Downloads/imports stage into <name>.part / <name>.partial then os.replace() into
place, so a kill mid-write leaves a discardable temp — never a corrupt final.
cleanup_orphaned_temp_files sweeps ones left behind under the images root, only
older than 6h so an in-flight download's staging file is never removed. Daily beat.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Deploys (docker SIGTERM→SIGKILL, default 10s) were killing Celery jobs
mid-flight. Give in-flight work room to drain and make interrupted work
resume cleanly instead of stalling.
- docker-compose.yml: stop_grace_period per lane (web 30s / worker 90s /
scheduler 60s / maintenance-long 180s / ml-worker 120s) so warm shutdown
can actually drain before SIGKILL.
- celery_app.py: task_reject_on_worker_lost=True — a task killed past the
grace window is re-queued (safe: idempotent + chunked, recovery sweeps
re-drive stragglers).
- interpreter_client.py: map 429/5xx (502/503/504) → InterpreterUnavailable
and parse Retry-After (delta-seconds or HTTP-date); a draining Interpreter
behind a reverse proxy no longer raises an opaque HTTPError.
- translation.py: thread retry_after out of _translate_batch; retranslate_posts
resumes after the Retry-After hint (or 60s default, capped 900s) on an
interrupt with _reset_done=True, self-terminating via the health gate.
- tests: 429/5xx mapping + Retry-After parse; interrupt-resume + default backoff.
No migration.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
tasks/translation.py — translate_posts: picks untranslated posts (title OR
description non-empty), per-post [title, description] batch via the Interpreter
client, stores translations + detected lang + engine_version; passthrough /
already-target posts are marked handled with no stored translation. 503 or a
connection error interrupts (retry next cycle), 400 stops (fix config), per-post
commit keeps progress; wall-clock bounded. Wired into celery (maintenance_long
lane) + a daily beat. No-op unless enabled + base URL set + healthy. GET
/settings/translation/status + POST .../run for the Settings card. Task tests
(stubbed client, monkeypatched session).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
scheduled_presentation_auto_apply (daily beat) runs presentation_auto_apply_sweep
— idempotent, so an interrupted run just re-runs next cycle (that's the recovery),
wall-clock bounded by soft/hard task time limits. prune_presentation_reviews
(daily beat) drops RESOLVED review flags older than 30 days (rule 89 retention).
Tests run both tasks via a monkeypatched session factory. Milestone 141 complete:
the presentation-chrome auto-hide + conflict-flagged review is now live end-to-end.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
Daily scheduled_retract_auto_tags re-scores standing auto-applied tags and drops
the ones the model no longer supports:
- retract_auto_applied_heads: per graduated head, re-score its source='head_auto'
images (bounded — only the images already carrying the auto-tag, not the whole
library) and remove ones now < auto_apply_threshold.
- retract_auto_applied_ccip: per source='ccip_auto' character tag, max-cosine the
image's figure vectors vs that character's prototypes; remove ones now below the
ccip auto-apply threshold.
Both SKIP operator-confirmed tags (TagPositiveConfirmation) and are SILENT — a low
score isn't proof the tag was wrong, so no hard negative is recorded (that's
reserved for an operator removal). No-op unless the relevant auto-apply switch is
on. New daily beat. sklearn-free tests for both paths + the disabled no-op.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
- refresh_character_prototypes celery task wraps the incremental builder (sync
ml worker); returns skipped / rebuilt=N removed=N.
- Beat: every ~15 min (cheap global-gate no-op when idle) + a nightly full=True
reconcile as belt-and-suspenders.
- train_heads enqueues it on success, so the Retrain button AND the nightly head
retrain refresh CCIP on the SAME trigger — unified lifecycle, as asked.
