cluster_name: ray-rllib-regression-tests

min_workers: 0
max_workers: 0

docker:
    image: anyscale/ray-ml:latest-gpu
    container_name: ray_container
    pull_before_run: True
    run_options: ["--ulimit nofile=1045876"]

# 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-0a2363a9cff180a64 # Custom ami
    # Set primary volume to 25 GiB
    BlockDeviceMappings:
        - DeviceName: /dev/sda1
          Ebs:
              VolumeSize: 100

worker_nodes:
    InstanceType: p3.16xlarge
    ImageId: ami-0a2363a9cff180a64 # Custom ami
    # 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:
    - apt-get install -y libglib2.0-0 libcudnn7=7.6.5.32-1+cuda10.1
    - pip install terminado
    - pip install torch==1.6 torchvision
    - pip install boto3==1.4.8 cython==0.29.0
    - "pip install https://s3-us-west-2.amazonaws.com/ray-wheels/releases/1.1.0/f591f6c1c8fa14af6df2adfdcf3255c59dff12b1/ray-1.1.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"
    - git clone https://github.com/ray-project/ray.git ray-cp
    - pip install -r ./ray-cp/release/rllib_tests/regression_tests/requirements.txt

# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
    - ray stop
    - OMP_NUM_THREADS=1 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
    - OMP_NUM_THREADS=1 ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076