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Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/a5c-ai/babysitter --skill ray-distributed-trainer명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
SKILL.md 표시 중
SOC 직업 분류 기준
| name | ray-distributed-trainer |
| description | Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management. |
| allowed-tools | ["Read","Write","Bash","Glob","Grep"] |
| graph | {"domains":["domain:data-science"],"specializations":["specialization:data-science-ml"],"skillAreas":["skill-area:machine-learning-frameworks","skill-area:hyperparameter-tuning-experiment-management"],"roles":["role:ml-engineer","role:ml-ops-engineer"],"workflows":["workflow:ml-model-lifecycle"]} |
Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management across clusters.
{
{
"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"
}
}
}
{
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'
}
}
}
}