Generated banner for Kermt Continue Pretrain
Generated logo for Kermt Continue Pretrain

Kermt Continue Pretrain

NVIDIAOfficialPublished by NVIDIA

  • Skill
  • Workflow
  • Data and AI
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Continue KERMT pretraining on a custom SMILES corpus with a grover_base, cmim, or hybrid checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized training and write model bundles, prepared data, logs, and checkpoints 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-continue-pretrain skill manages the end-to-end workflow for continuing the pretraining of KERMT models (grover_base, cmim, or hybrid). It handles input validation, data preparation, and the execution of training runs within a containerized environment. The skill supports both fresh-schedule training and true resumption of interrupted runs, ensuring that model architecture and vocabulary constraints are strictly maintained.

What it does

  • Validates system requirements and GPU availability for training.
  • Resolves and validates user-provided or released KERMT checkpoints.
  • Prepares custom SMILES data for training while enforcing vocabulary consistency.
  • Configures and launches detached training runs with hyperparameter overrides.
  • Provides progress monitoring and logging for long-running training tasks.
  • Generates a manifest for reproducible training runs.

When to use it

  • When you need to continue training an existing KERMT checkpoint on a new SMILES dataset.
  • When you want to perform a fresh-schedule continuation of a pretrained model.
  • When you need to resume an interrupted training run from a specific checkpoint.
  • When you want to leverage NVIDIA-released pretrained hybrid models for further training.

Advantages

  • Ensures architectural and vocabulary integrity by validating checkpoints before training.
  • Automates complex data preparation and container orchestration.
  • Supports flexible hyperparameter tuning while preventing invalid configuration overrides.
  • Provides clear error handling and redirection to appropriate workflows for incompatible models.

Usage examples

“Continue pretraining the hybrid model on my custom_data.csv.”
The agent will prompt for consent to download the released hybrid model, validate the CSV, prepare the data, and launch the training process.
“Resume my interrupted pretraining run from /path/to/checkpoint.pt.”
The agent will validate the checkpoint for resume compatibility and restart the training process from the exact state saved in the checkpoint.

Install

  1. Download the skill as a ZIP:

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

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

Documentation by NVIDIA

kermt-continue-pretrain

Continue pretraining from a user-supplied KERMT checkpoint (grover_base / cmim / hybrid). The skill is the workflow orchestrator: it validates inputs, prepares the corpus, launches the runner, and returns a run directory.

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–N CUDA-capable NVIDIA GPUs. The runner auto-detects via torch.cuda.device_count(); --gpus 0,2 overrides. On a single GPU the runner falls back to --batch_size 32 --save_interval 500; on multi-GPU it uses the defaults_pretrain.json values (currently batch_size 256). Note: --gpus N uses torch.cuda indexing, which can differ from nvidia-smi's display order on multi-GPU hosts (PCI bus vs. CUDA enumeration). To target a specific physical GPU, set CUDA_VISIBLE_DEVICES before invoking, or run python -c "import torch; print([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])" to confirm which device you're picking.

  • VRAM: the default --batch-size 256 is sized for A100-class hardware (80 GB VRAM). On smaller GPUs, downscale to avoid OOM:

    GPU classVRAMSuggested --batch-size
    L4, T4, V100 16 GB16–24 GB32–64
    A100 40 GB, L40, A4040–48 GB128
    A100 80 GB, H100, H20080 GB256 (default)

    These are rough starting points — pass --batch-size N to override.

  • Disk: tens of GB depending on corpus size + epochs (each checkpoint is several hundred MB).

  • Driver / CUDA: any host supporting CUDA 12.6 (the kermt image base). kermt-setup validates this up-front.

Inputs

Required:

  • --csv <path> — the pretrain CSV (single column smiles). If you have separate train/val CSVs, pass --val-csv <path> too.

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

  • --ckpt <path> — the input pretrain checkpoint to continue from. Must be a grover_base (with vocab heads), cmim, or hybrid ckpt; the validator rejects everything else with a redirect to the correct workflow. If omitted, the skill offers to download the released pretrained hybrid model nvidia/NV-KERMT-70M-v2 and continue-pretrain from it — see "Resolve & validate the checkpoint" (workflow step 3). The released bundle ships its three vocab files alongside the ckpt, so the authoritative-vocab pass-through (step 5) works automatically.

  • --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:

  • --val-csv <path> — separate validation CSV. Without it, the prep step auto-splits the input by --val-frac 0.1 (random shuffle with --seed).

  • --epochs N / --batch-size N / --init-lr F / --max-lr F / --final-lr F / --warmup-epochs F / --weight-decay F / --dropout F / --save-interval N / --seed N — training-hyperparameter overrides. Anything not given is filled from config/defaults_pretrain.json.

