1. Managed MLflow for Experiment Tracking on Databricks Entitlements


    To manage MLflow for experiment tracking on Databricks, you would establish an MLflow experiment within the Databricks workspace. MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It allows you to track experiments, record and compare parameters and results, and package and share models.

    In the context of Pulumi, we use the databricks.MlflowExperiment resource to create and manage MLflow experiments on Databricks. It helps to encapsulate an MLflow Experiment, which is a named set of MLflow Runs, and offers tracking and organizing functionalities for ML model experimentation.

    The following Pulumi Python program demonstrates how to create a managed MLflow experiment within Databricks:

    import pulumi import pulumi_databricks as databricks # Create a new Databricks MLflow experiment. mlflow_experiment = databricks.MlflowExperiment("example-experiment", name="my-mlflow-experiment", # 'description' is optional and provides a description for the experiment. description="A managed MLflow experiment for tracking.", # 'lifecycle_stage' denotes the stage of the experiment e.g. 'active'. lifecycle_stage="active", # 'artifact_location' specifies the location where artifacts for the experiment will be stored. # This can be a DBFS location or an S3 bucket. It's optional and if not provided, # Databricks sets a default location under the workspace's root location. artifact_location="dbfs:/my-experiments/my-mlflow-experiment" ) # Export the ID of the experiment which might be useful for integration with other systems/tools or for reference. pulumi.export('experiment_id', mlflow_experiment.experiment_id)

    This program defines an MLflow experiment using the databricks.MlflowExperiment class from the Databricks package within Pulumi. Here's a breakdown of what's happening:

    1. We import the required Pulumi module for Databricks.
    2. We create an MLflow experiment named "my-mlflow-experiment" and optionally provide a description. This helps in organizing and identifying the purpose of the experiment.
    3. The lifecycle_stage attribute denotes the stage of the experiment, which we set to "active". In MLflow, an experiment can either be active or deleted. An active experiment is where you can log runs under.
    4. Optionally, we define artifact_location where results and associated data of this experiment runs will be stored. If you don't provide this location, Databricks automatically assigns a default path within the workspace's dedicated DBFS storage.
    5. Finally, we use pulumi.export to output the experiment_id, which is the unique identifier for the experiment created. This ID can be used to reference the experiment in other operations or to integrate with external systems.

    Remember that this code snippet assumes that you have the appropriate Databricks workspace set up and configured with Pulumi. Your Databricks provider needs to be authenticated with the proper credentials to perform these operations. Make sure that you have your Databricks workspace URL and personal access token correctly configured in your environment to authenticate the Pulumi Databricks provider.