Keras

Keras 3 is a game changer: it is now multi-backend. You can write Keras code and run it on top of JAX, PyTorch, or TensorFlow.

When to Use

• Portability: Write once, run on any framework.
• Simplicity: model.fit() is still the cleanest API in the industry.
• XLA: Keras 3 enables XLA compilation on all backends by default.

Core Concepts

Backend Agnostic

The Model is just a blueprint. You choose the engine at runtime. os.environ["KERAS_BACKEND"] = "jax"

Functional API

Defining models as a graph of layers: x = Dense()(inputs).

Keras Core (keras.ops)

A numpy-like API that works across all frameworks (differentiable numpy).

Best Practices (2025)

Do:

• Use Keras 3: Migrate from tf.keras.
• Use JAX backend: For fastest training on TPUs/GPUs.
• Use PyTorch backend: If you need to integrate into a larger PyTorch codebase.

Don't:

• Don't mix tf.* ops: Use keras.ops.* to remain framework-agnostic.

References

• Keras Documentation