Pagmo2
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Polyaxon | 3,385 |  | 4 | 12 | 3 days ago | 377 | August 14, 2023 | 122 | apache-2.0 | |
| MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle | ||||||||||
| Pagmo2 | 743 | 5 | 3 months ago | 24 | August 03, 2021 | 42 | gpl-3.0 | C++ | ||
| A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model. | ||||||||||
| Rumale | 694 |  | 2 | 4 | a day ago | 67 | June 27, 2021 | bsd-3-clause | Ruby | |
| Rumale is a machine learning library in Ruby | ||||||||||
| Pygmo2 | 329 |  | 11 | 18 | 3 months ago | 22 | December 22, 2020 | 28 | mpl-2.0 | C++ |
| A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model. | ||||||||||
| Entangle | 89 | 2 years ago | 23 | June 17, 2021 | mit | Python | ||||
| A lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs. | ||||||||||
| Alphazero_gomoku_mpi | 86 | 4 years ago | 5 | Python | ||||||
| An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku | ||||||||||
| Distributeddeeplearning | 20 | 5 years ago | mit | Shell | ||||||
| Tutorials on running distributed deep learning on Batch AI | ||||||||||
| Gfsopt | 13 | 4 years ago | 3 | April 27, 2019 | mit | Python | ||||
| Convenient hyperparameter optimization | ||||||||||
| Ebic | 8 | 4 years ago | 1 | mit | C++ | |||||
| EBIC - AI-based parallel biclustering algorithm | ||||||||||
| Chapter15 Alphazero | 7 | 4 years ago | Python | |||||||
| Chapter 15 AlphaZero in book Deep Reinforcement Learning: code example of AlphaZero solving Gomoku game. | ||||||||||
pagmo
IMPORTANT NOTICE: pygmo, the Python bindings for pagmo, have been split off into a separate project, hosted here. Please update your bookmarks!
pagmo is a C++ scientific library for massively parallel optimization. It is built around the idea of providing a unified interface to optimization algorithms and to optimization problems and to make their deployment in massively parallel environments easy.
If you are using pagmo as part of your research, teaching, or other activities, we would be grateful if you could star the repository and/or cite our work. For citation purposes, you can use the following BibTex entry, which refers to the pagmo paper in the Journal of Open Source Software:
@article{Biscani2020,
doi = {10.21105/joss.02338},
url = {https://doi.org/10.21105/joss.02338},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {53},
pages = {2338},
author = {Francesco Biscani and Dario Izzo},
title = {A parallel global multiobjective framework for optimization: pagmo},
journal = {Journal of Open Source Software}
}
The DOI of the latest version of the software is available at this link.
The full documentation can be found here.
Upgrading from pagmo 1.x.x
If you were using the old pagmo, have a look here on some technical data on what and why a completely new API and code was developed: https://github.com/esa/pagmo2/wiki/From-1.x-to-2.x
You will find many tutorials in the documentation, we suggest to skim through them to realize the differences. The new pagmo (version 2) should be considered (and is) as an entirely different code.
