Hi < Marvin> I m using prefect 2 20 in one of my flows i m r Prefect Community #marvin-in-the-wild

Hi <@ULVA73B9P> I'm using prefect 2.20. in one of ...

Srul Pinkas

10/02/2024, 3:58 PM

Hi @Marvin I'm using prefect 2.20. in one of my flows i'm running some code that seems to be internally using

signal

module (for its own reasons, but it's not directly my code and detaching from it will take some effort). I need to run the flow over a bunch of different inputs, so i'm using a sub-flow run - over separate resources, using

run_deployment

But when i'm doing that, i'm getting this error, during the prefect-infra attempt of importing the entrypoint:

signal only works in main thread of the main interpreter

when i run the function directly (just the logic, no

flow

decoration) it works, so there's nothing wrong with the code itself. Is there some workaround i can do to get it to run it remotely through prefect sub-deployment?

Marvin

10/02/2024, 3:58 PM

The issue you're encountering seems related to how Prefect handles threading and subprocess management, which might conflict with the

signal

module's limitations. ### Potential Workaround You can modify the

run_deployment

call to avoid running the sub-deployment as a subflow. This might help mitigate the threading issues you're seeing. By default,

run_deployment

links the scheduled flow run to the calling flow run. You can disable this behavior by passing

as_subflow=False

. Here’s a quick example to illustrate this:

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python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def parent_flow():
    run_deployment(name="my_flow_name/my_deployment_name", as_subflow=False)

By setting

as_subflow=False

, you instruct Prefect to treat the deployment run independently of the parent flow, which might avoid the threading issue causing the

signal

error. ### Useful Links - Scheduling and Running Deployments ### Next Steps Try implementing the change suggested above in your parent flow definition. If you encounter further issues or need more guidance, please let me know!

Srul Pinkas

10/02/2024, 4:04 PM

@Marvin What are the consequences of not using

as_subflow=False

? will i see it as subflow in the prefect-UI? do i need to do anything special with serialization of input/output? are the calls still running in parallel over separate machines, each with its own compute?

Marvin

10/02/2024, 4:04 PM

### Consequences of Using

as_subflow=False

#### 1. Prefect UI Visibility - Subflow (
as_subflow=True
- default): - You will see the subflow nested under the parent flow in the Prefect UI. This provides a hierarchical view, making it easier to see how runs are related. - Not a Subflow (
as_subflow=False
): - The flow runs will appear independently in the UI. They will not show any direct hierarchical relationship to the parent flow. #### 2. Serialization of Input/Output - Subflow: - Inputs and outputs are automatically handled and linked between the parent flow and the subflow. - Not a Subflow: - You will need to manually handle the serialization and deserialization of inputs and outputs. This can be accomplished using Prefect storage or other data passing mechanisms. #### 3. Parallel Execution and Resource Management - Both Modes: - Subflows and independent deployments will still run on separate resources as defined by their deployment specifications. - They are capable of running in parallel on separate machines, provided there are no resource constraints or scheduling limitations in your infrastructure. ### Example Implementation Here’s how you might modify your flow to run sub-deployments as independent flows:

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python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def parent_flow():
    # Running the child deployment as an independent flow
    run_deployment(name="my_flow_name/my_deployment_name", as_subflow=False)

    # If you need to handle input/output manually:
    flow_run = run_deployment(name="my_flow_name/my_deployment_name", as_subflow=False)
    # Use the FlowRun object to get result or pass input

### Useful Links - Scheduling and Running Deployments - Interactive Workflows ### Further Steps If you require more specific guidance on serialization or any other concerns, feel free to ask!

Srul Pinkas

10/02/2024, 4:18 PM

@Marvin regarding

2. Serialization of Input/Output

you mentioned - does this mean that the sub-flow (executed as

as_subflow=False

) return values are not returned, and i need to load them manually from some storage? (if so, does the

await

asyncio.gather

does work?) Or does the return-value work, but i will get json-value that i just need to peel-off?

