Orchestrate Multi-Cloud AI Workloads with SkyPilot and Docker SDK

                      
                     
"""
Production implementation for orchestrating multi-cloud AI workloads using SkyPilot and Docker SDK.
Provides secure, scalable operations across multiple cloud environments.
"""
from typing import Dict, Any, List, Union
import os
import logging
import time
import requests
from docker import from_env as docker_client

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

class Config:
    """Configuration class to manage environment variables."""
    sky_pilot_project: str = os.getenv('SKY_PILOT_PROJECT')
    docker_image: str = os.getenv('DOCKER_IMAGE')
    api_endpoint: str = os.getenv('API_ENDPOINT')

# Initialize Docker client
client = docker_client()  # Connect to Docker daemon

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

async def sanitize_fields(data: Dict[str, Union[str, List[str]]]) -> Dict[str, str]:
    """Sanitize input fields to prevent XSS and injection attacks.
    
    Args:
        data: Raw input data
    Returns:
        Sanitized input data
    Raises:
        ValueError: If sanitization fails
    """
    sanitized = {k: str(v).strip() for k, v in data.items()}
    return sanitized

async def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch data from the specified API.
    
    Args:
        api_url: URL of the API to fetch data from
    Returns:
        Data from API as a dictionary
    Raises:
        requests.exceptions.RequestException: If fetching fails
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f'Error fetching data from API: {e}')
        raise

async def save_to_db(data: Dict[str, Any]) -> bool:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Returns:
        True if save is successful
    Raises:
        Exception: If save operation fails
    """
    # Placeholder for actual DB save logic
    try:
        logger.info('Saving data to database...')
        # Simulate DB save operation
        return True
    except Exception as e:
        logger.error(f'Error saving to database: {e}')
        return False

async def normalize_data(raw_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize data to a standard format.
    
    Args:
        raw_data: Raw input data
    Returns:
        Normalized data
    Raises:
        ValueError: If normalization fails
    """
    # Add normalization logic here
    normalized = [{k: v.lower() for k, v in record.items()} for record in raw_data]
    return normalized

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of data records.
    
    Args:
        data: List of data records to process
    Returns:
        Processed data
    Raises:
        Exception: If processing fails
    """
    # Simulate processing logic
    processed = [{'processed_data': record['data']} for record in data]
    return processed

class Orchestrator:
    """Main orchestrator class to manage multi-cloud AI workloads."""
    def __init__(self, config: Config):
        self.config = config

    async def run(self, input_data: Dict[str, Any]) -> None:
        """Run the orchestration workflow.
        
        Args:
            input_data: Input data for the orchestration
        """
        try:
            await validate_input(input_data)  # Validate input
            sanitized_data = await sanitize_fields(input_data)  # Sanitize fields
            logger.info('Fetching data from API...')
            raw_data = await fetch_data(self.config.api_endpoint)  # Fetch data
            normalized_data = await normalize_data(raw_data)  # Normalize data
            processed_data = await process_batch(normalized_data)  # Process data
            success = await save_to_db(processed_data)  # Save to DB
            if success:
                logger.info('Data saved successfully.')  # Log success
            else:
                logger.warning('Data save failed.')  # Log failure
        except Exception as e:
            logger.error(f'Error in orchestration: {e}')  # Handle errors

if __name__ == '__main__':
    # Example usage
    config = Config()
    orchestrator = Orchestrator(config)
    example_input = {'task_id': '123', 'parameters': ['param1', 'param2']}
    import asyncio
    asyncio.run(orchestrator.run(example_input))