1. Throttling Settings for Machine Learning APIs


    Throttling settings are crucial for managing the load and ensuring fair usage of machine learning APIs. In cloud platforms like Google Cloud Platform (GCP) and Azure, you can configure throttling as part of the machine learning service setup.

    In this context, throttling settings may involve specifying the number of requests that can be made to the API within a given timeframe, which helps in preventing over-utilization of resources and ensures that the API remains responsive for all users.

    When you're working with Pulumi to set up machine learning services and their respective APIs, you would typically manage these settings through specific properties in the resources that represent your machine learning endpoints or deployments. Both Azure and GCP offer machine learning services that can be managed via Pulumi.

    Let's consider an example where you might be setting up an Azure Machine Learning service using Pulumi, and you want to configure its online endpoint, which provides real-time serving of machine learning models. One of the resources you would use is OnlineEndpoint, part of the azure-native package, which represents a machine learning endpoint that can be accessed over the web.

    Here's a program that creates an Azure Machine Learning endpoint with throttling settings in Pulumi, using Python:

    import pulumi import pulumi_azure_native.machinelearningservices as ml # Set up a resource group if you don't already have one resource_group = ml.ResourceGroup("resource_group", resource_group_name="my_ml_resource_group") # Create an Azure Machine Learning Workspace ml_workspace = ml.Workspace("ml_workspace", resource_group_name=resource_group.name, location="eastus", sku=ml.SkuArgs(name="Enterprise")) # Create an Azure Machine Learning Online Endpoint # You can configure the compute type, instance count, and other settings here as per your requirements. online_endpoint = ml.OnlineEndpoint("online_endpoint", resource_group_name=resource_group.name, workspace_name=ml_workspace.name, location=ml_workspace.location, online_endpoint_properties=ml.OnlineEndpointPropertiesArgs( # Throttling settings can be managed under the deployment properties, # if available, by setting the appropriate properties for scaling, # instance counts or rate limiting. description="My ML Endpoint with Throttling Settings", auth_mode="Key", compute="Dedicated", traffic=ml.TrafficPercentileArgs( # Configuration for percentage-based traffic distribution among all deployments. ) )) pulumi.export('endpoint_name', online_endpoint.name) pulumi.export('endpoint_status', online_endpoint.provisioning_state)

    In this program:

    • We first create a resource group and a machine learning workspace as prerequisites for deploying online endpoints.
    • Then we define an online endpoint using the OnlineEndpoint resource, specifying properties including the compute type and the authentication mode.
    • In the online_endpoint_properties, you can further customize the settings related to throttling, like the number of instances and such. However, please note that exact throttling settings such as request per second (RPS) may need to come from configuring the underlying compute target or may be defined in other properties or sub-resources depending on updates in the Azure API which Pulumi reflects in its SDK.

    The traffic parameter within OnlineEndpointPropertiesArgs does not directly set the throttling but is related to traffic management. For throttling, you would look into parameters that control resource allocation, scaling, and rate limiting which might be part of the compute resource configuration or other services linked to the ML endpoint.

    It is important to refer to the Azure Machine Learning documentation to understand the full capabilities and configurations that can be applied, including any throttling settings available.

    Remember, cloud providers continuously evolve, and new features or properties may be added that allow for more direct control over API throttling. Keep an eye on the official documentation and Pulumi's updates for any such features.

    For Google Cloud Platform, you would use a similar approach with GCP-specific resources from Pulumi's gcp package. There is not a direct throttling resource in Pulumi as throttling settings are often properties of other resources. If you are working with GCP's AI platform, you would use resources like gcp.ml.EngineModel for deploying models, while the relevant throttling would be configured at the API management level using a service like Endpoints or API Gateway.