%global _empty_manifest_terminate_build 0 Name: python-kedro Version: 0.18.7 Release: 1 Summary: Kedro helps you build production-ready data and analytics pipelines License: Apache Software License (Apache 2.0) URL: https://github.com/kedro-org/kedro Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1c/e4/5be14c8372d0063264020d00b7337e1d1685e57b7f426109521783e17332/kedro-0.18.7.tar.gz BuildArch: noarch Requires: python3-anyconfig Requires: python3-attrs Requires: python3-cachetools Requires: python3-click Requires: python3-cookiecutter Requires: python3-dynaconf Requires: python3-gitpython Requires: python3-importlib-resources Requires: python3-jmespath Requires: python3-more-itertools Requires: python3-omegaconf Requires: python3-pip-tools Requires: python3-pluggy Requires: python3-PyYAML Requires: python3-rich Requires: python3-rope Requires: python3-setuptools Requires: python3-toml Requires: python3-toposort Requires: python3-importlib-metadata Requires: python3-fsspec Requires: python3-fsspec Requires: python3-importlib-metadata Requires: python3-Jinja2 Requires: python3-Pillow Requires: python3-PyYAML Requires: python3-SQLAlchemy Requires: python3-biopython Requires: python3-compress-pickle[lz4] Requires: python3-dask[complete] Requires: python3-delta-spark Requires: python3-docutils Requires: python3-geopandas Requires: python3-hdfs Requires: python3-holoviews Requires: python3-ipykernel Requires: python3-lxml Requires: python3-matplotlib Requires: python3-myst-parser Requires: python3-nbsphinx Requires: python3-nbstripout Requires: python3-networkx Requires: python3-opencv-python Requires: python3-openpyxl Requires: python3-pandas-gbq Requires: python3-pandas Requires: python3-plotly Requires: python3-pyarrow Requires: python3-pyproj Requires: python3-pyspark Requires: python3-redis Requires: python3-requests Requires: python3-s3fs Requires: python3-scikit-learn Requires: python3-scipy Requires: python3-sphinx-autodoc-typehints Requires: python3-sphinx-copybutton Requires: python3-sphinx-rtd-theme Requires: python3-sphinxcontrib-mermaid Requires: python3-sphinx Requires: python3-tensorflow Requires: python3-triad Requires: python3-tables Requires: python3-tables Requires: python3-requests Requires: python3-requests Requires: python3-biopython Requires: python3-biopython Requires: python3-dask[complete] Requires: python3-triad Requires: python3-dask[complete] Requires: python3-triad Requires: python3-docutils Requires: python3-sphinx Requires: python3-sphinx-rtd-theme Requires: python3-nbsphinx Requires: python3-nbstripout Requires: python3-sphinx-autodoc-typehints Requires: python3-sphinx-copybutton Requires: python3-ipykernel Requires: python3-sphinxcontrib-mermaid Requires: python3-myst-parser Requires: python3-Jinja2 Requires: python3-geopandas Requires: python3-pyproj Requires: python3-geopandas Requires: python3-pyproj Requires: python3-holoviews Requires: python3-holoviews Requires: python3-matplotlib Requires: python3-matplotlib Requires: python3-networkx Requires: python3-networkx Requires: python3-SQLAlchemy Requires: python3-lxml Requires: python3-openpyxl Requires: python3-pandas-gbq Requires: python3-pandas Requires: python3-pyarrow Requires: python3-pandas Requires: python3-pandas Requires: python3-openpyxl Requires: python3-pandas Requires: python3-pandas Requires: python3-pandas-gbq Requires: python3-pandas Requires: python3-pandas-gbq Requires: python3-pandas Requires: python3-pandas Requires: python3-tables Requires: python3-tables Requires: python3-pandas Requires: python3-pandas Requires: python3-pyarrow Requires: python3-pandas Requires: python3-SQLAlchemy Requires: python3-pandas Requires: python3-SQLAlchemy Requires: python3-pandas Requires: python3-lxml Requires: python3-tables Requires: python3-tables Requires: python3-compress-pickle[lz4] Requires: python3-compress-pickle[lz4] Requires: python3-Pillow Requires: python3-Pillow Requires: python3-pandas Requires: python3-plotly Requires: python3-plotly Requires: python3-pandas Requires: python3-plotly Requires: python3-redis Requires: python3-delta-spark Requires: python3-hdfs Requires: python3-pyspark Requires: python3-s3fs Requires: