ReddRadar
Agent skill
ray-distributed-trainer
Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management.
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/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'
}
}
}
}
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