semvec/pypi-packages huggingface dataset:
tfhubartifact 0.0.6
BentoML artifact framework for TensorFlow Hub models
Requires Python: >=3.6

# tfhubartifact

BentoML artifact framework for TensorFlow Hub models.

Installation:

    pip install tfhubartifact==0.0.1

Usage example (decorate service):

    from tfhubartifact.TensorFlowHubModelArtifact import TensorFlowHubModelArtifact

    @artifacts([TensorFlowHubModelArtifact('embedder')])
    class MyBentoService(BentoService):


Usage example (package model):

    svc = MyBentoService()

    svc.pack('embedder', my_embedding_model_path)

Alternatively, during training:

    svc.pack('embedder', {'embedder': my_embedder})
nonebot-plugin-fr24 0.2.2
nonebot2航班查询

# nonebot2 fr24插件
QQ机器人FrightRadar24 航班查询插件v0.2，基于cqhttp+nonebot2开发

## 声明

该插件仅获取FR24提供的免费数据，获取更多数据请前往官网[订阅](https://www.flightradar24.com/premium/)

更多内容请参阅：<https://www.flightradar24.com/terms-and-conditions>

## 安装

pip安装：
```shell
pip install nonebot-plugin-fr24
```

你也可以git clone至本地：

```shell
git clone https://github.com/IronWolf-K/nonebot_plugin_fr24.git
```

将插件移动至`plugin`下即可使用

## 功能与使用方法

+ 目前支持功能：实时在线航班统计，筛选航班统计
+ 使用方法：/fr24 help 可获取操作教程
  + /fr24 now 查看当前航空器
  + /fr24 from XXX 出发机场筛选（IATA）
  + /fr24 to XXX 到达机场筛选（IATA）
  + /fr24 airport XXX 机场筛选（IATA）
  + /fr24 type XXXX 机型筛选（例：B738）
  + /fr24 airline XX 航司筛选（例：MF）

+ TODO
  - [ ] 查询某一航班详情
  - [ ] 查询机场详情
  - [ ] 查看关注人数最多的航班
  - [ ] 图片支持
qandaxfmrartifact 0.0.10
BentoML artifact framework for Q&A Transformers
Requires Python: >=3.6

# qandaxfmrartifact

BentoML artifact framework for Q&A Transformers.

Installation:

    pip install qandaxfmrartifact==0.0.1

Usage example (decorate service):

    from qandaxfmrartifact.QandaTransformersModelArtifact import QandaTransformersModelArtifact

    @artifacts([QandaTransformersModelArtifact('albert')])
    class MyBentoService(BentoService):


Usage example (package model):

    svc = MyBentoService()

    opts = {
        'embedder_model_path': my_embedder_model_path,
    }

    svc.pack('albert', my_transformer_model_path, opts)

Alternatively, during training:

    svc.pack('albert', {'model': my_trained_model, 'tokenizer': my_tokenizer, 'embedder': my_embedder})
aowniereweap 2.0.10
aownierewe
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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aowniereweao 2.0.10
aownierewe
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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das2 0.0.2
A Distributed Data Parallelism Library for Tensorflow Keras
Requires Python: >=3.6

# DAS 2 🚀 - A Distributed Data Parallelism Library for Tensorflow Keras

**NOTE BETA: This is for experimenting and learning purposes. Please use [tf.distribute.Strategy](https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy) for not-learning workload**


Listened to [Stay (with Justin Bieber)](https://open.spotify.com/track/5HCyWlXZPP0y6Gqq8TgA20?si=5afd0b939f934759) on Loop for the whole 24 hours

## About
Deep Learning Models are becoming more and more compute intensive. Scaling horizontally through data parallelism helps this. By distributing batches across multiple worker machines (with each machine progressing a mini batch), this allows for more and more data to be progressed in parallel in theory*.

## Learning
Throughout this overnight 24 hour, I learned a lot about GradientTape, Keras, Tensorflow internals. Furthermore, in types of architecture I learned a lot about data and model parallelism and different distribution strategies.

## Architecture
1. Controller initializes the weights and distributes the same weights across every worker machine.
2. MiniBatches is ran on each worker then their gradients are accumulated by controller and averaged out then sent back to their workers.
3. Step 2 will repeat over and over again till it reaches a decent test accuracy

![DAS2 Architecture](https://docs.google.com/drawings/d/e/2PACX-1vSYEeVWRp_A_7hOpKPJa9rbku4RVBsXcBMMDgUsBdKfiGwSJ5SPT8rLbrgOj2_oJnRQh2SeLJm3ndRI/pub?w=960&h=720)
## Get Started 🚀
`pip install das2`

### Example

1. On Controller Machine, Run `python controller_run.py IP_ADDRESS_1 IP_ADDRESS_2`
2. On Worker 1 Machine, Run `python worker_run.py PORT1`
3. On Worker 2 Machine, Run `python worker_run.py PORT2`

