Deploying the AWS Panorama sample application - AWS Panorama

Deploying the AWS Panorama sample application

After you've set up your AWS Panorama Appliance or compatible device and upgraded its software, deploy a sample application. In the following sections, you import a sample application with the AWS Panorama Application CLI and deploy it with the AWS Panorama console.

The sample application uses a machine learning model to detect people in frames of video from a network camera. It uses the AWS Panorama Application SDK to load a model, get images, and run the model. The application then overlays the results on top of the original video and outputs it to a connected display.

In a retail setting, analyzing foot traffic patterns enables you to predict traffic levels. By combining the analysis with other data, you can plan for increased staffing needs around holidays and other events, measure the effectiveness of advertisements and sales promotions, or optimize display placement and inventory management.


To follow the procedures in this tutorial, you need a command line terminal or shell to run commands. In the code listings, commands are preceded by a prompt symbol ($) and the name of the current directory, when appropriate.

~/panorama-project$ this is a command this is output

For long commands, we use an escape character (\) to split a command over multiple lines.

On Linux and macOS, use your preferred shell and package manager. On Windows 10, you can install the Windows Subsystem for Linux to get a Windows-integrated version of Ubuntu and Bash. For help setting up a development environment in Windows, see Setting up a development environment in Windows.

You use Python to develop AWS Panorama applications and install tools with pip, Python's package manager. If you don't already have Python, install the latest version. If you have Python 3 but not pip, install pip with your operating system's package manager, or install a new version of Python, which comes with pip.

In this tutorial, you use Docker to build the container that runs your application code. Install Docker from the Docker website: Get Docker

This tutorial uses the AWS Panorama Application CLI to import the sample application, build packages, and upload artifacts. The AWS Panorama Application CLI uses the AWS Command Line Interface (AWS CLI) to call service API operations. If you already have the AWS CLI, upgrade it to the latest version. To install the AWS Panorama Application CLI and AWS CLI, use pip.

$ pip3 install --upgrade awscli panoramacli

Download the sample application, and extract it into your workspace.

Import the sample application

To import the sample application for use in your account, use the AWS Panorama Application CLI. The application's folders and manifest contain references to a placeholder account number. To update these with your account number, run the panorama-cli import-application command.

aws-panorama-sample$ panorama-cli import-application

The SAMPLE_CODE package, in the packages directory, contains the application's code and configuration, including a Dockerfile that uses the application base image, panorama-application. To build the application container that runs on the appliance, use the panorama-cli build-container command.

aws-panorama-sample$ ACCOUNT_ID=$(aws sts get-caller-identity --output text --query 'Account') aws-panorama-sample$ panorama-cli build-container --container-asset-name code_asset --package-path packages/${ACCOUNT_ID}-SAMPLE_CODE-1.0

The final step with the AWS Panorama Application CLI is to register the application's code and model nodes, and upload assets to an Amazon S3 access point provided by the service. The assets include the code's container image, the model, and a descriptor file for each. To register the nodes and upload assets, run the panorama-cli package-application command.

aws-panorama-sample$ panorama-cli package-application Uploading package model Registered model with patch version bc9c58bd6f83743f26aa347dc86bfc3dd2451b18f964a6de2cc4570cb6f891f9 Uploading package code Registered code with patch version 11fd7001cb31ea63df6aaed297d600a5ecf641a987044a0c273c78ceb3d5d806

Deploy the application

Use the AWS Panorama console to deploy the application to your appliance.

To deploy the application

  1. Open the AWS Panorama console Deployed applications page.

  2. Choose Deploy application.

  3. Paste the contents of the application manifest, graphs/aws-panorama-sample/graph.json, into the text editor. Choose Next.

  4. For Application name, enter aws-panorama-sample.

  5. Choose Proceed to deploy.

  6. Choose Begin deployment.

  7. Choose Next without selecting a role.

  8. Choose Select device, and then choose your appliance. Choose Next.

  9. On the Select data sources step, choose View input(s), and add your camera stream as a data source. Choose Next.

  10. On the Configure step, choose Next.

  11. Choose Deploy, and then choose Done.

  12. In the list of deployed applications, choose aws-panorama-sample.

Refresh this page for updates, or use the following script to monitor the deployment from the command line.


while true; do aws panorama list-application-instances --query 'ApplicationInstances[?Name==`aws-panorama-sample`]' sleep 10 done
[ { "Name": "aws-panorama-sample", "ApplicationInstanceId": "applicationInstance-x264exmpl33gq5pchc2ekoi6uu", "DefaultRuntimeContextDeviceName": "my-appliance", "Status": "DEPLOYMENT_PENDING", "HealthStatus": "NOT_AVAILABLE", "StatusDescription": "Deployment Workflow has been scheduled.", "CreatedTime": 1630010747.443, "Arn": "arn:aws:panorama:us-west-2:123456789012:applicationInstance/applicationInstance-x264exmpl33gq5pchc2ekoi6uu", "Tags": {} } ] [ { "Name": "aws-panorama-sample", "ApplicationInstanceId": "applicationInstance-x264exmpl33gq5pchc2ekoi6uu", "DefaultRuntimeContextDeviceName": "my-appliance", "Status": "DEPLOYMENT_PENDING", "HealthStatus": "NOT_AVAILABLE", "StatusDescription": "Deployment Workflow has completed data validation.", "CreatedTime": 1630010747.443, "Arn": "arn:aws:panorama:us-west-2:123456789012:applicationInstance/applicationInstance-x264exmpl33gq5pchc2ekoi6uu", "Tags": {} } ] ...

When the deployment is complete, the application starts processing the video stream and sends logs to CloudWatch.

To view logs in CloudWatch Logs

  1. Open the Log groups page of the CloudWatch Logs console.

  2. Find AWS Panorama application and appliance logs in the following groups:

    • Device logs/aws/panorama/devices/device-id

    • Application logs/aws/panorama/devices/device-id/applications/instance-id

If the application doesn't start running, check the application and device logs in Amazon CloudWatch Logs.

Enable the SDK for Python

The sample application uses the AWS SDK for Python (Boto) to send metrics to Amazon CloudWatch. To enable this functionality, create a role that grants the application permission to send metrics, and redeploy the application with the role attached.

The sample application includes a AWS CloudFormation template that creates a role with the permissions that it needs. To create the role, use the aws cloudformation deploy command.

$ aws cloudformation deploy --template-file aws-panorama-sample.yml --stack-name aws-panorama-sample-runtime --capabilities CAPABILITY_NAMED_IAM

To redeploy the application

  1. Open the AWS Panorama console Deployed applications page.

  2. Choose an application.

  3. Choose Replace.

  4. Complete the steps to deploy the application. In the Specify IAM role, choose the role that you created. Its name starts with aws-panorama-sample-runtime.

  5. When the deployment completes, open the CloudWatch console and view the metrics in the AWSPanoramaApplication namespace. Every 150 frames, the application logs and uploads metrics for frame processing and inference time.

Clean up

If you are done working with the sample application, you can use the AWS Panorama console to remove it from the appliance.

To remove the application from the appliance

  1. Open the AWS Panorama console Deployed applications page.

  2. Choose an application.

  3. Choose Delete from device.

Next steps

If you encountered errors while deploying or running the sample application, see Troubleshooting.

To learn more about the sample application's features and implementation, continue to the next topic.