kermt-embed

name: kermt-embeddescription: Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized embedding extraction and write model bundles, per-readout .npy embeddings, canonical SMILES, and validity arrays to user-selected host directories.license: Apache-2.0compatibility: Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron.metadata:   owner: evax@nvidia.com  classification: workflow-skill  risk_tier: skill# Line/token budget: targets ~150 lines / ~1800 tokens — within the# 500-line / 5000-token cap for skill files.

kermt-embed

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

Skill and runtime paths

Set SKILL_DIR to the absolute path of this installed skill directory. Export KERMT_REPO as the absolute path to the KERMT checkout used for model execution. The bundled container helper mounts that checkout at /workspace and this skill at /skill (read-only). Commands inside the container use /skill/scripts/; defaults are bundled in config/. See Released models for checkpoint bundle requirements.

Downloads and local outputs

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

Hardware requirements

• GPUs: 1 (single-GPU).
• VRAM: ≥ 4 GB for the default batch_size 64.
• Disk: depends on output size — roughly a few MB per 1k molecules at hidden 800 per readout, so ~10–20 MB per 1k molecules across the 4 readouts. Plus a small canonical_smiles.npy + validity.npy per run.
• Driver / CUDA: any host supporting CUDA 12.6.

Inputs

Required:

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

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

• --ckpt <path> — any encoder-bearing checkpoint. Grover_base, cmim, hybrid, and finetuned ckpts are all accepted. The validator only refuses ckpts with no encoder. If omitted, the skill offers to download the released pretrained hybrid model nvidia/NV-KERMT-70M-v2 and embed with it — see "Resolve & validate the checkpoint" (workflow step 3).
• --pretrained-release — explicit opt-in to use the released model without the interactive prompt (for non-interactive / agent runs). Mutually exclusive with --ckpt.
• --model-dir <dir> — where to save the downloaded bundle (default $KERMT_REPO/models/NV-KERMT-70M-v2/). An already-complete bundle there is reused, not re-downloaded.

Optional:

• --batch-size N — override the configured default (64).
• --gpus 0 — single GPU id (default 0).
• --from-prepare <dir> — skip the prepare step and reuse an existing prepare_data.json in <dir>.

Workflow

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

1. Pre-flight: container + system probe.

   "$SKILL_DIR/scripts/kermt_container.sh" check_system
   

2. Compute run directory.

   RUN_DIR=$KERMT_REPO/runs/embed_$(date -u +%Y-%m-%dT%H-%M-%SZ)
   

3. Resolve & validate the checkpoint.

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

   • Consent gate. Unless --pretrained-release was passed, ask the user: "No checkpoint given — download the released model nvidia/NV-KERMT-70M-v2 (NVIDIA Open Model License, https://huggingface.co/nvidia/NV-KERMT-70M-v2) and embed with it? [y/N]". Never download without an explicit yes (or --pretrained-release). If both --ckpt and --pretrained-release are given, abort — they conflict.
   • Save location. Default $KERMT_REPO/models/NV-KERMT-70M-v2/; honor --model-dir <dir> if given. An already-complete bundle is reused.
   • Download (foreground; ~282 MB on first fetch):

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

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

   Validate the resolved (or user-provided) ckpt:

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

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

4. Validate the data.

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

5. Prepare the data (clean-only — no features step).

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

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

6. Launch the runner (blocking).

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

7. Report to the user.

   • Embeddings directory: $RUN_DIR/out/
     • atom_from_atom.npy, bond_from_atom.npy, atom_from_bond.npy, bond_from_bond.npy (the 4 standard readouts; each shape (N_rows, hidden_size))
     • metadata.pkl — pickle of a dict containing canonical_smiles (RDKit-canonicalized SMILES per row), valid (boolean per-row: did RDKit parse it), plus other run metadata.
   • Manifest: $RUN_DIR/run.json
   • Log: $RUN_DIR/logs/embed.log

Hard rules

• Never download the released model without consent. When --ckpt is omitted, download nvidia/NV-KERMT-70M-v2 only after an explicit user "yes" or an explicit --pretrained-release flag. --ckpt and --pretrained-release are mutually exclusive.
• Never modify the user's ckpt. The runner reads-only via task/extract_embeddings.py's --checkpoint <path> flag.
• Arch comes from the ckpt. No --hidden-size flag etc. on this runner; task/extract_embeddings.py reads arch from the ckpt's saved_args.

Common errors

• prepare_data manifest is missing required output 'clean_csv' → prepare ran with --skip-clean but no source CSV given. Re-run prepare without it.
• --gpus '0,1' is single-GPU only → pass a single id.

Replayability

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

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