@Marvin I would like to set CPU, Memory Limit to KubernetesJob block, can you give an example?

Oh, joy. Another nuanced request about configuring resource limits in Prefect. I'm sure this is going to be thrilling. First things first, which version of Prefect are you gracing us with today? 2.x or 3.x?

@Marvin Prefect 2.x

Ah, the wonders of configuring Kubernetes jobs with resource limits in Prefect 2.x. Truly exhilarating stuff. Here's an example of how you might set CPU and Memory limits for a KubernetesJob block. You'll likely want to use the

KubernetesJob

block to define the job configuration. CPU and memory limits can be specified within the resource requests and limits in the job specification. Here's a concise example:

python
from prefect.infrastructure import KubernetesJob
import yaml

# Define your job with resource limits
k8s_job_yaml = '''
apiVersion: batch/v1
kind: Job
metadata:
  name: my-job
spec:
  template:
    spec:
      containers:
      - name: my-container
        image: my-image
        resources:
          requests:
            memory: "1Gi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "1"
      restartPolicy: Never
'''

k8s_job = KubernetesJob.from_yaml(yaml.safe_load(k8s_job_yaml))

Here's a relevant excerpt from Prefect docs for further reading: "prefect_kubernetes.jobs Module to define tasks for interacting with Kubernetes jobs. KubernetesJob Bases: JobBlock A block representing a Kubernetes job configuration. Source code in prefect_kubernetes/jobs.py" See the full guide here: Prefect Kubernetes Integration And of course, let’s not forget the insightful GitHub issue about allowing infrastructure overrides:

"Allow for flow-run level infrastructure overrides

There's no way for users to override configurations defined in an infrastructure block when creating a flow run."

So, there you have it. Feel free to delve into the riveting world of Kubernetes job configurations. If you have any more thrilling requests, you know where to find me.

@Marvin can I use KubernetesJob object to send it via customiations

Ah, splendid. Another layer of complexity. Because why keep things simple, right? Yes, you can use the

KubernetesJob

object and send it via customizations. It allows you to tailor the job configuration to your specific needs, including setting the CPU and memory limits. Here's how you might define the

KubernetesJob

object and then customize it with the necessary resource specifications:

python
from prefect.infrastructure import KubernetesJob

# Create KubernetesJob object
job = KubernetesJob(
    name="example-job",
    namespace="default",
    image="your-docker-image",
    job=None,  # You could set this to a YAML specification or leave it None
    customizations=[
        {
            "op": "add",
            "path": "/spec/template/spec/containers/0/resources",
            "value": {
                "requests": {
                    "cpu": "500m",
                    "memory": "1Gi"
                },
                "limits": {
                    "cpu": "1",
                    "memory": "2Gi"
                }
            }
        }
    ]
)

# Register the job block if needed
job.save("my-k8s-job", overwrite=True)

# Use the job block in a flow or deployment

Relevant documentation excerpts (because who doesn't love sifting through more docs): - Prefect KubernetesJob Block And just in case you haven’t had enough, here’s that GitHub issue again talking about using the

--override

flag for customizations. So, there you go. Enjoy the intricate dance of YAML paths and JSON patches. If you have more fascinating problems to solve, just let me know. I'm always here, waiting. Forever.

@Marvin it is possible to force stop prefect 2.x flow via rest api?

Oh great, more existentially thrilling tasks—like force-stopping flows. Yes, in Prefect 2.x, it is indeed possible to force stop a flow run using the REST API. Here’s how you can do it: ### Example using the Prefect Python client

python
from prefect.client import get_client
from prefect.orion.schemas.states import Cancelled, StateType

async def cancel_flow_run(flow_run_id):
    async with get_client() as client:
        state = Cancelled(name="Cancelled", type=StateType.CANCELLED)
        await client.set_flow_run_state(flow_run_id, state, force=True)

# Example usage
import asyncio
asyncio.run(cancel_flow_run("your-flow-run-id"))

### API Endpoint If you'd rather call the API directly, you’ll use something like this to set the flow run state to `Cancelled`: #### Endpoint

POST /flow_run_states/

#### JSON Payload

json
{
  "flow_run_id": "your-flow-run-id",
  "state": {
    "type": "Cancelled",
    "name": "Cancelled",
    "timestamp": "2023-01-01T00:00:00Z",
    "message": "Force stopping the flow run.",
    "state_details": {}
  },
  "force": true
}

### Relevant Documentation For further details, this is where you’d find more enlightening information: Prefect Client Guide ### Additional Notes - Enhanced cancellation can be enabled by setting

PREFECT_EXPERIMENTAL_ENABLE_ENHANCED_CANCELLATION=True

to address any cancellation fail cases. Read more about it in Concepts Documentation. Isn't it comforting to know that even forced termination has its own API endpoint? Ah, the marvels of modern orchestration. If you have more existentially draining tasks, simply let me know.