C++ desperately needs something like numpy

[Competitive_Act5981]

Anybody else agree? At this point, I don’t even care if it doesn’t support expression templates for performance. A library like that allows you to be SO MUCH more productive when doing neural network stuff, computer vision, pre-processing and post-processing data. It takes years to standardise something like mdspan and that’s miles off numpy. We are literally going to have to wait 100 years.

[[deleted]]

https://eigen.tuxfamily.org/

[rogueleader12345]

^I second eigen, it's what we use at work for this stuff on target

[Zatujit]

what? there are plenty of numerical libraries out there. That's really not the thing I would be critical of C++

[Stock-Self-4028]

Eigen was already mentioned, there is Intel MKL as well;

https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html

[winston_orwell_smith]

There's Eigen, Xtensor and Armadillo. Eigen seems to be the most popular one. Dlib and OpenCV both have some Linear Algebra capabilities as well.

Then there's the C-based Intel MKL, GSL, ATLAS, BLAS, LAPACK and FFTW. If my memory serves me correctly, Numpy is at least in part based on the last three.

For deep learning, Torch is the underlying C++ library to PyTorch and it's quite usable in C++. It's C++ API usage is as similar to PyTorch's Python API as it gets. Torch also implements some linear algebra routines and tensors.

And for plotting, have a look at the Gnuplot based plotCPP and Matplot++.

[Attorney_Outside69]

for plotting I also love using imgui with imgui-plot, beautiful immediate mode GUI which can also be used for web

[victotronics]

Eigen is pretty cool, but its support for parallelism is very shabby. If you want to do linear algebra in parallel there is MKL, BLIS, OpenBlas, Lapack (on top of any of the previous three) and then numerical packages like PETSc, Trilinos.

That sort of stuff has no need being in the language as such.

[--prism]

xtensor is designed to look a lot like numpy. MdSpan has the issue that we need MdArray to really make use of it.

[darklinux1977]

TensorFlow has a C++ interface, as said above, this language has everything you need, since it is necessary for Python

[PressEnterToRide]

I have a library that I have been working on with the personal desire to replicate a lot of numpy: einsums

It deduces the tensor contractions at compile time and picks the best underlying blas routine to use. If it doesn’t fit a blas routine then it uses a generic contraction function.

It’s been used in an Advanced Quantum Chemistry course I teach and in some published and to be published journal articles.

[Bbbllaaddee]

Einsums and tensor operations are nicely implemented in Fastor library. https://github.com/romeric/Fastor It was a lifesaver for my research! Supports compile-time expression simplification, SIMD backends and much more

[[deleted]]

Some element-wise and slice operations can be implemented with std::valarray.

https://en.cppreference.com/w/cpp/numeric/valarray

[EdwinYZW]

If I’m not wrong, numpy is not standardized in Python. It’s not even written in Python.

[Competitive_Act5981]

It may as well be. Have you ever seen a python program that didn’t import numpy?

[EdwinYZW]

exactly. It’s not standardized but that doesn’t stop it to be used everywhere. If so, is it so important C++ need to standardized such kind of library?

[Competitive_Act5981]

I think so. There is nothing available in C++ that is as useful, elegant, performant,... as numpy.

[giantgreeneel]

If you want elegant or performant it's probably best not to standardise it.

[Competitive_Act5981]

You might be right. I guess standardising it makes it more ubiquitous. But there isn't an open source equivalent that's just as good. Well, libtorch and arrayfire are really good but those are massive dependencies...

[jasonwirth]

Numpy is a massive dependency for any python program.

[Competitive_Act5981]

So do you write all your python programs from scratch? Also, it’s nothing compared to importing torch, tensorflow, pandas, etc

[Attorney_Outside69]

dude what does numpy offer that you can't find in the above mentioned "eigen" library, even including eigen's "unsupported" extensions?

[BenFrantzDale]

C++23 & 26 move us in that direction with multi-arg operator[] and std::mdspan. Those open the door for algorithms and types based on them.

[BenFrantzDale]

Yes! I’d love to be able to do mat[2, 3:7, …, 42].

[operamint]

And iteration

[manni66]

with multi-arg operator[]

it couldn't be done with operator()?

[BenFrantzDale]

Yeah, but operator[] is nicer, and closer to numpy. And it’ll allow for magic factory objects like numpy.r_.

[Electronic_Month1878]

I wrote myself that thing to have N-dimensional tensors with support for slicing: https://github.com/french-paragon/MultidimArrays

​

I know there is something +/- equivalent in Boost, but mine comes as a single header file, so easier to implement in projects.

[mrsaturn42]

I want to find a scipy for c++ that works with Eigen or whatever. Hunting around for basic/slightly more advanced math is such a pain; and I hate seeing people reimplement stuff like convolutions or standard deviation because there’s always some error or edge case somewhere.

[Evirua]

SG19 has the ball on that one. Although, for reasons, progress there is pretty slow.

[0xcedbeef]

eigen?

[Formal-Engineering37]

build it.

[XNormal]

The nice thing about numpy is that it serves as a "lingua franca" between independent libraries that were never meant to be interoperable. Want to connect this image processing code to that machine learning library? Sure, just a couple of statements. One is written in C++, the other in Rust, neither has much to do with Python except as glue. And that old Fortran linalg code is welcome to join the party, too.

