Architecture to Distribute Deep Learning Models on Containers and Virtual Machines for Industry 4.0

Abstract

eep learning (DL) is increasingly used in industry, especially in industry 4.0. Thanks to DL, it possible to better prevent breakdowns and manufacturing defects. DL models are becoming more and more complex and efficient, requiring significant compute resources and compute time. The use of Graphic Processing Units (GPUs) makes it possible to speed up processing but at a higher cost. An alternative to them is the use of distributed DL (DDL) which differs from Federated Deep Learning in that it focuses on accelerating calculations and does not address data privacy. DLL requires having several computing nodes. This is where cloud computing comes in. Cloud computing allows resources or virtual machines to be allocated on demand, which reduces costs. However, the allocation of GPU resources has a higher cost than CPU resources, which can be problematic for small businesses. This article proposes to exploit the DDL on CPUs via the on-demand allocation of virtual machines in order to reduce costs. In addition, a solution for deploying the software stack necessary for proper operation is proposed. This is achieved using a containerization which is only composed of the software suites needed to run the DDL to minimize the container transfer size and consequently minimize the container deployment time.