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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. Previous PBT implementations have been synchronized glass-box systems. We propose a general, black-box PBT framework that distributes many asynchronous "trials" (a small number of training steps with warm-starting) across a cluster, coordinated by the PBT controller. The black-box design does not make assumptions on model architectures, loss functions or training procedures. Our system supports dynamic hyperparameter schedules to optimize both differentiable and non-differentiable metrics. We apply our system to train a state-of-the-art WaveNet generative model for human voice synthesis. We show that our PBT system achieves better accuracy, less sensitivity and faster convergence compared to existing methods, given the same computational resource., 9 pages

Due to their complexity, modern systems expose many configuration parameters which users must tune to maximize performance. Auto-tuning has emerged as an alternative in which a black-box optimizer iteratively evaluates configurations to find efficient ones. Unfortunately, for many systems, such as distributed systems, evaluating performance takes too long and the space of configurations is too large for the optimizer to converge within a reasonable time. We present BOAT, a framework which allows developers to build efficient bespoke auto-tuners for their system, in situations where generic auto-tuners fail. At BOAT's core is structured Bayesian optimization (SBO), a novel extension of the Bayesian optimization algorithm. SBO leverages contextual information provided by system developers, in the form of a probabilistic model of the system's behavior, to make informed decisions about which configurations to evaluate. In a case study, we tune the scheduling of a neural network computation on a heterogeneous cluster. Our auto-tuner converges within ten iterations. The optimized configurations outperform those found by generic auto-tuners in thirty iterations by up to 2X.