Optimize Edge LLM Serving with vLLM and NVIDIA Model-Optimizer

                      
                     
"""
Production implementation for optimizing Edge LLM serving using vLLM and NVIDIA Model-Optimizer.
Provides secure, scalable operations for serving large language models efficiently.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import httpx
from pydantic import BaseModel, Field, ValidationError
from sqlalchemy import create_engine, Column, String, Integer
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, Session

# Logger setup with INFO level
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to manage environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///./test.db')  # Default to SQLite
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))  # Retry attempts for API calls

Base = declarative_base()

# SQLAlchemy model definition
class Record(Base):
    __tablename__ = 'records'
    id = Column(Integer, primary_key=True)
    data = Column(String, nullable=False)

# Create a database engine
engine = create_engine(Config.database_url)
Base.metadata.create_all(bind=engine)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

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 'input_text' not in data:
        raise ValueError('Missing input_text in data')  # Ensure required field
    return True  # Validation successful

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}  # Strip whitespace

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize the input data for processing.
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    normalized = {'input_text': data['input_text'].lower()}  # Lowercase for consistency
    return normalized

async def fetch_data(endpoint: str, payload: Dict[str, Any]) -> Dict[str, Any]:
    """Fetch data from an external API.
    Args:
        endpoint: API endpoint to fetch data
        payload: Data to send in the request
    Returns:
        Response data
    Raises:
        Exception: If request fails
    """
    async with httpx.AsyncClient() as client:
        response = await client.post(endpoint, json=payload)
        if response.status_code != 200:
            raise Exception(f'Failed to fetch data: {response.status_code}')  # Raise an error on failure
        return response.json()  # Return the JSON response

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of data.
    Args:
        data: List of input data to process
    Returns:
        Processed data
    """
    results = []
    for record in data:
        try:
            validated = await validate_input(record)  # Validate input
            sanitized = await sanitize_fields(record)  # Sanitize fields
            normalized = await normalize_data(sanitized)  # Normalize data
            results.append(normalized)  # Add processed record to results
        except Exception as e:
            logger.error(f'Error processing record: {e}')  # Log error
    return results  # Return all processed records

async def save_to_db(db: Session, records: List[Dict[str, Any]]) -> None:
    """Save processed records to the database.
    Args:
        db: Database session
        records: List of records to save
    """
    for record in records:
        db_record = Record(data=record['input_text'])  # Create a new record
        db.add(db_record)  # Add to the session
    db.commit()  # Commit changes to the database

async def handle_errors(error: Exception) -> None:
    """Log and handle errors appropriately.
    Args:
        error: Exception to handle
    """
    logger.error(f'An error occurred: {error}')  # Log the error

class LLMService:
    """Service class to handle LLM operations.
    """
    def __init__(self, db: Session):
        self.db = db

    async def run_pipeline(self, input_data: Dict[str, Any]) -> None:
        """Run the complete LLM processing pipeline.
        Args:
            input_data: Input data for processing
        """
        try:
            await validate_input(input_data)  # Validate input
            sanitized = await sanitize_fields(input_data)  # Sanitize fields
            results = await fetch_data('http://example.com/api', sanitized)  # Fetch data
            await save_to_db(self.db, results)  # Save results
        except Exception as e:
            await handle_errors(e)  # Handle errors gracefully

if __name__ == '__main__':
    # Example usage of the service
    with SessionLocal() as session:
        llm_service = LLMService(db=session)  # Instantiate service
        example_input = {'input_text': 'Hello World!'}  # Example input
        llm_service.run_pipeline(example_input)  # Execute pipeline