Continuous Integration for AI Systems using GitLab Runners

Implementing GitLab Runners for AI CI

To set up continuous integration (CI) for AI systems using GitLab Runners with Pulumi, you need to create GitLab Runner instances for your project. A GitLab Runner is an application that works with GitLab CI/CD to run jobs in a pipeline. For this purpose, we'll define a Pulumi program that does the following:

Registers a new GitLab Runner at the project or group level, which handles the execution of your CI jobs.
Ensures that necessary properties such as a registration token, tags, and Runner executor type are configured.

Let's go through the steps to set up a GitLab Runner using Pulumi and Python.

First, we will declare a GitLab Runner. For this Pulumi program to run, you need to have GitLab provider pre-configured for your Pulumi setup. This usually involves setting up the GitLab token and other authentication mechanisms inside your Pulumi project.

Here is a Pulumi program that creates a GitLab Runner. Please take note that you need to replace YOUR_REGISTRATION_TOKEN with a legitimate registration token obtained from your GitLab instance.

import pulumi
import pulumi_gitlab as gitlab

# Create a GitLab Runner.
runner = gitlab.Runner("ai-system-runner",
                       description="Runner for AI System",
                       active=True,  # The runner is active and able to run jobs.
                       locked=False,  # If the Runner is locked to current projects.
                       access_level="not_protected",
                       run_untagged=False,  # Defines if the runner can run untagged jobs.
                       tag_list=["ai", "ml"],  # Tags associated with the runner.
                       maximum_timeout=3600,  # Job execution timeout.
                       registration_token="YOUR_REGISTRATION_TOKEN"  # Registration token obtained from GitLab.
                       )

# Export the runner's token.
pulumi.export('runner_token', runner.token)

This Pulumi program will create a new Runner within your GitLab instance which can be used to run jobs. The tag_list parameter can be used to specify tags that are associated with the runner, which can be used to select specific runners for individual CI jobs - a common practice if you have different runners for different types of jobs, such as ones that require GPUs for AI / ML workloads.

The registration_token is a secret token that you must obtain from your GitLab project or group's settings under the CI/CD settings pane. This is used to register a Runner with your project or group so that it can execute jobs.

The locked parameter is set to False to indicate that the Runner is not exclusively locked to the current project, meaning it can be available to run jobs across different projects if required.

The maximum_timeout parameter is the maximum time a job can run before it is forcibly terminated. This might be relevant for AI systems where training runs can sometimes take significant time.

After executing the Pulumi program with the appropriate token, you'll have a registered GitLab Runner that will be triggered to run whenever a CI pipeline is executed.

Remember to secure your registration token and any other sensitive credentials. In a production setup, you should use Pulumi's secrets management to handle such sensitive data.

If you're new to Pulumi, you typically run this program using the Pulumi CLI. You would navigate to the directory containing this code and then run pulumi up, which prompts Pulumi to perform an 'update', provisioning resources as described in the code.