Accelerate Multi-Model Edge Inference on Intel Arc GPUs with IPEX-LLM and Triton

"""
Production implementation for Accelerating Multi-Model Edge Inference on Intel Arc GPUs with IPEX-LLM and Triton.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, conlist

# Logging configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    model_url: str = os.getenv('MODEL_URL', 'http://localhost:8000/model')
    db_url: str = os.getenv('DATABASE_URL', 'sqlite:///./test.db')

class InferenceRequest(BaseModel):
    """Request model for inference data."""
    inputs: conlist(float, min_items=1)

async def validate_input(data: InferenceRequest) -> bool:
    """Validate input data for inference.
    
    Args:
        data: InferenceRequest object to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if not data.inputs:
        raise ValueError('Input list cannot be empty')
    return True

async def fetch_data(url: str, json_data: Dict[str, Any]) -> Dict[str, Any]:
    """Fetch data from the model API.
    
    Args:
        url: API endpoint URL
        json_data: JSON payload to send
    Returns:
        Response data from the API
    Raises:
        HTTPException: If API call fails
    """
    try:
        response = requests.post(url, json=json_data)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f'API request failed: {e}')
        raise HTTPException(status_code=500, detail='API request failed')

async def process_batch(data: List[float]) -> Dict[str, Any]:
    """Process a batch of data for inference.
    
    Args:
        data: List of input data
    Returns:
        Inference results
    Raises:
        Exception: If processing fails
    """
    # Simulating a batch processing delay
    time.sleep(1)  # Simulate processing time
    logger.info('Batch processed successfully')
    return {'results': [d * 2 for d in data]}  # Mock inference logic

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate results from multiple inferences.
    
    Args:
        results: List of results from inference
    Returns:
        Aggregated metrics
    """
    aggregated = sum(result['results'][0] for result in results)
    logger.info('Metrics aggregated')
    return {'total': aggregated}

app = FastAPI()

@app.post('/inference/')
async def inference(request: InferenceRequest) -> Dict[str, Any]:
    """Endpoint for model inference.
    
    Args:
        request: InferenceRequest object
    Returns:
        Inference results
    Raises:
        HTTPException: If errors occur
    """
    try:
        await validate_input(request)
        results = await fetch_data(Config.model_url, request.dict())
        processed_results = await process_batch(results['inputs'])
        return processed_results
    except ValueError as ve:
        logger.warning(f'Validation error: {ve}')
        raise HTTPException(status_code=400, detail=str(ve))
    except Exception as e:
        logger.error(f'Inference failed: {e}')
        raise HTTPException(status_code=500, detail='Inference failed')

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)