autogluon
Fast and Accurate ML in 3 Lines of Code
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; GPU support requires additional setup per installation guide.
- 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 · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution in both open-source and commercial projects with minimal restrictions.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 10,596 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 305,993 downloads/mo, #7,789 on PyPI
Alternatives
Verify before relying
pip install autogluon
from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv", presets="best")
predictions = predictor.predict("test.csv")- 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.
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 on this page. Verify before relying on it.
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
autogluon 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 repository activity (10596 stars). Supports Python 3.10–3.13 across Linux, macOS, and Windows.
Requires Python 3.10 or later; GPU support requires additional setup per installation guide.
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")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 the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesautogluon.coreautogluon.featuresautogluon.tabularautogluon.multimodalautogluon.timeseries |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 305,993 / month, #7,789 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-1.6.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “no-code machine learning”
- autogluonAutoGluon automates machine learning model training and deployment…
- gradioGradio builds web interfaces for machine learning models, APIs, and…
- autogluon.tabularAutomates machine learning model training and prediction on tabular…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also autogluon.core · autogluon.features · autogluon.multimodal · autogluon.tabular · autogluon.text · autogluon.timeseries · pyglove · autogluon.common · autogluon.vision · tabpfn