Customize Python pipelines

This article describes how to customize building, testing, packaging, and delivering Python apps and code in Azure Pipelines. To create your first pipeline with Python, see the Python quickstart.

With Microsoft-hosted agents in Azure Pipelines, you can build your Python apps without having to set up your own infrastructure. Tools that you commonly use to build, test, and run Python apps, including pip, are preinstalled.

You might need to request the free grant of parallel jobs or purchase a parallel job to run your pipelines.

Use a specific Python version

To use a specific version of Python in your pipeline, add the Use Python version task to azure-pipelines.yml. The following example YAML pipeline definition sets the pipeline to use Python 3.11.

YAML

steps:
- task: UsePythonVersion@0
  inputs:
    versionSpec: '3.11'

Use multiple Python versions

To run a pipeline with multiple Python versions, for example to test a package against those versions, define a job with a matrix of Python versions. Then set the UsePythonVersion task to reference the matrix variable. For example:

YAML

jobs:
- job: 'Test'
  pool:
    vmImage: 'ubuntu-latest'
  strategy:
    matrix:
      Python38:
        python.version: '3.8'
      Python39:
        python.version: '3.9'
      Python310:
        python.version: '3.10'

  steps:
  - task: UsePythonVersion@0
    inputs:
      versionSpec: '$(python.version)'

You can add tasks that use each Python version in the matrix.

Run Python scripts

To run Python scripts from your repository, use a script element and specify a filename. For example:

YAML

- script: python src/example.py

You can also use the Python script task to run inline Python scripts.

YAML

- task: PythonScript@0
  inputs:
    scriptSource: 'inline'
    script: |
      print('Hello world 1')
      print('Hello world 2')

To parameterize script execution, use the PythonScript task with arguments values to pass arguments into the running process. You can use sys.argv or the more sophisticated argparse library to parse the arguments.

YAML

- task: PythonScript@0
  inputs:
    scriptSource: inline
    script: |
      import sys
      print ('Executing script file is:', str(sys.argv[0]))
      print ('The arguments are:', str(sys.argv))
      import argparse
      parser = argparse.ArgumentParser()
      parser.add_argument("--world", help="Provide the name of the world to greet.")
      args = parser.parse_args()
      print ('Hello ', args.world)
    arguments: --world Venus

Install dependencies

You can use scripts to install specific PyPI packages with pip. The following example installs or upgrades pip and the setuptools and wheel packages.

YAML

- script: python -m pip install --upgrade pip setuptools wheel
  displayName: 'Install tools'

Install requirements

After you update pip and friends, a typical next step is to install dependencies from requirements.txt.

YAML

- script: pip install -r requirements.txt
  displayName: 'Install requirements'

Run tests

You can use scripts to install and run various tests in your pipeline.

Run lint tests with flake8

The following YAML code installs or upgrades flake8 and uses it to run lint tests.

YAML

- script: |
    python -m pip install flake8
    flake8 .
  displayName: 'Run lint tests'

Test with pytest and collect coverage metrics with pytest-cov

The following YAML code installs pytest and pytest-cov and runs tests, outputting test results in JUnit format and outputting code coverage results in Cobertura XML format.

YAML

- script: |
    pip install pytest pytest-azurepipelines
    pip install pytest-cov
    pytest --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml
  displayName: 'pytest'

Run tests with Tox

Azure Pipelines can run parallel Tox test jobs to split up the work. On a development computer, you have to run your test environments in series. The following example uses tox -e py to run whichever version of Python is active for the current job.

YAML

- job:

  pool:
    vmImage: 'ubuntu-latest'
  strategy:
    matrix:
      Python38:
        python.version: '3.8'
      Python39:
        python.version: '3.9'
      Python310:
        python.version: '3.10'

  steps:
  - task: UsePythonVersion@0
    displayName: 'Use Python $(python.version)'
    inputs:
      versionSpec: '$(python.version)'

  - script: pip install tox
    displayName: 'Install Tox'

  - script: tox -e py
    displayName: 'Run Tox'

Publish test results

Add the Publish Test Results task to publish JUnit or xUnit test results to the server.

YAML

- task: PublishTestResults@2
  condition: succeededOrFailed()
  inputs:
    testResultsFiles: '**/test-*.xml'
    testRunTitle: 'Publish test results for Python $(python.version)'

Publish code coverage results

Add the Publish code coverage results task to publish code coverage results to the server. You can see coverage metrics in the build summary, and download HTML reports for further analysis.

YAML

- task: PublishCodeCoverageResults@2
  inputs:
    codeCoverageTool: Cobertura
    summaryFileLocation: '$(System.DefaultWorkingDirectory)/**/coverage.xml'

Package and deliver code

To authenticate with twine, use the Python twine upload authenticate task to store authentication credentials in the PYPIRC_PATH environment variable.

YAML

- task: TwineAuthenticate@0
  inputs:
    artifactFeed: '<Azure Artifacts feed name>'
    pythonUploadServiceConnection: '<twine service connection from external organization>'

Then add a custom script that uses twine to publish your packages.

YAML

- script: |
   twine upload -r "<feed or service connection name>" --config-file $(PYPIRC_PATH) <package path/files>

You can also use Azure Pipelines to build an image for your Python app and push it to a container registry.

Related content

• Azure DevOps plugin for PyCharm (IntelliJ)
• Getting Started with Python in VS Code
• Build and publish a Python app
• Azure Pipelines task reference
• Azure Pipelines agents