Getting Started¶

This guide walks you through setting up MLflow and creating your first widget.

Prerequisites¶

Install mlflow-widgets with MLflow:
```
pip install mlflow-widgets[mlflow]
```
Start an MLflow tracking server:
```
mlflow server --port 5000
```

Create some runs¶

Log a few training runs so there's data to visualize:

import mlflow

mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("my-experiment")

for trial in range(3):
    with mlflow.start_run(run_name=f"trial-{trial}"):
        mlflow.log_param("lr", 0.01 * (trial + 1))
        for step in range(50):
            mlflow.log_metric("loss", 1.0 / (step + 1 + trial), step=step)

Visualize with MlflowChart¶

from mlflow_widgets import MlflowChart

chart = MlflowChart(
    tracking_uri="http://localhost:5000",
    experiment_id="1",
    metric_key="loss",
)
chart

The chart will auto-poll every 5 seconds by default. Pass poll_seconds=None to disable auto-polling and show a manual refresh button instead.

Browse runs with MlflowRunTable¶

from mlflow_widgets import MlflowRunTable

table = MlflowRunTable(
    tracking_uri="http://localhost:5000",
    experiment_id="1",
)
table

Usage with Marimo¶

Wrap any widget with mo.ui.anywidget() to use it in a Marimo notebook:

import marimo as mo
from mlflow_widgets import MlflowChart

chart = MlflowChart(
    tracking_uri="http://localhost:5000",
    experiment_id="1",
    metric_key="loss",
)
widget = mo.ui.anywidget(chart)
widget

Next steps¶

Learn about each widget in the Widgets section
Browse the Examples for complete Marimo notebook demos
See the full API Reference for all parameters and methods