HiPlot fetcher plugin for MLflow experiment tracking.
Project description
A HiPlot experiment fetcher plugin for MLflow, to help visualise your tracked experiments.
Installation
Install this library with pip as:
pip install hiplot_mlflow
Usage
You can visualise experiments either in a Jupyter notebook or using HiPlot’s built in server.
Notebook
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")
experiments.display(force_full_width=True)
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)
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:
mlflow://name/experiment-name mlflow://id/0
You can also add tags=yes as a query string parameter to include tags in the output, for example:
mlflow://name/experiment-name?tags=yes
You can also use the multiple experiments loading syntax. Either the dictionary format (to define your own labels):
multi://{ "first-experiment": "mlflow://id/1", "another-experiment": "mlflow://name/another-experiment?tags=yes" }
or list format:
multi://[ "mlflow://id/1", "mlflow://name/another-experiment?tags=yes" ]
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