# Slightly modified version of example file here: https://github.com/ray-project/ray/blob/236951ee4ce1b2c582c209fe7cc40d9fe5d0dd92/python/ray/autoscaler/gcp/example-full.yaml

# A unique identifier for the head node and workers of this cluster.
cluster_name: default

# The maximum number of workers nodes to launch in addition to the head
# node.
max_workers: 4

# The autoscaler will scale up the cluster faster with higher upscaling speed.
# E.g., if the task requires adding more nodes then autoscaler will gradually
# scale up the cluster in chunks of upscaling_speed*currently_running_nodes.
# This number should be > 0.
upscaling_speed: 1.0

# 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: "rayproject/ray:latest-cpu" # Change to latest-gpu if you need GPU support (needs longer startup time)
  # image: rayproject/ray-ml:latest-gpu   # use this one if you need ML dependencies; beware, it's slower to pull
  container_name: "ray_container"
  # 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"
    - --ulimit nofile=65536:65536

  # Example of running a GPU head with CPU workers
  # head_image: "rayproject/ray-ml:latest-gpu"
  # Allow Ray to automatically detect GPUs

  # worker_image: "rayproject/ray-ml:latest-cpu"
  # worker_run_options: []

# 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: europe-west1    # Belgium
    availability_zone: europe-west1-b
    project_id: <your-project-id>  # Globally unique project id
    use_internal_ips: true  # We want the cluster to use internal IPs only

# 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

# Tell the autoscaler the allowed node types and the resources they provide.
# The key is the name of the node type, which is just for debugging purposes.
# The node config specifies the launch config and physical instance type.
available_node_types:
    ray_head_default:
        # The resources provided by this node type.
        resources: {"CPU": 2}
        # 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
        node_config:
            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/common-cpu
            networkInterfaces:
              - kind: compute#networkInterface
                subnetwork: projects/<your-project-id>/regions/europe-west1/subnetworks/ray-subnet-europe-west1
            # 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: []
    ray_worker_small:
        # The minimum number of worker nodes of this type to launch.
        # This number should be >= 0.
        min_workers: 3
        # The maximum number of worker nodes of this type to launch.
        # This takes precedence over min_workers.
        max_workers: 5
        # The resources provided by this node type.
        resources: {"CPU": 4}
        # 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
        node_config:
            machineType: c2-standard-4
            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/common-cpu
            networkInterfaces:
              - kind: compute#networkInterface
                subnetwork: projects/<your-project-id>/regions/europe-west1/subnetworks/ray-subnet-europe-west1
            # Run workers on preemtible instance by default.
            # Comment this out to use on-demand.
            scheduling:
              - preemptible: true
            # Un-Comment this to launch workers with the Service Account of the Head Node
            # serviceAccounts:
            # - email: ray-autoscaler-sa-v1@<project_id>.iam.gserviceaccount.com
            #   scopes:
            #   - https://www.googleapis.com/auth/cloud-platform

    # Additional options can be found in in the compute docs at
    # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert

# Specify the node type of the head node (as configured above).
head_node_type: ray_head_default

# 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

# Patterns for files to exclude when running rsync up or rsync down
rsync_exclude:
    - "**/.git"
    - "**/.git/**"

# Pattern files to use for filtering out files when running rsync up or rsync down. The file is searched for
# in the source directory and recursively through all subdirectories. For example, if .gitignore is provided
# as a value, the behavior will match git's behavior for finding and using .gitignore files.
rsync_filter:
    - ".gitignore"

# 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 a Docker image 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).
    # To run the nightly version of ray (as opposed to the latest), either use a rayproject docker image
    # that has the "nightly" (e.g. "rayproject/ray-ml:nightly-gpu") or uncomment the following line:
    # - pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_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
    - >-
      ray start
      --head
      --port=6379
      --object-manager-port=8076
      --dashboard-host '0.0.0.0'
      --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
    - >-
      ray start
      --address=$RAY_HEAD_IP:6379
      --object-manager-port=8076
head_node: {}
worker_nodes: {}