Editing CCU:GPU Cluster Quick Start (section)

== Overview ==

The GPU cluster runs on Kubernetes, which is a container orchestrator. That means that users can run docker containers, which are essentially light-weight virtual machines without the overhead of an operating system, i.e. they mostly make use of the OS of the host machine. The additional layer in between allows the container to bring their own libraries with them, and shields the host OS from interference from the container. The containers are assigned to the host machines automatically, but the user has some options to specify which machine or which kind of machine they want to end up on. There is a global file system which is running on a Ceph cluster, which is mounted on every host. The details are not important for you, but it means that there is plenty of fast NVMe storage available which you can use for your code and datasets. You have to mount the directories which you want to use inside the container.

The typical workflow if you want to run your own applications is as follows:

# Log in to the cluster and configure kubectl, the command line tool to talk to Kubernetes, to use your login credentials and namespace.
# Create a persistent container to access the file systems, and mount the Ceph volumes inside it. Use this container to transfer code and data to the cluster and back.
# (optional): Create your own custom container image with special libraries etc. which you need to run your code.
# Create a GPU-enabled container based on your own image or one of the ready-made images with Deep Learning toolkits or whatever workload you want to run.
# Start your workloads by logging into the container and running your code manually (only good for debugging), or by defining a job script which automatically runs a specified command inside the container until successful completion (recommended).

We will cover these points in more detail below.