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Re:DiffEqFlux.jl – A Julia Library for Neural Differential E... by xiaodai on Wednesday January 23, 2019 @03:58PM (#18982874)
I wonder if this type of collaboration is easy to achieve in other languages? Of course not many have an awesome ODE ecosystem like Julia's. But it feels like an unique strengths of Julia where it's easy to integrate heavy duty libraries and have them work together!

It reminds me of this post http://www.stochasticlifestyle.com/why-numba-and-cython-are-...

I think Numba and the Cythan ecosystem will struggle to achieve this with this level of (relative) ease, although I see the original implementation was done in PyTorch. The ODE and ML experts probably made it look easy!

Now I wonder about the relative performance of DiffEqFlux vs the original implementation

Re:DiffEqFlux.jl – A Julia Library for Neural Differential E... by marmaduke on Wednesday January 23, 2019 @05:12PM (#18983652)
the numerical performance ecosystem in Python is historically more siloed than in Julia, due to reliance on C-like language for performance. Relative latecomers like Numba and Autograd(compare to NumPy) didn't get enough traction for it all to cohere nicely.

(I don't use Julia, but have done several evaluations for our hpc work)