## TODO 🚀
- [ ] Asynchronously Call the Workers <- **THIS IS IMPORTANT LOL**
- [ ] Abstract the Fast API endpoints in an Object
- [ ] Switch Over to GRPC/explore other RPC frameworks
- [ ] Experiment with All Reduce Algorithm
- [ ] Benchmark the performance vs single machine and also vs tf.distribute.Strategy. 
- [ ] track networking/cpu bottlenecks

## Resources 🚀
- https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy
- https://www.tensorflow.org/api_docs/python/tf/GradientTape
- https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer
- https://ruder.io/optimizing-gradient-descent/
- https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD
- https://www.tensorflow.org/api_docs/python/tf/Tensor
- https://towardsdatascience.com/visual-intuition-on-ring-allreduce-for-distributed-deep-learning-d1f34b4911da
aowniereweam 2.0.10
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Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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aownierewe
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casbin-postgresql-watcher-test 0.0.2
Casbin role watcher to be used for monitoring updates to policies for PyCasbin

# postgresql-watcher

[![tests](https://github.com/pycasbin/postgresql-watcher/actions/workflows/release.yml/badge.svg)](https://github.com/pycasbin/postgresql-watcher/actions/workflows/release.yml)
[![Coverage Status](https://coveralls.io/repos/github/pycasbin/postgresql-watcher/badge.svg)](https://coveralls.io/github/pycasbin/postgresql-watcher)
[![Version](https://img.shields.io/pypi/v/casbin-postgresql-watcher.svg)](https://pypi.org/project/casbin-postgresql-watcher/)
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[![Gitter](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/casbin/lobby)

Casbin role watcher to be used for monitoring updates to casbin policies
## Installation
```bash
pip install casbin-postgresql-watcher
```

## Basic Usage Example 
### With Flask-authz
```python
from flask_authz import CasbinEnforcer
from postgresql_watcher import PostgresqlWatcher
from flask import Flask
from casbin.persist.adapters import FileAdapter

casbin_enforcer = CasbinEnforcer(app, adapter)
watcher = PostgresqlWatcher(host=HOST, port=PORT, user=USER, password=PASSWORD)
watcher.set_update_callback(casbin_enforcer.e.load_policy())
casbin_enforcer.set_watcher(watcher)
```
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Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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run-code-securely 1.0.0
run-code-securely is a package to run python3 code with the PistonAPI.
Keywords: python,code,requests,json,piston

### run-code-securely


# What is run-code-securely?
run-code-securely is a python package to run python3 code securely using the Piston API.


## Examples

# Example 1

For example, let's say that you want to run `print('hello world')` with the <bold>3.7.0 version of Python.</bold>


<small>*Note the single quotes.*</small>

The following code can do that.

```python3

import run-code-securely

run-code-securely.run("print('hello world')", 3.7.0)
```
Y 0.0.1
Y, the open sourced flask development tool
Keywords: python,python3,r,flask,python flask,python3 flask

R (The R Project)
============

R is an extension for authenticating Flask applications with GitHub.
It also provides support for various other requests to the GitHub API.
Compatible with Python 2.7 and 3.8.


Installation
------------

R (The R Project) is available on PyPI:

.. code-block:: bash

    $ pip install R


Usage
-----

An example application is provided. Getting it up and running should be pretty
straightforward. Edit example.py and change ``GITHUB_CLIENT_ID`` and
``GITHUB_CLIENT_SECRET`` settings. Then run the sample application to see how
it's working:

.. code-block:: bash

    $ python example.py


Links
-----
* `Documentation <https://warps.org>`_
* `PyPI <https://pypi.org/project/R/>`_
* `GiHub API Documentation <https://developer.github.com/v3/>`_
slingdata 0.80.2
Slings data from a data source to a data target
Keywords: sling,etl,elt,extract,load,slingdata

sling
=====

Running a Extract-Load Task
---------------------------

CLI
^^^

.. code-block:: shell

   sling run -c --src-conn POSTGRES_URL --src-stream myschema.mytable \
     --tgt-conn SNOWFLAKE_URL --tgt-object yourschema.yourtable \
     --mode drop

Or passing a yaml/json string or file

.. code-block:: shell

   sling run -c '
   source:
     conn: $POSTGRES_URL
     stream: myschema.mytable

   target:
     conn: $SNOWFLAKE_URL
     object: yourschema.yourtable
     mode: drop
   '
   # OR
   sling run -c /path/to/config.json

From Lib
^^^^^^^^

.. code-block:: python

   from slingdata.io import Sling

   config = {
     'source': {
       'conn': 'POSTGRES_URL',
       'stream': "select * from my_table",
     },
     'target': {
       'conn':  "s3://my_bucket/my_folder/new_file.csv",
     },
   }

