v24.10.0-rc5 #75

	name: ML Workflows Via Actions
	on: [push]
	jobs:
	build:
	runs-on: ubuntu-latest
	steps:

	# This copies the files in this repo, particulary the yaml workflow spec needed for Argo.
	- name: Step One - checkout files in repo
	uses: actions/checkout@master

	# Get credentials (the kubeconfig file) the k8 cluster. Copies kubeconfig into /github/workspace/.kube/config
	- name: Step Two - 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 }}

	###################################################
	# This is the action that submits the Argo Workflow
	- name: Step Three - Submit Argo Workflow from the .argo folder in this repo
	id: argo
	uses: machine-learning-apps/actions-argo@master
	with:
	argo_url: ${{ secrets.ARGO_URL }}
	# below is a reference to a YAML file in this repo that defines the workflow.
	workflow_yaml_path: ".argo/build.yml"
	parameter_file_path: ".argo/role.yaml"
	env:
	# KUBECONFIG tells kubectl where it can find your authentication information. A config file was saved to this path in Step Two.
	KUBECONFIG: '/github/workspace/.kube/config'

	# This step displays the Argo URL, and illustrates how you can use the output of the previous Action.
	- name: test argo outputs
	run: echo "Argo URL $WORKFLOW_URL"
	env:
	WORKFLOW_URL: ${{ steps.argo.outputs.WORKFLOW_URL }}

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