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.multimodal-1.0.1b20240330.tar.gz (335.4 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file autogluon.multimodal-1.0.1b20240330.tar.gz.

File metadata

File hashes

Hashes for autogluon.multimodal-1.0.1b20240330.tar.gz
Algorithm Hash digest
SHA256 79f170dd792baa62787ebab778963e6639915b430cfc95468e105641390b1c90
MD5 0ec16e5825c373d3fcb3b1ab8289f8f6
BLAKE2b-256 5c4c0406f54437b5d2ac8afd3193721fe1e2c83556f4304ae262f937ecce2766

See more details on using hashes here.

File details

Details for the file autogluon.multimodal-1.0.1b20240330-py3-none-any.whl.

File metadata

File hashes

Hashes for autogluon.multimodal-1.0.1b20240330-py3-none-any.whl
Algorithm Hash digest
SHA256 bedf2f61cbc2a089d86fd98c273ce1fa64855a650126ddb64125715d43d8dddf
MD5 7a766ec82c64731f043b46705f476182
BLAKE2b-256 803a4539a5599b4b588389f752e8f92d95f54412cc0042e9f5fa267a747e5e79

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