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, 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 3.9 - 3.12 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 Quick Start API
MultiModalPredictor Quick Start API
TimeSeriesPredictor Quick Start API

:mag: 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
: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

Articles

Train/Deploy AutoGluon in the Cloud

: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.

Release files for autogluon.features 1.4.1b20251012

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.features 1.4.1b20251012
File Size Uploaded
autogluon.features-1.4.1b20251012.tar.gz 48.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for autogluon.features 1.4.1b20251012
File Interpreter ABI Platform
autogluon.features-1.4.1b20251012-py3-none-any.whl Python 3 none any Details

Total release size: 112.9 kB

Release files / autogluon.features-1.4.1b20251012.tar.gz

Download URL autogluon.features-1.4.1b20251012.tar.gz
Size 48.6 kB
Tags Source
SHA-256 checksum
How to use checksums
af45813a13e7d354c74480e843f0ac97673dc31d3a6837f5e6b314140db11190
BLAKE2b-256 checksum
How to use checksums
a8f533da3bf4e724ecd844168436d99deb4a3ec583c424e2f2607ee53901aa8b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.9.23

Release files / autogluon.features-1.4.1b20251012-py3-none-any.whl

Download URL autogluon.features-1.4.1b20251012-py3-none-any.whl
Size 64.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
4ffa3a9888f7d28f4f5e89a72b17381043b3c48aaa5780167bfe0a962cf31de3
BLAKE2b-256 checksum
How to use checksums
c82386ca9a517d0d29ea90dbeea9b5b7a5d739b25c036ec4149b7f3becb02068
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.9.23

Release history Release notifications | RSS feed

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

This release

1.4.1b20251012 This release

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