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<@ULVA73B9P> why can't i call submit() on a task? ...
# ask-marvin
t
@Marvin why can't i call submit() on a task? do i need to import anything or return something from a task function?
m
To help you with this issue, I need to determine which version of Prefect you are using, as the approach may differ between Prefect 2.x and 3.x. Could you please let me know your Prefect version? If you're unsure, you can run 
prefect --version
in your terminal to find out.
t
@Marvin version is 3
m
In Prefect 3.x, you can indeed call the 
submit()
method on a task, but it's important to do this within the context of a flow. Tasks in Prefect operate within flows, which manage the execution of the tasks. Here's a brief overview of how you can correctly use the 
submit()
method: 1. Structure: Ensure that your task is called within a flow. A 
submit()
call on a task is valid within this scope. 2. Task Runners: If you do not specify a task runner, Prefect will use the default 
ThreadPoolTaskRunner
, which supports 
submit()
. You can specify others if needed, like 
DaskTaskRunner
. 3. Using PrefectFuture: When you use 
submit()
, it returns a 
PrefectFuture
object. You can call methods like 
wait()
on this object to handle the execution flow, but issues may arise if these aren't correctly managed. Here's an example based on Prefect 3.x:
Copy code
python
from prefect import flow, task

@task
def my_task(data):
    return data * 2

@flow
def my_flow():
    future = my_task.submit(10)
    result = future.wait()
    print(f"Task result: {result}")

my_flow()
### Common Pitfalls: - Make sure that the 
submit()
calls are properly awaited or managed, using 
PrefectFuture.wait()
to ensure tasks complete as expected. - Ensure you are using task runners that support concurrent execution if needed. For more details, you can check these sources: - Prefect 3.x Task Runners - GitHub Issue: Callable Task Name Generators If you face specific error messages or behavior, it may relate to known issues such as task retry logic or how Prefect manages futures across different runners, as seen in some GitHub discussions.
t
@Marvin write a a flow that has task 1 2 3 4, task 1 2 3 runs sequentially, and task 4 runs parallel with task 2. None of the task should take in any parameter or return anything. Is it possible?
m
Yes, it's possible to arrange tasks in a Prefect 3.x flow such that tasks 1, 2, and 3 run sequentially, while task 4 runs in parallel with task 2. This can be achieved using dependencies between tasks in the flow. Here's an example of how you can accomplish this:
Copy code
python
from prefect import flow, task

@task
def task_1():
    print("Task 1 is running.")

@task
def task_2():
    print("Task 2 is running.")

@task
def task_3():
    print("Task 3 is running.")

@task
def task_4():
    print("Task 4 is running.")

@flow
def my_flow():
    # Task 1 -> Task 2 -> Task 3: Sequential dependency
    t1 = task_1()
    t2 = task_2(wait_for=[t1])
    t3 = task_3(wait_for=[t2])
    
    # Task 4 runs parallelly with Task 2
    t4 = task_4(wait_for=[t1])  # Independent of Task 2

    # Ensure tasks 3 and 4 both complete before ending flow
    t3.wait()
    t4.wait()

my_flow()
### Explanation: - Task 1 executes first due to there being no dependencies. - Task 2 is set to start after Task 1 finishes using 
wait_for
. - Task 4 starts after Task 1, but independently of *Task 2*—running concurrently. - Task 3 only proceeds once Task 2 completes, creating a sequential link. You can adapt this structure to fit other flow scenarios as needed. For more detailed guidance, you can refer to Prefect's documentation on writing flows and managing task dependencies: - Prefect 3.x Writing Flows - Prefect 3.x Task Runners This setup will help ensure that your tasks are executed in the desired order.
t
@Marvin, how can I serve multiple flows? Each flow have differnt retry and schedule?
m
In Prefect 3.x, you can serve multiple flows, each with different retry policies and schedules, using Prefect's flow deployment utilities. Here's how you can manage this: ### Serving Multiple Flows You can use the 
serve()
method along with 
to_deployment()
to handle multiple flows within the same script. Each flow can be configured with specific scheduling and retry policies.
Copy code
python
from prefect import flow, serve
import time

@flow(retries=2, retry_delay_seconds=10)
def flow_with_retry():
    print("This flow will retry.")

