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Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only).Effortlessly build and train models for computer vision, natural language processing, audio processing,timeseries forecasting, recommender systems, etc.

• Accelerated model development: Ship deep learning solutions faster thanks to the high-level UX of Kerasand the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.
• State-of-the-art performance: By picking the backend that is the fastest for your model architecture (often JAX!),leverage speedups ranging from 20% to 350% compared to other frameworks.Benchmark here.
• Datacenter-scale training: Scale confidently from your laptop to large clusters of GPUs or TPUs.

Join nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3.

Keras 3 is available on PyPI askeras. Note that Keras 2 remains available as thetf-keras package.

1. Installkeras:

pip install keras --upgrade

2. Install backend package(s).

To usekeras, you should also install the backend of choice:tensorflow,jax, ortorch.Note thattensorflow is required for using certain Keras 3 features: certain preprocessing layersas well astf.data pipelines.

Keras 3 is compatible with Linux and macOS systems. For Windows users, we recommend using WSL2 to run Keras.To install a local development version:

1. Install dependencies:

pip install -r requirements.txt

2. Run installation command from the root directory.

python pip_build.py --install

3. Run API generation script when creating PRs that updatekeras_export public APIs:

./shell/api_gen.sh

Therequirements.txt file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we alsoprovide a separaterequirements-{backend}-cuda.txt for TensorFlow, JAX, and PyTorch. These install all CUDAdependencies viapip and expect a NVIDIA driver to be pre-installed. We recommend a clean Python environment for eachbackend to avoid CUDA version mismatches. As an example, here is how to create a JAX GPU environment withconda:

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

You can export the environment variableKERAS_BACKEND or you can edit your local config file at~/.keras/keras.jsonto configure your backend. Available backend options are:"tensorflow","jax","torch","openvino". Example:

export KERAS_BACKEND="jax"

In Colab, you can do:

importosos.environ["KERAS_BACKEND"]="jax"importkeras

Note: The backend must be configured before importingkeras, and the backend cannot be changed afterthe package has been imported.

Note: The OpenVINO backend is an inference-only backend, meaning it is designed only for running modelpredictions usingmodel.predict() method.

Keras 3 is intended to work as a drop-in replacement fortf.keras (when using the TensorFlow backend). Just take yourexistingtf.keras code, make sure that your calls tomodel.save() are using the up-to-date.keras format, and you'redone.

If yourtf.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 customtrain_step()), it is usually possible to convert itto 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.Dataset pipelines or PyTorchDataLoaders.

• 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-nativeModule 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.

Read more in theKeras 3 release announcement.