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

Communication bottlenecks in data parallel training

As we mentioned in Chapter 2, Parameter Server and All-Reduce, and Chapter 3, Building a Data Parallel Training and Serving Pipeline, we need to conduct a communication-heavy step, namely model synchronization, after each training iteration.

In this section, we will conduct the theoretical analysis for the total traffic needs that are to be transferred over the network. Then, we will identify network inefficiency in widely used communication protocols such as NCCL and Gloo.

Analyzing the communication workloads

Let's dive into this communication-heavy step a little deeper. The model synchronization step is performed for the following purposes:

  • Aggregating all the gradients that have been generated from all the workers
  • Updating the model weights of all the workers

Some notations that will be used in this section are as follows:

  • g_i: The local gradients that are generated from a single worker...