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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.
It supports a subset of numpy.ndarray interface.
For detailed instructions on installing CuPy, see the installation guide.
You can install CuPy using pip:
(Binary Package for CUDA 8.0)
$ pip install cupy-cuda80
(Binary Package for CUDA 9.0)
$ pip install cupy-cuda90
(Binary Package for CUDA 9.1)
$ pip install cupy-cuda91
(Binary Package for CUDA 9.2)
$ pip install cupy-cuda92
(Binary Package for CUDA 10.0)
$ pip install cupy-cuda100
(Binary Package for CUDA 10.1)
$ pip install cupy-cuda101
(Binary Package for CUDA 10.2)
$ pip install cupy-cuda102
(Source Package)
$ pip install cupyThe latest version of cuDNN and NCCL libraries are included in binary packages (wheels). For the source package, you will need to install cuDNN/NCCL before installing CuPy, if you want to use it.
We provide the official Docker image. Use nvidia-docker command to run CuPy image with GPU. You can login to the environment with bash, and run the Python interpreter.
$ nvidia-docker run -it cupy/cupy /bin/bash
Please see the contribution guide.
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.
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"
}