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huynhbaotoanhuynhbaotoan
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Merge pull request #5 from Merlion-Crew/ml_model_uc81
UC-81 Build Jenkins pipeline to deploy ML model directly to Azure App Service (without docker image)
2 parents 38168bd + 4aa8e1c commit be1eae4

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Lines changed: 229 additions & 63 deletions

‎.pipelines/build-model-container-image.Jenkinsfile‎

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pipeline {
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agent { label 'master' }
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environment {
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ML_IMAGE_FOLDER = 'imagefiles'
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IMAGE_NAME = 'mlmodelimage'
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MODEL_NAME = "${MODEL_NAME}"
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SCORE_SCRIPT = 'scoring/score.py'
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RESOURCE_GROUP = "${RESOURCE_GROUP}"
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WORKSPACE_NAME = "${WORKSPACE_NAME}"
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ML_CONTAINER_REGISTRY = "${ML_CONTAINER_REGISTRY}"
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}
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stages {
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stage('initialize') {
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steps {
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echo 'Remove the previous one!'
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}
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post {
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always {
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deleteDir() /* clean up our workspace */
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}
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}
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}
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stage ('Download build artifacts (get model name)') {
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environment {
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BUILD_ARTIFACT_FOLDER = "download"
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}
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steps {
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script {
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copyArtifacts(projectName: "${env.build_job_name}", selector: buildParameter("ml_model_selector"), target: "${env.BUILD_ARTIFACT_FOLDER}");
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def FILES_LIST = sh (script: "ls '${env.BUILD_ARTIFACT_FOLDER}'", returnStdout: true).trim()
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//DEBUG
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echo "FILES_LIST : ${FILES_LIST}"
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MODEL_NAME = sh (script: "cat ${env.BUILD_ARTIFACT_FOLDER}/model_name.txt",
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returnStdout: true).trim()
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sh "echo ${MODEL_NAME}"
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}
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}
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}
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stage('generate_dockerfile') {
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steps {
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echo "Hello docker image build ${env.BUILD_ID}"
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checkout scm
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//checkout([$class: 'GitSCM', branches: [[name: '*/ml_model_uc81']],
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// userRemoteConfigs: [[url: 'https://github.com/Merlion-Crew/MLOpsPython.git/']]])
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sh '''
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conda env create --file ./diabetes_regression/ci_dependencies.yml --force
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'''
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withCredentials([azureServicePrincipal("${AZURE_SP}")]) {
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sh '''#!/bin/bash -ex
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az login --service-principal -u $AZURE_CLIENT_ID -p $AZURE_CLIENT_SECRET -t $AZURE_TENANT_ID
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az account set -s $AZURE_SUBSCRIPTION_ID
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export SUBSCRIPTION_ID=$AZURE_SUBSCRIPTION_ID
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source /home/azureuser/anaconda3/bin/activate mlopspython_ci
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python3 -m ml_service.util.create_scoring_image
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'''
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}
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}
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}
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stage('build_and_push') {
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steps {
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echo "Build docker images"
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sh '''#!/bin/bash -ex
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az acr login --name $ML_CONTAINER_REGISTRY
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docker build -t $NEXUS_DOCKER_REGISTRY_URL/$IMAGE_NAME:$BUILD_ID ./diabetes_regression/scoring/$ML_IMAGE_FOLDER/
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'''
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withCredentials([[$class: 'UsernamePasswordMultiBinding', credentialsId: 'nexus-docker-repo',
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usernameVariable: 'USERNAME', passwordVariable: 'PASSWORD']]) {
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sh '''#!/bin/bash -ex
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docker login -u $USERNAME --password $PASSWORD https://$NEXUS_DOCKER_REGISTRY_URL
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docker push $NEXUS_DOCKER_REGISTRY_URL/$IMAGE_NAME:$BUILD_ID
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'''
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}
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}
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}
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stage('deploy') {
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steps {
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echo "Deploy to Azure App Service"
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withCredentials([[$class: 'UsernamePasswordMultiBinding', credentialsId: 'nexus-docker-repo',
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usernameVariable: 'USERNAME', passwordVariable: 'PASSWORD']]) {
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sh '''#!/bin/bash -ex
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az webapp config container set --name $AZURE_APPSERVICE_NAME --resource-group $APPSERVICE_RESOURCE_GROUP --docker-custom-image-name $NEXUS_DOCKER_REGISTRY_URL/$IMAGE_NAME:$BUILD_ID --docker-registry-server-url https://$NEXUS_DOCKER_REGISTRY_URL --docker-registry-server-user $USERNAME --docker-registry-server-password $PASSWORD
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az webapp restart --name $AZURE_APPSERVICE_NAME --resource-group $APPSERVICE_RESOURCE_GROUP
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'''
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}
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}
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}
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}
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}

