Merge branch 'main' of https://github.com/santhosh-rajashekar/mslearn…

…-mlops

.github/workflows/deploy-model-manual.yml

@@ -0,0 +1,37 @@
+name: Deploy Model Manually #intrdouced for quick testing
+
+on:
+  pull_request_target:
+    types: [labeled]
+
+env:
+  workspace-name: aml-ws-mlops
+  resource-group: rg-mlops
+  job-name: amusing_jackal_9w9m3ls2b2
+  model-name: azureml_amusing_jackal_9w9m3ls2b2_output_mlflow_log_model_583397521
+
+permissions:
+  id-token: write
+  contents: read
+
+jobs:
+
+  deploy-model:
+    name: Deploy Model
+    if: github.event.label.name == 'deploy-model'
+    runs-on: ubuntu-latest
+    environment:
+      name: prod
+    steps:
+      - name: Check out repo
+        uses: actions/checkout@main
+      - name: Install az ml extension
+        run: az extension add -n ml -y
+      - name: "Az CLI login"
+        uses: azure/login@v1
+        with:
+          client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+      - name: Trigger Azure ML job
+        run:  az ml online-deployment create -f src/deploy-endpoint.yml --all-traffic --workspace-name ${{ env.workspace-name }} --resource-group ${{ env.resource-group }}

.github/workflows/deploy-webapp.yml

@@ -0,0 +1,40 @@
+on:
+  workflow_dispatch:
+
+permissions:
+  id-token: write
+  contents: read
+
+env:
+  AZURE_WEBAPP_NAME: mlops-skaroti-appservice # set this to your web application's name
+  AZURE_WEBAPP_PACKAGE_PATH: 'src/web/myapp' # set this to the path to your web app project, defaults to the repository root
+
+jobs:
+  build:
+    environment:
+      name: 'prod'
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: azure/login@v1
+        with:
+          client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+
+      - name: Set up Python 3.x
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.x
+      - name: Install dependencies
+        run: |
+          pip install -r src/web/myapp/requirements.txt
+      - name: Deploy App
+        uses: azure/webapps-deploy@v2
+        with:
+          app-name: ${{ env.AZURE_WEBAPP_NAME }}
+          package: ${{ env.AZURE_WEBAPP_PACKAGE_PATH }}
+      - name: logout
+        run: |
+          az logout

.github/workflows/train-model.yml

@@ -1,8 +1,9 @@
 name: Train Model
 
 on:
-  pull_request_target:
-    types: [labeled]
+  workflow_dispatch:
+    branches: 
+      - main
 
 env:
   workspace-name: aml-ws-mlops

requirements.txt

@@ -1,5 +1,6 @@
-pytest==7.4.0
+pytest==7.3.1
 mlflow==2.6.0
-pandas==2.0.3
+pandas==2.0.1
 sklearn==0.0
-scikit-learn==1.3.0
+scikit-learn==1.3.0
+azureml-inference-server-http==1.2.2

src/deploy-endpoint.yml

@@ -1,6 +1,7 @@
 $schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
-name: mlflow-deployment
+name: mlops-mlflow-predication-v3
 endpoint_name: mlops-mlflow-endpoint-v1
-model: azureml:azureml_amusing_jackal_9w9m3ls2b2_output_mlflow_log_model_583397521:1
+model: azureml:azureml_polite_apple_qkbzxkd1kg_output_mlflow_log_model_299177315:1
 instance_type: Standard_F4s_v2 
-instance_count: 1
+instance_count: 1
+environment: azureml:AzureML-sklearn-0.24-ubuntu18.04-py37-cpu@latest

