services:
  decisionlab:
    build:
      context: .
      args:
        # CPU by default. For NVIDIA GPUs use https://download.pytorch.org/whl/cu128
        TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
    image: decisionlab:latest
    container_name: DecisionLab
    ports:
      # localhost only by default (DL-SA-009). To reach the lab from other machines set DLAB_BIND=0.0.0.0 in .env.
      - "${DLAB_BIND:-127.0.0.1}:9910:9910"
    environment:
      TRUSTED_MODELING_SHA256: ${TRUSTED_MODELING_SHA256:-}
      DEVICE: auto                      # auto | cuda | cpu
      # The three Hub models (shown in this order, then any models in ./models, marked "(local)"):
      LIGHTDEC_ARTHUR_REPO: Falconsai/LightDec_Arthur
      LIGHTDEC_V2_REPO: Falconsai/LightDec_V2
      REFLUX_LAYA_REPO: yasserrmd/enterprise-reflux-laya-v21
      LAYA_REPO: convaiinnovations/laya
      MODELS_DIR: /models               # LightDec and Arthur folders in ./models (mounted below) are added too
      LIGHTDEC_VARIANT: fp16            # fp16 | int8 (compact-int8 folder), for every LightDec model
      # HF_TOKEN: hf_xxx               # only needed for gated or private repos
    volumes:
      - decisionlab-hf:/data/hf         # model cache survives rebuilds
      - ./models:/models:ro             # unzip each FalconDec model into its own folder here
    restart: unless-stopped
    # Uncomment for an NVIDIA GPU (needs the NVIDIA Container Toolkit and the cu128 build arg above):
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: 1
    #           capabilities: [gpu]

volumes:
  decisionlab-hf: