Model Hubs & Serving

Hyperparameter Optimization of Machine Learning Algorithms

Code for hyperparameter tuning/optimization of machine learning and deep learning algorithms.

Status
UnmaintainedNo commits in 1434 days.
Last checked
Checked · Oct 10, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

This tool provides code to optimize the hyperparameters of various machine learning and deep learning models, improving their performance through systematic parameter tuning.

This tool offers a comprehensive approach to hyperparameter tuning, supporting both traditional machine learning and deep learning frameworks with various optimization methods.

When it fits — and when it doesn’t

A good fit for

  • Developers who need to optimize hyperparameters for a variety of ML and DL algorithms.

  • Projects where systematic parameter tuning can significantly improve model performance.

  • Teams working on deep learning projects that require efficient training processes.

Look elsewhere if

  • Users looking for a fully managed service for hyperparameter optimization.

  • Scenarios requiring real-time hyperparameter adjustments during model deployment.

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited support for non-Python ML frameworks

    Primary integration is with Python-based libraries like scikit-learn and TensorFlow, lacking native support for R or Julia frameworks

  2. 02

    Resource-intensive at scale

    Bayesian optimization can be computationally expensive when dealing with large datasets or complex models

What it does well

  • Supports a wide range of machine learning algorithms for hyperparameter tuning.

  • Includes methods like grid search, random search, and Bayesian optimization.

  • Optimized for both traditional ML models and deep learning frameworks.

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

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Setup steps, common gotchas and what to check after 30 days.

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