In this previous post, I described the first attempt to build a VDI platform for Deep Learning Applications. This post aggregates the information I used to build Linux Virtual Desktops for AI and Data Science. It’s a live-blog, which means I will update it when the process changes.
![](https://vhojan.nl/wp-content/uploads/2020/04/Screenshot-2020-04-16-at-20.54.33.png)
Building and Provisioning the desktop
The first step is to fully set up Horizon to use Linux Desktops. The following guide is quite thorough:
Set up docker
Setting up docker isn’t that hard. There are just a couple of things to take into account when installing (NVIDIA) Docker on a virtual desktop. The first step is to install docker and NVIDIA docker:
Docker installation: https://github.com/NVIDIA/nvidia-docker/issues/913
After the docker installation, the horizon agent needs to be adjusted:
Adjusting Horizon Agent after docker installation: https://communities.vmware.com/thread/597866
Deploying a container
In case you need to install a different CUDA version, check it out here:
CUDA installation for Ubuntu: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements
Selecting the proper CUDA runtime for Docker: https://github.com/NVIDIA/nvidia-docker/issues/861https://github.com/NVIDIA/nvidia-docker/wiki/CUDA#requirementshttps://askubuntu.com/questions/917356/how-to-verify-cuda-installation-in-16-04
Finally, you can pull a container from the NGC cloud:
Using NGC containers with vGPU: https://docs.nvidia.com/ngc/ngc-vgpu-setup-guide/
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