semvec/pypi-packages huggingface dataset:
fc-pruning 0.1.0
FC Pruning
Requires Python: >=3.8

# [FeatureCloud Pruning](https://github.com/AidaMehammed/FC_Pruning_App)
### Model Compression with Pruning

The FC Pruning package offers a streamlined approach to model compression using advanced pruning techniques. With support for federated learning frameworks and integration with the [Torch-Pruning](https://github.com/VainF/Torch-Pruning/tree/master) library, this package enables efficient distributed training, suitable for a wide range of machine learning tasks.
1. Train Local Model


    
2. Configure Quantization:
    - Set up initial quantization settings.
      ```python
        self.configure_pruning(pruning_ratio, model, reference_model, imp,ex_input,
                               ignored_layers)        
      ```



 

3. Send Data to Coordinator and perform Pruning:
    - Send the prepared model data to the coordinator.
   ```python
    self.send_data_to_coordinator(model, use_pruning=True, use_smpc=False, use_dp=False)
   ```
fc-quantization 0.1.1
FC Quantization
Requires Python: >=3.8

# [FeatureCloud Quantization](https://github.com/AidaMehammed/FC_Quantization_App)
### Model Compression with Quantization

The FC Quantization package provides a comprehensive solution for model compression through quantization techniques. Integrated with federated learning frameworks and built upon the capabilities of PyTorch's quantization functionalities, this package facilitates efficient distributed training suitable for various machine learning tasks.


1. Configure Quantization:
    - Set up initial quantization settings.
      ```python
        self.configure_quant(model, backend, quant_typ)
        ```

2. Train Local Model:
    - If using post-static quantization:
        - Train the model.
        - Prepare for post-static quantization.
         ```python
         prep_post_static_quant(model, train_loader, backend)
        ```

    - Else:
        - Prepare for quantization-aware training.
      ```python
      pf.prepare_qat(model,backend)        
      ```
        - Train the prepared model.


3. Reconfigure Quantization:
    - Update quantization settings for the trained model.
   ```python
        self.configure_quant(prepared_model, backend, quant_typ)
   ```

4. Send Data to Coordinator:
    - Send the prepared model data to the coordinator.
me-toolbox 0.0.18
Mechanical engineering design tools
Requires Python: >=3.9

# me-toolbox

**me-toolbox** is a Python library meant to simplify the tedious<br>
calculations of mechanical design and help speed up the design process.<br>
This library contains fatigue, gears, springs and fasteners analysis tools.<br>
I made this library for my own personal use<br>
**Use at your own discretion.**


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install me-toolbox.
```bash
 pip install me-toolbox 
```
<!--
--->

## Usage

```python
import me_toolbox.springs as springs 
import me_toolbox.gears as gears
import me_toolbox.fatigue as fatigue
import me_toolbox.fatigue as fatigue
import me_toolbox.fasteners as fasteners

compression_spring = springs.HelicalCompressionSpring(...)
extension_spring = springs.ExtensionSpring(...)
torsion_spring = springs.HelicalTorsionSpring(...)

gear = gears.SpurGear(...)
transmission = gears.Transmission(...)

se = fatigue.EnduranceLimit(...)
fatigue_analysis = fatigue.FatigueAnalysis(se, ...)

bolt = fasteners.Bolt(...)
fastener = fasteners.ThreadedFastener(bolt, ...)
pattern = fasteners.BoltPattern(fasteners, ...)
```

For more detailed examples:
https://github.com/OmriStein/me-toolbox/tree/master/examples

## License
[MIT](https://choosealicense.com/licenses/mit/)
m2m 0.4.0
M2M Daemons.
Requires Python: >=3.8

===========
M2M Daemons
===========

Copyright (c) 2022-2024 Jérémie DECOCK (www.jdhp.org)

* Web site: https://gitlab.com/jdhp/sensors-m2m-daemons
* Online documentation: https://jdhp.gitlab.io/m2m
* Examples: https://jdhp.gitlab.io/m2m/gallery/
* Source code: https://gitlab.com/jdhp/sensors-m2m-daemons
* Issue tracker: https://gitlab.com/jdhp/sensors-m2m-daemons/issues
* M2M Daemons on PyPI: https://pypi.org/project/m2m


Description
===========

M2M Daemons

Note:

    This project is still in beta stage, so the API is not finalized yet.


