# Lightning-AI/pytorch-lightning

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

Repository: https://github.com/Lightning-AI/pytorch-lightning
Canonical: https://ross.abutalabs.com/products/pytorch-lightning
Homepage: https://lightning.ai/pytorch-lightning/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme
Language: Python
License: Apache-2.0
License Family: permissive
Topics: python, deep-learning, artificial-intelligence, ai, pytorch, data-science, machine-learning
Last push: 2026-08-25T16:59:45+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 74, longevity 100
- inputs: {"age_days": 2713, "days_push": 8, "days_rel": 98, "gap_med": 45.0, "n_releases_24m": 13}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 31310, forks 3784 (observed 2026-08-28T04:11:56.703533+00:00)

## What it is
PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate for training, finetuning, and scaling AI models. It handles engineering concerns like backpropagation, mixed precision, and multi-GPU/distributed training with zero code changes.

## Use cases
- pretrain large AI models on single or thousands of GPUs
- finetune any model without changing code
- remove repetitive PyTorch training boilerplate
- scale training from one GPU to distributed clusters
- run mixed-precision training without manual setup

## When to choose
- you want structured, reproducible PyTorch training loops
- you need to scale training across many GPUs with minimal code changes
- you're an ML researcher tired of rewriting engineering code per project

## When to avoid
- you need full low-level control over every training step
- your project is tiny and plain PyTorch is simpler
- you're not using PyTorch as your backend

## Facets
- artifact type: library
- maturity: stable
- function: deep-learning, machine-learning, llm-training, gpu-computing
- domain: deep-learning, machine-learning, artificial-intelligence, data-science
- platform: python, cross-platform
- tags: pytorch, training-loop, distributed-training, mixed-precision, boilerplate-reduction, gpu

## Member repositories
- Lightning-AI/pytorch-lightning (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:56.703533+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 2026-08-29T16:52:18.225386+00:00, confidence not recorded.
  - readme: https://github.com/Lightning-AI/pytorch-lightning (fetched 2026-08-28T04:11:56.703533+00:00, sha acb26461f745)
  - homepage: https://lightning.ai/pytorch-lightning/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme (fetched 2026-08-29T07:49:23.887945+00:00, sha 21f0a0890a80)
  - registry_pypi: https://pypi.org/pypi/pytorch-lightning/json (fetched 2026-08-29T07:49:23.897180+00:00, sha 55c4c9f70f32)
- Data as of 2026-08-30T08:39:29.467469+00:00.