Hyperas
Simple wrapper for hyperparameter optimization in Python.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Hyperas?
Hyperas is a simple and convenient tool for optimizing hyperparameters of machine learning models. It wraps around Keras to provide an easy-to-use interface for tuning parameters, making it easier to improve model performance without manual trial and error.
Key differentiator
“Hyperas stands out for its simplicity and ease of integration with Keras, making it an accessible choice for optimizing machine learning models without extensive setup or configuration.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Hyperas wraps around Keras, making it incompatible with other deep learning frameworks like TensorFlow or PyTorch.
While genetic algorithms can be effective for some hyperparameter optimization tasks, they might not converge as quickly or effectively as gradient-based methods in certain scenarios.
Hyperas is an open-source project with a relatively small user base, which can lead to fewer contributions, slower bug fixes, and less comprehensive documentation compared to larger projects.
Fit analysis
Who is it for?
✓ Best for
Developers working with Keras who need to optimize hyperparameters without complex setup.
Projects where manual hyperparameter tuning is too time-consuming or impractical.
✕ Not a fit for
Users requiring real-time hyperparameter optimization in production environments.
Scenarios where the computational cost of genetic algorithms outweighs benefits.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Next step
Get Started with Hyperas
Step-by-step setup guide with code examples and common gotchas.