You can inspect your work pool using:

bash
prefect work-pool inspect "your-cloud-run-pool"

This will show you the current configuration and limits set at the work pool level, which your deployment-specific settings will override.

Based on the research, I can show you how to configure custom resources in your

prefect.yaml

file. Here's how to structure it:

yaml
deployments:
  - name: your-deployment-name
    version: null
    tags: []
    description: null
    schedule: null
    flow_name: null
    entrypoint: null
    parameters: {}
    work_pool:
      name: your-cloud-run-pool
      job_variables:
        resource_limits:
          cpu: "2"
          memory: "4Gi"

For multiple deployments with different resource requirements, you can specify them individually:

yaml
deployments:
  - name: small-deployment
    work_pool:
      name: your-cloud-run-pool
      job_variables:
        resource_limits:
          cpu: "1"
          memory: "2Gi"

  - name: large-deployment
    work_pool:
      name: your-cloud-run-pool
      job_variables:
        resource_limits:
          cpu: "4"
          memory: "8Gi"

Important notes: 1. The

resource_limits

configuration will override the work pool defaults only for the specific deployment(s) 2. Valid CPU values for Cloud Run: "1", "2", or "4" 3. Memory values should include units (e.g., "512Mi", "1Gi", "2Gi", "4Gi", etc.) 4. Make sure the values are within Cloud Run's supported limits for your region You can apply these changes by running:

bash
prefect deploy --all

Or for a specific deployment:

bash
prefect deploy -n your-deployment-name

To verify the configuration was applied correctly, you can check the deployment details in the Prefect UI or use:

bash
prefect deployment inspect "your-deployment-name"

Remember that Cloud Run has specific resource constraints: - CPU must be 1, 2, or 4 vCPUs - Memory limits depend on CPU configuration: - 1 vCPU: up to 4Gi - 2 vCPUs: up to 8Gi - 4 vCPUs: up to 32Gi

To properly set the resources that will be applied to Cloud Run, you need to: 1. Create a base job template JSON file (e.g.,

cloud-run-template.json

) with the resource configurations:

json
{
  "job_configuration": {
    "resource_limits": {
      "cpu": "2",
      "memory": "4Gi"
    }
  }
}

2. Update your work pool with these settings:

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

Alternatively, you can set this up in the Prefect UI: 1. Go to the Work Pools page 2. Select your Cloud Run work pool 3. Click "Edit" 4. In the "Base Job Template" section, add the resource configurations 5. Save the changes After setting this up at the work pool level, you can still override these settings for specific deployments in your

prefect.yaml

as shown earlier:

yaml
deployments:
  - name: your-deployment-name
    work_pool:
      name: your-cloud-run-pool
      job_variables:
        resource_limits:
          cpu: "4"
          memory: "8Gi"

Important notes: 1. The work pool configuration serves as the default for all deployments using that pool 2. Deployment-specific configurations in

prefect.yaml

will override the work pool defaults 3. Make sure your Cloud Run service account has the necessary permissions to create jobs with custom resource configurations 4. Remember Cloud Run's resource limits: - CPU: 1, 2, or 4 vCPUs - Memory: Must be appropriate for the CPU configuration (e.g., up to 8Gi for 2 vCPUs) If you're still seeing issues after making these changes: 1. Verify the work pool configuration is correct using:

bash
prefect work-pool inspect "your-cloud-run-pool"

2. Check the Cloud Run logs for any permission or resource-related errors 3. Ensure your Cloud Run worker is running with the latest configuration

Based on this, I can provide you with a template that only adds resource specifications while preserving all other defaults. Here's how to do it in your `prefect.yaml`:

yaml
deployments:
  - name: your-deployment-name
    work_pool:
      name: your-cloud-run-pool
      job_variables:
        cpu: "2"    # Specify your desired CPU
        memory: "4Gi"  # Specify your desired memory

This approach is safer because: 1. It only specifies the resource variables (

cpu

and

memory

) 2. All other default template variables remain untouched 3. The variables are inserted into the default template's

{{ cpu }}

and

{{ memory }}

placeholders You can also set these variables in a deployment using Python:

python
from prefect import flow
from prefect.deployments import Deployment

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

deployment = flow.from_source(
    source="your-source",
    work_pool_name="your-cloud-run-pool",
    job_variables={
        "cpu": "2",
        "memory": "4Gi"
    }
).deploy(name="my-deployment")

Important notes: 1. Valid CPU values for Cloud Run: "1", "2", or "4" 2. Memory values must include units and be appropriate for the CPU: - 1 CPU: up to 4Gi - 2 CPU: up to 8Gi - 4 CPU: up to 32Gi 3. All other configurations (timeouts, service accounts, etc.) will remain at their default values 4. The resource specifications will only apply to this specific deployment To verify the configuration: 1. Check the deployment in the Prefect UI 2. Look at the "Job Configuration" section to confirm only the resource settings were modified 3. Monitor the Cloud Run jobs to ensure they're running with the specified resources This approach ensures you're only modifying the resource allocations while keeping all other Cloud Run configurations at their default values.