  • --vocab-loss-weight F (hybrid only) / --latent-dim N / --contrastive-temperature F (cmim and hybrid only) — loss / decoder overrides.

  • --wandb-project NAME / --wandb-run-name NAME — optional Weights & Biases logging. When --wandb-project is set, rank 0 logs train/val losses; the run name is honored only alongside a project. Off by default. (Independent of the ckpt's wandb_run_id continuity handling under --resume.)

  • --resume — see "Modes" section below.

  • --gpus 0,2 — restrict to a GPU subset. Default uses all visible GPUs.

  • --from-prepare <dir> — skip the prepare step and reuse an existing prepare_data.json in <dir>. Useful when iterating on hyperparameters.

Modes

The runner has two modes for ingesting the input ckpt, dispatched on whether --resume is set. Pick based on intent:

Default (fresh-schedule continue-pretrain)

Use when: you have a finished pretrain ckpt and want to continue training it — on a new corpus, with a different objective, or just for more epochs than its original plan. The previous training's step counter and schedule shape are no longer relevant; you want a new learning-rate schedule for the new run.

What gets loaded from the ckpt:

  • ✓ Model weights (encoder + vocab heads + contrast head + decoder, whatever is there)

  • ✓ Optimizer state (Adam's running m1/m2 moments — warm-starts the new schedule so the first few hundred steps aren't dominated by noisy gradient-estimate startup)

  • ✗ Scheduler step counter (reset to 0)

  • ✗ Epoch counter (reset to 0)

  • ✗ Batch counter (reset to 0)

  • ✗ wandb run id (new wandb run, not a continuation)

Schedule shape (init/max/final LR, warmup epochs, total epochs): from your CLI args or defaults_pretrain.json. A fresh NoamLR is constructed from these values and starts at step 0.

--resume (true resume)

Use when: a previous run was interrupted (crash, OOM, Ctrl-C) and you want to pick up exactly where it left off — same dataset, same schedule, same training trajectory.

What gets loaded from the ckpt: everything in the save_model_for_restart format. Model weights + optimizer state + scheduler_step + epoch + batch_idx + wandb_run_id are all restored. The new run continues from the saved step in the saved schedule (which is recovered from the ckpt's saved_args). Mid-epoch resume works too — pretrain_ddp.py's sampler skip-count picks up at the saved batch index within the saved epoch.

Schedule shape: inherited from the ckpt's saved_args. CLI overrides of any schedule flag (--epochs / --warmup-epochs / --init-lr / --max-lr / --final-lr) are rejected with a hard error — pure resume means pure resume; if you want to change the schedule, drop --resume and start a fresh-schedule run.

Requirements: the ckpt must have been saved via save_model_for_restart (i.e., carry optimizer / scheduler_step / epoch / batch_idx keys). If any of these is missing, the runner errors with a clear message and suggests dropping --resume.

The default mode is the right choice ~90% of the time. Reach for --resume only when you genuinely need to continue a single interrupted training run.

Workflow

Let $KERMT_REPO be the path to your kermt repo checkout, and assume kermt-setup has already built kermt:latest. All paths below are on the host; the helper bind-mounts them at known container paths.

  1. Pre-flight: ensure container + system probe.

    "$SKILL_DIR/scripts/kermt_container.sh" check_system | python -c "
    import json, sys; d = json.load(sys.stdin)
    if not d['ok']:
        print('System check failed:', d['gaps']); sys.exit(1)
    print(f'OK: {len(d[\"gpus\"])} GPU(s); {d[\"disk\"][\"free_gb\"]} GB free; CUDA via container toolkit')
    "

    Surface any gaps to the user. Refuse to proceed if ok: false.

  2. Compute run directory.

    RUN_DIR=$KERMT_REPO/runs/continue-pretrain_$(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 continue-pretrain from 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. The bundle's three vocab files land in <save-dir> too, so step 5's --vocab-dir auto-detection (which looks in the ckpt's parent directory) finds them with no extra work.

    Validate the resolved (or user-provided) ckpt:

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

    Parse the JSON. Abort on ok: false, showing the error verbatim. The error message redirects the user to kermt-add-cmim-pretrain for encoder-only ckpts, or to kermt-finetune for finetuned ckpts.

  4. Validate the data.

    "$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> -- \
        "python /skill/scripts/check_data.py --mode pretrain --csv /data/<basename>"

    Abort on ok: false.

