PyTorch Systems Engineering: Architecture, Runtime Internals, and Scalable AI Infrastructure for Production Deep Learning - Softcover

Inhaltsangabe

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 model Tensor memory layout, storage semantics, and GPU data movement Autograd internals and dynamic computation graph construction CUDA execution, kernel optimization, and GPU performance tuning Mixed precision training and numerical stability in large models Compiler pipelines including graph capture and kernel fusion systems Distributed training architectures such as DDP and FSDP Transformer and large language model training systems Memory optimization strategies for large-scale workloads Production inference pipelines and deployment architectures PyTorch extensibility through C++ extensions and CUDA kernels Debugging distributed systems and runtime failure modes By 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.

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