mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
44 lines
1.2 KiB
YAML
44 lines
1.2 KiB
YAML
cluster_name: ray-rllib-regression-tests
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min_workers: 0
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max_workers: 0
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docker:
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image: anyscale/ray-ml:latest-gpu
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container_name: ray_container
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pull_before_run: True
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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: us-west-2a
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cache_stopped_nodes: False
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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: p2.xlarge # Cheaper 1GPU K80 instance
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# Set primary volume to 25 GiB
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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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# List of shell commands to run to set up nodes.
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setup_commands:
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- apt-get install -y libglib2.0-0 libcudnn7=7.6.5.32-1+cuda10.1 curl unzip gcc python3-dev
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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; OMP_NUM_THREADS=1 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; OMP_NUM_THREADS=1 ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
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