Implement Canary Model Deployments for Industrial AI with Seldon Core and ArgoCD

                      
                     
"""
Production implementation for Canary Model Deployments.
Provides secure, scalable operations using Seldon Core and ArgoCD.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import time
from contextlib import contextmanager

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    retry_delay: int = int(os.getenv('RETRY_DELAY', 2))

@contextmanager
def connect_db() -> None:
    """
    Context manager for database connection pooling.
    
    Yields:
        None
    """
    logger.info('Establishing database connection...')
    # Simulated connection logic (to be replaced with actual DB connection)
    try:
        yield
    finally:
        logger.info('Closing database connection...')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for the API.
    
    Args:
        data: Input dictionary to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'model_id' not in data:
        raise ValueError('Missing model_id')  # Input validation
    if not isinstance(data['model_id'], str):
        raise ValueError('model_id must be a string')  # Type validation
    return True

async def fetch_data(api_url: str, model_id: str) -> Dict[str, Any]:
    """Fetch model data from the API.
    
    Args:
        api_url: The API endpoint URL
        model_id: The ID of the model to fetch
    Returns:
        dict: The model data
    Raises:
        ConnectionError: If the API call fails
    """
    response = requests.get(f'{api_url}/models/{model_id}')  # API call
    if response.status_code != 200:
        raise ConnectionError(f'Failed to fetch data: {response.status_code}')  # Error handling
    return response.json()

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    logger.info('Saving data to database...')
    # Simulated save logic (replace with actual DB logic)
    if not data:
        raise Exception('No data to save')
    logger.info('Data saved successfully.')

async def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for processing.
    
    Args:
        data: List of records to transform
    Returns:
        List[Dict[str, Any]]: Transformed records
    """
    transformed = []
    for record in data:
        transformed.append({
            'id': record['id'],
            'transformed_value': record['value'] * 2  # Example transformation
        })
    return transformed

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of records.
    
    Args:
        data: List of records
    """
    transformed_data = await transform_records(data)  # Transform records
    await save_to_db(transformed_data)  # Save transformed data

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from data.
    
    Args:
        data: List of records
    Returns:
        Dict[str, Any]: Aggregated metrics
    """
    total = sum(record['value'] for record in data)  # Aggregate metric
    return {'total_value': total}

async def call_api(data: Dict[str, Any]) -> None:
    """Call external API with data.
    
    Args:
        data: Data to send
    Raises:
        ConnectionError: If the API call fails
    """
    response = requests.post(f'{Config.api_url}/process', json=data)  # API call
    if response.status_code != 200:
        raise ConnectionError(f'Failed to call API: {response.status_code}')  # Error handling

async def handle_errors(func):
    """Decorator to handle errors with retries.
    
    Args:
        func: The function to wrap
    Returns:
        Callable: Wrapped function
    """
    async def wrapper(*args, **kwargs):
        attempts = 0
        while attempts < Config.retry_attempts:
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                attempts += 1
                logger.error(f'Error occurred: {e}')  # Log error
                if attempts < Config.retry_attempts:
                    time.sleep(Config.retry_delay)  # Exponential backoff
        raise Exception('Max retry attempts reached')  # Raise after max retries
    return wrapper

class CanaryDeployment:
    """Class to manage canary deployments.
    
    This orchestrates the flow of data through validation, transformation,
    and processing using the configured methods.
    """
    @handle_errors
    async def run(self, data: Dict[str, Any]) -> None:
        """Main execution method for the canary deployment.
        
        Args:
            data: Input data for processing
        """
        await validate_input(data)  # Validate input data
        model_data = await fetch_data(Config.api_url, data['model_id'])  # Fetch model data
        await process_batch(model_data)  # Process the fetched data
        metrics = await aggregate_metrics(model_data)  # Aggregate metrics
        await call_api(metrics)  # Call external API with metrics

if __name__ == '__main__':
    # Example usage
    canary_deployment = CanaryDeployment()
    sample_data = {'model_id': '12345'}
    import asyncio
    asyncio.run(canary_deployment.run(sample_data))