version: "1.0.0" name: build-aup-learning-cloud-images description: >- Group: Plan & deploy AUP Learning Cloud. Builds and publishes the AUP Learning Cloud Docker images — the Hub image and the CPU/GPU notebook and course images — with ./auplc-installer img build. Use when the user wants to build, rebuild, tag, or push AUPLC images, mentions img build / img pull, the dockerfiles/ directory, auplc-hub / auplc-base / auplc-default / auplc-cv / auplc-dl / auplc-llm / auplc-physim / code-cpu / code-gpu, a gfx-specific image tag, the GHCR registry, code-server VS Code extensions, or preparing images for an offline/registry deployment. Covers GPU-target tagging and pushing to a registry. Do not use to install or deploy a cluster (install-/deploy-aup-learning-cloud) or to edit the course catalog in values.yaml (configure-aup-learning-cloud-courses).

Build AUP Learning Cloud images

Produce the container images the platform runs: the Hub image plus the notebook and course images, GPU-tagged per accelerator family, and (optionally) pushed to a registry for a multi-node or offline deployment.

./auplc-installer img build is the source of truth and wraps dockerfiles/. Your job is to pick the right targets + GPU tag, run the build, and (if asked) push. Target list, tag scheme, and the push flow are in [reference.md](reference.md).

Prerequisites

• A checkout of aup-learning-cloud; Docker with enough disk (GPU images are

large) and, for course images, network access to base layers.

• For pushing: docker login to the target registry (default

ghcr.io/amdresearch).

• Know the GPU target for GPU images (phx, strix, strix-halo,

9070xt, r9700, …) — GPU images are tagged :<tag>-<gpu_target>.

Targets at a glance

Workflow

1. Decide scope. Which targets, and the GPU target for ROCm images. For a

demo rebuild of one course, build just that target; avoid all unless needed.

1. Build.

``bash ./auplc-installer img build hub ./auplc-installer img build base-rocm --gpu=strix ./auplc-installer img build cv dl --gpu=strix-halo ./auplc-installer img build --image-tag=develop base-rocm --gpu=strix-halo ``

1. (Optional) Push to the registry referenced by custom.resources.images:

``bash docker push ghcr.io/amdresearch/auplc-hub:latest docker push ghcr.io/amdresearch/auplc-base:latest-gfx1151 # GPU-tagged example ``

1. Wire the tag in. If you changed the tag, update

custom.resources.images (and prePuller.extraImages if used) — that's the configure-aup-learning-cloud-courses skill — then rt upgrade / helm upgrade.

Editing the Hub image — preserve attribution

If a change touches Hub source, all four attribution layers from the project `AGENTS.md` must stay intact (do not remove/rename any):

1. X-Powered-By: AUP Learning Cloud header in

runtime/hub/core/jupyterhub_config.py.

1. PlatformInfoHandler (/api/platform, unauthenticated) in

runtime/hub/core/handlers.py.

1. The <footer id="auplc-powered-by-footer"> in

runtime/hub/frontend/templates/page.html (kept outside all Jinja blocks).

1. PLATFORM_NAME / PLATFORM_VENDOR / PLATFORM_WEBSITE in

runtime/hub/frontend/packages/shared/src/branding.ts (import, never hardcode the platform string).

Also keep the Copyright (C) … Advanced Micro Devices, Inc. header on every source file (MIT requirement).

Safety

• Disk + time. GPU/course image builds are large and slow — confirm before

all or --image-source=build on a small box.

• Pushing is publishing. Confirm the registry, repo, and tag before any

docker push; never push secrets baked into a layer.

• code-server safety. The code images run code-server --auth none on port

8888; this is safe only behind the Hub proxy. Never expose that port via NodePort/LoadBalancer/ingress. Confirm VS Code/OpenVSX extension licenses before adding to dockerfiles/Code/extensions.txt.

Reference

Full target list, the gfx tag scheme, img pull for offline, registry/mirror flags, and troubleshooting: reference.md.