| Name | pyopencl JSON |
| Version |
2025.2.6
JSON |
| download |
| home_page | None |
| Summary | Python wrapper for OpenCL |
| upload_time | 2025-07-27 19:58:23 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | ~=3.10 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
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| coveralls test coverage |
No coveralls.
|
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/actions/workflows/ci.yml/badge.svg
:alt: Github Build Status
:target: https://github.com/inducer/pyopencl/actions/workflows/ci.yml
.. |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 <https://mathema.tician.de/software/pycuda>`__:
* Object cleanup tied to lifetime of objects. This idiom, often
called `RAII <https://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>`__
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 <https://documen.tician.de/pyopencl>`__
as well as a `Wiki <https://wiki.tiker.net/PyOpenCL>`__.
* Liberal license. PyOpenCL is open-source under the
`MIT license <https://en.wikipedia.org/wiki/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 <https://documen.tician.de/pyopencl/misc.html#installation>`__
using Conda on Linux and macOS (that also install a working OpenCL implementation!)
can be found in the `documentation <https://documen.tician.de/pyopencl/>`__.
What you'll need if you do *not* want to use the convenient instructions above and
instead build from source:
* g++/clang new enough to be compatible with nanobind (specifically, full support of C++17 is needed)
* `numpy <https://numpy.org>`__, and
* an OpenCL implementation. (See this `howto <https://wiki.tiker.net/OpenCLHowTo>`__
for how to get one.)
Links
-----
* `Documentation <https://documen.tician.de/pyopencl>`__
(read how things work)
* `Python package index <https://pypi.python.org/pypi/pyopencl>`__
(download releases, including binary wheels for Linux, macOS, Windows)
* `Conda Forge <https://anaconda.org/conda-forge/pyopencl>`__
(download binary packages for Linux, macOS, Windows)
* `Github <https://github.com/inducer/pyopencl>`__
(get latest source code, file bugs)
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