kxy
A toolkit to boost the productivity of machine learning engineers.
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (15.2%) to scientific vocabulary
Keywords
Repository
A toolkit to boost the productivity of machine learning engineers.
Basic Info
- Host: GitHub
- Owner: kxytechnologies
- License: gpl-3.0
- Language: Python
- Default Branch: master
- Homepage: https://kxy.ai
- Size: 38.6 MB
Statistics
- Stars: 52
- Watchers: 2
- Forks: 12
- Open Issues: 5
- Releases: 32
Topics
Metadata Files
README.md
Boosting The Productivity of Machine Learning Engineers
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. 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
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 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
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 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.
Citation (CITATION.cff)
cff-version: 1.2.0
message: If you use this software, please cite it using these metadata.
authors:
- family-names: Kom Samo
given-names: Yves-Laurent
orcid: "https://orcid.org/0000-0003-2901-6930"
title: KXY: A Seemless API to 10x The Productivity of Machine Learning Engineers.
version: 1.4.3
date-released: "2021-10-12"
abstract: KXY is a powerful serverless analysis toolkit that takes trial-and-error out of machine learning projects.
url: "https://github.com/kxytechnologies/kxy-python"
license: GPL-3.0
GitHub Events
Total
- Watch event: 2
- Issue comment event: 1
- Fork event: 1
Last Year
- Watch event: 2
- Issue comment event: 1
- Fork event: 1
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 251
- Total Committers: 2
- Avg Commits per committer: 125.5
- Development Distribution Score (DDS): 0.486
Top Committers
| Name | Commits | |
|---|---|---|
| Yves-Laurent | yl@p****i | 129 |
| Yves-Laurent | yl@k****i | 122 |
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 8
- Total pull requests: 0
- Average time to close issues: about 2 months
- Average time to close pull requests: N/A
- Total issue authors: 7
- Total pull request authors: 0
- Average comments per issue: 1.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- zeydabadi (2)
- LJSthu (1)
- Johannesfjeldsaa (1)
- Admolly (1)
- pbeste18 (1)
- ddofer (1)
- Christinele14 (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 808 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 147
- Total maintainers: 2
pypi.org: kxy
A Powerful Serverless Pre-Learning and Post-Learning Analysis Toolkit
- Homepage: https://www.kxy.ai
- Documentation: https://www.kxy.ai/reference
- License: GPLv3
-
Latest release: 1.4.11
published over 3 years ago
Rankings
Dependencies
- halo *
- ipywidgets *
- numpy >=1.18.1
- pandarallel *
- pandas >=0.23.0
- requests >=2.22.0
- scipy >=1.4.1
- numpy >=1.13.1