Skill · em Dados, IA e pesquisa

ray-train

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter…

Procedência

Antes de instalar

2 arquivos · 23,7 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/ray-train/.

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

Global: instala em ~/.claude/skills/ray-train/.

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

Codex

Neste projeto: instala em .agents/skills/ray-train/.

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

Global: instala em ~/.agents/skills/ray-train/.

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

Antigravity

Neste projeto: instala em .agents/skills/ray-train/.

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

Global: instala em ~/.gemini/antigravity-cli/skills/ray-train/.

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

Peça ao Rook

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

Prévia do SKILL.md

---
name: ray-train
description: Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Ray Train, Distributed Training, Orchestration, Ray, Hyperparameter Tuning, Fault Tolerance, Elastic Scaling, Multi-Node, PyTorch, TensorFlow]
dependencies: ["ray[train]", torch, transformers]
---

# Ray Train - Distributed Training Orchestration

## Quick start

Ray Train scales machine learning training from single GPU to multi-node clusters with minimal code changes.

**Installation**:
```bash
pip install -U "ray[train]"
```

**Basic PyTorch training** (single node):

```python
import ray
from ray import train
from ray.train import ScalingConfig
from ray.train.torch import TorchTrainer
import torch
import torch.nn as nn

# Define training function
def train_func(config):
    # Your normal PyTorch code
    model = nn.Linear(10, 1)
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

    # Prepare for distributed (Ray handles device placement)
    model = train.torch.prepare_model(model)
…

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