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Item #:
152011565
AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch
Item #:
152011565
HUF 43678
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Transform complex AI systems into streamlined, high-impact AI solutions with proven optimization strategies.
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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.
Product Details
- 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 Weight | 2 lbs (910 grams) |
Who Should Buy?
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AI Researchers
Researchers looking to enhance model training efficiencies using advanced GPU techniques and frameworks like CUDA and PyTorch.
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Machine Learning Engineers
Engineers who develop and deploy machine learning models, needing optimizations for performance and resource utilization.
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Data Scientists
Data scientists with a focus on deep learning methodologies that require efficient computational strategies for large datasets.
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Beginners
Individuals new to AI or Machine Learning may find the content too advanced and complex without foundational knowledge.
Product Description
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HUF 43678
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Features & Benefits
- 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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