Creating a Low Priority Server in ML Studio

Question

What are the steps to create a low priority server in ML Studio? Is it possible to create one? Please provide any documentation or code snippets that may be helpful.

Answer 1

Hello @Miranda Ntimane

Thanks for reaching out to us, Azure allows you to use excess unutilized capacity as Low-Priority VMs across virtual machine scale sets, Batch, and the Machine Learning service. These allocations are pre-emptible but come at a reduced price compared to dedicated VMs. In general, we recommend using Low-Priority VMs for Batch workloads. You should also use them where interruptions are recoverable either through resubmits (for Batch Inferencing) or through restarts (for deep learning training with checkpointing).

Low-Priority VMs have a single quota separate from the dedicated quota value, which is by VM family. Learn more about AmlCompute quotas.

Low-Priority VMs don't work for compute instances, since they need to support interactive notebook experiences.

There is a one example of how to create a low-priority VM, please refer to the document - https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch?view=azureml-api-2&tabs=cli#creating-batch-deployments-with-low-priority-vms

compute_name = "low-pri-cluster"
compute_cluster = AmlCompute(
   name=compute_name, 
   description="Low priority compute cluster", 
   min_instances=0, 
   max_instances=2,
   tier='LowPriority'
)
 
ml_client.begin_create_or_update(compute_cluster)

Please have a try and let me know how it goes. I hope this helps.

Regards,

Yutong

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