Yes! You can configure the Docker image that the Cloud Run worker uses to create Cloud Run jobs. There are two main ways to do this: 1. Through the Work Pool Configuration: When creating or updating a Cloud Run work pool, you can specify the default image in the base job template. This will be used for all jobs unless overridden at the deployment level. 2. Through the Deployment Configuration: You can specify the image for specific deployments, which will override the work pool default. Here's how to do both: ### Option 1: Setting the image in the Work Pool When creating a Cloud Run work pool, you can specify the image in the base job template. Here's an example:

python
{
    "image": "your-docker-image:tag",
    "cpu": 1,
    "memory": 512,
    "memory_unit": "Mi",
    "region": "us-central1"
}

You can set this either through the Prefect UI or using the CLI:

bash
prefect work-pool create "my-cloud-run-pool" \
    --type cloud-run \
    --base-job-template path/to/template.json

### Option 2: Setting the image in the Deployment When creating a deployment, you can specify the image in the deployment configuration:

python
from prefect import flow
from prefect.deployments import Deployment

@flow
def my_flow():
    # your flow code here
    pass

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    work_pool_name="my-cloud-run-pool",
    job_variables={
        "image": "your-docker-image:tag"
    }
)

Or when using the modern deployment syntax in Prefect 3.x:

python
@flow
def my_flow():
    # your flow code here
    pass

if __name__ == "__main__":
    my_flow.deploy(
        name="my-deployment",
        work_pool_name="my-cloud-run-pool",
        job_variables={
            "image": "your-docker-image:tag"
        }
    )

Important notes: 1. The image must be accessible to your GCP Cloud Run service 2. If you're using a private container registry, make sure your Cloud Run service has the necessary permissions to pull the image 3. The image should contain all the dependencies required by your flow 4. Make sure the image has Python and the required Prefect version installed Would you like me to provide more specific details about any of these configuration options or help with setting up a particular approach?

To resolve this, please: 1. Verify Service Account: - Check which service account is being used in your GCP Credentials block - Ensure this service account exists and is active 2. Verify Permissions: - Go to the Google Cloud Console → IAM & Admin → IAM - Find your service account - Verify it has the necessary roles mentioned above - If using Workload Identity, ensure the federation is set up correctly 3. Check Region Configuration: - The error shows you're trying to use

asia-northeast3

- Make sure your service account has permissions in this region - Verify the region matches what's configured in your work pool Would you like me to help you verify any specific part of this setup? Please let me know: 1. Which service account you're using 2. Whether you're using Workload Identity 3. The specific GCP project ID This will help me provide more targeted assistance.

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✅ thought for 18.9 seconds

I see the 403 permission error for Cloud Run. Let me help you troubleshoot this systematically. Since your Cloud Run worker is running as a Cloud Run service itself, this is likely an issue with the service account that the Cloud Run service (worker) is using. Here's what we need to check: 1. First, verify the service account being used by your Cloud Run worker service:

bash
   gcloud run services describe YOUR_WORKER_SERVICE_NAME \
     --region asia-northeast3 \
     --format="get(serviceConfig.serviceAccountEmail)"

2. This service account needs the following permissions: -

roles/run.developer

- to create and manage Cloud Run jobs -

roles/run.invoker

- to invoke Cloud Run services -

roles/iam.serviceAccountUser

- to act as the service account You can grant these permissions using:

bash
# Replace YOUR_PROJECT_ID and SERVICE_ACCOUNT_EMAIL with actual values
gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
    --member="serviceAccount:SERVICE_ACCOUNT_EMAIL" \
    --role="roles/run.developer"

gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
    --member="serviceAccount:SERVICE_ACCOUNT_EMAIL" \
    --role="roles/run.invoker"

gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
    --member="serviceAccount:SERVICE_ACCOUNT_EMAIL" \
    --role="roles/iam.serviceAccountUser"

3. Additionally, check if the Cloud Run API is enabled:

bash
gcloud services enable <http://run.googleapis.com|run.googleapis.com>

4. Verify region permissions: - The error shows you're using

asia-northeast3

- Make sure your service account has permissions in this region - Confirm this matches your work pool configuration To help you further, could you please: 1. Confirm if you're using the default compute service account (

<mailto:PROJECT_NUMBER-compute@developer.gserviceaccount.com|PROJECT_NUMBER-compute@developer.gserviceaccount.com>

) or a custom service account? 2. Can you check if the Cloud Run API is enabled in your project? 3. Are you able to create Cloud Run jobs manually in the GCP Console with the same service account? This will help me provide more specific guidance for your setup. Also, as a temporary test, you could try granting the more permissive

roles/run.admin

role to verify if it's purely a permissions issue:

bash
gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
    --member="serviceAccount:SERVICE_ACCOUNT_EMAIL" \
    --role="roles/run.admin"

Note: While this would work for testing, it's recommended to use the more granular permissions mentioned above for production use.

I notice that while the service account has

Cloud Run Admin

and

Service Account User

roles, there might be a specific requirement for Cloud Run job creation. Let's modify the service account permissions to ensure it has all necessary permissions. Given your Terraform configuration, the service account needs additional permissions to create and manage Cloud Run jobs. Here's what you should add to your service account: 1. Add IAM permissions for job creation. You can add this to your Terraform configuration:

hcl
resource "google_project_iam_member" "prefect_server_cloudrun_jobs" {
  project = var.project
  role    = "roles/run.developer"
  member  = "serviceAccount:${var.prefect_server_service_account_email}"
}

# If not already present, add this for job invocation
resource "google_project_iam_member" "prefect_server_cloudrun_invoker" {
  project = var.project
  role    = "roles/run.invoker"
  member  = "serviceAccount:${var.prefect_server_service_account_email}"
}

2. Verify the service account has permission to create other service accounts (needed for job execution):

hcl
resource "google_project_iam_member" "prefect_server_sa_creator" {
  project = var.project
  role    = "roles/iam.serviceAccountCreator"
  member  = "serviceAccount:${var.prefect_server_service_account_email}"
}

3. Add permission to act as service accounts:

hcl
resource "google_project_iam_member" "prefect_server_sa_user" {
  project = var.project
  role    = "roles/iam.serviceAccountUser"
  member  = "serviceAccount:${var.prefect_server_service_account_email}"
}

4. Additionally, make sure your Cloud Run service has the necessary security context:

hcl
resource "google_cloud_run_v2_service" "prefect_worker" {
  # ... your existing configuration ...

  template {
    containers {
      # ... your existing container configuration ...

      # Add this if not present
      service_account = var.prefect_server_service_account_email
    }
  }
}

5. Enable the necessary APIs if not already enabled:

hcl
resource "google_project_service" "required_apis" {
  for_each = toset([
    "<http://run.googleapis.com|run.googleapis.com>",
    "<http://iam.googleapis.com|iam.googleapis.com>",
    "<http://cloudresourcemanager.googleapis.com|cloudresourcemanager.googleapis.com>"
  ])
  
  project = var.project
  service = each.value

  disable_on_destroy = false
}

After applying these changes: 1. Apply the Terraform changes 2. Restart your Cloud Run worker service 3. Try running a deployment again If you're still seeing issues, could you please: 1. Check the Cloud Run service logs for any additional error messages 2. Verify if the service account has the correct project-level permissions by running:

bash
gcloud projects get-iam-policy YOUR_PROJECT_ID \
    --flatten="bindings[].members" \
    --format='table(bindings.role)' \
    --filter="bindings.members:${var.prefect_server_service_account_email}"

This will show all roles assigned to your service account at the project level. Would you like me to provide any additional details about any of these configurations or help troubleshoot further if these changes don't resolve the issue?

I apologize for my previous response where I incorrectly included the

roles/iam.serviceAccountCreator

role. After researching further, I can confirm that this role is not required for the Cloud Run worker to function properly. For a Cloud Run worker to create and manage Cloud Run jobs, you only need these essential roles: 1.

roles/run.developer

- to create and manage Cloud Run jobs 2.

roles/run.invoker

- to invoke Cloud Run services 3.

roles/iam.serviceAccountUser

- to act as the service account Your current service account already has sufficient permissions with: - Cloud Run Admin (which is actually more permissive than needed) - Service Account User - Cloud SQL Client (for database access) - IAP-secured Web App User The

serviceAccountCreator

role is not necessary because: 1. The Cloud Run worker doesn't need to create new service accounts 2. It only needs to execute Cloud Run jobs using the existing service account Would you like me to provide a more minimal set of IAM permissions that would work for your use case? This could help improve security by following the principle of least privilege.

