Machine Learning Project Development Tool
About
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]
{loadData,train,score,push,pull,jupyter,plugin,bump,prime,exec}
...
Iris Example
Example Skelebot Project
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Version: 1.1.0
Environment: None
Skelebot Version: 1.0.2
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positional arguments:
{loadData,train,score,push,pull,jupyter,plugin,bump,prime,exec}
loadData Load the Iris Dataset and save it into the data folder for the train job to access (src/loadData.py)
train Use the data loaded in the loadData job to train the iris model (src/train.py)
score Use the model that was built in the train job to score new data against the iris model (src/score.py)
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
-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
Install
Install Skelebot with Pip:
pip install skelebot
Getting Started
To get started using Skelebot you can follow the documentation found here.
Contributing
Anyone is welcome to make contributions to the project. If you would like to make a contribution, please read our Contributor Guide.
Versioning
This project adheres to Semantic Versioning. Please refer to the Changelog for information regarding the differences between versions of the project.