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.

NOTE:

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+https://github.com/SwissDataScienceCenter/renku-python.git#egg=renku

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 and Linux is via Homebrew.

$ brew tap swissdatasciencecenter/renku
$ brew install renku

Install and execute Renku in an isolated environment using pipx. It will guarantee that there are no version conflicts with dependencies you are using for your work and research.

Install pipx and make sure that the $PATH is correctly configured.

$ python3 -m pip install --user pipx
$ pipx ensurepath

Once pipx is installed use following command to install renku.

$ pipx install renku
$ which renku
~/.local/bin/renku

Prevously we have recommended to use pipsi. You can still use it or migrate to **pipx**.

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.

Initialize a 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 https://raw.githubusercontent.com/SwissDataScienceCenter/renku-python/master/README.rst

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. The full documentation will soon be available at: https://renku-python.readthedocs.io/

For development it's convenient to install renku in editable mode. This is still possible with pipx. First clone the repository and then do:

$ pipx install \
    --editable \
    --spec <path-to-renku-python>[all] \
    renku

This will install all the extras for testing and debugging.

To run renku via e.g. the Visual Studio Code debugger you need run it via the python executable in whatever virtual environment was used to install renku. If there is a package needed for the debugger, you need to inject it into the virtual environment first, e.g.:

$ pipx inject renku ptvsd

Finally, run renku via the debugger:

$ ~/.local/pipx/venvs/renku/bin/python -m ptvsd --host localhost --wait -m renku <command>

If using Visual Studio Code, you may also want to set the Remote Attach configuration PathMappings so that it will find your source code, e.g.

{
        "name": "Python: Remote Attach",
        "type": "python",
        "request": "attach",
        "port": 5678,
        "host": "localhost",
        "pathMappings": [
            {
                "localRoot": "<path-to-renku-python-source-code>",
                "remoteRoot": "<path-to-renku-python-source-code>"
            }
        ]
    },