wehub-resource-sync
124 lines
5.3 KiB
YAML
124 lines
5.3 KiB
YAML
# An unique identifier for the head node and workers of this cluster.
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cluster_name: default
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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-ml:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_container"
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# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
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# if no cached version is present.
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pull_before_run: True
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run_options: [] # Extra options to pass into "docker run"
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# Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray-ml:latest-gpu"
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# Allow Ray to automatically detect GPUs
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# worker_image: "rayproject/ray-ml:latest-cpu"
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# worker_run_options: []
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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: aws
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region: us-west-2
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# Availability zone(s), comma-separated, that nodes may be launched in.
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# Nodes will be launched in the first listed availability zone and will
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# be tried in the subsequent availability zones if launching fails.
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availability_zone: us-west-2a,us-west-2b
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# Whether to allow node reuse. If set to False, nodes will be terminated
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# instead of stopped.
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cache_stopped_nodes: True # If not present, the default is True.
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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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# By default Ray creates a new private keypair, but you can also use your own.
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# If you do so, make sure to also set "KeyName" in the head and worker node
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# configurations below.
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# ssh_private_key: /path/to/your/key.pem
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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 such as SubnetId and KeyName.
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# For more documentation on available fields, see:
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# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
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worker_nodes:
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foo: bar
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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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}
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# Files or directories to copy from the head node to the worker nodes. The format is a
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# list of paths. The same path on the head node will be copied to the worker node.
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# This behavior is a subset of the file_mounts behavior. In the vast majority of cases
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# you should just use file_mounts. Only use this if you know what you're doing!
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cluster_synced_files: []
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# Whether changes to directories in file_mounts or cluster_synced_files in the head node
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# should sync to the worker node continuously
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file_mounts_sync_continuously: False
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# Patterns for files to exclude when running rsync up or rsync down
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rsync_exclude:
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- "**/.git"
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- "**/.git/**"
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# Pattern files to use for filtering out files when running rsync up or rsync down. The file is searched for
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# in the source directory and recursively through all subdirectories. For example, if .gitignore is provided
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# as a value, the behavior will match git's behavior for finding and using .gitignore files.
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rsync_filter:
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- ".gitignore"
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# List of commands that will be run before `setup_commands`. If docker is
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# enabled, these commands will run outside the container and before docker
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# is setup.
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initialization_commands: []
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# List of shell commands to run to set up nodes.
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setup_commands: []
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# Note: if you're developing Ray, you probably want to create a Docker image that
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# has your Ray repo pre-cloned. Then, you can replace the pip installs
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# below with a git checkout <your_sha> (and possibly a recompile).
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# Uncomment the following line if you want to run the nightly version of ray (as opposed to the latest)
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# - pip install -U 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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# 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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- 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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