python3-pyspark Requires: python3-hdfs Requires: python3-s3fs Requires: python3-delta-spark Requires: python3-pyspark Requires: python3-hdfs Requires: python3-s3fs Requires: python3-pyspark Requires: python3-hdfs Requires: python3-s3fs Requires: python3-pyspark Requires: python3-hdfs Requires: python3-s3fs Requires: python3-scikit-learn Requires: python3-scipy Requires: python3-scikit-learn Requires: python3-scipy Requires: python3-tensorflow Requires: python3-tensorflow Requires: python3-opencv-python Requires: python3-opencv-python Requires: python3-PyYAML Requires: python3-pandas Requires: python3-pandas Requires: python3-PyYAML %description ![Kedro Logo Banner](https://raw.githubusercontent.com/kedro-org/kedro/develop/static/img/kedro_banner.png) [![Python version](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue.svg)](https://pypi.org/project/kedro/) [![PyPI version](https://badge.fury.io/py/kedro.svg)](https://pypi.org/project/kedro/) [![Conda version](https://img.shields.io/conda/vn/conda-forge/kedro.svg)](https://anaconda.org/conda-forge/kedro) [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/kedro-org/kedro/blob/main/LICENSE.md) [![Slack Organisation](https://img.shields.io/badge/slack-chat-blueviolet.svg?label=Kedro%20Slack&logo=slack)](https://slack.kedro.org) ![CircleCI - Main Branch](https://img.shields.io/circleci/build/github/kedro-org/kedro/main?label=main) ![Develop Branch Build](https://img.shields.io/circleci/build/github/kedro-org/kedro/develop?label=develop) [![Documentation](https://readthedocs.org/projects/kedro/badge/?version=stable)](https://docs.kedro.org/) [![OpenSSF Best Practices](https://bestpractices.coreinfrastructure.org/projects/6711/badge)](https://bestpractices.coreinfrastructure.org/projects/6711) ## What is Kedro? Kedro is an open-source Python framework to create reproducible, maintainable, and modular data science code. It uses software engineering best practices to help you build production-ready data engineering and data science pipelines. Kedro is hosted by the [LF AI & Data Foundation](https://lfaidata.foundation/). ## How do I install Kedro? To install Kedro from the Python Package Index (PyPI) run: ``` pip install kedro ``` It is also possible to install Kedro using `conda`: ``` conda install -c conda-forge kedro ``` Our [Get Started guide](https://docs.kedro.org/en/stable/get_started/install.html) contains full installation instructions, and includes how to set up Python virtual environments. ## What are the main features of Kedro? ![Kedro-Viz Pipeline Visualisation](https://github.com/kedro-org/kedro-viz/blob/main/.github/img/banner.png) *A pipeline visualisation generated using [Kedro-Viz](https://github.com/kedro-org/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 to save and load 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. | | Pipeline Abstraction | Automatic resolution of dependencies between pure Python functions and data pipeline visualisation using [Kedro-Viz](https://github.com/kedro-org/kedro-viz). | | 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/PyCQA/isort) and [`black`](https://github.com/psf/black) and make use of the standard Python logging library. | | Flexible Deployment | Deployment strategies that include single or distributed-machine deployment as well as additional support for deploying on Argo, Prefect, Kubeflow, AWS Batch and Databricks. | ## How do I use Kedro? The [Kedro documentation](https://docs.kedro.org/en/stable/) first explains [how to install Kedro](https://docs.kedro.org/en/stable/get_started/install.html) and then introduces [key Kedro concepts](https://docs.kedro.org/en/stable/get_started/kedro_concepts.html). - The first example illustrates the [basics of a Kedro project](https://docs.kedro.org/en/stable/get_started/new_project.html) using the Iris dataset - You can then review the [spaceflights tutorial](https://docs.kedro.org/en/stable/tutorial/tutorial_template.html) to build a Kedro project for hands-on experience For new and intermediate Kedro users, there's a comprehensive section on [how to visualise Kedro projects using Kedro-Viz](https://docs.kedro.org/en/stable/visualisation/kedro-viz_visualisation.html) and [how to work with Kedro and Jupyter notebooks](https://docs.kedro.org/en/stable/notebooks_and_ipython/kedro_and_notebooks). Further documentation is available for more advanced Kedro usage and deployment. We also recommend the [glossary](https://docs.kedro.org/en/stable/resources/glossary.html) and the [API reference documentation](/kedro) for additional information. ## 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: - To address the main shortcomings of Jupyter notebooks, one-off scripts, and glue-code because there is a focus on creating **maintainable data science code** - To enhance **team collaboration** when different team members have varied exposure to software engineering concepts - To increase efficiency, because applied concepts like modularity and separation of concerns inspire the creation of **reusable analytics code** ## The humans behind Kedro The [Kedro product team](https://docs.kedro.org/en/stable/faq/faq.html#who-maintains-kedro) and a number of [open source contributors from across the world](https://github.com/kedro-org/kedro/releases) maintain Kedro. ## Can I contribute? Yes! Want to help build Kedro? Check out our [guide to contributing to Kedro](https://github.com/kedro-org/kedro/blob/main/CONTRIBUTING.md). ## Where can I learn more? There is a growing community around Kedro. Have a look at the [Kedro FAQs](https://docs.kedro.org/en/stable/faq/faq.html#how-can-i-find-out-more-about-kedro) to find projects using Kedro and links to articles, podcasts and talks. ## Who likes Kedro? There are Kedro users across the world, who work at start-ups, major enterprises and academic institutions like [Absa](https://www.absa.co.za/), [Acensi](https://acensi.eu/page/home), [Advanced Programming Solutions SL](https://www.linkedin.com/feed/update/urn:li:activity:6863494681372721152/), [AI Singapore](https://makerspace.aisingapore.org/2020/08/leveraging-kedro-in-100e/), [AMAI GmbH](https://www.am.ai/), [Augment Partners](https://www.linkedin.com/posts/augment-partners_kedro-cheat-sheet-by-augment-activity-6858927624631283712-Ivqk), [AXA UK](https://www.axa.co.uk/), [Belfius](https://www.linkedin.com/posts/vangansen_mlops-machinelearning-kedro-activity-6772379995953238016-JUmo), [Beamery](https://medium.com/hacking-talent/production-code-for-data-science-and-our-experience-with-kedro-60bb69934d1f), [Caterpillar](https://www.caterpillar.com/), [CRIM](https://www.crim.ca/en/), [Dendra Systems](https://www.dendra.io/), [Element AI](https://www.elementai.com/), [GetInData](https://getindata.com/blog/running-machine-learning-pipelines-kedro-kubeflow-airflow), [GMO](https://recruit.gmo.jp/engineer/jisedai/engineer/jisedai/engineer/jisedai/engineer/jisedai/engineer/jisedai/blog/kedro_and_mlflow_tracking/), [Indicium](https://medium.com/indiciumtech/how-to-build-models-as-products-using-mlops-part-2-machine-learning-pipelines-with-kedro-10337c48de92), [Imperial College London](https://github.com/dssg/barefoot-winnie-public), [ING](https://www.ing.com), [Jungle Scout](https://junglescouteng.medium.com/jungle-scout-case-study-kedro-airflow-and-mlflow-use-on-production-code-150d7231d42e), [Helvetas](https://www.linkedin.com/posts/lionel-trebuchon_mlflow-kedro-ml-ugcPost-6747074322164154368-umKw), [Leapfrog](https://www.lftechnology.com/blog/ai-pipeline-kedro/), [McKinsey & Company](https://www.mckinsey.com/alumni/news-and-insights/global-news/firm-news/kedro-from-proprietary-to-open-source), [Mercado Libre Argentina](https://www.mercadolibre.com.ar), [Modec](https://www.modec.com/), [Mosaic Data Science](https://www.youtube.com/watch?v=fCWGevB366g), [NaranjaX](https://www.youtube.com/watch?v=_0kMmRfltEQ), [NASA](https://github.com/nasa/ML-airport-taxi-out), [NHS AI Lab](https://nhsx.github.io/skunkworks/synthetic-data-pipeline), [Open Data Science LatAm](https://www.odesla.org/), [Prediqt](https://prediqt.co/), [QuantumBlack](https://medium.com/quantumblack/introducing-kedro-the-open-source-library-for-production-ready-machine-learning-code-d1c6d26ce2cf), [ReSpo.Vision](https://neptune.ai/customers/respo-vision), [Retrieva](https://tech.retrieva.jp/entry/2020/07/28/181414), [Roche](https://www.roche.com/), [Sber](https://www.linkedin.com/posts/seleznev-artem_welcome-to-kedros-documentation-kedro-activity-6767523561109385216-woTt), [Société