1. Versioning ML Data using Databricks Catalog for Reproducibility


    Versioning machine learning (ML) data is crucial for experiment reproducibility, allowing data scientists to return to a specific state of the data for training models, re-running experiments, and ensuring consistency across various team members' work. Databricks is a platform that provides various tools to assist with this, including the Databricks Catalog, which can be used to create and manage databases and the structural information about the data (metadata).

    Pulumi allows you to define such infrastructure as code, which adds to the reproducibility by allowing the same setup to be reproduced or updated in a controlled manner. Below is a Python program using Pulumi with the Databricks provider to create a catalog in Databricks. This catalog will serve as a way to organize and access various datasets that you may want to version for ML purposes.

    Here's how you can create a Databricks Catalog for ML data versioning with Pulumi:

    • The databricks.Catalog resource is used to create a catalog in Databricks.
    • The catalog can contain multiple databases, which in turn can contain tables and views that reference the ML data.
    • Properties of the catalog, such as its name, an optional description, and more, can be set through the Pulumi resource.

    We start by importing the required Pulumi libraries along with the databricks package. We then use the databricks.Catalog class to create a new catalog. In this example, we'll assume you have the right permissions and Databricks workspace already set up.

    import pulumi import pulumi_databricks as databricks # Create a Databricks catalog to organize ML data with versioning ml_data_catalog = databricks.Catalog("ml-data-catalog", name="ml-data-catalog", owner="your_databricks_username", # Replace with the actual owner comment="Catalog for ML data versioning", # Options for the catalog such as location of the underlying data, e.g, a S3 bucket on AWS # storageRoot is the root path in the object store where dbfs paths and dbfs file ids for # managed tables in databases in this catalog are stored. # metastoreId represents the metastore that backs the content of this catalog. # isolationMode signifies whether the managed tables in the metastore are accessible by multiple catalogs or not. # The parameters provided here are placeholders and should be replaced with actual values. options={ "location": "s3://your-bucket/path/to/ml-data/" }, metastoreId="your_metastore_id", isolationMode="SHARED" ) # Export the catalog URL for easy access pulumi.export('ml_data_catalog_url', ml_data_catalog.storage_root)

    In the example, replace the placeholder strings with actual values relevant to your setup:

    • "your_databricks_username" should be replaced with the Databricks username of the owner of the catalog.
    • "s3://your-bucket/path/to/ml-data/" should be replaced with the root path to where your ML data is (or will be) stored.
    • "your_metastore_id" should be replaced with the ID of the metastore that backs the content of this catalog.

    Note: You need to have a Databricks workspace and appropriate permissions configured for this to work.

    Once you run the Pulumi program, it will provision the catalog as defined, enabling you to start versioning your ML data for reproducibility. The URL to the storage root of the catalog is exported so that it can be easily accessed or used in subsequent operations.