> For the complete documentation index, see [llms.txt](https://docs.qpoint.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.qpoint.io/getting-started/qscan/installation/cloud-run.md).

# Cloud Run

QScan can be deployed as a Google Cloud Run Worker Pool. Worker pools are long-running instances suited for background processing workloads like QScan.

## Prerequisites

* Google Cloud project with Cloud Run API enabled
* `gcloud` CLI with beta components installed
* Secrets stored in Google Secret Manager

## CPU Configuration

Create a `worker.yaml` file:

```yaml
apiVersion: run.googleapis.com/v1
kind: WorkerPool
metadata:
  annotations:
    run.googleapis.com/launch-stage: BETA
    run.googleapis.com/scalingMode: manual
    run.googleapis.com/manualInstanceCount: "1"
  name: qscan-worker
spec:
  template:
    metadata:
      annotations:
        run.googleapis.com/execution-environment: gen2
        run.googleapis.com/cpu-throttling: "false"
    spec:
      containers:
        - name: qscan-worker
          image: us-docker.pkg.dev/qpoint-edge/public/qscan:latest
          env:
            - name: NUM_POLLERS
              value: "2"
            - name: NUM_SCANNERS
              value: "2"
            - name: LOG_LEVEL
              value: "info"
            - name: LOG_ENCODING
              value: "json"
            - name: METRICS_PORT
              value: "8080"
            - name: REGISTRATION_TOKEN
              valueFrom:
                secretKeyRef:
                  key: latest
                  name: qscan-registration-token
          livenessProbe:
            httpGet:
              path: /
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 10
          resources:
            limits:
              cpu: 6000m
              memory: 24Gi
          startupProbe:
            httpGet:
              path: /
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 30
            failureThreshold: 5
```

Deploy with:

{% code overflow="wrap" %}

```bash
gcloud beta run worker-pools replace worker.yaml --region=us-central1
```

{% endcode %}

## GPU Configuration

For GPU-accelerated scanning, modify the resource limits:

```yaml
          resources:
            limits:
              cpu: 4000m
              memory: 16Gi
              nvidia.com/gpu: "1"
```

{% hint style="info" %}
GPU support in Cloud Run requires a region that offers GPU instances. Check the [Cloud Run GPU documentation](https://cloud.google.com/run/docs/configuring/services/gpu) for available regions and GPU types.
{% endhint %}

## Secret Manager

Store sensitive values in Google Secret Manager and reference them in your worker configuration:

```bash
echo -n "your-registration-token" | \
  gcloud secrets create qscan-registration-token --data-file=-

echo -n "your-s3-access-key" | \
  gcloud secrets create qscan-s3-access-key --data-file=-
```

Reference secrets in the worker YAML using `secretKeyRef`:

```yaml
            - name: REGISTRATION_TOKEN
              valueFrom:
                secretKeyRef:
                  key: latest
                  name: qscan-registration-token
```

Ensure the Cloud Run service account has the `roles/secretmanager.secretAccessor` role.

## Updating the Image

To update the QScan image without modifying the full configuration:

{% code overflow="wrap" %}

```bash
gcloud beta run worker-pools update qscan-worker \
  --image=us-docker.pkg.dev/qpoint-edge/public/qscan:latest \
  --region=us-central1
```

{% endcode %}


---

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