[datanecdote]

As the document I linked to says, Jax autograd supports custom data types and custom gradients.

It’s honestly exhausting arguing with all you Julia boosters. You can down vote me to hell, I don’t care. I’m done engaging with this community.

You all are not winning over any market share from Python with your dismissive, arrogant, closed minded culture.

[cbkeller]

I understand you are frustrated, however, please remember

> Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that."

> Please don't comment about the voting on comments. It never does any good, and it makes boring reading.

> Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith.

https://news.ycombinator.com/newsguidelines.html

[krastanov]

I am confused why you assume I am a "Julia booster" or use such combative language. I love Python and Jax and use it for much of my research work, I just also like learning of other approaches. Please try to honestly address the sibling comments. We have repeatedly claimed that tools like Zygote.jl can autodifferentiate efficiently things that Jax can not (without a lot of extra special code and hand-defined backprop methods), e.g., an array of structs with scalar and vector properties over which a scalar cost is defined. Just give examples, so that we can both learn something new about these wonderful tools instead of using such offensive language. It is hard to not take your own comments as the ones being dismissive.

Also, look from where this conversation started. My claim was that jax does not work with "(scipy ode solvers, special functions, image processing libraries, special number types (mpmath), domain-specific libraries)". A julia library does not need to know of Zygote.jl to be autodifferentiable. A python library needs to be pure-python numpy-based library to work with jax.

In order to try to contribute to the discussion: I think this paper describes relatively well what is so special about the Julia autodiff tools: https://arxiv.org/abs/1810.07951

For a separate approach, which is also very original, check out https://github.com/jrevels/Cassette.jl