Skill · em Dados, IA e pesquisa

pytorch-lightning

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Procedência

Origem: davila7/claude-code-templates
Caminho: cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning
Versão fixada: 57f899e5394bb8ca166f38eacae8f0853cbfe033
Licença: MIT
Espelhado em 25/09/2026
2 downloads no Claude Code Templates (lido em 25/09/2026)

Antes de instalar

4 arquivos · 43,4 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/pytorch-lightning/.

d=".claude/skills/pytorch-lightning"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/callbacks.md" "$u/references/callbacks.md" \
  -o "$d/references/distributed.md" "$u/references/distributed.md" \
  -o "$d/references/hyperparameter-tuning.md" "$u/references/hyperparameter-tuning.md"

Global: instala em ~/.claude/skills/pytorch-lightning/.

d="$HOME/.claude/skills/pytorch-lightning"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/callbacks.md" "$u/references/callbacks.md" \
  -o "$d/references/distributed.md" "$u/references/distributed.md" \
  -o "$d/references/hyperparameter-tuning.md" "$u/references/hyperparameter-tuning.md"

Codex

Neste projeto: instala em .agents/skills/pytorch-lightning/.

d=".agents/skills/pytorch-lightning"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/callbacks.md" "$u/references/callbacks.md" \
  -o "$d/references/distributed.md" "$u/references/distributed.md" \
  -o "$d/references/hyperparameter-tuning.md" "$u/references/hyperparameter-tuning.md"

Global: instala em ~/.agents/skills/pytorch-lightning/.

d="$HOME/.agents/skills/pytorch-lightning"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/callbacks.md" "$u/references/callbacks.md" \
  -o "$d/references/distributed.md" "$u/references/distributed.md" \
  -o "$d/references/hyperparameter-tuning.md" "$u/references/hyperparameter-tuning.md"

Antigravity

Neste projeto: instala em .agents/skills/pytorch-lightning/.

d=".agents/skills/pytorch-lightning"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/callbacks.md" "$u/references/callbacks.md" \
  -o "$d/references/distributed.md" "$u/references/distributed.md" \
  -o "$d/references/hyperparameter-tuning.md" "$u/references/hyperparameter-tuning.md"

Global: instala em ~/.gemini/antigravity-cli/skills/pytorch-lightning/.

d="$HOME/.gemini/antigravity-cli/skills/pytorch-lightning"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/callbacks.md" "$u/references/callbacks.md" \
  -o "$d/references/distributed.md" "$u/references/distributed.md" \
  -o "$d/references/hyperparameter-tuning.md" "$u/references/hyperparameter-tuning.md"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.pytorch-lightning.ai-research

Prévia do SKILL.md

---
name: pytorch-lightning
description: High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [PyTorch Lightning, Training Framework, Distributed Training, DDP, FSDP, DeepSpeed, High-Level API, Callbacks, Best Practices, Scalable]
dependencies: [lightning, torch, transformers]
---

# PyTorch Lightning - High-Level Training Framework

## Quick start

PyTorch Lightning organizes PyTorch code to eliminate boilerplate while maintaining flexibility.

**Installation**:
```bash
pip install lightning
```

**Convert PyTorch to Lightning** (3 steps):

```python
import lightning as L
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset

# Step 1: Define LightningModule (organize your PyTorch code)
class LitModel(L.LightningModule):
    def __init__(self, hidden_size=128):
        super().__init__()
        self.model = nn.Sequential(
            nn.Linear(28 * 28, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, 10)
        )

    def training_step(self, batch, batch_idx):
…

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