Manage Industrial Model Fleets with Kubernetes Python Client and Seldon Core

                      
                     
"""
Production implementation for managing industrial model fleets using the Kubernetes Python Client and Seldon Core.
Provides secure, scalable operations for deploying and managing machine learning models.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import time
import requests
from kubernetes import client, config

# Logger setup for tracking application flow and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    
    Attributes:
        kube_config: str
        seldon_url: str
    """
    kube_config: str = os.getenv('KUBE_CONFIG_PATH', '~/.kube/config')
    seldon_url: str = os.getenv('SELFDON_CORE_URL', 'http://localhost:8000')

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

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

def fetch_data(url: str) -> Dict[str, Any]:
    """
    Fetch data from a given URL with error handling.
    
    Args:
        url: URL to fetch data from
    Returns:
        JSON response data
    Raises:
        Exception: If HTTP request fails
    """
    try:
        response = requests.get(url)
        response.raise_for_status()
        return response.json()
    except Exception as e:
        logger.error(f'Error fetching data from {url}: {str(e)}')
        raise

def save_to_db(data: Dict[str, Any]) -> None:
    """
    Placeholder function to save data to a database.
    
    Args:
        data: Data to save
    """
    logger.info('Saving data to database...')
    # Database save logic goes here

def call_api(endpoint: str, payload: Dict[str, Any]) -> Any:
    """
    Call an external API with the provided payload.
    
    Args:
        endpoint: API endpoint to call
        payload: Data to send
    Returns:
        API response
    Raises:
        Exception: If API call fails
    """
    try:
        response = requests.post(endpoint, json=payload)
        response.raise_for_status()
        return response.json()
    except Exception as e:
        logger.error(f'API call failed to {endpoint}: {str(e)}')
        raise

class ModelFleetManager:
    """
    Class to manage model fleets in Kubernetes using the Seldon Core.
    
    Methods:
        deploy_model(data: Dict[str, Any]) -> None
        get_model_status(model_name: str, namespace: str) -> Dict[str, Any]
    """
    def __init__(self):
        config.load_kube_config(Config.kube_config)
        self.kube_client = client.AppsV1Api()
        logger.info('Kubernetes client configured.')

    def deploy_model(self, data: Dict[str, Any]) -> None:
        """
        Deploy a model to the Seldon Core.
        
        Args:
            data: Model deployment data
        """
        try:
            validate_input(data)  # Validate the input data
            data = sanitize_fields(data)  # Sanitize the fields

            # Here we would create the deployment in Kubernetes
            logger.info(f'Deploying model {data["model_name"]} to namespace {data["namespace"]}.')
            # Deployment logic goes here

        except ValueError as e:
            logger.error(f'Validation error: {str(e)}')
        except Exception as e:
            logger.error(f'Failed to deploy model: {str(e)}')

    def get_model_status(self, model_name: str, namespace: str) -> Dict[str, Any]:
        """
        Get the status of a deployed model.
        
        Args:
            model_name: The name of the model
            namespace: The namespace where the model is deployed
        Returns:
            Model status information
        Raises:
            Exception: If fetching status fails
        """
        try:
            logger.info(f'Fetching status for model {model_name} in namespace {namespace}.')
            # Logic to get model status goes here
            return {"status": "unknown"}  # Placeholder

        except Exception as e:
            logger.error(f'Error fetching model status: {str(e)}')
            raise

if __name__ == '__main__':
    # Example usage
    fleet_manager = ModelFleetManager()  # Initialize the fleet manager
    model_data = {
        "model_name": "my_model",
        "namespace": "default"
    }
    fleet_manager.deploy_model(model_data)  # Deploy the model
    status = fleet_manager.get_model_status("my_model", "default")  # Get model status
    logger.info(f'Model status: {status}')