Why are these changes needed?

Before these changes vSphere provider was using vSphere SDK to create a Ray cluster on vSphere. However, with availability of K8s control plane within the vSphere hypervisor (Supervisor) it is possible to deploy services using k8s operator. These changes are done to make Ray as a Supervior service on vSphere. With these changes Ray cluster is exposed as a k8s custom resource (CR). Instead of using old vSphere SDK to create and manage the cluster, now, the provider create, update, and delete Ray cluster CRs through exposed k8s API.

1. Old provider (vSphere provider) replaced by the new vmray provider.( Updated node_provider.py)
2. Added cluster_operator_client.py: Wrapper for calling K8s APIs to manage Ray cluster. (Autoscaler through the node provider calls functions in this file to manage a Ray cluster)
3. Ray nodes are created through VM CR and not using pyvmomi, vsphere SDK (deleted files pyvmomi_sdk_provider.py, vsphere_sdk_provider.py, and associated test files)
4. Ray nodes are created through VM CR instead of using Frozen VM template ( changed configs (config.py) to support new configurations, deleted redundant gpu_utils.py and utils.py)
5. Updated default.yaml and other yaml files to support:
   • Supervisor (K8s) Namespace where the Ray cluster should be deployed
   • The Supervisor control plane VM IP (k8s API server)
   • VM image to be used to create Ray nodes
   • VM Storage class to be used for Ray nodes creation.

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• I've included any doc changes needed forhttps://docs.ray.io/en/master/.
  • I've added any new APIs to the API Reference. For example, if I added a
    method in Tune, I've added it indoc/source/tune/api/ under the
    corresponding.rst file.
• I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures athttps://flakey-tests.ray.io/
• Testing Strategy
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  • This PR is not tested :(