I just saw the news about Keras 3.0 supporting JAX, PyTorch, and TensorFlow backends. As someone who has strictly stayed within the TF ecosystem, I’m curious if this actually makes things easier or just adds more complexity. Does this mean I can write code once and run it anywhere without performance hits? How are you guys implementing this in your current deep learning projects?
3 answers
Keras 3.0 is a complete game-changer because it removes the framework lock-in that has plagued the industry for years. I recently converted a custom layer from a TF-specific implementation to the new Keras core ops, and it worked seamlessly on a JAX backend. The performance is actually quite impressive; in some cases, running a Keras model on JAX resulted in faster training times on TPUs compared to the native TF implementation. It allows you to leverage the best of each ecosystem—use PyTorch’s data loaders and JAX’s XLA optimization all within a familiar Keras syntax.
Are you finding that the debugging process is different when switching between the backends, especially when dealing with custom training loops?
It definitely reduces complexity. Not having to learn three different APIs to use three different frameworks is a massive time-saver for my small research team.
Spot on, Brian. It levels the playing field for developers who might not have the time to master every single framework's nuances.
Honestly, Steven, debugging feels more consistent now. Since the high-level API remains the same, the errors are usually related to the underlying backend's tensor handling. Using the Eager Execution mode in TensorFlow while developing, then switching to JAX for the final high-speed training run, has been my go-to strategy lately.