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I'd say stick with PT for now! If you're interested in deployment, might wanna look into TF, but I'd use PyTorch Live to build a mobile application before you do that!
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From other comments, a lot of JavaScript developers who want to use TensorFlow had never heard of TensorFlow.js or ml5.js!
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If you're interested in function transformations in PyTorch, try out functorch :) https://github.com/pytorch/functorch
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equinox
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
I just started using equinox for neural networks in jax. It's really simple. I'd highly recommend you take a look!
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pytorch-lightning
Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning] (by PyTorchLightning)
Is the problem the sheer number of options, or the fact that they are all together in one place? Would it be better if they were organized into the different trainer entrypoints (fit, validate, ...)? If that is the case, there was an RFC proposing this which you might find interesting, feel free to drop by and comment on the issue: https://github.com/PyTorchLightning/pytorch-lightning/issues/10444
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