# Scheduling Azure ML Compute Cluster

> Azure ML compute cluster schedules set the minimum node count to 0 at stop and restore the saved minimum at start. Idle nodes then deallocate on the cluster's own idle timeout. Running jobs are never killed; the schedule only changes where the autoscaler is allowed to settle.

Source: https://zop.dev/integrations/azure/ml-compute-cluster/schedule
Updated: 2026-08-19

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## A floor change, not a stop

Nothing here issues a stop verb. The schedule edits one number: the cluster's minimum node
count, set to 0 in the evening and back to its saved value in the morning. The AmlCompute
autoscaler does the actual work, deallocating nodes once they have sat idle for the cluster's
configured timeout. Of every mechanism in this directory, this is among the least invasive:
the schedule adjusts policy and the platform enforces it.

## Jobs are never interrupted

Because the mechanism is a floor and not a kill, a training job running at the stop time
simply keeps running; its node deallocates only after the job finishes and the idle timeout
passes. Teams nervous about evening cutoffs killing long experiments can relax here. The
worst case is a node that bills a few extra hours to finish real work, which is the correct
outcome.

## The idle timeout sets the lag

The stop takes effect gradually: min drops to 0 at 19:00, but nodes leave only after the idle
window (say 120 seconds to 30 minutes, depending on configuration) expires per node. A long
timeout delays the saving every single night. If the cluster's timeout was set generously to
keep nodes warm between rapid experiment iterations, consider whether that generosity should
apply at midnight.

## The restored minimum buys warm mornings

Restoring a nonzero minimum at 7am pre-provisions nodes before anyone submits, converting the
first job's cold-start wait into zero. Clusters whose minimum was already 0 around the clock
get nothing from the stop half of this schedule; for them the start-time pre-warm is the
entire value, and it is a real one on GPU SKUs with slow allocation.

## What deallocation leaves behind

Deallocated nodes stop compute billing. The workspace's storage account, container registry
and any attached datastores bill independently. Nothing about a compute floor touches those
meters.