keras
A multi-backend deep learning framework that enables effortless model development across JAX, TensorFlow, PyTorch, and OpenVINO.
What is keras?
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.
Overview
Deep Learning for humans
Use Cases
Best For
- Building and training deep learning models with a high-level, intuitive API
- Experimenting with different backends (JAX, TensorFlow, PyTorch) for performance comparisons
- Migrating existing tf.keras code to other backends with minimal changes
- Developing models for computer vision, NLP, audio, or timeseries applications
- Scaling training from a laptop to large GPU or TPU clusters
- Creating custom layers or models that can be used across multiple frameworks
Not Ideal For
- Projects requiring fine-grained, low-level control over training loops or hardware-specific optimizations without abstraction overhead
- Teams deeply invested in a single framework ecosystem with extensive custom tooling that bypasses high-level APIs
- Edge deployment scenarios where minimal binary size and dependencies are critical, as Keras with full backends can be resource-intensive
Pros & Cons
Pros
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.
Cons
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.
Frequently Asked Questions
Related Projects
Tensorflow - Open source software library for numerical computation using data flow graphsAn Open Source Machine Learning Framework for Everyone
PyTorch - Tensors and Dynamic neural networks in Python with strong GPU accelerationTensors and Dynamic neural networks in Python with strong GPU acceleration
streamlitStreamlit — A faster way to build and share data apps.
gradioBuild and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.