Distributed Machine Learning with Python
Cód:
491_9781801815697
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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, youll 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. Youll 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, youll see how to use distributed systems to enhance machine learning model training and serving speed. Youll 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, youll 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 hard
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