ray-train
Ray Train scales machine learning training from single GPU to multi-node clusters with minimal code changes.
Profile →npx skills add majiayu000/claude-skill-registry --skill ray-train --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
--- 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 dis
- Quick start
- Common workflows
- Workflow 1: Scale existing PyTorch code
- Workflow 2: HuggingFace Transformers integration
- Workflow 3: Hyperparameter tuning with Ray Tune
- Workflow 4: Checkpointing and fault tolerance
- Workflow 5: Multi-node training
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
pip install -U "ray[train]" On head node ray start --head --port=6379 On worker nodes ray start --address=<head-node-ip>:6379 ray status Should show: Restart head node ray stop ray start --head --port=6379 --dashboard-host=0.0.0.0
What does the ray-train skill do?
Ray Train scales machine learning training from single GPU to multi-node clusters with minimal code changes.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ray-train --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.