# GPU on HUNT Cloud

We offer GPU machine types in our IaaS tier allowing you to run the AI/ML workloads of your choice.

See GPU specification for more details on the specific GPU models we offer.

# Software

To get you started, the GPU machines come pre-installed with the software below. If you need to install any other tools or versions you can do this as well.

# How to use

Run the following command to call the NVIDIA System Management Interface (nvidia-smi) to manage and verify that things are working:

nvidia-smi

If you wish to use a specific version of CUDA, you can either install it manually or use the NVIDIA Container Toolkit to run a container of your choosing. Here is an example for CUDA version 11.0:

sudo docker run --rm --runtime=nvidia nvidia/cuda:11.0-base nvidia-smi

# Jupyter in Nvidia Docker

If you want to use Jupyterlab on GPU machine we recommend iot-salzburg/gpu-jupyter (opens new window) docker image. This project uses the NVIDIA CUDA image as the base image and installs their toolstack on top of it to enable GPU calculations in the Jupyter notebooks. Python packages Tensorflow and Pytorch are preinstalled to match the Cuda version (GPU drivers).

docker pull cschranz/gpu-jupyter:v1.4_cuda-11.2_ubuntu-20.04

docker run --rm -d --runtime=nvidia -v ${HOME}:${HOME} --workdir ${HOME} -e HOME=${HOME} -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes -p 8888:8888 --user root --name gpu-jupyter cschranz/gpu-jupyter:v1.4_cuda-11.2_ubuntu-20.04

docker ps

This starts an instance of GPU-Jupyter with the tag v1.4_cuda-11.2_ubuntu-20.04 at http://localhost:8888 (port 8888). The default password is gpu-jupyter.

More details about docker image can also be found on docker hub (opens new window).

# More info

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