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:
- 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.
- 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
.
stages:
evaluate:
cmd: python src/evaluate.py
deps: ...
plots:
- 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.