Offline installation for Machine Learning Server for Windows

By default, installers connect to Microsoft download sites to get required and updated components for Machine Learning Server 9.2.1 for Windows. If firewall constraints prevent the installer from reaching these sites, you can use an internet-connected device to download files, transfer files to an offline server, and then run setup.

Before you start, review the following articles for requirements and restrictions:

Downloads

On an internet-connected computer, download all of the following files.

Component Download Used for
Machine Learning Server setup Get serversetup.exe from one of these sites:

Visual Studio Dev Essentials
pending - Volume Licensing Service Center (VLSC)
pending - MSDN subscription downloads
R Server
Pre-trained Models MLM_9.2.1.0_1033.cab Pre-trained models, R or Python
Microsoft R Open 3.4.1.0 SRO_3.4.1.0_1033.cab R
Microsoft Python Open SPO_9.2.1.0_1033.cab Python
Microsoft Python Server SPS_9.2.1.0_1033.cab Python
Tip

Run ServerSetup.exe /offline from the command line to get links for the .cab files used during intallation.

Transfer and place files

Use a tool or device to transfer the files to the offline server. Extract the zipped executable for setup. Place files in the following locations:

  • Put the unzipped serversetup.exe in a convenient folder. It is not important where this file resides.
  • Put the CAB files in the setup user's temp folder: C:\Users\<user-name>\AppData\Local\Temp.

Run setup

After files are placed, use the wizard or run setup from the command line:

Check log files

If there are errors during Setup, check the log files located in the system temp directory. An easy way to get there is typing %temp% as a Run command or search operation in Windows. If you installed all components, your log file list looks similar to this screenshot:

Machine Learning Server setup log files

Connect and validate

Machine Learning Server executes on demand as R Server or as a Python application. As a verification step, connect to each application and run a script or function.

For R

R Server runs as a background process, as Microsoft ML Server Engine in Task Manager. Server startup occurs when a client application like R Tools for Visual Studio or Rgui.exe connects to the server.

  1. Go to C:\Program Files\Microsoft\ML Server\R_SERVER\bin\x64.
  2. Double-click Rgui.exe to start the R Console application.
  3. At the command line, type search() to show preloaded objects, including the RevoScaleR package.
  4. Type print(Revo.version) to show the software version.
  5. Type rxSummary(~., iris) to return summary statistics on the built-in iris sample dataset. The rxSummary function is from RevoScaleR.

For Python

Python runs when you execute a .py script or run commands in a Python console window.

  1. Go to C:\Program Files\Microsoft\ML Server\PYTHON_SERVER.
  2. Double-click Python.exe.
  3. At the command line, type help() to open interactive help.
  4. Type revoscalepy at the help prompt, followed by microsoftml to print the function list for each module.
  5. Paste in the following revoscalepy script to return summary statistics from the built-in AirlineDemo demo data:

    import os
    import revoscalepy 
    sample_data_path = revoscalepy.RxOptions.get_option("sampleDataDir")
    ds = revoscalepy.RxXdfData(os.path.join(sample_data_path, "AirlineDemoSmall.xdf"))
    summary = revoscalepy.rx_summary("ArrDelay+DayOfWeek", ds)  
    print(summary)
    

    Output from the sample dataset should look similar to the following:

    Summary Statistics Results for: ArrDelay+DayOfWeek
    File name: /opt/microsoft/mlserver/9.2.1/libraries/PythonServer/revoscalepy/data/sample_data/AirlineDemoSmall.xdf
    Number of valid observations: 600000.0
    
            Name       Mean     StdDev   Min     Max  ValidObs  MissingObs
    0  ArrDelay  11.317935  40.688536 -86.0  1490.0  582628.0     17372.0
    
    Category Counts for DayOfWeek
    Number of categories: 7
    
                Counts
    DayOfWeek         
    1          97975.0
    2          77725.0
    3          78875.0
    4          81304.0
    5          82987.0
    6          86159.0
    7          94975.0
    

Next steps

We recommend starting with any Quickstart tutorial listed in the contents pane.

See also