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how-to-deploy-amlarc-endpoint.sh
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how-to-deploy-amlarc-endpoint.sh
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## IMPORTANT: this file and accompanying assets are the source for snippets in https://docs.microsoft.com/azure/machine-learning!
## Please reach out to the Azure ML docs & samples team before before editing for the first time.
set -e
# <set_endpoint_name>
export ENDPOINT_NAME="<YOUR_ENDPOINT_NAME>"
# </set_endpoint_name>
export COMPUTE_ID = "<FULLY_QUALIFIED_COMPUTE_ID>"
export ENDPOINT_NAME=endpt-`echo $RANDOM`
$file = "endpoints/amlarc/managed/simple-flow/1-create-endpoint-with-blue.yml"
$compute_string = "{{fullyQualifiedComputeId}}"
sed -i.bak 's|${compute_string}|${COMPUTE_ID}|g' $file
# <deploy>
az ml endpoint create -n $ENDPOINT_NAME -f $file
# </deploy>
# <get_status>
az ml endpoint show -n $ENDPOINT_NAME
# </get_status>
# check if create was successful
endpoint_status=`az ml endpoint show --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 endpoint show --name $ENDPOINT_NAME --query "deployments[?name=='blue'].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 endpoint invoke -n $ENDPOINT_NAME --request-file endpoints/online/model-1/sample-request.json
# </test_endpoint>
# <get_logs>
az ml endpoint get-logs -n $ENDPOINT_NAME --deployment blue
# </get_logs>
# <get_scoring_uri>
az ml endpoint show -n $ENDPOINT_NAME --query "scoring_uri"
# </get_scoring_uri>
# <delete_endpoint>
az ml endpoint delete -n $ENDPOINT_NAME --yes --no-wait
# </delete_endpoint>