{"id":13533522,"url":"https://github.com/cupy/cupy","last_synced_at":"2025-05-12T22:25:34.996Z","repository":{"id":37622972,"uuid":"72523920","full_name":"cupy/cupy","owner":"cupy","description":"NumPy \u0026 SciPy for 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align=\"center\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/cupy/cupy/main/docs/image/cupy_logo_1000px.png\" width=\"400\"/\u003e\u003c/div\u003e\n\n# CuPy : NumPy \u0026 SciPy for GPU\n\n[![pypi](https://img.shields.io/pypi/v/cupy)](https://pypi.python.org/pypi/cupy)\n[![Conda](https://img.shields.io/badge/conda--forge-cupy-blue)](https://anaconda.org/conda-forge/cupy)\n[![GitHub license](https://img.shields.io/github/license/cupy/cupy)](https://github.com/cupy/cupy)\n[![Matrix](https://img.shields.io/matrix/cupy_community:gitter.im?server_fqdn=matrix.org)](https://gitter.im/cupy/community)\n[![Twitter](https://img.shields.io/twitter/follow/CuPy_Team?label=%40CuPy_Team)](https://twitter.com/CuPy_Team)\n[![Medium](https://img.shields.io/badge/Medium-CuPy-teal)](https://medium.com/cupy-team)\n\n[**Website**](https://cupy.dev/)\n| [**Install**](https://docs.cupy.dev/en/stable/install.html)\n| [**Tutorial**](https://docs.cupy.dev/en/stable/user_guide/basic.html)\n| [**Examples**](https://github.com/cupy/cupy/tree/main/examples)\n| [**Documentation**](https://docs.cupy.dev/en/stable/)\n| [**API Reference**](https://docs.cupy.dev/en/stable/reference/)\n| [**Forum**](https://groups.google.com/forum/#!forum/cupy)\n\nCuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python.\nCuPy acts as a [drop-in replacement](https://docs.cupy.dev/en/stable/reference/comparison.html) to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms.\n\n```py\n\u003e\u003e\u003e import cupy as cp\n\u003e\u003e\u003e x = cp.arange(6).reshape(2, 3).astype('f')\n\u003e\u003e\u003e x\narray([[ 0.,  1.,  2.],\n       [ 3.,  4.,  5.]], dtype=float32)\n\u003e\u003e\u003e x.sum(axis=1)\narray([  3.,  12.], dtype=float32)\n```\n\nCuPy also provides access to low-level CUDA features.\nYou can pass `ndarray` to existing CUDA C/C++ programs via [RawKernels](https://docs.cupy.dev/en/stable/user_guide/kernel.html#raw-kernels), use [Streams](https://docs.cupy.dev/en/stable/reference/cuda.html) for performance, or even call [CUDA Runtime APIs](https://docs.cupy.dev/en/stable/reference/cuda.html#runtime-api) directly.\n\n## Installation\n\n### Pip\n\nBinary packages (wheels) are available for Linux and Windows on [PyPI](https://pypi.org/org/cupy/).\nChoose the right package for your platform.\n\n| Platform              | Architecture      | Command                                                       |\n| --------------------- | ----------------- | ------------------------------------------------------------- |\n| CUDA 11.x (11.2+)     | x86_64 / aarch64  | `pip install cupy-cuda11x`                                    |\n| CUDA 12.x             | x86_64 / aarch64  | `pip install cupy-cuda12x`                                    |\n| ROCm 4.3 (*[experimental](https://docs.cupy.dev/en/latest/install.html#using-cupy-on-amd-gpu-experimental)*)          | x86_64            | `pip install cupy-rocm-4-3`                                   |\n| ROCm 5.0 (*[experimental](https://docs.cupy.dev/en/latest/install.html#using-cupy-on-amd-gpu-experimental)*)          | x86_64            | `pip install cupy-rocm-5-0`                                   |\n\n\u003e [!NOTE]\\\n\u003e 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`).\n\n### Conda\n\nBinary packages are also available for Linux and Windows on [Conda-Forge](https://anaconda.org/conda-forge/cupy).\n\n| Platform              | Architecture                | Command                                                       |\n| --------------------- | --------------------------- | ------------------------------------------------------------- |\n| CUDA                  | x86_64 / aarch64 / ppc64le  | `conda install -c conda-forge cupy`                           |\n\nIf you need a slim installation (without also getting CUDA dependencies installed), you can do `conda install -c conda-forge cupy-core`.\n\nIf you need to use a particular CUDA version (say 12.0), you can use the `cuda-version` metapackage to select the version, e.g. `conda install -c conda-forge cupy cuda-version=12.0`.\n\n\u003e [!NOTE]\\\n\u003e If you encounter any problem with CuPy installed from `conda-forge`, please feel free to report to [cupy-feedstock](https://github.com/conda-forge/cupy-feedstock/issues), and we will help investigate if it is just a packaging issue in `conda-forge`'s recipe or a real issue in CuPy.\n\n### Docker\n\nUse [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/overview.html) to run [CuPy container images](https://hub.docker.com/r/cupy/cupy).\n\n```\n$ docker run --gpus all -it cupy/cupy\n```\n\n## Resources\n\n- [Installation Guide](https://docs.cupy.dev/en/stable/install.html) - instructions on building from source\n- [Release Notes](https://github.com/cupy/cupy/releases)\n- [Projects using CuPy](https://github.com/cupy/cupy/wiki/Projects-using-CuPy)\n- [Contribution Guide](https://docs.cupy.dev/en/stable/contribution.html)\n- [GPU Acceleration in Python using CuPy and Numba (GTC November 2021 Technical Session)](https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31149/)\n- [GPU-Acceleration of Signal Processing Workflows using CuPy and cuSignal[^1] (ICASSP'21 Tutorial)](https://github.com/awthomp/cusignal-icassp-tutorial)\n\n[^1]: cuSignal is now part of CuPy starting v13.0.0.\n\n## License\n\nMIT License (see `LICENSE` file).\n\nCuPy is designed based on NumPy's API and SciPy's API (see `docs/source/license.rst` file).\n\nCuPy is being developed and maintained by [Preferred Networks](https://www.preferred.jp/en/) and [community contributors](https://github.com/cupy/cupy/graphs/contributors).\n\n## Reference\n\nRyosuke Okuta, Yuya Unno, Daisuke Nishino, Shohei Hido and Crissman Loomis.\n**CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations.**\n*Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS)*, (2017).\n[[PDF](http://learningsys.org/nips17/assets/papers/paper_16.pdf)]\n\n```bibtex\n@inproceedings{cupy_learningsys2017,\n  author       = \"Okuta, Ryosuke and Unno, Yuya and Nishino, Daisuke and Hido, Shohei and Loomis, Crissman\",\n  title        = \"CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations\",\n  booktitle    = \"Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS)\",\n  year         = \"2017\",\n  url          = \"http://learningsys.org/nips17/assets/papers/paper_16.pdf\"\n}\n```\n","funding_links":["https://github.com/sponsors/cupy"],"categories":["Python","Basic Components","Computations","Sensor Processing","This repo contains the list of awesome repo present in github. 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