wehub-resource-sync
188 lines
8.8 KiB
YAML
188 lines
8.8 KiB
YAML
# A 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 object 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: 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
|
|
# Set unique subnet mask or a random mask will be used.
|
|
# subnet_mask: 10.0.0.0/16
|
|
# Set unique id for resources in this cluster.
|
|
# If not set a default id will be generated based on the resource group and cluster name.
|
|
# unique_id: RAY1
|
|
# Set managed identity name and resource group;
|
|
# If not set, a default user-assigned identity will be generated in the resource group specified above.
|
|
# msi_name: ray-cluster-msi
|
|
# msi_resource_group: other-rg
|
|
# Set provisioning and use of public/private IPs for head and worker nodes;
|
|
# If both options below are true, only the head node will have a public IP address provisioned.
|
|
# use_internal_ips: True
|
|
# use_external_head_ip: True
|
|
|
|
# How Ray will authenticate with newly launched nodes.
|
|
auth:
|
|
ssh_user: ubuntu
|
|
# SSH keys will be auto-generated with Ray-specific names if not specified
|
|
# Uncomment and specify custom paths if you want to use different existing keys:
|
|
# ssh_private_key: /path/to/your/key.pem
|
|
# ssh_public_key: /path/to/your/key.pub
|
|
|
|
# You can make more specific customization to node configurations can be made using the ARM template azure-vm-template.json file.
|
|
# See this documentation here: https://docs.microsoft.com/en-us/azure/templates/microsoft.compute/2019-03-01/virtualmachines
|
|
# Changes to the local file will be used during deployment of the head node, however worker nodes deployment occurs
|
|
# on the head node, so changes to the template must be included in the wheel file used in setup_commands section below
|
|
|
|
# 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": 4}
|
|
# Provider-specific config, e.g. instance type.
|
|
node_config:
|
|
azure_arm_parameters:
|
|
vmSize: Standard_D4s_v3
|
|
# List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
|
|
imagePublisher: microsoft-dsvm
|
|
imageOffer: ubuntu-2204
|
|
imageSku: 2204-gen2
|
|
imageVersion: latest
|
|
|
|
# Or, use a custom image from Azure Compute Gallery.
|
|
# Note: if you use a custom image, then imagePublisher,
|
|
# imageOffer, imageSku, and imageVersion are ignored.
|
|
# imageId: /subscriptions/[subscription-id]/resourceGroups/[resource-group-id]/providers/Microsoft.Compute/galleries/[azure-compute-gallery-id]/images/[image-id]/versions/[image-version]
|
|
|
|
# Optionally set osDiskSize if you want to use a custom disk size.
|
|
# osDiskSize: 128
|
|
|
|
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 resources provided by this node type.
|
|
resources: {"CPU": 4}
|
|
# Provider-specific config, e.g. instance type.
|
|
node_config:
|
|
azure_arm_parameters:
|
|
vmSize: Standard_D4s_v3
|
|
# List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
|
|
imagePublisher: microsoft-dsvm
|
|
imageOffer: ubuntu-2204
|
|
imageSku: 2204-gen2
|
|
imageVersion: latest
|
|
# optionally set priority to use Spot instances
|
|
priority: Spot
|
|
# set a maximum price for spot instances if desired
|
|
# billingProfile:
|
|
# maxPrice: -1
|
|
|
|
# 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. Ray copies the same path on the head node 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:
|
|
# enable docker setup
|
|
- sudo usermod -aG docker $USER || true
|
|
- sleep 10 # delay to avoid docker permission denied errors
|
|
# get rid of annoying Ubuntu message
|
|
- touch ~/.sudo_as_admin_successful
|
|
|
|
# List of shell commands to run to set up nodes.
|
|
# NOTE: rayproject/ray-ml:latest has ray latest bundled
|
|
setup_commands: []
|
|
# Note: if you're developing Ray, you probably want to create a Docker image that
|
|
# has your Ray repo pre-cloned. Then, you can replace the pip installs
|
|
# below with a git checkout <your_sha> (and possibly a recompile).
|
|
# To run the nightly version of ray (as opposed to the latest), either use a rayproject docker image
|
|
# that has the "nightly" (e.g. "rayproject/ray-ml:nightly-gpu") or uncomment the following line:
|
|
# - pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp38-cp38-manylinux2014_x86_64.whl"
|
|
|
|
# Custom commands that will be run on the head node after common setup.
|
|
head_setup_commands:
|
|
- pip install -U azure-core==1.35.0 azure-cli-core==2.77.0 azure-identity==1.23.1 azure-mgmt-compute==35.0.0 azure-mgmt-network==29.0.0 azure-mgmt-resource==24.0.0 azure-common==1.1.28 msrest==0.7.1 msrestazure==0.6.4.post1
|
|
|
|
# Custom commands that will be run on worker nodes after common setup.
|
|
worker_setup_commands: []
|
|
|
|
# Command to start ray on the head node. You don't need to change this.
|
|
head_start_ray_commands:
|
|
- ray stop
|
|
- ray start --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml
|
|
|
|
# Command to start ray on worker nodes. You don't need to change this.
|
|
worker_start_ray_commands:
|
|
- ray stop
|
|
- ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
|