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.. DGL documentation master file, created by
   sphinx-quickstart on Fri Oct  5 14:18:01 2018.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

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Welcome to Deep Graph Library Tutorials and Documentation
=========================================================
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.. toctree::
   :maxdepth: 1
   :caption: Get Started
   :hidden:
   :glob:

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   install/index
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   tutorials/blitz/index
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.. toctree::
   :maxdepth: 2
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   :caption: Advanced Materials
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   :hidden:
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   :titlesonly:
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   :glob:
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   guide/index
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   guide_cn/index
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   guide_ko/index
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   tutorials/large/index
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   tutorials/cpu/index
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   tutorials/multi/index
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   tutorials/dist/index
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   tutorials/models/index
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.. toctree::
   :maxdepth: 2
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   :caption: API Reference
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   :hidden:
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   :glob:
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   api/python/dgl
   api/python/dgl.data
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   api/python/dgl.dataloading
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   api/python/dgl.DGLGraph
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   api/python/dgl.distributed
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   api/python/dgl.function
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   api/python/dgl.geometry
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   api/python/nn-pytorch
   api/python/nn-tensorflow
   api/python/nn-mxnet
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   api/python/nn.functional
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   api/python/dgl.ops
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   api/python/dgl.optim
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   api/python/dgl.sampling
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   api/python/dgl.multiprocessing
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   api/python/dgl.contrib.UnifiedTensor
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   api/python/transforms
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   api/python/udf
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.. toctree::
   :maxdepth: 1
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   :caption: Notes
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   :hidden:
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   :glob:

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   contribute
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   developer/ffi
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   performance
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.. toctree::
   :maxdepth: 1
   :caption: Misc
   :hidden:
   :glob:

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   faq
   env_var
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   resources

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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.
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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,

* `Learn DGL by examples <https://github.com/dmlc/dgl/tree/master/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.

Contribution
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-------------
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 <https://github.com/dmlc/dgl>`_ and check out our
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:doc:`contribution guidelines <contribute>`.
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Index
-----
* :ref:`genindex`