> ## Documentation Index
> Fetch the complete documentation index at: https://cloudanix.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Vertexai workbench cloud monitoring enabled remediation

### Triage and Remediation

<Tabs>
  <Tab title="Remediation">
    ### Remediation

    <AccordionGroup>
      <Accordion title="Using Console" defaultOpen="true">
        Below are the concise, console-based steps to ensure Cloud Monitoring is enabled for Vertex AI Workbench notebook instances.

        ***

        ## 1. Verify Cloud Monitoring & APIs

        1. Go to **Google Cloud console**: [https://console.cloud.google.com](https://console.cloud.google.com)
        2. In the top-left, select the correct **Project**.
        3. In the left menu, go to **APIs & Services → Library**.
        4. Confirm the following APIs are **Enabled** (enable if they are not):
           * **Cloud Monitoring API**
           * **Vertex AI API**

        ***

        ## 2. Enable Monitoring on a New Vertex AI Workbench Notebook

        1. Go to **Vertex AI → Workbench** in the console.
        2. Click **New notebook** → choose the type (e.g., **Managed notebooks**).
        3. Fill in the basic details (name, region, machine type, etc.).
        4. Expand **Advanced Settings** (or similar section, wording may slightly differ).
        5. Find the **Monitoring** or **Operations** section:
           * Check/enable **Cloud Monitoring** / **Ops Agent** / **Enable monitoring** option.
        6. Complete the rest of the configuration as desired.
        7. Click **Create**.

        Cloud Monitoring will now collect metrics from that notebook VM.

        ***

        ## 3. Enable Monitoring on an Existing Notebook Instance

        > You typically need to stop the instance to edit some settings.

        1. Go to **Vertex AI → Workbench**.
        2. Locate your notebook instance in the list.
        3. If it’s **Running**, click the **three‑dot menu** (⋮) → **Stop** and wait until status is **Stopped**.
        4. Again click the **three‑dot menu** (⋮) → **Edit**.
        5. In the edit page, expand **Advanced Settings**.
        6. Locate the **Monitoring** / **Ops Agent** / **Enable Cloud Monitoring** section and:
           * Check/enable the monitoring option.
        7. Save the changes (e.g., **Save**, **Update**, or **Save & Continue** depending on UI).
        8. Once updated, click **Start** to start the notebook instance again.

        ***

        ## 4. Confirm Metrics in Cloud Monitoring

        1. Go to **Monitoring → Metrics explorer** in the console.
        2. In **Resource type**, select:
           * `VM Instance` or `Vertex AI Workbench` (depending on what appears for your setup).
        3. Check that you see metrics (CPU, memory, disk, etc.) for your notebook VM.

        This ensures Cloud Monitoring is enabled and collecting data for your Vertex AI notebook instances via the GCP console.
      </Accordion>

      <Accordion title="Using CLI">
        Below is a practical CLI-based way to ensure Vertex AI notebook instances are sending metrics to Cloud Monitoring. The approach is different for:

        * **Managed Notebooks (Vertex AI Workbench managed)** – monitoring is on by default; you mainly need APIs enabled.
        * **User-Managed Notebooks (Vertex AI Workbench user-managed / legacy AI Platform Notebooks)** – these are GCE VMs; you enable monitoring by installing the Ops Agent on the VM.

        ***

        ## 1. Prerequisites (all Vertex AI notebooks)

        ### 1.1. Set default project and region/zone

        ```bash theme={null}
        PROJECT_ID="your-project-id"
        REGION="us-central1"       # for managed notebooks
        ZONE="us-central1-b"       # for user-managed (VM-based) notebooks

        gcloud config set project "$PROJECT_ID"
        ```

        ### 1.2. Enable required APIs

        ```bash theme={null}
        gcloud services enable \
          notebooks.googleapis.com \
          monitoring.googleapis.com
        ```

        ***

        ## 2. Managed Notebooks (Vertex AI Workbench – Managed)

        For **managed notebooks**, Google manages the underlying infra and Cloud Monitoring integration is enabled by default. The main remediation is just ensuring the APIs are on (done above).

        To verify instances:

        ```bash theme={null}
        gcloud notebooks instances list --location="$REGION"
        ```

        If you are only using **managed notebooks**, there is nothing more to “turn on” for Monitoring via CLI.

        ***

        ## 3. User-Managed Notebooks (VM-based) – Install Ops Agent

        For **user-managed notebooks**, each notebook is a Compute Engine VM. To enable Cloud Monitoring, install the Ops Agent on those VMs.

        ### 3.1. List user-managed notebook instances

        ```bash theme={null}
        gcloud notebooks instances list --location="$ZONE" --format="table(name, state, proxyUri)"
        ```

        Note: For user-managed notebooks, the **instance name** is typically the same as the underlying GCE VM name and uses the same **zone**.

