>Python has amazing strengths as a glue layer, and low-level bindings to C and C++ allow building libraries in C, C++ and many other languages with better performance characteristics. This is what has enabled things like numpy, TensorFlow and PyTorch and a vast number of other libraries in the ecosystem. Unfortunately, while this approach is an effective way to building high performance Python libraries, its approach comes with a cost: building these hybrid libraries is very complicated, requiring low-level understanding of the internals of cpython, requires knowledge of C/C++/… programming...
But the cost has already been paid. We have NumPy, we have PyTorch and TensorFlow. So I don't see the value-add here. Maybe there's something I'm missing.
But the cost has already been paid. We have NumPy, we have PyTorch and TensorFlow. So I don't see the value-add here. Maybe there's something I'm missing.