The agent workload is download-bound (download 400–5462ms vs GPU ~300–600ms),
so the old N-slot serial chain (each slot: lease→download→decode→GPU→submit)
left the fast GPU idle during every download. Rearchitect worker.py into a
producer/consumer pipeline:
downloader pool (autoscaled by BUFFER OCCUPANCY) → bounded queue → 1–2 GPU
consumers (detect+embed→submit)
- Downloaders are I/O-bound → many overlap; the autoscaler now tunes DOWNLOADER
count by buffer fill (empty = GPU starving → add; full = outpacing GPU → add a
2nd consumer if it has util/VRAM headroom and lifts throughput, else trim).
- Bounded buffer (12) = backpressure: a full buffer blocks downloaders, capping
RAM + lease look-ahead. VRAM pressure sheds a consumer immediately.
- Heartbeat thread keeps every held lease alive (buffered jobs wait on the GPU;
curator's 180s TTL would otherwise reclaim them mid-buffer).
- Preserves all resilience: lease exp-backoff, submit-path retry (#169),
release-on-stop, region caps + video early-exit (#171). Stop drains BOTH pools
and releases every held lease at once (single held-set as source of truth).
- Consumers SHARE one embedder + proposers instance (a 2nd consumer adds
concurrent inference, not N× VRAM — bounds the VRAM creep seen with N slots).
- UI reworked for the pipeline: tiles show downloaders · buffer · on-GPU ·
processed · errors, a buffer-occupancy meter, and a consumers/waited-out line;
the dial now tunes downloaders. Build marker 2026-07-01.1.
Also fix the operator-flagged detector warning: yolo11n + the comic-panel model
threw "'Conv' object has no attribute 'bn'" on every image (ultralytics' load-
time Conv+BN fusion on a version-mismatched graph), silently disabling 2 of 3
crop proposers and spamming the log per image. Disable that fusion (unfused
inference is correct, marginally slower) and permanently self-disable a proposer
on the first inference failure instead of re-throwing forever.
Refs milestone 122.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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