Orchestrate Distributed AI Workloads with Ray and Kubernetes Python Client

                      
                     
"""
Production implementation for orchestrating distributed AI workloads using Ray and Kubernetes.
Provides secure, scalable operations for large-scale data processing.
"""
from typing import Dict, Any, List, Union
import os
import logging
import ray
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Set up logging for tracking events and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration for the application, including environment variables.
    """
    db_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')
    ray_address: str = os.getenv('RAY_ADDRESS', 'auto')

# Initialize Ray with the given address for distributed processing
ray.init(address=Config.ray_address)

# Create a session factory for database operations
engine = create_engine(Config.db_url)
Session = sessionmaker(bind=engine)

async def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate input data for required fields.
    
    Args:
        data: Input data to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'model_id' not in data:
        raise ValueError('Missing required field: model_id')
    if 'data' not in data:
        raise ValueError('Missing required field: data')
    logger.info('Input validation passed.')
    return True

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

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

async def save_to_db(data: Dict[str, Any]) -> None:
    """
    Save processed data into the database.
    
    Args:
        data: Data to store in the database.
    """
    with Session() as session:
        session.execute(text("INSERT INTO ai_results (model_id, result) VALUES (:model_id, :result)"), data)
        session.commit()
    logger.info('Data saved to the database successfully.')

@ray.remote
async def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Process a batch of data using a distributed Ray task.
    
    Args:
        batch: List of data items to process.
    Returns:
        List of processed results.
    """
    results = []
    for item in batch:
        # Simulate some processing
        result = {'model_id': item['model_id'], 'result': item['data'] * 2}
        results.append(result)
    return results

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """
    Aggregate metrics from processed results.
    
    Args:
        results: List of processed results.
    Returns:
        Aggregated metrics.
    """
    total = sum(result['result'] for result in results)
    logger.info(f'Aggregated total: {total}')
    return {'total': total}

class AIOrchestrator:
    """
    Main orchestrator class for managing the workflow of AI tasks.
    """
    async def run(self, input_data: Dict[str, Any]) -> None:
        """
        Execute the complete workflow from input validation to results storage.
        
        Args:
            input_data: Input data for processing.
        """
        try:
            await validate_input(input_data)
            sanitized_data = await sanitize_fields(input_data)
            fetched_data = await fetch_data(sanitized_data['data'])
            results = await process_batch(fetched_data)
            aggregated_metrics = await aggregate_metrics(results)
            await save_to_db(aggregated_metrics)
        except Exception as e:
            logger.error(f'Error during orchestration: {e}')
            raise

if __name__ == '__main__':
    # Example usage of the orchestrator
    orchestrator = AIOrchestrator()
    example_data = {'model_id': 'model_123', 'data': 'http://example.com/api/data'}
    import asyncio
    asyncio.run(orchestrator.run(example_data))