1. Versioned Dataset Snapshots for Machine Learning in GKE


    Taking versioned dataset snapshots for machine learning involves storing data in a version-controlled manner, so that each snapshot of the dataset can be associated with a specific state or version. This is useful in machine learning on Google Kubernetes Engine (GKE) to manage the data used for training models, allowing reproducibility and rollbacks if needed.

    In the context of GKE and Google Cloud Platform (GCP), you would typically leverage Google Cloud Storage (GCS) for storing your datasets, as it is durable, highly available, and can interact smoothly with GKE.

    Within Pulumi, you can use the pulumi_gcp provider to interact with GCP services, including GCS. To create versioned snapshots, you can enable versioning on a GCS bucket, which will keep a history of changes to each object in the bucket.

    Below is a Pulumi program in Python that sets up a GCS bucket configured for versioning. It also includes a GKE cluster where you can run machine learning workloads that will utilize the versioned data.

    import pulumi import pulumi_gcp as gcp # Create a GCP bucket with versioning enabled. This bucket will be used to store versioned datasets. bucket = gcp.storage.Bucket("ml-dataset-bucket", versioning={ "enabled": True } ) # Output the GCS bucket URL pulumi.export("bucket_url", bucket.url) # Define the GKE cluster where machine learning workloads will be deployed. cluster = gcp.container.Cluster("ml-cluster", initial_node_count=1, node_config={ "machine_type": "n1-standard-1", "oauth_scopes": [ "https://www.googleapis.com/auth/cloud-platform", ], }, ) # Output the GKE cluster name pulumi.export("cluster_name", cluster.name) # If needed, you could define a Kubernetes Job or a Pod that uses this data for machine learning tasks.


    1. First, we import the necessary Pulumi modules.
    2. We create a Google Cloud Storage bucket with versioning enabled. The versioning parameter ensures that each update or delete of an object will result in a new, timestamped version of the data being retained.
    3. We output the URL of the bucket using pulumi.export, which allows you to see the generated resource's name in the Pulumi Console or CLI after deployment. You can use this URL to access the bucket.
    4. We define a GKE cluster with an initial node pool size. The node_config parameter specifies the type of machine to use for our cluster, as well as the OAuth scopes that enable access to cloud platform services.
    5. We output the name of the GKE cluster for reference.

    Running this Pulumi program will require you to have suitable permissions on GCP to create GCS buckets and GKE clusters. You'll also need to set up Pulumi with your GCP credentials and install the pulumi_gcp package in your Python environment.

    By following these steps, you're able to set up your infrastructure on GCP using Pulumi for versioned data management in machine learning workloads.