1. Scalable Machine Learning Workspaces with Databricks


    When you want to set up a scalable Machine Learning workspace with Databricks on a cloud provider, you would typically need to provision several resources. This includes a Databricks workspace itself, clusters for computation, as well as storage for your data and any other supporting infrastructure.

    In this guide, we will create a scalable Machine Learning workspace using the Pulumi Databricks provider. The Databricks workspace is an environment for accessing all of your Databricks assets. The workspace organizes objects (notebooks, libraries, and experiments) into folders and provides access to data and computational resources, such as clusters and jobs.

    Here's a step-by-step guide on how to use Pulumi to create a Databricks workspace and a scalable cluster:

    1. Databricks Workspace: This is the primary environment for your Machine Learning tasks. It comes with support for various data engineering, machine learning, and data science tasks.

    2. Databricks Cluster: Clusters are groups of computers that run your data engineering, machine learning, and data science workloads. You can configure them to scale automatically based on the workload.

    3. Storage: For storage, you might use Databricks DBFS (Databricks File System) or integrate with cloud-specific storage like AWS S3 or Azure Blob storage.

    In this example, I'll show you how to create these resources using Pulumi's Databricks provider. Note that you will need to have your Databricks account set up and have the necessary cloud provider credentials configured for Pulumi.

    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace databricks_workspace = databricks.Workspace("my-workspace", location="westus", sku="standard" ) # Create an all-purpose compute cluster, which can be used for various tasks, # such as running notebooks or scheduled jobs. # The cluster is set to auto-scale between 1-8 workers based on load. compute_cluster = databricks.Cluster("compute-cluster", autoscale=databricks.ClusterAutoscaleArgs( max_workers=8, min_workers=1, ), node_type_id="Standard_D3_v2", spark_version="7.3.x-scala2.12", cluster_name="compute-cluster" ) # Export the Databricks workspace URL for easy access pulumi.export('workspace_url', databricks_workspace.workspace_url) # Export the cluster ID for reference pulumi.export('cluster_id', compute_cluster.cluster_id)

    Let's explain what each part of the code is doing:

    • We import the necessary Pulumi libraries for Python, including the pulumi_databricks module, which contains all the functionality we need to interact with Databricks.
    • We then create a Workspace with a specified location and SKU level, which are required properties. The location is set to a value such as 'westus', and you should replace it with the region that's the best fit for your requirements. SKU defines the tier and performance characteristics of the workspace.
    • Next, we create a Cluster. The cluster definition includes an autoscaling configuration (how many workers will be auto-provisioned depending on the load), node type (the size of each worker node), and spark version.
    • Lastly, we use pulumi.export to print out the workspace URL and the cluster ID. These outputs can be useful if you're integrating Pulumi with CI/CD pipelines or want to access these resources programmatically after deployment.

    Note that no user authentication information has been provisioned through this guide. Databricks integrates with your cloud provider's IAM mechanics, and you should refer to Databricks and Pulumi documentation for the right way to manage access and credentials.

    Remember to replace placeholders like the region or node type with values that match your specific use case. For more complex setups, you may need to add additional configurations such as security groups, network settings, or more intricate cluster configurations.

    Before running this code, ensure your Pulumi CLI is installed and configured with access to the target cloud provider and Databricks. Please consult the Pulumi Databricks Provider documentation for details on configuration and advanced usage.