pytorch-trainer

name	pytorch-trainer
description	PyTorch model training skill with custom training loops, gradient management, and GPU optimization.
allowed-tools	["Read","Write","Bash","Glob","Grep"]
graph	{"domains":["domain:data-science"],"specializations":["specialization:data-science-ml"],"skillAreas":["skill-area:deep-learning-libraries","skill-area:machine-learning-frameworks"],"roles":["role:ml-engineer","role:data-scientist"],"workflows":["workflow:ml-model-lifecycle"]}

pytorch-trainer

Overview

PyTorch model training skill with custom training loops, gradient management, GPU optimization, and integration with experiment tracking systems.

Capabilities

• Custom training loop execution
• Learning rate scheduling (StepLR, CosineAnnealing, OneCycleLR, etc.)
• Gradient clipping and accumulation
• Mixed precision training (AMP)
• Checkpoint management and resumption
• DataLoader optimization
• Multi-GPU training (DataParallel, DistributedDataParallel)
• Early stopping with patience

Target Processes

• Model Training Pipeline with Experiment Tracking
• Distributed Training Orchestration
• AutoML Pipeline Orchestration

Tools and Libraries

• PyTorch
• PyTorch Lightning (optional)
• torchvision, torchaudio, torchtext
• CUDA toolkit

Input Schema

{
  "type": "object",
  "required": ["modelPath", "dataConfig", "trainingConfig"],
  "properties": {
    "modelPath": {
      "type": "string",
      "description": "Path to model definition file"
    },
    "dataConfig": {
      "type": "object",
      "properties": {
        "trainPath": { "type": "string" },
        "valPath": { "type": "string" },
        "batchSize":    
            
      
    
     
       
       
            
            
            
            
            
            
            
      
    
     
       
       
            
            
            
      
    
  

Output Schema

{
  "type": "object",
  "required": ["status", "metrics", "checkpointPath"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error", "early_stopped"]
    },
    "metrics": {
      "type": "object",
      "properties": {
        "trainLoss": { "type": "number" },
        "valLoss": { "type":  
            
            
            
            
      
    
     
       
    
     
       
       
         
         
              
              
              
        
      
    
  

Usage Example

{
  kind: 'skill',
  title: 'Train PyTorch model',
  skill: {
    name: 'pytorch-trainer',
    context: {
      modelPath: 'models/resnet.py',
      dataConfig: {
        trainPath: 'data/train',
        valPath: 'data/val',
        batchSize: 32,
        numWorkers: 4
      },
      trainingConfig: {
        epochs: 100,
        learningRate: 0.001,
        optimizer: 'AdamW',
        scheduler: 'cosine',
        mixedPrecision: true,
        gradientClipping: 1.0
      }
    }
  }
}