Deploy Quantized Models to Factory Edge Devices with vLLM and ExecuTorch

                      
                     
"""
Production implementation for deploying quantized models to factory edge devices.
Provides secure, scalable operations using vLLM and ExecuTorch.
"""

from typing import Dict, Any, List
import os
import logging
import json
import time
import requests
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from contextlib import contextmanager

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    model_url: str = os.getenv('MODEL_URL', 'http://localhost:8000/models')
    db_url: str = os.getenv('DATABASE_URL', 'sqlite:///models.db')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))

class ModelRequest(BaseModel):
    """
    Model request schema for input validation.
    """
    model_id: str = Field(..., description='The ID of the model to deploy')
    parameters: Dict[str, Any] = Field(..., description='Parameters for model deployment')

async def validate_input(data: ModelRequest) -> bool:
    """Validate the input data for model deployment.
    
    Args:
        data: The input data to validate.
    Returns:
        bool: True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if not data.model_id:
        raise ValueError('Model ID is required')  # Check if model_id is provided
    return True

async def fetch_model(model_id: str) -> Dict[str, Any]:
    """Fetch model details from the model registry.
    
    Args:
        model_id: The ID of the model to fetch.
    Returns:
        dict: Model details.
    Raises:
        HTTPException: If the model cannot be fetched.
    """
    try:
        response = requests.get(f'{Config.model_url}/{model_id}')
        response.raise_for_status()
        return response.json()
    except requests.HTTPError as e:
        logger.error(f'Error fetching model: {e}')
        raise HTTPException(status_code=500, detail='Model fetch failed')

async def deploy_model(model: Dict[str, Any], parameters: Dict[str, Any]) -> str:
    """Deploy a model to the edge device.
    
    Args:
        model: The model details.
        parameters: Deployment parameters.
    Returns:
        str: Deployment confirmation message.
    Raises:
        HTTPException: If deployment fails.
    """
    try:
        # Simulate deployment logic (this should be replaced with actual deployment code)
        logger.info(f'Deploying model {model['id']} with parameters {parameters}')
        time.sleep(2)  # Simulate deployment time
        return f'Model {model['id']} deployed successfully'
    except Exception as e:
        logger.error(f'Error deploying model: {e}')
        raise HTTPException(status_code=500, detail='Model deployment failed')

async def save_deployment_log(model_id: str, status: str) -> None:
    """Save deployment log to the database.
    
    Args:
        model_id: The ID of the deployed model.
        status: Deployment status.
    """
    # Simulate saving the log (to be replaced with actual DB code)
    logger.info(f'Saving deployment log for model {model_id} with status {status}')

@contextmanager
def connection_pool() -> None:
    """Context manager for managing database connections.
    """
    try:
        # Simulate opening a connection pool
        logger.info('Opening connection pool')
        yield
    finally:
        # Simulate closing a connection pool
        logger.info('Closing connection pool')

app = FastAPI()

@app.post('/deploy')
async def deploy_model_endpoint(request: ModelRequest):
    """Endpoint to deploy a quantized model.
    
    Args:
        request: ModelRequest object containing model ID and parameters.
    Returns:
        dict: Deployment result.
    """
    await validate_input(request)  # Validate the input data
    model = await fetch_model(request.model_id)  # Fetch the model details
    deployment_status = await deploy_model(model, request.parameters)  # Deploy the model
    await save_deployment_log(request.model_id, deployment_status)  # Save the deployment log
    return {'message': deployment_status}  # Return success message

if __name__ == '__main__':
    # Example usage
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)  # Start the FastAPI application