Ubuntu Linux Data Science Virtual Machine is now available is now released on the
What is the Data Science Virtual Machine
The Data Science team will continue to support CentOS and Windows Data Science Virtual Machine ‘DSVM’.
The team have made some major enhancement to the DSVM offering with the Ubuntu version. We have found from feedback Ubuntu is overwhelmingly the most popular Linux distro among data scientists and academics.
So the team are very excited and happy to now offer Ubuntu as a core Linux DSVM platform with CPU and GPU Images available on the
The Data Science Virtual Machine family of VM images on Azure includes the
DSVM for Windows
CentOS-based DSVM for Linux
, and an
Ubuntu-based DSVM for Linux
. These images come with popular data science and machine learning tools, including Microsoft R Server Developer Edition, Microsoft R Open, Anaconda Python, Julia, Jupyter notebooks, Visual Studio Code, RStudio, xgboost, and many more. A full list of tools for all editions of the DSVM is available
The DSVM has proven popular with many Academic data scientists as it helps them focus on teaching, learning and research and avoid delay due to IT Support and mundane steps around tool installation and configuration. The use of the DSVM has additionally helped many Institutions IT teams as it reduce the amount of support required in lab setup and preparation.
In addition to all the data science tools you love, you now have a choice of deep learning tools (CNTK, Tensorflow, MxNet, Caffe/Caffe2, Torch, Theano, Keras, NVidia Digits) on the Ubuntu version.
Are now available so you can deploy the same DSVM on a GPU VM (Azure NC-Series) or a CPU-Only VM. You just fall back to using the CPU when running the deep learning tools on CPU-only hardware. NVidia Drivers, CUDA etc are all on the VM image by default. So you can just get started with deep learning in matter of minutes. No need to download the framework source, fight with compiling those tools and installing the dependencies.
The Data Science team have also been working with with Facebook to be among the five partners featured in their announcement of open sourcing their latest deep learning framework called Caffe2 (a rewrite of Caffe) at their F8 conference.
You can login in to it with SSH client like Putty, SSH command line OR use X2GO for graphical interface. Jupyter/Jupyterhub, RStudio Server are available.
A side by side comparison of tools preinstalled on different editions Windows and Linux of the DSVM can be found
So I really look forward to how academica are using this and please do share your usage, support and feedback to help the Data Science team keep improving the DSVM and make it the best analytics development environment anywhere on the cloud. Please send any questions to or feedback to the