Set up experiment tracking

Experiment tracking is the process of saving all machine-learning related experiment information so that it is easy to find and compare past runs. Kedro-Viz supports native experiment tracking from version 4.1.1 onwards. When experiment tracking is enabled in your Kedro project, you will be able to access, edit and compare your experiments directly from the Kedro-Viz web app, as well as see how your metrics have changed over time.

Enabling experiment tracking features on Kedro-Viz relies on:

This tutorial will provide a step-by-step process to set up experiment tracking and access your logged metrics from each run on Kedro-Viz. It will use the starter outlined in the spaceflights tutorial. You can also jump directly to this section for direct reference in setting up experiment tracking for your Kedro project.

You can also access a more detailed Kedro-Viz live demo.

Set up a project


You can skip this step if you have followed all previous parts of the tutorial.

We assume that you have already installed Kedro and Kedro-Viz. To set up a new project using the spaceflights starter, run:

kedro new --starter=spaceflights

Feel free to name your project as you like, but this guide will assume the project is named Kedro Experiment Tracking Tutorial, and that your project is in a sub-folder in your working directory that was created by kedro new, named kedro-experiment-tracking-tutorial.

Set up the session store

In the domain of experiment tracking, each pipeline run is considered a session. A session store records all related metadata for each pipeline run, from logged metrics to other run-related data such as timestamp, git username and branch. The session store is a SQLite database that is generated during your first pipeline run after it has been set up in your project.

To set up the session store, go to the src/kedro-experiment-tracking-tutorial/ file and add the following:

from kedro_viz.integrations.kedro.sqlite_store import SQLiteStore
from pathlib import Path

SESSION_STORE_ARGS = {"path": str(Path(__file__).parents[2] / "data")}

This will specify the creation of the SQLiteStore under the /data subfolder, using the SQLiteStore setup from your installed Kedro-Viz plugin.

Please ensure that your installed version of Kedro-Viz is at least version 4.1.1 onwards. This step is crucial to enable experiment tracking features on Kedro-Viz, as it is the database used to serve all run data to the Kedro-Viz front-end. Once this step is complete, you can either proceed to set up the tracking datasets or set up your nodes and pipelines to log metrics; these two activities are interchangeable, but both should be completed to get a working experiment tracking setup.

Set up tracking datasets

There are two types of tracking datasets: tracking.MetricsDataSet and tracking.JSONDataSet. The tracking.MetricsDataSet should be used for tracking numerical metrics, and the tracking.JSONDataSet can be used for tracking any other JSON-compatible data like boolean or text-based data.

Set up two datasets to log r2 scores and parameters for each run by adding the following in the conf/base/catalog.yml file:

  type: tracking.MetricsDataSet
  filepath: data/09_tracking/metrics.json

  type: tracking.JSONDataSet
  filepath: data/09_tracking/companies_columns.json


These two datasets include namespaces to correspond to the pipeline setup. If you have a project without namespaces, you can still use experiment tracking.

Set up your nodes and pipelines to log metrics

Now that you have set up the tracking datasets to log experiment tracking data, next ensure that the data is returned from your nodes.

Set up the data to be logged for the metrics dataset - under of your data_science pipeline (/src/kedro-experiment-tracking-tutorial/pipelines/data_science/, add three different metrics to your evaluate_model function: score to log your r2 score, mae to log your mean absolute error, and me to log your max error, and returning those 3 metrics as a key value pair.

The new evaluate_model function would look like this:

from sklearn.metrics import mean_absolute_error, max_error

def evaluate_model(
    regressor: LinearRegression, X_test: pd.DataFrame, y_test: pd.Series
) -> Dict[str, float]:
    """Calculates and logs the coefficient of determination.

        regressor: Trained model.
        X_test: Testing data of independent features.
        y_test: Testing data for price.
    y_pred = regressor.predict(X_test)
    score = r2_score(y_test, y_pred)
    mae = mean_absolute_error(y_test, y_pred)
    me = max_error(y_test, y_pred)
    logger = logging.getLogger(__name__)"Model has a coefficient R^2 of %.3f on test data.", score)
    return {"r2_score": score, "mae": mae, "max_error": me}

Next, ensure that the dataset is also specified as an output of your evaluate_model node. In the src/kedro-experiment-tracking-tutorial/pipelines/data_science/ file, specify the output of your evaluate_model to be the metrics dataset. Note that the output dataset must exactly match the name of the tracking dataset specified in the catalog file.

