semvec/pypi-packages huggingface dataset:
apss 0.3.0
一种基于神经网络和启发式策略的深度学习模型分布式训练切分(3D parallelism)快速策略搜索算法
Requires Python: >=3.7

# APSS(for Training): Automatically Distributed Deep Learning Parallelism Strategies Search by Self Play

APSS 是一种基于神经网络和启发式策略的深度学习模型分布式训练切分(3D parallelism)快速策略搜索算法，它结合启发式策略和训练集群环境初步生成候选策略，然后通过深度管道策略网络（DPSN）为每个候选策略提供详细的pipeline划分，采用自我对弈的对比强化学习（CRLSP）进行离线训练，无需实际数据收集和后续应用中的微调。此仓库我们使用[Mindspore](https://www.mindspore.cn/)进行实现。

----------

## Context
- [Installation](#installation)
- [Usage and Examples](#usage-and-examples)
  - [生成PP问题的验证数据集](#生成PP问题的验证数据集)
  - [执行训练](#执行训练)
- [How It Works](#how-it-works)


## Installation
Requirements:  
 - Python >= 3.7
 - Mindspore >= 2.1.1

### Method 1: With pip
```
pip install apss
```

### Method 2: From source
```
git clone https://github.com/Cheny1m/APSS
cd APSS
pip install -e .
```

## Usage and Examples

### 一步执行训练

```
python -m apss.training.apss_run --graph_size 8 --num_split 3 --rebuild_data
```
* `graph_size` , `num_split` 分别代表了问题的层数大小和需要执行pipeline划分的数量，两个命令行参数共同描述了所训练问题的大小，可根据需求动态调整。
* `rebuild_data` 表示是否在执行训练前，从Data Synthesizer中生成训练数据，默认建议开启。如果需要从`.ckpt`中接续训练或无需改变之前生成的训练数据直接禁用`--rebuild_data`参数即可。训练数据可在/data目录下找到。
* 已经完成过执行训练后，`.ckpt`保存在/output文件夹下，日志保存在/log文件夹下，可以通过tensorboard_logger在浏览器中实时查看训练过程及其数据。

## How It Works
![The pipeline of APSS.](docs/apss_pipeline.png)
bagpipe-python 0.0.7
Bagpipe is a scikit-learn powered preprocessing library for listed dataframes based on the pipline structure of sklearn.
Requires Python: >=3.10

# BagPipe

Bagpipe is a scikit-learn powered preprocessing library for listed dataframes based on the pipline structure of sklearn. It provides a set of specialized new transformers which can be used in addition to exsisting sklearn transformers, making it easier for handling datasets containing different csv in a sequence based setting.
torob-integration 0.0.2
A Python module facilitating effortless interaction with the Torob API for seamless access to product data.
Requires Python: >=3.6

<h1 align="center">Python Torob Integration </h1>

# Overview

- [Introduction](#introduction)
- [Overview](#overview)
- [Installation](#installation)
- [Usage](#usage)
- [License](#license)


## Introduction
A Python module facilitating effortless interaction with the Torob API for seamless access to product suggestions, search results, detailed product information, special offers, and price chart data.
This module utilizes the Torob API available at [https://torob.com/](https://torob.com/). It is important to note that the usage of this module does not alter any data on the Torob website.


# Installation
A Python module facilitating effortless interaction with the Torob API for seamless access to product data. in order to use this module you have to install it by pip command or through setup.

```bash
pip install torob-integration
```
Import package into your project by:

```bash
from torob_integration.api import Torob
```
in order to use the module please consider looking at examples and documentations.

# Usage

import module and create object
```python
from torob_integration.api import Torob
torob_instance = Torob()
```

Fetch product suggestions based on a query
```python
from torob_integration.api import Torob
torob_instance = Torob()
suggestion_result = torob_instance.suggestion("laptop")
```

Perform a search for products based on a query and page number
```python
from torob_integration.api import Torob
torob_instance = Torob()
search_result = torob_instance.search("phone", page=0)
```

Get detailed information about a specific product. Note: Before running the `details` function, execute the `search` function to obtain the actual values for 'prk' and 'search_id'.
```python
from torob_integration.api import Torob
torob_instance = Torob()
details_result = torob_instance.details(prk='sample_prk', search_id='sample_search_id')
```

Fetch special offers for products with optional pagination based on page number
```python
from torob_integration.api import Torob
torob_instance = Torob()
special_offers_result = torob_instance.special_offers(page=0)
```

Get the price chart data for a specific product, Note: Before running the `price_chart` function, execute the `search` function to obtain the actual values for 'prk' and 'search_id'.
```python
from torob_integration.api import Torob
torob_instance = Torob()
price_chart_result = torob_instance.price_chart(prk='sample_prk', search_id='sample_search_id')
```


## license
MIT license
qurix-kafka-observer 0.2.0
qurix package for observing kafka messages
Requires Python: >=3.10, <4
Keywords: python

# KafkaObserver

## What is it?

The KafkaObserver package provides a set of tools for observing and retrieving information about  Kafka topics and consumer groups. It facilitates monitoring and managing Kafka infrastructure by offering insights into offset statuses, watermarks, and retention policies.

