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<@ULVA73B9P> how do i call an async flow as a fast...
# ask-marvin
r
@Marvin how do i call an async flow as a fastapi background task?
m
To call an asynchronous Prefect flow as a FastAPI background task, you can follow these steps: 1. Define Your Prefect Flow: Ensure your flow is defined as an asynchronous function using the 
@flow
decorator from Prefect. 2. Set Up FastAPI: Create a FastAPI application and define a route that will trigger the background task. 3. Use FastAPI's Background Tasks: Utilize FastAPI's background tasks to run your flow asynchronously. Here's a basic example:
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python
from fastapi import FastAPI, BackgroundTasks
from prefect import flow

app = FastAPI()

@flow
async def my_async_flow(name: str):
    print(f"Running flow for {name}")
    return f"Completed flow for {name}"

@app.get("/run-flow")
async def run_flow(background_tasks: BackgroundTasks, name: str):
    background_tasks.add_task(my_async_flow, name)
    return {"message": f"Flow for {name} is running in the background"}
### Key Points: - Use 
@flow
to define your Prefect flow. - Use 
BackgroundTasks
from FastAPI to run the flow asynchronously. - Ensure your flow function is asynchronous if it involves I/O-bound operations. For more detailed examples and information, you can refer to the Prefect Background Task Examples and the Prefect documentation. If you have any specific requirements or configurations, feel free to ask!
r
@Marvin For my code:
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config = MyConfig(...)
background_tasks.add_task(my_flow, config)
I’m getting:
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share/uv/python/cpython-3.13.1-linux-x86_64-gnu/lib/python3.13/threading.py:1041: RuntimeWarning: coroutine 'run_flow_async' was never awaited
  self.run()
m
The warning you're encountering, 
RuntimeWarning: coroutine 'run_flow_async' was never awaited
, suggests that the asynchronous flow is not being properly awaited when run as a background task. This can happen if the function you're trying to execute as a background task is a coroutine and isn't being awaited. To address this, you need to ensure that the coroutine is awaited. Since FastAPI's 
BackgroundTasks
does not inherently handle asynchronous functions, you have a couple of options: 1. Run the coroutine within an async function: You can create an async wrapper function that awaits the coroutine and then add this wrapper to the background tasks. 2. Use 
asyncio.create_task
: Directly create an asyncio task for the coroutine within your route. Here's an example of how you can modify your code using the second approach:
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python
from fastapi import FastAPI, BackgroundTasks
from prefect import flow
import asyncio

app = FastAPI()

@flow
async def my_flow(config):
    # Your flow logic here
    print(f"Running flow with config: {config}")

@app.get("/run-flow")
async def run_flow(background_tasks: BackgroundTasks, config):
    # Create an asyncio task for the coroutine
    asyncio.create_task(my_flow(config))
    return {"message": "Flow is running in the background"}
### Key Considerations: - Ensure that your flow function is defined with the 
@flow
decorator and is asynchronous. - Use 
asyncio.create_task
to directly handle the coroutine execution outside of FastAPI's 
BackgroundTasks
. For more details on handling async functions in Prefect and FastAPI, you can refer to Prefect's upgrade guide and Prefect Background Task Examples.
r
@Marvin hm is there a way i can just call the flow as a background task, ignoring fastapi? like similar to how tasks can be delayed? would that involve serving the flow? if so, how do i call the flow after serving it
m
To execute a Prefect flow as a background task without involving FastAPI, you can leverage the 
serve
functionality to run flows locally and trigger them programmatically. Here's how you can do it: ### Steps to Serve and Trigger a Flow: 1. Define and Serve the Flow: - Use the 
serve()
method to run your flow in a long-running process. This allows the flow to be ready to receive and execute tasks. 2. Trigger the Flow: - Once the flow is served, you can trigger it programmatically using the Prefect client. Here's a basic example of how you might set this up:
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python
from prefect import flow, serve, get_client

@flow
async def my_async_flow(config):
    print(f"Running flow with config: {config}")
    return f"Completed flow for {config}"

def main():
    # Serve the flow
    serve(my_async_flow)  # This will keep the flow ready to receive tasks

if __name__ == "__main__":
    main()
### Triggering the Flow: After serving the flow, you can trigger it using the Prefect client like so:
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python
import asyncio
from prefect import get_client

async def trigger_flow():
    client = await get_client()
    await client.create_flow_run_from_deployment(
        deployment_name="my_async_flow",
        parameters={"config": "your_config_here"}
    )

