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# 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 initial number of worker nodes to launch in addition to the head
# node. When the cluster is first brought up (or when it is refreshed with a
# subsequent `ray up`) this number of nodes will be started.
initial_workers: 0
# Whether or not to autoscale aggressively. If this is enabled, if at any point
# we would start more workers, we start at least enough to bring us to
# initial_workers.
autoscaling_mode: default
# 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: "" # e.g., tensorflow/tensorflow:1.5.0-py3
container_name: "" # e.g. ray_docker
run_options: [] # Extra options to pass into "docker run"
# Example of running a GPU head with CPU workers
# head_image: "tensorflow/tensorflow:1.13.1-py3"
# head_run_options:
# - --runtime=nvidia
# worker_image: "ubuntu:18.04"
# worker_run_options: []
# The autoscaler will scale up the cluster to this target fraction of resource
# usage. For example, if a cluster of 10 nodes is 100% busy and
# target_utilization is 0.8, it would resize the cluster to 13. This fraction
# can be decreased to increase the aggressiveness of upscaling.
# This value must be less than 1.0 for scaling to happen.
target_utilization_fraction: 0.8
# If a node is idle for this many minutes, it will be removed.
idle_timeout_minutes: 5
# Cloud-provider specific configuration.
provider:
type: aws
region: us-west-2
# Availability zone(s), comma-separated, that nodes may be launched in.
# Nodes are currently spread between zones by a round-robin approach,
# however this implementation detail should not be relied upon.
availability_zone: us-west-2a,us-west-2b
# 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.
# ssh_private_key: /path/to/your/key.pem
# Provider-specific config for the head node, e.g. instance type. By default
# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
# For more documentation on available fields, see:
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
head_node:
InstanceType: m5.large
ImageId: ami-05931d11d2bf831c3 # Deep Learning AMI (Ubuntu) Version 24.3
# You can provision additional disk space with a conf as follows
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeSize: 100
# Additional options in the boto docs.
# Provider-specific config for worker nodes, e.g. instance type. By default
# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
# For more documentation on available fields, see:
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
worker_nodes:
InstanceType: m5.large
ImageId: ami-05931d11d2bf831c3 # Deep Learning AMI (Ubuntu) Version 24.3
# Run workers on spot by default. Comment this out to use on-demand.
InstanceMarketOptions:
MarketType: spot
# Additional options can be found in the boto docs, e.g.
# SpotOptions:
# MaxPrice: MAX_HOURLY_PRICE
# Additional options in the boto docs.
# 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 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 'export PATH="$HOME/anaconda3/envs/tensorflow_p36/bin:$PATH"' >> ~/.bashrc
# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.8.0.dev6-cp27-cp27mu-manylinux1_x86_64.whl
# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.8.0.dev6-cp35-cp35m-manylinux1_x86_64.whl
- pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.8.0.dev6-cp36-cp36m-manylinux1_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 boto3==1.4.8 # 1.4.8 adds InstanceMarketOptions
# 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 --redis-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 --redis-address=$RAY_HEAD_IP:6379 --object-manager-port=8076
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