%global _empty_manifest_terminate_build 0 Name: python-databand Version: 1.0.12.12 Release: 1 Summary: Machine Learning Orchestration License: Apache Software License URL: https://github.com/databand-ai/dbnd Source0: https://mirrors.nju.edu.cn/pypi/web/packages/2a/b5/a45998bc15f9eb9cddf2e692889e13967c3e89f05bb4595d067316503bf3/databand-1.0.12.12.tar.gz BuildArch: noarch Requires: python3-dbnd Requires: python3-dbnd-airflow Requires: python3-dbnd-airflow-auto-tracking Requires: python3-dbnd-airflow-export Requires: python3-dbnd-airflow-versioned-dag Requires: python3-dbnd-aws Requires: python3-dbnd-azure Requires: python3-dbnd-databricks Requires: python3-dbnd-docker Requires: python3-dbnd-gcp Requires: python3-dbnd-hdfs Requires: python3-dbnd-docker Requires: python3-dbnd-luigi Requires: python3-dbnd-mlflow Requires: python3-dbnd-postgres Requires: python3-dbnd-qubole Requires: python3-dbnd-redshift Requires: python3-dbnd-snowflake Requires: python3-dbnd-spark Requires: python3-dbnd-tensorflow %description [![pipeline status](https://gitlab.com/databand-ai/dbnd/badges/master/pipeline.svg)](https://gitlab.com/databand-ai/dbnd/pipelines) ![PyPI - Downloads](https://img.shields.io/pypi/dm/dbnd) ![PyPI](https://img.shields.io/pypi/v/dbnd) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dbnd) ![PyPI - License](https://img.shields.io/pypi/l/dbnd) ![Code style: ](https://img.shields.io/badge/code%20style-black-000000.svg) # DBND DBND an open source framework for building and tracking data pipelines. DBND is used for processes ranging from data ingestion, preparation, machine learning model training and production. DBND includes a Python library, set of APIs, and CLI that enables you to collect metadata from your workflows, create a system of record for runs, and easily orchestrate complex processes. DBND simplifies the process of building and running data pipelines from dbnd import task ```python from dbnd import task @task def say_hello(name: str = "databand.ai") -> str: value = "Hello %s!" % name return value ``` And makes it easy to track your critical pipeline metadata ```python from dbnd import log_metric, log_dataframe log_dataframe("my_dataset", my_dataset) log_metric("r2", r2) ``` ## Getting Started See our [documentation](https://docs.databand.ai/docs) with examples and quickstart guides to get up and running with DBND. ## The Latest and Greatest For using DBND, we recommend that you work with a virtual environment like [Virtualenv](https://virtualenv.pypa.io/en/latest/) or [Conda](https://docs.conda.io/en/latest/). Update to the latest and greatest: ```shell script pip install dbnd ``` If you would like access to our latest features, or have any questions, feedback, or contributions we would love to here from you! Get in touch through contact@databand.ai %package -n python3-databand Summary: Machine Learning Orchestration Provides: python-databand BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-databand [![pipeline status](https://gitlab.com/databand-ai/dbnd/badges/master/pipeline.svg)](https://gitlab.com/databand-ai/dbnd/pipelines) ![PyPI - Downloads](https://img.shields.io/pypi/dm/dbnd) ![PyPI](https://img.shields.io/pypi/v/dbnd) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dbnd) ![PyPI - License](https://img.shields.io/pypi/l/dbnd) ![Code style: ](https://img.shields.io/badge/code%20style-black-000000.svg) # DBND DBND an open source framework for building and tracking data pipelines. DBND is used for processes ranging from data ingestion, preparation, machine learning model training and production. DBND includes a Python library, set of APIs, and CLI that enables you to collect metadata from your workflows, create a system of record for runs, and easily orchestrate complex processes. DBND simplifies the process of building and running data pipelines from dbnd import task ```python from dbnd import task @task def say_hello(name: str = "databand.ai") -> str: value = "Hello %s!" % name return value ``` And makes it easy to track your critical pipeline metadata ```python from dbnd import log_metric, log_dataframe log_dataframe("my_dataset", my_dataset) log_metric("r2", r2) ``` ## Getting Started See our [documentation](https://docs.databand.ai/docs) with examples and quickstart guides to get up and running with DBND. ## The Latest and Greatest For using DBND, we recommend that you work with a virtual environment like [Virtualenv](https://virtualenv.pypa.io/en/latest/) or [Conda](https://docs.conda.io/en/latest/). Update to the latest and greatest: ```shell script pip install dbnd ``` If you would like access to our latest features, or have any questions, feedback, or contributions we would love to here from you! Get in touch through contact@databand.ai %package help Summary: Development documents and examples for databand Provides: python3-databand-doc %description help [![pipeline status](https://gitlab.com/databand-ai/dbnd/badges/master/pipeline.svg)](https://gitlab.com/databand-ai/dbnd/pipelines) ![PyPI - Downloads](https://img.shields.io/pypi/dm/dbnd) ![PyPI](https://img.shields.io/pypi/v/dbnd) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dbnd) ![PyPI - License](https://img.shields.io/pypi/l/dbnd) ![Code style: ](https://img.shields.io/badge/code%20style-black-000000.svg) # DBND DBND an open source framework for building and tracking data pipelines. DBND is used for processes ranging from data ingestion, preparation, machine learning model training and production. DBND includes a Python library, set of APIs, and CLI that enables you to collect metadata from your workflows, create a system of record for runs, and easily orchestrate complex processes. DBND simplifies the process of building and running data pipelines from dbnd import task ```python from dbnd import task @task def say_hello(name: str = "databand.ai") -> str: value = "Hello %s!" % name return value ``` And makes it easy to track your critical pipeline metadata ```python from dbnd import log_metric, log_dataframe log_dataframe("my_dataset", my_dataset) log_metric("r2", r2) ``` ## Getting Started See our [documentation](https://docs.databand.ai/docs) with examples and quickstart guides to get up and running with DBND. ## The Latest and Greatest For using DBND, we recommend that you work with a virtual environment like [Virtualenv](https://virtualenv.pypa.io/en/latest/) or [Conda](https://docs.conda.io/en/latest/). Update to the latest and greatest: ```shell script pip install dbnd ``` If you would like access to our latest features, or have any questions, feedback, or contributions we would love to here from you! Get in touch through contact@databand.ai %prep %autosetup -n databand-1.0.12.12 %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-databand -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 1.0.12.12-1 - Package Spec generated