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https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
ca68bf1e93
* Updated scalability envelope tests for 1.4. * Update data processing release test for 1.4.
48 lines
1.2 KiB
YAML
48 lines
1.2 KiB
YAML
cluster_name: object-store-benchmarks
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min_workers: 0
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max_workers: 999999
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upscaling_speed: 9999999
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provider:
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type: aws
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region: us-west-2
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availability_zone: us-west-2a
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auth:
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ssh_user: ubuntu
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available_node_types:
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head_node:
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node_config:
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InstanceType: m4.4xlarge
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ImageId: ami-098555c9b343eb09c
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resources:
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node: 1
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max_workers: 999999
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worker_node:
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node_config:
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InstanceType: m4.xlarge
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ImageId: ami-098555c9b343eb09c
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resources:
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node: 1
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max_workers: 999999
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head_node_type: head_node
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worker_default_node_type: worker_node
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setup_commands:
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- pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/releases/1.4.0/6ac5e0e5ad45070e27c77aca7267bcee30cc4b4a/ray-1.4.0-cp37-cp37m-manylinux2014_x86_64.whl
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- pip install tqdm numpy
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idle_timeout_minutes: 5
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head_start_ray_commands:
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- ray stop
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- ulimit -n 1000000; 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 1000000; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
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