Distribute Model Training Across Clouds with Ray and SkyPilot

                      
                     
"""
Production implementation for distributed model training across clouds using Ray and SkyPilot.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import time
import ray
from ray import remote

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

class Config:
    """
    Configuration class for environment variables.
    """  
    def __init__(self):
        self.ray_address: str = os.getenv('RAY_ADDRESS', 'auto')
        self.num_clusters: int = int(os.getenv('NUM_CLUSTERS', '1'))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for model training.
    
    Args:
        data: Input data dictionary containing training parameters.
    Returns:
        True if valid, raises ValueError otherwise.
    Raises:
        ValueError: If validation fails.
    """
    if 'dataset_path' not in data:
        raise ValueError('Missing dataset_path')  # Ensure required field exists
    return True

@remote
def train_model(cluster_id: int, model_config: Dict[str, Any]) -> Dict[str, Any]:
    """Train a model on a specified cluster.
    
    Args:
        cluster_id: Identifier for the training cluster.
        model_config: Configuration for the model to train.
    Returns:
        Results of the training process.
    """
    logger.info(f'Starting training on cluster {cluster_id}')
    # Simulate training time
    time.sleep(5)  # Placeholder for actual training logic
    return {'cluster_id': cluster_id, 'status': 'success'}

def fetch_data(dataset_path: str) -> List[Dict[str, Any]]:
    """Fetch data from a specified path.
    
    Args:
        dataset_path: Path to the dataset.
    Returns:
        List of records fetched from the dataset.
    """  
    logger.info(f'Fetching data from {dataset_path}')
    # Simulate data fetching
    return [{'feature1': 1, 'feature2': 2}]  # Placeholder for actual data

def sanitize_fields(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Sanitize and normalize input data.
    
    Args:
        data: Raw data to sanitize.
    Returns:
        Sanitized data.
    """
    logger.debug('Sanitizing input data')
    # Placeholder for sanitization logic
    return data

def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate results from multiple clusters.
    
    Args:
        results: List of results from different training clusters.
    Returns:
        Aggregated metrics.
    """
    logger.info('Aggregating metrics from training results')
    return {'overall_status': 'success', 'num_clusters': len(results)}

class DistributedModelTrainer:
    """Orchestrator for distributed model training across clouds.
    """  
    def __init__(self, config: Config):
        self.config = config

    def train(self, dataset_path: str) -> Dict[str, Any]:
        """Main workflow for distributed training.
        
        Args:
            dataset_path: Path to the dataset for training.
        Returns:
            Aggregated results from all clusters.
        """  
        validate_input({'dataset_path': dataset_path})  # Validate input
        raw_data = fetch_data(dataset_path)  # Fetch data
        sanitized_data = sanitize_fields(raw_data)  # Sanitize data
        training_results = []  # Store results from each cluster
        for cluster_id in range(self.config.num_clusters):  # Iterate over clusters
            result = train_model.remote(cluster_id, {'data': sanitized_data})  # Train model asynchronously
            training_results.append(result)  # Collect results
        results = ray.get(training_results)  # Get results from all clusters
        return aggregate_metrics(results)  # Aggregate metrics

if __name__ == '__main__':
    # Initialize Ray
    ray.init(address=Config().ray_address)  # Connecting to Ray cluster
    config = Config()  # Load configuration
    trainer = DistributedModelTrainer(config)  # Instantiate trainer
    try:
        dataset_path = 'path/to/dataset'  # Example dataset path
        results = trainer.train(dataset_path)  # Start training
        logger.info(f'Training results: {results}')  # Log results
    except Exception as e:
        logger.error(f'Error during training: {e}')  # Log errors
    finally:
        ray.shutdown()  # Cleanup resources