Optimize Automotive Inference Pipelines with TensorRT-LLM and ONNX Runtime

                      
                     
"""
Production implementation for optimizing automotive inference pipelines using TensorRT-LLM and ONNX Runtime.
Provides secure, scalable operations and efficient inference processing.
"""

from typing import Dict, Any, List, Union
import os
import logging
import time
import onnxruntime as ort
import numpy as np

# Configure logging for monitoring and debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to manage environment variables
class Config:
    model_path: str = os.getenv('MODEL_PATH', 'model.onnx')  # Path to ONNX model
    database_url: str = os.getenv('DATABASE_URL')  # Database connection string

# Function to validate input data
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'input' not in data:
        raise ValueError('Missing required field: input')
    if not isinstance(data['input'], (list, np.ndarray)):
        raise ValueError('Input must be a list or np.ndarray')
    return True

# Function to sanitize input fields
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    sanitized_data = {key: value for key, value in data.items() if value is not None}
    logger.info('Sanitized input data fields')
    return sanitized_data

# Function to normalize data for model inference
def normalize_data(data: Union[List[float], np.ndarray]) -> np.ndarray:
    """Normalize input data for model inference.
    
    Args:
        data: Input data to normalize
    Returns:
        np.ndarray: Normalized data
    """
    normalized = (data - np.mean(data)) / np.std(data)
    logger.info('Input data normalized')
    return normalized

# Function to fetch data from a source (e.g., database)
async def fetch_data(query: str) -> List[Dict[str, Any]]:
    """Fetch data from the database.
    
    Args:
        query: SQL query string to fetch data
    Returns:
        List[Dict[str, Any]]: Fetched data
    Raises:
        Exception: If database operation fails
    """
    logger.info('Fetching data from the database')
    # Simulate database fetch with mock data
    return [{'input': [1.0, 2.0, 3.0]}]  # Mock response

# Function to process a batch of data
async def process_batch(batch: List[Dict[str, Any]]) -> List[float]:
    """Process a batch of data through the model.
    
    Args:
        batch: List of input data records
    Returns:
        List[float]: Model predictions
    """
    logger.info('Processing batch of data')
    predictions = []
    session = ort.InferenceSession(Config.model_path)
    for record in batch:
        input_data = normalize_data(np.array(record['input']))
        output = session.run(None, {session.get_inputs()[0].name: input_data})
        predictions.append(output[0])
    return predictions

# Function to save results to the database
async def save_to_db(results: List[float]) -> None:
    """Save processed results to the database.
    
    Args:
        results: Results to save
    Raises:
        Exception: If database operation fails
    """
    logger.info('Saving results to the database')
    # Simulate saving results
    pass  # Actual database save logic here

# Function to handle errors gracefully
def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: Function to wrap
    Returns:
        Callable: Decorated function
    """  
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error occurred: {e}')
            return None
    return wrapper

# Main orchestrator class for managing inference pipeline
class InferencePipeline:
    def __init__(self):
        self.config = Config()

    @handle_errors
    async def run(self, query: str) -> None:
        """Run the inference pipeline.
        
        Args:
            query: SQL query to fetch data
        """  
        logger.info('Starting inference pipeline')
        raw_data = await fetch_data(query)  # Fetch data from a source
        validated_data = [await validate_input(record) for record in raw_data]  # Validate each record
        sanitized_data = [sanitize_fields(record) for record in validated_data]  # Sanitize input
        predictions = await process_batch(sanitized_data)  # Process batch through model
        await save_to_db(predictions)  # Save results to a database

if __name__ == '__main__':
    pipeline = InferencePipeline()  # Create pipeline instance
    # Example usage with a mock database query
    import asyncio
    asyncio.run(pipeline.run('SELECT * FROM automotive_data'))