Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
ray-train
ray-train is an open-source workflow skill for Claude Code and compatible agents, published by Orchestra-Research. Its author describes it as: “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 trainin…”. The project has 12k stars on GitHub and is available under the MIT license. Add it to your setup with `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`.
What ray-train does
Ray Train scales machine learning training from single GPU to multi-node clusters with minimal code changes.
Installation
Add ray-train to your agent with:
/plugin marketplace add Orchestra-Research/AI-Research-SKILLsAlways review a skill's source before installing it. This command comes from the skill's public repository; the linked repo is the source of truth for exact setup steps.
What's inside
The SKILL.md for ray-train is organised into these sections:
- 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
When to use it
Reach for ray-train when you want workflow help from your agent without writing the same instructions every session. Load the skill and the agent picks it up automatically for relevant tasks.
Strengths
- Clear MIT license — safe to read and adapt
- Ships in Orchestra-Research/AI-Research-SKILLs, an established project with 11,807 GitHub stars
- Actively maintained (recent commits)
Topics
Frequently asked questions
- What does ray-train do?
- 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.
- How do I install ray-train?
- Run /plugin marketplace add Orchestra-Research/AI-Research-SKILLs in your agent, then reload your skills. Review the source at https://github.com/Orchestra-Research/AI-Research-SKILLs before installing.
- Is ray-train free to use?
- Yes. ray-train is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
- Where does ray-train come from?
- ray-train ships inside Orchestra-Research/AI-Research-SKILLs, a repository that contains 41 catalogued skills in total. The repository's 11,807 GitHub stars apply to that whole collection, not to this skill on its own.
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