pyopencl

Python wrapper for OpenCL

Project description

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

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This version

2022.3.1

Jan 4, 2023

2022.3

Nov 21, 2022

2022.2.4

Oct 14, 2022

2022.2.3

Sep 12, 2022

2022.2.2

Sep 11, 2022

2022.2.1

Sep 8, 2022

2022.2

Sep 8, 2022

2022.1.6

Jun 22, 2022

2022.1.5

May 19, 2022

2022.1.4

May 17, 2022

2022.1.3

Apr 19, 2022

2022.1.2

Apr 19, 2022

2022.1.1

Apr 19, 2022

2022.1

Feb 10, 2022

2021.2.13

Jan 17, 2022

2021.2.12

Jan 16, 2022

2021.2.11

Dec 30, 2021

2021.2.10

Dec 5, 2021

2021.2.9

Oct 18, 2021

2021.2.8

Oct 7, 2021

2021.2.7

Oct 6, 2021

2021.2.6

Jul 7, 2021

2021.2.5

Jun 27, 2021

2021.2.4 yanked

Jun 27, 2021

2021.2.3

Jun 24, 2021

2021.2.2

May 21, 2021

2021.2.1

May 17, 2021

2021.2 yanked

May 17, 2021

2021.1.6

Apr 5, 2021

2021.1.5

Apr 3, 2021

2021.1.4

Mar 15, 2021

2021.1.3

Mar 11, 2021

2021.1.2

Feb 17, 2021

2021.1.1

Jan 28, 2021

2021.1

Jan 25, 2021

2020.3.1

Nov 20, 2020

2020.3

Nov 18, 2020

2020.2.2

Sep 11, 2020

2020.2.1

Aug 16, 2020

2020.2

Jun 19, 2020

2020.1

Apr 30, 2020

2019.1.2

Nov 7, 2019

2019.1.1

Aug 12, 2019

2019.1

Jul 1, 2019

2018.2.5

Mar 8, 2019

2018.2.4

Feb 14, 2019

2018.2.3

Feb 13, 2019

2018.2.2

Dec 18, 2018

2018.2.1

Oct 23, 2018

2018.2

Sep 14, 2018

2018.1.1

Feb 24, 2018

2018.1

Jan 19, 2018

2017.2.2

Dec 17, 2017

2017.2.1

Dec 15, 2017

2017.2

Jul 2, 2017

2017.1.1

Jun 2, 2017

2017.1

May 24, 2017

2016.2.1

Dec 18, 2016

2016.2

Oct 12, 2016

2016.1

Apr 20, 2016

2015.2.4

Dec 21, 2015

2015.2.3

Dec 2, 2015

2015.2.2

Dec 1, 2015

2015.2.1

Nov 5, 2015

2015.2

Nov 4, 2015

2015.1

Feb 13, 2015

2014.1

May 29, 2014

2013.2

Oct 28, 2013

2013.1

Jul 4, 2013

2012.1

Jun 19, 2012

2011.2

Dec 2, 2011

2011.1.2

Jul 4, 2011

2011.1.1

Jun 26, 2011

2011.1

Jun 26, 2011

2011.1beta3 pre-release

Jan 6, 2011

2011.1beta2 pre-release

Jan 4, 2011

2011.1beta pre-release

Jan 1, 2011

0.92

Sep 20, 2010

0.91.5

Mar 2, 2010

0.91.4

Nov 29, 2009

0.91.3

Sep 28, 2009

0.91.2

Sep 18, 2009

0.91.1

Sep 18, 2009

0.91

Sep 14, 2009

0.90.4

Sep 11, 2009

0.90.3

Sep 8, 2009

0.90.2

Sep 3, 2009

0.90.1

Sep 2, 2009

0.90

Mar 11, 2009

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BLAKE2b-256	`53417ed1633cd6dfbfa093be9fa6dc4cb10859884cbabc6438e3ee220738bd18`

Hashes for pyopencl-2022.3.1-cp38-cp38-musllinux_1_1_x86_64.whl

Hashes for pyopencl-2022.3.1-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm	Hash digest
SHA256	`12684865a7efb65ed14058ecf11466d86dcd3936eb535a8aadc43ee92c98fc88`
MD5	`674e79473cbe839b7f027182f3ceec15`
BLAKE2b-256	`057d80de48b5d54522b3f1568d949072b9cfa37b035b38c6e7404203b6789653`

Hashes for pyopencl-2022.3.1-cp38-cp38-musllinux_1_1_i686.whl

Hashes for pyopencl-2022.3.1-cp38-cp38-musllinux_1_1_i686.whl
Algorithm	Hash digest
SHA256	`c675a8680199d1ab9725521993aca769952596da4cf58c3ad97814d3e144c7ab`
MD5	`b519cd5e702d4c750e4f6d219a122e54`
BLAKE2b-256	`5f1060aa63983d90c19890aad019ef83675f01652f755f862cdc60d8ccdbf506`

Hashes for pyopencl-2022.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Hashes for pyopencl-2022.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`aa24ed07984b9a1cd2a066c33db4b10eb5cbb02b14f1515c004c26a2ce50c660`
MD5	`7f8c4bd5e795d4159b47996452844189`
BLAKE2b-256	`a3a6762cddb1246d58ad0a681fb1fe8e6fa3290cc60d31871d5c1dccf75e43c0`

Hashes for pyopencl-2022.3.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl

Hashes for pyopencl-2022.3.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm	Hash digest
SHA256	`627182a8627c807379293527f2d42f94363286ca910b226703875bcc4392380b`
MD5	`10b265a4dadae56e49dc55ab1307a79a`
BLAKE2b-256	`46eb10b28cbe968b5e0e6bae2208c664636d6a622b88835762ce9df1d5dc6a80`

Hashes for pyopencl-2022.3.1-cp38-cp38-macosx_10_9_x86_64.whl

Hashes for pyopencl-2022.3.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`d1a84315388c781914843811c2d5895c66f2cb2128abf2415e5e44fa54befabf`
MD5	`b1b7311283f0184912cfebe229b5a33c`
BLAKE2b-256	`b98f42676ab14bf62c4da0a052da34d096f74a7177989e07186711f741e863e7`