semvec/pypi-packages huggingface dataset:
model-unpickler 0.1
A tool to load machine/deep learning models with security



A tool to load machine/deep learning models with security.

Many machine/deep learning libraries (PyTorch, Scikit-Learn and so on) save trained models solely based on Python pickle, while pickle is well known for its potential to execute malicious code when loading objects from untrusted sources.

This libary provides a secure tool to load pickled models by overriding the `find_class` method of standard python Unpickler class together with a series of global names -- __whilelist__. Only globals in the whilelist are allowed in loaded model objects, whereas the loading process interrupts when an untrusted global name is found to prevent any potential exploit.

This libary also provides utils to quickly update the global whilelist in case that the corresponding machine learning libraries are updated.
pome 0.0.5
Distributed accounting software

<p align="center"><a href="https://pome.gr/">
<img src="https://pome.gr/assets/pome-round.png" width="90px"/>
</a></p>

# What is pome?

`pome` is a distributed accounting software.

## Why pome?

We believe that accounting data is one of the most valuable data of a business.   

Hence, it seems crucial to us that:

1. Entrepreneurs should own their accounting data and not depend on a third-party cloud to access them. 
2. The accounting data should be written using a simple, documented and open human-readable format

This is what pome offers.

Our ambition is to provide the framework for an ecosystem that will let businesses get the most out of their accounting data.

# How does pome work?

`pome` reads and write accounting data locally on your computer in a regular folder that is also a [git repository](https://en.wikipedia.org/wiki/Git). This approach provides a powerful framework to collaboratively work on accounting data in a distributed way.

`pome` provides a web UI that you can access from your browser.

# Getting started

## Install pome

`pome` requires Python 3.9.

We recommend running the below command in a Python 3.9 [virtual environment](https://realpython.com/python-virtual-environments-a-primer/).

```
pip3.9 install pome
```

## Start your company's accounts pome repository

You can use example companies to get started:

```
git clone https://github.com/pome-gr/pome.git
cd pome
cp -r examples/companies/<choose_your_example_company> /path/to/your/new/company/repository
cd /path/to/your/new/company/repository
git init
git add -A
git commit -m "Initial commit"
pome
```

Then open your browser at [http://localholst:5000](http://localholst:5000) to launch the UI.

You can change pome's port by launching pome with the port as first argument: `pome <PORT_NUMBER>`.

## Using a remote git repository

It is common and useful to host your git repository on a server that all your collaborators can access.
Common ways to achieve this are to use services such as [https://github.com](https://github.com) or [https://gitlab.com](https://gitlab.com).

Once you have a remote setup, you can ask pome to pull and push automatically from and to it by setting `"git_communicate_with_remote": true` in `pome_settings.json` at the root of your company's account repository (create the file if it is not present and put in `.gitignore` if you don't want to propagate your settings to your collaborators).