%global _empty_manifest_terminate_build 0
Name: python-cgm-ml-common
Version: 3.1.7
Release: 1
Summary: ChildGrowthMonitor's ML Common code
License: GNU General Public License v3 (GPLv3)
URL: https://github.com/Welthungerhilfe/cgm-ml
Source0: https://mirrors.aliyun.com/pypi/web/packages/5b/d6/8d2c520d7073ef8aa08de60b403461e9637b37d55221b3de258ed2badd68/cgm-ml-common-3.1.7.tar.gz
BuildArch: noarch
%description
[](https://codecov.io/gh/Welthungerhilfe/cgm-ml)
[](https://github.com/Welthungerhilfe/cgm-ml/actions/workflows/continous-integration.yml)
[](https://pypi.python.org/pypi/cgm-ml-common)
[](https://mybinder.org/v2/gh/Welthungerhilfe/cgm-ml/HEAD)
[](https://www.codefactor.io/repository/github/welthungerhilfe/cgm-ml)
# Child Growth Monitor Machine Learning
[Child Growth Monitor (CGM)](https://childgrowthmonitor.org) is a game-changing app to detect malnutrition.
If you have questions about the project, reach out to `info@childgrowthmonitor.org`.
This is the Machine Learnine repository associated with the CGM project.
## Introduction
This project uses machine learning to identify malnutrition from 3D scans of children under 5 years of age.
This [one-minute video](https://www.youtube.com/watch?v=f2doV43jdwg) explains.
## Getting started
### Requirements
You will need:
* Python 3.6 or Python 3.7
* TensorFlow version 2
* other libraries
To install, run:
```bash
pip install -r requirements.txt
```
We use [Microsoft Azure ML](https://azure.microsoft.com/en-us/services/machine-learning/) to manage our datasets, experiments, and models internally.
You can also run most of the code without AzureML though.
### Dataset access
Data access is provided on as-needed basis following signature of the Welthungerhilfe Data Privacy & Commitment to
Maintain Data Secrecy Agreement. If you need data access (e.g. to train your machine learning models),
please contact [Markus Matiaschek](mailto:info@childgrowthmonitor.org) for details.
If you have access to scan data, you can use: `cgmml/data_utils` to understand and visualize the data.
## Repository structure
The source code is in `cgmml/`.
Due to AzureML, all code for a single experiment run needs to reside in one directory.
Example: All code for one specific training, e.g. a ResNet training, needs to be in this training directory.
However, many of our trainings (and also evaluation runs) share large portions of code.
In order to reduce code duplication, we copy shared(a.k.a. common) utility code with `copy_dir()` from `cgmml/common/` into the training/evaluation directory.
This way, during the experiment run, the code is in the directory and can be used during the run.
### Run linting / tests
```bash
# Make sure to be in the root dir of this repository
flake8 cgmml/
pytest
```
### Release cgm-ml-common
Common functionalities of this repo are released on pypi:
To release a new version of cgm-ml-common:
* Configure the version you wish to release in `setup.py`
* Publish the release using the pipeline `.github/workflows/pypi-release.yml`
## Contributing
Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.
## Versioning
Our [releases](https://github.com/Welthungerhilfe/cgm-ml/releases) use [semantic versioning](http://semver.org).
You can find a chronologically ordered list of notable changes in [CHANGELOG.md](CHANGELOG.md).
## License
This project is licensed under the GNU General Public License v3.0. See [LICENSE](LICENSE) for details and refer to [NOTICE](NOTICE) for additional licensing notes and use of third-party components.
%package -n python3-cgm-ml-common
Summary: ChildGrowthMonitor's ML Common code
Provides: python-cgm-ml-common
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-cgm-ml-common
[](https://codecov.io/gh/Welthungerhilfe/cgm-ml)
[](https://github.com/Welthungerhilfe/cgm-ml/actions/workflows/continous-integration.yml)
[](https://pypi.python.org/pypi/cgm-ml-common)
[](https://mybinder.org/v2/gh/Welthungerhilfe/cgm-ml/HEAD)
[](https://www.codefactor.io/repository/github/welthungerhilfe/cgm-ml)
# Child Growth Monitor Machine Learning
[Child Growth Monitor (CGM)](https://childgrowthmonitor.org) is a game-changing app to detect malnutrition.
If you have questions about the project, reach out to `info@childgrowthmonitor.org`.
This is the Machine Learnine repository associated with the CGM project.
## Introduction
This project uses machine learning to identify malnutrition from 3D scans of children under 5 years of age.
This [one-minute video](https://www.youtube.com/watch?v=f2doV43jdwg) explains.
## Getting started
### Requirements
You will need:
* Python 3.6 or Python 3.7
* TensorFlow version 2
* other libraries
To install, run:
```bash
pip install -r requirements.txt
```
We use [Microsoft Azure ML](https://azure.microsoft.com/en-us/services/machine-learning/) to manage our datasets, experiments, and models internally.
