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Accelerate model training and interference with order-of-magnitude time reduction

Learn state-of-the-art parallel schemes for both model training and serving

A detailed study of bottlenecks at distributed model training and serving stages

Description

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.

Who is this book for?

This book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial. You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.

What you will learn

Deploy distributed model training and serving pipelines

Get to grips with the advanced features in TensorFlow and PyTorch

Mitigate system bottlenecks during in-parallel model training and serving

Discover the latest techniques on top of classical parallelism paradigm

Explore advanced features in Megatron-LM and Mesh-TensorFlow

Use state-of-the-art hardware such as NVLink, NVSwitch, and GPUs

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Distributed Machine Learning with Python

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Amazon verified reviews

Hitesh Hinduja Aug 11, 2022

Interesting book with a need of hour in today's age of data. Must read for all the distributed systems enthusiasts.

Amazon Verified review

@maxgoff May 24, 2022

Although distributed computing has become de rigueur in most modern web applications, the fact remains that most training and reference materials for ML/AI programming still focus on single node architectures. One undeniable trend is the growing girth of data required to train some of the most interesting models emerging today. In order to rapidly innovate and compete, distributed ML will become table stakes in the near future as we move forward.If you write ML/AI code, implement smart data pipelines, architect systems in order to scale or simply want to learn techniques beyond the common core ML/AI training available, this book is a must-have for your shelf. Wang covers a lot of territory and does so clearly with excellent examples. He also provides the technical foundation for the WHY.As more data and processing capabilities accumulate at the edge, the exponentially expanding universe of data processing demands distributed computing. Machine Learning must follow a distributed pattern if it is to continue to provide value. Wang's text provides a solid foundation and reference point.Distributed computing is awesome. We use distributed computing applications every day. Wang's text provides the lessons you will need to ensure that modern ML innovations will utilize resources with much greater productivity. Time is our most precious resource. Distributed Machine Learning with Python will save you LOTS of it!

Wenyu Wang May 12, 2022

As someone who is interested in but has never learned about details of distributed ML, I find this book a great source of information regarding its principles (algorithmic and system design) and practice (in the context of Python implementation).

Jackson May 02, 2022

This book focused on the challenges of modern-day machine learning problems such as the increasing amount of training data and model size and provided solutions to address them.The author started with a broad overview of a typical ML pipeline and went on to dissect each stage of the pipeline and explained where the bottlenecks might be and why they would occur.Different parallelism paradigms were presented, interleaved with a lot of system design knowledge and code snippets such as model parallelism, parameter server architecture, NV-link channel, etc.Overall, this book is extremely helpful for anyone who's working on ML problems!

Amazon Customer Jun 19, 2022

This is an excellent book if you want to understand how to leverage Data parallelism and Model parallelism for any ML deployment or experimentation. It clearly explains how and why distributed computing concept works which is something everyone should know in ML/AI field. This is the future with the data growth we see every day.

Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems

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