1. Custom ML Model Training with Vertex AI Workbench


    To train a custom machine learning (ML) model using Vertex AI Workbench on Google Cloud Platform, you'll need to perform a series of steps. These include setting up a Vertex AI Workbench instance where you can run your data processing and model training code, creating a dataset, and potentially using other Vertex AI resources like Feature Store or TensorBoard.

    Let's go through a Pulumi program in Python that sets up the necessary Vertex AI resources for training a custom ML model. We'll create a Vertex AI Workbench instance and make sure it's ready to use for our machine learning tasks.

    Below is the Pulumi program that accomplishes these tasks:

    import pulumi import pulumi_gcp as gcp # Replace these variables with your actual project ID and region. project_id = 'my-gcp-project' region = 'us-central1' # Create a Vertex AI Dataset for storing data that will be used to train the model. ai_dataset = gcp.vertex.AiDataset("aiDataset", project=project_id, display_name="my_custom_model_dataset", metadata_schema_uri="gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml", labels={"env": "training"}) # Provision Vertex AI Workbench instance which will be used for running the training job. workbench_instance = gcp.notebooks.Instance("workbenchInstance", project=project_id, location=region, post_startup_script=""" # Here, you can add any shell commands to run after the instance starts. # For instance, you could git clone your model training code, set up # environments, install dependencies, etc. """, machine_type="n1-standard-4", vm_image=gcp.notebooks.InstanceVmImageArgs( project="deeplearning-platform-release", image_family="tf-latest-cpu", # Choose appropriate image for your training job. )) pulumi.export("ai_dataset_id", ai_dataset.id) pulumi.export("workbench_instance_name", workbench_instance.name)

    This program starts by setting up a AiDataset which represents the dataset you wish to use for your machine learning model. We specify a metadata_schema_uri which should be aligned with the type of data you're using. In this example, it's set for an image dataset, but you can find other URIs for different dataset types in the Vertex AI documentation.

    Next, we define a Instance which represents the Vertex AI Workbench machine where ML model training will occur. It's configured with a post-startup script placeholder where you can add commands to execute after the instance is created, such as installing dependencies or fetching your code repository.

    Each of these resources is tagged with a project ID and often a location/region, which you'll need to replace with the specific identifiers for your Google Cloud project.

    This program ends with exporting two important IDs - the ID of the dataset and the name of the Workbench instance. When the Pulumi program runs successfully, these values can be obtained from the Pulumi CLI output or the Pulumi Web Console. They are useful for identifying your resources and ensuring they are configured correctly.

    To actually train your model, you would write your training script in your preferred machine learning framework (like TensorFlow or PyTorch) and then run it on the Vertex AI Workbench instance after your environment is set up. Keep in mind that this program is just a starting point that provisions the infrastructure; you'll also need logic to handle model training and deployment.