autogluon.core
Fast and Accurate ML in 3 Lines of Code
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; GPU support and optional dependencies are available but require separate configuration per the installation guide.
- 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 · maintenance · safety
Apache-2.0 (permissive) — 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.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 10,596 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 561,418 downloads/mo, #5,997 on PyPI
Alternatives
Verify before relying
pip install autogluon.core
from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv", presets="best")
predictions = predictor.predict("test.csv")- 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
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 on this page. Verify before relying on it.
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
autogluon-core on PyPI
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.
Requires Python 3.10 or later; GPU support and optional dependencies are available but require separate configuration per the installation guide.
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")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 the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 12 packagesnumpyscipyscikit-learnnetworkxpandastqdmrequestsmatplotlibboto3typing-extensionsautogluon.commonautogluon.features |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 561,418 / month, #5,997 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Customer ServiceIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Financial and Insurance IndustryIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchIntended Audience :: Telecommunications IndustryOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Scientific/Engineering :: Information AnalysisTopic :: Software Development |
Evidence: autogluon_core-1.6.1-py3-none-any.whl
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See also autogluon · autogluon.common · autogluon.timeseries · autogluon.tabular · pyglove · autogluon.features · autogluon.multimodal · autogluon.vision · autogluon.text · tabpfn