Skip to content

GitLab

"git@developer.sourcefind.cn:OpenDAS/ollama.git" did not exist on "fc2a0715dfe2dd370fa9aa0e55e449130cf3e5d7"
Unverified Commit 5967d817 authored by Minjie Wang's avatar Minjie Wang Committed by GitHub
Browse files

[Doc] Re-organize API docs and tutorials (#1222) 

* reorg tutorials and api docs

* fix
parent f1a8f926
Hide whitespace changes
Inline Side-by-side
Showing with 57 additions and 166 deletions
docs/source/index.rst
View file @ 5967d817
...@@ -34,168 +34,60 @@ Relationship of DGL to other frameworks ...@@ -34,168 +34,60 @@ Relationship of DGL to other frameworks
--------------------------------------- ---------------------------------------
DGL is designed to be compatible and agnostic to the existing tensor DGL is designed to be compatible and agnostic to the existing tensor
frameworks. It provides a backend adapter interface that allows easy porting frameworks. It provides a backend adapter interface that allows easy porting
to other tensor-based, autograd-enabled frameworks. Currently, our prototype to other tensor-based, autograd-enabled frameworks.
works with MXNet/Gluon and PyTorch.
Get Started Getting Started
----------- ---------------
.. toctree:: Follow the :doc:`instructions<install/index>` to install DGL.
:maxdepth: 1 :doc:`DGL at a glance<tutorials/basics/1_first>` is the most common place to get started with.
:caption: Get Started It offers a broad experience of using DGL for deep learning on graph data.
:hidden: You can learn other basic concepts of DGL through the dedicated tutorials.
:glob:
install/index
install/backend
Follow the :doc:`instructions<install/index>` to install DGL. The :doc:`DGL at a glance<tutorials/basics/1_first>`
is the most common place to get started with. Each tutorial is accompanied with a runnable
python script and jupyter notebook that can be downloaded.
.. ================================================================================================
(start) MANUALLY INCLUDE THE GENERATED TUTORIALS/BASIC/INDEX.RST HERE TO EMBED THE EXAMPLES
================================================================================================
.. raw:: html
<div class="sphx-glr-thumbcontainer">
.. only:: html
.. figure:: /tutorials/basics/images/thumb/sphx_glr_1_first_thumb.png
:ref:`sphx_glr_tutorials_basics_1_first.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/basics/1_first
.. raw:: html
<div class="sphx-glr-thumbcontainer">
.. only:: html
.. figure:: /tutorials/basics/images/thumb/sphx_glr_2_basics_thumb.png
:ref:`sphx_glr_tutorials_basics_2_basics.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/basics/2_basics
.. raw:: html
<div class="sphx-glr-thumbcontainer">
.. only:: html
.. figure:: /tutorials/basics/images/thumb/sphx_glr_3_pagerank_thumb.png
:ref:`sphx_glr_tutorials_basics_3_pagerank.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/basics/3_pagerank
.. raw:: html
</div>
.. raw:: html
<div class="sphx-glr-thumbcontainer">
.. only:: html
.. figure:: /tutorials/basics/images/thumb/sphx_glr_4_batch_thumb.png
:ref:`sphx_glr_tutorials_basics_4_batch.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/basics/4_batch
.. raw:: html
<div class="sphx-glr-thumbcontainer">
.. only:: html
.. figure:: /tutorials/hetero/images/thumb/sphx_glr_1_basics_thumb.png
:ref:`sphx_glr_tutorials_hetero_1_basics.py`
.. raw:: html * Learn constructing graphs and set/get node and edge features :doc:`here<tutorials/basics/2_basics>`.
* Learn performing computation on graph using message passing :doc:`here<tutorials/basics/3_pagerank>`.
* Learn processing multiple graph samples in a batch :doc:`here<tutorials/basics/4_batch>`.
* Learn working with heterogeneous graph data :doc:`here<tutorials/basics/5_hetero>`.
</div> End-to-end model tutorials are other good starting points for learning DGL and popular
models on graphs. The model tutorials are categorized based on the way they utilize DGL APIs.
.. toctree::
:hidden:
/tutorials/hetero/1_basics
.. raw:: html
<div style='clear:both'></div>
.. ================================================================================================
(end) MANUALLY INCLUDE THE GENERATED TUTORIALS/BASIC/INDEX.RST HERE TO EMBED THE EXAMPLES
================================================================================================
Learning DGL through examples
-----------------------------
The model tutorials are categorized based on the way they utilize DGL APIs.
* :ref:`Graph Neural Network and its variant <tutorials1-index>`: Learn how to use DGL to train * :ref:`Graph Neural Network and its variant <tutorials1-index>`: Learn how to use DGL to train
popular **GNN models** on one input graph. popular **GNN models** on one input graph.
* :ref:`Dealing with many small graphs <tutorials2-index>`: Learn how to **batch** many * :ref:`Dealing with many small graphs <tutorials2-index>`: Learn how to train models for
