DGL Release 23.07

The NVIDIA container image for DGL, release 23.07, is available on NGC.

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:

• DGL 1.1+f635e2a
• PyTorch 23.07
• RAPIDS 23.06
• Ubuntu 20.04 including Python 3.8
• NVIDIA CUDA® 12.1.1
• NVIDIA cuBLAS 12.1.3.1
• NVIDIA cuDNN 8.9.3
• NVIDIA NCCL 2.18.3
• Apex
• rdma-core 39.0
• OpenMPI 4.1.4+
• GDRCopy 2.3
• Nsight Compute 2023.1.1.4
• Nsight Systems 2023.2.3.1001
• NVIDIA HPC-X 2.15
• TensorRT 8.6.1.6
• SHARP 2.5
• TensorBoard 2.7.0
• DALI 1.27.0

GPU Requirements

Release 23.07 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.

• DGL container image version 23.07 is based on DGL 1.1.1.

  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.

Known Issues

• 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.