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.
AutoGluon is supported on Python 3.10 - 3.13 and is available on Linux, MacOS, and Windows.
You can install AutoGluon with:
pip install autogluonVisit our Installation Guide for detailed instructions, including GPU support, Conda installs, and optional dependencies.
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 |  |
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| TimeSeriesPredictor | |
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| MultiModalPredictor | |
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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 |
- AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (Arxiv, 2020) (BibTeX)
- Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation (NeurIPS, 2020) (BibTeX)
- Benchmarking Multimodal AutoML for Tabular Data with Text Fields (NeurIPS, 2021) (BibTeX)
- XTab: Cross-table Pretraining for Tabular Transformers (ICML, 2023)
- AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting (AutoML Conf, 2023) (BibTeX)
- 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)
- Chronos: Learning the Language of Time Series (TMLR, 2024)
- Multi-layer Stack Ensembles for Time Series Forecasting (AutoML Conf, 2025) (BibTeX)
- Chronos-2: From Univariate to Universal Forecasting (Arxiv, 2025) (BibTeX)
- 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)
- 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)
- AutoGluon Cloud (Recommended)
- AutoGluon Deep Learning Containers (Security certified & maintained by the AutoGluon developers)
- AutoGluon Official Docker Container
- Amazon SageMaker Autopilot (Managed AutoGluon experience)
If you use AutoGluon in a scientific publication, please refer to our citation guide.
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.
This library is licensed under the Apache 2.0 License.