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CuPy : NumPy & SciPy for GPU

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

>>>importcupyascp>>>x=cp.arange(6).reshape(2,3).astype('f')>>>xarray([[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 passndarray to existing CUDA C/C++ programs viaRawKernels, useStreams for performance, or even callCUDA Runtime APIs directly.

Installation

Pip

Binary packages (wheels) are available for Linux and Windows onPyPI.Choose the right package for your platform.

Platform	Architecture	Command
CUDA 11.x (11.2+)	x86_64 / aarch64	`pip install cupy-cuda11x`
CUDA 12.x	x86_64 / aarch64	`pip install cupy-cuda12x`
ROCm 4.3 (experimental)	x86_64	`pip install cupy-rocm-4-3`
ROCm 5.0 (experimental)	x86_64	`pip install cupy-rocm-5-0`

Note

To install pre-releases, append--pre -U -f https://pip.cupy.dev/pre (e.g.,pip install cupy-cuda11x --pre -U -f https://pip.cupy.dev/pre).

Conda

Binary packages are also available for Linux and Windows onConda-Forge.

Platform	Architecture	Command
CUDA	x86_64 / aarch64 / ppc64le	`conda install -c conda-forge cupy`

If you need a slim installation (without also getting CUDA dependencies installed), you can doconda install -c conda-forge cupy-core.

If you need to use a particular CUDA version (say 12.0), you can use thecuda-version metapackage to select the version, e.g.conda install -c conda-forge cupy cuda-version=12.0.

Note

If you encounter any problem with CuPy installed fromconda-forge, please feel free to report tocupy-feedstock, and we will help investigate if it is just a packaging issue inconda-forge's recipe or a real issue in CuPy.

Docker

UseNVIDIA Container Toolkit to runCuPy container images.

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

Resources

Installation Guide - instructions on building from source
Release Notes
Projects using CuPy
Contribution Guide
GPU Acceleration in Python using CuPy and Numba (GTC November 2021 Technical Session)
GPU-Acceleration of Signal Processing Workflows using CuPy and cuSignal 1 (ICASSP'21 Tutorial)

License

MIT License (seeLICENSE file).

CuPy is designed based on NumPy's API and SciPy's API (seedocs/source/license.rst file).

CuPy is being developed and maintained byPreferred Networks andcommunity 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).[PDF]

@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"}