1. Collaborative Machine Learning with Databricks Notebooks


    To facilitate collaborative machine learning with Databricks Notebooks, you'll need to set up a Databricks workspace and instantiate notebooks within that environment. Databricks allows data scientists and engineers to work together seamlessly, with notebooks that support various languages like Python, R, Scala, and SQL.

    Let me walk you through how you would do this using Pulumi, focusing on setting up the necessary infrastructure.

    Firstly, you need to have a Databricks workspace in place. Pulumi can provision this for you if it doesn't already exist. In the following program, we will use the pulumi_databricks package to create a workspace and then add a notebook to that workspace.

    The resources involved are:

    1. Databricks Workspace: This is the primary environment where you can manage all your Databricks assets like notebooks, clusters, and jobs. It is a fully managed cloud service that provides a collaborative environment for running data engineering and data science workloads.

    2. Databricks Notebook: Once you have your workspace, you can create notebooks within it. A notebook is an interactive coding environment where you can run code, visualize data, and share results.

    Here's a Pulumi program that creates a Databricks workspace and a notebook:

    import pulumi import pulumi_databricks as databricks # Configuration notebook_content_path = './HelloWorld.py' # Path to your notebook file, typically a Python file. notebook_path_in_databricks = '/Shared/HelloWorld' # Path in Databricks workspace to save the notebook. # Create a Databricks workspace (adjust the args as needed) workspace = databricks.Workspace("my-workspace", location="westus", # Choose the region that fits you sku="standard" # The SKU represents the pricing tier (for example, 'standard' or 'premium') ) # Read the notebook file (should be in DBC format, Base64 encoded if binary format) with open(notebook_content_path, "r") as file: notebook_content = file.read() # Create a Databricks notebook within the workspace created above notebook = databricks.Notebook("my-notebook", content=notebook_content, path=notebook_path_in_databricks, language=databricks.NotebookLanguage.PYTHON, # Set the notebook language workspace_id=workspace.id ) # Export the URL where the notebook can be accessed pulumi.export('notebook_url', notebook.url)

    In the above program:

    • We start by importing the necessary Pulumi libraries.
    • The notebook content typically should be in a .py file for a Python notebook.
    • We define the location for our workspace and its SKU. These will impact where your workspace is located geographically, and the cost and features available to you.
    • We read the content of the notebook from a local file, which should be prepared in advance. The contents of this file are the code you wish to execute within the notebook.
    • We create the notebook in the workspace with the content read from the file.
    • Finally, we export the URL where the notebook can be accessed after deployment. This URL can be shared with your team for collaborative work.

    Make sure you have the Pulumi CLI installed and Pulumi Databricks provider configured with the proper credentials before running the above code.

    This Pulumi program sets up the infrastructure you need to start collaborating on machine learning projects using Databricks Notebooks. It's a starting point, and you'll likely need additional resources like clusters, jobs, and possibly permissions set up to fully utilize the collaborative features of Databricks.