FastEstimator is a high-level deep learning library built on TensorFlow2 and PyTorch. With the help of FastEstimator, you can easily build a high-performance deep learning model and run it anywhere. 😉
For more information, please visit our website.
| FastEstimator | Python | TensorFlow | PyTorch | CUDA | Installation Instruction |
|---|---|---|---|---|---|
| Nightly | 3.8-3.10 | 2.11.1 | 2.0.1 | 11.8 | master branch |
| 1.6 (recent stable) | 3.8-3.10 | 2.11.1 | 2.0.1 | 11.8 | r1.6 branch |
| 1.5 | 3.7-3.9 | 2.9.1 | 1.10.2 | 11.0 | r1.5 branch |
| 1.4 | 3.6-3.8 | 2.4.1 | 1.7.1 | 11.0 | r1.4 branch |
| 1.3 | 3.6-3.8 | 2.4.1 | 1.7.1 | 11.0 | r1.3 branch |
| 1.2 | 3.6-3.8 | 2.4.1 | 1.7.1 | 11.0 | r1.2 branch |
| 1.1 | 3.6-3.8 | 2.3.0 | 1.6.0 | 10.1 | r1.1 branch |
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Install TensorFlow
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Linux:
pip install tensorflow==2.11.1
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Mac (M1/M2): Please follow this installation guide
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Windows: Please follow this installation guide
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Install PyTorch
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CPU:
pip install torch==2.0.1+cpu torchvision==0.15.2+cpu torchaudio==2.0.2+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
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GPU:
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 -f https://download.pytorch.org/whl/cu118/torch_stable.html
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Extra Dependencies:
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Windows:
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Linux:
apt-get install libglib2.0-0 libsm6 libxrender1 libxext6
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Mac:
- Please follow this installation guide
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Stable:
pip install fastestimator
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Nightly (Linux/Mac):
pip install fastestimator-nightly
Docker containers create isolated virtual environments that share resources with a host machine. Docker provides an easy way to set up a FastEstimator environment. You can simply pull our image from Docker Hub and get started:
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Stable:
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GPU:
docker pull fastestimator/fastestimator:latest-gpu
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CPU:
docker pull fastestimator/fastestimator:latest-cpu
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Nighly:
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GPU:
docker pull fastestimator/fastestimator:nightly-gpu
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CPU:
docker pull fastestimator/fastestimator:nightly-cpu
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- Website: More info about FastEstimator API and news.
- Tutorial Series: Everything you need to know about FastEstimator.
- Application Hub: End-to-end deep learning examples in FastEstimator.
Please cite FastEstimator in your publications if it helps your research:
@misc{fastestimator,
title = {FastEstimator: A Deep Learning Library for Fast Prototyping and Productization},
author = {Xiaomeng Dong and Junpyo Hong and Hsi-Ming Chang and Michael Potter and Aritra Chowdhury and
Purujit Bahl and Vivek Soni and Yun-Chan Tsai and Rajesh Tamada and Gaurav Kumar and Caroline Favart and
V. Ratna Saripalli and Gopal Avinash},
note = {NeurIPS Systems for ML Workshop},
year = {2019},
url = {http://learningsys.org/neurips19/assets/papers/10_CameraReadySubmission_FastEstimator_final_camera.pdf}
}