def initialize(self) -> None:
+        """Initialize the model and tokenizer."""
+        if self.rank == 0:
+            logger.info(
+                f"Initializing PyTorch backend with {self.world_size} ranks")
+
+        # Initialize distributed if needed and not already initialized
+        if self.world_size > 1 and not dist.is_initialized():
+            dist.init_process_group("nccl")
+
+        # Set device and dtype
+        torch.cuda.set_device(self.local_rank)
+        torch.set_default_dtype(self.dtype)
+        torch.set_num_threads(self.config['num_threads'])
+        torch.manual_seed(self.config['seed'])
+
+        # Load model configuration
+        if not self.config_path.exists():
+            raise RuntimeError(
+                f"Model configuration not found at {self.config_path}")
+
+        with open(self.config_path) as f:
+            model_args_data = json.load(f)
+
+        # Override with our config
+        model_args_data['max_batch_size'] = self.config['batch_size']
+        model_args_data['max_seq_len'] = self.config['max_seq_len']
+
+        self.model_args = ModelArgs(**model_args_data)
+
+        if self.rank == 0:
+            logger.info(f"Model config: batch_size={self.model_args.max_batch_size}, "
+                        f"seq_len={self.model_args.max_seq_len}")
+
+        # Initialize model
+        with torch.device(self.config['device']):
+            self.model = Transformer(self.model_args)
+
+        # Load tokenizer (only rank 0 needs it for MLPerf, but all ranks need it for run_eval_mpi)
+        self.tokenizer = AutoTokenizer.from_pretrained(str(self.model_path), revision=self.config['model_revision'])
+
+        # Load model weights
+        checkpoint_file = self.model_path / \
+            f"model{self.rank}-mp{self.world_size}.safetensors"
+        load_model(self.model, str(checkpoint_file))
+
+        if self.rank == 0:
+            logger.info("Model loaded successfully")
+
+        # Mark as initialized
+        self.is_initialized = True
+
+    def sample(self, logits: torch.Tensor, temperature: float) -> torch.Tensor:
+        """Sample from logits with temperature."""
+        logits = logits / max(temperature, 1e-5)
+        probs = torch.softmax(logits, dim=-1)
+        return probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
+
+    @torch.inference_mode()
+    def _generate_batch(
+        self,
+        prompt_tokens: List[List[int]],
+    ) -> List[List[int]]:
+        """Generate tokens for a batch of prompts."""
+        prompt_lens = [len(t) for t in prompt_tokens]
+        max_prompt_len = max(prompt_lens)
+
+        # Check prompt length
+        if max_prompt_len > self.model.max_seq_len:
+            raise ValueError(
+                f"Prompt length {max_prompt_len} exceeds model maximum "
+                f"sequence length {self.model.max_seq_len}"
+            )
+
+        # Calculate total length
+        total_len = min(self.model.max_seq_len,
+                        self.config['max_new_tokens'] + max_prompt_len)
+
+        # Initialize tokens tensor
+        tokens = torch.full(
+            (len(prompt_tokens), total_len),
+            -1,
+            dtype=torch.long,
+            device=self.config['device']
+        )
+
+        # Fill in prompt tokens
+        for i, t in enumerate(prompt_tokens):
+            tokens[i, :len(t)] = torch.tensor(
+                t, dtype=torch.long, device=self.config['device'])
+
+        # Generation loop
+        prev_pos = 0
+        finished = torch.tensor(
+            [False] * len(prompt_tokens), device=self.config['device'])
+        prompt_mask = tokens != -1
+        eos_id = self.tokenizer.eos_token_id if self.tokenizer.eos_token_id is not None else -1
+
+        for cur_pos in range(min(prompt_lens), total_len):
+            logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
+
+            if self.config['temperature'] > 0:
+                next_token = self.sample(logits, self.config['temperature'])
+            else:
+                next_token = logits.argmax(dim=-1)
+
+            # Only generate for positions after prompt
+            next_token = torch.where(
+                prompt_mask[:, cur_pos],
+                tokens[:, cur_pos],
+                next_token
+            )
+            tokens[:, cur_pos] = next_token
+
+            # Check for EOS
+            finished |= torch.logical_and(
+                ~prompt_mask[:, cur_pos],
+                next_token == eos_id
+            )
+
+            prev_pos = cur_pos
+
+            if finished.all():
+                break
+
+        # Extract completion tokens
+        completion_tokens = []
+        for i, toks in enumerate(tokens.tolist()):
+            toks = toks[prompt_lens[i]:prompt_lens[i] +
+                        self.config['max_new_tokens']]
+            if eos_id in toks and eos_id != -1:
+                toks = toks[:toks.index(eos_id)]
+
+            # Validate response is not empty
+            if not toks:
+                prompt_preview = prompt_tokens[i][:50] if len(
+                    prompt_tokens[i]) > 50 else prompt_tokens[i]
+                raise RuntimeError(
+                    f"Empty response generated from PyTorch backend for prompt tokens: {prompt_preview}...")
+
+            completion_tokens.append(toks)
+
+        return completion_tokens
+
+    @require_initialized
+    def generate(self, tokenized_prompts: List[List[int]], **kwargs) -> List[Dict[str, Any]]:
+        """
+        Generate responses for a list of pre-tokenized prompts.
+
+        This method should only be called by rank 0 in distributed mode.
+
+        Args:
+            tokenized_prompts: List of pre-tokenized prompts (token IDs)
+            **kwargs: Additional generation parameters (ignored)
+
+        Returns:
+            List of dictionaries with generation results
+        """
+        # In distributed mode, only rank 0 should call this
+        if self.world_size > 1 and self.rank != 0:
+            raise RuntimeError(
+                "generate() should only be called by rank 0 in distributed mode")
+
+        results = []
+
+        # Process in batches
+        batch_size = self.config['batch_size']
+        for i in range(0, len(tokenized_prompts), batch_size):
+            batch_tokens = tokenized_prompts[i:i + batch_size]
+
+            # For distributed mode, we need to broadcast to other ranks
+            if self.world_size > 1:
+                objects_to_broadcast = [batch_tokens]
+                dist.broadcast_object_list(objects_to_broadcast, src=0)
+
+            # Generate tokens
+            generated_tokens = self._generate_batch(batch_tokens)
+
+            # Create results
+            for tokens in generated_tokens:
+                result = {
+                    'tokens': tokens,
+                }
+                results.append(result)
+
+        return results
+
+    @require_initialized
+    def generate_batch_distributed(self, batch_tokens: List[List[int]]) -> List[List[int]]:
+        """
+        Generate tokens for a batch in distributed mode.
+
+        This method is called by all ranks and handles the distributed communication.
+        Used by run_eval_mpi.py where all ranks participate.
+
+        Args:
+            batch_tokens: Tokenized prompts for this batch (None for non-rank-0)
+
+        Returns:
+            Generated tokens (only meaningful for rank 0)
+        """
+        # In distributed mode, broadcast the batch from rank 0
+        if self.world_size > 1:
+            if self.rank == 0:
+                objects_to_broadcast = [batch_tokens]
+            else:
+                objects_to_broadcast = [None]
+            dist.broadcast_object_list(objects_to_broadcast, src=0)
+
+            if self.rank != 0:
+                batch_tokens = objects_to_broadcast[0]
+
+        # All ranks generate
+        if batch_tokens:
+            return self._generate_batch(batch_tokens)
+        else:
+            return []
+
+    @require_initialized
+    def generate_async(self, tokenized_prompts: List[List[int]], **kwargs) -> List[asyncio.Future]:
+        """
+        Generate responses asynchronously.
+
+        For PyTorch backend, this is just a wrapper around sync generation.
+        In distributed mode, only rank 0 should call this method.
+        """
+        if self.world_size > 1 and self.rank != 0:
+            raise RuntimeError(
+                "generate_async() should only be called by rank 0 in distributed mode")
+
+        loop = asyncio.get_event_loop()
+
+        # Run sync generation in executor
+        future = loop.run_in_executor(
+            None,
+            self.generate,
+            tokenized_prompts
+        )
+
+        # Create individual futures for each prompt
+        futures = []
+        num_prompts = len(tokenized_prompts)
+
+        async def extract_result(idx):
+            results = await future
+            return results[idx]
+
+        for i in range(num_prompts):
+            prompt_future = loop.create_task(extract_result(i))
+            futures.append(prompt_future)
+
+        return futures
+
+    @require_initialized
+    def generate_batch_distributed_async(self, batch_tokens: List[List[int]]) -> asyncio.Future:
+        """
+        Generate tokens for a batch in distributed mode asynchronously.
+
+        This method is called by all ranks and handles the distributed communication.
+        Used by run_mlperf_mpi.py where all ranks participate.
+
+        Args:
+            batch_tokens: Tokenized prompts for this batch (None for non-rank-0)
+
+        Returns:
+            Future containing generated tokens (only meaningful for rank 0)
+        """
+        loop = asyncio.get_event_loop()
+
+        # Run distributed generation in executor
+        future = loop.run_in_executor(
+            None,
+            self.generate_batch_distributed,
+            batch_tokens
+        )
+
+        return future
+
+    def shutdown(self) -> None:
+        """Clean up resources."""
+        if self.rank == 0:
+            logger.info("Shutting down PyTorch backend")
+
+        # Clear model from memory
+        self.model = None
+        self.tokenizer = None
+
+        # Clean up CUDA memory
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+        # Mark as not initialized
+        self.is_initialized = False
+
+        # Note: We don't destroy the process group here because it might be
+        # managed by the calling script (run_eval_mpi.py)
diff --git a/language/deepseek-r1/backends/sglang_backend.py b/language/deepseek-r1/backends/sglang_backend.py
new file mode 100644
index 0000000000..06cf074a96
--- /dev/null
+++ b/language/deepseek-r1/backends/sglang_backend.py
@@ -0,0 +1,668 @@
+"""
+SGLang backend for DeepSeek model inference.
+
+This backend starts an SGLang server as a subprocess and communicates with it
+using the OpenAI-compatible API.
+"""
+
+import asyncio
+import json
+import os
+import signal
+import subprocess
+import sys
+import threading
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, AsyncIterator
+
+import httpx
+import numpy as np
+import requests
+import torch
+from openai import AsyncOpenAI, OpenAI
+from tqdm import tqdm
+from transformers import AutoTokenizer
+
+from .base_backend import BaseBackend, StreamingChunk
+from .utils import (
+    find_free_port,
+    get_cache_directory,
+    set_all_seeds,
+    setup_huggingface_cache,
+    TerminalDisplay,
+    LogMonitor
+)
+from utils.backend_registry import get_backend_config, apply_backend_env_vars
+from utils.validation import require_initialized, validate_prompts_input
+
+
+class SGLangBackend(BaseBackend):
+    """SGLang backend with server management and OpenAI-compatible API."""
+
+    def __init__(self, config: Dict[str, Any] = None):
+        """Initialize SGLang backend with configuration from registry."""
+        super().__init__()
+        # Get configuration from registry
+        self.config = get_backend_config('sglang')
+        # Allow override with passed config
+        if config:
+            self.config.update(config)
+
+        # Dynamic port allocation to avoid conflicts
+        self.port = find_free_port(30000)
+        self.config['port'] = self.port
+
+        # Server process management
+        self.server_process = None
+        self.server_log_file = None
+
+        # Client objects
+        self.client = None
+        self.async_client = None
+
+        # Tokenizer for string conversion
+        self.tokenizer = None
+
+        # Log monitoring
+        self._log_monitor = None
+        
+        # Shared semaphore for async concurrency control
+        self._async_semaphore = None
+
+        # Configure logging to suppress httpx INFO logs (only show warnings/errors)
+        import logging
+        logging.getLogger("httpx").setLevel(logging.WARNING)
+        logging.getLogger("openai").setLevel(logging.WARNING)
+        logging.getLogger("httpcore").setLevel(logging.WARNING)
+
+        self._setup_environment()
+
+    def _setup_environment(self) -> None:
+        """Set up environment variables and cache directories."""
+        # Use the utility function to get cache directory
+        cache_base = get_cache_directory()
+
+        # Set up HuggingFace cache environment variables
+        setup_huggingface_cache()
+
+        # Apply backend-specific environment variables from registry
+        apply_backend_env_vars('sglang')
+
+        # Set seeds for reproducibility
+        seed = self.config['random_seed']
+        set_all_seeds(seed)
+
+    def _check_server_alive(self) -> None:
+        """Check if server process is still alive, raise RuntimeError if not."""
+        if self.server_process and self.server_process.poll() is not None:
+            exit_code = self.server_process.returncode
+            raise RuntimeError(
+                f"SGLang server process has died with exit code: {exit_code}. "
+                f"Check server logs at: {self.server_log_file}"
+            )
+
+    def _build_server_command(self) -> List[str]:
+        """Build the SGLang server startup command."""
+        cmd = [
+            sys.executable, '-m', 'sglang.launch_server',
+            '--model-path', self.config['model'],
+            '--revision', self.config['model_revision'],
+            '--port', str(self.port),
+            '--host', self.config['host'],
+            '--tp', str(self.config['tensor_parallel_size']),
+            '--context-length', str(self.config['context_length']),
+            '--mem-fraction-static', str(self.config['mem_fraction_static']),
+            '--random-seed', str(self.config['random_seed']),
+            '--trust-remote-code',
+            '--dtype', self.config['dtype'],
+            '--served-model-name', self.config['served_model_name'],
+        ]
+
+        # Add optimization flags
+        if self.config['enable_torch_compile']:
+            cmd.append('--enable-torch-compile')
+
+        if self.config['enable_flashinfer']:
+            cmd.append('--attention-backend')
+            cmd.append('flashinfer')
+
+        if self.config['enable_dp_attention']:
+            cmd.extend(['--enable-dp-attention', '--dp', str(self.config['dp'])])
+
+        # Add performance settings
+        cmd.extend([
+            '--cuda-graph-max-bs', str(self.config['cuda_graph_max_bs']),
+            '--max-running-requests', str(self.config['max_running_requests'])
+        ])
+
+        return cmd
+
+    def _wait_for_server_ready(self, timeout: int = None) -> bool:
+        """Wait for SGLang server to become available with real-time log monitoring."""
+        if timeout is None:
+            timeout = self.config['server_startup_timeout']
+
+        start_time = time.time()
+        health_url = f"http://localhost:{self.port}/health"
+        check_interval = self.config['health_check_interval']
+
+        print(f"\n[SGLANG] Starting server on port {self.port}...")
+        print(f"[SGLANG] Server startup timeout: {timeout//60} minutes")
+        print(f"[SGLANG] Health check URL: {health_url}")
+
+        # Start log monitoring if log file exists
+        if self.server_log_file:
+            self._log_monitor = LogMonitor(
+                log_file_path=self.server_log_file,
+                prefix="SGLANG",
+                max_lines=5,
+                display_interval=1.0
+            )
+            # Start monitor and wait for it to set up display area
+            self._log_monitor.start(wait_for_file=True, file_wait_timeout=30.0)
+
+        # Reserve a line for progress indicator
+        print()  # Empty line for progress
+
+        last_check_time = 0
+        last_progress_update = 0
+        progress_idx = 0
+
+        while time.time() - start_time < timeout:
+            elapsed = int(time.time() - start_time)
+
+            # Update progress indicator every 0.5 seconds
+            if time.time() - last_progress_update >= 0.5:
+                last_progress_update = time.time()
+                progress_idx = (progress_idx + 1) % len(TerminalDisplay.PROGRESS_CHARS)
+                minutes = elapsed // 60
+                seconds = elapsed % 60
+                # Use carriage return to stay on the same line
+                progress_msg = f"[SGLANG] {TerminalDisplay.PROGRESS_CHARS[progress_idx]} Waiting for server... ({minutes}m {seconds}s elapsed)"
+                # Pad with spaces to clear any previous longer text
+                print(f"\r{progress_msg:<80}", end='', flush=True)
+
+            # Run health checks every check_interval seconds
+            if elapsed >= last_check_time + check_interval:
+                last_check_time = elapsed
+
+                try:
+                    response = requests.get(health_url, timeout=5)
+                    if response.status_code == 200:
+                        # Health check passed, now try a warmup query
+                        print(f"\r{' '*80}\r", end='', flush=True)
+                        print(f"\n[SGLANG] Health check passed, running warmup query...")
+
+                        # Try to send a simple warmup query using OpenAI client
+                        try:
+                            base_url = f"http://localhost:{self.port}/v1"
+                            api_key = self.config['api_key'] or "dummy-key"
+
+                            # Create a temporary client for warmup
+                            warmup_client = OpenAI(
+                                base_url=base_url,
+                                api_key=api_key,
+                                timeout=httpx.Timeout(timeout=30.0),
+                                max_retries=3
+                            )
+
+                            # Send a simple warmup request
+                            warmup_response = warmup_client.chat.completions.create(
+                                model=self.config['served_model_name'],
+                                messages=[{"role": "user", "content": "Hello"}],
+                                temperature=0.0,
+                                max_tokens=10,
+                                seed=self.config['seed']
+                            )
+
+                            # Check if we got a valid response
+                            if warmup_response.choices[0].message.content:
+                                print(f"[SGLANG] ✓ Warmup query successful! Response: {warmup_response.choices[0].message.content[:50]}...")
+
+                                # Stop log monitoring
+                                if self._log_monitor:
+                                    self._log_monitor.stop()
+                                    self._log_monitor = None
+
+                                print(f"\n[SGLANG] " + "="*60)
+                                print(f"[SGLANG] ✓ SERVER READY! (startup took {elapsed}s)")
+                                print(f"[SGLANG] " + "="*60)
+                                return True
+                            else:
+                                print(f"[SGLANG] Warmup query returned empty response, retrying...")
+
+                        except Exception as warmup_error:
+                            print(f"[SGLANG] Warmup query failed: {warmup_error}, retrying...")
+                            # Continue waiting, the server might not be fully ready yet
+
+                except requests.exceptions.RequestException:
+                    pass
+
+            # Check if server process is still alive
+            if self.server_process and self.server_process.poll() is not None:
+                if self._log_monitor:
+                    self._log_monitor.stop()
+                    self._log_monitor = None
+                # Clear progress line
+                print(f"\r{' '*80}\r", end='', flush=True)
+                print(f"\n[SGLANG] ✗ Server process died with exit code: {self.server_process.returncode}")
+                if self.server_log_file:
+                    print(f"[SGLANG] Check server logs at: {self.server_log_file}")
+                return False
+
+            time.sleep(0.1)  # Check every 100ms for smoother progress
+
+        # Timeout reached
+        if self._log_monitor:
+            self._log_monitor.stop()
+            self._log_monitor = None
+        # Clear progress line
+        print(f"\r{' '*80}\r", end='', flush=True)
+        print(f"\n[SGLANG] ✗ Server failed to start within {timeout} seconds")
+        return False
+
+    def _start_server(self) -> None:
+        """Start the SGLang server as a subprocess."""
+        print(f"\n[SGLANG] Starting SGLang server for {self.config['model']}...")
+        print(f"[SGLANG] Configuration:")
+        print(f"[SGLANG]   - Port: {self.port}")
+        print(f"[SGLANG]   - Tensor Parallel: {self.config['tensor_parallel_size']}")
+        print(f"[SGLANG]   - Context Length: {self.config['context_length']:,} tokens")
+        print(f"[SGLANG]   - dtype: {self.config['dtype']}")
+
+        # Create log file for server output
+        log_dir = Path("/work/logs")
+        log_dir.mkdir(exist_ok=True)
+        self.server_log_file = log_dir / f"sglang_server_{self.port}_{int(time.time())}.log"
+
+        cmd = self._build_server_command()
+        print(f"\n[SGLANG] Command: {' '.join(cmd)}")
+        print(f"[SGLANG] Server logs: {self.server_log_file}")
+
+        # Start server process
+        with open(self.server_log_file, 'w') as log_file:
+            self.server_process = subprocess.Popen(
+                cmd,
+                stdout=log_file,
+                stderr=subprocess.STDOUT,
+                preexec_fn=os.setsid  # Create new process group for clean shutdown
+            )
+
+        # Wait for server to be ready (with log monitoring)
+        if not self._wait_for_server_ready():
+            self._stop_server()
+            raise RuntimeError("Failed to start SGLang server")
+
+    def _stop_server(self) -> None:
+        """Stop the SGLang server gracefully."""
+        # Stop log monitoring first
+        if self._log_monitor:
+            self._log_monitor.stop()
+            self._log_monitor = None
+
+        if self.server_process:
+            print("\n[SGLANG] Stopping SGLang server...")
+
+            try:
+                # Send SIGTERM to process group
+                os.killpg(os.getpgid(self.server_process.pid), signal.SIGTERM)
+
+                # Wait for graceful shutdown
+                try:
+                    self.server_process.wait(timeout=30)
+                    print("[SGLANG] Server stopped gracefully")
+                except subprocess.TimeoutExpired:
+                    # Force kill if not stopped
+                    print("[SGLANG] Server didn't stop gracefully, forcing...")
+                    os.killpg(os.getpgid(self.server_process.pid), signal.SIGKILL)
+                    self.server_process.wait()
+                    print("[SGLANG] Server force stopped")
+            except ProcessLookupError:
+                # Process already dead
+                pass
+
+            self.server_process = None
+
+    def initialize(self) -> None:
+        """Initialize the SGLang backend by starting server and setting up clients."""
+        if self.is_initialized:
+            return
+
+        try:
+            # Load tokenizer for string conversion
+            print(f"[SGLANG] Loading tokenizer: {self.config['tokenizer']}...")
+            self.tokenizer = AutoTokenizer.from_pretrained(self.config['tokenizer'], revision=self.config['model_revision'])
+
+            # Start SGLang server (with log monitoring)
+            self._start_server()
+
+            # Initialize OpenAI clients
+            base_url = f"http://localhost:{self.port}/v1"
+            api_key = self.config['api_key'] or "dummy-key"
+
+            print(f"[SGLANG] Creating OpenAI clients with base URL: {base_url}")
+
+            # Configure timeout settings
+            timeout_config = httpx.Timeout(
+                timeout=self.config['request_timeout'],
+                connect=30.0,
+                read=None,
+                write=None,
+                pool=None
+            )
+
+            print(f"[SGLANG] Timeout configuration: {timeout_config}")
+
+            self.client = OpenAI(
+                base_url=base_url,
+                api_key=api_key,
+                timeout=timeout_config,
+                max_retries=10  # Use 10 retries as requested
+            )
+
+            print(f"[SGLANG] Created synchronous OpenAI client")
+
+            self.async_client = AsyncOpenAI(
+                base_url=base_url,
+                api_key=api_key,
+                timeout=timeout_config,
+                max_retries=10  # Use 10 retries as requested
+            )
+
+            print(f"[SGLANG] Created asynchronous OpenAI client")
+            
+            # Create shared semaphore for async concurrency control
+            self._async_semaphore = asyncio.Semaphore(self.config['max_running_requests'])
+            print(f"[SGLANG] Created async semaphore with limit: {self.config['max_running_requests']}")
+
+            # Server readiness was already verified by health endpoint in _wait_for_server_ready()
+            # No need to check models endpoint
+
+            # Only set initialized to True if we have valid clients
+            self.is_initialized = True
+            print("[SGLANG] Backend initialized successfully!")
+
+        except Exception as e:
+            # Clean up on failure
+            print(f"[SGLANG] Initialization failed: {e}")
+
+            # Clear any partially initialized state
+            self.client = None
+            self.async_client = None
+            self.tokenizer = None
+            self._async_semaphore = None
+
+            # Stop server if it was started
+            self._stop_server()
+
+            # Ensure is_initialized is False
+            self.is_initialized = False
+
+            # Re-raise the exception
+            raise
+
+    @require_initialized
+    def generate(self, 
+                tokenized_prompts: Optional[List[List[int]]] = None,
+                text_prompts: Optional[List[str]] = None,
+                **kwargs) -> List[Dict[str, Any]]:
+        """Generate responses synchronously."""
+        # Check if server process is still alive
+        self._check_server_alive()
+
+        # Check if client is properly initialized
+        if self.client is None:
+            raise RuntimeError("SGLang client is not initialized. Server may have failed to start.")
+
+        # Validate prompts using centralized validation
+        validate_prompts_input(
+            backend_name='sglang',
+            tokenized_prompts=tokenized_prompts,
+            text_prompts=text_prompts,
+            input_type='text'
+        )
+
+        # SGLang prefers text prompts
+        if text_prompts is None:
+            # Convert tokenized prompts to strings
+            prompt_strings = [
+                self.tokenizer.decode(tokens, skip_special_tokens=False)
+                for tokens in tokenized_prompts
+            ]
+        else:
+            prompt_strings = text_prompts
+
+        results = []
+
+        # Process prompts with progress bar
+        for prompt in tqdm(prompt_strings, desc="SGLang sync inference", unit="prompt"):
+            try:
+                completion = self.client.chat.completions.create(
+                    model=self.config['served_model_name'],
+                    messages=[{"role": "user", "content": prompt}],
+                    temperature=self.config['temperature'],
+                    top_p=self.config['top_p'],
+                    max_tokens=self.config['max_tokens'],
+                    seed=self.config['seed'],
+                )
+
+                # Get generated text
+                generated_text = completion.choices[0].message.content
+
+                # Validate response is not empty
+                if not generated_text:
+                    raise RuntimeError(f"Empty response received from SGLang server for prompt: {prompt[:100]}...")
+
+                # Tokenize the output to get token IDs
+                tokens = self.tokenizer.encode(generated_text)
+
+                results.append({
+                    'tokens': tokens,
+                    'text': generated_text
+                })
+
+            except Exception as e:
+                print(f"\nError generating completion: {e}")
+                raise RuntimeError(f"SGLang backend failed to generate tokens for prompt: {prompt[:100]}...")
+
+        return results
+
+    async def _async_generate_single(self, prompt: str, idx: int, semaphore: asyncio.Semaphore) -> Tuple[int, Dict[str, Any]]:
+        """Generate a single response asynchronously with semaphore control."""
+        # Check if async client is properly initialized
+        if self.async_client is None:
+            raise RuntimeError(f"SGLang async client is not initialized for prompt {idx}")
+
+        async with semaphore:
+            try:
+                completion = await self.async_client.chat.completions.create(
+                    model=self.config['served_model_name'],
+                    messages=[{"role": "user", "content": prompt}],
+                    temperature=self.config['temperature'],
+                    top_p=self.config['top_p'],
+                    max_tokens=self.config['max_tokens'],
+                    seed=self.config['seed'],
+                )
+
+                # Get generated text
+                generated_text = completion.choices[0].message.content
+
+                # Validate response is not empty
+                if not generated_text:
+                    raise RuntimeError(f"Empty response received from SGLang server for prompt: {prompt[:100]}...")
+
+                # Tokenize the output to get token IDs
+                tokens = self.tokenizer.encode(generated_text)
+
+                return idx, {'tokens': tokens, 'text': generated_text}
+
+            except Exception as e:
+                print(f"\nError generating completion for prompt {idx}: {e}")
+                raise RuntimeError(f"SGLang backend failed to generate tokens for prompt {idx}: {e}")
+
+    @require_initialized
+    def generate_async(self, 
+                      tokenized_prompts: Optional[List[List[int]]] = None,
+                      text_prompts: Optional[List[str]] = None,
+                      **kwargs) -> List[asyncio.Future]:
+        """Generate responses asynchronously using shared semaphore."""
+        # Check if server process is still alive
+        self._check_server_alive()
+
+        # Check if client is properly initialized
+        if self.async_client is None:
+            raise RuntimeError("SGLang async client is not initialized. Server may have failed to start.")
+
+        # Validate prompts using centralized validation
+        validate_prompts_input(
+            backend_name='sglang',
+            tokenized_prompts=tokenized_prompts,
+            text_prompts=text_prompts,
+            input_type='text'
+        )
+
+        # SGLang prefers text prompts
+        if text_prompts is None:
+            # Convert tokenized prompts to strings
+            prompt_strings = [
+                self.tokenizer.decode(tokens, skip_special_tokens=False)
+                for tokens in tokenized_prompts
+            ]
+        else:
+            prompt_strings = text_prompts
+
+        # Get the current event loop or create one
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+
+        futures = []
+        for idx, prompt in enumerate(prompt_strings):
+            # Create a task for each prompt using the shared semaphore
+            task = asyncio.create_task(self._async_generate_single(prompt, idx, self._async_semaphore))
+            
+            # Create a future that will hold the result
+            future = asyncio.Future()
+            
+            # Setup callback to extract just the result (not the index)
+            def make_callback(future_obj, expected_idx):
+                def callback(task_obj):
+                    try:
+                        idx, result = task_obj.result()
+                        if idx != expected_idx:
+                            future_obj.set_exception(Exception(f"Index mismatch: expected {expected_idx}, got {idx}"))
+                        else:
+                            future_obj.set_result(result)
+                    except Exception as e:
+                        future_obj.set_exception(e)
+                return callback
+            
+            task.add_done_callback(make_callback(future, idx))
+            futures.append(future)
+
+        return futures
+
+    async def generate_stream(self, 
+                            tokenized_prompts: Optional[List[List[int]]] = None,
+                            text_prompts: Optional[List[str]] = None,
+                            **kwargs) -> List[AsyncIterator[StreamingChunk]]:
+        """Generate responses for a list of prompts with streaming."""
+        if not self.is_initialized:
+            raise RuntimeError("Backend not initialized. Call initialize() first.")
+            
+        # Check if server process is still alive
+        self._check_server_alive()
+        
+        # Check if async client is properly initialized
+        if self.async_client is None:
+            raise RuntimeError("SGLang async client is not initialized. Server may have failed to start.")
+        
+        # Validate prompts
+        validate_prompts_input(
+            backend_name='sglang',
+            tokenized_prompts=tokenized_prompts,
+            text_prompts=text_prompts,
+            input_type='text'
+        )
+        
+        # SGLang prefers text prompts
+        if text_prompts is None:
+            # Convert tokenized prompts to strings
+            prompt_strings = [
+                self.tokenizer.decode(tokens, skip_special_tokens=False)
+                for tokens in tokenized_prompts
+            ]
+        else:
+            prompt_strings = text_prompts
+            
+        async def stream_single_prompt(prompt: str) -> AsyncIterator[StreamingChunk]:
+            try:
+                stream = await self.async_client.chat.completions.create(
+                    model=self.config['served_model_name'],
+                    messages=[{"role": "user", "content": prompt}],
+                    temperature=self.config.get('temperature'),
+                    top_p=self.config.get('top_p'),
+                    max_tokens=self.config.get('max_tokens'),
+                    seed=self.config.get('seed'),
+                    stream=True
+                )
+                
+                async for chunk in stream:
+                    if not chunk.choices:
+                        continue
+                        
+                    delta = chunk.choices[0].delta
+                    finish_reason = chunk.choices[0].finish_reason
+                    
+                    if delta.content:
+                        yield StreamingChunk(
+                            token=delta.content,
+                            token_ids=[],  # SGLang doesn't provide token IDs in streaming
+                            is_finished=finish_reason is not None,
+                            finish_reason=finish_reason
+                        )
+                    elif finish_reason:
+                        # Final chunk with no content but finish reason
+                        yield StreamingChunk(
+                            token="",
+                            token_ids=[],
+                            is_finished=True,
+                            finish_reason=finish_reason
+                        )
+            except Exception as e:
+                print(f"[SGLANG] Streaming error for prompt: {e}")
+                raise
+        
+        return [stream_single_prompt(prompt) for prompt in prompt_strings]
+
+    def shutdown(self) -> None:
+        """Clean up resources and shut down the backend."""
+        print("[SGLANG] Shutting down SGLang backend...")
+
+        # Stop log monitoring
+        if self._log_monitor:
+            self._log_monitor.stop()
+            self._log_monitor = None
+
+        # Close clients
+        self.client = None
+        self.async_client = None
+        
+        # Clear async semaphore
+        self._async_semaphore = None
+
+        # Stop server
+        self._stop_server()
+
+        # Clear tokenizer
+        self.tokenizer = None
+
+        # Clear CUDA cache
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+        self.is_initialized = False
+        print("[SGLANG] Backend shutdown complete")
\ No newline at end of file
diff --git a/language/deepseek-r1/backends/utils.py b/language/deepseek-r1/backends/utils.py
new file mode 100644
index 0000000000..0e4c7732da
--- /dev/null
+++ b/language/deepseek-r1/backends/utils.py
@@ -0,0 +1,381 @@
+"""
+Utility functions for MLPerf backends.
+"""
+
+import os
+import random
+import socket
+import subprocess
+import threading
+import time
+from pathlib import Path
+from typing import Optional, Union
+
+import numpy as np
+import torch
+from transformers import set_seed
+
+
+def get_cache_directory() -> Path:
+    """
+    Get the cache directory at /raid/data/$USER/.cache
+    
+    Returns:
+        Path: The cache directory path
+    """
+    # Get the current user
+    user = os.environ.get('USER', os.environ.get('USERNAME', 'unknown'))
+    
+    # Use /raid/data/$USER/.cache
+    cache_dir = Path(f'/raid/data/{user}/.cache')
+    
+    # Create the cache directory if it doesn't exist
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    
+    return cache_dir
+
+
+def setup_huggingface_cache() -> Path:
+    """
+    Set up HuggingFace cache environment variables using the preferred cache directory.
+    
+    Returns:
+        Path: The cache directory being used
+    """
+    cache_dir = get_cache_directory()
+    
+    # Set HuggingFace cache environment variables
+    os.environ['HF_HOME'] = str(cache_dir)
+    os.environ['HF_HUB_CACHE'] = str(cache_dir)
+    os.environ['HUGGINGFACE_HUB_CACHE'] = str(cache_dir)
+    
+    return cache_dir
+
+
+def find_free_port(start_port: int = 30000, max_attempts: int = 100) -> int:
+    """
+    Find a free port starting from start_port.
+    
+    Args:
+        start_port: The port number to start searching from
+        max_attempts: Maximum number of ports to try
+        
+    Returns:
+        int: A free port number
+        
+    Raises:
+        RuntimeError: If no free port is found after max_attempts
+    """
+    for i in range(max_attempts):
+        port = start_port + i
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        try:
+            sock.bind(('', port))
+            sock.close()
+            return port
+        except OSError:
+            continue
+    raise RuntimeError(f"Could not find free port after {max_attempts} attempts starting from {start_port}")
+
+
+def set_all_seeds(seed: int = 42) -> None:
+    """
+    Set seeds for all random number generators for reproducibility.
+    
+    Args:
+        seed: The seed value to use
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    set_seed(seed)
+
+
+def validate_prompts(tokenized_prompts: Optional[list] = None, 
+                    text_prompts: Optional[list] = None,
+                    backend_type: str = "") -> None:
+    """
+    Validate that at least one type of prompts is provided.
+    
+    Args:
+        tokenized_prompts: List of tokenized prompts
+        text_prompts: List of text prompts
+        backend_type: Name of the backend for error messages
+        
+    Raises:
+        ValueError: If neither prompt type is provided
+    """
+    if tokenized_prompts is None and text_prompts is None:
+        raise ValueError(f"{backend_type + ' backend' if backend_type else 'Backend'} requires either text_prompts or tokenized_prompts")
+
+
+# Terminal display utilities
+class TerminalDisplay:
+    """ANSI escape codes and utilities for terminal display formatting."""
+    
+    # ANSI escape codes for cursor control
+    CLEAR_SCREEN = "\033[2J"
+    MOVE_CURSOR_UP = "\033[{}A"
+    CLEAR_LINE = "\033[K"
+    SAVE_CURSOR = "\033[s"
+    RESTORE_CURSOR = "\033[u"
+    
+    # Progress spinner characters
+    PROGRESS_CHARS = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏']
+    
+    @staticmethod
+    def clear_lines(num_lines: int) -> None:
+        """Clear the specified number of lines above the cursor."""
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(num_lines), end='', flush=True)
+        for _ in range(num_lines):
+            print(TerminalDisplay.CLEAR_LINE)
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(num_lines), end='', flush=True)
+    
+    @staticmethod
+    def save_cursor_position() -> None:
+        """Save the current cursor position."""
+        print(TerminalDisplay.SAVE_CURSOR, end='', flush=True)
+    
+    @staticmethod
+    def restore_cursor_position() -> None:
+        """Restore the previously saved cursor position."""
+        print(TerminalDisplay.RESTORE_CURSOR, end='', flush=True)
+    
+    @staticmethod
+    def clear_current_line() -> None:
+        """Clear the current line."""
+        print("\r" + " " * 80 + "\r", end='', flush=True)
+    
+    @staticmethod
+    def truncate_line(line: str, max_length: int = 110) -> str:
+        """Truncate a line to fit within the specified length."""
+        if len(line) <= max_length:
+            return line
+        return line[:max_length - 3] + "..." 
+
+
+class LogMonitor:
+    """Real-time log file monitor with terminal display."""
+    
+    def __init__(self, 
+                 log_file_path: Union[str, Path],
+                 prefix: str = "LOG",
+                 max_lines: int = 5,
+                 display_interval: float = 1.0,
+                 header_text: Optional[str] = None):
+        """
+        Initialize the log monitor.
+        
+        Args:
+            log_file_path: Path to the log file to monitor
+            prefix: Prefix for display lines (e.g., "[SGLANG]")
+            max_lines: Maximum number of log lines to display
+            display_interval: How often to refresh the display (seconds)
+            header_text: Optional custom header text
+        """
+        self.log_file_path = Path(log_file_path)
+        self.prefix = prefix
+        self.max_lines = max_lines
+        self.display_interval = display_interval
+        self.header_text = header_text or f"Server startup logs (last {max_lines} lines):"
+        
+        # Threading control
+        self._monitor_thread = None
+        self._stop_event = None
+        self._ready_event = None
+        
+        # Display dimensions
+        self.total_lines = max_lines + 3  # 2 header lines + 1 blank separator
+        
+    def start(self, wait_for_file: bool = True, file_wait_timeout: float = 30.0) -> bool:
+        """
+        Start the log monitor in a background thread.
+        
+        Args:
+            wait_for_file: Whether to wait for the log file to exist
+            file_wait_timeout: How long to wait for the file (seconds)
+            
+        Returns:
+            bool: True if monitor started successfully
+        """
+        if self._monitor_thread is not None:
+            return True  # Already running
+            
+        self._stop_event = threading.Event()
+        self._ready_event = threading.Event()
+        
+        self._monitor_thread = threading.Thread(
+            target=self._monitor_loop,
+            args=(wait_for_file, file_wait_timeout),
+            daemon=True
+        )
+        self._monitor_thread.start()
+        
+        # Wait for the monitor to set up its display area
+        return self._ready_event.wait(timeout=2.0)
+    
+    def stop(self) -> None:
+        """Stop the log monitor and clean up display."""
+        if self._stop_event and self._monitor_thread:
+            self._stop_event.set()
+            self._monitor_thread.join(timeout=2)
+            self._monitor_thread = None
+            self._stop_event = None
+            self._ready_event = None
+    
+    def _monitor_loop(self, wait_for_file: bool, file_wait_timeout: float) -> None:
+        """Main monitoring loop that runs in a separate thread."""
+        # Wait for log file if requested
+        if wait_for_file:
+            start_time = time.time()
+            while not self.log_file_path.exists():
+                if time.time() - start_time > file_wait_timeout:
+                    print(f"[{self.prefix}] Warning: Log file not found after {file_wait_timeout}s: {self.log_file_path}")
+                    self._ready_event.set()
+                    return
+                time.sleep(0.5)
+        elif not self.log_file_path.exists():
+            print(f"[{self.prefix}] Warning: Log file not found: {self.log_file_path}")
+            self._ready_event.set()
+            return
+            
+        print(f"\n[{self.prefix}] Monitoring logs: {self.log_file_path.name}")
+        print(f"[{self.prefix}] " + "="*60)
+        
+        # Initialize display area
+        self._setup_display_area()
+        
+        # Signal that we're ready
+        self._ready_event.set()
+        
+        # Buffer for log lines
+        line_buffer = []
+        last_display_time = 0
+        
+        try:
+            # Use tail -f to follow the log file
+            process = subprocess.Popen(
+                ["tail", "-f", str(self.log_file_path)],
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                text=True,
+                bufsize=1,
+                universal_newlines=True
+            )
+            
+            while not self._stop_event.is_set():
+                if process.poll() is not None:
+                    break
+                    
+                # Read available lines without blocking
+                line_added = False
+                try:
+                    import select
+                    while select.select([process.stdout], [], [], 0)[0]:
+                        line = process.stdout.readline()
+                        if line:
+                            line_buffer.append(line.rstrip())
+                            if len(line_buffer) > self.max_lines:
+                                line_buffer.pop(0)
+                            line_added = True
+                        else:
+                            break
+                except:
+                    # Fallback for systems without select
+                    line = process.stdout.readline()
+                    if line:
+                        line_buffer.append(line.rstrip())
+                        if len(line_buffer) > self.max_lines:
+                            line_buffer.pop(0)
+                        line_added = True
+                
+                # Update display if needed
+                current_time = time.time()
+                if line_added or (current_time - last_display_time >= self.display_interval):
+                    last_display_time = current_time
+                    self._update_display(line_buffer)
+                    
+                time.sleep(0.1)
+                
+            # Clean up
+            process.terminate()
+            try:
+                process.wait(timeout=2)
+            except subprocess.TimeoutExpired:
+                process.kill()
+                
+        except Exception as e:
+            print(f"\n[{self.prefix}] Log monitor error: {e}")
+        finally:
+            self._cleanup_display()
+    
+    def _setup_display_area(self) -> None:
+        """Reserve and initialize the display area."""
+        # Reserve space
+        for _ in range(self.total_lines):
+            print()
+            
+        # Move back up to start of reserved area
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(self.total_lines), end='', flush=True)
+        
+        # Print initial display
+        print(f"\r[{self.prefix}] {self.header_text}", end='')
+        print(TerminalDisplay.CLEAR_LINE, flush=True)
+        print(f"\r[{self.prefix}] " + "-"*60, end='')
+        print(TerminalDisplay.CLEAR_LINE, flush=True)
+        
+        # Print empty lines
+        for _ in range(self.max_lines):
+            print(f"\r[{self.prefix}] ", end='')
+            print(TerminalDisplay.CLEAR_LINE, flush=True)
+            
+        # Print separator
+        print(f"\r", end='')
+        print(TerminalDisplay.CLEAR_LINE, flush=True)
+    
+    def _update_display(self, line_buffer: list) -> None:
+        """Update the display with current log lines."""
+        # Save cursor position
+        print(TerminalDisplay.SAVE_CURSOR, end='', flush=True)
+        
+        # Move to start of reserved area (cursor is on progress line, 1 below our area)
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(self.total_lines + 1), end='', flush=True)
+        
+        # Print header
+        print(f"\r[{self.prefix}] {self.header_text}", end='')
+        print(TerminalDisplay.CLEAR_LINE, flush=True)
+        print(f"\r[{self.prefix}] " + "-"*60, end='')
+        print(TerminalDisplay.CLEAR_LINE, flush=True)
+        
+        # Print log lines
+        for i in range(self.max_lines):
+            if i < len(line_buffer):
+                line = TerminalDisplay.truncate_line(line_buffer[i], 110)
+                print(f"\r[{self.prefix}] {line}", end='')
+            else:
+                print(f"\r[{self.prefix}] ", end='')
+            print(TerminalDisplay.CLEAR_LINE, flush=True)
+            
+        # Print separator
+        print(f"\r", end='')
+        print(TerminalDisplay.CLEAR_LINE, flush=True)
+        
+        # Restore cursor position
+        print(TerminalDisplay.RESTORE_CURSOR, end='', flush=True)
+    
+    def _cleanup_display(self) -> None:
+        """Clean up the display area on exit."""
+        print(TerminalDisplay.SAVE_CURSOR, end='', flush=True)
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(self.total_lines + 1), end='', flush=True)
+        
+        # Clear all reserved lines
+        for _ in range(self.total_lines):
+            print(f"\r", end='')
+            print(TerminalDisplay.CLEAR_LINE, flush=True)
+            
+        print(TerminalDisplay.RESTORE_CURSOR, end='', flush=True) 
\ No newline at end of file
diff --git a/language/deepseek-r1/backends/vllm_backend.py b/language/deepseek-r1/backends/vllm_backend.py
new file mode 100644
index 0000000000..4ac408042f
--- /dev/null
+++ b/language/deepseek-r1/backends/vllm_backend.py
@@ -0,0 +1,308 @@
+import asyncio
+import os
+import random
+import subprocess
+import time
+import gc
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.distributed
+from tqdm import tqdm
+from transformers import set_seed
+from vllm import LLM, SamplingParams
+
+from .base_backend import BaseBackend
+from .utils import get_cache_directory, setup_huggingface_cache, set_all_seeds, validate_prompts
+from utils.backend_registry import get_backend_config, apply_backend_env_vars
+from utils.validation import require_initialized, validate_prompts_input
+
+
+class VLLMBackend(BaseBackend):
+    """vLLM backend using synchronous LLM class with async wrapper support.
+
+    This backend only accepts text prompts, not tokenized prompts.
+    """
+
+    def __init__(self, config: Dict[str, Any] = None):
+        """Initialize vLLM backend with configuration from registry."""
+        super().__init__()  # Initialize base class
+        # Get configuration from registry
+        self.config = get_backend_config('vllm')
+        # Allow override with passed config
+        if config:
+            self.config.update(config)
+
+        # Set model and tokenizer names
+        self.model_name = self.config['model']
+        self.tokenizer_name = self.config['tokenizer']
+
+        self.llm = None
+        self.sampling_params = None
+        self._setup_environment()
+
+    def _setup_environment(self) -> None:
+        """Set up environment variables and cache directories."""
+        # Use the utility function to get cache directory
+        cache_base = get_cache_directory()
+
+        # Use models subdirectory to match user's example paths
+        self.cache_dir = cache_base.parent / 'models'
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
+        self.config['model_cache_dir'] = str(self.cache_dir)
+
+        # Set up HuggingFace cache environment variables
+        setup_huggingface_cache()
+
+        # Apply backend-specific environment variables from registry
+        apply_backend_env_vars('vllm')
+
+        # Set seeds for reproducibility
+        seed = self.config['seed']
+        set_all_seeds(seed)
+
+    def _ensure_model_cached(self) -> Path:
+        """Ensure model is available locally and return path."""
+        model_name = self.config['model']
+
+        # Create safe directory name from model path
+        model_dir_name = model_name.replace("/", "_")
+        checkpoint_path = self.cache_dir / model_dir_name
+
+        if not checkpoint_path.exists():
+            print(f"Model not found at {checkpoint_path}")
+            print(f"Downloading {model_name} from HuggingFace...")
+
+            # Create download command following user's exact steps
+            cmd = [
+                "huggingface-cli", "download",
+                model_name,
+                "--revision", self.config['model_revision'],
+                "--local-dir", str(checkpoint_path)
+            ]
+
+            # Set environment variable for faster downloads
+            env = os.environ.copy()
+            env['HF_HUB_ENABLE_HF_TRANSFER'] = '1'
+
+            try:
+                # Run download command
+                print(
+                    f"Running command: HF_HUB_ENABLE_HF_TRANSFER=1 {' '.join(cmd)}")
+                result = subprocess.run(
+                    cmd,
+                    env=env,
+                    capture_output=True,
+                    text=True,
+                    check=True
+                )
+                print(f"Model downloaded successfully to {checkpoint_path}")
+            except subprocess.CalledProcessError as e:
+                print(f"Error downloading model: {e}")
+                print(f"stdout: {e.stdout}")
+                print(f"stderr: {e.stderr}")
+                raise RuntimeError(f"Failed to download model: {e}")
+        else:
+            print(f"Using cached model at {checkpoint_path}")
+
+        return checkpoint_path
+
+    def initialize(self) -> None:
+        """Initialize the vLLM backend with LLM class."""
+        # Ensure model is cached locally
+        checkpoint_path = self._ensure_model_cached()
+
+        # Configure sampling parameters
+        self.sampling_params = SamplingParams(
+            n=1,
+            temperature=self.config['temperature'],
+            top_p=self.config['top_p'],
+            max_tokens=self.config['max_output_len'],
+            seed=self.config['seed'],
+        )
+
+        # Create LLM instance
+        print(f"Initializing vLLM with model: {self.model_name}")
+
+        try:
+            self.llm = LLM(
+                model=self.model_name,
+                tokenizer=self.tokenizer_name,
+                tensor_parallel_size=self.config['tensor_parallel_size'],
+                max_model_len=self.config['max_model_len'],
+                max_num_seqs=self.config['max_num_seqs'],
+                gpu_memory_utilization=self.config['gpu_memory_utilization'],
+                trust_remote_code=self.config['trust_remote_code'],
+                dtype=self.config['dtype'],
+                seed=self.config['seed'],
+                enforce_eager=self.config['enforce_eager'],
+                enable_prefix_caching=self.config['enable_prefix_caching'],
+                enable_chunked_prefill=self.config['enable_chunked_prefill'],
+            )
+            self.is_initialized = True
+            print("vLLM backend initialized successfully!")
+        except Exception as e:
+            raise RuntimeError(f"Failed to initialize vLLM: {e}")
+
+    @require_initialized
+    def generate(self,
+                tokenized_prompts: Optional[List[List[int]]] = None,
+                text_prompts: Optional[List[str]] = None,
+                **kwargs) -> List[Dict[str, Any]]:
+        """Generate responses synchronously using LLM.generate().
+
+        Note: vLLM backend only accepts text_prompts parameter.
+        """
+        # Validate prompts using centralized validation
+        validate_prompts_input(
+            backend_name='vllm',
+            tokenized_prompts=tokenized_prompts,
+            text_prompts=text_prompts,
+            input_type='text'
+        )
+
+        # Run generation
+        outputs = self.llm.generate(text_prompts, self.sampling_params)
+
+        # Convert outputs to standard format
+        results = []
+        for output in outputs:
+            # output is of type RequestOutput
+            # We assume n=1 in sampling_params, so take outputs[0]
+            completion = output.outputs[0]
+
+            # Validate response is not empty
+            if not completion.text:
+                # Get the corresponding prompt for context
+                prompt_idx = outputs.index(output)
+                prompt_preview = text_prompts[prompt_idx][:100] if len(text_prompts[prompt_idx]) > 100 else text_prompts[prompt_idx]
+                raise RuntimeError(f"Empty response received from vLLM for prompt: {prompt_preview}...")
+
+            results.append({
+                'tokens': list(completion.token_ids),  # Convert tuple to list for .copy() compatibility
+                'text': completion.text,
+                'finish_reason': completion.finish_reason
+            })
+
+        return results
+
+    @require_initialized
+    def generate_async(self,
+                      tokenized_prompts: Optional[List[List[int]]] = None,
+                      text_prompts: Optional[List[str]] = None,
+                      **kwargs) -> List[asyncio.Future]:
+        """Generate responses asynchronously, returning futures immediately.
+
+        Note: vLLM backend only accepts text_prompts parameter.
+        """
+        # Validate prompts using centralized validation
+        validate_prompts_input(
+            backend_name='vllm',
+            tokenized_prompts=tokenized_prompts,
+            text_prompts=text_prompts,
+            input_type='text'
+        )
+
+        if not text_prompts:
+            return []
+
+        # Create and return a future that will run the synchronous generation
+        loop = asyncio.get_event_loop()
+
+        # Run the synchronous generation in executor
+        future = loop.run_in_executor(None, self._generate_batch, text_prompts)
+
+        # Convert the single future to a list of futures, one per prompt
+        async def split_results():
+            results = await future
+            return results
+
+        # Create individual futures for each prompt
+        main_future = asyncio.ensure_future(split_results())
+
+        # Create a future for each prompt that extracts its result
+        prompt_futures = []
+        for i in range(len(text_prompts)):
+            async def get_result(idx=i):
+                results = await main_future
+                return results[idx]
+            prompt_futures.append(asyncio.ensure_future(get_result()))
+
+        return prompt_futures
+
+    def _generate_batch(self, text_prompts: List[str]) -> List[Dict[str, Any]]:
+        """Helper method to run synchronous generation for async wrapper."""
+        outputs = self.llm.generate(text_prompts, self.sampling_params)
+
+        # Convert outputs to standard format
+        results = []
+        for output in outputs:
+            completion = output.outputs[0]
+
+            # Validate response is not empty
+            if not completion.text:
+                # Get the corresponding prompt for context
+                prompt_idx = outputs.index(output)
+                prompt_preview = text_prompts[prompt_idx][:100] if len(text_prompts[prompt_idx]) > 100 else text_prompts[prompt_idx]
+                raise RuntimeError(f"Empty response received from vLLM for prompt: {prompt_preview}...")
+
+            results.append({
+                'tokens': list(completion.token_ids),  # Convert tuple to list for .copy() compatibility
+                'text': completion.text,
+                'finish_reason': completion.finish_reason
+            })
+
+        return results
+
+    def shutdown(self) -> None:
+        """Clean up resources and shut down the backend."""
+        print("Shutting down vLLM backend...")
+
+        # Delete the LLM instance first
+        if self.llm is not None:
+            # Access internal executor to ensure proper cleanup
+            if self.llm.llm_engine is not None:
+                try:
+                    # This helps cleanup vLLM's internal Ray/multiprocessing resources
+                    del self.llm.llm_engine.model_executor
+                except Exception as e:
+                    print(f"Warning: Failed to cleanup model executor: {e}")
+
+            del self.llm
+            self.llm = None
+
+        # Clear CUDA cache
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.synchronize()  # Ensure all CUDA operations are complete
+
+        # Clean up distributed resources if they exist
+        if torch.distributed.is_initialized():
+            try:
+                torch.distributed.destroy_process_group()
+                print("Destroyed process group")
+            except Exception as e:
+                print(f"Warning: Failed to destroy process group: {e}")
+
+        self.sampling_params = None
+        self.is_initialized = False
+
+        # Perform garbage collection
+        gc.collect()
+
+        # Check if Ray is initialized and shutdown if needed
+        # This helps with multiprocessing shared memory cleanup
+        try:
+            import ray
+            if ray.is_initialized():
+                ray.shutdown()
+                print("Ray shutdown completed")
+        except Exception as e:
+            print(f"Warning: Failed to shutdown Ray: {e}")
+
+        # Wait for cleanup to complete
+        time.sleep(0.5)
+
+        print("vLLM backend shutdown complete")
diff --git a/language/deepseek-r1/docker/Dockerfile.pytorch b/language/deepseek-r1/docker/Dockerfile.pytorch
new file mode 100644
index 0000000000..83656d57a1
--- /dev/null
+++ b/language/deepseek-r1/docker/Dockerfile.pytorch
@@ -0,0 +1,68 @@
+# PyTorch Backend Dockerfile
+FROM nvcr.io/nvidia/pytorch:25.04-py3
+# FROM pytorch/pytorch:2.4.1-cuda12.4-cudnn9-runtime
+
+# Set environment variables
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PIP_BREAK_SYSTEM_PACKAGES=1
+ENV MLPERF_BACKEND=pytorch
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    python3 \
+    python3-pip \
+    python3-dev \
+    git \
+    curl \
+    ca-certificates \
+    cmake \
+    build-essential \
+    pybind11-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install UV package manager system-wide
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    mv /root/.local/bin/uv /usr/local/bin/uv && \
+    mv /root/.local/bin/uvx /usr/local/bin/uvx && \
+    chmod 755 /usr/local/bin/uv /usr/local/bin/uvx && \
+    uv --version
+
+# Clone DeepSeek-V3 repository directly to /opt/ref_dsinfer
+RUN git clone https://github.com/deepseek-ai/DeepSeek-V3.git /opt/ref_dsinfer && \
+    cd /opt/ref_dsinfer && \
+    git checkout 4cc6253d5c225e2c5fea32c54573449c1c46470a 
+# echo "export PYTHONPATH=/opt:\$PYTHONPATH" >> /etc/bash.bashrc
+# touch /opt/ref_dsinfer/__init__.py && \
+# touch /opt/ref_dsinfer/inference/__init__.py && \
+
+RUN pip3 install --no-cache-dir \
+    triton \
+    transformers \
+    accelerate \
+    pandas \
+    numpy \
+    tqdm \
+    datasets \
+    huggingface-hub[hf_transfer] \
+    safetensors
+
+# Set Python path for the container
+ENV PYTHONPATH=/opt:${PYTHONPATH}
+
+# Set cache directory environment variables
+# These will be overridden at runtime if needed
+ENV HF_HOME=/raid/data/\$USER/.cache
+ENV HF_HUB_CACHE=/raid/data/\$USER/.cache
+ENV HUGGINGFACE_HUB_CACHE=/raid/data/\$USER/.cache
+
+# Enable HF Transfer for faster downloads
+ENV HF_HUB_ENABLE_HF_TRANSFER=1
+
+# Set working directory to /work for mounted workspace
+WORKDIR /work
+
+# Add /work to PATH so setup.sh can be run without ./
+ENV PATH="/work:${PATH}"
+
+# Set default command
+CMD ["/bin/bash"] 
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/Dockerfile.sglang b/language/deepseek-r1/docker/Dockerfile.sglang
new file mode 100644
index 0000000000..d2cfc92012
--- /dev/null
+++ b/language/deepseek-r1/docker/Dockerfile.sglang
@@ -0,0 +1,71 @@
+# SGLang Backend Dockerfile
+FROM nvidia/cuda:12.6.0-devel-ubuntu22.04
+
+# Set environment variables
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PIP_BREAK_SYSTEM_PACKAGES=1
+ENV MLPERF_BACKEND=sglang
+
+# Install Python and system dependencies
+RUN apt-get update && apt-get install -y \
+    software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa \
+    && apt-get update && apt-get install -y \
+    python3.10 \
+    python3.10-dev \
+    python3.10-distutils \
+    python3-pip \
+    git \
+    git-lfs \
+    curl \
+    wget \
+    ca-certificates \
+    cmake \
+    build-essential \
+    ninja-build \
+    pybind11-dev \
+    pkg-config \
+    sudo \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set Python 3.10 as default
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1 && \
+    update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1
+
+# Install pip for Python 3.10
+RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
+
+# Install UV package manager system-wide
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    mv /root/.local/bin/uv /usr/local/bin/uv && \
+    mv /root/.local/bin/uvx /usr/local/bin/uvx && \
+    chmod 755 /usr/local/bin/uv /usr/local/bin/uvx && \
+    uv --version
+
+# Install base Python packages
+RUN pip3 install --no-cache-dir \
+    numpy \
+    setuptools \
+    wheel \
+    "pybind11>=2.11.1"
+
+# Set CUDA environment for proper compilation
+ENV CUDA_HOME=/usr/local/cuda
+
+# Set cache directory environment variables
+# These will be overridden at runtime if needed
+ENV HF_HOME=/raid/data/\$USER/.cache/huggingface
+ENV HF_HUB_CACHE=/raid/data/\$USER/.cache/huggingface
+ENV HUGGINGFACE_HUB_CACHE=/raid/data/\$USER/.cache/huggingface
+
+# Create necessary directories
+RUN mkdir -p /work /work/logs
+
+# Set working directory to /work for mounted workspace
+WORKDIR /work
+
+# Add /work to PATH so setup.sh can be run without ./
+ENV PATH="/work:${PATH}"
+
+# Set default command
+CMD ["/bin/bash"] 
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/Dockerfile.vllm b/language/deepseek-r1/docker/Dockerfile.vllm
new file mode 100644
index 0000000000..adedcca37e
--- /dev/null
+++ b/language/deepseek-r1/docker/Dockerfile.vllm
@@ -0,0 +1,74 @@
+# vLLM Backend Dockerfile - Install from source
+FROM nvidia/cuda:12.6.0-devel-ubuntu22.04
+
+# Set environment variables to prevent interactive prompts during build
+ENV DEBIAN_FRONTEND=noninteractive
+# ENV PIP_BREAK_SYSTEM_PACKAGES=1
+ENV MLPERF_BACKEND=vllm
+# Set CUDA architectures for vLLM build (adjust based on your GPU)
+ENV TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0;8.6;8.9;9.0"
+
+# Install basic dependencies and build tools
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    python3-pip \
+    python3-dev \
+    git \
+    curl \
+    ca-certificates \
+    build-essential \
+    cmake \
+    ninja-build \
+    pybind11-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Upgrade pip to latest version to avoid dependency conflicts
+RUN python3 -m pip install --upgrade pip setuptools wheel
+
+# Install UV package manager system-wide
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    mv /root/.local/bin/uv /usr/local/bin/uv && \
+    mv /root/.local/bin/uvx /usr/local/bin/uvx && \
+    chmod 755 /usr/local/bin/uv /usr/local/bin/uvx && \
+    uv --version
+
+# Set up a working directory for vLLM installation
+WORKDIR /vllm-install
+
+# Clone the vLLM repository (main branch)
+RUN git clone https://github.com/vllm-project/vllm.git .
+
+# Install PyTorch with CUDA support
+RUN pip3 install --no-cache-dir torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu126
+
+# Install vLLM from source
+# This will install the latest version from the cloned main branch
+# RUN pip3 install --no-cache-dir -e .
+RUN pip3 install vllm==0.9.0
+
+# Install additional dependencies needed for MLPerf evaluation
+RUN pip3 install --no-cache-dir \
+    pandas \
+    numpy \
+    tqdm \
+    huggingface-hub[hf_transfer] \
+    datasets \
+    accelerate
+
+# Set cache directory environment variables
+# These will be overridden at runtime if needed
+ENV HF_HOME=/raid/data/\$USER/.cache
+ENV HF_HUB_CACHE=/raid/data/\$USER/.cache
+ENV HUGGINGFACE_HUB_CACHE=/raid/data/\$USER/.cache
+
+# Set working directory to /work for mounted workspace
+WORKDIR /work
+
+# Add /work to PATH so setup.sh can be run without ./
+ENV PATH="/work:${PATH}"
+
+# Expose the default vLLM API port
+EXPOSE 8000
+
+# Set default command
+CMD ["/bin/bash"]
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/evaluation_requirements.txt b/language/deepseek-r1/docker/evaluation_requirements.txt
new file mode 100644
index 0000000000..e7b5a84199
--- /dev/null
+++ b/language/deepseek-r1/docker/evaluation_requirements.txt
@@ -0,0 +1,10 @@
+# Evaluation requirements for MLPerf DeepSeek reference implementation
+# This file includes editable installs of evaluation submodules
+
+# Install build dependencies first
+numpy
+pylatexenc
+
+# Install evaluation submodules as editable packages
+-e submodules/LiveCodeBench
+-e submodules/prm800k
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/launch_scripts/launch_pytorch.sh b/language/deepseek-r1/docker/launch_scripts/launch_pytorch.sh
new file mode 100755
index 0000000000..540ee1860f
--- /dev/null
+++ b/language/deepseek-r1/docker/launch_scripts/launch_pytorch.sh
@@ -0,0 +1,223 @@
+#!/bin/bash
+
+# PyTorch Docker Launch Script
+# Replicates the functionality of launch_docker.sh for PyTorch backend
+
+# Set default values (can be overridden by environment variables)
+MOUNT_SELF_AS_LOADGEN=${MOUNT_SELF_AS_LOADGEN:-true}
+IMAGE_NAME=${IMAGE_NAME:-mlperf-pytorch}
+IMAGE_TAG=${IMAGE_TAG:-latest}
+LOCAL_USER=${LOCAL_USER:-1}
+WORK_DIR=${WORK_DIR:-$(dirname "$(realpath "$0")")/../..}
+CONTAINER_NAME=${CONTAINER_NAME:-pytorch}
+RUN_CMD=${RUN_CMD:-}
+
+# Get user information
+USER_ID=$(id --user)
+USER_NAME=$(id --user --name)
+GROUP_ID=$(id --group)
+GROUP_NAME=$(id --group --name)
+
+# Set image tag suffix for local user
+if [ "$LOCAL_USER" = "1" ]; then
+	IMAGE_TAG_SUFFIX="-$USER_NAME"
+else
+	IMAGE_TAG_SUFFIX=""
+fi
+
+# Base and final image names
+BASE_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}"
+FINAL_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}${IMAGE_TAG_SUFFIX}"
+
+# Generate random number for container name
+RANDOM_NUM=$RANDOM
+
+# Set ccache directory
+CCACHE_DIR=${CCACHE_DIR:-${WORK_DIR}/.ccache}
+
+# Docker run options
+DOCKER_RUN_OPTS="--rm -it --ipc=host --ulimit memlock=-1 --ulimit stack=67108864"
+DOCKER_RUN_ARGS="" # "-p 8000:8000"
+GPU_OPTS="--gpus=all"
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+	case $1 in
+	--image-name)
+		IMAGE_NAME="$2"
+		BASE_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}"
+		FINAL_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}${IMAGE_TAG_SUFFIX}"
+		shift 2
+		;;
+	--container-name)
+		CONTAINER_NAME="$2"
+		shift 2
+		;;
+	--gpu-count)
+		if [ "$2" != "all" ]; then
+			GPU_OPTS="--gpus=$2"
+		fi
+		shift 2
+		;;
+	--model-cache-dir)
+		MODEL_CACHE_DIR="$2"
+		shift 2
+		;;
+	--local-user)
+		LOCAL_USER="$2"
+		if [ "$LOCAL_USER" = "1" ]; then
+			IMAGE_TAG_SUFFIX="-$USER_NAME"
+		else
+			IMAGE_TAG_SUFFIX=""
+		fi
+		FINAL_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}${IMAGE_TAG_SUFFIX}"
+		shift 2
+		;;
+	--help)
+		echo "Usage: $0 [OPTIONS]"
+		echo "Options:"
+		echo "  --image-name NAME        Docker image name (default: mlperf-pytorch)"
+		echo "  --container-name NAME    Container name (default: pytorch)"
+		echo "  --gpu-count COUNT        GPU count (default: all)"
+		echo "  --model-cache-dir DIR    Model cache directory"
+		echo "  --local-user 0|1         Enable local user setup (default: 1)"
+		echo "  --help                   Show this help message"
+		exit 0
+		;;
+	*)
+		# Store remaining arguments for passing to container
+		RUN_CMD="$RUN_CMD $1"
+		shift
+		;;
+	esac
+done
+
+echo "Launching PyTorch Docker container..."
+echo "Base image: $BASE_IMAGE"
+echo "Final image: $FINAL_IMAGE"
+echo "Work directory: $WORK_DIR"
+echo "Container name: ${CONTAINER_NAME}-${RANDOM_NUM}-${USER_NAME}"
+echo "User: $USER_NAME (UID: $USER_ID)"
+
+# Build the base Docker image
+echo "Building base Docker image..."
+SCRIPT_DIR=$(dirname "$(realpath "$0")")
+
+# Build the base image using the Dockerfile for PyTorch
+docker build -f "$SCRIPT_DIR/../Dockerfile.pytorch" -t "$BASE_IMAGE" "$SCRIPT_DIR/../.."
+
+# Check if base build was successful
+if [ $? -ne 0 ]; then
+	echo "Error: Failed to build base Docker image"
+	exit 1
+fi
+
+echo "Successfully built base Docker image: $BASE_IMAGE"
+
+# Create user-specific image if LOCAL_USER is enabled
+if [ "$LOCAL_USER" = "1" ]; then
+	echo "Building user-specific image with user: $USER_NAME"
+
+	# Create a temporary Dockerfile.user
+	USER_DOCKERFILE="$SCRIPT_DIR/Dockerfile.user.tmp"
+	cat >"$USER_DOCKERFILE" <<EOF
+ARG BASE_IMAGE_WITH_TAG
+
+FROM \${BASE_IMAGE_WITH_TAG} as base
+
+# Alternative user
+ARG USER_ID=0
+ARG USER_NAME=root
+ARG GROUP_ID=0
+ARG GROUP_NAME=root
+
+ENV PATH="\$PATH:/home/\${USER_NAME}/.local/bin"
+RUN (getent group \${GROUP_ID} || groupadd --gid \${GROUP_ID} \${GROUP_NAME}) && \\
+    (getent passwd \${USER_ID} || useradd --gid \${GROUP_ID} --uid \${USER_ID} --create-home --no-log-init --shell /bin/bash \${USER_NAME})
+
+RUN adduser \${USER_NAME} sudo
+RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+
+USER \${USER_NAME}
+
+# Install basic user utilities
+RUN pip install --user --no-cache-dir ipython jupyter
+
+EOF
+
+	# Build user-specific image
+	docker build \
+		--build-arg BASE_IMAGE_WITH_TAG="$BASE_IMAGE" \
+		--build-arg USER_ID="$USER_ID" \
+		--build-arg USER_NAME="$USER_NAME" \
+		--build-arg GROUP_ID="$GROUP_ID" \
+		--build-arg GROUP_NAME="$GROUP_NAME" \
+		--file "$USER_DOCKERFILE" \
+		--tag "$FINAL_IMAGE" \
+		"$SCRIPT_DIR"
+
+	# Clean up temporary Dockerfile
+	rm -f "$USER_DOCKERFILE"
+
+	# Check if user build was successful
+	if [ $? -ne 0 ]; then
+		echo "Error: Failed to build user-specific Docker image"
+		exit 1
+	fi
+
+	echo "Successfully built user-specific image: $FINAL_IMAGE"
+else
+	FINAL_IMAGE="$BASE_IMAGE"
+fi
+
+# Check if final image exists
+if ! docker image inspect "$FINAL_IMAGE" >/dev/null 2>&1; then
+	echo "Error: Docker image '$FINAL_IMAGE' not found."
+	echo "Please check the build process above."
+	exit 1
+fi
+
+echo "Starting container with image: $FINAL_IMAGE"
+
+# Prepare inference mount if MOUNT_SELF_AS_LOADGEN is true
+INFERENCE_MOUNT=""
+if [ "$MOUNT_SELF_AS_LOADGEN" = "true" ]; then
+	INFERENCE_ROOT="$(dirname "$(realpath "$0")")/../../../../"
+	INFERENCE_MOUNT="-v ${INFERENCE_ROOT}:/inference"
+	echo "Mounting self to /inference: ${INFERENCE_ROOT} -> /inference"
+else
+	echo "Cloning loadgen repository to /tmp/inference (MOUNT_SELF_AS_LOADGEN=false)"
+	# Clone loadgen repository if it doesn't exist
+	INFERENCE_TMP="/tmp/inference-${USER_NAME}-${RANDOM_NUM}"
+	if [ ! -d "$INFERENCE_TMP" ]; then
+		echo "Cloning MLPerf Inference repository..."
+		git clone --depth 1 https://github.com/mlcommons/inference.git "$INFERENCE_TMP"
+		if [ $? -ne 0 ]; then
+			echo "Error: Failed to clone MLPerf Inference repository"
+			exit 1
+		fi
+		echo "Successfully cloned loadgen repository to: $INFERENCE_TMP"
+	else
+		echo "Using existing loadgen repository at: $INFERENCE_TMP"
+	fi
+	INFERENCE_MOUNT="-v ${INFERENCE_TMP}:/inference"
+fi
+
+# Run the Docker container with all mounts (same as main docker setup)
+docker run $DOCKER_RUN_OPTS $DOCKER_RUN_ARGS \
