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

Chapter 3: Building a Data Parallel Training and Serving Pipeline

In the previous chapter, we discussed the two main-stream data parallel training paradigms, parameter server and All-Reduce. Due to the shortcomings of the parameter server paradigm, the mainstream solution for data parallel training is the All-Reduce architecture. We will illustrate our implementation using the All-Reduce paradigm.

In this chapter, we will mainly focus on the coding side of data parallelism. Before we dive into the details, we will list the assumptions we have for the implementations in this chapter:

  • We will use homogenous hardware for all our training nodes.
  • All our training nodes will be exclusively used for a single job, which means no resource sharing in multi-tenant clusters.
  • The number of accelerators will always be sufficient for our needs.

First, we will describe the entire training pipeline and highlight the major components, which include data preprocessing, data...