Create a Data Science Virtual Machine
Activate the Learn sandbox
Use the Activate sandbox button in this module to activate the Learn sandbox. The activation creates an Azure resource group, a container for resources that relate to a single project domain. This resource group is available for one hour. Use the resource group to create a Data Science Virtual Machine, connect to it, and run programs.
Create a Data Science Virtual Machine
After you decide to use an Azure Data Science Virtual Machine (DSVM) to explore your domain, you must decide to use either Windows Server 2019 or Ubuntu Server 20.04 LTS as the operating system. You should also plan the hardware and network resources the DSVM needs.
Operating system choices
There are two DSVM images:
- Windows Server 2019
- Ubuntu Server 20.04 LTS
Both images contain various tools for data science and deep learning. Python programmers can use Visual Studio Code or PyCharm to program PyTorch, TensorFlow, and ONNX. R users can use CRAN-R. C#, TypeScript, and F# users can use Visual Studio Code. On the Windows image, they can use Visual Studio to program ML.NET, and SQL Server to store and retrieve their data.
Jupyter is installed on both machines with multiple language kernels. On the Ubuntu image, JupyterHub is installed to provide multi-user access.
Processor, RAM, and disk choices
Azure virtual machines come in various sizes, grouped by type:
| Type | Characteristics |
|---|---|
| General purpose | Balanced CPU-to-memory ratio. |
| GPU | Single or multiple GPUs. Best fit for statistical machine learning training and inferencing. |
| Memory optimized | High memory-to-CPU ratio. Good for in-memory analytics. |
| Storage optimized | High disk and I/O throughput. Good for big data. |
| High-performance compute | Powerful CPU machines. Good for tools with no GPU support. |
| Compute optimized | High CPU-to-memory ratio. Probably not a good fit for data science. GPU or high-performance compute is a preferred type. |
To take advantage of available GPU processing, the DSVMs are preconfigured with the drivers, library versions, and configurations.
Within a type, machines are typically associated with a letter-based series. For instance, GPU sizes typically begin with an "N": NC6, NC12s v3, NC24, NV12, and so on.
Before you create a DSVM, you should decide on a storage strategy. You can install the OS on a Premium SSD, a Standard SSD, or a Standard HDD. Additionally, you can either create or attach to one or more data disks. Again, you can choose the hard-drive technology, trading off the higher speed of SSDs against their cost per gigabyte.
You can resize your DSVM as your requirements change. For instance, you might choose a general-purpose or memory-optimized size when data acquisition and understanding are your primary focus. When modeling becomes the bottleneck, you can resize to a GPU offering.