#4646th of 75 in ML frameworks and libraries
autogluon
AutoML library for tabular, time series and multimodal data in Python
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- Stars
- 10.8k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- Python
10,096 on 31 Mar 202610,792 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
AutoGluon trains and ensembles models for tabular, time series and multimodal prediction through predictor classes such as TabularPredictor, TimeSeriesPredictor and MultiModalPredictor. It selects and combines models, from classic ML algorithms to foundation models, so a working pipeline takes a few lines of Python. It installs with pip on Python 3.10 to 3.13 across Linux, macOS and Windows.
Who it is for: Data scientists and ML engineers building models on tables or time series
Strengths
- Three task APIs (tabular, time series, multimodal) share a fit/predict pattern
- Combines classic ML models and foundation models in one ensemble
- Installs with pip or conda on Linux, macOS and Windows
- Cloud options: AutoGluon Cloud, official Docker image, AWS Deep Learning Containers
Weaknesses
- README gives no RAM, VRAM or training-time figures
- README does not say how much GPU helps or which tasks need it
- Not an LLM app; no chat UI, model server or agent runtime
- Supports Python 3.10 to 3.13 only
What it needs
- GPU optional
- Models: tabular models, time series forecasting, multimodal models
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