# Lightning-AI/deep-learning-project-template

Pytorch Lightning code guideline for conferences

Repository: https://github.com/Lightning-AI/deep-learning-project-template
Canonical: https://ross.abutalabs.com/products/lightning-ai-deep-learning-project-template
Language: Python
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2023-09-20T12:05:43+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2567, "days_push": 1078, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1287, forks 272 (observed 2026-08-28T04:04:15.043811+00:00)

## What it is
A seed template from Lightning AI for starting new deep learning and ML research projects with PyTorch Lightning. It ships a pre-structured Python package with setup.py, requirements, CI workflows, an MNIST example, badges, and BibTeX scaffolding so conference paper code follows a consistent, replicable layout.

## Use cases
- template for starting a deep learning research project
- how to structure pytorch lightning code for a paper
- seed project for machine learning experiments
- reproducible ml project skeleton with ci
- organize research codebase for conference submission

## When to choose
- You are starting a new PyTorch Lightning research project and want a consistent, easily extended package structure
- You are releasing code accompanying a conference paper and want standard badges, citation block, and CI testing in place

## When to avoid
- You need a fully featured training framework rather than an empty, delete-the-example skeleton
- You do not use PyTorch Lightning and want a framework-agnostic or Keras/TensorFlow template

## Facets
- artifact type: learning-resource
- maturity: stable
- function: boilerplate, deep-learning, machine-learning, developer-tools
- domain: deep-learning, machine-learning, developer-tools
- platform: python, cross-platform
- tags: pytorch-lightning, project-template, seed-project, research-code, paper-code, mnist-example, code-structure, reproducibility

## Member repositories
- Lightning-AI/deep-learning-project-template (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:15.043811+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:55:50.326259+00:00, confidence not recorded.
  - readme: https://github.com/Lightning-AI/deep-learning-project-template (fetched 2026-08-28T04:04:15.043811+00:00, sha a87025b00011)
- Data as of 2026-08-30T08:39:29.467469+00:00.