Kermt Setup
NVIDIAOfficialPublished by NVIDIA
- Skill
- Workflow
- Cloud and DevOps
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The kermt-setup skill provides a foundational workflow for initializing the KERMT agent environment. It performs a series of prerequisite checks, including verifying the host's Docker installation and NVIDIA container toolkit, ensuring GPU passthrough is functional, and building the required Docker image from the repository. The process concludes with a GPU smoke test inside the container to confirm that the environment is correctly configured for subsequent KERMT agent tasks.
What it does
- Verifies host Docker daemon connectivity and installation.
- Checks for NVIDIA container toolkit and GPU passthrough capability.
- Builds the kermt:latest Docker image from the local repository.
- Executes a GPU smoke test using PyTorch within the container.
- Reports final image details and environment readiness status.
When to use it
- When setting up the KERMT agent environment for the first time on a new machine.
- After modifying the Dockerfile or environment configuration files.
- When troubleshooting environment issues before running other kermt-* skills.
- When explicitly requested by the user to rebuild the agent environment.
Advantages
- Ensures all hardware and software dependencies are met before agent execution.
- Automates the complex setup of GPU-accelerated container environments.
- Provides clear diagnostic feedback if hardware or container configuration fails.
- Prevents redundant builds by verifying existing image status.
Usage examples
- “Set up the kermt environment.”
- The agent verifies Docker, checks GPU passthrough, builds the image if missing, and runs a smoke test to confirm CUDA availability.
- “Rebuild the kermt image.”
- The agent prompts for confirmation to remove the existing image and then triggers a fresh build process.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/kermt-setup/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip kermt-setup.zip -d .agents/skills
Documentation by NVIDIA
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 hostnvidia-smibefore invoking.Host docker: docker engine + nvidia-container-toolkit. Without the toolkit,
docker run --gpus allwill 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; thedocker imagesSize 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 callkermt_ensure_imagethemselves, 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="$SKILL_DIR/scripts/kermt_container.sh".
Verify docker is installed and the daemon is reachable.
"$HELPER" check_dockerExit 0 → continue. Non-zero → surface the error to the user (typically "docker not on PATH" or "daemon not reachable"); do not attempt step 2.
Verify GPU passthrough works.
"$HELPER" check_gpuThis runs
docker run --rm --gpus all nvidia/cuda:12.6.3-base-ubuntu22.04 nvidia-smiand checks the exit status. Non-zero → tell the user to installnvidia-container-toolkiton the host and confirm a CUDA-capable NVIDIA GPU is visible to the host (nvidia-smion the host should also work). Stop here; without GPU passthrough the kermt image will build but no workflow will run.Build or verify the kermt image.
"$HELPER" ensure_imageIf the image already exists, this returns immediately. Otherwise it builds from
$KERMT_REPO/Dockerfile. Warn the user before invoking that the first build takes ~10–20 minutes on a typical workstation and streams build logs to the console. Do not run this in the background — the user wants to see progress and any build failures must surface immediately.GPU smoke test inside the container. Quote the whole
pythoncommand as a single string — the helper passes args throughbash -c "$*", 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: Trueand a positivedevice_count. Ifcuda_availableisFalsedespite 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.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
Dockerfileorenvironment.ymlas 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-cacheor--pullflag 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_imageConfirm with the user before running docker image rm.
