feat(agent): idle-unload GPU models to free VRAM when the queue is idle

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

agent/fc_agent/config.py

@@ -51,6 +51,12 @@ class Config:
     bandwidth_limit_mb_s: float  # aggregate download cap in MEGABYTES/s across
     #                              all downloaders + video streams (0 = unlimited);
     #                              tunable live from the agent UI
+    idle_unload_seconds: float   # after this long with the GPU idle (nothing in
+    #                              flight, queue empty or Stopped), unload the
+    #                              SigLIP embedder + YOLO proposers to free their
+    #                              VRAM; they reload lazily on the next job. A
+    #                              24/7 agent otherwise squats on ~5GB doing
+    #                              nothing. 0 disables (keep models warm forever).
 
     @classmethod
     def from_env(cls) -> Config:
@@ -87,4 +93,8 @@ class Config:
             # link to ~1-1.5 MB/s per stream, browser included). Raise it (or 0)
             # from the agent UI on wired/faster networks.
             bandwidth_limit_mb_s=float(os.environ.get("BANDWIDTH_LIMIT_MB_S", "8")),
+            # 5 min: long enough that a lull between job bursts doesn't thrash the
+            # (few-second) reload, short enough that an agent left running with an
+            # empty queue hands its VRAM back promptly.
+            idle_unload_seconds=float(os.environ.get("IDLE_UNLOAD_SECONDS", "300")),
         )