services:
  applio-studio:
    build: .
    image: mike-ai/applio-studio:3.6.4
    container_name: mike-ai-applio-studio
    restart: "no"
    # PyTorch DataLoader workers exchange training batches through /dev/shm.
    # Docker's 64 MiB default is far too small and can make failed training
    # runs look successful because of an upstream Applio exit-code bug.
    shm_size: "16gb"
    labels:
      com.mike-ai.applio-worker: applio
    environment:
      NVIDIA_VISIBLE_DEVICES: ${VOICE_GPU_UUID:?set VOICE_GPU_UUID to the RTX 5080 UUID}
      NVIDIA_DRIVER_CAPABILITIES: compute,utility
      HF_HOME: /models/huggingface
      PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True
    ports:
      - "127.0.0.1:8011:6969"
    volumes:
      - /data/voice/applio/huggingface:/models/huggingface
      - /data/voice/applio/logs:/app/logs
      - /data/voice/applio/models:/app/rvc/models
      - /data/voice/applio/datasets:/app/assets/datasets
      - /data/voice/applio/config.json:/app/assets/config.json
    healthcheck:
      test: ["CMD-SHELL", "curl -fsS http://127.0.0.1:6969/ >/dev/null"]
      interval: 5s
      timeout: 3s
      start_period: 900s
      retries: 3
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ["${VOICE_GPU_UUID:?set VOICE_GPU_UUID to the RTX 5080 UUID}"]
              capabilities: [gpu]
    networks:
      frontend:
        aliases: [applio-studio]

networks:
  frontend:
    name: mike-ai_frontend
    external: true