Skip to main content

Python wrapper for OpenCL

Project description

Gitlab Build Status Github Build Status Python Package Index Release Page

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.)

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyopencl-2021.2.10.tar.gz (451.3 kB view details)

Uploaded Source

Built Distributions

pyopencl-2021.2.10-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pyopencl-2021.2.10-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pyopencl-2021.2.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (862.6 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pyopencl-2021.2.10-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (862.0 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

pyopencl-2021.2.10-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (879.7 kB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

pyopencl-2021.2.10-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (878.9 kB view details)

Uploaded CPython 3.6m manylinux: glibc 2.17+ x86-64

File details

Details for the file pyopencl-2021.2.10.tar.gz.

File metadata

  • Download URL: pyopencl-2021.2.10.tar.gz
  • Upload date:
  • Size: 451.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0+

File hashes

Hashes for pyopencl-2021.2.10.tar.gz
Algorithm Hash digest
SHA256 75a1f202741bace9606a8680bbbfac69bf8a73d4e7511fb1a6ce3e48185996ae
MD5 38053a2ed361774bb4e6739b1bc8f3aa
BLAKE2b-256 595d2450f9b63412cc99441b13a12a8a45cb84a915ec3772763f6e25a9cc3d41

See more details on using hashes here.

File details

Details for the file pyopencl-2021.2.10-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyopencl-2021.2.10-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ea991b6e566c215dce0016c6765f2f9160a69d06c2f30ec92d99955b80701f2e
MD5 3cfeabe8d82bd2ea17e264e890484a47
BLAKE2b-256 d8c488d60d17e70025dfb075a875ee802753f46fd2a215dc750d330ba4f515f9

See more details on using hashes here.

File details

Details for the file pyopencl-2021.2.10-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyopencl-2021.2.10-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6b2fc3ee18f154fff9a46d7a8d23ae5407ec0339282d57752fca55a23d6a3810
MD5 dfaacef128d7d0358664624842778cb8
BLAKE2b-256 258cdd9b7fc9667c4cae7b432e9d06762b385e89831a0fee4f6b3d916af3be5e

See more details on using hashes here.

File details

Details for the file pyopencl-2021.2.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyopencl-2021.2.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ba9f1a591f9770d656c9df3379c096e82867bbb10d1fb875ffb120e8926bd0ad
MD5 c7524256ca44240b971f745f6367c31a
BLAKE2b-256 97011831c4db5822d3f0909ebb352c2de32a6ad1e96bc7fcab4ae8aef43990b9

See more details on using hashes here.

File details

Details for the file pyopencl-2021.2.10-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyopencl-2021.2.10-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a10fbce5b10efa6fce4974958ad2adccee13a7ddf202cbcd5c3f23ef1b092f25
MD5 3163b1441c87316cfc295ce71a879d99
BLAKE2b-256 eb724f7b7b44ec20f6cd0118170554a76c69c6f36141c399e1372d80199fcdb2

See more details on using hashes here.

File details

Details for the file pyopencl-2021.2.10-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyopencl-2021.2.10-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 295213968ecffcc02ecbcf8b0ba18886c3d29312f66a64a6cdc04ee2efb09c66
MD5 d2f4d025e9c2568c8373402e139b5a38
BLAKE2b-256 4f1ae40f902d59b5ac795fe96182f241a79ceb4d0bed357192b91d1f83d0541f

See more details on using hashes here.

File details

Details for the file pyopencl-2021.2.10-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyopencl-2021.2.10-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7b06f9612079195cfd14347712c52792d8f036d4c6f6831a194cc888ce3dd139
MD5 3c35228ab6da52f25ef0488f5e5e94d6
BLAKE2b-256 4fe399d0ac6f9b5ff1bc0c6fa2db2bfaebf11a755df4f13091389935a9e1f621

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page