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autogluon.common

Fast and Accurate ML in 3 Lines of Code

autogluon-common v1.6.1 372.2K downloads/30d#7,165 on PyPI10,596
Permissive license Apache-2.0 Active released

What it is and what it does

autogluon.common is a shared utility package within the AutoGluon automated machine learning framework, developed by AWS AI. It provides common infrastructure and helper functions used across AutoGluon's various predictors (TabularPredictor, TimeSeriesPredictor, MultiModalPredictor) and other components. The package abstracts away boilerplate and reusable logic so that higher-level AutoGluon modules can focus on their specific tasks without duplicating code.

This is primarily a dependency package—it is installed as part of the AutoGluon ecosystem rather than used standalone. It depends on standard data science libraries (numpy, pandas, scikit-learn) and AWS integration tools (boto3), along with utilities for task scheduling (joblib), progress reporting (tqdm), and configuration management (pyyaml). The package is actively maintained, supports Python 3.10–3.13 across Linux, macOS, and Windows, and carries no known security vulnerabilities.

Use it for:

  • As a dependency when installing AutoGluon for tabular, time series, or multimodal machine learning tasks
  • Accessing shared data preprocessing or validation utilities if you are extending AutoGluon or building on top of its ecosystem
  • Leveraging common configuration and logging infrastructure across multiple AutoGluon-based workflows
  • Working with AWS S3 integration for model storage and data loading via boto3 bindings in the common layer

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Provides shared utilities and common infrastructure for AutoGluon's automated machine learning framework, supporting tabular, time series, multimodal, and image data tasks.

Yes—install it as part of AutoGluon if you are using any AutoGluon predictor (tabular, time series, or multimodal). It is a required dependency, actively maintained, has no security issues, and carries a permissive Apache-2.0 license. If you are not using AutoGluon, there is no reason to install this package directly.

Install

autogluon-common on PyPI

pip

pip install autogluon-common

uv

uv add autogluon-common

poetry

poetry add autogluon-common

Installing autogluon.common

Before you install

Low friction installation with a pure-Python wheel. Active maintenance with a recent release (8 days old) and strong repository signal (10596 stars). Depends on well-established libraries (numpy, pandas, scikit-learn, boto3, joblib) with no exotic or compiled dependencies.

License in practice

Apache-2.0 permissive license allows commercial and private use with minimal restrictions. You may use, modify, and distribute the code provided you retain license notices and include a copy of the license.

Quickstart

pip install autogluon.common

from autogluon.common import something
# Use shared utilities from AutoGluon ecosystem

Requires Python 3.10–3.13; earlier or later versions are not supported.

Verify before relying

  • What specific utilities and functions autogluon.common exports and what they are used for
  • Whether this package is intended for direct use or primarily as an internal dependency for other AutoGluon modules
  • Performance characteristics or scalability limits for the shared infrastructure

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 11 — numpy, pandas, pyarrow, boto3, psutil, tqdm, requests, joblib, pyyaml, packaging, scikit-learn
Maintenance actively maintained — 8 days since the last release
Last repo commit
First released
Downloads 372,186/month — #7,165 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

Evidence: autogluon_common-1.6.1-py3-none-any.whl

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

Tags

automl common utilitiesautogluon shared infrastructuremachine learning automation helpersml model training utilitiesautomated feature engineeringml pipeline orchestrationdata preprocessing for automl
automlaws-aiinfrastructure

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