Tao Run Automl Deft Pipeline

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.

When this skill applies

• 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)

The handoffs are:

• Phase 1 → Phase 2: a spec file AND the winning checkpoint. Retraining the same HPs in DEFT's baseline step is wasted compute, so the bridge deep-merges Phase 1's winning HPs onto baseline_spec.yaml, copies the winning checkpoint into ${RESULTS_DIR}/baseline/train/ under the filename DEFT expects, and pre-populates deft_state.json + loop_log.jsonl so DEFT resumes at baseline inference → evaluate → RCA → iter 1. DEFT itself stays plain-train (automl_policy: off preserved). Verbatim 4-step procedure in references/handoff.md.
• Phase 2 → Phase 3: a training CSV AND the iter winner's checkpoint. The CSV (train_combined_iter${N_final}.csv) is AutoML's training data; the checkpoint (iterations.<best>.best_ckpt_path from deft_state.json) is wired into each rec's train.pretrained_model_path so Phase 3 fine-tunes from Phase 2's winner rather than from scratch. Without this warm-start Phase 3 routinely regresses vs the iter winner. Phase 3's winning checkpoint is the deliverable — no separate retrain after Phase 3. See references/handoff.md.

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.md for the per-phase cost 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), as does tao-run-automl's shared launch preflight in Phases 1 and 3.

Before printing the gate the agent must read every downstream preflight section in full and run every read-only check those sections prescribe, surfacing each outcome in the summary. Running every step of the DEFT skill's ## Pre-Flight is mandatory — if any step is skipped the consolidated gate is invalid and the pipeline must not advance. The summary must include, in order: (1) workspace/host/platform/network, (2) credentials SET/UNSET status, (3) resolved container image URIs with PRESENT/MISSING, (4) dataset table with leakage check, (5) Phase 1 config, (6) Phase 2 config incl. pre-seeded baseline source, (7) Phase 3 config, (8) compute estimate, (9) the confirmation line. After the gate, pass every collected value through to each downstream skill so it has nothing to ask. The only allowed post-gate pauses are mid-run hard-stop safety gates (e.g. DEFT's KPI regression gate); call them out in the summary.

See references/preflight.md for the full build procedure, the exact mandatory contents of each summary section (with the GPU memory rule of thumb, DEFT loop defaults, and required inputs verbatim), the downstream gate-suppression inputs, and the fallback when an older skill-bank version hard-codes its own STOP gate.

Phase 1 — AutoML baseline

Invoke tao-skill-bank:tao-run-automl with:

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. Instead of retraining the same HPs in DEFT's baseline step, pre-seed DEFT's baseline state from Phase 1's outputs so DEFT starts at baseline inference → evaluate → RCA → iter 1. The four steps — write the merged baseline_spec_automl.yaml, copy the winning checkpoint into ${RESULTS_DIR}/baseline/train/, initialise deft_state.json with iterations.baseline.stage_completed == "train" (and append the matching loop_log.jsonl entry), then invoke DEFT — are given verbatim with the exact code in references/handoff.md. automl_policy: off inside the loop is preserved.

Quality check before handing off

Run a quick eval of the winning checkpoint against the held-out set: per-class prediction counts (if it collapsed to one class, evaluate the 2nd or 3rd best instead) and a comparison to a zero-shot ChangeNet baseline (if AutoML did not improve over zero-shot, surface that and pause). See references/handoff.md.

Phase 2 — DEFT loop (plain training, baseline pre-seeded from Phase 1)

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 above) 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:

• The Pre-Flight Summary display step — not a fresh user gate. The Consolidated Pre-Flight (above) is the single gate; the DEFT summary still prints as an audit-trail display of the pre-seeded baseline/train/ source but must not re-prompt, since every input was collected in the consolidated gate.
• Baseline inference → evaluate → RCA on the pre-seeded checkpoint, and the full per-iteration RCA → routing → SDG → mining → assemble → train cycle.
• KPI gating and stop conditions; ${RESULTS_DIR}/ layout, deft_state.json, loop_log.jsonl, 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
• 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:

The warm-start is mandatory: with no warm-start, every rec starts from random init with only 10-20 epochs to reconverge, Phase 3's val_loss regresses 0.03-0.05 vs iter1, and the _pick_best safety net silently rolls back to the iter winner — wasting Phase 3's compute. The concrete spec_overrides code (selecting the lowest-far_pct iteration, excluding any prior final_automl), the broad-exploration tradeoff, output to ${RESULTS_DIR}/final_automl/, and wiring Phase 3's checkpoint back into the DEFT report via iterations.final_automl + re-running prepare_inference_spec.py (with the _pick_best regression safety net) are all in references/handoff.md.

Pitfalls and quality checks

These apply to both AutoML phases — bake them into agent behavior, don't just paste once. The full detail is in references/pitfalls.md:

• Metric pitfalls (AOI is class-imbalanced). ChangeNet AOI is PASS-dominant; val_loss can mode-collapse to a zero-recall PASS-everything model. Prefer FAR @ 100%-recall directly, or gate val_loss with a pred_counts sanity check, or decide top-K by FAR @ 100%-recall. For balanced / regression tasks, val_loss is fine.
• Run-to-run noise. AutoML can show 2–3× metric variance for the same config. If the winner looks 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 (kpi/testing_set.csv), which stays untouched until DEFT's evaluate stage. Phase 3 trains on the augmented CSV but keeps the same val set so Phase 1 and Phase 3 numbers stay comparable.
• Compute budget. Surface the per-phase structure up front and only give a wall-clock range after the user supplies their per-job time.

Quick Start (AOI worked example)

When starting fresh from "run the AOI workflow", the agent delivers a three-phase worded message to the user (Phase 1 AutoML baseline → Phase 2 DEFT loop → Phase 3 AutoML refinement, with the cost framing and "OK to proceed?" close), then after confirmation invokes tao-run-automl (Phase 1), writes the merged spec, pre-seeds deft_state.json, invokes tao-run-deft-aoi (Phase 2) with every input pre-supplied, and invokes tao-run-automl again (Phase 3) — with no further pauses unless a downstream skill hits an unrecoverable hard-stop gate — then summarizes the trajectory (baseline AutoML best → DEFT iter 1 → ... → DEFT iter N_final → Phase 3 best).

See references/quick-start.md for the verbatim customer-facing message and the exact post-confirmation invoke sequence.

Non-AOI DEFT applications

The same three-phase pattern applies to other DEFT skills — swap network_arch, the Phase 2 DEFT skill, the spec/checkpoint path conventions, and the Phase 3 augmented-CSV path. The handoff shape (Phase 1 emits spec + checkpoint that pre-seeds the DEFT baseline, Phase 2 emits an augmented dataset, Phase 3 emits the final checkpoint) is identical, and the baseline-skip mechanism is generic to any DEFT-style loop with a resumable baseline state. See references/quick-start.md.

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/preflight.md — building the consolidated pre-flight gate
• references/handoff.md — Phase 1→2 pre-seed, Phase 2 quality check, Phase 3 warm-start + report wiring
• references/pitfalls.md — metric, noise, leakage, and compute-budget guidance
• references/quick-start.md — verbatim worked-example message and non-AOI variant