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%global _empty_manifest_terminate_build 0
Name: python-multiprocessing_on_dill
Version: 3.5.0a4
Release: 1
Summary: A friendly fork of multiprocessing which uses dill instead of pickle
License: PSFL
URL: https://github.com/sixty-north/multiprocessing_on_dill
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/86/4d/4b135e2e5cd0194eb29f2ed36e9a77a07596787a9a8ac2279bd4445398f2/multiprocessing_on_dill-3.5.0a4.tar.gz
BuildArch: noarch
%description
This project is a friendly fork – for Python 3 – of the Python Standard Library `multiprocessing
<https://docs.python.org/3/library/multiprocessing.html>`_ module, which uses the third-party
`dill <https://pypi.python.org/pypi/dill>`_ serializer instead of the standard ``pickle`` serializer. This overcomes
many shortcomings of ``pickle`` which prevent multiprocessing being used with lambdas, closures and other useful Python
objects.
The easiest way to use ``multiprocessing_on_dill`` in place of ``multiprocessing`` is simply to replace any import
statements like this::
import multiprocessing
with::
import multiprocessing_on_dill as multiprocessing
and import statements like this::
from multiprocessing import Pool
with::
from multiprocessing_on_dill import Pool
With such import changes in place, it will now be possible to use functions like ``Pool.map()`` with lambdas::
pool = Pool(12)
result = pool.map(lambda x: x*x, range(10000))
Everything else should be identical to the Python version.
You can determine from which version of the Python Standard Library ``multiprocessing_on_dill`` has been forked, by
examining the ``multiprocessing_on_dill.__version__`` attribute.
%package -n python3-multiprocessing_on_dill
Summary: A friendly fork of multiprocessing which uses dill instead of pickle
Provides: python-multiprocessing_on_dill
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-multiprocessing_on_dill
This project is a friendly fork – for Python 3 – of the Python Standard Library `multiprocessing
<https://docs.python.org/3/library/multiprocessing.html>`_ module, which uses the third-party
`dill <https://pypi.python.org/pypi/dill>`_ serializer instead of the standard ``pickle`` serializer. This overcomes
many shortcomings of ``pickle`` which prevent multiprocessing being used with lambdas, closures and other useful Python
objects.
The easiest way to use ``multiprocessing_on_dill`` in place of ``multiprocessing`` is simply to replace any import
statements like this::
import multiprocessing
with::
import multiprocessing_on_dill as multiprocessing
and import statements like this::
from multiprocessing import Pool
with::
from multiprocessing_on_dill import Pool
With such import changes in place, it will now be possible to use functions like ``Pool.map()`` with lambdas::
pool = Pool(12)
result = pool.map(lambda x: x*x, range(10000))
Everything else should be identical to the Python version.
You can determine from which version of the Python Standard Library ``multiprocessing_on_dill`` has been forked, by
examining the ``multiprocessing_on_dill.__version__`` attribute.
%package help
Summary: Development documents and examples for multiprocessing_on_dill
Provides: python3-multiprocessing_on_dill-doc
%description help
This project is a friendly fork – for Python 3 – of the Python Standard Library `multiprocessing
<https://docs.python.org/3/library/multiprocessing.html>`_ module, which uses the third-party
`dill <https://pypi.python.org/pypi/dill>`_ serializer instead of the standard ``pickle`` serializer. This overcomes
many shortcomings of ``pickle`` which prevent multiprocessing being used with lambdas, closures and other useful Python
objects.
The easiest way to use ``multiprocessing_on_dill`` in place of ``multiprocessing`` is simply to replace any import
statements like this::
import multiprocessing
with::
import multiprocessing_on_dill as multiprocessing
and import statements like this::
from multiprocessing import Pool
with::
from multiprocessing_on_dill import Pool
With such import changes in place, it will now be possible to use functions like ``Pool.map()`` with lambdas::
pool = Pool(12)
result = pool.map(lambda x: x*x, range(10000))
Everything else should be identical to the Python version.
You can determine from which version of the Python Standard Library ``multiprocessing_on_dill`` has been forked, by
examining the ``multiprocessing_on_dill.__version__`` attribute.
%prep
%autosetup -n multiprocessing_on_dill-3.5.0a4
%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-multiprocessing_on_dill -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 3.5.0a4-1
- Package Spec generated
|
