Updated Data Science Virtual Machine for Windows: GPU-enabled with Docker support

June 21, 2017
By

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

The Windows edition of the Data Science Virtual Machine (DSVM), the all-in-one virtual machine image with a wide-collection of open-source and Microsoft data science tools, has been updated to the Windows Server 2016 platform. This update brings built-in support for Docker containers and GPU-based deep learning. 

GPU-based Deep Learning. While prior editions of the DSVM could access GPU-based capabilities by installing additional components, everything is now configured and ready at launch. The DSVM now includes GPU-enabled builds of popular deep learning frameworks including CNTK, Tensorflow, and MXNET. It also includes Microsoft R Server 9.1, and several machine-learning functions in the MicrosoftML package can also take advantage of GPUs. Note that you will need to use an N-series Azure instance to benefit from GPU acceleration, but all of the tools in the DSVM will also work on regular CPU-based instances as well.

GPU screenshot

Docker Containers. Windows Server 2016 includes Windows Containers, and the DSVM also includes the Docker Engine. This means you can easily run Windows containers from Docker Hub or the Azure Container registry. This is limited to Windows-based containers right now, but you can use Linux-based containers on the Data Science Virtual Machine for Ubuntu Linux, which likewise supports GPU-enabled deep learning.

It's easy to launch a Windows 2016 DSVM on Azure, and the documentation will help you get started using it. (An Azure account is required, but free Azure credits are available to help get you started.) You can find more information about this update at the link below.

Cortana Intelligence and Machine Learning Blog: Introducing the new Data Science Virtual Machine on Windows Server 2016

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