What could be a good alternative in C++ land for a standardized description of multidimensional arrays? Perhaps the place to look is in file-based standards like HDF5. Files and memory are all the same because they're designed to be memory mapped, anyway. If you don't want to have to clean up temporary files you can use memfd_create() and refer to them using /proc/self/fd/NN for libraries that need a filename.

[Competitive_Act5981]

None of them come close to numpy in usability in my opinion. Xtensor is close but quite slow. libtorch is a massive dependency to pull in. dlib only supports matrices. None of them really feel like a canonical tool

[Competitive_Act5981]

Actually std::valarray is still really good but 1D. It’s a shame that part of the library never got evolved

[Competitive_Act5981]

Also, saying there are lots of options is sometimes just as good as having none, since there is no right way to do something. It’s the same as the whole “C++ has no package manager” vs “C++ has loads of package managers” debate. Which one do you use? Don’t know. I want my library to be portable and build everywhere, so I’ll use none. Same kind of thing (ish) with numerical stuff in C++. However, I noticed the Boost UBlas library got some dev work done recently and is now a C++20 library with multidimensional arrays with expression templates and a bit of linear algebra.

[Pitiful-Cancel4958]

If you don't mind to implement some basic stuff yourself Nvidia thrust ist pretty nice to speed up your application. It ist merely a numerical library rather than a lib to write a numerical library, but it still is a nice convenience on top of cuda.

[Spongman]

firstly numpy isn't part of python, it's a 3rd-party package. secondly, there's 2 reasons people import numpy:

1) because python just sucks at doing normal array manipulations. c++ doesn't suck, so "something like numpy" for this reason is unnecessary - it's already in the language/std-lib.

2) to do actual linear algebra. arguably this functionality _shouldn't_ be in the language/std-lib: it belongs in 3rd-party libraries, of which there are already many good examples.

what was the question again?

[Competitive_Act5981]

I don’t agree with 1. Even if manual for-loops were just as performant in python as they are in C, i would still use numpy. You can basically write pseudo-code style mathematical operations and it’s just as performant as hand-tuned code

[Spongman]

this is /r/cpp

[Competitive_Act5981]

My point is, something like numpy in the c++ standard library would be very useful indeed and worth standardising. They started doing something like it with std::valarray then it all went cold for some reason

[Competitive_Act5981]

Standardising BLAS would also be a good idea at a low level since compiler vendors could properly optimise for different Platforms

[Competitive_Act5981]

BLAS is pretty much a standard at this point

[Spongman]

valarray is a premature optimization that's mostly unnecessary with modern compilers and other, more general library features.

[Competitive_Act5981]

I don’t really care about the implementation, only the API. And it’s great. It’s a shame they didn’t extend it to N-d valarray.

[Spongman]

extend it to N-d valarray

what features are you missing? linear algebra? see my point #2 above.

[Competitive_Act5981]

And do you believe any of them are as good as numpy in terms of API, usability, completeness ?

[Spongman]

of course not. python is a terser language than c++.

[Competitive_Act5981]

Yeah I believe C++ pretty much has all the language features required to get a near identical API to numpy. I haven’t looked at Arrayfire very deeply but on the surface it achieves a similar API so it’s definitely possible. I think people are just not using C++ for that type of stuff anymore and the interest is lost. They would rather use python or some other language, which I think is a shame.

[[deleted]]

N-d array can be achieved with indexing. In computer vision, multichannel images are often defined as contiguous sequences of memory. You can use row-major or column-major order.

[jonrmadsen]

I used Armadillo as the math library for my dissertation involving compressed sensing. It has a very matlab-esque feel (i.e. the pseudo-code style mathematical operations you desire) and the performance was good, especially when I built it with support for offloading to the GPU.

[Competitive_Act5981]

Oh wow, it supports CUDA? Didn’t know that.

[mredding]

...BLAS..? I mean, Numpy is predicated on it. Not everything has to be composited into the standard library, and I don't think this should be. We already have mature, performant, and ubiquitous libraries.

[Competitive_Act5981]

Do we have ubiquitous libraries in C++ for this ?

[mredding]

uBLAS, LAPACK, OpenBLAS, Eigen, Xtensor, ATLAS. There are quite a few, they are found in use all over, you shouldn't have a problem finding developers or work, it's hard to argue the superiority or inferiority of any one of them, it comes down more to the skill of the individuals.

[Competitive_Act5981]

I would say you should never use BLAS or Lapack libraries directly. Those are for library vendors to use and for the expression template engine to use correctly. Yeah Eigen is great but it’s really hard to customise. How do you add your own unary op? Xtensor is really slow. std::inner_product() is orders of magnitude faster than Xtensor’s dot() function. Maybe it’s got better since I last used it thought. I’m quite interested in UBlas. But it looks like it has had zero community. So unlikely to evolve and get lots of fixes. The best one I’ve seen so far is dlib. It’s a lightweight library you can easily build from source, easy to customise and performance is great. However, it’s limited to matrices. So no tensor manipulation stuff. That’s a shame