feat(b3): ml-worker becomes optional — embed-only role, decoupled GPU coordination, cpu-embed switch

The ml-worker's ONLY processing role is now the CPU whole-image embed fallback
(tag_and_embed renamed embed_image — Camie tagging was retired #1189 and the
name kept implying otherwise; videos were already handled agent-style: frame
sampling + mean-pool). Detection/cropping/CCIP stay GPU-agent-only, and their
completion is judged per-pipeline: ccip by gpu_job rows, siglip by concept
regions at the current model version — never by image_record.siglip_embedding.
A CPU embed therefore can NEVER close crop work for the agent (regression test
pins this; only the whole-image 'embed' job, the same artifact, is satisfied).

Making removal actually safe (operator will drop the container):
- GPU-queue coordination (enqueue_gpu_backfill, recover_orphaned_gpu_jobs,
  reprocess_gpu_jobs) moved verbatim to tasks/gpu_queue.py on the maintenance
  quick lane — it lived on the 'ml' queue only by module colocation, which made
  the ml-worker a hard dependency of the whole agent pipeline.
- New ml_settings.cpu_embed_enabled (migration 0074, default ON so agent-less
  installs keep working): OFF stops the four import hooks queueing embed work
  nothing will consume and no-ops the manual backfill; switch lives on the
  renamed 'CPU embedding backfill' card.
- NB heads training / auto-apply still run on the ml image (sklearn) — a stack
  that removes the container gives those up too.

Deploy note: in-flight messages under the old task names are dropped by the
new workers; the 60s orphan sweep + hourly backfill re-fire under the new
names immediately.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM

backend/app/api/gpu.py

@@ -96,7 +96,7 @@ async def backfill():
     """Enqueue a job for every image that doesn't already have one for `task`."""
     body = await request.get_json(silent=True) or {}
     task = str(body.get("task") or "ccip")
-    from ..tasks.ml import enqueue_gpu_backfill
+    from ..tasks.gpu_queue import enqueue_gpu_backfill
 
     r = enqueue_gpu_backfill.delay(task)
     return jsonify({"celery_task_id": r.id, "task": task}), 202
@@ -109,7 +109,7 @@ async def reprocess():
     detectors). Heavy — the back-catalogue is otherwise skipped by the backfills."""
     body = await request.get_json(silent=True) or {}
     task = str(body.get("task") or "ccip")
-    from ..tasks.ml import reprocess_gpu_jobs
+    from ..tasks.gpu_queue import reprocess_gpu_jobs
 
     r = reprocess_gpu_jobs.delay(task)
     return jsonify({"celery_task_id": r.id, "task": task}), 202