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.
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.
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 | ||
TimeSeriesPredictor | ||
MultiModalPredictor |
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 |
If you use AutoGluon in a scientific publication, please refer to our citation guide.
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.
This library is licensed under the Apache 2.0 License.