1. Scalable Machine Learning Model Training on Databricks


    To set up a scalable machine learning model training environment on Databricks using Pulumi, you'll need to create a Databricks workspace, a cluster within the workspace where the training will occur, and potentially jobs that define your machine learning tasks.

    Databricks is a data analytics platform hosted in the cloud. It provides an environment to run large-scale data processing and machine learning workloads. With Pulumi, you can create, deploy, and manage a Databricks workspace, which is an environment for accessing all of your Databricks assets.

    Here's what we'll do in this Pulumi Python program:

    1. Create a Databricks workspace.
    2. Define a Databricks cluster configuration. It will be a scalable cluster configured to your needs that Databricks can automatically scale up or down based on the workload.
    3. Define a Databricks job (optional). This can be a specific machine learning training task that you want to execute, such as a Spark job or a notebook job.

    Below is the program to achieve these steps:

    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace # The workspace allows you to collaborate with others and access all Databricks assets. workspace = databricks.Workspace("my-workspace", name="my-db-workspace", sku="standard" # Choose between "standard", "premium", or other SKUs as per your requirement # Additional config like tags or resource_group_name can be added as needed. ) # Define scalable cluster settings, autoscaling from 1 to 8 worker nodes as an example. autoscale_settings = databricks.ClusterAutoscale( min_workers=1, max_workers=8 ) # Define the cluster where the model training will take place. cluster = databricks.Cluster("my-training-cluster", cluster_name="training-cluster", spark_version="latest-runtime", # Choose the runtime version you need for Spark and Databricks. node_type_id="Standard_D3_v2", # Choose the node type depending on your processing requirements. autoscale=autoscale_settings, # Apply the autoscaling configuration autotermination_minutes=60 # Automatically terminate the cluster after 60 minutes of inactivity. # You might add additional configuration such as custom_tags, driver_node_type_id, etc. ) # Define a Databricks job (if necessary) # This would be your machine learning model training task, which might be a notebook or script. # Note: Replace the content_url with the URL of your notebook or script. job = databricks.Job("my-model-training-job", name="Model Training", new_cluster=cluster.get_resource_config(), # Reference the cluster configuration notebook_task=databricks.JobNotebookTaskArgs( notebook_path=content_url ), # The job can also be triggered on a schedule or event by configuring a 'schedule' block. ) # Export the workspace URL for easy access pulumi.export("workspace_url", workspace.workspace_url) # Export the cluster ID to reference it easily pulumi.export("cluster_id", cluster.id) # Optionally export the job ID if you created a job pulumi.export("job_id", job.id)

    Let's break down the program:

    • We create a workspace with databricks.Workspace. This workspace is the central hub for all activities in Databricks.
    • We define a cluster in this workspace using databricks.Cluster. We enable autoscaling with min and max worker nodes. You can adjust the node type, runtime version, and other configurations based on your needs.
    • Optionally, we define a job with databricks.Job that specifies what machine learning task should run. We can specify this job to run a notebook or a script and reference the previously created cluster to run this job. Details like the path to the notebook and job scheduling options can be adjusted as needed.
    • Finally, we export useful information, such as workspace URL, cluster ID, and job ID, using pulumi.export. This info can be used to access the resources directly or in other Pulumi programs.

    Remember to replace placeholders like content_url with actual paths or URLs to your machine learning code or notebooks.

    This Pulumi program provides you with a foundation for a scalable machine learning training environment. You can expand it further by adding more complex automation, integrating with other services, or refining security and access controls.