Run the disk-backed DEFT AOI improvement loop for NVIDIA Cosmos Reason 3 / Cosmos3 models, using Nano by default and Edge or Super when explicitly requested: evaluate the base model on Proxy and frozen Benchmark splits, mine real image pairs from Proxy gaps, assemble a per-iteration Train JSON from selected Mining samples, train with cosmos-rl LoRA SFT, and repeat through the selected platform's submit/status/logs/cancel contract. This migration supports bare labels only: the assistant response must be exactly OK or NG. Use for "run Cosmos3 DEFT AOI", "CR3 AOI loop", or "improve Cosmos3 PCB inspection with bare OK/NG"; do not use for rich/reasoning annotation, one-off Cosmos training, or generic anomaly generation.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Run the disk-backed DEFT AOI improvement loop for NVIDIA Cosmos Reason 3 / Cosmos3 models, using Nano by default and Edge or Super when explicitly requested: evaluate the base model on Proxy and frozen Benchmark splits, mine real image pairs from Proxy gaps, assemble a per-iteration Train JSON from selected Mining samples, train with cosmos-rl LoRA SFT, and repeat through the selected platform's submit/status/logs/cancel contract. This migration supports bare labels only: the assistant response must be exactly OK or NG. Use for "run Cosmos3 DEFT AOI", "CR3 AOI loop", or "improve Cosmos3 PCB inspection with bare OK/NG"; do not use for rich/reasoning annotation, one-off Cosmos training, or generic anomaly generation.
license
Apache-2.0 AND CC-BY-4.0
compatibility
Requires the companion TAO skill-bank skills from `eval.config`, host Python with `pyarrow` and `yaml`, and the selected platform's native CLI.
metadata
{"author":"NVIDIA Corporation","version":"0.1.0"}
allowed-tools
Read Task Bash Write
tags
["application","workflow","deft","aoi","cosmos"]
Skill: tao-run-deft-aoi-cosmos3
Installation
Install this application as part of the full TAO skill-bank root, not as only
the companion skill folders: TAO_SKILL_BANK_PATH must point at a directory
containing versions.yaml, scripts/resolve_versions_key.py, and the
skills/{applications,models,data,platform,core}/... tree listed in
eval.config. Run bundled validation with the skill Python so dependencies
match runtime: PYTHON=$(scripts/deft_python.sh); "$PYTHON" -m unittest tests.test_cosmos3_bare. Resolve network mode first. Missing air-gap imports
are a hard stop; network-enabled setup lives only in
references/network-bootstrap.md.
Execution Contract
Treat a run as a disk-backed state machine.
Preserve every explicit user value and show the source of each effective
value (user, spec, or default) in the Pre-Flight Summary.
Ask which installed platform to use. Do not select Docker, SLURM,
Kubernetes, Brev, virtualenv, or an external platform by default.
Resolve network mode, then read exactly one of references/air-gap.md or
references/network-bootstrap.md. Run the selected platform skill's
Preflight and stop on a missing system/native-CLI prerequisite.
Before any mutation or launch, invoke tao-launch-workflow and show its
launch review plus this skill's Pre-Flight Summary. Wait for one explicit
approval.
After approval, initialize ${RESULTS_DIR}/deft_state.json exactly once
with scripts/init_deft_state.py. Pass the exact GPU model reported by the
selected platform's Preflight through --gpu-model (include accelerator
memory when available), plus the resolved network mode/source and selected
absolute Python. Never reinitialize a resumed run or edit
deft_state.json by hand.
Before every stage, after context compaction, and before a completion claim,
run scripts/deft_context.py --state ... --stage .... Use its durable
next_stage and the state file's status,
current_iteration, iterations.*.status, stage_completed, and latest
events entry to resume. Do not infer progress from assistant prose or
from an artifact that is not recorded in state.
Run every command that can install, fetch, log in, or launch a local
container through . In an
air-gap it rejects egress/package operations and enforces no-pull. Remote
platforms must apply the equivalent immutable no-pull/offline policy.
scripts/deft_exec.py --state ... -- <command>
Submit each GPU stage through the chosen platform's four verbs:
submit / status / logs / cancel. The submit verb must open the
job-record before native launch; the returned id is the only launch handle.
