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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 on data such as tables and time series, helping you achieve strong predictive performance with just a few lines of code.

From classic ML algorithms to foundation models, the options keep multiplying — but which one should you use? AutoGluon takes care of that: it finds the combination of models that works best for your use case.

💾 Installation

AutoGluon is supported on Python 3.10 - 3.13 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", presets="best")
predictions = predictor.predict("test.csv")
AutoGluon Task Quickstart API
TabularPredictor Quick Start API
TimeSeriesPredictor Quick Start API
MultiModalPredictor Quick Start API

🔍 Resources

More talks, articles, Kaggle solutions and papers using AutoGluon are listed in AWESOME.md.

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
📺 Structured Foundation Models Meets AutoML Expo Talk ICML 2025 2025/07/13
📺 AutoGluon 1.2: Advancing AutoML with Foundational Models and LLM Agents Expo Workshop NeurIPS 2024 2024/12/10
📺 AutoGluon: Towards No-Code Automated Machine Learning Tutorial AutoML 2024 2024/09/09
📺 AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code Tutorial AutoML 2023 2023/09/12
🔉 AutoGluon: The Story Podcast The AutoML Podcast 2023/09/05
📺 AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data Tutorial PyData Berlin 2023/06/20
📺 Solving Complex ML Problems in a few Lines of Code with AutoGluon Tutorial PyData Seattle 2023/06/20
📺 The AutoML Revolution Tutorial Fall AutoML School 2022 2022/10/18
Scientific Publications
Title Venue Year Citation
Beyond IID: How General Are Tabular Foundation Models, Really? NeurIPS 2026 BibTeX
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks NeurIPS 2026 BibTeX
fev-bench: A Realistic Benchmark for Time Series Forecasting NeurIPS 2026 BibTeX
MLZero: A Multi-Agent System for End-to-end Machine Learning Automation NeurIPS 2025
Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models NeurIPS 2025
TabArena: A Living Benchmark for Machine Learning on Tabular Data NeurIPS Spotlight 2025
Chronos-2: From Univariate to Universal Forecasting Arxiv 2025 BibTeX
Multi-layer Stack Ensembles for Time Series Forecasting AutoML Conf 2025 BibTeX
Chronos: Learning the Language of Time Series TMLR 2024
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models AutoML Conf 2024 BibTeX
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications AutoML Conf 2024
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting AutoML Conf 2023 BibTeX
XTab: Cross-table Pretraining for Tabular Transformers ICML 2023
Benchmarking Multimodal AutoML for Tabular Data with Text Fields NeurIPS 2021 BibTeX
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation NeurIPS 2020 BibTeX
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data Arxiv 2020 BibTeX
Train/Deploy AutoGluon in the Cloud

📝 Citing AutoGluon

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

👋 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.

🏛️ License

This library is licensed under the Apache 2.0 License.

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