Files
- ATTRIBUTION.mdAdded by AutoGineer829 B
- BENCHMARK.md7.4 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md6.5 KiB
- evals/evals.json4.4 KiB
- scripts/kermt_container.shScript18.9 KiB
- skill-card.md4.4 KiB
- skill.oms.sig4.7 KiB
scripts/kermt_container.sh
#!/usr/bin/env bash
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
# kermt_container.sh — bootstrap helper for the kermt agent skills.
#
# Two ways to use this file:
#
# 1. As a subcommand dispatcher (recommended for skills):
# "$SKILL_DIR/scripts/kermt_container.sh" ensure_image
# "$SKILL_DIR/scripts/kermt_container.sh" run --ckpt /host/ckpt.pt -- python -c 'import torch; print(torch.cuda.device_count())'
# "$SKILL_DIR/scripts/kermt_container.sh" run_detached --name foo --run-dir runs/foo -- bash train.sh
#
# 2. Sourced into a shell or another script, then call the kermt_* functions
# directly:
# source "$SKILL_DIR/scripts/kermt_container.sh"
# kermt_ensure_image
# kermt_run --ckpt /host/ckpt.pt -- python ...
#
# Configuration (override via env vars before invocation):
# KERMT_IMAGE docker image tag (default: kermt:latest)
# KERMT_REPO host path to the kermt repo checkout (default: auto-derived
# from this script's location)
# KERMT_GPUS value passed to docker --gpus (default: all)
#
# Mount flags accepted by kermt_run / kermt_run_detached:
# --data <path> bind to /data (read-only). If <path> is a file,
# its PARENT directory is mounted at /data so
# commands can use /data/<basename>; if <path> is a
# directory, it is mounted at /data directly.
# --ckpt <path> bind to /ckpt (read-only; the path is mounted as-is)
# --vocab-dir <dir> bind to /vocab (read-only)
# --run-dir <dir> bind to /runs (read-write; created on host if missing)
# --model-dir <dir> bind to /model (read-write; created on host if missing).
# Target for released-model downloads (fetch_released_model.py).
#
# Additional flags for kermt_run_detached:
# --name <name> docker container name (default: kermt-<UTC-timestamp>-<pid>)
#
# Everything after `--` is the command passed to the container. It runs inside
# the `kermt` conda environment (the image's default env).
set -o pipefail
: "${KERMT_IMAGE:=kermt:latest}"
: "${KERMT_GPUS:=all}"
# The skill may be installed outside the KERMT checkout. Mount its own helpers
# separately so container commands always execute the distributed skill copy.
_kermt_script_dir="$(cd "$(dirname "${BASH_SOURCE[0]:-$0}")" && pwd)"
_kermt_bundle_dir="$(cd "$_kermt_script_dir/.." && pwd)"
if [[ -z "${KERMT_REPO:-}" ]]; then
for _kermt_start in "$_kermt_script_dir" "$PWD"; do
_kermt_candidate="$_kermt_start"
while [[ "$_kermt_candidate" != / ]]; do
if [[ -f "$_kermt_candidate/main.py" && -d "$_kermt_candidate/kermt" ]]; then
KERMT_REPO="$_kermt_candidate"
break 2
fi
_kermt_candidate="$(dirname "$_kermt_candidate")"
done
done
unset _kermt_start _kermt_candidate
fi
unset _kermt_script_dir
_kermt_require_repo() {
if [[ -z "${KERMT_REPO:-}" || ! -f "$KERMT_REPO/main.py" || ! -d "$KERMT_REPO/kermt" ]]; then
echo "[kermt] Set KERMT_REPO to the KERMT checkout containing main.py and kermt/." >&2
return 1
fi
KERMT_REPO="$(cd "$KERMT_REPO" && pwd)" || return $?
export KERMT_REPO
}
# -----------------------------------------------------------------------------
# Host environment checks
# -----------------------------------------------------------------------------
kermt_check_docker() {
if ! command -v docker >/dev/null 2>&1; then
echo "[kermt] error: docker not found on PATH. Install Docker first." >&2
return 1
fi
if ! docker info >/dev/null 2>&1; then
echo "[kermt] error: docker daemon not reachable. Is the docker service running, and is your user in the 'docker' group?" >&2
