.. toctree:: :maxdepth: 1 :caption: Get Started :hidden: :glob: install/index tutorials/blitz/index
.. toctree:: :maxdepth: 2 :caption: Advanced Materials :hidden: :titlesonly: :glob: guide/index guide_cn/index tutorials/large/index tutorials/models/index
.. toctree:: :maxdepth: 2 :caption: API Reference :hidden: :glob: api/python/dgl api/python/dgl.data api/python/dgl.dataloading api/python/dgl.DGLGraph api/python/dgl.distributed api/python/dgl.function api/python/nn api/python/nn.functional api/python/dgl.ops api/python/dgl.optim api/python/dgl.sampling api/python/udf
.. toctree:: :maxdepth: 1 :caption: Developer Notes :hidden: :glob: contribute developer/ffi
.. toctree:: :maxdepth: 1 :caption: Misc :hidden: :glob: faq env_var resources
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.
For absolute beginners, start with the :doc:`Blitz Introduction to DGL <tutorials/blitz/index>`. 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,
- Learn DGL by examples.
- Read the :doc:`User Guide<guide/index>` (:doc:`中文版链接<guide_cn/index>`), which explains the concepts and usage of DGL in much more details.
- Go through the tutorials for :doc:`Stochastic Training of GNNs <tutorials/large/index>`, which covers the basic steps for training GNNs on large graphs in mini-batches.
- :doc:`Study classical papers <tutorials/models/index>` on graph machine learning alongside DGL.
- Search for the usage of a specific API in the :doc:`API reference manual <api/python/index>`, which organizes all DGL APIs by their namespace.
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 :doc:`contribution guidelines <contribute>`.