1. Distributed ML Model Training on Databricks


    Distributed Machine Learning (ML) Model Training on Databricks involves setting up an environment where you can train machine learning models across a cluster of virtual machines for better performance and speed. Pulumi enables you to define this infrastructure as code, which allows for repeatable and consistent deployments.

    To achieve this, you need a Databricks workspace where you can run your code, and within Databricks, you’ll need to set up a cluster that can host and run your ML training jobs. Optionally, you might also want to define jobs and notebooks to execute your training scripts.

    Here is a step-by-step explanation with a Pulumi Python program to set up such an environment:

    1. Databricks Workspace: This is the foundational element where all your Databricks resources are managed. You would typically set up a workspace in your cloud provider (e.g., AWS, Azure, GCP) using their respective Pulumi packages.

    2. Databricks Cluster: Within the workspace, we create a cluster that serves as the computing resource for running ML training jobs. You specify the type and number of machines, along with configuration specific to ML needs, such as pre-installed libraries or custom docker images.

    3. Databricks Notebook: You can use Databricks notebooks to build your ML models. The notebooks can be created and managed as code using Pulumi.

    4. Databricks Job: Jobs are used for running a sequence of computations or tasks, like training an ML model on a scheduled or triggered basis. We can define a job which points to the notebook or a script written for training.

    5. Exported Outputs: After setting up all required resources, you can export certain outputs like the workspace URL or cluster ID, which might be required for accessing or managing these resources afterward.

    Now, let's look at a Pulumi program that could set this up. The following code assumes you are already authenticated with your cloud provider and Databricks. It uses the pulumi_databricks Python package, which you should install using pip before running this program. The below program specifically outlines setting up a Databricks cluster, which is generally the most complicated part and is central to model training.

    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace using a cloud-specific provider. # In this example, we'll stick to common configurations. # Replace `<cloud-specific-resource>` with actual cloud provider's Databricks resource, # such as `aws_native.databricks.Workspace` or similar for Azure/GCP. workspace = databricks.Workspace("my-workspace", # Set provider-specific properties, e.g., location, SKU, etc. # ... ) # Create a Databricks cluster dedicated for ML model training. cluster = databricks.Cluster("my-ml-cluster", num_workers=2, # Specify the number of worker nodes in the cluster. autoscale=databricks.ClusterAutoscaleArgs( min_workers=2, max_workers=10, ), node_type_id="Standard_D3_v2", # Specify the VM type or set it based on your needs. spark_version="7.3.x-scala2.12", # Choose a Databricks runtime version that supports ML. autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, max_workers=10 ), spark_conf={"spark.databricks.mlflow.trackMLlib.enabled": "true"}, # Enable MLflow tracking for MLlib models. custom_tags={ "ResourceClass": "SingleNode" }, cluster_source="UI", spark_env_vars={ "PYSPARK_PYTHON": "/databricks/python3/bin/python3" }, init_scripts=[databricks.ClusterInitScriptsArgs( dbfs=databricks.ClusterInitScriptsArgsDbfsArgs( destination="dbfs:/databricks/scripts/init.sh", ) )], # Optional: Add any cluster initialization scripts ) # Optional: define a job to train an ML model. job = databricks.Job("my-ml-job", existing_cluster_id=cluster.id, # Define the job task, such as a spark job, python wheel task, etc. # In this case, we will assume we're executing a spark job that refers to a notebook. new_cluster=databricks.JobNewClusterArgs( spark_version=cluster.spark_version, node_type_id=cluster.node_type_id ), notebook_task=databricks.JobNotebookTaskArgs( notebook_path="/Users/your.email@example.com/ML_Train", # Path to the notebook in your workspace. ), ) # Export the Databricks workspace URL and the cluster ID. pulumi.export('workspace_url', workspace.url) pulumi.export('cluster_id', cluster.cluster_id)

    To apply this program, save it in a file (for example, main.py), ensure you have Pulumi installed and configured, run pulumi up, and follow the on-screen prompts.

    Please note: The above code is a template and simplification. You will need to replace placeholder values and add necessary configurations specific to your cloud provider and Databricks setup. Additionally, Databricks has its own permissions and configurations that you need to manage through its admin console or API. The code is intentionally generic because specifics can vary widely based on individual needs and cloud providers.