return 1
fi
}
kermt_check_system() {
# Probe host system and report GPU presence + VRAM + compute capability +
# driver / CUDA version + disk space. Emits a single JSON document to
# stdout that the calling skill consumes; exits 0 with `ok: false` and a
# populated `gaps` array when anything is below the per-workflow minimum,
# exits 1 only on unexpected internal errors. Uses host nvidia-smi + df +
# host python3 (stdlib only).
python3 - "$KERMT_REPO" "$KERMT_IMAGE" <<'PYEOF'
import json, os, shutil, subprocess, sys
repo, image = sys.argv[1], sys.argv[2]
result = {
"ok": True,
"gpus": [],
"disk": {"path": repo, "free_gb": None, "min_gb": 20},
"host": {"docker": None, "nvidia_smi": None, "container_toolkit": None},
"image": {"tag": image, "present_locally": None},
"gaps": [],
}
def _gap(msg):
result["ok"] = False
result["gaps"].append(msg)
# docker presence
try:
r = subprocess.run(["docker", "info"], capture_output=True, text=True, timeout=10)
result["host"]["docker"] = "ok" if r.returncode == 0 else f"failed: {r.stderr.strip().splitlines()[-1] if r.stderr else 'unknown'}"
if r.returncode != 0:
_gap("docker daemon not reachable (is the service running, and is your user in the 'docker' group?)")
except FileNotFoundError:
result["host"]["docker"] = "not found"
_gap("docker not on PATH; install Docker first")
except Exception as e:
result["host"]["docker"] = f"error: {e}"
_gap(f"docker probe failed: {e}")
# nvidia-smi (host driver)
try:
r = subprocess.run(
["nvidia-smi", "--query-gpu=name,memory.total,compute_cap,driver_version,uuid",
"--format=csv,noheader,nounits"],
capture_output=True, text=True, timeout=10,
)
if r.returncode == 0:
result["host"]["nvidia_smi"] = "ok"
for line in r.stdout.strip().splitlines():
parts = [p.strip() for p in line.split(",")]
if len(parts) >= 5:
try:
vram_mb = int(parts[1])
except ValueError:
vram_mb = None
result["gpus"].append({
"name": parts[0],
"vram_mb": vram_mb,
"compute_cap": parts[2],
"driver": parts[3],
"uuid": parts[4],
})
if not result["gpus"]:
_gap("nvidia-smi succeeded but reported no GPUs")
else:
result["host"]["nvidia_smi"] = "failed"
_gap("nvidia-smi found but failed; is the NVIDIA driver loaded?")
except FileNotFoundError:
result["host"]["nvidia_smi"] = "not found"
_gap("nvidia-smi not on PATH; install the NVIDIA driver")
except Exception as e:
result["host"]["nvidia_smi"] = f"error: {e}"
_gap(f"nvidia-smi probe failed: {e}")
# disk free at the repo location
try:
free_bytes = shutil.disk_usage(repo).free
free_gb = free_bytes // (1024**3)
result["disk"]["free_gb"] = free_gb
if free_gb < result["disk"]["min_gb"]:
_gap(f"disk free at {repo} is {free_gb} GB; need at least {result['disk']['min_gb']} GB for the kermt image")
except Exception as e:
_gap(f"could not check disk space at {repo}: {e}")
# image presence (informational only)
try:
r = subprocess.run(["docker", "image", "inspect", image], capture_output=True, text=True, timeout=10)
result["image"]["present_locally"] = (r.returncode == 0)
except Exception:
result["image"]["present_locally"] = None
# nvidia-container-toolkit probe — only meaningful if both docker and a
# locally-present image are available. Pick kermt:$tag first; fall back to
# the small CUDA base image if that's the only one present; otherwise skip
# (avoid pulling anything).
def _probe_image():
for img in (image, "nvidia/cuda:12.6.3-base-ubuntu22.04"):
r = subprocess.run(["docker", "image", "inspect", img], capture_output=True)
if r.returncode == 0:
return img
return None
probe_img = _probe_image()
if probe_img:
try:
r = subprocess.run(
["docker", "run", "--rm", "--gpus", "all", probe_img, "nvidia-smi"],
capture_output=True, text=True, timeout=60,
)
if r.returncode == 0:
result["host"]["container_toolkit"] = f"ok (probed via {probe_img})"
else:
result["host"]["container_toolkit"] = f"failed (probed via {probe_img})"
_gap("`docker run --gpus all` failed; install nvidia-container-toolkit and ensure the host driver supports it")
except Exception as e:
result["host"]["container_toolkit"] = f"error: {e}"
_gap(f"nvidia-container-toolkit probe failed: {e}")
else:
result["host"]["container_toolkit"] = "skipped (no probe image present locally; run ensure_image first)"
print(json.dumps(result, indent=2))
