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 how to implement a model training and serving pipeline using the data parallelism paradigm.

First, we illustrated the whole data parallel training pipeline and defined the key functions in each step. Then, we showed the implementation of data parallel training in both single-machine multi-GPUs and multi-machine multi-GPUs. We concluded that this multi-process implementation is better than a single process with multi-threading. Then, we discussed adding the fault tolerance feature to a data parallel training job. After that, we showed you how to conduct in-parallel model evaluation and hyperparameter tuning. Finally, we demonstrated how to implement data parallel model serving.

In the next chapter, we will discuss the bottlenecks in the current solutions for data parallelism. We will also provide solutions that can mitigate these bottleneck issues and boost end-to-end performance.