The initial (cold) full build loads the whole reference set once in the
background, never on a /suggestions request.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
Settings IA per the approved A3 design (the old layout was the two-app merge
fossilized):
- Import tab retired: ImportTriggerPanel + ImportTaskList deleted (manual
/import scans stay API-level; imports arrive via downloads/extension, heal
via the Layer-2 auto-refetch sweep, and show in Activity). ImportFiltersForm
moves to Maintenance → 'Ingestion & filters' and loads its own settings; the
import store shrinks to settings-only (no remaining consumers of the
scan/task-list machinery). Overview's pending banner now points at Activity.
- Maintenance regrouped: Ingestion & filters / GPU agent & embeddings
(GpuAgent, Failed processing, CPU embedding backfill) / Tagging (sliders,
Heads, Aliases) / Library health (MissingFiles, Thumbnails, DB, Archive
re-extract demoted last) / Storage.
- One extension home: BrowserExtensionCard moves from Settings → Overview to
Subscriptions → Settings, above the API key bar it authenticates.
- Single-color import filter WIRED: skip_single_color/threshold existed since
FC-2 but nothing read them (the audit module's docstring said as much) —
now enforced on both import paths via the audit's canonical predicate
(tolerance 30, matching the Cleanup card default; animated images exempt
like the transparency check). Default stays off; test added.
- Dead weight: PlaceholderView (zero refs) and the permanently-disabled
'Export failed logs (CSV — v2)' menu stub deleted; stale docs fixed
(celery queue docstring, threshold comment citing retired tasks, ml
package docstring, HeadsCard 'replaces Camie' blurb).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
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
The head-vs-centroid eval (#1130) existed to prove the 'frozen embedding +
trained head' spine; the operator accepted the tagging system and dropped the
harness. Removed per rule 22: TagEvalCard + store, /api/tag_eval blueprint,
tag_eval_run ml task, recover-stalled-tag-eval-runs sweep + beat entry,
TagEvalRun model + table (migration 0073), and its tests.
The eval's data loaders + metric helpers were NOT eval-specific — the nightly
heads trainer runs on them — so they moved verbatim to
services/ml/training_data.py (heads.py import updated; behavior unchanged).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
An errored GPU job's stored reason is a suspicion; the file probe is the
verdict. A 15-min beat sweep (triage_gpu_errors) runs verify_integrity's own
probe (sha256 + decode) on each errored image ONCE and writes both verdicts:
ImageRecord.integrity_status and the new GpuJob.triage_status ('defect' |
'file_ok', migration 0072). Every classification logs at WARNING so it
surfaces in Logs/System Activity.
- 'defect' rows are excluded from /retry_errors (re-running a known-bad file
burns agent time re-minting the tombstone); response now reports
defects_kept and the GpuAgentCard toast says so.
- GET /api/gpu/errors: triage view — reason buckets (classify_reason),
probe verdicts, per-job detail. POST /errors/triage runs the sweep now.
- POST /api/gpu/errors/<id>/recover: reuses the Layer-2 refetch pattern —
delete the defective copy + record (full cascade takes the tombstones too)
and re-poll its subscription Source so a fresh copy re-imports and re-enters
the pipeline; 'no_source' when nothing pollable resolves.
- New 'Failed processing' card (GpuTriageCard) in Maintenance: verdict counts,
reason summary, probe-now, defect list with thumbnails + per-image Recover.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
- ml-backfill-daily: the CPU tag_and_embed backfill raced the GPU agent's
daily embed backfill for the same NULL-embedding images at ~100x the cost
(B1 audit verdict, milestone #124). The backfill TASK stays — the manual
/api/ml/backfill button remains the deliberate CPU fallback pending B3.
- purge-legacy: one-time IR-migration cleanup, dry-run verified 0 targets on
the live library before removal (A2 audit, milestone #123). Fully retired
per rule 22: tile, store action, route, service fn, tests.
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
The v2 pivot replaced per-tag SigLIP centroids with learned heads + CCIP.
