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Migrate Execute R Script modules in Studio (classic)

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Important

Support for Machine Learning Studio (classic) will end on 31 August 2024. We recommend you transition to Azure Machine Learning by that date.

Beginning 1 December 2021, you will not be able to create new Machine Learning Studio (classic) resources (workspace and web service plan). Through 31 August 2024, you can continue to use the existing Machine Learning Studio (classic) experiments and web services.

ML Studio (classic) documentation is being retired and may not be updated in the future.

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.