wehub-resource-sync
165 lines
7.7 KiB
YAML
165 lines
7.7 KiB
YAML
# An unique identifier for the head node and workers of this cluster.
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cluster_name: default
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# The maximum number of workers nodes to launch in addition to the head
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# node.
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max_workers: 2
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# The autoscaler will scale up the cluster faster with higher upscaling speed.
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# E.g., if the task requires adding more nodes then autoscaler will gradually
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# scale up the cluster in chunks of upscaling_speed*currently_running_nodes.
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# This number should be > 0.
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upscaling_speed: 1.0
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# This executes all commands on all nodes in the docker container,
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# and opens all the necessary ports to support the Ray cluster.
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# Empty object means disabled.
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docker: {}
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 5
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# Cloud-provider specific configuration.
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provider:
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type: azure
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# https://azure.microsoft.com/en-us/global-infrastructure/locations
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location: westus2
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resource_group: ray-cluster
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# set subscription id otherwise the default from az cli will be used
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# subscription_id: 00000000-0000-0000-0000-000000000000
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# set unique subnet mask or a random mask will be used
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# subnet_mask: 10.0.0.0/16
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# set unique id for resources in this cluster
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# if not set a default id will be generated based on the resource group and cluster name
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# unique_id: RAY1
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# Availability zones for VM placement (comma-separated). Examples:
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# availability_zone: "1,2,3" # Use zones 1, 2, and 3
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# availability_zone: "1" # Use only zone 1
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# availability_zone: "none" # Explicitly disable zones
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availability_zone: "auto" # Let Azure automatically pick zones
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# How Ray will authenticate with newly launched nodes.
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auth:
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ssh_user: ubuntu
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# SSH keys will be auto-generated with Ray-specific names if not specified
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# Uncomment and specify custom paths if you want to use different existing keys:
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# ssh_private_key: /path/to/your/key.pem
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# ssh_public_key: /path/to/your/key.pub
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# More specific customization to node configurations can be made using the ARM template azure-vm-template.json file
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# See documentation here: https://docs.microsoft.com/en-us/azure/templates/microsoft.compute/2019-03-01/virtualmachines
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# Changes to the local file will be used during deployment of the head node, however worker nodes deployment occurs
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# on the head node, so changes to the template must be included in the wheel file used in setup_commands section below
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# Tell the autoscaler the allowed node types and the resources they provide.
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# The key is the name of the node type, which is just for debugging purposes.
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# The node config specifies the launch config and physical instance type.
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available_node_types:
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ray.head.default:
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resources: {"CPU": 2}
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# Provider-specific config, e.g. instance type.
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node_config:
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azure_arm_parameters:
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vmSize: Standard_D2s_v3
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# List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
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imagePublisher: microsoft-dsvm
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imageOffer: ubuntu-2204
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imageSku: 2204-gen2
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imageVersion: latest
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# Head node: explicitly disable availability zones
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availability_zone: "none"
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ray.worker.default:
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# The minimum number of nodes of this type to launch.
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# This number should be >= 0.
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min_workers: 0
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# The resources provided by this node type.
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resources: {"CPU": 2}
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# Provider-specific config, e.g. instance type.
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node_config:
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azure_arm_parameters:
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vmSize: Standard_D2s_v3
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# List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
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imagePublisher: microsoft-dsvm
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imageOffer: ubuntu-2204
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imageSku: 2204-gen2
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imageVersion: latest
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# comment lines below to not use Spot instances
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priority: Spot
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# set a maximum price for spot instances if desired
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# billingProfile:
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# maxPrice: -1
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# Workers: inherit provider availability_zone setting
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# Options: "1,2,3" for specific zones, "none" to disable zones,
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# or "auto" to let Azure pick zones automatically
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# Specify the node type of the head node (as configured above).
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head_node_type: ray.head.default
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# Files or directories to copy to the head and worker nodes. The format is a
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# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
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file_mounts: {
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# "/path1/on/remote/machine": "/path1/on/local/machine",
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# "/path2/on/remote/machine": "/path2/on/local/machine",
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}
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# Files or directories to copy from the head node to the worker nodes. The format is a
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# list of paths. The same path on the head node will be copied to the worker node.
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# This behavior is a subset of the file_mounts behavior. In the vast majority of cases
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# you should just use file_mounts. Only use this if you know what you're doing!
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cluster_synced_files: []
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# Whether changes to directories in file_mounts or cluster_synced_files in the head node
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# should sync to the worker node continuously
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file_mounts_sync_continuously: False
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# Patterns for files to exclude when running rsync up or rsync down
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rsync_exclude: []
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# Pattern files to use for filtering out files when running rsync up or rsync down. The file is searched for
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# in the source directory and recursively through all subdirectories. For example, if .gitignore is provided
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# as a value, the behavior will match git's behavior for finding and using .gitignore files.
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rsync_filter: []
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# List of commands that will be run before `setup_commands`. If docker is
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# enabled, these commands will run outside the container and before docker
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# is setup.
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initialization_commands:
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# get rid of annoying Ubuntu message
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- touch ~/.sudo_as_admin_successful
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# List of shell commands to run to set up nodes.
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setup_commands:
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# Note: if you're developing Ray, you probably want to create an AMI that
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# has your Ray repo pre-cloned. Then, you can replace the pip installs
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# below with a git checkout <your_sha> (and possibly a recompile).
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# Note: The Ubuntu 22.04 dsvm image has a few venvs already configured but
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# they all contain python modules that are not compatible with Ray at the moment.
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- (which conda && echo 'eval "$(conda shell.bash hook)"' >> ~/.bashrc) || true
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- conda tos accept
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- conda create -n ray-env python=3.10 -y
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- conda activate ray-env && echo 'conda activate ray-env' >> ~/.bashrc
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- which ray || pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp310-cp310-manylinux2014_x86_64.whl"
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# Consider uncommenting these if you also want to run apt-get commands during setup
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# - sudo pkill -9 apt-get || true
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# - sudo pkill -9 dpkg || true
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# - sudo dpkg --configure -a
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# Custom commands that will be run on the head node after common setup.
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head_setup_commands:
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- pip install -U azure-core==1.35.0 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
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# Custom commands that will be run on worker nodes after common setup.
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worker_setup_commands: []
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# Command to start ray on the head node. You don't need to change this.
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head_start_ray_commands:
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- ray stop
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- ulimit -n 65536; ray start --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml --dashboard-host=0.0.0.0
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# Command to start ray on worker nodes. You don't need to change this.
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worker_start_ray_commands:
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- ray stop
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- ulimit -n 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
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