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Distributed Machine Learning Python by Guanhua Wang (16 results)
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Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems (Paperback or Softback)
Published by Packt Publishing 5/13/2022, 2022
ISBN 10: 1801815690 ISBN 13: 9781801815697
Language: English
Paperback or Softback. Condition: New. Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems 1.08. Book.
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Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems
Published by Packt Publishing 2022-05, 2022
ISBN 10: 1801815690 ISBN 13: 9781801815697
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Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems
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Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems
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Add to basketCondition: New. Über den AutorrnrnGuanhua Wang is a final-year Computer Science PhD student in the RISELab at UC Berkeley, advised by Professor Ion Stoica. His research lies primarily in the Machine Learning Systems area including fast collective communica.
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Distributed Machine Learning with Python
Published by Packt Publishing Limited, 2022
ISBN 10: 1801815690 ISBN 13: 9781801815697
Language: English
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Distributed Machine Learning with Python
Published by Packt Publishing Limited, 2022
ISBN 10: 1801815690 ISBN 13: 9781801815697
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Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems
Published by Packt Publishing Limited, 2022
ISBN 10: 1801815690 ISBN 13: 9781801815697
Language: English
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Distributed Machine Learning with Python
Published by Packt Publishing, Limited, 2022
ISBN 10: 1801815690 ISBN 13: 9781801815697
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build and deploy an efficient data processing pipeline for machine learning model training in an elastic, in-parallel model training or multi-tenant cluster and cloudKey Features:Accelerate model training and interference with order-of-magnitude time reductionLearn state-of-the-art parallel schemes for both model training and servingA detailed study of bottlenecks at distributed model training and serving stagesBook 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.What You Will Learn:Deploy distributed model training and serving pipelinesGet to grips with the advanced features in TensorFlow and PyTorchMitigate system bottlenecks during in-parallel model training and servingDiscover the latest techniques on top of classical parallelism paradigmExplore advanced features in Megatron-LM and Mesh-TensorFlowUse state-of-the-art hardware such as NVLink, NVSwitch, and GPUsWho this book is 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.
