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 chapter, we discussed two major bottlenecks in the data parallel training process – communication and on-device memory.

Communication becomes a bottleneck during model synchronization. To make things even worse, the Ring All-Reduce solution also wastes some network links that cannot form a ring. Thus, we propose a tree-based All-Reduce solution, which is more efficient and can achieve faster model synchronization than ring-based solutions.

To mitigate the issue of memory, we discussed two major methods – recomputation and quantization.

In the next chapter, we will explore model parallelism, which is another kind of popular paradigm for in-parallel model training and inference. Instead of splitting the input data, model parallelism partitions the model itself.