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[autoscaler] Service and Ingress per worker pod (#9359)

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PidgeyBE
2020-08-10 13:14:30 -07:00
committed by SangBin Cho
parent 562dfba732
commit 4fc0452e7b
4 changed files with 417 additions and 3 deletions
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doc/source/cluster/cloud.rst
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@@ -153,6 +153,7 @@ Kubernetes
The cluster launcher can also be used to start Ray clusters on an existing Kubernetes cluster. First, install the Kubernetes API client (``pip install kubernetes``), then make sure your Kubernetes credentials are set up properly to access the cluster (if a command like ``kubectl get pods`` succeeds, you should be good to go).
Once you have ``kubectl`` configured locally to access the remote cluster, you should be ready to launch your cluster. The provided `ray/python/ray/autoscaler/kubernetes/example-full.yaml <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/kubernetes/example-full.yaml>`__ cluster config file will create a small cluster of one pod for the head node configured to autoscale up to two worker node pods, with all pods requiring 1 CPU and 0.5GiB of memory.
It's also possible to deploy service and ingress resources for each scaled worker pod. An example is provided in `ray/python/ray/autoscaler/kubernetes/example-ingress.yaml <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/kubernetes/example-ingress.yaml>`__.
Test that it works by running the following commands from your local machine: