1. Scalable Machine Learning Workflows with Databricks Notebooks


    To create scalable machine learning workflows with Databricks Notebooks using Pulumi, you'll need to orchestrate Databricks resources. Databricks is a platform that provides a collaborative environment for interactive analytics and machine learning. With notebooks in Databricks, data scientists can write code in multiple languages, explore data, and leverage powerful clusters to perform compute-heavy tasks.

    In this example, I'll show you how to define Databricks resources with Pulumi for a scalable machine learning workflow. We will define a Databricks cluster and a notebook that you can use to analyze data and run machine learning models. The Databricks cluster will be configured to autoscale, allowing it to handle varying workloads efficiently.

    Overview of Resources:

    • databricks.Cluster: Represents a cluster in Databricks. Clusters are compute resources where notebooks and libraries can execute.
    • databricks.Notebook: Represents a notebook in Databricks, which is a series of cells containing runnable code.

    Here's how you can create these resources with Pulumi in Python:

    import pulumi import pulumi_databricks as databricks # Create a Databricks cluster with autoscaling enabled # This allows for cost-effective handling of varying workloads cluster = databricks.Cluster("ml-cluster", autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, max_workers=8, ), node_type_id="Standard_DS3_v2", # This specifies the type of node to use spark_version="6.4.x-scala2.11", # This specifies the Databricks runtime version # Documentation: https://www.pulumi.com/registry/packages/databricks/api-docs/cluster/ ) # Create a Databricks notebook in Python language format notebook = databricks.Notebook("ml-notebook", content_base64="cHV0IHlvdXIgbm90ZWJvb2sgY29udGVudCBoZXJlIGVuY29kZWQgaW4gYmFzZTY0", path="/Users/pulumi_user/machine_learning_notebook", # Defines where the notebook is stored within the workspace language=databricks.NotebookLanguage.PYTHON, # Specifies the language of the notebook # Documentation: https://www.pulumi.com/registry/packages/databricks/api-docs/notebook/ ) # Export the URLs to access the cluster and notebook in the Databricks workspace pulumi.export('Cluster URL', pulumi.Output.concat("https://", cluster.workspace_url, "/?o=", cluster.workspace_id, "#job/", cluster.id)) pulumi.export('Notebook URL', pulumi.Output.concat("https://", notebook.workspace_url, "/?o=", notebook.workspace_id, "#notebook/", notebook.path))

    The above Pulumi program will set up a Databricks cluster and a notebook inside the Databricks workspace. Here's a step-by-step breakdown of the actions it performs:

    1. Define a Databricks Cluster: A cluster is specified with a certain node type and Databricks runtime version. We've enabled autoscaling, which means the cluster will automatically scale the number of worker nodes based on the workload, starting with one worker and scaling up to 8 workers as needed. The node type Standard_DS3_v2 is an Azure example, so if using AWS or GCP, you will need to use the equivalent for those clouds.

    2. Define a Databricks Notebook: A notebook is created with base64 encoded content, a designated path, and the notebook language set to Python. You need to replace cHV0IHlvdXIgbm90ZWJvb2sgY29udGVudCBoZXJlIGVuY29kZWQgaW4gYmFzZTY0 with the actual base64-encoded content of your notebook.

    3. Export URLs: When the cluster and notebook are provisioned, the program will export URLs that you can use to access them through the Databricks workspace UI. These URLs will directly open the provisioned cluster and notebook.

    Make sure to replace the placeholders for content and paths with your own. Also, ensure that the configuration suits your specific requirements and constraints, such as cluster size, node type, and runtime version.

    Once you run this Pulumi program with the pulumi up command, it will create the specified resources in the Databricks workspace. You will then be able to access the notebook via the URL and start developing your machine learning workflows on a scalable infrastructure.