Metadata-Version: 2.1
Name: kedro
Version: 0.16.1
Summary: Kedro helps you build production-ready data and analytics pipelines
Home-page: https://github.com/quantumblacklabs/kedro
Author: QuantumBlack Labs
License: Apache Software License (Apache 2.0)
Description: ![Kedro Logo Banner](https://raw.githubusercontent.com/quantumblacklabs/kedro/develop/img/kedro_banner.png)
        
        -----------------
        
        | 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/) |
        | Latest PyPI Release | [![PyPI version](https://badge.fury.io/py/kedro.svg)](https://pypi.org/project/kedro/) |
        | Latest Conda Release | [![Conda Version](https://img.shields.io/conda/vn/conda-forge/kedro.svg)](https://anaconda.org/conda-forge/kedro) |
        | `master` Branch Build | [![CircleCI](https://circleci.com/gh/quantumblacklabs/kedro/tree/master.svg?style=shield)](https://circleci.com/gh/quantumblacklabs/kedro/tree/master) |
        | `develop` Branch Build | [![CircleCI](https://circleci.com/gh/quantumblacklabs/kedro/tree/develop.svg?style=shield)](https://circleci.com/gh/quantumblacklabs/kedro/tree/develop) |
        | Documentation Build | [![Documentation](https://readthedocs.org/projects/kedro/badge/?version=latest)](https://kedro.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) |
        | Questions | [![Questions: Stackoverflow "kedro"](https://img.shields.io/badge/stackoverflow%20tag-kedro-yellow)](https://stackoverflow.com/questions/tagged/kedro) |
        
        
        ## What is Kedro?
        
        > "The centre of your data pipeline."
        
        Kedro is a development workflow framework that implements software engineering best-practice for data pipelines with an eye towards productionising machine learning models. We provide a standard approach so that you can:
         - Worry less about how to write production-ready code,
         - Spend more time building data pipelines that are robust, scalable, deployable, reproducible and versioned,
         - And, standardise the way that your team collaborates across your project.
        
        
        ## How do I install Kedro?
        
        `kedro` is a Python package. To install it from the Python Package Index (PyPI) simply run:
        
        ```
        pip install kedro
        ```
        
        It is also possible to install `kedro` using `conda`, a package and environment manager program bundled with Anaconda. With [`conda`](https://kedro.readthedocs.io/en/stable/02_getting_started/01_prerequisites.html#python-virtual-environments) already installed, simply run:
        
        ```
        conda install -c conda-forge kedro
        ```
        
        See more detailed installation instructions, including how to setup Python virtual environments, in our [installation guide](https://kedro.readthedocs.io/en/stable/02_getting_started/02_install.html) and get started with our ["Hello Word"](https://kedro.readthedocs.io/en/stable/02_getting_started/04_hello_world.html) example.
        
        ## What are the main features of Kedro?
        
        ![Kedro-Viz Pipeline Visualisation](https://raw.githubusercontent.com/quantumblacklabs/kedro/develop/img/pipeline_visualisation.png)
        *A pipeline visualisation generated using [Kedro-Viz](https://github.com/quantumblacklabs/kedro-viz)*
        
        
        | Feature | What is this? |
        |----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
        | Project Template | A standard, modifiable and easy-to-use project template based on [Cookiecutter Data Science](https://github.com/drivendata/cookiecutter-data-science/). |
        | Data Catalog | A series of lightweight data connectors used for saving and loading data across many different file formats and file systems including local and network file systems, cloud object stores, and HDFS. The Data Catalog also includes data and model versioning for file-based systems. Used with a Python or YAML API. |
        | Pipeline Abstraction | Automatic resolution of dependencies between pure Python functions and data pipeline visualisation using [Kedro-Viz](https://github.com/quantumblacklabs/kedro-viz). |
        | The Journal | An ability to reproduce pipeline runs with saved pipeline run results. |
        | Coding Standards | Test-driven development using [`pytest`](https://github.com/pytest-dev/pytest), produce well-documented code using [Sphinx](http://www.sphinx-doc.org/en/master/), create linted code with support for [`flake8`](https://github.com/PyCQA/flake8), [`isort`](https://github.com/timothycrosley/isort) and [`black`](https://github.com/psf/black) and make use of the standard Python logging library. |
        | Flexible Deployment | Deployment strategies that include the use of Docker with [Kedro-Docker](https://github.com/quantumblacklabs/kedro-docker), conversion of Kedro pipelines into Airflow DAGs with [Kedro-Airflow](https://github.com/quantumblacklabs/kedro-airflow), leveraging a REST API endpoint with Kedro-Server _(coming soon)_ and serving Kedro pipelines as a Python package. Kedro can be deployed locally, on-premise and cloud (AWS, Azure and Google Cloud Platform) servers, or clusters (EMR, EC2, Azure HDinsight and Databricks). |
        
        
        ## How do I use Kedro?
        
        Our [documentation](https://kedro.readthedocs.io/en/stable/) explains:
        
        - Best-practice on how to [get started using Kedro](https://kedro.readthedocs.io/en/stable/02_getting_started/01_prerequisites.html)
        - A ["Hello World" data and ML pipeline example](https://kedro.readthedocs.io/en/stable/02_getting_started/04_hello_world.html) based on the **Iris dataset**
        - A two-hour [Spaceflights tutorial](https://kedro.readthedocs.io/en/stable/03_tutorial/01_workflow.html) that teaches you beginner to intermediate functionality
        - How to [use the CLI](https://kedro.readthedocs.io/en/stable/06_resources/03_commands_reference.html) offered by `kedro_cli.py` (`kedro new`, `kedro run`, ...)
        - An overview of [Kedro architecture](https://kedro.readthedocs.io/en/stable/06_resources/02_architecture_overview.html)
        - [Frequently asked questions (FAQs)](https://kedro.readthedocs.io/en/stable/06_resources/01_faq.html)
        
        Documentation for the latest stable release can be found [here](https://kedro.readthedocs.io/en/stable/). You can also run `kedro docs` from your CLI and open the documentation for your current version of Kedro in a browser.
        