src/infra/main.bicep

@@ -0,0 +1,76 @@
+param location string = resourceGroup().location
+param keyVaultName string = 'amlwsmlokeyvault7251ed5b'
+
+@description('Reference an existing Key Vault')
+resource kv 'Microsoft.KeyVault/vaults@2023-02-01' existing = {
+  name: keyVaultName
+}
+
+@description('Create an App Service Plan with a Basic SKU')
+resource exampleAppServicePlan 'Microsoft.Web/serverfarms@2022-09-01' = {
+  name: 'mlflow-ASP'
+  location: location
+  kind: 'Linux'
+  properties: {
+    reserved: true
+  }
+  sku: {
+    tier: 'Basic'
+    name: 'B1'
+  }
+}
+
+@description('Create the App Service with a system-assigned identity')
+resource exampleAppService 'Microsoft.Web/sites@2022-09-01' = {
+  name: 'mlops-skaroti-appservice' // must be globally unique
+  location: location
+  kind: 'linux'
+  identity: {
+    type: 'SystemAssigned'
+  }
+  properties: {
+    serverFarmId: exampleAppServicePlan.id
+    reserved: true
+    siteConfig: {
+      linuxFxVersion: 'PYTHON|3.11'
+    }
+  }
+}
+
+@description('Configure the App Service with the Key Vault secrets')
+resource exampleAppServiceConfig 'Microsoft.Web/sites/config@2022-09-01' = {
+  name: 'web'
+  parent: exampleAppService
+  properties: {
+    appSettings: [
+      {
+        name: 'API_KEY'
+        value: '@Microsoft.KeyVault(SecretUri=https://${kv.name}.vault.azure.net/secrets/apiKey/)'
+      }
+      {
+        name: 'AZURE_ML_ENDPOINT_URL'
+        value: '@Microsoft.KeyVault(SecretUri=https://${kv.name}.vault.azure.net/secrets/endpointUrl/)'
+      }
+      {
+        name: 'SCM_DO_BUILD_DURING_DEPLOYMENT'
+        value: '1'
+      }
+    ]
+  }
+}
+
+@description('This is the built-in Key Vault Secrets User role. See https://docs.microsoft.com/azure/role-based-access-control/built-in-roles')
+resource keyVaultSecretsUser 'Microsoft.Authorization/roleDefinitions@2022-04-01' existing = {
+  name: '4633458b-17de-408a-b874-0445c86b69e6'
+}
+
+@description('Assign the Key Vault Secrets User role to the App Service')
+resource roleAssignment 'Microsoft.Authorization/roleAssignments@2022-04-01' = {
+  name: guid(kv.id, exampleAppService.id)
+  properties: {
+    principalId: exampleAppService.identity.principalId
+    roleDefinitionId: keyVaultSecretsUser.id
+    principalType: 'ServicePrincipal'
+  }
+  scope: kv
+}

src/job-prod.yml

@@ -10,6 +10,6 @@ inputs:
     path: azureml:diabetes-prod-folder:1
   reg_rate: 0.01
 environment: azureml:AzureML-sklearn-0.24-ubuntu18.04-py37-cpu@latest
-compute: azureml:aml-ws-compute-ds11-v2
+compute: azureml:aml-ws-compute-f4s-v2
 experiment_name: diabetes-classification-production
 description: Train a classification model to predict diabetes

src/myapp/myapp/app.py

@@ -0,0 +1,114 @@
+from flask import Flask, request, render_template
+import json
+import os
+import ssl
+import urllib.request
+
+app = Flask(__name__)
+
+
+def allowSelfSignedHttps(allowed):
+    # bypass the server certificate verification on client side
+    if (
+        allowed
+        and not os.environ.get("PYTHONHTTPSVERIFY", "")
+        and getattr(ssl, "_create_unverified_context", None)
+    ):
+        ssl._create_default_https_context = ssl._create_unverified_context
+
+
+allowSelfSignedHttps(True)
+# this line is needed if you use self-signed certificate
+# in your scoring service.
+
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    if request.method == "POST":
+        # Get user input
+        patient_id = request.form["PatientID"]
+        pregnancies = request.form["Pregnancies"]
+        plasma_glucose = request.form["PlasmaGlucose"]
+        diastolic_blood_pressure = request.form["DiastolicBloodPressure"]
+        triceps_thickness = request.form["TricepsThickness"]
+        serum_insulin = request.form["SerumInsulin"]
+        bmi = request.form["BMI"]
+        diabetes_pedigree = request.form["DiabetesPedigree"]
+        age = request.form["Age"]
+
+        # Format input data
+        input_data = {
+            "input_data": {
+                "columns": [
+                    "PatientID",
+                    "Pregnancies",
+                    "PlasmaGlucose",
+                    "DiastolicBloodPressure",
+                    "TricepsThickness",
+                    "SerumInsulin",
+                    "BMI",
+                    "DiabetesPedigree",
+                    "Age",
+                ],
+                "index": [0],
+                "data": [
+                    [
+                        patient_id,
+                        pregnancies,
+                        plasma_glucose,
+                        diastolic_blood_pressure,
+                        triceps_thickness,
+                        serum_insulin,
+                        bmi,
+                        diabetes_pedigree,
+                        age,
+                    ]
+                ],
+            }
+        }
+
+        body = str.encode(json.dumps(input_data))
+        # Get the AZURE_ML_ENDPOINT_URL environment variable passed from App Service Application settings
+        url = os.environ["AZURE_ML_ENDPOINT_URL"]
+        # Get the API_KEY environment variable passed from App Service Application settings
+        api_key = os.environ["API_KEY"]
+        if not api_key:
+            raise Exception("A key should be provided to invoke the endpoint")
+
+        # The azureml-model-deployment header will force the request
+        # to go to a specific deployment.
+        # Remove this header to have the request observe the endpoint
+        # traffic rules
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": ("Bearer " + api_key),
+            "azureml-model-deployment": "mlflow-deployment",
+        }
+
+        req = urllib.request.Request(url, body, headers)
+
+        try:
+            response = urllib.request.urlopen(req)
+
+            result = json.loads(response.read())
+
+            # Display result
+            if result[0] == 1:
+                return render_template("result.html", result="The patient is diabetic.")
+            else:
+                return render_template(
+                    "result.html", result="The patient is not diabetic."
+                )
+        except urllib.error.HTTPError as error:
+            print("The request failed with status code: " + str(error.code))
+
+            # Print the headers - they include the requert ID and the
+            # timestamp, which are useful for debugging the failure
+            print(error.info())
+            print(error.read().decode("utf8", "ignore"))
+    else:
+        return render_template("index.html")
+
+
+if __name__ == "__main__":
+    app.run()

