Book Image

Distributed Machine Learning with Python

By : Guanhua Wang
Book Image

Distributed Machine Learning with Python

By: Guanhua Wang

Overview of this book

Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.
Table of Contents (17 chapters)
Section 1 – Data Parallelism
Section 2 – Model Parallelism
Section 3 – Advanced Parallelism Paradigms

Job migration and multiplexing

Here, we'll discuss DNN training job migration and multiplexing. We will first discuss the motivation and operations for job migration.

Job migration

The first thing we will discuss here is why we need job migration. A simple example to understand this operation is shown in the following figure:

Figure 12.8 – A single training job is assigned to GPU 1 on Machine 1 and GPU 3 on Machine 2

As shown in the preceding figure, in a cloud environment, there is the case that a single DNN training job can be split across multiple machines. As per one of our assumptions at the beginning of this chapter, cross-machine communication bandwidth is low. Therefore, if we conduct frequent model synchronization between GPU 1 and GPU 3, the network communication latency is very high. Thus, the system utilization is very low.

Due to the low system efficiency, we want to move GPUs working on the same job into the minimum number...