Based on my research and the available information, I can provide a solution for customizing resources per deployment while using a shared job template in Kubernetes. Here's how you can do it: 1. First, define your base job template with common configurations in your work pool settings. This can include all shared configurations except for the resources. 2. When creating your deployments, you can override or extend the resource specifications using the

job_variables

parameter. Here's how:

python
from prefect import flow
from prefect.deployments import Deployment

@flow
def my_resource_intensive_flow():
    # Your flow code here
    pass

# Deploy with custom resource requirements
deployment = flow.from_source(
    source="your_source",
    name="resource-intensive-deployment",
    work_pool_name="your-k8s-workpool",
    job_variables={
        "job": {
            "spec": {
                "template": {
                    "spec": {
                        "containers": [{
                            "name": "prefect-job",  # This name must match your base template
                            "resources": {
                                "requests": {
                                    "memory": "2Gi",
                                    "cpu": "1000m"
                                },
                                "limits": {
                                    "memory": "4Gi",
                                    "cpu": "2000m"
                                }
                            }
                        }]
                    }
                }
            }
        }
    }
).deploy()

Key points to note: 1. The

job_variables

structure follows the Kubernetes Job specification format. 2. Make sure the container name (

prefect-job

in the example) matches the name in your base template. 3. You only need to specify the parts you want to override - the rest will be inherited from your base template. 4. The resources specification follows the standard Kubernetes resource request/limits format. For a deployment with different resource requirements, you would simply adjust the values in the

resources

section while keeping the rest of your base template intact. For example, for a lighter-weight deployment:

python
deployment = flow.from_source(
    source="your_source",
    name="lightweight-deployment",
    work_pool_name="your-k8s-workpool",
    job_variables={
        "job": {
            "spec": {
                "template": {
                    "spec": {
                        "containers": [{
                            "name": "prefect-job",
                            "resources": {
                                "requests": {
                                    "memory": "256Mi",
                                    "cpu": "100m"
                                },
                                "limits": {
                                    "memory": "512Mi",
                                    "cpu": "500m"
                                }
                            }
                        }]
                    }
                }
            }
        }
    }
).deploy()

This approach allows you to: 1. Maintain a single base job template with common configurations 2. Override resource specifications per deployment 3. Keep your deployment code DRY (Don't Repeat Yourself) 4. Maintain flexibility for different workload requirements Remember that the resource values you specify must be valid Kubernetes resource quantities and should align with your cluster's capacity and quotas.

❌ Error: No module named 'prefect_kubernetes'

❌ Error: No module named 'prefect_kubernetes'