###############################################################################
# Clinical Intelligence — Docker Compose
#
# Manages the infrastructure layer: LLM inference and protein structure
# prediction. OpenShell + OpenClaw run on the host.
#
# Quick start:
#   cp .env.example .env           # fill in NGC_API_KEY
#   make up                        # start all services
#   make setup                     # create sandbox + deploy config
#   make test                      # run test suite (levels 1-3)
#   make test-full                 # run all levels including agent tests
#
# Individual services:
#   docker compose up ollama -d    # just the LLM
#   docker compose up openfold3 -d # just protein prediction
#   docker compose logs -f ollama   # watch Ollama logs
###############################################################################

services:

  # ── Ollama (local LLM inference) ──────────────────────────────────
  # GPU pinning: LLM_GPU defaults to "0". On dual-GPU stations (e.g. RTX PRO
  # 6000 + GB300), set LLM_GPU in .env to the GB300 index — the RTX PRO 6000
  # is too small (98 GB) for Nemotron-3-Super (~94 GB resident).
  # Find the GB300 index with:
  #   nvidia-smi --query-gpu=index,name --format=csv,noheader | awk -F', ' '/GB300/{print $1; exit}'
  ollama:
    image: ollama/ollama:latest
    ports:
      - "${OLLAMA_PORT:-11434}:11434"
    volumes:
      - ollama-data:/root/.ollama
    environment:
      OLLAMA_HOST: "0.0.0.0"
      OLLAMA_KEEP_ALIVE: "4h"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ["${LLM_GPU:-0}"]
              capabilities: [gpu]
    healthcheck:
      test: ["CMD", "ollama", "list"]
      interval: 10s
      timeout: 5s
      retries: 30
      start_period: 30s
    restart: unless-stopped

  # ── Model puller (one-shot: pulls the model if missing) ──────────
  model-pull:
    image: ollama/ollama:latest
    depends_on:
      ollama:
        condition: service_healthy
    entrypoint: ["sh", "-c"]
    command:
      - |
        if ! ollama list 2>/dev/null | grep -q "${OLLAMA_MODEL:-nemotron-3-super:120b-a12b}"; then
          echo "Pulling ${OLLAMA_MODEL:-nemotron-3-super:120b-a12b}..."
          ollama pull "${OLLAMA_MODEL:-nemotron-3-super:120b-a12b}"
        else
          echo "Model already available."
        fi
    environment:
      OLLAMA_HOST: "http://ollama:11434"
    restart: "no"

  # ── OpenFold3 NIM (protein structure prediction) ──────────────────
  # GPU pinning: OPENFOLD_GPU defaults to "0". OpenFold3's PyTorch backend
  # crashes on multi-GPU containers with:
  #   "device >= 0 && device < num_gpus INTERNAL ASSERT FAILED"
  # Pinning to a single device avoids the crash loop. Set OPENFOLD_GPU in
  # .env to share or separate from the LLM GPU.
  # Image pull requires `docker login nvcr.io` first (see `make ngc-login`
  # or instructions.md Step 2). NGC_API_KEY in .env alone is not enough —
  # NGC_API_KEY is the runtime credential; docker login is the pull credential.
  openfold3:
    image: nvcr.io/nim/openfold/openfold3:latest
    ports:
      - "${OPENFOLD_PORT:-8000}:8000"
    environment:
      NGC_API_KEY: ${NGC_API_KEY:?Set NGC_API_KEY in .env}
      NIM_OPTIMIZED_BACKEND: torch_baseline
    shm_size: 16g
    ulimits:
      memlock: -1
      stack: 67108864
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ["${OPENFOLD_GPU:-0}"]
              capabilities: [gpu]
    healthcheck:
      test: ["CMD", "curl", "-sf", "http://localhost:8000/v1/health/ready"]
      interval: 30s
      timeout: 10s
      retries: 20
      start_period: 180s
    restart: unless-stopped

  # ── Test runner ───────────────────────────────────────────────────
  test:
    build:
      context: .
      dockerfile: docker/test/Dockerfile
    environment:
      OLLAMA_HOST: "ollama"
      OPENFOLD_HOST: "openfold3"
      SANDBOX_NAME: "${SANDBOX_NAME:-clinical-sandbox}"
    volumes:
      - ./test-results:/app/test-results
      - /var/run/docker.sock:/var/run/docker.sock:ro
    depends_on:
      ollama:
        condition: service_healthy
    profiles:
      - test
    entrypoint: ["bash", "scripts/test-all.sh"]
    command: ["--level", "3", "--verbose"]

volumes:
  ollama-data: