1. Interactive AI Research with Databricks Notebooks


    To accomplish your goal of setting up an environment for interactive AI research with Databricks Notebooks, you'll need to create Databricks workspaces and notebooks where your research and experiments can take place. We'll be working with Pulumi, a modern Infrastructure as Code tool that allows you to define your infrastructure using programming languages like Python.

    In this example, you will define a Databricks workspace within Azure, although similar resources exist for other cloud providers like AWS and GCP. The databricks workspace contains the environment to organize, manage, and run your Databricks notebooks. Once the workspace is in place, you can create a Databricks notebook resource within that workspace, where your research and AI models can be implemented and tested interactively.

    Here's how you would use Pulumi in Python to create a Databricks workspace and a notebook. Please adjust the values of the resource_group_name, location, workspace_name, and notebook_content variables to match your specific requirements. The notebook_content should be the base64-encoded content of your notebook.

    import pulumi import pulumi_azure_native as azure_native import pulumi_databricks as databricks # Define the Azure resource group where the resources will be deployed. resource_group = azure_native.resources.ResourceGroup('resource-group', resource_group_name='my-databricks-rg', location='West US') # Define the Azure Databricks workspace within the resource group. workspace = azure_native.databricks.Workspace('workspace', name='my-databricks-workspace', location=resource_group.location, resource_group_name=resource_group.name, sku=azure_native.databricks.SkuArgs(name='standard')) # Define a Databricks Notebook within the workspace with some base64-encoded content. # This content is typically the encoded .dbc file which contains your interactive notebook. notebook = databricks.Notebook('notebook', path="/Users/myuser@mydomain.com/MyNotebook", content_base64="base64-encoded-notebook-content", language=databricks.NotebookLanguage.PYTHON, format=databricks.NotebookFormat.SOURCE) # Export the URL of the Databricks workspace pulumi.export('databricks_workspace_url', workspace.workspace_url)

    In this program:

    • An Azure resource group is created to hold all related resources.
    • A Databricks workspace is then defined in that resource group.
    • Next, we create a notebook within that workspace using the databricks.Notebook resource. The notebook is defined with a specific path, content, and language.
    • notebook_content is supposed to be the base64-encoded content of the actual notebook you want to create. This content is typically derived from a .dbc file or similar, which is the format for Databricks notebooks.
    • Lastly, we export the URL of the Databricks workspace. This URL can be used to access the workspace and all its notebooks through a web browser.

    You'll need to encode your Databricks notebook content to base64 to set the content_base64 property. You can do this with many online tools or via the command line using a tool like base64 usually available on Linux and macOS systems.

    In practical usage, you may also want to include additional resources such as networking configurations, storage accounts, and more, depending on how isolated and secure you'd like your environment to be. If you're working within a team or an organization, you might also include additional role assignments and permissions setups to control access to the Databricks workspace and notebooks.

    Remember, the program above is an outline and will need specific details like the resource_group_name and content_base64 filled in, which will depend on your actual application and needs. Once complete, you would run this program using the Pulumi CLI to provision these resources on Azure.