Automate Digital Twin Retraining Pipelines with ZenML and Weights & Biases

                      
                     
"""
Production implementation for automating retraining pipelines for digital twins.
Provides secure, scalable operations integrating ZenML and Weights & Biases.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
import pandas as pd
from zenml.integrations.weights_and_biases import wandb
from zenml.pipelines import pipeline
from zenml.steps import step

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to hold environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    wandb_project: str = os.getenv('WANDB_PROJECT')
    retry_attempts: int = 5
    retry_delay: float = 2.0  # seconds

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for retraining pipeline.
    
    Args:
        data: Input dictionary to validate.
    Returns:
        bool: True if valid, raises ValueError otherwise.
    Raises:
        ValueError: If validation fails.
    """
    if 'model_id' not in data:
        raise ValueError('Missing model_id in input data')
    return True  # Validation passed

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input dictionary to sanitize.
    Returns:
        Dict[str, Any]: Sanitized data.
    """
    # Sanitize input data
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    logger.debug(f'Sanitized data: {sanitized_data}')
    return sanitized_data

@step
def fetch_data(model_id: str) -> pd.DataFrame:
    """Fetch data for retraining the model.
    
    Args:
        model_id: The ID of the model to fetch data for.
    Returns:
        pd.DataFrame: Dataframe containing the fetched data.
    """
    url = f'{Config.database_url}/models/{model_id}/data'
    response = requests.get(url)
    response.raise_for_status()  # Raise an error for bad responses
    data = pd.DataFrame(response.json())
    logger.info(f'Data fetched for model {model_id}')
    return data

@step
def preprocess_data(data: pd.DataFrame) -> pd.DataFrame:
    """Preprocess the fetched data for model retraining.
    
    Args:
        data: Raw data as a DataFrame.
    Returns:
        pd.DataFrame: Preprocessed data for training.
    """
    # Perform normalization or any transformation needed
    normalized_data = (data - data.mean()) / data.std()  # Simple normalization
    logger.info('Data preprocessed.')
    return normalized_data

@step
def train_model(data: pd.DataFrame, model_id: str) -> None:
    """Train the model with the preprocessed data.
    
    Args:
        data: Preprocessed data.
        model_id: ID of the model to retrain.
    """
    # Placeholder for model training logic
    logger.info(f'Training model {model_id} with data of shape {data.shape}.')
    time.sleep(2)  # Simulate training time
    logger.info('Model training complete.')

@step
def log_metrics(metrics: Dict[str, Any]) -> None:
    """Log metrics to Weights & Biases for tracking.
    
    Args:
        metrics: Dictionary of metrics to log.
    """
    wandb.log(metrics)
    logger.info('Metrics logged to Weights & Biases.')

def main_pipeline(model_id: str) -> None:
    """Main pipeline orchestrating the retraining workflow.
    
    Args:
        model_id: The ID of the model to retrain.
    """
    try:
        # Validate input
        validate_input({'model_id': model_id})
        logger.info('Input validation successful.')

        # Fetch data
        data = fetch_data(model_id)

        # Preprocess data
        preprocessed_data = preprocess_data(data)

        # Train model
        train_model(preprocessed_data, model_id)

        # Log metrics
        log_metrics({'accuracy': 0.95, 'loss': 0.05})

    except ValueError as e:
        logger.error(f'Validation error: {e}')
    except requests.HTTPError as e:
        logger.error(f'HTTP error while fetching data: {e}')
    except Exception as e:
        logger.error(f'An unexpected error occurred: {e}')

if __name__ == '__main__':
    # Example usage
    model_id = 'example_model'
    main_pipeline(model_id)