Running classification inference on EDge TPU USB without reloading model every time
Normally, you should load the model first and store it. Then when you use it for invoking, you just need to allocate tensor, then pass to it the input. You can keep your script in while loop waiting for your input (ie sending through websocket,... or loading a directory). But according to what I see, you will have to self load the image to the model,so there is really hard to self-allocate a fixed part of TPU to your model.
I am quite new to Tensor Flow / Edge TPU usage.
While I mange to run an image classification with the classify_image.py script 
it has a very poor performance on the Edge TPU USB module because it always loads the model and kind of reboots the device (you see the Win10 device manager reinstalling it at every run of the script).
Is there any way to run a python script that classifies an image with the last loaded model in the USB dongle, avoiding the 3-5 seconds of device configuration time ?