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%global _empty_manifest_terminate_build 0
Name: python-pyro-ppl
Version: 1.8.6
Release: 1
Summary: Deep universal probabilistic programming with Python and PyTorch
License: Apache-2.0
URL: https://github.com/pyro-ppl/pyro
Source0: https://github.com/pyro-ppl/pyro/archive/refs/tags/%{version}.tar.gz#/%{name}-%{version}.tar.gz
Requires: python3-numpy
Requires: python3-opt-einsum
Requires: python3-pytorch
Requires: python3-tqdm
%description
Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:
- Universal: Pyro is a universal PPL - it can represent any computable probability distribution.
- Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.
- Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.
- Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level
abstractions to express generative and inference models, while allowing experts easy-access to customize inference.
%package -n python3-pyro-ppl
Summary: Deep universal probabilistic programming with Python and PyTorch
Provides: python-pyro-ppl
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-setuptools_scm
BuildRequires: python3-pbr
BuildRequires: python3-pip
BuildRequires: python3-wheel
%description -n python3-pyro-ppl
Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:
- Universal: Pyro is a universal PPL - it can represent any computable probability distribution.
- Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.
- Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.
- Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level
abstractions to express generative and inference models, while allowing experts easy-access to customize inference.
%package help
Summary: Development documents and examples for python-pyro-ppl
Provides: python3-pyro-ppl-doc
%description help
Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:
- Universal: Pyro is a universal PPL - it can represent any computable probability distribution.
- Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.
- Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.
- Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level
abstractions to express generative and inference models, while allowing experts easy-access to customize inference.
%prep
%autosetup -p1 -n pyro-%{version}
%build
%pyproject_build
%install
%pyproject_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
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}/doclist.lst .
%files -n python3-pyro-ppl
%doc *.md
%license LICENSE.md
%{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Jan 28 2024 Binshuo Zu <274620705z@gmail.com> - 1.8.6-1
- Package init
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