  Feedback-driven optimization, such as traditional machine learning training,
is a static process that lacks real-time adaptability of hyperparameters.
Tuning solutions for optimization require trial and error paired with
checkpointing and schedulers, in many cases feedback from the algorithm is
overlooked. Adjusting hyperparameters during optimization usually requires the
program to be restarted, wasting utilization and time, while placing
unnecessary strain on memory and processors. We present LiveTune, a novel
framework allowing real-time parameter adjustment of optimization loops through
LiveVariables. Live Variables allow for continuous feedback-driven optimization
by storing parameters on designated ports on the system, allowing them to be
dynamically adjusted. Extensive evaluations of our framework on standard
machine learning training pipelines show saving up to 60 seconds and 5.4
Kilojoules of energy per hyperparameter change. We also show the feasibility
and value of LiveTune in a reinforcement learning application where the users
change the dynamics of the reward structure while the agent is learning showing
5x improvement over the baseline. Finally, we outline a fully automated
workflow to provide end-to-end, unsupervised feedback-driven optimization.
