NGC | Catalog
Logo for PyG
Features
Description
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.
Publisher
NVIDIA
Latest Tag
24.03-py3
Modified
April 8, 2024
Compressed Size
9.24 GB
Multinode Support
Yes
Multi-Arch Support
Yes
24.03-py3 (Latest) Security Scan Results

Linux / arm64

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Linux / amd64

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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 & 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 (see contents of PyTorch container)

Key Features in this release:

  • torch-frame integration

  • NVIDIAs syngen tool for synthetic graph data generation. See README.md for details

  • torch.compile accelerations.

    • We recommend using torch.compile on your GNN models for accelerating any example effortlessly.
    • Example: model = torch.compile(model)

Prerequisites

There are two main prerequisites for this container:

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