1. Databricks for AI Workflow Orchestration


    To orchestrate AI workflows in Databricks using Pulumi, you would typically set up a few core components:

    • A Databricks workspace, used as an environment to run your AI workflows.
    • Databricks clusters, these are the compute resources where your data processing and machine learning models run.
    • Jobs, which are workloads (like notebooks, JARs, Python scripts) scheduled to run on your Databricks clusters.
    • Potential additional resources such as tables, storage, and notebooks depending on the specifics of your workflow.

    In the following Pulumi program, I'll demonstrate how to create a Databricks workspace, spin up a cluster, and define a job to orchestrate an AI workflow. Note that for Databricks on Azure (azure-native.databricks.Workspace), you'll need to have an Azure account and follow additional steps to integrate with Databricks. For this example, we will focus on the databricks provider which is generally applicable but bear in mind that you will need to handle authentication and configure Databricks appropriately in your environment.

    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace (If you are using Azure-native Databricks, # you need to use azure_native.databricks.Workspace and provide the appropriate arguments) workspace = databricks.Workspace("ai-workspace", name="my-databricks-workspace", sku="premium", # Assuming 'premium' is your desired SKU ) # Create a Databricks cluster within the workspace cluster = databricks.Cluster("ai-cluster", cluster_name="my-ai-cluster", spark_version="6.4.x-scala2.11", node_type_id="Standard_D3_v2", # Choose an appropriate node type num_workers=2, # Starting with 2 worker nodes autotermination_minutes=20, # Auto-terminate idle clusters after 20 minutes ) # Define a Databricks job to run an AI model training notebook job = databricks.Job("ai-job", existing_cluster_id=cluster.id, # Reference to the cluster created above notebook_task=databricks.JobNotebookTaskArgs( notebook_path="/Workspace/path/to/your/notebook", # Specify the path to your notebook to be used for the job ), new_cluster=databricks.JobsClusterArgs( num_workers=2, # Specify the number of worker nodes for the job's cluster spark_version="6.4.x-scala2.11", node_type_id="Standard_D3_v2", ), name="my-ai-training-job", ) # Export the IDs of the resources pulumi.export('workspace_id', workspace.id) pulumi.export('cluster_id', cluster.id) pulumi.export('job_id', job.id)

    This program first declares a Databricks workspace where your data and ML (machine learning) models live. It's a good practice to assign proper names to your resources, and choose the appropriate SKUs and configurations that match your workload requirements and budget.

    Then, it creates a Databricks cluster, which is a group of virtual machines that will work together to run your AI workflows. Here we specify the number of workers, the Spark version, and the virtual machine types to use based on your computational needs.

    Lastly, we define a job that references the cluster we created. This job will be responsible for running an AI training notebook. You'll need to specify the notebook's path where your AI code resides. In the new_cluster block, if you want the job to run on a new cluster rather than an existing one, you'll configure it much like the main cluster; for simplicity, in this example, we are using the existing cluster created earlier.

    Remember to replace placeholders like "/Workspace/path/to/your/notebook" with the actual notebook paths and other configurations with the ones matching your workload.

    Also, keep in mind to configure your provider credentials for Pulumi to be able to authenticate against the Databricks workspace; this might involve setting environment variables, config values, or using a service principal depending on your cloud setup and preferences.

    After running this program with pulumi up, Pulumi will handle the creation and deployment of these resources in the specified order and output the IDs of the created resources. You can use these IDs to reference these resources in other Pulumi programs or perform actions using the Databricks API or CLI.