autogluon ORG

Fast and Accurate ML in 3 Lines of Code


Installation | Documentation | Release Notes

AutoGluon, developed by AWS AI, 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.

💾 Installation

AutoGluon is supported on Python 3.10 - 3.13 and is available on Linux, MacOS, and Windows.

You can install AutoGluon with:

python
pip install autogluon

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

:zap: Quickstart

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

python
from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv", presets="best")
predictions = predictor.predict("test.csv")

| AutoGluon Task | Quickstart | API |
|:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| TabularPredictor |  |  |
| TimeSeriesPredictor |  |  |
| MultiModalPredictor |  |  |

:mag: Resources

Hands-on Tutorials / Talks

Below is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available [here](AWESOME.md#videos--tutorials).

| Title | Format | Location | Date |
|--------------------------------------------------------------------------------------------------------------------------|----------|----------------------------------------------------------------------------------|------------|
| :tv: Structured Foundation Models Meets AutoML | Expo Talk | ICML 2025 | 2025/07/13 |
| :tv: AutoGluon 1.2: Advancing AutoML with Foundational Models and LLM Agents | Expo Workshop | NeurIPS 2024 | 2024/12/10 |
| :tv: AutoGluon: Towards No-Code Automated Machine Learning | Tutorial | AutoML 2024 | 2024/09/09 |
| :tv: AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code | Tutorial | AutoML 2023 | 2023/09/12 |
| :sound: AutoGluon: The Story | Podcast | The AutoML Podcast | 2023/09/05 |
| :tv: AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data | Tutorial | PyData Berlin | 2023/06/20 |
| :tv: Solving Complex ML Problems in a few Lines of Code with AutoGluon | Tutorial | PyData Seattle | 2023/06/20 |
| :tv: The AutoML Revolution | Tutorial | Fall AutoML School 2022 | 2022/10/18 |

Scientific Publications

• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (*Arxiv*, 2020) ([BibTeX](CITING.md#general-usage--autogluontabular))
• Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation (*NeurIPS*, 2020) ([BibTeX](CITING.md#tabular-distillation))
• Benchmarking Multimodal AutoML for Tabular Data with Text Fields (*NeurIPS*, 2021) ([BibTeX](CITING.md#autogluonmultimodal))
• XTab: Cross-table Pretraining for Tabular Transformers (*ICML*, 2023)
• AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting (*AutoML Conf*, 2023) ([BibTeX](CITING.md#autogluontimeseries))
• TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications (*AutoML Conf*, 2024)
• AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models (*AutoML Conf*, 2024) ([BibTeX](CITING.md#autogluonmultimodal))
• Chronos: Learning the Language of Time Series (*TMLR*, 2024)
• Multi-layer Stack Ensembles for Time Series Forecasting (*AutoML Conf*, 2025) ([BibTeX](CITING.md#autogluontimeseries))
• Chronos-2: From Univariate to Universal Forecasting (*Arxiv*, 2025) ([BibTeX](CITING.md#autogluontimeseries))
• TabArena: A Living Benchmark for Machine Learning on Tabular Data (*NeurIPS Spotlight*, 2025)
• Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models (*NeurIPS*, 2025)
• MLZero: A Multi-Agent System for End-to-end Machine Learning Automation (*NeurIPS*, 2025)
• fev-bench: A Realistic Benchmark for Time Series Forecasting (*Arxiv*, 2025)

Articles

• AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library (*Towards Data Science*, Jan 2024)
• AutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions (*AWS Open Source Blog*, Mar 2020)
• AutoGluon overview & example applications (*Towards Data Science*, Dec 2019)

Train/Deploy AutoGluon in the Cloud

• AutoGluon Cloud (Recommended)
• AutoGluon Deep Learning Containers (Security certified & maintained by the AutoGluon developers)
• AutoGluon Official Docker Container
• Amazon SageMaker Autopilot (Managed AutoGluon experience)

:pencil: Citing AutoGluon

If you use AutoGluon in a scientific publication, please refer to our [citation guide](CITING.md).

:wave: 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.

:classical_building: License

This library is licensed under the Apache 2.0 License.

Collapse