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CuPy : NumPy & SciPy for GPU

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CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms.

>>> import cupy as cp
>>> x = cp.arange(6).reshape(2, 3).astype('f')
>>> x
array([[ 0.,  1.,  2.],
       [ 3.,  4.,  5.]], dtype=float32)
>>> x.sum(axis=1)
array([  3.,  12.], dtype=float32)

CuPy also provides access to low-level CUDA features. You can pass ndarray to existing CUDA C/C++ programs via RawKernels, use Streams for performance, or even call CUDA Runtime APIs directly.

Installation

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

Platform Command
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
CUDA 11.3 pip install cupy-cuda113
CUDA 11.4 pip install cupy-cuda114
ROCm 4.0 (*) pip install cupy-rocm-4-0
ROCm 4.2 (*) pip install cupy-rocm-4-2
ROCm 4.3 (*) pip install cupy-rocm-4-3

(*) ROCm support is an experimental feature. Refer to the docs for details.

Use -f https://pip.cupy.dev/pre option to install pre-releases (e.g., pip install cupy-cuda114 -f https://pip.cupy.dev/pre). 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"
}