Deep learning optimization library for PyTorch
Project description
Deep Learning framework to accelerate the training of PyTorch models.
Boost • Docs • Community • Contribute
PyTorch-Boost is a deep learning optimization library, offering a lightweight and user-friendly wrapper for custom CUDA functions. Designed to accelerate PyTorch model training and inference, PyTorch-Boost facilitates the application of high-precision and low-precision modalities— encompassing 32-bit, 16-bit, 8-bit, 4-bit, and mixed-precision training—across multiple GPUs or machines with its distributed training capabilities.
For detailed usage, installation guidelines, and more, please refer to our comprehensive documentation.
Install PyTorch-Boost
Simple installation from PyPI
pip install pytorch-boost
Other installation options
Install with optional dependencies
pip install pytorch-boost['extra']
Conda
conda install pytorch-boost -c conda-forge
Install stable version
pip install https://github.com/kswain55/pytorch-boost/archive/refs/heads/release/stable.zip -U
Install bleeding-edge
pip install https://github.com/kswain55/pytorch-boost/archive/refs/heads/master.zip -U
Compatibility
Supported Platform:
- Linux
Supported Hardware:
- NVIDIA Hopper, Ada, Ampere, Volta, and Turing architecture
- AMD CDNA 2/3 and RDNA 2/3
Supported Software Layer:
- CUDA 11.8 - 12.2 (NVIDIA)
- ROCM 5.3 and HIP compilation tools (AMD)
Acknowledgements
We would like to thank Tim Dettmers for his work bitsandbytes and the team developing PyTorch, allowing us to make this possible. We would also like to thank NVIDIA and AMD for providng the neccessary hardware for testing.
How to cite us
If you found this library useful, please consider citing our work:
@article{boost2023pytorch,
title={Boost},
author={minyoung huh, kswain},
journal={arXiv preprint arXiv:2023.2023},
year={2023}
}
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