Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems free ebook download.

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

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Author(s):	Guanhua Wang

Date:	2022	Format:	pdf	Language:	English	ISBN/ASIN:	1801817219
Pages:	284	OCR:		Quality:		ISBN13:
Uploader:	rubybook	Upload Date:	7/26/2024 6:15:03 AM

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1801817219 pdf
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 FeaturesAccelerate model training and interference with order-of-magnitude time reductionAccelerate model training and interference with order-of-magnitude time reductionLearn state-of-the-art parallel schemes for both model training and servingLearn state-of-the-art parallel schemes for both model training and servingA detailed study of bottlenecks at distributed model training and serving stagesA detailed study of bottlenecks at distributed model training and serving stagesBook DescriptionReducing 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 learnDeploy distributed model training and serving pipelinesDeploy distributed model training and serving pipelinesGet to grips with the advanced features in TensorFlow and PyTorchGet to grips with the advanced features in TensorFlow and PyTorchMitigate system bottlenecks during in-parallel model training and servingMitigate system bottlenecks during in-parallel model training and servingDiscover the latest techniques on top of classical parallelism paradigmDiscover the latest techniques on top of classical parallelism paradigmExplore advanced features in Megatron-LM and Mesh-TensorFlowExplore advanced features in Megatron-LM and Mesh-TensorFlowUse state-of-the-art hardware such as NVLink, NVSwitch, and GPUsUse state-of-the-art hardware such as NVLink, NVSwitch, and GPUsWho this book is forThis 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.Table of ContentsSplitting Input Data Splitting Input Data Parameter Server and All-ReduceParameter Server and All-ReduceBuilding a Data Parallel Training and Serving PipelineBuilding a Data Parallel Training and Serving PipelineBottlenecks and SolutionsBottlenecks and SolutionsSplitting the ModelSplitting the ModelPipeline Input and Layer SplitPipeline Input and Layer SplitImplementing Model Parallel Training and Serving WorkflowsImplementing Model Parallel Training and Serving WorkflowsAchieving Higher Throughput and Lower LatencyAchieving Higher Throughput and Lower LatencyA Hybrid of Data and Model ParallelismA Hybrid of Data and Model ParallelismFederated Learning and Edge DevicesFederated Learning and Edge DevicesElastic Model Training and ServingElastic Model Training and ServingAdvanced Techniques for Further Speed-UpsAdvanced Techniques for Further Speed-Ups

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Tags: Accelerating distributed Distributed Learning Machine model Python serving systems training

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