auto-sklearn VS syne-tune

Compare auto-sklearn vs syne-tune and see what are their differences.

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auto-sklearn syne-tune
3 1
7,403 363
0.8% 1.4%
1.8 8.1
4 months ago 9 days ago
Python Python
BSD 3-clause "New" or "Revised" License Apache License 2.0
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.

auto-sklearn

Posts with mentions or reviews of auto-sklearn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-05-26.

syne-tune

Posts with mentions or reviews of syne-tune. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing auto-sklearn and syne-tune you can also consider the following projects:

autogluon - Fast and Accurate ML in 3 Lines of Code

SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Auto-PyTorch - Automatic architecture search and hyperparameter optimization for PyTorch

nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

tune-sklearn - A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

pymarl2 - Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)

on-policy - This is the official implementation of Multi-Agent PPO (MAPPO).

iterative-stratification - scikit-learn cross validators for iterative stratification of multilabel data