semvec/pypi-packages huggingface dataset:
bot-studio 1.4.0
Automation Library
License: Apache License 2.0

Bot Studio is a python library for runing bots built with DataKund Studio.
You can also train bots with DataKund to automate repetitive work without doing any programming.
It currently runs only on windows.

### Installation
```sh
pip install bot-studio
```

### Import Bot Studio
```
from bot_studio import *
```

### Create Object
Create object first to open browser.
```sh
dk=bot_studio.new()
or
dk=bot_studio.new(argument1=value,argument2=value,..)
```

### Browser Options
| Option         | Default Value              | Description							   |
| -------------  | ---------------------------|-------------------------------------------
| headless       |           False            | Can set it to ``True`` if wants headless  |
| proxy	         |         No proxy	          | Pass proxy value e.g ``98.0.2.5:4000`` 	|
| profile_path   | creates temporary profile  | Pass profile path e.g ``C:\\Users\\username\\AppData\\Local\\Google\\Chrome\\User Data\\``|
| user_agent     |      No user agent         | Pass user agent e.g ``python 2.7", "platform":"Windows``|
| download_folder|Downloads in default folder | If want to set download directory to custom e.g ``E:files\\``|


#### Run a bot
```sh
dk.amazon_login(email='put your email here', login_url='https://www.amazon.com/gp/sign-in.html', password='put password here')
```

#### Make Bot using Studio
To create your own bot follow below steps:-
* Run command "datakund" in command prompt
* A browser will get opened containing DK extension
* To open Extension press Alt+O
* Type the bot name e.g "google_search" and click on "Make New Bot"
* Click on Record and do actions like opening link, typing etc.
* Go back and then Run bot

---
**NOTE**

If you want to call your bots in your code, then consider bot names which do not contain space, special character etc.
---


#### Access Your bot through Code
To access your code:-
* Go to Code Section in extension
* Go to python and copy the code
* Now run that code, your bot will run

#### Make your bots public
To share your bots with public, so that anyone can run them follow these steps:-
* Go to your bot in Extension
* Go to More section and then Publish
* Write title, desc and image and click on Publish
* Here you go, now anyone can run this bot

#### Run public bot
Here we will run the bot we did made above
```sh
google_search(keyword="shoes")
```

### Send Feedback to Developers
```sh
bot_studio.send_feedback(feedback="Need help with this ......")
```

### Contact Us
* [Telegram](https://t.me/datakund)
* [Website](https://datakund.com)
hybridbackend-cpu 0.5.4
A high-performance framework for training wide-and-deep recommender systems on heterogeneous cluster
License: Apache License 2.0
Keywords: deep learning,recommendation system

# HybridBackend

[![cibuild: cpu](https://github.com/alibaba/HybridBackend/actions/workflows/cpu-cibuild.yaml/badge.svg?branch=main&event=push)](https://github.com/alibaba/HybridBackend/actions/workflows/cpu-cibuild.yaml)
[![readthedocs](https://readthedocs.org/projects/hybridbackend/badge/?version=latest)](https://hybridbackend.readthedocs.io/en/latest/?badge=latest)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](http://makeapullrequest.com)
[![license](https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg)](https://opensource.org/licenses/Apache-2.0)

HybridBackend is a high-performance framework for training wide-and-deep
recommender systems on heterogeneous cluster.

## Features

- Memory-efficient loading of categorical data

## Install

### Using pip packages

GLIBC    | CUDA | Python | Tensorflow      | Command
-------- | ---- | ------ | --------------- | ------------
`>= 2.4` | -    | 3.6    | `>=1.15, < 2.0` | `pip install hybridbackend-cpu`
`>= 2.4` | -    | 3.6    | `>=1.14, < 1.15` | `pip install hybridbackend-cpu-legacy`

### Build from source

See [Building Instructions](https://github.com/alibaba/HybridBackend/blob/main/BUILD.md).

## Usage

Please see [documentation](https://hybridbackend.readthedocs.io/en/latest/) for
more information.

## License

HybridBackend is licensed under the [Apache 2.0 License](LICENSE).

## Community

- Please see [Contributing Guide](https://github.com/alibaba/HybridBackend/blob/main/CONTRIBUTING.md)
before your first contribution.

- Please [register as an adopter](https://github.com/alibaba/HybridBackend/blob/main/ADOPTERS.md)
if your organization is interested in adoption. We will discuss new feature
requirements with registered adopters in advance.

- Please cite HybridBackend in your publications if it helps:

  ```text
  @article{zhang2022picasso,
    title={PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems},
    author={Zhang, Yuanxing and Chen, Langshi and Yang, Siran and Yuan, Man and Yi, Huimin and Zhang, Jie and Wang, Jiamang and Dong, Jianbo and Xu, Yunlong and Song, Yue and others},
    journal={arXiv preprint arXiv:2204.04903},
    year={2022}
  }
  ```

## Contact Us

If you would like to share your experiences with others, you are welcome to
contact us in DingTalk:

[<img src="https://github.com/alibaba/HybridBackend/raw/main/images/dingtalk.png" alt="dingtalk" width="200"/>](https://h5.dingtalk.com/circle/healthCheckin.html?dtaction=os&corpId=ding14f3e2ea4b79994cadf6428847a62d4a&51951ad=a84b419&cbdbhh=qwertyuiop)