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oefrhayesterday at 10:31 AM2 repliesview on HN

For much of the ML/scientific ecosystem, you're lucky to get all your deps working with the latest minor version of Python six months to a year after its release. Random ML projects with hundreds to thousands of stars on GitHub may only work with a specific, rather ancient version of Python.

> Because otherwise this problem is trivially solved by anyone competent. In particular, building and installing Python from source is just the standard configure / make / make install dance, and it Just Works. I have done it many times and never needed any help to figure it out even though it was the first thing I tried to build from C source after switching to Linux.

I compiled the latest GCC many times with the standard configure / make / make install dance when I just started learning *nix command line. I even compiled gmp, mpfr, etc. many times. It Just Works. Do you compile your GCC every time before you compile your Python? Why not? It Just Works.


Replies

klibertpyesterday at 12:06 PM

> Why not?

Time. CPython compiles in a few minutes on an underpowered laptop. I don't recall last time I compiled GCC, but I had to compile LLVM and Clang recently, and it took significantly longer than "a few minutes" on a high-end desktop.

zahlmanyesterday at 7:44 PM

> Random ML projects with hundreds to thousands of stars on GitHub may only work with a specific, rather ancient version of Python.

Can you name some?

> Do you compile your GCC every time before you compile your Python? Why not? It Just Works.

If I needed a different version of GCC to make Python work, then probably, yes. But I haven't yet.

Just like I barely ever need a different version of Python. I keep several mainly so that I can test/verify compatibility of my own code.