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Fast and Accurate ML in 3 Lines of Code

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Installation | Documentation | Release Notes

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.

πŸ’Ύ Installation

AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.

You can install AutoGluon with:

pip install autogluon

Visit our Installation Guide for detailed instructions, including GPU support, Conda installs, and optional dependencies.

⚑ Quickstart

Build accurate end-to-end ML models in just 3 lines of code!

from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv")
predictions = predictor.predict("test.csv")
AutoGluon Task Quickstart API
TabularPredictor Quick Start API
MultiModalPredictor Quick Start API
TimeSeriesPredictor Quick Start API

πŸ” Resources

Hands-on Tutorials / Talks

Below is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available here.

Title Format Location Date
πŸ“Ί AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code Tutorial AutoML Conf 2023 2023/09/12
πŸ”‰ AutoGluon: The Story Podcast The AutoML Podcast 2023/09/05
πŸ“Ί AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data Tutorial PyData Berlin 2023/06/20
πŸ“Ί Solving Complex ML Problems in a few Lines of Code with AutoGluon Tutorial PyData Seattle 2023/06/20
πŸ“Ί The AutoML Revolution Tutorial Fall AutoML School 2022 2022/10/18

Scientific Publications

Articles

Train/Deploy AutoGluon in the Cloud

πŸ“ Citing AutoGluon

If you use AutoGluon in a scientific publication, please refer to our citation guide.

πŸ‘‹ How to get involved

We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the Contributing Guide to get started.

πŸ›οΈ License

This library is licensed under the Apache 2.0 License.