---
id: autogluon
version: "1.6.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# autogluon — Fast and Accurate ML in 3 Lines of Code

License: permissive · Maintenance: active · Downloads: 306.0K/mo

## What it is and what it does

AutoGluon is an automated machine learning (AutoML) framework developed by AWS that eliminates the need to manually select, tune, and ensemble models. It handles the full ML pipeline—from data preprocessing and feature engineering to model selection and hyperparameter optimization—across multiple data types: structured tabular data, time series, images, text, and combinations thereof. You provide a dataset and a few configuration parameters, and AutoGluon trains and evaluates a suite of models, returning predictions or a deployable predictor object.

The package is designed for developers and data scientists who want strong predictive performance without deep expertise in model architecture or tuning. It abstracts away complexity while remaining flexible enough for advanced users to customize presets and ensemble strategies. The framework depends on five internal submodules (core, features, tabular, multimodal, timeseries) that handle task-specific logic, and it supports current Python versions on major operating systems.

Use it for:

- Train a tabular classification or regression model on structured data in seconds without manual feature engineering or hyperparameter tuning.
- Forecast future values in time series data using pre-configured ensemble strategies.
- Build multimodal predictors that combine text, images, and tabular features in a single model.
- Rapidly prototype ML solutions for business problems (finance, healthcare, customer service) where time-to-model matters.
- Benchmark multiple model architectures and ensembles automatically to find the best performer for your dataset.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

AutoGluon automates machine learning model training and deployment across tabular, time series, image, text, and multimodal data with minimal code.

Yes. AutoGluon is production-stable, actively maintained, permissively licensed, and has no known vulnerabilities. Install it if you need to train accurate ML models quickly across tabular, time series, or multimodal data without manual tuning. It is most valuable for rapid prototyping, benchmarking, and applications where AutoML's abstraction saves significant development time; less useful if you need full control over model internals or are working with highly specialized architectures.

## Install

pip install autogluon
uv add autogluon
poetry add autogluon

## Installing autogluon

Before you install: Installation is straightforward with low friction; the package is actively maintained with a recent release (8 days old) and strong repository activity (10596 stars). Supports Python 3.10–3.13 across Linux, macOS, and Windows.

License in practice: Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution in both open-source and commercial projects with minimal restrictions.

Quickstart:

pip install autogluon

from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv", presets="best")
predictions = predictor.predict("test.csv")

Requires Python 3.10 or later; GPU support requires additional setup per installation guide.

Verify before relying:

- Memory and compute requirements for large datasets or deep learning tasks.
- Actual training time and accuracy compared to manual model tuning.
- Whether all five runtime submodules (core, features, tabular, multimodal, timeseries) are required or can be installed selectively.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 306.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

automated machine learning, automl tabular data, time series forecasting, multimodal deep learning, ml model training automation, image text classification, no-code machine learning, automl, deep-learning, tabular-data

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