Setup and version your ML project

with MLV-tools and DVC

St├ęphanie Bracaloni & Sarah Diot-Girard

About Us

Sarah Diot-Girard
Data Scientist since 2012
Interested in DataOps and Ethics
PeopleDoc logo @SgdJlbl
St├ęphanie Bracaloni
Software Engineer since 2013
Automation and Code Quality
PeopleDoc logo @sbracaloni
PeopleDoc logo

Major issues:

  • Versioning (Data & Jupyter Notebooks)
  • Automate the pipeline execution
  • Reproduce & Share experiments

Setup a project using MLV-tools

  • Easily create DVC steps from command line pipeline steps
gen_dvc -i ./ -o ./commands/script_dvc

Setup a project using MLV-tools

  • Convert Jupyter Notebooks to
    executable and configurable Python3 scripts
ipynb_to_python -n ./notebook.ipynb -o ./

Setup a project using MLV-tools

  • Convert Jupyter Notebooks to DVC steps in one command
ipynb_to_dvc -n ./notebook.ipynb -o ./commands/script_dvc

[Tutorial time]

Clone MLV-tools tutorial repository


How to:

We are waiting for your Pull Requests!

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Fonts: White Rabbit by Matthew Welch
Capsuula by Henrich Fichna
Icons made by Smashicons, Good Ware, Designmodo, Freepik from; license CC 3.0 BY

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Time Optimization

Tutorial Part1: build docker images
cd mlv-tools-tutorial/resources/dvc_playground
docker-compose build
Tutorial Part2: build docker image (Optional)
(Can also be run on your computer)