Boosting The Productivity of Machine Learning Engineers

Documentation

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

Blog

https://blog.kxy.ai

Installation

From PyPi:

pip install kxy -U

From GitHub:

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

kxy configure

and follow the instructions. To get your own API key you need an account; you can sign up here. You'll then be automatically given an API key which you can find here.

Docker

The Docker image 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

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 in your browser. This docker environment comes with all examples available on the documentation website.

To start a Jupyter Notebook server from an existing directory of notebooks, run

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 in your browser.

You can also get the same Docker image from GitHub here.

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 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.

fudp/kxy-python