gcp.yaml

# Defaults: https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/gcp/defaults.yaml

# 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: 1

# Cloud-provider specific configuration.
provider:
    type: gcp
    region: us-west1
    availability_zone: us-west1-a
    project_id: decisions-as-a-service # Globally unique project id

# 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": 32}
        # 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: e2-highcpu-32
            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
            # Additional options can be found in in the compute docs at
            # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
    ray_worker_small:
        # The minimum number of worker nodes of this type to launch.
        # This number should be >= 0.
        min_workers: 0
        # The maximum number of worker nodes of this type to launch.
        # This takes precedence over min_workers.
        max_workers: 4
        # The resources provided by this node type.
        resources: {"CPU": 32}
        # 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: e2-highcpu-32
            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
            # 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

# 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.
file_mounts: {
    "/home/ubuntu/daas": "/Users/eugenhotaj/Desktop/daas"
}

# 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:
    - ".rayignore"

# List of shell commands to run to set up nodes.
setup_commands: 
    - sudo chown -R $(whoami) /opt/conda/*
    - conda create -y -n "ray" python=3.9.0
    - conda init bash
    - conda activate ray && echo 'conda activate ray' >> ~/.bashrc  
    - python3 -m pip install --upgrade pip
    - python3 -m pip install -e /home/ubuntu/daas/