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AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch
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AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch
Transform complex AI systems into streamlined, high-impact AI solutions with proven optimization strategies.
AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch
Item #: 194802945

AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch

88% of respondents would recommend this to a friend

Item #: 194802945

PLN 409

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What Stands Out

Performance Optimization
Enhances AI model training efficiency by leveraging GPU capabilities and CUDA technology, resulting in faster processing times for both training and inference workloads.
Framework Flexibility
Seamlessly integrates with PyTorch, allowing for comprehensive model development workflows and reducing barriers for developers transitioning to GPU-based architectures.
Scalability Solutions
Provides scalable solutions for deploying AI systems, ensuring optimal resources are utilized for diverse workloads, which improves overall performance and cost-effectiveness in data center environments.

Product Details

Shop AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch online at a best price in Poland. B0F47689K8
  • Elevate your AI system performance capabilities with this definitive guide to unlocking peak efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering equips professionals with actionable strategies to co-optimize hardware, software, and algorithms for high-performance and cost-effective AI systems. Authored by Chris Fregly, a performance-focused engineering and product leader, this comprehensive resource transforms complex systems into streamlined, high-impact AI solutions.Inside, you'll discover step-by-step methodologies for fine-tuning GPU CUDA kernels, PyTorch-based algorithms, and multinode training and inference systems. You'll also master the art of scaling GPU clusters for high performance, distributed model training jobs, and inference servers.Codesign and optimize hardware, software, and algorithms to achieve maximum throughput and cost savingsImplement cutting-edge inference strategies that reduce latency and boost throughput in real-world settingsUtilize industry-leading scalability tools and frameworksProfile, diagnose, and eliminate performance bottlenecks across complex AI pipelinesIntegrate full stack optimization techniques for robust, reliable AI system performanceWhether you're an engineer, researcher, or developer, AI Systems Performance Engineering offers a holistic roadmap for building resilient, scalable, and cost-effective AI systems that excel in both training and inference.
Publisher O'Reilly Media
Publication date 23 Dec. 2025
Language English
Print length 954 pages
ISBN-13 979-8341627789
Item weight 1.79 kg
Dimensions 17.78 x 5.08 x 23.34 cm

Who Should Buy?

Suitable For
  • Data Scientists

    Data scientists looking to enhance their model training and inference efficiency using GPUs and PyTorch will benefit greatly.

  • Machine Learning Engineers

    Machine learning engineers focused on increasing the performance of deep learning models through optimization techniques should use this product.

  • AI Researchers

    AI researchers seeking to push the boundaries of existing models and explore high-performance computing in research will find it valuable.

Not Suitable For
  • Beginners in AI

    Beginners in AI may find the technical depth and optimization complexities overwhelming and unsuitable for their learning curve.

Product Description

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Customer Questions & Answers

  • Question: What is the main focus of the book?

    Answer: The book focuses on maximizing efficiency and performance in AI systems through practical optimization strategies.
  • Question: Who would benefit from this guide?

    Answer: Engineers, researchers, and developers working with AI infrastructure will find this guide invaluable.
  • Question: Does it include hands-on methodologies?

    Answer: Yes, it includes step-by-step methodologies for optimizing various components of AI systems.

Higher Education Editorial Review

AI Systems Performance Engineering: Optimizing Model Training And Inference Workloads With GPUs, CUDA, And PyTorch is a comprehensive guide published by O'Reilly Media, coming in at an impressive 954 pages. This book is a must-have for anyone involved in AI model development, providing in-depth insights into optimizing workloads efficiently. The focus on GPUs, CUDA, and PyTorch ensures that readers can leverage powerful tools in the AI field. The publication, available in English, also includes ample technical details that cater to both newcomers and seasoned professionals looking to enhance their system performance.

Customer Reviews & Ratings

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Pros

  • In-depth exploration of AI optimization techniques
  • Focus on GPUs and CUDA usage
  • Comprehensive resource for model training
  • Practical insights for AI practitioners
  • Suitable for both beginners and experts

Cons

  • Heavy item, may not be portable for all readers

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PLN 409

Order now and get it around Thursday, October 08

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Only 3 items left in stock.
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Features & Benefits

  • Maximize AI system performance with actionable optimization strategies.
  • Learn to co-optimize hardware, software, and algorithms efficiently.
  • Receive step-by-step methodologies for GPU CUDA and PyTorch tuning.
  • Discover how to scale GPU clusters for high performance.
  • Access a comprehensive checklist of over 175 optimization techniques.
  • Implement cutting-edge inference strategies that reduce latency and boost throughput.

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