As we have previously described, DL Workbench is a tool that allows you to import Deep Learning models, evaluate their performance and accuracy, and perform different optimization tasks, like calibration for 8-bit integer inference. Profiling and model optimization are device-specific, therefore, to achieve maximum performance in a deployment environment, we need to perform these steps directly in that environment. DL Workbench helps you with accessing those capabilities on remote machines. Please note that if you want to have access to numerous hardware configurations ready for work and you do not have them locally or in your private lab, you can run DL Workbench in the Intel DevCloud for the Edge, where you can easily start experiments with available hardware. In this paper, we primarily focus on the case when you prepare the model for deployment and need to benchmark it on a specific hardware setup available in your private lab or a pre-production sandbox.

Introduction It's early morning and the sun is shining, but where is the birdsong? The new bird feeder should be filled with seeds, but it's empty, and a happy squirrel is scurrying up a nearby tree with the stolen goods. Unfortunately, most modern bird feeders have not been able to prevent this common problem. By bringing the bird feeder into the 21st century, we can examine how deep learning helps keep birdseed for the birds. In the following, we will explore how to design an image classification solution using the Deep Learning Workbench (DL Workbench).