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:
- Install Machine Learning Server 9.2.1 on Windows for an internet-connected installation.
- Command line installation for a scripted installation.
On an internet-connected computer, download all of the following files.
|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
|Pre-trained Models||MLM_184.108.40.206_1033.cab||Pre-trained models, R or Python|
|Microsoft R Open 220.127.116.11||SRO_18.104.22.168_1033.cab||R|
|Microsoft Python Open||SPO_22.214.171.124_1033.cab||Python|
|Microsoft Python Server||SPS_126.96.36.199_1033.cab||Python|
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:
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:
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.
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.
- Go to C:\Program Files\Microsoft\ML Server\R_SERVER\bin\x64.
- Double-click Rgui.exe to start the R Console application.
- At the command line, type
search()to show preloaded objects, including the
print(Revo.version)to show the software version.
rxSummary(~., iris)to return summary statistics on the built-in iris sample dataset. The
rxSummaryfunction is from
Python runs when you execute a .py script or run commands in a Python console window.
- Go to C:\Program Files\Microsoft\ML Server\PYTHON_SERVER.
- Double-click Python.exe.
- At the command line, type
help()to open interactive help.
revoscalepyat the help prompt, followed by
microsoftmlto print the function list for each module.
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
We recommend starting with any Quickstart tutorial listed in the contents pane.