dmlc--dgl
Zihao Ye
93ac29ce34
* upd * upd * upd * lint * fix * fix test * fix * fix * upd * upd * upd * upd * upd * upd * upd * upd * upd * upd * upd tutorial * upd * upd * fix kg * upd doc organization * refresh test * upd * refactor doc * fix lint Co-authored-by: Minjie Wang <minjie.wang@nyu.edu>
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161 行
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.. 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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Overview of DGL
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===============
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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 (e.g.
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PyTorch, MXNet, Gluon etc.).
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DGL reduces the implementation of graph neural networks into declaring a set
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of *functions* (or *modules* in PyTorch terminology). In addition, DGL
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provides:
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* Versatile controls over message passing, ranging from low-level operations
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such as sending along selected edges and receiving on specific nodes, to
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high-level control such as graph-wide feature updates.
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* Transparent speed optimization with automatic batching of computations and
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sparse matrix multiplication.
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* Seamless integration with existing deep learning frameworks.
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* Easy and friendly interfaces for node/edge feature access and graph
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structure manipulation.
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* Good scalability to graphs with tens of millions of vertices.
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To begin with, we have prototyped 10 models across various domains:
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semi-supervised learning on graphs (with potentially billions of nodes/edges),
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generative models on graphs, (previously) difficult-to-parallelize tree-based
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models like TreeLSTM, etc. We also implement some conventional models in DGL
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from a new graphical perspective yielding simplicity.
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Relationship of DGL to other frameworks
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---------------------------------------
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DGL is designed to be compatible and agnostic to the existing tensor
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frameworks. It provides a backend adapter interface that allows easy porting
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to other tensor-based, autograd-enabled frameworks.
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Getting Started
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---------------
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Follow the :doc:`instructions<install/index>` to install DGL.
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:doc:`DGL at a glance<tutorials/basics/1_first>` is the most common place to get started with.
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It offers a broad experience of using DGL for deep learning on graph data.
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You can learn other basic concepts of DGL through the dedicated tutorials.
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* Learn constructing graphs and set/get node and edge features :doc:`here<tutorials/basics/2_basics>`.
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* Learn performing computation on graph using message passing :doc:`here<tutorials/basics/3_pagerank>`.
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* Learn processing multiple graph samples in a batch :doc:`here<tutorials/basics/4_batch>`.
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* Learn working with heterogeneous graph data :doc:`here<tutorials/basics/5_hetero>`.
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End-to-end model tutorials are other good starting points for learning DGL and popular
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models on graphs. The model tutorials are categorized based on the way they utilize DGL APIs.
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* :ref:`Graph Neural Network and its variant <tutorials1-index>`: Learn how to use DGL to train
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popular **GNN models** on one input graph.
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* :ref:`Dealing with many small graphs <tutorials2-index>`: Learn how to train models for
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many graph samples such as sentence parse trees.
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* :ref:`Generative models <tutorials3-index>`: Learn how to deal with **dynamically-changing graphs**.
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* :ref:`Old (new) wines in new bottle <tutorials4-index>`: Learn how to combine DGL with tensor-based
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DGL framework in a flexible way. Explore new perspective on traditional models by graphs.
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* :ref:`Training on giant graphs <tutorials5-index>`: Learn how to train graph neural networks
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on giant graphs.
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Each tutorial is accompanied with a runnable python script and jupyter notebook that
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can be downloaded. If you would like the tutorials improved, please raise a github issue.
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API reference document lists more endetailed specifications of each API and GNN modules,
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a useful manual for in-depth developers.
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.. toctree::
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:maxdepth: 1
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:caption: Installation
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:hidden:
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:glob:
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install/index
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install/backend
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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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tutorials/basics/index
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.. toctree::
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:maxdepth: 3
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:caption: Tutorials
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:hidden:
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:glob:
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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/graph
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api/python/heterograph
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api/python/readout
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api/python/batch_heterograph
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api/python/nn
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api/python/function
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api/python/udf
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api/python/traversal
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api/python/propagate
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api/python/transform
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api/python/sampler
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api/python/data
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api/python/nodeflow
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api/python/random
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api/python/model_zoo
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.. toctree::
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:maxdepth: 1
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:caption: Developer 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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.. 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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Free software
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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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History
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-------
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Prototype of DGL started in early Spring, 2018, at NYU Shanghai by Prof. `Zheng
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Zhang <https://shanghai.nyu.edu/academics/faculty/directory/zheng-zhang>`_ and
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Quan Gan. Serious development began when `Minjie
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<https://jermainewang.github.io/>`_, `Lingfan <https://cs.nyu.edu/~lingfan/>`_
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and Prof. `Jinyang Li <http://www.news.cs.nyu.edu/~jinyang/>`_ from NYU's
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system group joined, flanked by a team of student volunteers at NYU Shanghai,
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Fudan and other universities (Yu, Zihao, Murphy, Allen, Qipeng, Qi, Hao), as
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well as early adopters at the CILVR lab (Jake Zhao). Development accelerated
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when AWS MXNet Science team joined force, with Da Zheng, Alex Smola, Haibin
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Lin, Chao Ma and a number of others. For full credit, see `here
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<https://www.dgl.ai/ack>`_.
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Index
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-----
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* :ref:`genindex`
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