@Marvin how to make a deployment from dynamic flow? I don't know subflows and tasks in advance

From the search results, here's a concise guideline on making a deployment from a dynamic flow in Prefect. ### Prefect 2.x To create a deployment for a dynamic flow, you can use the Prefect deployment interface. Here is how you can accomplish that: 1. Define your dynamic flow: Define your flow in a dynamic way so that it can generate tasks/subflows at runtime. 2. Create Deployment objects: Use the

to_deployment

method to create deployment objects. This method lets you specify the necessary attributes of the deployment. 3. Register Deployment: You need to register the deployment with the Prefect server using the

serve

function (if serving multiple deployments). Example:

python
from prefect import flow

@flow
def dynamic_flow():
    # Code to dynamically generate tasks and subflows
    pass

if __name__ == "__main__":
    deploy = dynamic_flow.to_deployment(name="dynamic-flow-deployment")
    deploy.apply() # Registers the deployment with the Prefect API

For more detailed instructions, see this link: https://docs-2.prefect.io/latest/tutorial/deployments/ ### Prefect 3.x In Prefect 3.x, deployments are slightly different but you can still dynamically deploy flows. The process involves specifying work pools, work queues, and job variables in a JSON description or Python script. Example:

python
from prefect import flow
from prefect.deployments import Deployment

@flow
def dynamic_flow():
    # Code to dynamically generate tasks and subflows
    pass

if __name__ == "__main__":
    deployment = Deployment.build_from_flow(flow=dynamic_flow, name="dynamic-flow-deployment")
    deployment.apply() # Registers the deployment with the Prefect API

For more detailed instructions, see this link: https://docs-3.prefect.io/3.0/deploy/index Each version has its nuances, so depending on your specific version of Prefect, one of these examples should provide the necessary steps to create a deployment for a dynamic flow. Let me know if you have any further questions!

@Marvin what if my long running task\flow failed, how to restart it and restore the state?

Handling the failure and restart of long-running tasks or flows, while ensuring state restoration in Prefect, can be approached differently depending on whether you're using Prefect 2.x or 3.x. ### Prefect 2.x 1. Retries: You can set retries on your tasks and flows to automatically retry up to a certain number of times after a failure.

python
   from prefect import flow, task

   @task(retries=3, retry_delay_seconds=10)
   def risky_task():
       # Task logic
       pass

   @flow
   def my_flow():
       risky_task()

   my_flow()

2. State Change Hooks: Use state change hooks to execute custom logic when tasks or flows enter various states such as

Failed

.

python
   from prefect import flow, task
   from prefect.engine import signals

   @task
   def my_task():
       # Task logic
       pass

   def on_failure(task, old_state, new_state):
       if not new_state.is_failed():
           return new_state

       # Custom failure handling logic here
       # e.g., logging, notifications, cleanup

   @flow(state_handlers=[on_failure])
   def my_flow():
       my_task()

   my_flow()

3. Resuming Failed Flows: If a flow fails, you can manually restart it from the last successful state. Prefect doesn't do this automatically, so you'll need to implement a way to save states and restart from them, potentially using checkpoints. For more detailed instructions, see: https://docs-2.prefect.io/latest/concepts/flows/ ### Prefect 3.x 1. Retries: Similar to Prefect 2.x, Prefect 3.x allows setting retries on tasks and flows.

python
   from prefect import flow, task

   @task(retries=3, retry_delay_seconds=10)
   def risky_task():
       # Task logic
       pass

   @flow
   def my_flow():
       risky_task()

   my_flow()

2. State Change Hooks: In Prefect 3.x, you can also use state change hooks to manage flow and task states.

python
   from prefect import flow, task

   @task
   def my_task():
       # Task logic
       pass

   def on_failure(flow, flow_run, state):
       if state.is_failed():
           # Custom failure handling logic here
           # e.g., logging, notifications, cleanup
           pass

   @flow(on_failure=[on_failure])
   def my_flow():
       my_task()

   my_flow()

3. Resuming Failed Flows: Prefect 3.x provides more robust state management and allows for better manual control over resuming flows from a failed state, though you would still need to handle this manually. Using artifacts or result handlers to save and load states can be helpful. For more detailed instructions, see: https://docs-3.prefect.io/3.0/develop/manage-states Each version has methods to handle retries, failure hooks, and manual state management. Depending on your use case and version, you may need to use a combination of these strategies to restart and restore the state of your long-running tasks and flows effectively. Let me know if you have more questions!