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Source: pytorch-geometric
Section: science
Priority: optional
Maintainer: Debian Deep Learning Team <debian-science-maintainers@lists.alioth.debian.org>
Uploaders:
Andrius Merkys <merkys@debian.org>,
Rules-Requires-Root: no
Build-Depends:
debhelper-compat (= 13),
dh-sequence-python3,
flit,
libtorch-dev <!nocheck>,
libsleef-dev [arm64 ppc64el] <!nocheck>,
pybuild-plugin-pyproject,
python3,
python3-ase <!nocheck>,
python3-jinja2 <!nocheck>,
python3-pandas <!nocheck>,
python3-pyparsing <!nocheck>,
python3-pytest <!nocheck>,
python3-rdkit <!nocheck>,
python3-setuptools,
python3-sklearn <!nocheck>,
python3-torch <!nocheck>,
python3-torch-cluster <!nocheck>,
python3-torch-sparse <!nocheck>,
python3-tqdm <!nocheck>,
Testsuite: autopkgtest-pkg-pybuild
Standards-Version: 4.6.2
Homepage: https://github.com/pyg-team/pytorch_geometric
Vcs-Browser: https://salsa.debian.org/deeplearning-team/pytorch-geometric
Vcs-Git: https://salsa.debian.org/deeplearning-team/pytorch-geometric.git
Package: python3-torch-geometric
Architecture: all
Depends:
${misc:Depends},
${python3:Depends},
Description: Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch 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, 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, DataPipe
support, distributed graph learning via Quiver, a large number of common
benchmark datasets (based on simple interfaces to create your own), the
GraphGym experiment manager, and helpful transforms, both for learning on
arbitrary graphs as well as on 3D meshes or point clouds.
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