G.O.D Framework

Introduction

The ai_monitoring_dashboard.py script is a centralized dashboard for visualizing real-time performance metrics and historical data collected by the AI monitoring system. It provides analytical insights into resource usage, inference results, and anomaly detection to empower developers and operators to monitor AI systems visually and interactively.

Purpose

The purpose of ai_monitoring_dashboard.py is to:

• Provide a web-based user interface for monitoring AI system metrics in real-time.
• Enable exploration and visualization of historical performance data, including resource usage and error rates.
• Facilitate anomaly detection and debugging workflows using graphs, alerts, and logs.
• Serve as an interactive tool for team collaboration during infrastructure performance analysis.

Key Features

• Intuitive Dashboard Layout: Displays key metrics such as CPU usage, memory usage, GPU activity, and latency.
• Graphical Visualizations: Provides real-time and historical data graphs using tools such as Matplotlib or Plotly.
• Alert System: Integrates with the G.O.D alerting module to highlight anomalies.
• Data Customization: Users can select specific datasets, visualizations, or time-frames to analyze.
• Integration Support: Supports connections with external logging or monitoring tools.

Logic and Implementation

The script provides a Flask-based web application that interacts with backend databases or monitoring systems to fetch and display data. It leverages real-time streaming for dynamic updates while supporting queries for historical analysis.

            from flask import Flask, render_template, request
            import datetime
            import random

            app = Flask(__name__)

            @app.route("/")
            def index():
                """
                Main dashboard route.
                """
                return render_template("dashboard.html", title="AI Monitoring Dashboard")

            @app.route("/api/realtime-metrics", methods=["GET"])
            def get_realtime_metrics():
                """
                Mock API for real-time metrics data.
                Replace with actual data retrieval logic.
                """
                data = {
                    "cpu_usage": random.uniform(10, 80),
                    "memory_usage": random.uniform(20, 90),
                    "gpu_usage": random.uniform(5, 50),
                    "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                }
                return data

            if __name__ == "__main__":
                app.run(debug=True, host="0.0.0.0", port=5000)
            

Dependencies

• Flask: Web framework for creating the dashboard interface and serving requests.
• Matplotlib or Plotly: Libraries for rendering graphs and charts.
• psutil: Used for system-level monitoring data if integrated directly with the monitoring system.
• Bootstrap: CSS framework for styling the HTML-based frontend interface (optional).

Usage

Run the script as a standalone Flask web application for visualization:

            # Navigate to the directory containing ai_monitoring_dashboard.py
            $ python ai_monitoring_dashboard.py
            # Access the dashboard at http://127.0.0.1:5000
            

The dashboard provides the following views:

• Main Dashboard: Provides an overview of real-time usage statistics.
• Historical Analysis: Select specific dates, files, or logs for in-depth analysis.

System Integration

The ai_monitoring_dashboard.py works with the following G.O.D modules:

• ai_monitoring.py: Consumes collected metrics for visualization.
• ai_alerting.py: Displays notifications and alerts for anomalies.
• ai_advanced_reporting.py: Supplements the dashboard with advanced reporting capabilities.

Future Enhancements

• Integrate WebSocket support for improved real-time data streaming.
• Enhance user authentication and role-based access control for the dashboard.
• Support exporting of analyzed data as reports (e.g., PDF, CSV).
• Add machine learning insights on anomalies using data from ai_anomaly_detection.py.