graph samples for max efficiency. many graph samples such as sentence parse trees.
* :ref:`Generative models <tutorials3-index>`: Learn how to deal with **dynamically-changing graphs**. * :ref:`Generative models <tutorials3-index>`: Learn how to deal with **dynamically-changing graphs**.
* :ref:`Old (new) wines in new bottle <tutorials4-index>`: Learn how to combine DGL with tensor-based * :ref:`Old (new) wines in new bottle <tutorials4-index>`: Learn how to combine DGL with tensor-based
DGL framework in a flexible way. Explore new perspective on traditional models by graphs. DGL framework in a flexible way. Explore new perspective on traditional models by graphs.
* :ref:`Training on giant graphs <tutorials5-index>`: Learn how to train graph neural networks * :ref:`Training on giant graphs <tutorials5-index>`: Learn how to train graph neural networks
on giant graphs. on giant graphs.
Or go through all of them :doc:`here <tutorials/models/index>`. Each tutorial is accompanied with a runnable python script and jupyter notebook that
can be downloaded. If you would like the tutorials improved, please raise a github issue.
API reference document lists more endetailed specifications of each API and GNN modules,
a useful manual for in-depth developers.
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1
:caption: Features :caption: Installation
:hidden: :hidden:
:glob: :glob:
features/builtin install/index
features/nn install/backend
.. toctree::
:maxdepth: 1
:caption: Get Started
:hidden:
:glob:
tutorials/basics/index
.. toctree:: .. toctree::
:maxdepth: 3 :maxdepth: 3
:caption: Model Tutorials :caption: Tutorials
:hidden: :hidden:
:glob: :glob:
...@@ -204,9 +96,25 @@ Or go through all of them :doc:`here <tutorials/models/index>`. ...@@ -204,9 +96,25 @@ Or go through all of them :doc:`here <tutorials/models/index>`.
.. toctree:: .. toctree::
:maxdepth: 2 :maxdepth: 2
:caption: API Reference :caption: API Reference
:hidden:
:glob: :glob:
api/python/index api/python/graph
api/python/heterograph
api/python/batch
api/python/batch_heterograph
api/python/nn
api/python/function
api/python/udf
api/python/subgraph
api/python/traversal
api/python/propagate
api/python/transform
api/python/sampler
api/python/data
api/python/nodeflow
api/python/random
api/python/model_zoo
.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1
......
tutorials/basics/2_basics.py
View file @ 5967d817
""" """
.. currentmodule:: dgl .. currentmodule:: dgl
DGL Basics DGLGraph and Node/edge Features
========== ===============================
**Author**: `Minjie Wang <https://jermainewang.github.io/>`_, Quan Gan, Yu Gai, **Author**: `Minjie Wang <https://jermainewang.github.io/>`_, Quan Gan, Yu Gai,
Zheng Zhang Zheng Zhang
......
tutorials/basics/3_pagerank.py
View file @ 5967d817
""" """
.. currentmodule:: dgl .. currentmodule:: dgl
PageRank with DGL message passing Message Passing Tutorial
================================= ========================
**Author**: `Minjie Wang <https://jermainewang.github.io/>`_, Quan Gan, Yu Gai, **Author**: `Minjie Wang <https://jermainewang.github.io/>`_, Quan Gan, Yu Gai,
Zheng Zhang Zheng Zhang
......
tutorials/basics/4_batch.py
View file @ 5967d817
""" """
.. currentmodule:: dgl .. currentmodule:: dgl
Tutorial: Batched graph classification with DGL Graph Classification Tutorial
================================================ =============================
**Author**: `Mufei Li <https://github.com/mufeili>`_, **Author**: `Mufei Li <https://github.com/mufeili>`_,
`Minjie Wang <https://jermainewang.github.io/>`_, `Minjie Wang <https://jermainewang.github.io/>`_,
......
tutorials/hetero/1_basics.py → tutorials/basics/5_hetero.py
View file @ 5967d817
""" """
.. currentmodule:: dgl .. currentmodule:: dgl
Working with Heterogeneous Graphs in DGL Working with Heterogeneous Graphs
======================================== =================================
**Author**: Quan Gan, `Minjie Wang <https://jermainewang.github.io/>`_, Mufei Li, **Author**: Quan Gan, `Minjie Wang <https://jermainewang.github.io/>`_, Mufei Li,
George Karypis, Zheng Zhang George Karypis, Zheng Zhang
......
tutorials/models/README.txt
View file @ 5967d817
Model overview
==============
We developed DGL with a broad range of applications in mind. Building
state-of-art models forces us to think hard on the most common and useful APIs,
learn the hard lessons, and push the system design.
We have prototyped altogether 10 different models, all of them are ready to run
out-of-box and some of them are very new graph-based algorithms. In most of the
cases, they demonstrate the performance, flexibility, and expressiveness of
DGL. For where we still fall in short, these exercises point to future
directions.
We categorize the models below, providing links to the original code and
tutorial when appropriate. As will become apparent, these models stress the use
of different DGL APIs.
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment