From e911aa5e119a7f699adb437079fe92f8b97810f1 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Fri, 5 May 2023 09:43:59 +0000 Subject: automatic import of python-pytorchrl --- .gitignore | 1 + python-pytorchrl.spec | 179 ++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 181 insertions(+) create mode 100644 python-pytorchrl.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..6169f68 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/pytorchrl-3.2.11.tar.gz diff --git a/python-pytorchrl.spec b/python-pytorchrl.spec new file mode 100644 index 0000000..9f7ce12 --- /dev/null +++ b/python-pytorchrl.spec @@ -0,0 +1,179 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pytorchrl +Version: 3.2.11 +Release: 1 +Summary: Disributed RL implementations with ray and pytorch. +License: MIT License +URL: https://github.com/PyTorchRL/pytorchrl/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b8/b0/dbd0fba3ddfa2b5cd8818e85497478b857f26a843aeba5cb35aa88cc2590/pytorchrl-3.2.11.tar.gz +BuildArch: noarch + +Requires: python3-gym[atari] +Requires: python3-gym[accept-rom-license] +Requires: python3-ray[default] +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scipy +Requires: python3-lz4 +Requires: python3-tqdm +Requires: python3-opencv-python +Requires: python3-wandb +Requires: python3-hydra-core + +%description +## PyTorchRL: A PyTorch library for reinforcement learning + +Deep Reinforcement learning (DRL) has been very successful in recent years but current methods still require vast amounts of data to solve non-trivial environments. Scaling to solve more complex tasks requires frameworks that are flexible enough to allow prototyping and testing of new ideas, yet avoiding the impractically slow experimental turnaround times associated to single-threaded implementations. PyTorchRL is a pytorch-based library for DRL that allows to easily assemble RL agents using a set of core reusable and easily extendable sub-modules as building blocks. To reduce training times, PyTorchRL allows scaling agents with a parameterizable component called Scheme, that permits to define distributed architectures with great flexibility by specifying which operations should be decoupled, which should be parallelized, and how parallel tasks should be synchronized. + +### Installation + +``` + conda create -y -n pytorchrl + conda activate pytorchrl + + conda install pytorch torchvision cudatoolkit -c pytorch + + pip install pytorchrl +``` + +### Documentation + +PyTorchRL documentation can be found [here](https://pytorchrl.readthedocs.io/en/latest/). + +### Citing PyTorchRL +Here is the [paper](https://arxiv.org/abs/2007.02622) + +``` +@misc{bou2021pytorchrl, + title={PyTorchRL: Modular and Distributed Reinforcement Learning in PyTorch}, + author={Albert Bou and Gianni De Fabritiis}, + year={2021}, + eprint={2007.02622}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + +%package -n python3-pytorchrl +Summary: Disributed RL implementations with ray and pytorch. +Provides: python-pytorchrl +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pytorchrl +## PyTorchRL: A PyTorch library for reinforcement learning + +Deep Reinforcement learning (DRL) has been very successful in recent years but current methods still require vast amounts of data to solve non-trivial environments. Scaling to solve more complex tasks requires frameworks that are flexible enough to allow prototyping and testing of new ideas, yet avoiding the impractically slow experimental turnaround times associated to single-threaded implementations. PyTorchRL is a pytorch-based library for DRL that allows to easily assemble RL agents using a set of core reusable and easily extendable sub-modules as building blocks. To reduce training times, PyTorchRL allows scaling agents with a parameterizable component called Scheme, that permits to define distributed architectures with great flexibility by specifying which operations should be decoupled, which should be parallelized, and how parallel tasks should be synchronized. + +### Installation + +``` + conda create -y -n pytorchrl + conda activate pytorchrl + + conda install pytorch torchvision cudatoolkit -c pytorch + + pip install pytorchrl +``` + +### Documentation + +PyTorchRL documentation can be found [here](https://pytorchrl.readthedocs.io/en/latest/). + +### Citing PyTorchRL +Here is the [paper](https://arxiv.org/abs/2007.02622) + +``` +@misc{bou2021pytorchrl, + title={PyTorchRL: Modular and Distributed Reinforcement Learning in PyTorch}, + author={Albert Bou and Gianni De Fabritiis}, + year={2021}, + eprint={2007.02622}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + +%package help +Summary: Development documents and examples for pytorchrl +Provides: python3-pytorchrl-doc +%description help +## PyTorchRL: A PyTorch library for reinforcement learning + +Deep Reinforcement learning (DRL) has been very successful in recent years but current methods still require vast amounts of data to solve non-trivial environments. Scaling to solve more complex tasks requires frameworks that are flexible enough to allow prototyping and testing of new ideas, yet avoiding the impractically slow experimental turnaround times associated to single-threaded implementations. PyTorchRL is a pytorch-based library for DRL that allows to easily assemble RL agents using a set of core reusable and easily extendable sub-modules as building blocks. To reduce training times, PyTorchRL allows scaling agents with a parameterizable component called Scheme, that permits to define distributed architectures with great flexibility by specifying which operations should be decoupled, which should be parallelized, and how parallel tasks should be synchronized. + +### Installation + +``` + conda create -y -n pytorchrl + conda activate pytorchrl + + conda install pytorch torchvision cudatoolkit -c pytorch + + pip install pytorchrl +``` + +### Documentation + +PyTorchRL documentation can be found [here](https://pytorchrl.readthedocs.io/en/latest/). + +### Citing PyTorchRL +Here is the [paper](https://arxiv.org/abs/2007.02622) + +``` +@misc{bou2021pytorchrl, + title={PyTorchRL: Modular and Distributed Reinforcement Learning in PyTorch}, + author={Albert Bou and Gianni De Fabritiis}, + year={2021}, + eprint={2007.02622}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + +%prep +%autosetup -n pytorchrl-3.2.11 + +%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-pytorchrl -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot - 3.2.11-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..dab17bf --- /dev/null +++ b/sources @@ -0,0 +1 @@ +209ea78ac2eae07549c65c3ecd423330 pytorchrl-3.2.11.tar.gz -- cgit v1.2.3