1. AI Model Training Orchestration with Azure Batch


    Azure Batch is a cloud computing service that enables large-scale parallel and high-performance computing batch jobs to be easily and efficiently run in the Azure cloud. You can use Azure Batch for tasks such as AI model training, where you often need to run high-performance calculations on many parallel tasks.

    In the context of AI model training with Azure Batch, you typically manage a pool of compute nodes (virtual machines), upload the necessary training data and application code, create a job that runs your training tasks across the compute nodes, and scales down the resources when the computation is done to manage costs effectively.

    The following program demonstrates how to provision necessary Azure Batch resources for AI model training orchestration using Pulumi in Python. The program includes:

    1. Creating an Azure Batch account to manage and execute batch workloads.
    2. Provisioning an Azure Batch pool of virtual machines which will execute the tasks.
    3. Setting up an Azure Batch job to run the tasks related to AI model training.

    This example assumes you have an existing resource group and the necessary training application and data files ready for use:

    import pulumi import pulumi_azure_native.batch as azure_batch from pulumi_azure_native.resources import ResourceGroup # Replace these variables with your own specific names and configurations resource_group_name = "my-resource-group" storage_account_name = "mystorageaccount" storage_container_name = "mydatacontainer" batch_account_name = "mybatchaccount" batch_pool_name = "mypoolofnodes" vm_size = "STANDARD_D2_V2" # this size supports Azure Batch AI tasks node_agent_sku_id = "batch.node.ubuntu 18.04" # node-agent that is compatible with Ubuntu 18.04 # Existing resource group to deploy the Batch resources in resource_group = ResourceGroup.get("existing-resource-group", resource_group_name) # Create an Azure Batch Account batch_account = azure_batch.BatchAccount("batch-account", resource_group_name=resource_group.name, location=resource_group.location, account_name=batch_account_name, ) # Create a pool of compute nodes in Azure Batch Account batch_pool = azure_batch.Pool("batch-pool", resource_group_name=resource_group.name, account_name=batch_account.name, pool_name=batch_pool_name, vm_size=vm_size, scale_settings=azure_batch.ScaleSettingsArgs( fixed_scale=azure_batch.FixedScaleSettingsArgs( target_dedicated_nodes=1, # Assuming a single node for simple tasks ), ), deployment_configuration=azure_batch.DeploymentConfigurationArgs( virtual_machine_configuration=azure_batch.VirtualMachineConfigurationArgs( image_reference=azure_batch.ImageReferenceArgs( publisher="Canonical", offer="UbuntuServer", sku="18.04-LTS", version="latest", ), node_agent_sku_id=node_agent_sku_id, ), ), ) # Create an Azure Batch Job where tasks for AI model training will be executed batch_job = azure_batch.Job("batch-job", resource_group_name=resource_group.name, account_name=batch_account.name, pool_info=azure_batch.PoolInformationArgs( pool_id=batch_pool.id, ), ) # Example of how to create a task in the job (the command line would be your training script) batch_task = azure_batch.Task("batch-task", resource_group_name=resource_group.name, account_name=batch_account.name, job_name=batch_job.name, command_line="python train-model.py", # Replace with the correct command line resource_files=[ azure_batch.ResourceFileArgs( http_url=f"https://{storage_account_name}.blob.core.windows.net/{storage_container_name}/train-model.py", file_path="train-model.py", ), # You can add other files needed for training as well ], ) # Export the URL of the Batch Account pulumi.export("batch_account_url", batch_account.account_endpoint)

    In this code:

    • We retrieve an existing resource group where the Azure Batch resources will be placed.
    • We create a BatchAccount, which provides a context to run our batch jobs and tasks.
    • We establish a Pool, which defines the set of compute nodes on which your tasks will run.
    • We define a Job under the Batch Account, which acts as a container for the tasks we will define and run.
    • We set up a Task within the Job, which is the actual execution unit. Here, it refers to a dummy training script you'd replace with your actual code.

    When you run this code as part of a Pulumi application, Pulumi will provision the Azure resources detailed above. Remember to replace the placeholders and command lines with actual values that correspond to your application-specific configuration, such as the name of your Python training script and the required resource files.