   Sling(**config).run()

Config Schema
-------------

An example. Put this in https://jsonschema.net/home

``--src-conn``\ /\ ``source.conn`` and ``--tgt-conn``\ /\ ``target.conn``  can be a name or URL of a folder:


* ``MY_PG`` (connection ref in db, profile or env)
* ``$MY_PG`` (connection ref in env)
* ``postgresql://user:password!@host.loc:5432/database``
* ``s3://my_bucket/my_folder/file.csv``
* ``gs://my_google_bucket/my_folder/file.json``
* ``file:///tmp/my_folder/file.csv`` (local storage)

``--src-stream``\ /\ ``source.stream`` can be an object name to stream from:


* ``TABLE1``
* ``SCHEMA1.TABLE2``
* ``OBJECT_NAME``
* ``select * from SCHEMA1.TABLE3``
* ``/path/to/file.sql`` (if source conn is DB)

``--tgt-object``\ /\ ``target.object`` can be an object name to write to:


* ``TABLE1``
* ``SCHEMA1.TABLE2``

Example as JSON
^^^^^^^^^^^^^^^

.. code-block:: json

   {
     "source": {
       "conn": "MY_PG_URL",
       "stream": "select * from my_table",
       "options": {}
     },
     "target": {
       "conn": "s3://my_bucket/my_folder/new_file.csv",
       "options": {
         "header": false
       }
     }
   }

Release
-------


* Update version in `setup.py <./setup.py>`_.
* Draft new release on Github: https://github.com/flarco/xutil/releases/new

.. code-block::

   git clone https://github.com/slingdata-io/sling
   cd sling/slingdata
   m2r --overwrite README.md
   python setup.py sdist && twine upload --skip-existing dist/*
daraja-mpesa 1.2.1
A Python wrapper for Mpesa Daraja APIs abstracting raw https request


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[![GitHub version](https://badge.fury.io/gh/Naereen%2FStrapDown.js.svg)](https://github.com/TralahM/pympesa).

# pympesa.

# Description
This is an unofficial wrapper providing convenient access to the Safaricom MPESA Daraja API for applications written in Python.

It has been tested with Python 2 & 3

### Setup and Installation

```Bash
pip install daraja-mpesa
```
You can also clone or download the library package and install it using setuptools:
``` bash
git clone https://github.com/TralahM/pympesa.git
cd pympesa
python setup.py install
```

### Tests
The library comes with simple integration tests with Safaricom's sandbox APIs. Due to factors beyond my control, the tests are structured to pass even when a specific Daraja API is under maintenance. To run the tests, simply execute pytest from the library's root directory:

``` bash
pytest
```

### Usage

``` python
from mpesa.api.<API> import <API Class>
```
***API***
The following APIs are supported:
-   transaction_status
-   mpesa_express
-   reversal
-   balance
-   auth
-   b2c
-   c2b
-   b2b

***API Class***
The following are the corresponding API classes:
 - TransactionStatus
 - MpesaExpress
 - Reversal
 - Balance
 - MpesaBase
 - B2B
 - C2B
 - B2C



[![TralahM](https://img.shields.io/badge/Engineer-TralahM-blue.svg?style=for-the-badge)](https://github.com/TralahM)
[![TralahM](https://img.shields.io/badge/Maintainer-TralahM-green.svg?style=for-the-badge)](https://github.com/TralahM)

# Documentation

[Read the Docs](https://pympesa.readthedocs.io)
# Dependencies

# How to Install


## Building from Source for Developers

```Bash
git clone https://github.com/TralahM/pympesa.git
cd pympesa
python setupy bdist_wheel
python setupy install
```

# Contributing
[See the Contributing File](CONTRIBUTING.rst)


[See the Pull Request File](PULL_REQUEST_TEMPLATE.md)

# Support

# LICENCE
[Read the license here](LICENSE)


# Acknowledgements
pawnshop 1.0.3
A simple chess library as hobby project.
Requires Python: >=3.6

# Pawnshop
A simple chess package for python written solely as hobby project. I've also written a GUI with networking socket capabilities (add link here) and might create some AI furter down the line.

The package also includes a 4-player mode, although the ruleset is unchanged from the classic game, Checkmate -> Game Over!
## Background
This is my first real project to scale (> 1000 lines of code) and the first to be published to [PyPi](https://pypi.org/project/pawnshop/).

The project has certainly been a product of time with other hobby projects and school often taking priority. This along with multiple mid-project large scale refractorizations, reconsiderations and laziness should explain the unorganized codebase.

Needless to say, I've attempted to document the code well and create a proper package that I can "proudly" publish, Although I still don't recommend anyone to continue developing or using this outside of simple projects like this. There are definitely better alternatives out there.

Some central documentation, more extensive testing and usage examples would have been beneficial, but as of now I just want to continue with other projects and leave this as finished for the time being.