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title titleSuffix description author manager ms.service ms.topic ms.date ms.collection ms.author ms.custom
How to install and run the Spatial Analysis container - Azure AI Vision
Azure AI services
The Spatial Analysis container lets you detect people and distances in a video feed.
PatrickFarley
nitinme
azure-ai-vision
conceptual
02/21/2025
ce-skilling-fresh-tier2, ce-skilling-ai-copilot
pafarley
devx-track-azurecli

Install and run the Spatial Analysis container (preview)

[!INCLUDE spatial-analysis-deprecation]

The Spatial Analysis container enables you to analyze real-time streaming video to understand spatial relationships between people, their movement, and interactions with objects in physical environments. Containers help you meet specific security and data governance requirements.

Prerequisites

  • Azure subscription - Create one for free
  • Once you have your Azure subscription, create a Computer Vision resource for the Standard S1 tier in the Azure portal to get your key and endpoint. After it deploys, select Go to resource.
    • You'll need the key and endpoint from the resource you create to run the Spatial Analysis container. You'll use your key and endpoint later.

Spatial Analysis container requirements

To run the Spatial Analysis container, you need a compute device with an NVIDIA CUDA Compute Capable GPU 6.0 or higher (for example, NVIDIA Tesla T4, A2, 1080Ti, or 2080Ti). We recommend that you use Azure Stack Edge with GPU acceleration, however the container runs on any other desktop machine that meets the minimum requirements. We refer to this device as the host computer.

Azure Stack Edge device

Azure Stack Edge is a hardware-as-a-service solution and an AI-enabled edge computing device with network data transfer capabilities. For detailed preparation and setup instructions, see the Azure Stack Edge documentation.

Desktop machine

Minimum hardware requirements

  • 4 GB of system RAM
  • 4 GB of GPU RAM
  • 8 core CPU
  • One NVIDIA CUDA Compute Capable GPU 6.0 or higher (for example, NVIDIA Tesla T4, A2, 1080Ti, or 2080Ti)
  • 20 GB of HDD space

Recommended hardware

  • 32 GB of system RAM
  • 16 GB of GPU RAM
  • 8 core CPU
  • Two NVIDIA CUDA Compute Capable GPUs 6.0 or higher (for example, NVIDIA Tesla T4, A2, 1080Ti, or 2080Ti)
  • 50 GB of SSD space

In this article, you'll download and install the following software packages. The host computer must be able to run the following (see below for instructions):

Azure VM with GPU

In our example, we utilize an NCv3 series VM that has one v100 GPU.

Requirement Description
Camera The Spatial Analysis container isn't tied to a specific camera brand. The camera device needs to: support Real-Time Streaming Protocol(RTSP) and H.264 encoding, be accessible to the host computer, and be capable of streaming at 15FPS and 1080p resolution.
Linux OS Ubuntu Desktop 18.04 LTS must be installed on the host computer.

Set up the host computer

We recommend that you use an Azure Stack Edge device for your host computer. Select Desktop Machine if you're configuring a different device, or Virtual Machine if you're utilizing a VM.

Azure Stack Edge device

Configure compute on the Azure Stack Edge portal

Spatial Analysis uses the compute features of the Azure Stack Edge to run an AI solution. To enable the compute features, make sure that:

  • You've connected and activated your Azure Stack Edge device.
  • You have a Windows client system running PowerShell 5.0 or later, to access the device.
  • To deploy a Kubernetes cluster, you need to configure your Azure Stack Edge device via the Local UI on the Azure portal:
    1. Enable the compute feature on your Azure Stack Edge device. To enable compute, go to the Compute page in the web interface for your device.
    2. Select a network interface that you want to enable for compute, then select Enable. This creates a virtual switch on your device, on that network interface.
    3. Leave the Kubernetes test node IP addresses and the Kubernetes external services IP addresses blank.
    4. Select Apply. This operation may take about two minutes.

Configure compute

Set up Azure Stack Edge role and create an IoT Hub resource

In the Azure portal, navigate to your Azure Stack Edge resource. On the Overview page or navigation list, select the Edge compute Get started button. In the Configure Edge compute tile, select Configure.

