Orchestrate GPU Prefill-Decode Inference for Factory AI with NVIDIA Dynamo and vLLM

"""
Production implementation for orchestrating GPU prefill-decode inference for Factory AI using NVIDIA Dynamo and vLLM.
Provides secure, scalable operations.
"""

from typing import Dict, Any, List
import os
import logging
import time
import requests
from concurrent.futures import ThreadPoolExecutor

# Logger setup for tracking application flow and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class for environment variables and settings
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///local.db')
    api_endpoint: str = os.getenv('API_ENDPOINT', 'http://localhost:8000/api')
    max_workers: int = int(os.getenv('MAX_WORKERS', 4))

# Validate input data for inference
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for inference.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'model_id' not in data:
        raise ValueError('Missing model_id')
    if 'payload' not in data:
        raise ValueError('Missing payload')
    return True

# Sanitize input fields to prevent injection attacks
async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to ensure safe processing.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    # Implement sanitization logic here
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    return sanitized_data

# Normalize data for processing
async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize request data for consistent processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    # Example normalization logic
    normalized_data = {k: v.lower() for k, v in data.items()}
    return normalized_data

# Process a batch of inference requests
async def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of requests asynchronously.
    
    Args:
        batch: List of input data dictionaries
    Returns:
        List of processed results
    """
    results = []
    for item in batch:
        result = await call_api(item)
        results.append(result)
    return results

# Fetch data from the API endpoint
async def call_api(data: Dict[str, Any]) -> Dict[str, Any]:
    """Call external API for inference.
    
    Args:
        data: Input data for inference
    Returns:
        API response
    Raises:
        Exception: If API call fails
    """
    try:
        response = requests.post(Config.api_endpoint, json=data)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f'API call failed: {e}')
        raise Exception('API call failed')

# Aggregate metrics for monitoring
async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from inference results.
    
    Args:
        results: List of results from inference
    Returns:
        Aggregated metrics
    """
    # Implement aggregation logic here
    metrics = {'total': len(results), 'success': sum(1 for r in results if r.get('status') == 'success')}
    return metrics

# Save inference results to the database (mock implementation)
async def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save processed results to the database.
    
    Args:
        results: Inference results to save
    """
    # Implement save logic here
    logger.info('Results saved to the database.')

# Main orchestrator class to tie everything together
class InferenceOrchestrator:
    def __init__(self):
        self.executor = ThreadPoolExecutor(max_workers=Config.max_workers)

    async def run_inference(self, data: Dict[str, Any]) -> None:
        """Run the inference workflow.
        
        Args:
            data: Input data for inference
        """
        try:
            await validate_input(data)  # Validate input
            sanitized_data = await sanitize_fields(data)  # Sanitize
            normalized_data = await normalize_data(sanitized_data)  # Normalize
            results = await process_batch([normalized_data])  # Process
            await save_to_db(results)  # Save results
            metrics = await aggregate_metrics(results)  # Aggregate
            logger.info(f'Metrics: {metrics}')
        except Exception as e:
            logger.error(f'Error in inference workflow: {e}')

if __name__ == '__main__':
    import asyncio
    orchestrator = InferenceOrchestrator()
    sample_data = {'model_id': '123', 'payload': {'input': 'data'}}
    loop = asyncio.get_event_loop()  # Create an event loop for async execution
    loop.run_until_complete(orchestrator.run_inference(sample_data))  # Run inference