A “Hello World” example

To learn how basic Kedro projects work, you can create a project interactively and explore it as you read this section. Feel free to name your project as you like, but this guide will assume the project is named getting-started.

Be sure to enter Y to include Kedro’s example so your new project template contains the well-known Iris dataset, to get you started.

The Iris dataset, generated in 1936 by the British statistician and biologist Ronald Fisher, is a simple, but frequently-referenced dataset. It contains 150 samples in total, comprising 50 samples of 3 different species of Iris plant (Iris Setosa, Iris Versicolour and Iris Virginica). For each sample, the flower measurements are recorded for the sepal length, sepal width, petal length and petal width.


Classification is a method, within the context of machine learning, to determine what group some object belongs to based on known categorisation of similar objects.

The Iris dataset can be used by a machine learning model to illustrate classification. The classification algorithm, once trained on data with known values of species, takes an input of sepal and petal measurements, and compares them to the values it has stored from its training data. It will then output a predictive classification of the Iris species.

Project directory structure

The project directory will be structured as shown. You are free to adapt the folder structure to your project’s needs, but the example shows a convenient starting point and some best-practices:

getting-started     # Parent directory of the template
├── .gitignore      # Prevent staging of unnecessary files to git
├── kedro_cli.py    # A collection of Kedro command line interface (CLI) commands
├── .kedro.yml      # Path to discover project context
├── README.md       # Project README
├── .ipython        # IPython startup scripts
├── conf            # Project configuration files
├── data            # Local project data (not committed to version control)
├── docs            # Project documentation
├── logs            # Project output logs (not committed to version control)
├── notebooks       # Project related Jupyter notebooks
├── references      # Sharable project references (tables, pdfs, etc.)
├── results         # Shareable results
└── src             # Project source code

If you opted to include Kedro’s built-in example when you created the project then the conf/, data/ and src/ directories will be pre-populated with an example configuration, input data and Python source code respectively.

Project source code

The project’s source code can be found in the src directory. It contains 2 subfolders:

  • getting_started/ - this is the Python package for your project:
    • pipelines/data_engineering/nodes.py and pipelines/data_science/nodes.py- Example node functions, which perform the actual operations on the data (more on this in the Example pipeline below)
    • pipelines/data_engineering/pipeline.py and pipelines/data_science/pipeline.py - Where each individual pipeline is created from the above nodes to form the business logic flow
    • pipeline.py - Where the project’s main pipelines are collated and named
    • run.py - The main entry point of the project, which brings all the components together and runs the pipeline
  • tests/: This is where you should keep the project unit tests. Newly generated projects are preconfigured to run these tests using pytest. To kick off project testing, simply run the following from the project’s root directory:
kedro test

Writing code

Use the notebooks folder for experimental code and move the code to src/ as it develops.

Project components

A kedro project consists of the following main components:

Component Description
Data Catalog A collection of datasets that can be used to form the data pipeline. Each dataset provides load and save capabilities for a specific data type, e.g. CSVS3DataSet loads and saves data to a csv file in S3.
Pipeline A collection of nodes. A pipeline takes care of node dependencies and execution order.
Node A Python function which executes some business logic, e.g. data cleaning, dropping columns, validation, model training, scoring, etc.
Runner An object that runs the kedro pipeline using the specified data catalog. Currently kedro supports 2 runner types: SequentialRunner and ParallelRunner.


You can store data under the appropriate layer in the data folder. We recommend that all raw data should go into raw and processed data should move to other layers according to data engineering convention.

Example pipeline

The getting-started project contains two pipelines: a data_engineering pipeline and data_science pipeline, found in src/getting_started/pipelines, with relevant example node functions pertaining to each of them. The following data-engineering nodes are provided in src/getting_started/pipelines/data_engineering/nodes.py:

Node Description Node Function Name
Split data Splits the example Iris dataset into train and test samples split_data

As well as data-science nodes in src/getting_started/pipelines/data_science/nodes.py:

Node Description Node Function Name
Train model Trains a simple multi-class logistic regression model train_model
Predict Makes class predictions given a pre-trained model and a test set predict
Report accuracy Reports the accuracy of the predictions performed by the previous node report_accuracy

Node execution order is determined by resolving the input and output data dependencies between the nodes and not by the order in which the nodes were passed into the pipeline.


There are two default folders for adding configuration - conf/base/ and conf/local/:

  • conf/base/ - Used for project-specific configuration
  • conf/local/ - Used for access credentials, personal IDE configuration or other sensitive / personal content

Project-specific configuration

There are three files used for project-specific configuration:

  • catalog.yml - The Data Catalog allows you to define the file paths and loading / saving configuration required for different datasets
  • logging.yml - Uses Python’s default logging library to set up logging
  • parameters.yml - Allows you to define parameters for machine learning experiments e.g. train / test split and number of iterations

Sensitive or personal configuration

As we described above, any access credentials, personal IDE configuration or other sensitive and personal content should be stored in conf/local/. By default, credentials.yml is generated in conf/base/ (because conf/local/ is ignored by git) and to populate and use the file, you should first move it to conf/local/. Further safeguards for preventing sensitive information from being leaked onto git are discussed in the FAQs.

Running the example

In order to run the getting-started project, simply execute the following from the root project directory:

kedro run

This command calls the run() method on the ProjectContext class defined in src/getting_started/run.py, which in turn does the following:

  1. Instantiates ProjectContext class:
    • Reads relevant configuration
    • Configures Python logging
    • Instantiates the DataCatalog and feeds a dictionary containing parameters config
  2. Instantiates the pipeline
  3. Instantiates the SequentialRunner and runs it by passing the following arguments:
    • Pipeline object
    • DataCatalog object

Upon successful completion, you should see the following log message in your console:

2019-02-13 16:59:26,293 - kedro.runner.sequential_runner - INFO - Completed 4 out of 4 tasks
2019-02-13 16:59:26,293 - kedro.runner.sequential_runner - INFO - Pipeline execution completed successfully.


Congratulations! In this chapter you have set up Kedro and used it to create a first example project, which has illustrated the basic concepts of using nodes to form a pipeline, a Data Catalog and the project configuration. This example uses a simple and familiar dataset, to keep your first experience very basic and easy to follow. In the next chapter, we will revisit the core concepts in more detail and walk through a more complex example.