Link

In the Configure Edge compute page, choose an existing IoT Hub, or choose to create a new one. By default, a Standard (S1) pricing tier is used to create an IoT Hub resource. To use a free tier IoT Hub resource, create one and then select it. The IoT Hub resource uses the same subscription and resource group that is used by the Azure Stack Edge resource

Select Create. The IoT Hub resource creation may take a couple of minutes. After the IoT Hub resource is created, the Configure Edge compute tile will update to show the new configuration. To confirm that the Edge compute role has been configured, select View config on the Configure compute tile.

When the Edge compute role is set up on the Edge device, it creates two devices: an IoT device and an IoT Edge device. Both devices can be viewed in the IoT Hub resource. The Azure IoT Edge Runtime will already be running on the IoT Edge device.

Note

Enable MPS on Azure Stack Edge

Follow these steps to remotely connect from a Windows client.

  1. Run a Windows PowerShell session as an administrator.

  2. Make sure that the Windows Remote Management service is running on your client. At the command prompt, type:

    winrm quickconfig

    For more information, see Installation and configuration for Windows Remote Management.

  3. Assign a variable to the connection string used in the hosts file.

    $Name = "<Node serial number>.<DNS domain of the device>"

    Replace <Node serial number> and <DNS domain of the device> with the node serial number and DNS domain of your device. You can get the values for node serial number from the Certificates page and DNS domain from the Device page in the local web UI of your device.

  4. To add this connection string for your device to the client’s trusted hosts list, type the following command:

    Set-Item WSMan:\localhost\Client\TrustedHosts $Name -Concatenate -Force

  5. Start a Windows PowerShell session on the device:

    Enter-PSSession -ComputerName $Name -Credential ~\EdgeUser -ConfigurationName Minishell -UseSSL

    If you see an error related to trust relationship, then check if the signing chain of the node certificate uploaded to your device is also installed on the client accessing your device.

  6. Provide the password when prompted. Use the same password that is used to sign into the local web UI. The default local web UI password is Password1. When you successfully connect to the device using remote PowerShell, you see the following sample output:

    Windows PowerShell
Copyright (C) Microsoft Corporation. All rights reserved.

PS C:\WINDOWS\system32> winrm quickconfig
WinRM service is already running on this machine.
PS C:\WINDOWS\system32> $Name = "1HXQG13.wdshcsso.com"
PS C:\WINDOWS\system32> Set-Item WSMan:\localhost\Client\TrustedHosts $Name -Concatenate -Force
PS C:\WINDOWS\system32> Enter-PSSession -ComputerName $Name -Credential ~\EdgeUser -ConfigurationName Minishell -UseSSL

WARNING: The Windows PowerShell interface of your device is intended to be used only for the initial network configuration. Please engage Microsoft Support if you need to access this interface to troubleshoot any potential issues you may be experiencing. Changes made through this interface without involving Microsoft Support could result in an unsupported configuration.
[1HXQG13.wdshcsso.com]: PS>

Desktop machine

Follow these instructions if your host computer isn't an Azure Stack Edge device.

Install NVIDIA CUDA Toolkit and NVIDIA graphics drivers on the host computer

Use the following bash script to install the required NVIDIA graphics drivers, and CUDA Toolkit.

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
sudo apt-get update
sudo apt-get -y install cuda

Reboot the machine, and run the following command.

nvidia-smi

You should see the following output.

NVIDIA driver output

Install Docker CE and nvidia-docker2 on the host computer

Install Docker CE on the host computer.

sudo apt-get update
sudo apt-get install -y apt-transport-https ca-certificates curl gnupg-agent software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io

Install the nvidia-docker-2 software package.

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install -y docker-ce nvidia-docker2
sudo systemctl restart docker

Enable NVIDIA MPS on the host computer

Tip

  • Don't install MPS if your GPU compute capability is less than 7.x (pre Volta). See CUDA Compatibility for reference.
  • Run the MPS instructions from a terminal window on the host computer. Not inside your Docker container instance.

For best performance and utilization, configure the host computer's GPU(s) for NVIDIA Multiprocess Service (MPS). Run the MPS instructions from a terminal window on the host computer.