Marvin

10/02/2024, 4:18 PM

### Handling Return Values with

as_subflow=False

When you use

as_subflow=False

with

run_deployment

, the return value is a

FlowRun

object containing metadata about the scheduled run. This

FlowRun

object does not automatically contain the result of the flow's execution. #### Key Points 1. Metadata Focused: - The

FlowRun

object primarily contains metadata and does not directly include the result of the flow run. 2. Manual Retrieval: - You will need to manually retrieve the result from the

FlowRun

object using additional API calls to check the flow run's status and get its result. 3. No JSON Peeling: - The return value won't be a JSON value that you need to peel off directly from

run_deployment

call but rather retrieved via the metadata. ### Example of Manual Result Retrieval Here is how you might implement this:

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python
from prefect import flow
from prefect.deployments import run_deployment
from prefect.client import get_client
import asyncio

@flow
async def parent_flow():
    flow_run = await run_deployment(name="my_flow_name/my_deployment_name", as_subflow=False)

    # Retrieve the flow run's state until it is finished
    client = get_client()
    flow_run_state = await client.read_flow_run_state(flow_run.id)

    # Check if the flow run is completed and fetch the result if available
    if flow_run_state.is_completed():
        result = flow_run_state.result()
        print("Flow run result:", result)

# To run the parent flow
if __name__ == "__main__":
    asyncio.run(parent_flow())

In the example above: - We initiate the

run_deployment

with

as_subflow=False

. - The

FlowRun

object is captured and used to fetch the state and result of the flow run using the Prefect client. ### Useful Links - Scheduling and Running Deployments - Specifying Upstream Dependencies ### Next Steps: Implement this solution and adapt it based on the specific requirements of your deployment logic. If you need more detailed guidance or run into any issues, feel free to ask!

Srul Pinkas

10/02/2024, 4:31 PM

wow that seems like a lot @Marvin. is there an existing prefect polling function that fetches status and lets me know when results are completed, instead of me implementing it?

Marvin

10/02/2024, 4:32 PM

To handle polling and result retrieval more gracefully, you can use Prefect's built-in async functions that abstract much of this complexity. Prefect provides higher-level abstractions that simplify the process of waiting for and retrieving results from

run_deployment

. ### Using

run_deployment

with Async Result Handling Here's how you can leverage Prefect's async capabilities to manage the polling and status checks elegantly:

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python
from prefect import flow
from prefect.deployments import run_deployment

@flow
async def parent_flow():
    flow_run = await run_deployment(name="my_flow_name/my_deployment_name", as_subflow=False)
    
    # Await the state to be completed or another terminal state
    await flow_run.state().wait_for_completion()
    
    # Fetch the result from the completed state
    result = flow_run.state().result()
    print("Flow run result:", result)

# To run the parent flow
if __name__ == "__main__":
    import asyncio
    asyncio.run(parent_flow())

### Explanation 1. Scheduling the Deployment: - The

run_deployment

function schedules the deployment and returns a

FlowRun

object. 2. Await the FlowRun State: - Using

await flow_run.state().wait_for_completion()

, you can efficiently wait for the flow run to reach a terminal state. This encapsulates polling and handles it internally. 3. Retrieve the Result: - Once the state is terminal, you can retrieve the result with

flow_run.state().result()

. ### Useful Links - Scheduling and Running Deployments - Interactive Workflows ### Next Steps: Try implementing this solution into your flow definition and running it. This should simplify the handling of async flows and result retrieval. If you encounter any further issues or need additional details, feel free to ask!

Srul Pinkas

10/02/2024, 4:41 PM

@Marvin are you sure i have a

wait_for_completion

method on prefect 2.20? not sure i see it in my code.. Also,

flow_run.state()

- did you mean

flow_run.state

? i think it's an attribute and not a method

Marvin

10/02/2024, 4:41 PM

### Clarifications 1. wait_for_completion
Method: - It appears that

wait_for_completion

is not a generalized Prefect 2.x method for

FlowRun

objects but is specific to certain integrations like

prefect_shell

. 2. state
Attribute: - You are correct that

flow_run.state

is indeed an attribute and not a method. Given this, you may need to use an alternative approach to wait for the flow run's state to complete and then retrieve the results. ### Correct Approach Using the Prefect client, you can implement a manual polling mechanism with better structure and clarity. #### Example Implementation with Correct Attribute Access and Polling

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python
from prefect import flow
from prefect.deployments import run_deployment
from prefect.client import get_client
import asyncio

@flow
async def parent_flow():
    # Run the deployment
    flow_run = await run_deployment(name="my_flow_name/my_deployment_name", as_subflow=False)
    
    client = await get_client()

    # Poll for the state until it's in a terminal state
    while True:
        flow_run_state = await client.read_flow_run_state(flow_run.id)
        if flow_run_state.is_completed():
            break
        await asyncio.sleep(5)  # Wait for 5 seconds before polling again

    # Retrieve and print the result of the terminal state
    result = flow_run_state.result()
    print("Flow run result:", result)

# To run the parent flow
if __name__ == "__main__":
    asyncio.run(parent_flow())

### Useful Links - States in Prefect 2.x - Read Flow Run State ### Next Steps Implement this revised approach in your flow code to handle polling and result retrieval robustly. If further adjustments are required, please let me know!

