emukit VS baybe

Compare emukit vs baybe and see what are their differences.

emukit

A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc. (by EmuKit)
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emukit baybe
1 1
565 179
0.2% 10.1%
5.0 9.9
6 days ago 3 days ago
Python Python
Apache License 2.0 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.

emukit

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

baybe

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

What are some alternatives?

When comparing emukit and baybe you can also consider the following projects:

monaco - Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.

modAL - A modular active learning framework for Python

manticore - Symbolic execution tool

vizier - Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

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

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

trimmed_match - This Python library implements Trimmed Match for analyzing randomized paired geo experiments and also implements Trimmed Match Design for designing randomized paired geo experiments.

awesome-experimental-standards-deep-learning - Repository collecting resources and best practices to improve experimental rigour in deep learning research.

pybads - PyBADS: Bayesian Adaptive Direct Search optimization algorithm for model fitting in Python

lumos - Code and data for "Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs"