chore: import upstream snapshot with attribution

python/ray/autoscaler/gcp/defaults.yaml

@@ -0,0 +1,171 @@
+# An unique identifier for the head node and workers of this cluster.
+cluster_name: default
+
+# The maximum number of workers nodes to launch in addition to the head
+# node.
+max_workers: 2
+
+# The autoscaler will scale up the cluster faster with higher upscaling speed.
+# E.g., if the task requires adding more nodes then autoscaler will gradually
+# scale up the cluster in chunks of upscaling_speed*currently_running_nodes.
+# This number should be > 0.
+upscaling_speed: 1.0
+
+# This executes all commands on all nodes in the docker container,
+# and opens all the necessary ports to support the Ray cluster.
+# Empty string means disabled.
+docker: {}
+
+# If a node is idle for this many minutes, it will be removed.
+idle_timeout_minutes: 5
+
+# Cloud-provider specific configuration.
+provider:
+    type: gcp
+    region: us-west1
+    availability_zone: us-west1-a
+    project_id: null # Globally unique project id
+
+# 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. This requires that you have added the key into the
+# project wide meta-data.
+#    ssh_private_key: /path/to/your/key.pem
+
+# Tell the autoscaler the allowed node types and the resources they provide.
+# The key is the name of the node type, which is just for debugging purposes.
+# The node config specifies the launch config and physical instance type.
+available_node_types:
+    ray_head_default:
+        # The resources provided by this node type.
+        resources: {"CPU": 2}
+        # Provider-specific config for this node type, e.g. instance type. By default
+        # Ray will auto-configure unspecified fields such as subnets and ssh-keys.
+        # For more documentation on available fields, see:
+        # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
+        node_config:
+            machineType: n1-standard-2
+            disks:
+              - boot: true
+                autoDelete: true
+                type: PERSISTENT
+                initializeParams:
+                  diskSizeGb: 50
+                  # See https://cloud.google.com/compute/docs/images for more images
+                  sourceImage: projects/deeplearning-platform-release/global/images/family/common-cpu
+
+            # Additional options can be found in in the compute docs at
+            # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
+
+            # If the network interface is specified as below in both head and worker
+            # nodes, the manual network config is used.  Otherwise an existing subnet is
+            # used.  To use a shared subnet, ask the subnet owner to grant permission
+            # for 'compute.subnetworks.use' to the ray autoscaler account...
+            # networkInterfaces:
+            #   - kind: compute#networkInterface
+            #     subnetwork: path/to/subnet
+            #     aliasIpRanges: []
+    ray_worker_small:
+        # The minimum number of nodes of this type to launch.
+        # This number should be >= 0.
+        min_workers: 0
+        # The resources provided by this node type.
+        resources: {"CPU": 2}
+        # Provider-specific config for this node type, e.g. instance type. By default
+        # Ray will auto-configure unspecified fields such as subnets and ssh-keys.
+        # For more documentation on available fields, see:
+        # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
+        node_config:
+            machineType: n1-standard-2
+            disks:
+              - boot: true
+                autoDelete: true
+                type: PERSISTENT
+                initializeParams:
+                  diskSizeGb: 50
+                  # See https://cloud.google.com/compute/docs/images for more images
+                  sourceImage: projects/deeplearning-platform-release/global/images/family/common-cpu
+            # Run workers on preemtible instance by default.
+            # Comment this out to use on-demand.
+            scheduling:
+              - preemptible: true
+
+    # Additional options can be found in in the compute docs at
+    # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
+
+# Specify the node type of the head node (as configured above).
+head_node_type: ray_head_default
+
+# Files or directories to copy to the head and worker nodes. The format is a
+# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
+file_mounts: {
+#    "/path1/on/remote/machine": "/path1/on/local/machine",
+#    "/path2/on/remote/machine": "/path2/on/local/machine",
+}
+
+# Files or directories to copy from the head node to the worker nodes. The format is a
+# list of paths. The same path on the head node will be copied to the worker node.
+# This behavior is a subset of the file_mounts behavior. In the vast majority of cases
+# you should just use file_mounts. Only use this if you know what you're doing!
+cluster_synced_files: []
+
+# Whether changes to directories in file_mounts or cluster_synced_files in the head node
+# should sync to the worker node continuously
+file_mounts_sync_continuously: False
+
+# Patterns for files to exclude when running rsync up or rsync down
+rsync_exclude: []
+
+# Pattern files to use for filtering out files when running rsync up or rsync down. The file is searched for
+# in the source directory and recursively through all subdirectories. For example, if .gitignore is provided
+# as a value, the behavior will match git's behavior for finding and using .gitignore files.
+rsync_filter: []
+
+# List of commands that will be run before `setup_commands`. If docker is
+# enabled, these commands will run outside the container and before docker
+# is setup.
+initialization_commands: []
+
+# List of shell commands to run to set up nodes.
+setup_commands:
+    # Note: if you're developing Ray, you probably want to create an AMI that
+    # has your Ray repo pre-cloned. Then, you can replace the pip installs
+    # below with a git checkout <your_sha> (and possibly a recompile).
+    # - echo 'export PATH="$HOME/anaconda3/envs/tensorflow_p36/bin:$PATH"' >> ~/.bashrc
+    # Install ray if not present
+    - >-
+        (stat /opt/conda/bin/ &> /dev/null &&
+        echo 'export PATH="/opt/conda/bin:$PATH"' >> ~/.bashrc) || true
+    - which ray || pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"
+
+
+# Custom commands that will be run on the head node after common setup.
+head_setup_commands:
+  - pip install google-api-python-client==1.7.8
+
+# Custom commands that will be run on worker nodes after common setup.
+worker_setup_commands: []
+
+# Command to start ray on the head node. You don't need to change this.
+head_start_ray_commands:
+    - ray stop
+    - >-
+      ulimit -n 65536;
+      ray start
+      --head
+      --port=6379
+      --object-manager-port=8076
+      --autoscaling-config=~/ray_bootstrap_config.yaml
+      --dashboard-host=0.0.0.0
+
+# Command to start ray on worker nodes. You don't need to change this.
+worker_start_ray_commands:
+    - ray stop
+    - >-
+      ulimit -n 65536;
+      ray start
+      --address=$RAY_HEAD_IP:6379
+      --object-manager-port=8076