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Python wrapper for OpenCL

Project description

PyOpenCL: Pythonic Access to OpenCL, with Arrays and Algorithms

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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
`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.)

Places on the web related to PyOpenCL:

* `Python package index <>`_ (download releases)

* `Documentation <>`_ (read how things work)
* `Conda Forge <>`_ (download binary packages for Linux, macOS, Windows)
* `C. Gohlke's Windows binaries <>`_ (download Windows binaries)
* `Github <>`_ (get latest source code, file bugs)
* `Wiki <>`_ (read installation tips, get examples, read FAQ)

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