AI-LLM-Dashboard

A sophisticated dashboard for monitoring and controlling Large Language Model (LLM) training processes with real-time metrics visualization and system monitoring.

Quick Start

# Clone the repository
git clone https://github.com/Maazaowski/AI-LLM-Dashboard

# Install dependencies
pip install -r requirements.txt

# Run the dashboard
python main.py

Features

• Real-time Training Metrics

  • Progress tracking
  • Loss visualization
  • Learning rate monitoring
  • Accuracy metrics
  • Training/Validation loss comparison

• System Monitoring

  • CPU usage tracking
  • Memory utilization
  • Disk usage statistics
  • Real-time updates

• Interactive UI Components

  • Progress bar with training status
  • Live updating graphs
  • Training control buttons (Pause/Stop/Export)
  • Detailed logging console
  • Model selection dropdown

Technical Requirements

Core Dependencies

• ttkbootstrap>=1.0.0
• matplotlib>=3.4.0
• psutil>=5.8.0
• pytest>=6.0.0

Optional Dependencies

• tensorboard>=2.6.0
• pandas>=1.3.0

Installation Guide

Prerequisites

• Python >=3.8
• pip (Python package manager)
• Git

Step-by-step Installation

1. Clone the Repository

   git clone https://github.com/Maazaowski/AI-LLM-Dashboard
   cd AI-LLM-Dashboard

2. Create Virtual Environment (Recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

Development Setup

Running Tests

pytest tests/

Code Style

• Follow PEP 8 guidelines
• Use type hints
• Document functions and classes

Troubleshooting Guide

Common Issues

1. UI Not Responding

   • Check system resources
   • Verify training thread status
   • Review log console for errors

2. Memory Issues

   • Adjust batch size
   • Monitor system metrics
   • Close unnecessary applications

Version History

• 1.0.0 (Build 001)
  • Real-time training visualization
  • System metrics monitoring
  • Training control interface

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open Pull Request

Support

For support, please open an issue on the GitHub repository.

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Last updated: 2024.12.10 Build: 001