GPU enablement (#872) cancelled — not worth the Pascal-specific build for a
modest CPU→GPU win on an old P4. Remove the dead GPU code (device.py, the CUDA
provider branch in tagger, the .to('cuda') path in embedder) so nothing carries
it forward.
Instead, bound CPU inference threads by default so the ml-worker is a predictable
core consumer on a SHARED node — the intended scaling model is multiple worker
replicas (each --concurrency=1, each its own cgroup limit), not one big
container. ONNX Runtime and torch otherwise size their thread pools to ALL host
cores, so each replica would grab every core and oversubscribe / starve the
co-located DB+web. Cap both to _INTRA_OP_THREADS=4 (matches the prior per-worker
cpus:4 unit): run N replicas where N×4 stays within the cores allotted to ML.
- tagger: ort.SessionOptions().intra_op_num_threads = 4 (CPUExecutionProvider).
- embedder: torch.set_num_threads(4).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Step 1 of GPU enablement (code only — CPU-safe, CI-green; the CUDA image is a
separate step pending the host driver version).
- New services/ml/device.py: FC_ML_DEVICE (auto|cuda|cpu) intent + VRAM knobs
(FC_ML_ONNX_GPU_MEM_GB, FC_ML_TORCH_MEM_FRACTION). Per-worker-host bootstrap →
env, not a DB setting (the GPU host runs CUDA, others CPU).
- tagger: use CUDAExecutionProvider (with gpu_mem_limit) when requested AND the
provider is actually present (onnxruntime-gpu), else CPUExecutionProvider. Logs
the active providers.
- embedder: move model + inputs to cuda when requested AND torch.cuda is
available; cap torch's VRAM share; .detach().cpu() before numpy. fp32 kept so
GPU embeddings stay in the same space as existing CPU ones.
Both AND the env intent with the framework's real availability, so on CPU
(CI / CPU onnxruntime / no GPU) they fall back cleanly — behavior unchanged.
The 8GB P4 is shared by both frameworks, hence the conservative default caps.
Tests: device env parsing. (tagger/embedder GPU paths are operator-verified on
the GPU host — models aren't in CI.)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The headline bug: aliases created from the modal NEVER resolved. Create
sent the normalized display name ('Sword', 'Uchiha Sasuke') while
resolution keys on the raw booru model key ('sword', 'uchiha_sasuke',
case-sensitive) — so the mapping was stored under a key nothing looks up,
and the prediction kept reappearing unaliased. The raw key wasn't even in
the /suggestions response, so the modal couldn't send it.
- Suggestion now carries raw_name (the model key an alias must use) and
via_alias (surfaced via an operator alias); both serialized by the API.
- Modal alias-create sends raw_name, not display_name (the fix). Aliased
suggestions show an 'alias' badge and a 'Remove alias' action; 'Treat as
alias for…' is hidden for centroid hits (no model key) and already-aliased
rows.
- Tag-side management: TagCard ⋮ → 'Aliases…' opens a dialog listing the
model keys that fold into a tag, with remove (GET /api/tags/<id>/aliases +
AliasService.list_for_tag). Creation stays in the modal suggestion flow.
Tests: full API round-trip locking the raw-key contract (raw_name exposed →
alias authored with it → resolves + via_alias on a later image);
list_for_tag (service + API); via_alias/raw_name on the existing service
suggestion tests. No migration.
Co-Authored-By: Claude Opus 4.8 (1M context) <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>
Consumer #4 of the store-floor change (#764). An allowlist tag can't
auto-apply more permissively than the ingest floor — predictions below
tagger_store_floor aren't stored, so a lower min_confidence behaves
identically to the floor. update_threshold now clamps to max(value, floor);
the AllowlistTable confidence input min-binds to the live floor and clamps
on edit. Keeps the stored threshold honest about actual apply behavior.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Promotes the prediction store-floor from the TAGGER_STORE_FLOOR env (default
0.05) to a DB-backed, Settings-UI-tunable ml_settings column (default 0.70).
Storing every tag down to 0.05 from a ~10k-tag tagger is what grew
image_record's TOAST to ~100 GB; the suggestion path already filters at 0.70
and the centroid/learned path covers lower-confidence preferred tags, so the
sub-0.70 tail is redundant. Foundation for plan-task #764 (backfill + reclaim
land next; this only changes the write gate for NEW imports).
