https://github.com/southernmethodistuniversity/pyg
ODSRCI Workshop: Graph Neural Networks (GNNs) using PyTorch-Geometric (PyG)
https://github.com/southernmethodistuniversity/pyg
gnns gpu graphs hpc python
Last synced: over 1 year ago
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ODSRCI Workshop: Graph Neural Networks (GNNs) using PyTorch-Geometric (PyG)
- Host: GitHub
- URL: https://github.com/southernmethodistuniversity/pyg
- Owner: SouthernMethodistUniversity
- License: mit
- Created: 2025-02-03T22:41:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-03T23:02:05.000Z (over 1 year ago)
- Last Synced: 2025-02-04T00:18:55.408Z (over 1 year ago)
- Topics: gnns, gpu, graphs, hpc, python
- Language: Python
- Homepage: https://southernmethodistuniversity.github.io/pyg/
- Size: 2.36 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SMU O'Donnell Data Science and Research Computing Institute PyTorch Geometric (PyG) Workshop
Welcome to the SMU [O’Donnell Data Science and Research Computing
Institute](https://www.smu.edu/provost/odonnell-institute) (ODSRCI) workshop on
PyTorch Geometric (PyG), a powerful library for deep learning on graphs and
irregular structures. PyG extends PyTorch with optimized data structures and
operations tailored for graph-based learning, enabling efficient implementation
of Graph Neural Networks (GNNs). In this session, we will explore PyG’s core
functionalities, including data handling, message passing, and model building
for tasks such as node classification, link prediction, and graph
classification. Whether you’re new to graph-based deep learning or looking to
enhance your PyTorch expertise, this workshop will provide hands-on guidance and
practical applications to help you leverage PyG effectively.
## Contribution
This workshop is open for contributions from the community. You can suggest
changes, report issues, or add new content to help enhance the learning
experience.