optimization-tutorial
sigopt-server
| optimization-tutorial | sigopt-server | |
|---|---|---|
| 1 | 1 | |
| 17 | 33 | |
| - | - | |
| 0.0 | 9.6 | |
| about 2 years ago | 3 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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optimization-tutorial
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
I will look into this algorithm. Thanks for the suggestion. I have some basic explanations of the optimization techniques and their parameters in a separate repository: https://github.com/SimonBlanke/optimization-tutorial
But there is still a lot of work to be done.
sigopt-server
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SigOpt (YC W15, Optimization Platform) is now fully open source
Hi, I am one of the founders of SigOpt (acquired by Intel in 2020) and I am happy to answer any questions people may have!
You can also jump right to the code here: https://github.com/sigopt/sigopt-server
What are some alternatives?
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
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.
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
SAP-HANA-AutoML - Python Automated Machine Learning library for tabular data.
BayesianOptimization - A Python implementation of global optimization with gaussian processes.
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Gradient-Free-Optimizers - Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
surrogate-models - A collection of surrogate models for sequence model based optimization techniques
pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.