1. Enforcing Code Standards in Machine Learning Projects on GitLab


    Enforcing code standards is an essential part of maintaining the quality and consistency of code in any software project, including machine learning projects. In GitLab, code standards can be enforced through a combination of GitLab features such as merge request approvals, protected branches, code reviews, push rules, and continuous integration (CI) pipelines with linting jobs.

    To help you get started with enforcing code standards in machine learning projects on GitLab using Pulumi, I will guide you through creating a new GitLab project with push rules that enforce code standards and setting up a CI pipeline that includes a linting job.

    In this program, we will cover the following:

    1. Creating a new GitLab project using the gitlab.Project resource.
    2. Applying push rules to the project with the pushRules property.
    3. Defining a CI pipeline with a linting stage for checking code standards.

    Here's how you can accomplish this with Pulumi in Python:

    import pulumi import pulumi_gitlab as gitlab # Step 1: Create a new GitLab project # The `gitlab.Project` resource is used to create and manage a project on GitLab. # It allows you to specify various settings like project name, visibility, and push rules # to enforce code standards such as file size limits and file name regex. ml_project = gitlab.Project("ml_project", name="my-ml-project", visibility_level="private", push_rules=gitlab.ProjectPushRulesArgs( # Prevent secret leaks by blocking keywords like "password" prevent_secrets=True, # Enforce that all commit messages must follow a regex pattern. # For example, must include a ticket number like "TICKET-1234: Commit message" commit_message_regex=r"TICKET-\d{4}: .*", # Reject commits with file names that do not follow a standard naming convention file_name_regex=r"([a-zA-Z0-9\s_\\.\-\(\):])+(.py|.ipynb)$", # Enforce maximum file size (in bytes) to avoid excessively large files max_file_size=1048576, # This is 1MB ) ) # Step 2: Set up a CI pipeline with linting # We can define a GitLab CI pipeline configuration as a string and use the `gitlab.Project` # resource's `ci_config_path` property to specify the path to the CI config file. # The following CI configuration defines a linting job using Flake8, a popular Python linting tool. ci_config = """ stages: - lint flake8-lint: stage: lint image: python:3.9 script: - pip install flake8 - flake8 . --count --ignore=E501,W503 --max-complexity=10 --max-line-length=127 --statistics """ ci_file = gitlab.ProjectEnvironment("ci_config_file", project=ml_project.id, name=".gitlab-ci.yml", content=ci_config ) # Export the URL of the project so that it can be accessed easily after deployment pulumi.export('project_url', ml_project.web_url)


    • First, we create a new GitLab project named my-ml-project with the gitlab.Project resource, specifying the project name and visibility level.
    • With the pushRules property, we set up rules that enforce:
      • Secrets prevention in code (prevent_secrets).
      • A commit message pattern (commit_message_regex).
      • A regex to allow only specific file extensions (.py for Python files, .ipynb for Jupyter notebooks).
      • A maximum file size (max_file_size).
    • Next, we define the CI pipeline configuration, which contains a lint stage with a flake8-lint job to perform linting using Flake8.
    • The gitlab.ProjectEnvironment named ci_config_file is used to create a .gitlab-ci.yml file within the project, linking the CI configuration to the project.
    • Lastly, we export the project's web URL for easy access after it has been provisioned.

    To run this Pulumi program, save the code in a file (e.g., main.py), ensure that GitLab is properly configured in your Pulumi environment, and then execute it with the Pulumi CLI. After applying this configuration, your machine learning project on GitLab will have enforced code standards that must be met for pushing code and a CI pipeline that will lint your code when changes are proposed.