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)
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Install TensorFlow
- Linux/MAC:
pip install tensorflow==2.9.1
- Windows: Please follow this installation guide.
- Linux/MAC:
-
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
- CPU:
-
Extra Dependencies:
-
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
- GPU:
- Nighly:
- GPU:
docker pull fastestimator/fastestimator:nightly-gpu
- CPU:
docker pull fastestimator/fastestimator:nightly-cpu
- GPU:
- 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}
}