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HiPlot fetcher plugin for MLflow experiment tracking.

Project description

A HiPlot experiment fetcher plugin for MLflow, to help visualise your tracked experiments.


Install this library with pip as:

pip install hiplot_mlflow


You can visualise experiments either in a Jupyter notebook or using HiPlot’s built in server.


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

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

You can also retrieve experiments by their MLflow experiment ID:

experiment = hiplot_mlflow.fetch_by_id(0)

By default, MLflow tags are not shown (only MLflow metrics and parameters are shown). To display them, pass include_tag=True to either of the fetch functions, for example:

experiment = hiplot_mlflow.fetch("my-lovely-experiment", include_tags=True)
Loading HiPlot in a notebook

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

HiPlot Server

To use HiPlot’s built in webserver with hiplot-mlflow, you can start it up with the custom experiment fetcher implemented by this package:

hiplot hiplot_mlflow.fetch_by_uri

You can then use the mlflow:// schema to access MLflow experiments in HiPlot by either experiment or name, for example:

Loading HiPlot server with experiment name

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. Either the dictionary format (to define your own labels):

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

or list format:

Multiple experiments in HiPlot server

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