mirror of
https://github.com/wassname/ray.git
synced 2026-06-27 20:53:14 +08:00
Eric Liang
128 lines
5.1 KiB
YAML
128 lines
5.1 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 initial number of worker nodes to launch in addition to the head
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# node. When the cluster is first brought up (or when it is refreshed with a
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# subsequent `ray up`) this number of nodes will be started.
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initial_workers: 0
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# Whether or not to autoscale aggressively. If this is enabled, if at any point
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# we would start more workers, we start at least enough to bring us to
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# initial_workers.
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autoscaling_mode: default
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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: "tensorflow/tensorflow:1.12.0-gpu-py3"
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container_name: "ray-nvidia-docker-test" # e.g. ray_docker
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run_options:
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- --runtime=nvidia
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# # Example of running a GPU head with CPU workers
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# head_image: "tensorflow/tensorflow:1.13.1-gpu-py3"
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# head_run_options:
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# - --runtime=nvidia
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# worker_image: "tensorflow/tensorflow:1.13.1-py3"
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# worker_run_options: []
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# The autoscaler will scale up the cluster to this target fraction of resource
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# usage. For example, if a cluster of 10 nodes is 100% busy and
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# target_utilization is 0.8, it would resize the cluster to 13. This fraction
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# can be decreased to increase the aggressiveness of upscaling.
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# This value must be less than 1.0 for scaling to happen.
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target_utilization_fraction: 0.8
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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 are currently spread between zones by a round-robin approach,
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# however this implementation detail should not be relied upon.
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availability_zone: us-west-2a,us-west-2b
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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 the head node, 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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head_node:
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InstanceType: p2.xlarge
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ImageId: ami-0b294f219d14e6a82 # Deep Learning AMI (Ubuntu) Version 21.0
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# You can provision additional disk space with a conf as follows
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BlockDeviceMappings:
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- DeviceName: /dev/sda1
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Ebs:
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VolumeSize: 100
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# Additional options in the boto docs.
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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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InstanceType: m5.large
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ImageId: ami-0b294f219d14e6a82 # Deep Learning AMI (Ubuntu) Version 21.0
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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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# Additional options can be found in the boto docs, e.g.
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# SpotOptions:
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# MaxPrice: MAX_HOURLY_PRICE
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# Additional options in the boto docs.
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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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# List of shell commands to run to set up nodes.
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setup_commands:
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# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.8.0.dev5-cp27-cp27mu-manylinux1_x86_64.whl
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- pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.8.0.dev5-cp35-cp35m-manylinux1_x86_64.whl
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# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.8.0.dev5-cp36-cp36m-manylinux1_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 boto3==1.4.8 # 1.4.8 adds InstanceMarketOptions
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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 --redis-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 --redis-address=$RAY_HEAD_IP:6379 --object-manager-port=8076
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