Monitor health status

Vertex AI Workbench instances provide several methods for monitoring the health of your notebooks. This page describes how to use each method.

Methods for monitoring health status

You can monitor the health of your Vertex AI Workbench instances in a few different ways. This page describes how to use the following methods:

• Use guest attributes to report system health

• Report custom metrics to Cloud Monitoring

• Report system and application metrics to Monitoring by installing Monitoring on a Vertex AI Workbench instance

• Use the diagnostic tool

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. Enable the Notebooks API.

   Enable the API

5. Install the Google Cloud CLI.

6. To initialize the gcloud CLI, run the following command:

   gcloud init

7. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Go to project selector

8. Make sure that billing is enabled for your Google Cloud project.

9. Enable the Notebooks API.

   Enable the API

10. Install the Google Cloud CLI.

11. To initialize the gcloud CLI, run the following command:

    gcloud init

Use guest attributes to report system health

You can use guest attributes to report the system health of the following core services:

• Docker service
• Docker reverse proxy agent
• Jupyter service
• Jupyter API

Guest attributes are a specific type of custom metadata that applications can write to while running on your Vertex AI Workbench instance. To learn more about guest attributes, see About VM metadata.

How instances use guest attributes to report system health

The notebooks-collection-agent service runs a Python process in the background that verifies the status of the Vertex AI Workbench instance's core services and updates the guest attributes as either 1 if no problems are detected or -1 if a failure is detected.

To use the notebooks-collection-agent service to report on your Vertex AI Workbench instance's health, you must enable the following guest attributes while creating a Vertex AI Workbench instance:

• enable-guest-attributes=TRUE: This enables guest attributes on your Vertex AI Workbench instance. All new instances enable this attribute by default.
• report-event-health=TRUE: This records system health check results to your guest attributes.

The notebooks-collection-agent service doesn't need any special permissions to write to the instance's guest attributes.

Create a Vertex AI Workbench instance with system health guest attributes enabled

To use system health guest attributes to report on your Vertex AI Workbench instance's health, you must select the Enable system health report checkbox when you create a Vertex AI Workbench instance.

You can enable the system health report by using the Google Cloud console.

1. In the Google Cloud console, go to the Instances page.

   Go to Instances

2. Click add_box Create new.

3. In the New instance dialog, click Advanced options.

4. In the Create instance dialog, in the Details section, provide the following information for your new instance:

   • Name: Provide a name for your new instance.
   • Region and Zone: Select a region and zone for the new instance. For best network performance, select the region that is geographically closest to you.

5. In the System health section, in Reporting, select Report system health.

6. Complete the rest of the instance creation dialog, and then click Create.

Monitor system health through guest attributes

For Vertex AI Workbench instances that have the related guest attributes enabled, you can retrieve the values of your system health guest attributes by using either the Google Cloud console, the Google Cloud CLI with Compute Engine commands, or the Google Cloud CLI with Vertex AI Workbench commands.

gcloud compute instances get-guest-attributes INSTANCE_NAME \
    --zone ZONE

 NAMESPACE   KEY                         VALUE
 notebooks   docker_proxy_agent_status   1
 notebooks   docker_status               1
 notebooks   jupyterlab_api_status       1
 notebooks   jupyterlab_status           1
 notebooks   system-health               1
 notebooks   updated                     2023-06-20 17:00:00.12345

 NAMESPACE   KEY                         VALUE
 notebooks   docker_proxy_agent_status   -1
 notebooks   docker_status               -1
 notebooks   jupyterlab_api_status       1
 notebooks   jupyterlab_status           1
 notebooks   system-health               -1
 notebooks   updated                     2023-06-20 17:00:00.12345

Report custom metrics to Monitoring

Vertex AI Workbench instances let you collect system status and JupyterLab metrics and report them to Cloud Monitoring. These custom metrics are different from the standard metrics that are reported when you install Monitoring on your Vertex AI Workbench instance.

The custom metrics reported to Monitoring include the following:

• The system health of these Vertex AI Workbench core services:

  • Docker service
  • Docker reverse proxy agent
  • Jupyter service
  • Jupyter API

• The following JupyterLab metrics:

  • Number of kernels
  • Number of terminals
  • Number of connections
  • Number of sessions
  • Maximum memory
  • High memory
  • Current memory

How instances report custom metrics to Monitoring

To report custom metrics to Monitoring, you must enable the report-notebook-metrics metadata setting while creating a Vertex AI Workbench instance.

You must also make sure that the Vertex AI Workbench instance's service account has Monitoring Metric Writer (roles/monitoring.metricWriter) permissions. For more information, see Manage access to projects, folders, and organizations.

Create a Vertex AI Workbench instance that reports custom metrics to Monitoring

To report custom metrics to Monitoring, you must select the Report custom metrics to Cloud Monitoring checkbox when you create a Vertex AI Workbench instance.

