%global _empty_manifest_terminate_build 0
Name: python-torch-geometric
Version: 2.3.0
Release: 1
Summary: Graph Neural Network Library for PyTorch
License: MIT License
URL: https://pypi.org/project/torch-geometric/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/43/b5/be9795db7756e6c1fa2606c8145ec637552487e72c6428ed0b231f8bcbd3/torch_geometric-2.3.0.tar.gz
BuildArch: noarch
%description
[![PyPI Version][pypi-image]][pypi-url]
[![Testing Status][testing-image]][testing-url]
[![Linting Status][linting-image]][linting-url]
[![Docs Status][docs-image]][docs-url]
[![Contributing][contributing-image]][contributing-url]
[![Slack][slack-image]][slack-url]
**[Documentation](https://pytorch-geometric.readthedocs.io)** | **[Paper](https://arxiv.org/abs/1903.02428)** | **[Colab Notebooks and Video Tutorials](https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html)** | **[External Resources](https://pytorch-geometric.readthedocs.io/en/latest/external/resources.html)** | **[OGB Examples](https://github.com/snap-stanford/ogb/tree/master/examples)**
**PyG** *(PyTorch Geometric)* is a library built upon [PyTorch](https://pytorch.org/) to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
It consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, from a variety of published papers.
In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, [multi GPU-support](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu), [`torch.compile`](https://pytorch-geometric.readthedocs.io/en/latest/tutorial/compile.html) support, [`DataPipe`](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py) support, a large number of common benchmark datasets (based on simple interfaces to create your own), the [GraphGym](https://pytorch-geometric.readthedocs.io/en/latest/advanced/graphgym.html) experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
**[Click here to join our Slack community!][slack-url]**
%package -n python3-torch-geometric
Summary: Graph Neural Network Library for PyTorch
Provides: python-torch-geometric
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-torch-geometric
[![PyPI Version][pypi-image]][pypi-url]
[![Testing Status][testing-image]][testing-url]
[![Linting Status][linting-image]][linting-url]
[![Docs Status][docs-image]][docs-url]
[![Contributing][contributing-image]][contributing-url]
[![Slack][slack-image]][slack-url]
**[Documentation](https://pytorch-geometric.readthedocs.io)** | **[Paper](https://arxiv.org/abs/1903.02428)** | **[Colab Notebooks and Video Tutorials](https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html)** | **[External Resources](https://pytorch-geometric.readthedocs.io/en/latest/external/resources.html)** | **[OGB Examples](https://github.com/snap-stanford/ogb/tree/master/examples)**
**PyG** *(PyTorch Geometric)* is a library built upon [PyTorch](https://pytorch.org/) to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
It consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, from a variety of published papers.
In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, [multi GPU-support](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu), [`torch.compile`](https://pytorch-geometric.readthedocs.io/en/latest/tutorial/compile.html) support, [`DataPipe`](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py) support, a large number of common benchmark datasets (based on simple interfaces to create your own), the [GraphGym](https://pytorch-geometric.readthedocs.io/en/latest/advanced/graphgym.html) experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
**[Click here to join our Slack community!][slack-url]**
%package help
Summary: Development documents and examples for torch-geometric
Provides: python3-torch-geometric-doc
%description help
[![PyPI Version][pypi-image]][pypi-url]
[![Testing Status][testing-image]][testing-url]
[![Linting Status][linting-image]][linting-url]
[![Docs Status][docs-image]][docs-url]
[![Contributing][contributing-image]][contributing-url]
[![Slack][slack-image]][slack-url]
**[Documentation](https://pytorch-geometric.readthedocs.io)** | **[Paper](https://arxiv.org/abs/1903.02428)** | **[Colab Notebooks and Video Tutorials](https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html)** | **[External Resources](https://pytorch-geometric.readthedocs.io/en/latest/external/resources.html)** | **[OGB Examples](https://github.com/snap-stanford/ogb/tree/master/examples)**
**PyG** *(PyTorch Geometric)* is a library built upon [PyTorch](https://pytorch.org/) to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
It consists of various methods for deep learning on graphs and other irregular structures, also known as *[geometric deep learning](http://geometricdeeplearning.com/)*, from a variety of published papers.
In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, [multi GPU-support](https://github.com/pyg-team/pytorch_geometric/tree/master/examples/multi_gpu), [`torch.compile`](https://pytorch-geometric.readthedocs.io/en/latest/tutorial/compile.html) support, [`DataPipe`](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/datapipe.py) support, a large number of common benchmark datasets (based on simple interfaces to create your own), the [GraphGym](https://pytorch-geometric.readthedocs.io/en/latest/advanced/graphgym.html) experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
**[Click here to join our Slack community!][slack-url]**
%prep
%autosetup -n torch-geometric-2.3.0
%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-torch-geometric -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot - 2.3.0-1
- Package Spec generated