Metadata-Version: 2.1
Name: kedro-mlflow
Version: 0.7.4
Summary: A kedro-plugin to use mlflow in your kedro projects
Home-page: https://github.com/Galileo-Galilei/kedro-mlflow
Author: Galileo-Galilei
License: Apache Software License (Apache 2.0)
Description: **General informations**
        
        [![Python Version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8-blue.svg)](https://pypi.org/project/kedro-mlflow/) [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Code Style: Black](https://img.shields.io/badge/code%20style-black-black.svg)](https://github.com/ambv/black)
        [![SemVer](https://img.shields.io/badge/semver-2.0.0-green)](https://semver.org/)
        
        ----------------------------------------------------------
        | Software repository | Latest release | Total downloads |
        |---------------------|----------------|-----------------|
        | Pypi | [![PyPI version](https://badge.fury.io/py/kedro-mlflow.svg)](https://pypi.org/project/kedro-mlflow/) | [![Downloads](https://pepy.tech/badge/kedro-mlflow)](https://pepy.tech/project/kedro-mlflow) |
        
        **Code health**
        
        ----------------------------------------------------------
        | Branch | Tests | Coverage | Links | Documentation | Deployment | Activity |
        |--------|-------|----------|-------|---------------|------------|----------|
        | `master` | [![test](https://github.com/Galileo-Galilei/kedro-mlflow/workflows/test/badge.svg?branch=master)](https://github.com/Galileo-Galilei/kedro-mlflow/actions?query=workflow%3Atest+branch%3Amaster) | [![codecov](https://codecov.io/gh/Galileo-Galilei/kedro-mlflow/branch/master/graph/badge.svg)](https://codecov.io/gh/Galileo-Galilei/kedro-mlflow/branch/master)|[![links](https://github.com/Galileo-Galilei/kedro-mlflow/workflows/check-links/badge.svg?branch=master)](https://github.com/Galileo-Galilei/kedro-mlflow/actions?query=workflow%3Acheck-links+branch%3Amaster)|[![Documentation](https://readthedocs.org/projects/kedro-mlflow/badge/?version=stable)](https://kedro-mlflow.readthedocs.io/en/stable/)|[![publish](https://github.com/Galileo-Galilei/kedro-mlflow/workflows/publish/badge.svg?branch=master)](https://github.com/Galileo-Galilei/kedro-mlflow/actions?query=branch%3Amaster+workflow%3Apublish)|[![commit](https://img.shields.io/github/commits-since/Galileo-Galilei/kedro-mlflow/0.7.4)](https://github.com/Galileo-Galilei/kedro-mlflow/compare/0.7.4...master)|
        
        # What is kedro-mlflow?
        
        ``kedro-mlflow`` is a [kedro-plugin](https://kedro.readthedocs.io/en/stable/07_extend_kedro/04_plugins.html) for lightweight and portable integration of [mlflow](https://mlflow.org/docs/latest/index.html) capabilities inside [kedro](https://kedro.readthedocs.io/en/stable/index.html) projects. It enforces [``Kedro`` principles](https://kedro.readthedocs.io/en/stable/12_faq/01_faq.html?highlight=principles#what-is-the-philosophy-behind-kedro) to make mlflow usage as production ready as possible. Its core functionalities are :
        
        - **versioning**: `kedro-mlflow` intends to enhance reproducibility for machine learning experimentation. With `kedro-mlflow` installed, 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](https://www.mlflow.org/docs/latest/tracking.html).
        - **model packaging**: ``kedro-mlflow`` intends to be be an agnostic machine learning framework for people who want to write portable, production ready machine learning pipelines. It offers a convenient API to convert a Kedro pipeline to 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](https://www.mlflow.org/docs/latest/models.html).
        
