From 4d6942a9917a5d7d7909affc3b8e73a570c85d4f Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 10:09:13 +0000 Subject: [PATCH] docs: preserve upstream English README --- README.en.md | 133 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 README.en.md diff --git a/README.en.md b/README.en.md new file mode 100644 index 0000000..52ecbec --- /dev/null +++ b/README.en.md @@ -0,0 +1,133 @@ +# 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 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/).