PYEOF
}
kermt_check_gpu() {
# Probes whether `docker --gpus all` is wired up (nvidia-container-toolkit).
# Image-selection priority (never pulls anything):
# 1) $KERMT_IMAGE if it exists locally,
# 2) else nvidia/cuda:12.6.3-base-ubuntu22.04 if it exists locally,
# 3) else skip with a warning (return 0). The smoke test inside kermt_run
# will catch broken GPU passthrough later anyway.
local probe_img=""
if docker image inspect "$KERMT_IMAGE" >/dev/null 2>&1; then
probe_img="$KERMT_IMAGE"
elif docker image inspect nvidia/cuda:12.6.3-base-ubuntu22.04 >/dev/null 2>&1; then
probe_img="nvidia/cuda:12.6.3-base-ubuntu22.04"
else
echo "[kermt] check_gpu: skipped — neither '$KERMT_IMAGE' nor 'nvidia/cuda:12.6.3-base-ubuntu22.04' is present locally. Run 'ensure_image' first, or this probe will be exercised by the in-container smoke test." >&2
return 0
fi
if ! docker run --rm --gpus all "$probe_img" nvidia-smi >/dev/null 2>&1; then
echo "[kermt] error: 'docker run --gpus all' failed (probe image: $probe_img). Install nvidia-container-toolkit and ensure the host has a CUDA-capable NVIDIA driver." >&2
return 1
fi
}
# -----------------------------------------------------------------------------
# Image build / verification
# -----------------------------------------------------------------------------
kermt_ensure_image() {
_kermt_require_repo || return $?
kermt_check_docker || return $?
if docker image inspect "$KERMT_IMAGE" >/dev/null 2>&1; then
local id
id=$(docker image inspect "$KERMT_IMAGE" --format '{{.Id}}' 2>/dev/null | cut -c1-19)
echo "[kermt] image '$KERMT_IMAGE' already present (${id:-unknown})"
return 0
fi
echo "[kermt] image '$KERMT_IMAGE' not found; building from $KERMT_REPO/Dockerfile"
echo "[kermt] first build typically takes 10-20 minutes on a typical workstation; subsequent runs reuse the cached image"
docker build -t "$KERMT_IMAGE" -f "$KERMT_REPO/Dockerfile" "$KERMT_REPO"
}
# -----------------------------------------------------------------------------
# Mount-flag parser, internal
# -----------------------------------------------------------------------------
# Reads flags from the caller's positional args until it hits '--', appending
# `-v src:dst[:ro]` pairs into the caller-provided array name (passed as $1).
# Returns the number of caller-provided args consumed via _kermt_consumed.
# This is bash-specific (uses nameref via `declare -n`).
_kermt_parse_mounts() {
local -n _out="$1"
shift
_kermt_consumed=0
while [[ $# -gt 0 ]]; do
case "$1" in
--)
return 0
;;
--data)
