Yantrashiksha: An Exciting New Open Source Autograd Library Bridging Python and C++
infrastructure#autograd📝 Blog|Analyzed: Apr 23, 2026 10:06•
Published: Apr 23, 2026 09:59
•1 min read
•r/deeplearningAnalysis
An incredibly ambitious and exciting project, Yantrashiksha brings a fresh approach to building neural network foundations by combining the accessibility of Python with the raw performance of C++. The developer's plan to seamlessly integrate the C++ autograd engine using pybind11 promises fantastic performance improvements for deep learning workflows. It is highly inspiring to see such innovative Open Source infrastructure being actively developed to push the boundaries of custom framework capabilities.
Key Takeaways
- •The project features two autograd engines: a highly complete Python version and an ambitious C++ version for optimized performance.
- •The C++ engine intelligently utilizes smart pointers to propagate gradients and is made accessible via pybind11 bindings.
- •Future plans include an exciting transition to shift the deep learning models entirely to the high-performance C++ autograd backend.
Reference / Citation
View Original"The other autograd framework is in C++ and you will find it under the folder Math. lemme explain this properly for you all: [...] Autograd: This is the main engine that performs the autograd. It uses a node class that uses smart pointers to propogate gradients. Bindings: this binds the code in Python using pybind11, making the syntax easy but the core in C++."
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