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* round robin on failure to launch

* still round-robin spot instances

* prioritize first AZ

* no more round-robining

* doc updates

* Order subnets by AZ

* add spot instance advisor link

* ensure we try all AZs

* fix typos
23 contributors

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@robertnishihara @ijrsvt @richardliaw @ericl @pcmoritz @DmitriGekhtman @alanwguo @barakmich @mfitton @edoakes @AdamGleave @simon-mo
# 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: 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" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
# image: rayproject/ray:latest-gpu # 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
# Example of running a GPU head with CPU workers
# head_image: "rayproject/ray-ml:latest-gpu"
# Allow Ray to automatically detect GPUs
# worker_image: "rayproject/ray-ml:latest-cpu"
# worker_run_options: []
# 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
# Whether to allow node reuse. If set to False, nodes will be terminated
# instead of stopped.
cache_stopped_nodes: True # If not present, the default is True.
# 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
# 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 node type's CPU and GPU resources are auto-detected based on AWS instance type.
# If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.
# You can also set custom resources.
# For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set
# resources: {"CPU": 1, "GPU": 1, "custom": 5}
resources: {}
# Provider-specific config for this node type, 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
node_config:
InstanceType: m5.large
ImageId: ami-0a2363a9cff180a64 # Deep Learning AMI (Ubuntu) Version 30
# You can provision additional disk space with a conf as follows
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeSize: 100
# Additional options in the boto docs.
ray.worker.default:
# 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: 2
# The node type's CPU and GPU resources are auto-detected based on AWS instance type.
# If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.
# You can also set custom resources.
# For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set
# resources: {"CPU": 1, "GPU": 1, "custom": 5}
resources: {}
# Provider-specific config for this node type, 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
node_config:
InstanceType: m5.large
ImageId: ami-0a2363a9cff180a64 # Deep Learning AMI (Ubuntu) Version 30
# Run workers on spot by default. Comment this out to use on-demand.
# NOTE: If relying on spot instances, it is best to specify multiple different instance
# types to avoid interruption when one instance type is experiencing heightened demand.
# Demand information can be found at https://aws.amazon.com/ec2/spot/instance-advisor/
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.
# 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:
- "**/.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:
- ".gitignore"
# 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 a Docker image 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).
# To run the nightly version of ray (as opposed to the latest), either use a rayproject docker image
# that has the "nightly" (e.g. "rayproject/ray-ml:nightly-gpu") or uncomment the following line:
# - pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"
# 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: []
# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
- ray stop
- 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
- ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
head_node: {}
worker_nodes: {}