GitLab CI template for Python

This project implements a GitLab CI/CD template to build, test and analyse your Python projects.

Usage

In order to include this template in your project, add the following to your gitlab-ci.yml:

include:
  - project: 'to-be-continuous/python'
    ref: '6.1.3'
    file: '/templates/gitlab-ci-python.yml'

Global configuration

The Python template uses some global configuration used throughout all jobs.

The cache policy also makes the necessary to manage pip cache (not to download Python dependencies over and over again).

Multi build-system support

The Python template supports the most popular dependency management & build systems.

By default it tries to auto-detect the build system used by the project (based on the presence of pyproject.toml and/or setup.py and/or requirements.txt), but the build system might also be set explicitly using the $PYTHON_BUILD_SYSTEM variable:

⚠️ You can explicitly set the build tool version by setting $PYTHON_BUILD_SYSTEM variable including a version identification. For example PYTHON_BUILD_SYSTEM="poetry==1.1.15"

Jobs

py-package job

This job allows building your Python project distribution packages.

It is bound to the build stage, it is disabled by default and can be enabled by setting $PYTHON_PACKAGE_ENABLED to true.

Lint jobs

py-pylint job

This job is disabled by default and performs code analysis based on pylint Python lib. It is activated by setting $PYLINT_ENABLED to true.

It is bound to the build stage, and uses the following variables:

In addition to a textual report in the console, this job produces the following reports, kept for one day:

Test jobs

The Python template features four alternative test jobs:

• py-unittest that performs tests based on unittest Python lib,
• or py-pytest that performs tests based on pytest Python lib,
• or py-nosetest that performs tests based on nose Python lib,
• or py-compile that performs byte code generation to check syntax if not tests are available.

py-unittest job

This job is disabled by default and performs tests based on unittest Python lib. It is activated by setting $UNITTEST_ENABLED to true.

In order to produce JUnit test reports, the tests are executed with the xmlrunner module.

It is bound to the build stage, and uses the following variables:

ℹ️ use a .coveragerc file at the root of your Python project to control the coverage settings.

Example:

[run]
# enables branch coverage
branch = True
# list of directories/packages to cover
source =
    module_1
    module_2

In addition to a textual report in the console, this job produces the following reports, kept for one day:

py-pytest job

This job is disabled by default and performs tests based on pytest Python lib. It is activated by setting $PYTEST_ENABLED to true.

It is bound to the build stage, and uses the following variables:

ℹ️ use a .coveragerc file at the root of your Python project to control the coverage settings.

Example:

[run]
# enables branch coverage
branch = True
# list of directories/packages to cover
source =
    module_1
    module_2

In addition to a textual report in the console, this job produces the following reports, kept for one day:

py-nosetest job

This job is disabled by default and performs tests based on nose Python lib. It is activated by setting $NOSETESTS_ENABLED to true.

It is bound to the build stage, and uses the following variables:

By default coverage will be run on all the project directories. You can restrict it to your packages by setting the $NOSE_COVER_PACKAGE variable. More info

ℹ️ use a .coveragerc file at the root of your Python project to control the coverage settings.

In addition to a textual report in the console, this job produces the following reports, kept for one day:

py-compile job

This job is a fallback if no unit test has been setup ($UNITTEST_ENABLED and $PYTEST_ENABLED and $NOSETEST_ENABLED are not set), and performs a compileall.

It is bound to the build stage, and uses the following variables:

py-bandit job (SAST)

This job is disabled by default and performs a Bandit analysis.

It is bound to the test stage, and uses the following variables:

In addition to a textual report in the console, this job produces the following reports, kept for one day:

py-safety job (dependency check)

This job is disabled by default and performs a dependency check analysis using Safety.

It is bound to the test stage, and uses the following variables:

py-trivy job (dependency check)

This job is disabled by default and performs a dependency check analysis using Trivy.

It is bound to the test stage, and uses the following variables:

In addition to a textual report in the console, this job produces the following reports, kept for one day:

py-sbom job

This job generates a SBOM file listing all dependencies using syft.

