.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/integrations/plot_skore_mlflow_project.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_integrations_plot_skore_mlflow_project.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_integrations_plot_skore_mlflow_project.py:


.. _example_skore_mlflow_project:

==========================================
Store and retrieve Skore reports in MLflow
==========================================

The primilarly goal of `skore` is to create data science artifacts in the form of
structured reports. Those reports can easily be used programmatically via the Python
API. A subsequent aim is to store those reports that you create during your experiment
cycle in a way that it is easy to retrieve them later on.

Skore provides two natives ways to store reports: locally or on Skore Hub. Skore Hub
provides additional interactivity features for you to explore, compare and share
visual insights.

In addition, Skore also provides an MLflow integration to store the content of
reports directly as MLflow artifacts. This example shows how to persist reports in
MLflow using :class:`~skore.Project` in ``mode="mlflow"``: log reports as MLflow runs
and inspect them.

To run this example against your own MLflow tracking server, use:

.. code-block:: bash

    TRACKING_URI=<tracking_uri> PROJECT=<project> python plot_skore_mlflow_project.py

To try it locally, start an MLflow server with ``uvx mlflow server`` and set
``TRACKING_URI=http://127.0.0.1:5000``. For more setup details, see the
`MLflow quickstart <https://mlflow.org/docs/latest/ml/getting-started/running-notebooks/>`_.

.. GENERATED FROM PYTHON SOURCE LINES 34-39

Create a Skore report
=====================

First, we start by creating a Skore report by evaluating a logistic regression model
on the iris dataset using some cross-validation.

.. GENERATED FROM PYTHON SOURCE LINES 40-51

.. code-block:: Python

    from sklearn.datasets import load_iris
    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler
    from skore import evaluate

    X, y = load_iris(return_X_y=True, as_frame=True)

    estimator = make_pipeline(StandardScaler(), LogisticRegression())
    report = evaluate(estimator, X, y, splitter=5)








.. GENERATED FROM PYTHON SOURCE LINES 52-58

Store the Skore reports as MLflow artifacts
===========================================

Now, we will store the different items of the Skore report as MLflow artifacts.
For this matter, you need to create a :class:`~skore.Project` in ``mode="mlflow"``
and pass the information regarding the MLflow tracking server.

.. GENERATED FROM PYTHON SOURCE LINES 59-73

.. code-block:: Python


    import io


    # MLflow/Alembic emits verbose DB initialization logs; silence them so the
    # example page focuses on skore usage rather than backend startup details.
    with redirect_stdout(io.StringIO()), redirect_stderr(io.StringIO()):
        # This creates an MLflow experiment with name `PROJECT`:
        project = Project(
            PROJECT,
            mode="mlflow",
            tracking_uri=TRACKING_URI,
        )








.. GENERATED FROM PYTHON SOURCE LINES 99-101

Once the project created, the same API used to store a report locally or on Skore Hub
applies.

.. GENERATED FROM PYTHON SOURCE LINES 101-103

.. code-block:: Python

    project.put("logistic-regression", report)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    2026/06/20 09:44:32 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.
    2026/06/20 09:44:34 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.
    2026/06/20 09:44:36 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.
    2026/06/20 09:44:38 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.
    2026/06/20 09:44:40 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.
    2026/06/20 09:44:43 WARNING mlflow.utils.environment: Failed to resolve installed pip version. ``pip`` will be added to conda.yaml environment spec without a version specifier.




.. GENERATED FROM PYTHON SOURCE LINES 104-119

Once that you stored the report, the artifacts will be available on the MLflow
tracking server at the following URL:
``http://<TRACKING_URI>/#/<PROJECT>/1/runs/<RUN_ID>/artifacts``

To find the run ID attributed by MLflow, you can check the section below.

Retrieve the Skore report from MLflow tracking server
=====================================================

Like for the other modes (local and Skore Hub), you can access what is stored in the
project via the :meth:`~skore.Project.summarize` method.

In MLflow mode, :meth:`~skore.Project.summarize` exposes the same
:class:`~skore.Summary` API as in local and hub mode: use
:meth:`~skore.Summary.frame` to get the underlying :class:`pandas.DataFrame`.

