[yazanobeidi]

Hi, first off thank you for your contributions, and this goes to the entire team. Keras is a wonderful tool and this was definitely the right move to do. No other package nails the “progressive disclosure” philosophy like Keras.

This caught my eye:

> “Right now, we use tf.nest (a Python data structure processing utility) extensively across the codebase, which requires the TensorFlow package. In the near future, we intend to turn tf.nest into a standalone package, so that you could use Keras Core without installing TensorFlow.”

I recently migrated a TF project to PyTorch (would have been great to have keras_core at the time) and used torch.nested. Could this not be an option?

A second question. For “customizing what happens in fit()”. Must this be written in either TF/PyTorch/Jax only, or can this be done with keras_core.ops, similar to the example shown for custom components? The idea would be you can reuse the same training loop logic across frameworks, like for custom components.

[kerasteam2]

At this time, there are no backend-agnostic APIs to implement training steps/training loops, because each backend handles training very differently so no shared abstraction can exist (expecially for JAX). So when customizing fit() you have to use backend-native APIs.

If you want to make a model with a custom train_step that is cross-backend, you can do something like:

  def train_step(self, *args, *kwargs):
    if keras.config.backend() == "tensorflow":
      return self._tf_train_step(*args, *kwargs)
    elif ...

BTW it looks the previous account is being rate-limited to less than 1 post / hour (maybe even locked for the day) so I will be very slow to answer questions.