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)
1
Section 1 – Data Parallelism
6
Section 2 – Model Parallelism
11
Section 3 – Advanced Parallelism Paradigms

Reducing bits in hardware

A recent study shows that using fewer bits to represent model weights will not introduce significant model test accuracy. Given this observation, we can use fewer bits to represent each weight value inside a DNN model. A simple example is shown here:

Figure 8.13 – Reducing bit representation per model weight

As shown in Figure 8.13, we can reduce the bit representation from FP32 to FP16. We can further reduce the bits by moving from FP16 to INT8.