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Asynchronous [black-box] Optimization

Project description

Current PyPi Version Supported Python Versions BSD 3-clause license Documentation Status Codecov Report Travis tests

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 ./ --mini-batch=50 looks like what you execute normally, now what you have to do looks like this:

orion -n experiment_name ./ --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:

Start by starring and forking our Github repo!

Thanks for the support!


You can find our roadmap here:


The project is licensed under the BSD license.

Project details

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