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Kermt Infer

NVIDIAOfficialPublished by NVIDIA

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

From the author

Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill validates that the input ckpt has task FFN heads (refuses pretrain ckpts with a redirect to kermt-finetune), validates the CSV, prepares the data (clean + rdkit_2d features), then launches main.py predict inside the kermt container (blocking, minutes-scale).
— the description by NVIDIA

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/kermt-infer/download
  2. Unzip 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 to kermt-finetune.

  • --csv <path> — SMILES-only CSV. First column is smiles; 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 existing prepare_data.json in <dir>.

Workflow

Let $KERMT_REPO be the path to your kermt repo checkout, and assume kermt-setup has built kermt:latest.

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

    "$SKILL_DIR/scripts/kermt_container.sh" check_system

    Refuse to proceed on ok: false.

  2. Compute run directory.

    RUN_DIR=$KERMT_REPO/runs/infer_$(date -u +%Y-%m-%dT%H-%M-%SZ)
  3. 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 to kermt-finetune.

  4. 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.

  5. 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.json with clean_csv + clean_npz paths (rdkit_2d_normalized features).

  6. 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.

  7. 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.log

    • Row 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 so main.py predict --checkpoint_dir picks 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.json but does not pass arch flags into main.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_applied field of run.json records 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; use kermt-finetune first.

  • prepare_data manifest reports ok=False → check the manifest errors for 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, not main.py directly.

  • --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

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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.

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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)
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