Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

Fast and Accurate ML in 3 Lines of Code

Latest Release Conda Forge Python Versions Downloads GitHub license Discord Twitter Continuous Integration Platform Tests

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

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

Articles

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.

Release files for autogluon.tabular 1.6.4b20260923

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for autogluon.tabular 1.6.4b20260923
File Size Uploaded
autogluon_tabular-1.6.4b20260923.tar.gz 484.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for autogluon.tabular 1.6.4b20260923
File Interpreter ABI Platform
autogluon_tabular-1.6.4b20260923-py3-none-any.whl Python 3 none any Details

Total release size: 1.0 MB

Release files / autogluon_tabular-1.6.4b20260923.tar.gz

Download URL autogluon_tabular-1.6.4b20260923.tar.gz
Size 484.1 kB
Tags Source
SHA-256 checksum
How to use checksums
66820145fc45433d2ce87751910451fd0a9eb5778e337de867c7ef4ffc51daf1
BLAKE2b-256 checksum
How to use checksums
f8be90094d6e101979eb1a9ab8e5d02e074e1379a506edac35fd7898d5c3c5c6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.11.16

Release files / autogluon_tabular-1.6.4b20260923-py3-none-any.whl

Download URL autogluon_tabular-1.6.4b20260923-py3-none-any.whl
Size 557.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
beff71d02918bf3e6943468a51a70fa9bcb3b087d2df156de67f968e333d675a
BLAKE2b-256 checksum
How to use checksums
d4f303110fe1059d2aa4c803b2a8f7914e269ec781d4177a4a33e5766d0197c0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.11.16

Release history Release notifications | RSS feed

This release

1.6.4b20260923 This release

2 release files

1.6.3

2 release files

1.6.2

2 release files

1.6.1

2 release files

1.6.0

2 release files

1.5.0

2 release files

1.4.0

2 release files

1.3.1

2 release files

1.3.0

2 release files

1.2

2 release files

1.1.1

2 release files

1.1.0

2 release files

1.0.0

2 release files

0.8.3

2 release files

0.8.2

2 release files

0.8.1

2 release files

0.8.0

2 release files

0.7.0

2 release files

0.6.2

2 release files

0.6.1

2 release files

0.6.0

2 release files

0.5.3

2 release files

0.5.2

2 release files

0.5.1

2 release files

0.5.0

2 release files

0.4.3

2 release files

0.4.2

2 release files

0.4.1

2 release files

0.4.0

2 release files

0.3.1

2 release files

0.3.0

2 release files

0.2.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page