hunk.code_window: # RLlib.
   #/python/ray/rllib/ @ray-project/rllib
   # ==== Build and CI ====
   # Bazel.
   #/BUILD.bazel @ray-project/ray-core
   #/WORKSPACE @ray-project/ray-core
  
hunk.labels: keep keep add keep keep keep keep keep
hunk.after_edit: # ML Docker Dependencies
   /python/requirements/ml/requirements_dl.txt @amogkam @sven1977 @richardliaw @matthewdeng
   /python/requirements_ml_docker.txt @amogkam @sven1977 @richardliaw @matthewdeng
  
hunk.file_path: .github/CODEOWNERS
hunk.type: add
file: # An unique identifier for the head node and workers of this cluster.
  cluster_name: transformer-cluster
  
  # The maximum number of workers nodes to launch in addition to the head
  # node. This takes precedence over min_workers. min_workers default to 0.
  min_workers: 3
  max_workers: 3
  
  # Cloud-provider specific configuration.
  provider:
      type: aws
      region: us-west-2
  
  # How Ray will authenticate with newly launched nodes.
  auth:
      ssh_user: ubuntu
  
  available_node_types:
      ray.head.default:
          min_workers: 0
          max_workers: 0
          node_config:
              InstanceType: g3.8xlarge
              ImageId: latest_dlami
              InstanceMarketOptions:
                  MarketType: spot
              BlockDeviceMappings:
                  - DeviceName: /dev/sda1
                    Ebs:
                        VolumeSize: 300
  
  
      ray.worker.default:
          min_workers: 3
          max_workers: 3
          node_config:
              InstanceType: g3.8xlarge
              ImageId: latest_dlami
              InstanceMarketOptions:
                  MarketType: spot
              BlockDeviceMappings:
                  - DeviceName: /dev/sda1
                    Ebs:
                        VolumeSize: 300
  
  
  setup_commands:
      # This replaces the standard anaconda Ray installation
      - pip install ray[tune]
      - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl
  
      # Install HuggingFace
      - git clone https://github.com/huggingface/transformers || true
      - cd transformers &&
        pip install -U . &&
        pip install -r ./examples/pytorch/text-classification/requirements.txt
  