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

Chapter 2: Parameter Server and All-Reduce

As described in Chapter 1, Splitting Input Data, to keep model consistency among all the GPUs/nodes involved in a data parallel training job, we need to conduct model synchronization. In terms of this model synchronization core, distributed system architectures for data parallel training must be built up.

To guarantee model consistency, two methodologies can be applied.

First, we can keep the model parameters in one place (a centralized node). Whenever a GPU/node needs to conduct model training, it pulls the parameters from the centralized node, trains the model, then pushes back model updates to the centralized node. Model consistency is guaranteed since all the GPUs/nodes are pulling from the same centralized node. This is what is called the parameter server paradigm.

Second, every GPU/node keeps a copy of the model parameters so we force the model copies to synchronize periodically. Each GPU trains its local model replica using...