Package: keras-nightly
Keras 3: Deep Learning for HumansKeras 3 is a multi-backend deep learning framework, with support for TensorFlow, JAX, and PyTorch.InstallationInstall with pipKeras 3 is available on PyPI askeras. Note that Keras 2 remains available as thetf-keraspackage.Installkeras:pip install keras --upgradeInstall backend package(s).To usekeras, you should also install the backend of choice:tensorflow,jax, ortorch.
Note thattensorflowis required for using certain Keras 3 features: certain preprocessing layers
as well astf.datapipelines.Local installationMinimal installationKeras 3 is compatible with Linux and MacOS systems. For Windows users, we recommend using WSL2 to run Keras.
To install a local development version:Install dependencies:pip install -r requirements.txtRun installation command from the root directory.python pip_build.py --installAdding GPU supportTherequirements.txtfile will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also
provide a separaterequirements-{backend}-cuda.txtfor TensorFlow, JAX, and PyTorch. These install all CUDA
dependencies viapipand 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 withconda:condacreate-y-nkeras-jaxpython=3.10
condaactivatekeras-jax
pipinstall-rrequirements-jax-cuda.txt
pythonpip_build.py--installConfiguring your backendYou can export the environment variableKERAS_BACKENDor you can edit your local config file at~/.keras/keras.jsonto configure your backend. Available backend options are:"tensorflow","jax","torch". Example:export KERAS_BACKEND="jax"In Colab, you can do:importosos.environ["KERAS_BACKEND"]="jax"importkerasNote:The backend must be configured before importingkeras, and the backend cannot be changed after
the package has been imported.Backwards compatibilityKeras 3 is intended to work as a drop-in replacement fortf.keras(when using the TensorFlow backend). Just take your
existingtf.kerascode, make sure that your calls tomodel.save()are using the up-to-date.kerasformat, and you're
done.If yourtf.kerasmodel 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 customtrain_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 existingtf.data.Datasetpipelines or PyTorchDataLoaders.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-nativeModuleor 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.Read more in theKeras 3 release announcement.