Monitor health status
Vertex AI Workbench user-managed notebooks is deprecated . On January 30, 2025, support for user-managed notebooks will end and the ability to create user-managed notebooks instances will be removed. Existing instances will continue to function but patches, updates, and upgrades won't be available. To continue using Vertex AI Workbench, we recommend that you migrate your user-managed notebooks instances to Vertex AI Workbench instances .
User-managed notebooks 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 user-managed notebooks instances in a few different ways. This page describes how to use the following methods:
Set up the gcloud CLI
To complete some of the steps on this page, you need to use the Google Cloud CLI.Install the Google Cloud CLI, then initialize it by running 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 user-managed notebooks 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 user-managed notebooks
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 user-managed notebooks instance's health,
you must enable the following guest attributes while
creating
a user-managed notebooks instance:
-
enable-guest-attributes=TRUE
: This enables guest attributes on your user-managed notebooks instance. All new instances enable this attribute by default. -
report-system-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 user-managed notebooks instance with system health guest attributes enabled
To use system health guest attributes to report on your user-managed notebooks instance's health, you must select the Enable system health reportcheckbox when you create a user-managed notebooks instance.
You can enable the system health report by using either the Google Cloud console or the Google Cloud CLI.
Before you begin
Before you can create a user-managed notebooks instance, you must have a Google Cloud project and enable the Notebooks API for that project.- 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.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project .
-
Make sure that billing is enabled for your Google Cloud project .
-
Enable the Notebooks API.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project .
-
Make sure that billing is enabled for your Google Cloud project .
-
Enable the Notebooks API.
- If you plan to use GPUs with your user-managed notebooks instance, check the quotas page in the Google Cloud console to ensure that you have enough GPUs available in your project. If GPUs are not listed on the quotas page, or you require additional GPU quota, you can request a quota increase. See Requesting an increase in quota on the Compute Engine Resource quotas page.
Required roles
If you created the project, you have the
Owner ( roles/owner
) IAM role on the project,
which includes all required permissions. Skip this section and
start creating your user-managed notebooks instance. If you didn't
create the project yourself, continue in this section.
To ensure that your user account has the necessary permissions to create a Vertex AI Workbench user-managed notebooks instance, ask your administrator to grant your user account the following IAM roles on the project:
- Notebooks Admin (
roles/notebooks.admin
) - Service Account User (
roles/iam.serviceAccountUser
)
For more information about granting roles, see Manage access .
Your administrator might also be able to give your user account the required permissions through custom roles or other predefined roles .
Create the instance
Console
-
In the Google Cloud console, go to the User-managed notebookspage. Or go to notebook.new (https://notebook.new) and skip the next step.
-
Click New notebook, and then select Customize.
-
On the Create a user-managed notebookpage, in the Detailssection, provide the following information for your new instance:
- Name: a name for your new instance
- Regionand Zone: Select a region and zone for the new instance. For best network performance, select the region that is geographically closest to you. See the available user-managed notebooks locations .
-
Select the System healthsection.
-
In the System health and reportingsection, select the Enable system health reportcheckbox.
-
Complete the rest of the instance creation dialog, and then click Create.
gcloud
-
From Cloud Shell or any environment where the Google Cloud CLI is installed, enter the following Google Cloud CLI command:
gcloud notebooks instances create INSTANCE_NAME \ --vm-image-project=deeplearning-platform-release \ --vm-image-family= IMAGE_FAMILY \ --machine-type= MACHINE_TYPE \ --location= ZONE \ --metadata=enable-guest-attributes=TRUE,report-system-health=TRUE
Replace the following:
-
INSTANCE_NAME
: the name of your new instance -
IMAGE_FAMILY
: the image family name that you want to use to create your instance -
MACHINE_TYPE
: the machine type of your instance's VM; for example,n1-standard-4
-
ZONE
: the zone where you want your new instance to be located, for example,us-west1-a
-
-
Access your instance from the Google Cloud console .
Monitor system health through guest attributes
For user-managed notebooks 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.
Console
-
In the Google Cloud console, go to the User-managed notebookspage.
-
Click the instance name that you want to view the system health status of.
-
On the Notebook detailspage, click the Healthtab. Review the status of your instance and its core services.
gcloud with Compute Engine
gcloud compute instances get-guest-attributes INSTANCE_NAME
\
--zone ZONE
Replace the following:
-
INSTANCE_NAME
: the name of your instance -
ZONE
: the zone where your instance is located
If your core services are healthy, the results look like the following.
A value of 1
means no failure was detected.
