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HiPlot demonstration

Given about 7000 experimental datapoints, we want to understand which parameters influence the metric we want to optimize: valid ppl. How can HiPlot help?

  • On the parallel plot, each line represents one datapoint. Slicing on the valid ppl axis reveals that higher values for lr lead to better models.

  • We will focus on higher values for the lr then. Un-slice the valid ppl axis by clicking on the axis, but outside of the current slice. Slice on the lr axis values above 1e-2, then click the Keep button.

  • Let’s see now how the training goes by adding a line plot. Right click the epoch axis title and select Set as X axis. Similarly, set valid ppl as the Y axis. Once you have done both, an XY line plot should appear below the parallel plot.

  • Slicing through the dropout, embedding_size and lr axis reveals how they can affect the training dynamics: convergence speed and maximum performance.

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