nni VS Sklearn-genetic-opt

Compare nni vs Sklearn-genetic-opt and see what are their differences.

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nni Sklearn-genetic-opt
5 6
13,708 272
0.8% -
6.7 4.8
about 1 month ago 30 days ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

nni

Posts with mentions or reviews of nni. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-04.

Sklearn-genetic-opt

Posts with mentions or reviews of Sklearn-genetic-opt. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-02.

What are some alternatives?

When comparing nni and Sklearn-genetic-opt you can also consider the following projects:

optuna - A hyperparameter optimization framework

genetic-algorithm-in-python - A genetic algorithm written in Python for educational purposes.

FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

evalml - EvalML is an AutoML library written in python.

autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code

sklearn-deap - Use evolutionary algorithms instead of gridsearch in scikit-learn

AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]

de-torch - Minimal PyTorch Library for Differential Evolution

hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python

FEDOT - Automated modeling and machine learning framework FEDOT

automlbenchmark - OpenML AutoML Benchmarking Framework

Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.