Centroids were still recomputed (on every tag merge + a daily beat) but NOTHING
read them — suggestions come from heads+CCIP and apply_allowlist_tags applies
via Camie predictions, not centroids. Pure dead wiring; remove it.
Removed: CentroidService, recompute_centroid/recompute_centroids tasks, the
daily beat, POST /api/ml/recompute-centroids, the recompute-on-merge trigger,
the tag_reference_embedding table + model, the centroid_similarity_threshold +
min_reference_images settings (migration 0066), the CentroidRecomputeCard +
its store action + MaintenancePanel tile, and the centroid slider in
MLThresholdSliders. _keep_as_alias drops its vestigial has-centroid branch (the
allowlist branch already covers "could re-emit"); tag merge no longer clears a
table that no longer exists.
NOT touched (still live, parallel to heads): the Camie tagger, ImagePrediction,
and the allowlist bulk-apply — accepting a suggestion still allowlists + applies
it across the library. The tag-eval "centroid" baseline metric is unrelated
(in-memory) and stays. (image_record.centroid_scores JSON column also remains —
separate legacy field, its own micro-cleanup.)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Make the SigLIP embedder an operator choice (drop-in to SigLIP 2:
google/siglip2-so400m-patch16-512 is a verified 1152-d model at 512px → no
schema change, better small-cue fidelity). A swap = set model + re-embed +
retrain, all operator-driven; the GPU agent does the re-embed so it's fast.
- settings: embedder_model_name is now a setting (migration 0065) alongside the
existing embedder_model_version; both editable + validated (non-empty) in the
ml admin API. The server embedder loads by HF name (AutoImageProcessor/Model,
model-agnostic), preferring the pre-downloaded local dir for the default so
existing deploys don't re-download; rebuilds on a name change.
- agent: new 'embed' job = whole-image SigLIP embedding (mean-pool video frames)
under the lease-announced model → POST /jobs/submit_embedding writes
image_record.siglip_embedding + siglip_model_version. The lease now announces
the model FROM THE SETTING (not a constant).
- re-embed routing: enqueue_gpu_backfill('embed') selects unembedded + stale-
version images; 'siglip' now re-embeds concept crops whose version != current
(so a swap re-triggers crops, not just the never-embedded back-catalogue). The
CPU ml-worker backfill no longer re-embeds on a version mismatch (it can't
churn the library at 512px) — the GPU agent owns version re-embeds. Daily
'embed' + 'siglip' beats self-heal.
- scoring: score_image only bags embeddings in the CURRENT model's space (whole-
image gated by siglip_model_version, concept regions by embedding_version) so a
mid-swap stale vector isn't scored by new-space heads; legacy NULL = current.
- UI: GpuAgentCard "Embedding model (advanced)" — edit name/version, Save, and
"Re-embed library (GPU)" (queues embed + siglip); points at SigLIP 2.
Tests: lease announces model + submit_embedding round-trip; enqueue 'embed'
selects stale/unembedded; stale-version excluded from scoring; embedder model
settable + empty rejected; siglip gate updated to current-version concept.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Lift recall on small/local concepts (glasses, cum, stomach-bulge, xray,
lactation) that the whole-image SigLIP vector washes out: the GPU agent now
embeds figure crops with SigLIP too, stored as kind='concept' regions, and the
suggestion rail scores each image as a BAG (whole-image + every concept crop),
taking each head's MAX over the bag. The whole-image vector is always in the
bag, so this can never score lower than before.
Model-agnostic by construction: the server ANNOUNCES the embedding model
(HF name + version) in the lease, so the agent loads whatever the heads were
trained in and stays in lock-step — a model swap is a server setting + a
re-embed migration, never an agent change.
- agent: model-agnostic CropEmbedder (torch/transformers get_image_features,
fp16 on CUDA, inference-locked); worker branches on job.task — 'ccip' emits
figure(CCIP)+concept(SigLIP) in one pass, 'siglip' emits concept-only so the
back-catalogue backfill never churns figure/CCIP regions; torch cu124 +
transformers in the image.
