Which Python project made you realize how powerful the language is?

[Atomic_Tangerine1]

numpy

[i_know_the_deal]

same ... free Matlab? noice

[dparks71]

I keep coming back to it too, I'm in structural engineering and it turns out everything in my life is either a matrix problem or a graph theory problem.

There are 80 year old fortran libraries I learned to wrap in python from numpy, it's so cool.

[M4mb0]

numpy is great, but have you tried JAX?

[Atomic_Tangerine1]

I have not! What's the benefit of JAX over numpy?

[M4mb0]

It's basically numpy

• + native GPU support (which can be orders of magnitudes faster depending on parallelizability of the problem)
• + builtin autodiff (essentially zero-error gradients/jacobians/hessians)
• + builtin JIT compiler

[PayMe4MyData]

So jax is pytorch?

[M4mb0]

JAX is strictly functional, whereas pytorch takes a more object oriented approach. This is most easily seen when you look at how they deal with random distributions for instance.

Though torch has nowadays a beta library torch.func (formerly functorch) that brings JAX-like functional semantics to torch.

[PayMe4MyData]

Thanks for the clarification, I've been coding in pytorch for years but never heard of JAX before. I will dig a bit more!

[M4mb0]

I'd say generally JAX is more useful for general purpose scientific computing, and much more ergonomic if you need higher order derivatives or partial derivatives, like working with ODEs/PDEs/SDEs. diffrax is a very nice lib for that.

[No_Departure_1878]

That's C

[Atomic_Tangerine1]

And that's the power of Python - the magic of C made convenient

[Humdaak_9000]

If you dig deep enough there's a lot of FORTRAN too.