Python wrapper for OpenCL

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PyOpenCL: Pythonic Access to OpenCL, with Arrays and Algorithms
---------------------------------------------------------------

.. image:: https://gitlab.tiker.net/inducer/pyopencl/badges/master/pipeline.svg
:target: https://gitlab.tiker.net/inducer/pyopencl/commits/master
.. image:: https://badge.fury.io/py/pyopencl.png
:target: http://pypi.python.org/pypi/pyopencl
(Also: `Travis CI <https://travis-ci.org/inducer/pyopencl/builds>`_ to build binary wheels for releases, see `#264 <https://github.com/inducer/pyopencl/pull/264>`_)

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 <http://mathema.tician.de/software/pycuda>`_:

* Object cleanup tied to lifetime of objects. This idiom, often
called
`RAII <http://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 <http://documen.tician.de/pyopencl>`_
as well as a `Wiki <http://wiki.tiker.net/PyOpenCL>`_.

* Liberal license. PyOpenCL is open-source under the
`MIT license <http://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:

* gcc/g++ new enough to be compatible with pybind11
(see their `FAQ <https://pybind11.readthedocs.io/en/stable/faq.html>`_)
* `numpy <http://numpy.org>`_, and
* an OpenCL implementation. (See this `howto <http://wiki.tiker.net/OpenCLHowTo>`_ for how to get one.)

Places on the web related to PyOpenCL:

* `Python package index <http://pypi.python.org/pypi/pyopencl>`_ (download releases)

* `Documentation <http://documen.tician.de/pyopencl>`_ (read how things work)
* `Conda Forge <https://anaconda.org/conda-forge/pyopencl>`_ (download binary packages for Linux, macOS, Windows)
* `C. Gohlke's Windows binaries <http://www.lfd.uci.edu/~gohlke/pythonlibs/#pyopencl>`_ (download Windows binaries)
* `Github <http://github.com/inducer/pyopencl>`_ (get latest source code, file bugs)
* `Wiki <http://wiki.tiker.net/PyOpenCL>`_ (read installation tips, get examples, read FAQ)


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Release history Release notifications | RSS feed

2024.3

2024.2.7

2024.2.6

2024.2.5

2024.2.4 yanked

Reason this release was yanked:

missing compyte https://github.com/inducer/pyopencl/issues/765

2024.2.2

2024.2.1

2024.2

2024.1

2023.1.4

2023.1.3

2023.1.2

2023.1.1

2023.1

2022.3.1

2022.3

2022.2.4

2022.2.3

2022.2.2

2022.2.1

2022.2

2022.1.6

2022.1.5

2022.1.4

2022.1.3

2022.1.2

2022.1.1

2022.1

2021.2.13

2021.2.12

2021.2.11

2021.2.10

2021.2.9

2021.2.8

2021.2.7

2021.2.6

2021.2.5

2021.2.4 yanked

Reason this release was yanked:

test_arithmetic_on_non_scalars fails on 3.6 (no dataclasses)

2021.2.3

2021.2.2

2021.2.1

2021.2 yanked

Reason this release was yanked:

https://github.com/inducer/pyopencl/pull/474

2021.1.6

2021.1.5

2021.1.4

2021.1.3

2021.1.2

2021.1.1

2021.1

2020.3.1

2020.3

2020.2.2

2020.2.1

2020.2

2020.1

2019.1.2

2019.1.1

2019.1

2018.2.5

This version

2018.2.4

2018.2.3

2018.2.2

2018.2.1

2018.2

2018.1.1

2018.1

2017.2.2

2017.2.1

2017.2

2017.1.1

2017.1

2016.2.1

2016.2

2016.1

2015.2.4

2015.2.3

2015.2.2

2015.2.1

2015.2

2015.1

2014.1

2013.2

2013.1

2012.1

2011.2

2011.1.2

2011.1.1

2011.1

2011.1beta3 pre-release

2011.1beta2 pre-release

2011.1beta pre-release

0.92

0.91.5

0.91.4

0.91.3

0.91.2

0.91.1

0.91

0.90.4

0.90.3

0.90.2

0.90.1

0.90

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