# Run the trigger function
asyncio.run(trigger_flow())
### Key Points: - 
serve()
: Keeps the flow ready to receive tasks, acting as a long-running process. - 
get_client()
: Use this to interact with the Prefect API and trigger flow runs. For more detailed information on serving and running flows, you can refer to the Prefect documentation on running flows in local processes. This approach allows you to run and trigger flows independently of FastAPI, providing a more flexible background execution model.
r
@Marvin do i need to run 
prefect server start
separately in a different terminal process first? or will 
flow.serve()
spin up a prefect server for me automatically?
m
The 
flow.serve()
method in Prefect does not automatically start a Prefect server for you. It is a utility to serve the flow locally, allowing it to be ready to receive tasks and execute them. However, it does not handle the orchestration layer, which means you need to have a Prefect server running separately to manage and orchestrate flow runs. ### Steps to Ensure Proper Setup: 1. Start Prefect Server: You need to run 
prefect server start
in a separate terminal to start the Prefect server. This server acts as the orchestration layer, managing the execution of your flows. 2. Serve the Flow: Use 
flow.serve()
in your script to keep the flow ready to receive and execute tasks. 3. Connect to the Server: Ensure that your Prefect client is configured to connect to the running server. This might involve setting environment variables or using configuration files to specify the server URL. For more detailed information on deploying and running flows, you can refer to the Prefect documentation on running flows in local processes. If you have any more questions or need further clarification, feel free to ask!
r
@Marvin ok, also, do i have to call it in like the if 
__name__ == "__main__"
of the flow file? or can i just call it in my app 
__init__
method? would it block the init from finishing there?
m
The 
flow.serve()
function is indeed a long-running process, which means it will block the execution of any code following it. Therefore, it is typically called in a context where you expect the process to run indefinitely, like in the 
if __name__ == "__main__"
block. This ensures that the script can be executed directly and the serve function will run as intended, without blocking other initialization code. ### Usage Considerations: - Blocking Behavior: Since 
flow.serve()
is blocking, calling it in an 
__init__
method would prevent the initialization from completing, as it would hold the execution there. - Recommended Setup: Use 
if __name__ == "__main__"
to ensure that the serve function is called only when the script is executed directly, allowing the rest of your application to initialize properly without being blocked. Here's an example setup:
Copy code
python
from prefect import flow, serve

@flow
async def my_async_flow(config):
    print(f"Running flow with config: {config}")
    return f"Completed flow for {config}"

def main():
    # Serve the flow
    serve(my_async_flow)  # This will keep the flow ready to receive tasks

if __name__ == "__main__":
    main()
In this setup, the 
serve
function will only be called when the script is executed directly, ensuring that it doesn't interfere with other parts of your application, such as initialization logic in an 
__init__
method.
r
@Marvin i don’t like that I need one terminal process for 
prefect server start
and then a second for serving my flow, and then a third for my actual fastapi server. is there a way to specify that when my prefect server starts, that it should serve a specific flow?
m
Currently, Prefect does not provide a built-in mechanism to automatically serve a specific flow when starting the Prefect server itself. The 
prefect server start
command is designed to launch the server, which acts as the orchestration layer, but it does not inherently serve flows. However, you can streamline your setup by using a script to manage all the necessary processes. Here's a suggestion on how you might achieve this: ### Using a Script to Manage Processes You can create a script to launch all necessary components in parallel. This script can use tools like Python's 
subprocess
module or 
concurrent.futures
to manage multiple processes. Here's a basic example using Python's `subprocess`:
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python
import subprocess

def start_prefect_server():
    return subprocess.Popen(["prefect", "server", "start"])

def serve_flow():
    return subprocess.Popen(["python", "your_flow_script.py"])

def start_fastapi():
    return subprocess.Popen(["uvicorn", "your_fastapi_app:app", "--reload"])

if __name__ == "__main__":
    server_process = start_prefect_server()
    flow_process = serve_flow()
    fastapi_process = start_fastapi()

    try:
        server_process.wait()
        flow_process.wait()
        fastapi_process.wait()
    except KeyboardInterrupt:
        server_process.terminate()
        flow_process.terminate()
        fastapi_process.terminate()
### Key Considerations: - Process Management: This script handles starting and stopping all processes. Ensure your flow script and FastAPI app are correctly specified. - Error Handling: You might want to add more robust error handling and logging to manage these processes in a production environment. This approach allows you to manage all components from a single entry point, reducing the overhead of multiple terminal windows. However, keep in mind that this is a workaround and not a built-in Prefect feature.
r
@Marvin hm ok so i ended up doing:
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background_tasks.add_task(get_client().create_flow_run, variant_scoring_flow, parameters={"config":config})
without serving the flow explicitly, i just want to call it in the background. it is stuck in pending though
m
When a flow run is stuck in a "Pending" state, it typically means that the flow is awaiting execution by a Prefect worker or agent. Here are some common reasons and solutions: ### Common Causes and Solutions: 1. No Active Worker: Ensure that you have a Prefect worker or agent running that can pick up and execute the flow. Without an active worker, the flow will remain in a pending state. 2. Concurrency Limits: Check if there are any concurrency limits set on your work queue or pool. If the limits are reached, new flow runs will remain pending until existing ones complete. 3. Infrastructure Issues: If using dynamic infrastructure (like Kubernetes or Docker), ensure that the infrastructure is correctly configured and able to spin up the necessary resources. 4. Network Issues: Ensure that there are no network connectivity issues between your Prefect server and the infrastructure running the worker or agent. ### Steps to Resolve: - Start a Worker: Run a Prefect worker or agent to pick up the pending flow. You can do this by executing a command like 
prefect agent start -q <queue_name>
in a terminal where 
<queue_name>
is the name of the work queue your flow is assigned to. - Check Logs: Review logs for any errors or warnings that might indicate why the flow isn't being executed. - Configuration Review: Ensure that your Prefect client and server configurations are correctly set up, including any API keys or URLs. ### Additional Resources: For more detailed troubleshooting, you can refer to the Prefect documentation on work pools and queues or check relevant GitHub issues like Flow gets stuck in a loop of "submitting flow" and "abort signal" and Prefect job stuck in Pending state. These issues provide insights into common problems and potential fixes. If you continue to face issues, providing logs or additional context can help in diagnosing the problem further.
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