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%global _empty_manifest_terminate_build 0
Name: torchrl
Version: 0.2.1
Release: 1
Summary: A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
License: MIT
URL: https://pytorch.org/rl
Source0: https://github.com/pytorch/rl/archive/refs/tags/v%{version}.tar.gz#/%{name}-%{version}.tar.gz
BuildRequires: g++
Requires: python3-numpy
Requires: python3-pytorch
Requires: python3-cloudpickle
Requires: python3-tensordict
%description
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch.
It provides pytorch and python-first, low and high level abstractions for RL that are intended to
be efficient, modular, documented and properly tested. The code is aimed at supporting research in RL.
Most of it is written in python in a highly modular way, such that researchers can easily swap components,
transform them or write new ones with little effort.
%package -n python3-torchrl
Summary: A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
Provides: python-torchrl
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-setuptools_scm
BuildRequires: python3-pbr
BuildRequires: python3-pip
BuildRequires: python3-wheel
BuildRequires: python3-hatchling
BuildRequires: python3-pytorch
BuildRequires: ninja-build
%description -n python3-torchrl
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch.
It provides pytorch and python-first, low and high level abstractions for RL that are intended to
be efficient, modular, documented and properly tested. The code is aimed at supporting research in RL.
Most of it is written in python in a highly modular way, such that researchers can easily swap components,
transform them or write new ones with little effort.
%package help
Summary: Development documents and examples for torchrl
Provides: python3-torchrl-doc
%description help
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch.
It provides pytorch and python-first, low and high level abstractions for RL that are intended to
be efficient, modular, documented and properly tested. The code is aimed at supporting research in RL.
Most of it is written in python in a highly modular way, such that researchers can easily swap components,
transform them or write new ones with little effort.
%prep
%autosetup -p1 -n rl-%{version}
%build
%pyproject_build
%install
%pyproject_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}
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-torchrl
%doc *.md
%license LICENSE
%{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Jan 28 2024 Binshuo Zu <274620705z@gmail.com> - 0.2.1-1
- Package init
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