[[ -e "$2" ]] || { echo "[kermt] --data path not found: $2" >&2; return 1; }
# If the user passes a file, mount its parent directory at /data so
# downstream commands can refer to /data/<basename>. Mounting a
# single file at /data makes the path-as-directory pattern in the
# skill examples (`--csv /data/<basename>`) fail with "not found".
if [[ -d "$2" ]]; then
_out+=("-v" "$(realpath "$2"):/data:ro")
else
_out+=("-v" "$(realpath "$(dirname "$2")"):/data:ro")
fi
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
--ckpt)
[[ -e "$2" ]] || { echo "[kermt] --ckpt path not found: $2" >&2; return 1; }
_out+=("-v" "$(realpath "$2"):/ckpt:ro")
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
--vocab-dir)
[[ -d "$2" ]] || { echo "[kermt] --vocab-dir not found or not a directory: $2" >&2; return 1; }
_out+=("-v" "$(realpath "$2"):/vocab:ro")
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
--run-dir)
mkdir -p "$2" || { echo "[kermt] failed to create --run-dir: $2" >&2; return 1; }
_out+=("-v" "$(realpath "$2"):/runs")
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
--model-dir)
mkdir -p "$2" || { echo "[kermt] failed to create --model-dir: $2" >&2; return 1; }
_out+=("-v" "$(realpath "$2"):/model")
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
*)
return 0
;;
esac
done
}
# -----------------------------------------------------------------------------
# Foreground / detached run
# -----------------------------------------------------------------------------
# Capture host-side git state for the repo and emit `-e KERMT_REPO_COMMIT=…
# -e KERMT_REPO_DIRTY=true|false` flags. Used by the run / run_detached
# wrappers so the runner's run.json manifest gets honest commit info even
# though `git -C /workspace` inside the container fails due to bind-mount
# ownership.
_kermt_git_env_flags() {
local commit="unknown"
local dirty="false"
if command -v git >/dev/null 2>&1 && [[ -d "$KERMT_REPO/.git" ]]; then
local c
c=$(git -C "$KERMT_REPO" rev-parse HEAD 2>/dev/null) && commit="$c"
# `--untracked-files=no` filters out user-private notes (e.g. a CLAUDE.md
# or RELEASE_PLAN_v2.0.md at the repo root) that wouldn't affect
# reproducibility — only modifications to tracked files do.
if [[ -n "$(git -C "$KERMT_REPO" status --porcelain --untracked-files=no 2>/dev/null | head -n 1)" ]]; then
dirty="true"
fi
fi
printf '%s\n%s\n%s\n%s\n' "-e" "KERMT_REPO_COMMIT=$commit" "-e" "KERMT_REPO_DIRTY=$dirty"
}
# Forward HF_TOKEN into the container when it is set, so fetch_released_model.py
# can authenticate to Hugging Face. The current release is public (no token
# needed); this only guards against shared-IP rate limits or a future gated
# repo. Emits nothing when HF_TOKEN is unset.
_kermt_hf_env_flags() {
if [[ -n "${HF_TOKEN:-}" ]]; then
printf '%s\n%s\n' "-e" "HF_TOKEN=$HF_TOKEN"
fi
}
kermt_run() {
kermt_ensure_image || return $?
local mount_args=()
_kermt_parse_mounts mount_args "$@" || return $?
shift "$_kermt_consumed"
if [[ "${1:-}" != "--" ]]; then
echo "[kermt] expected '--' separating mount flags from the command (got '${1:-}')" >&2
return 1
fi
shift
if [[ $# -eq 0 ]]; then
echo "[kermt] no command supplied after '--'" >&2
return 1
fi
local git_args=()
while IFS= read -r line; do git_args+=("$line"); done < <(_kermt_git_env_flags)
local hf_args=()
while IFS= read -r line; do hf_args+=("$line"); done < <(_kermt_hf_env_flags)
docker run --rm --gpus "$KERMT_GPUS" \
--user "$(id -u):$(id -g)" \
-v "$KERMT_REPO:/workspace" \
-v "$_kermt_bundle_dir:/skill:ro" \
"${mount_args[@]}" \
-w /workspace \
-e KERMT_REPO=/workspace \
-e PYTHONPATH=/workspace \
-e HOME=/tmp/kermt-home \
"${git_args[@]}" \
"${hf_args[@]}" \
"$KERMT_IMAGE" \
conda run -n kermt --no-capture-output bash -c "$*"
}
kermt_run_detached() {
kermt_ensure_image || return $?
local name=""
local mount_args=()
# Pull --name out first, then let the shared mount parser handle the rest.
while [[ $# -gt 0 ]]; do
case "$1" in
--name) name="$2"; shift 2 ;;
--) break ;;
--data|--ckpt|--vocab-dir|--run-dir|--model-dir) break ;;
*) break ;;
esac
done
_kermt_parse_mounts mount_args "$@" || return $?
