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The freedom of fast iterations of distributed deep learning tasks is crucialfor smaller companies to gain competitive advantages and market shares from bigtech giants. HorovodRunner brings this process to relatively accessible sparkclusters. There have been, however, no benchmark tests on HorovodRunner per se,nor specifically graph convolutional network (GCN, hereafter), and very limitedscalability benchmark tests on Horovod, the predecessor requiring custom builtGPU clusters. For the first time, we show that Databricks' HorovodRunnerachieves significant lift in scaling efficiency for the convolutional neuralnetwork (CNN, hereafter) based tasks on both GPU and CPU clusters, but not theoriginal GCN task. We also implemented the Rectified Adam optimizer for thefirst time in HorovodRunner.

Benchmark Tests of Convolutional Neural Network and Graph Convolutional Network on HorovodRunner Enabled Spark Clusters