Kermt Infer
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-infer skill provides a structured, containerized workflow for running molecular property predictions using finetuned KERMT checkpoints. It automates the end-to-end process, including system validation, checkpoint verification, data cleaning, RDKit feature generation, and model execution. By enforcing strict validation steps, it ensures that only compatible finetuned checkpoints are used, preventing common errors associated with pre-trained models or malformed input data.
What it does
- Validates that the provided checkpoint contains necessary task FFN heads.
- Cleans input SMILES data and generates RDKit 2D features.
- Executes model inference within a controlled, GPU-accelerated container environment.
- Generates a comprehensive run manifest including command replay and configuration details.
- Provides clear error reporting for data or checkpoint compatibility issues.
When to use it
- When you need to perform inference on a batch of molecules using a previously finetuned KERMT model.
- When you want to ensure data consistency and feature normalization before running predictions.
- When you require a reproducible, logged execution of a molecular prediction task.
- When you need to verify if a checkpoint is ready for inference or requires further finetuning.
Advantages
- Automates complex data preparation and validation steps.
- Ensures reproducibility through detailed run manifests and command replay capabilities.
- Prevents runtime failures by pre-validating checkpoints and input CSVs.
- Maintains integrity of the original checkpoint by using symlinked execution paths.
Usage examples
- “Run inference on my_molecules.csv using the finetuned checkpoint at ./models/my_model.ckpt”
- The skill validates the checkpoint and CSV, prepares the features, runs the inference in the container, and returns the path to the predictions CSV.
- “Perform inference with a batch size of 64 using my_model.ckpt”
- The skill executes the inference workflow using the specified batch size, overriding the default configuration.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/kermt-infer/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip kermt-infer.zip -d .agents/skills
Documentation by NVIDIA
kermt-infer
Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill is the workflow orchestrator: validate ckpt, validate CSV, prepare data, launch the runner blocking, return the predictions CSV.
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/.
Hardware requirements
GPUs: 1 (single-GPU). Multi-GPU inference is not currently supported.
VRAM: ≥ 4 GB for the default
batch_size 32.Disk: a few hundred MB per run (cleaned CSV + features + predictions).
Driver / CUDA: any host supporting CUDA 12.6 (the kermt image base).
Inputs
Required:
--ckpt <path>— finetuned checkpoint (must have task FFN heads). The validator refuses pretrain ckpts with a redirect tokermt-finetune.--csv <path>— SMILES-only CSV. First column issmiles; other columns are ignored.
Optional:
--batch-size N— override the configured default (32).--seed N— random seed for inference (deterministic featurization paths).--gpus 0— single GPU id (default 0). Multi-GPU rejected.--from-prepare <dir>— skip the prepare step and reuse an existingprepare_data.jsonin<dir>.
Workflow
Let $KERMT_REPO be the path to your kermt repo checkout, and assume
kermt-setup has built kermt:latest.
Pre-flight: ensure container + system probe.
"$SKILL_DIR/scripts/kermt_container.sh" check_systemRefuse to proceed on
ok: false.Compute run directory.
RUN_DIR=$KERMT_REPO/runs/infer_$(date -u +%Y-%m-%dT%H-%M-%SZ)Validate the checkpoint.
"$SKILL_DIR/scripts/kermt_container.sh" run --ckpt <user-ckpt> -- \ "python /skill/scripts/check_checkpoint.py --mode inference --ckpt /ckpt"Parse the JSON. Abort on
ok: false. The validator rejects pretrain ckpts (has_task_ffn: false) with a redirect tokermt-finetune.Validate the data.
"$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> -- \ "python /skill/scripts/check_data.py --mode inference --csv /data/<basename>"Abort on
ok: false.Prepare the data.
"$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> --run-dir $RUN_DIR -- \ "python /skill/scripts/prepare_data.py --mode inference \\ --csv /data/<basename> --out /runs/data"Outputs land at
$RUN_DIR/data/prepare_data.jsonwithclean_csv+clean_npzpaths (rdkit_2d_normalized features).Launch the runner (blocking).
"$SKILL_DIR/scripts/kermt_container.sh" run \\ --ckpt <user-ckpt> --run-dir $RUN_DIR -- \\ "python /skill/scripts/run_inference.py \\ --ckpt /ckpt \\ --prepare-manifest /runs/data/prepare_data.json \\ --out /runs \\ [--gpus 0 --batch-size N --seed N]"Returns the predictions CSV path on success.
Report to the user. Output a short summary:
Predictions:
$RUN_DIR/out/predictions.csv(smiles + per-target columns)Manifest:
$RUN_DIR/run.json(cmd_replay + image digest + applied args)Log:
$RUN_DIR/logs/inference.logRow count: <N> molecules predicted across <K> targets
Hard rules
Never modify the user's ckpt. The runner symlinks the ckpt into a unique
<out>/ckpt_link/subdir somain.py predict --checkpoint_dirpicks it up; the source file stays untouched.Arch comes from the ckpt, never from CLI/defaults. The runner records the validator's arch block in
run.jsonbut does not pass arch flags intomain.py predict— predict reads them from the loaded ckpt's saved_args.Single-GPU only. Multi-GPU inference is not currently supported.
Echo applied defaults. The
args_appliedfield ofrun.jsonrecords every flag's value + source (user / default-config). Surface a short summary of any default-filled flag.
Common errors
inference requires a finetuned ckpt with task FFN heads→ ckpt is a pretrain ckpt; usekermt-finetunefirst.prepare_data manifest reports ok=False→ check the manifesterrorsfor the failed step (typically clean_smiles or save_features).could not convert string to float: '<value>'from save_features or main.py predict → input CSV has a non-numeric passthrough column (e.g. a 'split' label). The prep step now strips the CSV to SMILES-only at inference; if this error still surfaces, the CSV is being read by a runner that bypassed prepare_data. Re-run via the skill, notmain.pydirectly.--gpus '0,1' is single-GPU only→ pass a single id.
Replayability
The run.json cmd_replay field is a single-line command that re-runs the
inference with the same inputs. To replay inside the kermt container:
$(jq -r .cmd_replay $RUN_DIR/run.json)If ok_to_replay: false (dirty kermt repo worktree at launch time), pin
the commit via repo.commit and git checkout it first.
Files
- ATTRIBUTION.mdAdded by AutoGineer829 B
- BENCHMARK.md7.4 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.9 KiB
- config/defaults_inference.json528 B
- evals/evals.json5.5 KiB
- scripts/_utils.pyScript14.1 KiB
- scripts/check_checkpoint.pyScript20.1 KiB
- scripts/check_data.pyScript11.9 KiB
- scripts/kermt_container.shScript18.9 KiB
- scripts/prepare_data.pyScript36.5 KiB
- scripts/run_inference.pyScript10.5 KiB
- skill-card.md4.4 KiB
- skill.oms.sig6.1 KiB
scripts/kermt_container.sh
#!/usr/bin/env bash
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
# kermt_container.sh — bootstrap helper for the kermt agent skills.
#
# Two ways to use this file:
#
# 1. As a subcommand dispatcher (recommended for skills):
# "$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-infer · 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 or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download kermt-infer.zip