1. Automated Model Training Workflows with GCP Cloud Build.


    In order to create an automated model training workflow with GCP Cloud Build, you would define a series of tasks that perform the necessary steps for training a machine learning model. These tasks might include fetching data, preprocessing, training, validation, and storing the model artifacts in a way that they can be deployed or used for inference.

    Within Google Cloud, Cloud Build is a service that allows you to build your software quickly across all languages. Google Cloud also provides AI Platform Training service, which lets you run your training application in a managed environment that supports multiple machine learning frameworks.

    Below is a sample Pulumi program in Python that sets up a basic automated model training workflow in GCP using Cloud Build and AI Platform. In this example, we create:

    1. A Cloud Build Trigger, which will respond to source changes and start a build that trains the model.
    2. A Google Cloud Storage Bucket, which will store the source code for training the model and the resulting model artifacts.

    Please note that in order to perform the training, you would have a source repository with the necessary code and configuration. The actual machine learning training code is not provided here as it depends heavily on your specific use case, the data you're working with, and the machine learning framework you're using (e.g., TensorFlow, PyTorch).

    Here is what the Pulumi program would look like:

    import pulumi from pulumi_gcp import cloudbuild, storage import pulumi_gcp as gcp # Define the Google Cloud project and location. project = gcp.config.project location = gcp.config.region # Create a Google Cloud Storage Bucket to store the training code and model artifacts. bucket = storage.Bucket("model-training-bucket", location=location, labels={"purpose": "model-training"}) # Define the Cloud Build configuration to train the machine learning model. # This is a basic configuration and will likely require adjustment to fit your training code and requirements. build_config = cloudbuild.BuildStepArgs( name="gcr.io/cloud-builders/gcloud", args=[ "ai-platform", "jobs", "submit", "training", "my_model_training_job", # This should be a unique job name. "--master-image-uri", "gcr.io/cloud-ml/algorithms/custom_container:latest", # Specify the container image for training here. "--region", location, # Make sure this matches the region of your AI Platform resources. "--", # Arguments after this are passed directly to the training application. # Add your application-specific training arguments here. ] ) # Create the trigger for Cloud Build to automate model training on source changes. # The source is assumed to be in a Google Cloud Source Repository. trigger = cloudbuild.BuildTrigger("model-training-trigger", descriptions="Trigger for model training", disabled=False, tags=["ml-training"], filename="cloudbuild.yaml", # This file in your repository contains build instructions for Cloud Build. substitutions={"_BUCKET_NAME": bucket.name}, # Pass the bucket name as a variable to the build. source=cloudbuild.BuildTriggerSourceArgs( repo_source=cloudbuild.BuildTriggerSourceRepoSourceArgs( project_id=project, repo_name="my-ml-repo", # Replace with your repository name. branch_name="main", # Replace with the branch you want to trigger from. ), ), steps=[build_config], # Use the build configuration defined earlier. included_files=["**"], # Run the trigger on changes to any file, adjust as needed for your workflow. ) # Export the bucket URL to access it later if needed. pulumi.export("bucket_url", bucket.url)

    This program performs the following actions:

    • Storage Bucket Creation: A Google Cloud Storage bucket is created to hold the source code and model artifacts.
    • Build Step Definition: A Cloud Build step is defined with the necessary arguments to submit a job to the AI Platform using the gcloud CLI.
    • Build Trigger Creation: A Cloud Build trigger is created that listens for changes to a specified branch in a source repository and starts the training process.

    Remember to replace placeholders like my_model_training_job, my-ml-repo, and the training arguments with your own values.

    Make sure to have the cloudbuild.yaml file in the root of your source repository with the detailed steps to perform when a build is triggered. The actual training and related steps would be specified in that file.

    Lastly, you'll need to have the Google Cloud CLI tools available in the container specified in master-image-uri for this to work. Depending on your workflow, you might need to create and use a custom container that includes your training code and any dependencies.