1. Managing Datasets and Model Artifacts in GitLab


    To manage datasets and model artifacts in GitLab using Pulumi, you will need to interact with various GitLab resources such as projects, repositories, files, and potentially CI/CD pipelines as well. Through these, you can structure datasets, store model artifacts, and define how they are built, tested, and deployed.

    Here is a comprehensive guide, followed by a Pulumi program in Python, to manage datasets and model artifacts within a new GitLab project. We will be using the pulumi_gitlab provider to accomplish these tasks.

    Step-by-Step Guide

    1. Project Creation: We will start by creating a new GitLab project to house our datasets and model artifacts. This is like creating a new repository which will serve as a container for your data and code.

    2. Repository Files: For managing files within this project (e.g., dataset files or model binaries), you can use the gitlab.ProjectFile resource. This lets you upload files to your GitLab project repository.

    3. CI/CD Pipeline: If you need to automate processes like testing your models or building artifacts, setting up a CI/CD pipeline within the project is necessary. This can be achieved by creating a file named .gitlab-ci.yml in your repository, which defines the pipeline's stages and jobs.

    4. Artifacts: GitLab CI/CD pipelines can produce artifacts that are the outputs of jobs. These could be data files, models, or any other files that you want to pass between jobs or store after a pipeline finishes.

    Pulumi Program

    Let's create a simple Pulumi program that sets up a new GitLab project and outlines how one can manage datasets and model artifacts.

    Firstly, you'd need to install the pulumi-gitlab Python package with the following command:

    pip install pulumi_gitlab

    Now, let's write the Pulumi program:

    import pulumi import pulumi_gitlab as gitlab # Create a new GitLab project to store datasets and model artifacts project_name = 'data-and-models-management' project = gitlab.Project(project_name, name=project_name, description="A project to manage datasets and model artifacts", visibility_level="private") # Assume we have dataset and model files locally that we want to upload to the project dataset_file_path = 'path/to/dataset.csv' model_artifact_path = 'path/to/model.pkl' # Upload a dataset file to the GitLab project repository dataset_file = gitlab.ProjectFile("dataset-file", project=project.id, file_path=dataset_file_path, # The contents should be the base64 encoded content of the file content=pulumi.FileAsset(dataset_file_path).as_base64(), branch="main") # Upload a model artifact file to the GitLab project repository model_artifact_file = gitlab.ProjectFile("model-artifact-file", project=project.id, file_path=model_artifact_path, # The contents should be the base64 encoded content of the file content=pulumi.FileAsset(model_artifact_path).as_base64(), branch="main") # Export the URLs to access these files pulumi.export("dataset_file_url", pulumi.Output.concat(project.web_url, "/", dataset_file_path)) pulumi.export("model_artifact_file_url", pulumi.Output.concat(project.web_url, "/", model_artifact_path))


    • We start by creating a new private GitLab project using the gitlab.Project resource. This project will be where we store our dataset and model artifacts.
    • Then we use gitlab.ProjectFile to upload two files to the project’s repository: one for the dataset and one for the model artifact. We specify the file paths and the content, which is the base64-encoded content of each file, obtained by reading it as a FileAsset.
    • Since we want to reference these files, we export their URLs which comprise the project's web URL and the respective file paths used in the repository.
    • Note: For managing more complex artifacts or automating the management process, we would integrate CI/CD pipeline configuration via a .gitlab-ci.yml file within this same project structure.

    Feel free to adapt and expand this program to suit the specific needs of your datasets and model artifacts management strategy within GitLab.