1. Managed ML Experimentation with GCP Vertex AI


    To manage machine learning (ML) experimentation on GCP using Vertex AI, you'll often need to utilize several resources that Vertex AI offers. These resources might include datasets (to manage your ML data), tensorboards (for experiment visualization), endpoints (for deploying ML models for consumption), and metadata stores (to catalog metadata for artifacts, executions, and contexts).

    Here's an overview of setting up a managed ML experimentation environment using Pulumi with the GCP provider:

    1. Datasets: In Vertex AI, a dataset is a collection of data that can be used for training and evaluating machine learning models. You define properties like display name and metadata schema (the format of your dataset).

    2. Tensorboards: Use Tensorboard to visualize metrics like loss and accuracy during the model training process. You can define a display name and encryption specifications if encryption is needed.

    3. Endpoints: An endpoint in Vertex AI allows you to serve predictions from deployed models. You can specify details about the network, description, and encryption requirements.

    4. Metadata Stores: A metadata store is used to record and retrieve metadata associated with your machine learning workflows in Vertex AI.

    Now, let's write a Pulumi program to provision these resources:

    import pulumi import pulumi_gcp as gcp # Define a dataset ai_dataset = gcp.vertex.AiDataset("my-ai-dataset", display_name="my_dataset", metadata_schema_uri="gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml", project="your-gcp-project-id", region="us-central1" ) # Create a Tensorboard ai_tensorboard = gcp.vertex.AiTensorboard("my-ai-tensorboard", display_name="my_tensorboard", project="your-gcp-project-id", region="us-central1" ) # Define an AI Endpoint ai_endpoint = gcp.vertex.AiEndpoint("my-ai-endpoint", display_name="my_endpoint", project="your-gcp-project-id", location="us-central1" ) # Initialize a Metadata Store ai_metadata_store = gcp.vertex.AiMetadataStore("my-ai-metadata-store", project="your-gcp-project-id", region="us-central1" ) # Exporting important information for further use pulumi.export('ai_dataset_id', ai_dataset.name) pulumi.export('ai_tensorboard_id', ai_tensorboard.name) pulumi.export('ai_endpoint_id', ai_endpoint.name) pulumi.export('ai_metadata_store_id', ai_metadata_store.name)

    Here's what the code does:

    • We import required modules: the pulumi base module gives us core functionalities, while pulumi_gcp is specifically for working with Google Cloud resources.
    • For each resource (dataset, tensorboard, endpoint, metadata store), we create an instance using corresponding classes from pulumi_gcp.
    • We specify required information for each resource, such as names and regions. Note that your-gcp-project-id should be replaced with your actual GCP project ID.
    • Finally, we export the resource IDs with pulumi.export, which will be visible as output after the program runs. This is useful for referencing these resources later on.

    Make sure to replace placeholders like your-gcp-project-id with your actual project ID and adjust the configuration as per your requirements. Once you run this code with Pulumi, it will provision the underlying infrastructure on GCP.

    To run this Pulumi program, you need to have the Pulumi CLI installed and configured with access to your GCP account. Save this code in a __main__.py file within a Pulumi project directory, and use the Pulumi CLI to run pulumi up to preview and apply the changes.