https://github.com/kxytechnologies/kxy-python
A toolkit to boost the productivity of machine learning engineers.
https://github.com/kxytechnologies/kxy-python
feature-engineering feature-selection information-theory machine-learning machine-learning-library model-compression python
Last synced: 6 months ago
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A toolkit to boost the productivity of machine learning engineers.
- Host: GitHub
- URL: https://github.com/kxytechnologies/kxy-python
- Owner: kxytechnologies
- License: gpl-3.0
- Created: 2020-04-08T01:44:58.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-06-06T13:31:18.000Z (almost 4 years ago)
- Last Synced: 2025-10-27T11:55:13.528Z (6 months ago)
- Topics: feature-engineering, feature-selection, information-theory, machine-learning, machine-learning-library, model-compression, python
- Language: Python
- Homepage: https://kxy.ai
- Size: 38.6 MB
- Stars: 52
- Watchers: 2
- Forks: 12
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION.cff
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README
-----------------
# Boosting The Productivity of Machine Learning Engineers
[](https://github.com/kxytechnologies/kxy-python/blob/master/LICENSE)
[](https://www.kxy.ai/)
[](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 && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `` 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=,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c "kxy configure && /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''"
```
where you should replace `` 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.