azureml-examples/cli/deploy-local-endpoint.sh

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set -e
# <set_endpoint_name>
export ENDPOINT_NAME="<YOUR_ENDPOINT_NAME>"
# </set_endpoint_name>
export ENDPOINT_NAME=endpt-`echo $RANDOM`
# <create_endpoint>
az ml online-endpoint create --local -n $ENDPOINT_NAME -f endpoints/online/managed/sample/endpoint.yml
# </create_endpoint>
# <create_deployment>
az ml online-deployment create --local -n blue --endpoint $ENDPOINT_NAME -f endpoints/online/managed/sample/blue-deployment.yml
# </create_deployment>
# <get_status>
az ml online-endpoint show -n $ENDPOINT_NAME --local
# </get_status>
# check if create was successful
endpoint_status=`az ml online-endpoint show --local --name $ENDPOINT_NAME --query "provisioning_state" -o tsv`
echo $endpoint_status
if [[ $endpoint_status == "Succeeded" ]]
then
echo "Endpoint created successfully"
else
echo "Endpoint creation failed"
exit 1
fi
deploy_status=`az ml online-deployment show --local --name blue --endpoint $ENDPOINT_NAME --query "provisioning_state" -o tsv`
echo $deploy_status
if [[ $deploy_status == "Succeeded" ]]
then
echo "Deployment completed successfully"
else
echo "Deployment failed"
exit 1
fi
# <test_endpoint>
az ml online-endpoint invoke --local --name $ENDPOINT_NAME --request-file endpoints/online/model-1/sample-request.json
# </test_endpoint>
# <test_endpoint_using_curl>
SCORING_URI=$(az ml online-endpoint show --local -n $ENDPOINT_NAME -o tsv --query scoring_uri)
curl --request POST "$SCORING_URI" --header 'Content-Type: application/json' --data @endpoints/online/model-1/sample-request.json
# </test_endpoint_using_curl>
# <get_logs>
az ml online-deployment get-logs --local -n blue --endpoint $ENDPOINT_NAME
# </get_logs>
# <delete_endpoint>
az ml online-endpoint delete --local --name $ENDPOINT_NAME --yes --no-wait
# </delete_endpoint>