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Machine Learning Project Development Tool

CircleCI token codecov Codacy Badge License: MIT Version PyPI PyPI - Downloads

Sean Shookman

Joao Moreira

Sherry Wang

Cody Hutchins

Kazi Tanzim Islam

Samuel Gaist



Skelebot is a command-line tool for developing machine learning projects and executing them in Docker. The purpose of Skelebot is to simply make the life of a Data Scientist easier by doing a lot of the legwork for mundane tasks automatically through a unified, consistent interface.

[/code/my-iris-model] > skelebot -h
usage: skelebot [-h] [-e ENV] [-s] [-n]

Iris Example
Example Skelebot Project
Version: 1.1.0
Environment: None
Skelebot Version: 1.8.5

positional arguments:
    loadData            Load the Iris Dataset and save it into the data folder for the train job to access (src/
    train               Use the data loaded in the loadData job to train the iris model (src/
    score               Use the model that was built in the train job to score new data against the iris model (src/
    push                Push an artifact to artifactory
    pull                Pull an artifact from artifactory
    jupyter             Spin up Jupyter in a Docker Container (port=8888, folder=.)
    plugin              Install a plugin for skelebot from a local zip file
    bump                Bump the skelebot.yaml project version
    prime               Generate Dockerfile and .dockerignore and build the docker image
    exec                Exec into the running Docker container

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         Display the version number of Skelebot
  -e ENV, --env ENV     Specify the runtime environment configurations
  -s, --skip-build      Skip the build process and attempt to use previous docker build
  -n, --native          Run natively instead of through Docker
  -c, --contact         Display the contact email of the Skelebot project


Install Skelebot with Pip:

pip install skelebot

Getting Started

To get started using Skelebot you can follow the Documentation.


Anyone is welcome to make contributions to the project. If you would like to make a contribution, please read our Contributor Guide.


This project adheres to Semantic Versioning. Please refer to the Changelog for information regarding the differences between versions of the project.