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Deploy Contoso-Support Promptflow #3

Deploy Contoso-Support Promptflow

Deploy Contoso-Support Promptflow #3

name: Deploy Contoso-Support Promptflow
on:
workflow_dispatch:
workflow_run:
workflows: ["run-support-eval-pf-pipeline"]
branches: [main]
types:
- completed
env:
GROUP: ${{secrets.GROUP}}
WORKSPACE: ${{secrets.WORKSPACE}}
SUBSCRIPTION: ${{secrets.SUBSCRIPTION}}
RUN_NAME: contoso_support
EVAL_RUN_NAME: contoso_support_eval
ENDPOINT_NAME: contoso_support
DEPLOYMENT_NAME: blue
KEY_VAULT_NAME: ${{secrets.KEY_VAULT_NAME}}
jobs:
create-endpoint-and-deploy-pf:
runs-on: ubuntu-latest
if: ${{ github.event_name == 'workflow_dispatch' || github.event.workflow_run.conclusion == 'success' }}
steps:
- name: Check out repo
uses: actions/checkout@v2
- name: Install az ml extension
run: az extension add -n ml -y
- name: Azure login
uses: azure/login@v1
with:
creds: ${{secrets.AZURE_CREDENTIALS}}
- name: List current directory
run: ls
- name: Set default subscription
run: |
az account set -s ${{env.SUBSCRIPTION}}
- name: Create Hash
run: echo "HASH=$(echo -n $RANDOM | sha1sum | cut -c 1-6)" >> "$GITHUB_ENV"
- name: Create unique endpoint name
run: echo "ENDPOINT_NAME=$(echo 'contoso-support-'$HASH)" >> "$GITHUB_ENV"
- name: Display endpoint name
run: echo "Endpoint name is:" ${{env.ENDPOINT_NAME}}
- name: Setup endpoint
run: az ml online-endpoint create --file deployment/support-endpoint.yaml --name ${{env.ENDPOINT_NAME}} -g ${{env.GROUP}} -w ${{env.WORKSPACE}}
- name: Update deployment PRT_CONFIG variable
run: |
PRT_CONFIG_OVERRIDE=deployment.subscription_id=${{ env.SUBSCRIPTION }},deployment.resource_group=${{ env.GROUP }},deployment.workspace_name=${{ env.WORKSPACE }},deployment.endpoint_name=${{ env.ENDPOINT_NAME }},deployment.deployment_name=${{ env.DEPLOYMENT_NAME }}
sed -i "s/PRT_CONFIG_OVERRIDE:.*/PRT_CONFIG_OVERRIDE: $PRT_CONFIG_OVERRIDE/g" deployment/support-deployment.yaml
- name: Setup deployment
run: az ml online-deployment create --file deployment/support-deployment.yaml --endpoint-name ${{env.ENDPOINT_NAME}} --all-traffic -g ${{env.GROUP}} -w ${{env.WORKSPACE}}
- name: Check the status of the endpoint
run: az ml online-endpoint show -n ${{env.ENDPOINT_NAME}} -g ${{env.GROUP}} -w ${{env.WORKSPACE}}
- name: Check the status of the deployment
run: az ml online-deployment get-logs --name contoso-support --endpoint-name ${{env.ENDPOINT_NAME}} -g ${{env.GROUP}} -w ${{env.WORKSPACE}}
- name: Read endpoint principal
run: |
az ml online-endpoint show -n ${{env.ENDPOINT_NAME}} -g ${{env.GROUP}} -w ${{env.WORKSPACE}} > endpoint.json
jq -r '.identity.principal_id' endpoint.json > principal.txt
echo "Principal is: $(cat principal.txt)"
- name: Assign Permission to Endpoint Principal
run: |
echo "assigning permissions to Principal to AzureML workspace.."
az role assignment create --assignee $(cat principal.txt) --role "AzureML Data Scientist" --scope "/subscriptions/${{ env.SUBSCRIPTION }}/resourcegroups/${{env.GROUP}}/providers/Microsoft.MachineLearningServices/workspaces/${{env.WORKSPACE}}"
az role assignment create --assignee $(cat principal.txt) --role "Azure Machine Learning Workspace Connection Secrets Reader" --scope "/subscriptions/${{ env.SUBSCRIPTION }}/resourcegroups/${{env.GROUP}}/providers/Microsoft.MachineLearningServices/workspaces/${{env.WORKSPACE}}/onlineEndpoints/${{env.ENDPOINT_NAME}}"
echo "assigning permissions to Principal to Key vault.."
az keyvault set-policy --name ${{secrets.KEY_VAULT_NAME}} --resource-group ${{env.GROUP}} --object-id $(cat principal.txt) --secret-permissions get list