hunk.code_window:                 return string.replace("~", self.home_dir, 1)
           return string
       def run_init(self, *, as_head, file_mounts):
           image = self.docker_config.get("image")
           image = self.docker_config.get(
               f"{'head' if as_head else 'worker'}_image", image)
           self._check_docker_installed()
  
hunk.labels: keep keep keep keep add keep keep keep keep keep
hunk.after_edit:         BOOTSTRAP_MOUNTS = [
               "~/ray_bootstrap_config.yaml", "~/ray_bootstrap_key.pem"
           ]
  
hunk.file_path: python/ray/autoscaler/command_runner.py
hunk.type: add
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
  
  # Whether or not to autoscale aggressively. If this is enabled, if at any point
  #   we would start more workers, we start at least enough to bring us to
  #   initial_workers.
  autoscaling_mode: default
  
  # 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., rayproject/ray:0.8.7
      container_name: "" # e.g. ray_docker
      # If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
      # if no cached version is present.
      pull_before_run: True
      run_options: []  # Extra options to pass into "docker run"
  
  
  # 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/deeplearning-platform-release/global/images/family/tf-1-13-cpu
  
      # Additional options can be found in in the compute docs at
      # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
  
      # If the network interface is specified as below in both head and worker
      # nodes, the manual network config is used.  Otherwise an existing subnet is
      # used.  To use a shared subnet, ask the subnet owner to grant permission
      # for 'compute.subnetworks.use' to the ray autoscaler account...
      # networkInterfaces:
      #   - kind: compute#networkInterface
      #     subnetwork: path/to/subnet
      #     aliasIpRanges: []
  
  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/deeplearning-platform-release/global/images/family/tf-1-13-cpu
      # 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",
  }
  
  # Files or directories to copy from the head node to the worker nodes. The format is a
  # list of paths. The same path on the head node will be copied to the worker node.
  # This behavior is a subset of the file_mounts behavior. In the vast majority of cases
  # you should just use file_mounts. Only use this if you know what you're doing!
  cluster_synced_files: []
  
  # Whether changes to directories in file_mounts or cluster_synced_files in the head node
  # should sync to the worker node continuously
  file_mounts_sync_continuously: False
  
  # List of commands that will be run before `setup_commands`. If docker is
  # enabled, these commands will run outside the container and before docker
  # is setup.
  initialization_commands: []
  
  # List of shell commands to run to set up nodes.
  setup_commands:
      # Note: if you're developing Ray, you probably want to create an AMI 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
  
      # Install MiniConda.
      - >-
        wget https://repo.anaconda.com/miniconda/Miniconda3-latest-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"' >> ~/.profile
  
      # Install ray
      - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-1.1.0.dev0-cp37-cp37m-manylinux1_x86_64.whl
  
  
  # Custom commands that will be run on the head node after common setup.
  head_setup_commands:
    - pip install google-api-python-client==1.7.8
  
  # 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
        --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
        --address=$RAY_HEAD_IP:6379
        --object-manager-port=8076
  