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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 user’s guide-101 for the simplest of demonstrations!


As simple and as complex you want

  • Simple and natural, but also explicit and verbose, search domain definitions
  • Minimal and non-intrusive client interface for reporting target function values
  • Database logging (currently powered by MongoDB)
  • Flexible configuration
  • Explicit experiment termination conditions
  • Algorithms algorithms algorithms: Skopt’s bayesian optimizers are at hand without writing. Random search is the default. only a single line of code.
  • More algorithms: Implementing and distributing algorithms is as easy as possible! Check developer’s guide-101. Expect algorithm plugins to pop out quickly!
  • Came up with an idea? Your intuition is still at play: Help your optima hunter now by a command line interface.
  • And other many more already there or coming soon!


Install Oríon by running:

pip install orion

For more information read the full installation docs.

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:

Start by starring and forking our Github repo!

Thanks for the support!


You can find our roadmap here: https://github.com/Epistimio/orion/blob/master/ROADMAP.md


If you use Oríon for published work, please cite our work using the following bibtex entry.

  author       = {Xavier Bouthillier and
                  Christos Tsirigotis and
                  François Corneau-Tremblay and
                  Pierre Delaunay and
                  Reyhane Askari and
                  Dendi Suhubdy and
                  Michael Noukhovitch and
                  Dmitriy Serdyuk and
                  Arnaud Bergeron and
                  Peter Henderson and
                  Pascal Lamblin and
                  Mirko Bronzi and
                  Christopher Beckham},
  title        = {Oríon - Asynchronous Distributed Hyperparameter Optimization},
  month        = oct,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {v0.1.7},
  doi          = {10.5281/zenodo.3478593},
  url          = {https://doi.org/10.5281/zenodo.3478593}