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".
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name
tao-run-automl-deft-pipeline
description
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".
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
Phase 1 (AutoML baseline) Phase 2 (DEFT loop, plain train) Phase 3 (AutoML refinement)
───────────────────────── ──────────────────────────────── ───────────────────────────
specs/baseline_spec.yaml (Phase 1 winner pre-seeds baseline ${RESULTS_DIR}/iter${N}/dataset/
train/base/training_set.csv — DEFT skips its baseline train) train_combined_iter${N}.csv
│ │ │
▼ ▼ ▼
[ AutoML HPO sweep ] [ DEFT: baseline-inference → RCA [ AutoML HPO sweep ]
N recommendations → iter 1..N (plain retrain) ] re-tunes HPs against the
pick best by val_loss / FAR RCA / route / SDG / mining DEFT-augmented dataset
│ │ │
▼ ▼ ▼
best HPs spec + ckpt ─────► DEFT-augmented CSV ───────────► final best checkpoint
+ iter winner checkpoint (the deliverable; no
(Phase 3 warm-starts from it) further retrain)
More from this repository
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/baseline_spec.yaml, copies the checkpoint into ${RESULTS_DIR}/baseline/train/, and pre-populates deft_state.json so DEFT skips its baseline train and resumes at baseline inference → evaluate → RCA → iter 1. DEFT stays plain-train (automl_policy: off preserved).
Phase 2 → Phase 3: a training CSV (train_combined_iter${N_final}.csv) AND the iter winner's checkpoint — the checkpoint is wired into each rec's train.pretrained_model_path so Phase 3 fine-tunes from Phase 2's winner. Phase 3's winning checkpoint is the deliverable; no separate retrain after Phase 3.
See references/phase-handoffs.md for the exact steps, code, and DEFT-honors-this-handoff details of both handoffs.
Why three phases instead of two
Phase 1 alone finds good HPs on the original training distribution, but the model still has the distributional gaps DEFT is designed to fill.
Phase 2 alone (just DEFT) fills the gaps but uses whatever HPs specs/baseline_spec.yaml was hand-authored with — usually not optimal.
Phase 3 alone would run AutoML against the augmented dataset, but without a tuned baseline the DEFT loop's iteration cost is higher (slower convergence, more iterations to hit the KPI).
Running all three: AutoML cheap-tunes once on the original data, DEFT does the heavy data work with reasonable HPs, then AutoML tunes again on the now-richer dataset. Phase 3 is the most important of the three for the final deployed FAR/recall.
Cost up-front
The pipeline is sequential. Total wall-clock ≈ Phase 1 (N_automl × per-rec train) + Phase 2 (M iterations × per-iter cost) + Phase 3 (N_automl × per-rec train).
Note that Phase 2 has no separate baseline train — Phase 1's winning checkpoint is reused as DEFT's baseline, so the baseline cost lands inside Phase 1's N_automl trainings rather than as an extra retrain. Surface this to the user before kickoff. Typically Phase 2's iterations still dominate (each includes SDG + retrain), but Phase 1 and Phase 3 each add several hours on a single-GPU box. Use the per-job estimate from the user's setup (if they have one) rather than guessing minutes. See references/pitfalls-and-quality-checks.md (Compute budget) for the per-phase term breakdown.
Consolidated Pre-Flight — one gate, all three phases
The pipeline has exactly one user gate. Before any side-effecting action (docker pull, docker login, any job-launch call delegated to a downstream skill, file mutations under ${RESULTS_DIR}/), the agent must produce a single consolidated Pre-Flight Summary that subsumes every downstream skill's preflight. Once the user approves, the run is autonomous through all three phases — no further interactive pauses.
The user explicitly does not want to be paged between phases. The DEFT loop's own inline ## Pre-Flight Summary gate becomes a zero-question display step (every value pre-supplied from this consolidated gate), as does tao-run-automl's shared launch preflight in Phase 1 and Phase 3.
Before printing the summary, the agent must open and read every downstream skill's preflight section in full, run every read-only check those sections prescribe, and surface the outcome of each check. The summary has nine mandatory sections (workspace/host/platform/network; credentials status; container images; dataset table; Phase 1 config; Phase 2 config; Phase 3 config; compute estimate; confirmation line). After the gate, every downstream interactive gate is suppressed by passing through the collected values. The only allowed post-gate pauses are mid-run hard-stop safety gates the downstream skill cannot bypass.
