Welcome to AutoGL’s documentation!


Actively under development by @THUMNLab

AutoGL is developed for researchers and developers to quickly conduct autoML on the graph datasets & tasks.

The workflow below shows the overall framework of AutoGL.


AutoGL uses AutoGL Dataset to maintain datasets for graph-based machine learning, which is based on the dataset in PyTorch Geometric with some support added to corporate with the auto solver framework.

Different graph-based machine learning tasks are solved by different AutoGL Solvers , which make use of four main modules to automatically solve given tasks, namely Auto Feature Engineer, Auto Model, Neural Architecture Search, HyperParameter Optimization, and Auto Ensemble.



Please make sure you meet the following requirements before installing AutoGL.

  1. Python >= 3.6.0

  2. PyTorch (>=1.6.0)

    see PyTorch for installation.

  3. PyTorch Geometric (>=1.7.0)

    see PyTorch Geometric for installation.


Install from pip & conda

Run the following command to install this package through pip.

pip install autogl

Install from source

Run the following command to install this package from the source.

git clone https://github.com/THUMNLab/AutoGL.git
cd AutoGL
python setup.py install

Install for development

If you are a developer of the AutoGL project, please use the following command to create a soft link, then you can modify the local package without installation again.

pip install -e .


In AutoGL, the tasks are solved by corresponding solvers, which in general do the following things:

  1. Preprocess and feature engineer the given datasets. This is done by the module named auto feature engineer, which can automatically add/delete useful/useless attributes in the given datasets. Some topological features may also be extracted & combined to form stronger features for current tasks.

  2. Find the best suitable model architectures through neural architecture search. This is done by modules named nas. AutoGL provides several search spaces, algorithms and estimators for finding the best architectures.

  1. Automatically train and tune popular models specified by users. This is done by modules named auto model and hyperparameter optimization. In the auto model, several commonly used graph deep models are provided, together with their hyperparameter spaces. These kinds of models can be tuned using hyperparameter optimization module to find the best hyperparameter for the current task.

  2. Find the best way to ensemble models found and trained in the last step. This is done by the module named auto ensemble. The suitable models available are ensembled here to form a more powerful learner.

Indices and tables