  Hyperparameter tuning of multi-stage pipelines introduces a significant
computational burden. Motivated by the observation that work can be reused
across pipelines if the intermediate computations are the same, we propose a
pipeline-aware approach to hyperparameter tuning. Our approach optimizes both
the design and execution of pipelines to maximize reuse. We design pipelines
amenable for reuse by (i) introducing a novel hybrid hyperparameter tuning
method called gridded random search, and (ii) reducing the average training
time in pipelines by adapting early-stopping hyperparameter tuning approaches.
We then realize the potential for reuse during execution by introducing a novel
caching problem for ML workloads which we pose as a mixed integer linear
program (ILP), and subsequently evaluating various caching heuristics relative
to the optimal solution of the ILP. We conduct experiments on simulated and
real-world machine learning pipelines to show that a pipeline-aware approach to
hyperparameter tuning can offer over an order-of-magnitude speedup over
independently evaluating pipeline configurations.
