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Alex
47 lines
1.2 KiB
YAML
47 lines
1.2 KiB
YAML
cluster_name: long-running-distributed-tests
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min_workers: 3
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max_workers: 3
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idle_timeout_minutes: 15
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docker:
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image: rayproject/ray-ml:1.2.0-gpu
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container_name: ray_container
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pull_before_run: True
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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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cache_stopped_nodes: False
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auth:
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ssh_user: ubuntu
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head_node:
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InstanceType: g3.8xlarge
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ImageId: ami-0828a1066dc750737
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worker_nodes:
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InstanceType: g3.8xlarge
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ImageId: ami-0828a1066dc750737
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InstanceMarketOptions:
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MarketType: spot
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setup_commands:
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- sudo apt-get install -y libglib2.0-0 libcudnn7=7.6.5.32-1+cuda10.1
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- pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl
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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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- export RAY_BACKEND_LOG_LEVEL=debug
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- 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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- export RAY_BACKEND_LOG_LEVEL=debug
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- ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
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