Chapter 3. Deep Learning Workflow
Although deep learning is making a big shift from just being in academia to being in industry and powering millions of users' requests every day, the new players in the field still struggle to set up a workflow for the deep learning pipeline. This chapter is designed to cover the portion of workflow that PyTorch can help with.
PyTorch was started as a research framework by a Facebook intern and it has grown to the stage where the backend is backed by a super-optimized Caffe2 core. So, in a nutshell, PyTorch can be used as a research or prototype framework and at the same time, it can be used to write an efficient model with serving modules, and it also can be deployable to single-board computers and mobile devices.
A typical deep learning workflow starts with ideation and research around a problem statement, which is where the architectural design and model decisions come into play. The theoretical model is then experimented...