Create a shared pool of Data Science Virtual Machines
In this article, you'll learn how to create a shared pool of Data Science Virtual Machines (DSVMs) for a team. The benefits of using a shared pool include better resource utilization, easier sharing and collaboration, and more effective management of DSVM resources.
You can use many methods and technologies to create a pool of DSVMs. This article focuses on pools for interactive virtual machines (VMs). An alternative managed compute infrastructure is Azure Machine Learning Compute. For more information, see Create compute cluster.
Interactive VM pool
A pool of interactive VMs that are shared by the whole AI/data science team allows users to log in to an available instance of the DSVM instead of having a dedicated instance for each set of users. This setup enables better availability and more effective utilization of resources.
You use Azure virtual machine scale sets technology to create an interactive VM pool. You can use scale sets to create and manage a group of identical, load-balanced, and autoscaling VMs.
The user logs in to the main pool's IP or DNS address. The scale set automatically routes the session to an available DSVM in the scale set. Because users want a consistent and familiar environment regardless of the VM they're logging in to, all instances of the VM in the scale set mount a shared network drive, like an Azure Files share or a Network File System (NFS) share. The user's shared workspace is normally kept on the shared file store that's mounted on each of the instances.
You can find a sample Azure Resource Manager template that creates a scale set with Ubuntu DSVM instances on GitHub. You'll find a sample of the parameter file for the Azure Resource Manager template in the same location.
You can create the scale set from the Azure Resource Manager template by specifying values for the parameter file in the Azure CLI:
az group create --name [[NAME OF RESOURCE GROUP]] --location [[ Data center. For eg: "West US 2"]
az deployment group create --resource-group [[NAME OF RESOURCE GROUP ABOVE]] --template-uri https://raw.githubusercontent.com/Azure/DataScienceVM/master/Scripts/CreateDSVM/Ubuntu/dsvm-vmss-cluster.json --parameters @[[PARAMETER JSON FILE]]
The preceding commands assume you have:
- A copy of the parameter file with the values specified for your instance of the scale set.
- The number of VM instances.
- Pointers to the Azure Files share.
- Credentials for the storage account that will be mounted on each VM.
The parameter file is referenced locally in the commands. You can also pass parameters inline or prompt for them in your script.
The preceding template enables the SSH and the JupyterHub port from the front-end scale set to the back-end pool of Ubuntu DSVMs. As a user, you log in to the VM on a Secure Shell (SSH) or on JupyterHub in the normal way. Because the VM instances can be scaled up or down dynamically, any state must be saved in the mounted Azure Files share. You can use the same approach to create a pool of Windows DSVMs.
The script that mounts the Azure Files share is also available in the Azure DataScienceVM repository in GitHub. The script mounts the Azure Files share at the specified mount point in the parameter file. The script also creates soft links to the mounted drive in the initial user's home directory. A user-specific notebook directory in the Azure Files share is soft-linked to the $HOME/notebooks/remote directory so that users can access, run, and save their Jupyter notebooks. You can use the same convention when you create additional users on the VM to point each user's Jupyter workspace to the Azure Files share.
Virtual machine scale sets support autoscaling. You can set rules about when to create additional instances and when to scale down instances. For example, you can scale down to zero instances to save on cloud hardware usage costs when the VMs are not used at all. The documentation pages of virtual machine scale sets provide detailed steps for autoscaling.