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 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:
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.
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
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.