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Python wrapper for OpenCL

Project description

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(Also: Travis CI to build binary wheels for releases, see #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:

  • 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.)

Places on the web related to PyOpenCL:

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