Metadata-Version: 2.1
Name: kxy
Version: 1.4.6
Summary: A Powerful Serverless Pre-Learning and Post-Learning Analysis Toolkit
Home-page: https://www.kxy.ai
Author: Dr. Yves-Laurent Kom Samo
Author-email: github@kxy.ai
License: GPLv3
Download-URL: https://github.com/kxytechnologies/kxy-python/archive/v1.4.6.tar.gz
Project-URL: Documentation, https://www.kxy.ai/reference
Project-URL: Source Code, https://github.com/kxytechnologies/kxy-python/
Keywords: Lean ML,AutoML,Pre-Learning,Post-Learning,Model-Free ML
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy (>=1.13.1)
Requires-Dist: scipy (>=1.4.1)
Requires-Dist: pandas (>=0.23.0)
Requires-Dist: requests (>=2.22.0)
Requires-Dist: pandarallel
Requires-Dist: halo
Requires-Dist: ipywidgets
Requires-Dist: scikit-learn

<div align="center">
  <img src="https://www.kxy.ai/theme/images/logos/logo.svg"><br>
</div>

-----------------

# Boosting The Productivity of Machine Learning Engineers
[![License](https://img.shields.io/badge/license-GPLv3%2B-blue)](https://github.com/kxytechnologies/kxy-python/blob/master/LICENSE)
[![PyPI Latest Release](https://img.shields.io/pypi/v/kxy.svg)](https://www.kxy.ai/)
[![Downloads](https://pepy.tech/badge/kxy)](https://www.kxy.ai/)


## Documentation
https://www.kxy.ai/reference/

## Blog
https://blog.kxy.ai


## Installation
From PyPi:
```Bash
pip install kxy -U
```
From GitHub:
```Bash
git clone https://github.com/kxytechnologies/kxy-python.git & cd ./kxy-python & pip install .
```
## Authentication
All heavy-duty computations are run on our serverless infrastructure and require an API key. To configure the package with your API key, run 
```Bash
kxy configure
```
and follow the instructions. To get your own API key you need an account; you can sign up [here](https://www.kxy.ai/signup/). You'll then be automatically given an API key which you can find [here](https://www.kxy.ai/portal/profile/identity/).


## Docker
The Docker image [kxytechnologies/kxy](https://hub.docker.com/repository/docker/kxytechnologies/kxy) has been built for your convenience, and comes with anaconda, auto-sklearn, and the kxy package. 

To start a Jupyter Notebook server from a sandboxed Docker environment, run
```Bash
docker run -i -t -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `<YOUR API KEY>` with your API key and navigate to [http://localhost:5555](http://localhost:5555) in your browser. This docker environment comes with [all examples available on the documentation website](https://www.kxy.ai/reference/latest/examples/).

To start a Jupyter Notebook server from an existing directory of notebooks, run
```Bash
docker run -i -t --mount src=</path/to/your/local/dir>,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure <YOUR API KEY> && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `</path/to/your/local/dir>` with the path to your local notebook folder and navigate to [http://localhost:5555](http://localhost:5555) in your browser.

You can also get the same Docker image from GitHub [here](https://github.com/kxytechnologies/kxy-python/pkgs/container/kxy-python).

## Other Programming Language
We plan to release friendly API client in more programming language. 

In the meantime, you can directly issue requests to our [RESTFul API](https://www.kxy.ai/reference/latest/api/index.html) using your favorite programming language. 

## Pricing 
All API keys are given a free quota (a few dozen backend tasks) that should be enough to try out the package and see if you love it. Beyond the free quota you will be billed a small fee per task. 

KXY is free for academic use; simply signup with your university email. 

KXY is also free for Kaggle competitions; sign up and email kaggle@kxy.ai to get a promotional code.


