Monitor Industrial LLM Inference Metrics with NVIDIA Dynamo and Prometheus Client

"""
Production implementation for monitoring industrial LLM inference metrics.
This module provides secure and scalable operations with NVIDIA Dynamo and Prometheus.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from prometheus_client import CollectorRegistry, Counter, Gauge, push_to_gateway

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

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'http://localhost:8000')
    prometheus_url: str = os.getenv('PROMETHEUS_URL', 'http://localhost:9091')

# Create a registry for Prometheus metrics
registry = CollectorRegistry()

# Define Prometheus metrics
inference_counter = Counter('inference_requests_total', 'Total number of inference requests', registry=registry)
inference_latency = Gauge('inference_latency_seconds', 'Latency of inference requests in seconds', registry=registry)

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

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    # This is a simple example; expand sanitation as needed
    return {key: str(value).strip() for key, value in data.items()}

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for consistent processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    # Perform normalization, e.g., converting all keys to lowercase
    return {key.lower(): value for key, value in data.items()}

async def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch data from the given URL and return the response.
    
    Args:
        url: URL to fetch data from
    Returns:
        Parsed JSON response
    Raises:
        Exception: If the request fails
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()  # Return JSON response
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')  # Log errors
        raise Exception('Failed to fetch data')

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of inference requests.
    
    Args:
        batch: List of requests to process
    """
    for record in batch:
        try:
            await validate_input(record)  # Validate each record
            sanitized_record = await sanitize_fields(record)  # Sanitize input
            normalized_record = await normalize_data(sanitized_record)  # Normalize data
            await call_api(normalized_record)  # Call the inference API
        except Exception as e:
            logger.warning(f'Error processing record {record}: {e}')  # Log error for each record

async def call_api(data: Dict[str, Any]) -> None:
    """Call the inference API and log the metrics.
    
    Args:
        data: The input data to process
    """
    start_time = time.time()  # Record start time for latency measurement
    try:
        # Simulate calling the inference API
        # In production, this would be an actual API call
        logger.info(f'Calling inference API with data: {data}')
        inference_counter.inc()  # Increment the counter for each request
        # Simulate some processing time
        await asyncio.sleep(0.1)
    finally:
        latency = time.time() - start_time  # Calculate latency
        inference_latency.set(latency)  # Set the latency metric
        push_to_gateway(Config.prometheus_url, job='inference_metrics', registry=registry)  # Push metrics to Prometheus

async def aggregate_metrics() -> None:
    """Aggregate and push metrics to Prometheus at regular intervals.
    """
    while True:
        # This function can be scheduled as a background task
        logger.info('Aggregating metrics...')
        await asyncio.sleep(30)  # Wait for 30 seconds before next aggregation

# Main orchestrator class to tie everything together
class MetricsMonitor:
    """Main class for monitoring inference metrics.
    """
    async def run(self):
        logger.info('Starting Metrics Monitor...')  # Start monitoring
        # Example batch processing; in production, this would be event-driven
        example_batch = [{'id': 1, 'data': 'example data'}, {'id': 2, 'data': 'example data'}]  # Sample input
        await process_batch(example_batch)  # Process the example batch

if __name__ == '__main__':
    import asyncio
    monitor = MetricsMonitor()  # Instantiate the monitor
    try:
        asyncio.run(monitor.run())  # Run the main monitoring process
    except Exception as e:
        logger.error(f'Error in main execution: {e}')  # Log any errors