ray/release/long_running_tests/larger.yaml

cluster_name: ray-release-long-running-tests

docker:
    image: anyscale/ray-ml:nightly
    container_name: ray_container
    pull_before_run: False

provider:
    type: aws
    region: us-west-2
    availability_zone: us-west-2a, us-west-2b, us-west-2c

auth:
    ssh_user: ubuntu

available_node_types:
    cpu_4_ondemand:
        node_config:
            InstanceType: m5.2xlarge
            TagSpecifications:
              - ResourceType: "instance"
                Tags:
                  - Key: anyscale-user
                    Value: "release-automation@anyscale.com"
                  - Key: anyscale-custodian
                    Value: "ignore"
                  - Key: anyscale-expiration
                    Value: '{{env["ANSYCALE_EXPIRATION"]}}'
            BlockDeviceMappings:
              - DeviceName: /dev/sda1
                Ebs:
                  VolumeSize: 200
        resources: {"CPU": 8}
        min_workers: 0
        max_workers: 0

head_node_type: cpu_4_ondemand
worker_default_node_type: cpu_4_ondemand


setup_commands:
   - sudo apt update && sudo apt install -y unzip
   - pip install --upgrade pip
   - pip install -U tensorflow==1.14
   - pip install -U {{env["RAY_WHEEL"]}}
   - pip install -q "ray[all]" "gym[atari]"


head_start_ray_commands:
    - ray stop