Deep Learning Lifecycle Management with Kubernetes, REST, and Python

Boris Tvaroska, Lenovo

Abstract: 

In this tutorial, we will learn the basics of using trained deep learning model in applications through REST API. We will deploy model with pure Python and with Tensorflow Serving. Each application should be as simple as possible. The most straightforward approach to build REST service with the keras model is just to put the model into web framework in python. Flask is the minimalistic framework and is a good choice for simple applications or MVP. To support multiple models, or multiple versions of one model at scale we will utilize TF Serving to build a scalable API able to serve hundreds and thousands of requests per second.

Prerequisites:
Python3, experience with Docker, and basic knowledge of REST web services.

BibTeX
@conference {233001,
author = {Boris Tvaroska},
title = {Deep Learning Lifecycle Management with Kubernetes, {REST}, and Python},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
month = may,
}