# This file is generated by `ray project create`.

# A unique identifier for the head node and workers of this cluster.
cluster_name: long-running-distributed-tests

# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
min_workers: 3
# The maximum number of workers nodes to launch in addition to the head
# node. This takes precedence over min_workers. min_workers defaults to 0.
max_workers: 3

# 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
    cache_stopped_nodes: False

# 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: g3.8xlarge
    ImageId: ami-0888a3b5189309429  # DLAMI 7/1/19
    BlockDeviceMappings:
        - DeviceName: /dev/sda1
          Ebs:
              VolumeSize: 150

worker_nodes:
  InstanceType: g3.8xlarge
  ImageId: ami-0888a3b5189309429  # DLAMI 7/1/19
  BlockDeviceMappings:
        - DeviceName: /dev/sda1
          Ebs:
              VolumeSize: 150
  InstanceMarketOptions:
    MarketType: spot

setup_commands:
  # Install ray.
  - pip install -U pip
  - ray || pip install -U {{ray-wheel}}
  # Installing this without -U to make sure we don't replace the existing Ray installation
  - pip install ray[rllib]
  - pip install -U ipdb
  # There have been some recent problems with torch 1.5 and torchvision 0.6
  # not recognizing GPUs.
  # So, we force install torch 1.4 and torchvision 0.5. 
  # https://github.com/pytorch/pytorch/issues/37212#issuecomment-623198624.
  - pip install torch==1.4.0 torchvision==0.5.0
  - echo set-window-option -g mouse on > ~/.tmux.conf
  - echo 'termcapinfo xterm* ti@:te@' > ~/.screenrc

# 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 --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:
    - ray stop
    - export RAY_BACKEND_LOG_LEVEL=debug
    - ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076