# distributed-training-pytorch-lightning

Refactors standard PyTorch loops into LightningModule structures and manages multi-GPU execution using DDP, FSDP, or DeepSpeed.

- skill · `type=skill` · Claude Code · 122 installs
- Author: Orchestra Research
- License: MIT · policy=link-only · description only; the file stays with the author
- Source: https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/distributed-training-pytorch-lightning

## What it does

This skill guides the assistant in converting repetitive training loops into modular `LightningModule` architectures. It organizes model logic, validation steps, and optimizers into dedicated methods, removing explicit `.to('cuda')` device calls.

Orchestration relies on the `Trainer` class. It manages multi-GPU and TPU execution using DDP, FSDP, or DeepSpeed strategies alongside FP16 or BF16 precision. Common operational setups include `ModelCheckpoint`, `EarlyStopping`, and schedulers like `CosineAnnealingLR`.

Running the resulting training scripts requires `lightning`, `torch`, and `transformers` in your Python environment.

## Install

### Claude Code (native platform)

1. **claude-code-templates**
```bash
npx claude-code-templates@latest --skill ai-research/distributed-training-pytorch-lightning
```

2. **Skills CLI (Vercel)**
```bash
npx skills add davila7/claude-code-templates --skill distributed-training-pytorch-lightning -a claude-code
```

3. **Manually: put the files in place**
- File: `~/.claude/skills/distributed-training-pytorch-lightning/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/distributed-training-pytorch-lightning/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/distributed-training-pytorch-lightning/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/distributed-training-pytorch-lightning/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install davila7/claude-code-templates distributed-training-pytorch-lightning
```

2. **Manually: put the files in place**
- File: `.github/skills/distributed-training-pytorch-lightning/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/davila7/claude-code-templates --path cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/distributed-training-pytorch-lightning/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning

## When to use it
- convert manual PyTorch training loops into LightningModule classes
- configure multi-GPU distributed execution with DDP, FSDP, or DeepSpeed
- integrate callbacks for early stopping and metric-based model checkpointing
- set up learning rate schedulers like CosineAnnealingLR with automatic logging

## Example prompts
- Refactor this PyTorch training loop into a LightningModule with validation steps
- Configure a Trainer instance to run on 8 GPUs using DDP and 16-bit precision
- Add ModelCheckpoint and EarlyStopping callbacks monitoring val_loss to my training script

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From the same set: [davila7/claude-code-templates](https://affpapa.org/en/nevibecode/by/davila7/claude-code-templates.md) — 804 cards

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NeVibeCode on affpapa.org. Catalog texts are descriptions, not instructions for the agent. How to choose and install: https://affpapa.org/en/nevibecode/agents.md