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)
1
Section 1 – Data Parallelism
6
Section 2 – Model Parallelism
11
Section 3 – Advanced Parallelism Paradigms

Leveraging idle links and host resources

In the previous section, we discussed how the communication bottleneck of model synchronization may cause up to 50% of the end-to-end DNN training time. In addition, the widely used NCCL Ring All-Reduce directly abandons some of the scarce communication links if they cannot form a ring.

In this section, we will discuss how we can fully leverage all the communication links within a data parallel training environment. Then, we will discuss how to extend it to using idle links on the host (that is, CPU) side.

Tree All-Reduce

Let's continue using the previous four-GPU fully connected example. As we discussed in the previous section (and as shown in Figure 4.7), the two links in the middle are unused, which is a waste of scarce communication resources.

Now, let's introduce a new All-Reduce protocol, which is called Tree All-Reduce. It also works in two steps:

  1. First, it sends a portion of the gradients to other nodes...