autogluon.tabular
Fast and Accurate ML in 3 Lines of Code
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries a permissive license, and solves a real problem—reducing the time and expertise needed to train tabular ML models. It is well-suited for rapid prototyping, production deployment, and learning. No known security vulnerabilities as of the query date.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10–3.13; depends on numpy, scipy, pandas, scikit-learn, networkx, and autogluon.core and autogluon.features packages.
- Low install friction with a pure Python wheel distribution.
- Actively maintained with a recent release; the repository shows strong community engagement with 10596 stars and continuous development.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most production and research applications.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 10,596 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 569,878 downloads/mo, #5,957 on PyPI
Alternatives
Verify before relying
pip install autogluon.tabular
from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv", presets="best")
predictions = predictor.predict("test.csv")- Whether the package supports GPU acceleration and what additional dependencies or setup are required for it.
- Memory and compute requirements for typical tabular datasets (size, feature count, model complexity).
- Specific model types and ensemble strategies used internally during automated training.
What it is and what it does
AutoGluon Tabular is an automated machine learning library that trains and deploys high-accuracy predictive models on structured (tabular) data with minimal user code. It abstracts away the typical ML workflow—data preprocessing, feature engineering, algorithm selection, and hyperparameter tuning—so you can fit and predict in just a few lines. The package is built on top of numpy, scipy, pandas, and scikit-learn, and integrates networkx for graph-based feature interactions and autogluon.core and autogluon.features for its automation engine.
The library is designed for developers and data scientists who want to quickly build production-ready models without deep expertise in tuning individual algorithms. It supports classification and regression tasks on tabular data, and is part of the broader AutoGluon ecosystem (which also handles time series and multimodal data). The package is actively maintained by AWS AI, supports Python 3.10–3.13 across Linux, macOS, and Windows, and carries an Apache-2.0 license.
Use it for
- Build a classification model for customer churn prediction from historical transaction and demographic data in minutes.
- Rapidly prototype a regression model for sales forecasting without manual feature selection or algorithm tuning.
- Deploy a production ML pipeline for fraud detection by fitting a model on labeled historical transactions.
- Compare multiple baseline models on a new tabular dataset to establish performance benchmarks quickly.
- Automate model retraining on fresh data with minimal code changes in a scheduled batch pipeline.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries a permissive license, and solves a real problem—reducing the time and expertise needed to train tabular ML models. It is well-suited for rapid prototyping, production deployment, and learning. No known security vulnerabilities as of the query date.
Install
autogluon-tabular on PyPI
Before you install
Low install friction with a pure Python wheel distribution. Actively maintained with a recent release; the repository shows strong community engagement with 10596 stars and continuous development.
Requires Python 3.10–3.13; depends on numpy, scipy, pandas, scikit-learn, networkx, and autogluon.core and autogluon.features packages.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for most production and research applications.
Quickstart
pip install autogluon.tabular
from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv", presets="best")
predictions = predictor.predict("test.csv")Verify before relying
- Whether the package supports GPU acceleration and what additional dependencies or setup are required for it.
- Memory and compute requirements for typical tabular datasets (size, feature count, model complexity).
- Specific model types and ensemble strategies used internally during automated training.
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 | 7 packagesnumpyscipypandasscikit-learnnetworkxautogluon.coreautogluon.features |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 569,878 / month, #5,957 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_tabular-1.6.1-py3-none-any.whl
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See also autogluon · pytabkit · autogluon.core · autogluon.features · autogluon.timeseries · pyglove · autogluon.text · autogluon.vision · tabicl · tabpfn