Hrefna (DHC) @hachyderm.io SRE at Google. Queer. Poly :potion\_polyamory: Trans :verified\_trans: :nonbinary\_potion: Engineer. Ace :flag\_ace: Member of AWU-CWA. #ActuallyAutistic :rainbowinfinity: #UnionStrong
Opinions my own. Does not suffer fools gladly.
Mostly its advantage as far as arrays go is its ability to push things out to an accelerator (GPU) without making code changes. Also its JIT functionality is a good bit faster than using pytorch's (at least anecdotally).
My experience with it is not at all related to ODEs (more things like MCMC) and I have no direct experience with its gradient functionality and only limited with its auto vectorization, so take my experience with a grain of salt.
Considering, it may be worth highlighting that tools like Jax exist as well (https://github.com/google/jax). These have even become an expected integration in some toolkits (e.g., numpyro)
It may not be the most elegant approach, but there's a lot of power in something that "mostly just works and then we can optimize narrowly once we find a problem"
It doesn't make a solution that solves this mess bad, but I do wonder about it being a narrow niche
In a real way it feels like there's a "hump" with language adoption. Some languages clear it, some don't, and I don't think we have a good feel as an industry for what makes a language "successful" in this regard.
Some things obviously help, other things obviously hurt, but mostly what succeeds or doesn't seems to be a matter of luck intersecting with need.