This is the second project of Udacity Cloud DevOps Engineer using Azure Nanodegree.

• A link to a Trello board for the project:

https://trello.com/b/mkRe5Rne/udacity-azure-course2

• A link to a spreadsheet that includes the original and final project plan:

https://docs.google.com/spreadsheets/d/1Q3iyPWt2bHowQ76-PuupFn5tF3dBx7mrWktgoqO_E2I/edit?usp=sharing

• Go to https://portal.azure.com/#cloudshell/ and run command:

ssh-keygen -t rsa
cat ~/.ssh/id_rsa.pub

• Add public key to GitHub. (GitHub > Settings > SSH and GPG keys > Paste > Add the key)
• Clone the repository

git clone [email protected]:voletri/udacity-azure-course2.git

• Create a virtual environment

python3 -m venv ~/.udacity-azure-course2
source ~/.udacity-azure-course2/bin/activate

• Run: make all

cd udacity-azure-course2
make all

• The following commands can be run in order to then test and start the application:

export FLASK_APP=app.py
flask run

• Go to https://portal.azure.com/#cloudshell/
• Run command to create webapp:

az webapp up --sku F1 -n udacity-azure-course2

• Confirm webapp is created successfully

  Confirm app is up and running

  https://udacity-azure-course2.azurewebsites.net
  

• GitHub > Actions > set up a workflow yourself

name: Python application test with Github Actions

on: [push]

jobs:
  build:

    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v3
    - name: Set up Python 3.7
      uses: actions/setup-python@v1
      with:
        python-version: 3.7
    - name: Install dependencies
      run: |
        make install
    - name: Lint with pylint
      run: |
        make lint
    - name: Test with pytest
      run: |
        make test

Note the official documentation should be referred to and double checked asyou setup CI/CD. Successful deploy of the project in Azure Pipelines.

• Logs from your running application here:

https://udacity-azure-course2.scm.azurewebsites.net/api/logs/docker

• Running Azure App Service from Azure Pipelines automatic deployment
• Successful prediction from deployed flask app in Azure Cloud Shell. Use this file as a template for the deployed prediction. The output should look similar to this:

(.udacity-azure-course2) tri@Azure:~/udacity-azure-course2$ sh make_predict_azure_app.sh
Port: 443
{"prediction":[20.35373177134412]

• Output of streamed log files from deployed application

Go to https://portal.azure.com/ -> App Service -> Select app -> In tab Monitoring -> Log stream

Or using command to display the logs of the server:

az webapp log tail -g trivl_rg_1166 -n udacity-azure-course2

• Output of locust I will implement Locust

Create file locustfile.py

Using command start a local service in your environment, default port is 8089

locust -f locustfile.py

• Improve error handling and tesing
• Add new pytest tests in the Makefile
• Setup different environments (DEV, Production)

Youtube demo link: https://youtu.be/B52y8BWTuRg