Kermt Setup skill

kermt-setup

Bootstrap the KERMT agent environment. Run this once on a fresh machine (or after the Dockerfile or environment.yml changes) before invoking any other kermt-* skill.

Skill and runtime paths

Set SKILL_DIR to the absolute path of this installed skill directory. Export KERMT_REPO as the absolute path to the KERMT checkout used for model execution. The bundled container helper mounts that checkout at /workspace and this skill at /skill (read-only). Commands inside the container use /skill/scripts/.

Hardware requirements

• GPU: at least one CUDA-capable NVIDIA GPU visible to the host. The image is based on nvidia/cuda:12.6.3-cudnn-devel-ubuntu22.04, so the host driver must support CUDA 12.6. Verify with host nvidia-smi before invoking.
• Host docker: docker engine + nvidia-container-toolkit. Without the toolkit, docker run --gpus all will fail at step 2 of the workflow below.
• Disk: ≈ 50 GB free for the built kermt image (docker image inspect --format '{{.Size}}' reports ≈ 44 GB; the docker images Size column can show ~100 GB because it counts shareable buildx attestation layers that are deduplicated across images). Plan for ~50 GB of unique on-disk storage; add a comfortable buffer if you're also keeping build cache.
• Memory: the build itself peaks at ~4 GB RAM during conda env solve.
• This skill does not run training/inference workloads itself; per-workflow hardware requirements (VRAM, GPU count) are declared in the respective kermt-<workflow> skills.

When to invoke

• User explicitly asks (/kermt-setup, "set up kermt", "build the kermt image", etc.).
• Or another kermt-* skill detected that the image does not exist and routed here. (Most other skills call kermt_ensure_image themselves, so this is usually only needed for the first-time setup, debugging, or a forced rebuild.)

Inputs

The skill takes no required arguments. Optional overrides (via env vars before invoking, or by setting them in the user's shell):

• KERMT_IMAGE — image tag to build/verify (default: kermt:latest).
• KERMT_REPO — host path of the kermt repo checkout (default: auto-derived from the script's location).

If the user has not specified a repo path and the current working directory is not inside a kermt repo clone, ask for the repo path before proceeding.

Workflow

All work goes through the bundled scripts/kermt_container.sh on the host. The script's subcommand dispatch can be invoked directly without sourcing — that is the preferred form for skill use.

Let HELPER="$SKILL_DIR/scripts/kermt_container.sh".

1. Verify docker is installed and the daemon is reachable.

   "$HELPER" check_docker
   

   Exit 0 → continue. Non-zero → surface the error to the user (typically "docker not on PATH" or "daemon not reachable"); do not attempt step 2.

2. Verify GPU passthrough works.

   "$HELPER" check_gpu
   

   This runs docker run --rm --gpus all nvidia/cuda:12.6.3-base-ubuntu22.04 nvidia-smi and checks the exit status. Non-zero → tell the user to install nvidia-container-toolkit on the host and confirm a CUDA-capable NVIDIA GPU is visible to the host (nvidia-smi on the host should also work). Stop here; without GPU passthrough the kermt image will build but no workflow will run.

3. Build or verify the kermt image.

   "$HELPER" ensure_image
   

   If the image already exists, this returns immediately. Otherwise it builds from $KERMT_REPO/Dockerfile. Warn the user before invoking that the first build takes ~10–20 minutes on a typical workstation and streams build logs to the console. Do not run this in the background — the user wants to see progress and any build failures must surface immediately.

4. GPU smoke test inside the container. Quote the whole python command as a single string — the helper passes args through bash -c "$*", so unquoted multi-word commands get re-parsed and any embedded quotes are collapsed.

   "$HELPER" run -- 'python -c "import torch; print(\"cuda_available:\", torch.cuda.is_available()); print(\"device_count:\", torch.cuda.device_count())"'
   

   Expected output: cuda_available: True and a positive device_count. If cuda_available is False despite step 2 passing, something is wrong with the container's CUDA wiring — report the full output to the user and stop; do not declare the environment ready.

5. Summary to user. Report:

   • Image tag and ID (docker image inspect $KERMT_IMAGE --format '{{.Id}}').
   • Image size (docker image inspect $KERMT_IMAGE --format '{{.Size}}').
   • GPU count detected inside the container.
   • "Ready" — the user can now invoke other kermt-* skills.

Hard rules

• Do not pull or push docker images. The kermt image is built locally only.
• Do not auto-delete or prune older kermt:* tags without the user's explicit confirmation — the user may be running a finetune or pretrain in another container that depends on a specific tag.
• Do not modify the host's docker daemon configuration, daemon.json, or user-group membership.
• Do not modify the Dockerfile or environment.yml as part of this skill. If the build fails because of a Dockerfile issue, surface the error and stop; let the user decide whether to edit.
• Do not rebuild the image when it already exists (i.e. do not pass a --no-cache or --pull flag to ensure_image) unless the user explicitly asks for a forced rebuild.

Forced rebuild

If the user explicitly asks to rebuild (e.g. after changing the Dockerfile or environment.yml), the cleanest path is to remove the old image first, then rerun ensure_image:

docker image rm $KERMT_IMAGE
"$HELPER" ensure_image

Confirm with the user before running docker image rm.