Deep Learning at Scale

eBook Details:

• Paperback: 400 pages
• Publisher: WOW! eBook (July 30, 2024)
• Language: English
• ISBN-10: 1098145283
• ISBN-13: 978-1098145286

eBook Description:

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

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.

You’ll gain a thorough understanding of:

• How data flows through the deep-learning network and the role the computation graphs play in building your model
• How accelerated computing speeds up your training and how best you can utilize the resources at your disposal
• How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism
• How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training
• Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training
• How to expedite the training lifecycle and streamline your feedback loop to iterate model development
• A set of data tricks and techniques and how to apply them to scale your training model
• How to select the right tools and techniques for your deep-learning project
• Options for managing the compute infrastructure when running at scale

This Deep Learning at Scale 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.