What is the Azure Data Science Virtual Machine for Linux and Windows?

The Data Science Virtual Machine (DSVM) is a customized VM image available on the Azure cloud platform, and it can handle data science. It has many popular data science tools preinstalled and preconfigured to jump-start building intelligent applications for advanced analytics.

The DSVM is available on:

  • Windows Server 2019
  • Windows Server 2022
  • Ubuntu 20.04 LTS

Additionally, we offer Azure DSVM for PyTorch - an Ubuntu 20.04 image from Azure Marketplace optimized for large, distributed deep learning workloads. This preinstalled DSVM comes validated with the latest PyTorch version, to reduce setup costs and accelerate time to value. It comes packaged with various optimization features:

  • ONNX Runtime​
  • DeepSpeed​
  • MSCCL​
  • ORTMoE​
  • Fairscale​
  • Nvidia Apex​
  • An up-to-date stack with the latest compatible versions of Ubuntu, Python, PyTorch, and CUDA

Comparison with Azure Machine Learning

The DSVM is a customized VM image for Data Science, but Azure Machine Learning is an end-to-end platform that covers:

  • Fully Managed Compute
    • Compute Instances
    • Compute Clusters for distributed ML tasks
    • Inference Clusters for real-time scoring
  • Datastores (for example Blob, ADLS Gen2, SQL DB)
  • Experiment tracking
  • Model management
  • Notebooks
  • Environments (manage conda and R dependencies)
  • Labeling
  • Pipelines (automate End-to-End Data science workflows)

Comparison with Azure Machine Learning Compute Instances

Azure Machine Learning Compute Instances are a fully configured and managed VM image, while the DSVM is an unmanaged VM.

Key differences between a DSVM and an Azure Machine Learning compute instance:

Feature Data Science
Azure Machine Learning
Compute Instance
Fully Managed No Yes
Language Support Python, R, Julia, SQL, C#,
Java, Node.js, F#
Python and R
Operating System Ubuntu
Pre-Configured GPU Option Yes Yes
Scale up option Yes Yes
SSH Access Yes Yes
RDP Access Yes No
Hosted Notebooks
(requires additional configuration)
Built-in SSO No
(requires additional configuration)
Built-in Collaboration No Yes
Preinstalled Tools Jupyter(lab), VS Code,
Visual Studio, PyCharm, Juno,
Power BI Desktop, SSMS,
Microsoft Office 365, Apache Drill

Sample DSVM customer use cases

Short-term experimentation and evaluation

The DSVM can evaluate or learn new data science tools. Try some of our published samples and walkthroughs.

Deep learning with GPUs

In the DSVM, your training models can use deep learning algorithms on graphics processing unit (GPU)-based hardware. If you take advantage of the VM scaling capabilities of the Azure platform, the DSVM helps you lever GPU-based hardware in the cloud, according to your needs. You can switch to a GPU-based VM when you train large models, or when you need high-speed computations while you keep the same OS disk. You can choose any of the N series GPU-enabled virtual machine SKUs with DSVM. Azure free accounts don't support GPU-enabled virtual machine SKUs.

A Windows-edition DSVM comes preinstalled with GPU drivers, frameworks, and GPU versions of deep learning frameworks. On the Linux editions, deep learning on GPUs is enabled on the Ubuntu DSVMs.

You can also deploy the Ubuntu or Windows DSVM editions to an Azure virtual machine that isn't based on GPUs. In this case, all the deep learning frameworks fall back to the CPU mode.

Learn more about available deep learning and AI frameworks.

Data science training and education

Enterprise trainers and educators who teach data science classes usually provide a virtual machine image. The image ensures that students both have a consistent setup and that the samples work predictably.

The DSVM creates an on-demand environment with a consistent setup, to ease the support and incompatibility challenges. Cases where these environments need to be built frequently, especially for shorter training classes, benefit substantially.

What does the DSVM include?

For more information, see this full list of tools on both Windows and Linux DSVMs.

Next steps

For more information, visit these resources: