Colossal-AI for Large-Scale Model Training (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Colossal-AI for Large-Scale Model Training" "Colossal-AI for Large-Scale Model Training" offers a definitive, in-depth exploration of the principles, architectures, and best practices that drive the next generation of large-scale artificial intelligence training. Beginning with a comprehensive overview of the evolution of model scaling in deep learning, the book methodically addresses the technical challenges inherent in distributed training-ranging from memory constraints and communication bottlenecks to the design of specialized parallelism strategies and scalable architectures. Readers are...
"Colossal-AI for Large-Scale Model Training" "Colossal-AI for Large-Scale Model Training" offers a definitive, in-depth exploration of the principles, architectures, and best practices that drive the next generation of large-scale artificial intelligence training. Beginning with a comprehensive overview of the evolution of model scaling in deep learning, the book methodically addresses the technical challenges inherent in distributed training-ranging from memory constraints and communication bottlenecks to the design of specialized parallelism strategies and scalable architectures. Readers are guided through detailed analyses of foundational system design principles and practical considerations drawn from leading AI organizations, positioning Colossal-AI within the broader ecosystem of advanced training frameworks. At its core, the book meticulously details the Colossal-AI system architecture, illuminating its layered design, extensible plugin system, and seamless hardware integration. Dedicated chapters break down sophisticated parallelism techniques-including tensor, pipeline, and hybrid parallelism-along with state-of-the-art memory and communication optimizations such as mixed precision computation, gradient checkpointing, and custom collective operations. Further, the book delves into robust, scalable data management methodologies, offering insights into distributed data loading, augmented data pipelines, and fault-tolerant operations vital for massive, real-world applications. Bridging theoretical underpinnings with hands-on guidance, the text culminates with practical case studies, deployment strategies for supercomputing environments, and forward-looking research trends. It emphasizes industry-proven solutions for model deployment, cost modeling, and resource optimization, while flagging emerging topics such as sustainable AI, new hardware accelerators, and lifelong learning workflows. Altogether, "Colossal-AI for Large-Scale Model Training" serves as an essential resource for engineers, researchers, and practitioners aiming to master efficient, scalable, and future-ready AI.
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