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 Skelebot with Pip:
pip install skelebot
To get started using Skelebot you can follow the documentation found here.
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.