Keras

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. 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. 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: 3. Run API generation script when creating PRs that update keras export public APIs: The following table lists the minimum supported versions of each backend for the latest stable release of Keras (v3.x): 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 : 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: 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. 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. 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 .