# keras

> Multi-backend Keras

- **URL**: https://www.freshcrate.ai/projects/keras
- **Author**: pypi
- **Category**: Frameworks
- **Latest version**: `v3.14.1` (2026-05-07)
- **License**: Apache License 2.0
- **Source**: https://github.com/keras-team/keras
- **Homepage**: https://pypi.org/project/keras/
- **Language**: Python
- **GitHub**: 64,025 stars, 19,749 forks
- **Registry**: pypi (`keras`)
- **Tags**: `pypi`

## Description

# Keras 3: Deep Learning for Humans

Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only).
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`. Additionally,
The `openvino` backend is available with support for model inference only.

### 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
```

## Backend Compatibility Table

The following table lists the minimum supported versions of each backend for the latest stable release of Keras (v3.x):

| Backend    | Minimum Supported Version |
|------------|---------------------------|
| TensorFlow | 2.16.1                    |
| JAX        | 0.4.20                    |
| PyTorch    | 2.1.0                     |
| OpenVINO   | 2025.3.0                  |

#### 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"`, `"openvino"`. 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.

**Note:** The OpenVINO backend is an inference-only backend, meaning it is designed only for running model
predictions using `model.predict()` method.

## 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

## Recent releases

| Version | Date | Urgency | Changes |
| --- | --- | --- | --- |
| `v3.14.1` | 2026-05-07 | High | ## Saving & Reloading * **Harden path and link resolution when extracting files from archives (#22839)**   * Fixed link resolution bug when validating links extracted from TAR archives.   * Fixed path confusion bug when validating files extracted from ZIP and TAR archives (including `.keras` files).   * Added path validation when extracting assets from Orbax checkpoints. * **Harden H5 validation code and apply it to legacy .h5 files (#22801)**   * Disallow external links and virtual datase |
| `3.14.0` | 2026-04-21 | Low | Imported from PyPI (3.14.0) |
| `v3.14.0` | 2026-04-03 | Medium | ## Highlights  - **Orbax Checkpoint Integration**: Full support for Orbax checkpoints, including sharding, remote paths, and step recovery. - **Quantization Upgrades**: Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization. - **Batch Renormalization in BatchNorm**: Added batch renormalization feature to the `BatchRenormalization` layer. - **New Optimizer**: Added `ScheduleFreeAdamW` optimizer. - **Gated Attention**: Introduced optional Ga |
| `v3.14.0` | 2026-04-03 | Medium | ## Highlights  - **Orbax Checkpoint Integration**: Full support for Orbax checkpoints, including sharding, remote paths, and step recovery. - **Quantization Upgrades**: Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization. - **Batch Renormalization in BatchNorm**: Added batch renormalization feature to the `BatchRenormalization` layer. - **New Optimizer**: Added `ScheduleFreeAdamW` optimizer. - **Gated Attention**: Introduced optional Ga |
| `v3.14.0` | 2026-04-03 | Medium | ## Highlights  - **Orbax Checkpoint Integration**: Full support for Orbax checkpoints, including sharding, remote paths, and step recovery. - **Quantization Upgrades**: Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization. - **Batch Renormalization in BatchNorm**: Added batch renormalization feature to the `BatchRenormalization` layer. - **New Optimizer**: Added `ScheduleFreeAdamW` optimizer. - **Gated Attention**: Introduced optional Ga |
| `v3.14.0` | 2026-04-03 | Medium | ## Highlights  - **Orbax Checkpoint Integration**: Full support for Orbax checkpoints, including sharding, remote paths, and step recovery. - **Quantization Upgrades**: Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization. - **Batch Renormalization in BatchNorm**: Added batch renormalization feature to the `BatchRenormalization` layer. - **New Optimizer**: Added `ScheduleFreeAdamW` optimizer. - **Gated Attention**: Introduced optional Ga |
| `v3.14.0` | 2026-04-03 | Medium | ## Highlights  - **Orbax Checkpoint Integration**: Full support for Orbax checkpoints, including sharding, remote paths, and step recovery. - **Quantization Upgrades**: Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization. - **Batch Renormalization in BatchNorm**: Added batch renormalization feature to the `BatchRenormalization` layer. - **New Optimizer**: Added `ScheduleFreeAdamW` optimizer. - **Gated Attention**: Introduced optional Ga |
| `v3.14.0` | 2026-04-03 | Medium | ## Highlights  - **Orbax Checkpoint Integration**: Full support for Orbax checkpoints, including sharding, remote paths, and step recovery. - **Quantization Upgrades**: Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization. - **Batch Renormalization in BatchNorm**: Added batch renormalization feature to the `BatchRenormalization` layer. - **New Optimizer**: Added `ScheduleFreeAdamW` optimizer. - **Gated Attention**: Introduced optional Ga |
| `v3.14.0` | 2026-04-03 | Medium | ## Highlights  - **Orbax Checkpoint Integration**: Full support for Orbax checkpoints, including sharding, remote paths, and step recovery. - **Quantization Upgrades**: Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization. - **Batch Renormalization in BatchNorm**: Added batch renormalization feature to the `BatchRenormalization` layer. - **New Optimizer**: Added `ScheduleFreeAdamW` optimizer. - **Gated Attention**: Introduced optional Ga |
| `v3.14.0` | 2026-04-03 | Medium | ## Highlights  - **Orbax Checkpoint Integration**: Full support for Orbax checkpoints, including sharding, remote paths, and step recovery. - **Quantization Upgrades**: Added support for Activation-aware Weight Quantization (AWQ) and Asymmetric INT4 Sub-Channel Quantization. - **Batch Renormalization in BatchNorm**: Added batch renormalization feature to the `BatchRenormalization` layer. - **New Optimizer**: Added `ScheduleFreeAdamW` optimizer. - **Gated Attention**: Introduced optional Ga |

## Citation

- HTML: https://www.freshcrate.ai/projects/keras
- Markdown: https://www.freshcrate.ai/projects/keras.md
- Dependencies JSON: https://www.freshcrate.ai/api/projects/keras/deps

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