1. InstanceTemplate for Batch Processing in Machine Learning


    To create an instance template for batch processing in machine learning, we can set up a cloud environment that provisions virtual machines (VMs) tailored for batch processing tasks. For example, on Azure, you might use Azure Machine Learning Compute to manage compute resources needed for machine learning workloads. On Google Cloud Platform (GCP), you might configure a Compute Engine instance template that is then used to create a managed instance group optimized for batch processing.

    The following program will illustrate creating an Azure Machine Learning Compute resource, which is well-suited for batch processing in machine learning scenarios. This resource is a managed compute infrastructure that allows you to easily run large-scale machine learning workloads on Azure.

    This program does the following:

    • Creates an Azure Resource Group to hold our resources.
    • Configures an Azure Machine Learning workspace, which is the foundational block in the cloud that you use to experiment, train, and deploy machine learning models.
    • Sets up an Azure Machine Learning Compute instance within the workspace with specific properties suited for batch processing tasks.

    Here's how you can accomplish this with Pulumi in Python:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure resource group for organizing related resources. resource_group = azure_native.resources.ResourceGroup("resource_group") # Create an Azure Machine Learning Workspace. This workspace will be the foundation # for all machine learning activities. ml_workspace = azure_native.machinelearningservices.Workspace("ml_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.machinelearningservices.SkuArgs(name="Enterprise"), workspace_name="my-ml-workspace") # Create a Machine Learning Compute instance within the workspace. # This is where the batch processing will actually take place. # You can define properties such as VM size, disk size, etc., suitable for your load. aml_compute = azure_native.machinelearningservices.Compute("aml_compute", resource_group_name=resource_group.name, workspace_name=ml_workspace.name, compute_name="my-aml-compute", properties=azure_native.machinelearningservices.ComputeInstanceSchema( # Use a specific schema for setting up the compute configuration compute_type="AmlCompute", # Define the type e.g., AmlCompute, AksCompute, etc. # Define the properties of the AmlCompute properties=azure_native.machinelearningservices.AmlComputeProperties( vm_size="STANDARD_DS3_V2", # Specify VM size vm_priority="Dedicated", # Specify the VM priority scale_settings=azure_native.machinelearningservices.ScaleSettings( max_node_count=4, # Scale up to 4 nodes min_node_count=0, # Can scale down to 0 nodes ), node_idle_time_before_scale_down="PT5M" # Node idle time before scaling down ) ) ) # Make the compute URL an output of our program. pulumi.export("compute_resource_id", aml_compute.id)

    This Pulumi program defines the necessary Azure resources for establishing a machine learning infrastructure suitable for batch processing. Each resource is represented by a class in the Pulumi Azure Native SDK, and we instantiate these classes to create the resources in the Azure cloud.

    The aml_compute object will be a managed compute target that can automatically scale up or down based on the job's requirements, making it efficient and cost-effective for running batch processing workloads. The VM size, priority, and scale settings are tailored to your needs by adjusting the respective properties of the AmlComputeProperties class.

    After running this Pulumi program, the cloud resources will be provisioned and ready for you to submit batch processing jobs for machine learning.