Skill · em Dados, IA e pesquisa

pytorch-fsdp

Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

Procedência

Antes de instalar

3 arquivos · 485,7 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-fsdp/.

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

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

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

Codex

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

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

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

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

Antigravity

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

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

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

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

Peça ao Rook

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

Prévia do SKILL.md

---
name: pytorch-fsdp
description: Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Distributed Training, PyTorch, FSDP, Data Parallel, Sharding, Mixed Precision, CPU Offloading, FSDP2, Large-Scale Training]
dependencies: [torch>=2.0, transformers]
---

# Pytorch-Fsdp Skill

Comprehensive assistance with pytorch-fsdp development, generated from official documentation.

## When to Use This Skill

This skill should be triggered when:
- Working with pytorch-fsdp
- Asking about pytorch-fsdp features or APIs
- Implementing pytorch-fsdp solutions
- Debugging pytorch-fsdp code
- Learning pytorch-fsdp best practices

## Quick Reference

### Common Patterns

**Pattern 1:** Generic Join Context Manager# Created On: Jun 06, 2025 | Last Updated On: Jun 06, 2025 The generic join context manager facilitates distributed training on uneven inputs. This page outlines the API of the relevant classes: Join, Joinable, and JoinHook. For a tutorial, see Distributed Training with Uneven…

```
Join
```

**Pattern 2:** Distributed communication package - torch.distributed# Created On: Jul 12, 2017 | Last Updated On: Sep 04, 2025 Note Please refer to PyTorch Distributed Overview for a brief introduction to all features related to distributed training. Backends# torch.distributed supports four built-in backends, each wit…

```
torch.distributed
```

**Pattern 3:** Initialization# The package needs to be initialized using the torch.distributed.init_process_group() or torch.distributed.device_mesh.init_device_mesh() function before calling any other methods. Both block until all processes have joined. Warning Initialization is not thread-safe. Process group creation…
…

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