1. NGINX as Reverse Proxy for Azure ML Models

    Python

    To create an NGINX reverse proxy for Azure Machine Learning (ML) models, you would leverage NGINX running on a virtual machine or a container service within Azure to direct traffic to your Azure ML Models. The Azure ML Models would be deployed as web services (either as an InferenceEndpoint or WebService resource, depending on whether you're using Managed Endpoints or Real-time Endpoints) which can be called through HTTP requests.

    Outlined below is the process of using Pulumi to configure such an infrastructure:

    1. Azure Machine Learning Models: Use the azure-native.machinelearningservices.InferenceEndpoint or azure-native.machinelearning.WebService based on your specific scenario to deploy the Azure ML models as endpoints that can be accessed over HTTP.

      • InferenceEndpoint is used for deploying models to inference clusters in Azure Machine Learning.
      • WebService is for deploying a real-time endpoint where the model is hosted for inference.

    2. Azure Virtual Machine or Container Instances: Set up Azure Compute resources like a Virtual Machine or Azure Container Instances where NGINX will run.

      • If using an Azure VM, you would use azure-native.compute.VirtualMachine to create the VM and install NGINX.
      • If using Azure Container Instances, the azure-native.containerinstance.ContainerGroup resource would be used to run a container image with NGINX.

    3. Networking: Ensure proper networking configuration through azure-native.network resources such as VirtualNetwork, Subnet, NetworkInterface, and PublicIPAddress to expose the NGINX service to the public and to communicate with the ML endpoints.

    4. NGINX Configuration: The NGINX server configuration will be set to relay requests to the appropriate Azure ML endpoints. This is not directly handled by Pulumi, but you can automate the deployment of the configuration to the compute resources using scripts or configuration management tools.

    Here's a high-level overview of how you might begin to set up these resources:

    import pulumi import pulumi_azure_native as azure_native # Step 1: Deploy an Azure Machine Learning InferenceEndpoint or WebService # This is a simplified example that assumes the workspace and other required dependencies are already set up. inference_endpoint = azure_native.machinelearningservices.InferenceEndpoint( "inferenceEndpoint", resource_group_name="resourceGroup", workspace_name="workspace", location="eastus", # You will need to fill in the properties based on the specifics of your model and environment ) # Step 2: Create an Azure Virtual Machine to host the NGINX reverse proxy # You would include additional configurations like network interfaces, OS image, admin credentials, etc. vm = azure_native.compute.VirtualMachine( "vm", resource_group_name="resourceGroup", location="eastus", # Additional configuration would go here, including the NGINX settings ) # Step 3: Networking for the NGINX Reverse Proxy Virtual Machine # Here you would create and configure a virtual network, public IP, and networking interfaces as required # Step 4: NGINX Configuration is typically a configuration file that you would deploy to the VM # or container where NGINX is running. This is often done through a script or init container and # is not directly managed through Pulumi. Example NGINX config snippets would be provided outside of Pulumi. # Exporting endpoint URLs or public IP address for accessing your NGINX reverse proxy pulumi.export("inference_endpoint_url", inference_endpoint.properties.apply(lambda props: props.scoring_uri)) pulumi.export("vm_public_ip", vm.id) # Assuming you have a public IP for the VM

    In this code:

    • The InferenceEndpoint is a placeholder for your ML model endpoint.
    • The VM and networking configurations are very minimal. You would need to complete them based on your specifics.
    • NGINX configuration would be managed outside of Pulumi. It would involve setting up the server to redirect to your InferenceEndpoint.

    Please adjust the resource configurations according to your actual setup and requirements. This Pulumi program is a starting point, and further configuration, particularly around networking and security, would be necessary for a production deployment.


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