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

Pipeline input

In this section, we will explain how pipeline parallelism works. At a high level, pipeline parallelism breaks each batch of training input into smaller micro-batches and conducts data pipelining over these micro-batches. To illustrate it more clearly, let's first describe how normal batch training works.

We will use the three-layer model example depicted in Figure 6.1. We will also maintain the GPU setup depicted in Figure 6.2.

Now assume that each training batch contains three input items: input 1, input 2, and input 3. We use this batch to feed in the model. We draw the forward propagation workflow as shown in Figure 6.7. It works as follows:

  1. After GPU1 receives the training batch of inputs 1, 2, and 3, GPU1 conducts forward propagation as F1i (forward propagation on input i on GPU1), which is, F11, F12, and F13.
  2. After GPU1 finishes the forward propagation of inputs 1, 2, and 3, it passes its layer output of F11, F12, F13 to GPU2. Based on GPU1...