semvec/pypi-packages huggingface dataset:
robbytorch 0.4.1
Cool package for robust AI
License: MIT
Requires Python: >=3.7, <4
Keywords: pytorch,neural-networks,deep-learning,robust-learning,transfer-learning

# Installation

`Robbytorch` requires Pytorch, however it's not specified in the dependencies - we recommend installing Pytorch manually via conda and only later installing Robbytorch by pip. Pytorch has to be in version `1.6` or higher.

Use your conda env or create a new one:

```
conda create --name <ENV NAME> python=3.8 pip
conda activate <ENV NAME>
```

Install [Pytorch](https://pytorch.org/). If you have older drivers for GPU you may require older version of CUDA, i.e.:

```
conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch -c conda-forge
```

or even [older Pytorch version](https://pytorch.org/get-started/previous-versions/):

```
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
```

Then run:

```
pip install robbytorch
```

# Usage

The basics of the `Robbytorch` library are explained in the [ipython/RobbytorchTutorial.ipynb](https://github.com/mim-solutions/robbytorch/blob/master/ipython/RobbytorchTutorial.ipynb) juputer notebook.

# TODO

- Streamline logging - currently we need to pass arguments to `MLFLowWriter` at init which we want to do outside of the `Trainer#train_model` function to keep reference to the `Writer` instance. We'd like to invent better flow that would allow us to pass config dict just once;
- Create helper for iterating over rows and cols and printing tensor:

```python
diffs = show - show.roll(shifts=1, dims=0)

cols, rows = 3, 6
for i in range(rows):
    curr = (show[i*cols:(i+1)*cols])
    diff = vis_grad(diffs[i*cols:(i+1)*cols])
    robby.get_image_table(curr, diff, size=(10,10))

robby.widen_outputs()
```

# Packaging

## Development mode

Run from this repository's root dir:
```
python setup.py develop
```

Now you can use the package as if it was installed by `pip`.

When you’re done with a given development task, you can remove the project source from a staging area using:

```
python setup.py develop --uninstall
```

## Creating python package

Use [this tutorial](https://packaging.python.org/tutorials/packaging-projects/). In short:

1. put your source files into `src/<PACKAGE NAME>/` dir and create files: `src/<PACKAGE NAME>/__init__.py`, `setup.py`, `pyproject.toml`, `README.md` and `LICENSE` (also optionally an empty `test` dir)
2. install required packages:
```
python3 -m pip install --upgrade twine build
```
3. build the package, i.e. run from root folder:
```
python3 -m build
```
4. upload the package:
```
python3 -m twine upload dist/*
```

In order to perform the last step you need to have a https://pypi.org/ account and create the API token, which could be conveniently placed in `$HOME/.pypirc`:

```
[pypi]
  username = __token__
  password = pypi-<API TOKEN>
```


## Warnings:
If you update the package you should increment the `version` param in `setup.py` and empty the `dist` dir.
rpe-api 0.3.0
Retency Privacy Engine API Python library
License: MIT

# RPE-API-library
Python library simplifying the use of Retency Privacy Engine API.

### Installation
```
pip install rpe-api
```

### Get started
How to login to Retency Privacy Engine and create your first join:

```Python
from rpe_api import Client, New Project, import_from_file

# Login in to RPE and create your session
user = Client('username', 'password')

# Create a new project
yourproject = NewProject(user, 'your_project_name')

# Import your data sets into your project
import_from_file('path/to/your/file.csv', yourproject, 'left')
import_from_file('path/to/your/otherfile.csv', yourproject, 'right')

# Select the attributes and join columns for your tables
yourproject.select_columns('left', ['attribute'],['id'])
yourproject.select_columns('right', ['attribute'],['id'])

# Launch the private join
project.private_join('left')
```
virtualwatts 0.1.1
VirtualWatts is a formula for a  software-defined power meter based on the PowerAPI framework.
License: MIT
Requires Python: >=3.7
Keywords: energy,powerapi,containers,performance counters,power meter

VirtualWatts is a software-defined power meter based on the PowerAPI toolkit.
VirtualWatts a configurable software that can estimate the power consumption of
process inside a VM in real-time.
VirtuallWatts is supposed to be used inside a VM. It need to receive two metrics :

- The energy consumption of the VM, usually made with
  [SmartWatts](https://github.com/powerapi-ng/smartwatts-formula)
- The cpu usage of the tracked processes and the global cpu usage, as provided
  by [procfs-sensor](https://github.com/powerapi-ng/procfs-sensor).

# About

VirtualWatts is an open-source project developed by the [Spirals research
group](https://team.inria.fr/spirals) (University of Lille 1 and Inria).

The documentation is not available yet.

## Contributing

If you would like to contribute code you can do so through GitHub by forking the
repository and sending a pull request.
You should start by reading the [contribution guide](https://github.com/powerapi-ng/virtualwatts/blob/main/contributing.md)

When submitting code, please check that it is conform by using `pylint` and
`flake8` with the configurations files at the root of the project.

## Acknowledgments

VirtualWatts is written in [Python](https://www.python.org/) (under [PSF
license](https://docs.python.org/3/license.html)) and built on top of
[PowerAPI](https://github.com/powerapi-ng/powerapi)

MIT License

Copyright (c) 2021 PowerAPI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
diskord-invites 0.2
Python library for simple invites tracking.
License: MIT
Requires Python: >=3.6
Keywords: discord diskord discord-invites invite tracker

<div>
  <h1 align='center'>
    diskord-invites
  </h1>
</div>
<div>
  <p align='center'>
    <img src=https://img.shields.io/pypi/dm/diskord-invites?color=success&label=PyPi%20Downloads&style=flat-square>
    <img src=https://shields.io/github/issues-raw/FlamptX/diskord-invites?color=success&label=Active%20Issues&style=flat-square>
    <img src=https://img.shields.io/pypi/v/diskord-invites>
  </p>
  <p align='center'>
    Python library for simple invites tracking.
  </p>
</div>
<br>

## Features
- Very easy to use
- Uses [diskord](https://github.com/diskord-dev/diskord)
- Actively maintained
## Links
- **[PyPi](https://pypi.org/project/diskord-invites)**
## Installation
You can easily install it using the python package manager `pip`

```
pip install diskord-invites
```
## Quickstart
Example usage (bot is diskord.ext.commands.Bot)
```python
from diskord_invites import Tracker

tracker = Tracker(bot)
bot.add_cog(tracker)

@tracker.member_join
async def member_join(member, inviter, channel):
    await channel.send(f"{member.name} was invited by {inviter.name}")
```

## Contributions
Feel free to open pull requests and improve the library. If you find any issues, please report it.