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Hypertunity is a lightweight, high-level library for hyperparameter optimisation. Among others, it supports:

  • Bayesian optimisation by wrapping GPyOpt
  • external or internal objective evaluation using a scheduler, also compatible with Slurm
  • real-time visualisation of results in Tensorboard using the HParams plugin.

The main guiding design principles are:

  • Modular: you can use any optimiser and reporter as well as schedule jobs locally or on Slurm without changes in the API.
  • Simple: the small codebase (just about 1000 LOC) and the flat subpackage hierarchy makes it easy to use, maintain and extend.
  • Extensible: base classes such as Optimiser, Job and Reporter allow for seamless implementation of customized functionality.

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