The SigLIP embedder + YOLO proposers load lazily then stay resident for the
container's whole lifetime — a 24/7 agent with an empty queue squats on ~5GB of
VRAM doing nothing (operator-observed: 4900MiB held at GPU-util 8% / P8). Sleep
mode only sheds downloaders + poll cadence; even a UI Stop left the models loaded.
Add a monitor thread that unloads the torch-owned models after
cfg.idle_unload_seconds (env IDLE_UNLOAD_SECONDS, default 300; 0 disables) with
the GPU genuinely idle (active==0, buffer drained, no job completed in the
window), then torch.cuda.empty_cache() to hand the blocks back to the driver.
They reload lazily on the next job via the existing _ensure_embedder /
_proposers_for. Covers both sleep-mode idle and a full Stop. Surfaced in
/status (models_loaded) and the agent UI pipe line; the VRAM meter drops too.
Residual: imgutils CCIP/person ONNX sessions + the CUDA context stay resident
(no clean unload API) — idle VRAM drops substantially, not to zero.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TbrA36zNczjVhrM6cWThQa
Two issues surfaced by the live logs (GPU pegged at ~0% util, 0.5 jobs/s,
truncated-image failures):
- BATCH the SigLIP embeds: collect all of an image's crops (figure + booru_yolo
components + panels) and embed them in ONE forward pass instead of one
forward+lock per crop. The per-crop path serialised every crop through the
inference lock and starved the GPU (≈0% util, autoscaler stuck oscillating);
batching gives a real GPU-bound workload + far higher throughput. CCIP still
runs per figure inline.
- LOAD_TRUNCATED_IMAGES in the agent (matches the server embedder): slightly-
truncated scraped images now load instead of failing the job 3× then erroring
("image file is truncated (N bytes not processed)").
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
bvandeusen