[generichuman]

It is unlikely people would use Python if it didn't rely on C code.

If your ML model takes hours when it can take minutes, or takes days when it can take hours, you will move away. You could move away to another language or a faster interpreter but that's a different discussion.

> more focus con optimizing the interpreter

This is good but there's an upper bound to performance of interpreted languages. Maybe the Python interpreter could be as fast as V8, but it is unlikely to be fast as JVM. People will need to drop down to C / Fortran for whatever compute intensive work they're doing.

[robertlagrant]

> If your ML model takes hours when it can take minutes, or takes days when it can take hours, you will move away. You could move away to another language or a faster interpreter but that's a different discussion.

How much of this is done in Python, vs constructing instructions in Python to run on a GPU at uber speed?

[generichuman]

Widely used libraries for python such as SciPy, PyTorch, TensorFlow, Numpy all drop down to lower level languages (C/C++/Fortran).

I haven't seen many people doing ML in Python without one of these libraries, so I'd say it is mostly constructing instructions in Python and offloading the actual intensive work to these libraries (which may or may not run on GPU depending on your hardware).