Enumerating Pre-install Packages

Introduction to Pre-installed Apps. And any dependency packages. This stages the appx package and configures it for pre-install. All dependencies.

The ELS application calls the MappingGetServices function to determine the services that are available on the operating system. The function can be used either to enumerate all available ELS services, or to filter the services based on application-provided search criteria. When services are no longer needed, the application calls MappingFreeServices.

Get All Supported Services

This code example illustrates the use of MappingGetServices and MappingFreeServices to enumerate and then free all available services on the operating system. To do this, the application passes NULL for the pOptions parameter of MappingGetServices.

Get Specific Services

The next example illustrates the use of MappingGetServices and MappingFreeServices to enumerate and then free all services of category 'Language Detection'. For more information about this service category, see Microsoft Language Detection.

Related topics

We already know that Azure Machine Learning(https://studio.azureml.net/) supports for R language which makes our life easier to use our own existing R code with in AML studio.

There are over 400 most popular R packages pre-install in the Azure Machine Learning Studio.

How to find the list of packages that are currently supported or installed, you can use the below script:

Use Execute R script task in AML studio and copy paste the below script:

out <- data.frame(installed.packages(,fields='Description'))
maml.mapOutputPort('out') The truth hurts torrent.

If you want, you can also convert the output to a CSV file using Convert to CSV:

Now the biggest issue is when we try to execute our existing R code which have R packages not pre-install or supported in AML. You will end up getting Error: could not find function '<packagename>' or package '<packagename>'is not available or there is no package called '<packagename>'

You need to upload/install the R package with Azure Machine Learning Studio to execute you code.

Here are the Steps How to install/upload outside R package in Azure Machine Learning studio:

I will be using dummies R package in my example for the installation:

1. Download the R package dummies or which you want to install, you can use R studio to do this if the package is not yet install in the R package library. Location will be: C:Users<username>DocumentsRwin-library3.2
2. Put dummies package into a .zip folder as shown below:

1. Now the most important step where most of us making mistake is we need to again put the .zip folder to another .zip folder. Which means dummies.zip -> dummies (2).zip
2. You can rename the final zip folder as per your wish let say in my case I renamed it to pack.zip

1. Upload the final pack.zip file to the Azure Machine Learning Data Set:
2. Under saved data set drag and drop the .zip package file
3. Use Execute R script task in AML studio and copy paste the below script:

   install.packages('src/pack/dummies.zip', lib = '.', repos = NULL, verbose = TRUE)
   library(dummies, lib.loc='.', verbose=TRUE)

4. This will successful install the dummies R package.

Please note if you are getting the below error while running you R script, please do zip of you zip, as mention in the step 3.

Error 0063: The following error occurred during evaluation of R script:

---------- Start of error message from R ----------

zip file 'src/dummies.zip' not found

I hope this blog will help you to know how to check the pre-install, and how to install you own R package within Azure ML Studio and handle ErrorCode':'0063.