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This repo provides example of deploying multiple applications using kuberay operator in kubernetes

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rayapps-kubernetes-deployment

This repo provides example of deploying multiple applications using kuberay operator in kubernetes

This repo contains the following artifacts

  • couple of ml applications text summarizer and translator that exposes end points for performing summarizing and translation (which are taken from ray documentation)

  • docker file with common dependencies and to dockerize the applications

  • rayservice.yaml that helps in creating the ray cluster and deploying apps using kuberay operator

  • client.py to test the service end points

Steps to deploy the apps in kubernetes using kuberay

  • kubernetes cluster with helm installed is a prerequisite to start with(This can be executed on local cluster using minikube)

  • install kuberay operator by running following commands (reference https://github.com/ray-project/kuberay-helm)

    helm repo add kuberay https://ray-project.github.io/kuberay-helm/
    helm repo update
    helm install kuberay kuberay/kuberay-operator

  • build the docker image with applications using the following command docker buildx build -t ml-apps:latest .

  • in case minikube is used as cluster, copy the image to minikube using the following command
    minikube image load ml-apps:latest

  • deploy the applications by running the following command
    kubectl -f apply rayservice.yaml

  • once the services is up and running, port-forward the dashboard port for head-svc and serve-svc to access the dashboard and testing the service. run the following command for port forwarding
    kubectl port-foward <svc_name> <host_port>:<pod_port>

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