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Welcome to Deep Graph Library Tutorials and Documentation

.. toctree::
   :maxdepth: 1
   :caption: Get Started
   :hidden:
   :glob:

   install/index
   tutorials/blitz/index

.. toctree::
   :maxdepth: 2
   :caption: Advanced Materials
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   :titlesonly:
   :glob:

   guide/index
   guide_cn/index
   tutorials/large/index
   tutorials/models/index

.. toctree::
   :maxdepth: 2
   :caption: API Reference
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   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/dgl.multiprocessing
   api/python/udf

.. toctree::
   :maxdepth: 1
   :caption: Developer Notes
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   contribute
   developer/ffi

.. toctree::
   :maxdepth: 1
   :caption: Misc
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   :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.

Getting Started

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,

Contribution

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>`.

Index