Python

In this serie I show you how you can create a PyPi ready solution which is stored on GitHub and has fully automatized build-test-deploy CI/CD pipeline in Azure DevOps. Furthermore you will be able to use it from command line like azure-cli or aws-cli.

Table of content

• Part 1: Basic Python Code
• Part 2: Basic Release diagram
• Part 3: Azure DevOps preparation
• Part 4: Create Azure DevOps pipeline – staging
• Part 5: Create Azure DevOps pipeline – master
• Part 6: Add test cases to project (current)
• Part 7: Expand Python project functionality

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In Part 5 more or less we reached our goal. Nevertheless several integrated test cases (unittest) are recommended in a well built code. Therefore Today we add a unittest to our project.

Before we start we need to know some facts about unittests in Python:

• Unittest implementation is not a rocket science, merely we have to follow the best practices
• During unittest the functions are really executing. This means when you test a function in unittest it will call in live not only for testing.
• Prepare your unittest for modular usage.

Then recommended to know the background of unittests:

• Unit testing framework (Python 2.7)
• Unit testing framework (Python 3.7)

We use the Python 2.7 related unittest in our project.

1. We create unittest for our Core module. Therefore the simplest thing if we put our unittest next to core module files inside core directory (<project directory>/pypiproject/core). Name if this file in our example is: pypiproject_core_unittest.py

2. Content of the file is very simple.

• unittest package import
• core module import for testing
• TestCase moduile class which contains the required test cases
• TestCase runner function – this is required because we won’t run unittest from __main__.

# Import unittest
import unittest

# Import core module main file for testing
from .pypiproject_core import *

# Test case Class
class TestCoreModule(unittest.TestCase):
    
    currentResult = None # holds last result object passed to run method

    def setUp(self):
        pass

    # teardown for manage test case results
    def tearDown(self):
        ok = self.currentResult.wasSuccessful()
        errors = self.currentResult.errors
        failures = self.currentResult.failures
        print ('All tests passed so far!' if ok else \
                ' %d errors and %d failures so far' % \
                (len(errors), len(failures)))

    def run(self, result=None):
        self.currentResult = result # remember result for use in tearDown
        unittest.TestCase.run(self, result) # call superclass run method

    # Test Case 01 - Write out text in string format with default value
    def test_input(self):
        self.assertEqual(getText("test text"), "test text")

    # Test Case 02 - Write out text in string and json format
    def test_output(self):
        self.assertEqual(getText("test text", "string"), "test text")
        self.assertEqual(getText("test text", "json"), {"text": "test text"})

# Test runner function
def runUnittests():
    suite = unittest.TestSuite()
    # Check test cases in TestCoreModule class
    for method in dir(TestCoreModule):
        # Add test collection tests which start with "test"
        if method.startswith("test"):
            suite.addTest(TestCoreModule(method))
    # Return with execution and result of testcases
    return unittest.TextTestRunner(verbosity=3).run(suite)

3. How can you run the test cases?

It is pretty easy and simple to automatize

Start a Python shell where our project is installed then execute the following commands:

from pypiproject.core.pypiproject_core_unittest import *
testResult = runUnittests()

and the result is awesome:

test_input (pypiproject.core.pypiproject_core_unittest.TestCoreModule) ... All tests passed so far!
ok
test_output (pypiproject.core.pypiproject_core_unittest.TestCoreModule) ... All tests passed so far!
ok

----------------------------------------------------------------------
Ran 2 tests in 0.003s

OK

Additionally the testResult variable contains the detailed result of execution.

It was easy and I hope make sense. If you have any further questions feel free to get in touch with your questions.

In this serie I show you how you can create a PyPi ready solution which is stored on GitHub and has fully automatized build-test-deploy CI/CD pipeline in Azure DevOps. Furthermore you will be able to use it from command line like azure-cli or aws-cli.

Table of content

• Part 1: Basic Python Code
• Part 2: Basic Release diagram
• Part 3: Azure DevOps preparation
• Part 4: Create Azure DevOps pipeline – staging
• Part 5: Create Azure DevOps pipeline – master (current)
• Part 6: Add test cases to project
• Part 7: Expand Python project functionality

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Last time we created our first pipeline for staging branch. Now we create the pipeline in Azure DevOps for master branch.

This it the pipeline which transports our code to PRODUCTION (live). As last time I mentioned Staging is important from quality perspective. Because this ensures to check your code before production (live).

Accordinglycreate a pipeline which starts when a pull (merge) request is executed into master branch on Git. Then it compiles the code then uploads to Prod-PyPi.

