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

Section 3 – Advanced Parallelism Paradigms

In this section, we will learn state-of-the-art techniques on top of traditional data and model parallelism approaches. First, we will understand the concept of hybrid data-model parallelism. Second, we will discuss federated learning and edge device learning. Third, we will discuss elastic and in-parallel model training/inference in multitenant clusters or cloud environments. Finally, we will look at some more advanced techniques for further accelerating in-parallel model training and serving.

This section comprises the following chapters:

  • Chapter 9, A Hybrid of Data and Model Parallelism
  • Chapter 10, Federated Learning and Edge Devices
  • Chapter 11, Elastic Model Training and Serving
  • Chapter 12, Advanced Techniques for Further Speed-Ups