StateSet Synthetic Data Studio Architecture Guide

class GRPOArchitecture:
    """Core GRPO training architecture"""
    
    components = {
        "trajectory_generator": {
            "purpose": "Generates multiple response trajectories",
            "features": ["Parallel generation", "Memory efficient"]
        },
        "reward_computer": {
            "purpose": "Hierarchical reward calculation",
            "features": ["Multi-objective", "Custom functions"]
        },
        "advantage_estimator": {
            "purpose": "Group-relative advantage computation",
            "features": ["Baseline normalization", "Variance reduction"]
        },
        "policy_optimizer": {
            "purpose": "PPO-based policy updates",
            "features": ["Gradient clipping", "KL control"]
        },
        "kl_controller": {
            "purpose": "Adaptive KL divergence control",
            "features": ["Dynamic adjustment", "Stability monitoring"]
        }
    }

// Code splitting with lazy loading
const TrainingDashboard = lazy(() => 
  import('./pages/TrainingDashboard')
);

// Bundle optimization
optimization: {
  splitChunks: {
    chunks: 'all',
    cacheGroups: {
      vendor: {
        test: /[\\/]node_modules[\\/]/,
        priority: 10
      }
    }
  }
}

// Service Worker caching
serviceWorkerRegistration.register({
  onUpdate: registration => {
    // Handle updates
  }
});

// Virtual scrolling for large lists
<VirtualList
  height={600}
  itemCount={10000}
  itemSize={50}
  renderItem={renderRow}
/>

# Connection pooling
engine = create_async_engine(
    DATABASE_URL,
    pool_size=20,
    max_overflow=40,
    pool_pre_ping=True,
    pool_recycle=3600
)

# Query optimization
@cached(ttl=300)
async def get_user_models(user_id: str):
    return await db.execute(
        select(Model)
        .options(selectinload(Model.metrics))
        .where(Model.user_id == user_id)
        .order_by(Model.created_at.desc())
    )

# Async I/O throughout
async def process_request(request: Request):
    async with httpx.AsyncClient() as client:
        tasks = [
            fetch_user_data(client, user_id),
            fetch_model_data(client, model_id),
            fetch_metrics(client, metric_ids)
        ]
        results = await asyncio.gather(*tasks)

# Multi-GPU configuration
class DistributedTrainer:
    def __init__(self, num_gpus=4):
        self.device_ids = list(range(num_gpus))
        self.model = nn.DataParallel(
            model, 
            device_ids=self.device_ids
        )
    
    def train(self, dataloader):
        # Gradient accumulation
        accumulation_steps = 4
        
        for i, batch in enumerate(dataloader):
            loss = self.model(batch) / accumulation_steps
            loss.backward()
            
            if (i + 1) % accumulation_steps == 0:
                optimizer.step()
                optimizer.zero_grad()

# Mixed precision training
scaler = torch.cuda.amp.GradScaler()

with autocast():
    output = model(input)
    loss = criterion(output, target)

scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()

"""
Python Coding Standards
"""

# 1. Follow PEP 8 style guide
from typing import List, Optional, Dict, Any
import asyncio
from datetime import datetime

# 2. Type hints for all functions
async def process_training_job(
    job_id: str,
    config: Dict[str, Any],
    timeout: Optional[int] = 3600
) -> TrainingResult:
    """
    Process a training job asynchronously.
    
    Args:
        job_id: Unique job identifier
        config: Training configuration
        timeout: Maximum execution time in seconds
        
    Returns:
        TrainingResult object with metrics
        
    Raises:
        TrainingError: If training fails
        TimeoutError: If timeout exceeded
    """
    try:
        async with timeout_context(timeout):
            result = await train_model(job_id, config)
            return result
    except asyncio.TimeoutError:
        raise TimeoutError(f"Job {job_id} exceeded timeout")
    except Exception as e:
        logger.error(f"Training failed: {e}")
        raise TrainingError(str(e))

# 3. Comprehensive error handling
class TrainingError(Exception):
    """Custom exception for training errors"""
    pass

# 4. Async/await for I/O operations
async def fetch_training_data(dataset_id: str) -> Dataset:
    async with get_db_session() as session:
        return await session.get(Dataset, dataset_id)

/**
 * TypeScript Coding Standards
 */

// 1. Strict TypeScript settings
// tsconfig.json: "strict": true

// 2. Interface definitions
interface TrainingJob {
  id: string;
  status: 'pending' | 'running' | 'completed' | 'failed';
  progress: number;
  config: TrainingConfig;
  metrics?: TrainingMetrics;
  createdAt: Date;
  updatedAt: Date;
}

// 3. Component typing
interface DashboardProps {
  userId: string;
  onJobSelect: (jobId: string) => void;
}

const Dashboard: React.FC<DashboardProps> = ({ 
  userId, 
  onJobSelect 
}) => {
  // 4. Custom hooks for logic reuse
  const { jobs, loading, error } = useTrainingJobs(userId);
  
  // 5. Error boundaries
  if (error) {
    return <ErrorBoundary error={error} />;
  }
  
  return (
    <div className="dashboard">
      {/* Component implementation */}
    </div>
  );
};

// 6. Async handling with proper types
const fetchJobs = async (
  userId: string
): Promise<TrainingJob[]> => {
  try {
    const response = await api.get<TrainingJob[]>(
      `/users/${userId}/jobs`
    );
    return response.data;
  } catch (error) {
    console.error('Failed to fetch jobs:', error);
    throw new Error('Failed to load training jobs');
  }
};

# RESTful API Design Principles

# 1. Consistent naming conventions
/api/v1/resources              # Plural for collections
/api/v1/resources/{id}         # Singular for items

# 2. HTTP methods usage
GET     - Read operations
POST    - Create operations
PUT     - Full updates
PATCH   - Partial updates
DELETE  - Delete operations

# 3. Status codes
200 OK                  - Successful GET/PUT/PATCH
201 Created             - Successful POST
204 No Content          - Successful DELETE
400 Bad Request         - Invalid request
401 Unauthorized        - Authentication required
403 Forbidden           - Insufficient permissions
404 Not Found           - Resource not found
422 Unprocessable       - Validation errors
429 Too Many Requests   - Rate limit exceeded
500 Internal Error      - Server error

# 4. Response format
{
  "data": {
    "id": "123",
    "type": "training_job",
    "attributes": {
      "status": "running",
      "progress": 0.75
    }
  },
  "meta": {
    "timestamp": "2025-01-20T10:00:00Z",
    "version": "1.0.0"
  }
}

# 5. Error format
{
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Invalid training configuration",
    "details": {
      "field": "batch_size",
      "reason": "Must be between 1 and 128"
    }
  }
}