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.

HiPlot documentation¶

Contents:

Table Of Contents

HiPlot demonstration¶

HiPlot documentation¶

Indices and tables¶