1. AI Model Batch Scoring on Azure Batch


    To accomplish AI model batch scoring on Azure Batch using Pulumi, you would need to create a few resources on Azure:

    1. Azure Batch Account: This is the top-level resource in Azure Batch, which provides you with access to the Batch service.
    2. Azure Batch Pool: This pool consists of a set of compute nodes (virtual machines). Your batch jobs and tasks run on these compute nodes.
    3. Azure Batch Job: A job is a collection of tasks. You would submit tasks to this job to run your model scoring.
    4. Azure Batch Tasks: These are the individual units of execution. For model scoring, each task would score a different chunk of data using your AI model.

    Below is a detailed Pulumi program in Python, demonstrating how to set up AI model batch scoring on Azure Batch.

    import pulumi import pulumi_azure_native as azure_native # Creating a new resource group resource_group = azure_native.resources.ResourceGroup('resourceGroup') # Creating an Azure Batch account batch_account = azure_native.batch.Account('batchAccount', resource_group_name=resource_group.name, location=resource_group.location, # The pool allocation mode also determines how users manage virtual machine sizes. # If set to 'BatchService', pools can be created without specifying a particular VM size. # In this case, 'BatchService' is being used. pool_allocation_mode=azure_native.batch.PoolAllocationMode.BATCH_SERVICE, ) # Creating an Azure Batch pool # The pool allows provisioning to resize down to zero nodes when there is no job in the pool requiring a VM. batch_pool = azure_native.batch.Pool('batchPool', account_name=batch_account.name, resource_group_name=resource_group.name, vm_size="STANDARD_A1_v2", # This is a small VM size for demo purposes. scale_settings=azure_native.batch.ScaleSettingsArgs( fixed_scale=azure_native.batch.FixedScaleSettingsArgs( target_dedicated_nodes=0, target_low_priority_nodes=1 ), ), ) # Creating an Azure Batch job batch_job = azure_native.batch.Job('batchJob', resource_group_name=resource_group.name, account_name=batch_account.name, # Specify the ID of the Pool where this Job needs to run pool_info=azure_native.batch.PoolInformationArgs( pool_id=batch_pool.id ), ) # Batch scoring typically involves multiple tasks that can be executed in parallel. # Here we are adding just one task as a demonstration. batch_task = azure_native.batch.Task('batchTask', resource_group_name=resource_group.name, account_name=batch_account.name, job_name=batch_job.name, command_line="cmd /c echo Hello world from the Batch job!", # This is a Windows command example; replace with scoring script call. ) # Exporting the names and IDs of the created resources pulumi.export('resource_group_name', resource_group.name) pulumi.export('batch_account_name', batch_account.name) pulumi.export('batch_pool_id', batch_pool.id) pulumi.export('batch_job_id', batch_job.id)

    This is a basic setup for a batch scoring scenario.

    • Note that the batch_task command line is a placeholder. In a real-world scenario, you'd replace this with the command to execute your scoring script, which might be a Python script, a Docker container invocation, or any other command that can run on the Azure VM.
    • Depending on your particular use case and requirements, you might also include command-line arguments to the script, references to Azure Blob Storage where your data and model are stored, amongst other customizations.
    • The VM size STANDARD_A1_v2 is chosen for demonstration purposes. You'd typically use a VM size that matches your workload's CPU, memory and GPU requirements.
    • The pool is set to scale to 0 dedicated nodes to save on costs when not in use, but provision 1 low-priority node which is cheaper and can be preempted. Adjust the scaling according to your job frequency and performance requirements.

    This Pulumi program should be placed in a Python file, after which you can run pulumi up to provision the resources on Azure. If you have your Azure credentials configured, Pulumi will use them to authenticate and create resources.

    Now you have a Pulumi program that sets up Azure Batch resources for AI model batch scoring. You can continue to refine and expand upon this program to tailor it to your specific needs, such as handling inputs and outputs, scaling policies, and more.