+	$GPU_OPTS \
+	-v /home/mlperf_inference_storage:/home/mlperf_inference_storage \
+	-v /raid/data:/raid/data \
+	-e HISTFILE="${WORK_DIR}/.bash_history" \
+	--env "CCACHE_DIR=${CCACHE_DIR}" \
+	--env "USER=${USER_NAME}" \
+	--env "HF_HOME=/raid/data/${USER_NAME}/.cache" \
+	--env "HF_HUB_CACHE=/raid/data/${USER_NAME}/.cache" \
+	--env "HUGGINGFACE_HUB_CACHE=/raid/data/${USER_NAME}/.cache" \
+	--workdir /work \
+	-v "${WORK_DIR}:/work" \
+	$INFERENCE_MOUNT \
+	--hostname "$(hostname)-docker" \
+	--name "${CONTAINER_NAME}-${RANDOM_NUM}-${USER_NAME}" \
+	--tmpfs /tmp:exec \
+	"$FINAL_IMAGE" $RUN_CMD
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/launch_scripts/launch_sglang.sh b/language/deepseek-r1/docker/launch_scripts/launch_sglang.sh
new file mode 100755
index 0000000000..2d21a0dcd8
--- /dev/null
+++ b/language/deepseek-r1/docker/launch_scripts/launch_sglang.sh
@@ -0,0 +1,223 @@
+#!/bin/bash
+
+# SGLang Docker Launch Script
+# Replicates the functionality of launch_docker.sh for SGLang backend
+
+# Set default values (can be overridden by environment variables)
+MOUNT_SELF_AS_LOADGEN=${MOUNT_SELF_AS_LOADGEN:-true}
+IMAGE_NAME=${IMAGE_NAME:-mlperf-sglang}
+IMAGE_TAG=${IMAGE_TAG:-latest}
+LOCAL_USER=${LOCAL_USER:-1}
+WORK_DIR=${WORK_DIR:-$(dirname "$(realpath "$0")")/../..}
+CONTAINER_NAME=${CONTAINER_NAME:-sglang}
+RUN_CMD=${RUN_CMD:-}
+
+# Get user information
+USER_ID=$(id --user)
+USER_NAME=$(id --user --name)
+GROUP_ID=$(id --group)
+GROUP_NAME=$(id --group --name)
+
+# Set image tag suffix for local user
+if [ "$LOCAL_USER" = "1" ]; then
+    IMAGE_TAG_SUFFIX="-$USER_NAME"
+else
+    IMAGE_TAG_SUFFIX=""
+fi
+
+# Base and final image names
+BASE_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}"
+FINAL_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}${IMAGE_TAG_SUFFIX}"
+
+# Generate random number for container name
+RANDOM_NUM=$RANDOM
+
+# Set ccache directory
+CCACHE_DIR=${CCACHE_DIR:-${WORK_DIR}/.ccache}
+
+# Docker run options
+DOCKER_RUN_OPTS="--rm -it --ipc=host --ulimit memlock=-1 --ulimit stack=67108864"
+DOCKER_RUN_ARGS="-p 8000:8000"
+GPU_OPTS="--gpus=all"
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --image-name)
+            IMAGE_NAME="$2"
+            BASE_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}"
+            FINAL_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}${IMAGE_TAG_SUFFIX}"
+            shift 2
+            ;;
+        --container-name)
+            CONTAINER_NAME="$2"
+            shift 2
+            ;;
+        --gpu-count)
+            if [ "$2" != "all" ]; then
+                GPU_OPTS="--gpus=$2"
+            fi
+            shift 2
+            ;;
+        --model-cache-dir)
+            MODEL_CACHE_DIR="$2"
+            shift 2
+            ;;
+        --local-user)
+            LOCAL_USER="$2"
+            if [ "$LOCAL_USER" = "1" ]; then
+                IMAGE_TAG_SUFFIX="-$USER_NAME"
+            else
+                IMAGE_TAG_SUFFIX=""
+            fi
+            FINAL_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}${IMAGE_TAG_SUFFIX}"
+            shift 2
+            ;;
+        --help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --image-name NAME        Docker image name (default: mlperf-sglang)"
+            echo "  --container-name NAME    Container name (default: sglang)"
+            echo "  --gpu-count COUNT        GPU count (default: all)"
+            echo "  --model-cache-dir DIR    Model cache directory"
+            echo "  --local-user 0|1         Enable local user setup (default: 1)"
+            echo "  --help                   Show this help message"
+            exit 0
+            ;;
+        *)
+            # Store remaining arguments for passing to container
+            RUN_CMD="$RUN_CMD $1"
+            shift
+            ;;
+    esac
+done
+
+echo "Launching SGLang Docker container..."
+echo "Base image: $BASE_IMAGE"
+echo "Final image: $FINAL_IMAGE"
+echo "Work directory: $WORK_DIR"
+echo "Container name: ${CONTAINER_NAME}-${RANDOM_NUM}-${USER_NAME}"
+echo "User: $USER_NAME (UID: $USER_ID)"
+
+# Build the base Docker image
+echo "Building base Docker image..."
+SCRIPT_DIR=$(dirname "$(realpath "$0")")
+
+# Build the base image using the Dockerfile for SGLang
+docker build -f "$SCRIPT_DIR/../Dockerfile.sglang" -t "$BASE_IMAGE" "$SCRIPT_DIR/../.."
+
+# Check if base build was successful
+if [ $? -ne 0 ]; then
+    echo "Error: Failed to build base Docker image"
+    exit 1
+fi
+
+echo "Successfully built base Docker image: $BASE_IMAGE"
+
+# Create user-specific image if LOCAL_USER is enabled
+if [ "$LOCAL_USER" = "1" ]; then
+    echo "Building user-specific image with user: $USER_NAME"
+    
+    # Create a temporary Dockerfile.user
+    USER_DOCKERFILE="$SCRIPT_DIR/Dockerfile.user.tmp"
+    cat > "$USER_DOCKERFILE" << EOF
+ARG BASE_IMAGE_WITH_TAG
+
+FROM \${BASE_IMAGE_WITH_TAG} as base
+
+# Alternative user
+ARG USER_ID=0
+ARG USER_NAME=root
+ARG GROUP_ID=0
+ARG GROUP_NAME=root
+
+ENV PATH="\$PATH:/home/\${USER_NAME}/.local/bin"
+RUN (getent group \${GROUP_ID} || groupadd --gid \${GROUP_ID} \${GROUP_NAME}) && \\
+    (getent passwd \${USER_ID} || useradd --gid \${GROUP_ID} --uid \${USER_ID} --create-home --no-log-init --shell /bin/bash \${USER_NAME})
+
+RUN adduser \${USER_NAME} sudo
+RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+
+USER \${USER_NAME}
+
+# Install basic user utilities
+RUN pip install --user --no-cache-dir ipython jupyter
+
+EOF
+
+    # Build user-specific image
+    docker build \
+        --build-arg BASE_IMAGE_WITH_TAG="$BASE_IMAGE" \
+        --build-arg USER_ID="$USER_ID" \
+        --build-arg USER_NAME="$USER_NAME" \
+        --build-arg GROUP_ID="$GROUP_ID" \
+        --build-arg GROUP_NAME="$GROUP_NAME" \
+        --file "$USER_DOCKERFILE" \
+        --tag "$FINAL_IMAGE" \
+        "$SCRIPT_DIR"
+    
+    # Clean up temporary Dockerfile
+    rm -f "$USER_DOCKERFILE"
+    
+    # Check if user build was successful
+    if [ $? -ne 0 ]; then
+        echo "Error: Failed to build user-specific Docker image"
+        exit 1
+    fi
+    
+    echo "Successfully built user-specific image: $FINAL_IMAGE"
+else
+    FINAL_IMAGE="$BASE_IMAGE"
+fi
+
+# Check if final image exists
+if ! docker image inspect "$FINAL_IMAGE" >/dev/null 2>&1; then
+    echo "Error: Docker image '$FINAL_IMAGE' not found."
+    echo "Please check the build process above."
+    exit 1
+fi
+
+echo "Starting container with image: $FINAL_IMAGE"
+
+# Prepare inference mount if MOUNT_SELF_AS_LOADGEN is true
+INFERENCE_MOUNT=""
+if [ "$MOUNT_SELF_AS_LOADGEN" = "true" ]; then
+    INFERENCE_ROOT="$(dirname "$(realpath "$0")")/../../../../"
+    INFERENCE_MOUNT="-v ${INFERENCE_ROOT}:/inference"
+    echo "Mounting self to /inference: ${INFERENCE_ROOT} -> /inference"
+else
+    echo "Cloning loadgen repository to /tmp/inference (MOUNT_SELF_AS_LOADGEN=false)"
+    # Clone loadgen repository if it doesn't exist
+    INFERENCE_TMP="/tmp/inference-${USER_NAME}-${RANDOM_NUM}"
+    if [ ! -d "$INFERENCE_TMP" ]; then
+        echo "Cloning MLPerf Inference repository..."
+        git clone --depth 1 https://github.com/mlcommons/inference.git "$INFERENCE_TMP"
+        if [ $? -ne 0 ]; then
+            echo "Error: Failed to clone MLPerf Inference repository"
+            exit 1
+        fi
+        echo "Successfully cloned loadgen repository to: $INFERENCE_TMP"
+    else
+        echo "Using existing loadgen repository at: $INFERENCE_TMP"
+    fi
+    INFERENCE_MOUNT="-v ${INFERENCE_TMP}:/inference"
+fi
+
+# Run the Docker container with all mounts (same as main docker setup)
+docker run $DOCKER_RUN_OPTS $DOCKER_RUN_ARGS \
+    $GPU_OPTS \
+    -v /home/mlperf_inference_storage:/home/mlperf_inference_storage \
+    -v /raid/data:/raid/data \
+    -e HISTFILE="${WORK_DIR}/.bash_history" \
+    --env "CCACHE_DIR=${CCACHE_DIR}" \
+    --env "USER=${USER_NAME}" \
+    --env "HF_HOME=/raid/data/${USER_NAME}/.cache" \
+    --env "HF_HUB_CACHE=/raid/data/${USER_NAME}/.cache" \
+    --env "HUGGINGFACE_HUB_CACHE=/raid/data/${USER_NAME}/.cache" \
+    --workdir /work \
+    -v "${WORK_DIR}:/work" \
+    $INFERENCE_MOUNT \
+    --hostname "$(hostname)-docker" \
+    --name "${CONTAINER_NAME}-${RANDOM_NUM}-${USER_NAME}" \
+    --tmpfs /tmp:exec \
+    "$FINAL_IMAGE" $RUN_CMD 
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/launch_scripts/launch_vllm.sh b/language/deepseek-r1/docker/launch_scripts/launch_vllm.sh
new file mode 100755
index 0000000000..966c1ab516
--- /dev/null
+++ b/language/deepseek-r1/docker/launch_scripts/launch_vllm.sh
@@ -0,0 +1,223 @@
+#!/bin/bash
+
+# vLLM Docker Launch Script
+# Replicates the functionality of launch_docker.sh for vLLM backend
+
+# Set default values (can be overridden by environment variables)
+MOUNT_SELF_AS_LOADGEN=${MOUNT_SELF_AS_LOADGEN:-true}
+IMAGE_NAME=${IMAGE_NAME:-mlperf-vllm}
+IMAGE_TAG=${IMAGE_TAG:-latest}
+LOCAL_USER=${LOCAL_USER:-1}
+WORK_DIR=${WORK_DIR:-$(dirname "$(realpath "$0")")/../..}
+CONTAINER_NAME=${CONTAINER_NAME:-vllm}
+RUN_CMD=${RUN_CMD:-}
+
+# Get user information
+USER_ID=$(id --user)
+USER_NAME=$(id --user --name)
+GROUP_ID=$(id --group)
+GROUP_NAME=$(id --group --name)
+
+# Set image tag suffix for local user
+if [ "$LOCAL_USER" = "1" ]; then
+    IMAGE_TAG_SUFFIX="-$USER_NAME"
+else
+    IMAGE_TAG_SUFFIX=""
+fi
+
+# Base and final image names
+BASE_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}"
+FINAL_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}${IMAGE_TAG_SUFFIX}"
+
+# Generate random number for container name
+RANDOM_NUM=$RANDOM
+
+# Set ccache directory
+CCACHE_DIR=${CCACHE_DIR:-${WORK_DIR}/.ccache}
+
+# Docker run options
+DOCKER_RUN_OPTS="--rm -it --ipc=host --ulimit memlock=-1 --ulimit stack=67108864"
+DOCKER_RUN_ARGS=""
+GPU_OPTS="--gpus=all"
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --image-name)
+            IMAGE_NAME="$2"
+            BASE_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}"
+            FINAL_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}${IMAGE_TAG_SUFFIX}"
+            shift 2
+            ;;
+        --container-name)
+            CONTAINER_NAME="$2"
+            shift 2
+            ;;
+        --gpu-count)
+            if [ "$2" != "all" ]; then
+                GPU_OPTS="--gpus=$2"
+            fi
+            shift 2
+            ;;
+        --model-cache-dir)
+            MODEL_CACHE_DIR="$2"
+            shift 2
+            ;;
+        --local-user)
+            LOCAL_USER="$2"
+            if [ "$LOCAL_USER" = "1" ]; then
+                IMAGE_TAG_SUFFIX="-$USER_NAME"
+            else
+                IMAGE_TAG_SUFFIX=""
+            fi
+            FINAL_IMAGE="${IMAGE_NAME}:${IMAGE_TAG}${IMAGE_TAG_SUFFIX}"
+            shift 2
+            ;;
+        --help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --image-name NAME        Docker image name (default: mlperf-vllm)"
+            echo "  --container-name NAME    Container name (default: vllm)"
+            echo "  --gpu-count COUNT        GPU count (default: all)"
+            echo "  --model-cache-dir DIR    Model cache directory"
+            echo "  --local-user 0|1         Enable local user setup (default: 1)"
+            echo "  --help                   Show this help message"
+            exit 0
+            ;;
+        *)
+            # Store remaining arguments for passing to container
+            RUN_CMD="$RUN_CMD $1"
+            shift
+            ;;
+    esac
+done
+
+echo "Launching vLLM Docker container..."
+echo "Base image: $BASE_IMAGE"
+echo "Final image: $FINAL_IMAGE"
+echo "Work directory: $WORK_DIR"
+echo "Container name: ${CONTAINER_NAME}-${RANDOM_NUM}-${USER_NAME}"
+echo "User: $USER_NAME (UID: $USER_ID)"
+
+# Build the base Docker image
+echo "Building base Docker image..."
+SCRIPT_DIR=$(dirname "$(realpath "$0")")
+
+# Build the base image using the Dockerfile for vLLM
+docker build -f "$SCRIPT_DIR/../Dockerfile.vllm" -t "$BASE_IMAGE" "$SCRIPT_DIR/../.."
+
+# Check if base build was successful
+if [ $? -ne 0 ]; then
+    echo "Error: Failed to build base Docker image"
+    exit 1
+fi
+
+echo "Successfully built base Docker image: $BASE_IMAGE"
+
+# Create user-specific image if LOCAL_USER is enabled
+if [ "$LOCAL_USER" = "1" ]; then
+    echo "Building user-specific image with user: $USER_NAME"
+    
+    # Create a temporary Dockerfile.user
+    USER_DOCKERFILE="$SCRIPT_DIR/Dockerfile.user.tmp"
+    cat > "$USER_DOCKERFILE" << EOF
+ARG BASE_IMAGE_WITH_TAG
+
+FROM \${BASE_IMAGE_WITH_TAG} as base
+
+# Alternative user
+ARG USER_ID=0
+ARG USER_NAME=root
+ARG GROUP_ID=0
+ARG GROUP_NAME=root
+
+ENV PATH="\$PATH:/home/\${USER_NAME}/.local/bin"
+RUN (getent group \${GROUP_ID} || groupadd --gid \${GROUP_ID} \${GROUP_NAME}) && \\
+    (getent passwd \${USER_ID} || useradd --gid \${GROUP_ID} --uid \${USER_ID} --create-home --no-log-init --shell /bin/bash \${USER_NAME})
+
+RUN adduser \${USER_NAME} sudo
+RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+
+USER \${USER_NAME}
+
+# Install basic user utilities
+RUN pip install --user --no-cache-dir ipython jupyter
+
+EOF
+
+    # Build user-specific image
+    docker build \
+        --build-arg BASE_IMAGE_WITH_TAG="$BASE_IMAGE" \
+        --build-arg USER_ID="$USER_ID" \
+        --build-arg USER_NAME="$USER_NAME" \
+        --build-arg GROUP_ID="$GROUP_ID" \
+        --build-arg GROUP_NAME="$GROUP_NAME" \
+        --file "$USER_DOCKERFILE" \
+        --tag "$FINAL_IMAGE" \
+        "$SCRIPT_DIR"
+    
+    # Clean up temporary Dockerfile
+    rm -f "$USER_DOCKERFILE"
+    
+    # Check if user build was successful
+    if [ $? -ne 0 ]; then
+        echo "Error: Failed to build user-specific Docker image"
+        exit 1
+    fi
+    
+    echo "Successfully built user-specific image: $FINAL_IMAGE"
+else
+    FINAL_IMAGE="$BASE_IMAGE"
+fi
+
+# Check if final image exists
+if ! docker image inspect "$FINAL_IMAGE" >/dev/null 2>&1; then
+    echo "Error: Docker image '$FINAL_IMAGE' not found."
+    echo "Please check the build process above."
+    exit 1
+fi
+
+echo "Starting container with image: $FINAL_IMAGE"
+
+# Prepare inference mount if MOUNT_SELF_AS_LOADGEN is true
+INFERENCE_MOUNT=""
+if [ "$MOUNT_SELF_AS_LOADGEN" = "true" ]; then
+    INFERENCE_ROOT="$(dirname "$(realpath "$0")")/../../../../"
+    INFERENCE_MOUNT="-v ${INFERENCE_ROOT}:/inference"
+    echo "Mounting self to /inference: ${INFERENCE_ROOT} -> /inference"
+else
+    echo "Cloning loadgen repository to /tmp/inference (MOUNT_SELF_AS_LOADGEN=false)"
+    # Clone loadgen repository if it doesn't exist
+    INFERENCE_TMP="/tmp/inference-${USER_NAME}-${RANDOM_NUM}"
+    if [ ! -d "$INFERENCE_TMP" ]; then
+        echo "Cloning MLPerf Inference repository..."
+        git clone --depth 1 https://github.com/mlcommons/inference.git "$INFERENCE_TMP"
+        if [ $? -ne 0 ]; then
+            echo "Error: Failed to clone MLPerf Inference repository"
+            exit 1
+        fi
+        echo "Successfully cloned loadgen repository to: $INFERENCE_TMP"
+    else
+        echo "Using existing loadgen repository at: $INFERENCE_TMP"
+    fi
+    INFERENCE_MOUNT="-v ${INFERENCE_TMP}:/inference"
+fi
+
+# Run the Docker container with all mounts (same as main docker setup)
+docker run $DOCKER_RUN_OPTS $DOCKER_RUN_ARGS \
+    $GPU_OPTS \
+    -v /home/mlperf_inference_storage:/home/mlperf_inference_storage \
+    -v /raid/data:/raid/data \
+    -e HISTFILE="${WORK_DIR}/.bash_history" \
+    --env "CCACHE_DIR=${CCACHE_DIR}" \
+    --env "USER=${USER_NAME}" \
+    --env "HF_HOME=/raid/data/${USER_NAME}/.cache" \
+    --env "HF_HUB_CACHE=/raid/data/${USER_NAME}/.cache" \
+    --env "HUGGINGFACE_HUB_CACHE=/raid/data/${USER_NAME}/.cache" \
+    --workdir /work \
+    -v "${WORK_DIR}:/work" \
+    $INFERENCE_MOUNT \
+    --hostname "$(hostname)-docker" \
+    --name "${CONTAINER_NAME}-${RANDOM_NUM}-${USER_NAME}" \
+    --tmpfs /tmp:exec \
+    "$FINAL_IMAGE" $RUN_CMD 
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/setup_scripts/common.sh b/language/deepseek-r1/docker/setup_scripts/common.sh
new file mode 100755
index 0000000000..863e982de0
--- /dev/null
+++ b/language/deepseek-r1/docker/setup_scripts/common.sh
@@ -0,0 +1,348 @@
+#!/bin/bash
+# Common setup functions for MLPerf DeepSeek evaluation environment
+# This script contains shared functionality for all backends
+
+set -e # Exit on error
+
+# Common function to check if uv is installed
+check_uv_installed() {
+	if ! command -v uv &>/dev/null; then
+		echo "Error: uv is not installed. Please ensure the Docker image has uv installed."
+		exit 1
+	fi
+}
+
+# Common function to setup virtual environment
+setup_virtual_environment() {
+	local VENV_DIR="$1"
+
+	# Check if we're already in the /work directory
+	if [ "$PWD" != "/work" ]; then
+		echo "Changing to /work directory..."
+		cd /work
+	fi
+
+	# Check if virtual environment already exists
+	if [ -d "$VENV_DIR" ]; then
+		echo "Virtual environment already exists at $VENV_DIR"
+	else
+		echo "Creating new UV virtual environment..."
+		uv venv --system-site-packages "$VENV_DIR"
+	fi
+}
+
+# Common function to install build dependencies
+install_build_dependencies() {
+	echo "Installing build dependencies in venv..."
+	VIRTUAL_ENV=$VENV_DIR uv pip install numpy setuptools
+}
+
+# Common function to create missing __init__.py files in prm800k
+create_prm800k_init_files() {
+	echo "Creating missing __init__.py files in prm800k..."
+
+	# List of directories that need __init__.py files
+	local PRM800K_DIRS=(
+		"/work/submodules/prm800k"
+		"/work/submodules/prm800k/prm800k"
+		"/work/submodules/prm800k/prm800k/grading"
+		"/work/submodules/prm800k/prm800k/eval"
+		"/work/submodules/prm800k/prm800k/data"
+		"/work/submodules/prm800k/prm800k/instructions"
+		"/work/submodules/prm800k/prm800k/math_splits"
+	)
+
+	for dir in "${PRM800K_DIRS[@]}"; do
+		if [ -d "$dir" ] && [ ! -f "$dir/__init__.py" ]; then
+			echo "Creating __init__.py in $dir"
+			touch "$dir/__init__.py"
+		fi
+	done
+
+	echo "Finished creating __init__.py files"
+}
+
+# Common function to patch prm800k setup.py
+patch_prm800k_setup() {
+	echo "Checking and patching prm800k setup.py if necessary..."
+
+	# First, create missing __init__.py files
+	create_prm800k_init_files
+
+	PRM800K_SETUP="/work/submodules/prm800k/setup.py"
+	if [ -f "$PRM800K_SETUP" ]; then
+		echo "Found prm800k setup.py at: $PRM800K_SETUP"
+		# Check if the file still has the problematic import
+		if grep -q "import numpy" "$PRM800K_SETUP"; then
+			echo "Patching prm800k setup.py to fix numpy import issue..."
+			# Create a backup
+			cp "$PRM800K_SETUP" "$PRM800K_SETUP.bak"
+			# Remove the numpy import  line and add it to install_requires
+			cat >"$PRM800K_SETUP" <<'EOF'
+from setuptools import setup, find_packages
+
+setup(
+    name='prm800k',
+    packages=find_packages(),
+    version='0.0.1',
+    install_requires=[
+        'numpy',
+    ],
+)
+EOF
+			echo "Patch applied successfully!"
+			echo "New content of setup.py:"
+			cat "$PRM800K_SETUP"
+		else
+			echo "prm800k setup.py already patched or doesn't need patching."
+		fi
+	else
+		echo "WARNING: prm800k setup.py not found at $PRM800K_SETUP"
+	fi
+}
+
+# Common function to install evaluation requirements
+install_evaluation_requirements() {
+	echo "Installing evaluation requirements..."
+	if [ -f "/work/docker/evaluation_requirements.txt" ]; then
+		VIRTUAL_ENV=$VENV_DIR uv pip install -r /work/docker/evaluation_requirements.txt
+		echo "Evaluation requirements installed successfully!"
+	else
+		echo "Warning: evaluation_requirements.txt not found at /work/docker/evaluation_requirements.txt"
+		echo "Please ensure the workspace is properly mounted."
+	fi
+}
+
+# Common function to check build tools
+check_build_tools() {
+	echo "Checking if required build tools are available..."
+	for tool in cmake make git g++; do
+		if ! command -v $tool &>/dev/null; then
+			echo "Error: $tool is not installed. MLPerf LoadGen installation cannot proceed."
+			echo "Please ensure build dependencies are installed in the Docker image."
+			exit 1
+		fi
+	done
+}
+
+# Common function to install MLPerf LoadGen
+install_mlperf_loadgen() {
+	local FORCE_REBUILD="$1"
+	local BACKEND="$2"
+
+	echo ""
+	echo "=== Installing MLPerf LoadGen for backend: $BACKEND ==="
+
+	check_build_tools
+
+	# Set build directory
+	MLPERF_BUILD_DIR="/inference/loadgen/build"
+	WHEEL_DIR="$MLPERF_BUILD_DIR/wheels"
+
+	# Create build directories
+	mkdir -p $MLPERF_BUILD_DIR
+	mkdir -p $WHEEL_DIR
+
+	# Check if force rebuild is requested
+	if [ "$FORCE_REBUILD" = "true" ]; then
+		echo "Force rebuild requested. Removing cached wheels for backend $BACKEND..."
+		rm -f $WHEEL_DIR/mlcommons_loadgen*_${BACKEND}.whl
+	fi
+
+	# Check if we already have a built wheel for this backend
+	if ls $WHEEL_DIR/mlcommons_loadgen*_${BACKEND}.whl 1>/dev/null 2>&1; then
+		echo "Found existing MLPerf LoadGen wheel for backend $BACKEND in $WHEEL_DIR"
+
+		# Get Python version info for compatibility check
+		PYTHON_VERSION=$(python3 -c "import sys; print(f'cp{sys.version_info.major}{sys.version_info.minor}')")
+		PLATFORM=$(python3 -c "import platform; print(platform.machine())")
+
+		# Check if the wheel is compatible with current Python version
+		WHEEL_FILE=$(ls $WHEEL_DIR/mlcommons_loadgen*_${BACKEND}.whl | head -n1)
+		WHEEL_NAME=$(basename "$WHEEL_FILE")
+
+		echo "Current Python version tag: $PYTHON_VERSION"
+		echo "Current platform: $PLATFORM"
+		echo "Found wheel: $WHEEL_NAME"
+
+		# Temporarily rename wheel to remove backend suffix for installation
+		ORIGINAL_WHEEL_NAME=$(echo "$WHEEL_NAME" | sed "s/_${BACKEND}\.whl$/.whl/")
+		TEMP_WHEEL_PATH="$WHEEL_DIR/$ORIGINAL_WHEEL_NAME"
+
+		echo "Temporarily renaming wheel for installation: $WHEEL_NAME -> $ORIGINAL_WHEEL_NAME"
+		cp "$WHEEL_FILE" "$TEMP_WHEEL_PATH"
+		pip install --force-reinstall "$TEMP_WHEEL_PATH"
+
+		# Clean up temporary wheel (cached wheel with backend suffix is preserved)
+		rm -f "$TEMP_WHEEL_PATH"
+		echo "Preserved cached wheel: $WHEEL_NAME"
+
+		# Verify installation
+		if python3 -c "import mlperf_loadgen" 2>/dev/null; then
+			echo "MLPerf LoadGen installed successfully from cached wheel for backend $BACKEND!"
+			return 0
+		else
+			echo "Installation verification failed. Will rebuild..."
+			rm -f $WHEEL_DIR/mlcommons_loadgen*_${BACKEND}.whl
+			NEED_BUILD=true
+		fi
+	else
+		echo "No cached wheel found for backend $BACKEND."
+		NEED_BUILD=true
+	fi
+
+	# Build from source if needed
+	if [ "${NEED_BUILD:-true}" = "true" ]; then
+		echo "Building MLPerf LoadGen from source for backend $BACKEND..."
+
+		# Check for MLPerf inference repository at /inference, clone if needed
+		if [ ! -d "/inference" ] || [ ! -d "/inference/loadgen" ]; then
+			echo "MLPerf inference repository not found or incomplete at /inference"
+			echo "Cloning MLPerf inference repository from main branch..."
+
+			# Remove existing incomplete directory if it exists
+			if [ -d "/inference" ]; then
+				echo "Removing incomplete /inference directory..."
+				rm -rf /inference
+			fi
+
+			# Clone the repository
+			echo "Cloning https://github.com/mlcommons/inference.git to /inference..."
+			git clone --depth 1 --branch main https://github.com/mlcommons/inference.git /inference
+
+			if [ $? -ne 0 ]; then
+				echo "Error: Failed to clone MLPerf inference repository"
+				echo "Please check your internet connection or clone manually:"
+				echo "  git clone https://github.com/mlcommons/inference.git /inference"
+				exit 1
+			fi
+
+			echo "Successfully cloned MLPerf inference repository"
+		else
+			echo "Using existing MLPerf inference repository at /inference"
+		fi
+
+		# Navigate to loadgen directory
+		LOADGEN_DIR="/inference/loadgen"
+		if [ ! -d "$LOADGEN_DIR" ]; then
+			echo "Error: loadgen directory not found at $LOADGEN_DIR even after cloning"
+			echo "The repository structure may have changed. Please check manually."
+			exit 1
+		fi
+
+		cd $LOADGEN_DIR
+
+		# Build loadgen C++ library first
+		echo "Building loadgen C++ library..."
+		mkdir -p build
+		cd build
+		cmake -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DCMAKE_BUILD_TYPE=Release ..
+		make -j$(nproc)
+
+		# Go back to loadgen directory to build wheel
+		cd $LOADGEN_DIR
+
+		# Build the wheel using UV to avoid dependency conflicts
+		echo "Building MLPerf LoadGen wheel for backend $BACKEND..."
+		# First, ensure we have the build dependencies
+		VIRTUAL_ENV=$VENV_DIR uv pip install setuptools wheel "pybind11>=2.11.1"
+
+		# Build the wheel using python setup.py directly to avoid pip's dependency resolver conflicts
+		CFLAGS="-std=c++14 -O3" python3 setup.py bdist_wheel --dist-dir=$WHEEL_DIR
+
+		# Rename the wheel to include backend suffix
+		ORIGINAL_WHEEL=$(ls $WHEEL_DIR/mlcommons_loadgen*.whl | head -n1)
+		if [ -f "$ORIGINAL_WHEEL" ]; then
+			WHEEL_NAME=$(basename "$ORIGINAL_WHEEL")
+			# Extract the base name and extension
+			WHEEL_BASE="${WHEEL_NAME%.whl}"
+			BACKEND_WHEEL="${WHEEL_BASE}_${BACKEND}.whl"
+			BACKEND_WHEEL_PATH="$WHEEL_DIR/$BACKEND_WHEEL"
+
+			echo "Renaming wheel from $WHEEL_NAME to $BACKEND_WHEEL"
+			mv "$ORIGINAL_WHEEL" "$BACKEND_WHEEL_PATH"
+			WHEEL_FILE="$BACKEND_WHEEL_PATH"
+		else
+			echo "Error: No wheel file found after build!"
+			exit 1
+		fi
+
+		# Install the wheel using pip directly
+		echo "Installing MLPerf LoadGen wheel for backend $BACKEND..."
+
+		# Temporarily rename wheel to remove backend suffix for installation
+		ORIGINAL_WHEEL_NAME=$(echo "$(basename "$WHEEL_FILE")" | sed "s/_${BACKEND}\.whl$/.whl/")
+		TEMP_WHEEL_PATH="$WHEEL_DIR/$ORIGINAL_WHEEL_NAME"
+
+		echo "Temporarily renaming wheel for installation: $(basename "$WHEEL_FILE") -> $ORIGINAL_WHEEL_NAME"
+		cp "$WHEEL_FILE" "$TEMP_WHEEL_PATH"
+
+		pip install --force-reinstall "$TEMP_WHEEL_PATH"
+
+		# Clean up temporary wheel (backend-suffixed wheel is preserved for caching)
+		rm -f "$TEMP_WHEEL_PATH"
+		echo "Preserved cached wheel for future use: $(basename "$WHEEL_FILE")"
+
+		# Verify installation
+		if python3 -c "import mlperf_loadgen" 2>/dev/null; then
+			echo "MLPerf LoadGen built and installed successfully for backend $BACKEND!"
+		else
+			echo "Error: MLPerf LoadGen installation verification failed!"
+			exit 1
+		fi
+	fi
+
+	# Return to work directory
+	cd /work
+}
+
+# Common function to print final setup info
+print_setup_info() {
+	local VENV_DIR="$1"
+	local BACKEND="$2"
+	local ACTIVATE_VENV="$3"
+
+	echo ""
+	echo "=== Setup completed successfully ==="
+	echo "Virtual environment created at: $VENV_DIR"
+	echo "Backend: $BACKEND"
+	echo ""
+	echo "IMPORTANT USAGE NOTES:"
+	echo "====================="
+
+	if [ "$ACTIVATE_VENV" = "true" ]; then
+		echo "Virtual environment has been activated for this backend."
+		echo ""
+		echo "For all commands with this backend, the virtual environment is active:"
+		case "$BACKEND" in
+		"pytorch")
+			echo "   (.venv_pytorch) \$ torchrun --nproc_per_node=8 run_eval_mpi.py ..."
+			echo "   (.venv_pytorch) \$ torchrun --nproc_per_node=8 run_mlperf_mpi.py ..."
+			;;
+		"vllm")
+			echo "   (.venv_vllm) \$ python run_eval.py ..."
+			echo "   (.venv_vllm) \$ python run_mlperf.py ..."
+			;;
+		"sglang")
+			echo "   (.venv_sglang) \$ python run_eval.py ..."
+			echo "   (.venv_sglang) \$ python run_mlperf.py ..."
+			;;
+		esac
+		echo "   \$ python eval_accuracy.py ..."
+	else
+		echo "Virtual environment NOT activated for this backend ($BACKEND)."
+		echo ""
+		echo "For regular inference and MLPerf runs with this backend:"
+		echo "   DO NOT activate the virtual environment. Run directly:"
+		echo ""
+		echo "For accuracy evaluation ONLY, activate the virtual environment:"
+		echo "   \$ source $VENV_DIR/bin/activate"
+		echo "   (.venv_BACKEND) \$ python eval_accuracy.py ..."
+		echo "   (.venv_BACKEND) \$ deactivate"
+	fi
+
+	echo ""
+	echo "MLPerf LoadGen installed is installed as system package."
+	echo "Build artifacts cached at: /inference/loadgen/build"
+	echo "Wheels cached at: /inference/loadgen/build/wheels"
+}
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/setup_scripts/setup_pytorch.sh b/language/deepseek-r1/docker/setup_scripts/setup_pytorch.sh
new file mode 100755
index 0000000000..ab271f2dd3
--- /dev/null
+++ b/language/deepseek-r1/docker/setup_scripts/setup_pytorch.sh
@@ -0,0 +1,131 @@
+#!/bin/bash
+# Setup script for MLPerf DeepSeek evaluation environment - PyTorch Backend
+# This script sets up the PyTorch backend with virtual environment activated
+
+set -e  # Exit on error
+
+# Source common functions
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+source "$SCRIPT_DIR/common.sh"
+
+# Parse command line arguments
+FORCE_REBUILD=false
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --force-rebuild)
+            FORCE_REBUILD=true
+            shift
+            ;;
+        --help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --force-rebuild    Force rebuild of MLPerf LoadGen from source"
+            echo "  --help            Show this help message"
+            echo ""
+            echo "PyTorch Backend Setup:"
+            echo "- Creates and activates virtual environment for all operations"
+            echo "- Installs accuracy evaluation dependencies"
+            echo "- Sets up MLPerf LoadGen"
+            echo "- Virtual environment remains active after setup"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+echo "=== Setting up MLPerf DeepSeek evaluation environment - PyTorch Backend ==="
+echo "=== Virtual environment will be active for all PyTorch operations ==="
+
+# Check if uv is installed
+check_uv_installed
+
+# Set the virtual environment directory
+VENV_DIR="/work/.venv_pytorch"
+
+# Setup virtual environment
+setup_virtual_environment "$VENV_DIR"
+
+# Activate the virtual environment for setup and subsequent use
+echo "Activating virtual environment for PyTorch backend..."
+source "$VENV_DIR/bin/activate"
+
+# Install build dependencies
+install_build_dependencies
+
+# Apply patch to fix prm800k setup.py
+patch_prm800k_setup
+
+# Install evaluation requirements
+install_evaluation_requirements
+
+# Install MLPerf LoadGen
+install_mlperf_loadgen "$FORCE_REBUILD" "$MLPERF_BACKEND"
+
+# PyTorch-specific setup
+echo ""
+echo "=== PyTorch Backend-Specific Setup ==="
+
+VIRTUAL_ENV=$VENV_DIR uv pip install -r /opt/ref_dsinfer/inference/requirements.txt
+
+# Verify PyTorch is available (should be from base image)
+if python3 -c "import torch" 2>/dev/null; then
+    TORCH_VERSION=$(python3 -c "import torch; print(torch.__version__)")
+    echo "PyTorch is available: version $TORCH_VERSION"
+else
+    echo "Warning: PyTorch not found in the environment"
+fi
+
+# Set PYTHONPATH to include ref_dsinfer paths for checks
+export PYTHONPATH="/opt:/opt/ref_dsinfer/inference:${PYTHONPATH}"
+
+# Verify ref_dsinfer package is available
+if python3 -c "import ref_dsinfer" 2>/dev/null; then
+    echo "DeepSeek-V3 ref_dsinfer package is available"
+else
+    echo "Warning: ref_dsinfer package not found. Check PYTHONPATH and /opt/ref_dsinfer"
+fi
+
+# Check if kernel module is importable
+if python3 -c "import sys; sys.path.append('/opt/ref_dsinfer/inference'); from ref_dsinfer.inference.model import Transformer, ModelArgs" 2>/dev/null; then
+    echo "✓ ref_dsinfer inference model imports successfully"
+else
+    echo "✗ ref_dsinfer inference model import failed. Checking kernel module..."
+    
+    # Try to import kernel directly to get more detailed error
+    if python3 -c "import sys; sys.path.append('/opt/ref_dsinfer/inference'); import kernel" 2>/dev/null; then
+        echo "✓ kernel module is accessible directly"
+    else
+        echo "✗ kernel module import failed. This may be expected if CUDA extensions need compilation."
+        echo "The kernel.py file should contain fallback implementations for CPU."
+    fi
+fi
+
+# Model download and conversion
+echo ""
+echo "=== Model Download and Conversion ==="
+echo "Downloading DeepSeek-R1 model..."
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download deepseek-ai/DeepSeek-R1 --revision 56d4cbbb4d29f4355bab4b9a39ccb717a14ad5ad --local-dir /raid/data/${USER}/models/deepseek-ai_DeepSeek-R1
+
+echo "Converting model to inference format..."
+# Check if converted model already exists
+CONVERTED_MODEL_PATH="/raid/data/${USER}/models/deepseek-ai_DeepSeek-R1-Demo"
+if [ -d "$CONVERTED_MODEL_PATH" ] && [ -f "$CONVERTED_MODEL_PATH/model0-mp8.safetensors" ] && [ -f "$CONVERTED_MODEL_PATH/tokenizer.json" ]; then
+    echo "Converted model already exists at $CONVERTED_MODEL_PATH, skipping conversion..."
+else
+    echo "Converting model to inference format..."
+    python /opt/ref_dsinfer/inference/convert.py --hf-ckpt-path /raid/data/${USER}/models/deepseek-ai_DeepSeek-R1 --save-path /raid/data/${USER}/models/deepseek-ai_DeepSeek-R1-Demo --n-experts 256 --model-parallel 8
+fi
+
+echo "Model download and conversion completed."
+
+# Print setup information (with venv activated)
+print_setup_info "$VENV_DIR" "pytorch" "true"
+
+echo ""
+echo "=== PyTorch Backend Setup Complete ==="
+echo "Virtual environment is now active and will remain active."
+echo "Ready for PyTorch distributed inference and MLPerf runs." 
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/setup_scripts/setup_sglang.sh b/language/deepseek-r1/docker/setup_scripts/setup_sglang.sh
new file mode 100755
index 0000000000..1fc83738ce
--- /dev/null
+++ b/language/deepseek-r1/docker/setup_scripts/setup_sglang.sh
@@ -0,0 +1,162 @@
+#!/bin/bash
+# Setup script for MLPerf DeepSeek evaluation environment - SGLang Backend
+# This script sets up the SGLang backend with virtual environment activated
+
+set -e  # Exit on error
+
+# Source common functions
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+source "$SCRIPT_DIR/common.sh"
+
+# Parse command line arguments
+FORCE_REBUILD=false
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --force-rebuild)
+            FORCE_REBUILD=true
+            shift
+            ;;
+        --help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --force-rebuild    Force rebuild of MLPerf LoadGen from source"
+            echo "  --help            Show this help message"
+            echo ""
+            echo "SGLang Backend Setup:"
+            echo "- Creates and activates virtual environment for all operations"
+            echo "- Installs accuracy evaluation dependencies"
+            echo "- Sets up MLPerf LoadGen"
+            echo "- Installs SGLang-specific optimizations"
+            echo "- Virtual environment remains active after setup"
+            echo ""
+            echo "Note: SGLang server may take 20+ minutes to start for large models"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+echo "=== Setting up MLPerf DeepSeek evaluation environment - SGLang Backend ==="
+echo "=== Virtual environment will be active for all SGLang operations ==="
+
+# Check if uv is installed
+check_uv_installed
+
+# Set the virtual environment directory
+VENV_DIR="/work/.venv_sglang"
+
+# Setup virtual environment
+setup_virtual_environment "$VENV_DIR"
+
+# Activate the virtual environment for setup and subsequent use
+echo "Activating virtual environment for SGLang backend..."
+source "$VENV_DIR/bin/activate"
+
+# Install build dependencies
+install_build_dependencies
+
+# Apply patch to fix prm800k setup.py
+patch_prm800k_setup
+
+# Install evaluation requirements
+install_evaluation_requirements
+
+# Install MLPerf LoadGen
+install_mlperf_loadgen "$FORCE_REBUILD" "$MLPERF_BACKEND"
+
+# SGLang-specific setup
+echo ""
+echo "=== SGLang Backend-Specific Setup ==="
+
+# Install core dependencies for SGLang
+echo "Installing core dependencies for SGLang..."
+VIRTUAL_ENV=$VENV_DIR uv pip install \
+    transformers \
+    pandas \
+    numpy \
+    tqdm \
+    huggingface_hub \
+    datasets \
+    accelerate \
+    openai \
+    httpx \
+    requests \
+    torch==2.6.0 \
+    sglang[all]==0.4.6.post5 
+
+# Verify SGLang installation
+if python3 -c "import sglang" 2>/dev/null; then
+    SGLANG_VERSION=$(python3 -c "import sglang; print(sglang.__version__)")
+    echo "SGLang installed successfully: version $SGLANG_VERSION"
+else
+    echo "Error: SGLang installation failed"
+    exit 1
+fi
+
+# Install sgl-kernel for optimizations
+echo "Installing sgl-kernel..."
+VIRTUAL_ENV=$VENV_DIR uv pip install sgl-kernel --no-deps --force-reinstall || echo "Warning: sgl-kernel installation failed"
+
+# Verify sgl_kernel installation
+if python3 -c "import sgl_kernel" 2>/dev/null; then
+    echo "sgl_kernel installed successfully"
+else
+    echo "Warning: sgl_kernel import failed - this optimization may not be available"
+fi
+
+# Verify torch is available for SGLang
+if python3 -c "import torch" 2>/dev/null; then
+    TORCH_VERSION=$(python3 -c "import torch; print(torch.__version__)")
+    echo "PyTorch is available: version $TORCH_VERSION"
+    
+    # Check CUDA availability
+    if python3 -c "import torch; print('CUDA available:', torch.cuda.is_available())" 2>/dev/null; then
+        GPU_COUNT=$(python3 -c "import torch; print('GPU count:', torch.cuda.device_count())" 2>/dev/null || echo "GPU count: 0")
+        echo "$GPU_COUNT"
+    fi
+else
+    echo "Warning: PyTorch not found in the environment"
+fi
+
+# Create logs directory for server output
+echo "Creating logs directory for SGLang server..."
+mkdir -p /work/logs
+
+# Print component installation summary
+echo ""
+echo "=== Component Installation Summary ==="
+echo -n "SGLang: "
+python3 -c "import sglang; print('✓ Installed')" 2>/dev/null || echo "✗ Not installed"
+
+echo -n "FlashInfer: "
+python3 -c "import flashinfer; print('✓ Installed')" 2>/dev/null || echo "✗ Not installed"
+
+echo -n "sgl_kernel: "
+python3 -c "import sgl_kernel; print('✓ Installed')" 2>/dev/null || echo "✗ Not installed"
+
+echo -n "DeepGEMM: "
+python3 -c "import deepgemm; print('✓ Installed')" 2>/dev/null || echo "✗ Not installed"
+
+echo -n "MLPerf LoadGen: "
+python3 -c "import mlperf_loadgen; print('✓ Installed')" 2>/dev/null || echo "✗ Not installed"
+
+echo ""
+
+# Print setup information (with venv activated)
+print_setup_info "$VENV_DIR" "sglang" "true"
+
+echo ""
+echo "=== SGLang Backend Setup Complete ==="
+echo "Virtual environment is now active and will remain active."
+echo ""
+echo "IMPORTANT: SGLang Server Startup Time"
+echo "The SGLang server may take 20-30 minutes to start for large models like DeepSeek-R1."
+echo "The backend will show a progress bar during server startup."
+echo ""
+echo "Server logs will be saved to: /work/logs/sglang_server_*.log"
+echo ""
+echo "Ready for SGLang inference and MLPerf runs." 
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/setup_scripts/setup_vllm.sh b/language/deepseek-r1/docker/setup_scripts/setup_vllm.sh
new file mode 100755
index 0000000000..fa3f7849fa
--- /dev/null
+++ b/language/deepseek-r1/docker/setup_scripts/setup_vllm.sh
@@ -0,0 +1,105 @@
+#!/bin/bash
+# Setup script for MLPerf DeepSeek evaluation environment - vLLM Backend
+# This script sets up the vLLM backend with virtual environment activated
+
+set -e  # Exit on error
+
+# Source common functions
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+source "$SCRIPT_DIR/common.sh"
+
+# Parse command line arguments
+FORCE_REBUILD=false
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --force-rebuild)
+            FORCE_REBUILD=true
+            shift
+            ;;
+        --help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --force-rebuild    Force rebuild of MLPerf LoadGen from source"
+            echo "  --help            Show this help message"
+            echo ""
+            echo "vLLM Backend Setup:"
+            echo "- Creates and activates virtual environment for all operations"
+            echo "- Installs accuracy evaluation dependencies"
+            echo "- Sets up MLPerf LoadGen"
+            echo "- Virtual environment remains active after setup"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+echo "=== Setting up MLPerf DeepSeek evaluation environment - vLLM Backend ==="
+echo "=== Virtual environment will be active for all vLLM operations ==="
+
+# Check if uv is installed
+check_uv_installed
+
+# Set the virtual environment directory
+VENV_DIR="/work/.venv_vllm"
+
+# Setup virtual environment
+setup_virtual_environment "$VENV_DIR"
+
+# Activate the virtual environment for setup and subsequent use
+echo "Activating virtual environment for vLLM backend..."
+source "$VENV_DIR/bin/activate"
+
+# Install build dependencies
+install_build_dependencies
+
+# Apply patch to fix prm800k setup.py
+patch_prm800k_setup
+
+# Install evaluation requirements
+install_evaluation_requirements
+
+# Install MLPerf LoadGen
+install_mlperf_loadgen "$FORCE_REBUILD" "$MLPERF_BACKEND"
+
+# vLLM-specific setup
+echo ""
+echo "=== vLLM Backend-Specific Setup ==="
+
+# Verify vLLM is available (should be from base image)
+if python3 -c "import vllm" 2>/dev/null; then
+    VLLM_VERSION=$(python3 -c "import vllm; print(vllm.__version__)")
+    echo "vLLM is available: version $VLLM_VERSION"
+else
+    echo "Warning: vLLM not found in the environment"
+fi
+
+# Install flash attention for improved performance
+echo "Installing flash attention..."
+VIRTUAL_ENV=$VENV_DIR uv pip install https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.5%2Bcu128torch2.7-cp38-abi3-linux_x86_64.whl
+
+
+# Verify torch is available for vLLM
+if python3 -c "import torch" 2>/dev/null; then
+    TORCH_VERSION=$(python3 -c "import torch; print(torch.__version__)")
+    echo "PyTorch is available: version $TORCH_VERSION"
+    
+    # Check CUDA availability
+    if python3 -c "import torch; print('CUDA available:', torch.cuda.is_available())" 2>/dev/null; then
+        GPU_COUNT=$(python3 -c "import torch; print('GPU count:', torch.cuda.device_count())" 2>/dev/null || echo "GPU count: 0")
+        echo "$GPU_COUNT"
+    fi
+else
+    echo "Warning: PyTorch not found in the environment"
+fi
+
+# Print setup information (with venv activated)
+print_setup_info "$VENV_DIR" "vllm" "true"
+
+echo ""
+echo "=== vLLM Backend Setup Complete ==="
+echo "Virtual environment is now active and will remain active."
+echo "Ready for vLLM inference and MLPerf runs." 
\ No newline at end of file
diff --git a/language/deepseek-r1/eval_accuracy.py b/language/deepseek-r1/eval_accuracy.py
new file mode 100644
index 0000000000..1bb31b9076
--- /dev/null
+++ b/language/deepseek-r1/eval_accuracy.py
@@ -0,0 +1,830 @@
+#!/usr/bin/env python3
+"""
+Standalone evaluation script for mlperf-inference deepseek-r1 dataset.
+
+Expected input format (pickle file with DataFrame):
+- model_output: The model's response text
+- tok_model_output_len: The length of the model's response tokens
+- ground_truth: The expected answer
+- dataset: Dataset name (e.g., 'gpqa', 'mmlu_pro', 'math500', 'livecodebench', 'aime')
+- question: The question text
+
+Output adds two columns:
+- extracted_answer: Parsed answer from model output
+- prompt_accuracy: 100.0 if correct, 0.0 if incorrect
+"""
+
+import sys
+import os
+import argparse
+import logging
+import pickle
+import json
+import re
+import shutil
+import time
+from functools import lru_cache
+from typing import Dict, Any, Optional, Tuple, Union
+import pandas as pd
+from tqdm import tqdm
+from concurrent.futures import ProcessPoolExecutor, as_completed
+import multiprocessing
+from pathlib import Path
+
+# MLPerf log processing imports
+import numpy as np
+from transformers import AutoTokenizer
+
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+
+# =============================================================================
+# MLPerf Log Accuracy Processing
+# =============================================================================
+
+def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
+                               dataset_file: Union[str, Path],
+                               checkpoint_path: str,
+                               dtype: str = "int32",
+                               output_dir: Optional[Union[str, Path]] = None,
+                               base_filename: Optional[str] = None) -> Tuple[pd.DataFrame, str]:
+    """Process MLPerf log accuracy file and evaluate results.
+
+    Args:
+        mlperf_log_file: Path to mlperf_log_accuracy.json
+        dataset_file: Path to ground truth dataset pickle file
+        checkpoint_path: Path to tokenizer checkpoint
+        dtype: Data type for numpy conversion ("int32", "int64", "float") - default "int32" matches MLPerf SUT
+        output_dir: Directory to save evaluated results
+        base_filename: Base filename for output file
+
+    Returns:
+        Tuple of (evaluated_dataframe, saved_file_path)
+    """
+    # Validate input files
+    mlperf_log_file = Path(mlperf_log_file)
+    dataset_file = Path(dataset_file)
+
+    if not mlperf_log_file.exists():
+        raise FileNotFoundError(f"MLPerf log file not found: {mlperf_log_file}")
+    if not dataset_file.exists():
+        raise FileNotFoundError(f"Dataset file not found: {dataset_file}")
+
+    logger.info(f"Processing MLPerf log: {mlperf_log_file}")
+    logger.info(f"Using dataset: {dataset_file}")
+    logger.info(f"Using checkpoint: {checkpoint_path}")
+
+    # Load tokenizer
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(
+            checkpoint_path,
+            model_max_length=22000,
+            padding_side="left",
+            use_fast=False,
+        )
+        logger.info("Tokenizer loaded successfully")
+    except Exception as e:
+        raise RuntimeError(f"Failed to load tokenizer from {checkpoint_path}: {e}")
+
+    # Load ground truth dataset
+    try:
+        with open(dataset_file, 'rb') as f:
+            dataset_df = pickle.load(f)
+
+        if isinstance(dataset_df, pd.DataFrame):
+            if 'gt_output' in dataset_df.columns:
+                ground_truths = dataset_df['gt_output'].tolist()
+            elif 'ground_truth' in dataset_df.columns:
+                ground_truths = dataset_df['ground_truth'].tolist()
+            else:
+                raise ValueError("Dataset must contain 'gt_output' or 'ground_truth' column")
+
+            # Get other required columns with fallbacks
+            if 'dataset' in dataset_df.columns:
+                datasets = dataset_df['dataset'].tolist()
+            elif 'metric' in dataset_df.columns:
+                # Infer dataset from metric names
+                datasets = [metric.replace('_em', '').replace('_', '') for metric in dataset_df['metric'].tolist()]
+            else:
+                datasets = ['unknown'] * len(ground_truths)
+
+            if 'question' in dataset_df.columns:
+                questions = dataset_df['question'].tolist()
+            elif 'input' in dataset_df.columns:
+                questions = dataset_df['input'].tolist()
+            else:
+                questions = [''] * len(ground_truths)
+        else:
+            # Fallback for other formats
+            ground_truths = dataset_df if isinstance(dataset_df, list) else []
+            datasets = ['unknown'] * len(ground_truths)
+            questions = [''] * len(ground_truths)
+
+        logger.info(f"Loaded {len(ground_truths)} ground truth entries")
+    except Exception as e:
+        raise RuntimeError(f"Failed to load dataset file: {e}")
+
+    # Set numpy dtype - default to int32 to match MLPerf SUT output format
+    eval_dtype = np.int32
+    if dtype == "int64":
+        eval_dtype = np.int64
+    elif dtype == "float":
+        eval_dtype = np.float32
+
+    # Load MLPerf log
+    try:
+        mlperf_results = []
+        with open(mlperf_log_file, 'r') as f:
+            for line_num, line in enumerate(f, 1):
+                line = line.strip()
+                if not line:  # Skip empty lines
+                    continue
+                try:
+                    mlperf_results.append(json.loads(line))
+                except json.JSONDecodeError as e:
+                    logger.warning(f"Failed to parse line {line_num}: {e}")
+                    continue
+        logger.info(f"Loaded {len(mlperf_results)} MLPerf results")
+    except Exception as e:
+        raise RuntimeError(f"Failed to load MLPerf log file: {e}")
+
+    # Process MLPerf results
+    target_required = []
+    datasets_required = []
+    questions_required = []
+    preds_token_ids = []
+    tok_lengths = []
+
+    seen_qsl_idx = set()
+
+    for pred_entry in mlperf_results:
+        qsl_idx = pred_entry["qsl_idx"]
+
+        # Skip duplicates
+        if qsl_idx in seen_qsl_idx:
+            continue
+        seen_qsl_idx.add(qsl_idx)
+
+        # Validate qsl_idx is within bounds
+        if qsl_idx >= len(ground_truths):
+            logger.warning(f"qsl_idx {qsl_idx} out of bounds, skipping")
+            continue
+
+        # Extract and convert hex data to token IDs
+        try:
+            hex_data = pred_entry["data"]
+            token_ids = np.frombuffer(bytes.fromhex(hex_data), eval_dtype)
+            tok_lengths.append(len(token_ids))
+            preds_token_ids.append(token_ids)
+
+            # Map to ground truth
+            target_required.append(ground_truths[qsl_idx])
+            datasets_required.append(datasets[qsl_idx])
+            questions_required.append(questions[qsl_idx])
+
+        except Exception as e:
+            logger.warning(f"Error processing entry with qsl_idx {qsl_idx}: {e}")
+            continue
+
+    if not preds_token_ids:
+        raise ValueError("No valid predictions found in MLPerf log")
+
+    logger.info(f"Processing {len(preds_token_ids)} valid predictions")
+
+    # Decode tokens to text
+    try:
+        preds_decoded_text = tokenizer.batch_decode(
+            preds_token_ids, skip_special_tokens=True
+        )
+        logger.info("Token decoding completed")
+    except Exception as e:
+        raise RuntimeError(f"Failed to decode tokens: {e}")
+
+    # Create DataFrame for evaluation
+    df_data = {
+        'model_output': preds_decoded_text,
+        'ground_truth': target_required,
+        'dataset': datasets_required,
+        'question': questions_required,
+        'tok_model_output_len': tok_lengths
+    }
+
+    df = pd.DataFrame(df_data)
+    logger.info(f"Created DataFrame with {len(df)} rows")
+
+    # Process and save using existing unified function
+    df_evaluated, saved_file_path = process_and_save_dataframe(
+        df,
+        output_dir=output_dir,
+        base_filename=base_filename
+    )
+
+    # Print results using unified function
+    print_evaluation_results(df_evaluated, logger)
+
+    return df_evaluated, saved_file_path
+
+
+# =============================================================================
+# Input Validation
+# =============================================================================
+
+def validate_dataframe(df: pd.DataFrame) -> None:
+    """Validate input DataFrame has required columns."""
+    if not isinstance(df, pd.DataFrame):
+        raise ValueError("Input must be a pandas DataFrame")
+
+    required_cols = ['model_output', 'dataset', 'ground_truth', 'tok_model_output_len']
+    missing_cols = [col for col in required_cols if col not in df.columns]
+    if missing_cols:
+        raise ValueError(f"Missing required columns: {missing_cols}")
+
+
+def validate_text_input(text: Any) -> str:
+    """Validate and convert text input to string."""
+    if pd.isna(text) or text is None:
+        return ""
+    return str(text).strip()
+
+
+def validate_dataset_name(dataset: Any) -> str:
+    """Validate dataset name."""
+    if pd.isna(dataset) or not dataset:
+        raise ValueError("Dataset name cannot be empty")
+    return str(dataset).lower()
+
+
+# =============================================================================
+# Answer Parsing Functions
+# =============================================================================
+
+def parse_multiple_choice(text: str, max_option: str = 'D') -> Optional[str]:
+    """Parse multiple choice answer (A-D or A-J)."""
+    text = validate_text_input(text)
+    if not text:
+        return None
+
+    # Clean artifacts
+    if text.startswith(("['", '["')) and text.endswith(("']", '"]')):
+        text = text[2:-2].strip()
+
+    text = text.replace(r'\n', '\n').replace(r'\'', "'")
+
+    # Find ANSWER/FINAL ANSWER pattern
+    pattern = rf"\b(?:ANSWER|FINAL\s*ANSWER)\b\s*[:=]?\s*(?:\(?\s*([A-{max_option}])\s*\)?)(?:\s*$|[^A-Za-z])"
+    matches = list(re.finditer(pattern, text, re.IGNORECASE))
+
+    if matches:
+        return matches[-1].group(1).upper()
+
+    # MMLU-Pro fallback: standalone letter
+    if max_option == 'J':
+        fallback_matches = list(re.finditer(
+            r"\b([A-J])\b", text, re.IGNORECASE))
+        if fallback_matches:
+            return fallback_matches[-1].group(1).upper()
+
+    return None
+
+
+def parse_boxed_math(text: str) -> Optional[str]:
+    """Parse \\boxed{answer} format."""
+    text = validate_text_input(text)
+    if not text:
+        return None
+
+    idx = text.rfind(r"\boxed{")
+    if idx == -1:
+        return None
+
+    # Find matching brace
+    depth, i = 0, idx + 7
+    content_start = i
+    while i < len(text):
+        if text[i] == '{':
+            depth += 1
+        elif text[i] == '}':
+            if depth == 0:
+                return text[content_start:i].strip()
+            depth -= 1
+        i += 1
+    return None
+
+
+def parse_aime_answer(text: str) -> Optional[int]:
+    """Parse AIME integer answer (0-999)."""
+    text = validate_text_input(text)
+    if not text:
+        return None
+
+    # Priority 1: \boxed{digits}
+    boxed_matches = list(re.finditer(r"\\boxed{\s*(\d+)\s*}", text))
+    if boxed_matches:
+        extracted_str = boxed_matches[-1].group(1)
+    else:
+        # Priority 2: Answer: <digits>
+        answer_matches = list(re.finditer(
+            r"Answer:\s*(\d+)(?!\.)\b", text, re.IGNORECASE | re.MULTILINE))
+        if not answer_matches:
+            return None
+        extracted_str = answer_matches[-1].group(1)
+
+    try:
+        val = int(extracted_str)
+        if 0 <= val <= 999:
+            return val
+    except ValueError:
+        pass
+
+    return None
+
+
+def parse_code(text: str) -> Optional[str]:
+    """Parse code from ```python code block."""
+    text = validate_text_input(text)
+    if not text:
+        return None
+
+    match = re.search(r"```python(.*?)```", text, re.DOTALL)
+    return match.group(1).strip() if match else None
+
+
+# =============================================================================
+# Answer Evaluation Functions
+# =============================================================================
+
+def evaluate_multiple_choice(parsed: Optional[str], ground_truth: str, valid_options: str) -> bool:
+    """Evaluate multiple choice answer."""
+    if not parsed or not ground_truth:
+        return False
+
+    parsed = parsed.upper()
+    ground_truth = ground_truth.upper()
+
+    return parsed in valid_options and parsed == ground_truth
+
+
+def evaluate_math500(parsed: Optional[str], ground_truth: str) -> bool:
+    """Evaluate MATH-500 using PRM800K grader."""
+    if not parsed or not ground_truth:
+        return False
+
+    parsed = str(parsed).strip()
+    ground_truth = str(ground_truth)
+
+    if not parsed:
+        return False
+
+    # Use sys.path approach for proper module importing
+    workspace_path = os.path.dirname(os.path.abspath(__file__))
+    prm800k_module_path = os.path.join(workspace_path, "submodules", "prm800k", "prm800k")
+
+    if not os.path.exists(prm800k_module_path):
+        raise FileNotFoundError(f"PRM800K module not found at: {prm800k_module_path}")
+
+    # Save current directory and sys.path
+    original_cwd = os.getcwd()
+    original_syspath = sys.path.copy()
+
+    try:
+        # Add prm800k module path to sys.path
+        if prm800k_module_path not in sys.path:
+            sys.path.insert(0, prm800k_module_path)
+        
+        # Change directory as some imports might use relative paths
+        os.chdir(prm800k_module_path)
+        
+        # Now import should work
+        from grading.grader import grade_answer
+        result = grade_answer(given_answer=parsed, ground_truth=ground_truth)
+    except ImportError as e:
+        raise ImportError(f"Failed to import PRM800K grader: {e}")
+    finally:
+        # Always restore original directory and sys.path
+        os.chdir(original_cwd)
+        sys.path[:] = original_syspath
+
+    return result
+
+
+def evaluate_aime(parsed: Optional[int], ground_truth: Any) -> bool:
+    """Evaluate AIME integer answer."""
+    if parsed is None:
+        return False
+
+    try:
+        gt_int = int(ground_truth)
+        return int(parsed) == gt_int
+    except (ValueError, TypeError):
+        return False
+
+
+@lru_cache(maxsize=1)
+def load_lcb_benchmark() -> Dict[str, Any]:
+    """Load LiveCodeBench benchmark with caching."""
+    lcb_dir = os.path.abspath(os.path.join(
+        os.path.dirname(__file__), "submodules", "LiveCodeBench"))
+
+    if not os.path.isdir(lcb_dir):
+        raise FileNotFoundError(
+            f"LiveCodeBench submodule required at: {lcb_dir}")
+
+    original_cwd = os.getcwd()
+    os.chdir(lcb_dir)
+
+    if lcb_dir not in sys.path:
+        sys.path.insert(0, lcb_dir)
+
+    try:
+        os.environ['TQDM_DISABLE'] = '1'
+
+        from lcb_runner.utils.scenarios import Scenario
+        from lcb_runner.runner.scenario_router import build_prompt_benchmark
+
+        mock_args = argparse.Namespace(
+            scenario=Scenario.codegeneration, release_version="release_v1",
+            subset="code_generation", language="python", not_fast=False,
+            start_date=None, end_date=None, k=[1], num_samples=1,
+            timeout=60, num_workers=1, num_process_evaluate=1,
+            model_name="standalone_eval", output_dir="/tmp",
+            prompt_type="custom", continue_existing=False, evaluate=True
+        )
+
+        full_benchmark, _ = build_prompt_benchmark(mock_args)
+        return {inst.question_id: inst for inst in full_benchmark}
+
+    finally:
+        os.chdir(original_cwd)
+        os.environ.pop('TQDM_DISABLE', None)
+
+
+def evaluate_livecodebench(code: Optional[str], question_id: str) -> bool:
+    """Evaluate LiveCodeBench code generation."""
+    if not code or not question_id:
+        return False
+
+    lcb_dir = os.path.abspath(os.path.join(
+        os.path.dirname(__file__), "submodules", "LiveCodeBench"))
+    benchmark_map = load_lcb_benchmark()
+
+    instance = benchmark_map.get(question_id)
+    if not instance:
+        return False
+
+    original_cwd = os.getcwd()
+    temp_dir = f"/tmp/temp_lcb_eval_{question_id}_{int(time.time())}"
+    os.makedirs(temp_dir, exist_ok=True)
+
+    try:
+        os.chdir(lcb_dir)
+        os.environ['TQDM_DISABLE'] = '1'
+
+        from lcb_runner.utils.scenarios import Scenario
+        from lcb_runner.evaluation import extract_instance_results
+        from lcb_runner.runner.scenario_router import sort_and_extract_save_results, get_metrics
+
+        mock_args = argparse.Namespace(
+            scenario=Scenario.codegeneration, release_version="release_v1",
+            subset="code_generation", language="python", not_fast=False,
+            start_date=None, end_date=None, k=[1], num_samples=1,
+            timeout=60, num_workers=1, num_process_evaluate=1,
+            model_name="inline_handler_eval", output_dir=temp_dir,
+            prompt_type="custom", continue_existing=False, evaluate=True,
+        )
+
+        batch_benchmark = [instance]
+        batch_custom_outputs = [[code]]
+
+        save_results = [inst.insert_output(output, output)
+                        for inst, output in zip(batch_benchmark, batch_custom_outputs)]
+
+        _, combined_results = sort_and_extract_save_results(
+            mock_args.scenario, save_results)
+        _, instance_results, _ = get_metrics(
+            mock_args.scenario, mock_args, batch_benchmark, combined_results
+        )
+
+        graded = extract_instance_results(instance_results)
+        return graded and graded[0] and graded[0][0]
+
+    finally:
+        os.chdir(original_cwd)
+        shutil.rmtree(temp_dir, ignore_errors=True)
+        os.environ.pop('TQDM_DISABLE', None)
+
+
+def evaluate_livecodebench_worker(args: Tuple[str, str]) -> Tuple[str, bool]:
+    """Worker function for parallel LiveCodeBench evaluation."""
+    code, question_id = args
+
+    try:
+        return question_id, evaluate_livecodebench(code, question_id)
+    except Exception:
+        return question_id, False
+
+
+# =============================================================================
+# Dataset Configuration
+# =============================================================================
+
+DATASET_EVALUATORS = {
+    'gpqa': {
+        'parse': lambda text: parse_multiple_choice(text, 'D'),
+        'evaluate': lambda parsed, gt: evaluate_multiple_choice(parsed, gt, 'ABCD')
+    },
+    'mmlu_pro': {
+        'parse': lambda text: parse_multiple_choice(text, 'J'),
+        'evaluate': lambda parsed, gt: evaluate_multiple_choice(parsed, gt, 'ABCDEFGHIJ')
+    },
+    'math500': {
+        'parse': parse_boxed_math,
+        'evaluate': evaluate_math500
+    },
+    'aime': {
+        'parse': parse_aime_answer,
+        'evaluate': evaluate_aime
+    },
+    'livecodebench': {
+        'parse': parse_code,
+        'evaluate': evaluate_livecodebench
+    }
+}
+
+
+def get_evaluator(dataset_name: str) -> Dict[str, Any]:
+    """Get evaluator functions for dataset."""
+    dataset_lower = validate_dataset_name(dataset_name)
+
+    for key, evaluator in DATASET_EVALUATORS.items():
+        if key in dataset_lower:
+            return evaluator
+
+    raise ValueError(f"No evaluator found for dataset: {dataset_name}")
+
+
+# =============================================================================
+# Main Processing Functions
+# =============================================================================
+
+def process_row(row: pd.Series) -> Dict[str, Any]:
+    """Process a single row and return extracted answer and accuracy."""
+    dataset_name = validate_dataset_name(row['dataset'])
+    raw_output = validate_text_input(row['model_output'])
+    ground_truth = row['ground_truth']
+
+    evaluator = get_evaluator(dataset_name)
+    extracted = evaluator['parse'](raw_output)
+
+    is_correct = False
+    if extracted is not None and not pd.isna(ground_truth):
+        is_correct = evaluator['evaluate'](extracted, ground_truth)
+
+    return {
+        'extracted_answer': extracted,
+        'prompt_accuracy': 100.0 if is_correct else 0.0
+    }
+
+
+def process_livecodebench_parallel(df: pd.DataFrame, group_indices: pd.Index) -> Tuple[int, int]:
+    """Process LiveCodeBench items in parallel."""
+    # Prepare work items
+    work_items = []
+    for idx in group_indices:
+        row = df.loc[idx]
+        extracted = row.get('extracted_answer')
+        ground_truth = row.get('ground_truth')
+
+        if extracted is not None and not pd.isna(ground_truth):
+            work_items.append((idx, extracted, ground_truth))
+
+    if not work_items:
+        return 0, 0
+
+    # Process in parallel
+    max_workers = min(multiprocessing.cpu_count(), len(work_items))
+    logger.info(
+        f"Evaluating {len(work_items)} LiveCodeBench items with {max_workers} workers")
+
+    correct_count = 0
+    total_evaluated = 0
+
+    with ProcessPoolExecutor(max_workers=max_workers) as executor:
+        future_to_idx = {
+            executor.submit(evaluate_livecodebench_worker, (code, question_id)): idx
+            for idx, code, question_id in work_items
+        }
+
+        for future in tqdm(as_completed(future_to_idx, timeout=1200),
+                           total=len(future_to_idx), desc="Evaluating LiveCodeBench"):
+            idx = future_to_idx[future]
+
+            try:
+                question_id, is_correct = future.result(timeout=30)
+                df.at[idx, 'prompt_accuracy'] = 100.0 if is_correct else 0.0
+                total_evaluated += 1
+                if is_correct:
+                    correct_count += 1
+            except Exception as e:
+                logger.error(f"Error evaluating row {idx}: {e}")
+                df.at[idx, 'prompt_accuracy'] = 0.0
+                total_evaluated += 1
+
+    return correct_count, total_evaluated
+
+
+def process_dataframe(df: pd.DataFrame) -> pd.DataFrame:
+    """Process entire dataframe with optimized batch processing."""
+    validate_dataframe(df)
+
+    df_output = df.copy()
+    df_output['extracted_answer'] = None
+    df_output['prompt_accuracy'] = 0.0
+
+    # Process by dataset
+    for dataset_name, group_indices in tqdm(df_output.groupby('dataset').groups.items(),
+                                            desc="Processing datasets"):
+        evaluator = get_evaluator(dataset_name)
+
+        # Parse answers for all rows in this dataset
+        logger.info(
+            f"Processing {len(group_indices)} rows for dataset '{dataset_name}'")
+        for idx in group_indices:
+            row = df_output.loc[idx]
+            raw_output = validate_text_input(row['model_output'])
+            df_output.at[idx, 'extracted_answer'] = evaluator['parse'](
+                raw_output)
+
+        # Evaluate answers
+        if 'livecodebench' in dataset_name.lower():
+            correct_count, total_evaluated = process_livecodebench_parallel(
+                df_output, group_indices)
+        else:
+            # Sequential evaluation for other datasets
+            correct_count = 0
+            total_evaluated = 0
+
+            for idx in group_indices:
+                row = df_output.loc[idx]
+                extracted = row['extracted_answer']
+                ground_truth = row['ground_truth']
+
+                if extracted is not None and not pd.isna(ground_truth):
+                    is_correct = evaluator['evaluate'](extracted, ground_truth)
+                    df_output.at[idx,
+                                 'prompt_accuracy'] = 100.0 if is_correct else 0.0
+                    total_evaluated += 1
+                    if is_correct:
+                        correct_count += 1
+
+        # Log results
+        if total_evaluated > 0:
+            accuracy = correct_count / total_evaluated * 100
+            logger.info(
+                f"{dataset_name} results: {correct_count}/{total_evaluated} correct ({accuracy:.1f}% accuracy)")
+
+    return df_output
+
+
+# =============================================================================
+# Unified Evaluation Utilities
+# =============================================================================
+
+def print_evaluation_results(df_evaluated: pd.DataFrame, logger: Optional[logging.Logger] = None) -> Dict[str, Any]:
+    """Print evaluation results in a unified format.
+
+    Args:
+        df_evaluated: DataFrame with evaluated results
+        logger: Optional logger instance (uses module logger if not provided)
+
+    Returns:
+        Dictionary with evaluation statistics
+    """
+    if logger is None:
+        logger = logging.getLogger(__name__)
+
+    # Calculate statistics
+    evaluated = df_evaluated['extracted_answer'].notna().sum()
+    correct = (df_evaluated['prompt_accuracy'] > 0).sum()
+    accuracy = df_evaluated['prompt_accuracy'].mean()
+
+    # tok_model_output_len is now a required column
+    mean_output_len = float(df_evaluated['tok_model_output_len'].mean())
+
+    results = {
+        # 'evaluated': int(evaluated),
+        # 'correct': int(correct),
+        'mean-accuracy': float(accuracy),
+        'mean-output-tok-len': mean_output_len,
+        'num-samples': len(df_evaluated),
+    }
+
+    result_str = json.dumps(results, indent=2)
+    logger.info(f"\nEvaluation Results: {result_str}")
+
+    return results
+
+
+def process_and_save_dataframe(df: pd.DataFrame,
+                              output_dir: Optional[Union[str, Path]] = None,
+                              base_filename: Optional[str] = None) -> Tuple[pd.DataFrame, str]:
+    """Process dataframe for evaluation and save the results.
+
+    Args:
+        df: Input DataFrame to evaluate
+        output_dir: Directory to save the evaluated pickle file (defaults to same dir as source)
+        base_filename: Base filename for output (defaults to auto-generated)
+
+    Returns:
+        Tuple of (evaluated_dataframe, saved_file_path)
+    """
+    # Process the dataframe
+    df_evaluated = process_dataframe(df)
+
+    # Determine output path
+    if output_dir is None:
+        # Try to infer from existing path info in the dataframe or use current directory
+        output_dir = Path.cwd()
+    else:
+        output_dir = Path(output_dir)
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Generate filename if not provided
+    if base_filename is None:
+        timestamp = time.strftime("%Y%m%d_%H%M%S")
+        base_filename = f"results_evaluated_{timestamp}.pkl"
+    elif not base_filename.endswith('_evaluated.pkl'):
+        # Ensure it ends with _evaluated.pkl
+        if base_filename.endswith('.pkl'):
+            base_filename = base_filename[:-4] + '_evaluated.pkl'
+        else:
+            base_filename = base_filename + '_evaluated.pkl'
+
+    output_path = output_dir / base_filename
+
+    # Save the evaluated dataframe
+    with open(output_path, 'wb') as f:
+        pickle.dump(df_evaluated, f)
+
+    logger.info(f"Evaluated results saved to: {output_path}")
+
+    return df_evaluated, str(output_path)
+
+
+# =============================================================================
+# Main Function
+# =============================================================================
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Evaluate model outputs with parallel LiveCodeBench processing")
+    parser.add_argument("--input-file", required=True,
+                        help="Input pickle file")
+    parser.add_argument(
+        "--output-file", help="Output pickle file (defaults to <input-file>_evaluated.pkl)")
+    parser.add_argument("--verbose", action="store_true",
+                        help="Verbose logging")
+
+    args = parser.parse_args()
+
+    if args.verbose:
+        logging.getLogger().setLevel(logging.DEBUG)
+
+    if not os.path.exists(args.input_file):
+        raise FileNotFoundError(f"Input file not found: {args.input_file}")
+
+    # Determine output file path
+    if args.output_file:
+        output_path = Path(args.output_file)
+        output_dir = output_path.parent
+        output_filename = output_path.name
+    else:
+        input_path = Path(args.input_file)
+        output_dir = input_path.parent
+        output_filename = input_path.stem + "_evaluated.pkl"
+
+    logger.info(f"Processing: {args.input_file}")
+
+    # Load and process data
+    with open(args.input_file, 'rb') as f:
+        df = pickle.load(f)
+
+    logger.info(f"Loaded {len(df)} rows")
+
+    # Process and save with unified function
+    df_evaluated, saved_file_path = process_and_save_dataframe(
+        df,
+        output_dir=output_dir,
+        base_filename=output_filename
+    )
+
+    # Print evaluation results with unified function
+    print_evaluation_results(df_evaluated, logger)
+
+    logger.info("Done!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/language/deepseek-r1/launch_docker.sh b/language/deepseek-r1/launch_docker.sh
new file mode 100755
index 0000000000..fdb6c3935e
--- /dev/null
+++ b/language/deepseek-r1/launch_docker.sh
@@ -0,0 +1,136 @@
+#!/bin/bash
+
+# MLPerf DeepSeek Reference Implementation Docker Launcher
+# Top-level script to launch different backend containers
+
+set -e
+
+# Default values
+BACKEND=""
+WORK_DIR=${WORK_DIR:-$(dirname "$(realpath "$0")")}
+SCRIPT_DIR="$WORK_DIR/docker/launch_scripts"
+
+# Available backends
+AVAILABLE_BACKENDS=("pytorch" "vllm" "sglang")
+
+# Additional mount directories (can be customized)
+EXTRA_MOUNTS=${EXTRA_MOUNTS:-""}
+
+# Function to display usage
+show_usage() {
+    echo "Usage: $0 --backend <backend> [OPTIONS]"
+    echo ""
+    echo "Required:"
+    echo "  --backend BACKEND        Backend to use: pytorch, vllm, sglang"
+    echo ""
+    echo "Options:"
+    echo "  --image-name NAME        Docker image name"
+    echo "  --container-name NAME    Container name"
+    echo "  --gpu-count COUNT        GPU count (default: all)"
+    echo "  --model-cache-dir DIR    Model cache directory"
+    echo "  --local-user 0|1         Enable local user setup (default: 1)"
+    echo "  --extra-mounts MOUNTS    Additional mount directories (format: 'host1:container1,host2:container2')"
+    echo "  --help                   Show this help message"
+    echo ""
+    echo "Available backends:"
+    for backend in "${AVAILABLE_BACKENDS[@]}"; do
+        echo "  - $backend"
+    done
+    echo ""
+    echo "Examples:"
+    echo "  $0 --backend pytorch"
+    echo "  $0 --backend vllm --gpu-count 2"
+    echo "  $0 --backend sglang --extra-mounts '/data:/data,/models:/models'"
+}
+
+# Function to check if backend is valid
+is_valid_backend() {
+    local backend="$1"
+    for valid_backend in "${AVAILABLE_BACKENDS[@]}"; do
+        if [[ "$backend" == "$valid_backend" ]]; then
+            return 0
+        fi
+    done
+    return 1
+}
+
+# Function to get launch script path
+get_launch_script() {
+    local backend="$1"
+    echo "$SCRIPT_DIR/launch_${backend}.sh"
+}
+
+# Parse command line arguments
+BACKEND_ARGS=()
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --backend)
+            BACKEND="$2"
+            if ! is_valid_backend "$BACKEND"; then
+                echo "Error: Invalid backend '$BACKEND'"
+                echo "Available backends: ${AVAILABLE_BACKENDS[*]}"
+                exit 1
+            fi
+            shift 2
+            ;;
+        --extra-mounts)
+            EXTRA_MOUNTS="$2"
+            shift 2
+            ;;
+        --help)
+            show_usage
+            exit 0
+            ;;
+        --image-name|--container-name|--gpu-count|--model-cache-dir|--local-user)
+            # Pass these arguments to the backend script
+            BACKEND_ARGS+=("$1" "$2")
+            shift 2
+            ;;
+        *)
+            # Pass any other arguments to the backend script
+            BACKEND_ARGS+=("$1")
+            shift
+            ;;
+    esac
+done
+
+# Check if backend was specified
+if [[ -z "$BACKEND" ]]; then
+    echo "Error: --backend is required"
+    echo ""
+    show_usage
+    exit 1
+fi
+
+# Get the launch script for the specified backend
+LAUNCH_SCRIPT=$(get_launch_script "$BACKEND")
+
+# Check if launch script exists
+if [[ ! -f "$LAUNCH_SCRIPT" ]]; then
+    echo "Error: Launch script not found: $LAUNCH_SCRIPT"
+    exit 1
+fi
+
+# Make sure the launch script is executable
+chmod +x "$LAUNCH_SCRIPT"
+
+echo "MLPerf DeepSeek Reference Implementation Docker Launcher"
+echo "======================================================="
+echo "Backend: $BACKEND"
+echo "Launch script: $LAUNCH_SCRIPT"
+echo "Work directory: $WORK_DIR"
+
+# Set environment variables for extra mounts if specified
+if [[ -n "$EXTRA_MOUNTS" ]]; then
+    echo "Extra mounts: $EXTRA_MOUNTS"
+    export EXTRA_MOUNTS
+fi
+
+# Set work directory environment variable
+export WORK_DIR
+
+echo "======================================================="
+echo ""
+
+# Execute the backend-specific launch script with all arguments
+exec "$LAUNCH_SCRIPT" "${BACKEND_ARGS[@]}"
\ No newline at end of file
diff --git a/language/deepseek-r1/mlperf/__init__.py b/language/deepseek-r1/mlperf/__init__.py
new file mode 100644
index 0000000000..33b3154f6b
--- /dev/null
+++ b/language/deepseek-r1/mlperf/__init__.py
@@ -0,0 +1,29 @@
+"""MLPerf inference SUT implementations and dataset utilities."""
+
+from .base_sut import BaseSUT
+from .offline_sut import OfflineSUT
+from .server_sut import ServerSUT
+
+# Import QSL implementations
+from .qsl import QuerySampleLibrary, DistributedQuerySampleLibrary
+
+# Import MLPerf utilities
+from .utils import (
+    prepare_mlperf_dataset,
+    process_mlperf_results,
+    create_mlperf_output_dataframe
+)
+
+__all__ = [
+    # SUTs
+    'BaseSUT', 
+    'OfflineSUT', 
+    'ServerSUT',
+    # QSL
+    'QuerySampleLibrary',
+    'DistributedQuerySampleLibrary',
+    # Utilities
+    'prepare_mlperf_dataset',
+    'process_mlperf_results',
+    'create_mlperf_output_dataframe'
+] 
\ No newline at end of file
diff --git a/language/deepseek-r1/mlperf/base_sut.py b/language/deepseek-r1/mlperf/base_sut.py
new file mode 100644
index 0000000000..7249207aab
--- /dev/null
+++ b/language/deepseek-r1/mlperf/base_sut.py
@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+"""Base System Under Test (SUT) class for MLPerf inference benchmarks."""
+
+import abc
+import logging
+from typing import List, Dict, Any, Optional
+import mlperf_loadgen as lg
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseSUT(abc.ABC):
+    """Base class for MLPerf inference System Under Test (SUT).
+    
+    This class defines the interface that all SUTs must implement for MLPerf
+    inference benchmarks. It provides two main methods:
+    - issue_queries: to enqueue prompt tokens
