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This README provides comprehensive information about the project, including its purpose, usage instructions, and configuration details.

The Dockerfile sets up a Docker image for running a Python application that utilizes NVIDIA CUDA for GPU computation. It includes the following steps:

• Starts from the NVIDIA CUDA image with Python support (nvidia/cuda:12.3.2-cudnn9-runtime-ubuntu20.04).
• Installs Python, pip, and virtualenv.
• Creates a virtual environment and activates it.
• Upgrades pip in the virtual environment.
• Copies the requirements.txt file into the container and installs dependencies.
• Makes sure the appuser owns the application directory.
• Copies the gpu_metrics.py script into the container.
• Exposes port 8888.
• Specifies the command to run the script (CMD ["python3", "gpu_metrics.py"]).

To use the Docker image:

1. Clone or download the repository containing the Dockerfile and other necessary files.
2. Build the Docker image:

docker build -t nvidia-cuda-exporter .

4. Run a Docker container based on the image:

docker run --name nvidia-cuda-exporter --gpus all -p 8888:8888 -v /usr/local/nvidia:/usr/local/nvidia nvidia-cuda-exporter

6. Access the Python application through a web browser or programmatically: http://localhost:<host-port>

Replace <host-port> with the desired port on your host machine.

The gpu_metrics_collector.py script collects GPU metrics using NVIDIA Management Library (NVML) and exposes them as Prometheus metrics via an HTTP server. It includes the following functionality:

• Collects various GPU metrics such as memory usage, utilization, temperature, power usage, fan speed, clock speeds, PCIe transmission speed, and more.
• Exposes the metrics at http://localhost:8888/metrics.

To use the script:

1. Clone or download the repository containing the script.
2. Install the required dependencies: pip install prometheus_client pynvml
3. Run the script: python gpu_metrics_collector.py

Access the metrics at http://localhost:8888/metrics.