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%global _empty_manifest_terminate_build 0
%define debug_package %{nil}
Name: rl
Version: 0.0.4
Release: 2
Summary: A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
License: BSD-3
URL: https://github.com/pytorch/rl
Source0: https://atomgit.com/havefun/rl/raw/master/rl-0.0.4.tar.gz
BuildRequires: g++
Requires: python3-future
Requires: python3-numpy
%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-rl
Summary: A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
Provides: python-rl
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-setuptools_scm
BuildRequires: python3-pbr
BuildRequires: python3-pip
BuildRequires: python3-wheel
BuildRequires: python3-hatchling
BuildRequires: python3-astunparse
BuildRequires: python3-numpy
BuildRequires: python3-pyyaml
BuildRequires: cmake
BuildRequires: python3-typing-extensions
BuildRequires: python3-requests
BuildRequires: python3-pytorch
AutoReqProv: no
%description -n python3-rl
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.
%package help
Summary: Development documents and examples for torch
Provides: python3-rl-doc
%description help
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.
%prep
%autosetup -p1 -n %{name}-%{version}
%build
%py3_build
%install
%define _unpackaged_files_terminate_build 0
%py3_install
%files -n python3-rl
%doc *.md
%license LICENSE
%{python3_sitearch}/*
%changelog
* Tue April 15 2024 Hongyu Li<543306408@qq.com>
- Package init
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