OpenCL integration for Python, plus shiny features
OTHER License
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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 <https://mathema.tician.de/software/pycuda>
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Object cleanup tied to lifetime of objects. This idiom, often
called RAII <https://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>
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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>
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as well as a Wiki <https://wiki.tiker.net/PyOpenCL>
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Liberal license. PyOpenCL is open-source under the
MIT license <https://en.wikipedia.org/wiki/MIT_License>
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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>
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using Conda on Linux and macOS (that also install a working OpenCL implementation!)
can be found in the documentation <https://documen.tician.de/pyopencl/>
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What you'll need if you do not want to use the convenient instructions above and instead build from source:
numpy <https://numpy.org>
__, andhowto <https://wiki.tiker.net/OpenCLHowTo>
__Documentation <https://documen.tician.de/pyopencl>
__Python package index <https://pypi.python.org/pypi/pyopencl>
__Conda Forge <https://anaconda.org/conda-forge/pyopencl>
__Github <https://github.com/inducer/pyopencl>
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