1. Distributed Deep Learning Inference Serving


    To set up a distributed deep learning inference serving, you would typically need the following components:

    1. Machine Learning Model: This is the actual deep learning model you've previously trained and wish to serve for inference.
    2. Inference Service: A scalable service that can serve the model to process incoming inference requests.
    3. Load Balancing/Request Routing: To distribute the inference requests efficiently across the inference service instances.

    We'll be using Azure as the cloud provider for this illustration. Specifically, we'll use the following Azure services:

    • Azure Machine Learning Inference Endpoint: This will allow us to deploy our trained models as web services, making them accessible for real-time and batch predictions.
    • Azure Machine Learning Inference Pool: This represents a pool of compute resources for serving the inference endpoints.

    Here is a Pulumi program in Python that sets up a basic distributed deep learning inference serving on Azure using Azure Machine Learning. Please note that you'll need to have your machine learning model ready and accessible for this setup to be complete.

    import pulumi import pulumi_azure_native as azure_native # Configure Azure resource group resource_group = azure_native.resources.ResourceGroup('inference_resource_group') # Provision Azure Machine Learning Workspace workspace = azure_native.machinelearningservices.Workspace( "inference_workspace", resource_group_name=resource_group.name, location=resource_group.location, identity=azure_native.machinelearningservices.IdentityArgs( type="SystemAssigned" ) ) # Create an Inference Pool for hosting the inference compute resources inference_pool = azure_native.machinelearningservices.InferencePool( "inference_pool", resource_group_name=resource_group.name, location=resource_group.location, workspace_name=workspace.name, sku=azure_native.machinelearningservices.SkuArgs( name="Standard_D3_v2" ), inference_pool_properties=azure_native.machinelearningservices.InferencePoolPropertiesArgs( # Configuration for the code and model artifacts code_configuration=azure_native.machinelearningservices.CodeConfigurationArgs( code_id="<your-code-artifact-id>", scoring_script="<your-scoring-script-path>" ), # Model details model_configuration=azure_native.machinelearningservices.ModelConfigurationArgs( model_id="<your-model-artifact-id>" ), # Other settings can be specified, such as environment configuration ) ) # Deploy the inference endpoint inference_endpoint = azure_native.machinelearningservices.InferenceEndpoint( "inference_endpoint", resource_group_name=resource_group.name, location=resource_group.location, workspace_name=workspace.name, sku=azure_native.machinelearningservices.SkuArgs( name="Standard" ), inference_endpoint_properties=azure_native.machinelearningservices.InferenceEndpointPropertiesArgs( auth_mode="AMLToken", # Authentication mode for the endpoint # Group ID ties the endpoint to a specific inference pool group_id=inference_pool.name, ) ) # Export the HTTP endpoint of the deployed model, which will be used to send inference requests pulumi.export("http_endpoint", inference_endpoint.properties.apply(lambda props: props["scoring_uri"]))

    In this program:

    • We start by creating a new resource group in Azure to organize resources related to our inference service.
    • We then set up an Azure Machine Learning Workspace, which is a foundational block for machine learning operations in Azure.
    • We create an Inference Pool, which specifies the type and amount of resources that will be used to serve our deep learning model.
    • The code_configuration parameter should point to the code that will process the inference requests, and model_configuration should reference the trained model.
    • Finally, we deploy the Inference Endpoint, which is the endpoint that applications will call to perform inference using our trained model.
    • We export the Scoring URI as http_endpoint, which can be used to send inference requests to the model.

    Please replace <your-code-artifact-id>, <your-scoring-script-path>, and <your-model-artifact-id> with actual values for your artifacts, code, and model.

    Remember to perform proper authentication and setup before running this Pulumi program, which includes logging in to Azure CLI and setting up the required Azure credentials to work with Pulumi.