‎.pipelines/deploy-model-to-appservice.Jenkinsfile‎

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pipeline {
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agent { label 'master' }
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environment {
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ML_IMAGE_FOLDER = 'imagefiles'
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IMAGE_NAME = 'mlmodelimage'
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MODEL_NAME = "${MODEL_NAME}"
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SCORE_SCRIPT = 'scoring/score.py'
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RESOURCE_GROUP = "${RESOURCE_GROUP}"
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WORKSPACE_NAME = "${WORKSPACE_NAME}"
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PACKAGE_FOLDER = './download'
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}
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stages {
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stage('initialize') {
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steps {
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echo 'Remove the previous one!'
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}
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post {
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always {
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deleteDir() /* clean up our workspace */
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}
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}
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}
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stage('download_model') {
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steps {
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echo "Downloading..."
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checkout scm
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withCredentials([azureServicePrincipal("${AZURE_SP}")]) {
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sh '''#!/bin/bash -ex
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az login --service-principal -u $AZURE_CLIENT_ID -p $AZURE_CLIENT_SECRET -t $AZURE_TENANT_ID
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az account set -s $AZURE_SUBSCRIPTION_ID
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MODEL_ID=$(az ml model list --workspace-name $WORKSPACE_NAME --model-name $MODEL_NAME --resource-group $RESOURCE_GROUP --latest --query [0].id)
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az ml model download --resource-group $RESOURCE_GROUP --workspace-name $WORKSPACE_NAME --model-id $MODEL_ID --target-dir $PACKAGE_FOLDER
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'''
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}
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}
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}
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stage('build_zip_package') {
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steps {
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echo "Packaging..."
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sh '''#!/bin/bash -ex
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cp ./ml_service/util/scoring/* $PACKAGE_FOLDER/
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cp ./diabetes_regression/$SCORE_SCRIPT $PACKAGE_FOLDER/
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cd $PACKAGE_FOLDER/
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zip -r deployment-${BUILD_NUMBER}.zip .
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'''
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}
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}
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stage('prod') {
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steps {
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echo 'Deploying!'
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sh '''
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cd $PACKAGE_FOLDER/
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curl -X POST -u '\$'$AZURE_APPSERVICE_NAME:$AZURE_APPSERVICE_DEPLOYMENT_PASSWORD --data-binary @"deployment-${BUILD_NUMBER}.zip" https://$AZURE_APPSERVICE_NAME.scm.azurewebsites.net/api/zipdeploy
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'''
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}
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}
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}
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}

‎diabetes_regression/ci_dependencies.yml‎

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- pip:
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# dependencies with versions aligned with conda_dependencies.yml.
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- azureml-sdk==1.13.*
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- azureml-sdk
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# Additional pip dependencies for the CI environment.
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- pytest==5.4.*

‎diabetes_regression/conda_dependencies.yml‎

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- pip:
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# Base AzureML SDK
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- azureml-sdk==1.13.*
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- azureml-sdk
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# Minimum required for the scoring environment. Must match AzureML SDK version.
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# https://docs.microsoft.com/en-us/azure/machine-learning/concept-environments
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- azureml-defaults==1.13.*
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- azureml-defaults
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# Training deps
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- scikit-learn

‎ml_service/pipelines/build-model-container-image.Jenkinsfile‎

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This file was deleted.

‎ml_service/util/create_scoring_image.py‎

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)
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args = parser.parse_args()
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model = Model(ws, name=e.model_name, version=e.model_version)
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model = Model(ws, name=e.model_name)
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sources_dir = e.sources_directory_train
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if (sources_dir is None):
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sources_dir = 'diabetes_regression'

‎ml_service/util/scoring/application.py‎

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from flask import Flask, request, make_response, jsonify
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import main
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app = Flask(__name__)
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main.init()
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@app.route("/")
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def hello():
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return "Hello World!"
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@app.route('/score', methods=['POST'])
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def score_realtime():
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print(request.get_json()['data'])
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#require request data in json format
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if 'Content-Type' not in request.headers or request.headers['Content-Type'] != 'application/json':
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return make_response(
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'Expects Content-Type to be application/json!',
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415
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)
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response = main.run(request.get_json()['data'], request.headers)
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headers = {"Content-Type": "application/json"}
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return make_response(
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jsonify(response),
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200,
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{"Content-Type": "application/json"}
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)

‎ml_service/util/scoring/main.py‎

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import os
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import inspect
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import importlib.util as imp
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import logging
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import sys
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script_location = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'score.py')
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driver_module_spec = imp.spec_from_file_location('service_driver', script_location)
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driver_module = imp.module_from_spec(driver_module_spec)
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driver_module_spec.loader.exec_module(driver_module)
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def run(http_body, request_headers):
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global run_supports_request_headers
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arguments = {run_input_parameter_name: http_body}
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if run_supports_request_headers:
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arguments["request_headers"] = request_headers
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return_obj = driver_module.run(**arguments)
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return return_obj
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def init():
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global run_input_parameter_name
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global run_supports_request_headers
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run_args = inspect.signature(driver_module.run).parameters.keys()
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run_args_list = list(run_args)
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run_input_parameter_name = run_args_list[0] if run_args_list[0] != "request_headers" else run_args_list[1]
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run_supports_request_headers = "request_headers" in run_args_list
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driver_module.init()

‎ml_service/util/scoring/requirements.txt‎

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numpy==1.18.*
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joblib==0.16.0
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azureml-sdk==1.14.*
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azureml-defaults==1.14.*
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inference-schema[numpy-support]
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sklearn

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