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Re:PyTorch vs. TensorFlow in 2022 by mrdoops on Tuesday December 14, 2021 @12:51PM (#29554720)
Yep and this approach also allows languages like Julia and Elixir to compile their expressions into valid compute graphs that target JAX/XLA. That polyglot capability opens up cutting edge machine learning into quite a bit more ecosystems with another level of capabilities in distribution and fault tolerance as is the case with Elixir + Nx.
Re:PyTorch vs. TensorFlow in 2022 by servytor on Tuesday December 14, 2021 @12:59PM (#29554848)
Could you or someone elaborate more on how other languages can hook into JAX/XLA?
Re:PyTorch vs. TensorFlow in 2022 by nestorD on Tuesday December 14, 2021 @02:37PM (#29556340)
Julia has XLA.jl [0] which interoperate with their deep-learning stack and Elixir has NX [1] which is higher level (basically JAX but in Elixir). I would love to see someone do something like that in Rust...

[0]: https://github.com/FluxML/XLA.jl

[1]: https://github.com/elixir-nx/nx/tree/main/nx