wehub-resource-sync
56 lines
1.9 KiB
YAML
56 lines
1.9 KiB
YAML
# Minimal configuration for an automatically managed on-premise cluster.
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# To use, run the script at ray/python/ray/autoscaler/local/coordinator_server.py:
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# $ python coordinator_server.py --ips <list_of_node_ips> --host <HOST> --port <PORT>
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# Copy the address from the output into the coordinator_address field.
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# A unique identifier for the head node and workers of this cluster.
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cluster_name: minimal-automatic
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provider:
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type: local
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coordinator_address: COORDINATOR_HOST:COORDINATOR_PORT
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# The minimum number of workers nodes to add to the Ray cluster in addition to the head
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# node. This number should be >= 0.
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# Set to 0 by default.
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min_workers: 0
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# The maximum number of worker nodes to add to the Ray cluster in addition to the head node.
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# This takes precedence over min_workers.
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# Required for automatically managed clusters.
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max_workers: 2
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# How Ray will authenticate with newly launched nodes.
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auth:
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ssh_user: YOUR_USERNAME
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# Optional if an ssh private key is necessary to ssh to the cluster.
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# ssh_private_key: ~/.ssh/id_rsa
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# The above configuration assumes Ray is installed on your on-prem cluster.
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# If Ray is not already installed on your cluster, you can use setup
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# commands to install it.
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# For the latest Python 3.7 Linux wheels:
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# setup_commands:
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# - if [ $(which ray) ]; then pip uninstall ray -y; fi
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# - pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"
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# Defaults are empty to avoid any surprise changes to on-prem cluster's state.
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# Refer to example yamls for examples of ray installation in setup commands.
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initialization_commands: []
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setup_commands: []
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head_setup_commands: []
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worker_setup_commands: []
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available_node_types: {}
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head_node_type: {}
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head_start_ray_commands: []
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worker_start_ray_commands: []
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file_mounts: {}
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cluster_synced_files: []
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file_mounts_sync_continuously: false
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rsync_exclude: []
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rsync_filter: []
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