Google Research

Population Based Training as a Service

  • Ang Li
  • Ola Spyra
  • Sagi Perel
  • Valentin Dalibard
  • Max Jaderberg
  • Chenjie Gu
  • David Budden
  • Tim Harley
  • Pramod Gupta
NIPS Systems for ML Workshop (2018) (to appear)

Abstract

Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training. We present in this extended abstract a generalized black-box service framework for Population Based Training. We argue that the black-box PBT service design has benefits in system scalability and required engineering effort. We perform a case study on WaveNet speech synthesis to demonstrate the effectiveness of our PBT service.

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