How to add or remove R and Python packages on Machine Learning Server for Windows

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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

You can add open-source and third-party R and Python packages to the same local repository containing the product-specific packages for Machine Learning Server, allowing you to call functions from any library in the same script. Any packages you add to the repository must be compatible with the base R and Anaconda distributions upon which Microsoft's R and Python libraries are built.

Version requirements

Product-specific packages like RevoScaleR and revoscalepy are built on base libraries of R and Python respectively. Any new packages that you add must be compatible with the base libraries installed with the product.

Upgrading or downgrading the base R or Python libraries installed by setup is not supported. Microsoft's proprietary packages are built on specific distributions and versions of the base libraries. Substituting different versions of those libraries could destabilize your installation.

Base R is distributed through Microsoft R Open, as installed by Machine Learning Server or R Server. Python is distributed though Anaconda, also installed by Machine Learning server.

Product version R version Anaconda/Python version
9.4 3.5.2 Miniconda 4.5.12/3.7.1
9.3 3.4.3 4.2/3.5
9.2 3.4.1 4.2/3.5
9.1 3.3.3 not applicable

To verify the base library versions on your system, start a command-line tool and check the version information displayed when the tool starts.

  1. For R, start R.exe from \Program Files\Microsoft\ML Server\R_SERVER\bin\x64.

    Loading the R execution environment displays base R version information, similar to this output.

    R version 3.4.3 (2017-11-30) -- "Kite-Eating Tree" Copyright (C) 2017 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit)

  2. For Python, start Python.exe from \Program Files\Microsoft\ML Server\PYTHON_SERVER.

    Loading the R execution environment displays base R version information, similar to this output.

    Python 3.5.2 |Anaconda 4.2.0 (64-bit)| (default, Jul 5 2016, 11:41:13) [MSC v.1900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information.

Package location

On Windows, packages installed and used by Machine Learning Server can be found at these locations:

  • For R: C:\Program Files\Microsoft\ML Server\R_SERVER\library

  • For Python: C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\Lib\site-packages

Add or remove R packages

R packages tend to have with multiple dependencies so we generally recommend using a tool like miniCran. For more information and alternative methodologies, see R package management.

Add or remove Python packages

Run the following commands from an administrator prompt.

Using pip

# Add a package
cd C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\Scripts
pip install <packagename>

# Remove a package
cd C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\Scripts
pip uninstall <packagename>

Using conda

# Add a package
cd C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\Scripts
conda install <packagename>

# Remove a package
cd C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\Scripts
conda uninstall <packagename>

See also