A web interface for monitoring NVIDIA GPUs
Project description
GPUs
A real-time web interface for monitoring NVIDIA GPUs.
Features
- Real-time monitoring of NVIDIA GPU statistics
- Clean, modern, responsive web interface
- Historical utilization graphs
- Process monitoring
- Command-line interface
- Background server mode
Requirements
- Python 3.6+
- NVIDIA GPU with installed drivers
- NVIDIA Management Library (NVML)
Installation
pip install gpus
Usage
Starting the Web Interface
# Start the web interface in the foreground (default port 5000)
gpus
# Specify a different port
gpus --port 8080
# or with the short option
gpus -P 8080
# Specify update interval (in seconds)
gpus --update-interval 2.0
# or with the short option
gpus -U 2.0
# Specify history length (in seconds)
gpus --history-length 600
# or with the short option
gpus -L 600
# Specify history resolution (in seconds)
gpus --history-resolution 1.0
# or with the short option
gpus -R 1.0
Then open your web browser and navigate to http://localhost:5000 (or the port you specified).
Managing Background Server
You can run the server in the background and manage it with subcommands:
# Start the server in the background
gpus start
# Check if the server is running
gpus status
# Stop the background server
gpus stop
The background server uses the same command-line options as the foreground server:
# Start the background server on a specific port
gpus -P 8080 start
Using as a Python Package
from gpus.app import GPUMonitorApp
# Create the application
app = GPUMonitorApp(
update_interval=2.0,
history_length=300,
history_resolution=1.0
)
# Run the application
app.run(host='0.0.0.0', port=5000)
Development
Setup Development Environment
# Clone the repository
git clone https://github.com/fakerybakery/gpus
cd gpus
# Create a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install in development mode
pip install -e .
License
MIT License
Disclaimer: This was mostly a vibe-coded project. Use at your own risk.
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