base on Deep Learning for humans # 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`. 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"`, `"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. To use `openvino` backend, install the required dependencies from the `requirements-openvino.txt` file. ## 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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