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diff --git a/python-torch-geometric.spec b/python-torch-geometric.spec new file mode 100644 index 0000000..22c5af1 --- /dev/null +++ b/python-torch-geometric.spec @@ -0,0 +1,108 @@ +%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]** +<p align="center"> + <a href="https://medium.com/stanford-cs224w"><img style="max-width=: 941px" src="https://data.pyg.org/img/cs224w_tutorials.png" /></a> + +%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]** +<p align="center"> + <a href="https://medium.com/stanford-cs224w"><img style="max-width=: 941px" src="https://data.pyg.org/img/cs224w_tutorials.png" /></a> + +%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]** +<p align="center"> + <a href="https://medium.com/stanford-cs224w"><img style="max-width=: 941px" src="https://data.pyg.org/img/cs224w_tutorials.png" /></a> + +%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 <Python_Bot@openeuler.org> - 2.3.0-1 +- Package Spec generated |