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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.
+
+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
+   :hidden:
+   :titlesonly:
+   :glob:
+
+   stochastic_training/index
+   guide/index
+   guide_cn/index
+   guide_ko/index
+   graphtransformer/index
+   notebooks/sparse/index
+   tutorials/cpu/index
+   tutorials/multi/index
+   tutorials/dist/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/dgl.geometry
+   api/python/dgl.graphbolt
+   api/python/nn-pytorch
+   api/python/nn.functional
+   api/python/dgl.ops
+   api/python/dgl.optim
+   api/python/dgl.sampling
+   api/python/dgl.sparse_v0
+   api/python/dgl.multiprocessing
+   api/python/transforms
+   api/python/udf
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Notes
+   :hidden:
+   :glob:
+
+   contribute
+   developer/ffi
+   performance
+
+.. 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.
+
+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 <notebooks/stochastic_training/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
+-------------
+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
+:doc:`contribution guidelines <contribute>`.
+
+Index
+-----
+* :ref:`genindex`