Autoscale Industrial AI Services Based on Inference Queue Depth with KServe and Prometheus Client

"""
Production implementation for autoscaling industrial AI services based on inference queue depth.
Provides secure, scalable operations leveraging KServe and Prometheus Client.
"""
from typing import Dict, Any, List
import os
import logging
import httpx
import asyncio
from prometheus_client import CollectorRegistry, Gauge, push_to_gateway

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

# Configuration class to handle environment variables
class Config:
    kserve_url: str = os.getenv('KSERVE_URL', 'http://localhost:8080')
    prometheus_gateway: str = os.getenv('PROMETHEUS_GATEWAY', 'http://localhost:9091')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    retry_delay: float = float(os.getenv('RETRY_DELAY', 2))

# Initialize Prometheus metrics
registry = CollectorRegistry()
queue_depth_gauge = Gauge('inference_queue_depth', 'Current depth of the inference queue', registry=registry)

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

async def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch data from a given URL.
    
    Args:
        url: URL to fetch data from
    Returns:
        JSON response as a dictionary
    Raises:
        httpx.HTTPStatusError: If the request fails
    """
    async with httpx.AsyncClient() as client:
        response = await client.get(url)
        response.raise_for_status()  # Raises an error for bad responses
        return response.json()

async def save_to_db(data: Dict[str, Any]) -> None:
    """Mock function to save processed data to a database.
    
    Args:
        data: Data to save
    Raises:
        Exception: For demonstration purposes only
    """
    logger.info('Saving data to the database...')
    # Simulate a save operation
    await asyncio.sleep(1)  # Simulating delay
    logger.info('Data saved successfully!')

async def call_api(model_name: str, request_data: Dict[str, Any]) -> Dict[str, Any]:
    """Call the AI model API.
    
    Args:
        model_name: Name of the model to call
        request_data: Data to send
    Returns:
        Model response as a dictionary
    Raises:
        Exception: On API call failure
    """
    url = f'{Config.kserve_url}/v1/models/{model_name}:predict'
    logger.info(f'Calling API: {url}')
    # Retry logic
    for attempt in range(Config.retry_attempts):
        try:
            async with httpx.AsyncClient() as client:
                response = await client.post(url, json=request_data)
                response.raise_for_status()
                return response.json()
        except httpx.HTTPStatusError as e:
            logger.warning(f'Attempt {attempt + 1} failed: {e}')
            await asyncio.sleep(Config.retry_delay * (2 ** attempt))  # Exponential backoff
    raise Exception('Max retries exceeded')

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input data for processing.
    
    Args:
        data: Raw input data
    Returns:
        Normalized data
    """
    logger.info('Normalizing data...')
    # Normalize data here
    return data  # Placeholder for normalization logic

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of requests.
    
    Args:
        data: List of input data
    Returns:
        Processed results
    """
    results = []
    for record in data:
        await validate_input(record)  # Validate each record
        normalized = await normalize_data(record)  # Normalize data
        response = await call_api(normalized['model_name'], normalized['request_data'])  # Call AI model
        results.append(response)  # Collect results
    return results

async def aggregate_metrics() -> None:
    """Aggregate metrics for Prometheus.
    
    Returns:
        None
    """
    queue_depth = len(await fetch_data(f'{Config.kserve_url}/queue_depth'))  # Hypothetical endpoint
    queue_depth_gauge.set(queue_depth)  # Set the gauge value
    logger.info(f'Queue depth set to {queue_depth}')

async def handle_errors() -> None:
    """Handle errors and cleanup resources.
    
    Returns:
        None
    """
    logger.error('An error occurred, cleaning up...')
    # Perform cleanup actions here

class InferenceService:
    """Main orchestrator class for inference services.
    
    Attributes:
        data: List of incoming data
    """
    def __init__(self, data: List[Dict[str, Any]]):
        self.data = data

    async def run(self) -> None:
        """Run the inference process.
        
        Returns:
            None
        """
        try:
            await aggregate_metrics()  # Update metrics
            results = await process_batch(self.data)  # Process requests
            await save_to_db(results)  # Save results
        except Exception as e:
            await handle_errors()  # Handle errors
            logger.error(f'Error in inference process: {e}')

if __name__ == '__main__':
    # Example usage
    input_data = [
        {'model_name': 'model1', 'request_data': {'input': 'data1'}},
        {'model_name': 'model2', 'request_data': {'input': 'data2'}}
    ]
    service = InferenceService(input_data)
    asyncio.run(service.run())