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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.
Latest Tag
May 24, 2024
Compressed Size
9.21 GB
Multinode Support
Multi-Arch Support
24.05-py3 (Latest) Security Scan Results

Linux / arm64

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Linux / amd64

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What Is In This Container?

This container image includes the complete source of the NVIDIA version of PyG in /opt/pyg/pytorch_geometric. It is prebuilt and installed as a system Python module. The /workspace/examples folder is copied from /opt/pyg/pytorch_geometric/examples for users starting to run PyG. For an introductory example about training a GCN, try python /workspace/examples/gcn.py

See /workspace/README.md for details

The container also uses torch-geometric 2.4.0 and pyg-lib 0.2.0. This container also contains the GNN Platform (/opt/pyg/gnn-platform), an NVIDIA project that provides a low-code API for rapid GNN experimentation and training/deploying end-to-end GNN pipelines. Examples can be found at /workspace/gnn-platform-examples. For more details about the GNN Platform, see /opt/pyg/gnn-platform/README.md.

This container is built on NVIDIA PyTorch container. For the full list of contents see the PyG Container Release Notes.

Open source code

Running the container

Use the following commands to run the container, where <xx.xx> is the container version.

docker run --gpus all -it --rm nvcr.io/nvidia/pyg:xx.xx-py3

For example, 23.11 for November 2023 release:

docker run --gpus all -it --rm nvcr.io/nvidia/pyg:23.11-py3

Security CVEs

To review known CVEs on this image, refer to the Security Scanning tab on this page.


By pulling and using the container, you accept the terms and conditions of this End User License Agreement.