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this post was submitted on 15 Jul 2024
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Programming
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@maegul
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
@tschenkel @astrojuanlu @programming
@tschenkel
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.
@maegul @astrojuanlu @programming
@hrefna @tschenkel @astrojuanlu @programming
Yea ... it seems that things like this are part of Julia's problem ...
that for many the "two language problem" is actually the "two language solution" that's working just fine and as intended, or as you say, well enough to make an ecosystem jump seem too costly.