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  • Azure Machine Learning monitoring

Azure Machine Learning monitoring

Dynatrace ingests metrics for multiple preselected namespaces, including Azure Machine Learning. You can view metrics for each service instance, split metrics into multiple dimensions, and create custom charts that you can pin to your dashboards.

Prerequisites

  • Dynatrace version 1.200+
  • Environment ActiveGate version 1.195+

Enable monitoring

To enable monitoring for this service, you first need to set up integration with Azure Monitor.

Add the service to monitoring

In order to view the service metrics, you must add the service to monitoring in your Dynatrace environment.

To add a service to monitoring
  1. In the Dynatrace menu, go to Settings > Cloud and virtualization and select Azure.
  2. On the Azure overview page, select Edit for the desired Azure instance.
  3. Go to Services and select Add service, choose the desired service name from the list, and select Add service.
  4. Select Save changes to save your configuration.

Monitor resources based on tags

You can choose to monitor resources based on existing Azure tags, as Dynatrace automatically imports them from service instances.

To monitor resources based on tags

  1. In the Dynatrace menu, go to Settings > Cloud and virtualization > Azure and select Edit for the desired Azure instance.
  2. For Resources to be monitored, select Monitor resources selected by tags.
  3. Enter the Key and Value.
  4. Select Save to save your configuration.

Note: To import the Azure tags automatically into Dynatrace, enable Capture Azure tags automatically.

Configure service metrics

Once you add a service, Dynatrace starts automatically collecting a suite of metrics for this particular service. These are recommended metrics.

Recommended metrics:

  • Are enabled by default
  • Can't be disabled
  • Can have recommended dimensions (enabled by default, can't be disabled)
  • Can have optional dimensions (disabled by default, can be enabled).

Apart from the recommended metrics, most services have the possibility of enabling optional metrics.

Optional metrics:

  • Can be added and configured manually
To add and configure metrics
  1. In the Dynatrace menu, go to Settings > Cloud and virtualization and select Azure.
  2. On the Azure overview page, scroll down and select Edit for the desired Azure instance.
  3. Go to Services and select Manage services.
  4. To add a metric, select the service for which you want to add metrics.
  5. Select Add new metric.
  6. From the menu, select the metric you want.
  7. Select Add metric to add the metric to monitoring.
  8. To configure a metric, select Edit.
  9. Select Apply to save your configuration.

View service metrics

You can view the service metrics in your Dynatrace environment either on the custom device overview page or on your Dashboards page.

View metrics on the custom device overview page

To access the custom device overview page

  1. In the Dynatrace menu, go to Technologies.
  2. Filter by service name and select the relevant custom device group.
  3. Once you select the custom device group, you're on the custom device group overview page.
  4. The custom device group overview page lists all instances (custom devices) belonging to the group. Select an instance to view the custom device overview page.

View metrics on your dashboard

Once you add a service to monitoring, a preset dashboard for the respective service containing all recommended metrics is automatically created on your Dashboards page. You can look for specific dashboards by filtering by Preset and then by Name.

Note: For existing monitored services, you might need to resave your credentials for the preset dashboard to appear on the Dashboards page. To resave your credentials, go to Settings > Cloud and virtualization > Azure, select the desired Azure instance, then select Save.

You can't make changes on a preset dashboard directly, but you can clone and edit it. To clone a dashboard, open the browse menu (…) and select Clone.
To remove a dashboard from the dashboards list, you can hide it. To hide a dashboard, open the browse menu (…) and select Hide.

Note: Hiding a dashboard doesn't affect other users.

