Python vs. Rust for Neural Networks
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Python vs. Rust for Neural Networks
1–10 of 149 posts
Re: Python vs. Rust for Neural Networks
#2>> Is rust suitable for data science workflows?
>> Right now I have to say that the answer is “not yet”. I’ll definitely reach for rust in the future when I need to write optimized low-level code with minimal dependencies. However using it as a full replacement for python or C++ will require a more stabilized and well-developed ecosystem of packages.
I'm not sure rust is really aiming to be something used for data science workflows. I'm not sure the community will be putting much effort into making this a reality.
Re: Python vs. Rust for Neural Networks
#3>> Is rust suitable for data science workflows? >> Right now I have to say that the answer is “not yet”. I’ll definitely reach for rust in the future when I need to write optimized low-level code with minimal dependencies. However using it as a full replacement for python or C++ will require a more stabilized and well-developed ecosystem of packages. I'm not sure rust is really aiming to be something used for data sc…
Fine, but it seems reasonable for someone to check. It's much easier to make a decision about whether a programming language is what you want if you've got evidence from someone who has tried something similar to you, as opposed to just hearing people say "Language X is awesome because of unimaginably low-level (from my point of view) feature Y".
Rust seems great, to be honest, just not universally so. Nothing wrong with defining the boundaries.
Re: Python vs. Rust for Neural Networks
#4This seems to be comparing a hand implemented neural network in python (and numpy) and one in rust. Even in this simple case, the author discovers that in the python case, most of the time is spent in non-python linear algebra libraries.
Most of the major deep learning frameworks for python (tensorflow, keras, torch, mxnet, etc) will not normally be spending the majority of their time in python. Typically, the strategy is to use python to declare the overall structure of the net, and where to load data from, and then the actual heavy lifting will be done in optimized libraries written probably in C++ (or fortran, I seem to recall BLAS used fortran).
Re: Python vs. Rust for Neural Networks
#5This seems to be comparing a hand implemented neural network in python (and numpy) and one in rust. Even in this simple case, the author discovers that in the python case, most of the time is spent in non-python linear algebra libraries. Most of the major deep learning frameworks for python (tensorflow, keras, torch, mxnet, etc) will not normally be spending the majority of their time in python. Typically, the strate…
I think BLAS is a spec rather than one library. So your version may or may not be fortran. I do think the original "reference implementation" was written in fortran, which is sometimes called "The BLAS library" but I think most BLAS you see in the wild are not that.
Re: Python vs. Rust for Neural Networks
#6This seems to be comparing a hand implemented neural network in python (and numpy) and one in rust. Even in this simple case, the author discovers that in the python case, most of the time is spent in non-python linear algebra libraries. Most of the major deep learning frameworks for python (tensorflow, keras, torch, mxnet, etc) will not normally be spending the majority of their time in python. Typically, the strate…
This is the strength of python, though, and one you cannot ignore. Python is old, and has fast C ops for everything you may want to accomplish. There's no shame in that method in benchmarks.
Re: Python vs. Rust for Neural Networks
#7This seems to be comparing a hand implemented neural network in python (and numpy) and one in rust. Even in this simple case, the author discovers that in the python case, most of the time is spent in non-python linear algebra libraries. Most of the major deep learning frameworks for python (tensorflow, keras, torch, mxnet, etc) will not normally be spending the majority of their time in python. Typically, the strate…
I think BLAS is a spec rather than one library. So your version may or may not be fortran. I do think the original "reference implementation" was written in fortran, which is sometimes called "The BLAS library" but I think most BLAS you see in the wild are not that.
Yes. BLAS was originally specified in Fortran.
But many BLAS implementation (like cuBLAS, the CUDA/Nvidia version for GPU's) don't use Fortran at all.
Re: Python vs. Rust for Neural Networks
#8This seems to be comparing a hand implemented neural network in python (and numpy) and one in rust. Even in this simple case, the author discovers that in the python case, most of the time is spent in non-python linear algebra libraries. Most of the major deep learning frameworks for python (tensorflow, keras, torch, mxnet, etc) will not normally be spending the majority of their time in python. Typically, the strate…
I think BLAS is a spec rather than one library. So your version may or may not be fortran. I do think the original "reference implementation" was written in fortran, which is sometimes called "The BLAS library" but I think most BLAS you see in the wild are not that.
> I think BLAS is a spec rather than one library.
Yep; poor wording on my part. Thanks. :)
Re: Python vs. Rust for Neural Networks
#9This approach I think is missing the point. You will write highly optimized libraries in Rust, and then use those in Python.
This is why Python has eaten the world. Not because its the best at any one thing, except bringing all those things together - at which it is unparalleled, and is unlikely to be surpassed anytime soon.
numpy, scipy, pandas, tensorflow all those have very little actual Python code, its c++ and even fortran here and there.
This whole Python vs Bla thing is just silly nonsense. I know Python and some Bla, and so should you. Tonight someone will release SuperFantasticNewThing implemented in Bla, tomorrow someone else will wrap that in Python, and tomorrow night the rest of us will use PySuperFantasticNewThing, and that's exactly how it should be.
Re: Python vs. Rust for Neural Networks
#10Neural network libraries (Tensorflow, Pytorch) have a C++ backend and a Python interface. Which is great - you get a performant compiled language as the backend and a flexible user-friendly language as the interface.
Rust vs Python is a weird question because in reality no one writes their own neural network with numpy, and no one expects Rust to act like an interpreted language suitable for data science workflows. It would be more apt to compare Rust and C++.