+    - flush_queries: to await completion of all issued queries
+    """
+    
+    def __init__(self, name: str = "BaseSUT"):
+        """Initialize the base SUT.
+        
+        Args:
+            name: Name of the SUT for logging purposes
+        """
+        self.name = name
+        self.sut = None
+        logger.info(f"Initializing {self.name}")
+    
+    @abc.abstractmethod
+    def issue_queries(self, query_samples: List[lg.QuerySample]) -> None:
+        """Issue queries to the SUT.
+        
+        This method should enqueue the provided query samples for processing.
+        It should return immediately without waiting for completion.
+        
+        Args:
+            query_samples: List of MLPerf LoadGen query samples to process
+        """
+        raise NotImplementedError("Subclasses must implement issue_queries")
+    
+    @abc.abstractmethod
+    def flush_queries(self) -> None:
+        """Flush all pending queries.
+        
+        This method should wait for all previously issued queries to complete
+        before returning. It's called by LoadGen to ensure all work is done.
+        """
+        raise NotImplementedError("Subclasses must implement flush_queries")
+    
+    def start(self) -> lg.ConstructSUT:
+        """Start the SUT and return the LoadGen SUT handle.
+        
+        Returns:
+            LoadGen SUT handle for use with LoadGen
+        """
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        logger.info(f"{self.name} started")
+        return self.sut
+    
+    def stop(self) -> None:
+        """Stop the SUT and clean up resources."""
+        if self.sut:
+            lg.DestroySUT(self.sut)
+            self.sut = None
+            logger.info(f"{self.name} stopped")
+    
+    def __enter__(self):
+        """Context manager entry."""
+        return self.start()
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit."""
+        self.stop() 
\ No newline at end of file
diff --git a/language/deepseek-r1/mlperf/offline_sut.py b/language/deepseek-r1/mlperf/offline_sut.py
new file mode 100644
index 0000000000..db1c4feea1
--- /dev/null
+++ b/language/deepseek-r1/mlperf/offline_sut.py
@@ -0,0 +1,310 @@
+#!/usr/bin/env python3
+"""Offline SUT implementation for MLPerf inference benchmarks."""
+
+import asyncio
+import logging
+import threading
+from typing import List, Dict, Any, Optional
+import numpy as np
+import mlperf_loadgen as lg
+
+from .base_sut import BaseSUT
+from backends import BaseBackend
+from utils.backend_registry import uses_text_input
+
+
+logger = logging.getLogger(__name__)
+
+
+class OfflineSUT(BaseSUT):
+    """Offline scenario SUT implementation.
+
+    This SUT uses async inference APIs similar to run_eval.py pattern:
+    - Collects all queries into a batch
+    - Calls backend.generate_async() once with all prompts
+    - Processes futures as they complete out-of-order
+    """
+
+    def __init__(self,
+                 backend: BaseBackend,
+                 dataset: List[List[int]],
+                 dataset_strings: Optional[List[str]] = None,
+                 name: str = "OfflineSUT"):
+        """Initialize the offline SUT.
+
+        Args:
+            backend: Backend instance to use for inference
+            dataset: List of tokenized prompts
+            dataset_strings: List of original text prompts (for backends that use text)
+            name: Name of the SUT
+        """
+        super().__init__(name)
+        self.backend = backend
+        self.dataset = dataset
+        self.dataset_strings = dataset_strings
+
+        # Determine backend type using registry
+        self.backend_name = getattr(backend, 'backend_name', type(backend).__name__.lower())
+        self.uses_text_prompts = uses_text_input(self.backend_name)
+
+        if self.uses_text_prompts and dataset_strings is None:
+            raise ValueError(f"Backend {self.backend_name} requires text prompts but dataset_strings was not provided")
+
+        # Async event loop and thread
+        self.loop = None
+        self.loop_thread = None
+
+        # Query collection for batch processing (like run_eval.py)
+        self.query_samples = []
+        self.queries_lock = threading.Lock()
+        self.batch_processed = threading.Event()
+
+        # Results storage
+        self.results = {}
+
+        # Track index to sample ID mapping
+        self.index_to_id = {}
+
+        # Initialize batch future to None
+        self.batch_future = None
+
+    def issue_queries(self, query_samples: List[lg.QuerySample]) -> None:
+        """Issue queries - collect all queries for batch processing like run_eval.py.
+
+        Args:
+            query_samples: List of MLPerf LoadGen query samples
+        """
+        logger.info(f"Issuing {len(query_samples)} queries")
+
+        # Store queries for batch processing
+        with self.queries_lock:
+            self.query_samples = query_samples
+            # Track index to ID mapping
+            for sample in query_samples:
+                self.index_to_id[sample.index] = sample.id
+
+        # Schedule batch processing in the async loop (like run_eval.py)
+        future = asyncio.run_coroutine_threadsafe(
+            self._process_all_queries_async(),
+            self.loop
+        )
+
+        # Store the future so we can wait for it in flush_queries
+        self.batch_future = future
+
+    def flush_queries(self) -> None:
+        """Wait for all queries to complete."""
+        logger.info("Flushing queries...")
+
+        # Wait for the batch processing to complete
+        if self.batch_future is not None:
+            try:
+                self.batch_future.result()  # Wait for completion
+                logger.info("Batch processing completed")
+            except Exception as e:
+                logger.error(f"Error waiting for batch completion: {e}")
+            finally:
+                # Reset batch_future
+                self.batch_future = None
+
+    async def _process_all_queries_async(self):
+        """Process all queries in a single batch using run_eval.py pattern."""
+        try:
+            with self.queries_lock:
+                query_samples = self.query_samples.copy()
+
+            if not query_samples:
+                logger.warning("No queries to process")
+                return
+
+            logger.info(f"Processing {len(query_samples)} queries in batch")
+
+            # Prepare prompts for batch processing (like run_eval.py)
+            if self.uses_text_prompts:
+                # Use text prompts for vLLM and SGLang
+                prompts = [self.dataset_strings[sample.index] for sample in query_samples]
+                futures = self.backend.generate_async(text_prompts=prompts)
+            else:
+                # Use tokenized prompts for other backends
+                prompts = [self.dataset[sample.index] for sample in query_samples]
+                futures = self.backend.generate_async(tokenized_prompts=prompts)
+
+            logger.info(f"Got {len(futures)} futures from backend")
+
+            # Process futures as they complete (same pattern as run_eval.py)
+            results = [None] * len(futures)
+            indexed_futures = [(i, future) for i, future in enumerate(futures)]
+            completed_indices = set()
+
+            # Use asyncio.wait with FIRST_COMPLETED to handle out-of-order completion
+            pending = {future for _, future in indexed_futures}
+
+            while pending:
+                # Wait for at least one future to complete
+                done, pending = await asyncio.wait(pending, return_when=asyncio.FIRST_COMPLETED)
+
+                # Process all completed futures in this batch
+                for completed_future in done:
+                    # Find the original index for this completed future
+                    original_idx = None
+                    for idx, future in indexed_futures:
+                        if future is completed_future:
+                            original_idx = idx
+                            break
+
+                    if original_idx is None:
+                        logger.error("Could not find original index for completed future")
+                        continue
+
+                    # Check for duplicate completion
+                    if original_idx in completed_indices:
+                        logger.warning(f"Prompt {original_idx} completed multiple times!")
+                        continue
+
+                    try:
+                        # Get the result from the completed future
+                        result = await completed_future
+
+                        # Store the result in the correct position
+                        results[original_idx] = result
+                        completed_indices.add(original_idx)
+
+                        # Send result to LoadGen immediately
+                        sample = query_samples[original_idx]
+                        await self._send_result_to_loadgen(sample, result)
+
+                    except Exception as e:
+                        logger.error(f"Error processing prompt {original_idx}: {type(e).__name__}: {e}")
+                        # Raise the error instead of handling empty responses
+                        raise RuntimeError(f"Backend failed to generate tokens for prompt {original_idx}: {e}")
+
+            # Verify all results are populated
+            if len(completed_indices) != len(futures):
+                missing_count = len(futures) - len(completed_indices)
+                raise RuntimeError(f"Missing results: completed {len(completed_indices)} != {len(futures)} total ({missing_count} missing)")
+
+            for i, result in enumerate(results):
+                if result is None:
+                    raise RuntimeError(f"Missing result for prompt {i}")
+
+            logger.info(f"Completed all {len(completed_indices)} prompts successfully")
+
+        except Exception as e:
+            logger.error(f"Error during batch processing: {type(e).__name__}: {e}")
+            import traceback
+            traceback.print_exc()
+            raise  # Re-raise instead of sending empty responses
+
+    async def _send_result_to_loadgen(self, sample: lg.QuerySample, result: Dict[str, Any]):
+        """Send a single result to LoadGen."""
+        try:
+            # Validate that tokens exist - raise error if missing
+            tokens = result.get('tokens')
+            if tokens is None:
+                raise ValueError(f"Backend result missing 'tokens' key for query {sample.id}")
+            if not isinstance(tokens, (list, tuple)) or len(tokens) == 0:
+                raise ValueError(f"Backend returned empty or invalid tokens for query {sample.id}: {tokens}")
+
+            # Create a copy of tokens before numpy conversion
+            tokens_copy = tokens.copy()
+
+            # Convert tokens to bytes for LoadGen
+            token_array = np.array(tokens, dtype=np.int32)
+            n_tokens = len(tokens)
+
+            # Create LoadGen response
+            response = lg.QuerySampleResponse(
+                sample.id,
+                token_array.ctypes.data if token_array.size > 0 else 0,
+                token_array.nbytes,
+                n_tokens,
+            )
+
+            # Store result with the tokens copy for later access
+            self.results[sample.id] = {
+                'tokens': tokens_copy,
+                'text': result.get('text', '')
+            }
+
+            # Send response to LoadGen
+            lg.QuerySamplesComplete([response])
+
+            logger.debug(f"Sent {n_tokens} tokens to LoadGen for query {sample.id}")
+
+        except Exception as e:
+            logger.error(f"Error sending result to LoadGen for query {sample.id}: {e}")
+            # Raise the error instead of sending empty response
+            raise RuntimeError(f"Failed to send result to LoadGen for query {sample.id}: {e}")
+
+    def _run_event_loop(self):
+        """Run the async event loop in a separate thread."""
+        asyncio.set_event_loop(self.loop)
+        self.loop.run_forever()
+
+    def start(self) -> lg.ConstructSUT:
+        """Start the SUT and async event loop."""
+        # Create and start event loop in separate thread
+        self.loop = asyncio.new_event_loop()
+        self.loop_thread = threading.Thread(target=self._run_event_loop)
+        self.loop_thread.start()
+
+        # Call parent start
+        return super().start()
+
+    def stop(self) -> None:
+        """Stop the SUT and clean up."""
+        # Stop the event loop
+        if self.loop:
+            self.loop.call_soon_threadsafe(self.loop.stop)
+
+        # Wait for thread to finish
+        if self.loop_thread and self.loop_thread.is_alive():
+            self.loop_thread.join()
+
+        # Close the loop
+        if self.loop:
+            self.loop.close()
+            self.loop = None
+
+        # Call parent stop
+        super().stop()
+
+    def get_results(self) -> List[Dict[str, Any]]:
+        """Get all results in order of dataset indices using stored backend results.
+
+        Returns:
+            List of result dictionaries with model_output, tok_model_output, and tok_model_output_len
+        """
+        # Create a list to hold results in dataset order
+        ordered_results = []
+
+        # Only process results for samples that were actually queried
+        # Sort by index to maintain dataset order
+        queried_indices = sorted(self.index_to_id.keys())
+
+        logger.info(f"Retrieving results for {len(queried_indices)} queried samples")
+
+        # Process results in order of dataset indices using stored results
+        for i in queried_indices:
+            # Get the sample ID for this index
+            sample_id = self.index_to_id[i]
+
+            # Get the stored result from backend
+            result = self.results.get(sample_id, {})
+
+            if result:
+                tokens = result['tokens']
+                output_text = result.get('text', '')
+                if not output_text and self.backend.tokenizer:
+                    output_text = self.backend.tokenizer.decode(result['tokens'], skip_special_tokens=True)
+
+                ordered_results.append({
+                    'model_output': output_text,
+                    'tok_model_output': tokens,
+                    'tok_model_output_len': len(tokens)
+                })
+            else:
+                # No backend result for this sample
+                raise RuntimeError(f"No backend result stored for dataset index {i}, sample_id {sample_id}")
+
+        return ordered_results
\ No newline at end of file
diff --git a/language/deepseek-r1/mlperf/qsl.py b/language/deepseek-r1/mlperf/qsl.py
new file mode 100644
index 0000000000..59bc5e36a8
--- /dev/null
+++ b/language/deepseek-r1/mlperf/qsl.py
@@ -0,0 +1,87 @@
+"""QuerySampleLibrary implementations for MLPerf LoadGen."""
+
+import logging
+from typing import List, Optional
+import mlperf_loadgen as lg
+
+
+class QuerySampleLibrary:
+    """MLPerf QuerySampleLibrary implementation for single-process execution."""
+    
+    def __init__(self, dataset: List[List[int]], dataset_strings: List[str], 
+                 name: str = "QSL"):
+        """
+        Initialize QSL with dataset.
+        
+        Args:
+            dataset: List of tokenized prompts
+            dataset_strings: List of original prompt strings
+            name: Name for logging
+        """
+        self.dataset = dataset
+        self.dataset_strings = dataset_strings
+        self.count = len(dataset)
+        self.perf_count = self.count
+        self.name = name
+        self.logger = logging.getLogger(__name__)
+        
+        # Create LoadGen QSL
+        self.qsl = lg.ConstructQSL(
+            self.count,
+            self.perf_count,
+            lambda x: None,  # LoadSamplesToRam
+            lambda x: None   # UnloadSamplesFromRam
+        )
+        self.logger.info(f"Created {self.name} with {self.count} samples")
+    
+    def __del__(self):
+        """Cleanup QSL."""
+        if self.qsl is not None:
+            lg.DestroyQSL(self.qsl)
+            self.logger.info(f"{self.name} destroyed")
+
+
+class DistributedQuerySampleLibrary:
+    """QuerySampleLibrary for distributed execution (MPI/torchrun)."""
+    
+    def __init__(self, dataset: List[List[int]], dataset_strings: List[str],
+                 rank: int, world_size: int, name: str = "DistributedQSL"):
+        """
+        Initialize distributed QSL.
+        
+        Args:
+            dataset: List of tokenized prompts
+            dataset_strings: List of original prompt strings
+            rank: Process rank
+            world_size: Total number of processes
+            name: Name for logging
+        """
+        self.dataset = dataset
+        self.dataset_strings = dataset_strings
+        self.count = len(dataset)
+        self.perf_count = self.count
+        self.rank = rank
+        self.world_size = world_size
+        self.name = name
+        self.logger = logging.getLogger(__name__)
+        
+        # Track if this is rank zero explicitly
+        self.is_rank_zero = (self.rank == 0)
+        
+        # Only rank 0 creates the actual QSL
+        if self.is_rank_zero:
+            self.qsl = lg.ConstructQSL(
+                self.count,
+                self.perf_count,
+                lambda x: None,
+                lambda x: None
+            )
+            self.logger.info(f"Created {self.name} with {self.count} samples on rank 0")
+        else:
+            self.qsl = None
+    
+    def __del__(self):
+        """Cleanup QSL on rank 0."""
+        if self.is_rank_zero and self.qsl is not None:
+            lg.DestroyQSL(self.qsl)
+            self.logger.info(f"{self.name} destroyed on rank 0") 
\ No newline at end of file
diff --git a/language/deepseek-r1/mlperf/server_sut.py b/language/deepseek-r1/mlperf/server_sut.py
new file mode 100644
index 0000000000..75699e208f
--- /dev/null
+++ b/language/deepseek-r1/mlperf/server_sut.py
@@ -0,0 +1,427 @@
+#!/usr/bin/env python3
+"""Server SUT implementation for MLPerf inference benchmarks."""
+
+import asyncio
+import logging
+import threading
+import time
+from typing import List, Dict, Any, Optional, Set, AsyncIterator
+from dataclasses import dataclass, field
+import numpy as np
+import mlperf_loadgen as lg
+from utils import supports_streaming
+
+from .base_sut import BaseSUT
+from backends import BaseBackend
+from backends.base_backend import StreamingChunk
+from utils.backend_registry import uses_text_input
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class QueryInfo:
+    """Information about a single query from LoadGen."""
+    query_id: int                     # Unique query ID from LoadGen
+    index: int                        # Index into the dataset
+    issued_time: float               # When the query was issued
+    sample: lg.QuerySample           # Original LoadGen query sample
+    result: Optional[Dict[str, Any]] = None  # Results after completion
+
+
+@dataclass
+class StreamingQueryState:
+    """Track state of a streaming query."""
+    query_info: QueryInfo
+    stream_gen: AsyncIterator[StreamingChunk]
+    first_token_time: Optional[float] = None
+    first_token_sent: bool = False
+    accumulated_text: str = ""
+    accumulated_tokens: List[int] = field(default_factory=list)
+    start_time: float = field(default_factory=time.perf_counter)
+    task: Optional[asyncio.Task] = None  # Track the processing task
+
+
+class ServerSUT(BaseSUT):
+    """Server scenario SUT implementation with streaming support.
+
+    This SUT requires backends to support streaming for server mode.
+    All processing happens asynchronously without blocking LoadGen.
+    """
+
+    def __init__(self,
+                 backend: BaseBackend,
+                 dataset: List[List[int]],
+                 dataset_strings: Optional[List[str]] = None,
+                 name: str = "ServerSUT"):
+        """Initialize the server SUT.
+
+        Args:
+            backend: Backend instance to use for inference
+            dataset: List of tokenized prompts
+            dataset_strings: List of original text prompts (for backends that use text)
+            name: Name of the SUT
+        """
+        super().__init__(name)
+        self.backend = backend
+        self.dataset = dataset
+        self.dataset_strings = dataset_strings
+
+        # Determine backend type using registry
+        self.backend_name = getattr(backend, 'backend_name', type(backend).__name__.lower())
+        self.uses_text_prompts = uses_text_input(self.backend_name)
+
+        if self.uses_text_prompts and dataset_strings is None:
+            raise ValueError(f"Backend {self.backend_name} requires text prompts but dataset_strings was not provided")
+
+        # Async event loop and thread
+        self.loop = None
+        self.loop_thread = None
+
+        # Track active streaming queries
+        self.active_streams: Dict[int, StreamingQueryState] = {}
+        self.active_streams_lock = asyncio.Lock()
+
+        # Track all active tasks for proper cleanup
+        self.active_tasks: Set[asyncio.Task] = set()
+        self.active_tasks_lock = asyncio.Lock()
+
+        # Results storage
+        self.all_results: Dict[int, Dict[str, Any]] = {}
+        self.results_lock = asyncio.Lock()
+
+
+
+    def issue_queries(self, query_samples: List[lg.QuerySample]) -> None:
+        """Issue queries in streaming mode with batching."""
+        if not supports_streaming():
+            # Fallback to async if streaming not supported
+            super().issue_queries(query_samples)
+            return
+
+        logger.debug(f"Issuing {len(query_samples)} queries (non-blocking)")
+
+        # Schedule async processing without blocking
+        for sample in query_samples:
+            query_info = QueryInfo(
+                query_id=sample.id,
+                index=sample.index,
+                issued_time=time.time(),
+                sample=sample
+            )
+
+            # Fire and forget - start streaming without blocking
+            if self.loop:
+                asyncio.run_coroutine_threadsafe(
+                    self._start_streaming_query(query_info),
+                    self.loop
+                )
+
+    async def _start_streaming_query(self, query_info: QueryInfo) -> None:
+        """Start streaming for a single query."""
+        try:
+            # Verify streaming support
+            if not supports_streaming():
+                raise RuntimeError(f"Backend {self.backend_name} does not support streaming required for server mode")
+
+            # Prepare prompt based on backend type
+            if self.uses_text_prompts:
+                prompt = [self.dataset_strings[query_info.index]]
+                stream_gens = await self.backend.generate_stream(text_prompts=prompt)
+            else:
+                prompt = [self.dataset[query_info.index]]
+                stream_gens = await self.backend.generate_stream(tokenized_prompts=prompt)
+
+            # Create streaming state
+            state = StreamingQueryState(
+                query_info=query_info,
+                stream_gen=stream_gens[0]
+            )
+
+            # Register active stream
+            async with self.active_streams_lock:
+                self.active_streams[query_info.query_id] = state
+
+            # Start processing the stream and track the task
+            task = asyncio.create_task(self._process_stream(state))
+            state.task = task
+
+            # Track the task for proper cleanup
+            async with self.active_tasks_lock:
+                self.active_tasks.add(task)
+
+            # Add callback to remove task from active set when done
+            task.add_done_callback(self._remove_task_from_active)
+
+        except Exception as e:
+            logger.error(f"Error starting stream for query {query_info.query_id}: {e}")
+            raise RuntimeError(f"Failed to start streaming for query {query_info.query_id}: {e}")
+
+    def _remove_task_from_active(self, task: asyncio.Task) -> None:
+        """Remove a completed task from the active set."""
+        # This runs in the event loop thread context
+        asyncio.create_task(self._async_remove_task_from_active(task))
+
+    async def _async_remove_task_from_active(self, task: asyncio.Task) -> None:
+        """Async version of task removal."""
+        async with self.active_tasks_lock:
+            self.active_tasks.discard(task)
+
+    async def _process_stream(self, state: StreamingQueryState) -> None:
+        """Process a streaming response, reporting first token immediately."""
+        try:
+            async for chunk in state.stream_gen:
+                current_time = time.perf_counter()
+
+                # Accumulate text and tokens
+                if chunk.token:
+                    state.accumulated_text += chunk.token
+                if chunk.token_ids:
+                    state.accumulated_tokens.extend(chunk.token_ids)
+
+                # Report first token immediately for TTFT measurement
+                if not state.first_token_sent and (chunk.token or chunk.token_ids):
+                    state.first_token_time = current_time - state.start_time
+                    state.first_token_sent = True
+
+                    # Send first token response to LoadGen immediately
+                    # This allows LoadGen to measure TTFT accurately
+                    await self._send_first_token_response(state)
+
+                # Check if generation is complete
+                if chunk.is_finished:
+                    # Send final response with complete text
+                    await self._send_final_response(state)
+                    break
+
+        except asyncio.CancelledError:
+            # Task was cancelled, clean up gracefully
+            logger.debug(f"Stream processing cancelled for query {state.query_info.query_id}")
+            # Close the async generator properly (assume aclose exists in our containerized environment)
+            try:
+                await state.stream_gen.aclose()
+            except Exception:
+                pass
+            raise
+        except Exception as e:
+            logger.error(f"Error processing stream for query {state.query_info.query_id}: {e}")
+            raise RuntimeError(f"Stream processing failed for query {state.query_info.query_id}: {e}")
+        finally:
+            # Clean up active stream
+            async with self.active_streams_lock:
+                self.active_streams.pop(state.query_info.query_id, None)
+
+    async def _send_first_token_response(self, state: StreamingQueryState) -> None:
+        """Send first token notification to LoadGen for TTFT measurement."""
+        logger.debug(f"First token received for query {state.query_info.query_id} at {state.first_token_time:.3f}s")
+
+        # Convert first tokens to proper format for LoadGen
+        if state.accumulated_tokens:
+            output_tokens = np.ascontiguousarray(state.accumulated_tokens, dtype=np.int32)
+        else:
+            # If no token IDs available, encode the text
+            if hasattr(self.backend, 'tokenizer') and state.accumulated_text:
+                tokens = self.backend.tokenizer.encode(state.accumulated_text)
+                output_tokens = np.ascontiguousarray(tokens, dtype=np.int32)
+            else:
+                raise RuntimeError(f"No token IDs available for first token response for query {state.query_info.query_id}")
+
+        output_seq_len = len(output_tokens)
+        output_toks_ptr = output_tokens.ctypes.data if output_seq_len > 0 else 0
+        output_toks_size = output_seq_len * output_tokens.itemsize
+
+        # Send first token response using LoadGen API
+        lg.FirstTokenComplete([
+            lg.QuerySampleResponse(
+                state.query_info.query_id,
+                output_toks_ptr,
+                output_toks_size,
+                output_seq_len
+            )
+        ])
+
+    async def _send_final_response(self, state: StreamingQueryState) -> None:
+        """Send final response with complete generated text."""
+        try:
+            # For MLPerf server mode, we need complete tokens
+            if state.accumulated_tokens:
+                # Create a copy of tokens before numpy conversion
+                tokens_to_send = state.accumulated_tokens.copy()
+                token_array = np.array(state.accumulated_tokens, dtype=np.int32)
+            else:
+                # If no tokens, encode the text
+                if hasattr(self.backend, 'tokenizer') and state.accumulated_text:
+                    tokens = self.backend.tokenizer.encode(state.accumulated_text)
+                    # Create a copy of tokens before numpy conversion
+                    tokens_to_send = tokens.copy()
+                    token_array = np.array(tokens, dtype=np.int32)
+                else:
+                    raise RuntimeError(f"No tokens or tokenizer available for query {state.query_info.query_id}")
+
+            # Validate we have tokens
+            if len(token_array) == 0:
+                raise RuntimeError(f"No tokens generated for query {state.query_info.query_id}")
+
+
+
+            # Create LoadGen response
+            response = lg.QuerySampleResponse(
+                state.query_info.query_id,
+                token_array.ctypes.data if token_array.size > 0 else 0,
+                token_array.nbytes,
+                len(token_array)
+            )
+
+            # Send response to LoadGen
+            lg.QuerySamplesComplete([response])
+
+            # Store result for later retrieval with the tokens copy
+            async with self.results_lock:
+                state.query_info.result = {
+                    'text': state.accumulated_text,
+                    'tokens': tokens_to_send,  # Use the copy
+                    'first_token_time': state.first_token_time,
+                    'total_time': time.perf_counter() - state.start_time,
+                    'index': state.query_info.index  # Store the dataset index
+                }
+                self.all_results[state.query_info.query_id] = state.query_info.result
+
+            logger.debug(f"Sent {len(token_array)} tokens to LoadGen for query {state.query_info.query_id}")
+
+        except Exception as e:
+            logger.error(f"Error sending final response for query {state.query_info.query_id}: {e}")
+            raise RuntimeError(f"Failed to send final response for query {state.query_info.query_id}: {e}")
+
+    def flush_queries(self) -> None:
+        """Wait for all active streams to complete."""
+        logger.info("Flushing queries...")
+
+        # Create a task to wait for all streams
+        async def wait_for_streams():
+            max_wait = 300  # 5 minutes timeout
+            start_time = time.time()
+
+            while time.time() - start_time < max_wait:
+                async with self.active_streams_lock:
+                    if not self.active_streams:
+                        break
+                    active_count = len(self.active_streams)
+
+                logger.debug(f"Waiting for {active_count} active streams...")
+                await asyncio.sleep(0.1)
+
+            async with self.active_streams_lock:
+                if self.active_streams:
+                    logger.warning(f"Timeout: {len(self.active_streams)} streams still active")
+
+        # Run the wait task in the event loop
+        if self.loop and not self.loop.is_closed():
+            future = asyncio.run_coroutine_threadsafe(wait_for_streams(), self.loop)
+            try:
+                future.result(timeout=310)  # Slightly longer than internal timeout
+            except Exception as e:
+                logger.error(f"Error waiting for streams to complete: {e}")
+
+    def _run_event_loop(self):
+        """Run the async event loop in a separate thread."""
+        asyncio.set_event_loop(self.loop)
+
+        # Run until stopped
+        self.loop.run_forever()
+
+    def start(self) -> lg.ConstructSUT:
+        """Start the SUT and async event loop."""
+        # Create new event loop
+        self.loop = asyncio.new_event_loop()
+
+        # Start event loop thread
+        self.loop_thread = threading.Thread(target=self._run_event_loop)
+        self.loop_thread.start()
+
+        # Call parent start
+        return super().start()
+
+    def stop(self) -> None:
+        """Stop the SUT and clean up."""
+        # Cancel all active tasks before stopping the loop
+        if self.loop and self.loop_thread:
+            # Cancel all active tasks
+            async def cancel_all_tasks():
+                async with self.active_tasks_lock:
+                    tasks_to_cancel = list(self.active_tasks)
+
+                if tasks_to_cancel:
+                    logger.info(f"Cancelling {len(tasks_to_cancel)} active streaming tasks...")
+                    for task in tasks_to_cancel:
+                        task.cancel()
+
+                    # Wait for all tasks to complete cancellation
+                    await asyncio.gather(*tasks_to_cancel, return_exceptions=True)
+                    logger.info("All streaming tasks cancelled")
+
+                # Clear the active tasks set
+                async with self.active_tasks_lock:
+                    self.active_tasks.clear()
+
+            # Run the cancellation in the event loop
+            future = asyncio.run_coroutine_threadsafe(cancel_all_tasks(), self.loop)
+            try:
+                future.result(timeout=10.0)  # Give tasks time to cancel
+            except Exception as e:
+                logger.error(f"Error cancelling tasks: {e}")
+
+            # Now stop the loop
+            self.loop.call_soon_threadsafe(self.loop.stop)
+
+            # Wait for thread to finish
+            self.loop_thread.join(timeout=5.0)
+
+            # Close loop
+            if not self.loop.is_closed():
+                self.loop.close()
+            self.loop = None
+
+        # Call parent stop
+        super().stop()
+
+    def get_results(self) -> List[Dict[str, Any]]:
+        """Get all results in order of dataset indices using stored backend results.
+
+        Returns:
+            List of result dictionaries with model_output, tok_model_output, and tok_model_output_len
+        """
+        # Create a list to hold results in dataset order
+        ordered_results = []
+
+        # Create index to result mapping from all stored results
+        index_to_result = {}
+
+        # Map results by their stored dataset index
+        for query_id, result in self.all_results.items():
+            if result and 'index' in result:
+                index_to_result[result['index']] = result
+
+        # Only process results for samples that were actually queried
+        # Sort by index to maintain dataset order
+        queried_indices = sorted(index_to_result.keys())
+        
+        logger.info(f"Retrieving results for {len(queried_indices)} queried samples")
+
+        # Process results in order of dataset indices using stored backend results
+        for i in queried_indices:
+            result = index_to_result[i]
+
+            # Get tokens from the backend result
+            tokens = result['tokens']
+            output_text = result.get('text', '')
+            if not output_text and self.backend.tokenizer:
+                output_text = self.backend.tokenizer.decode(result['tokens'], skip_special_tokens=True)
+
+            ordered_results.append({
+                'model_output': output_text,
+                'tok_model_output': tokens,
+                'tok_model_output_len': len(tokens)
+            })
+
+        return ordered_results
\ No newline at end of file
diff --git a/language/deepseek-r1/mlperf/user.conf b/language/deepseek-r1/mlperf/user.conf
new file mode 100644
index 0000000000..0c248804bd
--- /dev/null
+++ b/language/deepseek-r1/mlperf/user.conf
@@ -0,0 +1,12 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+
+*.Offline.target_qps = 1.0
+*.Offline.min_duration = 600000
+*.Offline.min_query_count = 4388
+
+*.Server.target_qps = 1.0
+*.Server.min_duration = 600000
+*.Server.min_query_count = 4388
\ No newline at end of file
diff --git a/language/deepseek-r1/mlperf/utils.py b/language/deepseek-r1/mlperf/utils.py
new file mode 100644
index 0000000000..973e46c201
--- /dev/null
+++ b/language/deepseek-r1/mlperf/utils.py
@@ -0,0 +1,133 @@
+"""MLPerf-specific utilities for tokenization and dataset handling."""
+
+import pandas as pd
+from typing import List, Dict, Any, Optional, Tuple
+from utils import load_dataset, validate_dataset
+from utils.backend_registry import uses_text_input, uses_chat_template
+from utils.tokenization import StandardTokenizer
+
+
+def prepare_mlperf_dataset(input_file: str, 
+                          backend_name: Optional[str] = None,
+                          tokenizer: StandardTokenizer = None,
+                          num_samples: Optional[int] = None,
+                          skip_samples: int = 0,
+                          use_chat_template: Optional[bool] = None) -> Dict[str, Any]:
+    """
+    Prepare dataset for MLPerf inference.
+    
+    Args:
+        input_file: Path to input pickle file
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+                      (Kept for backward compatibility but not used in our codebase)
+        tokenizer: StandardTokenizer instance
+        num_samples: Number of samples to use
+        skip_samples: Number of samples to skip
+        use_chat_template: Whether to use chat template (if None, determined by registry)
+        
+    Returns:
+        Dictionary with prepared dataset components
+    """
+    if backend_name is None:
+        from utils.backend_registry import detect_backend
+        backend_name = detect_backend()
+    
+    # Load and validate dataset
+    df = load_dataset(input_file, num_samples, skip_samples)
+    validate_dataset(df)
+    
+    prompts = df['text_input'].tolist()
+    print(f"[MLPerf] Loaded {len(prompts)} prompts from dataset")
+    
+    # Check if backend uses text prompts from registry
+    uses_text_prompts = uses_text_input()
+    
+    # Determine chat template usage from registry if not specified
+    if use_chat_template is None:
+        use_chat_template = uses_chat_template()
+        print(f"[MLPerf] Using chat template from registry: {use_chat_template}")
+    
+    if uses_text_prompts:
+        print(f"[MLPerf] Backend {backend_name} uses text prompts directly")
+        return {
+            'dataframe': df,
+            'prompts': prompts,
+            'tokenized_prompts': prompts,  # For compatibility
+            'processed_strings': prompts,
+            'uses_text_prompts': True
+        }
+    else:
+        print(f"[MLPerf] Tokenizing prompts for {backend_name} backend...")
+        tokenized_prompts, processed_strings = tokenizer.tokenize_prompts(
+            prompts, use_chat_template
+        )
+        print(f"[MLPerf] Tokenized {len(tokenized_prompts)} prompts")
+        
+        return {
+            'dataframe': df,
+            'prompts': prompts,
+            'tokenized_prompts': tokenized_prompts,
+            'processed_strings': processed_strings,
+            'uses_text_prompts': False
+        }
+
+
+def process_mlperf_results(sut_results: List[Dict[str, Any]],
+                          tokenizer: Optional[StandardTokenizer] = None,
+                          backend_name: Optional[str] = None,
+                          uses_text_prompts: Optional[bool] = None) -> List[Dict[str, Any]]:
+    """
+    Process MLPerf SUT results into standardized format.
+    
+    Args:
+        sut_results: Raw results from MLPerf SUT
+        tokenizer: StandardTokenizer for decoding
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+                      (Kept for backward compatibility but not used in our codebase)
+        uses_text_prompts: Whether backend uses text prompts (if None, determined by registry)
+        
+    Returns:
+        List of processed result dictionaries
+    """
+    from utils.tokenization import process_inference_results
+    
+    if backend_name is None:
+        from utils.backend_registry import detect_backend
+        backend_name = detect_backend()
+    
+    # Determine text prompt usage from registry if not specified
+    if uses_text_prompts is None:
+        uses_text_prompts = uses_text_input()
+    
+    # Reuse the general inference result processing
+    return process_inference_results(sut_results, tokenizer, uses_text_prompts=uses_text_prompts)
+
+
+def create_mlperf_output_dataframe(input_df: pd.DataFrame,
+                                  results: List[Dict[str, Any]],
+                                  backend_name: Optional[str] = None) -> pd.DataFrame:
+    """
+    Create output dataframe with MLPerf results.
+    
+    Args:
+        input_df: Input dataframe
+        results: Processed MLPerf results
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+                      (Kept for backward compatibility but not used in our codebase)
+        
+    Returns:
+        Output dataframe with results
+    """
+    if backend_name is None:
+        from utils.backend_registry import detect_backend
+        backend_name = detect_backend()
+    
+    df_output = input_df.copy()
+    
+    # Add result columns
+    df_output['model_output'] = [r['model_output'] for r in results]
+    df_output['tok_model_output'] = [r['tok_model_output'] for r in results]
+    df_output['tok_model_output_len'] = [r['tok_model_output_len'] for r in results]
+    df_output['model_backend'] = backend_name
+    
+    return df_output 
\ No newline at end of file
diff --git a/language/deepseek-r1/run_eval.py b/language/deepseek-r1/run_eval.py
new file mode 100755
index 0000000000..169b3473e4
--- /dev/null
+++ b/language/deepseek-r1/run_eval.py
@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+import argparse
+import asyncio
+import os
+import sys
+from typing import Any, Dict, List, Optional
+import pandas as pd
+from tqdm import tqdm
+from tqdm.asyncio import tqdm as async_tqdm
+
+# Disable tokenizers parallelism to avoid forking issues
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+from backends import BaseBackend
+from utils import (
+    load_dataset, save_results, validate_dataset, generate_timestamped_filename,
+    validate_runner_for_backend, uses_text_input, uses_chat_template,
+    StandardTokenizer, process_inference_results,
+    get_backend_instance, create_base_argument_parser, print_runner_header,
+    setup_output_paths, validate_runner_args, handle_runner_error,
+    validate_dataset_extended, supports_async
+)
+
+
+def create_argument_parser() -> argparse.ArgumentParser:
+    """Create argument parser with shared arguments only."""
+    parser = create_base_argument_parser(
+        "Modular backend evaluation system for MLPerf DeepSeek reference implementation"
+    )
+    
+    # Add runner-specific arguments
+    parser.add_argument("--async", action="store_true",
+                       help="Use async generation instead of synchronous")
+    
+    return parser
+
+
+async def run_async_inference(backend: BaseBackend, 
+                            tokenized_prompts: List[List[int]],
+                            text_prompts: Optional[List[str]] = None) -> List[Dict[str, Any]]:
+    """Run async inference with proper error handling and progress bar that updates as tasks complete."""
+    try:
+        # Get futures from backend
+        if uses_text_input():
+            futures = backend.generate_async(text_prompts=text_prompts)
+        else:
+            futures = backend.generate_async(tokenized_prompts=tokenized_prompts)
+        
+        # Create a list to store results in order
+        results = [None] * len(futures)
+        
+        # Create enumerated futures with their original indices for tracking
+        indexed_futures = [(i, future) for i, future in enumerate(futures)]
+        
+        # Track completion for debugging
+        completed_indices = set()
+        
+        # Process tasks with progress bar that updates as tasks complete
+        with async_tqdm(total=len(futures), desc="Async inference", unit="prompt") as pbar:
+            # Use asyncio.wait with FIRST_COMPLETED to handle out-of-order completion
+            pending = {future for _, future in indexed_futures}
+            
+            while pending:
+                # Wait for at least one future to complete
+                done, pending = await asyncio.wait(pending, return_when=asyncio.FIRST_COMPLETED)
+                
+                # Process all completed futures in this batch
+                for completed_future in done:
+                    # Find the original index for this completed future
+                    original_idx = None
+                    for idx, future in indexed_futures:
+                        if future is completed_future:
+                            original_idx = idx
+                            break
+                    
+                    if original_idx is None:
+                        print(f"\nWarning: Could not find original index for completed future")
+                        continue
+                    
+                    # Check for duplicate completion
+                    if original_idx in completed_indices:
+                        print(f"\nWarning: Prompt {original_idx} completed multiple times!")
+                        continue
+                    
+                    try:
+                        # Get the result from the completed future
+                        result = await completed_future
+                        
+                        # Store the result in the correct position
+                        results[original_idx] = result
+                        completed_indices.add(original_idx)
+                        
+                    except Exception as e:
+                        print(f"\nError processing prompt {original_idx}: {type(e).__name__}: {e}")
+                        import traceback
+                        traceback.print_exception(type(e), e, e.__traceback__)
+                        
+                        # Raise the error instead of using empty tokens
+                        raise RuntimeError(f"Backend failed to generate tokens for prompt {original_idx}: {e}")
+                    
+                    # Update progress bar after each completion
+                    pbar.update(1)
+        
+        # Verify all results are populated
+        if len(completed_indices) != len(futures):
+            missing_count = len(futures) - len(completed_indices)
+            raise RuntimeError(f"Missing results: completed {len(completed_indices)} != {len(futures)} total ({missing_count} missing)")
+        
+        for i, result in enumerate(results):
+            if result is None:
+                raise RuntimeError(f"Missing result for prompt {i}")
+        
+        print(f"\nCompleted all {len(completed_indices)} prompts successfully")
+        
+        return results
+    except Exception as e:
+        print(f"Error during async inference: {type(e).__name__}: {e}")
+        import traceback
+        traceback.print_exc()
+        raise
+
+
+def run_sync_inference(backend: BaseBackend, 
+                      tokenized_prompts: List[List[int]],
+                      text_prompts: Optional[List[str]] = None) -> List[Dict[str, Any]]:
+    """Run sync inference with proper error handling."""
+    try:
+        if uses_text_input():
+            results = backend.generate(text_prompts=text_prompts)
+        else:
+            results = backend.generate(tokenized_prompts=tokenized_prompts)
+        return results
+    except Exception as e:
+        print(f"Error during sync inference: {e}")
+        raise
+
+
+def main():
+    """Main evaluation function."""
+    # Parse arguments
+    parser = create_argument_parser()
+    args = parser.parse_args()
+    
+    try:
+        # Validate arguments
+        validate_runner_args(args, 'eval')
+        
+        # Detect backend early
+        backend_name = validate_runner_for_backend('eval')
+        
+        # Set up output paths
+        output_dir, output_file = setup_output_paths(args)
+        if args.output_file is None:
+            args.output_file = output_file
+        
+        # Generate the actual filename with timestamp that will be used for saving
+        actual_output_file = generate_timestamped_filename(args.output_file, add_timestamp=True)
+        
+        # Get async flag using getattr since 'async' is a reserved keyword
+        use_async = getattr(args, 'async', False)
+        
+        # Check if backend supports async
+        if use_async and not supports_async():
+            raise RuntimeError(f"Backend {backend_name} does not support async generation")
+        
+        # Print header
+        print_runner_header("Modular Backend Evaluation System", backend_name, args)
+        print(f"Mode: {'Async' if use_async else 'Sync'}")
+        print("=" * 80)
+        
+        # Load and validate dataset
+        df = load_dataset(args.input_file, args.num_samples, args.skip_samples)
+        validate_dataset_extended(df)
+        
+        prompts = df['text_input'].tolist()
+        
+        # Initialize tokenizer
+        tokenizer = StandardTokenizer()
+        
+        # Determine whether to use chat template based on registry
+        use_chat_template = uses_chat_template()
+        
+        # For text-prompt backends, we'll pass the prompts directly
+        # For tokenized-prompt backends, we need to tokenize first
+        if uses_text_input():
+            print(f"Backend {backend_name} uses text prompts directly")
+            tokenized_prompts = None
+            processed_strings = prompts
+        else:
+            # Tokenize prompts before initializing backend
+            print("Tokenizing prompts...")
+            print(f"Using chat template: {use_chat_template}")
+            tokenized_prompts, processed_strings = tokenizer.tokenize_prompts(
+                prompts, use_chat_template
+            )
+            print(f"Tokenized {len(tokenized_prompts)} prompts")
+            print(f"Tokenizer Max length: {tokenizer.max_length}")
+        
+        # Initialize backend using registry
+        print(f"\nInitializing {backend_name.upper()} backend...")
+        backend = get_backend_instance(backend_name)
+        
+        with backend:
+            # Create new output dataframe with only required columns
+            df_output = pd.DataFrame()
+            
+            # Copy all columns from input dataframe first
+            for col in df.columns:
+                df_output[col] = df[col]
+            
+            # Run inference with appropriate prompt format
+            if use_async:
+                print("Running async inference...")
+                raw_results = asyncio.run(run_async_inference(
+                    backend, tokenized_prompts, text_prompts=prompts))
+            else:
+                print("Running sync inference...")
+                raw_results = run_sync_inference(
+                    backend, tokenized_prompts, text_prompts=prompts)
+            
+            # Process raw results into standardized format using shared utility
+            print("Processing results...")
+            standardized_results = process_inference_results(
+                raw_results, tokenizer
+            )
+            
+            # Add generated columns
+            df_output['model_output'] = [r['model_output'] for r in standardized_results]
+            df_output['tok_model_output'] = [r['tok_model_output'] for r in standardized_results]
+            df_output['tok_model_output_len'] = [r['tok_model_output_len'] for r in standardized_results]
+            df_output['model_backend'] = [r['model_backend'] for r in standardized_results]
+            
+            # Save results
+            output_file = save_results(df_output, args.output_file, add_timestamp=True)
+            
+            print(f"\nEvaluation completed successfully!")
+            print(f"Results saved to: {output_file}")
+            print(f"Output columns: {list(df_output.columns)}")
+            
+    except KeyboardInterrupt:
+        print("\nEvaluation interrupted by user")
+        sys.exit(1)
+    except Exception as e:
+        handle_runner_error(e, "run_eval.py")
+
+
+if __name__ == "__main__":
+    main() 
\ No newline at end of file
diff --git a/language/deepseek-r1/run_eval_mpi.py b/language/deepseek-r1/run_eval_mpi.py
new file mode 100644
index 0000000000..37425526e1
--- /dev/null
+++ b/language/deepseek-r1/run_eval_mpi.py
@@ -0,0 +1,237 @@
+#!/usr/bin/env python3
+import os
+import sys
+import argparse
+import pandas as pd
+from typing import Optional
+import builtins
+
+import torch
+import torch.distributed as dist
+
+# Import utilities and backend registry
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from backends.pytorch_backend import PyTorchBackend
+from utils import save_results, generate_timestamped_filename, StandardTokenizer
+from utils.backend_registry import uses_chat_template, get_backend_instance, detect_backend, validate_runner_for_backend
+from utils.runner_utils import create_base_argument_parser, print_runner_header
+from utils.validation import validate_runner_args, ValidationError
+from utils.data_utils import load_dataset
+from backends import BaseBackend
+
+
+def main(
+    input_pickle_path: str,
+    output_pickle_path: str,
+    num_samples: Optional[int] = None,
+    skip_samples: int = 0,
+) -> None:
+    """
+    Main function to load the model, process prompts from a DataFrame, and save results.
+    """
+    _print = builtins.print  # Capture the original built-in print
+
+    world_size = int(os.getenv("WORLD_SIZE", "1"))
+    rank = int(os.getenv("RANK", "0"))
+    local_rank = int(os.getenv("LOCAL_RANK", "0"))
+
+    # Override print for non-rank 0 processes
+    if rank != 0:
+        print = lambda *_, **__: None
+
+    # Detect backend from environment
+    backend_name = detect_backend()
+    
+    # Validate backend
+    validate_runner_for_backend('eval_mpi')
+
+    # Get chat template usage from registry
+    use_chat_template = uses_chat_template()
+
+    # Generate the actual filename with timestamp that will be used for saving
+    actual_output_file = generate_timestamped_filename(output_pickle_path, add_timestamp=True)
+
+    if rank == 0:
+        _print("=" * 80)
+        _print("Distributed PyTorch Backend Evaluation System")
+        _print("=" * 80)
+        _print(f"Backend: {backend_name}")
+        _print(f"World size: {world_size}")
+        _print(f"Input file: {input_pickle_path}")
+        _print(f"Output file: {actual_output_file}")
+        if num_samples:
+            _print(f"Sample limit: {num_samples}")
+        if skip_samples:
+            _print(f"Skip samples: {skip_samples}")
+        _print(f"Chat template: {'enabled' if use_chat_template else 'disabled'} (from registry)")
+        _print("=" * 80)
+
+    # Initialize PyTorch backend
+    backend = PyTorchBackend()
+    backend.initialize()
+    
+    # Initialize StandardTokenizer
+    tokenizer = StandardTokenizer()
+
+    # Only rank 0 handles data
+    prompts_text_list = []
+    df_for_results = None
+
+    if rank == 0:
+        # Load DataFrame
+        _print(f"Loading input DataFrame from {input_pickle_path}...")
+        try:
+            df_for_results = pd.read_pickle(input_pickle_path)
+            _print(f"Loaded DataFrame with {len(df_for_results)} rows and columns: {df_for_results.columns.tolist()}")
+            
+            # Apply skip_samples if specified
+            if skip_samples > 0:
+                if skip_samples >= len(df_for_results):
+                    _print(f"Error: skip_samples ({skip_samples}) is greater than or equal to total samples ({len(df_for_results)})")
+                    backend.shutdown()
+                    if world_size > 1:
+                        dist.destroy_process_group()
+                    return
+                _print(f"Skipping first {skip_samples} samples")
+                df_for_results = df_for_results.iloc[skip_samples:].copy()
+                # Reset index to ensure sequential indices starting from 0
+                df_for_results = df_for_results.reset_index(drop=True)
+            
+            # Apply num_samples limit if specified
+            if num_samples is not None and num_samples < len(df_for_results):
+                _print(f"Limiting to first {num_samples} samples (out of {len(df_for_results)} total after skipping)")
+                df_for_results = df_for_results.head(num_samples).copy()
+                # Reset index to ensure sequential indices starting from 0
+                df_for_results = df_for_results.reset_index(drop=True)
+                
+        except Exception as e:
+            _print(f"Error loading input pickle file: {e}")
+            backend.shutdown()
+            if world_size > 1:
+                dist.destroy_process_group()
+            return
+
+        if 'text_input' not in df_for_results.columns:
+            _print("Error: 'text_input' column not found in the input DataFrame.")
+            backend.shutdown()
+            if world_size > 1:
+                dist.destroy_process_group()
+            return
+
+        prompts_text_list = df_for_results['text_input'].tolist()
+        _print(f"Extracted {len(prompts_text_list)} prompts from 'text_input' column.")
+
+        # Pre-initialize output columns
+        df_for_results['model_output'] = ""
+        df_for_results['tok_model_output'] = None
+        df_for_results['tok_model_output'] = df_for_results['tok_model_output'].astype('object')
+        df_for_results['tok_model_output_len'] = 0
+        df_for_results['model_backend'] = backend_name
+
+    # Broadcast the number of prompts to all ranks
+    if world_size > 1:
+        if rank == 0:
+            num_prompts_tensor = torch.tensor(len(prompts_text_list), dtype=torch.long, device="cuda")
+        else:
+            num_prompts_tensor = torch.empty(1, dtype=torch.long, device="cuda")
+        dist.broadcast(num_prompts_tensor, src=0)
+        num_total_prompts = num_prompts_tensor.item()
+    else:
+        num_total_prompts = len(prompts_text_list)
+
+    batch_size = backend.config['batch_size']
+
+    # Process prompts in batches
+    for i in range(0, num_total_prompts, batch_size):
+        current_batch_num = (i // batch_size) + 1
+        current_batch_prompt_texts = None
+        current_batch_prompt_tokens = None
+
+        if rank == 0:
+            current_batch_prompt_texts = prompts_text_list[i:i+batch_size]
+            # Tokenize on rank 0 using StandardTokenizer
+            current_batch_prompt_tokens, _ = tokenizer.tokenize_prompts(
+                current_batch_prompt_texts, use_chat_template
+            )
+            
+            _print(f"Processing batch {current_batch_num}, size {len(current_batch_prompt_tokens)}")
+
+        # All ranks call generate_batch_distributed
+        generated_tokens_for_batch = backend.generate_batch_distributed(
+            current_batch_prompt_tokens if rank == 0 else None
+        )
+
+        if rank == 0:
+            # Validate that we received valid tokens
+            if not generated_tokens_for_batch:
+                raise RuntimeError(f"Backend returned empty tokens for batch {current_batch_num}")
+            
+            for batch_idx, tokens in enumerate(generated_tokens_for_batch):
+                if not isinstance(tokens, (list, tuple)) or len(tokens) == 0:
+                    raise RuntimeError(f"Backend returned empty or invalid tokens for batch {current_batch_num}, item {batch_idx}: {tokens}")
+            
+            # Decode tokens to text using StandardTokenizer
+            decoded_texts_for_batch = tokenizer.batch_decode(
+                generated_tokens_for_batch
+            )
+
+            # Update DataFrame for the current batch
+            start_index_in_df = i
+            num_items_in_batch_output = len(decoded_texts_for_batch)
+
+            for batch_idx in range(num_items_in_batch_output):
+                original_df_idx = start_index_in_df + batch_idx
+                if original_df_idx < len(df_for_results):
+                    # Use at for assignments with list values
+                    df_for_results.at[original_df_idx, 'model_output'] = decoded_texts_for_batch[batch_idx]
+                    df_for_results.at[original_df_idx, 'tok_model_output'] = generated_tokens_for_batch[batch_idx]
+                    df_for_results.at[original_df_idx, 'tok_model_output_len'] = len(generated_tokens_for_batch[batch_idx])
+
+            _print(f"Batch {current_batch_num} completed.")
+
+    if rank == 0 and df_for_results is not None:
+        _print(f"All batches processed. Saving results...")
+        
+        # Keep only required columns in the same order as run_eval.py
+        output_columns = ['text_input', 'ground_truth', 'question', 'dataset', 'model_output', 'tok_model_output', 'tok_model_output_len', 'model_backend']
+        # Filter to only columns that exist
+        output_columns = [col for col in output_columns if col in df_for_results.columns]
+        df_output = df_for_results[output_columns]
+        
+        try:
+            saved_file = save_results(df_output, output_pickle_path, add_timestamp=True)
+            _print(f"Successfully saved results to {saved_file}")
+        except Exception as e:
+            _print(f"Error saving output pickle file: {e}")
+
+    # Clean up
+    backend.shutdown()
+    if world_size > 1:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    parser = create_base_argument_parser(
+        "Run distributed inference with PyTorch backend using MPI/torchrun"
+    )
+    # Note: --no-chat-template is not in the base parser and no longer needed
+
+    args = parser.parse_args()
+
+    # Validate arguments
+    try:
+        validate_runner_args(args, 'eval_mpi')
+    except ValidationError as e:
+        print(f"Argument validation error: {e}")
+        sys.exit(1)
+
+    # Set default output file if not specified
+    if args.output_file is None:
+        args.output_file = "data/pytorch_output.pkl"
+
+    main(
+        args.input_file,
+        args.output_file,
+        args.num_samples,
+        args.skip_samples,
+    ) 
\ No newline at end of file
diff --git a/language/deepseek-r1/run_mlperf.py b/language/deepseek-r1/run_mlperf.py
new file mode 100755
index 0000000000..7f484e725e
--- /dev/null
+++ b/language/deepseek-r1/run_mlperf.py
@@ -0,0 +1,400 @@
+#!/usr/bin/env python3
+import argparse
+import json
+import logging
+import os
+import sys
+from pathlib import Path
+from typing import Dict, List, Union, Optional, Any
+
+# Disable tokenizers parallelism to avoid forking issues
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import mlperf_loadgen as lg
+import numpy as np
+import pandas as pd
+
+from backends import BaseBackend
+from mlperf import (
+    OfflineSUT, ServerSUT, BaseSUT,
+    QuerySampleLibrary,
+    prepare_mlperf_dataset,
+    process_mlperf_results,
+    create_mlperf_output_dataframe
+)
+from utils import (
+    validate_runner_for_backend, uses_text_input, uses_chat_template,
+    load_dataset, save_results, print_runner_header, StandardTokenizer,
+    get_backend_instance, create_base_argument_parser,
+    setup_output_paths, validate_runner_args, handle_runner_error,
+    validate_dataset_extended, generate_timestamped_filename
+)
+from eval_accuracy import process_dataframe, print_evaluation_results, process_and_save_dataframe, process_mlperf_log_accuracy
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+def create_argument_parser() -> argparse.ArgumentParser:
+    """Create argument parser for MLPerf runner."""
+    parser = create_base_argument_parser(
+        "Run MLPerf inference benchmarks with modular backends (async pattern)"
+    )
+
+    # Scenario selection (no backend argument, auto-detected)
+    parser.add_argument("--mode", type=str, default="offline",
+                       choices=["offline", "server"],
+                       help="MLPerf scenario mode")
+
+    # MLPerf configuration
+    parser.add_argument("--mlperf-conf", type=str, default="/inference/mlperf.conf",
+                       help="Path to MLPerf configuration file")
+
+    parser.add_argument("--user-conf", type=str, default="mlperf/user.conf",
+                       help="Path to user configuration file")
+
+    parser.add_argument("--scenario", type=str, default=None,
+                       choices=["Offline", "Server"],
+                       help="MLPerf scenario (overrides --mode)")
+
+    parser.add_argument("--accuracy", action="store_true",
+                       help="Run accuracy mode instead of performance")
+
+    # Output configuration
+    parser.add_argument("--output-dir", type=str, default="mlperf_results",
+                       help="Directory for MLPerf output logs")
+
+    parser.add_argument("--log-dir", type=str, default=None,
+                       help="Directory for detailed logs")
+
+    return parser
+
+
+def configure_loadgen(scenario: str,
+                     accuracy_mode: bool,
+                     mlperf_conf: Optional[str] = None,
+                     user_conf: Optional[str] = None,
+                     log_dir: Optional[str] = None,
+                     model_name: str = "deepseek-r1") -> lg.TestSettings:
+    """Configure LoadGen test settings.
+
+    Args:
+        scenario: MLPerf scenario ("offline" or "server")
+        accuracy_mode: Whether to run in accuracy mode
+        mlperf_conf: Path to MLPerf config file
+        user_conf: Path to user config file
+        log_dir: Directory for logs
+        model_name: Model name for configuration (default: deepseek-r1)
+
+    Returns:
+        LoadGen TestSettings
+    """
+    settings = lg.TestSettings()
+
+    # Set scenario
+    if scenario.lower() == "offline":
+        settings.scenario = lg.TestScenario.Offline
+    elif scenario.lower() == "server":
+        settings.scenario = lg.TestScenario.Server
+    else:
+        raise ValueError(f"Unknown scenario: {scenario}")
+
+    # Set mode
+    if accuracy_mode:
+        settings.mode = lg.TestMode.AccuracyOnly
+    else:
+        settings.mode = lg.TestMode.PerformanceOnly
+
+    # Load configurations if files exist
+    if mlperf_conf and Path(mlperf_conf).exists():
+        settings.FromConfig(mlperf_conf, model_name, scenario, 2)
+    if user_conf and Path(user_conf).exists():
+        settings.FromConfig(user_conf, model_name, scenario, 1)
+
+    return settings
+
+
+def run_loadgen_test(sut: Union[OfflineSUT, ServerSUT],
+                    qsl: QuerySampleLibrary,
+                    settings: lg.TestSettings,
+                    log_settings: lg.LogSettings) -> None:
+    """Run LoadGen test.
+
+    Args:
+        sut: System Under Test instance
+        qsl: Query Sample Library
+        settings: Test settings
+        log_settings: Log settings
+    """
+    # Start the test
+    logger.info("Starting LoadGen test with async generation pattern")
+    lg.StartTestWithLogSettings(sut.sut, qsl.qsl, settings, log_settings)
+    logger.info("LoadGen test completed")
+
+
+def main():
+    """Main function."""
+    import gc
+    import torch
+
+    parser = create_argument_parser()
+    args = parser.parse_args()
+
+    try:
+        # Validate arguments
+        validate_runner_args(args, 'mlperf')
+
+        # Detect backend early
+        backend_name = validate_runner_for_backend('mlperf')
+
+        # Handle scenario override
+        if args.scenario:
+            args.mode = args.scenario.lower()
+
+        # Create output directories
+        output_dir = Path(args.output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        if args.log_dir:
+            log_dir = Path(args.log_dir)
+        else:
+            log_dir = output_dir / args.mode / ("accuracy" if args.accuracy else "performance")
+        log_dir.mkdir(parents=True, exist_ok=True)
+
+        # Set up output paths with mode information
+        _, output_file_base = setup_output_paths(args, mode=args.mode)
+        if args.output_file is None:
+            # Create output file path in the log directory
+            mode_str = "accuracy" if args.accuracy else "performance"
+            output_file_base = str(log_dir / f"{backend_name}_mlperf_{args.mode}_{mode_str}_output.pkl")
+        else:
+            output_file_base = args.output_file
+
+        # Generate the actual filename with timestamp that will be used for saving
+        actual_output_file = generate_timestamped_filename(output_file_base, add_timestamp=True)
+
+        # Ensure the parent directory of the output file exists
+        output_file_parent = Path(actual_output_file).parent
+        output_file_parent.mkdir(parents=True, exist_ok=True)
+        logger.info(f"Ensured output file directory exists: {output_file_parent}")
+
+        logger.info("=" * 80)
+        logger.info("MLPerf Inference Benchmark Runner (Async Pattern)")
+        logger.info("=" * 80)
+        logger.info(f"Backend: {backend_name}")
+        logger.info(f"Mode: {args.mode}")
+        logger.info(f"Accuracy: {args.accuracy}")
+        logger.info(f"Input file: {args.input_file}")
+        logger.info(f"Output directory: {output_dir}")
+        logger.info(f"Output file: {actual_output_file}")
+        logger.info(f"Uses text prompts: {uses_text_input()}")
+        logger.info("=" * 80)
+
+        # Initialize tokenizer
+        tokenizer = StandardTokenizer()
+
+        # Determine whether to use chat template based on registry
+        use_chat_template = uses_chat_template()
+
+        # Prepare dataset using new utility
+        logger.info("Preparing MLPerf dataset...")
+        dataset_info = prepare_mlperf_dataset(
+            args.input_file,
+            tokenizer=tokenizer,
+            use_chat_template=use_chat_template,
+        )
+
+        # Extract components
+        df = dataset_info['dataframe']
+        prompts = dataset_info['prompts']
+        tokenized_prompts = dataset_info['tokenized_prompts']
+        processed_strings = dataset_info['processed_strings']
+        uses_text_prompts = dataset_info['uses_text_prompts']
+
+        logger.info(f"Loaded {len(prompts)} prompts from dataset")
+
+        # For backends that use text prompts, we pass the processed strings
+        # For tokenized backends, we pass the tokenized prompts
+        if uses_text_prompts:
+            logger.info(f"Backend {backend_name} will use text prompts directly")
+            dataset_for_sut = tokenized_prompts
+            strings_for_sut = processed_strings
+        else:
+            logger.info(f"Backend {backend_name} will use tokenized prompts")
+            dataset_for_sut = tokenized_prompts
+            strings_for_sut = processed_strings # This is what gets used for generation now
+
+        # Create backend using registry
+        logger.info(f"Initializing {backend_name} backend...")
+        backend = get_backend_instance(backend_name)
+
+        # Use backend context manager to ensure initialization and cleanup
+        with backend:
+            # Create SUT with proper data
+            if args.mode == "offline":
+                sut = OfflineSUT(
+                    backend=backend,
+                    dataset=dataset_for_sut,
+                    dataset_strings=strings_for_sut,
+                    name=f"{backend_name}_offline_sut"
+                )
+            else:  # server
+                sut = ServerSUT(
+                    backend=backend,
+                    dataset=dataset_for_sut,
+                    dataset_strings=strings_for_sut,
+                    name=f"{backend_name}_server_sut"
+                )
+
+            # Create QSL
+            qsl = QuerySampleLibrary(dataset_for_sut, strings_for_sut)
+
+            # Configure LoadGen
+            settings = configure_loadgen(
+                scenario=args.mode,
+                accuracy_mode=args.accuracy,
+                mlperf_conf=args.mlperf_conf,
+                user_conf=args.user_conf,
+                log_dir=str(log_dir)
+            )
+
+            # Update settings with dataset info
+            # TODO(vir): these should be in mlperf.conf
+            settings.max_query_count = len(tokenized_prompts)
+            settings.min_query_count = len(tokenized_prompts)
+            settings.use_token_latencies = True
+            settings.server_coalesce_queries = True
+
+            # Configure logging
+            log_settings = lg.LogSettings()
+            log_settings.log_output.outdir = str(log_dir)
+            log_settings.log_output.copy_summary_to_stdout = True
+            log_settings.enable_trace = False
+
+            # Start the SUT
+            logger.info("Starting SUT with async generation support...")
+            sut.start()
+
+            try:
+                # Run test
+                logger.info("Running LoadGen test with async backend...")
+                run_loadgen_test(sut, qsl, settings, log_settings)
+                logger.info("LoadGen test completed successfully")
+
+                # Ensure all queries are flushed and async operations complete
+                logger.info("Flushing queries to ensure completion...")
+                sut.flush_queries()
+
+                # Get results BEFORE stopping the SUT
+                logger.info("Retrieving results from SUT...")
+                sut_results = sut.get_results()
+                logger.info(f"Retrieved {len(sut_results)} results from SUT")
+            finally:
+                # Stop the SUT
+                logger.info("Stopping SUT...")
+                sut.stop()
+
+                # Explicitly destroy QSL to ensure proper cleanup order
+                if qsl.qsl is not None:
+                    logger.info("Destroying QSL...")
+                    lg.DestroyQSL(qsl.qsl)
+                    qsl.qsl = None
+
+            logger.info(f"MLPerf results saved to: {log_dir}")
+
+            # Save results to pickle file (always, regardless of mode)
+            logger.info("Processing results for output file...")
+
+            # Initialize df_output to None for safety
+            df_output = None
+
+            try:
+                # Get results from SUT - must have valid results
+                if not sut_results:
+                    raise RuntimeError("No results available from SUT - backend failed to generate tokens")
+
+                # Process results using new utility
+                processed_results = process_mlperf_results(
+                    sut_results, tokenizer
+                )
+
+                # Create output dataframe using new utility
+                df_output = create_mlperf_output_dataframe(
+                    df, processed_results, backend_name
+                )
+
+                # Save results
+                # FIXME(vir): output pickle is empty so dont save, okay since accuracy run anyways uses mlperf_log_accuracy.json
+                # saved_file = save_results(df_output, output_file_base, add_timestamp=True)
+                # logger.info(f"Results saved to: {saved_file}")
+                # logger.info(f"Output columns: {list(df_output.columns)}")
+
+            except Exception as e:
+                logger.error(f"Error processing results: {e}")
+                raise
+
+            # If in accuracy mode, run evaluation
+            if args.accuracy and df_output is not None:
+                logger.info("=" * 80)
+                logger.info("Running accuracy evaluation...")
+                logger.info("=" * 80)
+
+                # Check for MLPerf log accuracy file first
+                mlperf_log_file = log_dir / "mlperf_log_accuracy.json"
+
+                if mlperf_log_file.exists():
+                    logger.info(f"Found MLPerf log accuracy file: {mlperf_log_file}")
+                    logger.info("Using MLPerf log for accuracy evaluation...")
+
+                    # Get checkpoint path from backend configuration
+                    backend_config = get_backend_instance(backend_name).config
+
+                    # Determine checkpoint path based on backend type
+                    if hasattr(get_backend_instance(backend_name), 'model_path'):
+                        # PyTorch backend has model_path
+                        checkpoint_path = str(get_backend_instance(backend_name).model_path)
+                    elif 'model' in backend_config:
+                        # Other backends use model name directly
+                        checkpoint_path = backend_config['model']
+                    elif 'model_name' in backend_config:
+                        # PyTorch backend config
+                        checkpoint_path = backend_config['model_name']
+                    else:
+                        # Fallback to default
+                        checkpoint_path = "deepseek-ai/DeepSeek-R1"
+
+                    # Process MLPerf log accuracy
+                    df_evaluated, evaluated_file = process_mlperf_log_accuracy(
