Version-Controlled Machine Learning Pipelines on Databricks

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Pulumi · Accepted Answer

To set up version-controlled machine learning (ML) pipelines on Databricks using Pulumi and Python, you'll be leveraging several key resources from the Databricks provider within Pulumi.

Here's how these resources will be used to establish your ML pipelines:

databricks.Repo - This resource is used to manage code repositories in Databricks. It's necessary for version control, where you can commit your ML models and pipeline code.
databricks.Cluster - A Databricks cluster is a set of computation resources and configurations on which you run your data engineering, data science, and data analytics workloads. It's the environment where your ML models will be trained and evaluated.
databricks.Job - It's used to create and manage jobs in Databricks, which can be one-off tasks or scheduled to run periodically. You could use jobs to orchestrate your ML training and evaluation tasks.
databricks.Notebook - Databricks notebooks are used for interactive data exploration and visualization. You might use a notebook to craft your ML models and then transfer the code into a more production-ready Python file.
databricks.Library - Libraries are packages or modules that provide additional functionality to your clusters. For ML pipelines, you might need libraries such as TensorFlow, PyTorch, or scikit-learn.

For a basic Pulumi Python program, these resources will be defined and connected to create your ML pipeline. The following program demonstrates how to set up these resources using Pulumi.

Please replace placeholder values like <REPOSITORY_URL>, <BRANCH_NAME>, <CLUSTER_NAME>, and <NOTEBOOK_PATH> with actual values relevant to your environment and project.

Let's begin by writing the Pulumi program:

import pulumi
import pulumi_databricks as databricks

# Creating a new Databricks repository for version-controlled ML codes.
# Replace `<REPOSITORY_URL>` with the URL of your Git repository.
repo = databricks.Repo("ml-repository",
    url="<REPOSITORY_URL>",
    path="/Repos/your-user/your-project",
    branch="<BRANCH_NAME>"
)

# Creating a Databricks cluster to execute ML tasks.
# Replace `<CLUSTER_NAME>` and `<NODE_TYPE>` with appropriate values for your scenario.
cluster = databricks.Cluster("ml-cluster",
    cluster_name="<CLUSTER_NAME>",
    spark_version="7.3.x-scala2.12",  # Choose a Spark version compatible with your ML workloads.
    node_type_id="<NODE_TYPE>",  # Choose a node type based on your computation needs.
    autotermination_minutes=20,  # Automatically terminate the idle cluster to save costs.
    num_workers=3  # Specify the number of worker nodes in the cluster.
)

# Attaching necessary ML libraries, such as TensorFlow or PyTorch, to the cluster.
# Replace the library package details with specific versions you need.
library = databricks.Library("ml-library",
    cluster_id=cluster.id,
    pypi=databricks.LibraryPypiArgs(
        package="tensorflow",
        repo="https://pypi.python.org/simple"
    )
)

# Creating a Databricks notebook for interactive ML model development.
# Replace `<NOTEBOOK_PATH>` with the location you wish to store your notebook.
notebook = databricks.Notebook("ml-notebook",
    path="<NOTEBOOK_PATH>",
    # Below, assuming the notebook content is in a file named 'ML_Notebook.py'.
    content_base64=pulumi.FileAsset("./ML_Notebook.py").hash
)

# Creating a job to run ML pipelines, which could be a sequence of tasks such as data preprocessing,
# model training, evaluation, etc.
# Attach the cluster and library configurations created earlier.
job = databricks.Job("ml-job",
    new_cluster=databricks.JobNewClusterArgs(  # Use the cluster configuration above.
        spark_version=cluster.spark_version,
        node_type_id=cluster.node_type_id,
        num_workers=cluster.num_workers,
    ),
    libraries=[databricks.JobLibrariesArgs(
        pypi=databricks.JobLibrariesPypiArgs(
            package="tensorflow"
        )
    )],
    notebook_task=databricks.JobNotebookTaskArgs(
        notebook_path=notebook.path
    )
)

# Exporting the URL of the Databricks workspace and job for easy access.
pulumi.export("databricksWorkspaceUrl", cluster.workspace_url)
pulumi.export("databricksJobUrl", job.url)

To execute this Pulumi program, you'll need to have:

The Pulumi CLI installed and your cloud provider configured.
The pulumi_databricks package installed in your Python environment.
Databricks credentials configured properly either via the Pulumi config or through environment variables.

This program will set up a complete ML pipeline in Databricks, where your code is version controlled, and you have a cluster ready to process your ML tasks. It also sets up a job that can be manually triggered or scheduled to run at particular intervals, allowing for automated ML workflows to be established.

Remember to customize the configurations for cluster size, node types, and library versions to match the resource needs of your ML workloads.