puncoz-bookmarks/learndrake

An R package that teaches drake

Machine learning workflow management with drake

Machine learning workflows can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the drake R package. drake resolves the dependency structure of your analysis pipeline, skips tasks that are already up to date, executes the rest with optional distributed computing, and organizes the output so you rarely have to think about data files. This workshop will teach you how to create and maintain machine learning projects with drake-powered automation.

Installation

To obtain the workshop materials, simply install the learndrake package.

install.packages("remotes")
library(remotes)
install_github("wlandau/learndrake")

Usage

The functions in learndrake help navigate and deploy the workshop materials.

Function	Purpose
view_slides()	Open the introductory slides in a web browser.
save_notebooks()	Save the tutorials to your computer: R notebooks and supporting files.
launch_app()	Launch a Shiny app that accompanies a tutorial.
save_app()	Save the app files so you can deploy to shinyapps.io or Shiny Server.

Introductory slides

The workshop begins with an introductory presentation on drake. View the slides at https://wlandau.github.io/learndrake/index.html or open them yourself in a browser with view_slides().

Tutorials

After the introductory presentation, students work through a sequence of R notebooks in order. Use save_notebooks() to save the notebooks and supporting files to your computer.

Notebook	Topic
1-churn.Rmd	Deep learning case study
2-setup.Rmd	Convert the case study to a new drake project.
3-flow.Rmd	Develop, work, and iterate on the project.
4-plans	A deep dive into drake plans.
5-files	Custom input and output data files.
6-reports	Special considerations of knitr and R Markdown reports.
7-hpc	High-performance computing

Apps

Notebooks 3-flow.Rmd and 4-plans.Rmd come with supporting Shiny apps to conduct the learning exercises.

App	Notebook	Deploy locally	Public URL
Iterate on a drake workflow	3-flow.Rmd	launch_app("flow")	http://wlandau.shinyapps.io/learndrakeflow
Exercises on drake plans	4-plans.Rmd	launch_app("plans")	http://wlandau.shinyapps.io/learndrakeplans
Visualize drake projects	4-plans.Rmd	launch_app("drakeplanner")	http://wlandau.shinyapps.io/drakeplanner

Thanks

Thanks to	For
Edgar Ruiz	Uniting drake and keras at https://github.com/sol-eng/tensorflow-w-r and providing valuable advice on the construction of the workshop.
Matt Dancho	Publishing the original blog post with the workshop’s underlying case study.
Eric Nantz	Reviewing and providing feedback on this workshop.