wehub-resource-sync
133 lines
5.5 KiB
YAML
133 lines
5.5 KiB
YAML
# An unique identifier for the head node and workers of this cluster.
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cluster_name: gpu-docker
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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 string means disabled.
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docker:
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image: "rayproject/ray-ml:latest-gpu"
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_nvidia_docker"
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# # Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray-ml:latest-gpu"
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# worker_image: "rayproject/ray-ml:latest"
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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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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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# 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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# 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.gpu:
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# The resources provided by this node type.
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resources: {"CPU": 6, "GPU": 1}
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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_NC6s_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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ray.worker.gpu:
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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 maximum number of workers nodes of this type to launch.
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# This takes precedence over min_workers.
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max_workers: 2
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# The resources provided by this node type.
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resources: {"CPU": 6, "GPU": 1}
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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_NC6s_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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# optionally set priority to 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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# Specify the node type of the head node (as configured above).
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head_node_type: ray.head.gpu
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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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# 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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# enable docker setup
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- sudo usermod -aG docker $USER || true
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- sleep 10 # delay to avoid docker permission denied errors
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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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# NOTE: rayproject/ray-ml:latest has ray latest bundled
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setup_commands: []
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# - 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"
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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-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
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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
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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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