Auto-scaling AI Inference Services with Nomad

Question

Pulumi · Accepted Answer

Auto-scaling an AI inference service requires infrastructure to deploy machine learning models, manage compute resources, and dynamically adjust to the demand for inference requests. Pulumi provides a way to define and manage this infrastructure as code, using various cloud providers.

For the scenario of scaling AI inference services with Nomad, you would typically be looking into deploying a containerized solution that can be scheduled and managed by Nomad, a workload orchestrator. Alongside Nomad, you would need a cloud provider to provision resources. However, Pulumi doesn't have direct support for Nomad. Instead, we can achieve the desired outcome by deploying to a Kubernetes cluster and using its auto-scaling capabilities or by using a cloud service like Azure Machine Learning or AWS SageMaker that supports auto-scaling.

Since the Pulumi Registry Results point to cloud providers with machine learning services, I'm going to use Azure as an example. Azure offers a service named Azure Machine Learning, which can be used to deploy AI models and supports auto-scaling. With Pulumi's Azure Native provider, you can define an Azure ML Operationalization Cluster that scales as needed.

### Detailed Explanation of the Program

The following Pulumi program in Python will:

1. Import the necessary Pulumi modules for Azure.
2. Create an Azure resource group to organize all the resources.
3. Define an Azure Machine Learning workspace, which is a foundational resource for machine learning in Azure.
4. Set up an Azure ML Compute Cluster that will be used for running inference services.
5. Enable autoscaling on the compute cluster.

Please replace the placeholders such as `<insert_resource_name>` with actual names relevant to your project.

```python
import pulumi
import pulumi_azure_native as azure_native
from pulumi_azure_native.machinelearningcompute import OperationalizationCluster

# Create an Azure resource group to organize the resources
resource_group = azure_native.resources.ResourceGroup('ai_resource_group')

# Define an Azure Machine Learning workspace
ml_workspace = azure_native.machinelearningservices.Workspace(
    "ml_workspace",
    resource_group_name=resource_group.name,
    sku=azure_native.machinelearningservices.SkuArgs(name="Standard"),
    location=resource_group.location
)

# Set up an Azure ML Compute Cluster for AI inference
ml_compute_cluster = OperationalizationCluster(
    "ml_compute_cluster",
    location=resource_group.location,
    resource_group_name=resource_group.name,
    cluster_name="myInferenceCluster",
    cluster_type="ACS",  # Specifies an Azure Container Service-based cluster
    container_service=OperationalizationCluster.ContainerServiceArgs(
        agent_count=2,
        agent_vm_size="Standard_DS3_v2",
        master_count=1,
        orchestrator_type="Kubernetes"
    ),
    global_service_configuration=OperationalizationCluster.GlobalServiceConfigurationArgs(
        auto_scale=OperationalizationCluster.GlobalServiceConfigurationArgsAutoScaleArgs(
            status="Enabled",  # Enables auto-scaling
            min_replicas=1,
            max_replicas=10,
            target_utilization=0.7
        )
    ),
    workspace=ml_workspace.name
)

pulumi.export('cluster_endpoint', ml_compute_cluster.cluster_endpoint)
```

In this program:

- We first create a resource group as a logical container for the AI resources we will create.
- Then, we establish a Machine Learning workspace within Azure to coordinate the creation, processing, and deployment of machine learning models.
- Next, we set up an Operationalization Cluster as our compute infrastructure. This is where your AI models are going to run and provide inference services.
- We configure autoscaling settings to ensure the compute resources adjust to the workload, reducing costs and ensuring efficiency.

The `OperationalizationCluster` resource allows for a rich configuration that we have simplified here for brevity. Depending on your needs, you may need to adjust compute sizes, the number of nodes, network settings, and so forth.

This cluster endpoint can be used by your application to interact with the deployed AI inference service.

Once you have this Pulumi program, you can run it using the Pulumi CLI:

1. Initialize a new Pulumi project in Python.
2. Replace the contents of `__main__.py` with the code provided.
3. Run `pulumi up` to deploy your infrastructure.

After deployment, Pulumi will output the endpoint of the inference cluster as a URL which you can use to send inference requests.

Keep in mind, we've assumed that you have already configured your Pulumi CLI with the necessary credentials to access your Azure subscription. If you haven't, you'll need to run `pulumi login` and `az login` to set them up before running the Pulumi program.