DGL Container Early Access
Deep Graph Library (DGL) is a framework-neutral, easy-to-use, and scalable Python library used for implementing and training Graph Neural Networks (GNN). Being framework-neutral, DGL is easily integrated into an existing PyTorch, TensorFlow, or an Apache MXNet workflow.
To enable developers to quickly take advantage of GNNs, we’ve partnered with the DGL team to provide a containerized solution that includes DGL, PyTorch, NVIDIA RAPIDS™ (cudf, xgboost, rmm, cuml, and cugraph)—which can be used to accelerate ETL operations—and a set of tested dependencies. Our early release includes two containers, a ready-to-use DGL container, and an 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. Apply for early access to our DGL container or the SE(3)-Transformer for DGL container.