- ml_settings.tagger_store_floor (migration 0044, default 0.70)
- tagger.Tagger.infer(store_floor=...); ml task passes settings.tagger_store_floor
- ML admin GET/PATCH expose it; PATCH rejects a category suggestion threshold
below the floor (nothing below the floor is stored, so the gap surfaces
nothing) — server backstop for the UI slider clamp
- Settings → ML: store-floor slider + caption; category sliders min-bound to it
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The typed dropdown sourced the threshold-filtered panel list (>= 0.70 general),
so low-confidence actions/features the model DID predict never appeared — forcing
hand-typed custom tags instead of accepting the model's canonical formatting.
Add a threshold override: SuggestionService.for_image(threshold_override=) and
GET /images/<id>/suggestions?min=<f> surface EVERY stored prediction (down to the
0.05 store floor), alias-resolved and normalized, still excluding applied/rejected
and unsurfaced categories. The suggestions store gains allByCategory + loadAll
(min=0); the dropdown searches that full set (cap 20), while the Suggestions panel
stays curated at the configured threshold. Accept/dismiss drop from both lists.
Operator-asked 2026-06-09. Test: a 0.30 general prediction is hidden by default
but surfaced with threshold_override=0.0; unsurfaced categories still excluded.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Rule 8 'no letters -> drop' was over-eager: bare digit tags like '2005'
returned None even though they're legitimate (booru year-tag shape).
Widen the keep-condition to any alphanumeric. Emoticons (':/', '^_^',
'+_+') still drop since they contain neither letters nor digits.
Camie's booru-style vocab strings (`uchiha_sasuke_(naruto)`,
`#unicus_(idolmaster)`, `1000-nen_ikiteru_(vocaloid)`, `:/`) were
surfacing raw in SuggestionsPanel — and worse, the SAME raw string was
written to tag.name on Accept, polluting the DB with `underscored_lowercase`
names that don't match the operator's "Title Case" tag convention.
Add backend/app/services/ml/tag_name.py with a single normalize()
applying nine rules (strip leading junk #/./+/;/~/_/ws, drop trailing
_(disambiguator) blocks iteratively, strip wrapping quotes, underscores
to spaces, space after colon, title-case each word's first char,
preserve hyphens/apostrophes/digits, drop entries with no letters).
Wire into SuggestionService.for_image:
- raw Camie key kept for alias_map lookup (alias rows are hand-curated
against raw keys; don't disturb)
- display_name = normalize(raw); None means drop the candidate
- existing-tag lookup widened to case-insensitive match against BOTH
raw and normalized forms so legacy underscore-named Tag rows accepted
before this change still surface as "existing" not "+ new"
Closes the last two findings from the 2026-06-02 audit (G5.1 + G5.4).
G5.1 — Centroid version no longer drifts:
CentroidService now reads MLSettings.embedder_model_version (the DB
row tag_and_embed already writes from) for both the centroid model-
version stamp and the drift-detection comparison. Previously the
centroid sites imported MODEL_VERSION from env, so the version stamped
on centroids could disagree with the version stamped on the embeddings
they were built from. By construction those now match, so list_drifted
won't silently miss the env-vs-DB drift case.
embedder.py keeps MODEL_VERSION as an env-driven constant for the
actual model loader — that's a different concern (which weights are
loaded) from the version-stamp that gets persisted alongside data.
G5.4 — Modal is a Pinia-only overlay:
The previous URL↔modal sync in GalleryView and ArtistGalleryTab
leaked the modal across route changes (RouterLink to /artist/<slug>
left the modal mounted on top of the new route) and re-opened it
on history back/forward with stale ?image=N entries.
Now: openImage() just calls modal.open(id) — no URL push.
GalleryView's dead closeImage helper is deleted. A route.name
watcher in App.vue closes the modal whenever the route changes,
which auto-fixes RouterLink-in-modal and back/forward.
Backward-compat: ?image=N is still honored on initial mount as a
one-shot deep-link opener, then router.replace strips the query so
the URL doesn't re-trigger and no extra history entry is added.
Existing bookmarks / shared URLs keep working; new opens stay
Pinia-only.
Four coupled operator-asked changes to the view modal (Scribe plan #509):
1. **Autofocus tag entry on modal open** — TagAutocomplete grabs focus
in onMounted/nextTick so the caret is in the input the moment the
modal renders. No click needed to start typing.
2. **General suggestions expanded by default** — SuggestionsPanel's
general-category group now mounts with `:default-open="true"`.
Operator can collapse if too noisy, but the v1 frame shows them.
3. **Lower general threshold default 0.95 → 0.50** — MLSettings.
suggestion_threshold_general default matches character. Alembic
0029 also bumps the existing singleton row's value if it's still
at the old 0.95. Operator can re-tune from Settings → ML.
