Comment by vkazanov

When the first scientific libraries were written for python, most alternatives didn't even consider being readable, or convenient. The choice was more like C/Cpp/Fortran vs Python.

And then Python went into a self-reinforcing loop, with scientific community coming up with more and more ways to improve Python support for the kind of interactive work that was required for data analysis. Think ipython -> jupyter -> jupyter forks and other python-centric notebook systems.

So when data analysis evolved into data science and machine learning, gpu-first library vendors already faced a crowd of people knowing python.

It is crazy how right now one can utilize 100s of gpus through these bits of dirty python wrapped in json.