'Is anyone else experiencing issues using Cloud Tools for Visual Studio to deploy GKE workloads?

For the past week we are seeing the error "Could not determine if the selected GCP project is ready for deployment, please close the dialog and try again", image below: error

It happens more often than not, and it is now impacting our deployment capability. This is from Visual Studio 2019 and 2022.

We tried reinstalling/updating the Cloud Tools add-in, and also upgrading Google SDK and kubectl. Unfortunately, the issue persists.

Any advice on you to troubleshoot and resolve this? Thanks!



Solution 1:[1]

[UPDATE] google cloud visual studio tool are not supported anymore https://github.com/GoogleCloudPlatform/google-cloud-visualstudio/issues/1121

Here my Workaround

  1. first of all you need to install docker desktop

  2. right click on project -> Add -> Docker Supoport

  3. righ click on project-> open in Terminal

  4. you need first time authentication

gcloud auth login
gcloud container clusters get-credentials {your cluster} --region {region} --project {project} gcloud auth configure-docker

  1. create a script ps1

     $version = [DateTimeOffset]::Now.ToUnixTimeSeconds()
    
     docker build -t gcr.io/{project}/{your deployment name}:$version .
    
     docker push gcr.io/{project}/{your deployment name}:$version
    
     $deployment_yaml = (Get-Content .\Conf\Prod\deployment.yaml) -replace "{{version}}", $version
    
     $deployment_yaml | kubectl apply -f -
    

here an example of deployment.yaml please note the I have added {version} placeholder

apiVersion: apps/v1
kind: Deployment
metadata:
  name: {your deployment name}
spec:
  replicas: 2
  selector:
    matchLabels:
      run: {your deployment name}
  template:
    metadata:
      creationTimestamp: null
      labels:
        run: {your deployment name}
    spec:
      containers:
      - image: gcr.io/{project}/{your deployment name}:{{version}}
        imagePullPolicy: IfNotPresent
        name: {your deployment name}
        ports:
        - containerPort: 8080
          protocol: TCP
        resources:
          limits:
            cpu: "2"
            memory: 2Gi
          requests:
            cpu: "2"
            memory: 2Gi

Sources

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Source: Stack Overflow

Solution Source
Solution 1