# Rollen "gpu-embed" und "gpu-small" (GPU-VMs mit Quadro P2000 + M4000): llama.cpp-Server (Task 6.1).
# Maxwell (sm_52) und Pascal (sm_61) brauchen CUDA 12.x – daher eigener Build mit festen Architekturen,
# derselbe Stand wie im Benchmark (ADR-0006): b6000 = Commit 4762ad7, CUDA 12.4.0, sm_52;61.
# Profile: embed (Embedding + Rerank, gpu-pve44/46), chat (verifier-small/signature-extractor, gpu-pve48).
# Ausgerollt von der Ansible-Rolle gpu-llamacpp (.env mit COMPOSE_PROFILES und Karten-UUIDs).
#
# Kartenwahl: je Dienst device_ids (UUID oder Index aus `nvidia-smi -L`). Ohne Angabe Index 1 = P2000
# (in gpu-pve44/46/48 ist Index 0 die M4000, sie bleibt frei). `count: 1` griffe die M4000.
# Netz: Host-Netz wie nex-speech. Veröffentlichte Docker-Ports liefen über DNAT und umgingen ufw;
# so gelten spätere ufw-Regeln der VM (Task 4.4).
# Datenschutz: kein --verbose (der loggt Prompts), /slots aus (zeigt Prompt-Inhalte), keine Web-Oberfläche.
name: knaster-gpu-llamacpp

x-logging: &logging
  driver: local
  options: {max-size: "5m", max-file: "2"}

x-llamacpp: &llamacpp
  build:
    context: https://github.com/ggml-org/llama.cpp.git#${LLAMACPP_COMMIT:-4762ad7316dcdec20016ab5985fb46a27902204d}
    dockerfile: .devops/cuda.Dockerfile
    target: server
    args:
      CUDA_VERSION: ${CUDA_VERSION:-12.4.0}
      CUDA_DOCKER_ARCH: ${CUDA_DOCKER_ARCH:-52;61}
  image: knaster/llamacpp-server:${LLAMACPP_REF:-b6000}-cu${CUDA_VERSION:-12.4.0}
  restart: unless-stopped
  network_mode: host
  read_only: true
  tmpfs: [/tmp:size=64m]
  labels: {autoheal: "true"}
  volumes:
    - ${MODELS_DIR:-/srv/models}:/models:ro
  logging: *logging

services:
  llamacpp-embed:
    <<: *llamacpp
    container_name: llamacpp-embed
    profiles: ["embed"]
    command: ["-m", "/models/${EMBED_GGUF:-Qwen3-Embedding-0.6B-Q8_0.gguf}", "--embedding",
              "--pooling", "${EMBED_POOLING:-last}", "-c", "8192", "-b", "8192", "-ub", "8192", "-np", "1",
              "-ngl", "99", "--alias", "embed-default", "--metrics", "--no-slots", "--no-webui",
              "--host", "0.0.0.0", "--port", "8081"]
    deploy:
      resources:
        reservations:
          devices: [{driver: nvidia, device_ids: ["${EMBED_GPU:-1}"], capabilities: [gpu]}]
    healthcheck: &hc
      test: ["CMD", "curl", "-fsS", "http://127.0.0.1:8081/health"]
      interval: 30s
      timeout: 5s
      retries: 3
      start_period: 120s

  llamacpp-rerank:
    <<: *llamacpp
    container_name: llamacpp-rerank
    profiles: ["embed"]
    command: ["-m", "/models/${RERANK_GGUF:-bge-reranker-v2-m3-Q8_0.gguf}", "--reranking",
              "-c", "8192", "-b", "8192", "-ub", "8192", "-np", "1", "-ngl", "99", "--alias", "rerank-default",
              "--metrics", "--no-slots", "--no-webui", "--host", "0.0.0.0", "--port", "8082"]
    deploy:
      resources:
        reservations:
          devices: [{driver: nvidia, device_ids: ["${RERANK_GPU:-1}"], capabilities: [gpu]}]
    healthcheck:
      <<: *hc
      test: ["CMD", "curl", "-fsS", "http://127.0.0.1:8082/health"]

  # Qwen3 ohne Denkphase (enable_thinking=false): Verifier und Signatur brauchen kurze, direkte Antworten,
  # und der Canary-Prober fragt mit max_tokens=5 nach "OK".
  llamacpp-chat:
    <<: *llamacpp
    container_name: llamacpp-chat
    profiles: ["chat"]
    command: ["-m", "/models/${CHAT_GGUF:-Qwen3-4B-Q4_K_M.gguf}", "-c", "8192", "-np", "1", "-ngl", "99",
              "--jinja", "--chat-template-kwargs", "{\"enable_thinking\": false}",
              "--alias", "verifier-small", "--metrics", "--no-slots", "--no-webui",
              "--host", "0.0.0.0", "--port", "8080"]
    deploy:
      resources:
        reservations:
          devices: [{driver: nvidia, device_ids: ["${CHAT_GPU:-1}"], capabilities: [gpu]}]
    healthcheck:
      <<: *hc
      test: ["CMD", "curl", "-fsS", "http://127.0.0.1:8080/health"]

  # Startet Container mit Label autoheal=true neu, wenn ihr Healthcheck dauerhaft scheitert
  # (restart: unless-stopped greift nur beim Absturz). Läuft in jedem Profil mit.
  autoheal:
    image: willfarrell/autoheal:1.2.0
    container_name: llamacpp-autoheal
    restart: unless-stopped
    environment: {AUTOHEAL_CONTAINER_LABEL: autoheal}
    volumes: ["/var/run/docker.sock:/var/run/docker.sock"]
    logging: *logging

# Exporter (node_exporter 9100, nvidia_gpu_exporter 9835) laufen nativ in der VM (Rolle gpu-exporters).