# An 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: 5

# 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-ml:latest"
    # image: rayproject/ray:latest   # use this one if you don't need ML dependencies, it's faster 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

# If a node is idle for this many minutes, it will be removed.
idle_timeout_minutes: 5

# Cloud-provider specific configuration.
provider:
    type: vsphere

# How Ray will authenticate with newly launched nodes.
auth:
    ssh_user: ray
# 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.
    ssh_private_key: ~/ray-bootstrap-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:
        # You can override the resources here. Adding GPU to the head node is not recommended.
        # resources: { "CPU": 2, "Memory": 4096}
        resources: {}
        node_config: {"vm_class": "best-effort-xlarge"}
    worker:
        # The minimum number of nodes of this type to launch.
        # This number should be >= 0.
        min_workers: 1
        max_workers: 3
        # You can override the resources here. For GPU, currently only NVIDIA GPU is supported. If no ESXi host can
        # fulfill the requirement, the Ray node creation will fail. The number of created nodes may not meet the desired
        # minimum number. The vSphere node provider will not distinguish the GPU type. It will just count the quantity:
        # mount the first k random available NVIDIA GPU to the VM, if the user set {"GPU": k}.
        # resources: {"CPU": 2, "Memory": 4096, "GPU": 1}
        resources: {}
        node_config: {"vm_class": "best-effort-xlarge"}
    worker_2:
         # The minimum number of nodes of this type to launch.
         # This number should be >= 0.
         min_workers: 1
         max_workers: 2
         # You can override the resources here. For GPU, currently only NVIDIA GPU is supported. If no ESXi host can
         # fulfill the requirement, the Ray node creation will fail. The number of created nodes may not meet the desired
         # minimum number. The vSphere node provider will not distinguish the GPU type. It will just count the quantity:
         # mount the first k random available NVIDIA GPU to the VM, if the user set {"GPU": k}.
         # resources: {"CPU": 2, "Memory": 4096, "GPU": 1}
         resources: {}
         node_config: {"vm_class": "best-effort-xlarge"}

# 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: []

# 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: []

# 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: []

# Custom commands that will be run on the head node after common setup.
head_setup_commands:
    - pip install 'git+https://github.com/vmware/vsphere-automation-sdk-python.git'

# 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 --autoscaling-config=~/ray_bootstrap_config.yaml --dashboard-host=0.0.0.0

# 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