Générale](https://www.societegenerale.com/en), [Telkomsel](https://medium.com/life-at-telkomsel/how-we-build-a-production-grade-data-pipeline-7004e56c8c98), [Universidad Rey Juan Carlos](https://github.com/vchaparro/MasterThesis-wind-power-forecasting/blob/master/thesis.pdf), [UrbanLogiq](https://urbanlogiq.com/), [Wildlife Studios](https://wildlifestudios.com), [WovenLight](https://www.wovenlight.com/) and [XP](https://youtu.be/wgnGOVNkXqU?t=2210). Kedro won [Best Technical Tool or Framework for AI](https://awards.ai/the-awards/previous-awards/the-4th-ai-award-winners/) in the 2019 Awards AI competition and a merit award for the 2020 [UK Technical Communication Awards](https://uktcawards.com/announcing-the-award-winners-for-2020/). It is listed on the 2020 [ThoughtWorks Technology Radar](https://www.thoughtworks.com/radar/languages-and-frameworks/kedro) and the 2020 [Data & AI Landscape](https://mattturck.com/data2020/). Kedro has received an [honorable mention in the User Experience category in Fast Company’s 2022 Innovation by Design Awards](https://www.fastcompany.com/90772252/user-experience-innovation-by-design-2022). ## How can I cite Kedro? If you're an academic, Kedro can also help you, for example, as a tool to solve the problem of reproducible research. Use the "Cite this repository" button on [our repository](https://github.com/kedro-org/kedro) to generate a citation from the [CITATION.cff file](https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-citation-files). %package -n python3-kedro Summary: Kedro helps you build production-ready data and analytics pipelines Provides: python-kedro BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-kedro ![Kedro Logo Banner](https://raw.githubusercontent.com/kedro-org/kedro/develop/static/img/kedro_banner.png) [![Python version](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue.svg)](https://pypi.org/project/kedro/) [![PyPI version](https://badge.fury.io/py/kedro.svg)](https://pypi.org/project/kedro/) [![Conda version](https://img.shields.io/conda/vn/conda-forge/kedro.svg)](https://anaconda.org/conda-forge/kedro) [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/kedro-org/kedro/blob/main/LICENSE.md) [![Slack Organisation](https://img.shields.io/badge/slack-chat-blueviolet.svg?label=Kedro%20Slack&logo=slack)](https://slack.kedro.org) ![CircleCI - Main Branch](https://img.shields.io/circleci/build/github/kedro-org/kedro/main?label=main) ![Develop Branch Build](https://img.shields.io/circleci/build/github/kedro-org/kedro/develop?label=develop) [![Documentation](https://readthedocs.org/projects/kedro/badge/?version=stable)](https://docs.kedro.org/) [![OpenSSF Best Practices](https://bestpractices.coreinfrastructure.org/projects/6711/badge)](https://bestpractices.coreinfrastructure.org/projects/6711) ## What is Kedro? Kedro is an open-source Python framework to create reproducible, maintainable, and modular data science code. It uses software engineering best practices to help you build production-ready data engineering and data science pipelines. Kedro is hosted by the [LF AI & Data Foundation](https://lfaidata.foundation/). ## How do I install Kedro? To install Kedro from the Python Package Index (PyPI) run: ``` pip install kedro ``` It is also possible to install Kedro using `conda`: ``` conda install -c conda-forge kedro ``` Our [Get Started guide](https://docs.kedro.org/en/stable/get_started/install.html) contains full installation instructions, and includes how to set up Python virtual environments. ## What are the main features of Kedro? ![Kedro-Viz Pipeline Visualisation](https://github.com/kedro-org/kedro-viz/blob/main/.github/img/banner.png) *A pipeline visualisation generated using [Kedro-Viz](https://github.com/kedro-org/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 to save and load 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. | | Pipeline Abstraction | Automatic resolution of dependencies between pure Python functions and data pipeline visualisation using [Kedro-Viz](https://github.com/kedro-org/kedro-viz). | | 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/PyCQA/isort) and [`black`](https://github.com/psf/black) and make use of the standard Python logging library. | | Flexible Deployment | Deployment strategies that include single or distributed-machine deployment as well as additional support for deploying on Argo, Prefect, Kubeflow, AWS Batch and Databricks. | ## How do I use Kedro? The [Kedro documentation](https://docs.kedro.org/en/stable/) first explains [how to install Kedro](https://docs.kedro.org/en/stable/get_started/install.html) and then introduces [key Kedro concepts](https://docs.kedro.org/en/stable/get_started/kedro_concepts.html). - The first example illustrates the [basics of a Kedro project](https://docs.kedro.org/en/stable/get_started/new_project.html) using the Iris dataset - You can then review the [spaceflights tutorial](https://docs.kedro.org/en/stable/tutorial/tutorial_template.html) to build a Kedro project for hands-on experience For new and intermediate Kedro users, there's a comprehensive section on [how to visualise Kedro projects using Kedro-Viz](https://docs.kedro.org/en/stable/visualisation/kedro-viz_visualisation.html) and [how to work with Kedro and Jupyter notebooks](https://docs.kedro.org/en/stable/notebooks_and_ipython/kedro_and_notebooks). Further documentation is available for more advanced Kedro usage and deployment. We also recommend the [glossary](https://docs.kedro.org/en/stable/resources/glossary.html) and the [API reference documentation](/kedro) for additional information. ## 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: - To address the main shortcomings of Jupyter notebooks, one-off scripts, and glue-code because there is a focus on creating **maintainable data science code** - To enhance **team collaboration** when different team members have varied exposure to software engineering concepts - To increase efficiency, because applied concepts like modularity and separation of concerns inspire the creation of **reusable analytics code** ## The humans behind Kedro The [Kedro product team](https://docs.kedro.org/en/stable/faq/faq.html#who-maintains-kedro) and a number of [open source contributors from across the world](https://github.com/kedro-org/kedro/releases) maintain Kedro. ## Can I contribute? Yes! Want to help build Kedro? Check out our [guide to contributing to Kedro](https://github.com/kedro-org/kedro/blob/main/CONTRIBUTING.md). ## Where can I learn more? There is a growing community around Kedro. Have a look at the [Kedro FAQs](https://docs.kedro.org/en/stable/faq/faq.html#how-can-i-find-out-more-about-kedro) to find projects using Kedro and links to articles, podcasts and talks. ## Who likes Kedro? There are Kedro users across the world, who work at start-ups, major enterprises and academic institutions like [Absa](https://www.absa.co.za/), [Acensi](https://acensi.eu/page/home), [Advanced Programming Solutions SL](https://www.linkedin.com/feed/update/urn:li:activity:6863494681372721152/), [AI Singapore](https://makerspace.aisingapore.org/2020/08/leveraging-kedro-in-100e/), [AMAI GmbH](https://www.am.ai/), [Augment Partners](https://www.linkedin.com/posts/augment-partners_kedro-cheat-sheet-by-augment-activity-6858927624631283712-Ivqk), [AXA UK](https://www.axa.co.uk/), [Belfius](https://www.linkedin.com/posts/vangansen_mlops-machinelearning-kedro-activity-6772379995953238016-JUmo), [Beamery](https://medium.com/hacking-talent/production-code-for-data-science-and-our-experience-with-kedro-60bb69934d1f), [Caterpillar](https://www.caterpillar.com/), [CRIM](https://www.crim.ca/en/), [Dendra Systems](https://www.dendra.io/), [Element AI](https://www.elementai.com/), [GetInData](https://getindata.com/blog/running-machine-learning-pipelines-kedro-kubeflow-airflow), [GMO](https://recruit.gmo.jp/engineer/jisedai/engineer/jisedai/engineer/jisedai/engineer/jisedai/engineer/jisedai/blog/kedro_and_mlflow_tracking/), [Indicium](https://medium.com/indiciumtech/how-to-build-models-as-products-using-mlops-part-2-machine-learning-pipelines-with-kedro-10337c48de92), [Imperial College