+                        mlperf_log_file=mlperf_log_file,
+                        dataset_file=args.input_file,
+                        checkpoint_path=checkpoint_path,
+                        output_dir=log_dir,
+                        base_filename="mlperf_accuracy_evaluated.pkl"
+                    )
+
+                    logger.info(f"MLPerf accuracy evaluation saved to: {evaluated_file}")
+                else:
+                    logger.info("No MLPerf log accuracy file found, using standard DataFrame evaluation...")
+                    raise RuntimeError("No MLPerf log accuracy file found, using standard DataFrame evaluation...")
+
+        # Ensure clean exit
+        gc.collect()
+
+        # Ensure all CUDA operations are complete
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+            torch.cuda.empty_cache()
+
+    except KeyboardInterrupt:
+        logger.info("Test interrupted by user")
+        sys.exit(1)
+    except Exception as e:
+        handle_runner_error(e, "run_mlperf.py")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/language/deepseek-r1/run_mlperf_mpi.py b/language/deepseek-r1/run_mlperf_mpi.py
new file mode 100755
index 0000000000..66196645ed
--- /dev/null
+++ b/language/deepseek-r1/run_mlperf_mpi.py
@@ -0,0 +1,642 @@
+#!/usr/bin/env python3
+import argparse
+import json
+import logging
+import os
+import sys
+from pathlib import Path
+from typing import Dict, List, Union, Optional, Any
+import builtins
+import time
+
+# Disable tokenizers parallelism to avoid forking issues
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import mlperf_loadgen as lg
+import numpy as np
+import pandas as pd
+import torch
+import torch.distributed as dist
+from transformers import AutoTokenizer
+
+from backends.pytorch_backend import PyTorchBackend
+from mlperf import (
+    OfflineSUT, ServerSUT, BaseSUT,
+    DistributedQuerySampleLibrary,
+    prepare_mlperf_dataset,
+    process_mlperf_results,
+    create_mlperf_output_dataframe
+)
+from utils import (
+    StandardTokenizer,
+    validate_dataset,
+    process_inference_results
+)
+from utils.runner_utils import create_base_argument_parser, print_runner_header
+from utils.backend_registry import (
+    uses_chat_template, get_backend_instance, detect_backend,
+    validate_runner_for_backend
+)
+from utils.validation import (
+    validate_runner_args, ValidationError,
+    validate_dataset_extended
+)
+from utils.data_utils import (
+    load_dataset, save_results,
+    generate_timestamped_filename
+)
+from eval_accuracy import process_dataframe, print_evaluation_results, process_and_save_dataframe, process_mlperf_log_accuracy
+
+
+# Configure logging - only for rank 0
+def setup_logging(rank: int):
+    """Setup logging based on rank."""
+    if rank == 0:
+        logging.basicConfig(
+            level=logging.INFO,
+            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        )
+    else:
+        # Disable logging for non-rank 0 processes
+        logging.disable(logging.CRITICAL)
+
+    return logging.getLogger(__name__)
+
+
+class DistributedOfflineSUT(BaseSUT):
+    """Distributed Offline SUT implementation for PyTorch backend.
+
+    Only rank 0 interacts with LoadGen, but all ranks participate in inference.
+    """
+
+    def __init__(self,
+                 backend: 'PyTorchBackend',
+                 dataset: List[List[int]],
+                 prompt_strings: Optional[List[str]] = None,
+                 name: str = "DistributedOfflineSUT",
+                 rank: int = 0,
+                 world_size: int = 1):
+        """Initialize the distributed offline SUT.
+
+        Args:
+            backend: Backend instance to use for inference
+            dataset: List of tokenized prompts
+            prompt_strings: Optional list of prompt strings
+            name: Name of the SUT
+            rank: Process rank
+            world_size: Total number of processes
+        """
+        super().__init__(name)
+        self.backend = backend
+        self.dataset = dataset
+        self.prompt_strings = prompt_strings
+        self.rank = rank
+        self.world_size = world_size
+
+        # Results storage (only rank 0)
+        if self.rank == 0:
+            self.results = {}
+            self.index_to_id = {}
+
+        # Flag to signal other ranks to exit
+        self._should_exit = False
+
+    def issue_queries(self, query_samples: List[lg.QuerySample]) -> None:
+        """Issue queries for processing.
+
+        Only called on rank 0 by LoadGen.
+
+        Args:
+            query_samples: List of MLPerf LoadGen query samples
+        """
+        if self.rank != 0:
+            return
+
+        logger = logging.getLogger(__name__)
+        logger.info(f"Issuing {len(query_samples)} queries")
+
+        # Process queries in batches
+        batch_size = self.backend.config['batch_size']
+
+        for i in range(0, len(query_samples), batch_size):
+            batch_samples = query_samples[i:i+batch_size]
+
+            # Prepare batch tokens
+            batch_tokens = []
+            batch_ids = []
+
+            for sample in batch_samples:
+                # Track index to ID mapping
+                self.index_to_id[sample.index] = sample.id
+
+                # Get tokens for this sample
+                tokens = self.dataset[sample.index]
+                batch_tokens.append(tokens)
+                batch_ids.append(sample.id)
+
+            # Signal other ranks to participate in generation
+            if self.world_size > 1:
+                signal = ["generate"]
+                dist.broadcast_object_list(signal, src=0)
+
+            # Generate using distributed backend
+            # This will broadcast to all ranks internally
+            generated_tokens = self.backend.generate_batch_distributed(batch_tokens)
+
+            # Process results and send to LoadGen
+            for j, (sample_id, tokens) in enumerate(zip(batch_ids, generated_tokens)):
+                # Create a copy of tokens before numpy conversion
+                tokens_copy = tokens.copy()
+
+                # Convert tokens to bytes for LoadGen
+                token_array = np.array(tokens, dtype=np.int32)
+                n_tokens = len(tokens)
+
+                # Create LoadGen response
+                response = lg.QuerySampleResponse(
+                    sample_id,
+                    token_array.ctypes.data,
+                    token_array.nbytes,
+                    n_tokens,
+                )
+
+                # Store result with the tokens copy
+                self.results[sample_id] = {
+                    'tokens': tokens_copy,
+                }
+
+                # Send response to LoadGen
+                lg.QuerySamplesComplete([response])
+
+            # Send idle signal to other ranks after batch completes
+            if self.world_size > 1:
+                idle_signal = [None]
+                dist.broadcast_object_list(idle_signal, src=0)
+
+    def flush_queries(self) -> None:
+        """Flush any pending queries."""
+        # Nothing to flush in this implementation
+        pass
+
+    def start(self) -> lg.ConstructSUT:
+        """Start the SUT."""
+        # Signal that we're starting
+        if self.rank == 0:
+            logger = logging.getLogger(__name__)
+            logger.info("Starting Distributed Offline SUT")
+
+        return super().start()
+
+    def stop(self) -> None:
+        """Stop the SUT."""
+        # Signal other ranks to exit is now handled in the main loop
+        # after LoadGen test completes
+
+        super().stop()
+
+    def get_results(self) -> List[Dict[str, Any]]:
+        """Get all results in order of dataset indices.
+
+        Returns:
+            List of result dictionaries with model_output, tok_model_output, and tok_model_output_len
+        """
+        if self.rank != 0:
+            return []
+
+        # Create a list to hold results in dataset order
+        ordered_results = []
+
+        # Process results in order of dataset indices
+        for i in range(len(self.dataset)):
+            # Get the sample ID for this index
+            sample_id = self.index_to_id.get(i)
+
+            if sample_id is not None and sample_id in self.results:
+                result = self.results[sample_id]
+                if 'tokens' in result and result['tokens']:
+                    tokens = result['tokens']
+
+                    # Decode tokens to get text output
+                    output_text = ''
+                    if self.backend.tokenizer:
+                        output_text = self.backend.tokenizer.decode(tokens, skip_special_tokens=True)
+
+                    ordered_results.append({
+                        'model_output': output_text,
+                        'tok_model_output': tokens,
+                        'tok_model_output_len': len(tokens)
+                    })
+                else:
+                    # Result exists but no tokens - this is an error
+                    raise RuntimeError(f"No tokens in result for dataset index {i}, sample_id {sample_id}")
+            else:
+                # No result for this index - this is an error
+                raise RuntimeError(f"No result for dataset index {i}, sample_id {sample_id}")
+
+        return ordered_results
+
+
+
+
+def create_argument_parser() -> argparse.ArgumentParser:
+    """Create argument parser for distributed MLPerf runner."""
+    parser = argparse.ArgumentParser(
+        description="Run MLPerf inference benchmarks with distributed PyTorch backend",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    # Dataset arguments
+    parser.add_argument("--input-file", type=str,
+                       default="data/final_output.pkl",
+                       help="Input pickle file with prompts")
+
+    # MLPerf configuration
+    parser.add_argument("--mlperf-conf", type=str, default="/inference/mlperf.conf",
+                       help="Path to MLPerf configuration file")
+
+    parser.add_argument("--user-conf", type=str, default="mlperf/user.conf",
+                       help="Path to user configuration file")
+
+    parser.add_argument("--mode", type=str, default="offline",
+                       choices=["offline", "server"],
+                       help="MLPerf scenario mode (only offline supported for distributed)")
+
+    parser.add_argument("--accuracy", action="store_true",
+                       help="Run accuracy mode instead of performance")
+
+    # Output configuration
+    parser.add_argument("--output-dir", type=str, default="mlperf_results",
+                       help="Directory for MLPerf output logs")
+
+    parser.add_argument("--log-dir", type=str, default=None,
+                       help="Directory for detailed logs")
+
+    parser.add_argument("--output-file", type=str, default=None,
+                       help="Output pickle file path (auto-generated if not specified)")
+
+    # Note: --no-chat-template is removed (chat template usage determined by backend registry)
+
+    return parser
+
+
+def configure_loadgen(scenario: str,
+                     accuracy_mode: bool,
+                     mlperf_conf: Optional[str] = None,
+                     user_conf: Optional[str] = None,
+                     log_dir: Optional[str] = None,
+                     model_name: str = "deepseek-r1") -> lg.TestSettings:
+    """Configure LoadGen test settings.
+
+    Args:
+        scenario: MLPerf scenario ("offline" or "server")
+        accuracy_mode: Whether to run in accuracy mode
+        mlperf_conf: Path to MLPerf config file
+        user_conf: Path to user config file
+        log_dir: Directory for logs
+        model_name: Model name for configuration (default: deepseek-r1)
+
+    Returns:
+        LoadGen TestSettings
+    """
+    settings = lg.TestSettings()
+
+    # Set scenario
+    if scenario.lower() == "offline":
+        settings.scenario = lg.TestScenario.Offline
+    elif scenario.lower() == "server":
+        settings.scenario = lg.TestScenario.Server
+    else:
+        raise ValueError(f"Unknown scenario: {scenario}")
+
+    # Set mode
+    if accuracy_mode:
+        settings.mode = lg.TestMode.AccuracyOnly
+    else:
+        settings.mode = lg.TestMode.PerformanceOnly
+
+    # Load configurations if files exist
+    if mlperf_conf and Path(mlperf_conf).exists():
+        settings.FromConfig(mlperf_conf, model_name, scenario, 2)
+    if user_conf and Path(user_conf).exists():
+        settings.FromConfig(user_conf, model_name, scenario, 1)
+
+    return settings
+
+
+def run_loadgen_test(sut: DistributedOfflineSUT,
+                    qsl: DistributedQuerySampleLibrary,
+                    settings: lg.TestSettings,
+                    log_settings: lg.LogSettings,
+                    rank: int,
+                    logger) -> None:
+    """Run LoadGen test (only on rank 0).
+
+    Args:
+        sut: System Under Test instance
+        qsl: Query Sample Library
+        settings: Test settings
+        log_settings: Log settings
+        rank: Process rank
+        logger: Logger instance
+    """
+    if rank == 0:
+        # Start the test
+        logger.info("Starting LoadGen test")
+        lg.StartTestWithLogSettings(sut.sut, qsl.qsl, settings, log_settings)
+        logger.info("LoadGen test completed")
+
+
+def main():
+    """Main function."""
+    _print = builtins.print  # Capture the original built-in print
+
+    # Get distributed environment info
+    world_size = int(os.getenv("WORLD_SIZE", "1"))
+    rank = int(os.getenv("RANK", "0"))
+    local_rank = int(os.getenv("LOCAL_RANK", "0"))
+
+    # Override print for non-rank 0 processes
+    if rank != 0:
+        print = lambda *_, **__: None
+
+    # Setup logging
+    logger = setup_logging(rank)
+
+    # Detect backend from environment
+    backend_name = detect_backend()
+
+    # Validate backend
+    validate_runner_for_backend('mlperf_mpi')
+
+    # Get chat template usage from registry
+    use_chat_template = uses_chat_template()
+
+    # Parse arguments
+    parser = create_argument_parser()
+    args = parser.parse_args()
+
+    # Validate arguments
+    if rank == 0:
+        try:
+            validate_runner_args(args, 'mlperf_mpi')
+        except ValidationError as e:
+            _print(f"Argument validation error: {e}")
+            sys.exit(1)
+
+    # Validate mode for distributed
+    if args.mode != "offline":
+        if rank == 0:
+            logger.error("Only offline mode is supported for distributed execution")
+        sys.exit(1)
+
+    # Create output directories (only rank 0)
+    if rank == 0:
+        output_dir = Path(args.output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        if args.log_dir:
+            log_dir = Path(args.log_dir)
+        else:
+            log_dir = output_dir / args.mode / ("accuracy" if args.accuracy else "performance")
+        log_dir.mkdir(parents=True, exist_ok=True)
+
+        # Determine output file path
+        if args.output_file:
+            output_file_base = args.output_file
+        else:
+            mode_str = "accuracy" if args.accuracy else "performance"
+            output_file_base = str(log_dir / f"{backend_name}_mlperf_{args.mode}_{mode_str}_output.pkl")
+
+        # Generate the actual filename with timestamp
+        actual_output_file = generate_timestamped_filename(output_file_base, add_timestamp=True)
+
+        # Ensure the parent directory of the output file exists
+        output_file_parent = Path(actual_output_file).parent
+        output_file_parent.mkdir(parents=True, exist_ok=True)
+        logger.info(f"Ensured output file directory exists: {output_file_parent}")
+
+        logger.info("=" * 80)
+        logger.info("MLPerf Inference Benchmark Runner (Distributed PyTorch)")
+        logger.info("=" * 80)
+        logger.info(f"Backend: {backend_name}")
+        logger.info(f"World size: {world_size}")
+        logger.info(f"Mode: {args.mode}")
+        logger.info(f"Accuracy: {args.accuracy}")
+        logger.info(f"Input file: {args.input_file}")
+        logger.info(f"Output directory: {output_dir}")
+        logger.info(f"Output file: {actual_output_file}")
+        logger.info(f"Chat template: {'enabled' if use_chat_template else 'disabled'} (from registry)")
+        logger.info("=" * 80)
+    else:
+        log_dir = None
+        actual_output_file = None
+
+    try:
+        # Initialize PyTorch backend
+        backend = PyTorchBackend()
+        backend.initialize()
+
+        # Initialize tokenizer (only on rank 0)
+        tokenizer = None
+        if rank == 0:
+            tokenizer = StandardTokenizer()
+
+        # Only rank 0 handles dataset loading
+        df = None
+        tokenized_prompts = []
+        processed_strings = []
+
+        if rank == 0:
+            # Prepare dataset using new utility
+            logger.info("Preparing MLPerf dataset...")
+            dataset_info = prepare_mlperf_dataset(
+                args.input_file,
+                tokenizer=tokenizer,
+                use_chat_template=use_chat_template
+            )
+
+            # Extract components
+            df = dataset_info['dataframe']
+            tokenized_prompts = dataset_info['tokenized_prompts']
+            processed_strings = dataset_info['processed_strings']
+
+            logger.info(f"Loaded {len(tokenized_prompts)} prompts from dataset")
+
+        # Create SUT
+        sut = DistributedOfflineSUT(
+            backend=backend,
+            dataset=tokenized_prompts if rank == 0 else [],
+            prompt_strings=processed_strings if rank == 0 else [],
+            name=f"{backend_name}_distributed_offline_sut",
+            rank=rank,
+            world_size=world_size
+        )
+
+        # Create QSL (only rank 0 needs the actual QSL)
+        qsl = DistributedQuerySampleLibrary(
+            tokenized_prompts if rank == 0 else [],
+            processed_strings if rank == 0 else [],
+            rank,
+            world_size
+        )
+
+        # Only rank 0 configures and runs LoadGen
+        if rank == 0:
+            # Configure LoadGen
+            settings = configure_loadgen(
+                scenario=args.mode,
+                accuracy_mode=args.accuracy,
+                mlperf_conf=args.mlperf_conf,
+                user_conf=args.user_conf,
+                log_dir=str(log_dir)
+            )
+
+            # Update settings with dataset info
+            # TODO(vir): these should be in mlperf.conf
+            settings.max_query_count = len(tokenized_prompts)
+            settings.min_query_count = len(tokenized_prompts)
+            settings.use_token_latencies = True
+            settings.server_coalesce_queries = True
+
+            # Configure logging
+            log_settings = lg.LogSettings()
+            log_settings.log_output.outdir = str(log_dir)
+            log_settings.log_output.copy_summary_to_stdout = True
+            log_settings.enable_trace = False
+
+        # Start the SUT
+        sut.start()
+
+        try:
+            if rank == 0:
+                # Run test (only rank 0)
+                logger.info("Running test...")
+                run_loadgen_test(sut, qsl, settings, log_settings, rank, logger)
+                logger.info("Completed test...")
+
+                # Ensure all queries are flushed and async operations complete
+                logger.info("Flushing queries to ensure completion...")
+                sut.flush_queries()
+
+                # Send exit signal to other ranks
+                if world_size > 1:
+                    exit_signal = [True]
+                    dist.broadcast_object_list(exit_signal, src=0)
+            else:
+                # Non-rank 0 processes participate in distributed generation
+                # They wait for signals from rank 0 and participate in generate_batch_distributed
+                while True:
+                    # First, check if we should exit
+                    # We use a separate broadcast to signal exit
+                    exit_check = [None]
+                    dist.broadcast_object_list(exit_check, src=0)
+
+                    if exit_check[0] is True:
+                        # Exit signal received
+                        break
+                    elif exit_check[0] == "generate":
+                        # Signal to participate in generation
+                        # The actual batch tokens will be broadcast inside generate_batch_distributed
+                        backend.generate_batch_distributed(None)
+                    # If exit_check[0] is None, continue waiting
+        finally:
+            # Stop the SUT
+            sut.stop()
+
+            if rank == 0:
+                logger.info(f"Results saved to: {log_dir}")
+
+                # Print summary
+                summary_file = log_dir / "mlperf_log_summary.txt"
+                if summary_file.exists():
+                    logger.info("\nTest Summary:")
+                    logger.info("-" * 40)
+                    with open(summary_file, 'r') as f:
+                        _print(f.read())
+
+                # Save results to pickle file (always, regardless of mode)
+                if df is not None:
+                    logger.info("Processing results for output file...")
+
+                    # Initialize df_output to None for safety
+                    df_output = None
+
+                    try:
+                        # Get results from SUT (if available)
+                        logger.info("Retrieving results from distributed SUT...")
+                        sut_results = sut.get_results()
+                        logger.info(f"Retrieved {len(sut_results)} results from distributed SUT")
+
+                        # Process results using new utility
+                        processed_results = process_mlperf_results(
+                            sut_results, tokenizer
+                        )
+
+                        # Create output dataframe using new utility
+                        df_output = create_mlperf_output_dataframe(
+                            df, processed_results, backend_name
+                        )
+
+                        # Save results
+                        # FIXME(vir): output pickle is empty so dont save, okay since accuracy run anyways uses mlperf_log_accuracy.json
+                        # saved_file = save_results(df_output, output_file_base, add_timestamp=True)
+                        # logger.info(f"Results saved to: {saved_file}")
+                        # logger.info(f"Output columns: {list(df_output.columns)}")
+
+                    except Exception as e:
+                        logger.error(f"Error processing results: {e}")
+                        raise
+
+                    # If in accuracy mode, run evaluation
+                    if args.accuracy and df_output is not None:
+                        logger.info("=" * 80)
+                        logger.info("Running accuracy evaluation...")
+                        logger.info("=" * 80)
+
+                        # Check for MLPerf log accuracy file first
+                        mlperf_log_file = log_dir / "mlperf_log_accuracy.json"
+
+                        if mlperf_log_file.exists():
+                            logger.info(f"Found MLPerf log accuracy file: {mlperf_log_file}")
+                            logger.info("Using MLPerf log for accuracy evaluation...")
+
+                            # For PyTorch backend (only one supported in MPI), get model path
+                            checkpoint_path = str(backend.model_path) if hasattr(backend, 'model_path') else backend.config.get('model_name', 'deepseek-ai/DeepSeek-R1')
+
+                            # Process MLPerf log accuracy
+                            df_evaluated, evaluated_file = process_mlperf_log_accuracy(
+                                mlperf_log_file=mlperf_log_file,
+                                dataset_file=args.input_file,
+                                checkpoint_path=checkpoint_path,
+                                output_dir=log_dir,
+                                base_filename="mlperf_accuracy_evaluated.pkl"
+                            )
+
+                            logger.info(f"MLPerf accuracy evaluation saved to: {evaluated_file}")
+                        else:
+                            logger.info("No MLPerf log accuracy file found, using standard DataFrame evaluation...")
+                            raise RuntimeError("No MLPerf log accuracy file found, using standard DataFrame evaluation...")
+
+    except KeyboardInterrupt:
+        if rank == 0:
+            logger.info("Test interrupted by user")
+        backend.shutdown()
+        if world_size > 1:
+            dist.destroy_process_group()
+        sys.exit(1)
+    except Exception as e:
+        if rank == 0:
+            logger.error(f"Test failed: {e}", exc_info=True)
+        backend.shutdown()
+        if world_size > 1:
+            dist.destroy_process_group()
+        sys.exit(1)
+
+    # Clean up
+    backend.shutdown()
+    if world_size > 1:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/language/deepseek-r1/setup.sh b/language/deepseek-r1/setup.sh
new file mode 100755
index 0000000000..76a1d47dfe
--- /dev/null
+++ b/language/deepseek-r1/setup.sh
@@ -0,0 +1,278 @@
+#!/bin/bash
+# Main setup script for MLPerf DeepSeek evaluation environment
+# This script detects the backend and calls the appropriate setup script
+#
+# Backend detection methods (in order):
+# 1. MLPERF_BACKEND environment variable (recommended)
+# 2. Automatic detection based on installed packages
+#
+# Backend-specific behavior:
+# - pytorch, vllm, sglang: Virtual environment activated after setup
+
+set -e  # Exit on error
+
+# Function to detect backend based on installed packages and environment
+detect_backend() {
+    echo "Detecting backend environment..." >&2
+    
+    # Check for backend-specific markers or environment variables first
+    if [ -n "$MLPERF_BACKEND" ]; then
+        echo "Using MLPERF_BACKEND environment variable: $MLPERF_BACKEND" >&2
+        # Validate the backend value
+        case "$MLPERF_BACKEND" in
+            pytorch|vllm|sglang)
+                echo "$MLPERF_BACKEND"
+                return 0
+                ;;
+            *)
+                echo "Warning: Invalid MLPERF_BACKEND value: $MLPERF_BACKEND" >&2
+                echo "Valid values: pytorch, vllm, sglang" >&2
+                echo "Continuing with automatic detection..." >&2
+                ;;
+        esac
+    fi
+    
+    # Check for vLLM in system Python
+    if python3 -c "import vllm" >/dev/null 2>&1; then
+        echo "Detected vLLM in system Python" >&2
+        echo "vllm"
+        return 0
+    fi
+    
+    # Check for PyTorch with ref_dsinfer (DeepSeek-V3 reference implementation)
+    if python3 -c "import torch" >/dev/null 2>&1 && python3 -c "import ref_dsinfer" >/dev/null 2>&1; then
+        echo "Detected PyTorch with ref_dsinfer in system Python" >&2
+        echo "pytorch"
+        return 0
+    fi
+    
+    # For SGLang, check CUDA version and specific PyTorch version
+    # SGLang containers typically have CUDA 12.6 and PyTorch 2.6.0
+    if command -v nvcc >/dev/null 2>&1; then
+        CUDA_VERSION=$(nvcc --version | grep "release" | awk '{print $6}' | cut -d',' -f1)
+        if [[ "$CUDA_VERSION" == "12.6" ]]; then
+            # Check if PyTorch 2.6.0 is in system (SGLang specific)
+            if python3 -c "import torch; exit(0 if torch.__version__.startswith('2.6.0') else 1)" >/dev/null 2>&1; then
+                # Additional check: no vLLM
+                if ! python3 -c "import vllm" >/dev/null 2>&1; then
+                    echo "Detected SGLang environment (CUDA 12.6, PyTorch 2.6.0)" >&2
+                    echo "sglang"
+                    return 0
+                fi
+            fi
+        fi
+    fi
+    
+    # Check for general PyTorch environment
+    if python3 -c "import torch" >/dev/null 2>&1; then
+        echo "Detected PyTorch in system Python" >&2
+        echo "pytorch"
+        return 0
+    fi
+    
+    # Only check virtual environments as a last resort
+    # and verify they are actually valid
+    for backend in sglang vllm pytorch; do
+        VENV_DIR="/work/.venv_$backend"
+        if [ -d "$VENV_DIR" ] && [ -x "$VENV_DIR/bin/python3" ]; then
+            # Check if the venv Python is actually working
+            if "$VENV_DIR/bin/python3" -c "import sys" >/dev/null 2>&1; then
+                echo "Found working $backend virtual environment" >&2
+                echo "$backend"
+                return 0
+            else
+                echo "Found broken $backend virtual environment at $VENV_DIR" >&2
+            fi
+        fi
+    done
+    
+    # If no specific backend detected, return unknown
+    echo "unknown"
+    return 1
+}
+
+# Function to clean up broken virtual environments
+cleanup_broken_venvs() {
+    echo "Checking for broken virtual environments..." >&2
+    for backend in pytorch vllm sglang; do
+        VENV_DIR="/work/.venv_$backend"
+        if [ -d "$VENV_DIR" ]; then
+            if [ ! -x "$VENV_DIR/bin/python3" ] || ! "$VENV_DIR/bin/python3" -c "import sys" >/dev/null 2>&1; then
+                echo "Found broken virtual environment at $VENV_DIR" >&2
+                echo -n "Remove it? [y/N] " >&2
+                read -r response
+                if [[ "$response" =~ ^[Yy]$ ]]; then
+                    rm -rf "$VENV_DIR"
+                    echo "Removed $VENV_DIR" >&2
+                fi
+            fi
+        fi
+    done
+}
+
+# Parse command line arguments
+FORCE_REBUILD=false
+BACKEND_OVERRIDE=""
+CLEANUP_VENVS=false
+
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --force-rebuild)
+            FORCE_REBUILD=true
+            shift
+            ;;
+        --backend)
+            BACKEND_OVERRIDE="$2"
+            shift 2
+            ;;
+        --cleanup-venvs)
+            CLEANUP_VENVS=true
+            shift
+            ;;
+        --help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  --force-rebuild    Force rebuild of MLPerf LoadGen from source"
+            echo "  --backend BACKEND  Override backend detection (pytorch|vllm|sglang)"
+            echo "  --cleanup-venvs    Check and remove broken virtual environments"
+            echo "  --help            Show this help message"
+            echo ""
+            echo "Backend Detection:"
+            echo "  1. Set MLPERF_BACKEND environment variable (recommended):"
+            echo "     export MLPERF_BACKEND=sglang"
+            echo "     $0"
+            echo ""
+            echo "  2. Use --backend command line argument:"
+            echo "     $0 --backend sglang"
+            echo ""
+            echo "  3. Automatic detection (may fail with multiple backends)"
+            echo ""
+            echo "Backend-specific behavior:"
+            echo "  pytorch, vllm, sglang: Virtual environment activated after setup"
+            echo ""
+            echo "Supported backends: pytorch, vllm, sglang"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+echo "=== MLPerf DeepSeek Environment Setup ==="
+
+# Check if cleanup requested
+if [ "$CLEANUP_VENVS" = "true" ]; then
+    cleanup_broken_venvs
+    echo ""
+fi
+
+# Detect or use override backend
+if [ -n "$BACKEND_OVERRIDE" ]; then
+    BACKEND="$BACKEND_OVERRIDE"
+    echo "Using backend override: $BACKEND"
+else
+    BACKEND=$(detect_backend)
+    if [ $? -eq 0 ]; then
+        echo "Detected backend: $BACKEND"
+    else
+        echo "ERROR: Could not detect backend automatically."
+        echo ""
+        echo "This usually happens when:"
+        echo "  - Multiple backends are installed in the same container"
+        echo "  - The container environment doesn't match expected patterns"
+        echo "  - Old virtual environments exist from previous runs"
+        echo ""
+        echo "SOLUTION: Set the MLPERF_BACKEND environment variable:"
+        echo "  export MLPERF_BACKEND=sglang  # For SGLang backend"
+        echo "  export MLPERF_BACKEND=vllm    # For vLLM backend"
+        echo "  export MLPERF_BACKEND=pytorch # For PyTorch backend"
+        echo ""
+        echo "Or use the --backend option:"
+        echo "  $0 --backend sglang"
+        echo ""
+        echo "To clean up broken virtual environments:"
+        echo "  $0 --cleanup-venvs"
+        exit 1
+    fi
+fi
+
+# Validate backend
+case "$BACKEND" in
+    pytorch|vllm|sglang)
+        echo "Setting up for $BACKEND backend..."
+        ;;
+    *)
+        echo "Error: Unknown backend '$BACKEND'"
+        echo "Supported backends: pytorch, vllm, sglang"
+        exit 1
+        ;;
+esac
+
+# Get the directory where this script is located
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+SETUP_SCRIPTS_DIR="$SCRIPT_DIR/docker/setup_scripts"
+
+# Check if setup scripts directory exists
+if [ ! -d "$SETUP_SCRIPTS_DIR" ]; then
+    echo "Error: Setup scripts directory not found at $SETUP_SCRIPTS_DIR"
+    echo "Please ensure the setup_scripts directory exists and contains backend-specific scripts."
+    exit 1
+fi
+
+# Call the appropriate backend setup script
+BACKEND_SCRIPT="$SETUP_SCRIPTS_DIR/setup_$BACKEND.sh"
+
+if [ ! -f "$BACKEND_SCRIPT" ]; then
+    echo "Error: Backend setup script not found: $BACKEND_SCRIPT"
+    echo "Available setup scripts:"
+    ls -1 "$SETUP_SCRIPTS_DIR"/setup_*.sh 2>/dev/null || echo "  None found"
+    exit 1
+fi
+
+echo "Running backend-specific setup: $BACKEND_SCRIPT"
+echo ""
+
+# Make sure the script is executable (only if we have permission to change it)
+if [ ! -x "$BACKEND_SCRIPT" ]; then
+    if chmod +x "$BACKEND_SCRIPT" 2>/dev/null; then
+        echo "Made script executable: $BACKEND_SCRIPT"
+    else
+        echo "Note: Could not make script executable, but will run with bash"
+    fi
+fi
+
+# Pass through the force rebuild flag if set
+if [ "$FORCE_REBUILD" = "true" ]; then
+    bash "$BACKEND_SCRIPT" --force-rebuild
+else
+    bash "$BACKEND_SCRIPT"
+fi
+
+echo ""
+echo "=== Main Setup Complete ==="
+echo "Backend: $BACKEND"
+
+# Provide activation instructions for backends that use virtual environments
+case "$BACKEND" in
+    pytorch|vllm|sglang)
+        echo ""
+        echo "IMPORTANT: To activate the virtual environment, run:"
+        echo "   source /work/.venv_${BACKEND}/bin/activate"
+        echo ""
+        echo ""
+        if [ "$BACKEND" = "pytorch" ]; then
+            echo "   (.venv_${BACKEND}) torchrun --nproc_per_node=8 run_eval_mpi.py ..."
+            echo "   (.venv_${BACKEND}) torchrun --nproc_per_node=8 run_mlperf_mpi.py ..."
+        else
+            echo "   (.venv_${BACKEND}) python run_eval.py ..."
+            echo "   (.venv_${BACKEND}) python run_mlperf.py ..."
+        fi
+        ;;
+esac
+
+echo ""
+echo "For usage instructions, see the output above or run:"
+echo "  $BACKEND_SCRIPT --help"
\ No newline at end of file
diff --git a/language/deepseek-r1/submodules/LiveCodeBench b/language/deepseek-r1/submodules/LiveCodeBench
new file mode 160000
index 0000000000..b1e7cab44d
--- /dev/null
+++ b/language/deepseek-r1/submodules/LiveCodeBench
@@ -0,0 +1 @@
+Subproject commit b1e7cab44d610bbc2e10d36d270cd0c89c600492
diff --git a/language/deepseek-r1/submodules/prm800k b/language/deepseek-r1/submodules/prm800k
new file mode 160000
index 0000000000..7ecc794703
--- /dev/null
+++ b/language/deepseek-r1/submodules/prm800k
@@ -0,0 +1 @@
+Subproject commit 7ecc794703b2877f63226f2477a49b34f9b25163
diff --git a/language/deepseek-r1/utils/__init__.py b/language/deepseek-r1/utils/__init__.py
new file mode 100644
index 0000000000..ce8d10e9fd
--- /dev/null
+++ b/language/deepseek-r1/utils/__init__.py
@@ -0,0 +1,104 @@
+"""
+Utility functions for data handling and tokenization.
+
+Provides common functionality for data handling and tokenization.
+"""
+
+from .data_utils import load_dataset, save_results, generate_timestamped_filename
+from .data_utils import prepare_output_dataframe, add_standardized_columns, validate_dataset
+
+# Import tokenization utilities
+from .tokenization import (
+    StandardTokenizer,
+    process_inference_results
+)
+
+# Import backend registry functions
+from .backend_registry import (
+    get_backend_config,
+    get_backend_class_path,
+    uses_text_input,
+    uses_chat_template,
+    get_supported_backends,
+    get_backend_instance,
+    is_backend_compatible_with_runner,
+    get_backend_env_vars,
+    apply_backend_env_vars,
+    # Backend detection and validation
+    detect_backend,
+    validate_backend,
+    validate_runner_for_backend,
+    # Feature check functions
+    supports_streaming,
+    supports_async,
+    requires_torchrun
+)
+
+# Import runner utilities
+from .runner_utils import (
+    create_base_argument_parser,
+    print_runner_header,
+    setup_output_paths
+)
+
+# Import validation utilities
+from .validation import (
+    BackendError,
+    BackendNotInitializedError,
+    ValidationError,
+    require_initialized,
+    validate_prompts_input,
+    validate_dataset_extended,
+    validate_runner_args
+)
+
+# Import error handling utilities
+from .error_handling import (
+    handle_backend_error,
+    handle_runner_error
+)
+
+__all__ = [
+    # Data utilities
+    'load_dataset',
+    'save_results',
+    'generate_timestamped_filename',
+    'prepare_output_dataframe',
+    'add_standardized_columns',
+    'validate_dataset',
+    # Backend utilities
+    'detect_backend',
+    'validate_backend',
+    'validate_runner_for_backend',
+    # Tokenization
+    'StandardTokenizer',
+    'process_inference_results',
+    # Backend registry
+    'get_backend_config',
+    'get_backend_class_path',
+    'uses_text_input',
+    'uses_chat_template',
+    'get_supported_backends',
+    'get_backend_instance',
+    'is_backend_compatible_with_runner',
+    'get_backend_env_vars',
+    'apply_backend_env_vars',
+    'supports_streaming',
+    'supports_async',
+    'requires_torchrun',
+    # Runner utilities
+    'create_base_argument_parser',
+    'print_runner_header',
+    'setup_output_paths',
+    # Validation
+    'BackendError',
+    'BackendNotInitializedError',
+    'ValidationError',
+    'require_initialized',
+    'validate_prompts_input',
+    'validate_dataset_extended',
+    'validate_runner_args',
+    # Error handling
+    'handle_backend_error',
+    'handle_runner_error'
+]
\ No newline at end of file
diff --git a/language/deepseek-r1/utils/backend_registry.py b/language/deepseek-r1/utils/backend_registry.py
new file mode 100644
index 0000000000..73aee587e6
--- /dev/null
+++ b/language/deepseek-r1/utils/backend_registry.py
@@ -0,0 +1,464 @@
+"""Central registry for all backend configurations and metadata."""
+from typing import Dict, List, Optional, Any
+import os
+from pathlib import Path
+
+# Configuration constants
+MAX_ISL = 3136  # max input sequence length
+MAX_OSL = 32 * 1024  # max output sequence length
+MAX_TEMPLATE_TOKS = 4  # max template tokens
+MODEL_REVISION = "56d4cbbb4d29f4355bab4b9a39ccb717a14ad5ad"
+
+# Backend Registry with all metadata
+BACKEND_REGISTRY = {
+    'pytorch': {
+        'class_path': 'backends.pytorch_backend.PyTorchBackend',
+        'input_type': 'tokenized',
+        'uses_chat_template': True,
+        'supports_async': False,
+        'supports_streaming': False,
+        'compatible_runners': ['eval_mpi', 'mlperf_mpi'],
+        'required_torchrun': True,
+        'config': {
+            "model_name": "deepseek-ai/DeepSeek-R1",
+            "model_revision": MODEL_REVISION,
+            "batch_size": 16,
+            "max_seq_len": MAX_ISL + MAX_OSL + MAX_TEMPLATE_TOKS,
+            "max_new_tokens": MAX_OSL,
+            "temperature": 0.0,
+            "device": "cuda",
+            "dtype": "bfloat16",
+            "seed": 965,  # pytorch will use special seed (same as ref-outputs)
+            "num_threads": 8,
+        },
+        'env_vars': {
+            # PyTorch reads REF_DSINFER_PATH but doesn't set it
+            # Also reads WORLD_SIZE, RANK, LOCAL_RANK from torchrun/mpirun
+            'OMP_NUM_THREADS': '8',
+        }
+    },
+    'vllm': {
+        'class_path': 'backends.vllm_backend.VLLMBackend',
+        'input_type': 'text',
+        'uses_chat_template': True,
+        'supports_async': True,
+        'supports_streaming': False,
+        'compatible_runners': ['eval', 'mlperf'],
+        'required_torchrun': False,
+        'config': {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "model_revision": MODEL_REVISION,
+            "tokenizer": "deepseek-ai/DeepSeek-R1",
+            "tensor_parallel_size": 8,
+            "max_num_seqs": 64,
+            "gpu_memory_utilization": 0.90,
+            "trust_remote_code": True,
+            "dtype": "auto",
+            "max_input_len": MAX_ISL + MAX_TEMPLATE_TOKS,
+            "max_output_len": MAX_OSL,
+            "max_model_len": MAX_ISL + MAX_OSL + MAX_TEMPLATE_TOKS,
+            "temperature": 0.0,
+            "top_p": 1.0,
+            "seed": 42,
+            "enforce_eager": False,
+            "enable_prefix_caching": True,
+            "enable_chunked_prefill": True,
+        },
+        'env_vars': {
+            'CUDA_MODULE_LOADING': 'LAZY',
+            'NCCL_TREE_THRESHOLD': '0',
+            'OMP_NUM_THREADS': '8',
+            'VLLM_ATTENTION_BACKEND': 'FLASHINFER',
+            'VLLM_DISABLE_TQDM': '1',
+            'DISABLE_TQDM': '1',
+            'VLLM_USE_V1': '0',
+            'VLLM_ENGINE_ITERATION_TIMEOUT_S': '0',
+            'VLLM_API_TIMEOUT': '0',
+            'VLLM_RPC_TIMEOUT': '0',
+            'VLLM_WORKER_TIMEOUT': '0',
+            'VLLM_ASYNC_ENGINE_TIMEOUT': '0',
+        }
+    },
+    'sglang': {
+        'class_path': 'backends.sglang_backend.SGLangBackend',
+        'input_type': 'text',
+        'uses_chat_template': False,
+        'supports_async': True,
+        'supports_streaming': True,
+        'compatible_runners': ['eval', 'mlperf'],
+        'required_torchrun': False,
+        'config': {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "model_revision": MODEL_REVISION,
+            "served_model_name": "deepseek-r1",
+            "tokenizer": "deepseek-ai/DeepSeek-R1",
+            "host": "0.0.0.0",
+            "api_key": None,
+            "tensor_parallel_size": 8,
+            # NOTE(vir): sg-lang crash without +2 additional
+            "context_length": MAX_ISL + MAX_OSL + MAX_TEMPLATE_TOKS + 2,
+            "max_tokens": MAX_OSL,
+            "mem_fraction_static": 0.90,
+            "random_seed": 42,
+            "dtype": "auto",
+            "trust_remote_code": True,
+            "enable_torch_compile": True,
+            "enable_flashinfer": True,
+            "enable_dp_attention": True,
+            "dp": 8,
+            "cuda_graph_max_bs": 512,
+            "temperature": 0.0,
+            "top_p": 1.0,
+            "seed": 42,
+            "max_running_requests": 512,  # concurrency
+            "request_timeout": None,
+            "server_startup_timeout": 1800,
+            "health_check_interval": 5,
+        },
+        'env_vars': {
+            'CUDA_MODULE_LOADING': 'LAZY',
+            'NCCL_TREE_THRESHOLD': '0',
+        }
+    }
+}
+
+
+def detect_backend() -> str:
+    """
+    Detect backend from MLPERF_BACKEND environment variable.
+
+    Returns:
+        Backend name from MLPERF_BACKEND
+
+    Raises:
+        RuntimeError: If MLPERF_BACKEND is not set or invalid
+    """
+    if "MLPERF_BACKEND" not in os.environ:
+        supported = get_supported_backends()
+        raise RuntimeError(
+            "MLPERF_BACKEND environment variable is required but not set.\n\n"
+            "Please set it to one of the supported backends:\n" +
+            "\n".join(
+                f"  export MLPERF_BACKEND={backend}" for backend in supported)
+        )
+
+    backend = os.environ["MLPERF_BACKEND"]
+
+    # Validate the backend value
+    supported = get_supported_backends()
+    if backend not in supported:
+        raise RuntimeError(
+            f"Invalid MLPERF_BACKEND value: {backend}\n\n"
+            f"Supported backends: {', '.join(supported)}\n\n"
+            "Please set it to one of:\n" +
+            "\n".join(f"  export MLPERF_BACKEND={b}" for b in supported)
+        )
+
+    return backend
+
+
+def validate_backend(backend: str) -> None:
+    """
+    Validate that the backend name is supported.
+
+    Args:
+        backend: Backend name to validate
+
+    Raises:
+        ValueError: If backend is not supported
+    """
+    supported_backends = get_supported_backends()
+    if backend not in supported_backends:
+        raise ValueError(
+            f"Unknown backend '{backend}'. Supported backends: {', '.join(supported_backends)}")
+
+
+def _get_compatibility_error_message(backend: str, runner_type: str, compatible: List[str]) -> str:
+    """
+    Generate error message for incompatible backend/runner combinations.
+
+    Args:
+        backend: Detected backend name
+        runner_type: Type of runner
+        compatible: List of compatible backends for this runner
+
+    Returns:
+        Error message string
+    """
+    if runner_type in ['eval_mpi', 'mlperf_mpi']:
+        return (
+            f"MPI runners (run_eval_mpi.py, run_mlperf_mpi.py) only support: {', '.join(compatible)}.\n"
+            f"Detected backend: {backend}\n\n"
+            f"To use {backend} backend, use the non-MPI runners:\n"
+            f"  - run_eval.py\n"
+            f"  - run_mlperf.py\n\n"
+            f"To use MPI runners:\n"
+            f"  export MLPERF_BACKEND=pytorch"
+        )
+    else:
+        # For non-MPI runners
+        if backend == 'pytorch':
+            return (
+                f"For PyTorch backend, use the MPI runner instead:\n"
+                f"  torchrun --nproc_per_node=8 run_eval_mpi.py [args]\n\n"
+                f"{runner_type} runner only supports: {', '.join(compatible)}"
+            )
+        else:
+            return (
+                f"{runner_type} runner does not support {backend} backend.\n"
+                f"Compatible backends: {', '.join(compatible)}"
+            )
+
+
+def validate_runner_for_backend(runner_type: str) -> str:
+    """
+    Validate that the runner is compatible with the detected backend.
+
+    Args:
+        runner_type: One of 'eval', 'mlperf', 'eval_mpi', 'mlperf_mpi'
+
+    Returns:
+        Backend name from MLPERF_BACKEND env var (if valid)
+
+    Raises:
+        RuntimeError: If MLPERF_BACKEND not set or runner incompatible with backend
+    """
+    backend = detect_backend()
+
+    # Check compatibility using registry
+    if not is_backend_compatible_with_runner(backend, runner_type):
+        supported = get_supported_backends()
+        compatible = [
+            b for b in supported if is_backend_compatible_with_runner(b, runner_type)]
+        error_msg = _get_compatibility_error_message(
+            backend, runner_type, compatible)
+        raise RuntimeError(error_msg)
+
+    return backend
+
+
+def supports_streaming(backend_name: Optional[str] = None) -> bool:
+    """
+    Check if backend supports streaming generation.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+
+    Returns:
+        True if backend supports streaming
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unknown backend: {backend_name}. Available backends: {list(BACKEND_REGISTRY.keys())}")
+
+    return BACKEND_REGISTRY[backend_name].get('supports_streaming', False)
+
+
+def supports_async(backend_name: Optional[str] = None) -> bool:
+    """
+    Check if backend supports async generation.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+
+    Returns:
+        True if backend supports async
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unknown backend: {backend_name}. Available backends: {list(BACKEND_REGISTRY.keys())}")
+
+    return BACKEND_REGISTRY[backend_name].get('supports_async', False)
+
+
+def requires_torchrun(backend_name: Optional[str] = None) -> bool:
+    """
+    Check if backend requires torchrun for execution (supports MPI/distributed execution).
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+
+    Returns:
+        True if backend requires torchrun
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unknown backend: {backend_name}. Available backends: {list(BACKEND_REGISTRY.keys())}")
+
+    return BACKEND_REGISTRY[backend_name].get('required_torchrun', False)
+
+
+def get_backend_config(backend_name: Optional[str] = None) -> Dict[str, Any]:
+    """Get configuration for a specific backend.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+
+    Returns:
+        Backend configuration dictionary
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unknown backend: {backend_name}. Available backends: {list(BACKEND_REGISTRY.keys())}")
+    return BACKEND_REGISTRY[backend_name]['config'].copy()
+
+
+def get_backend_class_path(backend_name: Optional[str] = None) -> str:
+    """Get the class path for a backend.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+
+    Returns:
+        Backend class path string
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unknown backend: {backend_name}. Available backends: {list(BACKEND_REGISTRY.keys())}")
+    return BACKEND_REGISTRY[backend_name]['class_path']
+
+
+def uses_text_input(backend_name: Optional[str] = None) -> bool:
+    """Check if a backend uses text input (vs tokenized).
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+
+    Returns:
+        True if backend uses text input, False if tokenized
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unknown backend: {backend_name}. Available backends: {list(BACKEND_REGISTRY.keys())}")
+    return BACKEND_REGISTRY[backend_name]['input_type'] == 'text'
+
+
+def uses_chat_template(backend_name: Optional[str] = None) -> bool:
+    """Check if a backend uses chat templates.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+
+    Returns:
+        True if backend uses chat templates
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unknown backend: {backend_name}. Available backends: {list(BACKEND_REGISTRY.keys())}")
+    return BACKEND_REGISTRY[backend_name]['uses_chat_template']
+
+
+def get_supported_backends() -> List[str]:
+    """Get list of all supported backends."""
+    return list(BACKEND_REGISTRY.keys())
+
+
+def get_backend_instance(backend_name: Optional[str] = None):
+    """Create and return a backend instance.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+
+    Returns:
+        Backend instance
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unknown backend: {backend_name}. Available backends: {list(BACKEND_REGISTRY.keys())}")
+
+    class_path = get_backend_class_path(backend_name)
+    module_path, class_name = class_path.rsplit('.', 1)
+
+    # Import the module and get the class
+    import importlib
+    module = importlib.import_module(module_path)
+    backend_class = getattr(module, class_name)
+
+    return backend_class()
+
+
+def is_backend_compatible_with_runner(backend_name: Optional[str] = None, runner_type: str = None) -> bool:
+    """Check if a backend is compatible with a specific runner type.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+        runner_type: Type of runner to check compatibility with
+
+    Returns:
+        True if backend is compatible with the runner type
+    """
+    if runner_type is None:
+        raise ValueError("runner_type is required")
+
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        return False
+    return runner_type in BACKEND_REGISTRY[backend_name]['compatible_runners']
+
+
+def get_backend_env_vars(backend_name: Optional[str] = None) -> Dict[str, str]:
+    """Get environment variables for a backend.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+
+    Returns:
+        Dictionary of environment variables
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    if backend_name not in BACKEND_REGISTRY:
+        raise ValueError(
+            f"Unknown backend: {backend_name}. Available backends: {list(BACKEND_REGISTRY.keys())}")
+
+    # Get static env vars
+    env_vars = BACKEND_REGISTRY[backend_name]['env_vars'].copy()
+
+    # Handle dynamic env vars (e.g., OMP_NUM_THREADS based on tensor_parallel_size)
+    if backend_name == 'vllm':
+        config = get_backend_config(backend_name)
+        env_vars['OMP_NUM_THREADS'] = str(
+            config.get('tensor_parallel_size', 8))
+
+    return env_vars
+
+
+def apply_backend_env_vars(backend_name: Optional[str] = None) -> None:
+    """Apply environment variables for a backend.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+    """
+    if backend_name is None:
+        backend_name = detect_backend()
+
+    env_vars = get_backend_env_vars(backend_name)
+    for key, value in env_vars.items():
+        os.environ[key] = value
\ No newline at end of file
diff --git a/language/deepseek-r1/utils/data_utils.py b/language/deepseek-r1/utils/data_utils.py
new file mode 100644
index 0000000000..80acb5c8ce
--- /dev/null
+++ b/language/deepseek-r1/utils/data_utils.py
@@ -0,0 +1,196 @@
+"""
+Data utilities for loading datasets and saving results.
+
+Provides common functionality for data handling across all backends.
+"""
+
+import os
+import pickle
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+import pandas as pd
+import numpy as np
+from utils.validation import ValidationError, validate_dataset_extended
+
+
+def generate_timestamped_filename(output_file: str, add_timestamp: bool = True) -> str:
+    """
+    Generate the actual filename that will be used when saving, with timestamp if requested.
+    
+    Args:
+        output_file: Base output file path
+        add_timestamp: Whether to add timestamp to filename
+        
+    Returns:
+        Actual filename that will be used for saving
+    """
+    if not add_timestamp:
+        return output_file
+        
+    timestamp_suffix = time.strftime("%Y%m%d_%H%M%S")
+    base_name, ext = os.path.splitext(output_file)
+    return f"{base_name}_{timestamp_suffix}{ext}"
+
+
+def load_dataset(file_path: str, num_samples: Optional[int] = None, skip_samples: int = 0) -> pd.DataFrame:
+    """
+    Load dataset from pickle file.
+    
+    Args:
+        file_path: Path to the pickle file
+        num_samples: Optional limit on number of samples to load
+        skip_samples: Number of samples to skip from the beginning
+        
+    Returns:
+        Loaded DataFrame
+        
+    Raises:
+        ValidationError: If file doesn't exist or validation fails
+        Exception: If file can't be loaded
+    """
+    if not os.path.exists(file_path):
+        raise ValidationError(f"Input file not found: {file_path}")
+    
+    print(f"Loading dataset from {file_path}...")
+    
+    try:
+        with open(file_path, "rb") as f:
+            df = pd.read_pickle(f)
+    except Exception as e:
+        raise ValidationError(f"Failed to load dataset: {str(e)}")
+    
+    print(f"Loaded {len(df)} samples")
+    
+    # Skip samples if specified
+    if skip_samples > 0:
+        if skip_samples >= len(df):
+            raise ValidationError(
+                f"skip_samples ({skip_samples}) must be less than total samples ({len(df)})"
+            )
+        original_length = len(df)
+        df = df.iloc[skip_samples:].reset_index(drop=True)
+        print(f"Skipped first {skip_samples} samples (from {original_length} total)")
+    
+    # Limit number of samples if specified
+    if num_samples is not None:
+        original_length = len(df)
+        df = df.head(num_samples)
+        print(f"Limited to {len(df)} samples (from {original_length} total after skipping)")
+    
+    return df
+
+
+def save_results(df: pd.DataFrame, 
+                output_file: str, 
+                add_timestamp: bool = True) -> str:
+    """
+    Save results DataFrame to pickle file.
+    
+    Args:
+        df: DataFrame to save
+        output_file: Output file path
+        add_timestamp: Whether to add timestamp to filename
+        
+    Returns:
+        Actual output file path used
+        
+    Raises:
+        ValidationError: If save operation fails
+    """
+    # Add timestamp to filename if requested
+    if add_timestamp:
+        timestamp_suffix = time.strftime("%Y%m%d_%H%M%S")
+        base_name, ext = os.path.splitext(output_file)
+        output_file = f"{base_name}_{timestamp_suffix}{ext}"
+    
+    # Ensure output directory exists
+    os.makedirs(os.path.dirname(output_file), exist_ok=True)
+    
+    print(f"Saving results to {output_file}...")
+    
+    # Reset index before saving
+    df_to_save = df.reset_index(drop=True)
+    
+    try:
+        with open(output_file, "wb") as f:
+            pickle.dump(df_to_save, f)
+        print(f"Save completed: {len(df_to_save)} samples saved to {output_file}")
+    except Exception as e:
+        raise ValidationError(f"Failed to save results: {str(e)}")
+    
+    return output_file
+
+
+def prepare_output_dataframe(input_df: pd.DataFrame, 
+                           backend_name: Optional[str] = None) -> pd.DataFrame:
+    """
+    Prepare output DataFrame by cleaning up old columns.
+    
+    Args:
+        input_df: Input DataFrame
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+        
+    Returns:
+        Cleaned DataFrame ready for new results
+    """
+    if backend_name is None:
+        from utils.backend_registry import detect_backend
+        backend_name = detect_backend()
+    
+    df_output = input_df.copy()
+    
+    # Define columns to drop (old model outputs and unwanted columns)
+    columns_to_drop = [
+        # specify columns to drop here
+    ]
+    
+    # Also drop any existing backend-specific columns
+    backend_columns = [col for col in df_output.columns if col.startswith(f'{backend_name}_')]
+    columns_to_drop.extend(backend_columns)
+    
+    # Drop columns that exist
+    df_output = df_output.drop(
+        columns=[col for col in columns_to_drop if col in df_output.columns]
+    )
+    
+    return df_output
+
+
+def add_standardized_columns(df: pd.DataFrame, 
+                           results: List[Dict[str, Any]],
+                           tokenized_prompts: List[List[int]] = None) -> pd.DataFrame:
+    """
+    Add standardized output columns to DataFrame.
+    
+    Args:
+        df: Input DataFrame
+        results: List of result dictionaries from backend
+        tokenized_prompts: List of tokenized input prompts (deprecated, not used)
+        
+    Returns:
+        DataFrame with added standardized columns
+    """
+    # Add results columns with new naming convention
+    df['model_output'] = [r.get('model_output', '') for r in results]
+    df['tok_model_output'] = [r.get('tok_model_output', []) for r in results]
+    df['tok_model_output_len'] = [r.get('tok_model_output_len', 0) for r in results]
+    df['model_backend'] = [r.get('model_backend', '') for r in results]
+    
+    return df
+
+
+def validate_dataset(df: pd.DataFrame, backend_name: Optional[str] = None) -> None:
+    """
+    Validate that the dataset has required columns.
+    
+    Args:
+        df: DataFrame to validate
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+        
+    Raises:
+        ValidationError: If required columns are missing or validation fails
+    """
+    # Use centralized validation function
+    validate_dataset_extended(df, backend_name) 
\ No newline at end of file
diff --git a/language/deepseek-r1/utils/error_handling.py b/language/deepseek-r1/utils/error_handling.py
new file mode 100644
index 0000000000..54ca580135
--- /dev/null
+++ b/language/deepseek-r1/utils/error_handling.py
@@ -0,0 +1,48 @@
+"""Centralized error handling utilities."""
+import sys
+import traceback
+from typing import Optional, Type
+from .validation import BackendError, ValidationError
+
+
+def handle_backend_error(e: Exception, backend_name: str, operation: str) -> None:
+    """
+    Standardized error handling for backend operations.
+    
+    Args:
+        e: The exception that occurred
+        backend_name: Name of the backend
+        operation: Description of the operation that failed
+    """
+    error_msg = f"\n[{backend_name.upper()}] Error during {operation}: {type(e).__name__}: {str(e)}"
+    
+    if isinstance(e, (RuntimeError, ValueError)):
+        # Known errors - just print the message
+        print(error_msg)
+    else:
+        # Unexpected errors - print full traceback
+        print(error_msg)
+        traceback.print_exc()
+
+
+def handle_runner_error(e: Exception, runner_name: str) -> None:
+    """
+    Standardized error handling for runners.
+    
+    Args:
+        e: The exception that occurred
+        runner_name: Name of the runner
+    """
+    if isinstance(e, KeyboardInterrupt):
+        print(f"\n{runner_name} interrupted by user")
+        sys.exit(1)
+    elif isinstance(e, ValidationError):
+        print(f"\nValidation error: {e}")
+        sys.exit(1)
+    elif isinstance(e, BackendError):
+        print(f"\nBackend error: {e}")
+        sys.exit(1)
+    else:
+        print(f"\n{runner_name} failed: {e}")
+        traceback.print_exc()
+        sys.exit(1) 
\ No newline at end of file
diff --git a/language/deepseek-r1/utils/runner_utils.py b/language/deepseek-r1/utils/runner_utils.py
new file mode 100644
index 0000000000..8c90deb515
--- /dev/null
+++ b/language/deepseek-r1/utils/runner_utils.py
@@ -0,0 +1,106 @@
+"""Common utilities for all runners."""
+import argparse
+from pathlib import Path
+from typing import Tuple, Optional
+from datetime import datetime
+import os
+
+
+def create_base_argument_parser(description: str) -> argparse.ArgumentParser:
+    """Create argument parser with common arguments."""
+    parser = argparse.ArgumentParser(
+        description=description,
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    
+    # Common dataset arguments
+    parser.add_argument("--input-file", type=str,
+                       default="data/final_output.pkl",
+                       help="Input pickle file with prompts")
+    
+    parser.add_argument("--output-file", type=str, default=None,
+                       help="Output pickle file path (auto-generated if not specified)")
+    
+    parser.add_argument("--num-samples", type=int, default=None,
+                       help="Number of samples to process from dataset")
+    
+    parser.add_argument("--skip-samples", type=int, default=0,
+                       help="Number of samples to skip from the beginning")
+    
+    # NOTE: --no-chat-template flag is NOT included (chat template usage determined by backend registry)
+    
+    return parser
+
+
+def print_runner_header(runner_name: str, backend_name: Optional[str] = None, args: argparse.Namespace = None) -> None:
+    """Print standardized header for runners.
+    
+    Args:
+        runner_name: Name of the runner
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+        args: Parsed command line arguments
+    """
+    if backend_name is None:
+        from .backend_registry import detect_backend
+        backend_name = detect_backend()
+    
+    print("=" * 80)
+    print(f"{runner_name}")
+    print("=" * 80)
+    print(f"Backend: {backend_name}")
+    if args:
+        print(f"Input file: {args.input_file}")
+        if hasattr(args, 'output_file') and args.output_file:
+            print(f"Output file: {args.output_file}")
+        if hasattr(args, 'num_samples') and args.num_samples is not None:
+            print(f"Number of samples: {args.num_samples}")
+        if hasattr(args, 'skip_samples') and args.skip_samples > 0:
+            print(f"Skipping samples: {args.skip_samples}")
+    print("=" * 80)
+
+
+def setup_output_paths(args: argparse.Namespace, backend_name: Optional[str] = None, mode: Optional[str] = None) -> Tuple[Path, str]:
+    """
+    Set up output directories and file paths.
+    
+    Args:
+        args: Parsed command line arguments
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+        mode: Optional mode (e.g., 'offline', 'server' for MLPerf)
+        
+    Returns:
+        Tuple of (output_dir, output_file_path)
+    """
+    if backend_name is None:
+        from .backend_registry import detect_backend
+        backend_name = detect_backend()
+    
+    # Determine output directory
+    if hasattr(args, 'output_dir') and args.output_dir:
+        output_dir = Path(args.output_dir)
+    else:
+        # Default output directory based on runner type
+        if mode:
+            output_dir = Path(f"outputs/{backend_name}/{mode}")
+        else:
+            output_dir = Path(f"outputs/{backend_name}")
+    
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    # Determine output file path
+    if args.output_file:
+        output_file = args.output_file
+    else:
+        # Generate timestamp-based filename
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        if hasattr(args, 'num_samples') and args.num_samples:
+            suffix = f"_{args.num_samples}samples"
+        else:
+            suffix = "_full"
+        
+        if mode:
+            output_file = str(output_dir / f"{backend_name}_{mode}_output_{timestamp}{suffix}.pkl")
+        else:
+            output_file = str(output_dir / f"{backend_name}_output_{timestamp}{suffix}.pkl")
+    
+    return output_dir, output_file 
\ No newline at end of file
diff --git a/language/deepseek-r1/utils/tokenization.py b/language/deepseek-r1/utils/tokenization.py
new file mode 100644
index 0000000000..c5fa77d69d
--- /dev/null
+++ b/language/deepseek-r1/utils/tokenization.py
@@ -0,0 +1,143 @@
+"""Shared tokenization utilities for all runners."""
+
+from typing import List, Tuple, Optional
+from transformers import AutoTokenizer
+from utils.backend_registry import uses_chat_template, detect_backend, get_backend_config, get_supported_backends
+
+
+class StandardTokenizer:
+    """Standard tokenizer for DeepSeek models."""
+    
+    # Standard configuration used across all runners
+    DEFAULT_MODEL = "deepseek-ai/DeepSeek-R1"
+    DEFAULT_MAX_LENGTH = 32 * 1024
+    
+    def __init__(self, model_name: str = None, max_length: int = None):
+        """
+        Initialize tokenizer.
+        
+        Args:
+            model_name: HuggingFace model name
+            max_length: Maximum sequence length
+        """
+        self.model_name = model_name or self.DEFAULT_MODEL
+        self.max_length = max_length or self.DEFAULT_MAX_LENGTH
+        self._tokenizer = None
+    
+    @property
+    def tokenizer(self):
+        """Lazy load tokenizer."""
+        if self._tokenizer is None:
+            print(f"Loading tokenizer: {self.model_name}")
+            self._tokenizer = AutoTokenizer.from_pretrained(self.model_name, revision="56d4cbbb4d29f4355bab4b9a39ccb717a14ad5ad")
+        return self._tokenizer
+    
+    def tokenize_prompts(self, prompts: List[str], 
+                        use_chat_template: Optional[bool] = None,
+                        backend_name: Optional[str] = None) -> Tuple[List[List[int]], List[str]]:
+        """
+        Tokenize prompts with backend-specific handling.
+        
+        Args:
+            prompts: List of text prompts
+            use_chat_template: Whether to use chat template (if None and backend_name provided, uses registry)
+            backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+            
+        Returns:
+            Tuple of (tokenized_prompts, processed_strings)
+        """
+        # Auto-detect backend if not provided
+        if backend_name is None:
+            backend_name = detect_backend()
+        
+        # Determine chat template usage from registry if backend_name provided
+        if use_chat_template is None:
+            use_chat_template = uses_chat_template(backend_name)
+            print(f"[{backend_name}] Using chat template from registry: {use_chat_template}")
+        
+        tokenized = []
+        processed_strings = []
+        
+        for prompt in prompts:
+            if use_chat_template and hasattr(self.tokenizer, 'apply_chat_template'):
+                tokens = self.tokenizer.apply_chat_template(
+                    [{"role": "user", "content": prompt}],
+                    add_generation_prompt=True,
+                    max_length=self.max_length,
+                    truncation=True
+                )
+                processed_string = self.tokenizer.decode(tokens, skip_special_tokens=False)
+            else:
+                tokens = self.tokenizer.encode(
+                    prompt,
+                    truncation=True,
+                    max_length=self.max_length
+                )
+                processed_string = prompt
+            
+            tokenized.append(tokens)
+            processed_strings.append(processed_string)
+        
+        return tokenized, processed_strings
+    
+    def decode_tokens(self, tokens: List[int], skip_special_tokens: bool = True) -> str:
+        """Decode tokens to text."""
+        return self.tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
+    
+    def batch_decode(self, token_lists: List[List[int]], 
+                    skip_special_tokens: bool = True) -> List[str]:
+        """Batch decode multiple token lists."""
+        return self.tokenizer.batch_decode(token_lists, skip_special_tokens=skip_special_tokens)
+
+
+def process_inference_results(raw_results: List[dict], 
+                            tokenizer: Optional[StandardTokenizer] = None,
+                            backend_name: Optional[str] = None,
+                            uses_text_prompts: bool = False) -> List[dict]:
+    """
+    Process raw inference results into standardized format.
+    
+    Args:
+        raw_results: Raw results from backend
+        tokenizer: Tokenizer for decoding
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+        uses_text_prompts: Whether backend uses text prompts
+        
+    Returns:
+        List of standardized result dictionaries
+    """
+    # Auto-detect backend if not provided
+    if backend_name is None:
+        backend_name = detect_backend()
+    
+    if backend_name not in get_supported_backends():
+        raise ValueError(f"Backend {backend_name} is not supported")
+    
+    backend_config = get_backend_config(backend_name)
+    
+    standardized_results = []
+    
+    for raw_result in raw_results:
+        # Handle text-prompt backends
+        if uses_text_prompts and 'text' in raw_result:
+            text = raw_result['text']
+            tokens = raw_result.get('tokens', [])
+        else:
+            # Decode tokens to get text
+            tokens = raw_result.get('tokens', [])
+            text = ''
+            if tokenizer and tokens:
+                try:
+                    text = tokenizer.decode_tokens(tokens)
+                except:
+                    pass
+        
+        standardized = {
+            'model_output': text,
+            'tok_model_output': tokens,
+            'tok_model_output_len': len(tokens),
+            'model_backend': backend_name,
+        }
+        standardized_results.append(standardized)
+    
+    return standardized_results 
\ No newline at end of file
diff --git a/language/deepseek-r1/utils/validation.py b/language/deepseek-r1/utils/validation.py
new file mode 100644
index 0000000000..29bebef4f1
--- /dev/null
+++ b/language/deepseek-r1/utils/validation.py
@@ -0,0 +1,145 @@
+"""Centralized validation utilities."""
+from functools import wraps
+from typing import Optional, List, Dict, Any
+import pandas as pd
+import argparse
+
+
+class BackendError(RuntimeError):
+    """Base exception for backend errors."""
+    pass
+
+
+class BackendNotInitializedError(BackendError):
+    """Raised when backend operation is called before initialization."""
+    def __init__(self, backend_name: str = "Backend"):
+        super().__init__(f"{backend_name} not initialized. Call initialize() first.")
+
+
+class ValidationError(ValueError):
+    """Base exception for validation errors."""
+    pass
+
+
+def require_initialized(method):
+    """Decorator to ensure backend is initialized before method execution."""
+    @wraps(method)
+    def wrapper(self, *args, **kwargs):
+        if not getattr(self, 'is_initialized', False):
+            backend_name = getattr(self, 'backend_name', 'Backend')
+            raise BackendNotInitializedError(backend_name)
+        return method(self, *args, **kwargs)
+    return wrapper
+
+
+def validate_prompts_input(backend_name: Optional[str] = None,
+                          tokenized_prompts: Optional[List[List[int]]] = None,
+                          text_prompts: Optional[List[str]] = None,
+                          input_type: str = None) -> None:
+    """
+    Centralized prompt validation with backend-specific requirements.
+
+    Args:
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+        tokenized_prompts: List of tokenized prompts
+        text_prompts: List of text prompts
+        input_type: Expected input type from registry ('text' or 'tokenized')
+
+    Raises:
+        ValidationError: If validation fails
+    """
+    if backend_name is None:
+        from .backend_registry import detect_backend
+        backend_name = detect_backend()
+
+    if tokenized_prompts is None and text_prompts is None:
+        raise ValidationError(f"{backend_name} backend requires either text_prompts or tokenized_prompts")
+
+    if input_type == 'text' and tokenized_prompts is not None and text_prompts is None:
+        raise ValidationError(f"{backend_name} backend requires text_prompts, not tokenized_prompts")
+
+    if input_type == 'tokenized' and text_prompts is not None and tokenized_prompts is None:
+        raise ValidationError(f"{backend_name} backend requires tokenized_prompts, not text_prompts")
+
+    # Additional validation for tokenized prompts
+    if tokenized_prompts is not None:
+        if not tokenized_prompts:
+            raise ValidationError("tokenized_prompts cannot be empty")
+        for i, prompt in enumerate(tokenized_prompts):
+            if not isinstance(prompt, list):
+                raise ValidationError(f"tokenized_prompts[{i}] must be a list of integers")
+            if not prompt:
+                raise ValidationError(f"tokenized_prompts[{i}] cannot be empty")
+
+    # Additional validation for text prompts
+    if text_prompts is not None:
+        if not text_prompts:
+            raise ValidationError("text_prompts cannot be empty")
+        for i, prompt in enumerate(text_prompts):
+            if not isinstance(prompt, str):
+                raise ValidationError(f"text_prompts[{i}] must be a string")
+
+
+def validate_dataset_extended(df: pd.DataFrame,
+                            backend_name: Optional[str] = None,
+                            required_columns: Optional[List[str]] = None) -> None:
+    """
+    Extended dataset validation with backend-specific requirements.
+
+    Args:
+        df: DataFrame to validate
+        backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
+        required_columns: Optional list of required columns (defaults to ['text_input'])
+
+    Raises:
+        ValidationError: If validation fails
+    """
+    if backend_name is None:
+        from .backend_registry import detect_backend
+        backend_name = detect_backend()
+
+    if required_columns is None:
+        required_columns = ['text_input']
+
+    missing_columns = [col for col in required_columns if col not in df.columns]
+    if missing_columns:
+        raise ValidationError(f"Dataset missing required columns: {missing_columns}")
+
+    # Check for empty prompts
+    empty_prompts = df['text_input'].isna().sum()
+    if empty_prompts > 0:
+        print(f"Warning: Found {empty_prompts} empty text_input in dataset")
+
+    # Backend-specific validation could be added here
+    if backend_name:
+        # Example: validate max sequence length for specific backends
+        from .backend_registry import get_backend_config, BACKEND_REGISTRY
+        if backend_name in BACKEND_REGISTRY:
+            config = get_backend_config(backend_name)
+            # Add backend-specific validation based on config if needed
+
+    print(f"Dataset validation passed: {len(df)} samples with required columns")
+
+
+def validate_runner_args(args: argparse.Namespace, runner_type: str) -> None:
+    """
+    Centralized argument validation for runners.
+
+    Args:
+        args: Parsed command line arguments
+        runner_type: Type of runner for specific validation
+
+    Raises:
+        ValidationError: If validation fails
+    """
+    # Common validations
+    if hasattr(args, 'num_samples') and args.num_samples is not None and args.num_samples <= 0:
+        raise ValidationError("--num-samples must be positive")
+
+    if hasattr(args, 'skip_samples') and args.skip_samples < 0:
+        raise ValidationError("--skip-samples cannot be negative")
+
+    # Runner-specific validations
+    if runner_type in ['mlperf', 'mlperf_mpi']:
+        if hasattr(args, 'mode') and args.mode not in ['offline', 'server']:
+            raise ValidationError(f"Invalid mode: {args.mode}. Must be 'offline' or 'server'")
\ No newline at end of file

