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116 lines
3.2 KiB
ReStructuredText
116 lines
3.2 KiB
ReStructuredText
.. DGL documentation master file, created by
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sphinx-quickstart on Fri Oct 5 14:18:01 2018.
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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Welcome to Deep Graph Library Tutorials and Documentation
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=========================================================
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.. toctree::
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:maxdepth: 1
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:caption: Get Started
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:hidden:
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:glob:
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install/index
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tutorials/blitz/index
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.. toctree::
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: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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stochastic_training/index
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guide/index
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guide_cn/index
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guide_ko/index
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graphtransformer/index
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notebooks/sparse/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::
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: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
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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/dgl.graphbolt
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api/python/nn-pytorch
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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.sparse_v0
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api/python/dgl.multiprocessing
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api/python/transforms
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api/python/udf
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.. toctree::
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: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::
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:maxdepth: 1
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:caption: Misc
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:hidden:
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:glob:
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faq
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env_var
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resources
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Deep Graph Library (DGL) is a Python package built for easy implementation of
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graph neural network model family, on top of existing DL frameworks (currently
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supporting PyTorch, MXNet and TensorFlow). It offers a versatile control of message passing,
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speed optimization via auto-batching and highly tuned sparse matrix kernels,
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and multi-GPU/CPU training to scale to graphs of hundreds of millions of
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nodes and edges.
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Getting Started
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---------------
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For absolute beginners, start with the :doc:`Blitz Introduction to DGL <tutorials/blitz/index>`.
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It covers the basic concepts of common graph machine learning tasks and a step-by-step
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on building Graph Neural Networks (GNNs) to solve them.
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For acquainted users who wish to learn more advanced usage,
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* `Learn DGL by examples <https://github.com/dmlc/dgl/tree/master/examples>`_.
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* Read the :doc:`User Guide<guide/index>` (:doc:`中文版链接<guide_cn/index>`), which explains the concepts
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and usage of DGL in much more details.
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* Go through the tutorials for :doc:`Stochastic Training of GNNs <notebooks/stochastic_training/index>`,
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which covers the basic steps for training GNNs on large graphs in mini-batches.
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* :doc:`Study classical papers <tutorials/models/index>` on graph machine learning alongside DGL.
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* Search for the usage of a specific API in the :doc:`API reference manual <api/python/index>`,
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which organizes all DGL APIs by their namespace.
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Contribution
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-------------
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DGL is free software; you can redistribute it and/or modify it under the terms
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of the Apache License 2.0. We welcome contributions.
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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
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-----
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* :ref:`genindex`
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