1. Databricks Jobs for Parallel Model Training


    In order to set up Databricks jobs for parallel model training, you would typically follow these steps:

    1. Define a Databricks workspace where your data scientists and engineers can collaborate.
    2. Create a Databricks cluster that will be used to run the training jobs. The cluster could be a new cluster for each job or an existing cluster.
    3. Define the jobs within Databricks that will execute your training scripts. This can include things like notebooks, JARs, or Python scripts, along with the computational resources needed.

    Using Pulumi, you can automate the provisioning of Databricks workspaces, clusters, and jobs. The pulumi_databricks Python package provides all the necessary classes to define your Databricks resources in code.

    Here's a Pulumi program in Python that sets up a Databricks job for parallel model training:

    import pulumi import pulumi_databricks as databricks # Create a Databricks workspace (assume that databricks.Workspace has been set up previously). # This step is usually done manually or using Pulumi's databricks.Workspace resource. # Setup a new Databricks cluster or reference an existing one. # For simplicity, let's create a new cluster for training. cluster = databricks.Cluster("training-cluster", num_workers=2, spark_version="7.3.x-scala2.12", node_type_id="Standard_DS3_v2", autotermination_minutes=20, spark_conf={ "spark.databricks.delta.preview.enabled": True }, custom_tags={ "Environment": "staging", "Job": "Model-Training" } ) # Define a Databricks job to run parallel model training tasks. # Replace '<your-notebook-path>' with the actual path of your Databricks notebook and # ensure that your training script is compatible with parallel execution. job = databricks.Job("parallel-training-job", existing_cluster_id=cluster.id, notebook_task=databricks.JobNotebookTaskArgs( notebook_path="<your-notebook-path>", base_parameters={"param1": "value1"} ), new_cluster=databricks.JobNewClusterArgs( spark_version="7.3.x-scala2.12", node_type_id="Standard_DS3_v2", num_workers=2, autotermination_minutes=20, ), email_notifications=databricks.JobEmailNotificationsArgs( on_failure=["admin@example.com"] ), timeout_seconds=3600, max_concurrent_runs=4 ) pulumi.export('job_name', job.name) pulumi.export('job_id', job.id)

    In this example:

    • A databricks.Cluster is created to define the computational resources used for model training. You specify the type of nodes the cluster should use (Standard_DS3_v2), the number of workers (2), and other configurations like autotermination to save on costs.

    • A databricks.Job is defined to execute the training tasks using the cluster. The job specifies a notebook task, which should point to a notebook within your Databricks workspace that contains the model training logic. You also configure notifications and concurrency settings.

    • Finally, Pulumi exports the job's name and ID, which can be helpful for further automation or monitoring.

    Replace "<your-notebook-path>" with the path to your actual Databricks notebook. Also, adjust parameters such as num_workers, spark_version, and node_type_id according to your computation needs and Databricks cluster configurations.

    For more information on each of these resources and their properties, you can consult the Databricks Provider documentation. The example provided sets up the infrastructure; you may need to write and test the actual parallel model training scripts separately within your Databricks environment.