London](https://github.com/dssg/barefoot-winnie-public), [ING](https://www.ing.com), [Jungle Scout](https://junglescouteng.medium.com/jungle-scout-case-study-kedro-airflow-and-mlflow-use-on-production-code-150d7231d42e), [Helvetas](https://www.linkedin.com/posts/lionel-trebuchon_mlflow-kedro-ml-ugcPost-6747074322164154368-umKw), [Leapfrog](https://www.lftechnology.com/blog/ai-pipeline-kedro/), [McKinsey & Company](https://www.mckinsey.com/alumni/news-and-insights/global-news/firm-news/kedro-from-proprietary-to-open-source), [Mercado Libre Argentina](https://www.mercadolibre.com.ar), [Modec](https://www.modec.com/), [Mosaic Data Science](https://www.youtube.com/watch?v=fCWGevB366g), [NaranjaX](https://www.youtube.com/watch?v=_0kMmRfltEQ), [NASA](https://github.com/nasa/ML-airport-taxi-out), [NHS AI Lab](https://nhsx.github.io/skunkworks/synthetic-data-pipeline), [Open Data Science LatAm](https://www.odesla.org/), [Prediqt](https://prediqt.co/), [QuantumBlack](https://medium.com/quantumblack/introducing-kedro-the-open-source-library-for-production-ready-machine-learning-code-d1c6d26ce2cf), [ReSpo.Vision](https://neptune.ai/customers/respo-vision), [Retrieva](https://tech.retrieva.jp/entry/2020/07/28/181414), [Roche](https://www.roche.com/), [Sber](https://www.linkedin.com/posts/seleznev-artem_welcome-to-kedros-documentation-kedro-activity-6767523561109385216-woTt), [Société Générale](https://www.societegenerale.com/en), [Telkomsel](https://medium.com/life-at-telkomsel/how-we-build-a-production-grade-data-pipeline-7004e56c8c98), [Universidad Rey Juan Carlos](https://github.com/vchaparro/MasterThesis-wind-power-forecasting/blob/master/thesis.pdf), [UrbanLogiq](https://urbanlogiq.com/), [Wildlife Studios](https://wildlifestudios.com), [WovenLight](https://www.wovenlight.com/) and [XP](https://youtu.be/wgnGOVNkXqU?t=2210). Kedro won [Best Technical Tool or Framework for AI](https://awards.ai/the-awards/previous-awards/the-4th-ai-award-winners/) in the 2019 Awards AI competition and a merit award for the 2020 [UK Technical Communication Awards](https://uktcawards.com/announcing-the-award-winners-for-2020/). It is listed on the 2020 [ThoughtWorks Technology Radar](https://www.thoughtworks.com/radar/languages-and-frameworks/kedro) and the 2020 [Data & AI Landscape](https://mattturck.com/data2020/). Kedro has received an [honorable mention in the User Experience category in Fast Company’s 2022 Innovation by Design Awards](https://www.fastcompany.com/90772252/user-experience-innovation-by-design-2022). ## How can I cite Kedro? If you're an academic, Kedro can also help you, for example, as a tool to solve the problem of reproducible research. Use the "Cite this repository" button on [our repository](https://github.com/kedro-org/kedro) to generate a citation from the [CITATION.cff file](https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-citation-files). %package help Summary: Development documents and examples for kedro Provides: python3-kedro-doc %description help ![Kedro Logo Banner](https://raw.githubusercontent.com/kedro-org/kedro/develop/static/img/kedro_banner.png) [![Python version](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue.svg)](https://pypi.org/project/kedro/) [![PyPI version](https://badge.fury.io/py/kedro.svg)](https://pypi.org/project/kedro/) [![Conda version](https://img.shields.io/conda/vn/conda-forge/kedro.svg)](https://anaconda.org/conda-forge/kedro) [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/kedro-org/kedro/blob/main/LICENSE.md) [![Slack Organisation](https://img.shields.io/badge/slack-chat-blueviolet.svg?label=Kedro%20Slack&logo=slack)](https://slack.kedro.org) ![CircleCI - Main Branch](https://img.shields.io/circleci/build/github/kedro-org/kedro/main?label=main) ![Develop Branch Build](https://img.shields.io/circleci/build/github/kedro-org/kedro/develop?label=develop) [![Documentation](https://readthedocs.org/projects/kedro/badge/?version=stable)](https://docs.kedro.org/) [![OpenSSF Best Practices](https://bestpractices.coreinfrastructure.org/projects/6711/badge)](https://bestpractices.coreinfrastructure.org/projects/6711) ## What is Kedro? Kedro is an open-source Python framework to create reproducible, maintainable, and