From 30eb03a75b8ded552422e783b1b185dd2886243c Mon Sep 17 00:00:00 2001
From: Viraat Chandra <viraatc@nvidia.com>
Date: Mon, 9 Jun 2025 15:37:27 -0700
Subject: [PATCH 295/305] add deepseek-r1 (#2201)

---
 language/deepseek-r1/docker/Dockerfile.sglang         |  4 +++-
 .../deepseek-r1/docker/setup_scripts/setup_sglang.sh  | 11 ++++-------
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/language/deepseek-r1/docker/Dockerfile.sglang b/language/deepseek-r1/docker/Dockerfile.sglang
index d2cfc92012..02703901d7 100644
--- a/language/deepseek-r1/docker/Dockerfile.sglang
+++ b/language/deepseek-r1/docker/Dockerfile.sglang
@@ -26,6 +26,8 @@ RUN apt-get update && apt-get install -y \
     pybind11-dev \
     pkg-config \
     sudo \
+    libnuma-dev \
+    htop \
     && rm -rf /var/lib/apt/lists/*
 
 # Set Python 3.10 as default
@@ -68,4 +70,4 @@ WORKDIR /work
 ENV PATH="/work:${PATH}"
 
 # Set default command
-CMD ["/bin/bash"] 
\ No newline at end of file
+CMD ["/bin/bash"]
\ No newline at end of file
diff --git a/language/deepseek-r1/docker/setup_scripts/setup_sglang.sh b/language/deepseek-r1/docker/setup_scripts/setup_sglang.sh
index 1fc83738ce..9bd2e7677e 100755
--- a/language/deepseek-r1/docker/setup_scripts/setup_sglang.sh
+++ b/language/deepseek-r1/docker/setup_scripts/setup_sglang.sh
@@ -86,7 +86,8 @@ VIRTUAL_ENV=$VENV_DIR uv pip install \
     httpx \
     requests \
     torch==2.6.0 \
-    sglang[all]==0.4.6.post5 
+    sglang[all]==0.4.6.post5 \
+    sgl_kernel
 
 # Verify SGLang installation
 if python3 -c "import sglang" 2>/dev/null; then
@@ -97,10 +98,6 @@ else
     exit 1
 fi
 
-# Install sgl-kernel for optimizations
-echo "Installing sgl-kernel..."
-VIRTUAL_ENV=$VENV_DIR uv pip install sgl-kernel --no-deps --force-reinstall || echo "Warning: sgl-kernel installation failed"
-
 # Verify sgl_kernel installation
 if python3 -c "import sgl_kernel" 2>/dev/null; then
     echo "sgl_kernel installed successfully"
@@ -112,7 +109,7 @@ fi
 if python3 -c "import torch" 2>/dev/null; then
     TORCH_VERSION=$(python3 -c "import torch; print(torch.__version__)")
     echo "PyTorch is available: version $TORCH_VERSION"
-    
+
     # Check CUDA availability
     if python3 -c "import torch; print('CUDA available:', torch.cuda.is_available())" 2>/dev/null; then
         GPU_COUNT=$(python3 -c "import torch; print('GPU count:', torch.cuda.device_count())" 2>/dev/null || echo "GPU count: 0")
@@ -159,4 +156,4 @@ echo "The backend will show a progress bar during server startup."
 echo ""
 echo "Server logs will be saved to: /work/logs/sglang_server_*.log"
 echo ""
-echo "Ready for SGLang inference and MLPerf runs." 
\ No newline at end of file
+echo "Ready for SGLang inference and MLPerf runs."
\ No newline at end of file

From 341b190f7feca88f92c7ae3f61d621ecdac48d47 Mon Sep 17 00:00:00 2001
From: Viraat Chandra <viraatc@nvidia.com>
Date: Mon, 9 Jun 2025 20:14:26 -0700
Subject: [PATCH 296/305] update deepseek-r1 (#2202)

---
 language/deepseek-r1/eval_accuracy.py | 51 ++++++++++++++++++++++-----
 1 file changed, 43 insertions(+), 8 deletions(-)

diff --git a/language/deepseek-r1/eval_accuracy.py b/language/deepseek-r1/eval_accuracy.py
index 1bb31b9076..55647b7ca5 100644
--- a/language/deepseek-r1/eval_accuracy.py
+++ b/language/deepseek-r1/eval_accuracy.py
@@ -135,17 +135,52 @@ def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
 
     # Load MLPerf log
     try:
-        mlperf_results = []
+        # First, check if this is a JSON array format or newline-delimited JSON
         with open(mlperf_log_file, 'r') as f:
-            for line_num, line in enumerate(f, 1):
-                line = line.strip()
-                if not line:  # Skip empty lines
-                    continue
+            first_line = f.readline().strip()
+            
+        if first_line == '[':
+            # JSON array format - load the entire file
+            logger.info("Detected JSON array format")
+            with open(mlperf_log_file, 'r') as f:
                 try:
-                    mlperf_results.append(json.loads(line))
+                    mlperf_results = json.load(f)
                 except json.JSONDecodeError as e:
-                    logger.warning(f"Failed to parse line {line_num}: {e}")
-                    continue
+                    # If full file parsing fails, try to parse line by line, skipping brackets
+                    logger.warning(f"Failed to parse as complete JSON array: {e}")
+                    logger.info("Attempting line-by-line parsing")
+                    mlperf_results = []
+                    with open(mlperf_log_file, 'r') as f2:
+                        for line_num, line in enumerate(f2, 1):
+                            line = line.strip()
+                            # Skip empty lines, opening/closing brackets
+                            if not line or line == '[' or line == ']':
+                                continue
+                            # Remove trailing comma if present
+                            if line.endswith(','):
+                                line = line[:-1]
+                            try:
+                                mlperf_results.append(json.loads(line))
+                            except json.JSONDecodeError as e:
+                                logger.warning(f"Failed to parse line {line_num}: {e}")
+                                continue
+        else:
+            # Newline-delimited JSON format
+            logger.info("Detected newline-delimited JSON format")
+            mlperf_results = []
+            with open(mlperf_log_file, 'r') as f:
+                # Reset to beginning since we read the first line
+                f.seek(0)
+                for line_num, line in enumerate(f, 1):
+                    line = line.strip()
+                    if not line:  # Skip empty lines
+                        continue
+                    try:
+                        mlperf_results.append(json.loads(line))
+                    except json.JSONDecodeError as e:
+                        logger.warning(f"Failed to parse line {line_num}: {e}")
+                        continue
+                        
         logger.info(f"Loaded {len(mlperf_results)} MLPerf results")
     except Exception as e:
         raise RuntimeError(f"Failed to load MLPerf log file: {e}")

From 44d91928890d10b1f11b12d7849c1d0270e7b775 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Tue, 10 Jun 2025 21:39:46 +0530
Subject: [PATCH 297/305] Call existing submission generation workflow from
 mlperf-automations repo (#2199)

---
 .../workflows/test-submission-generation.yml  | 46 +++----------------
 1 file changed, 6 insertions(+), 40 deletions(-)

diff --git a/.github/workflows/test-submission-generation.yml b/.github/workflows/test-submission-generation.yml
index 0bb2ad353b..219d8c2bd4 100644
--- a/.github/workflows/test-submission-generation.yml
+++ b/.github/workflows/test-submission-generation.yml
@@ -9,44 +9,10 @@ on:
       - '.github/workflows/test-submission-generation.yml'
       - '**'  
       - '!**.md'
+      
 jobs:
-  submission_generation:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      fail-fast: false
-      matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: [ "3.12" ]
-        division: ["closed", "open", "closed-open"]
-        category: ["datacenter", "edge"]
-        case: ["closed"]
-        action: ["run", "docker"]
-        exclude:
-          - os: macos-latest
-          - os: windows-latest
-          - category: "edge"
-            
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        pip install mlc-scripts
-    - name: Pull repo where test cases are uploaded
-      run: |
-        git clone -b submission-generation-examples https://github.com/mlcommons/inference.git submission_generation_examples
-    - name: Run Submission Generation - ${{ matrix.case }} ${{ matrix.action }} ${{ matrix.category }} ${{ matrix.division }} 
-      continue-on-error: true
-      run: |
-        if [ "${{ matrix.case }}" == "closed" ]; then
-          description="Test submission - contains closed edge and datacenter"
-        elif [ "${{ matrix.case }}" == "closed-power" ]; then
-          description="Test submission - contains closed-power edge and datacenter results"
-        fi
-        # Dynamically set the log group to simulate a dynamic step name
-        echo "::group::$description"
-        mlc ${{ matrix.action }} script --tags=generate,inference,submission  --adr.compiler.tags=gcc --version=v5.0 --clean --preprocess_submission=yes --submission_base_dir=mysubmissions --results_dir=$PWD/submission_generation_tests/${{ matrix.case }}/ --run-checker --submitter=MLCommons --tar=yes --division=${{ matrix.division }} --env.MLC_DETERMINE_MEMORY_CONFIGURATION=yes --quiet
-        mlc ${{ matrix.action }} script --tags=run,submission,checker --submitter_id_off=mysubmitter_id --tar=yes --submission_dir=mysubmissions/submissions --submission_tar_file=mysubmission.tar.gz
+  run-tests:
+    uses: mlcommons/mlperf-automations/.github/workflows/test-mlperf-inference-submission-generation.yml@dev
+    with:
+      ref: ${{ github.event.pull_request.head.ref }}
+      repo-url: ${{ github.event.pull_request.head.repo.html_url }}

From a8cd2122f14a396ce6bae7e8712c23ee74ff270c Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Tue, 10 Jun 2025 12:13:33 -0500
Subject: [PATCH 298/305] Add deepseek configuration + v5.1 Readme (#2203)

* Add deepseek configuration + v5.1 Readme

* Update README.md
---
 README.md           | 26 ++++++++++++++++++++++++++
 loadgen/mlperf.conf | 17 +++++++++++++++++
 2 files changed, 43 insertions(+)

diff --git a/README.md b/README.md
index 6cab3e2f63..4df1853e8f 100644
--- a/README.md
+++ b/README.md
@@ -17,6 +17,32 @@ Please see the [MLPerf Inference benchmark paper](https://arxiv.org/abs/1911.025
 
 Please see [here](https://docs.mlcommons.org/inference/benchmarks/) for the MLPerf inference documentation website which includes automated commands to run MLPerf inference benchmarks using different implementations.
 
+## MLPerf Inference v5.1 (submission deadline July 25, 2025)
+
+For submissions, please use the master branch and any commit since the [5.1 seed release (soon to be released)]() although it is best to use the latest commit in the [master branch](https://github.com/mlcommons/inference).
+
+For power submissions please use [SPEC PTD 1.11.1](https://github.com/mlcommons/power) (needs special access) and any commit of the power-dev repository after the [code-freeze](https://github.com/mlcommons/power-dev/commit/c4b3ad8202fbd8ac28d77149e5e7aeadb725bbf2)
+
+
+| model | reference app | framework | dataset | category
+| ---- | ---- | ---- | ---- | ---- |
+| retinanet 800x800 | [vision/classification_and_detection](https://github.com/mlcommons/inference/tree/master/vision/classification_and_detection) | pytorch, onnx | openimages resized to 800x800| edge,datacenter |
+| bert | [language/bert](https://github.com/mlcommons/inference/tree/master/language/bert) | tensorflow, pytorch, onnx | squad-1.1 | edge |
+| dlrm-v2 | [recommendation/dlrm_v2](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm_v2/pytorch) | pytorch | Multihot Criteo Terabyte | datacenter |
+| 3d-unet | [vision/medical_imaging/3d-unet-kits19](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-kits19) | pytorch, tensorflow, onnx | KiTS19 | datacenter |
+| stable-diffusion-xl | [text_to_image](https://github.com/mlcommons/inference/tree/master/text_to_image) | pytorch | COCO 2014| edge,datacenter |
+| llama2-70b | [language/llama2-70b](https://github.com/mlcommons/inference/tree/master/language/llama2-70b) | pytorch | OpenOrca | datacenter |
+| llama3.1-405b | [language/llama3-405b](https://github.com/mlcommons/inference/tree/master/language/llama3.1-405b) | pytorch | LongBench, LongDataCollections, Ruler, GovReport | datacenter |
+| mixtral-8x7b | [language/mixtral-8x7b](https://github.com/mlcommons/inference/tree/master/language/mixtral-8x7b) | pytorch | OpenOrca, MBXP, GSM8K | datacenter |
+| rgat | [graph/rgat](https://github.com/mlcommons/inference/tree/master/graph/R-GAT) | pytorch | IGBH | datacenter |
+| pointpainting | [automotive/3d-object-detection](https://github.com/mlcommons/inference/tree/master/automotive/3d-object-detection) | pytorch, onnx | Waymo Open Dataset | edge |
+| llama3.1-8b | [language/llama3.1-8b](https://github.com/mlcommons/inference/tree/master/language/llama3.1-8b)| pytorch | CNN-Daily Mail | edge,datacenter |
+| deepseek-r1 | [language/deepseek-r1](https://github.com/mlcommons/inference/tree/master/language/deepseek-r1)| pytorch | mlperf_deepseek_r1 | datacenter |
+| whisper | [speech2text](https://github.com/mlcommons/inference/tree/master/speech2text)| pytorch | LibriSpeech | edge,datacenter |
+
+* Framework here is given for the reference implementation. Submitters are free to use their own frameworks to run the benchmark.
+
+
 ## MLPerf Inference v5.0 (submission deadline February 28, 2025)
 
 For submissions, please use the master branch and any commit since the [5.0 seed release](https://github.com/mlcommons/inference/commit/5d83ed5de438ffb55bca4cdb2966fba90a9dbca6) although it is best to use the latest commit in the [master branch](https://github.com/mlcommons/inference).
diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index 57190c6c37..e7d6d7744f 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -20,6 +20,8 @@ llama3_1-405b.*.performance_sample_count_override = 8313
 stable-diffusion-xl.*.performance_sample_count_override = 5000
 rgat.*.performance_sample_count_override = 788379
 pointpainting.*.performance_sample_count_override = 1024
+deepseek-r1.*.performance_sample_count_override = 4388
+deepseek-r1-interactive.*.performance_sample_count_override = 4388
 # set to 0 to let entire sample set to be performance sample
 3d-unet.*.performance_sample_count_override = 0
 
@@ -55,6 +57,8 @@ llama2-70b.*.sample_concatenate_permutation = 1
 llama2-70b-interactive.*.sample_concatenate_permutation = 1
 mixtral-8x7b.*.sample_concatenate_permutation = 1
 llama3_1-405b.*.sample_concatenate_permutation = 1
+deepseek-r1.*.sample_concatenate_permutation = 1
+deepseek-r1-interactive.*.sample_concatenate_permutation = 1
 
 *.Server.target_latency = 10
 *.Server.target_latency_percentile = 99
@@ -73,6 +77,9 @@ llama2-70b.*.use_token_latencies = 1
 llama2-70b-interactive.*.use_token_latencies = 1
 mixtral-8x7b.*.use_token_latencies = 1
 llama3_1-405b.*.use_token_latencies = 1
+deepseek-r1.*.use_token_latencies = 1
+deepseek-r1-interactive.*.use_token_latencies = 1
+
 # gptj benchmark infers token latencies
 gptj.*.infer_token_latencies = 1
 gptj.*.token_latency_scaling_factor = 69
@@ -94,6 +101,14 @@ llama3_1-405b.Server.target_latency = 0
 llama3_1-405b.Server.ttft_latency = 6000
 llama3_1-405b.Server.tpot_latency = 175
 
+deepseek-r1.Server.target_latency = 0
+deepseek-r1.Server.ttft_latency = 2000
+deepseek-r1.Server.tpot_latency = 80
+
+deepseek-r1-interactive.Server.target_latency = 0
+deepseek-r1-interactive.Server.ttft_latency = 1000
+deepseek-r1-interactive.Server.tpot_latency = 40
+
 *.Offline.target_latency_percentile = 90
 *.Offline.min_duration = 600000
 
@@ -114,6 +129,8 @@ llama2-70b.Offline.min_query_count = 24576
 llama3_1-405b.Offline.min_query_count = 8313
 mixtral-8x7b.Offline.min_query_count = 15000
 rgat.Offline.min_query_count = 788379
+deepseek-r1.Offline.min_query_count = 4388
+deepseek-r1-interactive.Offline.min_query_count = 4388
 
 # These fields should be defined and overridden by user.conf.
 *.SingleStream.target_latency = 10

From 088bb828894558a1118fd82825c2cabb60d76428 Mon Sep 17 00:00:00 2001
From: arjunsuresh <arjunsuresh@users.noreply.github.com>
Date: Tue, 10 Jun 2025 17:13:42 +0000
Subject: [PATCH 299/305] Increment version to 5.0.18

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index d9150afa6e..9ecb9afd6e 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.17
+5.0.18

From e0fdceab4dc3d99f66a69a58eb90e7d74936dd9a Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Tue, 10 Jun 2025 23:37:01 +0530
Subject: [PATCH 300/305] Updated readme with mlc commands for
 model,dataset,accuracy and submission generation (#2143)

* Updated with mlc commands for model,dataset,accuracy,submission
---
 .github/workflows/auto-update-dev.yml         |   2 +-
 .github/workflows/build_wheels.yml            |   1 -
 .github/workflows/test-bert.yml               |   2 +-
 automotive/3d-object-detection/README.md      |   4 +
 graph/R-GAT/README.md                         |   4 +
 language/bert/README.md                       |  49 +++++
 language/deepseek-r1/backends/__init__.py     |   2 +-
 .../deepseek-r1/backends/pytorch_backend.py   |  52 ++---
 .../deepseek-r1/backends/sglang_backend.py    | 168 +++++++++------
 language/deepseek-r1/backends/utils.py        | 173 +++++++++-------
 language/deepseek-r1/backends/vllm_backend.py |  33 +--
 language/deepseek-r1/eval_accuracy.py         |  74 ++++---
 language/deepseek-r1/mlperf/__init__.py       |   6 +-
 language/deepseek-r1/mlperf/base_sut.py       |  28 +--
 language/deepseek-r1/mlperf/offline_sut.py    |  70 ++++---
 language/deepseek-r1/mlperf/qsl.py            |  25 +--
 language/deepseek-r1/mlperf/server_sut.py     | 100 +++++----
 language/deepseek-r1/mlperf/utils.py          |  69 ++++---
 language/deepseek-r1/run_eval.py              | 165 ++++++++-------
 language/deepseek-r1/run_eval_mpi.py          |  99 +++++----
 language/deepseek-r1/run_mlperf.py            | 116 ++++++-----
 language/deepseek-r1/run_mlperf_mpi.py        | 195 ++++++++++--------
 language/deepseek-r1/utils/__init__.py        |   2 +-
 .../deepseek-r1/utils/backend_registry.py     |  11 +-
 language/deepseek-r1/utils/data_utils.py      | 108 +++++-----
 language/deepseek-r1/utils/error_handling.py  |  11 +-
 language/deepseek-r1/utils/runner_utils.py    |  58 +++---
 language/deepseek-r1/utils/tokenization.py    |  87 ++++----
 language/deepseek-r1/utils/validation.py      |  44 ++--
 language/gpt-j/README.md                      |  32 ++-
 language/llama2-70b/README.md                 |  49 ++++-
 language/llama3.1-405b/README.md              |  50 +++--
 language/mixtral-8x7b/README.md               |  41 +++-
 recommendation/dlrm_v2/pytorch/README.md      |  32 ++-
 text_to_image/README.md                       |  12 ++
 tools/submission/submission_checker.py        |  23 ++-
 vision/classification_and_detection/README.md |  80 ++++++-
 .../medical_imaging/3d-unet-kits19/README.md  |  53 +++++
 38 files changed, 1368 insertions(+), 762 deletions(-)

diff --git a/.github/workflows/auto-update-dev.yml b/.github/workflows/auto-update-dev.yml
index 58298ec86a..697fdf8080 100644
--- a/.github/workflows/auto-update-dev.yml
+++ b/.github/workflows/auto-update-dev.yml
@@ -3,7 +3,7 @@ name: Auto-Update Dev Branches from Master
 on:
   push:
     branches:
-      - master  # Trigger workflow on commits to 'dev' branch
+      - master  # Trigger workflow on commits to 'master' branch
 
 jobs:
   update-dev:
diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
index 3004ecc2d5..f7c0b1d5ce 100644
--- a/.github/workflows/build_wheels.yml
+++ b/.github/workflows/build_wheels.yml
@@ -1,5 +1,4 @@
 name: Build loadgen wheels and release them into PYPI
-
 on:
   release:
     types: [published]
diff --git a/.github/workflows/test-bert.yml b/.github/workflows/test-bert.yml
index 27fcee0d3f..2a8b609c4c 100755
--- a/.github/workflows/test-bert.yml
+++ b/.github/workflows/test-bert.yml
@@ -5,7 +5,7 @@ name: Test for MLPerf inference bert submission generation using MLC script auto
 
 on:
   pull_request:
-    branches: [ "master", "dev" ]
+    branches: [ "master_off", "dev_off" ]
     paths:
       - language/bert/**
       - tools/submission/**
diff --git a/automotive/3d-object-detection/README.md b/automotive/3d-object-detection/README.md
index e1190e8132..d0430d444c 100644
--- a/automotive/3d-object-detection/README.md
+++ b/automotive/3d-object-detection/README.md
@@ -101,3 +101,7 @@ Please click [here](https://github.com/mlcommons/inference/blob/master/automotiv
 ```
 python accuracy_waymo.py --mlperf-accuracy-file <path to accuracy file>/mlperf_log_accuracy.json --waymo-dir /waymo/kitti_format/
 ```
+
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
\ No newline at end of file
diff --git a/graph/R-GAT/README.md b/graph/R-GAT/README.md
index 1380cb047d..d9f5fafa44 100644
--- a/graph/R-GAT/README.md
+++ b/graph/R-GAT/README.md
@@ -181,6 +181,10 @@ mlcr process,mlperf,accuracy,_igbh --result_dir=<Path to directory where files a
 
 Please click [here](https://github.com/mlcommons/inference/blob/dev/graph/R-GAT/tools/accuracy_igbh.py) to view the Python script for evaluating accuracy for the IGBH dataset.
 
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
+
 #### Run using docker
 
 Not implemented yet
diff --git a/language/bert/README.md b/language/bert/README.md
index e60745d45e..80fc8189ea 100644
--- a/language/bert/README.md
+++ b/language/bert/README.md
@@ -24,6 +24,44 @@ Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/l
 ## Disclaimer
 This benchmark app is a reference implementation that is not meant to be the fastest implementation possible.
 
+## Automated command to run the benchmark via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/bert/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
+
+### Download model through MLCFlow Automation
+
+**Pytorch Framework**
+
+```
+mlcr get,ml-model,bert-large,_pytorch --outdirname=<path_to_download> -j
+```
+
+**Onnx Framework**
+
+```
+mlcr get,ml-model,bert-large,_onnx --outdirname=<path_to_download> -j
+```
+
+**TensorFlow Framework**
+
+```
+mlcr get,ml-model,bert-large,_tensorflow --outdirname=<path_to_download> -j
+```
+
+### Download dataset through MLCFlow Automation
+
+**Validation**
+```
+mlcr get,dataset,squad,validation  --outdirname=<path_to_download> -j
+```
+
+**Calibration**
+```
+mlcr get,dataset,squad,_calib1 --outdirname=<path_to_download> -j
+```
+
 ## Commands
 
 Please run the following commands:
@@ -45,6 +83,17 @@ Please run the following commands:
 - The script [tf_freeze_bert.py] freezes the TensorFlow model into pb file.
 - The script [bert_tf_to_pytorch.py] converts the TensorFlow model into the PyTorch `BertForQuestionAnswering` module in [HuggingFace Transformers](https://github.com/huggingface/transformers) and also exports the model to [ONNX](https://github.com/onnx/onnx) format.
 
