Run the self-contained DEFT improvement loop for NVIDIA TAO CLIP / SigLIP2 Image Attribute Augmentation (IAA): dataset preparation, zero-shot evaluation, attribute gap analysis, caption-space k-NN mining, history-aware selection, retraining, and re-evaluation against an IAA retrieval KPI. Use for requests to run or resume the IAA DEFT loop or improve an IAA model until a metric target or iteration budget is reached. Treat `tao-deft-iaa` as shorthand for this canonical `tao-run-deft-iaa` workflow. Do not use for standalone CLIP training, one-off evaluation or embedding, generic k-NN mining, or AOI/ChangeNet DEFT workflows.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Instruções da origem · Visualização somente leitura
name
tao-run-deft-iaa
description
Run the self-contained DEFT improvement loop for NVIDIA TAO CLIP / SigLIP2 Image Attribute Augmentation (IAA): dataset preparation, zero-shot evaluation, attribute gap analysis, caption-space k-NN mining, history-aware selection, retraining, and re-evaluation against an IAA retrieval KPI. Use for requests to run or resume the IAA DEFT loop or improve an IAA model until a metric target or iteration budget is reached. Treat `tao-deft-iaa` as shorthand for this canonical `tao-run-deft-iaa` workflow. Do not use for standalone CLIP training, one-off evaluation or embedding, generic k-NN mining, or AOI/ChangeNet DEFT workflows.
license
Apache-2.0 AND CC-BY-4.0
compatibility
Requires Docker, NVIDIA Container Toolkit, accessible NVIDIA GPUs, the two IAA dataset export archives, and Python 3.9+ with the documented runtime dependencies.
Standalone install? If this session was not initialized by the TAO skill
bank plugin, run the tao-setup skill first for host preflight, credential
checks, and cross-skill discovery.
Run the canonical IAA flow as one resumable, disk-backed workflow. All IAA
workflow logic, templates, and host adapters ship with this skill; customers do
not need a separate source checkout. The bundled scripts make stage calls
deterministic without adding another orchestration layer.
This skill supports local Docker only. Do not ask the user to choose a
platform or silently translate the workflow to SLURM, Kubernetes, or Brev.
Entry Contract
tao-deft-iaa and tao-run-deft-iaa select this same workflow. IAA supports
local Docker only, so that declaration is the platform selection. State it and
do not ask the user to choose a platform.
Use two intake phases:
Perform only bounded, lightweight path discovery. Two explicit archive file
paths win and suppress all archive searching. An explicit archive directory
is treated as one approved search root. Otherwise, resolve the conventional
~/workspace and use only these deduplicated search roots: ~/iaa, the
workspace root, and its iaa/, input/, and inputs/ children. Check the
workspace root itself only; beneath each other search root, inspect the root
and directories at most two levels below it. This AOI-style bounded-subtree
lookup supports nested export/drop directories without turning into a home
or repository scan. Never follow symlinks or add the current checkout,
source repositories, tutorial/notebook trees, or workspace dataset-output
trees as implicit search roots.
An archive candidate contains direct regular-file children
images_raw.tar and meta.tar.gz; the workflow needs one such pair, not
multiple dataset directories. Follow the classification and provenance
format in references/preflight.md so the user sees every search root, its
reason and depth bound, the directory in which a pair was found, and whether
it is archive-only or mixed with extracted data.
Enumerate run-state files only at
<workspace>/results/run_*/deft_state.json. Read the minimal identity fields
and present a resume candidate only when workflow is exactly
tao-run-deft-iaa; never offer AOI or unidentified DEFT state as IAA.
Do not validate large archives to EOF or inspect Docker, images, GPUs, or
credentials yet.
If max_iterations or a time budget is absent, ask one consolidated
question for that required value and any genuinely ambiguous path/run
choice. State the documented defaults and that a complete read-only
preflight plus approval summary follows. Do not ask whether the user wants
optional KPI, authentication, or parameter overrides; apply their defaults
unless the prompt already supplies an override.
After required intake is resolved, discover and validate:
workspace and either a new ${RESULTS_DIR} or one existing run to resume;
images_raw.tar and meta.tar.gz; SHA256SUMS is optional;
max_iterations, or a user-supplied time budget from which an iteration
limit can be estimated;
metric name, query type, operator, and optional target;
whether the deployment requires authenticated Hugging Face model access;
any explicit epoch, GPU, mining, continual-learning, or visualization
overrides.
For a new run, RESULTS_DIR must be a child of WORKSPACE. DATASET_ROOT
must be below a workspace data directory (for example
$WORKSPACE/data/iaa_v31_tao_ft), not directly below the workspace; neither
path may contain the other. All approved paths are absolute and non-symlink.
Never replace an explicit value with a heuristic. Defaults apply only to
unspecified values and must be identified as defaults in the pre-flight
summary. max_iterations has no default. An absent metric target means an
ungated run that stops after max_iterations. Hugging Face token forwarding
defaults to disabled because the bundled model is public.
If required information remains missing after full discovery, ask one
consolidated follow-up. A normal invocation should need no knowledge of stage
modules, container mounts, state files, or bundled-runtime function
signatures.
Safety Gate
Perform only read-only discovery before approval: resolve paths, inspect file
metadata and archives, check process-environment variable presence, inspect
local images, inspect GPUs, and audit an existing run. Credentials come only
from the launching process environment. Never open, source, grep, or copy a
credential file. If a required variable is absent, tell the user which name to
export in the shell that launches the agent; never ask for its value in chat.
Do not inspect credential-file metadata when no credential is needed. If the
user explicitly asks for a file-permission check, stat only that named file,
warn about group/other readability, and still do not load it.
