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Python vs. Rust for Neural Networks

ngoldbaum.github.io

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Re: Python vs. Rust for Neural Networks

#81

Post by blub »

jimbokun wrote: post #68
tanilama wrote: post #12

Nobody writing NN in Python, they are just describing it. For NN or DL in general, the correctness doesn't really lie too much on the code quality level, like ownership Rust people love to talk about. It is more about Numeric stability under/overflow and such. Choice of programming language offers limited help here. I don't think Rust has a killer app for ML/DL community to offer as of now, the focus is vastly differ…

Not a data scientist, but isn’t Julia better positioned to challenge Python for ML workflows than Rust?

Rust isn't challenging anything for ML workflows, let's be serious.

Re: Python vs. Rust for Neural Networks

#82

Post by echelon »

kuzehanka wrote: post #20
tanilama wrote: post #12

Nobody writing NN in Python, they are just describing it. For NN or DL in general, the correctness doesn't really lie too much on the code quality level, like ownership Rust people love to talk about. It is more about Numeric stability under/overflow and such. Choice of programming language offers limited help here. I don't think Rust has a killer app for ML/DL community to offer as of now, the focus is vastly differ…

I've had a few Rust lovers come and mention this project to me recently. None of them had any data science or ML experience. None of them knew that Python is just used to define the high level architecture. At the same time, comparatively tedious languages like Rust will never attract data science practitioners. They don't care about the kind of safety it brings, they don't care about improving performance in a compo…

I think this project has immediate merits when it comes to productionizing small, sparse networks for running on the CPU.

I do research and prototyping in Python, but I have to deploy on mobile devices. I was going to roll my own implementation, but now that this exists, it's something I'm going to look into.

Re: Python vs. Rust for Neural Networks

#83

Post by echelon »

skohan wrote: post #41
kuzehanka wrote: post #20

Earlier quoted context omitted.

I've had a few Rust lovers come and mention this project to me recently. None of them had any data science or ML experience. None of them knew that Python is just used to define the high level architecture. At the same time, comparatively tedious languages like Rust will never attract data science practitioners. They don't care about the kind of safety it brings, they don't care about improving performance in a compo…

This is why I think Swift is a much better choice to replace or at least compliment Python than Rust. It has a modern, powerful type system and all the quality of life advantages which come with it, but it manages this with a lot more usability than Rust. A well written swift framework almost becomes a DSL for the problem domain, which is a great property for a data science tool to have.

Swift is not fun on non-Mac platforms. It feels a lot like Google's Dart in terms of how it was positioned and advocated.

Modern Rust isn't difficult to use. This is becoming a really tired meme from detractors. The compiler is incredibly helpful, non-lexical lifetimes are a thing, and unless you're doing a lot of sharing and parallelism, you can avoid many borrow checker problems until you learn RAII.

Re: Python vs. Rust for Neural Networks

#84

Post by justinclift »

sjwright wrote: post #79
corndoge wrote: post #72

Earlier quoted context omitted.

Good lord, hopefully latency isn't 2.5 seconds!

I can’t even. How could you ever get 2500 msec on transit? That’s like circling the globe ten times.

Maybe a bunch of SSL cert exchanges through some very low bandwidth connections? ;)

Still, it's more likely a figure used for exaggeration, for effect.

Re: Python vs. Rust for Neural Networks

#85

Post by [deleted] »

justinclift wrote: post #84
sjwright wrote: post #79

Earlier quoted context omitted.

I can’t even. How could you ever get 2500 msec on transit? That’s like circling the globe ten times.

Maybe a bunch of SSL cert exchanges through some very low bandwidth connections? ;) Still, it's more likely a figure used for exaggeration, for effect.

[deleted]

Re: Python vs. Rust for Neural Networks

#86

Post by skohan »

echelon wrote: post #83
skohan wrote: post #41

Earlier quoted context omitted.

This is why I think Swift is a much better choice to replace or at least compliment Python than Rust. It has a modern, powerful type system and all the quality of life advantages which come with it, but it manages this with a lot more usability than Rust. A well written swift framework almost becomes a DSL for the problem domain, which is a great property for a data science tool to have.

Swift is not fun on non-Mac platforms. It feels a lot like Google's Dart in terms of how it was positioned and advocated. Modern Rust isn't difficult to use. This is becoming a really tired meme from detractors. The compiler is incredibly helpful, non-lexical lifetimes are a thing, and unless you're doing a lot of sharing and parallelism, you can avoid many borrow checker problems until you learn RAII.

It's a fair criticism of Swift that it is not well supported across platforms. The story is getting better, but it is still miles behind Rust in this regard. I am a huge fan of Swift as a language, but I am very critical of the tooling.

