Pytorch systems engineering di nexley devlin (4 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Buch. Condizione: Neu. Neuware.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - PyTorch Systems Engineering is a deep, architecture-driven exploration of PyTorch as a modern execution platform for large-scale AI systems. It moves beyond model building and API usage to focus on the internal mechanisms that define performance, scalability, and reliability in production deep learning workloads.As AI systems grow in complexity-from large language models to distributed multimodal pipelines-the challenges shift away from model design and toward systems engineering: how tensors are executed across heterogeneous hardware, how autograd constructs and traverses dynamic computation graphs, how GPU memory is allocated and optimized under pressure, and how distributed training systems maintain correctness and efficiency at scale.This book treats PyTorch not as a library, but as a full runtime system composed of tightly integrated subsystems: a dynamic execution engine, a tensor computation backend, a compiler pipeline, a distributed coordination layer, and a GPU-accelerated memory management system.Throughout the book, readers will gain a practical understanding of how PyTorch actually behaves under production workloads, including its performance characteristics, internal abstractions, and failure modes.You will learn how to reason about deep learning systems in terms of execution flow, memory behavior, and infrastructure constraints rather than isolated model architectures.Key topics include:PyTorch runtime architecture and execution modelTensor memory layout, storage semantics, and GPU data movementAutograd internals and dynamic computation graph constructionCUDA execution, kernel optimization, and GPU performance tuningMixed precision training and numerical stability in large modelsCompiler pipelines including graph capture and kernel fusion systemsDistributed training architectures such as DDP and FSDPTransformer and large language model training systemsMemory optimization strategies for large-scale workloadsProduction inference pipelines and deployment architecturesPyTorch extensibility through C++ extensions and CUDA kernelsDebugging distributed systems and runtime failure modesBy the end of this book, readers will be able to design, optimize, and operate production-grade AI systems built on PyTorch, with a clear understanding of the tradeoffs that govern performance, scalability, and system reliability.This is not an introductory guide to machine learning. It is a systems engineering manual for building and scaling modern AI infrastructure with PyTorch.…

Taschenbuch. Condizione: Neu. PyTorch Systems Engineering | Architecture, Runtime Internals, and Scalable AI Infrastructure for Production Deep Learning | Devlin Nexley | Taschenbuch | Englisch | 2026 | Devlin Nexley | EAN 9788675164159 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …