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

Vanilla model parallelism is inefficient

As mentioned in a huge number of papers from academia and technical reports from the industry, vanilla model parallelism is very inefficient regarding GPU computation and memory utilization. To illustrate why vanilla model parallelism is not efficient, let's look at a simple DNN model, which is shown in Figure 6.1:

Figure 6.1 – A simple NLP model with three layers

As shown in Figure 6.1, given the training input, we pass it into our three-layer NLP model. The layers are denoted as Layer 1, Layer 2, and Layer 3. After the forward propagation, the model will generate some output.

Now let's assume we use three GPUs. Each GPU only holds one layer of the original model. It is shown in Figure 6.2:

Figure 6.2 – Model partition on three GPUs

In Figure 6.2, we have GPU1 holding Layer 1 of the model. Similarly, we have GPU2 holding Layer 2 and GPU3 holding Layer 3.

Now, we...