Edit

Monitor online endpoints

  • Article

Azure Machine Learning uses integration with Azure Monitor to track and monitor metrics and logs for online endpoints. You can view metrics in charts, compare between endpoints and deployments, pin to Azure portal dashboards, configure alerts, query from log tables, and push logs to supported targets. You can also use Application Insights to analyze events from user containers.

  • Metrics: For endpoint-level metrics such as request latency, requests per minute, new connections per second, and network bytes, you can drill down to see details at the deployment level or status level. Deployment-level metrics such as CPU/GPU utilization and memory or disk utilization can also be drilled down to instance level. Azure Monitor allows tracking these metrics in charts and setting up dashboards and alerts for further analysis.

  • Logs: You can send metrics to the Log Analytics workspace where you can query the logs using Kusto query syntax. You can also send metrics to Azure Storage accounts and/or Event Hubs for further processing. In addition, you can use dedicated log tables for online endpoint related events, traffic, and console (container) logs. Kusto query allows complex analysis and joining of multiple tables.

  • Application insights: Curated environments include integration with Application Insights, and you can enable or disable this integration when you create an online deployment. Built-in metrics and logs are sent to Application Insights, and you can use the built-in features of Application Insights (such as Live metrics, Transaction search, Failures, and Performance) for further analysis.

In this article you learn how to:

  • Choose the right method to view and track metrics and logs
  • View metrics for your online endpoint
  • Create a dashboard for your metrics
  • Create a metric alert
  • View logs for your online endpoint
  • Use Application Insights to track metrics and logs

Prerequisites

  • Deploy an Azure Machine Learning online endpoint.
  • You must have at least Reader access on the endpoint.

Metrics

You can view metrics pages for online endpoints or deployments in the Azure portal. An easy way to access these metrics pages is through links available in the Azure Machine Learning studio user interface—specifically in the Details tab of an endpoint's page. Following these links will take you to the exact metrics page in the Azure portal for the endpoint or deployment. Alternatively, you can also go into the Azure portal to search for the metrics page for the endpoint or deployment.

To access the metrics pages through links available in the studio:

  1. Go to the Azure Machine Learning studio.

  2. In the left navigation bar, select the Endpoints page.

  3. Select an endpoint by clicking its name.

  4. Select View metrics in the Attributes section of the endpoint to open up the endpoint's metrics page in the Azure portal.

  5. Select View metrics in the section for each available deployment to open up the deployment's metrics page in the Azure portal.

    A screenshot showing how to access the metrics of an endpoint and deployment from the studio UI.

To access metrics directly from the Azure portal:

  1. Sign in to the Azure portal.

  2. Navigate to the online endpoint or deployment resource.

    Online endpoints and deployments are Azure Resource Manager (ARM) resources that can be found by going to their owning resource group. Look for the resource types Machine Learning online endpoint and Machine Learning online deployment.

  3. In the left-hand column, select Metrics.

Available metrics

Depending on the resource that you select, the metrics that you see will be different. Metrics are scoped differently for online endpoints and online deployments.

Metrics at endpoint scope

  • Request Latency
  • Request Latency P50 (Request latency at the 50th percentile)
  • Request Latency P90 (Request latency at the 90th percentile)
  • Request Latency P95 (Request latency at the 95th percentile)
  • Requests per minute
  • New connections per second
  • Active connection count
  • Network bytes

Split on the following dimensions:

  • Deployment
  • Status Code
  • Status Code Class

For example, you can split along the deployment dimension to compare the request latency of different deployments under an endpoint.

Bandwidth throttling

Bandwidth will be throttled if the quota limits are exceeded for managed online endpoints. For more information on limits, see the article on limits for online endpoints. To determine if requests are throttled:

  • Monitor the "Network bytes" metric
  • The response trailers will have the fields: ms-azureml-bandwidth-request-delay-ms and ms-azureml-bandwidth-response-delay-ms. The values of the fields are the delays, in milliseconds, of the bandwidth throttling. For more information, see Bandwidth limit issues.

Metrics at deployment scope

  • CPU Utilization Percentage
  • Deployment Capacity (the number of instances of the requested instance type)
  • Disk Utilization
  • GPU Memory Utilization (only applicable to GPU instances)
  • GPU Utilization (only applicable to GPU instances)
  • Memory Utilization Percentage

Split on the following dimension:

  • Instance Id

For instance, you can compare CPU and/or memory utilization between difference instances for an online deployment.

Create dashboards and alerts

Azure Monitor allows you to create dashboards and alerts, based on metrics.

Create dashboards and visualize queries

You can create custom dashboards and visualize metrics from multiple sources in the Azure portal, including the metrics for your online endpoint. For more information on creating dashboards and visualizing queries, see Dashboards using log data and Dashboards using application data.

Create alerts

You can also create custom alerts to notify you of important status updates to your online endpoint:

  1. At the top right of the metrics page, select New alert rule.

    Screenshot showing 'New alert rule' button surrounded by a red box.

  2. Select a condition name to specify when your alert should be triggered.

    Screenshot showing 'Configure signal logic' button surrounded by a red box.

  3. Select Add action groups > Create action groups to specify what should happen when your alert is triggered.

  4. Choose Create alert rule to finish creating your alert.

For more information, see Create Azure Monitor alert rules.

Logs

There are three logs that can be enabled for online endpoints:

  • AmlOnlineEndpointTrafficLog: You could choose to enable traffic logs if you want to check the information of your request. Below are some cases:

    • If the response isn't 200, check the value of the column "ResponseCodeReason" to see what happened. Also check the reason in the "HTTPS status codes" section of the Troubleshoot online endpoints article.

