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

Freezing layers

The first technique we introduce here is called layer freezing. At a high level, we have the assumption that different layers of a model may converge at different stages of the training process. Thus, we can freeze the layers that converge earlier.

Here, freezing refers to the following two operations:

  • We abandon the intermediate results on particular layers during forward propagation.
  • We may also avoid generating gradients during backward propagation.

We illustrate this technique in the following diagram:

Figure 8.1 – Simplified illustration of a three-layer language model

As shown in the preceding diagram, we assume the input data has already been tokenized and can be directly fed into the model for either model training or model serving stages. We have a three-layer model. Each layer is an independent transformer layer, and each single transformer layer is allocated on a separate GPU.

Now, let's discuss...