The ml-worker's ONLY processing role is now the CPU whole-image embed fallback
(tag_and_embed renamed embed_image — Camie tagging was retired #1189 and the
name kept implying otherwise; videos were already handled agent-style: frame
sampling + mean-pool). Detection/cropping/CCIP stay GPU-agent-only, and their
completion is judged per-pipeline: ccip by gpu_job rows, siglip by concept
regions at the current model version — never by image_record.siglip_embedding.
A CPU embed therefore can NEVER close crop work for the agent (regression test
pins this; only the whole-image 'embed' job, the same artifact, is satisfied).
Making removal actually safe (operator will drop the container):
- GPU-queue coordination (enqueue_gpu_backfill, recover_orphaned_gpu_jobs,
reprocess_gpu_jobs) moved verbatim to tasks/gpu_queue.py on the maintenance
quick lane — it lived on the 'ml' queue only by module colocation, which made
the ml-worker a hard dependency of the whole agent pipeline.
- New ml_settings.cpu_embed_enabled (migration 0074, default ON so agent-less
installs keep working): OFF stops the four import hooks queueing embed work
nothing will consume and no-ops the manual backfill; switch lives on the
renamed 'CPU embedding backfill' card.
- NB heads training / auto-apply still run on the ml image (sklearn) — a stack
that removes the container gives those up too.
Deploy note: in-flight messages under the old task names are dropped by the
new workers; the 60s orphan sweep + hourly backfill re-fire under the new
names immediately.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
Heads + CCIP are the tag source and head auto-apply is the earned propagation.
The Camie tagger ran only to feed the allowlist bulk-apply (its ImagePrediction
rows had no other consumer), and the allowlist was a SECOND, un-earned auto-apply
path firing in parallel with heads on every accept — exactly the un-earned spray
the v2 pivot replaced. Retire both.
Behavior change: accepting a suggestion now applies the tag to THAT image only
(source='ml_accepted', a head-training positive) — it no longer allowlists +
fans the tag across the library via Camie. Propagation is heads' earned
auto-apply. (Loses instant cold-start propagation for booru-vocab tags; that was
un-earned and bypassed the precision gate.)
- tag_and_embed is now EMBED-ONLY (no Camie load/infer, no ImagePrediction
writes); backfill enqueues it for images with no embedding.
- Removed: services/ml/tagger.py, apply_allowlist_tags + helpers + daily beat +
every enqueue caller (accept/alias/merge/per-image), api/allowlist.py +
blueprint, ImagePrediction + TagAllowlist models/tables (migration 0067),
AllowlistTable.vue + allowlist store, the accept coverage-projection payload.
- AllowlistService gutted to accept/dismiss/undismiss/reject (the rejection store
the rail still needs); accept returns nothing, API returns {accepted, tag_id}.
- tag merge no longer repoints/triggers the allowlist; _keep_as_alias now keys on
ML-applied image_tag sources (incl. head_auto) instead of the allowlist.
- UI: MLBackfillCard relabelled to embedding-only; accept toast simplified;
MaintenancePanel drops the allowlist tile.
Left for a follow-up hygiene pass (now-inert, harmless): the dead settings
columns (tagger_store_floor, tagger_model_version, suggestion_threshold_*,
video_min_tag_frames), image_record.tagger_model_version, MLThresholdSliders
trim, and the Camie model download in download_models.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Video tag noise root cause: frames were a FIXED count (6) max-pooled — a tag
firing on one frame survived at peak confidence, and a fixed count under-samples
long multi-scene videos so real scene-local tags looked like noise.
Redesign (operator-steered):
- Sample at a fixed CADENCE — one frame every `video_frame_interval_seconds`
(default 4) across the 5–95% window — so a tag's frame-presence reflects real
screen time independent of video length. Capped at `video_max_frames` (default
64): a long video stretches the spacing instead of exploding into hundreds of
inferences, bounding per-video cost on the single ml-worker (per-frame ffmpeg
timeout also cut 60s→30s).
- Aggregate with `_aggregate_video_predictions`: keep a tag only if it appears in
>= `video_min_tag_frames` sampled frames (≈ that many × interval seconds on
screen — duration-independent noise rejection), with confidence = MEAN over the
frames it appears in (not max). Clamps the threshold to the sample count so a
1–2-frame short video still tags.
- All three knobs are DB-backed ml_settings (migration 0053), patchable via
/api/ml/settings + sliders in the ML settings card — replaces the
VIDEO_ML_FRAMES env var (product-not-project).
Tests: aggregation drops one-frame noise + means corroborated tags + clamps on
short videos; settings round-trip + min>max validation. Replaced the
_maxpool_predictions unit test.
NOTE: this is the QUALITY half of #747. The perf half — the ml-worker runs
CPU-only — is GPU enablement, tracked separately in #872.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Read cutover verified in prod (suggestions + allowlist read image_prediction;
backfill complete at 908k rows / 51k images). Removes the old JSON column and
everything that fed it:
- ImageRecord.tagger_predictions column removed; migration 0046 DROPs it.
tagger_model_version kept as the "tagged / current?" signal the backfill
sweep reads (needs-tagging check switched to tagger_model_version IS NULL).
- tag_and_embed no longer dual-writes the JSON — image_prediction is the only
write path.
- importer re-import reset drops the JSON line (image_prediction rows are
already deleted on re-import).
- Retired the one-time #768 backfill task + the #764 prune task, their admin
endpoints, and their Maintenance cards (Backfill/PrunePredictionsCard).
- Tests seed/assert via image_prediction; stale column refs removed.
Disk reclaim is NOT automatic: DROP COLUMN is a catalog change. Run
`VACUUM FULL image_record` off-hours afterward to return the ~100 GB to the OS
so DB backups go small (#739). image_prediction (~90 MB) stays in pg_dump — it's
the source of truth now.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Switch every prediction READER off the JSON column onto the normalized
image_prediction table. Parity by construction: each reader loads the same
{raw_name: {category, confidence}} dict it consumed before (via small
_load_predictions helpers), so all downstream threshold/alias/merge/consensus
logic is byte-identical — only the data source changed.
- suggestions.SuggestionService.for_image (and for_selection via it)
- ml.apply_allowlist_tags (iterates images that have prediction rows)
- importer re-import reset deletes the image's prediction rows
The tagger_predictions JSON column is still dual-written (step 1) so it stays
valid during transition; the backfill task's NULL check still works. Removing
the JSON write + DROP column + retiring the #764 prune is the cleanup
follow-up (needs a quiesced-worker window for the DROP lock).
Tests: shared tests/_prediction_helpers.seed_predictions seeds the table;
read-path tests (suggestions, bulk consensus, allowlist apply, API) seed there
instead of ImageRecord.tagger_predictions.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
apply_allowlist_tags: 4 modes (tag-only / image-only / both / full sweep),
matches a tag to a prediction either by direct name or via alias
(name, category) resolution, gates on per-tag min_confidence, skips
applied/rejected, applies source='ml_auto'. recompute_centroid /
recompute_centroids: async-bridged calls into CentroidService, delta-gated.
Beat: daily backfill, daily centroid recompute, daily allowlist sweep.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
tag_and_embed: Camie + SigLIP on one image (video → 10-frame sample,
max-pool tags, mean-pool embeddings), stores predictions/embedding with
model versions, then enqueues per-image allowlist apply. backfill:
keyset-paginated discovery of images missing predictions/embeddings for
the current model versions (restart-safe). apply_allowlist_tags stub
included so .delay() resolves between commits (filled in Task 9).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusen