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

Introducing adaptive model training

Here, we'll discuss elastic model training. In the following sections, we may use adaptive and elastic interchangeably, as they have similar meanings.

Adaptive model training is where we can change the number of GPUs during the training process. To better illustrate what we mean by changing the number of GPUs during the training process, we'll first describe how traditional distributed DNN training works with a fixed number of GPUs.

Traditional data parallel training

In normal distributed data parallel training, we assign our training job to a fixed number of GPUs, as shown in the following figure:

Figure 11.1 – AllReduce-based data parallel training with four workers

As shown in the preceding figure, one data parallel training paradigm is AllReduce-based. In this setting, we fix the number of workers to four. Therefore, for each training iteration, we do the following:

  1. Feed four batches...