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

Chapter 5: Splitting the Model

In this chapter, we will discuss how to train giant models with model parallelism. Giant models refers to models that are too large to fit into a single GPU's memory. Some examples of giant models include Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-Trainer Transformer (GPT): GPT-2 and GPT-3.

In contrast to data parallel workloads, model parallelism is often adopted for language models. Language models are a specific type of deep learning model that works in the Natural Language Processing (NLP) domain. Here, the input data is usually text sequences. The model outputs predictions for tasks such as question answering and next sentence prediction.

NLP model training is often segregated into two different types, pre-training and fine-tuning. Pre-training means training the whole giant model from scratch, which may need a huge amount of data and plenty of training epochs. Fine-tuning uses pre-trained models as...