https://github.com/keras-team/keras-nlp/tree/master/keras_nl... https://github.com/keras-team/keras-cv/tree/master/keras_cv/...
Fast forward to now, and my biggest pain point is that all the new models are released on PyTorch, but the PyTorch serving story is still far behind TF Serving. Can this help convert a PyTorch model into a servable SavedModel?
To use pretrained models, you can take a look at KerasCV and KerasNLP, they have all the classics, like BERT, T5, OPT, Whisper, StableDiffusion, EfficientNet, YOLOv8, etc. They're adding new models regularly.
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.
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 ...
Will Keras Core support direct deployment to edge devices like RPi or Arduino?
Will the experience of defining and training a model in JAX/PyTorch and then deploying to edge devices be seamless?
Anything related on the roadmap?