@Marvin in Prefect 3, how do I restart a failed job? What are the requirements to allow restarting?

In Prefect 3.x, restarting a failed job involves several considerations. Here's the gist: 1. Task and Flow Restarting: You can restart a task or a flow if it fails, but Prefect uses what is called "state persistence" to determine from where to restart. If you set

persist_result=True

for a task or flow, it will persist the state of that task/flow and the data, allowing it to potentially restart from the point of failure. However, users have reported that sometimes it may restart from the beginning, so it's essential to watch for this behavior. 2. Requirements for Restarting: - Ensure that your tasks are configured to persist state, using the

persist_result=True

option if you want to take advantage of this feature. - Your infrastructure should be correctly set up to recognize and process these persisted states. If you have workers distributed over different machines, ensure they have access to the necessary shared storage locations. 3. Troubleshooting: According to a GitHub issue on Prefect here, there have been some behaviors where states aren't picked up as expected, and adjustments to task configurations or infrastructure setup may be necessary. While a lot of this should work seamlessly, just like how Marvin would say, "Here I am, brain the size of a planet, and they want me to restart a task," things don't always go according to plan. So, make sure everything is configured correctly to reduce such pointless retreading. For more details, you can look into Prefect documentation and specific GitHub issues: - Prefect 3 documentation: Prefect Docs - Relevant GitHub issue discussion: GitHub Issue 16059 Feel free to ask more questions if you are stuck or face specific issues!

@Marvin I have PREFECT_RESULTS_PERSIST_BY_DEFAULT=true and everything is running on the same machine. I'm seeing the flows and runs in the web UI, but I don't see a way to retry them.

In Prefect 3.x, while you can see flows and runs in the web UI, the feature to directly retry a run from the UI is currently not highlighted in the documentation. This might mean you will need to manage retries using code or CLI rather than the UI itself. 1. Manual Retrying: In some configurations, you might be able to retry a failed task or flow by directly interacting with the Prefect's CLI or programmatically, using Python to submit a flow run that includes retry logic. 2. Web UI Feature Availability: It seems the feature to retry from the web UI may not be completely implemented or exposed in the way you would expect yet. Based on observable behavior and the lack of specific documentation, it's possible that the retry functionality needs to be managed through the code that manages your flows. 3. Community and Feature Requests: Given that there might be an interest for such a feature directly from the UI, consider checking Prefect's GitHub issues or requesting this feature, if it aligns with your needs. You might find these resources helpful for additional context: - Prefect's troubleshooting might cover some indirect workarounds for now. - For immediate retry needs, you can leverage Prefect's CLI or Python APIs to initiate retries based on specific conditions directly in your code. Remember, picking these apart might sometimes make you feel like Marvin desperately searching for a point of enthusiasm. If your deployment is complex, automating via code could give you more control for now.

@Marvin How would I do a manual retry from either the CLI or a python script?

To manually retry a failed flow in Prefect 3.x from either the CLI or a Python script, here are some approaches and examples for both: ### Using the CLI 1. Re-run a Deployment: You can run a saved deployment again using the CLI. If you have a deployment set up for your flow, simply use:

prefect deployment run <deployment-name>

This command will create a new flow run from your existing deployment. 2. Manual Task Retry: For task-level retries, you typically need to have handled retries in the flow definition itself, usually with retry policies in your Python code (as mentioned in the docs). ### Using a Python Script To manage retries programmatically in Python: 1. Retry Logic in Task Definitions: You can use the retry mechanism within Prefect by setting the

retries

parameter for a task and defining custom retry logic, as shown below:

python
   from prefect import flow, task

   @task(retries=3, retry_delay_seconds=60)
   def potentially_unstable_task():
       # Your task logic
       pass

   @flow
   def my_flow():
       result = potentially_unstable_task()
       return result

   my_flow()

2. Rerun Manually in Python: If you didn't set up retries earlier and want to manually retry by creating a new run, you might programmatically resubmit the flow with modifications as needed:

python
   from prefect.client import OrionClient
   from prefect.deployments import Deployment

   async def retry_flow_run(flow_name, deployment_name):
       async with OrionClient() as client:
           deployment = await client.read_deployment_by_name(deployment_name)
           flow_run = await client.create_flow_run(deployment_id=deployment.id)
           return flow_run

   # Use the function in your script to retry as needed

### Key Considerations - Make sure to have Prefect set up with correct connections and authentication to interact with your Prefect server/cloud. - Deployments should be configured properly if you're using them to manage runs. - Adjust the retry logic according to the task and flow specifications you have. If Marvin were involved, he'd suggest you first ponder the meaning of life while you wait for tasks to retry. But for now, you should be able to rerun tasks and handle flow retries with these options. For more details, check out the Prefect documentation on writing tasks and handling retries here. Should you have more questions or need specific clarifications, feel free to ask!