cluster_name: ray-rllib-regression-tests

min_workers: 0
max_workers: 0

# Cloud-provider specific configuration.
provider:
    type: aws
    region: us-west-2
    availability_zone: us-west-2a
    cache_stopped_nodes: False

# How Ray will authenticate with newly launched nodes.
auth:
    ssh_user: ubuntu

head_node:
    InstanceType: p3.16xlarge
    ImageId: ami-07728e9e2742b0662 # Deep Learning AMI (Ubuntu 16.04)

    # Set primary volume to 25 GiB
    BlockDeviceMappings:
        - DeviceName: /dev/sda1
          Ebs:
              VolumeSize: 100


# List of shell commands to run to set up nodes.
setup_commands:
    - wget --quiet https://s3-us-west-2.amazonaws.com/ray-wheels/{{ray_branch}}/{{commit}}/ray-{{ray_version}}-cp36-cp36m-manylinux1_x86_64.whl
    - conda uninstall -y terminado
    - source activate tensorflow_p36 && pip install -U ray-{{ray_version}}-cp36-cp36m-manylinux1_x86_64.whl
    - source activate tensorflow_p36 && pip install ray[rllib] ray[debug]
    - source activate tensorflow_p36 && pip install boto3==1.4.8 cython==0.29.0

# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
    - source activate tensorflow_p36 && ray stop
    - ulimit -n 65536; source activate tensorflow_p36 && OMP_NUM_THREADS=1 ray start --head --redis-port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml

# Command to start ray on worker nodes. You don't need to change this.
worker_start_ray_commands:
    - source activate tensorflow_p36 && ray stop
    - ulimit -n 65536; source activate tensorflow_p36 && OMP_NUM_THREADS=1 ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076