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

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

License: permissive · Maintenance: active · Downloads: 561.4K/mo

## What it is and what it does

AutoGluon Core is the foundational package for AWS's AutoML framework, handling the core automation logic that powers model training across multiple data modalities. It abstracts away hyperparameter tuning, feature engineering, and model selection so that developers can train competitive machine learning models with just a few lines of code. The package integrates with scikit-learn, pandas, numpy, and other standard ML libraries, and includes support for distributed training via boto3 and AWS infrastructure.

The package is designed for developers and data scientists who want to build predictive models quickly without deep expertise in algorithm tuning. It works by automatically exploring multiple model architectures and configurations, then stacking or ensembling the best performers. While the core package provides the infrastructure, it is typically used alongside companion packages (autogluon.tabular, autogluon.timeseries, autogluon.multimodal) that specialize in specific data types.

Use it for:

- Train a tabular classification or regression model from CSV data in minutes without manual hyperparameter tuning
- Forecast time series data using automated ensemble methods and foundation models
- Build multimodal predictive models that combine text, images, and structured features
- Rapidly prototype ML solutions for business problems in finance, healthcare, or customer service
- Benchmark multiple model architectures automatically to find the best performer for your dataset

## Worth the install?

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

AutoGluon Core provides the foundational infrastructure for automated machine learning, enabling training of high-accuracy models on tabular, time series, image, and text data with minimal code.

Yes, if you need to train accurate ML models quickly and want to avoid manual hyperparameter tuning. The package is production-stable, actively maintained, permissively licensed, and has no known vulnerabilities. Install it when you're building prototypes or production systems where speed and ease of use matter more than full control over every modeling decision. It's especially valuable for teams without dedicated ML engineers.

## Install

pip install autogluon-core
uv add autogluon-core
poetry add autogluon-core

## Installing autogluon.core

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

License in practice: Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions; you may use, modify, and distribute the software freely as long as you include the license notice.

Quickstart:

pip install autogluon.core

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 and optional dependencies are available but require separate configuration per the installation guide.

Verify before relying:

- Specific performance benchmarks or accuracy comparisons against other AutoML frameworks
- Memory and compute requirements for different dataset sizes and model types
- Whether all advertised data types (image, text, time series, tabular) are equally mature in this core release

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 561.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

automated machine learning framework, automl for tabular data, fast model training automation, multi-modal machine learning, time series forecasting automl, deep learning automation, end-to-end ml pipeline, automl, ensemble-learning, aws-native

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