Dependencies
============

C.f. requirements.txt


.. _install:

Installation (development environment)
======================================

Posix (Linux, MacOSX, WSL, ...)
-------------------------------

From the M2M Daemons source code::

    conda deactivate         # Only if you use Anaconda...
    python3 -m venv env
    source env/bin/activate
    python3 -m pip install --upgrade pip
    python3 -m pip install -r requirements-dev.txt


Windows
-------

From the M2M Daemons source code::

    conda deactivate         # Only if you use Anaconda...
    python3 -m venv env
    env\Scripts\activate.bat
    python3 -m pip install --upgrade pip
    python3 -m pip install -r requirements-dev.txt


Installation (production environment)
=====================================

::

    python3 -m pip install m2m


Documentation
=============

* Online documentation: https://jdhp.gitlab.io/m2m
* API documentation: https://jdhp.gitlab.io/m2m/api.html


Example usage
=============

::

    mqtt2influx --dry --verbose --config-path examples/config.yml


Build and run the Python Docker image
=====================================

Build the docker image
----------------------

From the M2M Daemons source code::

    docker build -t m2m:latest .

Run unit tests from the docker container
----------------------------------------

From the M2M Daemons source code::

    docker run m2m pytest

Run an example from the docker container
----------------------------------------

From the M2M Daemons source code::

    docker run m2m mqtt2influx

Bug reports
===========

To search for bugs or report them, please use the M2M Daemons Bug Tracker at:

    https://gitlab.com/jdhp/sensors-m2m-daemons/issues


License
=======

This project is provided under the terms and conditions of the `MIT License`_.


.. _MIT License: http://opensource.org/licenses/MIT
.. _command prompt: https://en.wikipedia.org/wiki/Cmd.exe
random-credit-card 0.1.0
Generate random but theoretically valid credit card numbers.
Requires Python: <4.0,>=3.12

# Random Credit Card

Generate random but theoretically valid credit card numbers.

### /!\ Disclamer

This code is intended for educational / regulated testing purposes only. It is provided "as is" without any warranties. Use at your own risk and ensure compliance with applicable laws.

## Install


Random Credit Card can be installed from [PyPI]().

```sh
python3 -m pip install random-credit-card
```

## Use as a script

If package is installed

```
python3 -m random_credit_card.credit_cards
```

Or download and run [credit_cards.py](random_credit_card/credit_cards.py).

```sh
python3 credit_cards.py
```



## Use as a Python module

```py
from random_credit_card import CreditCardNumber, ExpirationDate, generate_csc

...
number = CreditCardNumber() # Randomly generated
exp = ExpirationDate() # Randomly generated
code = generate_csc() # Randomly generated
```
pirate 0.1.1a1
Pirate is a Python package that aims to be a unified framework for all things information retrieval. Currently, it serves as a go-to library for negative mining.
Requires Python: <4.0,>=3.10

# Pirate

![Pirate Logo](./imgs/logo.png)

Pirate is a Python package that aims to be a unified framework for all things information retrieval. Currently, it serves as a go-to library for negative mining. The package provides a set of classes and methods to handle and manipulate data in various formats, making the process of information retrieval and negative mining easy and efficient.

## Installation

Pirate can be installed using pip. Simply run the following command:

```sh
pip install pirate
```

## Features

* **Data Handling:** Pirate provides classes like Passages, Queries, Triples, and Ranking to handle different types of data. These classes can load data from various formats such as JSON, CSV, and lists, and also save data to these formats.
* **Negative Mining:** Pirate is designed to make the process of negative mining easy and efficient. It provides methods to load and manipulate data, making it a powerful tool for negative mining tasks.

## Example Usage

Here's a simple example of how you can use the Passages class to load data from a JSON file:

```py
from pirate.data import Passages

# Load passages from a JSON file
passages = Passages("path_to_your_json_file.json") 
```

Same principle can be applied to load Queries, Triples, and Ranking data.

## Running Tests

Pirate uses pytest for testing. To run the tests, navigate to the project directory and run the following command:

```sh
PYTHONPATH=$PYTHONPATH:. pytest
```

This will run all the tests in the tests directory.

## Future Plans

While Pirate is currently focused on negative mining, the ultimate goal is to make it a comprehensive framework for all things related to information retrieval. Future updates will include more features and tools to handle a wider range of information retrieval tasks.

## License

Pirate is licensed under the Apache License 2.0. For more details, see the [LICENSE](./LICENSE) file.