kedro-mlflow 0.3.0

A kedro-plugin to use mlflow in your kedro projects

General informations

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Software repository	Latest release	Total downloads
Pypi

Code health

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Branch	Tests	Coverage	Documentation	Deployment
develop
master

Main contributors

The following people actively maintain, enhance and discuss design to make this package as good as possible. Many thanks to them!

• Yolan Honoré-Rougé
• Kajetan Maurycy Olszewski
• Adrian Piotr Kruszewski
• Takieddine Kadiri

Release and roadmap

The release history centralizes packages improvements across time. The main features coming in next releases are listed on github milestones. Feel free to upvote/downvote and discuss prioritization in associated issues.

What is kedro-mlflow?

kedro-mlflow is a kedro-plugin for lightweight and portable integration of mlflow capabilities inside kedro projects. It enforces Kedro principles to make mlflow usage as production ready as possible. Its core functionalities are :

• versioning: you can effortlessly register your parameters or your datasets with minimal configuration in a kedro run. Later, you will be able to browse your runs in the mlflow UI, and retrieve the runs you want. This is directly linked to Mlflow Tracking.
• model packaging: kedro-mlflow offers a convenient API to register a pipeline as a model in the mlflow sense. Consequently, you can API-fy or serve your kedro pipeline with one line of code, or share a model with without worrying of the preprocessing to be made for further use. This is directly linked to Mlflow Models.

How do I install kedro-mlflow?

Important: kedro-mlflow is only compatible with kedro>0.16.0. If you have a project created with an older version of Kedro, see this migration guide.

kedro-mlflow is available on PyPI, so you can install it with pip:

pip install kedro-mlflow

If you want to use the develop version of the package which is the most up to date, you can install the package from github:

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

I strongly recommend to use conda (a package manager) to create an environment and to read kedro installation guide.

Getting started:

The documentation contains:

• A "hello world" example which demonstrates how you to setup your project, version parameters and datasets, and browse your runs in the UI.
• A more detailed tutorial to show more advanced features (mlflow configuration through the plugin, package and serve a kedro Pipeline...)

Some frequently asked questions on more advanced features:

• You want to log additional metrics to the run? -> Try MlflowMetricsDataSet !
• You want to log nice dataviz of your pipeline that you register with MatplotlibWriter? -> Try MlflowArtifactDataSet to log any local files (.png, .pkl, .csv...) automagically!
• You want to create easily an API to share your awesome model to anyone? -> See if pipeline_ml_factory can fit your needs
• You want to do something that is not straigthforward with current implementation? Open an issue, and let's see what happens!

Can I contribute?

I'd be happy to receive help to maintain and improve the package. Please check the contributing guidelines.