GPU Container Job
What a GPU Container Job is, when to use one, and how you work with it.
A GPU Container Job runs your workload inside a KubeVirt virtual machine instance (VMI) with a GPU and shell access. You connect to it and work on it like a remote computer, installing what you need and running your code interactively.
When to use one
A GPU Container Job fits when you want direct, interactive shell access to a GPU to run a workload, such as training and fine-tuning, experiments, processing your data, etc.
If you only want to call a language model over an API, a Managed Inference Job is the better fit. It serves the model for you, so you don't set up or run the environment yourself.
What you get
- Shell access, so you install what you need and run your own commands.
- VM-level isolation, where each job runs in its own VMI.
CosmicAC schedules the job on your cluster and hands you a ready GPU. If you want the component-level picture of how that happens, see Architecture.
How you connect
You work with a running job from the CLI, which opens a shell on the container. From there the job behaves like any remote machine. You run scripts, start processes, and inspect output directly.
For the commands that open a shell, see Access a GPU Container.