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Chapter 4. Monitoring model performance

4.1. Viewing performance metrics for all models on a model server
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In OpenShift AI, you can monitor the following metrics for all the models that are deployed on a model server:

  • HTTP requests - The number of HTTP requests that have failed or succeeded for all models on the server.

    Note: You can also view the number of HTTP requests that have failed or succeeded for a specific model, as described in Viewing HTTP request metrics for a deployed model.

  • Average response time (ms) - For all models on the server, the average time it takes the model server to respond to requests.
  • CPU utilization (%) - The percentage of the CPU’s capacity that is currently being used by all models on the server.
  • Memory utilization (%) - The percentage of the system’s memory that is currently being used by all models on the server.

You can specify a time range and a refresh interval for these metrics to help you determine, for example, when the peak usage hours are and how the models are performing at a specified time.

Prerequisites

  • You have installed Red Hat OpenShift AI.
  • On the OpenShift cluster where OpenShift AI is installed, user workload monitoring is enabled.
  • You have logged in to OpenShift AI.
  • If you are using specialized OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins) in OpenShift.
  • There are deployed data science models in your data science project.

Procedure

  1. From the OpenShift AI dashboard navigation menu, click Data Science Projects and then select the project that contains the data science models that you want to monitor.
  2. On the Components page, scroll down to the Models and model servers section.
  3. In the row for the model server that you are interested in, click the action menu (⋮) and then select View model server metrics.

  4. Optional: On the metrics page for the model server, set the following options:

    • Time range - Specifies how long to track the metrics. You can select one of these values: 1 hour, 24 hours, 7 days, and 30 days.
    • Refresh interval - Specifies how frequently the graphs on the metrics page are refreshed (to show the latest data). You can select one of these values: 15 seconds, 30 seconds, 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, 2 hours, and 1 day.
  5. Scroll down to view data graphs for HTTP requests, average response time, CPU utilization, and memory utilization.

Verification

On the metrics page for the model server, the graphs provide performance metric data.

4.2. Viewing HTTP request metrics for a deployed model
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You can view a graph that illustrates the HTTP requests that have failed or succeeded for a specific model.

Prerequisites

  • You have installed Red Hat OpenShift AI.
  • On the OpenShift cluster where OpenShift AI is installed, user workload monitoring is enabled.
  • You have logged in to OpenShift AI.
  • If you are using specialized OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins) in OpenShift.
  • You have deployed a model in a data science project.

Procedure

  1. From the OpenShift AI dashboard navigation menu, select Model Serving.
  2. On the Deployed models page, select the model that you are interested in.

  3. Optional: On the Endpoint performance tab, set the following options:

    • Time range - Specifies how long to track the metrics. You can select one of these values: 1 hour, 24 hours, 7 days, and 30 days.
    • Refresh interval - Specifies how frequently the graphs on the metrics page are refreshed (to show the latest data). You can select one of these values: 15 seconds, 30 seconds, 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, 2 hours, and 1 day.

Verification

The Endpoint performance tab shows a graph of the HTTP metrics for the model.

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