auto-sklearn VS Auto-PyTorch

Compare auto-sklearn vs Auto-PyTorch and see what are their differences.

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auto-sklearn Auto-PyTorch
3 4
7,394 2,274
0.7% 1.5%
1.8 0.0
4 months ago 17 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.

Auto-PyTorch

Posts with mentions or reviews of Auto-PyTorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-09.

What are some alternatives?

When comparing auto-sklearn and Auto-PyTorch you can also consider the following projects:

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

autogluon - AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data [Moved to: https://github.com/autogluon/autogluon]

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

lightning-flash - Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains

syne-tune - Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.

carefree-learn - Deep Learning ❤️ PyTorch

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

cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

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

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

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