- server: lease announces embed_model_name/embed_version; score_image is
max-over-bag (version-filtered region embeddings); enqueue_gpu_backfill
'siglip' gates on a missing concept region (drains the back-catalogue,
retries failures, no double-enqueue); daily siglip-backfill beat; UI button;
/api/ccip/overview reports images_with_concept_siglip.
- v1 scope: suggestion rail only — auto-apply stays whole-image (conservative;
heads' thresholds were calibrated on whole-image). Bulk-apply bag = follow-up.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Works through the optional CCIP ideas + the "keep moving even if I forget" ask:
AUTOMATION (no button needed):
- Hourly beat auto-enqueues CCIP backfill — new images get embedded (and errored
ones retried) on their own; the queue never goes idle waiting for a click.
- CCIP auto-apply: a daily sweep tags confident matches (source='ccip_auto') so
identity tags keep flowing. ON by default (opt-out, like head auto-apply);
ml_settings.ccip_auto_apply_enabled + _threshold (0.92, above the suggest cut),
migration 0064. Vectorized (one matmul + reduceat per image), reversible, skips
already-applied/rejected. Switch + threshold in the GPU agent card; GET/PATCH
/api/ml/settings; auto_applied count in /api/ccip/overview.
REFERENCE QUALITY (the over-fire root cause):
- character_references now draws ONLY from single-character images — on a
multi-character image the tag is image-level, so every figure would otherwise
pollute each character's prototypes (a 2-char image tagged 'Velma' made
Daphne's figure a Velma reference). This is the contamination behind residual
over-firing.
- Cached on a cheap signature (char-tag count + ccip-region count/max-id) so the
reference load isn't redone on every modal open.
Tests: multi-character image not used as a reference; auto-apply tags a confident
match as ccip_auto.
NEXT (not done, confirmed): comic-panel cropping + SigLIP concept crops ("spot
interesting content").
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
So work an agent orphaned gets picked back up quickly, three layers:
- GpuJobService.release(): a graceful agent stop hands its still-leased jobs back
to pending instantly (POST /api/gpu/jobs/release), no waiting out the lease.
- GpuJobService.recover_orphaned() + recover_orphaned_gpu_jobs Celery task on a
60s beat: resets expired leases (a hard-crashed agent) to pending and keeps the
queue counts honest even when nothing is leasing.
- Lease TTL 300→180s: still well above any single job (a capped-frame video embed
is tens of seconds, and a live worker heartbeats), but a hard crash recovers
faster once the sweep fires.
Tests: release returns-to-pending (token-scoped), recover_orphaned resets only
expired leases, release API round-trip.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Auto-apply is now ON by default (operator-asked: opt-OUT, not opt-in) — migration
0059 + model default flipped. The support (>=30) + measured-precision gates keep
it safe and every auto-tag is reversible.
Observability so the operator can tune from real data:
- MISFIRE = an auto-applied (source='head_auto') tag the operator later removes.
UNDER-FIRE = a tag with a head the operator adds by hand (the head missed it).
Both captured at correction time in TagService.add_to_image/remove_from_image
(source is lost on delete) into durable per-tag counters (head_metric), keyed
by tag so they survive head retrain/prune.
- Daily snapshot_head_metrics writes a per-concept time-series point
(head_metrics_snapshot): auto-applied volume + cumulative misfires/under-fires
+ head quality; 180-day retention; daily beat.
- GET /api/heads/metrics: per-concept current counts + realized misfire rate +
head quality, plus the snapshot time-series — the report to tune the precision
target + support floor.
Migration 0060. Tests: misfire/under-fire counting (and the negatives — manual
removal isn't a misfire, headless manual add isn't an under-fire), snapshot
time-series, metrics API.
What's the autofire threshold? There's no single number — each graduated head
derives its OWN probability cutoff from its PR curve: the operating point that
holds precision >= head_auto_apply_precision (0.97) at max recall. The global
knobs are that target + the >=30 support floor.
NEXT (slice 3): UI — enable toggle, dry-run preview, per-concept trends.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Graduated heads can now apply their tag without a human — gated so it's safe:
- FIRING GATE: a head fires only when the master switch (head_auto_apply_enabled,
default OFF) is on AND it has >= head_auto_apply_min_positives (default 30)
clean labels. A precise-looking but under-supported low-N head can't spray tags.
- auto_apply_sweep (heads.py): streams every embedded image in chunks, scores
against the eligible heads (numpy, no sklearn), applies each head's tag where
score >= its auto_apply_threshold and the tag isn't already applied/rejected,
with source='head_auto' (distinguishable + reversible). dry_run counts only.
- HeadAutoApplyRun (migration 0059) tracks each sweep / preview; apply_head_tags
task (ml queue) + scheduled_apply_head_tags daily beat (no-op unless enabled)
+ recovery sweep + retention(20).
- API: POST /api/heads/auto-apply {dry_run} (202 / 409 running / 400 disabled),
GET /api/heads/auto-apply (recent runs + per-concept report). Settings
head_auto_apply_enabled + min_positives via /api/ml/settings.
Tests: sweep applies above threshold, dry-run writes nothing, skips under-
supported + ungraduated heads; API disabled/dry-run/conflict guards.
NEXT (slice 2): the observability the operator asked for — per-concept misfire
(auto-applied-then-removed) + under-fire tracking, time-series snapshots, and a
reporting API to tune. Slice 3: the UI (enable, preview, trends).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Two cadences for keeping heads in sync with your tagging:
- PASSIVE: a nightly `scheduled_train_heads` beat (skips if a run is already
in flight; creates+commits the run row before dispatching train_heads so the
ml worker always finds it). Folds the day's accepts/rejects + newly-eligible
concepts into the heads without anyone clicking.
- ACTIVE: a "Retrain heads" button in the Explore trail bar — bank the +/-
feedback you just gave while walking content, without a trip to Settings.
Shared logic in a new useHeadTraining composable (trigger + poll + start/finish
toasts), used by the Explore button; reflects an already-running run (incl. the
nightly one) on mount.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
The eval (#1130) proved the frozen-embedding + trained-head spine; this lands
its production form (the first of three slices that make heads the suggestion
source, replacing Camie + centroid).
- tag_head: one logistic-regression head per general/character concept with
enough labelled positives. Weights (pgvector), honest CV-derived suggest
threshold + earned-auto-apply point, and per-concept quality metrics.
- head_training_run: persisted batch lifecycle (mirrors tag_eval_run) so the
admin card shows live + historical status across navigation.
- services/ml/heads.py: TRAIN (sync, ml worker, reuses tag_eval's proven data
loaders + metric math so production heads match measured eval numbers) and
SCORE (async, API worker — numpy via pgvector, no scikit-learn): score one
image's embedding against all heads → the rail's suggestions, cached on
(count, max trained_at) so a retrain invalidates without per-request loads.
- tasks.ml.train_heads (ml queue, commits per head so a kill leaves progress)
+ recover_stalled_head_training_runs sweep + retention(20) + 5-min beat
(rule 89).
- api/heads.py: POST /api/heads/train (one run at a time, 409 guard) + GET
/api/heads (count, graduated, last-trained, running, per-concept table,
recent runs).
- ml_settings: head_min_positives + head_auto_apply_precision, tunable via
/api/ml/settings.
Scoring isn't wired into the rail yet (slice C) and the admin UI is slice B —
this slice makes training + scoring exist and CI-verifiable. 'precision' column
stored as precision_cv (SQL reserved word). Migration 0058.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Slice 1 of milestone #114 (tagging v2). Proves the frozen-embedding + trained-
head spine on the operator's own data, reusing the SigLIP embeddings already
stored on image_record — no re-embedding, no GPU.
