ray-distributed-trainer
Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management.
npx skills add a5c-ai/babysitter --skill ray-distributed-trainer --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.
# ray-distributed-trainer ## Overview Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management across clusters. ## Capabilities - Ray Train for distributed training - Ray Tune for hyperparameter search at scale - Cluster resource management - Fault tolerance and checkpointing - Actor-based parallelism - Integration with PyTorch and TensorFlow - Elastic training support - Multi-node orchestration ## Target Processes - Distributed Training Orchestration - AutoML Pipeline Orchestration - Model Training Pipeline ## Tools and Libraries - Ray - Ray Train - Ray Tune - Ray Cluster ## Input Schema ```json { "type": "object", "required": ["mode", "config"], "properties": { "mode": { "type": "string", "enum": ["train", "tune", "cluster"], "description": "Ray operation mode" }, "config": { "type": "object", "properties": { "numWorkers": { "type": "integer" }, "useGpu": { "type": "boolean" }, "resourcesPerWorker": { "type": "object", "properties": { "cpu": { "type": "number" }, "gpu": { "type": "number" } } } } }, "trainConfig": { "type": "object", "properties": { "trainerPath": { "type": "string" }, "framework": { "type": "string", "enum": ["p
- Overview
- Capabilities
- Target Processes
- Tools and Libraries
- Input Schema
- Output Schema
- Usage Example
What does the ray-distributed-trainer skill do?
Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill ray-distributed-trainer --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 a5c-ai/babysitter, a repository with 1,642 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.