103 строки
2.9 KiB
Bash
Executable File
103 строки
2.9 KiB
Bash
Executable File
#!/bin/bash
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set -e
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BASE_PATH=endpoints/online/custom-container/r/multimodel-plumber
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DEPLOYMENT_NAME=r-deployment
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# <set_endpoint_name>
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export ENDPOINT_NAME="<YOUR_ENDPOINT_NAME>"
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# </set_endpoint_name>
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export ENDPOINT_NAME=endpt-r-`echo $RANDOM`
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# <get_workspace_details>
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WORKSPACE=$(az config get --query "defaults[?name == 'workspace'].value" -o tsv)
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ACR_NAME=$(az ml workspace show -n $WORKSPACE --query container_registry -o tsv | cut -d'/' -f9-)
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if [[ $ACR_NAME == "" ]]
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then
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echo "Getting ACR name failed, exiting"
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exit 1
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fi
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# </get_workspace_details>
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# <build_image>
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az acr login -n $ACR_NAME
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IMAGE_TAG=${ACR_NAME}.azurecr.io/r_server
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az acr build $BASE_PATH -f $BASE_PATH/r.dockerfile -t $IMAGE_TAG -r $ACR_NAME
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# </build_image>
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# Clean up utility
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cleanup(){
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sed -i 's/'$ACR_NAME'/{{ACR_NAME}}/' $BASE_PATH/r-deployment.yml
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az ml online-endpoint delete -n $ENDPOINT_NAME -y
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az ml model archive -n plumber -v 1 || true
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}
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# <run_locally>
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docker run --rm -d -p 8000:8000 -v "$PWD/$BASE_PATH/scripts:/var/azureml-app" \
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-v "$PWD/$BASE_PATH/models":"/var/azureml-models/models" \
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-e AZUREML_MODEL_DIR=/var/azureml-models \
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-e AML_APP_ROOT=/var/azureml-app \
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-e AZUREML_ENTRY_SCRIPT=plumber.R \
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--name="r_server" $IMAGE_TAG
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sleep 10
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# </run_locally>
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# <test_locally>
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# Check liveness, readiness, scoring locally
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curl "http://localhost:8000/live"
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curl "http://localhost:8000/ready"
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curl -d @$BASE_PATH/sample_request.json -H 'Content-Type: application/json' http://localhost:8000/score
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docker stop r_server
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# </test_locally>
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# Fill in placeholders in deployment YAML
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sed -i 's/{{ACR_NAME}}/'$ACR_NAME'/' $BASE_PATH/r-deployment.yml
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# <create_endpoint>
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az ml online-endpoint create --name $ENDPOINT_NAME -f $BASE_PATH/r-endpoint.yml
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# </create_endpoint>
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# <check_endpoint_status>
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endpoint_status=`az ml online-endpoint show --name $ENDPOINT_NAME --query "provisioning_state" -o tsv`
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echo $endpoint_status
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if [[ $endpoint_status == "Succeeded" ]]
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then
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echo "Endpoint created successfully"
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else
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echo "Endpoint creation failed"
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exit 1
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fi
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# </check_endpoint_status>
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# <create_deployment>
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az ml online-deployment create --name r-deployment --endpoint $ENDPOINT_NAME -f $BASE_PATH/r-deployment.yml --all-traffic --skip-script-validation
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# </create_deployment>
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# Check if deployment was successful
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deploy_status=`az ml online-deployment show --name r-deployment --endpoint $ENDPOINT_NAME --query "provisioning_state" -o tsv`
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echo $deploy_status
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if [[ $deploy_status == "Succeeded" ]]
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then
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echo "Deployment completed successfully"
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else
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echo "Deployment failed"
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cleanup
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exit 1
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fi
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# <test_endpoint>
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echo "Testing endpoint"
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for model in {a..c}
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do
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RESPONSE=$(az ml online-endpoint invoke -n $ENDPOINT_NAME --request-file <(jq --arg m "$model" '.model=$m' $BASE_PATH/sample_request.json))
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echo "Model $model tested successfully, response was $RESPONSE."
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done
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# </test_endpoint>
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# Clean up
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cleanup
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