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.
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.
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:
Security Common Vulnerabilities and Exposures (CVEs)
Please review the Security Scanning tab on NGC to view the latest security scan results. For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.
Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure this container meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
License
By pulling and using the container, you accept the terms and conditions of this End User License Agreement and Product-Specific Terms.