Fast and Accurate ML in 3 Lines of Code
Project description
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.
: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")
predictions = predictor.predict("test.csv")
AutoGluon Task | Quickstart | API |
---|---|---|
TabularPredictor | ||
MultiModalPredictor | ||
TimeSeriesPredictor |
: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: AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code | Tutorial | AutoML Conf 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 (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)
: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.
Project details
Release history Release notifications | RSS feed
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
Hashes for autogluon.timeseries-1.1.1b20240428.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | ce2611237eb35f4fe66bbde5ebc05dc09aa5142a3b48b7b18bf5ed9c228d3fa8 |
|
MD5 | 6841677b1286050823d9aa903224ac41 |
|
BLAKE2b-256 | d72e483ce3f3bed5c213a8f72ef4023cd0e2820a19737d2a5c66c51297d31e53 |
Hashes for autogluon.timeseries-1.1.1b20240428-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9d21e5cad314ac0c2e5b4afc9dbfe59140e9b50252bd3ccbafa43b066734bd8f |
|
MD5 | 0e51f80f5dd5bbcf53ec3294e3fdd0d2 |
|
BLAKE2b-256 | 047c6d47624c20659094cdc6a47e0262f8f897ce5225ed68ce2924228d4b60e5 |