Kermt Embed
NVIDIAOfficialPublished by NVIDIA
- Skill
- Workflow
- Science and research
- Apache-2.0
- Medium risk
- Has scripts
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
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/kermt-embed/downloadUnzip 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 800per readout, so ~10–20 MB per 1k molecules across the 4 readouts. Plus a smallcanonical_smiles.npy+validity.npyper run.Driver / CUDA: any host supporting CUDA 12.6.
Inputs
Required:
--csv <path>— SMILES CSV. First column issmiles; 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 existingprepare_data.jsonin<dir>.
Workflow
Let $KERMT_REPO be the path to your kermt repo checkout.
Pre-flight: container + system probe.
"$SKILL_DIR/scripts/kermt_container.sh" check_systemCompute run directory.
RUN_DIR=$KERMT_REPO/runs/embed_$(date -u +%Y-%m-%dT%H-%M-%SZ)Resolve & validate the checkpoint.
Resolve — only if
--ckptwas omitted. Default to the released pretrained hybrid model nvidia/NV-KERMT-70M-v2:Consent gate. Unless
--pretrained-releasewas 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--ckptand--pretrained-releaseare 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(surfaceerrors). On success set<user-ckpt> = <save-dir>/kermt_contrastive_v2.0.pt.
Validate the resolved (or user-provided) ckpt:
"$SKILL_DIR/scripts/kermt_container.sh" run --ckpt <user-ckpt> -- \ "python /skill/scripts/check_checkpoint.py --mode embed --ckpt /ckpt"Parse JSON. Abort on
ok: false. The validator only refuses encoder-less ckpts (rare).Validate the data.
"$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> -- \ "python /skill/scripts/check_data.py --mode embed --csv /data/<basename>"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
$RUN_DIR/data/prepare_data.jsonwith a singleclean_csvpath.task/extract_embeddings.pyfeaturizes from SMILES on the fly.Launch the runner (blocking).
"$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]"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 containingcanonical_smiles(RDKit-canonicalized SMILES per row),valid(boolean per-row: did RDKit parse it), plus other run metadata.
Manifest:
$RUN_DIR/run.jsonLog:
$RUN_DIR/logs/embed.log
Hard rules
Never download the released model without consent. When
--ckptis omitted, downloadnvidia/NV-KERMT-70M-v2only after an explicit user "yes" or an explicit--pretrained-releaseflag.--ckptand--pretrained-releaseare 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-sizeflag etc. on this runner;task/extract_embeddings.pyreads arch from the ckpt's saved_args.
Common errors
prepare_data manifest is missing required output 'clean_csv'→ prepare ran with--skip-cleanbut 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 $RUN_DIR/run.json)If ok_to_replay: false (dirty kermt repo worktree at launch time), pin
the commit via repo.commit and git checkout it first.
Files
- ATTRIBUTION.mdAdded by AutoGineer829 B
- BENCHMARK.md7.6 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md7.5 KiB
- config/defaults_embed.json718 B
- config/released_model.json398 B
- evals/evals.json6.8 KiB
- references/released-models.md1.6 KiB
- scripts/_utils.pyScript14.1 KiB
- scripts/check_checkpoint.pyScript20.1 KiB
- scripts/check_data.pyScript11.9 KiB
- scripts/fetch_released_model.pyScript7.9 KiB
- scripts/kermt_container.shScript18.9 KiB
- scripts/prepare_data.pyScript36.5 KiB
- scripts/run_extract_embeddings.pyScript8.8 KiB
- skill-card.md4.2 KiB
- skill.oms.sig6.8 KiB
scripts/kermt_container.sh
#!/usr/bin/env bash
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
# kermt_container.sh — bootstrap helper for the kermt agent skills.
#
# Two ways to use this file:
#
# 1. As a subcommand dispatcher (recommended for skills):
# "$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 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-embed · 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-embed.zip