  5. Prepare the data (skip if --from-prepare given). Pass the ckpt's vocab through. Look in the ckpt's parent directory for the conventional pretrain_atom_vocab.{json,pkl}, pretrain_bond_vocab.{json,pkl}, and pretrain_smiles_vocab.pkl files (the bundling convention for released models; see references/released-models.md). If all three are present, auto-pass via --vocab-dir <ckpt_parent_dir>. If only some are present, pass them via explicit flags (--atom-vocab, --bond-vocab, --smiles-vocab). If none are present, ask the user for --vocab-dir — or refuse to proceed, because rebuilding a fresh vocab from the new corpus would silently mismatch the ckpt's vocab heads (the ckpt's vocab is authoritative for continue-pretrain).

    Note the two-layer mount pattern: pass the host directory to kermt_container.sh --vocab-dir (which mounts it at /vocab inside the container), and reference /vocab from the inner prepare_data.py command. The same pattern applies to every host path the inner command needs to read (--data <host-csv> → /data/<basename>, --ckpt <host-ckpt> → /ckpt).

    VOCAB_DIR=$(dirname <user-ckpt>)
    "$SKILL_DIR/scripts/kermt_container.sh" run \
        --data <user-csv> --vocab-dir $VOCAB_DIR --run-dir $RUN_DIR -- \
        "python /skill/scripts/prepare_data.py --mode pretrain \\
             --csv /data/<basename> --out /runs/data \\
             --vocab-dir /vocab \\
             [--val-csv /data/<val-basename>] [--val-frac 0.1] [--seed 0]"

    Outputs land at $RUN_DIR/data/prepare_data.json with vocab_source: "user_provided". The runner step 7 will verify the vocab files' entry counts match the ckpt's vocab-head sizes and refuse to launch on mismatch.

  6. Estimate runtime + confirm with user.

    • Pretrain wall time depends on corpus size × epochs × GPU count.

    • Tell the user the estimate; ask "proceed?" unless --yes flag was given (agent-non-interactive case).

    • Example estimate template: ~N hours on K GPUs for E epochs over M molecules (~steps/epoch × seconds/step).

  7. Launch the runner detached.

    "$SKILL_DIR/scripts/kermt_container.sh" run_detached \\
        --name kermt-continue-pretrain-<ts> \\
        --ckpt <user-ckpt> --run-dir $RUN_DIR -- \\
        "python /skill/scripts/run_pretrain_local.py \\
             --ckpt /ckpt \\
             --prepare-manifest /runs/data/prepare_data.json \\
             --out /runs \\
             [--epochs N --batch-size N --init-lr F ...]"

    Returns the container name + id + log file path.

  8. Report to the user. Output a short summary:

    • Container name + id

    • $RUN_DIR/run.json (the manifest with cmd_replay + image digest)

    • Log file: $RUN_DIR/logs/pretrain_ddp.log

    • TensorBoard: $RUN_DIR/logs/tb (open with tensorboard --logdir $RUN_DIR/logs/tb)

    • Suggest invoking kermt-monitor <RUN_DIR> to check progress.

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 input ckpt. The runner symlinks it into the save_dir; the symlink is what pretrain_ddp.py auto-resumes from. The source file stays untouched.

  • Never silently override arch. If the user passes a --hidden-size etc. that doesn't match the ckpt-derived value, the runner aborts loudly. Arch params come from the ckpt, period.

  • Never block on the long-running pretrain itself. The runner is invoked via run_detached; the skill returns immediately after step 8. Use kermt-monitor for progress.

  • Echo applied defaults back to the user. The args_applied field of run.json records every flag's value + source (user / default-config / auto-1gpu / auto-multi-gpu). Skill should surface a summary of any flag not user-specified so the user knows what was assumed.

Common errors

  • model_type='finetuned' rejected → the ckpt is a downstream finetune, not a pretrain. The error redirects to the relevant workflow.

  • grover_base ckpt has no vocab head → encoder-only ckpt (e.g. the original-grover grover_base.pt). The error redirects to kermt-add-cmim-pretrain.

  • prepare_data manifest is missing required outputs → user passed --from-prepare to a directory where prepare was run with --skip-vocab or --skip-split. Re-run prepare without those flags.

  • --gpus all not available → install nvidia-container-toolkit; check kermt_container.sh check_system.

Replayability

The run.json cmd_replay field is a single-line command that re-runs the pretrain with the same inputs, hyperparameters, and arch. To replay:

# Inside the kermt container:
$(jq -r .cmd_replay $RUN_DIR/run.json)

If ok_to_replay: false in the manifest (because the kermt repo working tree was dirty at launch time), the replay may not be bit-exact — pin the exact commit via the repo.commit field 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.

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-continue-pretrain · 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-continue-pretrain.zip