  We describe a light-weight yet performant system for hyper-parameter
optimization that approximately minimizes an overall scalar cost function that
is obtained by combining multiple performance objectives using a
target-priority-limit scalarizer. It also supports a trade-off mode, where the
goal is to find an appropriate trade-off among objectives by interacting with
the user. We focus on the common scenario where there are on the order of tens
of hyper-parameters, each with various attributes such as a range of continuous
values, or a finite list of values, and whether it should be treated on a
linear or logarithmic scale. The system supports multiple asynchronous
simulations and is robust to simulation stragglers and failures.
