vh
a262477a61
stacks/intern-decision: compose (GPU 1, :8033, hard VRAM cap as the single .env knob, healthcheck, Homepage group 'AI - Eval & Retrieval'), .env.example and README. dns: intern-decision.fv.internal -> fv-ml1 (synced to ana/esh/nh3). acceptance on fv-ml1 GPU 3, 3 fresh processes: bit-identical to the Jev bench's native rows (pooled 240/259, Wyrd 79/84, 0/560 flips, Δp 0), negative control 10/122/14, 0 flips across restarts; largest accepted request 200 at a 10,134 MiB card peak under a 9.25 GiB cap; 503 and recovery proven at a tight cap. GPU 1 deploy held: nvidia-smi Free on GPU 1 is 15,442 MiB.
65 lines
2.7 KiB
YAML
65 lines
2.7 KiB
YAML
# intern-decision: Intern-Decision-4B (internlm, Apache-2.0) behind intern-decision-serve, on
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# fv-ml1 GPU 1 (the utility card, beside vllm-coder, the erp/meromero seats and scriberr).
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# Replaces semif (Prime, 2026-09-30: "replace semif with intern-decision now").
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#
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# One forward pass per call, scored by the checkpoint's OWN inference.py (sha256-pinned); the
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# service keeps semif-serve's HTTP surface (/decide, /decide/shared, /health). Service code +
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# contract: services/intern-decision-serve/ (intern-decision-serve.contract.md). Image built on
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# fv-ml1 from that dir.
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#
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# ⚠ VRAM_CAP_GIB is a HARD cap on torch's allocator (per-process memory fraction), set so the
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# container's WHOLE nvidia-smi footprint, CUDA context included, stays <= 10,300 MiB whatever
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# the request (Prime/infra-ops budget with scriberr, 2026-09-30). A request that needs more
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# gets 503 out_of_memory and the service stays up. See the README before changing it.
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#
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# .env (tunables): IMAGE, PORT, GPU_ID, VRAM_CAP_GIB, HOST_IP, INTERN_DECISION_API_TOKEN
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# (vault intern-decision/api-token, >= 32 chars).
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name: intern-decision
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services:
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intern-decision:
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image: ${IMAGE:?set IMAGE}
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container_name: intern-decision
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restart: unless-stopped
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ports:
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- "${PORT:-8033}:8000"
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environment:
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INTERN_DECISION_API_TOKEN: ${INTERN_DECISION_API_TOKEN:?set INTERN_DECISION_API_TOKEN}
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INTERN_DECISION_DEVICE: cuda
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INTERN_DECISION_VRAM_CAP_GIB: ${VRAM_CAP_GIB:?set VRAM_CAP_GIB}
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INTERN_DECISION_MAX_TOKENS: ${MAX_TOKENS:-8192}
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INTERN_DECISION_MAX_DECISIONS: ${MAX_DECISIONS:-64}
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# POSTs in progress (queued + scoring) before new ones get 429 busy.
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INTERN_DECISION_MAX_QUEUE: ${MAX_QUEUE:-32}
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volumes:
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# Pinned weights AND the checkpoint's inference.py, read offline. Never downloads.
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- /tank/aimodels/huggingface:/hf:ro
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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device_ids: ["${GPU_ID:-1}"]
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capabilities: [gpu]
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8000/health', timeout=5)"]
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interval: 30s
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timeout: 10s
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retries: 3
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# Startup loads ~9 GB of weights and scores the warm-up three times before it serves.
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start_period: 300s
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networks:
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- tnet
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labels:
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- homepage.group=AI - Eval & Retrieval
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- homepage.name=Intern-Decision — typed decisions
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- homepage.icon=mdi-scale-balance
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- homepage.description=Typed decisions from one forward pass (Intern-Decision-4B, fv-ml1 GPU1)
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- homepage.href=http://${HOST_IP:-10.251.50.54}:${PORT:-8033}/health
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networks:
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tnet:
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name: traefik-net
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external: true
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