mirror of
https://github.com/wassname/ray.git
synced 2026-07-08 06:26:37 +08:00
Robert Nishihara
74 lines
2.1 KiB
YAML
74 lines
2.1 KiB
YAML
# An unique identifier for the head node and workers of this cluster.
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cluster_name: sgd-pytorch
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# The maximum number of workers nodes to launch in addition to the head
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# node. This takes precedence over min_workers. min_workers default to 0.
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min_workers: 1
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initial_workers: 1
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max_workers: 1
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target_utilization_fraction: 0.9
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 20
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# docker:
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# image: tensorflow/tensorflow:1.5.0-py3
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# container_name: ray_docker
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# Cloud-provider specific configuration.
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provider:
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type: aws
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region: us-east-1
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availability_zone: us-east-1f
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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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head_node:
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InstanceType: p3.8xlarge
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ImageId: ami-0757fc5a639fe7666
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# InstanceMarketOptions:
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# MarketType: spot
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# SpotOptions:
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# MaxPrice: "9.0"
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worker_nodes:
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InstanceType: p3.8xlarge
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ImageId: ami-0757fc5a639fe7666
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# InstanceMarketOptions:
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# MarketType: spot
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# SpotOptions:
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# MaxPrice: "9.0"
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# # Run workers on spot by default. Comment this out to use on-demand.
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# InstanceMarketOptions:
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# MarketType: spot
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setup_commands:
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- ray || pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.8.0.dev4-cp36-cp36m-manylinux1_x86_64.whl
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- conda install -y pytorch torchvision cudatoolkit=9.0 -c pytorch
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- pip install -U ipdb ray[rllib]
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file_mounts: {
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}
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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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# 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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- ray start --head --redis-port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml --object-store-memory=1000000000
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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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- ray start --redis-address=$RAY_HEAD_IP:6379 --object-manager-port=8076 --object-store-memory=1000000000
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