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> The ecosystem is still pretty barebones compared to Python There's help: https://github.com/JuliaPy/PyCall.jl https://github.com/JuliaPy/PyCall.jl https://gi

> The ecosystem is still pretty barebones compared to Python There's help: https://github.com/JuliaPy/PyCall.jl https://github.com/JuliaPy/PyCall.jl https://github.com/JuliaInterop/RCall.jl https://github.com/JuliaInterop/RCall.jl Works like a charm.

davnn 6y ago
That seems to work really well. I didn't have use cases where I would prefer to call Python from Julia instead of using Python directly. Additionally, I think you also have to think about things like Documentation and Tooling.
swagonomixxx 6y ago
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?
- dunefox 6y 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.
- amkkma 6y 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/ https://sciml.ai/ Even has relatively mature web frameworks (https://github.com/GenieFramework/Genie.jl https://github.com/GenieFramework/Genie.jl)

davnn 6y ago

That seems to work really well. I didn't have use cases where I would prefer to call Python from Julia instead of using Python directly. Additionally, I think you also have to think about things like Documentation and Tooling.

swagonomixxx 6y ago

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?

dunefox 6y 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.
amkkma 6y 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/ https://sciml.ai/ Even has relatively mature web frameworks (https://github.com/GenieFramework/Genie.jl https://github.com/GenieFramework/Genie.jl)