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
Reporterallow for seamless implementation of customized functionality.