AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch
暫譯: AI 系統性能工程：使用 GPUs、CUDA 和 PyTorch 優化模型訓練與推論工作負載

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.