Renku Python Library, CLI and Service

A Python library for the Renku collaborative data science platform. It includes a CLI and SDK for end-users as well as a service backend. It provides functionality for the creation and management of projects and datasets, and simple utilities to capture data provenance while performing analysis tasks.

NOTE:

renku-python is the python library and core service for Renku - it does not start the Renku platform itself - for that, refer to the Renku docs on running the platform.

Renku for Users

Installation

Renku releases and development versions are available from PyPI. You can install it using any tool that knows how to handle PyPI packages. Our recommendation is to use :code:pipx.

pipx

First, 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

pipx installs renku into its own virtual environment, making sure that it does not pollute any other packages or versions that you may have already installed.

Note

If you install renku as a dependency in a virtual environment and the environment is active, your shell will default to the version installed in the virtual environment, not the version installed by pipx.

To install a development release:

$ pipx install --pip-args pre renku

pip

$ pip install renku

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

$ pip install --pre 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.

Docker

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.

CLI Example

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/

Renku as a Service

This repository includes a renku-core RPC service written as a Flask application that provides (almost) all of the functionality of the Renku CLI. This is used to provide one of the backends for the RenkuLab web UI. The service can be deployed in production as a Helm chart (see helm-chart.

Developing Renku

For testing the functionality from source it is convenient to install renku in editable mode using pipx. Clone the repository and then do:

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

This will install all the extras for testing and debugging.

Running tests

To run tests locally with specific version of Python:

$ pyenv install 3.7.5rc1
$ pipenv --python ~/.pyenv/versions/3.7.5rc1/bin/python install
$ pipenv run tests

To recreate environment with different version of Python, it's easy to do so with the following commands:

$ pipenv --rm
$ pyenv install 3.6.9
$ pipenv --python ~/.pyenv/versions/3.6.9/bin/python install
$ pipenv run tests

Using External Debuggers

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.cli <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>"
            }
        ]
    },