+### Evaluate the accuracy through MLCFlow Automation
+```bash
+mlcr process,mlperf,accuracy,_squad --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+Please click [here](https://github.com/mlcommons/inference/blob/master/language/bert/accuracy-squad.py) to view the Python script for evaluating accuracy for the squad dataset.
+
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
+
 ## Loadgen over the Network 
 
 ```
diff --git a/language/deepseek-r1/backends/__init__.py b/language/deepseek-r1/backends/__init__.py
index 61ad96a3f2..865ed3bd53 100644
--- a/language/deepseek-r1/backends/__init__.py
+++ b/language/deepseek-r1/backends/__init__.py
@@ -11,4 +11,4 @@
 # to avoid dependency issues when only using certain backends
 __all__ = [
     'BaseBackend',
-]
\ No newline at end of file
+]
diff --git a/language/deepseek-r1/backends/pytorch_backend.py b/language/deepseek-r1/backends/pytorch_backend.py
index c1e426185d..0742882bca 100644
--- a/language/deepseek-r1/backends/pytorch_backend.py
+++ b/language/deepseek-r1/backends/pytorch_backend.py
@@ -1,3 +1,17 @@
+from utils.validation import require_initialized, BackendNotInitializedError
+from utils.backend_registry import get_backend_config
+from .utils import get_cache_directory
+from .base_backend import BaseBackend
+from transformers import AutoTokenizer
+import torch.distributed as dist
+import torch
+from pathlib import Path
+import asyncio
+from typing import Any, Dict, List, Optional
+import logging
+import json
+from ref_dsinfer.inference.model import Transformer, ModelArgs
+from safetensors.torch import load_model
 import os
 import sys
 
@@ -6,23 +20,6 @@
     'REF_DSINFER_PATH', '/opt/ref_dsinfer/inference')
 sys.path.append(ref_dsinfer_path)
 
-from safetensors.torch import load_model
-from ref_dsinfer.inference.model import Transformer, ModelArgs
-import json
-import logging
-from typing import Any, Dict, List, Optional
-import asyncio
-from pathlib import Path
-
-import torch
-import torch.distributed as dist
-from transformers import AutoTokenizer
-
-from .base_backend import BaseBackend
-from .utils import get_cache_directory
-from utils.backend_registry import get_backend_config
-from utils.validation import require_initialized, BackendNotInitializedError
-
 
 logger = logging.getLogger(__name__)
 
@@ -115,8 +112,10 @@ def initialize(self) -> None:
         with torch.device(self.config['device']):
             self.model = Transformer(self.model_args)
 
-        # Load tokenizer (only rank 0 needs it for MLPerf, but all ranks need it for run_eval_mpi)
-        self.tokenizer = AutoTokenizer.from_pretrained(str(self.model_path), revision=self.config['model_revision'])
+        # Load tokenizer (only rank 0 needs it for MLPerf, but all ranks need
+        # it for run_eval_mpi)
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            str(self.model_path), revision=self.config['model_revision'])
 
         # Load model weights
         checkpoint_file = self.model_path / \
@@ -133,7 +132,8 @@ def sample(self, logits: torch.Tensor, temperature: float) -> torch.Tensor:
         """Sample from logits with temperature."""
         logits = logits / max(temperature, 1e-5)
         probs = torch.softmax(logits, dim=-1)
-        return probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
+        return probs.div_(torch.empty_like(
+            probs).exponential_(1)).argmax(dim=-1)
 
     @torch.inference_mode()
     def _generate_batch(
@@ -222,7 +222,8 @@ def _generate_batch(
         return completion_tokens
 
     @require_initialized
-    def generate(self, tokenized_prompts: List[List[int]], **kwargs) -> List[Dict[str, Any]]:
+    def generate(
+            self, tokenized_prompts: List[List[int]], **kwargs) -> List[Dict[str, Any]]:
         """
         Generate responses for a list of pre-tokenized prompts.
 
@@ -265,7 +266,8 @@ def generate(self, tokenized_prompts: List[List[int]], **kwargs) -> List[Dict[st
         return results
 
     @require_initialized
-    def generate_batch_distributed(self, batch_tokens: List[List[int]]) -> List[List[int]]:
+    def generate_batch_distributed(
+            self, batch_tokens: List[List[int]]) -> List[List[int]]:
         """
         Generate tokens for a batch in distributed mode.
 
@@ -296,7 +298,8 @@ def generate_batch_distributed(self, batch_tokens: List[List[int]]) -> List[List
             return []
 
     @require_initialized
-    def generate_async(self, tokenized_prompts: List[List[int]], **kwargs) -> List[asyncio.Future]:
+    def generate_async(
+            self, tokenized_prompts: List[List[int]], **kwargs) -> List[asyncio.Future]:
         """
         Generate responses asynchronously.
 
@@ -331,7 +334,8 @@ async def extract_result(idx):
         return futures
 
     @require_initialized
-    def generate_batch_distributed_async(self, batch_tokens: List[List[int]]) -> asyncio.Future:
+    def generate_batch_distributed_async(
+            self, batch_tokens: List[List[int]]) -> asyncio.Future:
         """
         Generate tokens for a batch in distributed mode asynchronously.
 
diff --git a/language/deepseek-r1/backends/sglang_backend.py b/language/deepseek-r1/backends/sglang_backend.py
index 06cf074a96..10be6e1dcd 100644
--- a/language/deepseek-r1/backends/sglang_backend.py
+++ b/language/deepseek-r1/backends/sglang_backend.py
@@ -66,11 +66,12 @@ def __init__(self, config: Dict[str, Any] = None):
 
         # Log monitoring
         self._log_monitor = None
-        
+
         # Shared semaphore for async concurrency control
         self._async_semaphore = None
 
-        # Configure logging to suppress httpx INFO logs (only show warnings/errors)
+        # Configure logging to suppress httpx INFO logs (only show
+        # warnings/errors)
         import logging
         logging.getLogger("httpx").setLevel(logging.WARNING)
         logging.getLogger("openai").setLevel(logging.WARNING)
@@ -128,7 +129,8 @@ def _build_server_command(self) -> List[str]:
             cmd.append('flashinfer')
 
         if self.config['enable_dp_attention']:
-            cmd.extend(['--enable-dp-attention', '--dp', str(self.config['dp'])])
+            cmd.extend(['--enable-dp-attention',
+                       '--dp', str(self.config['dp'])])
 
         # Add performance settings
         cmd.extend([
@@ -175,7 +177,8 @@ def _wait_for_server_ready(self, timeout: int = None) -> bool:
             # Update progress indicator every 0.5 seconds
             if time.time() - last_progress_update >= 0.5:
                 last_progress_update = time.time()
-                progress_idx = (progress_idx + 1) % len(TerminalDisplay.PROGRESS_CHARS)
+                progress_idx = (
+                    progress_idx + 1) % len(TerminalDisplay.PROGRESS_CHARS)
                 minutes = elapsed // 60
                 seconds = elapsed % 60
                 # Use carriage return to stay on the same line
@@ -192,7 +195,8 @@ def _wait_for_server_ready(self, timeout: int = None) -> bool:
                     if response.status_code == 200:
                         # Health check passed, now try a warmup query
                         print(f"\r{' '*80}\r", end='', flush=True)
-                        print(f"\n[SGLANG] Health check passed, running warmup query...")
+                        print(
+                            f"\n[SGLANG] Health check passed, running warmup query...")
 
                         # Try to send a simple warmup query using OpenAI client
                         try:
@@ -210,7 +214,8 @@ def _wait_for_server_ready(self, timeout: int = None) -> bool:
                             # Send a simple warmup request
                             warmup_response = warmup_client.chat.completions.create(
                                 model=self.config['served_model_name'],
-                                messages=[{"role": "user", "content": "Hello"}],
+                                messages=[
+                                    {"role": "user", "content": "Hello"}],
                                 temperature=0.0,
                                 max_tokens=10,
                                 seed=self.config['seed']
@@ -218,23 +223,28 @@ def _wait_for_server_ready(self, timeout: int = None) -> bool:
 
                             # Check if we got a valid response
                             if warmup_response.choices[0].message.content:
-                                print(f"[SGLANG] ✓ Warmup query successful! Response: {warmup_response.choices[0].message.content[:50]}...")
+                                print(
+                                    f"[SGLANG] ✓ Warmup query successful! Response: {warmup_response.choices[0].message.content[:50]}...")
 
                                 # Stop log monitoring
                                 if self._log_monitor:
                                     self._log_monitor.stop()
                                     self._log_monitor = None
 
-                                print(f"\n[SGLANG] " + "="*60)
-                                print(f"[SGLANG] ✓ SERVER READY! (startup took {elapsed}s)")
-                                print(f"[SGLANG] " + "="*60)
+                                print(f"\n[SGLANG] " + "=" * 60)
+                                print(
+                                    f"[SGLANG] ✓ SERVER READY! (startup took {elapsed}s)")
+                                print(f"[SGLANG] " + "=" * 60)
                                 return True
                             else:
-                                print(f"[SGLANG] Warmup query returned empty response, retrying...")
+                                print(
+                                    f"[SGLANG] Warmup query returned empty response, retrying...")
 
                         except Exception as warmup_error:
-                            print(f"[SGLANG] Warmup query failed: {warmup_error}, retrying...")
-                            # Continue waiting, the server might not be fully ready yet
+                            print(
+                                f"[SGLANG] Warmup query failed: {warmup_error}, retrying...")
+                            # Continue waiting, the server might not be fully
+                            # ready yet
 
                 except requests.exceptions.RequestException:
                     pass
@@ -246,9 +256,11 @@ def _wait_for_server_ready(self, timeout: int = None) -> bool:
                     self._log_monitor = None
                 # Clear progress line
                 print(f"\r{' '*80}\r", end='', flush=True)
-                print(f"\n[SGLANG] ✗ Server process died with exit code: {self.server_process.returncode}")
+                print(
+                    f"\n[SGLANG] ✗ Server process died with exit code: {self.server_process.returncode}")
                 if self.server_log_file:
-                    print(f"[SGLANG] Check server logs at: {self.server_log_file}")
+                    print(
+                        f"[SGLANG] Check server logs at: {self.server_log_file}")
                 return False
 
             time.sleep(0.1)  # Check every 100ms for smoother progress
@@ -264,17 +276,21 @@ def _wait_for_server_ready(self, timeout: int = None) -> bool:
 
     def _start_server(self) -> None:
         """Start the SGLang server as a subprocess."""
-        print(f"\n[SGLANG] Starting SGLang server for {self.config['model']}...")
+        print(
+            f"\n[SGLANG] Starting SGLang server for {self.config['model']}...")
         print(f"[SGLANG] Configuration:")
         print(f"[SGLANG]   - Port: {self.port}")
-        print(f"[SGLANG]   - Tensor Parallel: {self.config['tensor_parallel_size']}")
-        print(f"[SGLANG]   - Context Length: {self.config['context_length']:,} tokens")
+        print(
+            f"[SGLANG]   - Tensor Parallel: {self.config['tensor_parallel_size']}")
+        print(
+            f"[SGLANG]   - Context Length: {self.config['context_length']:,} tokens")
         print(f"[SGLANG]   - dtype: {self.config['dtype']}")
 
         # Create log file for server output
         log_dir = Path("/work/logs")
         log_dir.mkdir(exist_ok=True)
-        self.server_log_file = log_dir / f"sglang_server_{self.port}_{int(time.time())}.log"
+        self.server_log_file = log_dir / \
+            f"sglang_server_{self.port}_{int(time.time())}.log"
 
         cmd = self._build_server_command()
         print(f"\n[SGLANG] Command: {' '.join(cmd)}")
@@ -315,7 +331,10 @@ def _stop_server(self) -> None:
                 except subprocess.TimeoutExpired:
                     # Force kill if not stopped
                     print("[SGLANG] Server didn't stop gracefully, forcing...")
-                    os.killpg(os.getpgid(self.server_process.pid), signal.SIGKILL)
+                    os.killpg(
+                        os.getpgid(
+                            self.server_process.pid),
+                        signal.SIGKILL)
                     self.server_process.wait()
                     print("[SGLANG] Server force stopped")
             except ProcessLookupError:
@@ -332,7 +351,8 @@ def initialize(self) -> None:
         try:
             # Load tokenizer for string conversion
             print(f"[SGLANG] Loading tokenizer: {self.config['tokenizer']}...")
-            self.tokenizer = AutoTokenizer.from_pretrained(self.config['tokenizer'], revision=self.config['model_revision'])
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.config['tokenizer'], revision=self.config['model_revision'])
 
             # Start SGLang server (with log monitoring)
             self._start_server()
@@ -341,7 +361,8 @@ def initialize(self) -> None:
             base_url = f"http://localhost:{self.port}/v1"
             api_key = self.config['api_key'] or "dummy-key"
 
-            print(f"[SGLANG] Creating OpenAI clients with base URL: {base_url}")
+            print(
+                f"[SGLANG] Creating OpenAI clients with base URL: {base_url}")
 
             # Configure timeout settings
             timeout_config = httpx.Timeout(
@@ -371,10 +392,12 @@ def initialize(self) -> None:
             )
 
             print(f"[SGLANG] Created asynchronous OpenAI client")
-            
+
             # Create shared semaphore for async concurrency control
-            self._async_semaphore = asyncio.Semaphore(self.config['max_running_requests'])
-            print(f"[SGLANG] Created async semaphore with limit: {self.config['max_running_requests']}")
+            self._async_semaphore = asyncio.Semaphore(
+                self.config['max_running_requests'])
+            print(
+                f"[SGLANG] Created async semaphore with limit: {self.config['max_running_requests']}")
 
             # Server readiness was already verified by health endpoint in _wait_for_server_ready()
             # No need to check models endpoint
@@ -403,17 +426,18 @@ def initialize(self) -> None:
             raise
 
     @require_initialized
-    def generate(self, 
-                tokenized_prompts: Optional[List[List[int]]] = None,
-                text_prompts: Optional[List[str]] = None,
-                **kwargs) -> List[Dict[str, Any]]:
+    def generate(self,
+                 tokenized_prompts: Optional[List[List[int]]] = None,
+                 text_prompts: Optional[List[str]] = None,
+                 **kwargs) -> List[Dict[str, Any]]:
         """Generate responses synchronously."""
         # Check if server process is still alive
         self._check_server_alive()
 
         # Check if client is properly initialized
         if self.client is None:
-            raise RuntimeError("SGLang client is not initialized. Server may have failed to start.")
+            raise RuntimeError(
+                "SGLang client is not initialized. Server may have failed to start.")
 
         # Validate prompts using centralized validation
         validate_prompts_input(
@@ -436,7 +460,8 @@ def generate(self,
         results = []
 
         # Process prompts with progress bar
-        for prompt in tqdm(prompt_strings, desc="SGLang sync inference", unit="prompt"):
+        for prompt in tqdm(
+                prompt_strings, desc="SGLang sync inference", unit="prompt"):
             try:
                 completion = self.client.chat.completions.create(
                     model=self.config['served_model_name'],
@@ -452,7 +477,8 @@ def generate(self,
 
                 # Validate response is not empty
                 if not generated_text:
-                    raise RuntimeError(f"Empty response received from SGLang server for prompt: {prompt[:100]}...")
+                    raise RuntimeError(
+                        f"Empty response received from SGLang server for prompt: {prompt[:100]}...")
 
                 # Tokenize the output to get token IDs
                 tokens = self.tokenizer.encode(generated_text)
@@ -464,15 +490,18 @@ def generate(self,
 
             except Exception as e:
                 print(f"\nError generating completion: {e}")
-                raise RuntimeError(f"SGLang backend failed to generate tokens for prompt: {prompt[:100]}...")
+                raise RuntimeError(
+                    f"SGLang backend failed to generate tokens for prompt: {prompt[:100]}...")
 
         return results
 
-    async def _async_generate_single(self, prompt: str, idx: int, semaphore: asyncio.Semaphore) -> Tuple[int, Dict[str, Any]]:
+    async def _async_generate_single(
+            self, prompt: str, idx: int, semaphore: asyncio.Semaphore) -> Tuple[int, Dict[str, Any]]:
         """Generate a single response asynchronously with semaphore control."""
         # Check if async client is properly initialized
         if self.async_client is None:
-            raise RuntimeError(f"SGLang async client is not initialized for prompt {idx}")
+            raise RuntimeError(
+                f"SGLang async client is not initialized for prompt {idx}")
 
         async with semaphore:
             try:
@@ -490,7 +519,8 @@ async def _async_generate_single(self, prompt: str, idx: int, semaphore: asyncio
 
                 # Validate response is not empty
                 if not generated_text:
-                    raise RuntimeError(f"Empty response received from SGLang server for prompt: {prompt[:100]}...")
+                    raise RuntimeError(
+                        f"Empty response received from SGLang server for prompt: {prompt[:100]}...")
 
                 # Tokenize the output to get token IDs
                 tokens = self.tokenizer.encode(generated_text)
@@ -499,20 +529,22 @@ async def _async_generate_single(self, prompt: str, idx: int, semaphore: asyncio
 
             except Exception as e:
                 print(f"\nError generating completion for prompt {idx}: {e}")
-                raise RuntimeError(f"SGLang backend failed to generate tokens for prompt {idx}: {e}")
+                raise RuntimeError(
+                    f"SGLang backend failed to generate tokens for prompt {idx}: {e}")
 
     @require_initialized
-    def generate_async(self, 
-                      tokenized_prompts: Optional[List[List[int]]] = None,
-                      text_prompts: Optional[List[str]] = None,
-                      **kwargs) -> List[asyncio.Future]:
+    def generate_async(self,
+                       tokenized_prompts: Optional[List[List[int]]] = None,
+                       text_prompts: Optional[List[str]] = None,
+                       **kwargs) -> List[asyncio.Future]:
         """Generate responses asynchronously using shared semaphore."""
         # Check if server process is still alive
         self._check_server_alive()
 
         # Check if client is properly initialized
         if self.async_client is None:
-            raise RuntimeError("SGLang async client is not initialized. Server may have failed to start.")
+            raise RuntimeError(
+                "SGLang async client is not initialized. Server may have failed to start.")
 
         # Validate prompts using centralized validation
         validate_prompts_input(
@@ -542,44 +574,49 @@ def generate_async(self,
         futures = []
         for idx, prompt in enumerate(prompt_strings):
             # Create a task for each prompt using the shared semaphore
-            task = asyncio.create_task(self._async_generate_single(prompt, idx, self._async_semaphore))
-            
+            task = asyncio.create_task(
+                self._async_generate_single(
+                    prompt, idx, self._async_semaphore))
+
             # Create a future that will hold the result
             future = asyncio.Future()
-            
+
             # Setup callback to extract just the result (not the index)
             def make_callback(future_obj, expected_idx):
                 def callback(task_obj):
                     try:
                         idx, result = task_obj.result()
                         if idx != expected_idx:
-                            future_obj.set_exception(Exception(f"Index mismatch: expected {expected_idx}, got {idx}"))
+                            future_obj.set_exception(
+                                Exception(f"Index mismatch: expected {expected_idx}, got {idx}"))
                         else:
                             future_obj.set_result(result)
                     except Exception as e:
                         future_obj.set_exception(e)
                 return callback
-            
+
             task.add_done_callback(make_callback(future, idx))
             futures.append(future)
 
         return futures
 
-    async def generate_stream(self, 
-                            tokenized_prompts: Optional[List[List[int]]] = None,
-                            text_prompts: Optional[List[str]] = None,
-                            **kwargs) -> List[AsyncIterator[StreamingChunk]]:
+    async def generate_stream(self,
+                              tokenized_prompts: Optional[List[List[int]]] = None,
+                              text_prompts: Optional[List[str]] = None,
+                              **kwargs) -> List[AsyncIterator[StreamingChunk]]:
         """Generate responses for a list of prompts with streaming."""
         if not self.is_initialized:
-            raise RuntimeError("Backend not initialized. Call initialize() first.")
-            
+            raise RuntimeError(
+                "Backend not initialized. Call initialize() first.")
+
         # Check if server process is still alive
         self._check_server_alive()
-        
+
         # Check if async client is properly initialized
         if self.async_client is None:
-            raise RuntimeError("SGLang async client is not initialized. Server may have failed to start.")
-        
+            raise RuntimeError(
+                "SGLang async client is not initialized. Server may have failed to start.")
+
         # Validate prompts
         validate_prompts_input(
             backend_name='sglang',
@@ -587,7 +624,7 @@ async def generate_stream(self,
             text_prompts=text_prompts,
             input_type='text'
         )
-        
+
         # SGLang prefers text prompts
         if text_prompts is None:
             # Convert tokenized prompts to strings
@@ -597,8 +634,9 @@ async def generate_stream(self,
             ]
         else:
             prompt_strings = text_prompts
-            
-        async def stream_single_prompt(prompt: str) -> AsyncIterator[StreamingChunk]:
+
+        async def stream_single_prompt(
+                prompt: str) -> AsyncIterator[StreamingChunk]:
             try:
                 stream = await self.async_client.chat.completions.create(
                     model=self.config['served_model_name'],
@@ -609,14 +647,14 @@ async def stream_single_prompt(prompt: str) -> AsyncIterator[StreamingChunk]:
                     seed=self.config.get('seed'),
                     stream=True
                 )
-                
+
                 async for chunk in stream:
                     if not chunk.choices:
                         continue
-                        
+
                     delta = chunk.choices[0].delta
                     finish_reason = chunk.choices[0].finish_reason
-                    
+
                     if delta.content:
                         yield StreamingChunk(
                             token=delta.content,
@@ -635,7 +673,7 @@ async def stream_single_prompt(prompt: str) -> AsyncIterator[StreamingChunk]:
             except Exception as e:
                 print(f"[SGLANG] Streaming error for prompt: {e}")
                 raise
-        
+
         return [stream_single_prompt(prompt) for prompt in prompt_strings]
 
     def shutdown(self) -> None:
@@ -650,7 +688,7 @@ def shutdown(self) -> None:
         # Close clients
         self.client = None
         self.async_client = None
-        
+
         # Clear async semaphore
         self._async_semaphore = None
 
@@ -665,4 +703,4 @@ def shutdown(self) -> None:
             torch.cuda.empty_cache()
 
         self.is_initialized = False
-        print("[SGLANG] Backend shutdown complete")
\ No newline at end of file
+        print("[SGLANG] Backend shutdown complete")
diff --git a/language/deepseek-r1/backends/utils.py b/language/deepseek-r1/backends/utils.py
index 0e4c7732da..ebd6ce3719 100644
--- a/language/deepseek-r1/backends/utils.py
+++ b/language/deepseek-r1/backends/utils.py
@@ -19,50 +19,50 @@
 def get_cache_directory() -> Path:
     """
     Get the cache directory at /raid/data/$USER/.cache
-    
+
     Returns:
         Path: The cache directory path
     """
     # Get the current user
     user = os.environ.get('USER', os.environ.get('USERNAME', 'unknown'))
-    
+
     # Use /raid/data/$USER/.cache
     cache_dir = Path(f'/raid/data/{user}/.cache')
-    
+
     # Create the cache directory if it doesn't exist
     cache_dir.mkdir(parents=True, exist_ok=True)
-    
+
     return cache_dir
 
 
 def setup_huggingface_cache() -> Path:
     """
     Set up HuggingFace cache environment variables using the preferred cache directory.
-    
+
     Returns:
         Path: The cache directory being used
     """
     cache_dir = get_cache_directory()
-    
+
     # Set HuggingFace cache environment variables
     os.environ['HF_HOME'] = str(cache_dir)
     os.environ['HF_HUB_CACHE'] = str(cache_dir)
     os.environ['HUGGINGFACE_HUB_CACHE'] = str(cache_dir)
-    
+
     return cache_dir
 
 
 def find_free_port(start_port: int = 30000, max_attempts: int = 100) -> int:
     """
     Find a free port starting from start_port.
-    
+
     Args:
         start_port: The port number to start searching from
         max_attempts: Maximum number of ports to try
-        
+
     Returns:
         int: A free port number
-        
+
     Raises:
         RuntimeError: If no free port is found after max_attempts
     """
@@ -75,13 +75,14 @@ def find_free_port(start_port: int = 30000, max_attempts: int = 100) -> int:
             return port
         except OSError:
             continue
-    raise RuntimeError(f"Could not find free port after {max_attempts} attempts starting from {start_port}")
+    raise RuntimeError(
+        f"Could not find free port after {max_attempts} attempts starting from {start_port}")
 
 
 def set_all_seeds(seed: int = 42) -> None:
     """
     Set seeds for all random number generators for reproducibility.
-    
+
     Args:
         seed: The seed value to use
     """
@@ -96,73 +97,76 @@ def set_all_seeds(seed: int = 42) -> None:
     set_seed(seed)
 
 
-def validate_prompts(tokenized_prompts: Optional[list] = None, 
-                    text_prompts: Optional[list] = None,
-                    backend_type: str = "") -> None:
+def validate_prompts(tokenized_prompts: Optional[list] = None,
+                     text_prompts: Optional[list] = None,
+                     backend_type: str = "") -> None:
     """
     Validate that at least one type of prompts is provided.
-    
+
     Args:
         tokenized_prompts: List of tokenized prompts
         text_prompts: List of text prompts
         backend_type: Name of the backend for error messages
-        
+
     Raises:
         ValueError: If neither prompt type is provided
     """
     if tokenized_prompts is None and text_prompts is None:
-        raise ValueError(f"{backend_type + ' backend' if backend_type else 'Backend'} requires either text_prompts or tokenized_prompts")
+        raise ValueError(
+            f"{backend_type + ' backend' if backend_type else 'Backend'} requires either text_prompts or tokenized_prompts")
 
 
 # Terminal display utilities
 class TerminalDisplay:
     """ANSI escape codes and utilities for terminal display formatting."""
-    
+
     # ANSI escape codes for cursor control
     CLEAR_SCREEN = "\033[2J"
     MOVE_CURSOR_UP = "\033[{}A"
     CLEAR_LINE = "\033[K"
     SAVE_CURSOR = "\033[s"
     RESTORE_CURSOR = "\033[u"
-    
+
     # Progress spinner characters
     PROGRESS_CHARS = ['⠋', '⠙', '⠹', '⠸', '⠼', '⠴', '⠦', '⠧', '⠇', '⠏']
-    
+
     @staticmethod
     def clear_lines(num_lines: int) -> None:
         """Clear the specified number of lines above the cursor."""
-        print(TerminalDisplay.MOVE_CURSOR_UP.format(num_lines), end='', flush=True)
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(
+            num_lines), end='', flush=True)
         for _ in range(num_lines):
             print(TerminalDisplay.CLEAR_LINE)
-        print(TerminalDisplay.MOVE_CURSOR_UP.format(num_lines), end='', flush=True)
-    
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(
+            num_lines), end='', flush=True)
+
     @staticmethod
     def save_cursor_position() -> None:
         """Save the current cursor position."""
         print(TerminalDisplay.SAVE_CURSOR, end='', flush=True)
-    
+
     @staticmethod
     def restore_cursor_position() -> None:
         """Restore the previously saved cursor position."""
         print(TerminalDisplay.RESTORE_CURSOR, end='', flush=True)
-    
+
     @staticmethod
     def clear_current_line() -> None:
         """Clear the current line."""
         print("\r" + " " * 80 + "\r", end='', flush=True)
-    
+
     @staticmethod
     def truncate_line(line: str, max_length: int = 110) -> str:
         """Truncate a line to fit within the specified length."""
         if len(line) <= max_length:
             return line
-        return line[:max_length - 3] + "..." 
+        return line[:max_length - 3] + "..."
 
 
 class LogMonitor:
     """Real-time log file monitor with terminal display."""
-    
-    def __init__(self, 
+
+    def __init__(self,
                  log_file_path: Union[str, Path],
                  prefix: str = "LOG",
                  max_lines: int = 5,
@@ -170,7 +174,7 @@ def __init__(self,
                  header_text: Optional[str] = None):
         """
         Initialize the log monitor.
-        
+
         Args:
             log_file_path: Path to the log file to monitor
             prefix: Prefix for display lines (e.g., "[SGLANG]")
@@ -183,42 +187,43 @@ def __init__(self,
         self.max_lines = max_lines
         self.display_interval = display_interval
         self.header_text = header_text or f"Server startup logs (last {max_lines} lines):"
-        
+
         # Threading control
         self._monitor_thread = None
         self._stop_event = None
         self._ready_event = None
-        
+
         # Display dimensions
         self.total_lines = max_lines + 3  # 2 header lines + 1 blank separator
-        
-    def start(self, wait_for_file: bool = True, file_wait_timeout: float = 30.0) -> bool:
+
+    def start(self, wait_for_file: bool = True,
+              file_wait_timeout: float = 30.0) -> bool:
         """
         Start the log monitor in a background thread.
-        
+
         Args:
             wait_for_file: Whether to wait for the log file to exist
             file_wait_timeout: How long to wait for the file (seconds)
-            
+
         Returns:
             bool: True if monitor started successfully
         """
         if self._monitor_thread is not None:
             return True  # Already running
-            
+
         self._stop_event = threading.Event()
         self._ready_event = threading.Event()
-        
+
         self._monitor_thread = threading.Thread(
             target=self._monitor_loop,
             args=(wait_for_file, file_wait_timeout),
             daemon=True
         )
         self._monitor_thread.start()
-        
+
         # Wait for the monitor to set up its display area
         return self._ready_event.wait(timeout=2.0)
-    
+
     def stop(self) -> None:
         """Stop the log monitor and clean up display."""
         if self._stop_event and self._monitor_thread:
@@ -227,36 +232,39 @@ def stop(self) -> None:
             self._monitor_thread = None
             self._stop_event = None
             self._ready_event = None
-    
-    def _monitor_loop(self, wait_for_file: bool, file_wait_timeout: float) -> None:
+
+    def _monitor_loop(self, wait_for_file: bool,
+                      file_wait_timeout: float) -> None:
         """Main monitoring loop that runs in a separate thread."""
         # Wait for log file if requested
         if wait_for_file:
             start_time = time.time()
             while not self.log_file_path.exists():
                 if time.time() - start_time > file_wait_timeout:
-                    print(f"[{self.prefix}] Warning: Log file not found after {file_wait_timeout}s: {self.log_file_path}")
+                    print(
+                        f"[{self.prefix}] Warning: Log file not found after {file_wait_timeout}s: {self.log_file_path}")
                     self._ready_event.set()
                     return
                 time.sleep(0.5)
         elif not self.log_file_path.exists():
-            print(f"[{self.prefix}] Warning: Log file not found: {self.log_file_path}")
+            print(
+                f"[{self.prefix}] Warning: Log file not found: {self.log_file_path}")
             self._ready_event.set()
             return
-            
+
         print(f"\n[{self.prefix}] Monitoring logs: {self.log_file_path.name}")
-        print(f"[{self.prefix}] " + "="*60)
-        
+        print(f"[{self.prefix}] " + "=" * 60)
+
         # Initialize display area
         self._setup_display_area()
-        
+
         # Signal that we're ready
         self._ready_event.set()
-        
+
         # Buffer for log lines
         line_buffer = []
         last_display_time = 0
-        
+
         try:
             # Use tail -f to follow the log file
             process = subprocess.Popen(
@@ -267,11 +275,11 @@ def _monitor_loop(self, wait_for_file: bool, file_wait_timeout: float) -> None:
                 bufsize=1,
                 universal_newlines=True
             )
-            
+
             while not self._stop_event.is_set():
                 if process.poll() is not None:
                     break
-                    
+
                 # Read available lines without blocking
                 line_added = False
                 try:
@@ -285,7 +293,7 @@ def _monitor_loop(self, wait_for_file: bool, file_wait_timeout: float) -> None:
                             line_added = True
                         else:
                             break
-                except:
+                except BaseException:
                     # Fallback for systems without select
                     line = process.stdout.readline()
                     if line:
@@ -293,65 +301,69 @@ def _monitor_loop(self, wait_for_file: bool, file_wait_timeout: float) -> None:
                         if len(line_buffer) > self.max_lines:
                             line_buffer.pop(0)
                         line_added = True
-                
+
                 # Update display if needed
                 current_time = time.time()
-                if line_added or (current_time - last_display_time >= self.display_interval):
+                if line_added or (
+                        current_time - last_display_time >= self.display_interval):
                     last_display_time = current_time
                     self._update_display(line_buffer)
-                    
+
                 time.sleep(0.1)
-                
+
             # Clean up
             process.terminate()
             try:
                 process.wait(timeout=2)
             except subprocess.TimeoutExpired:
                 process.kill()
-                
+
         except Exception as e:
             print(f"\n[{self.prefix}] Log monitor error: {e}")
         finally:
             self._cleanup_display()
-    
+
     def _setup_display_area(self) -> None:
         """Reserve and initialize the display area."""
         # Reserve space
         for _ in range(self.total_lines):
             print()
-            
+
         # Move back up to start of reserved area
-        print(TerminalDisplay.MOVE_CURSOR_UP.format(self.total_lines), end='', flush=True)
-        
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(
+            self.total_lines), end='', flush=True)
+
         # Print initial display
         print(f"\r[{self.prefix}] {self.header_text}", end='')
         print(TerminalDisplay.CLEAR_LINE, flush=True)
-        print(f"\r[{self.prefix}] " + "-"*60, end='')
+        print(f"\r[{self.prefix}] " + "-" * 60, end='')
         print(TerminalDisplay.CLEAR_LINE, flush=True)
-        
+
         # Print empty lines
         for _ in range(self.max_lines):
             print(f"\r[{self.prefix}] ", end='')
             print(TerminalDisplay.CLEAR_LINE, flush=True)
-            
+
         # Print separator
         print(f"\r", end='')
         print(TerminalDisplay.CLEAR_LINE, flush=True)
-    
+
     def _update_display(self, line_buffer: list) -> None:
         """Update the display with current log lines."""
         # Save cursor position
         print(TerminalDisplay.SAVE_CURSOR, end='', flush=True)
-        
-        # Move to start of reserved area (cursor is on progress line, 1 below our area)
-        print(TerminalDisplay.MOVE_CURSOR_UP.format(self.total_lines + 1), end='', flush=True)
-        
+
+        # Move to start of reserved area (cursor is on progress line, 1 below
+        # our area)
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(
+            self.total_lines + 1), end='', flush=True)
+
         # Print header
         print(f"\r[{self.prefix}] {self.header_text}", end='')
         print(TerminalDisplay.CLEAR_LINE, flush=True)
-        print(f"\r[{self.prefix}] " + "-"*60, end='')
+        print(f"\r[{self.prefix}] " + "-" * 60, end='')
         print(TerminalDisplay.CLEAR_LINE, flush=True)
-        
+
         # Print log lines
         for i in range(self.max_lines):
             if i < len(line_buffer):
@@ -360,22 +372,23 @@ def _update_display(self, line_buffer: list) -> None:
             else:
                 print(f"\r[{self.prefix}] ", end='')
             print(TerminalDisplay.CLEAR_LINE, flush=True)
-            
+
         # Print separator
         print(f"\r", end='')
         print(TerminalDisplay.CLEAR_LINE, flush=True)
-        
+
         # Restore cursor position
         print(TerminalDisplay.RESTORE_CURSOR, end='', flush=True)
-    
+
     def _cleanup_display(self) -> None:
         """Clean up the display area on exit."""
         print(TerminalDisplay.SAVE_CURSOR, end='', flush=True)
-        print(TerminalDisplay.MOVE_CURSOR_UP.format(self.total_lines + 1), end='', flush=True)
-        
+        print(TerminalDisplay.MOVE_CURSOR_UP.format(
+            self.total_lines + 1), end='', flush=True)
+
         # Clear all reserved lines
         for _ in range(self.total_lines):
             print(f"\r", end='')
             print(TerminalDisplay.CLEAR_LINE, flush=True)
-            
-        print(TerminalDisplay.RESTORE_CURSOR, end='', flush=True) 
\ No newline at end of file
+
+        print(TerminalDisplay.RESTORE_CURSOR, end='', flush=True)
diff --git a/language/deepseek-r1/backends/vllm_backend.py b/language/deepseek-r1/backends/vllm_backend.py
index 4ac408042f..ec49227f41 100644
--- a/language/deepseek-r1/backends/vllm_backend.py
+++ b/language/deepseek-r1/backends/vllm_backend.py
@@ -148,9 +148,9 @@ def initialize(self) -> None:
 
     @require_initialized
     def generate(self,
-                tokenized_prompts: Optional[List[List[int]]] = None,
-                text_prompts: Optional[List[str]] = None,
-                **kwargs) -> List[Dict[str, Any]]:
+                 tokenized_prompts: Optional[List[List[int]]] = None,
+                 text_prompts: Optional[List[str]] = None,
+                 **kwargs) -> List[Dict[str, Any]]:
         """Generate responses synchronously using LLM.generate().
 
         Note: vLLM backend only accepts text_prompts parameter.
@@ -177,11 +177,14 @@ def generate(self,
             if not completion.text:
                 # Get the corresponding prompt for context
                 prompt_idx = outputs.index(output)
-                prompt_preview = text_prompts[prompt_idx][:100] if len(text_prompts[prompt_idx]) > 100 else text_prompts[prompt_idx]
-                raise RuntimeError(f"Empty response received from vLLM for prompt: {prompt_preview}...")
+                prompt_preview = text_prompts[prompt_idx][:100] if len(
+                    text_prompts[prompt_idx]) > 100 else text_prompts[prompt_idx]
+                raise RuntimeError(
+                    f"Empty response received from vLLM for prompt: {prompt_preview}...")
 
             results.append({
-                'tokens': list(completion.token_ids),  # Convert tuple to list for .copy() compatibility
+                # Convert tuple to list for .copy() compatibility
+                'tokens': list(completion.token_ids),
                 'text': completion.text,
                 'finish_reason': completion.finish_reason
             })
@@ -190,9 +193,9 @@ def generate(self,
 
     @require_initialized
     def generate_async(self,
-                      tokenized_prompts: Optional[List[List[int]]] = None,
-                      text_prompts: Optional[List[str]] = None,
-                      **kwargs) -> List[asyncio.Future]:
+                       tokenized_prompts: Optional[List[List[int]]] = None,
+                       text_prompts: Optional[List[str]] = None,
+                       **kwargs) -> List[asyncio.Future]:
         """Generate responses asynchronously, returning futures immediately.
 
         Note: vLLM backend only accepts text_prompts parameter.
@@ -245,11 +248,14 @@ def _generate_batch(self, text_prompts: List[str]) -> List[Dict[str, Any]]:
             if not completion.text:
                 # Get the corresponding prompt for context
                 prompt_idx = outputs.index(output)
-                prompt_preview = text_prompts[prompt_idx][:100] if len(text_prompts[prompt_idx]) > 100 else text_prompts[prompt_idx]
-                raise RuntimeError(f"Empty response received from vLLM for prompt: {prompt_preview}...")
+                prompt_preview = text_prompts[prompt_idx][:100] if len(
+                    text_prompts[prompt_idx]) > 100 else text_prompts[prompt_idx]
+                raise RuntimeError(
+                    f"Empty response received from vLLM for prompt: {prompt_preview}...")
 
             results.append({
-                'tokens': list(completion.token_ids),  # Convert tuple to list for .copy() compatibility
+                # Convert tuple to list for .copy() compatibility
+                'tokens': list(completion.token_ids),
                 'text': completion.text,
                 'finish_reason': completion.finish_reason
             })
@@ -265,7 +271,8 @@ def shutdown(self) -> None:
             # Access internal executor to ensure proper cleanup
             if self.llm.llm_engine is not None:
                 try:
-                    # This helps cleanup vLLM's internal Ray/multiprocessing resources
+                    # This helps cleanup vLLM's internal Ray/multiprocessing
+                    # resources
                     del self.llm.llm_engine.model_executor
                 except Exception as e:
                     print(f"Warning: Failed to cleanup model executor: {e}")
diff --git a/language/deepseek-r1/eval_accuracy.py b/language/deepseek-r1/eval_accuracy.py
index 55647b7ca5..a0b546b600 100644
--- a/language/deepseek-r1/eval_accuracy.py
+++ b/language/deepseek-r1/eval_accuracy.py
@@ -45,11 +45,11 @@
 # =============================================================================
 
 def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
-                               dataset_file: Union[str, Path],
-                               checkpoint_path: str,
-                               dtype: str = "int32",
-                               output_dir: Optional[Union[str, Path]] = None,
-                               base_filename: Optional[str] = None) -> Tuple[pd.DataFrame, str]:
+                                dataset_file: Union[str, Path],
+                                checkpoint_path: str,
+                                dtype: str = "int32",
+                                output_dir: Optional[Union[str, Path]] = None,
+                                base_filename: Optional[str] = None) -> Tuple[pd.DataFrame, str]:
     """Process MLPerf log accuracy file and evaluate results.
 
     Args:
@@ -68,7 +68,8 @@ def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
     dataset_file = Path(dataset_file)
 
     if not mlperf_log_file.exists():
-        raise FileNotFoundError(f"MLPerf log file not found: {mlperf_log_file}")
+        raise FileNotFoundError(
+            f"MLPerf log file not found: {mlperf_log_file}")
     if not dataset_file.exists():
         raise FileNotFoundError(f"Dataset file not found: {dataset_file}")
 
@@ -86,7 +87,8 @@ def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
         )
         logger.info("Tokenizer loaded successfully")
     except Exception as e:
-        raise RuntimeError(f"Failed to load tokenizer from {checkpoint_path}: {e}")
+        raise RuntimeError(
+            f"Failed to load tokenizer from {checkpoint_path}: {e}")
 
     # Load ground truth dataset
     try:
@@ -99,14 +101,20 @@ def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
             elif 'ground_truth' in dataset_df.columns:
                 ground_truths = dataset_df['ground_truth'].tolist()
             else:
-                raise ValueError("Dataset must contain 'gt_output' or 'ground_truth' column")
+                raise ValueError(
+                    "Dataset must contain 'gt_output' or 'ground_truth' column")
 
             # Get other required columns with fallbacks
             if 'dataset' in dataset_df.columns:
                 datasets = dataset_df['dataset'].tolist()
             elif 'metric' in dataset_df.columns:
                 # Infer dataset from metric names
-                datasets = [metric.replace('_em', '').replace('_', '') for metric in dataset_df['metric'].tolist()]
+                datasets = [
+                    metric.replace(
+                        '_em',
+                        '').replace(
+                        '_',
+                        '') for metric in dataset_df['metric'].tolist()]
             else:
                 datasets = ['unknown'] * len(ground_truths)
 
@@ -138,7 +146,7 @@ def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
         # First, check if this is a JSON array format or newline-delimited JSON
         with open(mlperf_log_file, 'r') as f:
             first_line = f.readline().strip()
-            
+
         if first_line == '[':
             # JSON array format - load the entire file
             logger.info("Detected JSON array format")
@@ -146,8 +154,10 @@ def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
                 try:
                     mlperf_results = json.load(f)
                 except json.JSONDecodeError as e:
-                    # If full file parsing fails, try to parse line by line, skipping brackets
-                    logger.warning(f"Failed to parse as complete JSON array: {e}")
+                    # If full file parsing fails, try to parse line by line,
+                    # skipping brackets
+                    logger.warning(
+                        f"Failed to parse as complete JSON array: {e}")
                     logger.info("Attempting line-by-line parsing")
                     mlperf_results = []
                     with open(mlperf_log_file, 'r') as f2:
@@ -162,7 +172,8 @@ def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
                             try:
                                 mlperf_results.append(json.loads(line))
                             except json.JSONDecodeError as e:
-                                logger.warning(f"Failed to parse line {line_num}: {e}")
+                                logger.warning(
+                                    f"Failed to parse line {line_num}: {e}")
                                 continue
         else:
             # Newline-delimited JSON format
@@ -180,7 +191,7 @@ def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
                     except json.JSONDecodeError as e:
                         logger.warning(f"Failed to parse line {line_num}: {e}")
                         continue
-                        
+
         logger.info(f"Loaded {len(mlperf_results)} MLPerf results")
     except Exception as e:
         raise RuntimeError(f"Failed to load MLPerf log file: {e}")
@@ -220,7 +231,8 @@ def process_mlperf_log_accuracy(mlperf_log_file: Union[str, Path],
             questions_required.append(questions[qsl_idx])
 
         except Exception as e:
-            logger.warning(f"Error processing entry with qsl_idx {qsl_idx}: {e}")
+            logger.warning(
+                f"Error processing entry with qsl_idx {qsl_idx}: {e}")
             continue
 
     if not preds_token_ids:
@@ -271,7 +283,11 @@ def validate_dataframe(df: pd.DataFrame) -> None:
     if not isinstance(df, pd.DataFrame):
         raise ValueError("Input must be a pandas DataFrame")
 
-    required_cols = ['model_output', 'dataset', 'ground_truth', 'tok_model_output_len']
+    required_cols = [
+        'model_output',
+        'dataset',
+        'ground_truth',
+        'tok_model_output_len']
     missing_cols = [col for col in required_cols if col not in df.columns]
     if missing_cols:
         raise ValueError(f"Missing required columns: {missing_cols}")
@@ -390,7 +406,8 @@ def parse_code(text: str) -> Optional[str]:
 # Answer Evaluation Functions
 # =============================================================================
 
-def evaluate_multiple_choice(parsed: Optional[str], ground_truth: str, valid_options: str) -> bool:
+def evaluate_multiple_choice(
+        parsed: Optional[str], ground_truth: str, valid_options: str) -> bool:
     """Evaluate multiple choice answer."""
     if not parsed or not ground_truth:
         return False
@@ -414,10 +431,12 @@ def evaluate_math500(parsed: Optional[str], ground_truth: str) -> bool:
 
     # Use sys.path approach for proper module importing
     workspace_path = os.path.dirname(os.path.abspath(__file__))
-    prm800k_module_path = os.path.join(workspace_path, "submodules", "prm800k", "prm800k")
+    prm800k_module_path = os.path.join(
+        workspace_path, "submodules", "prm800k", "prm800k")
 
     if not os.path.exists(prm800k_module_path):
-        raise FileNotFoundError(f"PRM800K module not found at: {prm800k_module_path}")
+        raise FileNotFoundError(
+            f"PRM800K module not found at: {prm800k_module_path}")
 
     # Save current directory and sys.path
     original_cwd = os.getcwd()
@@ -427,10 +446,10 @@ def evaluate_math500(parsed: Optional[str], ground_truth: str) -> bool:
         # Add prm800k module path to sys.path
         if prm800k_module_path not in sys.path:
             sys.path.insert(0, prm800k_module_path)
-        
+
         # Change directory as some imports might use relative paths
         os.chdir(prm800k_module_path)
-        
+
         # Now import should work
         from grading.grader import grade_answer
         result = grade_answer(given_answer=parsed, ground_truth=ground_truth)
@@ -622,7 +641,8 @@ def process_row(row: pd.Series) -> Dict[str, Any]:
     }
 
 
-def process_livecodebench_parallel(df: pd.DataFrame, group_indices: pd.Index) -> Tuple[int, int]:
+def process_livecodebench_parallel(
+        df: pd.DataFrame, group_indices: pd.Index) -> Tuple[int, int]:
     """Process LiveCodeBench items in parallel."""
     # Prepare work items
     work_items = []
@@ -726,7 +746,8 @@ def process_dataframe(df: pd.DataFrame) -> pd.DataFrame:
 # Unified Evaluation Utilities
 # =============================================================================
 
-def print_evaluation_results(df_evaluated: pd.DataFrame, logger: Optional[logging.Logger] = None) -> Dict[str, Any]:
+def print_evaluation_results(df_evaluated: pd.DataFrame,
+                             logger: Optional[logging.Logger] = None) -> Dict[str, Any]:
     """Print evaluation results in a unified format.
 
     Args:
@@ -762,8 +783,8 @@ def print_evaluation_results(df_evaluated: pd.DataFrame, logger: Optional[loggin
 
 
 def process_and_save_dataframe(df: pd.DataFrame,
-                              output_dir: Optional[Union[str, Path]] = None,
-                              base_filename: Optional[str] = None) -> Tuple[pd.DataFrame, str]:
+                               output_dir: Optional[Union[str, Path]] = None,
+                               base_filename: Optional[str] = None) -> Tuple[pd.DataFrame, str]:
     """Process dataframe for evaluation and save the results.
 
     Args:
@@ -779,7 +800,8 @@ def process_and_save_dataframe(df: pd.DataFrame,
 
     # Determine output path
     if output_dir is None:
-        # Try to infer from existing path info in the dataframe or use current directory
+        # Try to infer from existing path info in the dataframe or use current
+        # directory
         output_dir = Path.cwd()
     else:
         output_dir = Path(output_dir)
diff --git a/language/deepseek-r1/mlperf/__init__.py b/language/deepseek-r1/mlperf/__init__.py
index 33b3154f6b..bfe95c35e3 100644
--- a/language/deepseek-r1/mlperf/__init__.py
+++ b/language/deepseek-r1/mlperf/__init__.py
@@ -16,8 +16,8 @@
 
 __all__ = [
     # SUTs
-    'BaseSUT', 
-    'OfflineSUT', 
+    'BaseSUT',
+    'OfflineSUT',
     'ServerSUT',
     # QSL
     'QuerySampleLibrary',
@@ -26,4 +26,4 @@
     'prepare_mlperf_dataset',
     'process_mlperf_results',
     'create_mlperf_output_dataframe'
-] 
\ No newline at end of file
+]
diff --git a/language/deepseek-r1/mlperf/base_sut.py b/language/deepseek-r1/mlperf/base_sut.py
index 7249207aab..f1d32eb869 100644
--- a/language/deepseek-r1/mlperf/base_sut.py
+++ b/language/deepseek-r1/mlperf/base_sut.py
@@ -12,65 +12,65 @@
 
 class BaseSUT(abc.ABC):
     """Base class for MLPerf inference System Under Test (SUT).
-    
+
     This class defines the interface that all SUTs must implement for MLPerf
     inference benchmarks. It provides two main methods:
     - issue_queries: to enqueue prompt tokens
     - flush_queries: to await completion of all issued queries
     """
-    
+
     def __init__(self, name: str = "BaseSUT"):
         """Initialize the base SUT.
-        
+
         Args:
             name: Name of the SUT for logging purposes
         """
         self.name = name
         self.sut = None
         logger.info(f"Initializing {self.name}")
-    
+
     @abc.abstractmethod
     def issue_queries(self, query_samples: List[lg.QuerySample]) -> None:
         """Issue queries to the SUT.
-        
+
         This method should enqueue the provided query samples for processing.
         It should return immediately without waiting for completion.
-        
+
         Args:
             query_samples: List of MLPerf LoadGen query samples to process
         """
         raise NotImplementedError("Subclasses must implement issue_queries")
-    
+
     @abc.abstractmethod
     def flush_queries(self) -> None:
         """Flush all pending queries.
-        
+
         This method should wait for all previously issued queries to complete
         before returning. It's called by LoadGen to ensure all work is done.
         """
         raise NotImplementedError("Subclasses must implement flush_queries")
-    
+
     def start(self) -> lg.ConstructSUT:
         """Start the SUT and return the LoadGen SUT handle.
-        
+
         Returns:
             LoadGen SUT handle for use with LoadGen
         """
         self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
         logger.info(f"{self.name} started")
         return self.sut
-    
+
     def stop(self) -> None:
         """Stop the SUT and clean up resources."""
         if self.sut:
             lg.DestroySUT(self.sut)
             self.sut = None
             logger.info(f"{self.name} stopped")
-    
+
     def __enter__(self):
         """Context manager entry."""
         return self.start()
-    
+
     def __exit__(self, exc_type, exc_val, exc_tb):
         """Context manager exit."""
-        self.stop() 
\ No newline at end of file
+        self.stop()
diff --git a/language/deepseek-r1/mlperf/offline_sut.py b/language/deepseek-r1/mlperf/offline_sut.py
index db1c4feea1..00382f4660 100644
--- a/language/deepseek-r1/mlperf/offline_sut.py
+++ b/language/deepseek-r1/mlperf/offline_sut.py
@@ -44,11 +44,15 @@ def __init__(self,
         self.dataset_strings = dataset_strings
 
         # Determine backend type using registry
-        self.backend_name = getattr(backend, 'backend_name', type(backend).__name__.lower())
+        self.backend_name = getattr(
+            backend,
+            'backend_name',
+            type(backend).__name__.lower())
         self.uses_text_prompts = uses_text_input(self.backend_name)
 
         if self.uses_text_prompts and dataset_strings is None:
-            raise ValueError(f"Backend {self.backend_name} requires text prompts but dataset_strings was not provided")
+            raise ValueError(
+                f"Backend {self.backend_name} requires text prompts but dataset_strings was not provided")
 
         # Async event loop and thread
         self.loop = None
@@ -122,12 +126,15 @@ async def _process_all_queries_async(self):
             # Prepare prompts for batch processing (like run_eval.py)
             if self.uses_text_prompts:
                 # Use text prompts for vLLM and SGLang
-                prompts = [self.dataset_strings[sample.index] for sample in query_samples]
+                prompts = [self.dataset_strings[sample.index]
+                           for sample in query_samples]
                 futures = self.backend.generate_async(text_prompts=prompts)
             else:
                 # Use tokenized prompts for other backends
-                prompts = [self.dataset[sample.index] for sample in query_samples]
-                futures = self.backend.generate_async(tokenized_prompts=prompts)
+                prompts = [self.dataset[sample.index]
+                           for sample in query_samples]
+                futures = self.backend.generate_async(
+                    tokenized_prompts=prompts)
 
             logger.info(f"Got {len(futures)} futures from backend")
 
@@ -136,7 +143,8 @@ async def _process_all_queries_async(self):
             indexed_futures = [(i, future) for i, future in enumerate(futures)]
             completed_indices = set()
 
-            # Use asyncio.wait with FIRST_COMPLETED to handle out-of-order completion
+            # Use asyncio.wait with FIRST_COMPLETED to handle out-of-order
+            # completion
             pending = {future for _, future in indexed_futures}
 
             while pending:
@@ -153,12 +161,14 @@ async def _process_all_queries_async(self):
                             break
 
                     if original_idx is None:
-                        logger.error("Could not find original index for completed future")
+                        logger.error(
+                            "Could not find original index for completed future")
                         continue
 
                     # Check for duplicate completion
                     if original_idx in completed_indices:
-                        logger.warning(f"Prompt {original_idx} completed multiple times!")
+                        logger.warning(
+                            f"Prompt {original_idx} completed multiple times!")
                         continue
 
                     try:
@@ -174,36 +184,44 @@ async def _process_all_queries_async(self):
                         await self._send_result_to_loadgen(sample, result)
 
                     except Exception as e:
-                        logger.error(f"Error processing prompt {original_idx}: {type(e).__name__}: {e}")
+                        logger.error(
+                            f"Error processing prompt {original_idx}: {type(e).__name__}: {e}")
                         # Raise the error instead of handling empty responses
-                        raise RuntimeError(f"Backend failed to generate tokens for prompt {original_idx}: {e}")
+                        raise RuntimeError(
+                            f"Backend failed to generate tokens for prompt {original_idx}: {e}")
 
             # Verify all results are populated
             if len(completed_indices) != len(futures):
                 missing_count = len(futures) - len(completed_indices)
-                raise RuntimeError(f"Missing results: completed {len(completed_indices)} != {len(futures)} total ({missing_count} missing)")
+                raise RuntimeError(
+                    f"Missing results: completed {len(completed_indices)} != {len(futures)} total ({missing_count} missing)")
 
             for i, result in enumerate(results):
                 if result is None:
                     raise RuntimeError(f"Missing result for prompt {i}")
 
-            logger.info(f"Completed all {len(completed_indices)} prompts successfully")
+            logger.info(
+                f"Completed all {len(completed_indices)} prompts successfully")
 
         except Exception as e:
-            logger.error(f"Error during batch processing: {type(e).__name__}: {e}")
+            logger.error(
+                f"Error during batch processing: {type(e).__name__}: {e}")
             import traceback
             traceback.print_exc()
             raise  # Re-raise instead of sending empty responses
 
-    async def _send_result_to_loadgen(self, sample: lg.QuerySample, result: Dict[str, Any]):
+    async def _send_result_to_loadgen(
+            self, sample: lg.QuerySample, result: Dict[str, Any]):
         """Send a single result to LoadGen."""
         try:
             # Validate that tokens exist - raise error if missing
             tokens = result.get('tokens')
             if tokens is None:
-                raise ValueError(f"Backend result missing 'tokens' key for query {sample.id}")
+                raise ValueError(
+                    f"Backend result missing 'tokens' key for query {sample.id}")
             if not isinstance(tokens, (list, tuple)) or len(tokens) == 0:
-                raise ValueError(f"Backend returned empty or invalid tokens for query {sample.id}: {tokens}")
+                raise ValueError(
+                    f"Backend returned empty or invalid tokens for query {sample.id}: {tokens}")
 
             # Create a copy of tokens before numpy conversion
             tokens_copy = tokens.copy()
@@ -229,12 +247,15 @@ async def _send_result_to_loadgen(self, sample: lg.QuerySample, result: Dict[str
             # Send response to LoadGen
             lg.QuerySamplesComplete([response])
 
-            logger.debug(f"Sent {n_tokens} tokens to LoadGen for query {sample.id}")
+            logger.debug(
+                f"Sent {n_tokens} tokens to LoadGen for query {sample.id}")
 
         except Exception as e:
-            logger.error(f"Error sending result to LoadGen for query {sample.id}: {e}")
+            logger.error(
+                f"Error sending result to LoadGen for query {sample.id}: {e}")
             # Raise the error instead of sending empty response
-            raise RuntimeError(f"Failed to send result to LoadGen for query {sample.id}: {e}")
+            raise RuntimeError(
+                f"Failed to send result to LoadGen for query {sample.id}: {e}")
 
     def _run_event_loop(self):
         """Run the async event loop in a separate thread."""
@@ -282,7 +303,8 @@ def get_results(self) -> List[Dict[str, Any]]:
         # Sort by index to maintain dataset order
         queried_indices = sorted(self.index_to_id.keys())
 
-        logger.info(f"Retrieving results for {len(queried_indices)} queried samples")
+        logger.info(
+            f"Retrieving results for {len(queried_indices)} queried samples")
 
         # Process results in order of dataset indices using stored results
         for i in queried_indices:
@@ -296,7 +318,8 @@ def get_results(self) -> List[Dict[str, Any]]:
                 tokens = result['tokens']
                 output_text = result.get('text', '')
                 if not output_text and self.backend.tokenizer:
-                    output_text = self.backend.tokenizer.decode(result['tokens'], skip_special_tokens=True)
+                    output_text = self.backend.tokenizer.decode(
+                        result['tokens'], skip_special_tokens=True)
 
                 ordered_results.append({
                     'model_output': output_text,
@@ -305,6 +328,7 @@ def get_results(self) -> List[Dict[str, Any]]:
                 })
             else:
                 # No backend result for this sample
-                raise RuntimeError(f"No backend result stored for dataset index {i}, sample_id {sample_id}")
+                raise RuntimeError(
+                    f"No backend result stored for dataset index {i}, sample_id {sample_id}")
 
-        return ordered_results
\ No newline at end of file
+        return ordered_results
diff --git a/language/deepseek-r1/mlperf/qsl.py b/language/deepseek-r1/mlperf/qsl.py
index 59bc5e36a8..d4c9405a4e 100644
--- a/language/deepseek-r1/mlperf/qsl.py
+++ b/language/deepseek-r1/mlperf/qsl.py
@@ -7,12 +7,12 @@
 
 class QuerySampleLibrary:
     """MLPerf QuerySampleLibrary implementation for single-process execution."""
-    
-    def __init__(self, dataset: List[List[int]], dataset_strings: List[str], 
+
+    def __init__(self, dataset: List[List[int]], dataset_strings: List[str],
                  name: str = "QSL"):
         """
         Initialize QSL with dataset.
-        
+
         Args:
             dataset: List of tokenized prompts
             dataset_strings: List of original prompt strings
@@ -24,7 +24,7 @@ def __init__(self, dataset: List[List[int]], dataset_strings: List[str],
         self.perf_count = self.count
         self.name = name
         self.logger = logging.getLogger(__name__)
-        
+
         # Create LoadGen QSL
         self.qsl = lg.ConstructQSL(
             self.count,
@@ -33,7 +33,7 @@ def __init__(self, dataset: List[List[int]], dataset_strings: List[str],
             lambda x: None   # UnloadSamplesFromRam
         )
         self.logger.info(f"Created {self.name} with {self.count} samples")
-    
+
     def __del__(self):
         """Cleanup QSL."""
         if self.qsl is not None:
@@ -43,12 +43,12 @@ def __del__(self):
 
 class DistributedQuerySampleLibrary:
     """QuerySampleLibrary for distributed execution (MPI/torchrun)."""
-    
+
     def __init__(self, dataset: List[List[int]], dataset_strings: List[str],
                  rank: int, world_size: int, name: str = "DistributedQSL"):
         """
         Initialize distributed QSL.
-        
+
         Args:
             dataset: List of tokenized prompts
             dataset_strings: List of original prompt strings
@@ -64,10 +64,10 @@ def __init__(self, dataset: List[List[int]], dataset_strings: List[str],
         self.world_size = world_size
         self.name = name
         self.logger = logging.getLogger(__name__)
-        
+
         # Track if this is rank zero explicitly
         self.is_rank_zero = (self.rank == 0)
-        
+
         # Only rank 0 creates the actual QSL
         if self.is_rank_zero:
             self.qsl = lg.ConstructQSL(
@@ -76,12 +76,13 @@ def __init__(self, dataset: List[List[int]], dataset_strings: List[str],
                 lambda x: None,
                 lambda x: None
             )
-            self.logger.info(f"Created {self.name} with {self.count} samples on rank 0")
+            self.logger.info(
+                f"Created {self.name} with {self.count} samples on rank 0")
         else:
             self.qsl = None
-    
+
     def __del__(self):
         """Cleanup QSL on rank 0."""
         if self.is_rank_zero and self.qsl is not None:
             lg.DestroyQSL(self.qsl)
-            self.logger.info(f"{self.name} destroyed on rank 0") 
\ No newline at end of file
+            self.logger.info(f"{self.name} destroyed on rank 0")
diff --git a/language/deepseek-r1/mlperf/server_sut.py b/language/deepseek-r1/mlperf/server_sut.py
index 75699e208f..e3acb2bde8 100644
--- a/language/deepseek-r1/mlperf/server_sut.py
+++ b/language/deepseek-r1/mlperf/server_sut.py
@@ -69,11 +69,15 @@ def __init__(self,
         self.dataset_strings = dataset_strings
 
         # Determine backend type using registry
-        self.backend_name = getattr(backend, 'backend_name', type(backend).__name__.lower())
+        self.backend_name = getattr(
+            backend,
+            'backend_name',
+            type(backend).__name__.lower())
         self.uses_text_prompts = uses_text_input(self.backend_name)
 
         if self.uses_text_prompts and dataset_strings is None:
-            raise ValueError(f"Backend {self.backend_name} requires text prompts but dataset_strings was not provided")
+            raise ValueError(
+                f"Backend {self.backend_name} requires text prompts but dataset_strings was not provided")
 
         # Async event loop and thread
         self.loop = None
@@ -91,8 +95,6 @@ def __init__(self,
         self.all_results: Dict[int, Dict[str, Any]] = {}
         self.results_lock = asyncio.Lock()
 
-
-
     def issue_queries(self, query_samples: List[lg.QuerySample]) -> None:
         """Issue queries in streaming mode with batching."""
         if not supports_streaming():
@@ -123,7 +125,8 @@ async def _start_streaming_query(self, query_info: QueryInfo) -> None:
         try:
             # Verify streaming support
             if not supports_streaming():
-                raise RuntimeError(f"Backend {self.backend_name} does not support streaming required for server mode")
+                raise RuntimeError(
+                    f"Backend {self.backend_name} does not support streaming required for server mode")
 
             # Prepare prompt based on backend type
             if self.uses_text_prompts:
@@ -155,8 +158,10 @@ async def _start_streaming_query(self, query_info: QueryInfo) -> None:
             task.add_done_callback(self._remove_task_from_active)
 
         except Exception as e:
-            logger.error(f"Error starting stream for query {query_info.query_id}: {e}")
-            raise RuntimeError(f"Failed to start streaming for query {query_info.query_id}: {e}")
+            logger.error(
+                f"Error starting stream for query {query_info.query_id}: {e}")
+            raise RuntimeError(
+                f"Failed to start streaming for query {query_info.query_id}: {e}")
 
     def _remove_task_from_active(self, task: asyncio.Task) -> None:
         """Remove a completed task from the active set."""
@@ -181,7 +186,8 @@ async def _process_stream(self, state: StreamingQueryState) -> None:
                     state.accumulated_tokens.extend(chunk.token_ids)
 
                 # Report first token immediately for TTFT measurement
-                if not state.first_token_sent and (chunk.token or chunk.token_ids):
+                if not state.first_token_sent and (
+                        chunk.token or chunk.token_ids):
                     state.first_token_time = current_time - state.start_time
                     state.first_token_sent = True
 
@@ -197,35 +203,43 @@ async def _process_stream(self, state: StreamingQueryState) -> None:
 
         except asyncio.CancelledError:
             # Task was cancelled, clean up gracefully
-            logger.debug(f"Stream processing cancelled for query {state.query_info.query_id}")
-            # Close the async generator properly (assume aclose exists in our containerized environment)
+            logger.debug(
+                f"Stream processing cancelled for query {state.query_info.query_id}")
+            # Close the async generator properly (assume aclose exists in our
+            # containerized environment)
             try:
                 await state.stream_gen.aclose()
             except Exception:
                 pass
             raise
         except Exception as e:
-            logger.error(f"Error processing stream for query {state.query_info.query_id}: {e}")
-            raise RuntimeError(f"Stream processing failed for query {state.query_info.query_id}: {e}")
+            logger.error(
+                f"Error processing stream for query {state.query_info.query_id}: {e}")
+            raise RuntimeError(
+                f"Stream processing failed for query {state.query_info.query_id}: {e}")
         finally:
             # Clean up active stream
             async with self.active_streams_lock:
                 self.active_streams.pop(state.query_info.query_id, None)
 
-    async def _send_first_token_response(self, state: StreamingQueryState) -> None:
+    async def _send_first_token_response(
+            self, state: StreamingQueryState) -> None:
         """Send first token notification to LoadGen for TTFT measurement."""
-        logger.debug(f"First token received for query {state.query_info.query_id} at {state.first_token_time:.3f}s")
+        logger.debug(
+            f"First token received for query {state.query_info.query_id} at {state.first_token_time:.3f}s")
 
         # Convert first tokens to proper format for LoadGen
         if state.accumulated_tokens:
-            output_tokens = np.ascontiguousarray(state.accumulated_tokens, dtype=np.int32)
+            output_tokens = np.ascontiguousarray(
+                state.accumulated_tokens, dtype=np.int32)
         else:
             # If no token IDs available, encode the text
             if hasattr(self.backend, 'tokenizer') and state.accumulated_text:
                 tokens = self.backend.tokenizer.encode(state.accumulated_text)
                 output_tokens = np.ascontiguousarray(tokens, dtype=np.int32)
             else:
-                raise RuntimeError(f"No token IDs available for first token response for query {state.query_info.query_id}")
+                raise RuntimeError(
+                    f"No token IDs available for first token response for query {state.query_info.query_id}")
 
         output_seq_len = len(output_tokens)
         output_toks_ptr = output_tokens.ctypes.data if output_seq_len > 0 else 0
@@ -248,22 +262,25 @@ async def _send_final_response(self, state: StreamingQueryState) -> None:
             if state.accumulated_tokens:
                 # Create a copy of tokens before numpy conversion
                 tokens_to_send = state.accumulated_tokens.copy()
-                token_array = np.array(state.accumulated_tokens, dtype=np.int32)
+                token_array = np.array(
+                    state.accumulated_tokens, dtype=np.int32)
             else:
                 # If no tokens, encode the text
-                if hasattr(self.backend, 'tokenizer') and state.accumulated_text:
-                    tokens = self.backend.tokenizer.encode(state.accumulated_text)
+                if hasattr(self.backend,
+                           'tokenizer') and state.accumulated_text:
+                    tokens = self.backend.tokenizer.encode(
+                        state.accumulated_text)
                     # Create a copy of tokens before numpy conversion
                     tokens_to_send = tokens.copy()
                     token_array = np.array(tokens, dtype=np.int32)
                 else:
-                    raise RuntimeError(f"No tokens or tokenizer available for query {state.query_info.query_id}")
+                    raise RuntimeError(
+                        f"No tokens or tokenizer available for query {state.query_info.query_id}")
 
             # Validate we have tokens
             if len(token_array) == 0:
-                raise RuntimeError(f"No tokens generated for query {state.query_info.query_id}")
-
-
+                raise RuntimeError(
+                    f"No tokens generated for query {state.query_info.query_id}")
 
             # Create LoadGen response
             response = lg.QuerySampleResponse(
@@ -287,11 +304,14 @@ async def _send_final_response(self, state: StreamingQueryState) -> None:
                 }
                 self.all_results[state.query_info.query_id] = state.query_info.result
 
-            logger.debug(f"Sent {len(token_array)} tokens to LoadGen for query {state.query_info.query_id}")
+            logger.debug(
+                f"Sent {len(token_array)} tokens to LoadGen for query {state.query_info.query_id}")
 
         except Exception as e:
-            logger.error(f"Error sending final response for query {state.query_info.query_id}: {e}")
-            raise RuntimeError(f"Failed to send final response for query {state.query_info.query_id}: {e}")
+            logger.error(
+                f"Error sending final response for query {state.query_info.query_id}: {e}")
+            raise RuntimeError(
+                f"Failed to send final response for query {state.query_info.query_id}: {e}")
 
     def flush_queries(self) -> None:
         """Wait for all active streams to complete."""
@@ -313,13 +333,16 @@ async def wait_for_streams():
 
             async with self.active_streams_lock:
                 if self.active_streams:
-                    logger.warning(f"Timeout: {len(self.active_streams)} streams still active")
+                    logger.warning(
+                        f"Timeout: {len(self.active_streams)} streams still active")
 
         # Run the wait task in the event loop
         if self.loop and not self.loop.is_closed():
-            future = asyncio.run_coroutine_threadsafe(wait_for_streams(), self.loop)
+            future = asyncio.run_coroutine_threadsafe(
+                wait_for_streams(), self.loop)
             try:
-                future.result(timeout=310)  # Slightly longer than internal timeout
+                # Slightly longer than internal timeout
+                future.result(timeout=310)
             except Exception as e:
                 logger.error(f"Error waiting for streams to complete: {e}")
 
@@ -352,7 +375,8 @@ async def cancel_all_tasks():
                     tasks_to_cancel = list(self.active_tasks)
 
                 if tasks_to_cancel:
-                    logger.info(f"Cancelling {len(tasks_to_cancel)} active streaming tasks...")
+                    logger.info(
+                        f"Cancelling {len(tasks_to_cancel)} active streaming tasks...")
                     for task in tasks_to_cancel:
                         task.cancel()
 
@@ -365,7 +389,8 @@ async def cancel_all_tasks():
                     self.active_tasks.clear()
 
             # Run the cancellation in the event loop
-            future = asyncio.run_coroutine_threadsafe(cancel_all_tasks(), self.loop)
+            future = asyncio.run_coroutine_threadsafe(
+                cancel_all_tasks(), self.loop)
             try:
                 future.result(timeout=10.0)  # Give tasks time to cancel
             except Exception as e:
@@ -405,10 +430,12 @@ def get_results(self) -> List[Dict[str, Any]]:
         # Only process results for samples that were actually queried
         # Sort by index to maintain dataset order
         queried_indices = sorted(index_to_result.keys())
-        
-        logger.info(f"Retrieving results for {len(queried_indices)} queried samples")
 
-        # Process results in order of dataset indices using stored backend results
+        logger.info(
+            f"Retrieving results for {len(queried_indices)} queried samples")
+
+        # Process results in order of dataset indices using stored backend
+        # results
         for i in queried_indices:
             result = index_to_result[i]
 
@@ -416,7 +443,8 @@ def get_results(self) -> List[Dict[str, Any]]:
             tokens = result['tokens']
             output_text = result.get('text', '')
             if not output_text and self.backend.tokenizer:
-                output_text = self.backend.tokenizer.decode(result['tokens'], skip_special_tokens=True)
+                output_text = self.backend.tokenizer.decode(
+                    result['tokens'], skip_special_tokens=True)
 
             ordered_results.append({
                 'model_output': output_text,
@@ -424,4 +452,4 @@ def get_results(self) -> List[Dict[str, Any]]:
                 'tok_model_output_len': len(tokens)
             })
 
-        return ordered_results
\ No newline at end of file
+        return ordered_results
diff --git a/language/deepseek-r1/mlperf/utils.py b/language/deepseek-r1/mlperf/utils.py
index 973e46c201..f4fbedda41 100644
--- a/language/deepseek-r1/mlperf/utils.py
+++ b/language/deepseek-r1/mlperf/utils.py
@@ -7,15 +7,15 @@
 from utils.tokenization import StandardTokenizer
 
 
-def prepare_mlperf_dataset(input_file: str, 
-                          backend_name: Optional[str] = None,
-                          tokenizer: StandardTokenizer = None,
-                          num_samples: Optional[int] = None,
-                          skip_samples: int = 0,
-                          use_chat_template: Optional[bool] = None) -> Dict[str, Any]:
+def prepare_mlperf_dataset(input_file: str,
+                           backend_name: Optional[str] = None,
+                           tokenizer: StandardTokenizer = None,
+                           num_samples: Optional[int] = None,
+                           skip_samples: int = 0,
+                           use_chat_template: Optional[bool] = None) -> Dict[str, Any]:
     """
     Prepare dataset for MLPerf inference.
-    
+
     Args:
         input_file: Path to input pickle file
         backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
@@ -24,29 +24,30 @@ def prepare_mlperf_dataset(input_file: str,
         num_samples: Number of samples to use
         skip_samples: Number of samples to skip
         use_chat_template: Whether to use chat template (if None, determined by registry)
-        
+
     Returns:
         Dictionary with prepared dataset components
     """
     if backend_name is None:
         from utils.backend_registry import detect_backend
         backend_name = detect_backend()
-    
+
     # Load and validate dataset
     df = load_dataset(input_file, num_samples, skip_samples)
     validate_dataset(df)
-    
+
     prompts = df['text_input'].tolist()
     print(f"[MLPerf] Loaded {len(prompts)} prompts from dataset")
-    
+
     # Check if backend uses text prompts from registry
     uses_text_prompts = uses_text_input()
-    
+
     # Determine chat template usage from registry if not specified
     if use_chat_template is None:
         use_chat_template = uses_chat_template()
-        print(f"[MLPerf] Using chat template from registry: {use_chat_template}")
-    
+        print(
+            f"[MLPerf] Using chat template from registry: {use_chat_template}")
+
     if uses_text_prompts:
         print(f"[MLPerf] Backend {backend_name} uses text prompts directly")
