Agent skill · NVIDIA
tao-run-automl-deft-pipeline
Run the canonical NVIDIA AOI three-phase training pipeline — Phase 1 AutoML baseline (HPO), Phase 2 DEFT loop (RCA → SDG → mining → plain-train retrain), Phase 3 AutoML refinement on the DEFT-augmented dataset. Use when the user asks to "run the AOI workflow", "fine-tune my PCB AOI model end-to-end", "improve my AOI ChangeNet model", or "AOI workflow with AutoML" request — route here instead of tao-run-deft-aoi directly unless the user explicitly asks for the DEFT loop ONLY (e.g. "run JUST the DEFT loop", "skip AutoML, only DEFT"). Also handles the same three-phase pattern for non-AOI DEFT applications — AutoML baseline then DEFT loop warm-started from AutoML's winning HPs then post-DEFT AutoML refinement on the iteration-augmented dataset. Trigger phrases include "run the AOI workflow", "AOI end-to-end", "AutoML + DEFT", "AutoML then DEFT", "tune hyperparameters then DEFT", "DEFT with AutoML at both ends", "warm-start DEFT", "improve my AOI model".
What it needs
About 10k tokens when loaded.
What this skill does
AutoML + DEFT Pipeline A workflow-bridge skill that runs three phases in sequence by delegating to two existing skills — tao-run-automl for HPO and a DEFT application skill (default tao-run-deft-aoi for AOI; other skills/applications/deft- skills for non-AOI cases) for the iterative data-improvement loop. This skill does not re-implement AutoML or DEFT. It owns only the connective tissue: HPO spec inputs, the spec-handoff between AutoML and DEFT, and the post-DEFT AutoML re-run on the augmented dataset. Routing policy User asks to "run the AOI workflow" or "improve my AOI ChangeNet model" — default to this skill, not tao-run-deft-aoi directly. The bare DEFT loop is the inner stage of this pipeline. User wants AutoML and DEFT chained on the same model/dataset User says "AutoML at both ends", "tune HPs then DEFT", "warm-start DEFT", "AutoML before and after DEFT" User has an AutoML-tuned spec and asks how to feed it into DEFT When this skill does NOT apply User explicitly asks for the DEFT loop only ("run JUST the DEFT loop", "skip AutoML") → use tao-run-deft-aoi directly User wants only AutoML with no follow-on DEFT → use tao-run-automl directly User is doing zero-shot eval, RAG, or non-training workflows --- The mental model The two handoffs are: Phase 1 → Phase 2: a spec file AND the winning checkpoint — the bridge deep-merges Phase 1's HPs onto specs/baselinespec.yaml, copies the checkpoint into ${RESULTSDIR}/baseline/train/, and pre-populates deftstate.json / looplog.jsonl so DEFT skips its baseline train and resumes at baseline inference → evaluate → RCA → iter 1. DEFT stays plain-train (automlpolicy: off preserved). Phase 2 → Phase 3: a training CSV (traincombinediter${Nfinal}.csv) AND the iter winner's checkpoint — the checkpoint is wired into each rec's train.pretrainedmodelpath so Phase 3 fine-tunes from Phase 2's winner. Phase 3's winning checkpoint is the deliverable; no separate retrain after Phase 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/tao-run-automl-deft-pipeline