        ### 3.2. Enable OS Login (recommended, if not already)

        ```bash theme={null}
        gcloud compute instances add-metadata "INSTANCE_NAME" \
          --zone="$ZONE" \
          --metadata=enable-oslogin=TRUE
        ```

        Replace `INSTANCE_NAME` with your notebook VM name.

        ### 3.3. SSH into the notebook VM

        ```bash theme={null}
        gcloud compute ssh "INSTANCE_NAME" --zone="$ZONE"
        ```

        ### 3.4. Install the Ops Agent (Cloud Monitoring + Logging)

        Once logged into the VM:

        **Debian/Ubuntu (most Vertex AI notebook images are Debian-based):**

        ```bash theme={null}
        curl -sSO https://dl.google.com/cloudagents/add-google-cloud-ops-agent-repo.sh
        sudo bash add-google-cloud-ops-agent-repo.sh --also-install
        ```

        **RHEL/CentOS:**

        ```bash theme={null}
        curl -sSO https://dl.google.com/cloudagents/add-google-cloud-ops-agent-repo.sh
        sudo bash add-google-cloud-ops-agent-repo.sh --also-install --distribution=el
        ```

        The agent will automatically send system metrics and logs to Cloud Monitoring/Logging.

        Exit the VM when done:

        ```bash theme={null}
        exit
        ```

        ### 3.5. (Optional) Verify the Ops Agent status

        From the VM:

        ```bash theme={null}
        sudo systemctl status google-cloud-ops-agent
        ```

        You should see the service as `active (running)`.

        ***

        ## 4. Verify in Cloud Monitoring

        After a few minutes, verify metrics:

        ```bash theme={null}
        # List monitored resource types
        gcloud monitoring resource-descriptors list --format="value(type)" | grep notebook
        ```

        Then check metrics in the console:

        * Go to **Monitoring → Metrics explorer**
        * Resource type: `gce_instance`
        * Filter by instance name of your notebook VM.

        This ensures Cloud Monitoring is receiving metrics for your Vertex AI notebook instances.
      </Accordion>

      <Accordion title="Using Python">
        Below is a practical way to ensure Vertex AI Workbench (user‑managed) notebook VMs are correctly configured for Cloud Monitoring using Python.

        For Vertex AI Workbench user‑managed notebooks, monitoring is essentially:

        * The VM must have the **Cloud Monitoring** and **Cloud Logging** OAuth scopes.
        * The **Ops Agent / Monitoring agent** must be installed (on current images it usually is by default).

        **Important:** Scopes on a VM cannot be changed in-place. If an instance was created without monitoring scopes, you must recreate it with the right config. The code below shows how to:

        1. Check scopes for existing notebook VMs.
        2. Create/recreate a notebook instance with Monitoring & Logging enabled.

        ***

        ## 1. Setup

        ```bash theme={null}
        pip install google-cloud-notebooks google-api-python-client
        gcloud auth application-default login
        ```

        Ensure your default credentials have `roles/notebooks.admin` and `roles/compute.viewer` at least.

        ***

        ## 2. Check if existing notebook instances have Monitoring enabled

        Vertex AI Workbench user-managed notebooks are backed by **Compute Engine VMs**. We’ll:

        * List notebook instances via the Notebooks API.
        * For each, find the underlying VM and inspect its OAuth scopes.
        * Flag instances missing `monitoring.write` and `logging.write`.

        ```python theme={null}
        from google.cloud import notebooks_v1
        from googleapiclient import discovery
        from google.auth import default

        PROJECT_ID = "your-project-id"
        LOCATION = "us-central1"   # change as needed
        PARENT = f"projects/{PROJECT_ID}/locations/{LOCATION}"

        def list_notebook_instances():
            client = notebooks_v1.NotebookServiceClient()
            for inst in client.list_instances(parent=PARENT):
                yield inst

        def get_compute_client():
            creds, _ = default()
            return discovery.build("compute", "v1", credentials=creds, cache_discovery=False)

        def check_monitoring_for_instance(instance, compute):
            # User-managed notebooks have a single VM behind them.
            # The VM name is usually the same as the notebook instance ID.
            # The zone is in instance.gce_setup.zone (or instance.vm_image.gce_zone on some versions).
            try:
                gce_setup = instance.gce_setup
                zone = gce_setup.zone.split("/")[-1]  # e.g. "projects/.../zones/us-central1-b"
                vm_name = gce_setup.vm_name or instance.name.split("/")[-1]
            except AttributeError:
                # Fallback: parse zone from instance.name if needed
                # Adjust if your instances look different
                print(f"Could not determine VM info for {instance.name}")
                return

            vm = compute.instances().get(project=PROJECT_ID, zone=zone, instance=vm_name).execute()

