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 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.)
  • 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)


OpenCL integration for Python, plus shiny features

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