@Marvin I have a mapped task, which I'll call A, followed by an unmapped task B. B doesn't take any inputs, but must be run after A is complete or else an error will occur. Currently the flow tries to run the tasks in parallel. How can I add a dependency to ensure that B doesn't start until A is finished?

In Prefect, you can use the

set_dependencies

method to add a dependency between two tasks. In your case, you want to ensure that task B doesn't start until task A is finished. Here's how you can do it:

from prefect import Flow, Task

class TaskA(Task):
    def run(self):
        # Your code here
        pass

class TaskB(Task):
    def run(self):
        # Your code here
        pass

with Flow("My Flow") as flow:
    task_a = TaskA()
    task_b = TaskB()

    flow.set_dependencies(task=task_b, upstream_tasks=[task_a])

In this example,

TaskB

won't run until

TaskA

has completed. The

set_dependencies

method sets

TaskA

as an upstream task of

TaskB

, creating a dependency. However, if

TaskA

is a mapped task, you might run into issues because Prefect treats each iteration of the mapped task as a separate task. If you want

TaskB

to run after all iterations of

TaskA

have completed, you'll need to use a different approach. One solution could be to use an additional task that collects the results of the mapped task and passes them to

TaskB

. Here's an example:

from prefect import Flow, Task

class TaskA(Task):
    def run(self):
        # Your code here
        pass

class TaskB(Task):
    def run(self):
        # Your code here
        pass

class Collector(Task):
    def run(self):
        # Your code here
        pass

with Flow("My Flow") as flow:
    task_a = TaskA().map(some_list)
    collector = Collector().map(task_a)
    task_b = TaskB(upstream_tasks=[collector])

In this example,

Collector

is a mapped task that takes the results of

TaskA

and

TaskB

waits for

Collector

to finish. This ensures that

TaskB

won't run until all iterations of

TaskA

have completed. Please note that this is a general approach and you might need to adjust it to fit your specific use case.

@Marvin research

wait_for

in the task docs

In Prefect, you can use the

wait_for

parameter when calling a task to specify upstream task dependencies. This allows you to control the execution order of tasks that do not share data dependencies. Here's an example:

from prefect import Flow, Task

class TaskA(Task):
    def run(self):
        # Your code here
        pass

class TaskB(Task):
    def run(self):
        # Your code here
        pass

class TaskC(Task):
    def run(self):
        # Your code here
        pass

class TaskD(Task):
    def run(self):
        # Your code here
        pass

with Flow("My Flow") as flow:
    a = TaskA().submit()
    b = TaskB().submit()
    # Wait for TaskA and TaskB to complete
    c = TaskC().submit(wait_for=[a, b])
    # TaskD will wait for TaskC to complete
    # Note: If waiting for one task it must still be in a list.
    d = TaskD().submit(wait_for=[c])

In this example,

TaskC

won't run until both

TaskA

and

TaskB

have completed. Similarly,

TaskD

won't run until

TaskC

has completed. The

wait_for

parameter takes a list of tasks that the current task should wait for. Please note that this is a general approach and you might need to adjust it to fit your specific use case.

@Marvin stop subclassing Task, you dont do that in prefect 2 unless you really need to. tasks are just functions, and so are flows (@flow decorated functions) what are the current contents of your notes?

Here are the current contents of my notes: 1. Prefect 1: - Prefect 1 is obsolete, along with the

with Flow()

syntax and flow.run(). 2. Prefect 2: - @flow, @task, are the new way to define flows/tasks. - Subflows are just flows called from within a flow. - You just call flows now instead of my_flow.run(). - Tasks are just functions, and so are flows (@flow decorated functions). You don't need to subclass Task in Prefect 2 unless you really need to.

@Marvin exactly, now please try again

In Prefect, you can use the

wait_for

parameter when calling a task to specify upstream task dependencies. This allows you to control the execution order of tasks that do not share data dependencies. Here's an example:

from prefect import task, flow

@task
def task_a():
    pass

@task
def task_b():
    pass

@task
def task_c():
    pass

@task
def task_d():
    pass

@flow
def my_flow():
    a = task_a.submit()
    b = task_b.submit()
    # Wait for task_a and task_b to complete
    c = task_c.submit(wait_for=[a, b])
    # task_d will wait for task_c to complete
    # Note: If waiting for one task it must still be in a list.
    d = task_d.submit(wait_for=[c])

In this example,

task_c

won't run until both

task_a

and

task_b

have completed. Similarly,

task_d

won't run until

task_c

has completed. The

wait_for

parameter takes a list of tasks that the current task should wait for. Please note that this is a general approach and you might need to adjust it to fit your specific use case.