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We have renamed Views to Projects in Iterative Studio.

Accordingly, Views dashboard is now called Projects dashboard; View settings are now called Project settings; and so on.

Visualize and Compare Experiments

You can visualize and compare experiments using plots, images, charts, etc. You can also export the project table as CSV, so that you can use the data with any external reporting or visualization tool of your choice.

Display plots and images

You can visualize certain metrics of machine learning experiments as plots. Usual plot examples are AUC curves, loss functions, and confusion matrices, among others. For DVC repositories, the plots are defined in dvc.yaml (plots field). Refer to the DVC plots documentation for details on how to add plots to your repositories.

Types of plots

Iterative Studio can work with two types of plots files in your repository:

  1. Data series files, which can be JSON, YAML, CSV or TSV. Data from these files will populate your AUC curves, loss functions, confusion matrices and other metric plots.
  2. Image files in JPEG, GIF, or PNG format. These images will be displayed as-is in Iterative Studio.

Plots can be pipeline outputs or top-level. Below is a sample dvc.yaml file with 2 plots in the evaluate stage and a top-level plot using data from runtime_logs/logs.csv.

    cmd: python src/evaluate.py
    deps: ...
      - output/predictions.json:
          template: confusion
          x: actual
          y: predicted
      - output/misclassified_samples/:
          cache: false
plots: runtime_logs/logs.csv

As you can see,

  • metrics from output/predictions.json will be plotted in a confusion matrix,
  • images in the output/misclassified_samples/ directory will be displayed directly,
  • data from runtime_logs/logs.json will be rendered using the default (linear) template.

For images, you can also specify a single image file (eg, output/misclassified_sample1.png) instead of a directory.

How to generate plots

To generate the plots, select one or more experiments (represented by the commits), and click on the Show plots button.

The plots will appear in the plots pane. If you have selected more than one experiment, you can use the plots to compare them.

Live plots

You can send live updates to your plots with DVCLive. The number of recent updates to the live metrics are displayed in the Live icon. Live plots are also shown and updated in real-time in the plots pane along with all other plots.

Generate trend charts

Click on the Trends button to generate a plot of how the metrics changed over the course of the different experiments. For each metric, the trend charts show how the metric changed from one commit to another. You can include one or more branches in the trend chart.

Compare experiments

To compare different experiments, select two experiments (represented by the commits), and click on the Compare button. The metrics, parameters and files in the selected experiments will be displayed side by side for easy comparison.


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