Scale Industrial LLM Serving Across GPU Clusters with NVIDIA Dynamo and Ray

"""
Production implementation for scaling industrial LLM serving across GPU clusters using NVIDIA Dynamo and Ray.
Provides secure, scalable operations for real-time inference.
"""
from typing import Dict, Any, List
import os
import logging
import time
import ray
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field

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

# Configuration settings from environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    backoff_factor: float = float(os.getenv('BACKOFF_FACTOR', 0.5))

# Initialize FastAPI app
app = FastAPI()

# Input validation model using Pydantic
class InputData(BaseModel):
    prompt: str = Field(..., min_length=1, description="Prompt for the LLM.")
    user_id: str = Field(..., description="Unique user identifier.")

def validate_input(data: InputData) -> None:
    """Validate request data.
    Args:
        data: Input data model
    Raises:
        ValueError: If validation fails
    """
    if not data.prompt:
        raise ValueError('Prompt cannot be empty')

async def fetch_data(user_id: str) -> Dict[str, Any]:
    """Fetch user data from the database.
    Args:
        user_id: Unique identifier for the user
    Returns:
        User data as a dictionary
    Raises:
        ValueError: If user is not found
    """
    logger.info(f'Fetching data for user: {user_id}')
    # Simulate database fetch with a placeholder
    user_data = {'preferences': 'default'}  # Simulated data
    if user_data is None:
        raise ValueError(f'User {user_id} not found')
    return user_data

async def save_to_db(user_id: str, result: str) -> None:
    """Save inference result to the database.
    Args:
        user_id: Unique identifier for the user
        result: Inference result to save
    """
    logger.info(f'Saving result for user: {user_id}')
    # Simulated database save
    # db.save_result(user_id, result)

async def call_api(prompt: str) -> str:
    """Call the LLM API to get the result.
    Args:
        prompt: Prompt for the LLM
    Returns:
        Inference result
    Raises:
        RuntimeError: If API call fails
    """
    logger.info(f'Calling LLM API with prompt: {prompt}')
    # Simulate API call
    response = "Simulated LLM response"  # Placeholder response
    return response

async def process_batch(data: List[InputData]) -> List[str]:
    """Process a batch of inputs asynchronously.
    Args:
        data: List of input data models
    Returns:
        List of results from the LLM
    """
    results = []
    for item in data:
        try:
            validate_input(item)
            user_data = await fetch_data(item.user_id)
            result = await call_api(item.prompt)
            await save_to_db(item.user_id, result)
            results.append(result)
        except Exception as e:
            logger.error(f'Error processing item: {item}, error: {str(e)}')
            results.append(f'Error: {str(e)}')  # Append error message
    return results

@app.post('/process', response_model=List[str])
async def process_request(data: List[InputData]) -> List[str]:
    """Process incoming requests.
    Args:
        data: List of input data models
    Returns:
        List of results from the LLM
    Raises:
        HTTPException: If validation or processing fails
    """
    try:
        logger.info('Received data for processing')
        results = await process_batch(data)
        return results
    except Exception as e:
        logger.error(f'Processing error: {str(e)}')
        raise HTTPException(status_code=500, detail='Processing error')

if __name__ == '__main__':
    # Example usage
    ray.init()  # Initialize Ray for distributed computation
    logger.info('Ray initialized')
    # Run the FastAPI application
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)