1. Managing AI Model Versioning Data in GCP Datastore

    Python

    To manage AI model versioning data in GCP Datastore using Pulumi, we'll utilize the pulumi_gcp module. Specifically, we will work with the DatastoreIndex resource, which allows us to define a Datastore index that your queries can use.

    Google Cloud Datastore is a NoSQL document database built for automatic scaling, high performance, and ease of application development. To manage versioning data for AI models, we can store each version's metadata within the Datastore as entities. Each entity represents a version of the AI model, containing properties such as the model's version number, training dataset descriptions, accuracy metrics, etc.

    Here's how you can set up an index for querying AI model versioning data stored in GCP Datastore:

    1. Define the DataStoreIndex resource within your Pulumi program that specifies the kind of entities and the properties by which you would like to index and query your data.
    2. Create entities in the Datastore that contain the versioning information for your AI models, making sure the data is structured so that the defined indexes can be used for efficient queries.

    Below is a detailed Pulumi program that does just that in Python.

    import pulumi import pulumi_gcp as gcp # Configure the Google Cloud provider config = pulumi.Config() project = config.require("project") # Ensure the project is configured in Pulumi # Create a Datastore index for AI model versions # The entity kind might be "AIModelVersion" to represent different versions of an AI model. # We assume each model version entity would have properties like version number, accuracy, and deploymentStatus. model_version_index = gcp.datastore.DataStoreIndex("aiModelVersionIndex", project=project, kind="AIModelVersion", # The kind of entity this index applies to properties=[ # List of properties that we want to index {"name": "versionNumber", "direction": "ASCENDING"}, {"name": "accuracy", "direction": "DESCENDING"}, {"name": "deploymentStatus", "direction": "ASCENDING"}, ] ) # Export the index's ID, which can be used to refer to the index in queries pulumi.export('model_version_index_id', model_version_index.id)

    In the above code:

    • We begin by importing the necessary Pulumi modules.
    • We configure the Google Cloud provider and ensure that the GCP project is specified in the Pulumi configuration.
    • We define the DataStoreIndex resource, which is used to create a new index in Google Cloud Datastore. We've named this index aiModelVersionIndex for clarity.
    • kind specifies the entity kind to which the index applies. We used "AIModelVersion" as a placeholder; this should match the kind you assign to your AI model version entities.
    • properties is a list of objects where each object describes an indexed property. In our example, we're indexing by versionNumber in ascending order, accuracy in descending order, and deploymentStatus in ascending order.
    • We mark the index's ID as an output export, which is useful if we need to reference this index elsewhere.

    To actually manage the versioning data of AI models, you would put entities into the Datastore with these properties, and run queries against those properties using the index we've created.

    Remember to apply appropriate IAM roles and permissions to allow your application to interact with Datastore.

    For more details on managing resources in Google Cloud with Pulumi, you may refer to the GCP Datastore documentation.


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