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 3.10 - 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.features 1.5.1b20260717

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 1.5.1b20260717
File Size Uploaded Provenance Actions
autogluon_features-1.5.1b20260717.tar.gz 97.0 kB Without provenance Details for autogluon_features-1.5.1b20260717.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for autogluon.features 1.5.1b20260717
File Interpreter ABI Platform Actions
autogluon_features-1.5.1b20260717-py3-none-any.whl Python 3 none any Details for autogluon_features-1.5.1b20260717-py3-none-any.whl

Total release size: 213.5 kB

Release files / autogluon_features-1.5.1b20260717.tar.gz

Download URL autogluon_features-1.5.1b20260717.tar.gz
Size 97.0 kB
Tags Source
SHA-256 checksum
How to use checksums
d57c67edcfcda06867dc92d1e9b6f2e7d991c97bd18d645eb7be2062a31d23c3
BLAKE2b-256 checksum
How to use checksums
36343e191bb5dcd6f0ed29857ad2ae333ecc7fd60a3448478965540745905c6e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Release files / autogluon_features-1.5.1b20260717-py3-none-any.whl

Download URL autogluon_features-1.5.1b20260717-py3-none-any.whl
Size 116.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
3628bb1bdc8a9b57c118a6fbdac0944422cd89992e8b1caa2d3ef67de0f94f65
BLAKE2b-256 checksum
How to use checksums
b6e49444b4655c22a6ddab9b57eca540d7c37ee7a89be0c267e59ce8947101e8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.15

Security for autogluon.features 1.5.1b20260717

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release
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