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.

• Python >=3.7
• Nvidia Driver >= 450 (GPU only)
• CUDA >= 11.0 (GPU only)

• Install TensorFlow

  • Linux/MAC:

    pip install tensorflow==2.9.1

  • Windows: Please follow this installation guide.

• Install PyTorch

  • CPU:

    pip install torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2 -f https://download.pytorch.org/whl/cpu/torch_stable.html

  • GPU:

    pip install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/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:

    $ echo No extra dependency needed ":)"

• Stable (Linux/Mac):

  $ pip install fastestimator

• Stable (Windows):

  First download zip file from available releases

  $ pip install fastestimator-x.x.x.zip

• Nightly (Linux/Mac):

  $ pip install fastestimator-nightly

• Nightly (Windows):

  First download zip file here

  $ pip install fastestimator-master.zip

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

• 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}
}

Apache License 2.0