1. GPU-accelerated AI Batch Processing on Azure


    When creating GPU-accelerated AI batch processing on Azure, you can leverage Azure Machine Learning and Azure Batch services. Azure Machine Learning is a fully managed service that enables you to easily build, train, and deploy machine learning models. Batch processing with GPU acceleration often involves training machine learning models or running parallel high-performance computing tasks, which can be done with the Azure Batch AI service, a part of Azure Batch. Azure Batch provides a platform for large-scale parallel and high-performance computing applications efficiently in the cloud.

    The following program shows how to create a GPU-accelerated AI batch processing job on Azure using Pulumi. The first thing we'll set up is an Azure Machine Learning workspace and compute cluster. We'll then define a batch job to run on this compute cluster.

    The key components in your Pulumi Python program would be:

    1. Workspace - Controls the Azure Machine Learning workspace where all the assets will be associated.
    2. Compute - Defines a compute target on Azure, we'll use a GPU-enabled virtual machine size for AI workloads.
    3. BatchDeployment or Job - Orchestrates the actual batch processing; this may depend on the specific Azure service suitable for your workload.

    Before running the following Pulumi code, ensure you have:

    • Installed the Pulumi CLI on your system.
    • A configured Azure environment with appropriate credentials.

    Here's the Pulumi program that will set up Azure resources for GPU-accelerated AI batch processing:

    import pulumi import pulumi_azure_native as azure_native # Setup Azure Machine Learning Workspace workspace = azure_native.machinelearningservices.Workspace("workspace", resource_group_name=azure_native.ResourceGroupName("resource-group"), location="East US", # Choose the appropriate Azure location sku=azure_native.machinelearningservices.SkuArgs( name="Standard", # Choose an appropriate SKU ), # Other properties can be set as needed ) # Create the GPU-enabled compute cluster gpu_cluster = azure_native.machinelearningservices.Compute("gpuCluster", resource_group_name=workspace.resource_group_name, workspace_name=workspace.name, properties=azure_native.machinelearningservices.ComputeArgs( # Define properties for GPU compute compute_type="AmlCompute", properties=azure_native.machinelearningservices.AmlComputeArgs( vm_size="Standard_NC6", # NC-series VM for GPU acceleration vm_priority="Dedicated", scale_settings=azure_native.machinelearningservices.ScaleSettingsArgs( min_node_count=0, max_node_count=4, # Scale as per the batch job needs ), ), ), ) # Define a batch processing job using the compute cluster batch_job = azure_native.machinelearningservices.Job("batchJob", resource_group_name=workspace.resource_group_name, workspace_name=workspace.name, job_base_properties=azure_native.machinelearningservices.JobBasePropertiesArgs( compute_binding=azure_native.machinelearningservices.ComputeBindingArgs( target=compute_id.apply(lambda id: id), # Bind the job to the GPU compute cluster ), environment_variables={ "BATCH_SIZE": "64", # Example environment variable, use as needed # Additional variables as required by batch job }, # Include the details for the specific batch processing tasks here # This would typically be the ML training script or batch processing application. ), ) pulumi.export("workspace_name", workspace.name) pulumi.export("gpu_cluster_name", gpu_cluster.name) pulumi.export("batch_job_name", batch_job.name)

    In the above program:

    • We initialize a new Workspace which represents the container for your ML experiments, models, images, etc.
    • We create a Compute cluster named gpuCluster using AmlCompute, which is a managed compute infrastructure that allows you to easily create a cluster of GPU-enabled virtual machines.
    • The BatchDeployment or Job would contain details about the batch tasks you need to perform, like ML model training with GPU acceleration. In this example, a ComputeBinding is used to bind the job to the GPU compute cluster we defined.

    After you've written this program, save it to a file named __main__.py, and run pulumi up from the terminal in the same directory. Pulumi will perform the deployment to Azure by creating the AI and Batch resources as defined. Remember to replace any placeholder values, like resource group name, with actual values that correspond to your Azure setup.

    Consider this a starting point; based on your specific GPU-accelerated workloads, you may need to adjust VM sizes, scaling options, job parameters, and more.

    Before deploying this to a production environment, testing and proper Azure role and policy setup are recommended to ensure the service functions as expected. Additionally, consult Azure pricing to understand potential costs.