wehub-resource-sync
58 lines
2.0 KiB
YAML
58 lines
2.0 KiB
YAML
# Unique identifier for the head node and workers of this cluster.
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cluster_name: nightly-cpu-minimal-centralus
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max_workers: 2
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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: centralus
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resource_group: ray-nightly-cpu-minimal-centralus
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cache_stopped_nodes: False
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auth:
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ssh_user: ubuntu
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ssh_private_key: "~/.ssh/ray-autoscaler-tests-ssh-key"
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ssh_public_key: "~/.ssh/ray-autoscaler-tests-ssh-key.pub"
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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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node_config:
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azure_arm_parameters:
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vmSize: Standard_D2s_v3
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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.default:
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min_workers: 2
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max_workers: 2
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resources: {"CPU": 2}
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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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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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# 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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setup_commands:
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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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file_mounts_sync_continuously: False
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file_mounts: {
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"~/.ssh/ray-autoscaler-tests-ssh-key.pub": "~/.ssh/ray-autoscaler-tests-ssh-key.pub"
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}
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head_setup_commands:
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- pip install 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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