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.common-1.0.1b20240305.tar.gz (54.4 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file autogluon.common-1.0.1b20240305.tar.gz.

File metadata

File hashes

Hashes for autogluon.common-1.0.1b20240305.tar.gz
Algorithm Hash digest
SHA256 f01e87a15088be8b8511a23f59fbbb7938098d6aa0263e4077756969f15518c2
MD5 a39f3b5464abf79c76cb1c7c09dc48df
BLAKE2b-256 4745f81a962cfd6580eb6dc027a77902a2dc67838f5c20635b8479cee36be215

See more details on using hashes here.

File details

Details for the file autogluon.common-1.0.1b20240305-py3-none-any.whl.

File metadata

File hashes

Hashes for autogluon.common-1.0.1b20240305-py3-none-any.whl
Algorithm Hash digest
SHA256 05da7c7782e927dfb5e81d50d2834cd96d4c2627f5359d815273f9c8238be188
MD5 8fd563b08f5107a96d615dc94b7627d7
BLAKE2b-256 f6b337eb8595499a46c7e2dee0aa252a80a9519aa0b897f7ba34fed915a4df00

See more details on using hashes here.

Supported by

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