PyOpenCL: Pythonic Access to OpenCL, with Arrays and Algorithms =============================================================== .. |badge-gitlab-ci| image:: https://gitlab.tiker.net/inducer/pyopencl/badges/main/pipeline.svg :alt: Gitlab Build Status :target: https://gitlab.tiker.net/inducer/pyopencl/commits/main .. |badge-github-ci| image:: https://github.com/inducer/pyopencl/workflows/CI/badge.svg?branch=main&event=push :alt: Github Build Status :target: https://github.com/inducer/pyopencl/actions?query=branch%3Amain+workflow%3ACI+event%3Apush .. |badge-pypi| image:: https://badge.fury.io/py/pyopencl.svg :alt: Python Package Index Release Page :target: https://pypi.org/project/pyopencl/ .. |badge-zenodo| image:: https://zenodo.org/badge/1575307.svg :alt: Zenodo DOI for latest release :target: https://zenodo.org/badge/latestdoi/1575307 |badge-gitlab-ci| |badge-github-ci| |badge-pypi| |badge-zenodo| PyOpenCL lets you access GPUs and other massively parallel compute devices from Python. It tries to offer computing goodness in the spirit of its sister project `PyCUDA `__: * Object cleanup tied to lifetime of objects. This idiom, often called `RAII `__ in C++, makes it much easier to write correct, leak- and crash-free code. * Completeness. PyOpenCL puts the full power of OpenCL's API at your disposal, if you wish. Every obscure ``get_info()`` query and all CL calls are accessible. * Automatic Error Checking. All CL errors are automatically translated into Python exceptions. * Speed. PyOpenCL's base layer is written in C++, so all the niceties above are virtually free. * Helpful and complete `Documentation `__ as well as a `Wiki `__. * Liberal license. PyOpenCL is open-source under the `MIT license `__ and free for commercial, academic, and private use. * Broad support. PyOpenCL was tested and works with Apple's, AMD's, and Nvidia's CL implementations. Simple 4-step `install instructions `__ using Conda on Linux and macOS (that also install a working OpenCL implementation!) can be found in the `documentation `__. What you'll need if you do *not* want to use the convenient instructions above and instead build from source: * gcc/g++ new enough to be compatible with pybind11 (see their `FAQ `__) * `numpy `__, and * an OpenCL implementation. (See this `howto `__ for how to get one.) Links ----- * `Documentation `__ (read how things work) * `Conda Forge `__ (download binary packages for Linux, macOS, Windows) * `Python package index `__ (download releases) * `C. Gohlke's Windows binaries `__ (download Windows binaries) * `Github `__ (get latest source code, file bugs)