See references/consolidated-preflight.md for: the full list of preflight sections to read, the required DEFT ## Pre-Flight run, the exact nine-section summary contents, the value pass-through for gate suppression, and the procedure when the skill bank version doesn't yet support gate suppression.
Phase 1 — AutoML baseline
Invoke tao-skill-bank:tao-run-automl with:
Input
AOI default
Notes
network_arch
visual-changenet
Same model the DEFT loop expects
train_dataset_uri
<workspace>/train/base/training_set.csv
Same training set DEFT will start from
eval_dataset_uri
<workspace>/train/base/validation_set.csv
Held-out — must NOT be the KPI test set (<workspace>/kpi/testing_set.csv), since that set is reserved for DEFT's final reporting
metric
FAR @ 100% recall (preferred) or val_loss
See Metric pitfalls in references/pitfalls-and-quality-checks.md — ChangeNet AOI is class-imbalanced, val_loss alone can mode-collapse
algorithm
bayesian
LLM-brain or autoresearch if compute is tight
automl_max_recommendations
5–10 for AOI
More recs = better HPs but linear in compute
spec_overrides
Pin epochs / batch_size; sweep optimizer-related HPs only
Otherwise AutoML wanders into long-train regimes that blow Phase 2's budget
After the sweep finishes, AutoML's result["best"]["specs"] is the winning hyperparameter dict.
Handoff to Phase 2
Phase 1 hands over two artifacts: the winning spec and the winning checkpoint. Retraining the same HPs in DEFT's baseline step is wasted compute — instead, pre-seed DEFT's baseline state from Phase 1's outputs so DEFT starts at baseline inference → evaluate → RCA → iter 1. This is a four-step bridge (write merged spec → pre-seed baseline/train/ → initialise deft_state.json with baseline already done → invoke DEFT), followed by a quality check of the winning checkpoint (per-class prediction counts; compare to zero-shot ChangeNet).
See references/phase-handoffs.md for the verbatim Steps 1–4 (including the cp command and the deft_state.json pre-seed code) and the quality-check checklist.
Invoke tao-skill-bank:tao-run-deft-aoi (read its SKILL.md for the full interface). For non-AOI applications, invoke the matching DEFT skill; the handoff shape is the same.
The DEFT loop's baseline-train sub-step is skipped. Phase 1 already produced a checkpoint trained at the winning HPs, and Phase 1's handoff (see references/phase-handoffs.md) pre-populated ${RESULTS_DIR}/baseline/train/ and ${RESULTS_DIR}/deft_state.json so DEFT resumes at baseline inference → evaluate → RCA → iter 1. The rest of the DEFT loop runs unchanged. Do not modify its automl_policy: off invariant.
The DEFT loop owns: its Pre-Flight Summary display step (not a fresh user gate — the Consolidated Pre-Flight above is the single gate; the DEFT summary still records the pre-seeded baseline/train/ source and must not re-prompt); baseline inference → evaluate → RCA on the pre-seeded checkpoint; the full per-iteration RCA → routing → SDG → mining → assemble → train cycle; KPI gating and stop conditions; and the ${RESULTS_DIR}/ layout (deft_state.json, DEFT_Loop_Report.html).
After the loop exits (KPI met or max_iterations reached), capture two values from deft_state.json: iterations.<best>.best_ckpt_path (the loop's best plain-train checkpoint) and the final iteration label N_final (used to locate the augmented training CSV).
If the DEFT loop hard-stops on an unrecoverable gate, skip Phase 3. There is no validated augmented CSV to feed AutoML.
Phase 3 — AutoML refinement on the DEFT-augmented dataset
Re-invoke tao-skill-bank:tao-run-automl with the augmented training CSV as the train dataset, the same held-out validation CSV as before, and Phase 2's iter winner checkpoint as the warm-start:
Same as Phase 1 (<workspace>/train/base/validation_set.csv) — keep the comparison apples-to-apples
metric
Same metric as Phase 1
algorithm
Same as Phase 1
automl_max_recommendations
5–10
Initial spec
Start from <workspace>/specs/baseline_spec_automl.yaml (Phase 1's winner) — gives the sweep a strong centroid to refine around
Warm-start checkpoint
iterations.<best>.best_ckpt_path from ${RESULTS_DIR}/deft_state.json — set spec_overrides["train"]["pretrained_model_path"] to this path. Each Phase 3 rec then fine-tunes from Phase 2's winner instead of training from scratch.
