semvec/pypi-packages huggingface dataset:
OmarStyle-sel 0.1
Omar_Style _ 〄🇵🇸


    
<h1 style="color:red; text-align: center;">Omar_Style</h1>




<!-- ___
| Name | code | email |
|----|----|---|
|omar|10|omar@gamli.com|
___ -->
___
May the peace, blessings, and mercy of God be upon you. Welcome to this nice library
Also, using this library is very simple
See, in the second example, how is his office?
## note

<!-- This library is used with a library <g style="color:red; font-size: 20px;" >tkinter</g > -->

___
## Download the library
``` bash 

``` 
___
``` python

```
____

## Any problem contact us
[Telegram](https://t.me/OmarStyle1)

### The developer of this library
Omar_Style _ م€„
experiment-results-manager 0.1.2
Light-weight experiment tracker
Requires Python: >=3.8

# 🔬 ERM: Experiment Results Manager

Light-weight alternative to `mlflow` experiment tracking that doesn't require kubernetes. Useful tool to compare metrics between training attempts in your model training workflow

## ✨ Features

- 📈 Track plots, metrics, & other data
- 👀 Side-by-side comparison 
- 💾 Experiment registry 
- ⛅️ Supports S3, GCS, Azure and others (via `fsspec`)

## 🚀 Examples & Demos
- Quick and easy: [serialize_and_deserialize.ipynb](examples/serialize_and_deserialize.ipynb)
- Practical but more involved: [compare_runs.ipynb](examples/compare_runs.ipynb)
- Browse the registry: [browse_registry.ipynb](examples/browse_registry.ipynb)

<p align="center">
<img src="https://user-images.githubusercontent.com/1297369/233723764-c52cf948-ec4d-4b94-916d-77cadababae8.png" height="400">
</p>

## ✅ Get Started
#### Installation
```sh

pip install experiment-results-manager \
  gcsfs \
  s3fs
# install s3fs if you plan to store data in s3
# install gcsfs if you plan to store data in google cloud storage
```

#### Basic Usage
```python
import experiment_results_manager as erm

# Create an experiment run
er = erm.ExperimentRun(
    experiment_id="my_experiment",
    variant_id="main"
)

# Log relevant data
er.log_param("objective", "rmse")
er.log_metric("rmse", "0.9")
er.log_figure(mpl_fig, "ROC Curve")
er.log_text("lorem ipsum...", "text")

# Display the report (if you are in a notebook)
html = erm.compare_runs(er)
display(HTML(html))

# Save to registry
saved_path = erm.save_run_to_registry(er, "s3://erm-registry")

```
<hr>
<p align="center" style="text-align: center; color: gray; font-size: 10px;">
Made with ❤️ in Berlin
</p>
simple-spider-tool 0.0.19
一些简易、好用的爬虫工具，减少代码与文件冗余，提升工作的效率和代码整洁度
Requires Python: >=3.5
Keywords: datetime,jsonpath,md5,spider,timestamp,tool

# simple spider tool

在实际工作中，沉淀的一些简易、好用的爬虫工具，减少重复代码与文件冗余，希望一样能为使用者带来益处。如果您也想贡献好的代码片段，请将代码以及描述，通过邮箱（ [xingcys@gmail.com](mailto:xingc<xingcys@gmail.com>) ）发送给我。代码格式是遵循自我主观，如存在不足敬请指出！

## 安装

```shell
pip install simple-spider-tool
```
## 简单使用

```python
from simple_spider_tool import format_json, jsonpath

data = {
    "code": 200,
    "data": [
        {
            "id": 1,
            "username": "admin",
            "level": "boss"
        },
        {
            "id": 2,
            "username": "user",
            "level": "staff"
        }
    ]
}

boss_name = jsonpath(data, '$.data[?(@.level=="boss")].username', first=True)
all_user_info = jsonpath(data, '$.data[*].username')

print(boss_name)
print(format_json(all_user_info))
```
## 兼容使用
在`0.0.18`对包目录发生改变，由`simple_spider_tools`更改为`simple_spider_tool`，如有使用过低于`0.0.18`版本，请通过安装兼容扩展包以达到兼容使用
```shell
pip install simple-spider-tool[seventeen]
```

## 链接
Github：https://github.com/xingcweb/simple-spider-tool

在线文档：https://simple-spider-tool.xingc.top/
pruebaPyPiPedro 0.0.0
Esta es una descripcion corta de prueba
Requires Python: >=3.10

1. bajar el repositorio
      - git clone https://github.com/soypedroamaya/docker-ubuntu-python-pkgs.git    
2. nos metemos dentro del repo
      - cd docker-ubuntu-python-pkgs
3. construimos la imagen
      - docker build -t img-docker-ubuntu-python-pkgs .

4. construimos el contenedor basado en la imagen, con interaccion en la terminal y pegada a nuestro directorio local 
      - docker run --name cnt-docker-ubuntu-python-pkgs -it -v $(pwd):/docker-ubuntu-python-pkgs img-docker-ubuntu-python-pkgs bash

4.1 nos conectamos desde visual studio y con la extension "dev containers" a nuestro contenedor corriendo

5. nos movemos a la rama que necesitemos:
    - git checkout exp

6. creamos el entorno virtual de la rama
    - python3.10 -m venv exp

7. activamos el entorno virtual anterior
    - source exp/bin/activate

8. bajamos las librerias necesarias y que estan segun la rama
    - pip install --no-cache-dir -r requirements.txt

    - (opcional) => pip freeze > requirements.txt
    nota: para sacar una lista con las versiones exactas de las librerias que ocupa ese entorno.

9. creamos las librerias externas
    - npm install

------- conf de sphinx ----
mkdir docs -> en la raiz del proyecto
cd docs
sphinx-quickstart -> llenar las preguntas (aqui me quede...)
cd .. -> te salis hasta el directorio raiz
sphinx-apidoc -o docs .
cd docs

-- agregar la palabra "modules" en el archivo index.rst, en el area del toctree

make html
meter en un browser:
file:///Volumes/MicroSD4GB/Codigo/public/soypedroamaya/docker-ubuntu-python-pkgs/docs/_build/html/index.html


-- commitizen
primera vez
    cz bump --yes --changelog