Distributed Model Serving with Low-Latency Response

Question

Pulumi · Accepted Answer

Distributed model serving refers to a setup where machine learning models are deployed across multiple servers or endpoints, allowing parallel processing of incoming requests to deliver low-latency responses. Low-latency response is crucial in real-time applications where decisions need to be made quickly, like in financial transactions, online gaming, or real-time analytics.

Pulumi can be used to define, deploy, and manage cloud infrastructure that supports distributed model serving with a low-latency response. Below we will write a program in Python that uses Pulumi to create such infrastructure. We will use various cloud resources suitable for our scenario.

In this example, I'll choose to use the azure-native.machinelearningcompute.OperationalizationCluster resource from Azure as it provides a scalable and efficient way to serve predictions from trained models, which fits our requirement for low-latency response in a distributed model serving setup. This cluster will be responsible for deploying and managing machine learning models at scale.

Here's how you can define this with Pulumi:

Operationalization Cluster: This resource is an Azure resource that allows you to deploy machine learning models at scale. It is optimized to serve models with low latency and can be set up to auto-scale based on traffic, which is perfect for handling varying loads.
Container Registry: An Azure Container Registry will be needed to store and manage the Docker container images that contain the machine learning models.
Container Service: This will define the properties related to the containers that will serve the machine learning models, including the number of agents (nodes) and the size of the virtual machines that they run on.
App Insights: Application Insights is used for monitoring the performance and detecting issues of the applications that are serving the machine learning models.

Let's go ahead and write a Pulumi program that sets up the Operationalization Cluster along with the necessary components. We will assume that the machine learning models have already been containerized and are ready to be served.

import pulumi
import pulumi_azure_native as azure_native

# Define the resource group where all resources will be provisioned
resource_group = azure_native.resources.ResourceGroup("model_serving_rg")

# Create an Azure Container Registry to store Docker images
container_registry = azure_native.containerregistry.Registry(
    "model_serving_registry",
    resource_group_name=resource_group.name,
    sku=azure_native.containerregistry.SkuArgs(
        name="Basic"  # Choose a SKU that fits your needs
    ),
    admin_user_enabled=True,
)

# Create an Azure Container Service (AKS) to host the containers serving the models
aks_cluster = azure_native.containerservice.ManagedCluster(
    "model_serving_aks_cluster",
    resource_group_name=resource_group.name,
    agent_pool_profiles=[{
        "count": 3,  # Number of VMs to handle the model serving, scale as needed
        "vm_size": "Standard_DS2_v2",  # Choose an appropriate VM size
        "name": "agentpool",
    }],
    dns_prefix="model-serving-cluster",
)

# Set up Application Insights for monitoring
app_insights = azure_native.insights.Component(
    "model_serving_app_insights",
    resource_group_name=resource_group.name,
    kind="web",
    application_type="web",
)

# Define the Operationalization Cluster to deploy and manage models at scale
operationalization_cluster = azure_native.machinelearningcompute.OperationalizationCluster(
    "model_serving_cluster",
    resource_group_name=resource_group.name,
    cluster_type="ACS",
    container_service={
        "agent_count": 3,
        "agent_vm_size": "Standard_DS2_v2",
        "orchestrator_type": "Kubernetes",
    },
    container_registry={
        "resourceId": container_registry.id
    },
    app_insights={
        "resourceId": app_insights.id
    },
    storage_account={
        # Ensure you set up a storage account and fill the properties accordingly
        "resourceId": "<storage-account-resource-id>"
    },
)

# Export the endpoints for convenience
pulumi.export('AKS Cluster Endpoint', aks_cluster.private_fqdn)
pulumi.export('Operationalization Cluster Endpoint', operationalization_cluster.scoring_uri)

Here's what this program does:

We have set up a resource group in Azure to manage all of our resources in one logical group.
We have set up a container registry for our Docker images containing machine learning models.
We have created an AKS cluster to run our containers which will actually serve the machine learning models.
We have configured Application Insights for monitoring our service's health and performance.
Finally, we set up the Operationalization Cluster which will manage the deployment of our machine learning models at scale and allow for auto-scaling based on the traffic it receives to maintain low latency.

Please remember to replace placeholders such as <storage-account-resource-id> with your actual Azure Storage Account resource ID.

After deploying this program with Pulumi, your distributed model serving infrastructure will be provisioned and ready to use on Azure. Be sure to monitor and tune performance metrics in Application Insights to maintain the desired low-latency response.