The warm-start is mandatory: without it every rec starts from random init with only 10-20 epochs to reconverge, val_loss regresses by 0.03-0.05 vs iter1, and the _pick_best safety net silently rolls back to the iter winner. Output goes to ${RESULTS_DIR}/final_automl/; the winning checkpoint of this sweep is the pipeline's deliverable. After the sweep, register Phase 3's checkpoint under iterations.final_automl in deft_state.json and re-run prepare_inference_spec.py so the handoff sees it (falling back to the loop's best if Phase 3 regressed).
See references/phase-handoffs.md for: the full "why the warm-start is mandatory" rationale and tradeoff, the concrete spec_overrides selection code, the exact two-step wiring of Phase 3's output back into the DEFT report, and the safety note on regression.
Pitfalls and quality checks
These apply to both AutoML phases. Bake them into agent behavior — don't just paste once. The full detail lives in references/pitfalls-and-quality-checks.md; in brief:
Metric pitfalls — AOI is class-imbalanced. ChangeNet AOI datasets are PASS-dominant (90%+), so a val_loss winner can be a mode-collapsed model. Prefer FAR @ 100%-recall directly, or guard val_loss with a pred_counts sanity check, or eval top-K by FAR @ 100%-recall before picking. For balanced / regression tasks, val_loss is fine.
Run-to-run noise. AutoML can show 2–3× variance for the same HP config. If the winner is suspiciously better than the runner-up, re-run with a fresh seed before committing the spec to Phase 2.
Cleanliness (data leakage). Both AutoML phases use a validation set distinct from the KPI test set (<workspace>/kpi/testing_set.csv), which is reserved for DEFT's final reporting. Phase 3 trains on the augmented CSV but keeps the same validation set so Phase 1 and Phase 3 numbers stay comparable.
Compute budget. Phase 1 N_automl × per-rec train; Phase 2 M_iter × (RCA + SDG + mining + retrain) (usually largest); Phase 3 N_automl × per-rec train on the larger augmented dataset. Ask the user for their per-job time before quoting wall-clock.
Quick Start (AOI worked example)
When starting fresh from "run the AOI workflow", the agent presents a three-phase plan to the user (Phase 1 AutoML baseline → Phase 2 DEFT loop → Phase 3 AutoML refinement), states the total cost structure (no extra baseline retrain at the front, no extra retrain at the end), asks for the user's per-run time for a wall-clock estimate, and waits for approval. After confirmation it invokes Phase 1, writes the merged spec, pre-seeds deft_state.json, invokes the DEFT loop with every input pre-supplied, then invokes Phase 3 — with no further pauses unless a downstream skill hits an unrecoverable hard-stop. It summarizes the trajectory at the end (baseline AutoML best → DEFT iter 1 → ... → DEFT iter N_final → Phase 3 best).
See references/quick-start-example.md for the verbatim customer-facing message block and the exact post-confirmation invocation sequence.
Non-AOI DEFT applications
Same three-phase pattern applies to other DEFT skills. Swap:
network_arch to the relevant model
The DEFT skill invoked in Phase 2
The "best HP spec file" and "best HP checkpoint" path conventions to whatever the target DEFT skill expects
The augmented-CSV path in Phase 3 to whatever the target DEFT skill produces
The handoff shape — Phase 1 emits a spec + checkpoint (the checkpoint pre-seeds the DEFT baseline), Phase 2 consumes both and emits an augmented dataset, Phase 3 emits the final checkpoint — is identical. The Phase 1 → Phase 2 baseline-skip mechanism is generic: any DEFT-style loop that exposes a resumable baseline state can be seeded the same way.
See also
tao-skill-bank:tao-run-automl — AutoML interface, algorithms, HP ranges
tao-skill-bank:tao-run-deft-aoi — full DEFT AOI loop (Phase 2 default)
tao-skill-bank:tao-train-visual-changenet — underlying ChangeNet train/eval/infer skill (used by both AutoML and DEFT)
Other skills/applications/deft-* skills — non-AOI Phase 2 targets
references/consolidated-preflight.md — the single-gate preflight in full
references/phase-handoffs.md — both handoffs, baseline pre-seed, and Phase 3 warm-start, verbatim
