Babysitter ray-distributed-trainer

Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management.

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/data-science-ml/skills/ray-distributed-trainer" ~/.claude/skills/a5c-ai-babysitter-ray-distributed-trainer && rm -rf "$T"

manifest: library/specializations/data-science-ml/skills/ray-distributed-trainer/SKILL.md

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

{
  "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": ["pytorch", "tensorflow", "xgboost"] },
        "scalingConfig": { "type": "object" }
      }
    },
    "tuneConfig": {
      "type": "object",
      "properties": {
        "searchSpace": { "type": "object" },
        "scheduler": { "type": "string" },
        "numSamples": { "type": "integer" },
        "metric": { "type": "string" },
        "mode": { "type": "string", "enum": ["min", "max"] }
      }
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "results"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error", "partial"]
    },
    "results": {
      "type": "object",
      "properties": {
        "bestConfig": { "type": "object" },
        "bestMetric": { "type": "number" },
        "numTrials": { "type": "integer" },
        "completedTrials": { "type": "integer" }
      }
    },
    "checkpointPath": {
      "type": "string"
    },
    "clusterStatus": {
      "type": "object",
      "properties": {
        "numNodes": { "type": "integer" },
        "totalCpu": { "type": "number" },
        "totalGpu": { "type": "number" }
      }
    },
    "trainingTime": {
      "type": "number"
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Distributed hyperparameter tuning',
  skill: {
    name: 'ray-distributed-trainer',
    context: {
      mode: 'tune',
      config: {
        numWorkers: 4,
        useGpu: true,
        resourcesPerWorker: { cpu: 2, gpu: 1 }
      },
      tuneConfig: {
        searchSpace: {
          lr: { type: 'loguniform', min: 1e-5, max: 1e-1 },
          batchSize: { type: 'choice', values: [16, 32, 64] }
        },
        scheduler: 'asha',
        numSamples: 100,
        metric: 'val_loss',
        mode: 'min'
      }
    }
  }
}