semvec/pypi-packages huggingface dataset:
TurtleGlide 0.1.0
Comandos para crear archivos HTML, CSS y JS

TurtleGlide
================

**Introducción**
---------------

TurtleGlide es una herramienta para crear archivos dentro de la carpeta `templates` o `static` de una app de Django.

**Instalación del paquete**
-------------------------

### Instalación en tu proyecto Django

Instalar TurtleGlide en tu proyecto Django:

```bash
pip install TurtleGlide
```

Luego, agregar TurtleGlide como una app de Django en `INSTALLED_APPS`:

```python
INSTALLED_APPS = [
    'TurtleGlide'
]
```

**Uso de TurtleGlide**
---------------------

### Parámetros obligatorios

Hay tres parámetros obligatorios para el uso del comando `create_archive`:

* `app_name`: Nombre de la app
* `file_path`: Ruta del componente HTML
* `--template` para archivos en la carpeta `templates`
* `--static` para archivos en la carpeta `static`

### Comando de ejemplo

```bash
python manage.py create_archive "app_name" --static "file_path"
```

**Otras funciones el uso de commandos**
---------------------

| Función | Descripción | Código de ejemplo |
| --- | --- | --- |
| [create_archive](#funciones) | Crea archivos en las carpetas `static` y `templates` | [Ver ejemplo](#create-archive) |

**Funciones**
---------------------

### create_archive

Crea multiples archivos en las carpetas `static` y `templates` de una app de Django.

```bash
python manage.py create_archive home --static css/app.css --template layouts/main.html
```

**Instalación para desarrolladores**
---------------------------------

### Paso 1: Instalar dependencias y crear entorno virtual

Llamar al script `setup.sh` para instalar las dependencias y crear el entorno virtual:

```bash
./setup.sh
```

### Paso 2: Activar entorno virtual

Activar el entorno virtual:

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

### Listo para empezar

Ya estás listo para empezar a trabajar con TurtleGlide sin problemas.
json-explore 2.0.1
A lightweight CLI tool for interactively exploring JSON files and dictionaries.
Requires Python: >=3.6

# JSON-Explore

A lightweight CLI tool for interactively exploring JSON files and dictionaries.

## Installation

Install using pip:
```bash
pip install json-explore
```

## Import Instructions

```python
import json_explore
```

## Usage

```python
import json_explore

json_example: dict = \
{"menu": {
  "id": 29,
  "value": "File",
  "popup": {
    "menuitem": [
      {"value": "New", "onclick": "CreateNewDoc()"},
      {"value": "Open", "onclick": "OpenDoc()"},
      {"value": "Close", "onclick": "CloseDoc()"}
    ]
  }
}}
print(f"Let's explore the above json using the json explore function")
print()
json_explore.json_explore_json(json_example)

print(f"Now lets explore the same json using the file path function")
print()
json_explore.json_explore_fp("test.json")
```

## CLI example
```bash
json-explore path/to/your/file.json
```

## Navigation
Enter a key name or an element number to navigate into a that json level
Enter a ^ to navigate up
Enter Q to quit

When at the top the JSON level name will be Top

## Example

```text
Q to quit
^ to go up
Type key string or element number to go into lower level

JSON level: Top dict:
	menu: dict

:menu
JSON level: menu dict:
	id: 29: <class 'int'>
	value: File: <class 'str'>
	popup: dict

:popup
JSON level: popup dict:
	menuitem: list

:menuitem
JSON level: menuitem list:
	Element: 0: dict
	Element: 1: dict
	Element: 2: dict

:1
JSON level: 1 dict:
	value: Open: <class 'str'>
	onclick: OpenDoc(): <class 'str'>

:^
JSON level: menuitem list:
	Element: 0: dict
	Element: 1: dict
	Element: 2: dict

:^
JSON level: popup dict:
	menuitem: list

:Q
```


# Relase Notes

## 2.0.0 (Current)
- now works in the command line
- improved setup.py for pypi discriptions
- added more detailed README
ipgelo 0.1.0
A Python module that can get geolocation data from an IP Address.
Requires Python: >=3.7

# ipgelo

Ipgelo is a super simple Python module that can make getting IP Geolocation much easier.

## Installation

pip install ipgelo

##  Example

import ipgelo

geo = ipgelo.geo('8.8.8.8')

## Variables

country, countrycode, city, zip, lat, lon, timezone
These are the variables that are defined after running ipgelo.geo(''). This can be either IPv4, or IPv6.
kazemlstack 0.1.0
Kaze MLOps stack
Requires Python: >=3.12

# Kaze's MLOps stack

This is an evolving stack of tool I uses for my ML workflow. It is going to be very opinionated, not necessarily always up to date (considering the pace of the field, I think this is fair), and not always the best choice for your use case. I choose this stack because it fits my philosophy and my workflow.

The main purpose of this repo is to serve as a reference to retrace my steps whenever I need, instead of a template which I just copy and deploy to the next project. I have no intention of making this a full fledge library.

## Stack

- **Machine learning framework**: [Jax](https://jax.readthedocs.io/en/latest/) (with [Flax](https://flax.readthedocs.io/en/latest/) for neural networks)
- **Hyperparameter tuning** : [Optax](https://optax.readthedocs.io/en/latest/) (for optimizers) + [Optuna](https://optuna.org/) (for hyperparameter tuning)
- **Object storage**: [MinIO](https://min.io/) (for storing data and models)
- **Database**: [PostgreSQL](https://www.postgresql.org/) (for storing metadata and results)
- **Experiment tracking**: [MLflow](https://mlflow.org/) (for tracking experiments and models)
- **Data versioning**: [DVC](https://dvc.org/) (for data versioning and pipelines)
- **Orchestration**: [Dagster](https://dagster.io/) (for orchestrating the pipeline)
- **Deployment**: [BentoML](https://docs.bentoml.org/en/latest/) (for deploying the model as a service)
- **Monitoring**: [Prometheus](https://prometheus.io/) (for monitoring the service) + [Grafana](https://grafana.com/) (for visualizing the metrics)