# Set GPU(s) compute mode to EXCLUSIVE_PROCESS
sudo nvidia-smi --compute-mode=EXCLUSIVE_PROCESS

# Cronjob for setting GPU(s) compute mode to EXCLUSIVE_PROCESS on boot
echo "SHELL=/bin/bash" > /tmp/nvidia-mps-cronjob
echo "PATH=/usr/local/sbin:/usr/local/bin:/sbin:/bin:/usr/sbin:/usr/bin" >> /tmp/nvidia-mps-cronjob
echo "@reboot   root    nvidia-smi --compute-mode=EXCLUSIVE_PROCESS" >> /tmp/nvidia-mps-cronjob

sudo chown root:root /tmp/nvidia-mps-cronjob
sudo mv /tmp/nvidia-mps-cronjob /etc/cron.d/

# Service entry for automatically starting MPS control daemon
echo "[Unit]" > /tmp/nvidia-mps.service
echo "Description=NVIDIA MPS control service" >> /tmp/nvidia-mps.service
echo "After=cron.service" >> /tmp/nvidia-mps.service
echo "" >> /tmp/nvidia-mps.service
echo "[Service]" >> /tmp/nvidia-mps.service
echo "Restart=on-failure" >> /tmp/nvidia-mps.service
echo "ExecStart=/usr/bin/nvidia-cuda-mps-control -f" >> /tmp/nvidia-mps.service
echo "" >> /tmp/nvidia-mps.service
echo "[Install]" >> /tmp/nvidia-mps.service
echo "WantedBy=multi-user.target" >> /tmp/nvidia-mps.service

sudo chown root:root /tmp/nvidia-mps.service
sudo mv /tmp/nvidia-mps.service /etc/systemd/system/

sudo systemctl --now enable nvidia-mps.service

Configure Azure IoT Edge on the host computer

To deploy the Spatial Analysis container on the host computer, create an instance of an Azure IoT Hub service using the Standard (S1) or Free (F0) pricing tier.

Use the Azure CLI to create an instance of Azure IoT Hub. Replace the parameters where appropriate. Alternatively, you can create the Azure IoT Hub on the Azure portal.

curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash
sudo az login

sudo az account set --subscription "<name or ID of Azure Subscription>"

sudo az group create --name "<resource-group-name>" --location "<your-region>"

See Region Support for available regions.

sudo az iot hub create --name "<iothub-group-name>" --sku S1 --resource-group "<resource-group-name>"

sudo az iot hub device-identity create --hub-name "<iothub-name>" --device-id "<device-name>" --edge-enabled

You need to install Azure IoT Edge version 1.0.9. Follow these steps to download the correct version:

Ubuntu Server 18.04:

curl https://packages.microsoft.com/config/ubuntu/18.04/multiarch/prod.list > ./microsoft-prod.list

Copy the generated list.

sudo cp ./microsoft-prod.list /etc/apt/sources.list.d/

Install the Microsoft GPG public key.

curl https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > microsoft.gpg
sudo cp ./microsoft.gpg /etc/apt/trusted.gpg.d/

Update the package lists on your device.

sudo apt-get update

Install the 1.0.9 release:

sudo apt-get install iotedge=1.1* libiothsm-std=1.1*

Next, register the host computer as an IoT Edge device in your IoT Hub instance, using a connection string.

You need to connect the IoT Edge device to your Azure IoT Hub. You need to copy the connection string from the IoT Edge device you created earlier. Alternatively, you can run the below command in the Azure CLI.

sudo az iot hub device-identity connection-string show --device-id my-edge-device --hub-name test-iot-hub-123

On the host computer open /etc/iotedge/config.yaml for editing. Replace ADD DEVICE CONNECTION STRING HERE with the connection string. Save and close the file. Run this command to restart the IoT Edge service on the host computer.

sudo systemctl restart iotedge

Deploy the Spatial Analysis container as an IoT Module on the host computer, either from the Azure portal or Azure CLI. If you're using the portal, set the image URI to the location of your Azure Container Registry.

Use the below steps to deploy the container using the Azure CLI.

Azure VM with GPU

An Azure Virtual Machine with a GPU can also be used to run Spatial Analysis. The example below will use a NCv3 series VM that has one v100 GPU.

Create the VM

Open the Create a Virtual Machine wizard in the Azure portal.

Give your VM a name and select the region to be (US) West US 2.

Important

Be sure to set Availability Options to "No infrastructure redundancy required". Refer to the below figure for the complete configuration and the next step for help locating the correct VM size.