shift "$_kermt_consumed"
if [[ "${1:-}" != "--" ]]; then
echo "[kermt] expected '--' separating mount flags from the command (got '${1:-}')" >&2
return 1
fi
shift
if [[ $# -eq 0 ]]; then
echo "[kermt] no command supplied after '--'" >&2
return 1
fi
if [[ -z "$name" ]]; then
name="kermt-$(date -u +%Y%m%dT%H%M%SZ)-$$"
fi
local cid
local git_args=()
while IFS= read -r line; do git_args+=("$line"); done < <(_kermt_git_env_flags)
local hf_args=()
while IFS= read -r line; do hf_args+=("$line"); done < <(_kermt_hf_env_flags)
cid=$(docker run -d --gpus "$KERMT_GPUS" \
--user "$(id -u):$(id -g)" \
--name "$name" \
-v "$KERMT_REPO:/workspace" \
-v "$_kermt_bundle_dir:/skill:ro" \
"${mount_args[@]}" \
-w /workspace \
-e KERMT_REPO=/workspace \
-e PYTHONPATH=/workspace \
-e HOME=/tmp/kermt-home \
"${git_args[@]}" \
"${hf_args[@]}" \
"$KERMT_IMAGE" \
conda run -n kermt --no-capture-output bash -c "$*") || return $?
echo "[kermt] container started: name=$name id=$cid"
echo "[kermt] follow logs: docker logs -f $name"
echo "[kermt] wait for exit: docker wait $name"
echo "[kermt] stop: docker stop $name"
echo "$cid"
}
# -----------------------------------------------------------------------------
# Subcommand dispatch when invoked directly (not sourced)
# -----------------------------------------------------------------------------
if [[ "${BASH_SOURCE[0]:-$0}" == "${0}" ]]; then
cmd="${1:-}"; shift || true
case "$cmd" in
check_docker) kermt_check_docker "$@" ;;
check_gpu) kermt_check_gpu "$@" ;;
check_system) kermt_check_system "$@" ;;
ensure_image) kermt_ensure_image "$@" ;;
run) kermt_run "$@" ;;
run_detached) kermt_run_detached "$@" ;;
""|-h|--help)
cat >&2 <<EOF
usage: $0 <subcommand> [args...]
Subcommands:
check_docker Verify docker is installed and the daemon is reachable.
check_gpu Verify 'docker --gpus all' works (nvidia-container-toolkit).
check_system Emit a JSON probe of host GPU + VRAM + compute_cap +
driver + disk space + container toolkit + image presence.
Exits 0 with ok=false + a 'gaps' list when anything's
below the per-workflow minimum.
ensure_image Build kermt:latest from \$KERMT_REPO/Dockerfile if missing.
run [flags] -- ... Run a command inside the container (foreground, --rm).
run_detached [flags] -- ...
Run detached; prints container name + id + log hint.
Mount flags (for run / run_detached):
--data <path> bind to /data (read-only)
--ckpt <path> bind to /ckpt (read-only)
--vocab-dir <dir> bind to /vocab (read-only)
--run-dir <dir> bind to /runs (read-write; created on host if missing)
--model-dir <dir> bind to /model (read-write; released-model download target)
Additional flags for run_detached:
--name <name> container name (default: kermt-<timestamp>-<pid>)
Environment overrides:
KERMT_IMAGE default kermt:latest
KERMT_REPO checkout path; otherwise discovered above the skill or working directory
KERMT_GPUS default all
EOF
exit 1
;;
*)
echo "[kermt] unknown subcommand: $cmd" >&2
echo "[kermt] run '$0 --help' for usage" >&2
exit 1
;;
esac
fi
Requirements and compatibility
Written for: Claude Code, OpenAI Codex
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs a command-line tool, Needs Python
The author says: Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- NVIDIA
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/bionemo-kermt-setup · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download kermt-setup.zip