The pipeline creation for master branch has the next steps:

1. Create basic pipeline (check changes on Git)
2. Compile and upload package
3. Merge Staging to Master on Git
4. Check result on Azure DevOps
5. Check result on Prod PyPi

1. Create basic pipeline (check changes on Git)

1. Let’s open our project landing page.

And click on “Pipelines” menu item on left side of screen.

We already have a pipeline (for staging). You can see the following screen.

2. Here you merely click on + New button above the staging pipeline and choose New build pipeline.

3. On next screen you choose the “Use the classic editor” option under the three other code.

4. Choose GitHub option at Select source screen.

5. Click on three point “…” at Repository part then select the right repository (pypi-project) and right branch (master) from GitHub. Then click on Continue button.

6. On next screen you should define the pipeline template. Remember we would like to create a pipeline which will build and deploy Python package to Pypi. Therefore you should choose the Python Package template from the list. Then click on Apply button.

Wow. As you can see on next screen we have a pipeline which seem pretty good. 🙂

7. Here we need to add a Name for our pipeline. (Because this is the master related pipeline, my suggestion is: master – pypi-project)
Then choose Hosted VS2017 from list under Agent pool.

8. Now we have to delete some steps and stages from the default pipeline. So please click on (right button of the mouse) then choose Remove selected the following items:

• Flake
• pytest
• Publish Test Results **/test-results.xml

And keep only the Build and Test stage/job.

9. Rename Build and Test stage to Build and Publish. Then choose the None under Parallelism section. And under Run this job choose the Only when all previous jobs have succeeded option.

10. Click on Triggers tab and under Continuous integration choose your git repository. Then on the right side tick Enable continuous integration. And be sure the master branch is the selected under Branch filters.

2. Compile and upload package

In this section we configure steps inside pipeline to compile our code the upload to Prod-PyPi.

11. Click on Variables tab and modify the python.version value to 2.7

12. Go back to Tasks tab then click on Install dependencies task and replace the Script content to this:

python -m pip install --upgrade pip && pip install -r pypi/requirements.txt

13. Now click on + to add a new Command Line task to Build and Publish stage. Then click on Add button.

14. Select newly added task, rename it to Build sdist & bdist_wheel. Then insert into script section the following:

python setup.py sdist bdist_wheel

This task is required to build python package.

15. Add a new Python Twine Upload Authenticate task with + on Tasks tab. Then click to Add button.

16. Select newly added task, rename it to Twine Authenticate to PyPi – MASTER. Then under Feeds from external organizations choose the Pypi related connection. (pypi-project on PyPi prod)

This will create the connection with Prod PyPi to upload your freshly built package.

17. Finally add a new Command Line task with + on Tasks tab. Then click to Add button.

18. Select newly added task, rename it to Use a custom twine task to publish. Then insert into script section the following:

twine upload dist/* -r the1bitdemo --config-file $(PYPIRC_PATH)

Please be sure the value after -r is same you defined at EndpointName in prod PyPi connection.

This uploads out package to PyPi where we configured the connection in Part 3: Azure DevOps preparation.

19. Last but not least save out pipeline so click on Save button on screen.

20. In the screen we should add the comment information. Then click on Save button.

3. Merge Staging to Master on Git

In this steps we do a standard Pull request from Staging branch to Master branch.

21. Open your project on Git.

22. Choose Pull requests from menu under project name

23. Click on New pull request

24. Under “Comparing changes” you can see the destination and the source branches for merge. Please be sure you can see the following:

Destination: master
Source: staging

25. Then click on Create pull request

26. On the next screen you write a comment then click on Create pull request button.

27. Now Git checks whether the merge is possible or not. If there is no conflict (everything is green) you can finalize the merge. So click on Merge pull request button.

28. Finally confirm merge

29. Done. Lets check master branch on Git.

And now let’s check our pipeline in Azure DevOps.

4. Check result on Azure DevOps

30. In Azure DevOps click on Builds on left side of screen then choose master – pypi-project you can see the status of your build. (It was initiated by Pull request on Git)

It’s great…Let’s check it on Prod PyPi

5. Check result on PyPi

31. Open in your browser the https://pypi.org/

32. Type pypi-project into search field then hit enter.

33. Choose the from the list

Awesome!…This is ours. And anybody can install it….We are done. 🙂

Next time we will improve our project with some test case related steps. Now you can implement your pipelines. 🙂

In this serie I show you how you can create a PyPi ready solution which is stored on GitHub and has fully automatized build-test-deploy CI/CD pipeline in Azure DevOps. Furthermore you will be able to use it from command line like azure-cli or aws-cli.

Table of content

• Part 1: Basic Python Code
• Part 2: Basic Release diagram
• Part 3: Azure DevOps preparation
• Part 4: Create Azure DevOps pipeline – staging (current)
• Part 5: Create Azure DevOps pipeline – master
• Part 6: Add test cases to project
• Part 7: Expand Python project functionality

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Last time we prepared our Azure DevOps project and created the external connections for Github and PyPi. 

