Monitor Physics ML Model Drift in Digital Twins with PhysicsNeMo and Weights and Biases

"""
Production implementation for monitoring ML model drift in digital twins using PhysicsNeMo and Weights and Biases.
This code provides secure and scalable operations to track changes in model performance.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import requests
from time import sleep

# Setup logger for tracking the process
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    
    Attributes:
        physics_nemo_api: str = API endpoint for PhysicsNeMo.
        wandb_api_key: str = Weights and Biases API key.
        model_id: str = Identifier for the ML model.
    """
    physics_nemo_api: str = os.getenv('PHYSICS_NEMO_API')
    wandb_api_key: str = os.getenv('WANDB_API_KEY')
    model_id: str = os.getenv('MODEL_ID')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for model drift monitoring.
    
    Args:
        data: Input data for validation.
    Returns:
        bool: True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if not isinstance(data, dict) or 'metrics' not in data:
        raise ValueError('Invalid input: Expected a dictionary with metrics.')
    return True

def fetch_model_metrics() -> Dict[str, float]:
    """Fetch the latest model metrics from PhysicsNeMo API.
    
    Returns:
        Dict[str, float]: A dictionary containing model metrics.
    Raises:
        Exception: For any errors while fetching data.
    """ 
    try:
        response = requests.get(f'{Config.physics_nemo_api}/metrics/{Config.model_id}')
        response.raise_for_status()
        logger.info('Metrics fetched successfully.')
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching metrics: {e}')
        raise

def transform_metrics(metrics: Dict[str, float]) -> Dict[str, float]:
    """Transform the fetched metrics if necessary.
    
    Args:
        metrics: Raw metrics from the model.
    Returns:
        Dict[str, float]: Transformed metrics.
    """
    # In this case, we assume no transformation is needed.
    return metrics

def save_to_wandb(metrics: Dict[str, float]) -> None:
    """Log metrics to Weights and Biases.
    
    Args:
        metrics: Metrics to log.
    """
    import wandb
    wandb.login(key=Config.wandb_api_key)  # Authenticate
    wandb.init(project='model-drift-monitoring')
    wandb.log(metrics)
    logger.info('Metrics logged to Weights and Biases.')

def monitor_drift() -> None:
    """Monitor model drift in a loop.
    
    This function repeatedly fetches metrics and checks for drift.
    """
    while True:
        try:
            metrics = fetch_model_metrics()  # Fetch metrics
            validate_input(metrics)  # Validate metrics
            transformed_metrics = transform_metrics(metrics)  # Transform metrics
            save_to_wandb(transformed_metrics)  # Log to Weights & Biases

            # Check for drift (example threshold)
            if transformed_metrics['accuracy'] < 0.8:
                logger.warning('Model drift detected! Accuracy below threshold.')

        except Exception as e:
            logger.error(f'Error in monitoring: {e}')  # Handle errors gracefully

        sleep(60)  # Wait before the next check

if __name__ == '__main__':
    # Main block to run the monitoring process
    logger.info('Starting model drift monitoring...')
    monitor_drift()  # Start monitoring