Load Balancing for Distributed ML Model Inferences

Question

Pulumi · Accepted Answer

When deploying distributed machine learning (ML) model inferences, load balancing is critical to efficiently distribute the inferencing workload across multiple instances. It helps in achieving higher availability and fault tolerance. You would use a load balancer to direct incoming inference requests to different servers that host your ML model, based on factors like current load, server health, or other criteria.

For this goal, let's consider a scenario where you are using Azure as your cloud provider, and you want to set up load balancing for ML model inferences. Specifically, we will use Azure Machine Learning Inference service resources for deploying the ML models, along with an Inference Endpoint that will act as a load balancer to route incoming requests to the backing inference services.

Here's a breakdown of what we'll do:

1. **Azure Machine Learning Workspace**: This resource provides a centralized place for your ML training, scoring, deploying, and management activities.

2. **Azure Machine Learning Inference Cluster**: The inference cluster will host the deployed ML models and handle the inferencing workloads.

3. **Azure Machine Learning Inference Endpoint**: This resource works as a front-facing load balancer, directing incoming scoring requests to different nodes within the inference cluster.

Now, let's translate this into a Pulumi program:

```python
import pulumi
import pulumi_azure_native.machinelearningservices as ml_services

# Create an Azure Machine Learning Workspace
ml_workspace = ml_services.Workspace(
    "mlWorkspace",
    resource_group_name="myResourceGroup",  # Replace with your resource group name
    location="East US",  # Replace with your preferred location
    identity={
        "type": "SystemAssigned",
    },
    sku={
        "name": "Basic",  # Choose between Basic, Enterprise, or Dev-test based on your needs
    }
)

# Create an Azure Machine Learning Inference Cluster
# The cluster will consist of multiple nodes capable of serving the ML models
inference_cluster = ml_services.InferenceCluster(
    "inferenceCluster",
    identity={
        "type": "SystemAssigned",
    },
    resource_group_name="myResourceGroup",  # Replace with your resource group name
    location="East US",  # Replace with your preferred location
    workspace_name=ml_workspace.name,
    sku={
        # Define the size, tier and other specifications for the VMs in the inference cluster
        "name": "Standard_DS3_v2",
        "tier": "Standard",
        "size": "DS3_v2",
    },
)

# Create an Azure Machine Learning Inference Endpoint
# The inference endpoint is the load balancer that directs incoming requests to the inference services
inference_endpoint = ml_services.InferenceEndpoint(
    "inferenceEndpoint",
    identity={
        "type": "SystemAssigned",
    },
    resource_group_name="myResourceGroup",  # Replace with your resource group name
    location="East US",  # Replace with your preferred location
    workspace_name=ml_workspace.name,
    inferencing_capacity=1,  # Define the capacity of simultaneous inferencing operations
    compute={"target": inference_cluster.id},  # Associate the cluster with the endpoint
)

# Export the inference endpoint URL so it can be used to make prediction requests
pulumi.export("endpoint_url", inference_endpoint.properties.apply(lambda props: props["scoring_uri"]))
```

In this program, we are creating necessary resources for deploying and load balancing ML model inferences using Azure Native providers.

- We start by creating an Azure Machine Learning Workspace, where all our ML assets will reside.
- Then, we set up an Inference Cluster, which consists of the necessary compute resources to host and serve our models.
- Finally, we establish an Inference Endpoint, which essentially serves as our load balancer. It exposes a URI that clients can use to send inferencing requests, and it directs those requests to the underlying compute resources efficiently.

Once you have this setup, deploy it using the Pulumi CLI, and configure your clients to send the ML inferencing requests to the `endpoint_url` that the Pulumi program exports.

Remember that we've used placeholder values for resource group name and location; you should replace these with your actual Azure resource group and preferred location.

This program sets the foundation for deploying your ML models and serving inferences at scale while leveraging Azure's robust load balancing to ensure optimal resource usage and responsiveness.