The NVIDIA® Deep Learning SDK accelerates widely-used deep learning frameworks such as DGL. DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet, or TensorFlow.
There are two containers available:
- DGL container - consists of the latest versions of DGL and PyTorch, their dependencies, and the latest performance optimizations to run your code with GPU-accelerated performance immediately.
- SE(3)-Transformer for DGL container - accelerated neural network training environment based on DGL, SE(3)-Transformer, and PyTorch and suited for recognizing 3-dimensional shapes.
This is useful for segmenting LIDAR point clouds or in pharmaceutical and drug discovery research, for example.
The GPU-accelerated NVIDIA DGL containers help developers and data scientists who work with Graph Neural Networks (GNN) on large, heterogeneous graphs with billions of edges. These containers allow developers to work more efficiently in an integrated, GPU-accelerated environment that combines DGL and PyTorch. Instead of using home-grown software that is expensive to maintain, developers can use end-to-end GNN solutions through tested, validated, and supported containers.
This document describes the key features, software enhancements and improvements, known issues, and how to run this container.