73 lines
2.9 KiB
Markdown
73 lines
2.9 KiB
Markdown
# Keras Core: A new multi-backend Keras
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Keras Core is a new multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.
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**WARNING:** At this time, this package is experimental.
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It has rough edges and not everything might work as expected.
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We are currently hard at work improving it.
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Once ready, this package will become Keras 3.0 and subsume `tf.keras`.
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## Local installation
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Keras Core is compatible with Linux and MacOS systems. To install a local development version:
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1. Install dependencies:
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```
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pip install -r requirements.txt
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```
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2. Run installation command from the root directory.
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```
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python pip_build.py --install
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```
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You should also install your backend of choice: `tensorflow`, `jax`, or `torch`.
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Note that `tensorflow` is required for using certain Keras Core features: certain preprocessing layers as
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well as `tf.data` pipelines.
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## Configuring your backend
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You can export the environment variable `KERAS_BACKEND` or you can edit your local config file at `~/.keras/keras.json`
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to configure your backend. Available backend options are: `"tensorflow"`, `"jax"`, `"torch"`. Example:
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```
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export KERAS_BACKEND="jax"
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```
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In Colab, you can do:
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```python
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import os
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os.environ["KERAS_BACKEND"] = "jax"
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import keras_core as keras
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```
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## Backwards compatibility
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Keras Core is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your
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existing `tf.keras` code, change the `keras` imports to `keras_core`, make sure that your calls to `model.save()` are using
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the up-to-date `.keras` format, and you're done.
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If your `tf.keras` model does not include custom components, you can start running it on top of JAX or PyTorch immediately.
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If it does include custom components (e.g. custom layers or a custom `train_step()`), it is usually possible to convert it
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to a backend-agnostic implementation in just a few minutes.
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In addition, Keras models can consume datasets in any format, regardless of the backend you're using:
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you can train your models with your existing `tf.data.Dataset` pipelines or PyTorch `DataLoaders`.
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## Why use Keras Core?
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- Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework,
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e.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.
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- Write custom components (e.g. layers, models, metrics) that you can use in low-level workflows in any framework.
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- You can take a Keras model and train it in a training loop written from scratch in native TF, JAX, or PyTorch.
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- 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.
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- Make your ML code future-proof by avoiding framework lock-in.
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- As a PyTorch user: get access to power and usability of Keras, at last!
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- As a JAX user: get access to a fully-featured, battle-tested, well-documented modeling and training library.
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