Based on the error and the Cloud Run V2 API requirements, here's how to fix this issue. You need to provide a proper job template in your work pool configuration. Here's the correct template structure for Cloud Run V2:

json
{
    "job": {
        "template": {
            "template": {
                "containers": [
                    {
                        "image": "${image}",
                        "resources": {
                            "cpu_limit": "1",
                            "memory_limit": "512Mi"
                        },
                        "env": [
                            {
                                "name": "PREFECT_API_URL",
                                "value": "${prefect_api_url}"
                            },
                            {
                                "name": "PREFECT_API_KEY",
                                "value": "${prefect_api_key}"
                            }
                        ]
                    }
                ],
                "serviceAccount": "${service_account_name}",
                "timeoutSeconds": "600"
            }
        }
    }
}

You can update your work pool in one of two ways: 1. Using the UI: - Go to your work pool settings - Click "Edit" - In the "Base Job Template" section, paste the above JSON - Update the values as needed 2. Using the CLI:

bash
# First save the template to a file named template.json
prefect work-pool create "your-pool-name" \
    --type cloud-run-v2 \
    --base-job-template template.json

Key points about the template: 1. Note the nested

template

structure - this is required for Cloud Run V2 2. The outer

job

wrapper is required 3. Variables in

${...}

will be replaced with actual values at runtime 4. Make sure to set appropriate resource limits for your needs Variables you can use in the template: - `${image}`: Docker image to use - `${prefect_api_url}`: Your Prefect API URL - `${prefect_api_key}`: API key if needed - `${service_account_name}`: The service account email Would you like me to: 1. Provide a more detailed template with additional configuration options? 2. Help you troubleshoot the specific values in your current configuration? 3. Show how to verify the configuration is correct before deploying?

Your message was too long, here's your message at the allowed limit: Here's my work pool's Base Job Template ```{ variables { type object properties { cpu { type string title CPU default 1000m description The CPU to allocate to the Cloud Run job } env { type object title Environment Variables description Environment variables to set when starting a flow run additionalProperties { type string } } args { type array items { type string } title Args description The arguments to pass to the Cloud Run Job V2's entrypoint command } name { type string title Name description Name given to infrastructure created by a worker } image { type string title Image Name default asia-northeast3-docker pkg dev/solvook-infra/ml/prefect latest description The image to use for the Cloud Run job If not provided the default Prefect image will be used } labels { type object title Labels description Labels applied to infrastructure created by a worker additionalProperties { type string } } memory { type string title Memory default 512Mi example 512Mi pattern ^\\d+(? G|Gi|M|Mi)$ description The memory to allocate to the Cloud Run job along with the units whichcould be G Gi M Mi } region { type string title Region default asia-northeast3 description The region in which to run the Cloud Run job } command { type string title Command description The command to use when starting a flow run In most cases this should be left blank and the command will be automatically generated by the worker } timeout { type integer title Job Timeout default 600 maximum 86400 description The length of time that Prefect will wait for a Cloud Run Job to complete before raising an exception (maximum of 86400 seconds 1 day) exclusiveMinimum 0 } keep_job { type boolean title Keep Job After Completion default true description Keep the completed Cloud run job on Google Cloud Platform } credentials { allOf [ { $ref #/definitions/GcpCredentials } ] title GCP Credentials default { $ref { block_document_id 52eb905d-1563-4850-af2b-bbe6720da9e7 } } description The GCP Credentials used to connect to Cloud Run If not provided credentials will be inferred from the local environment } max_retries { type integer title Max Retries default 0 description The number of times to retry the Cloud Run job } launch_stage { enum [ ALPHA BETA GA DEPRECATED EARLY_ACCESS PRELAUNCH UNIMPLEMENTED LAUNCH_TAG_UNSPECIFIED ] type string title Launch Stage default BETA description The launch stage of the Cloud Run Job V2 See <https //cloud google com/run/docs/about-features-categories> for additional details } vpc_connector_name { type string title VPC Connector Name description The name of the VPC connector to use for the Cloud Run job } service_account_name { type string title Service Account Name default <mailto prefect-server-service-account@solvook-infra iam gserviceaccount com|prefect-server-service-account@solvook-infra iam gserviceaccount com> example <mailto prefect-server-service-account@solvook-infra iam gserviceaccount com|prefect-server-service-account@solvook-infra iam gserviceaccount com> description The name of the service account to use for the task execution of Cloud Run Job By default Cloud Run jobs run as the default Compute Engine Service Account } } definitions { GcpCredentials { type object title GcpCredentials properties { project { type string

Based on the research and the Cloud Run V2 API requirements, here's the correct template structure. The key difference is in how the template is structured for V2:

json
{
    "template": {
        "containers": [
            {
                "image": "${image}",
                "resources": {
                    "limits": {
                        "cpu": "1",
                        "memory": "512Mi"
                    }
                },
                "env": [
                    {
                        "name": "PREFECT_API_URL",
                        "value": "${prefect_api_url}"
                    },
                    {
                        "name": "PREFECT_API_KEY",
                        "value": "${prefect_api_key}"
                    }
                ]
            }
        ],
        "serviceAccount": "${service_account_name}",
        "timeoutSeconds": 600
    }
}

