Renku CLI and SDK for Python Documentation Status

A Python library for the Renku collaborative data science platform. It allows the user to create projects, manage datasets, and capture data provenance while performing analysis tasks.

renku-python is the python library for Renku that provides an SDK and a command-line interface (CLI). It does not start the Renku platform itself - for that, refer to the Renku docs on running the platform.


The latest release is available on PyPI and can be installed using pip:

$ pip install renku

The latest development versions are available on PyPI or from the Git repository:

$ pip install --dev renku
# - OR -
$ pip install -e git+

Use following installation steps based on your operating system and preferences if you would like to work with the command line interface and you do not need the Python library to be importable.


The recommended way of installing Renku on MacOS is via Homebrew.

$ brew tap swissdatasciencecenter/renku
$ brew install renku

Pip Script Installer (pipsi)

You can use pipsi to isolate dependencies and to guarantee that there are no version conflicts. Make sure you have the pipsi command correctly installed and ~/.local/bin is in your $PATH.

$ pipsi install renku
$ which renku


The containerized version of the CLI can be launched using Docker command.

$ docker run -it -v "$PWD":"$PWD" -w="$PWD" renku/renku-python renku

It makes sure your current directory is mounted to the same place in the container.

For more information about the Renku API see its documentation.

Use the Renku command line

Interaction with the platform can take place via the command-line interface (CLI).

Start by creating for folder where you want to keep your Renku project:

$ mkdir -p ~/temp/my-renku-project
$ cd ~/temp/my-renku-project
$ renku init

Create a dataset and add data to it:

$ renku dataset create my-dataset
$ renku dataset add my-dataset

Run an analysis:

$ renku run wc < data/my-dataset/README.rst > wc_readme

Trace the data provenance:

$ renku log wc_readme

These are the basics, but there is much more that Renku allows you to do with your data analysis workflows.

For more information about using renku, refer to the Renku command line instructions.