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 2020-10-01 17:00:00.12345
If any of the four core services fail, system-health reports a -1
value to indicate system failure. In most cases,
a system failure means that JupyterLab is not accessible.
An example of a failure result might look like the following.
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 2020-10-01 17:00:00.12345
gcloud with Vertex AI Workbench
To monitor your system health, you can use the getInstanceHealth method to retrieve the values of your guest attributes.
The following example shows how to do this using the gcloud CLI.
gcloud notebooks instances is-healthy example-instance \
--location= ZONE
Replace ZONE
with the zone where your instance
is located, for example, us-west1-a
.
If your core services are healthy, the results look like the following.
A value of 1
means no failure was detected.
{ "health_state": HEALTHY, "docker-proxy-agent": 1, "docker-service": 1, "jupyter-service": 1, "jupyter-api": 1, "last-updated": "2020-10-01 17:00:30.12345" }
An example of a failure result might look like the following.
{ "healthy": UNHEALTHY, "docker-proxy-agent": 1, "docker-service": 1, "jupyter-service": -1, "jupyter-api": -1, "last-updated": "2020-10-01 17:00:30.12345" }
Report custom metrics to Monitoring
User-managed notebooks 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 user-managed notebooks instance .
The custom metrics reported to Monitoring include the following:
-
The system health of these user-managed notebooks 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 user-managed notebooks instance.
You must also make sure that the user-managed notebooks
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 user-managed notebooks instance that reports custom metrics to Monitoring
To report custom metrics to Monitoring, you must select the Report custom metrics to Cloud Monitoringcheckbox when you create a user-managed notebooks instance.
You can enable reporting custom metrics to Cloud Monitoring by using either the Google Cloud console or the Google Cloud CLI.
Before you begin
Before you can create a user-managed notebooks instance, you must have a Google Cloud project and enable the Notebooks API for that project.- 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.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project .
-
Make sure that billing is enabled for your Google Cloud project .
-
Enable the Notebooks API.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project .
-
Make sure that billing is enabled for your Google Cloud project .
-
Enable the Notebooks API.
- If you plan to use GPUs with your user-managed notebooks instance, check the quotas page in the Google Cloud console to ensure that you have enough GPUs available in your project. If GPUs are not listed on the quotas page, or you require additional GPU quota, you can request a quota increase. See Requesting an increase in quota on the Compute Engine Resource quotas page.
Required roles
If you created the project, you have the
Owner ( roles/owner
) IAM role on the project,
which includes all required permissions. Skip this section and
start creating your user-managed notebooks instance. If you didn't
create the project yourself, continue in this section.
To ensure that your user account has the necessary permissions to create a Vertex AI Workbench user-managed notebooks instance, ask your administrator to grant your user account the following IAM roles on the project:
- Notebooks Admin (
roles/notebooks.admin
) - Service Account User (
roles/iam.serviceAccountUser
)
For more information about granting roles, see Manage access .
Your administrator might also be able to give your user account the required permissions through custom roles or other predefined roles .
Create the instance
Console
-
In the Google Cloud console, go to the User-managed notebookspage. Or go to notebook.new (https://notebook.new) and skip the next step.
-
Click New notebook, and then select Customize.
-
On the Create a user-managed notebookpage, in the Detailssection, provide the following information for your new instance:
- Name: a name for your new instance
- Regionand Zone: Select a region and zone for the new instance. For best network performance, select the region that is geographically closest to you. See the available user-managed notebooks locations .
-
Select the System healthsection.
-
In the System health and reportingsection, select the Report custom metrics to Cloud Monitoringcheckbox.
-
Complete the rest of the instance creation dialog, and then click Create.
gcloud
-
From Cloud Shell or any environment where the Google Cloud CLI is installed, enter the following Google Cloud CLI command:
gcloud notebooks instances create INSTANCE_NAME \ --vm-image-project=deeplearning-platform-release \ --vm-image-family= IMAGE_FAMILY \ --machine-type= MACHINE_TYPE \ --location= ZONE \ --metadata=report-notebook-metrics=TRUE
Replace the following:
-
INSTANCE_NAME
: the name of your new instance -
IMAGE_FAMILY
: the image family name that you want to use to create your instance -
MACHINE_TYPE
: the machine type of your instance's VM, for example,n1-standard-4
-
ZONE
: the zone where you want your new instance to be located, for example,us-west1-a
-
-
Access your instance from the Google Cloud console .
Grant Monitoring Metric Writer permissions to the service account
After you've created
your new user-managed notebooks instance,
grant Monitoring Metric Writer permissions
( roles/monitoring.metricWriter
) to
the service account for
the user-managed notebooks instance.
