semvec/pypi-packages huggingface dataset:
pyPhasesRecordloaderNox 0.1.0
Adds a record loaders to the pyPhases package
Requires Python: >=3.5

# Extension for pyPhasesRecordloader



## Usage

In a phase you can access the data through the `RecordLoader`:

Add the plugins and config values to your project.yaml::

```yaml
name: NoxProject
plugins:
  - pyPhasesML
  - pyPhasesRecordloaderNox
  - pyPhasesRecordloader

phases:
  - name: MyPhase

config:
  nox-path: C:/datasets/recordings

```

In a phase (`phases/MyPhase.py`) you can access the records using the `RecordLoader`:

```python
from pyPhasesRecordloader import RecordLoader
from pyPhases import Phase

class MyPhase(Phase):
    def run(self):
      recordIds = recordLoader.getRecordList()
      for recordId in recordIds:
        record = recordLoader.getRecord(recordId)
```

Run your project with `python -m phases run MyPhase`.
FedModelKit 0.7.0
Tools to scaffold and run privacy-preserving federated learning experiments across distributed data sites.
Requires Python: >=3.12

# 🌐 FedModelKit (fmk)

**FedModelKit** is a Python library designed to streamline the execution of **federated learning tasks** within a network of collaborating partners.  
It provides a guided workflow that helps users quickly set up experiments, manage dependencies, and follow step-by-step instructions through generated resources.

---

## 📑 Table of Contents
- [Overview](#-overview)
- [Features](#-features)
- [Dependencies](#-Dependencies)
- [Installation](#-installation)
- [Quick Start](#-quick-start)
- [Next Steps](#-next-steps)

---

## 🔎 Overview

Federated learning enables multiple partners to train machine learning models collaboratively **without sharing raw data**.  
The **FedModelKit** library simplifies this process by:

- Automating project initialization  
- Creating a reproducible experiment directory with all required dependencies
- Offering a clear, step-by-step workflow to run experiments in a federated network    

---

## 🧩 Dependencies

These are the dependencies of the packages, including the required Python version:

| Dependency        | Version     | Description                                      |
|-------------------|------------|--------------------------------------------------|
| **Python**        | >=3.13        | Required Python interpreter version              |
| `flwr[simulations]` | ==1.17.0  | Federated learning framework and simulations with Flower |
| `flwr_datasets`   | >=0.5.0     | Datasets compatible with Flower                  |
| `mlflow`          | >=3.1.1     | For experiment tracking and logging              |
| `pandas`          | >=2.3.0     | Data manipulation and analysis                   |


---

## ⚙️ Installation

It is recommended to install `FedModelKit` inside a virtual environment.  
We suggest using [uv](https://github.com/astral-sh/uv) for fast and reproducible setups:


1. Create and activate a virtual environment
    ```bash
    uv venv
    source .venv/bin/activate
    ```
    or in case of Windows OS
    ```bash
    uv venv
    .venv\Scripts\activate
    ```

2. Install `FedModelKit`
    ```bash
    uv pip install FedModelKit

    ```

## 🚀 Quick Start

Initialize a new federated learning experiment with:

```bash
fmk init -n my_experiment

```
This will generate in the present directory the project for a new FL experiment.

## 📖 Next Steps

👉 Once the experiment directory has been created, open the file `README.md` that has been generated in your directory and follow the guided workflow to set up and run your federated learning tasks.
nominatim-db 5.2.0
A tool for building a database of OpenStreetMap for geocoding and for searching the database. Database backend.
Requires Python: >=3.9
Keywords: OpenStreetMap, geocoding, search

# Nominatim - DB Backend

Nominatim is a tool to search OpenStreetMap data
by name and address (geocoding) and to generate synthetic addresses of
OSM points (reverse geocoding).

This module implements the database backend for Nominatim and the
command-line tool for importing and maintaining the database.

## Installation

### Prerequisites

Nominatim requires [osm2pgsql](https://osm2pgsql.org/) (>=1.8) for reading
OSM data and [PostgreSQL](https://www.postgresql.org/) (>=9.6) to store the data.

On Ubuntu (>=23.04) and Debian (when using backports), you can install them with:

    sudo apt-get install osm2pgsql postgresql-postgis

### Installation from pypi

To install Nominatim from pypi, run:

    pip install nominatim-db


## Quick start

First create a project directory for your new Nominatim database, which
is the space for additional configuration and customization:

    mkdir nominatim-project

Make sure you run all nominatim commands from within the project directory:

    cd nominatim-project

Download an appropriate data extract, for example from
[Geofabrik](https://download.geofabrik.de/) and import the file:

    nominatim import --osm-file <downlaoded-osm-data.pbf>

You will need to install the [`nominatim-api`](https://pypi.org/project/nominatim-api/)
package to query the database.

## Documentation

A HTML version of the documentation can be found at
https://nominatim.org/release-docs/latest/ .

## License

The source code is available under a GPLv3 license.
nominatim-api 5.2.0
A tool for building a database of OpenStreetMap for geocoding and for searching the database. Search library.
Requires Python: >=3.9
Keywords: OpenStreetMap, geocoding, search

# Nominatim - Frontend Library

Nominatim is a tool to search OpenStreetMap data
by name and address (geocoding) and to generate synthetic addresses of
OSM points (reverse geocoding).

This module implements the library for searching a Nominatim database
imported with the [`nominatim-db`](https://pypi.org/project/nominatim-db/) package.

## Installation

To install the Nominatim API from pypi, run:

    pip install nominatim-api

## Running a Nominatim server

You need Falcon or Starlette to run Nominatim as a service, as well as
an ASGI-capable server like uvicorn. To install them from pypi run:

    pip install falcon uvicorn

You need to have a Nominatim database imported with the 'nominatim-db'
package. Go to the project directory, then run uvicorn as:

    uvicorn --factory nominatim.server.falcon.server:run_wsgi

## Documentation

The full documentation for the Nominatim library can be found at:
https://nominatim.org/release-docs/latest/library/Getting-Started/

The v1 API of the server is documented at:
https://nominatim.org/release-docs/latest/api/Overview/

## License

The source code is available under a GPLv3 license.