Per concept: train a logistic-regression HEAD (positives + negatives = explicit
rejections + sampled unlabeled) vs the old single-CENTROID baseline; report
cross-validated precision/recall/AP for both, a LEARNING CURVE (AP/F1 as tagged
positives grow 10→30→100→300), and example image ids (head-would-suggest /
head-doubts-positive) to eyeball.
Persisted so the report SURVIVES navigation (operator-flagged): the run + full
report live in a new tag_eval_run row (mirrors library_audit_run); the admin
card will rehydrate from GET on mount, not transient state.
- models.TagEvalRun + migration 0056; runs on the ml queue (only worker with
numpy/sklearn) — numpy/sklearn lazy-imported so the API can still enqueue.
- services/ml/tag_eval (compute + start helper, one-running guard), tasks.ml
.tag_eval_run, api/tag-eval (POST create, GET history light / detail w/ report).
- recover_stalled_tag_eval_runs sweep + retention (keep last 20) + 5-min beat
(rule 89). scikit-learn added to requirements-ml.
- tests: param normalization + the rehydrate read-path + create/conflict.
Frontend admin card (trigger + render persisted report) follows next.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A swarm overlay-network blip after the :latest redeploy left Redis healthy but
transiently unreachable; a worker starting in that window crash-looped on the
initial broker connect (kombu OperationalError) and needed a manual Redis reset
to recover.
Retry the broker forever on startup + at runtime (broker_connection_max_retries
=None), add redis-transport socket options to the broker (short connect timeout,
TCP keepalive, retry_on_timeout, periodic health check), and mirror the same on
the Redis result backend. Now a transient outage self-heals when overlay routing
returns instead of the worker exiting.
Test pins the key resilience settings.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XCUHUGQLrBrkgyk1t49kpX
tasks/external.py drives the external_link ledger:
- fetch_external_link(link_id): atomic claim (pending/failed→downloading, so a
duplicate enqueue no-ops), per-host Redis serialize lock (#720 pattern;
requeue-with-countdown if busy), fetch via external_fetch into the artist
library tree, then route each file through importer.attach_in_place via a
synthesized sidecar so it links to the SAME post (archive→ImageRecords,
else→PostAttachment; on-disk original removed for captured files, art stays);
thumbnail+ML enqueue for new images; status downloaded | failed | dead with
attempts/last_error/completed_at/duration.
- sweep_external_links(): enqueue a bounded batch of actionable links.
- recover_external_links() + prune_external_links(): recovery + retention (#89).
- per-host enable read via getattr (forward-compatible; Settings UI adds the
columns in 4d — defaults on, rule #26).
Wiring: celery include + route (download lane) + beat (sweep 10m, recover +
prune daily); download_service phase 3 enqueues a sweep after recording links.
Integration tests: download+attach, failure, dead-letter, non-claimable, sweep.
mega still needs the MEGAcmd binary in the runtime image (Phase 4c). Refs #830.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Even chunked, a single concurrency-1 maintenance lane is fragile — a 30-min DB
backup or a multi-chunk library audit holds the slot and delays the quick
self-healing recovery sweeps / vacuum (operator-flagged 2026-06-07: long runs
must never block quick maintenance).
Route the long one-shots — backup.*, admin.* (normalize/re-extract/cascade-
delete), library_audit.* — to a new `maintenance_long` queue served by a
dedicated worker (concurrency 1), added to docker-compose (+ dev override). The
scheduler keeps the quick `maintenance` lane (sweeps, vacuum, cleanup) for
itself, so a backup can no longer starve a 5-min vacuum. UI queue list +
routing tests updated.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The TABLESAMPLE showcase reads physical blocks (bloat-sensitive), and the
periodic prune/backfill/recovery tasks churn dead tuples faster than
autovacuum always keeps up — so explicit maintenance earns its keep here.
