Oríon is an asynchronous framework for black-box function optimization.
Its purpose is to serve as a meta-optimizer for machine learning models and training, as well as a flexible experimentation platform for large scale asynchronous optimization procedures.
Core design value is the minimum disruption of a researcher’s workflow. It allows fast and efficient tuning, providing minimum simple non-intrusive (not even necessary!) helper client interface for a user’s script.
So if ./run.py --mini-batch=50 looks like what you execute normally, now what you have to do looks like this:
orion -n experiment_name ./run.py --mini-batch~'randint(32, 256)'
Check out our getting started guide or this presentation for an overview, or our scikit-learn example for a more hands-on experience. Finally we encourage you to browse our documentation.
Why Oríon?
Effortless to adopt, deeply customizable
Adopt it with a single line of code
Natively asynchronous, thus resilient and easy to parallelize
Offers the latest established hyperparameter algorithms
Elegant and rich search-space definitions
Comprehensive configuration system with smart defaults
Transparent persistence in local or remote database
Integrate seamlessly your own hyper-optimization algorithms
Language and configuration file agnostic
Installation
Install Oríon by running $ pip install orion. For more information consult the installation guide.
Presentations
Contribute or Ask
Do you have a question or issues? Do you want to report a bug or suggest a feature? Name it! Please contact us by opening an issue in our repository below and checkout our contribution guidelines:
Issue Tracker: https://github.com/epistimio/orion/issues
Source Code: https://github.com/epistimio/orion
Start by starring and forking our Github repo!
Thanks for the support!
Citation
If you use Oríon for published work, please cite our work using the following bibtex entry.
@software{xavier_bouthillier_2023_0_2_7,
author = {Xavier Bouthillier and
Christos Tsirigotis and
François Corneau-Tremblay and
Thomas Schweizer and
Lin Dong and
Pierre Delaunay and
Fabrice Normandin and
Mirko Bronzi and
Dendi Suhubdy and
Reyhane Askari and
Michael Noukhovitch and
Chao Xue and
Satya Ortiz-Gagné and
Olivier Breuleux and
Arnaud Bergeron and
Olexa Bilaniuk and
Steven Bocco and
Hadrien Bertrand and
Guillaume Alain and
Dmitriy Serdyuk and
Peter Henderson and
Pascal Lamblin and
Christopher Beckham},
title = {{Epistimio/orion: Asynchronous Distributed Hyperparameter Optimization}},
month = march,
year = 2023,
publisher = {Zenodo},
version = {v0.2.7,
doi = {10.5281/zenodo.3478592},
url = {https://doi.org/10.5281/zenodo.3478592}
}Roadmap
See ROADMAP.md.
License
The project is licensed under the BSD license.
Release files for orion 0.2.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| orion-0.2.7.tar.gz | 29.5 MB | Details |
Release files / orion-0.2.7.tar.gz
| Download URL | orion-0.2.7.tar.gz |
|---|---|
| Size | 29.5 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.2 CPython/3.9.16
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