This project involves building a Github repository from scratch and creating a scaffolding that will be used to perform both Continuous Integration and Continuous Delivery for a Python-based machine learning application using the Flask web framework. It requires the use of Github Actions along with a Makefile, requirements.txt and application code to perform an initial lint, test, and install cycle. Further, the project is integrated with Azure Pipelines to enable Continuous Delivery to Azure App Service.

Below are the links to Trello board and spreadsheet for the project.

• Project Trello Board

• Initial project spreadsheet

• Final Project Spreadsheet

Below is the architectural diag the project.

Setup the project repository on Github with the initial project scaffold:

• Makefile
• Tests
• code
• requirements.txt

• Launch an Azure Cloud Development Shell Development Environment
• create ssh-keys
• upload public ssh key to the github account hosting the project repository

Below is the screenshot of the project repo cloned into the azure shell development environment.

• Update the project files: Makefile, requirements.txt and include the project code files

• Create a Python virtual environment as follows.

  • python3 -m venv ~/.myrepo

• Activate the virtual environment by running the fowwing command.

• source ~/.myrepo/bin/activate

• Create the project sript file and test files

• Test the code by running make all
• A screenshot showing passing the test is shown below:

Go to the Github repo and do the following:

• Enable Github Actions in the project
• Replace yml code autogenerated by enabling Github Actions with the provided yml code
• Verify remote tests pass in Github Actions UI as shown below:

Configure Azure Pipelines to deploy the Flask starter code to Azure App Services as follows.

• Replace scaffolding code with Flask ML code
• Run the app locally as follows:

  • activate virtual environmen by running the following code:

    • source ~/.myrepo/bin/activate followed by
    • python app.py

  • Open another cloud shell environment

    • acitvate the virtual environment and run
    • ./make_prediction.sh

    The expected output is

• Deploy the app by running
• az webapp up -n bostonpredictionservice
  • here, bostonpredictionservice is the name of the app
• copy our the generated url: https://bostonpredictionservice.azurewebsites.net to the browser and the following message will be displayed.

• To make remote prediction: modify the project code file make_predict_azure_app.sh with your chosen app name.
• Verify prediction by running the code ./make_predict_azure_app.sh
• The output message is as follows:

• Get log from the deployed app by running the command az webapp log tail and it displays the following output.

• Checking performance validation with locust
  • modify the host name in the locustfile.py with the url of the deployed app.
  • Ensure the key names in the json under the predict function is the same as those in app.py.
• Run locust -f locustfile.py --headless -u 20 -r 5 -t 20s
• The expected output is

• Go to the Azure DevOps organization in the portal

• Click on My Azure DeOps Organizations

• Sign in

• Create a new project and give it a name, for e.g ci_cd_project2

• Make sure that Git is selected as the version control and create

• Create a new service connection under the Pipelines tab after clicking on project settings

• choose Create service connection --> Azure Resource Manager --> Service principal (automatic) --> --> subsription

• Choose Service connection name

• login

• Choose the same resource group where the app is deployed

• Make sure the ci_cd_prGrant access permission to all pipelines is checked and save

• Go to Pieplines and create a new pipeline

• Choose Github

• Configure the pipeline as Python to Linux Wepp App on Azure

• Choose MSDN Platform Supsription and continue

• Sign in to Github

• Choose the Project Repo in Github

• Validate and configure

• Check the package version then save and run

• The output is as follows.

Some of the ways the project can be improved in the future are:

• Ensure that all needed packages are specified in the requirements.txt file.
• Provide starter codes for deep learming frameworks such as Pytorch and TensorFlow.

Video Link