Parallel Data Processing for AI on Azure Batch

Question

Pulumi · Accepted Answer

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:

```python
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:
   ```
   pulumi-azure-native
   ```
3. Create a Python virtual environment and install the dependencies:
   ```shell
   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.