MlflowRunSelector¶

Pure Python helper that fetches MLflow experiment runs and returns them as a pandas DataFrame. Designed for interactive run selection workflows, especially with Marimo's mo.ui.table.

Note

MlflowRunSelector is not a widget -- it's a plain Python class. It does not render anything on its own.

Basic usage¶

from mlflow_widgets import MlflowRunSelector

selector = MlflowRunSelector(
    tracking_uri="http://localhost:5000",
    experiment_id="1",
)
df = selector.to_dataframe()

The returned DataFrame includes columns: run_id, run_name, status, start_time, duration_s, plus param.<key> and metric.<key> for each parameter and metric.

Interactive selection with Marimo¶

The primary use case is selecting runs via checkboxes, then passing selected IDs to a chart:

import marimo as mo
from mlflow_widgets import MlflowRunSelector, MlflowChart

selector = MlflowRunSelector(
    tracking_uri="http://localhost:5000",
    experiment_id="1",
)
table = mo.ui.table(selector.to_dataframe(), selection="multi")

In another cell:

selected_ids = MlflowRunSelector.get_run_ids(table.value)
chart = MlflowChart(runs=selected_ids, metric_key="loss")
mo.ui.anywidget(chart)

Requirements¶

pandas must be installed to use to_dataframe(). It will raise ImportError with a helpful message if missing.

API reference¶

See MlflowRunSelector for the full API.