Today we do something exciting… After several preparation steps today we will create our staging pipeline in Azure DevOps.

Accordinglycreate a pipeline which observes if something is changing in your code in staging branch on Git. Then it compiles the code then uploads to Test-PyPi for testing purpose.

Staging is important from quality perspective. Because this ensures to check your code before production (live).

The pipeline creation for staging branch has the next steps:

1. Create basic pipeline (check changes on Git)
2. Compile and upload package
3. Check result on Azure DevOps
4. Check result on Test PyPi
5. Test pipeline

1. Create basic pipeline (check changes on Git)

1. Let’s open our project landing page.

And click on “Pipelines” menu item on left side of screen.

Because we don’t have any pipeline you can see the following screen.

2. Here you merely click on New pipeline button

3. On next screen you choose the “Use the visual designer” option under the three other code.

4. Choose GitHub option at Select source screen.

5. Click on three point “…” at Repository part then select the right repository (pypi-project) and right branch (staging) from GitHub. Then click on Continue button.

6. On next screen you should define the pipeline template. Remember we would like to create a pipeline which will build and deploy Python package to Pypi. Therefore you should choose the Python Package template from the list. Then click on Apply button.

Wow. As you can see on next screen we have a pipeline which seem pretty good. 🙂

7. Here we need to add a Name for our pipeline. (Because this is the staging related pipeline, my suggestion is: staging – pypi-project)
Then choose Hosted VS2017 from list under Agent pool.

8. Now we configure – again – the code source. Click on Get sources on middle of screen.

9. In this step you need to repeat the steps you did in point 5. Additional activity to tick Report build status which is important from debug and tracing point of view.

10. Now we have to delete some steps and stages from the default pipeline. So please click on then choose Remove selected the following items:

• Flake
• pytest

And keep only the Build and Test stage.

11. Rename Build and Test stage to Build and Publish. Then choose the None under Parallelism section. And under Run this job choose the Only when all previous jobs have succeeded option.

12. Click on Triggers tab and under Continuous integration choose your git repository. Then on the right side tick Enable continuous integration. And be sure the staging branch is the selected under Branch filters.

2. Compile and upload package

In this section we configure steps inside pipeline to compile our code the upload to Test-PyPi.

13. Click on Variables tab and modify the python.version value to 2.7

14. Go back to Tasks tab then click on Install dependencies task and replace the Script content to this:

python -m pip install --upgrade pip && pip install -r pypi/requirements.txt

15. Now click on + to add a new Command Line task to Build and Publish stage. Then click on Add button.

16. Select newly added task, rename it to Build sdist & bdist_wheel. Then insert into script section the following:

python setup.py sdist bdist_wheel

This task is required to build python package.

17. Add a new Python Twine Upload Authenticate task with + on Tasks tab. Then click to Add button.

18. Select newly added task, rename it to Twine Authenticate to test PyPi – STAGING. Then under Feeds from external organizations choose the test Pypi related connection. (pypi-project on PyPi test)

This will create the connection with Test PyPi to upload your freshly built package.

19. Finally add a new Command Line task with + on Tasks tab. Then click to Add button.

20. Select newly added task, rename it to Use a custom twine task to publish. Then insert into script section the following:

twine upload dist/* -r the1bitdemo --config-file $(PYPIRC_PATH)

Please be sure the value after -r is same you defined at EndpointName in test PyPi connection.

This uploads out package to PyPi where we configured the connection in previous step.

21. Last but not least save out pipeline so click on Save & queue button on screen.

22. In the screen we should add some information to starting such as comment and commit. Then click on Save & queue button. It is in queue now.

3. Check result on Azure DevOps

It is in queue now.

23. To click on Builds on left side of screen then choose staging – pypi-project you can see the status of your build.

It’s great…Let’s check it on Test PyPi

4. Check result on Test PyPi

24. Open in your browser the https://test.pypi.org/

25. Type pypi-project into search field then hit enter.

26. Choose the from the list

YEEEEESSSSS!…This is ours. And anybody can install it.

5. Test pipeline

The final activity for today is testing our pipeline in work.

27. Open your code in a Visual Studio Code. It must be the staging branch.

28. Modify the version number to 0.0.0.3 in the right places.

# Modify: modules/version.py
__version__ = "0.0.0.3"

# Add this to docs/readme.md
* version 0.0.0.3
    * Test Azure DevOps pipeline

29. Push the changes to Git

30. Check your pipeline

31. Check the latest package on Test PyPi

We are done. 🙂

Next time we will create the pipeline for master branch. Have a great time until that. 🙂