Key differences in this V2 template: 1. The structure is flatter - no nested

job

wrapper 2. Resources are specified under

limits

3. The template is directly at the root level You can update your work pool using one of these methods: 1. Using the UI: - Go to your work pool settings - Click "Edit" - Replace the entire base job template with the above JSON - Save the changes 2. Using the CLI:

bash
# Save the template to a file
echo '{
    "template": {
        "containers": [
            {
                "image": "${image}",
                "resources": {
                    "limits": {
                        "cpu": "1",
                        "memory": "512Mi"
                    }
                },
                "env": [
                    {
                        "name": "PREFECT_API_URL",
                        "value": "${prefect_api_url}"
                    },
                    {
                        "name": "PREFECT_API_KEY",
                        "value": "${prefect_api_key}"
                    }
                ]
            }
        ],
        "serviceAccount": "${service_account_name}",
        "timeoutSeconds": 600
    }
}' > template.json

prefect work-pool create "your-pool-name" \
    --type cloud-run-v2 \
    --base-job-template template.json

Optional additional configurations you can add:

json
{
    "template": {
        "containers": [
            {
                // ... existing configuration ...
                "args": ["${command}"],  // If you need to pass specific commands
                "labels": {              // If you need labels
                    "<http://prefect.io/flow-run-id|prefect.io/flow-run-id>": "${flow_run_id}"
                }
            }
        ],
        "vpc": {                        // If you need VPC access
            "connector": "your-vpc-connector",
            "egress": "ALL_TRAFFIC"
        }
    }
}

Would you like me to: 1. Help you troubleshoot if this template still doesn't work? 2. Explain any specific part of the configuration in more detail? 3. Show how to add additional configurations for your specific needs?

For a Cloud Run V2 worker, you need to configure credentials in two places: 1. Work Pool Configuration: You need to specify a GCP Credentials block in your work pool settings. Here's how to set it up: a. First, create a GCP Credentials block if you haven't already:

bash
   prefect block register -m prefect_gcp

b. Then create the credentials block either through the UI or Python:

python
   from prefect_gcp import GcpCredentials
   
   credentials = GcpCredentials(
       service_account_info=None,  # Leave as None when running on GCP
       service_account_file=None,  # Leave as None when running on GCP
       project="your-project-id"   # Your GCP project ID
   )
   credentials.save("gcp-credentials-block-name")

2. Worker Service Account: Since your worker is running as a Cloud Run service, it should use the default compute service account or a custom service account. This service account needs:

bash
   # Check the current service account being used by your worker
   gcloud run services describe prefect-worker \
     --region YOUR_REGION \
     --format="get(serviceConfig.serviceAccountEmail)"

Ensure this service account has these roles:

bash
   # Replace SERVICE_ACCOUNT_EMAIL with the email from above command
   gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
     --member="serviceAccount:SERVICE_ACCOUNT_EMAIL" \
     --role="roles/run.developer"

   gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
     --member="serviceAccount:SERVICE_ACCOUNT_EMAIL" \
     --role="roles/run.invoker"

   gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
     --member="serviceAccount:SERVICE_ACCOUNT_EMAIL" \
     --role="roles/iam.serviceAccountUser"

3. Update Work Pool Configuration: Make sure your work pool has the credentials block configured. In the work pool settings, you should see a field for GCP Credentials. Make sure it's set to your credentials block. You can also update it via CLI:

bash
   prefect work-pool update "your-pool-name" \
     --set variables='{"credentials": "gcp-credentials-block-name"}'

To troubleshoot: 1. Check Worker Logs:

bash
   gcloud run services logs read prefect-worker --region YOUR_REGION

Look for more detailed error messages about the credentials issue. 2. Verify Work Pool Configuration:

bash
   prefect work-pool inspect "your-pool-name"

Check that the credentials block is properly referenced. 3. Test Service Account Permissions:

bash
   # Get the service account currently being used
   SA_EMAIL=$(gcloud run services describe prefect-worker \
     --region YOUR_REGION \
     --format="get(serviceConfig.serviceAccountEmail)")
   
   # List its roles
   gcloud projects get-iam-policy YOUR_PROJECT_ID \
     --flatten="bindings[].members" \
     --format='table(bindings.role)' \
     --filter="bindings.members:$SA_EMAIL"

Could you please: 1. Check if you see the credentials block properly configured in your work pool settings? 2. Share any specific error message you're seeing about the missing credentials? 3. Confirm whether you're using a custom service account or the default compute service account? This will help me provide more targeted assistance for your setup.