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Comment by thecatspaw | Hacker News Reader

Im sorry, but this very much feels like a fluff piece. Yes we _may_ need a new programming language, or we may not.

Half of this article is about hardware, while only a small part of it is about programming languages.

> There are several projects at Google, Facebook, and other places to kind of design such a compiled language that can be efficient for deep learning, but it’s not clear at all that the community will follow, because people just want to use Python

Are the libraries not implemented in native code? The brief mention that python gets does not really detail what is wrong with it, aside from not beeing compiled I guess. But that is no issue if the libraries are native, so I dont see a reason to move away from python, let alone create a new programming language for it

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You don't have to check baby's diaper to know they went number 2.

Python ML code is a bit of a joke. If you're any kind of semi professional developer who sees deep learning code for the first time and doesn't say "what the fuck would you do it like that for", I simply don't know what to tell you.

Im not saying a new language will fix this problem but some new primitives and idiomatic options protecting people from themselves probably wouldn't hurt the industry.

Likewise, something more ergonomic might actually improve onboarding new developers into the space.

I agree. Python is fine for short programs, but please give me a type safe language for developing more complex programs. BTW, as an unofficial library, some devs at Google do provide Haskell TensorFlow bindings.

I am not a Swift developer but I have looked at Swift enough to know that I like it better than Python.

Common Lisp is another interesting language: the SBCL compiler’s warnings will help you catch many errors early. So, maybe Python would be much better with MyPy type annotations and JIT compilation.

What do you find offensive about, for example, the following CNN written in Python?

https://github.com/keras-team/keras/blob/master/examples/cif...

"I simply don't know what to tell you." Please try! You can at least provide one example of the jokes you are referring to?

That code sets stack vars and calls out to the actual work.

This is a perfect example of what's perplexing me about the "learning" industry.

https://i.redd.it/00824ycw3hwy.jpg

That code also uses pickle to serialize to disk, which is a gross, irritating pattern I've seen from Pythonistas: just pickle it to disk, it works on my laptop.

Seems like Logo programming, to me.

That criticism is surprising because Python doesn't have stack variables and the code serializes the model in h5-format.

poo-poo, people will insult your sensibilities using whatever language you choose, the "problem" is not them, or the language or its idioms, its you.

If you write ML code for money, you are a pro, the end. Python is not perfect, but everything else is worse, far worse. What is the product that falls out of a ML pipeline ? ML pro-tip: its not hand-crafted artisanal code.

My complaints about ML prettiness were unclear but to clarify it's usually criticism aimed at library authors who often come from very academic backgrounds with very poor sense (lack of exp?) of just how lazy professional developers are. Case in point, you don't care if it comes out as poop because you're getting shit done. Hell it seems like you don't even care if you have to wade in it for another 5 years.

Kudos, but what if i told you if the tooling authors could have ensured you never wrote poop but still got shit done?

The end product is a model.

If you can't describe how that model makes its way into a product and is consumed, then you aren't a pro, the end.

From the product's perspective, all you produce is a configuration file.

The libraries are native code in a similar sense to python being native code. The kernels are native and typically highly optimised but there is a possibility that better optimised kernels can be generated on the fly once the computational graph is known.

>> that better optimised kernels can be generated on the fly once the computational graph is known.

Which is already done by python libraries.

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