Clone hide azure

Machine

Learning

Available metrics

NameDescriptionDimensionsUnitRecommended
Active CoresNumber of active cores.Scenario, ClusterNameCount✔️
Active NodesNumber of active nodes. These are the nodes which are actively running a job.Scenario, ClusterNameCount✔️
Cancel Requested RunsNumber of runs where cancel was requested for this workspace. Count is updated when cancellation request has been received for a run.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount
Cancelled RunsNumber of runs cancelled for this workspace. Count is updated when a run is successfully cancelled.Scenario, RunType, PublishedPipelineI, ComputeType, PipelineStepTypeCount
Completed RunsNumber of runs completed successfully for this workspace. Count is updated when a run has completed and output has been collected.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount✔️
CpuUtilizationPercentage of memory utilization on a CPU node. Utilization is reported at one minute intervals.Scenario, runId, NodeId, ClusterNamePercent✔️
ErrorsNumber of run errors in this workspace. Count is updated whenever run encounters an error.ScenarioCount✔️
Failed RunsNumber of runs failed for this workspace. Count is updated when a run fails.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount✔️
Finalizing RunsNumber of runs entered finalizing state for this workspace. Count is updated when a run has completed but output collection still in progress.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount✔️
GpuUtilizationPercentage of memory utilization on a GPU node. Utilization is reported at one-minute intervals.Scenario, runId, NodeId, DeviceId, ClusterNamePercent✔️
Idle CoresNumber of idle cores.Scenario, ClusterNameCount✔️
Idle NodesNumber of idle nodes. Idle nodes are the nodes which are not running any jobs but can accept new job if available.Scenario, ClusterNameCount✔️
Leaving CoresNumber of leaving coresScenario, ClusterNameCount✔️
Leaving NodesNumber of leaving nodes. Leaving nodes are the nodes which just finished processing a job and will go to Idle state.Scenario, ClusterNameCount✔️
Model Deploy FailedNumber of model deployments that failed in this workspace.Scenario, StatusCodeCount✔️
Model Deploy StartedNumber of model deployments started in this workspace.ScenarioCount✔️
Model Deploy SucceededNumber of model deployments that succeeded in this workspace.ScenarioCount✔️
Model Register FailedNumber of model registrations that failed in this workspace.Scenario, StatusCodeCount✔️
Model Register SucceededNumber of model registrations that succeeded in this workspace.ScenarioCount✔️
Not Responding RunsNumber of runs not responding for this workspace. Count is updated when a run enters Not Responding state.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount✔️
Not Started RunsNumber of runs in Not Started state for this workspace. Count is updated when a request is received to create a run but run information has not yet been populated.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount✔️
Preempted CoresNumber of preempted coresScenario, ClusterNameCount✔️
Preempted NodesNumber of preempted nodes. These nodes are the low priority nodes which are taken away from the available node pool.Scenario, ClusterNameCount✔️
Preparing RunsNumber of runs that are preparing for this workspace. Count is updated when a run enters Preparing state while the run environment is being prepared.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount
Provisioning RunsNumber of runs that are provisioning for this workspace. Count is updated when a run is waiting on compute target creation or provisioning.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount
Queued RunsNumber of runs that are queued for this workspace. Count is updated when a run is queued in compute target. Can occur when waiting for required compute nodes to be ready.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount✔️
Quota Utilization PercentagePercent of quota utilized.Scenario, ClusterName, VmFamilyName, VmPriorityPercent✔️
Started RunsNumber of runs running for this workspace. Count is updated when a run starts running on required resources.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount✔️
Starting RunsNumber of runs started for this workspace. Count is updated after request to create run and run info, such as the Run Id, has been populated.Scenario, RunType, PublishedPipelineId, ComputeType, PipelineStepTypeCount✔️
Total CoresNumber of total cores.Scenario, ClusterNameCount✔️
Total NodesNumber of total nodes. This total includes some of Active Nodes, Idle Nodes, Unusable Nodes, Preempted Nodes, Leaving Nodes.Scenario, ClusterNameCount✔️
Unusable CoresNumber of unusable cores.Scenario, ClusterNameCount✔️
Unusable NodesNumber of unusable nodes. Unusable nodes are not functional due to some unresolvable issue. Azure will recycle these nodes.Scenario, ClusterNameCount✔️
WarningsNumber of run warnings in this workspace. Count is updated whenever a run encounters a warning.ScenarioCount✔️