migrate-execute-r-script.md

title	description	services	ms.service	ms.subservice	ms.topic	author	ms.author	ms.date
ML Studio (classic): Migrate Execute R Script	Rebuild Studio (classic) Execute R script modules to run on Azure Machine Learning.	machine-learning	machine-learning	studio-classic	how-to	xiaoharper	zhanxia	03/08/2021

Migrate Execute R Script modules in Studio (classic)

[!INCLUDE ML Studio (classic) retirement]

In this article, you learn how to rebuild a Studio (classic) Execute R Script module in Azure Machine Learning.

For more information on migrating from Studio (classic), see the migration overview article.

Execute R Script

Azure Machine Learning designer now runs on Linux. Studio (classic) runs on Windows. Due to the platform change, you must adjust your Execute R Script during migration, otherwise the pipeline will fail.

To migrate an Execute R Script module from Studio (classic), you must replace the maml.mapInputPort and maml.mapOutputPortinterfaces with standard functions.

The following table summarizes the changes to the R Script module:

Feature	Studio (classic)	Azure Machine Learning designer
Script Interface	maml.mapInputPort and maml.mapOutputPort	Function interface
Platform	Windows	Linux
Internet Accessible	No	Yes
Memory	14 GB	Dependent on Compute SKU

How to update the R script interface

Here are the contents of a sample Execute R Script module in Studio (classic):

# Map 1-based optional input ports to variables 
dataset1 <- maml.mapInputPort(1) # class: data.frame 
dataset2 <- maml.mapInputPort(2) # class: data.frame 

# Contents of optional Zip port are in ./src/ 
# source("src/yourfile.R"); 
# load("src/yourData.rdata"); 

# Sample operation 
data.set = rbind(dataset1, dataset2); 

 
# You'll see this output in the R Device port. 
# It'll have your stdout, stderr and PNG graphics device(s). 

plot(data.set); 

# Select data.frame to be sent to the output Dataset port 
maml.mapOutputPort("data.set"); 

Here are the updated contents in the designer. Notice that the maml.mapInputPort and maml.mapOutputPort have been replaced with the standard function interface azureml_main.

azureml_main <- function(dataframe1, dataframe2){ 
    # Use the parameters dataframe1 and dataframe2 directly 
    dataset1 <- dataframe1 
    dataset2 <- dataframe2 

    # Contents of optional Zip port are in ./src/ 
    # source("src/yourfile.R"); 
    # load("src/yourData.rdata"); 

    # Sample operation 
    data.set = rbind(dataset1, dataset2); 


    # You'll see this output in the R Device port. 
    # It'll have your stdout, stderr and PNG graphics device(s). 
    plot(data.set); 

  # Return datasets as a Named List 

  return(list(dataset1=data.set)) 
} 

For more information, see the designer Execute R Script module reference.

Install R packages from the internet

Azure Machine Learning designer lets you install packages directly from CRAN.

This is an improvement over Studio (classic). Since Studio (classic) runs in a sandbox environment with no internet access, you had to upload scripts in a zip bundle to install more packages.

Use the following code to install CRAN packages in the designer's Execute R Script module:

  if(!require(zoo)) { 
      install.packages("zoo",repos = "http://cran.us.r-project.org") 
  } 
  library(zoo) 

Next steps

In this article, you learned how to migrate Execute R Script modules to Azure Machine Learning.

See the other articles in the Studio (classic) migration series:

1. Migration overview.
2. Migrate dataset.
3. Rebuild a Studio (classic) training pipeline.
4. Rebuild a Studio (classic) web service.
5. Integrate a Machine Learning web service with client apps.
6. Migrate Execute R Script modules.