botorch
Bayesian optimization in PyTorch (by pytorch)
optimas
Optimization at scale, powered by libEnsemble (by optimas-org)
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| botorch | optimas | |
|---|---|---|
| 5 | 1 | |
| 2,949 | 20 | |
| 1.5% | - | |
| 9.4 | 9.7 | |
| 5 days ago | 4 days ago | |
| Jupyter Notebook | Python | |
| MIT License | GNU General Public License v3.0 or later |
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.
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.
botorch
Posts with mentions or reviews of botorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-06.
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botorch VS SMT - a user suggested alternative
2 projects | 6 Dec 2023
- BoTorch – Bayesian Optimization in PyTorch
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[D] Uncertainty estimation with calibration set (with MC Dropout)
The true answer for this is modelling the problem bayesian in the first place using, for example, https://botorch.org/ and https://gpytorch.ai/.
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Bayesian Optimization Book
Yes, I'm using a binary outcome, since that's what I get from playing a game. To get probabilities I'd have to play a lot of games with the same settings/features/point and take the mean, but it seems that defeats the point of Bayesian optimization finding the best point to evaluate for each iteration.
The SPSA method seems to work quite well with binary outcomes. This is what I was trying to beat. Unfortunately I was never able to converge faster than SPSA (or even close to that) even increasing the number of samples.
I got some feedback form the botorch team back then: https://github.com/pytorch/botorch/issues/347#:~:text=thomas...
optimas
Posts with mentions or reviews of optimas.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-31.
-
BoTorch – Bayesian Optimization in PyTorch
It's also very useful for simulation-based optimization. As an example, we use it extensively for the design of particle accelerators, where the simulations are typically expensive and need to run on supercomputers. We have built our own library[0] for enabling this, which in the end uses BoTorch (through Ax[1]) under the hood.
[0]: https://github.com/optimas-org/optimas
What are some alternatives?
When comparing botorch and optimas you can also consider the following projects:
stat_rethinking_2022 - Statistical Rethinking course winter 2022
Ax - Adaptive Experimentation Platform
noisy-bayesian-optimization - Bayesian Optimization for very Noisy functions
smt - Surrogate Modeling Toolbox