Autoscale LLM Inference Endpoints with vLLM and KServe

                      
                     
"""
Production implementation for Autoscale LLM Inference Endpoints with vLLM and KServe.
Provides secure, scalable, and efficient operations for large language model inference.
"""

from fastapi import FastAPI, HTTPException, Request
from pydantic import BaseModel, ValidationError
import os
import logging
import httpx
import asyncio

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

class Config:
    """Configuration class for application settings."""
    inference_url: str = os.getenv('INFERENCE_URL', 'http://localhost:8080/inference')
    max_retries: int = int(os.getenv('MAX_RETRIES', 3))
    retry_delay: int = int(os.getenv('RETRY_DELAY', 2))  # seconds

class InferenceRequest(BaseModel):
    """Model for inference requests."""
    input_text: str

async def validate_input(data: dict) -> bool:
    """Validate the input data for the inference request.
    
    Args:
        data: Input data to validate.
    Returns:
        bool: True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'input_text' not in data:
        raise ValueError('Missing input_text in request')
    return True

async def fetch_inference(data: InferenceRequest) -> dict:
    """Fetch inference from the model endpoint.
    
    Args:
        data: InferenceRequest object containing input text.
    Returns:
        dict: Inference results.
    Raises:
        HTTPException: If fetching inference fails.
    """
    for attempt in range(Config.max_retries):
        try:
            async with httpx.AsyncClient() as client:
                response = await client.post(Config.inference_url, json=data.dict())
                response.raise_for_status()  # Raise an error for bad responses
                return response.json()
        except httpx.HTTPStatusError as e:
            logger.warning(f'Attempt {attempt + 1}: Error during inference: {e}')
            await asyncio.sleep(Config.retry_delay)
    raise HTTPException(status_code=500, detail='Inference service is unavailable after retries')

async def process_inference(request: Request) -> dict:
    """Process inference request from the client.
    
    Args:
        request: FastAPI request object.
    Returns:
        dict: Inference results.
    Raises:
        HTTPException: If request processing fails.
    """
    try:
        body = await request.json()
        await validate_input(body)  # Validate input data
        inference_request = InferenceRequest(**body)
        result = await fetch_inference(inference_request)  # Fetch inference
        return result
    except ValidationError as ve:
        logger.error(f'Validation error: {ve}')
        raise HTTPException(status_code=400, detail=ve.errors())
    except Exception as e:
        logger.error(f'Unexpected error: {e}')
        raise HTTPException(status_code=500, detail='Internal server error')

app = FastAPI()

@app.post('/inference')
async def inference_endpoint(request: Request) -> dict:
    """Endpoint for LLM inference.
    
    Args:
        request: FastAPI request object.
    Returns:
        dict: Inference results from the model.
    """
    return await process_inference(request)  # Process inference request

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