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https://github.com/machine-learning-apps/gke-kubeconfig

GitHub Action that Fetches Kubeconfig From GKE and saves to `$GITHUB_WORKSPACE/.kube/config`
https://github.com/machine-learning-apps/gke-kubeconfig

Last synced: 23 days ago
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GitHub Action that Fetches Kubeconfig From GKE and saves to `$GITHUB_WORKSPACE/.kube/config`

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![Actions Status](https://github.com/machine-learning-apps/gke-kubeconfig/workflows/Build/badge.svg)



## This Action retrieves a [kubeconfig file](https://kubernetes.io/docs/tasks/access-application-cluster/configure-access-multiple-clusters/) from [Google Kubernetes Engine (GKE)](https://cloud.google.com/kubernetes-engine/)



### This action saves the kubeconfig file into the following directory:



`$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](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl)



## Notes on the kubeconfig file:



From [this explanation](https://ahmet.im/blog/mastering-kubeconfig/):



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`



## Usage



### Example Workflow That Uses This Action



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



```yaml

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'

```



### Mandatory Arguments



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.



## Keywords

 MLOps, Machine Learning, Data Science