AutoGluon

Полная документация ниже — автоматически из репозитория autogluon/autogluon (ветка master). Оригинальные материалы проекта распространяются на условиях его лицензии.

Fast and Accurate ML in 3 Lines of Code

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.

:zap: 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
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.

Scientific Publications

• 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)

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.

: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.