1. Machine Learning Model Training on Databricks Clusters


    To train a machine learning model on Databricks clusters through Pulumi, you would need to define a Databricks cluster configuration and set up the machine learning environment with necessary libraries, datasets, and scripts.

    In Pulumi, the databricks.Cluster resource represents a Databricks cluster that you can spin up and configure according to your needs for machine learning training. Using the databricks.Cluster resource, you can declare the desired state of your Databricks cluster, including the node type, number of workers, spark version, scaling properties etc. After defining a cluster, you can use the databricks.Libray resource to install libraries such as PyPi packages, Maven artifacts, or other custom libraries needed for your machine learning model. You can also attach notebooks and scripts to your cluster that contain your training code.

    Here is a program written in Python that leverages Pulumi to set up a Databricks cluster, install libraries, and configure a notebook which you can then use to train your machine learning model:

    import pulumi import pulumi_databricks as databricks # Create a new Databricks cluster configuration cluster = databricks.Cluster("ml-training-cluster", num_workers=2, spark_version="7.3.x-scala2.12", node_type_id="i3.xlarge", autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, max_workers=3, ), spark_env_vars={ "PYSPARK_PYTHON": "/databricks/python3/bin/python3", }, # Enable auto termination after 120 minutes of inactivity to save costs autotermination_minutes=120 # More configuration settings can be added as required ) # Install common machine learning libraries onto the Databricks cluster libraries = databricks.Library("ml-libraries", cluster_id=cluster.id, pypi=databricks.LibraryPypiArgs( package="scikit-learn", ), # You can install additional libraries as needed ) # Notebook setup (if necessary, not specifically for training, more for preparatory work) notebook = databricks.Notebook("ml-training-notebook", path="/Users/you@example.com/ML_Training", content_base64=pulumi.Output.secret("encoded_notebook_content"), # content_base64 should be set with the base64 encoded content of your notebook ) # Additional resources like models, data sources, job configurations, etc. can also be included here. # Export the Databricks cluster URL for easy access pulumi.export("databricks_cluster_url", cluster.cluster_url)

    Detailed Explanation

    1. Cluster Setup: The databricks.Cluster resource creates a new Databricks cluster. The num_workers parameter is set to 2 which means there will be two worker nodes by default. The autoscale option allows the cluster to scale between 1 and 3 workers automatically based on the workload.

    2. Spark Version and Node Types: We specify the Spark version compatible with our training code and select the appropriate instance type for the nodes. The spark_env_vars sets the environment variables for Spark, like pointing PYSPARK_PYTHON to the Python 3 executable.

    3. Auto Termination: To control costs, the autotermination_minutes is set to 120 which means if the cluster is inactive for 120 minutes, it will shut down automatically.

    4. Installing Libraries: The databricks.Library resource is used to install machine learning libraries like scikit-learn on the cluster. You can add more libraries by specifying the appropriate package name under the pypi option.

    5. Notebook Configuration: The optional databricks.Notebook resource is for adding a notebook to your Databricks workspace. This is useful if you have preparatory or analysis work that you'd like to perform in a notebook environment. The content_base64 parameter should be filled with the base64-encoded content of your Databricks notebook.

    6. Exports: Finally, we export the cluster URL which can be used to easily access the Databricks workspace. This export provides an output after deploying our Pulumi stack which will have the generated URL of our Databricks cluster.

    Once you deploy this code with Pulumi, you will have a running Databricks cluster ready for training your machine learning model with the required libraries installed. You will then upload your datasets and machine learning scripts to the Databricks workspace and initiate your model training runs.

    Keep in mind that you should have the Databricks provider configured in your Pulumi setup before running this code. Also, this code does not handle data uploading or initiating the actual training job as those steps are typically performed through the Databricks workspace interface or using their API.