# Keras 3: Deep Learning for Humans

Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, and PyTorch.
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 Keras
and 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](https://keras.io/getting_started/benchmarks/).
- **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.


## Installation

### Install with pip

Keras 3 is available on PyPI as `keras`. Note that Keras 2 remains available as the `tf-keras` package.

1. Install `keras`:

```
pip install keras --upgrade
```

2. Install backend package(s).

To use `keras`, you should also install the backend of choice: `tensorflow`, `jax`, or `torch`.
Note that `tensorflow` is required for using certain Keras 3 features: certain preprocessing layers
as well as `tf.data` pipelines.

### Local installation

#### Minimal installation

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 update `keras_export` public APIs:

```
./shell/api_gen.sh
```

#### Adding GPU support

The `requirements.txt` file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also
provide a separate `requirements-{backend}-cuda.txt` for TensorFlow, JAX, and PyTorch. These install all CUDA
dependencies via `pip` and expect a NVIDIA driver to be pre-installed. We recommend a clean python environment for each
backend to avoid CUDA version mismatches. As an example, here is how to create a Jax GPU environment with `conda`:

```shell
conda create -y -n keras-jax python=3.10
conda activate keras-jax
pip install -r requirements-jax-cuda.txt
python pip_build.py --install
```

## Configuring your backend

You can export the environment variable `KERAS_BACKEND` or you can edit your local config file at `~/.keras/keras.json`
to configure your backend. Available backend options are: `"tensorflow"`, `"jax"`, `"torch"`. Example:

```
export KERAS_BACKEND="jax"
```

In Colab, you can do:

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

import keras
```

**Note:** The backend must be configured before importing `keras`, and the backend cannot be changed after 
the package has been imported.

## Backwards compatibility

Keras 3 is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your
existing `tf.keras` code, make sure that your calls to `model.save()` are using the up-to-date `.keras` format, and you're
done.

If your `tf.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 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 PyTorch `DataLoaders`.

## Why use Keras 3?

- 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 `Module` 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 the [Keras 3 release announcement](https://keras.io/keras_3/).