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 11: Elastic Model Training and Serving

The one big challenge in distributed DNN training is determining how many GPUs or accelerators to use for a single training or inference job. If we assign too many GPUs to a single job, it may waste computational resources. If we assign too few GPUs to a particular job, it may lead to an insanely long training time. In addition, this choice of the number of GPUs is also highly relevant to choosing the corresponding hyperparameters (such as batch size and learning rate) during the whole DNN training session. How to choose the appropriate quantity of accelerators is the main topic we cover in this chapter. In addition, we will also explore hyperparameter tuning accordingly.

Before we discuss anything further, we want to list our assumptions, as follows:

  • We assume you have an infinite number of GPUs or TPUs or other accelerators to use for DNN training and inference.
  • We assume you use homogeneous GPUs or other kinds of accelerators...