(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:

• 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)