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
Nº de artículo: 152011565

AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch

Nº de artículo: 152011565

COP 481575

COP 546048

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What Stands Out

GPU Optimization
Leverage advanced GPU capabilities to enhance model training efficiency, drastically reducing time and resources required for deployment and iterative improvement.
CUDA Utilization
Utilizes CUDA for parallel computing, maximizing hardware performance and enabling faster processing of large datasets, which significantly boosts overall productivity for AI engineers.
PyTorch Integration
Seamlessly integrates with PyTorch, providing developers with user-friendly tools and techniques for robust model building and accelerated inference, driving innovation in AI applications.

Detalles de producto

Shop AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch online at a best price in Colombia. B0F47689K8
  • 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
Item Weight2 lbs (910 grams)

Who Should Buy?

Suitable For
  • AI Researchers

    Researchers looking to enhance model training efficiencies using advanced GPU techniques and frameworks like CUDA and PyTorch.

  • Machine Learning Engineers

    Engineers who develop and deploy machine learning models, needing optimizations for performance and resource utilization.

  • Data Scientists

    Data scientists with a focus on deep learning methodologies that require efficient computational strategies for large datasets.

Not Suitable For
  • Beginners

    Individuals new to AI or Machine Learning may find the content too advanced and complex without foundational knowledge.

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COP 481575

COP 546048

Haz tu pedido ahora y recíbelo por ahí Monday, Octubre 19

QTY:

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Fastest cross-border delivery

características y beneficios

  • 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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