Welcome to Deep Graph Library Tutorials and Documentationο
Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of existing DL frameworks (currently supporting PyTorch, MXNet and TensorFlow). It offers a versatile control of message passing, speed optimization via auto-batching and highly tuned sparse matrix kernels, and multi-GPU/CPU training to scale to graphs of hundreds of millions of nodes and edges.
Getting Startedο
For absolute beginners, start with the Blitz Introduction to DGL. It covers the basic concepts of common graph machine learning tasks and a step-by-step on building Graph Neural Networks (GNNs) to solve them.
For acquainted users who wish to learn more advanced usage,
Read the User Guide (δΈζηιΎζ₯), which explains the concepts and usage of DGL in much more details.
Go through the tutorials for Stochastic Training of GNNs, which covers the basic steps for training GNNs on large graphs in mini-batches.
Study classical papers on graph machine learning alongside DGL.
Search for the usage of a specific API in the API reference manual, which organizes all DGL APIs by their namespace.
Contributionο
DGL is free software; you can redistribute it and/or modify it under the terms of the Apache License 2.0. We welcome contributions. Join us on GitHub and check out our contribution guidelines.