# 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/).
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"description": "# Keras 3: Deep Learning for Humans\n\nKeras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, and PyTorch.\nEffortlessly build and train models for computer vision, natural language processing, audio processing,\ntimeseries forecasting, recommender systems, etc.\n\n- **Accelerated model development**: Ship deep learning solutions faster thanks to the high-level UX of Keras\nand the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.\n- **State-of-the-art performance**: By picking the backend that is the fastest for your model architecture (often JAX!),\nleverage speedups ranging from 20% to 350% compared to other frameworks. [Benchmark here](https://keras.io/getting_started/benchmarks/).\n- **Datacenter-scale training**: Scale confidently from your laptop to large clusters of GPUs or TPUs.\n\nJoin nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3.\n\n\n## Installation\n\n### Install with pip\n\nKeras 3 is available on PyPI as `keras`. Note that Keras 2 remains available as the `tf-keras` package.\n\n1. Install `keras`:\n\n```\npip install keras --upgrade\n```\n\n2. Install backend package(s).\n\nTo use `keras`, you should also install the backend of choice: `tensorflow`, `jax`, or `torch`.\nNote that `tensorflow` is required for using certain Keras 3 features: certain preprocessing layers\nas well as `tf.data` pipelines.\n\n### Local installation\n\n#### Minimal installation\n\nKeras 3 is compatible with Linux and MacOS systems. For Windows users, we recommend using WSL2 to run Keras.\nTo install a local development version:\n\n1. Install dependencies:\n\n```\npip install -r requirements.txt\n```\n\n2. Run installation command from the root directory.\n\n```\npython pip_build.py --install\n```\n\n3. Run API generation script when creating PRs that update `keras_export` public APIs:\n\n```\n./shell/api_gen.sh\n```\n\n#### Adding GPU support\n\nThe `requirements.txt` file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also\nprovide a separate `requirements-{backend}-cuda.txt` for TensorFlow, JAX, and PyTorch. These install all CUDA\ndependencies via `pip` and expect a NVIDIA driver to be pre-installed. We recommend a clean python environment for each\nbackend to avoid CUDA version mismatches. As an example, here is how to create a Jax GPU environment with `conda`:\n\n```shell\nconda create -y -n keras-jax python=3.10\nconda activate keras-jax\npip install -r requirements-jax-cuda.txt\npython pip_build.py --install\n```\n\n## Configuring your backend\n\nYou can export the environment variable `KERAS_BACKEND` or you can edit your local config file at `~/.keras/keras.json`\nto configure your backend. Available backend options are: `\"tensorflow\"`, `\"jax\"`, `\"torch\"`. Example:\n\n```\nexport KERAS_BACKEND=\"jax\"\n```\n\nIn Colab, you can do:\n\n```python\nimport os\nos.environ[\"KERAS_BACKEND\"] = \"jax\"\n\nimport keras\n```\n\n**Note:** The backend must be configured before importing `keras`, and the backend cannot be changed after \nthe package has been imported.\n\n## Backwards compatibility\n\nKeras 3 is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your\nexisting `tf.keras` code, make sure that your calls to `model.save()` are using the up-to-date `.keras` format, and you're\ndone.\n\nIf your `tf.keras` model does not include custom components, you can start running it on top of JAX or PyTorch immediately.\n\nIf it does include custom components (e.g. custom layers or a custom `train_step()`), it is usually possible to convert it\nto a backend-agnostic implementation in just a few minutes.\n\nIn addition, Keras models can consume datasets in any format, regardless of the backend you're using:\nyou can train your models with your existing `tf.data.Dataset` pipelines or PyTorch `DataLoaders`.\n\n## Why use Keras 3?\n\n- Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework,\ne.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.\n- Write custom components (e.g. layers, models, metrics) that you can use in low-level workflows in any framework.\n - You can take a Keras model and train it in a training loop written from scratch in native TF, JAX, or PyTorch.\n - 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.\n- Make your ML code future-proof by avoiding framework lock-in.\n- As a PyTorch user: get access to power and usability of Keras, at last!\n- As a JAX user: get access to a fully-featured, battle-tested, well-documented modeling and training library.\n\n\nRead more in the [Keras 3 release announcement](https://keras.io/keras_3/).\n",
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