@flow
def quick_flow():
    print("This flow executes quickly.")

if __name__ == "__main__":
    flow_with_retry_deploy = flow_with_retry.to_deployment(
        name="retry-flow",
        interval=60  # Example schedule every 60 seconds
    )
    
    quick_flow_deploy = quick_flow.to_deployment(
        name="quick-flow",
        interval=30  # Example schedule every 30 seconds
    )
    
    serve(flow_with_retry_deploy, quick_flow_deploy)
### Setting Custom Retry Policies Prefect tasks can have custom retry policies using the 
@task
decorator. You customize the number of retries, delay between retries, and more. Example:
Copy code
python
from prefect import task

@task(retries=3, retry_delay_seconds=[1, 10, 30])
def my_task():
    print("Retry task logic here.")
### Scheduling Flows You can schedule flows using various approaches: - Inline Deployment: As shown in the example, use 
interval
or 
cron
expressions with 
to_deployment()
. - Prefect UI: Adjust schedules dynamically via the Prefect UI. - CLI and 
prefect.yaml
: Manage schedules through command-line interface or configuration files. To learn more, refer to these resources: - Running Flows in Local Processes - Task Retry Configuration - Adding Schedules to Deployments By utilizing these tools, you can effectively manage multiple flows with distinct operational requirements.
t
@Marvin, can you create and run a task without a flow?
m
In Prefect, tasks are designed to be run within the context of a flow, as flows provide the orchestration capabilities such as retry policies, concurrency management, and state tracking. Running a task outside a flow would bypass these important features. However, for testing or experimenting purposes, you can technically call a task as a regular Python function, but this will not leverage Prefect's orchestration capabilities such as retries and logging:
Copy code
python
from prefect import task

@task
def my_task():
    print("Task is running.")

# Directly calling the task function (outside Prefect's orchestrated environment)
my_task.fn()  # Use .fn() to call the underlying function of a task
### Considerations: - No Orchestration: Running as a standalone Python function means you won't have Prefect's orchestration features, like state management, retries, or concurrency. - No Flow Features: Features like monitoring, logging, or dashboard visualization are unavailable. - Primarily for Testing: It's usually done for testing the task logic itself without orchestration. For full functionality, including retries and scheduling, you should integrate tasks within flows. This ensures best practices in workflow management are adhered to. For more on orchestrating tasks, see the Prefect Documentation on Tasks.
t
@Marvin Hi, if I schedule a flow to execute, say every 1 hour, through serve api, my terminal calling the python Prefect code is hung to run that Prefect flow. If I ctrl + c to exit, the flow will stop running. If I have 100 flows, then I will have 100 hung terminals. There has to be a better way to schedule once and only manage the scheduled flows on the UI. How could I do it?
m
You're correct that managing multiple flows using the 
serve()
command in a terminal session can be cumbersome, especially when scaling up to multiple flows. Prefect offers a more scalable and manageable approach through its deployment and orchestration features. Here’s how you can handle this more efficiently: ### Use Prefect Cloud or Prefect Server Instead of using 
serve
from a terminal, you can deploy your flows to Prefect Cloud or a self-hosted Prefect Server. This way, the scheduling and execution of your flows are managed centrally, and you don't need to keep your local environment running continuously. 1. Deploy Flows Using Prefect CLI: Prefect’s CLI allows you to deploy and schedule flows without running them locally. This method deploys the flows to Prefect's orchestration layer (either Prefect Cloud or a self-hosted instance).
Copy code
bash
   prefect deployment build my_flow.py:my_flow -n "My Flow"
   prefect deployment apply my_flow-deployment.yaml
This creates a deployment spec that includes scheduling information, which Prefect will manage. 2. Schedule Through UI/CLI: Use the Prefect UI or CLI to manage schedules. Schedules can be dynamically adjusted through the Prefect Cloud or Server UI, or specified when deploying flows via command line or configuration files. - CLI Command Example:
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bash
     prefect deployment schedule "My Flow" --cron "0 * * * *"
3. Utilize Prefect Agents: Prefect agents can run as lightweight services on your infrastructure. They poll the orchestration backend for scheduled work and execute flows. You only need to run agents, which can handle multiple flows. ### Steps to Set Up - Set up an Agent: Deploy agents on your infrastructure that are connected to Prefect Cloud or your on-premise server to execute flows. - Use Work Queues: Assign flows to work queues, and agents will handle execution based on these queues. - Centralized Monitoring and Logging: With Prefect's cloud or server setup, you get centralized logging, flow state management, and the ability to adjust flows and schedules without dealing with each flow individually via terminal. ### Resources - Prefect Deployments - Prefect Agents - Managing Schedules By using these features, you can efficiently manage large numbers of flows and benefit from Prefect's full orchestration capabilities without manually running each flow in a terminal session.
t
@Marvin if I run task in the background, aka deferred task, does it run outside of a flow
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