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Pytorch Adapt

PyTorch Adapt is a Python library for building and customizing machine-learning domain-adaptation workflows with PyTorch. It provides algorithms, loss functions, hooks, adapters and validation methods that developers can combine to adapt existing models to new data domains.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pytorch Adapt — 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 it when an existing machine-learning model needs to be adapted to a new data domain and you want modular PyTorch components for training or validation.

Who it is for

  • PyTorch developers adapting existing models to new data domains.
  • Machine-learning practitioners assembling or customizing domain-adaptation training and validation workflows.
  • Developers who want to use PyTorch Lightning or PyTorch Ignite wrappers with an adaptation workflow.

Documented uses

  • Combine adaptation algorithms and loss functions into a training workflow.
  • Add domain-adaptation hooks to existing PyTorch models, optimizers and dataloaders.
  • Wrap an adapter for use with PyTorch Lightning or PyTorch Ignite.
  • Evaluate predictions with a validator, including SNDValidator.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed. The README documents installation through pip and does not document a Conda installation; it says Conda is coming soon.

  • The library expects users to supply their own models, optimizers and data loaders for the demonstrated vanilla PyTorch workflow.
  • Framework integrations are installed through separate pip extras for Lightning and Ignite.
  • The README includes example notebooks and points to setup.py for dependency details.

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 PyTorch Adapt with pip

Install the library, or add the documented framework extra needed for a Lightning or Ignite integration.

Before you start

  • Python environment with pip.

Steps

  1. Install the base package with the command below.

    Documented command
    pip install pytorch-adapt

  2. For the Lightning integration, install pytorch-adapt[lightning] instead.

  3. For the Ignite integration, install pytorch-adapt[ignite] instead.

Expected outcome

The package installation includes the selected library components and, when chosen, the documented framework integration extra.

Guide limitations

  • This covers installation only, not running a training or validation workflow.
  • Dependency details are referenced in the project's setup.py; no specific Python or PyTorch version requirement is stated here.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
KevinMusgrave/pytorch-adapt · Official homepage title · retained in its source language
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