Track Twin Model Performance with Weights & Biases and AWS IoT TwinMaker SDK

                      
                     
"""
Production implementation for tracking twin model performance using Weights & Biases and AWS IoT TwinMaker SDK.
Provides secure, scalable operations and integrates with cloud services.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests
import json
from dataclasses import dataclass

# Logger setup for tracking the application flow and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to manage environment variables
@dataclass
class Config:
    weights_and_biases_api_key: str = os.getenv('WANDB_API_KEY')
    aws_twinmaker_endpoint: str = os.getenv('TWINMAKER_ENDPOINT')

# Validate input data for the tracking process
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for twin model performance tracking.
    
    Args:
        data: Input JSON data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    # Check for required fields in data
    required_fields = ['model_id', 'metrics']
    for field in required_fields:
        if field not in data:
            raise ValueError(f'Missing field: {field}')  # Raise error for missing fields
    return True

# Sanitize input data fields
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    # Simple sanitation for demonstration purposes
    return {key: str(value).strip() for key, value in data.items()}

# Normalize metrics for consistent storage
def normalize_data(metrics: Dict[str, float]) -> Dict[str, float]:
    """Normalize metrics for tracking.
    
    Args:
        metrics: Metrics to normalize
    Returns:
        Normalized metrics
    """
    # Example normalization logic
    normalized_metrics = {key: value / 100 for key, value in metrics.items() if value > 0}
    return normalized_metrics

# Fetch data from AWS IoT TwinMaker
def fetch_data(twinmaker_endpoint: str, model_id: str) -> Dict[str, Any]:
    """Fetch twin data from AWS IoT TwinMaker.
    
    Args:
        twinmaker_endpoint: AWS endpoint for TwinMaker
        model_id: Model identifier
    Returns:
        JSON response from AWS TwinMaker
    Raises:
        ConnectionError: If unable to fetch data
    """
    try:
        response = requests.get(f'{twinmaker_endpoint}/{model_id}')
        response.raise_for_status()  # Raises HTTPError for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error('Error fetching data from TwinMaker: %s', e)
        raise ConnectionError('Failed to fetch data from AWS IoT TwinMaker')  # Handle connection error

# Save metrics to Weights & Biases
def save_to_wandb(metrics: Dict[str, Any], project: str) -> None:
    """Save metrics to Weights & Biases.
    
    Args:
        metrics: Metrics to log
        project: The project name in W&B
    Raises:
        RuntimeError: If saving fails
    """
    try:
        import wandb
        wandb.init(project=project)
        wandb.log(metrics)  # Log metrics to W&B
        wandb.finish()  # Finish logging
    except Exception as e:
        logger.error('Error saving metrics to W&B: %s', e)
        raise RuntimeError('Failed to save metrics to Weights & Biases')  # Handle save error

# Process a batch of data for performance tracking
async def process_batch(data: Dict[str, Any]) -> None:
    """Process batch data for performance tracking.
    
    Args:
        data: Input data for processing
    """
    # Validate and sanitize input data
    await validate_input(data)
    sanitized_data = sanitize_fields(data)
    metrics = normalized_data(sanitized_data['metrics'])  # Normalize metrics
    save_to_wandb(metrics, project='TwinModelPerformance')  # Save to W&B

# Main orchestrator class for tracking twin model performance
class TwinModelPerformanceTracker:
    def __init__(self, config: Config):
        self.config = config

    def track_performance(self, model_id: str) -> None:
        """Track the performance of the specified twin model.
        
        Args:
            model_id: The ID of the model to track
        """
        try:
            # Fetch data from TwinMaker
            data = fetch_data(self.config.aws_twinmaker_endpoint, model_id)
            # Process the fetched batch
            await process_batch(data)
        except Exception as e:
            logger.error('Error in tracking performance: %s', e)

if __name__ == '__main__':
    # Example usage of the performance tracker
    config = Config()
    tracker = TwinModelPerformanceTracker(config)
    tracker.track_performance('model_123')  # Track a specific model