G.O.D Framework

Introduction

The ai_monitoring.py script is responsible for real-time monitoring of AI systems and machine learning models during execution. It captures metrics like system usage (CPU, GPU, memory), latency, inference times, throughput, and error rates. This module plays a critical role in proactively identifying issues and bottlenecks by providing actionable insights into the operational status of the AI ecosystem within the G.O.D Framework.

Purpose

The ai_monitoring.py script serves multiple objectives:

• Track performance metrics for deployed AI models and pipelines.
• Monitor resource utilization (e.g., CPU, GPU, memory) in real-time.
• Log errors, crashes, and exceptions for debugging purposes.
• Provide alerts and notifications for anomalies or deviations from expected behaviors.
• Enable logging and visualization for analysis and decision-making.

Key Features

• Real-Time Monitoring: Tracks key system and model metrics live during inference or training.
• Comprehensive Metrics: Captures latency, throughput, error rates, accuracy, and resource consumption data.
• Integration: Easily integrates with other modules such as ai_alerting.py for anomaly notification and ai_advanced_reporting.py for detailed visualizations.
• Plugin Support: Extensible to support monitoring tools like Prometheus, Grafana, or Elastic Stack.
• Error Logging Dashboard: Centralized storage of real-time logs for debugging and troubleshooting.

Logic and Implementation

This script includes a monitoring service that continuously collects performance metrics and logs vital statistics. The `psutil` library is used for system-level monitoring, while AI-specific performance metrics (e.g., inference latency, accuracy) are captured using hooks in processing pipelines or model APIs. The data is periodically logged or streamed to external tools for visualization and alerting. Below is the implementation of the key features:

            import psutil
            import time
            import logging
            from datetime import datetime

            class MonitoringService:
                """
                System and AI service monitoring utility.
                """

                def __init__(self, log_file='monitoring_logs.txt', interval=5):
                    self.interval = interval  # seconds between metric captures
                    self.log_file = log_file
                    logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s - %(message)s')
                    print(f"Monitoring service initialized. Logging to {log_file}.")

                def get_resource_usage(self):
                    """
                    Obtain system resource usage metrics.
                    """
                    cpu_usage = psutil.cpu_percent(interval=1)
                    memory = psutil.virtual_memory()
                    gpu_usage = self.get_gpu_usage()  # Placeholder for actual GPU monitoring integration
                    return {'cpu': cpu_usage, 'memory': memory.percent, 'gpu': gpu_usage}

                def get_gpu_usage(self):
                    """
                    Placeholder function to simulate GPU metrics.
                    Integrate with libraries like GPUtil or NVIDIA's NVML for actual metrics.
                    """
                    return 0  # Simulated GPU usage (0%)

                def monitor(self):
                    """
                    Start the monitoring process.
                    """
                    try:
                        while True:
                            metrics = self.get_resource_usage()
                            logging.info(f"CPU: {metrics['cpu']}%, Memory: {metrics['memory']}%, GPU: {metrics['gpu']}%")
                            print(f"Metrics Captured - CPU: {metrics['cpu']}%, Memory: {metrics['memory']}%, GPU: {metrics['gpu']}%")
                            time.sleep(self.interval)
                    except KeyboardInterrupt:
                        print("Monitoring service stopped.")

            # Example Usage
            if __name__ == "__main__":
                monitor = MonitoringService()
                monitor.monitor()
            

Dependencies

• psutil: Python library for system-level monitoring such as CPU and memory usage.
• logging: Built-in Python module for capturing and saving logs.
• time: Standard library for time-based operations (e.g., sleep intervals).

Usage

The ai_monitoring.py script can be run as a standalone program for real-time monitoring or integrated into pipeline scripts to monitor specific workflows.

            # Example usage to start monitoring:
            monitor = MonitoringService(log_file='system_monitor.log', interval=10)
            monitor.monitor()
            

System Integration

The ai_monitoring.py module can be seamlessly integrated with other G.O.D Framework modules and tools:

• Alerting: Pair with ai_alerting.py for sending notifications when preset thresholds are breached.
• Cloud Dashboards: Stream data to platforms like Prometheus or Elastic Stack for further analysis and visualization.
• Error Metrics: Collect error rates and exception data from ai_anomaly_detection.py or related modules.

Future Enhancements

• Integrate with external GPU monitoring libraries such as NVIDIA’s NVML or GPUtil.
• Add support for detailed AI-specific inference monitoring (e.g., latency per model).
• Push monitoring data to cloud platforms like AWS CloudWatch or Google Cloud Monitoring.
• Develop a WebSocket or API interface for retrieval of live metrics.