Should we consider adding bayesmark to the benchmarking suite
kveerama opened this issue · 1 comments
kveerama commented
The bayesmark
package is another wrapper
hyper parameter tuning library. We can add this to our benchmarking suite. Per their documentation, they wrap around:
The builtin optimizers are wrappers on the following projects:
HyperOpt
Nevergrad
OpenTuner
PySOT
Scikit-optimize
https://github.com/uber/bayesmark/
And we already benchmark against HyperOpt
. Note that OpenTuner
is a previous package developed at MIT in 2014.
kveerama commented
We have in the past tried Nevergrad
. Alternatively, we can just add Nevergrad
, Scitkit-optimize
and PySOT
individually.