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Runtime execution vs. model export 
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It's very important to understand the distinction between running your
model directly within a development environment (typically on your own
PC) and running an exported version on an embedded target.

Runtime execution (development mode)
====================================

During development and testing, you'll mostly run your AI models
directly within the Aidge framework.

This allows you to:

-  **Iterate quickly**: test changes, debug, and validate model behavior
   without the need for a full export process.

-  **Access full debugging tools**: utilize the rich debugging capabilities
   of the development environment.

This "runtime execution" is ideal for the iterative development cycle,
but it doesn't represent how the model will perform on a constrained
embedded device.

Export (deployment mode) 
========================

Once your model is refined and ready for deployment, Aidge's export
strategy comes into play. The goal of exporting is to generate a source
code tailored for specific hardware, allowing inference of your AI model on embedded device.

Then, you could also use this exported version in a runtime execution
mode. To do so, create a **backend plugin** by converting the export of
one operator into a compiled kernel library.

Summary 
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.. image:: /source/_static/runtime-vs-export.PNG
      :scale: 60%