## Main Features

- **Watermark Information:** Retrieve information about the watermark (low and high offsets) for a specified topic.

- **Retention Policy Information:** Retrieve information about the retention policy (time and size) for a specified topic.

- **Topic Information:** Obtain details about Kafka topics, including basic information and extended details like watermark information and retention policies.

- **Consumer Groups Information:** Retrieve information about existing consumer groups.

- **Offset Status:** Get the offset status (current, committed, etc.) for a specific consumer and topic. Plot the offset status for visualization.


## Requirements

- `confluent-kafka`
- `openpyxl`

You can install these dependencies manually or use the provided `requirement.txt` file in the repository.

## Installation

1. Create a New Virtual Environment (named `.venv` in this case):

```bash
python3 -m venv venv
```

2. Activate the Virtual Environment:

```bash
source venv/bin/activate
```

3. Install the Package:

To install the `qurix-kafka-observer` package, use `pip`:

```bash
pip install qurix-kafka-observer
```

## Usage

### Kafka Observer

To use the KafkaObserver class:

```python
from qurix.kafka.observer import KafkaObserver
from qurix.kafka.entities.config import ConsumerConfig

# Create a ConsumerConfig object with your Kafka settings
BOOTSTRAP_SERVERS = "localhost:9092"
consumer_config = ConsumerConfig(
    bootstrap_servers=BOOTSTRAP_SERVERS,
    # ...
)

observer = KafkaObserver(consumer_config)

# Get consumer groups
observer.get_consumer_groups()

# Get consumer groups with offsets
observer.get_consumer_groups_offsets()

from confluent_kafka import Consumer

my_consumer = Consumer({"bootstrap.servers": BOOTSTRAP_SERVERS, "group.id": "your_consumer_group"})

# Get offset status
observer.get_offset_status(my_consumer, "some_topic")
```

The observer has also the possibility to plot important water mark and offset information for a certain topic:

```python
# Plot offset status
observer.plot_offset_status(my_consumer, "some_topic")


## Contact

For any inquiries or questions, feel free [reach out](https://qurix.tech/about_us.html).
latexcalc 0.8.1
LaTeXcalc is a LaTeX-Calculation-library for python3 accepting an optional values-dict for variables
Requires Python: >=3.7, <4
Keywords: TeX,LaTeX,latexcalc,texcalc,calctex,calclatex,calculate

# LaTeXcalc

## What is this?

LaTeXcalc is a LaTeX-Calculation-library for python3.

This calculates a mathematical formula written as a LaTeX-string.
It can use a given value-dict for variables in the formula.
The return-value is int or float.

## Supported features

LaTeXcalc can handle the following LaTeX-operations:

- apply `+`, `-`, `*`, `/` operators and the `-` as algebraic sign
- multiplication and division first, then addition and subtraction
- power `^`
- brackets `()`, `[]` and `{}` (also with `\left(`..`\right)`)
- squareroot `\sqrt{}`
- trigonometric functions like `\sin` or `\arctan` (radian measure)
- exponential numbers linke `1e+12`
- `\ln` (natural log), `\log` (base 10) and `e^`
- `\pi` or `pi` and `e` as default-values `math.pi` and `math.e`
- `\frac{}{}` (works as abbreviated `\frac 1 2` as well)

make sure you always use `*` (or `\cdot`), because `xy` != `x*y`, in that case xy would be a value!

## How to use

You can install this by using pip: `pip install latexcalc` or alternatively by including the folder `latexcalc` in your python3-project.

Then all you need to do is

```
import latexcalc

c = latexcalc.calc('\sqrt{3^2+4^2}')
# or with variables:
c = latexcalc.calc('\sqrt(a^2+b^2)', {'a':3,'b':4})
print(c) # 5.0
```

## Limitations

All testet cases work, but there might still be wrong calculations in
special cases, so test it for your needs!

## Anything wrong? Something missing?

Feel free to submit an issue if you find a bug or if you think something important is missing.

Just check out the latest code on GitHub and test your issue as it is done for some cases in the `test.py`. Then please add a Line for the `test.py` to your report to show the desired operation!

## Alternatives

There are some other libraries out there doing similar things, but at time of writing I didn't find anything that fulfilled my needs (lightweight, working with variables, ...).
So depending on your requirements this or maybe something else might be the right choise, thank's for cheching this out!
bothlent-asr-eval 1.1.0
An asr evaluation tool targetting for testing Portuguese(Brazil) sentence.
Requires Python: >=3.7, <4
Keywords: sample,setuptools,development

# bothlent_asr_eval

#### 介绍
借助EditDistance算法，计算wer，ser指标。

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目
5.  Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)