1. Distributed Machine Learning Model Serving via Databricks


    To serve a distributed Machine Learning model using Databricks as a platform, you would need to set up several components within the Databricks environment. The high-level steps involved are:

    1. Provision a Databricks Workspace: Where you would manage all your resources.
    2. Create a Databricks Cluster: To run your computation and machine learning tasks.
    3. Upload and Setup your Machine Learning Model: Package your model and its dependencies, and upload it to Databricks.
    4. Create Jobs to Serve the Model: Set up a job that can invoke the model to serve predictions, potentially via API calls or scheduled batch processing.

    Below is a Pulumi program written in Python to achieve this. The program sets up a Databricks workspace, provisions a cluster to run machine learning computations, and prepares a job to serve the model. Note that the specifics of how you serve your machine learning model — whether it's via an API call, a Databricks notebook, or another method — will affect the exact configuration of your Pulumi resources. For the sake of this example, let's assume you have a model packaged as a Databricks job.

    import pulumi import pulumi_databricks as databricks # Step 1: Provision a Databricks Workspace # Depending on your cloud provider, you would use the corresponding Pulumi provider to set this up. # For example, in Azure, you would use 'azure-native.databricks.Workspace'. workspace = databricks.Workspace('ai-distributed-ml-workspace', location='westus', sku='standard', # Other required properties... ) # Step 2: Create a Databricks Cluster cluster = databricks.Cluster('ai-distributed-ml-cluster', spark_version='7.3.x-scala2.12', node_type_id='Standard_D3_v2', autotermination_minutes=20, autoscale=databricks.AutoscaleArgs( min_workers=1, max_workers=5, ), # Add any other needed configurations such as libraries or spark configs... ) # Step 3: Setup your Machine Learning Model # This step is more abstract in implementation. It depends on how your model and its dependencies are packaged. # For demonstration purposes, we're assuming you have a wheel file for a Python package containing your ML model. library = databricks.Library('ai-ml-model', whl='dbfs:/my_ml_model.whl', cluster_id=cluster.id, ) # Step 4: Create Jobs to Serve the Model job = databricks.Job('ai-ml-model-serving-job', existing_cluster_id=cluster.id, libraries=[{'whl': 's3://my-ml-model/my_ml_model.whl'}], # Use the wheel file uploaded in previous step tasks=[databricks.JobTaskArgs( notebook_task=databricks.JobTaskNotebookTaskArgs( notebook_path='/Users/user@example.com/MyNotebook', base_parameters={'param': 'value'}, ), )], # setup any triggers or schedules as required ) # Output the necessary details to access the job and workspace later pulumi.export('databricks_workspace_url', workspace.workspace_url) pulumi.export('databricks_model_serving_job_id', job.job_id)

    Detailed Explanation:

    • Workspace: The workspace acts as a container for all your Databricks assets such as notebooks, libraries, and clusters. It is a virtual environment for organizing your cloud resources. The workspace resource sets the location and SKU, among other necessary configuration options.

    • Cluster: The cluster you create is where the model will run. This cluster is set up with an autoscaling option, which will provide efficient resource utilization. Clusters can be defined with various properties, such as node_type_id to specify the size/type of the VMs, spark_version to set the version of Spark, and autotermination_minutes to automatically terminate the cluster after a period of inactivity to save costs.

    • Library: Machine Learning models and their dependencies are often packaged in libraries and can be in various forms such as Python wheel files. Here, Library is a resource type that allows you to attach these dependencies to your cluster.

    • Job: This represents an execution of a specific task. In the context of machine learning, a job could be scheduled to score new data using your trained model or for retraining purposes. In this sample, I've created a job that references a Databricks notebook where the model serving logic would be implemented. This job is associated with the cluster by the existing_cluster_id property. The notebook itself would contain the code to use our ML model to make predictions.

    Remember that you would need to replace placeholders like 'dbfs:/my_ml_model.whl' and '/Users/user@example.com/MyNotebook' with actual paths to your model wheel file and notebook.

    Please note that the code above is a high-level skeleton to illustrate the process and resources involved in serving a distributed Machine Learning model using Pulumi and Databricks. You will need to customize it according to your specific model, deployment needs, and cloud provider requirements.