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Auto Pytorch

Auto-PyTorch is an open-source Python AutoML framework that searches neural-network architectures and training hyperparameters for deep-learning tasks. Its documented focus is tabular classification and regression, plus time-series forecasting. The workflow processes user-provided data, evaluates baselines, runs a budgeted SMAC search with Hyperband cutoff rules, and builds an ensemble from the observed results.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Auto Pytorch — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Auto-PyTorch when you have a supported tabular or time-series task and want a framework to automate configuration search and model selection within a Python workflow.

Who it is for

  • Python developers building tabular classification or regression models.
  • Practitioners developing time-series forecasting models.
  • Teams seeking automated model-configuration search within a PyTorch-oriented workflow.

Documented uses

  • Search architecture and training settings for tabular classification or regression.
  • Automate model search for time-series forecasting.
  • Compare searched configurations and produce an ensemble from search observations.
Before you choose

Requirements & limits

The repository identifies the project as open source under the Apache-2.0 license and documents installation through PyPI.

  • The project supports tabular classification and regression as well as time-series forecasting, with a separate forecasting dependency extra.
  • Its documented workflow includes data processing, cross-validation or holdout splits, baseline evaluation, budgeted search and ensemble construction.
  • The README warns that moving from version 0.0.2 to 0.1.0 breaks compatibility; it points users seeking the old API to the master-old branch.
  • The repository documents Python installation and a PyTorch-centered framework; the project description identifies Python as its implementation language.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Auto-PyTorch for tabular work

Install the framework from PyPI as documented in its README.

Before you start

  • Python development environment with pip.

Steps

  1. Run the command below.

    Documented command
    pip install autoPyTorch

  2. Consult the project documentation for task-specific API usage.

Expected outcome

Auto-PyTorch is installed from the package distribution for subsequent development.

Guide limitations

  • This setup guide does not execute a model search.
  • The README specifies additional dependencies for time-series forecasting.
Documentation 1 ↗
Source-based practical guide

Install Auto-PyTorch with forecasting dependencies

Install the package with the additional dependency set specified for time-series forecasting.

Before you start

  • Python development environment with pip.

Steps

  1. Run the command below.

    Documented command
    pip install autoPyTorch[forecasting]

  2. Consult the project documentation for the forecasting API and input format.

Expected outcome

The package is installed with the documented forecasting extra.

Guide limitations

  • This setup guide does not execute a forecast or verify an environment.
  • The README documents dataset formats and task examples separately.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
automl/Auto-PyTorch · Official homepage title · retained in its source language
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