semvec/pypi-packages huggingface dataset:
sau-emeka.ugwuanyi 0.0.0
The SAU Exporter is a Python-based application designed to collect and expose AWS EC2 and EBS volume metrics for monitoring and observability. It utilizes the Prometheus client library to provide an HTTP endpoint for scraping metrics.
Requires Python: >=3.8

# SAU Exporter
## Overview

The SAU Exporter is a Python-based application designed to collect and expose AWS EC2 and EBS volume metrics for monitoring and observability. It utilizes the Prometheus client library to provide an HTTP endpoint for scraping metrics.

## Features
* **EC2 Metrics**: Collects information about stopped EC2 instances in different AWS regions.
* **EBS Volume Metrics**: Retrieves data on unattached/errored EBS volumes, including volume state, type, size, and region.
* **Prometheus Integration**: Exposes metrics in the Prometheus format, making it compatible with Prometheus monitoring systems.

## Getting Started

### Prerequisites:
* Python 3.8+ installed on your system.
* Ensure the required Python libraries are installed by running the following command.
```bash
pip install -r requirements.txt
```

### Configuration:
Create a YAML configuration file with the necessary settings. 
#### Configuration Options
* **Regions**: Specify the AWS regions for which you want to collect metrics.
* **Exporter Port**: Define the port on which the exporter will expose metrics (default: 9000).
* **Logging Configuration**: Customize logging settings, such as log file directory and retention.
See example below.
```yaml
# list of regions to scrape: REQUIRED
regions:
  - eu-central-1
  - eu-west-1

# exporter port. Defaults to 9000
exporter_port: 9000

# Logging configuration
logging:
  # Number of log files to retain after log rotation
  retention: 7
  # log directory. defaults to current working directory
  directory: "."
  # log level (debug, info, warn, error). Defaults to info
  level: info
```

### Running the Exporter:
Execute the exporter by providing the path to the configuration file:

```bash
python3 sau_exporter.py -c /path/to/your/config.yaml
```

#### Metrics Endpoint:
Once the exporter is running, metrics can be accessed at `http://localhost:<exporter_port>/`.

## Customization
Feel free to extend or customize the exporter to meet your specific requirements. You can modify the provided code or add additional collectors to gather more AWS resource metrics.

## License
This project is licensed under the MIT License. Feel free to use, modify, and distribute it according to the terms of the license.

## Author
Created by Emeka Ugwuanyi. For questions or feedback, please contact me.


**Happy monitoring!**
dvf1312 4112023
dvf1312
Requires Python: >=3.7

# dvf1312

## Installation

Install dependencies (requests, OSMPythonTools)

## Utilisation

in command line: 

python -m dvf1312.dvf1312 -f "31390 Carbonne" -p 31390

in code:

from dvf1312 import dvf1312

result=dvf1312("31390 Carbonne", 31390)

print(result[0]) #json bati

print(result[1]) #json non bati

If you encounter an error with postcode on a specific commune, 
you can try to rename the commune entered in input or tweak 
the code so it downloads based on lat,lon.

For fine tuning automation on downloading public data or
inquiry on developing algorithms to spatially identify 
zones where prices are below or above a treshold,
you can contact me
dislib 0.9.0
The distributed computing library on top of PyCOMPSs

<h1 align="center">  
    <img src="https://github.com/bsc-wdc/dislib/raw/master/docs/logos/dislib-logo-full.png" alt="The Distributed 
    Computing Library" height="90px">    
</h1>

<h3 align="center">Distributed computing library implemented over PyCOMPSs programming model for HPC.</h3>

<p align="center">
  <a href="https://dislib.bsc.es/en/latest/?badge=latest">
    <img src="https://readthedocs.org/projects/dislib/badge/?version=stable"
         alt="Documentation Status"/>
  </a>  
  <a href="https://github.com/bsc-wdc/dislib">
    <img src="https://compss.bsc.es/jenkins/buildStatus/icon?job=dislib_multibranch%2Fmaster"
         alt="Build Status">
  </a> 
  <a href="https://codecov.io/gh/bsc-wdc/dislib">
    <img src="https://codecov.io/gh/bsc-wdc/dislib/branch/master/graph/badge.svg"
         alt="Code Coverage"/>
  </a>
  <a href="https://badge.fury.io/py/dislib">
      <img src="https://badge.fury.io/py/dislib.svg" alt="PyPI version" height="18">
  </a>   
  <a href="https://badge.fury.io/py/dislib">
      <img src="https://img.shields.io/badge/python-3.6-blue.svg" alt="Python version" height="18">
  </a>       
</p>

<p align="center"><b>
    <a href="https://dislib.bsc.es">Website</a> •  
    <a href="https://dislib.bsc.es/en/stable/api-reference.html">Documentation</a> •
    <a href="https://github.com/bsc-wdc/dislib/releases">Releases</a> •
    <a href="https://bit.ly/bsc-wdc-community">Slack</a>
</b></p>


## Introduction


The Distributed Computing Library (dislib) provides distributed algorithms ready to use as a library. So far, dislib is highly focused on machine learning algorithms, and it is greatly inspired by [scikit-learn](https://scikit-learn.org/). However, other types of numerical algorithms might be added in the future. The library has been implemented on top of [PyCOMPSs programming model](http://compss.bsc.es), and it is being developed by the [Workflows and Distributed Computing group](https://github.com/bsc-wdc) of the [Barcelona Supercomputing Center](https://www.bsc.es/). dislib allows easy local development through docker. Once the code is finished, it can be run directly on any distributed platform without any further changes. This includes clusters, supercomputers, clouds, and containerized platforms.