![]() Low priority VMs have a separate vCPU quota that differs from the one for dedicated VMs.If VMs are preempted or unavailable, batch deployment jobs attempt to replace the lost capacity by queuing the failed tasks to the cluster. Batch deployment jobs automatically seek the target number of VMs in the available compute cluster based on the number of tasks to submit.Once a deployment is associated with a low priority VMs' cluster, all the jobs produced by such deployment will use low priority VMs. Batch deployment jobs consume low priority VMs by running on Azure Machine Learning compute clusters created with low priority VMs.How batch deployment works with low priority VMsĪzure Machine Learning Batch Deployments provides several capabilities that make it easy to consume and benefit from low priority VMs: For pricing details, see Azure Machine Learning pricing. Low priority VMs are offered at a significantly reduced price compared with dedicated VMs. For this reason, they are most suitable for batch and asynchronous processing workloads where the job completion time is flexible and the work is distributed across many VMs. The tradeoff for using them is that those VMs may not always be available to be allocated, or may be preempted at any time, depending on available capacity. ![]() ![]() When you specify low priority VMs in your pools, Azure can use this surplus, when available. Low priority VMs take advantage of surplus capacity in Azure. Low priority VMs enable a large amount of compute power to be used for a low cost. APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)Īzure Batch Deployments supports low priority VMs to reduce the cost of batch inference workloads.
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