diff --git a/README.md b/README.md index 4354557..bebfd55 100644 --- a/README.md +++ b/README.md @@ -7,15 +7,16 @@ An autoML framework & toolkit for machine learning on graphs. Feel free to open issues or contact us at autogl@tsinghua.edu.cn if you have any comments or suggestions! [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) -[![Documentation Status](https://readthedocs.org/projects/autogl/badge/?version=latest)](https://autogl.readthedocs.io/en/latest/?badge=latest) +[![Documentation Status](http://mn.cs.tsinghua.edu.cn/autogl/documentation/?badge=latest)](http://mn.cs.tsinghua.edu.cn/autogl/documentation/?badge=latest) ## News! - 2021.12.31 New Version! v0.3.0-pre is here! - - AutoGL now support backend [__Deep Graph Library (DGL)__](https://www.dgl.ai/) to be interface-friendly for DGL users! All the homogeneous node classification task, link prediction task, and graph classification task are supported currently under DGL backend. - - The __heterogeneous__ node classification tasks are now supported! See [hetero tutorial]() for more details. - - To make the whole library more scalable, the module `model` is now __decoupled__ to two additional sub-modules named `encoder` and `decoder`. `encoder` is more related to data and graph structure, responsible for feature extracting. `decoder` is more related to tasks, responsible for task solving. Under the __decoupled__ design, one `encoder` can be used to solve all kinds of tasks, relieving much burden for developing and user expanding/contributing. -- 2021.07.11 New version! v0.2.0-pre is here! In this new version, AutoGL supports [neural architecture search (NAS)](https://autogl.readthedocs.io/en/latest/docfile/tutorial/t_nas.html) to customize architectures for the given datasets and tasks. AutoGL also supports [sampling](https://autogl.readthedocs.io/en/latest/docfile/tutorial/t_trainer.html#node-classification-with-sampling) now to perform tasks on large datasets, including node-wise sampling, layer-wise sampling, and sub-graph sampling. The link prediction task is now also supported! Learn more in our [tutorial](https://autogl.readthedocs.io/en/latest/index.html). + - AutoGL now support [__Deep Graph Library (DGL)__](https://www.dgl.ai/) backend to be interface-friendly for DGL users! All the homogeneous node classification task, link prediction task, and graph classification task are currently supported under DGL backend. AutoGL is also compatible with PyG 2.0 now. + - The __heterogeneous__ node classification tasks are now supported! See [hetero tutorial](TODO) for more details. + - To make the library more flexible, the module `model` now supports __decoupled__ to two additional sub-modules named `encoder` and `decoder`. `encoder` is more related to graph structure and is responsible for extracting node features. `decoder` is more related to specific tasks and is responsible for task solving. Under the __decoupled__ design, one `encoder` can be used to solve all kinds of tasks, relieving burdens for developing and user expanding/contributing. + - We enrich our supported NAS algorithms such as [AutoAttend](TODO), [GASSO](TODO), [hardware-aware algorithm](TODO), etc. +- 2021.07.11 New version! v0.2.0-pre is here! In this new version, AutoGL supports [neural architecture search (NAS)](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_nas.html) to customize architectures for the given datasets and tasks. AutoGL also supports [sampling](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_trainer.html#node-classification-with-sampling) now to perform tasks on large datasets, including node-wise sampling, layer-wise sampling, and sub-graph sampling. The link prediction task is now also supported! Learn more in our [tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/index.html). - 2021.04.16 Our survey paper about automated machine learning on graphs is accepted by IJCAI! See more [here](http://arxiv.org/abs/2103.00742). - 2021.04.10 Our paper [__AutoGL: A Library for Automated Graph Learning__](https://arxiv.org/abs/2104.04987) is accepted by _ICLR 2021 Workshop on Geometrical and Topological Representation Learning_! You can cite our paper following methods [here](#Cite). @@ -27,7 +28,7 @@ The workflow below shows the overall framework of AutoGL. -AutoGL uses `datasets` to maintain datasets for graph-based machine learning, which is based on Dataset in PyTorch Geometric with some functions added to support the auto solver framework. +AutoGL uses `datasets` to maintain datasets for graph-based machine learning, which is based on Dataset in PyTorch Geometric or Deep Graph Library with some functions added to support the auto solver framework. Different graph-based machine learning tasks are handled by different `AutoGL solvers`, which make use of five main modules to automatically solve given tasks, namely `auto feature engineer`, `neural architecture search`, `auto model`, `hyperparameter optimization`, and `auto ensemble`. @@ -83,15 +84,15 @@ Please make sure you meet the following requirements before installing AutoGL. see for installation. -If you want to use PyTorch Geometric backend, you need to install +3. Graph Library Backend -3. PyTorch Geometric (>=1.7.0) +You will need either PyTorch Geometric (PyG) or Deep Graph Library (DGL) as the backend. You can select a backend following [here](TODO) if you install both. - see for installation. +3.1 PyTorch Geometric (>=1.7.0) -If you want to use Deep Graph Library, you need to install + see for installation. -3. Deep Graph Library (>=0.7.0) +3.2 Deep Graph Library (>=0.7.0) see for installation. @@ -166,4 +167,4 @@ You may also find our [survey paper](http://arxiv.org/abs/2103.00742) helpful: ``` ## License -Notice that we follow [Apache license](LICENSE) across the entire codebase from v0.2. +We follow [Apache license](LICENSE) across the entire codebase from v0.2.