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Maggy: Asynchronous distributed hyperparameter optimization based on Apache Spark

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Maggy: Asynchronous distributed hyperparameter optimization based on Apache Spark
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Asynchronous algorithms on a bulk-synchronous system
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490
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CC Attribution 2.0 Belgium:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Abstract
Maggy is an open-source framework built on Apache Spark, for asynchronous parallel execution of trials for machine learning experiments. In this talk, we will present our work to tackle search as a general purpose method efficiently with Maggy, focusing on hyperparameter optimization. We show that an asynchronous system enables state-of-the-art optimization algorithms and allows extensive early stopping in order to increase the number of trials that can be performed in a given period of time on a fixed amount of resources.