Unify Experiment Tracking and Model Versioning for Digital Twins with Weights & Biases and MLflow

                      
                     
"""
Production implementation for Unifying Experiment Tracking and Model Versioning for Digital Twins.
Provides secure, scalable integrations with Weights & Biases and MLflow.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from contextlib import contextmanager

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

class Config:
    """Configuration class to manage environment variables."""
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI')
    wandb_api_key: str = os.getenv('WANDB_API_KEY')

@contextmanager
def db_connection_pool():
    """Context manager for database connection pooling."
    # Simulate connection pool creation
    try:
        logger.info("Creating connection pool.")
        yield "database_connection"
    finally:
        logger.info("Closing connection pool.")

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate incoming data for experiments.
    Args:
        data: Input dictionary to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails, e.g., missing fields.
    """
    if 'experiment_id' not in data:
        raise ValueError('Missing required field: experiment_id')
    return True

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for processing.
    Args:
        data: Raw input data.
    Returns:
        Normalized data dictionary.
    """
    logger.debug("Normalizing data.")
    # Example transformation
    return {k: str(v).strip() for k, v in data.items()}

async def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from an external API.
    Args:
        endpoint: API endpoint to call.
    Returns:
        JSON response from the API.
    Raises:
        RuntimeError: If the request fails.
    """
    try:
        logger.info(f"Fetching data from {endpoint}.")
        response = requests.get(endpoint)
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        logger.error(f"Error fetching data: {str(e)}")
        raise RuntimeError(f"Failed to fetch data: {str(e)}")

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save experiment data to the database.
    Args:
        data: Data to store in the database.
    Raises:
        RuntimeError: If saving fails.
    """
    with db_connection_pool() as conn:
        logger.info("Saving data to database.")
        # Simulate data saving
        if not data:
            raise RuntimeError("No data to save.")
        logger.debug(f"Data saved: {data}")

def call_api(api_url: str) -> None:
    """Call external API for tracking.
    Args:
        api_url: The API URL to be called.
    """
    logger.info(f"Calling API at {api_url}.")
    # Simulate an API call
    time.sleep(1)  # Simulating latency
    logger.info("API call completed.")

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of experiments.
    Args:
        batch: List of experiment data dictionaries.
    """
    for record in batch:
        try:
            await validate_input(record)  # Validate each record
            normalized = normalize_data(record)  # Normalize data
            await save_to_db(normalized)  # Save to DB
            logger.info(f"Processed record: {normalized}")
        except Exception as e:
            logger.error(f"Error processing record {record}: {str(e)}")

async def aggregate_metrics(metrics: List[int]) -> float:
    """Aggregate given metrics.
    Args:
        metrics: List of metric values.
    Returns:
        Average of metrics.
    """
    if not metrics:
        logger.warning("No metrics provided to aggregate.")
        return 0.0
    average = sum(metrics) / len(metrics)
    logger.info(f"Aggregated metrics: {average}")
    return average

class ExperimentTracker:
    """Main orchestrator class for tracking experiments.
    Handles integration with Weights & Biases and MLflow.
    """
    def __init__(self):
        self.config = Config()
        logger.info("ExperimentTracker initialized.")

    def start_tracking(self, experiment_id: str) -> None:
        """Start tracking an experiment.
        Args:
            experiment_id: The ID of the experiment to track.
        """
        # Simulate starting an experiment tracking
        logger.info(f"Starting tracking for experiment: {experiment_id}")
        # Initialize Weights & Biases here
        os.environ['WANDB_API_KEY'] = self.config.wandb_api_key
        # Similar initialization for MLflow
        logger.info("Tracking started.")

    async def run_experiments(self, data: List[Dict[str, Any]]) -> None:
        """Run experiments using provided data.
        Args:
            data: List of experiment data.
        """
        await process_batch(data)  # Process the batch of experiments

if __name__ == '__main__':
    # Example usage of the ExperimentTracker
    tracker = ExperimentTracker()
    tracker.start_tracking('experiment_001')
    sample_data = [{'experiment_id': 'exp1', 'param': 'value1'}, {'experiment_id': 'exp2', 'param': 'value2'}]
    import asyncio
    asyncio.run(tracker.run_experiments(sample_data))