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Modern deep-learning stacks rely on a computation graph to represent a neural network as a directed acyclic graph (DAG) whose nodes are tensor operations and whose edges carry multi-dimensional arrays (tensors). This abstraction allows the framework to reason about the entire model ahead of time, insert missing backward operations, schedule ker ...

Understanding PyTorch for Deep Learning PyTorch has emerged as one of the leading frameworks in deep learning, particularly favored in research and academia. Its dynamic computation graph and intuitive design make it a preferred choice for prototyping and experimentation. In contrast to static-graph alternatives, PyTorch enables developers to m ...

Modern AI frameworks rely on computation graphs as the fundamental abstraction for representing and executing neural network models. By using universal data structures like tensors to interpret and perform neural network operations, computation graphs enable systematic analysis and optimization of AI systems. Motivation: Challenges in AI Engine ...