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0 pointsswagonomixxx5y ago0 comments

While this is an decent interim solution, I wouldn't want to call out to Python for anything running in production from Julia. Good for prototyping at home perhaps.

A lot of the "scientific" Python packages (NumPy, SciPy, etc.) actually just call out to C libraries for the majority of their computation. I imagine Julia can do that already, or it can call out to something similar in the stdlib, so it doesn't really need integration with these Python packages other than just API familiarity. But is that worth the cost in performance?

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2 comments · 2 top-level

amkkma5y ago

People definitely have used PyCall in production.

Julia at this point covers everything in NumPy, SciPy and much more. For optimization, bayesian stuff, scientific, and the convergence of the above with ML, it's far ahead- https://sciml.ai/

Even has relatively mature web frameworks (https://github.com/GenieFramework/Genie.jl)

dunefox5y ago

I don't care about production or performance much; I care about data analysis and machine/deep learning for NLP. Whatever lets me use the best language and packages is best and so far it's Julia with the ecosystems and tools from Python and R.

I'm very certain that you can just import the underlying packages directly but this way is easiest, especially since I'm not familiar with R.

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