cupy/cupy repository overview

⁠CuPy : NumPy & SciPy for GPU

Website⁠ | Install⁠ | Tutorial⁠ | Examples⁠ | Documentation⁠ | API Reference⁠ | Forum⁠

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 and Windows. Refer to the Installation Guide⁠ for more details.

⁠Run on Docker

Use NVIDIA Container Toolkit⁠ to run CuPy image with GPU.

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

⁠More information

• Release Notes⁠
• Projects using CuPy⁠
• Contribution Guide⁠

⁠License

MIT License

⁠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"
}