I want to deploy multiple ML models in different pods within the same namespace. But whenever I pull a new image from aws ECR and deploy it using helm it terminates the current running pod and makes a new one. So I am unable to deploy multiple models. Every time it kills the previous one and makes a new pod.
helm upgrade --install tf-serving ./charts/tf-serving/ --namespace mlhubOR
helm upgrade --recreate-pods --install tf-serving ./charts/tf-serving/ --namespace mlhubtf-serving-8559fb87d-2twwl 1/1 Running 0 37s
tf-serving-8559fb87d-m6hgs 0/1 Terminating 0 45sIt kills the previous one and makes a new, but the images of both models are different with different tags also.
You can use one Helm chart to create multiple Releases. For example to deploy first model:
helm install ./charts/tf-serving/ --name tf-serving --namespace mlhubAnd if you later want to add another one:
helm install ./charts/tf-serving/ --name tf-serving2 --namespace mlhubNow when you run helm list you will be able to see both tf-serving and tf-serving2.
Be aware that you can not have multiple Kubernetes resources of the same Kind with the same name, so I would recommend using {{ .Release.Name }} value in your chart, as a prefix for all deployed resources.