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Languages supported on the Data Science Virtual Machine

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The Data Science Virtual Machine (DSVM) comes with several pre-built languages and development tools for building your artificial intelligence (AI) applications. Here are some of the notable ones.

Python

Category Value
Language versions supported Python 3.8
Supported DSVM editions Windows Server 2019, Linux
How is it configured / installed on the DSVM? There is multiple conda environments whereby each of these has different Python packages pre-installed. To list all available environments in your machine, run conda env list.

How to use and run it

  • Run at a command prompt:

    Open a command prompt and use one of the following methods, depending on the version of Python you want to run:

    conda activate <conda_environment_name>
python --version

  • Use in an IDE:

    The DSVM images have several IDEs installed such as VS.Code or PyCharm. You can use them to edit, run and debug your Python scripts.

  • Use in Jupyter Lab:

    Open a Launcher tab in Jupyter Lab and select the type and kernel of your new document. If you want your document to be placed in a certain folder, navigate to that folder in the File Browser on the left side first.

  • Install Python packages:

    To install a new package, you need to activate the right environment first. The environment is the place where your new package will be installed, and the package will then only be available in that environment.

    To activate an environment, run conda activate <environment_name>. Once the environment is activated, you can use a package manager like conda or pip to install or update a package.

    As an alternative, if you are using Jupyter, you can also install packages directly by running !pip install --upgrade <package_name> in a cell.

R

Category Value
Language versions supported CRAN R 4.0.5
Supported DSVM editions Linux, Windows

How to use and run it

  • Run at a command prompt:

    Open a command prompt and type R.

  • Use in Jupyter Lab

    Open a Launcher tab in Jupyter Lab and select the type and kernel of your new document. If you want your document to be placed in a certain folder, navigate to that folder in the File Browser on the left side first.

  • Install R packages:

    You can install new R packages by using the install.packages() function.

Julia

Category Value
Language versions supported 1.0.5
Supported DSVM editions Linux, Windows

How to use and run it

  • Run at a command prompt

    Open a command prompt and run julia.

  • Use in Jupyter:

    Open a Launcher tab in Jupyter and select the type and kernel of your new document. If you want your document to be placed in a certain folder, navigate to that folder in the File Browser on the left side first.

  • Install Julia packages:

    You can use Julia package manager commands like Pkg.add() to install or update packages.

Other languages

C#: Available on Windows and accessible through the Visual Studio Community edition or at the Developer Command Prompt for Visual Studio, where you can run the csc command.

Java: OpenJDK is available on both the Linux and Windows editions of the DSVM and is set on the path. To use Java, type the javac or java command at a command prompt in Windows or on the bash shell in Linux.

Node.js: Node.js is available on both the Linux and Windows editions of the DSVM and is set on the path. To access Node.js, type the node or npm command at a command prompt in Windows or on the bash shell in Linux. On Windows, the Visual Studio extension for the Node.js tools is installed to provide a graphical IDE to develop your Node.js application.

F#: Available on Windows and accessible through the Visual Studio Community edition or at a Developer Command Prompt for Visual Studio, where you can run the fsc command.