[mbauman]

The key for me — as someone who has been around for a long time and is at JuliaHub — is that Julia excels most at problems that don't already have an efficient library implementation.

If your work is well-served by existing libraries, great! There's no need to compete against something that's already working well. But that's frequently not the case for modeling, simulation, differential equations, and SciML.

[catgary]

The ODEs stuff in Julia is nice, but I think diffusers/JAX is a reasonable backbone to copy over whatever you need from there. I do think Julia is doing will in stats and has gotten some mindshare from R in that regard.

But I think a reasonably compentent Python/JAX programmer can roll out whatever they need relatively easily (especially if you want to use the GPU). I do miss Tullio, though.

[ssivark]

But how does one get the composability of multiple dispatch? A problem with the Python world IME is the need to reinvent everything in Jax ecosystem, in the Pytorch ecosystem, etc -- because of fundamental language limitations. This is tedious, and feels like a lot of wasted effort.

Another example: It's frustrating that Flax had to implement it's own "lifted" transformations instead of being able to just use jax transformations -- which makes it impossible to just slot a Flax model into a jax library that integrates ODEs. Equinox might be better on this front, but that means that all the models now need to be re-implemented in Equinox. The fragmentation and churn in the Python ecosystem is outrageous -- the only reason it doesn't collapse under its own weight is how much funding and manpower ML stakeholders are able to pour into the ecosystem.

Given how much the ecosystem depends on that sponsored effort, the popular frameworks will likely prioritize ML applications, and corollary use cases will be second class citizens in case of design tradeoffs. Eg: framework overheads matter less when one is trying to use large NN models -vs- when one is trying to use small models, or other parametric approaches.

[lagrange77]

> But I think a reasonably compentent Python/JAX programmer can roll out whatever they need relatively easily

You mean in terms of the ODE stuff, Julia provides?