amdresearch/aup-learning-cloud/configure-aup-learning-cloud-courses

Configure AUP Learning Cloud courses

Change what users can spawn and who can see it, by editing the custom: block of the JupyterHub values and re-applying with Helm. One coherent surface: course images, their resource requirements, the spawn-UI metadata, accelerator selectors, team mappings, and quota.

Edit a values overlay (e.g. runtime/values-basic-example.yaml or values.local.yaml), never the chart defaults blindly. The key map and the full field guide are in [reference.md](reference.md).

Prerequisites

• A checkout of aup-learning-cloud; a running Hub (single- or multi-node).
• helm + kubectl against the cluster, or ./auplc-installer on a

single-node box.

• Know which keys already exist: custom.resources.images is the catalog;

course keys are cpu, gpu, code-cpu, code-gpu, and Course-CV, Course-DL, Course-LLM, Course-PhySim.

The four places a course lives

A course key must be consistent across all of these or the spawn UI breaks:

1. custom.resources.images.<key> — the container image.
2. custom.resources.requirements.<key> — cpu, memory, and amd.com/gpu.
3. custom.resources.metadata.<key> — spawn-UI group, description,

accelerator, acceleratorKeys, allowGitClone, launchMode, resourceType.

1. custom.teams.mapping.<team> — the teams allowed to launch it.

Workflow

1. Read the current state. Open runtime/values.yaml for the canonical

shape, and the active overlay for what is deployed. Confirm the exact key you are changing.

1. Make the edit in the overlay. Add/modify the key in all four places

above (or, for accelerators/quota, the relevant block). Keep acceleratorKeys pointing at real custom.accelerators keys (phx, strix, strix-halo, 9070xt, r9700).

1. Keep accelerator selectors honest. Each custom.accelerators.<key>.nodeSelector

must equal a real node label — confirm with kubectl describe node <node> | grep amd.com/gpu.product-name.

1. Validate the render before applying. `helm template jupyterhub

./runtime/chart -f runtime/values.yaml -f <overlay> must succeed; the repo also ships runtime/chart/values.schema.json`.

1. Apply. Single-node: ./auplc-installer rt upgrade. Multi/manual:

helm upgrade --install jupyterhub ./runtime/chart -n jupyterhub -f runtime/values.yaml -f <overlay>.

1. Verify. Reload the spawn page; the course appears in its group for the

mapped teams only, and a launched pod gets the expected resources/node.

Safety

• Edit overlays, not secrets. Never put OAuth secrets or tokens in tracked

files. Never commit a values.local.yaml that carries site config.

• Removing a course hides it and can strand running servers on that image —

confirm with the user and check for active spawns first.

• Quota changes apply cluster-wide. Lowering minimumToStart / cpuRate

or editing refreshRules affects every user; confirm before applying.

• A helm upgrade restarts the Hub pod (brief auth blip). Confirm timing for a

live class.

Reference

Course-key map, every metadata/requirements field, the accelerator block, team-mapping semantics, and the quota knobs: reference.md.