Create a user-managed notebooks instance

Console

To create a user-managed notebooks instance by using the Google Cloud console, do the following:

1. In the Google Cloud console, go to the User-managed notebookspage. Or go to notebook.new (https://notebook.new) and skip the next step.

   Go to User-managed notebooks

2. Click add_box Create new.

   The New instancewindow opens.

3. In the New instancewindow, click Advanced options.

4. In the Detailssection, provide the following information for your new instance:

   • Name: Provide a name for your new instance. The name must start with a letter followed by up to 62 lowercase letters, numbers, or hyphens (-), and cannot end with a hyphen.
   • 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 .
   • Labels: Optional. Provide custom key-value labels for the instance.
   • Tags: Optional. Provide tags for the instance.

5. In the Environmentsection, provide the following:

   • Operating system: Select the operating system that you want to use.
   • Environment: Select the environment that you want to use.
   • Version: Use the latest version or a previous version of user-managed notebooks instances.
   • Post-startup script: Optional. Click Browseto select a script to run one time, after the instance is created. The path must be a URL or Cloud Storage path, for example: gs:// PATH_TO_FILE / FILE_NAME .
   • Metadata: Optional. Provide custom metadata keys for the instance.

6. In the Machine typesection, provide the following:

   • Machine type: Select the number of CPUs and amount of RAM for your new instance. Vertex AI Workbench provides monthly cost estimates for each machine type that you select.

   • GPU: Optional. If you want GPUs, select the GPUtype and Number of GPUsfor your new instance. For information about the different GPUs, see GPUs on Compute Engine .

     Select Install NVIDIA GPU driver automatically for me.

   • Shielded VM: Optional. Select or clear the following:

     • Secure Boot
     • Virtual Trusted Platform Module (vTPM)
     • Integrity monitoring

   • Idle shutdown: Optional.

     • To change the number of minutes before shutdown, in the Time of inactivity before shutdown (Minutes)field, change the value to an integer from 10 through 1440.

     • To turn off idle shutdown, clear Enable Idle Shutdown.

7. In the Diskssection, provide the following:

   • Disks: Optional. To change the default boot or data disk settings, select a Boot disk type, Boot disk size in GB, Data disk type, and Data disk size in GB. For more information about disk types, see Storage options .

   • Delete to trash: Optional. Select this checkbox to use the operating system's default trash behavior, If you use the default trash behavior, files deleted by using the JupyterLab user interface are recoverable but these deleted files do use disk space.

   • Backup: Optional. To sync a Cloud Storage location with your instance's data disk, click Browseand specify the Cloud Storage location. To learn about storage costs, see Cloud Storage pricing .

   • Encryption: Select Google-managed encryption keyor Customer-managed encryption key (CMEK). To use CMEK, see Customer-managed encryption keys .

8. In the Networkingsection, provide the following:

   • Networking: Adjust the network options to use a network in your current project or a Shared VPC network from a host project, if one is configured. If you are using a Shared VPC in the host project, you must also grant the Compute Network User role ( roles/compute.networkUser ) to the Notebooks Service Agent in the host project.

     1. In the Networkfield, select the network that you want. You can select a VPC network, as long as the network has Private Google Access enabled or can access the internet. For more information, see network configuration options .

     2. In the Subnetworkfield, select the subnetwork that you want.

   • To turn off the external IP address, clear the Assign external IP addresscheckbox.

   • To turn off proxy access, clear the Allow proxy accesscheckbox.

9. In the IAM and securitysection, provide the following:

   • IAM and security: To grant access to the instance's JupyterLab interface, complete one of the following steps:

     • To grant access to JupyterLab through a service account, select Service account.

       • To use the default Compute Engine service account, select Use default Compute Engine service account.

       • To use a custom service account, clear Use default Compute Engine service account, and then, in the Service account emailfield, enter your custom service account email address.

     • To grant a single user access to the JupyterLab interface, do the following:

       1. Select Single user, and then, in the User emailfield, enter the user account that you want to grant access. If the specified user is not the creator of the instance, you must grant the specified user the Service Account User role ( roles/iam.serviceAccountUser ) on the instance's service account.

       2. Your instance uses a service account to interact with Google Cloud services and APIs.

          • To use the default Compute Engine service account, select Use default Compute Engine service account.

          • To use a custom service account, clear Use default Compute Engine service account, and then, in the Service account emailfield, enter your custom service account email address.

     To learn more about granting access, see Manage access .

   • Security options: Select or clear the following checkboxes:

     • Root access to the instance
     • nbconvert
     • File downloading
     • Terminal access

10. In the System healthsection, provide the following:

    • Environment upgrade and system health: To automatically upgrade to newly released environment versions, select Environment auto-upgradeand complete the Upgrade schedule.

    • In Reporting, select or clear the following checkboxes:

      • Report system health
      • Report custom metrics to Cloud Monitoring
      • Install Cloud Monitoring
      • Report DNS status for required Google domains

11. Click Create.

    Vertex AI Workbench creates and automatically starts your user-managed notebooks instance. When the instance is ready to use, Vertex AI Workbench activates an Open JupyterLablink.

gcloud

By default, the Google Cloud CLI creates a user-managed notebooks instance with an external IP address. To create an instance without an external IP address, you must specify a subnet that has Private Google Access enabled. For more information, see networking configuration options .

1. To create a user-managed notebooks instance from the command line, from Cloud Shell or any terminal where the Google Cloud CLI is installed, use the gcloud notebooks instances create command:

   gcloud notebooks instances create INSTANCE_NAME 
   \
     --vm-image-project=deeplearning-platform-release \
     --vm-image-family= VM_IMAGE_FAMILY 
   \
     --machine-type= MACHINE_TYPE 
   \
     --location= LOCATION 
   

   Replace the following:

   • INSTANCE_NAME : the name of your new instance; must start with a letter followed by up to 62 lowercase letters, numbers, or hyphens (-), and cannot end with a hyphen.
   • VM_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
   • LOCATION : the Google Cloud location where you want your new instance to be

2. Access your instance from the Google Cloud console .

For information about all commands for creating an instance from the command line, see the gcloud CLI documentation .

Terraform

The following sample uses the google_notebooks_instance Terraform resource to create a new user-managed notebooks instance named notebooks-instance-basic .

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands .

 resource "google_notebooks_instance" "basic_instance" {
  name         = "notebooks-instance-basic"
  location     = "us-central1-a"
  machine_type = "e2-medium"

  vm_image {
    project      = "deeplearning-platform-release"
    image_family = "tf-ent-2-9-cu113-notebooks"
  }
}