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.

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

The NVIDIA PyG Container is optimized for use with NVIDIA GPUs, and contains the following software for GPU acceleration:

• CUDA
• cuBLAS
• NVIDIA cuDNN
• NVIDIA NCCL (optimized for NVLink)
• RAPIDS
• NVIDIA Data Loading Library (DALI)
• TensorRT
• Torch-TensorRT

The software stack in this container has been validated for compatibility, and does not require any additional installation or compilation from the end user. This container can help accelerate your deep learning workflow from end to end.

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, 24.07 for the July 2024 release:

docker run --gpus all -it --rm nvcr.io/nvidia/pyg:24.07-py3

Suggested Reading

For the latest Release Notes, see the PyG Release Notes.

For a full list of the supported software and specific versions that come packaged with this framework based on the container image, see the Frameworks Support Matrix. For more information about PyG, including tutorials, documentation, and examples, see:

• PyG website
• PyG project
• PyG Documentation
• PyG Tutorials

Security CVEs

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

Ethical AI

NVIDIA's platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model's developer to ensure:

• The model meets the requirements for the relevant industry and use case
• The necessary instruction and documentation are provided to understand error rates, confidence intervals, and results
• The model is being used under the conditions and in the manner intended.

License

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