# intern-decision: Intern-Decision-4B (internlm, Apache-2.0) behind intern-decision-serve, on
# fv-ml1 GPU 1 (the utility card, beside vllm-coder, the erp/meromero seats and scriberr).
# 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 beside scriberr's peak
#   (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: ${MAX_QUEUE:-32}
    volumes:
      # Pinned weights AND the checkpoint's inference.py, read offline. Never downloads.
      - /tank/aimodels/huggingface:/hf:ro
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ["${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