1. Asynchronous Batch Processing for Machine Learning


    Asynchronous batch processing is a common pattern in machine learning (ML) workflows. It allows you to process large volumes of data without waiting for each operation to complete before starting the next one. This approach can lead to significant performance improvements, especially when using cloud resources that can scale on demand.

    In the context of cloud infrastructure and Pulumi, you might set up asynchronous batch processing using cloud services such as AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform. These services provide the necessary tools to train machine learning models, process data in batches, and orchestrate the workflow.

    In this example, we will be using Azure Machine Learning services to illustrate how to set up the infrastructure for asynchronous batch processing for a machine learning workflow with Pulumi. We'll create a Machine Learning Workspace and a Compute Cluster within that workspace that can be used to run batch jobs.

    The resources we use are:

    • azure_native.machinelearningservices.Workspace to create a new ML workspace.
    • azure_native.machinelearningservices.ComputeCluster to create a compute cluster in the workspace.

    Here's what a basic Pulumi program setting up the infrastructure for an asynchronous batch processing machine learning workflow on Azure might look like:

    import pulumi import pulumi_azure as azure from pulumi_azure_native import machinelearningservices # Create an Azure Resource Group resource_group = azure.core.ResourceGroup('ml_resource_group') # Create Machine Learning Workspace ml_workspace = machinelearningservices.Workspace( 'ml_workspace', resource_group_name=resource_group.name, location=resource_group.location, sku=machinelearningservices.SkuArgs(name='Standard') ) # Create a Machine Learning Compute Cluster for Batch Processing ml_compute_cluster = machinelearningservices.ComputeCluster( 'ml_compute_cluster', resource_group_name=resource_group.name, workspace_name=ml_workspace.name, compute_name='mlcomputecluster', location=resource_group.location, sku=machinelearningservices.SkuArgs(name='Standard_D3_v2'), properties=machinelearningservices.ComputeClusterPropertiesArgs( vm_size='STANDARD_D3_V2', vm_priority='Dedicated', scale_settings=machinelearningservices.ScaleSettingsArgs( max_node_count=4, min_node_count=0, node_idle_time_before_scale_down='PT5M' ), ) ) # Export the Azure Machine Learning Workspace URL pulumi.export('ml_workspace_url', pulumi.Output.concat( 'https://ml.azure.com/workspaces/', ml_workspace.name, '?cloud=AzurePublicCloud&wsid=/subscriptions/', pulumi.Config('azure').get('subscriptionId'), '/resourceGroups/', resource_group.name, '/providers/Microsoft.MachineLearningServices/workspaces/', ml_workspace.name ))

    In this code:

    • We define a ResourceGroup which acts as a container for all the resources we create.
    • We then create a Workspace under machine learning services that act as a foundational block, giving a context for the data, compute resources, code, models, etc.
    • The ComputeCluster is set up with a specific VM size and scale settings that enable auto-scaling. It scales from 0 when not in use to a maximum of four nodes when jobs are queued.
    • Finally, we export the URL of the Machine Learning Workspace that can be used to navigate to the workspace in the Azure portal.

    Remember to set up the Azure provider and configure your credentials to deploy this Pulumi program successfully. The pulumi.Config class is used to read configuration settings which would include your Azure Subscription ID in a real-world scenario.

    This setup is the starting point, and you would typically deploy your machine learning models, datasets, and other components inside the workspace to take full advantage of the cloud capabilities for ML workflows.