It is bound to the test stage, and uses the following variables:

In addition to logs in the console, this job produces the following reports, kept for one week:

SonarQube analysis

If you're using the SonarQube template to analyse your Python code, here is a sample sonar-project.properties file:

# see: https://docs.sonarqube.org/latest/analyzing-source-code/test-coverage/python-test-coverage/
# set your source directory(ies) here (relative to the sonar-project.properties file)
sonar.sources=.
# exclude unwanted directories and files from being analysed
sonar.exclusions=**/test_*.py

# set your tests directory(ies) here (relative to the sonar-project.properties file)
sonar.tests=.
sonar.test.inclusions=**/test_*.py

# tests report: xUnit format
sonar.python.xunit.reportPath=reports/TEST-*.xml
# coverage report: Cobertura format
sonar.python.coverage.reportPaths=reports/py-coverage.cobertura.xml
# pylint: parseable format (if enabled)
sonar.python.pylint.reportPaths=reports/py-lint.parseable.txt
# Bandit: CSV format (if enabled)
sonar.python.bandit.reportPaths=reports/py-bandit.bandit.csv

More info:

• Python language support
• test coverage & execution parameters
• third-party issues

py-release job

This job is disabled by default and allows to perform a complete release of your Python code:

1. increase the Python project version,
2. Git commit changes and create a Git tag with the new version number,
3. build the Python packages,
4. publish the built packages to a PyPI compatible repository (GitLab packages by default).

The Python template supports two packaging systems:

• Poetry: uses Poetry-specific version, build and publish commands.
• Setuptools: uses Bumpversion as version management, build as package builder and Twine to publish.

The release job is bound to the publish stage, appears only on production and integration branches and uses the following variables:

Setuptools tip

If you're using a setup.cfg declarative file for your project Setuptools configuration, then you will have to write a .bumpversion.cfg file to workaround a bug that prevents Bumpversion from updating the project version in your setup.cfg file.

Example of .bumpversion.cfg file:

[bumpversion]
# same version as in your setup.cfg
current_version = 0.5.0

[bumpversion:file:setup.cfg]
# any additional config here
# see: https://github.com/peritus/bumpversion#file-specific-configuration

semantic-release integration

If you activate the semantic-release-info job from the semantic-release template, the py-release job will rely on the generated next version info. Thus, a release will be performed only if a next semantic release is present.

You should disable the semantic-release job (as it's the py-release job that will perform the release and so we only need the semantic-release-info job) by setting SEMREL_RELEASE_DISABLED to true.

Finally, the semantic-release integration can be disabled with the PYTHON_SEMREL_RELEASE_DISABLED variable.

Git authentication

A Python release involves some Git push operations.

You can either use a SSH key or user/password credentials.

Using a SSH key

We recommend you to use a project deploy key with write access to your project.

The key should not have a passphrase (see how to generate a new SSH key pair).

Specify 🔒 $GIT_PRIVATE_KEY as secret project variable with the private part of the deploy key.

-----BEGIN 0PENSSH PRIVATE KEY-----
blablabla
-----END OPENSSH PRIVATE KEY-----

The template handles both classic variable and file variable.

Using user/password credentials

Simply specify 🔒 $GIT_USERNAME and 🔒 $GIT_PASSWORD as secret project variables.

Note that the password should be an access token (preferably a project or group access token) with write_repository scope and Maintainer role.

Pip repositories

When depending on Python packages published in GitLab's packages registry, it could be useful to configure a group level Package. But such repository will require an authenticated access.

To do so, simply set the PIP_INDEX_URL and use the CI job token.

variables:
  PIP_INDEX_URL: "${CI_SERVER_PROTOCOL}://gitlab-ci-token:${CI_JOB_TOKEN}@${CI_SERVER_HOST}:${CI_SERVER_PORT}/api/v4/groups/<group-id>/-/packages/pypi/simple"

In a corporate environment, you can be faced to two repositories: the corporate proxy-cache and the project repository.

Simply use both PIP_INDEX_URL and PIP_EXTRA_INDEX_URL.

variables:
  PIP_INDEX_URL: "https://cache.corp/repository/pypi/simple"
  PIP_EXTRA_INDEX_URL: "${CI_SERVER_PROTOCOL}://gitlab-ci-token:${CI_JOB_TOKEN}@${CI_SERVER_HOST}:${CI_SERVER_PORT}/api/v4/groups/<group-id>/-/packages/pypi/simple"