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Kermt Embed

NVIDIAOfficialPublished by NVIDIA

  • Skill
  • Workflow
  • Science and research
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized embedding extraction and write model bundles, per-readout .npy embeddings, canonical SMILES, and validity arrays to user-selected host directories.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The kermt-embed skill provides a structured, containerized workflow for generating per-molecule embeddings from KERMT model checkpoints. It manages the entire pipeline, including system validation, data preparation, checkpoint verification, and execution. The skill supports both custom checkpoints and the automatic, consent-based download of pretrained NVIDIA KERMT models. It outputs standardized .npy embedding files for four distinct readouts, along with canonical SMILES and validity metadata, ensuring consistent results for downstream chemical analysis.

What it does

  • Validates system requirements and container environment for embedding extraction.
  • Automates the download and verification of pretrained KERMT model bundles.
  • Cleans and prepares input SMILES data from CSV files for model processing.
  • Executes embedding extraction on a single GPU using specified or default checkpoints.
  • Generates standardized .npy embedding arrays and metadata for molecular analysis.
  • Provides replayable run manifests for reproducibility.

When to use it

  • When you need to generate numerical vector representations for a set of molecules.
  • When you want to use a pretrained KERMT model to featurize chemical data.
  • When you have a custom encoder-bearing KERMT checkpoint and need to extract embeddings from it.
  • When you require a reproducible, containerized pipeline for molecular embedding tasks.

Advantages

  • Ensures consistent environment configuration via containerization.
  • Automates complex multi-step validation of checkpoints and input data.
  • Provides clear, standardized output formats for downstream integration.
  • Includes built-in safety checks for model downloads and hardware compatibility.

Usage examples

“Extract embeddings for the molecules in my_molecules.csv using the default pretrained model.”
The agent will prompt for consent to download the NV-KERMT-70M-v2 model, validate the CSV, and generate embedding files in a new run directory.
“Run embedding extraction on molecules.csv using my local checkpoint at /path/to/model.pt.”
The agent validates the local checkpoint and input CSV, then executes the extraction workflow using the provided model file.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/kermt-embed/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip kermt-embed.zip -d .agents/skills

Documentation by NVIDIA

kermt-embed

Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint. The skill is the workflow orchestrator: validate ckpt, validate CSV, clean SMILES, launch the runner blocking, return the per-readout .npy files.

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/; defaults are bundled in config/. See Released models for checkpoint bundle requirements.

Downloads and local outputs

The optional released-model branch reads config/released_model.json for the Hugging Face repository, pinned revision, and filenames. The bundled scripts/fetch_released_model.py downloads the model bundle over HTTPS into the host directory the user selects. Public models work without credentials; if HF_TOKEN is set, the container helper forwards it for Hugging Face authentication. Prepared data, logs, and workflow results go into the chosen run directory.

Hardware requirements

  • GPUs: 1 (single-GPU).

  • VRAM: ≥ 4 GB for the default batch_size 64.

  • Disk: depends on output size — roughly a few MB per 1k molecules at hidden 800 per readout, so ~10–20 MB per 1k molecules across the 4 readouts. Plus a small canonical_smiles.npy + validity.npy per run.

  • Driver / CUDA: any host supporting CUDA 12.6.

Inputs

Required:

  • --csv <path> — SMILES CSV. First column is smiles; other columns are ignored (no targets needed).

Checkpoint (optional — defaults to the released model if omitted):

  • --ckpt <path> — any encoder-bearing checkpoint. Grover_base, cmim, hybrid, and finetuned ckpts are all accepted. The validator only refuses ckpts with no encoder. If omitted, the skill offers to download the released pretrained hybrid model nvidia/NV-KERMT-70M-v2 and embed with it — see "Resolve & validate the checkpoint" (workflow step 3).

  • --pretrained-release — explicit opt-in to use the released model without the interactive prompt (for non-interactive / agent runs). Mutually exclusive with --ckpt.

  • --model-dir <dir> — where to save the downloaded bundle (default $KERMT_REPO/models/NV-KERMT-70M-v2/). An already-complete bundle there is reused, not re-downloaded.

Optional:

  • --batch-size N — override the configured default (64).

  • --gpus 0 — single GPU id (default 0).

  • --from-prepare <dir> — skip the prepare step and reuse an existing prepare_data.json in <dir>.