            service_accounts = vm.get("serviceAccounts", [])
            if not service_accounts:
                print(f"{instance.name}: NO service account configured – monitoring/ logging are disabled")
                return

            scopes = set()
            for sa in service_accounts:
                scopes.update(sa.get("scopes", []))

            needs_monitoring = not any("monitoring.write" in s for s in scopes)
            needs_logging = not any("logging.write" in s for s in scopes)

            if needs_monitoring or needs_logging:
                print(f"{instance.name}: missing scopes -> "
                      f"{'monitoring' if needs_monitoring else ''} "
                      f"{'logging' if needs_logging else ''}")
            else:
                print(f"{instance.name}: OK (Monitoring & Logging scopes present)")

        def main():
            compute = get_compute_client()
            for inst in list_notebook_instances():
                check_monitoring_for_instance(inst, compute)

        if __name__ == "__main__":
            main()
        ```

        Any instance printed as `missing scopes` should be **recreated** with the correct scopes.

        ***

        ## 3. Create (or recreate) a notebook with Cloud Monitoring enabled

        The simplest way to “enable Cloud Monitoring” is to ensure the notebook’s underlying VM has:

        ```text theme={null}
        https://www.googleapis.com/auth/logging.write
        https://www.googleapis.com/auth/monitoring.write
        ```

        and that you’re using a recent Vertex AI Workbench image (which includes the Ops Agent).

        ```python theme={null}
        from google.cloud import notebooks_v1
        from google.protobuf import field_mask_pb2

        PROJECT_ID = "your-project-id"
        LOCATION = "us-central1-b"  # zone; user-managed notebooks are zonal
        PARENT = f"projects/{PROJECT_ID}/locations/{LOCATION}"
        INSTANCE_ID = "my-monitored-notebook"

        SERVICE_ACCOUNT_EMAIL = "my-sa@your-project-id.iam.gserviceaccount.com"  # or default

        def create_notebook_with_monitoring():
            client = notebooks_v1.NotebookServiceClient()

            instance = notebooks_v1.Instance(
                name=f"{PARENT}/instances/{INSTANCE_ID}",
                machine_type=f"projects/{PROJECT_ID}/zones/{LOCATION}/machineTypes/n1-standard-4",
                service_account=SERVICE_ACCOUNT_EMAIL,
                service_account_scopes=[
                    "https://www.googleapis.com/auth/cloud-platform",         # superset
                    "https://www.googleapis.com/auth/logging.write",
                    "https://www.googleapis.com/auth/monitoring.write",
                ],
                vm_image=notebooks_v1.VmImage(
                    project="deeplearning-platform-release",
                    image_family="common-cpu-notebooks",  # pick an appropriate family
                ),
                install_gpu_driver=False,
                no_public_ip=True,
            )

            op = client.create_instance(
                parent=PARENT,
                instance_id=INSTANCE_ID,
                instance=instance,
            )
            print("Creating instance...")
            result = op.result()
            print(f"Created instance: {result.name}")

        if __name__ == "__main__":
            create_notebook_with_monitoring()
        ```

        If you want to **recreate** an existing instance:

        1. Export current settings (machine type, disks, network, etc.).
        2. Delete the old instance.
        3. Call `create_instance` with the same settings plus the Monitoring & Logging scopes as above.

        ***

        ## 4. (Optional) Verify in Cloud Monitoring

        After the instance is running:

        * In the console: Monitoring → Metrics Explorer → find `agent.googleapis.com` metrics for the VM.
        * Or via API, list time series for `agent.googleapis.com/agent/uptime` for that VM.

        ***

        If you can share whether you’re using **user‑managed** or **managed** notebooks and what your current instance config looks like (or a sample `Instance` JSON), I can adjust the Python exactly to your environment.
      </Accordion>

      <Accordion title="Using Terraform">
        ```hcl theme={null}
        # Enabling the built‑in “Cloud Monitoring” feature on Vertex AI Workbench
        # notebook instances is not currently exposed as a Terraform argument on
        # google_notebooks_instance / google_notebooks_runtime, so it cannot be
        # remediated directly in Terraform.

        # You must turn this on via the GCP Console or API:
        # Console:
        #   Vertex AI → Workbench → (select notebook instance) → Edit →
        #   Monitoring → Enable Cloud Monitoring → Save.
        #
        # Or via the Notebooks/Vertex AI API using the corresponding field, outside
        # of Terraform control.

        # No valid Terraform arguments exist today that map to this specific toggle,
        # so any Terraform change here would not actually satisfy the check.
        ```

        After this, `terraform plan` should show **no changes related to Cloud Monitoring** for the notebook instances, because the setting is not managed by the provider.
      </Accordion>
    </AccordionGroup>
  </Tab>
</Tabs>