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|
%global _empty_manifest_terminate_build 0
Name: python-pycuda
Version: 2022.2.2
Release: 1
Summary: Python wrapper for Nvidia CUDA
License: MIT
URL: http://mathema.tician.de/software/pycuda
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/78/09/9df5358ffb74d225243b56a65ffe196de481fcd8f731f55e41f2d5d36015/pycuda-2022.2.2.tar.gz
BuildArch: noarch
%description
PyCUDA lets you access `Nvidia <https://nvidia.com>`_'s `CUDA
<https://nvidia.com/cuda/>`_ parallel computation API from Python.
Several wrappers of the CUDA API already exist-so what's so special
about 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. PyCUDA knows about dependencies, too, so (for
example) it won't detach from a context before all memory
allocated in it is also freed.
* Convenience. Abstractions like pycuda.driver.SourceModule and
pycuda.gpuarray.GPUArray make CUDA programming even more
convenient than with Nvidia's C-based runtime.
* Completeness. PyCUDA puts the full power of CUDA's driver API at
your disposal, if you wish. It also includes code for
interoperability with OpenGL.
* Automatic Error Checking. All CUDA errors are automatically
translated into Python exceptions.
* Speed. PyCUDA's base layer is written in C++, so all the niceties
above are virtually free.
* Helpful `Documentation <https://documen.tician.de/pycuda>`_.
Relatedly, like-minded computing goodness for `OpenCL <https://www.khronos.org/registry/OpenCL/>`_
is provided by PyCUDA's sister project `PyOpenCL <https://pypi.org/project/pyopencl>`_.
%package -n python3-pycuda
Summary: Python wrapper for Nvidia CUDA
Provides: python-pycuda
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pycuda
PyCUDA lets you access `Nvidia <https://nvidia.com>`_'s `CUDA
<https://nvidia.com/cuda/>`_ parallel computation API from Python.
Several wrappers of the CUDA API already exist-so what's so special
about 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. PyCUDA knows about dependencies, too, so (for
example) it won't detach from a context before all memory
allocated in it is also freed.
* Convenience. Abstractions like pycuda.driver.SourceModule and
pycuda.gpuarray.GPUArray make CUDA programming even more
convenient than with Nvidia's C-based runtime.
* Completeness. PyCUDA puts the full power of CUDA's driver API at
your disposal, if you wish. It also includes code for
interoperability with OpenGL.
* Automatic Error Checking. All CUDA errors are automatically
translated into Python exceptions.
* Speed. PyCUDA's base layer is written in C++, so all the niceties
above are virtually free.
* Helpful `Documentation <https://documen.tician.de/pycuda>`_.
Relatedly, like-minded computing goodness for `OpenCL <https://www.khronos.org/registry/OpenCL/>`_
is provided by PyCUDA's sister project `PyOpenCL <https://pypi.org/project/pyopencl>`_.
%package help
Summary: Development documents and examples for pycuda
Provides: python3-pycuda-doc
%description help
PyCUDA lets you access `Nvidia <https://nvidia.com>`_'s `CUDA
<https://nvidia.com/cuda/>`_ parallel computation API from Python.
Several wrappers of the CUDA API already exist-so what's so special
about 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. PyCUDA knows about dependencies, too, so (for
example) it won't detach from a context before all memory
allocated in it is also freed.
* Convenience. Abstractions like pycuda.driver.SourceModule and
pycuda.gpuarray.GPUArray make CUDA programming even more
convenient than with Nvidia's C-based runtime.
* Completeness. PyCUDA puts the full power of CUDA's driver API at
your disposal, if you wish. It also includes code for
interoperability with OpenGL.
* Automatic Error Checking. All CUDA errors are automatically
translated into Python exceptions.
* Speed. PyCUDA's base layer is written in C++, so all the niceties
above are virtually free.
* Helpful `Documentation <https://documen.tician.de/pycuda>`_.
Relatedly, like-minded computing goodness for `OpenCL <https://www.khronos.org/registry/OpenCL/>`_
is provided by PyCUDA's sister project `PyOpenCL <https://pypi.org/project/pyopencl>`_.
%prep
%autosetup -n pycuda-2022.2.2
%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-pycuda -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 2022.2.2-1
- Package Spec generated
|