Poll the backend, not the job-record, and map state to
PENDING RUNNING COMPLETE ERROR CANCELED UNKNOWN.
Commit every completed or failed DEFT stage with
scripts/commit_stage.py. It verifies the stage inputs and atomically
updates both the resume snapshot and ordered events array in state. Every
commit requires a positive, measured --duration-sec: use backend elapsed
wall time for submitted jobs and a host wall-clock timer for inline stages.
Missing or zero durations are rejected.
Claim completion only after scripts/finalize_run.py verifies final
Benchmark evidence, successfully commits loop_stop, and a fresh
read of deft_state.json shows status == "complete",
iterations.baseline.status == "complete", and the final iteration's
status == "complete".
Never place secrets in a spec, command, transcript, job-record, or chat. Check
credential presence only, for example
[ -n "$HF_TOKEN" ] && echo SET || echo UNSET. Credentials come from the
user's exported shell environment or from a user-approved env file —
~/.tao/secrets.env, ~/.config/tao/.env, or a path the user points at, never
one merely found in the workspace — loaded with
set -a; source /path/to/.env; set +a. Never print the file's contents or any
credential value.
Cosmos3 Model Contract
Model skill: tao-finetune-cosmos-reason.
Supported canonical base models:
nvidia/Cosmos3-Nano — default;
nvidia/Cosmos3-Edge — only when explicitly requested;
nvidia/Cosmos3-Super — only when explicitly requested.
Normalize the user aliases nano, edge, and super to those canonical
IDs. Preserve any variant selected in the prompt. When no variant is
selected, use Nano.
Give hardware recommendations for the selected variant and report when the
available compute is insufficient. If the prompt asks for a variant
recommendation based on hardware or workload, recommend one with the
tradeoff, but require an explicit selection before state initialization.
Never silently switch or fall back to another variant.
Keep the selected canonical ID as source-model lineage, but do not pass the
native online checkpoint directly to Cosmos-RL.
The published Cosmos Reason 3 reasoners ship in Cosmos3's own native Omni
format (model_type="cosmos3_omni"), which Cosmos-RL cannot load. After
launch approval and before baseline evaluation, run the model skill's
scripts/prepare_cosmos3_vlm_checkpoint.py to convert the selected reasoner
into a Qwen3-VL safetensors PTM, or validate and reuse an existing prepared
output.
Use the prepared PTM consistently for zero-shot evaluation, Train
policy.model_name_or_path, and LoRA model.base_model_path. The model
being trained is still the selected Cosmos Reason 3 reasoner — keep its
canonical ID as checkpoint lineage; the Qwen3-VL PTM is only the on-disk
format Cosmos-RL consumes.
Nano may use the helper's packaged Qwen3-VL default. Edge and Super require
a variant-specific, validated VLM base; never reuse Nano's conversion
arguments.
Container key: images.tao_toolkit.cosmos_rl in versions.yaml.
The pinned image caps vLLM evaluation at one image per prompt, which this
two-image contract cannot satisfy. Run scripts/patch_eval_image_cap.py
before the first evaluate job and mount its output read-only into every
evaluation container; see references/cosmos-reason.md.
Workflow override: automl_policy: off. DEFT owns iteration and checkpoint
selection; this is a workflow argument, not a TOML key.
Default adaptation: LoRA over the language-side projections
["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], leaving the vision tower's pretrained weights untouched. The
schema also accepts "all-linear", which additionally adapts the vision
linear layers; use it only when the user explicitly requests it. Derive all
other Train defaults from the model skill's current template.
Every spec is a nested dictionary serialized to TOML. Never write literal
flat dotted keys into a spec.
Do not mount user data over /workspace; cosmos-rl is installed there.
Run every container that writes into the results tree as the invoking user,
not root, or the run's own outputs become undeletable by their owner. See
references/cosmos-reason.md.
Read skills/models/tao-finetune-cosmos-reason/SKILL.md and its
references/skill_info.yaml before authoring a spec. Start from the model
skill's current packaged template for the selected action and apply only the
AOI workflow overrides in references/cosmos-reason.md. Replace every
dataset/output path with the chosen platform's compute-frame path. Prove that
the selected Cosmos-RL image can load the prepared PTM and train the requested
variant; do not reuse Nano conversion, parallelism, or memory assumptions for
Edge or Super.