:::image type="content" source="media/spatial-analysis/virtual-machine-instance-details.jpg" alt-text="Virtual machine configuration details." lightbox="media/spatial-analysis/virtual-machine-instance-details.jpg":::

To locate the VM size, select "See all sizes" and then view the list for "N-Series" and select NC6s_v3, shown below.

:::image type="content" source="media/spatial-analysis/virtual-machine-sizes.png" alt-text="Virtual machine sizes." lightbox="media/spatial-analysis/virtual-machine-sizes.png":::

Next, Create the VM. Once created, navigate to the VM resource in the Azure portal and select Extensions from the left pane. Select on "Add" to bring up the extensions window with all available extensions. Search for and select NVIDIA GPU Driver Extension, select create, and complete the wizard.

Once the extension is successfully applied, navigate to the VM main page in the Azure portal and select Connect. The VM can be accessed either through SSH or RDP. RDP is helpful as it enables viewing of the visualizer window (explained later). Configure the RDP access by following these steps and opening a remote desktop connection to the VM.

Verify Graphics Drivers are Installed

Run the following command to verify that the graphics drivers have been successfully installed.

nvidia-smi

You should see the following output.

NVIDIA driver output

Install Docker CE and nvidia-docker2 on the VM

Run the following commands one at a time in order to install Docker CE and nvidia-docker2 on the VM.

Install Docker CE on the host computer.

sudo apt-get update
sudo apt-get install -y apt-transport-https ca-certificates curl gnupg-agent software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io

Install the nvidia-docker-2 software package.

DISTRIBUTION=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$DISTRIBUTION/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install -y docker-ce nvidia-docker2
sudo systemctl restart docker

Now that you have set up and configured your VM, follow the steps below to configure Azure IoT Edge.

Configure Azure IoT Edge on the VM

To deploy the Spatial Analysis container on the VM, create an instance of an Azure IoT Hub service using the Standard (S1) or Free (F0) pricing tier.

Use the Azure CLI to create an instance of Azure IoT Hub. Replace the parameters where appropriate. Alternatively, you can create the Azure IoT Hub on the Azure portal.

curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash
sudo az login

sudo az account set --subscription "<name or ID of Azure Subscription>"

sudo az group create --name "<resource-group-name>" --location "<your-region>"

See Region Support for available regions.

sudo az iot hub create --name "<iothub-name>" --sku S1 --resource-group "<resource-group-name>"

sudo az iot hub device-identity create --hub-name "<iothub-name>" --device-id "<device-name>" --edge-enabled

You need to install Azure IoT Edge version 1.0.9. Follow these steps to download the correct version:

Ubuntu Server 18.04:

curl https://packages.microsoft.com/config/ubuntu/18.04/multiarch/prod.list > ./microsoft-prod.list

Copy the generated list.

sudo cp ./microsoft-prod.list /etc/apt/sources.list.d/

Install the Microsoft GPG public key.

curl https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > microsoft.gpg
sudo cp ./microsoft.gpg /etc/apt/trusted.gpg.d/

Update the package lists on your device.

sudo apt-get update

Install the 1.0.9 release:

sudo apt-get install iotedge=1.1* libiothsm-std=1.1*

Next, register the VM as an IoT Edge device in your IoT Hub instance, using a connection string.

You need to connect the IoT Edge device to your Azure IoT Hub. You need to copy the connection string from the IoT Edge device you created earlier. Alternatively, you can run the below command in the Azure CLI.

sudo az iot hub device-identity connection-string show --device-id my-edge-device --hub-name test-iot-hub-123

On the VM open /etc/iotedge/config.yaml for editing. Replace ADD DEVICE CONNECTION STRING HERE with the connection string. Save and close the file. Run this command to restart the IoT Edge service on the VM.

sudo systemctl restart iotedge

Deploy the Spatial Analysis container as an IoT Module on the VM, either from the Azure portal or Azure CLI. If you're using the portal, set the image URI to the location of your Azure Container Registry.

Use the below steps to deploy the container using the Azure CLI.

IoT Deployment manifest

To streamline container deployment on multiple host computers, you can create a deployment manifest file to specify the container creation options, and environment variables. You can find an example of a deployment manifest for Azure Stack Edge, other desktop machines, and Azure VM with GPU on GitHub.

