Deploy and activate the MRS parcel and custom service descriptor (CSD)
Important
This content is being retired and may not be updated in the future. The support for Machine Learning Server will end on July 1, 2022. For more information, see What's happening to Machine Learning Server?
Applies to: R Server 9.1 on the Cloudera distribution of Apache Hadoop (CDH)
A parcel installation of Microsoft R Server on CDH is a 2-part process. In part 1, you generated a parcel and Custom Service Descriptor (CSD) for Microsoft R Server (MRS) 9.1. This article is part 2. Because you copied the parcel and CSD to the Cloudera repositories, you can now use Cloudera Manager to deploy the parcel, activate and roll out R Server, and add MRS as a service administered within Cloudera Manager.
Step 1: Distribute the MRS parcel
In Cloudera Manager, click the parcel icon on the top right menu bar.
Find MRS in the parcel list. If you don't see it, check the parcel-repo folder (by default, /opt/cloudera/parcel-repo) for
MRS-9.1.0-el7.parcelandMRS-9.1.0-el7.parcel.sha. The machine should be the master node of the cluster.
In the parcel details page, MRS should have a status of Downloaded with an option to Distribute. Click Distribute to roll out MRS on available data nodes.
Status changes to distributed. Click Activate on the button to make MRS operational in the cluster.
You are finished with this task when status is "distributed, activated" and the next available action is Deactivate.
Step 2: Add MRS as a service
In Cloudera Manager home page, click the down arrow by the cluster name and choose Add Service.
Find and select Microsoft R Server and click Continue to start a wizard for adding services.
In the next page, add role assignments on all nodes used to run the service, both edge and data nodes. Click Continue.
On the last page, click Finish to start the service.
R Server should now be available to use.
Next Steps
Review the following walkthroughs to move forward with using R Server and the RevoScaleR package in Spark and MapReduce processing models.