Automate Factory AI Container Lifecycle with Docker SDK and Kubernetes Python Client

                      
                     
"""
Production implementation for Automating Factory AI Container Lifecycle using Docker SDK and Kubernetes Python Client.
Provides secure, scalable operations for managing AI container lifecycles.
"""

from typing import Dict, Any, List, Optional
import os
import logging
import time
import docker
import kubernetes
from kubernetes import client, config

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

class Config:
    docker_host: str = os.getenv('DOCKER_HOST', 'unix:///var/run/docker.sock')
    kubernetes_context: str = os.getenv('K8S_CONTEXT', 'default')

# Validate the input data for container creation
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for container creation.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """  
    if 'image' not in data:
        raise ValueError('Missing image')
    if 'name' not in data:
        raise ValueError('Missing container name')
    return True

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

# Transform data for creating a container
async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform input data for the container.
    
    Args:
        data: Raw input data
    Returns:
        Transformed data ready for Docker SDK
    """  
    return {
        'name': data['name'],
        'image': data['image'],
    }

# Fetch data from an external API or database if needed
async def fetch_data(source: str) -> Dict[str, Any]:
    """Fetch data from an external source.
    
    Args:
        source: The data source URL
    Returns:
        Fetched data
    Raises:
        RuntimeError: If data fetching fails
    """  
    try:
        # Simulate data fetching
        return {'image': 'nginx:latest', 'name': 'my-nginx'}
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise RuntimeError('Data fetching failed')

# Process a batch of containers
async def process_batch(data_list: List[Dict[str, Any]]) -> None:
    """Process and create containers from a list of data.
    
    Args:
        data_list: List of data to process
    """  
    for data in data_list:
        await create_container(data)

# Create a Docker container
async def create_container(data: Dict[str, Any]) -> None:
    """Create a Docker container based on the input data.
    
    Args:
        data: Data for the container
    """  
    try:
        await validate_input(data)
        sanitized_data = await sanitize_fields(data)
        transformed_data = await transform_records(sanitized_data)
        client = docker.from_env()
        container = client.containers.run(transformed_data['image'], name=transformed_data['name'], detach=True)
        logger.info(f'Container created: {container.name}')
    except Exception as e:
        logger.error(f'Error creating container: {e}')

# Main orchestrator class to manage the lifecycle
class FactoryAIContainerManager:
    def __init__(self):
        # Load Kubernetes configuration
        config.load_kube_config()  # Load from ~/.kube/config
        self.k8s_client = client.CoreV1Api()
        self.docker_client = docker.from_env()

    async def manage_lifecycle(self, data: Dict[str, Any]) -> None:
        """Manage the lifecycle of the AI container.
        
        Args:
            data: Input data for container management
        """  
        try:
            await create_container(data)
            logger.info('Container lifecycle managed successfully.')
        except Exception as e:
            logger.error(f'Error managing container lifecycle: {e}')

# Main block for execution
if __name__ == '__main__':
    # Example usage
    manager = FactoryAIContainerManager()
    data = {'image': 'nginx:latest', 'name': 'my-nginx'}
    import asyncio
    asyncio.run(manager.manage_lifecycle(data))