Metadata-Version: 2.1
Name: kedro_mlflow
Version: 0.2.1
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: 
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        | Theme | Status |
        |------------------------|------------------------|
        | Python Version | [![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/) |
        | Latest PyPI Release | [![PyPI version](https://badge.fury.io/py/kedro-mlflow.svg)](https://pypi.org/project/kedro-mlflow/) |
        | Code quality check | [![Development workflow - Check code quality (lint, test)](https://github.com/Galileo-Galilei/kedro-mlflow/workflows/Development%20workflow%20-%20Check%20code%20quality%20(lint,%20test)/badge.svg)](https://github.com/Galileo-Galilei/kedro-mlflow/actions?query=workflow%3A%22Development+workflow+-+Check+code+quality+%28lint%2C+test%29%22) |
        | `master` Branch Build | [![Production workflow - Build and deploy package](https://github.com/Galileo-Galilei/kedro-mlflow/workflows/Production%20workflow%20-%20Build%20and%20deploy%20package/badge.svg)](https://github.com/Galileo-Galilei/kedro-mlflow/actions?query=workflow%3A%22Production+workflow+-+Build+and+deploy+package%22) |
        | Documentation Build | [![Documentation](https://readthedocs.org/projects/kedro-mlflow/badge/?version=latest)](https://kedro-mlflow.readthedocs.io/) |
        | License | [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) |
        | Code Style | [![Code Style: Black](https://img.shields.io/badge/code%20style-black-black.svg)](https://github.com/ambv/black) |
        
        # Release
        The [release history](https://github.com/Galileo-Galilei/kedro-mlflow/blob/develop/CHANGELOG.md) centralizes packages improvements across time. **Coming soon:**
        - enhanced documentation, especially with detailed tutorials for ``PipelineML`` class and advanced versioning parametrisation
        - better integration to [Mlflow Projects](https://www.mlflow.org/docs/latest/projects.html)
        - better integration to [Mlflow Model Registry](https://www.mlflow.org/docs/latest/model-registry.html)
        - better CLI experience and bug fixes
        - ability to retrieve parameters / re-run a former run for reproducibility / collaboration
        
        
        
        # What is kedro-mlflow?
        ``kedro-mlflow`` is a [kedro-plugin](https://kedro.readthedocs.io/en/stable/04_user_guide/10_developing_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]() 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](https://www.mlflow.org/docs/latest/tracking.html)
        - **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](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``. 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 ``develop`` version of the package which is the most up to date, you can install the package from github:
        ```console
        pip install --upgrade git+https://github.com/quantumblacklabs/kedro.git@develop
        ```
        
        I strongly recommend to use ``conda`` (a package manager) to create an environment and to read [``kedro`` installation guide](https://kedro.readthedocs.io/en/stable/02_getting_started/01_prerequisites.html).
        
        
        
        # Getting started:
        The documentation contains:
        - [A "hello world" example](https://kedro-mlflow.readthedocs.io/en/latest/source/02_hello_world_example/index.html) which demonstrates how you to **setup your project**, **version parameters** and **datasets**, and browse your runs in the UI.
        - A more [detailed tutorial](https://kedro-mlflow.readthedocs.io/en/latest/source/03_tutorial/index.html) 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? -> [See ``mlflow.log_metric`` and add it to your functions](https://www.mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_metric) !
        - You want to log nice dataviz of your pipeline that you register with ``MatplotlibWriter``? -> [Try ``MlflowDataSet`` to log any local files (.png, .pkl, .csv...) *automagically*](https://kedro-mlflow.readthedocs.io/en/latest/source/02_hello_world_example/02_first_steps.html#artifacts)!
        - You want to create easily an API to share your awesome model to anyone? -> [See if ``pipeline_ml`` 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!
        
        # Can I contribute?
        
        I'd be happy to receive help to maintain and improve the package. Please check the [contributing guidelines](https://github.com/Galileo-Galilei/kedro-mlflow/blob/develop/CONTRIBUTING.md).
        
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
