semvec/pypi-packages huggingface dataset:
pymoa-remote 0.1.0
PyMoa-Remote is a library to remotely execute functions e.g. on a Raspberry Pi.
License: MIT

PyMoa-Remote
============

PyMoa-Remote is a library to remotely execute functions e.g. on a Raspberry Pi.

For more information: https://matham.github.io/pymoa-remote/index.html

To install https://matham.github.io/pymoa-remote/installation.html

.. image:: https://img.shields.io/pypi/pyversions/pymoa-remote.svg
    :target: https://pypi.python.org/pypi/pymoa-remote/
    :alt: Supported Python versions

.. image:: https://img.shields.io/pypi/v/pymoa-remote.svg
    :target: https://pypi.python.org/pypi/pymoa-remote/
    :alt: Latest Version on PyPI

.. image:: https://coveralls.io/repos/github/matham/pymoa-remote/badge.svg?branch=master
    :target: https://coveralls.io/github/matham/pymoa-remote?branch=master
    :alt: Coverage status

.. image:: https://github.com/matham/pymoa-remote/workflows/Python%20application/badge.svg
    :target: https://github.com/matham/pymoa-remote/actions
    :alt: Github action status
graphbook_huggingface 0.0.6
Graphbook Hugging Face Plugin for no-code Hugging Face AI pipelines
License: MIT
Requires Python: <4.0,>=3.9
Keywords: huggingface, ml, workflow, pipelines, pytorch, data science, machine learning, ai

<p align="center">
  <a href="https://graphbook.ai">
    <img src="assets/graphbook-hf-banner.png" alt="Logo" width=512>
  </a>

  <h1 align="center">Graphbook Hugging Face</h1>

  <p align="center">
    Build No Code Hugging Face AI Pipelines
  </p>
</p>

You can build efficient DAG workflows or AI pipelines without any code. This is a Graphbook plugin that lets you drag and drop Hugging Face models and datasets onto Graphbook workflows. This plugin contains a web panel for searching and drag-and-dropping models and datasets from [Huggingface Hub](https://huggingface.co/) onto their graphbook workflows.

<img src="assets/example-hf-pipeline.png" alt="Example Pipeline with Hugging Face" with=1024>

## Packaged Nodes

Graphbook Hugging Face contains the following nodes:

* `TransformersPipeline` step for model usage from transformers package
* `HuggingfaceDataset` step for dataset usage from the datasets package
* And numerous `Post Processing/*` steps for post processing of model outputs

## Getting started
1. `pip install graphbook_huggingface graphbook transformers datasets`
1. `graphbook --config hf.config.yaml`