Scalable Machine Learning Workflows on Databricks

Question

Pulumi · Accepted Answer

Creating scalable machine learning workflows on Databricks involves orchestrating various components such as compute clusters, jobs for running ML tasks, and notebooks for interactive development. Pulumi provides an infrastructure as code approach to define and manage these resources in a reproducible and version-controlled manner.

In Pulumi, you can use the Databricks provider to provision and manage resources like clusters, jobs, and notebooks. Below is a program that demonstrates how to create these resources for a scalable machine learning workflow.

First, we'll need a Databricks workspace where we can create our resources. For this example, we'll assume the workspace is already set up.

Here's what each part of the program does:
1. **Cluster**: This is the compute resource where machine learning tasks will be executed. The cluster can be autoscaled to meet workload demands and optimized for ML workloads with specific node types.
2. **Jobs**: These represent tasks that will run on the clusters. In ML workflows, a job might represent a data preprocessing step, training a model, or evaluating model performance.
3. **Notebook**: This is used for interactive development, such as data exploration, model prototyping, and analysis. It can be linked to a job to execute predefined analyses or ML training.

Let's proceed with the Pulumi program written in Python:

```python
import pulumi
import pulumi_databricks as databricks

# Define a Databricks cluster configured for machine learning
ml_cluster = databricks.Cluster("ml-cluster",
    autoscale=databricks.ClusterAutoscaleArgs(
        min_workers=2,
        max_workers=50,
    ),
    node_type_id="Standard_D3_v2", # Choose an appropriate VM size
    spark_version="7.3.x-scala2.12", # Choose a Databricks runtime that supports ML; versions might change over time
    enable_elastic_disk=True, # Enables elastic disk for the cluster
    spark_conf={
        "spark.databricks.mlflow.trackMLlib.enabled": "true",  # Enables MLflow tracking for MLlib workloads
        "spark.databricks.repl.allowedLanguages": "sql,python,r",  # Allows SQL, Python, and R in this cluster
    }
)

# Define a job to run a machine learning task, such as training a model
ml_job = databricks.Job("ml-job",
    new_cluster=databricks.JobClusterArgs(
        spark_version=ml_cluster.spark_version,
        node_type_id=ml_cluster.node_type_id,
        autoscale=databricks.JobClusterAutoscaleArgs(
            min_workers=ml_cluster.autoscale.min_workers,
            max_workers=ml_cluster.autoscale.max_workers,
        ),
    ),
    notebook_task=databricks.JobNotebookTaskArgs(
        notebook_path="/Shared/my_notebook", # Path to the notebook within the Databricks workspace
    ),
    name="ML Model Training",
    max_concurrent_runs=1, # Limit the number of concurrent runs for this job
)

# Define a Databricks notebook for interactive ML development
ml_notebook = databricks.Notebook("ml-notebook",
    path="/Shared/my_notebook", # The path in Databricks workspace where the notebook will be stored
    content_base64=pulumi.FileAsset("my_notebook.py").apply(lambda file: file.read_text()), # The content of the notebook, encoded in base64, from a local file
)

# Export the cluster and job IDs
pulumi.export("cluster_id", ml_cluster.cluster_id)
pulumi.export("job_id", ml_job.job_id)
```

In this program:
- We defined a machine learning-oriented cluster with autoscaling capabilities, so it can automatically adjust the number of worker nodes based on the workload.
- We specified a job that uses a notebook to perform a machine learning task, such as training a machine learning model. The notebook should contain the necessary code for this task (you would replace the `"my_notebook.py"` with your actual notebook file).
- We initiated a Databricks notebook that will contain our ML development code. Notebooks are great for iterative development and experimentation.
- Finally, we exported the cluster and job IDs for easy reference.

To use this program, replace `"my_notebook.py"` with the path to your actual notebook file that contains your machine learning code. Make sure to adjust the cluster configuration and notebook path to fit your needs. This program assumes you have the necessary Databricks provider set up and authenticated.