base on NumPy & SciPy for GPU <div align="center"><img src="https://raw.githubusercontent.com/cupy/cupy/main/docs/image/cupy_logo_1000px.png" width="400"/></div> # CuPy : NumPy & SciPy for GPU [](https://pypi.python.org/pypi/cupy) [](https://anaconda.org/conda-forge/cupy) [](https://github.com/cupy/cupy) [](https://gitter.im/cupy/community) [](https://twitter.com/CuPy_Team) [](https://medium.com/cupy-team) [**Website**](https://cupy.dev/) | [**Install**](https://docs.cupy.dev/en/stable/install.html) | [**Tutorial**](https://docs.cupy.dev/en/stable/user_guide/basic.html) | [**Examples**](https://github.com/cupy/cupy/tree/main/examples) | [**Documentation**](https://docs.cupy.dev/en/stable/) | [**API Reference**](https://docs.cupy.dev/en/stable/reference/) | [**Forum**](https://groups.google.com/forum/#!forum/cupy) CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy 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. ```py >>> 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](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. ## Installation ### Pip Binary packages (wheels) are available for Linux and Windows on [PyPI](https://pypi.org/org/cupy/). 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](https://docs.cupy.dev/en/latest/install.html#using-cupy-on-amd-gpu-experimental)*) | x86_64 | `pip install cupy-rocm-4-3` | | 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` | > [!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 on [Conda-Forge](https://anaconda.org/conda-forge/cupy). | 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 do `conda install -c conda-forge cupy-core`. If 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`. > [!NOTE]\ > 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. ### Docker Use [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). ``` $ docker run --gpus all -it cupy/cupy ``` ## Resources - [Installation Guide](https://docs.cupy.dev/en/stable/install.html) - instructions on building from source - [Release Notes](https://github.com/cupy/cupy/releases) - [Projects using CuPy](https://github.com/cupy/cupy/wiki/Projects-using-CuPy) - [Contribution Guide](https://docs.cupy.dev/en/stable/contribution.html) - [GPU Acceleration in Python using CuPy and Numba (GTC November 2021 Technical Session)](https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31149/) - [GPU-Acceleration of Signal Processing Workflows using CuPy and cuSignal[^1] (ICASSP'21 Tutorial)](https://github.com/awthomp/cusignal-icassp-tutorial) [^1]: cuSignal is now part of CuPy starting v13.0.0. ## License MIT License (see `LICENSE` file). CuPy is designed based on NumPy's API and SciPy's API (see `docs/source/license.rst` file). CuPy is being developed and maintained by [Preferred Networks](https://www.preferred.jp/en/) and [community contributors](https://github.com/cupy/cupy/graphs/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](http://learningsys.org/nips17/assets/papers/paper_16.pdf)] ```bibtex @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" } ``` ", Assign "at most 3 tags" to the expected json: {"id":"11760","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"