         return {
@@ -62,7 +63,7 @@ def prepare_mlperf_dataset(input_file: str,
             prompts, use_chat_template
         )
         print(f"[MLPerf] Tokenized {len(tokenized_prompts)} prompts")
-        
+
         return {
             'dataframe': df,
             'prompts': prompts,
@@ -73,61 +74,63 @@ def prepare_mlperf_dataset(input_file: str,
 
 
 def process_mlperf_results(sut_results: List[Dict[str, Any]],
-                          tokenizer: Optional[StandardTokenizer] = None,
-                          backend_name: Optional[str] = None,
-                          uses_text_prompts: Optional[bool] = None) -> List[Dict[str, Any]]:
+                           tokenizer: Optional[StandardTokenizer] = None,
+                           backend_name: Optional[str] = None,
+                           uses_text_prompts: Optional[bool] = None) -> List[Dict[str, Any]]:
     """
     Process MLPerf SUT results into standardized format.
-    
+
     Args:
         sut_results: Raw results from MLPerf SUT
         tokenizer: StandardTokenizer for decoding
         backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
                       (Kept for backward compatibility but not used in our codebase)
         uses_text_prompts: Whether backend uses text prompts (if None, determined by registry)
-        
+
     Returns:
         List of processed result dictionaries
     """
     from utils.tokenization import process_inference_results
-    
+
     if backend_name is None:
         from utils.backend_registry import detect_backend
         backend_name = detect_backend()
-    
+
     # Determine text prompt usage from registry if not specified
     if uses_text_prompts is None:
         uses_text_prompts = uses_text_input()
-    
+
     # Reuse the general inference result processing
-    return process_inference_results(sut_results, tokenizer, uses_text_prompts=uses_text_prompts)
+    return process_inference_results(
+        sut_results, tokenizer, uses_text_prompts=uses_text_prompts)
 
 
 def create_mlperf_output_dataframe(input_df: pd.DataFrame,
-                                  results: List[Dict[str, Any]],
-                                  backend_name: Optional[str] = None) -> pd.DataFrame:
+                                   results: List[Dict[str, Any]],
+                                   backend_name: Optional[str] = None) -> pd.DataFrame:
     """
     Create output dataframe with MLPerf results.
-    
+
     Args:
         input_df: Input dataframe
         results: Processed MLPerf results
         backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
                       (Kept for backward compatibility but not used in our codebase)
-        
+
     Returns:
         Output dataframe with results
     """
     if backend_name is None:
         from utils.backend_registry import detect_backend
         backend_name = detect_backend()
-    
+
     df_output = input_df.copy()
-    
+
     # Add result columns
     df_output['model_output'] = [r['model_output'] for r in results]
     df_output['tok_model_output'] = [r['tok_model_output'] for r in results]
-    df_output['tok_model_output_len'] = [r['tok_model_output_len'] for r in results]
+    df_output['tok_model_output_len'] = [
+        r['tok_model_output_len'] for r in results]
     df_output['model_backend'] = backend_name
-    
-    return df_output 
\ No newline at end of file
+
+    return df_output
diff --git a/language/deepseek-r1/run_eval.py b/language/deepseek-r1/run_eval.py
index 169b3473e4..8965101bd4 100755
--- a/language/deepseek-r1/run_eval.py
+++ b/language/deepseek-r1/run_eval.py
@@ -1,4 +1,13 @@
 #!/usr/bin/env python3
+from utils import (
+    load_dataset, save_results, validate_dataset, generate_timestamped_filename,
+    validate_runner_for_backend, uses_text_input, uses_chat_template,
+    StandardTokenizer, process_inference_results,
+    get_backend_instance, create_base_argument_parser, print_runner_header,
+    setup_output_paths, validate_runner_args, handle_runner_error,
+    validate_dataset_extended, supports_async
+)
+from backends import BaseBackend
 import argparse
 import asyncio
 import os
@@ -11,59 +20,51 @@
 # Disable tokenizers parallelism to avoid forking issues
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
-from backends import BaseBackend
-from utils import (
-    load_dataset, save_results, validate_dataset, generate_timestamped_filename,
-    validate_runner_for_backend, uses_text_input, uses_chat_template,
-    StandardTokenizer, process_inference_results,
-    get_backend_instance, create_base_argument_parser, print_runner_header,
-    setup_output_paths, validate_runner_args, handle_runner_error,
-    validate_dataset_extended, supports_async
-)
-
 
 def create_argument_parser() -> argparse.ArgumentParser:
     """Create argument parser with shared arguments only."""
     parser = create_base_argument_parser(
         "Modular backend evaluation system for MLPerf DeepSeek reference implementation"
     )
-    
+
     # Add runner-specific arguments
     parser.add_argument("--async", action="store_true",
-                       help="Use async generation instead of synchronous")
-    
+                        help="Use async generation instead of synchronous")
+
     return parser
 
 
-async def run_async_inference(backend: BaseBackend, 
-                            tokenized_prompts: List[List[int]],
-                            text_prompts: Optional[List[str]] = None) -> List[Dict[str, Any]]:
+async def run_async_inference(backend: BaseBackend,
+                              tokenized_prompts: List[List[int]],
+                              text_prompts: Optional[List[str]] = None) -> List[Dict[str, Any]]:
     """Run async inference with proper error handling and progress bar that updates as tasks complete."""
     try:
         # Get futures from backend
         if uses_text_input():
             futures = backend.generate_async(text_prompts=text_prompts)
         else:
-            futures = backend.generate_async(tokenized_prompts=tokenized_prompts)
-        
+            futures = backend.generate_async(
+                tokenized_prompts=tokenized_prompts)
+
         # Create a list to store results in order
         results = [None] * len(futures)
-        
+
         # Create enumerated futures with their original indices for tracking
         indexed_futures = [(i, future) for i, future in enumerate(futures)]
-        
+
         # Track completion for debugging
         completed_indices = set()
-        
+
         # Process tasks with progress bar that updates as tasks complete
         with async_tqdm(total=len(futures), desc="Async inference", unit="prompt") as pbar:
-            # Use asyncio.wait with FIRST_COMPLETED to handle out-of-order completion
+            # Use asyncio.wait with FIRST_COMPLETED to handle out-of-order
+            # completion
             pending = {future for _, future in indexed_futures}
-            
+
             while pending:
                 # Wait for at least one future to complete
                 done, pending = await asyncio.wait(pending, return_when=asyncio.FIRST_COMPLETED)
-                
+
                 # Process all completed futures in this batch
                 for completed_future in done:
                     # Find the original index for this completed future
@@ -72,46 +73,51 @@ async def run_async_inference(backend: BaseBackend,
                         if future is completed_future:
                             original_idx = idx
                             break
-                    
+
                     if original_idx is None:
-                        print(f"\nWarning: Could not find original index for completed future")
+                        print(
+                            f"\nWarning: Could not find original index for completed future")
                         continue
-                    
+
                     # Check for duplicate completion
                     if original_idx in completed_indices:
-                        print(f"\nWarning: Prompt {original_idx} completed multiple times!")
+                        print(
+                            f"\nWarning: Prompt {original_idx} completed multiple times!")
                         continue
-                    
+
                     try:
                         # Get the result from the completed future
                         result = await completed_future
-                        
+
                         # Store the result in the correct position
                         results[original_idx] = result
                         completed_indices.add(original_idx)
-                        
+
                     except Exception as e:
-                        print(f"\nError processing prompt {original_idx}: {type(e).__name__}: {e}")
+                        print(
+                            f"\nError processing prompt {original_idx}: {type(e).__name__}: {e}")
                         import traceback
                         traceback.print_exception(type(e), e, e.__traceback__)
-                        
+
                         # Raise the error instead of using empty tokens
-                        raise RuntimeError(f"Backend failed to generate tokens for prompt {original_idx}: {e}")
-                    
+                        raise RuntimeError(
+                            f"Backend failed to generate tokens for prompt {original_idx}: {e}")
+
                     # Update progress bar after each completion
                     pbar.update(1)
-        
+
         # Verify all results are populated
         if len(completed_indices) != len(futures):
             missing_count = len(futures) - len(completed_indices)
-            raise RuntimeError(f"Missing results: completed {len(completed_indices)} != {len(futures)} total ({missing_count} missing)")
-        
+            raise RuntimeError(
+                f"Missing results: completed {len(completed_indices)} != {len(futures)} total ({missing_count} missing)")
+
         for i, result in enumerate(results):
             if result is None:
                 raise RuntimeError(f"Missing result for prompt {i}")
-        
+
         print(f"\nCompleted all {len(completed_indices)} prompts successfully")
-        
+
         return results
     except Exception as e:
         print(f"Error during async inference: {type(e).__name__}: {e}")
@@ -120,9 +126,9 @@ async def run_async_inference(backend: BaseBackend,
         raise
 
 
-def run_sync_inference(backend: BaseBackend, 
-                      tokenized_prompts: List[List[int]],
-                      text_prompts: Optional[List[str]] = None) -> List[Dict[str, Any]]:
+def run_sync_inference(backend: BaseBackend,
+                       tokenized_prompts: List[List[int]],
+                       text_prompts: Optional[List[str]] = None) -> List[Dict[str, Any]]:
     """Run sync inference with proper error handling."""
     try:
         if uses_text_input():
@@ -140,46 +146,52 @@ def main():
     # Parse arguments
     parser = create_argument_parser()
     args = parser.parse_args()
-    
+
     try:
         # Validate arguments
         validate_runner_args(args, 'eval')
-        
+
         # Detect backend early
         backend_name = validate_runner_for_backend('eval')
-        
+
         # Set up output paths
         output_dir, output_file = setup_output_paths(args)
         if args.output_file is None:
             args.output_file = output_file
-        
-        # Generate the actual filename with timestamp that will be used for saving
-        actual_output_file = generate_timestamped_filename(args.output_file, add_timestamp=True)
-        
+
+        # Generate the actual filename with timestamp that will be used for
+        # saving
+        actual_output_file = generate_timestamped_filename(
+            args.output_file, add_timestamp=True)
+
         # Get async flag using getattr since 'async' is a reserved keyword
         use_async = getattr(args, 'async', False)
-        
+
         # Check if backend supports async
         if use_async and not supports_async():
-            raise RuntimeError(f"Backend {backend_name} does not support async generation")
-        
+            raise RuntimeError(
+                f"Backend {backend_name} does not support async generation")
+
         # Print header
-        print_runner_header("Modular Backend Evaluation System", backend_name, args)
+        print_runner_header(
+            "Modular Backend Evaluation System",
+            backend_name,
+            args)
         print(f"Mode: {'Async' if use_async else 'Sync'}")
         print("=" * 80)
-        
+
         # Load and validate dataset
         df = load_dataset(args.input_file, args.num_samples, args.skip_samples)
         validate_dataset_extended(df)
-        
+
         prompts = df['text_input'].tolist()
-        
+
         # Initialize tokenizer
         tokenizer = StandardTokenizer()
-        
+
         # Determine whether to use chat template based on registry
         use_chat_template = uses_chat_template()
-        
+
         # For text-prompt backends, we'll pass the prompts directly
         # For tokenized-prompt backends, we need to tokenize first
         if uses_text_input():
@@ -195,19 +207,19 @@ def main():
             )
             print(f"Tokenized {len(tokenized_prompts)} prompts")
             print(f"Tokenizer Max length: {tokenizer.max_length}")
-        
+
         # Initialize backend using registry
         print(f"\nInitializing {backend_name.upper()} backend...")
         backend = get_backend_instance(backend_name)
-        
+
         with backend:
             # Create new output dataframe with only required columns
             df_output = pd.DataFrame()
-            
+
             # Copy all columns from input dataframe first
             for col in df.columns:
                 df_output[col] = df[col]
-            
+
             # Run inference with appropriate prompt format
             if use_async:
                 print("Running async inference...")
@@ -217,26 +229,31 @@ def main():
                 print("Running sync inference...")
                 raw_results = run_sync_inference(
                     backend, tokenized_prompts, text_prompts=prompts)
-            
+
             # Process raw results into standardized format using shared utility
             print("Processing results...")
             standardized_results = process_inference_results(
                 raw_results, tokenizer
             )
-            
+
             # Add generated columns
-            df_output['model_output'] = [r['model_output'] for r in standardized_results]
-            df_output['tok_model_output'] = [r['tok_model_output'] for r in standardized_results]
-            df_output['tok_model_output_len'] = [r['tok_model_output_len'] for r in standardized_results]
-            df_output['model_backend'] = [r['model_backend'] for r in standardized_results]
-            
+            df_output['model_output'] = [r['model_output']
+                                         for r in standardized_results]
+            df_output['tok_model_output'] = [r['tok_model_output']
+                                             for r in standardized_results]
+            df_output['tok_model_output_len'] = [
+                r['tok_model_output_len'] for r in standardized_results]
+            df_output['model_backend'] = [r['model_backend']
+                                          for r in standardized_results]
+
             # Save results
-            output_file = save_results(df_output, args.output_file, add_timestamp=True)
-            
+            output_file = save_results(
+                df_output, args.output_file, add_timestamp=True)
+
             print(f"\nEvaluation completed successfully!")
             print(f"Results saved to: {output_file}")
             print(f"Output columns: {list(df_output.columns)}")
-            
+
     except KeyboardInterrupt:
         print("\nEvaluation interrupted by user")
         sys.exit(1)
@@ -245,4 +262,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main() 
\ No newline at end of file
+    main()
diff --git a/language/deepseek-r1/run_eval_mpi.py b/language/deepseek-r1/run_eval_mpi.py
index 37425526e1..4edeae5f8f 100644
--- a/language/deepseek-r1/run_eval_mpi.py
+++ b/language/deepseek-r1/run_eval_mpi.py
@@ -1,4 +1,11 @@
 #!/usr/bin/env python3
+from backends import BaseBackend
+from utils.data_utils import load_dataset
+from utils.validation import validate_runner_args, ValidationError
+from utils.runner_utils import create_base_argument_parser, print_runner_header
+from utils.backend_registry import uses_chat_template, get_backend_instance, detect_backend, validate_runner_for_backend
+from utils import save_results, generate_timestamped_filename, StandardTokenizer
+from backends.pytorch_backend import PyTorchBackend
 import os
 import sys
 import argparse
@@ -11,13 +18,6 @@
 
 # Import utilities and backend registry
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from backends.pytorch_backend import PyTorchBackend
-from utils import save_results, generate_timestamped_filename, StandardTokenizer
-from utils.backend_registry import uses_chat_template, get_backend_instance, detect_backend, validate_runner_for_backend
-from utils.runner_utils import create_base_argument_parser, print_runner_header
-from utils.validation import validate_runner_args, ValidationError
-from utils.data_utils import load_dataset
-from backends import BaseBackend
 
 
 def main(
@@ -41,7 +41,7 @@ def main(
 
     # Detect backend from environment
     backend_name = detect_backend()
-    
+
     # Validate backend
     validate_runner_for_backend('eval_mpi')
 
@@ -49,7 +49,8 @@ def main(
     use_chat_template = uses_chat_template()
 
     # Generate the actual filename with timestamp that will be used for saving
-    actual_output_file = generate_timestamped_filename(output_pickle_path, add_timestamp=True)
+    actual_output_file = generate_timestamped_filename(
+        output_pickle_path, add_timestamp=True)
 
     if rank == 0:
         _print("=" * 80)
@@ -63,13 +64,14 @@ def main(
             _print(f"Sample limit: {num_samples}")
         if skip_samples:
             _print(f"Skip samples: {skip_samples}")
-        _print(f"Chat template: {'enabled' if use_chat_template else 'disabled'} (from registry)")
+        _print(
+            f"Chat template: {'enabled' if use_chat_template else 'disabled'} (from registry)")
         _print("=" * 80)
 
     # Initialize PyTorch backend
     backend = PyTorchBackend()
     backend.initialize()
-    
+
     # Initialize StandardTokenizer
     tokenizer = StandardTokenizer()
 
@@ -82,12 +84,14 @@ def main(
         _print(f"Loading input DataFrame from {input_pickle_path}...")
         try:
             df_for_results = pd.read_pickle(input_pickle_path)
-            _print(f"Loaded DataFrame with {len(df_for_results)} rows and columns: {df_for_results.columns.tolist()}")
-            
+            _print(
+                f"Loaded DataFrame with {len(df_for_results)} rows and columns: {df_for_results.columns.tolist()}")
+
             # Apply skip_samples if specified
             if skip_samples > 0:
                 if skip_samples >= len(df_for_results):
-                    _print(f"Error: skip_samples ({skip_samples}) is greater than or equal to total samples ({len(df_for_results)})")
+                    _print(
+                        f"Error: skip_samples ({skip_samples}) is greater than or equal to total samples ({len(df_for_results)})")
                     backend.shutdown()
                     if world_size > 1:
                         dist.destroy_process_group()
@@ -96,14 +100,15 @@ def main(
                 df_for_results = df_for_results.iloc[skip_samples:].copy()
                 # Reset index to ensure sequential indices starting from 0
                 df_for_results = df_for_results.reset_index(drop=True)
-            
+
             # Apply num_samples limit if specified
             if num_samples is not None and num_samples < len(df_for_results):
-                _print(f"Limiting to first {num_samples} samples (out of {len(df_for_results)} total after skipping)")
+                _print(
+                    f"Limiting to first {num_samples} samples (out of {len(df_for_results)} total after skipping)")
                 df_for_results = df_for_results.head(num_samples).copy()
                 # Reset index to ensure sequential indices starting from 0
                 df_for_results = df_for_results.reset_index(drop=True)
-                
+
         except Exception as e:
             _print(f"Error loading input pickle file: {e}")
             backend.shutdown()
@@ -119,21 +124,25 @@ def main(
             return
 
         prompts_text_list = df_for_results['text_input'].tolist()
-        _print(f"Extracted {len(prompts_text_list)} prompts from 'text_input' column.")
+        _print(
+            f"Extracted {len(prompts_text_list)} prompts from 'text_input' column.")
 
         # Pre-initialize output columns
         df_for_results['model_output'] = ""
         df_for_results['tok_model_output'] = None
-        df_for_results['tok_model_output'] = df_for_results['tok_model_output'].astype('object')
+        df_for_results['tok_model_output'] = df_for_results['tok_model_output'].astype(
+            'object')
         df_for_results['tok_model_output_len'] = 0
         df_for_results['model_backend'] = backend_name
 
     # Broadcast the number of prompts to all ranks
     if world_size > 1:
         if rank == 0:
-            num_prompts_tensor = torch.tensor(len(prompts_text_list), dtype=torch.long, device="cuda")
+            num_prompts_tensor = torch.tensor(
+                len(prompts_text_list), dtype=torch.long, device="cuda")
         else:
-            num_prompts_tensor = torch.empty(1, dtype=torch.long, device="cuda")
+            num_prompts_tensor = torch.empty(
+                1, dtype=torch.long, device="cuda")
         dist.broadcast(num_prompts_tensor, src=0)
         num_total_prompts = num_prompts_tensor.item()
     else:
@@ -148,13 +157,14 @@ def main(
         current_batch_prompt_tokens = None
 
         if rank == 0:
-            current_batch_prompt_texts = prompts_text_list[i:i+batch_size]
+            current_batch_prompt_texts = prompts_text_list[i:i + batch_size]
             # Tokenize on rank 0 using StandardTokenizer
             current_batch_prompt_tokens, _ = tokenizer.tokenize_prompts(
                 current_batch_prompt_texts, use_chat_template
             )
-            
-            _print(f"Processing batch {current_batch_num}, size {len(current_batch_prompt_tokens)}")
+
+            _print(
+                f"Processing batch {current_batch_num}, size {len(current_batch_prompt_tokens)}")
 
         # All ranks call generate_batch_distributed
         generated_tokens_for_batch = backend.generate_batch_distributed(
@@ -164,12 +174,14 @@ def main(
         if rank == 0:
             # Validate that we received valid tokens
             if not generated_tokens_for_batch:
-                raise RuntimeError(f"Backend returned empty tokens for batch {current_batch_num}")
-            
+                raise RuntimeError(
+                    f"Backend returned empty tokens for batch {current_batch_num}")
+
             for batch_idx, tokens in enumerate(generated_tokens_for_batch):
                 if not isinstance(tokens, (list, tuple)) or len(tokens) == 0:
-                    raise RuntimeError(f"Backend returned empty or invalid tokens for batch {current_batch_num}, item {batch_idx}: {tokens}")
-            
+                    raise RuntimeError(
+                        f"Backend returned empty or invalid tokens for batch {current_batch_num}, item {batch_idx}: {tokens}")
+
             # Decode tokens to text using StandardTokenizer
             decoded_texts_for_batch = tokenizer.batch_decode(
                 generated_tokens_for_batch
@@ -183,23 +195,36 @@ def main(
                 original_df_idx = start_index_in_df + batch_idx
                 if original_df_idx < len(df_for_results):
                     # Use at for assignments with list values
-                    df_for_results.at[original_df_idx, 'model_output'] = decoded_texts_for_batch[batch_idx]
-                    df_for_results.at[original_df_idx, 'tok_model_output'] = generated_tokens_for_batch[batch_idx]
-                    df_for_results.at[original_df_idx, 'tok_model_output_len'] = len(generated_tokens_for_batch[batch_idx])
+                    df_for_results.at[original_df_idx,
+                                      'model_output'] = decoded_texts_for_batch[batch_idx]
+                    df_for_results.at[original_df_idx,
+                                      'tok_model_output'] = generated_tokens_for_batch[batch_idx]
+                    df_for_results.at[original_df_idx, 'tok_model_output_len'] = len(
+                        generated_tokens_for_batch[batch_idx])
 
             _print(f"Batch {current_batch_num} completed.")
 
     if rank == 0 and df_for_results is not None:
         _print(f"All batches processed. Saving results...")
-        
+
         # Keep only required columns in the same order as run_eval.py
-        output_columns = ['text_input', 'ground_truth', 'question', 'dataset', 'model_output', 'tok_model_output', 'tok_model_output_len', 'model_backend']
+        output_columns = [
+            'text_input',
+            'ground_truth',
+            'question',
+            'dataset',
+            'model_output',
+            'tok_model_output',
+            'tok_model_output_len',
+            'model_backend']
         # Filter to only columns that exist
-        output_columns = [col for col in output_columns if col in df_for_results.columns]
+        output_columns = [
+            col for col in output_columns if col in df_for_results.columns]
         df_output = df_for_results[output_columns]
-        
+
         try:
-            saved_file = save_results(df_output, output_pickle_path, add_timestamp=True)
+            saved_file = save_results(
+                df_output, output_pickle_path, add_timestamp=True)
             _print(f"Successfully saved results to {saved_file}")
         except Exception as e:
             _print(f"Error saving output pickle file: {e}")
@@ -234,4 +259,4 @@ def main(
         args.output_file,
         args.num_samples,
         args.skip_samples,
-    ) 
\ No newline at end of file
+    )
diff --git a/language/deepseek-r1/run_mlperf.py b/language/deepseek-r1/run_mlperf.py
index 7f484e725e..2345cf5b9b 100755
--- a/language/deepseek-r1/run_mlperf.py
+++ b/language/deepseek-r1/run_mlperf.py
@@ -1,4 +1,23 @@
 #!/usr/bin/env python3
+from eval_accuracy import process_dataframe, print_evaluation_results, process_and_save_dataframe, process_mlperf_log_accuracy
+from utils import (
+    validate_runner_for_backend, uses_text_input, uses_chat_template,
+    load_dataset, save_results, print_runner_header, StandardTokenizer,
+    get_backend_instance, create_base_argument_parser,
+    setup_output_paths, validate_runner_args, handle_runner_error,
+    validate_dataset_extended, generate_timestamped_filename
+)
+from mlperf import (
+    OfflineSUT, ServerSUT, BaseSUT,
+    QuerySampleLibrary,
+    prepare_mlperf_dataset,
+    process_mlperf_results,
+    create_mlperf_output_dataframe
+)
+from backends import BaseBackend
+import pandas as pd
+import numpy as np
+import mlperf_loadgen as lg
 import argparse
 import json
 import logging
@@ -10,26 +29,6 @@
 # Disable tokenizers parallelism to avoid forking issues
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
-import mlperf_loadgen as lg
-import numpy as np
-import pandas as pd
-
-from backends import BaseBackend
-from mlperf import (
-    OfflineSUT, ServerSUT, BaseSUT,
-    QuerySampleLibrary,
-    prepare_mlperf_dataset,
-    process_mlperf_results,
-    create_mlperf_output_dataframe
-)
-from utils import (
-    validate_runner_for_backend, uses_text_input, uses_chat_template,
-    load_dataset, save_results, print_runner_header, StandardTokenizer,
-    get_backend_instance, create_base_argument_parser,
-    setup_output_paths, validate_runner_args, handle_runner_error,
-    validate_dataset_extended, generate_timestamped_filename
-)
-from eval_accuracy import process_dataframe, print_evaluation_results, process_and_save_dataframe, process_mlperf_log_accuracy
 
 # Configure logging
 logging.basicConfig(
@@ -47,39 +46,39 @@ def create_argument_parser() -> argparse.ArgumentParser:
 
     # Scenario selection (no backend argument, auto-detected)
     parser.add_argument("--mode", type=str, default="offline",
-                       choices=["offline", "server"],
-                       help="MLPerf scenario mode")
+                        choices=["offline", "server"],
+                        help="MLPerf scenario mode")
 
     # MLPerf configuration
     parser.add_argument("--mlperf-conf", type=str, default="/inference/mlperf.conf",
-                       help="Path to MLPerf configuration file")
+                        help="Path to MLPerf configuration file")
 
     parser.add_argument("--user-conf", type=str, default="mlperf/user.conf",
-                       help="Path to user configuration file")
+                        help="Path to user configuration file")
 
     parser.add_argument("--scenario", type=str, default=None,
-                       choices=["Offline", "Server"],
-                       help="MLPerf scenario (overrides --mode)")
+                        choices=["Offline", "Server"],
+                        help="MLPerf scenario (overrides --mode)")
 
     parser.add_argument("--accuracy", action="store_true",
-                       help="Run accuracy mode instead of performance")
+                        help="Run accuracy mode instead of performance")
 
     # Output configuration
     parser.add_argument("--output-dir", type=str, default="mlperf_results",
-                       help="Directory for MLPerf output logs")
+                        help="Directory for MLPerf output logs")
 
     parser.add_argument("--log-dir", type=str, default=None,
-                       help="Directory for detailed logs")
+                        help="Directory for detailed logs")
 
     return parser
 
 
 def configure_loadgen(scenario: str,
-                     accuracy_mode: bool,
-                     mlperf_conf: Optional[str] = None,
-                     user_conf: Optional[str] = None,
-                     log_dir: Optional[str] = None,
-                     model_name: str = "deepseek-r1") -> lg.TestSettings:
+                      accuracy_mode: bool,
+                      mlperf_conf: Optional[str] = None,
+                      user_conf: Optional[str] = None,
+                      log_dir: Optional[str] = None,
+                      model_name: str = "deepseek-r1") -> lg.TestSettings:
     """Configure LoadGen test settings.
 
     Args:
@@ -119,9 +118,9 @@ def configure_loadgen(scenario: str,
 
 
 def run_loadgen_test(sut: Union[OfflineSUT, ServerSUT],
-                    qsl: QuerySampleLibrary,
-                    settings: lg.TestSettings,
-                    log_settings: lg.LogSettings) -> None:
+                     qsl: QuerySampleLibrary,
+                     settings: lg.TestSettings,
+                     log_settings: lg.LogSettings) -> None:
     """Run LoadGen test.
 
     Args:
@@ -162,7 +161,8 @@ def main():
         if args.log_dir:
             log_dir = Path(args.log_dir)
         else:
-            log_dir = output_dir / args.mode / ("accuracy" if args.accuracy else "performance")
+            log_dir = output_dir / args.mode / \
+                ("accuracy" if args.accuracy else "performance")
         log_dir.mkdir(parents=True, exist_ok=True)
 
         # Set up output paths with mode information
@@ -170,17 +170,21 @@ def main():
         if args.output_file is None:
             # Create output file path in the log directory
             mode_str = "accuracy" if args.accuracy else "performance"
-            output_file_base = str(log_dir / f"{backend_name}_mlperf_{args.mode}_{mode_str}_output.pkl")
+            output_file_base = str(
+                log_dir / f"{backend_name}_mlperf_{args.mode}_{mode_str}_output.pkl")
         else:
             output_file_base = args.output_file
 
-        # Generate the actual filename with timestamp that will be used for saving
-        actual_output_file = generate_timestamped_filename(output_file_base, add_timestamp=True)
+        # Generate the actual filename with timestamp that will be used for
+        # saving
+        actual_output_file = generate_timestamped_filename(
+            output_file_base, add_timestamp=True)
 
         # Ensure the parent directory of the output file exists
         output_file_parent = Path(actual_output_file).parent
         output_file_parent.mkdir(parents=True, exist_ok=True)
-        logger.info(f"Ensured output file directory exists: {output_file_parent}")
+        logger.info(
+            f"Ensured output file directory exists: {output_file_parent}")
 
         logger.info("=" * 80)
         logger.info("MLPerf Inference Benchmark Runner (Async Pattern)")
@@ -220,13 +224,14 @@ def main():
         # For backends that use text prompts, we pass the processed strings
         # For tokenized backends, we pass the tokenized prompts
         if uses_text_prompts:
-            logger.info(f"Backend {backend_name} will use text prompts directly")
+            logger.info(
+                f"Backend {backend_name} will use text prompts directly")
             dataset_for_sut = tokenized_prompts
             strings_for_sut = processed_strings
         else:
             logger.info(f"Backend {backend_name} will use tokenized prompts")
             dataset_for_sut = tokenized_prompts
-            strings_for_sut = processed_strings # This is what gets used for generation now
+            strings_for_sut = processed_strings  # This is what gets used for generation now
 
         # Create backend using registry
         logger.info(f"Initializing {backend_name} backend...")
@@ -315,7 +320,8 @@ def main():
             try:
                 # Get results from SUT - must have valid results
                 if not sut_results:
-                    raise RuntimeError("No results available from SUT - backend failed to generate tokens")
+                    raise RuntimeError(
+                        "No results available from SUT - backend failed to generate tokens")
 
                 # Process results using new utility
                 processed_results = process_mlperf_results(
@@ -347,16 +353,19 @@ def main():
                 mlperf_log_file = log_dir / "mlperf_log_accuracy.json"
 
                 if mlperf_log_file.exists():
-                    logger.info(f"Found MLPerf log accuracy file: {mlperf_log_file}")
+                    logger.info(
+                        f"Found MLPerf log accuracy file: {mlperf_log_file}")
                     logger.info("Using MLPerf log for accuracy evaluation...")
 
                     # Get checkpoint path from backend configuration
                     backend_config = get_backend_instance(backend_name).config
 
                     # Determine checkpoint path based on backend type
-                    if hasattr(get_backend_instance(backend_name), 'model_path'):
+                    if hasattr(get_backend_instance(
+                            backend_name), 'model_path'):
                         # PyTorch backend has model_path
-                        checkpoint_path = str(get_backend_instance(backend_name).model_path)
+                        checkpoint_path = str(
+                            get_backend_instance(backend_name).model_path)
                     elif 'model' in backend_config:
                         # Other backends use model name directly
                         checkpoint_path = backend_config['model']
@@ -376,10 +385,13 @@ def main():
                         base_filename="mlperf_accuracy_evaluated.pkl"
                     )
 
-                    logger.info(f"MLPerf accuracy evaluation saved to: {evaluated_file}")
+                    logger.info(
+                        f"MLPerf accuracy evaluation saved to: {evaluated_file}")
                 else:
-                    logger.info("No MLPerf log accuracy file found, using standard DataFrame evaluation...")
-                    raise RuntimeError("No MLPerf log accuracy file found, using standard DataFrame evaluation...")
+                    logger.info(
+                        "No MLPerf log accuracy file found, using standard DataFrame evaluation...")
+                    raise RuntimeError(
+                        "No MLPerf log accuracy file found, using standard DataFrame evaluation...")
 
         # Ensure clean exit
         gc.collect()
@@ -397,4 +409,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/language/deepseek-r1/run_mlperf_mpi.py b/language/deepseek-r1/run_mlperf_mpi.py
index 66196645ed..176be41710 100755
--- a/language/deepseek-r1/run_mlperf_mpi.py
+++ b/language/deepseek-r1/run_mlperf_mpi.py
@@ -1,4 +1,37 @@
 #!/usr/bin/env python3
+from eval_accuracy import process_dataframe, print_evaluation_results, process_and_save_dataframe, process_mlperf_log_accuracy
+from utils.data_utils import (
+    load_dataset, save_results,
+    generate_timestamped_filename
+)
+from utils.validation import (
+    validate_runner_args, ValidationError,
+    validate_dataset_extended
+)
+from utils.backend_registry import (
+    uses_chat_template, get_backend_instance, detect_backend,
+    validate_runner_for_backend
+)
+from utils.runner_utils import create_base_argument_parser, print_runner_header
+from utils import (
+    StandardTokenizer,
+    validate_dataset,
+    process_inference_results
+)
+from mlperf import (
+    OfflineSUT, ServerSUT, BaseSUT,
+    DistributedQuerySampleLibrary,
+    prepare_mlperf_dataset,
+    process_mlperf_results,
+    create_mlperf_output_dataframe
+)
+from backends.pytorch_backend import PyTorchBackend
+from transformers import AutoTokenizer
+import torch.distributed as dist
+import torch
+import pandas as pd
+import numpy as np
+import mlperf_loadgen as lg
 import argparse
 import json
 import logging
@@ -12,41 +45,6 @@
 # Disable tokenizers parallelism to avoid forking issues
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
-import mlperf_loadgen as lg
-import numpy as np
-import pandas as pd
-import torch
-import torch.distributed as dist
-from transformers import AutoTokenizer
-
-from backends.pytorch_backend import PyTorchBackend
-from mlperf import (
-    OfflineSUT, ServerSUT, BaseSUT,
-    DistributedQuerySampleLibrary,
-    prepare_mlperf_dataset,
-    process_mlperf_results,
-    create_mlperf_output_dataframe
-)
-from utils import (
-    StandardTokenizer,
-    validate_dataset,
-    process_inference_results
-)
-from utils.runner_utils import create_base_argument_parser, print_runner_header
-from utils.backend_registry import (
-    uses_chat_template, get_backend_instance, detect_backend,
-    validate_runner_for_backend
-)
-from utils.validation import (
-    validate_runner_args, ValidationError,
-    validate_dataset_extended
-)
-from utils.data_utils import (
-    load_dataset, save_results,
-    generate_timestamped_filename
-)
-from eval_accuracy import process_dataframe, print_evaluation_results, process_and_save_dataframe, process_mlperf_log_accuracy
-
 
 # Configure logging - only for rank 0
 def setup_logging(rank: int):
@@ -119,7 +117,7 @@ def issue_queries(self, query_samples: List[lg.QuerySample]) -> None:
         batch_size = self.backend.config['batch_size']
 
         for i in range(0, len(query_samples), batch_size):
-            batch_samples = query_samples[i:i+batch_size]
+            batch_samples = query_samples[i:i + batch_size]
 
             # Prepare batch tokens
             batch_tokens = []
@@ -141,10 +139,12 @@ def issue_queries(self, query_samples: List[lg.QuerySample]) -> None:
 
             # Generate using distributed backend
             # This will broadcast to all ranks internally
-            generated_tokens = self.backend.generate_batch_distributed(batch_tokens)
+            generated_tokens = self.backend.generate_batch_distributed(
+                batch_tokens)
 
             # Process results and send to LoadGen
-            for j, (sample_id, tokens) in enumerate(zip(batch_ids, generated_tokens)):
+            for j, (sample_id, tokens) in enumerate(
+                    zip(batch_ids, generated_tokens)):
                 # Create a copy of tokens before numpy conversion
                 tokens_copy = tokens.copy()
 
@@ -219,7 +219,8 @@ def get_results(self) -> List[Dict[str, Any]]:
                     # Decode tokens to get text output
                     output_text = ''
                     if self.backend.tokenizer:
-                        output_text = self.backend.tokenizer.decode(tokens, skip_special_tokens=True)
+                        output_text = self.backend.tokenizer.decode(
+                            tokens, skip_special_tokens=True)
 
                     ordered_results.append({
                         'model_output': output_text,
@@ -228,16 +229,16 @@ def get_results(self) -> List[Dict[str, Any]]:
                     })
                 else:
                     # Result exists but no tokens - this is an error
-                    raise RuntimeError(f"No tokens in result for dataset index {i}, sample_id {sample_id}")
+                    raise RuntimeError(
+                        f"No tokens in result for dataset index {i}, sample_id {sample_id}")
             else:
                 # No result for this index - this is an error
-                raise RuntimeError(f"No result for dataset index {i}, sample_id {sample_id}")
+                raise RuntimeError(
+                    f"No result for dataset index {i}, sample_id {sample_id}")
 
         return ordered_results
 
 
-
-
 def create_argument_parser() -> argparse.ArgumentParser:
     """Create argument parser for distributed MLPerf runner."""
     parser = argparse.ArgumentParser(
@@ -247,44 +248,45 @@ def create_argument_parser() -> argparse.ArgumentParser:
 
     # Dataset arguments
     parser.add_argument("--input-file", type=str,
-                       default="data/final_output.pkl",
-                       help="Input pickle file with prompts")
+                        default="data/final_output.pkl",
+                        help="Input pickle file with prompts")
 
     # MLPerf configuration
     parser.add_argument("--mlperf-conf", type=str, default="/inference/mlperf.conf",
-                       help="Path to MLPerf configuration file")
+                        help="Path to MLPerf configuration file")
 
     parser.add_argument("--user-conf", type=str, default="mlperf/user.conf",
-                       help="Path to user configuration file")
+                        help="Path to user configuration file")
 
     parser.add_argument("--mode", type=str, default="offline",
-                       choices=["offline", "server"],
-                       help="MLPerf scenario mode (only offline supported for distributed)")
+                        choices=["offline", "server"],
+                        help="MLPerf scenario mode (only offline supported for distributed)")
 
     parser.add_argument("--accuracy", action="store_true",
-                       help="Run accuracy mode instead of performance")
+                        help="Run accuracy mode instead of performance")
 
     # Output configuration
     parser.add_argument("--output-dir", type=str, default="mlperf_results",
-                       help="Directory for MLPerf output logs")
+                        help="Directory for MLPerf output logs")
 
     parser.add_argument("--log-dir", type=str, default=None,
-                       help="Directory for detailed logs")
+                        help="Directory for detailed logs")
 
     parser.add_argument("--output-file", type=str, default=None,
-                       help="Output pickle file path (auto-generated if not specified)")
+                        help="Output pickle file path (auto-generated if not specified)")
 
-    # Note: --no-chat-template is removed (chat template usage determined by backend registry)
+    # Note: --no-chat-template is removed (chat template usage determined by
+    # backend registry)
 
     return parser
 
 
 def configure_loadgen(scenario: str,
-                     accuracy_mode: bool,
-                     mlperf_conf: Optional[str] = None,
-                     user_conf: Optional[str] = None,
-                     log_dir: Optional[str] = None,
-                     model_name: str = "deepseek-r1") -> lg.TestSettings:
+                      accuracy_mode: bool,
+                      mlperf_conf: Optional[str] = None,
+                      user_conf: Optional[str] = None,
+                      log_dir: Optional[str] = None,
+                      model_name: str = "deepseek-r1") -> lg.TestSettings:
     """Configure LoadGen test settings.
 
     Args:
@@ -324,11 +326,11 @@ def configure_loadgen(scenario: str,
 
 
 def run_loadgen_test(sut: DistributedOfflineSUT,
-                    qsl: DistributedQuerySampleLibrary,
-                    settings: lg.TestSettings,
-                    log_settings: lg.LogSettings,
-                    rank: int,
-                    logger) -> None:
+                     qsl: DistributedQuerySampleLibrary,
+                     settings: lg.TestSettings,
+                     log_settings: lg.LogSettings,
+                     rank: int,
+                     logger) -> None:
     """Run LoadGen test (only on rank 0).
 
     Args:
@@ -386,7 +388,8 @@ def main():
     # Validate mode for distributed
     if args.mode != "offline":
         if rank == 0:
-            logger.error("Only offline mode is supported for distributed execution")
+            logger.error(
+                "Only offline mode is supported for distributed execution")
         sys.exit(1)
 
     # Create output directories (only rank 0)
@@ -397,7 +400,8 @@ def main():
         if args.log_dir:
             log_dir = Path(args.log_dir)
         else:
-            log_dir = output_dir / args.mode / ("accuracy" if args.accuracy else "performance")
+            log_dir = output_dir / args.mode / \
+                ("accuracy" if args.accuracy else "performance")
         log_dir.mkdir(parents=True, exist_ok=True)
 
         # Determine output file path
@@ -405,15 +409,18 @@ def main():
             output_file_base = args.output_file
         else:
             mode_str = "accuracy" if args.accuracy else "performance"
-            output_file_base = str(log_dir / f"{backend_name}_mlperf_{args.mode}_{mode_str}_output.pkl")
+            output_file_base = str(
+                log_dir / f"{backend_name}_mlperf_{args.mode}_{mode_str}_output.pkl")
 
         # Generate the actual filename with timestamp
-        actual_output_file = generate_timestamped_filename(output_file_base, add_timestamp=True)
+        actual_output_file = generate_timestamped_filename(
+            output_file_base, add_timestamp=True)
 
         # Ensure the parent directory of the output file exists
         output_file_parent = Path(actual_output_file).parent
         output_file_parent.mkdir(parents=True, exist_ok=True)
-        logger.info(f"Ensured output file directory exists: {output_file_parent}")
+        logger.info(
+            f"Ensured output file directory exists: {output_file_parent}")
 
         logger.info("=" * 80)
         logger.info("MLPerf Inference Benchmark Runner (Distributed PyTorch)")
@@ -425,7 +432,8 @@ def main():
         logger.info(f"Input file: {args.input_file}")
         logger.info(f"Output directory: {output_dir}")
         logger.info(f"Output file: {actual_output_file}")
-        logger.info(f"Chat template: {'enabled' if use_chat_template else 'disabled'} (from registry)")
+        logger.info(
+            f"Chat template: {'enabled' if use_chat_template else 'disabled'} (from registry)")
         logger.info("=" * 80)
     else:
         log_dir = None
@@ -460,7 +468,8 @@ def main():
             tokenized_prompts = dataset_info['tokenized_prompts']
             processed_strings = dataset_info['processed_strings']
 
-            logger.info(f"Loaded {len(tokenized_prompts)} prompts from dataset")
+            logger.info(
+                f"Loaded {len(tokenized_prompts)} prompts from dataset")
 
         # Create SUT
         sut = DistributedOfflineSUT(
@@ -511,7 +520,8 @@ def main():
             if rank == 0:
                 # Run test (only rank 0)
                 logger.info("Running test...")
-                run_loadgen_test(sut, qsl, settings, log_settings, rank, logger)
+                run_loadgen_test(
+                    sut, qsl, settings, log_settings, rank, logger)
                 logger.info("Completed test...")
 
                 # Ensure all queries are flushed and async operations complete
@@ -524,7 +534,8 @@ def main():
                     dist.broadcast_object_list(exit_signal, src=0)
             else:
                 # Non-rank 0 processes participate in distributed generation
-                # They wait for signals from rank 0 and participate in generate_batch_distributed
+                # They wait for signals from rank 0 and participate in
+                # generate_batch_distributed
                 while True:
                     # First, check if we should exit
                     # We use a separate broadcast to signal exit
@@ -536,7 +547,8 @@ def main():
                         break
                     elif exit_check[0] == "generate":
                         # Signal to participate in generation
-                        # The actual batch tokens will be broadcast inside generate_batch_distributed
+                        # The actual batch tokens will be broadcast inside
+                        # generate_batch_distributed
                         backend.generate_batch_distributed(None)
                     # If exit_check[0] is None, continue waiting
         finally:
@@ -563,9 +575,11 @@ def main():
 
                     try:
                         # Get results from SUT (if available)
-                        logger.info("Retrieving results from distributed SUT...")
+                        logger.info(
+                            "Retrieving results from distributed SUT...")
                         sut_results = sut.get_results()
-                        logger.info(f"Retrieved {len(sut_results)} results from distributed SUT")
+                        logger.info(
+                            f"Retrieved {len(sut_results)} results from distributed SUT")
 
                         # Process results using new utility
                         processed_results = process_mlperf_results(
@@ -597,11 +611,19 @@ def main():
                         mlperf_log_file = log_dir / "mlperf_log_accuracy.json"
 
                         if mlperf_log_file.exists():
-                            logger.info(f"Found MLPerf log accuracy file: {mlperf_log_file}")
-                            logger.info("Using MLPerf log for accuracy evaluation...")
-
-                            # For PyTorch backend (only one supported in MPI), get model path
-                            checkpoint_path = str(backend.model_path) if hasattr(backend, 'model_path') else backend.config.get('model_name', 'deepseek-ai/DeepSeek-R1')
+                            logger.info(
+                                f"Found MLPerf log accuracy file: {mlperf_log_file}")
+                            logger.info(
+                                "Using MLPerf log for accuracy evaluation...")
+
+                            # For PyTorch backend (only one supported in MPI),
+                            # get model path
+                            checkpoint_path = str(
+                                backend.model_path) if hasattr(
+                                backend,
+                                'model_path') else backend.config.get(
+                                'model_name',
+                                'deepseek-ai/DeepSeek-R1')
 
                             # Process MLPerf log accuracy
                             df_evaluated, evaluated_file = process_mlperf_log_accuracy(
@@ -612,10 +634,13 @@ def main():
                                 base_filename="mlperf_accuracy_evaluated.pkl"
                             )
 
-                            logger.info(f"MLPerf accuracy evaluation saved to: {evaluated_file}")
+                            logger.info(
+                                f"MLPerf accuracy evaluation saved to: {evaluated_file}")
                         else:
-                            logger.info("No MLPerf log accuracy file found, using standard DataFrame evaluation...")
-                            raise RuntimeError("No MLPerf log accuracy file found, using standard DataFrame evaluation...")
+                            logger.info(
+                                "No MLPerf log accuracy file found, using standard DataFrame evaluation...")
+                            raise RuntimeError(
+                                "No MLPerf log accuracy file found, using standard DataFrame evaluation...")
 
     except KeyboardInterrupt:
         if rank == 0:
@@ -639,4 +664,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/language/deepseek-r1/utils/__init__.py b/language/deepseek-r1/utils/__init__.py
index ce8d10e9fd..65f575da29 100644
--- a/language/deepseek-r1/utils/__init__.py
+++ b/language/deepseek-r1/utils/__init__.py
@@ -101,4 +101,4 @@
     # Error handling
     'handle_backend_error',
     'handle_runner_error'
-]
\ No newline at end of file
+]
diff --git a/language/deepseek-r1/utils/backend_registry.py b/language/deepseek-r1/utils/backend_registry.py
index 73aee587e6..aa8d62a18e 100644
--- a/language/deepseek-r1/utils/backend_registry.py
+++ b/language/deepseek-r1/utils/backend_registry.py
@@ -173,7 +173,8 @@ def validate_backend(backend: str) -> None:
             f"Unknown backend '{backend}'. Supported backends: {', '.join(supported_backends)}")
 
 
-def _get_compatibility_error_message(backend: str, runner_type: str, compatible: List[str]) -> str:
+def _get_compatibility_error_message(
+        backend: str, runner_type: str, compatible: List[str]) -> str:
     """
     Generate error message for incompatible backend/runner combinations.
 
@@ -401,7 +402,8 @@ def get_backend_instance(backend_name: Optional[str] = None):
     return backend_class()
 
 
-def is_backend_compatible_with_runner(backend_name: Optional[str] = None, runner_type: str = None) -> bool:
+def is_backend_compatible_with_runner(
+        backend_name: Optional[str] = None, runner_type: str = None) -> bool:
     """Check if a backend is compatible with a specific runner type.
 
     Args:
@@ -441,7 +443,8 @@ def get_backend_env_vars(backend_name: Optional[str] = None) -> Dict[str, str]:
     # Get static env vars
     env_vars = BACKEND_REGISTRY[backend_name]['env_vars'].copy()
 
-    # Handle dynamic env vars (e.g., OMP_NUM_THREADS based on tensor_parallel_size)
+    # Handle dynamic env vars (e.g., OMP_NUM_THREADS based on
+    # tensor_parallel_size)
     if backend_name == 'vllm':
         config = get_backend_config(backend_name)
         env_vars['OMP_NUM_THREADS'] = str(
@@ -461,4 +464,4 @@ def apply_backend_env_vars(backend_name: Optional[str] = None) -> None:
 
     env_vars = get_backend_env_vars(backend_name)
     for key, value in env_vars.items():
-        os.environ[key] = value
\ No newline at end of file
+        os.environ[key] = value
diff --git a/language/deepseek-r1/utils/data_utils.py b/language/deepseek-r1/utils/data_utils.py
index 80acb5c8ce..0eb4cd3dcd 100644
--- a/language/deepseek-r1/utils/data_utils.py
+++ b/language/deepseek-r1/utils/data_utils.py
@@ -15,54 +15,56 @@
 from utils.validation import ValidationError, validate_dataset_extended
 
 
-def generate_timestamped_filename(output_file: str, add_timestamp: bool = True) -> str:
+def generate_timestamped_filename(
+        output_file: str, add_timestamp: bool = True) -> str:
     """
     Generate the actual filename that will be used when saving, with timestamp if requested.
-    
+
     Args:
         output_file: Base output file path
         add_timestamp: Whether to add timestamp to filename
-        
+
     Returns:
         Actual filename that will be used for saving
     """
     if not add_timestamp:
         return output_file
-        
+
     timestamp_suffix = time.strftime("%Y%m%d_%H%M%S")
     base_name, ext = os.path.splitext(output_file)
     return f"{base_name}_{timestamp_suffix}{ext}"
 
 
-def load_dataset(file_path: str, num_samples: Optional[int] = None, skip_samples: int = 0) -> pd.DataFrame:
+def load_dataset(
+        file_path: str, num_samples: Optional[int] = None, skip_samples: int = 0) -> pd.DataFrame:
     """
     Load dataset from pickle file.
-    
+
     Args:
         file_path: Path to the pickle file
         num_samples: Optional limit on number of samples to load
         skip_samples: Number of samples to skip from the beginning
-        
+
     Returns:
         Loaded DataFrame
-        
+
     Raises:
         ValidationError: If file doesn't exist or validation fails
         Exception: If file can't be loaded
     """
     if not os.path.exists(file_path):
         raise ValidationError(f"Input file not found: {file_path}")
-    
+
     print(f"Loading dataset from {file_path}...")
-    
+
     try:
         with open(file_path, "rb") as f:
             df = pd.read_pickle(f)
     except Exception as e:
         raise ValidationError(f"Failed to load dataset: {str(e)}")
-    
+
     print(f"Loaded {len(df)} samples")
-    
+
     # Skip samples if specified
     if skip_samples > 0:
         if skip_samples >= len(df):
@@ -71,31 +73,33 @@ def load_dataset(file_path: str, num_samples: Optional[int] = None, skip_samples
             )
         original_length = len(df)
         df = df.iloc[skip_samples:].reset_index(drop=True)
-        print(f"Skipped first {skip_samples} samples (from {original_length} total)")
-    
+        print(
+            f"Skipped first {skip_samples} samples (from {original_length} total)")
+
     # Limit number of samples if specified
     if num_samples is not None:
         original_length = len(df)
         df = df.head(num_samples)
-        print(f"Limited to {len(df)} samples (from {original_length} total after skipping)")
-    
+        print(
+            f"Limited to {len(df)} samples (from {original_length} total after skipping)")
+
     return df
 
 
-def save_results(df: pd.DataFrame, 
-                output_file: str, 
-                add_timestamp: bool = True) -> str:
+def save_results(df: pd.DataFrame,
+                 output_file: str,
+                 add_timestamp: bool = True) -> str:
     """
     Save results DataFrame to pickle file.
-    
+
     Args:
         df: DataFrame to save
         output_file: Output file path
         add_timestamp: Whether to add timestamp to filename
-        
+
     Returns:
         Actual output file path used
-        
+
     Raises:
         ValidationError: If save operation fails
     """
@@ -104,93 +108,99 @@ def save_results(df: pd.DataFrame,
         timestamp_suffix = time.strftime("%Y%m%d_%H%M%S")
         base_name, ext = os.path.splitext(output_file)
         output_file = f"{base_name}_{timestamp_suffix}{ext}"
-    
+
     # Ensure output directory exists
     os.makedirs(os.path.dirname(output_file), exist_ok=True)
-    
+
     print(f"Saving results to {output_file}...")
-    
+
     # Reset index before saving
     df_to_save = df.reset_index(drop=True)
-    
+
     try:
         with open(output_file, "wb") as f:
             pickle.dump(df_to_save, f)
-        print(f"Save completed: {len(df_to_save)} samples saved to {output_file}")
+        print(
+            f"Save completed: {len(df_to_save)} samples saved to {output_file}")
     except Exception as e:
         raise ValidationError(f"Failed to save results: {str(e)}")
-    
+
     return output_file
 
 
-def prepare_output_dataframe(input_df: pd.DataFrame, 
-                           backend_name: Optional[str] = None) -> pd.DataFrame:
+def prepare_output_dataframe(input_df: pd.DataFrame,
+                             backend_name: Optional[str] = None) -> pd.DataFrame:
     """
     Prepare output DataFrame by cleaning up old columns.
-    
+
     Args:
         input_df: Input DataFrame
         backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
-        
+
     Returns:
         Cleaned DataFrame ready for new results
     """
     if backend_name is None:
         from utils.backend_registry import detect_backend
         backend_name = detect_backend()
-    
+
     df_output = input_df.copy()
-    
+
     # Define columns to drop (old model outputs and unwanted columns)
     columns_to_drop = [
         # specify columns to drop here
     ]
-    
+
     # Also drop any existing backend-specific columns
-    backend_columns = [col for col in df_output.columns if col.startswith(f'{backend_name}_')]
+    backend_columns = [
+        col for col in df_output.columns if col.startswith(f'{backend_name}_')]
     columns_to_drop.extend(backend_columns)
-    
+
     # Drop columns that exist
     df_output = df_output.drop(
         columns=[col for col in columns_to_drop if col in df_output.columns]
     )
-    
+
     return df_output
 
 
-def add_standardized_columns(df: pd.DataFrame, 
-                           results: List[Dict[str, Any]],
-                           tokenized_prompts: List[List[int]] = None) -> pd.DataFrame:
+def add_standardized_columns(df: pd.DataFrame,
+                             results: List[Dict[str, Any]],
+                             tokenized_prompts: List[List[int]] = None) -> pd.DataFrame:
     """
     Add standardized output columns to DataFrame.
-    
+
     Args:
         df: Input DataFrame
         results: List of result dictionaries from backend
         tokenized_prompts: List of tokenized input prompts (deprecated, not used)
-        
+
     Returns:
         DataFrame with added standardized columns
     """
     # Add results columns with new naming convention
     df['model_output'] = [r.get('model_output', '') for r in results]
     df['tok_model_output'] = [r.get('tok_model_output', []) for r in results]
-    df['tok_model_output_len'] = [r.get('tok_model_output_len', 0) for r in results]
+    df['tok_model_output_len'] = [
+        r.get(
+            'tok_model_output_len',
+            0) for r in results]
     df['model_backend'] = [r.get('model_backend', '') for r in results]
-    
+
     return df
 
 
-def validate_dataset(df: pd.DataFrame, backend_name: Optional[str] = None) -> None:
+def validate_dataset(df: pd.DataFrame,
+                     backend_name: Optional[str] = None) -> None:
     """
     Validate that the dataset has required columns.
-    
+
     Args:
         df: DataFrame to validate
         backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
-        
+
     Raises:
         ValidationError: If required columns are missing or validation fails
     """
     # Use centralized validation function
-    validate_dataset_extended(df, backend_name) 
\ No newline at end of file
+    validate_dataset_extended(df, backend_name)
diff --git a/language/deepseek-r1/utils/error_handling.py b/language/deepseek-r1/utils/error_handling.py
index 54ca580135..6b588b9c20 100644
--- a/language/deepseek-r1/utils/error_handling.py
+++ b/language/deepseek-r1/utils/error_handling.py
@@ -5,17 +5,18 @@
 from .validation import BackendError, ValidationError
 
 
-def handle_backend_error(e: Exception, backend_name: str, operation: str) -> None:
+def handle_backend_error(e: Exception, backend_name: str,
+                         operation: str) -> None:
     """
     Standardized error handling for backend operations.
-    
+
     Args:
         e: The exception that occurred
         backend_name: Name of the backend
         operation: Description of the operation that failed
     """
     error_msg = f"\n[{backend_name.upper()}] Error during {operation}: {type(e).__name__}: {str(e)}"
-    
+
     if isinstance(e, (RuntimeError, ValueError)):
         # Known errors - just print the message
         print(error_msg)
@@ -28,7 +29,7 @@ def handle_backend_error(e: Exception, backend_name: str, operation: str) -> Non
 def handle_runner_error(e: Exception, runner_name: str) -> None:
     """
     Standardized error handling for runners.
-    
+
     Args:
         e: The exception that occurred
         runner_name: Name of the runner
@@ -45,4 +46,4 @@ def handle_runner_error(e: Exception, runner_name: str) -> None:
     else:
         print(f"\n{runner_name} failed: {e}")
         traceback.print_exc()
-        sys.exit(1) 
\ No newline at end of file
+        sys.exit(1)
diff --git a/language/deepseek-r1/utils/runner_utils.py b/language/deepseek-r1/utils/runner_utils.py
index 8c90deb515..fc2d4ad7f2 100644
--- a/language/deepseek-r1/utils/runner_utils.py
+++ b/language/deepseek-r1/utils/runner_utils.py
@@ -12,29 +12,31 @@ def create_base_argument_parser(description: str) -> argparse.ArgumentParser:
         description=description,
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
-    
+
     # Common dataset arguments
     parser.add_argument("--input-file", type=str,
-                       default="data/final_output.pkl",
-                       help="Input pickle file with prompts")
-    
+                        default="data/final_output.pkl",
+                        help="Input pickle file with prompts")
+
     parser.add_argument("--output-file", type=str, default=None,
-                       help="Output pickle file path (auto-generated if not specified)")
-    
+                        help="Output pickle file path (auto-generated if not specified)")
+
     parser.add_argument("--num-samples", type=int, default=None,
-                       help="Number of samples to process from dataset")
-    
+                        help="Number of samples to process from dataset")
+
     parser.add_argument("--skip-samples", type=int, default=0,
-                       help="Number of samples to skip from the beginning")
-    
-    # NOTE: --no-chat-template flag is NOT included (chat template usage determined by backend registry)
-    
+                        help="Number of samples to skip from the beginning")
+
+    # NOTE: --no-chat-template flag is NOT included (chat template usage
+    # determined by backend registry)
+
     return parser
 
 
-def print_runner_header(runner_name: str, backend_name: Optional[str] = None, args: argparse.Namespace = None) -> None:
+def print_runner_header(
+        runner_name: str, backend_name: Optional[str] = None, args: argparse.Namespace = None) -> None:
     """Print standardized header for runners.
-    
+
     Args:
         runner_name: Name of the runner
         backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
@@ -43,7 +45,7 @@ def print_runner_header(runner_name: str, backend_name: Optional[str] = None, ar
     if backend_name is None:
         from .backend_registry import detect_backend
         backend_name = detect_backend()
-    
+
     print("=" * 80)
     print(f"{runner_name}")
     print("=" * 80)
@@ -59,22 +61,23 @@ def print_runner_header(runner_name: str, backend_name: Optional[str] = None, ar
     print("=" * 80)
 
 
-def setup_output_paths(args: argparse.Namespace, backend_name: Optional[str] = None, mode: Optional[str] = None) -> Tuple[Path, str]:
+def setup_output_paths(args: argparse.Namespace,
+                       backend_name: Optional[str] = None, mode: Optional[str] = None) -> Tuple[Path, str]:
     """
     Set up output directories and file paths.
-    
+
     Args:
         args: Parsed command line arguments
         backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
         mode: Optional mode (e.g., 'offline', 'server' for MLPerf)
-        
+
     Returns:
         Tuple of (output_dir, output_file_path)
     """
     if backend_name is None:
         from .backend_registry import detect_backend
         backend_name = detect_backend()
-    
+
     # Determine output directory
     if hasattr(args, 'output_dir') and args.output_dir:
         output_dir = Path(args.output_dir)
@@ -84,9 +87,9 @@ def setup_output_paths(args: argparse.Namespace, backend_name: Optional[str] = N
             output_dir = Path(f"outputs/{backend_name}/{mode}")
         else:
             output_dir = Path(f"outputs/{backend_name}")
-    
+
     output_dir.mkdir(parents=True, exist_ok=True)
-    
+
     # Determine output file path
     if args.output_file:
         output_file = args.output_file
@@ -97,10 +100,13 @@ def setup_output_paths(args: argparse.Namespace, backend_name: Optional[str] = N
             suffix = f"_{args.num_samples}samples"
         else:
             suffix = "_full"
-        
+
         if mode:
-            output_file = str(output_dir / f"{backend_name}_{mode}_output_{timestamp}{suffix}.pkl")
+            output_file = str(
+                output_dir /
+                f"{backend_name}_{mode}_output_{timestamp}{suffix}.pkl")
         else:
-            output_file = str(output_dir / f"{backend_name}_output_{timestamp}{suffix}.pkl")
-    
-    return output_dir, output_file 
\ No newline at end of file
+            output_file = str(output_dir /
+                              f"{backend_name}_output_{timestamp}{suffix}.pkl")
+
+    return output_dir, output_file
diff --git a/language/deepseek-r1/utils/tokenization.py b/language/deepseek-r1/utils/tokenization.py
index c5fa77d69d..ec67e1e2eb 100644
--- a/language/deepseek-r1/utils/tokenization.py
+++ b/language/deepseek-r1/utils/tokenization.py
@@ -7,15 +7,15 @@
 
 class StandardTokenizer:
     """Standard tokenizer for DeepSeek models."""
-    
+
     # Standard configuration used across all runners
     DEFAULT_MODEL = "deepseek-ai/DeepSeek-R1"
     DEFAULT_MAX_LENGTH = 32 * 1024
-    
+
     def __init__(self, model_name: str = None, max_length: int = None):
         """
         Initialize tokenizer.
-        
+
         Args:
             model_name: HuggingFace model name
             max_length: Maximum sequence length
@@ -23,50 +23,54 @@ def __init__(self, model_name: str = None, max_length: int = None):
         self.model_name = model_name or self.DEFAULT_MODEL
         self.max_length = max_length or self.DEFAULT_MAX_LENGTH
         self._tokenizer = None
-    
+
     @property
     def tokenizer(self):
         """Lazy load tokenizer."""
         if self._tokenizer is None:
             print(f"Loading tokenizer: {self.model_name}")
-            self._tokenizer = AutoTokenizer.from_pretrained(self.model_name, revision="56d4cbbb4d29f4355bab4b9a39ccb717a14ad5ad")
+            self._tokenizer = AutoTokenizer.from_pretrained(
+                self.model_name, revision="56d4cbbb4d29f4355bab4b9a39ccb717a14ad5ad")
         return self._tokenizer
-    
-    def tokenize_prompts(self, prompts: List[str], 
-                        use_chat_template: Optional[bool] = None,
-                        backend_name: Optional[str] = None) -> Tuple[List[List[int]], List[str]]:
+
+    def tokenize_prompts(self, prompts: List[str],
+                         use_chat_template: Optional[bool] = None,
+                         backend_name: Optional[str] = None) -> Tuple[List[List[int]], List[str]]:
         """
         Tokenize prompts with backend-specific handling.
-        
+
         Args:
             prompts: List of text prompts
             use_chat_template: Whether to use chat template (if None and backend_name provided, uses registry)
             backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
-            
+
         Returns:
             Tuple of (tokenized_prompts, processed_strings)
         """
         # Auto-detect backend if not provided
         if backend_name is None:
             backend_name = detect_backend()
-        
+
         # Determine chat template usage from registry if backend_name provided
         if use_chat_template is None:
             use_chat_template = uses_chat_template(backend_name)
-            print(f"[{backend_name}] Using chat template from registry: {use_chat_template}")
-        
+            print(
+                f"[{backend_name}] Using chat template from registry: {use_chat_template}")
+
         tokenized = []
         processed_strings = []
-        
+
         for prompt in prompts:
-            if use_chat_template and hasattr(self.tokenizer, 'apply_chat_template'):
+            if use_chat_template and hasattr(
+                    self.tokenizer, 'apply_chat_template'):
                 tokens = self.tokenizer.apply_chat_template(
                     [{"role": "user", "content": prompt}],
                     add_generation_prompt=True,
                     max_length=self.max_length,
                     truncation=True
                 )
-                processed_string = self.tokenizer.decode(tokens, skip_special_tokens=False)
+                processed_string = self.tokenizer.decode(
+                    tokens, skip_special_tokens=False)
             else:
                 tokens = self.tokenizer.encode(
                     prompt,
@@ -74,49 +78,52 @@ def tokenize_prompts(self, prompts: List[str],
                     max_length=self.max_length
                 )
                 processed_string = prompt
-            
+
             tokenized.append(tokens)
             processed_strings.append(processed_string)
-        
+
         return tokenized, processed_strings
-    
-    def decode_tokens(self, tokens: List[int], skip_special_tokens: bool = True) -> str:
+
+    def decode_tokens(self, tokens: List[int],
+                      skip_special_tokens: bool = True) -> str:
         """Decode tokens to text."""
-        return self.tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
-    
-    def batch_decode(self, token_lists: List[List[int]], 
-                    skip_special_tokens: bool = True) -> List[str]:
+        return self.tokenizer.decode(
+            tokens, skip_special_tokens=skip_special_tokens)
+
+    def batch_decode(self, token_lists: List[List[int]],
+                     skip_special_tokens: bool = True) -> List[str]:
         """Batch decode multiple token lists."""
-        return self.tokenizer.batch_decode(token_lists, skip_special_tokens=skip_special_tokens)
+        return self.tokenizer.batch_decode(
+            token_lists, skip_special_tokens=skip_special_tokens)
 
 
-def process_inference_results(raw_results: List[dict], 
-                            tokenizer: Optional[StandardTokenizer] = None,
-                            backend_name: Optional[str] = None,
-                            uses_text_prompts: bool = False) -> List[dict]:
+def process_inference_results(raw_results: List[dict],
+                              tokenizer: Optional[StandardTokenizer] = None,
+                              backend_name: Optional[str] = None,
+                              uses_text_prompts: bool = False) -> List[dict]:
     """
     Process raw inference results into standardized format.
-    
+
     Args:
         raw_results: Raw results from backend
         tokenizer: Tokenizer for decoding
         backend_name: Optional backend name override. If None, uses MLPERF_BACKEND env var.
         uses_text_prompts: Whether backend uses text prompts
-        
+
     Returns:
         List of standardized result dictionaries
     """
     # Auto-detect backend if not provided
     if backend_name is None:
         backend_name = detect_backend()
-    
+
     if backend_name not in get_supported_backends():
         raise ValueError(f"Backend {backend_name} is not supported")
-    
+
     backend_config = get_backend_config(backend_name)
-    
+
     standardized_results = []
-    
+
     for raw_result in raw_results:
         # Handle text-prompt backends
         if uses_text_prompts and 'text' in raw_result:
@@ -129,9 +136,9 @@ def process_inference_results(raw_results: List[dict],
             if tokenizer and tokens:
                 try:
                     text = tokenizer.decode_tokens(tokens)
-                except:
+                except BaseException:
                     pass
-        
+
         standardized = {
             'model_output': text,
             'tok_model_output': tokens,
@@ -139,5 +146,5 @@ def process_inference_results(raw_results: List[dict],
             'model_backend': backend_name,
         }
         standardized_results.append(standardized)
-    
-    return standardized_results 
\ No newline at end of file
+
+    return standardized_results
diff --git a/language/deepseek-r1/utils/validation.py b/language/deepseek-r1/utils/validation.py
index 29bebef4f1..768427ada1 100644
--- a/language/deepseek-r1/utils/validation.py
+++ b/language/deepseek-r1/utils/validation.py
@@ -12,8 +12,10 @@ class BackendError(RuntimeError):
 
 class BackendNotInitializedError(BackendError):
     """Raised when backend operation is called before initialization."""
+
     def __init__(self, backend_name: str = "Backend"):
-        super().__init__(f"{backend_name} not initialized. Call initialize() first.")
+        super().__init__(
+            f"{backend_name} not initialized. Call initialize() first.")
 
 
 class ValidationError(ValueError):
@@ -33,9 +35,9 @@ def wrapper(self, *args, **kwargs):
 
 
 def validate_prompts_input(backend_name: Optional[str] = None,
-                          tokenized_prompts: Optional[List[List[int]]] = None,
-                          text_prompts: Optional[List[str]] = None,
-                          input_type: str = None) -> None:
+                           tokenized_prompts: Optional[List[List[int]]] = None,
+                           text_prompts: Optional[List[str]] = None,
+                           input_type: str = None) -> None:
     """
     Centralized prompt validation with backend-specific requirements.
 
@@ -53,13 +55,16 @@ def validate_prompts_input(backend_name: Optional[str] = None,
         backend_name = detect_backend()
 
     if tokenized_prompts is None and text_prompts is None:
-        raise ValidationError(f"{backend_name} backend requires either text_prompts or tokenized_prompts")
+        raise ValidationError(
+            f"{backend_name} backend requires either text_prompts or tokenized_prompts")
 
     if input_type == 'text' and tokenized_prompts is not None and text_prompts is None:
-        raise ValidationError(f"{backend_name} backend requires text_prompts, not tokenized_prompts")
+        raise ValidationError(
+            f"{backend_name} backend requires text_prompts, not tokenized_prompts")
 
     if input_type == 'tokenized' and text_prompts is not None and tokenized_prompts is None:
-        raise ValidationError(f"{backend_name} backend requires tokenized_prompts, not text_prompts")
+        raise ValidationError(
+            f"{backend_name} backend requires tokenized_prompts, not text_prompts")
 
     # Additional validation for tokenized prompts
     if tokenized_prompts is not None:
@@ -67,9 +72,11 @@ def validate_prompts_input(backend_name: Optional[str] = None,
             raise ValidationError("tokenized_prompts cannot be empty")
         for i, prompt in enumerate(tokenized_prompts):
             if not isinstance(prompt, list):
-                raise ValidationError(f"tokenized_prompts[{i}] must be a list of integers")
+                raise ValidationError(
+                    f"tokenized_prompts[{i}] must be a list of integers")
             if not prompt:
-                raise ValidationError(f"tokenized_prompts[{i}] cannot be empty")
+                raise ValidationError(
+                    f"tokenized_prompts[{i}] cannot be empty")
 
     # Additional validation for text prompts
     if text_prompts is not None:
@@ -81,8 +88,8 @@ def validate_prompts_input(backend_name: Optional[str] = None,
 
 
 def validate_dataset_extended(df: pd.DataFrame,
-                            backend_name: Optional[str] = None,
-                            required_columns: Optional[List[str]] = None) -> None:
+                              backend_name: Optional[str] = None,
+                              required_columns: Optional[List[str]] = None) -> None:
     """
     Extended dataset validation with backend-specific requirements.
 
@@ -101,9 +108,11 @@ def validate_dataset_extended(df: pd.DataFrame,
     if required_columns is None:
         required_columns = ['text_input']
 
-    missing_columns = [col for col in required_columns if col not in df.columns]
+    missing_columns = [
+        col for col in required_columns if col not in df.columns]
     if missing_columns:
-        raise ValidationError(f"Dataset missing required columns: {missing_columns}")
+        raise ValidationError(
+            f"Dataset missing required columns: {missing_columns}")
 
     # Check for empty prompts
     empty_prompts = df['text_input'].isna().sum()
@@ -118,7 +127,8 @@ def validate_dataset_extended(df: pd.DataFrame,
             config = get_backend_config(backend_name)
             # Add backend-specific validation based on config if needed
 
-    print(f"Dataset validation passed: {len(df)} samples with required columns")
+    print(
+        f"Dataset validation passed: {len(df)} samples with required columns")
 
 
 def validate_runner_args(args: argparse.Namespace, runner_type: str) -> None:
@@ -133,7 +143,8 @@ def validate_runner_args(args: argparse.Namespace, runner_type: str) -> None:
         ValidationError: If validation fails
     """
     # Common validations
-    if hasattr(args, 'num_samples') and args.num_samples is not None and args.num_samples <= 0:
+    if hasattr(
+            args, 'num_samples') and args.num_samples is not None and args.num_samples <= 0:
         raise ValidationError("--num-samples must be positive")
 
     if hasattr(args, 'skip_samples') and args.skip_samples < 0:
@@ -142,4 +153,5 @@ def validate_runner_args(args: argparse.Namespace, runner_type: str) -> None:
     # Runner-specific validations
     if runner_type in ['mlperf', 'mlperf_mpi']:
         if hasattr(args, 'mode') and args.mode not in ['offline', 'server']:
-            raise ValidationError(f"Invalid mode: {args.mode}. Must be 'offline' or 'server'")
\ No newline at end of file
+            raise ValidationError(
+                f"Invalid mode: {args.mode}. Must be 'offline' or 'server'")
diff --git a/language/gpt-j/README.md b/language/gpt-j/README.md
index cfcf068791..9c952b65db 100644
--- a/language/gpt-j/README.md
+++ b/language/gpt-j/README.md
@@ -1,9 +1,28 @@
 # GPT-J Reference Implementation
 
-Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/gpt-j) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+## Automated command to run the benchmark via MLCFlow
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/gpt-j/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
+
+### Download model through MLCFlow Automation
+
+```
+mlcr get,ml-model,gptj,_pytorch --outdirname=<path_to_download> -j
+```
+
+### Download dataset through MLCFlow Automation
+
+**Validation Dataset**
+```
+mlcr get,dataset,cnndm,_validation --outdirname=<path_to_download> -j
+```
+
+**Calibration Dataset**
+```
+mlcr get,dataset,cnndm,_calibration --outdirname=<path_to_download> -j
+```
 
 
 ### Setup Instructions
@@ -113,6 +132,13 @@ Evaluates the ROGUE scores from the accuracy logs. Only applicable when specifyi
 python evaluation.py --mlperf-accuracy-file ./build/logs/mlperf_log_accuracy.json --dataset-file ./data/cnn_eval.json
 ```
 
