Scalable Machine Learning Model Training on Databricks

Question

Pulumi · Accepted Answer

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:

```python
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.