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.

Metadata

Release files for autogluon.tabular 1.6.4b20260930

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 1.6.4b20260930
File Size Uploaded
autogluon_tabular-1.6.4b20260930.tar.gz 493.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for autogluon.tabular 1.6.4b20260930
File Interpreter ABI Platform
autogluon_tabular-1.6.4b20260930-py3-none-any.whl Python 3 none any Details

Total release size: 1.1 MB

Release files / autogluon_tabular-1.6.4b20260930.tar.gz

Download URL autogluon_tabular-1.6.4b20260930.tar.gz
Size 493.2 kB
Tags Source
SHA-256 checksum
How to use checksums
fb559dd035c49e92a3c4e4970378f742ca3073c70517bd64f744180ed4b226d4
BLAKE2b-256 checksum
How to use checksums
8137cebaa228e0120726ef504d80f77d18869c7de3968465bf3db68b6fa46ccd
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-1.6.4b20260930-py3-none-any.whl

Download URL autogluon_tabular-1.6.4b20260930-py3-none-any.whl
Size 570.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
1e791672c3bec0189a7d047e03359bbf4557fb1ada37bebb8560679690f219ce
BLAKE2b-256 checksum
How to use checksums
614e0dfaff30527b6ed558bfd686b984f9408d08f9c2313e9f642316e2b93b5c
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

1.6.4b20260930 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