Examples¶

mlflow-widgets ships with several Marimo notebook demos in the examples/ directory. Each file is a self-contained Marimo notebook with inline dependency metadata.

Running the demos¶

# Install demo dependencies
pip install mlflow-widgets[demo]

# Start an MLflow server
mlflow server --port 5000 &

# Run a specific demo
MLFLOW_TRACKING_URI=http://localhost:5000 marimo edit examples/demo.py

# Run all demos with hot-reload
MLFLOW_TRACKING_URI=http://localhost:5000 marimo edit --watch .

Available demos¶

demo.py -- Mock experiments + chart¶

Generates mock training runs with random metrics, then visualizes them with MlflowChart. A good starting point to see the chart in action.

live_tracking.py -- Live tracking¶

Tracks an experiment on-the-fly and shows live chart updates as metrics are logged. Demonstrates the auto-polling feature.

table_demo.py -- Run table¶

Displays all runs in an experiment using MlflowRunTable with sortable columns, status badges, and parameter/metric columns.

nested_demo.py -- Nested runs¶

Creates parent/child nested runs and displays them in MlflowRunTable's tree view with collapsible groups.

parallel_demo.py -- Parallel coordinates¶

Shows a parallel coordinates chart with status filtering. Each axis represents a hyperparameter or metric.

combo_demo.py -- Selector + table + chart¶

End-to-end workflow: MlflowRunSelector produces a DataFrame, mo.ui.table enables checkbox selection, and MlflowChart visualizes the selected runs. Demonstrates how the components compose together.