This release is a pre-release and may not be stable for production use.
AutoGluon automates machine learning on data such as tables and time series, helping you achieve strong predictive performance with just a few lines of code.
From classic ML algorithms to foundation models, the options keep multiplying — but which one should you use? AutoGluon takes care of that: it finds the combination of models that works best for your use case.
💾 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.
: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", presets="best")
predictions = predictor.predict("test.csv")
| AutoGluon Task | Quickstart | API |
|---|---|---|
| TabularPredictor |  |
 |
| TimeSeriesPredictor | |
|
| MultiModalPredictor | |
|
: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: Structured Foundation Models Meets AutoML | Expo Talk | ICML 2025 | 2025/07/13 |
| :tv: AutoGluon 1.2: Advancing AutoML with Foundational Models and LLM Agents | Expo Workshop | NeurIPS 2024 | 2024/12/10 |
| :tv: AutoGluon: Towards No-Code Automated Machine Learning | Tutorial | AutoML 2024 | 2024/09/09 |
| :tv: AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code | Tutorial | AutoML 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
- 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)
- Chronos: Learning the Language of Time Series (TMLR, 2024)
- Multi-layer Stack Ensembles for Time Series Forecasting (AutoML Conf, 2025) (BibTeX)
- Chronos-2: From Univariate to Universal Forecasting (Arxiv, 2025) (BibTeX)
- TabArena: A Living Benchmark for Machine Learning on Tabular Data (NeurIPS Spotlight, 2025)
- Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models (NeurIPS, 2025)
- MLZero: A Multi-Agent System for End-to-end Machine Learning Automation (NeurIPS, 2025)
- fev-bench: A Realistic Benchmark for Time Series Forecasting (Arxiv, 2025)
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 Deep Learning Containers (Security certified & maintained by the AutoGluon developers)
- AutoGluon Official Docker Container
- Amazon SageMaker Autopilot (Managed AutoGluon experience)
: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.
1 maintainer
Release files for autogluon.common 1.6.2b20260908
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| autogluon_common-1.6.2b20260908.tar.gz | 88.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| autogluon_common-1.6.2b20260908-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 187.9 kB
Release files / autogluon_common-1.6.2b20260908.tar.gz
| Download URL | autogluon_common-1.6.2b20260908.tar.gz |
|---|---|
| Size | 88.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
e36bb461794b51cb9c8eb7e6b8dc1bc2bd83f303b61cfd10f503fb2f0b076e83
|
|
BLAKE2b-256 checksum How to use checksums |
1c14b8cf8a1464b83be77ad751870eb612d4e1a3d82fc1760b8f0a96b8558be5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.11.16
|
Release files / autogluon_common-1.6.2b20260908-py3-none-any.whl
| Download URL | autogluon_common-1.6.2b20260908-py3-none-any.whl |
|---|---|
| Size | 99.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
6c0f846a75e03a249595fcafbea02d14f5a43927dcdac9050162ffd907c46eba
|
|
BLAKE2b-256 checksum How to use checksums |
17cd41149101f93eef6bc254efe7b153ed288521dd40dd1cddab7db93d2202fd
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.11.16
|
