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Keras
What Keras is
Keras is a free Python deep learning API for developing and training machine-learning models. Its building blocks include layers, metrics, loss functions, optimizers, callbacks, and workflows for fitting, evaluating, and predicting. Keras 3 works with JAX, TensorFlow, or PyTorch; OpenVINO is available for inference only. Training and prediction can use NumPy arrays, Pandas dataframes, TensorFlow datasets, PyTorch DataLoaders, and Keras PyDataset objects. Models can also be used as PyTorch modules, exported as TensorFlow SavedModels, or represented as stateless JAX functions. The distribution API supports data and model parallelism through JAX. KerasHub supplies implementations of popular architectures and pretrained checkpoints, while KerasTuner offers Bayesian Optimization, Hyperband, and Random Search. The getting-started materials include more than 150 notebooks on computer vision, natural language processing, and generative AI. Keras runs on Linux, macOS, and Windows, with GPU acceleration and multiple deployment targets listed. Installation uses pip, but Keras 3 also requires a separately installed backend configured before importing the API.
Who it suits
Keras suits machine-learning engineers building models in Python, particularly those who want guides and examples for common ML use cases. It can also suit teams working across supported backends or data formats.
What it does well
- Free plan and Linux, macOS, and Windows support.
- Works with JAX, TensorFlow, and PyTorch.
- Accepts NumPy, Pandas, TensorFlow, and PyTorch data inputs.
- More than 150 example notebooks are available.
- KerasTuner includes three search algorithms.
What to know first
- Keras 3 requires a separately installed and configured backend.
- OpenVINO support is for inference only.
- Distribution API is currently implemented for JAX.
Verdict
Keras combines model-building and training workflows with multiple backend and data-input options. Account for the separate backend installation, and note that distribution support is currently implemented for JAX.
Facts from Keras's own pages
- Backends
- Keras 3 runs on JAX, TensorFlow, and PyTorch, and offers an OpenVINO backend for inference. · keras.io, 30 Sept 2026
- Community support
- Keras provides a Google Group, community meetings, Discord, and a Google AI Forum for discussion and updates. · keras.io, 30 Sept 2026
- Compatibility limit
- The Keras distribution API supports model parallelism through JAX; TensorFlow and PyTorch support is described as coming soon on the Keras 3 launch page. · keras.io, 30 Sept 2026
- Contributions
- The Keras site invites code, ideas, and feedback and links to its roadmap, contribution guide, and GitHub repository. · keras.io, 30 Sept 2026
- Data inputs
- Keras 3 training, evaluation, and prediction routines support tf.data.Dataset, PyTorch DataLoader, NumPy arrays, and Pandas dataframes. · keras.io, 30 Sept 2026
- Data integrations
- Keras models can use NumPy arrays, Pandas dataframes, TensorFlow tf.data datasets, PyTorch DataLoaders, and Keras PyDataset objects. · keras.io, 30 Sept 2026
- Distribution
- The distribution API supports data and model parallelism and is currently implemented for the JAX backend. · keras.io, 30 Sept 2026
- Examples
- The getting-started page offers over 150 example notebooks covering computer vision, natural language processing, and generative AI. · keras.io, 30 Sept 2026
- Founded
- 2015 · keras.io, 28 Sept 2026
- Frameworks
- Keras 3 runs on JAX, TensorFlow, or PyTorch, and supports OpenVINO for inference only. · keras.io, 30 Sept 2026
- Hyperparameter tuning
- KerasTuner includes Bayesian Optimization, Hyperband, and Random Search algorithms and can be extended with new search algorithms. · keras.io, 30 Sept 2026
- Installation
- Keras installs from PyPI with pip install --upgrade keras; using Keras 3 also requires installing a backend framework. · keras.io, 30 Sept 2026
- Intended users
- Keras describes its audience as machine learning engineers and presents guides and examples for model development across common ML use cases. · keras.io, 30 Sept 2026
- Model building
- Developers can build models with the Sequential API, the Functional API, or model subclassing. · keras.io, 30 Sept 2026
- Model interoperability
- Keras 3 models can be used as PyTorch modules, exported as TensorFlow SavedModels, or instantiated as stateless JAX functions. · keras.io, 30 Sept 2026
- Model portability
- Keras 3 models can be used as PyTorch modules, exported as TensorFlow SavedModels, or instantiated as stateless JAX functions. · keras.io, 30 Sept 2026
- Pretrained models
- KerasHub provides Keras 3 implementations of popular architectures and pretrained checkpoints on Kaggle Models for training and inference. · keras.io, 30 Sept 2026
- Product
- Keras is a Python deep learning API focused on readable, maintainable code and fast model iteration. · keras.io, 30 Sept 2026
- Purpose
- Keras is a Python deep learning API designed to make model development concise, readable, and easier to debug. · keras.io, 30 Sept 2026
- Requirement
- Keras 3 requires a separately installed backend framework, and the backend must be configured before importing Keras. · keras.io, 30 Sept 2026
- Security and compliance
- The Keras pages reviewed do not state security certifications or compliance claims. · keras.io, 30 Sept 2026
- Support
- The Keras site directs users to its Google Group for questions and development discussion, and GitHub issues for bug reports and feature requests. · keras.io, 30 Sept 2026
- Training
- Keras provides built-in fit, evaluate, and predict workflows for training, evaluation, and inference. · keras.io, 30 Sept 2026
Where Keras stands on Freedom251
- Deep Learning Software#23 of 35