feat(intern-decision): decision seat swapped to h2o-lightning-4b on fv-ml1 GPU 1; eval seat down

The :8033 seat now serves h2oai/h2o-lightning-4b @ b43f759b on vLLM 0.30.0 with
byte-pinned KV (4.48 GiB, 135,558 tokens) behind services/h2o-decision-front:
32k refuse-not-trim admission, the seat's bearer token, and /decide +
/decide/shared translated onto /v1/systemone for albok-nemi's judge. URL, port,
token and stack name are unchanged. INC down 13:08:18, h2o healthy 13:11:27.

Post-checks on the live seat: /health at the pinned revision; JevBench public
205/231 (= bench); 422 over the cap; 16q at the cap warm 2.0-2.16 s; seat peak
16,164 MiB under 4 concurrent 16q x 32k calls against a 16,847 budget.

compose.inc.yaml + rollback-to-inc.sh restore Intern-Decision-4B in one step.
The temporary GPU 3 eval seat (stacks/h2o-eval) is down.
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2026-10-12 13:19:46 -07:00
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# intern-decision: Intern-Decision-4B (internlm, Apache-2.0) behind intern-decision-serve, on
# fv-ml1 GPU 1 (the utility card, beside vllm-coder and the erp/meromero seats; scriberr moved to
# GPU 3 on 2026-09-30 1322, Prime, to free this card's headroom for 32k-token calls).
# Replaces semif (Prime, 2026-09-30: "replace semif with intern-decision now").
#
# One forward pass per call, scored by the checkpoint's OWN inference.py (sha256-pinned); the
# service keeps semif-serve's HTTP surface (/decide, /decide/shared, /health). Service code +
# contract: services/intern-decision-serve/ (intern-decision-serve.contract.md). Image built on
# fv-ml1 from that dir.
#
# ⚠ VRAM_CAP_GIB is a HARD cap on torch's allocator (per-process memory fraction), set so the
# container's WHOLE nvidia-smi footprint, CUDA context included, fits GPU 1's free memory beside
# the static vLLM seats (infra-ops budget, 2026-09-30); MAX_TOKENS keeps every accepted call under the cap. A request that needs more
# gets 503 out_of_memory and the service stays up. See the README before changing it.
#
# .env (tunables): IMAGE, PORT, GPU_ID, VRAM_CAP_GIB, MAX_TOKENS, HOST_IP, INTERN_DECISION_API_TOKEN
# (vault intern-decision/api-token, >= 32 chars).
name: intern-decision
services:
intern-decision:
image: ${IMAGE:?set IMAGE}
container_name: intern-decision
restart: unless-stopped
ports:
- "${PORT:-8033}:8000"
environment:
INTERN_DECISION_API_TOKEN: ${INTERN_DECISION_API_TOKEN:?set INTERN_DECISION_API_TOKEN}
INTERN_DECISION_DEVICE: cuda
INTERN_DECISION_VRAM_CAP_GIB: ${VRAM_CAP_GIB:?set VRAM_CAP_GIB}
# Coupled to VRAM_CAP_GIB: the largest call measured to fit under the cap (README "VRAM").
INTERN_DECISION_MAX_TOKENS: ${MAX_TOKENS:?set MAX_TOKENS}
INTERN_DECISION_MAX_DECISIONS: ${MAX_DECISIONS:-64}
# POSTs in progress (queued + scoring) before new ones get 429 busy.
INTERN_DECISION_MAX_QUEUE: ${MAX_QUEUE:-32}
volumes:
# Pinned weights AND the checkpoint's inference.py, read offline. Never downloads.
- /tank/aimodels/huggingface:/hf:ro
# Triton/fla autotune cache (README "Cold-shape latency"): the ~6.5 s first-call-per-bucket
# autotune results survive RECREATES (deploys, image upgrades, .env edits), not just restarts.
# The mount point exists in the image owned by 10001, so the fresh volume is intern-writable.
- triton-cache:/tmp/triton-cache
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ["${GPU_ID:-1}"]
capabilities: [gpu]
healthcheck:
test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://127.0.0.1:8000/health', timeout=5)"]
interval: 30s
timeout: 10s
retries: 3
# Startup loads ~9 GB of weights and scores the warm-up three times before it serves.
start_period: 300s
networks:
- tnet
labels:
- homepage.group=AI - Eval & Retrieval
- homepage.name=Intern-Decision — typed decisions
- homepage.icon=mdi-scale-balance
- homepage.description=Typed decisions from one forward pass (Intern-Decision-4B, fv-ml1 GPU1)
- homepage.href=http://${HOST_IP:-10.251.50.54}:${PORT:-8033}/health
networks:
tnet:
name: traefik-net
external: true
volumes:
# -> intern-decision_triton-cache on the host
triton-cache: