Copy.deepcopy() vs clone() in Pytorch

PyTorch has become a popular deep learning framework in the machine learning community. Creating duplicates of items is a common requirement for developers and researchers using PyTorch. Understanding the distinctions between the copies is essential for retaining a model’s state, providing data augmentation, or enabling parallel processing. It is essential to use the copy.deepcopy() and clone() methods.

In this article, we examine the nuances of various object copying methods in PyTorch and their applications, performance issues, and best practices for …

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