Create a new Kedro project¶

Summary¶

There are a few ways to create a new project once you have set up Kedro:

You can use kedro new to create a basic Kedro project containing project directories and basic code, but empty to extend as you need.
You can use kedro new and pass in a configuration file to manually control project details such as the name, folder and package name.
You can create a Kedro project populated with template code that acts as a starter example. This guide illustrates with the pandas-iris starter, and there is a range of Kedro starter projects.

Once you’ve created a project:

You need to navigate to its project folder and install its dependencies: pip install -r src/requirements.txt
To run the project: kedro run
To visualise the project: kedro viz

Create a new empty project¶

The simplest way to create a default Kedro project is to navigate to your preferred directory and type:

kedro new

Enter a name for the project, which can be human-readable and may contain alphanumeric symbols, spaces, underscores and hyphens. It must be at least two characters long.

It’s best to keep the name simple because the choice is set as the value of project_name and is also used to generate the folder and package names for the project automatically.

So, if you enter “Get Started”, the folder for the project (repo_name) is automatically set to be get-started, and the Python package name (python_package) for the project is set to be get_started.

Description	Setting	Example
A human-readable name for the new project	`project_name`	`Get Started`
Local directory to store the project	`repo_name`	`get-started`
The Python package name for the project (short, all-lowercase)	`python_package`	`get_started`

The output of kedro new is a directory containing all the project files and subdirectories required for a basic Kedro project, ready to extend with the code.

Create a new project from a configuration file¶

To customise a new project’s directory and package name, use a configuration file to specify those values. The configuration file must contain:

output_dir The path in which to create the project directory
project_name
repo_name
python_package

The output_dir can be set to customised. For example, ~ for the home directory or . for the current working directory. Here is an example config.yml, which assumes that a directory named ~/code already exists:

output_dir: ~/code
project_name: My First Kedro Project
repo_name: testing-kedro
python_package: test_kedro

To create this new project:

kedro new --config=<path>/config.yml

Create a new project containing example code¶

Use a Kedro starter to create a project containing template code, to run as-is or to adapt and extend.

The following illustrates a project created with example code based on the familiar Iris dataset.

The first step is to create the Kedro project using a starter to add the example code and data.

kedro new --starter=pandas-iris

Run the project¶

However you create a Kedro project, once kedro new has completed, the next step is to navigate to the project folder (cd <project-name>) and install dependencies with pip as follows:

pip install -r src/requirements.txt

Now run the project:

kedro run

Note

The first time you type a kedro command in a new project, you will be asked whether you wish to opt into usage analytics. Your decision is recorded in the .telemetry file so that subsequent calls to kedro in this project do not ask this question again.

Visualise a Kedro project¶

This section swiftly introduces project visualisation using Kedro-Viz. See the visualisation documentation for more detail.

The Kedro-Viz package needs to be installed into your virtual environment separately as it is not part of the standard Kedro installation:

pip install kedro-viz

To start Kedro-Viz, enter the following in your terminal:

kedro viz

This command automatically opens a browser tab to serve the visualisation at http://127.0.0.1:4141/.

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.

Where next?¶

You have completed the section on Kedro project creation for new users. Now choose how to learn more:

Understand more about Kedro: The following page explains the fundamental Kedro concepts.
Learn hands-on: If you prefer to learn hands-on, move on to the spaceflights tutorial. The tutorial illustrates how to set up a working project, add dependencies, create nodes, register pipelines, set up the Data Catalog, add documentation, and package the project.
How-to guides: The documentation sections following the tutorial explain how to visualise a Kedro project and how to combine Kedro with a Jupyter notebook.

If you’ve worked through the documentation listed and are unsure where to go next, review the Kedro repositories on GitHub and Kedro’s Slack channels.

More information about the `pandas-iris` example project¶

If you used the pandas-iris starter to create an example project, the rest of this page gives further information.

Expand for more details.

Background information¶

The Iris dataset was generated in 1936 by the British statistician and biologist Ronald Fisher. The dataset contains 150 samples, comprising 50 each 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.

A machine learning model can use the Iris dataset to illustrate classification (a method used to determine the type of an object by comparison with similar objects that have previously been categorised). Once trained on known data, the machine learning model can make a predictive classification by comparing a test object to the output of its training data.

The Kedro starter contains a single pipeline comprising three nodes responsible for splitting the data into training and testing samples, running a 1-nearest neighbour classifier algorithm to make predictions and accuracy-reporting.

The nodes are stored in src/get_started/nodes.py:

Node	Description
`split_data`	Splits the example Iris dataset into train and test samples
`make_predictions`	Makes class predictions (using 1-nearest neighbour classifier and train-test set)
`report_accuracy`	Reports the accuracy of the predictions performed by the previous node.

Iris example: visualisation¶

If you visualise your project with Kedro-Viz you should see the following: