Track Factory Model Experiments Across Sites with MLflow and DVC

                      
                     
"""
Production implementation for tracking factory model experiments across sites using MLflow and DVC.
Provides secure, scalable operations.
"""
import os
import logging
import mlflow
import dvc.api
import pandas as pd
from typing import Dict, Any, List

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

class Config:
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI', 'http://localhost:5000')
    dvc_repo_url: str = os.getenv('DVC_REPO_URL', 'https://example.com/repo.git')
    dvc_remote: str = os.getenv('DVC_REMOTE', 's3://mybucket')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'experiment_id' not in data:
        raise ValueError('Missing experiment_id')
    if 'model_name' not in data:
        raise ValueError('Missing model_name')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

async def fetch_data(experiment_id: str) -> pd.DataFrame:
    """Fetch data from DVC.
    Args:
        experiment_id: ID of the experiment
    Returns:
        DataFrame containing experiment data
    """
    logger.info('Fetching data for experiment %s', experiment_id)
    # Simulated DVC fetch
    data = pd.DataFrame({'metric': [0.1, 0.2, 0.3], 'timestamp': [1, 2, 3]})
    return data

async def save_to_db(data: pd.DataFrame, model_name: str) -> None:
    """Save model data to database.
    Args:
        data: DataFrame to save
        model_name: Name of the model
    Returns:
        None
    """
    logger.info('Saving data for model %s', model_name)
    # Simulated database save operation
    pass

async def log_experiment(model_name: str, metrics: Dict[str, float]) -> None:
    """Log experiment results to MLflow.
    Args:
        model_name: Name of the model
        metrics: Metrics to log
    Returns:
        None
    Raises:
        mlflow.exceptions.MlflowException: If logging fails
    """
    try:
        mlflow.start_run()
        mlflow.log_param('model_name', model_name)
        for key, value in metrics.items():
            mlflow.log_metric(key, value)
        mlflow.end_run()
        logger.info('Logged metrics for model %s', model_name)
    except Exception as e:
        logger.error('Failed to log experiment: %s', e)
        raise mlflow.exceptions.MlflowException('Logging failed')

async def aggregate_metrics(data: pd.DataFrame) -> Dict[str, float]:
    """Aggregate metrics from data.
    Args:
        data: DataFrame containing metrics
    Returns:
        Dictionary of aggregated metrics
    """
    return {'mean_metric': data['metric'].mean()}

class ExperimentTracker:
    """Class to track experiments across sites.
    """
    def __init__(self):
        self.config = Config()

    async def run_experiment(self, data: Dict[str, Any]) -> None:
        """Run an experiment tracking.
        Args:
            data: Input data for the experiment
        Returns:
            None
        """
        try:
            await validate_input(data)
            sanitized_data = await sanitize_fields(data)
            logger.info('Sanitized data: %s', sanitized_data)
            experiment_id = sanitized_data['experiment_id']
            model_name = sanitized_data['model_name']
            fetched_data = await fetch_data(experiment_id)
            metrics = await aggregate_metrics(fetched_data)
            await log_experiment(model_name, metrics)
            await save_to_db(fetched_data, model_name)
        except ValueError as ve:
            logger.warning('Validation error: %s', ve)
        except Exception as e:
            logger.error('Error in experiment run: %s', e)

if __name__ == '__main__':
    tracker = ExperimentTracker()
    sample_data = {'experiment_id': '123', 'model_name': 'MyModel'}
    import asyncio
    asyncio.run(tracker.run_experiment(sample_data))