1. Automated ML Workflow Orchestration with Databricks


    Automating ML workflows involves creating and managing resources such as compute instances, data storage, and jobs for running various stages of a machine learning pipeline. Databricks is a platform that provides these capabilities, simplifying the orchestration of complex data analytics workflows. In the following guide, I will walk you through creating a simple automated machine learning workflow using the Databricks provider in Pulumi.

    The fundamental resources to manage with the Databricks provider are:

    • Databricks Cluster: Compute resource for executing analytics operations.
    • Databricks Job: Represents an automated task such as an ML model training job.
    • Databricks Notebook: A collaborative document that contains runnable code and narrative text.

    To start with, you'll need to configure Pulumi for use with your Databricks and cloud provider. Make sure your Databricks workspace and cloud provider (AWS, Azure, GCP) credentials are set up correctly.

    Below is a Pulumi program that sets up a simple Databricks cluster and runs a job on it. In this example, we will not be diving deep into ML specifics but rather focusing on creating a cluster and running a notebook job that can be part of an ML workflow.

    Pulumi Program for Databricks

    import pulumi import pulumi_databricks as databricks # Create a new Databricks cluster cluster = databricks.Cluster("ml-cluster", # Specify the properties for the cluster num_workers=2, cluster_name="pulumi-ml-cluster", spark_version="7.3.x-scala2.12", node_type_id="Standard_D3_v2", # This is an example node type autotermination_minutes=20, # Auto-terminate the cluster after 20 minutes of inactivity ) # Create a new Databricks notebook notebook = databricks.Notebook("ml-notebook", # Define the content of the notebook (usually loaded from an actual notebook file) content_base64="cHJpbnQoIkhlbGxvLCB3b3JsZCIpCg==", # Base64-encoded string of the notebook content path="/Shared/pulumi_ml_notebook", # Path in the workspace for the notebook ) # Create a new Databricks job that uses the cluster and notebook we created job = databricks.Job("ml-job", # Define the properties of the job existing_cluster_id=cluster.id, notebook_task=databricks.JobNotebookTaskArgs( notebook_path=notebook.path, ), ) # Export the cluster ID and notebook path for easy access pulumi.export("cluster_id", cluster.id) pulumi.export("notebook_path", notebook.path)

    Now, let's break down what this Pulumi program does:

    1. We import the necessary Pulumi packages for Databricks.
    2. We create a Databricks cluster with 2 workers using the standard D3 node type and set it to auto-terminate after 20 minutes of inactivity.
    3. A Databricks notebook is created, which is where you would normally input your machine learning code. The content_base64 is a Base64-encoded representation of the notebook's contents, which could include Python or Scala code to train an ML model.
    4. We create a Databricks job, which is configured to run on the created cluster and execute the notebook we've set up.

    The pulumi.export lines will output some important values such as the cluster ID and the path to the notebook in the Databricks workspace once the deployment is completed.

    This code demonstrates the orchestration capabilities that Pulumi offers for Databricks resources. The focus here is on the infrastructure and orchestration, not the ML code itself, as this can vary widely based on the specific use case. The power of Pulumi lies in the ability to easily provision and manage the supporting infrastructure for ML workflows across cloud providers with full support for languages like Python.