Skip to main content

AutoML for Image, Text, and Tabular Data

Project description

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.

: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")
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 1.0: Shattering the AutoML Ceiling with Zero Lines of Code Tutorial AutoML Conf 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 history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autogluon.tabular-1.0.1b20240325.tar.gz (256.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

autogluon.tabular-1.0.1b20240325-py3-none-any.whl (305.9 kB view details)

Uploaded Python 3

File details

Details for the file autogluon.tabular-1.0.1b20240325.tar.gz.

File metadata

File hashes

Hashes for autogluon.tabular-1.0.1b20240325.tar.gz
Algorithm Hash digest
SHA256 eda15caa9f83a574a5cd31f7e2d3a23bf9281e031c26204228a23ce1c6e6bf76
MD5 63bd340bdcd8444a7c788efaa5da8a5a
BLAKE2b-256 b17fe6c0ff4a09fd1f69a1ac5cf650deab27cf206c9b788a6df155a55e7e4563

See more details on using hashes here.

File details

Details for the file autogluon.tabular-1.0.1b20240325-py3-none-any.whl.

File metadata

File hashes

Hashes for autogluon.tabular-1.0.1b20240325-py3-none-any.whl
Algorithm Hash digest
SHA256 734bcd17c0e72659fb56adf20671e076f3aca3ca58a0645e3cdc639c3d3d9ff8
MD5 45f3ab4b930a27f884e2ed12fa707f80
BLAKE2b-256 40bbbc7ece55a4921093e6e9dd8e3178d063232279991ca522ba4422027d6ce6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page