PyTorch is a Python-based machine learning library used for applications like deep learning and natural language processing.
We recently stumbled upon a long form essay on PyTorch internals by NYU adjunct faculty Edward Yang that’s a brilliant resource for people who wish to contribute to PyTorch but find the codebase a tad bit daunting.
The behemoth C++ codebase can be overwhelming to quite a few.
The essay breaks it down by telling the basic conceptual structure of a tensor library that supports automatic differentiation.
It also gives potential contributors tools and tricks to find their way around the codebase.
Divided into 2 parts, the first part focuses on the concepts to help better understand how things work under the hood and the second part gets into the nitty gritty details of the code.
Yang has put in several illustrations for examples which makes for really engaging content. We highly recommend!
Credits: PyTorch on Twitter
TO DO: Peruse E. Yang’s long form essay on PyTorch internals to be able to contribute to codebase.
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