chore: import upstream snapshot with attribution

python/ray/autoscaler/aws/example-minimal.yaml

@@ -0,0 +1,48 @@
+# An unique identifier for the head node and workers of this cluster.
+cluster_name: aws-example-minimal
+
+# Cloud-provider specific configuration.
+provider:
+    type: aws
+    region: us-west-2
+
+# The maximum number of workers nodes to launch in addition to the head
+# node.
+max_workers: 3
+
+# Tell the autoscaler the allowed node types and the resources they provide.
+# The key is the name of the node type, which is for debugging purposes.
+# The node config specifies the launch config and physical instance type.
+available_node_types:
+    ray.head.default:
+        # The node type's CPU and GPU resources are auto-detected based on AWS instance type.
+        # If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.
+        # You can also set custom resources.
+        # For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set
+        # resources: {"CPU": 1, "GPU": 1, "custom": 5}
+        resources: {}
+        # Provider-specific config for this node type, e.g., instance type. By default
+        # Ray auto-configures 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
+        node_config:
+            InstanceType: m5.large
+    ray.worker.default:
+        # The minimum number of worker nodes of this type to launch.
+        # This number should be >= 0.
+        min_workers: 3
+        # The maximum number of worker nodes of this type to launch.
+        # This parameter takes precedence over min_workers.
+        max_workers: 3
+        # The node type's CPU and GPU resources are auto-detected based on AWS instance type.
+        # If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler.
+        # You can also set custom resources.
+        # For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set
+        # resources: {"CPU": 1, "GPU": 1, "custom": 5}
+        resources: {}
+        # Provider-specific config for this node type, e.g., instance type. By default
+        # Ray auto-configures 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
+        node_config:
+            InstanceType: m5.large