Offline installation for Machine Learning Server for Windows
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: Machine Learning Server 9.2.1 | 9.3 | 9.4
By default, installers connect to Microsoft download sites to get required and updated components for Machine Learning Server 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 article for requirements and general information about setup: Install Machine Learning Server on Windows.
9.4.7 Downloads
On an internet-connected computer, download all of the following files.
| Component | Download | Used for |
|---|---|---|
| Machine Learning Server setup | Get Machine Learning Server for Windows (en_machine_learning_server_for_windows_x64_.zip) from Volume Licensing Service Center (VLSC) | ML Server |
| MLM | MLM_9.4.7.0_1033.cab | Pre-trained models, R or Python |
| Microsoft R Open | SRO_3.5.2.0_1033.cab | R |
| Microsoft Python Open | SPO_4.5.12.0_1033.cab (see note below) | Python |
| Microsoft Python Server | SPS_9.4.7.0_1033.cab | Python |
| Python script | Install-PyForMLS | Python |
Note
If you are performing offline installation using the Python script Install-PyForMLS.ps1, then after you download SPO_4.5.12.0_1033.cab, rename the file to SPO_9.4.7.0_1033.cab. The installation script expects this filename.
9.3.0 Downloads
On an internet-connected computer, download all of the following files.
| Component | Download | Used for |
|---|---|---|
| Machine Learning Server setup | Get Machine Learning Server for Windows (en_machine_learning_server_for_windows_x64_.zip) from Volume Licensing Service Center (VLSC) | R Server |
| Pre-trained Models | MLM_9.3.0.0_1033.cab | Pre-trained models, R or Python |
| Microsoft R Open 3.4.3.0 | SRO_3.4.3.0_1033.cab | R |
| Microsoft Python Open | SPO_9.3.0.0_1033.cab | Python |
| Microsoft Python Server | SPS_9.3.0.0_1033.cab | Python |
| Python script | Install-PyForMLS | Python |
9.2.1 Downloads
If you require the previous version, use these links instead.
| Component | Download | Used for |
|---|---|---|
| Machine Learning Server setup | Get Machine Learning Server for Windows (en_machine_learning_server_for_windows_x64_.zip) from Volume Licensing Service Center (VLSC) | R Server |
| Pre-trained Models | MLM_9.2.1.0_1033.cab | Pre-trained models, R or Python |
| Microsoft R Open 3.4.3.0 | 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 |
| Python script | Install-PyForMLS | Python |
Transfer and place files
Use a tool or device to transfer the files to the offline server.
- Put the unzipped en_machine_learning_server_for_windows_x64_.zip file in a convenient folder.
- Right-click Extract All to unpack the file. You should see a folder named MLS93Win. This folder contains ServerSetup.exe.
- Put the CAB files in the setup user's temp folder: C:\Users<user-name>\AppData\Local\Temp.
Tip
On the offline server, run ServerSetup.exe /offline from the command line to get links for the .cab files used during installation. The list of .cab files appears in the installation wizard, after you select which components to install.
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:
Set environment variables
Create an MKL_CBWR environment variable to ensure consistent output from Intel Math Kernel Library (MKL) calculations.
In Control Panel, click System and Security > System > Advanced System Settings > Environment Variables.
Create a new User or System variable.
- Set variable name to
MKL_CBWR - Set the variable value to
AUTO
This step requires a server restart.
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 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 theRevoScaleRpackage. - Type
print(Revo.version)to show the software version. - Type
rxSummary(~., iris)to return summary statistics on the built-in iris sample dataset. TherxSummaryfunction is fromRevoScaleR.
For Python
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.Type
revoscalepyat the help prompt, followed bymicrosoftmlto 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/9.4.0/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
Verify CLI
Note
Before you continue, reboot the machine.
Open an Administrator command prompt.
Enter the following command to check availability of the CLI:
az ml admin --help. If you receive the following error:az: error argument _command_package: invalid choice: ml, follow the instructions to re-add the extension to the CLI.
Next steps
We recommend starting with any Quickstart tutorial listed in the contents pane.