Skip to main content

Fast and Accurate ML in 3 Lines of Code

Project description

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 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-1.1.0b20240411.tar.gz (54.4 kB view details)

Uploaded Source

Built Distribution

autogluon.common-1.1.0b20240411-py3-none-any.whl (64.4 kB view details)

Uploaded Python 3

File details

Details for the file autogluon.common-1.1.0b20240411.tar.gz.

File metadata

File hashes

Hashes for autogluon.common-1.1.0b20240411.tar.gz
Algorithm Hash digest
SHA256 7f7fd430de4a39a446bab6b8cad8c9dd712412f6d85962745301d14524ec556d
MD5 7abaef002defe6fb795710d632fb18c7
BLAKE2b-256 030d422819f5e207a45b747e96da116f2c86f5bf6a4b95b6c5e3aa3b4e9dc0f0

See more details on using hashes here.

File details

Details for the file autogluon.common-1.1.0b20240411-py3-none-any.whl.

File metadata

File hashes

Hashes for autogluon.common-1.1.0b20240411-py3-none-any.whl
Algorithm Hash digest
SHA256 a157c16d41870a821b649256a9c56c68af78467463cb26961e0f8544d539fdaa
MD5 d2922599ad5a4870e73b8f98aa1dc268
BLAKE2b-256 a19c55ba080fadcc077716cc592f66e375d9f6e0953d9fb8bc0ad81b4e6bb055

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