The node of the evaluate_model on the pipeline should look like this:

    inputs=["regressor", "X_test", "y_test"],

Repeat the same steps to set up the companies_column dataset. For this dataset, log the column that contains the list of companies as outlined in the companies.csv file under the /data/01_raw directory. Modify the preprocess_companies node under the data_processing pipeline (src/kedro-experiment-tracking-tutorial/pipelines/data_processing/ to return the data under a key value pair, as shown below:

from typing import Tuple, Dict

def preprocess_companies(companies: pd.DataFrame) -> Tuple[pd.DataFrame, Dict]:
    """Preprocesses the data for companies.

        companies: Raw data.
        Preprocessed data, with `company_rating` converted to a float and
        `iata_approved` converted to boolean.
    companies["iata_approved"] = _is_true(companies["iata_approved"])
    companies["company_rating"] = _parse_percentage(companies["company_rating"])
    return companies, {"columns": companies.columns.tolist(), "data_type": "companies"}

Again, you must ensure that the dataset is also specified as an output on the file under the data_processing pipeline (src/kedro-experiment-tracking-tutorial/pipelines/data_processing/, as follows:

    outputs=["preprocessed_companies", "companies_columns"],

Having set up both datasets, you can now generate your first set of experiment tracking data!

Generate the Run data

The beauty of native experiment tracking in Kedro is that all tracked data is generated and stored each time you do a Kedro run. Hence, to generate the data, you need only execute:

kedro run

After the run completes, under data/09_tracking, you will now see two folders, companies_column.json and metrics.json. On performing a pipeline run after setting up the tracking datasets, Kedro will generate a folder with the dataset name for each tracked dataset. Each folder of the tracked dataset will contain folders named by the timestamp of each pipeline run to store the saved metrics of the dataset, and each future pipeline run will generate a new timestamp folder with the JSON file of the saved metrics under the folder of its subsequent tracked dataset.

You will also see the session_store.db generated from your first pipeline run after enabling experiment tracking, which is used to store all the generated run metadata, alongside the tracking dataset, to be used for exposing experiment tracking to Kedro-Viz.

Try to execute kedro run a few times to generate a larger set of experiment data. You can also play around with setting up different tracking datasets, and check the logged data via the generated JSON data files.

Access run data and compare runs

Here comes the fun part of accessing your run data on Kedro-Viz. Having ensured that you are using Kedro-Viz >=4.1.1 (you can confirm your Kedro-Viz version by running kedro info), run:

kedro viz

When you open the Kedro-Viz web app, you will see an experiment tracking icon on your left. Click the icon to go to the experiment tracking page (you can also access the page via, where you will now see the set of experiment data generated from your previous runs:

You can now access, compare and pin your runs by toggling the Compare runs button:

View and compare plot data

From Kedro-Viz version 5.0.0 experiment tracking also supports the display and comparison of plots, such as Plotly and Matplotlib.

Add a new node to the data_processing nodes (src/kedro-experiment-tracking-tutorial/pipelines/data_processing/

import matplotlib.pyplot as plt
import seaborn as sn

def create_confusion_matrix(companies: pd.DataFrame):
    actuals = [0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1]
    predicted = [1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1]
    data = {"y_Actual": actuals, "y_Predicted": predicted}
    df = pd.DataFrame(data, columns=["y_Actual", "y_Predicted"])
    confusion_matrix = pd.crosstab(
        df["y_Actual"], df["y_Predicted"], rownames=["Actual"], colnames=["Predicted"]
    sn.heatmap(confusion_matrix, annot=True)
    return plt

You might have to execute pip install seaborn if the seaborn library is not installed yet.

And now add this node to the data_processing pipeline (src/kedro-experiment-tracking-tutorial/pipelines/data_processing/


In the catalog add the confusion_matrix data definition, making sure to set the versioned flag to true within the project catalog to include the plot in experiment tracking.

# conf/base/catalog.yml

  type: matplotlib.MatplotlibWriter
  filepath: data/09_tracking/confusion_matrix.png
  versioned: true

After running the pipeline with kedro run, the plot will be saved and you will be able to see the plot in the experiment tracking panel when you execute kedro viz. Clicking on a plot will expand it. When in comparison view, expanding a plot will show all the plots in that view for them to be compared side-by-side.

View your metrics timeline

Additionally, you can monitor the changes to metrics over time from the pipeline visualisation tab . Clicking on any MetricsDataset node will open a side panel displaying how the metric value has changed over time.

Keep an eye out on the Kedro-Viz release page for the upcoming releases on this experiment tracking functionality.