#################################################################### # All nodes in this cluster will auto-terminate in 1 hour #################################################################### # An unique identifier for the head node and workers of this cluster. cluster_name: stress-testing # The minimum number of workers nodes to launch in addition to the head # node. This number should be >= 0. min_workers: 105 # The maximum number of workers nodes to launch in addition to the head # node. This takes precedence over min_workers. max_workers: 105 # The autoscaler will scale up the cluster to this target fraction of resource # usage. For example, if a cluster of 10 nodes is 100% busy and # target_utilization is 0.8, it would resize the cluster to 13. This fraction # can be decreased to increase the aggressiveness of upscaling. # This value must be less than 1.0 for scaling to happen. target_utilization_fraction: 0.8 # If a node is idle for this many minutes, it will be removed. idle_timeout_minutes: 5 # Cloud-provider specific configuration. provider: type: aws region: us-west-2 availability_zone: us-west-2a # How Ray will authenticate with newly launched nodes. auth: ssh_user: ubuntu # By default Ray creates a new private keypair, but you can also use your own. # If you do so, make sure to also set "KeyName" in the head and worker node # configurations below. # ssh_private_key: /path/to/your/key.pem # Provider-specific config for the head node, e.g. instance type. By default # Ray will auto-configure unspecified fields such as SubnetId and KeyName. # For more documentation on available fields, see: # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances head_node: InstanceType: m5.12xlarge ImageId: ami-0def3275 # Default Ubuntu 16.04 AMI. # Set primary volume to 25 GiB BlockDeviceMappings: - DeviceName: /dev/sda1 Ebs: VolumeSize: 50 # Additional options in the boto docs. # Provider-specific config for worker nodes, e.g. instance type. By default # Ray will auto-configure unspecified fields such as SubnetId and KeyName. # For more documentation on available fields, see: # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances worker_nodes: InstanceType: m5.large ImageId: ami-0def3275 # Default Ubuntu 16.04 AMI. # Set primary volume to 25 GiB BlockDeviceMappings: - DeviceName: /dev/sda1 Ebs: VolumeSize: 50 # Run workers on spot by default. Comment this out to use on-demand. InstanceMarketOptions: MarketType: spot # Additional options can be found in the boto docs, e.g. # SpotOptions: # MaxPrice: MAX_HOURLY_PRICE # Additional options in the boto docs. # Files or directories to copy to the head and worker nodes. The format is a # dictionary from REMOTE_PATH: LOCAL_PATH, e.g. file_mounts: { # "/path1/on/remote/machine": "/path1/on/local/machine", # "/path2/on/remote/machine": "/path2/on/local/machine", } # List of shell commands to run to set up nodes. setup_commands: # Consider uncommenting these if you run into dpkg locking issues # - sudo pkill -9 apt-get || true # - sudo pkill -9 dpkg || true # - sudo dpkg --configure -a # Install basics. - sudo apt-get update - sudo apt-get install -y build-essential curl unzip # Install Anaconda. - wget https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh || true - bash Anaconda3-5.0.1-Linux-x86_64.sh -b -p $HOME/anaconda3 || true - echo 'export PATH="$HOME/anaconda3/bin:$PATH"' >> ~/.bashrc # # Build Ray. # - git clone https://github.com/ray-project/ray || true # - ray/ci/travis/install-bazel.sh - pip install boto3==1.4.8 cython==0.29.0 # - cd ray/python; git checkout master; git pull; pip install -e . --verbose - pip install https://s3-us-west-2.amazonaws.com/ray-wheels/releases/<<>>/<<>>/ray-<<>>-cp36-cp36m-manylinux1_x86_64.whl - echo "sudo halt" | at now + 60 minutes # Custom commands that will be run on the head node after common setup. head_setup_commands: [] # Custom commands that will be run on worker nodes after common setup. worker_setup_commands: [] # Command to start ray on the head node. You don't need to change this. head_start_ray_commands: - ray stop - ulimit -n 65536; ray start --head --num-redis-shards=5 --redis-port=6379 --autoscaling-config=~/ray_bootstrap_config.yaml # Command to start ray on worker nodes. You don't need to change this. worker_start_ray_commands: - ray stop - ulimit -n 65536; ray start --redis-address=$RAY_HEAD_IP:6379 --num-gpus=100