DGL Release 24.08
This container is only available to NVAIE customers.
This DGL container release is intended for use on the NVIDIA® Hopper Architecture GPU, NVIDIA H100, the NVIDIA® Ampere Architecture GPU, NVIDIA A100, and the associated NVIDIA CUDA® 12 and NVIDIA cuDNN 9 libraries.
Driver Requirements
Release 24.08 is based on CUDA 12.6 which requires NVIDIA Driver release 560 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 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545).
The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545 and R555 drivers, which are not forward-compatible with CUDA 12.6. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.
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 2.3.
- RAPIDS 24.06
- This container also contains WholeGraph 24.06 with NVSHMEM support. WholeGraph is a part of the NVIDIA RAPIDS library which provides an underlying graph storage structure to enhance GNN training, especially optimized for NVIDIA hardware.
- NVIDIA CUDA® 12.6
- NVIDIA cuBLAS 12.6.0.22
- NVIDIA cuDNN 9.3.0.75
- NVIDIA NCCL 2.22.3
- NVIDIA RAPIDS™ 24.06
- rdma-core 39.0
- NVIDIA HPC-X 2.19
- OpenMPI 4.1.7
- GDRCopy 2.3
- TensorBoard 2.16.2
- Nsight Compute 2024.3.0.15
- Nsight Systems 2024.4.2.133
- NVIDIA TensorRT™ 10.3.0.26
- Torch-TensorRT 2.5.0.a0
- NVIDIA DALI® 1.40
- nvImageCodec 0.2.0.7
- MAGMA 2.6.2
- JupyterLab 2.4.2 including Jupyter-TensorBoard
- PyTorch quantization wheel v2.1.2
- TransformerEngine v1.9
- TensorRT Model Optimizer 0.15.0
GPU Requirements
Release 24.08 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.
- The DGL container supports distributed in-memory sampling and feature gathering with WholeGraph. It can be easily integrated with DGL GraphBolt dataloader, enabling out-of-the-box distributed GNN training.
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, a DGLError will occur.