Pyopencl Save

OpenCL integration for Python, plus shiny features

Project README

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)
Open Source Agenda is not affiliated with "Pyopencl" Project. README Source: inducer/pyopencl