jupyterlab-python-docker-stack/docker-compose.yml at main · empowerai/jupyterlab-python-docker-stack · GitHub

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version: "3.8"
services:
  gpu-jupyter-noconda-py311:
    deploy:
      resources:
        reservations:
          devices:
            - capabilities:
              - gpu
    # # Set hardware limits: one GPU, max. 48GB RAM, max. 31 GPUs
    #         - driver: nvidia
    #           capabilities: [gpu]
    #           device_ids: ["0"]  # select one GPU
    #     limits:
    #       cpus: "31.0"
    #       memory: 48g
    ports:
      - 8888:8888
      - 5005:5000
    volumes:
      - /home/nvadmin/data:/home/${NB_USER}/work
      - hf_cache:/home/${NB_USER}/.cache/huggingface/hub
    working_dir: /home/${NB_USER}
    environment:
      # enable sudo permissions
      GRANT_SUDO: "yes"
      CHOWN_HOME: "yes"
      CHOWN_HOME_OPTS: "-R"
      # CHOWN_EXTRA: "/home/${NB_USER}/.cache"
      # CHOWN_OPTS: "-R"
      # JUPYTER_ENABLE_LAB: "yes"
    # .env vars
      NB_USER: $NB_USER
      NB_UID: $NB_UID
      NB_GID: $NB_GID
      MLFLOW_S3_ENDPOINT_URL: $MLFLOW_S3_ENDPOINT_URL
      MLFLOW_TRACKING_URI: $MLFLOW_TRACKING_URI
      AWS_ACCESS_KEY_ID: $AWS_ACCESS_KEY_ID`
      AWS_SECRET_ACCESS_KEY: $AWS_SECRET_ACCESS_KEY
      CHROMA_API_IMPL: $CHROMA_API_IMPL
      CHROMA_SERVER_HOST: $CHROMA_SERVER_HOST
      CHROMA_SERVER_HTTP_PORT: $CHROMA_SERVER_HTTP_PORT

    user:
      "root"
    restart: always
    shm_size: '2gb'
    image: ncicto/gpu-jupyter-noconda-py311:latest
    container_name: gpu-jupyter-noconda-py311
    # image: cschranz/gpu-jupyter:v1.5_cuda-11.6_ubuntu-20.04_python-only
    # container_name: gpu-jupyter-cschranz
    # Public bridge neccesary when running on same host as mlflow service
    networks:
      - public
networks:
  public:
    driver: bridge
volumes:
  hf_cache: