$GITHUB_WORKSPACE/.kube/config

• Inside a Docker container running in Actions, $GITHUB_WORKSPACE is mounted such that this directory always equals /github/workspace. Therefore, the kubeconfig file in a subsequent action running in a Docker container can be found at /github/workspace/.kube/config

• Note that outside a Docker container $GITHUB_WORKSPACE is not /github/workspace (you must reference the environment variable to see the directory).

The purpose of this action is to retrieve a kubeconfig file so that other actions can use kubectl to interact with your GKE cluster. An overview of how to retrieve this config file manually are located in these docs

From this explanation:

If you’re using kubectl, here’s the preference that takes effect while determining which kubeconfig file is used.

1. use --kubeconfig flag, if specified
2. use KUBECONFIG environment variable, if specified
3. use $HOME/.kube/config file

This action is the third step in the below example: Submit Argo Deployment

name: Test GKE kubeconfig
on: [push]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      # this action will save the kubeconfig file to `/github/workspace/.kube/config`, which is visible to subsequent steps.
    - name: Get kubeconfig file from GKE
      uses: machine-learning-apps/gke-kubeconfig@master
      with:
        application_credentials: ${{ secrets.APPLICATION_CREDENTIALS }}
        project_id: ${{ secrets.PROJECT_ID }}
        location_zone: ${{ secrets.LOCATION_ZONE }}
        cluster_name: ${{ secrets.CLUSTER_NAME }}
        
    - name: Test Kubeconfig File
      uses: docker://bitnami/kubectl:latest
      with:
        args: get namespaces
      env:
        # Setting the environment variable KUBECONFIG sets the default config file that kubectl looks for.
        KUBECONFIG: '/github/workspace/.kube/config'

1. APPLICATION_CREDENTIALS: base64 encoded GCP application credentials (https://cloud.google.com/sdk/docs/authorizing). To base64 encode your credentails, you can run this command:

   cat your-gke-credentials.json | base64

2. PROJECT_ID: Name of the GCP Project where the GKE K8s cluster resides.

3. LOCATION_ZONE: The location-zone where your GKE K8s cluster resides, for example, us-west1-a

4. CLUSTER_NAME: The name of your GKE K8s cluster.

MLOps, Machine Learning, Data Science