Visualise the spaceflights 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.

If you haven’t installed Kedro follow the documentation to get set up.

Then, in your terminal window, navigate to the folder you want to store the project.

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 anything, but we will assume Spaceflights 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

The next step is optional, but useful to check that all is working. Run the full set of pipelines for the tutorial project:

kedro run

To start Kedro-Viz, type the following into your terminal from the project directory:

kedro viz

The command 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, refresh the view, and check that your tutorial project code is complete if you’ve not generated it from the starter template. If you still don’t see the visualisation, the Kedro community can help:

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 Python or YAML file changes in the project. Add the flag to the command you use to start Kedro-Viz:

kedro viz --autoreload

The autoreload flag reflects changes to the project as they happen. For example, commenting out create_model_input_table_node in pipeline.py will trigger a re-render of the pipeline:

autoreload

Visualise layers

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.

Open 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:

Share a pipeline visualisation

You can share a the pipeline structure within a Kedro-Viz visualisation as a JSON file from the terminal:

kedro viz --save-file=my_shareable_pipeline.json

This command will save a visualisation of the __default__ pipeline as a JSON file called my_shareable_pipeline.json. It doesn’t share data, such as that in the code panel, nor can you share images or charts.

To visualise the shared file, type the following to load it from the terminal:

kedro viz --load-file=my_shareable_pipeline.json