1. CI/CD for ML Model Training Pipelines with GitLab


    Creating a CI/CD (Continuous Integration/Continuous Delivery) pipeline for ML (Machine Learning) model training with GitLab involves setting up a version control repository for your ML code, configuring a runner for executing your pipeline tasks, and defining the pipeline stages for training your model.

    Here's what each step entails in the context of Pulumi and GitLab:

    1. Version Control Repository: You'll need a GitLab project to host your ML codebase. This will be used to track changes, manage code reviews, and serve as the source for your CI/CD pipeline.

    2. GitLab Runner: A runner is an agent that runs your CI jobs and sends the results back to GitLab. It can be installed on a server you manage or run on a cloud instance.

    3. Pipeline Configuration: GitLab pipelines are defined in a .gitlab-ci.yml file in the root of your repository. This file contains the definitions of the different stages your code goes through, such as linting, testing, training, and possibly deploying your model.

    4. Cluster or Server for Training: Depending on your needs, you'll either train your model directly on a runner or send jobs to a dedicated server or cluster, such as a Kubernetes cluster. Integration with a Kubernetes cluster can be done through the GitLab Kubernetes Cluster integration.

    Below is a Pulumi Python program that sets up a basic GitLab project and a Kubernetes cluster to run your ML model training pipelines. This includes creating a new project, enabling a runner, and setting up a Kubernetes cluster.

    import pulumi import pulumi_gitlab as gitlab # Create a new GitLab project for the ML codebase ml_project = gitlab.Project("ml_project", name="ml-model-training", description="Project to host ML model training pipeline", visibility_level="private") # Assuming you already have a GitLab runner set up, # you can associate it with your project. # Replace the 'runner_id' with your existing runner's ID. project_runner_enablement = gitlab.ProjectRunnerEnablement("project_runner", project=ml_project.id, runner_id=12345) # Use your actual runner id here # Create a project cluster where the CI jobs will run. This is an example and might require # additional details depending on your specific Kubernetes cluster configuration. # Note: You'll need to have access to a Kubernetes cluster and have the details like API URL and token. project_cluster = gitlab.ProjectCluster("project_cluster", name="ml-model-training-cluster", project=ml_project.id, kubernetes_api_url="https://your.k8s.api.url", # Replace with your Kubernetes API URL kubernetes_token="your_kubernetes_token", # Replace with your Kubernetes token kubernetes_ca_cert="your_kubernetes_ca_cert", # Replace with your Kubernetes CA Certificate enabled=True, managed=True) pulumi.export('gitlab_ml_project_url', ml_project.web_url) pulumi.export('gitlab_ml_project_runner_enablement', project_runner_enablement.runner_id) pulumi.export('gitlab_ml_project_cluster', project_cluster.name)

    This program will create a new private project in GitLab for hosting your machine learning codebase. It sets up a project runner, assuming you have a runner available, and it connects the GitLab project with a Kubernetes cluster where your CI jobs for training the model will be executed.

    Please replace 'runner_id', 'https://your.k8s.api.url', 'your_kubernetes_token', and 'your_kubernetes_ca_cert' with your actual runner ID and Kubernetes cluster details.

    Put this code inside a Python file, for instance, ci_cd_ml_pipeline.py, and then run it with Pulumi:

    pulumi up

    The pulumi.export lines will output the resulting URLs and IDs on the console after you run the Pulumi program. Keep in mind that more sophisticated setups will require additional configuration and resources depending on your specific ML pipeline needs, such as volume mounts for datasets, GPU resource scheduling, or integration with data pipeline tools.