Metadata-Version: 2.4
Name: keras-rs
Version: 0.1.0.dev0
Summary: Multi-backend recommender systems with Keras 3.
Author-email: Keras RS team <keras-rs@google.com>
License: Apache License 2.0
Project-URL: Home, https://keras.io/
Project-URL: Repository, https://github.com/keras-team/keras-rs
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: keras
Requires-Dist: ml-dtypes

# Keras Recommenders

Keras Recommenders is a library for building recommender systems on top of
Keras 3. Keras Recommenders works natively with TensorFlow, JAX, or PyTorch. It
provides a collection of building blocks which help with the full workflow of
creating a recommender system. As it's built on Keras 3, models can be trained
and serialized in any framework and re-used in another without costly
migrations.

This library is an extension of the core Keras API; all high-level modules
receive that same level of polish as core Keras. If you are familiar with Keras,
congratulations! You already understand most of Keras Recommenders.

## Installation

Keras Recommenders is available on PyPI as `keras-rs`:

```bash
pip install keras-rs
```

To try out the latest version of Keras Recommenders, you can use our nightly
package:

```bash
pip install keras-rs-nightly
```

Read [Getting started with Keras](https://keras.io/getting_started/) for more
information on installing Keras 3 and compatibility with different frameworks.

> [!IMPORTANT]
> We recommend using Keras Recommenders with TensorFlow 2.16 or later, as
> TF 2.16 packages Keras 3 by default.

## Configuring your backend

If you have Keras 3 installed in your environment (see installation above), you
can use Keras Recommenders with any of JAX, TensorFlow and PyTorch. To do so,
set the `KERAS_BACKEND` environment variable. For example:

```shell
export KERAS_BACKEND=jax
```

Or in Colab, with:

```python
import os
os.environ["KERAS_BACKEND"] = "jax"

import keras_rs
```

> [!IMPORTANT]
> Make sure to set the `KERAS_BACKEND` **before** importing any Keras libraries;
> it will be used to set up Keras when it is first imported.

## Compatibility

We follow [Semantic Versioning](https://semver.org/), and plan to provide
backwards compatibility guarantees both for code and saved models built with our
components. While we continue with pre-release `0.y.z` development, we may break
compatibility at any time and APIs should not be considered stable.

## Citing Keras Recommenders

If Keras Recommenders helps your research, we appreciate your citations.
Here is the BibTeX entry:

```bibtex
@misc{kerasrecommenders2024,
  title={KerasRecommenders},
  author={Hertschuh, Fabien and  Chollet, Fran\c{c}ois and Sharma, Abheesht and others},
  year={2024},
  howpublished={\url{https://github.com/keras-team/keras-rs}},
}
```

## Acknowledgements

Thank you to all of our wonderful contributors!

<a href="https://github.com/keras-team/keras-rs/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=keras-team/keras-rs" />
</a>
