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file: # An unique identifier for the head node and workers of this cluster.
  cluster_name: default
  
  # The minimum number of workers nodes to launch in addition to the head
  # node. This number should be >= 0.
  min_workers: 0
  
  # The maximum number of workers nodes to launch in addition to the head
  # node. This takes precedence over min_workers.
  max_workers: 2
  
  # The initial number of worker nodes to launch in addition to the head
  # node. When the cluster is first brought up (or when it is refreshed with a
  # subsequent `ray up`) this number of nodes will be started.
  initial_workers: 0
  
  # This executes all commands on all nodes in the docker container,
  # and opens all the necessary ports to support the Ray cluster.
  # Empty string means disabled.
  docker:
      image: "" # e.g., tensorflow/tensorflow:1.5.0-py3
      container_name: "" # e.g. ray_docker
  
  
  # The autoscaler will scale up the cluster to this target fraction of resource
  # usage. For example, if a cluster of 10 nodes is 100% busy and
  # target_utilization is 0.8, it would resize the cluster to 13. This fraction
  # can be decreased to increase the aggressiveness of upscaling.
  # This value must be less than 1.0 for scaling to happen.
  target_utilization_fraction: 0.8
  
  # If a node is idle for this many minutes, it will be removed.
  idle_timeout_minutes: 5
  
  # Cloud-provider specific configuration.
  provider:
      type: gcp
      region: us-west1
      availability_zone: us-west1-a
      project_id: null # Globally unique project id
  
  # How Ray will authenticate with newly launched nodes.
  auth:
      ssh_user: ubuntu
  # By default Ray creates a new private keypair, but you can also use your own.
  # If you do so, make sure to also set "KeyName" in the head and worker node
  # configurations below. This requires that you have added the key into the
  # project wide meta-data.
  #    ssh_private_key: /path/to/your/key.pem
  
  # Provider-specific config for the head node, e.g. instance type. By default
  # Ray will auto-configure unspecified fields such as subnets and ssh-keys.
  # For more documentation on available fields, see:
  # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
  head_node:
      machineType: n1-standard-2
      disks:
        - boot: true
          autoDelete: true
          type: PERSISTENT
          initializeParams:
            diskSizeGb: 50
            # See https://cloud.google.com/compute/docs/images for more images
            sourceImage: projects/ubuntu-os-cloud/global/images/family/ubuntu-1604-lts  # Ubuntu
  
      # Additional options can be found in in the compute docs at
      # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
  
  worker_nodes:
      machineType: n1-standard-2
      disks:
        - boot: true
          autoDelete: true
          type: PERSISTENT
          initializeParams:
            diskSizeGb: 50
            # See https://cloud.google.com/compute/docs/images for more images
            sourceImage: projects/ubuntu-os-cloud/global/images/family/ubuntu-1604-lts  # Ubuntu
      # Run workers on preemtible instance by default.
      # Comment this out to use on-demand.
      scheduling:
        - preemptible: true
  
      # Additional options can be found in in the compute docs at
      # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
  
  # Files or directories to copy to the head and worker nodes. The format is a
  # dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
  file_mounts: {
  #    "/path1/on/remote/machine": "/path1/on/local/machine",
  #    "/path2/on/remote/machine": "/path2/on/local/machine",
  }
  
  # List of shell commands to run to set up nodes.
  setup_commands:
      # Consider uncommenting these if you also want to run apt-get commands during setup
      # - sudo pkill -9 apt-get || true
      # - sudo pkill -9 dpkg || true
      # - sudo dpkg --configure -a
  
      # Install basics.
      - sudo apt-get update
      - >-
        sudo apt-get install -y
        cmake
        pkg-config
        build-essential
        autoconf
        curl
        libtool
        unzip
        flex
        bison
        python
      # Install Anaconda.
      - >-
        wget https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh -O ~/anaconda3.sh
        || true
      - bash ~/anaconda3.sh -b -p ~/anaconda3 || true
      - rm ~/anaconda3.sh
      - echo 'export PATH="$HOME/anaconda3/bin:$PATH"' >> ~/.bashrc
  
      # Build Ray.
      # Note: if you're developing Ray, you probably want to create a boot-disk
      # that has your Ray repo pre-cloned. Then, you can replace the pip installs
      # below with a git checkout <your_sha> (and possibly a recompile).
      - echo 'export PATH="$HOME/anaconda3/envs/tensorflow_p36/bin:$PATH"' >> ~/.bashrc
      - >-
        pip install
        google-api-python-client==1.6.7
        cython==0.29.0
      # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.2-cp27-cp27mu-manylinux1_x86_64.whl
      # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.2-cp35-cp35m-manylinux1_x86_64.whl
      # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.6.2-cp36-cp36m-manylinux1_x86_64.whl
      - >-
        cd ~
        && git clone https://github.com/ray-project/ray || true
      - >-
        cd ~/ray/python
        && pip install -e . --verbose
  
  # Custom commands that will be run on the head node after common setup.
  head_setup_commands: []
  
  # Custom commands that will be run on worker nodes after common setup.
  worker_setup_commands: []
  
  # Command to start ray on the head node. You don't need to change this.
  head_start_ray_commands:
      - ray stop
      - >-
        ulimit -n 65536;
        ray start
        --head
        --redis-port=6379
        --object-manager-port=8076
        --autoscaling-config=~/ray_bootstrap_config.yaml
  
  # Command to start ray on worker nodes. You don't need to change this.
  worker_start_ray_commands:
      - ray stop
      - >-
        ulimit -n 65536;
        ray start
        --redis-address=$RAY_HEAD_IP:6379
        --object-manager-port=8076
  