Auto-PyTorch

Summary

Auto-PyTorch is a free, self-hosted machine-learning toolkit based on PyTorch. It automates model selection and tuning, including neural-network architecture and training hyperparameters, within a user-set search budget. Its search methods include Bayesian optimization, meta-learning, SMAC, and Hyperband. Supported tasks include tabular classification, tabular regression, and time-series forecasting. For tabular work, preprocessing can include imputation, categorical encoding, scaling, and feature processing, with related hyperparameters tuned during search. Auto-PyTorch can build ensembles based on model predictions for a validation set; users can configure ensemble size and candidate limits. Memory limits for estimators and an overall wall-time limit are configurable. Parallel Bayesian optimization is available through Dask.distributed, but workers need shared filesystem access to training data and models. Installation documentation specifies Linux, Python 3.7 or later, a C++11-capable compiler, and SWIG 3.0; SWIG 4.0 or later is not supported. Forecasting needs additional dependencies. The project provides a Docker image and states that it uses the 3-clause BSD license.

Who it is for

Auto-PyTorch suits machine-learning practitioners who want to automate model search in a code-based, self-hosted workflow. Its documented tasks cover tabular data and time-series forecasting.

Compared on AutoML software

Free plan

Yesautoml.github.io

Feature engineering

Yesautoml.github.io

Automated model selection

Yesautoml.github.io

Workflow interface

codeautoml.github.io

Hosting model

self_hostedautoml.github.io

Facts

What it does

Auto-PyTorch is an automated machine-learning toolkit based on PyTorch that helps users automate algorithm selection and hyperparameter tuning.automl.github.io · 3 Oct 2026

Optimization methods

It uses Bayesian optimization, meta-learning, and ensemble construction to search for models.automl.github.io · 3 Oct 2026

Supported tasks

The documentation describes tabular classification, tabular regression, and time-series forecasting tasks.automl.github.io · 3 Oct 2026

Data preparation

For tabular tasks, its preprocessing includes imputation, categorical encoding, scaling, and feature preprocessing, with corresponding hyperparameters tuned during search.automl.github.io · 3 Oct 2026

Ensembling

It builds ensembles by selecting among models based on their predictions for a validation set, and users can configure ensemble size and candidate limits.automl.github.io · 3 Oct 2026

Resource controls

Users can set a memory limit for estimators and a total wall-time limit for model search.automl.github.io · 3 Oct 2026

Parallel processing

It supports parallel Bayesian optimization using Dask.distributed, and parallel workers need access to a shared file system for training data and models.automl.github.io · 3 Oct 2026

Integration

The documented ecosystem includes PyTorch, scikit-learn transformers, Dask.distributed, and threadpoolctl.automl.github.io · 3 Oct 2026

Installation

The installation documentation specifies Linux, Python 3.7 or later, a C++11-capable compiler, and SWIG 3.0.*, and also documents a Docker image.automl.github.io · 3 Oct 2026

Forecasting dependencies

Time-series forecasting requires additional dependencies beyond the base installation.automl.github.io · 3 Oct 2026

License

The documentation states that Auto-PyTorch is licensed under the 3-clause BSD license.automl.github.io · 3 Oct 2026

Support and contribution

The project invites bug reports, documentation improvements, and feature contributions through its GitHub issue tracker.automl.github.io · 3 Oct 2026

Maker

The GitHub project says Auto-PyTorch is developed by the AutoML Groups of the University of Freiburg and Hannover.github.com · 3 Oct 2026

Purpose

Auto-PyTorch is an automated machine learning toolkit based on PyTorch that automates algorithm selection and hyperparameter tuning.automl.github.io · 3 Oct 2026

Optimization

It jointly optimizes neural network architecture and training hyperparameters for automated deep learning.github.com · 3 Oct 2026

Search methods

Its search process uses Bayesian optimization, meta-learning, SMAC, and Hyperband to explore pipeline configurations within a user-set budget.github.com · 3 Oct 2026

Integrations

The documentation describes using Dask.distributed for parallel Bayesian optimization and sklearn column transformers for data preprocessing.automl.github.io · 3 Oct 2026

Deployment

The project provides a Docker image and can be installed from PyPI or manually in a Python environment.automl.github.io · 3 Oct 2026

Platform requirement

The installation documentation lists Linux, Python 3.7 or later, a C++11-capable compiler, and SWIG 3.0 as system requirements.automl.github.io · 3 Oct 2026

Compatibility limit

The installation documentation says SWIG 4.0 or later is not supported.automl.github.io · 3 Oct 2026

Parallel computing requirement

When using multiple workers, the documentation says they must have access to a shared file system for training data and models.automl.github.io · 3 Oct 2026

Support and contributions

The project invites bug reports and documentation contributions through its GitHub issue tracker and recommends contacting developers by opening an issue before starting feature work.automl.github.io · 3 Oct 2026

Where it ranks on Everything Xiaomi

• Best AutoML Software in 2026#6 of 28

Sources

• automl.github.io/Auto-PyTorch/master/· checked 3 Oct 2026
• automl.github.io/Auto-PyTorch/master/manual.html· checked 3 Oct 2026
• automl.github.io/Auto-PyTorch/master/installation.html· checked 3 Oct 2026
• github.com/automl/Auto-PyTorch· checked 3 Oct 2026