semvec/pypi-packages huggingface dataset:
doccano-client-patched 1.2.9
A patched fork of doccano-client with updated dependency bounds
License: MIT
Requires Python: <4.0,>=3.8

# doccano client

[![Codacy Badge](https://app.codacy.com/project/badge/Grade/cf90190126b948e09362048b63600b06)](https://www.codacy.com/gh/doccano/doccano-client/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=doccano/doccano-client&amp;utm_campaign=Badge_Grade) [![Tests](https://github.com/doccano/doccano-client/actions/workflows/ci.yml/badge.svg)](https://github.com/doccano/doccano-client/actions/workflows/ci.yml)

A simple client for the doccano API.

## Installation

To install `doccano-client`, simply run:

```bash
pip install doccano-client
```

## Usage

```python
from doccano_client import DoccanoClient

# instantiate a client and log in to a Doccano instance
client = DoccanoClient('http://doccano.example.com')
client.login(username='username', password='password')

# get basic information about the authorized user
user = client.get_profile()

# list all projects
projects = client.list_projects()
```

Please see the [documentation](https://doccano.github.io/doccano-client/) for further details.

## Doccano API BETA Client

We're introducing a newly revamped Doccano API Client that features more Pythonic interaction as well as more testing and documentation. It also adds more regulated compatibility with specific Doccano release versions.

You can find the documentation on usage of the beta client [here](doccano_client/beta/README.md).
dist-classicrl 0.1.17
A high-performance Python package for distributed classical reinforcement learning algorithms with support for single-threaded, parallel, and MPI-distributed Q-Learning training.
License: MIT
Requires Python: >=3.13

==============
dist_classicrl
==============

A Python package for distributed classical reinforcement learning algorithms.

.. image:: https://img.shields.io/pypi/v/dist_classicrl.svg
    :alt: PyPI-Server
    :target: https://pypi.org/project/dist_classicrl/
.. image:: https://img.shields.io/badge/license-MIT-blue.svg
    :alt: License
    :target: https://github.com/j-moralejo-pinas/dist_classicrl/blob/main/LICENSE.txt
.. image:: https://img.shields.io/badge/python-3.8+-blue.svg
    :alt: Python Version
    :target: https://www.python.org/downloads/

|

🎯 **Project Description**
--------------------------

**dist_classicrl** provides high-performance, scalable implementations of classic reinforcement learning algorithms with support for distributed training. The library focuses on Q-Learning with multiple execution modes: single-threaded, parallel (multiprocessing), and distributed (MPI) training.

🚀 **Key Features**
-------------------

- 🚀 **Multiple Execution Modes**: Single-threaded, parallel, and MPI-distributed training
- ⚡ **High Performance**: Optimized implementations with vectorized operations and performance benchmarking
- 🎮 **Multi-Agent Support**: Built-in support for multi-agent environments
- 🔧 **Flexible Architecture**: Abstract base classes for easy extension and custom environments
- 🌐 **Standards Compliant**: Compatible with Gymnasium and PettingZoo environments (coming soon)

📚 **Documentation**
--------------------

**Essential Guides:**

- 📦 `Installation Guide <docs/installation.rst>`_ - Setup instructions and requirements
- 📊 `Performance Benchmarks <docs/benchmarks.rst>`_ - Benchmarking results and analysis
- 📚 `Tutorials <docs/tutorials.rst>`_ - Step-by-step guides for using the library
- 🤝 `Contributing Guidelines <CONTRIBUTING.rst>`_ - Development standards and contribution process
- 📄 `License <LICENSE.txt>`_ - License terms and usage rights
- 👥 `Authors <AUTHORS.rst>`_ - Project contributors and maintainers
- 📜 `Changelog <CHANGELOG.rst>`_ - Project history and version changes
- 📜 `Code of Conduct <CODE_OF_CONDUCT.rst>`_ - Guidelines for participation and conduct

---