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cupy
5.1K Stars 469 Forks MIT License 21.7K Commits 342 Opened issues

Description

A NumPy-compatible array library accelerated by CUDA

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CuPy : A NumPy-compatible array library accelerated by CUDA

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CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class,

cupy.ndarray
, and many functions on it.

Installation

Wheels (precompiled binary packages) are available for Linux (x86_64) and Windows (amd64). Choose the right package for your platform.

| Platform | Command | | --------- | ------------------------------ | | CUDA 9.0 |

pip install cupy-cuda90
| | CUDA 9.2 |
pip install cupy-cuda92
| | CUDA 10.0 |
pip install cupy-cuda100
| | CUDA 10.1 |
pip install cupy-cuda101
| | CUDA 10.2 |
pip install cupy-cuda102
| | CUDA 11.0 |
pip install cupy-cuda110
| | CUDA 11.1 |
pip install cupy-cuda111
| | CUDA 11.2 |
pip install cupy-cuda112
| | ROCm 4.0 |
pip install cupy-rocm-4-0
(experimental; see docs for details) |

See the Installation Guide if you are using Conda/Anaconda or building from source.

Run on Docker

Use NVIDIA Container Toolkit to run CuPy image with GPU.

$ docker run --gpus all -it cupy/cupy

More information

License

MIT License (see

LICENSE
file).

CuPy is designed based on NumPy's API and SciPy's API (see

docs/LICENSE_THIRD_PARTY
file).

CuPy is being maintained and developed by Preferred Networks Inc. and community contributors.

Reference

Ryosuke Okuta, Yuya Unno, Daisuke Nishino, Shohei Hido and Crissman Loomis. CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations. Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS), (2017). URL

@inproceedings{cupy_learningsys2017,
  author       = "Okuta, Ryosuke and Unno, Yuya and Nishino, Daisuke and Hido, Shohei and Loomis, Crissman",
  title        = "CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations",
  booktitle    = "Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS)",
  year         = "2017",
  url          = "http://learningsys.org/nips17/assets/papers/paper_16.pdf"
}

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