Exercise - Deploy and build the solution

  • 20 minutes

In the first exercise, you have already installed the Azure IoT Edge runtime on your Linux computer. Make sure you have installed the following software development tools.

Create Azure AI services

In this module, you use Azure AI Custom Vision and Azure AI Speech services.

  • Azure AI Custom Vision service is used to create a machine learning model with fruit images provided. Then the model is exported and added to the project folder.

  • Azure AI Speech is used to generate speech from label of the item. You will add the speech key in deployment template.

The multi-service resource is listed under Azure AI services > Azure AI services multi-service account in the portal. To create a multi-service resource follow these instructions:

  1. Sign into the Azure portal.

  2. Select this link to create a multi-service resource: https://portal.azure.com/#create/Microsoft.CognitiveServicesAllInOne

  3. On the Create page, provide the following information:

    Project details Description
    Subscription Select one of your available Azure subscriptions.
    Resource group The Azure resource group that will contain your Azure AI services resource. You can create a new group or add it to a pre-existing group.
    Region The location of your Azure AI service instance. Different locations may introduce latency, but have no impact on the runtime availability of your resource.
    Name A descriptive name for your Azure AI services resource. For example, MyAzureAIServicesResource.
    Pricing tier The cost of your Azure AI services account depends on the options you choose and your usage. For more information, see the API pricing details.

    Screenshot of creating a multi-service resource.

  4. Configure other settings for your resource as needed, read and accept the conditions (as applicable), and then select Review + create.

Tip

If your subscription doesn't allow you to create an Azure AI services resource, you may need to enable the privilege of that Azure resource provider using the Azure portal, PowerShell command or an Azure CLI command. If you are not the subscription owner, ask the Subscription Owner or someone with a role of admin to complete the registration for you or ask for the /register/action privileges to be granted to your account.

Install the Docker Registry on the Linux computer

Azure IoT Edge relies on Docker images being distributed from a Docker Registry. In production, you would deploy Docker images from a registry such as Azure Container Registry.

When you are developing an Azure IoT Edge module, it is faster to install a local container registry on the device and deploy Docker images from the local registry to Azure IoT Edge.

Open the terminal in your Linux computer and run the following command to set up a local Docker Registry.

docker run -d -p 5000:5000 --restart=always --name registry registry:2

Clone the image recognition solution to the Linux computer

  1. Clone this GitHub repository.

    git clone https://github.com/MicrosoftDocs/mslearn-oxford.create-image-recognition-with-azure-iot-edge

  2. Open the solution from the Visual Studio Code menu.

Update the Azure AI Speech key

Open the deployment.template.json file and update the azureSpeechServicesKey with the key you copied from the Azure Speech service.

The illustration shows how to update speech service key.

Confirm the processor

You need to ensure the image you plan to build matches the target processor architecture. In our case, we are going to build for amd64. Confirm processor architecture.

From the Visual Studio Code, bottom bar click the currently selected processor architecture, then from the popup select amd64.

The illustration shows how to select a processor.

Build the solution

Build and Push the solution to Docker by right mouse clicking the deployment.template.json file and select "Build and Push IoT Edge Solution". The first build will be slow as Docker needs to pull the base layers to your local machine.

If you are cross-compiling to amd64 then the first build will be very slow as OpenCV and Python requirements need to be compiled. On a fast Intel i7-8750H processor, cross-compiling this solution will take approximately 40 minutes.

The illustration shows how to build and push the solution.

Deploy the solution

When the Docker Build and Push process has been completed, select the Azure IoT Hub device you want to deploy the solution to. Right mouse click the deployment.json file found in the config folder and select the target device from the drop-down list.

The illustration shows how to create a deployment.