Show the summary defined in references/preflight.md, including every
parameter and source, planned file creation/extraction, image pulls, estimated
runtime, and resume status. Wait for explicit approval before Docker login or
pulls, package installation, archive extraction, config/state creation, or any
write under the workspace.
If an approved parameter later changes, show the changed summary rows and get
approval again before continuing. No confirmation is needed between unchanged,
already-approved stages.
Execution Contract
After approval, follow references/preflight.md to prepare the runtime,
materialize the immutable run config, and initialize state once. Never
reinitialize an existing run.
Before every stage, after context compaction, and before any completion
claim, run:
Continue only when the audit reports IN_PROGRESS and its next_action
matches the intended stage. For INVALID, stop launching work and report
the listed inconsistency. For nonterminal FAILED whose next_action is
loop_stop, follow the pipeline reference and commit hard_stop; for
terminal FAILED, report the failure and launch no more work. For
COMPLETE, do not rerun a stage.
Read only the current stage reference named by read_before_action. Use
run_iaa_stage.py for bundled IAA host stages and
run_deft_container.py for every TAO container command. These wrappers
reconstruct paths and images from state on each call, so do not depend on a
previous cd or export.
A command succeeds only when its exit status is zero and its documented
output checks pass. Redirect verbose output to the wrapper-owned log;
inspect the final error block or at most the last 40 lines.
Commit each successful or terminally failed stage exactly once with
commit_stage.py. It validates artifact structure, freshness, iteration
scope, command evidence, leakage, and transition order before applying a
recoverable, journaled update to deft_state.json and loop_log.jsonl.
Never call
log_stage.py or record_metric_result.py directly, and never hand-edit
canonical state, log, metric, or history files.
Render the HTML report with render_deft_report.py. Reporting is a
deterministic read of audited state; it does not require another agent.
All recorded artifact paths are absolute host paths under ${RESULTS_DIR}.
Baseline artifacts live under zs/, iteration N under iter_N/, and run-wide
splits, source embeddings, and history files at the run root.
Workflow
The normal path is linear, with only two meaningful decisions:
pre-flight approval
-> dataset_setup -> pool_embed -> baseline evaluate
-> KPI met? yes: loop_stop
no: baseline gap_analysis
-> for N = 1..max_iterations:
data_mining -> history_select -> visualize -> train -> evaluate
-> KPI met? yes: loop_stop
no and N < max: gap_analysis -> next iteration
no and N = max: loop_stop
-> final audit -> HTML report
Stage ownership and exact commands are in
references/scripts-and-agents.md. Read the detailed reference only when its
stage is next:
Stage
Reference
Required result
dataset setup
references/data-layout.md
verified rebuilt dataset, five split files, non-empty source-pool parquet
pool embedding and mining
references/mining.md
fresh command evidence plus non-empty, schema-checked parquet outputs
successful TAO status, bound metric evidence; for train, a fresh best and normalized checkpoint
gap analysis
references/gap-analysis.md
non-empty iteration-scoped gaps parquet
history selection
references/mining.md
budgeted mined set, cumulative/history entry, zero eval leakage
visualization
references/visualization.md
enabled artifacts and command evidence, or a config-authorized skip
visualize is the only optional stage. Commit it with --skip only when both
visualization settings are false in the approved config. Do not turn off a
failing visualization mid-run without revising and reapproving the config.
Loop and Recovery Rules
The loop is bounded by max_iterations; never create an iteration outside
that range. Once the KPI passes, the only legal next transition is
loop_stop. Do not mine or train again.
Do not use open-ended polling. For a deliberately backgrounded container,
retain the process handle and poll no more often than every 30 seconds while
continuing to update the user.
Never repeat an unchanged failed command speculatively. Classify the failure,
inspect its final log block, make one evidence-based correction permitted by
the stage reference, then retry once. Stable command status records persist
attempt 1/2, and the wrappers refuse a third attempt after context loss. The
audit must pass before advancing.
A transient registry/network interruption or GPU contention may be retried
once after evidence shows the condition changed. A second equivalent failure
is terminal for the run.
Never retry checksum or rebuild verification failure, zero-row mining,
schema/cardinality failure, missing eval-split evidence, eval leakage,
history conflict that fails documented resume, metric-contract mismatch,
stale/cross-iteration output, or an unsupported GPU architecture. Commit an
error when state permits and stop with the exact recovery action.
If a command wrote outputs but crashed before stage commit, run the audit.
Reuse outputs only when their command status is successful, fresh, correctly
scoped, and all stage validators pass. History selection has one supported
recovery: rerun the deterministic adapter with --resume; never delete or
edit a history entry by hand.
Manual modification or truncation of deft_state.json or loop_log.jsonl
invalidates the run. Preserve it for diagnosis and start a new results
directory.
Metric and Stop Semantics
The approved metric contract is immutable for the run. Evaluation must parse
the exact iteration's nvidia_iaa_metrics_aggregate.csv; the result records
its source path and is re-derived during commit and audit. Checkpoint ranking
and best-run reporting follow the approved operator (>=/> chooses the
higher value, <=/< the lower), not a hard-coded metric convention.
Successful completion is exactly one of:
loop_stop(reason=kpi_met) after a bound evaluation satisfies its target;
loop_stop(reason=max_iterations) after the final allowed evaluation when
the target is unmet or absent.
hard_stop is a failed terminal outcome, not successful completion.
Completion
At a normal loop boundary:
commit loop_stop with the audit-selected reason;
render ${RESULTS_DIR}/DEFT_Loop_Report.html with trigger loop-end;
Report the stop reason, baseline and best metric, best iteration/checkpoint,
completed iteration count, report path, and any warnings. A checkpoint, CSV,
HTML file, or assistant statement alone is not completion evidence.
