Thank you for sharing experience!.

My intuition is that C++ python extensions make it easier to to easy things (then in C++) but make it harder to do hard things.

People always go for convenience first and then move to something more fundamental and flexible.

Data Science was mostly in R and MATLAB about 12-15 years ago. Then people moved to more general python. Next step is a compiled language with static types imo.

Just seek to replace a python part with something that is slower (obviously) but not like way slower then Pytorch vectorized ops.

And to have freedom to use more native structures and a bit less thinking about how to vectorize every algorithm possible

Mostly use vectorized pytorch operations.

Sometimes use just native loops and indexing.

Yes, unfortunately there are specific data preprocessing cases where I have to do stuff outside of pytorch, it's just more convenient.

And even when mostly using pytorch I still want the freedom to just use native functionality of a language without a huge hit to a performance.

But I know pytorch vectorized ops will still be faster and are suitable for majority of tasks

But I do want to use pytorch, I like it very much.

I just usually have a lot of specific modifications to data and find myself avoiding native python and loops/indexing, because it makes things way slower.

It would still be slower if I implemented it in C++ then in pytorch, but at least not like way slower and would not create a bottleneck

Flexible = more freedom as I understand ot. GIL = less freedom. Maybe you have different definition.

I do understand it's needed for python to be what it is now and to become so popular.

Agree, what I am trying to say is C++ gives you more freedom here. In python it's just no way you can use native features in anything you want to run fast.

Vectorized operations are goated, I agree. But I don't want to be constrained to always use them. Especially when I have to write a lot of very specific modifications to data before I feed it to a NN.

I understand this. This is not answering my question.

Using python front end I must implement any algorithm I have in mind in terms of vectorized pytorch operations. I can't use loops and indexing and other python libraries, or my code will be slow and only executed with 1 core.

How that supposed to make my job easier?

Again, in C++ I am not so much constrained to only use Pytorch functions. I can use other libraries and native features.

In python I basically must express any algorithm I have in my head in terms of vectorized pytorch operations with broadcasting. Not the case in C++. Am I wrong here?

I am not taking about researchers, I am talking more about businesses. No problem with researchers using python.

Every time I need to manipulate tensors I have to describe my algorithm in terms of vectorized pytorch functions and broadcasting, instead of more straightforward loops and indexing.

How is that supposed to make my job easier?

Nothing but the fact that everywhere I go to work they have most of ML code base in python so I have to use it.

1. But you have to use vectorized operations instead of loops, pytorch functions for everything you want to be fast etc... This is different from just using a library. It's ONLY using the library
2. There are better languages that are also flexible and popular. And python is not so flexible anyway (GIL).