Bare OK/NG Contract
This migration supports one annotation mode: bare_okng.
Each record is ShareGPT JSON with exactly two images in
[AOI, golden_reference] order.
The first human/user turn contains the inspection prompt.
The final assistant/gpt response is exactly OK or NG; reasoning,
prefixes, explanations, and final-answer wrappers are invalid training
labels.
NG is the positive class. NG -> OK is a false accept; OK -> NG is a
false reject.
Evaluation may normalize a model response by its last standalone OK/NG
token, but training labels remain exact.
Rich, reasoning, BCQ/MCQ, and task fan-out modes are outside this migration.
Stop instead of silently accepting them.
Run scripts/validate_sharegpt.py on Proxy, Benchmark, Mining, and each
generated iteration training file. There is no input Train annotation.
Run scripts/validate_split_contract.py to prove that Proxy, Benchmark, and
Mining targets are disjoint and that the frozen Benchmark annotation hash has
not changed. When a generated Train file is supplied, the same validator
requires its targets to come from Mining, the immediate --previous-train
seed, or the current iteration's --synthetic AnomalyGen output, and to remain
disjoint from Proxy and Benchmark. For iteration N>1, --previous-train is
required and the validator proves that every preceding Train record was
retained.
KPI Isolation
Proxy:annotations/proxy_kpi.json. It is the only error source
for RCCA, routing, mining targets, and data-mixture decisions. It never stops
the loop.
Benchmark:annotations/benchmark_kpi.json. It is frozen, evaluated
at baseline and every iteration, and is the only stop-gate source. Benchmark
sample errors never feed routing or mining.
Default gate: recall_ng >= 1.0. If the user asks for accuracy, use
accuracy >= <target>.
Unknown model responses block the gate through the
unknown_predictions <= 0 metric constraint.
scripts/analyze_gaps.py writes Proxy RCCA artifacts or Benchmark aggregate
metrics plus metric_result.json. scripts/record_metric_result.py binds the
Benchmark metric evidence to the configured metric contract.
Workspace Contract
workspace/
├── annotations/ # user-supplied
│ ├── benchmark_kpi.json
│ ├── proxy_kpi.json
│ └── mining_pool.json
├── images/ # user-supplied
└── specs/ # produced by this workflow, after approval
├── train_spec.toml
├── evaluate_spec_proxy.toml
└── evaluate_spec_benchmark.toml
Per-role evaluate specs are preferred over one shared evaluate_spec.toml;
both are accepted. See references/data-layout.md.
The user supplies annotations and images. The specs are not an input to
ask for: build them from the tao-finetune-cosmos-reason templates plus the
AOI overrides, and write them after the approval gate and before
init_deft_state.py, which refuses to initialize without them. A workspace
carrying its own specs is still valid — reuse them rather than overwriting —
but their absence is normal and is never a reason to stop and ask the user for
a TOML file.
Non-default paths are valid when passed explicitly to
scripts/init_deft_state.py; downstream stages must read the recorded paths
instead of re-inferring conventions. Record absolute host/compute-frame
artifact paths under ${RESULTS_DIR}/baseline or
${RESULTS_DIR}/iterN.
Read references/data-layout.md for the dataset roles, allowed source
categories, and commercial-training eligibility.
Launch Intake and Pre-Flight
Read references/preflight.md and run every ordered check:
select and preflight the platform;
resolve workspace, annotations, media root, and max_iterations;
validate bare ShareGPT and Proxy/Benchmark/Mining target isolation;
hash and freeze Benchmark annotations;
resolve current Cosmos-RL and data-services images from versions.yaml;
plan conversion of the selected Cosmos Reason 3 reasoner into a Qwen3-VL
PTM, and that output's platform-visible path;
check only required environment-variable presence;
construct Proxy / Benchmark TOML specs and validate the Train template;
verify compute shape and path visibility from the selected platform;
run the model/platform launch preflight;
show the full Pre-Flight Summary and stop for approval.
