feat(agent): GPU load readout + live worker-count tuning (#114)
Control UI gains what the operator asked for: - GPU load (nvidia-smi): util %, VRAM used/total + bar, temp — so you can see how hard the card is working while you're at the desktop. - Worker count is now a live − / + control (POST /concurrency), not just an env: the worker is a pool of independent slots (shared model, so slots add concurrent inference, not N× VRAM). Dial up for speed, down to free the card. Replaces pause/resume with Start/Stop + the worker dial. - Graceful release on stop / pool-shrink: a slot hands its still-leased jobs back via client.release() so they're re-picked immediately (pairs with the server recovery sweep). Not CI-tested (agent/ outside CI) — verified by running. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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@@ -8,7 +8,8 @@ class Config:
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fc_url: str # base URL of the FabledCurator web service
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token: str # the bearer token from Settings → Tagging → GPU agent
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agent_id: str # identifies this agent's leases
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batch_size: int # jobs leased per round (concurrency is still 1)
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batch_size: int # jobs a worker leases per round
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concurrency: int # INITIAL parallel workers (tunable live from the UI)
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ccip_model: str # imgutils CCIP model name ("" → imgutils default)
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detector_level: str # imgutils person-detector level: n|s|m|x
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poll_idle_seconds: float # wait between empty leases
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@@ -20,6 +21,7 @@ class Config:
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token=os.environ.get("FC_TOKEN", ""),
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agent_id=os.environ.get("AGENT_ID", "desktop-agent"),
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batch_size=int(os.environ.get("BATCH_SIZE", "4")),
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concurrency=int(os.environ.get("CONCURRENCY", "1")),
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ccip_model=os.environ.get("CCIP_MODEL", ""),
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detector_level=os.environ.get("DETECTOR_LEVEL", "m"),
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poll_idle_seconds=float(os.environ.get("POLL_IDLE_SECONDS", "10")),
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