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

Layer split

In this section, we will discuss another kind of approach to improve model parallelism training efficiency called intra-layer model parallelism. Generally speaking, the data structure for holding each layer's neurons can be represented as matrices. One common function during NLP model training and serving is matrix multiplication. Therefore, we can split a layer's matrix in some way to enable in-parallel execution.

Let's discuss it with a simple example. Let's just focus on Layer 1 of any model. It takes the training data as input, and after forward propagation, it generates some outputs to the following layers. We can draw this Layer 1 as shown in Figure 6.11:

Figure 6.11 – Weights matrix for Layer 1 of an NLP model

As shown in Figure 6.11, we illustrate the data structure that represents Layer 1 of an NLP model. Here, each column represents a neuron. Each weight within a column is a neuron weight. Basically, in this...