200 Seconds of Delay: The Hidden Cost of H2O Dataframe Cleanup

In a data science project using h2o, a loop of heatmap visualizations was set up to measure overfitting. The function designed to return these heatmaps experienced a significant delay of around 200 seconds when returning the figure. This delay was traced back to the garbage collection process of the h2o dataframe. When the function included the command `h2o.remove(shocked_hf)`, it confirmed that this cleanup was the cause of the delay. The H2OFrame in question was relatively small, with only 625 rows and 107 columns. Despite this, the removal process was slow, suggesting inefficiencies in the cleanup mechanism. The developer considered alternatives like manual garbage collection or integrating the loop within the function, but these solutions were not ideal. The statistics highlight the unexpected performance impact of data management in h2o, particularly when dealing with even modestly sized datasets.

Source: stackoverflow.com