pyopencl 2024.2.7

Description:

pyopencl 2024.2.7

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:

g++/clang new enough to be compatible with nanobind (specifically, full support of C++17 is needed)
numpy, and
an OpenCL implementation. (See this howto
for how to get one.)


Links

Documentation
(read how things work)
Python package index
(download releases, including binary wheels for Linux, macOS, Windows)
Conda Forge
(download binary packages for Linux, macOS, Windows)
Github
(get latest source code, file bugs)