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Comment by kerasteam | Hacker News Reader

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

You're my favorite Python developer. Very intuitive API design.

Any chance a keras.linalg suite of ops is in the works?

Cross-platform differences between the behavior of tf.linalg and torch.linalg have cost me a lot of time over the years.

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 any `numpy.linalg` op that turns out to be useful (or you could open a PR for any op you need).

Hey Francois, congrats to you and the team on the launch! I've generally chosen Pytorch over Tensorflow for my day to day, but now that Keras is framework agnostic I'm excited to revisit it.

One thing I'm wondering about is if it's possible (or necessary?) to use Keras in concert with Pytorch Lightning. In some ways, Lightning evolved to be "Keras for Pytorch," so what is the path forward in a world where both exist as options for Pytorch users—do they interoperate or are they competitors/alternatives to each other?

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 not tried this with Lightning, but we've had a good experience with custom PyTorch Modules.

More broadly, it's feasible to use Keras components with any framework built on PyTorch or JAX in the sense that it's always possible to write "adapter layers" that wrap a Keras layer and make it usable by another framework, or the other way around. We have folks doing this to use Flax components (from JAX) as Keras layers, and inversely, to use Keras layers as Flax Modules.

Hi Francois, will there be breaking changes between 2.0 & 3.0 API

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, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/

What is Keras 3.0 (not) ideal for? Why not use pyTorch or Flax?

How much focus was there in the direction of TFLite?

Were any improvements made?

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, distribution, and training performance.

Thanks for info.

I hugely rely on TFLite for a bunch of hobby projects.

Congrats on the release!

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