agentleFS
liuhlabliuhlab/liulab-skills/skills/lab-containers/SKILL.md

lab-containers

Skill1 starsChanged 14 days agoMITView on GitHub

Pull, build, update, version-check or use the lab's Singularity container images (SIF, ghcr.io liulab-runtime) on ircbc: run commands, pixi-env shells or Jupyter inside them, and the $LIU_LAB_PACKAGES shared store. ircbc compute nodes have no internet, so: crane pull on the login node, sbatch build from docker-archive, digest-based version checks.

The file

Files
SKILL.md

Lab container images: check → pull → build → test → use

Repeatable procedure for liulab-runtime environments on ircbc as Singularity images. Follow lab-hpc first (step-0 preflight, personal.md, the VPN caveat, confirm every sbatch and srun with the user), and its references/ircbc-hpc.md for the facts underneath — network topology, $LIU_LAB_PACKAGES layout, proxy gotchas.

Scope: ircbc only. Its compute nodes have no internet and all real work there runs inside SIFs. Do not apply these recipes to arc — arc runs the same liulab-runtime environments natively via pixi (see lab-hpc); reach for a SIF there only if the user explicitly asks.

Where each step runs

Work belongs on a compute node. This skill holds the one honest exception: crane talks to ghcr.io, and ircbc compute nodes have no internet, so the pull and the remote digest check can only happen on the login node — by necessity, not convenience. The build, the smoke test, and every actual use of an image are compute-node work.

The whole Slurm client (squeue, sbatch, scancel, srun) runs on ircbc compute nodes, so if you already hold a node, submit from there rather than hopping back. $LIU_LAB_PACKAGES lives on /share and is readable from both, so only the network fetch is login-bound.

Version check first — never rebuild blindly

Each SIF carries a sidecar recording the digest it was built from: liulab-runtime_<env>.sif.digest. List the store instead of assuming what exists, then compare local against remote:

ssh ircbc 'ls -lh $LIU_LAB_PACKAGES/liulab-runtime_*.sif* 2>/dev/null; \
  echo "local:  $(cat $LIU_LAB_PACKAGES/liulab-runtime_<env>.sif.digest 2>/dev/null || echo none)"; \
  echo "remote: $($LIU_LAB_PACKAGES/bin/crane digest ghcr.io/liuhlab/liulab-runtime:<env>)"'
  • Digests match → up to date. Do not rebuild; go use it.
  • Digests differ → show the user the SIF's build date and both digests, and ask whether to keep the local image or update.
  • No SIF for that env → update, pinning the remote digest just printed.

Env names such as ml, align-rna and align-dna are examples, not a catalogue — the ls -lh above is the authority on what is actually built and how much space each image takes. These are shared lab images: never delete or overwrite someone else's without asking, keep the store group-writable (dirs carry setgid), and mind the shared disk before building another SIF.

Where the rest lives

  • references/update-image.md — pull the tarball, build the SIF, smoke-test it, write the sidecar.
  • references/using-images.md — run a command, open an interactive pixi-env shell, start Jupyter.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.