1. Automated ML Model Building with GCP Cloud Build


    To set up an automated machine learning (ML) model building pipeline with GCP using Pulumi, you would typically need to set up several GCP services such as Cloud Storage, Cloud Build, and AI Platform. The process involves storing your ML code in a source repository, configuring Cloud Build to automatically train your ML models when new code is pushed, and finally deploying the trained models to AI Platform for serving predictions.

    Below is an explanation of how you can automate ML model building with GCP Cloud Build, followed by a Pulumi program written in Python that sets up the necessary infrastructure.

    Detailed Explanation

    1. Cloud Storage: Create a bucket to store source code, training data, and trained model artifacts.
    2. Source Repositories: Although not explicitly set in Pulumi, you need a source code repository such as Cloud Source Repositories or GitHub where your ML code is stored.
    3. Cloud Build: Create a Cloud Build trigger that listens for changes to the source repository and starts a build process when changes are pushed. The build process can include steps for training the ML model using your code.
    4. AI Platform: Deploy the trained models to AI Platform to serve predictions. AI Platform also offers jobs and models resources for training and deploying ML models.

    In the following Pulumi program, we will focus on setting up Cloud Storage and Cloud Build as part of the automated ML building pipeline:

    import pulumi import pulumi_gcp as gcp # Create a GCP Cloud Storage bucket to store source code, training data, and trained model artifacts. ml_storage_bucket = gcp.storage.Bucket("ml_storage_bucket", location="us-central1" ) # Cloud Build configuration to automate the model training with AI Platform. # Replace `<your_source_repository_url>` and `<your_training_application_folder>` with your own values. cloud_build_trigger = gcp.cloudbuild.Trigger("ml_build_trigger", description="Trigger for ML model training", filename="cloudbuild.yaml", included_files=[ "<your_training_application_folder>/**/*.py", "<your_training_application_folder>/**/requirements.txt", ], substitutions={ "_BUCKET_NAME": ml_storage_bucket.name, }, source=gcp.cloudbuild.TriggerSourceArgs( repo_source=gcp.cloudbuild.TriggerSourceRepoSourceArgs( branch_name="main", repo_name="<your_source_repository_url>" ), ) ) # Export the bucket name and Cloud Build trigger ID for reference pulumi.export("bucket_name", ml_storage_bucket.name) pulumi.export("build_trigger_id", cloud_build_trigger.id)

    In this program,

    • We create a GCP storage bucket named ml_storage_bucket where we can store ML-related files like datasets, model artifacts, etc.
    • We set up a Cloud Build trigger ml_build_trigger that listens for updates in the source code repository. The trigger is configured to start a build when there are changes in the Python files or the requirements.txt within the specified application folder. The build instructions are assumed to be in a file named cloudbuild.yaml in the root of the repository.
    • We use build substitutions to replace placeholders in the cloudbuild.yaml file with actual values, such as the name of the storage bucket. These substitutions would be used within the cloudbuild.yaml to reference the appropriate GCP resources.
    • Finally, we export the bucket name and the Cloud Build trigger ID so that they can be easily retrieved if needed.

    Before running this Pulumi code, you must have cloudbuild.yaml in the root of your repository with the necessary steps to train and optionally deploy your model to AI Platform.

    Please replace <your_source_repository_url> and <your_training_application_folder> with the actual URL to your source repository and the path inside your repository where your training application resides. The branch_name should reflect the branch you want to build from; in this example, we use main.

    This program sets up the necessary infrastructure for your automated ML model building with GCP Cloud Build. You can extend it further by adding resources like AI Platform jobs or model deployment steps, depending on your specific requirements.