noahgift/flask-ml-azure-serverless

Deploy Flask Machine Learning Application on Azure App Services

flask-ml-azure-serverless

Deploy Flask Machine Learning Application on Azure App Services

If you run into problems

• Build the container using Docker commands in the Makefile
• Rebuild the model using a later version of sklearn and update requirements.txt with your version of sklearn

To run it locally follow these steps (on Python 3.8, there are issues on later version of Python)

1. Create virtual environment and source

python3 -m venv ~/.flask-ml-azure
source ~/.flask-ml-azure/bin/activate

2. Run make install

3. Run python app.py

4. In a separate shell run: ./make_prediction.sh

To run it in Azure Pipelines

1. Refer to Azure Official Documentation guide here throughout

2. Launch Azure Shell

3. Create Github Repo with Azure Pipelines Enabled (Could be a fork of this repo)

4. Clone the repo into Azure Cloud Shell

Note: You make need to follow this YouTube video guide on how to setup SSH keys and configure cloudshell environment

5. Create virtual environment and source

python3 -m venv ~/.flask-ml-azure
source ~/.flask-ml-azure/bin/activate

2. Run make install

3. Create an app service and initially deploy your app in Cloud Shell

az webapp up -n <your-appservice>

4. Verify deployed application works by browsing to deployed url: https://<your-appservice>.azurewebsites.net/

You will see this output:

5. Verify Machine Learning predictions work

Change the line in make_predict_azure_app.sh to match the deployed prediction -X POST https://<yourappname>.azurewebsites.net:$PORT/predict

6. Create an Azure DevOps project and connect to Azure, (as official documentation describes)

7. Connect to Azure Resource Manager

8. Configure connection to previously deployed resource group

9. Create new Python Pipeline with Github Integration

This process will create a YAML file that looks roughly like the YAML output shown below. Refer to the official Azure Pipeline YAML documentation for more information about it.

# Python to Linux Web App on Azure
# Build your Python project and deploy it to Azure as a Linux Web App.
# Change python version to one thats appropriate for your application.
# https://docs.microsoft.com/azure/devops/pipelines/languages/python

trigger:
- master

variables:
  # Azure Resource Manager connection created during pipeline creation
  azureServiceConnectionId: '<youridhere>'
  
  # Web app name
  webAppName: 'flask-ml-service'

  # Agent VM image name
  vmImageName: 'ubuntu-latest'

  # Environment name
  environmentName: 'flask-ml-service'

  # Project root folder. Point to the folder containing manage.py file.
  projectRoot: $(System.DefaultWorkingDirectory)
  
  # Python version: 3.7
  pythonVersion: '3.7'

stages:
- stage: Build
  displayName: Build stage
  jobs:
  - job: BuildJob
    pool:
      vmImage: $(vmImageName)
    steps:
    - task: UsePythonVersion@0
      inputs:
        versionSpec: '$(pythonVersion)'
      displayName: 'Use Python $(pythonVersion)'
    
    - script: |
        python -m venv antenv
        source antenv/bin/activate
        python -m pip install --upgrade pip
        pip install setup
        pip install -r requirements.txt
      workingDirectory: $(projectRoot)
      displayName: "Install requirements"

    - task: ArchiveFiles@2
      displayName: 'Archive files'
      inputs:
        rootFolderOrFile: '$(projectRoot)'
        includeRootFolder: false
        archiveType: zip
        archiveFile: $(Build.ArtifactStagingDirectory)/$(Build.BuildId).zip
        replaceExistingArchive: true

    - upload: $(Build.ArtifactStagingDirectory)/$(Build.BuildId).zip
      displayName: 'Upload package'
      artifact: drop

- stage: Deploy
  displayName: 'Deploy Web App'
  dependsOn: Build
  condition: succeeded()
  jobs:
  - deployment: DeploymentJob
    pool:
      vmImage: $(vmImageName)
    environment: $(environmentName)
    strategy:
      runOnce:
        deploy:
          steps:
          
          - task: UsePythonVersion@0
            inputs:
              versionSpec: '$(pythonVersion)'
            displayName: 'Use Python version'

          - task: AzureWebApp@1
            displayName: 'Deploy Azure Web App : flask-ml-service'
            inputs:
              azureSubscription: $(azureServiceConnectionId)
              appName: $(webAppName)
              package: $(Pipeline.Workspace)/drop/$(Build.BuildId).zip

10. Verify Continuous Delivery of Azure Pipelines by changing app.py

You can watch this YouTube Walkthrough of this process

11. Add a lint step (this gates your code against syntax failure)

    - script: |
        python -m venv antenv
        source antenv/bin/activate
        make install
        make lint
      workingDirectory: $(projectRoot)
      displayName: 'Run lint tests'

You can watch this YouTube Walkthrough of this process

Cloud Computing for Data Analysis Book

This book is being written "just in time", with a weekly release schedule.