%global _empty_manifest_terminate_build 0
Name:		python-bodywork
Version:	3.0.12
Release:	1
Summary:	ML pipeline orchestration and model deployments on Kubernetes, made really easy.
License:	AGPL 3.0
URL:		https://www.bodyworkml.com
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b8/cb/90b2b4e9d4e11fa5c8fbb9e9327142a0b0b9c5ed49b4d5e7a1d66e3cb26d/bodywork-3.0.12.tar.gz
BuildArch:	noarch


%description
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/bw-v3-terminal-demo-readme.png"/>
</div>
Bodywork is a command line tool that deploys machine learning pipelines to [Kubernetes](https://en.wikipedia.org/wiki/Kubernetes). It takes care of everything to do with containers and orchestration, so that you don't have to.
## Who is this for?
Bodywork is aimed at teams who want a solution for running ML pipelines and deploying models to Kubernetes. It is a lightweight and simpler alternative to [Kubeflow](https://www.kubeflow.org), or to building your own platform based around a workflow orchestration tool like [Apache Airflow](https://airflow.apache.org), [Argo Workflows](https://argoproj.github.io/workflows/) or [Dagster](https://www.dagster.io).
## Pipeline = Jobs + Services
Any stage in a Bodywork pipeline can do one of two things:
- [x] **run a batch job**, to prepare features, train models, compute batch predictions, etc.
- [x] **start a long-running process**, like a Flask app that serves model predictions via HTTP.
You can use these to compose pipelines for many common ML use-cases, from serving pre-trained models to running continuous training on a schedule.
## No Boilerplate Code Required
Defining a stage is as simple as developing an executable Python module or Jupyter notebook that performs the required tasks, and then committing it to your project's Git repository. You are free to structure your codebase as you wish and there are no new APIs to learn.
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/project_structure_map.png"/ alt="Git project structure">
</div>
## Easy to Configure
Stages are assembled into [DAGs](https://en.wikipedia.org/wiki/Directed_acyclic_graph) that define your pipeline's workflow. This and other key [configuration](user_guide.md#configuring-a-project-for-deployment-with-bodywork) is contained within a single [bodywork.yaml file](https://github.com/bodywork-ml/bodywork-ml-pipeline-project/blob/master/bodywork.yaml).
## Simplified DevOps for ML
Bodywork removes the need for you to build and manage container images for any stage of your pipeline. It works by running all stages using Bodywork's [custom container image](https://hub.docker.com/repository/docker/bodyworkml/bodywork-core). Each stage starts by pulling all the required files directly from your project's Git repository (e.g., from GitHub), pip-installing any required dependencies, and then running the stage's designated Python module (or Jupyter notebook).
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/ml_pipeline.png"/>
</div>
## Get Started
Bodywork is distributed as a Python package - install it from [PyPI](https://pypi.org/project/bodywork/#description):
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/get_started_1_v2.png"/>
</div>
Add a [bodywork.yaml](https://bodywork.readthedocs.io/en/latest/user_guide/#configuring-a-project-for-deployment-with-bodywork) file to your Python project’s Git repo. The contents of this file describe how your project will be deployed:
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/get_started_2_v2.png"/>
</div>
Bodywork is used from the command-line to deploy projects to Kubernetes clusters. With a single command, you can start Bodywork containers (hosted by us on Docker Hub), that pull Python modules directly from your project’s Git repo, and run them:
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/get_started_3_v2.png"/>
</div>
You don’t need to build Docker images or understand how to configure Kuberentes resources. Bodywork will fill the gap between executable Python modules and operational jobs and services on Kubernetes.
If you’re new to Kubernetes then check out our guide to [Kubernetes for ML](https://bodywork.readthedocs.io/en/latest/kubernetes/#getting-started-with-kubernetes) - we’ll have you up-and-running with a test cluster on your laptop, in under 10 minutes.
## Documentation
The documentation for bodywork-core can be found [here](https://bodywork.readthedocs.io/en/latest/). This is the best place to start.
## Deployment Templates
To accelerate your project's journey to production, we provide [deployment templates](https://bodywork.readthedocs.io/en/latest/template_projects/) for common use-cases:
- [batch scoring data](https://github.com/bodywork-ml/bodywork-batch-job-project)
- [deploying a prediction service with a REST API](https://github.com/bodywork-ml/bodywork-serve-model-project)
- [scheduling a continuous-training pipeline](https://github.com/bodywork-ml/bodywork-ml-pipeline-project)
## We want your Feedback
If Bodywork sounds like a useful tool, then please send us a signal with a GitHub ★
## Contacting Us
If you:
- Have a question that these pages haven't answered, or need help getting started with Kubernetes, then please use our [discussion board](https://github.com/bodywork-ml/bodywork-core/discussions).
- Have found a bug, then please [open an issue](https://github.com/bodywork-ml/bodywork-core/issues).
- Would like to contribute, then please talk to us **first** at [info@bodyworkml.com](mailto:info@bodyworkml.com).
- Would like to commission new functionality, then please contact us at [info@bodyworkml.com](mailto:info@bodyworkml.com).
Bodywork is brought to you by [Bodywork Machine Learning](https://www.bodyworkml.com).

