This release is a pre-release and may not be stable for production use.
Fast and Accurate ML in 3 Lines of Code
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.
⚡ 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 |  |
 |
| MultiModalPredictor | |
|
| TimeSeriesPredictor | |
|
🔍 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 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 |
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 (Under Review, 2024)
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.timeseries 1.1.2b20240911
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.timeseries-1.1.2b20240911.tar.gz | 125.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| autogluon.timeseries-1.1.2b20240911-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 274.2 kB
Release files / autogluon.timeseries-1.1.2b20240911.tar.gz
| Download URL | autogluon.timeseries-1.1.2b20240911.tar.gz |
|---|---|
| Size | 125.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
820f1d3f493dc6b60c7bf602d06fcc1c4b9b6cbcce1c6899dfac193b50181370
|
|
BLAKE2b-256 checksum How to use checksums |
4883e7b5ce2b9f5150201010c6f400f1a9ce79b9576db078c085b0feab06c723
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.8.18
|
Release files / autogluon.timeseries-1.1.2b20240911-py3-none-any.whl
| Download URL | autogluon.timeseries-1.1.2b20240911-py3-none-any.whl |
|---|---|
| Size | 148.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1158d70a404826b3b170271621d3161253d80b85603ec9be78b9ba43805ebcef
|
|
BLAKE2b-256 checksum How to use checksums |
049ffacac95c262dfb766782da0ff0179184c411d072e74fbcfd80157bf62ac8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.8.18
|
