Deep Learning at Scale: At the Intersection of Hardware, Software, and Data

類型： 簡裝書 語言： 英語（English） 作者： Mall, Suneeta 頁數： 448 頁 出版于： 2024-07-02 ISBN13： 9781098145286 ISBN10： 1098145283 出版社： O'Reilly Media 規格： 23.3426 x 17.78 x 2.3114 cm 重量： 707 克

This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project.
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<p>Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. <p> This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. <p> You'll gain a thorough understanding of: <ul> <li>How data flows through the deep-learning network and the role the computation graphs play in building your model <li>How accelerated computing speeds up your training and how best you can utilize the resources at your disposal <li>How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism <li>How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training <li>Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training <li>How to expedite the training lifecycle and streamline your feedback loop to iterate model development <li>A set of data tricks and techniques and how to apply them to scale your training model <li>How to select the right tools and techniques for your deep-learning project <li>Options for managing the compute infrastructure when running at scale

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