1. DBFS as a Repository for ML Experimentation Artifacts


    DBFS, or Databricks File System, is a distributed file system that runs on top of Databricks, simplifying data access within Databricks workflows. If you're using Pulumi for automating cloud infrastructure, you might integrate DBFS as a repository for your machine learning experimentation artifacts by using the databricks.DbfsFile resource from the pulumi_databricks package. This resource allows you to manage files on DBFS, making it easy to store and retrieve ML artifacts like models, datasets, or logs.

    Below is a Pulumi Python program that demonstrates how to set up a DBFS file, which could be used for ML experimentation. Remember to have the Pulumi CLI installed and have Databricks credentials configured in your environment for this Pulumi program to work.

    import pulumi import pulumi_databricks as databricks # Create a new DBFS file which can be used to store ML experimentation artifacts # Here we are simulating an ML experiment's serialized model file dbfs_ml_experiment_artifact = databricks.DbfsFile("mlExperimentArtifact", content_base64=pulumi.Output.secret("<base64-encoded-content>"), path="/mnt/experiments/artifacts/my_model.bin" ) # Export the URL that can be used to access the ML experimentation artifact pulumi.export("artifact_url", dbfs_ml_experiment_artifact.path)


    • The databricks.DbfsFile resource is used to create a new file on DBFS. The name mlExperimentArtifact becomes the logical name for this resource within your Pulumi application.

    • The content_base64 parameter is expected to be a base64-encoded string that represents the content of your file. In a real-world scenario, this content will be the serialized form of a machine learning model or any other experiment artifact.

    • The path parameter specifies the location on DBFS where the file will be stored. This example stores the artifact in a mounted directory at /mnt/experiments/artifacts/my_model.bin. You'd usually mount a durable storage service like AWS S3 or Azure Blob Storage to DBFS to persist your data outside the lifetime of a Databricks cluster.

    • pulumi.export makes the path of your DBFS file available as an output of your Pulumi program which you can access after deployment through the Pulumi CLI or through the Pulumi Service Console.

    • <base64-encoded-content> is a placeholder for the content you would like to place in the DBFS file. You would base64-encode your actual file content and insert it into the code to create the DBFS file with the provided content.

    Remember to replace <base64-encoded-content> with the Base64 encoded version of your ML model or other artifacts you wish to store in this file. This content is marked secret to prevent it from being logged or stored in plaintext in the Pulumi state.

    This program provides a starting point, and you may need to adapt it to your specific machine learning experimentation workflow requirements.