No results directory, state file, spec mutation, dependency install, image
pull, or native launch is allowed before this gate, except the TAO policy's
small-Python-helper remediation.
Workflow
The full transition graph is in references/pipeline-and-state.md.
The frozen Benchmark gate is always evaluated before any Proxy work. Proxy
evaluate and RCCA exist only to seed the next iteration's mining, so they run
only when the gate is unmet. A run that passes the gate stops without spending
a Proxy evaluation.
Baseline starts with zero-shot frozen Benchmark evaluation of the unmodified
base model, which establishes the zero-shot KPI:
evaluate_benchmark
benchmark_metrics — stop here when the gate already passes.
evaluate_proxy — only when the gate is unmet.
proxy_rcca
For each iterN when the frozen Benchmark gate is unmet:
routing — derive mining targets from Proxy false accepts/rejects only.
Write both formats from the same rows: mining_targets.json for state
(--mining-targets takes the JSON) and a filepath[,label] parquet for the
embedding container. Gap rows carry no image paths, so join back to Proxy by
id — see references/gap-analysis.md.
anomalygen — generate synthetic defects with paidf-anomalygen in
inference_only mode, then turn each generated pair into a bare NG
record with scripts/emit_sdg_sharegpt.py. --skip is permitted only when
the driving Proxy RCCA recorded zero false accepts, and even then generating
is often still worthwhile — see references/paidf-anomalygen.md.
data_mining — invoke tao-mine-aoi-images, apply the configured cosine
floor with scripts/filter_mined_by_cosine.py, then run the mapped skill's
history-aware post-processing so a filepath selected by a prior iteration
cannot enter Train again. The default top-K remains 5; preserve an explicit
user value and increase it only when the history summary shows low novelty.
assemble_data — align mined target paths to Mining source prompts,
golden references, and exact labels with scripts/emit_mined_sharegpt.py;
create train_iter_1.json from the mined and synthetic records only after
Proxy RCA and Mining selection, then append monotonically into
train_iter_N.json in later iterations with
scripts/assemble_training_json.py.
validate_data — validate exact bare labels, files, duplicates, and
generated-Train lineage plus Proxy/Benchmark leakage.
train
evaluate_benchmark
benchmark_metrics — stop here when the gate passes or
N = max_iterations.
evaluate_proxy — only when the loop continues.
proxy_rcca
init_deft_state.py writes the first DEFT_Loop_Report.html; every successful
commit_stage.py call then refreshes it through the deterministic
scripts/render_report.py post-commit hook. Stop when the Benchmark contract
passes, max_iterations is reached, or a hard stop occurs. For an ordinary
stop, run scripts/finalize_run.py with the explicit reason, then run
render_report.py --require-terminal after optional token alignment.
The Cosmos-only report addition is a bounded prompt showcase sourced from
recorded annotations; keep every other visual convention aligned with
ChangeNet. See references/REPORT_RENDERING.md. Never delegate or hand-author
report rendering.
Stage References
Stage
Producer
Read first
Train
tao-finetune-cosmos-reason train, automl_policy: off
Commit an error stage and do not auto-retry for: invalid disk state; a rich or
non-exact training label; a JSONL or non-array annotation input; an
an unconverted Cosmos Reason 3 checkpoint still in native Omni format at a
Cosmos-RL boundary;
missing/ambiguous mined-to-source alignment; missing/tampered mining history,
cross-iteration mined filepath duplication; target overlap among
Proxy/Benchmark/Mining; a generated Train target outside Mining and AnomalyGen
output, or overlapping Proxy/Benchmark; a changed Benchmark hash; any Benchmark
error used for routing; missing/empty mining output; a failed or empty
AnomalyGen run while Proxy false accepts remain outstanding; an anomalygen
skip not backed by zero false accepts in the driving RCCA; a synthetic record
whose label is not NG or whose paired image is missing; a checkpoint outside
the iteration result tree; an invalid nested TOML spec; unknown evaluator
ground truth; or a program error.
Infrastructure errors may follow the chosen platform skill's bounded retry
policy with a new job-record linked by --retry-of; the DEFT stage is committed
only once, after a successful terminal backend result.
