Using a Data Science Virtual Machine

If you have been provisioned a Data Science Virtual Machine (DSVM) as a Virtual Machine, it will arrive in your workspace with the most common Data Science tools already installed and ready to go. You can still install your own software, and everything will work in the same way as a standard Virtual Machine.

You can read more about how to access your Virtual Machine here and how to use your Virtual Machine here.

Performance and Compute power

The standard set of specifications for a DSVM are 4 vCPU, 16 GB memory, and 28 GB temp storage. Should you require additional power for you project, a higher spec machine can be provided: see this article for details. Some features of the DSVM may require additional computing power, for example when GPU is added, it can take advantage of the preinstalled libraries and tools for running Machine Learning tasks.

If you run into any issues or have any questions about Data Science Virtual Machines, please contact your Workspace Administrator or Aridhia Service Desk.

What’s included in your Data science Virtual Machine

You can find the full list of tools available in a DSVM, as well as more information about each specific tool on Microsoft's website.

Please note that Visual Studio 2019 community, Microsoft Teams, and Microsoft365 will not be included in a Windows DSVM due to licensing issues.