A multi-backend deep learning framework that enables effortless model development across JAX, TensorFlow, PyTorch, and OpenVINO.
Keras 3 is a multi-backend deep learning framework that enables developers to build and train models for computer vision, NLP, audio processing, timeseries forecasting, and recommender systems using a high-level, unified API. It solves the problem of framework lock-in by supporting JAX, TensorFlow, PyTorch, and OpenVINO backends, allowing users to leverage the strengths of each while maintaining code portability.
Deep learning practitioners, researchers, and developers who need a flexible, high-level framework for model development across multiple backends, from startups to global enterprises.
Developers choose Keras for its high-level usability, multi-backend flexibility, and performance optimizations, enabling faster development and future-proofing ML code without being tied to a single framework ecosystem.
Deep Learning for humans
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Supports JAX, TensorFlow, PyTorch, and OpenVINO, allowing developers to avoid framework lock-in and leverage the best backend for performance, as highlighted in the backend compatibility section.
Offers an intuitive API that accelerates model development and debugging, with easy-to-use runtimes like PyTorch eager execution, enabling faster shipping of solutions.
Enables speedups of 20% to 350% by choosing the fastest backend, often JAX, backed by benchmarks linked in the README for informed decision-making.
Scales from laptops to large GPU/TPU clusters and works as a drop-in replacement for tf.keras, making migration seamless for existing codebases.
The backend must be set before importing Keras and cannot be changed afterward, limiting dynamic workflow adjustments and requiring careful planning.
Installing GPU support requires separate clean environments for each backend to avoid CUDA version conflicts, adding setup overhead and maintenance complexity.
OpenVINO backend is inference-only, restricting training options and necessitating additional backends for full model development, which can fragment workflows.