You can enable reporting custom metrics to Cloud Monitoring by using the Google Cloud console.

1. In the Google Cloud console, go to the Instances page.

   Go to Instances

2. Click add_box Create new.

3. In the New instance dialog, click Advanced options.

4. In the Create instance dialog, in the Details section, provide the following information for your new instance:

   • Name: Provide a name for your new instance.
   • Region and Zone: Select a region and zone for the new instance. For best network performance, select the region that is geographically closest to you.

5. In the System health section, in Reporting, select Report custom metrics to Cloud Monitoring.

6. Complete the rest of the instance creation dialog, and then click Create.

Grant Monitoring Metric Writer permissions to the service account

After you've created your new Vertex AI Workbench instance, grant Monitoring Metric Writer permissions (roles/monitoring.metricWriter) to the service account for the Vertex AI Workbench instance. For more information, see Manage access to projects, folders, and organizations.

Monitor custom metrics through Monitoring

For Vertex AI Workbench instances that have reporting custom metrics enabled, you can monitor your custom metrics by using the Google Cloud console.

1. In the Google Cloud console, go to the Instances page.

   Go to Instances

2. Click the instance name that you want to view the custom metrics of.

3. On the Instance details page, click the Monitoring tab. Review the custom metrics for your instance.

Install Monitoring on an instance

This option automatically installs Monitoring. The installation requires 256 MB of disk space. An internet connection is required for the metrics to be reported to Monitoring.

How instances report system and application metrics

To report system and application metrics by installing Cloud Monitoring on your Vertex AI Workbench instance, you must select the Install Cloud Monitoring agent checkbox when you create a Vertex AI Workbench instance. These metrics are different from the custom metrics that are reported when you enable the report-notebook-metrics metadata setting.

Create a Vertex AI Workbench instance that reports system and application metrics to Monitoring

To install Monitoring on your Vertex AI Workbench instance, you can use the Google Cloud console.

1. In the Google Cloud console, go to the Instances page.

   Go to Instances

2. Click add_box Create new.

3. In the New instance dialog, click Advanced options.

4. In the Create instance dialog, in the Details section, provide the following information for your new instance:

   • Name: Provide a name for your new instance.
   • Region and Zone: Select a region and zone for the new instance. For best network performance, select the region that is geographically closest to you.

5. In the System health section, in Reporting, select Install Cloud Monitoring.

6. Complete the rest of the instance creation dialog, and then click Create.

Monitor system and application metrics through Monitoring

For Vertex AI Workbench instances that have Monitoring installed, you can monitor your system and application metrics by using the Google Cloud console:

1. In the Google Cloud console, go to the Instances page.

   Go to Instances

2. Click the instance name that you want to view the system and application metrics of.

3. On the Instance details page, click the Monitoring tab. Review the system and application metrics for your instance. To learn how to interpret these metrics, see Review resource metrics.

Use the diagnostic tool to monitor system health

Vertex AI Workbench instances include a built-in diagnostic tool that can help you monitor the system health of your instances.

Tasks performed by the diagnostic tool

The diagnostic tool performs the following tasks:

• Verifies the status of the following Vertex AI Workbench core services:

  • Docker service
  • Docker reverse proxy agent
  • Jupyter service
  • Jupyter API

• Checks whether the disk space for boot and data disks is used beyond an 85% threshold.

• Installs lsof (internet connection required).

• Collects the following instance logs:

  • Network information (ifconfig, netstat)
  • Logs in the /var/log/ folder
  • Docker status information
  • lsof (open files) data
  • Docker service status
  • Proxy reverse agent status
  • Jupyter service status
  • Jupyter API status
  • Proxy agent configuration file
  • Python processes

• Runs the following commands and collects the results:

  • pip freeze
  • conda list
  • gcloud compute instances describe INSTANCE_NAME
  • gcloud config list

Run the diagnostic tool

If your instance uses a custom container, see Run the diagnostic tool in the Vertex AI Workbench custom container documentation.

To run the diagnostic tool in an instance that doesn't use a custom container, complete the following steps:

1. Use ssh to connect to your Vertex AI Workbench instance.

2. In the SSH terminal, run the following commands:

   sudo -i
   cd /opt/deeplearning/bin/
   ./diagnostic_tool.sh

   The diagnostic tool collects the logs, compresses them in a .tar.gz file, and places the file in the /tmp/ folder.

3. Extract the file and then evaluate the contents. The contents include:

   • log folder: Logs from the var/log/ folder
   • report.log: Output for all commands collected
   • proxy-agent-config.json: Proxy configuration information
   • Docker log: A -json.log file that includes Docker container logs

You can use the following options with the diagnostic tool.

What's next

• Learn more about VM metadata.

• Learn more about Monitoring.