dmlc--dgl
Minjie Wang
90e78c5854
* WIP: import tvm runtime node system * WIP: object system * containers * tested basic container composition * tested custom object * fix setattr bug * tested object container return * fix lint * some comments about get/set state * fix lint * fix lint * update cython * fix cython * ffi doc * fix doc
232 行
6.4 KiB
ReStructuredText
232 行
6.4 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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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. Currently, our prototype
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works with MXNet/Gluon and PyTorch.
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Get Started
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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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Follow the :doc:`instructions<install/index>` to install DGL. The :doc:`DGL at a glance<tutorials/basics/1_first>`
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is the most common place to get started with. Each tutorial is accompanied with a runnable
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python script and jupyter notebook that can be downloaded.
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.. ================================================================================================
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(start) MANUALLY INCLUDE THE GENERATED TUTORIALS/BASIC/INDEX.RST HERE TO EMBED THE EXAMPLES
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================================================================================================
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:ref:`sphx_glr_tutorials_basics_1_first.py`
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:ref:`sphx_glr_tutorials_basics_2_basics.py`
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:ref:`sphx_glr_tutorials_basics_3_pagerank.py`
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.. figure:: /tutorials/basics/images/thumb/sphx_glr_4_batch_thumb.png
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:ref:`sphx_glr_tutorials_basics_4_batch.py`
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<div style='clear:both'></div>
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.. ================================================================================================
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(end) MANUALLY INCLUDE THE GENERATED TUTORIALS/BASIC/INDEX.RST HERE TO EMBED THE EXAMPLES
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================================================================================================
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Learning DGL through examples
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-----------------------------
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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 **batch** many
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graph samples for max efficiency.
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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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Or go through all of them :doc:`here <tutorials/models/index>`.
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.. toctree::
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:maxdepth: 1
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:caption: Features
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:hidden:
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:glob:
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features/builtin
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.. toctree::
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:maxdepth: 3
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:caption: Model 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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:glob:
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api/python/index
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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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