Get started with Kedro-Viz¶
Kedro-Viz is a key part of Kedro. It displays data and nodes, and the connections between them, to visualise the structure of the pipelines in a Kedro project.
This section assumes you are familiar with the basic Kedro concepts described in the spaceflights tutorial. If you have not yet worked through the tutorial, you can still follow this example.
Generate a copy of the spaceflights tutorial project with all the code in place by using the Kedro starter for the spaceflights tutorial:
kedro new --starter=spaceflights
When prompted for a project name, you can enter any name, but we will assume
Kedro Tutorial throughout.
When your project is ready, navigate to the root directory of the project and install the dependencies for the project, which include Kedro-Viz:
pip install -r src/requirements.txt
Visualise the spaceflights project¶
To run Kedro-Viz, type the following into your terminal from the project directory:
The command automatically opens a browser tab to serve the visualisation at http://127.0.0.1:4141/.
You should see the following:
If a visualisation panel opens up and a pipeline is not visible, then please check that your tutorial project code is complete if you’ve not generated it from the starter template.
If you still can’t see the visualisation, the Kedro community can help!
Exit an open visualisation¶
To exit the visualisation, close the browser tab. To regain control of the terminal, enter
⌘+c on Mac or
Ctrl+c on Windows or Linux machines.
Automatic visualisation updates¶
You can use the
--autoreload flag to autoreload Kedro-Viz when a
YAML file changes in the project. Add the flag to the command you use to start Kedro-Viz:
kedro viz --autoreload
autoreload flag reflects changes to the project as they happen. For example, commenting out
pipeline.py will trigger a re-render of the pipeline:
By convention, a pipeline can be defined as having different layers according to how data is processed, which makes it easier to collaborate.
For example, the data engineering convention labels datasets according to the stage of the pipeline (e.g. whether the data has been cleaned).
You can add a
layer attribute to the datasets in the Data Catalog, which is reflected in the Kedro-Viz visualisation.
catalog.yml for the completed spaceflights tutorial and replace the existing code with the following:
companies: type: pandas.CSVDataSet filepath: data/01_raw/companies.csv layer: raw reviews: type: pandas.CSVDataSet filepath: data/01_raw/reviews.csv layer: raw shuttles: type: pandas.ExcelDataSet filepath: data/01_raw/shuttles.xlsx layer: raw preprocessed_companies: type: pandas.ParquetDataSet filepath: data/02_intermediate/preprocessed_companies.pq layer: intermediate preprocessed_shuttles: type: pandas.ParquetDataSet filepath: data/02_intermediate/preprocessed_shuttles.pq layer: intermediate model_input_table: type: pandas.ParquetDataSet filepath: data/03_primary/model_input_table.pq layer: primary regressor: type: pickle.PickleDataSet filepath: data/06_models/regressor.pickle versioned: true layer: models
The visualisation now includes the layers: