1. Parallel Data Processing for AI on Azure Batch


    Parallel data processing is a powerful capability for running large-scale computations efficiently. Microsoft Azure Batch provides a managed service for running large-scale parallel and high-performance computing applications efficiently in the cloud.

    Azure Batch enables you to run large-scale applications efficiently by providing job scheduling and auto-scaling of a pool of virtual machines. In this context, AI applications can benefit from Azure Batch as it can process multiple concurrent workloads and manage compute resources.

    Below is a Pulumi program written in Python that demonstrates how you can set up Azure Batch for parallel data processing for AI. The program performs the following actions:

    1. Creates an Azure Batch account, which is required to define and manage pools, jobs, and tasks.
    2. Creates a pool of compute nodes in the Batch account to execute tasks. The vmSize attribute specifies the size of the VMs in the pool.
    3. Creates a job within the Batch account. The job oversees the execution of tasks and can be configured to handle task dependencies efficiently.
    4. Each task can execute a command or run a script that performs a unit of work in the context of AI processing, such as training a model or processing a batch of data.

    Let's now look at the program:

    import pulumi import pulumi_azure_native.batch as azure_batch # Note: Azure configuration and authentication should already be set up # in the Pulumi CLI environment prior to running this program. # Create an Azure Batch Account batch_account = azure_batch.BatchAccount("my-batch-account", resource_group_name="my-resource-group", location="eastus", account_name="mybatchaccount" ) # Create a pool of Azure compute nodes (VMs) for processing tasks compute_pool = azure_batch.Pool("my-compute-pool", account_name=batch_account.name, pool_name="mypool", vm_size="STANDARD_A1_v2", # You can choose the appropriate VM size for your workload scale_settings=azure_batch.AutoScaleSettingsArgs( auto_scale=azure_batch.AutoScaleArgs( formula="startingNumberOfVMs=1", evaluation_interval="PT5M" ) ), resource_group_name="my-resource-group" ) # Create a job within the Batch account to manage the execution of tasks batch_job = azure_batch.Job("my-batch-job", account_name=batch_account.name, job_name="myjob", pool_info=azure_batch.PoolInformationArgs( pool_id=compute_pool.id ), resource_group_name="my-resource-group" ) # Create a task within the job # Here, you would specify the command line that performs the data processing # For AI applications, it could be a python script that trains a model or process data task = azure_batch.Task("my-task", account_name=batch_account.name, job_name=batch_job.name, task_name="mytask", command_line="python myscript.py", resource_files=[ azure_batch.ResourceFileArgs( http_url="https://my-storage.blob.core.windows.net/my-container/myscript.py", file_path="myscript.py" ) ], resource_group_name="my-resource-group" ) # Export the URL of the Batch account pulumi.export("batch_account_url", batch_account.account_endpoint)

    In the above program:

    • We create a BatchAccount resource to set up an Azure Batch account.
    • A Pool is created under this Batch account, specifying the type and size of VMs to be used for parallel execution.
    • We define a Job within this Batch account to oversee task execution.
    • A Task is created with a command line that points to the Python script you want to run. The script is made available via a ResourceFile, which is referenced by its URL.

    The command_line in the task should be the script or command that kicks off your data processing job. The http_url is the HTTP URL where the script file is located.

    To run this Pulumi program:

    1. Install Pulumi and set up authentication with Azure.
    2. Create a requirements.txt file containing the Pulumi Azure Native package:
    3. Create a Python virtual environment and install the dependencies:
      python -m venv venv source venv/bin/activate pip install -r requirements.txt
    4. Save the above program to a file (e.g., __main__.py).
    5. Run pulumi up to deploy your Azure Batch configuration.
    6. Once your tasks have successfully completed, you can optionally run pulumi destroy to clean up resources.

    By following these steps, you can create a scalable system for parallel data processing for your AI applications on Azure with Pulumi.