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I'm also surprised that XLA.jl doesn't seem to have had continued development: https://github.com/FluxML/XLA.jl

When in doubt, piggybacking on (or at least interoperating with) what the large technology companies are investing in is probably savvy, sort of what the OP did.

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XLA.jl was kind of a solution looking for a problem. If you want fast code in Julia, you can just write Julia.

That's incorrect. If you work with mid-sized neural networks and MCMC sampling, allocations start to play a significant role (And Flux.jl is bad at preallocation). Prealloc.jl does not work properly. Zygote.jl adds even more allocations to the mix...

Jax/XLA completely solves this problem. Yes, it's annoying that you have to work with a static graph but if your problem fits the description... it's great.

There's work being done to solve this in Julia. See escapeanalysis.jl and the immutable array pr in base

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