For more information, see Manage access to projects, folders, and organizations
.
Monitor custom metrics through Monitoring
For user-managed notebooks instances that have reporting custom metrics enabled , you can monitor your custom metrics by using the Google Cloud console.
-
In the Google Cloud console, go to the User-managed notebookspage.
-
Click the instance name that you want to view the custom metrics of.
-
On the Notebook detailspage, click the Monitoringtab. 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 user-managed notebooks instance,
you must select the Install Cloud Monitoring agentcheckbox when you create
a user-managed notebooks instance.
These metrics are different from the custom metrics that are reported when
you enable the report-notebook-metrics
metadata
setting
.
Create a user-managed notebooks instance that reports system and application metrics to Monitoring
To install Monitoring on your user-managed notebooks instance, you can use either the Google Cloud console or the Google Cloud CLI.
Before you begin
Before you can create a user-managed notebooks instance, you must have a Google Cloud project and enable the Notebooks API for that project.- 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.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project .
-
Make sure that billing is enabled for your Google Cloud project .
-
Enable the Notebooks API.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project .
-
Make sure that billing is enabled for your Google Cloud project .
-
Enable the Notebooks API.
- If you plan to use GPUs with your user-managed notebooks instance, check the quotas page in the Google Cloud console to ensure that you have enough GPUs available in your project. If GPUs are not listed on the quotas page, or you require additional GPU quota, you can request a quota increase. See Requesting an increase in quota on the Compute Engine Resource quotas page.
Required roles
If you created the project, you have the
Owner ( roles/owner
) IAM role on the project,
which includes all required permissions. Skip this section and
start creating your user-managed notebooks instance. If you didn't
create the project yourself, continue in this section.
To ensure that your user account has the necessary permissions to create a Vertex AI Workbench user-managed notebooks instance, ask your administrator to grant your user account the following IAM roles on the project:
- Notebooks Admin (
roles/notebooks.admin
) - Service Account User (
roles/iam.serviceAccountUser
)
For more information about granting roles, see Manage access .
Your administrator might also be able to give your user account the required permissions through custom roles or other predefined roles .
Create the instance
Console
-
In the Google Cloud console, go to the User-managed notebookspage. Or go to notebook.new (https://notebook.new) and skip the next step.
-
Click New notebook, and then select Customize.
-
On the Create a user-managed notebookpage, in the Detailssection, provide the following information for your new instance:
- Name: a name for your new instance
- Regionand Zone: Select a region and zone for the new instance. For best network performance, select the region that is geographically closest to you. See the available user-managed notebooks locations .
-
Select the System healthsection.
-
In the System health and reportingsection, select the Install Cloud Monitoring agentcheckbox.
-
Complete the rest of the instance creation dialog, and then click Create.
gcloud
-
From Cloud Shell or any environment where the Google Cloud CLI is installed, enter the following Google Cloud CLI command:
gcloud notebooks instances create INSTANCE_NAME \ --vm-image-project=deeplearning-platform-release \ --vm-image-family= IMAGE_FAMILY \ --machine-type= MACHINE_TYPE \ --location= ZONE \ --metadata=install-monitoring-agent=TRUE
Replace the following:
-
INSTANCE_NAME
: the name of your new instance -
IMAGE_FAMILY
: the image family name that you want to use to create your instance -
MACHINE_TYPE
: the machine type of your instance's VM; for example,n1-standard-4
-
ZONE
: the zone where you want your new instance to be located, for example,us-west1-a
-
-
Access your instance from the Google Cloud console .
Monitor system and application metrics through Monitoring
For user-managed notebooks instances that have Monitoring installed , you can monitor your system and application metrics by using the Google Cloud console:
-
In the Google Cloud console, go to the User-managed notebookspage.
-
Click the instance name that you want to view the system and application metrics of.
-
On the Notebook detailspage, click the Monitoringtab. 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
User-managed notebooks 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 user-managed notebooks 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
- Network information (
-
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
To run the diagnostic tool, complete the following steps:
-
Use ssh to connect to your user-managed notebooks instance .
-
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. -
Extract the file and then evaluate the contents. The contents include:
-
log
folder : Logs from thevar/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.
Option | Description |
---|---|
-r | A repair option that tries to restore failed user-managed notebooks core services status |
-s | Runs without a confirmation |
-b | Uploads the .tar.gz
file
to a Cloud Storage bucket. |
-v | A debug option for troubleshooting the tool in case of failures |
-c | Captures 30 seconds of packet traffic into your user-managed notebooks instance, filtering SSH |
-d | A destination folder in which to save the logs |
-h | Help |