Keras 3: A new multi-backend Keras

Keras 3 is a new multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.

Installation

Install with pip

Keras 3 is available as a preview release on PyPI named keras-core. Keras 2 (tf.keras) is distributed along with the tensorflow package.

Install keras-core:

pip install keras-core

Install backend package(s).

To use keras-core, you should also install the backend of choice: tensorflow, jax, or torch. Note that tensorflow is required for using certain Keras 3 features: certain preprocessing layers as well as tf.data pipelines.

Note: If you are using the keras-core package you also need to switch your Keras import. Use import keras_core as keras. This is a temporary step until the release of Keras 3 on PyPI.

Local installation

Keras 3 is compatible with Linux and MacOS systems. To install a local development version:

Install dependencies:

pip install -r requirements.txt

Run installation command from the root directory.

python pip_build.py --install

The requirements.txt file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also provide a separate requirements-{backend}-cuda.txt for TensorFlow, JAX, and PyTorch. These install all CUDA dependencies via pip and expect a NVIDIA driver to be pre-installed. We recommend a clean python environment for each backend to avoid CUDA version mismatches. As an example, here is how to create a Jax GPU environment with conda:

conda create -y -n keras-jax python=3.10
conda activate keras-jax
pip install -r requirements-jax-cuda.txt
python pip_build.py --install

Configuring your backend

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: "tensorflow", "jax", "torch". Example:

export KERAS_BACKEND="jax"

In Colab, you can do:

import os
os.environ["KERAS_BACKEND"] = "jax"

import keras_core as keras

Note: The backend must be configured before importing keras, and the backend cannot be changed after the package has been imported.

Backwards compatibility

Keras 3 is intended to work as a drop-in replacement for tf.keras (when using the TensorFlow backend). Just take your existing tf.keras code, make sure that your calls to model.save() are using the up-to-date .keras format, and you're done.

If your tf.keras model does not include custom components, you can start running it on top of JAX or PyTorch immediately.

If it does include custom components (e.g. custom layers or a custom train_step()), it is usually possible to convert it to a backend-agnostic implementation in just a few minutes.

In addition, Keras models can consume datasets in any format, regardless of the backend you're using: you can train your models with your existing tf.data.Dataset pipelines or PyTorch DataLoaders.

Keras 3 timeline

At the moment, we are releasing Keras 3 as a preview release with under the keras-core name on PyPI. We encourage anyone interested in the future of the library to try it out and give feedback.

You can find the current stable release of Keras 2 at the tf-keras repository.

We will share updates on the release timeline as soon as they are available.

Why use Keras 3?

Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework, e.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.
Write custom components (e.g. layers, models, metrics) that you can use in low-level workflows in any framework.
- You can take a Keras model and train it in a training loop written from scratch in native TF, JAX, or PyTorch.
- You can take a Keras model and use it as part of a PyTorch-native Module or as part of a JAX-native model function.
Make your ML code future-proof by avoiding framework lock-in.
As a PyTorch user: get access to power and usability of Keras, at last!
As a JAX user: get access to a fully-featured, battle-tested, well-documented modeling and training library.

Metadata

Release files for keras-nightly 3.0.0.dev2023102503

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for keras-nightly 3.0.0.dev2023102503
File	Size	Uploaded
keras-nightly-3.0.0.dev2023102503.tar.gz	705.6 kB	Oct 25, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for keras-nightly 3.0.0.dev2023102503
File	Interpreter	ABI	Platform	Reset
keras_nightly-3.0.0.dev2023102503-py3-none-any.whl 976.7 kB Oct 25, 2023	Python 3	none	any	Details

Total release size: 1.7 MB

Release files / keras-nightly-3.0.0.dev2023102503.tar.gz

Download URL	keras-nightly-3.0.0.dev2023102503.tar.gz
Size	705.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`c0b1116fe41971261f7ddd5cbf270f416da49005e6339ec0b04e06bcea2737df`
BLAKE2b-256 checksum How to use checksums	`951141ce9ed7da81b34f25c22bbd2b17582b1d4df8b5f69518f53e66952059b3`
Upload date	Oct 25, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.11.6`

Release files / keras_nightly-3.0.0.dev2023102503-py3-none-any.whl

Download URL	keras_nightly-3.0.0.dev2023102503-py3-none-any.whl
Size	976.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`d4f68e3ee337f67009d29e4a12910046e21d6243da40f05b9559df445ef6a7d0`
BLAKE2b-256 checksum How to use checksums	`2b00f6bb380e94aaf099f5688e5dd3ab080c4138b279de6ecc98e7b835c9cde8`
Upload date	Oct 25, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.11.6`