and since you mentioned, you want to use ECS, here is a tutorial on setting up ECS agent: https://towardsdatascience.com/how-to-cut-your-aws-ecs-costs-with-fargate-spot-and-prefect-1a1ba5d2e2df as well as this docs page: https://docs.prefect.io/orchestration/agents/ecs.html#running-ecs-agent-in-production

@Anna Geller Sorry to bother you again, but I've tried out the ECS agent tutorial you wrote up (great read btw 👏). I'm still getting around on some AWS concepts, we usually have dedicated DevOps people handling these sort of tasks, but since I'm experimenting with this feature .. it'll be up to me 😅 Anyway, I changed the necessary variables under the bash script shared and ran a couple of basic tasks. So far so good ! What I initially assumed based on the read and diagram is that each task within the flow would trigger the agent to spin up a new ECS machine 😅 Which I thought would be limited to how many concurrent tasks we've allowed in our Prefect settings, hence the Prefect ECS agent would dynamically spin up and down the necessary ECS containers based on Flow. This is the code I'm currently testing out to test my hypothesis:

from random import randrange
from prefect.storage import S3
from prefect.run_configs import ECSRun
from prefect import task, Flow

FLOW_NAME = "ecs-parallel"
STORAGE = S3(
    bucket="prefect-datasets-2",
    key=f"flows/{FLOW_NAME}.py",
    stored_as_script=True,
    # this will ensure to upload the Flow script to S3 during registration
    local_script_path=f"{FLOW_NAME}.py",
)
RUN_CONFIG = ECSRun(
    labels=["test"],
    task_role_arn="arn:aws:iam::115627025420:role/prefectTaskRole",
    run_task_kwargs=dict(cluster="prefectEcsCluster", launchType="FARGATE", ),
)

@task
def random_num(stop):
    number = randrange(stop)
    print(f"Your number is {number}")
    return number


@task
def sum_numbers(numbers):
    print(sum(numbers))


with Flow(FLOW_NAME, storage=STORAGE, run_config=RUN_CONFIG, ) as flow:
    stop = 100

    number_1 = random_num(stop)
    number_2 = random_num(stop)
    number_3 = random_num(stop)
    number_4 = random_num(stop)
    number_5 = random_num(stop)
    number_6 = random_num(stop)

    sum_numbers = sum_numbers(numbers=[number_1, number_2, number_3, number_4, number_5, number_6])

if __name__ == "__main__":
    flow.register(project_name="elt",
                  idempotency_key=flow.serialized_hash())

However that wasn't the case (I think), the agent seemed to have launched a new container, run the flow and then exited. I went ahead and brought up the

Desired Count

of the prefectECSAgent to 3 to see what happens. Oddly enough, Prefect cloud could only see TWO ECS agents instead of three. Hence I bumped the count up to 5, and still only 2 agents were visible. So just to confirm: • Can a single Flow be run by multiple (similar and dissimilar) agents ? If that's not the case, would having multiple agents mean being able to run multiple FLOWS in concurrency ? And if I want parallelism within a single flow, I should rely on the Dask executor ? • Do you have an idea why bringing up the desired count of the prefectEcsAgent did not correctly reflect the number of agents available ? (I feel it may not be designed to be scaled in that manner) Looking forward to your reply !

And if I want parallelism within a single flow, I should rely on the Dask executor ?

Correct, you could e.g. use LocalDaskExecutor. But you would need to use mapping to make your current flow example parallel:

from prefect import task, Flow
from prefect.executors import LocalDaskExecutor


@task
def generate_random_numbers():
    return list(range(1, 5))


@task
def add_one(x):
    return x + 1


@task(log_stdout=True)
def print_results(res):
    print(res)


with Flow("mapping-example", executor=LocalDaskExecutor()) as flow:
    numbers = generate_random_numbers()
    result = add_one.map(numbers)
    print_results(result)

This entire flow will still run within one ECS task and will parallelize across existing threads