Today we announce the general availability of Syne Tune, an open-source Python library for large-scale distributed hyperparameter and neural architecture optimization. It provides implementations of several state-of-the-art global optimizers, such as Bayesian optimization, Hyperband, and population-based training. Additionally, it supports constrained and multi-objective optimization, and allows you to bring your own global optimization algorithm.
With Syne Tune, you can run hyperparameter and neural architecture tuning jobs locally on your machine or remotely on Amazon SageMaker by changing just one line of code. The former is a well-suited backend for smaller workloads and fast experimentation on local CPUs or GPUs. The latter is well-suited for larger workloads, which come with a substantial amount of implementation overhead. Syne Tune makes it easy to use SageMaker as a backend to reduce wall clock time by evaluating a large number of configurations on parallel Amazon Elastic Compute Cloud (Amazon EC2) instances, while taking advantage

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