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Ask HN: How did Python become the lingua franca of ML/AI?

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Re: Ask HN: How did Python become the lingua franca of ML/AI?

#41

Post by z3phyr »

auntienomen wrote: post #33

Python's a lingua franca in AI/NN because it was already a dominant language in scientific computing. Its dominance in scientific computing grew steadily through the 1990s and 2000s, for a few reasons: 1) Python -- specifically CPython -- made it easy to wrap existing, thoroughly tested high performance libraries in Python APIs. So, you got easy access to things like GSL and BLAS and LAPACK, but you get to call numpy…

Can you link any source on the python scientific usage in the 90s and early 2000s? I think the dominant language in science at that time was a mishmash of MATLAB, Java, C++, FORTRAN and Perl (In Biology at least, perl was the goto glue language due to its excellent string processing capabilities)

Re: Ask HN: How did Python become the lingua franca of ML/AI?

#42

Post by powersnail »

The use of python in most ML/AI research is not even a "glue" language.

It is used as a shell. It's merely an interface to some gigantic, highly optimized libraries (numpy, scipy, and later, Tensorflow, Pytorch, etc.), and it does a very decent job at being an interface.

- The language is easy to grasp, at least the part that is used in data science and ML;

- The syntax is "familiar", as compared with R;

- There are many more general purpose libraries in Python than in R;

- There's no memory management problems;

- The standard library is packed with batteries;

- No compiling, which is important for being a shell;

- It's better than bash etc. at dealing with non-text data, especially numerical values;

- The community was already writing extensions in C;

Some other language could work well, too, had someone written a numpy for it at the time. But there really aren't that many people who are capable, interested, and invested enough to write such a marvelous library.

Re: Ask HN: How did Python become the lingua franca of ML/AI?

#43

Post by lokimedes »

I can only provide anecdotal material, but back in 2007-2014 when I was a particle physics researcher, we saw a high uptake of python for steering data analysis jobs. The actual calculations were done in C++. Gradually over the years, as more students joined the LHC conquest, our tools evolved to allow more of the analyses to be directly programmed in Python. R was never a thing among the 10000+ physicists in our community. These people have since then drifted around the world working on Big Data, ML and recently Data Science. It’s hard to keep count, but I routinely recognize fellow particle physicists at various ML companies.

For the curious, our primary hammer was “ROOT” https://root.cern - note its well-evolved ability to connect Python and C++ code.

Re: Ask HN: How did Python become the lingua franca of ML/AI?

#44

Post by mynameisash »

cgb223 wrote: post #28
bbulkow wrote: post #22

Another way to analyze the problem: what other language would it have been, given the moment ml hit? You say compared to other scripting languages'. Let's list them. Ruby: no numeric support Go: unnecessary typing, modest numeric support, shitty generics Bash: ha ha ha Scala, java, c, cpp: not a scripting language, complex Tcl, php: out of favor Rust: hadn't happened yet R: in memory bias, not as simple Other languag…

Non ML/AI coder here: Why does ML/AI work need to be written in a scripting language? Why can’t it be something like C++ etc instead?

It doesn't need to / you could theoretically do it in C++. It's just that Python (as with other scripting languages) provides really nice, high-level expressiveness and also has a decent module system. You can write code in the REPL or just write a quick-and-dirty script and test it out without write-compile-run cycles.

NumPy is highly optimized for things like matrix math. You get great speed with the C-level module, and you drive it with really simple Python code. So you want to multiply two matrices? The code literally looks no different than multiplying two scalars. That's Python's superpower.

I haven't written C++ in nearly 20 years; maybe it's good enough to be able to do ML work. But the heavy lifting library in C/C++ plus the high-level driving Python is a really good fit.

Re: Ask HN: How did Python become the lingua franca of ML/AI?

#45

Post by danwills »

auntienomen wrote: post #27
bbulkow wrote: post #26

Earlier quoted context omitted.

I left out JavaScript. It is not a simple language, with it's service heritage. It is bound up in the node runtime in a way that doesn't really work right for data processing.

lol, you forgot perl too.

Yeah and what about awk!?

Partially joking, but not totally, and I can appreciate why people might say this: https://news.ycombinator.com/item?id=5725291

(I pasted the HN link because the original seems to be down)

Re: Ask HN: How did Python become the lingua franca of ML/AI?

