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 8: Achieving Higher Throughput and Lower Latency

Generally speaking, model parallelism is less efficient than data parallelism. The main reasons are twofold, as outlined here.

First, the sequential dependency among deep neural network (DNN) layers holding onto different graphics processing units (GPUs) limits the performance. One GPU may not start working until its predecessor finishes generating outputs.

Second, the limited GPU memory makes it impossible to train a large input batch in each training iteration. Due to the large size of the model parameters, we can only train small batches of data per training iteration.

Given the preceding two challenges, we try to improve throughput and latency performance by adopting state-of-the-art (SOTA) techniques, such as freezing layers, model distillations, and more. Before we dive into the details, we'll first illustrate the assumptions for the materials of this chapter, as follows:

  • We assume there are no...