Scalable Inference Clusters for AI Services

Question

Pulumi · Accepted Answer

To create scalable inference clusters for AI services, you can use cloud providers like Microsoft Azure, which offers Machine Learning services that allow for creating and managing scalable AI inference clusters. These clusters can consist of multiple nodes that can serve predictions from trained machine learning models efficiently.

Below, I will present a program written in Python using Pulumi with the Azure Native provider. The program will deploy an inference pool, which is part of Azure Machine Learning, and is designed to handle high amounts of inference requests for deploying machine learning models at scale.

The key components we'll use in the program are:
- **Inference Pool**: This is a group of one or more nodes, managed by Azure Machine Learning, that can be used to serve models for inference. Each node in the pool can run one or more instances of a containerized model.
- **Workspace**: Before creating an inference pool, you need an Azure Machine Learning workspace, which is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models.

Let's go step by step through the Pulumi program:

1. **Setup Azure Native Provider**: Import `pulumi_azure_native`, which contains the necessary components for working with Azure resources.
2. **Create an Inference Pool**: Define your inference pool, specifying the node type and size, and other configurations necessary for the inference service.
3. **Infrastructure as Code**: The entire Azure Machine Learning workspace and related infrastructure will be programmatically defined and can be versioned, shared, and reused.

```python
import pulumi
from pulumi_azure_native import machinelearningservices as ml

# Configuration Variables (hard-coded for simplicity, but could be parameterized)
resource_group_name = 'myResourceGroup'
workspace_name = 'myMLWorkspace'
location = 'EastUS'
inference_pool_name = 'myInferencePool'

# Create a Machine Learning Workspace
workspace = ml.Workspace(
    "workspace",
    resource_group_name=resource_group_name,
    workspace_name=workspace_name,
    location=location,
    sku="Standard",
    identity=ml.IdentityArgs(
        type="SystemAssigned"
    ),
)

# Create an Inference Pool within the workspace
inference_pool = ml.InferencePool(
    "inferencePool",
    resource_group_name=resource_group_name,
    workspace_name=workspace.name,
    location=location,
    inference_pool_name=inference_pool_name,
    sku=ml.SkuArgs(
        name="Standard_D3_v2",
        tier="Standard",
        size="Standard_D3_v2",
        family="D",
        capacity=1
    ),
    properties=ml.InferencePoolPropertiesArgs(
        description="My Inference Pool for serving ML models",
        code_configuration=ml.CodeConfigurationArgs(
            code_id="/subscriptions/your_subscription/resourceGroups/your_resource_group/providers/Microsoft.MachineLearningServices/workspaces/your_workspace/codes/your_code/versions/your_code_version",
            scoring_script="score.py"
        ),
        # You can add more configurations for models, environments, etc.
    ),
    tags={
        "Environment": "Production",
        "Purpose": "Inference"
    }
)

# Export the inference pool's name and other properties
pulumi.export("inference_pool_name", inference_pool.name)
pulumi.export("inference_pool_description", inference_pool.properties.description)
```

In this program:
- We first import the necessary Pulumi components to interact with Azure resources.
- Then, a new Machine Learning workspace is created with a specific name, location, and identity type.
- Afterwards, we create an Inference Pool within our Machine Learning workspace, specifying the hardware specs (SKU), and configure it for serving ML models with a scoring script.
- Finally, we export the name and description of the created inference pool, which is useful for querying via the Pulumi CLI or integrating with other systems.

Remember, this code is designed to run without modification assuming your Pulumi CLI is set up with the correct Azure credentials. This Pulumi program could be expanded with additional properties and integrated with other Azure services based on the specific requirements of the scalable inference clusters you intend to create for your AI services.