README.md
In mrkaye97/lightMLFlow: A lightweight R wrapper for the MLFlow REST API

lightMLFlow

A lightweight R wrapper for the MLFlow REST API

lightMLFlow is not on CRAN. You can install it from Github as follows:

# install.packages("devtools")

devtools::install_github("mrkaye97/lightMLFlow")

This package differs from the CRAN mlflow package in a few important ways.

First, there are some things that the full CRAN mlflow package has that this package doesn’t:

mlflow supports more configurable artifact stores, while lightMLFlow only supports S3 as the artifact store.
mlflow allows you to run install_mlflow() to install MLFlow on your machine. lightMLFlow assumes you already have MLFlow installed. (In other words, there’s no install_mlflow() function in this package).
mlflow lets you run the MLFlow UI directly from the package, which lightMLFlow does not. lightMLFlow is made to be used with a deployed MLFlow instance, which means that your instance needs to already exist (either on local on on a cloud server).

However, there are also significant advantages to using lightMLFlow over mlflow:

lightMLFlow features a friendlier API, with significantly fewer functions, no mlflow::mlflow_* function prefixing (following Tidyverse conventions, lightMLFlow function names are verbs), and improved error handling.
lightMLFlow fixes some bugs in mlflow’s API wrapping functions.
lightMLFlow is significantly more lightweight than mlflow. It doesn’t depend on httpuv, reticulate, or swagger, and has a far more minimal footprint in general.
lightMLFlow uses aws.s3 to put and save objects to and from S3, which means you don’t need to have a boto3 install on your machine running your MLFlow code. This is an essential change, as it means that lightMLFlow does not require any Python infrastructure, as opposed to mlflow, which does.

A couple goals for this package as it matures:

Supporting Tidymodels model formats in save_model and log_model. Since Tidymodels provides a uniform API for modeling, it should play nicely with MLFlow by allowing us to save an arbitrary model in .rds format (as long as a predict method exists), and then load the .rds file and use predict(model, data) when serving the model.