4. **Retire `copyright` + `artist` as ML suggestion categories** —
neither feeds a Tag.kind (`artist` retired in FC-2d-vii-c, never
really existed as a copyright tag-kind). They were surfaced in the
suggestions pipeline + threshold settings UI but had no follow-
through. Drop from SURFACED_CATEGORIES, suggestions._threshold_for,
ml_admin GET/PATCH allowlist, MLSettings columns (alembic 0029
drops the two columns), frontend CATEGORY_ORDER + CATEGORY_LABELS,
SuggestionsPanel.peopleCats, AliasPickerDialog kind-check, and
MLThresholdSliders rows.
Out of scope (intentional): `tag_kind` Postgres enum still includes
`artist` for historic Tag row queryability (per the model comment);
no operator pain reported, no enum-shrink needed.
Tests:
- test_surfaced_categories asserts {character, general}, excludes
artist + copyright.
- test_threshold_for_artist_is_unsurfaced extended to cover copyright.
- test_get_and_patch_settings asserts new 0.50 default and the absent
artist + copyright keys in the GET payload.
The HF repo Camais03/camie-tagger-v2 has camie-tagger-v2.onnx (789 MB)
+ camie-tagger-v2-metadata.json (7.77 MB) at root, NOT model.onnx +
selected_tags.csv. Tags ship as nested JSON (dataset_info.tag_mapping)
not CSV. Per the published onnx_inference.py reference: input is NCHW
not NHWC, normalize with ImageNet mean/std, pad-square color (124,116,
104), sigmoid the second output (refined predictions) not the first.
Operator hit this during the IR migration ML backfill — download_models
silently fetched only 3 json files (allow_patterns matched nothing
useful), tagger.load() then raised RuntimeError. Fetched the actual
v2 layout via WebFetch, rewrote tagger to match published reference.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
onnxruntime is in requirements-ml.txt only (deliberately kept out of the
lean web image and CI). The top-level `import onnxruntime` broke pytest
collection of test_ml_tagger / test_ml_suggestions / test_tasks_ml even
though those are pure-logic/integration-marked, because collection
imports the module.
Mirrors the embedder's lazy-torch pattern: onnxruntime is imported inside
Tagger.load(), placed AFTER the file-existence checks so
test_load_raises_when_model_missing still gets RuntimeError (not
ModuleNotFoundError) in onnxruntime-less environments. self._session
annotation dropped to a comment to avoid an eval-time ort reference.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
AllowlistService: accept (apply ml_accepted + add to allowlist + clear
rejection; returns whether newly-added so API can kick retro-apply),
add_alias_and_accept, dismiss, reject_applied_tag (remove + record
rejection so the allowlist won't re-apply), threshold update, remove,
list_all.
TagService.rename: refuses on (name, kind, fandom_id) collision with a
message pointing at FC-2c merge. Tests marked integration.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The read path: load tagger_predictions, drop unsurfaced categories
(rating/meta/year), apply per-category thresholds, batch-resolve aliases,
skip applied + rejected, augment with centroid hits above the similarity
threshold, merge duplicate signals (take max score, mark source 'both'),
group by category, sort by score DESC. Tests marked integration.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
recompute_for_tag (mean of member embeddings, eligible-kind + min-refs
gated, upsert), list_drifted (the delta-gate: member-count mismatch OR
missing OR wrong model version), find_similar_tags (pgvector cosine
distance, similarity = 1 - distance). Tests marked integration.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
resolve() / resolve_many() (batch, used by the suggestion read path),
idempotent create, remove, list_all. Category-scoped so 'naruto' as
character vs copyright map to different canonicals. Tests marked
integration (real DB).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Direct port of ImageRepo's siglip.py. Lazy torch/transformers import so
the web container can import the module (for enqueue logic) without the
torch cost. EMBED_DIM=1152 asserted against the schema's Vector(1152)
columns. Real inference runs in the local integration suite.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
CPU-only, lazy-loaded, process-singleton ONNX session. Parses Camie's
string-category selected_tags.csv (vs WD14's integer Danbooru ids).
STORE_FLOOR (0.05) keeps the stored predictions JSON compact;
SURFACED_CATEGORIES gates which categories the suggestion UI shows
(meta/rating/year stored but never surfaced).
Inference itself isn't unit-tested (1GB model not in CI); the missing-
model error path and pure-logic surface are. Full inference runs in the
local integration suite.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusen