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https://github.com/wassname/ray.git
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Robert Nishihara
22460ff7af
* Use Anaconda for autoscaling example and add example config for development. * Install Python2 for building the web ui.
74 lines
3.3 KiB
YAML
74 lines
3.3 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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# 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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# 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: m5.large
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ImageId: ami-3b6bce43 # Amazon Deep Learning AMI (Ubuntu)
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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-3b6bce43 # Amazon Deep Learning AMI (Ubuntu)
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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 initialize the head node.
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head_init_commands:
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# Note: if you're developing Ray, you probably want to create an AMI 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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- PATH=/home/ubuntu/anaconda3/bin:$PATH pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/f5ea44338eca392df3a868035df3901829cc2ca1/ray-0.3.0-cp36-cp36m-manylinux1_x86_64.whl
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- PATH=/home/ubuntu/anaconda3/bin:$PATH pip install boto3==1.4.8 # 1.4.8 adds InstanceMarketOptions
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- PATH=/home/ubuntu/anaconda3/bin:$PATH ray stop
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- PATH=/home/ubuntu/anaconda3/bin:$PATH ray start --head --redis-port=6379 --autoscaling-config=~/ray_bootstrap_config.yaml
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# List of shell commands to run to initialize workers.
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worker_init_commands:
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- PATH=/home/ubuntu/anaconda3/bin:$PATH pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/f5ea44338eca392df3a868035df3901829cc2ca1/ray-0.3.0-cp36-cp36m-manylinux1_x86_64.whl
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- PATH=/home/ubuntu/anaconda3/bin:$PATH ray stop
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- PATH=/home/ubuntu/anaconda3/bin:$PATH ray start --redis-address=$RAY_HEAD_IP:6379
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