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Manual package installation (Microsoft R Server for Linux or Hadoop)

  • Straipsnis

An alternative to running the install.sh script at the command line is to manually install each package and component.

Syntax

With root privilege, you can use a package manager to install each package in the R Server distribution. The following example illustrates the syntax.

  • On RHEL: rpm -i microsoft-r-open-mro-3.3.x86_64
  • On Ubuntu: apt-get install microsoft-r-open-mro-3.3
  • On SUSE: zypper install microsoft-r-open-mro-3.3

Prerequisites

A manual package installation is similar to an offline installation. As a first step, review the instructions for offline installation for system prerequisites and for downloading and unpacking the distribution.

After you unpack the distribution, you should see packages for RPM and DEB in the /tmp/MRS91Hadoop directory.

Steps

  1. Log in as root or as a user with super user privileges (sudo -s). The following instructions assume user privileges with the sudo override.

  2. Use a package manager to verify system repositories are up-to-date:

    • On RHEL use yum: [root@localhost tmp] $ yum expire-cache
    • On Ubuntu use apt-get: [root@localhost tmp] $ apt-get autoclean

  3. Install the .NET Core package from https://www.microsoft.com/net/core. This component is required for machine learning, remote execution, web service deployment, and configuration of web and compute nodes.

  4. Install Microsoft R Server packages. You should have the following packages, which should be installed in this order:

    microsoft-r-open-mro-3.3.3.x86_64.rpm microsoft-r-server-packages-9.1.rpm microsoft-r-server-hadoop-9.1.rpm microsoft-r-server-mml-9.1.rpm microsoft-r-server-mlm-9.1.rpm microsoft-r-server-config-rserve-9.1.rpm microsoft-r-server-computenode-9.1.rpm microsoft-r-server-webnode-9.1.rpm microsoft-r-server-adminutil-9.1.rpm

Create folders and set permissions

A manual or custom installation must create the appropriate folders and set permissions.

RPM or DEB Installers

  • /var/RevoShare/ and hdfs://user/RevoShare must exist and have folders for each user running Microsoft R Server in Hadoop or Spark.
  • /var/RevoShare/<user> and hdfs://user/RevoShare/<user> must have full permissions (all read, write, and executive permissions for all users).

Cloudera Parcel Installers

  1. Create /var/RevoShare/ and hdfs://user/RevoShare. Parcels cannot create them for you.
  2. Give /var/RevoShare/<user> and hdfs://user/RevoShare/<user> for each user.
  3. Grant full permissions to both /var/RevoShare/<user> and hdfs://user/RevoShare/<user>.

Install additional packages on each node using rxExec

Once you have R Server installed on a node, you can use the rxExec function in RevoScaleR to install additional packages, including third-party packages from CRAN or another repository.

For example, to install the SuppDists package on all the nodes of your cluster, call rxExec as follows:

rxExec(install.packages, "SuppDists")

Enable Remote Connections and Analytic Deployment

The server can be used as-is if you install and use an R IDE on the same box, but to benefit from the deployment and consumption of web services with Microsoft R Server, then you must configure R Server after installation to act as a deployment server and host analytic web services. Possible configurations are a one-box setup or an enterprise setup. Doing so also enables remote execution, allowing you to connect to R Server from an R Client workstation and execute code on the server.

Next Steps

Review the following walkthroughs to move forward with using R Server and the RevoScaleR package in Spark and MapReduce processing models.