| benchmarks | ||
| examples | ||
| integration_tests | ||
| keras_core | ||
| shell | ||
| .gitignore | ||
| LICENSE | ||
| pip_build.py | ||
| pyproject.toml | ||
| README.md | ||
| requirements.txt | ||
| setup.cfg | ||
| setup.py | ||
Keras Core: a new multi-backend Keras
Keras Core is a new multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.
Local installation
Run python3 pip_build.py --install from the root directory.
Backwards compatibility
Keras Core is intend to work as a drop-in replacement for tf.keras (when using the TensorFlow backend).
If your tf.keras model does not include custom compoments, 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 Torch DataLoaders.
Why use Keras Core?
- 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
Moduleor 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.
Credits
TODO