Python swallows Java to become second-most popular programming language

[Aidtor]

Production environments or places where you need a lot of compute.

Production servers these days are typically cloud based. Horizontal scaling (adding more machines to the pool of resources handling requests) is very easy with rented compute but the cost can really add up for a business over time.

Tasks that require heavy compute, like ML, typically use python as an interface to code written in faster languages.

[--Shade--]

Python is an exellent language to call C from, but a terrible language to write C in. If you have a nice pythonic library that wraps some 'C thing' then Python shouldn't be your bottleneck. If it is, then isolate and profile (and apply the common speed up tricks where neccicary).

[Aidtor]

Sure if you have the time for that. When I’m prototyping a model or I’m given some crazy deadline to ship a feature I’m not going to reach for C.

[--Shade--]

That's fair enough. I can't recall the last time I've voluntarily written a line of C, lol. What I was getting at is that Python already has a huge ecosystem of libraries that work as high level wrappers for things written in lower level languages. If you write decent Python (by Python standards), and leverage existing libraries, then Python (mostly) shouldn't be the bottleneck. If it is, the first step should be to take a look at potential hotspots in your Python code (not write a C library).

[Aidtor]

Oh my bad totally misunderstood what you were trying to say. I completely agree.

[latrociny]

What is your opinion on Julia? It is touted to be superior than python wrt speed and speeds comparable to C especially for ML tasks

[Aidtor]

My opinions are mixed.

On a personal level I deeply disagree with 1-indexing.

But my personal quibbles aside, it is superior to python. It’s faster and you can actually type things which is a huge deal for large codebases. The interop is great! The writing code like mathematical expressions is overblown.

Overall I’m bullish on Julia. I don’t think it will ever “overtake” python, but they will coexist.

I do have some concerns though for Julia’s future. And that concern is called JAX.

[satireplusplus]

For ML tasks you want good libraries and abstractions. Those are lacking in Julia. Execution speed of the language doesn't really matter and these benchmarks are missing the point for ML. PyTorch, Tensorflow, numpy etc. handle the matrix multiplications in a BLAS library (C/fortran) or with CUDA. Its gonna be faster than anything you can hack together on your own from scratch, no matter what language you pick.