---
name: ray-distributed-trainer-a72493780a2b
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]

---

# 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": ["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

```json
{
  "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

```javascript
{
  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'
      }
    }
  }
}
```


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Engineer, Data Scientist, ML / AI Engineer
Specialties: API
Tools: Ray
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: afd8a4ff42b53c06f26da48947883a4bb404747564c66a685d512bb45b964f9e