Deploy Quantized LLMs to Industrial Sensors with CTranslate2 and Triton

                      
                     
"""
Production implementation for deploying quantized LLMs to industrial sensors using CTranslate2 and Triton.
Provides secure, scalable operations.
"""

from typing import Dict, Any, List
import os
import logging
import time
import requests

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

class Config:
    """
    Configuration class to load environment variables.
    """
    ctranslate_model_path: str = os.getenv('CTRANSLATE_MODEL_PATH', '/models/')
    triton_server_url: str = os.getenv('TRITON_SERVER_URL', 'http://localhost:8000')
    db_url: str = os.getenv('DATABASE_URL')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for model inference.

    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'sensor_id' not in data:
        raise ValueError('Missing sensor_id')  # Validate presence of sensor_id
    if 'input_data' not in data:
        raise ValueError('Missing input_data')  # Validate presence of 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:
        Sanitized input data
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    return sanitized_data

def fetch_data(sensor_id: str) -> Dict[str, Any]:
    """Fetch data from a sensor.

    Args:
        sensor_id: ID of the sensor to fetch data from
    Returns:
        Data fetched from the sensor
    Raises:
        Exception: If data fetching fails
    """
    try:
        response = requests.get(f'{Config.triton_server_url}/sensors/{sensor_id}/data')
        response.raise_for_status()  # Raise error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data for sensor {sensor_id}: {e}')
        raise Exception('Failed to fetch data')

def normalize_data(data: List[float]) -> List[float]:
    """Normalize data for model input.

    Args:
        data: Raw input data
    Returns:
        Normalized data
    """
    max_val = max(data)
    normalized = [x / max_val for x in data]  # Simple normalization
    return normalized

def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform records into required format for CTranslate2.

    Args:
        data: Input data to transform
    Returns:
        Transformed data suitable for model inference
    """
    return {'input_ids': [int(x) for x in data['input_data']]}  # Example transformation

def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of input data for inference.

    Args:
        batch: List of input data dictionaries
    Returns:
        List of inference results
    """
    results = []
    for record in batch:
        try:
            validate_input(record)  # Validate each record
            sanitized = sanitize_fields(record)  # Sanitize for security
            normalized = normalize_data(sanitized['input_data'])  # Normalize the data
            transformed = transform_records({'input_data': normalized})  # Transform the data
            results.append(transformed)  # Store the result
        except Exception as e:
            logger.warning(f'Processing failed for record {record}: {e}')  # Log warnings for processing errors
    return results

def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save inference results to the database.

    Args:
        results: Inference results to save
    Raises:
        Exception: If saving to DB fails
    """
    # Placeholder for database saving logic
    pass

class InferenceOrchestrator:
    """Orchestrates the inference process for deploying quantized LLMs.
    """
    def __init__(self):
        self.config = Config()  # Load configuration

    def run_inference(self, sensor_id: str) -> None:
        """Run inference for a given sensor_id.

        Args:
            sensor_id: ID of the sensor
        """
        try:
            raw_data = fetch_data(sensor_id)  # Fetch data from the sensor
            results = process_batch([raw_data])  # Process the fetched data
            save_to_db(results)  # Save results to DB
        except Exception as e:
            logger.error(f'Inference failed for sensor {sensor_id}: {e}')  # Log errors

if __name__ == '__main__':
    orchestrator = InferenceOrchestrator()  # Create orchestrator instance
    # Example usage
    sensor_id = 'sensor_1'
    orchestrator.run_inference(sensor_id)  # Run inference for example sensor