vh
6b201e1d4a
Prime 2026-09-30: move scriberr to GPU 3 and extend the Jev endpoint to 32k tokens. Scriberr holds 0 VRAM idle; verified a 20-min job on GPU 3 at 5,496 MiB. With GPU 1 freed, intern-decision's measured card peak at MAX_TOKENS=32768 is 15,220 MiB against a 15,437 MiB budget (n=3, 1 and 16 questions); 32,769 tokens is refused 422 up front. JevBench v1.2.16 via /v1/systemone unchanged: 202/231, 0 diffs vs the bench.
67 lines
3.0 KiB
YAML
67 lines
3.0 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 and the erp/meromero seats; scriberr moved to
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# GPU 3 on 2026-09-30 1322, Prime, to free this card's headroom for 32k-token calls).
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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, fits GPU 1's free memory beside
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# the static vLLM seats (infra-ops budget, 2026-09-30); MAX_TOKENS keeps every accepted call under the cap. 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, MAX_TOKENS, 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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# Coupled to VRAM_CAP_GIB: the largest call measured to fit under the cap (README "VRAM").
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INTERN_DECISION_MAX_TOKENS: ${MAX_TOKENS:?set MAX_TOKENS}
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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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