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Chapter 12: Distributed Deep Learning — Training Across GPUs, Machines, and Servers
An interview-focused guide to distributed deep-learning training, covering device placement, GPU memory, parameter servers, synchronous and asynchronous training, data and model parallelism, AllReduce, input pipelines, communication bottlenecks, scaling efficiency, and Staff-level ML system design.
Deepak Mishra19 min read
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