CI/CD for ML Model Deployment with GitLab Infrastructure Runner

CI/CD Pipelines for ML Model Deployment on GitLab

To set up a CI/CD pipeline for ML Model Deployment with a GitLab Infrastructure Runner using Pulumi, we will proceed with the following steps:

Create a GitLab project: We need a GitLab project where our machine learning model and its associated code will reside. This project will also contain the CI/CD pipeline configuration file .gitlab-ci.yml.
Register a Runner to the project: GitLab Runners are agents that run the jobs defined in .gitlab-ci.yml. They can be specific to a project or shared across multiple projects. In a machine learning context, runners might have specific hardware like GPUs attached for model training and testing.
Configure the CI/CD pipeline: In .gitlab-ci.yml, we define the steps that are automatically executed upon code changes. This might include building the model, running tests, and deploying it.

In this Pulumi program, we create a new GitLab project and register a GitLab Runner to it. We assume you have a GitLab token with sufficient permissions to create projects and manage runners.

Below is a Pulumi program in Python that accomplishes this:

import pulumi
import pulumi_gitlab as gitlab

# This is the name of the GitLab project we will create.
project_name = "ml-model-deployment"

# Create a new GitLab project.
ml_project = gitlab.Project("MLProject",
    name=project_name,
    # You can add additional properties to tailor the project setup.
    description="Machine Learning Model Deployment Project",
    visibility_level="private")

# Register a new Runner for the project. You will need a registration token
# which can be retrieved from the GitLab Web UI under Settings > CI/CD > Runners.
runner = gitlab.ProjectRunner("MLRunner",
    project=ml_project.id,
    # Replace `token` with your registration token.
    token="YOUR_REGISTRATION_TOKEN",
    # A runner can use a specific executor, such as 'shell' or 'docker+machine' for ML workloads.
    # See: https://docs.gitlab.com/runner/executors/
    runner_type="docker+machine",
    # Additional configurations like docker image can be specified here.
    # For example, if you need GPUs, use a CUDA-enabled image and machine with GPU.
    # You may also specify tags to select the right runner based on job tags in the CI/CD pipeline definition.
    tags=["docker", "machine"],
    # Provide the locked status indicating if the Runner is locked to the current project.
    locked=True)

pulumi.export("project_name", ml_project.name)
pulumi.export("runner_id", runner.runner_id)

This program sets up the basic GitLab project infrastructure and Runner registration. To activate the continuous integration piece, you will need to add a .gitlab-ci.yml file to your repository with your ML model workflow steps.

The .gitlab-ci.yml might look something like this for a simple ML deployment pipeline:

stages:
  - build
  - test
  - deploy

build_model:
  stage: build
  script:
    - echo "Building the ML model"
    # Add commands to build your model
  tags:
    - docker

test_model:
  stage: test
  script:
    - echo "Testing the ML model"
    # Add commands to test your model
  tags:
    - docker

deploy_model:
  stage: deploy
  script:
    - echo "Deploying the ML model"
    # Add commands to deploy your model
  tags:
    - machine

In this .gitlab-ci.yml, there are three stages: build, test, and deploy. Each stage has some scripts that will be executed within the context of a GitLab runner when changes are pushed to the repository. By specifying tags, we ensure that the jobs are picked up by a runner that has the specified tags, which allows us to control the environment in which each job runs.

Remember that adjusting the GitLab CI/CD pipeline to fit the ML deployment process would likely involve specific steps such as setting up environments, managing dependencies, training models, running evaluations, and deploying trained models to production. Each of these steps should be tailored to the specifics of the ML project and the technologies being used.

In order to use Pulumi to accomplish this, you need to have Pulumi CLI installed and set up with your cloud credentials. You also need to ensure that your GitLab token is set up in the Pulumi program. The program can then be executed by running pulumi up in the command line, which will provision the resources in the appropriate cloud provider.