Jul 11, 2023 · Specifically, this guide teaches you how to use jax.sharding APIs to train Keras models, with minimal changes to your code, on multiple GPUs or TPUS (typically 2 to 16) …

Jun 25, 2023 · Keras provides default training and evaluation loops, fit() and evaluate(). Their usage is covered in the guide Training & evaluation with the built-in methods.

You can export the environment variable KERAS_BACKEND or you can edit your local config file at ~/.keras/keras.json to configure your backend. Available backend options are: "jax", …

This layer accepts JAX models in the form of a function, call_fn, which must take the following arguments with these exact names: params: trainable parameters of the model.

Keras 3 is a full rewrite of Keras that enables you to run your Keras workflows on top of either JAX, TensorFlow, PyTorch, or OpenVINO (for inference-only), and that unlocks brand new …

Jun 27, 2023 · A core principle of Keras is progressive disclosure of complexity. You should always be able to get into lower-level workflows in a gradual way. You shouldn't fall off a cliff if …

Nov 7, 2023 · The keras.distribution.DeviceMesh class in Keras distribution API represents a cluster of computational devices configured for distributed computation. It aligns with similar …

We benchmark the three backends of Keras 3 (TensorFlow, JAX, PyTorch) alongside Keras 2 with TensorFlow. Find code and setup details for reproducing our results here.

Jun 3, 2025 · With the JAX backend, we need to preprocess the inputs to convert them to a hardware-dependent format required for use with SparseCores. We'll do this by wrapping the …

Aug 7, 2025 · This tutorial will guide you through the integration of Keras with Flax's NNX (Neural Networks JAX) module system, demonstrating how it significantly enhances variable handling …