semvec/pypi-packages huggingface dataset:
evduty-api 1.1.0
Library to communicate with EVduty REST api
Requires Python: >=3.11

[![Build, test and publish](https://github.com/happydev-ca/evduty-api/actions/workflows/publish.yml/badge.svg)](https://github.com/happydev-ca/evduty-api/actions/workflows/publish.yml)

# evduty-api

Library to communicate with EVduty REST API.

## Usage

```python
import aiohttp
import asyncio
import os

from evdutyapi import EVDutyApi


async def run():
    async with aiohttp.ClientSession() as session:
        api = EVDutyApi(os.environ['EMAIL'], os.environ['PASSWORD'], session)
        stations = await api.async_get_stations()
        print(stations)


asyncio.run(run())
```

### Build and test locally

```shell
make venv
source .venv/bin/activate
make test
make build
```

### Logging

Enable debug level to log API requests and responses.
```python
import logging
logging.basicConfig(level=logging.DEBUG)
```

### Release version

```shell
make release bump=patch|minor|major
```
rats-devtools 0.16.0
Rats Development Tools
Requires Python: <4.0,>=3.10
Keywords: pipelines, machine learning, research

# introduction

The `rats-devtools` component provides a suite of tools to automate and streamline the development
workflow for any application. It offers commands for tasks such as CI pipeline checks,
container image building, documentation generation, and other essential development operations that
support, but are not directly part of, your product. While originally developed for this project,
`rats-devtools` is now designed for general use and can help teams enforce best practices and
maintain high-quality development environments across different projects.

Typically, the commands provided by this component are installed repo-wide, and are able to support
both monorepos, and single-component code bases. The tools for the development of `rats` components
can be installed with the `rats.setup` script found in the `bin/` folder.

```bash
cd rats
direnv allow .
rats.setup
```

You can configure shell-completion for the duration of your session with the commands below:

``` bash
eval "$(_RATS_AML_COMPLETE=zsh_source rats-aml)"
eval "$(_RATS_CI_COMPLETE=zsh_source rats-ci)"
eval "$(_RATS_DOCS_COMPLETE=zsh_source rats-docs)"
eval "$(_RATS_EZ_COMPLETE=zsh_source rats-ez)"
eval "$(_RATS_RUNTIME_COMPLETE=zsh_source rats-runtime)"
```

The commands in this component are also used by our CI pipelines, so will give you a good way
to validate your changes before submitting a pull request.
keras-remote 0.0.1
Run Keras models remotely on TPU seamlessly.
Requires Python: >=3.9

# Keras Remote

Run Keras models remotely on TPU as seamlessly as running the same code locally.

## Prerequisites

1.  **Google Cloud SDK**: Install the `gcloud` CLI.
2.  **Authentication**: Run `gcloud auth login` and `gcloud auth application-default login`.
3.  **Permissions**: Ensure your GCP user has permissions to create and manage TPU VMs.

## Installation

```bash
pip install -r requirements.txt
```

## Running the Demo

The `demo_train.py` script demonstrates how to run a Keras model on a remote TPU.

```bash
# Optional: Set your GCP project and zone
export KERAS_REMOTE_PROJECT="your-project-id"
export KERAS_REMOTE_ZONE="us-central1-f"  # or other zones like europe-west4-a

python demo_train.py
```

**Note:** TPU availability varies by zone. If you encounter a `RESOURCE_EXHAUSTED` error, try a different zone or accelerator type (e.g., `v2-8` vs `v3-8`).

The `@keras_remote.run` decorator handles:
1.  Packaging your local code.
2.  Provisioning (or finding) a TPU VM.
3.  Uploading your code and dependencies.
4.  Executing the function inside a Docker container on the TPU VM.
imane-moviessdk 0.0.3
Software Development Kit (SDK) for MovieLens API
Requires Python: >=3.12

# MovieLens SDK (imane-moviessdk)

Un SDK Python simple pour interagir avec l'API REST MovieLens. Il est conçu pour les **Data Analysts** et **Data Scientists**, avec une prise en charge native de **Pydantic**, **dictionnaires** et **DataFrames Pandas**.
[![PyPI version](https://badge.fury.io/py/imane-moviessdk.svg)](https://badge.fury.io/py/imane-moviessdk)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
## Installation
```bash
pip install moviesdk
```

## Configuration
``` Python 
from imane_moviessdk import MovieClient, MovieConfig

# Configuration avec l'URL de votre API (Render ou locale)
config = MovieConfig(movie_base_url="http://localhost")
client = MovieClient(config=config)
```

-----

## Tester le SDK
## 1. Health check
``` Python
client.health_check()
# Retourne : {"status": "ok"}
```
## 2. Récupérer un film
``` Python
movie = client.get_movie(1)
print(movie.title)
```
## 3. Liste de films au format DataFrame
``` Python
df = client.list_movies(limit=5, output_format="pandas")
print(df.head())
```

-----
## Modes de sortie disponibles
Toutes les méthodes de liste (`list_movies`, `list_ratings`, etc.) peuvent retourner :

-des objets **Pydantic** (valeur par défaut)
-des **dictionnaires**
-des **DataFrames Pandas**

Exemple:
```python
client.list_movies(limit=10, output_format="dict")
client.list_ratings(limit=10, output_format="pandas")
```
----
## Tester en local
Vous pouvez aussi utiliser une API locale : 
```python
config = MovieConfig(movie_base_url="http://localhost:80")
client = MovieClient(config=config)
```

----
## Public cible
-Data Analysts
-Data Scientists
-Étudiants et curieux en Data
-Développeurs Python

---

## Licence
MIT License

----

## Liens Utiles
-PyPI : [https://pypi.org/project/imane-moviessdk](https://pypi.org/project/imane-moviessdk)