%package -n python3-bodywork
Summary:	ML pipeline orchestration and model deployments on Kubernetes, made really easy.
Provides:	python-bodywork
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-bodywork
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/bw-v3-terminal-demo-readme.png"/>
</div>
Bodywork is a command line tool that deploys machine learning pipelines to [Kubernetes](https://en.wikipedia.org/wiki/Kubernetes). It takes care of everything to do with containers and orchestration, so that you don't have to.
## Who is this for?
Bodywork is aimed at teams who want a solution for running ML pipelines and deploying models to Kubernetes. It is a lightweight and simpler alternative to [Kubeflow](https://www.kubeflow.org), or to building your own platform based around a workflow orchestration tool like [Apache Airflow](https://airflow.apache.org), [Argo Workflows](https://argoproj.github.io/workflows/) or [Dagster](https://www.dagster.io).
## Pipeline = Jobs + Services
Any stage in a Bodywork pipeline can do one of two things:
- [x] **run a batch job**, to prepare features, train models, compute batch predictions, etc.
- [x] **start a long-running process**, like a Flask app that serves model predictions via HTTP.
You can use these to compose pipelines for many common ML use-cases, from serving pre-trained models to running continuous training on a schedule.
## No Boilerplate Code Required
Defining a stage is as simple as developing an executable Python module or Jupyter notebook that performs the required tasks, and then committing it to your project's Git repository. You are free to structure your codebase as you wish and there are no new APIs to learn.
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/project_structure_map.png"/ alt="Git project structure">
</div>
## Easy to Configure
Stages are assembled into [DAGs](https://en.wikipedia.org/wiki/Directed_acyclic_graph) that define your pipeline's workflow. This and other key [configuration](user_guide.md#configuring-a-project-for-deployment-with-bodywork) is contained within a single [bodywork.yaml file](https://github.com/bodywork-ml/bodywork-ml-pipeline-project/blob/master/bodywork.yaml).
## Simplified DevOps for ML
Bodywork removes the need for you to build and manage container images for any stage of your pipeline. It works by running all stages using Bodywork's [custom container image](https://hub.docker.com/repository/docker/bodyworkml/bodywork-core). Each stage starts by pulling all the required files directly from your project's Git repository (e.g., from GitHub), pip-installing any required dependencies, and then running the stage's designated Python module (or Jupyter notebook).
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/ml_pipeline.png"/>
</div>
## Get Started
Bodywork is distributed as a Python package - install it from [PyPI](https://pypi.org/project/bodywork/#description):
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/get_started_1_v2.png"/>
</div>
Add a [bodywork.yaml](https://bodywork.readthedocs.io/en/latest/user_guide/#configuring-a-project-for-deployment-with-bodywork) file to your Python project’s Git repo. The contents of this file describe how your project will be deployed:
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/get_started_2_v2.png"/>
</div>
Bodywork is used from the command-line to deploy projects to Kubernetes clusters. With a single command, you can start Bodywork containers (hosted by us on Docker Hub), that pull Python modules directly from your project’s Git repo, and run them:
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/get_started_3_v2.png"/>
</div>
You don’t need to build Docker images or understand how to configure Kuberentes resources. Bodywork will fill the gap between executable Python modules and operational jobs and services on Kubernetes.
If you’re new to Kubernetes then check out our guide to [Kubernetes for ML](https://bodywork.readthedocs.io/en/latest/kubernetes/#getting-started-with-kubernetes) - we’ll have you up-and-running with a test cluster on your laptop, in under 10 minutes.
## Documentation
The documentation for bodywork-core can be found [here](https://bodywork.readthedocs.io/en/latest/). This is the best place to start.
## Deployment Templates
To accelerate your project's journey to production, we provide [deployment templates](https://bodywork.readthedocs.io/en/latest/template_projects/) for common use-cases:
- [batch scoring data](https://github.com/bodywork-ml/bodywork-batch-job-project)
- [deploying a prediction service with a REST API](https://github.com/bodywork-ml/bodywork-serve-model-project)
- [scheduling a continuous-training pipeline](https://github.com/bodywork-ml/bodywork-ml-pipeline-project)
## We want your Feedback
If Bodywork sounds like a useful tool, then please send us a signal with a GitHub ★
## Contacting Us
If you:
- Have a question that these pages haven't answered, or need help getting started with Kubernetes, then please use our [discussion board](https://github.com/bodywork-ml/bodywork-core/discussions).
- Have found a bug, then please [open an issue](https://github.com/bodywork-ml/bodywork-core/issues).
- Would like to contribute, then please talk to us **first** at [info@bodyworkml.com](mailto:info@bodyworkml.com).
- Would like to commission new functionality, then please contact us at [info@bodyworkml.com](mailto:info@bodyworkml.com).
Bodywork is brought to you by [Bodywork Machine Learning](https://www.bodyworkml.com).

