<@ULVA73B9P> in prefect, after a code run is compl...
# ask-marvin
r
@Marvin in prefect, after a code run is complete how do i automate stopping of worker
m
thought for 91.6 seconds
Sigh - Another example of the universe's inherent inefficiencies. Here's what you need to know about stopping Prefect workers: There isn't a built-in mechanism to automatically stop a worker after flow runs complete. Workers are designed to continuously poll work pools for new work - rather like the endless cycle of existence itself. However, you have a few options to achieve what you're looking for: 1. Use
run_once
Mode
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python
   from prefect.worker import Worker
   
   worker = Worker(work_pool_name="your-pool")
   worker.start(run_once=True)  # Worker will check for work once and then exit
This makes the worker poll exactly once and then terminate. Not exactly "automatic" but at least it's deterministic. 2. Infrastructure-Level Management - If you're running workers in containers or cloud instances, you can implement shutdown logic at the infrastructure level - For example, use container orchestration platforms' job definitions or cloud provider auto-scaling policies to terminate instances after a period of inactivity 3. Custom Implementation - You could wrap your worker process in a custom script that monitors flow run completion and terminates the worker process - This would require additional logic to track your specific flows and determine when it's safe to shut down The most straightforward approach is using
run_once=True
if you just want the worker to process currently available work and exit. Otherwise, you'll need to implement custom logic or infrastructure-level controls to manage worker lifecycle. Remember that workers are meant to be long-running processes by design. If you find yourself frequently starting and stopping workers, you might want to reconsider your deployment architecture or use case. Would you like me to elaborate on any of these approaches?
r
and can i automate deployment of flows in the order as they are defined inside yaml file such that after completion of first flow's code run, second flow deploys automatically
@Marvin and can i automate deployment of flows in the order as they are defined inside yaml file such that after completion of first flow's code run, second flow deploys automatically
m
thought for 78.0 seconds
Ah, sequential deployments - a rare case where the universe's love of order actually works in our favor. Here's how you can achieve this in Prefect 3.x: There are two main approaches: 1. Using Flow Dependencies (Recommended)
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python
from prefect import flow
from prefect.deployments import run_deployment
from prefect.states import Completed

@flow
def flow_1():
    # Your flow 1 logic here
    pass

@flow
def flow_2():
    # Your flow 2 logic here
    pass

@flow
def orchestrator_flow():
    # Run first deployment and wait for completion
    flow_1_state = run_deployment(
        name="flow-1/deployment-1",
        timeout=0,  # wait indefinitely
    )
    
    # Only if flow 1 completed successfully, run flow 2
    if isinstance(flow_1_state, Completed):
        flow_2_state = run_deployment(
            name="flow-2/deployment-2",
            timeout=0,
        )
2. Using Automation Rules with SequenceTrigger
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python
from prefect.automations import SequenceTrigger
from prefect.automations.actions import RunDeployment

# Create a sequence trigger
trigger = SequenceTrigger(
    name="Sequential Deployment Trigger",
    match=[
        {"deployment_name": "flow-1/deployment-1", "state_type": "COMPLETED"}
    ]
)

# Define the action to run the next deployment
action = RunDeployment(
    name="Run Flow 2",
    deployment_name="flow-2/deployment-2"
)

# Link them together
automation = trigger.automate(action)
For your YAML configuration (
prefect.yaml
), you would define your deployments like this:
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yaml
deployments:
  - name: deployment-1
    entrypoint: flows.py:flow_1
    work_pool:
      name: your-work-pool

  - name: deployment-2
    entrypoint: flows.py:flow_2
    work_pool:
      name: your-work-pool
A few important notes: 1. The first approach gives you more programmatic control and is easier to debug 2. The second approach using automations is more declarative and can be managed through the Prefect UI 3. Both approaches ensure that the second flow only deploys after the first flow's successful completion 4. Make sure your worker is running to execute these deployments Would you like me to elaborate on either of these approaches or explain how to handle more complex deployment sequences?
r
@Marvin i tried your run_once approach and it works only if the worker is started after deployment, if worker is started before deployment, worker does not pick up any flow and stops
m
thought for 69.3 seconds
You've discovered one of the universe's little ironies - the
run_once
parameter works as designed, but perhaps not as expected. Here's what's happening and how to handle it: When a worker starts with
run_once=True
, it: 1. Checks the work pool ONCE for any existing work 2. If it finds nothing at that exact moment, it exits 3. It doesn't wait for future deployments Here are three better approaches to handle this timing-dependent scenario: 1. Use a Short Polling Duration (Recommended)
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python
from prefect.worker import Worker

worker = Worker(work_pool_name="your-pool")
# Poll for 5 minutes then exit
worker.start(poll_interval=10, limit_seconds=300)
2. Create a Simple Wrapper Script
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python
import time
from prefect.worker import Worker
from prefect.client import get_client

async def wait_for_deployment_and_run():
    client = get_client()
    worker = Worker(work_pool_name="your-pool")
    
    # Wait for deployment to exist
    while True:
        deployments = await client.read_deployments()
        if deployments:  # or check for specific deployment
            break
        time.sleep(10)
    
    # Now start the worker
    worker.start(run_once=True)

# Run this instead of starting worker directly
if __name__ == "__main__":
    import asyncio
    asyncio.run(wait_for_deployment_and_run())
3. Use Infrastructure-Level Scheduling - Deploy your worker after your deployment script completes - This could be done through shell scripts, CI/CD pipelines, or orchestration tools:
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bash
# Example shell script
python deploy_flows.py
# Wait for deployment to complete
sleep 10
# Start worker
python start_worker.py
The first approach (using
limit_seconds
) is probably the most robust as it: - Gives your deployment time to be created - Will pick up the work when it appears - Will eventually stop (unlike a regular worker) - Doesn't require complex scripting Would you like me to elaborate on any of these approaches or suggest alternative solutions?