# 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 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-gpu"
    # image: rayproject/ray:latest-gpu   # use this one if you don't need ML dependencies, it's faster to pull
    container_name: "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: False
    run_options: []  # Extra options to pass into "docker run"

    # Example of running a GPU head with CPU workers
    # head_image: "rayproject/ray-ml:latest-gpu"

    # worker_image: "rayproject/ray-ml:latest"

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

# Cloud-provider specific configuration.
provider:
    type: azure
    # https://azure.microsoft.com/en-us/global-infrastructure/locations
    location: westus2
    resource_group: ray-cluster
    # set subscription id otherwise the default from az cli will be used
    # subscription_id: 00000000-0000-0000-0000-000000000000   

# How Ray will authenticate with newly launched nodes.
auth:
    ssh_user: ubuntu
    # you must specify paths to matching private and public key pair files
    # use `ssh-keygen -t rsa -b 4096` to generate a new ssh key pair
    ssh_private_key: ~/.ssh/id_rsa
    # changes to this should match what is specified in file_mounts
    ssh_public_key: ~/.ssh/id_rsa.pub

# Provider-specific config for the head node, e.g. instance type. By default
# Ray will auto-configure unspecified fields using defaults.yaml
head_node:
    azure_arm_parameters:
        vmSize: Standard_NC6
        # List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
        imagePublisher: microsoft-dsvm
        imageOffer: ubuntu-1804
        imageSku: "1804"
        imageVersion: 20.07.06

# Provider-specific config for worker nodes, e.g. instance type. By default
# Ray will auto-configure unspecified fields using defaults.yaml
worker_nodes:
    azure_arm_parameters:
        vmSize: Standard_NC6
        # List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
        imagePublisher: microsoft-dsvm
        imageOffer: ubuntu-1804
        imageSku: "1804"
        imageVersion: 20.07.06

# 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",
     "/home/ubuntu/.ssh/id_rsa.pub": "~/.ssh/id_rsa.pub"
}

# 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:
    # get rid of annoying Ubuntu message
    - touch ~/.sudo_as_admin_successful

# 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 'eval "$(conda shell.bash hook)"' >> ~/.bashrc
    # - echo 'conda activate py37_pytorch' >> ~/.bashrc
    - echo 'conda activate py37_tensorflow' >> ~/.bashrc
    - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-1.2.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl
    # 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

# Custom commands that will be run on the head node after common setup.
head_setup_commands: 
    - pip install azure-cli-core==2.4.0 azure-mgmt-compute==12.0.0 azure-mgmt-msi==1.0.0 azure-mgmt-network==10.1.0

# 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