- tasks.maintenance.vacuum_analyze: VACUUM (ANALYZE) over high-churn tables
(VACUUM_TABLES) on an AUTOCOMMIT connection (VACUUM can't run in a txn).
Scheduled weekly via Beat; also operator-triggerable.
- _sync_engine.get_sync_engine(): expose the process engine for the
autocommit connection.
- GET /api/admin/maintenance/db-stats: per-table n_live/n_dead/dead_pct +
last (auto)vacuum/analyze from pg_stat_user_tables — visibility, not a
black box.
- POST /api/admin/maintenance/vacuum: enqueue the task on demand.
Tests: vacuum task runs + reports tables; db-stats shape; trigger queues.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Five small G5 findings from the 2026-06-02 audit. Each is local and
follows an established FC pattern.
- download_service: replace hardcoded ('discord','pixiv') tuple with
auth_type_for(platform) == 'token'. A 7th token-platform now picks
up the right credential path without touching this site.
- /api/tags/<source_id>/merge enqueues recompute_centroid.delay after
merge so the target's centroid reflects its new image set
immediately. Daily list_drifted catches it within 24h, but eager
recompute closes the suggestion-quality dip in the meantime.
- backfill_thumbnails added to beat_schedule (daily). The task
docstring claimed periodic Beat but the entry was never registered,
so the library got no self-healing thumbnail repair; only the
manual admin-UI button fired it.
- modal.createAndAdd pushes a kind='fandom' tag into
tagsStore.fandomCache so FandomPicker sees the new fandom on next
open. Was: cache-gated load (length===0) skipped refetch, new
fandom invisible until full page reload.
- cleanup cluster:
- Drop .webp from cleanup_service.unlink — thumbnailer only writes
.jpg/.png; the third tuple member was dead code.
- Drop effective_date from /api/gallery/scroll response — no FE
consumer reads it. Service still computes the attribute for
timeline ordering; this just trims the JSON.
- Rename store.recentMinute → store.recentRuns across the
systemActivity store + three consumers (SystemActivitySummary,
QueuesTable, SystemActivityTab). The data is the last 200 runs
(not actually "last minute"), so the name lied.
NOT in this bundle: the duplicate tag-merge endpoint
(/api/tags vs /api/admin/tags) is harder — has 1 FE caller and 3 tests
on the admin variant; consolidation is its own change.
Plugs the FC long-running-entity discipline gaps the 2026-06-02 audit
flagged: every entity that can get stuck now has recovery + retention +
timeout, and the long-runners no longer collide with the FC-3i sweep.
Recovery sweeps (every 5 min):
- recover_stalled_backup_runs — flips BackupRun stuck in
running/restoring past 7h (covers the 6.5h images-backup hard
limit) to error. prune_backups docstring corrected — the FC-3i
TaskRun sweep never touched BackupRun rows.
- recover_stalled_library_audit_runs — flips LibraryAuditRun stuck
past 135 min (10-min buffer above scan_library_for_rule's 2h5m
hard limit) to error. Previously a SIGKILL'd row blocked all
future audits until manual DB surgery.
- recover_stalled_import_batches — finalizes ImportBatch rows
stuck running >2h whose child tasks are all terminal (orphan case
where the orchestrator crashed before the closing UPDATE). Uses
the same EXISTS predicate /api/system/stats already had.
Retention (daily):
- prune_library_audit_runs — 30-day window. Audit rows carry
matched_ids JSONB blobs that can hold tens of thousands of ids.
