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why would you use Jax over pytorch? even if it has technical merits it lacks an ecosystem of readily available models to study and tweak.



At some point you stop caring about being able to import a set of imagnet pretrained weights and start caring about extreme flexibility. Think about implement ting, say "Scene Representation Networks" https://arxiv.org/abs/1906.01618 in each of the three frameworks. Tf is a pig, pytorch is slow, and Jax is going to crush the problem.

The lack of say, keras.applications is a shame, but it won't last, and if you have a GPU or 8 the power of optimized (p/v)map definitely makes up for it.


I mean, the authors implemented it in pytorch: https://github.com/vsitzmann/scene-representation-networks

Do you have any particular evidence that PyTorch is slow here?




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