🖥️ GPU Monitor Board

📋 Description

GPU Monitor Board is a cutting-edge web application that provides real-time monitoring of NVIDIA GPUs across multiple servers. With its intuitive interface, you can effortlessly track GPU utilization, memory usage, temperature, and user information for each GPU on connected servers.

✨ Features

• 🔄 Real-time monitoring of multiple servers
• 📊 Comprehensive GPU information display:
  • GPU index and name
  • Temperature
  • Utilization percentage
  • Memory usage and total memory
  • Current user (if available)
• 🎨 Color-coded utilization for quick status identification
• ⏱️ Automatic updates every 60 seconds
• 📑 Tabbed interface for seamless navigation between servers

🛠️ Prerequisites

• Python 3.9+
• FastAPI
• Uvicorn
• Paramiko
• NVIDIA GPU drivers installed on monitored servers

🚀 Installation

1. Clone the repository:

   git clone https://github.com/scottsuk0306/gpu-board.git
   cd gpu-board

2. Install the required Python packages:

   pip install fastapi uvicorn paramiko

3. Create a config.json file based on the provided template:

   cp config.json.example config.json

   Edit config.json with your server details.

⚙️ Configuration

Edit the config.json file to include your server details:

{
  "servers": [
    {"hostname": "server1.example.com", "username": "user1", "password": "pass1", "port": 22},
    {"hostname": "server2.example.com", "username": "user2", "port": 22, "key_filename": "path/to/key.pem"}
  ]
}

🖱️ Usage

1. Start the server:

   uvicorn app:app --host 0.0.0.0 --port 8000

2. Open a web browser and navigate to http://localhost:8000

🔒 Security Notes

• Ensure that config.json is not exposed to public access.
• For production use, consider using environment variables or a secure secrets management system for sensitive information.
• Ensure that the SSH keys have appropriate permissions (typically 600).

🤝 Contributing

Contributions to the GPU Monitor Board project are welcome! Please feel free to submit pull requests or create issues for bugs and feature requests.

📄 License

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

🙏 Acknowledgements

• This project uses Bootstrap for styling.
• NVIDIA-SMI is used for gathering GPU information.

📞 Support

If you encounter any issues or have questions, please open an issue on our GitHub repository.

🚀 Roadmap

• Support installation through pip
• Dockerize