ray_cluster_template.yaml

# An unique identifier for the head node and workers of this cluster.
cluster_name: <USER>_<IDENTIFIER>

## NOTE: Typically for local clusters, min_workers == initial_workers == max_workers.

# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
# Typically, min_workers == initial_workers == max_workers.
min_workers: 1
# The initial number of worker nodes to launch in addition to the head node.
# Typically, min_workers == initial_workers == max_workers.
initial_workers: 1

# The maximum number of workers nodes to launch in addition to the head node.
# This takes precedence over min_workers.
# Typically, min_workers == initial_workers == max_workers.
max_workers: 1

# Autoscaling parameters.
# Ignore this if min_workers == initial_workers == max_workers.
autoscaling_mode: default
target_utilization_fraction: 0.8
idle_timeout_minutes: 5

# 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. Assumes Docker is installed.
docker:
  image: "" # e.g., tensorflow/tensorflow:1.5.0-py3
  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"

# Local specific configuration.
provider:
  type: local
  head_ip: <HEAD_IP>
  worker_ips:
    - <WORKER_IP_1>
    - <WORKER_IP_2>
    - <WORKER_IP_3>

# How Ray will authenticate with newly launched nodes.
auth:
  ssh_user: <USER>
  ssh_private_key: ~/.ssh/id_rsa

# Leave this empty.
head_node: {}

# Leave this empty.
worker_nodes: {}

# 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",
# }

# 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 each nodes.
setup_commands: []

# Custom commands that will be run on the head node after common setup.
head_setup_commands: []

# Custom commands that will be run on worker nodes after common setup.
worker_setup_commands: []

# NOTE: Modified the following commands to use the tf2-gpu environment
# and to use specific ports that have been opened for this purpose
# by Andrew Sedler (asedler3@gatech.edu)

# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
  - conda activate <CONDA-ENV> && ray stop
  - conda activate <CONDA-ENV> && ulimit -c unlimited && ray start --head --redis-port=10000 --redis-shard-ports=10001 --node-manager-port=10002 --object-manager-port=10003 --gcs-server-port=10004 --min-worker-port=10005 --max-worker-port=10099 --autoscaling-config=~/ray_bootstrap_config.yaml

# Command to start ray on worker nodes. You don't need to change this.
worker_start_ray_commands:
  - conda activate <CONDA-ENV> && ray stop
  - conda activate <CONDA-ENV> && ray start --address=$RAY_HEAD_IP:10000 --node-manager-port=10002 --object-manager-port=10003 --min-worker-port=10005 --max-worker-port=10099