semvec/pypi-packages huggingface dataset:
PyModelManager 0.47.0
ModelManager API
Requires Python: >=3.6

# PyModelManager
PyModelManager это Python библиотека для взаимодействия с ModelManager API.

## Установка

```bash
$ pip install PyModelManager
```

## Пример использования

```python
from modelmanager import ModelManager

# Сначала создаем экземпляр ModelManager
mm = ModelManager(base_url='http://{hostname}/api', login='{login}', password='{password}')

# Получим список проектов
projects = mm.get_projects()

# Получим все существующие версии моделей всех проектов
models = [project.get_models() for project in projects]

# Выводим в stdout все модели
for model in models:
    print(model)

# Получим реестр признаков
registry = mm.get_feature_registry()

# Получим список всех признаков
features = registry.all()

# Выводим в stdout все признаки
for feature in features:
    print('Feature: %s, %s' % (feature.name, feature.description))
    
# Создадим в реестре новый признак
feature = registry.create('birthday', "Дата рождения в формате 'yyyy-mm-dd'")

# Изменим признак
feature.description = "Дата рождения в формате 'yyyy.mm.dd"
feature.update()

# Удалим признак
registry.delete(feature)

# Создадим модель описание модели
model = project.create_model(
            author='RAffghi',
            name='Africa Economic, Banking and Systemic Crisis',
            title='Africa Economic, Banking and Systemic Crisis',
            description='Africa Economic, Banking and Systemic Crisis : A Logistic Regression Model Approch',
            algorithm='LogisticRegression',
            label='no_crisis',
            features=[feature(name) for name in ['case', 'cc3', 'country', 'year', 'systemic_crisis', 'exch_usd',
             'domestic_debt_in_default', 'sovereign_external_debt_default',
             'gdp_weighted_default', 'inflation_annual_cpi', 'independence',
             'currency_crises', 'inflation_crises', 'banking_crisis']],
            characteristics={}
        )

# Удаление модели
model.delete()

# Загрузка бинарного файла модели
file = '/your/model/file/path'
model.upload(file)

# Скачивание бинарного фала модели
bytes = model.download()
```

## Документация

TBD

## Как собрать пакет
```bash
python3 setup.py sdist
python3 -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
```
comp-lead 0.1.15
Display a handful of tech leaders and companies
Requires Python: >=3

# Comp-Lead [![PyPI](https://img.shields.io/pypi/v/comp-lead?color=blue)](https://pypi.org/project/comp-lead/)

This is my first PyPI package, so this is very simple. This package displays a handful of tech leaders and companies (five to be exact).
The reason why I decided to create this package was to just understand the way packages are built and how they are published.

### Usage:

Installation:

```sh
pip install comp-lead
```

Import:

```python
from comp_lead import sample
```

Run: 

```python
sample.obj.displayLead()

sample.com.displayComp()
```
gt-dicom-connector 0.0.2
A DICOM Node Connector for Python
Requires Python: >=3.6.0

# gt-dicom-connector [![Build Status](https://travis-ci.org/gyrofx/gt-dicom-connector-py.svg?branch=master)](https://travis-ci.org/gyrofx/gt-dicom-connector-py)

gt-dicom-connector is a python library to retiev and send data to dicom nodes

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install gt-dicom-connector.

```bash
pip install gt-dicom-connector
```

Currently gt-dicom-connector supports python 3.6 and 3.7.


## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
cctools 0.0.1
Large scale distributed computing on clusters, clouds, and grids


The Cooperative Computing Tools (cctools) is a software
package for enabling large scale distributed computing
on clusters, clouds, and grids.  It is used primarily for
attacking large scale problems in science and engineering.

You can read more about this software at:
   http://ccl.cse.nd.edu/software

The software is developed by members of the Cooperative
Computing Lab at the University of Notre Dame, led by
Prof. Douglas Thain.  The file CREDITS lists the many
people that have contributed to the software over the years.

For complete instructions on how to build, install, and use
the software, see here:
   http://ccl.cse.nd.edu/software/manuals

If you are impatient, the following commands will build
and install the software in your home directory:

   ./configure --prefix ${HOME}/cctools
   make clean
   make install
   export PATH=${PATH}:${HOME}/cctools/bin
   chirp_status

------------------------------------------------------------
This software package is
Copyright (c) 2003-2004 Douglas Thain
Copyright (c) 2005-2019 The University of Notre Dame
This software is distributed under the GNU General Public License.
See the file COPYING for details.
------------------------------------------------------------
This product includes software developed by and/or derived
from the Globus Project (http://www.globus.org/)
to which the U.S. Government retains certain rights.
------------------------------------------------------------
This product includes code derived from the RSA Data
Security, Inc. MD5 Message-Digest Algorithm.
------------------------------------------------------------
This product includes public domain code for the
SHA1 algorithm written by Peter Gutmann, David Ireland,
and A. M. Kutchman.
------------------------------------------------------------
This product includes the source code for the MT19937-64
Mersenne Twister pseudorandom number generator, written by 
Makoto Matsumoto and Takuji Nishimura.