Launch your initial computer vision application on AWS Panorama within five minutes
is a project template designed to automate and simplify the creation, setup, and deployment process of AWS Panorama applications. AWS Panorama is an edge computing appliance and SDK from Amazon that enables computer vision applications to run locally on-premises.
What Does Do?
typically automates:
- The creation of a standardized project structure for AWS Panorama applications
- Configuration of necessary AWS resources, project files, and deployment scripts
- Integration with AWS services needed for computer vision workflows on Panorama devices
- Streamlined packaging and deployment processes to edge devices
By using (a templating tool for Python), this template drastically reduces manual setup time and errors, making it easier for developers to focus on building their computer vision models rather than dealing with infrastructure details.
Getting Started
To get started with creating a Panorama application project, the command-line tool must be installed using pip, conda, or pipx (recommended). Once installed, you can use to generate a new project.
You'll be prompted to enter a few parameters, such as the name of the directory where the project will be generated, and the name of an existing S3 bucket in your account with read/write privileges.
Key Features
Among the tasks provided by are:
- Initializing a git repository
- Installing required build tools
- Importing the project to AWS
- Building the project and creating a dummy deep learning model
- Uploading the compiled application container and deep learning model to AWS
Useful Resources
For concrete, up-to-date details or examples of usage, I recommend checking the official AWS Panorama GitHub or AWS documentation for or starter templates related to Panorama. A step-by-step guide on deploying an object-detector application on AWS Panorama was published on Towards Data Science over a year ago.
About the Project
The open-source project of was supported by Neosperience. Janos Tolgyesi, the author of the article, is an AWS Community Builder and Machine Learning Team Leader at Neosperience with expertise in ML technologies and AWS.
Conclusion
With , developers can streamline the process of creating, setting up, and deploying AWS Panorama applications. By automating many of the repetitive tasks involved in creating a Panorama application, allows developers to focus more on building their computer vision models and less on infrastructure details.
Technology plays a crucial role in the project, as it uses a templating tool called SageMaker Clinical Trial Manager (CTM) for Python, which helps automate the setup process of AWS Panorama applications.
By using technology, developers can save time on manual setup and reduce errors, allowing them to focus more on building their computer vision models without having to worry about infrastructure details.
