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)