+### Evaluate the accuracy through MLCFlow Automation
+```bash
+mlcr process,mlperf,accuracy,_cnndm --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+Please click [here](https://github.com/mlcommons/inference/blob/master/language/gpt-j/evaluation.py) to view the Python script for evaluating accuracy for the cnndm dataset.
+
 ### Reference Model - ROUGE scores
 The following are the rouge scores obtained when evaluating the GPT-J fp32 model on the entire validation set (13368 samples) using beam search, beam_size=4
 
@@ -122,6 +148,10 @@ ROUGE 2 - 20.1235
 
 ROUGE L - 29.9881
 
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
+
 ### License:
 Apache License Version 2.0.
 
diff --git a/language/llama2-70b/README.md b/language/llama2-70b/README.md
index 506423cc2c..bbd9889564 100644
--- a/language/llama2-70b/README.md
+++ b/language/llama2-70b/README.md
@@ -7,6 +7,9 @@
         - For server scenario, it is necessary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
         - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
 
+
+## Automated command to run the benchmark via MLCFlow
+
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama2-70b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
 You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
@@ -65,9 +68,11 @@ CPU-only setup, as well as any GPU versions for applicable libraries like PyTorc
 ### MLCommons Members Download
 MLCommons hosts the model and preprocessed dataset for download **exclusively by MLCommons Members**. You must first agree to the [confidentiality notice](https://llama2.mlcommons.org) using your organizational email address, then you will receive a link to a directory containing Rclone download instructions. _If you cannot access the form but you are part of a MLCommons Member organization, submit the [MLCommons subscription form](https://mlcommons.org/community/subscribe/) with your organizational email address and [associate a Google account](https://accounts.google.com/SignUpWithoutGmail) with your organizational email address._
 
-Once you have the access, you can download the model automatically via the below command
+
+### Download model through MLCFlow Automation
+
 ```
-mlcr get,ml-model,llama2 --outdirname=${CHECKPOINT_PATH} -j
+mlcr get,ml-model,llama2-70b,_pytorch -j --outdirname=<Download path> -j
 ```
 
 ### External Download (Not recommended for official submission)
@@ -82,6 +87,34 @@ git clone https://huggingface.co/meta-llama/Llama-2-70b-chat-hf ${CHECKPOINT_PAT
 
 ## Get Dataset
 
+### Download Preprocessed dataset through MLCFlow Automation
+
+**Validation**
+
+```
+mlcr get,dataset,preprocessed,openorca,_validation --outdirname=<path_to_download> -j
+```
+
+**Calibration**
+
+```
+mlcr get,dataset,preprocessed,openorca,_calibration --outdirname=<path_to_download> -j
+```
+
+### Download Unprocessed dataset through MLCFlow Automation
+
+**Validation**
+
+```
+mlcr get,dataset,openorca,_validation --outdirname=<path_to_download> -j
+```
+
+**Calibration**
+
+```
+mlcr get,dataset,openorca,_calibration --outdirname=<path_to_download> -j
+```
+
 ### Preprocessed
 
 You can use Rclone to download the preprocessed dataset from a Cloudflare R2 bucket.
@@ -244,6 +277,18 @@ scale from a 0.0-1.0 scale):
 
 This was run on a DGX-H100 node. Total runtime was ~4.5 days.
 
+### Evaluate the accuracy through MLCFlow Automation
+```bash
+mlcr process,mlperf,accuracy,_openorca --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+Please click [here](https://github.com/mlcommons/inference/blob/master/language/llama2-70b/evaluate-accuracy.py) to view the Python script for evaluating accuracy for the Waymo dataset.
+
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
+
+
 # Run llama2-70b-interactive benchmark
 
 For official, Llama2-70b submissions it is also possible to submit in the interactive category. This sets a more strict latency requirements for Time to First Token (ttft) and Time per Output Token (tpot). Specifically, the interactive category requires loadgen to enforce `ttft <= 450ms` and `ttft <= 40ms`
diff --git a/language/llama3.1-405b/README.md b/language/llama3.1-405b/README.md
index 50668263c4..65cf226d03 100644
--- a/language/llama3.1-405b/README.md
+++ b/language/llama3.1-405b/README.md
@@ -7,11 +7,12 @@
         - For server scenario, it is necessary to call `lg.FirstTokenComplete(response)` for each query. This way the first token will be reported and it's latency will be measured.
         - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
 
-## Automated command to run the benchmark via MLFlow
+## Automated command to run the benchmark via MLCFlow
 
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/llama3_1-405b/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
-You can also do pip install mlc-scripts and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
+
 
 ## Prepare environment
 
@@ -99,11 +100,24 @@ pip install -e ../../loadgen
 ## Get Model
 ### MLCommons Members Download (Recommended for official submission)
 
-You need to request for access to [MLcommons](http://llama3-1.mlcommons.org/) and you'll receive an email with the download instructions. You can download the model automatically via the below command
+You need to request for access to [MLcommons](http://llama3-1.mlcommons.org/) and you'll receive an email with the download instructions. 
+
+### Download model through MLCFlow Automation
+
+**From MLCOMMONS Google Drive**
+
 ```
 mlcr get,ml-model,llama3 --outdirname=${CHECKPOINT_PATH} -j
 ```
 
+**From HuggingFace**
+
+```
+mlcr get,ml-model,llama3,_hf --outdirname=${CHECKPOINT_PATH} --hf_token=<huggingface access token> -j
+```
+
+**Note:**
+Downloading llama3.1-405B model from Hugging Face will require an [**access token**](https://huggingface.co/settings/tokens) which could be generated for your account. Additionally, ensure that your account has access to the [llama3.1-405B](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) model. 
 
 ### External Download (Not recommended for official submission)
 + First go to [llama3.1-request-link](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and make a request, sign in to HuggingFace (if you don't have account, you'll need to create one). **Please note your authentication credentials** as you may be required to provide them when cloning below.
@@ -115,16 +129,22 @@ git clone https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct ${CHECKPOINT
 cd ${CHECKPOINT_PATH} && git checkout be673f326cab4cd22ccfef76109faf68e41aa5f1
 ```
 
-### Download huggingface model through MLC
+
+## Get Dataset
+
+### Download dataset through MLCFlow Automation
+
+**Validation**
 
 ```
-mlcr get,ml-model,llama3,_hf --outdirname=${CHECKPOINT_PATH} --hf_token=<huggingface access token> -j
+mlcr get,dataset,mlperf,inference,llama3,_validation --outdirname=<path to download> -j
 ```
 
-**Note:**
-Downloading llama3.1-405B model from Hugging Face will require an [**access token**](https://huggingface.co/settings/tokens) which could be generated for your account. Additionally, ensure that your account has access to the [llama3.1-405B](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) model. 
+**Calibration**
 
-## Get Dataset
+```
+mlcr get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
+```
 
 ### Preprocessed
 
@@ -144,11 +164,6 @@ You can then navigate in the terminal to your desired download directory and run
 ```
 rclone copy mlc-inference:mlcommons-inference-wg-public/llama3.1_405b/mlperf_llama3.1_405b_dataset_8313_processed_fp16_eval.pkl ./ -P
 ```
-**MLC Command**
-
-```
-mlcr get,dataset,mlperf,inference,llama3,_validation --outdirname=<path to download> -j
-```
 
 You can also download the calibration dataset from the Cloudflare R2 bucket by running the following command:
 
@@ -156,11 +171,6 @@ You can also download the calibration dataset from the Cloudflare R2 bucket by r
 rclone copy mlc-inference:mlcommons-inference-wg-public/llama3.1_405b/mlperf_llama3.1_405b_calibration_dataset_512_processed_fp16_eval.pkl ./ -P
 ```
 
-**MLC Command**
-```
-mlcr get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
-```
-
 
 ## Run Performance Benchmarks
 
@@ -267,3 +277,7 @@ Running the GPU implementation in FP16 precision resulted in the following FP16
 }
 ```
 The accuracy target is 99% for rougeL and exact_match, and 90% for tokens_per_sample
+
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
\ No newline at end of file
diff --git a/language/mixtral-8x7b/README.md b/language/mixtral-8x7b/README.md
index 74935a4dc2..2bdcceb12c 100644
--- a/language/mixtral-8x7b/README.md
+++ b/language/mixtral-8x7b/README.md
@@ -9,7 +9,11 @@
         - For all scenarios, when calling `lg.QuerySamplesComplete(response)`, it is necessary that each of the elements in response is a `lg.QuerySampleResponse` that contains the number of tokens (can be create this way: `lg.QuerySampleResponse(qitem.id, bi[0], bi[1], n_tokens)`). The number of tokens reported should match with the number of tokens on your answer and this will be checked in [TEST06](../../compliance/nvidia/TEST06/)
 
 
-Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/mixtral-8x7b) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+## Automated command to run the benchmark via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/language/mixtral-8x7b/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
 
 ## Prepare environment
 
@@ -66,6 +70,12 @@ CPU-only setup, as well as any GPU versions for applicable libraries like PyTorc
 
 **Important Note:** Files and configurations of the model have changed, and might change in the future. If you are going to get the model from Hugging Face or any external source, use a version of the model that exactly matches the one in this [commit](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1/commit/a60832cb6c88d5cb6e507680d0e9996fbad77050). We strongly recommend to get the model following the steps in the next section:
 
+### Download model through MLCFlow Automation
+
+```
+mlcr get,ml-model,mixtral --outdirname=<path_to_download> -j
+```
+
 ### Get Checkpoint
 
 #### Using Rclone
@@ -87,6 +97,22 @@ rclone copy mlc-inference:mlcommons-inference-wg-public/mixtral_8x7b/mixtral-8x7
 
 ## Get Dataset
 
+### Download Preprocessed dataset through MLCFlow Automation
+
+**Validation**
+
+```
+mlcr get,dataset-mixtral,openorca-mbxp-gsm8k-combined,_validation --outdirname=<path to download> -j
+```
+
+**Calibration**
+
+```
+mlcr get,dataset-mixtral,openorca-mbxp-gsm8k-combined,_calibration --outdirname=<path to download> -j
+```
+
+- Adding `_wget` tag to the run command will change the download tool from `rclone` to `wget`.
+
 ### Preprocessed
 
 #### Using Rclone
@@ -228,6 +254,15 @@ fi
 
 The ServerSUT was not tested for GPU runs.
 
+## Accuracy Evaluation
+
+### Evaluate the accuracy through MLCFlow Automation
+```bash
+mlcr process,mlperf,accuracy,_openorca-gsm8k-mbxp-combined --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+Please click [here](https://github.com/mlcommons/inference/blob/master/language/mixtral-8x7b/evaluate-accuracy.py) to view the Python script for evaluating accuracy for the Waymo dataset.
+
 ### Evaluation
 Recreating the enviroment for evaluating the quality metrics can be quite tedious. Therefore we provide a dockerfile and recommend using docker for this task.
 1. Build the evaluation container
@@ -269,3 +304,7 @@ For official submissions, 99% of each reference score is enforced. Additionally,
 ```json
 {'tokens_per_sample': 144.84}
 ```
+
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
\ No newline at end of file
diff --git a/recommendation/dlrm_v2/pytorch/README.md b/recommendation/dlrm_v2/pytorch/README.md
index 6f09e26ded..9a0c523334 100755
--- a/recommendation/dlrm_v2/pytorch/README.md
+++ b/recommendation/dlrm_v2/pytorch/README.md
@@ -2,8 +2,12 @@
 
 This is the reference implementation for MLCommons Inference benchmarks.
 
+## Automated command to run the benchmark via MLCFlow
+
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/recommendation/dlrm-v2/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
+
 ### Supported Models
 
 **TODO: Decide benchmark name**
@@ -71,7 +75,13 @@ CFLAGS="-std=c++14" python setup.py develop --user
 
 ### Download preprocessed Dataset
 
-Download the preprocessed dataset using Rclone.
+#### Download dataset through MLCFlow Automation
+
+```
+mlcr get,preprocessed,dataset,criteo,_validation --outdirname=<path_to_download> -j
+```
+
+#### Download the preprocessed dataset using Rclone.
 
 To run Rclone on Windows, you can download the executable [here](https://rclone.org/install/#windows).
 To install Rclone on Linux/macOS/BSD systems, run:
@@ -102,13 +112,10 @@ framework | Size in bytes (`du *`) | MD5 hash (`md5sum *`)
 N/A | pytorch | <2GB | -
  pytorch | 97.31GB | -
 
-#### MLC method
-
-The following MLCommons MLC commands can be used to programmatically download the model checkpoint. 
+#### Download model through MLCFlow Automation
 
 ```
-pip install mlc-scripts
-mlcr get,ml-model,dlrm,_pytorch,_weight_sharded,_rclone -j
+mlcr get,ml-model,get,ml-model,dlrm,_pytorch,_weight_sharded,_rclone --outdirname=<path_to_download> -j
 ```
 
 #### Manual method
@@ -312,6 +319,15 @@ In the reference implementation, each sample is mapped to 100-700 user-item pair
 
 ### Running accuracy script
 
+#### Evaluate the accuracy through MLCFlow Automation
+
+```bash
+mlcr process,mlperf,accuracy,_terabyte --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+Please click [here](https://github.com/mlcommons/inference/blob/master/recommendation/dlrm_v2/pytorch/tools/accuracy-dlrm.py) to view the Python script for evaluating accuracy for the Waymo dataset.
+
+
 To get the accuracy from a LoadGen accuracy json log file,
 
 1. If your SUT outputs the predictions and the ground truth labels in a packed format like the reference implementation then run
@@ -414,6 +430,10 @@ usage: main.py [-h]
 
 `--find-peak-performance` determine the maximum QPS for the Server, while not applicable to other scenarios.
 
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
+
 ## License
 
 [Apache License 2.0](LICENSE)
diff --git a/text_to_image/README.md b/text_to_image/README.md
index b00595785b..b11873049e 100644
--- a/text_to_image/README.md
+++ b/text_to_image/README.md
@@ -164,3 +164,15 @@ Add the `--accuracy` to the command to run the benchmark
 ```bash
 python3 main.py --dataset "coco-1024" --dataset-path coco2014 --profile stable-diffusion-xl-pytorch --accuracy --model-path model/ [--dtype <fp32, fp16 or bf16>] [--device <cuda or cpu>] [--time <time>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
+
+### Evaluate the accuracy through MLCFlow Automation
+```bash
+mlcr process,mlperf,accuracy,_coco2014 --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+Please click [here](https://github.com/mlcommons/inference/blob/master/text_to_image/tools/accuracy_coco.py) to view the Python script for evaluating accuracy for the Waymo dataset.
+
+
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
\ No newline at end of file
diff --git a/tools/submission/submission_checker.py b/tools/submission/submission_checker.py
index 74dbf89baa..40c83eecc5 100755
--- a/tools/submission/submission_checker.py
+++ b/tools/submission/submission_checker.py
@@ -482,7 +482,7 @@
             "rgat": 788379,
             "pointpainting": 1024,
         },
-        "dataset-size" : {
+        "dataset-size": {
             "resnet": 50000,
             "retinanet": 24781,
             "bert-99": 10833,
@@ -1023,7 +1023,7 @@ def get_min_query_count(self, model, scenario):
         if model not in self.min_queries:
             raise ValueError("model not known: " + model)
         return self.min_queries[model].get(scenario)
-    
+
     def get_dataset_size(self, model):
         model = self.get_mlperf_model(model)
         if model not in self.dataset_size:
@@ -2153,7 +2153,8 @@ def log_result(
             if filter_submitter and submitter != filter_submitter:
                 continue
             results_path = os.path.join(division, submitter, "results")
-            measurements_path = os.path.join(division, submitter, "measurements")
+            measurements_path = os.path.join(
+                division, submitter, "measurements")
             systems_path = os.path.join(division, submitter, "systems")
             if not os.path.exists(results_path):
                 continue
@@ -2259,7 +2260,8 @@ def log_result(
                         extra_model_mapping = json.load(fp)
 
             if not config.skip_all_systems_with_results:
-                measurement_diff = list(set(list_dir(measurements_path)) - set(list_dir(results_path)))
+                measurement_diff = list(
+                    set(list_dir(measurements_path)) - set(list_dir(results_path)))
                 systems_diff = list(
                     set(
                         [
@@ -2290,9 +2292,12 @@ def log_result(
 
             #  Check for calibration documentation
             if not config.skip_calibration_check and division not in ["open"]:
-                calibration_path_root = os.path.join(division, submitter, "calibration.md")
-                calibration_path_doc = os.path.join(division, submitter, "documentation", "calibration.md")
-                if not (os.path.exists(calibration_path_root)) and (not os.path.exists(calibration_path_doc)):
+                calibration_path_root = os.path.join(
+                    division, submitter, "calibration.md")
+                calibration_path_doc = os.path.join(
+                    division, submitter, "documentation", "calibration.md")
+                if not (os.path.exists(calibration_path_root)) and (
+                        not os.path.exists(calibration_path_doc)):
                     log.error(
                         "%s/%s: has not calibration file. One of %s or %s is required",
                         division,
@@ -3246,8 +3251,8 @@ def main():
         args.extra_model_benchmark_map,
         ignore_uncommited=args.submission_exceptions,
         skip_power_check=args.skip_power_check,
-        skip_all_systems_with_results = args.skip_all_systems_have_results_check,
-        skip_calibration_check = args.skip_calibration_check
+        skip_all_systems_with_results=args.skip_all_systems_have_results_check,
+        skip_calibration_check=args.skip_calibration_check
     )
 
     if args.scenarios_to_skip:
diff --git a/vision/classification_and_detection/README.md b/vision/classification_and_detection/README.md
index 12320b5fde..42c50f14b0 100755
--- a/vision/classification_and_detection/README.md
+++ b/vision/classification_and_detection/README.md
@@ -2,8 +2,11 @@
 
 This is the reference implementation for MLPerf Inference Classification and Object Detection benchmarks
 
-## Automated Run Commands
-Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+## Automated command to run the benchmark via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
 
 ### ResNet50
 
@@ -71,6 +74,55 @@ This benchmark app is a reference implementation that is not meant to be the fas
 It is written in python which might make it less suitable for lite models like mobilenet or large number of cpu's.
 There is a [C++ implementation](https://github.com/mlcommons/cm4mlops/tree/mlperf-inference/script/app-mlperf-inference-mlcommons-cpp) available which currently supports onnxruntime backend for CPUs and Nvidia GPUs.
 
+### Download model through MLCFlow Automation
+
+
+```
+mlcr get,ml-model,pointpainting --outdirname=<path_to_download> -j
+```
+
+### Download dataset through MLCFlow Automation
+
+#### Imagenet Dataset (Preprocessed)
+
+```
+mlcr get,dataset,image-classification,imagenet,preprocessed,_pytorch --outdirname=<path_to_download> -j
+```
+
+#### Imagenet Dataset (Unprocessed)
+
+**Validation**
+
+```
+mlcr get,dataset,imagenet,validation --outdirname=<path_to_download> -j
+```
+
+**Calibration**
+
+```
+mlcr get,dataset,imagenet,calibration --outdirname=<path_to_download> -j
+```
+
+#### Openimages Dataset (Preprocessed)
+
+```
+get,dataset,object-detection,open-images,openimages,preprocessed,_validation --outdirname=<path_to_download> -j 
+```
+
+#### Openimages Dataset (Unprocessed)
+
+**Validation**
+
+```
+mlcr get,dataset,openimages,original,_validation --outdirname=<path_to_download> -j  
+```
+
+**Calibration**
+
+```
+mlcr get,dataset,openimages,original,_calibration --outdirname=<path_to_download> -j 
+```
+
 ## Tools for preparing datasets and validating accuracy
 The reference implementation includes all required pre-processing of datasets.
 It also includes a ```--accuracy``` option to validate accuracy as required by mlperf.
@@ -89,11 +141,29 @@ python upscale_coco.py --inputs /data/coco/ --outputs /data/coco-1200 --size 120
 to come.
 
 ### Validate accuracy for resnet50 and mobilenet benchmarks
-The tool is [here](tools/accuracy-imagenet.py). You can run it like:
+
+The tool is [here](tools/accuracy-imagenet.py). 
+
+#### Evaluate the accuracy through MLCFlow Automation
+```bash
+mlcr process,mlperf,accuracy,_imagenet --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+#### Manual method
 ```
 python tools/accuracy-imagenet.py --mlperf-accuracy-file mlperf_log_accuracy.json --imagenet-val-file /data/imagenet2012/val_map.txt
 ```
 
+### Validate accuracy for retinanet
+
+### Evaluate the accuracy through MLCFlow Automation
+```bash
+mlcr process,mlperf,accuracy,_openimages --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+Please click [here](https://github.com/mlcommons/inference/blob/master/vision/classification_and_detection/tools/accuracy-openimages.py) to view the Python script for evaluating accuracy for the Waymo dataset.
+
+
 ### Validate accuracy for ssd-mobilenet and ssd-resnet34 benchmarks
 The tool is [here](tools/accuracy-coco.py). You can run it like:
 ```
@@ -276,6 +346,10 @@ comma separated list of which latencies (in seconds) we try to reach in the 99 p
 maximum batchsize we generate to backend (default: 128).
 
 
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
+
 ## License
 
 [Apache License 2.0](LICENSE)
diff --git a/vision/medical_imaging/3d-unet-kits19/README.md b/vision/medical_imaging/3d-unet-kits19/README.md
index ae2b816079..dfb808d800 100644
--- a/vision/medical_imaging/3d-unet-kits19/README.md
+++ b/vision/medical_imaging/3d-unet-kits19/README.md
@@ -6,8 +6,11 @@ This model performing KiTS19 dataset for kidney tumor segmentation task is propo
 
 [3D-UNet BraTS19 model](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet-brats19), which has been used for MLPerf-Inference v0.7 and v1.0, is the valid model for the submissions until the end of year 2021. Please use BraTS19 model for v.1.1 submission.
 
+## Automated command to run the benchmark via MLCFlow
+
 Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks/medical_imaging/3d-unet/) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
 
+You can also do `pip install mlc-scripts` and then use `mlcr` commands for downloading the model and datasets using the commands given in the later sections.
 
 ## Prerequisites
 
@@ -29,6 +32,45 @@ If you would like to run on NVIDIA GPU, you will need:
 This benchmark app is a reference implementation that is not meant to be the fastest implementation possible.
 This benchmark app is built by refactoring lots of codes from [MLPerf-Training 3D-UNet](https://github.com/mlcommons/training/blob/master/image_segmentation/pytorch) and [MLPerf-Inference 3D-UNet BraTS](https://github.com/mlcommons/inference/tree/master/vision/medical_imaging/3d-unet)
 
+
+### Download model through MLCFlow Automation
+
+**PyTorch**
+```
+mlcr get,ml-model,3d-unet,_pytorch --outdirname=<path_to_download> -j
+```
+
+**Onnx**
+```
+mlcr get,ml-model,3d-unet,_onnx --outdirname=<path_to_download> -j
+```
+
+**TensorFlow**
+```
+mlcr get,ml-model,3d-unet,_tensorflow --outdirname=<path_to_download> -j
+```
+
+### Download dataset through MLCFlow Automation
+
+#### Unprocessed dataset
+
+**Validation**
+```
+mlcr get,dataset,kits19,_validation --outdirname=<path_to_download> -j
+```
+
+**Calibration**
+```
+mlcr get,dataset,kits19,_calibration --outdirname=<path_to_download> -j
+```
+
+#### Preprocessed dataset
+
+```
+mlcr get,dataset,kits19,preprocessed --outdirname=<path_to_download> -j
+```
+
+
 ## Commands
 
 Please run the following commands, in the suggested order:
@@ -52,6 +94,17 @@ Other useful commands:
 - `python3 run.py --backend=[tensorflow|pytorch|pytorch_checkpoint|onnxruntime] --scenario=[Offline|SingleStream|MultiStream|Server] [--accuracy] --model=[path/to/model]`: run the harness inside the docker container. Performance or Accuracy results will be printed in console
 - `python3 accuracy_kits.py --log_file=<LOADGEN_LOG>`: compute accuracy from a LoadGen accuracy JSON log file
 
+### Evaluate the accuracy through MLCFlow Automation
+```bash
+mlcr process,mlperf,accuracy,_kits --result_dir=<Path to directory where files are generated after the benchmark run>
+```
+
+Please click [here](https://github.com/mlcommons/inference/blob/master/vision/medical_imaging/3d-unet-kits19/accuracy_kits.py) to view the Python script for evaluating accuracy for the Waymo dataset.
+
+## Automated command for submission generation via MLCFlow
+
+Please see the [new docs site](https://docs.mlcommons.org/inference/submission/) for an automated way to generate submission through MLCFlow. 
+
 ## Details
 
 - Baseline SUT that connects LoadGen and QSL is in [base_SUT.py](base_SUT.py). QSL implementation is in [kits_QSL.py](kits_QSL.py).

From 771b789b0e2d86622e1affa85892ddfc72f37f66 Mon Sep 17 00:00:00 2001
From: ANANDHU S <71482562+anandhu-eng@users.noreply.github.com>
Date: Tue, 10 Jun 2025 23:41:44 +0530
Subject: [PATCH 301/305] Docs - Update disk space for reference implementation
  (#2159)

* update value of disk space
---
 main.py | 55 +++++++++++++++++++++++++++++++++----------------------
 1 file changed, 33 insertions(+), 22 deletions(-)

diff --git a/main.py b/main.py
index 0020d8c4ec..d2f625fcf4 100755
--- a/main.py
+++ b/main.py
@@ -434,6 +434,8 @@ def get_test_query_count(model, implementation, device, num_devices=1):
         return p_range
 
     def get_min_system_requirements(spaces, model, implementation, device):
+        if implementation.lower() not in ['reference', 'nvidia']:
+            return ""
         model = model.lower()
         min_sys_req_content = ""
         min_sys_req_content += f"{spaces}<details>\n"
@@ -445,42 +447,50 @@ def get_min_system_requirements(spaces, model, implementation, device):
             if implementation.lower() == "nvidia":
                 if "dlrm" in model:
                     device_memory = "24GB"
-                elif "llama2-70b" in model or "mixtral" in model:
+                elif "llama2-70b" in model:
+                    device_memory = "2x80GB"
+                elif "mixtral" in model:
                     device_memory = "80GB"
                 elif "sdxl" in model or "gptj" in model:
                     device_memory = "16GB"
                 else:
-                    device_memory = "8GB"
+                    device_memory = "To be updated"
             elif implementation.lower() == "reference":
                 if "dlrm" in model:
                     device_memory = "2x80GB"
-                elif "llama2-70b" in model:
-                    device_memory = "8x80GB"
                 elif "mixtral" in model:
                     device_memory = "4x80GB"
                 elif "sdxl" in model:
                     device_memory = "24GB(fp32), 16GB(fp16)"
                 elif "gptj" in model:
                     device_memory = "80GB(fp32). 40GB(fp16)"
-                elif "pointpainting" in model:
-                    device_memory = "To be updated"
                 else:
-                    device_memory = "8GB"
+                    device_memory = "To be updated"
             min_sys_req_content += f"{spaces}* **Device Memory**: {device_memory}\n\n"
-        # disk space
-        if "dlrm" in model:
-            disk_space = "500GB"
-        elif "llama2-70b" in model:
-            disk_space = "700GB"
-        elif "mixtral" in model:
-            disk_space = "100GB"
-        elif "retinanet" in model:
-            disk_space = "200GB"
-        elif "pointpainting" in model:
+
+        if implementation.lower() == "reference":
+            # disk space
+            ds = {
+                "dlrm": "500GB",
+                "pointpainting": "500GB",
+                "llama2-70b": "600GB",
+                "llama3_1-405b": "2.3TB",
+                "mixtral": "100GB",
+                "retinanet": "200GB",
+                "gptj": "50GB",
+                "resnet50": "50GB",
+                "sdxl": "50GB",
+                "3d-unet": "60GB",
+                "rgat": "2.3TB",
+                "bert": "50GB"
+            }
             disk_space = "To be updated"
-        else:
-            disk_space = "50GB"
-        min_sys_req_content += f"{spaces}* **Disk Space**: {disk_space}\n\n"
+            for key in ds:
+                if key in model:
+                    disk_space = ds[key]
+                    break
+
+            min_sys_req_content += f"{spaces}* **Disk Space**: {disk_space}\n\n"
         # System memory
         if "dlrm" in model:
             system_memory = "512GB"
@@ -680,6 +690,7 @@ def mlperf_inference_run_command(
                 f"\\\n{pre_space} --server_target_qps=<SERVER_TARGET_QPS>"
             )
 
+        rerun = "--rerun" if execution_mode == "test" else ""
         run_cmd_extra = get_run_cmd_extra(
             f_pre_space,
             model,
@@ -737,7 +748,7 @@ def mlperf_inference_run_command(
 {pre_space} --framework={framework} \\
 {pre_space} --category={category} {scenario_option} \\
 {pre_space} --execution_mode=test \\
-{pre_space} --device={device} {docker_cmd_suffix}
+{pre_space} --device={device} {docker_cmd_suffix} {rerun}
 {f_pre_space}```\n"""
 
             return docker_setup_cmd + run_cmd_extra
@@ -783,7 +794,7 @@ def mlperf_inference_run_command(
 {pre_space} --framework={framework} \\
 {pre_space} --category={category} {scenario_option} \\
 {pre_space} --execution_mode={execution_mode} \\
-{pre_space} --device={device} {cmd_suffix}
+{pre_space} --device={device} {cmd_suffix} {rerun}
 {f_pre_space}```\n"""
 
             return run_cmd + run_cmd_extra

From 3ab3b5b027d7cf7e94ac5746054d74b46b087df1 Mon Sep 17 00:00:00 2001
From: Nathan Wasson <nathanw@mlcommons.org>
Date: Tue, 10 Jun 2025 17:42:34 -0500
Subject: [PATCH 302/305] Update Waymo access instructions (#2148)

* Update Waymo access instructions in README.md
---
 automotive/3d-object-detection/README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/automotive/3d-object-detection/README.md b/automotive/3d-object-detection/README.md
index d0430d444c..7f354cd557 100644
--- a/automotive/3d-object-detection/README.md
+++ b/automotive/3d-object-detection/README.md
@@ -31,7 +31,9 @@ mlcr get,dataset,waymo --outdirname=<path_to_download> -j
 ```
 
 ## Downloading the dataset and model checkpoints
-Contact [MLCommons](https://waymo.mlcommons.org/) support for accessing the Waymo Open Dataset along with the model checkpoints for the reference implementation. You will need to accept a license agreement and will be given directions to download the data with rclone. You will need to place the kitti_format folder under a directory named waymo. There are four total checkpoints 2 for pytorch and 2 for onnx.
+MLCommons hosts the dataset and model checkpoints for download **exclusively by MLCommons Members**. You must first agree to the [confidentiality notice](https://waymo.mlcommons.org) using your organizational email address, then you will receive a link to a directory containing Rclone download instructions. _If you cannot access the form but you are part of a MLCommons Member organization, submit the [MLCommons subscription form](https://mlcommons.org/community/subscribe/) with your organizational email address and [associate a Google account](https://accounts.google.com/SignUpWithoutGmail) with your organizational email address._
+
+Once you download the dataset and model checkpoints, you will need to place the kitti_format folder under a directory named waymo. There are four total checkpoints 2 for pytorch and 2 for onnx.
 
 After downloading, the structure of the data should look like below:
 

From 9e271f9ade8e49c33fd3de08bd505489d0fa7ffd Mon Sep 17 00:00:00 2001
From: Arjun Suresh <arjun@gateoverflow.com>
Date: Wed, 11 Jun 2025 16:56:23 +0100
Subject: [PATCH 303/305] Update verify_performance.py | Refactor the code
 (#2073)

* Update verify_performance.py | Refactor the code

* Update verify_performance.py

* [Automated Commit] Format Codebase

* [Automated Commit] Format Codebase

* [Automated Commit] Format Codebase

* Update build_wheels.yml

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Pablo Gonzalez <pablo.gonzalez@factored.ai>
---
 .../nvidia/TEST01/verify_performance.py       | 182 +++++++-----------
 1 file changed, 68 insertions(+), 114 deletions(-)

diff --git a/compliance/nvidia/TEST01/verify_performance.py b/compliance/nvidia/TEST01/verify_performance.py
index 4b527730b1..318582a3dd 100644
--- a/compliance/nvidia/TEST01/verify_performance.py
+++ b/compliance/nvidia/TEST01/verify_performance.py
@@ -1,5 +1,5 @@
 #! /usr/bin/env python3
-# Copyright 2018-2022 The MLPerf Authors. All Rights Reserved.
+# Copyright 2018-2025 The MLPerf Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,140 +13,94 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =============================================================================
+
 import json
 import argparse
 import os
 import sys
 import re
 
-sys.path.append(os.getcwd())
+sys.path.append(
+    os.path.abspath(
+        os.path.join(
+            os.path.dirname(__file__),
+            "..",
+            "..",
+            "..",
+            "tools",
+            "submission")))
+from log_parser import MLPerfLog  # noqa
+
+RESULT_FIELD = {
+    "Offline": "result_samples_per_second",
+    "SingleStream": "early_stopping_latency_ss",
+    "MultiStream": "early_stopping_latency_ms",
+    "Server": "result_completed_samples_per_sec",
+}
+
+
+def result_log(file_path):
+    score, target_latency = 0, None
+    score = float(mlperf_log[RESULT_FIELD[scenario]])
+
+    mlperf_log = MLPerfLog(file_path)
+    scenario = mlperf_log["effective_scenario"]
+
+    if not (
+        "result_validity" in mlperf_log.get_keys()
+        and mlperf_log["result_validity"] == "VALID"
+    ):
+        sys.exit("TEST FAIL: Invalid results in {}".format(file_path))
+
+    if mlperf_log.has_error():
+        print(
+            "WARNING: {} ERROR reported in {}".format(
+                line.split()[0],
+                file_path))
+
+    res = float(mlperf_log[RESULT_FIELD[scenario]])
+    if scenario == "Server":
+        target_latency = mlperf_log["effective_target_latency_ns"]
+
+    return scenario, score, target_latency
 
 
 def main():
-    # Parse arguments to identify the path to the accuracy logs from
-    #   the accuracy and performance runs
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--reference_summary",
         "-r",
-        help="Specifies the path to the summary log for the performance run.",
-        default="",
-    )
+        "--reference_log_details",
+        help="Path to reference performance log_details file.",
+        required=True)
     parser.add_argument(
-        "--test_summary",
         "-t",
-        help="Specifies the path to the summary log for this test.",
-        default="",
-    )
+        "--test_log_details",
+        help="Path to test performance log_details file.",
+        required=True)
     args = parser.parse_args()
 
     print("Verifying performance.")
-    ref_file = open(args.reference_summary, "r")
-    test_file = open(args.test_summary, "r")
-    ref_score = 0
-    test_score = 0
-    ref_mode = ""
-    test_mode = ""
-
-    for line in ref_file:
-        if re.match("Scenario", line):
-            ref_mode = line.split(": ", 1)[1].strip()
-            continue
-
-        if ref_mode == "SingleStream":
-            if re.match(
-                    ".*Early stopping (90th|90.0th|99.9th) percentile estimate", line):
-                ref_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if ref_mode == "MultiStream":
-            if re.match(
-                    ".*Early stopping (99th|99.0th) percentile estimate", line):
-                ref_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if ref_mode == "Server":
-            if re.match("Completed samples per second", line):
-                ref_score = line.split(": ", 1)[1].strip()
-                continue
-            if re.match("target_latency (ns)", line):
-                ref_target_latency = line.split(": ", 1)[1].strip()
-                continue
-
-        if ref_mode == "Offline":
-            if re.match("Samples per second", line):
-                ref_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if re.match("Result is", line):
-            valid = line.split(": ", 1)[1].strip()
-            if valid == "INVALID":
-                sys.exit("TEST FAIL: Reference results are invalid")
-
-        if re.match("\\d+ ERROR", line):
-            error = line.split(" ", 1)[0].strip()
-            print("WARNING: " + error + " ERROR reported in reference results")
-
-    for line in test_file:
-        if re.match("Scenario", line):
-            test_mode = line.split(": ", 1)[1].strip()
-            continue
-        if test_mode == "SingleStream":
-            if re.match(
-                    ".*Early stopping (90th|90.0th|99.9th) percentile estimate", line):
-                test_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if test_mode == "MultiStream":
-            if re.match(
-                    ".*Early stopping (99th|99.0th) percentile estimate", line):
-                test_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if test_mode == "Server":
-            if re.match("Completed samples per second", line):
-                test_score = line.split(": ", 1)[1].strip()
-                continue
-            if re.match("target_latency (ns)", line):
-                test_target_latency = line.split(": ", 1)[1].strip()
-                if test_target_latency != ref_target_latency:
-                    print("TEST FAIL: Server target latency mismatch")
-                    sys.exit()
-                continue
-
-        if test_mode == "Offline":
-            if re.match("Samples per second", line):
-                test_score = line.split(": ", 1)[1].strip()
-                continue
-
-        if re.match("Result is", line):
-            valid = line.split(": ", 1)[1].strip()
-            if valid == "INVALID":
-                sys.exit("TEST FAIL: Test results are invalid")
-
-        if re.match("\\d+ ERROR", line):
-            error = line.split(" ", 1)[0].strip()
-            print("WARNING: " + error + " ERROR reported in test results")
-
-    if test_mode != ref_mode:
-        sys.exit("Test and reference scenarios do not match!")
-
-    print("reference score = {}".format(ref_score))
-    print("test score = {}".format(test_score))
+    ref_scenario, ref_score, ref_target_latency = parse_result_log(
+        args.reference_log_details)
+    test_scenario, test_score, test_target_latency = parse_result_log(
+        args.test_log_details)
 
-    threshold = 0.10
+    if test_scenario != ref_scenario:
+        sys.exit("TEST FAIL: Test and reference scenarios do not match!")
 
-    # In single-/multi-stream mode, latencies can be very short for high performance systems
-    # and run-to-run variation due to external disturbances (OS) can be significant.
-    # In this case we relax pass threshold to 20%
-    if (ref_mode == "SingleStream" and float(ref_score) <= 200000) or (
-        ref_mode == "MultiStream" and float(ref_score) <= 1600000
-    ):
+    if ref_mode == "Server" and test_target_latency != ref_target_latency:
+        sys.exit("TEST FAIL: Server target latency mismatch")
+
+    print(f"Reference score = {ref_score}")
+    print(f"Test score = {test_score}")
+
+    threshold = 0.10
+    if (ref_scenario == "SingleStream" and ref_score <= 200000) or (
+            ref_scenario == "MultiStream" and ref_score <= 1600000):
         threshold = 0.20
 
-    if float(test_score) < float(ref_score) * (1 + threshold) and float(
-        test_score
-    ) > float(ref_score) * (1 - threshold):
+    if ref_score * (1 - threshold) <= test_score <= ref_score * \
+            (1 + threshold):
         print("TEST PASS")
     else:
         print("TEST FAIL: Test score invalid")

From 93cebdac8d21efdfd8d3253ed65eb34291cfa10a Mon Sep 17 00:00:00 2001
From: Pablo Gonzalez <pablo.gonzalez@factored.ai>
Date: Wed, 11 Jun 2025 14:36:29 -0500
Subject: [PATCH 304/305] Update mlperf.conf with final deepseek configuration
 (#2208)

---
 loadgen/mlperf.conf | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/loadgen/mlperf.conf b/loadgen/mlperf.conf
index e7d6d7744f..ba7f9b77db 100644
--- a/loadgen/mlperf.conf
+++ b/loadgen/mlperf.conf
@@ -21,7 +21,6 @@ stable-diffusion-xl.*.performance_sample_count_override = 5000
 rgat.*.performance_sample_count_override = 788379
 pointpainting.*.performance_sample_count_override = 1024
 deepseek-r1.*.performance_sample_count_override = 4388
-deepseek-r1-interactive.*.performance_sample_count_override = 4388
 # set to 0 to let entire sample set to be performance sample
 3d-unet.*.performance_sample_count_override = 0
 
@@ -58,7 +57,6 @@ llama2-70b-interactive.*.sample_concatenate_permutation = 1
 mixtral-8x7b.*.sample_concatenate_permutation = 1
 llama3_1-405b.*.sample_concatenate_permutation = 1
 deepseek-r1.*.sample_concatenate_permutation = 1
-deepseek-r1-interactive.*.sample_concatenate_permutation = 1
 
 *.Server.target_latency = 10
 *.Server.target_latency_percentile = 99
@@ -78,7 +76,6 @@ llama2-70b-interactive.*.use_token_latencies = 1
 mixtral-8x7b.*.use_token_latencies = 1
 llama3_1-405b.*.use_token_latencies = 1
 deepseek-r1.*.use_token_latencies = 1
-deepseek-r1-interactive.*.use_token_latencies = 1
 
 # gptj benchmark infers token latencies
 gptj.*.infer_token_latencies = 1
@@ -105,10 +102,6 @@ deepseek-r1.Server.target_latency = 0
 deepseek-r1.Server.ttft_latency = 2000
 deepseek-r1.Server.tpot_latency = 80
 
-deepseek-r1-interactive.Server.target_latency = 0
-deepseek-r1-interactive.Server.ttft_latency = 1000
-deepseek-r1-interactive.Server.tpot_latency = 40
-
 *.Offline.target_latency_percentile = 90
 *.Offline.min_duration = 600000
 
@@ -130,7 +123,6 @@ llama3_1-405b.Offline.min_query_count = 8313
 mixtral-8x7b.Offline.min_query_count = 15000
 rgat.Offline.min_query_count = 788379
 deepseek-r1.Offline.min_query_count = 4388
-deepseek-r1-interactive.Offline.min_query_count = 4388
 
 # These fields should be defined and overridden by user.conf.
 *.SingleStream.target_latency = 10

From ab2b81063c920bbbc61a577f7a0766e07a0ce0c4 Mon Sep 17 00:00:00 2001
From: arjunsuresh <arjunsuresh@users.noreply.github.com>
Date: Wed, 11 Jun 2025 19:36:38 +0000
Subject: [PATCH 305/305] Increment version to 5.0.19

---
 loadgen/VERSION.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/loadgen/VERSION.txt b/loadgen/VERSION.txt
index 9ecb9afd6e..f3eb74e2ba 100644
--- a/loadgen/VERSION.txt
+++ b/loadgen/VERSION.txt
@@ -1 +1 @@
-5.0.18
+5.0.19