- 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)
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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