modular data science code. It uses software engineering best practices to help you build production-ready data engineering and data science pipelines. Kedro is hosted by the [LF AI & Data Foundation](https://lfaidata.foundation/). ## How do I install Kedro? To install Kedro from the Python Package Index (PyPI) run: ``` pip install kedro ``` It is also possible to install Kedro using `conda`: ``` conda install -c conda-forge kedro ``` Our [Get Started guide](https://docs.kedro.org/en/stable/get_started/install.html) contains full installation instructions, and includes how to set up Python virtual environments. ## What are the main features of Kedro? ![Kedro-Viz Pipeline Visualisation](https://github.com/kedro-org/kedro-viz/blob/main/.github/img/banner.png) *A pipeline visualisation generated using [Kedro-Viz](https://github.com/kedro-org/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 to save and load 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. | | Pipeline Abstraction | Automatic resolution of dependencies between pure Python functions and data pipeline visualisation using [Kedro-Viz](https://github.com/kedro-org/kedro-viz). | | 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/PyCQA/isort) and [`black`](https://github.com/psf/black) and make use of the standard Python logging library. | | Flexible Deployment | Deployment strategies that include single or distributed-machine deployment as well as additional support for deploying on Argo, Prefect, Kubeflow, AWS Batch and Databricks. | ## How do I use Kedro? The [Kedro documentation](https://docs.kedro.org/en/stable/) first explains [how to install Kedro](https://docs.kedro.org/en/stable/get_started/install.html) and then introduces [key Kedro concepts](https://docs.kedro.org/en/stable/get_started/kedro_concepts.html). - The first example illustrates the [basics of a Kedro project](https://docs.kedro.org/en/stable/get_started/new_project.html) using the Iris dataset - You can then review the [spaceflights tutorial](https://docs.kedro.org/en/stable/tutorial/tutorial_template.html) to build a Kedro project for hands-on experience For new and intermediate Kedro users, there's a comprehensive section on [how to visualise Kedro projects using Kedro-Viz](https://docs.kedro.org/en/stable/visualisation/kedro-viz_visualisation.html) and [how to work with Kedro and Jupyter notebooks](https://docs.kedro.org/en/stable/notebooks_and_ipython/kedro_and_notebooks). Further documentation is available for more advanced Kedro usage and deployment. We also recommend the [glossary](https://docs.kedro.org/en/stable/resources/glossary.html) and the [API reference documentation](/kedro) for additional information. ## 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: - To address the main shortcomings of Jupyter notebooks, one-off scripts, and glue-code because there is a focus on creating **maintainable data science code** - To enhance **team collaboration** when different team members have varied exposure to software engineering concepts - To increase efficiency, because applied concepts like modularity and separation of concerns inspire the creation of **reusable analytics code** ## The humans behind Kedro The [Kedro product team](https://docs.kedro.org/en/stable/faq/faq.html#who-maintains-kedro) and a number of [open source contributors from across the world](https://github.com/kedro-org/kedro/releases) maintain Kedro. ## Can I contribute? Yes! Want to help build Kedro? Check out our [guide to contributing to Kedro](https://github.com/kedro-org/kedro/blob/main/CONTRIBUTING.md). ## Where can I learn more? There is a growing community around Kedro. Have a look at the [Kedro FAQs](https://docs.kedro.org/en/stable/faq/faq.html#how-can-i-find-out-more-about-kedro) to find projects using Kedro and links to articles, podcasts and talks. ## Who likes Kedro? There are Kedro users across the world, who work at start-ups, major enterprises and academic institutions like [Absa](https://www.absa.co.za/), [Acensi](https://acensi.eu/page/home), [Advanced Programming Solutions SL](https://www.linkedin.com/feed/update/urn:li:activity:6863494681372721152/), [AI