PyG Release 24.03

This PyG container release is intended for use on the NVIDIA® Ampere Architecture GPU, NVIDIA A100, and the associated NVIDIA CUDA® 12 and NVIDIA cuDNN 8 libraries.

Driver Requirements

Release 24.03 is based on NVIDIA CUDA 12.4.0.41, which requires NVIDIA Driver release 535 or later. However, if you are running on a data center GPU (for example, T4 or any other data center GPU), you can use NVIDIA driver release 450.51 (or later R450), 470.57 (or later R470), 510.47 (or later R510), or 525.85 (or later R525), or 535.86 (or later R535). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R460, and R520 drivers, which are not forward-compatible with CUDA 12.2. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

Contents of the PyG 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 example, to run the gcn.py example:

            

            
/workspace/examples# python gcn.py
        

See /workspace/README.md for details. The container also includes the following:

• torch-geometric 2.5.0
• pyg-lib 0.4.0
• 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
• Built on PyTorch 24.03, which contains the following:
  • Ubuntu 22.04 including Python 3.10
  • NVIDIA CUDA 12.4.0.41
  • NVIDIA cuBLAS 12.4.2.65
  • NVIDIA cuDNN 9.0.0.306
  • NVIDIA NCCL 2.20
  • NVIDIA RAPIDS™ 24.02
  • Apex
  • rdma-core 39.0
  • NVIDIA HPC-X 2.18
  • OpenMPI 4.1.4+
  • GDRCopy 2.3
  • TensorBoard 2.9.0
  • Nsight Compute 2024.1.0.13
  • Nsight Systems 2024.2.1.38
  • NVIDIA TensorRT™ 8.6.3
  • Torch-TensorRT 2.3.0a0
  • NVIDIA DALI® 1.35.
  • MAGMA 2.6.2
  • JupyterLab 2.3.2 including Jupyter-TensorBoard
  • TransformerEngine v1.4
  • PyTorch quantization wheel 2.1.2

GPU Requirements

Release 24.03 supports CUDA compute capability 6.0 and later. This corresponds to GPUs in the NVIDIA Pascal, NVIDIA Volta™, NVIDIA Turing™, NVIDIA Ampere architecture, and NVIDIA Hopper™ architecture families. For a list of GPUs to which this compute capability corresponds, see CUDA GPUs. For additional support details, see Deep Learning Frameworks Support Matrix.

Key Features and Enhancements

• New native PyG recsys example with python3 /workspace/examples/hetero/recommender_system.py.
• Includes RelBench (https://relbench.stanford.edu/) with examples for node and link level tasks. See /workspace/README.md for details.
• If trying to use /workspace/examples/ogbn_papers100m.py or /workspace/examples/multi_gpu/papers100m_gcn.py, we recommend using the respective python code from https://github.com/pyg-team/pytorch_geometric/pull/8173 instead. Please copy and paste the desired code example and run it with python3 <example_name>.py

Announcements

There are no announces for PyG in this release.

NVIDIA PyG Container Versions

The PyG container supports the same version of Ubuntu and CUDA as the PyTorch container.

Known Issues

• None