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I still don't really understand why someone would choose to use NumPy when the numerical story in Rust is now so good.

by okman 9y ago

losteric 9y ago
Numpy is great because of all the frameworks built on top. What's the story with Rust?
kevmo314 9y ago
Perhaps for engineers, but for the rest of the scientific community it's pretty obvious: Rust doesn't have nearly the coverage that NumPy does. A quick check, I couldn't find a GMRES solver in Rust, which is extremely useful for solving large linear systems and hardly an obscure algorithm. FWIW this is the same situation NumPy was in a while back ago, but instead of boldly asserting that what they had was enough, they looked to other prevalent languages to figure out what was missing and packages like pandas and statsmodels came out.
empath75 9y ago
As someone who likes rust, python is just easier to code for exploration and the kinds of advantages you get for using rust aren’t particularly relevant.
beisner 9y ago
TensorFlow doesn’t have a Rust interface.
- seventydwarves 9y ago
  Isn't this a Rust interface for TensorFlow? https://github.com/tensorflow/rust https://github.com/tensorflow/rust
ubernostrum 9y ago
The entire surrounding ecosystem. Which includes not just the core numpy/scipy libraries but the stats, modeling, ML, plotting, etc. Plus the wealth of other Python stuff it can integrate with (I work at an almost entirely Python shop -- data science people work in the same language as pipeline and application people, which is really an overlooked thing). Plus the ease of installation and management from Anaconda. Plus the notebook format for easy sharing. Plus... well, lots and lots and lots of things. "We have a fast numerical library" is step one of about ten thousand to achieving parity with what the Python/numpy/scipy ecosystem does.
marmaduke 9y ago
They don’t compete since you could reimplement your compute kernels in Rust and use them directly or as (g)ufuncs with the numpy container, used by the whole science and data stacks (visualization stats etc). Numpy compute APIs are for prototyping or compute which isn’t CPU bottleneck. So for the majority of numpy users Rust competes with the likes of Numba, Cython, etc, which are hard to beat if you’re already coding in Python.
__s 9y ago
https://news.ycombinator.com/item?id=15483267 https://news.ycombinator.com/item?id=15483267 Every once in awhile /r/rust will get a numerics post & discussion will mostly concede that there's still a ways to go to make this field ergonomic
Derbasti 9y ago
Numerical computation is just one part of scientific code. Linear Algebra, data analysis, modelling, and visualization, are equally important.
na85 9y ago
I avoid rust due to the annoying, symbol-heavy syntax that requires too much Shift-key usage.

I still don't really understand why someone would choose to use NumPy when the numerical story in Rust is now so good.

by okman 9y ago

I still don't really understand why someone would choose to use NumPy when the numerical story in Rust is now so good.

losteric 9y ago

Numpy is great because of all the frameworks built on top. What's the story with Rust?

kevmo314 9y ago

Perhaps for engineers, but for the rest of the scientific community it's pretty obvious: Rust doesn't have nearly the coverage that NumPy does. A quick check, I couldn't find a GMRES solver in Rust, which is extremely useful for solving large linear systems and hardly an obscure algorithm. FWIW this is the same situation NumPy was in a while back ago, but instead of boldly asserting that what they had was enough, they looked to other prevalent languages to figure out what was missing and packages like pandas and statsmodels came out.

empath75 9y ago

As someone who likes rust, python is just easier to code for exploration and the kinds of advantages you get for using rust aren’t particularly relevant.

beisner 9y ago

TensorFlow doesn’t have a Rust interface.

seventydwarves 9y ago
Isn't this a Rust interface for TensorFlow? https://github.com/tensorflow/rust https://github.com/tensorflow/rust

ubernostrum 9y ago

The entire surrounding ecosystem. Which includes not just the core numpy/scipy libraries but the stats, modeling, ML, plotting, etc. Plus the wealth of other Python stuff it can integrate with (I work at an almost entirely Python shop -- data science people work in the same language as pipeline and application people, which is really an overlooked thing). Plus the ease of installation and management from Anaconda. Plus the notebook format for easy sharing. Plus... well, lots and lots and lots of things. "We have a fast numerical library" is step one of about ten thousand to achieving parity with what the Python/numpy/scipy ecosystem does.

marmaduke 9y ago

They don’t compete since you could reimplement your compute kernels in Rust and use them directly or as (g)ufuncs with the numpy container, used by the whole science and data stacks (visualization stats etc). Numpy compute APIs are for prototyping or compute which isn’t CPU bottleneck. So for the majority of numpy users Rust competes with the likes of Numba, Cython, etc, which are hard to beat if you’re already coding in Python.

__s 9y ago

https://news.ycombinator.com/item?id=15483267 https://news.ycombinator.com/item?id=15483267 Every once in awhile /r/rust will get a numerics post & discussion will mostly concede that there's still a ways to go to make this field ergonomic

Derbasti 9y ago

Numerical computation is just one part of scientific code. Linear Algebra, data analysis, modelling, and visualization, are equally important.

na85 9y ago

I avoid rust due to the annoying, symbol-heavy syntax that requires too much Shift-key usage.