Singapore](https://makerspace.aisingapore.org/2020/08/leveraging-kedro-in-100e/), [AMAI GmbH](https://www.am.ai/), [Augment Partners](https://www.linkedin.com/posts/augment-partners_kedro-cheat-sheet-by-augment-activity-6858927624631283712-Ivqk), [AXA UK](https://www.axa.co.uk/), [Belfius](https://www.linkedin.com/posts/vangansen_mlops-machinelearning-kedro-activity-6772379995953238016-JUmo), [Beamery](https://medium.com/hacking-talent/production-code-for-data-science-and-our-experience-with-kedro-60bb69934d1f), [Caterpillar](https://www.caterpillar.com/), [CRIM](https://www.crim.ca/en/), [Dendra Systems](https://www.dendra.io/), [Element AI](https://www.elementai.com/), [GetInData](https://getindata.com/blog/running-machine-learning-pipelines-kedro-kubeflow-airflow), [GMO](https://recruit.gmo.jp/engineer/jisedai/engineer/jisedai/engineer/jisedai/engineer/jisedai/engineer/jisedai/blog/kedro_and_mlflow_tracking/), [Indicium](https://medium.com/indiciumtech/how-to-build-models-as-products-using-mlops-part-2-machine-learning-pipelines-with-kedro-10337c48de92), [Imperial College London](https://github.com/dssg/barefoot-winnie-public), [ING](https://www.ing.com), [Jungle Scout](https://junglescouteng.medium.com/jungle-scout-case-study-kedro-airflow-and-mlflow-use-on-production-code-150d7231d42e), [Helvetas](https://www.linkedin.com/posts/lionel-trebuchon_mlflow-kedro-ml-ugcPost-6747074322164154368-umKw), [Leapfrog](https://www.lftechnology.com/blog/ai-pipeline-kedro/), [McKinsey & Company](https://www.mckinsey.com/alumni/news-and-insights/global-news/firm-news/kedro-from-proprietary-to-open-source), [Mercado Libre Argentina](https://www.mercadolibre.com.ar), [Modec](https://www.modec.com/), [Mosaic Data Science](https://www.youtube.com/watch?v=fCWGevB366g), [NaranjaX](https://www.youtube.com/watch?v=_0kMmRfltEQ), [NASA](https://github.com/nasa/ML-airport-taxi-out), [NHS AI Lab](https://nhsx.github.io/skunkworks/synthetic-data-pipeline), [Open Data Science LatAm](https://www.odesla.org/), [Prediqt](https://prediqt.co/), [QuantumBlack](https://medium.com/quantumblack/introducing-kedro-the-open-source-library-for-production-ready-machine-learning-code-d1c6d26ce2cf), [ReSpo.Vision](https://neptune.ai/customers/respo-vision), [Retrieva](https://tech.retrieva.jp/entry/2020/07/28/181414), [Roche](https://www.roche.com/), [Sber](https://www.linkedin.com/posts/seleznev-artem_welcome-to-kedros-documentation-kedro-activity-6767523561109385216-woTt), [Société Générale](https://www.societegenerale.com/en), [Telkomsel](https://medium.com/life-at-telkomsel/how-we-build-a-production-grade-data-pipeline-7004e56c8c98), [Universidad Rey Juan Carlos](https://github.com/vchaparro/MasterThesis-wind-power-forecasting/blob/master/thesis.pdf), [UrbanLogiq](https://urbanlogiq.com/), [Wildlife Studios](https://wildlifestudios.com), [WovenLight](https://www.wovenlight.com/) and [XP](https://youtu.be/wgnGOVNkXqU?t=2210). Kedro won [Best Technical Tool or Framework for AI](https://awards.ai/the-awards/previous-awards/the-4th-ai-award-winners/) in the 2019 Awards AI competition and a merit award for the 2020 [UK Technical Communication Awards](https://uktcawards.com/announcing-the-award-winners-for-2020/). It is listed on the 2020 [ThoughtWorks Technology Radar](https://www.thoughtworks.com/radar/languages-and-frameworks/kedro) and the 2020 [Data & AI Landscape](https://mattturck.com/data2020/). Kedro has received an [honorable mention in the User Experience category in Fast Company’s 2022 Innovation by Design Awards](https://www.fastcompany.com/90772252/user-experience-innovation-by-design-2022). ## How can I cite Kedro? If you're an academic, Kedro can also help you, for example, as a tool to solve the problem of reproducible research. Use the "Cite this repository" button on [our repository](https://github.com/kedro-org/kedro) to generate a citation from the [CITATION.cff file](https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-citation-files). %prep %autosetup -n kedro-0.18.7 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-kedro -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Apr 21 2023 Python_Bot - 0.18.7-1 - Package Spec generated