chore: import upstream snapshot with attribution

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

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+# 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: 5
+
+# Cloud-provider specific configuration.
+provider:
+    type: vsphere
+
+# How Ray will authenticate with newly launched nodes.
+auth:
+    ssh_user: ray
+# 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: ~/ray-bootstrap-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:
+        # You can override the resources here. Adding GPU to the head node is not recommended.
+        # resources: { "CPU": 2, "Memory": 4096}
+        resources: {}
+    ray.worker.default:
+        # The minimum number of nodes of this type to launch.
+        # This number should be >= 0.
+        min_workers: 1
+        max_workers: 3
+        # You can override the resources here. For GPU, currently only NVIDIA GPU is supported. If no ESXi host can
+        # fulfill the requirement, the Ray node creation will fail. The number of created nodes may not meet the desired
+        # minimum number. The vSphere node provider will not distinguish the GPU type. It will just count the quantity:
+        # mount the first k random available NVIDIA GPU to the VM, if the user set {"GPU": k}.
+        # resources: {"CPU": 2, "Memory": 4096, "GPU": 1}
+        resources: {}
+
+# Specify the node type of the head node (as configured above).
+head_node_type: ray.head.default