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
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
Answers "how are videos/all media handled by the GPU worker": a job is per ITEM,
but the agent fans a VIDEO into per-frame instances (ffmpeg in the agent, the
existing cadence), each stored with a timestamp — so a video becomes a BAG of
frame embeddings (fixes the mean-embedding muddle) instead of one washed-out
vector. Stills → frame_time NULL; animated GIF/WebP treated like short video.
- image_region.frame_time (migration 0061, not yet deployed so folded in): the
source frame's seconds for video/animated media; NULL for stills. RegionService
passes it through. A whole frame is just kind='frame'.
- gpu_job + GpuJobService (migration 0062): the durable work list that keeps the
desktop agent HTTP-only — enqueue (dedupes (image,task)) / lease (FOR UPDATE
SKIP LOCKED, re-claims expired leases so the queue self-heals) / heartbeat /
complete / fail (re-queues until MAX_ATTEMPTS then 'error'). The server enqueues;
the agent leases+submits over the web API; Redis/Postgres stay private.
Tests: enqueue dedupe, lease-then-skip-when-held, expired-lease reclaim, scoped
heartbeat, complete, fail-requeue-then-error. region test now covers frame_time.
NEXT: the thin HTTP API (lease/submit/heartbeat) + bearer-token auth, then the
agent container + control UI.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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