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


In this section, we will explain several classic NLP models used nowadays, namely ELMo, BERT, and GPT.

Before we dive into these complicated model structures, we will first illustrate the basic concept of a Recurrent Neural Network (RNN) and how it works. Then, we will move on to the transformers. This section will cover the following topics:

  • Basic concepts
  • RNN
  • ELMo
  • BERT
  • GPT

We will start with introducing RNNs.

Basic concepts

Here, we will dive into the world of RNNs. At a high-level, different from CNNs, an RNN usually needs to maintain the states from previous input. It is just like memory for human beings.

We will illustrate what we mean with the following examples:

Figure 5.7 – One-to-one problems

As shown in the preceding figure, one-to-one is a typical problem format in the computer vision domain. Basically, assuming we have a CNN model, we input an image as Input 1, as shown in Figure...