> Modern Rust isn't difficult to use. This is becoming a really tired meme from detractors.

I beg to differ. I've been programming professionally for over a decade, and I have shipped projects in a variety of languages, and I can safely say that Rust has a steeper learning curve and requires more cognitive overhead to use than many other languages. I find it relatively nice to work with rust in spite of this because the tooling is so great, but it's undeniable that Rust has made tradeoffs which sacrifice ease of use in favor of safety and performance.

Re: Python vs. Rust for Neural Networks

#87

Post by mplanchard »

archgoon wrote: post #62
timClicks wrote: post #43

I think it's quite impressive actually that someone can pick up Rust and manage to out-perform Numpy in their first project. BLAS implementations are decades-long exercises in optimization. In my own experience, Rust has been excellent for the more boring side of data science - churning through TBs of input data.

> I think it's quite impressive actually that someone can pick up Rust and manage to out-perform Numpy in their first project. They didn't. At the end of the article they discuss this. "In fact it’s worse than that. One of the exercises in the book is to rewrite the Python code to use vectorized matrix multiplication. In this approach the backpropagation for all of the samples in each mini-batch happens in a single s…

That is a different algorithm, though, right? They didn’t rewrite the rust code to use matrix multiplication, so it’s no longer a direct comparison, and is instead comparing unoptimized rust with a less efficient algorithm to super optimized C++ with a more efficient algorithm (being called from python).

In which case, IMO, it’s fairly surprising that the rust implementation is only 2x slower.

Re: Python vs. Rust for Neural Networks

#88

Post by danielscrubs »

kuzehanka wrote: post #65
danielscrubs wrote: post #51

Earlier quoted context omitted.

I am a data scientist and I care. The time when you could just do proof of concepts or a PowerPoint presentation is long behind us. So now we have to start to take it into production, which means we get the exact same problems as SE has always had. Iff Rust helps us take it into production we will use it. But it’s a lot of land to cover to reach Pythons libraries so I’m not holding my breath. That said, Pythons perfo…

I must be missing something. Modern data science workloads involve fanning out data and code across dozens to hundreds of nodes. The bottlenecks, in order, are: inter-node comms, gpu/compute, on-disk shuffling, serialisation, pipeline starvation, and finally the runtime. Why worry about optimising the very top of the perf pyramid which will make the least difference? Why worry if you spent 1ms pushing data to numpy w…

Every small thing counts when you have big data which is exactly why you need performance everywhere, if Rust can help with that I don’t mind switching my team to that.

The problem are usually when you do novel feature engineering not the actual model training.

But I was a C++ dev before checking the assembly for performance optimization so I guess I have more wiggle room to see when things are not up to snuff. If I got a cent for every: -you are not better than the compiler writers you can’t improve this.

Especially from the Java folks. They simply don’t want to learn shit, which is fine if they just where not so quick with the lies/excuses when proven wrong.

Re: Python vs. Rust for Neural Networks

#89

Post by chillee »

wokwokwok wrote: post #47
chillee wrote: post #40

Earlier quoted context omitted.

Just because fast.ai has some investment in Swift does not mean that S4TF has attracted mind share. The bulk of fast.ai is still taught on Pytorch.

The parent comment literally said data science practitioners don't care about speed or safety because the GPU is where all the real work happens; that's false, I've provided an example of it being false from a respected party. What do you want me to say? eh, I give up. Believe whatever you want to believe.

I'm not saying that no data science practitioners care about speed and safety, and its easy for me to come up with cases where they should/do care.

My point is that even fast.ai still views S4TF as somewhat niche, and that data science practitioners as a whole still don't care.

Re: Python vs. Rust for Neural Networks

#90

Post by danielscrubs »

smallnamespace wrote: post #56
Nullabillity wrote: post #54

Earlier quoted context omitted.

I'm by no means a specialized data scientist, but I've done some (very surface-level) text crunching with both the TF/Numpy stack and Rust. To me, the nice thing about switching to Rust for that kind of stuff was that it dramatically raised the bar of what I could do before reaching for those hyper-optimized descriptive libraries. Want to calculate the levenshtein distances of the cross product of 100k strings? Sure,…

> But if I'm doing something that I don't know will fit into an existing Python mold (that I know about) then I'll strongly consider using it. The thing is, for anything performance intensive and scientific, you're almost guaranteed to find a Python binding. It has all these bindings because scientists are almost always writing either Python, C++, or Fortran (with a smattering of R or Octave on the side). Want to do…

And they will cover a small part of what is found in research papers.

Python has the best ecosystem, but Rust was made by a competent team so we will root for it.

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