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|
%global _empty_manifest_terminate_build 0
Name: python-dbnd
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/94/5e/fd2f9b732b11553c2b39aa543c81deb5d1fad960ecc967050a10828174b5/dbnd-1.0.12.12.tar.gz
BuildArch: noarch
Requires: python3-tzlocal
Requires: python3-six
Requires: python3-more-itertools
Requires: python3-attrs
Requires: python3-pyyaml
Requires: python3-pytz
Requires: python3-pytzdata
Requires: python3-requests
Requires: python3-configparser
Requires: python3-pygments
Requires: python3-dateutil
Requires: python3-sqlparse
Requires: python3-Jinja2
Requires: python3-typing
Requires: python3-colorama
Requires: python3-qtconsole
Requires: python3-nbconvert
Requires: python3-nbformat
Requires: python3-jupyter
Requires: python3-traitlets
Requires: python3-IPython
Requires: python3-jupyter-contrib-nbextensions
Requires: python3-openpyxl
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-tables
Requires: python3-feather-format
Requires: python3-pyarrow
Requires: python3-scikit-learn
Requires: python3-matplotlib
Requires: python3-scikit-learn
Requires: python3-matplotlib
Requires: python3-coverage
Requires: python3-tox
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-mock
Requires: python3-wheel
%description
[](https://gitlab.com/databand-ai/dbnd/pipelines)
 
 
# 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-dbnd
Summary: Machine Learning Orchestration
Provides: python-dbnd
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-dbnd
[](https://gitlab.com/databand-ai/dbnd/pipelines)
 
 
# 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 dbnd
Provides: python3-dbnd-doc
%description help
[](https://gitlab.com/databand-ai/dbnd/pipelines)
 
 
# 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 dbnd-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-dbnd -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.12.12-1
- Package Spec generated
|
