Launch your initial computer vision application on AWS Panorama within five minutes
In the realm of computer vision and video analytics, AWS Panorama offers a hardware and software platform that enables running applications on IP cameras. One of the latest tools to streamline the creation and deployment of AWS Panorama applications is the open-source project, cookiecutter-panorama.
This command-line utility, which can be installed using pip, conda, or pipx, provides a pre-configured project template that automates the setup and deployment of AWS Panorama applications. The template engine generates the project in a directory named after the parameter for the Panorama Application.
The process begins by initializing a git repository, installing required build tools (aws-cli, panorama-cli, and docker), and importing the project to AWS. The template engine then builds the project, creating a dummy deep learning model, and uploads the compiled application container and packaged deep learning model to AWS. The compiled manifest JSON, generated during this process, can be used to deploy the application in the AWS Panorama Console.
The Panorama application is loosely coupled to a set of deep learning and business logic packages bound together by the manifest file. This collection of packaged deep learning models, business logic code, and a manifest structure form a Panorama Application.
The cookiecutter-panorama tool offers a standardized and repeatable approach to deploy AWS Panorama projects, reducing setup time and potential errors. It manages tasks like generating application manifests, deploying edge node applications, and handling continuous integration/deployment pipelines, thereby simplifying the deployment process of video analytics applications on AWS Panorama devices.
Two parameters need to be set during the creation of a Panorama application with cookiecutter-panorama: one for the name of the Panorama Application, and another for the name of an existing S3 bucket in the account with read/write privileges.
For a step-by-step guide on deploying an object-detector application on AWS Panorama, you can refer to the comprehensive article published on Towards Data Science by Janos Tolgyesi, an AWS Community Builder and Machine Learning Team Leader at Neosperience with over 5 years of expertise in ML technologies and 8+ years on AWS. The guide, although long, takes approximately 17 minutes to read and offers a detailed walkthrough of the process.
The open-source project of cookiecutter-panorama was supported by Neosperience. For questions, issues, or suggestions, you can open an issue in the cookiecutter-panorama GitHub project or use the provided contacts.
As of now, there are over 7000 cookiecutter project templates published on GitHub, with cookiecutter-panorama adding the Panorama application template to this list. The Makefile within the generated project provides a rich set of functionalities, including configuring a git repository, building the application container and the deep learning model, uploading the packages to AWS, running the application on a development workstation, and safeguarding the AWS account id from leaking in public repositories.
In conclusion, the cookiecutter-panorama tool is a valuable addition to the AWS Panorama ecosystem, offering a simplified and efficient way to create, configure, and deploy AWS Panorama applications.
Technology plays a crucial role in the deployment of AWS Panorama applications, with the command-line utility, cookiecutter-panorama, serving as an essential tool. This technology streamlines the process by providing a pre-configured project template for data-and-cloud-computing, automating the setup and deployment of AWS Panorama applications. It simplifies the deployment of video analytics applications on AWS Panorama devices.