        > *Note:* The CLI is a convenient tool for being able to run `kedro` commands but you can also invoke the Kedro CLI as a Python module with `python -m kedro`
        
        *Note:* Read our [FAQs](https://kedro.readthedocs.io/en/stable/06_resources/01_faq.html#how-does-kedro-compare-to-other-projects) to learn how we differ from workflow managers like Airflow and Luigi.
        
        
        ## Why does Kedro exist?
        
        Kedro is built upon our collective best-practice (and mistakes) trying to deliver real-world ML applications that have vast amounts of raw unvetted data. We developed Kedro to achieve the following:
        
         - **Collaboration** on an analytics codebase when different team members have varied exposure to software engineering best-practice
         - Focussing on **maintainable data and ML pipelines** as the standard, instead of a singular activity of deploying models in production
         - A way to inspire the creation of **reusable analytics code** so that we never start from scratch when working on a new project
         - **Efficient use of time** because we're able to quickly move from experimentation into production
        
        ## The humans behind Kedro
        
        Kedro was originally designed by [Aris Valtazanos](https://github.com/arisvqb) and [Nikolaos Tsaousis](https://github.com/tsanikgr) to solve challenges they faced in their project work.
        Their work was later turned into an internal product by [Peteris Erins](https://github.com/Pet3ris), [Ivan Danov](https://github.com/idanov), [Nikolaos Kaltsas](https://github.com/nikos-kal), [Meisam Emamjome](https://github.com/misamae) and [Nikolaos Tsaousis](https://github.com/tsanikgr).
        
        Currently the core Kedro team consists of:
        * [Yetunde Dada](https://github.com/yetudada)
        * [Ivan Danov](https://github.com/idanov)
        * [Richard Westenra](https://github.com/richardwestenra)
        * [Dmitrii Deriabin](https://github.com/DmitryDeryabin)
        * [Lorena Balan](https://github.com/lorenabalan)
        * [Kiyohito Kunii](https://github.com/921kiyo)
        * [Zain Patel](https://github.com/mzjp2)
        * [Lim Hoang](https://github.com/limdauto)
        * [Andrii Ivaniuk](https://github.com/andrii-ivaniuk)
        
        Former core team members with significant contributions are:
        [Gordon Wrigley](https://github.com/tolomea), [Jo Stichbury](https://github.com/stichbury), [Nasef Khan](https://github.com/nakhan98) and [Anton Kirilenko](https://github.com/Flid).
        
        And last but not least, all the open-source contributers whose work went into all Kedro [releases](https://github.com/quantumblacklabs/kedro/blob/master/RELEASE.md).
        
        ## Can I contribute?
        
        Yes! Want to help build Kedro? Check out our guide to [contributing](https://github.com/quantumblacklabs/kedro/blob/master/CONTRIBUTING.md).
        
        
        ## Where can I learn more?
        
        There is a growing community around Kedro. Have a look at our [FAQs](https://kedro.readthedocs.io/en/stable/06_resources/01_faq.html#where-can-i-learn-more) to find projects using Kedro and links to articles, podcasts and talks.
        
        
        ## What licence do you use?
        
        Kedro is licensed under the [Apache 2.0](https://github.com/quantumblacklabs/kedro/blob/master/LICENSE.md) License.
        
        
        ## We're hiring!
        
        Do you want to be part of the team that builds Kedro and [other great products](https://quantumblack.com/labs) at QuantumBlack? If so, you're in luck! QuantumBlack is currently hiring Software Engineers who love using data to drive their decisions. Take a look at [our open positions](https://www.quantumblack.com/careers/current-openings#content) and see if you're a fit.
        
Keywords: 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: api
Provides-Extra: biosequence
Provides-Extra: dask
Provides-Extra: docs
Provides-Extra: geopandas
Provides-Extra: matplotlib
Provides-Extra: networkx
Provides-Extra: notebook_templates
Provides-Extra: pandas
Provides-Extra: pillow
Provides-Extra: profilers
Provides-Extra: spark
Provides-Extra: api.APIDataSet
Provides-Extra: biosequence.BioSequenceDataSet
Provides-Extra: dask.ParquetDataSet
Provides-Extra: geopandas.GeoJSONDataSet
Provides-Extra: matplotlib.MatplotlibWriter
Provides-Extra: networkx.NetworkXDataSet
Provides-Extra: pandas.CSVDataSet
Provides-Extra: pandas.ExcelDataSet
Provides-Extra: pandas.FeatherDataSet
Provides-Extra: pandas.GBQTableDataSet
Provides-Extra: pandas.HDFDataSet
Provides-Extra: pandas.JSONDataSet
Provides-Extra: pandas.ParquetDataSet
Provides-Extra: pandas.SQLTableDataSet
Provides-Extra: pillow.ImageDataSet
Provides-Extra: spark.SparkDataSet
Provides-Extra: spark.SparkHiveDataSet
Provides-Extra: spark.SparkJDBCDataSet
Provides-Extra: all