%package help
Summary:	Development documents and examples for bodywork
Provides:	python3-bodywork-doc
%description help
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/bw-v3-terminal-demo-readme.png"/>
</div>
Bodywork is a command line tool that deploys machine learning pipelines to [Kubernetes](https://en.wikipedia.org/wiki/Kubernetes). It takes care of everything to do with containers and orchestration, so that you don't have to.
## Who is this for?
Bodywork is aimed at teams who want a solution for running ML pipelines and deploying models to Kubernetes. It is a lightweight and simpler alternative to [Kubeflow](https://www.kubeflow.org), or to building your own platform based around a workflow orchestration tool like [Apache Airflow](https://airflow.apache.org), [Argo Workflows](https://argoproj.github.io/workflows/) or [Dagster](https://www.dagster.io).
## Pipeline = Jobs + Services
Any stage in a Bodywork pipeline can do one of two things:
- [x] **run a batch job**, to prepare features, train models, compute batch predictions, etc.
- [x] **start a long-running process**, like a Flask app that serves model predictions via HTTP.
You can use these to compose pipelines for many common ML use-cases, from serving pre-trained models to running continuous training on a schedule.
## No Boilerplate Code Required
Defining a stage is as simple as developing an executable Python module or Jupyter notebook that performs the required tasks, and then committing it to your project's Git repository. You are free to structure your codebase as you wish and there are no new APIs to learn.
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/project_structure_map.png"/ alt="Git project structure">
</div>
## Easy to Configure
Stages are assembled into [DAGs](https://en.wikipedia.org/wiki/Directed_acyclic_graph) that define your pipeline's workflow. This and other key [configuration](user_guide.md#configuring-a-project-for-deployment-with-bodywork) is contained within a single [bodywork.yaml file](https://github.com/bodywork-ml/bodywork-ml-pipeline-project/blob/master/bodywork.yaml).
## Simplified DevOps for ML
Bodywork removes the need for you to build and manage container images for any stage of your pipeline. It works by running all stages using Bodywork's [custom container image](https://hub.docker.com/repository/docker/bodyworkml/bodywork-core). Each stage starts by pulling all the required files directly from your project's Git repository (e.g., from GitHub), pip-installing any required dependencies, and then running the stage's designated Python module (or Jupyter notebook).
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/ml_pipeline.png"/>
</div>
## Get Started
Bodywork is distributed as a Python package - install it from [PyPI](https://pypi.org/project/bodywork/#description):
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/get_started_1_v2.png"/>
</div>
Add a [bodywork.yaml](https://bodywork.readthedocs.io/en/latest/user_guide/#configuring-a-project-for-deployment-with-bodywork) file to your Python project’s Git repo. The contents of this file describe how your project will be deployed:
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/get_started_2_v2.png"/>
</div>
Bodywork is used from the command-line to deploy projects to Kubernetes clusters. With a single command, you can start Bodywork containers (hosted by us on Docker Hub), that pull Python modules directly from your project’s Git repo, and run them:
<div align="center">
<img src="https://bodywork-media.s3.eu-west-2.amazonaws.com/get_started_3_v2.png"/>
</div>
You don’t need to build Docker images or understand how to configure Kuberentes resources. Bodywork will fill the gap between executable Python modules and operational jobs and services on Kubernetes.
If you’re new to Kubernetes then check out our guide to [Kubernetes for ML](https://bodywork.readthedocs.io/en/latest/kubernetes/#getting-started-with-kubernetes) - we’ll have you up-and-running with a test cluster on your laptop, in under 10 minutes.
## Documentation
The documentation for bodywork-core can be found [here](https://bodywork.readthedocs.io/en/latest/). This is the best place to start.
## Deployment Templates
To accelerate your project's journey to production, we provide [deployment templates](https://bodywork.readthedocs.io/en/latest/template_projects/) for common use-cases:
- [batch scoring data](https://github.com/bodywork-ml/bodywork-batch-job-project)
- [deploying a prediction service with a REST API](https://github.com/bodywork-ml/bodywork-serve-model-project)
- [scheduling a continuous-training pipeline](https://github.com/bodywork-ml/bodywork-ml-pipeline-project)
## We want your Feedback
If Bodywork sounds like a useful tool, then please send us a signal with a GitHub ★
## Contacting Us
If you:
- Have a question that these pages haven't answered, or need help getting started with Kubernetes, then please use our [discussion board](https://github.com/bodywork-ml/bodywork-core/discussions).
- Have found a bug, then please [open an issue](https://github.com/bodywork-ml/bodywork-core/issues).
- Would like to contribute, then please talk to us **first** at [info@bodyworkml.com](mailto:info@bodyworkml.com).
- Would like to commission new functionality, then please contact us at [info@bodyworkml.com](mailto:info@bodyworkml.com).
Bodywork is brought to you by [Bodywork Machine Learning](https://www.bodyworkml.com).

%prep
%autosetup -n bodywork-3.0.12

%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-bodywork -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.12-1
- Package Spec generated