Accelerate Industrial Multimodal Model Inference with Fused Kernels Using DeepSpeed-Inference and Triton

"""
Production implementation for Accelerating Industrial Multimodal Model Inference with Fused Kernels using DeepSpeed-Inference and Triton.
Provides secure, scalable operations for model inference workflows.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests
from contextlib import contextmanager

# Configuring logging for the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    model_url: str = os.getenv('MODEL_URL')
    inference_timeout: int = int(os.getenv('INFERENCE_TIMEOUT', 30))  # Default: 30 seconds

@contextmanager
def connect_to_service() -> None:
    """Context manager for managing connections to the inference service.
    
    Yields:
        None
    """
    logger.info('Connecting to the inference service...')
    try:
        # Simulating connection setup
        yield
    except Exception as e:
        logger.error(f'Connection failed: {e}')
        raise
    finally:
        logger.info('Connection closed.')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate incoming request data for inference.
    
    Args:
        data: Input data containing features for model inference
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'features' not in data or not isinstance(data['features'], list):
        raise ValueError('Invalid input: features must be a list.')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to ensure safe processing.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}

async def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from a given endpoint.
    
    Args:
        endpoint: URL of the API endpoint
    Returns:
        Response data as dictionary
    Raises:
        RequestException: If the request fails
    """
    logger.info(f'Fetching data from {endpoint}')
    response = requests.get(endpoint)
    response.raise_for_status()  # Raise an error for bad responses
    return response.json()

async def call_inference_service(data: Dict[str, Any]) -> Dict[str, Any]:
    """Call the inference service with the provided data.
    
    Args:
        data: Input data for the inference
    Returns:
        Model inference results
    Raises:
        RuntimeError: If the inference call fails
    """
    try:
        logger.info('Calling inference service...')
        response = requests.post(Config.model_url, json=data, timeout=Config.inference_timeout)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.Timeout:
        logger.error('Inference service timeout.')
        raise RuntimeError('Inference service timed out.')
    except requests.exceptions.RequestException as e:
        logger.error(f'Inference service error: {e}')
        raise RuntimeError('Failed to call inference service.')

async def process_inference(data: Dict[str, Any]) -> Dict[str, Any]:
    """Process inference by validating, sanitizing, and calling the inference service.
    
    Args:
        data: Raw input data
    Returns:
        Inference results
    Raises:
        ValueError: If input validation fails
    """
    await validate_input(data)  # Validate input data
    sanitized_data = await sanitize_fields(data)  # Sanitize data
    result = await call_inference_service(sanitized_data)  # Call the service
    return result

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from multiple inference results.
    
    Args:
        results: List of inference results
    Returns:
        Aggregated metrics
    """
    aggregated = {'mean': sum(res.get('score', 0) for res in results) / len(results)}
    return aggregated

if __name__ == '__main__':
    # Example usage
    sample_data = {'features': [0.1, 0.2, 0.3]}
    try:
        with connect_to_service():
            inference_result = process_inference(sample_data)  # Process inference
            logger.info(f'Inference Result: {inference_result}')
    except Exception as e:
        logger.error(f'Error during inference: {e}')  # Handle errors gracefully