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Comment by nextos | Hacker News Reader

Julia is growing a really good GPU support ecosystem.

Thus, the advantage is to have all your codebase in a single language vs a 2-language solution (e.g. Python && C++).

We live in an age of libraries like cudnn which are vendor provided/optimized, for 99.9% of ML applications you won't need to write custom CUDA code. Further as I mentioned earlier with Intermediate Representation languages like TVM you are guaranteed to get the best underlying constructs across different computing architectures.

Julia might still be good enough MATLAB replacement for Computational Simulation style tasks, but its clearly not suited for Machine learning.

Neither is Python, but people still use it.

Julia, despite a pretty small userbase, has some great ML libraries. See for example Knet:

https://github.com/denizyuret/Knet.jl

You are right, but for Python, you usually don't have to touch C++, because someone already took care of that part.

That's true when you're lucky. But if you're someone who does package development and methods research, you're usually not lucky. This is why pretty much all of the top / mainly used Python packages have lots of C++ and Fortran in there. As someone who develops algorithms rather than just using packages, I found this to infringe on my productivity in Python, whereas making Julia packages "production-quality" is straight forward, so even with the smaller userbase I think it's worthwhile to develop in Julia instead.

Julia marginally makes sense in some academic environments. Outside of that? Not really.

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