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You can absolutely serve with Keras if your inference server is in Python. For instance, if you're looking for a basic solution, you can just set up a Flask app that calls `predict…

We made sure that TFLite workflows would run smoothly with Keras 3 models. We did not come up with any TFLite related improvements. The focus was on the multi-backend architecture,…

Yes, Keras can be used to build LLMs. In fact this is one of the main use cases. There are some tutorials about how to do it "from scratch", like this: https://keras.io/examples/nl…

We don't have a separate `ops.linalg` package, but we do include `numpy.linalg` ops as part of `keras.ops`. For now only 2 ops are supported: `qr` and `solve`. We're open to adding…

All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467 You can use this migration guide to identify and fix each of these issues (and further, makin…

Thanks! Hope you'll find the new Keras useful! So far the export story focuses on SavedModel and the services that consume that format, e.g. TFLite, TFjs and TFServing. You can jus…

Both Keras models/layers (with the PyTorch backend) and Lightning Modules are PyTorch Modules, so they should be able to interoperate with each other in a PyTorch workflow. We have…

According to PyPI downloads and user surveys (like the yearly StackOverflow survey) the two main frameworks are TensorFlow and PyTorch for Deep Learning, and Scikit-Learn for class…

This means that the API, the abstractions, the workflows are battle-tested. The codebase itself went through 2 months of private beta and 5 months of public beta. It is already use…

Yeah, that never happened. We process dozens of bug reports and feature requests every week, and we listen to them.

To clarify, I have never attacked PyTorch, on Twitter or otherwise. What happened is that I was a target of online harassment campaign from 2017 to January 2021 (when it stopped ab…

Francois from the Keras team here -- happy to answer questions!

That's right, if the model is backend-agnostic you can train it with a PyTorch training loop and then reload it and use it with TF ecosystem tools, like serve it with TF-Serving or…

Yes, you can check out KerasCV and KerasNLP which host pretrained models like ResNet, BERT, and many more. They run on all backends as of the latest releases (today), and convertin…

For a Keras Core model to be usable with the TF Serving ecosystem, it must be implemented either via Keras APIs (Keras layers and Keras ops) or via TF APIs. To use pretrained model…

Yes, model weights saved with Keras Core are backend-agnostic. You can train a model in one backend and reload it in another. Coral TPU could be used with Keras Core, but via the T…

I worked on the project, happy to answer any questions!