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.
: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
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file autogluon_timeseries-1.6.0b20260805.tar.gz.
File metadata
- Download URL: autogluon_timeseries-1.6.0b20260805.tar.gz
- Upload date:
- Size: 230.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/7.0.0 CPython/3.11.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ceed0b70619fa3ea36747014993cf2e2cd8d56ef1f8aa47cb7b3c869e5b6f349
|
|
| MD5 |
9c47b5a0710fcc9b4d7f596c9d805990
|
|
| BLAKE2b-256 |
1e73c70a6fa031d7dc7c78bc0b0e298a35a8153d7ecbe3eccb0d637d39c0a5ea
|
File details
Details for the file autogluon_timeseries-1.6.0b20260805-py3-none-any.whl.
File metadata
- Download URL: autogluon_timeseries-1.6.0b20260805-py3-none-any.whl
- Upload date:
- Size: 277.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/7.0.0 CPython/3.11.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3037dc7d6709dd956c80f0c695dff07539f2dc791929c7ba116e4b22d7274f9f
|
|
| MD5 |
035250176c0195d2ef1bd0fdbdd790d2
|
|
| BLAKE2b-256 |
165ec0b66cb707a5f9f78bc5518b56ffebdd68531922204f02fd8556a9154417
|