#46

Post by auntienomen »

auntienomen wrote: post #33

Python's a lingua franca in AI/NN because it was already a dominant language in scientific computing. Its dominance in scientific computing grew steadily through the 1990s and 2000s, for a few reasons: 1) Python -- specifically CPython -- made it easy to wrap existing, thoroughly tested high performance libraries in Python APIs. So, you got easy access to things like GSL and BLAS and LAPACK, but you get to call numpy…

Extending the comment a little: I'm crediting CPython for the nice APIs, but really I should be crediting David Beazley's SWIG. That got the ball rolling on a lot of projects.

Re: Ask HN: How did Python become the lingua franca of ML/AI?

#47

Post by jandrewrogers »

Python became the de facto glue language for supercomputing a very long time ago because you could easily bind C code into it. If you needed linear algebra etc to run on a massive supercomputer, there were highly optimized Python libraries for that so the researcher didn’t have to write C/C++/Fortran. This massively improved iteration times for a lot of scientific computing efforts with only a modest loss of performance. By the time data science/ML/AI/etc became a thing these tools were already very mature and also relevant.

The tl;dr: Python had the advantage of a mature legacy in supercomputing doing many of the same types of computations done in AI/ML. Those libraries and bindings provided a massive leg up versus other scripting languages that did not have this kind of capability effectively built-in.

Re: Ask HN: How did Python become the lingua franca of ML/AI?

#48

Post by auntienomen »

z3phyr wrote: post #41
auntienomen wrote: post #33

Python's a lingua franca in AI/NN because it was already a dominant language in scientific computing. Its dominance in scientific computing grew steadily through the 1990s and 2000s, for a few reasons: 1) Python -- specifically CPython -- made it easy to wrap existing, thoroughly tested high performance libraries in Python APIs. So, you got easy access to things like GSL and BLAS and LAPACK, but you get to call numpy…

Can you link any source on the python scientific usage in the 90s and early 2000s? I think the dominant language in science at that time was a mishmash of MATLAB, Java, C++, FORTRAN and Perl (In Biology at least, perl was the goto glue language due to its excellent string processing capabilities)

I'm not sure I could find sources on the web any more easily than you. (Maybe start by looking at references in David Beazley's old talks?) I was in physics at the time, and what happened there was that Python basically enveloped Fortran & C++, letting people use the existing code without getting bogged down in complicated invocations.

Re: Ask HN: How did Python become the lingua franca of ML/AI?

#49

Post by perth »

friedman23 wrote: post #37
perth wrote: post #13

Earlier quoted context omitted.

The most insane thing about Python is how you can override single methods in classes and use the class like normal. One time I was working on getting FIFO working on Windows, and none of the Python built-ins were set up to handle any random process writing to a named pipe that wasn't within the same Python instance. So what I did was I took the closest implementation Python offered, which was in the multiprocessing m…

Monkey patching is a terrible practice outside of unit testing and can lead to extremely difficult to debug bugs. Also monkey patching isn't unique to python.

FWIW I tried looking a few up and standard library seemed hit-or-miss:

JavaScript it didn't work:

  class testClass {
    constructor() { }
    
    callHoHe() {
      console.log('ho', 'he');
    }
  }

  let hi = new testClass();
  hi.callHoHe();

  testClass.callHoHe = () => {
    console.log('haha');
  }

  let heh = new testClass();
  heh.callHoHe();
This ended up just printing 'ho', 'he' twice,

For Java people didn't think it was possible:

https://stackoverflow.com/questions/47006118/is-there-any-wa...

For Java they said here that you just have to use your own similar implementation.

And for C# they have some pretty intense restrictions on overriding standard library stuff:

https://stackoverflow.com/questions/21302768/where-we-can-ov...

Golang doesn't seem to have this functionality as well:

https://stackoverflow.com/questions/37079225/golang-monkey-p...

Ps. it would have been nice to have monkey patching when dealing with btoa and atob in JavaScript, since they have different function on NodeJS vs the browser.

Re: Ask HN: How did Python become the lingua franca of ML/AI?

#50

Post by oivey »

Python had basically already won numerical computing thanks to NumPy, SciPy, and Matplotlib before ML really blew up. The other two serious contenders were R and Matlab. Python is a much better general purpose language than either of those, and Matlab is proprietary.
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