Discussed Low-Pri VMs

Author: lobrien

articles/machine-learning/how-to-set-up-training-targets.md

@@ -89,10 +89,12 @@ Now that you've attached the compute and configured your run, the next step is t
 
 Azure Machine Learning Compute is a managed-compute infrastructure that allows the user to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users in your workspace. The compute scales up automatically when a job is submitted, and can be put in an Azure Virtual Network. The compute executes in a containerized environment and packages your model dependencies in a [Docker container](https://www.docker.com/why-docker).
 
-You can use Azure Machine Learning Compute to distribute the training process across a cluster of CPU or GPU compute nodes in the cloud. For more information on the VM sizes that include GPUs, see [GPU-optimized virtual machine sizes](https://docs.microsoft.com/azure/virtual-machines/linux/sizes-gpu).
+You can use Azure Machine Learning Compute to distribute the training process across a cluster of CPU or GPU compute nodes in the cloud. For more information on the VM sizes that include GPUs, see [GPU-optimized virtual machine sizes](https://docs.microsoft.com/azure/virtual-machines/linux/sizes-gpu). 
 
 Azure Machine Learning Compute has default limits, such as the number of cores that can be allocated. For more information, see [Manage and request quotas for Azure resources](https://docs.microsoft.com/azure/machine-learning/how-to-manage-quotas).
 
+You may also choose to use low-priority VMs to run some or all of your workloads. These VMs do not have guaranteed availability and may be pre-empted while in use. A pre-empted job is restarted, not resumed.  Low-priority VMs have discounted rates compared to normal VMs, see [Plan and manage costs](https://docs.microsoft.com/azure/machine-learning/concept-plan-manage-cost).
+
 > [!TIP]
 > Clusters can generally scale up to 100 nodes as long as you have enough quota for the number of cores required. By default clusters are setup with inter-node communication enabled between the nodes of the cluster to support MPI jobs for example. However you can scale your clusters to 1000s of nodes by simply [raising a support ticket](https://portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade/newsupportrequest), and requesting to whitelist your subscription, or workspace, or a specific cluster for disabling inter-node communication.