You can also run most of the code without AzureML though.
### Dataset access
Data access is provided on as-needed basis following signature of the Welthungerhilfe Data Privacy & Commitment to
Maintain Data Secrecy Agreement. If you need data access (e.g. to train your machine learning models),
please contact [Markus Matiaschek](mailto:info@childgrowthmonitor.org) for details.
If you have access to scan data, you can use: `cgmml/data_utils` to understand and visualize the data.
## Repository structure
The source code is in `cgmml/`.
Due to AzureML, all code for a single experiment run needs to reside in one directory.
Example: All code for one specific training, e.g. a ResNet training, needs to be in this training directory.
However, many of our trainings (and also evaluation runs) share large portions of code.
In order to reduce code duplication, we copy shared(a.k.a. common) utility code with `copy_dir()` from `cgmml/common/` into the training/evaluation directory.
This way, during the experiment run, the code is in the directory and can be used during the run.
### Run linting / tests
```bash
# Make sure to be in the root dir of this repository
flake8 cgmml/
pytest
```
### Release cgm-ml-common
Common functionalities of this repo are released on pypi:
To release a new version of cgm-ml-common:
* Configure the version you wish to release in `setup.py`
* Publish the release using the pipeline `.github/workflows/pypi-release.yml`
## Contributing
Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.
## Versioning
Our [releases](https://github.com/Welthungerhilfe/cgm-ml/releases) use [semantic versioning](http://semver.org).
You can find a chronologically ordered list of notable changes in [CHANGELOG.md](CHANGELOG.md).
## License
This project is licensed under the GNU General Public License v3.0. See [LICENSE](LICENSE) for details and refer to [NOTICE](NOTICE) for additional licensing notes and use of third-party components.
%package help
Summary: Development documents and examples for cgm-ml-common
Provides: python3-cgm-ml-common-doc
%description help
[](https://codecov.io/gh/Welthungerhilfe/cgm-ml)
[](https://github.com/Welthungerhilfe/cgm-ml/actions/workflows/continous-integration.yml)
[](https://pypi.python.org/pypi/cgm-ml-common)
[](https://mybinder.org/v2/gh/Welthungerhilfe/cgm-ml/HEAD)
[](https://www.codefactor.io/repository/github/welthungerhilfe/cgm-ml)
# Child Growth Monitor Machine Learning
[Child Growth Monitor (CGM)](https://childgrowthmonitor.org) is a game-changing app to detect malnutrition.
If you have questions about the project, reach out to `info@childgrowthmonitor.org`.
This is the Machine Learnine repository associated with the CGM project.
## Introduction
This project uses machine learning to identify malnutrition from 3D scans of children under 5 years of age.
This [one-minute video](https://www.youtube.com/watch?v=f2doV43jdwg) explains.
## Getting started
### Requirements
You will need:
* Python 3.6 or Python 3.7
* TensorFlow version 2
* other libraries
To install, run:
```bash
pip install -r requirements.txt
```
We use [Microsoft Azure ML](https://azure.microsoft.com/en-us/services/machine-learning/) to manage our datasets, experiments, and models internally.
You can also run most of the code without AzureML though.
### Dataset access
Data access is provided on as-needed basis following signature of the Welthungerhilfe Data Privacy & Commitment to
Maintain Data Secrecy Agreement. If you need data access (e.g. to train your machine learning models),
please contact [Markus Matiaschek](mailto:info@childgrowthmonitor.org) for details.
If you have access to scan data, you can use: `cgmml/data_utils` to understand and visualize the data.
## Repository structure
The source code is in `cgmml/`.
Due to AzureML, all code for a single experiment run needs to reside in one directory.
Example: All code for one specific training, e.g. a ResNet training, needs to be in this training directory.
However, many of our trainings (and also evaluation runs) share large portions of code.
In order to reduce code duplication, we copy shared(a.k.a. common) utility code with `copy_dir()` from `cgmml/common/` into the training/evaluation directory.
This way, during the experiment run, the code is in the directory and can be used during the run.
### Run linting / tests
```bash
# Make sure to be in the root dir of this repository
flake8 cgmml/
pytest
```
### Release cgm-ml-common
Common functionalities of this repo are released on pypi:
To release a new version of cgm-ml-common:
* Configure the version you wish to release in `setup.py`
* Publish the release using the pipeline `.github/workflows/pypi-release.yml`
## Contributing
Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details.
## Versioning
Our [releases](https://github.com/Welthungerhilfe/cgm-ml/releases) use [semantic versioning](http://semver.org).
You can find a chronologically ordered list of notable changes in [CHANGELOG.md](CHANGELOG.md).
## License
This project is licensed under the GNU General Public License v3.0. See [LICENSE](LICENSE) for details and refer to [NOTICE](NOTICE) for additional licensing notes and use of third-party components.
%prep
%autosetup -n cgm-ml-common-3.1.7
%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-cgm-ml-common -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot - 3.1.7-1
- Package Spec generated