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

What this book covers

Chapter 1, Splitting Input Data, shows how to distribute machine learning training or serving workload on the input data dimension, which is called data parallelism.

Chapter 2, Parameter Server and All-Reduce, describes two widely-adopted model synchronization schemes in the data parallel training process.

Chapter 3, Building a Data Parallel Training and Serving Pipeline, illustrates how to implement data parallel training and the serving workflow.

Chapter 4, Bottlenecks and Solutions, describes how to improve data parallelism performance with some advanced techniques, such as more efficient communication protocols, reducing the memory footprint.

Chapter 5, Splitting the Model, introduces the vanilla model parallel approach in general.

Chapter 6, Pipeline Input and Layer Split, shows how to improve system efficiency with pipeline parallelism.

Chapter 7, Implementing Model Parallel Training and Serving Workflows, discusses how to implement model parallel training and serving in detail.

Chapter 8, Achieving Higher Throughput and Lower Latency, covers advanced schemes to reduce computation and memory consumption in model parallelism.

Chapter 9, A Hybrid of Data and Model Parallelism, combines data and model parallelism together as an advanced in-parallel model training/serving scheme.

Chapter 10, Federated Learning and Edge Devices, talks about federated learning and how edge devices are involved in this process.

Chapter 11, Elastic Model Training and Serving, describes a more efficient scheme that can change the number of accelerators used on the fly.

Chapter 12, Advanced Techniques for Further Speed-Ups, summarizes several useful tools, such as a performance debugging tool, job multiplexing, and heterogeneous model training.