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.

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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.