This directory builds a convolutional neural net to classify the MNIST dataset using the tf.data, tf.estimator.Estimator, and tf.layers APIs.
To begin, you'll simply need the latest version of TensorFlow installed.
First make sure you've added the models folder to your Python path; otherwise you may encounter an error like ImportError: No module named mnist.
Then to train the model, run the following:
python mnist.py
Distributed Training on Gradient
Just run the example with following parameters:
"name": "Mnist Sample",
"projectHandle": "<your project handle",
"parameterServerContainer": "tensorflow/tensorflow:1.13.1-gpu-py3",
"parameterServerMachineType": "K80",
"parameterServerCount": 1,
"workerCommand": "python mnist.py",
"workerContainer": "tensorflow/tensorflow:1.13.1-gpu-py3",
"workspaceUrl": "git+https://github.com/paperspace/mnist-sample.git",
"workerMachineType": "K80",
"workerCount": 2,
"parameterServerCommand": "python mnist.py"
Gradient will generate TF_CONFIG in base64 format for each node so all you need to do in your other projects:
paperspace_tf_config = json.loads(base64.urlsafe_b64decode(os.environ.get('TF_CONFIG')).decode('utf-8'))
You can export the model into Tensorflow SavedModel format by using the argument --export_dir:
python mnist.py --export_dir /tmp/mnist_saved_model
If you are training on Tensorflow using a GPU but would like to export the model for use in Tensorflow Serving on a CPU-only server you can train and/or export the model using --data_format=channels_last:
python mnist.py --data_format=channels_last
The SavedModel will be saved in a timestamped directory under /tmp/mnist_saved_model/ (e.g. /tmp/mnist_saved_model/1513630966/).
Getting predictions with SavedModel
Use saved_model_cli to inspect and execute the SavedModel.