semvec/pypi-packages huggingface dataset:
gluetube 0.1.1
A lightweight python script scheduler
License: MIT

# gluetube
A simple python script scheduler with a shared local key-value database. Meant to enable easy automation and integration of infrastructure and systems. Like cron, but with more bells and whistles.

## installation

There are two ways to deploy gluetube. 

* docker
* virtual machine / bare metal

### docker

1. `docker volume create gluetube-cfg`
2. `docker volume create gluetube-db`
3. `docker run -d --init --name gluetube -v gluetube-cfg:/home/gluetube/.gluetube/etc -v gluetube-db:/home/gluetube/.gluetube/db ctomkow/gluetube`
4. `docker exec -it gluetube bash`
5. `gt db --rekey`

### VM

1. `adduser gluetube`
2. `pip install --user gluetube`
3. `gt --configure`
4. `gt db --init`
5. `gt daemon --background`
6. `gt db --rekey`

## example usage

> `gt --help`

> `gt summary`

> `gt schedule 1 --now`

## pipeline development

You are meant to develop your own pipelines in python for gluetube. The following is a brief description of how to get your development environment setup. These instructions assume you use **VS code** and **docker**.

1. `docker pull ctomkow/gluetube`

2. `docker volume create gluetube-db`

3. `docker volume create gluetube-cfg`

4. `docker run -itd --name gluetube --net=host -v gluetube-db:/home/gluetube/.gluetube/db -v gluetube-cfg:/home/gluetube/.gluetube/etc ctomkow/gluetube:latest`

5. Open VS code. In the docker-> containers section, right-click on the running gluetube container and `attach visual studio code` to running container. 

6. Within the container terminal, enter the pipelines directory. `cd pipelines`

7. Clone a repository. `git clone <url> .` Note the `.` at the end of the git clone. This is important because we don't want the project folder as a subdirectory.
 
8. Now you can develop your pipelines (.py files) while having a live gluetube daemon running within the container. All the gluetube cli commands are available to test out your pipeline code in a production-like environment. `gt --help`

## roadmap

[gluetube roadmap](https://github.com/ctomkow/gluetube/wiki/Roadmap)
bert-for-tf2e 0.14.13
A TensorFlow 2.11.0 Keras implementation of BERT.
License: MIT
Requires Python: >=3.6
Keywords: tensorflow keras bert

BERT for TensorFlow 2.11.0
======================

This is a modification version of the original `bert-for-tf2` created by `kpe`. I made a minor change to the code 
to make it work with never version of TensorFlow, following the solution that i found in github community.
Resolving the TypeError issue. Last time checked at 1/23/2023 - it worked fine.

This repo contains a `TensorFlow 2.11.0`_ `Keras`_ implementation of `google-research/bert`_
with support for loading of the original `pre-trained weights`_,
and producing activations **numerically identical** to the one calculated by the original model.

`ALBERT`_ and `adapter-BERT`_ are also supported by setting the corresponding
configuration parameters (``shared_layer=True``, ``embedding_size`` for `ALBERT`_
and ``adapter_size`` for `adapter-BERT`_). Setting both will result in an adapter-ALBERT
by sharing the BERT parameters across all layers while adapting every layer with layer specific adapter.

The implementation is build from scratch using only basic tensorflow operations,
following the code in `google-research/bert/modeling.py`_
(but skipping dead code and applying some simplifications). It also utilizes `kpe/params-flow`_ to reduce
common Keras boilerplate code (related to passing model and layer configuration arguments).

`bert-for-tf2e`_ should work with both `TensorFlow 2.11.0`_ and `TensorFlow 1.14`_ or newer.

Install
-------

``bert-for-tf2e`` bert for tensorflow 2.0 (extended) is on the Python Package Index (PyPI):

::

    pip install bert-for-tf2e


For more detail please check the original version:
---------
- `SOURCE`_ - https://github.com/kpe/bert-for-tf2
syntaxedit 0.3.0
Syntax highlighting Qt text widget
License: MIT
Requires Python: >=3.7.2,<3.12
Keywords: qt,pyside,pyqt,syntax highlighting

# syntaxedit

[![test](https://github.com/davidwinter/syntaxedit/workflows/ci_cd/badge.svg)](https://github.com/davidwinter/syntaxedit/actions?query=workflow%3Aci_cd) [![PyPI](https://img.shields.io/pypi/v/syntaxedit)](https://pypi.org/project/syntaxedit/)

> A simple Python Qt syntax highlighting widget

![syntaxedit](https://raw.githubusercontent.com/davidwinter/syntaxedit/main/example.png)

## Features

- Extensive [syntax](https://pygments.org/languages/) and [theme](https://pygments.org/styles/) support - powered by [Pygments](https://pygments.org)
- Set font and font size
- Set indentation size

## Usage

1. Install package

   ```shell
   pip install syntaxedit
   ```

   Or

   ```shell
   poetry add syntaxedit
   ```

2. In your app, include the package, and create a `SyntaxEdit` widget:

    ```python
    from syntaxedit.core import SyntaxEdit

    code = """# Todo list

    - [ ] Go shopping
    - [x] Walk the dog"""

    widget = SyntaxEdit(code)
    ```

### Available options

- `content`: the initial content for the widget. **Default:** `""`
- `parent`: parent Qt widget for SyntaxEdit. **Default:** `None`
- `font`: the font family for the widget. **Default:** `"Courier New"`
- `font_size`: size to use for the font. **Default:** `13`
- `syntax`: the code [syntax](https://pygments.org/languages/) to use. **Default:** `"Markdown"`
- `theme`: the syntax [theme](https://pygments.org/styles/) to use. **Default:** `"solarized-light"`
- `indentation_size`: the size for indentation. **Default:** `4`
- `use_theme_background`: whether to use the background colour from the theme or to not set one. **Default:** `True`

## Authors

By [David Winter](https://github.com/davidwinter)

## License

MIT