This release is a pre-release and may not be stable for production use.
AutoGluon, developed by AWS AI, 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 3.10 - 3.13 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.
⚡ 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", presets="best")
predictions = predictor.predict("test.csv")
| AutoGluon Task | Quickstart | API |
|---|---|---|
| TabularPredictor |  |
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| TimeSeriesPredictor | |
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| MultiModalPredictor | |
|
🔍 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 |
|---|---|---|---|
| 📺 AutoGluon: Towards No-Code Automated Machine Learning | Tutorial | AutoML 2024 | 2024/09/09 |
| 📺 AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code | Tutorial | AutoML 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 |
Scientific Publications
- AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (Arxiv, 2020) (BibTeX)
- Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation (NeurIPS, 2020) (BibTeX)
- Benchmarking Multimodal AutoML for Tabular Data with Text Fields (NeurIPS, 2021) (BibTeX)
- XTab: Cross-table Pretraining for Tabular Transformers (ICML, 2023)
- AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting (AutoML Conf, 2023) (BibTeX)
- TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications (AutoML Conf, 2024)
- AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models (AutoML Conf, 2024) (BibTeX)
- Multi-layer Stack Ensembles for Time Series Forecasting (AutoML Conf, 2025) (BibTeX)
- Chronos-2: From Univariate to Universal Forecasting (Arxiv, 2025) (BibTeX)
Articles
- AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library (Towards Data Science, Jan 2024)
- AutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions (AWS Open Source Blog, Mar 2020)
- AutoGluon overview & example applications (Towards Data Science, Dec 2019)
Train/Deploy AutoGluon in the Cloud
- AutoGluon Cloud (Recommended)
- AutoGluon on SageMaker AutoPilot
- AutoGluon on Amazon SageMaker
- AutoGluon Deep Learning Containers (Security certified & maintained by the AutoGluon developers)
- AutoGluon Official Docker Container
- AutoGluon-Tabular on AWS Marketplace (Not maintained by us)
📝 Citing AutoGluon
If you use AutoGluon in a scientific publication, please refer to our citation guide.
👋 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.
🏛️ License
This library is licensed under the Apache 2.0 License.
1 maintainer
Metadata
1 maintainer
Release files for autogluon.common 1.5.1b20260116
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
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Total release size: 147.6 kB
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