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Kermt Setup

NVIDIAOfficialPublished by NVIDIA

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Bootstrap the KERMT agent environment — verify host docker + nvidia-container-toolkit, build the kermt:latest image from the repo's Dockerfile if it doesn't yet exist, and run a GPU smoke test inside the container. Every other kermt-* skill depends on this; invoke it first.
— the description by NVIDIA

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

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/kermt-setup/download
  2. Unzip 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 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.

Files

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.

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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

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download kermt-setup.zip