ray/release/long_running_distributed_tests/cluster.yaml

cluster_name: long-running-distributed-tests

min_workers: 3
max_workers: 3

target_utilization_fraction: 0.8
idle_timeout_minutes: 15

docker:
    image: anyscale/ray-ml:latest-gpu
    container_name: ray_container
    pull_before_run: True

provider:
    type: aws
    region: us-west-2
    availability_zone: us-west-2a
    cache_stopped_nodes: False

auth:
    ssh_user: ubuntu

head_node:
    InstanceType: g3.8xlarge

worker_nodes:
  InstanceType: g3.8xlarge
  InstanceMarketOptions:
    MarketType: spot

setup_commands:
  - apt-get install -y libglib2.0-0 libcudnn7=7.6.5.32-1+cuda10.1
  - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-1.2.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl

# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
    - ray stop
    - export RAY_BACKEND_LOG_LEVEL=debug
    - ray start --head --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:
    - ray stop
    - export RAY_BACKEND_LOG_LEVEL=debug
    - ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076