1. Deploying Deep Learning Inference Models on UniFi AI-NVR.


    To deploy deep learning inference models on a UniFi AI-NVR (Network Video Recorder), it's important to note that Pulumi itself does not directly interact with networking hardware or NVR systems. However, Pulumi can be used to orchestrate cloud resources and services that could interact with such devices indirectly, for example, through IoT services or serverless functions that process data and send commands.

    Since UniFi AI-NVR devices are typically managed locally or via UniFi's own cloud service, we’ll instead focus on what is feasible with Pulumi. We could deploy a virtual machine or a container in a cloud service that processes video streams and runs inference models, which could then interact with UniFi AI-NVR devices through an API, if available.

    For demo purposes, let's assume you want to set up a cloud-based environment that processes video data with deep learning models, and then outputs inference results that could potentially be sent to an NVR. In this setup, we'll use Azure as the cloud provider and set up an Azure Machine Learning workspace, where you can manage and deploy your machine learning models.

    Here’s how you might set up an Azure Machine Learning workspace with Pulumi in Python. The code will create a resource group, a machine learning workspace, and an inference cluster where the models will be deployed:

    import pulumi import pulumi_azure_native as azure_native # Creating a new resource group resource_group = azure_native.resources.ResourceGroup('myresourcegroup') # Creating an Azure Machine Learning workspace within the resource group ml_workspace = azure_native.machinelearningservices.Workspace( 'mymlworkspace', resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.machinelearningservices.SkuArgs( name='Basic', ), # Other properties like description, friendly name, etc can be set as needed. ) # Creating an Inference Cluster to deploy machine learning models # Note: The specific details like subnet ID should be defined or referencing existing infrastructure. inference_cluster = azure_native.machinelearningservices.InferenceCluster( 'myinferencecluster', resource_group_name=resource_group.name, location=ml_workspace.location, workspace_name=ml_workspace.name, properties=azure_native.machinelearningservices.InferenceClusterPropertiesArgs( description='Cluster for deploying deep learning models' ), # Sku, Identity, and other configurations as per your requirement. ) # Exporting the Azure Machine Learning workspace URL for easy access pulumi.export('ml_workspace_url', ml_workspace.workspace_url)

    This program defines three main resources for deployment:

    1. Resource Group: A container that holds related resources for an Azure solution.
    2. Machine Learning Workspace: The top-level resource for Azure Machine Learning, providing a space where you can collaborate and work with machine learning models.
    3. Inference Cluster: A compute target that can be used to host deployed machine learning models and provide REST endpoints for inference.

    Pulumi will manage the state and dependencies between these resources, ensuring they are created, updated, or deleted in the correct order and with the correct configurations.

    To apply this configuration, you would need to have Pulumi and the Azure CLI installed, and you must be logged into your Azure account through the CLI. With those prerequisites met, you could run pulumi up to deploy the resources specified in the script.

    This script is just a basic foundation for deploying machine learning infrastructure on Azure with Pulumi. It can be expanded to include further details, such as configuring the underlying virtual network settings for the inference cluster or incorporating automated pipelines and CI/CD processes for your machine learning model development workflow.