src/myapp/myapp/requirements.txt

@@ -0,0 +1,2 @@
+Flask==2.2.5
+requests==2.31.0

src/myapp/myapp/templates/index.html

@@ -0,0 +1,30 @@
+<!DOCTYPE html>
+<html>
+    <head>
+        <title>Diabetes Prediction</title>
+    </head>
+    <body>
+        <h1>Diabetes Prediction</h1>
+        <form method="post">
+            <label for="PatientID">Patient ID:</label><br>
+            <input type="text" id="PatientID" name="PatientID"><br><br>
+            <label for="Pregnancies">Pregnancies:</label><br>
+            <input type="text" id="Pregnancies" name="Pregnancies"><br><br>
+            <label for="PlasmaGlucose">Plasma Glucose:</label><br>
+            <input type="text" id="PlasmaGlucose" name="PlasmaGlucose"><br><br>
+            <label for="DiastolicBloodPressure">Diastolic Blood Pressure:</label><br>
+            <input type="text" id="DiastolicBloodPressure" name="DiastolicBloodPressure"><br><br>
+            <label for="TricepsThickness">Triceps Thickness:</label><br>
+            <input type="text" id="TricepsThickness" name="TricepsThickness"><br><br>
+            <label for="SerumInsulin">Serum Insulin:</label><br>
+            <input type="text" id="SerumInsulin" name="SerumInsulin"><br><br>
+            <label for="BMI">BMI:</label><br>
+            <input type="text" id="BMI" name="BMI"><br><br>
+            <label for="DiabetesPedigree">Diabetes Pedigree:</label><br>
+            <input type="text" id="DiabetesPedigree" name="DiabetesPedigree"><br><br>
+            <label for="Age">Age:</label><br>
+            <input type="text" id="Age" name="Age"><br><br>
+            <input type="submit" value="Submit">
+        </form> 
+    </body>
+</html>

src/myapp/myapp/templates/result.html

@@ -0,0 +1,10 @@
+<!DOCTYPE html>
+<html>
+    <head>
+        <title>Diabetes Prediction Result</title>
+    </head>
+    <body>
+        <h1>Diabetes Prediction Result</h1>
+        <p>{{ result }}</p>
+    </body>
+</html>

src/myapp/myapp/web.config

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="utf-8"?>
+<configuration>
+  <system.webServer>
+    <handlers>
+      <add name="httpPlatformHandler" path="*" verb="*" modules="httpPlatformHandler" resourceType="Unspecified" />
+    </handlers>
+    <httpPlatform processPath="%INTERPRETERPATH%" arguments="%INTERPRETER_ARGUMENTS%" stdoutLogEnabled="true" stdoutLogFile="\\?\%home%\LogFiles\python.log" startupTimeLimit="60" processesPerApplication="16">
+      <environmentVariables>
+        <environmentVariable name="PORT" value="%HTTP_PLATFORM_PORT%" />
+        <environmentVariable name="PYTHONPATH" value="%HOME%\site\wwwroot" />
+      </environmentVariables>
+    </httpPlatform>
+  </system.webServer>
+</configuration>