        # How do I install kedro-mlflow?
        
        **Important: ``kedro-mlflow`` is only compatible with ``kedro>=0.16.0`` and ``mlflow>=1.0.0``. If you have a project created with an older version of ``Kedro``, see this [migration guide](https://github.com/quantumblacklabs/kedro/blob/master/RELEASE.md#migration-guide-from-kedro-015-to-016).**
        
        ``kedro-mlflow`` is available on PyPI, so you can install it with ``pip``:
        
        ```console
        pip install kedro-mlflow
        ```
        
        If you want to use the most up to date version of the package which is under development and not released yet, you can install the package from github:
        
        ```console
        pip install --upgrade git+https://github.com/Galileo-Galilei/kedro-mlflow.git
        ```
        
        I strongly recommend to use ``conda`` (a package manager) to create an environment and to read [``kedro`` installation guide](https://kedro.readthedocs.io/en/latest/02_get_started/02_install.html).
        
        # Getting started
        
        The documentation contains:
        
        - [A  "hello world" example](https://kedro-mlflow.readthedocs.io/en/latest/source/03_getting_started/index.html) which demonstrates how you to **setup your project**, **version parameters** and **datasets**, and browse your runs in the UI.
        - A section for [advanced machine learning versioning](https://kedro-mlflow.readthedocs.io/en/latest/source/04_experimentation_tracking/index.html) to show more advanced features (mlflow configuration through the plugin, package and serve a kedro ``Pipeline``...)
        - A section to demonstrate how to use `kedro-mlflow` as a [machine learning framework](https://kedro-mlflow.readthedocs.io/en/latest/source/05_framework_ml/index.html) to deliver production ready pipelines and serve them. This section comes with [an example repo](https://github.com/Galileo-Galilei/kedro-mlflow-tutorial) you can clone and try out.
        
        Some frequently asked questions on more advanced features:
        
        - You want to log additional metrics to the run? -> [Try ``MlflowMetricsDataSet``](https://kedro-mlflow.readthedocs.io/en/stable/source/04_experimentation_tracking/05_version_metrics.html) !
        - 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*](https://kedro-mlflow.readthedocs.io/en/stable/source/04_experimentation_tracking/03_version_datasets.html)!
        - You want to create easily an API to share your awesome model to anyone? -> [See if ``pipeline_ml_factory`` can fit your needs](https://github.com/Galileo-Galilei/kedro-mlflow/issues/16)
        - You want to do something that is not straigthforward with current implementation? Open an issue, and let's see what happens!
        
        # Release and roadmap
        
        The [release history](https://github.com/Galileo-Galilei/kedro-mlflow/blob/master/CHANGELOG.md) centralizes packages improvements across time. The main features coming in next releases are [listed on github milestones](https://github.com/Galileo-Galilei/kedro-mlflow/milestones). Feel free to upvote/downvote and discuss prioritization in associated issues.
        
        # Disclaimer
        
        This package is still in active development. We use [SemVer](https://semver.org/) principles to version our releases. Until we reach `1.0.0` milestone, breaking changes will lead to `<minor>` version number increment, while releases which do not introduce breaking changes in the API will lead to `<patch>` version number increment.
        
        The user must be aware that we will not reach `1.0.0` milestone before Kedro does (mlflow has already reached `1.0.0`).
        
        If you want to see how to migrate from one version of `kedro-mlflow` to another, see the [migration guide](https://kedro-mlflow.readthedocs.io/en/stable/source/02_installation/03_migration_guide.html).
        
        # Can I contribute?
        
        We'd be happy to receive help to maintain and improve the package. Any PR will be considered (from typo in the docs to core features add-on) Please check the [contributing guidelines](https://github.com/Galileo-Galilei/kedro-mlflow/blob/master/CONTRIBUTING.md).
        
        # Main contributors
        
        The following people actively maintain, enhance and discuss design to make this package as good as possible:
        
        - [Yolan Honoré-Rougé](https://github.com/galileo-galilei)
        - [Takieddine Kadiri](https://github.com/takikadiri)
        
        Many thanks to [Adrian Piotr Kruszewski](https://github.com/akruszewski) for his past work on the repo.
        
Keywords: kedro plugin,mlflow,model versioning,model packaging,pipelines,machine learning,data pipelines,data science,data engineering
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6, <3.9
Description-Content-Type: text/markdown
Provides-Extra: doc
Provides-Extra: test
Provides-Extra: dev
