Keras 3 is a Python deep-learning API that lets you build and train models using JAX, TensorFlow, or PyTorch as the backend. The shared Keras API can make models and workflows easier to move between those frameworks, but portability depends on your custom code, data pipeline, and the capabilities available on your target backend. Keras also describes OpenVINO as an inference-only backend.
What is Keras 3?
Keras is a high-level API for building and training deep-learning models. Keras 3 is a full rewrite that supports multiple backends, rather than tying Keras workflows to TensorFlow alone. Its central idea is that you can use familiar Keras components and interfaces while relying on the backend that best fits your project.
The supported training backends are JAX, TensorFlow, and PyTorch. Keras’s announcement also describes OpenVINO as an inference-only backend, so do not treat it as another option for training models. See the Keras 3 announcement and the Keras overview.
Which backends does Keras 3 support?
| Backend | What it means in Keras 3 |
|---|---|
| JAX | Supported for training and use of Keras workflows. |
| TensorFlow | Supported for training and use of Keras workflows. TensorFlow 2.16 and later use Keras 3 by default. |
| PyTorch | Supported for training and use of Keras workflows. |
| OpenVINO | Described by Keras as inference-only; some operations may not be supported. |
Backend choice is not a universal performance contest. Keras’s own announcement says benchmark outcomes vary by model and notes cases where TensorFlow outperforms JAX on GPU. That is Keras’s characterization, not an independent performance guarantee. Consider your existing framework ecosystem, target devices, custom operations, input pipeline, distributed-training needs, and dependencies when choosing.
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How portable are Keras 3 models and workflows?
Portability is strongest when a model uses built-in Keras layers and backend-agnostic APIs. Keras provides keras.ops for common operations; custom layers and other components built with Keras APIs can often be reused across supported backends. If custom code calls TensorFlow, JAX, or PyTorch operations directly, that code may need backend-specific replacements.
Keras describes its .keras model files as backend-agnostic. However, a file’s portability does not make its custom objects portable: custom layers and functions still need backend-agnostic implementations to load successfully under another backend.
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Training routines can accept several input sources, including NumPy arrays, Pandas data, tf.data.Dataset, PyTorch DataLoader, and keras.utils.PyDataset. There is an important caveat for TensorFlow input pipelines: a tf.data dataset can feed training on other backends, but support for mapping arbitrary Keras layers or models inside tf.data is more limited outside TensorFlow.
How to set up Keras 3 with a backend
Install Keras and the framework you want to use, then select the backend before importing Keras. Keras documents the setup and compatibility considerations in its getting-started guide.
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- Install the Keras package and a supported backend framework in your Python environment.
- Choose the backend by setting the
KERAS_BACKENDenvironment variable or configuring it locally. - Set that configuration before importing
keras. Keras cannot switch backends after it has been imported in the process. - Check the current Keras and backend compatibility guidance for your environment rather than relying on old version pairs shown in setup examples.
For example, set KERAS_BACKEND to torch before starting the Python process if PyTorch is your chosen backend. The exact framework versions to install depend on current compatibility guidance and your deployment environment.
How to migrate from Keras 2 to Keras 3
Many projects using public Keras APIs need only modest changes, but larger codebases can hit incompatibilities—especially where they rely on private or deprecated APIs, TensorFlow-specific code, or layers that create state at the wrong point in their lifecycle. The official Keras 2 to Keras 3 migration guide covers these changes.
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- Update imports: replace imports such as
from tensorflow import keraswithimport keras, and changetf.keras.*references tokeras.*where appropriate. - Review custom components: use backend-agnostic Keras operations where possible if you intend to support more than one backend. Framework-specific operations may limit portability.
- Move layer state creation: create variables and other layer state in
__init__()orbuild(), not incall(). This structure helps Keras determine a layer’s state before training. - Test GPU execution: the migration guide says
jit_compiledefaults toTrueon GPU. If a TensorFlow operation in your model is unsupported by XLA and causes an error, settingjit_compile=Falsemay resolve it. This is a targeted workaround, not a required change for every project. - Run your project tests: verify model construction, training, saving, loading, and inference in the environment and on the backend you plan to deploy.
TensorFlow 2.16 and later use Keras 3 by default. Projects that require legacy Keras 2 can use the separately installed tf_keras package. With TensorFlow 2.16 or later, setting TF_USE_LEGACY_KERAS=1 directs tf.keras to that legacy package; this can also affect other packages that import tf.keras in the same process. Consult the current setup guidance before changing an environment shared by multiple packages.
What changes for distributed training?
Keras describes data-parallel training support across JAX, TensorFlow, and PyTorch. Its keras.distribution model-parallel functionality, by contrast, is JAX-specific in the announcement. If your project relies on a particular distribution strategy, check that it is supported by your selected backend instead of assuming the same API and capabilities apply everywhere.
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When Keras 3 is a good fit
- Your team wants a high-level Keras workflow while keeping the option to use JAX, TensorFlow, or PyTorch.
- Your models mainly use built-in Keras layers and backend-agnostic operations.
- You need to work with input sources such as NumPy, Pandas, PyTorch data loaders, or TensorFlow datasets.
Expect more migration work when a project depends heavily on framework-specific operations, custom components, private or deprecated APIs, or assumptions about one backend’s input and distribution features. Treat backend portability as something to verify through tests, not as a guarantee that every project can switch frameworks unchanged.
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