@Marvin DaskTaskrunner modeul not found

If you encounter an error stating that the

DaskTaskRunner

module is not found, it may be due to several reasons. Firstly, ensure that you have installed the

prefect-dask

package, as the

DaskTaskRunner

class is part of this package. You can install it using:

bash
pip install prefect-dask

Once installed, here's a basic example of how to use the `DaskTaskRunner`:

python
import time
from prefect import flow, task
from prefect_dask import DaskTaskRunner

@task
def shout(number):
    time.sleep(0.5)
    print(f"#{number}")

@flow(task_runner=DaskTaskRunner())
def count_to(highest_number):
    for number in range(highest_number):
        shout.submit(number)

if __name__ == "__main__":
    count_to(10)

Make sure when using

DaskTaskRunner

in scripts, you follow the

if __name__ == "__main__":

pattern to avoid warnings and errors. You can find more information and examples in the Prefect Dask and Ray Task Runners documentation and on the Prefect Dask GitHub page.

@Marvin when i submit a task to Daks it says module not found

It appears there are no recent GitHub issues in the Prefect repository that match the problem of a "module not found" during task submission to Dask. This could suggest that the issue may not be widespread or commonly reported. Nonetheless, a "module not found" error during task submission to Dask may suggest a problem with how your environment is set up, particularly concerning Python's module visibility. Here are a few steps to troubleshoot this issue: 1. Ensure Proper Package Installation: Make sure that all required packages are installed in the environment from which you are running your Dask workers. This includes both

prefect

and any other Python packages your tasks depend on. 2. Check Python Environment: Ensure that the Python environment (virtual environment or conda environment) being used by your Dask scheduler and workers is the same as the one being used to run Prefect. Differences in the environments can lead to module not found errors. 3. Serialization of Functions: When using Dask with Prefect, the task functions are serialized and sent to Dask workers. If your task functions rely on imports or context that isn't available on the Dask workers, you'll encounter issues. Make sure all necessary modules and context are importable on worker nodes. 4. Use

if __name__ == "__main__":

: If you're using Dask with scripts, make sure to protect your flow and task definitions with

if __name__ == "__main__":

, which is required to correctly serialize and run code with Dask. If these steps do not resolve the issue, it may be helpful to provide a more detailed description or code snippet of how you are submitting tasks to Dask. This can help in providing a more tailored solution to the problem.