Using HiPlot

Experiment visualisation with high dimensional plotting.

HiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways to represent information. Our hiplot-mlflow library implements an MLFlow experiment fetcher plugin for HiPlot to enable visualising experiments.

To use hiplot-mlflow, install it on your server, or add it as a Python 3 package in a custom environment.

For details on how to use HiPlot’s interactive visualisation, please refer to their documentation.

Using HiPlot in a notebook

In Jupyter notebooks use the hiplot_mlflow.fetch function to retrieve an MLflow experiment by name, and display it with HiPlot:

import hiplot_mlflow
experiments = hiplot_mlflow.fetch("my-lovely-experiment")

By default MLflow tags are not shown (only MLflow metrics and parameters). To display tags as well, pass include_tag=True to the fetching functions, for example:

experiment = hiplot_mlflow.fetch("my-lovely-experiment", include_tags=True)

See more about what you can do with the returned hiplot.Experiment object in the HiPlot documentation.

Using HiPlot as a stand-alone app

You can also use HiPlot’s built in webserver with hiplot-mlflow as a custom app on the Platform.

To do this, create a start-up script, here we’ll call it for example, somewhere in your workspace with the following contents.


exec hiplot hiplot_mlflow.fetch_by_uri --port 8888

Then create an environment that installs hiplot-mlflow as a Python 3 package.

Finally, set up the custom script app app to use that environment, and run the script.

If you the navigate to the app’s URL, you can then use the mlflow:// schema to access MLflow experiments in HiPlot by either experiment name or ID, for example:


You can also add tags=yes as a query string parameter to include tags in the output, for example:


You can also use the multiple experiments loading syntax of HiPlot, either the list format:


or the dictionary format, where you can define your own experiment labels:

    "first-experiment": "mlflow://id/1",
    "another-experiment": "mlflow://name/another-experiment?tags=yes"

If you encounter any issue when plotting, such as a “HTTP error, check server logs / javascript console” message in the HiPlot interface, you can troubleshoot by navigating in the app’s control interface’s Monitor / App Logs view.