Workflow

Let $KERMT_REPO be the path to your kermt repo checkout.

  1. Pre-flight: container + system probe.

    "$SKILL_DIR/scripts/kermt_container.sh" check_system
  2. Compute run directory.

    RUN_DIR=$KERMT_REPO/runs/embed_$(date -u +%Y-%m-%dT%H-%M-%SZ)
  3. Resolve & validate the checkpoint.

    Resolve — only if --ckpt was omitted. Default to the released pretrained hybrid model nvidia/NV-KERMT-70M-v2:

    • Consent gate. Unless --pretrained-release was passed, ask the user: "No checkpoint given — download the released model nvidia/NV-KERMT-70M-v2 (NVIDIA Open Model License, https://huggingface.co/nvidia/NV-KERMT-70M-v2) and embed with it? [y/N]". Never download without an explicit yes (or --pretrained-release). If both --ckpt and --pretrained-release are given, abort — they conflict.

    • Save location. Default $KERMT_REPO/models/NV-KERMT-70M-v2/; honor --model-dir <dir> if given. An already-complete bundle is reused.

    • Download (foreground; ~282 MB on first fetch):

      "$SKILL_DIR/scripts/kermt_container.sh" run --model-dir <save-dir> -- \
          "python /skill/scripts/fetch_released_model.py --out /model"

      Parse the JSON; abort on ok: false (surface errors). On success set <user-ckpt> = <save-dir>/kermt_contrastive_v2.0.pt.

    Validate the resolved (or user-provided) ckpt:

    "$SKILL_DIR/scripts/kermt_container.sh" run --ckpt <user-ckpt> -- \
        "python /skill/scripts/check_checkpoint.py --mode embed --ckpt /ckpt"

    Parse JSON. Abort on ok: false. The validator only refuses encoder-less ckpts (rare).

  4. Validate the data.

    "$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> -- \
        "python /skill/scripts/check_data.py --mode embed --csv /data/<basename>"
  5. Prepare the data (clean-only — no features step).

    "$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> --run-dir $RUN_DIR -- \
        "python /skill/scripts/prepare_data.py --mode embed \\
             --csv /data/<basename> --out /runs/data"

    Outputs land at $RUN_DIR/data/prepare_data.json with a single clean_csv path. task/extract_embeddings.py featurizes from SMILES on the fly.

  6. Launch the runner (blocking).

    "$SKILL_DIR/scripts/kermt_container.sh" run \\
        --ckpt <user-ckpt> --run-dir $RUN_DIR -- \\
        "python /skill/scripts/run_extract_embeddings.py \\
             --ckpt /ckpt \\
             --prepare-manifest /runs/data/prepare_data.json \\
             --out /runs \\
             [--gpus 0 --batch-size N]"
  7. Report to the user.

    • Embeddings directory: $RUN_DIR/out/

      • atom_from_atom.npy, bond_from_atom.npy, atom_from_bond.npy, bond_from_bond.npy (the 4 standard readouts; each shape (N_rows, hidden_size))

      • metadata.pkl — pickle of a dict containing canonical_smiles (RDKit-canonicalized SMILES per row), valid (boolean per-row: did RDKit parse it), plus other run metadata.

    • Manifest: $RUN_DIR/run.json

    • Log: $RUN_DIR/logs/embed.log

Hard rules

  • Never download the released model without consent. When --ckpt is omitted, download nvidia/NV-KERMT-70M-v2 only after an explicit user "yes" or an explicit --pretrained-release flag. --ckpt and --pretrained-release are mutually exclusive.

  • Never modify the user's ckpt. The runner reads-only via task/extract_embeddings.py's --checkpoint <path> flag.

  • Arch comes from the ckpt. No --hidden-size flag etc. on this runner; task/extract_embeddings.py reads arch from the ckpt's saved_args.

Common errors

  • prepare_data manifest is missing required output 'clean_csv' → prepare ran with --skip-clean but no source CSV given. Re-run prepare without it.

  • --gpus '0,1' is single-GPU only → pass a single id.

Replayability

$(jq -r .cmd_replay $RUN_DIR/run.json)

If ok_to_replay: false (dirty kermt repo worktree at launch time), pin the commit via repo.commit and git checkout it first.

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 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-embed · 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-embed.zip