DGL Release 24.05

NVIDIA Optimized Frameworks (Latest Release) Download PDF

This DGL 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..

Contents of the DGL container

This container image contains the complete source of the version of DGL in /opt/dgl/dgl-source. It is pre-built and installed as a system Pyton module. The container includes the following:

GPU Requirements

Release 24.05 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

This DGL release includes the following key features and enhancements.

  • In this release of the NVIDIA DGL container, NVIDIA enhances support for distributed feature gathering by integrating NVSHMEM, further improving on the feature fetching performance for distributed GNN tasks. Check out the examples located at: /workspace/examples/wholegraph-examples
  • Add NVIDIA Synthetic Graph Generation tool for generating graphs with an arbitrary size, including node and edge tabular features.

    The major features of the release can be found in the DGL release notes.

Announcements

None.

NVIDIA DGL Container Versions

The following table shows what versions of Ubuntu, CUDA, DGL, and TensorRT are supported in each NVIDIA containers for DGL. For older container versions, refer to the Frameworks Support Matrix.

Container Version Ubuntu CUDA Toolkit DGL PyTorch
24.05 22.04 NVIDIA CUDA 12.4.1 2.2+22aea5c 24.05
24.04 2.1+e1f7738 24.04
24.03 NVIDIA CUDA 12.4.0.41 2.1+7c51cd16 24.03
24.01 NVIDIA CUDA 12.3.2 1.2+c660f5c 24.01
23.11 NVIDIA CUDA 12.3.0 1.1.2 23.11
23.09 NVIDIA CUDA 12.2.1 1.1.2 23.09
23.07 NVIDIA CUDA 12.1.1 1.1.1 23.07

Known Issues

  • When cpu sampling is enabled (use_uva=False and num_workers>0), DGL sampling process would initialize cuda instance (issue-6561), which could result in a segmentation fault with the current cuda driver in the container.
  • The tensors that are used as node features must be contiguous and cannot be views of other tensors when the use_uva flag is set to True in the dgl.dataloading.Dataloader class.

    When you attempt to use a graph with a non-contiguous or view tensors for edata or ndata, aDGLError will occur.

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