Challenge 4: Work with linting and unit testing
Challenge scenario
Code quality can be assessed in two ways: linting and unit testing. Use linting to check for any stylistic errors and unit testing to verify your functions.
Prerequisites
If you haven’t, complete the previous challenge before you continue.
You’ll complete the workflow created in the previous challenge.
Objectives
By completing this challenge, you’ll learn how to:
- Run linters and unit tests with GitHub Actions.
- Troubleshoot errors to improve your code.
Important! Each challenge is designed to allow you to explore how to implement DevOps principles when working with machine learning models. Some instructions may be intentionally vague, inviting you to think about your own preferred approach. If for example, the instructions ask you to create an Azure Machine Learning workspace, it’s up to you to explore and decide how you want to create it. To make it the best learning experience for you, it’s up to you to make it as simple or as challenging as you want.
Challenge Duration
- Estimated Time: 45 minutes
Instructions
In the tests folder, you’ll find files that will perform linting and unit testing on your code. The flake8 lints your code to check for stylistic errors. The test_train.py performs unit tests on your code to check whether the functions behave as expected.
- Go to the Actions tab in your GitHub repo and trigger the Code checks workflow manually. Inspect the output and fix your code where necessary.
Hint
Whenever the linter finds an error, the GitHub Actions step will fail with exit code 1. Inspect the output of the workflow to see the specific error codes for the linter. Next to the error code, the output will also list the source file with the line number and column number to help you find the cause of the error.
- Add linting and unit tests jobs to the workflow you created in the previous challenge. The workflow should be triggered by the creation of a new pull request. The workflow should run the Flake8 linter and run the Pytest unit tests.
Hint
To include unit testing in your workflow, install Pytest (using the
requirements.txt), and run the tests with pytest tests/. By default, Pytest uses test files that are prefixed with test.
- Create (or edit) a branch protection rule to require the two code checks to be successful before merging a pull request to the main branch.
Hint
To configure checks to be required to pass before merging, you can enable status checks in a branch protection rule. To find the checks, your jobs need to have a name. To ensure the checks run whenever a pull request is created, your checks should be part of a GitHub Actions workflow triggered by a
pull_request event.
To trigger the workflow, do the following:
- Make a change and push it. For example, change the hyperparameter value.
- Create a pull request, showing the integrated code checks.
Success criteria
To complete this challenge successfully, you should be able to show:
- Both the Linting and Unit tests checks are completed successfully without any errors. The successful checks should be shown in a newly created pull request.
Useful resources
- Flake8 documentation, including error codes and their descriptions.
- A beginner’s guide to Python testing.
- Learn more about test infrastructure using Azure ML and how to create tests.
- Learn more about testing with Pytest.
In this challenge, all testing is executed with GitHub Actions. Optionally, you can learn how to verify your code locally with Visual Studio Code. Running linters and unit tests locally is not required for this challenge.