Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.
KerasTuner
KerasTuner searches hyperparameters to optimize machine learning models built with Keras.

About KerasTuner
KerasTuner is an open-source hyperparameter optimization framework for Keras machine learning models. It simplifies hyperparameter search by letting users define search spaces with a define-by-run syntax, then uses built-in algorithms to identify optimal hyperparameter values for their models. The framework includes pre-built Bayesian Optimization, Hyperband, and Random Search algorithms, and is designed to be easily extended by researchers developing new search methods. It requires Python 3.8+ and TensorFlow 2.0+, and is installable via pip.
Key features
- Define-by-run syntax for hyperparameter search space configuration
- Built-in Bayesian Optimization, Hyperband, and Random Search algorithms
- Extensible design for custom search algorithm development
- Native integration with Keras and TensorFlow 2.0+ workflows
- Open-source release under Apache-2.0 license
- User-friendly, scalable design that addresses common hyperparameter search pain points
- Includes three pre-built search algorithms for immediate use
- Extensible structure supports custom search algorithm development by researchers
- Requires Python 3.8+ and TensorFlow 2.0+ to operate
- Only supports hyperparameter optimization for Keras-based models
KerasTuner at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Good for
- Developers, Researchers
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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KerasTuner FAQ
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