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

Case study of Megatron-LM

Megatron-LM is a large-scale DNN training system developed at NVIDIA. It uses data parallelism and model parallelism together.

Let's first talk about how Megatron-LM splits models using model parallelism. Then, we will discuss how it is extended to use data parallelism as well.

Layer split for model parallelism

We will first illustrate how Megatron-LM uses model parallelism within a multi-GPU machine. Let's focus on a simple matrix multiplication case.

General Matrix Multiply (GEMM) is widely used in the DNN layers of language models.

Suppose we have matrix A, as shown in the following diagram:

Figure 9.1 – Weight matrix of a layer in a language model

As shown in the preceding diagram, for one particular layer of a language model, we have a weight matrix. We call the weight matrix A. A is a 4x4 weight matrix.

Now, let's assume we have some input data for this DNN layer. We call the input data...