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|
%global _empty_manifest_terminate_build 0
Name: python-pyopencl
Version: 2022.3.1
Release: 1
Summary: Python wrapper for OpenCL
License: MIT
URL: http://mathema.tician.de/software/pyopencl
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/dd/4c/7f60601cb3e55d21cff151da2121d7d19e8f9f69e3519dc8fca2593668b2/pyopencl-2022.3.1.tar.gz
Requires: python3-numpy
Requires: python3-pytools
Requires: python3-platformdirs
Requires: python3-oclgrind-binary-distribution
Requires: python3-pocl-binary-distribution
Requires: python3-pytest
Requires: python3-Mako
%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 <https://mathema.tician.de/software/pycuda>`__:
* Object cleanup tied to lifetime of objects. This idiom, often
called `RAII <https://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>`__
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 <https://documen.tician.de/pyopencl>`__
as well as a `Wiki <https://wiki.tiker.net/PyOpenCL>`__.
* Liberal license. PyOpenCL is open-source under the
`MIT license <https://en.wikipedia.org/wiki/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 <https://documen.tician.de/pyopencl/misc.html#installation>`__
using Conda on Linux and macOS (that also install a working OpenCL implementation!)
can be found in the `documentation <https://documen.tician.de/pyopencl/>`__.
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 <https://pybind11.readthedocs.io/en/stable/faq.html>`__)
* `numpy <https://numpy.org>`__, and
* an OpenCL implementation. (See this `howto <https://wiki.tiker.net/OpenCLHowTo>`__
for how to get one.)
%package -n python3-pyopencl
Summary: Python wrapper for OpenCL
Provides: python-pyopencl
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-pyopencl
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 <https://mathema.tician.de/software/pycuda>`__:
* Object cleanup tied to lifetime of objects. This idiom, often
called `RAII <https://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>`__
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 <https://documen.tician.de/pyopencl>`__
as well as a `Wiki <https://wiki.tiker.net/PyOpenCL>`__.
* Liberal license. PyOpenCL is open-source under the
`MIT license <https://en.wikipedia.org/wiki/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 <https://documen.tician.de/pyopencl/misc.html#installation>`__
using Conda on Linux and macOS (that also install a working OpenCL implementation!)
can be found in the `documentation <https://documen.tician.de/pyopencl/>`__.
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 <https://pybind11.readthedocs.io/en/stable/faq.html>`__)
* `numpy <https://numpy.org>`__, and
* an OpenCL implementation. (See this `howto <https://wiki.tiker.net/OpenCLHowTo>`__
for how to get one.)
%package help
Summary: Development documents and examples for pyopencl
Provides: python3-pyopencl-doc
%description help
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 <https://mathema.tician.de/software/pycuda>`__:
* Object cleanup tied to lifetime of objects. This idiom, often
called `RAII <https://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>`__
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 <https://documen.tician.de/pyopencl>`__
as well as a `Wiki <https://wiki.tiker.net/PyOpenCL>`__.
* Liberal license. PyOpenCL is open-source under the
`MIT license <https://en.wikipedia.org/wiki/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 <https://documen.tician.de/pyopencl/misc.html#installation>`__
using Conda on Linux and macOS (that also install a working OpenCL implementation!)
can be found in the `documentation <https://documen.tician.de/pyopencl/>`__.
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 <https://pybind11.readthedocs.io/en/stable/faq.html>`__)
* `numpy <https://numpy.org>`__, and
* an OpenCL implementation. (See this `howto <https://wiki.tiker.net/OpenCLHowTo>`__
for how to get one.)
%prep
%autosetup -n pyopencl-2022.3.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-pyopencl -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Apr 07 2023 Python_Bot <Python_Bot@openeuler.org> - 2022.3.1-1
- Package Spec generated
|
