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Max Fitton
88 lines
3.3 KiB
YAML
88 lines
3.3 KiB
YAML
# An unique identifier for the head node and workers of this cluster.
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cluster_name: gpu-docker
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# The minimum number of workers nodes to launch in addition to the head
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# node. This number should be >= 0.
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min_workers: 0
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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.
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max_workers: 2
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# The autoscaler will scale up the cluster faster with higher upscaling speed.
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# E.g., if the task requires adding more nodes then autoscaler will gradually
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# scale up the cluster in chunks of upscaling_speed*currently_running_nodes.
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# This number should be > 0.
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upscaling_speed: 1.0
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# This executes all commands on all nodes in the docker container,
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled.
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docker:
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image: "rayproject/ray:latest-gpu"
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container_name: "ray_nvidia_docker" # e.g. ray_docker
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# # Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray:latest-gpu"
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# worker_image: "rayproject/ray:latest"
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# If a node is idle for this many minutes, it will be removed.
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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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location: westus2
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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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# you must specify paths to matching private and public key pair files
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# use `ssh-keygen -t rsa -b 4096` to generate a new ssh key pair
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ssh_private_key: ~/.ssh/id_rsa
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# changes to this should match what is specified in file_mounts
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ssh_public_key: ~/.ssh/id_rsa.pub
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# Provider-specific config for the head node, e.g. instance type. By default
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# Ray will auto-configure unspecified fields using defaults.yaml
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head_node:
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azure_arm_parameters:
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vmSize: Standard_NC6s_v3
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# Provider-specific config for worker nodes, e.g. instance type. By default
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# Ray will auto-configure unspecified fields using defaults.yaml
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worker_nodes:
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azure_arm_parameters:
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vmSize: Standard_NC6s_v3
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# Files or directories to copy to the head and worker nodes. The format is a
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# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
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file_mounts: {
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# "/path1/on/remote/machine": "/path1/on/local/machine",
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# "/path2/on/remote/machine": "/path2/on/local/machine",
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"/home/ubuntu/.ssh/id_rsa.pub": "~/.ssh/id_rsa.pub"
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}
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# List of shell commands to run to set up nodes.
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# NOTE: rayproject/ray:latest has ray latest bundled
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setup_commands: []
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# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-1.2.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl
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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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- pip install azure-cli-core==2.4.0 azure-mgmt-compute==12.0.0 azure-mgmt-msi==1.0.0 azure-mgmt-network==10.1.0
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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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- ulimit -n 65536; 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 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
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