  Modern learning models are characterized by large hyperparameter spaces and
long training times. These properties, coupled with the rise of parallel
computing and the growing demand to productionize machine learning workloads,
motivate the need to develop mature hyperparameter optimization functionality
in distributed computing settings. We address this challenge by first
introducing a simple and robust hyperparameter optimization algorithm called
ASHA, which exploits parallelism and aggressive early-stopping to tackle
large-scale hyperparameter optimization problems. Our extensive empirical
results show that ASHA outperforms existing state-of-the-art hyperparameter
optimization methods; scales linearly with the number of workers in distributed
settings; and is suitable for massive parallelism, as demonstrated on a task
with 500 workers. We then describe several design decisions we encountered,
along with our associated solutions, when integrating ASHA in Determined AI's
end-to-end production-quality machine learning system that offers
hyperparameter tuning as a service.
