CuPy : NumPy-like API accelerated with 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. It supports a subset of numpy.ndarray interface.

Installation

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

(Source Package)
$ pip install cupy

The 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.

Run with Docker

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

Development

Please see the contribution guide.

More information

• Release notes
• Projects using CuPy

License

MIT License (see LICENSE file).

CuPy is designed based on NumPy's API and SciPy's API (see docs/LICENSE_THIRD_PARTY file).

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"
}