The following table shows the various Environment Variables used by the IoT Edge Module. You can also set them in the deployment manifest linked above, using the env attribute in spatialanalysis:

Setting Name Value Description
ARCHON_LOG_LEVEL Info; Verbose Logging level, select one of the two values
ARCHON_SHARED_BUFFER_LIMIT 377487360 Do not modify
ARCHON_PERF_MARKER false Set this to true for performance logging, otherwise this should be false
ARCHON_NODES_LOG_LEVEL Info; Verbose Logging level, select one of the two values
OMP_WAIT_POLICY PASSIVE Do not modify
QT_X11_NO_MITSHM 1 Do not modify
APIKEY your API Key Collect this value from Azure portal from your Vision resource. You can find it in the Key and endpoint section for your resource.
BILLING your Endpoint URI Collect this value from Azure portal from your Vision resource. You can find it in the Key and endpoint section for your resource.
EULA accept This value needs to be set to accept for the container to run
DISPLAY :1 This value needs to be same as the output of echo $DISPLAY on the host computer. Azure Stack Edge devices don't have a display. This setting is not applicable
KEY_ENV ASE Encryption key Add this environment variable if Video_URL is an obfuscated string
IV_ENV Initialization vector Add this environment variable if Video_URL is an obfuscated string

Important

The Eula, Billing, and ApiKey options must be specified to run the container; otherwise, the container won't start. For more information, see Billing.

Once you update the Deployment manifest for Azure Stack Edge devices, a desktop machine or Azure VM with GPU with your own settings and selection of operations, you can use the below Azure CLI command to deploy the container on the host computer, as an IoT Edge Module.

sudo az login
sudo az extension add --name azure-iot
sudo az iot edge set-modules --hub-name "<iothub-name>" --device-id "<device-name>" --content DeploymentManifest.json --subscription "<name or ID of Azure Subscription>"
Parameter Description
--hub-name Your Azure IoT Hub name.
--content The name of the deployment file.
--target-condition Your IoT Edge device name for the host computer.
-–subscription Subscription ID or name.

This command will start the deployment. Navigate to the page of your Azure IoT Hub instance in the Azure portal to see the deployment status. The status may show as 417 – The device's deployment configuration is not set until the device finishes downloading the container images and starts running.

Validate that the deployment is successful

There are several ways to validate that the container is running. Locate the Runtime Status in the IoT Edge Module Settings for the Spatial Analysis module in your Azure IoT Hub instance on the Azure portal. Validate that the Desired Value and Reported Value for the Runtime Status is Running.

Example deployment verification

Once the deployment is complete and the container is running, the host computer starts sending events to the Azure IoT Hub. If you used the .debug version of the operations, you'll see a visualizer window for each camera you configured in the deployment manifest. You can now define the lines and zones you want to monitor in the deployment manifest and follow the instructions to deploy again.

Configure the operations performed by Spatial Analysis

You need to use Spatial Analysis operations to configure the container to use connected cameras, configure the operations, and more. For each camera device you configure, the operations for Spatial Analysis generates an output stream of JSON messages, sent to your instance of Azure IoT Hub.

Use the output generated by the container

If you want to start consuming the output generated by the container, see the following articles:

  • Use the Azure Event Hubs SDK for your chosen programming language to connect to the Azure IoT Hub endpoint and receive the events. For more information, see Read device-to-cloud messages from the built-in endpoint.
  • Set up Message Routing on your Azure IoT Hub to send the events to other endpoints or save the events to Azure Blob Storage, etc.

Troubleshooting

[!INCLUDE Diagnostic container]

Billing

The Spatial Analysis container sends billing information to Azure, using a Vision resource on your Azure account. The use of Spatial Analysis in public preview is currently free.

Azure AI containers aren't licensed to run without being connected to the metering / billing endpoint. You must always enable the containers to communicate billing information with the billing endpoint. Azure AI containers don't send customer data, such as the video or image that's being analyzed, to Microsoft.

Summary

In this article, you learned concepts and workflow for downloading, installing, and running the Spatial Analysis container. In summary:

  • Spatial Analysis is a Linux container for Docker.
  • Container images are downloaded from the Microsoft Container Registry.
  • Container images run as IoT Modules in Azure IoT Edge.
  • Configure the container and deploy it on a host machine.

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