Welcome to fluentopt documentation!

Fluentopt is a flexible hyper-parameter optimization library.

Most hyper-parameter optimization libraries impose three main restrictions :

  • they control the optimization loop
  • they force the inputs to be represented by vectors
  • the priors are very restricted, e.g gaussian, uniform or discrete uniform

the goal of fluentopt is to provide hyper-parameter optimization library where :

  • the optimization loop is controlled by the user (but we will provide also helpers).

  • the inputs can be represented by a python dictionary to express conditionals rather than just a list (or vector), but in case not needed they can also just be a list or a scalar. The dictionaries can contain strings, varying length lists and special objects like ‘None’.

  • the priors of the hyper-parameters are not restricted to some pre-defined probability distributions. Users will just provide samplers as a python function, that is, a function that takes a seed and returns a python dictionary.

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