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

Fregly, Chris

/ ISBN 13: 9798341627789

Edité par O'Reilly Media, 2025

N° de réf. du vendeur NDQ9QSSNNH

Synopsis :

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 savings
• Implement cutting-edge inference strategies that reduce latency and boost throughput in real-world settings
• Utilize industry-leading scalability tools and frameworks
• Profile, diagnose, and eliminate performance bottlenecks across complex AI pipelines
• Integrate full stack optimization techniques for robust, reliable AI system performance

Whether 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.

À propos de l?auteur: Chris Fregly is a passionate performance engineer and AI product leader with a proven track record of driving innovation at leading tech companies like Netflix, Databricks, and Amazon Web Services (AWS). He's led performance-focused engineering teams that built advanced AI/ML products, scaled go-to-market initiatives, and reduced cost for large-scale generative AI and analytics workloads. He is also co-author of 2 O'Reilly books: Data Science on AWS and Generative AI on AWS - as well as the creator of the O'Reilly online course titled, "High Performance AI in Production with Nvidia GPUs".

Détails bibliographiques

Titre : AI Systems Performance Engineering: ...
Éditeur : O'Reilly Media
Date d'édition : 2025
Reliure : Couverture souple
Etat : new