Ai Systems Performance Engineering: Optimizing Model Training and Inference Workloads With Gpus, Cuda, and Pytorch - Couverture souple
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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".
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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AI Systems Performance Engineering
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AI Systems Performance Engineering
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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 (Paperback)
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Paperback. Etat : new. Paperback. Elevate your AI system performance capabilities with this definitive guide to maximizing efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering provides engineers, researchers, and developers with a hands-on set of actionable optimization strategies. Learn to co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems that excel in both training and inference. Authored by Chris Fregly, a performance-focused engineering and product leader, this resource transforms complex AI 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. The book ends with a 175+-item checklist of proven, ready-to-use optimizations.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 performance Authored by Chris Fregly, a performance-focused engineering and product leader, this resource transforms complex AI 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798341627789
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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
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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 (eng)
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AI Systems Performance Engineering
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Paperback. Etat : New. Elevate your AI system performance capabilities with this definitive guide to maximizing efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering provides engineers, researchers, and developers with a hands-on set of actionable optimization strategies. Learn to co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems that excel in both training and inference. Authored by Chris Fregly, a performance-focused engineering and product leader, this resource transforms complex AI 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. The book ends with a 175+-item checklist of proven, ready-to-use optimizations.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 performance. N° de réf. du vendeur LU-9798341627789
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AI Systems Performance Engineering: Optimizing Hardware, Software, and Algorithms for Efficient Training and Inference
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