Although there are number of packages already available on Azure ML Studio for various purposes like cleaning, feature selection, training, etc.
But sometimes while performing experiment we may need the packages that are not available on Azure ML studio. So we need to first upload that particular package/packages on the Azure ML and then install the same package for using it in the experiment.
Following are the steps to upload and install the package that are not already available/supported on/by the Azure ML:
- Depending upon the package that we need, download the package from their sources or install the package locally first using Command prompt or R studio(in case of R package) . For example :
If you need to install R package, you need to install this package locally first by using
or you can directly download the zip file of that package from the CRAN.
- Build the zip file of the required packages that are downloaded from their respective sources or installed locally.
Important : Build the zip file with all dependencies of the package.
- Now again build the zip file of the zip file that we got from step 2. Sometimes we do miss this step and get a range of errors. But this is the most important step and package will not be installed without following this step.
- Upload this final zip file on the Azure ML by clicking on the '+' at the bottom of the Azure ML studio interface. Upload the package by going to DATASET > FROM LOCAL FILE. When you click on the FROM LOCAL FILE, following pop up will appear.You can choose the package from Local by clicking on the Choose File. Fill in the fields and select the different fields available on this popup based on the requirement.The zip file will be available in the datasets section of the Azure ML Studio(Screenshot).
- Now in order to install the package in the Azure ML, you need to drag and drop the zip file and Execute R script and connect them as shown in the below screenshot. And write the below r script in the Execute R script and execute.You can tune the parameters based on the requirement. Just in case you have multiple packages in the zip file then you have to install all the packages one by one using R script. Remember you need to install th dependencies first.