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

Fregly, Chris

/ ISBN 13: 9798341627789

Verlag: O'Reilly Media, 2025

Bestandsnummer des Verkäufers NDQ9QSSNNH

Inhaltsangabe:

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.

Über die Autorin bzw. den Autor: Chris Fregly is a performance engineer and AI product leader who has driven innovations at Netflix, Databricks, Amazon Web Services (AWS), and multiple startups. He has led performance-focused engineering teams that built AI/ML products, scaled go-to-market initiatives, and reduced cost for large-scale generative-AI and analytics workloads. Chris is coauthor of the Oâ€(TM)Reilly books Data Science on AWS and Generative AI on AWS, and creator of the Oâ€(TM)Reilly course "High-Performance AI in Production with NVIDIA GPUs."� His work spans kernel-level tuning, compiler-driven acceleration, distributed training, and high-throughput inference. Chris is the organizer of the global AI Performance Engineering meetup with over 100,000 members worldwide.

Bibliografische Details

Titel: AI Systems Performance Engineering: ...
Verlag: O'Reilly Media
Erscheinungsdatum: 2025
Einband: Softcover
Zustand: new