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