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

Issues with the parameter server

In recent years, fewer and fewer machine learning practitioners have been using the parameter server paradigm for their data parallel training jobs. The main reason for this decrease in the popularity of the parameter server architecture is twofold.

Given N nodes, it is unclear what the best ratio is between the parameter server and workers.

As we've mentioned previously, in the parameter server architecture, we have two roles:

  • Parameter server:
    • Never do training, 0 training BW
    • More PS, higher communication BW, less model synchronization latency
  • Worker:
    • More Workers, higher training BW
    • More Workers, more data transfer, higher model synchronization overhead

We need to balance training throughput and communication latency. We will discuss this trade-off in the following two cases.

Case 1 – more parameter servers

If we assign more nodes as parameter servers, we have fewer data to communicate since we have fewer...