[D] Are you using PyTorch or TensorFlow going into 2022?

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/MachineLearning

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  • live

    PyTorch Live is an easy to use library of tools for creating on-device ML demos on Android and iOS. With Live, you can build a working mobile ML prototype in minutes. (by pytorch)

    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!

  • ml5-library

    Friendly machine learning for the web! 🤖

    From other comments, a lot of JavaScript developers who want to use TensorFlow had never heard of TensorFlow.js or ml5.js!

  • SonarLint

    Deliver Cleaner and Safer Code - Right in Your IDE of Choice!. SonarLint is a free and open source IDE extension that identifies and catches bugs and vulnerabilities as you code, directly in the IDE. Install from your favorite IDE marketplace today.

  • functorch

    functorch is JAX-like composable function transforms for PyTorch.

    If you're interested in function transformations in PyTorch, try out functorch :) https://github.com/pytorch/functorch

  • equinox

    Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.

    I just started using equinox for neural networks in jax. It's really simple. I'd highly recommend you take a look!

  • pytorch-lightning

    The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

    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

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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