  Prior research in resource scheduling for machine learning training workloads
has largely focused on minimizing job completion times. Commonly, these model
training workloads collectively search over a large number of parameter values
that control the learning process in a hyperparameter search. It is preferable
to identify and maximally provision the best-performing hyperparameter
configuration (trial) to achieve the highest accuracy result as soon as
possible.
  To optimally trade-off evaluating multiple configurations and training the
most promising ones by a fixed deadline, we design and build HyperSched -- a
dynamic application-level resource scheduler to track, identify, and
preferentially allocate resources to the best performing trials to maximize
accuracy by the deadline. HyperSched leverages three properties of a
hyperparameter search workload over-looked in prior work - trial disposability,
progressively identifiable rankings among different configurations, and
space-time constraints - to outperform standard hyperparameter search
algorithms across a variety of benchmarks.
