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

Pros and cons of pipeline parallelism

In the preceding sections, we discussed how pipeline parallelism works in both forward and backward propagation during model parallelism training. In this section, we will discuss the advantages and disadvantages of pipeline parallelism.

Advantages of pipeline parallelism

The most important advantage of pipeline parallelism is that it helps to reduce the GPU idle time during model parallelism training. Here, we list all the advantages:

  • Reduces overall training time
  • Reduces GPU idle time while waiting for the predecessor or successor's GPU output
  • Not much coding complexity to implement pipeline parallelism
  • Can be generally adapted to any kind of DNN model
  • Simple and easy to understand

Disadvantages of pipeline parallelism

In the preceding section, we discussed the advantages of pipeline parallelism. Now let's look at the disadvantages of pipeline parallelism:

  • The CPU needs to send more instructions...