OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Developer Tools & SDKs

Autogl

AutoGL is an open-source Python framework and toolkit for automated machine learning on graph datasets and tasks, with modules for feature engineering, graph neural network models, neural architecture search, hyperparameter optimization and ensembles.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Autogl — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use AutoGL when building or experimenting with automated learning workflows for graph data, particularly when model search, hyperparameter optimization or ensembling are part of the task.

Who it is for

  • Researchers developing graph-learning experiments.
  • Developers building AutoML workflows or graph-based machine-learning models.

Documented uses

  • Organize graph datasets and task-specific solver workflows.
  • Automate feature engineering and graph-model selection.
  • Search graph neural network architectures and tune hyperparameters.
  • Combine models using voting or stacking ensembles.
  • Implement custom AutoML or graph-learning methods within a framework.
Before you choose

Requirements & limits

The repository is public and identifies the project as Apache-2.0 licensed. Its README provides pip and source-installation instructions.

  • Requires Python 3.6 or later and PyTorch 1.6 or later, plus one of the documented graph backends.
  • The README lists PyTorch Geometric and DGL as backend options; choose and install a backend before using the toolkit.
  • The framework exposes distinct modules for feature engineering, neural architecture search, hyperparameter optimization and ensembling, which may suit workflows that need more than a standalone graph model.
  • AutoGL-light is a separate lightweight library described as supporting easier customization of graph AutoML pipelines.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install AutoGL from pip

Install the package after preparing the documented Python, PyTorch and graph-backend prerequisites.

Before you start

  • Python 3.6 or later
  • PyTorch 1.6 or later
  • Either PyTorch Geometric 1.7 or later or Deep Graph Library 0.7 or later

Steps

  1. Install PyTorch following its installation instructions.

  2. Install either PyTorch Geometric or Deep Graph Library following the respective project instructions.

  3. Run the command below.

    Documented command
    pip install autogl

Expected outcome

The AutoGL package is installed through pip.

Guide limitations

  • These steps cover installation only; they do not configure a dataset, select a solver or run a graph-learning workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
THUMNLab/AutoGL · Official homepage title · retained in its source language
How we review sourcesSuggest a correction