dmlc--dgl
Haibin Lin
89 行
3.0 KiB
ReStructuredText
89 行
3.0 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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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 checkout our `contribution guidelines <https://github.com/dmlc/dgl/blob/master/CONTRIBUTING.md>`_.
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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 and Quan Gan. Serious development began when Minjie, Lingfan and Prof
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Jinyang Li from NYU's system group joined, flanked by a team of student
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volunteers at NYU Shanghai, Fudan and other universities (Yu, Zihao, Murphy,
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Allen, Qipeng, Qi, Hao), as well as early adopters at the CILVR lab (Jake
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Zhao). Development accelerated when AWS MXNet Science team joined force, with
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Da Zheng, Alex Smola, Haibin Lin, Chao Ma and a number of others. For full
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credit, see `here <https://www.dgl.ai/ack>`_.
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.. toctree::
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:maxdepth: 1
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:caption: Get Started
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:glob:
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install/index
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.. toctree::
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:maxdepth: 2
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:caption: Tutorials
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:glob:
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tutorials/basics/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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:glob:
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api/python/index
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.. toctree::
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:maxdepth: 1
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:glob:
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faq
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env_var
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Index
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-----
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* :ref:`genindex`
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