- prune_import_batches — 30-day window. Cascades to ImportTask via
the model relationship.
time_limits on five long-runners that previously had none (the
audit's headline finding — every one of these collided with the
recover_stalled_task_runs 5-min default and could be marked
'error' mid-flight):
- scan_directory: 60m soft / 70m hard
- verify_integrity: 60m / 70m
- backfill_phash: 30m / 35m
- apply_allowlist_tags: 30m / 35m
- recompute_centroids: 30m / 35m
QUEUE_STUCK_THRESHOLD_MINUTES now covers maintenance (75) and scan
(75) — above the longest task on each — with per-task overrides
for the outliers (backup_images_task 420, restore_images_task 420,
scan_library_for_rule 130).
start_audit_run guard is now age-aware: a 'running' row older than
the audit hard limit doesn't block a new run (the sweep will catch
it within 5 min). Previously a SIGKILL'd row blocked forever.
/api/import/status now uses the same EXISTS predicate
/api/system/stats does, so the two endpoints no longer disagree on
the active-batch question.
DownloadEvent.started_at resets on pending→running so a freshly-
promoted event from a busy queue isn't measured against its
original enqueue time (was racing recover_stalled_download_events
on heavy-queue days).
The scan tick (scan.py:_tick_due_sources_async) inserts
DownloadEvent(status='pending') and fires download_source.delay(). If the
task dies before finalizing the event — worker OOM/SIGKILL, lost task, or
a gallery-dl that didn't unwind on the 1200s hard time_limit — the event
stays in-flight forever. Every later tick then skips the source via the
in-flight guard (scan.py:168), so Source.last_checked_at is never written
and the operator sees "last check never" in the Subscriptions health
column, permanently.
cleanup_old_download_events only prunes terminal events (by design); no
existing sweep covered the pending/running case. Operator confirmed
2026-05-29 with a diagnostic query: all 43 "never checked" sources were
stranded behind stale in-flight events (eligible_stuck_inflight = 43,
every other bucket zero).
New recover_stalled_download_events task (Beat every 5 min):
- Flips DownloadEvent rows pending/running > 30 min (10 min past the
download_source 1200s hard kill, so legitimately-running tasks are
never touched) to status='error' with a sentinel message.
- Bumps each affected Source's consecutive_failures ONCE per source —
backoff is 2^N on that counter so per-event bumps would needlessly
inflate the next interval — sets last_error, stamps last_checked_at.
UPDATE...RETURNING source_id avoids a SELECT-then-UPDATE-WHERE-IN that
would hit the psycopg 65535-param ceiling on a large strand pile.
Net: the 43 currently-stranded sources unstick on the first sweep after
deploy, their health dots flip amber instead of unchecked, and the next
scan tick re-queues them.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The celery_app.py beat-schedule edit failed with a stale-read error in
the Task 9 commit, so the ML daily jobs weren't registered. Adds
ml-backfill-daily, recompute-centroids-daily, apply-allowlist-sweep-daily.
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>
recover_interrupted_tasks runs every 5 minutes, finds ImportTask rows
stuck in 'processing' for >30 minutes (well above any legitimate import
duration), and re-queues them. cleanup_old_tasks runs daily and deletes
finished tasks older than 7 days so the task table stays an operational
view rather than an archive.
Both thresholds match ImageRepo's precedent. The 30-min stuck threshold
is documented inline so a future reader can adjust it intentionally
rather than mistaking it for a 'magic number'.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
scan_directory walks ImportSettings.import_scan_path, creates an
ImportBatch, enumerates supported files into ImportTasks, and enqueues
import_media_file per task. import_media_file moves the task through
its state machine (pending → queued → processing → complete/skipped/failed),
updates ImportBatch counters atomically (UPDATE ... SET col = col + 1),
enqueues a thumbnail task on success, and marks the batch complete when
the last task drains.
generate_thumbnail runs on its own queue (thumbnail) so big imports
don't starve thumbnail throughput; failure here is logged and does not
fail the import.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Routes are pre-declared for FC-2/FC-3 task modules (import, ml, thumbnail,
download, scan, maintenance). Queue lanes match the ImageRepo pattern where
beat+maintenance run on a separate worker so long imports don't starve
periodic tasks. Smoke ping task confirms the wiring in eager mode for CI.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusen