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Quality of HN comments are getting bad for a few months. This is nothing to do with python ML ecosystem and what you have to realize llmcpp doing is it is infer
by v3ss0n 3y ago
Quality of HN comments are getting bad for a few months. This is nothing to do with python ML ecosystem and what you have to realize llmcpp doing is it is inferencing already built models- which is running the models.
Building (training) machine learning, deep learning models are much more complex , order of magnitude complex than just running the models and doing that in C or C++ would take you years which would take just a few month with python.
And complexity of `pip install` is nothing compared to that.
That's why no real ETL+Deep learning, training work is done in c or c++.
- cztomsik 3y agoYou're not entirely wrong but pretty much everything you use from python is written in C++ anyway, so what's your point?
- ageofwant 3y agoThe point is, as you pointed out, that you code against the appropriate level of abstraction. You write a ML workflow appropriate language like Python in something like C++/rust, and ML flows in Python. That should really not be that hard to understand.
- v3ss0n 3y agoIt is same argument as "Every HTML, CSS , JAVASCRIPT" development you do is written in C/C++ anyways .
- cztomsik 3y agoI am not an expert, but from what I've seen, PyTorch is mostly a thin wrapper over the C++ libtorch. The same is true for DOM as well, but nobody uses DOM directly, whereas everybody uses PyTorch. The big shift is Jupyter, but that's mainly for exploratory programming. If you already know what you're doing, there's no reason why C++ should be worse than Python for training. It's likely that most ML engineers do not have experience with C++. I don't have that experience either but from what I've seen, C++ is very powerful, so once you subtract the jupyter, there's not really too much left. BTW: You cannot use DOM/CSSOM from C++, the only API is JS, so your argument is theoretical.