semvec/pypi-packages huggingface dataset:
ast-slizovskaia 0.1.4
Test exercise AST model on the ESC-50 dataset
Requires Python: >=3.8,<4.0

# Test implementation for Audio Spectrogram Transformer by Olga Slizovskaia

This repository provides a test implementation of the Audio Spectrogram Transformer described in the original [paper](https://arxiv.org/pdf/2104.01778.pdf).
Please, note, that this implementation is lacking several important details compared to the original paper, such as dataset normalization, data augmentation routines and optimal hyperparameters selection. 
The results that you will obtain using the code provided in this repository, will differ severely from the results reported in the original paper. 

## Requirements

This repository requires a working python3.9 installation and uses poetry for dependency management and packaging.  
Please, install [poetry](https://python-poetry.org/docs/#installation) using the official guidelines.

You also need to download the [ESC-50 dataset](https://dagshub.com/kinkusuma/esc50-dataset) and 
specify the path to the dataset as ```dataset_dir``` parameter in [hparams.py](./ast_slizovskaia/hparams.py) configuration file.

## Installation

To install all necessary dependencies, run:

```poetry env use 3.9```

```poetry install```

## Usage

We use the standard 5-fold cross-validation scheme for evaluating the classification model. The folds are defined in the datasets meta file and hardcoded for training.
To train and evaluate the model, run:

```python train.py```

or

```poetry run python train.py ```.

## Results

The best test accuracy score achieved with this model without any pretraining is 0.39 as you can see in the following plot:

![test_accuracy](./ast_slizovskaia/data/test_accuracy.png)

The model overfits singnificantly reaching training loss values as low as 1.8 and only reaching validation and test loss values about 2.3.
PAPyA 1.1.6
A library for performance analysis of sql based RDF graph processing systems
Keywords: python,benchmarking,rdf systems,big data,apache spark


<p align="center">
<a href='https://datasystemsgrouput.github.io/PAPyA/'><img src="https://github.com/DataSystemsGroupUT/PAPyA/blob/main/figs/papayalogo.png" width="100"/> </a>
</p>

# <a href='https://datasystemsgrouput.github.io/PAPyA/'>PAPyA</a>

Prescriptive Performance Analysis  in Python Actions 

[![DOI](https://zenodo.org/badge/487547762.svg)](https://zenodo.org/badge/latestdoi/487547762)

This library provides prescriptive analysis for the complex solution space of (RDF relational schema, Partitioning, and Storage Formats) that emerges with querying large RDF graphs over Relational Big Data (BD) System, e.g., Apache Spark-SQL.

* Documentation and more details about <a href='https://datasystemsgrouput.github.io/PAPyA/'> PAPya </a>
modbus-tcprtu 1.0.0
A ModbusRTU Over TCP/IP library, depends on modbus_tk.
Keywords: modbus,modbus_tk,modbus_tcp,modbus_rtu

# modbus_tcprtu

A ModbusRTU Over TCP/IP library, depends on modbus_tk. It's often use with 485-to-TCP converter.

```python

# must install modbus_tk
import modbus_tk.defines as cst
from modbus_tcprtu import TcpRtuMaster

# create a master
master = TcpRtuMaster(host='127.0.0.1', port=9001)
master.set_timeout(2)
for i in range(10):
    # read data from modbus
    # just use as modbus_tk.Master
    print(i, master.execute(1, cst.READ_HOLDING_REGISTERS, 0, i + 1))

```


CHANGES
===============
1.0.0

- First version
mkdocs-manuals-theme 0.10.0
A theme for rendering manuals using mkdocs
Requires Python: >=3.7

# Theme for creating team manuals

This theme is based on top of mkdocs-basic-theme.

https://github.com/mkdocs/mkdocs-basic-theme


## Usage

Install the theme

```bash
pip install mkdocs-manuals-theme
```

Use the theme in `mkdocs.yml`

```yaml
theme:
    name: manuals-theme
```


## Development

Setting up dev environment:

```bash
# create and activate virtual env
python3 -m venv .venv
source .venv/bin/activate

# install requirements
pip install '.[dev]'
```

## Summary of understanding

The documentation around templating is pretty good. It is all on one page :)
https://www.mkdocs.org/dev-guide/themes/#navigation-objects

There are 2 global variables on each page:
`nav` and `toc`

Nav is an iterable of 3 kinds of page: `section`, `page` and `link`
salomon 0.0.8
Sage mAker modeL cOpy froM One account to aNother
Requires Python: >=3.7

# Salomon
Sage mAker modeL cOpy froM One account to aNother

Solves everlasting issues with Amazon SageMaker:
1. Make copy of a SageMaker model from one account to another (or from one environment to another)
2. Freeze SageMaker model, so that model used for SageMaker endpoints is immutable and safe from incidental modifications by data scientists
3. Single-command operation for model copy (fills gaps present in Terraform, CloudFormation, etc.) 

(currently works only with SageMaker Model Package. TODO: add SageMaker Model support.)

## Installation

```shell
pip install salomon
```

## Make a copy of SageMaker Model Package

Use `copy_model_package()` function. It makes a copy of SageMaker Model Package.
    
    1. Reads source_arn SageMaker Model Package
    2. Replaces paths for data files with `dst_s3_path`
    3. Replaces docker image URIs with `dst_ecr`
    4. Makes a copy of data files to `dst_s3_path`
    5. Pulls docker images and then pushes to `dst_ecr`
    6. Creates new SageMaker Model Package in current AWS account.

Example:

1. Prepare IAM role that can access both source and destination SageMaker resources, ECRs and S3 files
2. Authenticate to all source docker registries, for example:
```shell
aws ecr get-login-password --region eu-central-1 | docker login --username AWS --password-stdin 492215442770.dkr.ecr.eu-central-1.amazonaws.com
# all other ECRs ...
```
3. Execute below code with IAM role created in point 1.

```python
from salomon import copy_model_package

copy_model_package(
    source_arn="arn:aws:sagemaker:eu-central-1:1111111111:model-package/source-model-package/1",
    dst_group_name="copy-of-model-package",
    dst_s3_path="s3://bucket-in-22222222222/copy-of-model-package",
    dst_ecr="22222222222.dkr.ecr.eu-central-1.amazonaws.com/copy-of-model-package"   
)
```
asyncpy 1.2.0
Use asyncio and aiohttp's concatenated web crawler framework
Requires Python: >=3.6
Keywords: python web crawl asyncio

安装: pip install asyncpy

## Asynpy


Asynpy 是一个异步请求框架，基于asyncio和aiohttp，实现便捷高效的从网络上抓取数据

"With reference to the design pattern of scrapy, an asynchronous co-processing method is adopted to simplify some operations and complete the tasks of web crawler more easily and efficiently"



### asyncio
asyncio是Python 3.4版本引入用来编写并发代码的标准库，直接内置了对异步IO的支持。
asyncio的编程模型就是一个单线程的消息循环。
从asyncio模块中直接获取一个EventLoop的引用，然后把需要执行的协程扔到EventLoop中执行，就实现了异步IO。
 
 

### aiohttp
aiohttp - 基于asyncio实现的HTTP框架。
aiohttp强调的是异步并发。提供了对asyncio/await的支持，可以实现单线程并发IO操作。







#### 单线程的asyncio为什么速度会更快呢？
- 多线程是基于系统的，协程是基于线程的，多线程上下文切换有陷入内核态的消耗，协程就没有，协程更轻量
- Python的GIL阻止两个线程在同一个程序中同时执行，有时候多线程并没有单线程速度快。
- 而Scrapy是基于twited的单线程框架，消息队列和事件循环在一个线程来回切换，downloader是多线程的



#### 详细操作文档请参考博客或者github:

https://blog.csdn.net/weixin_43582101/article/details/106258518

https://github.com/lixi5338619/asyncpy


Thanks : Scrapy、Ruia、Looter、asyncio、aiohttp


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