Collaborative Development of AI Pipelines on GitLab

Building AI Pipelines with GitLab Infrastructure-as-Code

To set up a collaborative development environment for AI pipelines on GitLab using Pulumi, you need to create the necessary resources on GitLab to host and run the pipelines. This includes creating a project, setting up CI/CD runners, and perhaps managing environments and protected branches. With Pulumi, you can manage these resources as infrastructure as code, which is beneficial for maintaining consistency, repeatability, and version control in your setup.

In the following Pulumi Python program, I'll create a GitLab project where you and your collaborators can push AI pipeline code. Then, I'll set up a runner, which is necessary for executing the CI/CD pipelines. Finally, I'll touch on environments, which can be useful for managing deployments of your AI models.

Let's start by setting up a new project:

import pulumi
import pulumi_gitlab as gitlab

# Create a new GitLab project for the AI pipelines.
project = gitlab.Project("ai_pipeline_project",
    name="ai-pipeline-project",
    path="ai-pipeline-project",
    description="A project for AI pipeline collaboration",
    visibility_level="private",  # You might want to keep the project private.
    merge_method="merge",       # This is the strategy used when merging MRs.
    issues_enabled=True,        # Enables GitLab issues for task tracking.
    merge_requests_enabled=True # Enables merge requests for code reviews.
)

# Register a new runner for the CI/CD pipelines.
runner = gitlab.Runner("ai_pipeline_runner",
    description="Runner for AI pipeline project",
    active=True,
    locked=False,            # When locked, the runner is exclusively assigned to the project.
    tag_list=["ai", "ml"],   # Tags help to classify runners for specific jobs.
    run_untagged=True,       # This runner can execute jobs that don't have tags.
    access_level="protected", # Protected runners can only be used by protected branches and tags.
    maximum_timeout=3600,    # Maximum job timeout in seconds. Set this according to your pipeline's needs.
    registration_token=project.runners_token  # Use the registration token from the project.
)

# Output the URLs which are useful to access the project and the runner settings.
pulumi.export("project_web_url", project.web_url)
pulumi.export("runner_registration_token", project.runners_token)

In this program:

We created a GitLab project by instantiating gitlab.Project from the pulumi_gitlab package. This sets up a new repository with options for issues and merge requests which are essential for collaboration on code.
Next, we registered a GitLab runner with gitlab.Runner. CI/CD runners are the engines that process your CI/CD jobs defined in .gitlab-ci.yml. They are required to execute the jobs that test, build, and deploy your AI models.
pulumi.export statements output the URLs for easy access to the project and the registration token for the runner.

In a collaborative environment, these building blocks are used to establish the infrastructure to handle the development cycle of code changes. From here, you might extend the setup by adding branch protection rules, creating different environments for staging and production, and more. However, as a starting point, this will get the project and a runner up and running on GitLab.

Remember to execute the Pulumi program with your GitLab provider configuration in place, ensuring that Pulumi has the necessary permissions to manage resources in your GitLab account.