Installation guide

Pre-requisites

Create a virtual environment

I strongly recommend to create a virtual environment in order to avoid version conflicts between packages. I use conda in this tutorial.

I also recommend to read Kedro installation guide to set up your Kedro project.

conda create -n <your-environment-name> python=<3.[6-8].X>

For the rest of the section, we assume the environment is activated:

conda activate <your-environment-name>

Check your kedro version

If you have an existing environment with kedro already installed, make sure its version is above 0.16.0. kedro-mlflow cannot be used with kedro<0.16.0, and if you install it in an existing environment, it will reinstall a more up-to-date version of kedro and likely mess your project up until you reinstall the proper version of kedro (the one you originally created the project with).

pip show kedro

should return:

Name: kedro
Version: <your-kedro-version>  # <-- make sure it is above 0.16.0, <0.17.0
Summary: Kedro helps you build production-ready data and analytics pipelines
Home-page: https://github.com/quantumblacklabs/kedro
Author: QuantumBlack Labs
Author-email: None
License: Apache Software License (Apache 2.0)
Location: <...>\anaconda3\envs\<your-environment-name>\lib\site-packages
Requires: pip-tools, cachetools, fsspec, toposort, anyconfig, PyYAML, click, pluggy, jmespath, python-json-logger, jupyter-client, setuptools, cookiecutter

Install the plugin

There are versions of the plugin compatible up to kedro>=0.16.0 and mlflow>=0.8.0. kedro-mlflow stops adding features to a minor version 2 to 6 months after a new kedro release.

Install with pip / uv

You can install kedro-mlflow plugin from PyPi with pip:

pip install --upgrade kedro-mlflow

If you prefer uv and have it installed, you can use:

uv pip install --upgrade kedro-mlflow

Install with conda / mamba / micromamba

You can install kedro-mlflow plugin with conda from the conda-forge channel:

conda install kedro-mlflow -c conda-forge

Install from github

You may want to install the master branch from source which has unreleased features:

pip install git+https://github.com/Galileo-Galilei/kedro-mlflow.git

Check the installation

Enter kedro info in a terminal with the activated virtual env to check the installation. If it has succeeded, you should see the following ascii art:

 _            _
| | _____  __| |_ __ ___
| |/ / _ \/ _` | '__/ _ \
|   <  __/ (_| | | | (_) |
|_|\_\___|\__,_|_|  \___/
v0.<minor>.<patch>

kedro allows teams to create analytics
projects. It is developed as part of
the Kedro initiative at QuantumBlack.

Installed plugins:
kedro_mlflow: 0.14.0 (hooks:global,project)

The version 0.14.0 of the plugin is installed and has both global and project commands.

That’s it! You are now ready to go!

Available commands

With the kedro mlflow -h command outside of a kedro project, you now see the following output:

Usage: kedro mlflow [OPTIONS] COMMAND [ARGS]...

  Use mlflow-specific commands inside kedro project.

Options:
  -h, --help  Show this message and exit.