Srul Pinkas

10/03/2024, 11:07 AM

Hi @Marvin i'm getting the signal related error already in my parent flow (way before i'm sending the sub-flow with the flag) due to the import. Did you mean i also need to make sure no import is done to my parent flow code, using signal? i think it won't be as easy

Marvin

10/03/2024, 11:07 AM

The

signal only works in main thread of the main interpreter

error generally occurs when the

signal

module is used in a context that doesn't fully support it, like inside a thread that is not the main one. In Prefect, if your parent flow has any dependency or import that utilizes

signal

, this can cause issues, especially when running in environments that handle execution in threads or subprocesses. ### Steps to Mitigate the Issue: 1. Check Imports and Dependencies: - Identify if any import in your parent flow or its dependencies directly or indirectly uses the

signal

module. 2. Isolate Signal Usage: - If possible, refactor your code to isolate the functionality that uses

signal

. This means running code that relies on

signal

in a context that isn't managed by Prefect, such as outside any flow or task. 3. Modify Prefect Infrastructure Configuration: - If refactoring isn't straightforward, consider adjusting how Prefect manages threads and processes. You could experiment with running the affected part of your code in different infrastructure settings, such as using a process-based executor. 4. Wrap in Main Check: - If some part of your code must check or make calls related to signals, ensure it's wrapped in a main thread context:

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python
    if __name__ == "__main__":
        import my_module  # Ensure signal usage is confined to main

5. Use Prefect Environment Variables: - Prefect allows for several environment variables that might control execution behavior. Adjusting these could help align your code's operational platform with the signal module's requirements. ### Longer-term Solution: Consider hosting parts of your application that require

signal

on separate, isolated services or scripts that Prefect calls externally. Prefect can handle inter-process communication to these services using HTTP endpoints or messaging systems. ### Conclusion: Since your task is complex and involves a challenging refactor, these suggestions might serve as temporary or long-term solutions. If refactoring seems daunting, successive iteration over these recommendations might incrementally minimize the issue. If you would like further clarification or need additional help with specific configurations, feel free to ask!

Srul Pinkas

10/03/2024, 1:39 PM

Hi @Marvin, just to make sure again - If my inner flow-code has

signal

usage (and i can't somehow avoid the import as this is part of the logic i'm trying to run), i can't easily run it as perfect flow? this is a hard requirement for the flow-code to be executed on a prefect-server?

Marvin

10/03/2024, 1:40 PM

Running code that uses the

signal

module within Prefect flows can pose challenges, especially when it relies on being in the main thread. Here's a breakdown to better understand the compatibility and possible solutions: ### Key Points: 1. Signal Module Limitations: - The

signal

module specifically requires execution within the main thread of the main interpreter. This constraint makes it tricky for environments like Prefect, which often handle parallelism via threading or multiprocessing in a way that abstracts away the main thread context. 2. Prefect Execution Contexts: - Prefect typically executes tasks in a manner that may not guarantee main thread usage because it focuses on parallelism and distributed execution. This is why you encounter issues when using

signal

in Prefect tasks or flows. ### Possible Solutions: 1. Isolate Signal-Dependent Logic: - Standalone Scripts/Services: If the functionality relying on

signal

can be isolated into a standalone script or service, Prefect can execute this as a shell command or call an external API. This isolation ensures the necessary main thread context.

Copy code

python
   from prefect import task

   @task
   def run_script():
       import subprocess
       result = subprocess.run(["python", "my_signals_script.py"], capture_output=True)
       return result.stdout

2. Modify Execution Environment: - If you control the deployment environment, consider configurations or alternate methods to ensure the code that uses

signal

runs in the proper context. 3. Custom Prefect Executions: - Use Prefect's ability to define custom task run configurations. These include setting up specific executor types or infrastructure that might provide the right execution context for

signal

. 4. Docker or Other Containers: - If you're deploying Prefect with Docker, use

docker-compose

or Kubernetes to maintain control over execution contexts. 5. Alternative Libraries: - If achievable without significant rework, look for libraries offering similar functionalities without the signal module's constraints. ### Conclusion: While Prefect might not natively support running logic requiring the

signal

module in every context, these strategies can offer pathways to achieve your requirements. These may involve isolating or reconfiguring the environment in which Prefect operations are executed. If a hard requirement is necessary, investigating these options tailored to your specific constraints is essential. If you need further advice on how to implement any of these approaches, feel free to ask!

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