Ah, I see the issue here. In Prefect 3.x, the Dask integration has been moved to a separate package. You'll need to: 1. First install the package if you haven't already:

bash
pip install prefect-dask

2. Then, you can access the Dask client and scheduler info through the task runner's context. Here's how:

python
from prefect import flow
from prefect_dask import DaskTaskRunner

@flow(task_runner=DaskTaskRunner(address="your-cluster-address"))
def my_flow():
    # Access the Dask client within the flow
    client = get_client()
    # Get scheduler info
    scheduler_info = client.scheduler_info()
    print(scheduler_info)

The

scheduler_info()

method will return a dictionary containing detailed information about your Dask cluster, including: - Workers and their status - Memory usage - Task counts and statistics - Other cluster metrics If you need to access this information outside of a flow context, you can use Dask's native client:

python
from dask.distributed import Client

client = Client("your-cluster-address")
scheduler_info = client.scheduler_info()

Note: Make sure your cluster address is in the correct format (e.g., "tcp://ip:port" or the appropriate protocol for your ECS setup). Let me know if you need more specific information about particular scheduler metrics you're interested in monitoring!

Ah yes, my apologies for the oversight. You need to import the

get_client

function from

distributed

. Here's the correct code with all necessary imports:

python
from prefect import flow
from prefect_dask import DaskTaskRunner
from distributed import get_client  # Add this import!

@flow(task_runner=DaskTaskRunner(address="your-cluster-address"))
def my_flow():
    client = get_client()
    scheduler_info = client.scheduler_info()
    print(scheduler_info)

Alternatively, you can also access the client directly from the task runner itself within the flow:

python
from prefect import flow
from prefect_dask import DaskTaskRunner

@flow(task_runner=DaskTaskRunner(address="your-cluster-address"))
def my_flow():
    # Access client through the task runner
    client = my_flow.task_runner.client
    scheduler_info = client.scheduler_info()
    print(scheduler_info)

Both approaches will work, but the second one might be preferable as it's more explicit about where the client is coming from. Choose whichever style you prefer!