1. Interactive AI Research Environments on Databricks


    Creating an interactive AI research environment on Databricks involves setting up various resources including a Databricks workspace, clusters to run computations, and notebooks to write and execute code. Below is a Pulumi program written in Python that sets up a Databricks workspace with a cluster and an empty notebook. This will provide the foundation on which you can build your AI research environment.

    First, you require a Databricks workspace, which is a fundamental building block that provides an environment for building and running your data pipelines and machine learning models. The workspace contains notebooks and tools for managing your clusters.

    Second, you need a Databricks cluster, which is a set of computation resources that can be used to run your notebooks and data processing workloads. You can configure the cluster depending on the computation power you need.

    Finally, the Databricks notebook is where you write your code. These notebooks support different languages like Python, Scala, SQL, and R, and you can interactively run your code on the attached cluster.

    Please install the required Pulumi Databricks provider before running the program below:

    pip install pulumi_databricks

    Here's the comprehensive Pulumi Python program:

    import pulumi import pulumi_databricks as databricks # Initialize a new Databricks workspace resource. # More on Databricks Workspace can be found here: https://www.pulumi.com/registry/packages/databricks/api-docs/workspace/ databricks_workspace = databricks.Workspace("ai-research-workspace", location="West US 2", # Choose a location that is closer to your region sku="standard", # The SKU determines the pricing tier and available features managed_resource_group_id="/subscriptions/{subscription-id}/resourceGroups/{resource-group}", # Replace with your subscription and resource group details ) # Initialize a new Databricks cluster resource. # More on Databricks Clusters can be found here: https://www.pulumi.com/registry/packages/databricks/api-docs/cluster/ databricks_cluster = databricks.Cluster("ai-research-cluster", num_workers=1, # Define the number of workers, this can be scaled according to the need spark_version="9.1.x-scala2.12", # Choose a version that supports your need node_type_id="Standard_F4s", # Choose a node type based on required CPU and memory cluster_name="AI Research Cluster", ) # Initialize a new Databricks notebook resource. # More on Databricks Notebooks can be found here: https://www.pulumi.com/registry/packages/databricks/api-docs/notebook/ databricks_notebook = databricks.Notebook("ai-research-notebook", content="print('Hello World')", # Initial content of the notebook, you can start with a simple print statement language="PYTHON", # Specify the language of the notebook, PYTHON is selected here path="/Users/{username}/AI_Research", # Set the location in the workspace where the notebook will be saved ) # Export the Databricks workspace URL so you can easily access it later pulumi.export('databricks_workspace_url', databricks_workspace.workspace_url) # Export the Databricks cluster ID for reference pulumi.export('databricks_cluster_id', databricks_cluster.id)

    This program initializes the three main components for the research environment:

    • A workspace acts as a container for all your Databricks assets.
    • A cluster provides the computational resources needed to process data and train models.
    • A notebook is where you will write Python code for your research and experiments.

    After you run this program with the Pulumi CLI, you will receive output links that you can use to access your Databricks workspace directly and view your cluster's ID.

    Remember to replace the placeholders with your actual subscription ID, resource group, and username. To run this program, save it to a file named __main__.py, navigate to the directory containing the file in your terminal, and execute pulumi up to provision the resources in your Databricks account. Make sure you have set up your Databricks provider configuration with the necessary credentials.