Performance Monitor

Overview

This tool monitors the performance of a set of machines in a local network. It collects metrics using Prometheus and displays them on a Flask-based dashboard.

Features

• Real-time performance monitoring
• Metrics collection using Prometheus
• Dashboard visualization with Flask and Chart.js

Prerequisites

1. Python 3.11
2. Docker and Docker Compose
3. Prometheus Node Exporter installed and running on each node (refer to NODES.md for setup instructions)

Configuration

All configurable parameters are stored in config.yaml.

Example config.yaml

servers:
  - name: ServerOne
    address: http://ServerOne:9100
    network_interface: eno1
  - name: ServerTwo
    address: http://ServerTwo:9100
    network_interface: enp7s0

dashboard:
  host: 0.0.0.0
  port: 5000
  debug: true

metrics_port: 8000

redis:
  host: localhost
  port: 6379
  db: 0

Installation

1. Clone the repository:

   git clone https://github.com/SolidRusT/srt-inference-monitoring.git
   cd srt-inference-monitoring

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

Running the Application

Using Docker Compose

1. Ensure Docker and Docker Compose are installed.

2. Start the application:

   docker-compose up -d --build

3. Access the dashboard: Open a web browser and navigate to http://localhost:5000.

Manually

1. Start the metrics collector and Flask app:

   python start.py

2. Access the dashboard: Open a web browser and navigate to http://localhost:5000.

Running Tests

1. Run unit tests:

   python -m unittest discover tests

Logging

Logs will provide information about the metrics collection process and any errors encountered. Check the logs for messages confirming that metrics are being collected and pushed to the Redis (Valkey) server.

Troubleshooting

• Metrics not displaying: Ensure that the Prometheus Node Exporter is running and accessible on each node. Check the logs for any errors in metrics collection.
• Scheduler not running: Confirm that the scheduler is starting and running the collect_metrics function.

License

MIT License. See LICENSE for more details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

Author

Suparious, SolidRusT Networks