Skip to main content
Pre-release

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

AutoML for Image, Text, Time Series, and Tabular Data

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

Install Instructions | Documentation | Release Notes

AutoGluon 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 3.8 - 3.11 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")
predictions = predictor.predict("test.csv")
AutoGluon Task Quickstart API
TabularPredictor Quick Start API
MultiModalPredictor Quick Start API
TimeSeriesPredictor 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
📺 AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code Tutorial AutoML Conf 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 1.0.1b20240208

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 1.0.1b20240208
File Size Uploaded
autogluon.tabular-1.0.1b20240208.tar.gz 257.7 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for autogluon.tabular 1.0.1b20240208
File Interpreter ABI Platform
autogluon.tabular-1.0.1b20240208-py3-none-any.whl Python 3 none any Details

Total release size: 564.5 kB

Release files / autogluon.tabular-1.0.1b20240208.tar.gz

Download URL autogluon.tabular-1.0.1b20240208.tar.gz
Size 257.7 kB
Tags Source
SHA-256 checksum
How to use checksums
78dc046a2193f50856c917b53bb339b4d77e3b19ec0626d4a1a22ba991705069
BLAKE2b-256 checksum
How to use checksums
cb600c9c9a2b16eeb71999f61208c8e019e7c4c99f2cd5d2b84f4accf39bb7ed
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.8.18

Release files / autogluon.tabular-1.0.1b20240208-py3-none-any.whl

Download URL autogluon.tabular-1.0.1b20240208-py3-none-any.whl
Size 306.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
733a1a0764b096f5f7d98ce564d42bf64a8cfbbe1e869a5c7cb9504b90436cfd
BLAKE2b-256 checksum
How to use checksums
dc9ddbe5b229c14aeddc46a5bb8520a0163082abdc05602de21d10983bd62044
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.8.18

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

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

This release

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