    • You could check the response code and response reason of your model from the column "ModelStatusCode" and "ModelStatusReason".

    • You want to check the duration of the request like total duration, the request/response duration, and the delay caused by the network throttling. You could check it from the logs to see the breakdown latency.

    • If you want to check how many requests or failed requests recently. You could also enable the logs.

  • AmlOnlineEndpointConsoleLog: Contains logs that the containers output to the console. Below are some cases:

    • If the container fails to start, the console log can be useful for debugging.

    • Monitor container behavior and make sure that all requests are correctly handled.

    • Write request IDs in the console log. Joining the request ID, the AmlOnlineEndpointConsoleLog, and AmlOnlineEndpointTrafficLog in the Log Analytics workspace, you can trace a request from the network entry point of an online endpoint to the container.

    • You can also use this log for performance analysis in determining the time required by the model to process each request.

  • AmlOnlineEndpointEventLog: Contains event information regarding the container's life cycle. Currently, we provide information on the following types of events:

    Name Message
    BackOff Back-off restarting failed container
    Pulled Container image "<IMAGE_NAME>" already present on machine
    Killing Container inference-server failed liveness probe, will be restarted
    Created Created container image-fetcher
    Created Created container inference-server
    Created Created container model-mount
    LivenessProbeFailed Liveness probe failed: <FAILURE_CONTENT>
    ReadinessProbeFailed Readiness probe failed: <FAILURE_CONTENT>
    Started Started container image-fetcher
    Started Started container inference-server
    Started Started container model-mount
    Killing Stopping container inference-server
    Killing Stopping container model-mount

How to enable/disable logs

Important

Logging uses Azure Log Analytics. If you do not currently have a Log Analytics workspace, you can create one using the steps in Create a Log Analytics workspace in the Azure portal.

  1. In the Azure portal, go to the resource group that contains your endpoint and then select the endpoint.

  2. From the Monitoring section on the left of the page, select Diagnostic settings and then Add settings.

  3. Select the log categories to enable, select Send to Log Analytics workspace, and then select the Log Analytics workspace to use. Finally, enter a Diagnostic setting name and select Save.

    Screenshot of the diagnostic settings dialog.

    Important

    It may take up to an hour for the connection to the Log Analytics workspace to be enabled. Wait an hour before continuing with the next steps.

  4. Submit scoring requests to the endpoint. This activity should create entries in the logs.

  5. From either the online endpoint properties or the Log Analytics workspace, select Logs from the left of the screen.

  6. Close the Queries dialog that automatically opens, and then double-click the AmlOnlineEndpointConsoleLog. If you don't see it, use the Search field.

    Screenshot showing the log queries.

  7. Select Run.

    Screenshots of the results after running a query.

Example queries

You can find example queries on the Queries tab while viewing logs. Search for Online endpoint to find example queries.

Screenshot of the example queries.

Log column details

The following tables provide details on the data stored in each log:

AmlOnlineEndpointTrafficLog

Property Description
Method The requested method from client.
Path The requested path from client.
SubscriptionId The machine learning subscription ID of the online endpoint.
AzureMLWorkspaceId The machine learning workspace ID of the online endpoint.
AzureMLWorkspaceName The machine learning workspace name of the online endpoint.
EndpointName The name of the online endpoint.
DeploymentName The name of the online deployment.
Protocol The protocol of the request.
ResponseCode The final response code returned to the client.
ResponseCodeReason The final response code reason returned to the client.
ModelStatusCode The response status code from model.
ModelStatusReason The response status reason from model.
RequestPayloadSize The total bytes received from the client.
ResponsePayloadSize The total bytes sent back to the client.
UserAgent The user-agent header of the request, including comments but truncated to a max of 70 characters.
XRequestId The request ID generated by Azure Machine Learning for internal tracing.
XMSClientRequestId The tracking ID generated by the client.
TotalDurationMs Duration in milliseconds from the request start time to the last response byte sent back to the client. If the client disconnected, it measures from the start time to client disconnect time.
RequestDurationMs Duration in milliseconds from the request start time to the last byte of the request received from the client.
ResponseDurationMs Duration in milliseconds from the request start time to the first response byte read from the model.
RequestThrottlingDelayMs Delay in milliseconds in request data transfer due to network throttling.
ResponseThrottlingDelayMs Delay in milliseconds in response data transfer due to network throttling.

AmlOnlineEndpointConsoleLog

Property Description
TimeGenerated The timestamp (UTC) of when the log was generated.
OperationName The operation associated with log record.
InstanceId The ID of the instance that generated this log record.
DeploymentName The name of the deployment associated with the log record.
ContainerName The name of the container where the log was generated.
Message The content of the log.

AmlOnlineEndpointEventLog

Property Description
TimeGenerated The timestamp (UTC) of when the log was generated.
OperationName The operation associated with log record.
InstanceId The ID of the instance that generated this log record.
DeploymentName The name of the deployment associated with the log record.
Name The name of the event.
Message The content of the event.

Using Application Insights

Curated environments include integration with Application Insights, and you can enable or disable this integration when you create an online deployment. Built-in metrics and logs are sent to Application Insights, and you can use the built-in features of Application Insights (such as Live metrics, Transaction search, Failures, and Performance) for further analysis.

See Application Insights overview for more.

In the studio, you can use the Monitoring tab on an online endpoint's page to see high-level activity monitor graphs for the managed online endpoint. To use the monitoring tab, you must select Enable Application Insight diagnostic and data collection when you create your endpoint.

A screenshot of monitoring endpoint-level metrics in the studio.

Related content