1. Load Balancing for Distributed AI Model Inference


    To set up a load balancing system for distributed AI model inference, you would typically need the following components:

    1. AI Models: Prepared and trained models that are ready to be used for inference.
    2. Inference Servers: A set of servers that will run the AI models and process incoming inference requests.
    3. Load Balancer: A system that distributes incoming inference requests across the available inference servers evenly or based on certain rules.

    We can use cloud services to set up these components. Pulumi offers resources that allow us to define and deploy the necessary infrastructure in a programmatic and repeatable way. For this specific use case, we can utilize Azure's Machine Learning services to manage and deploy AI models, along with Azure's load balancing solutions to distribute the workload.

    Below is a Pulumi program written in Python that sets up an Inference Pool using Azure Machine Learning services, which is designed to handle distributed AI model inference. The inference pool acts as an abstraction to manage a set of inference servers and can be implicitly load balanced by the platform.

    Here is how these resources are set up:

    • An Inference Pool will be the resource where the models are deployed and served. It's an Azure resource that will manage the underlying infrastructure needed for model inference, such as compute resources.

    • Inference Endpoint represents a service that allows client applications to perform predictions by using the deployed models.

    The following program assumes that you have already trained an AI model, which is identified by a modelId. The code below will set up the infrastructure to enable you to create an inference endpoint where the model can be hosted:

    import pulumi import pulumi_azure_native as azure_native # Configure the Azure location to deploy resources to. location = 'East US' # Create an Azure resource group to contain all the resources. resource_group = azure_native.resources.ResourceGroup('ai_resource_group', resource_group_name='ai_inference_rg', location=location) # Create an Azure Machine Learning Workspace. ml_workspace = azure_native.machinelearningservices.Workspace('ml_workspace', location=location, resource_group_name=resource_group.name, workspace_name='ml_inference_workspace', sku=azure_native.machinelearningservices.SkuArgs(name="Enterprise")) # Create an Inference Pool for distributing AI model inference. inference_pool = azure_native.machinelearningservices.InferencePool( 'inference_pool', location=location, resource_group_name=resource_group.name, workspace_name=ml_workspace.name, sku=azure_native.machinelearningservices.SkuArgs(name="Dedicated", tier="Standard"), inference_pool_name='my_inference_pool', inference_pool_properties=azure_native.machinelearningservices.InferencePoolPropertiesArgs( nodeSkuType="Standard_DS3_v2", code_configuration=azure_native.machinelearningservices.CodeConfigurationArgs( scoring_script='score.py' # This script should contain the entry point for the inference. ), model_configuration=azure_native.machinelearningservices.ModelConfigurationArgs( model_id='my-model-id' # Replace with the actual model ID. ), ) ) # An Inference Endpoint to expose the inference pool for client requests. inference_endpoint = azure_native.machinelearningservices.InferenceEndpoint( 'inference_endpoint', location=location, resource_group_name=resource_group.name, workspace_name=ml_workspace.name, endpoint_name='my_inference_endpoint', inference_endpoint_properties=azure_native.machinelearningservices.InferenceEndpointPropertiesArgs( auth_mode=azure_native.machinelearningservices.EndpointAuthMode.KEY, groupId='my-inference-endpoint' # The association with the Inference Pool. ) ) # Export the inference endpoint URL for clients to perform predictions. pulumi.export('endpoint_url', inference_endpoint.properties.apply(lambda props: props['scoring_uri']))

    In this Pulumi program, we first create an Azure Resource Group to organize the resources necessary for the AI inference. Then, we set up a Machine Learning Workspace, which is a foundational resource for all Azure Machine Learning services.

    The InferencePool object represents the compute resources that will run our AI model. Note that we have specified details like the nodeSkuType to define the compute specifications for the nodes within the pool and a code_configuration that references our scoring script. The model_configuration should link to the model that you've trained by specifying its model_id.

    Once the inference pool is established, we create an InferenceEndpoint. This is the actually exposed service that clients will send requests to, and it is connected to our inference pool via the groupId.

    Finally, we export the endpoint_url which clients can use to perform model predictions. This URL is dynamically retrieved from the properties of the InferenceEndpoint.

    Please replace placeholders like 'my-model-id', 'score.py' with actual values relevant to your model and its entry point script.

    To run this program:

    1. Ensure you have Pulumi installed and configured with your Azure account.
    2. Save the above code to a file named __main__.py.
    3. Run pulumi up to preview and deploy the resources.

    This program sets the foundation for an AI model inference on Azure. The actual implementation of model training, scoring script development, and client integration would be separate from this infrastructure setup.