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

Recomputation and quantization

To reduce the memory footprint during DNN training, we have two main kinds of methodology – recomputation and quantization.

Recomputation refers to the process where, if some tensors are not used for a certain period, we can delete the tensors and then recompute the result once we need it later.

At a high level, quantization means that we use fewer physical bits to represent a single value. For example, if a normal integer value consumes 4 bytes, by conducting quantization over this integer value, we use 2 bytes or even fewer bits to represent the same value. Quantization is lossy optimization, which means it may lose some information while shrinking the bits so that they represent the weights/gradients.

A comparison between these two approaches is illustrated in the following table:

Figure 4.16 – A comparison of the two methods for reducing memory footprints

Recomputation is performed to reproduce the previous...