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Pytorch Lightning Template

An open-source project template for adapting existing PyTorch training code to PyTorch Lightning, with separate interfaces for models and datasets.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pytorch Lightning Template — 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 PyTorch project needs a Lightning-oriented scaffold and the codebase benefits from separating model and dataset implementations.

Who it is for

  • Developers adapting an existing PyTorch training project to Lightning.
  • Researchers or engineers organizing projects with multiple model and dataset files.

Documented uses

  • Adapt model and dataset code into a reusable Lightning project structure.
  • Customize training, validation and optimizer functions in a shared model interface.
  • Route model and dataset choices through arguments and corresponding interface classes.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. The README says to clone it locally and copy the relevant template into a project; it describes no separate installation step.

  • The README describes classification and super-resolution templates and recommends classification as a starting point for tasks not listed.
  • Automatic model and dataset selection depends on the documented snake_case file and CamelCase class naming convention.
  • The template leaves project-specific training behavior to functions including configure_optimizers, training_step and validation_step.

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

Set up the template in a project directory

Clone the repository and copy a template for your problem type into your project.

Before you start

  • A local environment where Git can clone the repository.

Steps

  1. Clone the repository with the command below.

    Documented command
    git clone https://github.com/miracleyoo/pytorch-lightning-template.git

  2. Choose a problem type, such as classification.

  3. Copy the corresponding template into your project directory.

Expected outcome

The chosen problem-type template is available in your project directory for adaptation.

Guide limitations

  • The README says the available specialized templates are classification and super-resolution; for an unlisted task, it suggests starting from classification.
  • This setup guidance does not specify how to run or test a project.
Documentation 1 ↗
Source-based practical guide

Adapt model and dataset modules in the template

Connect project-specific model and dataset classes through the template interfaces.

Before you start

  • A copied template and project-specific model and dataset files.

Steps

  1. Place dataset modules in data and model modules in model, following the template’s interface structure.

  2. Create or adapt DInterface to import the dataset class, instantiate it in setup(), and provide the train, validation, and test dataloader functions.

  3. Create or adapt MInterface to import the model class and modify the training-related functions, such as configure_optimizers, training_step, and validation_step.

  4. Use snake_case filenames for model and dataset modules, and corresponding CamelCase class names so the interfaces can parse them by --model_name and --dataset.

Expected outcome

Model and dataset implementations are organized behind shared Lightning interfaces, with training behavior and dataloaders defined in those interfaces.

Guide limitations

  • The README describes interface structure and customization points but does not provide a complete project-specific implementation or execution procedure.
  • The documented automatic parsing depends on the specified filename and class-name conventions.
Documentation 1 ↗
Evidence and transparency

Sources & review

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