Specifiction
For vendors
AdvertiseTop spots, featured rows, home sponsor Get listedAdd a product we don't cover yet Claim a listingFree: verify the domain, then correct your page Vendor dashboardNumbers, orders and placements
List your tool
Vendor dashboard
Tech riderRev. 3 Oct 2026

Auto-PyTorch

Ch 106.8of 28 AutoML Software
  1. 1Runs onLinux, self-hosted
  2. 2CostsFree plan
  3. 3Feature engineeringYes
  4. 4Automated model selectionYes
  5. 5Workflow interfacecode
  6. 6Hosting modelself hosted
6 lines stated Written from the maker's own pages: automl.github.io
Visit site Alternatives
The Auto-PyTorch homepage

Overview

Auto-PyTorch is ranked #10 of 28 in AutoML software on Specifiction. It runs on Linux, Self-hosted. There is a free plan.

Auto-PyTorch plans and pricing

All plans
Auto-PyTorch Free 3-clause BSD license automl.github.io · 3 Oct 2026

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

Best Auto-PyTorch alternatives

See all 20
All accessCh 01 BigML Free planFree trialAPI from $833.33/mo7.3 All accessCh 02 JADBio Free planFree trialAPI from $183.25/mo7.3 All accessCh 03 Amazon SageMaker Autopilot Free trialAPIWeb 7.2 All accessCh 04 FEDOT Free planAPILinux Free to start7.2 All accessCh 05 AutoKeras Free planLinuxMac Free to start7.1 All accessCh 06 LightAutoML Free planLinuxMac Free to start7.1

Where it ranks on Specifiction

Is Auto-PyTorch yours?

Claim it for free: prove the domain, then correct facts, plans and screenshots. An editor reviews every change.

Claim it free Promote Auto-PyTorch

Sources