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
AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch
Č. položky: 194802945

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

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Č. položky: 194802945

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  • 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

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