.. GENERATED FROM PYTHON SOURCE LINES 119-126

.. code-block:: Python




    summary = project.summarize()
    pandas_summary = summary.frame().reset_index()
    pandas_summary[["id", "key", "report_type", "learner", "dataset"]]






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>id</th>
          <th>key</th>
          <th>report_type</th>
          <th>learner</th>
          <th>dataset</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>a396f25b6000460ab33733c9cc642b36</td>
          <td>logistic-regression</td>
          <td>cross-validation</td>
          <td>LogisticRegression</td>
          <td>8f9eb48c</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 132-133

Then, you can retrieve a Skore report using the `"id"` column:

.. GENERATED FROM PYTHON SOURCE LINES 133-137

.. code-block:: Python

    (run_id,) = pandas_summary["id"]
    loaded_report = project.get(run_id)
    loaded_report.metrics.summarize().frame()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead tr th {
            text-align: left;
        }

        .dataframe thead tr:last-of-type th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th></th>
          <th></th>
          <th colspan="2" halign="left">LogisticRegression</th>
        </tr>
        <tr>
          <th></th>
          <th></th>
          <th>mean</th>
          <th>std</th>
        </tr>
        <tr>
          <th>Metric</th>
          <th>Label</th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>Score</th>
          <th></th>
          <td>0.960000</td>
          <td>0.043461</td>
        </tr>
        <tr>
          <th>Accuracy</th>
          <th></th>
          <td>0.960000</td>
          <td>0.043461</td>
        </tr>
        <tr>
          <th rowspan="3" valign="top">Precision</th>
          <th>0</th>
          <td>1.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.945455</td>
          <td>0.081312</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0.944444</td>
          <td>0.078567</td>
        </tr>
        <tr>
          <th rowspan="3" valign="top">Recall</th>
          <th>0</th>
          <td>1.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.940000</td>
          <td>0.089443</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0.940000</td>
          <td>0.089443</td>
        </tr>
        <tr>
          <th rowspan="3" valign="top">ROC AUC</th>
          <th>0</th>
          <td>1.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0.996000</td>
          <td>0.005477</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0.996000</td>
          <td>0.005477</td>
        </tr>
        <tr>
          <th>Log loss</th>
          <th></th>
          <td>0.150357</td>
          <td>0.040373</td>
        </tr>
        <tr>
          <th>Fit time (s)</th>
          <th></th>
          <td>0.002438</td>
          <td>0.000250</td>
        </tr>
        <tr>
          <th>Predict time (s)</th>
          <th></th>
          <td>0.000336</td>
          <td>0.000025</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 138-140

You can directly use MLflow to access information stored in the MLflow tracking
server.

.. GENERATED FROM PYTHON SOURCE LINES 140-145

.. code-block:: Python

    import mlflow

    mlflow_run = mlflow.get_run(run_id)
    mlflow_run.data.metrics





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    {'accuracy': 0.96, 'accuracy_std': 0.043461349368017654, 'log_loss': 0.1503572043685131, 'log_loss_std': 0.0403727742084397, 'recall': 0.96, 'recall_std': 0.043461349368017654, 'precision': 0.96, 'precision_std': 0.043461349368017654, 'roc_auc': 0.9977777777777778, 'roc_auc_std': 0.0025153847605937046, 'fit_time': 0.002438224000007949, 'fit_time_std': 0.0002496825460605965, 'predict_time': 0.0003314581999973143, 'predict_time_std': 3.2789543088645514e-05}



.. GENERATED FROM PYTHON SOURCE LINES 146-155

Conclusion
==========

Skore offers native integrations locally and with Skore Hub. However, if you are
already using MLflow, you can use the ``mode="mlflow"`` option to store reports as
MLflow artifacts directly inside the tracking server.

However, you will not benefit from the interactive user interface provided by Skore
Hub.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 18.995 seconds)


.. _sphx_glr_download_auto_examples_integrations_plot_skore_mlflow_project.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_skore_mlflow_project.ipynb <plot_skore_mlflow_project.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_skore_mlflow_project.py <plot_skore_mlflow_project.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_skore_mlflow_project.zip <plot_skore_mlflow_project.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_