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FastEstimator

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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.

Support Matrix

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

Installation

1. Install Dependencies

  • Install TensorFlow

  • Install PyTorch

    • 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

    • 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

  • Extra Dependencies:

    • Windows:

      • Install Build Tools for Visual Studio 2019 here.

      • Install latest Visual C++ redistributable here and choose x86 for 32 bit OS, x64 for 64 bit OS.

    • Linux:

      apt-get install libglib2.0-0 libsm6 libxrender1 libxext6

    • Mac:

2. Install FastEstimator

  • Stable:

    pip install fastestimator

  • Nightly (Linux/Mac):

    pip install fastestimator-nightly

Docker Hub

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:

  • Stable:

    • GPU:

      docker pull fastestimator/fastestimator:latest-gpu

    • CPU:

      docker pull fastestimator/fastestimator:latest-cpu

  • Nighly:

    • GPU:

      docker pull fastestimator/fastestimator:nightly-gpu

    • CPU:

      docker pull fastestimator/fastestimator:nightly-cpu

Useful Links

  • 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.

Citation

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}
}

License

Apache License 2.0